Martingale + Grid DCA Strategy [YinYangAlgorithms]This Strategy focuses on strategically Martingaling when the price has dropped X% from your current Dollar Cost Average (DCA). When it does Martingale, it will create a Purchase Grid around this location to likewise attempt to get you a better DCA. Likewise following the Martingale strategy, it will sell when your Profit has hit your target of X%.
Martingale may be an effective way to lower your DCA. This is due to the fact that if your initial purchase; or in our case, initial Grid, all went through and the price kept going down afterwards, that you may purchase more to help lower your DCA even more. By doing so, you may bring your DCA down and effectively may make it easier and quicker to reach your target profit %.
Grid trading may be an effective way of reducing risk and lowering your DCA as you are spreading your purchases out over multiple different locations. Likewise we offer the ability to ‘Stack Grids’. What this means, is that if a single bar was to go through 20 grids, the purchase amount would be 20x what each grid is valued at. This may help get you a lower DCA as rather than creating 20 purchase orders at each grid location, we create a single purchase order at the lowest grid location, but for 20x the amount.
By combining both Martingale and Grid DCA techniques we attempt to lower your DCA strategically until you have reached your target profit %.
Before we start, we just want to make it known that first off, this Strategy features 8% Commission Fees, you may change this in the Settings to better reflect the Commission Fees of your exchange. On a similar note, due to Commission Fees being one of the number one profit killers in fast swing trade strategies, this strategy doesn’t focus on low trades, but the ideology of it may result in low amounts of trades. Please keep in mind this is not a bad thing. Since it has the ability to ‘Stack Grid Purchases’ it may purchase more for less and result in more profit, less commission fees, and likewise less # of trades.
Tutorial:
In this example above, we have it set so we Martingale twice, and we use 100 grids between the upper and lower level of each martingale; for a total of 200 Grids. This strategy will take total capital (initial capital + net profit) and divide it by the amount of grids. This will result in the $ amount purchased per grid. For instance, say you started with $10,000 and you’ve made $2000 from this Strategy so far, your total capital is $12,000. If you likewise are implementing 200 grids within your Strategy, this will result in $12,000 / 200 = $60 per grid. However, please note, that the further down the grid / martingale is, the more volume it is able to purchase for $60.
The white line within the Strategy represents your DCA. As the Strategy makes purchases, this will continue to get lower as will your Target Profit price (Blue Line). When the Close goes above your Target Profit price, the Strategy will close all open positions and claim the profit. This profit is then reinvested back into the Strategy, which may exponentially help the Strategy become more profitable the longer it runs for.
In the example above, we’ve zoomed in on the first example. In this we want to focus on how the Strategy got back into the trades shortly after it sold. Currently within the Settings we have it set so our entry is when the Lowest with a length of 3 is less than the previous Lowest with a length of 3. This is 100% customizable and there are multiple different entry options you can choose from and customize such as:
EMA 7 Crossover EMA 21
EMA 7 Crossunder EMA 21
RSI 14 Crossover RSI MA 14
RSI 14 Crossunder RSI MA 14
MFI 14 Crossover MFI MA 14
MFI 14 Crossunder MFI MA 14
Lowest of X Length < Previous Lowest of X Length
Highest of X Length > Previous Highest of X Length
All of these entry options may be tailored to be checked for on a different Time Frame than the one you are currently using the Strategy on. For instance, you may be running the Strategy on the 15 minute Time Frame yet decide you want the RSI to cross over the RSI MA on the 1 Day to be a valid entry location.
Please keep in mind, this Strategy focuses on DCA, this means you may not want the initial purchase to be the best location. You may want to buy when others think it is a good time to sell. This is because there may be strong bearish momentum which drives the price down drastically and potentially getting you a good DCA before it corrects back up.
We will continue to add more Entry options as time goes on, and if you have any in mind please don’t hesitate to let us know.
Now, back to the example above, if we refer to the Yellow circle, you may see that the Lowest of a length of 3 was less than its previous lowest, this triggered the martingales to create their grids. Only a few bars later, the price went into the first grid and went a little lower than its midpoint (Yellow line). This caused about 60% of the first grid to be purchased. Shortly after the price went even lower into this grid and caused the entire first martingale grid to be purchased. However, if you notice, the white line (your DCA) is lower than the midpoint of the first grid. This is due to the fact that we have ‘Stack Grid Purchases’ enabled. This allows the Strategy to purchase more when a single bar crosses through multiple grid locations; and effectively may lower your average more than if it simply executed a purchase order at each grid.
Still looking at the same location within our next example, if we simply increase the Martingale amount from 2 to 3 we can see something strange happens. What happened is our Target Profit price was reached, then our entry condition was met, which caused all of the martingale grids to be formed; however, the price continued to increase afterwards. This may not be a good thing, sure the price could correct back down to these grid locations, but what if it didn’t and it just kept increasing? This would result in this Strategy being stuck and unable to make any trades. For this reason we have implemented a Failsafe in the Settings called ‘Reset Grids if no purchase happens after X bars’.
We have enabled our Failsafe ‘Reset Grids if no purchase happens after X bars’ in this example above. By default it is set to 100 bars, but you can change this to whatever works best for you. If you set it to 0, this Failsafe will be disabled and act like the example prior where it is possible to be stuck with no trades executing.
This Failsafe may be an important way to ensure the Strategy is able to make purchases, however it may also mean the Grids increase in price when it is used, and if a massive correction were to occur afterwards, you may lose out on potential profit.
This Strategy was designed with WebHooks in mind. WebHooks allow you to send signals from the Strategy to your exchange. Simply set up a Custom TradingView Bot within the OKX exchange or 3Commas platform (which has your exchange API), enter the data required from the bot into the settings here, select your bot type in ‘Webhook Alert Type’, and then set up the alert. After that you’re good to go and this Strategy will fully automate all of its trades within your exchange for you. You need to format the Alert a certain way for it to work, which we will go over in the next example.
Add an alert for this Strategy and simply modify the alert message so all it says is:
{{strategy.order.alert_message}}
Likewise change from the Alert ‘Settings’ to Alert ‘Notifications’ at the top of the alert popup. Within the Notifications we will enable ‘Webhook URL’ and then we will pass the URL we are sending the Webhook to. In this example we’ve put OKX exchange Webhook URL, however if you are using 3Commas you’ll need to change this to theirs.
OKX Webhook URL:
www.okx.com
3Commas Webhook URL:
app.3commas.io
Make sure you click ‘Create’ to actually create this alert. After that you’re all set! There are many Tutorials videos you can watch if you are still a little confused as to how Webhook trading works.
Due to the nature of this Strategy and how it is designed to work, it has the ability to never sell unless there it will make profit. However, because of this it also may be stuck waiting in trades for quite a long period of time (usually a few months); especially when your Target Profit % is 15% like in the example above. However, this example above may be a good indication that it may maintain profitability for a long period of time; considering this ‘Deep Backtest’ is from 2017-8-17.
We will conclude the tutorial here. Hopefully you understand how this Strategy has the potential to make calculated and strategic DCA Grid purchases for you and then based on a traditional Martingale fashion, bulk sell at the desired Target Profit Percent.
Settings:
Purchase Settings:
Only Purchase if its lower than DCA: Generally speaking, we want to lower our Average, and therefore it makes sense to only buy when the close is lower than our current DCA and a Purchase Condition is met.
Purchase Condition: When creating the initial buy location you must remember, you want to Buy when others are Fearful and Sell when others are Greedy. Therefore, many of the Buy conditions involve times many would likewise Sell. This is one of the bonuses to using a Strategy like this as it will attempt to get you a good entry location at times people are selling.
Lower / Upper Change Length: This Lower / Upper Length is only used if the Purchase Condition is set to 'Lower Changed' or 'Upper Changed'. This is when the Lowest or Highest of this length changes. Lowest would become lower or Highest would become higher.
Purchase Resolution: Purchase Resolution is the Time Frame that the Purchase Condition is calculated on. For instance, you may only want to start a new Purchase Order when the RSI Crosses RSI MA on the 1 Day, but yet you run this Strategy on the 15 minutes.
Sell Settings:
Trailing Take Profit: Trailing Take Profit is where once your Target Profit Percent has been hit, this will trail up to attempt to claim even more profit.
Target Profit Percent: What is your Target Profit Percent? The Strategy will close all positions when the close price is greater than your DCA * this Target Profit Percent.
Grid Settings:
Stack Grid Purchases: If a close goes through multiple Buy Grids in one bar, should we amplify its purchase amount based on how many grids it went through?
Reset Grids if no purchase happens after X Bars: Set this to 0 if you never want to reset. This is very useful in case the price is very bullish and continues to increase after our Target Profit location is hit. What may happen is, Target Profit location is hit, then the Entry condition is met but the price just keeps increasing afterwards. We may not want to be sitting waiting for the price to drop, which may never happen. This is more of a failsafe if anything. You may set it very large, like 500+ if you only want to use it in extreme situations.
Grid % Less than Initial Purchase Price: How big should our Buy Grid be? For instance if we bought at 0.25 and this value is set to 20%, that means our Buy Grid spans from 0.2 - 0.25.
Grid Amounts: How many Grids should we create within our Buy location?
Martingale Settings:
Amount of Times 'Planned' to Martingale: The more Grids + the More Martingales = the less $ spent per grid, however the less risk. Remember it may be better to be right and take your time than risk too much and be stuck too long.
Martingale Percent: When the current price is this percent less than our DCA, lets create another Buy Grid so we can lower our average more. This will make our profit location less.
Webhook Alerts:
Webhook Alert Type: How should we format this Alert? 3Commas and OKX take their alerts differently, so please select the proper one or your webhooks won't work.
3Commas Webhook Alerts:
3Commas Bot ID: The 3Commas Bot ID is needed so we know which BOT ID we are sending this webhook too.
3Commas Email Token: The 3Commas Email Token is needed for your webhooks to work properly as it is linked to your account.
OKX Webhook Alerts:
OKX Signal Token: This Signal Token is attached to your OKX bot and will be used to access it within OKX.
If you have any questions, comments, ideas or concerns please don't hesitate to contact us.
HAPPY TRADING!
Cerca negli script per "profit"
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
Easy Trade Pro [Buy and Sell Strategy + Backtesting System]Hello Traders,
Easy Trade Pro is a comprehensive tool that combines multiple technical indicators into a single customizable one. This tool is the culmination of an extensive trading career, it is designed to help traders navigate the markets in any timeframe and financial asset, like Equities, Futures, Crypto, Forex and Commodities.
Before we deep dive into the comprehensive guide on what Easy Trade Pro is, let's kick off by showcasing the strategy used in this example. Please note, we have adopted an extremely conservative approach strictly following the Tradingview House Rules, which you can review here: www.tradingview.com
The backtest strategy parameters:
Currency pair: EUR USD
Timeframe: 15-min chart
Market: Spot, no leverage
Broker: FXCM
Trading range: 2022-09-01 07:30 — 2023-06-26 20:00
Backtesting range: 2022-08-31 23:00 — 2023-06-26 20:00
Initial Capital: $10,000
Buy Order Size: 20% of the capital, $2,000
Stop Loss: 0.50%
Sell orders: Four different take profits where we unload the position by 25% each time
Broker Fees: Commission set at 0.08$
Slippage: 10 ticks
Understanding FXCM Commissions and Setting Realistic Slippage for EUR/USD Spot Trading:
◉I would like to provide some clarity on the commission structure and slippage setting used in the study for trading the EUR/USD pair on the FXCM spot market. Based on the information available, FXCM charges a commission of $4.00 per standard lot (100,000) on both sides of the trade (meaning at open and close) for the EUR/USD pair. Since the study involve an order size of $2,000 USD, which is equivalent to 0.02 lots, the commission fee for one side of the trade (either buying or selling) would be calculated as $4.00 multiplied by 0.02, which is $0.08. This means that for each individual trade, whether it be a buy or sell, the commission fee would be $0.08.
◉As for slippage, it is crucial to account for the inherent uncertainty in the execution price due to market fluctuations. In the forex market, the EUR/USD pair is quoted with a precision of five decimal places, with the smallest price change being a "pipette" (0.00001). Given that slippage can vary based on market conditions, it is considered fair practice to use a slippage of around 10 ticks under normal market conditions for the EUR/USD pair. This allows for a more realistic representation of the execution price, especially in a liquid and fast-moving market such as forex.
More detailed information about FXCM fees structure in the link below:
docs.fxcorporate.com
Enter a Trade conditions:
For our buy order, we utilize a custom buy signal called 'Bullish Reversal'. A detailed explanation of this and other buy orders can be found later in the guide, specifically in section 1).
To enhance realism in our trading strategy, we have implemented a confirmation mechanism. When utilizing the strategy tester, you have the option to input a value to determine the number of confirmation candles to consider.
For example, if you set the input to 1, the system will check if the next candle following the signal meets the criteria for confirmation. If set to 2, the system will evaluate the second candle, and so on for higher values. The confirmation is determined by comparing the closing or opening price of the selected buy signal candle with the corresponding closing price of the confirmation candle.
In this case we choose as buy signal: 'Bullish Reversal' + 2 candle of confirmation
Exit a trade conditions:
On the sell side, we exit a trade in four different types of sell orders where we take profits. Inside '', you will encounter unique labels attributed to our custom sell signals. A detailed explanation of these sell orders can be found later in the guide, specifically in section 1). We used custom order called:
1TP 'Good Sell'
2TP 'Good Sell'
3TP 'Good Sell'
4TP 'Bearish Reversal' + 4 confirmation candles
Our confirmation logic, for sell signals, is applied only to 'Bearish Reversal' signal. The confirmation is determined by comparing the closing or opening price of the selected 'Bearish Reversal' candle with the corresponding closing price of the confirmation candle. In this case, we wait for the fourth candle from the 'Bearish Reversal' signal to confirm the sell trade.
