Strategy Myth-Busting #5 - POKI+GTREND+ADX - [MYN]This is part of a new series we are calling "Strategy Myth-Busting" where we take open public manual trading strategies and automate them. The goal is to not only validate the authenticity of the claims but to provide an automated version for traders who wish to trade autonomously.
Our fifth one we are automating is one of the strategies from "The Best 3 Buy And Sell Indicators on Tradingview + Confirmation Indicators ( The Golden Ones ))" from "Online Trading Signals (Scalping Channel)". No formal backtesting was done by them and resuructo messaged me asking if we could validate their claims.
Originally, we mimic verbatim the settings Online Trading Signals was using however weren't getting promising results. So before we stopped there we thought we might want to see if this could be improved on. So we adjusted the Renko Assignment modifier from ATR to Traditional and adjusted the value to be higher from 30 to 47. We also decided to try adding another signal confirmation to eliminate some of the ranged market conditions so we choose our favorite, ADX . Also, given we are using this on a higher time-frame we adjusted the G-Channel Trend detection source from close to OHLC4 to get better average price action indication and more accurate trend direction.
This strategy uses a combination of 2 open-source public indicators:
poki buy and sell Take profit and stop loss by RafaelZioni
G-Channel Trend Detection by jaggedsoft
Trading Rules
15m - 4h timeframe. We saw best results at the recommended 1 hour timeframe.
Long Entry:
When POKI triggers a buy signal
When G-Channel Trend Detection is in an upward trend (Green)
ADX Is above 25
Short Entry:
When POKI triggers a sell signal
When G-Channel Trend Detection is in an downward trend (red)
ADX Is above 25
If you know of or have a strategy you want to see myth-busted or just have an idea for one, please feel free to message me.
Полосы и каналы
Flying Dragon Trend StrategyFlying Dragon Trend Strategy can be used to indicate the trend on all timeframes by finetuning the input settings.
The Flying Dragon Trend family includes both the strategy and the indicator, where the strategy supports of selecting the optimal set of inputs for the indicator in each scenario. Highly recommended to get familiar with the strategy first to get the best out of the indicator.
Flying Dragon Trend plots the trend bands into the ribbon, where the colours indicate the trend of each band. The plotting of the bands can be turned off in the input settings. Based on the user selectable Risk Level the strategy is executed when the price crosses the certain moving average line, or at the Lowest risk level all the bands have the same colour.
The main idea is to combine two different moving averages to cross each other at the possible trend pivot point, but trying to avoid any short term bounces to affect the trend indication. The ingenuity resides in the combination of selected moving average types, lengths and especially the offsets. The trend bands give visual hint for the user while observing the price interaction with the bands, one could say that when "the Dragon swallows the candles the jaws wide open", then there is high possibility for the pivot. The leading moving average should be fast while the lagging moving average should be, well, lagging behind the leading one. There is Offset selections for each moving average, three for leading one and one for the lagging one, those are where the magic happens. After user has selected preferred moving average types and lengths, by tuning each offset the optimal sweet spot for each timeframe and equity will be found. The default values are good enough starting points for longer (4h and up) timeframes, but shorter timeframes (minutes to hours) require different combination of settings, some hints are provided in tooltips. Basically the slower the "leading" moving average (like HMA75 or HMA115) and quicker the "lagging" moving average (like SMA12 or SMA5) become, the better performance at the Lowest risk level on minute scales. This "reversed" approach at the minute scales is shown also as reversed colour for the "lagging" moving average trend band, which seems to make it work surprisingly well.
The Flying Dragon Trend does not necessarily work well on zig zag and range bounce scenarios without additional finetuning of the input settings to fit the current condition.
Strategy direction selector by DashTrader.
Channels Strategy [Dimkud]Channels trading Strategy. Based on "Channels Strategy" by JoseMetal.
To the original strategy added additional options and filters : Static SL/TP in percents (%), time delay between orders, ATR Filter, second Keltner Channel (Multi TimeFrame).
Interface translated to English.
Were good backtest results on many crypto tokens on 15m - 45m - 1h periods.
Mostly with configuration: Keltner Channel (optimise parameters for every token) + Static SL/TP (optimise values for every token) + "Enter Condition" = "Wick out of band".
The better is to optimise paramaters separately for Short and Long trading. And run two separate bots (in settings enable only Long or only Short.)
Tested on real automated trading on few online bot platforms. (3comm, revenuebot, veles).
Later I will make tutorial how to connect strategy to these platforms or contact me if you need help.
Hull Kaufman SuperTrend Cloud (HKST Cloud)TLDR: This is a long only trend following system that uses highest and lowest values of three trend following indicators to form a "cloud". Enter when the candle high crosses above the highest band. Close if the low or close crosses below the lowest band.
3 indicators
1. Kaufman Adaptive moving Average - set at 20
2. Hull Moving Average (of the Kaufman Adaptive moving average) - set at 20
3. SuperTrend - I believe this is set at 5 periods and 3*atr but this can be changed
Cloud
the upper band is the highest of the 3 indicators
the lower band is the lowest of the 3 indicators
Entry and Exit:
Enter when the high crosses above the upper band of the cloud.
(This means you will only get a long signal if the high was previously below the upper band of the cloud and then crosses over.)
Exit when the low crosses below the lower band of the cloud .
