Buffett Quality Score [Consumer Discretionary]Evaluating Consumer Discretionary Companies with the Buffett Quality Score
The consumer discretionary sector, characterized by its sensitivity to economic cycles and consumer spending patterns, demands a robust framework for financial evaluation. The Buffett Quality Score offers a comprehensive assessment of financial health and performance specifically tailored to this dynamic industry. This scoring system combines critical financial ratios uniquely relevant to consumer discretionary companies, providing investors and analysts with a reliable tool for evaluation.
Selected Financial Metrics and Criteria
1. Altman Z-Score > 2.0
Relevance: The Altman Z-Score assesses bankruptcy risk, combining profitability, leverage, liquidity, solvency, and activity ratios. For consumer discretionary companies, which often face volatile market conditions, a score above 2.0 indicates financial stability and the ability to withstand economic downturns. This metric is particularly important in this sector due to the high variability in consumer spending.
2. Piotroski F-Score > 6.0
Relevance: The Piotroski F-Score evaluates fundamental strength based on profitability, leverage, liquidity, and operating efficiency. In the consumer discretionary sector, where rapid changes in consumer preferences can impact performance, a score above 6.0 highlights strong fundamental performance and resilience. This score is crucial for identifying companies with robust financial foundations in a highly competitive environment.
3. Asset Turnover > 1.0
Relevance: Asset Turnover measures the efficiency of asset use in generating sales. For consumer discretionary companies, a ratio above 1.0 signifies effective utilization of assets to drive revenue growth. Given the sector's reliance on high sales volumes and rapid inventory turnover, this metric is key to assessing operational efficiency.
4. Current Ratio > 1.5
Relevance: The Current Ratio assesses liquidity by comparing current assets to current liabilities. A ratio above 1.5 ensures that consumer discretionary companies can meet short-term obligations. This liquidity is essential for maintaining operational stability and flexibility to adapt to market changes, especially during economic fluctuations.
5. Debt to Equity Ratio < 1.0
Relevance: A lower Debt to Equity Ratio indicates prudent financial management and reduced reliance on debt. This is particularly important for consumer discretionary companies, which need to maintain financial flexibility to invest in new trends and innovations without overleveraging. Lower debt levels also reduce risk during economic downturns.
6. EBITDA Margin > 15.0%
Relevance: The EBITDA Margin measures operating profitability. A margin above 15.0% indicates efficient operations and the ability to generate sufficient earnings before interest, taxes, depreciation, and amortization. This is crucial for sustaining profitability in a competitive and fluctuating market, ensuring the company can reinvest in growth and innovation.
7. EPS One-Year Growth > 5.0%
Relevance: EPS growth reflects the company’s ability to increase earnings per share over the past year. For consumer discretionary companies, growth exceeding 5.0% signals positive earnings momentum, which is vital for investor confidence and the ability to fund future growth initiatives. This metric highlights companies that are successfully increasing profitability.
8. Gross Margin > 25.0%
Relevance: Gross Margin represents the profitability of sales after production costs. A margin exceeding 25.0% indicates strong pricing power and effective cost management, crucial for maintaining profitability while adapting to changing consumer demands. High gross margins are indicative of a company’s ability to control costs and price products competitively.
9. Net Margin > 10.0%
Relevance: Net Margin measures overall profitability after all expenses. A margin above 10.0% highlights the company’s ability to maintain strong profit levels, ensuring financial health and stability. This is essential for sustaining operations and investing in new opportunities, reflecting the company's efficiency in converting revenue into actual profit.
10.Return on Equity (ROE) > 15.0%
Relevance: ROE indicates how effectively a company uses equity to generate profits. An ROE above 15.0% signifies strong shareholder value creation. This metric is key for evaluating long-term performance in the consumer discretionary sector, where investor returns are closely tied to the company’s ability to innovate and grow. High ROE demonstrates effective management and profitable use of equity capital.
Interpreting the Buffett Quality Score
0-4 Points: Indicates potential weaknesses across multiple financial areas, warranting further investigation and risk assessment.
5 Points: Suggests average performance based on sector-specific criteria, indicating a need for cautious optimism.
6-10 Points: Signifies strong financial health and quality, meeting or exceeding most performance thresholds, making the company a potentially attractive investment.
Conclusion
The Buffett Quality Score provides a structured approach to evaluating financial health and performance. By focusing on these essential financial metrics, stakeholders can make informed decisions, identifying companies that are well-positioned to thrive in the competitive and economically sensitive consumer discretionary sector.
Disclaimer: The Buffett Quality Score serves as a tool for financial evaluation and analysis. It is not a substitute for professional financial advice or investment recommendations. Investors should conduct thorough research and seek personalized guidance based on individual circumstances.
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[Joy] Jasmine Strategy for Bitcoin and CryptoIt is my strategy I use for spot and future trading, mostly for BTCUSD
Notable parameters used:
INDEX:BTCUSD
Data: 2017 - today
Long trade margin/leverage: 8x (50/8 = 6.25)
Short trade margin/leverage: 1x (50/50 = 1)
Commission: 0.075%
Initial Capital: $15, 000
Results:
Net profit: 832.74 %
Buy & Hold: 602.56 %. It beats the buy and hold.
Percent profitability: 88 % . It means 8 out of 10 trades resulted in profits.
Margin Calls: 0 (i.e. Never had a margin call according to backtest from 2017 till today)
Total closes trades: 25
Profit factor: 8.238
Avg Winning Trade: 43.08 %
Largest Winning Trade: 334.85 %
Avg # Bars in Winning Trades: 44 (i.e. 88 days)
Sharpe Ratio: 0.61. A Sharpe ratio under 1.0 is considered sub-optimal. Because of the big swings, I cannot make the Sharpe ratio any better at this time.
Sortino Ratio: 5.153. I think a Sortino ratio of 3.0 or higher is considered excellent. Do your research.
I am using 7.1% stop loss on long trades. However, you can turn off the stop loss and note the profitability remains the same.
Do remember there may be other costs, such as funding costs.
Description:
The strategy hunts for a few market features, namely breakouts, abnormal wicks relative to the body, abnormal volume relative to the candle characteristics, and possible confirmation of all these. It also hunts for more aspects. It gives a relative score of each of the characteristics. Finally, it tries to draw a guesstimate. In the end, it is only a guesstimate. Users see the final outcome (buy/sell etc). The whole logic happens at the background.
The strategy is not to be used for scalping, day trading or swing trading. In other words, it is not suitable for trading in a lower timeframe. It is to be used for Positional Trading For example, if one is trying this for BTCUSD, one may only try this for BTCUSD in a 2day timeframe and not in lower timeframes (such as 4 hours or 1 hour etc.) I am primarily interested in BTC for my research. However, it may be tested on other cryptos as well with varying degrees of results.
Please remember that past performance does not be indicative of future results. Different types of investments involve varying degrees of risk. There can be no assurance that the information referred to directly or indirectly in this strategy will be profitable, equal to any corresponding historical performance level(s), or suitable for you in any form or shape. Market condition changes very fast. Moreover, it would be best if you did not assume that any discussion or information contained here serves as the receipt of, or as a substitute for, personalized investment advice. I am not a financial advisor. I have no qualifications to be a financial advisor. It is only for educational and research purposes. Readers are encouraged to consult with a professional advisor of his/her choosing. Neither I nor my indicators or strategies take any responsibility for any misuse of the information for any actual trading. Even though this strategy did 88% profitability from 2017-2021, it may do poorly and may even be NOT profitable in the future.
Backtesting & Trading Engine [PineCoders]The PineCoders Backtesting and Trading Engine is a sophisticated framework with hybrid code that can run as a study to generate alerts for automated or discretionary trading while simultaneously providing backtest results. It can also easily be converted to a TradingView strategy in order to run TV backtesting. The Engine comes with many built-in strats for entries, filters, stops and exits, but you can also add you own.
If, like any self-respecting strategy modeler should, you spend a reasonable amount of time constantly researching new strategies and tinkering, our hope is that the Engine will become your inseparable go-to tool to test the validity of your creations, as once your tests are conclusive, you will be able to run this code as a study to generate the alerts required to put it in real-world use, whether for discretionary trading or to interface with an execution bot/app. You may also find the backtesting results the Engine produces in study mode enough for your needs and spend most of your time there, only occasionally converting to strategy mode in order to backtest using TV backtesting.
As you will quickly grasp when you bring up this script’s Settings, this is a complex tool. While you will be able to see results very quickly by just putting it on a chart and using its built-in strategies, in order to reap the full benefits of the PineCoders Engine, you will need to invest the time required to understand the subtleties involved in putting all its potential into play.
Disclaimer: use the Engine at your own risk.
Before we delve in more detail, here’s a bird’s eye view of the Engine’s features:
More than 40 built-in strategies,
Customizable components,
Coupling with your own external indicator,
Simple conversion from Study to Strategy modes,
Post-Exit analysis to search for alternate trade outcomes,
Use of the Data Window to show detailed bar by bar trade information and global statistics, including some not provided by TV backtesting,
Plotting of reminders and generation of alerts on in-trade events.
By combining your own strats to the built-in strats supplied with the Engine, and then tuning the numerous options and parameters in the Inputs dialog box, you will be able to play what-if scenarios from an infinite number of permutations.
USE CASES
You have written an indicator that provides an entry strat but it’s missing other components like a filter and a stop strategy. You add a plot in your indicator that respects the Engine’s External Signal Protocol, connect it to the Engine by simply selecting your indicator’s plot name in the Engine’s Settings/Inputs and then run tests on different combinations of entry stops, in-trade stops and profit taking strats to find out which one produces the best results with your entry strat.
You are building a complex strategy that you will want to run as an indicator generating alerts to be sent to a third-party execution bot. You insert your code in the Engine’s modules and leverage its trade management code to quickly move your strategy into production.
You have many different filters and want to explore results using them separately or in combination. Integrate the filter code in the Engine and run through different permutations or hook up your filtering through the external input and control your filter combos from your indicator.
You are tweaking the parameters of your entry, filter or stop strat. You integrate it in the Engine and evaluate its performance using the Engine’s statistics.
You always wondered what results a random entry strat would yield on your markets. You use the Engine’s built-in random entry strat and test it using different combinations of filters, stop and exit strats.
You want to evaluate the impact of fees and slippage on your strategy. You use the Engine’s inputs to play with different values and get immediate feedback in the detailed numbers provided in the Data Window.
You just want to inspect the individual trades your strategy generates. You include it in the Engine and then inspect trades visually on your charts, looking at the numbers in the Data Window as you move your cursor around.
You have never written a production-grade strategy and you want to learn how. Inspect the code in the Engine; you will find essential components typical of what is being used in actual trading systems.
You have run your system for a while and have compiled actual slippage information and your broker/exchange has updated his fees schedule. You enter the information in the Engine and run it on your markets to see the impact this has on your results.
FEATURES
Before going into the detail of the Inputs and the Data Window numbers, here’s a more detailed overview of the Engine’s features.
Built-in strats
The engine comes with more than 40 pre-coded strategies for the following standard system components:
Entries,
Filters,
Entry stops,
2 stage in-trade stops with kick-in rules,
Pyramiding rules,
Hard exits.
While some of the filter and stop strats provided may be useful in production-quality systems, you will not devise crazy profit-generating systems using only the entry strats supplied; that part is still up to you, as will be finding the elusive combination of components that makes winning systems. The Engine will, however, provide you with a solid foundation where all the trade management nitty-gritty is handled for you. By binding your custom strats to the Engine, you will be able to build reliable systems of the best quality currently allowed on the TV platform.
On-chart trade information
As you move over the bars in a trade, you will see trade numbers in the Data Window change at each bar. The engine calculates the P&L at every bar, including slippage and fees that would be incurred were the trade exited at that bar’s close. If the trade includes pyramided entries, those will be taken into account as well, although for those, final fees and slippage are only calculated at the trade’s exit.
You can also see on-chart markers for the entry level, stop positions, in-trade special events and entries/exits (you will want to disable these when using the Engine in strategy mode to see TV backtesting results).
Customization
You can couple your own strats to the Engine in two ways:
1. By inserting your own code in the Engine’s different modules. The modular design should enable you to do so with minimal effort by following the instructions in the code.
2. By linking an external indicator to the engine. After making the proper selections in the engine’s Settings and providing values respecting the engine’s protocol, your external indicator can, when the Engine is used in Indicator mode only:
Tell the engine when to enter long or short trades, but let the engine’s in-trade stop and exit strats manage the exits,
Signal both entries and exits,
Provide an entry stop along with your entry signal,
Filter other entry signals generated by any of the engine’s entry strats.
Conversion from strategy to study
TradingView strategies are required to backtest using the TradingView backtesting feature, but if you want to generate alerts with your script, whether for automated trading or just to trigger alerts that you will use in discretionary trading, your code has to run as a study since, for the time being, strategies can’t generate alerts. From hereon we will use indicator as a synonym for study.
Unless you want to maintain two code bases, you will need hybrid code that easily flips between strategy and indicator modes, and your code will need to restrict its use of strategy() calls and their arguments if it’s going to be able to run both as an indicator and a strategy using the same trade logic. That’s one of the benefits of using this Engine. Once you will have entered your own strats in the Engine, it will be a matter of commenting/uncommenting only four lines of code to flip between indicator and strategy modes in a matter of seconds.
Additionally, even when running in Indicator mode, the Engine will still provide you with precious numbers on your individual trades and global results, some of which are not available with normal TradingView backtesting.
Post-Exit Analysis for alternate outcomes (PEA)
While typical backtesting shows results of trade outcomes, PEA focuses on what could have happened after the exit. The intention is to help traders get an idea of the opportunity/risk in the bars following the trade in order to evaluate if their exit strategies are too aggressive or conservative.
After a trade is exited, the Engine’s PEA module continues analyzing outcomes for a user-defined quantity of bars. It identifies the maximum opportunity and risk available in that space, and calculates the drawdown required to reach the highest opportunity level post-exit, while recording the number of bars to that point.
Typically, if you can’t find opportunity greater than 1X past your trade using a few different reasonable lengths of PEA, your strategy is doing pretty good at capturing opportunity. Remember that 100% of opportunity is never capturable. If, however, PEA was finding post-trade maximum opportunity of 3 or 4X with average drawdowns of 0.3 to those areas, this could be a clue revealing your system is exiting trades prematurely. To analyze PEA numbers, you can uncomment complete sets of plots in the Plot module to reveal detailed global and individual PEA numbers.
Statistics
The Engine provides stats on your trades that TV backtesting does not provide, such as:
Average Profitability Per Trade (APPT), aka statistical expectancy, a crucial value.
APPT per bar,
Average stop size,
Traded volume .
It also shows you on a trade-by-trade basis, on-going individual trade results and data.
In-trade events
In-trade events can plot reminders and trigger alerts when they occur. The built-in events are:
Price approaching stop,
Possible tops/bottoms,
Large stop movement (for discretionary trading where stop is moved manually),
Large price movements.
Slippage and Fees
Even when running in indicator mode, the Engine allows for slippage and fees to be included in the logic and test results.
Alerts
The alert creation mechanism allows you to configure alerts on any combination of the normal or pyramided entries, exits and in-trade events.
Backtesting results
A few words on the numbers calculated in the Engine. Priority is given to numbers not shown in TV backtesting, as you can readily convert the script to a strategy if you need them.
We have chosen to focus on numbers expressing results relative to X (the trade’s risk) rather than in absolute currency numbers or in other more conventional but less useful ways. For example, most of the individual trade results are not shown in percentages, as this unit of measure is often less meaningful than those expressed in units of risk (X). A trade that closes with a +25% result, for example, is a poor outcome if it was entered with a -50% stop. Expressed in X, this trade’s P&L becomes 0.5, which provides much better insight into the trade’s outcome. A trade that closes with a P&L of +2X has earned twice the risk incurred upon entry, which would represent a pre-trade risk:reward ratio of 2.
The way to go about it when you think in X’s and that you adopt the sound risk management policy to risk a fixed percentage of your account on each trade is to equate a currency value to a unit of X. E.g. your account is 10K USD and you decide you will risk a maximum of 1% of it on each trade. That means your unit of X for each trade is worth 100 USD. If your APPT is 2X, this means every time you risk 100 USD in a trade, you can expect to make, on average, 200 USD.
By presenting results this way, we hope that the Engine’s statistics will appeal to those cognisant of sound risk management strategies, while gently leading traders who aren’t, towards them.
We trade to turn in tangible profits of course, so at some point currency must come into play. Accordingly, some values such as equity, P&L, slippage and fees are expressed in currency.
Many of the usual numbers shown in TV backtests are nonetheless available, but they have been commented out in the Engine’s Plot module.
Position sizing and risk management
All good system designers understand that optimal risk management is at the very heart of all winning strategies. The risk in a trade is defined by the fraction of current equity represented by the amplitude of the stop, so in order to manage risk optimally on each trade, position size should adjust to the stop’s amplitude. Systems that enter trades with a fixed stop amplitude can get away with calculating position size as a fixed percentage of current equity. In the context of a test run where equity varies, what represents a fixed amount of risk translates into different currency values.
Dynamically adjusting position size throughout a system’s life is optimal in many ways. First, as position sizing will vary with current equity, it reproduces a behavioral pattern common to experienced traders, who will dial down risk when confronted to poor performance and increase it when performance improves. Second, limiting risk confers more predictability to statistical test results. Third, position sizing isn’t just about managing risk, it’s also about maximizing opportunity. By using the maximum leverage (no reference to trading on margin here) into the trade that your risk management strategy allows, a dynamic position size allows you to capture maximal opportunity.
To calculate position sizes using the fixed risk method, we use the following formula: Position = Account * MaxRisk% / Stop% [, which calculates a position size taking into account the trade’s entry stop so that if the trade is stopped out, 100 USD will be lost. For someone who manages risk this way, common instructions to invest a certain percentage of your account in a position are simply worthless, as they do not take into account the risk incurred in the trade.
The Engine lets you select either the fixed risk or fixed percentage of equity position sizing methods. The closest thing to dynamic position sizing that can currently be done with alerts is to use a bot that allows syntax to specify position size as a percentage of equity which, while being dynamic in the sense that it will adapt to current equity when the trade is entered, does not allow us to modulate position size using the stop’s amplitude. Changes to alerts are on the way which should solve this problem.
In order for you to simulate performance with the constraint of fixed position sizing, the Engine also offers a third, less preferable option, where position size is defined as a fixed percentage of initial capital so that it is constant throughout the test and will thus represent a varying proportion of current equity.
Let’s recap. The three position sizing methods the Engine offers are:
1. By specifying the maximum percentage of risk to incur on your remaining equity, so the Engine will dynamically adjust position size for each trade so that, combining the stop’s amplitude with position size will yield a fixed percentage of risk incurred on current equity,
2. By specifying a fixed percentage of remaining equity. Note that unless your system has a fixed stop at entry, this method will not provide maximal risk control, as risk will vary with the amplitude of the stop for every trade. This method, as the first, does however have the advantage of automatically adjusting position size to equity. It is the Engine’s default method because it has an equivalent in TV backtesting, so when flipping between indicator and strategy mode, test results will more or less correspond.
3. By specifying a fixed percentage of the Initial Capital. While this is the least preferable method, it nonetheless reflects the reality confronted by most system designers on TradingView today. In this case, risk varies both because the fixed position size in initial capital currency represents a varying percentage of remaining equity, and because the trade’s stop amplitude may vary, adding another variability vector to risk.
Note that the Engine cannot display equity results for strategies entering trades for a fixed amount of shares/contracts at a variable price.
SETTINGS/INPUTS
Because the initial text first published with a script cannot be edited later and because there are just too many options, the Engine’s Inputs will not be covered in minute detail, as they will most certainly evolve. We will go over them with broad strokes; you should be able to figure the rest out. If you have questions, just ask them here or in the PineCoders Telegram group.
Display
The display header’s checkbox does nothing.
For the moment, only one exit strategy uses a take profit level, so only that one will show information when checking “Show Take Profit Level”.
Entries
You can activate two simultaneous entry strats, each selected from the same set of strats contained in the Engine. If you select two and they fire simultaneously, the main strat’s signal will be used.
The random strat in each list uses a different seed, so you will get different results from each.
The “Filter transitions” and “Filter states” strats delegate signal generation to the selected filter(s). “Filter transitions” signals will only fire when the filter transitions into bull/bear state, so after a trade is stopped out, the next entry may take some time to trigger if the filter’s state does not change quickly. When you choose “Filter states”, then a new trade will be entered immediately after an exit in the direction the filter allows.
If you select “External Indicator”, your indicator will need to generate a +2/-2 (or a positive/negative stop value) to enter a long/short position, providing the selected filters allow for it. If you wish to use the Engine’s capacity to also derive the entry stop level from your indicator’s signal, then you must explicitly choose this option in the Entry Stops section.
Filters
You can activate as many filters as you wish; they are additive. The “Maximum stop allowed on entry” is an important component of proper risk management. If your system has an average 3% stop size and you need to trade using fixed position sizes because of alert/execution bot limitations, you must use this filter because if your system was to enter a trade with a 15% stop, that trade would incur 5 times the normal risk, and its result would account for an abnormally high proportion in your system’s performance.
Remember that any filter can also be used as an entry signal, either when it changes states, or whenever no trade is active and the filter is in a bull or bear mode.
Entry Stops
An entry stop must be selected in the Engine, as it requires a stop level before the in-trade stop is calculated. Until the selected in-trade stop strat generates a stop that comes closer to price than the entry stop (or respects another one of the in-trade stops kick in strats), the entry stop level is used.
It is here that you must select “External Indicator” if your indicator supplies a +price/-price value to be used as the entry stop. A +price is expected for a long entry and a -price value will enter a short with a stop at price. Note that the price is the absolute price, not an offset to the current price level.
In-Trade Stops
The Engine comes with many built-in in-trade stop strats. Note that some of them share the “Length” and “Multiple” field, so when you swap between them, be sure that the length and multiple in use correspond to what you want for that stop strat. Suggested defaults appear with the name of each strat in the dropdown.
In addition to the strat you wish to use, you must also determine when it kicks in to replace the initial entry’s stop, which is determined using different strats. For strats where you can define a positive or negative multiple of X, percentage or fixed value for a kick-in strat, a positive value is above the trade’s entry fill and a negative one below. A value of zero represents breakeven.
Pyramiding
What you specify in this section are the rules that allow pyramiding to happen. By themselves, these rules will not generate pyramiding entries. For those to happen, entry signals must be issued by one of the active entry strats, and conform to the pyramiding rules which act as a filter for them. The “Filter must allow entry” selection must be chosen if you want the usual system’s filters to act as additional filtering criteria for your pyramided entries.
Hard Exits
You can choose from a variety of hard exit strats. Hard exits are exit strategies which signal trade exits on specific events, as opposed to price breaching a stop level in In-Trade Stops strategies. They are self-explanatory. The last one labelled When Take Profit Level (multiple of X) is reached is the only one that uses a level, but contrary to stops, it is above price and while it is relative because it is expressed as a multiple of X, it does not move during the trade. This is the level called Take Profit that is show when the “Show Take Profit Level” checkbox is checked in the Display section.
While stops focus on managing risk, hard exit strategies try to put the emphasis on capturing opportunity.
Slippage
You can define it as a percentage or a fixed value, with different settings for entries and exits. The entry and exit markers on the chart show the impact of slippage on the entry price (the fill).
Fees
Fees, whether expressed as a percentage of position size in and out of the trade or as a fixed value per in and out, are in the same units of currency as the capital defined in the Position Sizing section. Fees being deducted from your Capital, they do not have an impact on the chart marker positions.
In-Trade Events
These events will only trigger during trades. They can be helpful to act as reminders for traders using the Engine as assistance to discretionary trading.
Post-Exit Analysis
It is normally on. Some of its results will show in the Global Numbers section of the Data Window. Only a few of the statistics generated are shown; many more are available, but commented out in the Plot module.
Date Range Filtering
Note that you don’t have to change the dates to enable/diable filtering. When you are done with a specific date range, just uncheck “Date Range Filtering” to disable date filtering.
Alert Triggers
Each selection corresponds to one condition. Conditions can be combined into a single alert as you please. Just be sure you have selected the ones you want to trigger the alert before you create the alert. For example, if you trade in both directions and you want a single alert to trigger on both types of exits, you must select both “Long Exit” and “Short Exit” before creating your alert.
Once the alert is triggered, these settings no longer have relevance as they have been saved with the alert.
When viewing charts where an alert has just triggered, if your alert triggers on more than one condition, you will need the appropriate markers active on your chart to figure out which condition triggered the alert, since plotting of markers is independent of alert management.
Position sizing
You have 3 options to determine position size:
1. Proportional to Stop -> Variable, with a cap on size.
2. Percentage of equity -> Variable.
3. Percentage of Initial Capital -> Fixed.
External Indicator
This is where you connect your indicator’s plot that will generate the signals the Engine will act upon. Remember this only works in Indicator mode.
DATA WINDOW INFORMATION
The top part of the window contains global numbers while the individual trade information appears in the bottom part. The different types of units used to express values are:
curr: denotes the currency used in the Position Sizing section of Inputs for the Initial Capital value.
quote: denotes quote currency, i.e. the value the instrument is expressed in, or the right side of the market pair (USD in EURUSD ).
X: the stop’s amplitude, itself expressed in quote currency, which we use to express a trade’s P&L, so that a trade with P&L=2X has made twice the stop’s amplitude in profit. This is sometimes referred to as R, since it represents one unit of risk. It is also the unit of measure used in the APPT, which denotes expected reward per unit of risk.
X%: is also the stop’s amplitude, but expressed as a percentage of the Entry Fill.
The numbers appearing in the Data Window are all prefixed:
“ALL:” the number is the average for all first entries and pyramided entries.
”1ST:” the number is for first entries only.
”PYR:” the number is for pyramided entries only.
”PEA:” the number is for Post-Exit Analyses
Global Numbers
Numbers in this section represent the results of all trades up to the cursor on the chart.
Average Profitability Per Trade (X): This value is the most important gauge of your strat’s worthiness. It represents the returns that can be expected from your strat for each unit of risk incurred. E.g.: your APPT is 2.0, thus for every unit of currency you invest in a trade, you can on average expect to obtain 2 after the trade. APPT is also referred to as “statistical expectancy”. If it is negative, your strategy is losing, even if your win rate is very good (it means your winning trades aren’t winning enough, or your losing trades lose too much, or both). Its counterpart in currency is also shown, as is the APPT/bar, which can be a useful gauge in deciding between rivalling systems.
Profit Factor: Gross of winning trades/Gross of losing trades. Strategy is profitable when >1. Not as useful as the APPT because it doesn’t take into account the win rate and the average win/loss per trade. It is calculated from the total winning/losing results of this particular backtest and has less predictive value than the APPT. A good profit factor together with a poor APPT means you just found a chart where your system outperformed. Relying too much on the profit factor is a bit like a poker player who would think going all in with two’s against aces is optimal because he just won a hand that way.
Win Rate: Percentage of winning trades out of all trades. Taken alone, it doesn’t have much to do with strategy profitability. You can have a win rate of 99% but if that one trade in 100 ruins you because of poor risk management, 99% doesn’t look so good anymore. This number speaks more of the system’s profile than its worthiness. Still, it can be useful to gauge if the system fits your personality. It can also be useful to traders intending to sell their systems, as low win rate systems are more difficult to sell and require more handholding of worried customers.
Equity (curr): This the sum of initial capital and the P&L of your system’s trades, including fees and slippage.
Return on Capital is the equivalent of TV’s Net Profit figure, i.e. the variation on your initial capital.
Maximum drawdown is the maximal drawdown from the highest equity point until the drop . There is also a close to close (meaning it doesn’t take into account in-trade variations) maximum drawdown value commented out in the code.
The next values are self-explanatory, until:
PYR: Avg Profitability Per Entry (X): this is the APPT for all pyramided entries.
PEA: Avg Max Opp . Available (X): the average maximal opportunity found in the Post-Exit Analyses.
PEA: Avg Drawdown to Max Opp . (X): this represents the maximum drawdown (incurred from the close at the beginning of the PEA analysis) required to reach the maximal opportunity point.
Trade Information
Numbers in this section concern only the current trade under the cursor. Most of them are self-explanatory. Use the description’s prefix to determine what the values applies to.
PYR: Avg Profitability Per Entry (X): While this value includes the impact of all current pyramided entries (and only those) and updates when you move your cursor around, P&L only reflects fees at the trade’s last bar.
PEA: Max Opp . Available (X): It’s the most profitable close reached post-trade, measured from the trade’s Exit Fill, expressed in the X value of the trade the PEA follows.
PEA: Drawdown to Max Opp . (X): This is the maximum drawdown from the trade’s Exit Fill that needs to be sustained in order to reach the maximum opportunity point, also expressed in X. Note that PEA numbers do not include slippage and fees.
EXTERNAL SIGNAL PROTOCOL
Only one external indicator can be connected to a script; in order to leverage its use to the fullest, the engine provides options to use it as either an entry signal, an entry/exit signal or a filter. When used as an entry signal, you can also use the signal to provide the entry’s stop. Here’s how this works:
For filter state: supply +1 for bull (long entries allowed), -1 for bear (short entries allowed).
For entry signals: supply +2 for long, -2 for short.
For exit signals: supply +3 for exit from long, -3 for exit from short.
To send an entry stop level with an entry signal: Send positive stop level for long entry (e.g. 103.33 to enter a long with a stop at 103.33), negative stop level for short entry (e.g. -103.33 to enter a short with a stop at 103.33). If you use this feature, your indicator will have to check for exact stop levels of 1.0, 2.0 or 3.0 and their negative counterparts, and fudge them with a tick in order to avoid confusion with other signals in the protocol.
Remember that mere generation of the values by your indicator will have no effect until you explicitly allow their use in the appropriate sections of the Engine’s Settings/Inputs.
An example of a script issuing a signal for the Engine is published by PineCoders.
RECOMMENDATIONS TO ASPIRING SYSTEM DESIGNERS
Stick to higher timeframes. On progressively lower timeframes, margins decrease and fees and slippage take a proportionally larger portion of profits, to the point where they can very easily turn a profitable strategy into a losing one. Additionally, your margin for error shrinks as the equilibrium of your system’s profitability becomes more fragile with the tight numbers involved in the shorter time frames. Avoid <1H time frames.
Know and calculate fees and slippage. To avoid market shock, backtest using conservative fees and slippage parameters. Systems rarely show unexpectedly good returns when they are confronted to the markets, so put all chances on your side by being outrageously conservative—or a the very least, realistic. Test results that do not include fees and slippage are worthless. Slippage is there for a reason, and that’s because our interventions in the market change the market. It is easier to find alpha in illiquid markets such as cryptos because not many large players participate in them. If your backtesting results are based on moving large positions and you don’t also add the inevitable slippage that will occur when you enter/exit thin markets, your backtesting will produce unrealistic results. Even if you do include large slippage in your settings, the Engine can only do so much as it will not let slippage push fills past the high or low of the entry bar, but the gap may be much larger in illiquid markets.
Never test and optimize your system on the same dataset , as that is the perfect recipe for overfitting or data dredging, which is trying to find one precise set of rules/parameters that works only on one dataset. These setups are the most fragile and often get destroyed when they meet the real world.
Try to find datasets yielding more than 100 trades. Less than that and results are not as reliable.
Consider all backtesting results with suspicion. If you never entertained sceptic tendencies, now is the time to begin. If your backtest results look really good, assume they are flawed, either because of your methodology, the data you’re using or the software doing the testing. Always assume the worse and learn proper backtesting techniques such as monte carlo simulations and walk forward analysis to avoid the traps and biases that unchecked greed will set for you. If you are not familiar with concepts such as survivor bias, lookahead bias and confirmation bias, learn about them.
Stick to simple bars or candles when designing systems. Other types of bars often do not yield reliable results, whether by design (Heikin Ashi) or because of the way they are implemented on TV (Renko bars).
Know that you don’t know and use that knowledge to learn more about systems and how to properly test them, about your biases, and about yourself.
Manage risk first , then capture opportunity.
Respect the inherent uncertainty of the future. Cleanse yourself of the sad arrogance and unchecked greed common to newcomers to trading. Strive for rationality. Respect the fact that while backtest results may look promising, there is no guarantee they will repeat in the future (there is actually a high probability they won’t!), because the future is fundamentally unknowable. If you develop a system that looks promising, don’t oversell it to others whose greed may lead them to entertain unreasonable expectations.
Have a plan. Understand what king of trading system you are trying to build. Have a clear picture or where entries, exits and other important levels will be in the sort of trade you are trying to create with your system. This stated direction will help you discard more efficiently many of the inevitably useless ideas that will pop up during system design.
Be wary of complexity. Experienced systems engineers understand how rapidly complexity builds when you assemble components together—however simple each one may be. The more complex your system, the more difficult it will be to manage.
Play! . Allow yourself time to play around when you design your systems. While much comes about from working with a purpose, great ideas sometimes come out of just trying things with no set goal, when you are stuck and don’t know how to move ahead. Have fun!
@LucF
NOTES
While the engine’s code can supply multiple consecutive entries of longs or shorts in order to scale positions (pyramid), all exits currently assume the execution bot will exit the totality of the position. No partial exits are currently possible with the Engine.
Because the Engine is literally crippled by the limitations on the number of plots a script can output on TV; it can only show a fraction of all the information it calculates in the Data Window. You will find in the Plot Module vast amounts of commented out lines that you can activate if you also disable an equivalent number of other plots. This may be useful to explore certain characteristics of your system in more detail.
When backtesting using the TV backtesting feature, you will need to provide the strategy parameters you wish to use through either Settings/Properties or by changing the default values in the code’s header. These values are defined in variables and used not only in the strategy() statement, but also as defaults in the Engine’s relevant Inputs.
If you want to test using pyramiding, then both the strategy’s Setting/Properties and the Engine’s Settings/Inputs need to allow pyramiding.
If you find any bugs in the Engine, please let us know.
THANKS
To @glaz for allowing the use of his unpublished MA Squize in the filters.
To @everget for his Chandelier stop code, which is also used as a filter in the Engine.
To @RicardoSantos for his pseudo-random generator, and because it’s from him that I first read in the Pine chat about the idea of using an external indicator as input into another. In the PineCoders group, @theheirophant then mentioned the idea of using it as a buy/sell signal and @simpelyfe showed a piece of code implementing the idea. That’s the tortuous story behind the use of the external indicator in the Engine.
To @admin for the Volatility stop’s original code and for the donchian function lifted from Ichimoku .
To @BobHoward21 for the v3 version of Volatility Stop .
To @scarf and @midtownsk8rguy for the color tuning.
To many other scripters who provided encouragement and suggestions for improvement during the long process of writing and testing this piece of code.
To J. Welles Wilder Jr. for ATR, used extensively throughout the Engine.
To TradingView for graciously making an account available to PineCoders.
And finally, to all fellow PineCoders for the constant intellectual stimulation; it is a privilege to share ideas with you all. The Engine is for all TradingView PineCoders, of course—but especially for you.
Look first. Then leap.
Great Expectations [LucF]Great Expectations helps traders answer the question: What is possible? It is a powerful question, yet exploration of the unknown always entails risk. A more complete set of questions better suited to traders could be:
What opportunity exists from any given point on a chart?
What portion of this opportunity can be realistically captured?
What risk will be incurred in trying to do so, and how long will it take?
Great Expectations is the result of an exploration of these questions. It is a trade simulator that generates visual and quantitative information to help strategy modelers visually identify and analyse areas of optimal expectation on charts, whether they are designing automated or discretionary strategies.
WARNING: Great Expectations is NOT an indicator that helps determine the current state of a market. It works by looking at points in the past from which the future is already known. It uses one definition of repainting extensively (i.e. it goes back in the past to print information that could not have been know at the time). Repainting understood that way is in fact almost all the indicator does! —albeit for what I hope is a noble cause. The indicator is of no use whatsoever in analyzing markets in real-time. If you do not understand what it does, please stay away!
This is an indicator—not a strategy that uses TradingView’s backtesting engine. It works by simulating trades, not unlike a backtest, but with the crucial difference that it assumes a trade (either long or short) is entered on all bars in the historic sample. It walks forward from each bar and determines possible outcomes, gathering individual trade statistics that in turn generate precious global statistics from all outcomes tested on the chart.
Great Expectations provides numbers summarizing trade results on all simulations run from the chart. Those numbers cannot be compared to backtest-produced numbers since all non-filtered bars are examined, even if an entry was taken on the bar immediately preceding the current one, which never happens in a backtest. This peculiarity does NOT invalidate Great Expectations calculations; it just entails that results be considered under a different light. Provided they are evaluated within the indicator’s context, they can be useful—sometimes even more than backtesting results, e.g. in evaluating the impact of parameter-fitting or variations in entry, exit or filtering strats.
Traders and strategy modelers are creatures of hope often suffering from blurred vision; my hope is that Great Expectations will help them appraise the validity of their setup and strat intuitions in a realistic fashion, preventing confirmation bias from obstructing perspective—and great expectations from turning into financial great deceptions.
USE CASES
You’ve identified what looks like a promising setup on other indicators. You load Great Expectations on the chart and evaluate if its high-expectation areas match locations where your setup’s conditions occur. Unless today is your lucky day, chances are the indicator will help you realize your setup is not as promising as you had hoped.
You want to get a rough estimate of the optimal trade duration for a chart and you don’t mind using the entry and exit strategies provided with the indicator. You use the trade length readouts of the indicator.
You’re experimenting with a new stop strategy and want to know how long it will keep you in trades, on average. You integrate your stop strategy in the indicator’s code and look at the average trade length it produces and the TST ratio to evaluate its performance.
You have put together your own entry and exit criteria and are looking for a filter that will help you improve backtesting results. You visually ascertain the suitability of your filter by looking at its results on the charts with great Expectations, to see if your filter is choosing its areas correctly.
You have a strategy that shows backtested trades on your chart. Great Expectations can help you evaluate how well your strategy is benefitting from high-opportunity areas while avoiding poor expectation spots.
You want more complete statistics on your set of strategies than what backtesting will provide. You use Great Expectations, knowing that it tests all bars in the sample that correspond to your criteria, as opposed to backtesting results which are limited to a subset of all possible entries.
You want to fool your friends into thinking you’ve designed the holy grail of indicators, something that identifies optimal opportunities on any chart; you show them the P&L cloud.
FEATURES
For one trade
At any given point on the chart, assuming a trade is entered there, Great Expectations shows you information specific to that trade simulation both on the chart and in the Data Window.
The chart can display:
the P & L Cloud which shows whether the trade ended profitably or not, and by how much,
the Opportunity & Risk Cloud which the maximum opportunity and risk the simulation encountered. When superimposed over the P & L cloud, you will see what I call the managed opportunity and risk, i.e the portion of maximum opportunity that was captured and the portion of the maximum risk that was incurred,
the target and if it was reached,
a background that uses a gradient to show different levels of trade length, P&L or how frequently the target was reached during simulation.
The Data Window displays more than 40 values on individual trades and global results. For any given trade you will know:
Entry/Exit levels, including slippage impact,
It’s outcome and duration,
P/L achieved,
The fraction of the maximum opportunity/risk managed by the trade.
For all trades
After going through all the possible trades on the chart, the indicator will provide you with a rare view of all outcomes expressed with the P&L cloud, which allows us to instantly see the most/least profitable areas of a chart using trade data as support, while also showing its relationship with the opportunity/risk encountered during the simulation. The difference between the two clouds is the managed opportunity and risk.
The Data Window will present you with numbers which we will go through later. Some of them are: average stop size, P/L, win rate, % opportunity managed, trade lengths for different types of trade outcomes and the TST (Target:Stop Travel) ratio.
Let’s see Great Expectations in action… and remember to open your Data Window!
INPUTS
Trade direction : You must first choose if you wish to look at long or short trades. Because of the way the indicator works and the amount of visual information on the chart, it is only practical to look at one type of trades at a time. The default is Longs.
Maximum trade Length (MaxL) : This is the maximum walk forward distance the simulator will go in analyzing outcomes from any given point in the past. It also determines the size of the dead zone among the chart’s last bars. A red background line identifies the beginning of the dead zone for which not enough bars have elapsed to analyze outcomes for the maximum trade length defined. If an ATR-based entry stop is used, that length is added to the wait time before beginning simulations, so that the first entry starts with a clean ATR value. On a sample of around 16000 bars, my tests show that the indicator runs into server errors at lengths of around 290, i.e. having completed ~4,6M simulation loop iterations. That is way too high a length anyways; 100 will usually be amply enough to ring out all the possibilities out of a simulation, and on shorter time frames, 30 can be enough. While making it unduly small will prevent simulations of expressing the market’s potential, the less you use, the faster the indicator will run. The default is 40.
Unrealized P&L base at End of Trade (EOT) : When a simulation ends and the trade is still open, we calculate unrealized P&L from an exit order executed from either the last in-trade stop on the previous bar, or the close of the last bar. You can readily see the impact of this selection on the chart, with the P&L cloud. The default is on the close.
Display : The check box besides the title does nothing.
Show target : Shows a green line displaying the trade’s target expressed as a multiple of X, i.e. the amplitude of the entry stop. I call this value “X” and use it as a unit to express profit and loss on a trade (some call it “R”). The line is highlighted for trades where the close reached the target during the trade, whether the trade ended in profit or loss. This is also where you specify the multiple of X you wish to use in calculating targets. The multiple is used even if targets are not displayed.
Show P&L Cloud : The cloud allows traders to see right away the profitable areas of the chart. The only line printed with the cloud is the “end of trade line” (EOT). The EOT line is the only way one can see the level where a trade ended on the chart (in the Data Window you can see it as the “Exit Fill” value). The EOT level for the trade determines if the trade ended in a profit or a loss. Its value represents one of the following:
- fill from order executed at close of bar where stop is breached during trade (which produces “Realized P/L”),
- simulation of a fill pseudo-fill at the user-defined EOT level (last close or stop level) if the trade runs its course through MaxL bars without getting stopped (producing Unrealized P/L).
The EOT line and the cloud fill print in green when the trade’s outcome is profitable and in red when it is not. If the trade was closed after breaching the stop, the line appears brighter.
Show Opportunity&Risk Cloud : Displays the maximum opportunity/risk that was present during the trade, i.e. the maximum and minimum prices reached.
Background Color Scheme : Allows you to choose between 3 different color schemes for the background gradients, to accommodate different types of chart background/candles. Select “None” if you don’t want a background.
Background source : Determines what value will be used to generate the different intensities of the gradient. You can choose trade length (brighter is shorter), Trade P&L (brighter is higher) or the number of times the target was reached during simulation (brighter is higher). The default is Trade Length.
Entry strat : The check box besides the title does nothing. The default strat is All bars, meaning a trade will be simulated from all bars not excluded by the filters where a MaxL bars future exists. For fun, I’ve included a pseudo-random entry strat (an indirect way of changing the seed is to vary the starting date of the simulation).
Show Filter State : Displays areas where the combination of filters you have selected are allowing entries. Filtering occurs as per your selection(s), whether the state is displayed or not. The effect of multiple selections is additive. The filters are:
1. Bar direction: Longs will only be entered if close>open and vice versa.
2. Rising Volume: Applies to both long and shorts.
3. Rising/falling MA of the length you choose over the number of bars you choose.
4. Custom indicator: You can feed your own filtering signal through this from another indicator. It must produce a signal of 1 to allow long entries and 0 to allow shorts.
Show Entry Stops :
1. Multiple of user-defined length ATR.
2. Fixed percentage.
3. Fixed value.
All entry stops are calculated using the entry fill price as a reference. The fill price is calculated from the current bar’s open, to which slippage is added if configured. This simulates the case where the strategy issued the entry signal on the previous bar for it to be executed at the next bar’s open.
The entry stop remains active until the in-trade stop becomes the more aggressive of the two stops. From then on, the entry stop will be ignored, unless a bar close breaches the in-trade stop, in which case the stop will be reset with a new entry stop and the process repeats.
Show In-trade stops : Displays in bright red the selected in-trade stop (be sure to read the note in this section about them).
1. ATR multiple: added/subtracted from the average of the two previous bars minimum/maximum of open/close.
2. A trailing stop with a deviation expressed as a multiple of entry stop (X).
3. A fixed percentage trailing stop.
Trailing stops deviations are measured from the highest/lowest high/low reached during the trade.
Note: There is a twist with the in-trade stops. It’s that for any given bar, its in-trade stop can hold multiple values, as each successive pass of the advancing simulation loops goes over it from a different entry points. What is printed is the stop from the loop that ended on that bar, which may have nothing to do with other instances of the trade’s in-trade stop for the same bar when visited from other starting points in previous simulations. There is just no practical way to print all stop values that were used for any given bar. While the printed entry stops are the actual ones used on each bar, the in-trade stops shown are merely the last instance used among many.
Include Slippage : if checked, slippage will be added/subtracted from order price to yield the fill price. Slippage is in percentage. If you choose to include slippage in the simulations, remember to adjust it by considering the liquidity of the markets and the time frame you’ll be analyzing.
Include Fees : if checked, fees will be subtracted/added to both realized an unrealized trade profits/losses. Fees are in percentage. The default fees work well for crypto markets but will need adjusting for others—especially in Forex. Remember to modify them accordingly as they can have a major impact on results. Both fees and slippage are included to remind us of their importance, even if the global numbers produced by the indicator are not representative of a real trading scenario composed of sequential trades.
Date Range filtering : the usual. Just note that the checkbox has to be selected for date filtering to activate.
DATA WINDOW
Most of the information produced by this indicator is made available in the Data Window, which you bring up by using the icon below the Watchlist and Alerts buttons at the right of the TV UI. Here’s what’s there.
Some of the information presented in the Data Window is standard trade data; other values are not so standard; e. g. the notions of managed opportunity and risk and Target:Stop Travel ratio. The interplay between all the values provided by Great Expectations is inherently complex, even for a static set of entry/filter/exit strats. During the constant updating which the habitual process of progressive refinement in building strategies that is the lot of strategy modelers entails, another level of complexity is no doubt added to the analysis of this indicator’s values. While I don’t want to sound like Wolfram presenting A New Kind of Science , I do believe that if you are a serious strategy modeler and spend the time required to get used to using all the information this indicator makes available, you may find it useful.
Trade Information
Entry Order : This is the open of the bar where simulation starts. We suppose that an entry signal was generated at the previous bar.
Entry Fill (including slip.) : The actual entry price, including slippage. This is the base price from which other values will be calculated.
Exit Order : When a stop is breached, an exit order is executed from the close of the bar that breached the stop. While there is no “In-trade stop” value included in the Data Window (other than the End of trade Stop previously discussed), this “Exit Order” value is how we can know the level where the trade was stopped during the simulation. The “Trade Length” value will then show the bar where the stop was breached.
Exit Fill (including slip.) : When the exit order is simulated, slippage is added to the order level to create the fill.
Chart: Target : This is the target calculated at the beginning of the simulation. This value also appear on the chart in teal. It is controlled by the multiple of X defined under the “Show Target” checkbox in the Inputs.
Chart: Entry Stop : This value also appears on the chart (the red dots under points where a trade was simulated). Its value is controlled by the Entry Strat chosen in the Inputs.
X (% Fill, including Fees) and X (currency) : This is the stop’s amplitude (Entry Fill – Entry Stop) + Fees. It represents the risk incurred upon entry and will be used to express P&L. We will show R expressed in both a percentage of the Entry Fill level (this value), and currency (the next value). This value represents the risk in the risk:reward ratio and is considered to be a unit of 1 so that RR can be expressed as a single value (i.e. “2” actually meaning “1:2”).
Trade Length : If trade was stopped, it’s the number of bars elapsed until then. The trade is then considered “Closed”. If the trade ends without being stopped (there is no profit-taking strat implemented, so the stop is the only exit strat), then the trade is “Open”, the length is MaxL and it will show in orange. Otherwise the value will print in green/red to reflect if the trade is winning/losing.
P&L (X) : The P&L of the trade, expressed as a multiple of X, which takes into account fees paid at entry and exit. Given our default target setting at 2 units of “X”, a trade that closes at its target will have produced a P&L of +2.0, i.e. twice the value of X (not counting fees paid at exit ). A trade that gets stopped late 50% further that the entry stop’s level will produce a P&L of -1.5X.
P&L (currency, including Fees) : same value as above, but expressed in currency.
Target first reached at bar : If price closed above the target during the trade (even if it occurs after the trade was stopped), this will show when. This value will be used in calculating our TST ratio.
Times Stop/Target reached in sim. : Includes all occurrences during the complete simulation loop.
Opportunity (X) : The highest/lowest price reached during a simulation, i.e. the maximum opportunity encountered, whether the trade was previously stopped or not, expressed as a multiple of X.
Risk (X) : The lowest/highest price reached during a simulation, i.e. the maximum risk encountered, whether the trade was previously stopped or not, expressed as a multiple of X.
Risk:Opportunity : The greater this ratio, the greater Opportunity is, compared to Risk.
Managed Opportunity (%) : The portion of Opportunity that was captured by the highest/low stop position, even if it occurred after a previous stop closed the trade.
Managed Risk (%) : The portion of risk that was protected by the lowest/highest stop position, even if it occurred after a previous stop closed the trade. When this value is greater than 100%, it means the trade’s stop is protecting more than the maximum risk, which is frequent. You will, however, never see close to those values for the Managed Opportunity value, since the stop would have to be higher than the Maximum opportunity. It is much easier to alleviate the risk than it is to lock in profits.
Managed Risk:Opportunity : The ratio of the two preceding values.
Managed Opp. vs. Risk : The Managed Opportunity minus the Managed Risk. When it is negative, which is most often is, it means your strat is protecting a greater portion of the risk than it captures opportunity.
Global Numbers
Win Rate(%) : Percentage of winning trades over all entries. Open trades are considered winning if their last stop/close (as per user selection) locks in profits.
Avg X%, Avg X (currency) : Averages of previously described values:.
Avg Profitability/Trade (APPT) : This measures expectation using: Average Profitability Per Trade = (Probability of Win × Average Win) − (Probability of Loss × Average Loss) . It quantifies the average expectation/trade, which RR alone can’t do, as the probabilities of each outcome (win/lose) must also be used to calculate expectancy. The APPT combine the RR with the win rate to yield the true expectancy of a strategy. In my usual way of expressing risk with X, APPT is the equivalent of the average P&L per trade expressed in X. An APPT of -1.5 means that we lose on average 1.5X/trade.
Equity (X), Equity (currency) : The cumulative result of all trade outcomes, expressed as a multiple of X. Multiplied by the Average X in currency, this yields the Equity in currency.
Risk:Opportunity, Managed Risk:Opportunity, Managed Opp. vs. Risk : The global values of the ones previously described.
Avg Trade Length (TL) : One of the most important values derived by going through all the simulations. Again, it is composed of either the length of stopped trades, or MaxL when the trade isn’t stopped (open). This value can help systems modelers shape the characteristics of the components they use to build their strategies.
Avg Closed Win TL and Avg Closed Lose TL : The average lengths of winning/losing trades that were stopped.
Target reached? Avg bars to Stop and Target reached? Avg bars to Target : For the trades where the target was reached at some point in the simulation, the number of bars to the first point where the stop was breached and where the target was reached, respectively. These two values are used to calculate the next value.
TST (Target:Stop Travel Ratio) : This tracks the ratio between the two preceding values (Bars to first stop/Bars to first target), but only for trades where the target was reached somewhere in the loop. A ratio of 2 means targets are reached twice as fast as stops.
The next values of this section are counts or percentages and are self-explanatory.
Chart Plots
Contains chart plots of values already describes.
NOTES
Optimization/Overfitting: There is a fine line between optimizing and overfitting. Tools like this indicator can lead unsuspecting modelers down a path of overfitting that often turns strategies into over-specialized beasts that do not perform elegantly when confronted to the real-world. Proven testing strategies like walk forward analysis will go a long way in helping modelers alleviate this risk.
Input tuning: Because the results generated by the indicator will vary with the parameters used in the active entry, filtering and exit strats, it’s important to realize that although it may be fun at first, just slapping the default settings on a chart and time frame will not yield optimal nor reliable results. While using ATR as often as possible (as I do in this indicator) is a good way to make strat parametrization adaptable, it is not a foolproof solution.
There is no data for the last MaxL bars of the chart, since not enough trade future has elapsed to run a simulation from MaxL bars back.
Modifying the code: I have tried to structure the code modularly, even if that entails a larger code base, so that you can adapt it to your needs. I’ve included a few token components in each of the placeholders designed for entry strategies, filters, entry stops and in-trade stops. This will hopefully make it easier to add your own. In the same spirit, I have also commented liberally.
You will find in the code many instances of standard trade management tasks that can be lifted to code TV strategies where, as I do in mine, you manage everything yourself and don’t rely on built-in Pine strategy functions to act on your trades.
Enjoy!
THANKS
To @scarf who showed me how plotchar() could be used to plot values without ruining scale.
To @glaz for the suggestion to include a Chandelier stop strat; I will.
To @simpelyfe for the idea of using an indicator input for the filters (if some day TV lets us use more than one, it will be useful in other modules of the indicator).
To @RicardoSantos for the random generator used in the random entry strat.
To all scripters publishing open source on TradingView; their code is the best way to learn.
To my trading buddies Irving and Bruno; who showed me way back how pro traders get it done.
SFC Indicator_2507使用說明書 / User Manual
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概述 / Overview
繁體中文：
SFC Indicator 是一個多功能技術分析指標，整合了布林通道、移動平均線、真實波幅均值(ATR)、箱型理論以及 Bollinger Bandit 策略。此指標專為 TradingView 平台設計，提供全面的交易信號與風險管理工具。
English:
SFC Indicator is a comprehensive technical analysis indicator that integrates Bollinger Bands, Moving Averages, Average True Range (ATR), Box Theory, and Bollinger Bandit strategy. Designed for the TradingView platform, it provides comprehensive trading signals and risk management tools.
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主要功能 / Key Features
1. 布林通道 (Bollinger Bands)
繁體中文：
- 支援雙標準差設定 (σ1, σ2)
- 預設週期：21
- 提供上軌、中軌、下軌顯示
- 自動生成買賣信號
English:
- Supports dual standard deviation settings (σ1, σ2)
- Default period: 21
- Displays upper, middle, and lower bands
- Automatic buy/sell signal generation
2. 移動平均線 (Moving Averages)
繁體中文：
- 三條可配置的移動平均線：快線(10)、中線(20)、慢線(60)
- 支援 SMA 和 EMA 切換
- 自定義顏色設定
English:
- Three configurable moving averages: Fast (10), Medium (20), Slow (60)
- Supports SMA and EMA switching
- Customizable color settings
3. 真實波幅均值 (Average True Range - ATR)
繁體中文：
- 預設週期：14
- 支援多種平滑方式：RMA、SMA、EMA、WMA
- 動態止損計算
- 風險管理工具
English:
- Default period: 14
- Multiple smoothing methods: RMA, SMA, EMA, WMA
- Dynamic stop-loss calculation
- Risk management tool
4. 箱型理論 (Box Theory)
繁體中文：
- 自動識別價格區間
- 視覺化箱型範圍
- 突破信號提示
English:
- Automatic price range identification
- Visual box range display
- Breakout signal alerts
5. Bollinger Bandit 策略
繁體中文：
- 動態追蹤止損
- ROC (變動率) 確認
- 自適應平倉機制
English:
- Dynamic trailing stop
- ROC (Rate of Change) confirmation
- Adaptive position closing mechanism
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參數設定 / Parameter Settings
布林通道設定 / Bollinger Bands Settings
- 布林通道均線週期 BB Period: 21 (計算週期)
- 標準差 σ1: 1 (第一標準差倍數)
- 標準差 σ2: 2 (第二標準差倍數)
移動平均線設定 / Moving Average Settings
- 快線 Fast MA: 10 (短期移動平均)
- 中線 Medium MA: 20 (中期移動平均)
- 慢線 Slow MA: 60 (長期移動平均)
- 使用 EMA: false (切換至指數移動平均)
ATR 設定 / ATR Settings
- 週期 Period: 14 (ATR 計算週期)
- 平滑方式 Smoothing: RMA (平滑算法選擇)
Bollinger Bandit 設定 / Bollinger Bandit Settings
- 布林通道週期 BB Period: 50 (Bandit 策略布林週期)
- 初始平倉均線週期 Initial Exit MA: 50 (初始止損均線週期)
- ROC 週期 ROC Period: 30 (變動率計算週期)
箱型理論設定 / Box Theory Settings
- 箱型週期 Box Period: 21 (價格區間計算週期)
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交易信號說明 / Trading Signals
布林通道信號 / Bollinger Bands Signals
繁體中文：
- 做多信號：價格連續 X 根 K 棒低於下軌後，突破下軌向上
- 做空信號：價格連續 X 根 K 棒高於上軌後，跌破上軌向下
- 止盈目標：上軌 (Profit1) 和上 2 軌 (Profit2) 或下軌和下 2 軌
- 止損點：下 2 軌減去 ATR 值或上 2 軌加上 ATR 值
English:
- Buy Signal: Price crosses above lower band after staying below for X consecutive bars
- Sell Signal: Price crosses below upper band after staying above for X consecutive bars
- Take Profit: Upper band (Profit1) and upper 2σ band (Profit2) or lower equivalents
- Stop Loss: Lower 2σ band minus ATR value or upper 2σ band plus ATR value
Bollinger Bandit 信號 / Bollinger Bandit Signals
繁體中文：
- 做多條件：ROC > 0 且價格突破 Bandit 上軌
- 做空條件：ROC < 0 且價格跌破 Bandit 下軌
- 動態止損：使用自適應移動平均線作為追蹤止損
English:
- Buy Condition: ROC > 0 and price breaks above Bandit upper band
- Sell Condition: ROC < 0 and price breaks below Bandit lower band
- Dynamic Stop: Uses adaptive moving average as trailing stop
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視覺元素說明 / Visual Elements
圖表元素 / Chart Elements
- 綠色三角形 ▲：做多信號 (Buy Signal)
- 紅色倒三角形 ▼：做空信號 (Sell Signal)
- 橘色線：動態止損均線 (Dynamic Stop Loss MA)
- 藍色圓點：ROC > 0 (向上動能 / Upward Momentum)
- 橘色圓點：ROC < 0 (向下動能 / Downward Momentum)
資訊面板 / Information Panel
繁體中文：
右上角顯示即時交易資訊，包含：
- 當前交易方向 (做多/做空/空手)
- 進場價格 (Entry)
- 止損價格 (Loss)
- 止盈目標 1 和 2 (Profit1, Profit2)
- Bandit 上下軌價格 (Upper, Lower)
- 動態止損線數值 (Dynamic)
English:
Top-right panel displays real-time trading information:
- Current position (Long/Short/Flat)
- Entry price
- Stop-loss price
- Take-profit targets 1 and 2
- Bandit upper/lower band prices
- Dynamic stop-loss line value
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使用建議 / Usage Recommendations
最佳實踐 / Best Practices
繁體中文：
1. 多時間框架確認：建議在更高時間框架確認趨勢方向
2. 風險管理：嚴格遵守止損設定，建議單筆風險不超過資金的2%
3. 信號過濾：結合ROC指標過濾假信號
4. 市場環境：在趨勢明確的市場中效果較佳
5. 回測驗證：使用前請先進行充分的歷史回測
English:
1. Multi-timeframe Confirmation: Confirm trend direction on higher timeframes
2. Risk Management: Strictly follow stop-loss settings, limit single trade risk to 2% of capital
3. Signal Filtering: Use ROC indicator to filter false signals
4. Market Conditions: Works best in trending markets
5. Backtesting: Conduct thorough historical backtesting before use
適用市場 / Suitable Markets
- 股票 Stock Markets
- 外匯 Forex Markets
- 期貨 Futures Markets
- 加密貨幣 Cryptocurrency Markets
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警示設定 / Alert Settings
繁體中文：
指標內建兩種警示功能：
- 做多信號警示：「做多訊號，來也!!!」
- 做空信號警示：「做空訊號，來也!!!」
可在 TradingView 中設定價格警示，當信號出現時自動通知。
English:
Built-in alert functions:
- Buy signal alert: "做多訊號，來也!!!" (Buy Signal Alert!)
- Sell signal alert: "做空訊號，來也!!!" (Sell Signal Alert!)
Set up price alerts in TradingView for automatic notifications when signals occur.
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技術資訊 / Technical Information
指標作者 / Indicator Author: @lincheng_SFC
版本 / Version: Pine Script v6
授權 / License: Mozilla Public License 2.0
發布平台 / Platform: TradingView
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免責聲明 / Disclaimer
繁體中文：
本指標僅供教育和分析用途，不構成投資建議。交易涉及風險，過往表現不代表未來結果。請在使用前進行充分測試，並根據個人風險承受能力謹慎投資。
English:
This indicator is for educational and analytical purposes only and does not constitute investment advice. Trading involves risks, and past performance does not guarantee future results. Please conduct thorough testing before use and invest carefully according to your risk tolerance.
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聯絡資訊 / Contact Information
作者 Author: @lincheng_SFC
如有問題或建議，歡迎在TradingView上聯繫
For questions or suggestions, feel free to contact on TradingView
Machine Learning: Donchian DCA Grid Strategy [YinYangAlgorithms]This strategy uses a Machine Learning approach on the Donchian Channels with a DCA and Grid purchase/sell Strategy. Not only that, but it uses a custom Bollinger calculation to determine its Basis which is used as a mild sell location. This strategy is a pure DCA strategy in the sense that no shorts are used and theoretically it can be used in webhooks on most exchanges as it’s only using Spot Orders. The idea behind this strategy is we utilize both the Highest Highs and Lowest Lows within a Machine Learning standpoint to create Buy and Sell zones. We then fraction these zones off into pieces to create Grids. This allows us to ‘micro’ purchase as it enters these zones and likewise ‘micro’ sell as it goes up into the upper (sell) zones.
You have the option to set how many grids are used, by default we use 100 with max 1000. These grids can be ‘stacked’ together if a single bar is to go through multiple at the same time. For instance, if a bar goes through 30 grids in one bar, it will have a buy/sell power of 30x. Stacking Grid Buy and (sometimes) Sells is a very crucial part of this strategy that allows it to purchase multitudes during crashes and capitalize on sales during massive pumps.
With the grids, you’ll notice there is a middle line within the upper and lower part that makes the grid. As a Purchase Type within our Settings this is identified as ‘Middle of Zone Purchase Amount In USDT’. The middle of the grid may act as the strongest grid location (aside from maybe the bottom). Therefore there is a specific purchase amount for this Grid location.
This DCA Strategy also features two other purchase methods. Most importantly is its ‘Purchase More’ type. Essentially it will attempt to purchase when the Highest High or Lowest Low moves outside of the Outer band. For instance, the Lowest Low becomes Lower or the Higher High becomes Higher. When this happens may be a good time to buy as it is featuring a new High or Low over an extended period.
The last but not least Purchase type within this Strategy is what we call a ‘Strong Buy’. The reason for this is its verified by the following:
The outer bounds have been pushed (what causes a ‘Purchase More’)
The Price has crossed over the EMA 21
It has been verified through MACD, RSI or MACD Historical (Delta) using Regular and Hidden Divergence (Note, only 1 of these verifications is required and it can be any).
By default we don’t have Purchase Amount for ‘Strong Buy’ set, but that doesn’t mean it can’t be viable, it simply means we have only seen a few pairs where it actually proved more profitable allocating money there rather than just increasing the purchase amount for ‘Purchase More’ or ‘Grids’.
Now that you understand where we BUY, we should discuss when we SELL.
This Strategy features 3 crucial sell locations, and we will discuss each individually as they are very important.
1. ‘Sell Some At’: Here there are 4 different options, by default its set to ‘Both’ but you can change it around if you want. Your options are:
‘Both’ - You will sell some at both locations. The amount sold is the % used at ‘Sell Some %’.
‘Basis Line’ - You will sell some when the price crosses over the Basis Line. The amount sold is the % used at ‘Sell Some %’.
‘Percent’ - You will sell some when the Close is >= X% between the Lower Inner and Upper Inner Zone.
‘None’ - This simply means don’t ever Sell Some.
2. Sell Grids. Sell Grids are exactly like purchase grids and feature the same amount of grids. You also have the ability to ‘Stack Grid Sells’, which basically means if a bar moves multiple grids, it will stack the amount % wise you will sell, rather than just selling the default amount. Sell Grids use a DCA logic but for selling, which we deem may help adjust risk/reward ratio for selling, especially if there is slow but consistent bullish movement. It causes these grids to constantly push up and therefore when the close is greater than them, accrue more profit.
3. Take Profit. Take profit occurs when the close first goes above the Take Profit location (Teal Line) and then Closes below it. When Take Profit occurs, ALL POSITIONS WILL BE SOLD. What may happen is the price enters the Sell Grid, doesn’t go all the way to the top ‘Exiting it’ and then crashes back down and closes below the Take Profit. Take Profit is a strong location which generally represents a strong profit location, and that a strong momentum has changed which may cause the price to revert back to the buy grid zone.