Protect your capital:
This super-conservative study involves a clear low risk, with the use of $2,000, 20% of our capital. If the stop loss of 0.5% were triggered, we lose 10$, equating to 0.10% of $10,000 - thus affecting only 0.10% of our capital.
Super Conservative Approach & Results:
With 353 closed trades, we achieved a net profit of 2.03%, or $203.34$ relative to our initial $10,000 capital, and a win rate of 73.37%.
Less Conservative Approach & Results:
We could also consider increasing our risk to 0.5% of our capital per trade. We would maintain our stop loss at 0.50%, but we would need to use all our capital to enter the market. If the stop loss of 0.5% will be triggered, we would lose 50$, equating to 0.5% of $10,000.
In this scenario, our net profit would have increased to 10.15%, equivalent to $1015.
Please be aware:
While fully automated strategies can bring considerable advantages, they are not without their cons. For one, relying solely on an automated system may not take into account the potential confluence of other strategies or indicators, such as the significance of support and resistance zones. These elements often require a more nuanced, human understanding of the markets and cannot always be perfectly replicated by an algorithm.
Additionally, it's essential to remember that a significant percentage of traders are not consistently profitable. As such, prudent risk management, a conservative approach, and acceptance of a reasonable profit are crucial aspects of successful trading. While the allure of high returns can be tempting, the sustainability of your trading strategy should always take precedence. Achieving steady, reliable profits over time often outweighs the appeal of a risky, high-return strategy that could potentially lead to substantial losses.
So, while automation can be a powerful tool in your trading arsenal, it's also important to consider other strategies and factors. Always ensure you're managing your risk effectively and approaching trading with a realistic and informed perspective.
------------------------------------------------------------------------ Why Easy Trade Pro is Original? ----------------------------------------------------------------------------------
We developed Easy Trade Pro as a unique and comprehensive solution, and we decided to protect our code to preserve its originality. We invested significant time and effort into making it a realistic trading strategy simulator. The standout features that set Easy Trade Pro apart include:
☀ Versatile Stop Loss Mechanisms: Stop loss execution can be complex and often requires careful coding to work as intended. In most freely available open-source codes, stop losses are implemented using the Average True Range (ATR). ATR can be beneficial but has limitations:
☁ Lagging Indicator - Like most technical indicators, the ATR is a lagging indicator. This means it is based on past data, and so it may not accurately reflect future market volatility. If market conditions change rapidly, the ATR may not adjust quickly enough, potentially leading to suboptimal stop loss levels.
☁ No Directional Information - The ATR measures volatility, but it does not provide any indication of the direction of the trend. Therefore, it should not be used as a standalone tool for making trading decisions, but should be used in conjunction with other technical analysis tools that can provide directional cues.
☁ Inefficiency in Trending Markets - In strongly trending markets, ATR-based stops can sometimes be too far from the current price level. This could lead to larger losses if the price moves against your trade before hitting the stop loss. On the flip side, in less volatile, sideways markets, an ATR-based stop might be set too close to the entry point, leading to premature stop outs.
☁ Overoptimization Risk - If you're backtesting a trading strategy, there's a risk of overoptimizing your stop loss settings by fine-tuning them to past data. The best ATR multiplier that worked in the past might not necessarily work in the future, leading to potential performance issues.
☀ We countered these by implementing four different types of 'protect the trade' mechanisms:
✔ Fixed Percentage Stop Loss
✔ Trailing Stop Loss
✔ Stop Loss Moved to Entry Upon Reaching Certain Gain
✔ Stop Loss Moved to Entry Upon Reaching First Take Profit Order ("Custom Order").
☀ Dual Exit Strategy: We incorporated two distinct methods of exiting a trade. The first uses our custom signals, while the second triggers exit at a certain percentage of gain.
☀ Multiple Take Profit Orders: You have the flexibility to establish up to four different sell orders. This feature enables you to fractionate your exit strategy according to your needs. You can choose to trigger these fractions based on our custom signals or determine your own exit points by setting targeted gains at a fixed percentage.
☀ Confirmation Candle System: This feature enhances trade precision by requiring confirmation candles after a buy or sell signal. This confirmation, dependent on the next candle's closing price, helps reduce false signals and improves entry and exit points. While our confirmation system is applicable to all custom buy signals, it's solely dedicated for the bearish reversal when it comes to sell signals.
☀ Universal Compatibility: Easy Trade Pro's Strategy Tester works perfectly with any asset class. The code can handle different contract types, including the SPX contracts and fractional assets like Bitcoin. It's optimized to ensure proper execution of trades without rounding issues.
☀ Bullish and Bearish Reversal candles: Our method of detecting these pivotal candles combines conditions from buy and sell signals with pertinent divergences in Price, RSI, and Volume (OBV). The distinguishing factor, however, lies in recognizing significant shifts in market structure and liquidity grabs. To further enhance the credibility of our indicator, we've incorporated Bollinger Bands, serving as an additional layer in spotting potential trend reversals, particularly when aligned with long-wick candlesticks, engulfing patterns, and morning or evening star formations.
☀ Non-Repainting Indicator: Our indicator signals are designed not to repaint. Once a signal appears, it stays fixed, offering a reliable tool for your trading decisions.
================================================== EXTENSIVE TECHNICAL DESCRIPTION ====================================================
Easy Trade Pro is versatile, allowing you to analyze market trends across any financial asset. With its rigorous testing, our tool can be used confidently on any timeframe, from 1D to 1min, whether you prefer longer-term or shorter-term trades.
Although we recommend trading on timeframes between 1D and 1min, higher timeframes like 1W chart, can also provide broader insights.
Our study combines a variety of popular technical indicators, such as RSI, Stochastic RSI, MACD, DMI, Bollinger Bands as well as relevant EMAs. On the volume side OBV and MFI. Using a data-driven approach, “Easy Trade Pro” analyzes historical market trends to identify optimal ways to combine these indicators with significant divergences between price and oscillators. On top of that the code considers relevant changes in market structure and liquidity grabs, to generate reliable and accurate signals for potential buy and sell opportunities.
* ☎ --> Please not that MACD, BBs, and EMAs account for a minimal part of our script <--- ☎, If you're looking for a simpler tool, consider checking out our open-source indicator, 'RSI, SRSI, MACD, and DMI cross - Open source code'. You can find it here:
With our customizable system, traders will be able to identify:
1) Three types of buy signals🐂,💰,💎 and sell signals 🐻,🔨,💀
2) Bullish and bearish reversal candles with support and resistance lines
3) Bull and bear momentum signals
4) A function that utilizes Color bars to identify the strength of the trend
5) Three customizable moving averages
6) Alerts direct to your email or phone
7) Advanced and customizable settings menu
8) Our software also includes a backtesting system that that allows users to test their trading strategies on historical data, to check how they would have performed in real-world market conditions. This can help refine a trading strategy and make more informed decisions.
------------------------------------------------------------------------------ 1) BUY AND SELL SIGNALS ---------------------------------------------------------------------------------
Our buy and sell signals are generated using a custom combination of RSI, MFI, and Stochastic RSI levels, as well as relevant MACD and Stochastic RSI crosses. These indicators are carefully analyzed to identify potential trading opportunities and determine optimal entry and exit points for trades.
RSI (Relative strength index) measures the strength of a security's price action, while the SRSI (Stochastic Relative Strength Index) is a momentum oscillator that measures the current price relative to its high and low range over a set period. The Money Flow Index (MFI) is another momentum indicator that uses both price and volume data to measure buying and selling pressure. MACD (Moving Average Convergence Divergence) is a popular technical indicator used in financial markets to analyze price trends and momentum.
▶ With our system, you'll be able to identify three different levels of buy signals:
◉ The first level of buy signal is represented by a 🐂 emoji and is a "Good Buy". This signal indicates a possible buying opportunity. It indicates that could be a good opportunity to enter in a long trade. It's important to note that, the "Good Buy" signal can sometimes be supplemented with a green "Bull" text and a flag plotshape positioned beneath the signal. In these scenarios, we categorize this as a "Good Buy Bull" signal.
◉ The second level of buy signal is represented by a 💰 emoji and is a "Great Buy". This signal indicates a stronger buying opportunity than the "Good Buy" signal.
◉ The third and strongest buy signal is represented by a 💎 emoji and is an "Incredible Buy". This signal indicates a stronger buying opportunity than the "Good Buy" and "Great Buy" signals
▶ With our system, you'll be able to identify three different levels of sell signals:
◉ On the sell side, the first level is represented by a 🐻 emoji and is a "Good Sell". This signal indicates a possible selling opportunity. It indicates that could be a good opportunity to exit a trade or open a short position. It's important to note that, the "Good Sell" signal can occasionally be accompanied by a red "Bear" text and a flag plotshape positioned beneath the signal. In such instances, we refer to this as a "Good Sell Bear" signal.
◉ The second sell signal is represented by a 🔨 emoji and is a "Great Sell". This signal indicates a stronger selling opportunity than the "Good Sell" signal.
◉ The third and strongest sell signal is represented by a 💀 emoji and is an "Incredible Sell". This signal indicates a stronger selling opportunity than the "Good Sell" and "Great Sell" signals.
------------------------------------------2) "BULLISH AND BEARISH REVERSAL CANDLES PLUS SUPPORT AND RESISTANCE LINES" ------------------------------------------------
Bullish and bearish reversal candles are specific candles that have more probability to reverse the trend.
Our trading indicator is designed to identify bullish and bearish reversal candles. Our method of detecting these pivotal candles combines conditions from buy and sell signals with pertinent divergences in Price, RSI, and Volume (OBV). The distinguishing factor, however, lies in recognizing significant shifts in market structure and liquidity grabs. To further enhance the credibility of our indicator, we've incorporated Bollinger Bands, serving as an additional layer in spotting potential trend reversals, particularly when aligned with long-wick candlesticks, engulfing patterns, and morning or evening star formations.
These candles are represented by blue and orange colors respectively by default. Additionally, the indicator also uses lines that are drawn at either the opening or closing of candles to help identify pivot points of support or resistance. These candles, lines color or shape are customizable in the settings menu.
How can I benefit the most from bullish reversal candles? To make the most of bullish reversal candles, a powerful strategy is:
E.g, 1D chart - Wait for the next 1 or 2 candles to close above the support line linked to the bullish reversal candle. For lower timeframes, it is recommended to wait for 2 or 3 candles before making a trading decision. A good tip is also to look for other signals (confluence), like a buy signal. Traders should decide based on their risk tolerance.
Here below we can see an example of a bullish reversal candle in the BTC/USDT, 1D, chart. The system identify a bullish reversal candle (blue color), the next 2 candles are green and closed above the support blue line, in addition we have other bullish signals (confluence).
How can I benefit the most from bullish reversal lines? Bullish reversal lines can help traders to identify key level of support and maintain control of their position until a clear break below occurs.
In the example below we se how the price retrace to the support line:
After touching the price bounce up.
How can I benefit the most from bearish reversal candles? To make the most of bearish reversal candles, a powerful strategy is:
E.g, 1D chart - Wait for the next 1 or 2 candles to close below the resistance line linked to the bearish reversal candle. For lower timeframes, it is recommended to wait for 2 or 3 candles before making a trading decision. Traders should decide based on their risk tolerance.
Here below we can see an example of a bearish reversal candle in the ETH/USDT, 1D, chart. The system identify a bearish reversal candle (orange color), the next candle is red and closes below the resistance orange line. A good tip is also to look for other signals (confluence), like a sell signal.
How can I benefit the most from bearish reversal lines? Bearish reversal lines can help traders to identify key level of resistance and maintain control of their position until a clear break above occurs.
In the example below we se how the price bounce back to the resistance line and get rejected.
------------------------------------------------------------------------- 3) BULL AND BEAR MOMENTUM SIGNALS -----------------------------------------------------------------------
We analyzed factors such as buy or sell signals, long or short confirmation signals, DMI crossup or crossdown and breaks of market structure (BOS) or change of character (CHoCh) to determine the strength and direction of the trend. These study give us bull trend or bear trend signals that can help traders identify potential trading opportunities and make informed decisions.
These conditions are represented by a green word "BULL" and a flag shape below (bull momentum) and by a red word "BEAR" and a flag shape above (bear momentum) respectively by default. These plots shapes are customizable in the settings menu.
How can I benefit the most from bull momentum signals? To make the most of bull momentum signals, a powerful strategy is:
E.g, 1D chart - Look for confluence. If bull signal comes with a "Good Buy 🐂" in the same candle the signal is more strong. Another good combo is to look for a bullish reversal candle prior or after this signal, usually within a range of 1/2 candles. For lower timeframes, it is recommended to wait 2/3 candles before making a trading decision.
In the picture below we can see an example of a bull momentum signal in the US500, 1D, chart.
How can I benefit the most from bear momentum signals? To make the most of bear momentum signals, a powerful strategy is:
E.g, 1D chart - Look for confluence. If bear signal comes with a "Good Sell 🐻" in the same candle the signal is more strong. Another good combo is to look for a bearish reversal candle prior or after this signal, usually within a range of 1/2 candles. For lower timeframes, it is recommended to wait 2/3 candles before making a trading decision.
In the picture below we can see an example of a bear momentum signal in combo with a sell signal, NETFLIX, 1D, chart.
-------------------------------------------------------------- 4) "COLOR BARS THAT INDICATE THE STRENGTH OF THE TREND -----------------------------------------------------
This code is responsible for changing the color of the bars on a chart based on certain conditions. The gradient colors are defined for green and red, and the algorithm checks if the current bar is within a certain range of either a bearish reversal or bullish reversal candle and whether the price is above or below certain exponential moving averages or if important break of market structure occurs.