(This means that this rule will only close if the low was previously above the upper band and then crosses under)
The "Exit" may not trigger if the low (the wick) never gets above the band. In this case the long order will close if the closing price is below the cloud.
Logic
Kaufamn is the best moving average i have found at responding to changes in volatility. This means it moves up or down quickly during expansive moves but becoming very flat during relative choppiness. However, getting flat causes the the Kaufman AMA to trigger a lot of false signals when volatility is transitioning from high to low. This is why the Hull moving average (with its rounded turns) keeps a lot of the false continuations contained because usually prices need to decisively move higher in order to over come the Hull moving average which continues to increase/decrease during the candles after an expansive move.
The super trend places a stagnant floor and ceiling which acts as a great stoploss or trigger as price action attempts to trend in a certain direction. The super trend allows for the user to adjust the likely hood of this cloud indicator changing from bullish to bearish based on the volatility of the asset that is being analyzed.
individually all of these are great. Together the trader can use this cloud to create a trend following or anlysis system that captures the bulk of moves.
Not my best explanation but this indicator is actually pretty simple.
Hope this helps. Happy Trades
-Snarky Puppy
Ichimoku Cloud and ADX with Trailing Stop Loss (by Coinrule)The Ichimoku Cloud is a collection of technical indicators that show support and resistance levels, as well as momentum and trend direction. It does this by taking multiple averages and plotting them on a chart. It also uses these figures to compute a “cloud” that attempts to forecast where the price may find support or resistance in the future.
The Ichimoku Cloud was developed by Goichi Hosoda, a Japanese journalist, and published in the late 1960s. It provides more data points than the standard candlestick chart. While it seems complicated at first glance, those familiar with how to read the charts often find it easy to understand with well-defined trading signals.
The Ichimoku Cloud is composed of five lines or calculations, two of which comprise a cloud where the difference between the two lines is shaded in.
The lines include a nine-period average, a 26-period average, an average of those two averages, a 52-period average, and a lagging closing price line.
The cloud is a key part of the indicator. When the price is below the cloud, the trend is down. When the price is above the cloud, the trend is up.
The above trend signals are strengthened if the cloud is moving in the same direction as the price. For example, during an uptrend, the top of the cloud is moving up, or during a downtrend, the bottom of the cloud is moving down.
DMI is simple to interpret. When +DI > - DI, it means the price is trending up. On the other hand, when -DI > +DI, the trend is weak or moving on the downside. The ADX does not give an indication of the direction but about the strength of the trend.
Typically values of ADX above 25 mean that the trend is steeply moving up or down, based on the -DI and +DI positioning. This script aims to capture swings in the DMI, and thus, in the trend of the asset, using a contrarian approach.
Trading on high values of ADX, the strategy tries to spot extremely oversold and overbought conditions. Values of ADX above 45 may suggest that the trend has overextended and is maybe about to reverse.
This strategy combines the Ichimoku Cloud with the ADX indicator to better enter trades.
Long orders are placed when these basic signals are triggered.
Long Position:
Tenkan-Sen is above the Kijun-Sen
Chikou-Span is above the close of 26 bars ago
Close is above the Kumo Cloud
MACD line crosses over the signal line
-DI is greater than +DI
ADX is greater than 45
Close Position:
3% increase trailing
3% decrease trailing
The script is backtested from December 2022 and provides good returns.
A trading fee of 0.1% is also taken into account and is aligned to the base fee applied on Binance.
MA Grid StrategyThis trading script uses adjusted 5 min MA40 bands.
The bands are the MA value adjusted by a percentage.
When the symbol closes below the lower bands it buys in increments.
When the symbol closes above the upper bands it sells percentages of the open positions.
Theoretically, this script will work best in bullish price movements, for this reason, ADX and DI were implemented to only trade when the current time frame shows bullish signals.
The returns on this script are minimal, If you have any improvement ideas I would love to hear them, I have only started looking into grid trading.
Kioseff Trading - AI-Powered Strategy Optimizer Introducing the Kioseff Trading AI-Powered Strategy Optimizer
Optimize and build your trading strategy with ease, no matter your experience level. The Kioseff Trading AI-Powered Strategy Optimizer allows traders to efficiently test and refine strategies with thousands of different profit targets and stop loss settings. Integrated with TradingView's backtester, this tool simplifies strategy optimization, strategy testing, and alert setting, enabling you to enhance your strategy with AI-driven insights.
Key Features:
Comprehensive Testing : Simultaneously test thousands of profit targets and stop losses to fine-tune your strategy.
Dual Strategy Optimization : Adjust and optimize both long and short strategies for balanced performance.
AI Integration : Elevate your strategy with heuristic-based adaptive learning, turning it into a smart, AI-assisted system.
Detailed Analysis : View critical metrics like profit factor, win rate, max drawdown, and equity curve, presented in a strategy script format.
Customizable Alerts : Set alerts for the best version of your strategy.
Flexible Risk Management : Optimize various stop loss types, including profit targets, limit orders, OCO orders, trailing stops, and fixed stops.
Targeted Goals : Choose optimization goals like highest win rate, maximum net profit, or most efficient profit.
Indicator Compatibility : Integrate any strategy/indicator, whether it’s your creation, a favorite author’s, or any public TradingView indicator.
Accessible Design : Navigate a user-friendly interface suitable for traders of all skill levels. No code required.
Precision Lock-In : “Lock” your optimal profit target or stop loss to drill down into precision testing of other variables.