Keep in mind, if you have (by default) ‘Only Sell If Profit’ toggled, all sell locations will only create sell orders when it is profitable to do so. Just cause it may be a good time to sell, doesn’t mean based on your DCA it is. In our opinion, only selling when it is profitable to do so is a key part of the DCA purchase strategy.
You likewise have the ability to ‘Only Buy If Lower than DCA’, which is likewise by default. These two help keep the Yin and Yang by balancing each other out where you’re only purchasing and selling when it makes logical sense too, even if that involves ignoring a signal and waiting for a better opportunity.
Tutorial:
Like most of our Strategies, we try to capitalize on lower Time Frames, generally the 15 minutes so we may find optimal entry and exit locations while still maintaining a strong correlation to trend patterns.
First off, let’s discuss examples of how this Strategy works prior to applying Machine Learning (enabled by default).
In this example above we have disabled the showing of ‘Potential Buy and Sell Signals’ so as to declutter the example. In here you can see where actual trades had gone through for both buying and selling and get an idea of how the strategy works. We also have disabled Machine Learning for this example so you can see the hard lines created by the Donchian Channel. You can also see how the Basis line ‘white line’ may act as a good location to ‘Sell Some’ and that it moves quite irregularly compared to the Donchian Channel. This is due to the fact that it is based on two custom Bollinger Bands to create the basis line.
Here we zoomed out even further and moved back a bit to where there were dense clusters of buy and sell orders. Sometimes when the price is rather volatile you’ll see it ‘Ping Pong’ back and forth between the buy and sell zones quite quickly. This may be very good for your trades and profit as a whole, especially if ‘Only Buy If Lower Than DCA’ and ‘Only Sell If Profit’ are both enabled; as these toggles will ensure you are:
Always lowering your Average when buying
Always making profit when selling
By default 8% commission is added to the Strategy as well, to simulate the cost effects of if these trades were taking place on an actual exchange.
In this example we also turned on the visuals for our ‘Purchase More’ (orange line) and ‘Take Profit’ (teal line) locations. These are crucial locations. The Purchase More makes purchases when the bottom of the grid has been moved (may dictate strong price movement has occurred and may be potential for correction). Our Take Profit may help secure profit when a momentum change is happening and all of the Sell Grids weren’t able to be used.
In the example above we’ve enabled Buy and Sell Signals so that you can see where the Take Profit and Purchase More signals have occurred. The white circle demonstrates that not all of the Position Size was sold within the Sell Grids, and therefore it was ALL CLOSED when the price closed below the Take Profit Line (Teal).
Then, when the bottom of the Donchian Channel was pushed further down due to the close (within the yellow circle), a Purchase More Signal was triggered.
When the close keeps pushing the bottom of the Buy Grid lower, it can cause multiple Purchase More Signals to occur. This is normal and also a crucial part of this strategy to help lower your DCA. Please note, the Purchase More won’t trigger a Buy if the Close is greater than the DCA and you have ‘Only Purchase If Lower Than DCA’ activated.
By turning on Machine Learning (default settings) the Buy and Sell Grid Zones are smoothed out more. It may cause it to look quite a bit different. Machine Learning although it looks much worse, may help increase the profit this Strategy can produce. Previous results DO NOT mean future results, but in this example, prior to turning on Machine Learning it had produced 37% Profit in ~5 months and with Machine Learning activated it is now up to 57% Profit in ~5 months.
Machine Learning causes the Strategy to focus less on Grids and more on Purchase More when it comes to getting its entries. However, if you likewise attempt to focus on Purchase More within non Machine Learning, the locations are different and therefore the results may not be as profitable.
PLEASE NOTE:
By default this strategy uses 1,000,000 as its initial capital. The amount it purchases in its Settings is relevant to this Initial capital. Considering this is a DCA Strategy, we only want to ‘Micro’ Buy and ‘Micro’ Sell whenever conditions are met.
Therefore, if you increase the Initial Capital, you’ll likewise want to increase the Purchase Amounts within the Settings and Vice Versa. For instance, if you wish to set the Initial Capital to 10,000, you should likewise can the amounts in the Settings to 1% of what they are to account for this.
We may change the Purchase Amounts to be based on %’s in a later update if it is requested.
We will conclude this Tutorial here, hopefully you can see how a DCA Grid Purchase Model applied to Machine Learning Donchian Channels may be useful for making strategic purchases in low and high zones.
Settings:
Display Data:
Show Potential Buy Locations: These locations are where 'Potentially' orders can be placed. Placement of orders is dependant on if you have 'Only Buy If Lower Than DCA' toggled and the Price is lower than DCA. It also is effected by if you actually have any money left to purchase with; you can't buy if you have no money left!
Show Potential Sell Locations: These locations are where 'Potentially' orders will be sold. If 'Only Sell If Profit' is toggled, the sell will only happen if you'll make profit from it!
Show Grid Locations: Displaying won't affect your trades but it can be useful to see where trades will be placed, as well as which have gone through and which are left to be purchased. Max 100 Grids, but visuals will only be shown if its 20 or less.
Purchase Settings:
Only Buy if its lower than DCA: Generally speaking, we want to lower our Average, and therefore it makes sense to only buy when the close is lower than our current DCA and a Purchase Condition is met.
Compound Purchases: Compounding Purchases means reinvesting profit back into your trades right away. It drastically increases profits, but it also increases risk too. It will adjust your Purchase Amounts for the Purchase Type you have set at the same % rate of strategy initial_capital to the amounts you have set.
Adjust Purchase Amount Ratio to Maintain Risk level: By adjusting purchase levels we generally help maintain a safe risk level. Basically we generally want to reserve X amount of % for each purchase type being used and relocate money when there is too much in one type. This helps balance out purchase amounts and ensure the types selected have a correct ratio to ensure they can place the right amount of orders.
Stack Grid Buys: Stacking Buy Grids is when the Close crosses multiple Buy Grids within the same bar. Should we still only purchase the value of 1 Buy Grid OR stack the grid buys based on how many buy grids it went through.
Purchase Type: Where do you want to make Purchases? We recommend lowering your risk by combining All purchase types, but you may also customize your trading strategy however you wish.
Strong Buy Purchase Amount In USDT: How much do you want to purchase when the 'Strong Buy' signal appears? This signal only occurs after it has at least entered the Buy Zone and there have been other verifications saying it's now a good time to buy. Our Strong Buy Signal is a very strong indicator that a large price movement towards the Sell Zone will likely occur. It almost always results in it leaving the Buy Zone and usually will go to at least the White Basis line where you can 'Sell Some'.
Buy More Purchase Amount In USDT: How much should you purchase when the 'Purchase More' signal appears? This 'Purchase More' signal occurs when the lowest level of the Buy Zone moves lower. This is a great time to buy as you're buying the dip and generally there is a correction that will allow you to 'Sell Some' for some profit.
Amount of Grid Buy and Sells: How many Grid Purchases do you want to make? We recommend having it at the max of 10, as it will essentially get you a better Average Purchase Price, but you may adjust it to whatever you wish. This amount also only matters if your Purchase Type above incorporates Grid Purchases. Max 100 Grids, but visuals will only be shown if it's 20 or less.
Each Grid Purchase Amount In USDT: How much should you purchase after closing under a grid location? Keep in mind, if you have 10 grids and it goes through each, it will be this amount * 10. Grid purchasing is a great way to get a good entry, lower risk and also lower your average.
Middle Of Zone Purchase Amount In USDT: The Middle Of Zone is the strongest grid location within the Buy Zone. This is why we have a unique Purchase Amount for this Grid specifically. Please note you need to have 'Middle of Zone is a Grid' enabled for this Purchase Amount to be used.
Sell:
Only Sell if its Profit: There is a chance that during a dump, all your grid buys when through, and a few Purchase More Signals have appeared. You likely got a good entry. A Strong Buy may also appear before it starts to pump to the Sell Zone. The issue that may occur is your Average Purchase Price is greater than the 'Sell Some' price and/or the Grids in the Sell Zone and/or the Strong Sell Signal. When this happens, you can either take a loss and sell it, or you can hold on to it and wait for more purchase signals to therefore lower your average more so you can take profit at the next sell location. Please backtest this yourself within our YinYang Purchase Strategy on the pair and timeframe you are wanting to trade on. Please also note, that previous results will not always reflect future results. Please assess the risk yourself. Don't trade what you can't afford to lose. Sometimes it is better to strategically take a loss and continue on making profit than to stay in a bad trade for a long period of time.
Stack Grid Sells: Stacking Sell Grids is when the Close crosses multiple Sell Grids within the same bar. Should we still only sell the value of 1 Sell Grid OR stack the grid sells based on how many sell grids it went through.
Stop Loss Type: This is when the Close has pushed the Bottom of the Buy Grid More. Do we Stop Loss or Purchase More?? By default we recommend you stay true to the DCA part of this strategy by Purchasing More, but this is up to you.
Sell Some At: Where if selected should we 'Sell Some', this may be an important way to sell a little bit at a good time before the price may correct. Also, we don't want to sell too much incase it doesn't correct though, so its a 'Sell Some' location. Basis Line refers to our Moving Basis Line created from 2 Bollinger Bands and Percent refers to a Percent difference between the Lower Inner and Upper Inner bands.
Sell Some At Percent Amount: This refers to how much % between the Lower Inner and Upper Inner bands we should well at if we chose to 'Sell Some'.
Sell Some Min %: This refers to the Minimum amount between the Lower Inner band and Close that qualifies a 'Sell Some'. This acts as a failsafe so we don't 'Sell Some' for too little.
Sell % At Strong Sell Signal: How much do we sell at the 'Strong Sell' Signal? It may act as a strong location to sell, but likewise Grid Sells could be better.
Grid and Donchian Settings:
Donchian Channel Length: How far back are we looking back to determine our Donchian Channel.
Extra Outer Buy Width %: How much extra should we push the Outer Buy (Low) Width by?
Extra Inner Buy Width %: How much extra should we push the Inner Buy (Low) Width by?
Extra Inner Sell Width %: How much extra should we push the Inner Sell (High) Width by?
Extra Outer Sell Width %: How much extra should we push the Outer Sell (High) Width by?
Machine Learning:
Rationalized Source Type: Donchians usually use High/Low. What Source is our Rationalized Source using?
Machine Learning Type: Are we using a Simple ML Average, KNN Mean Average, KNN Exponential Average or None?
Machine Learning Length: How far back is our Machine Learning going to keep data for.
k-Nearest Neighbour (KNN) Length: How many k-Nearest Neighbours will we account for?
Fast ML Data Length: What is our Fast ML Length?? This is used with our Slow Length to create our KNN Distance.
Slow ML Data Length: What is our Slow ML Length?? This is used with our Fast Length to create our KNN Distance.
If you have any questions, comments, ideas or concerns please don't hesitate to contact us.
HAPPY TRADING!
GKD-C Adaptive-Lookback Phase Change Index [Loxx]Giga Kaleidoscope GKD-C Adaptive-Lookback Phase Change Index is a Confirmation module included in Loxx's "Giga Kaleidoscope Modularized Trading System".
█ GKD-C Adaptive-Lookback Phase Change Index
What is the Phase Change Index?
The Phase Change Index (PCI) is a technical indicator that has gained popularity among traders in recent years. It is used to identify market phases and make profitable trades based on momentum and price data. The PCI was developed by M.H. Pee and first introduced in the Stocks & Commodities magazine in 2004.
The PCI is calculated using the 35-day momentum and the 35-day price channel index (PCI). The momentum is the difference between the current day's close and the close 35 days ago, while the PCI measures the distance between the highest high and lowest low over a period of 35 days. By combining these two indicators, traders can identify six possible market phases, each with its own trading strategy.
The formula for calculating the Phase Change Index (PCI) is as follows:
PCI = 100 * (C - L) / (H - L)
Where:
- C is the closing price of the current day
- L is the lowest low over a period of 35 days
- H is the highest high over a period of 35 days
The formula for calculating momentum is as follows:
Momentum = C - Cn
Where:
- C is the closing price of the current day
- Cn is the closing price n days ago, where n = 35 in this case.
The first two phases are characterized by negative momentum, with phase one having a low PCI value (less than 20) and phase two having a high PCI value (greater than 80). In these phases, traders should enter short positions. The next two phases have positive momentum, with phase three having a low PCI value and phase four having a high PCI value. In these phases, traders should enter long positions.
The final two phases are characterized by neutral momentum, with phase five having a low PCI value and phase six having a high PCI value. In these phases, traders should maintain their previous positions until there is a clear signal to enter or exit.
Traders can also use other technical indicators in conjunction with the PCI to confirm signals or filter out false signals. For example, some traders use moving averages or trendlines to confirm trend direction before entering a trade based on the PCI.
In conclusion, the Phase Change Index is a powerful technical indicator that can help traders identify market phases and make profitable trades. By combining momentum and price data, traders can enter long or short positions based on the six possible market phases. Backtesting results have shown that the PCI is robust across parameters, markets, and years. However, it is important to use proper risk management and not rely solely on past profitability when making trading decisions.
What is the Jurik Filter?
The Jurik Filter is a technical analysis tool that is used to filter out market noise and identify trends in financial markets. It was developed by Mark Jurik in the 1990s and is based on a non-linear smoothing algorithm that provides a more accurate representation of price movements.
Traditional moving averages, such as the Simple Moving Average ( SMA ) or Exponential Moving Average ( EMA ), are linear filters that produce a lag between price and the moving average line. This can cause false signals during periods of market volatility , which can result in losses for traders and investors.
The Jurik Filter is designed to address this issue by incorporating a damping factor into the smoothing algorithm. This damping factor adjusts the filter's responsiveness to the changes in price, allowing it to filter out market noise without overshooting price peaks and valleys.
The Jurik Filter is calculated using a mathematical formula that takes into account the current and past prices of an asset, as well as the volatility of the market. This formula incorporates the damping factor and produces a smoother price curve than traditional moving average filters.
One of the advantages of the Jurik Filter is its ability to adjust to changing market conditions. The damping factor can be adjusted to suit different securities and time frames, making it a versatile tool for traders and investors.
Traders and investors often use the Jurik Filter in conjunction with other technical analysis tools, such as the MACD or RSI , to confirm or complement their trading strategies. By filtering out market noise and identifying trends in the financial markets, the Jurik Filter can help improve the accuracy of trading signals and reduce the risks of false signals during periods of market volatility .
Overall, the Jurik Filter is a powerful technical analysis tool that can help traders and investors make more informed decisions about buying and selling securities. By providing a smoother price curve and reducing false signals, it can help improve trading performance and reduce risk in volatile markets.
What is the Adaptive Lookback Period?
The adaptive lookback period is a technique used in technical analysis to adjust the period of an indicator based on changes in market conditions. This technique is particularly useful in volatile or rapidly changing markets where a fixed period may not be optimal for detecting trends or signals.
The concept of the adaptive lookback period is relatively simple. By adjusting the lookback period based on changes in market conditions, traders can more accurately identify trends and signals. This can help traders to enter and exit trades at the right time and improve the profitability of their trading strategies.
The adaptive lookback period works by identifying potential swing points in the market. Once these points are identified, the lookback period is calculated based on the number of swings and a speed parameter. The swing count parameter determines the number of swings that must occur before the lookback period is adjusted. The speed parameter controls the rate at which the lookback period is adjusted, with higher values indicating a more rapid adjustment.
The adaptive lookback period can be applied to a wide range of technical indicators, including moving averages, oscillators, and trendlines. By adjusting the period of these indicators based on changes in market conditions, traders can reduce the impact of noise and false signals, leading to more profitable trades.
In summary, the adaptive lookback period is a powerful technique for traders and analysts looking to optimize their technical indicators. By adjusting the period based on changes in market conditions, traders can more accurately identify trends and signals, leading to more profitable trades. While there are various ways to implement the adaptive lookback period, the basic concept remains the same, and traders can adapt and customize the technique to suit their individual needs and trading styles.
What is the Adaptive-Lookback Phase Change Index?
The combination of adaptive lookback and Jurik filtering is an effective technique used in technical analysis to filter out market noise and improve the accuracy of trading signals. When applied to the Phase Change Index (PCI) indicator, the adaptive lookback period can be used to adjust the period of the indicator based on changes in market conditions. Jurik filtering can then be used to filter out market noise and improve the accuracy of the signals produced by the PCI indicator.
The adaptive lookback period is particularly useful in volatile or rapidly changing markets where a fixed period may not be optimal for detecting trends or signals. By adjusting the lookback period based on changes in market conditions, traders can more accurately identify trends and signals, leading to more profitable trades.
Jurik filtering is a more advanced filtering technique that uses a combination of smoothing and phase shift to produce a more accurate signal. This technique is particularly useful in filtering out market noise and improving the accuracy of trading signals. Jurik filtering can be applied to various indicators, including moving averages, oscillators, and trendlines.
Overall, the combination of adaptive lookback and Jurik filtering is a powerful technique used in technical analysis to filter out market noise and improve the accuracy of trading signals. When applied to the Phase Change Index (PCI) indicator, this technique is particularly effective in identifying trend changes and producing more accurate signals for entry and exit points in trading strategies.
Keep in mind, this is an inverse indicator meaning that above the middle-line/signal is short, below is long.
Additional Features
This indicator allows you to select from 33 source types. They are as follows:
Close
Open
High
Low
Median
Typical
Weighted
Average
Average Median Body
Trend Biased
Trend Biased (Extreme)
HA Close
HA Open
HA High
HA Low
HA Median
HA Typical
HA Weighted
HA Average
HA Average Median Body
HA Trend Biased
HA Trend Biased (Extreme)
HAB Close
HAB Open
HAB High
HAB Low
HAB Median
HAB Typical
HAB Weighted
HAB Average
HAB Average Median Body
HAB Trend Biased
HAB Trend Biased (Extreme)
What are Heiken Ashi "better" candles?
Heiken Ashi "better" candles are a modified version of the standard Heiken Ashi candles, which are a popular charting technique used in technical analysis. Heiken Ashi candles help traders identify trends and potential reversal points by smoothing out price data and reducing market noise. The "better formula" was proposed by Sebastian Schmidt in an article published by BNP Paribas in Warrants & Zertifikate, a German magazine, in August 2004. The aim of this formula is to further improve the smoothing of the Heiken Ashi chart and enhance its effectiveness in identifying trends and reversals.
Standard Heiken Ashi candles are calculated using the following formulas:
Heiken Ashi Close = (Open + High + Low + Close) / 4
Heiken Ashi Open = (Previous Heiken Ashi Open + Previous Heiken Ashi Close) / 2
Heiken Ashi High = Max (High, Heiken Ashi Open, Heiken Ashi Close)
Heiken Ashi Low = Min (Low, Heiken Ashi Open, Heiken Ashi Close)
The "better formula" modifies the standard Heiken Ashi calculation by incorporating additional smoothing, which can help reduce noise and make it easier to identify trends and reversals. The modified formulas for Heiken Ashi "better" candles are as follows:
Better Heiken Ashi Close = (Open + High + Low + Close) / 4
Better Heiken Ashi Open = (Previous Better Heiken Ashi Open + Previous Better Heiken Ashi Close) / 2
Better Heiken Ashi High = Max (High, Better Heiken Ashi Open, Better Heiken Ashi Close)
Better Heiken Ashi Low = Min (Low, Better Heiken Ashi Open, Better Heiken Ashi Close)
Smoothing Factor = 2 / (N + 1), where N is the chosen period for smoothing
Smoothed Better Heiken Ashi Open = (Better Heiken Ashi Open * Smoothing Factor) + (Previous Smoothed Better Heiken Ashi Open * (1 - Smoothing Factor))
Smoothed Better Heiken Ashi Close = (Better Heiken Ashi Close * Smoothing Factor) + (Previous Smoothed Better Heiken Ashi Close * (1 - Smoothing Factor))
The smoothed Better Heiken Ashi Open and Close values are then used to calculate the smoothed Better Heiken Ashi High and Low values, resulting in "better" candles that provide a clearer representation of the market trend and potential reversal points.
It's important to note that, like any other technical analysis tool, Heiken Ashi "better" candles are not foolproof and should be used in conjunction with other indicators and analysis techniques to make well-informed trading decisions.
Heiken Ashi "better" candles, as mentioned previously, provide a clearer representation of market trends and potential reversal points by reducing noise and smoothing out price data. When using these candles in conjunction with other technical analysis tools and indicators, traders can gain valuable insights into market behavior and make more informed decisions.
To effectively use Heiken Ashi "better" candles in your trading strategy, consider the following tips:
Trend Identification: Heiken Ashi "better" candles can help you identify the prevailing trend in the market. When the majority of the candles are green (or another color, depending on your chart settings) and there are no or few lower wicks, it may indicate a strong uptrend. Conversely, when the majority of the candles are red (or another color) and there are no or few upper wicks, it may signal a strong downtrend.
Trend Reversals: Look for potential trend reversals when a change in the color of the candles occurs, especially when accompanied by longer wicks. For example, if a green candle with a long lower wick is followed by a red candle, it could indicate a bearish reversal. Similarly, a red candle with a long upper wick followed by a green candle may suggest a bullish reversal.
Support and Resistance: You can use Heiken Ashi "better" candles to identify potential support and resistance levels. When the candles are consistently moving in one direction and then suddenly change color with longer wicks, it could indicate the presence of a support or resistance level.
Stop-Loss and Take-Profit: Using Heiken Ashi "better" candles can help you manage risk by determining optimal stop-loss and take-profit levels. For instance, you can place your stop-loss below the low of the most recent green candle in an uptrend or above the high of the most recent red candle in a downtrend.
Confirming Signals: Heiken Ashi "better" candles should be used in conjunction with other technical indicators, such as moving averages, oscillators, or chart patterns, to confirm signals and improve the accuracy of your analysis.
In this implementation, you have the choice of AMA, KAMA, or T3 smoothing. These are as follows:
Kaufman Adaptive Moving Average (KAMA)
The Kaufman Adaptive Moving Average (KAMA) is a type of adaptive moving average used in technical analysis to smooth out price fluctuations and identify trends. The KAMA adjusts its smoothing factor based on the market's volatility, making it more responsive in volatile markets and smoother in calm markets. The KAMA is calculated using three different efficiency ratios that determine the appropriate smoothing factor for the current market conditions. These ratios are based on the noise level of the market, the speed at which the market is moving, and the length of the moving average. The KAMA is a popular choice among traders who prefer to use adaptive indicators to identify trends and potential reversals.
Adaptive Moving Average
The Adaptive Moving Average (AMA) is a type of moving average that adjusts its sensitivity to price movements based on market conditions. It uses a ratio between the current price and the highest and lowest prices over a certain lookback period to determine its level of smoothing. The AMA can help reduce lag and increase responsiveness to changes in trend direction, making it useful for traders who want to follow trends while avoiding false signals. The AMA is calculated by multiplying a smoothing constant with the difference between the current price and the previous AMA value, then adding the result to the previous AMA value.
T3
The T3 moving average is a type of technical indicator used in financial analysis to identify trends in price movements. It is similar to the Exponential Moving Average (EMA) and the Double Exponential Moving Average (DEMA), but uses a different smoothing algorithm.
The T3 moving average is calculated using a series of exponential moving averages that are designed to filter out noise and smooth the data. The resulting smoothed data is then weighted with a non-linear function to produce a final output that is more responsive to changes in trend direction.
The T3 moving average can be customized by adjusting the length of the moving average, as well as the weighting function used to smooth the data. It is commonly used in conjunction with other technical indicators as part of a larger trading strategy.
█ Giga Kaleidoscope Modularized Trading System
Core components of an NNFX algorithmic trading strategy
The NNFX algorithm is built on the principles of trend, momentum, and volatility. There are six core components in the NNFX trading algorithm:
1. Volatility - price volatility; e.g., Average True Range, True Range Double, Close-to-Close, etc.
2. Baseline - a moving average to identify price trend
3. Confirmation 1 - a technical indicator used to identify trends
4. Confirmation 2 - a technical indicator used to identify trends
5. Continuation - a technical indicator used to identify trends
6. Volatility/Volume - a technical indicator used to identify volatility/volume breakouts/breakdown
7. Exit - a technical indicator used to determine when a trend is exhausted
What is Volatility in the NNFX trading system?
In the NNFX (No Nonsense Forex) trading system, ATR (Average True Range) is typically used to measure the volatility of an asset. It is used as a part of the system to help determine the appropriate stop loss and take profit levels for a trade. ATR is calculated by taking the average of the true range values over a specified period.
True range is calculated as the maximum of the following values:
-Current high minus the current low
-Absolute value of the current high minus the previous close
-Absolute value of the current low minus the previous close
ATR is a dynamic indicator that changes with changes in volatility. As volatility increases, the value of ATR increases, and as volatility decreases, the value of ATR decreases. By using ATR in NNFX system, traders can adjust their stop loss and take profit levels according to the volatility of the asset being traded. This helps to ensure that the trade is given enough room to move, while also minimizing potential losses.
Other types of volatility include True Range Double (TRD), Close-to-Close, and Garman-Klass
What is a Baseline indicator?
The baseline is essentially a moving average, and is used to determine the overall direction of the market.
The baseline in the NNFX system is used to filter out trades that are not in line with the long-term trend of the market. The baseline is plotted on the chart along with other indicators, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR).
Trades are only taken when the price is in the same direction as the baseline. For example, if the baseline is sloping upwards, only long trades are taken, and if the baseline is sloping downwards, only short trades are taken. This approach helps to ensure that trades are in line with the overall trend of the market, and reduces the risk of entering trades that are likely to fail.
By using a baseline in the NNFX system, traders can have a clear reference point for determining the overall trend of the market, and can make more informed trading decisions. The baseline helps to filter out noise and false signals, and ensures that trades are taken in the direction of the long-term trend.
What is a Confirmation indicator?
Confirmation indicators are technical indicators that are used to confirm the signals generated by primary indicators. Primary indicators are the core indicators used in the NNFX system, such as the Average True Range (ATR), the Moving Average (MA), and the Relative Strength Index (RSI).
The purpose of the confirmation indicators is to reduce false signals and improve the accuracy of the trading system. They are designed to confirm the signals generated by the primary indicators by providing additional information about the strength and direction of the trend.
Some examples of confirmation indicators that may be used in the NNFX system include the Bollinger Bands, the MACD (Moving Average Convergence Divergence), and the MACD Oscillator. These indicators can provide information about the volatility, momentum, and trend strength of the market, and can be used to confirm the signals generated by the primary indicators.
In the NNFX system, confirmation indicators are used in combination with primary indicators and other filters to create a trading system that is robust and reliable. By using multiple indicators to confirm trading signals, the system aims to reduce the risk of false signals and improve the overall profitability of the trades.
What is a Continuation indicator?
In the NNFX (No Nonsense Forex) trading system, a continuation indicator is a technical indicator that is used to confirm a current trend and predict that the trend is likely to continue in the same direction. A continuation indicator is typically used in conjunction with other indicators in the system, such as a baseline indicator, to provide a comprehensive trading strategy.
What is a Volatility/Volume indicator?
Volume indicators, such as the On Balance Volume (OBV), the Chaikin Money Flow (CMF), or the Volume Price Trend (VPT), are used to measure the amount of buying and selling activity in a market. They are based on the trading volume of the market, and can provide information about the strength of the trend. In the NNFX system, volume indicators are used to confirm trading signals generated by the Moving Average and the Relative Strength Index. Volatility indicators include Average Direction Index, Waddah Attar, and Volatility Ratio. In the NNFX trading system, volatility is a proxy for volume and vice versa.
By using volume indicators as confirmation tools, the NNFX trading system aims to reduce the risk of false signals and improve the overall profitability of trades. These indicators can provide additional information about the market that is not captured by the primary indicators, and can help traders to make more informed trading decisions. In addition, volume indicators can be used to identify potential changes in market trends and to confirm the strength of price movements.
What is an Exit indicator?
The exit indicator is used in conjunction with other indicators in the system, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR), to provide a comprehensive trading strategy.
The exit indicator in the NNFX system can be any technical indicator that is deemed effective at identifying optimal exit points. Examples of exit indicators that are commonly used include the Parabolic SAR, the Average Directional Index (ADX), and the Chandelier Exit.
The purpose of the exit indicator is to identify when a trend is likely to reverse or when the market conditions have changed, signaling the need to exit a trade. By using an exit indicator, traders can manage their risk and prevent significant losses.
In the NNFX system, the exit indicator is used in conjunction with a stop loss and a take profit order to maximize profits and minimize losses. The stop loss order is used to limit the amount of loss that can be incurred if the trade goes against the trader, while the take profit order is used to lock in profits when the trade is moving in the trader's favor.
Overall, the use of an exit indicator in the NNFX trading system is an important component of a comprehensive trading strategy. It allows traders to manage their risk effectively and improve the profitability of their trades by exiting at the right time.
How does Loxx's GKD (Giga Kaleidoscope Modularized Trading System) implement the NNFX algorithm outlined above?
Loxx's GKD v1.0 system has five types of modules (indicators/strategies). These modules are:
1. GKD-BT - Backtesting module (Volatility, Number 1 in the NNFX algorithm)
2. GKD-B - Baseline module (Baseline and Volatility/Volume, Numbers 1 and 2 in the NNFX algorithm)
3. GKD-C - Confirmation 1/2 and Continuation module (Confirmation 1/2 and Continuation, Numbers 3, 4, and 5 in the NNFX algorithm)
4. GKD-V - Volatility/Volume module (Confirmation 1/2, Number 6 in the NNFX algorithm)
5. GKD-E - Exit module (Exit, Number 7 in the NNFX algorithm)
(additional module types will added in future releases)
Each module interacts with every module by passing data between modules. Data is passed between each module as described below:
GKD-B => GKD-V => GKD-C(1) => GKD-C(2) => GKD-C(Continuation) => GKD-E => GKD-BT
That is, the Baseline indicator passes its data to Volatility/Volume. The Volatility/Volume indicator passes its values to the Confirmation 1 indicator. The Confirmation 1 indicator passes its values to the Confirmation 2 indicator. The Confirmation 2 indicator passes its values to the Continuation indicator. The Continuation indicator passes its values to the Exit indicator, and finally, the Exit indicator passes its values to the Backtest strategy.
This chaining of indicators requires that each module conform to Loxx's GKD protocol, therefore allowing for the testing of every possible combination of technical indicators that make up the six components of the NNFX algorithm.
What does the application of the GKD trading system look like?
Example trading system:
Backtest: Strategy with 1-3 take profits, trailing stop loss, multiple types of PnL volatility, and 2 backtesting styles
Baseline: Hull Moving Average
Volatility/Volume: Hurst Exponent
Confirmation 1: Adaptive-Lookback Phase Change Index as shown on the chart above
Confirmation 2: Williams Percent Range
Continuation: Fisher Transform
Exit: Rex Oscillator
Each GKD indicator is denoted with a module identifier of either: GKD-BT, GKD-B, GKD-C, GKD-V, or GKD-E. This allows traders to understand to which module each indicator belongs and where each indicator fits into the GKD protocol chain.
Giga Kaleidoscope Modularized Trading System Signals (based on the NNFX algorithm)
Standard Entry
1. GKD-C Confirmation 1 Signal
2. GKD-B Baseline agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume agrees
Baseline Entry
1. GKD-B Baseline signal
2. GKD-C Confirmation 1 agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume agrees
6. GKD-C Confirmation 1 signal was less than 7 candles prior
Volatility/Volume Entry
1. GKD-V Volatility/Volume signal
2. GKD-C Confirmation 1 agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 2 agrees
5. GKD-B Baseline agrees
6. GKD-C Confirmation 1 signal was less than 7 candles prior
Continuation Entry
1. Standard Entry, Baseline Entry, or Pullback; entry triggered previously
2. GKD-B Baseline hasn't crossed since entry signal trigger
3. GKD-C Confirmation Continuation Indicator signals
4. GKD-C Confirmation 1 agrees
5. GKD-B Baseline agrees
6. GKD-C Confirmation 2 agrees
1-Candle Rule Standard Entry
1. GKD-C Confirmation 1 signal
2. GKD-B Baseline agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
Next Candle:
1. Price retraced (Long: close < close or Short: close > close )
2. GKD-B Baseline agrees
3. GKD-C Confirmation 1 agrees
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume agrees
1-Candle Rule Baseline Entry
1. GKD-B Baseline signal
2. GKD-C Confirmation 1 agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 1 signal was less than 7 candles prior
Next Candle:
1. Price retraced (Long: close < close or Short: close > close )
2. GKD-B Baseline agrees
3. GKD-C Confirmation 1 agrees
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume Agrees
1-Candle Rule Volatility/Volume Entry
1. GKD-V Volatility/Volume signal
2. GKD-C Confirmation 1 agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 1 signal was less than 7 candles prior
Next Candle:
1. Price retraced (Long: close < close or Short: close > close)
2. GKD-B Volatility/Volume agrees
3. GKD-C Confirmation 1 agrees
4. GKD-C Confirmation 2 agrees
5. GKD-B Baseline agrees
PullBack Entry
1. GKD-B Baseline signal
2. GKD-C Confirmation 1 agrees
3. Price is beyond 1.0x Volatility of Baseline
Next Candle:
1. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
2. GKD-C Confirmation 1 agrees
3. GKD-C Confirmation 2 agrees
4. GKD-V Volatility/Volume Agrees
]█ Setting up the GKD
The GKD system involves chaining indicators together. These are the steps to set this up.
Use a GKD-C indicator alone on a chart
1. Inside the GKD-C indicator, change the "Confirmation Type" setting to "Solo Confirmation Simple"
Use a GKD-V indicator alone on a chart
**nothing, it's already useable on the chart without any settings changes
Use a GKD-B indicator alone on a chart
**nothing, it's already useable on the chart without any settings changes
Baseline (Baseline, Backtest)
1. Import the GKD-B Baseline into the GKD-BT Backtest: "Input into Volatility/Volume or Backtest (Baseline testing)"
2. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "Baseline"
Volatility/Volume (Volatility/Volume, Backte st)
1. Inside the GKD-V indicator, change the "Testing Type" setting to "Solo"
2. Inside the GKD-V indicator, change the "Signal Type" setting to "Crossing" (neither traditional nor both can be backtested)
3. Import the GKD-V indicator into the GKD-BT Backtest: "Input into C1 or Backtest"
4. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "Volatility/Volume"
5. Inside the GKD-BT Backtest, a) change the setting "Backtest Type" to "Trading" if using a directional GKD-V indicator; or, b) change the setting "Backtest Type" to "Full" if using a directional or non-directional GKD-V indicator (non-directional GKD-V can only test Longs and Shorts separately)
6. If "Backtest Type" is set to "Full": Inside the GKD-BT Backtest, change the setting "Backtest Side" to "Long" or "Short
7. If "Backtest Type" is set to "Full": To allow the system to open multiple orders at one time so you test all Longs or Shorts, open the GKD-BT Backtest, click the tab "Properties" and then insert a value of something like 10 orders into the "Pyramiding" settings. This will allow 10 orders to be opened at one time which should be enough to catch all possible Longs or Shorts.
Solo Confirmation Simple (Confirmation, Backtest)
1. Inside the GKD-C indicator, change the "Confirmation Type" setting to "Solo Confirmation Simple"
1. Import the GKD-C indicator into the GKD-BT Backtest: "Input into Backtest"
2. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "Solo Confirmation Simple"
Solo Confirmation Complex without Exits (Baseline, Volatility/Volume, Confirmation, Backtest)
1. Inside the GKD-V indicator, change the "Testing Type" setting to "Chained"
2. Import the GKD-B Baseline into the GKD-V indicator: "Input into Volatility/Volume or Backtest (Baseline testing)"
3. Inside the GKD-C indicator, change the "Confirmation Type" setting to "Solo Confirmation Complex"
4. Import the GKD-V indicator into the GKD-C indicator: "Input into C1 or Backtest"
5. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "GKD Full wo/ Exits"
6. Import the GKD-C into the GKD-BT Backtest: "Input into Exit or Backtest"
Solo Confirmation Complex with Exits (Baseline, Volatility/Volume, Confirmation, Exit, Backtest)
1. Inside the GKD-V indicator, change the "Testing Type" setting to "Chained"
2. Import the GKD-B Baseline into the GKD-V indicator: "Input into Volatility/Volume or Backtest (Baseline testing)"
3. Inside the GKD-C indicator, change the "Confirmation Type" setting to "Solo Confirmation Complex"
4. Import the GKD-V indicator into the GKD-C indicator: "Input into C1 or Backtest"
5. Import the GKD-C indicator into the GKD-E indicator: "Input into Exit"
6. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "GKD Full w/ Exits"
7. Import the GKD-E into the GKD-BT Backtest: "Input into Backtest"
Full GKD without Exits (Baseline, Volatility/Volume, Confirmation 1, Confirmation 2, Continuation, Backtest)
1. Inside the GKD-V indicator, change the "Testing Type" setting to "Chained"
2. Import the GKD-B Baseline into the GKD-V indicator: "Input into Volatility/Volume or Backtest (Baseline testing)"
3. Inside the GKD-C 1 indicator, change the "Confirmation Type" setting to "Confirmation 1"
4. Import the GKD-V indicator into the GKD-C 1 indicator: "Input into C1 or Backtest"
5. Inside the GKD-C 2 indicator, change the "Confirmation Type" setting to "Confirmation 2"
6. Import the GKD-C 1 indicator into the GKD-C 2 indicator: "Input into C2"
7. Inside the GKD-C Continuation indicator, change the "Confirmation Type" setting to "Continuation"
8. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "GKD Full wo/ Exits"
9. Import the GKD-E into the GKD-BT Backtest: "Input into Exit or Backtest"
Full GKD with Exits (Baseline, Volatility/Volume, Confirmation 1, Confirmation 2, Continuation, Exit, Backtest)
1. Inside the GKD-V indicator, change the "Testing Type" setting to "Chained"
2. Import the GKD-B Baseline into the GKD-V indicator: "Input into Volatility/Volume or Backtest (Baseline testing)"
3. Inside the GKD-C 1 indicator, change the "Confirmation Type" setting to "Confirmation 1"
4. Import the GKD-V indicator into the GKD-C 1 indicator: "Input into C1 or Backtest"
5. Inside the GKD-C 2 indicator, change the "Confirmation Type" setting to "Confirmation 2"
6. Import the GKD-C 1 indicator into the GKD-C 2 indicator: "Input into C2"
7. Inside the GKD-C Continuation indicator, change the "Confirmation Type" setting to "Continuation"
8. Import the GKD-C Continuation indicator into the GKD-E indicator: "Input into Exit"
9. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "GKD Full w/ Exits"
10. Import the GKD-E into the GKD-BT Backtest: "Input into Backtest"
Baseline + Volatility/Volume (Baseline, Volatility/Volume, Backtest)
1. Inside the GKD-V indicator, change the "Testing Type" setting to "Baseline + Volatility/Volume"
2. Inside the GKD-V indicator, make sure the "Signal Type" setting is set to "Traditional"
3. Import the GKD-B Baseline into the GKD-V indicator: "Input into Volatility/Volume or Backtest (Baseline testing)"
4. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "Baseline + Volatility/Volume"
5. Import the GKD-V into the GKD-BT Backtest: "Input into C1 or Backtest"
6. Inside the GKD-BT Backtest, change the setting "Backtest Type" to "Full". For this backtest, you must test Longs and Shorts separately
7. To allow the system to open multiple orders at one time so you can test all Longs or Shorts, open the GKD-BT Backtest, click the tab "Properties" and then insert a value of something like 10 orders into the "Pyramiding" settings. This will allow 10 orders to be opened at one time which should be enough to catch all possible Longs or Shorts.
Requirements
Inputs
Confirmation 1: GKD-V Volatility / Volume indicator
Confirmation 2: GKD-C Confirmation indicator
Continuation: GKD-C Confirmation indicator
Solo Confirmation Simple: GKD-B Baseline
Solo Confirmation Complex: GKD-V Volatility / Volume indicator
Solo Confirmation Super Complex: GKD-V Volatility / Volume indicator
Stacked 1: None
Stacked 2+: GKD-C, GKD-V, or GKD-B Stacked 1
Outputs
Confirmation 1: GKD-C Confirmation 2 indicator
Confirmation 2: GKD-C Continuation indicator
Continuation: GKD-E Exit indicator
Solo Confirmation Simple: GKD-BT Backtest
Solo Confirmation Complex: GKD-BT Backtest or GKD-E Exit indicator
Solo Confirmation Super Complex: GKD-C Continuation indicator
Stacked 1: GKD-C, GKD-V, or GKD-B Stacked 2+
Stacked 2+: GKD-C, GKD-V, or GKD-B Stacked 2+ or GKD-BT Backtest
Additional features will be added in future releases.
Gap Filling Strategy Gaps are market prices structures that appear frequently in the stock market, and can be detected when the opening price is different from the previous closing price, this is why gaps are also called "opening price jumps". While gaps can occur frequently, some of them are more significant than others, and can be observed when looking at a long term chart.
The following strategy is based on the exploitation of significant gaps occurring during a new session, and posses various options that can return a wide variety of results.
Type Of Gaps And Occurence
I'am not a professional when it comes to gaps, but as you know the stock market close for the day, however it is still possible to place orders, your broker will hold them until the market open back. Once the market reopen the broker execute the pending orders, and when many orders where pending the market register really high volume and the price might differ from the precedent close.
Gaps are generally broken down into four types:
Common : Gaps occurring within a certain price range, mostly occurs during ranging markets.
Break Away : Gaps breaking a support and resistance, making a new higher high/lower low.
Runaway : Gaps occurring within a trend, followed by a continuation of the trend.
Exhaustion : Gaps occurring at the end of a trend, followed by a reversal.
As said before, some gaps are more significant than others, the significance of a gap can be determined by comparing the opening price with the previous high/low price and by looking at volume. Significant up gaps will have an opening price greater than the previous high, while significant down gap will have an opening price lower than the previous low with both high volume accompanying them.
After a gap, when the price go back to the point previous to the gap we say that it has been "filled", this characteristic is what will be exploited in this strategy.
Strategy Rules & Logic
In this strategy, the significance of a gap is determined by the position of the opening price relative to the previous high/low and make sure the bar following the gap don't fill it.
When the setting invert is set to false the strategy interpret the detected gaps as being exhaustion gaps, therefore when an up gap occur a short position is opened, when a down gap occur a long position is opened. When invert is set to true gaps are considered to be runaway or break away gaps, therefore the contrary positions are opened. Positions are exited when the gap has been filled, which in the chart is show'n when the price cross the red level who act as either a take profit (invert = false) or as a stop loss (invert = true).
There are various closing conditions available that the user can select from the "close when" setting.
New Session : This option close all previous positions when the market is in a new session.
New Gap : This option close all previous position when a new gap has been detected.
Reverse Position : This option close all previous position when a contrary position to the current one is opened. This option would reduce the number of trades.
Testing On Some Stocks
The analysis will be tested in different tech stocks with a main TF of 15 minutes with no spread and commissions applied. Default settings will be used. We'll be making our first analysis using AMD, who has recently formed a full reverse HS pattern, where the neckline has been crossed by the price. (by the way i have a bad feeling about it, hey ! feeling filling ! Lame jokes!)
Profit: $ -12.22
Trades: 272
Profitability: 65.07 %
We can see negative results, with an heavily decreasing balance. Using invert would return positive results.
We will now test the strategy on NVDA, the company is one of the biggest when it comes to the Gpu market.
Profit: $ -215.54
Trades: 297
Profitability: 60.27 %
Not better, using invert would of course create better results. Like AMD the balance is heavily decreasing.
Finally we will test the strategy on Seagate technology, a company mostly known for their mechanical hard drives.
Profit: $ -4.32
Trades: 261
Profitability: 65.9 %
Here the balance does not appear so heavily decreasing and even managed to reach back the initial balance before going down again.
Summary
A strategy based on gap filling has been briefly introduced and tested with 3 tech stocks. The results show that using invert option might be better. The advantage of this strategy against ones using technical indicators is that this one does not heavily depend on user settings, which make it way more efficient, this a big advantage of patterns based strategies.
Thx to LucF for helping with the "process_orders_on_close" element, since i had to use closing price i had to remove it tho, was afraid results would differ even more from a more realistic backtest. And thx for those who continuously support me, more cool stuff is coming up.
Thx for reading and i hope you'll have learned something new today !
Strategy Builder Pro [ChartPrime]ChartPrime Strategy Creator Overview
The ChartPrime Strategy Builder offers traders an innovative, structured approach to building and testing strategies. The Strategy Creator allows users to combine, test, and automate complex strategies with many parameters.
Key Features of the ChartPrime Strategy Builder
1. Customizable Buy and Sell Conditions
The Strategy Creator provides flexibility in establishing entry and exit rules, with separate sections for long and short strategies. Traders can combine multiple conditions in each section to fine-tune when positions are opened or closed. For instance, they might choose to only buy when the indicator signals a buy and the Dynamic Reactor (a low lag filter) indicator shows a bullish trend. Users are able to pick, mix and match the following list of features:
Signal Mode: Select the type of assistive signals you are requiring. Provided are both trend following signals with self optimization using backtest results as well as reversal signals, aiming to provide real time tops and bottoms in markets. Both these signal modes can be fine tuned using the tuning input to refine signals to a trader's liking. ChartPrime Trend Signals leverage audio engineering inspired techniques and low-pass filters in order to achieve and attempt to produce lower lag response times and therefore are designed to have a uniqueness when compared to more classical trend following approaches.
The Dynamic Reactor: provides a simple band passing through the chart. This can provide assistance in support and resistance locations as well as identifying the trend direction expressed via green and red colors. Taking a moving average and applying unique adaptivity calculations gives this plot a unique and fast behavior.
Candlestick structures: analyze candlestick formation putting a spin on classical candlestick patterns and provide the most relevant formations on the chart. These are not classical and are filtered by further analyzing market activity. A trader's classic with a spin.
The Prime Trend Assistant: provides a trend following dynamic support and resistance level. This makes it perfect to use in confluence or as a filter for other supporting indicators. This is an adaptive trend following system designed to handle volatility leveraging filter kernels as opposed to low pass filters.
Money Flow: with further filters applied for early response to money flow changes in the market. This can be a great filter in trends.
Oscillator reversals: are built in leveraging an oscillator focusing on market momentum allowing users to enter based on market shifts and trends along with reversals.
Volume-Inspired Signals: determine overbought and oversold conditions, adding another layer of analysis to the oscillator. These appear as orange labels, providing a simple reading into a possible reversal.
The Volume Matrix: is a volume oscillator that shows whether money is flowing into or out of the market. Green suggests an uptrend with buyers in control, while red indicates a majority of sellers. By incorporating smoothed volume analysis, it distinguishes between bullish and bearish volumes, offering an early indication of potential trend reversals.
The True 7: is a middle-ranking system that evaluates the strength of a move and the overall trend, offering a numeric or visual representation of trend strength. It can also indicate when a trend is starting to reverse, providing leading signals for potential market shifts. Rather than using an oscillator, this offers the unique edge of falling into set categories, making understanding it simple. This can be a great confluence point when designing a strategy.
Take profits: These offer real-time suggestions from our algorithm on when it might be a good time to take profit. Using these as part of a strategy allows for great entries at bottoms and tops of trends.
Using features such as the Dynamic reactor have dual purposes. Traders can use this as both a filter and an entry condition. This allows for true interoperability when using the Strategy Builder. The above conditions are duplicated for short entries too allowing for symmetrical trading systems. By disabling all of the entry conditions on either long or short areas of the settings will create a strategy that only takes a single type of position. For example; a trader that just wants to take longs can disable all short options.
2. Layered Entries
Layered entries, a feature to enhance the uniqueness in the tool. It allows traders to average into positions as the market moves, rather than committing all capital at once. This feature is particularly useful for volatile markets where prices may fluctuate substantially. The Strategy Builder lets users adjust the number of layered entries, which can help in managing risk and optimizing entry points as well as the aggressiveness of the safety orders. With each safety order placed the system will automatically and dynamically scale into positions reducing the average entry price and hence dynamically adjust the potential take profits. Due to the potential complexities of exiting during multiple orders, a smart system is employed to automatically take profits on the layered system aiming to take profits at peaks of trends.
Users are able to override this smart TP system at the bottom of the settings instead targeting percentage profits for both short and long positions.
Entries lowering average buy price
The ability to adjust how quickly the system layers into positions can also be adjusted via the layered entries drop down between fast and slow mode where the slow mode will be more cautious when producing new orders.
3. Flexible Take Profit (TP) and Stop Loss (SL) Options
Traders can set their TP and SL levels according to various parameters, including ATR (Average True Range), risk-reward ratio, trailing stops, or specific price changes. If layered entries are active, an automatic TP method is applied by default, though traders can manually specify TP values if they prefer. This setup allows for precise control over trade exits, tailored to the strategy’s risk profile.
Provided options
The ability to use external take profits and stop losses is also provided. By loading an indicator of your choice the plots will be added to the chart. By navigating to the external sources area of the settings, users can select this plot and use it as part of a wider trading system.
Example: Let’s say a user has entries based on the inbuilt trend signals and wishes to exit whenever the RSI crosses above 70, they can add RSI to the chart, select crossing up and enter the value of 70.
4. Integrated Reinvestment for Compounding Gains
The reinvestment option allows traders to reinvest a portion of their gains into future trades, increasing trade size over time and benefiting from compounding. For example, a user might set 30% of each trade's profit to reinvest, with the remaining 70% allocated for risk management or additional safety orders. This approach can enhance long-term growth while balancing risk.
Generally in trading it can be a good approach to take profits so we suggest a healthy balance. This setting is generally best used for slow steady strategies with the long term aim of accumulating as much of the asset as possible.
5. Leverage and Position Sizing
Users can configure leverage and position sizing to simulate varying risk levels and capital allocations. A dashboard on the interface displays margin requirements based on the selected leverage, allowing traders to estimate trade sizes relative to their available capital. Whenever using leverage especially with layered entries it’s important to keep a close eye on the position sizes to avoid potential liquidations.
6. Pre-Configured Strategies for Immediate Testing
For users seeking a starting point, ChartPrime includes a range of preset strategies. These were developed and backtested by ChartPrime’s team. This allows traders to start with a stable base and adapt it to their own preferences. It is vital to understand that historical performance doesn't guarantee future success, and traders should be mindful of overfitting. These pre-built configurations offer a structured way and base to design strategies off of. These are also subject to changing results as new price action arrives and they become outdated. They serve the purpose of simply being example use cases.
7. In-Depth Specific Backtesting Ranges
The Strategy Builder includes backtesting capabilities, providing a clear view of how different setups would have performed over specified time periods. Traders can select date ranges to target specific market conditions, then review results on TradingView to see how their strategies perform across different market trends.
Example Use Case: Developing a Strategy
Consider a trader who is focused on long positions only and prefers a lower-risk strategy (note these tools can be used for all assets; we are using an undisclosed asset as an example). Using the Strategy Builder, they could:
- Disable short conditions.
- Set long entry rules to trigger when both the ChartPrime oscillator and Quantum Reactor indicators show bullish signals.
- Enable layered entries to improve average entry prices by adding to positions during market dips.
- Run a backtest over a two-year period to see historical performance trends, making adjustments as needed.
The backtest will show where entries and exits would have occurred and how layered entries may have impacted profitability.
8. Iterative design
Strategy builders and creating a strategy is often an iterative process. By experimenting and using logic; a trader can arrive at a more sustainable system. Analyzing the shortcomings of your strategy and iteratively designing and filtering them out is the goal. For example; let’s say a strategy has high drawdown, a user would want to tighten stop losses for example to reduce this and find a balance point between optimizing winning trades and reducing the drawdown. When designing a strategy there are generally tradeoffs and optimizing taking into consideration a wide range of factors is key. This also applies to filtering techniques, entries and exits and every variable in the strategy.
Let’s say a strategy was taking too many long positions in a downtrend and after you’ve analyzed the data, you come to the conclusion this needs to be solved. Filtering these using built in trend following tools can be a great approach and refining with logic is a great approach.
The Strategy Builder also takes into consideration those who seek to automate especially via reinvesting and leverage features.
Considerations
The ChartPrime Strategy Builder aims to help traders build clear, rule-based strategies without excessive complexity. As with all backtesting tools, it's crucial to understand that historical performance doesn't guarantee future success, and traders should be mindful of overfitting. This tool offers a structured way to test strategies against various market conditions, helping traders refine their approaches with data-driven insights. Traders should also ensure they enter the correct fees when designing strategies and ensure usage on standard candle types.
Quantify [Entry Model] | FractalystWhat’s the indicator’s purpose and functionality?
Quantify is a machine learning entry model designed to help traders identify high-probability setups to refine their strategies.
➙ Simply pick your bias, select your entry timeframes, and let Quantify handle the rest for you.
Can the indicator be applied to any market approach/trading strategy?
Absolutely, all trading strategies share one fundamental element: Directional Bias
Once you’ve determined the market bias using your own personal approach, whether it’s through technical analysis or fundamental analysis, select the trend direction in the Quantify user inputs.
The algorithm will then adjust its calculations to provide optimal entry levels aligned with your chosen bias. This involves analyzing historical patterns to identify setups with the highest potential expected values, ensuring your setups are aligned with the selected direction.
Can the indicator be used for different timeframes or trading styles?
Yes, regardless of the timeframe you’d like to take your entries, the indicator adapts to your trading style.
Whether you’re a swing trader, scalper, or even a position trader, the algorithm dynamically evaluates market conditions across your chosen timeframe.
How can this indicator help me to refine my trading strategy?
1. Focus on Positive Expected Value
• The indicator evaluates every setup to ensure it has a positive expected value, helping you focus only on trades that statistically favor long-term profitability.
2. Adapt to Market Conditions
• By analyzing real-time market behavior and historical patterns, the algorithm adjusts its calculations to match current conditions, keeping your strategy relevant and adaptable.
3. Eliminate Emotional Bias
• With clear probabilities, expected values, and data-driven insights, the indicator removes guesswork and helps you avoid emotional decisions that can damage your edge.
4. Optimize Entry Levels
• The indicator identifies optimal entry levels based on your selected bias and timeframes, improving robustness in your trades.
5. Enhance Risk Management
• Using tools like the Kelly Criterion, the indicator suggests optimal position sizes and risk levels, ensuring that your strategy maintains consistency and discipline.
6. Avoid Overtrading
• By highlighting only high-potential setups, the indicator keeps you focused on quality over quantity, helping you refine your strategy and avoid unnecessary losses.
How can I get started to use the indicator for my entries?
1. Set Your Market Bias
• Determine whether the market trend is Bullish or Bearish using your own approach.
• Select the corresponding bias in the indicator’s user inputs to align it with your analysis.
2. Choose Your Entry Timeframes
• Specify the timeframes you want to focus on for trade entries.
• The indicator will dynamically analyze these timeframes to provide optimal setups.
3. Let the Algorithm Analyze
• Quantify evaluates historical data and real-time price action to calculate probabilities and expected values.
• It highlights setups with the highest potential based on your selected bias and timeframes.
4. Refine Your Entries
• Use the insights provided—entry levels, probabilities, and risk calculations—to align your trades with a math-driven edge.
• Avoid overtrading by focusing only on setups with positive expected value.
5. Adapt to Market Conditions
• The indicator continuously adapts to real-time market behavior, ensuring its recommendations stay relevant and precise as conditions change.
How does the indicator calculate the current range?
The indicator calculates the current range by analyzing swing points from the very first bar on your charts to the latest available bar it identifies external liquidity levels, also known as BSLQ (buy-side liquidity levels) and SSLQ (sell-side liquidity levels).
What's the purpose of these levels? What are the underlying calculations?
1. Understanding Swing highs and Swing Lows
Swing High: A Swing High is formed when there is a high with 2 lower highs to the left and right.
Swing Low: A Swing Low is formed when there is a low with 2 higher lows to the left and right.
2. Understanding the purpose and the underlying calculations behind Buyside, Sellside and Pivot levels.
3. Identifying Discount and Premium Zones.
4. Importance of Risk-Reward in Premium and Discount Ranges
How does the script calculate probabilities?
The script calculates the probability of each liquidity level individually. Here's the breakdown:
1. Upon the formation of a new range, the script waits for the price to reach and tap into pivot level level. Status: "■" - Inactive
2. Once pivot level is tapped into, the pivot status becomes activated and it waits for either liquidity side to be hit. Status: "▶" - Active
3. If the buyside liquidity is hit, the script adds to the count of successful buyside liquidity occurrences. Similarly, if the sellside is tapped, it records successful sellside liquidity occurrences.
4. Finally, the number of successful occurrences for each side is divided by the overall count individually to calculate the range probabilities.
Note: The calculations are performed independently for each directional range. A range is considered bearish if the previous breakout was through a sellside liquidity. Conversely, a range is considered bullish if the most recent breakout was through a buyside liquidity.
What does the multi-timeframe functionality offer?
You can incorporate up to 4 higher timeframe probabilities directly into the table.
This feature allows you to analyze the probabilities of buyside and sellside liquidity across multiple timeframes, without the need to manually switch between them.
By viewing these higher timeframe probabilities in one place, traders can spot larger market trends and refine their entries and exits with a better understanding of the overall market context.
What are the multi-timeframe underlying calculations?
The script uses the same calculations (mentioned above) and uses security function to request the data such as price levels, bar time, probabilities and booleans from the user-input timeframe.
How does the Indicator Identifies Positive Expected Values?
Quantify instantly calculates whether a trade setup has the potential to generate positive expected value (EV).
To determine a positive EV setup, the indicator uses the formula:
EV = ( P(Win) × R(Win) ) − ( P(Loss) × R(Loss))
where:
- P(Win) is the probability of a winning trade.
- R(Win) is the reward or return for a winning trade, determined by the current risk-to-reward ratio (RR).
- P(Loss) is the probability of a losing trade.
- R(Loss) is the loss incurred per losing trade, typically assumed to be -1.
By calculating these values based on historical data and the current trading setup, the indicator helps you understand whether your trade has a positive expected value.
How can I know that the setup I'm going to trade with has a positive EV?
If the indicator detects that the adjusted pivot and buy/sell side probabilities have generated positive expected value (EV) in historical data, the risk-to-reward (RR) label within the range box will be colored blue and red .
If the setup does not produce positive EV, the RR label will appear gray.
This indicates that even the risk-to-reward ratio is greater than 1:1, the setup is not likely to yield a positive EV because, according to historical data, the number of losses outweighs the number of wins relative to the RR gain per winning trade.
What is the confidence level in the indicator, and how is it determined?
The confidence level in the indicator reflects the reliability of the probabilities calculated based on historical data. It is determined by the sample size of the probabilities used in the calculations. A larger sample size generally increases the confidence level, indicating that the probabilities are more reliable and consistent with past performance.
How does the confidence level affect the risk-to-reward (RR) label?
The confidence level (★) is visually represented alongside the probability label. A higher confidence level indicates that the probabilities used to determine the RR label are based on a larger and more reliable sample size.
How can traders use the confidence level to make better trading decisions?
Traders can use the confidence level to gauge the reliability of the probabilities and expected value (EV) calculations provided by the indicator. A confidence level above 95% is considered statistically significant and indicates that the historical data supporting the probabilities is robust. This high confidence level suggests that the probabilities are reliable and that the indicator’s recommendations are more likely to be accurate.
In data science and statistics, a confidence level above 95% generally means that there is less than a 5% chance that the observed results are due to random variation. This threshold is widely accepted in research and industry as a marker of statistical significance. Studies such as those published in the Journal of Statistical Software and the American Statistical Association support this threshold, emphasizing that a confidence level above 95% provides a strong assurance of data reliability and validity.
Conversely, a confidence level below 95% indicates that the sample size may be insufficient and that the data might be less reliable. In such cases, traders should approach the indicator’s recommendations with caution and consider additional factors or further analysis before making trading decisions.
How does the sample size affect the confidence level, and how does it relate to my TradingView plan?
The sample size for calculating the confidence level is directly influenced by the amount of historical data available on your charts. A larger sample size typically leads to more reliable probabilities and higher confidence levels.
Here’s how the TradingView plans affect your data access:
Essential Plan
The Essential Plan provides basic data access with a limited amount of historical data. This can lead to smaller sample sizes and lower confidence levels, which may weaken the robustness of your probability calculations. Suitable for casual traders who do not require extensive historical analysis.
Plus Plan
The Plus Plan offers more historical data than the Essential Plan, allowing for larger sample sizes and more accurate confidence levels. This enhancement improves the reliability of indicator calculations. This plan is ideal for more active traders looking to refine their strategies with better data.
Premium Plan
The Premium Plan grants access to extensive historical data, enabling the largest sample sizes and the highest confidence levels. This plan provides the most reliable data for accurate calculations, with up to 20,000 historical bars available for analysis. It is designed for serious traders who need comprehensive data for in-depth market analysis.
PRO+ Plans
The PRO+ Plans offer the most extensive historical data, allowing for the largest sample sizes and the highest confidence levels. These plans are tailored for professional traders who require advanced features and significant historical data to support their trading strategies effectively.
For many traders, the Premium Plan offers a good balance of affordability and sufficient sample size for accurate confidence levels.
What is the HTF probability table and how does it work?
The HTF (Higher Time Frame) probability table is a feature that allows you to view buy and sellside probabilities and their status from timeframes higher than your current chart timeframe.
Here’s how it works:
Data Request: The table requests and retrieves data from user-defined higher timeframes (HTFs) that you select.
Probability Display: It displays the buy and sellside probabilities for each of these HTFs, providing insights into the likelihood of price movements based on higher timeframe data.
Detailed Tooltips: The table includes detailed tooltips for each timeframe, offering additional context and explanations to help you understand the data better.
What do the different colors in the HTF probability table indicate?
The colors in the HTF probability table provide visual cues about the expected value (EV) of trading setups based on higher timeframe probabilities:
Blue: Suggests that entering a long position from the HTF user-defined pivot point, targeting buyside liquidity, is likely to result in a positive expected value (EV) based on historical data and sample size.
Red: Indicates that entering a short position from the HTF user-defined pivot point, targeting sellside liquidity, is likely to result in a positive expected value (EV) based on historical data and sample size.
Gray: Shows that neither long nor short trades from the HTF user-defined pivot point are expected to generate positive EV, suggesting that trading these setups may not be favorable.
What machine learning techniques are used in Quantify?
Quantify offers two main machine learning approaches:
1. Adaptive Learning (Fixed Sample Size): The algorithm learns from the entire dataset without resampling, maintaining a stable model that adapts to the latest market conditions.
2. Bootstrap Resampling: This method creates multiple subsets of the historical data, allowing the model to train on varying sample sizes. This technique enhances the robustness of predictions by ensuring that the model is not overfitting to a single dataset.
How does machine learning affect the expected value calculations in Quantify?
Machine learning plays a key role in improving the accuracy of expected value (EV) calculations. By analyzing historical price action, liquidity hits, and market bias patterns, the model continuously adjusts its understanding of risk and reward, allowing the expected value to reflect the most likely market movements. This results in more precise EV predictions, helping traders focus on setups that maximize profitability.
What is the Kelly Criterion, and how does it work in Quantify?
The Kelly Criterion is a mathematical formula used to determine the optimal position size for each trade, maximizing long-term growth while minimizing the risk of large drawdowns. It calculates the percentage of your portfolio to risk on a trade based on the probability of winning and the expected payoff.
Quantify integrates this with user-defined inputs to dynamically calculate the most effective position size in percentage, aligning with the trader’s risk tolerance and desired exposure.
How does Quantify use the Kelly Criterion in practice?
Quantify uses the Kelly Criterion to optimize position sizing based on the following factors:
1. Confidence Level: The model assesses the confidence level in the trade setup based on historical data and sample size. A higher confidence level increases the suggested position size because the trade has a higher probability of success.
2. Max Allowed Drawdown (User-Defined): Traders can set their preferred maximum allowed drawdown, which dictates how much loss is acceptable before reducing position size or stopping trading. Quantify uses this input to ensure that risk exposure aligns with the trader’s risk tolerance.
3. Probabilities: Quantify calculates the probabilities of success for each trade setup. The higher the probability of a successful trade (based on historical price action and liquidity levels), the larger the position size suggested by the Kelly Criterion.
What is a trailing stoploss, and how does it work in Quantify?
A trailing stoploss is a dynamic risk management tool that moves with the price as the market trend continues in the trader’s favor. Unlike a fixed take profit, which stays at a set level, the trailing stoploss automatically adjusts itself as the market moves, locking in profits as the price advances.
In Quantify, the trailing stoploss is enhanced by incorporating market structure liquidity levels (explain above). This ensures that the stoploss adjusts intelligently based on key price levels, allowing the trader to stay in the trade as long as the trend remains intact, while also protecting profits if the market reverses.
Why would a trader prefer a trailing stoploss based on liquidity levels instead of a fixed take-profit level?
Traders who use trailing stoplosses based on liquidity levels prefer this method because:
1. Market-Driven Flexibility: The stoploss follows the market structure rather than being static at a pre-defined level. This means the stoploss is less likely to be hit by small market fluctuations or false reversals. The stoploss remains adaptive, moving as the market moves.