Ultimately, this feature helps traders visually identify potential trends and market shifts and avoid getting distracted by price fluctuations. Please note that every gradient of color can be customize by the user. We set 3 different bullish colors and 3 different bearish colors.
Below the picture of the settings menu related to the bar color.
----------------------------------------------------------------------5)THREE CUSTOMIZABLE MOVING AVERAGES ----------------------------------------------------------------------
You can choose up to three moving averages, any length and any type like SMA, EMA, WMA, HMA, RMA, SWMA and VWMA. Furthermore, you have the freedom to adjust the color and width of the lines to your preference.
Below the picture of the settings menu related to the moving averages.
----------------------------------------------------------------------6) ALERTS DIRECT TO YOUR EMAIL OR PHONE --------------------------------------------------------------------
Our alert feature sends real-time notifications directly to your email or phone when a signal is generated, allowing you to take immediate action and stay ahead of the market.
With our system, you first establish your own rules for trading in the strategy tester - this includes your criteria for entering and exiting trades.
Once you've defined these conditions, our system will start sending you alerts. These alerts will be triggered whenever your specified conditions are met. So, if the market matches your 'enter trade' conditions, you'll receive an alert prompting. Similarly, when your 'exit trade' conditions are met, you'll receive another alert.
Remember, these alerts are purely based on the conditions you set.
Once the condition is met, you will receive alerts directly to your email or phone when enter and exit a trade based on your custom conditions. To make sure you receive these notifications click on notifications tab.
---------------------------------------------------------------7) ADVANCED AND CUSTOMIZABLE SETTINGS MENU----------------------------------------------------------------------
We designed Easy Trade indicators with traders in mind, so it's user-friendly, easy to navigate and users can customize inputs, style, and colors of every feature in the indicator's settings menu.
-----------------------------------------------------------------------8) EASY TRADE PRO - BACKTESTING SYSTEM----------------------------------------------------------------------
Easy Trade Pro features a highly effective and realistic backtesting system, designed to mirror as closely as possible the real-world scenarios of entering and exiting trades.
Step 1:
Open the settings menu of the Indicator.
Once opened the settings menu click on properties.
Decide on the capital you wish to invest. Choose whether to use contracts or USD and determine the size of your orders. For the sake of realism, we recommend not exceeding 25% of your capital per order. However, if you decide to utilize your entire capital, make sure to adjust your stop loss accordingly. For instance, if you have a capital of 10K and use 10K with a stop loss at 2%, your potential loss would be $200. Conversely, if you use only 2K of your 10K capital with a stop loss at 10%, you would still lose the same 2% of your capital. To make your simulation even more authentic, consider incorporating broker fees or commissions into your calculations. For example, spot market fees are typically around 0.10%. If you're backtesting markets with low liquidity, consider factoring in slippage as well.
Step 2:
Navigate to the 'Inputs' section and scroll down until you come across 'Backtesting System - Strategy Test'. Once you locate this, click on the box and activate the 'USE STRATEGY SYSTEM' option by checking the tick box.
Also You will then need to set a 'Start Date' and 'End Date', establishing a specific time period during which you wish to test your strategy.
Otherwise you can consider to use the deep backtesting feature.
Step 3:
It's now time to establish the conditions for entering a trade. You can choose from five different types of custom buy signals: Good Buy, Good Buy Bull, Great Buy, Incredible Buy, and Bullish Reversal. Note that 'Great Buy' and 'Incredible Buy' are rare signals, so we advise against using them frequently in mechanical strategy tests; instead, consider them more for manual live tests. For more consistent results, we recommend using the other buy signals.
After determining your preferred buy signal, you can choose how many confirmation candles you wish to wait for before entering a trade. A 'confirmation' means that if the next candle closes above the opening or closing price of the chosen buy signal, it's considered a confirmation. This could be the opening or closing price, depending on whether the candle is green (close > open) or red.
You can set the number of confirmation candles in different time frames: below 2h, between 2h and 10h, and above 10h.
Step 4:
It's now time to safeguard your trade by managing risk. You can choose to implement a stop loss, expressed in percentage terms, or opt for a trailing stop. A trailing stop is a type of stop loss order that moves with the market price. It is designed to protect gains by enabling a trade to remain open and continue to profit as long as the market price is moving in a favorable direction. However, the trade closes if the market price changes direction by a specified amount (the 'trailing stop distance').
Additionally, you can minimize losses and move the stop loss to your entry point once the price reaches a certain percentage of profit. This strategy can help secure potential gains while limiting the potential for losses.
Step 5:
Now it's time to set the conditions for exiting the trade. You have the option to divide your exit into a maximum of four parts, with each part representing 25% of the position size. For each take profit point, you can choose from three different custom sell signals: Good Sell, Good Sell Bear, and Bearish Reversal.
Similarly, the concept of confirmation candles also applies here, but in this case, the candles are not closing above. A 'confirmation' for a sell signal means that if the next candle closes below the opening or closing price of the selected sell signal, it's considered a confirmation. This could be the opening or closing price, depending on whether the candle is green (open > close) or red (close < open).
So, when you're looking to sell, a confirmation would occur if the next candlestick's closing price is lower than the opening or closing price of the candlestick that triggered the sell signal. This indicates a potential bearish trend, providing the confirmation to execute the sell order.
Additionally, we've introduced a feature that allows you to move your stop loss to the entry point whenever the first take profit (1TP) is reached, which equates to hitting one custom sell signal.
Step 6:
We've also designed an alternative method for taking profits. With this approach, you can choose to exit your position once a fixed percentage gain from the entry point is reached. For instance, you might decide to exit when a 10% profit is achieved. Similarly to the previous method, this approach allows you to choose up to four exit points and determine the proportion of your position you want to close at each stage.
Conclusion:
Easy Trade Pro provides users with various options for entering and exiting trades. To effectively utilize the indicator, we strongly recommend conducting thorough backtesting and considering the results across your preferred trading pairs. It is advisable to analyze a substantial number of trades, ideally exceeding 100 trades, to obtain reliable insights into the indicator's performance. This approach will help you gain a better understanding of how Easy Trade Pro aligns with your trading strategy and objectives.
❗Keep attention❗
It is important to note that no trading indicator or strategy is foolproof, and there is always a risk of losses in trading. While this indicator may provide useful information for making conclusions, it should not be used as the sole basis for making trading decisions. Traders should always use proper risk management techniques and consider multiple factors when making trading decisions.
It is also important to be aware of the limitations of simulated performance results. Hypothetical or simulated results do not represent actual trading, and since trades have not been executed, results may be over- or under-compensated for market factors such as lack of liquidity. Simulated trading programs are also designed with the benefit of hindsight, and no representation is being made that any account will achieve profits or losses similar to those shown. Therefore, our indicators are for informative purposes only and not intended to be used as financial advice.
We encourage traders to use our indicators as part of a well-rounded trading strategy and to always be aware of the risks involved in trading. Remember that past performance is not indicative of future results and always trade responsibly.
GKD-BT Giga Confirmation Stack Backtest [Loxx]Giga Kaleidoscope GKD-BT Giga Confirmation Stack Backtest is a Backtesting module included in Loxx's "Giga Kaleidoscope Modularized Trading System".
█ GKD-BT Giga Confirmation Stack Backtest
The Giga Confirmation Stack Backtest module allows users to perform backtesting on Long and Short signals from the confluence between GKD-C Confirmation 1 and GKD-C Confirmation 2 indicators. This module encompasses two types of backtests: Trading and Full. The Trading backtest permits users to evaluate individual trades, whether Long or Short, one at a time. Conversely, the Full backtest allows users to analyze either Longs or Shorts separately by toggling between them in the settings, enabling the examination of results for each signal type. The Trading backtest emulates actual trading conditions, while the Full backtest assesses all signals, regardless of being Long or Short.
Additionally, this backtest module provides the option to test using indicators 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 module 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.
To utilize this strategy, follow these steps:
1. Adjust the "Confirmation Type" in the GKD-C Confirmation 1 Indicator to "GKD New."
2. 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 Giga Confirmation Stack Backtest module setting named "Import GKD-C Confirmation 1."
3. Adjust the "Confirmation Type" in the GKD-C Confirmation 2 Indicator to "GKD New."
4. 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 Giga Confirmation Stack Backtest module setting named "Import GKD-C Confirmation 2."
█ Giga Confirmation Stack Backtest Entries
Entries are generated from the confluence of a GKD-C Confirmation 1 and GKD-C Confirmation 2 indicators. The Confirmation 1 gives the signal and the Confirmation 2 indicator filters or "approves" the the Confirmation 1 signal. If Confirmation 1 gives a long signal and Confirmation 2 shows a downtrend, then the long signal is rejected. If Confirmation 1 gives a long signal and Confirmation 2 shows an uptrend, then the long signal is approved and sent to the backtest execution engine.
█ Volatility Types Included
The GKD system utilizes volatility-based take profits and stop losses. Each take profit and stop loss is calculated as a multiple of volatility. Users can also adjust the multiplier values in the settings.
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: Confiramtion Stack Backtest
Baseline: Hull Moving Average
Volatility/Volume: Hurst Exponent
Confirmation 1: Fisher Transform as shown on the chart above
Confirmation 2: uf2018 as shown on the chart above
Continuation: Vortex
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-BT Giga Stacks Backtest [Loxx]Giga Kaleidoscope GKD-BT Giga Stacks Backtest is a Backtesting module included in Loxx's "Giga Kaleidoscope Modularized Trading System".
█ GKD-BT Giga Stacks Backtest
The Giga Stacks Backtest module allows users to perform backtesting on Long and Short signals from the confluence of GKD-B Baseline, GKD-C Confirmation, and GKD-V Volatility/Volume indicators. This module encompasses two types of backtests: Trading and Full. The Trading backtest permits users to evaluate individual trades, whether Long or Short, one at a time. Conversely, the Full backtest allows users to analyze either Longs or Shorts separately by toggling between them in the settings, enabling the examination of results for each signal type. The Trading backtest emulates actual trading conditions, while the Full backtest assesses all signals, regardless of being Long or Short.
Additionally, this backtest module provides the option to test using indicators 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 module 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.
To utilize this strategy, follow these steps (where "Stack XX" denotes the number of the Stack):
GKD-B Baseline Import: Import the value "Input into NEW GKD-BT Backtest" from the GKD-B Baseline module into the GKD-BT Giga Stacks Backtest module setting named "Stack XX: Import GKD-C, GKD-B, or GKD-V."
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 Giga Stacks Backtest module setting named "Stack XX: Import GKD-C, GKD-B, or GKD-V."
GKD-C Confirmation Import: 1) Adjust the "Confirmation Type" in the GKD-C Confirmation Indicator to "GKD New."; 2) Import the value "Input into NEW GKD-BT Backtest" from the GKD-C Confirmation module into the GKD-BT Giga Stacks Backtest module setting named "Stack XX: Import GKD-C, GKD-B, or GKD."
█ Giga Stacks Backtest Entries
Entries are generated form the confluence of up to six GKD-B Baseline, GKD-C Confirmation, and GKD-V Volatility/Volume indicators. Signals are generated when all Stacks reach uptrend or downtrend together.
Here's how this works. Assume we have the following Stacks and their respective trend on the current candle:
Stack 1 indicator is in uptreend
Stack 2 indicator is in downtrend
Stack 3 indicator is in uptreend
Stack 4 indicator is in uptreend
All stacks are in uptrend except for Stack 2. If Stack 2 reaches uptrend while Stacks 1, 3, and 4 stay in uptrend, then a long signal is generated. The last Stack to align with all other Stacks will generate a long or short signal.
█ Volatility Types Included
The GKD system utilizes volatility-based take profits and stop losses. Each take profit and stop loss is calculated as a multiple of volatility. Users can also adjust the multiplier values in the settings.
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: Stacks Backtest
Baseline: Hull Moving Average
Volatility/Volume: Hurst Exponent
Confirmation 1: Vorext
Confirmation 2: Coppock Curve
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 system.
GKD-BT Solo Confirmation Super Complex Backtest [Loxx]Giga Kaleidoscope GKD-BT Solo Confirmation Super Complex Backtest is a Backtesting module included in Loxx's "Giga Kaleidoscope Modularized Trading System".
█ GKD-BT Solo Confirmation Super Complex Backtest
The Solo Confirmation Super Complex Backtest module allows users to perform backtesting on Full GKD Long and Short signals using GKD-C confirmation indicators. These signals are further refined by GKD-B Baseline and GKD-V Volatility/Volume indicators and augmented by an additional GKD-C Confirmation indicator acting as a Continuation indicator. This module serves as a comprehensive tool that falls just below a Full GKD trading system. The key difference is that the GKD-BT Solo Confirmation Super Complex utilizes a single GKD-C Confirmation indicator, while the Full GKD system employs two GKD-C Confirmation indicators. Both the Solo Confirmation Super Complex and the Full GKD systems incorporate an extra GKD-C Confirmation indicator to identify Continuation signals, which provide both longs and shorts on developing trends following an initial trend change.
This module encompasses two types of backtests: Trading and Full. The Trading backtest permits users to evaluate individual trades, whether Long or Short, one at a time. Conversely, the Full backtest allows users to analyze either Longs or Shorts separately by toggling between them in the settings, enabling the examination of results for each signal type. The Trading backtest emulates actual trading conditions, while the Full backtest assesses all signals, regardless of being Long or Short.
Additionally, this backtest module provides the option to test the core GKD-C Confirmation and GKD-C Continuation indicators 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 module 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.
