All Instrument Swing Trader with Pyramids, DCA and Leverage
Introduction
This is my most advanced Pine 4 script so far. It combines my range trader algorithms with my trend following pyramids all on a single interval. This script includes my beta tested DCA feature along with simulated leverage and buying power calculations. It has a twin study with several alerts. The features in this script allow you to experiment with different risk strategies and evaluate the approximate impact on your account capital. The script is flexible enough to run on instruments from different markets and at various bar intervals. This strategy can be run in three different modes: long, short and bidirectional. The bidirectional mode has two split modes (Ping Pong and BiDir). It also generates a summary report label with information not available in the TradingView Performance report such as Rate Of Return Standard Deviation and other Sharpe Ratio input values. Notable features include the following:
- Swing Trading Paradigm
- Uni or Bidirectional trading modes
- Calculation presets for Crypto, Stocks and Forex
- Conditional Minimum Profit
- Hard stop loss field
- Two types of DCA (Positive and Negative)
- Discretionary Pyramid levels with threshold adjustment and limiter
- Consecutive loss counter with preset and label
- Reentry loss limiter and trade entry caution fields
- Simulated Leverage and margin call warning label (approximation only)
- Buying power report labels (approximation only)
- Rate Of Return report with input values for Sharpe Ratio, Sortino and others
- Summary report label with real-time status indicators
- Trend follow bias modes (Its still range trading)
- Six anti-chop settings
- Single interval strategy to reduce repaint occurrence
This is a swing trading strategy so the behavior of this script is to buy on weakness and sell on strength. As such trade orders are placed in a counter direction to price pressure. What you will see on the chart is a short position on peaks and a long position on valleys. Just to be clear, the range as well as trends are merely illusions as the chart only receives prices. However, this script attempts to calculate pivot points from the price stream. Rising pivots are shorts and falling pivots are longs. I refer to pivots as a vertex in this script which adds structural components to the chart formation (point, sides and a base). When trading in “Ping Pong” mode long and short positions are intermingled continuously as long as there exists a detectable vertex. Unfortunately, this can work against your backtest profitability on long duration trends where prices continue in a single direction without pullback. I have designed various features in the script to compensate for this event. A well configured script should perform in a range bound market and minimize losses in a trend. For a range trader the trend is most certainly not your friend. I also have a trend following version of this script for those not interested in trading the range.
This script makes use of the TradingView pyramid feature accessible from the properties tab. Additional trades can be placed in the draw-down space increasing the position size and thereby increasing the profit or loss when the position finally closes. Each individual add on trade increases its order size as a multiple of its pyramid level. This makes it easy to comply with NFA FIFO Rule 2-43(b) if the trades are executed here in America. The inputs dialog box contains various settings to adjust where the add on trades show up, under what circumstances and how frequent if at all. Please be advised that pyramiding is an advanced feature and can wipe out your account capital if your not careful. You can use the “Performance Bond Leverage” feature to stress test your account capital with varying pyramid levels during the backtest. Use modest settings with realistic capital until you discover what you think you can handle. See the“Performance Bond Leverage” description for more information.
In addition to pyramiding this script employs DCA which enables users to experiment with loss recovery techniques. This is another advanced feature which can increase the order size on new trades in response to stopped out or winning streak trades. The script keeps track of debt incurred from losing trades. When the debt is recovered the order size returns to the base amount specified in the TV properties tab. The inputs for this feature include a limiter to prevent your account from depleting capital during runaway markets. The main difference between DCA and pyramids is that this implementation of DCA applies to new trades while pyramids affect open positions. DCA is a popular feature in crypto trading but can leave you with large “bags” if your not careful. In other markets, especially margin trading, you’ll need a well funded account and much experience.
To be sure pyramiding and dollar cost averaging is as close to gambling as you can get in respectable trading exchanges. However, if you are looking to compete in a Forex contest or want to add excitement to your trading life style those features could find a place in your strategies. Although your backtest may show spectacular gains don’t expect your live trading account to do the same. Every backtest has some measure to data mining bias. Please remember that.
This script is equipped with a consecutive loss counter. A limit field is provided in the report section of the input dialog box. This is a whole number value that, when specified, will generate a label on the chart when consecutive losses exceed the threshold. Every stop hit beyond this limit will be reported on a version 4 label above the bar where the stop is hit. Use the location of the labels along with the summary report tally to improve the adaptability of system. Don’t simply fit the chart. A good trading system should adapt to ever changing market conditions. On the study version the consecutive loss limit can be used to halt live trading on the broker side (managed manually).
This script can simulate leverage applied to your account capital. Basically, you want to know if the account capital you specified in the properties tab is sufficient to trade this script with the order size, pyramid and DCA parameters needed. TradingView does not halt trading when the account capital is depleted nor do you receive notification of such an event. Input the leverage you intend to trade with and simulate the stress on your account capital. When the check box labeled “Report Margin Call” is enabled a marker will plot on the chart at the location where the threshold was breached. Additionally, the Summary Report will indicated such a breach has occurred during the backtest. Please note that the margin calculation uses a performance bond contract model which is the same type of leverage applied to Forex accounts. This is not the same leverage as stock margin accounts since shares are not actually borrowed. It is also not applicable to futures contracts since we do not calculate maintenance margin. Also note that the account margin and buying power are calculated using the U.S. Dollar as a funding currency. Margin rules across the globe vary considerably so use this feature as an approximation. The “Report Margin Call” plot only appears on negative buying power which is well beyond the NFA enforced margin closeout price. Vary the order size and account capital and activate the buying power plot to get as close as you can to the desired margin call threshold. Also keep in mind that rollover fees, commissions, spreads, etc affect the margin call in actual live trading. This feature does not include any of those costs.
Inputs
The script input dialog box is divided into five sections. The last section, Section 5, contains all of the script reporting options. Notable reporting options are the inputs which provide support for calculating actual Sharpe Ratios and other risk / performance metrics. The TradingView performance report does not produce a scalable Sharpe Ratio which is unfortunate considering the limited data supplied to the backtest. Three report fields made available in this section are intended to enable users to measure the performance of this script using various industry standard risk metrics. In particular, The Sharpe Ratio, Sortino Ratio, Alpha Calculation, Beta Calculation, R-Squared and Monthly Standard Deviation. The following fields are dedicated to this effort:
– ROR Sample Period - Integer number which specifies the rate of return period. This number is a component of the Sharpe Ratio and determines the number of sample periods divisible in the chart data. The number specified here is the length of the period measured in bar intervals. Since the quantity of TradingView historical data is limited this number should reflect the scalar value applied to your Sharpe calculation. When the checkbox “Report Period ROR” is enabled red boxes plot on the dates corresponding to the ROR sample period. The red boxes display information useful in calculating various risk and performance models. Ongoing buying power is included in the period report which is especially useful in assessing the DCA stress on account capital. Important: When the “ROR Sample Period” is specified the script computes the ROR mean value and displays the result in the summary report label on the live end of the chart. Use this number to calculate the historical standard deviation of period returns.
– Return Mean Value - This is the ROR mean value which is displayed in the summary report field “ROR Mean”. Enter the value shown in the summary report here in order to calculate the standard deviation of returns. Once calculated the result is displayed in the summary report field “Standard Dev”. Please note that ROR and standard deviation are calculated on the quote currency of the chart and not the account currency. If you intend to calculate risk metrics based on other denominated returns use the period calculations in a spreadsheet. Important: Do not change the account denomination on the properties tab simply to force a dollar calculation. It will alter the backtest itself since the minimum profit, stop-loss and other variables are always measured in the quote currency of the chart.
– Report Period ROR - This checkbox is used to display the ROR period report which plots a red label above the bars corresponding to the ROR sample period. The sample period is defined by the value entered into the “ROR Sample Period” field. This checkbox only determines if the period labels plot on the chart. It does not enable or disable the ROR calculation itself. Please see input description“ROR Sample Period” for a detailed description of this feature.
Design
This script uses twelve indicators on a single time frame. The original trading algorithms are a port from a much larger program on another trading platform. I’ve converted some of the statistical functions to use standard indicators available on TradingView. The setups make heavy use of the Hull Moving Average in conjunction with EMAs that form the Bill Williams Alligator as described in his book “New Trading Dimensions” Chapter 3. Lag between the Hull and the EMAs form the basis of the entry and exit points. The vertices are calculated using one of five featured indicators. Each indicator is actually a composite of calculations which produce a distinct mean. This mathematical distinction enables the script to be useful on various instruments which belong to entirely different markets. In other words, at least one of these indicators should be able generate pivots on an arbitrarily selected instrument. Try each one to find the best fit.
The entire script is around 2200 lines of Pine code which pushes the limits of what can be created on this platform given the TradingView maximums for: local scopes, run-time duration and compile time. This script incorporates code from both my range trader and trend following published programs. Both have been in development for nearly two years and have been in beta test for the last several months. During the beta test of the range trading script it was discovered that by widening the stop and delaying the entry, add on trading opportunities appeared on the chart. I determined that by sacrificing a few minor features code space could be made available for pyramiding capability in the range trader. The module has been through several refactoring passes and makes extensive use of ternary statements. As such, It takes a full three minutes to compile after adding it to a chart. Please wait for the hovering dots to disappear before attempting to bring up the input dialog box. For the most part the same configuration settings for the range script can be applied to this script.
Inputs to the script use cone centric measurements in effort to avoid exposing adjustments to the various internal indicators. The goal was to keep the inputs relevant to the actual trade entry and exit locations as opposed to a series of MA input values and the like. As a result the strategy exposes over 70 inputs grouped into long or short sections. Inputs are available for the usual minimum profit and stop-loss as well as safeguards, trade frequency, pyramids, DCA, modes, presets, reports and lots of calibrations. The inputs are numerous, I know. Unfortunately, at this time, TradingView does not offer any other method to get data in the script. The usual initialization files such as cnf, cfg, ini, json and xml files are currently unsupported.
I have several example configuration settings that I use for my own trading. They include cryptocurrencies and forex instruments on various time frames.
Indicator Repainting and Anomalies
Indicator repainting is an industry wide problem which mainly occurs when you mix backtest data with real-time data. It doesn't matter which platform you use some form of this condition will manifest itself on your chart over time. The critical aspect being whether live trades on your broker’s account continue to match your TradingView study.
Based on my experience with Pine, most of the problems stem from TradingView’s implementation of multiple interval access. Whereas most platforms provide a separate bar series for each interval requested, the Pine language interleaves higher time frames with the primary chart interval. The problem is exacerbated by allowing a look-ahead parameter to the Security function. The goal of my repaint prevention is simply to ensure that my signal trading bias remains consistent between the strategy, study and broker. That being said this is what I’ve done address this issue in this script:
1. This script uses only 1 time frame. The chart interval.
2. Every entry and exit condition is evaluated on closed bars only.
3. No security functions are called to avoid a look-ahead possibility.
4. Every contributing factor specified in the TradingView wiki regarding this issue has been addressed.
5. Entry and exit setups are not reliant on crossover conditions.
6. I’ve run a 10 minute chart live for a week and compared it to the same chart periodically reloaded. The two charts were highly correlated with no instances of completely opposite real-time signals. I do have to say that there were differences in the location of some trades between the backtest and the study. But, I think mostly those differences are attributable to trading off closed bars in the study and the use of strategy functions in the backtest.
The study does indeed bring up the TV warning dialog. The only reason for this is because the script uses an EMA indicator which according to TradingView is due to “peculiarities of the algorithm”. I use the EMA for the Bill Williams Alligator so there is no way to remove it.
One issue that comes up when comparing the strategy with the study is that the strategy trades show on the chart one bar later than the study. This problem is due to the fact that “strategy.entry()” and “strategy_exit()” do not execute on the same bar called. The study, on the other hand, has no such limitation since there are no position routines.
Please be aware that the data source matters. Cryptocurrency has no central tick repository so each exchange supplies TradingView its feed. Even though it is the same symbol the quality of the data and subsequently the bars that are supplied to the chart varies with the exchange. This script will absolutely produce different results on different data feeds of the same symbol. Be sure to backtest this script on the same data you intend to receive alerts for. Any example settings I share with you will always have the exchange name used to generate the test results.
Usage
The following steps provide a very brief set of instructions that will get you started but will most certainly not produce the best backtest. A trading system that you are willing to risk your hard earned capital will require a well crafted configuration that involves time, expertise and clearly defined goals. As previously mentioned, I have several example configs that I use for my own trading that I can share with you. To get hands on experience in setting up your own symbol from scratch please follow the steps below.
The input dialog box contains over 70 inputs separated into five sections. Each section is identified as such with a makeshift separator input. There are three main areas that must to be configured: long side, short side and settings that apply to both. The rest of the inputs apply to pyramids, DCA, reporting and calibrations. The following steps address these three main areas only. You will need to get your backtest in the black before moving on to the more advanced features.
Step 1. Setup the Base currency and order size in the properties tab.
Step 2. Select the calculation presets in the Instrument Type field.
Step 3. Select “No Trade” in the Trading Mode field.
Step 4. Select the Histogram indicator from Section 2. You will be experimenting with different ones so it doesn’t matter which one you try first.
Step 5. Turn on Show Markers in Section 2.
Step 6. Go to the chart and checkout where the markers show up. Blue is up and red is down. Long trades show up along the red markers and short trades on the blue.
Step 7. Make adjustments to “Base To Vertex” and “Vertex To Base” net change and roc in Section 3. Use these fields to move the markers to where you want trades to be.
Step 8. Try a different indicator from Section 2 and repeat Step 7 until you find the best match for this instrument on this interval. This step is complete when the Vertex settings and indicator combination produce the most favorable results.
Step 9. Go to Section 3 and enable “Apply Red Base To Base Margin”.
Step 10. Go to Section 4 and enable “Apply Blue Base To Base Margin”.
Step 11. Go to Section 2 and adjust “Minimum Base To Base Blue” and “Minimum Base To Base Red”. Observe the chart and note where the markers move relative to each other. Markers further apart will produce less trades but will reduce cutoffs in “Ping Pong” mode.
Step 12. Return to Section 3 and 4 and turn off “Base To Base Margin” which was enabled in steps 9 and 10.
Step 13. Turn off Show Markers in Section 2.
Step 14. Put in your Minimum Profit and Stop Loss in the first section. This is in pips or currency basis points (chart right side scale). Percentage is not currently supported. This is a fixed value minimum profit and stop loss. Also note that the profit is taken as a conditional exit on a market order not a fixed limit. The actual profit taken will almost always be greater than the amount specified (due to the exit condition). The stop loss, on the other hand, is indeed a hard number which is executed by the TradingView broker simulator when the threshold is breached. On the study version, the stop is executed at the close of the bar.
Step 15. Return to step 3 and select a Trading Mode (Long, Short, BiDir, Ping Pong). If you are planning to trade bidirectionally its best to configure long first then short. Combine them with “BiDir” or “Ping Pong” after setting up both sides of the trade individually. The difference between “BiDir” and “Ping Pong” is that “Ping Pong” uses position reversal and can cut off opposing trades less than the specified minimum profit. As a result “Ping Pong” mode produces the greatest number of trades.
Step 16. Take a look at the chart. Trades should be showing along the markers plotted earlier.
Step 17. Make adjustments to the Vertex fields in Section 2 until the TradingView performance report is showing a profit. This includes the “Minimum Base To Base” fields. If a profit cannot be achieved move on to Step 18. Other adjustments may make a crucial difference.
Step 18. Improve the backtest profitability by adjusting the “Entry Net Change” and “Entry ROC” in Section 3 and 4.
Step 19. Enable the “Mandatory Snap” checkbox in Section 3 and 4 and adjust the “Snap Candle Delta” and “Snap Fractal Delta” in Section 2. This should reduce some chop producing unprofitable reversals.
Step 20. Increase the distance between opposing trades by adding an “Interleave Delta” in Sections 3 and 4. This is a floating point value which starts at 0.01 and typically does not exceed 2.0.
Step 21. Increase the distance between opposing trades even further by adding a “Decay Minimum Span” in Sections 3 and 4. This is an absolute value specified in the symbol’s quote currency (right side scale of the chart). This value is similar to the minimum profit and stop loss fields in Section 1.
Step 22. Improve the backtest profitability by adjusting the “Sparse Delta” in Section 3 and 4.
Step 23. Improve the backtest profitability by adjusting the “Chase Delta” in Section 3 and 4.
Step 24. Improve the backtest profitability by adjusting the “Adherence Delta” in Section 3 and 4. This field requires the “Adhere to Rising Trend” checkbox to be enabled.
Step 25. Try each checkbox in Section 3 and 4. See if it improves the backtest profitability. The “Caution Lackluster” checkbox only works when “Caution Mode” is enabled.
Step 26. Enable the reporting conditions in Section 5. Look for long runs of consecutive losses or high debt sequences. These are indications that your trading system cannot withstand sudden changes in market sentiment.
Step 27. Examine the chart and see that trades are being placed in accordance with your desired trading goals. This is an important step. If your desired model requires multiple trades per day then you should be seeing hundreds of trades on the chart. Alternatively, you may be looking to trade fewer steep peaks and deep valleys in which case you should see trades at major turning points. Don’t simply settle for what the backtest serves you. Work your configuration until the system aligns with your desired model. Try changing indicators and even intervals if you cannot reach your simulation goals. Generally speaking, the histogram and Candle indicators produce the most trades. The Macro indicator captures the tallest peaks and valleys.
Step 28. Apply the backtest settings to the study version and perform forward testing.
This script is open for beta testing. After successful beta test it will become a commercial application available by subscription only. I’ve invested quite a lot of time and effort into making this the best possible signal generator for all of the instruments I intend to trade. I certainly welcome any suggestions for improvements. Thank you all in advance.
One final note. I'm not a fan of having the Performance Overview (blue wedge) automatically show up at the end of the publish page since it could be misleading. On the EUR/USD backtest showing here I used a minimum profit of 65 pips, a stop of 120 pips, the candle indicator and a 5 pyramid max value. Also Mark Pyramid Levels (blue triangles) are enabled along with a 720 ROR Sample Period (red labels).
Cerca negli script per "profit"
Strategy Chameleon [theUltimator5]Have you ever looked at an indicator and wondered to yourself "Is this indicator actually profitable?" Well now you can test it out for yourself with the Strategy Chameleon!
Strategy Chameleon is a versatile, signal-agnostic trading strategy designed to adapt to any external indicator or trading system. Like a chameleon changes colors to match its environment, this strategy adapts to match any buy/sell signals you provide, making it the ultimate backtesting and automation tool for traders who want to test multiple strategies without rewriting code.