2. Riding the Trend: Traders can capture more profit during a sustained trend because the trailing stop will adjust only when the trend starts to reverse significantly, based on key liquidity levels. This allows them to hold positions longer without prematurely locking in profits.
3. Avoiding Premature Exits: Fixed stoploss levels may exit a trade too early in volatile markets, while liquidity-based trailing stoploss levels respect the natural flow of price action, preventing the trader from exiting too soon during pullbacks or minor retracements.
🎲 Becoming the House: Gaining an Edge Over the Market
In American roulette, the casino has a 5.26% edge due to the presence of the 0 and 00 pockets. On even-money bets, players face a 47.37% chance of winning, while true 50/50 odds would require a 50% chance. This edge—the gap between the payout odds and the true probabilities—ensures that, statistically, the casino will always win over time, even if individual players win occasionally.
From a Trader’s Perspective
In trading, your edge comes from identifying and executing setups with a positive expected value (EV). For example:
• If you identify a setup with a 55.48% chance of winning and a 1:1 risk-to-reward (RR) ratio, your trade has a statistical advantage over a neutral (50/50) probability.
This edge works in your favor when applied consistently across a series of trades, just as the casino’s edge ensures profitability across thousands of spins.
🎰 Applying the Concept to Trading
Like casinos leverage their mathematical edge in games of chance, you can achieve long-term success in trading by focusing on setups with positive EV and managing your trades systematically. Here’s how:
1. Probability Advantage: Prioritize trades where the probability of success (win rate) exceeds the breakeven rate for your chosen risk-to-reward ratio.
• Example: With a 1:1 RR, you need a win rate above 50% to achieve positive EV.
2. Risk-to-Reward Ratio (RR): Even with a win rate below 50%, you can gain an edge by increasing your RR (e.g., a 40% win rate with a 2:1 RR still has positive EV).
3. Consistency and Discipline: Just as casinos profit by sticking to their mathematical advantage over thousands of spins, traders must rely on their edge across many trades, avoiding emotional decisions or overleveraging.
By targeting favorable probabilities and managing trades effectively, you “become the house” in your trading. This approach allows you to leverage statistical advantages to enhance your overall performance and achieve sustainable profitability.
What Makes the Quantify Indicator Original?
1. Data-Driven Edge
Unlike traditional indicators that rely on static formulas, Quantify leverages probability-based analysis and machine learning. It calculates expected value (EV) and confidence levels to help traders identify setups with a true statistical edge.
2. Integration of Market Structure
Quantify uses market structure liquidity levels to dynamically adapt. It identifies key zones like swing highs/lows and liquidity traps, enabling users to align entries and exits with where the market is most likely to react. This bridges the gap between price action analysis and quantitative trading.
3. Sophisticated Risk Management
The Kelly Criterion implementation is unique. Quantify allows traders to input their maximum allowed drawdown, dynamically adjusting risk exposure to maintain optimal position sizing. This ensures risk is scientifically controlled while maximizing potential growth.
4. Multi-Timeframe and Liquidity-Based Trailing Stops
The indicator doesn’t just suggest fixed profit-taking levels. It offers market structure-based trailing stop-loss functionality, letting traders ride trends as long as liquidity and probabilities favor the position, which is rare in most tools.
5. Customizable Bias and Adaptive Learning
• Directional Bias: Traders can set a bullish or bearish bias, and the indicator recalculates probabilities to align with the trader’s market outlook.
• Adaptive Learning: The machine learning model adapts to changes in data (via resampling or bootstrap methods), ensuring that predictions stay relevant in evolving markets.
6. Positive EV Focus
The focus on positive EV setups differentiates it from reactive indicators. It shifts trading from chasing signals to acting on setups that statistically favor profitability, akin to how professional quant funds operate.
7. User Empowerment
Through features like customizable timeframes, real-time probability updates, and visualization tools, Quantify empowers users to make data-informed decisions.
Terms and Conditions | Disclaimer
Our charting tools are provided for informational and educational purposes only and should not be construed as financial, investment, or trading advice. They are not intended to forecast market movements or offer specific recommendations. Users should understand that past performance does not guarantee future results and should not base financial decisions solely on historical data.
Built-in components, features, and functionalities of our charting tools are the intellectual property of @Fractalyst use, reproduction, or distribution of these proprietary elements is prohibited.
By continuing to use our charting tools, the user acknowledges and accepts the Terms and Conditions outlined in this legal disclaimer and agrees to respect our intellectual property rights and comply with all applicable laws and regulations.
GKD-BT Multi-Ticker Baseline Backtest [Loxx]The Giga Kaleidoscope GKD-BT Multi-Ticker Baseline Backtest is a backtesting module included in Loxx's "Giga Kaleidoscope Modularized Trading System."
█ Giga Kaleidoscope GKD-BT Multi-Ticker Baseline Backtest
The Multi-Ticker SCSC Backtest is a Solo Confirmation Super Complex backtest that allows traders to test GKD-B Multi-Ticker Baseline series baselines indicators filtered. The purpose of this backtest is to enable traders to quickly evaluate the viability of a Baseline across hundreds of tickers within 30-60 minutes.
The backtest module supports testing with 1 take profit and 1 stop loss. It also offers the option to limit testing to a specific date range, allowing simulated forward testing using historical data. This backtest module only includes standard long and short signals. Additionally, users can choose to display or hide a trading panel that provides relevant information about the backtest, statistics, and the current trade. Traders can also select a highlighting threshold for Total Percent Wins and Percent Profitable, and Profit Factor.
To use this indicator:
1. Import 1-10 tickers into the GKD-B Multi-Ticker Baseline indicator
2. Import the value "Input into NEW GKD-BT Multi-ticker Backtest" from the GKD-B Multi-Ticker Baseline indicator (Volatility-Adaptive, Stepped, etc.) into the GKD-BT Multi-Ticker Baseline Backtest.
3. Import the same 1-10 tickers from number step 1 above into the GKD-BT Multi-Ticker Baseline Backtest indicator into the text area field "Input Tickers separated by commas".
3. When importing tickers, ensure that you import the same type of tickers for all 1-10 tickers. For example, test only FX or Cryptocurrency or Stocks. Do not combine different tradable asset types.
4. Make sure that your chart is set to a ticker that corresponds to the tradable asset type. For cryptocurrency testing, set the chart to BTCUSDT. For Forex testing, set the chart to EURUSD.
This backtest includes the following metrics:
1. Net profit: Overall profit or loss achieved.
2. Total Closed Trades: Total number of closed trades, both winning and losing.
3. Total Percent Wins: Total wins, whether long or short, for the selected time interval regardless of commissions and other profit-modifying add-ons.
4. Percent Profitable: Total wins, whether long or short, that are also profitable, taking commissions into account.
5. Profit Factor: The ratio of gross profits to gross losses, indicating how much money the strategy made for every unit of money it lost.
6. Average Profit per Trade: The average gain or loss per trade, calculated by dividing the net profit by the total number of closed trades.
7. Average Number of Bars in Trade: The average number of bars that elapsed during trades for all closed trades.
Summary of notable settings:
Input Tickers separated by commas: Allows the user to input tickers separated by commas, specifying the symbols or tickers of financial instruments used in the backtest. The tickers should follow the format "EXCHANGE:TICKER" (e.g., "NASDAQ:AAPL, NYSE:MSFT").
Import GKD-B Baseline: Imports the "GKD-B Multi-Ticker Baseline" indicator.
Initial Capital: Represents the starting account balance for the backtest, denominated in the base currency of the trading account.
Order Size: Determines the quantity of contracts traded in each trade.
Order Type: Specifies the type of order used in the backtest, either "Contracts" or "% Equity."
Commission: Represents the commission per order or transaction cost incurred in each trade.
**the backtest data rendered to the chart above uses $5 commission per trade and 10% equity per trade with $1 million initial capital. Each backtest result for each ticker assumes these same inputs. The results are NOT cumulative, they are separate and isolated per ticker and trading side, long or short**
█ Volatility Types included
The GKD system utilizes volatility-based take profits and stop losses. Each take profit and stop loss is calculated as a multiple of volatility. You can change the values of the multipliers in the settings as well.
This module includes 17 types of volatility:
Close-to-Close
Parkinson
Garman-Klass
Rogers-Satchell
Yang-Zhang
Garman-Klass-Yang-Zhang
Exponential Weighted Moving Average
Standard Deviation of Log Returns
Pseudo GARCH(2,2)
Average True Range
True Range Double
Standard Deviation
Adaptive Deviation
Median Absolute Deviation
Efficiency-Ratio Adaptive ATR
Mean Absolute Deviation
Static Percent
Various volatility estimators and indicators that investors and traders can use to measure the dispersion or volatility of a financial instrument's price. Each estimator has its strengths and weaknesses, and the choice of estimator should depend on the specific needs and circumstances of the user.
Close-to-Close
Close-to-Close volatility is a classic and widely used volatility measure, sometimes referred to as historical volatility.
Volatility is an indicator of the speed of a stock price change. A stock with high volatility is one where the price changes rapidly and with a larger amplitude. The more volatile a stock is, the riskier it is.
Close-to-close historical volatility is calculated using only a stock's closing prices. It is the simplest volatility estimator. However, in many cases, it is not precise enough. Stock prices could jump significantly during a trading session and return to the opening value at the end. That means that a considerable amount of price information is not taken into account by close-to-close volatility.
Despite its drawbacks, Close-to-Close volatility is still useful in cases where the instrument doesn't have intraday prices. For example, mutual funds calculate their net asset values daily or weekly, and thus their prices are not suitable for more sophisticated volatility estimators.
Parkinson
Parkinson volatility is a volatility measure that uses the stock’s high and low price of the day.
The main difference between regular volatility and Parkinson volatility is that the latter uses high and low prices for a day, rather than only the closing price. This is useful as close-to-close prices could show little difference while large price movements could have occurred during the day. Thus, Parkinson's volatility is considered more precise and requires less data for calculation than close-to-close volatility.
One drawback of this estimator is that it doesn't take into account price movements after the market closes. Hence, it systematically undervalues volatility. This drawback is addressed in the Garman-Klass volatility estimator.
Garman-Klass
Garman-Klass is a volatility estimator that incorporates open, low, high, and close prices of a security.
Garman-Klass volatility extends Parkinson's volatility by taking into account the opening and closing prices. As markets are most active during the opening and closing of a trading session, it makes volatility estimation more accurate.
Garman and Klass also assumed that the process of price change follows a continuous diffusion process (Geometric Brownian motion). However, this assumption has several drawbacks. The method is not robust for opening jumps in price and trend movements.
Despite its drawbacks, the Garman-Klass estimator is still more effective than the basic formula since it takes into account not only the price at the beginning and end of the time interval but also intraday price extremes.
Researchers Rogers and Satchell have proposed a more efficient method for assessing historical volatility that takes into account price trends. See Rogers-Satchell Volatility for more detail.
Rogers-Satchell
Rogers-Satchell is an estimator for measuring the volatility of securities with an average return not equal to zero.
Unlike Parkinson and Garman-Klass estimators, Rogers-Satchell incorporates a drift term (mean return not equal to zero). As a result, it provides better volatility estimation when the underlying is trending.
The main disadvantage of this method is that it does not take into account price movements between trading sessions. This leads to an underestimation of volatility since price jumps periodically occur in the market precisely at the moments between sessions.
A more comprehensive estimator that also considers the gaps between sessions was developed based on the Rogers-Satchel formula in the 2000s by Yang-Zhang. See Yang Zhang Volatility for more detail.
Yang-Zhang
Yang Zhang is a historical volatility estimator that handles both opening jumps and the drift and has a minimum estimation error.
Yang-Zhang volatility can be thought of as a combination of the overnight (close-to-open volatility) and a weighted average of the Rogers-Satchell volatility and the day’s open-to-close volatility. It is considered to be 14 times more efficient than the close-to-close estimator.
Garman-Klass-Yang-Zhang
Garman-Klass-Yang-Zhang (GKYZ) volatility estimator incorporates the returns of open, high, low, and closing prices in its calculation.
GKYZ volatility estimator takes into account overnight jumps but not the trend, i.e., it assumes that the underlying asset follows a Geometric Brownian Motion (GBM) process with zero drift. Therefore, the GKYZ volatility estimator tends to overestimate the volatility when the drift is different from zero. However, for a GBM process, this estimator is eight times more efficient than the close-to-close volatility estimator.
Exponential Weighted Moving Average
The Exponentially Weighted Moving Average (EWMA) is a quantitative or statistical measure used to model or describe a time series. The EWMA is widely used in finance, with the main applications being technical analysis and volatility modeling.
The moving average is designed such that older observations are given lower weights. The weights decrease exponentially as the data point gets older – hence the name exponentially weighted.
The only decision a user of the EWMA must make is the parameter lambda. The parameter decides how important the current observation is in the calculation of the EWMA. The higher the value of lambda, the more closely the EWMA tracks the original time series.
Standard Deviation of Log Returns
This is the simplest calculation of volatility. It's the standard deviation of ln(close/close(1)).
Pseudo GARCH(2,2)
This is calculated using a short- and long-run mean of variance multiplied by ?.
avg(var;M) + (1 ? ?) avg(var;N) = 2?var/(M+1-(M-1)L) + 2(1-?)var/(M+1-(M-1)L)
Solving for ? can be done by minimizing the mean squared error of estimation; that is, regressing L^-1var - avg(var; N) against avg(var; M) - avg(var; N) and using the resulting beta estimate as ?.
Average True Range
The average true range (ATR) is a technical analysis indicator, introduced by market technician J. Welles Wilder Jr. in his book New Concepts in Technical Trading Systems, that measures market volatility by decomposing the entire range of an asset price for that period.
The true range indicator is taken as the greatest of the following: current high less the current low; the absolute value of the current high less the previous close; and the absolute value of the current low less the previous close. The ATR is then a moving average, generally using 14 days, of the true ranges.
True Range Double
A special case of ATR that attempts to correct for volatility skew.
Standard Deviation
Standard deviation is a statistic that measures the dispersion of a dataset relative to its mean and is calculated as the square root of the variance. The standard deviation is calculated as the square root of variance by determining each data point's deviation relative to the mean. If the data points are further from the mean, there is a higher deviation within the data set; thus, the more spread out the data, the higher the standard deviation.
Adaptive Deviation
By definition, the Standard Deviation (STD, also represented by the Greek letter sigma ? or the Latin letter s) is a measure that is used to quantify the amount of variation or dispersion of a set of data values. In technical analysis, we usually use it to measure the level of current volatility.
Standard Deviation is based on Simple Moving Average calculation for mean value. This version of standard deviation uses the properties of EMA to calculate what can be called a new type of deviation, and since it is based on EMA, we can call it EMA deviation. Additionally, Perry Kaufman's efficiency ratio is used to make it adaptive (since all EMA type calculations are nearly perfect for adapting).
The difference when compared to the standard is significant--not just because of EMA usage, but the efficiency ratio makes it a "bit more logical" in very volatile market conditions.
Median Absolute Deviation
The median absolute deviation is a measure of statistical dispersion. Moreover, the MAD is a robust statistic, being more resilient to outliers in a data set than the standard deviation. In the standard deviation, the distances from the mean are squared, so large deviations are weighted more heavily, and thus outliers can heavily influence it. In the MAD, the deviations of a small number of outliers are irrelevant.
Because the MAD is a more robust estimator of scale than the sample variance or standard deviation, it works better with distributions without a mean or variance, such as the Cauchy distribution.
For this indicator, a manual recreation of the quantile function in Pine Script is used. This is so users have a full inside view into how this is calculated.
Efficiency-Ratio Adaptive ATR
Average True Range (ATR) is a widely used indicator for many occasions in technical analysis. It is calculated as the RMA of the true range. This version adds a "twist": it uses Perry Kaufman's Efficiency Ratio to calculate adaptive true range.
Mean Absolute Deviation
The mean absolute deviation (MAD) is a measure of variability that indicates the average distance between observations and their mean. MAD uses the original units of the data, which simplifies interpretation. Larger values signify that the data points spread out further from the average. Conversely, lower values correspond to data points bunching closer to it. The mean absolute deviation is also known as the mean deviation and average absolute deviation.
This definition of the mean absolute deviation sounds similar to the standard deviation (SD). While both measure variability, they have different calculations. In recent years, some proponents of MAD have suggested that it replace the SD as the primary measure because it is a simpler concept that better fits real life.
█ Giga Kaleidoscope Modularized Trading System
Core components of an NNFX algorithmic trading strategy
The NNFX algorithm is built on the principles of trend, momentum, and volatility. There are six core components in the NNFX trading algorithm:
1. Volatility - price volatility; e.g., Average True Range, True Range Double, Close-to-Close, etc.
2. Baseline - a moving average to identify price trend
3. Confirmation 1 - a technical indicator used to identify trends
4. Confirmation 2 - a technical indicator used to identify trends
5. Continuation - a technical indicator used to identify trends
6. Volatility/Volume - a technical indicator used to identify volatility/volume breakouts/breakdown
7. Exit - a technical indicator used to determine when a trend is exhausted
8. Metamorphosis - a technical indicator that produces a compound signal from the combination of other GKD indicators*
*(not part of the NNFX algorithm)
What is Volatility in the NNFX trading system?
In the NNFX (No Nonsense Forex) trading system, ATR (Average True Range) is typically used to measure the volatility of an asset. It is used as a part of the system to help determine the appropriate stop loss and take profit levels for a trade. ATR is calculated by taking the average of the true range values over a specified period.
True range is calculated as the maximum of the following values:
-Current high minus the current low
-Absolute value of the current high minus the previous close
-Absolute value of the current low minus the previous close
ATR is a dynamic indicator that changes with changes in volatility. As volatility increases, the value of ATR increases, and as volatility decreases, the value of ATR decreases. By using ATR in NNFX system, traders can adjust their stop loss and take profit levels according to the volatility of the asset being traded. This helps to ensure that the trade is given enough room to move, while also minimizing potential losses.
Other types of volatility include True Range Double (TRD), Close-to-Close, and Garman-Klass
What is a Baseline indicator?
The baseline is essentially a moving average, and is used to determine the overall direction of the market.
The baseline in the NNFX system is used to filter out trades that are not in line with the long-term trend of the market. The baseline is plotted on the chart along with other indicators, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR).
Trades are only taken when the price is in the same direction as the baseline. For example, if the baseline is sloping upwards, only long trades are taken, and if the baseline is sloping downwards, only short trades are taken. This approach helps to ensure that trades are in line with the overall trend of the market, and reduces the risk of entering trades that are likely to fail.
By using a baseline in the NNFX system, traders can have a clear reference point for determining the overall trend of the market, and can make more informed trading decisions. The baseline helps to filter out noise and false signals, and ensures that trades are taken in the direction of the long-term trend.
What is a Confirmation indicator?
Confirmation indicators are technical indicators that are used to confirm the signals generated by primary indicators. Primary indicators are the core indicators used in the NNFX system, such as the Average True Range (ATR), the Moving Average (MA), and the Relative Strength Index (RSI).
The purpose of the confirmation indicators is to reduce false signals and improve the accuracy of the trading system. They are designed to confirm the signals generated by the primary indicators by providing additional information about the strength and direction of the trend.
Some examples of confirmation indicators that may be used in the NNFX system include the Bollinger Bands, the MACD (Moving Average Convergence Divergence), and the MACD Oscillator. These indicators can provide information about the volatility, momentum, and trend strength of the market, and can be used to confirm the signals generated by the primary indicators.
In the NNFX system, confirmation indicators are used in combination with primary indicators and other filters to create a trading system that is robust and reliable. By using multiple indicators to confirm trading signals, the system aims to reduce the risk of false signals and improve the overall profitability of the trades.
What is a Continuation indicator?
In the NNFX (No Nonsense Forex) trading system, a continuation indicator is a technical indicator that is used to confirm a current trend and predict that the trend is likely to continue in the same direction. A continuation indicator is typically used in conjunction with other indicators in the system, such as a baseline indicator, to provide a comprehensive trading strategy.
What is a Volatility/Volume indicator?
Volume indicators, such as the On Balance Volume (OBV), the Chaikin Money Flow (CMF), or the Volume Price Trend (VPT), are used to measure the amount of buying and selling activity in a market. They are based on the trading volume of the market, and can provide information about the strength of the trend. In the NNFX system, volume indicators are used to confirm trading signals generated by the Moving Average and the Relative Strength Index. Volatility indicators include Average Direction Index, Waddah Attar, and Volatility Ratio. In the NNFX trading system, volatility is a proxy for volume and vice versa.
By using volume indicators as confirmation tools, the NNFX trading system aims to reduce the risk of false signals and improve the overall profitability of trades. These indicators can provide additional information about the market that is not captured by the primary indicators, and can help traders to make more informed trading decisions. In addition, volume indicators can be used to identify potential changes in market trends and to confirm the strength of price movements.
What is an Exit indicator?
The exit indicator is used in conjunction with other indicators in the system, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR), to provide a comprehensive trading strategy.
The exit indicator in the NNFX system can be any technical indicator that is deemed effective at identifying optimal exit points. Examples of exit indicators that are commonly used include the Parabolic SAR, and the Average Directional Index (ADX).
The purpose of the exit indicator is to identify when a trend is likely to reverse or when the market conditions have changed, signaling the need to exit a trade. By using an exit indicator, traders can manage their risk and prevent significant losses.
In the NNFX system, the exit indicator is used in conjunction with a stop loss and a take profit order to maximize profits and minimize losses. The stop loss order is used to limit the amount of loss that can be incurred if the trade goes against the trader, while the take profit order is used to lock in profits when the trade is moving in the trader's favor.
Overall, the use of an exit indicator in the NNFX trading system is an important component of a comprehensive trading strategy. It allows traders to manage their risk effectively and improve the profitability of their trades by exiting at the right time.
What is an Metamorphosis indicator?
The concept of a metamorphosis indicator involves the integration of two or more GKD indicators to generate a compound signal. This is achieved by evaluating the accuracy of each indicator and selecting the signal from the indicator with the highest accuracy. As an illustration, let's consider a scenario where we calculate the accuracy of 10 indicators and choose the signal from the indicator that demonstrates the highest accuracy.
The resulting output from the metamorphosis indicator can then be utilized in a GKD-BT backtest by occupying a slot that aligns with the purpose of the metamorphosis indicator. The slot can be a GKD-B, GKD-C, or GKD-E slot, depending on the specific requirements and objectives of the indicator. This allows for seamless integration and utilization of the compound signal within the GKD-BT framework.
How does Loxx's GKD (Giga Kaleidoscope Modularized Trading System) implement the NNFX algorithm outlined above?
Loxx's GKD v2.0 system has five types of modules (indicators/strategies). These modules are:
1. GKD-BT - Backtesting module (Volatility, Number 1 in the NNFX algorithm)
2. GKD-B - Baseline module (Baseline and Volatility/Volume, Numbers 1 and 2 in the NNFX algorithm)
3. GKD-C - Confirmation 1/2 and Continuation module (Confirmation 1/2 and Continuation, Numbers 3, 4, and 5 in the NNFX algorithm)
4. GKD-V - Volatility/Volume module (Confirmation 1/2, Number 6 in the NNFX algorithm)
5. GKD-E - Exit module (Exit, Number 7 in the NNFX algorithm)
6. GKD-M - Metamorphosis module (Metamorphosis, Number 8 in the NNFX algorithm, but not part of the NNFX algorithm)
(additional module types will added in future releases)
Each module interacts with every module by passing data to A backtest module wherein the various components of the GKD system are combined to create a trading signal.
That is, the Baseline indicator passes its data to Volatility/Volume. The Volatility/Volume indicator passes its values to the Confirmation 1 indicator. The Confirmation 1 indicator passes its values to the Confirmation 2 indicator. The Confirmation 2 indicator passes its values to the Continuation indicator. The Continuation indicator passes its values to the Exit indicator, and finally, the Exit indicator passes its values to the Backtest strategy.
This chaining of indicators requires that each module conform to Loxx's GKD protocol, therefore allowing for the testing of every possible combination of technical indicators that make up the six components of the NNFX algorithm.
What does the application of the GKD trading system look like?
Example trading system:
Backtest: Multi-Ticker CC Backtest
Baseline: Hull Moving Average
Volatility/Volume: Hurst Exponent
Confirmation 1: Advance Trend Pressure as shown on the chart above
Confirmation 2: uf2018
Continuation: Coppock Curve
Exit: Rex Oscillator
Metamorphosis: Baseline Optimizer
Each GKD indicator is denoted with a module identifier of either: GKD-BT, GKD-B, GKD-C, GKD-V, GKD-M, or GKD-E. This allows traders to understand to which module each indicator belongs and where each indicator fits into the GKD system.
█ Giga Kaleidoscope Modularized Trading System Signals
Standard Entry
1. GKD-C Confirmation gives signal
2. Baseline agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Volatility/Volume agrees
1-Candle Standard Entry
1a. GKD-C Confirmation gives signal
2a. Baseline agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
Next Candle
1b. Price retraced
2b. Baseline agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Baseline Entry
1. GKD-B Baseline gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Volatility/Volume agrees
7. Confirmation 1 signal was less than 'Maximum Allowable PSBC Bars Back' prior
1-Candle Baseline Entry
1a. GKD-B Baseline gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSBC Bars Back' prior
Next Candle
1b. Price retraced
2b. Baseline agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Volatility/Volume Entry
1. GKD-V Volatility/Volume gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Baseline agrees
7. Confirmation 1 signal was less than 7 candles prior
1-Candle Volatility/Volume Entry
1a. GKD-V Volatility/Volume gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSVVC Bars Back' prior
Next Candle
1b. Price retraced
2b. Volatility/Volume agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Baseline agrees
Confirmation 2 Entry
1. GKD-C Confirmation 2 gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Volatility/Volume agrees
6. Baseline agrees
7. Confirmation 1 signal was less than 7 candles prior
1-Candle Confirmation 2 Entry
1a. GKD-C Confirmation 2 gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSC2C Bars Back' prior
Next Candle
1b. Price retraced
2b. Confirmation 2 agrees
3b. Confirmation 1 agrees
4b. Volatility/Volume agrees
5b. Baseline agrees
PullBack Entry
1a. GKD-B Baseline gives signal
2a. Confirmation 1 agrees
3a. Price is beyond 1.0x Volatility of Baseline
Next Candle
1b. Price inside Goldie Locks Zone Minimum
2b. Price inside Goldie Locks Zone Maximum
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Continuation Entry
1. Standard Entry, 1-Candle Standard Entry, Baseline Entry, 1-Candle Baseline Entry, Volatility/Volume Entry, 1-Candle Volatility/Volume Entry, Confirmation 2 Entry, 1-Candle Confirmation 2 Entry, or Pullback entry triggered previously
2. Baseline hasn't crossed since entry signal trigger
4. Confirmation 1 agrees
5. Baseline agrees
6. Confirmation 2 agrees
GKD-BT Multi-Ticker CC Backtest [Loxx]The Giga Kaleidoscope GKD-BT Multi-Ticker CC Backtest is a backtest module included in Loxx's "Giga Kaleidoscope Modularized Trading System."
█ Giga Kaleidoscope GKD-BT Multi-Ticker CC Backtest
This backtest allows you to test GKD-C Confirmation 1 and GKD-C Confirmation 2 indicators together without the hassle of adding additional confluence indicators. The backtest includes 1 take profit and 1 SL and various types of volatility. The backtest results on the chart are using 10% equity of 1 million total equity and $5 commission per trade.
To use this indicator:
1. Import the value "Input into NEW GKD-BT Backtest" from the GKD-C Confirmation 1 indicator into the GKD-BT Multi-Ticker CC Backtest.
2. Import the value "Input into NEW GKD-BT Backtest" from the GKD-C Confirmation 2 indicator into the GKD-BT Multi-Ticker CC Backtest.
This backtest includes the following metrics:
1. Net profit: Overall profit or loss achieved.
2. Total Closed Trades: Total number of closed trades, both winning and losing.
3. Total Percent Wins: Total wins, whether long or short, for the selected time interval regardless of commissions and other profit-modifying addons.
4. Percent Profitable: Total wins, whether long or short, that are also profitable, taking commissions into account.
5. Profit Factor: The ratio of gross profits to gross losses, indicating how much money the strategy made for every unit of money it lost.
6. Average Profit per Trade: The average gain or loss per trade, calculated by dividing the net profit by the total number of closed trades.
7. Average Number of Bars in Trade: The average number of bars that elapsed during trades for all closed trades.
Summary of notable settings:
Input Tickers separated by commas: Allows the user to input tickers separated by commas, specifying the symbols or tickers of financial instruments used in the backtest. The tickers should follow the format "EXCHANGE:TICKER" (e.g., "NASDAQ:AAPL, NYSE:MSFT").
Import GKD-B Baseline: Imports the "GKD-B Baseline" indicator.
Import GKD-V Volatility/Volume: Imports the "GKD-V Volatility/Volume" indicator.
Import GKD-C Confirmation: Imports the "GKD-C Confirmation" indicator.
Import GKD-C Continuation: Imports the "GKD-C Continuation" indicator.
Initial Capital: Represents the starting account balance for the backtest, denominated in the base currency of the trading account.
Order Size: Determines the quantity of contracts traded in each trade.
Order Type: Specifies the type of order used in the backtest, either "Contracts" or "% Equity."
Commission: Represents the commission per order or transaction cost incurred in each trade.
█ Giga Kaleidoscope Modularized Trading System
Core components of an NNFX algorithmic trading strategy
The NNFX algorithm is built on the principles of trend, momentum, and volatility. There are six core components in the NNFX trading algorithm:
1. Volatility - price volatility; e.g., Average True Range, True Range Double, Close-to-Close, etc.
2. Baseline - a moving average to identify price trend
3. Confirmation 1 - a technical indicator used to identify trends
4. Confirmation 2 - a technical indicator used to identify trends
5. Continuation - a technical indicator used to identify trends
6. Volatility/Volume - a technical indicator used to identify volatility/volume breakouts/breakdown
7. Exit - a technical indicator used to determine when a trend is exhausted
8. Metamorphosis - a technical indicator that produces a compound signal from the combination of other GKD indicators*
*(not part of the NNFX algorithm)
What is Volatility in the NNFX trading system?
In the NNFX (No Nonsense Forex) trading system, ATR (Average True Range) is typically used to measure the volatility of an asset. It is used as a part of the system to help determine the appropriate stop loss and take profit levels for a trade. ATR is calculated by taking the average of the true range values over a specified period.
True range is calculated as the maximum of the following values:
-Current high minus the current low
-Absolute value of the current high minus the previous close
-Absolute value of the current low minus the previous close
ATR is a dynamic indicator that changes with changes in volatility. As volatility increases, the value of ATR increases, and as volatility decreases, the value of ATR decreases. By using ATR in NNFX system, traders can adjust their stop loss and take profit levels according to the volatility of the asset being traded. This helps to ensure that the trade is given enough room to move, while also minimizing potential losses.
Other types of volatility include True Range Double (TRD), Close-to-Close, and Garman-Klass
What is a Baseline indicator?
The baseline is essentially a moving average, and is used to determine the overall direction of the market.
The baseline in the NNFX system is used to filter out trades that are not in line with the long-term trend of the market. The baseline is plotted on the chart along with other indicators, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR).
Trades are only taken when the price is in the same direction as the baseline. For example, if the baseline is sloping upwards, only long trades are taken, and if the baseline is sloping downwards, only short trades are taken. This approach helps to ensure that trades are in line with the overall trend of the market, and reduces the risk of entering trades that are likely to fail.
By using a baseline in the NNFX system, traders can have a clear reference point for determining the overall trend of the market, and can make more informed trading decisions. The baseline helps to filter out noise and false signals, and ensures that trades are taken in the direction of the long-term trend.
What is a Confirmation indicator?
Confirmation indicators are technical indicators that are used to confirm the signals generated by primary indicators. Primary indicators are the core indicators used in the NNFX system, such as the Average True Range (ATR), the Moving Average (MA), and the Relative Strength Index (RSI).
The purpose of the confirmation indicators is to reduce false signals and improve the accuracy of the trading system. They are designed to confirm the signals generated by the primary indicators by providing additional information about the strength and direction of the trend.
Some examples of confirmation indicators that may be used in the NNFX system include the Bollinger Bands, the MACD (Moving Average Convergence Divergence), and the MACD Oscillator. These indicators can provide information about the volatility, momentum, and trend strength of the market, and can be used to confirm the signals generated by the primary indicators.
In the NNFX system, confirmation indicators are used in combination with primary indicators and other filters to create a trading system that is robust and reliable. By using multiple indicators to confirm trading signals, the system aims to reduce the risk of false signals and improve the overall profitability of the trades.
What is a Continuation indicator?
In the NNFX (No Nonsense Forex) trading system, a continuation indicator is a technical indicator that is used to confirm a current trend and predict that the trend is likely to continue in the same direction. A continuation indicator is typically used in conjunction with other indicators in the system, such as a baseline indicator, to provide a comprehensive trading strategy.
What is a Volatility/Volume indicator?
Volume indicators, such as the On Balance Volume (OBV), the Chaikin Money Flow (CMF), or the Volume Price Trend (VPT), are used to measure the amount of buying and selling activity in a market. They are based on the trading volume of the market, and can provide information about the strength of the trend. In the NNFX system, volume indicators are used to confirm trading signals generated by the Moving Average and the Relative Strength Index. Volatility indicators include Average Direction Index, Waddah Attar, and Volatility Ratio. In the NNFX trading system, volatility is a proxy for volume and vice versa.
By using volume indicators as confirmation tools, the NNFX trading system aims to reduce the risk of false signals and improve the overall profitability of trades. These indicators can provide additional information about the market that is not captured by the primary indicators, and can help traders to make more informed trading decisions. In addition, volume indicators can be used to identify potential changes in market trends and to confirm the strength of price movements.
What is an Exit indicator?
The exit indicator is used in conjunction with other indicators in the system, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR), to provide a comprehensive trading strategy.
The exit indicator in the NNFX system can be any technical indicator that is deemed effective at identifying optimal exit points. Examples of exit indicators that are commonly used include the Parabolic SAR, the Average Directional Index (ADX), and the Chandelier Exit.
The purpose of the exit indicator is to identify when a trend is likely to reverse or when the market conditions have changed, signaling the need to exit a trade. By using an exit indicator, traders can manage their risk and prevent significant losses.
In the NNFX system, the exit indicator is used in conjunction with a stop loss and a take profit order to maximize profits and minimize losses. The stop loss order is used to limit the amount of loss that can be incurred if the trade goes against the trader, while the take profit order is used to lock in profits when the trade is moving in the trader's favor.
Overall, the use of an exit indicator in the NNFX trading system is an important component of a comprehensive trading strategy. It allows traders to manage their risk effectively and improve the profitability of their trades by exiting at the right time.
What is an Metamorphosis indicator?
The concept of a metamorphosis indicator involves the integration of two or more GKD indicators to generate a compound signal. This is achieved by evaluating the accuracy of each indicator and selecting the signal from the indicator with the highest accuracy. As an illustration, let's consider a scenario where we calculate the accuracy of 10 indicators and choose the signal from the indicator that demonstrates the highest accuracy.
The resulting output from the metamorphosis indicator can then be utilized in a GKD-BT backtest by occupying a slot that aligns with the purpose of the metamorphosis indicator. The slot can be a GKD-B, GKD-C, or GKD-E slot, depending on the specific requirements and objectives of the indicator. This allows for seamless integration and utilization of the compound signal within the GKD-BT framework.
How does Loxx's GKD (Giga Kaleidoscope Modularized Trading System) implement the NNFX algorithm outlined above?
Loxx's GKD v2.0 system has five types of modules (indicators/strategies). These modules are:
1. GKD-BT - Backtesting module (Volatility, Number 1 in the NNFX algorithm)
2. GKD-B - Baseline module (Baseline and Volatility/Volume, Numbers 1 and 2 in the NNFX algorithm)
3. GKD-C - Confirmation 1/2 and Continuation module (Confirmation 1/2 and Continuation, Numbers 3, 4, and 5 in the NNFX algorithm)
4. GKD-V - Volatility/Volume module (Confirmation 1/2, Number 6 in the NNFX algorithm)
5. GKD-E - Exit module (Exit, Number 7 in the NNFX algorithm)
6. GKD-M - Metamorphosis module (Metamorphosis, Number 8 in the NNFX algorithm, but not part of the NNFX algorithm)
(additional module types will added in future releases)
Each module interacts with every module by passing data to A backtest module wherein the various components of the GKD system are combined to create a trading signal.
That is, the Baseline indicator passes its data to Volatility/Volume. The Volatility/Volume indicator passes its values to the Confirmation 1 indicator. The Confirmation 1 indicator passes its values to the Confirmation 2 indicator. The Confirmation 2 indicator passes its values to the Continuation indicator. The Continuation indicator passes its values to the Exit indicator, and finally, the Exit indicator passes its values to the Backtest strategy.
This chaining of indicators requires that each module conform to Loxx's GKD protocol, therefore allowing for the testing of every possible combination of technical indicators that make up the six components of the NNFX algorithm.
What does the application of the GKD trading system look like?
Example trading system:
Backtest: Multi-Ticker CC Backtest as shown on the chart above
Baseline: Hull Moving Average
Volatility/Volume: Hurst Exponent
Confirmation 1: Fisher Transform as shown on the chart above
Confirmation 2: uf2018 as shown on the chart above
Continuation: Coppock Curve
Exit: Rex Oscillator
Metamorphosis: Baseline Optimizer
Each GKD indicator is denoted with a module identifier of either: GKD-BT, GKD-B, GKD-C, GKD-V, GKD-M, or GKD-E. This allows traders to understand to which module each indicator belongs and where each indicator fits into the GKD system.
█ Giga Kaleidoscope Modularized Trading System Signals
Standard Entry
1. GKD-C Confirmation gives signal
2. Baseline agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Volatility/Volume agrees
1-Candle Standard Entry
1a. GKD-C Confirmation gives signal
2a. Baseline agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
Next Candle
1b. Price retraced
2b. Baseline agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Baseline Entry
1. GKD-B Baseline gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Volatility/Volume agrees
7. Confirmation 1 signal was less than 'Maximum Allowable PSBC Bars Back' prior
1-Candle Baseline Entry
1a. GKD-B Baseline gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSBC Bars Back' prior
Next Candle
1b. Price retraced
2b. Baseline agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Volatility/Volume Entry
1. GKD-V Volatility/Volume gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Baseline agrees
7. Confirmation 1 signal was less than 7 candles prior
1-Candle Volatility/Volume Entry
1a. GKD-V Volatility/Volume gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSVVC Bars Back' prior
Next Candle
1b. Price retraced
2b. Volatility/Volume agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Baseline agrees
Confirmation 2 Entry
1. GKD-C Confirmation 2 gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Volatility/Volume agrees
6. Baseline agrees
7. Confirmation 1 signal was less than 7 candles prior
1-Candle Confirmation 2 Entry
1a. GKD-C Confirmation 2 gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSC2C Bars Back' prior
Next Candle
1b. Price retraced
2b. Confirmation 2 agrees
3b. Confirmation 1 agrees
4b. Volatility/Volume agrees
5b. Baseline agrees
PullBack Entry
1a. GKD-B Baseline gives signal
2a. Confirmation 1 agrees
3a. Price is beyond 1.0x Volatility of Baseline
Next Candle
1b. Price inside Goldie Locks Zone Minimum
2b. Price inside Goldie Locks Zone Maximum
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Continuation Entry
1. Standard Entry, 1-Candle Standard Entry, Baseline Entry, 1-Candle Baseline Entry, Volatility/Volume Entry, 1-Candle Volatility/Volume Entry, Confirmation 2 Entry, 1-Candle Confirmation 2 Entry, or Pullback entry triggered previously
2. Baseline hasn't crossed since entry signal trigger
4. Confirmation 1 agrees
5. Baseline agrees
6. Confirmation 2 agrees
█ Connecting to Backtests
All GKD indicators are chained indicators meaning you export the value of the indicators to specialized backtest to creat your GKD trading system. Each indicator contains a proprietary signal generation algo that will only work with GKD backtests. You can find these backtests using the links below.
GKD-BT Giga Confirmation Stack Backtest
GKD-BT Giga Stacks Backtest
GKD-BT Full Giga Kaleidoscope Backtest
GKD-BT Solo Confirmation Super Complex Backtest
GKD-BT Solo Confirmation Complex Backtest
GKD-BT Solo Confirmation Simple Backtest
GKD-M Baseline Optimizer
GKD-M Accuracy Alchemist
GKD-BT Multi-Ticker SCC Backtest
GKD-BT Multi-Ticker SCS Backtest
GKD-BT Multi-Ticker SCS Backtest
GKD-BT Multi-Ticker Full GKD Backtest
GKD-BT Optimizer SCC Backtest [Loxx]The Giga Kaleidoscope GKD-BT Optimizer SCC Backtest is a backtesting module included in Loxx's "Giga Kaleidoscope Modularized Trading System."
█ Giga Kaleidoscope GKD-BT Optimizer SCC Backtest
The Optimizer SCC Backtest is a Solo Confirmation Complex backtest that allows traders to test single GKD-C Confirmation indicator with GKD-B Baseline and GKD-V Volatility/Volume filtering across 10 varying inputs. The purpose of this backtest is to enable traders to optimize a GKD-C indicator given varying inputs.
The backtest module supports testing with 1 take profit and 1 stop loss. It also offers the option to limit testing to a specific date range, allowing simulated forward testing using historical data. This backtest module only includes standard long and short signals. Additionally, users can choose to display or hide a trading panel that provides relevant information about the backtest, statistics, and the current trade. Traders can also select a highlighting treshold for Total Percent Wins and Percent Profitable, and Profit Factor.
To use this indicator:
1. Import the value "Input into NEW GKD-BT Backtest" from the GKD-B Baseline indicator into the GKD-BT Optimizer SCC Backtest.
2. Import the value "Input into NEW GKD-BT Backtest" from the GKD-V Volatility/Volume indicator into the GKD-BT Optimizer SCC Backtest.
3. Select the "Optimizer" option in the GKD-C Confirmation indicator
4. Import a GKD-C indicator "Input into NEW GKD-BT Optimizer Backtest Signals" into the GKD-C Indicator Signals dropdown
5. Import a GKD-C indicator "Input into NEW GKD-BT Optimizer Backtest Start" into the GKD-C Indicator Start dropdown
6. Import a GKD-C indicator "Input into NEW GKD-BT Optimizer Backtest Skip" into the GKD-C Indicator Skip dropdown
This backtest includes the following metrics:
1. Net profit: Overall profit or loss achieved.
2. Total Closed Trades: Total number of closed trades, both winning and losing.
3. Total Percent Wins: Total wins, whether long or short, for the selected time interval regardless of commissions and other profit-modifying addons.
4. Percent Profitable: Total wins, whether long or short, that are also profitable, taking commissions into account.
5. Profit Factor: The ratio of gross profits to gross losses, indicating how much money the strategy made for every unit of money it lost.
6. Average Profit per Trade: The average gain or loss per trade, calculated by dividing the net profit by the total number of closed trades.
7. Average Number of Bars in Trade: The average number of bars that elapsed during trades for all closed trades.
Summary of notable settings:
Input Tickers separated by commas: Allows the user to input tickers separated by commas, specifying the symbols or tickers of financial instruments used in the backtest. The tickers should follow the format "EXCHANGE:TICKER" (e.g., "NASDAQ:AAPL, NYSE:MSFT").
Import GKD-B Baseline: Imports the "GKD-B Baseline" indicator.
Import GKD-V Volatility/Volume: Imports the "GKD-V Volatility/Volume" indicator.
Import GKD-C Confirmation: Imports the "GKD-C Confirmation" indicator.
Import GKD-C Continuation: Imports the "GKD-C Continuation" indicator.
Initial Capital: Represents the starting account balance for the backtest, denominated in the base currency of the trading account.
Order Size: Determines the quantity of contracts traded in each trade.
Order Type: Specifies the type of order used in the backtest, either "Contracts" or "% Equity."
Commission: Represents the commission per order or transaction cost incurred in each trade.
**the backtest data rendered to the chart above uses $5 commission per trade and 10% equity per trade with $1 million initial capital. Each backtest result for each ticker assumes these same inputs. The results are NOT cumulative, they are separate and isolate per ticker and trading side, long or short**
█ Volatility Types included
The GKD system utilizes volatility-based take profits and stop losses. Each take profit and stop loss is calculated as a multiple of volatility. You can change the values of the multipliers in the settings as well.
This module includes 17 types of volatility:
Close-to-Close
Parkinson
Garman-Klass
Rogers-Satchell
Yang-Zhang
Garman-Klass-Yang-Zhang
Exponential Weighted Moving Average
Standard Deviation of Log Returns
Pseudo GARCH(2,2)
Average True Range
True Range Double
Standard Deviation
Adaptive Deviation
Median Absolute Deviation
Efficiency-Ratio Adaptive ATR
Mean Absolute Deviation
Static Percent
Various volatility estimators and indicators that investors and traders can use to measure the dispersion or volatility of a financial instrument's price. Each estimator has its strengths and weaknesses, and the choice of estimator should depend on the specific needs and circumstances of the user.
Close-to-Close
Close-to-Close volatility is a classic and widely used volatility measure, sometimes referred to as historical volatility.
Volatility is an indicator of the speed of a stock price change. A stock with high volatility is one where the price changes rapidly and with a larger amplitude. The more volatile a stock is, the riskier it is.
Close-to-close historical volatility is calculated using only a stock's closing prices. It is the simplest volatility estimator. However, in many cases, it is not precise enough. Stock prices could jump significantly during a trading session and return to the opening value at the end. That means that a considerable amount of price information is not taken into account by close-to-close volatility.
Despite its drawbacks, Close-to-Close volatility is still useful in cases where the instrument doesn't have intraday prices. For example, mutual funds calculate their net asset values daily or weekly, and thus their prices are not suitable for more sophisticated volatility estimators.
Parkinson
Parkinson volatility is a volatility measure that uses the stock’s high and low price of the day.
The main difference between regular volatility and Parkinson volatility is that the latter uses high and low prices for a day, rather than only the closing price. This is useful as close-to-close prices could show little difference while large price movements could have occurred during the day. Thus, Parkinson's volatility is considered more precise and requires less data for calculation than close-to-close volatility.
One drawback of this estimator is that it doesn't take into account price movements after the market closes. Hence, it systematically undervalues volatility. This drawback is addressed in the Garman-Klass volatility estimator.
Garman-Klass
Garman-Klass is a volatility estimator that incorporates open, low, high, and close prices of a security.
Garman-Klass volatility extends Parkinson's volatility by taking into account the opening and closing prices. As markets are most active during the opening and closing of a trading session, it makes volatility estimation more accurate.
Garman and Klass also assumed that the process of price change follows a continuous diffusion process (Geometric Brownian motion). However, this assumption has several drawbacks. The method is not robust for opening jumps in price and trend movements.
Despite its drawbacks, the Garman-Klass estimator is still more effective than the basic formula since it takes into account not only the price at the beginning and end of the time interval but also intraday price extremes.
Researchers Rogers and Satchell have proposed a more efficient method for assessing historical volatility that takes into account price trends. See Rogers-Satchell Volatility for more detail.
Rogers-Satchell
Rogers-Satchell is an estimator for measuring the volatility of securities with an average return not equal to zero.
Unlike Parkinson and Garman-Klass estimators, Rogers-Satchell incorporates a drift term (mean return not equal to zero). As a result, it provides better volatility estimation when the underlying is trending.
The main disadvantage of this method is that it does not take into account price movements between trading sessions. This leads to an underestimation of volatility since price jumps periodically occur in the market precisely at the moments between sessions.
A more comprehensive estimator that also considers the gaps between sessions was developed based on the Rogers-Satchel formula in the 2000s by Yang-Zhang. See Yang Zhang Volatility for more detail.
Yang-Zhang
Yang Zhang is a historical volatility estimator that handles both opening jumps and the drift and has a minimum estimation error.
Yang-Zhang volatility can be thought of as a combination of the overnight (close-to-open volatility) and a weighted average of the Rogers-Satchell volatility and the day’s open-to-close volatility. It is considered to be 14 times more efficient than the close-to-close estimator.
Garman-Klass-Yang-Zhang
Garman-Klass-Yang-Zhang (GKYZ) volatility estimator incorporates the returns of open, high, low, and closing prices in its calculation.
GKYZ volatility estimator takes into account overnight jumps but not the trend, i.e., it assumes that the underlying asset follows a Geometric Brownian Motion (GBM) process with zero drift. Therefore, the GKYZ volatility estimator tends to overestimate the volatility when the drift is different from zero. However, for a GBM process, this estimator is eight times more efficient than the close-to-close volatility estimator.
Exponential Weighted Moving Average
The Exponentially Weighted Moving Average (EWMA) is a quantitative or statistical measure used to model or describe a time series. The EWMA is widely used in finance, with the main applications being technical analysis and volatility modeling.
The moving average is designed such that older observations are given lower weights. The weights decrease exponentially as the data point gets older – hence the name exponentially weighted.
The only decision a user of the EWMA must make is the parameter lambda. The parameter decides how important the current observation is in the calculation of the EWMA. The higher the value of lambda, the more closely the EWMA tracks the original time series.
Standard Deviation of Log Returns
This is the simplest calculation of volatility. It's the standard deviation of ln(close/close(1)).
Pseudo GARCH(2,2)
This is calculated using a short- and long-run mean of variance multiplied by ?.
?avg(var;M) + (1 ? ?) avg(var;N) = 2?var/(M+1-(M-1)L) + 2(1-?)var/(M+1-(M-1)L)
Solving for ? can be done by minimizing the mean squared error of estimation; that is, regressing L^-1var - avg(var; N) against avg(var; M) - avg(var; N) and using the resulting beta estimate as ?.
Average True Range
The average true range (ATR) is a technical analysis indicator, introduced by market technician J. Welles Wilder Jr. in his book New Concepts in Technical Trading Systems, that measures market volatility by decomposing the entire range of an asset price for that period.
The true range indicator is taken as the greatest of the following: current high less the current low; the absolute value of the current high less the previous close; and the absolute value of the current low less the previous close. The ATR is then a moving average, generally using 14 days, of the true ranges.
True Range Double
A special case of ATR that attempts to correct for volatility skew.
Standard Deviation
Standard deviation is a statistic that measures the dispersion of a dataset relative to its mean and is calculated as the square root of the variance. The standard deviation is calculated as the square root of variance by determining each data point's deviation relative to the mean. If the data points are further from the mean, there is a higher deviation within the data set; thus, the more spread out the data, the higher the standard deviation.
Adaptive Deviation
By definition, the Standard Deviation (STD, also represented by the Greek letter sigma ? or the Latin letter s) is a measure that is used to quantify the amount of variation or dispersion of a set of data values. In technical analysis, we usually use it to measure the level of current volatility.
Standard Deviation is based on Simple Moving Average calculation for mean value. This version of standard deviation uses the properties of EMA to calculate what can be called a new type of deviation, and since it is based on EMA, we can call it EMA deviation. Additionally, Perry Kaufman's efficiency ratio is used to make it adaptive (since all EMA type calculations are nearly perfect for adapting).
The difference when compared to the standard is significant--not just because of EMA usage, but the efficiency ratio makes it a "bit more logical" in very volatile market conditions.
Median Absolute Deviation
The median absolute deviation is a measure of statistical dispersion. Moreover, the MAD is a robust statistic, being more resilient to outliers in a data set than the standard deviation. In the standard deviation, the distances from the mean are squared, so large deviations are weighted more heavily, and thus outliers can heavily influence it. In the MAD, the deviations of a small number of outliers are irrelevant.
Because the MAD is a more robust estimator of scale than the sample variance or standard deviation, it works better with distributions without a mean or variance, such as the Cauchy distribution.
For this indicator, a manual recreation of the quantile function in Pine Script is used. This is so users have a full inside view into how this is calculated.
Efficiency-Ratio Adaptive ATR
Average True Range (ATR) is a widely used indicator for many occasions in technical analysis. It is calculated as the RMA of the true range. This version adds a "twist": it uses Perry Kaufman's Efficiency Ratio to calculate adaptive true range.
Mean Absolute Deviation
The mean absolute deviation (MAD) is a measure of variability that indicates the average distance between observations and their mean. MAD uses the original units of the data, which simplifies interpretation. Larger values signify that the data points spread out further from the average. Conversely, lower values correspond to data points bunching closer to it. The mean absolute deviation is also known as the mean deviation and average absolute deviation.
This definition of the mean absolute deviation sounds similar to the standard deviation (SD). While both measure variability, they have different calculations. In recent years, some proponents of MAD have suggested that it replace the SD as the primary measure because it is a simpler concept that better fits real life.
█ Giga Kaleidoscope Modularized Trading System
Core components of an NNFX algorithmic trading strategy
The NNFX algorithm is built on the principles of trend, momentum, and volatility. There are six core components in the NNFX trading algorithm:
1. Volatility - price volatility; e.g., Average True Range, True Range Double, Close-to-Close, etc.
2. Baseline - a moving average to identify price trend
3. Confirmation 1 - a technical indicator used to identify trends
4. Confirmation 2 - a technical indicator used to identify trends
5. Continuation - a technical indicator used to identify trends
6. Volatility/Volume - a technical indicator used to identify volatility/volume breakouts/breakdown
7. Exit - a technical indicator used to determine when a trend is exhausted
8. Metamorphosis - a technical indicator that produces a compound signal from the combination of other GKD indicators*
*(not part of the NNFX algorithm)
What is Volatility in the NNFX trading system?
In the NNFX (No Nonsense Forex) trading system, ATR (Average True Range) is typically used to measure the volatility of an asset. It is used as a part of the system to help determine the appropriate stop loss and take profit levels for a trade. ATR is calculated by taking the average of the true range values over a specified period.
True range is calculated as the maximum of the following values:
-Current high minus the current low
-Absolute value of the current high minus the previous close
-Absolute value of the current low minus the previous close
ATR is a dynamic indicator that changes with changes in volatility. As volatility increases, the value of ATR increases, and as volatility decreases, the value of ATR decreases. By using ATR in NNFX system, traders can adjust their stop loss and take profit levels according to the volatility of the asset being traded. This helps to ensure that the trade is given enough room to move, while also minimizing potential losses.
Other types of volatility include True Range Double (TRD), Close-to-Close, and Garman-Klass
What is a Baseline indicator?
The baseline is essentially a moving average, and is used to determine the overall direction of the market.
The baseline in the NNFX system is used to filter out trades that are not in line with the long-term trend of the market. The baseline is plotted on the chart along with other indicators, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR).
Trades are only taken when the price is in the same direction as the baseline. For example, if the baseline is sloping upwards, only long trades are taken, and if the baseline is sloping downwards, only short trades are taken. This approach helps to ensure that trades are in line with the overall trend of the market, and reduces the risk of entering trades that are likely to fail.
By using a baseline in the NNFX system, traders can have a clear reference point for determining the overall trend of the market, and can make more informed trading decisions. The baseline helps to filter out noise and false signals, and ensures that trades are taken in the direction of the long-term trend.
What is a Confirmation indicator?
Confirmation indicators are technical indicators that are used to confirm the signals generated by primary indicators. Primary indicators are the core indicators used in the NNFX system, such as the Average True Range (ATR), the Moving Average (MA), and the Relative Strength Index (RSI).
The purpose of the confirmation indicators is to reduce false signals and improve the accuracy of the trading system. They are designed to confirm the signals generated by the primary indicators by providing additional information about the strength and direction of the trend.
Some examples of confirmation indicators that may be used in the NNFX system include the Bollinger Bands, the MACD (Moving Average Convergence Divergence), and the MACD Oscillator. These indicators can provide information about the volatility, momentum, and trend strength of the market, and can be used to confirm the signals generated by the primary indicators.
In the NNFX system, confirmation indicators are used in combination with primary indicators and other filters to create a trading system that is robust and reliable. By using multiple indicators to confirm trading signals, the system aims to reduce the risk of false signals and improve the overall profitability of the trades.
What is a Continuation indicator?
In the NNFX (No Nonsense Forex) trading system, a continuation indicator is a technical indicator that is used to confirm a current trend and predict that the trend is likely to continue in the same direction. A continuation indicator is typically used in conjunction with other indicators in the system, such as a baseline indicator, to provide a comprehensive trading strategy.
What is a Volatility/Volume indicator?
Volume indicators, such as the On Balance Volume (OBV), the Chaikin Money Flow (CMF), or the Volume Price Trend (VPT), are used to measure the amount of buying and selling activity in a market. They are based on the trading volume of the market, and can provide information about the strength of the trend. In the NNFX system, volume indicators are used to confirm trading signals generated by the Moving Average and the Relative Strength Index. Volatility indicators include Average Direction Index, Waddah Attar, and Volatility Ratio. In the NNFX trading system, volatility is a proxy for volume and vice versa.
By using volume indicators as confirmation tools, the NNFX trading system aims to reduce the risk of false signals and improve the overall profitability of trades. These indicators can provide additional information about the market that is not captured by the primary indicators, and can help traders to make more informed trading decisions. In addition, volume indicators can be used to identify potential changes in market trends and to confirm the strength of price movements.
What is an Exit indicator?
The exit indicator is used in conjunction with other indicators in the system, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR), to provide a comprehensive trading strategy.
The exit indicator in the NNFX system can be any technical indicator that is deemed effective at identifying optimal exit points. Examples of exit indicators that are commonly used include the Parabolic SAR, the Average Directional Index (ADX), and the Chandelier Exit.
The purpose of the exit indicator is to identify when a trend is likely to reverse or when the market conditions have changed, signaling the need to exit a trade. By using an exit indicator, traders can manage their risk and prevent significant losses.
In the NNFX system, the exit indicator is used in conjunction with a stop loss and a take profit order to maximize profits and minimize losses. The stop loss order is used to limit the amount of loss that can be incurred if the trade goes against the trader, while the take profit order is used to lock in profits when the trade is moving in the trader's favor.
Overall, the use of an exit indicator in the NNFX trading system is an important component of a comprehensive trading strategy. It allows traders to manage their risk effectively and improve the profitability of their trades by exiting at the right time.
What is an Metamorphosis indicator?
The concept of a metamorphosis indicator involves the integration of two or more GKD indicators to generate a compound signal. This is achieved by evaluating the accuracy of each indicator and selecting the signal from the indicator with the highest accuracy. As an illustration, let's consider a scenario where we calculate the accuracy of 10 indicators and choose the signal from the indicator that demonstrates the highest accuracy.
The resulting output from the metamorphosis indicator can then be utilized in a GKD-BT backtest by occupying a slot that aligns with the purpose of the metamorphosis indicator. The slot can be a GKD-B, GKD-C, or GKD-E slot, depending on the specific requirements and objectives of the indicator. This allows for seamless integration and utilization of the compound signal within the GKD-BT framework.
How does Loxx's GKD (Giga Kaleidoscope Modularized Trading System) implement the NNFX algorithm outlined above?
Loxx's GKD v2.0 system has five types of modules (indicators/strategies). These modules are:
1. GKD-BT - Backtesting module (Volatility, Number 1 in the NNFX algorithm)
2. GKD-B - Baseline module (Baseline and Volatility/Volume, Numbers 1 and 2 in the NNFX algorithm)
3. GKD-C - Confirmation 1/2 and Continuation module (Confirmation 1/2 and Continuation, Numbers 3, 4, and 5 in the NNFX algorithm)
4. GKD-V - Volatility/Volume module (Confirmation 1/2, Number 6 in the NNFX algorithm)
5. GKD-E - Exit module (Exit, Number 7 in the NNFX algorithm)
6. GKD-M - Metamorphosis module (Metamorphosis, Number 8 in the NNFX algorithm, but not part of the NNFX algorithm)
(additional module types will added in future releases)
Each module interacts with every module by passing data to A backtest module wherein the various components of the GKD system are combined to create a trading signal.
That is, the Baseline indicator passes its data to Volatility/Volume. The Volatility/Volume indicator passes its values to the Confirmation 1 indicator. The Confirmation 1 indicator passes its values to the Confirmation 2 indicator. The Confirmation 2 indicator passes its values to the Continuation indicator. The Continuation indicator passes its values to the Exit indicator, and finally, the Exit indicator passes its values to the Backtest strategy.
This chaining of indicators requires that each module conform to Loxx's GKD protocol, therefore allowing for the testing of every possible combination of technical indicators that make up the six components of the NNFX algorithm.
What does the application of the GKD trading system look like?
Example trading system:
Backtest: Optimizer Full GKD Backtest as shown on the chart above
Baseline: Hull Moving Average
Volatility/Volume: Hurst Exponent
Confirmation 1: Fisher Transofrm as shown on the chart above
Confirmation 2: uf2018
Continuation: Coppock Curve
Exit: Rex Oscillator
Metamorphosis: Baseline Optimizer
Each GKD indicator is denoted with a module identifier of either: GKD-BT, GKD-B, GKD-C, GKD-V, GKD-M, or GKD-E. This allows traders to understand to which module each indicator belongs and where each indicator fits into the GKD system.
█ Giga Kaleidoscope Modularized Trading System Signals
Standard Entry
1. GKD-C Confirmation gives signal
2. Baseline agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Volatility/Volume agrees
1-Candle Standard Entry
1a. GKD-C Confirmation gives signal
2a. Baseline agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
Next Candle
1b. Price retraced
2b. Baseline agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Baseline Entry
1. GKD-B Baseline gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Volatility/Volume agrees
7. Confirmation 1 signal was less than 'Maximum Allowable PSBC Bars Back' prior
1-Candle Baseline Entry
1a. GKD-B Baseline gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSBC Bars Back' prior
Next Candle
1b. Price retraced
2b. Baseline agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Volatility/Volume Entry
1. GKD-V Volatility/Volume gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Baseline agrees
7. Confirmation 1 signal was less than 7 candles prior
1-Candle Volatility/Volume Entry
1a. GKD-V Volatility/Volume gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSVVC Bars Back' prior
Next Candle
1b. Price retraced
2b. Volatility/Volume agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Baseline agrees
Confirmation 2 Entry
1. GKD-C Confirmation 2 gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Volatility/Volume agrees
6. Baseline agrees
7. Confirmation 1 signal was less than 7 candles prior
1-Candle Confirmation 2 Entry
1a. GKD-C Confirmation 2 gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSC2C Bars Back' prior
Next Candle
1b. Price retraced
2b. Confirmation 2 agrees
3b. Confirmation 1 agrees
4b. Volatility/Volume agrees
5b. Baseline agrees
PullBack Entry
1a. GKD-B Baseline gives signal
2a. Confirmation 1 agrees
3a. Price is beyond 1.0x Volatility of Baseline
Next Candle
1b. Price inside Goldie Locks Zone Minimum
2b. Price inside Goldie Locks Zone Maximum
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Continuation Entry
1. Standard Entry, 1-Candle Standard Entry, Baseline Entry, 1-Candle Baseline Entry, Volatility/Volume Entry, 1-Candle Volatility/Volume Entry, Confirmation 2 Entry, 1-Candle Confirmation 2 Entry, or Pullback entry triggered previously
2. Baseline hasn't crossed since entry signal trigger
4. Confirmation 1 agrees
5. Baseline agrees
6. Confirmation 2 agrees
█ Connecting to Backtests
All GKD indicators are chained indicators meaning you export the value of the indicators to specialized backtest to creat your GKD trading system. Each indicator contains a proprietary signal generation algo that will only work with GKD backtests. You can find these backtests using the links below.
GKD-BT Giga Confirmation Stack Backtest
GKD-BT Giga Stacks Backtest
GKD-BT Full Giga Kaleidoscope Backtest
GKD-BT Solo Confirmation Super Complex Backtest
GKD-BT Solo Confirmation Complex Backtest
GKD-BT Solo Confirmation Simple Backtest
GKD-M Baseline Optimizer
GKD-M Accuracy Alchemist
GKD-BT Optimizer SCC Backtest
GKD-BT Optimizer SCC Backtest
GKD-BT Optimizer SCC Backtest
GKD-C GKD-BT Optimizer Full GKD Backtest
GKD-BT Optimizer SCS Backtest [Loxx]The Giga Kaleidoscope GKD-BT Optimizer SCS Backtest is a backtesting module included in Loxx's "Giga Kaleidoscope Modularized Trading System."
█ Giga Kaleidoscope GKD-BT Optimizer SCS Backtest
The Optimizer SCS Backtest is a Solo Confirmation Simple backtest that allows traders to test single GKD-C confirmation indicators across 10 varying inputs. The purpose of this backtest is to enable traders to optimize a GKD-C indicator given varying inputs.
The backtest module supports testing with 1 take profit and 1 stop loss. It also offers the option to limit testing to a specific date range, allowing simulated forward testing using historical data. This backtest module only includes standard long and short signals. Additionally, users can choose to display or hide a trading panel that provides relevant information about the backtest, statistics, and the current trade. Traders can also select a highlighting treshold for Total Percent Wins and Percent Profitable, and Profit Factor.