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 Solo Confirmation Super Complex 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 Solo Confirmation Super Complex Backtest module setting named "Import GKD-V Volatility/Volume."
3. Adjust the "Confirmation Type" in the GKD-C Confirmation Indicator to "GKD New."
4. GKD-C Confirmation Import: Import the value "Input into NEW GKD-BT Backtest" from the GKD-C Confirmation module into the GKD-BT Solo Confirmation Super Complex Backtest module setting named "Import GKD-C Confirmation."
5. Adjust the "Confirmation Type" in the GKD-C Continuation Indicator to "GKD New."
6. GKD-C Continuation Import: Import the value "Input into NEW GKD-BT Backtest" from the GKD-C Continuation module into the GKD-BT Solo Confirmation Super Complex Backtest module setting named "Import GKD-C Continuation."
The GKD system utilizes volatility-based take profits and stop losses. Each take profit and stop loss is calculated as a multiple of volatility. Users can also adjust the multiplier values in the settings.
In a future update, the option to include a GKD-E Exit indicator will be added to this module to complete a full trading strategy.
█ Solo Confirmation Super Complex Backtest Entries
Within this module, there are eight 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
PullBack Entry
Continuation Entry
Each of these entry types can generate either long or short signals, resulting in a total of 16 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. You'll notice that these signals are different form the core GKD signals mentioned towards the end of this description. Signals from the GKD-BT Solo Confirmation Super Complex Backtest are modifided to add additional qualifications to make your finalized trading strategy more dynamic and robust.
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. 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. 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. Volatility/Volume agrees
6. Confirmation 1 signal was less than 'Maximum Allowable PSBC Bars Back' prior
1-Candle Baseline Entry
1a. GKD-B Basline 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. 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. Baseline agrees
6. 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. 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. 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, or Pullback entry triggered previously
2. Baseline hasn't crossed since entry signal trigger
4. Confirmation 1 agrees
5. Baseline 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: Solo Confirmation Complex Backtest as shown on the chart above
Baseline: Hull Moving Average as shown on the chart above
Volatility/Volume: Hurst Exponent as shown on the chart above
Confirmation 1: Fisher Trasnform as shown on the chart above
Confirmation 2: Williams Percent Range
Continuation: Vortex 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.
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-BT Solo Confirmation Complex Backtest [Loxx]Giga Kaleidoscope GKD-BT Solo Confirmation Complex Backtest is a Backtesting module included in Loxx's "Giga Kaleidoscope Modularized Trading System".
█ GKD-BT Solo Confirmation Complex Backtest
The Solo Confirmation Complex Backtest module enables users to perform backtesting on Standard Long and Short signals from GKD-C confirmation indicators, filtered by GKD-B Baseline and GKD-V Volatility/Volume indicators. This module represents a complex form of the Solo Confirmation Backtest in the GKD trading system. It includes two types of backtests: Trading and Full. The Trading backtest allows users to test individual trades, both Long and Short, one at a time. On the other hand, the Full backtest allows users to test either Longs or Shorts by toggling between them in the settings to view the results for each signal type. The Trading backtest simulates real trading, while the Full backtest tests all signals, whether Long or Short.
Additionally, this backtest module provides the option to test the GKD-C Confirmation 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 module 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.
The GKD system utilizes volatility-based take profits and stop losses. Each take profit and stop loss is calculated as a multiple of volatility. Users can also adjust the multiplier values in the settings.
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 Solo Confirmation Complex Backtest module setting named "Import GKD-B Baseline indicator."
Adjust the "Confirmation Type" in the GKD-C Confirmation Indicator to "GKD New."
2. GKD-C Confirmation Import: Import the value "Input into NEW GKD-BT Backtest" from the GKD-C Confirmation module into the GKD-BT Solo Confirmation Complex Backtest module setting named "Import GKD-C Confirmation indicator."
3. 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 Solo Confirmation Complex Backtest module setting named "Import GKD-V Volatility/Volume indicator."
4. The Solo Confirmation Complex Backtest module exclusively supports Standard Entries, both Long and Short. However, please note that this module uses a modified version of the Standard Entry. In this modified version, long and short signals are directly imported from the Confirmation indicator, and then baseline and volatility filtering is applied.
The GKD-B Baseline filter ensures that only trades aligning with the GKD-B Baseline's current trend are accepted. This filter takes into consideration the Goldie Locks Zone, which allows trades where the closing price of the last candle has moved within a minimum XX volatility and a maximum YY volatility range. The GKD-V Volatility/Volume filter allows only trades that meet a minimum threshold of ZZ GKD-V Volatility/Volume, which varies based on the specific GKD-V Volatility/Volume indicator used.
The Solo Confirmation Complex Backtest execution engine determines whether signals from the GKD-C Confirmation indicator are accepted or rejected based on two criteria:
1. The GKD-C Confirmation signal must be qualified by the direction of the GKD-B Baseline trend and the GKD-B Baseline's sweet-spot Goldie Locks Zone.
2. Sufficient Volatility/Volume, as indicated by the GKD-V Volatility/Volume indicator, must be present to execute a trade.
The purpose of the Solo Confirmation Complex Backtest is to test a GKD-C Confirmation indicator in the presence of macro trend and volatility/volume filtering.
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
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: Solo Confirmation Complex Backtest as shown on the chart above
Baseline: Hull Moving Average as shown on the chart above
Volatility/Volume: Hurst Exponent as shown on the chart above
Confirmation 1: Fisher Trasnform as shown on the chart above
Confirmation 2: Williams Percent Range
Continuation: Volatility-Adaptive Rapid RSI T3
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.
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-BT Solo Confirmation Simple Backtest [Loxx]Giga Kaleidoscope GKD-BT Solo Confirmation Simple Backtest is a Backtesting module included in Loxx's "Giga Kaleidoscope Modularized Trading System".
█ GKD-BT Solo Confirmation Simple Backtest
The Solo Confirmation Simple Backtest module enables users to perform Standard Long and Short signals on GKD-C confirmation indicators. This module represents the simplest form of Backtest in the GKD trading system. It includes two types of backtests: Trading and Full. The Trading backtest allows users to test individual trades, both long and short, one at a time. On the other hand, the Full backtest allows users to test either longs or shorts by toggling between them in the settings to view the results for each signal type. The Trading backtest simulates real trading, while the Full backtest tests all signals, whether long or short.
Additionally, this backtest module provides the option to test the GKD-C 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.
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. Adjust the "Confirmation Type" in the GKD-C Confirmation Indicator to "GKD New."
2. Import the value "Input into NEW GKD-BT Backtest" into the GKD-BT Solo Confirmation Simple Backtest module (this strategy backtest).
**The GKD-BT Solo Confirmation Simple Backtest module exclusively supports Standard Entries, both Long and Short. However, please note that this module uses a modified version of the standard entry, where long and short signals are directly imported from the Confirmation indicator without any baseline or volatility filtering applied.**
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
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: Solo Confirmation Simple Backtest as shown on the chart above
Baseline: Hull Moving Average
Volatility/Volume: Hurst Exponent
Confirmation 1: Fisher Trasnform as shown on the chart above
Confirmation 2: Williams Percent Range
Continuation: Volatility-Adaptive Rapid RSI T3
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.
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
[Fedra Algotrading Strategy]English / Spanish
Algotrading strategy optimized for cryptocurrencies. Originally conceived to trade automatically through bots (that's how I use it), it also works to get signals and trade manually in any exchange.
It works in spot. It does not repaint. Works in 15M, 30M, 1H and 4H (I prefer short periods).
Features:
Buy the dip:
Attempts to buy on the dip, finding entries when the price makes abrupt dips that break the linear regression of the last periods (default 40).
Trailing Take Profit:
Once the percentage established for the take profit is reached, the strategy follows the price if it is rising until it stops rising and only then makes the sale.
Trend Detection:
Determines whether the market is in an uptrend or downtrend by crossing 2 SMAs. This affects the performance of the strategy. This works as a filter to avoid making entries in a downtrend.
Trailing Break Even:
If the market enters a downtrend with an open trade, a Trailing Break Even is triggered, (configurable, default 1.5%). The intention is to close the trade as soon as possible, but without losses. The value of 1.5% is intended to cover commission costs and a possible spread. Like the Take Profit, the Trailing Break Even follows the price as it rises until it stops doing so before closing the trade.
How to use this strategy?
In the properties of the strategy you assign the amount you will trade (default 100), the percentage of the total capital you will use in each trade (default 100%) and the value of the commissions (default 1%).
Select the pair to trade. The strategy is optimized for trading pairs with stable coins. The strategy benefits from volatility so choosing among currencies with a market cap between 50M and 10,000M gives better profits than with top 10 currencies.
In the strategy options, disable the stoploss by setting it to 100% to be able to concentrate on the Take Profit.
With an eye on the "Net Profit" of the strategy, start with the take profit at 3% (for lower percentages there is the Break Even) and increase it 1 by 1 until determining which is the best for our pair (the one that gives us a better net profit).
Once the Take Profit is established, enable the StopLoss starting from 1 and choosing the best parameter looking for the balance that makes us feel comfortable between the Net Profit and the total of closed operations.
Test this same with candles of different periods (I trade with 15M and 30M candles).
Tip:
To trade automatically using a bot, I recommend using pairs in which the strategy has a profitability higher than 80%.
To counteract possible overfitting, when the strategy has given me a 30% profit, I recalculate the optimal parameters.
If you are interested in auromatizing it to trade on Binance, Binance US, AAX, Kucoin, Liquid, Okex, Bitfinex, Bittrex, Coinbase Pro, Gemini, HitBTC, Kraken or Poloniex, I recommend using Quadency bots, they are free and the ones I use.
This will eventually be a paid script, but you can request free access for now.
I am still working on optimizations, improvements, and more features.
DCA version coming soon.
I leave some optimizations of the spares I am trading at the moment (On 15M candles):
PAIR SL TP Bars Profit Profit Rentability
OM 3 4 96 70 2703.41% 88.57%
NU 4 3 96 81 1170.38% 86.42%
ONE 4 4 192 83 756% 91.57%
FTM 8 4 192 80 900.00% 92.50%
LUNA 3 8 192 78 410.98% 83.33%
OMG 6 4 192 72 408.75% 88.89%
FRONT 2 5 96 61 406% 85.25%
SOL 5 10 96 84 381.78% 83.33%
UTK 2 4 192 59 520.00% 88.06%
NMR 2 3 96 76 279% 80.26%
STPT 1 4 96 84 272.34% 79.76%
ROSE 5 4 96 59 478.00% 88.00%
clv 4 5 192 46 216% 78.26%
XTZ 4 6 96 87 216.00% 82.76%
C98 1 6 96 36 184.46% 80.56%
ALGO 7 4 192 61 222.00% 88.52%
ATOM 6 4 96 73 160.40% 86.30%
DOT 3 6 96 75 156.54% 84%
REEF 4 4 96 67 154.90% 85.07%
AUDIO 10 5 192 62 128.48% 83.87%
DYDX 1 10 96 20 120.76% 90%
DOT 4 6 96 77 111.33% 83.12%
KEEP 7 5 96 69 110% 87%
MINA 7 6 96 23 100.29% 86.96%
OPUL 1 5 96 18 95.26% 100%
HBAR 1 3 192 76 91.82% 81.58%
VRA/USDT 7 4 96 81 89.35% 81.48%
XEC 3 14 96 27 89.24% 85.19%
*****************************************************SPANISH*****************************************
Estrategia de Algotrading optimizada para criptomonedas. Originalmente concebida para operar de manera automática mediante bots (así la utilizo yo), funciona también para obtener señales y operar manualmente en cualquier exchange.
Funciona en spot. No repinta. Funciona en 15M, 30M, 1H y 4H (Yo prefiero periodos cortos)
Características:
Buy the dip:
Intenta comprar en el dip, encontrando entradas cuando el precio hace bajadas abruptas que rompen la regresión lineal de los últimos periodos (por defecto 40)
Trailing Take Profit:
Una vez alcanzado el porcentaje establecido para el take profit, la estrategia acompaña al precio si está en ascenso hasta que deja de subir y recién ahí realiza la venta.
Detección de Trend:
Determina si el mercado tiene una tendencia alcista o bajista mediante el cruce de 2 SMAs. Esto afecta el funcionamiento de la estrategia. Esto funciona como filtro para evitar realizar entradas en una tendencia bajista.
Trailing Break Even:
Si el mercado entra en tendencia bajista con una operación abierta, se activa un Trailing Break Even, (configurable, por defecto 1.5%). La intención es cerrar la operación lo antes posible, pero sin pérdidas. El valor de 1.5% está pensado para cubrir los costos de comisiones y un posible spread. Al igual que el Take Profit, El Trailing Break Even acompaña al precio mientras sube hasta que deja de hacerlo antes de cerrar la operación.
Cómo utilizar esta estrategia?
En las propiedades de la estrategia se le asigna el monto con el que va a operar (por defecto 100), el porcentaje del total de capital que utilizará en cada operación (por defecto 100%) y el valor de las comisiones (por defecto 1%)
Seleccionar el par a operar. La estrategia está optimizada para operar en pares con stablecoins. La estrategia se beneficia con la volatilidad por lo que elegir entre las monedas con un market cap de entre 50M y 10.000M da mejores beneficios que con monedas del top 10
En las opciones de la estrategia, deshabilitar el stoploss configurándolo en 100% para poder concentrarnos en el Take Profit.
Con un ojo en el “Beneficio Neto” de la estrategia, comenzar con el take profit en 3% (para porcentajes menores está el Break Even) e ir aumentándolo de 1 en 1 hasta determinar cuál es el mejor para nuestro par (el que nos proporciona un major beneficio neto).