How it works
It's important to remember that merely having the AI-Powered Strategy Optimizer on your chart doesn't automatically provide you with the best strategy. You need to follow the AI's guidance through an iterative process to discover the optimal settings for your strategy.
The Trading Strategy Optimizer is a versatile tool tailored for both non-coding traders and seasoned algorithmic trading professionals. Let's start with no-code-required instructions on how to use the optimizer.
Instructions: How To Optimize Your Strategy Without Code
1. Build your strategy in the settings
The image above shows explanations for each key setting.
Note: This example uses the RSI indicator to initiate a long trade whenever it dips below the 30 mark.
Ensure that the indicator you wish to optimize is already applied to your chart . This enables the Trading Strategy Optimizer to interact with the indicator and finetune profit targets and stop losses effectively.
Because the indicator is plotted on the chart I can access the indicator with the Trading Strategy Optimizer and optimize profit targets and stop losses for it.
2. Leverage AI Recommendations
Optimization Prompt: After you load your strategy, the tool advises you on new TP and SL levels that could be more profitable.
When your strategy is set, the tool gives you tips for where to set your profit goal (TP) and your stop loss to help you optimize your strategy. It'll tell you if there's a better range for these settings based on past results.
Follow Suggestions: Keep updating your TP and SL according to the tool's suggestions until it says "Best Found".
Final Result: The last image shows the best settings found by the indicator.
(Optional Step 3)
3. Lock the profit target or stop loss to further fine tune your strategy
Continue following the AI’s suggestion until “Best Found” is displayed.
Note: you can select lock either your stop loss or profit target for fine tuning. For this demonstration we will lock our profit target.
Code-Required Instructions (Optional)
You can backtest more code-intensive strategies, such as harmonic patterns, traditional chart patterns, candlestick patterns, Elliot wave, etc., by coding the entry condition in your own script and loading it into the Trading Strategy Optimizer. Let's dial in on how to achieve this!
1. You must create an integer variable in your script with an initial value of "0".
2. Define your entry condition in the code. Once complete, assign the value "1" to the variable you created if the entry condition is fulfilled.
3. Plot your variable.
4. Select the plotted variable in the settings for the Trading Strategy Optimizer
The image above shows a coded entry condition for the linear regression channel (which can be any indicator). When price crosses under and closes below the lower line our variable "strategyEntryVariable" is assigned the value "1".
The Trading Strategy Optimizer will treat this change in value from "0" to "1" as an entry signal and enter long/short up to 1000 times at the price where the entry condition was fulfilled.
5. Test Your Strategy
The image above shows the completion of the process! Keep applying the steps we described. Stick with the AI's recommendations until you see “Best Found” show up.
By following these instructions, you can build, test, and optimize almost any trading indicator or strategy!
So, just note that the Trading Strategy Optimizer considers a change in value of a plotted variable from "0" to "1" as an entry signal! So long as you follow this rule you should be able to test and optimize any conceivable, Pine Script compatible strategy!
AI Mode
AI Mode incorporates Heuristic-Based Adaptive Learning to fine-tune trading strategies in a continuous manner. This feature consists of two main components:
Heuristic-Based Decision Making: The algorithm evaluates multiple versions of your strategy using specific metrics such as Profit and Loss (PNL), Win Rate, and Most Efficient Profit. These metrics act as heuristics to assist the algorithm in identifying suitable profit targets and stop losses for trade execution.
Online Learning: The algorithm updates the performance evaluations of each strategy based on incoming market data. This enables the system to adapt to current market conditions.
Incorporating both heuristic-based decision-making and online learning, this feature aims to provide a framework for trading strategy optimization.
Settings
AI Mode Aggressiveness:
Description: The "AI Mode Aggressiveness" setting allows you to fine-tune the AI's trading behavior. This setting ranges from "Low" To "High, with higher aggressiveness indicating a more assertive trading approach.
Functionality: This feature filters trading strategies based on a proprietary evaluation method. A higher setting narrows down the strategies that the AI will consider, leaning towards more aggressive trading. Conversely, a lower setting allows for a more conservative approach by broadening the pool of potential strategies.
Adaptive Learning Aggressiveness:
Description: When Adaptive Learning is enabled, the "Adaptive Learning Aggressiveness" setting controls how dynamically the AI adapts to market conditions using selected performance metrics.
Functionality: This setting impacts the AI's responsiveness to shifts in strategy performance. By adjusting this setting, you can control how quickly the AI moves away from strategies that may have been historically successful but are currently underperforming, towards strategies that are showing current promise.
Additional Settings
Optimization
Trading system optimization is immensely advantageous when executed with prudence.
Technical-oriented, mechanical trading systems work when a valid correlation is methodical to the extent that an objective, precisely-defined ruleset can consistently exploit it. If no such correlation exists, or a technical-oriented system is erroneously designed to exploit an illusory correlation (absent predictive utility), the trading system will fail.
Evaluate results practically and test parameters rigorously after discovery. Simply mining the best-performing parameters and immediately trading them is unlikely a winning strategy. Put as much effort into testing strong-performing parameters and building an accompanying system as you would any other trading strategy. Automated optimization involves curve fitting - it's the responsibility of the trader to validate a replicable sequence or correlation and the trading system that exploits it.
supertrend with multiple filter strategythis indicator filters buy and sell signal from the supertrend base on various condition that the user can manually select.
as of now the following filter are included
buy and sell filter;
-Macd
-CCI
-EMA200
-LUX TRAMA
-Stochastic rsi
-MFI
EXIT SIGNAL CAN BE CHOSE BETWEEN ATR BAND OR BOLLINGER BAND
i am planning on keeping to add filters so if you have suggestion fell free to message me.