🎯 Key Features
1) Connects ANY external indicator's buy/sell signals
Works with RSI, MACD, moving averages, custom indicators, or any Pine Script output
Simply connect your indicator's signal output to the strategy inputs
2) Multiple Stop Loss Types:
Percentage-based stops
ATR (Average True Range) dynamic stops
Fixed point stops
3) Advanced Trailing Stop System:
Percentage trailing
ATR-based trailing
Fixed point trailing
4) Flexible Take Profit Options:
Risk:Reward ratio targeting
Percentage-based profits
ATR-based profits
Fixed point profits
5) Trading Direction Control
Long Only - Bull market strategies
Short Only - Bear market strategies
Both - Full market strategies
6) Time-Based Filtering
Optional trading session restrictions
Customize active trading hours
Perfect for day trading strategies
📈 How It Works
Signal Detection: The strategy monitors your connected buy/sell signals
Entry Logic: Executes trades when signals trigger during valid time periods
Risk Management: Automatically applies your chosen stop loss and take profit levels
Trailing System: Dynamically adjusts stops to lock in profits
Performance Tracking: Real-time statistics table showing win rate and performance
⚙️ Setup Instructions
0) Add indicator you want to test, then add the Strategy to your chart
Connect Your Signals:
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Go to strategy settings → Signal Sources
1) Set "Buy Signal Source" to your indicator's buy output
2) Set "Sell Signal Source" to your indicator's sell output
3) Choose table position - This simply changes the table location on the screen
4) Set trading direction preference - Buy only? Sell only? Both directions?
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5) Set your preferred stop loss type and level
You can set the stop loss to be either percentage based or ATR and fully configurable.
6) Enable trailing stops if desired
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7) Configure take profit settings
8) Toggle time filter to only consider specific time windows or trading sessions.
🚀 Use Cases
Test various indicators to determine feasibility and/or profitability.
Compare different signal sources quickly
Validate trading ideas with consistent risk management
Portfolio Management
Apply uniform risk management across different strategies
Standardize stop loss and take profit rules
Monitor performance consistently
Automation Ready
Built-in alert conditions for automated trading
Compatible with trading bots and webhooks
Easy integration with external systems
⚠️ Important Notes
This strategy requires external signals to function
Default settings use 10% of equity per trade
Pyramiding is disabled (one position at a time)
Strategy calculates on bar close, not every tick
🔗 Integration Examples
Works perfectly with:
RSI strategies (connect RSI > 70 for sells, RSI < 30 for buys)
Moving average crossovers
MACD signal line crosses
Bollinger Band strategies
Custom oscillators and indicators
Multi-timeframe strategies
📋 Default Settings
Position Size: 10% of equity
Stop Loss: 2% percentage-based
Trailing Stop: 1.5% percentage-based (enabled)
Take Profit: Disabled (optional)
Trade Direction: Both long and short
Time Filter: Disabled
Quantify [Trading Model] | FractalystNote: In this description, "TM" refers to Trading Model (not trademark) and "EM" refers to Entry Model
What’s the indicator’s purpose and functionality?
You know how to identify market bias but always struggle with figuring out the best exit method, or even hesitating to take your trades?
I've been there. That's why I built this solution—once and for all—to help traders who know the market bias but need a systematic and quantitative approach for their entries and trade management.
A model that shows you real-time market probabilities and insights, so you can focus on execution with confidence—not doubt or FOMO.
How does this Quantify differentiate from Quantify ?
Have you managed to code or even found an indicator that identifies the market bias for you, so you don’t have to manually spend time analyzing the market and trend?
Then that’s exactly why you might need the Quantify Trading Model.
With the Trading Model (TM) version, the script automatically uses your given bias identification method to determine the trend (bull vs bear and neutral), detect the bias, and provide instant insight into the trades you could’ve taken.
To avoid complications from consecutive signals, it uses a kNN machine learning algorithm that processes market structure and probabilities to predict the best future patterns.
(You don’t have to deal with any complexity—it’s all taken care of for you.)
Quantify TM uses the k-Nearest Neighbors (kNN) machine learning algorithm to learn from historical market patterns and adapt to changing market structures. This means it can recognize similar market conditions from the past and apply those lessons to current trading decisions.
On the other hand, Quantify EM requires you to manually select your directional bias. It then focuses solely on generating entry signals based on that pre-determined bias.
While the entry model version (EM) uses your manual bias selection to determine the trend, it then provides insights into trades you could’ve taken and should be taking.
Trading Model (TM)
- Uses `input.source()` to incorporate your personal methodology for identifying market bias
- Automates everything—from bias detection to entry and exit decisions
- Adapts to market bias changes through kNN machine learning optimization
- Reduces human intervention in trading decisions, limiting emotional interference
Entry Model (EM)
- Focuses specifically on optimizing entry points within your pre-selected directional bias
- Requires manual input for determining market bias
- Provides entry signals without automating alerts or bias rules
Can the indicator be applied to any market approach/trading strategy?
Yes, if you have clear rules for identifying the market bias, then you can code your bias detection and then use the input.source() user input to retrieve the direction from your own indicator, then the Quantify uses machine-learning identify the best setups for you.
Here's an example:
//@version=6
indicator('Moving Averages Bias', overlay = true)
// Input lengths for moving averages
ma10_length = input.int(10, title = 'MA 10 Length')
ma20_length = input.int(20, title = 'MA 20 Length')
ma50_length = input.int(50, title = 'MA 50 Length')
// Calculate moving averages
ma10 = ta.sma(close, ma10_length)
ma20 = ta.sma(close, ma20_length)
ma50 = ta.sma(close, ma50_length)
// Identify bias
var bias = 0
if close > ma10 and close > ma20 and close > ma50 and ma10 > ma20 and ma20 > ma50
bias := 1 // Bullish
bias
else if close < ma10 and close < ma20 and close < ma50 and ma10 < ma20 and ma20 < ma50
bias := -1 // Bearish
bias
else
bias := 0 // Neutral
bias
// Plot the bias
plot(bias, title = 'Identified Bias', color = color.blue,display = display.none)
Once you've created your custom bias indicator, you can integrate it with Quantify :
- Add your bias indicator to your chart
- Open the Quantify settings
- Set the Bias option to "Auto"
- Select your custom indicator as the bias source
The machine learning algorithms will then analyze historical price action and identify optimal setups based on your defined bias parameters. Performance statistics are displayed in summary tables, allowing you to evaluate effectiveness across different timeframes.
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 Quantify Helps You Trade Profitably?
The Quantify Trading Model offers several powerful features that can significantly improve your trading profitability when used correctly:
Real-Time Edge Assessment
It displays real-time probability of price moving in your favor versus hitting your stoploss
This gives you immediate insight into risk/reward dynamics before entering trades
You can make more informed decisions by knowing the statistical likelihood of success
Historical Edge Validation
Instantly shows whether your trading approach has demonstrated an edge in historical data
Prevents you from trading setups that historically haven't performed well
Gives confidence when entering trades that have proven statistical advantages
Optimized Position Sizing
Analyzes each setup's success rate to determine the adjusted Kelly criterion formula
Customizes position sizing based on your selected maximum drawdown tolerance
Helps prevent account-destroying losses while maximizing growth potential
Advanced Exit Management
Utilizes market structure-based trailing stop-loss mechanisms
Maximizes the average risk-reward ratio profit per winning trade
Helps capture larger moves while protecting gains during market reversals
Emotional Discipline Enforcement
Eliminates emotional bias by adhering to your pre-defined rules for market direction
Prevents impulsive decisions by providing objective entry and exit signals
Creates psychological distance between your emotions and trading decisions
Overtrading Prevention
Highlights only setups that demonstrate positive expectancy
Reduces frequency of low-probability trades
Conserves capital for higher-quality opportunities
Systematic Approach Benefits
By combining machine learning algorithms with your personal bias identification methods, Quantify helps transform discretionary trading approaches into more systematic, probability-based strategies.
What Entry Models are used in Quantify Trading Model version?
The Quantify Trading Model utilizes two primary entry models to identify high-probability trade setups:
Breakout Entry Model
- Identifies potential trade entries when price breaks through significant swing highs and swing lows
- Captures momentum as price moves beyond established trading ranges
- Particularly effective in trending markets when combined with the appropriate bias detection
- Optimized by machine learning to filter false breakouts based on historical performance
Fractals Entry Model
- Utilizes fractal patterns to identify potential reversal or continuation points
- Also uses swing levels to determine optimal entry locations
- Based on the concept that market structure repeats across different timeframes
- Identifies local highs and lows that form natural entry points
- Enhanced by machine learning to recognize the most profitable fractal formations
- These entry models work in conjunction with your custom bias indicator to ensure trades are taken in the direction of the overall market trend. The machine learning component analyzes historical performance of these entry types across different market conditions to optimize entry timing and signal quality.
How Does This Indicator Identify Market Structure?
1. Swing Detection
• The indicator identifies key swing points on the chart. These are local highs or lows where the price reverses direction, forming the foundation of market structure.
2. Structural Break Validation
• A structural break is flagged when a candle closes above a previous swing high (bullish) or below a previous swing low (bearish).
• Break Confirmation Process:
To confirm the break, the indicator applies the following rules:
• Valid Swing Preceding the Break: There must be at least one valid swing point before the break.
3. Numeric Labeling
• Each confirmed structural break is assigned a unique numeric ID starting from 1.
• This helps traders track breaks sequentially and analyze how the market structure evolves over time.
4. Liquidity and Invalidation Zones
• For every confirmed structural break, the indicator highlights two critical zones:
1. Liquidity Zone (LIQ): Represents the structural liquidity level.
2. Invalidation Zone (INV): Acts as Invalidation point if the structure fails to hold.
How does the trailing stop-loss work? what are the underlying calculations?
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.
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.
How can I get started to use the indicator?
1. Set Your Market Bias
• Choose Auto.
• Select the source you want Quantify to use as for bias identification method (explained above)
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. Choose Your Entry Model and BE/TP Levels
• Choose a model that suits your personality
• Choose a level where you'd like the script to take profit or move stop-loss to BE
4. Set and activate the alerts
What tables are used in the Quantify?
• Quarterly
• Monthly
• Weekly
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 Unauthorized 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.
Daily Coin Purchase StrategyThis indicator helps investors track the profitability of their Dollar-Cost Averaging (DCA) strategy when buying crypto assets periodically over time. Users can customize the start date, purchase amount per cycle, and the frequency of purchases based on different timeframes (e.g., 15m, 1H, 4H, 1D).
Features:
✅ Flexible Configuration: Allows users to set the start date, purchase amount per cycle, and select the desired timeframe (15m, 1H, 4H, 1D, etc.).
✅ Automated Profit Calculation: Displays total investment, total coins accumulated, average cost per coin, and profit based on the current market price.
✅ Supports Multiple Timeframes: Users can choose to buy at intervals based on 15m, 1H, 4H, or 1D candlesticks.
✅ Helps Plan Investment Capital: Calculates the total capital needed to follow a DCA strategy over a given period, allowing for better financial planning.
✅ Visualizes Investment Profitability: Provides clear insights into the accumulation process and the overall performance of the strategy.
✅ Intuitive Charts: Displays the accumulation process and investment performance over time.
How to Use:
Set the start date for purchases.
Enter the desired amount to invest per cycle.
Select the timeframe for purchases (15m, 1H, 4H, 1D, etc.).
The indicator will automatically calculate the number of coins accumulated, the average cost per coin, and the overall profit.
🔹 Use Cases:
Monitor the effectiveness of the DCA strategy over time.
Compare profitability across different purchase intervals.
Optimize long-term asset accumulation strategies.
Plan financial resources effectively for long-term investment strategies.
This version keeps all the key details while ensuring clarity and accuracy. Let me know if you need any modifications! 🚀
TheRookAlgoPROThe Rook Algo PRO is an automated strategy that uses ICT dealing ranges to get in sync with potential market trends. It detects the market sentiment and then place a sell or a buy trade in premium/discount or in breakouts with the desired risk management.
Why is useful?
This algorithm is designed to help traders to quickly identify the current state of the market and easily back test their strategy over longs periods of time and different markets its ideal for traders that want to profit on potential expansions and want to avoid consolidations this algo will tell you when the expansion is likely to begin and when is just consolidating and failing moves to avoid trading.
How it works and how it does it?
The Algo detects the current and previous market structure to identify current ranges and ICT dealing ranges that are created when the market takes buyside liquidity and sellside liquidity, it will tell if the market is in a consolidation, expansion, retracement or in a potential turtle soup environment, it will tell if the range is small or big compared to the previous one. Is important to use it in a trending markets because when is ranging the signals lose effectiveness.
This algo is similar to the previously released the Rook algo with the additional features that is an automated strategy that can take trades using filters with the desired risk reward and different entry types and trade management options.
Also this version plots FVGS(fair value gaps) during expansions, and detects consolidations with a box and the mid point or average. Some bars colors are available to help in the identification of the market state. It has the option to show colors of the dealing ranges first detected state.
How to use it?
Start selecting the desired type of entry you want to trade, you can choose to take Discount longs, premium sells, breakouts longs and sells, this first four options are the selected by default. You can enable riskier options like trades without confirmation in premium and discount or turtle soup of the current or previous dealing range. This last ones are ideal for traders looking to enter on a counter trend but has to be used with caution with a higher timeframe reference.
In the picture below we can see a premium sell signal configuration followed by a discount buy signal It display the stop break even level and take profit.
This next image show how the riskier entries work. Because we are not waiting for a confirmation and entering on a counter trend is normal to experience some stop losses because the stop is very tight. Should only be used with a clear Higher timeframe reference as support of the trade idea. This algo has the option to enable standard deviations from the normal stop point to prevent liquidity sweeps. The purple or blue arrows indicate when we are in a potential turtle soup environment.
The algo have a feature called auto-trade enable by default that allow for a reversal of the current trade in case it meets the criteria. And also can take all possible buys or all possible sells that are riskier entries if you just want to see the market sentiment. This is useful when the market is very volatile but is moving not just ranging.
Then we configure the desired trade filters. We have the options to trade only when dealing ranges are in sync for a more secure trend, or we can disable it to take riskier trades like turtle soup trades. We can chose the minimum risk reward to take the trade and the target extension from the current range and the exit type can be when we hit the level or in a retracement that is the default setting. These setting are the most important that determine profitability of the strategy, they has be adjusted depending on the timeframe and market we are trading.
The stop and target levels can also be configured with standard deviations from the current range that way can be adapted to the market volatility.
The Algo allow the user to chose if it want to place break even, or trail the stop. In the picture below we can see it in action. This can work when the trend is very strong if not can lead to multiple reentries or loses.
The last option we can configure is the time where the trades are going to be taken, if we trade usually in the morning then we can just add the morning time by default is set to the morning 730am to 1330pm if you want to trade other times you should change this. Or if we want to enter on the ICT macro times can also be added in a filter. Trade taken with the macro times only enable is visible in the picture below.
Strategy Results
The results are obtained using 2000usd in the MNQ! In the 15minutes timeframe 1 contract per trade. Commission are set to 2USD, slippage to 1tick, the backtesting range is from May 2 2024 to March 2025 for a total of 119 trades, this Strategy default settings are designed to take trades on the daily expansions, trail stop and Break even is activated the exit on profit is on a retracement, and for loses when the stop is hit. The auto-trade option is enable to allow to detect quickly market changes. The strategy give realistic results, makes around 200% of the account in around a year. 1.4 profit factor with around 37% profitable trades. These results can be further improve and adapted to the specific style of trading using the filters.
Remember entries constitute only a small component of a complete winning strategy. Other factors like risk management, position-sizing, trading frequency, trading fees, and many others must also be properly managed to achieve profitability. Past performance doesn’t guarantee future results.
Summary of features
-Easily Identify the current dealing range and market state to avoid consolidations
-Recognize expansions with FVGs and consolidation with shaded boxes
-Recognize turtle soups scenarios to avoid fake out breakout
-Configurable automated trades in premium/discount or breakouts
-Auto-trade option that allow for reversal of the current trade when is no longer valid
-Time filter to allow only entries around the times you trade or on the macro times.
-Risk Reward filter to take the automated trades with visible stop and take profit levels
-Customizable trade management take profit, stop, breakeven level with standard deviations
-Trail stop option to secure profit when price move in your favor
-Option to exit on a close, retracement or reversal after hitting the take profit level
-Option to exit on a close or reversal after hitting stop loss
-Dashboard with instant statistics about the strategy current settings and market sentiment
Smart MA Crossover BacktesterSmart MA Crossover Backtester - Strategy Overview
Strategy Name: Smart MA Crossover Backtester
Published on: TradingView
Applicable Markets: Works well on crypto (tested profitably on ETH)
Strategy Concept
The Smart MA Crossover Backtester is an improved Moving Average (MA) crossover strategy that incorporates a trend filter and an ATR-based stop loss & take profit mechanism for better risk management. It aims to capture trends efficiently while reducing false signals by only trading in the direction of the long-term trend.
Core Components & Logic
Moving Averages (MA) for Entry Signals
Fast Moving Average (9-period SMA)
Slow Moving Average (21-period SMA)
A trade signal is generated when the fast MA crosses the slow MA.
Trend Filter (200-period SMA)
Only enters long positions if price is above the 200-period SMA (bullish trend).
Only enters short positions if price is below the 200-period SMA (bearish trend).
This helps in avoiding counter-trend trades, reducing whipsaws.
ATR-Based Stop Loss & Take Profit
Uses the Average True Range (ATR) with a multiplier of 2 to calculate stop loss.
Risk-Reward Ratio = 1:2 (Take profit is set at 2x ATR).
This ensures dynamic stop loss and take profit levels based on market volatility.
Trading Rules
✅ Long Entry (Buy Signal):
Fast MA (9) crosses above Slow MA (21)
Price is above the 200 MA (bullish trend filter active)
Stop Loss: Below entry price by 2× ATR
Take Profit: Above entry price by 4× ATR
✅ Short Entry (Sell Signal):
Fast MA (9) crosses below Slow MA (21)
Price is below the 200 MA (bearish trend filter active)
Stop Loss: Above entry price by 2× ATR
Take Profit: Below entry price by 4× ATR
Why This Strategy Works Well for Crypto (ETH)?
🔹 Crypto markets are highly volatile – ATR-based stop loss adapts dynamically to market conditions.
🔹 Long-term trend filter (200 MA) ensures trading in the dominant direction, reducing false signals.
🔹 Risk-reward ratio of 1:2 allows for profitable trades even with a lower win rate.
This strategy has been tested on Ethereum (ETH) and has shown profitable performance, making it a strong choice for crypto traders looking for trend-following setups with solid risk management. 🚀
Trading IQ - Razor IQIntroducing TradingIQ's first dip buying/shorting all-in-one trading system: Razor IQ.
Razor IQ is an exclusive trading algorithm developed by TradingIQ, designed to trade upside/downside price dips of varying significance in trending markets. By integrating artificial intelligence and IQ Technology, Razor IQ analyzes historical and real-time price data to construct a dynamic trading system adaptable to various asset and timeframe combinations.
