XPloRR S&P500 Stock Market Crash Detection Strategy v2XPloRR S&P500 Stock Market Crash Detection Strategy v2
Long-Term Trailing-Stop strategy detecting S&P500 Stock Market Crashes/Corrections and showing Volatility as warning signal for upcoming crashes
Detecting or avoiding stock market crashes seems to be the 'Holy Grail' of strategies.
Since none of the strategies that I tested can beat the long term Buy&Hold strategy, the purpose was to detect a stock market crash on the S&P500 and step out in time to minimize losses and beat the Buy&Hold strategy. So beat the Buy&Hold strategy with around 10 trades. 100% capitalize sold trade into new trade.
With the default parameters the strategy generates 10262% profit (starting at 01/01/1962 until release date), with 10 closed trades, 100% profitable, while the Buy&Hold strategy only generates 3633% profit, so this strategy beats the Buy&Hold strategy by 2.82 times !
Also the strategy detects all major S&P500 stock market crashes and corrections since 1962 depending on the Trailing Stop Smoothness parameter, and steps out in time to cut losses and steps in again after the bottom has been reached. The 5 major crashes/corrections of 1987, 1990, 2001, 2008 and 2010 were successfully detected with the default parameters.
The script was first released on November 03 2019 and detected the Corona Crash on March 04 2020 with a Volatility crash-alert and a Sell crash-alert.
I have also created an Alerter Study Script based on the engine of this script, which generates Buy, Sell and Volatility signals.
If you are interested in this Alerter version script, please drop me a mail.
The script shows a lot of graphical information:
the Close value is shown in light-green. When the Close value is temporarily lower than the Buy value, the Close value is shown in light-red. This way it is possible to evaluate the virtual losses during the current trade.
the Trailing Stop value is shown in dark-green. When the Sell value is lower than the Buy value, the last color of the trade will be red (best viewed when zoomed)
the EMA and SMA values for both Buy and Sell signals are shown as colored graphs
the Buy signals are labeled in blue and the Sell signals are labeled in purple
the Volatility is shown below in green and red. The Alert Threshold (red) is default set to 2 (see Volatility Threshold parameter below)
How to use this Strategy?
Select the SPX (S&P500) graph and add this script to the graph.
Look in the strategy tester overview to optimize the values Percent Profitable and Net Profit (using the strategy settings icon, you can increase/decrease the parameters), then keep using these parameters for future Buy/Sell signals on the S&P500.
More trades don't necessarily generate more overall profit. It is important to detect only the major crashes and avoid closing trades on the smaller corrections. Bearing the smaller corrections generates a higher profit.
Watch out for the Volatility Alerts generated at the bottom (red). The Threshold can by changed by the Volatility Threshold parameter (default=2% ATR). In almost all crashes/corrections there is an alert ahead of the crash.
Although the signal doesn't predict the exact timing of the crash/correction, it is a clear warning signal that bearish times are ahead!
The correction in December 2018 was not a major crash but there was already a red Volatility warning alert. If the Volatility Alert repeats the next weeks/months, chances are higher that a bigger crash or correction is near. As can be seen in the graphic, the deeper the crash is, the higher and wider the red Volatility signal goes. So keep an eye on the red flag!
Here are the parameters:
Fast MA Buy: buy trigger when Fast MA Buy crosses over the Slow MA Buy value (use values between 10-20)
Slow MA Buy: buy trigger when Fast MA Buy crosses over the Slow MA Buy value (use values between 21-50)
Minimum Buy Strength: minimum upward trend value of the Fast MA Buy value (directional coefficient)(use values between 10-100)
Fast MA Sell: sell trigger when Fast MA Sell crosses under the Slow MA Sell value (use values between 10-20)
Slow MA Sell: sell trigger when Fast MA Sell crosses under the Slow MA Sell value (use values between 21-50)
Minimum Sell Strength: minimum downward trend value of the Fast MA Sell value (directional coefficient)(use values between 10-100)
Trailing Stop ATR: trailing stop % distance from the smoothed Close value (use values between 2-20)
Trailing Stop Smoothness: MA value for smoothing out the Trailing Stop close value
Buy On Start Date: force Buy on start date even without Buy signal (default: true)
Sell On End Date: force Sell on end date even without Sell signal (default: true)
Volatility EMA Period: MA value of the Volatility value (default 15)
Volatility Threshold: Threshold value to change volatility graph to red (default 2)
Volatility Graph Scaler: Scaling of the volatility graph (default 5)
Important : optimizing and using these parameters is no guarantee for future winning trades!
"the strat" için komut dosyalarını ara
Bollinger Bands BAT/USDT 30minThis is ready to use Bollinger Band strategy that was backtested on the data from the previous year 2019.
The main purpose of this strategy is to determine trades with the highest probability of success, to keep a consistent portfolio growth throughout the year. This strategy cherry-picks the most reliable points of entry on a particular timeframe (30m) for the particular asset (BAT/USDT). The backtest shows a great result of 78.95% profitability with the maximum drawdown of -4.02%. This is one of my strategies out of the group of automated strategies that helps to grow my portfolio steadily.
You are welcome to change inputs and backtest the following strategy. Any comments or ideas would be appreciated.
If you are happy with existing results and would like to automate the strategy, which can be done through alerts, then you need to convert it to study and add alerts in the code.
Let me know if you are interested in that and I will create a study based on this strategy.
Strategy VS Buy & HoldSUMMARY:
A strategy wrapper that makes a detailed and visual comparison between a given strategy and the buy & hold returns of the traded security.
DESCRIPTION:
TradingView has a "Buy & Hold Return" metric in the strategy tester that is often enough to assess how our strategy compares to a simple buy hold. However, one may want more information on how and when your strategy beats or is beaten by a simple buy & hold strategy. This script aims to show such detail by providing a more comprehensive metrics and charting the profit/loss of the given strategy against buy & hold.
As seen in the script, it plots/draws 4 elements:
1) Strategy P/L: strategy net profit + strategy open profit
2) Buy & Hold P/L: unrealized return
3) Difference: Strategy P/L - Buy & Hold P/L
4) Strategy vs Buy Hold Stats
> Percent of bars strategy P/L is above Buy & Hold
> Percent of bars strategy P/L is below Buy & Hold
> All Time Average Difference
ADJUSTABLE PARAMETERS:
All labels/panels can be disabled by unchecking these two options:
>bnh_info_panel = input(true, title='Enable Info Panel')
>bnh_indicator_panel = input(true, title='Enable Indicator Panel')
Comparison Date Range can be changed to better isolate specific areas:
>From Year, From Month, From Day
default: 1970 01 01
>To Year, To Month, To Day
default: 2050 12 31
Default settings basically covers all historical data.
HOW TO USE:
The default script contains a simple 50-200 SMA cross strategy, just delete and replace it. Those are everything between these lines:
/////////////////////////////////////////////////////////////////////////////////////
//////////////////////////////STRATEGY SCRIPT START//////////////////////////////////
(STRATEGY SCRIPT GOES HERE)
//////////////////////////////STRATEGY SCRIPT END////////////////////////////////////
/////////////////////////////////////////////////////////////////////////////////////
Removing all plots and drawings from your strategy is advisable.
If you are going to use the Comparison Date Range, apply "bnh_timeCond" to your strategy to align the dates. A sample on how it’s applied can be seen on the Placeholder MA cross strategy.
Note: bnh_timeCond returns a boolean series
Full Range Trading Strategy with DCA - Crypto, Forex, Stocks
Introduction
This is a Pine 4 range trading strategy. It has a twin study with several alerts. The design intent is to produce a commercial grade signal generator that can be adapted to any symbol and interval. Ideally, the script is reliable enough to be the basis of an automated trading system web-hooked to a server with API access to crypto, forex and stock brokerages. The strategy can be run in three different modes: long, short and bidirectional.
As a range trading strategy, the behavior of the 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. 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. I also have a trend following version of this script for those not interested in trading the range. Please be aware these are two types of traders. You should know who you are.