BankNifty_Bullish_Intraday
The script uses following mechanism to give a signal of BUY if multiple parameters evaluated are all passed.
ENTRY-
1. 5 min MACD should be more than its previous tick
2. 15 min MACD should be more than its previous tick
3. 60 min MACD should be more than its previous tick
4. ADX should be more than 12
5. RSI should be more than 60
6. Stochastic %k should have cross over with %d
7. Bollinger band upper band value should be more than previous tick
EXIT
If the 5 min bar price closes below 5 min EMA , it gives an exit signal.
BankNifty_Bearish_Intraday
The script uses following mechanism to give a signal of SELL if multiple parameters evaluated are all passed.
ENTRY-
1. 5 min MACD should be less than its previous tick
2. 15 min MACD should be less than its previous tick
3. 60 min MACD should be less than its previous tick
4. ADX should be more than 12
5. RSI should be less than 40
6. Stochastic %k should have negative cross over with %d
7. Bollinger band lower band value should be less than previous tick
EXIT
If the 5 min bar price closes above 5 min EMA , it gives an exit signal.
Macro Score - DFMA-BasedA "macro score", as defined here, is created by giving various weights to different signals and adding them together to get one smooth score. Positive or negative values are assigned to each of the signals depending on if the statement is true or false (e.g. DPO > 0: +1, DPO < 0: -1). This manner of strategy allows for a subset of the available signals to be present at one time as opposed to every technical signal having to be active in order for a long/short signal to trigger.
The DFMA - Democratic Fibonacci Moving Average - is a separate indicator that we have released that takes 10 different Fibonacci MAs (lengths of 3 to 233, at Fibonacci intervals) and averages them to form the DFMA line. This helps by creating a consensus on the trend based on moving averages alone. Crossovers of the DFMA with the various Fib MA lengths as well as a cross of the price source and these lines can provide adequate long and short signals.
This strategy has the signals and weights pre-determined in the code. Heaviest weights have been given to crosses of the DFMA line/Fib MA (233) as well as the crosses of the Fib MA (3)/DFMA. Additionally, there are thresholds for DPO ( Detrended Price Oscillator , above or below 0), CMO ( Chande Momentum Oscillator , above or below 0), Jurik Volatility Bands (above or below 0), and Stoch RSI (above or below 50). These foursignals hold a lighter weight than the MA cross signals.
The macro score itself is printed in an underlay as a white line that goes between -10 and 10 for this strategy. In addition to the macro score line, a blue momentum line (sourced by the macro score itself) has been included. A crossover/crossunder of the macro score and the macro momentum line is included into the long/short signal syntax in addition to a threshold for the macro score (-5/5).
Take profit, stop loss, and trailing percentages are also included, found at the bottom of the Input tab under “TT and TTP” as well as “Stop Loss”. Make sure to understand the TP/SL ratio that you desire before use, as the desired hit rate/profitability percentage will be affected accordingly. This strategy does NOT guarantee future returns. Apply caution in trading regardless of discretionary or algorithmic. Understand the concepts of risk/reward and the intricacies of each strategy choice before utilizing them in your personal trading.
Profitview Settings:
If you wish to utilize Profitview’s automation system, find the included “Profitview Settings” under the Input tab of the strategy settings menu. If not, skip this section entirely as it can be left blank. Options will be “OPEN LONG TITLE”, “OPEN SHORT TITLE”, “CLOSE LONG TITLE”, and “CLOSE SHORT TITLE”. If you wished to trade SOL, for example, you would put “SOL LONG”, “SOL SHORT”, “SOL CLOSE LONG”, and “SOL CLOSE SHORT” in these areas. Within your Profitview extension, ensure that your Alerts all match these titles. To set an alert for use with Profitview, go to the “Alerts” tab in TradingView, then create an alert. Make sure that your desired asset and timeframe are currently displayed on your screen when creating the alert. Under the “Condition” option of the alert, select the strategy, then select the expiration time. If using TradingView Premium, this can be open-ended. Otherwise, select your desired expiration time and date. This can be updated whenever desired to ensure the strategy does not expire. Under “Alert actions”, nothing necessarily needs to be selected unless so desired. Leave the “Alert name” option empty. For the “Message”, delete the generated message and replace it with {{strategy.order.alert_message}} and nothing else.
Default Properties, for AVAX 20M:
DPO - 40, uncentered
CMO - 25, open
K/D - 3/3
RSI Stoch Length - 3
Stoch Length - 4
Stoch Source - open
JVB Length - 25
JVB Smoothing - 2
DFMA source - close
Macro Length - 13
TP % - 1.5%
TTP % - 0.005%
SL % - 2%
MACD Optimizer Pro [Kioseff Trading]Massive update! This script now includes 12 different moving averages and 30+ built-in technical indicators to enhance your trading strategy optimization! (:
This script (MACD Optimizer Pro) allows the user to optimize and test hundreds of MACD strategies, simultaneously, in under 40 seconds. Of course, theoretically, an unlimited number of trading strategies can be tested with the MACD Optimizer Pro. After the optimization period - the MACD Optimizer Pro will show the most profitable MACD strategy or, should you choose, the highest win-rate MACD strategy or the most-efficient MACD strategy!