Philosophy of Razor IQ
Razor IQ operates on a single premise: Trends must retrace, and these retracements offer traders an opportunity to join in the overarching trend. At some point traders will enter against a trend in aggregate and traders in profitable positions entered during the trend will scale out. When occurring simultaneously, a trend will retrace against itself, offering an opportunity for traders not yet in the trend to join in the move and continue the trend.
Razor IQ is designed to work straight out of the box. In fact, its simplicity requires just a few user settings to manage output, making it incredibly straightforward to manage.
Long Limit Order Stop Loss and Minimum ATR TP/SL are the only settings that manage the performance of Razor IQ!
Traders don’t have to spend hours adjusting settings and trying to find what works best - Razor IQ handles this on its own.
Key Features of Razor IQ
Self-Learning Retracement Detection
Employs AI and IQ Technology to identify notable price dips in real-time.
AI-Generated Trading Signals
Provides retracement trading signals derived from self-learning algorithms.
Comprehensive Trading System
Offers clear entry and exit labels.
Performance Tracking
Records and presents trading performance data, easily accessible for user analysis.
Self-Learning Trading Exits
Razor IQ learns where to exit positions.
Long and Short Trading Capabilities
Supports both long and short positions to trade various market conditions.
How It Works
Razor IQ operates on a straightforward heuristic: go long during the retracement of significant upside price moves and go short during the retracement of significant downside price moves.
IQ Technology, TradingIQ's proprietary AI algorithm, defines what constitutes a “trend” and a “retracement” and what’s considered a tradable dip buying/shorting opportunity. For Razor IQ, this algorithm evaluates all historical trends and retracements, how much trends generally retrace and how long trends generally persist. For instance, the "dip" following an uptrend is measured and learned from, including the significance of the identified trend level (how long it has been active, how much price has increased, etc). By analyzing these patterns, Razor IQ adapts to identify and trade similar future retracements and trends.
In simple terms, Razor IQ clusters previous trend and retracement data in an attempt to trade similar price sequences when they repeat in the future. Using this knowledge, it determines the optimal, current price level where joining in the current trend (during a retracement) has a calculated chance of not stopping out before trend continuation.
For long positions, Razor IQ enters using a market order at the AI-identified long entry price point. If price closes beneath this level a market order will be placed and a long position entered. Of course, this is how the algorithm trades, users can elect to use a stop-limit order amongst other order types for position entry. After the position is entered TP1 is placed (identifiable on the price chart). TP1 has a twofold purpose:
Acts as a legitimate profit target to exit 50% of the position.
Once TP1 is achieved, a stop-loss order is immediately placed at breakeven, and a trailing stop loss controls the remainder of the trade. With this, so long as TP1 is achieved, the position will not endure a loss. So long as price continues to uptrend, Razor IQ will remain in the position.
For short positions, Razor IQ provides an AI-identified short entry level. If price closes above this level a market order will be placed and a short position entered. Again, this is how the algorithm trades, users can elect to use a stop-limit order amongst other order types for position entry. Upon entry Razor IQ implements a TP order and SL order (identifiable on the price chart).
Downtrends, in most markets, usually operate differently than uptrends. With uptrends, price usually increases at a modest pace with consistency over an extended period of time. Downtrends behave in an opposite manner - price decreases rapidly for a much shorter duration.
With this observation, the long dip entry heuristic differs slightly from the short dip entry heuristic.
The long dip entry heuristic specializes in identifying larger, long-term uptrends and entering on retracement of the uptrends. With a dedicated trailing stop loss, so long as the uptrend persists, Razor IQ will remain in the position.
The short dip entry heuristic specializes in identifying sharp, significant downside price moves, and entering short on upside volatility during these moves. A fixed stop loss and profit target are implemented for short positions - no trailing stop is used.
As a trading system, Razor IQ exits all TP orders using a limit order, with all stop losses exited as stop market orders.
What Classifies As a Tradable Dip?
For Razor IQ, tradable price dips are not manually set but are instead learned by the system. What qualifies as an exploitable price dip in one market might not hold the same significance in another. Razor IQ continuously analyzes historical and current trends (if one exists), how far price has moved during the trend, the duration of the trend, the raw-dollar price move of price dips during trends, and more, to determine which future price retracements offer a smart chance to join in any current price trend.
The image above illustrates the Razor Line Long Entry point.
The green line represents the Long Retracement Entry Point.
The blue upper line represents the first profit target for the trade.
The blue lower line represents the trailing stop loss start point for the long position.
The position is entered once price closes below the green line.
The green Razor Lazor long entry point will only appear during uptrends.
The image above shows a long position being entered after the Long Razor Lazor was closed beneath.
Green arrows indicate that the strategy entered a long position at the highlighted price level.
Blue arrows indicate that the strategy exited a position, whether at TP1, the initial stop loss, or at the trailing stop.
Blue lines above the entry price indicate the TP1 level for the current long trade. Blue lines below the current price indicate the initial stop loss price.
If price reaches TP1, a stop loss will be immediately placed at breakeven, and the in-built trailing stop will determine the future exit price.
A blue line (similar to the blue line shown for TP1) will trail price and correspond to the trailing stop price of the trade.
If the trailing stop is above the breakeven stop loss, then the trailing stop will be hit before the breakeven stop loss, which means the remainder of the trade will be exited at a profit.
If the breakeven stop loss is above the trailing stop, then the breakeven stop loss will be hit first. In this case, the remainder of the position will be exited at breakeven.
The image above shows the trailing stop price, represented by a blue line, and the breakeven stop loss price, represented by a pink line, used for the long position!
You can also hover over the trade labels to get more information about the trade—such as the entry price and exit price.
The image above exemplifies Razor IQ's output when a downtrend is active.
When a downtrend is active, Razor IQ will switch to "short mode". In short mode, Razor IQ will display a neon red line. This neon red line indicates the Razor Lazor short entry point. When price closes above the red Razor Lazor line a short position is entered.
The image above shows Razor IQ during an active short position.
The image above shows Razor IQ after completing a short trade.
Red arrows indicate that the strategy entered a short position at the highlighted price level.
Blue arrows indicate that the strategy exited a position, whether at the profit target or the fixed stop loss.
Blue lines indicate the profit target level for the current trade when below price. and blue lines above the current price indicate the stop loss level for the short trade.
Short traders do not utilize a trailing stop - only a fixed profit target and fixed stop loss are used.
You can also hover over the trade labels to get more information about the trade—such as the entry price and exit price.
Minimum Profit Target And Stop Loss
The Minimum ATR Profit Target and Minimum ATR Stop Loss setting control the minimum allowed profit target and stop loss distance. On most timeframes users won’t have to alter these settings; however, on very-low timeframes such as the 1-minute chart, users can increase these values so gross profits exceed commission.
After changing either setting, Razor IQ will retrain on historical data - accounting for the newly defined minimum profit target or stop loss.
AI Direction
The AI Direction setting controls the trade direction Razor IQ is allowed to take.
“Trade Longs” allows for long trades.
“Trade Shorts” allows for short trades.
Verifying Razor IQ’s Effectiveness
Razor IQ automatically tracks its performance and displays the profit factor for the long strategy and the short strategy it uses. This information can be found in the table located in the top-right corner of your chart showing.
This table shows the long strategy profit factor and the short strategy profit factor.
The image above shows the long strategy profit factor and the short strategy profit factor for Razor IQ.
A profit factor greater than 1 indicates a strategy profitably traded historical price data.
A profit factor less than 1 indicates a strategy unprofitably traded historical price data.
A profit factor equal to 1 indicates a strategy did not lose or gain money when trading historical price data.
Using Razor IQ
While Razor IQ is a full-fledged trading system with entries and exits - manual traders can certainly make use of its on chart indications and visualizations.
The hallmark feature of Razor IQ is its ability to signal an acceptable dip entry opportunity - for both uptrends and downtrends. Long entries are often signaled near the bottom of a retracement for an uptrend; short entries are often signaled near the top of a retracement for a downtrend.
Razor IQ will always operate on exact price levels; however, users can certainly take advantage of Razor IQ's trend identification mechanism and retracement identification mechanism to use as confluence with their personally crafted trading strategy.
Of course, every trend will reverse at some point, and a good dip buying/shorting strategy will often trade the reversal in expectation of the prior trend continuing (retracement). It's important not to aggressively filter retracement entries in hopes of avoiding an entry when a trend reversal finally occurs, as this will ultimately filter out good dip buying/shorting opportunities. This is a reality of any dip trading strategy - not just Razor IQ.
Of course, you can set alerts for all Razor IQ entry and exit signals, effectively following along its systematic conquest of price movement.
Big Candle Identifier with RSI Divergence and Advanced Stops1. Strategy Objective
The main goal of this strategy is to:
Identify significant price momentum (big candles).
Enter trades at opportune moments based on market signals (candlestick patterns and RSI divergence).
Limit initial risk through a fixed stop loss.
Maximize profits by using a trailing stop that activates only after the trade moves a specified distance in the profitable direction.
2. Components of the Strategy
A. Big Candle Identification
The strategy identifies big candles as indicators of strong momentum.
A big candle is defined as:
The body (absolute difference between close and open) of the current candle (body0) is larger than the bodies of the last five candles.
The candle is:
Bullish Big Candle: If close > open.
Bearish Big Candle: If open > close.
Purpose: Big candles signal potential continuation or reversal of trends, serving as the primary entry trigger.
B. RSI Divergence
Relative Strength Index (RSI): A momentum oscillator used to detect overbought/oversold conditions and divergence.
Fast RSI: A 5-period RSI, which is more sensitive to short-term price movements.
Slow RSI: A 14-period RSI, which smoothens fluctuations over a longer timeframe.
Divergence: The difference between the fast and slow RSIs.
Positive divergence (divergence > 0): Bullish momentum.
Negative divergence (divergence < 0): Bearish momentum.
Visualization: The divergence is plotted on the chart, helping traders confirm momentum shifts.
C. Stop Loss
Initial Stop Loss:
When entering a trade, an immediate stop loss of 200 points is applied.
This stop loss ensures the maximum risk is capped at a predefined level.
Implementation:
Long Trades: Stop loss is set below the entry price at low - 200 points.
Short Trades: Stop loss is set above the entry price at high + 200 points.
Purpose:
Prevents significant losses if the price moves against the trade immediately after entry.
D. Trailing Stop
The trailing stop is a dynamic risk management tool that adjusts with price movements to lock in profits. Here’s how it works:
Activation Condition:
The trailing stop only starts trailing when the trade moves 200 ticks (profit) in the right direction:
Long Position: close - entry_price >= 200 ticks.
Short Position: entry_price - close >= 200 ticks.
Trailing Logic:
Once activated, the trailing stop:
For Long Positions: Trails behind the price by 150 ticks (trail_stop = close - 150 ticks).
For Short Positions: Trails above the price by 150 ticks (trail_stop = close + 150 ticks).
Exit Condition:
The trade exits automatically if the price touches the trailing stop level.
Purpose:
Ensures profits are locked in as the trade progresses while still allowing room for price fluctuations.
E. Trade Entry Logic
Long Entry:
Triggered when a bullish big candle is identified.
Stop loss is set at low - 200 points.
Short Entry:
Triggered when a bearish big candle is identified.
Stop loss is set at high + 200 points.
F. Trade Exit Logic
Trailing Stop: Automatically exits the trade if the price touches the trailing stop level.
Fixed Stop Loss: Exits the trade if the price hits the predefined stop loss level.
G. 21 EMA
The strategy includes a 21-period Exponential Moving Average (EMA), which acts as a trend filter.
EMA helps visualize the overall market direction:
Price above EMA: Indicates an uptrend.
Price below EMA: Indicates a downtrend.
H. Visualization
Big Candle Identification:
The open and close prices of big candles are plotted for easy reference.
Trailing Stop:
Plotted on the chart to visualize its progression during the trade.
Green Line: Indicates the trailing stop for long positions.
Red Line: Indicates the trailing stop for short positions.
RSI Divergence:
Positive divergence is shown in green.
Negative divergence is shown in red.
3. Key Parameters
trail_start_ticks: The number of ticks required before the trailing stop activates (default: 200 ticks).
trail_distance_ticks: The distance between the trailing stop and price once the trailing stop starts (default: 150 ticks).
initial_stop_loss_points: The fixed stop loss in points applied at entry (default: 200 points).
tick_size: Automatically calculates the minimum tick size for the trading instrument.
4. Workflow of the Strategy
Step 1: Entry Signal
The strategy identifies a big candle (bullish or bearish).
If conditions are met, a trade is entered with a fixed stop loss.
Step 2: Initial Risk Management
The trade starts with an initial stop loss of 200 points.
Step 3: Trailing Stop Activation
If the trade moves 200 ticks in the profitable direction:
The trailing stop is activated and follows the price at a distance of 150 ticks.
Step 4: Exit the Trade
The trade is exited if:
The price hits the trailing stop.
The price hits the initial stop loss.
5. Advantages of the Strategy
Risk Management:
The fixed stop loss ensures that losses are capped.
The trailing stop locks in profits after the trade becomes profitable.
Momentum-Based Entries:
The strategy uses big candles as entry triggers, which often indicate strong price momentum.
Divergence Confirmation:
RSI divergence helps validate momentum and avoid false signals.
Dynamic Profit Protection:
The trailing stop adjusts dynamically, allowing the trade to capture larger moves while protecting gains.
6. Ideal Market Conditions
This strategy performs best in:
Trending Markets:
Big candles and momentum signals are more effective in capturing directional moves.
High Volatility:
Larger price swings improve the probability of reaching the trailing stop activation level (200 ticks).
TheHorsyAlgoPROThe Horsy algo is an automated strategy that uses any minute Higher timeframe range as reference and search for a purge of liquidity on the HTF high or low where buyside or sell side liquidity is, the algo only search this at specific desired times that can be configured according to the time you usually trade, the strategy is known as Turtle soup purge and reverse or lately as CRT.
Why is useful?
The purpose of this Algorithm is to help turtle soup traders to quickly identify when the market is likely to reverse the algo evaluates if the opportunity is worth it, base on risk reward and other desired filters. Also this strategy can help to quickly backtest the trader strategy it can be configured in different timeframes and adapt to the trader personality, they can easily see the results and statistics and notice if its profitable or not.
This algo is useful for intraday traders looking for a purge and reverse at a key times and at key HTF price levels this only looks the previous HTF highs and lows but is important to also monitor Order blocks, FVGs, gaps, or wicks to have the best results.
How it works and how it does it?
The Horsy algo simply Jumps from one type of liquidity to another one buyside to sell side or vice versa. In order for the algo to trigger an entry it has to meet these conditions
1. Take HTF liquidity, trade above a HTF high or below a HTF low in the selected time window
2. Make a change in the state of delivery with a close below the previous candle low for shorts and close above previous candle high for longs.
3. Allow for a reasonable risk reward, it will use the highest high for shorts and the lowest low for longs. The default take profit is the opposite side of the range.
4. Validate others user filters this include enter only trades aligned with the HTF bias, or trades aligned with the LTF bias or booth. The algo have the option to enter only premium and discount entries. And finally, an option to allow for different contract sizes depending of the maximum percent of the account we want to risk default is 1%. For this last option is important to check the initial balance and leverage are configured correctly, is disable by default because it requires more capital to perform well.
We can see the algo performing in the picture below with a short trade, notice there are some white lines, they are the high or the low of HTF candle that start generating inside candles in the HTF meaning a possible consolidation. The algo plots the HTF ranges in a shaded boxes as you can see below
The HTF bias as you can see in the picture is calculated based on the last close of the HTF meaning close above previous HTF high is bullish close below previous HTF low is bearish. This HTF bias level is also the last HTF mid-price or 50%. By default, this line is enabled.
The LTF bias is calculated based on the range created from the expansion outside the previous HTF range is also the mid-price. If the LTF close above previous HTF high is bullish and if the LTF close below previous HTF low is bearish. By default this LTF bias line is disable.
This strategy includes an original and personal developed code that uses dealing ranges to recognize if the market is expanding, retracing, reversing or consolidating. This allow the algo to exit the position when it detects a retracement or at the end of the expansion. This is the default exit type.
You can monitor the previous dealing ranges created in history with an option than can be enable, by default is disable, this ranges are created after price takes buyside and then sell side or vice versa. So this dealing ranges can be useful also to identify minor pools of liquidity and premium and discount in the lower timeframe.
The picture below is a long example, the exit in this case is just at the high of the range. The normal take profit is in a blue line for longs.
How to use it?
First select the desired HTF timeframe recommended is from 30min to 240min then you setup the chart on the lower timeframe you want to trade recommended is from 1min to 15min to enter. By default This strategy is designed to work for intraday during key times when price take stops and then moves quickly away from them. You can select as much as 6 different times or just one. After you select the desired time window where the algo will look for the purge and reverse, They are highlighted in the candles that change colors excluding the gray ones that indicates consolidation.
Then the Algo allow to performs several additional filters in the entries you can select if you want to trade only longs or shorts trades, you can select when to move the stop loss to Break even. In deviations of the risk or you can just select to remove risk when price hits the 50% of previous HTF range.
You can select the minimum desired risk reward of the trade before is allow to be taken. Once is configured correctly the algo should trigger signals with a triangle up or down plus the strategy entry.
At the beginning of the picture there are some blue lines in the HTF high low and close, this is to easily identify that the market is in the Asia session, the time can be configured by the user, these lines are normally gray.
On the right top of the screen you can see some statistics about the strategy how many trades it took, ARR is an approximated value of the accumulated total risk reward of all the trades when they get closed in the simulation.
Profit factor and percent profitable are also shown should be green it means that the strategy makes money over time. But apart from that is important to notice how it makes money it is stable over time? it is a roller coaster? that why I Include this other measurements MxcsTps is the maximum consecutives take profits and Mxcsls is the maximum consecutive stop losses it takes, the slash number after it is the consecutive Break evens. So this way you know what to expect and what is normal in the strategy.
The algo shows all the times the stop loss, take profit and break even level if enable in the colored red lines for short and blue lines for longs. You can also select how price will manage the profit or stoploss point meaning that you can choose to wait for the candle to close to invalidate your idea or to take profit. This is good to avoid liquidity sweeps but can also lead to mayor loses if the idea is wrong. The default setting is to close the trade when price takes the high or low where the stoploss is, the take profit is taken after a retracement to allow to profit on expansions. You can select also to exit on a reversal if you want to ride all the move. This last option has to be used with caution because sometimes price just retrace or reverse very fast decreasing the trade profit and overall strategy performance.
The algo have the option to use standard deviation from the normal risk if you prefer to prevent liquidity sweeps near the stop level this make wider stops but can lead to increased loses so it has to be used carefully.