This script employs a DCA feature which enables users to experiment with loss recovery techniques. This is an 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. This implementation of DCA does not use pyramid levels. Only the order size on subsequent new trades are affected. Pyramids on the other hand increase the size of open positions. If you are interested in seeing pyramids in action please see the trend version of this script which features both DCA and pyramids. While DCA is a popular feature in crypto trading, it can make you a “bag” holder if your not careful. In other markets, especially margin trading, you’ll need a well funded account and much trading experience to manage this feature safely.
Consecutive loss limit can be set to report a breach of the threshold value. 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).
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 1800 lines of Pine code which is the maximum incidental size given the TradingView limits: local scopes, run-time duration and compile time. I’ve been working on this script for nearly two years and have tested it on various instruments stocks, forex and crypto. It performs well on higher liquidity markets that have at least a year of historical data. Although the script can be implemented on any interval, it has been optimized for small time frames down to 5 minutes. The 10 minute BTC/USD produces around 500 trades in 2 ½ months. The 1 hour BTC/USD produces around 1300 trades in 1 ½ years. Originally, this script contained both range trading and trend following logic but had to be broken into separate scripts due to the aforementioned limitations.
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 50 inputs grouped into long or short sections. Inputs are available for the usual minimum profit and stop-loss as well as safeguards, trade frequency, DCA, modes, presets, reports and lots of calibrations. The inputs are numerous, I’m aware. 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.
Example configurations for various instruments along with a detailed PDF user manual is available.
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.
Tackling this repainting issue has been a major project goal of this script. Based on my experience with Pine, most of the problems stem from TradingView’s implementation of multiple interval access. Whereas most platform 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. 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.
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”.
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 along with a PDF which describes each input in detail. To get hands on experience in setting up your own symbol from scratch please follow the steps below.
The input dialog box contains over 50 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 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. 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.
Step 18. Improve the backtest profitability by adjusting the “Long Entry Net Change” and “Long Entry ROC” in Section 3.
Step 19. Improve the backtest profitability by adjusting the “Short Entry Net Change” and “Short Entry ROC” in Section 4.
Step 20. Improve the backtest profitability by adjusting the “Sparse Long Delta” in Section 3.
Step 21. Improve the backtest profitability by adjusting the “Chase Long Delta” in Section 3.
Step 22. Improve the backtest profitability by adjusting the “Long Adherence Delta” in Section 3. This field requires the “Adhere to Rising Trend” checkbox to be enabled.
Step 23. Try each checkbox in Section 3 and see if it improves the backtest profitability. The “Caution Lackluster Longs” checkbox only works when “Long Caution Mode” is enabled.
Step 24. Improve the backtest profitability by adjusting the “Sparse Short Delta” in Section 4.
Step 25. Improve the backtest profitability by adjusting the “Chase Short Delta” in Section 4.
Step 26. Improve the backtest profitability by adjusting the “Short Adherence Delta” in Section 4. This field requires the “Adhere to Falling Trend” checkbox to be enabled.
Step 27. Try each checkbox in Section 4 and see if it improves the backtest profitability. The “Caution Lackluster Shorts” checkbox only works when “Short Caution Mode” is enabled.
Step 28. 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 29. 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 30. 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.
Total Trend Follow Strategy with Pyramid and DCA
Introduction
This is a Pine 4 trend following strategy. It has a twin study with several alerts. The design intent is to produce a commercial grade signal generator that can be adapted to any symbol and interval. Ideally, the script is reliable enough to be the basis of an automated trading system web-hooked to a server with API access to crypto, forex and stock brokerages. The strategy can be run in three different modes: long, short and bidirectional.
As a trend following strategy, the behavior of the script is to buy on strength and sell on weakness. As such the trade orders maintain its directional bias according to price pressure. What you will see on the chart is long positions on the left side of the mountain and short on the right. Long and short positions are not intermingled as long as there exists a detectable trend. This is extremely beneficial feature in long running bull or bear markets. The script uses multiple setups to avoid the situation where you got in on the trend, took a small profit but couldn’t get back in because the logic is waiting for a pullback or some other intricate condition.
Deep draw-downs are a characteristic of trend following systems and this system is no different. However, 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. During the backtest use modest setting with realistic capital until you discover what you think you can handle.
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.
Consecutive loss limit can be set to report a breach of the threshold value. 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).
Design
This script uses nine indicators on two time frames. The chart (primary) interval and one higher time frame which is based on the primary. The higher time frame identifies the trend for which the primary will trade. I’ve tried to keep the higher time frame around five times greater than the primary. 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 alligator itself is used to identify the trend main body.
The entire script is around 1700 lines of Pine code which is the maximum incidental size given the TradingView limits: local scopes, run-time duration and compile time. I’ve been working on this script for over a year and have tested it on various instruments stocks, forex and crypto. It performs well on higher liquidity markets that have at least a year of historical data. Though it can be configured to work on any interval between 5 minutes and 1 day, trend trading is generally a longer term paradigm. For day trading the 10 to 15 minute interval will allow you to catch momentum breakouts. For intraweek trades 30 minutes to 1 hour should give you a trade every other a day. Four hours and above are for seasoned deep pocket traders. Originally, this script contained both range trading and trend following logic but had to be broken into separate scripts due to the aforementioned limitations.
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 50 inputs grouped into long or short sections. Inputs are available for the usual minimum profit and stop-loss as well as safeguards, trade frequency, DCA, modes, presets, reports and lots of calibrations. The inputs are numerous, I’m aware. 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.
Example configurations for various instruments along with a detailed PDF user manual is available.
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. Since this trading system is featured as two separate scripts, indicator repainting is addressed in the study version. The strategy (this script) is intended to be used on historical data to determine the appropriate trading inputs to apply in the study. As such, the higher time frame of this strategy will indeed repaint. Please do not attempt to trade from the strategy. Please see the study version for more information.
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. However, alerts that are subsequently fired off when triggered in the study are dispatched from the TradingView servers one bar later from the study plot. Therefore the alert you actually receive on your cell phone matches the strategy plot but is one bar later than the study plot. A lot can happen in four hours if you are trading off a 240 bar.
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 along with a PDF which describes each input in detail. To get hands on experience in setting up your own symbol from scratch please follow the steps below.
The input dialog box contains over 50 inputs separated into seven 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 3. 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 3.
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 blue markers and short trades on the red.
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. Blue is long and red is short.
Step 8. Try a different indicator from section 3 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. Turn off Show Markers in Section 3.
Step 10. Enable the Symmetrical and Deviation calculation models at the top of section 5 and 6 (Symmetrical, Deviation).
Step 11. Put in your Minimum Profit and Stop Loss in the first section. This is in pips or currency basis points (chart right side scale)
Step 12. Return to step 3 and select a Trading Mode (Long, Short, BiDir, Flip Flop). If you are planning to trade bidirectionally its best to configure long first then short. Combine them with BiDir or Flip Flop after setting up both sides of the trade individually.
Step 13. Trades should be showing on the chart.
Step 14. Make adjustments to the Vertex fields in section 3 until the TradingView performance report is showing a profit.
Step 15. Change indicators and repeat step 14. Pick the best indicator.
Step 16. Use the check boxes in sections 5 and 6 to improve the performance of each side.
Step 17. Try adding the Correlation calculation model to either side. This model can sometimes produce a negative result but can be improved by enabling “Adhere To Markers” or “Narrow Correlation Scope” in the sections 5 and 6.
Step 18. Enable the reporting conditions in section 7. 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 19. Examine the chart and see that trades are being placed in accordance with your desired trading model.
Step 20. 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.
Combo Backtest 123 Reversal & Bill Williams Averages. 3Lines This is combo strategies for get
a cumulative signal. Result signal will return 1 if two strategies
is long, -1 if all strategies is short and 0 if signals of strategies is not equal.
First strategy
This System was created from the Book "How I Tripled My Money In The
Futures Market" by Ulf Jensen, Page 183. This is reverse type of strategies.
The strategy buys at market, if close price is higher than the previous close
during 2 days and the meaning of 9-days Stochastic Slow Oscillator is lower than 50.
The strategy sells at market, if close price is lower than the previous close price
during 2 days and the meaning of 9-days Stochastic Fast Oscillator is higher than 50.