Optimization results can be backtested and verified using the native TradingView backtester - which is included in the MACD Optimizer Pro - and made easy to use! This feature makes settings alerts a simple practice!
Features
Test hundreds of MACD strategies, simultaneously, in under 40 seconds.
Optimize long MACD strategies and short MACD strategies.
12 different built-in moving averages included to improve your MACD strategy.
30+ built-in technical indicators to improve your MACD strategy.
Runs as a strategy script - profit factor, PnL , win-rate, number of trades, max drawdown, equity curve and other pertinent statistics shown.
Alerts
Optimize any MACD setting
Profit targets, trailing stops, fixed stop losses, and a binary MACD strategy can all be tested.
Strategies can be optimized for highest win rate, highest net profit, most efficient profit.
Limit orders can be simulated.
External indicators can be used for optimization i.e. your own, custom-built indicator, an indicator from your favorite author, or almost any publicly available
TradingView indicator.
Date range for optimization and backtesting are configurable.
Explanation
The image above shows a list of configurations for the optimizer. You can
You can test hundreds of different MACD settings in under 40 seconds on any timeframe, asset, etc.
The image above shows additional settings to filter the outcome of your optimization testing. Additionally, you can test an unlimited number of profit targets and stop losses!
You can add one of several built-in TradingView indicators to filter trade entries.
The image above shows all built-in moving averages and TradingView indicators that can be incorporated into your MACD strategy.
Additionally, you can add your own, custom indicator to the optimization test, your favorite indicator by your favorite author or almost any publicly available indicator on TradingView.
The image above shows the settings section in which you can implement this feature.
The image above shows an example of the custom indicator feature! In this instance, I am using the public indicator titled "Self-Optimizing" RSI and requiring it to measure below a level prior to entry! Almost any custom indicator, your favorite indicator, etc. is compatible with this feature!
The MACD Optimizer has improved user friendliness over previous versions. The optimizer can be as simple or complex as you'd like - capable of handling both "easy" and "difficult" tasks at your discretion.
Additionally, you can configure the optimizer to prioritize MACD strategies that earn profit most efficiently!
The image above shows this feature in action.
You can also configure the optimizer to prioritize MACD strategies that achieve the highest win rate!
The image above shows this feature in action.
Instructions
The instructions below show a rudimentary approach to using the optimizer.
1. Build your strategy in the settings.
You should also disable the "Run a Backtest" feature to improve load times during optimization.
The image above shows my custom strategy settings.
Now that you've got some data on your chart - you should try "Freezing" the "Smoothing" setting for MACD . When doing this, the optimizer will test hundreds of MACD settings with a fixed "Smoothing" setting. Try using the best "Smoothing" setting you were able to find for your initial testing.
2. Take the best "Smoothing" setting and test various MACD and Signal Lengths.
The image above shows me configuring the MACD Optimizer to test different MACD line lengths and Signal line lengths with a fixed "smoothing" setting.
From the results, we can see that there are better MACD settings than what was shown in our initial test!
With this information we can execute a TradingView backtest.
3. Execute a TradingView Backtest.
You must enable the "Run a Backtest" feature to perform a TradingView backtest. Additionally, it's advised to enable the "STOP OPTIMIZATION" feature when performing a TradingView backtest. Enabling this feature will improve load times for the backtest to only a few seconds (since the optimizer won't look for the best setting when this feature is enabled).
The image above shows completion of the process!
From here, you can perform further testing, set alerts, etc.
Backtest Settings Shown
Initial Capital: The initial capital used for the shown backtests is $3,500 USD. Set the initial capital to replicate your true starting capital (: PnL for the MACD strategies (listed in table) is calculated using a starting capital of $10,000 USD.
Slippage: The slippage settings for the displayed backtest was set to 2 ticks.
Commission: Commission was adjusted to 0.1%.
Verify Price for Limit Orders was set to 2 ticks.
Optimization
Trading system optimization is immensely advantageous when executed with prudence.
Technical-oriented, mechanical trading systems work when a valid correlation is methodical to the extent that an objective, precisely-defined ruleset can consistently exploit it. If no such correlation exists, or a technical-oriented system is erroneously designed to exploit an illusory correlation (absent predictive utility), the trading system will fail.
Evaluate results practically and test parameters rigorously after discovery. Simply mining the best-performing parameters and immediately trading them is unlikely a winning strategy. Put as much effort into testing strong-performing parameters and building an accompanying system as you would any other trading strategy. Automated optimization involves curve fitting - it's the responsibility of the trader to validate a replicable sequence or correlation and the trading system that exploits it.
Thanks for checking this out!
Kioseff Trading - AI-Optimized Supertrend
AI-Optimized Supertrend
Introducing AI-Optimized Supertrend: a streamlined solution for traders of any skill level seeking to rapidly test and optimize Supertrend. Capable of analyzing thousands of strategies, this tool cuts through the complexity to identify the most profitable, reliable, or efficient approaches.
Paired with TradingView's native backtesting capabilities, the AI-Optimized Supertrend learns from historical performance data. Set up is easy for all skill levels, and it makes fine-tuning trading alerts and Supertrend straightforward.