Below is a picture that show the entry stop and take profit levels with an exit on a retracement activated.
Strategy Results
The backtesting results are obtained simulating a 2000usd account in the Micro Nasdaq using 1 contract per trade. Commission are set to 2usd per contract, slippage to 1tick. You can see in list of trades we are not risking more than 1 % percent of the account. The backtested range is from august to November 2024. This strategy doesn’t generate too much trades because of the time filters and conditions that has to be meet to take an entry but you can see the results of the last 4months with the available data that are around 32 trades.
The default settings for this strategy is HTF as 240min designed to work on a LTF 5min chart, the default purge times are 245-300, 745-800, 845-900, 1045-1100 and 1245-1300 UTC-4, the algo will look for shorts or longs, with a minimum risk reward of 2.0. With an additional filter of the HTFBias. The take profit is by default taken on the first retracement after hitting the target. The default settings are optimized to work on the Nasdaq or Spy, but can also perform well in other assets with the correct adjustments.
Remember entries constitute only a small component of a complete winning strategy. Other factors like risk management, position-sizing, trading frequency, trading fees, and many others must also be properly managed to achieve profitability. Past performance doesn’t guarantee future results. To really take advantage of this strategy you have to study turtle soup and the HTF key levels use this only as a confirmation that your overall idea will play out and use it to backtest your model.
Summary of features
·Adaptable strategy to different HTF timeframes from 1-1440min
· Select up to 6 different purge time windows UTC-4, UTC-5
· Choose desired Risk Reward per trade
· Easily see the HTF high low close and 50% key levels in the LTF
· Identify HTF consolidations that generate key major liquidity pools
· HTF/LTF bias filters to trade in favor of the big trend or in sync
· Shaded boxes that indicate if the market is bullish, bearish or consolidating
· See the current midpoint of the last expansion move
· Optimal trade entry filter to trade only in a discount or premium
· Customizable trade management take profit, stop, breakeven level
· Option to exit on a close, retracement or reversal after hitting the take profit level
· Option to exit on a close or reversal after hitting stop loss
· Configurable breakeven point with standard deviations or at 50% of the HTF
· Calculate different contract sizes depending of a percentage of the initial balance
· Standard deviations from normal risk can be used to prevent liquidity sweeps
· See dealing ranges history to check minor pools of liquidity and premium or discount
· Dashboard with instant statistics about the strategy current settings
Martingale with MACD+KDJ opening conditionsStrategy Overview:
This strategy is based on a Martingale trading approach, incorporating MACD and KDJ indicators. It features pyramiding, trailing stops, and dynamic profit-taking mechanisms, suitable for both long and short trades. The strategy increases position size progressively using a Multiplier, a key feature of Martingale systems.
Key Concepts:
Martingale Strategy: A trading system where positions are doubled or increased after a loss to recover previous losses with a single successful trade. In this script, the position size is incremented using a Multiplier for each addition.
Pyramiding: Allows adding to existing trades when market conditions are favorable, enhancing profitability during trends.
Settings:
Basic Inputs:
Initial Order: Defines the starting size of the position.
Default: 150.0
MACD Settings: Customize the fast, slow, and signal smoothing lengths.
Default: Fast Length: 9, Slow Length: 26, Signal Smoothing: 9
KDJ Settings: Customize the length and smoothing parameters for KDJ.
Default: Length: 14, Smooth K: 3, Smooth D: 3
Max Additions: Sets the number of additional positions (pyramiding).
Default: 5 (Min: 1, Max: 10)
Position Sizing: Percent to add to positions on favorable conditions.
Default: 1.0%
Martingale Multiplier:
Add Multiplier: This value controls the scaling of additional positions according to the Martingale principle. After each loss, a new position is added, and its size is increased by the Multiplier factor. For example, with a multiplier of 2, each new addition will be twice as large as the previous one, accelerating recovery if the price moves favorably.
Default: 1.0 (no multiplication)
Can be adjusted up to 10x to aggressively increase position size after losses.
Trade Execution:
Long Trades:
Entry Condition: A long position is opened when the MACD line crosses over the signal line, and the KDJ’s %K crosses above %D.
Additions (Martingale): After the initial long position, new positions are added if the price drops by the defined percentage, and each new addition is increased using the Multiplier. This continues up to the set Max Additions.
Short Trades:
Entry Condition: A short position is opened when the MACD line crosses under the signal line, and the KDJ’s %K crosses below %D.
Additions (Martingale): After the initial short position, new positions are added if the price rises by the defined percentage, and each new addition is increased using the Multiplier.
Exit Conditions:
Take Profit: Exits are triggered when the price reaches the take-profit threshold.
Stop Loss: If the price moves unfavorably, the position will be closed at the set stop-loss level.
Trailing Stop: Adjusts dynamically as the price moves in favor of the trade to lock in profits.
On-Chart Visuals:
Long Signals: Blue triangles below the bars indicate long entries, and green triangles mark additional long positions.
Short Signals: Red triangles above the bars indicate short entries, and orange triangles mark additional short positions.
Information Table:
The strategy displays a table with key metrics:
Open Price: The entry price of the trade.
Average Price: The average price of the current position.
Additions: The number of additional positions taken.
Next Add Price: The price level for the next position.
Take Profit: The price at which profits will be taken.
Stop Loss: The stop-loss level to minimize risk.
Usage Instructions:
Adjust the parameters to your trading style using the input settings.
The Multiplier amplifies your position size after each addition, so use it cautiously, especially in volatile markets.
Monitor the signals and table on the chart for entry/exit decisions and trade management.
Mxwll OptAlgoIntroducing the Mxwll OptAlgo
Mxwll OptAlgo is a sophisticated algorithmic trading tool designed to identify potential long and short signals. It leverages an optimized combination of the M-Swift average, M-Smooth average, and M-RSI to fine-tune custom lengths and improve signal accuracy. The Mxwll OptAlgo provides long and short signals across various trading assets and timeframes. Additionally, it features optimized Take Profit (TP) and Stop Loss (SL) settings to help traders manage risk.
Key Features
Step-by-Step Complete Optimization: A systematic approach to optimize trading parameters.
Buy/Sell Signals: Clear indicators for long and short positions.
Easy to Use: User-friendly interface for seamless trading.
Predictive counter trend channels
Integrated trend following system and counter trend trading system
3-optimized strategies working cooperatively
Alerts and auto trading capabilities
How It Works
The Mxwll OptAlgo is comprised of three strategies:
Trend following using the OptAlgo
AI Reversal counter trend trading
Market crash shorting
Mxwll OptAlgo can be used for market analysis and trading similarly to any moving average.
The Mxwll OptAlgo MA is composed of two distinct moving averages to be used for trend following strategies.
M-Swift Average: The M-Swift Average accounts for volume and weights current price movement heavier than older price movement - allowing for improved responsiveness to current price movement. Volume is additionally weighted to the average to determine the significance of the price move and the resulting response of the M-Swift average. The M-Swift average consists of an HVWMA with OBV weighting. The HVWMA is used to create a moving average that adapts to volume, attempting to respond to significant price moves with high volume quicker and significant price moves with low volume slower - which might not be indicative of the start of a strong trend. To further reduce the M-Swift average’s responsiveness to weak volume price moves, the average is weighted with a normalized OBV. With this, the M-Swift moving average uses these two indicators to create a responsive moving average to significant price moves with high volume.
M-Smooth Average: The M-Smooth average consists of a McGinley average.
The McGinley Average is designed to address some of the limitations of traditional moving averages, such as the Simple Moving Average (SMA) or Exponential Moving Average (EMA), by reducing their lag and more accurately reflecting the market's true movements, especially during periods of volatility.
The McGinley Dynamic automatically adjusts its smoothing factor based on market speed. This means it responds more quickly to fast-moving markets and slows down during periods of consolidation, reducing the likelihood of false signals.
Unlike traditional moving averages that have a fixed period and can lag significantly behind fast-moving prices, the McGinley Dynamic adjusts dynamically, which helps to reduce lag and keeps the moving average closer to the price action.
The M-Smooth average uses bar low prices as a series during an uptrend - bar high prices as a series during a downtrend. A cross above the M-Smooth average indicates an uptrend, while a cross below the M-Smooth average indicates a downtrend. When this cross event occurs the M-Smooth average will “flip” from calculating on lows to highs, or highs to lows, contingent on the direction of the trend. The expectation is that a cross event of the M-Smooth average requires a substantial price move and, subsequent to this cross, price will continue to trend in the direction of the cross.
OptAlgo: The OptAlgo is simply the average of the M-Swift average and the M-smooth average.
By combining the M-Swift average and the M-Smooth average, the final output results in an average that slows during ranging markets and quickly adjusts to high volume breakouts and high volume reversals that initiate a trend. Due to the combination, the average will keep up quickly with a trend but remain at an appropriate distance from the current price - requiring a significant counter trend price move to change the direction of the OptAlgo average.
How does the OptAlgo follow trends?
The OptAlgo, comprising the two moving averages above, considers a cross event of the OptAlgo as a change in trend indication. The OptAlgo can be thought of as a moving average that significantly deviates from price. For price to cross the OptAlgo, a substantial price move must occur, and this event is treated as a "strong trend" or "new trend" indication.
M-RSI: The M-RSI is a fundamental component of the trend following strategy. Prior to a trend following “long” or “short” signal, the M-RSI must generate a signal in confluence with an OptAlgo cross event. When price crosses over the opt algo its color will change to green, indicating an uptrend. A buy signal will generate should the M-RSI provide a similar indication. The M-RSI portion of the trend following strategy is explained below. When price crosses under the opt algo its color will change to green, indicating a downtrend, and a sell signal becomes eligible. The foundational logic for using the Opt Algo as a trend following strategy is to treat crossovers/crossunders of the Opt Algo as strong trend indications, and trade them.
Steps to generate a trend following long signal:
1: M-RSI extends into oversold territory
2: Price crosses over the OptAlgo
Steps to generate a trend following short signal:
1: M-RSI extends into overbought territory
2: Price crosses under the OptAlgo
Our trend following strategy considers crossovers/crossunders at key market turning points as buy/sell opportunities. This strategy integrates the Mxwll RSI and Mxwll OptAlgo MA to determine entry points in anticipation of trend continuation.
The Mxwll RSI must move below/above the optimized OB/OS level prior to a cross event for a long/short signal to be considered. Entry points for this strategy are marked as "Long" or "Short".
At its core, the OptAlgo trend following strategy tries to enter a trend as close to the origin point as possible. As with any trend following strategy, price may not continue to move in the expected direction following entry, resulting in a losing trade.
AI Reversal Predictions
Our AI reversals strategy uses AI suggested turning points to capitalize on price reversions back towards the OptAlgo. These levels are considered by the AI on the selected days, and entry points at these levels are marked as "LLO" or "SLO".
How AI reversals work
Our AI reversals strategy attempts to trade price reversions back toward the Opt Algo.
These levels are calculated on specific days of the week, but can be traded any day. The internal algorithm determines which HTF highs/lows are most likely to function as tradable support/resistance levels. For instance, if Friday consists of heavy trading activity and high/low prices are tracked/recorded as causing significant support / resistance when tested in the future, the algorithm will consider support and resistance levels created on Friday as future tradable levels.
Additionally, if support/resistance levels created on Wednesday are recorded as weak or unpredictable when traded at in the future, the algorithm will not consider support/resistance levels generated on Thursday as tradable, and will not generate long or shit signals for these levels.
In the background, the AI reversals strategy is tracking success rates at multiple support and resistance levels. The best performers, if there are any, will be considered tradable. A “best performer” is calculated as the raw price move up to a threshold (i.e. 0.5%) that occurs subsequent to a test of the level.
Crash Short
The "Crash Short" strategy prioritizes short positions during retracements of a sell off. A simple yet effective strategy.
How Crash Short Works
The Crash Short strategy uses a customized momentum indicator (similar to ROC, MOM, etc.) to identify strong downside price moves. When our customized momentum indicator gives strong sell indications, the RSI is then referenced to identify an upside retracement. When the RSI exceeds a user-inputted level, a “Crash Short” signal is generated.
What is the customized momentum indicator?
The customized momentum indicator is the RoCR (Rate of Change Ratio). Instead of classic ROC, which is close - close , the RoCR divides the current close by a previous close. This formula creates a ratio that is more normalized than a simple price difference. This ratio is used to determine upside/downside momentum, with values greater than 1 indicating bullish momentum and values less than 1 indicating bearish momentum. The RoCR looks for deviating values to the downside (less than 1) to identify strong selling. From there, once the RSI crosses over an optimized level (such as 35), the indicator will print a sell signal titled "Crash Short".
Predictive Countertrend Channels
Our Predictive Countertrend Channel applies a two-stage recursive filter to smooth data using exponential decay and periodic adjustments for trend extraction. Our counter trend channels aren't directly used for signal processing; however, these channels provide useful visual cues for extended market moves.
Instructions for Optimization
Step 1: Optimize Mxwll OptAlgo
Begin by optimizing the M-Swift and M-Smooth averages for better signal accuracy.
This step simply finds better performing M-Swift and M-Smooth lookbacks. Again, if the strategy is unprofitable you will be notified and from there decide not to use the strategy.
Step 2: Optimize Mxwll RSI
Refine the Mxwll RSI settings to explore potential adjustments in smoothness and signal output. This step aims to evaluate whether these adjustments could improve the accuracy of the signals generated by Mxwll OptAlgo, while being mindful of any potential impacts.
Step 3: Optimize TP/SL
Consider adjusting the Take Profit and Stop Loss settings to potentially manage risk.
Step 4: Optimize Bars Between Trades
Set the number of bars between trades to regulate the frequency of trade executions. This adjustment may help in reducing the risk of overtrading and support a more disciplined trading strategy.
Step 5: Optimize Trade Flip
Adjust the trade flip parameters to potentially improve the management of transitions between long and short positions. This adjustment is intended to help achieve smoother trade executions, though outcomes may vary.
Step 6: Optimize RSI OB/OB Levels
Consider adjusting the overbought (OB) and oversold (OS) RSI levels to explore potential improvements in signal sensitivity. Careful calibration of these levels may help refine the accuracy of trend reversal signals, although results may depend on market conditions.
Finished!
From this point, consider setting alerts to make the most of the Mxwll Opt Algo's potential accuracy.
The effectiveness of the Opt Algo signal output can be evaluated using the "PF" table, which indicates the profit factor score for the strategy. A profit factor (PF) of less than or equal to 1 suggests that the strategy may not be profitable.
Disclaimer
No strategy works on any timeframe on any asset, so, if the Opt Algo underperforms for the asset/timeframe you're analyzing, the Opt Algo PF table lets you know it hasn't been generating accurate signals, in which case you can decide not to use it!
Optimization Disclaimer
Optimization can be tricky. It's helpful to test numerous strategies in aggregate to see if a strategy has potential. Despite this, optimization can cause overfitting. Overfitting occurs when a strategy is too closely fit to the data it's trading. Overfit backtests are deceptively phenomenal. While the historical performance looks great, the future expectancy of the strategy remains unpredictable - an overfit strategy will profit from periods of random price movement which, being random, are irreproducible and cannot be profited from other than their initial occurrence. When a strategy trades random price movement profitably, any and all profit earned can be reduced to chance. Keep this in mind when using the in-built optimization system. Optimization should be kept to a minimum, a tool to point you in the right direction, whether confirming potential or signifying a useless system.
SL ManagerSTOP LOSS MANAGER
Overview:
The "SL Manager" indicator is designed to assist traders in managing their stop loss (SL) and take profit (TP) levels for both long and short positions. This tool helps you visualize intermediate levels, enhancing your trading decisions by providing crucial information on the chart.
Usage:
This indicator is particularly useful for traders who want to manage their trades more effectively by visualizing potential adjustment points for their stop loss and take profit levels. It helps in making informed decisions to maximize profits and minimize risks by providing clear levels to take partial profits and adjust stop losses.
Features:
Position Input: Select between "long" and "short" positions.
Entry Price: Specify the entry price of your trade.
Take Profit: Define the price level at which you want to take profit.
Stop Loss: Set the stop loss price level to manage your risk.
Intermediate Levels:
For both long and short positions, the indicator calculates and plots the following intermediate levels:
50% Take Profit (TP 50%): Midway between the entry price and the take profit level, where you can take partial profits and move your SL up to the 25% mark.
75% Take Profit (TP 75%): Three-quarters of the way from the entry price to the take profit level, where you can take partial profits and move your SL to breakeven.
Stop Loss Move to 25% (SL Move to 25%): A level where the stop loss can be adjusted to lock in profits.
Visualization:
The indicator plots the calculated levels directly on the chart, provided the data for the current day is available. Different color codes and line styles distinguish between the various levels:
TP 50% and TP 75% are plotted in green.
SL Move to 25% is plotted in red .
Entry/Breakeven is plotted in blue.
Stock Rating [TrendX_]# OVERVIEW
This Stock Rating indicator provides a thorough evaluation of a company (NON-FINANCIAL ONLY) ranging from 0 to 5. The rating is the average of six core financial metrics: efficiency, profitability, liquidity, solvency, valuation, and technical ratings. Each metric encompasses several financial measurements to ensure a robust and holistic evaluation of the stock.
## EFFICIENCY METRICS
1. Asset-to-Liability Ratio : Measures a company's ability to cover its liabilities with its assets.
2. Equity-to-Liability Ratio : Indicates the proportion of equity used to finance the company relative to liabilities.
3. Net Margin : Shows the percentage of revenue that translates into profit.
4. Operating Expense : Reflects the costs required for normal business operations.
5. Operating Expense Ratio : Compares operating expenses to total revenue.
6. Operating Profit Ratio : Measures operating profit as a percentage of revenue.
7. PE to Industry Relative PE/PB : Compares the company's PE ratio to the industry average.
## PROFITABILITY METRICS
1. ROA : Indicates how efficiently a company uses its assets to generate profit.
2. ROE : Measures profitability relative to shareholders' equity.
3. EBITDA : Reflects a company's operational profitability.
4. Free Cash Flow Margin : Shows the percentage of revenue that remains as free cash flow.
5. Revenue Growth : Measures the percentage increase in revenue over a period.
6. Gross Margin : Reflects the percentage of revenue exceeding the cost of goods sold.
7. Net Margin : Percentage of revenue that is net profit.
8. Operating Margin : Measures the percentage of revenue that is operating profit.
## LIQUIDITY METRICS
1. Current Ratio : Indicates the ability to cover short-term obligations with short-term assets.
2. Interest Coverage Ratio : Measures the ability to pay interest on outstanding debt.
3. Debt-to-EBITDA : Compares total debt to EBITDA.
4. Debt-to-Equity Ratio : Indicates the relative proportion of debt and equity financing.
## SOLVENCY METRICS
1. Altman Z-score : Predicts bankruptcy risk
2. Beneish M-score : Detects earnings manipulation.
3. Fulmer H-factor : Predicts business failure risk.
## VALUATION METRICS
1. Industry Relative PE/PB Comparison : Compares the company's PE and PB ratios to industry averages.
2. Momentum of PE, PB, and EV/EBITDA Multiples : Tracks the trends of PE, PB, and EV/EBITDA ratios over time.
## TECHNICAL METRICS
1. Relative Strength Index (RSI) : Measures the speed and change of price movements.
2. Supertrend : Trend-following indicator that identifies market trends.
3. Moving Average Golden-Cross : Occurs when a short-term MA crosses above mid-term and long-term MA which are determined by half-PI increment in smoothing period.