Establecido el Take Profit, habilitar el StopLoss partiendo de 1 y eligiendo el mejor parámetro buscando el equilibrio que nos haga sentir cómodos entre el Beneficio Neto y el total de operaciones cerradas.
Probar esto mismo con velas de diferentes periodos (Yo opero con velas de 15M y 30M)
Consejo:
Para operar de manera automática mediante un bot, recomiendo utilizar pares en los que la estrategia tenga una rentabilidad superior al 80%
Para contrarestar posible overfiting, cuando la estrategia me ha dado un 30% de profit, vuelvo a calcular los parámetros óptimos.
Si te interesa auromatizarla para operar en Binance, Binance US, AAX, Kucoin, Liquid, Okex, Bitfinex, Bittrex, Coinbase Pro, Gemini, HitBTC, Kraken o Poloniex, recomiendo usar los bots de Quadency, son gratiutos y los que yo utilizo.
Este será eventualmente un script pago, pero puedes solicitar acceso gratuito por ahora.
Sigo trabajando en optimizaciones, mejoras, y más funciones.
Próximamente versión DCA.
Dejo algunas optimizaciones de lo spares que yo estoy operando en este momento (En velas de 15M contra BUSD):
PAR SL TP Bars Operaciones Profit Rentabilidad
OM 3 4 96 70 2703.41% 88.57%
NU 4 3 96 81 1170.38% 86.42%
ONE 4 4 192 83 756% 91.57%
FTM 8 4 192 80 900.00% 92.50%
LUNA 3 8 192 78 410.98% 83.33%
OMG 6 4 192 72 408.75% 88.89%
FRONT 2 5 96 61 406% 85.25%
SOL 5 10 96 84 381.78% 83.33%
UTK 2 4 192 59 520.00% 88.06%
NMR 2 3 96 76 279% 80.26%
STPT 1 4 96 84 272.34% 79.76%
ROSE 5 4 96 59 478.00% 88.00%
clv 4 5 192 46 216% 78.26%
XTZ 4 6 96 87 216.00% 82.76%
C98 1 6 96 36 184.46% 80.56%
ALGO 7 4 192 61 222.00% 88.52%
ATOM 6 4 96 73 160.40% 86.30%
DOT 3 6 96 75 156.54% 84%
REEF 4 4 96 67 154.90% 85.07%
AUDIO 10 5 192 62 128.48% 83.87%
DYDX 1 10 96 20 120.76% 90%
DOT 4 6 96 77 111.33% 83.12%
KEEP 7 5 96 69 110% 87%
MINA 7 6 96 23 100.29% 86.96%
OPUL 1 5 96 18 95.26% 100%
HBAR 1 3 192 76 91.82% 81.58%
VRA/USDT 7 4 96 81 89.35% 81.48%
XEC 3 14 96 27 89.24% 85.19%
Multi-Market Swing Trader Webhook Ready [HullBuster]
Introduction
This is an all symbol swing trading strategy intended for webhook integration to live accounts. This script employs an adjustable bandwidth ping pong algorithm which can be run in long only, short only or bidirectional modes. Additionally, this script provides advanced features such as pyramiding and DCA. It has been in development for nearly three years and exposes over 90 inputs to accommodate varying risk reward ratios. Equipped with a proper configuration it is suitable for professional traders seeking quality trades from a cloud based platform. This is my most advanced Pine Script to date which combines my RangeV3 and TrendV2 scripts. Using this combination it tries to bridge the gap between range bound and trending markets. I have put a lot of time into creating a system that could transition by itself so as to require less human intervention and thus be able to withstand long periods in full automation mode.
As a Pine strategy, hypothetical performance can be easily back-tested. Allowing you to Iron out the configuration of your target instrument. Now with recent advancements from the Pine development team this same script can be connected to a webhook through the alert mechanism. The requirement of a separate study script has been completely removed. This really makes things a lot easier to get your trading system up and running. I would like to also mention that TradingView has made significant advancements to the back-end over the last year. Notably, compile times are much faster now permitting more complex algorithms to be implemented. Thank you TradingView!
I used QuantConnect as my role model and strived to produce a base script which could compete with higher end cloud based platforms while being attractive to similarly experienced traders. The versatility of the Pine Language combined with the greater selection of end point execution systems provides a powerful alternative to other cloud based platforms. At the very least, with the features available today, a modular trading system for everyday use is a reality. I hope you'll agree.
This is a swing trading strategy so the behavior of this script is to buy on weakness and sell on strength. In trading parlance this is referred to as Support and Resistance Trading. Support being the point at which prices stop falling and start rising. Resistance being the point at which prices stop rising and fall. The chart real estate between these two points being defined as the range. This script seeks to implement strategies to profit from placing trades within this region. Short positions at resistance and long positions at support. 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 interleaved continuously as long as there exists a detectable vertex.
This is a non-hedging script so those of us subject to NFA FIFO Rule 2-43(b) should be generally safe to webhook into signals emitted from this script. However, as covered later in this document, there are some technical limitations to this statement. I have tested this script on various instruments for over two years and have configurations for forex, crypto and stocks. This script along with my TrendV2 script are my daily trading vehicles as a webhook into my forex and crypto accounts. This script employs various high risk features that could wipe out your account if not used judiciously. You should absolutely not use this script if you are a beginner or looking for a get-rich-quick strategy. Also please see my CFTC RULE 4.41 disclosure statement at the end of the document. Really!
Does this script repaint? The short answer is yes, it does, despite my best efforts to the contrary. EMAs are central to my strategy and TradingView calculates from the beginning of the series so there is just no getting around this. However, Pine is improving everyday and I am hopeful that this issue will be address from an architectural level at some point in the future. I have programmed my webhook to compensate for this occurrence so, in the mean time, this my recommended way to handle it (at the endpoint and before the broker).
Design
This strategy uses a ping pong algorithm of my own design. Basically, trades bounce off each other along the price stream. Trades are produced as a series of reversals. The point at which a trade reverses is a pivot calculation. A measurement is made between the recent valley to peak which results in a standard deviation value. This value is an input to implied probability calculation.Yes, the same implied probability used in sports betting. Odds are then calculated to determine the likelihood of price action continuing or retracing to the pivot. Based on where the account is at alert time, the action could be an entry, take profit or pyramid signal. In this design, trades must occur in alternating sequence. A long followed by a short then another long followed by a short and so on. In range bound price action trades appear along the outer bands of the channel in the aforementioned sequence. Shorts on the top and longs at the bottom. Generally speaking, the widths of the trading bands can be adjusted using the vertex dynamics in Section 2. There are a dozen inputs in this section used to describe the trading range. It is not a simple adjustment. If pyramids are enabled the strategy overrides the ping pong reversal pattern and begins an accumulation sequence. In this case you will see a series of same direction trades.
This script uses twelve indicators on a single time frame. The original trading algorithms are a port from a C++ program on proprietary 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 play a key role in identifying the pivot points. I really like the Hull Moving Average. I use it in all my systems, including 3 other platforms. It’s is an excellent leading indicator and a relatively light calculation.
The trend detection algorithms rely on several factors:
1. Smoothed EMAs in a Willams Alligator pattern.
2. Number of pivots encountered in a particular direction.
3. Which side debt is being incurred.
4. Settings in Section 4 and 5 (long and short)
The strategy uses these factors to determine the probability of prices continuing in the most recent direction. My TrendV2 script uses a higher time frame to determine trend direction. I can’t use that method in this script without exceeding various TradingView limitations on code size. However, the higher time frame is the best way to know which trend is worth pursuing or better to bet against.
The entire script is around 2400 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. The module has been through numerous refactoring passes and makes extensive use of ternary statements. As such, It takes a full minute 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. Scrolling the chart quickly may bring up an hour glass.
Regardless of the market conditions: range or trend. The behavior of the script is governed entirely by the 91 inputs. Depending on the settings, bar interval and symbol, you can configure a system to trade in small ranges producing a thousand or more trades. If you prefer wider ranges with fewer trades then the vertex detection settings in Section 2 should employ stiffer values. To make the script more of a trend follower, adjustments are available in Section 4 and 5 (long and short respectively). Overall this script is a range trader and the setups want to get in that way. It cannot be made into a full blown trend trading system. My TrendV2 is equipped for that purpose. Conversely, this script cannot be effectively deployed as a scalper either. The vertex calculation require too much data for high frequency trading. That doesn’t work well for retail customers anyway. The script is designed to function in bar intervals between 5 minutes and 4 hours. However, larger intervals require more backtest data in order to create reliable configurations. TradingView paid plans (Pro) only provide 10K bars which may not be sufficient. Please keep that in mind.
The transition from swing trader to trend follower typically happens after a stop is hit. That means that your account experiences a loss first and usually with a pyramid stack so the loss could be significant. Even then the script continues to alternate trades long and short. The difference is that the strategy tries to be more long on rising prices and more short on falling prices as opposed to simply counter trend trading. Otherwise, a continuous period of rising prices results in a distinctly short pyramid stack. This is much different than my TrendV2 script which stays long on peaks and short on valleys. Basically, the plan is to be profitable in range bound markets and just lose less when a trend comes along. How well this actually plays out will depend largely on the choices made in the sectioned input parameters.
Sections
The input dialog for this script contains 91 inputs separated into six sections.
Section 1: Global settings for the strategy including calculation model, trading direction, exit levels, pyramid and DCA settings. This is where you specify your minimum profit and stop levels. You should setup your Properties tab inputs before working on any of the sections. It’s really important to get the Base Currency right before doing any work on the strategy inputs. It is important to understand that the “Minimum Profit” and “Limit Offset” are conditional exits. To exit at a profit, the specified value must be exceeded during positive price pressure. On the other hand, the “Stop Offset” is a hard limit.
Section 2: Vertex dynamics. The script is equipped with four types of pivot point indicators. Histogram, candle, fractal and transform. Despite how the chart visuals may seem. The chart only receives prices. It’s up to the strategy to interpret patterns from the number stream. The quality of the feed and the symbol’s bar characteristics vary greatly from instrument to instrument. Each indicator uses a fundamentally different pattern recognition algorithm. Use trial and error to determine the best fit for your configuration. After selecting an indicator type, there are eight analog fields that must be configured for that particular indicator. This is the hardest part of the configuration process. The values applied to these fields determine how the range will be measured. They have a big effect on the number of trades your system will generate. To see the vertices click on the “Show Markers” check box in this section. Red markers are long positions and blue markers are short. This will give you an idea of where trades will be placed in natural order.
Section 3: Event thresholds. Price spikes are used to enter and exit trades. The magnitude which define these spikes are configured here. The rise and fall events are primarily for pyramid placement. The rise and fall limits determine the exit threshold for the conditional “Limit Offset” field found in Section 1. These fields should be adjusted one at a time. Use a zero value to disengage every one but the one you are working on. Use the fill colors found in Section 6 to get a visual on the values applied to these fields. To make it harder for pyramids to enter stiffen the Event values. This is more of a hack as the formal pyramid parameters are in Section 1.
Section 4 and 5: Long and short settings. These are mirror opposite settings with all opposing fields having the same meaning. Its really easy to introduce data mining bias into your configuration through these fields. You must combat against this tendency by trying to keep your settings as uniform as possible. Wildly different parameters for long and short means you have probably fitted the chart. There are nine analog and thirteen Boolean fields per trade direction. This section is all about how the trades themselves will be placed along the range defined in Section 2. Generally speaking, more restrictive settings will result in less trades but higher quality. Remember that this strategy will enter long on falling prices and short on rising prices. So getting in the trade too early leads to a draw-down. However, this could be what you want if pyramiding is enabled. I, personally, have found that the best configurations come from slightly skewing one side. I just accept that the other side will be sub-par.
Section 6: Chart rendering. This section contains one analog and four Boolean fields. More or less a diagnostic tool. Of particular interest is the “Symbol Debt Sequence” field. This field contains a whole number which paints regions that have sustained a run of bad trades equal or greater than specified value. It is useful when DCA is enabled. In this script Dollar Cost Averaging on new positions continues only until the symbol debt is recouped. To get a better understanding on how this works put a number in this field and activate DCA. You should notice how the trade size increases in the colored regions. The “Summary Report” checkbox displays a blue information box at the live end of the chart. It exposes several metrics which you may find useful if manually trading this strategy from audible alerts or text messages.
Pyramids
This script features a downward pyramiding strategy which increases your position size on losing trades. On purely margin trades, this feature can be used to, hypothetically, increase the profit factor of positions (not individual trades). On long only markets, such as crypto, you can use this feature to accumulate coins at depressed prices. The way it works is the stop offset, applied in the Section 1 inputs, determines the maximum risk you intend to bear. Additional trades will be placed at pivot points calculated all the way down to the stop price. The size of each add on trade is increased by a multiple of its interval. The maximum number of intervals is limited by the “Pyramiding” field in the properties tab. The rate at which pyramid positions are created can be adjusted in Section 1. To see the pyramids click on the “Mark Pyramid Levels” check box in the same section. Blue triangles are painted below trades other than the primary.
Unlike traditional Martingale strategies, the result of your trade is not dependent on the profit or loss from the last trade. The position must recover the R1 point in order to close. Alternatively, you can set a “Pyramid Bale Out Offset” in Section 1 which will terminate the trade early. However, the bale out must coincide with a pivot point and result in a profitable exit in order to actually close the trade. Should the market price exceed the stop offset set in Section 1, the full value of the position, multiplied by the accepted leverage, will be realized as a loss to the trading account. A series of such losses will certainly wipe out your account.
Pyramiding is an advanced feature intended for professional traders with well funded accounts and an appropriate mindset. The availability of this feature is not intended to endorse or promote my use of it. Use at your own risk (peril).