Second strategy
This indicator calculates 3 Moving Averages for default values of
13, 8 and 5 days, with displacement 8, 5 and 3 days: Median Price (High+Low/2).
The most popular method of interpreting a moving average is to compare
the relationship between a moving average of the security's price with
the security's price itself (or between several moving averages).
WARNING:
- For purpose educate only
- This script to change bars colors.
Combo Backtest 123 Reversal & Bear Power This is combo strategies for get
a cumulative signal. Result signal will return 1 if two strategies
is long, -1 if all strategies is short and 0 if signals of strategies is not equal.
First strategy
This System was created from the Book "How I Tripled My Money In The
Futures Market" by Ulf Jensen, Page 183. This is reverse type of strategies.
The strategy buys at market, if close price is higher than the previous close
during 2 days and the meaning of 9-days Stochastic Slow Oscillator is lower than 50.
The strategy sells at market, if close price is lower than the previous close price
during 2 days and the meaning of 9-days Stochastic Fast Oscillator is higher than 50.
Second strategy
Bear Power Indicator
To get more information please see "Bull And Bear Balance Indicator"
by Vadim Gimelfarb.
WARNING:
- For purpose educate only
- This script to change bars colors.
Combo Backtest 123 Reversal & (H-L)/C Histogram This is combo strategies for get
a cumulative signal. Result signal will return 1 if two strategies
is long, -1 if all strategies is short and 0 if signals of strategies is not equal.
First strategy
This System was created from the Book "How I Tripled My Money In The
Futures Market" by Ulf Jensen, Page 183. This is reverse type of strategies.
The strategy buys at market, if close price is higher than the previous close
during 2 days and the meaning of 9-days Stochastic Slow Oscillator is lower than 50.
The strategy sells at market, if close price is lower than the previous close price
during 2 days and the meaning of 9-days Stochastic Fast Oscillator is higher than 50.
Second strategy
This histogram displays (high-low)/close
Can be applied to any time frame.
WARNING:
- For purpose educate only
- This script to change bars colors.
Combo Backtest 123 Reversal & Bandpass FilterThis is combo strategies for get
a cumulative signal. Result signal will return 1 if two strategies
is long, -1 if all strategies is short and 0 if signals of strategies is not equal.
First strategy
This System was created from the Book "How I Tripled My Money In The
Futures Market" by Ulf Jensen, Page 183. This is reverse type of strategies.
The strategy buys at market, if close price is higher than the previous close
during 2 days and the meaning of 9-days Stochastic Slow Oscillator is lower than 50.
The strategy sells at market, if close price is lower than the previous close price
during 2 days and the meaning of 9-days Stochastic Fast Oscillator is higher than 50.
Second strategy
The related article is copyrighted material from
Stocks & Commodities Mar 2010
You can use in the xPrice any series: Open, High, Low, Close, HL2, HLC3, OHLC4 and ect...
WARNING:
- For purpose educate only
- This script to change bars colors.
Combo Backtest 123 Reversal & Average True Range Trailing Stops This is combo strategies for get
a cumulative signal. Result signal will return 1 if two strategies
is long, -1 if all strategies is short and 0 if signals of strategies is not equal.
First strategy
This System was created from the Book "How I Tripled My Money In The
Futures Market" by Ulf Jensen, Page 183. This is reverse type of strategies.
The strategy buys at market, if close price is higher than the previous close
during 2 days and the meaning of 9-days Stochastic Slow Oscillator is lower than 50.
The strategy sells at market, if close price is lower than the previous close price
during 2 days and the meaning of 9-days Stochastic Fast Oscillator is higher than 50.
Secon strategy
Average True Range Trailing Stops Strategy, by Sylvain Vervoort
The related article is copyrighted material from Stocks & Commodities Jun 2009
WARNING:
- For purpose educate only
- This script to change bars colors.
Combo Backtest 123 Reversal and ADXR This is combo strategies for get
a cumulative signal. Result signal will return 1 if two strategies
is long, -1 if all strategies is short and 0 if signals of strategies is not equal.
First strategy
This System was created from the Book "How I Tripled My Money In The
Futures Market" by Ulf Jensen, Page 183. This is reverse type of strategies.
The strategy buys at market, if close price is higher than the previous close
during 2 days and the meaning of 9-days Stochastic Slow Oscillator is lower than 50.
The strategy sells at market, if close price is lower than the previous close price
during 2 days and the meaning of 9-days Stochastic Fast Oscillator is higher than 50.
Secon strategy
The Average Directional Movement Index Rating (ADXR) measures the strength
of the Average Directional Movement Index (ADX). It's calculated by taking
the average of the current ADX and the ADX from one time period before
(time periods can vary, but the most typical period used is 14 days).
Like the ADX, the ADXR ranges from values of 0 to 100 and reflects strengthening
and weakening trends. However, because it represents an average of ADX, values
don't fluctuate as dramatically and some analysts believe the indicator helps
better display trends in volatile markets.
WARNING:
- For purpose educate only
- This script to change bars colors.
Combo Backtest 123 Reversal and Accelerator Oscillator (AC) This is combo strategies for get
a cumulative signal. Result signal will return 1 if two strategies
is long, -1 if all strategies is short and 0 if signals of strategies is not equal.
First strategy
This System was created from the Book "How I Tripled My Money In The
Futures Market" by Ulf Jensen, Page 183. This is reverse type of strategies.
The strategy buys at market, if close price is higher than the previous close
during 2 days and the meaning of 9-days Stochastic Slow Oscillator is lower than 50.
The strategy sells at market, if close price is lower than the previous close price
during 2 days and the meaning of 9-days Stochastic Fast Oscillator is higher than 50.
Second strategy
The Accelerator Oscillator has been developed by Bill Williams
as the development of the Awesome Oscillator. It represents the
difference between the Awesome Oscillator and the 5-period moving
average, and as such it shows the speed of change of the Awesome
Oscillator, which can be useful to find trend reversals before the
Awesome Oscillator does.
WARNING:
- For purpose educate only
- This script to change bars colors.
Combo Backtest 123 Reversal and Absolute Price Oscillator (APO) This is combo strategies for get
a cumulative signal. Result signal will return 1 if two strategies
is long, -1 if all strategies is short and 0 if signals of strategies is not equal.
First strategy
This System was created from the Book "How I Tripled My Money In The
Futures Market" by Ulf Jensen, Page 183. This is reverse type of strategies.
The strategy buys at market, if close price is higher than the previous close
during 2 days and the meaning of 9-days Stochastic Slow Oscillator is lower than 50.
The strategy sells at market, if close price is lower than the previous close price
during 2 days and the meaning of 9-days Stochastic Fast Oscillator is higher than 50.
Secon strategy
The Absolute Price Oscillator displays the difference between two exponential
moving averages of a security's price and is expressed as an absolute value.
How this indicator works
APO crossing above zero is considered bullish, while crossing below zero is bearish.
A positive indicator value indicates an upward movement, while negative readings
signal a downward trend.
Divergences form when a new high or low in price is not confirmed by the Absolute Price
Oscillator (APO). A bullish divergence forms when price make a lower low, but the APO
forms a higher low. This indicates less downward momentum that could foreshadow a bullish
reversal. A bearish divergence forms when price makes a higher high, but the APO forms a
lower high. This shows less upward momentum that could foreshadow a bearish reversal.
WARNING:
- For purpose educate only
- This script to change bars colors.
Combo Strategies 123 Reversal and 3-Bar-Reversal-Pattern This is combo strategies for get
a cumulative signal. Result signal will return 1 if two strategies
is long, -1 if all strategies is short and 0 if signals of strategies is not equal.
First strategy
This System was created from the Book "How I Tripled My Money In The
Futures Market" by Ulf Jensen, Page 183. This is reverse type of strategies.
The strategy buys at market, if close price is higher than the previous close
during 2 days and the meaning of 9-days Stochastic Slow Oscillator is lower than 50.
The strategy sells at market, if close price is lower than the previous close price
during 2 days and the meaning of 9-days Stochastic Fast Oscillator is higher than 50.