Features
Rapid Supertrend Strategy Testing : Quickly evaluate thousands of Supertrend strategies to find the most effective ones.
AI-Assisted Optimization : Leverage AI recommendations to fine-tune strategies for superior results.
Multi-Objective Optimization : Prioritize Supertrend based on your preference for the highest win rate, maximum profit, or efficiency.
Comprehensive Analytics : The strategy script provides an array of statistics such as profit factor, PnL, win rate, trade counts, max drawdown, and an equity curve to gauge performance accurately.
Alerts Setup : Conveniently set up alerts to be notified about critical trade signals or changes in performance metrics.
Versatile Stop Strategies : Experiment with profit targets, trailing stops, and fixed stop losses.
Binary Supertrend Exploration : Test binary Supertrend strategies.
Limit Orders : Analyze the impact of limit orders on your trading strategy.
Integration with External Indicators : Enhance strategy refinement by incorporating custom or publicly available indicators from TradingView into the optimization process.
Key Settings
The image above shows explanations for a list of key settings for the optimizer.
Set the Factor Range Limits : The AI suggests optimal upper and lower limits for the Factor range, defining the sensitivity of the Supertrend to price fluctuations. A wider range tests a greater variety, while a narrower range focuses on fine-tuning.
Adjust the ATR Range : Use the AI's recommendations to establish the upper and lower bounds for the Average True Range (ATR), which influences the Supertrend's volatility threshold.
ATR Flip : This option lets you interchange the order of ATR and Factor values to quicky test different sequences, giving you the flexibility to explore various combinations and their impact on the Supertrend indicator's performance.
Strategies Evaluated : Adjust this setting to determine how many Supertrend strategies you want to assess and compare.
Enable AI Mode : Turn this feature on to allow the AI to determine and employ the optimal Supertrend strategy with the desired performance metric, such as the highest win rate or maximum profitability.
Target Metric : Adjust this to direct the AI towards optimizing for maximum profit, top win rates, or the most efficient profits.
AI Mode Aggressiveness : Set how assertively the AI pursues the chosen performance goal, such as highest profit or win rate.
Strategy Direction : Choose to focus the AI's testing and optimization on either long or short Supertrend strategies.
Stop Loss Type : Specify the stop loss approach for optimization—fixed value, a trailing stop, or Supertrend direction changes.
Limit Order : Decide if you want to execute trades using limit orders for setting your profit targets, stop losses, or apply them to both.
Profit Target : Define your desired profit level when using either a fixed stop loss or a trailing stop.
Stop Loss : Define your desired stop loss when using either a fixed stop loss or a trailing stop.
How to: Find the best Supertrend for trading
It's important to remember that merely having the AI-Optimized Supertrend on your chart doesn't automatically provide you with the best strategy. You need to follow the AI's guidance through an iterative process to discover the optimal Supertrend settings and strategy.
Optimizing Supertrend involves adjusting two key parameters: the Factor and the Average True Range (ATR). These parameters significantly influence the Supertrend indicator's sensitivity and responsiveness to price movements.
Factor : This parameter multiplies the ATR to determine the distance of the Supertrend line from the price. Higher values will create a wider band, potentially leading to fewer trade signals, while lower values create a narrower band, which may result in more signals but also more noise.
ATR (Average True Range) : ATR measures market volatility. By using the ATR, the Supertrend adapts to changing market volatility; a higher ATR value means a more volatile market, so the Supertrend adjusts accordingly.
During the optimization process, these parameters are systematically varied to determine the combination that yields the best performance based on predefined criteria such as profitability, win rate, or risk management efficiency. The optimization aims to find the optimal Factor and ATR settings.
1.Starting Your Strategy Setup
Begin by deciding your goals for each trade: your profit target and stop loss, or if all trades exit when Supertrend changes direction. You'll also choose how to manage your stops – whether they stay put (fixed) or move with the price (trailing), and whether you want to exit trades at a specific price (limit orders). Keep the initial settings for Supertrend Factor Range and Supertrend ATR Range at their default to give the tool a broad testing field. The AI's guidance will refine these settings to pinpoint the most effective ones through a process of comprehensive testing.
Demonstration Start: We'll begin with the settings outlined in the key settings section, using Supertrend's direction change to the downside as our exit signal for all trades.
2. Continue applying the AI’s suggestions
Keep updating your optimization settings based on the AI's recommendations. Proceed with this iterative optimization until the "Best Found" message is displayed, signaling that the most effective strategy has been identified.
While following the AI's suggestions, we've been prompted with a new suggestion: increase the
number of strategies evaluated. Keep following the AI's new suggestions to evaluate more strategies. Do this until the "Best Found" message shows up.
Success! We continued to follow the AI’s suggestions until “Best Found” was indicated!
AI Mode
AI Mode incorporates Heuristic-Based Adaptive Learning to fine-tune trading strategies in a continuous manner. This feature consists of two main components:
Heuristic-Based Decision Making: The algorithm evaluates multiple Supertrend-based trading strategies using metrics such as Profit and Loss (PNL), Win Rate, and Most Efficient Profit. These metrics act as heuristics to assist the algorithm in identifying suitable strategies for trade execution.
Online Learning: The algorithm updates the performance evaluations of each strategy based on incoming market data. This enables the system to adapt to current market conditions.
Incorporating both heuristic-based decision-making and online learning, this feature aims to provide a framework for trading strategy optimization.