DCA
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 properties tab. The inputs for this feature are found in section 3 and 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 spot trading contest or just 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 of data mining bias. Please remember that.
Webhook Integration
The TradingView alerts dialog provides a way to connect your script to an external system which could actually execute your trade. This is a fantastic feature that enables you to separate the data feed and technical analysis from the execution and reporting systems. Using this feature it is possible to create a fully automated trading system entirely on the cloud. Of course, there is some work to get it all going in a reliable fashion. To that end this script has several things going for it. First off, it is a strategy type script. That means that the strategy place holders such as {{strategy.position_size}} can be embedded in the alert message text. There are more than 10 variables which can write internal script values into the message for delivery to the specified endpoint. Additionally, my scripts output the current win streak and debt loss counts in the {{strategy.order.alert_message}} field. Depending on the condition, this script will output other useful values in the JSON “comment” field of the alert message. Here is an excerpt of the fields I use in my webhook signal:
"broker_id": "kraken",
"account_id": "XXX XXXX XXXX XXXX",
"symbol_id": "XMRUSD",
"action": "{{strategy.order.action}}",
"strategy": "{{strategy.order.id}}",
"lots": "{{strategy.order.contracts}}",
"price": "{{strategy.order.price}}",
"comment": "{{strategy.order.alert_message}}",
"timestamp": "{{time}}"
Though TradingView does a great job in dispatching your alert this feature does come with a few idiosyncrasies. Namely, a single transaction call in your script may cause multiple transmissions to the endpoint. If you are using placeholders each message describes part of the transaction sequence. A good example is closing a pyramid stack. Although the script makes a single strategy.close() call, the endpoint actually receives a close message for each pyramid trade. The broker, on the other hand, only requires a single close. The incongruity of this situation is exacerbated by the possibility of messages being received out of sequence. Depending on the type of order designated in the message, a close or a reversal. This could have a disastrous effect on your live account. This broker simulator has no idea what is actually going on at your real account. Its just doing the job of running the simulation and sending out the computed results. If your TradingView simulation falls out of alignment with the actual trading account lots of really bad things could happen. Like your script thinks your are currently long but the account is actually short. Reversals from this point forward will always be wrong with no one the wiser. Human intervention will be required to restore congruence. But how does anyone find out this is occurring? In closed systems engineering this is known as entropy. In practice your webhook logic should be robust enough to detect these conditions. Be generous with the placeholder usage and give the webhook code plenty of information to compare states. Both issuer and receiver. Don’t blindly commit incoming signals without verifying system integrity.
Operation
This is a swing trading strategy so the fundamental 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. This is slightly misleading since a range as well as a trend are best recognized, in hindsight, after the patterns occur on the chart. In the middle of a trade, one never knows how deep valleys will drop or how high peaks will rise. For certain, long trades will continue to trigger as the market prices fall and short trades on rising prices. This means that the maximum efficiency of this strategy is achieved in choppy markets where the price doesn’t extend very far from its adjacent pivot point. Conversely, this strategy will be the least efficient when market conditions exhibit long continuous single direction price pressure. Especially, when measured in weeks. Translation, the trend is not your friend with this strategy. Internally, the script attempts to recognize prolonged price pressure and changes tactics accordingly. However, at best, the goal is to weather the trend until the range bound market returns. At worst, trend detection fails and pyramid trades continue to be placed until the limit specified in the Properties tab is reached. In all likelihood this could trigger a margin call and if it hits the stop it could wipe out your account.
This script has been in beta test four times since inception. During all that time no one has been successful in creating a configuration from scratch. Most people give up after an hour or so. To be perfectly honest, the configuration process is a bear. I know that but there is no way, currently, to create libraries in Pine. There is also no way specify input parameters other than the flattened out 2-D inputs dialog. And the publish rules clearly state that script variations addressing markets or symbols (suites) are not permitted. I suppose the problem is systemic to be-all-end-all solutions like my script is trying to be. I needed a cloud strategy for all the symbols that I trade and since Pine does not support library modules, include files or inter process communication this script and its unruly inputs are my weapon of choice in the war against the market forces. It takes me about six hours to configure a new symbol. Also not all the symbols I configure are equally successful. I should mention that I have a facsimile of this strategy written in another platform which allows me to run a backtest on 10 years of historical data. The results provide me a sanity check on the inputs I select on this platform.
My personal configurations use a 10 minute bar interval on forex instruments and 15 minutes on crypto. I try to align my TradingView scripts to employ standard intervals available from the broker so that I can backtest longer durations than those available on TradingView. For example, Bitcoin at 15 minute bars is downloadable from several sources. I really like the 10 minute bar. It provides lots of detectable patterns and is easy to store many years in an SQL database.
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 configurations that I use for my own trading that I can share with you if you like. To get hands on experience in setting up your own symbol from scratch please follow the steps below.
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 2. 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 4 and enable “Apply Red Base To Base Margin”.
Step 10. Go to Section 5 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. Turn off Show Markers in Section 2.
Step 13. 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. 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. 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.
Step 14. 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 15. Take a look at the chart. Trades should be showing along the markers plotted earlier.
Step 16. 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 17.
Step 17. Improve the backtest profitability by adjusting the “Entry Net Change” and “Entry ROC” in Section 4 and 5.
Step 18. Enable the “Mandatory Snap” checkbox in Section 4 and 5 and adjust the “Snap Candle Delta” and “Snap Fractal Delta” in Section 2. This should reduce some chop producing unprofitable reversals.
Step 19. Increase the distance between opposing trades by adding an “Interleave Delta” in Sections 4 and 5. This is a floating point value which starts at 0.01 and typically does not exceed 2.0.
Step 20. Increase the distance between opposing trades even further by adding a “Decay Minimum Span” in Sections 4 and 5. 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 21. The “Buy Composite Strength” input works in tandem with “Long Decay Minimum Span” in Section 4. Try enabling and see if it improves the performance. This field is only relevant when there is a value in “Long Decay Minimum Span”.
Step 22. The “Sell Composite Weakness” input works in tandem with “Short Decay Minimum Span” in Section 5. Try enabling and see if it improves the performance. This field is only relevant when there is a value in “Short Decay Minimum Span”.
Step 23. Improve the backtest profitability by adjusting the “Adherence Delta” in Section 4 and 5. This field requires the “Adhere to Rising Trend” checkbox to be enabled.
Step 24. At this point your strategy should be more or less working. Experiment with the remaining check boxes in Section 4 and 5. Keep the ones which seem to improve the performance.
Step 25. 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 Fractal indicator captures the tallest peaks and valleys. The Transform indicator is the most reliable but doesn’t well work on all instruments.
Example Settings
To reproduce the performance shown on the chart please use the following configuration:
1. Select XBTUSD Kraken as the chart symbol.
2. On the properties tab set the Order Size to: 0.01 Bitcoin
3. On the properties tab set the Pyramiding to: 10
4. In Section 1: Select “Forex” for the Instrument Type
5. In Section 1: Select “Ping Pong” for the Trading Mode
6. In Section 1: Input 1200 for the Minimum Profit
7. In Section 1: Input 15000 for the Stop Offset
8. In Section 1: Input 1200 for the Pyramid Minimum Span
9. In Section 1: Check mark the Ultra Wide Pyramids
10. In Section 2: Check mark the Use Transform Indicator
So to be clear, I used a base position size of one - one hundredth of a Bitcoin and allow the script to add up to 10 downward pyramids. The example back-test did hit eight downward pyramids. That means the account would have to be able to withstand a base position size (0.01) times 28. The resulting position size is 0.28 of a Bitcoin. If the price of Bitcoin is 35K then the draw down amount (not including broker fees) would be $9800 dollars. Since I have a premium subscription my backtest chart includes 20K historical bars. That's roughly six months of data. As of today, pro accounts only get 10K bars so the performance cannot be exactly matched with such a difference in historical data. Please keep that in mind.
There are, of course, various ways to reduce the risk incurred from accumulating pyramids. You can increase the “Pyramid Minimum Span” input found in Section 2 which increases the space between each pyramid trade. Also you can set a “Pyramid Bale Out Offset” in the same input section. This lets you out of the trade faster on position recovery. For example: Set a value of 8000 into this input and the number of trades increase to 178 from 157. Since the positions didn’t go full term, more trades were created at less profit each. The total brute force approach would be to simply limit the number of pyramids in the Properties tab.
It should be noted that since this is crypto, accumulating on the long side may be what you want. If you are not trading on margin and thus outright buying coins on the Kraken exchange you likely are interested in increasing your Bitcoin position at depressed prices. This is a popular feature on some of the other crypto trading packages like CryptoHopper and Profit Trailer. Click on Enable TV Long Only Rule in Section 1. This switches the signal emitter to long only. However, you may still see short trades on the chart. They are treated as a close instead of a reversal.
Feel free to PM me with any questions related to this script. Thank you and happy trading!
CFTC RULE 4.41
These results are based on simulated or hypothetical performance results that have certain inherent limitations. Unlike the results shown in an actual performance record, these results do not represent actual trading. Also, because these trades have not actually been executed, these results may have under-or over-compensated for the impact, if any, of certain market factors, such as lack of liquidity. Simulated or hypothetical trading programs in general are also subject to the fact that they are designed with the benefit of hindsight. No representation is being made that any account will or is likely to achieve profits or losses similar to these being shown.
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).
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. 🚀
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
Gabriel's Witcher Strategy [65 Minute Trading Bot]Strategy Description: Gabriel's Witcher Strategy
Author: Gabriel
Platform: TradingView Pine Script (Version 5)
Backtested Asset: Avalanche (Coinbase Brokage for Volume adjustment)
Timeframe: 65 Minutes
Strategy Type: Comprehensive Trend-Following and Momentum Strategy with Scalping and Risk Management Features
Overview
Gabriel's Witcher Strategy is an advanced trading bot designed for the Avalanche pair on a 65-minute timeframe. This strategy integrates a multitude of technical indicators to identify and execute high-probability trading opportunities. By combining trend-following, momentum, volume analysis, and range filtering, the strategy aims to capitalize on both long and short market movements. Additionally, it incorporates scalping mechanisms and robust risk management features, including take-profit (TP) levels and commission considerations, to optimize trade performance and profitability.
====Key Components====
Source Selection:
Custom Source Flexibility: Allows traders to select from a wide range of price and volume sources (e.g., Close, Open, High, Low, HL2, HLC3, OHLC4, VWAP, On-Balance Volume, etc.) for indicator calculations, enhancing adaptability to various trading styles.
Various curves of Volume Analysis are employed:
Tick Volume Calculation: Utilizes tick volume as a fallback when actual volume data is unavailable, ensuring consistency across different data feeds.
Volume Indicators: Incorporates multiple volume-based indicators such as On-Balance Volume (OBV), Accumulation/Distribution (AccDist), Negative Volume Index (NVI), Positive Volume Index (PVI), and Price Volume Trend (PVT) for comprehensive market analysis.
Trend Indicators:
ADX (Average Directional Index): Measures trend strength using either the Classic or Masanakamura method, with customizable length and threshold settings. It's used to open positions when the mesured trend is strong, or exit when its weak.
Jurik Moving Average (JMA): A smooth moving average that reduces lag, configurable with various parameters including source, resolution, and repainting options.
Parabolic SAR: Identifies potential reversals in market trends with adjustable start, increment, and maximum settings.
Custom Trend Indicator: Utilizes highest and lowest price points over a specified timeframe to determine current and previous trend bases, visually represented with color-filled areas.
Momentum Indicators:
Relative Strength Index (RSI): Evaluates the speed and change of price movements, smoothed with a custom length and source. It's used to not enter the market for shorts in oversold or longs for overbought conditions, and to enter for long in oversold or shorts for overboughts.
Momentum-Based Calculations: Employs both Double Exponential Moving Averages (DEMA) on a MACD-based RSI to enhance momentum signal accuracy which is then further accelerated by a Hull MA. This is the technical analysis tool that determines bearish or bullish momentum.
OBV-Based Momentum Conditions: Uses two exponential moving averages of OBV to determine bullish or bearish momentum shifts, anomalities, breakouts where banks flow their funds in or Smart Money Concepts trade.
Moving Averages (MA):
Multiple MA Types: Includes Simple Moving Average (SMA), Exponential Moving Average (EMA), Weighted Moving Average (WMA), Hull Moving Average (HMA), and Volume-Weighted Moving Average (VWMA), selectable via input parameters.
MA Speed Calculation: Measures the percentage change in MA values to determine the direction and speed of the trend.
Range Filtering:
Variance-Based Filter: Utilizes variance and moving averages to filter out trades during low-volatility periods, enhancing trade quality.
Color-Coded Range Indicators: Visualizes range filtering with color changes on the chart for quick assessment.
Scalping Mechanism:
Heikin-Ashi Candles: Optionally uses Heikin-Ashi candles for smoother price action analysis.
EMA-Based Trend Detection: Employs fast, medium, and slow EMAs to determine trend direction and potential entry points.
Fractal-Based Filtering: Detects regular or BW (Black & White) fractals to confirm trade signals.
Take Profit (TP) Management:
Dynamic TP Levels: Calculates TP levels based on the number of consecutive long or short entries, adjusting targets to maximize profits.
TP Signals and Re-Entry: Plots TP signals on the chart and allows for automatic re-entry upon TP hit, maintaining continuous trade flow.
Risk Management:
Commission Integration: Accounts for trading commissions to ensure net profitability.
Position Sizing: Configured to use a percentage of equity for each trade, adjustable via input parameters.
Pyramiding: Allows up to one additional position per direction to enhance gains during strong trends.
Alerts and Visual Indicators:
Buy/Sell Signals: Plots visual indicators (triangles and flags) on the chart to signify entry and TP points.