4. On-Balance Volume Golden-Cross : Measures cumulative buying and selling pressure.
Luxmi AI Ultimate 1 Min Option ScalperThe Luxmi AI Ultimate 1 Min Option Scalper is a specialized trading indicator designed for use in options trading. This tool is particularly focused on providing actionable signals to option buyers within a one-minute timeframe, making it highly suitable for scalping—a trading strategy aimed at profiting from small price changes. Below is an elaboration on how this indicator functions and its significance in trading decisions:
### Key Features of Luxmi AI Ultimate 1 Min Option Scalper
1. **Enter and Don't Signals:**
- **Enter Signals:** These signals indicate the optimal moments to enter a trade, suggesting when to buy an option. They are typically based on sophisticated algorithms that analyze price movements, volume, volatility, and other relevant market data.
- **Don't Signals:** These signals advise traders to refrain from entering a trade. This could be due to market conditions that are not conducive to profitable trading, such as high volatility, low liquidity, or unclear directional trends.
2. **Directional Trading Strategy:**
- The Luxmi AI Ultimate 1 Min Option Scalper focuses on directional trading, which involves making trades based on the expected direction of the market. For option buyers, this means taking positions that profit from upward (call options) or downward (put options) movements in the price of the underlying asset.
3. **Scalping Approach:**
- Scalping is a short-term trading strategy that involves making numerous trades over the course of a trading session, aiming to capitalize on small price changes. The one-minute timeframe is particularly suited for scalping, as it allows traders to quickly enter and exit positions to capture minimal but frequent profits.
### Functionality and Benefits
1. **Real-Time Analysis:**
- The indicator provides real-time analysis and signals, ensuring that traders receive timely information to make quick trading decisions. This is crucial in the fast-paced environment of scalping, where delays can significantly impact profitability.
2. **Automated Decision-Making Support:**
- By automating the signal generation process, the Luxmi AI Ultimate 1 Min Option Scalper helps reduce the cognitive load on traders. This automation can lead to more consistent trading performance, as it mitigates the impact of emotional and psychological factors that often influence human decision-making.
3. **Market Adaptability:**
- The indicator is designed to adapt to changing market conditions, adjusting its signals based on the latest data. This adaptability enhances its effectiveness in various market environments, whether trending, ranging, or highly volatile.
4. **Risk Management:**
- Incorporating "Don't" signals as part of the strategy helps traders avoid entering trades in unfavorable conditions, thereby managing risk more effectively. This feature is particularly valuable in preventing losses and preserving capital.
5. **Educational Value:**
- For less experienced traders, using the Luxmi AI Ultimate 1 Min Option Scalper can provide a learning experience. By observing the signals and their outcomes, traders can develop a better understanding of market dynamics and refine their trading strategies.
### Practical Application
- **Setup:** Traders integrate the Luxmi AI Ultimate 1 Min Option Scalper into their trading platforms. This setup typically involves installing the indicator and configuring it to monitor the specific options and market data relevant to the trader's strategy.
- **Monitoring:** During trading hours, traders monitor the signals provided by the indicator. They prepare to act quickly on "Enter" signals and heed "Don't" signals to avoid unnecessary risks.
- **Execution:** When an "Enter" signal is generated, traders execute the recommended trade, buying the corresponding option. They then manage their positions closely, ready to exit based on their predetermined profit targets or stop-loss levels.
In summary, the Luxmi AI Ultimate 1 Min Option Scalper is a powerful tool for option buyers, providing critical buy and hold signals in a highly time-sensitive manner. Its primary benefits include enhancing decision-making speed, improving trading consistency, and managing risk, all of which are essential for successful scalping in options trading.
Advanced Trend Strategy [BITsPIP]The BITsPIP team is super excited to share our latest trading gem with you all. We're all about diving deep and ensuring our strategies can stand the test of time. So, we invite you to join us in exploring the awesome potential of this new strategy and really put it through its pace with some deep backtesting. This isn't just another strategy; it boasts a profit factor hovering around 1.5 across over 1000 trades, which is quite an achievement. Consider integrating it with your trading bots to further enhance your trading efficiency and profit generation. Curious? Ask for trial access or drop by our website for more details.
I. Deep Backtesting
We're all in on transparency and solid results, which is why we didn't stop at 100... or even 500 trades. We went over 1000, making sure this strategy is as robust as they come. No flimsy forecasts or sneaky repainting here. Just good, solid strategy that's ready for the real deal. Curious about the details? Check out our detailed backtesting screenshot for the BINANCE:BTCUSDT in a 5-minute timeframe. It's all about giving you the clear picture.
#No Overfitting
#No Repainting
Backtesting Screenshot
II. Algorithmic Trading
Thinking of trading as a manual game? Think again! Manual trading is a bit like rolling the dice - fun, but kind of risky if you're aiming for consistent wins. Instead, why not lean into the future with algorithmic trading? It's all about trusting the market's rhythm over the long term. By integrating your strategy with a trading bot, you can enjoy peace of mind, rest easy, and keep those emotional trades at bay.
III) Applications
Dive into the Advanced Trend Strategy, your versatile tool for navigating the market's waters. This strategy shines in under an hour timeframes, offering adaptability across stocks, commodities, forex, and cryptocurrencies. Initially fine-tuned for low-volatility cryptos like BINANCE:BTCUSDT , its default settings are a solid starting point.
But here's where your expertise comes into play. Each market beats to its own drum, necessitating nuanced adjustments to stop loss and take profit settings. This customization is key to maximizing the strategy's effectiveness in your chosen arena.
IV) Strategy's Logic
The Advanced Trend Strategy is a powerhouse, blending the precision of Hull Suite, RSI, and our unique trend detector technique. At its core, it’s designed for savvy risk management, aiming to lock in substantial profits while steering clear of minor market ripples. It utilizes stop-loss and take-profit thresholds to form a profit channel, providing a safety net for each trade. This is a trend-following strategy at heart, where these profit channels play a critical role in maximizing returns by securing positions within these "warranty channels."
1. Trend-Following
The market's complexity, influenced by countless factors, makes small movements seem almost chaotic. Yet, the principle of #Trend-Following shines in less volatile markets in long term. The strategy excels by pinpointing the ideal moments to enter the market, coupled with refined risk management to secure profits. It’s tailored for you, the individual trader, enabling you to ride the waves of market trends upwards or downwards.
2. Risk Management
A key facet of the strategy is its emphasis on pragmatic risk management. Traders are empowered to establish practical stop-loss and take-profit levels, tailoring these crucial parameters to the specific market they are engaging in. This customization is instrumental in optimizing long-term profitability, ensuring that the strategy adapts fluidly to the unique characteristics and volatility patterns of different trading environments.
V) Strategy's Input Settings and Default Values
1. Alerts
The strategy comes equipped with a flexible alert system designed to keep you informed and ready to act. Within the settings, you’ll find options to configure order/exit and comment/alert messages to your preference. This feature is particularly useful for staying on top of the strategy’s activities without constant manual oversight.
2. Hull Suite
i. Hull Suite Length: Designed for capturing long-term trends, the Hull Suite Length is configured at 1000. Functioning comparably to moving averages, the Hull Suite features upper and lower bands. Currently, it is set to 1000.
ii. Length Multiplier: It's advisable to maintain a minimal value for the Length Multiplier, prioritizing the optimization of the Hull Suite Length. Presently, it is set to 1.
3. RSI Indicator
i. The RSI is a widely recognized tool in trading. Adapt the oversold and overbought thresholds to better match the specifics of your market for optimal results.
4. StopLoss and TakeProfit
i. StopLoss and TakeProfit Settings: Two distinct approaches are available. Semi-Automatic StopLoss/TakeProfit Setting and Manual StopLoss/TakeProfit Setting. The Semi-Automatic mode streamlines the process by allowing you to input values for a 5-minute timeframe, subsequently auto-adjusting these values across various timeframes, both lower and higher. Conversely, the Manual mode offers full control, enabling you to meticulously define TakeProfit values for each individual timeframe.
ii. TakeProfit Threshold # and TakeProfit Value #: Imagine this mechanism as an ascending staircase. Each step represents a range, with the lower boundary (TakeProfit Value) designed to close the trade upon being reached, and the upper boundary (TakeProfit Threshold) upon being hit, propelling the trade to the next level, and forming a new range. This stair-stepping approach enhances risk management and increases profitability. The pre-set configurations are tailored for $BINANCE:BTCUSDT. It's advisable to devote time to tailoring these settings to your specific market, aiming to achieve optimal results based on backtesting.
iii. StopLoss Value: In line with its name, this value marks the limit of loss you're prepared to accept should the market trend go against your expectations. It's crucial to note that once your asset reaches the first TakeProfit range, the initial StopLoss value becomes obsolete, supplanted by the first TakeProfit Value. The default StopLoss value is pegged at 1.6(%), a figure worth considering in your trading strategy.
VI) Entry Conditions
The primary signal for entry is generated by our custom trend detection mechanism and hull suite values (ascending/descending). This is supported by additional indicators acting as confirmation.
VII) Exit Conditions
The strategy stipulates exit conditions primarily governed by stop loss and take profit parameters. On infrequent occasions, if the trend lacks confirmation post-entry, the strategy mandates an exit upon the issuance of a reverse signal (whether confirmed or unconfirmed) by the strategy itself.
BITsPIP
Strategy - Plus / Connectable [Azullian]Discover the advanced capabilities of Strategy Plus, an essential component of the connectable indicator system designed for fast-paced strategy testing, visualization, and building within TradingView. This enhanced version of our foundational connectable strategy indicator seamlessly integrates with all connectable indicators . By utilizing the TradingView input source as a signal connector , it facilitates the linking of indicators to form a cohesive strategy. Each connectable indicator within the system sends signal weight to the next node, culminating in a comprehensive strategy that incorporates advanced customization options, sophisticated signal interpretation, and elaborate backtest labeling. Strategy Plus stands out by offering improved position management and extensive alert messaging capabilities, ensuring effective strategy refinement and backend integration.
█ DISTINCTIVE FEATURES
The Connectable Strategy Plus enhances risk mitigation within the connectable system through its advanced features and capabilities:
• Refined Signal Input Management: Tailor and precisely connect up to two signal filters with enhanced input flexibility, gain control, and strategic direction settings.
• Strategic Position Investment Control: Optimize positioning with versatile investment bases, custom investment percentages, and direction-specific investments for effective risk management.
• Advanced Exit Stop Loss Configuration: Implement custom stop loss tactics with diverse base modes and trailing options for tailored risk management.
• Strategic Exit Take Profit Settings: Apply precision-driven take profit strategies with various calculation modes and dynamic trailing functionality.
• Calibrated Entry Position Allocation: Optimize investment distribution for entry positions, including DCA and BRO trades, for strategic market response.
• Refined Order Setting Customization: Ensure exchange compliance with adjustable order settings, enhancing backtest accuracy and strategy reliability.
• Comprehensive Condition Settings: Define precise conditions for strategy execution, including date range filtering and order/loss limitations.
• Intuitive Visualization: Enhance strategy clarity with customizable visual elements and trade visualization features.
• Advanced Alert Configurations: Stay informed with comprehensive and customizable alerts for effective backend integration.
• Backend Integration With JSON Format: Leverage elaborate and structured data in JSON format for advanced analytics, enhancing decision-making and strategy optimization outside TradingView.
Let's review the separate parts of this indicator.
█ STRATEGY INPUTS
We've provided 2 inputs for connecting a signal filter or indicators or chains (1→, 2→) which are all set to 'Close' by default.
An input has several controls:
• Enable disable: Toggle the entire input on or off
• Input: Connect indicators or signal filter here, choose indicators with a compatible : Signal connector.
• G - Gain: Increase or reduce the strength of the incoming signal by a factor.
• SM - Signal Mode: Choose a trading direction compatible with the settings in your signal filter
• XM - Exit Mode: Determine when to allow to exit your open trade
○ Always: Doesn't take the restrictions into account, this ignores all the settings chosen in ML or MP
○ Restricted: Use both ML and MP conditions
○ Loss: Use the ML condition only, for example: Position will be exited and the exit signal will be allowed only when the loss exceeds the ML parameter
○ Profit: Use the MP condition only for example: Exits will only be allowed when the profit of the position exceeds the condition of the MP parameter
█ POSITION INVESTMENT
Determine the percentage of your trading budget you would like to use in each position based on the strategy's profit or loss.
• LINVB - Loss Investment Base: Choose which base to use to determine the investment percentage when the strategy is in a loss.
○ Equity: Use the equity as the base for percentage calculation.
○ Initial capital: Use the initial capital as the base for percentage calculation.
• LINV% - Loss Investment Percentage: Set a percentage of the chosen investment base as the investment for a new position.
○ For example, when 10% in loss, and a initial capital of $100, and the investment base is set to equity with a percentage of 50%, your investment will be 50% of $90, $45.
• PINVB - Profit Investment Base: Choose which base to use to determine the investment percentage when the strategy is in profit.
○ Equity: Use the equity as the base for percentage calculation.
○ Initial capital: Use the initial capital as the base for percentage calculation.
• PINV% - Profit Investment Percentage: Set a percentage of the chosen investment base as the investment for a new position.
○ For example, when 10% in profit, and an initial capital of $100, and the investment base is set to equity with a percentage of 100%, your investment will be 100% of $110, $110.
• XINVB - Custom Profit Investment Base: Choose which base to use to determine the investment percentage when the strategy is above a custom profit threshold (XT).
○ Equity: Use the equity as the base for percentage calculation.
○ Initial capital: Use the initial capital as the base for percentage calculation.
• XINV% - Custom Profit Investment Percentage: Set a percentage of the chosen investment base as the investment for a new position.
○ For example, when 100% in profit, exceeding the XT threshold of 50%, and an initial capital of $100, and the investment base is set to equity with a percentage of 50%, your investment will be 50% of $200, $100.
• XT% - Custom Profit Threshold: Determine how much profit triggers these custom profit investment settings.
• ELIB% - Entry Long Investment Base: Following previous settings, you can further restrict the investment according to the long trading direction.
○ For instance, if the previous calculation resulted in $45 to be used as an investment, and you've set the ELIB% to 50%, your long position will use 50% of $45, which is $22.5.
• ESIB% - Entry Short Investment Base: Following previous settings, you can further restrict the investment according to the short trading direction.
○ For example, if the previous calculation resulted in $45 to be used as an investment, and you've set the ESIB% to 50%, your short position will use 50% of $45, which is $22.5.
• RISK% - Risk Percentage:
○ Determine how much of the calculated position investment is at risk when the stop-loss is hit.
- For example, 1% of $45 represents a maximum loss of $0.45.
○ Risk percentage works together with the stop loss and the max leverage.
• MXLVG - Maximum Leverage:
○ Investigate the trading rules for your trading pair and use the maximum allowed amount of leverage.
○ To determine the number of contracts to be bought or sold, considering the stop loss and the specified risk percentage, the maximum leverage available will constrain the amount of leverage utilized to ensure that the maximum risk threshold is not exceeded. For instance, suppose the stop loss is set at 1%, and the risk percentage is defined as 10%. Initially, the calculated leverage to be used would be 10. However, if there is a maximum leverage cap set at 5, it would constrain the calculated leverage of 10 to adhere to the maximum limit of 5.
█ EXIT STOP LOSS
Determine the Stop Loss price based on your selected configuration.
As the stop loss is an integral part of the ordered contracts calculation used in conjunction with the Risk and Max leverage, you'll always need to provide a stop loss price.
• SLLB - Stop Loss Long Base: Choose a stop loss mode for calculating stop loss prices in long positions.
○ Risk: Determines the price using the Risk parameter (RISK%) and maximum leverage (MXLVG). In this case, SLLB% will not have any impact.
○ Price Entry + Offset: Calculates the stop loss price based on a offset percentage (SLLB%) from the entry price of the position.
○ Source: Computes the stop loss price based on an external indicator defined in SLLSRC.
- If this results in an invalid price, the calculation will revert to using the price entry + offset.
○ Source + Offset: Determines the stop loss price based on a positive or negative offset percentage (SLLB%) from an external indicator defined in SLLSRC.
- If this results in an invalid price, the calculation will fall back to using the price entry + offset.
• SLLB% - Stop Loss Long Base Percentage: Define an offset percentage that will be applied in the price entry + offset and source + offset stop loss modes.
• SLLSRC - Stop Loss Long Source: Connect an external indicator as the source for stop loss (only those providing price values eg: bollinger bands, moving averages...).
• SLLT - Stop Loss Long Trailing:
○ Fixed: The initial stop loss will be kept and no trailing stop loss will be applied.
○ Trail Stop: Takes into account all settings defined in SLLB and SLLB% and recalculates them with each candle.
- If a better stop loss is computed, it replaces the existing stop loss. In this mode SLLT% will be disregarded.
○ Trail Stop till BE: Similar to trailing stop mode, but it stops trailing when the stop loss reaches the break-even point.
○ Trail Stop from BE: Similar to trailing stop mode, but it starts trailing when the stop loss reaches the break-even point.
○ Trail Price: Computes the trailing stop loss price based on an offset percentage (SLLT%) from the closing price of the current candle.
- If a better stop loss price is calculated, it will be set as the new stop loss price.
○ Trail Price till BE: Similar to the Trail Price mode, but it stops trailing when the stop loss reaches the break-even point.
○ Trail Price from BE: Similar to Trail Price mode, but it starts trailing when the stop loss reaches the break-even point.
○ Trail Incr: Adapts the trailing stop loss price based on the offset percentage (SLLT%).
- Each price change in favor of your position will incrementally adapt the trailing stop loss with SLLT%.
○ Trail Incr till BE: Similar to the Trail Incr mode, but it stops trailing when the stop loss reaches the break-even point.
• SLLT% - Stop Loss Long Trailing Percentage: This percentage serves as an offset or increment depending on your chosen trailing mode.
• SLSB - Stop Loss Short Base: Functions similarly to SLLB but for short positions.
• SLSB% - Stop Loss Short Base Percentage: Functions similarly to SLLB% but for short positions.
• SLSSRC - Stop Loss Short Source: Functions similarly to SLLSRC but for short positions.