AI Mode Settings
AI Mode Aggressiveness:
Description: The "AI Mode Aggressiveness" setting allows you to fine-tune the AI's trading behavior. This setting ranges from “Low” to “High”, with “High” indicating a more assertive trading approach.
Functionality: This feature filters trading strategies based on a proprietary evaluation method. A higher setting narrows down the strategies that the AI will consider, leaning towards more aggressive trading. Conversely, a lower setting allows for a more conservative approach by broadening the pool of potential strategies.
Optimization
Trading system optimization is immensely advantageous when executed with prudence.
Technical-oriented, mechanical trading systems work when a valid correlation is methodical to the extent that an objective, precisely-defined ruleset can consistently exploit it. If no such correlation exists, or a technical-oriented system is erroneously designed to exploit an illusory correlation (absent predictive utility), the trading system will fail.
Evaluate results practically and test parameters rigorously after discovery. Simply mining the best-performing parameters and immediately trading them is unlikely a winning strategy. Put as much effort into testing strong-performing parameters and building an accompanying system as you would any other trading strategy. Automated optimization involves curve fitting - it's the responsibility of the trader to validate a replicable sequence or correlation and the trading system that exploits it.
M0PB (Momentum Pullback)Long/short strategy that identifies extreme readings on the rsi as a *momentum signal*, unlike most RSI strategies the script will look to buy or sell the first pullback in the direction of the extreme RSI reading.
Enters positions on the first pullback to the 5ema(low)/ 5ema(high) and exits at rolling 12 bar high/ low. The rolling high/ low feature means that if the price enters into a prolonged consolidation the profit target will begin to reduce with each new bar. The best trades tend to work within 2-6 bars.
Built for use on 5 min intervals on FX, Indexes, and Crypto. Lower than 5 minute time frames tend to be noisier and mean more commissions and a higher risk of slippage so the suggested timeframe is 5 mins.
Hard stop is X ATR (users can experiment with this) from the position entry price. This can be adjusted in user inputs.
There is a lot of slack left in entries and exits but the overall strategy is fairly robust across timeframes and markets and has between 60%-70% win rate with larger winners.
Signals that occur from economic news volatility are best avoided.
I11L - Reversal Trading Ideas by Larry ConnorsThis is my own Twist on Larry Connors Simple Tradingideas.
It Combines the RSI, Averaging In and the Lowest Bars in a Single System.
The current Configuration is designed for the Daily Timeframe.
Feel free to play with the Parameters and keep in mind that Larry Intended to buy fear and sell the greed!
The Rules are the following:
---Buy---
Buy, if the lowest Bar of your Configuration has been hit (default is set to 7).
---Average Down--
Buy, if the lowest Bar of your Configuration * Your open trades has been hit AND only if the buyin is atleast 1% cheaper.
---Close---
Close, if the RSI closes above a certain level (default is set to 70).
---TP---
There is no TP
---SL---
There is no SL, so be cautions of your tail Risk!
Extended Recursive Bands StrategyThe original indicator was created by alexgrover .
All credit goes to alexgrover for creating the indicator that this strategy uses.
This strategy was posted because there were multiple requests for it, and no strategy based on this indicator exists yet.
The Recursive Bands Indicator, an indicator specially created to be extremely efficient, I think you already know that calculation time is extra important in algorithmic trading, and this is the principal motivation for the creation of the proposed indicator. Originally described in Alex's paper "Pierrefeu, Alex (2019): Recursive Bands - A New Indicator For Technical Analysis", the indicator framework has been widely used in his previous uploaded indicators, however it would have been a shame to not upload it, however user experience being a major concern for me, I decided to add extra options, which explain the term "extended".
The Indicator
The indicator displays one upper and one lower band, every common usages applied to bands indicators such as support/resistance , breakout, trailing stop, etc, can also be applied to this one. Length controls how reactive the bands are, higher values will make the bands cross the price less often.
In order to provide more flexibility for the user alexgrover added the option to use various methods for the calculation of the indicator, therefore the indicator can use the average true range , standard deviation, average high-low range, and one totally exclusive method specially designed for this indicator.
Added logic:
We have implemented a logic that checks whether the bands have been following in the same direction for a set amount of bars. This logic must be true before it can enter trades. This is completely new code that was written by us entirely, and it makes a huge difference on strategy performance.
Strategy Long conditions:
1 — Price low is below the the lower band.
2 — The lower band keeps increasing in value until the 'lookback' setting amount of bars is reached.
Strategy Short conditions:
1 — Price high is above the upper band.
2 — The upper band keeps decreasing in value until the 'lookback' setting amount of bars is reached.
Strategy Properties:
We have set a default commission of 0.06% because these are Bybit's fees. The strategy uses an order size of 10% of equity, since drawdown is very low like this. We also use a 10 tick slippage to keep results realistic and account for this. All other settings were left as default apart from initial capital, just to decrease the size of the numbers.
Bollinger Band strategy with split, limit, stopEntering a short position after breaking the upper Bollinger Band, entering a long position when entering after breaking the lower Bollinger Band
Provides templates for how to display position average price, stop loss, and profit price using the plot function on the chart, and how to buy splits
After entering the position, if the price crosses the mid-band line, the stop loss is adjusted to the mid-band line.