To use this indicator:
1. Import a GKD-C indicator "Input into NEW GKD-BT Optimizer Backtest Signals" into the GKD-C Indicator Signals dropdown
1. Import a GKD-C indicator "Input into NEW GKD-BT Optimizer Backtest Start" into the GKD-C Indicator Start dropdown
1. Import a GKD-C indicator "Input into NEW GKD-BT Optimizer Backtest Skip" into the GKD-C Indicator Skip dropdown
This backtest includes the following metrics:
1. Net profit: Overall profit or loss achieved.
2. Total Closed Trades: Total number of closed trades, both winning and losing.
3. Total Percent Wins: Total wins, whether long or short, for the selected time interval regardless of commissions and other profit-modifying addons.
4. Percent Profitable: Total wins, whether long or short, that are also profitable, taking commissions into account.
5. Profit Factor: The ratio of gross profits to gross losses, indicating how much money the strategy made for every unit of money it lost.
6. Average Profit per Trade: The average gain or loss per trade, calculated by dividing the net profit by the total number of closed trades.
7. Average Number of Bars in Trade: The average number of bars that elapsed during trades for all closed trades.
Summary of notable settings:
Input Tickers separated by commas: Allows the user to input tickers separated by commas, specifying the symbols or tickers of financial instruments used in the backtest. The tickers should follow the format "EXCHANGE:TICKER" (e.g., "NASDAQ:AAPL, NYSE:MSFT").
Import GKD-B Baseline: Imports the "GKD-B Multi-Ticker Baseline" indicator.
Import GKD-V Volatility/Volume: Imports the "GKD-V Volatility/Volume" indicator.
Import GKD-C Confirmation: Imports the "GKD-C Confirmation" indicator.
Import GKD-C Continuation: Imports the "GKD-C Continuation" indicator.
Initial Capital: Represents the starting account balance for the backtest, denominated in the base currency of the trading account.
Order Size: Determines the quantity of contracts traded in each trade.
Order Type: Specifies the type of order used in the backtest, either "Contracts" or "% Equity."
Commission: Represents the commission per order or transaction cost incurred in each trade.
**the backtest data rendered to the chart above uses $5 commission per trade and 10% equity per trade with $1 million initial capital. Each backtest result for each ticker assumes these same inputs. The results are NOT cumulative, they are separate and isolate per ticker and trading side, long or short**
█ Volatility Types included
The GKD system utilizes volatility-based take profits and stop losses. Each take profit and stop loss is calculated as a multiple of volatility. You can change the values of the multipliers in the settings as well.
This module includes 17 types of volatility:
Close-to-Close
Parkinson
Garman-Klass
Rogers-Satchell
Yang-Zhang
Garman-Klass-Yang-Zhang
Exponential Weighted Moving Average
Standard Deviation of Log Returns
Pseudo GARCH(2,2)
Average True Range
True Range Double
Standard Deviation
Adaptive Deviation
Median Absolute Deviation
Efficiency-Ratio Adaptive ATR
Mean Absolute Deviation
Static Percent
Various volatility estimators and indicators that investors and traders can use to measure the dispersion or volatility of a financial instrument's price. Each estimator has its strengths and weaknesses, and the choice of estimator should depend on the specific needs and circumstances of the user.
Close-to-Close
Close-to-Close volatility is a classic and widely used volatility measure, sometimes referred to as historical volatility.
Volatility is an indicator of the speed of a stock price change. A stock with high volatility is one where the price changes rapidly and with a larger amplitude. The more volatile a stock is, the riskier it is.
Close-to-close historical volatility is calculated using only a stock's closing prices. It is the simplest volatility estimator. However, in many cases, it is not precise enough. Stock prices could jump significantly during a trading session and return to the opening value at the end. That means that a considerable amount of price information is not taken into account by close-to-close volatility.
Despite its drawbacks, Close-to-Close volatility is still useful in cases where the instrument doesn't have intraday prices. For example, mutual funds calculate their net asset values daily or weekly, and thus their prices are not suitable for more sophisticated volatility estimators.
Parkinson
Parkinson volatility is a volatility measure that uses the stock’s high and low price of the day.
The main difference between regular volatility and Parkinson volatility is that the latter uses high and low prices for a day, rather than only the closing price. This is useful as close-to-close prices could show little difference while large price movements could have occurred during the day. Thus, Parkinson's volatility is considered more precise and requires less data for calculation than close-to-close volatility.
One drawback of this estimator is that it doesn't take into account price movements after the market closes. Hence, it systematically undervalues volatility. This drawback is addressed in the Garman-Klass volatility estimator.
Garman-Klass
Garman-Klass is a volatility estimator that incorporates open, low, high, and close prices of a security.
Garman-Klass volatility extends Parkinson's volatility by taking into account the opening and closing prices. As markets are most active during the opening and closing of a trading session, it makes volatility estimation more accurate.
Garman and Klass also assumed that the process of price change follows a continuous diffusion process (Geometric Brownian motion). However, this assumption has several drawbacks. The method is not robust for opening jumps in price and trend movements.
Despite its drawbacks, the Garman-Klass estimator is still more effective than the basic formula since it takes into account not only the price at the beginning and end of the time interval but also intraday price extremes.
Researchers Rogers and Satchell have proposed a more efficient method for assessing historical volatility that takes into account price trends. See Rogers-Satchell Volatility for more detail.
Rogers-Satchell
Rogers-Satchell is an estimator for measuring the volatility of securities with an average return not equal to zero.
Unlike Parkinson and Garman-Klass estimators, Rogers-Satchell incorporates a drift term (mean return not equal to zero). As a result, it provides better volatility estimation when the underlying is trending.
The main disadvantage of this method is that it does not take into account price movements between trading sessions. This leads to an underestimation of volatility since price jumps periodically occur in the market precisely at the moments between sessions.
A more comprehensive estimator that also considers the gaps between sessions was developed based on the Rogers-Satchel formula in the 2000s by Yang-Zhang. See Yang Zhang Volatility for more detail.
Yang-Zhang
Yang Zhang is a historical volatility estimator that handles both opening jumps and the drift and has a minimum estimation error.
Yang-Zhang volatility can be thought of as a combination of the overnight (close-to-open volatility) and a weighted average of the Rogers-Satchell volatility and the day’s open-to-close volatility. It is considered to be 14 times more efficient than the close-to-close estimator.
Garman-Klass-Yang-Zhang
Garman-Klass-Yang-Zhang (GKYZ) volatility estimator incorporates the returns of open, high, low, and closing prices in its calculation.
GKYZ volatility estimator takes into account overnight jumps but not the trend, i.e., it assumes that the underlying asset follows a Geometric Brownian Motion (GBM) process with zero drift. Therefore, the GKYZ volatility estimator tends to overestimate the volatility when the drift is different from zero. However, for a GBM process, this estimator is eight times more efficient than the close-to-close volatility estimator.
Exponential Weighted Moving Average
The Exponentially Weighted Moving Average (EWMA) is a quantitative or statistical measure used to model or describe a time series. The EWMA is widely used in finance, with the main applications being technical analysis and volatility modeling.
The moving average is designed such that older observations are given lower weights. The weights decrease exponentially as the data point gets older – hence the name exponentially weighted.
The only decision a user of the EWMA must make is the parameter lambda. The parameter decides how important the current observation is in the calculation of the EWMA. The higher the value of lambda, the more closely the EWMA tracks the original time series.
Standard Deviation of Log Returns
This is the simplest calculation of volatility. It's the standard deviation of ln(close/close(1)).
Pseudo GARCH(2,2)
This is calculated using a short- and long-run mean of variance multiplied by ?.
?avg(var;M) + (1 ? ?) avg(var;N) = 2?var/(M+1-(M-1)L) + 2(1-?)var/(M+1-(M-1)L)
Solving for ? can be done by minimizing the mean squared error of estimation; that is, regressing L^-1var - avg(var; N) against avg(var; M) - avg(var; N) and using the resulting beta estimate as ?.
Average True Range
The average true range (ATR) is a technical analysis indicator, introduced by market technician J. Welles Wilder Jr. in his book New Concepts in Technical Trading Systems, that measures market volatility by decomposing the entire range of an asset price for that period.
The true range indicator is taken as the greatest of the following: current high less the current low; the absolute value of the current high less the previous close; and the absolute value of the current low less the previous close. The ATR is then a moving average, generally using 14 days, of the true ranges.
True Range Double
A special case of ATR that attempts to correct for volatility skew.
Standard Deviation
Standard deviation is a statistic that measures the dispersion of a dataset relative to its mean and is calculated as the square root of the variance. The standard deviation is calculated as the square root of variance by determining each data point's deviation relative to the mean. If the data points are further from the mean, there is a higher deviation within the data set; thus, the more spread out the data, the higher the standard deviation.
Adaptive Deviation
By definition, the Standard Deviation (STD, also represented by the Greek letter sigma ? or the Latin letter s) is a measure that is used to quantify the amount of variation or dispersion of a set of data values. In technical analysis, we usually use it to measure the level of current volatility.
Standard Deviation is based on Simple Moving Average calculation for mean value. This version of standard deviation uses the properties of EMA to calculate what can be called a new type of deviation, and since it is based on EMA, we can call it EMA deviation. Additionally, Perry Kaufman's efficiency ratio is used to make it adaptive (since all EMA type calculations are nearly perfect for adapting).
The difference when compared to the standard is significant--not just because of EMA usage, but the efficiency ratio makes it a "bit more logical" in very volatile market conditions.
Median Absolute Deviation
The median absolute deviation is a measure of statistical dispersion. Moreover, the MAD is a robust statistic, being more resilient to outliers in a data set than the standard deviation. In the standard deviation, the distances from the mean are squared, so large deviations are weighted more heavily, and thus outliers can heavily influence it. In the MAD, the deviations of a small number of outliers are irrelevant.
Because the MAD is a more robust estimator of scale than the sample variance or standard deviation, it works better with distributions without a mean or variance, such as the Cauchy distribution.
For this indicator, a manual recreation of the quantile function in Pine Script is used. This is so users have a full inside view into how this is calculated.
Efficiency-Ratio Adaptive ATR
Average True Range (ATR) is a widely used indicator for many occasions in technical analysis. It is calculated as the RMA of the true range. This version adds a "twist": it uses Perry Kaufman's Efficiency Ratio to calculate adaptive true range.
Mean Absolute Deviation
The mean absolute deviation (MAD) is a measure of variability that indicates the average distance between observations and their mean. MAD uses the original units of the data, which simplifies interpretation. Larger values signify that the data points spread out further from the average. Conversely, lower values correspond to data points bunching closer to it. The mean absolute deviation is also known as the mean deviation and average absolute deviation.
This definition of the mean absolute deviation sounds similar to the standard deviation (SD). While both measure variability, they have different calculations. In recent years, some proponents of MAD have suggested that it replace the SD as the primary measure because it is a simpler concept that better fits real life.
█ Giga Kaleidoscope Modularized Trading System
Core components of an NNFX algorithmic trading strategy
The NNFX algorithm is built on the principles of trend, momentum, and volatility. There are six core components in the NNFX trading algorithm:
1. Volatility - price volatility; e.g., Average True Range, True Range Double, Close-to-Close, etc.
2. Baseline - a moving average to identify price trend
3. Confirmation 1 - a technical indicator used to identify trends
4. Confirmation 2 - a technical indicator used to identify trends
5. Continuation - a technical indicator used to identify trends
6. Volatility/Volume - a technical indicator used to identify volatility/volume breakouts/breakdown
7. Exit - a technical indicator used to determine when a trend is exhausted
8. Metamorphosis - a technical indicator that produces a compound signal from the combination of other GKD indicators*
*(not part of the NNFX algorithm)
What is Volatility in the NNFX trading system?
In the NNFX (No Nonsense Forex) trading system, ATR (Average True Range) is typically used to measure the volatility of an asset. It is used as a part of the system to help determine the appropriate stop loss and take profit levels for a trade. ATR is calculated by taking the average of the true range values over a specified period.
True range is calculated as the maximum of the following values:
-Current high minus the current low
-Absolute value of the current high minus the previous close
-Absolute value of the current low minus the previous close
ATR is a dynamic indicator that changes with changes in volatility. As volatility increases, the value of ATR increases, and as volatility decreases, the value of ATR decreases. By using ATR in NNFX system, traders can adjust their stop loss and take profit levels according to the volatility of the asset being traded. This helps to ensure that the trade is given enough room to move, while also minimizing potential losses.
Other types of volatility include True Range Double (TRD), Close-to-Close, and Garman-Klass
What is a Baseline indicator?
The baseline is essentially a moving average, and is used to determine the overall direction of the market.
The baseline in the NNFX system is used to filter out trades that are not in line with the long-term trend of the market. The baseline is plotted on the chart along with other indicators, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR).
Trades are only taken when the price is in the same direction as the baseline. For example, if the baseline is sloping upwards, only long trades are taken, and if the baseline is sloping downwards, only short trades are taken. This approach helps to ensure that trades are in line with the overall trend of the market, and reduces the risk of entering trades that are likely to fail.
By using a baseline in the NNFX system, traders can have a clear reference point for determining the overall trend of the market, and can make more informed trading decisions. The baseline helps to filter out noise and false signals, and ensures that trades are taken in the direction of the long-term trend.
What is a Confirmation indicator?
Confirmation indicators are technical indicators that are used to confirm the signals generated by primary indicators. Primary indicators are the core indicators used in the NNFX system, such as the Average True Range (ATR), the Moving Average (MA), and the Relative Strength Index (RSI).
The purpose of the confirmation indicators is to reduce false signals and improve the accuracy of the trading system. They are designed to confirm the signals generated by the primary indicators by providing additional information about the strength and direction of the trend.
Some examples of confirmation indicators that may be used in the NNFX system include the Bollinger Bands, the MACD (Moving Average Convergence Divergence), and the MACD Oscillator. These indicators can provide information about the volatility, momentum, and trend strength of the market, and can be used to confirm the signals generated by the primary indicators.
In the NNFX system, confirmation indicators are used in combination with primary indicators and other filters to create a trading system that is robust and reliable. By using multiple indicators to confirm trading signals, the system aims to reduce the risk of false signals and improve the overall profitability of the trades.
What is a Continuation indicator?
In the NNFX (No Nonsense Forex) trading system, a continuation indicator is a technical indicator that is used to confirm a current trend and predict that the trend is likely to continue in the same direction. A continuation indicator is typically used in conjunction with other indicators in the system, such as a baseline indicator, to provide a comprehensive trading strategy.
What is a Volatility/Volume indicator?
Volume indicators, such as the On Balance Volume (OBV), the Chaikin Money Flow (CMF), or the Volume Price Trend (VPT), are used to measure the amount of buying and selling activity in a market. They are based on the trading volume of the market, and can provide information about the strength of the trend. In the NNFX system, volume indicators are used to confirm trading signals generated by the Moving Average and the Relative Strength Index. Volatility indicators include Average Direction Index, Waddah Attar, and Volatility Ratio. In the NNFX trading system, volatility is a proxy for volume and vice versa.
By using volume indicators as confirmation tools, the NNFX trading system aims to reduce the risk of false signals and improve the overall profitability of trades. These indicators can provide additional information about the market that is not captured by the primary indicators, and can help traders to make more informed trading decisions. In addition, volume indicators can be used to identify potential changes in market trends and to confirm the strength of price movements.
What is an Exit indicator?
The exit indicator is used in conjunction with other indicators in the system, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR), to provide a comprehensive trading strategy.
The exit indicator in the NNFX system can be any technical indicator that is deemed effective at identifying optimal exit points. Examples of exit indicators that are commonly used include the Parabolic SAR, the Average Directional Index (ADX), and the Chandelier Exit.
The purpose of the exit indicator is to identify when a trend is likely to reverse or when the market conditions have changed, signaling the need to exit a trade. By using an exit indicator, traders can manage their risk and prevent significant losses.
In the NNFX system, the exit indicator is used in conjunction with a stop loss and a take profit order to maximize profits and minimize losses. The stop loss order is used to limit the amount of loss that can be incurred if the trade goes against the trader, while the take profit order is used to lock in profits when the trade is moving in the trader's favor.
Overall, the use of an exit indicator in the NNFX trading system is an important component of a comprehensive trading strategy. It allows traders to manage their risk effectively and improve the profitability of their trades by exiting at the right time.
What is an Metamorphosis indicator?
The concept of a metamorphosis indicator involves the integration of two or more GKD indicators to generate a compound signal. This is achieved by evaluating the accuracy of each indicator and selecting the signal from the indicator with the highest accuracy. As an illustration, let's consider a scenario where we calculate the accuracy of 10 indicators and choose the signal from the indicator that demonstrates the highest accuracy.
The resulting output from the metamorphosis indicator can then be utilized in a GKD-BT backtest by occupying a slot that aligns with the purpose of the metamorphosis indicator. The slot can be a GKD-B, GKD-C, or GKD-E slot, depending on the specific requirements and objectives of the indicator. This allows for seamless integration and utilization of the compound signal within the GKD-BT framework.
How does Loxx's GKD (Giga Kaleidoscope Modularized Trading System) implement the NNFX algorithm outlined above?
Loxx's GKD v2.0 system has five types of modules (indicators/strategies). These modules are:
1. GKD-BT - Backtesting module (Volatility, Number 1 in the NNFX algorithm)
2. GKD-B - Baseline module (Baseline and Volatility/Volume, Numbers 1 and 2 in the NNFX algorithm)
3. GKD-C - Confirmation 1/2 and Continuation module (Confirmation 1/2 and Continuation, Numbers 3, 4, and 5 in the NNFX algorithm)
4. GKD-V - Volatility/Volume module (Confirmation 1/2, Number 6 in the NNFX algorithm)
5. GKD-E - Exit module (Exit, Number 7 in the NNFX algorithm)
6. GKD-M - Metamorphosis module (Metamorphosis, Number 8 in the NNFX algorithm, but not part of the NNFX algorithm)
(additional module types will added in future releases)
Each module interacts with every module by passing data to A backtest module wherein the various components of the GKD system are combined to create a trading signal.
That is, the Baseline indicator passes its data to Volatility/Volume. The Volatility/Volume indicator passes its values to the Confirmation 1 indicator. The Confirmation 1 indicator passes its values to the Confirmation 2 indicator. The Confirmation 2 indicator passes its values to the Continuation indicator. The Continuation indicator passes its values to the Exit indicator, and finally, the Exit indicator passes its values to the Backtest strategy.
This chaining of indicators requires that each module conform to Loxx's GKD protocol, therefore allowing for the testing of every possible combination of technical indicators that make up the six components of the NNFX algorithm.
What does the application of the GKD trading system look like?
Example trading system:
Backtest: Optimizer Full GKD Backtest as shown on the chart above
Baseline: Hull Moving Average
Volatility/Volume: Hurst Exponent
Confirmation 1: Fisher Transofrm as shown on the chart above
Confirmation 2: uf2018
Continuation: Coppock Curve
Exit: Rex Oscillator
Metamorphosis: Baseline Optimizer
Each GKD indicator is denoted with a module identifier of either: GKD-BT, GKD-B, GKD-C, GKD-V, GKD-M, or GKD-E. This allows traders to understand to which module each indicator belongs and where each indicator fits into the GKD system.
█ Giga Kaleidoscope Modularized Trading System Signals
Standard Entry
1. GKD-C Confirmation gives signal
2. Baseline agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Volatility/Volume agrees
1-Candle Standard Entry
1a. GKD-C Confirmation gives signal
2a. Baseline agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
Next Candle
1b. Price retraced
2b. Baseline agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Baseline Entry
1. GKD-B Baseline gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Volatility/Volume agrees
7. Confirmation 1 signal was less than 'Maximum Allowable PSBC Bars Back' prior
1-Candle Baseline Entry
1a. GKD-B Baseline gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSBC Bars Back' prior
Next Candle
1b. Price retraced
2b. Baseline agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Volatility/Volume Entry
1. GKD-V Volatility/Volume gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Baseline agrees
7. Confirmation 1 signal was less than 7 candles prior
1-Candle Volatility/Volume Entry
1a. GKD-V Volatility/Volume gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSVVC Bars Back' prior
Next Candle
1b. Price retraced
2b. Volatility/Volume agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Baseline agrees
Confirmation 2 Entry
1. GKD-C Confirmation 2 gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Volatility/Volume agrees
6. Baseline agrees
7. Confirmation 1 signal was less than 7 candles prior
1-Candle Confirmation 2 Entry
1a. GKD-C Confirmation 2 gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSC2C Bars Back' prior
Next Candle
1b. Price retraced
2b. Confirmation 2 agrees
3b. Confirmation 1 agrees
4b. Volatility/Volume agrees
5b. Baseline agrees
PullBack Entry
1a. GKD-B Baseline gives signal
2a. Confirmation 1 agrees
3a. Price is beyond 1.0x Volatility of Baseline
Next Candle
1b. Price inside Goldie Locks Zone Minimum
2b. Price inside Goldie Locks Zone Maximum
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Continuation Entry
1. Standard Entry, 1-Candle Standard Entry, Baseline Entry, 1-Candle Baseline Entry, Volatility/Volume Entry, 1-Candle Volatility/Volume Entry, Confirmation 2 Entry, 1-Candle Confirmation 2 Entry, or Pullback entry triggered previously
2. Baseline hasn't crossed since entry signal trigger
4. Confirmation 1 agrees
5. Baseline agrees
6. Confirmation 2 agrees
█ Connecting to Backtests
All GKD indicators are chained indicators meaning you export the value of the indicators to specialized backtest to creat your GKD trading system. Each indicator contains a proprietary signal generation algo that will only work with GKD backtests. You can find these backtests using the links below.
GKD-BT Giga Confirmation Stack Backtest
GKD-BT Giga Stacks Backtest
GKD-BT Full Giga Kaleidoscope Backtest
GKD-BT Solo Confirmation Super Complex Backtest
GKD-BT Solo Confirmation Complex Backtest
GKD-BT Solo Confirmation Simple Backtest
GKD-M Baseline Optimizer
GKD-M Accuracy Alchemist
GKD-BT Optimizer SCC Backtest
GKD-BT Optimizer SCS Backtest
GKD-BT Optimizer SCS Backtest
GKD-C GKD-BT Optimizer Full GKD Backtest
GKD-BT Multi-Ticker Full GKD Backtest [Loxx]The Giga Kaleidoscope GKD-BT Multi-Ticker Full GKD Backtest is a backtesting module included in Loxx's "Giga Kaleidoscope Modularized Trading System."
█ Giga Kaleidoscope GKD-BT Multi-Ticker Full GKD Backtest
The Multi-Ticker Full GKD Backtest is a Full GKD backtest that allows traders to test single GKD-C Confirmation indicator filtered by a GKD-B Multi-Ticker Baseline, GKD-V Volatility/Volume, and GKD-C Confirmation 2 indicator across 1-10 tickers. In addition. this module adds on various other long and short signls that fall outside the normal GKD standard long and short signals. These additional signals are formed using the GKD-B Multi-Ticker Baseline, GKD-V Volatility/Volume, GKD-C Confirmation 2, and GKD-C Continuation indicators. The purpose of this backtest is to enable traders to quickly evaluate a Baseline, Volatility/Volume, Confirmation 2, and Continuation indicators filtered GKD-C Confirmation 1 indicator across hundreds of tickers within 30-60 minutes.
The backtest module supports testing with 1 take profit and 1 stop loss. It also offers the option to limit testing to a specific date range, allowing simulated forward testing using historical data. This backtest module only includes standard long and short signals. Additionally, users can choose to display or hide a trading panel that provides relevant information about the backtest, statistics, and the current trade. Traders can also select a highlighting threshold for Total Percent Wins and Percent Profitable, and Profit Factor.
To use this indicator:
1. Import 1-10 tickers into the GKD-B Multi-Ticker Baseline indicator
2. Import the value "Input into NEW GKD-BT Multi-ticker Backtest" from the GKD-B Multi-Ticker Baseline indicator into the GKD-BT Multi-Ticker Full GKD Backtest.
3. Select the "Multi-ticker" option in the GKD-V Volatility/Volume indicator
4. Import 1-10 tickers into the GKD-V Volatility/Volume indicator
5. Import the value "Input into NEW GKD-BT Multi-ticker Backtest" from the GKD-V Volatility/Volume indicator into the GKD-BT Multi-Ticker Full GKD Backtest.
6. Select the "Multi-ticker" option in the GKD-C Confirmation 1 indicator.
7. Import 1-10 tickers into the GKD-C Confirmation 1 indicator.
8. Import the same 1-10 indicators into the GKD-BT Multi-Ticker Full GKD Backtest.
9. Import the value "Input into NEW GKD-BT Multi-ticker Backtest" from the GKD-C Confirmation 1 indicator into the GKD-BT Multi-Ticker Full GKD Backtest.
10. Import 1-10 tickers into the GKD-C Confirmation 2 indicator.
11. Import the same 1-10 indicators into the GKD-BT Multi-Ticker Full GKD Backtest.
12. Import the value "Input into NEW GKD-BT Multi-ticker Backtest" from the GKD-C Confirmation 2 indicator into the GKD-BT Multi-Ticker Full GKD Backtest.
13. Import 1-10 tickers into the GKD-C Continuation indicator.
14. Import the same 1-10 indicators into the GKD-BT Multi-Ticker Full GKD Backtest.
15. Import the value "Input into NEW GKD-BT Multi-ticker Backtest" from the GKD-C Continuation indicator into the GKD-BT Multi-Ticker Full GKD Backtest.
16. When importing tickers, ensure that you import the same type of tickers for all 1-10 tickers. For example, test only FX or Cryptocurrency or Stocks. Do not combine different tradable asset types.
17. Make sure that your chart is set to a ticker that corresponds to the tradable asset type. For cryptocurrency testing, set the chart to BTCUSDT. For Forex testing, set the chart to EURUSD.
This backtest includes the following metrics:
1. Net profit: Overall profit or loss achieved.
2. Total Closed Trades: Total number of closed trades, both winning and losing.
3. Total Percent Wins: Total wins, whether long or short, for the selected time interval regardless of commissions and other profit-modifying addons.
4. Percent Profitable: Total wins, whether long or short, that are also profitable, taking commissions into account.
5. Profit Factor: The ratio of gross profits to gross losses, indicating how much money the strategy made for every unit of money it lost.
6. Average Profit per Trade: The average gain or loss per trade, calculated by dividing the net profit by the total number of closed trades.
7. Average Number of Bars in Trade: The average number of bars that elapsed during trades for all closed trades.
Summary of notable settings:
Input Tickers separated by commas: Allows the user to input tickers separated by commas, specifying the symbols or tickers of financial instruments used in the backtest. The tickers should follow the format "EXCHANGE:TICKER" (e.g., "NASDAQ:AAPL, NYSE:MSFT").
Import GKD-B Baseline: Imports the "GKD-B Multi-Ticker Baseline" indicator.
Import GKD-V Volatility/Volume: Imports the "GKD-V Volatility/Volume" indicator.
Import GKD-C Confirmation: Imports the "GKD-C" indicator.
Activate Baseline: Activates the GKD-B Multi-Ticker Baseline.
Activate Goldie Locks Zone Minimum Threshold: Activates the inner Goldie Locks Zone from the GKD-B Multi-Ticker Baseline
Activate Goldie Locks Zone Maximum Threshold: Activates the outer Goldie Locks Zone from the GKD-B Multi-Ticker Baseline
Activate Volatility/Volume: Activates the GKD-V Volatility/Volume indicator.
Initial Capital: Represents the starting account balance for the backtest, denominated in the base currency of the trading account.
Order Size: Determines the quantity of contracts traded in each trade.
Order Type: Specifies the type of order used in the backtest, either "Contracts" or "% Equity."
Commission: Represents the commission per order or transaction cost incurred in each trade.
**the backtest data rendered to the chart above uses $5 commission per trade and 10% equity per trade with $1 million initial capital. Each backtest result for each ticker assumes these same inputs. The results are NOT cumulative, they are separate and isolate per ticker and trading side, long or short**
█ Volatility Types included
The GKD system utilizes volatility-based take profits and stop losses. Each take profit and stop loss is calculated as a multiple of volatility. You can change the values of the multipliers in the settings as well.
This module includes 17 types of volatility:
Close-to-Close
Parkinson
Garman-Klass
Rogers-Satchell
Yang-Zhang
Garman-Klass-Yang-Zhang
Exponential Weighted Moving Average
Standard Deviation of Log Returns
Pseudo GARCH(2,2)
Average True Range
True Range Double
Standard Deviation
Adaptive Deviation
Median Absolute Deviation
Efficiency-Ratio Adaptive ATR
Mean Absolute Deviation
Static Percent
Various volatility estimators and indicators that investors and traders can use to measure the dispersion or volatility of a financial instrument's price. Each estimator has its strengths and weaknesses, and the choice of estimator should depend on the specific needs and circumstances of the user.
Close-to-Close
Close-to-Close volatility is a classic and widely used volatility measure, sometimes referred to as historical volatility.
Volatility is an indicator of the speed of a stock price change. A stock with high volatility is one where the price changes rapidly and with a larger amplitude. The more volatile a stock is, the riskier it is.
Close-to-close historical volatility is calculated using only a stock's closing prices. It is the simplest volatility estimator. However, in many cases, it is not precise enough. Stock prices could jump significantly during a trading session and return to the opening value at the end. That means that a considerable amount of price information is not taken into account by close-to-close volatility.
Despite its drawbacks, Close-to-Close volatility is still useful in cases where the instrument doesn't have intraday prices. For example, mutual funds calculate their net asset values daily or weekly, and thus their prices are not suitable for more sophisticated volatility estimators.
Parkinson
Parkinson volatility is a volatility measure that uses the stock’s high and low price of the day.
The main difference between regular volatility and Parkinson volatility is that the latter uses high and low prices for a day, rather than only the closing price. This is useful as close-to-close prices could show little difference while large price movements could have occurred during the day. Thus, Parkinson's volatility is considered more precise and requires less data for calculation than close-to-close volatility.
One drawback of this estimator is that it doesn't take into account price movements after the market closes. Hence, it systematically undervalues volatility. This drawback is addressed in the Garman-Klass volatility estimator.
Garman-Klass
Garman-Klass is a volatility estimator that incorporates open, low, high, and close prices of a security.
Garman-Klass volatility extends Parkinson's volatility by taking into account the opening and closing prices. As markets are most active during the opening and closing of a trading session, it makes volatility estimation more accurate.
Garman and Klass also assumed that the process of price change follows a continuous diffusion process (Geometric Brownian motion). However, this assumption has several drawbacks. The method is not robust for opening jumps in price and trend movements.
Despite its drawbacks, the Garman-Klass estimator is still more effective than the basic formula since it takes into account not only the price at the beginning and end of the time interval but also intraday price extremes.
Researchers Rogers and Satchell have proposed a more efficient method for assessing historical volatility that takes into account price trends. See Rogers-Satchell Volatility for more detail.
Rogers-Satchell
Rogers-Satchell is an estimator for measuring the volatility of securities with an average return not equal to zero.
Unlike Parkinson and Garman-Klass estimators, Rogers-Satchell incorporates a drift term (mean return not equal to zero). As a result, it provides better volatility estimation when the underlying is trending.
The main disadvantage of this method is that it does not take into account price movements between trading sessions. This leads to an underestimation of volatility since price jumps periodically occur in the market precisely at the moments between sessions.
A more comprehensive estimator that also considers the gaps between sessions was developed based on the Rogers-Satchel formula in the 2000s by Yang-Zhang. See Yang Zhang Volatility for more detail.
Yang-Zhang
Yang Zhang is a historical volatility estimator that handles both opening jumps and the drift and has a minimum estimation error.
Yang-Zhang volatility can be thought of as a combination of the overnight (close-to-open volatility) and a weighted average of the Rogers-Satchell volatility and the day’s open-to-close volatility. It is considered to be 14 times more efficient than the close-to-close estimator.
Garman-Klass-Yang-Zhang
Garman-Klass-Yang-Zhang (GKYZ) volatility estimator incorporates the returns of open, high, low, and closing prices in its calculation.
GKYZ volatility estimator takes into account overnight jumps but not the trend, i.e., it assumes that the underlying asset follows a Geometric Brownian Motion (GBM) process with zero drift. Therefore, the GKYZ volatility estimator tends to overestimate the volatility when the drift is different from zero. However, for a GBM process, this estimator is eight times more efficient than the close-to-close volatility estimator.
Exponential Weighted Moving Average
The Exponentially Weighted Moving Average (EWMA) is a quantitative or statistical measure used to model or describe a time series. The EWMA is widely used in finance, with the main applications being technical analysis and volatility modeling.
The moving average is designed such that older observations are given lower weights. The weights decrease exponentially as the data point gets older – hence the name exponentially weighted.
The only decision a user of the EWMA must make is the parameter lambda. The parameter decides how important the current observation is in the calculation of the EWMA. The higher the value of lambda, the more closely the EWMA tracks the original time series.
Standard Deviation of Log Returns
This is the simplest calculation of volatility. It's the standard deviation of ln(close/close(1)).
Pseudo GARCH(2,2)
This is calculated using a short- and long-run mean of variance multiplied by ?.
?avg(var;M) + (1 ? ?) avg(var;N) = 2?var/(M+1-(M-1)L) + 2(1-?)var/(M+1-(M-1)L)
Solving for ? can be done by minimizing the mean squared error of estimation; that is, regressing L^-1var - avg(var; N) against avg(var; M) - avg(var; N) and using the resulting beta estimate as ?.
Average True Range
The average true range (ATR) is a technical analysis indicator, introduced by market technician J. Welles Wilder Jr. in his book New Concepts in Technical Trading Systems, that measures market volatility by decomposing the entire range of an asset price for that period.
The true range indicator is taken as the greatest of the following: current high less the current low; the absolute value of the current high less the previous close; and the absolute value of the current low less the previous close. The ATR is then a moving average, generally using 14 days, of the true ranges.
True Range Double
A special case of ATR that attempts to correct for volatility skew.
Standard Deviation
Standard deviation is a statistic that measures the dispersion of a dataset relative to its mean and is calculated as the square root of the variance. The standard deviation is calculated as the square root of variance by determining each data point's deviation relative to the mean. If the data points are further from the mean, there is a higher deviation within the data set; thus, the more spread out the data, the higher the standard deviation.
Adaptive Deviation
By definition, the Standard Deviation (STD, also represented by the Greek letter sigma ? or the Latin letter s) is a measure that is used to quantify the amount of variation or dispersion of a set of data values. In technical analysis, we usually use it to measure the level of current volatility.
Standard Deviation is based on Simple Moving Average calculation for mean value. This version of standard deviation uses the properties of EMA to calculate what can be called a new type of deviation, and since it is based on EMA, we can call it EMA deviation. Additionally, Perry Kaufman's efficiency ratio is used to make it adaptive (since all EMA type calculations are nearly perfect for adapting).
The difference when compared to the standard is significant--not just because of EMA usage, but the efficiency ratio makes it a "bit more logical" in very volatile market conditions.
Median Absolute Deviation
The median absolute deviation is a measure of statistical dispersion. Moreover, the MAD is a robust statistic, being more resilient to outliers in a data set than the standard deviation. In the standard deviation, the distances from the mean are squared, so large deviations are weighted more heavily, and thus outliers can heavily influence it. In the MAD, the deviations of a small number of outliers are irrelevant.
Because the MAD is a more robust estimator of scale than the sample variance or standard deviation, it works better with distributions without a mean or variance, such as the Cauchy distribution.
For this indicator, a manual recreation of the quantile function in Pine Script is used. This is so users have a full inside view into how this is calculated.
Efficiency-Ratio Adaptive ATR
Average True Range (ATR) is a widely used indicator for many occasions in technical analysis. It is calculated as the RMA of the true range. This version adds a "twist": it uses Perry Kaufman's Efficiency Ratio to calculate adaptive true range.
Mean Absolute Deviation
The mean absolute deviation (MAD) is a measure of variability that indicates the average distance between observations and their mean. MAD uses the original units of the data, which simplifies interpretation. Larger values signify that the data points spread out further from the average. Conversely, lower values correspond to data points bunching closer to it. The mean absolute deviation is also known as the mean deviation and average absolute deviation.
This definition of the mean absolute deviation sounds similar to the standard deviation (SD). While both measure variability, they have different calculations. In recent years, some proponents of MAD have suggested that it replace the SD as the primary measure because it is a simpler concept that better fits real life.
█ Giga Kaleidoscope Modularized Trading System
Core components of an NNFX algorithmic trading strategy
The NNFX algorithm is built on the principles of trend, momentum, and volatility. There are six core components in the NNFX trading algorithm:
1. Volatility - price volatility; e.g., Average True Range, True Range Double, Close-to-Close, etc.
2. Baseline - a moving average to identify price trend
3. Confirmation 1 - a technical indicator used to identify trends
4. Confirmation 2 - a technical indicator used to identify trends
5. Continuation - a technical indicator used to identify trends
6. Volatility/Volume - a technical indicator used to identify volatility/volume breakouts/breakdown
7. Exit - a technical indicator used to determine when a trend is exhausted
8. Metamorphosis - a technical indicator that produces a compound signal from the combination of other GKD indicators*
*(not part of the NNFX algorithm)
What is Volatility in the NNFX trading system?
In the NNFX (No Nonsense Forex) trading system, ATR (Average True Range) is typically used to measure the volatility of an asset. It is used as a part of the system to help determine the appropriate stop loss and take profit levels for a trade. ATR is calculated by taking the average of the true range values over a specified period.
True range is calculated as the maximum of the following values:
-Current high minus the current low
-Absolute value of the current high minus the previous close
-Absolute value of the current low minus the previous close
ATR is a dynamic indicator that changes with changes in volatility. As volatility increases, the value of ATR increases, and as volatility decreases, the value of ATR decreases. By using ATR in NNFX system, traders can adjust their stop loss and take profit levels according to the volatility of the asset being traded. This helps to ensure that the trade is given enough room to move, while also minimizing potential losses.
Other types of volatility include True Range Double (TRD), Close-to-Close, and Garman-Klass
What is a Baseline indicator?
The baseline is essentially a moving average, and is used to determine the overall direction of the market.
The baseline in the NNFX system is used to filter out trades that are not in line with the long-term trend of the market. The baseline is plotted on the chart along with other indicators, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR).
Trades are only taken when the price is in the same direction as the baseline. For example, if the baseline is sloping upwards, only long trades are taken, and if the baseline is sloping downwards, only short trades are taken. This approach helps to ensure that trades are in line with the overall trend of the market, and reduces the risk of entering trades that are likely to fail.
By using a baseline in the NNFX system, traders can have a clear reference point for determining the overall trend of the market, and can make more informed trading decisions. The baseline helps to filter out noise and false signals, and ensures that trades are taken in the direction of the long-term trend.
What is a Confirmation indicator?
Confirmation indicators are technical indicators that are used to confirm the signals generated by primary indicators. Primary indicators are the core indicators used in the NNFX system, such as the Average True Range (ATR), the Moving Average (MA), and the Relative Strength Index (RSI).
The purpose of the confirmation indicators is to reduce false signals and improve the accuracy of the trading system. They are designed to confirm the signals generated by the primary indicators by providing additional information about the strength and direction of the trend.
Some examples of confirmation indicators that may be used in the NNFX system include the Bollinger Bands, the MACD (Moving Average Convergence Divergence), and the MACD Oscillator. These indicators can provide information about the volatility, momentum, and trend strength of the market, and can be used to confirm the signals generated by the primary indicators.
In the NNFX system, confirmation indicators are used in combination with primary indicators and other filters to create a trading system that is robust and reliable. By using multiple indicators to confirm trading signals, the system aims to reduce the risk of false signals and improve the overall profitability of the trades.
What is a Continuation indicator?
In the NNFX (No Nonsense Forex) trading system, a continuation indicator is a technical indicator that is used to confirm a current trend and predict that the trend is likely to continue in the same direction. A continuation indicator is typically used in conjunction with other indicators in the system, such as a baseline indicator, to provide a comprehensive trading strategy.
What is a Volatility/Volume indicator?
Volume indicators, such as the On Balance Volume (OBV), the Chaikin Money Flow (CMF), or the Volume Price Trend (VPT), are used to measure the amount of buying and selling activity in a market. They are based on the trading volume of the market, and can provide information about the strength of the trend. In the NNFX system, volume indicators are used to confirm trading signals generated by the Moving Average and the Relative Strength Index. Volatility indicators include Average Direction Index, Waddah Attar, and Volatility Ratio. In the NNFX trading system, volatility is a proxy for volume and vice versa.
By using volume indicators as confirmation tools, the NNFX trading system aims to reduce the risk of false signals and improve the overall profitability of trades. These indicators can provide additional information about the market that is not captured by the primary indicators, and can help traders to make more informed trading decisions. In addition, volume indicators can be used to identify potential changes in market trends and to confirm the strength of price movements.
What is an Exit indicator?
The exit indicator is used in conjunction with other indicators in the system, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR), to provide a comprehensive trading strategy.
The exit indicator in the NNFX system can be any technical indicator that is deemed effective at identifying optimal exit points. Examples of exit indicators that are commonly used include the Parabolic SAR, the Average Directional Index (ADX), and the Chandelier Exit.
The purpose of the exit indicator is to identify when a trend is likely to reverse or when the market conditions have changed, signaling the need to exit a trade. By using an exit indicator, traders can manage their risk and prevent significant losses.
In the NNFX system, the exit indicator is used in conjunction with a stop loss and a take profit order to maximize profits and minimize losses. The stop loss order is used to limit the amount of loss that can be incurred if the trade goes against the trader, while the take profit order is used to lock in profits when the trade is moving in the trader's favor.
Overall, the use of an exit indicator in the NNFX trading system is an important component of a comprehensive trading strategy. It allows traders to manage their risk effectively and improve the profitability of their trades by exiting at the right time.
What is an Metamorphosis indicator?
The concept of a metamorphosis indicator involves the integration of two or more GKD indicators to generate a compound signal. This is achieved by evaluating the accuracy of each indicator and selecting the signal from the indicator with the highest accuracy. As an illustration, let's consider a scenario where we calculate the accuracy of 10 indicators and choose the signal from the indicator that demonstrates the highest accuracy.
The resulting output from the metamorphosis indicator can then be utilized in a GKD-BT backtest by occupying a slot that aligns with the purpose of the metamorphosis indicator. The slot can be a GKD-B, GKD-C, or GKD-E slot, depending on the specific requirements and objectives of the indicator. This allows for seamless integration and utilization of the compound signal within the GKD-BT framework.
How does Loxx's GKD (Giga Kaleidoscope Modularized Trading System) implement the NNFX algorithm outlined above?
Loxx's GKD v2.0 system has five types of modules (indicators/strategies). These modules are:
1. GKD-BT - Backtesting module (Volatility, Number 1 in the NNFX algorithm)
2. GKD-B - Baseline module (Baseline and Volatility/Volume, Numbers 1 and 2 in the NNFX algorithm)
3. GKD-C - Confirmation 1/2 and Continuation module (Confirmation 1/2 and Continuation, Numbers 3, 4, and 5 in the NNFX algorithm)
4. GKD-V - Volatility/Volume module (Confirmation 1/2, Number 6 in the NNFX algorithm)
5. GKD-E - Exit module (Exit, Number 7 in the NNFX algorithm)
6. GKD-M - Metamorphosis module (Metamorphosis, Number 8 in the NNFX algorithm, but not part of the NNFX algorithm)
(additional module types will added in future releases)
Each module interacts with every module by passing data to A backtest module wherein the various components of the GKD system are combined to create a trading signal.
That is, the Baseline indicator passes its data to Volatility/Volume. The Volatility/Volume indicator passes its values to the Confirmation 1 indicator. The Confirmation 1 indicator passes its values to the Confirmation 2 indicator. The Confirmation 2 indicator passes its values to the Continuation indicator. The Continuation indicator passes its values to the Exit indicator, and finally, the Exit indicator passes its values to the Backtest strategy.
This chaining of indicators requires that each module conform to Loxx's GKD protocol, therefore allowing for the testing of every possible combination of technical indicators that make up the six components of the NNFX algorithm.
What does the application of the GKD trading system look like?
Example trading system:
Backtest: Multi-Ticker Full GKD Backtest as shown on the chart above
Baseline: Hull Moving Average as shown on the chart above
Volatility/Volume: Hurst Exponent as shown on the chart above
Confirmation 1: Fisher Transform as shown on the chart above
Confirmation 2: uf2018 as shown on the chart above
Continuation: Coppock Curve as shown on the chart above
Exit: Rex Oscillator
Metamorphosis: Baseline Optimizer
Each GKD indicator is denoted with a module identifier of either: GKD-BT, GKD-B, GKD-C, GKD-V, GKD-M, or GKD-E. This allows traders to understand to which module each indicator belongs and where each indicator fits into the GKD system.
█ Giga Kaleidoscope Modularized Trading System Signals
Standard Entry
1. GKD-C Confirmation gives signal
2. Baseline agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Volatility/Volume agrees
1-Candle Standard Entry
1a. GKD-C Confirmation gives signal
2a. Baseline agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
Next Candle
1b. Price retraced
2b. Baseline agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Baseline Entry
1. GKD-B Basline gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Volatility/Volume agrees
7. Confirmation 1 signal was less than 'Maximum Allowable PSBC Bars Back' prior
1-Candle Baseline Entry
1a. GKD-B Baseline gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSBC Bars Back' prior
Next Candle
1b. Price retraced
2b. Baseline agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Volatility/Volume Entry
1. GKD-V Volatility/Volume gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Baseline agrees
7. Confirmation 1 signal was less than 7 candles prior
1-Candle Volatility/Volume Entry
1a. GKD-V Volatility/Volume gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSVVC Bars Back' prior
Next Candle
1b. Price retraced
2b. Volatility/Volume agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Baseline agrees
Confirmation 2 Entry
1. GKD-C Confirmation 2 gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Volatility/Volume agrees
6. Baseline agrees
7. Confirmation 1 signal was less than 7 candles prior
1-Candle Confirmation 2 Entry
1a. GKD-C Confirmation 2 gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSC2C Bars Back' prior
Next Candle
1b. Price retraced
2b. Confirmation 2 agrees
3b. Confirmation 1 agrees
4b. Volatility/Volume agrees
5b. Baseline agrees
PullBack Entry
1a. GKD-B Baseline gives signal
2a. Confirmation 1 agrees
3a. Price is beyond 1.0x Volatility of Baseline
Next Candle
1b. Price inside Goldie Locks Zone Minimum
2b. Price inside Goldie Locks Zone Maximum
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Continuation Entry
1. Standard Entry, 1-Candle Standard Entry, Baseline Entry, 1-Candle Baseline Entry, Volatility/Volume Entry, 1-Candle Volatility/Volume Entry, Confirmation 2 Entry, 1-Candle Confirmation 2 Entry, or Pullback entry triggered previously
2. Baseline hasn't crossed since entry signal trigger
4. Confirmation 1 agrees
5. Baseline agrees
6. Confirmation 2 agrees
█ Connecting to Backtests
All GKD indicators are chained indicators meaning you export the value of the indicators to specialized backtest to creat your GKD trading system. Each indicator contains a proprietary signal generation algo that will only work with GKD backtests. You can find these backtests using the links below.
GKD-BT Giga Confirmation Stack Backtest
GKD-BT Giga Stacks Backtest
GKD-BT Full Giga Kaleidoscope Backtest
GKD-BT Solo Confirmation Super Complex Backtest
GKD-BT Solo Confirmation Complex Backtest
GKD-BT Solo Confirmation Simple Backtest
GKD-M Baseline Optimizer
GKD-M Accuracy Alchemist
GKD-BT Multi-Ticker SCC Backtest
GKD-BT Multi-Ticker SCS Backtest
GKD-BT Multi-Ticker SCSC Backtest [Loxx]The Giga Kaleidoscope GKD-BT Multi-Ticker SCSC Backtest is a backtesting module included in Loxx's "Giga Kaleidoscope Modularized Trading System."
█ Giga Kaleidoscope GKD-BT Multi-Ticker SCSC Backtest
The Multi-Ticker SCSC Backtest is a Solo Confirmation Super Complex backtest that allows traders to test single GKD-C Confirmation indicator filtered by both a GKD-B Multi-Ticker Baseline and GKD-V Volatility/Volume indicator across 1-10 tickers. In addition. this module adds on various other long and short signls that fall outside the normal GKD standard long and short signals. These additional signals are formed using the GKD-B Multi-Ticker Baseline, GKD-V Volatility/Volume, and GKD-C Continuation indicators. The purpose of this backtest is to enable traders to quickly evaluate a Baseline, Volatility/Volume, and Continuation indicators filtered GKD-C Confirmation 1 indicator across hundreds of tickers within 30-60 minutes.
The backtest module supports testing with 1 take profit and 1 stop loss. It also offers the option to limit testing to a specific date range, allowing simulated forward testing using historical data. This backtest module only includes standard long and short signals. Additionally, users can choose to display or hide a trading panel that provides relevant information about the backtest, statistics, and the current trade. Traders can also select a highlighting threshold for Total Percent Wins and Percent Profitable, and Profit Factor.
To use this indicator:
1. Import 1-10 tickers into the GKD-B Multi-Ticker Baseline indicator
2. Import the value "Input into NEW GKD-BT Multi-ticker Backtest" from the GKD-B Multi-Ticker Baseline indicator into the GKD-BT Multi-Ticker SCSC Backtest.
3. Select the "Multi-ticker" option in the GKD-V Volatility/Volume indicator
4. Import 1-10 tickers into the GKD-V Volatility/Volume indicator
5. Import the value "Input into NEW GKD-BT Multi-ticker Backtest" from the GKD-V Volatility/Volume indicator into the GKD-BT Multi-Ticker SCSC Backtest.
6. Select the "Multi-ticker" option in the GKD-C Confirmation indicator.
7. Import 1-10 tickers into the GKD-C Confirmation indicator.
8. Import the same 1-10 indicators into the GKD-BT Multi-Ticker SCSC Backtest.
9. Import the value "Input into NEW GKD-BT Multi-ticker Backtest" from the GKD-C Confirmation indicator into the GKD-BT Multi-Ticker SCSC Backtest.
10. Import 1-10 tickers into the GKD-C Continuation indicator.
11. Import the same 1-10 indicators into the GKD-BT Multi-Ticker SCSC Backtest.
12. Import the value "Input into NEW GKD-BT Multi-ticker Backtest" from the GKD-C Continuation indicator into the GKD-BT Multi-Ticker SCSC Backtest.
13. When importing tickers, ensure that you import the same type of tickers for all 1-10 tickers. For example, test only FX or Cryptocurrency or Stocks. Do not combine different tradable asset types.
14. Make sure that your chart is set to a ticker that corresponds to the tradable asset type. For cryptocurrency testing, set the chart to BTCUSDT. For Forex testing, set the chart to EURUSD.
This backtest includes the following metrics:
1. Net profit: Overall profit or loss achieved.
2. Total Closed Trades: Total number of closed trades, both winning and losing.
3. Total Percent Wins: Total wins, whether long or short, for the selected time interval regardless of commissions and other profit-modifying addons.
4. Percent Profitable: Total wins, whether long or short, that are also profitable, taking commissions into account.
5. Profit Factor: The ratio of gross profits to gross losses, indicating how much money the strategy made for every unit of money it lost.
6. Average Profit per Trade: The average gain or loss per trade, calculated by dividing the net profit by the total number of closed trades.
7. Average Number of Bars in Trade: The average number of bars that elapsed during trades for all closed trades.
Summary of notable settings:
Input Tickers separated by commas: Allows the user to input tickers separated by commas, specifying the symbols or tickers of financial instruments used in the backtest. The tickers should follow the format "EXCHANGE:TICKER" (e.g., "NASDAQ:AAPL, NYSE:MSFT").
Import GKD-B Baseline: Imports the "GKD-B Multi-Ticker Baseline" indicator.
Import GKD-V Volatility/Volume: Imports the "GKD-V Volatility/Volume" indicator.
Import GKD-C Confirmation: Imports the "GKD-C Confirmation" indicator.
Import GKD-C Continuation: Imports the "GKD-C Continuation" indicator.
Initial Capital: Represents the starting account balance for the backtest, denominated in the base currency of the trading account.
Order Size: Determines the quantity of contracts traded in each trade.
Order Type: Specifies the type of order used in the backtest, either "Contracts" or "% Equity."
Commission: Represents the commission per order or transaction cost incurred in each trade.
**the backtest data rendered to the chart above uses $5 commission per trade and 10% equity per trade with $1 million initial capital. Each backtest result for each ticker assumes these same inputs. The results are NOT cumulative, they are separate and isolate per ticker and trading side, long or short**
█ Volatility Types included
The GKD system utilizes volatility-based take profits and stop losses. Each take profit and stop loss is calculated as a multiple of volatility. You can change the values of the multipliers in the settings as well.
This module includes 17 types of volatility:
Close-to-Close
Parkinson
Garman-Klass
Rogers-Satchell
Yang-Zhang
Garman-Klass-Yang-Zhang
Exponential Weighted Moving Average
Standard Deviation of Log Returns
Pseudo GARCH(2,2)
Average True Range
True Range Double
Standard Deviation
Adaptive Deviation
Median Absolute Deviation
Efficiency-Ratio Adaptive ATR
Mean Absolute Deviation
Static Percent
Various volatility estimators and indicators that investors and traders can use to measure the dispersion or volatility of a financial instrument's price. Each estimator has its strengths and weaknesses, and the choice of estimator should depend on the specific needs and circumstances of the user.
Close-to-Close
Close-to-Close volatility is a classic and widely used volatility measure, sometimes referred to as historical volatility.
Volatility is an indicator of the speed of a stock price change. A stock with high volatility is one where the price changes rapidly and with a larger amplitude. The more volatile a stock is, the riskier it is.
Close-to-close historical volatility is calculated using only a stock's closing prices. It is the simplest volatility estimator. However, in many cases, it is not precise enough. Stock prices could jump significantly during a trading session and return to the opening value at the end. That means that a considerable amount of price information is not taken into account by close-to-close volatility.
Despite its drawbacks, Close-to-Close volatility is still useful in cases where the instrument doesn't have intraday prices. For example, mutual funds calculate their net asset values daily or weekly, and thus their prices are not suitable for more sophisticated volatility estimators.
Parkinson
Parkinson volatility is a volatility measure that uses the stock’s high and low price of the day.
The main difference between regular volatility and Parkinson volatility is that the latter uses high and low prices for a day, rather than only the closing price. This is useful as close-to-close prices could show little difference while large price movements could have occurred during the day. Thus, Parkinson's volatility is considered more precise and requires less data for calculation than close-to-close volatility.
One drawback of this estimator is that it doesn't take into account price movements after the market closes. Hence, it systematically undervalues volatility. This drawback is addressed in the Garman-Klass volatility estimator.
Garman-Klass
Garman-Klass is a volatility estimator that incorporates open, low, high, and close prices of a security.
Garman-Klass volatility extends Parkinson's volatility by taking into account the opening and closing prices. As markets are most active during the opening and closing of a trading session, it makes volatility estimation more accurate.
Garman and Klass also assumed that the process of price change follows a continuous diffusion process (Geometric Brownian motion). However, this assumption has several drawbacks. The method is not robust for opening jumps in price and trend movements.
Despite its drawbacks, the Garman-Klass estimator is still more effective than the basic formula since it takes into account not only the price at the beginning and end of the time interval but also intraday price extremes.
Researchers Rogers and Satchell have proposed a more efficient method for assessing historical volatility that takes into account price trends. See Rogers-Satchell Volatility for more detail.
Rogers-Satchell
Rogers-Satchell is an estimator for measuring the volatility of securities with an average return not equal to zero.
Unlike Parkinson and Garman-Klass estimators, Rogers-Satchell incorporates a drift term (mean return not equal to zero). As a result, it provides better volatility estimation when the underlying is trending.
The main disadvantage of this method is that it does not take into account price movements between trading sessions. This leads to an underestimation of volatility since price jumps periodically occur in the market precisely at the moments between sessions.
A more comprehensive estimator that also considers the gaps between sessions was developed based on the Rogers-Satchel formula in the 2000s by Yang-Zhang. See Yang Zhang Volatility for more detail.
Yang-Zhang
Yang Zhang is a historical volatility estimator that handles both opening jumps and the drift and has a minimum estimation error.
Yang-Zhang volatility can be thought of as a combination of the overnight (close-to-open volatility) and a weighted average of the Rogers-Satchell volatility and the day’s open-to-close volatility. It is considered to be 14 times more efficient than the close-to-close estimator.
Garman-Klass-Yang-Zhang
Garman-Klass-Yang-Zhang (GKYZ) volatility estimator incorporates the returns of open, high, low, and closing prices in its calculation.
GKYZ volatility estimator takes into account overnight jumps but not the trend, i.e., it assumes that the underlying asset follows a Geometric Brownian Motion (GBM) process with zero drift. Therefore, the GKYZ volatility estimator tends to overestimate the volatility when the drift is different from zero. However, for a GBM process, this estimator is eight times more efficient than the close-to-close volatility estimator.
Exponential Weighted Moving Average
The Exponentially Weighted Moving Average (EWMA) is a quantitative or statistical measure used to model or describe a time series. The EWMA is widely used in finance, with the main applications being technical analysis and volatility modeling.
The moving average is designed such that older observations are given lower weights. The weights decrease exponentially as the data point gets older – hence the name exponentially weighted.
The only decision a user of the EWMA must make is the parameter lambda. The parameter decides how important the current observation is in the calculation of the EWMA. The higher the value of lambda, the more closely the EWMA tracks the original time series.
Standard Deviation of Log Returns
This is the simplest calculation of volatility. It's the standard deviation of ln(close/close(1)).
Pseudo GARCH(2,2)
This is calculated using a short- and long-run mean of variance multiplied by ?.
?avg(var;M) + (1 ? ?) avg(var;N) = 2?var/(M+1-(M-1)L) + 2(1-?)var/(M+1-(M-1)L)
Solving for ? can be done by minimizing the mean squared error of estimation; that is, regressing L^-1var - avg(var; N) against avg(var; M) - avg(var; N) and using the resulting beta estimate as ?.
Average True Range
The average true range (ATR) is a technical analysis indicator, introduced by market technician J. Welles Wilder Jr. in his book New Concepts in Technical Trading Systems, that measures market volatility by decomposing the entire range of an asset price for that period.
The true range indicator is taken as the greatest of the following: current high less the current low; the absolute value of the current high less the previous close; and the absolute value of the current low less the previous close. The ATR is then a moving average, generally using 14 days, of the true ranges.
True Range Double
A special case of ATR that attempts to correct for volatility skew.
Standard Deviation
Standard deviation is a statistic that measures the dispersion of a dataset relative to its mean and is calculated as the square root of the variance. The standard deviation is calculated as the square root of variance by determining each data point's deviation relative to the mean. If the data points are further from the mean, there is a higher deviation within the data set; thus, the more spread out the data, the higher the standard deviation.
Adaptive Deviation
By definition, the Standard Deviation (STD, also represented by the Greek letter sigma ? or the Latin letter s) is a measure that is used to quantify the amount of variation or dispersion of a set of data values. In technical analysis, we usually use it to measure the level of current volatility.
Standard Deviation is based on Simple Moving Average calculation for mean value. This version of standard deviation uses the properties of EMA to calculate what can be called a new type of deviation, and since it is based on EMA, we can call it EMA deviation. Additionally, Perry Kaufman's efficiency ratio is used to make it adaptive (since all EMA type calculations are nearly perfect for adapting).
The difference when compared to the standard is significant--not just because of EMA usage, but the efficiency ratio makes it a "bit more logical" in very volatile market conditions.
Median Absolute Deviation
The median absolute deviation is a measure of statistical dispersion. Moreover, the MAD is a robust statistic, being more resilient to outliers in a data set than the standard deviation. In the standard deviation, the distances from the mean are squared, so large deviations are weighted more heavily, and thus outliers can heavily influence it. In the MAD, the deviations of a small number of outliers are irrelevant.
Because the MAD is a more robust estimator of scale than the sample variance or standard deviation, it works better with distributions without a mean or variance, such as the Cauchy distribution.
For this indicator, a manual recreation of the quantile function in Pine Script is used. This is so users have a full inside view into how this is calculated.
Efficiency-Ratio Adaptive ATR
Average True Range (ATR) is a widely used indicator for many occasions in technical analysis. It is calculated as the RMA of the true range. This version adds a "twist": it uses Perry Kaufman's Efficiency Ratio to calculate adaptive true range.
Mean Absolute Deviation
The mean absolute deviation (MAD) is a measure of variability that indicates the average distance between observations and their mean. MAD uses the original units of the data, which simplifies interpretation. Larger values signify that the data points spread out further from the average. Conversely, lower values correspond to data points bunching closer to it. The mean absolute deviation is also known as the mean deviation and average absolute deviation.
This definition of the mean absolute deviation sounds similar to the standard deviation (SD). While both measure variability, they have different calculations. In recent years, some proponents of MAD have suggested that it replace the SD as the primary measure because it is a simpler concept that better fits real life.
█ Giga Kaleidoscope Modularized Trading System
Core components of an NNFX algorithmic trading strategy
The NNFX algorithm is built on the principles of trend, momentum, and volatility. There are six core components in the NNFX trading algorithm:
1. Volatility - price volatility; e.g., Average True Range, True Range Double, Close-to-Close, etc.
2. Baseline - a moving average to identify price trend
3. Confirmation 1 - a technical indicator used to identify trends
4. Confirmation 2 - a technical indicator used to identify trends
5. Continuation - a technical indicator used to identify trends
6. Volatility/Volume - a technical indicator used to identify volatility/volume breakouts/breakdown
7. Exit - a technical indicator used to determine when a trend is exhausted
8. Metamorphosis - a technical indicator that produces a compound signal from the combination of other GKD indicators*
*(not part of the NNFX algorithm)
What is Volatility in the NNFX trading system?
In the NNFX (No Nonsense Forex) trading system, ATR (Average True Range) is typically used to measure the volatility of an asset. It is used as a part of the system to help determine the appropriate stop loss and take profit levels for a trade. ATR is calculated by taking the average of the true range values over a specified period.
True range is calculated as the maximum of the following values:
-Current high minus the current low
-Absolute value of the current high minus the previous close
-Absolute value of the current low minus the previous close
ATR is a dynamic indicator that changes with changes in volatility. As volatility increases, the value of ATR increases, and as volatility decreases, the value of ATR decreases. By using ATR in NNFX system, traders can adjust their stop loss and take profit levels according to the volatility of the asset being traded. This helps to ensure that the trade is given enough room to move, while also minimizing potential losses.
Other types of volatility include True Range Double (TRD), Close-to-Close, and Garman-Klass
What is a Baseline indicator?
The baseline is essentially a moving average, and is used to determine the overall direction of the market.
The baseline in the NNFX system is used to filter out trades that are not in line with the long-term trend of the market. The baseline is plotted on the chart along with other indicators, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR).
Trades are only taken when the price is in the same direction as the baseline. For example, if the baseline is sloping upwards, only long trades are taken, and if the baseline is sloping downwards, only short trades are taken. This approach helps to ensure that trades are in line with the overall trend of the market, and reduces the risk of entering trades that are likely to fail.
By using a baseline in the NNFX system, traders can have a clear reference point for determining the overall trend of the market, and can make more informed trading decisions. The baseline helps to filter out noise and false signals, and ensures that trades are taken in the direction of the long-term trend.
What is a Confirmation indicator?
Confirmation indicators are technical indicators that are used to confirm the signals generated by primary indicators. Primary indicators are the core indicators used in the NNFX system, such as the Average True Range (ATR), the Moving Average (MA), and the Relative Strength Index (RSI).
The purpose of the confirmation indicators is to reduce false signals and improve the accuracy of the trading system. They are designed to confirm the signals generated by the primary indicators by providing additional information about the strength and direction of the trend.
Some examples of confirmation indicators that may be used in the NNFX system include the Bollinger Bands, the MACD (Moving Average Convergence Divergence), and the MACD Oscillator. These indicators can provide information about the volatility, momentum, and trend strength of the market, and can be used to confirm the signals generated by the primary indicators.
In the NNFX system, confirmation indicators are used in combination with primary indicators and other filters to create a trading system that is robust and reliable. By using multiple indicators to confirm trading signals, the system aims to reduce the risk of false signals and improve the overall profitability of the trades.
What is a Continuation indicator?
In the NNFX (No Nonsense Forex) trading system, a continuation indicator is a technical indicator that is used to confirm a current trend and predict that the trend is likely to continue in the same direction. A continuation indicator is typically used in conjunction with other indicators in the system, such as a baseline indicator, to provide a comprehensive trading strategy.
What is a Volatility/Volume indicator?
Volume indicators, such as the On Balance Volume (OBV), the Chaikin Money Flow (CMF), or the Volume Price Trend (VPT), are used to measure the amount of buying and selling activity in a market. They are based on the trading volume of the market, and can provide information about the strength of the trend. In the NNFX system, volume indicators are used to confirm trading signals generated by the Moving Average and the Relative Strength Index. Volatility indicators include Average Direction Index, Waddah Attar, and Volatility Ratio. In the NNFX trading system, volatility is a proxy for volume and vice versa.