• SLST - Stop Loss Short Trailing: Functions similarly to SLLT but for short positions.
• SLST% - Stop Loss Short Trailing Percentage: Functions similarly to SLLT% but for short positions.
█ EXIT TAKE PROFIT
Determine the Take Profit price based on your selected configuration.
• TPLB - Take Profit Long Base: Choose a take profit mode for calculating take profit prices in long positions.
○ Reward: Determines the take profit price using the Risk parameter (RISK%) and the calculated Stop Loss price and the set reward percentage (TPLB%).
- For example: Risk 1%, Calculated Stop loss price: $90, Entry price: $100, Reward (TPLB%): 2%, will result in a take profit price on $120.
○ Price Entry + Offset: Calculates the take profit price based on a offset percentage (TPLB%) from the entry price of the position.
- For example: Entry price: $100, Offset (TPLB%): 2%, will result in a take profit price on $102.
○ Source: Computes the take profit price based on an external input from another indicator defined in TPLSRC.
- If this results in an invalid price, the calculation will revert to using the price entry + offset.
○ Source + Offset: Determines the take profit price based on a positive or negative offset percentage (TPLB%) from an external indicator inpuy defined in TPLSRC.
- If this results in an invalid price, the calculation will fall back to using the price entry + offset.
• TPLB% - Take Profit Long Base Percentage: Define an offset percentage that will be applied in the price entry + offset and source + offset take profit modes.
• TPLSRC - Take Profit Long Source: Choose to connect an external indicator as the source for take profit (of course only those which provide price values eg: bollinger bands, moving averages... but not oscillators).
• TPLT - Take Profit Long Trailing:
○ Fixed: The initial take profit will be kept and no trailing take profit will be applied.
○ Trail Profit: Takes into account all settings defined in TPLB and TPLB% and recalculates them with each candle.
- If an applicable take profit is computed, it replaces the existing take profit. In this mode TPLT% will be disregarded.
○ Trail Profit till BE: Similar to trailing profit mode, but it stops trailing when the take profit reaches the break-even point.
○ Trail Profit from BE: Similar to trailing profit mode, but it starts trailing when the take profit reaches the break-even point.
○ Trail Price: Computes the trailing take profit price based on an offset percentage (TPLT%) from the closing price of the current candle.
- If an applicable take profit price is calculated, it will be set as the new take profit price.
○ Trail Price till BE: Similar to the Trail Price mode, but it stops trailing when the take profit reaches the break-even point.
○ Trail Price from BE: Similar to Trail Price mode, but it starts trailing when the take profit reaches the break-even point.
○ Trail Incr: Adapts the trailing take profit price based on the offset percentage (TPLT%). Each price change against your position will incrementally adapt the trailing take profit with TPLT%.
○ Trail Incr till BE: Similar to the Trail Incr mode, but it stops trailing when the take profit reaches the break-even point.
• TPLT% - Take Profit Long Trailing Percentage: This percentage serves as an offset or increment depending on your chosen trailing mode.
• TPSB - Take Profit Short Base: Functions similarly to TPLB but for short positions.
• TPSB% - Take Profit Short Base Percentage: Functions similarly to TPLB% but for short positions.
• TPSSRC - Take Profit Short Source: Functions similarly to TPLSRC but for short positions.
• TPST - Take Profit Short Trailing: Functions similarly to TPLT but for short positions.
• TPST% - Take Profit Short Trailing Percentage: Functions similarly to TPLT% but for short positions.
█ ENTRY INVESTMENT DISTRIBUTION
Based on your position investment calculation you can distribute the position investment accross the initial opening trade of the position (SIG%) or the follow up Dollar Cost Averaging (DCA%) or Break Out (BRO%) trades.
For example: SIG%: 10%, DCA%: 45%, BRO%: 45% and the calculated Position Investment is $100, then the initial trade will receive $10, DCA will receive $45, and BRO will receive $45 to work with. Disable BRO and or DCA by setting them to 0%. Keep in mind that the sum of SIG, BRO and DCA may not exceed 100%.
• SIG% - Initial order investment percentage based on the signal: The percentage of the position investment distributed over normal trades.
• DCA% - Dollar Cost Averaging investment percentage: The percentage of the position investment distributed to DCA trades.
• BRO% - Break Out investment percentage: The percentage of the position investment distributed to BRO trades.
█ ENTRY DCA
DCA (Dollar-Cost Averaging) is a risk mitigation strategy where the allocated DCA% budget from the Entry Investment Distribution is distributed among x levels (DCA#) based on calculated prices (DPLM) and order sizes (DOSM), when prices move against your position.
• DCA# - Maximum DCA levels: Set the maximum number of DCA levels.
• DPLM - DCA Price Level Mode: Choose a price level mode that determines at which prices the additional purchases are distributed:
○ Linear: Entry prices are evenly spaced at regular intervals.
○ QuadIn: Entry prices are front-loaded, with more at the beginning and fewer later.
○ QuadOut: Entry prices are back-loaded, with fewer at the beginning and more later.
○ QuadInOut: Entry prices start front-loaded, then become back-loaded.
○ CubicIn: Similar to QuadIn but with a smoother front-loaded distribution.
○ CubicOut: Similar to QuadOut but with a smoother back-loaded distribution.
○ ExpoIn: Entry prices are exponentially increasing, starting small and growing.
○ ExpoOut: Entry prices are exponentially decreasing, starting large and reducing.
○ ExpoInOut: Entry prices start exponentially increasing, then decrease exponentially.
• DOSM - DCA Order Size Mode: Choose a DCA budget distribution mode for order sizes:
○ Linear: Order sizes are evenly spaced at regular intervals.
○ QuadIn: Order sizes are front-loaded, with larger orders at the beginning and smaller ones later.
○ QuadOut: Order sizes are back-loaded, with smaller orders at the beginning and larger ones later.
○ QuadInOut: Order sizes start front-loaded and transition to back-loaded.
○ CubicIn: Similar to QuadIn but with a smoother front-loaded distribution of order sizes.
○ CubicOut: Similar to QuadOut but with a smoother back-loaded distribution of order sizes.
○ ExpoIn: Order sizes exponentially increase, starting small and growing.
○ ExpoOut: Order sizes exponentially decrease, starting large and reducing.
○ ExpoInOut: Order sizes start exponentially increasing, then decrease exponentially.
For a visual representation of the price or order size distribution modes, refer to online easing curves.
█ ENTRY BRO
BRO (Break Out) is a risk mitigation strategy where the allocated BRO% budget from the Entry Investment Distribution is distributed among x levels (BRO#) based on calculated prices (BPLM) and order sizes (BOSM), when prices move in favor of your position.
• BRO# - Maximum BRO levels: Set the maximum number of BRO levels.
• BPLM - BRO Price Level Mode: Choose a price level mode that determines at which prices the additional purchases are distributed:
○ Distribution easing modes work similar as the DCA easing modes.
• BOSM - BRO Order Size Mode: Choose a BRO budget distribution mode for order sizes:
○ Distribution easing modes work similar as the DCA easing modes.
█ ORDER SETTINGS
Fine-tune accuracy to match your exchange's trading constraints, enhancing backtest precision with these settings, default settings are least restrictive for crypto trading pairs.
• MINP - Mininmum Position Notional Value: Exchange-defined minimum notional value for positions:
○ Calculated based on your exchange's rules and is the minimum total value your position must hold to meet their requirements It is calculated by multiplying Quantity with price and leverage.
○ It helps ensure your trades align with your exchange's standards.
• MAXP - Maximum Position Notional Value: Exchange-defined maximum notional value for positions:
○ Similar to MINP, this value is calculated based on your exchange's rules and represents the maximum total value allowed for your position.
• MINQ - Mininmum Order Quantity: Least permissible order quantity based on exchange rules:
○ This is the smallest quantity of an asset that your exchange allows you to trade in a single order.
• MAXQ - Maximum Order Quantity: Highest permissible order quantity according to exchange rules:
○ Opposite of MINQ, this is the largest quantity of an asset you can trade in a single order as defined by your exchange.
• DECP - Decimals in Order Price: Allowed decimal places in order prices as per exchange specifications:
○ This value specifies the number of decimal places you can use when specifying the price of an order.
• DECQ - Decimals in Order Quantity: Permitted decimal places in order quantities according to exchange specifications:
○ Similar to DECP, this value indicates the number of decimal places you can use when specifying the quantity of an asset in an order.
█ STRATEGY CONDITIONS
Specify when the strategy is permitted to execute trades.
• DATE: Enable the Date Range filter to restrict entries to a specific date range.
○ START: Set a start date and hour to commence trading.
○ END: Set an end date and hour to conclude trading within the defined range.
• IDO - Maximum Intraday Orders: Limit the number of orders the strategy can place within a single trading day. Upon reaching this limit, the strategy temporarily halts further entries for the day.
• DL% - Maximum Intraday Loss%: Set a threshold for the maximum allowable intraday loss as a percentage of equity. When exceeded, the strategy temporarily suspends trading for the day.
• CLD - Maximum Consecutive Loss Days: Define the maximum number of consecutive days the strategy can incur losses. Upon reaching this limit, the strategy halts trading and avoids new entries.
• DD% - Maximum Drawdown: Specify the maximum permissible drawdown as a percentage of equity. If this limit is met, the strategy halts trading and refrains from placing additional entries.
• TP% - Total Profit %: Establish a target for the total profit percentage the strategy aims to achieve. Once this target is attained, the strategy halts trading and refrains from initiating new entries.
• TL% - Total Loss %: Define a limit for the total loss percentage relative to the initial capital. If this limit is exceeded, the strategy discontinues trading and refrains from placing further entries.
■ VISUALS
• LINE: Activate a colored dashed diagonal line to visually connect the entry and exit points of positions.
• SLTP: Enable visualization of stop loss, take profit, and break-even levels.
• PNL: Enable Break-Even and Close Lines along with a colored area in between to visualize profit and loss.
• ☼: Brightness % : Adjust the opacity of the plotted trading visuals.
• P - Profit Color : Choose the color for profit-related elements.
• L - Loss Color: Choose the color for loss-related elements.
• B - Breakeven Color : Select the color for break-even points.
• EL - Long Color: Specify the color for long positions.
• ES - Short Color: Specify the color for short positions.
• TRADE LABELING: For better analysis we've labeled all entries and exits conform with the type of order your strategy has executed, some examples:
○ EL-SIG0-124: Enter Long - Signal 0 - Position 124
○ EL-BRO1-130: Enter Long - BRO1 - Position 130
○ EL-BRO2-130: Enter Long - BRO2 - Position 130
○ ES-DCA1-140: Enter Short - DCA1 - Position 140
○ XS-DCA2-140: Exit Short - DCA2 - Position 140
○ XL-TP-150: Exit Long - Take Profit - Position 150
○ XS-TP-154: Exit Short - Take Profit - Position 154
○ XL-SL-160: Exit Long - Stop Loss - Position 160
○ XS-SL-164: Exit Short - Stop Loss - Position 164
○ XS-CND-165: Exit Short - Strategy Condition - Max intraday loss - Position 165
■ ALERT SETTINGS
For developers and those who wish to integrate TradingView alerts into their backend systems, we offer comprehensive labeling options.
• ALID: A unique identifier you've assigned to your alert.
• NAME: A structured name you've given to this strategy.
• LAYOUT: The layout key of the strategy, allowing direct chart linking from your backend.
• SYMBOL: The symbol on which the strategy operates.
○ ONCE: You can choose to include this information only in the first message to reduce message size and repetition in follow-up messages. (max. 4096 characters)
• TICK: The ticker for the strategy.
• CHART: The chart parameter containing the timeframe.period and timeframe.multiplier.
○ ONCE: You can choose to include this information only in the first message to reduce message size and repetition in follow-up messages. (max. 4096 characters)
• BAR: Includes bar information in the alert message.
• STRATEGY: Adds strategy inputs to the alert message.
○ ONCE: You can choose to include this information only in the first message to reduce message size and repetition in follow-up messages. (max. 4096 characters)
• PERFORMANCE: Incorporates strategy performance data into the alert message.
• SIGNAL: Appends received signal weights (EL, XL, ES, XS) to the alert message.
• ORDERS: Includes order details in the alert message.
• TAGS: Adds up to 6 tags and their corresponding values to the alert message.
○ ONCE: You can choose to include this information only in the first message to reduce message size and repetition in follow-up messages. (max. 4096 characters)
Of course we can't neglect letting you in on how this juicy JSON would look (without the // comments):
{
"id": 20726, // Message Id
"t": "2023-11-01T10:35:00Z", // Message Time
"al": { // Alert
"id": "639bfa9a-5f01-4031-8880-7ec01e972055", // Alert Id
"n": "TEST04", // Name
"l": "ABC123" // Layout
},
"sym": { // Symbol
"typ": "crypto", // Type
"r": "DOGEUSD.PM", // Root
"pre": "KRAKEN", // Prefix
"tc": "DOGEUSD.PM", // Ticker
"bc": "DOGE", // BaseCurrency
"c": "USD", // Currency
"d": "DOGEUSD Multi Collateral Perpetual Futures Contract", // Description
"mtc": 0.000001, // MinTick
"pv": 1, // PointValue
"ct": "PF_DOGEUSD" // CustomTicker
},
"ch": { // Chart
"pd": "1", // Period
"mul": 1 // Multiplier
},
"bar": { // Bar
"id": 20725, // Index
"t": "2023-11-01T10:33:00Z", // Time
"o": 0.066799, // Open
"h": 0.066799, // High
"l": 0.066799, // Low
"c": 0.066799, // Close
"v": 2924 // Vol
},
"strat": { // Strategy
"n": "Strategy - Plus / Connectable ", // Name
"sig": { // Signal
"c1e": true, // Connector1Enabled
"c1s": 500500.500501, // Connector1Source
"c1g": 1, // Connector1Gain
"c2e": false, // Connector2Enabled
"c2s": 0.067043, // Connector2Source
"c2g": 1, // Connector2Gain
"sm": "Swing (EL, ES)", // SignalMode
"xm": "Always", // ExitMode
"mlp": 0.01, // ExitModeMinPercLoss
"mpp": 0.01 // ExitModeMinPercProfit
},
"inv": { // Investment
"lb": "Equity", // LossBase
"lp": 50, // LossPerc
"pb": "Equity", // ProfitBase
"pp": 100, // ProfitPerc
"pcb": "Equity", // ProfitCustomBase
"pcp": 100, // ProfitCustomPerc
"pct": 10000, // ProfitCustomThreshold
"elp": 100, // LongPerc
"esp": 100, // ShortPerc
"rsk": 1, // MaxRisk
"lvg": 10 // MaxLeverage
},
"sl": { // StopLoss
"lb": "Price Entry + Offset", // LongBase
"lp": 0.2, // LongPerc
"lsrc": 0.067043, // LongSource
"lt": "Trail Stop", // LongTrailMode
"ltp": 0.2, // LongTrailPerc
"sb": "Price Entry + Offset", // ShortBase
"sp": 0.2, // ShortPerc
"ssrc": 0.067043, // ShortSource
"st": "Trail Stop", // ShortTrailMode
"stp": 0.2 // ShortTrailPerc
},
"tp": { // TakeProfit
"lb": "Price Entry + Offset", // LongBase
"lp": 1, // LongPerc
"lsrc": 0.067043, // LongSource
"lt": "Fixed", // LongTrailMode
"ltp": 1, // LongTrailPerc
"sb": "Price Entry + Offset", // ShortBase
"sp": 1, // ShortPerc
"ssrc": 0.067043, // ShortSource
"st": "Fixed", // ShortTrailMode
"stp": 1 // ShortTrailPerc
},
"dis": { // Distribution
"sigp": 10, // SignalPerc
"dcap": 0, // DCAPerc
"brop": 90 // BROPerc
},
"dca": { // DCA
"lvl": 3, // Levels
"pl": "linear", // ModePriceLevel
"os": "linear" // ModeOrderSize
},
"bro": { // BRO
"lvl": 3, // Levels
"pl": "expoIn", // ModePriceLevel
"os": "cubicOut" // ModeOrderSize
},
"ord": { // OrderSettings
"pmin": 5, // PNVMin
"pmax": 30000000, // PNVMax
"qmin": 0, // QtyMin
"qmax": 1000000000, // QtyMax
"dp": 6, // DecPrice
"dq": 6 // DecQty
},
"cnd": { // Conditions
"de": true, // DateRangeEnabled
"start": "2023-11-01T10:30:00Z", // StartTime
"end": "2024-12-31T23:30:00Z", // EndTime
"idoe": false, // MaxIntradayOrdersEnabled
"ido": 100, // MaxIntradayOrders
"dle": false, // MaxIntradayLossEnabled
"dl": 10, // MaxIntradayLossPerc
"clde": false, // MaxConsLossDaysEnabled
"cld": false, // MaxConsLossDays
"dde": false, // MaxDrawdownEnabled
"dd": 100, // MaxDrawdownPerc
"mpe": false, // MaxProfitEnabled
"mp": 200, // MaxProfitPerc
"mle": false, // MaxLossEnabled
"ml": -50 // MaxLossPerc
}
},
"perf": { // Performance
"ic": 1000, // InitialCapital
"eq": 1000, // Equity
"np": 0, // NetProfit
"op": 0, // OpenProfit
"ct": 0, // ClosedTrades
"ot": 0, // OpenTrades
"p": "FLAT", // MarketPosition
"ps": 0, // MarketPositionSize
"pp": "FLAT", // PreviousMarketPosition
"pps": 0 // PreviousMarketPositionSize
},
"sig": { // Signal
"el": 0, // EL
"xl": 0, // XL
"es": 6, // ES
"xs": 0 // XS
},
"ord": ,
"tag":
}
█ USAGE OF CONNECTABLE INDICATORS
■ Connectable chaining mechanism
Connectable indicators can be connected directly to the signal monitor, signal filter or strategy , or they can be daisy chained to each other while the last indicator in the chain connects to the signal monitor, signal filter or strategy. When using a signal filter you can chain the filter to the strategy input to make your chain complete.
• Direct chaining: Connect an indicator directly to the signal monitor, signal filter or strategy through the provided inputs (→).
• Daisy chaining: Connect indicators using the indicator input (→). The first in a daisy chain should have a flow (⌥) set to 'Indicator only'. Subsequent indicators use 'Both' to pass the previous weight. The final indicator connects to the signal monitor, signal filter, or strategy.
■ Set up this indicator with signals and a signal filter
The indicator provides visual cues based on signal conditions. However, its weight system is best utilized when paired with a connectable signal filter, monitor, or strategy .