Secon strategy
This startegy based on 3-day pattern reversal described in "Are Three-Bar
Patterns Reliable For Stocks" article by Thomas Bulkowski, presented in
January,2000 issue of Stocks&Commodities magazine.
That pattern conforms to the following rules:
- It uses daily prices, not intraday or weekly prices;
- The middle day of the three-day pattern has the lowest low of the three days, with no ties allowed;
- The last day must have a close above the prior day's high, with no ties allowed;
- Each day must have a nonzero trading range.
WARNING:
- For purpose educate only
- This script to change bars colors.
Combo Backtest 123 Reversal and 2/20 EMA This is combo strategies for get
a cumulative signal. Result signal will return 1 if two strategies
is long, -1 if all strategies is short and 0 if signals of strategies is not equal.
First strategy
This System was created from the Book "How I Tripled My Money In The
Futures Market" by Ulf Jensen, Page 183. This is reverse type of strategies.
The strategy buys at market, if close price is higher than the previous close
during 2 days and the meaning of 9-days Stochastic Slow Oscillator is lower than 50.
The strategy sells at market, if close price is lower than the previous close price
during 2 days and the meaning of 9-days Stochastic Fast Oscillator is higher than 50.
Secon strategy
This indicator plots 2/20 exponential moving average. For the Mov
Avg X 2/20 Indicator, the EMA bar will be painted when the Alert criteria is met.
Please, use it only for learning or paper trading. Do not for real trading.
WARNING:
- For purpose educate only
- This script to change bars colors.
XPloRR MA-Buy ATR-Trailing-Stop Long Term Strategy Beating B&HXPloRR MA-Buy ATR-MA-Trailing-Stop Strategy
Long term MA Trailing Stop strategy to beat Buy&Hold strategy
None of the strategies that I tested can beat the long term Buy&Hold strategy. That's the reason why I wrote this strategy.
Purpose: beat Buy&Hold strategy with around 10 trades. 100% capitalize sold trade into new trade.
My buy strategy is triggered by the EMA(blue) crossing over the SMA curve(orange).
My sell strategy is triggered by another EMA(lime) of the close value crossing the trailing stop(green) value.
The trailing stop value(green) is set to a multiple of the ATR(15) value.
ATR(15) is the SMA(15) value of the difference between high and low values.
Every stock has it's own "DNA", so first thing to do is find the right parameters to get the best strategy values voor EMA, SMA and Trailing Stop.
Then keep using these parameter for future buy/sell signals only for that particular stock.
Do the same for other stocks.
Here are the parameters:
Exponential MA: buy trigger when crossing over the SMA value (use values between 11-50)
Simple MA: buy trigger when EMA crosses over the SMA value (use values between 20 and 200)
Stop EMA: sell trigger when Stop EMA of close value crosses under the trailing stop value (use values between 8 and 16)
Trailing Stop #ATR: defines the trailing stop value as a multiple of the ATR(15) value
Example parameters for different stocks (Start capital: 1000, Order=100% of equity, Period 1/1/2005 to now):
BAR(Barco): EMA=11, SMA=82, StopEMA=12, Stop#ATR=9
Buy&HoldProfit: 45.82%, NetProfit: 294.7%, #Trades:8, %Profit:62.5%, ProfitFactor: 12.539
AAPL(Apple): EMA=12, SMA=45, StopEMA=12, Stop#ATR=6
Buy&HoldProfit: 2925.86%, NetProfit: 4035.92%, #Trades:10, %Profit:60%, ProfitFactor: 6.36
BEKB(Bekaert): EMA=12, SMA=42, StopEMA=12, Stop#ATR=7
Buy&HoldProfit: 81.11%, NetProfit: 521.37%, #Trades:10, %Profit:60%, ProfitFactor: 2.617
SOLB(Solvay): EMA=12, SMA=63, StopEMA=11, Stop#ATR=8
Buy&HoldProfit: 43.61%, NetProfit: 151.4%, #Trades:8, %Profit:75%, ProfitFactor: 3.794
PHIA(Philips): EMA=11, SMA=80, StopEMA=8, Stop#ATR=10
Buy&HoldProfit: 56.79%, NetProfit: 198.46%, #Trades:6, %Profit:83.33%, ProfitFactor: 23.07
I am very curious to see the parameters for your stocks and please make suggestions to improve this strategy.
CLEVER V151. What it is
Type: Full strategy, not just a visual indicator.
Name: CLEVER v15.
Overlay: Plots directly on the price chart.
Core idea:
Generate long/short signals from either:
Heikin‑Ashi open/close cross, or
EMA cross on ATR‑based Renko bricks,
then manage trades with ATR‑based multi‑take‑profit / stop‑loss, plus rich dashboards.
2. Entry logic (two modes)
Controlled by setup Type:
OANDA:XAUUSD OANDA:BTCUSD OANDA:EURUSD OANDA:USDCNH TVC:USOIL
Open/Close mode (“Open/Close”)
Uses Heikin‑Ashi candles on a higher timeframe (my_time).
Buy condition BUYOC: Heikin‑Ashi close crosses above open (bullish body flip).
Sell condition SELLOC: Heikin‑Ashi close crosses below open (bearish body flip).
Signals must also pass the RSI/ATR filter (trendType, see section 3).
Renko mode (“Renko”)
Builds Renko bricks (ATR‑based by default).
Computes EMAs on Renko close:
EMA1 (fast, length 2),
EMA2 (slow, length 10).
Buy BUYR: EMA1 crosses over EMA2 on Renko close (bullish).
Sell SELLR: EMA1 crosses under EMA2 on Renko close (bearish).
Again, trades only fire when the RSI/ATR filter allows it.
Both modes create unified booleans:
buy_entry = either BUYOC or BUYR
sel_entry = either SELLOC or SELLR
These then drive all trade/TP/SL logic.
3. RSI & ATR “sideways / trend” filter
Configurable via typefilter (“Sideways Filtering Input”), using:
RSI: 7‑period RSI with:
toplimitrsi (default 45),
botlimitrsi (default 10).
ATR filter:
ATR length 5 on the symbol.
ATR MA (EMA or SMA, length 5).
It defines combinations like:
ATR “high vs MA” → trending vs quiet.
RSI “outside band” vs “inside band” → trending vs sideways.
Options:
Filter by ATR only.
Filter by RSI only.
ATR or RSI.
ATR and RSI.
No filtering.
Only enter in sideways market (by ATR/RSI or by both).
Result is trendType (boolean).
If trendType is false, no trades are allowed.
4. Trade types / management modes
Controlled by TPSType:
ATR mode (“ATR”) – full multi‑TP risk management
Uses ATR(20) to set three take‑profit levels and a stop:
TP1 ≈ 1×(factor×ATR)
TP2 ≈ 2×(factor×ATR)
TP3 ≈ 3×(factor×ATR)
SL roughly symmetric the other way.
On a valid long/short entry:
Opens one position ("Long" or "Short").
Scales out using three strategy.exit orders:
TP1: 50% of position.
TP2: 30%.
TP3: 20%.
Uses a persistent state variable condition to track which TP/SL has been hit.
Draws lines and labels for:
Entry, SL, TP1/TP2/TP3.
Fills between Entry–TP area (profit zone) and Entry–SL area (risk zone).
Fires alerts for:
Long/Short entries, exits.
TP1/2/3 hits.
SL hits.
Trailing mode (“Trailing”) – signal‑to‑signal reversals
On buy:
Closes existing "Short", opens "Long".
On sell:
Closes "Long", opens "Short".
No fixed ATR TP/SL here; exit is mainly via opposite signal.
Options mode (“Options”) – long‑only style
On buy:
Opens "Long"; does not automatically close shorts (those lines are commented out).
On sell:
Closes "Long"; does not open new shorts.
Useful if you only want long trades (e.g., options, long‑only instruments).
Backtesting date range is also restricted by fromDate and toDate.
5. Visual elements on the chart
Bar colors
One layer colors bars by trend (up / down) from Heikin‑Ashi or Renko context.
Another layer colors bars simply by close > open vs close < open.