By using volume indicators as confirmation tools, the NNFX trading system aims to reduce the risk of false signals and improve the overall profitability of trades. These indicators can provide additional information about the market that is not captured by the primary indicators, and can help traders to make more informed trading decisions. In addition, volume indicators can be used to identify potential changes in market trends and to confirm the strength of price movements.
What is an Exit indicator?
The exit indicator is used in conjunction with other indicators in the system, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR), to provide a comprehensive trading strategy.
The exit indicator in the NNFX system can be any technical indicator that is deemed effective at identifying optimal exit points. Examples of exit indicators that are commonly used include the Parabolic SAR, the Average Directional Index (ADX), and the Chandelier Exit.
The purpose of the exit indicator is to identify when a trend is likely to reverse or when the market conditions have changed, signaling the need to exit a trade. By using an exit indicator, traders can manage their risk and prevent significant losses.
In the NNFX system, the exit indicator is used in conjunction with a stop loss and a take profit order to maximize profits and minimize losses. The stop loss order is used to limit the amount of loss that can be incurred if the trade goes against the trader, while the take profit order is used to lock in profits when the trade is moving in the trader's favor.
Overall, the use of an exit indicator in the NNFX trading system is an important component of a comprehensive trading strategy. It allows traders to manage their risk effectively and improve the profitability of their trades by exiting at the right time.
What is an Metamorphosis indicator?
The concept of a metamorphosis indicator involves the integration of two or more GKD indicators to generate a compound signal. This is achieved by evaluating the accuracy of each indicator and selecting the signal from the indicator with the highest accuracy. As an illustration, let's consider a scenario where we calculate the accuracy of 10 indicators and choose the signal from the indicator that demonstrates the highest accuracy.
The resulting output from the metamorphosis indicator can then be utilized in a GKD-BT backtest by occupying a slot that aligns with the purpose of the metamorphosis indicator. The slot can be a GKD-B, GKD-C, or GKD-E slot, depending on the specific requirements and objectives of the indicator. This allows for seamless integration and utilization of the compound signal within the GKD-BT framework.
How does Loxx's GKD (Giga Kaleidoscope Modularized Trading System) implement the NNFX algorithm outlined above?
Loxx's GKD v2.0 system has five types of modules (indicators/strategies). These modules are:
1. GKD-BT - Backtesting module (Volatility, Number 1 in the NNFX algorithm)
2. GKD-B - Baseline module (Baseline and Volatility/Volume, Numbers 1 and 2 in the NNFX algorithm)
3. GKD-C - Confirmation 1/2 and Continuation module (Confirmation 1/2 and Continuation, Numbers 3, 4, and 5 in the NNFX algorithm)
4. GKD-V - Volatility/Volume module (Confirmation 1/2, Number 6 in the NNFX algorithm)
5. GKD-E - Exit module (Exit, Number 7 in the NNFX algorithm)
6. GKD-M - Metamorphosis module (Metamorphosis, Number 8 in the NNFX algorithm, but not part of the NNFX algorithm)
(additional module types will added in future releases)
Each module interacts with every module by passing data to A backtest module wherein the various components of the GKD system are combined to create a trading signal.
That is, the Baseline indicator passes its data to Volatility/Volume. The Volatility/Volume indicator passes its values to the Confirmation 1 indicator. The Confirmation 1 indicator passes its values to the Confirmation 2 indicator. The Confirmation 2 indicator passes its values to the Continuation indicator. The Continuation indicator passes its values to the Exit indicator, and finally, the Exit indicator passes its values to the Backtest strategy.
This chaining of indicators requires that each module conform to Loxx's GKD protocol, therefore allowing for the testing of every possible combination of technical indicators that make up the six components of the NNFX algorithm.
What does the application of the GKD trading system look like?
Example trading system:
Backtest: Multi-Ticker SCSC Backtest as shown on the chart above
Baseline: Hull Moving Average as shown on the chart above
Volatility/Volume: Hurst Exponent as shown on the chart above
Confirmation 1: Fisher Transform as shown on the chart above
Confirmation 2: uf2018
Continuation: Coppock Curve as shown on the chart above
Exit: Rex Oscillator
Metamorphosis: Baseline Optimizer
Each GKD indicator is denoted with a module identifier of either: GKD-BT, GKD-B, GKD-C, GKD-V, GKD-M, or GKD-E. This allows traders to understand to which module each indicator belongs and where each indicator fits into the GKD system.
█ Giga Kaleidoscope Modularized Trading System Signals
Standard Entry
1. GKD-C Confirmation gives signal
2. Baseline agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Volatility/Volume agrees
1-Candle Standard Entry
1a. GKD-C Confirmation gives signal
2a. Baseline agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
Next Candle
1b. Price retraced
2b. Baseline agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Baseline Entry
1. GKD-B Basline gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Volatility/Volume agrees
7. Confirmation 1 signal was less than 'Maximum Allowable PSBC Bars Back' prior
1-Candle Baseline Entry
1a. GKD-B Baseline gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSBC Bars Back' prior
Next Candle
1b. Price retraced
2b. Baseline agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Volatility/Volume Entry
1. GKD-V Volatility/Volume gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Baseline agrees
7. Confirmation 1 signal was less than 7 candles prior
1-Candle Volatility/Volume Entry
1a. GKD-V Volatility/Volume gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSVVC Bars Back' prior
Next Candle
1b. Price retraced
2b. Volatility/Volume agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Baseline agrees
Confirmation 2 Entry
1. GKD-C Confirmation 2 gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Volatility/Volume agrees
6. Baseline agrees
7. Confirmation 1 signal was less than 7 candles prior
1-Candle Confirmation 2 Entry
1a. GKD-C Confirmation 2 gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSC2C Bars Back' prior
Next Candle
1b. Price retraced
2b. Confirmation 2 agrees
3b. Confirmation 1 agrees
4b. Volatility/Volume agrees
5b. Baseline agrees
PullBack Entry
1a. GKD-B Baseline gives signal
2a. Confirmation 1 agrees
3a. Price is beyond 1.0x Volatility of Baseline
Next Candle
1b. Price inside Goldie Locks Zone Minimum
2b. Price inside Goldie Locks Zone Maximum
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Continuation Entry
1. Standard Entry, 1-Candle Standard Entry, Baseline Entry, 1-Candle Baseline Entry, Volatility/Volume Entry, 1-Candle Volatility/Volume Entry, Confirmation 2 Entry, 1-Candle Confirmation 2 Entry, or Pullback entry triggered previously
2. Baseline hasn't crossed since entry signal trigger
4. Confirmation 1 agrees
5. Baseline agrees
6. Confirmation 2 agrees
█ Connecting to Backtests
All GKD indicators are chained indicators meaning you export the value of the indicators to specialized backtest to creat your GKD trading system. Each indicator contains a proprietary signal generation algo that will only work with GKD backtests. You can find these backtests using the links below.
GKD-BT Giga Confirmation Stack Backtest
GKD-BT Giga Stacks Backtest
GKD-BT Full Giga Kaleidoscope Backtest
GKD-BT Solo Confirmation Super Complex Backtest
GKD-BT Solo Confirmation Complex Backtest
GKD-BT Solo Confirmation Simple Backtest
GKD-M Baseline Optimizer
GKD-M Accuracy Alchemist
GKD-BT Multi-Ticker SCC Backtest
GKD-BT Multi-Ticker SCS Backtest
GKD-BT Multi-Ticker SCC Backtest [Loxx]The Giga Kaleidoscope GKD-BT Multi-Ticker SCC Backtest is a backtesting module included in Loxx's "Giga Kaleidoscope Modularized Trading System."
The Multi-Ticker SCC Backtest is a Solo Confirmation Complex backtest that allows traders to test single GKD-C confirmation indicator filtered by both a GKD-B Multi-Ticker Baseline and GKD-V Volatility/Volume indicator across 1-10 tickers. The purpose of this backtest is to enable traders to quickly evaluate a Baseline and Volatility/Volume filtered GKD-C Confirmation indicator across hundreds of tickers within 30-60 minutes.
The backtest module supports testing with 1 take profit and 1 stop loss. It also offers the option to limit testing to a specific date range, allowing simulated forward testing using historical data. This backtest module only includes standard long and short signals. Additionally, users can choose to display or hide a trading panel that provides relevant information about the backtest, statistics, and the current trade. Traders can also select a highlighting threshold for Total Percent Wins and Percent Profitable, and Profit Factor.
To use this indicator:
1. Import 1-10 tickers into the GKD-B Multi-Ticker Baseline indicator
2. Import the value "Input into NEW GKD-BT Multi-ticker Backtest" from the GKD-B Multi-Ticker Baseline indicator into the GKD-BT Multi-Ticker SCC Backtest.
3. Select the "Multi-ticker" option in the GKD-V Volatility/Volume indicator
4. Import 1-10 tickers into the GKD-V Volatility/Volume indicator
5. Import the value "Input into NEW GKD-BT Multi-ticker Backtest" from the GKD-V Volatility/Volume indicator into the GKD-BT Multi-Ticker SCC Backtest.
6. Select the "Multi-ticker" option in the GKD-C Confirmation indicator.
7. Import 1-10 tickers into the GKD-C Confirmation indicator.
8. Import the same 1-10 indicators into the GKD-BT Multi-Ticker SCC Backtest.
9. Import the value "Input into NEW GKD-BT Multi-ticker Backtest" from the GKD-C Confirmation indicator into the GKD-BT Multi-Ticker SCC Backtest.
10. When importing tickers, ensure that you import the same type of tickers for all 1-10 tickers. For example, test only FX or Cryptocurrency or Stocks. Do not combine different tradable asset types.
11. Make sure that your chart is set to a ticker that corresponds to the tradable asset type. For cryptocurrency testing, set the chart to BTCUSDT. For Forex testing, set the chart to EURUSD.
This backtest includes the following metrics:
1. Net profit: Overall profit or loss achieved.
2. Total Closed Trades: Total number of closed trades, both winning and losing.
3. Total Percent Wins: Total wins, whether long or short, for the selected time interval regardless of commissions and other profit-modifying addons.
4. Percent Profitable: Total wins, whether long or short, that are also profitable, taking commissions into account.
5. Profit Factor: The ratio of gross profits to gross losses, indicating how much money the strategy made for every unit of money it lost.
6. Average Profit per Trade: The average gain or loss per trade, calculated by dividing the net profit by the total number of closed trades.
7. Average Number of Bars in Trade: The average number of bars that elapsed during trades for all closed trades.
Summary of notable settings:
Input Tickers separated by commas: Allows the user to input tickers separated by commas, specifying the symbols or tickers of financial instruments used in the backtest. The tickers should follow the format "EXCHANGE:TICKER" (e.g., "NASDAQ:AAPL, NYSE:MSFT").
Import GKD-B Baseline: Imports the "GKD-B Multi-Ticker Baseline" indicator.
Import GKD-V Volatility/Volume: Imports the "GKD-V Volatility/Volume" indicator.
Import GKD-C Confirmation: Imports the "GKD-C" indicator.
Activate Baseline: Activates the GKD-B Multi-Ticker Baseline.
Activate Goldie Locks Zone Minimum Threshold: Activates the inner Goldie Locks Zone from the GKD-B Multi-Ticker Baseline
Activate Goldie Locks Zone Maximum Threshold: Activates the outer Goldie Locks Zone from the GKD-B Multi-Ticker Baseline
Activate Volatility/Volume: Activates the GKD-V Volatility/Volume indicator.
Initial Capital: Represents the starting account balance for the backtest, denominated in the base currency of the trading account.
Order Size: Determines the quantity of contracts traded in each trade.
Order Type: Specifies the type of order used in the backtest, either "Contracts" or "% Equity."
Commission: Represents the commission per order or transaction cost incurred in each trade.
**the backtest data rendered to the chart above uses $5 commission per trade and 10% equity per trade with $1 million initial capital. Each backtest result for each ticker assumes these same inputs. The results are NOT cumulative, they are separate and isolate per ticker and trading side, long or short**
Volatility Types included
The GKD system utilizes volatility-based take profits and stop losses. Each take profit and stop loss is calculated as a multiple of volatility. You can change the values of the multipliers in the settings as well.
This module includes 17 types of volatility:
Close-to-Close
Parkinson
Garman-Klass
Rogers-Satchell
Yang-Zhang
Garman-Klass-Yang-Zhang
Exponential Weighted Moving Average
Standard Deviation of Log Returns
Pseudo GARCH(2,2)
Average True Range
True Range Double
Standard Deviation
Adaptive Deviation
Median Absolute Deviation
Efficiency-Ratio Adaptive ATR
Mean Absolute Deviation
Static Percent
Various volatility estimators and indicators that investors and traders can use to measure the dispersion or volatility of a financial instrument's price. Each estimator has its strengths and weaknesses, and the choice of estimator should depend on the specific needs and circumstances of the user.
Close-to-Close
Close-to-Close volatility is a classic and widely used volatility measure, sometimes referred to as historical volatility.
Volatility is an indicator of the speed of a stock price change. A stock with high volatility is one where the price changes rapidly and with a larger amplitude. The more volatile a stock is, the riskier it is.
Close-to-close historical volatility is calculated using only a stock's closing prices. It is the simplest volatility estimator. However, in many cases, it is not precise enough. Stock prices could jump significantly during a trading session and return to the opening value at the end. That means that a considerable amount of price information is not taken into account by close-to-close volatility.
Despite its drawbacks, Close-to-Close volatility is still useful in cases where the instrument doesn't have intraday prices. For example, mutual funds calculate their net asset values daily or weekly, and thus their prices are not suitable for more sophisticated volatility estimators.
Parkinson
Parkinson volatility is a volatility measure that uses the stock’s high and low price of the day.
The main difference between regular volatility and Parkinson volatility is that the latter uses high and low prices for a day, rather than only the closing price. This is useful as close-to-close prices could show little difference while large price movements could have occurred during the day. Thus, Parkinson's volatility is considered more precise and requires less data for calculation than close-to-close volatility.
One drawback of this estimator is that it doesn't take into account price movements after the market closes. Hence, it systematically undervalues volatility. This drawback is addressed in the Garman-Klass volatility estimator.
Garman-Klass
Garman-Klass is a volatility estimator that incorporates open, low, high, and close prices of a security.
Garman-Klass volatility extends Parkinson's volatility by taking into account the opening and closing prices. As markets are most active during the opening and closing of a trading session, it makes volatility estimation more accurate.
Garman and Klass also assumed that the process of price change follows a continuous diffusion process (Geometric Brownian motion). However, this assumption has several drawbacks. The method is not robust for opening jumps in price and trend movements.
Despite its drawbacks, the Garman-Klass estimator is still more effective than the basic formula since it takes into account not only the price at the beginning and end of the time interval but also intraday price extremes.
Researchers Rogers and Satchell have proposed a more efficient method for assessing historical volatility that takes into account price trends. See Rogers-Satchell Volatility for more detail.
Rogers-Satchell
Rogers-Satchell is an estimator for measuring the volatility of securities with an average return not equal to zero.
Unlike Parkinson and Garman-Klass estimators, Rogers-Satchell incorporates a drift term (mean return not equal to zero). As a result, it provides better volatility estimation when the underlying is trending.
The main disadvantage of this method is that it does not take into account price movements between trading sessions. This leads to an underestimation of volatility since price jumps periodically occur in the market precisely at the moments between sessions.
A more comprehensive estimator that also considers the gaps between sessions was developed based on the Rogers-Satchel formula in the 2000s by Yang-Zhang. See Yang Zhang Volatility for more detail.
Yang-Zhang
Yang Zhang is a historical volatility estimator that handles both opening jumps and the drift and has a minimum estimation error.
Yang-Zhang volatility can be thought of as a combination of the overnight (close-to-open volatility) and a weighted average of the Rogers-Satchell volatility and the day’s open-to-close volatility. It is considered to be 14 times more efficient than the close-to-close estimator.
Garman-Klass-Yang-Zhang
Garman-Klass-Yang-Zhang (GKYZ) volatility estimator incorporates the returns of open, high, low, and closing prices in its calculation.
GKYZ volatility estimator takes into account overnight jumps but not the trend, i.e., it assumes that the underlying asset follows a Geometric Brownian Motion (GBM) process with zero drift. Therefore, the GKYZ volatility estimator tends to overestimate the volatility when the drift is different from zero. However, for a GBM process, this estimator is eight times more efficient than the close-to-close volatility estimator.
Exponential Weighted Moving Average
The Exponentially Weighted Moving Average (EWMA) is a quantitative or statistical measure used to model or describe a time series. The EWMA is widely used in finance, with the main applications being technical analysis and volatility modeling.
The moving average is designed such that older observations are given lower weights. The weights decrease exponentially as the data point gets older – hence the name exponentially weighted.
The only decision a user of the EWMA must make is the parameter lambda. The parameter decides how important the current observation is in the calculation of the EWMA. The higher the value of lambda, the more closely the EWMA tracks the original time series.
Standard Deviation of Log Returns
This is the simplest calculation of volatility. It's the standard deviation of ln(close/close(1)).
Pseudo GARCH(2,2)
This is calculated using a short- and long-run mean of variance multiplied by ?.
?avg(var;M) + (1 ? ?) avg(var;N) = 2?var/(M+1-(M-1)L) + 2(1-?)var/(M+1-(M-1)L)
Solving for ? can be done by minimizing the mean squared error of estimation; that is, regressing L^-1var - avg(var; N) against avg(var; M) - avg(var; N) and using the resulting beta estimate as ?.
Average True Range
The average true range (ATR) is a technical analysis indicator, introduced by market technician J. Welles Wilder Jr. in his book New Concepts in Technical Trading Systems, that measures market volatility by decomposing the entire range of an asset price for that period.
The true range indicator is taken as the greatest of the following: current high less the current low; the absolute value of the current high less the previous close; and the absolute value of the current low less the previous close. The ATR is then a moving average, generally using 14 days, of the true ranges.
True Range Double
A special case of ATR that attempts to correct for volatility skew.
Standard Deviation
Standard deviation is a statistic that measures the dispersion of a dataset relative to its mean and is calculated as the square root of the variance. The standard deviation is calculated as the square root of variance by determining each data point's deviation relative to the mean. If the data points are further from the mean, there is a higher deviation within the data set; thus, the more spread out the data, the higher the standard deviation.
Adaptive Deviation
By definition, the Standard Deviation (STD, also represented by the Greek letter sigma ? or the Latin letter s) is a measure that is used to quantify the amount of variation or dispersion of a set of data values. In technical analysis, we usually use it to measure the level of current volatility.
Standard Deviation is based on Simple Moving Average calculation for mean value. This version of standard deviation uses the properties of EMA to calculate what can be called a new type of deviation, and since it is based on EMA, we can call it EMA deviation. Additionally, Perry Kaufman's efficiency ratio is used to make it adaptive (since all EMA type calculations are nearly perfect for adapting).
The difference when compared to the standard is significant--not just because of EMA usage, but the efficiency ratio makes it a "bit more logical" in very volatile market conditions.
Median Absolute Deviation
The median absolute deviation is a measure of statistical dispersion. Moreover, the MAD is a robust statistic, being more resilient to outliers in a data set than the standard deviation. In the standard deviation, the distances from the mean are squared, so large deviations are weighted more heavily, and thus outliers can heavily influence it. In the MAD, the deviations of a small number of outliers are irrelevant.
Because the MAD is a more robust estimator of scale than the sample variance or standard deviation, it works better with distributions without a mean or variance, such as the Cauchy distribution.
For this indicator, a manual recreation of the quantile function in Pine Script is used. This is so users have a full inside view into how this is calculated.
Efficiency-Ratio Adaptive ATR
Average True Range (ATR) is a widely used indicator for many occasions in technical analysis. It is calculated as the RMA of the true range. This version adds a "twist": it uses Perry Kaufman's Efficiency Ratio to calculate adaptive true range.
Mean Absolute Deviation
The mean absolute deviation (MAD) is a measure of variability that indicates the average distance between observations and their mean. MAD uses the original units of the data, which simplifies interpretation. Larger values signify that the data points spread out further from the average. Conversely, lower values correspond to data points bunching closer to it. The mean absolute deviation is also known as the mean deviation and average absolute deviation.
This definition of the mean absolute deviation sounds similar to the standard deviation (SD). While both measure variability, they have different calculations. In recent years, some proponents of MAD have suggested that it replace the SD as the primary measure because it is a simpler concept that better fits real life.
█ Giga Kaleidoscope Modularized Trading System
Core components of an NNFX algorithmic trading strategy
The NNFX algorithm is built on the principles of trend, momentum, and volatility. There are six core components in the NNFX trading algorithm:
1. Volatility - price volatility; e.g., Average True Range, True Range Double, Close-to-Close, etc.
2. Baseline - a moving average to identify price trend
3. Confirmation 1 - a technical indicator used to identify trends
4. Confirmation 2 - a technical indicator used to identify trends
5. Continuation - a technical indicator used to identify trends
6. Volatility/Volume - a technical indicator used to identify volatility/volume breakouts/breakdown
7. Exit - a technical indicator used to determine when a trend is exhausted
8. Metamorphosis - a technical indicator that produces a compound signal from the combination of other GKD indicators*
*(not part of the NNFX algorithm)
What is Volatility in the NNFX trading system?
In the NNFX (No Nonsense Forex) trading system, ATR (Average True Range) is typically used to measure the volatility of an asset. It is used as a part of the system to help determine the appropriate stop loss and take profit levels for a trade. ATR is calculated by taking the average of the true range values over a specified period.
True range is calculated as the maximum of the following values:
-Current high minus the current low
-Absolute value of the current high minus the previous close
-Absolute value of the current low minus the previous close
ATR is a dynamic indicator that changes with changes in volatility. As volatility increases, the value of ATR increases, and as volatility decreases, the value of ATR decreases. By using ATR in NNFX system, traders can adjust their stop loss and take profit levels according to the volatility of the asset being traded. This helps to ensure that the trade is given enough room to move, while also minimizing potential losses.
Other types of volatility include True Range Double (TRD), Close-to-Close, and Garman-Klass
What is a Baseline indicator?
The baseline is essentially a moving average, and is used to determine the overall direction of the market.
The baseline in the NNFX system is used to filter out trades that are not in line with the long-term trend of the market. The baseline is plotted on the chart along with other indicators, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR).
Trades are only taken when the price is in the same direction as the baseline. For example, if the baseline is sloping upwards, only long trades are taken, and if the baseline is sloping downwards, only short trades are taken. This approach helps to ensure that trades are in line with the overall trend of the market, and reduces the risk of entering trades that are likely to fail.
By using a baseline in the NNFX system, traders can have a clear reference point for determining the overall trend of the market, and can make more informed trading decisions. The baseline helps to filter out noise and false signals, and ensures that trades are taken in the direction of the long-term trend.
What is a Confirmation indicator?
Confirmation indicators are technical indicators that are used to confirm the signals generated by primary indicators. Primary indicators are the core indicators used in the NNFX system, such as the Average True Range (ATR), the Moving Average (MA), and the Relative Strength Index (RSI).
The purpose of the confirmation indicators is to reduce false signals and improve the accuracy of the trading system. They are designed to confirm the signals generated by the primary indicators by providing additional information about the strength and direction of the trend.
Some examples of confirmation indicators that may be used in the NNFX system include the Bollinger Bands, the MACD (Moving Average Convergence Divergence), and the MACD Oscillator. These indicators can provide information about the volatility, momentum, and trend strength of the market, and can be used to confirm the signals generated by the primary indicators.
In the NNFX system, confirmation indicators are used in combination with primary indicators and other filters to create a trading system that is robust and reliable. By using multiple indicators to confirm trading signals, the system aims to reduce the risk of false signals and improve the overall profitability of the trades.
What is a Continuation indicator?
In the NNFX (No Nonsense Forex) trading system, a continuation indicator is a technical indicator that is used to confirm a current trend and predict that the trend is likely to continue in the same direction. A continuation indicator is typically used in conjunction with other indicators in the system, such as a baseline indicator, to provide a comprehensive trading strategy.
What is a Volatility/Volume indicator?
Volume indicators, such as the On Balance Volume (OBV), the Chaikin Money Flow (CMF), or the Volume Price Trend (VPT), are used to measure the amount of buying and selling activity in a market. They are based on the trading volume of the market, and can provide information about the strength of the trend. In the NNFX system, volume indicators are used to confirm trading signals generated by the Moving Average and the Relative Strength Index. Volatility indicators include Average Direction Index, Waddah Attar, and Volatility Ratio. In the NNFX trading system, volatility is a proxy for volume and vice versa.
By using volume indicators as confirmation tools, the NNFX trading system aims to reduce the risk of false signals and improve the overall profitability of trades. These indicators can provide additional information about the market that is not captured by the primary indicators, and can help traders to make more informed trading decisions. In addition, volume indicators can be used to identify potential changes in market trends and to confirm the strength of price movements.
What is an Exit indicator?
The exit indicator is used in conjunction with other indicators in the system, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR), to provide a comprehensive trading strategy.
The exit indicator in the NNFX system can be any technical indicator that is deemed effective at identifying optimal exit points. Examples of exit indicators that are commonly used include the Parabolic SAR, the Average Directional Index (ADX), and the Chandelier Exit.
The purpose of the exit indicator is to identify when a trend is likely to reverse or when the market conditions have changed, signaling the need to exit a trade. By using an exit indicator, traders can manage their risk and prevent significant losses.
In the NNFX system, the exit indicator is used in conjunction with a stop loss and a take profit order to maximize profits and minimize losses. The stop loss order is used to limit the amount of loss that can be incurred if the trade goes against the trader, while the take profit order is used to lock in profits when the trade is moving in the trader's favor.
Overall, the use of an exit indicator in the NNFX trading system is an important component of a comprehensive trading strategy. It allows traders to manage their risk effectively and improve the profitability of their trades by exiting at the right time.
What is an Metamorphosis indicator?
The concept of a metamorphosis indicator involves the integration of two or more GKD indicators to generate a compound signal. This is achieved by evaluating the accuracy of each indicator and selecting the signal from the indicator with the highest accuracy. As an illustration, let's consider a scenario where we calculate the accuracy of 10 indicators and choose the signal from the indicator that demonstrates the highest accuracy.
The resulting output from the metamorphosis indicator can then be utilized in a GKD-BT backtest by occupying a slot that aligns with the purpose of the metamorphosis indicator. The slot can be a GKD-B, GKD-C, or GKD-E slot, depending on the specific requirements and objectives of the indicator. This allows for seamless integration and utilization of the compound signal within the GKD-BT framework.
How does Loxx's GKD (Giga Kaleidoscope Modularized Trading System) implement the NNFX algorithm outlined above?
Loxx's GKD v2.0 system has five types of modules (indicators/strategies). These modules are:
1. GKD-BT - Backtesting module (Volatility, Number 1 in the NNFX algorithm)
2. GKD-B - Baseline module (Baseline and Volatility/Volume, Numbers 1 and 2 in the NNFX algorithm)
3. GKD-C - Confirmation 1/2 and Continuation module (Confirmation 1/2 and Continuation, Numbers 3, 4, and 5 in the NNFX algorithm)
4. GKD-V - Volatility/Volume module (Confirmation 1/2, Number 6 in the NNFX algorithm)
5. GKD-E - Exit module (Exit, Number 7 in the NNFX algorithm)
6. GKD-M - Metamorphosis module (Metamorphosis, Number 8 in the NNFX algorithm, but not part of the NNFX algorithm)
(additional module types will added in future releases)
Each module interacts with every module by passing data to A backtest module wherein the various components of the GKD system are combined to create a trading signal.
That is, the Baseline indicator passes its data to Volatility/Volume. The Volatility/Volume indicator passes its values to the Confirmation 1 indicator. The Confirmation 1 indicator passes its values to the Confirmation 2 indicator. The Confirmation 2 indicator passes its values to the Continuation indicator. The Continuation indicator passes its values to the Exit indicator, and finally, the Exit indicator passes its values to the Backtest strategy.
This chaining of indicators requires that each module conform to Loxx's GKD protocol, therefore allowing for the testing of every possible combination of technical indicators that make up the six components of the NNFX algorithm.
What does the application of the GKD trading system look like?
Example trading system:
Backtest: Multi-Ticker SCC Backtest as shown on the chart above
Baseline: Hull Moving Average as shown on the chart above
Volatility/Volume: Hurst Exponent as shown on the chart above
Confirmation 1: Fisher Trasnform as shown on the chart above
Confirmation 2: uf2018
Continuation: Vortex
Exit: Rex Oscillator
Metamorphosis: Baseline Optimizer
Each GKD indicator is denoted with a module identifier of either: GKD-BT, GKD-B, GKD-C, GKD-V, GKD-M, or GKD-E. This allows traders to understand to which module each indicator belongs and where each indicator fits into the GKD system.
█ Giga Kaleidoscope Modularized Trading System Signals
Standard Entry
1. GKD-C Confirmation gives signal
2. Baseline agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Volatility/Volume agrees
1-Candle Standard Entry
1a. GKD-C Confirmation gives signal
2a. Baseline agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
Next Candle
1b. Price retraced
2b. Baseline agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Baseline Entry
1. GKD-B Basline gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Volatility/Volume agrees
7. Confirmation 1 signal was less than 'Maximum Allowable PSBC Bars Back' prior
1-Candle Baseline Entry
1a. GKD-B Baseline gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSBC Bars Back' prior
Next Candle
1b. Price retraced
2b. Baseline agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Volatility/Volume Entry
1. GKD-V Volatility/Volume gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Baseline agrees
7. Confirmation 1 signal was less than 7 candles prior
1-Candle Volatility/Volume Entry
1a. GKD-V Volatility/Volume gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSVVC Bars Back' prior
Next Candle
1b. Price retraced
2b. Volatility/Volume agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Baseline agrees
Confirmation 2 Entry
1. GKD-C Confirmation 2 gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Volatility/Volume agrees
6. Baseline agrees
7. Confirmation 1 signal was less than 7 candles prior
1-Candle Confirmation 2 Entry
1a. GKD-C Confirmation 2 gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSC2C Bars Back' prior
Next Candle
1b. Price retraced
2b. Confirmation 2 agrees
3b. Confirmation 1 agrees
4b. Volatility/Volume agrees
5b. Baseline agrees
PullBack Entry
1a. GKD-B Baseline gives signal
2a. Confirmation 1 agrees
3a. Price is beyond 1.0x Volatility of Baseline
Next Candle
1b. Price inside Goldie Locks Zone Minimum
2b. Price inside Goldie Locks Zone Maximum
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Continuation Entry
1. Standard Entry, 1-Candle Standard Entry, Baseline Entry, 1-Candle Baseline Entry, Volatility/Volume Entry, 1-Candle Volatility/Volume Entry, Confirmation 2 Entry, 1-Candle Confirmation 2 Entry, or Pullback entry triggered previously
2. Baseline hasn't crossed since entry signal trigger
4. Confirmation 1 agrees
5. Baseline agrees
6. Confirmation 2 agrees
█ Connecting to Backtests
All GKD indicators are chained indicators meaning you export the value of the indicators to specialized backtest to creat your GKD trading system. Each indicator contains a proprietary signal generation algo that will only work with GKD backtests. You can find these backtests using the links below.
GKD-BT Giga Confirmation Stack Backtest
GKD-BT Giga Stacks Backtest
GKD-BT Full Giga Kaleidoscope Backtest
GKD-BT Solo Confirmation Super Complex Backtest
GKD-BT Solo Confirmation Complex Backtest
GKD-BT Solo Confirmation Simple Backtest
GKD-M Baseline Optimizer
GKD-M Accuracy Alchemist
GKD-BT Multi-Ticker SCS Backtest [Loxx]The Giga Kaleidoscope GKD-BT Multi-Ticker SCS Backtest is a backtesting module included in Loxx's "Giga Kaleidoscope Modularized Trading System."
The Multi-Ticker SCS Backtest is a Solo Confirmation Simple backtest that allows traders to test single GKD-C confirmation indicators across 1-10 tickers. The purpose of this backtest is to enable traders to quickly evaluate GKD-C across hundreds of tickers within 30-60 minutes.
The backtest module supports testing with 1 take profit and 1 stop loss. It also offers the option to limit testing to a specific date range, allowing simulated forward testing using historical data. This backtest module only includes standard long and short signals. Additionally, users can choose to display or hide a trading panel that provides relevant information about the backtest, statistics, and the current trade. Traders can also select a highlighting treshold for Total Percent Wins and Percent Profitable, and Profit Factor.
To use this indicator:
1. Select the "Multi-ticker" option in the GKD-C Confirmation indicator.
2. Import 1-10 tickers into the GKD-C Confirmation indicator.
3. Import the same 1-10 indicators into the GKD-BT Multi-Ticker SCS Backtest.
4. Import the value "Input into NEW GKD-BT Multi-ticker Backtest" from the GKD-C Confirmation indicator into the GKD-BT Multi-Ticker SCS Backtest.
5. When importing tickers, ensure that you import the same type of tickers for all 1-10 tickers. For example, test only FX or Cryptocurrency or Stocks. Do not combine different tradable asset types.
6. Make sure that your chart is set to a ticker that corresponds to the tradable asset type. For cryptocurrency testing, set the chart to BTCUSDT. For Forex testing, set the chart to EURUSD.
This backtest includes the following metrics:
1. Net profit: Overall profit or loss achieved.
2. Total Closed Trades: Total number of closed trades, both winning and losing.
3. Total Percent Wins: Total wins, whether long or short, for the selected time interval regardless of commissions and other profit-modifying addons.
4. Percent Profitable: Total wins, whether long or short, that are also profitable, taking commissions into account.
5. Profit Factor: The ratio of gross profits to gross losses, indicating how much money the strategy made for every unit of money it lost.
6. Average Profit per Trade: The average gain or loss per trade, calculated by dividing the net profit by the total number of closed trades.
7. Average Number of Bars in Trade: The average number of bars that elapsed during trades for all closed trades.
Summary of notable settings:
Input Tickers separated by commas: Allows the user to input tickers separated by commas, specifying the symbols or tickers of financial instruments used in the backtest. The tickers should follow the format "EXCHANGE:TICKER" (e.g., "NASDAQ:AAPL, NYSE:MSFT").
Import GKD-C: Imports the "GKD-C" source, which provides signals or data for the backtest.
Initial Capital: Represents the starting account balance for the backtest, denominated in the base currency of the trading account.
Order Size: Determines the quantity of contracts traded in each trade.
Order Type: Specifies the type of order used in the backtest, either "Contracts" or "% Equity."
Commission: Represents the commission per order or transaction cost incurred in each trade.
**the backtest data rendered to the chart above uses $5 commission per trade and 10% equity per trade with $1 million initial capital. Each backtest result for each ticker assumes these same inputs. The results are NOT cumulative, they are separate and isolate per ticker and trading side, long or short**
Volatility Types included
The GKD system utilizes volatility-based take profits and stop losses. Each take profit and stop loss is calculated as a multiple of volatility. You can change the values of the multipliers in the settings as well.
This module includes 17 types of volatility:
Close-to-Close
Parkinson
Garman-Klass
Rogers-Satchell
Yang-Zhang
Garman-Klass-Yang-Zhang
Exponential Weighted Moving Average
Standard Deviation of Log Returns
Pseudo GARCH(2,2)
Average True Range
True Range Double
Standard Deviation
Adaptive Deviation
Median Absolute Deviation
Efficiency-Ratio Adaptive ATR
Mean Absolute Deviation
Static Percent
Various volatility estimators and indicators that investors and traders can use to measure the dispersion or volatility of a financial instrument's price. Each estimator has its strengths and weaknesses, and the choice of estimator should depend on the specific needs and circumstances of the user.
Close-to-Close
Close-to-Close volatility is a classic and widely used volatility measure, sometimes referred to as historical volatility.
Volatility is an indicator of the speed of a stock price change. A stock with high volatility is one where the price changes rapidly and with a larger amplitude. The more volatile a stock is, the riskier it is.
Close-to-close historical volatility is calculated using only a stock's closing prices. It is the simplest volatility estimator. However, in many cases, it is not precise enough. Stock prices could jump significantly during a trading session and return to the opening value at the end. That means that a considerable amount of price information is not taken into account by close-to-close volatility.
Despite its drawbacks, Close-to-Close volatility is still useful in cases where the instrument doesn't have intraday prices. For example, mutual funds calculate their net asset values daily or weekly, and thus their prices are not suitable for more sophisticated volatility estimators.
Parkinson
Parkinson volatility is a volatility measure that uses the stock’s high and low price of the day.
The main difference between regular volatility and Parkinson volatility is that the latter uses high and low prices for a day, rather than only the closing price. This is useful as close-to-close prices could show little difference while large price movements could have occurred during the day. Thus, Parkinson's volatility is considered more precise and requires less data for calculation than close-to-close volatility.
One drawback of this estimator is that it doesn't take into account price movements after the market closes. Hence, it systematically undervalues volatility. This drawback is addressed in the Garman-Klass volatility estimator.
Garman-Klass
Garman-Klass is a volatility estimator that incorporates open, low, high, and close prices of a security.
Garman-Klass volatility extends Parkinson's volatility by taking into account the opening and closing prices. As markets are most active during the opening and closing of a trading session, it makes volatility estimation more accurate.
Garman and Klass also assumed that the process of price change follows a continuous diffusion process (Geometric Brownian motion). However, this assumption has several drawbacks. The method is not robust for opening jumps in price and trend movements.
Despite its drawbacks, the Garman-Klass estimator is still more effective than the basic formula since it takes into account not only the price at the beginning and end of the time interval but also intraday price extremes.
Researchers Rogers and Satchell have proposed a more efficient method for assessing historical volatility that takes into account price trends. See Rogers-Satchell Volatility for more detail.
Rogers-Satchell
Rogers-Satchell is an estimator for measuring the volatility of securities with an average return not equal to zero.
Unlike Parkinson and Garman-Klass estimators, Rogers-Satchell incorporates a drift term (mean return not equal to zero). As a result, it provides better volatility estimation when the underlying is trending.
The main disadvantage of this method is that it does not take into account price movements between trading sessions. This leads to an underestimation of volatility since price jumps periodically occur in the market precisely at the moments between sessions.
A more comprehensive estimator that also considers the gaps between sessions was developed based on the Rogers-Satchel formula in the 2000s by Yang-Zhang. See Yang Zhang Volatility for more detail.
Yang-Zhang
Yang Zhang is a historical volatility estimator that handles both opening jumps and the drift and has a minimum estimation error.
Yang-Zhang volatility can be thought of as a combination of the overnight (close-to-open volatility) and a weighted average of the Rogers-Satchell volatility and the day’s open-to-close volatility. It is considered to be 14 times more efficient than the close-to-close estimator.
Garman-Klass-Yang-Zhang
Garman-Klass-Yang-Zhang (GKYZ) volatility estimator incorporates the returns of open, high, low, and closing prices in its calculation.
GKYZ volatility estimator takes into account overnight jumps but not the trend, i.e., it assumes that the underlying asset follows a Geometric Brownian Motion (GBM) process with zero drift. Therefore, the GKYZ volatility estimator tends to overestimate the volatility when the drift is different from zero. However, for a GBM process, this estimator is eight times more efficient than the close-to-close volatility estimator.
Exponential Weighted Moving Average
The Exponentially Weighted Moving Average (EWMA) is a quantitative or statistical measure used to model or describe a time series. The EWMA is widely used in finance, with the main applications being technical analysis and volatility modeling.
The moving average is designed such that older observations are given lower weights. The weights decrease exponentially as the data point gets older – hence the name exponentially weighted.
The only decision a user of the EWMA must make is the parameter lambda. The parameter decides how important the current observation is in the calculation of the EWMA. The higher the value of lambda, the more closely the EWMA tracks the original time series.
Standard Deviation of Log Returns
This is the simplest calculation of volatility. It's the standard deviation of ln(close/close(1)).
Pseudo GARCH(2,2)
This is calculated using a short- and long-run mean of variance multiplied by ?.
?avg(var;M) + (1 ? ?) avg(var;N) = 2?var/(M+1-(M-1)L) + 2(1-?)var/(M+1-(M-1)L)
Solving for ? can be done by minimizing the mean squared error of estimation; that is, regressing L^-1var - avg(var; N) against avg(var; M) - avg(var; N) and using the resulting beta estimate as ?.
Average True Range
The average true range (ATR) is a technical analysis indicator, introduced by market technician J. Welles Wilder Jr. in his book New Concepts in Technical Trading Systems, that measures market volatility by decomposing the entire range of an asset price for that period.
The true range indicator is taken as the greatest of the following: current high less the current low; the absolute value of the current high less the previous close; and the absolute value of the current low less the previous close. The ATR is then a moving average, generally using 14 days, of the true ranges.
True Range Double
A special case of ATR that attempts to correct for volatility skew.
Standard Deviation
Standard deviation is a statistic that measures the dispersion of a dataset relative to its mean and is calculated as the square root of the variance. The standard deviation is calculated as the square root of variance by determining each data point's deviation relative to the mean. If the data points are further from the mean, there is a higher deviation within the data set; thus, the more spread out the data, the higher the standard deviation.
Adaptive Deviation
By definition, the Standard Deviation (STD, also represented by the Greek letter sigma ? or the Latin letter s) is a measure that is used to quantify the amount of variation or dispersion of a set of data values. In technical analysis, we usually use it to measure the level of current volatility.
Standard Deviation is based on Simple Moving Average calculation for mean value. This version of standard deviation uses the properties of EMA to calculate what can be called a new type of deviation, and since it is based on EMA, we can call it EMA deviation. Additionally, Perry Kaufman's efficiency ratio is used to make it adaptive (since all EMA type calculations are nearly perfect for adapting).
The difference when compared to the standard is significant--not just because of EMA usage, but the efficiency ratio makes it a "bit more logical" in very volatile market conditions.
Median Absolute Deviation
The median absolute deviation is a measure of statistical dispersion. Moreover, the MAD is a robust statistic, being more resilient to outliers in a data set than the standard deviation. In the standard deviation, the distances from the mean are squared, so large deviations are weighted more heavily, and thus outliers can heavily influence it. In the MAD, the deviations of a small number of outliers are irrelevant.
Because the MAD is a more robust estimator of scale than the sample variance or standard deviation, it works better with distributions without a mean or variance, such as the Cauchy distribution.
For this indicator, a manual recreation of the quantile function in Pine Script is used. This is so users have a full inside view into how this is calculated.
Efficiency-Ratio Adaptive ATR
Average True Range (ATR) is a widely used indicator for many occasions in technical analysis. It is calculated as the RMA of the true range. This version adds a "twist": it uses Perry Kaufman's Efficiency Ratio to calculate adaptive true range.
Mean Absolute Deviation
The mean absolute deviation (MAD) is a measure of variability that indicates the average distance between observations and their mean. MAD uses the original units of the data, which simplifies interpretation. Larger values signify that the data points spread out further from the average. Conversely, lower values correspond to data points bunching closer to it. The mean absolute deviation is also known as the mean deviation and average absolute deviation.
This definition of the mean absolute deviation sounds similar to the standard deviation (SD). While both measure variability, they have different calculations. In recent years, some proponents of MAD have suggested that it replace the SD as the primary measure because it is a simpler concept that better fits real life.
█ Giga Kaleidoscope Modularized Trading System
Core components of an NNFX algorithmic trading strategy
The NNFX algorithm is built on the principles of trend, momentum, and volatility. There are six core components in the NNFX trading algorithm:
1. Volatility - price volatility; e.g., Average True Range, True Range Double, Close-to-Close, etc.
2. Baseline - a moving average to identify price trend
3. Confirmation 1 - a technical indicator used to identify trends
4. Confirmation 2 - a technical indicator used to identify trends
5. Continuation - a technical indicator used to identify trends
6. Volatility/Volume - a technical indicator used to identify volatility/volume breakouts/breakdown
7. Exit - a technical indicator used to determine when a trend is exhausted
8. Metamorphosis - a technical indicator that produces a compound signal from the combination of other GKD indicators*
*(not part of the NNFX algorithm)
What is Volatility in the NNFX trading system?
In the NNFX (No Nonsense Forex) trading system, ATR (Average True Range) is typically used to measure the volatility of an asset. It is used as a part of the system to help determine the appropriate stop loss and take profit levels for a trade. ATR is calculated by taking the average of the true range values over a specified period.
True range is calculated as the maximum of the following values:
-Current high minus the current low
-Absolute value of the current high minus the previous close
-Absolute value of the current low minus the previous close
ATR is a dynamic indicator that changes with changes in volatility. As volatility increases, the value of ATR increases, and as volatility decreases, the value of ATR decreases. By using ATR in NNFX system, traders can adjust their stop loss and take profit levels according to the volatility of the asset being traded. This helps to ensure that the trade is given enough room to move, while also minimizing potential losses.
Other types of volatility include True Range Double (TRD), Close-to-Close, and Garman-Klass
What is a Baseline indicator?
The baseline is essentially a moving average, and is used to determine the overall direction of the market.
The baseline in the NNFX system is used to filter out trades that are not in line with the long-term trend of the market. The baseline is plotted on the chart along with other indicators, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR).
Trades are only taken when the price is in the same direction as the baseline. For example, if the baseline is sloping upwards, only long trades are taken, and if the baseline is sloping downwards, only short trades are taken. This approach helps to ensure that trades are in line with the overall trend of the market, and reduces the risk of entering trades that are likely to fail.
By using a baseline in the NNFX system, traders can have a clear reference point for determining the overall trend of the market, and can make more informed trading decisions. The baseline helps to filter out noise and false signals, and ensures that trades are taken in the direction of the long-term trend.
What is a Confirmation indicator?
Confirmation indicators are technical indicators that are used to confirm the signals generated by primary indicators. Primary indicators are the core indicators used in the NNFX system, such as the Average True Range (ATR), the Moving Average (MA), and the Relative Strength Index (RSI).
The purpose of the confirmation indicators is to reduce false signals and improve the accuracy of the trading system. They are designed to confirm the signals generated by the primary indicators by providing additional information about the strength and direction of the trend.
Some examples of confirmation indicators that may be used in the NNFX system include the Bollinger Bands, the MACD (Moving Average Convergence Divergence), and the MACD Oscillator. These indicators can provide information about the volatility, momentum, and trend strength of the market, and can be used to confirm the signals generated by the primary indicators.
In the NNFX system, confirmation indicators are used in combination with primary indicators and other filters to create a trading system that is robust and reliable. By using multiple indicators to confirm trading signals, the system aims to reduce the risk of false signals and improve the overall profitability of the trades.
What is a Continuation indicator?
In the NNFX (No Nonsense Forex) trading system, a continuation indicator is a technical indicator that is used to confirm a current trend and predict that the trend is likely to continue in the same direction. A continuation indicator is typically used in conjunction with other indicators in the system, such as a baseline indicator, to provide a comprehensive trading strategy.
What is a Volatility/Volume indicator?
Volume indicators, such as the On Balance Volume (OBV), the Chaikin Money Flow (CMF), or the Volume Price Trend (VPT), are used to measure the amount of buying and selling activity in a market. They are based on the trading volume of the market, and can provide information about the strength of the trend. In the NNFX system, volume indicators are used to confirm trading signals generated by the Moving Average and the Relative Strength Index. Volatility indicators include Average Direction Index, Waddah Attar, and Volatility Ratio. In the NNFX trading system, volatility is a proxy for volume and vice versa.
By using volume indicators as confirmation tools, the NNFX trading system aims to reduce the risk of false signals and improve the overall profitability of trades. These indicators can provide additional information about the market that is not captured by the primary indicators, and can help traders to make more informed trading decisions. In addition, volume indicators can be used to identify potential changes in market trends and to confirm the strength of price movements.
What is an Exit indicator?
The exit indicator is used in conjunction with other indicators in the system, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR), to provide a comprehensive trading strategy.
The exit indicator in the NNFX system can be any technical indicator that is deemed effective at identifying optimal exit points. Examples of exit indicators that are commonly used include the Parabolic SAR, the Average Directional Index (ADX), and the Chandelier Exit.
The purpose of the exit indicator is to identify when a trend is likely to reverse or when the market conditions have changed, signaling the need to exit a trade. By using an exit indicator, traders can manage their risk and prevent significant losses.
In the NNFX system, the exit indicator is used in conjunction with a stop loss and a take profit order to maximize profits and minimize losses. The stop loss order is used to limit the amount of loss that can be incurred if the trade goes against the trader, while the take profit order is used to lock in profits when the trade is moving in the trader's favor.
Overall, the use of an exit indicator in the NNFX trading system is an important component of a comprehensive trading strategy. It allows traders to manage their risk effectively and improve the profitability of their trades by exiting at the right time.
What is an Metamorphosis indicator?
The concept of a metamorphosis indicator involves the integration of two or more GKD indicators to generate a compound signal. This is achieved by evaluating the accuracy of each indicator and selecting the signal from the indicator with the highest accuracy. As an illustration, let's consider a scenario where we calculate the accuracy of 10 indicators and choose the signal from the indicator that demonstrates the highest accuracy.
The resulting output from the metamorphosis indicator can then be utilized in a GKD-BT backtest by occupying a slot that aligns with the purpose of the metamorphosis indicator. The slot can be a GKD-B, GKD-C, or GKD-E slot, depending on the specific requirements and objectives of the indicator. This allows for seamless integration and utilization of the compound signal within the GKD-BT framework.
How does Loxx's GKD (Giga Kaleidoscope Modularized Trading System) implement the NNFX algorithm outlined above?
Loxx's GKD v2.0 system has five types of modules (indicators/strategies). These modules are:
1. GKD-BT - Backtesting module (Volatility, Number 1 in the NNFX algorithm)
2. GKD-B - Baseline module (Baseline and Volatility/Volume, Numbers 1 and 2 in the NNFX algorithm)
3. GKD-C - Confirmation 1/2 and Continuation module (Confirmation 1/2 and Continuation, Numbers 3, 4, and 5 in the NNFX algorithm)
4. GKD-V - Volatility/Volume module (Confirmation 1/2, Number 6 in the NNFX algorithm)
5. GKD-E - Exit module (Exit, Number 7 in the NNFX algorithm)
6. GKD-M - Metamorphosis module (Metamorphosis, Number 8 in the NNFX algorithm, but not part of the NNFX algorithm)
(additional module types will added in future releases)
Each module interacts with every module by passing data to A backtest module wherein the various components of the GKD system are combined to create a trading signal.
That is, the Baseline indicator passes its data to Volatility/Volume. The Volatility/Volume indicator passes its values to the Confirmation 1 indicator. The Confirmation 1 indicator passes its values to the Confirmation 2 indicator. The Confirmation 2 indicator passes its values to the Continuation indicator. The Continuation indicator passes its values to the Exit indicator, and finally, the Exit indicator passes its values to the Backtest strategy.
This chaining of indicators requires that each module conform to Loxx's GKD protocol, therefore allowing for the testing of every possible combination of technical indicators that make up the six components of the NNFX algorithm.
What does the application of the GKD trading system look like?
Example trading system:
Backtest: Multi-Ticker SCS Backtest
Baseline: Hull Moving Average
Volatility/Volume: Hurst Exponent
Confirmation 1: Kase Peak Oscillator
Confirmation 2: uf2018
Continuation: Vortex
Exit: Rex Oscillator
Metamorphosis: Baseline Optimizer
Each GKD indicator is denoted with a module identifier of either: GKD-BT, GKD-B, GKD-C, GKD-V, GKD-M, or GKD-E. This allows traders to understand to which module each indicator belongs and where each indicator fits into the GKD system.
█ Giga Kaleidoscope Modularized Trading System Signals
Standard Entry
1. GKD-C Confirmation gives signal
2. Baseline agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Volatility/Volume agrees
1-Candle Standard Entry
1a. GKD-C Confirmation gives signal
2a. Baseline agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
Next Candle
1b. Price retraced
2b. Baseline agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Baseline Entry
1. GKD-B Basline gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Volatility/Volume agrees
7. Confirmation 1 signal was less than 'Maximum Allowable PSBC Bars Back' prior
1-Candle Baseline Entry
1a. GKD-B Baseline gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSBC Bars Back' prior
Next Candle
1b. Price retraced
2b. Baseline agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Volatility/Volume Entry
1. GKD-V Volatility/Volume gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Baseline agrees
7. Confirmation 1 signal was less than 7 candles prior
1-Candle Volatility/Volume Entry
1a. GKD-V Volatility/Volume gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSVVC Bars Back' prior
Next Candle
1b. Price retraced
2b. Volatility/Volume agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Baseline agrees
Confirmation 2 Entry
1. GKD-C Confirmation 2 gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Volatility/Volume agrees
6. Baseline agrees
7. Confirmation 1 signal was less than 7 candles prior
1-Candle Confirmation 2 Entry
1a. GKD-C Confirmation 2 gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSC2C Bars Back' prior
Next Candle
1b. Price retraced
2b. Confirmation 2 agrees
3b. Confirmation 1 agrees
4b. Volatility/Volume agrees
5b. Baseline agrees
PullBack Entry
1a. GKD-B Baseline gives signal
2a. Confirmation 1 agrees
3a. Price is beyond 1.0x Volatility of Baseline
Next Candle
1b. Price inside Goldie Locks Zone Minimum
2b. Price inside Goldie Locks Zone Maximum
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Continuation Entry
1. Standard Entry, 1-Candle Standard Entry, Baseline Entry, 1-Candle Baseline Entry, Volatility/Volume Entry, 1-Candle Volatility/Volume Entry, Confirmation 2 Entry, 1-Candle Confirmation 2 Entry, or Pullback entry triggered previously
2. Baseline hasn't crossed since entry signal trigger
4. Confirmation 1 agrees
5. Baseline agrees
6. Confirmation 2 agrees
█ Connecting to Backtests
All GKD indicators are chained indicators meaning you export the value of the indicators to specialized backtest to creat your GKD trading system. Each indicator contains a proprietary signal generation algo that will only work with GKD backtests. You can find these backtests using the links below.
GKD-BT Giga Confirmation Stack Backtest
GKD-BT Giga Stacks Backtest
GKD-BT Full Giga Kaleidoscope Backtest
GKD-BT Solo Confirmation Super Complex Backtest
GKD-BT Solo Confirmation Complex Backtest
GKD-BT Solo Confirmation Simple Backtest
GKD-M Baseline Optimizer
GKD-M Accuracy Alchemist
GKD-C Adaptive-Lookback Variety RSI [Loxx]Giga Kaleidoscope GKD-C Adaptive-Lookback Variety RSI is a Confirmation module included in Loxx's "Giga Kaleidoscope Modularized Trading System".
█ GKD-C Adaptive-Lookback Variety RSI
What is the Adaptive Lookback Period?
The adaptive lookback period is a technique used in technical analysis to adjust the period of an indicator based on changes in market conditions. This technique is particularly useful in volatile or rapidly changing markets where a fixed period may not be optimal for detecting trends or signals.
The concept of the adaptive lookback period is relatively simple. By adjusting the lookback period based on changes in market conditions, traders can more accurately identify trends and signals. This can help traders to enter and exit trades at the right time and improve the profitability of their trading strategies.
The adaptive lookback period works by identifying potential swing points in the market. Once these points are identified, the lookback period is calculated based on the number of swings and a speed parameter. The swing count parameter determines the number of swings that must occur before the lookback period is adjusted. The speed parameter controls the rate at which the lookback period is adjusted, with higher values indicating a more rapid adjustment.
The adaptive lookback period can be applied to a wide range of technical indicators, including moving averages, oscillators, and trendlines. By adjusting the period of these indicators based on changes in market conditions, traders can reduce the impact of noise and false signals, leading to more profitable trades.
In summary, the adaptive lookback period is a powerful technique for traders and analysts looking to optimize their technical indicators. By adjusting the period based on changes in market conditions, traders can more accurately identify trends and signals, leading to more profitable trades. While there are various ways to implement the adaptive lookback period, the basic concept remains the same, and traders can adapt and customize the technique to suit their individual needs and trading styles.
This indicator includes 10 types of RSI
1. Regular RSI
2. Slow RSI
3. Ehlers Smoothed RSI
4. Cutler's RSI
5. Rapid RSI
6. Harris' RSI
7. RSI DEMA
8. RSI TEMA
9. RSI T3
10. Jurik RSX
Regular RSI
The Relative Strength Index (RSI) is a widely used technical indicator in the field of financial market analysis. Developed by J. Welles Wilder Jr. in 1978, the RSI is a momentum oscillator that measures the speed and change of price movements. It helps traders identify potential trend reversals, overbought, and oversold conditions in a market.
The RSI is calculated based on the average gains and losses of an asset over a specified period, typically 14 days. The formula for calculating the RSI is as follows:
RSI = 100 - (100 / (1 + RS))
Where:
RS (Relative Strength) = Average gain over the specified period / Average loss over the specified period
The RSI ranges from 0 to 100, with values above 70 generally considered overbought (potentially indicating that the asset is overvalued and may experience a price decline) and values below 30 considered oversold (potentially indicating that the asset is undervalued and may experience a price increase).
Slow RSI
Slow RSI is a modified version of the Relative Strength Index (RSI) indicator that aims to provide a smoother, more consistent signal than the traditional RSI. The Slow RSI is designed to be less sensitive to sudden price movements, which can cause false signals.
To calculate Slow RSI, we first calculate the up and down values, just like in traditional RSI and Ehlers RSI. The up and down values are calculated by comparing the current price to the previous price, and then adding up the positive and negative differences.
Next, we calculate the Slow RSI value using the formula:
SlowRSI = 100 * up / (up + dn)
where "up" and "dn" are the total positive and negative differences, respectively.
This formula is similar to the one used in traditional RSI, but the dynamic lookback period based on the average of the up and down values is used to smooth out the signal.
Finally, we apply smoothing to the Slow RSI value by taking an exponential moving average (EMA) of the Slow RSI values over a specified period. This EMA helps to reduce the impact of sudden price movements and provide a smoother, more consistent signal over time.
Ehler's Smoothed RSI
Ehlers RSI is a modified version of the Relative Strength Index (RSI) indicator created by John Ehlers, a well-known technical analyst and author. The purpose of Ehlers RSI is to reduce lag and improve the responsiveness of the traditional RSI indicator.
To calculate Ehlers RSI, we first smooth the prices by taking a weighted average of the current price and the two previous prices. This smoothing helps to reduce noise in the data and produce a more accurate signal.
Next, we calculate the up and down values differently than in traditional RSI. In traditional RSI, the up and down values are based on the difference between the current price and the previous price. In Ehlers RSI, the up and down values are based on the difference between the current price and the price two bars ago. This approach helps to reduce lag and produce a more responsive indicator.
Finally, we calculate Ehlers RSI using the formula:
EhlersRSI = 50 * (up - down) / (up + down) + 50
The result is a more timely signal that can help traders identify potential trends and reversals in the market. However, as with any technical indicator, Ehlers RSI should be used in conjunction with other analysis tools and should not be relied on as the sole basis for trading decisions.
Cutler's RSI
Cutler's RSI (Relative Strength Index) is a variation of the traditional RSI, a popular technical analysis indicator used to measure the speed and change of price movements. The main difference between Cutler's RSI and the traditional RSI is the calculation method used to smooth the data. While the traditional RSI uses an exponential moving average (EMA) to smooth the data, Cutler's RSI uses a simple moving average (SMA).
Here's the formula for Cutler's RSI:
1. Calculate the price change: Price Change = Current Price - Previous Price
2. Calculate the average gain and average loss over a specified period (usually 14 days):
If Price Change > 0, add it to the total gains.
If Price Change < 0, add the absolute value to the total losses.
3. Calculate the average gain and average loss by dividing the totals by the specified period: Average Gain = Total Gains / Period, Average Loss = Total Losses / Period
4. Calculate the Relative Strength (RS): RS = Average Gain / Average Loss
5. Calculate Cutler's RSI: Cutler's RSI = 100 - (100 / (1 + RS))
Cutler's RSI is not necessarily better than the regular RSI; it's just a different variation of the traditional RSI that uses a simple moving average (SMA) instead of an exponential moving average (EMA) quantifiedstrategies.com. The main advantage of Cutler's RSI is that it is not data length dependent, meaning it returns consistent results regardless of the length of the period, or the starting point within a data file quantifiedstrategies.com.
However, it's worth noting that Cutler's RSI does not necessarily outperform the traditional RSI. In fact, backtests reveal that Cutler's RSI is no improvement compared to Wilder's RSI quantifiedstrategies.com. Additionally, using an SMA instead of an EMA in Cutler's RSI may result in the loss of the "believed" advantage of weighting the most recent price action aaii.com.
Both Cutler's RSI and the traditional RSI can be used to identify overbought/oversold levels, support and resistance, spot divergences for possible reversals, and confirm the signals from other indicators investopedia.com. Ultimately, the choice between Cutler's RSI and the traditional RSI depends on personal preference and the specific trading strategy being employed.
Rapid RSI
Rapid RSI is a technical analysis indicator that is a modified version of the Relative Strength Index (RSI). It was developed by Andrew Cardwell and was first introduced in the October 2006 issue of Technical Analysis of Stocks & Commodities magazine.
The Rapid RSI improves upon the regular RSI by modifying the way the average gains and losses are calculated. Here's a general breakdown of the Rapid RSI calculation:
1. Calculate the upward change (when the price has increased) and the downward change (when the price has decreased) for each period.
2. Calculate the simple moving average (SMA) of the upward changes and the SMA of the downward changes over the specified period.
3. Divide the SMA of the upward changes by the SMA of the downward changes to get the relative strength (RS).
4. Calculate the Rapid RSI by transforming the relative strength (RS) into a value ranging from 0 to 100.
By using the simple moving average (SMA) instead of the slow exponential moving average (RMA) as in the regular RSI, the Rapid RSI tends to be more responsive to recent price changes. This can help traders identify overbought and oversold conditions more quickly, potentially leading to earlier entry and exit points. However, it is important to note that a faster indicator may also produce more false signals.
Harris' RSI
Harris RSI (Relative Strength Index) is a technical indicator used in financial analysis to measure the strength or weakness of a security over time. It was developed by Larry Harris in 1986 as an alternative to the traditional RSI, which measures the price change of a security over a given period.
The Harris RSI uses a slightly different formula from the traditional RSI, but it is based on the same principles. It calculates the ratio of the average gain to the average loss over a specified period, typically 14 days. The result is then plotted on a scale of 0 to 100, with high values indicating overbought conditions and low values indicating oversold conditions.
The Harris RSI is believed to be more responsive to short-term price movements than the traditional RSI, making it useful for traders who are looking for quick trading opportunities. However, like any technical indicator, it should be used in conjunction with other forms of analysis to make informed trading decisions.
The calculation of the Harris RSI involves several steps:
1. Calculate the price change over the specified period (usually 14 days) using the following formula:
Price Change = Close Price - Prior Close Price
2. Calculate the average gain and average loss over the same period, using separate formulas for each:
Average Gain = (Sum of Gains over the Period) / Period
Average Loss = (Sum of Losses over the Period) / Period
Gains are calculated as the sum of all positive price changes over the period, while losses are calculated as the sum of all negative price changes over the period.
3. Calculate the Relative Strength (RS) as the ratio of the Average Gain to the Average Loss:
RS = Average Gain / Average Loss
4. Calculate the Harris RSI using the following formula:
Harris RSI = 100 - (100 / (1 + RS))
The resulting Harris RSI value is a number between 0 and 100, which is plotted on a chart to identify overbought or oversold conditions in the security. A value above 70 is generally considered overbought, while a value below 30 is generally considered oversold.
DEMA RSI
DEMA RSI is a variation of the Relative Strength Index (RSI) technical indicator that incorporates the Double Exponential Moving Average (DEMA) for smoothing. Like the regular RSI, the DEMA RSI is a momentum oscillator used to measure the speed and change of price movements, and it ranges from 0 to 100. Readings below 30 typically indicate oversold conditions, while readings above 70 indicate overbought conditions.
The DEMA RSI aims to improve upon the regular RSI by addressing its limitations, such as lag and false signals. By using the DEMA, a more responsive and faster RSI can be achieved. Here's a general breakdown of the DEMA RSI calculation:
1. Calculate the price change for each period, as well as the absolute value of the change.
2. Apply the DEMA smoothing technique to both the price change and its absolute value, separately. This involves calculating two sets of exponential moving averages and combining them to create a double-weighted moving average with reduced lag.
3. Divide the smoothed price change by the smoothed absolute value of the price change.
4. Transform the result into a value ranging from 0 to 100 to obtain the DEMA RSI.
The DEMA RSI is considered an improvement over the regular RSI because it provides faster and more responsive signals. This can help traders identify overbought and oversold conditions more accurately and potentially avoid false signals.
In summary, the main advantages of these RSI variations over the regular RSI are their ability to reduce noise, provide smoother lines, and be more responsive to price changes. This can lead to more accurate signals and fewer false positives in different market conditions.
TEMA RSI
TEMA RSI is a variation of the Relative Strength Index (RSI) technical indicator that incorporates the Triple Exponential Moving Average (TEMA) for smoothing. Like the regular RSI, the TEMA RSI is a momentum oscillator used to measure the speed and change of price movements, and it ranges from 0 to 100. Readings below 30 typically indicate oversold conditions, while readings above 70 indicate overbought conditions.