Let's connect the Strategy - Plus to a connectable signal filter and connectable indicators :
1. Load all relevant indicators
• Load MA - Plus / Connectable
• Load Signal filter - Plus / Connectable
• Load Strategy - Plus / Connectable
2. Signal Filter Plus: Connect the MA - Plus to the Signal Filter
• Open the signal filter settings
• Choose one of the five input dropdowns (1→, 2→, 3→, 4→, 5→) and choose : MA - Plus / Connectable: Signal Connector
• Toggle the enable box before the connected input to enable the incoming signal
3. Signal Filter: Update the filter settings if needed
• The default filter mode for the trading direction is SWING, and is compatible with the default settings in the strategy and indicators.
4. Signal Filter: Update the weight threshold settings if needed
• All connectable indicators load by default with a score of 6 for each direction (EL, XL, ES, XS)
• By default, weight threshold is 'ABOVE' Threshold 1 (TH1) and Threshold 2 (TH2), both set at 5. This allows each occurrence to score, as the default score is 1 point above the threshold.
5. Strategy Plus: Connect one of the strategy plus inputs to the signal filters signal connector in the strategy settings
• Select a strategy input → and select the Signal filter - Plus: Signal connector
6. Strateg Plus: Enable filter compatible directions
• As the default setting of the filter is SWING, we should also set the SM (Strategy mode) to SWING.
7. Strateg Plus: You're ready to start optimizing
• Dive into all parameters and start optimizing your backtesting results.
█ BENEFITS
• Adaptable Modular Design: Arrange indicators in diverse structures via direct or daisy chaining, allowing tailored configurations to align with your analysis approach.
• Streamlined Backtesting: Simplify the iterative process of testing and adjusting combinations, facilitating a smoother exploration of potential setups.
• Intuitive Interface: Navigate TradingView with added ease. Integrate desired indicators, adjust settings, and establish alerts without delving into complex code.
• Signal Weight Precision: Leverage granular weight allocation among signals, offering a deeper layer of customization in strategy formulation.
• Advanced Signal Filtering: Define entry and exit conditions with more clarity, granting an added layer of strategy precision.
• Clear Visual Feedback: Distinct visual signals and cues enhance the readability of charts, promoting informed decision-making.
• Standardized Defaults: Indicators are equipped with universally recognized preset settings, ensuring consistency in initial setups across different types like momentum or volatility.
• Reliability: Our indicators are meticulously developed to prevent repainting. We strictly adhere to TradingView's coding conventions, ensuring our code is both performant and clean.
█ COMPATIBLE INDICATORS
Each indicator that incorporates our open-source 'azLibConnector' library and adheres to our conventions can be effortlessly integrated and used as detailed above.
For clarity and recognition within the TradingView platform, we append the suffix ' / Connectable' to every compatible indicator.
█ COMMON MISTAKES, CLARIFICATIONS AND TIPS
• Removing an indicator from a chain: Deleting a linked indicator and confirming the "remove study tree" alert will also remove all underlying indicators in the object tree. Before removing one, disconnect the adjacent indicators and move it to the object stack's bottom.
• Point systems: The azLibConnector provides 500 points for each direction (EL: Enter long, XL: Exit long, ES: Enter short, XS: Exit short) Remember this cap when devising a point structure.
• Flow misconfiguration: In daisy chains the first indicator should always have a flow (⌥) setting of 'indicator only' while other indicator should have a flow (⌥) setting of 'both'.
• Hide attributes: As connectable indicators send through quite some information you'll notice all the arguments are taking up some screenwidth and cause some visual clutter. You can disable arguments in Chart Settings / Status line.
• Layout and abbreviations: To maintain a consistent structure, we use abbreviations for each input. While this may initially seem complex, you'll quickly become familiar with them. Each abbreviation is also explained in the inline tooltips.
• Inputs: Connecting a connectable indicator directly to the strategy delivers the raw signal without a weight threshold, meaning every signal will trigger a trade.
• Layout and Abbreviations: Abbreviations streamline structure and input identification. Although they may seem complex initially, inline tooltips provide explanations, facilitating quick acclimatization.
• Total Trade Limit Error & Date-Time Filter: For deep backtesting, be mindful of the total trade limit. Utilize the date-time filter to narrow the test scope and avoid TradingView order limits.
• Calculation Timeout: Encounter a timeout? Adjust any parameter slightly to restart the calculation process.
• Message Character Limit: To stay within message character limits, consider turning off certain features or setting some to 'once'.
• Direct Indicator-to-Strategy Connection: When connecting an indicator directly to a strategy without thresholds, the strategy will default to long if weights are equally assigned.
• Pyramid Enabling with DCA and BRO: Activate pyramid orders, enabling you to optimize your strategy during Dollar Cost Averaging and Break Out trades.
• Recalculate & Fill Orders Properties: Adjusting these default settings in strategy properties tab may lead to unexpected behavior when backtesting. Approach with caution.
• Optimized for Crypto: Our indicators have been optimized and tested primarily on cryptocurrency markets. Results in other markets may vary.
• Inline Tooltips Documentation: Detailed documentation and guidance are available via inline tooltips for immediate assistance.
• Strategy Settings Margin: Set margin to 1 to be able to apply leverage.
• Styling Panel: Explore the styling panel to disable labels or any other visual cues to reduce clutter on busy charts, enhancing visual clarity and personalization.
• Applying Leverage on Spot Markets: Ensure that maximum leverage on spot markets is configured to 1.
• Unrealistic Order Sizes: Verify that the order book can accommodate your backtested order sizes.
█ A NOTE OF GRATITUDE
Through years of exploring TradingView and Pine Script, we've drawn immense inspiration from the community's knowledge and innovation. Thank you for being a constant source of motivation and insight.
█ RISK DISCLAIMER
Azullian's content, tools, scripts, articles, and educational offerings are presented purely for educational and informational uses. Please be aware that past performance should not be considered a predictor of future results.
Smart DCA StrategyINSPIRATION
While Dollar Cost Averaging (DCA) is a popular and stress-free investment approach, I noticed an opportunity for enhancement. Standard DCA involves buying consistently, regardless of market conditions, which can sometimes mean missing out on optimal investment opportunities. This led me to develop the Smart DCA Strategy – a 'set and forget' method like traditional DCA, but with an intelligent twist to boost its effectiveness.
The goal was to build something more profitable than a standard DCA strategy so it was equally important that this indicator could backtest its own results in an A/B test manner against the regular DCA strategy.
WHY IS IT SMART?
The key to this strategy is its dynamic approach: buying aggressively when the market shows signs of being oversold, and sitting on the sidelines when it's not. This approach aims to optimize entry points, enhancing the potential for better returns while maintaining the simplicity and low stress of DCA.
WHAT THIS STRATEGY IS, AND IS NOT
This is an investment style strategy. It is designed to improve upon the common standard DCA investment strategy. It is therefore NOT a day trading strategy. Feel free to experiment with various timeframes, but it was designed to be used on a daily timeframe and that's how I recommend it to be used.
You may also go months without any buy signals during bull markets, but remember that is exactly the point of the strategy - to keep your buying power on the sidelines until the markets have significantly pulled back. You need to be patient and trust in the historical backtesting you have performed.
HOW IT WORKS
The Smart DCA Strategy leverages a creative approach to using Moving Averages to identify the most opportune moments to buy. A trigger occurs when a daily candle, in its entirety including the high wick, closes below the threshold line or box plotted on the chart. The indicator is designed to facilitate both backtesting and live trading.
HOW TO USE
Settings:
The input parameters for tuning have been intentionally simplified in an effort to prevent users falling into the overfitting trap.
The main control is the Buying strictness scale setting. Setting this to a lower value will provide more buying days (less strict) while higher values mean less buying days (more strict). In my testing I've found level 9 to provide good all round results.
Validation days is a setting to prevent triggering entries until the asset has spent a given number of days (candles) in the overbought state. Increasing this makes entries stricter. I've found 0 to give the best results across most assets.
In the backtest settings you can also configure how much to buy for each day an entry triggers. Blind buy size is the amount you would buy every day in a standard DCA strategy. Smart buy size is the amount you would buy each day a Smart DCA entry is triggered.
You can also experiment with backtesting your strategy over different historical datasets by using the Start date and End date settings. The results table will not calculate for any trades outside what you've set in the date range settings.
Backtesting:
When backtesting you should use the results table on the top right to tune and optimise the results of your strategy. As with all backtests, be careful to avoid overfitting the parameters. It's better to have a setup which works well across many currencies and historical periods than a setup which is excellent on one dataset but bad on most others. This gives a much higher probability that it will be effective when you move to live trading.
The results table provides a clear visual representation as to which strategy, standard or smart, is more profitable for the given dataset. You will notice the columns are dynamically coloured red and green. Their colour changes based on which strategy is more profitable in the A/B style backtest - green wins, red loses. The key metrics to focus on are GOA (Gain on Account) and Avg Cost .
Live Trading:
After you've finished backtesting you can proceed with configuring your alerts for live trading.
But first, you need to estimate the amount you should buy on each Smart DCA entry. We can use the Total invested row in the results table to calculate this. Assuming we're looking to trade on BITSTAMP:BTCUSD
Decide how much USD you would spend each day to buy BTC if you were using a standard DCA strategy. Lets say that is $5 per day
Enter that USD amount in the Blind buy size settings box
Check the Blind Buy column in the results table. If we set the backtest date range to the last 10 years, we would expect the amount spent on blind buys over 10 years to be $18,250 given $5 each day
Next we need to tweak the value of the Smart buy size parameter in setting to get it as close as we can to the Total Invested amount for Blind Buy
By following this approach it means we will invest roughly the same amount into our Smart DCA strategy as we would have into a standard DCA strategy over any given time period.
After you have calculated the Smart buy size , you can go ahead and set up alerts on Smart DCA buy triggers.
BOT AUTOMATION
In an effort to maintain the 'set and forget' stress-free benefits of a standard DCA strategy, I have set my personal Smart DCA Strategy up to be automated. The bot runs on AWS and I have a fully functional project for the bot on my GitHub account. Just reach out if you would like me to point you towards it. You can also hook this into any other 3rd party trade automation system of your choice using the pre-configured alerts within the indicator.
PLANNED FUTURE DEVELOPMENTS
Currently this is purely an accumulation strategy. It does not have any sell signals right now but I have ideas on how I will build upon it to incorporate an algorithm for selling. The strategy should gradually offload profits in bull markets which generates more USD which gives more buying power to rinse and repeat the same process in the next cycle only with a bigger starting capital. Watch this space!
MARKETS
Crypto:
This strategy has been specifically built to work on the crypto markets. It has been developed, backtested and tuned against crypto markets and I personally only run it on crypto markets to accumulate more of the coins I believe in for the long term. In the section below I will provide some backtest results from some of the top crypto assets.
Stocks:
I've found it is generally more profitable than a standard DCA strategy on the majority of stocks, however the results proved to be a lot more impressive on crypto. This is mainly due to the volatility and cycles found in crypto markets. The strategy makes its profits from capitalising on pullbacks in price. Good stocks on the other hand tend to move up and to the right with less significant pullbacks, therefore giving this strategy less opportunity to flourish.
Forex:
As this is an accumulation style investment strategy, I do not recommend that you use it to trade Forex.
STRATEGY IN ACTION
Here you see the indicator running on the BITSTAMP:BTCUSD pair. You can read the indicator as follows:
Vertical green bands on historical candles represents where buy signals triggered in the past
Table on the top right represents the results of the A/B backtest against a standard DCA strategy
Green Smart Buy column shows that Smart DCA was more profitable than standard DCA on this backtest. That is shown by the percentage GOA (Gain on Account) and the Avg Cost
Smart Buy Zone label marks the threshold which the entire candle must be below to trigger a buy signal (line can be changed to a box under plotting settings)
Green color of Smart Buy Zone label represents that the open candle is still valid for a buy signal. A signal will only be generated if the candle closes while this label is still green
Below is the same BITSTAMP:BTCUSD chart a couple of days later. Notice how the threshold has been broken and the Smart Buy Zone label has turned from green to red. No buy signal can be triggered for this day - even if the candle retraced and closed below the threshold before daily candle close.
Notice how the green vertical bands tend to be present after significant pullbacks in price. This is the reason the strategy works! Below is the same BITSTAMP:BTCUSD chart, but this time zoomed out to present a clearer picture of the times it would invest vs times it would sit out of the market. You will notice it invests heavily in bear markets and significant pullbacks, and does not buy anything during bull markets.
Finally, to visually demonstrate the indicator on an asset other than BTC, here is an example on CRYPTO:ETHUSD . In this case the current daily high has not touched the threshold so it is still possible for this to be a valid buy trigger on daily candle close. The vertical green band will not print until the buy trigger is confirmed.
BACKTEST RESULTS
Now for some backtest results to demonstrate the improved performance over a standard DCA strategy using all non-stablecoin assets in the top 30 cryptos by marketcap.
I've used the TradingView ticker (exchange name denoted as CRYPTO in the symbol search) for every symbol tested with the exception of BTCUSD because there was some dodgy data at the beginning of the TradingView BTCUSD chart which overinflated the effectiveness of the Smart DCA strategy on that ticker. For BTCUSD I've used the BITSTAMP exchange data. The symbol links below will take you to the correct chart and exchange used for the test.
I'm using the GOA (Gain on Account) values to present how each strategy performed.
The value on the left side is the standard DCA result and the right is the Smart DCA result.
✅ means Smart DCA strategy outperformed the standard DCA strategy
❌ means standard DCA strategy outperformed the Smart DCA strategy
To avoid overfitting, and to prove that this strategy does not suffer from overfitting, I've used the exact same input parameters for every symbol tested below. The settings used in these backtests are:
Buying strictness scale: 9
Validation days: 0
You can absolutely tweak the values per symbol to further improve the results of each, however I think using identical settings on every pair tested demonstrates a higher likelihood that the results will be similar in the live markets.
I'm presenting results for two time periods:
First price data available for trading pair -> closing candle on Friday 26th Jan 2024 (ALL TIME)
Opening candle on Sunday 1st Jan 2023 -> closing candle on Friday 26th Jan 2024 (JAN 2023 -> JAN 2024)
ALL TIME:
BITSTAMP:BTCUSD 80,884% / 133,582% ✅
CRYPTO:ETHUSD 17,231% / 36,146% ✅
CRYPTO:BNBUSD 5,314% / 2,702% ❌
CRYPTO:SOLUSD 1,745% / 1,171% ❌
CRYPTO:XRPUSD 2,585% / 4,544% ✅
CRYPTO:ADAUSD 338% / 353% ✅
CRYPTO:AVAXUSD 130% / 160% ✅
CRYPTO:DOGEUSD 13,690% / 16,432% ✅
CRYPTO:TRXUSD 414% / 466% ✅
CRYPTO:DOTUSD -16% / -7% ✅
CRYPTO:LINKUSD 1,161% / 2,164% ✅
CRYPTO:TONUSD 25% / 47% ✅
CRYPTO:MATICUSD 1,769% / 1,587% ❌
CRYPTO:ICPUSD 70% / 50% ❌
CRYPTO:SHIBUSD -20% / -19% ✅
CRYPTO:LTCUSD 486% / 718% ✅
CRYPTO:BCHUSD -4% / 3% ✅
CRYPTO:LEOUSD 102% / 151% ✅
CRYPTO:ATOMUSD 46% / 91% ✅
CRYPTO:UNIUSD -16% / 1% ✅
CRYPTO:ETCUSD 283% / 414% ✅
CRYPTO:OKBUSD 1,286% / 1,935% ✅
CRYPTO:XLMUSD 1,471% / 1,592% ✅
CRYPTO:INJUSD 830% / 1,035% ✅
CRYPTO:OPUSD 138% / 195% ✅
CRYPTO:NEARUSD 23% / 44% ✅
Backtest result analysis:
Assuming we have an initial investment amount of $10,000 spread evenly across each asset since the creation of each asset, it would have provided the following results.
Standard DCA Strategy results:
Average percent return: 4,998.65%
Profit: $499,865
Closing balance: $509,865
Smart DCA Strategy results:
Average percent return: 7,906.03%
Profit: $790,603
Closing balance: $800,603
JAN 2023 -> JAN 2024:
BITSTAMP:BTCUSD 47% / 66% ✅
CRYPTO:ETHUSD 26% / 33% ✅
CRYPTO:BNBUSD 15% / 17% ✅
CRYPTO:SOLUSD 272% / 394% ✅
CRYPTO:XRPUSD 7% / 12% ✅
CRYPTO:ADAUSD 43% / 59% ✅
CRYPTO:AVAXUSD 116% / 151% ✅
CRYPTO:DOGEUSD 8% / 14% ✅
CRYPTO:TRXUSD 48% / 65% ✅
CRYPTO:DOTUSD 24% / 35% ✅
CRYPTO:LINKUSD 83% / 124% ✅
CRYPTO:TONUSD 7% / 21% ✅
CRYPTO:MATICUSD -3% / 7% ✅
CRYPTO:ICPUSD 161% / 196% ✅
CRYPTO:SHIBUSD 1% / 8% ✅
CRYPTO:LTCUSD -15% / -7% ✅
CRYPTO:BCHUSD 47% / 68% ✅
CRYPTO:LEOUSD 9% / 11% ✅
CRYPTO:ATOMUSD 1% / 15% ✅
CRYPTO:UNIUSD 9% / 23% ✅
CRYPTO:ETCUSD 27% / 40% ✅
CRYPTO:OKBUSD 21% / 30% ✅
CRYPTO:XLMUSD 11% / 19% ✅
CRYPTO:INJUSD 477% / 446% ❌
CRYPTO:OPUSD 77% / 91% ✅
CRYPTO:NEARUSD 78% / 95% ✅
Backtest result analysis:
Assuming we have an initial investment amount of $10,000 spread evenly across each asset for the duration of 2023, it would have provided the following results.
Standard DCA Strategy results:
Average percent return: 61.42%
Profit: $6,142
Closing balance: $16,142
Smart DCA Strategy results:
Average percent return: 78.19%
Profit: $7,819
Closing balance: $17,819
GKD-C Zero-lag TEMA Crosses [Loxx]The Giga Kaleidoscope GKD-C Zero-lag TEMA Crosses is a confirmation module included in Loxx's "Giga Kaleidoscope Modularized Trading System."
█ GKD-C Zero-lag TEMA Crosses
Zero-lag TEMA Crosses is a spinoff of a the Zero-lag MA as described by David Stendahl in the April 2000 issue of the journal "Technical Analysis of Stocks and Commodities". This indicator uses TEMA calculation mode in order to make the lag lesser compared to the original Zero-lag MA, and that makes this version even faster than the Zero-lag DEMA too. This indicator is the difference between a Fast and Slow Zero-lag TEMA. This indicator is very useful for lower timeframe scalping.
What is the Zero-lag MA?
The Zero-lag MA (Zero-Lag Moving Average) is a technical indicator that was introduced in the April 2000 issue of the journal "Technical Analysis of Stocks and Commodities" by David Stendahl.