Bar Coloring: Changes bar colors based on ADX and trend conditions for immediate visual cues.
Price Levels: Marks significant price levels related to TP and position entries with cross styles.
Input Parameters
Source Settings:
Custom Sources (srcinput): Choose from various price and volume sources to tailor indicator calculations.
ADX Settings:
ADX Type (ADX_options): Select between 'CLASSIC' and 'MASANAKAMURA' methods.
ADX Length (ADX_len): Defines the period for ADX calculation.
ADX Threshold (th): Sets the minimum ADX value to consider a strong trend.
RSI Settings:
RSI Length (len_3): Period for RSI calculation.
RSI Source (src_3): Source data for RSI.
Trend Strength Settings:
Channel Length (n1): Period for trend channel calculation.
Average Length (n2): Period for smoothing trend strength.
Jurik Moving Average (JMA) Settings:
JMA Source (inp): Source data for JMA.
JMA Resolution (reso): Timeframe for JMA calculation.
JMA Repainting (rep): Option to allow JMA to repaint.
JMA Length (lengths): Period for JMA.
Parabolic SAR Settings:
SAR Start (start): Initial acceleration factor.
SAR Increment (increment): Acceleration factor increment.
SAR Maximum (maximum): Maximum acceleration factor.
SAR Point Width (width): Visual width of SAR points.
Trend Indicator Settings:
Trend Timeframe (timeframe): Period for trend indicator calculations.
Momentum Settings:
Source Type (srcType): Select between 'Price' and 'VWAP'.
Momentum Source (srcPrice): Source data for momentum calculations.
RSI Length (rsiLen): Period for momentum RSI.
Smooth Length (sLen): Smoothing period for momentum RSI.
OBV Settings:
OBV Line 1 (e1): EMA period for OBV line 1.
OBV Line 2 (e2): EMA period for OBV line 2.
Moving Average (MA) Settings:
MA Length (length): Period for MA calculations.
MA Type (matype): Select MA type (1: SMA, 2: EMA, 3: HMA, 4: WMA, 5: VWMA).
Range Filter Settings:
Range Filter Length (length0): Period for range filtering.
Range Filter Multiplier (mult): Multiplier for range variance.
Take Profit (TP) Settings:
TP Long (tp_long0): Percentage for long TP.
TP Short (tp_short0): Percentage for short TP.
Scalping Settings:
Scalping Activation (ACT_SCLP): Enable or disable scalping.
Scalping Length (HiLoLen): Period for scalping indicators.
Fast EMA Length (fastEMAlength): Period for fast EMA in scalping.
Medium EMA Length (mediumEMAlength): Period for medium EMA in scalping.
Slow EMA Length (slowEMAlength): Period for slow EMA in scalping.
Filter (filterBW): Enable or disable additional fractal filtering.
Pullback Lookback (Lookback): Number of bars for pullback consideration.
Use Heikin-Ashi Candles (UseHAcandles): Option to use Heikin-Ashi candles for smoother trend analysis.
Strategy Logic
Indicator Calculations:
Volume and Source Selection: Determines the primary data source based on user input, ensuring flexibility and adaptability.
ADX Calculation: Computes ADX using either the Classic or Masanakamura method to assess trend strength.
RSI Calculation: Evaluates market momentum using RSI, further smoothed with custom periods.
Trend Strength Assessment: Utilizes trend channel and average lengths to gauge the robustness of current trends.
Jurik Moving Average (JMA): Smooths price data to reduce lag and enhance trend detection.
Parabolic SAR: Identifies potential trend reversals with adjustable parameters for sensitivity.
Momentum Analysis: Combines RSI with DEMA and OBV-based conditions to confirm bullish or bearish momentum.
Moving Averages: Employs multiple MA types to determine trend direction and speed.
Range Filtering: Filters out low-volatility periods to focus on high-probability trades.
Trade Conditions:
Long Entry Conditions:
ADX Confirmation: ADX must be above the threshold, indicating a strong uptrend.
RSI and Momentum: RSI below 70 and positive momentum signals.
JMA and SAR: JMA indicates an uptrend, and Parabolic SAR is below the price.
Trend Indicator: Confirms the current trend direction.
Range Filter: Ensures market is in an upward range.
Scalping Option: If enabled, additional scalping conditions must be met.
Short Entry Conditions:
ADX Confirmation: ADX must be above the threshold, indicating a strong downtrend.
RSI and Momentum: RSI above 30 and negative momentum signals.
JMA and SAR: JMA indicates a downtrend, and Parabolic SAR is above the price.
Trend Indicator: Confirms the current trend direction.
Range Filter: Ensures market is in a downward range.
Scalping Option: If enabled, additional scalping conditions must be met.
Position Management:
Entry Execution: Places long or short orders based on the identified conditions and user-selected position types (Longs, Shorts, or Both).
Take Profit (TP): Automatically sets TP levels based on predefined percentages, adjusting dynamically with consecutive trades.
Re-Entry Mechanism: Allows for automatic re-entry upon TP hit, maintaining active trading positions.
Exit Conditions: Closes positions when TP levels are reached or when opposing trend signals are detected.
Visual Indicators:
Bar Coloring: Highlights bars in green for bullish conditions, red for bearish, and orange for neutral.
Plotting Price Levels: Marks significant price levels related to TP and trade entries with cross symbols.
Signal Shapes: Displays triangle and flag shapes on the chart to indicate trade entries and TP hits.
Alerts:
Custom Alerts: Configured to notify traders of long entries, short entries, and TP hits, enabling timely trade management and execution.
Usage Instructions
Setup:
Apply the Strategy: Add the script to your TradingView chart set to BTCUSDT with a 65-minute timeframe.
Configure Inputs: Adjust the input parameters under their respective groups (e.g., Source Settings, ADX, RSI, Trend Strength, etc.) to match your trading preferences and risk tolerance.
Position Selection:
Choose Position Type: Use the Position input to select Longs, Shorts, or Both based on your market outlook.
Execution: The strategy will automatically execute and manage positions according to the selected type, ensuring targeted trading actions.
Signal Interpretation:
Buy Signals: Blue triangles below the bars indicate potential long entry points.
Sell Signals: Red triangles above the bars indicate potential short entry points.
Take Profit Signals: Flags above or below the bars signify TP hits for long and short positions, respectively.
Bar Colors: Green bars suggest bullish conditions, red bars indicate bearish conditions, and orange bars represent neutral or consolidating markets.
Risk Management:
Default Position Size: Set to 100% of equity. Adjust the default_qty_value as needed for your risk management strategy.
Commission: Accounts for a 0.1% commission per trade. Adjust the commission_value to match your broker's fees.
Pyramiding: Allows up to one additional position per direction to enhance gains during strong trends.
Backtesting and Optimization:
Historical Testing: Utilize TradingView's backtesting features to evaluate the strategy's performance over historical data.
Parameter Tuning: Optimize input parameters to align the strategy with current market dynamics and personal trading objectives.
Alerts Configuration:
Set Up Alerts: Enable and configure alerts based on the predefined alertcondition statements to receive real-time notifications of trade signals and TP hits.
Additional Features
Comprehensive Indicator Integration: Combines multiple technical indicators to provide a holistic view of market conditions, enhancing trade signal accuracy.
Scalping Options: Offers an optional scalping mechanism to capitalize on short-term price movements, increasing trading flexibility.
Dynamic Take Profit Levels: Adjusts TP targets based on the number of consecutive trades, maximizing profit potential during favorable trends.
Advanced Volume Analysis: Utilizes various volume indicators to confirm trend strength and validate trade signals.
Customizable Range Filtering: Filters trades based on market volatility, ensuring trades are taken during optimal conditions.
Heikin-Ashi Candle Support: Optionally uses Heikin-Ashi candles for smoother price action analysis and reduced noise.
====Recommendations====
Thorough Backtesting:
Historical Performance: Before deploying the strategy in a live trading environment, perform comprehensive backtesting to understand its performance under various market conditions. These are the premium settings for Avalanche Coinbase.
Optimization: Regularly review and adjust input parameters to ensure the strategy remains effective amidst changing market volatility and trends. Backtest the strategy for each crypto and make sure you are in the right brokage when using the volume sources as it will affect the overall outcome of the trading strategy.
Risk Management:
Position Sizing: Adjust the default_qty_value to align with your risk tolerance and account size.
Stop-Loss Implementation: Although the strategy includes TP levels, they're also consided to be a stop-loss mechanisms to protect against adverse market movements.
Commission Adjustment: Ensure the commission_value accurately reflects your broker's fees to maintain realistic backtesting results. Generally, 0.1~0.3% are most of the average broker's comission fees.
Slipage: The slip comssion is 1 Tick, since the strategy is adjusted to only enter/exit on bar close where most positions are available.
Continuous Monitoring:
Strategy Performance: Regularly monitor the strategy's performance to ensure it operates as expected and make adjustments as needed. A max-drawndown hit has been added to operate in case the premium Avalanche settings go wrong, but you can turn it off an adjust the equity percentage to 50% if you are confortable with the high volatile max-drown or even 100% if your account allows you to borrow cash.
Customization:
Indicator Parameters: Tailor indicator settings (e.g., ADX length, RSI period, MA types) to better fit your specific trading style and market conditions.
Scalping Options: Enable or disable scalping based on your trading preferences and risk appetite.
Conclusion
Gabriel's Witcher Strategy is a robust and versatile trading solution designed to navigate the complexities of the Crypto market. By integrating a wide array of technical indicators and providing extensive customization options, this strategy empowers traders to execute informed and strategic trades. Its comprehensive approach, combining trend analysis, momentum detection, volume evaluation, and range filtering, ensures that trades are taken during optimal market conditions. Additionally, the inclusion of scalping features and dynamic take-profit management enhances the strategy's adaptability and profitability potential. Unlike any trading strategy, with both diligent testing and continuous monitoring under the strategy tester, it's possible to achieve sustained success by adjusting the settings to the individual Crypto that need it, for example this one is preset for Avalanche Coinbase 65 Miinutes but it can be adjust for BTCUSD or Etherium if you backtest and search for the right 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.
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
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.
Crypto Tipster v2---------------------
Crypto Tipster v2
Hello again! We're back with a drastically improved Crypto Tipster v2 Indicator using over a dozen all new algorithms based around Technical Analysis, Price Action, Momentum Swings and Reversal Detection.
We've taken our time with version 2 of Crypto Tipster, putting all our best practices to work and ensuring it performs superbly across numerous crypto markets and timeframes - we have focused our efforts towards the larger timeframes, 12H, 1D, 2D for example as we believe these to be the most consistent and predictable, and therefore the most profitable.
Trading on longer timeframes also reduces the overal cost of trading fee's as you'll be placing fewer trades over any given time period, whilst catching bigger swings and therefore earning a higher percentage per winning trade. Due to these bigger price swings you can de-leverage your trades too, making them inherintly safer and more controlled.
The final benefit to placing trades on longer timeframes is that you will not be tied down to your PC or laptop for hours on end waiting for a perfect entry or exit point, which increases the odds of placing bad/panic trades or even placing trades due to boredom! If you trade with Crypto Tipster v2 on a 1D timeframe, you will only ever have work to do once per day, at bar close; this is when trades are placed or exited, or stop losses/take profits are updated to new levels - easy!
Crypto Tipster v2 can help consistently catch tops and bottoms of trending markets whilst avoiding placing trades through choppy or ranging areas, this helps to not only maximise profits (what we're all after!) but also to minimise losses (equally important). We've tirelessly tested Crypto Tipster using literally thousands of variables across dozens of built-in algorithms over hundreds of trading pairs - lots of data to process!
The outcome is rather stunning and well worth checking out - we're rather proud of what we've achieved here, and we're pretty sure you're going to love it too!
---------------------
What's Included
- Chart Settings
The first section you'll come across, Chart Settings.
Here you'll find a few options regarding how your chosen market chart will look within TradingView and how Crypto Tipster will interact with this chart.
One of the most important Tick boxes is first on the list - "Show Backtest Results". This will change Crypto Tipster from displaying simple but easy-to-follow "Buy/Sell" labels into Strategy mode in which you can set up more complicated Stop Loss / Take profit settings as well as setting up Alerts for auto trading and other more complex functions (see How It Works for more info!
We've also included a "Trend Strength Bar Color" tick box which changes the color of the chart bars based on how strong Crypto Tipster is perceiving the current trend and in which direction.
- Trend Settings
"Trading Frequency" represents how often Crypto Tipster will be looking for a new trend / change in trend direction, and therefore how often it will be placing trades. By default this is set to "Normal" but can be changed to "Rapid" using the drop down menu.
"Entry Trend Strength" also determines how frequently trades are placed by selecting the strength of trend required before a trade is placed. The scale ranges from "1-5", with 1 being a low trend strength required, 5 being a very strong trend strength required.
Within the Trend Settings section you'll also find an "Avg Trend Strength over Bars" option. This allows you to average (mean) the current trend strength over a pre-determined amount (1-5) of previous chart bars - thus providing a potentially more consistent signal.
- Trade Settings
Trade Settings help Crypto Tipster determine what type of trades you're looking to place.
The overall "Trade Direction" will decide to either target only Long trades, only Short trades, or Both (default).
"Consecutive Trades in Same Direction" allows for pyramiding - whereby you can specify to allow for multiple trades of the same direction. Set to "1" as default allows for no extra pyramiding, max setting of "10".
- Trade Protection
Currently consisting of two functions, our Trade Protection section can help to achieve both the removal of false signals (whipsaws), and the extension of good trades without confusion during minor retracements.