Renko “ribbon” / cloud (almaRibbon)
When enabled, plots paired series (Renko or HA open/close) as circles and fills between them.
Cloud color switches with brick/candle trend.
EMA Cloud
Higher‑timeframe EMAs:
ema (48),
ema2 (2),
ema3 (21).
Uses either current TF or a higher TF (useHTF).
Plots EMAs and fills between them with different colors for uptrend vs downtrend using i_emaCloudColorUp/Down.
Gives a visual trend cloud around price.
DEMA ATR line (BackQuant module)
Computes a double‑EMA smoothed ATR‑based line (DemaAtr) from either HA close or standard close.
Constrains the line within ATR bands.
Colors green when rising, red when falling.
Optional visibility via showAtr.
Labels
Entry labels: “Long”, “Short”.
Exit labels: “Close”.
TP labels: “TP1”, “TP2”, “TP3”.
SL label: “SL”.
Line‑end labels showing numeric values and target pips for TP3/SL/Entry.
6. Dashboards / performance tables
All in the Dashboards group:
Strategy Performance panel
Shows after backtest completes:
Total trades, win rate.
Start/ending capital.
Average win/loss.
Profit factor.
Max run‑up.
Return % and max drawdown %.
Weekly Performance table
Day‑of‑week breakdown (Sun–Sat).
Per day:
Total trades,
Wins, losses,
Win rate %.
You choose whether to classify trades by open time or close time, and which timezone.
Monthly Performance table (QuantNomad style)
Month‑by‑month and year‑by‑year P&L (%).
Heatmap style table by year (rows) vs months (columns) + yearly column.
7. Alerts
The script fires TradingView alert() events for:
Any entry/exit (combined “Any Alert”).
Long Entry / Short Entry.
Long Exit / Short Exit.
Plus, when using TPSType == "ATR", the strategy.exit orders can each carry separate webhook messages for TP/SL.
Summary
This script is an advanced multi‑timeframe, Renko/Heikin‑Ashi EMA‑cross strategy with:
RSI + ATR‑based market‑state filter,
Three trade‑management modes (ATR multi‑TP, simple reversal, long‑only),
Rich visuals (Renko/EMA clouds, DEMA ATR, TP/SL areas),
And several performance dashboards (overall, weekly, monthly).
YCGH Ultimate Stocks Breakout Sniper📈 YCGH Ultimate Stocks Breakout Sniper
Overview
A sophisticated momentum-based breakout strategy designed to capture high-probability directional moves during volatility expansion phases. This system identifies breakout opportunities when price decisively breaks through established ranges, combining multiple technical filters to enhance signal quality and minimize false breakouts.
🎯 Strategy Features
Core Methodology:
Proprietary breakout detection algorithm
Multi-layered confirmation filters for signal validation
Adaptive trailing stops for profit protection
Systematic risk management with daily drawdown controls
Key Components:
✅ Volatility Expansion Filter - Only trades during periods of elevated market volatility to avoid choppy, range-bound conditions
✅ Optional Trend Alignment - Configurable trend filter (EMA/SMA/RMA/WMA) to align entries with broader market direction
✅ ROC Momentum Filter - Daily rate-of-change filter to capture strong momentum days (optional)
✅ Comprehensive Exit Strategy:
Fixed stop-loss (default 2%)
Take-profit targets (default 9%)
Dynamic trailing stops (2% activation, 0.5% offset)
✅ Flexible Direction Trading:
Auto-detect mode: Long+Short for perpetuals, Long-only for spot/equities
Manual override options available
Suitable for both crypto and stock markets
📊 Market Applicability
Optimized for: Cryptocurrency perpetual contracts and equity markets (1H-4H timeframes)
Also effective on: Futures and high-liquidity spot markets
The strategy adapts to different market regimes through configurable volatility and trend filters, making it versatile across various trading instruments and timeframes.
⚙️ Risk Management
Position Sizing: Percentage-based allocation with leverage support
Intraday Loss Limit: Maximum 10% drawdown protection (configurable)
Realistic Cost Modeling: 0.025% commission + 1 tick slippage
No Pyramiding: Single position management for controlled risk exposure
📈 Performance Visualization
Includes a comprehensive monthly returns table displaying:
Year-by-year performance breakdown
Monthly profit/loss percentages
Visual color-coding (green for profits, red for losses)
Clean, modern design with transparent styling
🔐 Access & Pricing
This is a PROTECTED, invite-only strategy.
The source code is not open-source and requires paid access for usage.
How to Get Access:
📧 Email: brijamohanjha@gmail.com
Include in your email:
Your TradingView username
Markets/assets you plan to trade
Preferred timeframe
What You'll Receive:
Full strategy access with invite-only permissions
Complete parameter documentation
Setup and optimization guidance
Implementation support
⚠️ Important Disclosures
Backtesting Parameters:
Commission: 0.025% per trade
Slippage: 1 tick
Results reflect realistic trading conditions
Risk Warning:
Past performance does not guarantee future results. This strategy involves substantial risk and may not be suitable for all investors. Users should thoroughly understand the risks and customize parameters based on their risk tolerance and market conditions.
📞 Contact for Access
Email: brijamohanjha@gmail.com
For questions about functionality, pricing, optimization, or market-specific settings, please reach out via email.
Note: This is a premium, paid strategy. Access is granted manually after consultation and payment confirmation.
Pro Bollinger Bands Strategy [Breno]This strategy excels in highly volatile financial instruments, including cryptocurrencies, high-beta stocks, commodity futures, and certain exchange-traded funds (ETFs) that exhibit clear mean-reversion characteristics around their Bollinger Bands. The system's ability to utilize scaling (position averaging) and an ATR-based stop loss makes it particularly effective in markets with significant price swings, allowing the trader to capture profits from price extremes while managing increased volatility-related risk.
Core Strategy Logic
This Strategy implements a comprehensive trend-following and mean-reversion strategy primarily leveraging the Bollinger Bands (BB) indicator for entry and exit signals, complemented by an Average True Range (ATR)-based Stop Loss mechanism and an optional EMA filter. It is designed with robust features for capital management, including configurable leverage and a sophisticated position averaging (scaling) system.
Long Entry: A long position is initiated when the closing price crosses over the Lower Bollinger Band (ta.crossover(close,lowerBB)). This signals a potential mean-reversion opportunity following a price dip.
Short Entry: A short position is initiated when the closing price crosses under the Upper Bollinger Band (ta.crossunder(close,upperBB)). (Note: Short entries are disabled by default in the script inputs).
Exit Conditions (Profit Target): Long positions aim to exit upon interaction with the Upper Bollinger Band. Users can select from three exit methods:
"Close When Touch": Exits when close≥upperBB.
"Close Above then Below": Exits when the previous close was above the upper band, and the current close is below it (a reversal signal).
"High Above": Exits when high>upperBB. The strategy features an optional profitOnly setting, which restricts all exits to only occur if the trade is currently in profit (i.e., close is above the strategy.position_avg_price for longs).
Key Features and Customization
Bollinger Bands & Filters -
Customizable BB Parameters: The Length and Deviation of the Bollinger Bands are fully adjustable, allowing users to fine-tune the sensitivity of the entry and exit signals.
Optional EMA Filter: An optional EMA Filter can be enabled to align entries with the prevailing trend, where a Long entry is only permitted if close≥EMA(EmaFilterRange).
Risk and Capital Management -
Equity Allocation: Position size is dynamically calculated based on a Percentage of Equity (capitalPerc) combined with the set Leverage multiplier.
Dynamic Stop Loss (ATR-Based):
An optional Stop Loss (SL) is calculated using a multiple (slAtrInput) of the Average True Range (ATR).
The SL is set relative to the entry price upon trade activation, providing a volatility-adjusted risk management layer.
Position Averaging (Scaling): The script supports the addition of multiple units (pyramiding) to an existing position based on three user-selected criteria:
"No": No averaging.
"Percent": Adds to the position if the price has dropped by a set percentage (addPct) from the average price.
"ATR": Adds to the position if the current price is significantly below a calculated ATR-based support level from the average price.