The Zero-lag MA is a type of moving average (MA) that is designed to reduce or eliminate the lag that is typically associated with traditional moving averages. Moving averages are a widely used technical analysis tool that helps traders to identify trends and potential trading opportunities. They work by calculating the average price of a security over a given period of time, and then plotting that average on a chart. The most commonly used moving averages are simple moving averages (SMAs) and exponential moving averages (EMAs).
The problem with traditional moving averages is that they can be slow to respond to changes in market conditions. This lag can cause traders to miss out on potential trading opportunities, or to enter or exit trades at the wrong time. The Zero-lag MA was developed as a solution to this problem.
The Zero-lag MA is calculated using a combination of two EMAs and a subtraction formula. The first step in calculating the Zero-lag MA is to calculate two exponential moving averages: a fast EMA and a slow EMA. The fast EMA is calculated over a shorter period of time than the slow EMA. The exact period lengths will depend on the trader's preferences and the security being analyzed.
Once the two EMAs have been calculated, the next step is to take the difference between them. This difference represents the current market trend, with a positive value indicating an uptrend and a negative value indicating a downtrend. However, this difference alone is not enough to create a useful indicator, as it can still suffer from lag.
To further reduce lag, the difference between the two EMAs is multiplied by a factor derived from a third, slower EMA. This slower EMA acts as a smoothing factor, helping to reduce noise and make the indicator more accurate. The exact period length of the slower EMA will depend on the trader's preferences and the security being analyzed.
The final step in calculating the Zero-lag MA is to add the result of the multiplication to the fast EMA. This produces a final value that represents the current market trend with reduced lag. The Zero-lag MA can be plotted on a chart like any other moving average, and can be used to identify trends, potential trading opportunities, and support and resistance levels.
Overall, the Zero-lag MA is designed to provide traders with a more accurate representation of current market conditions by reducing the lag time between price changes and the moving average. By doing so, it can help traders to make more informed trading decisions and improve their overall profitability.
What is the TEMA?
The triple exponential moving average (TEMA) is a technical analysis indicator that was developed to reduce the lag of traditional moving averages, such as the simple moving average (SMA) or the exponential moving average (EMA). The TEMA was first introduced by Patrick Mulloy in the January 1994 issue of the "Technical Analysis of Stocks and Commodities" magazine.
The TEMA is a type of moving average that is calculated by applying multiple exponential smoothing techniques to price data. Unlike traditional moving averages, which apply a single smoothing factor to price data, the TEMA applies three smoothing factors to produce a more responsive and accurate indicator.
To calculate the TEMA, the following steps are taken:
Calculate the single exponential moving average (SMA) of the price data over a given period.
Calculate the double exponential moving average (DEMA) of the SMA over the same period.
Calculate the triple exponential moving average (TEMA) of the DEMA over the same period.
The formula for calculating the TEMA is:
TEMA = 3 * EMA(SMA) - 3 * EMA(EMA(SMA)) + EMA(EMA(EMA(SMA)))
where EMA is the exponential moving average and SMA is the simple moving average.
The TEMA is designed to reduce the lag associated with traditional moving averages by applying multiple smoothing factors to the price data. This helps to filter out short-term price fluctuations and provide a smoother indicator of the underlying trend. The TEMA is also less susceptible to whipsaws, which occur when a security's price moves in one direction and then quickly reverses, causing false trading signals.
The TEMA can be used in a variety of ways in technical analysis. It can be used to identify trends, determine support and resistance levels, and generate trading signals. When the TEMA is rising, it is generally interpreted as a bullish signal, indicating that the price is trending higher. When the TEMA is falling, it is generally interpreted as a bearish signal, indicating that the price is trending lower.
In summary, the TEMA is a more responsive and accurate indicator than traditional moving averages, designed to reduce lag and provide a smoother representation of the underlying trend. It is a useful tool for technical analysts and traders looking to identify trends, support and resistance levels, and potential trading opportunities.
█ 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
Baseline: Hull Moving Average
Volatility/Volume: Hurst Exponent
Confirmation 1: Zero-lag TEMA Crosses 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 Baseline Backtest [Loxx]The Giga Kaleidoscope GKD-BT Baseline Backtest is a backtesting module included in Loxx's "Giga Kaleidoscope Modularized Trading System."
█ GKD-BT Baseline Backtest
The GKD-BT Baseline Backtest allows traders to backtest the Regular and Stepped baselines used in the GKD trading system. This module includes 65+ moving averages and 15+ types of volatility to choose from.
Additionally, this backtest module provides the option to test the GKD-B indicator with 1 to 3 take profits and 1 stop loss. The Trading backtest allows for the use of 1 to 3 take profits, while the Full backtest is limited to 1 take profit. The Trading backtest also offers the capability to apply a trailing take profit.
In terms of the percentage of trade removed at each take profit, this backtest module has the following hardcoded values:
Take profit 1: 50% of the trade is removed
Take profit 2: 25% of the trade is removed
Take profit 3: 25% of the trade is removed
Stop loss: 100% of the trade is removed
After each take profit is achieved, the stop loss level is adjusted. When take profit 1 is reached, the stop loss is moved to the entry point. Similarly, when take profit 2 is reached, the stop loss is shifted to take profit 1. The trailing take profit feature comes into play after take profit 2 or take profit 3, depending on the number of take profits selected in the settings. The trailing take profit is always activated on the final take profit when 2 or more take profits are chosen.
The backtest also offers the capability to restrict by a specific date range, allowing for simulated forward testing based on past data. Additionally, users have the option to display or hide a trading panel that provides relevant information about the backtest, statistics, and the current trade. It is also possible to activate alerts and toggle sections of the trading panel on or off. On the chart, historical take profit and stop loss levels are represented by horizontal lines overlaid for reference.
This backtest also includes an optional GKD-E Exit indicator that can be used to test early exits.
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.
To utilize this strategy, follow these steps:
1. (Required) Import the value "Input into NEW GKD-BT Backtest" from the GKD-B Baseline indicator into the GKD-BT Baseline Backtest field "Import GKD-B Baseline"
2. (Optional) Import the value "Input into NEW GKD-BT Backtest" from the GKD-E Exit indicator into the GKD-BT Baseline Backtest field "Import GKD-E Exit". You can toggle the Exit on or off using the "Activate GKD-E Exit" option.
Baselines that are compatible with this backtest module:
GKD-B Baseline
GKD-B Stepped Baseline
Volatility Types Included
17 types of volatility are included in this indicator
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
█ 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: GKD-BT Baseline Backtest as shown on the chart above
Baseline: Hull Moving Average as shown on the chart above
Volatility/Volume: Hurst Exponent
Confirmation 1: Sherif's HiLo
Confirmation 2: uf2018
Continuation: Coppock Curve
Exit: Fisher Transform as shown on the chart above
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-C Adaptive-Lookback Stochastic [Loxx]Giga Kaleidoscope GKD-C Adaptive-Lookback Stochastic is a Metamorphosis module included in Loxx's "Giga Kaleidoscope Modularized Trading System".
█ GKD-C Adaptive-Lookback Stochastic
The Adaptive-Lookback Stochastic uses a swing pivot lookback algorithm to adjust the periiod input bar-bar-bar thereby converting the regular Stochasitc oscillator into an adaptive Stochatic oscillator.
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.
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 Stochastic Oscillator?
The Stochastic Oscillator is a popular technical analysis indicator developed by George Lane in the 1950s. It is a momentum indicator that compares a security's closing price to its price range over a specified period. The main idea behind the Stochastic Oscillator is that, in an upward trending market, prices tend to close near their high, while in a downward trending market, prices tend to close near their low. The Stochastic Oscillator ranges from 0 to 100 and is primarily used to identify overbought and oversold conditions or potential trend reversals.
The Stochastic Oscillator is calculated using the following formula:
%K = ((C - L14) / (H14 - L14)) * 100
Where:
%K: The Stochastic Oscillator value.
C: The most recent closing price.
L14: The lowest price of the last 14 periods (or any other chosen period).
H14: The highest price of the last 14 periods (or any other chosen period).
Additionally, a moving average of %K, called %D, is calculated to provide a signal line:
%D = Simple Moving Average of %K over 'n' periods
The Stochastic Oscillator generates signals based on the following conditions:
1. Overbought and Oversold Levels: The Stochastic Oscillator typically uses 80 and 20 as overbought and oversold levels, respectively. When the oscillator is above 80, it is considered overbought, indicating that the market may be overvalued and a price decline is possible. When the oscillator is below 20, it is considered oversold, indicating that the market may be undervalued and a price rise is possible.
2. Bullish and Bearish Divergences: A bullish divergence occurs when the price makes a lower low, but the Stochastic Oscillator makes a higher low, suggesting a potential trend reversal to the upside. A bearish divergence occurs when the price makes a higher high, but the Stochastic Oscillator makes a lower high, suggesting a potential trend reversal to the downside.
3. Crosses: Buy signals are generated when %K crosses above %D, indicating upward momentum. Sell signals are generated when %K crosses below %D, indicating downward momentum.
The Stochastic Oscillator is commonly used in combination with other technical analysis tools to confirm signals and improve the accuracy of predictions.
When using the Stochastic Oscillator, it's important to consider a few best practices and additional insights:
1. Confirmation with other indicators: While the Stochastic Oscillator can provide valuable insights into potential trend reversals and overbought/oversold conditions, it is generally more effective when used in conjunction with other technical indicators, such as moving averages, RSI (Relative Strength Index), or MACD (Moving Average Convergence Divergence). This can help confirm signals and reduce the chances of false signals or whipsaws.
2. Timeframes: The Stochastic Oscillator can be applied to various timeframes, such as daily, weekly, or intraday charts. Adjusting the lookback period for the calculation can also alter the sensitivity of the indicator. A shorter lookback period will make the oscillator more sensitive to price movements, while a longer lookback period will make it less sensitive. Traders should choose a timeframe and lookback period that aligns with their trading strategy and risk tolerance.
3. Variations: There are two primary variations of the Stochastic Oscillator: Fast Stochastic and Slow Stochastic. The Fast Stochastic uses the original %K and %D calculations, while the Slow Stochastic smooths %K with an additional moving average and uses this smoothed %K as the new %D. The Slow Stochastic is generally considered to generate fewer false signals due to the additional smoothing.
4. Overbought and Oversold: It's important to remember that overbought and oversold conditions can persist for an extended period, especially during strong trends. This means that the Stochastic Oscillator alone should not be relied upon as a definitive buy or sell signal. Instead, traders should wait for additional confirmation from other indicators or price action before entering or exiting a trade.
The Stochastic Oscillator is a valuable momentum indicator that helps traders identify potential trend reversals and overbought/oversold conditions in the market. However, it is most effective when used in combination with other technical analysis tools and should be adapted to suit the specific needs of the individual trader's strategy and risk tolerance.
█ 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: Full GKD Backtest
Baseline: Hull Moving Average
Volatility/Volume: Hurst Exponent
Confirmation 1: Composite RSI
Confirmation 2: uf2018
Continuation: Vortex
Exit: Rex Oscillator
Metamorphosis: Fisher Transform, Universal Oscillator, Aroon, Vortex .. combined
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-BT Full Giga Kaleidoscope Backtest [Loxx]Giga Kaleidoscope GKD-BT Full Giga Kaleidoscope Backtest is a Backtesting module included in Loxx's "Giga Kaleidoscope Modularized Trading System".
█ GKD-BT Full Giga Kaleidoscope Backtest
The Full Giga Kaleidoscope Backtest module enables users to backtest Full GKD Long and Short signals, allowing the creation of a comprehensive NNFX trading system consisting of two confirmation indicators, a baseline, a measure of volatility/volume, and continuations.
This module offers two types of backtests: Trading and Full. The Trading backtest allows users to evaluate individual Long and Short trades one by one. On the other hand, the Full backtest enables the analysis of Longs or Shorts separately by toggling between them in the settings, providing insights into the results for each signal type. The Trading backtest simulates actual trading conditions, while the Full backtest evaluates all signals regardless of their Long or Short nature.
Additionally, the backtest module allows testing with 1 to 3 take profits and 1 stop loss. The Trading backtest supports 1 to 3 take profits, while the Full backtest is limited to 1 take profit. The Trading backtest also includes a trailing take profit feature.
Regarding the percentage of trade removed at each take profit, the backtest module incorporates the following predefined values:
Take profit 1: 50% of the trade is removed.
Take profit 2: 25% of the trade is removed.
Take profit 3: 25% of the trade is removed.
Stop loss: 100% of the trade is removed.
After achieving each take profit, the stop loss level is adjusted accordingly. When take profit 1 is reached, the stop loss is moved to the entry point. Similarly, when take profit 2 is reached, the stop loss is shifted to take profit 1. The trailing take profit feature comes into effect after take profit 2 or take profit 3, depending on the number of take profits selected in the settings. The trailing take profit is always activated on the final take profit when 2 or more take profits are chosen.
The backtest module also provides the option to restrict testing to a specific date range, allowing for simulated forward testing using past data. Additionally, users can choose to display or hide a trading panel that provides relevant information about the backtest, statistics, and the current trade. It is also possible to activate alerts and toggle sections of the trading panel on or off. Historical take profit and stop loss levels are displayed as overlaid horizontal lines on the chart for reference.
To utilize this strategy, follow these steps:
1. GKD-B Baseline Import: Import the value "Input into NEW GKD-BT Backtest" from the GKD-B Baseline module into the GKD-BT Full Giga Kaleidoscope Backtest module setting named "Import GKD-B Baseline."
2. GKD-V Volatility/Volume Import: Import the value "Input into NEW GKD-BT Backtest" from the GKD-V Volatility/Volume module into the GKD-BT Full Giga Kaleidoscope Backtest module setting named "Import GKD-V Volatility/Volume."
3. Adjust the "Confirmation 1 Type" in the GKD-C Confirmation Indicator to "GKD New."
4. GKD-C Confirmation 1 Import: Import the value "Input into NEW GKD-BT Backtest" from the GKD-C Confirmation 1 module into the GKD-BT Full Giga Kaleidoscope Backtest module setting named "Import GKD-C Confirmation 1."
5. Adjust the "Confirmation 2 Type" in the GKD-C Confirmation 2 Indicator to "GKD New."
6. GKD-C Confirmation 2 Import: Import the value "Input into NEW GKD-BT Backtest" from the GKD-C Confirmation 2 module into the GKD-BT Full Giga Kaleidoscope Backtest module setting named "Import GKD-C Confirmation 2."
7. Adjust the "Confirmation Type" in the GKD-C Continuation Indicator to "GKD New."
8. GKD-C Continuation Import: Import the value "Input into NEW GKD-BT Backtest" from the GKD-C Continuation module into the GKD-BT Full Giga Kaleidoscope Backtest module setting named "Import GKD-C Confirmation."
The GKD system utilizes volatility-based take profits and stop losses, where each take profit and stop loss is calculated as a multiple of volatility. Users have the flexibility to adjust the multiplier values in the settings to suit their preferences.
In a future update, the Full Giga Kaleidoscope Backtest module will include the option to incorporate a GKD-E Exit indicator, completing the full trading strategy.
█ Full Giga Kaleidoscope Backtest Entries
Within this module, there are ten distinct types of entries available, which are outlined below:
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
PullBack Entry
Continuation Entry
Each of these entry types can generate either long or short signals, resulting in a total of 20 signal variations. The user has the flexibility to enable or disable specific entry types and choose which qualifying rules within each entry type are applied to price to determine the final long or short signal.
The following section provides an overview of the various entry types and their corresponding qualifying rules:
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
█ Volatility Types Included
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
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.
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.
Static Percent
Static Percent allows the user to insert their own constant percent that will then be used to create take profits and stoploss
█ 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 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)
(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: Full Giga Kaleidoscope 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: Vorext as shown on the chart above
Confirmation 2: Coppock Curve as shown on the chart above
Continuation: Fisher Transform as shown on the chart above
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 system.
GKD-C Variety Filters w/ Dynamic Zones [Loxx]Giga Kaleidoscope GKD-C Variety Filters w/ Dynamic Zones 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: Variety Filters w/ Dynamic Zones 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
█ GKD-C Variety Filters w/ Dynamic Zones
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 levels. 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 Variety Filters w/ Dynamic Zones?
This indicator first smooths price with one of 65+ moving averages and then injects that output into the Dynamic Zones algorithm to create levels of significances. These levels are used to generate trading signals.
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.
Most Power V5 Most Power V5
The MOST indicator is an indicator used as a Moving Stoploss. The MOST indicator also generates a buy signal in case the prices rise, unlike the traditional indicators that allow you to make stop losses.In the MOST POWER V5 indicator, stoploss generates its signals using moving averages such as 'SMA', 'EMA', 'WMA', 'VWMA', 'HMA', 'SMMA', 'DEMA' rather than just the current price. In this way, it aims to prevent false signals that may be produced by excessive price movements during the day.
MOST POWER Indicator follows the average by preserving the stop loss distance in the movement of the moving average in the same direction.
The second variable that creates the MOST POWER curve is the stop loss distance that gives power to MOST with the moving average used and the shift rate that provides the stop signal. In this version, this distance is prepared with 2 separate inputs for both buy and sell.You can set these values as "percentage for long trend" and "percentage for short trend" in the indicator input properties.
You can also enter your Take Profit-1, Take Profit-2-, Take Profit-3- and Stop Loss levels in the indicator input properties, especially if you are interested in algo trading or to take advantage of the wonderful alarm setup features of tradingview and get the chance to get maximum profit. (If you enter Take Profit-1, Take Profit-2-, Take Profit-3- and Stop Loss levels as 0. Only buy and sell labels will appear on the indicator.)
Also, activate the amplitude filter option from the indicator input settings to get less signal and filter our inputs.
In addition, the entry price, take profit1, take profit2, take profit3 values for the last transaction opened to make things easier are located in the upper right corner of your graph as a table.
Important note: No indicator guarantees investment. That's why the tests you will do before real trades are very important in this indicator. I wish you all successful trades.
i will show how works with examples
Example 1
If you enter Take Profit-1, Take Profit-2-, Take Profit-3- and Stop Loss levels greater than 0
Example 2
If you enter Take Profit-1, Take Profit-2-, Take Profit-3- and Stop Loss levels as 0 (u will see only buy - sell labels)
Example 3
if select Amplitude Filter is on . you will see less signal
how works Amplitude Filter whats logic ?
if Amplitude Filter is active the signal from mostpower will be combined with Amplitude Filter.
for example, if the amplitude value is 2, the highest price and the lowest price of the last 2 bars are calculated. then this amplitude value is calculated for sma with its source high and low. Based on these values, the trend is determined in the amplitude of the price movement.
Important note: No indicator guarantees investment. That's why the tests you will do before real trades are very important in this indicator. I wish you all successful trades.