The TEMA RSI aims to improve upon the regular RSI by addressing its limitations, such as lag and false signals. By using the TEMA, a more responsive and faster RSI can be achieved. Here's a general breakdown of the TEMA RSI calculation:
1. Calculate the price change for each period, as well as the absolute value of the change.
2. Apply the TEMA smoothing technique to both the price change and its absolute value, separately. This involves calculating two sets of exponential moving averages and combining them to create a double-weighted moving average with reduced lag.
3. Divide the smoothed price change by the smoothed absolute value of the price change.
4. Transform the result into a value ranging from 0 to 100 to obtain the TEMA RSI.
The TEMA RSI is considered an improvement over the regular RSI because it provides faster and more responsive signals. This can help traders identify overbought and oversold conditions more accurately and potentially avoid false signals.
T3 RSI
T3 RSI is a variation of the Relative Strength Index (RSI) technical indicator that incorporates the Tilson T3 for smoothing. Like the regular RSI, the T3 RSI is a momentum oscillator used to measure the speed and change of price movements, and it ranges from 0 to 100. Readings below 30 typically indicate oversold conditions, while readings above 70 indicate overbought conditions.
The T3 RSI aims to improve upon the regular RSI by addressing its limitations, such as lag and false signals. By using the T3, a more responsive and faster RSI can be achieved. Here's a general breakdown of the T3 RSI calculation:
1. Calculate the price change for each period, as well as the absolute value of the change.
2. Apply the T3 smoothing technique to both the price change and its absolute value, separately. This involves calculating two sets of exponential moving averages and combining them to create a double-weighted moving average with reduced lag.
3. Divide the smoothed price change by the smoothed absolute value of the price change.
4. Transform the result into a value ranging from 0 to 100 to obtain the T3 RSI.
The T3 RSI is considered an improvement over the regular RSI because it provides faster and more responsive signals. This can help traders identify overbought and oversold conditions more accurately and potentially avoid false signals.
Jurik RSX
The Jurik RSX is a technical indicator developed by Mark Jurik to measure the momentum and strength of price movements in financial markets, such as stocks, commodities, and currencies. It is an advanced version of the traditional Relative Strength Index (RSI), designed to offer smoother and less lagging signals compared to the standard RSI.
The main advantage of the Jurik RSX is that it provides more accurate and timely signals for traders and analysts, thanks to its improved calculation methods that reduce noise and lag in the indicator's output. This enables better decision-making when analyzing market trends and potential trading opportunities.
What is Adaptive-Lookback Variety RSI
This indicator allows the user to select from 9 different RSI types and 33 source types. The various RSI types is enhanced by injecting an adaptive lookback period into the caculation making the RSI able to adaptive to differing market conditions.
Additional Features
This indicator allows you to select from 33 source types. They are as follows:
Close
Open
High
Low
Median
Typical
Weighted
Average
Average Median Body
Trend Biased
Trend Biased (Extreme)
HA Close
HA Open
HA High
HA Low
HA Median
HA Typical
HA Weighted
HA Average
HA Average Median Body
HA Trend Biased
HA Trend Biased (Extreme)
HAB Close
HAB Open
HAB High
HAB Low
HAB Median
HAB Typical
HAB Weighted
HAB Average
HAB Average Median Body
HAB Trend Biased
HAB Trend Biased (Extreme)
What are Heiken Ashi "better" candles?
Heiken Ashi "better" candles are a modified version of the standard Heiken Ashi candles, which are a popular charting technique used in technical analysis. Heiken Ashi candles help traders identify trends and potential reversal points by smoothing out price data and reducing market noise. The "better formula" was proposed by Sebastian Schmidt in an article published by BNP Paribas in Warrants & Zertifikate, a German magazine, in August 2004. The aim of this formula is to further improve the smoothing of the Heiken Ashi chart and enhance its effectiveness in identifying trends and reversals.
Standard Heiken Ashi candles are calculated using the following formulas:
Heiken Ashi Close = (Open + High + Low + Close) / 4
Heiken Ashi Open = (Previous Heiken Ashi Open + Previous Heiken Ashi Close) / 2
Heiken Ashi High = Max (High, Heiken Ashi Open, Heiken Ashi Close)
Heiken Ashi Low = Min (Low, Heiken Ashi Open, Heiken Ashi Close)
The "better formula" modifies the standard Heiken Ashi calculation by incorporating additional smoothing, which can help reduce noise and make it easier to identify trends and reversals. The modified formulas for Heiken Ashi "better" candles are as follows:
Better Heiken Ashi Close = (Open + High + Low + Close) / 4
Better Heiken Ashi Open = (Previous Better Heiken Ashi Open + Previous Better Heiken Ashi Close) / 2
Better Heiken Ashi High = Max (High, Better Heiken Ashi Open, Better Heiken Ashi Close)
Better Heiken Ashi Low = Min (Low, Better Heiken Ashi Open, Better Heiken Ashi Close)
Smoothing Factor = 2 / (N + 1), where N is the chosen period for smoothing
Smoothed Better Heiken Ashi Open = (Better Heiken Ashi Open * Smoothing Factor) + (Previous Smoothed Better Heiken Ashi Open * (1 - Smoothing Factor))
Smoothed Better Heiken Ashi Close = (Better Heiken Ashi Close * Smoothing Factor) + (Previous Smoothed Better Heiken Ashi Close * (1 - Smoothing Factor))
The smoothed Better Heiken Ashi Open and Close values are then used to calculate the smoothed Better Heiken Ashi High and Low values, resulting in "better" candles that provide a clearer representation of the market trend and potential reversal points.
It's important to note that, like any other technical analysis tool, Heiken Ashi "better" candles are not foolproof and should be used in conjunction with other indicators and analysis techniques to make well-informed trading decisions.
Heiken Ashi "better" candles, as mentioned previously, provide a clearer representation of market trends and potential reversal points by reducing noise and smoothing out price data. When using these candles in conjunction with other technical analysis tools and indicators, traders can gain valuable insights into market behavior and make more informed decisions.
To effectively use Heiken Ashi "better" candles in your trading strategy, consider the following tips:
Trend Identification: Heiken Ashi "better" candles can help you identify the prevailing trend in the market. When the majority of the candles are green (or another color, depending on your chart settings) and there are no or few lower wicks, it may indicate a strong uptrend. Conversely, when the majority of the candles are red (or another color) and there are no or few upper wicks, it may signal a strong downtrend.
Trend Reversals: Look for potential trend reversals when a change in the color of the candles occurs, especially when accompanied by longer wicks. For example, if a green candle with a long lower wick is followed by a red candle, it could indicate a bearish reversal. Similarly, a red candle with a long upper wick followed by a green candle may suggest a bullish reversal.
Support and Resistance: You can use Heiken Ashi "better" candles to identify potential support and resistance levels. When the candles are consistently moving in one direction and then suddenly change color with longer wicks, it could indicate the presence of a support or resistance level.
Stop-Loss and Take-Profit: Using Heiken Ashi "better" candles can help you manage risk by determining optimal stop-loss and take-profit levels. For instance, you can place your stop-loss below the low of the most recent green candle in an uptrend or above the high of the most recent red candle in a downtrend.
Confirming Signals: Heiken Ashi "better" candles should be used in conjunction with other technical indicators, such as moving averages, oscillators, or chart patterns, to confirm signals and improve the accuracy of your analysis.
In this implementation, you have the choice of AMA, KAMA, or T3 smoothing. These are as follows:
Kaufman Adaptive Moving Average (KAMA)
The Kaufman Adaptive Moving Average (KAMA) is a type of adaptive moving average used in technical analysis to smooth out price fluctuations and identify trends. The KAMA adjusts its smoothing factor based on the market's volatility, making it more responsive in volatile markets and smoother in calm markets. The KAMA is calculated using three different efficiency ratios that determine the appropriate smoothing factor for the current market conditions. These ratios are based on the noise level of the market, the speed at which the market is moving, and the length of the moving average. The KAMA is a popular choice among traders who prefer to use adaptive indicators to identify trends and potential reversals.
Adaptive Moving Average
The Adaptive Moving Average (AMA) is a type of moving average that adjusts its sensitivity to price movements based on market conditions. It uses a ratio between the current price and the highest and lowest prices over a certain lookback period to determine its level of smoothing. The AMA can help reduce lag and increase responsiveness to changes in trend direction, making it useful for traders who want to follow trends while avoiding false signals. The AMA is calculated by multiplying a smoothing constant with the difference between the current price and the previous AMA value, then adding the result to the previous AMA value.
T3
The T3 moving average is a type of technical indicator used in financial analysis to identify trends in price movements. It is similar to the Exponential Moving Average (EMA) and the Double Exponential Moving Average (DEMA), but uses a different smoothing algorithm.
The T3 moving average is calculated using a series of exponential moving averages that are designed to filter out noise and smooth the data. The resulting smoothed data is then weighted with a non-linear function to produce a final output that is more responsive to changes in trend direction.
The T3 moving average can be customized by adjusting the length of the moving average, as well as the weighting function used to smooth the data. It is commonly used in conjunction with other technical indicators as part of a larger trading strategy.
█ Giga Kaleidoscope Modularized Trading System
Core components of an NNFX algorithmic trading strategy
The NNFX algorithm is built on the principles of trend, momentum, and volatility. There are six core components in the NNFX trading algorithm:
1. Volatility - price volatility; e.g., Average True Range, True Range Double, Close-to-Close, etc.
2. Baseline - a moving average to identify price trend
3. Confirmation 1 - a technical indicator used to identify trends
4. Confirmation 2 - a technical indicator used to identify trends
5. Continuation - a technical indicator used to identify trends
6. Volatility/Volume - a technical indicator used to identify volatility/volume breakouts/breakdown
7. Exit - a technical indicator used to determine when a trend is exhausted
What is Volatility in the NNFX trading system?
In the NNFX (No Nonsense Forex) trading system, ATR (Average True Range) is typically used to measure the volatility of an asset. It is used as a part of the system to help determine the appropriate stop loss and take profit levels for a trade. ATR is calculated by taking the average of the true range values over a specified period.
True range is calculated as the maximum of the following values:
-Current high minus the current low
-Absolute value of the current high minus the previous close
-Absolute value of the current low minus the previous close
ATR is a dynamic indicator that changes with changes in volatility. As volatility increases, the value of ATR increases, and as volatility decreases, the value of ATR decreases. By using ATR in NNFX system, traders can adjust their stop loss and take profit levels according to the volatility of the asset being traded. This helps to ensure that the trade is given enough room to move, while also minimizing potential losses.
Other types of volatility include True Range Double (TRD), Close-to-Close, and Garman-Klass
What is a Baseline indicator?
The baseline is essentially a moving average, and is used to determine the overall direction of the market.
The baseline in the NNFX system is used to filter out trades that are not in line with the long-term trend of the market. The baseline is plotted on the chart along with other indicators, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR).
Trades are only taken when the price is in the same direction as the baseline. For example, if the baseline is sloping upwards, only long trades are taken, and if the baseline is sloping downwards, only short trades are taken. This approach helps to ensure that trades are in line with the overall trend of the market, and reduces the risk of entering trades that are likely to fail.
By using a baseline in the NNFX system, traders can have a clear reference point for determining the overall trend of the market, and can make more informed trading decisions. The baseline helps to filter out noise and false signals, and ensures that trades are taken in the direction of the long-term trend.
What is a Confirmation indicator?
Confirmation indicators are technical indicators that are used to confirm the signals generated by primary indicators. Primary indicators are the core indicators used in the NNFX system, such as the Average True Range (ATR), the Moving Average (MA), and the Relative Strength Index (RSI).
The purpose of the confirmation indicators is to reduce false signals and improve the accuracy of the trading system. They are designed to confirm the signals generated by the primary indicators by providing additional information about the strength and direction of the trend.
Some examples of confirmation indicators that may be used in the NNFX system include the Bollinger Bands, the MACD (Moving Average Convergence Divergence), and the MACD Oscillator. These indicators can provide information about the volatility, momentum, and trend strength of the market, and can be used to confirm the signals generated by the primary indicators.
In the NNFX system, confirmation indicators are used in combination with primary indicators and other filters to create a trading system that is robust and reliable. By using multiple indicators to confirm trading signals, the system aims to reduce the risk of false signals and improve the overall profitability of the trades.
What is a Continuation indicator?
In the NNFX (No Nonsense Forex) trading system, a continuation indicator is a technical indicator that is used to confirm a current trend and predict that the trend is likely to continue in the same direction. A continuation indicator is typically used in conjunction with other indicators in the system, such as a baseline indicator, to provide a comprehensive trading strategy.
What is a Volatility/Volume indicator?
Volume indicators, such as the On Balance Volume (OBV), the Chaikin Money Flow (CMF), or the Volume Price Trend (VPT), are used to measure the amount of buying and selling activity in a market. They are based on the trading volume of the market, and can provide information about the strength of the trend. In the NNFX system, volume indicators are used to confirm trading signals generated by the Moving Average and the Relative Strength Index. Volatility indicators include Average Direction Index, Waddah Attar, and Volatility Ratio. In the NNFX trading system, volatility is a proxy for volume and vice versa.
By using volume indicators as confirmation tools, the NNFX trading system aims to reduce the risk of false signals and improve the overall profitability of trades. These indicators can provide additional information about the market that is not captured by the primary indicators, and can help traders to make more informed trading decisions. In addition, volume indicators can be used to identify potential changes in market trends and to confirm the strength of price movements.
What is an Exit indicator?
The exit indicator is used in conjunction with other indicators in the system, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR), to provide a comprehensive trading strategy.
The exit indicator in the NNFX system can be any technical indicator that is deemed effective at identifying optimal exit points. Examples of exit indicators that are commonly used include the Parabolic SAR, the Average Directional Index (ADX), and the Chandelier Exit.
The purpose of the exit indicator is to identify when a trend is likely to reverse or when the market conditions have changed, signaling the need to exit a trade. By using an exit indicator, traders can manage their risk and prevent significant losses.
In the NNFX system, the exit indicator is used in conjunction with a stop loss and a take profit order to maximize profits and minimize losses. The stop loss order is used to limit the amount of loss that can be incurred if the trade goes against the trader, while the take profit order is used to lock in profits when the trade is moving in the trader's favor.
Overall, the use of an exit indicator in the NNFX trading system is an important component of a comprehensive trading strategy. It allows traders to manage their risk effectively and improve the profitability of their trades by exiting at the right time.
How does Loxx's GKD (Giga Kaleidoscope Modularized Trading System) implement the NNFX algorithm outlined above?
Loxx's GKD v1.0 system has five types of modules (indicators/strategies). These modules are:
1. GKD-BT - Backtesting module (Volatility, Number 1 in the NNFX algorithm)
2. GKD-B - Baseline module (Baseline and Volatility/Volume, Numbers 1 and 2 in the NNFX algorithm)
3. GKD-C - Confirmation 1/2 and Continuation module (Confirmation 1/2 and Continuation, Numbers 3, 4, and 5 in the NNFX algorithm)
4. GKD-V - Volatility/Volume module (Confirmation 1/2, Number 6 in the NNFX algorithm)
5. GKD-E - Exit module (Exit, Number 7 in the NNFX algorithm)
(additional module types will added in future releases)
Each module interacts with every module by passing data between modules. Data is passed between each module as described below:
GKD-B => GKD-V => GKD-C(1) => GKD-C(2) => GKD-C(Continuation) => GKD-E => GKD-BT
That is, the Baseline indicator passes its data to Volatility/Volume. The Volatility/Volume indicator passes its values to the Confirmation 1 indicator. The Confirmation 1 indicator passes its values to the Confirmation 2 indicator. The Confirmation 2 indicator passes its values to the Continuation indicator. The Continuation indicator passes its values to the Exit indicator, and finally, the Exit indicator passes its values to the Backtest strategy.
This chaining of indicators requires that each module conform to Loxx's GKD protocol, therefore allowing for the testing of every possible combination of technical indicators that make up the six components of the NNFX algorithm.
What does the application of the GKD trading system look like?
Example trading system:
Backtest: Strategy with 1-3 take profits, trailing stop loss, multiple types of PnL volatility, and 2 backtesting styles
Baseline: Hull Moving Average
Volatility/Volume: Hurst Exponent
Confirmation 1: Adaptive-Lookback Variety RSI as shown on the chart above
Confirmation 2: Williams Percent Range
Continuation: Adaptive-Lookback Variety RSI
Exit: Rex Oscillator
Each GKD indicator is denoted with a module identifier of either: GKD-BT, GKD-B, GKD-C, GKD-V, or GKD-E. This allows traders to understand to which module each indicator belongs and where each indicator fits into the GKD protocol chain.
Giga Kaleidoscope Modularized Trading System Signals (based on the NNFX algorithm)
Standard Entry
1. GKD-C Confirmation 1 Signal
2. GKD-B Baseline agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume agrees
Baseline Entry
1. GKD-B Baseline signal
2. GKD-C Confirmation 1 agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume agrees
6. GKD-C Confirmation 1 signal was less than 7 candles prior
Volatility/Volume Entry
1. GKD-V Volatility/Volume signal
2. GKD-C Confirmation 1 agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 2 agrees
5. GKD-B Baseline agrees
6. GKD-C Confirmation 1 signal was less than 7 candles prior
Continuation Entry
1. Standard Entry, Baseline Entry, or Pullback; entry triggered previously
2. GKD-B Baseline hasn't crossed since entry signal trigger
3. GKD-C Confirmation Continuation Indicator signals
4. GKD-C Confirmation 1 agrees
5. GKD-B Baseline agrees
6. GKD-C Confirmation 2 agrees
1-Candle Rule Standard Entry
1. GKD-C Confirmation 1 signal
2. GKD-B Baseline agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
Next Candle:
1. Price retraced (Long: close < close or Short: close > close )
2. GKD-B Baseline agrees
3. GKD-C Confirmation 1 agrees
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume agrees
1-Candle Rule Baseline Entry
1. GKD-B Baseline signal
2. GKD-C Confirmation 1 agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 1 signal was less than 7 candles prior
Next Candle:
1. Price retraced (Long: close < close or Short: close > close )
2. GKD-B Baseline agrees
3. GKD-C Confirmation 1 agrees
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume Agrees
1-Candle Rule Volatility/Volume Entry
1. GKD-V Volatility/Volume signal
2. GKD-C Confirmation 1 agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 1 signal was less than 7 candles prior
Next Candle:
1. Price retraced (Long: close < close or Short: close > close)
2. GKD-B Volatility/Volume agrees
3. GKD-C Confirmation 1 agrees
4. GKD-C Confirmation 2 agrees
5. GKD-B Baseline agrees
PullBack Entry
1. GKD-B Baseline signal
2. GKD-C Confirmation 1 agrees
3. Price is beyond 1.0x Volatility of Baseline
Next Candle:
1. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
2. GKD-C Confirmation 1 agrees
3. GKD-C Confirmation 2 agrees
4. GKD-V Volatility/Volume Agrees
]█ Setting up the GKD
The GKD system involves chaining indicators together. These are the steps to set this up.
Use a GKD-C indicator alone on a chart
1. Inside the GKD-C indicator, change the "Confirmation Type" setting to "Solo Confirmation Simple"
Use a GKD-V indicator alone on a chart
**nothing, it's already useable on the chart without any settings changes
Use a GKD-B indicator alone on a chart
**nothing, it's already useable on the chart without any settings changes
Baseline (Baseline, Backtest)
1. Import the GKD-B Baseline into the GKD-BT Backtest: "Input into Volatility/Volume or Backtest (Baseline testing)"
2. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "Baseline"
Volatility/Volume (Volatility/Volume, Backte st)
1. Inside the GKD-V indicator, change the "Testing Type" setting to "Solo"
2. Inside the GKD-V indicator, change the "Signal Type" setting to "Crossing" (neither traditional nor both can be backtested)
3. Import the GKD-V indicator into the GKD-BT Backtest: "Input into C1 or Backtest"
4. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "Volatility/Volume"
5. Inside the GKD-BT Backtest, a) change the setting "Backtest Type" to "Trading" if using a directional GKD-V indicator; or, b) change the setting "Backtest Type" to "Full" if using a directional or non-directional GKD-V indicator (non-directional GKD-V can only test Longs and Shorts separately)
6. If "Backtest Type" is set to "Full": Inside the GKD-BT Backtest, change the setting "Backtest Side" to "Long" or "Short
7. If "Backtest Type" is set to "Full": To allow the system to open multiple orders at one time so you test all Longs or Shorts, open the GKD-BT Backtest, click the tab "Properties" and then insert a value of something like 10 orders into the "Pyramiding" settings. This will allow 10 orders to be opened at one time which should be enough to catch all possible Longs or Shorts.
Solo Confirmation Simple (Confirmation, Backtest)
1. Inside the GKD-C indicator, change the "Confirmation Type" setting to "Solo Confirmation Simple"
1. Import the GKD-C indicator into the GKD-BT Backtest: "Input into Backtest"
2. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "Solo Confirmation Simple"
Solo Confirmation Complex without Exits (Baseline, Volatility/Volume, Confirmation, Backtest)
1. Inside the GKD-V indicator, change the "Testing Type" setting to "Chained"
2. Import the GKD-B Baseline into the GKD-V indicator: "Input into Volatility/Volume or Backtest (Baseline testing)"
3. Inside the GKD-C indicator, change the "Confirmation Type" setting to "Solo Confirmation Complex"
4. Import the GKD-V indicator into the GKD-C indicator: "Input into C1 or Backtest"
5. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "GKD Full wo/ Exits"
6. Import the GKD-C into the GKD-BT Backtest: "Input into Exit or Backtest"
Solo Confirmation Complex with Exits (Baseline, Volatility/Volume, Confirmation, Exit, Backtest)
1. Inside the GKD-V indicator, change the "Testing Type" setting to "Chained"
2. Import the GKD-B Baseline into the GKD-V indicator: "Input into Volatility/Volume or Backtest (Baseline testing)"
3. Inside the GKD-C indicator, change the "Confirmation Type" setting to "Solo Confirmation Complex"
4. Import the GKD-V indicator into the GKD-C indicator: "Input into C1 or Backtest"
5. Import the GKD-C indicator into the GKD-E indicator: "Input into Exit"
6. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "GKD Full w/ Exits"
7. Import the GKD-E into the GKD-BT Backtest: "Input into Backtest"
Full GKD without Exits (Baseline, Volatility/Volume, Confirmation 1, Confirmation 2, Continuation, Backtest)
1. Inside the GKD-V indicator, change the "Testing Type" setting to "Chained"
2. Import the GKD-B Baseline into the GKD-V indicator: "Input into Volatility/Volume or Backtest (Baseline testing)"
3. Inside the GKD-C 1 indicator, change the "Confirmation Type" setting to "Confirmation 1"
4. Import the GKD-V indicator into the GKD-C 1 indicator: "Input into C1 or Backtest"
5. Inside the GKD-C 2 indicator, change the "Confirmation Type" setting to "Confirmation 2"
6. Import the GKD-C 1 indicator into the GKD-C 2 indicator: "Input into C2"
7. Inside the GKD-C Continuation indicator, change the "Confirmation Type" setting to "Continuation"
8. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "GKD Full wo/ Exits"
9. Import the GKD-E into the GKD-BT Backtest: "Input into Exit or Backtest"
Full GKD with Exits (Baseline, Volatility/Volume, Confirmation 1, Confirmation 2, Continuation, Exit, Backtest)
1. Inside the GKD-V indicator, change the "Testing Type" setting to "Chained"
2. Import the GKD-B Baseline into the GKD-V indicator: "Input into Volatility/Volume or Backtest (Baseline testing)"
3. Inside the GKD-C 1 indicator, change the "Confirmation Type" setting to "Confirmation 1"
4. Import the GKD-V indicator into the GKD-C 1 indicator: "Input into C1 or Backtest"
5. Inside the GKD-C 2 indicator, change the "Confirmation Type" setting to "Confirmation 2"
6. Import the GKD-C 1 indicator into the GKD-C 2 indicator: "Input into C2"
7. Inside the GKD-C Continuation indicator, change the "Confirmation Type" setting to "Continuation"
8. Import the GKD-C Continuation indicator into the GKD-E indicator: "Input into Exit"
9. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "GKD Full w/ Exits"
10. Import the GKD-E into the GKD-BT Backtest: "Input into Backtest"
Baseline + Volatility/Volume (Baseline, Volatility/Volume, Backtest)
1. Inside the GKD-V indicator, change the "Testing Type" setting to "Baseline + Volatility/Volume"
2. Inside the GKD-V indicator, make sure the "Signal Type" setting is set to "Traditional"
3. Import the GKD-B Baseline into the GKD-V indicator: "Input into Volatility/Volume or Backtest (Baseline testing)"
4. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "Baseline + Volatility/Volume"
5. Import the GKD-V into the GKD-BT Backtest: "Input into C1 or Backtest"
6. Inside the GKD-BT Backtest, change the setting "Backtest Type" to "Full". For this backtest, you must test Longs and Shorts separately
7. To allow the system to open multiple orders at one time so you can test all Longs or Shorts, open the GKD-BT Backtest, click the tab "Properties" and then insert a value of something like 10 orders into the "Pyramiding" settings. This will allow 10 orders to be opened at one time which should be enough to catch all possible Longs or Shorts.
Requirements
Inputs
Confirmation 1: GKD-V Volatility / Volume indicator
Confirmation 2: GKD-C Confirmation indicator
Continuation: GKD-C Confirmation indicator
Solo Confirmation Simple: GKD-B Baseline
Solo Confirmation Complex: GKD-V Volatility / Volume indicator
Solo Confirmation Super Complex: GKD-V Volatility / Volume indicator
Stacked 1: None
Stacked 2+: GKD-C, GKD-V, or GKD-B Stacked 1
Outputs
Confirmation 1: GKD-C Confirmation 2 indicator
Confirmation 2: GKD-C Continuation indicator
Continuation: GKD-E Exit indicator
Solo Confirmation Simple: GKD-BT Backtest
Solo Confirmation Complex: GKD-BT Backtest or GKD-E Exit indicator
Solo Confirmation Super Complex: GKD-C Continuation indicator
Stacked 1: GKD-C, GKD-V, or GKD-B Stacked 2+
Stacked 2+: GKD-C, GKD-V, or GKD-B Stacked 2+ or GKD-BT Backtest
Additional features will be added in future releases.
GKD-C Adaptive Digital Kahler Variety RSI w/ DZ [Loxx]Giga Kaleidoscope GKD-C Adaptive Digital Kahler Variety RSI w/ DZ is a Confirmation module included in Loxx's "Giga Kaleidoscope Modularized Trading System".
█ Giga Kaleidoscope Modularized Trading System
What is Loxx's "Giga Kaleidoscope Modularized Trading System"?
The Giga Kaleidoscope Modularized Trading System is a trading system built on the philosophy of the NNFX (No Nonsense Forex) algorithmic trading.
What is the NNFX algorithmic trading strategy?
The NNFX (No-Nonsense Forex) trading system is a comprehensive approach to Forex trading that is designed to simplify the process and remove the confusion and complexity that often surrounds trading. The system was developed by a Forex trader who goes by the pseudonym "VP" and has gained a significant following in the Forex community.
The NNFX trading system is based on a set of rules and guidelines that help traders make objective and informed decisions. These rules cover all aspects of trading, including market analysis, trade entry, stop loss placement, and trade management.
Here are the main components of the NNFX trading system:
1. Trading Philosophy: The NNFX trading system is based on the idea that successful trading requires a comprehensive understanding of the market, objective analysis, and strict risk management. The system aims to remove subjective elements from trading and focuses on objective rules and guidelines.
2. Technical Analysis: The NNFX trading system relies heavily on technical analysis and uses a range of indicators to identify high-probability trading opportunities. The system uses a combination of trend-following and mean-reverting strategies to identify trades.
3. Market Structure: The NNFX trading system emphasizes the importance of understanding the market structure, including price action, support and resistance levels, and market cycles. The system uses a range of tools to identify the market structure, including trend lines, channels, and moving averages.
4. Trade Entry: The NNFX trading system has strict rules for trade entry. The system uses a combination of technical indicators to identify high-probability trades, and traders must meet specific criteria to enter a trade.
5. Stop Loss Placement: The NNFX trading system places a significant emphasis on risk management and requires traders to place a stop loss order on every trade. The system uses a combination of technical analysis and market structure to determine the appropriate stop loss level.
6. Trade Management: The NNFX trading system has specific rules for managing open trades. The system aims to minimize risk and maximize profit by using a combination of trailing stops, take profit levels, and position sizing.
Overall, the NNFX trading system is designed to be a straightforward and easy-to-follow approach to Forex trading that can be applied by traders of all skill levels.
Core components of an NNFX algorithmic trading strategy
The NNFX algorithm is built on the principles of trend, momentum, and volatility. There are six core components in the NNFX trading algorithm:
1. Volatility - price volatility; e.g., Average True Range, True Range Double, Close-to-Close, etc.
2. Baseline - a moving average to identify price trend
3. Confirmation 1 - a technical indicator used to identify trends
4. Confirmation 2 - a technical indicator used to identify trends
5. Continuation - a technical indicator used to identify trends
6. Volatility/Volume - a technical indicator used to identify volatility/volume breakouts/breakdown
7. Exit - a technical indicator used to determine when a trend is exhausted
What is Volatility in the NNFX trading system?
In the NNFX (No Nonsense Forex) trading system, ATR (Average True Range) is typically used to measure the volatility of an asset. It is used as a part of the system to help determine the appropriate stop loss and take profit levels for a trade. ATR is calculated by taking the average of the true range values over a specified period.
True range is calculated as the maximum of the following values:
-Current high minus the current low
-Absolute value of the current high minus the previous close
-Absolute value of the current low minus the previous close
ATR is a dynamic indicator that changes with changes in volatility. As volatility increases, the value of ATR increases, and as volatility decreases, the value of ATR decreases. By using ATR in NNFX system, traders can adjust their stop loss and take profit levels according to the volatility of the asset being traded. This helps to ensure that the trade is given enough room to move, while also minimizing potential losses.
Other types of volatility include True Range Double (TRD), Close-to-Close, and Garman-Klass
What is a Baseline indicator?
The baseline is essentially a moving average, and is used to determine the overall direction of the market.
The baseline in the NNFX system is used to filter out trades that are not in line with the long-term trend of the market. The baseline is plotted on the chart along with other indicators, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR).
Trades are only taken when the price is in the same direction as the baseline. For example, if the baseline is sloping upwards, only long trades are taken, and if the baseline is sloping downwards, only short trades are taken. This approach helps to ensure that trades are in line with the overall trend of the market, and reduces the risk of entering trades that are likely to fail.
By using a baseline in the NNFX system, traders can have a clear reference point for determining the overall trend of the market, and can make more informed trading decisions. The baseline helps to filter out noise and false signals, and ensures that trades are taken in the direction of the long-term trend.
What is a Confirmation indicator?
Confirmation indicators are technical indicators that are used to confirm the signals generated by primary indicators. Primary indicators are the core indicators used in the NNFX system, such as the Average True Range (ATR), the Moving Average (MA), and the Relative Strength Index (RSI).
The purpose of the confirmation indicators is to reduce false signals and improve the accuracy of the trading system. They are designed to confirm the signals generated by the primary indicators by providing additional information about the strength and direction of the trend.
Some examples of confirmation indicators that may be used in the NNFX system include the Bollinger Bands, the MACD (Moving Average Convergence Divergence), and the MACD Oscillator. These indicators can provide information about the volatility, momentum, and trend strength of the market, and can be used to confirm the signals generated by the primary indicators.
In the NNFX system, confirmation indicators are used in combination with primary indicators and other filters to create a trading system that is robust and reliable. By using multiple indicators to confirm trading signals, the system aims to reduce the risk of false signals and improve the overall profitability of the trades.
What is a Continuation indicator?
In the NNFX (No Nonsense Forex) trading system, a continuation indicator is a technical indicator that is used to confirm a current trend and predict that the trend is likely to continue in the same direction. A continuation indicator is typically used in conjunction with other indicators in the system, such as a baseline indicator, to provide a comprehensive trading strategy.
What is a Volatility/Volume indicator?
Volume indicators, such as the On Balance Volume (OBV), the Chaikin Money Flow (CMF), or the Volume Price Trend (VPT), are used to measure the amount of buying and selling activity in a market. They are based on the trading volume of the market, and can provide information about the strength of the trend. In the NNFX system, volume indicators are used to confirm trading signals generated by the Moving Average and the Relative Strength Index. Volatility indicators include Average Direction Index, Waddah Attar, and Volatility Ratio. In the NNFX trading system, volatility is a proxy for volume and vice versa.
By using volume indicators as confirmation tools, the NNFX trading system aims to reduce the risk of false signals and improve the overall profitability of trades. These indicators can provide additional information about the market that is not captured by the primary indicators, and can help traders to make more informed trading decisions. In addition, volume indicators can be used to identify potential changes in market trends and to confirm the strength of price movements.
What is an Exit indicator?
The exit indicator is used in conjunction with other indicators in the system, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR), to provide a comprehensive trading strategy.
The exit indicator in the NNFX system can be any technical indicator that is deemed effective at identifying optimal exit points. Examples of exit indicators that are commonly used include the Parabolic SAR, the Average Directional Index (ADX), and the Chandelier Exit.
The purpose of the exit indicator is to identify when a trend is likely to reverse or when the market conditions have changed, signaling the need to exit a trade. By using an exit indicator, traders can manage their risk and prevent significant losses.
In the NNFX system, the exit indicator is used in conjunction with a stop loss and a take profit order to maximize profits and minimize losses. The stop loss order is used to limit the amount of loss that can be incurred if the trade goes against the trader, while the take profit order is used to lock in profits when the trade is moving in the trader's favor.
Overall, the use of an exit indicator in the NNFX trading system is an important component of a comprehensive trading strategy. It allows traders to manage their risk effectively and improve the profitability of their trades by exiting at the right time.
How does Loxx's GKD (Giga Kaleidoscope Modularized Trading System) implement the NNFX algorithm outlined above?
Loxx's GKD v1.0 system has five types of modules (indicators/strategies). These modules are:
1. GKD-BT - Backtesting module (Volatility, Number 1 in the NNFX algorithm)
2. GKD-B - Baseline module (Baseline and Volatility/Volume, Numbers 1 and 2 in the NNFX algorithm)
3. GKD-C - Confirmation 1/2 and Continuation module (Confirmation 1/2 and Continuation, Numbers 3, 4, and 5 in the NNFX algorithm)
4. GKD-V - Volatility/Volume module (Confirmation 1/2, Number 6 in the NNFX algorithm)
5. GKD-E - Exit module (Exit, Number 7 in the NNFX algorithm)
(additional module types will added in future releases)
Each module interacts with every module by passing data between modules. Data is passed between each module as described below:
GKD-B => GKD-V => GKD-C(1) => GKD-C(2) => GKD-C(Continuation) => GKD-E => GKD-BT
That is, the Baseline indicator passes its data to Volatility/Volume. The Volatility/Volume indicator passes its values to the Confirmation 1 indicator. The Confirmation 1 indicator passes its values to the Confirmation 2 indicator. The Confirmation 2 indicator passes its values to the Continuation indicator. The Continuation indicator passes its values to the Exit indicator, and finally, the Exit indicator passes its values to the Backtest strategy.
This chaining of indicators requires that each module conform to Loxx's GKD protocol, therefore allowing for the testing of every possible combination of technical indicators that make up the six components of the NNFX algorithm.
What does the application of the GKD trading system look like?
Example trading system:
Backtest: Strategy with 1-3 take profits, trailing stop loss, multiple types of PnL volatility, and 2 backtesting styles
Baseline: Hull Moving Average
Volatility/Volume: Hurst Exponent
Confirmation 1: Adaptive Digital Kahler Variety RSI w/ DZ as shown on the chart above
Confirmation 2: Williams Percent Range
Continuation: Fisher Transform
Exit: Rex Oscillator
Each GKD indicator is denoted with a module identifier of either: GKD-BT, GKD-B, GKD-C, GKD-V, or GKD-E. This allows traders to understand to which module each indicator belongs and where each indicator fits into the GKD protocol chain.
Giga Kaleidoscope Modularized Trading System Signals (based on the NNFX algorithm)
Standard Entry
1. GKD-C Confirmation 1 Signal
2. GKD-B Baseline agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume agrees
Baseline Entry
1. GKD-B Baseline signal
2. GKD-C Confirmation 1 agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume agrees
6. GKD-C Confirmation 1 signal was less than 7 candles prior
Continuation Entry
1. Standard Entry, Baseline Entry, or Pullback; entry triggered previously
2. GKD-B Baseline hasn't crossed since entry signal trigger
3. GKD-C Confirmation Continuation Indicator signals
4. GKD-C Confirmation 1 agrees
5. GKD-B Baseline agrees
6. GKD-C Confirmation 2 agrees
1-Candle Rule Standard Entry
1. GKD-C Confirmation 1 signal
2. GKD-B Baseline agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
Next Candle:
1. Price retraced (Long: close < close or Short: close > close )
2. GKD-B Baseline agrees
3. GKD-C Confirmation 1 agrees
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume agrees
1-Candle Rule Baseline Entry
1. GKD-B Baseline signal
2. GKD-C Confirmation 1 agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 1 signal was less than 7 candles prior
Next Candle:
1. Price retraced (Long: close < close or Short: close > close )
2. GKD-B Baseline agrees
3. GKD-C Confirmation 1 agrees
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume Agrees
PullBack Entry
1. GKD-B Baseline signal
2. GKD-C Confirmation 1 agrees
3. Price is beyond 1.0x Volatility of Baseline
Next Candle:
1. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
3. GKD-C Confirmation 1 agrees
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume Agrees
█ GKD-C Adaptive Digital Kahler Variety RSI w/ DZ
What is Digital Kahler?
From Philipp Kahler's article for www.traders-mag.com, August 2008. "A Classic Indicator in a New Suit: Digital Stochastic"
Digital Indicators
Whenever you study the development of trading systems in particular, you will be struck in an extremely unpleasant way by the seemingly unmotivated indentations and changes in direction of each indicator. An experienced trader can recognise many false signals of the indicator on the basis of his solid background; a stupid trading system usually falls into any trap offered by the unclear indicator course. This is what motivated me to improve even further this and other indicators with the help of a relatively simple procedure. The goal of this development is to be able to use this indicator in a trading system with as few additional conditions as possible. Discretionary traders will likewise be happy about this clear course, which is not nerve-racking and makes concentrating on the essential elements of trading possible.
How Is It Done?
The digital stochastic is a child of the original indicator. We owe a debt of gratitude to George Lane for his idea to design an indicator which describes the position of the current price within the high-low range of the historical price movement. My contribution to this indicator is the changed pattern which improves the quality of the signal without generating too long delays in giving signals. The trick used to generate this “digital” behavior of the indicator. It can be used with most oscillators like RSI or CCI.
First of all, the original is looked at. The indicator always moves between 0 and 100. The precise position of the indicator or its course relative to the trigger line are of no interest to me, I would just like to know whether the indicator is quoted below or above the value 50. This is tantamount to the question of whether the market is just trading above or below the middle of the high-low range of the past few days. If the market trades in the upper half of its high-low range, then the digital stochastic is given the value 1; if the original stochastic is below 50, then the value –1 is given. This leads to a sequence of 1/-1 values – the digital core of the new indicator. These values are subsequently smoothed by means of a short exponential moving average . This way minor false signals are eliminated and the indicator is given its typical form.
This indicator contains 7 different types of RSI:
RSX
Regular
Slow
Rapid
Harris
Cuttler
Ehlers Smoothed
What is RSI?
RSI stands for Relative Strength Index . It is a technical indicator used to measure the strength or weakness of a financial instrument's price action.
The RSI is calculated based on the price movement of an asset over a specified period of time, typically 14 days, and is expressed on a scale of 0 to 100. The RSI is considered overbought when it is above 70 and oversold when it is below 30.
Traders and investors use the RSI to identify potential buy and sell signals. When the RSI indicates that an asset is oversold, it may be considered a buying opportunity, while an overbought RSI may signal that it is time to sell or take profits.
It's important to note that the RSI should not be used in isolation and should be used in conjunction with other technical and fundamental analysis tools to make informed trading decisions.
What is RSX?
Jurik RSX is a technical analysis indicator that is a variation of the Relative Strength Index Smoothed ( RSX ) indicator. It was developed by Mark Jurik and is designed to help traders identify trends and momentum in the market.
The Jurik RSX uses a combination of the RSX indicator and an adaptive moving average (AMA) to smooth out the price data and reduce the number of false signals. The adaptive moving average is designed to adjust the smoothing period based on the current market conditions, which makes the indicator more responsive to changes in price.
The Jurik RSX can be used to identify potential trend reversals and momentum shifts in the market. It oscillates between 0 and 100, with values above 50 indicating a bullish trend and values below 50 indicating a bearish trend . Traders can use these levels to make trading decisions, such as buying when the indicator crosses above 50 and selling when it crosses below 50.
The Jurik RSX is a more advanced version of the RSX indicator, and while it can be useful in identifying potential trade opportunities, it should not be used in isolation. It is best used in conjunction with other technical and fundamental analysis tools to make informed trading decisions.
What is Slow RSI?
Slow RSI is a variation of the traditional Relative Strength Index ( RSI ) indicator. It is a more smoothed version of the RSI and is designed to filter out some of the noise and short-term price fluctuations that can occur with the standard RSI .
The Slow RSI uses a longer period of time than the traditional RSI , typically 21 periods instead of 14. This longer period helps to smooth out the price data and makes the indicator less reactive to short-term price fluctuations.
Like the traditional RSI , the Slow RSI is used to identify potential overbought and oversold conditions in the market. It oscillates between 0 and 100, with values above 70 indicating overbought conditions and values below 30 indicating oversold conditions. Traders often use these levels as potential buy and sell signals.
The Slow RSI is a more conservative version of the RSI and can be useful in identifying longer-term trends in the market. However, it can also be slower to respond to changes in price, which may result in missed trading opportunities. Traders may choose to use a combination of both the Slow RSI and the traditional RSI to make informed trading decisions.
What is Rapid RSI?
Same as regular RSI but with a faster calculation method
What is Harris RSI?
Harris RSI is a technical analysis indicator that is a variation of the Relative Strength Index ( RSI ). It was developed by Larry Harris and is designed to help traders identify potential trend changes and momentum shifts in the market.
The Harris RSI uses a different calculation formula compared to the traditional RSI . It takes into account both the opening and closing prices of a financial instrument, as well as the high and low prices. The Harris RSI is also normalized to a range of 0 to 100, with values above 50 indicating a bullish trend and values below 50 indicating a bearish trend .
Like the traditional RSI , the Harris RSI is used to identify potential overbought and oversold conditions in the market. It oscillates between 0 and 100, with values above 70 indicating overbought conditions and values below 30 indicating oversold conditions. Traders often use these levels as potential buy and sell signals.
The Harris RSI is a more advanced version of the RSI and can be useful in identifying longer-term trends in the market. However, it can also generate more false signals than the standard RSI . Traders may choose to use a combination of both the Harris RSI and the traditional RSI to make informed trading decisions.
What is Cuttler RSI?
Cuttler RSI is a technical analysis indicator that is a variation of the Relative Strength Index ( RSI ). It was developed by Curt Cuttler and is designed to help traders identify potential trend changes and momentum shifts in the market.
The Cuttler RSI uses a different calculation formula compared to the traditional RSI . It takes into account the difference between the closing price of a financial instrument and the average of the high and low prices over a specified period of time. This difference is then normalized to a range of 0 to 100, with values above 50 indicating a bullish trend and values below 50 indicating a bearish trend .
Like the traditional RSI , the Cuttler RSI is used to identify potential overbought and oversold conditions in the market. It oscillates between 0 and 100, with values above 70 indicating overbought conditions and values below 30 indicating oversold conditions. Traders often use these levels as potential buy and sell signals.
The Cuttler RSI is a more advanced version of the RSI and can be useful in identifying longer-term trends in the market. However, it can also generate more false signals than the standard RSI . Traders may choose to use a combination of both the Cuttler RSI and the traditional RSI to make informed trading decisions.
What is Ehlers Smoothed RSI?
Ehlers smoothed RSI is a technical analysis indicator that is a variation of the Relative Strength Index ( RSI ). It was developed by John Ehlers and is designed to help traders identify potential trend changes and momentum shifts in the market.
The Ehlers smoothed RSI uses a different calculation formula compared to the traditional RSI . It uses a smoothing algorithm that is designed to reduce the noise and random fluctuations that can occur with the standard RSI . The smoothing algorithm is based on a concept called "digital signal processing" and is intended to improve the accuracy of the indicator.
Like the traditional RSI , the Ehlers smoothed RSI is used to identify potential overbought and oversold conditions in the market. It oscillates between 0 and 100, with values above 70 indicating overbought conditions and values below 30 indicating oversold conditions. Traders often use these levels as potential buy and sell signals.
The Ehlers smoothed RSI can be useful in identifying longer-term trends and momentum shifts in the market. However, it can also generate more false signals than the standard RSI . Traders may choose to use a combination of both the Ehlers smoothed RSI and the traditional RSI to make informed trading decisions.
What is a Vertical Horizontal Filter?
The Vertical Horizontal Filter (VHF) is a technical indicator used in trading to identify whether a market is trending or in a sideways trading range. It was developed by Adam White, and is based on the concept that markets tend to exhibit more volatility when they are trending, and less volatility when they are in a sideways range.
The VHF is calculated by taking the ratio of the range of the high and low prices over a specified period to the total range of prices over the same period. The resulting ratio is then multiplied by 100 to create a percentage value.
If the VHF is above a certain threshold, typically 60, it is considered to be indicating a trending market. If it is below the threshold, it is indicating a sideways trading range.
Traders use the VHF to help identify market conditions and to adjust their trading strategies accordingly. In a trending market, traders may look for opportunities to enter or exit positions based on the direction of the trend, while in a sideways trading range, traders may look for opportunities to buy at the bottom of the range and sell at the top.
The VHF can also be used in conjunction with other technical indicators, such as moving averages or momentum indicators, to help confirm trading signals. For example, if the VHF is indicating a trending market and the moving average is also indicating a trend, this may provide a stronger signal to enter or exit a trade.
One potential limitation of the VHF is that it can be less effective in markets that are transitioning between trending and sideways trading ranges. During these periods, the VHF may not accurately reflect the current market conditions, and traders may need to use other indicators or methods to help identify the current trend.
In summary, the Vertical Horizontal Filter (VHF) is a technical indicator used in trading to identify whether a market is trending or in a sideways trading range. It is based on the concept that markets exhibit more volatility when they are trending, and less volatility when they are in a sideways range. Traders use the VHF to help identify market conditions and adjust their trading strategies accordingly.
What are Dynamic Zones?
As explained in "Stocks & Commodities V15:7 (306-310): Dynamic Zones by Leo Zamansky, Ph .D., and David Stendahl"
Most indicators use a fixed zone for buy and sell signals. Here’ s a concept based on zones that are responsive to past levels of the indicator.
One approach to active investing employs the use of oscillators to exploit tradable market trends. This investing style follows a very simple form of logic: Enter the market only when an oscillator has moved far above or below traditional trading lev- els. However, these oscillator- driven systems lack the ability to evolve with the market because they use fixed buy and sell zones. Traders typically use one set of buy and sell zones for a bull market and substantially different zones for a bear market. And therein lies the problem.
Once traders begin introducing their market opinions into trading equations, by changing the zones, they negate the system’s mechanical nature. The objective is to have a system automatically define its own buy and sell zones and thereby profitably trade in any market — bull or bear. Dynamic zones offer a solution to the problem of fixed buy and sell zones for any oscillator-driven system.
An indicator’s extreme levels can be quantified using statistical methods. These extreme levels are calculated for a certain period and serve as the buy and sell zones for a trading system. The repetition of this statistical process for every value of the indicator creates values that become the dynamic zones. The zones are calculated in such a way that the probability of the indicator value rising above, or falling below, the dynamic zones is equal to a given probability input set by the trader.
To better understand dynamic zones, let's first describe them mathematically and then explain their use. The dynamic zones definition:
Find V such that:
For dynamic zone buy: P{X <= V}=P1
For dynamic zone sell: P{X >= V}=P2
where P1 and P2 are the probabilities set by the trader, X is the value of the indicator for the selected period and V represents the value of the dynamic zone.
The probability input P1 and P2 can be adjusted by the trader to encompass as much or as little data as the trader would like. The smaller the probability, the fewer data values above and below the dynamic zones. This translates into a wider range between the buy and sell zones. If a 10% probability is used for P1 and P2, only those data values that make up the top 10% and bottom 10% for an indicator are used in the construction of the zones. Of the values, 80% will fall between the two extreme levels. Because dynamic zone levels are penetrated so infrequently, when this happens, traders know that the market has truly moved into overbought or oversold territory.
Calculating the Dynamic Zones
The algorithm for the dynamic zones is a series of steps. First, decide the value of the lookback period t. Next, decide the value of the probability Pbuy for buy zone and value of the probability Psell for the sell zone.
For i=1, to the last lookback period, build the distribution f(x) of the price during the lookback period i. Then find the value Vi1 such that the probability of the price less than or equal to Vi1 during the lookback period i is equal to Pbuy. Find the value Vi2 such that the probability of the price greater or equal to Vi2 during the lookback period i is equal to Psell. The sequence of Vi1 for all periods gives the buy zone. The sequence of Vi2 for all periods gives the sell zone.
In the algorithm description, we have: Build the distribution f(x) of the price during the lookback period i. The distribution here is empirical namely, how many times a given value of x appeared during the lookback period. The problem is to find such x that the probability of a price being greater or equal to x will be equal to a probability selected by the user. Probability is the area under the distribution curve. The task is to find such value of x that the area under the distribution curve to the right of x will be equal to the probability selected by the user. That x is the dynamic zone.
What is Adaptive Digital Kahler Variety RSI w/ DZ?
We first calculate the VHF filter, we then inject that period output into an RSI calculation, we apply a Digital Kahler filter to this output, and finally, we create Dynamic Zones to determine oscillator extremes. There are four types of signals: Slope, Static Zero-line, Dynamic Levels, and Dynamic Middle
Requirements
Inputs
Confirmation 1 and Solo Confirmation: GKD-V Volatility / Volume indicator
Confirmation 2: GKD-C Confirmation indicator
Outputs
Confirmation 2 and Solo Confirmation Complex: GKD-E Exit indicator
Confirmation 1: GKD-C Confirmation indicator
Continuation: GKD-E Exit indicator
Solo Confirmation Simple: GKD-BT Backtest strategy
Additional features will be added in future releases.
GKD-C RSI of Fast Discrete Cosine Transform [Loxx]Giga Kaleidoscope GKD-C RSI of Fast Discrete Cosine Transform is a Confirmation module included in Loxx's "Giga Kaleidoscope Modularized Trading System".
█ Giga Kaleidoscope Modularized Trading System
What is Loxx's "Giga Kaleidoscope Modularized Trading System"?
The Giga Kaleidoscope Modularized Trading System is a trading system built on the philosophy of the NNFX (No Nonsense Forex) algorithmic trading.
What is the NNFX algorithmic trading strategy?
The NNFX (No-Nonsense Forex) trading system is a comprehensive approach to Forex trading that is designed to simplify the process and remove the confusion and complexity that often surrounds trading. The system was developed by a Forex trader who goes by the pseudonym "VP" and has gained a significant following in the Forex community.
The NNFX trading system is based on a set of rules and guidelines that help traders make objective and informed decisions. These rules cover all aspects of trading, including market analysis, trade entry, stop loss placement, and trade management.
Here are the main components of the NNFX trading system:
1. Trading Philosophy: The NNFX trading system is based on the idea that successful trading requires a comprehensive understanding of the market, objective analysis, and strict risk management. The system aims to remove subjective elements from trading and focuses on objective rules and guidelines.
2. Technical Analysis: The NNFX trading system relies heavily on technical analysis and uses a range of indicators to identify high-probability trading opportunities. The system uses a combination of trend-following and mean-reverting strategies to identify trades.
3. Market Structure: The NNFX trading system emphasizes the importance of understanding the market structure, including price action, support and resistance levels, and market cycles. The system uses a range of tools to identify the market structure, including trend lines, channels, and moving averages.
4. Trade Entry: The NNFX trading system has strict rules for trade entry. The system uses a combination of technical indicators to identify high-probability trades, and traders must meet specific criteria to enter a trade.
5. Stop Loss Placement: The NNFX trading system places a significant emphasis on risk management and requires traders to place a stop loss order on every trade. The system uses a combination of technical analysis and market structure to determine the appropriate stop loss level.
6. Trade Management: The NNFX trading system has specific rules for managing open trades. The system aims to minimize risk and maximize profit by using a combination of trailing stops, take profit levels, and position sizing.
Overall, the NNFX trading system is designed to be a straightforward and easy-to-follow approach to Forex trading that can be applied by traders of all skill levels.
Core components of an NNFX algorithmic trading strategy
The NNFX algorithm is built on the principles of trend, momentum, and volatility. There are six core components in the NNFX trading algorithm:
1. Volatility - price volatility; e.g., Average True Range, True Range Double, Close-to-Close, etc.
2. Baseline - a moving average to identify price trend
3. Confirmation 1 - a technical indicator used to identify trends
4. Confirmation 2 - a technical indicator used to identify trends
5. Continuation - a technical indicator used to identify trends
6. Volatility/Volume - a technical indicator used to identify volatility/volume breakouts/breakdown
7. Exit - a technical indicator used to determine when a trend is exhausted
What is Volatility in the NNFX trading system?
In the NNFX (No Nonsense Forex) trading system, ATR (Average True Range) is typically used to measure the volatility of an asset. It is used as a part of the system to help determine the appropriate stop loss and take profit levels for a trade. ATR is calculated by taking the average of the true range values over a specified period.
True range is calculated as the maximum of the following values:
-Current high minus the current low
-Absolute value of the current high minus the previous close
-Absolute value of the current low minus the previous close
ATR is a dynamic indicator that changes with changes in volatility. As volatility increases, the value of ATR increases, and as volatility decreases, the value of ATR decreases. By using ATR in NNFX system, traders can adjust their stop loss and take profit levels according to the volatility of the asset being traded. This helps to ensure that the trade is given enough room to move, while also minimizing potential losses.
Other types of volatility include True Range Double (TRD), Close-to-Close, and Garman-Klass
What is a Baseline indicator?
The baseline is essentially a moving average, and is used to determine the overall direction of the market.
The baseline in the NNFX system is used to filter out trades that are not in line with the long-term trend of the market. The baseline is plotted on the chart along with other indicators, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR).
Trades are only taken when the price is in the same direction as the baseline. For example, if the baseline is sloping upwards, only long trades are taken, and if the baseline is sloping downwards, only short trades are taken. This approach helps to ensure that trades are in line with the overall trend of the market, and reduces the risk of entering trades that are likely to fail.
By using a baseline in the NNFX system, traders can have a clear reference point for determining the overall trend of the market, and can make more informed trading decisions. The baseline helps to filter out noise and false signals, and ensures that trades are taken in the direction of the long-term trend.
What is a Confirmation indicator?
Confirmation indicators are technical indicators that are used to confirm the signals generated by primary indicators. Primary indicators are the core indicators used in the NNFX system, such as the Average True Range (ATR), the Moving Average (MA), and the Relative Strength Index (RSI).
The purpose of the confirmation indicators is to reduce false signals and improve the accuracy of the trading system. They are designed to confirm the signals generated by the primary indicators by providing additional information about the strength and direction of the trend.
Some examples of confirmation indicators that may be used in the NNFX system include the Bollinger Bands, the MACD (Moving Average Convergence Divergence), and the Stochastic Oscillator. These indicators can provide information about the volatility, momentum, and trend strength of the market, and can be used to confirm the signals generated by the primary indicators.
In the NNFX system, confirmation indicators are used in combination with primary indicators and other filters to create a trading system that is robust and reliable. By using multiple indicators to confirm trading signals, the system aims to reduce the risk of false signals and improve the overall profitability of the trades.
What is a Continuation indicator?
In the NNFX (No Nonsense Forex) trading system, a continuation indicator is a technical indicator that is used to confirm a current trend and predict that the trend is likely to continue in the same direction. A continuation indicator is typically used in conjunction with other indicators in the system, such as a baseline indicator, to provide a comprehensive trading strategy.
What is a Volatility/Volume indicator?
Volume indicators, such as the On Balance Volume (OBV), the Chaikin Money Flow (CMF), or the Volume Price Trend (VPT), are used to measure the amount of buying and selling activity in a market. They are based on the trading volume of the market, and can provide information about the strength of the trend. In the NNFX system, volume indicators are used to confirm trading signals generated by the Moving Average and the Relative Strength Index. Volatility indicators include Average Direction Index, Waddah Attar, and Volatility Ratio. In the NNFX trading system, volatility is a proxy for volume and vice versa.
By using volume indicators as confirmation tools, the NNFX trading system aims to reduce the risk of false signals and improve the overall profitability of trades. These indicators can provide additional information about the market that is not captured by the primary indicators, and can help traders to make more informed trading decisions. In addition, volume indicators can be used to identify potential changes in market trends and to confirm the strength of price movements.
What is an Exit indicator?
The exit indicator is used in conjunction with other indicators in the system, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR), to provide a comprehensive trading strategy.
The exit indicator in the NNFX system can be any technical indicator that is deemed effective at identifying optimal exit points. Examples of exit indicators that are commonly used include the Parabolic SAR, the Average Directional Index (ADX), and the Chandelier Exit.
The purpose of the exit indicator is to identify when a trend is likely to reverse or when the market conditions have changed, signaling the need to exit a trade. By using an exit indicator, traders can manage their risk and prevent significant losses.
In the NNFX system, the exit indicator is used in conjunction with a stop loss and a take profit order to maximize profits and minimize losses. The stop loss order is used to limit the amount of loss that can be incurred if the trade goes against the trader, while the take profit order is used to lock in profits when the trade is moving in the trader's favor.
Overall, the use of an exit indicator in the NNFX trading system is an important component of a comprehensive trading strategy. It allows traders to manage their risk effectively and improve the profitability of their trades by exiting at the right time.
How does Loxx's GKD (Giga Kaleidoscope Modularized Trading System) implement the NNFX algorithm outlined above?
Loxx's GKD v1.0 system has five types of modules (indicators/strategies). These modules are:
1. GKD-BT - Backtesting module (Volatility, Number 1 in the NNFX algorithm)
2. GKD-B - Baseline module (Baseline and Volatility/Volume, Numbers 1 and 2 in the NNFX algorithm)
3. GKD-C - Confirmation 1/2 and Continuation module (Confirmation 1/2 and Continuation, Numbers 3, 4, and 5 in the NNFX algorithm)
4. GKD-V - Volatility/Volume module (Confirmation 1/2, Number 6 in the NNFX algorithm)
5. GKD-E - Exit module (Exit, Number 7 in the NNFX algorithm)
(additional module types will added in future releases)
Each module interacts with every module by passing data between modules. Data is passed between each module as described below:
GKD-B => GKD-V => GKD-C(1) => GKD-C(2) => GKD-C(Continuation) => GKD-E => GKD-BT
That is, the Baseline indicator passes its data to Volatility/Volume. The Volatility/Volume indicator passes its values to the Confirmation 1 indicator. The Confirmation 1 indicator passes its values to the Confirmation 2 indicator. The Confirmation 2 indicator passes its values to the Continuation indicator. The Continuation indicator passes its values to the Exit indicator, and finally, the Exit indicator passes its values to the Backtest strategy.
This chaining of indicators requires that each module conform to Loxx's GKD protocol, therefore allowing for the testing of every possible combination of technical indicators that make up the six components of the NNFX algorithm.
What does the application of the GKD trading system look like?
Example trading system:
Backtest: Strategy with 1-3 take profits, trailing stop loss, multiple types of PnL volatility, and 2 backtesting styles
Baseline: Hull Moving Average
Volatility/Volume: Hurst Exponent
Confirmation 1: RSI of Fast Discrete Cosine Transform as shown on the chart above
Confirmation 2: Williams Percent Range
Continuation: Fisher Transform
Exit: Rex Oscillator
Each GKD indicator is denoted with a module identifier of either: GKD-BT, GKD-B, GKD-C, GKD-V, or GKD-E. This allows traders to understand to which module each indicator belongs and where each indicator fits into the GKD protocol chain.
Giga Kaleidoscope Modularized Trading System Signals (based on the NNFX algorithm)
Standard Entry
1. GKD-C Confirmation 1 Signal
2. GKD-B Baseline agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume agrees
Baseline Entry
1. GKD-B Baseline signal
2. GKD-C Confirmation 1 agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume agrees
6. GKD-C Confirmation 1 signal was less than 7 candles prior
Continuation Entry
1. Standard Entry, Baseline Entry, or Pullback; entry triggered previously
2. GKD-B Baseline hasn't crossed since entry signal trigger
3. GKD-C Confirmation Continuation Indicator signals
4. GKD-C Confirmation 1 agrees
5. GKD-B Baseline agrees
6. GKD-C Confirmation 2 agrees
1-Candle Rule Standard Entry
1. GKD-C Confirmation 1 signal
2. GKD-B Baseline agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
Next Candle:
1. Price retraced (Long: close < close or Short: close > close )
2. GKD-B Baseline agrees
3. GKD-C Confirmation 1 agrees
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume agrees
1-Candle Rule Baseline Entry
1. GKD-B Baseline signal
2. GKD-C Confirmation 1 agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 1 signal was less than 7 candles prior
Next Candle:
1. Price retraced (Long: close < close or Short: close > close )
2. GKD-B Baseline agrees
3. GKD-C Confirmation 1 agrees
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume Agrees
PullBack Entry
1. GKD-B Baseline signal
2. GKD-C Confirmation 1 agrees
3. Price is beyond 1.0x Volatility of Baseline
Next Candle:
1. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
3. GKD-C Confirmation 1 agrees
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume Agrees
█ Fast Discrete Cosine Transform
What is the Fast Discrete Cosine Transform?
Algolib is a C++ library for algorithmic trading that provides various algorithms for processing and analyzing financial data. The library includes a Fast Discrete Cosine Transform (FDCT) implementation, which is a fast version of the Discrete Cosine Transform (DCT) algorithm used for signal processing and data compression.
The FDCT implementation in Algolib is based on the FFT (Fast Fourier Transform) algorithm, which is a widely used method for computing the DCT. The implementation is optimized for performance and can handle large datasets efficiently. It uses the standard divide-and-conquer approach to compute the DCT recursively and combines the resulting coefficients to obtain the final DCT of the input signal.
The input to the FDCT algorithm in Algolib is a one-dimensional array of real numbers, which represents a time series or a financial signal. The algorithm then computes the DCT of the input sequence and returns a one-dimensional array of DCT coefficients, which represent the frequency components of the signal.
The implementation of the FDCT algorithm in Algolib uses C++ templates to provide a generic implementation that can work with different data types. It also includes various optimizations, such as loop unrolling, to improve the performance of the algorithm.
The steps involved in the FDCT algorithm in Algolib are:
-Divide the input sequence into even and odd parts.
-Compute the DCT of the even and odd parts recursively.
-Combine the DCT coefficients of the even and odd parts to obtain the final DCT coefficients.
-The implementation of the FDCT algorithm in Algolib uses the FFTW (Fastest Fourier Transform in the West) library to perform the FFT computations, which is a highly optimized library for computing Fourier transforms.
In summary, the Fast Discrete Cosine Transform implementation in Algolib is a fast and efficient implementation of the DCT algorithm, which is used for processing financial signals and time series data. The implementation is optimized for performance and uses the FFT algorithm for fast computation. The implementation is generic and can work with different data types, and includes optimizations such as loop unrolling to improve the performance of the algorithm.
What is the Fast Discrete Cosine Transform in terms of Forex trading?
The Fast Discrete Cosine Transform (FDCT) is an algorithm used for signal processing and data compression that can also be applied in trading forex. The FDCT is used to transform financial data into a set of coefficients that represent the data in terms of cosine functions of different frequencies. These coefficients can be used to analyze the frequency components of financial signals and to develop trading strategies based on these components.
In trading forex, the FDCT can be applied to various financial signals, such as price data, volume data, and technical indicators. By applying the FDCT to these signals, traders can identify the dominant frequency components of the signals and use this information to develop trading strategies.
For example, traders can use the FDCT to identify cycles in the market and use this information to develop trend-following strategies. The FDCT can also be used to identify short-term fluctuations in the market and develop mean-reversion strategies based on these fluctuations.
The FDCT can also be used in combination with other technical analysis tools, such as moving averages, to improve the accuracy of trading signals. For example, traders can apply the FDCT to the moving average of a financial signal to identify the dominant frequency components of the moving average and use this information to develop trading signals.
The FDCT can also be used in conjunction with machine learning algorithms to develop predictive models for financial markets. By applying the FDCT to financial data and using the resulting coefficients as inputs to a machine learning algorithm, traders can develop models that predict future price movements and identify profitable trading opportunities.