Volume Momentum Strategy [MA/VWAP Cross]Deconstructing the Volume Momentum Strategy: An Analysis of MA-VWAP Cross Mechanics
Introduction
The "Volume Momentum Strategy " is a technical trading algorithm programmed in Pine Script v6 for the TradingView platform. At its core, the strategy is a trend-following system that utilizes the interaction between a specific Moving Average (MA) and the Volume Weighted Average Price (VWAP) to generate trade signals. While the primary execution logic relies on price crossovers, the strategy incorporates a sophisticated secondary layer of analysis using the Commodity Channel Index (CCI) and Stochastic Oscillator. Uniquely, these secondary indicators are applied to volume data rather than price, serving as a gauge for market participation and momentum intensity.
The Core Engine: MA and VWAP Crossover
The primary engine driving the strategy's buy and sell decisions is the crossover relationship between a user-defined Moving Average and the VWAP.
1. The Anchor (VWAP): The strategy calculates the Volume Weighted Average Price based on the HLC3 (High, Low, Close divided by 3) source. VWAP serves as the dynamic benchmark for "fair value" throughout the trading session.
2. The Trigger (Moving Average): The script allows for flexibility in defining the "fast" line, offering options such as Simple (SMA), Exponential (EMA), or Hull Moving Averages.
3. The Signal:
o A Long (Buy) signal is generated when the chosen MA crosses over the VWAP. This suggests that short-term price momentum is exceeding the average volume-weighted price of the session, indicating bullish sentiment.
o A Short (Sell) signal is generated when the MA crosses under the VWAP, indicating bearish pressure where price is being pushed below the session's volume-weighted average.
The Role of CCI and Stochastic: Analyzing Volume Momentum
The prompt specifically inquires about how the CCI and Stochastic indicators fit into this process. In standard technical analysis, these oscillators are used to identify overbought or oversold price conditions. However, this strategy repurposes them to analyze Volume Momentum.
1. The Calculation
Instead of using close prices as the input source, the script passes volume data into both indicator functions:
• Volume CCI: Calculated as ta.cci(volume, cciLength). This measures the deviation of current volume from its statistical average.
• Volume Stochastic: Calculated as ta.stoch(volume, volume, volume, stochLength). This gauges the current volume relative to its recent range.
2. The "Volume Spike" Condition
The strategy combines these two indicators to define a specific market condition labeled isVolumeSpike. A volume spike is confirmed only when both conditions are met simultaneously:
• The Volume CCI must be greater than a defined threshold (default: 100).
• The Volume Stochastic must be greater than a defined threshold (default: 80).
3. Integration into the Process
It is critical to note how this script currently applies this "Volume Spike" logic:
• Visual Confirmation: In the current version of the code, the isVolumeSpike boolean is used strictly for visual feedback. When a spike is detected, the script paints the specific price bar yellow and plots a small triangle marker below the bar.
• Strategic Implication: While the code calculates these metrics, the variables long_condition and short_condition currently rely solely on the MA/VWAP crossover. The developer has left the volume logic as a visual overlay, noting in the comments that it serves as a "visual/alert" or a potential filter.
• Potential Alpha: Conceptually, this setup implies that a trader should look for the MA/VWAP crossover to occur coincidentally with—or shortly after—a "Volume Spike" (yellow bar). This would confirm that the price move is backed by significant institutional participation (volume) rather than just retail noise.
Risk Management and Time Constraints
The strategy wraps these technical signals in a robust risk management framework. It includes hard-coded time windows (start/stop trading times) and a "Close All" function to prevent holding positions overnight. Furthermore, it employs both percentage-based and dollar-based Stop Loss and Take Profit mechanisms, ensuring that every entry—whether generated by a high-momentum crossover or a standard trend move—has a predefined exit plan.
Conclusion
The "Volume Momentum Strategy" is a hybrid system. It executes trades based on the reliable trend signal of MA crossing VWAP but informs the trader with advanced volume analytics. By processing volume through the CCI and Stochastic calculations, it provides a "heads-up" display regarding the intensity of market participation, allowing the trader to distinguish between low-volume drifts and high-volume breakout moves.
TrendSight📌 TrendSight — The All-in-One Multi-Timeframe Trend Engine
Key Features & Logic
Multi-Timeframe Trend Confirmation:
Entries are filtered by confirming bullish/bearish alignment across three distinct Supertrend timeframes (e.g., 5-min, 15-min, 45-min, etc.), combined with an EMA and volatility filter, to ensure high-conviction trades that's a powerful combination! Designing the entire strategy around the 15-minute timeframe (M15) and focusing on high-volatility coins maximizes the strategy's effectiveness .
Guaranteed Single-Entry per Signal:
The strategy uses a powerful manual flag and counter system to ensure trades fire only once when a new signal begins. It absolutely prevents immediate re-entry if the signal remains true, waiting instead for the entire trend condition to reset to false.
Dynamic Trailing Stop Loss:
The Stop Loss is set to a moving Supertrend line (current_supertrend), ensuring tight risk management that trails the price as the trade moves into profit.Guaranteed Take Profit (4% Run-up): Uses a precise Limit Order via strategy.exit() to capture profits instantly at a 4% run-up. This ensures accurate profit capture, even on sudden spikes (wicks).
Automated Risk Management:
Position size is dynamically calculated based on a fixed risk percentage (default 2% of equity) relative to the distance to the trailing stop.
🔥 Core Components
1. Adaptive Multi-Timeframe SuperTrend Dashboard
The backbone of mTrendSight is a fully customizable SuperTrend system, enhanced with a multi-timeframe confirmation table displaying ST direction & value.
This compact “Trend Dashboard” provides instant clarity on higher-timeframe direction, trend strength, and market bias.
2. Dynamic Support & Resistance Channels
Automatically detects the strongest support/resistance zones using pivot clustering.
Key Features:
Clustered S/R Channels instead of thin lines
Adaptive width based on recent swings
Breakout markers (optional) for continuation signals
Helps identify structural zones, retest areas, and liquidity pockets
3. Multi-Timeframe Color-Coded EMAs
Plot up to three EMAs, each optionally pulled from a higher timeframe.
Benefits:
Instant visual trend alignment
Bullish/Bearish dynamic color shifts
Precision EMA value table for trade planning
Works perfectly with ST & RSI for multi-layer confirmation
4. Linear Regression Trend Channel
A statistically driven trend channel that measures the most probable path of price action.
Highlights:
Uses Pearson’s R to determine trend reliability
Provides a Confidence Level to judge whether trend slope is credible
Ideal for determining over-extension and mean-reversion zones
5. ATR Volatility Analyzer
A lightweight but powerful volatility classifier using ATR.
Features:
Detects High, Low, or Normal volatility
Clean table display
Helps filter entries during low-energy markets
Strengthens trend-following filters when volatility expands
6. RSI Momentum & Trend Classifier
A significantly improved RSI with multi-layer smoothing and structure-based classification.
Provides:
Bullish / Bearish / Neutral momentum states
Short-term momentum vs long-term RSI trend
Perfect for early trend shifts, pullback entries, and momentum confirmation
⚙️ How the Strategy Works (Execution Logic)
📌 Multi-Timeframe Supertrend + EMA + Volatility Confirmation
Entries are only triggered when:
Multiple Supertrend timeframes align (e.g., 5m + 15m + 45m)
EMA direction aligns with the trend
Volatility conditions (ATR filter) is not Low allow high-probability moves
This ensures strong directional confluence before every trade.
📌 Guaranteed Single-Entry Logic
The strategy uses a flag + counter system to ensure:
Only one entry is allowed per trend signal
Re-entries do not happen until the entire trend condition resets
The Strategy Tester remains clean, without duplicate overlapping trades
This eliminates revenge trades, repeated fills, and choppy overtrading.