"Chop Removal" can help to remove some whipsaw trades during ranging market conditions, therefore improving overal profitability by only targeting stronger trends. You have an option to choose from either "Weak" or "Strong" Chop Removal.
"Protection Filter" uses current trading criteria as defined by you, and uses it to check against a higher time frame than you're currently viewing. This can help to eliminate some bad trades at the expense of a potential lag on good trades.
- Stop Loss / Take Profit
Stop Losses should be a crucial aspect of everyone's trading system. They help prevent any trade from going too far in the wrong direction and limit losses.
Our "Stop Loss (%)" is quick and easy to set up, simply set the percentage offset from the entry price of trades and a fixed Stop Loss will be in place on all trades.
"Take Profit (%)" works in the same way as the Stop Loss mentioned above - simply set the percentage you'd like to exit a profitable trade at.
The "Trailing Stop (%)" is a little more complicated in that it will follow the trend of the trade a certain percentage away from the current market price - this is great for keeping yourself in a trade for as long as the trade is moving in the right direction.
- Extra Tools & Indicators
This is the section of Crypto Tipster that enables you to add some chart visuals to assist you with your preferred trading style.
"Potential Pivot Points" are not the same as actual pivot points - Potential pivot points will paint on the chart at bar close, giving you an immediate alert to potential tops/bottoms of market trends. You can choose to display only the strongest potential points, or include some of the weaker signals too.
"Actual Pivot Points" are inherintly more accurate than Potential pivot points, but do not paint on the chart until after a pre-determined amount of time has passed. These are great for placing stop losses/take profits or watching the market for breakouts or reversals.
"Support/Resistance Levels" plots up to 6 support and resistance horizontal lines based on recent price tops/bottoms. Use these to determine areas where price could rebound or break-through.
"Bollinger Band Breakout" - Bollinger bands are a tried and tested technical analysis tool, similar to pivot points and support/resistance lines, thee are another great tool to determine where price may retrace, consolidate or breakout.
- Ichimoku Cloud
Somewhat confusing and intimidating when you first come across this technical analysis indicator, the "Ichimoku Cloud" is one of our favorites. Assisting with the detection of Dynamic Support and Resistance levels, Momentum and Trend Direction all in one super indicator.
Although certain aspects of the Ichimoku Cloud are already present within Crypto Tipster v2 algorithms in order to offer you the best possible signals, we've also included a user-definable section of it's own so you can manually set up and use the cloud for your own trading needs, all cloud signals (and there are many) are available to set up as Alerts for your own needs or an Auto-Trading Bot.
- Custom Alerts for Any Signal
We've endeavoured to ensure that all signals, not just the Buy/Sell signals, are ready and available to create Alerts with; giving you the most opportunity to create a fully custom trading engine that suits your exact trading requirements.
This means you can set Alerts for any and all signals you can see on the chart when using Crypto Tipster v2, this includes Buy/Sell Signals, Trend Strength Signals, Choppy Market Signals, Stop Loss/Take Profit Signals, Pivot Points, S/R levels crossed above & below, Bollinger Band Breakout and several Ichimoku Cloud Signals.. the list goes on!
---------------------
We've tried to make Crypto Tipster as comprehensive and easy to understand as possible, we are however always in search of progression; we do really love to hear your feedback :)
For more information and a free 8-day trial please visit the link in our signature
Happy Trading Guys
[Pt] TICK Supertrend Strategy, 5 minBackground:
It is well known that the indices such as SPY and QQQ follow/represent market sentiment. The TICK index literally represents the market sentiment as it compares the number of stocks that are rising and falling on the NYSE. By default, the TICK index is a short term indicator. Therefore it isn't reliable for swing trading or long term strategies. However, it is perfect for scalping.
Although TICK is well known, many does not know how to use it effectively. As part of the background mechanism of this script, I’ve divided TICK into 5 major zones based on the close of each candle: Overbought (neutral with bearish bias), Bullish, Neutral, Bearish, and Oversold (neutral with bullish bias). Along with the use of Heikin Ashi technique, RSI, moving averages and candle analysis, this strategy aims to provide accurate representation of market sentiment and profitable entry and exit points. *** At the time of publication, this strategy has proved to be consistently profitable. HOWEVER, this DOES NOT guarantee future profitability. So use at your own risk! ***
What is it showing?
This strategy is an intraday scalping strategy that uses TICK data to predict market directions for optimal entry and exit points. It is displayed similarly to the famous Supertrend indicator, which is one of the most common ATR based trailing stop indicators, so visually it is easy to read. This strategy is suitable for trading indices such as SPX , SPY , SPX500USD , QQQ , DJI and any other tickers that have high positive correlation with TICK.
Script is proprietary, but as mentioned it incorporates the following elements with additional candlestick analysis, pattern recognition, stop-loss and profit taking strategy:
- NYSE TICK data
- Heikin Ashi candle technique
- ATR
- RSI
- Moving Averages
Bullish trend is determined by a confluence of said indicators and analyses, and is displayed as a green line under the price action. The distance is defined by an adjustable value that is based on a percentage of the previous daily ATR value. When a long order is in play, that line also acts as the stop-loss level. Bearish trend is the opposite and is displayed in red, by default.
What's unique?
Detecting a ranging market structure and avoiding overtrading in a choppy market has always proven to be difficult, even for the most professional traders. This strategy has built-in “choppiness” and volatility filtering scripts that attempts to help reduce the number of false entries. These elements are what makes this strategy unique and different from other indictors mashup strategies.
In addition, this strategy takes previous trades into account and “learn” from past trades when determining the optimal stop-loss level to maximize profitability. This allows this strategy to better adapts to changing and evolving market conditions.
Strategy statistics
All parameters are designed for 5min time frame.
At the time of publication, this strategy has proved to be consistently profitable through limited back testing data.
Initial capital = $10000
Pyramiding = 1
Slippage = 3 ticks to account for spread
Default leverage shown = 9x
Quantity per trade = 100% of account
Back testing period at time of publication = Apr 11, 2022 - July 22, 2022
Trading Session = 1000 - 1530 Mon-Fri
Timeframe = 5 min
Gain = 1338.48%
Total trades = 253
% Profitable = 45.85%
Profit Factor = 2.506
Max Drawdown = 19.36%
Extras
This release includes default AutoView alerts for trading SPX500USD on Oanda. It includes both long and short order entry alerts, and trailing stop-loss alerts.
Please DM for free trial.
Customizable OCC Non Repainting Scalper Bot v7.0bThis strategy is intended to be used on an automated trading platform and should be run on a one minute chart for fastest confirmations and signal relay to crypto automation platform. The strategy has been modded to only go long at this time to focus on profitability for one direction. The open long and close long text fields allow you to use your own webhook message for this purpose.
I have spent quite a bit of time and I figured I would put it out to the community to share the work and also get some feedback.
Ok, so let me say that I have done absolutely everything I can to make the strategy not repaint while still maintaining it's profitability. It has been a challenge so I am publishing this to the community to help test this.
What I have observed: the strategy will not repaint in real time. That is, if you have the chart open and keep it open, the signals are the same as the ones that are sent out by the strategy. In certain cases, when I reload the chart- the signals might be off from what was sent. In some ways, that is repainting, but it is repainting based on losing the real time data and recalculating from a different set of bars- since I am running it on a one minute chart then the start becomes different when you refresh.
To address repainting while keeping the strategy calculating as quickly as possibly I have altered the logic in the following ways:
I have made an assumption which might not work for everyone- at the first tick of the next bar, you can almost safefly assume in crypto that if you are looking at the previous bar for information, the open of the current bar was the close of the previous bar. This for the most part holds true in crypto with good liquidity. If you are trading a pair that jumps around due to low volume- this might not be the strategy to use. I might publish a different version with a different logic.
I have altered the security repaint to use isbarconfirmed, so at the very end of the bar (as soon as the bar is confirmed), we recalculate to the higher time frames. So as soon as the data is available, it is at that point that we can then safely calculate higher time frames. This is unique and experimental, but seems to do well at creating good signals for entry.
I have employed my own intervals by utilizing the resolution as an integer (used by the previous authors)- but in this case, I use the interval to take a snapshot of the higher time frame. With open close cross, the different moving averages can cause the repainting as they change to show the exact point of the cross. The interval feature I created minimizes this by utilizing the previous bar info until the interval is closed and then we recalculate the variants. You can use the interval offset feature to denote which minute is the one that starts and ends the interval. So for instance, Trading View uses minue 1 and minute 31 for 30 minute intervals. If you offset your 30 minute interval would start on minute 16 and do its calculations based on the last 30 minutes,
As with most of my scripts, I have started using filters and a "show data" feature that will give you the ability to see the values of indicators that you cannot plot in the overlay. This allows you to figure out how to filter losing trades or market conditions.
I have also added a trailing stop and created a fixed stop loss as seems to perform better than the original occ strategy. The original one seemed to repaint enough that it would close too quickly and not give the posiition enough time to become profitable. In certain cases where there was a large move, it would perform well, but for the most part the trades would not close profitably even though the backtest said that it did - probably due to the delay in execution and pinescript not having a confirmation on what the actual position price was.
This is still in beta mode, so please forward test first and use at your own risk.
If you spot repaint issues, please send me a message and try to explain the situation.
[Joy] Aladdin Long Trading Strategy 1.0.0 AlphaAladdin's Long trading strategy is to test out Aladdin for long trades only
This strategy is mainly used to test whether Aladdin is suitable for a coin/stocks/futures or for any trading. The profitability, average drawdown, average profits, etc are used by me to decide whether to use it for trading.
What is Aladdin and what does it do?
Using the volume and gradual flow of non-interrupted data (wicks and body of the candles), it tries to detect the macro condition of the market so that one may know in which direction the market is flowing.
* Bearish / Sell sign: On the candle's close, I open a short position
* Bullish sign: On the candle's close, I open a long position
* I take at least 50% profit when the indicator indicates to do so. One can configure that value as desired from the configuration depending on one's risk/money management. I might even convert some portion of the position into stable coins.
FAQ
Q: Does it use some EMA /MA/etc.? Does it use any indicator with tweaked settings?
Answer: No.
Q: What does it mostly depend on?
Answer: Volume and gradual flow of non-interrupted data. The logic depends purely on volume , price bars and the wicks.
Q: Does it work with all coins, stocks, futures, instruments?
Answer: I prefer to use the exchange with the best possible data. Then backtest out to find the best possible timeframe, stop loss and target all derived from this script data.
Q: Can you make it free or make it open source?
Answer: There is no free lunch in this world. I will never reveal or share the source code!
Q: Do you provide ongoing support for the indicator?
Answer: Yes, as long as I can, I will continue updating the indicator
Q: Are the bullish /buy & the bearish/sell markers automatic?
Answer: I have no control over the markers. It is driven purely by logic from the script.
Q: Is this financial advice?
Answer: This is not financial advice. I do not guarantee any profit or loss. I am not responsible for any of your losses or profits. My indicators do not assure profit or loss. It also does not auto-open or auto-close a trade.
Assumptions:
Only long trades are opened and closed. No short trades.
Starting Capital: $20,000
Order Size: 20% of Capital
Data used: Whatever data is available from 2011 till today on Trading view
Findings:
INDEX: BTCUSD 83% profitability using 2day tf
54 closed trades
Profit factor: 16
Sortino Ratio: 5.2
Average Winning Trade: 30%
Average Losing Trade: 9.12%
Largest Winning Trade: 1218%
Largest Losing Trade: 20.25%
Below are the profitability rate for the timeframe and the coins listed as found by running the trading strategy over the following as of today (Aug 1st 2021 12:40 pm Sydney Time).
⚜️ INDEX:BTCUSD 83% using 2day tf
⚜️INDEX:ETHUSD 80% using 1day tf
⚜️FTTUSD 81% using 2day tf
⚜️SRMUSD 71% using 1day tf
⚜️ADAUSDT 81% using 2day tf
⚜️ALGOUSD > 90% using 2day tf
⚜️ALTPERP 81% using 2day tf
⚜️AVAXUSDT 75% using 1day tf
⚜️BANDUSD > 90% using 2day tf
⚜️BCHUSD 82% using 2day tf
⚜️BNBUSD 79% using 1day tf
⚜️BNBUSD 85% using 2day tf
⚜️CHZUSD 71% using 1day tf
⚜️COMPUSD 81% using 1day tf
⚜️DOGEUSD 77% using 1day tf
⚜️EXCHPERP 83% using 1day tf
⚜️FILUSD > 90% using 1day tf
⚜️FTMUSD 70% using 2day tf
⚜️HTUSDT 75% using 2day tf
⚜️KINUSD >90% using 2day tf
⚜️LINKPERP 85% using 2day tf
⚜️LTCUSD 80% using 2day tf
⚜️MATICUSD 77% using 2day tf
⚜️NEOUSD 80% using 1day tf
⚜️NEXOUSD > 90% using 1day tf
⚜️OKBUSD 71% using 1day tf
⚜️OMGUSD 75% using 1day tf
⚜️RSRUSD 87% using 1day tf
⚜️RUNEUSD > 90% using 1day tf
⚜️SHITPERP > 90% using 1day tf
⚜️SOLUSD 84% using 1day tf
⚜️SUSHIUSD 71% using 1day tf
⚜️THETAUSD > 90% using 2day tf
⚜️UNIPERP 83% using 1day tf
⚜️VERTPERP > 90% using 1day tf
⚜️XAUUSD 63% using 2day tf
⚜️XTZUSD 83% using 2day tf
⚜️ZECUSD 72% using 2day tf
Disclaimer:
No one knows what will happen in the future. DYOR and decide on your own conditions. Do realize that neither I nor my indicator can guarantee any profit or loss. And there is no assurance that any trade will ever result in any profit. It is not financial advice.