In summary, the FDCT can be applied in trading forex to analyze the frequency components of financial signals and develop trading strategies based on these components. The FDCT can be used in conjunction with other technical analysis tools and machine learning algorithms to improve the accuracy of trading signals and develop predictive models for financial markets.
What is the Fast Discrete Cosine Transform in terms of Forex trading?
The Fast Discrete Cosine Transform (FDCT) is an algorithm used for signal processing and data compression that can also be applied in trading forex. The FDCT is used to transform financial data into a set of coefficients that represent the data in terms of cosine functions of different frequencies. These coefficients can be used to analyze the frequency components of financial signals and to develop trading strategies based on these components.
In trading forex, the FDCT can be applied to various financial signals, such as price data, volume data, and technical indicators. By applying the FDCT to these signals, traders can identify the dominant frequency components of the signals and use this information to develop trading strategies.
For example, traders can use the FDCT to identify cycles in the market and use this information to develop trend-following strategies. The FDCT can also be used to identify short-term fluctuations in the market and develop mean-reversion strategies based on these fluctuations.
The FDCT can also be used in combination with other technical analysis tools, such as moving averages, to improve the accuracy of trading signals. For example, traders can apply the FDCT to the moving average of a financial signal to identify the dominant frequency components of the moving average and use this information to develop trading signals.
The FDCT can also be used in conjunction with machine learning algorithms to develop predictive models for financial markets. By applying the FDCT to financial data and using the resulting coefficients as inputs to a machine learning algorithm, traders can develop models that predict future price movements and identify profitable trading opportunities.
In summary, the FDCT can be applied in trading forex to analyze the frequency components of financial signals and develop trading strategies based on these components. The FDCT can be used in conjunction with other technical analysis tools and machine learning algorithms to improve the accuracy of trading signals and develop predictive models for financial markets.
█ Relative Strength Index (RSI)
This indicator contains 7 different types of RSI .
RSX
Regular
Slow
Rapid
Harris
Cuttler
Ehlers Smoothed
What is RSI?
RSI stands for Relative Strength Index . It is a technical indicator used to measure the strength or weakness of a financial instrument's price action.
The RSI is calculated based on the price movement of an asset over a specified period of time, typically 14 days, and is expressed on a scale of 0 to 100. The RSI is considered overbought when it is above 70 and oversold when it is below 30.
Traders and investors use the RSI to identify potential buy and sell signals. When the RSI indicates that an asset is oversold, it may be considered a buying opportunity, while an overbought RSI may signal that it is time to sell or take profits.
It's important to note that the RSI should not be used in isolation and should be used in conjunction with other technical and fundamental analysis tools to make informed trading decisions.
What is RSX?
Jurik RSX is a technical analysis indicator that is a variation of the Relative Strength Index Smoothed ( RSX ) indicator. It was developed by Mark Jurik and is designed to help traders identify trends and momentum in the market.
The Jurik RSX uses a combination of the RSX indicator and an adaptive moving average (AMA) to smooth out the price data and reduce the number of false signals. The adaptive moving average is designed to adjust the smoothing period based on the current market conditions, which makes the indicator more responsive to changes in price.
The Jurik RSX can be used to identify potential trend reversals and momentum shifts in the market. It oscillates between 0 and 100, with values above 50 indicating a bullish trend and values below 50 indicating a bearish trend . Traders can use these levels to make trading decisions, such as buying when the indicator crosses above 50 and selling when it crosses below 50.
The Jurik RSX is a more advanced version of the RSX indicator, and while it can be useful in identifying potential trade opportunities, it should not be used in isolation. It is best used in conjunction with other technical and fundamental analysis tools to make informed trading decisions.
What is Slow RSI?
Slow RSI is a variation of the traditional Relative Strength Index ( RSI ) indicator. It is a more smoothed version of the RSI and is designed to filter out some of the noise and short-term price fluctuations that can occur with the standard RSI .
The Slow RSI uses a longer period of time than the traditional RSI , typically 21 periods instead of 14. This longer period helps to smooth out the price data and makes the indicator less reactive to short-term price fluctuations.
Like the traditional RSI , the Slow RSI is used to identify potential overbought and oversold conditions in the market. It oscillates between 0 and 100, with values above 70 indicating overbought conditions and values below 30 indicating oversold conditions. Traders often use these levels as potential buy and sell signals.
The Slow RSI is a more conservative version of the RSI and can be useful in identifying longer-term trends in the market. However, it can also be slower to respond to changes in price, which may result in missed trading opportunities. Traders may choose to use a combination of both the Slow RSI and the traditional RSI to make informed trading decisions.
What is Rapid RSI?
Same as regular RSI but with a faster calculation method
What is Harris RSI?
Harris RSI is a technical analysis indicator that is a variation of the Relative Strength Index ( RSI ). It was developed by Larry Harris and is designed to help traders identify potential trend changes and momentum shifts in the market.
The Harris RSI uses a different calculation formula compared to the traditional RSI . It takes into account both the opening and closing prices of a financial instrument, as well as the high and low prices. The Harris RSI is also normalized to a range of 0 to 100, with values above 50 indicating a bullish trend and values below 50 indicating a bearish trend .
Like the traditional RSI , the Harris RSI is used to identify potential overbought and oversold conditions in the market. It oscillates between 0 and 100, with values above 70 indicating overbought conditions and values below 30 indicating oversold conditions. Traders often use these levels as potential buy and sell signals.
The Harris RSI is a more advanced version of the RSI and can be useful in identifying longer-term trends in the market. However, it can also generate more false signals than the standard RSI . Traders may choose to use a combination of both the Harris RSI and the traditional RSI to make informed trading decisions.
What is Cuttler RSI?
Cuttler RSI is a technical analysis indicator that is a variation of the Relative Strength Index ( RSI ). It was developed by Curt Cuttler and is designed to help traders identify potential trend changes and momentum shifts in the market.
The Cuttler RSI uses a different calculation formula compared to the traditional RSI . It takes into account the difference between the closing price of a financial instrument and the average of the high and low prices over a specified period of time. This difference is then normalized to a range of 0 to 100, with values above 50 indicating a bullish trend and values below 50 indicating a bearish trend .
Like the traditional RSI , the Cuttler RSI is used to identify potential overbought and oversold conditions in the market. It oscillates between 0 and 100, with values above 70 indicating overbought conditions and values below 30 indicating oversold conditions. Traders often use these levels as potential buy and sell signals.
The Cuttler RSI is a more advanced version of the RSI and can be useful in identifying longer-term trends in the market. However, it can also generate more false signals than the standard RSI . Traders may choose to use a combination of both the Cuttler RSI and the traditional RSI to make informed trading decisions.
What is Ehlers Smoothed RSI?
Ehlers smoothed RSI is a technical analysis indicator that is a variation of the Relative Strength Index ( RSI ). It was developed by John Ehlers and is designed to help traders identify potential trend changes and momentum shifts in the market.
The Ehlers smoothed RSI uses a different calculation formula compared to the traditional RSI . It uses a smoothing algorithm that is designed to reduce the noise and random fluctuations that can occur with the standard RSI . The smoothing algorithm is based on a concept called "digital signal processing" and is intended to improve the accuracy of the indicator.
Like the traditional RSI , the Ehlers smoothed RSI is used to identify potential overbought and oversold conditions in the market. It oscillates between 0 and 100, with values above 70 indicating overbought conditions and values below 30 indicating oversold conditions. Traders often use these levels as potential buy and sell signals.
The Ehlers smoothed RSI can be useful in identifying longer-term trends and momentum shifts in the market. However, it can also generate more false signals than the standard RSI . Traders may choose to use a combination of both the Ehlers smoothed RSI and the traditional RSI to make informed trading decisions.
█ GKD-C RSI of Fast Discrete Cosine Transform
What is the RSI of Fast Discrete Cosine Transform in terms of Forex trading?
The Relative Strength Index (RSI) is a popular technical indicator used in trading forex to measure the strength of a trend and identify potential trend reversals. While the Fast Discrete Cosine Transform (FDCT) is not directly related to the RSI, it can be used to analyze the frequency components of the price data used to calculate the RSI and improve its accuracy.
The RSI is calculated by comparing the average gains and losses of a financial instrument over a given period of time. The RSI value ranges from 0 to 100, with values above 70 indicating an overbought market and values below 30 indicating an oversold market.
One limitation of the RSI is that it only considers the average gains and losses over a fixed period of time, which may not capture the complex patterns and dynamics of financial markets. This is where the FDCT can be useful.
By applying the FDCT to the price data used to calculate the RSI, traders can identify the dominant frequency components of the price data and use this information to adjust the RSI calculation. For example, traders can weight the gains and losses based on the frequency components identified by the FDCT, giving more weight to the dominant frequencies and less weight to the lower frequencies.
This approach can improve the accuracy of the RSI calculation and provide traders with more reliable signals for identifying trends and potential trend reversals. Traders can also use the frequency components identified by the FDCT to develop more advanced trading strategies, such as identifying cycles in the market and using this information to develop trend-following strategies.
In summary, while the FDCT is not directly related to the RSI, it can be used to analyze the frequency components of the price data used to calculate the RSI and improve its accuracy. Traders can use the FDCT to identify dominant frequency components and adjust the RSI calculation accordingly, providing more reliable signals for identifying trends and potential trend reversals.
This indicator has period lengths that are powers of powers of 2. There is also a features to increase the resolution of the FDCT.
Requirements
Inputs
Confirmation 1 and Solo Confirmation: GKD-V Volatility / Volume indicator
Confirmation 2: GKD-C Confirmation indicator
Outputs
Confirmation 2 and Solo Confirmation Complex: GKD-E Exit indicator
Confirmation 1: GKD-C Confirmation indicator
Continuation: GKD-E Exit indicator
Solo Confirmation Simple: GKD-BT Backtest strategy
Additional features will be added in future releases.
Titan Investments|Quantitative THEMIS|Pro|BINANCE:BTCUSDTP:4hInvestment Strategy (Quantitative Trading)
| 🛑 | Watch "LIVE" and 'COPY' this strategy in real time:
🔗 Link: www.tradingview.com
Hello, welcome, feel free 🌹💐
Since the stone age to the most technological age, one thing has not changed, that which continues impress human beings the most, is the other human being!
Deep down, it's all very simple or very complicated, depends on how you look at it.
I believe that everyone was born to do something very well in life.
But few are those who have, let's use the word 'luck' .
Few are those who have the 'luck' to discover this thing.
That is why few are happy and successful in their jobs and professions.
Thank God I had this 'luck' , and discovered what I was born to do well.
And I was born to program. 👨💻
📋 Summary : Project Titan
0️⃣ : 🦄 Project Titan
1️⃣ : ⚖️ Quantitative THEMIS
2️⃣ : 🏛️ Titan Community
3️⃣ : 👨💻 Who am I ❔
4️⃣ : ❓ What is Statistical/Probabilistic Trading ❓
5️⃣ : ❓ How Statistical/Probabilistic Trading works ❓
6️⃣ : ❓ Why use a Statistical/Probabilistic system ❓
7️⃣ : ❓ Why the human brain is not prepared to do Trading ❓
8️⃣ : ❓ What is Backtest ❓
9️⃣ : ❓ How to build a Consistent system ❓
🔟 : ❓ What is a Quantitative Trading system ❓
1️⃣1️⃣ : ❓ How to build a Quantitative Trading system ❓
1️⃣2️⃣ : ❓ How to Exploit Market Anomalies ❓
1️⃣3️⃣ : ❓ What Defines a Robust, Profitable and Consistent System ❓
1️⃣4️⃣ : 🔧 Fixed Technical
1️⃣5️⃣ : ❌ Fixed Outputs : 🎯 TP(%) & 🛑SL(%)
1️⃣6️⃣ : ⚠️ Risk Profile
1️⃣7️⃣ : ⭕ Moving Exits : (Indicators)
1️⃣8️⃣ : 💸 Initial Capital
1️⃣9️⃣ : ⚙️ Entry Options
2️⃣0️⃣ : ❓ How to Automate this Strategy ❓ : 🤖 Automation : 'Third-Party Services'
2️⃣1️⃣ : ❓ How to Automate this Strategy ❓ : 🤖 Automation : 'Exchanges
2️⃣2️⃣ : ❓ How to Automate this Strategy ❓ : 🤖 Automation : 'Messaging Services'
2️⃣3️⃣ : ❓ How to Automate this Strategy ❓ : 🤖 Automation : '🧲🤖Copy-Trading'
2️⃣4️⃣ : ❔ Why be a Titan Pro 👽❔
2️⃣5️⃣ : ❔ Why be a Titan Aff 🛸❔
2️⃣6️⃣ : 📋 Summary : ⚖️ Strategy: Titan Investments|Quantitative THEMIS|Pro|BINANCE:BTCUSDTP:4h
2️⃣7️⃣ : 📊 PERFORMANCE : 🆑 Conservative
2️⃣8️⃣ : 📊 PERFORMANCE : Ⓜ️ Moderate
2️⃣9️⃣ : 📊 PERFORMANCE : 🅰 Aggressive
3️⃣0️⃣ : 🛠️ Roadmap
3️⃣1️⃣ : 🧻 Notes ❕
3️⃣2️⃣ : 🚨 Disclaimer ❕❗
3️⃣3️⃣ : ♻️ ® No Repaint
3️⃣4️⃣ : 🔒 Copyright ©️
3️⃣5️⃣ : 👏 Acknowledgments
3️⃣6️⃣ : 👮 House Rules : 📺 TradingView
3️⃣7️⃣ : 🏛️ Become a Titan Pro member 👽
3️⃣8️⃣ : 🏛️ Be a member Titan Aff 🛸
0️⃣ : 🦄 Project Titan
This is the first real, 100% automated Quantitative Strategy made available to the public and the pinescript community for TradingView.
You will be able to automate all signals of this strategy for your broker , centralized or decentralized and also for messaging services : Discord, Telegram or Twitter .
This is the first strategy of a larger project, in 2023, I will provide a total of 6 100% automated 'Quantitative' strategies to the pinescript community for TradingView.
The future strategies to be shared here will also be unique , never before seen, real 'Quantitative' bots with real, validated results in real operation.
Just like the 'Quantitative THEMIS' strategy, it will be something out of the loop throughout the pinescript/tradingview community, truly unique tools for building mutual wealth consistently and continuously for our community.
1️⃣ : ⚖️ Quantitative THEMIS : Titan Investments|Quantitative THEMIS|Pro|BINANCE:BTCUSDTP:4h
This is a truly unique and out of the curve strategy for BTC /USD .
A truly real strategy, with real, validated results and in real operation.
A unique tool for building mutual wealth, consistently and continuously for the members of the Titan community.
Initially we will operate on a monthly, quarterly, annual or biennial subscription service.
Our goal here is to build a great community, in exchange for an extremely fair value for the use of our truly unique tools, which bring and will bring real results to our community members.
With this business model it will be possible to provide all Titan users and community members with the purest and highest degree of sophistication in the market with pinescript for tradingview, providing unique and truly profitable strategies.
My goal here is to offer the best to our members!
The best 'pinescript' tradingview service in the world!
We are the only Start-Up in the world that will decentralize real and full access to truly real 'quantitative' tools that bring and will bring real results for mutual and ongoing wealth building for our community.
2️⃣ : 🏛️ Titan Community : 👽 Pro 🔁 Aff 🛸
Become a Titan Pro 👽
To get access to the strategy: "Quantitative THEMIS" , and future Titan strategies in a 100% automated way, along with all tutorials for automation.
Pro Plans: 30 Days, 90 Days, 12 Months, 24 Months.
👽 Pro 🅼 Monthly
👽 Pro 🆀 Quarterly
👽 Pro🅰 Annual
👽 Pro👾Two Years
You will have access to a truly unique system that is out of the curve .
A 100% real, 100% automated, tested, validated, profitable, and in real operation strategy.
Become a Titan Affiliate 🛸
By becoming a Titan Affiliate 🛸, you will automatically receive 50% of the value of each new subscription you refer .
You will receive 50% for any of the above plans that you refer .
This way we will encourage our community to grow in a fair and healthy way, because we know what we have in our hands and what we deliver real value to our users.
We are at the highest level of sophistication in the market, the consistency here and the results here speak for themselves.
So growing our community means growing mutual wealth and raising collective conscience.
Wealth must be created not divided.
And here we are creating mutual wealth on all ends and in all ways.
A non-zero sum system, where everybody wins.
3️⃣ : 👨💻 Who am I ❔
My name is FilipeSoh I am 26 years old, Technical Analyst, Trader, Computer Engineer, pinescript Specialist, with extensive experience in several languages and technologies.
For the last 4 years I have been focusing on developing, editing and creating pinescript indicators and strategies for Tradingview for people and myself.
Full-time passionate workaholic pinescript developer with over 10,000 hours of pinescript development.
• Pinescript expert ▬Tradingview.
• Specialist in Automated Trading
• Specialist in Quantitative Trading.
• Statistical/Probabilistic Trading Specialist - Mark Douglas Scholl.
• Inventor of the 'Classic Forecast' Indicators.
• Inventor of the 'Backtest Table'.
4️⃣ : ❓ What is Statistical/Probabilistic Trading ❓
Statistical/probabilistic trading is the only way to get a positive mathematical expectation regarding the market and consequently that is the only way to make money consistently from it.
I will present below some more details about the Quantitative THEMIS strategy, it is a real strategy, tested, validated and in real operation, 'Skin in the Game' , a consistent way to make money with statistical/probabilistic trading in a 100% automated.
I am a Technical Analyst , I used to be a Discretionary Trader , today I am 100% a Statistical Trader .
I've gotten rich and made a lot of money, and I've also lost a lot with 'leverage'.
That was a few years ago.
The book that changed everything for me was "Trading in The Zone" by Mark Douglas.
That's when I understood that the market is just a game of statistics and probability, like a casino!
It was then that I understood that the human brain is not prepared for trading, because it involves triggers and mental emotions.
And emotions in trading and in making trading decisions do not go well together, not in the long run, because you always have the burden of being wrong with the outcome of that particular position.
But remembering that the market is just a statistical game!
5️⃣ : ❓ How Statistical/Probabilistic Trading works ❓
Let's use a 'coin' as an example:
If we toss a 'coin' up 10 times.
Do you agree that it is impossible for us to know exactly the result of the 'plays' before they actually happen?
As in the example above, would you agree, that we cannot "guess" the outcome of a position before it actually happens?
As much as we cannot "guess" whether the coin will drop heads or tails on each flip.
We can analyze the "backtest" of the 10 moves made with that coin:
If we analyze the 10 moves and count the number of times the coin fell heads or tails in a specific sequence, we then have a percentage of times the coin fell heads or tails, so we have a 'backtest' of those moves.
Then on the next flip we can now assume a point or a favorable position for one side, the side with the highest probability .
In a nutshell, this is more or less how probabilistic statistical trading works.
As Statistical Traders we can never say whether such a Trader/Position we take will be a winner or a loser.
But still we can have a positive and consistent result in a "sequence" of trades, because before we even open a position, backtests have already been performed so we identify an anomaly and build a system that will have a positive statistical advantage in our favor over the market.
The advantage will not be in one trade itself, but in the "sequence" of trades as a whole!
Because our system will work like a casino, having a positive mathematical expectation relative to the players/market.
Design, develop, test models and systems that can take advantage of market anomalies, until they change.
Be the casino! - Mark Douglas
6️⃣ : ❓ Why use a Statistical/Probabilistic system ❓
In recent years I have focused and specialized in developing 100% automated trading systems, essentially for the cryptocurrency market.
I have developed many extremely robust and efficient systems, with positive mathematical expectation towards the market.
These are not complex systems per se , because here we want to avoid 'over-optimization' as much as possible.
As Da Vinci said: "Simplicity is the highest degree of sophistication".
I say this because I have tested, tried and developed hundreds of systems/strategies.
I believe I have programmed more than 10,000 unique indicators/strategies, because this is my passion and purpose in life.
I am passionate about what I do, completely!
I love statistical trading because it is the only way to get consistency in the long run!
This is why I have studied, applied, developed, and specialized in 100% automated cryptocurrency trading systems.
The reason why our systems are extremely "simple" is because, as I mentioned before, in statistical trading we want to exploit the market anomaly to the maximum, that is, this anomaly will change from time to time, usually we can exploit a trading system efficiently for about 6 to 12 months, or for a few years, that is; for fixed 'scalpers' systems.
Because at some point these anomalies will be identified , and from the moment they are identified they will be exploited and will stop being anomalies .
With the system presented here; you can even copy the indicators and input values shared here;
However; what I have to offer you is: it is me , our team , and our community !
That is, we will constantly monitor this system, for life , because our goal here is to create a unique , perpetual , profitable , and consistent system for our community.
Myself , our team and our community will keep this script periodically updated , to ensure the positive mathematical expectation of it.
So we don't mind sharing the current parameters and values , because the real value is also in the future updates that this system will receive from me and our team , guided by our culture and our community of real users !
As we are hosted on 'tradingview', all future updates for this strategy, will be implemented and updated automatically on your tradingview account.
What we want here is: to make sure you get gains from our system, because if you get gains , our ecosystem will grow as a whole in a healthy and scalable way, so we will be generating continuous mutual wealth and raising the collective consciousness .
People Need People: 3️⃣🅿
7️⃣ : ❓ Why the human brain is not prepared to do Trading ❓
Today my greatest skill is to develop statistically profitable and 100% automated strategies for 'pinescript' tradingview.
Note that I said: 'profitable' because in fact statistical trading is the only way to make money in a 'consistent' way from the market.
And consequently have a positive wealth curve every cycle, because we will be based on mathematics, not on feelings and news.
Because the human brain is not prepared to do trading.
Because trading is connected to the decision making of the cerebral cortex.
And the decision making is automatically linked to emotions, and emotions don't match with trading decision making, because in those moments, we can feel the best and also the worst sensations and emotions, and this certainly affects us and makes us commit grotesque mistakes!
That's why the human brain is not prepared to do trading.
If you want to participate in a fully automated, profitable and consistent trading system; be a Titan Pro 👽
I believe we are walking an extremely enriching path here, not only in terms of financial returns for our community, but also in terms of knowledge about probabilistic and automated statistical trading.
You will have access to an extremely robust system, which was built upon very strong concepts and foundations, and upon the world's main asset in a few years: Bitcoin .
We are the tip of the best that exists in the cryptocurrency market when it comes to probabilistic and automated statistical trading.
Result is result! Me being dressed or naked.
This is just the beginning!
But there is a way to consistently make money from the market.
Being the Casino! - Mark Douglas
8️⃣ : ❓ What is Backtest ❓
Imagine the market as a purely random system, but even in 'randomness' there are patterns.
So now imagine the market and statistical trading as follows:
Repeating the above 'coin' example, let's think of it as follows:
If we toss a coin up 10 times again.
It is impossible to know which flips will have heads or tails, correct?
But if we analyze these 10 tosses, then we will have a mathematical statistic of the past result, for example, 70 % of the tosses fell 'heads'.
That is:
7 moves fell on "heads" .
3 moves fell on "tails" .
So based on these conditions and on the generic backtest presented here, we could adopt " heads " as our system of moves, to have a statistical and probabilistic advantage in relation to the next move to be performed.
That is, if you define a system, based on backtests , that has a robust positive mathematical expectation in relation to the market you will have a profitable system.
For every move you make you will have a positive statistical advantage in your favor over the market before you even make the move.
Like a casino in relation to all its players!
The casino does not have an advantage over one specific player, but over all players, because it has a positive mathematical expectation about all the moves that night.
The casino will always have a positive statistical advantage over its players.
Note that there will always be real players who will make real, million-dollar bankrolls that night, but this condition is already built into the casino's 'strategy', which has a pre-determined positive statistical advantage of that night as a whole.
Statistical trading is the same thing, as long as you don't understand this you will keep losing money and consistently.
9️⃣ : ❓ How to build a Consistent system ❓
See most traders around the world perform trades believing that that specific position taken will make them filthy rich, because they simply believe faithfully that the position taken will be an undoubted winner, based on a trader's methodology: 'trading a trade' without analyzing the whole context, just using 'empirical' aspects in their system.
But if you think of trading, as a sequence of moves.
You see, 'a sequence' !
When we think statistically, it doesn't matter your result for this , or for the next specific trade , but the final sequence of trades as a whole.
As the market has a random system of results distribution , if your system has a positive statistical advantage in relation to the market, at the end of that sequence you'll have the biggest probability of having a winning bank.
That's how you do real trading!
And with consistency!
Trading is a long term game, but when you change the key you realize that it is a simple game to make money in a consistent way from the market, all you need is patience.
Even more when we are based on Bitcoin, which has its 'Halving' effect where, in theory, we will never lose money in 3 to 4 years intervals, due to its scarcity and the fact that Bitcoin is the 'discovery of digital scarcity' which makes it the digital gold, we believe in this thesis and we follow Satoshi's legacy.
So align Bitcoin with a probabilistic statistical trading system with a positive mathematical expectation of the market and 100% automated with the long term, and all you need is patience, and you will become rich.
In fact Bitcoin by itself is already a path, buy, wait for each halving and your wealth will be maintained.
No inflation, unlike fiat currencies.
This is a complete and extremely robust strategy, with the most current possible and 'not possible' techniques involved and applied here.
Today I am at another level in developing 100% automated 'quantitative' strategies.
I was born for this!
🔟 : ❓ What is a Quantitative Trading system ❓
In addition to having access to a revolutionary strategy you will have access to disruptive 100% multifunctional tables with the ability to perform 'backtests' for better tracking and monitoring of your system on a customized basis.
I would like to emphasize one thing, and that is that you keep this in mind.
Today my greatest skill in 'pinescript' is to build indicators, but mainly strategies, based on statistical and probabilistic trading, with a postive mathematical expectation in relation to the market, in a 100% automated way.
This with the goal of building a consistent and continuous positive equity curve through mathematics using data, converting it into statistical / probabilistic parameters and applying them to a Quantitative model.
Before becoming a Quantitative Trader , I was a Technical Analyst and a Discretionary Trader .
First as a position trader and then as a day trader.
Before becoming a Trader, I trained myself as a Technical Analyst , to masterly understand the shape and workings of the market in theory.
But everything changed when I met 'Mark Douglas' , when I got to know his works, that's when my head exploded 🤯, and I started to understand the market for good!
The market is nothing more than a 'random' system of distributing results.
See that I said: 'random' .
Do yourself a mental exercise.
Is there really such a thing as random ?
I believe not, as far as we know maybe the 'singularity'.
So thinking this way, to translate, the market is nothing more than a game of probability, statistics and pure mathematics.
Like a casino!
What happens is that most traders, whenever they take a position, take it with all the empirical certainty that such position will win or lose, and do not take into consideration the total sequence of results to understand their place in the market.
Understanding your place in the market gives you the ability to create and design systems that can exploit the present market anomaly, and thus make money statistically, consistently, and 100% automated.
Thinking of it this way, it is easy to make money from the market.
There are many ways to make money from the market, but the only consistent way I know of is through 'probabilistic and automated statistical trading'.
1️⃣1️⃣ : ❓ How to build a Quantitative Trading system ❓
There are some fundamental points that must be addressed here in order to understand what makes up a system based on statistics and probability applied to a quantitative model.
When we talk about 'discretionary' trading, it is a trading system based on human decisions after the defined 'empirical' conditions are met.
It is quite another thing to build a fully automated system without any human interference/interaction .
That said:
Building a statistically profitable system is perfectly possible, but this is a high level task , but with possible high rewards and consistent gains.
Here you will find a real "Skin In The Game" strategy.
With all due respect, but the vast majority of traders who post strategies on TradingView do not understand what they are doing.
Most of them do not understand the minimum complexity involved in the main variable for the construction of a real strategy, the mother variable: "strategy".
I say this by my own experience, because I have analyzed practically all the existing publications of TradingView + 200,000 indicators and strategies.
I breathe pinescript, I eat pinescript, I sleep pinescript, I bathe pinescript, I live TradingView.
But the main advantage for the TradingView users, is that all entry and exit orders made by this strategy can be checked and analyzed thoroughly, to validate and prove the veracity of this strategy, because this is a 100% real strategy.
Here there is a huge world of possibilities, but only one way to build a 'pinescript strategy' that will work correctly aligned to the real world with real results .
There are some fundamental points to take into consideration when building a profitable trading system:
The most important of these for me is: 'DrawDown' .
Followed by: 'Hit Rate' .
And only after that we use the parameter: 'Profit'.
See, this is because here, we are dealing with the 'imponderable' , and anything can happen in this scenario.
But there is one thing that makes us sleep peacefully at night, and that is: controlling losses .
That is, in other words: controlling the DrawDown .
The amateur is concerned with 'winning', the professional is concerned with conserving capital.
If we have the losses under control, then we can move on to the other two parameters: hit rate and profit.
See, the second most important factor in building a system is the hit rate.
I say this from my own experience.
I have worked with many systems with a 'low hit rate', but extremely profitable.
For example: systems with hit rates of 40 to 50%.
But as much as statistically and mathematically the profit is rewarding, operating systems with a low hit rate is always very stressful psychologically.
That's why there are two big reasons why when I build an automated trading system, I focus on the high hit rate of the system, they are
1 - To reduce psychological damage as much as possible .
2 - And more important , when we create a system with a 'high hit rate' , there is a huge intrinsic advantage here, that most statistic traders don't take in consideration.
That is: knowing more quickly when the system stops being functional.
The main advantage of a system with a high hit rate is: to identify when the system stops being functional and stop exploiting the market's anomaly.
Look: When we are talking about trading and random distribution of results on the market, do you agree that when we create a trading system, we are focused on exploring some anomaly of that market?
When that anomaly is verified by the market, it will stop being functional with time.
That's why trading systems, 'scalpers', especially for cryptocurrencies, need constant monitoring, quarterly, semi-annually or annually.
Because market movements change from time to time.
Because we go through different cycles from time to time, such as congestion cycles, accumulation , distribution , volatility , uptrends and downtrends .
1️⃣2️⃣ : ❓ How to Exploit Market Anomalies ❓
You see there is a very important point that must be stressed here.
As we are always trying to exploit an 'anomaly' in the market.
So the 'number' of indicators/tools that will integrate the system is of paramount importance.
But most traders do not take this into consideration.
To build a professional, robust, consistent, and profitable system, you don't need to use hundreds of indicators to build your setup.
This will actually make it harder to read when the setup stops working and needs some adjustment.
So focusing on a high hit rate is very important here, this is a fundamental principle that is widely ignored , and with a high hit rate, we can know much more accurately when the system is no longer functional much faster.
As Darwin said: "It is not the strongest or the most intelligent that wins the game of life, it is the most adapted.
So simple systems, as contradictory as it may seem, are more efficient, because they help to identify inflection points in the market much more quickly.
1️⃣3️⃣ : ❓ What Defines a Robust, Profitable and Consistent System ❓
See I have built, hundreds of thousands of indicators and 'pinescript' strategies, hundreds of thousands.
This is an extremely professional, robust and profitable system.
Based on the currency pairs: BTC /USDT
There are many ways and avenues to build a profitable trading setup/system.
And actually this is not a difficult task, taking in consideration, as the main factor here, that our trading and investment plan is for the long term, so consequently we will face scenarios with less noise.
He who is in a hurry eats raw.
As mentioned before.
Defining trends in pinescript is technically a simple task, the hardest task is to determine congestion zones with low volume and volatility, it's in these moments that many false signals are generated, and consequently is where most setups face their maximum DrawDown.
That's why this strategy was strictly and thoroughly planned, built on a very solid foundation, to avoid as much noise as possible, for a positive and consistent equity curve in each market cycle, 'Consistency' is our 'Mantra' around here.
1️⃣4️⃣ : 🔧 Fixed Technical
• Strategy: Titan Investments|Quantitative THEMIS|Pro|BINANCE:BTCUSDTP:4h
• Pair: BTC/USDTP
• Time Frame: 4 hours
• Broker: Binance (Recommended)
For a more conservative scenario, we have built the Quantitative THEMIS for the 4h time frame, with the main focus on consistency.
So we can avoid noise as much as possible!
1️⃣5️⃣ : ❌ Fixed Outputs : 🎯 TP(%) & 🛑SL(%)
In order to build a 'perpetual' system specific to BTC/USDT, it took a lot of testing, and more testing, and a lot of investment and research.
There is one initial and fundamental point that we can address to justify the incredible consistency presented here.
That fundamental point is our exit via Take Profit or Stop Loss percentage (%).
🎯 Take Profit (%)
🛑 Stop Loss (%)
See, today I have been testing some more advanced backtesting models for some cryptocurrency systems.
In which I perform 'backtest of backtest', i.e. we use a set of strategies each focused on a principle, operating individually, but they are part of something unique, i.e. we do 'backtests' of 'backtests' together.
What I mean is that we do a lot of backtesting around here.
I can assure you, that always the best output for a trading system is to set fixed output values!
In other words:
🎯 Take Profit (%)
🛑 Stop Loss (%)
This happens because statistically setting fixed exit structures in the vast majority of times, presents a superior result on the capital/equity curve, throughout history and for the vast majority of setups compared to other exit methods.
This is due to a mathematical principle of simplicity, 'avoiding more noise'.
Thus whenever the Quantitative THEMIS strategy takes a position it has a target and a defined maximum stop percentage.
1️⃣6️⃣ : ⚠️ Risk Profile
The strategy, currently has 3 risk profiles ⚠️ patterns for 'fixed percentage exits': Take Profit (%) and Stop Loss (%) .
They are: ⚠️ Rich's Profiles
✔️🆑 Conservative: 🎯 TP=2.7 % 🛑 SL=2.7 %
❌Ⓜ️ Moderate: 🎯 TP=2.8 % 🛑 SL=2.7 %
❌🅰 Aggressive: 🎯 TP=1.6 % 🛑 SL=6.9 %
You will be able to select and switch between the above options and profiles through the 'input' menu of the strategy by navigating to the "⚠️ Risk Profile" menu.
You can then select, test and apply the Risk Profile above that best suits your risk management, expectations and reality , as well as customize all the 'fixed exit' values through the TP and SL menus below.
1️⃣7️⃣ : ⭕ Moving Exits : (Indicators)
The strategy currently also has 'Moving Exits' based on indicator signals.
These are Moving Exits (Indicators)
📈 LONG : (EXIT)
🧃 (MAO) Short : true
📉 SHORT : (EXIT)
🧃 (MAO) Long: false
You can select and toggle between the above options through the 'input' menu of the strategy by navigating to the "LONG : Exit" and "SHORT : Exit" menu.
1️⃣8️⃣ : 💸 Initial Capital
By default the "Initial Capital" set for entries and backtests of this strategy is: 10000 $
You can set another value for the 'Starting Capital' through the tradingview menu under "properties" , and edit the value of the "Initial Capital" field.
This way you can set and test other 'Entry Values' for your trades, tests and backtests.
1️⃣9️⃣ : ⚙️ Entry Options
By default the 'order size' set for this strategy is 100 % of the 'initial capital' on each new trade.
You can set and test other entry options like : contracts , cash , % of equity
You should make these changes directly in the input menu of the strategy by navigating to the menu "⚙️ Properties : TradingView" below.
⚙️ Properties : (TradingView)
📊 Strategy Type: strategy.position_size != 1
📝💲 % Order Type: % of equity
📝💲 % Order Size: 100
Leverage: 1
So you can define and test other 'Entry Options' for your trades, tests and backtests.
2️⃣0️⃣ : ❓ How to Automate this Strategy ❓ : 🤖 Automation : 'Third-Party Services'
It is possible to automate the signals of this strategy for any centralized or decentralized broker, as well as for messaging services: Discord, Telegram and Twitter.
All in an extremely simple and uncomplicated way through the tutorials available in PDF /VIDEO for our Titan Pro 👽 subscriber community.
With our tutorials in PDF and Video it will be possible to automate the signals of this strategy for the chosen service in an extremely simple way with less than 10 steps only.
Tradingview naturally doesn't count with native integration between brokers and tradingview.
But it is possible to use 'third party services' to do the integration and automation between Tradingview and your centralized or decentralized broker.
Here are the standard, available and recommended 'third party services' to automate the signals from the 'Quantitative THEMIS' strategy on the tradingview for your broker:
1) Wundertrading (Recommended):
2) 3commas:
3) Zignaly:
4) Aleeert.com (Recommended):
5) Alertatron:
Note! 'Third party services' cannot perform 'withdrawals' via their key 'API', they can only open positions, so your funds will always be 'safe' in your brokerage firm, being traded via the 'API', when they receive an entry and exit signal from this strategy.
2️⃣1️⃣ : ❓ How to Automate this Strategy ❓ : 🤖 Automation : 'Exchanges
You can automate this strategy for any of the brokers below, through your broker's 'API' by connecting it to the 'third party automation services' for tradingview available and mentioned in the menu above:
1) Binance (Recommended)
2) Bitmex
3) Bybit
4) KuCoin
5) Deribit
6) OKX
7) Coinbase
8) Huobi
9) Bitfinex
10) Bitget
11) Bittrex
12) Bitstamp
13) Gate. io
14) Kraken
15) Gemini
16) Ascendex
17) VCCE
2️⃣2️⃣ : ❓ How to Automate this Strategy ❓ : 🤖 Automation : 'Messaging Services'
You can also automate and monitor the signals of this strategy much more efficiently by sending them to the following popular messaging services:
1) Discord
2) Telegram
3) Twitter
2️⃣3️⃣ : ❓ How to Automate this Strategy ❓ : 🤖 Automation : '🧲🤖Copy-Trading'
It will also be possible to copy/replicate the entries and exits of this strategy to your broker in an extremely simple and agile way, through the available copy-trader services.
This way it will be possible to replicate the signals of this strategy at each entry and exit to your broker through the API connecting it to the integrated copy-trader services available through the tradingview automation services below:
1) Wundetrading:
2) Zignaly:
2️⃣4️⃣ : ❔ Why be a Titan Pro 👽❔
I believe that today I am at another level in 'pinescript' development.
I consider myself today a true unicorn as a pinescript developer, someone unique and very rare.
If you choose another tool or another pinescript service, this tool will be just another one, with no real results.
But if you join our Titan community, you will have access to a unique tool! And you will get real results!
I already earn money consistently with statistical and automated trading and as an expert pinescript developer.
I am here to evolve my skills as much as possible, and one day become a pinescript 'Wizard'.
So excellence, quality and professionalism will always be my north here.
You will never find a developer like me, and who will take so seriously such a revolutionary project as this one. A Maverick! ▬ The man never stops!
Here you will find the highest degree of sophistication and development in the market for 'pinescript'.
You will get the best of me and the best of pinescript possible.
Let me show you how a professional in my field does it.
Become a Titan Pro Member 👽 and get Full Access to this strategy and all the Automation Tutorials.
Be the Titan in your life!
2️⃣5️⃣ : ❔ Why be a Titan Aff 🛸❔
Get financial return for your referrals, Decentralize the World, and raise the collective consciousness.
2️⃣6️⃣ : 📋 Summary : ⚖️ Strategy: Titan Investments|Quantitative THEMIS|Pro|BINANCE:BTCUSDTP:4h
® Titan Investimentos | Quantitative THEMIS ⚖️ | Pro 👽 2.6 | Dev: © FilipeSoh 🧙 | 🤖 100% Automated : Discord, Telegram, Twitter, Wundertrading, 3commas, Zignaly, Aleeert, Alertatron, Uniswap-v3 | BINANCE:BTCUSDTPERP 4h
🛒 Subscribe this strategy ❗️ Be a Titan Member 🏛️
🛒 Titan Pro 👽 🏛️ Titan Pro 👽 Version with ✔️100% Integrated Automation 🤖 and 📚 Automation Tutorials ✔️100% available at: (PDF/VIDEO)
🛒 Titan Affiliate 🛸 🏛️ Titan Affiliate 🛸 (Subscription Sale) 🔥 Receive 50% commission
📋 Summary : QT THEMIS ⚖️
🕵️♂️ Check This Strategy..................................................................0
🦄 ® Titan Investimentos...............................................................1
👨💻 © Developer..........................................................................2
📚 Signal Automation Tutorials : (PDF/VIDEO).......................................3
👨🔧 Revision...............................................................................4
📊 Table : (BACKTEST)..................................................................5
📊 Table : (INFORMATIONS).............................................................6
⚙️ Properties : (TRADINGVIEW)........................................................7
📆 Backtest : (TRADINGVIEW)..........................................................8
⚠️ Risk Profile...........................................................................9
🟢 On 🔴 Off : (LONG/SHORT).......................................................10
📈 LONG : (ENTRY)....................................................................11
📉 SHORT : (ENTRY)...................................................................12
📈 LONG : (EXIT).......................................................................13
📉 SHORT : (EXIT)......................................................................14
🧩 (EI) External Indicator.............................................................15
📡 (QT) Quantitative...................................................................16
🎠 (FF) Forecast......................................................................17
🅱 (BB) Bollinger Bands................................................................18
🧃 (MAP) Moving Average Primary......................................................19
🧃 (MAP) Labels.........................................................................20
🍔 (MAQ) Moving Average Quaternary.................................................21
🍟 (MACD) Moving Average Convergence Divergence...............................22
📣 (VWAP) Volume Weighted Average Price........................................23
🪀 (HL) HILO..........................................................................24
🅾 (OBV) On Balance Volume.........................................................25
🥊 (SAR) Stop and Reverse...........................................................26
🛡️ (DSR) Dynamic Support and Resistance..........................................27
🔊 (VD) Volume Directional..........................................................28
🧰 (RSI) Relative Momentum Index.................................................29
🎯 (TP) Take Profit %..................................................................30
🛑 (SL) Stop Loss %....................................................................31
🤖 Automation Selected...............................................................32
📱💻 Discord............................................................................33
📱💻 Telegram..........................................................................34
📱💻 Twitter...........................................................................35
🤖 Wundertrading......................................................................36
🤖 3commas............................................................................37
🤖 Zignaly...............................................................................38
🤖 Aleeert...............................................................................39
🤖 Alertatron...........................................................................40
🤖 Uniswap-v3..........................................................................41
🧲🤖 Copy-Trading....................................................................42
♻️ ® No Repaint........................................................................43
🔒 Copyright ©️..........................................................................44
🏛️ Be a Titan Member..................................................................45
Nº Active Users..........................................................................46
⏱ Time Left............................................................................47
| 0 | 🕵️♂️ Check This Strategy
🕵️♂️ Version Demo: 🐄 Version with ❌non-integrated automation 🤖 and 📚 Tutorials for automation ❌not available
🕵️♂️ Version Pro: 👽 Version with ✔️100% Integrated Automation 🤖 and 📚 Automation Tutorials ✔️100% available at: (PDF/VIDEO)
| 1 | 🦄 ® Titan Investimentos
Decentralizing the World 🗺
Raising the Collective Conscience 🗺
🦄Site:
🦄TradingView: www.tradingview.com
🦄Discord:
🦄Telegram:
🦄Youtube:
🦄Twitter:
🦄Instagram:
🦄TikTok:
🦄Linkedin:
🦄E-mail:
| 2 | 👨💻 © Developer
🧠 Developer: @FilipeSoh🧙
📺 TradingView: www.tradingview.com
☑️ Linkedin:
✅ Fiverr:
✅ Upwork:
🎥 YouTube:
🐤 Twitter:
🤳 Instagram:
| 3 | 📚 Signal Automation Tutorials : (PDF/VIDEO)
📚 Discord: 🔗 Link: 🔒Titan Pro👽
📚 Telegram: 🔗 Link: 🔒Titan Pro👽
📚 Twitter: 🔗 Link: 🔒Titan Pro👽
📚 Wundertrading: 🔗 Link: 🔒Titan Pro👽
📚 3comnas: 🔗 Link: 🔒Titan Pro👽
📚 Zignaly: 🔗 Link: 🔒Titan Pro👽
📚 Aleeert: 🔗 Link: 🔒Titan Pro👽
📚 Alertatron: 🔗 Link: 🔒Titan Pro👽
📚 Uniswap-v3: 🔗 Link: 🔒Titan Pro👽
📚 Copy-Trading: 🔗 Link: 🔒Titan Pro👽
| 4 | 👨🔧 Revision
👨🔧 Start Of Operations: 01 Jan 2019 21:00 -0300 💡 Start Of Operations (Skin in the game) : Revision 1.0
👨🔧 Previous Review: 01 Jan 2022 21:00 -0300 💡 Previous Review : Revision 2.0
👨🔧 Current Revision: 01 Jan 2023 21:00 -0300 💡 Current Revision : Revision 2.6
👨🔧 Next Revision: 28 May 2023 21:00 -0300 💡 Next Revision : Revision 2.7
| 5 | 📊 Table : (BACKTEST)
📊 Table: true
🖌️ Style: label.style_label_left
📐 Size: size_small
📏 Line: defval
🎨 Color: #131722
| 6 | 📊 Table : (INFORMATIONS)
📊 Table: false
🖌️ Style: label.style_label_right
📐 Size: size_small
📏 Line: defval
🎨 Color: #131722
| 7 | ⚙️ Properties : (TradingView)
📊 Strategy Type: strategy.position_size != 1
📝💲 % Order Type: % of equity
📝💲 % Order Size: 100 %
🚀 Leverage: 1
| 8 | 📆 Backtest : (TradingView)
🗓️ Mon: true
🗓️ Tue: true
🗓️ Wed: true
🗓️ Thu: true
🗓️ Fri: true
🗓️ Sat: true
🗓️ Sun: true
📆 Range: custom
📆 Start: UTC 31 Oct 2008 00:00
📆 End: UTC 31 Oct 2030 23:45
📆 Session: 0000-0000
📆 UTC: UTC
| 9 | ⚠️ Risk Profile
✔️🆑 Conservative: 🎯 TP=2.7 % 🛑 SL=2.7 %
❌Ⓜ️ Moderate: 🎯 TP=2.8 % 🛑 SL=2.7 %
❌🅰 Aggressive: 🎯 TP=1.6 % 🛑 SL=6.9 %
| 10 | 🟢 On 🔴 Off : (LONG/SHORT)
🟢📈 LONG: true
🟢📉 SHORT: true
| 11 | 📈 LONG : (ENTRY)
📡 (QT) Long: true
🧃 (MAP) Long: false
🅱 (BB) Long: false
🍟 (MACD) Long: false
🅾 (OBV) Long: false
| 12 | 📉 SHORT : (ENTRY)
📡 (QT) Short: true
🧃 (MAP) Short: false
🅱 (BB) Short: false
🍟 (MACD) Short: false
🅾 (OBV) Short: false
| 13 | 📈 LONG : (EXIT)
🧃 (MAP) Short: true
| 14 | 📉 SHORT : (EXIT)
🧃 (MAP) Long: false
| 15 | 🧩 (EI) External Indicator
🧩 (EI) Connect your external indicator/filter: false
🧩 (EI) Connect your indicator here (Study mode only): close
🧩 (EI) Connect your indicator here (Study mode only): close
| 16 | 📡 (QT) Quantitative
📡 (QT) Quantitative: true
📡 (QT) Market: BINANCE:BTCUSDTPERP
📡 (QT) Dice: openai
| 17 | 🎠 (FF) Forecast
🎠 (FF) Include current unclosed current candle: true
🎠 (FF) Forecast Type: flat
🎠 (FF) Nº of candles to use in linear regression: 3
| 18 | 🅱 (BB) Bollinger Bands
🅱 (BB) Bollinger Bands: true
🅱 (BB) Type: EMA
🅱 (BB) Period: 20
🅱 (BB) Source: close
🅱 (BB) Multiplier: 2
🅱 (BB) Linewidth: 0
🅱 (BB) Color: #131722
| 19 | 🧃 (MAP) Moving Average Primary
🧃 (MAP) Moving Average Primary: true
🧃 (MAP) BarColor: false
🧃 (MAP) Background: false
🧃 (MAP) Type: SMA
🧃 (MAP) Source: open
🧃 (MAP) Period: 100
🧃 (MAP) Multiplier: 2.0
🧃 (MAP) Linewidth: 2
🧃 (MAP) Color P: #42bda8
🧃 (MAP) Color N: #801922
| 20 | 🧃 (MAP) Labels
🧃 (MAP) Labels: true
🧃 (MAP) Style BUY ZONE: shape.labelup
🧃 (MAP) Color BUY ZONE: #42bda8
🧃 (MAP) Style SELL ZONE: shape.labeldown
🧃 (MAP) Color SELL ZONE: #801922
| 21 | 🍔 (MAQ) Moving Average Quaternary
🍔 (MAQ) Moving Average Quaternary: true
🍔 (MAQ) BarColor: false
🍔 (MAQ) Background: false
🍔 (MAQ) Type: SMA
🍔 (MAQ) Source: close
🍔 (MAQ) Primary: 14
🍔 (MAQ) Secondary: 22
🍔 (MAQ) Tertiary: 44
🍔 (MAQ) Quaternary: 16
🍔 (MAQ) Linewidth: 0
🍔 (MAQ) Color P: #42bda8
🍔 (MAQ) Color N: #801922
| 22 | 🍟 (MACD) Moving Average Convergence Divergence
🍟 (MACD) Macd Type: EMA
🍟 (MACD) Signal Type: EMA
🍟 (MACD) Source: close
🍟 (MACD) Fast: 12
🍟 (MACD) Slow: 26
🍟 (MACD) Smoothing: 9
| 23 | 📣 (VWAP) Volume Weighted Average Price
📣 (VWAP) Source: close
📣 (VWAP) Period: 340
📣 (VWAP) Momentum A: 84
📣 (VWAP) Momentum B: 150
📣 (VWAP) Average Volume: 1
📣 (VWAP) Multiplier: 1
📣 (VWAP) Diviser: 2
| 24 | 🪀 (HL) HILO
🪀 (HL) Type: SMA
🪀 (HL) Function: Maverick🧙
🪀 (HL) Source H: high
🪀 (HL) Source L: low
🪀 (HL) Period: 20
🪀 (HL) Momentum: 26
🪀 (HL) Diviser: 2
🪀 (HL) Multiplier: 1
| 25 | 🅾 (OBV) On Balance Volume
🅾 (OBV) Type: EMA
🅾 (OBV) Source: close
🅾 (OBV) Period: 16
🅾 (OBV) Diviser: 2
🅾 (OBV) Multiplier: 1
| 26 | 🥊 (SAR) Stop and Reverse
🥊 (SAR) Source: close
🥊 (SAR) High: 1.8
🥊 (SAR) Mid: 1.6
🥊 (SAR) Low: 1.6
🥊 (SAR) Diviser: 2
🥊 (SAR) Multiplier: 1
| 27 | 🛡️ (DSR) Dynamic Support and Resistance
🛡️ (DSR) Source D: close
🛡️ (DSR) Source R: high
🛡️ (DSR) Source S: low
🛡️ (DSR) Momentum R: 0
🛡️ (DSR) Momentum S: 2
🛡️ (DSR) Diviser: 2
🛡️ (DSR) Multiplier: 1
| 28 | 🔊 (VD) Volume Directional
🔊 (VD) Type: SMA
🔊 (VD) Period: 68
🔊 (VD) Momentum: 3.8
🔊 (VD) Diviser: 2
🔊 (VD) Multiplier: 1
| 29 | 🧰 (RSI) Relative Momentum Index
🧰 (RSI) Type UP: EMA
🧰 (RSI) Type DOWN: EMA
🧰 (RSI) Source: close
🧰 (RSI) Period: 29
🧰 (RSI) Smoothing: 22
🧰 (RSI) Momentum R: 64
🧰 (RSI) Momentum S: 142
🧰 (RSI) Diviser: 2
🧰 (RSI) Multiplier: 1
| 30 | 🎯 (TP) Take Profit %
🎯 (TP) Take Profit: false
🎯 (TP) %: 2.2
🎯 (TP) Color: #42bda8
🎯 (TP) Linewidth: 1
| 31 | 🛑 (SL) Stop Loss %
🛑 (SL) Stop Loss: false
🛑 (SL) %: 2.7
🛑 (SL) Color: #801922
🛑 (SL) Linewidth: 1
| 32 | 🤖 Automation : Discord | Telegram | Twitter | Wundertrading | 3commas | Zignaly | Aleeert | Alertatron | Uniswap-v3
🤖 Automation Selected : Discord
| 33 | 🤖 Discord
🔗 Link Discord: discord.com
🔗 Link 📚 Automation: 🔒Titan Pro👽
📱💻 Discord ▬ Enter Long: 🔒Titan Pro👽
📱💻 Discord ▬ Exit Long: 🔒Titan Pro👽
📱💻 Discord ▬ Enter Short: 🔒Titan Pro👽
📱💻 Discord ▬ Exit Short: 🔒Titan Pro👽
| 34 | 🤖 Telegram
🔗 Link Telegram: telegram.org
🔗 Link 📚 Automation: 🔒Titan Pro👽
📱💻 Telegram ▬ Enter Long: 🔒Titan Pro👽
📱💻 Telegram ▬ Exit Long: 🔒Titan Pro👽
📱💻 Telegram ▬ Enter Short: 🔒Titan Pro👽
📱💻 Telegram ▬ Exit Short: 🔒Titan Pro👽
| 35 | 🤖 Twitter
🔗 Link Twitter: twitter.com
🔗 Link 📚 Automation: 🔒Titan Pro👽
📱💻 Twitter ▬ Enter Long: 🔒Titan Pro👽
📱💻 Twitter ▬ Exit Long: 🔒Titan Pro👽
📱💻 Twitter ▬ Enter Short: 🔒Titan Pro👽
📱💻 Twitter ▬ Exit Short: 🔒Titan Pro👽
| 36 | 🤖 Wundertrading : Binance | Bitmex | Bybit | KuCoin | Deribit | OKX | Coinbase | Huobi | Bitfinex | Bitget
🔗 Link Wundertrading: wundertrading.com
🔗 Link 📚 Automation: 🔒Titan Pro👽
📱💻 Wundertrading ▬ Enter Long: 🔒Titan Pro👽
📱💻 Wundertrading ▬ Exit Long: 🔒Titan Pro👽
📱💻 Wundertrading ▬ Enter Short: 🔒Titan Pro👽
📱💻 Wundertrading ▬ Exit Short: 🔒Titan Pro👽
| 37 | 🤖 3commas : Binance | Bybit | OKX | Bitfinex | Coinbase | Deribit | Bitmex | Bittrex | Bitstamp | Gate.io | Kraken | Gemini | Huobi | KuCoin
🔗 Link 3commas: 3commas.io
🔗 Link 📚 Automation: 🔒Titan Pro👽
📱💻 3commas ▬ Enter Long: 🔒Titan Pro👽
📱💻 3commas ▬ Exit Long: 🔒Titan Pro👽
📱💻 3commas ▬ Enter Short: 🔒Titan Pro👽
📱💻 3commas ▬ Exit Short: 🔒Titan Pro👽
| 38 | 🤖 Zignaly : Binance | Ascendex | Bitmex | Kucoin | VCCE
🔗 Link Zignaly: zignaly.com
🔗 Link 📚 Automation: 🔒Titan Pro👽
🤖 Type Automation: Profit Sharing
🤖 Type Provider: Webook
🔑 Key: 🔒Titan Pro👽
🤖 pair: BTCUSDTP
🤖 exchange: binance
🤖 exchangeAccountType: futures
🤖 orderType: market
🚀 leverage: 1x
% positionSizePercentage: 100 %
💸 positionSizeQuote: 10000 $
🆔 signalId: @Signal1234
| 39 | 🤖 Aleeert : Binance
🔗 Link Aleeert: aleeert.com
🔗 Link 📚 Automation: 🔒Titan Pro👽
📱💻 Aleeert ▬ Enter Long: 🔒Titan Pro👽
📱💻 Aleeert ▬ Exit Long: 🔒Titan Pro👽
📱💻 Aleeert ▬ Enter Short: 🔒Titan Pro👽
📱💻 Aleeert ▬ Exit Short: 🔒Titan Pro👽
| 40 | 🤖 Alertatron : Binance | Bybit | Deribit | Bitmex
🔗 Link Alertatron: alertatron.com
🔗 Link 📚 Automation: 🔒Titan Pro👽
📱💻 Alertatron ▬ Enter Long: 🔒Titan Pro👽
📱💻 Alertatron ▬ Exit Long: 🔒Titan Pro👽
📱💻 Alertatron ▬ Enter Short: 🔒Titan Pro👽
📱💻 Alertatron ▬ Exit Short: 🔒Titan Pro👽
| 41 | 🤖 Uniswap-v3
🔗 Link Alertatron: uniswap.org
🔗 Link 📚 Automation: 🔒Titan Pro👽
📱💻 Uniswap-v3 ▬ Enter Long: 🔒Titan Pro👽
📱💻 Uniswap-v3 ▬ Exit Long: 🔒Titan Pro👽
📱💻 Uniswap-v3 ▬ Enter Short: 🔒Titan Pro👽
📱💻 Uniswap-v3 ▬ Exit Short: 🔒Titan Pro👽
| 42 | 🧲🤖 Copy-Trading : Zignaly | Wundertrading
🔗 Link 📚 Copy-Trading: 🔒Titan Pro👽
🧲🤖 Copy-Trading ▬ Zignaly: 🔒Titan Pro👽
🧲🤖 Copy-Trading ▬ Wundertrading: 🔒Titan Pro👽
| 43 | ♻️ ® Don't Repaint!
♻️ This Strategy does not Repaint!: ® Signs Do not repaint❕
♻️ This is a Real Strategy!: Quality : ® Titan Investimentos
📋️️ Get more information about Repainting here:
| 44 | 🔒 Copyright ©️
🔒 Copyright ©️: Copyright © 2023-2024 All rights reserved, ® Titan Investimentos
🔒 Copyright ©️: ® Titan Investimentos
🔒 Copyright ©️: Unique and Exclusive Strategy. All rights reserved
| 45 | 🏛️ Be a Titan Members
🏛️ Titan Pro 👽 Version with ✔️100% Integrated Automation 🤖 and 📚 Automation Tutorials ✔️100% available at: (PDF/VIDEO)
🏛️ Titan Affiliate 🛸 (Subscription Sale) 🔥 Receive 50% commission
| 46 | ⏱ Time Left
Time Left Titan Demo 🐄: ⏱♾ | ⏱ : ♾ Titan Demo 🐄 Version with ❌non-integrated automation 🤖 and 📚 Tutorials for automation ❌not available
Time Left Titan Pro 👽: 🔒Titan Pro👽 | ⏱ : Pro Plans: 30 Days, 90 Days, 12 Months, 24 Months. (👽 Pro 🅼 Monthly, 👽 Pro 🆀 Quarterly, 👽 Pro🅰 Annual, 👽 Pro👾Two Years)
| 47 | Nº Active Users
Nº Active Subscribers Titan Pro 👽: 5️⃣6️⃣ | 1✔️ 5✔️ 10✔️ 100❌ 1K❌ 10K❌ 50K❌ 100K❌ 1M❌ 10M❌ 100M❌ : ⏱ Active Users is updated every 24 hours (Check on indicator)
Nº Active Affiliates Titan Aff 🛸: 6️⃣ | 1✔️ 5✔️ 10❌ 100❌ 1K❌ 10K❌ 50K❌ 100K❌ 1M❌ 10M❌ 100M❌ : ⏱ Active Users is updated every 24 hours (Check on indicator)
2️⃣7️⃣ : 📊 PERFORMANCE : 🆑 Conservative
📊 Exchange: Binance
📊 Pair: BINANCE: BTCUSDTPERP
📊 TimeFrame: 4h
📊 Initial Capital: 10000 $
📊 Order Type: % equity
📊 Size Per Order: 100 %
📊 Commission: 0.03 %
📊 Pyramid: 1
• ⚠️ Risk Profile: 🆑 Conservative: 🎯 TP=2.7 % | 🛑 SL=2.7 %
• 📆All years: 🆑 Conservative: 🚀 Leverage 1️⃣x
📆 Start: September 23, 2019
📆 End: January 11, 2023
📅 Days: 1221
📅 Bars: 7325
Net Profit:
🟢 + 1669.89 %
💲 + 166989.43 USD
Total Close Trades:
⚪️ 369
Percent Profitable:
🟡 64.77 %
Profit Factor:
🟢 2.314
DrawDrown Maximum:
🔴 -24.82 %
💲 -10221.43 USD
Avg Trade:
💲 + 452.55 USD
✔️ Trades Winning: 239
❌ Trades Losing: 130
✔️ Average Gross Win: + 12.31 %
❌ Average Gross Loss: - 9.78 %
✔️ Maximum Consecutive Wins: 9
❌ Maximum Consecutive Losses: 6
% Average Gain Annual: 499.33 %
% Average Gain Monthly: 41.61 %
% Average Gain Weekly: 9.6 %
% Average Gain Day: 1.37 %
💲 Average Gain Annual: 49933 $
💲 Average Gain Monthly: 4161 $
💲 Average Gain Weekly: 960 $
💲 Average Gain Day: 137 $
• 📆 Year: 2020: 🆑 Conservative: 🚀 Leverage 1️⃣x
• 📆 Year: 2021: 🆑 Conservative: 🚀 Leverage 1️⃣x
• 📆 Year: 2022: 🆑 Conservative: 🚀 Leverage 1️⃣x
2️⃣8️⃣ : 📊 PERFORMANCE : Ⓜ️ Moderate
📊 Exchange: Binance
📊 Pair: BINANCE: BTCUSDTPERP
📊 TimeFrame: 4h
📊 Initial Capital: 10000 $
📊 Order Type: % equity
📊 Size Per Order: 100 %
📊 Commission: 0.03 %
📊 Pyramid: 1
• ⚠️ Risk Profile: Ⓜ️ Moderate: 🎯 TP=2.8 % | 🛑 SL=2.7 %
• 📆 All years: Ⓜ️ Moderate: 🚀 Leverage 1️⃣x
📆 Start: September 23, 2019
📆 End: January 11, 2023
📅 Days: 1221
📅 Bars: 7325
Net Profit:
🟢 + 1472.04 %
💲 + 147199.89 USD
Total Close Trades:
⚪️ 362
Percent Profitable:
🟡 63.26 %
Profit Factor:
🟢 2.192
DrawDrown Maximum:
🔴 -22.69 %
💲 -9269.33 USD
Avg Trade:
💲 + 406.63 USD
✔️ Trades Winning: 229
❌ Trades Losing : 133
✔️ Average Gross Win: + 11.82 %
❌ Average Gross Loss: - 9.29 %
✔️ Maximum Consecutive Wins: 9
❌ Maximum Consecutive Losses: 8
% Average Gain Annual: 440.15 %
% Average Gain Monthly: 36.68 %
% Average Gain Weekly: 8.46 %
% Average Gain Day: 1.21 %
💲 Average Gain Annual: 44015 $
💲 Average Gain Monthly: 3668 $
💲 Average Gain Weekly: 846 $
💲 Average Gain Day: 121 $
• 📆 Year: 2020: Ⓜ️ Moderate: 🚀 Leverage 1️⃣x
• 📆 Year: 2021: Ⓜ️ Moderate: 🚀 Leverage 1️⃣x
• 📆 Year: 2022: Ⓜ️ Moderate: 🚀 Leverage 1️⃣x
2️⃣9️⃣ : 📊 PERFORMANCE : 🅰 Aggressive
📊 Exchange: Binance
📊 Pair: BINANCE: BTCUSDTPERP
📊 TimeFrame: 4h
📊 Initial Capital: 10000 $
📊 Order Type: % equity
📊 Size Per Order: 100 %
📊 Commission: 0.03 %
📊 Pyramid: 1
• ⚠️ Risk Profile: 🅰 Aggressive: 🎯 TP=1.6 % | 🛑 SL=6.9 %
• 📆 All years: 🅰 Aggressive: 🚀 Leverage 1️⃣x
📆 Start: September 23, 2019
📆 End: January 11, 2023
📅 Days: 1221
📅 Bars: 7325
Net Profit:
🟢 + 989.38 %
💲 + 98938.38 USD
Total Close Trades:
⚪️ 380
Percent Profitable:
🟢 84.47 %
Profit Factor:
🟢 2.156
DrawDrown Maximum:
🔴 -17.88 %
💲 -9182.84 USD
Avg Trade:
💲 + 260.36 USD
✔️ Trades Winning: 321
❌ Trades Losing: 59
✔️ Average Gross Win: + 5.75 %
❌ Average Gross Loss: - 14.51 %
✔️ Maximum Consecutive Wins: 21
❌ Maximum Consecutive Losses: 6
% Average Gain Annual: 295.84 %
% Average Gain Monthly: 24.65 %
% Average Gain Weekly: 5.69 %
% Average Gain Day: 0.81 %
💲 Average Gain Annual: 29584 $
💲 Average Gain Monthly: 2465 $
💲 Average Gain Weekly: 569 $
💲 Average Gain Day: 81 $
• 📆 Year: 2020: 🅰 Aggressive: 🚀 Leverage 1️⃣x
• 📆 Year: 2021: 🅰 Aggressive: 🚀 Leverage 1️⃣x
• 📆 Year: 2022: 🅰 Aggressive: 🚀 Leverage 1️⃣x
3️⃣0️⃣ : 🛠️ Roadmap
🛠️• 14/ 01 /2023 : Titan THEMIS Launch
🛠️• Updates January/2023 :
• 📚 Tutorials for Automation 🤖 already Available : ✔️
• ✔️ Discord
• ✔️ Wundertrading
• ✔️ Zignaly
• 📚 Tutorials for Automation 🤖 In Preparation : ⭕
• ⭕ Telegram
• ⭕ Twitter
• ⭕ 3comnas
• ⭕ Aleeert
• ⭕ Alertatron
• ⭕ Uniswap-v3
• ⭕ Copy-Trading
🛠️• Updates February/2023 :
• 📰 Launch of advertising material for Titan Affiliates 🛸
• 🛍️🎥🖼️📊 (Sales Page/VSL/Videos/Creative/Infographics)
🛠️• 28/05/2023 : Titan THEMIS update ▬ Version 2.7
🛠️• 28/05/2023 : BOT BOB release ▬ Version 1.0
• (Native Titan THEMIS Automation - Through BOT BOB, a bot for automation of signals, indicators and strategies of TradingView, of own code ▬ in validation.