📌 Dynamic Supertrend Trailing Stop
Stop Loss is anchored to current Supertrend value, creating:
Automatic trailing
Tight downside control
Protection against deep pullbacks
High responsiveness during volatility expansions
📌 Precision Take-Profit (4% Run-Up Capture)
A dedicated global exit block ensures:
Take Profit triggers exactly at 4% price run-up
Uses strategy.exit() with limit orders to catch spikes (wicks)
Works consistently on all timeframes & assets
📌 Automated Position Sizing (2% Risk Default)
Position size is dynamically calculated based on:
Account Equity
Distance to trailing stop
Configured risk %
This enforces proper risk management without manual adjustments.
📈 How to Interpret Results
Reliable Exits: All exits are globally managed, so stops and take profits trigger accurately on every bar.
Clean Trade History: Because of single-entry logic, backtests show one trade per valid signal.
Consistency: Multi-timeframe logic ensures only high-quality, structured trades.
Grok/Claude Turtle Soup Strategy # 🥣 Turtle Soup Strategy (Enhanced)
## A Mean-Reversion Strategy Based on Failed Breakouts
---
## Historical Origins
### The Original Turtle Traders (1983-1988)
The Turtle Trading system is one of the most famous experiments in trading history. In 1983, legendary commodities trader **Richard Dennis** made a bet with his partner **William Eckhardt** about whether great traders were born or made. Dennis believed trading could be taught; Eckhardt believed it was innate.
To settle the debate, Dennis recruited 23 ordinary people through newspaper ads—including a professional blackjack player, a fantasy game designer, and an accountant—and taught them his trading system in just two weeks. He called them "Turtles" after turtle farms he had visited in Singapore, saying *"We are going to grow traders just like they grow turtles in Singapore."*
The results were extraordinary. Over the next five years, the Turtles reportedly earned over **$175 million in profits**. The experiment proved Dennis right: trading could indeed be taught.
#### The Original Turtle Rules:
- **Entry:** Buy when price breaks above the 20-day high (System 1) or 55-day high (System 2)
- **Exit:** Sell when price breaks below the 10-day low (System 1) or 20-day low (System 2)
- **Stop Loss:** 2x ATR (Average True Range) from entry
- **Position Sizing:** Based on volatility (ATR)
- **Philosophy:** Pure trend-following—catch big moves by riding breakouts
The Turtle system was a **trend-following** strategy that assumed breakouts would lead to sustained trends. It worked brilliantly in trending markets but suffered during choppy, range-bound conditions.
---
### The Turtle Soup Strategy (1990s)
In the 1990s, renowned trader **Linda Bradford Raschke** (along with Larry Connors) observed something interesting: many of the breakouts that the Turtle system traded actually *failed*. Price would spike above the 20-day high, trigger Turtle buy orders, then immediately reverse—trapping the breakout traders.
Raschke realized these failed breakouts were predictable and tradeable. She developed the **Turtle Soup** strategy, which does the *exact opposite* of the original Turtle system:
> *"Instead of buying the breakout, we wait for it to fail—then fade it."*
The name "Turtle Soup" is a clever play on words: the strategy essentially "eats" the Turtles by trading against them when their breakouts fail.
#### Original Turtle Soup Rules:
- **Setup:** Price makes a new 20-day high (or low)
- **Qualifier:** The previous 20-day high must be at least 3-4 days old (not a fresh breakout)
- **Entry Trigger:** Price reverses back inside the channel (failed breakout)
- **Entry:** Go SHORT (against the failed breakout above), or LONG (against the failed breakdown below)
- **Philosophy:** Mean-reversion—fade false breakouts and profit from trapped traders
#### Turtle Soup Plus One Variant:
Raschke also developed a more conservative variant called "Turtle Soup Plus One" which waits for the *next bar* after the breakout to confirm the failure before entering. This reduces false signals but may miss some opportunities.
---
## Our Enhanced Turtle Soup Strategy
We have taken the classic Turtle Soup concept and enhanced it with modern technical indicators and filters to improve signal quality and adapt to today's markets.
### Core Logic Preserved
The fundamental strategy remains true to Raschke's original concept:
| Turtle (Original) | Turtle Soup (Our Strategy) |
|-------------------|---------------------------|
| BUY breakout above 20-day high | SHORT when that breakout FAILS |
| SELL breakout below 20-day low | LONG when that breakdown FAILS |
| Trend-following | Mean-reversion |
| "The trend is your friend" | "Failed breakouts trap traders" |
---
### Enhancements & Improvements
#### 1. RSI Exhaustion Filter
**Addition:** RSI must confirm exhaustion before entry
- **For SHORT entries:** RSI > 60 (buyers exhausted)
- **For LONG entries:** RSI < 40 (sellers exhausted)
**Why:** The original Turtle Soup had no momentum filter. Adding RSI ensures we only fade breakouts when the market is showing signs of exhaustion, significantly reducing false signals. This enhancement was inspired by later traders who found RSI extremes (originally 90/10, softened to 60/40) dramatically improved win rates.
#### 2. ADX Trending Filter
**Addition:** ADX must be > 20 for trades to execute
**Why:** While the original Turtle Soup was designed for ranging markets, we found that requiring *some* trend strength (ADX > 20) actually improves results. This ensures we're trading in markets with enough directional movement to create meaningful failed breakouts, rather than random noise in dead markets.
#### 3. Heikin Ashi Smoothing
**Addition:** Optional Heikin Ashi calculations for breakout detection
**Why:** Heikin Ashi candles smooth out price noise and make trend reversals more visible. When enabled, the strategy uses HA values to detect breakouts and failures, reducing whipsaws from erratic price spikes.
#### 4. Dynamic Donchian Channels with Regime Detection
**Addition:** Color-coded channels based on market regime
- 🟢 **Green:** Bullish regime (uptrend + DI+ > DI- + OBV bullish)
- 🔴 **Red:** Bearish regime (downtrend + DI- > DI+ + OBV bearish)
- 🟡 **Yellow:** Neutral regime
**Why:** Visual regime detection helps traders understand the broader market context. The original Turtle Soup had no regime awareness—our enhancement lets traders see at a glance whether conditions favor the strategy.
#### 5. Volume Spike Detection (Optional)
**Addition:** Optional filter requiring volume surge on the breakout bar
**Why:** Failed breakouts are more significant when they occur on high volume. A volume spike on the breakout bar (default 1.2x average) indicates more traders got trapped, creating stronger reversal potential.
#### 6. ATR-Based Stops and Targets
**Addition:** Configurable ATR-based stop losses and profit targets
- **Stop Loss:** 1.5x ATR (default)
- **Profit Target:** 2.0x ATR (default)
**Why:** The original Turtle Soup used fixed stop placement. ATR-based stops adapt to current volatility, providing tighter stops in calm markets and wider stops in volatile conditions.
#### 7. Signal Cooldown
**Addition:** Minimum bars between trades (default 5)
**Why:** Prevents overtrading during choppy conditions where multiple failed breakouts might occur in quick succession.
#### 8. Real-Time Info Panel
**Addition:** Comprehensive dashboard showing:
- Current regime (Bullish/Bearish/Neutral)
- RSI value and zone
- ADX value and trending status
- Breakout status
- Bars since last high/low
- Current setup status
- Position status
**Why:** Gives traders instant visibility into all strategy conditions without needing to check multiple indicators.
---
## Entry Rules Summary
### SHORT Entry (Fading Failed Breakout Above)
1. ✅ Price breaks ABOVE the 20-period Donchian high
2. ✅ Previous 20-period high was at least 1 bar ago
3. ✅ Price closes back BELOW the Donchian high (failed breakout)
4. ✅ RSI > 60 (exhausted buyers)
5. ✅ ADX > 20 (trending market)
6. ✅ Cooldown period met
→ **Enter SHORT**, betting the breakout will fail
### LONG Entry (Fading Failed Breakdown Below)
1. ✅ Price breaks BELOW the 20-period Donchian low
2. ✅ Previous 20-period low was at least 1 bar ago
3. ✅ Price closes back ABOVE the Donchian low (failed breakdown)
4. ✅ RSI < 40 (exhausted sellers)
5. ✅ ADX > 20 (trending market)
6. ✅ Cooldown period met
→ **Enter LONG**, betting the breakdown will fail
---
## Exit Rules
1. **ATR Stop Loss:** Position closed if price moves 1.5x ATR against entry
2. **ATR Profit Target:** Position closed if price moves 2.0x ATR in favor
3. **Channel Exit:** Position closed if price breaks the exit channel in the opposite direction
4. **Mid-Channel Exit:** Position closed if price returns to channel midpoint
---
## Best Market Conditions
The Turtle Soup strategy performs best when:
- ✅ Markets are prone to false breakouts
- ✅ Volatility is moderate (not too low, not extreme)
- ✅ Price is oscillating within a broader range
- ✅ There are clear support/resistance levels
The strategy may struggle when:
- ❌ Strong trends persist (breakouts follow through)
- ❌ Volatility is extremely low (no meaningful breakouts)
- ❌ Markets are in news-driven directional moves
---
## Default Settings
| Parameter | Default | Description |
|-----------|---------|-------------|
| Lookback Period | 20 | Donchian channel period |
| Min Bars Since Extreme | 1 | Bars since last high/low |
| RSI Length | 14 | RSI calculation period |
| RSI Short Level | 60 | RSI must be above this for shorts |
| RSI Long Level | 40 | RSI must be below this for longs |
| ADX Length | 14 | ADX calculation period |
| ADX Threshold | 20 | Minimum ADX for trades |
| ATR Period | 20 | ATR calculation period |
| ATR Stop Multiplier | 1.5 | Stop loss distance in ATR |
| ATR Target Multiplier | 2.0 | Profit target distance in ATR |
| Cooldown Period | 5 | Minimum bars between trades |
| Volume Multiplier | 1.2 | Volume spike threshold |
---
## Philosophy
> *"The Turtle system made millions by following breakouts. The Turtle Soup strategy makes money when those breakouts fail. In trading, there's always someone on the other side of the trade—this strategy profits by being the smart money that fades the trapped breakout traders."*
The beauty of the Turtle Soup strategy is its elegant simplicity: it exploits a known, repeatable pattern (failed breakouts) while using modern filters (RSI, ADX) to improve timing and reduce false signals.
---
## Credits
- **Original Turtle System:** Richard Dennis & William Eckhardt (1983)
- **Turtle Soup Strategy:** Linda Bradford Raschke & Larry Connors (1990s)
- **RSI Enhancement:** Various traders who discovered RSI extremes improve reversal detection
- **This Implementation:** Enhanced with Heikin Ashi smoothing, regime detection, ADX filtering, and comprehensive visualization
---
*"We're not following the turtles—we're making soup out of them."* 🥣
Liquidity Sweep & FVG StrategyThis strategy combines higher-timeframe liquidity levels, stop-hunt (sweep) logic, Fair Value Gaps (FVGs) and structure-based take-profits into a single execution engine.
It is not a simple mash-up of indicators: every module (HTF levels, sweeps, FVGs, ZigZag, sessions) feeds the same entry/exit logic.
1. Core Idea
The script looks for situations where price:
Sweeps a higher-timeframe high/low (takes liquidity around obvious levels),
Then forms a displacement candle with a gap (FVG) in the opposite direction,
Then uses the edge of that FVG as a limit entry,
And manages exits using unswept structural levels (ZigZag swings or HTF levels) as targets.
The intent is to systematically trade failed breakouts / stop hunts with a defined structure and risk model.
It is a backtesting / study tool, not a signal service.
2. How the Logic Works (Conceptual)
a) Higher-Timeframe Liquidity Engine
Daily, Weekly and Monthly highs/lows are pulled via request.security() and stored as HTF liquidity levels.
Each level is drawn as a line with optional label (1D/1W/1M High/Low).
A level is marked as “swept” once price trades through it; swept levels may be removed or shortened depending on settings.
b) Sweep & Manipulation Filter
A low sweep occurs when the current low trades through a stored HTF low.
A high sweep occurs when the current high trades through a stored HTF high.
If both a high and a low are swept in the same bar, the script flags this as “manipulation” and blocks new entries around that noise.
The script also tracks the sweep wick, bar index and HTF timeframe for later use in SL placement and labels.
c) FVG Detection & Management
FVGs are defined using a 3-candle displacement model:
Bullish FVG: high < low
Bearish FVG: low > high
Only gaps larger than a minimum size (ATR-based if no manual value is set) are kept.
FVGs are stored in arrays as boxes with: top, bottom, mid (CE), direction, and state (filled / reclaimed).
Boxes are auto-extended and visually faded when price is far away, or deleted when filled.
d) Entry Conditions (Sweep + FVG)
For each recent sweep window:
After a low sweep, the script searches for the nearest bullish FVG below price and uses its top edge as a long limit entry.
After a high sweep, it searches for the nearest bearish FVG above price and uses its bottom edge as a short limit entry.
A “knife protection” check blocks trades where price is already trading through the proposed stop.
Only one entry per sweep is allowed; entries are only placed inside the configured NY trading sessions and only if no manipulation flag is active and EOD protection allows it.
e) Stop-Loss Placement (“Tick-Free” SL)
The stop is not placed directly on the HTF level; instead, the script scans a window around the sweep bar to find a local extreme:
Longs: lowest low in a configurable bar window around the sweep.
Shorts: highest high in that window.
This produces a structure-based SL that is generally outside the main sweep wick.
f) Take-Profit Logic (ZigZag + HTF Levels)
A lightweight ZigZag engine tracks swing highs/lows and removes levels that have already been broken.
For intraday timeframes (< 1h), TP candidates come from unswept ZigZag swings above/below the entry.
For higher timeframes (≥ 1h), TP candidates fall back to unswept HTF liquidity levels.
The script picks up to two targets:
TP1: nearest valid target in the trade direction (or a 2R fallback if none exists),
TP2: second target (or a 4R fallback if none exists).
A multi-TP model is used: typically 50% at TP1, remainder managed towards TP2 with breakeven plus offset once TP1 is hit.
g) Session & End-of-Day Filters
Three predefined NY sessions (Early, Open, Afternoon) are available; entries are only allowed inside active sessions.
An End-of-Day filter checks a user-defined NY close time and:
Blocks new entries close to the end of the day,
Optionally forces flat before the close.
3. Inputs Overview (Conceptual)
Liquidity settings: which HTF levels to track (1D/1W/1M), how many to show, and sweep priority (highest TF vs nearest vs any).
FVG settings: visibility radius, search window after a sweep, minimum FVG size.
ZigZag settings: swing length used for TP discovery.
Execution & protection: limit order timeout, breakeven offset, EOD protection.
Visuals: labels, sweep markers, manipulation warning, session highlighting, TP lines, etc.
For exact meaning of each input, please refer to the inline comments in the open-source code.
4. Strategy Properties & Backtesting Notes
Default strategy properties in this script:
Initial capital: 100,000
Order size: 10% of equity (strategy.percent_of_equity)
Commission: 0.01% per trade (adjust as needed for your broker/asset)
Slippage: must be set manually in the Strategy Tester (recommended: at least a few ticks on fast markets).
Even though the order size is 10% of equity, actual risk per trade depends on the SL distance and is typically much lower than 10% of the account. You should still adjust these values to keep risk within what you personally consider sustainable (e.g. somewhere in the 1–2% range per trade).
For more meaningful results:
Test on liquid instruments (e.g. major indices, FX, or liquid futures).
Use enough history to reach 100+ closed trades on your market/timeframe.
Always include realistic commission and slippage.
Do not assume that past performance will continue.
5. How to Use
Apply the strategy to your preferred symbol and timeframe.
Set broker-like commission and slippage in the Strategy Tester.
Adjust:
HTF levels (1D/1W/1M),
Sessions (NY windows),
FVG search window and minimum size,
ZigZag length and EOD filter.
Observe how entries only appear:
After a HTF sweep,
In the configured session,
At a FVG edge,
With TP lines anchored at unswept structure / liquidity.
Use this primarily as a research and backtesting tool to study how your own ICT / SMC ideas behave over a large sample of trades.
6. Disclaimer
This script is for educational and research purposes only.
It does not constitute financial advice, and it does not guarantee profitability. Always validate results with realistic assumptions and use your own judgment before trading live.