• BOT BOB
Automation/Connection :
• API - For Centralized Brokers.
• Smart Contracts - Wallet Web - For Decentralized Brokers.
• This way users can automate any indicator or strategy of TradingView and Titan in a decentralized, secure and simplified way.
• Without having the need to use 'third party services' for automating TradingView indicators and strategies like the ones available above.
🛠️• 28/05/2023 : Release ▬ Titan Culture Guide 📝
3️⃣1️⃣ : 🧻 Notes ❕
🧻 • Note ❕ The "Demo 🐄" version, ❌does not have 'integrated automation', to automate the signals of this strategy and enjoy a fully automated system, you need to have access to the Pro version with '100% integrated automation' and all the tutorials for automation available. Become a Titan Pro 👽
🧻 • Note ❕ You will also need to be a "Pro User or higher on Tradingview", to be able to use the webhook feature available only for 'paid' profiles on the platform.
With the webhook feature it is possible to send the signals of this strategy to almost anywhere, in our case to centralized or decentralized brokerages, also to popular messaging services such as: Discord, Telegram or Twiter.
3️⃣2️⃣ : 🚨 Disclaimer ❕❗
🚨 • Disclaimer ❕❕ Past positive result and performance of a system does not guarantee its positive result and performance for the future!
🚨 • Disclaimer ❗❗❗ When using this strategy: Titan Investments is totally Exempt from any claim of liability for losses. The responsibility on the management of your funds is solely yours. This is a very high risk/volatility market! Understand your place in the market.
3️⃣3️⃣ : ♻️ ® No Repaint
This Strategy does not Repaint! This is a real strategy!
3️⃣4️⃣ : 🔒 Copyright ©️
Copyright © 2022-2023 All rights reserved, ® Titan Investimentos
3️⃣5️⃣ : 👏 Acknowledgments
I want to start this message in thanks to TradingView and all the Pinescript community for all the 'magic' created here, a unique ecosystem! rich and healthy, a fertile soil, a 'new world' of possibilities, for a complete deepening and improvement of our best personal skills.
I leave here my immense thanks to the whole community: Tradingview, Pinecoders, Wizards and Moderators.
I was not born Rich .
Thanks to TradingView and pinescript and all its transformation.
I could develop myself and the best of me and the best of my skills.
And consequently build wealth and patrimony.
Gratitude.
One more story for the infinite book !
If you were born poor you were born to be rich !
Raising🔼 the level and raising🔼 the ruler! 📏
My work is my 'debauchery'! Do better! 💐🌹
Soul of a first-timer! Creativity Exudes! 🦄
This is the manifestation of God's magic in me. This is the best of me. 🧙
You will copy me, I know. So you owe me. 💋
My mission here is to raise the consciousness and self-esteem of all Titans and Titanids! Welcome! 🧘 🏛️
The only way to accomplish great work is to do what you love ! Before I learned to program I was wasting my life!
Death is the best creation of life .
Now you are the new , but in the not so distant future you will gradually become the old . Here I stay forever!
Playing the game like an Athlete! 🖼️ Enjoy and Enjoy 🍷 🗿
In honor of: BOB ☆
1 name, 3 letters, 3 possibilities, and if read backwards it's the same thing, a palindrome. ☘
Gratitude to the oracles that have enabled me the 'luck' to get this far: Dal&Ni&Fer
3️⃣6️⃣ : 👮 House Rules : 📺 TradingView
House Rules : This publication and strategy follows all TradingView house guidelines and rules:
📺 TradingView House Rules: www.tradingview.com
📺 Script publication rules: www.tradingview.com
📺 Vendor requirements: www.tradingview.com
📺 Links/References rules: www.tradingview.com
3️⃣7️⃣ : 🏛️ Become a Titan Pro member 👽
🟩 Titan Pro 👽 🟩
3️⃣8️⃣ : 🏛️ Be a member Titan Aff 🛸
🟥 Titan Affiliate 🛸 🟥
Titan Investments|Quantitative THEMIS|Demo|BINANCE:BTCUSDTP:4hInvestment Strategy (Quantitative Trading)
| 🛑 | Watch "LIVE" and 'COPY' this strategy in real time:
🔗 Link: www.tradingview.com
Hello, welcome, feel free 🌹💐
Since the stone age to the most technological age, one thing has not changed, that which continues impress human beings the most, is the other human being!
Deep down, it's all very simple or very complicated, depends on how you look at it.
I believe that everyone was born to do something very well in life.
But few are those who have, let's use the word 'luck' .
Few are those who have the 'luck' to discover this thing.
That is why few are happy and successful in their jobs and professions.
Thank God I had this 'luck' , and discovered what I was born to do well.
And I was born to program. 👨💻
📋 Summary : Project Titan
0️⃣ : 🦄 Project Titan
1️⃣ : ⚖️ Quantitative THEMIS
2️⃣ : 🏛️ Titan Community
3️⃣ : 👨💻 Who am I ❔
4️⃣ : ❓ What is Statistical/Probabilistic Trading ❓
5️⃣ : ❓ How Statistical/Probabilistic Trading works ❓
6️⃣ : ❓ Why use a Statistical/Probabilistic system ❓
7️⃣ : ❓ Why the human brain is not prepared to do Trading ❓
8️⃣ : ❓ What is Backtest ❓
9️⃣ : ❓ How to build a Consistent system ❓
🔟 : ❓ What is a Quantitative Trading system ❓
1️⃣1️⃣ : ❓ How to build a Quantitative Trading system ❓
1️⃣2️⃣ : ❓ How to Exploit Market Anomalies ❓
1️⃣3️⃣ : ❓ What Defines a Robust, Profitable and Consistent System ❓
1️⃣4️⃣ : 🔧 Fixed Technical
1️⃣5️⃣ : ❌ Fixed Outputs : 🎯 TP(%) & 🛑SL(%)
1️⃣6️⃣ : ⚠️ Risk Profile
1️⃣7️⃣ : ⭕ Moving Exits : (Indicators)
1️⃣8️⃣ : 💸 Initial Capital
1️⃣9️⃣ : ⚙️ Entry Options
2️⃣0️⃣ : ❓ How to Automate this Strategy ❓ : 🤖 Automation : 'Third-Party Services'
2️⃣1️⃣ : ❓ How to Automate this Strategy ❓ : 🤖 Automation : 'Exchanges
2️⃣2️⃣ : ❓ How to Automate this Strategy ❓ : 🤖 Automation : 'Messaging Services'
2️⃣3️⃣ : ❓ How to Automate this Strategy ❓ : 🤖 Automation : '🧲🤖Copy-Trading'
2️⃣4️⃣ : ❔ Why be a Titan Pro 👽❔
2️⃣5️⃣ : ❔ Why be a Titan Aff 🛸❔
2️⃣6️⃣ : 📋 Summary : ⚖️ Strategy: Titan Investments|Quantitative THEMIS|Demo|BINANCE:BTCUSDTP:4h
2️⃣7️⃣ : 📊 PERFORMANCE : 🆑 Conservative
2️⃣8️⃣ : 📊 PERFORMANCE : Ⓜ️ Moderate
2️⃣9️⃣ : 📊 PERFORMANCE : 🅰 Aggressive
3️⃣0️⃣ : 🛠️ Roadmap
3️⃣1️⃣ : 🧻 Notes ❕
3️⃣2️⃣ : 🚨 Disclaimer ❕❗
3️⃣3️⃣ : ♻️ ® No Repaint
3️⃣4️⃣ : 🔒 Copyright ©️
3️⃣5️⃣ : 👏 Acknowledgments
3️⃣6️⃣ : 👮 House Rules : 📺 TradingView
3️⃣7️⃣ : 🏛️ Become a Titan Pro member 👽
3️⃣8️⃣ : 🏛️ Be a member Titan Aff 🛸
0️⃣ : 🦄 Project Titan
This is the first real, 100% automated Quantitative Strategy made available to the public and the pinescript community for TradingView.
You will be able to automate all signals of this strategy for your broker , centralized or decentralized and also for messaging services : Discord, Telegram or Twitter .
This is the first strategy of a larger project, in 2023, I will provide a total of 6 100% automated 'Quantitative' strategies to the pinescript community for TradingView.
The future strategies to be shared here will also be unique , never before seen, real 'Quantitative' bots with real, validated results in real operation.
Just like the 'Quantitative THEMIS' strategy, it will be something out of the loop throughout the pinescript/tradingview community, truly unique tools for building mutual wealth consistently and continuously for our community.
1️⃣ : ⚖️ Quantitative THEMIS : Titan Investments|Quantitative THEMIS|Demo|BINANCE:BTCUSDTP:4h
This is a truly unique and out of the curve strategy for BTC /USD .
A truly real strategy, with real, validated results and in real operation.
A unique tool for building mutual wealth, consistently and continuously for the members of the Titan community.
Initially we will operate on a monthly, quarterly, annual or biennial subscription service.
Our goal here is to build a great community, in exchange for an extremely fair value for the use of our truly unique tools, which bring and will bring real results to our community members.
With this business model it will be possible to provide all Titan users and community members with the purest and highest degree of sophistication in the market with pinescript for tradingview, providing unique and truly profitable strategies.
My goal here is to offer the best to our members!
The best 'pinescript' tradingview service in the world!
We are the only Start-Up in the world that will decentralize real and full access to truly real 'quantitative' tools that bring and will bring real results for mutual and ongoing wealth building for our community.
2️⃣ : 🏛️ Titan Community : 👽 Pro 🔁 Aff 🛸
Become a Titan Pro 👽
To get access to the strategy: "Quantitative THEMIS" , and future Titan strategies in a 100% automated way, along with all tutorials for automation.
Pro Plans: 30 Days, 90 Days, 12 Months, 24 Months.
👽 Pro 🅼 Monthly
👽 Pro 🆀 Quarterly
👽 Pro🅰 Annual
👽 Pro👾Two Years
You will have access to a truly unique system that is out of the curve .
A 100% real, 100% automated, tested, validated, profitable, and in real operation strategy.
Become a Titan Affiliate 🛸
By becoming a Titan Affiliate 🛸, you will automatically receive 50% of the value of each new subscription you refer .
You will receive 50% for any of the above plans that you refer .
This way we will encourage our community to grow in a fair and healthy way, because we know what we have in our hands and what we deliver real value to our users.
We are at the highest level of sophistication in the market, the consistency here and the results here speak for themselves.
So growing our community means growing mutual wealth and raising collective conscience.
Wealth must be created not divided.
And here we are creating mutual wealth on all ends and in all ways.
A non-zero sum system, where everybody wins.
3️⃣ : 👨💻 Who am I ❔
My name is FilipeSoh I am 26 years old, Technical Analyst, Trader, Computer Engineer, pinescript Specialist, with extensive experience in several languages and technologies.
For the last 4 years I have been focusing on developing, editing and creating pinescript indicators and strategies for Tradingview for people and myself.
Full-time passionate workaholic pinescript developer with over 10,000 hours of pinescript development.
• Pinescript expert ▬Tradingview.
• Specialist in Automated Trading
• Specialist in Quantitative Trading.
• Statistical/Probabilistic Trading Specialist - Mark Douglas Scholl.
• Inventor of the 'Classic Forecast' Indicators.
• Inventor of the 'Backtest Table'.
4️⃣ : ❓ What is Statistical/Probabilistic Trading ❓
Statistical/probabilistic trading is the only way to get a positive mathematical expectation regarding the market and consequently that is the only way to make money consistently from it.
I will present below some more details about the Quantitative THEMIS strategy, it is a real strategy, tested, validated and in real operation, 'Skin in the Game' , a consistent way to make money with statistical/probabilistic trading in a 100% automated.
I am a Technical Analyst , I used to be a Discretionary Trader , today I am 100% a Statistical Trader .
I've gotten rich and made a lot of money, and I've also lost a lot with 'leverage'.
That was a few years ago.
The book that changed everything for me was "Trading in The Zone" by Mark Douglas.
That's when I understood that the market is just a game of statistics and probability, like a casino!
It was then that I understood that the human brain is not prepared for trading, because it involves triggers and mental emotions.
And emotions in trading and in making trading decisions do not go well together, not in the long run, because you always have the burden of being wrong with the outcome of that particular position.
But remembering that the market is just a statistical game!
5️⃣ : ❓ How Statistical/Probabilistic Trading works ❓
Let's use a 'coin' as an example:
If we toss a 'coin' up 10 times.
Do you agree that it is impossible for us to know exactly the result of the 'plays' before they actually happen?
As in the example above, would you agree, that we cannot "guess" the outcome of a position before it actually happens?
As much as we cannot "guess" whether the coin will drop heads or tails on each flip.
We can analyze the "backtest" of the 10 moves made with that coin:
If we analyze the 10 moves and count the number of times the coin fell heads or tails in a specific sequence, we then have a percentage of times the coin fell heads or tails, so we have a 'backtest' of those moves.
Then on the next flip we can now assume a point or a favorable position for one side, the side with the highest probability .
In a nutshell, this is more or less how probabilistic statistical trading works.
As Statistical Traders we can never say whether such a Trader/Position we take will be a winner or a loser.
But still we can have a positive and consistent result in a "sequence" of trades, because before we even open a position, backtests have already been performed so we identify an anomaly and build a system that will have a positive statistical advantage in our favor over the market.
The advantage will not be in one trade itself, but in the "sequence" of trades as a whole!
Because our system will work like a casino, having a positive mathematical expectation relative to the players/market.
Design, develop, test models and systems that can take advantage of market anomalies, until they change.
Be the casino! - Mark Douglas
6️⃣ : ❓ Why use a Statistical/Probabilistic system ❓
In recent years I have focused and specialized in developing 100% automated trading systems, essentially for the cryptocurrency market.
I have developed many extremely robust and efficient systems, with positive mathematical expectation towards the market.
These are not complex systems per se , because here we want to avoid 'over-optimization' as much as possible.
As Da Vinci said: "Simplicity is the highest degree of sophistication".
I say this because I have tested, tried and developed hundreds of systems/strategies.
I believe I have programmed more than 10,000 unique indicators/strategies, because this is my passion and purpose in life.
I am passionate about what I do, completely!
I love statistical trading because it is the only way to get consistency in the long run!
This is why I have studied, applied, developed, and specialized in 100% automated cryptocurrency trading systems.
The reason why our systems are extremely "simple" is because, as I mentioned before, in statistical trading we want to exploit the market anomaly to the maximum, that is, this anomaly will change from time to time, usually we can exploit a trading system efficiently for about 6 to 12 months, or for a few years, that is; for fixed 'scalpers' systems.
Because at some point these anomalies will be identified , and from the moment they are identified they will be exploited and will stop being anomalies .
With the system presented here; you can even copy the indicators and input values shared here;
However; what I have to offer you is: it is me , our team , and our community !
That is, we will constantly monitor this system, for life , because our goal here is to create a unique , perpetual , profitable , and consistent system for our community.
Myself , our team and our community will keep this script periodically updated , to ensure the positive mathematical expectation of it.
So we don't mind sharing the current parameters and values , because the real value is also in the future updates that this system will receive from me and our team , guided by our culture and our community of real users !
As we are hosted on 'tradingview', all future updates for this strategy, will be implemented and updated automatically on your tradingview account.
What we want here is: to make sure you get gains from our system, because if you get gains , our ecosystem will grow as a whole in a healthy and scalable way, so we will be generating continuous mutual wealth and raising the collective consciousness .
People Need People: 3️⃣🅿
7️⃣ : ❓ Why the human brain is not prepared to do Trading ❓
Today my greatest skill is to develop statistically profitable and 100% automated strategies for 'pinescript' tradingview.
Note that I said: 'profitable' because in fact statistical trading is the only way to make money in a 'consistent' way from the market.
And consequently have a positive wealth curve every cycle, because we will be based on mathematics, not on feelings and news.
Because the human brain is not prepared to do trading.
Because trading is connected to the decision making of the cerebral cortex.
And the decision making is automatically linked to emotions, and emotions don't match with trading decision making, because in those moments, we can feel the best and also the worst sensations and emotions, and this certainly affects us and makes us commit grotesque mistakes!
That's why the human brain is not prepared to do trading.
If you want to participate in a fully automated, profitable and consistent trading system; be a Titan Pro 👽
I believe we are walking an extremely enriching path here, not only in terms of financial returns for our community, but also in terms of knowledge about probabilistic and automated statistical trading.
You will have access to an extremely robust system, which was built upon very strong concepts and foundations, and upon the world's main asset in a few years: Bitcoin .
We are the tip of the best that exists in the cryptocurrency market when it comes to probabilistic and automated statistical trading.
Result is result! Me being dressed or naked.
This is just the beginning!
But there is a way to consistently make money from the market.
Being the Casino! - Mark Douglas
8️⃣ : ❓ What is Backtest ❓
Imagine the market as a purely random system, but even in 'randomness' there are patterns.
So now imagine the market and statistical trading as follows:
Repeating the above 'coin' example, let's think of it as follows:
If we toss a coin up 10 times again.
It is impossible to know which flips will have heads or tails, correct?
But if we analyze these 10 tosses, then we will have a mathematical statistic of the past result, for example, 70 % of the tosses fell 'heads'.
That is:
7 moves fell on "heads" .
3 moves fell on "tails" .
So based on these conditions and on the generic backtest presented here, we could adopt " heads " as our system of moves, to have a statistical and probabilistic advantage in relation to the next move to be performed.
That is, if you define a system, based on backtests , that has a robust positive mathematical expectation in relation to the market you will have a profitable system.
For every move you make you will have a positive statistical advantage in your favor over the market before you even make the move.
Like a casino in relation to all its players!
The casino does not have an advantage over one specific player, but over all players, because it has a positive mathematical expectation about all the moves that night.
The casino will always have a positive statistical advantage over its players.
Note that there will always be real players who will make real, million-dollar bankrolls that night, but this condition is already built into the casino's 'strategy', which has a pre-determined positive statistical advantage of that night as a whole.
Statistical trading is the same thing, as long as you don't understand this you will keep losing money and consistently.
9️⃣ : ❓ How to build a Consistent system ❓
See most traders around the world perform trades believing that that specific position taken will make them filthy rich, because they simply believe faithfully that the position taken will be an undoubted winner, based on a trader's methodology: 'trading a trade' without analyzing the whole context, just using 'empirical' aspects in their system.
But if you think of trading, as a sequence of moves.
You see, 'a sequence' !
When we think statistically, it doesn't matter your result for this , or for the next specific trade , but the final sequence of trades as a whole.
As the market has a random system of results distribution , if your system has a positive statistical advantage in relation to the market, at the end of that sequence you'll have the biggest probability of having a winning bank.
That's how you do real trading!
And with consistency!
Trading is a long term game, but when you change the key you realize that it is a simple game to make money in a consistent way from the market, all you need is patience.
Even more when we are based on Bitcoin, which has its 'Halving' effect where, in theory, we will never lose money in 3 to 4 years intervals, due to its scarcity and the fact that Bitcoin is the 'discovery of digital scarcity' which makes it the digital gold, we believe in this thesis and we follow Satoshi's legacy.
So align Bitcoin with a probabilistic statistical trading system with a positive mathematical expectation of the market and 100% automated with the long term, and all you need is patience, and you will become rich.
In fact Bitcoin by itself is already a path, buy, wait for each halving and your wealth will be maintained.
No inflation, unlike fiat currencies.
This is a complete and extremely robust strategy, with the most current possible and 'not possible' techniques involved and applied here.
Today I am at another level in developing 100% automated 'quantitative' strategies.
I was born for this!
🔟 : ❓ What is a Quantitative Trading system ❓
In addition to having access to a revolutionary strategy you will have access to disruptive 100% multifunctional tables with the ability to perform 'backtests' for better tracking and monitoring of your system on a customized basis.
I would like to emphasize one thing, and that is that you keep this in mind.
Today my greatest skill in 'pinescript' is to build indicators, but mainly strategies, based on statistical and probabilistic trading, with a postive mathematical expectation in relation to the market, in a 100% automated way.
This with the goal of building a consistent and continuous positive equity curve through mathematics using data, converting it into statistical / probabilistic parameters and applying them to a Quantitative model.
Before becoming a Quantitative Trader , I was a Technical Analyst and a Discretionary Trader .
First as a position trader and then as a day trader.
Before becoming a Trader, I trained myself as a Technical Analyst , to masterly understand the shape and workings of the market in theory.
But everything changed when I met 'Mark Douglas' , when I got to know his works, that's when my head exploded 🤯, and I started to understand the market for good!
The market is nothing more than a 'random' system of distributing results.
See that I said: 'random' .
Do yourself a mental exercise.
Is there really such a thing as random ?
I believe not, as far as we know maybe the 'singularity'.
So thinking this way, to translate, the market is nothing more than a game of probability, statistics and pure mathematics.
Like a casino!
What happens is that most traders, whenever they take a position, take it with all the empirical certainty that such position will win or lose, and do not take into consideration the total sequence of results to understand their place in the market.
Understanding your place in the market gives you the ability to create and design systems that can exploit the present market anomaly, and thus make money statistically, consistently, and 100% automated.
Thinking of it this way, it is easy to make money from the market.
There are many ways to make money from the market, but the only consistent way I know of is through 'probabilistic and automated statistical trading'.
1️⃣1️⃣ : ❓ How to build a Quantitative Trading system ❓
There are some fundamental points that must be addressed here in order to understand what makes up a system based on statistics and probability applied to a quantitative model.
When we talk about 'discretionary' trading, it is a trading system based on human decisions after the defined 'empirical' conditions are met.
It is quite another thing to build a fully automated system without any human interference/interaction .
That said:
Building a statistically profitable system is perfectly possible, but this is a high level task , but with possible high rewards and consistent gains.
Here you will find a real "Skin In The Game" strategy.
With all due respect, but the vast majority of traders who post strategies on TradingView do not understand what they are doing.
Most of them do not understand the minimum complexity involved in the main variable for the construction of a real strategy, the mother variable: "strategy".
I say this by my own experience, because I have analyzed practically all the existing publications of TradingView + 200,000 indicators and strategies.
I breathe pinescript, I eat pinescript, I sleep pinescript, I bathe pinescript, I live TradingView.
But the main advantage for the TradingView users, is that all entry and exit orders made by this strategy can be checked and analyzed thoroughly, to validate and prove the veracity of this strategy, because this is a 100% real strategy.
Here there is a huge world of possibilities, but only one way to build a 'pinescript strategy' that will work correctly aligned to the real world with real results .
There are some fundamental points to take into consideration when building a profitable trading system:
The most important of these for me is: 'DrawDown' .
Followed by: 'Hit Rate' .
And only after that we use the parameter: 'Profit'.
See, this is because here, we are dealing with the 'imponderable' , and anything can happen in this scenario.
But there is one thing that makes us sleep peacefully at night, and that is: controlling losses .
That is, in other words: controlling the DrawDown .
The amateur is concerned with 'winning', the professional is concerned with conserving capital.
If we have the losses under control, then we can move on to the other two parameters: hit rate and profit.
See, the second most important factor in building a system is the hit rate.
I say this from my own experience.
I have worked with many systems with a 'low hit rate', but extremely profitable.
For example: systems with hit rates of 40 to 50%.
But as much as statistically and mathematically the profit is rewarding, operating systems with a low hit rate is always very stressful psychologically.
That's why there are two big reasons why when I build an automated trading system, I focus on the high hit rate of the system, they are
1 - To reduce psychological damage as much as possible .
2 - And more important , when we create a system with a 'high hit rate' , there is a huge intrinsic advantage here, that most statistic traders don't take in consideration.
That is: knowing more quickly when the system stops being functional.
The main advantage of a system with a high hit rate is: to identify when the system stops being functional and stop exploiting the market's anomaly.
Look: When we are talking about trading and random distribution of results on the market, do you agree that when we create a trading system, we are focused on exploring some anomaly of that market?
When that anomaly is verified by the market, it will stop being functional with time.
That's why trading systems, 'scalpers', especially for cryptocurrencies, need constant monitoring, quarterly, semi-annually or annually.
Because market movements change from time to time.
Because we go through different cycles from time to time, such as congestion cycles, accumulation , distribution , volatility , uptrends and downtrends .
1️⃣2️⃣ : ❓ How to Exploit Market Anomalies ❓
You see there is a very important point that must be stressed here.
As we are always trying to exploit an 'anomaly' in the market.
So the 'number' of indicators/tools that will integrate the system is of paramount importance.
But most traders do not take this into consideration.
To build a professional, robust, consistent, and profitable system, you don't need to use hundreds of indicators to build your setup.
This will actually make it harder to read when the setup stops working and needs some adjustment.
So focusing on a high hit rate is very important here, this is a fundamental principle that is widely ignored , and with a high hit rate, we can know much more accurately when the system is no longer functional much faster.
As Darwin said: "It is not the strongest or the most intelligent that wins the game of life, it is the most adapted.
So simple systems, as contradictory as it may seem, are more efficient, because they help to identify inflection points in the market much more quickly.
1️⃣3️⃣ : ❓ What Defines a Robust, Profitable and Consistent System ❓
See I have built, hundreds of thousands of indicators and 'pinescript' strategies, hundreds of thousands.
This is an extremely professional, robust and profitable system.
Based on the currency pairs: BTC /USDT
There are many ways and avenues to build a profitable trading setup/system.
And actually this is not a difficult task, taking in consideration, as the main factor here, that our trading and investment plan is for the long term, so consequently we will face scenarios with less noise.
He who is in a hurry eats raw.
As mentioned before.
Defining trends in pinescript is technically a simple task, the hardest task is to determine congestion zones with low volume and volatility, it's in these moments that many false signals are generated, and consequently is where most setups face their maximum DrawDown.
That's why this strategy was strictly and thoroughly planned, built on a very solid foundation, to avoid as much noise as possible, for a positive and consistent equity curve in each market cycle, 'Consistency' is our 'Mantra' around here.
1️⃣4️⃣ : 🔧 Fixed Technical
• Strategy: Titan Investments|Quantitative THEMIS|Demo|BINANCE:BTCUSDTP:4h
• Pair: BTC/USDTP
• Time Frame: 4 hours
• Broker: Binance (Recommended)
For a more conservative scenario, we have built the Quantitative THEMIS for the 4h time frame, with the main focus on consistency.
So we can avoid noise as much as possible!
1️⃣5️⃣ : ❌ Fixed Outputs : 🎯 TP(%) & 🛑SL(%)
In order to build a 'perpetual' system specific to BTC/USDT, it took a lot of testing, and more testing, and a lot of investment and research.
There is one initial and fundamental point that we can address to justify the incredible consistency presented here.
That fundamental point is our exit via Take Profit or Stop Loss percentage (%).
🎯 Take Profit (%)
🛑 Stop Loss (%)
See, today I have been testing some more advanced backtesting models for some cryptocurrency systems.
In which I perform 'backtest of backtest', i.e. we use a set of strategies each focused on a principle, operating individually, but they are part of something unique, i.e. we do 'backtests' of 'backtests' together.
What I mean is that we do a lot of backtesting around here.
I can assure you, that always the best output for a trading system is to set fixed output values!
In other words:
🎯 Take Profit (%)
🛑 Stop Loss (%)
This happens because statistically setting fixed exit structures in the vast majority of times, presents a superior result on the capital/equity curve, throughout history and for the vast majority of setups compared to other exit methods.
This is due to a mathematical principle of simplicity, 'avoiding more noise'.
Thus whenever the Quantitative THEMIS strategy takes a position it has a target and a defined maximum stop percentage.
1️⃣6️⃣ : ⚠️ Risk Profile
The strategy, currently has 3 risk profiles ⚠️ patterns for 'fixed percentage exits': Take Profit (%) and Stop Loss (%) .
They are: ⚠️ Rich's Profiles
✔️🆑 Conservative: 🎯 TP=2.7 % 🛑 SL=2.7 %
❌Ⓜ️ Moderate: 🎯 TP=2.8 % 🛑 SL=2.7 %
❌🅰 Aggressive: 🎯 TP=1.6 % 🛑 SL=6.9 %
You will be able to select and switch between the above options and profiles through the 'input' menu of the strategy by navigating to the "⚠️ Risk Profile" menu.
You can then select, test and apply the Risk Profile above that best suits your risk management, expectations and reality , as well as customize all the 'fixed exit' values through the TP and SL menus below.
1️⃣7️⃣ : ⭕ Moving Exits : (Indicators)
The strategy currently also has 'Moving Exits' based on indicator signals.
These are Moving Exits (Indicators)
📈 LONG : (EXIT)
🧃 (MAO) Short : true
📉 SHORT : (EXIT)
🧃 (MAO) Long: false
You can select and toggle between the above options through the 'input' menu of the strategy by navigating to the "LONG : Exit" and "SHORT : Exit" menu.
1️⃣8️⃣ : 💸 Initial Capital
By default the "Initial Capital" set for entries and backtests of this strategy is: 10000 $
You can set another value for the 'Starting Capital' through the tradingview menu under "properties" , and edit the value of the "Initial Capital" field.
This way you can set and test other 'Entry Values' for your trades, tests and backtests.
1️⃣9️⃣ : ⚙️ Entry Options
By default the 'order size' set for this strategy is 100 % of the 'initial capital' on each new trade.
You can set and test other entry options like : contracts , cash , % of equity
You should make these changes directly in the input menu of the strategy by navigating to the menu "⚙️ Properties : TradingView" below.
⚙️ Properties : (TradingView)
📊 Strategy Type: strategy.position_size != 1
📝💲 % Order Type: % of equity
📝💲 % Order Size: 100
Leverage: 1
So you can define and test other 'Entry Options' for your trades, tests and backtests.
2️⃣0️⃣ : ❓ How to Automate this Strategy ❓ : 🤖 Automation : 'Third-Party Services'
It is possible to automate the signals of this strategy for any centralized or decentralized broker, as well as for messaging services: Discord, Telegram and Twitter.
All in an extremely simple and uncomplicated way through the tutorials available in PDF /VIDEO for our Titan Pro 👽 subscriber community.
With our tutorials in PDF and Video it will be possible to automate the signals of this strategy for the chosen service in an extremely simple way with less than 10 steps only.
Tradingview naturally doesn't count with native integration between brokers and tradingview.
But it is possible to use 'third party services' to do the integration and automation between Tradingview and your centralized or decentralized broker.
Here are the standard, available and recommended 'third party services' to automate the signals from the 'Quantitative THEMIS' strategy on the tradingview for your broker:
1) Wundertrading (Recommended):
2) 3commas:
3) Zignaly:
4) Aleeert.com (Recommended):
5) Alertatron:
Note! 'Third party services' cannot perform 'withdrawals' via their key 'API', they can only open positions, so your funds will always be 'safe' in your brokerage firm, being traded via the 'API', when they receive an entry and exit signal from this strategy.
2️⃣1️⃣ : ❓ How to Automate this Strategy ❓ : 🤖 Automation : 'Exchanges
You can automate this strategy for any of the brokers below, through your broker's 'API' by connecting it to the 'third party automation services' for tradingview available and mentioned in the menu above:
1) Binance (Recommended)
2) Bitmex
3) Bybit
4) KuCoin
5) Deribit
6) OKX
7) Coinbase
8) Huobi
9) Bitfinex
10) Bitget
11) Bittrex
12) Bitstamp
13) Gate. io
14) Kraken
15) Gemini
16) Ascendex
17) VCCE
2️⃣2️⃣ : ❓ How to Automate this Strategy ❓ : 🤖 Automation : 'Messaging Services'
You can also automate and monitor the signals of this strategy much more efficiently by sending them to the following popular messaging services:
1) Discord
2) Telegram
3) Twitter
2️⃣3️⃣ : ❓ How to Automate this Strategy ❓ : 🤖 Automation : '🧲🤖Copy-Trading'
It will also be possible to copy/replicate the entries and exits of this strategy to your broker in an extremely simple and agile way, through the available copy-trader services.
This way it will be possible to replicate the signals of this strategy at each entry and exit to your broker through the API connecting it to the integrated copy-trader services available through the tradingview automation services below:
1) Wundetrading:
2) Zignaly:
2️⃣4️⃣ : ❔ Why be a Titan Pro 👽❔
I believe that today I am at another level in 'pinescript' development.
I consider myself today a true unicorn as a pinescript developer, someone unique and very rare.
If you choose another tool or another pinescript service, this tool will be just another one, with no real results.
But if you join our Titan community, you will have access to a unique tool! And you will get real results!
I already earn money consistently with statistical and automated trading and as an expert pinescript developer.
I am here to evolve my skills as much as possible, and one day become a pinescript 'Wizard'.
So excellence, quality and professionalism will always be my north here.
You will never find a developer like me, and who will take so seriously such a revolutionary project as this one. A Maverick! ▬ The man never stops!
Here you will find the highest degree of sophistication and development in the market for 'pinescript'.
You will get the best of me and the best of pinescript possible.
Let me show you how a professional in my field does it.
Become a Titan Pro Member 👽 and get Full Access to this strategy and all the Automation Tutorials.
Be the Titan in your life!
2️⃣5️⃣ : ❔ Why be a Titan Aff 🛸❔
Get financial return for your referrals, Decentralize the World, and raise the collective consciousness.
2️⃣6️⃣ : 📋 Summary : ⚖️ Strategy: Titan Investments|Quantitative THEMIS|Demo|BINANCE:BTCUSDTP:4h
® Titan Investimentos | Quantitative THEMIS ⚖️ | Demo 🐄 2.6 | Dev: © FilipeSoh 🧙 | 🤖 100% Automated : Discord, Telegram, Twitter, Wundertrading, 3commas, Zignaly, Aleeert, Alertatron, Uniswap-v3 | BINANCE:BTCUSDTPERP 4h
🛒 Subscribe this strategy ❗️ Be a Titan Member 🏛️
🛒 Titan Pro 👽 🔗 🏛️ Titan Pro 👽 Version with ✔️100% Integrated Automation 🤖 and 📚 Automation Tutorials ✔️100% available at: (PDF/VIDEO)
🛒 Titan Affiliate 🛸 🔗 🏛️ Titan Affiliate 🛸 (Subscription Sale) 🔥 Receive 50% commission
📋 Summary : QT THEMIS ⚖️
🕵️♂️ Check This Strategy..................................................................0
🦄 ® Titan Investimentos...............................................................1
👨💻 © Developer..........................................................................2
📚 Signal Automation Tutorials : (PDF/VIDEO).......................................3
👨🔧 Revision...............................................................................4
📊 Table : (BACKTEST)..................................................................5
📊 Table : (INFORMATIONS).............................................................6
⚙️ Properties : (TRADINGVIEW)........................................................7
📆 Backtest : (TRADINGVIEW)..........................................................8
⚠️ Risk Profile...........................................................................9
🟢 On 🔴 Off : (LONG/SHORT).......................................................10
📈 LONG : (ENTRY)....................................................................11
📉 SHORT : (ENTRY)...................................................................12
📈 LONG : (EXIT).......................................................................13
📉 SHORT : (EXIT)......................................................................14
🧩 (EI) External Indicator.............................................................15
📡 (QT) Quantitative...................................................................16
🎠 (FF) Forecast......................................................................17
🅱 (BB) Bollinger Bands................................................................18
🧃 (MAP) Moving Average Primary......................................................19
🧃 (MAP) Labels.........................................................................20
🍔 (MAQ) Moving Average Quaternary.................................................21
🍟 (MACD) Moving Average Convergence Divergence...............................22
📣 (VWAP) Volume Weighted Average Price........................................23
🪀 (HL) HILO..........................................................................24
🅾 (OBV) On Balance Volume.........................................................25
🥊 (SAR) Stop and Reverse...........................................................26
🛡️ (DSR) Dynamic Support and Resistance..........................................27
🔊 (VD) Volume Directional..........................................................28
🧰 (RSI) Relative Momentum Index.................................................29
🎯 (TP) Take Profit %..................................................................30
🛑 (SL) Stop Loss %....................................................................31
🤖 Automation Selected...............................................................32
📱💻 Discord............................................................................33
📱💻 Telegram..........................................................................34
📱💻 Twitter...........................................................................35
🤖 Wundertrading......................................................................36
🤖 3commas............................................................................37
🤖 Zignaly...............................................................................38
🤖 Aleeert...............................................................................39
🤖 Alertatron...........................................................................40
🤖 Uniswap-v3..........................................................................41
🧲🤖 Copy-Trading....................................................................42
♻️ ® No Repaint........................................................................43
🔒 Copyright ©️..........................................................................44
🏛️ Be a Titan Member..................................................................45
Nº Active Users..........................................................................46
⏱ Time Left............................................................................47
| 0 | 🕵️♂️ Check This Strategy
🕵️♂️ Version Demo: 🐄 Version with ❌non-integrated automation 🤖 and 📚 Tutorials for automation ❌not available
🕵️♂️ Version Pro: 👽 Version with ✔️100% Integrated Automation 🤖 and 📚 Automation Tutorials ✔️100% available at: (PDF/VIDEO)
| 1 | 🦄 ® Titan Investimentos
Decentralizing the World 🗺
Raising the Collective Conscience 🗺
🦄Site:
🦄TradingView: www.tradingview.com
🦄Discord:
🦄Telegram:
🦄Youtube:
🦄Twitter:
🦄Instagram:
🦄TikTok:
🦄Linkedin:
🦄E-mail:
| 2 | 👨💻 © Developer
🧠 Developer: @FilipeSoh🧙
📺 TradingView: www.tradingview.com
☑️ Linkedin:
✅ Fiverr:
✅ Upwork:
🎥 YouTube:
🐤 Twitter:
🤳 Instagram:
| 3 | 📚 Signal Automation Tutorials : (PDF/VIDEO)
📚 Discord: 🔗 Link: 🔒Titan Pro👽
📚 Telegram: 🔗 Link: 🔒Titan Pro👽
📚 Twitter: 🔗 Link: 🔒Titan Pro👽
📚 Wundertrading: 🔗 Link: 🔒Titan Pro👽
📚 3comnas: 🔗 Link: 🔒Titan Pro👽
📚 Zignaly: 🔗 Link: 🔒Titan Pro👽
📚 Aleeert: 🔗 Link: 🔒Titan Pro👽
📚 Alertatron: 🔗 Link: 🔒Titan Pro👽
📚 Uniswap-v3: 🔗 Link: 🔒Titan Pro👽
📚 Copy-Trading: 🔗 Link: 🔒Titan Pro👽
| 4 | 👨🔧 Revision
👨🔧 Start Of Operations: 01 Jan 2019 21:00 -0300 💡 Start Of Operations (Skin in the game) : Revision 1.0
👨🔧 Previous Review: 01 Jan 2022 21:00 -0300 💡 Previous Review : Revision 2.0
👨🔧 Current Revision: 01 Jan 2023 21:00 -0300 💡 Current Revision : Revision 2.6
👨🔧 Next Revision: 28 May 2023 21:00 -0300 💡 Next Revision : Revision 2.7
| 5 | 📊 Table : (BACKTEST)
📊 Table: true
🖌️ Style: label.style_label_left
📐 Size: size_small
📏 Line: defval
🎨 Color: #131722
| 6 | 📊 Table : (INFORMATIONS)
📊 Table: false
🖌️ Style: label.style_label_right
📐 Size: size_small
📏 Line: defval
🎨 Color: #131722
| 7 | ⚙️ Properties : (TradingView)
📊 Strategy Type: strategy.position_size != 1
📝💲 % Order Type: % of equity
📝💲 % Order Size: 100 %
🚀 Leverage: 1
| 8 | 📆 Backtest : (TradingView)
🗓️ Mon: true
🗓️ Tue: true
🗓️ Wed: true
🗓️ Thu: true
🗓️ Fri: true
🗓️ Sat: true
🗓️ Sun: true
📆 Range: custom
📆 Start: UTC 31 Oct 2008 00:00
📆 End: UTC 31 Oct 2030 23:45
📆 Session: 0000-0000
📆 UTC: UTC
| 9 | ⚠️ Risk Profile
✔️🆑 Conservative: 🎯 TP=2.7 % 🛑 SL=2.7 %
❌Ⓜ️ Moderate: 🎯 TP=2.8 % 🛑 SL=2.7 %
❌🅰 Aggressive: 🎯 TP=1.6 % 🛑 SL=6.9 %
| 10 | 🟢 On 🔴 Off : (LONG/SHORT)
🟢📈 LONG: true
🟢📉 SHORT: true
| 11 | 📈 LONG : (ENTRY)
📡 (QT) Long: true
🧃 (MAP) Long: false
🅱 (BB) Long: false
🍟 (MACD) Long: false
🅾 (OBV) Long: false
| 12 | 📉 SHORT : (ENTRY)
📡 (QT) Short: true
🧃 (MAP) Short: false
🅱 (BB) Short: false
🍟 (MACD) Short: false
🅾 (OBV) Short: false
| 13 | 📈 LONG : (EXIT)
🧃 (MAP) Short: true
| 14 | 📉 SHORT : (EXIT)
🧃 (MAP) Long: false
| 15 | 🧩 (EI) External Indicator
🧩 (EI) Connect your external indicator/filter: false
🧩 (EI) Connect your indicator here (Study mode only): close
🧩 (EI) Connect your indicator here (Study mode only): close
| 16 | 📡 (QT) Quantitative
📡 (QT) Quantitative: true
📡 (QT) Market: BINANCE:BTCUSDTPERP
📡 (QT) Dice: openai
| 17 | 🎠 (FF) Forecast
🎠 (FF) Include current unclosed current candle: true
🎠 (FF) Forecast Type: flat
🎠 (FF) Nº of candles to use in linear regression: 3
| 18 | 🅱 (BB) Bollinger Bands
🅱 (BB) Bollinger Bands: true
🅱 (BB) Type: EMA
🅱 (BB) Period: 20
🅱 (BB) Source: close
🅱 (BB) Multiplier: 2
🅱 (BB) Linewidth: 0
🅱 (BB) Color: #131722
| 19 | 🧃 (MAP) Moving Average Primary
🧃 (MAP) Moving Average Primary: true
🧃 (MAP) BarColor: false
🧃 (MAP) Background: false
🧃 (MAP) Type: SMA
🧃 (MAP) Source: open
🧃 (MAP) Period: 100
🧃 (MAP) Multiplier: 2.0
🧃 (MAP) Linewidth: 2
🧃 (MAP) Color P: #42bda8
🧃 (MAP) Color N: #801922
| 20 | 🧃 (MAP) Labels
🧃 (MAP) Labels: true
🧃 (MAP) Style BUY ZONE: shape.labelup
🧃 (MAP) Color BUY ZONE: #42bda8
🧃 (MAP) Style SELL ZONE: shape.labeldown
🧃 (MAP) Color SELL ZONE: #801922
| 21 | 🍔 (MAQ) Moving Average Quaternary
🍔 (MAQ) Moving Average Quaternary: true
🍔 (MAQ) BarColor: false
🍔 (MAQ) Background: false
🍔 (MAQ) Type: SMA
🍔 (MAQ) Source: close
🍔 (MAQ) Primary: 14
🍔 (MAQ) Secondary: 22
🍔 (MAQ) Tertiary: 44
🍔 (MAQ) Quaternary: 16
🍔 (MAQ) Linewidth: 0
🍔 (MAQ) Color P: #42bda8
🍔 (MAQ) Color N: #801922
| 22 | 🍟 (MACD) Moving Average Convergence Divergence
🍟 (MACD) Macd Type: EMA
🍟 (MACD) Signal Type: EMA
🍟 (MACD) Source: close
🍟 (MACD) Fast: 12
🍟 (MACD) Slow: 26
🍟 (MACD) Smoothing: 9
| 23 | 📣 (VWAP) Volume Weighted Average Price
📣 (VWAP) Source: close
📣 (VWAP) Period: 340
📣 (VWAP) Momentum A: 84
📣 (VWAP) Momentum B: 150
📣 (VWAP) Average Volume: 1
📣 (VWAP) Multiplier: 1
📣 (VWAP) Diviser: 2
| 24 | 🪀 (HL) HILO
🪀 (HL) Type: SMA
🪀 (HL) Function: Maverick🧙
🪀 (HL) Source H: high
🪀 (HL) Source L: low
🪀 (HL) Period: 20
🪀 (HL) Momentum: 26
🪀 (HL) Diviser: 2
🪀 (HL) Multiplier: 1
| 25 | 🅾 (OBV) On Balance Volume
🅾 (OBV) Type: EMA
🅾 (OBV) Source: close
🅾 (OBV) Period: 16
🅾 (OBV) Diviser: 2
🅾 (OBV) Multiplier: 1
| 26 | 🥊 (SAR) Stop and Reverse
🥊 (SAR) Source: close
🥊 (SAR) High: 1.8
🥊 (SAR) Mid: 1.6
🥊 (SAR) Low: 1.6
🥊 (SAR) Diviser: 2
🥊 (SAR) Multiplier: 1
| 27 | 🛡️ (DSR) Dynamic Support and Resistance
🛡️ (DSR) Source D: close
🛡️ (DSR) Source R: high
🛡️ (DSR) Source S: low
🛡️ (DSR) Momentum R: 0
🛡️ (DSR) Momentum S: 2
🛡️ (DSR) Diviser: 2
🛡️ (DSR) Multiplier: 1
| 28 | 🔊 (VD) Volume Directional
🔊 (VD) Type: SMA
🔊 (VD) Period: 68
🔊 (VD) Momentum: 3.8
🔊 (VD) Diviser: 2
🔊 (VD) Multiplier: 1
| 29 | 🧰 (RSI) Relative Momentum Index
🧰 (RSI) Type UP: EMA
🧰 (RSI) Type DOWN: EMA
🧰 (RSI) Source: close
🧰 (RSI) Period: 29
🧰 (RSI) Smoothing: 22
🧰 (RSI) Momentum R: 64
🧰 (RSI) Momentum S: 142
🧰 (RSI) Diviser: 2
🧰 (RSI) Multiplier: 1
| 30 | 🎯 (TP) Take Profit %
🎯 (TP) Take Profit: false
🎯 (TP) %: 2.2
🎯 (TP) Color: #42bda8
🎯 (TP) Linewidth: 1
| 31 | 🛑 (SL) Stop Loss %
🛑 (SL) Stop Loss: false
🛑 (SL) %: 2.7
🛑 (SL) Color: #801922
🛑 (SL) Linewidth: 1
| 32 | 🤖 Automation : Discord | Telegram | Twitter | Wundertrading | 3commas | Zignaly | Aleeert | Alertatron | Uniswap-v3
🤖 Automation Selected : Discord
| 33 | 🤖 Discord
🔗 Link Discord:
🔗 Link 📚 Automation: 🔒Titan Pro👽
📱💻 Discord ▬ Enter Long: 🔒Titan Pro👽
📱💻 Discord ▬ Exit Long: 🔒Titan Pro👽
📱💻 Discord ▬ Enter Short: 🔒Titan Pro👽
📱💻 Discord ▬ Exit Short: 🔒Titan Pro👽
| 34 | 🤖 Telegram
🔗 Link Telegram:
🔗 Link 📚 Automation: 🔒Titan Pro👽
📱💻 Telegram ▬ Enter Long: 🔒Titan Pro👽
📱💻 Telegram ▬ Exit Long: 🔒Titan Pro👽
📱💻 Telegram ▬ Enter Short: 🔒Titan Pro👽
📱💻 Telegram ▬ Exit Short: 🔒Titan Pro👽
| 35 | 🤖 Twitter
🔗 Link Twitter:
🔗 Link 📚 Automation: 🔒Titan Pro👽
📱💻 Twitter ▬ Enter Long: 🔒Titan Pro👽
📱💻 Twitter ▬ Exit Long: 🔒Titan Pro👽
📱💻 Twitter ▬ Enter Short: 🔒Titan Pro👽
📱💻 Twitter ▬ Exit Short: 🔒Titan Pro👽
| 36 | 🤖 Wundertrading : Binance | Bitmex | Bybit | KuCoin | Deribit | OKX | Coinbase | Huobi | Bitfinex | Bitget
🔗 Link Wundertrading:
🔗 Link 📚 Automation: 🔒Titan Pro👽
📱💻 Wundertrading ▬ Enter Long: 🔒Titan Pro👽
📱💻 Wundertrading ▬ Exit Long: 🔒Titan Pro👽
📱💻 Wundertrading ▬ Enter Short: 🔒Titan Pro👽
📱💻 Wundertrading ▬ Exit Short: 🔒Titan Pro👽
| 37 | 🤖 3commas : Binance | Bybit | OKX | Bitfinex | Coinbase | Deribit | Bitmex | Bittrex | Bitstamp | Gate.io | Kraken | Gemini | Huobi | KuCoin
🔗 Link 3commas:
🔗 Link 📚 Automation: 🔒Titan Pro👽
📱💻 3commas ▬ Enter Long: 🔒Titan Pro👽
📱💻 3commas ▬ Exit Long: 🔒Titan Pro👽
📱💻 3commas ▬ Enter Short: 🔒Titan Pro👽
📱💻 3commas ▬ Exit Short: 🔒Titan Pro👽
| 38 | 🤖 Zignaly : Binance | Ascendex | Bitmex | Kucoin | VCCE
🔗 Link Zignaly:
🔗 Link 📚 Automation: 🔒Titan Pro👽
🤖 Type Automation: Profit Sharing
🤖 Type Provider: Webook
🔑 Key: 🔒Titan Pro👽
🤖 pair: BTCUSDTP
🤖 exchange: binance
🤖 exchangeAccountType: futures
🤖 orderType: market
🚀 leverage: 1x
% positionSizePercentage: 100 %
💸 positionSizeQuote: 10000 $
🆔 signalId: @Signal1234
| 39 | 🤖 Aleeert : Binance
🔗 Link Aleeert:
🔗 Link 📚 Automation: 🔒Titan Pro👽
📱💻 Aleeert ▬ Enter Long: 🔒Titan Pro👽
📱💻 Aleeert ▬ Exit Long: 🔒Titan Pro👽
📱💻 Aleeert ▬ Enter Short: 🔒Titan Pro👽
📱💻 Aleeert ▬ Exit Short: 🔒Titan Pro👽
| 40 | 🤖 Alertatron : Binance | Bybit | Deribit | Bitmex
🔗 Link Alertatron:
🔗 Link 📚 Automation: 🔒Titan Pro👽
📱💻 Alertatron ▬ Enter Long: 🔒Titan Pro👽
📱💻 Alertatron ▬ Exit Long: 🔒Titan Pro👽
📱💻 Alertatron ▬ Enter Short: 🔒Titan Pro👽
📱💻 Alertatron ▬ Exit Short: 🔒Titan Pro👽
| 41 | 🤖 Uniswap-v3
🔗 Link Alertatron:
🔗 Link 📚 Automation: 🔒Titan Pro👽
📱💻 Uniswap-v3 ▬ Enter Long: 🔒Titan Pro👽
📱💻 Uniswap-v3 ▬ Exit Long: 🔒Titan Pro👽
📱💻 Uniswap-v3 ▬ Enter Short: 🔒Titan Pro👽
📱💻 Uniswap-v3 ▬ Exit Short: 🔒Titan Pro👽
| 42 | 🧲🤖 Copy-Trading : Zignaly | Wundertrading
🔗 Link 📚 Copy-Trading: 🔒Titan Pro👽
🧲🤖 Copy-Trading ▬ Zignaly: 🔒Titan Pro👽
🧲🤖 Copy-Trading ▬ Wundertrading: 🔒Titan Pro👽
| 43 | ♻️ ® Don't Repaint!
♻️ This Strategy does not Repaint!: ® Signs Do not repaint❕
♻️ This is a Real Strategy!: Quality : ® Titan Investimentos
📋️️ Get more information about Repainting here:
| 44 | 🔒 Copyright ©️
🔒 Copyright ©️: Copyright © 2023-2024 All rights reserved, ® Titan Investimentos
🔒 Copyright ©️: ® Titan Investimentos
🔒 Copyright ©️: Unique and Exclusive Strategy. All rights reserved
| 45 | 🏛️ Be a Titan Members
🏛️ Titan Pro 👽 Version with ✔️100% Integrated Automation 🤖 and 📚 Automation Tutorials ✔️100% available at: (PDF/VIDEO)
🏛️ Titan Affiliate 🛸 (Subscription Sale) 🔥 Receive 50% commission
| 46 | ⏱ Time Left
Time Left Titan Demo 🐄: ⏱♾ | ⏱ : ♾ Titan Demo 🐄 Version with ❌non-integrated automation 🤖 and 📚 Tutorials for automation ❌not available
Time Left Titan Pro 👽: 🔒Titan Pro👽 | ⏱ : Pro Plans: 30 Days, 90 Days, 12 Months, 24 Months. (👽 Pro 🅼 Monthly, 👽 Pro 🆀 Quarterly, 👽 Pro🅰 Annual, 👽 Pro👾Two Years)
| 47 | Nº Active Users
Nº Active Subscribers Titan Pro 👽: 5️⃣6️⃣ | 1✔️ 5✔️ 10✔️ 100❌ 1K❌ 10K❌ 50K❌ 100K❌ 1M❌ 10M❌ 100M❌ : ⏱ Active Users is updated every 24 hours (Check on indicator)
Nº Active Affiliates Titan Aff 🛸: 6️⃣ | 1✔️ 5✔️ 10❌ 100❌ 1K❌ 10K❌ 50K❌ 100K❌ 1M❌ 10M❌ 100M❌ : ⏱ Active Users is updated every 24 hours (Check on indicator)
2️⃣7️⃣ : 📊 PERFORMANCE : 🆑 Conservative
📊 Exchange: Binance
📊 Pair: BINANCE: BTCUSDTPERP
📊 TimeFrame: 4h
📊 Initial Capital: 10000 $
📊 Order Type: % equity
📊 Size Per Order: 100 %
📊 Commission: 0.03 %
📊 Pyramid: 1
• ⚠️ Risk Profile: 🆑 Conservative: 🎯 TP=2.7 % | 🛑 SL=2.7 %
• 📆All years: 🆑 Conservative: 🚀 Leverage 1️⃣x
📆 Start: September 23, 2019
📆 End: January 11, 2023
📅 Days: 1221
📅 Bars: 7325
Net Profit:
🟢 + 1669.89 %
💲 + 166989.43 USD
Total Close Trades:
⚪️ 369
Percent Profitable:
🟡 64.77 %
Profit Factor:
🟢 2.314
DrawDrown Maximum:
🔴 -24.82 %
💲 -10221.43 USD
Avg Trade:
💲 + 452.55 USD
✔️ Trades Winning: 239
❌ Trades Losing: 130
✔️ Average Gross Win: + 12.31 %
❌ Average Gross Loss: - 9.78 %
✔️ Maximum Consecutive Wins: 9
❌ Maximum Consecutive Losses: 6
% Average Gain Annual: 499.33 %
% Average Gain Monthly: 41.61 %
% Average Gain Weekly: 9.6 %
% Average Gain Day: 1.37 %
💲 Average Gain Annual: 49933 $
💲 Average Gain Monthly: 4161 $
💲 Average Gain Weekly: 960 $
💲 Average Gain Day: 137 $
• 📆 Year: 2020: 🆑 Conservative: 🚀 Leverage 1️⃣x
• 📆 Year: 2021: 🆑 Conservative: 🚀 Leverage 1️⃣x
• 📆 Year: 2022: 🆑 Conservative: 🚀 Leverage 1️⃣x
2️⃣8️⃣ : 📊 PERFORMANCE : Ⓜ️ Moderate
📊 Exchange: Binance
📊 Pair: BINANCE: BTCUSDTPERP
📊 TimeFrame: 4h
📊 Initial Capital: 10000 $
📊 Order Type: % equity
📊 Size Per Order: 100 %
📊 Commission: 0.03 %
📊 Pyramid: 1
• ⚠️ Risk Profile: Ⓜ️ Moderate: 🎯 TP=2.8 % | 🛑 SL=2.7 %
• 📆 All years: Ⓜ️ Moderate: 🚀 Leverage 1️⃣x
📆 Start: September 23, 2019
📆 End: January 11, 2023
📅 Days: 1221
📅 Bars: 7325
Net Profit:
🟢 + 1472.04 %
💲 + 147199.89 USD
Total Close Trades:
⚪️ 362
Percent Profitable:
🟡 63.26 %
Profit Factor:
🟢 2.192
DrawDrown Maximum:
🔴 -22.69 %
💲 -9269.33 USD
Avg Trade:
💲 + 406.63 USD
✔️ Trades Winning: 229
❌ Trades Losing : 133
✔️ Average Gross Win: + 11.82 %
❌ Average Gross Loss: - 9.29 %
✔️ Maximum Consecutive Wins: 9
❌ Maximum Consecutive Losses: 8
% Average Gain Annual: 440.15 %
% Average Gain Monthly: 36.68 %
% Average Gain Weekly: 8.46 %
% Average Gain Day: 1.21 %
💲 Average Gain Annual: 44015 $
💲 Average Gain Monthly: 3668 $
💲 Average Gain Weekly: 846 $
💲 Average Gain Day: 121 $
• 📆 Year: 2020: Ⓜ️ Moderate: 🚀 Leverage 1️⃣x
• 📆 Year: 2021: Ⓜ️ Moderate: 🚀 Leverage 1️⃣x
• 📆 Year: 2022: Ⓜ️ Moderate: 🚀 Leverage 1️⃣x
2️⃣9️⃣ : 📊 PERFORMANCE : 🅰 Aggressive
📊 Exchange: Binance
📊 Pair: BINANCE: BTCUSDTPERP
📊 TimeFrame: 4h
📊 Initial Capital: 10000 $
📊 Order Type: % equity
📊 Size Per Order: 100 %
📊 Commission: 0.03 %
📊 Pyramid: 1
• ⚠️ Risk Profile: 🅰 Aggressive: 🎯 TP=1.6 % | 🛑 SL=6.9 %
• 📆 All years: 🅰 Aggressive: 🚀 Leverage 1️⃣x
📆 Start: September 23, 2019
📆 End: January 11, 2023
📅 Days: 1221
📅 Bars: 7325
Net Profit:
🟢 + 989.38 %
💲 + 98938.38 USD
Total Close Trades:
⚪️ 380
Percent Profitable:
🟢 84.47 %
Profit Factor:
🟢 2.156
DrawDrown Maximum:
🔴 -17.88 %
💲 -9182.84 USD
Avg Trade:
💲 + 260.36 USD
✔️ Trades Winning: 321
❌ Trades Losing: 59
✔️ Average Gross Win: + 5.75 %
❌ Average Gross Loss: - 14.51 %
✔️ Maximum Consecutive Wins: 21
❌ Maximum Consecutive Losses: 6
% Average Gain Annual: 295.84 %
% Average Gain Monthly: 24.65 %
% Average Gain Weekly: 5.69 %
% Average Gain Day: 0.81 %
💲 Average Gain Annual: 29584 $
💲 Average Gain Monthly: 2465 $
💲 Average Gain Weekly: 569 $
💲 Average Gain Day: 81 $
• 📆 Year: 2020: 🅰 Aggressive: 🚀 Leverage 1️⃣x
• 📆 Year: 2021: 🅰 Aggressive: 🚀 Leverage 1️⃣x
• 📆 Year: 2022: 🅰 Aggressive: 🚀 Leverage 1️⃣x
3️⃣0️⃣ : 🛠️ Roadmap
🛠️• 14/ 01 /2023 : Titan THEMIS Launch
🛠️• Updates January/2023 :
• 📚 Tutorials for Automation 🤖 already Available : ✔️
• ✔️ Discord
• ✔️ Wundertrading
• ✔️ Zignaly
• 📚 Tutorials for Automation 🤖 In Preparation : ⭕
• ⭕ Telegram
• ⭕ Twitter
• ⭕ 3comnas
• ⭕ Aleeert
• ⭕ Alertatron
• ⭕ Uniswap-v3
• ⭕ Copy-Trading
🛠️• Updates February/2023 :
• 📰 Launch of advertising material for Titan Affiliates 🛸
• 🛍️🎥🖼️📊 (Sales Page/VSL/Videos/Creative/Infographics)
🛠️• 28/05/2023 : Titan THEMIS update ▬ Version 2.7
🛠️• 28/05/2023 : BOT BOB release ▬ Version 1.0
• (Native Titan THEMIS Automation - Through BOT BOB, a bot for automation of signals, indicators and strategies of TradingView, of own code ▬ in validation.
• BOT BOB
Automation/Connection :
• API - For Centralized Brokers.
• Smart Contracts - Wallet Web - For Decentralized Brokers.
• This way users can automate any indicator or strategy of TradingView and Titan in a decentralized, secure and simplified way.
• Without having the need to use 'third party services' for automating TradingView indicators and strategies like the ones available above.
🛠️• 28/05/2023 : Release ▬ Titan Culture Guide 📝
3️⃣1️⃣ : 🧻 Notes ❕
🧻 • Note ❕ The "Demo 🐄" version, ❌does not have 'integrated automation', to automate the signals of this strategy and enjoy a fully automated system, you need to have access to the Pro version with '100% integrated automation' and all the tutorials for automation available. Become a Titan Pro 👽
🧻 • Note ❕ You will also need to be a "Pro User or higher on Tradingview", to be able to use the webhook feature available only for 'paid' profiles on the platform.
With the webhook feature it is possible to send the signals of this strategy to almost anywhere, in our case to centralized or decentralized brokerages, also to popular messaging services such as: Discord, Telegram or Twiter.
3️⃣2️⃣ : 🚨 Disclaimer ❕❗
🚨 • Disclaimer ❕❕ Past positive result and performance of a system does not guarantee its positive result and performance for the future!
🚨 • Disclaimer ❗❗❗ When using this strategy: Titan Investments is totally Exempt from any claim of liability for losses. The responsibility on the management of your funds is solely yours. This is a very high risk/volatility market! Understand your place in the market.
3️⃣3️⃣ : ♻️ ® No Repaint
This Strategy does not Repaint! This is a real strategy!
3️⃣4️⃣ : 🔒 Copyright ©️
Copyright © 2022-2023 All rights reserved, ® Titan Investimentos
3️⃣5️⃣ : 👏 Acknowledgments
I want to start this message in thanks to TradingView and all the Pinescript community for all the 'magic' created here, a unique ecosystem! rich and healthy, a fertile soil, a 'new world' of possibilities, for a complete deepening and improvement of our best personal skills.
I leave here my immense thanks to the whole community: Tradingview, Pinecoders, Wizards and Moderators.
I was not born Rich .
Thanks to TradingView and pinescript and all its transformation.
I could develop myself and the best of me and the best of my skills.
And consequently build wealth and patrimony.
Gratitude.
One more story for the infinite book !
If you were born poor you were born to be rich !
Raising🔼 the level and raising🔼 the ruler! 📏
My work is my 'debauchery'! Do better! 💐🌹
Soul of a first-timer! Creativity Exudes! 🦄
This is the manifestation of God's magic in me. This is the best of me. 🧙
You will copy me, I know. So you owe me. 💋
My mission here is to raise the consciousness and self-esteem of all Titans and Titanids! Welcome! 🧘 🏛️
The only way to accomplish great work is to do what you love ! Before I learned to program I was wasting my life!
Death is the best creation of life .
Now you are the new , but in the not so distant future you will gradually become the old . Here I stay forever!
Playing the game like an Athlete! 🖼️ Enjoy and Enjoy 🍷 🗿
In honor of: BOB ☆
1 name, 3 letters, 3 possibilities, and if read backwards it's the same thing, a palindrome. ☘
Gratitude to the oracles that have enabled me the 'luck' to get this far: Dal&Ni&Fer
3️⃣6️⃣ : 👮 House Rules : 📺 TradingView
House Rules : This publication and strategy follows all TradingView house guidelines and rules:
📺 TradingView House Rules: www.tradingview.com
📺 Script publication rules: www.tradingview.com
📺 Vendor requirements: www.tradingview.com
📺 Links/References rules: www.tradingview.com
3️⃣7️⃣ : 🏛️ Become a Titan Pro member 👽
🟩 Titan Pro 👽 🟩
3️⃣8️⃣ : 🏛️ Be a member Titan Aff 🛸
🟥 Titan Affiliate 🛸 🟥