ILDA FINALY_BOT_V1conversionPeriods = input.int(9, minval=1, title="Conversion Line Length")
basePeriods = input.int(26, minval=1, title="Base Line Length")
laggingSpan2Periods = input.int(52, minval=1, title="Leading Span B Length")
displacement = input.int(26, minval=1, title="Lagging Span")
donchian ( len ) => math. avg (ta.lowest( len ), ta.highest( len ))
conversionLine = donchian (conversionPeriods)
baseLine = donchian (basePeriods)
leadLine1 = math. avg (conversionLine, baseLine)
leadLine2 = donchian (laggingSpan2Periods)
plot(conversionLine, color=#2962FF, title="Conversion Line")
plot(baseLine, color=#B71C1C, title="Base Line")
plot(close, offset = -displacement + 1, color=#43A047, title="Lagging Span")
p1 = plot(leadLine1, offset = displacement - 1, color=#A5D6A7,
title="Leading Span A")
p2 = plot(leadLine2, offset = displacement - 1, color=#EF9A9A,
title="Leading Span B")
plot(leadLine1 > leadLine2 ? leadLine1 : leadLine2, offset = displacement - 1, title = "Kumo Cloud Upper Line", display = display.none)
plot(leadLine1 < leadLine2 ? leadLine1 : leadLine2, offset = displacement - 1, title = "Kumo Cloud Lower Line", display = display.none)
fill(p1, p2, color = leadLine1 > leadLine2 ? color. rgb (67, 160, 71, 90) : color. rgb (244, 67, 54, 90))
ILDA FINALY_BOTconversionPeriods = input.int(9, minval=1, title="Conversion Line Length")
basePeriods = input.int(26, minval=1, title="Base Line Length")
laggingSpan2Periods = input.int(52, minval=1, title="Leading Span B Length")
displacement = input.int(26, minval=1, title="Lagging Span")
donchian(len) => math.avg(ta.lowest(len), ta.highest(len))
conversionLine = donchian(conversionPeriods)
baseLine = donchian(basePeriods)
leadLine1 = math.avg(conversionLine, baseLine)
leadLine2 = donchian(laggingSpan2Periods)
plot(conversionLine, color=#2962FF, title="Conversion Line")
plot(baseLine, color=#B71C1C, title="Base Line")
plot(close, offset = -displacement + 1, color=#43A047, title="Lagging Span")
p1 = plot(leadLine1, offset = displacement - 1, color=#A5D6A7,
title="Leading Span A")
p2 = plot(leadLine2, offset = displacement - 1, color=#EF9A9A,
title="Leading Span B")
plot(leadLine1 > leadLine2 ? leadLine1 : leadLine2, offset = displacement - 1, title = "Kumo Cloud Upper Line", display = display.none)
plot(leadLine1 < leadLine2 ? leadLine1 : leadLine2, offset = displacement - 1, title = "Kumo Cloud Lower Line", display = display.none)
fill(p1, p2, color = leadLine1 > leadLine2 ? color.rgb(67, 160, 71, 90) : color.rgb(244, 67, 54, 90))
Strategy Myth-Busting #20 - HalfTrend+HullButterfly - [MYN]#20 on the Myth-Busting bench, we are automating the " I Found Super Easy 1 Minute Scalping System And Backtest It 100 Times " strategy from " Jessy Trading " who claims 30.58% net profit over 100 trades in a couple of weeks with a 51% win rate and profit factor of 1.56 on EURUSD .
This one surprised us quite a bit. Despite the title of this strategy indicating this is on the 1 min timeframe, the author demonstrates the backtesting manually on the 5 minute timeframe. Given the simplicity of this strategy only incorporating a couple of indicators, it's robustness being able to be profitable in both low and high timeframes and on multiple symbols was quite refreshing.
The 3 settings which we need to pay most attention to here is the Hull Butterfly length, HalfTrend amplitude and the Max Number Of Bars Between Hull and HalfTrend Trigger. Depending on the timeframe and symbol, these settings greatly impact the performance outcomes of the strategy. I've listed a couple of these below.
And as always, If you know of or have a strategy you want to see myth-busted or just have an idea for one, please feel free to message me.
This strategy uses a combination of 3 open-source public indicators:
Hull Butterfly Oscillator by LuxAlgo
HalfTrend by Everget
Trading Rules
5 min candles but higher / lower candles work too.
Stop loss at swing high/low
Take Profit 1.5x the risk
Long
Hull Butterfly gives us green column, Wait for HalfTrend to present an up arrow and enter trade.
Short
Hull Butterfly gives us a red column , Wait for HalfTrend to present a down arrow and enter trade.
Alternative Trading Settings for different time frames
1 Minute Timeframe
Move the Hull Butterfly length from the default 11 to 9
Move the HalfTrend Amplitude from the default 2 to 1
Enabling ADX Filter with a 25 threshold
2 Hour Timeframe
Move the HalfTrend Amplitude from the default 2 to 1
Laddered Take Profits from 14.5% to 19% with an 8% SL
[Floride] 4LBS Strategy - COPPERHEAD**Hello. Because of my poor English skill, there may be many grammatically incorrect sentences in the description below. Thank you in advance for your understanding. **
Copperhead
This is a strategy created by combining three 4LBS channels, with the goal of catching the most volatile points, the points that are profitable as soon as you enter the position. The goal is to target the minimum number of entries and the vital point without missing it.
Characteristics
Each channel is L1 in 4LBS channels using a period of Fibonacci multiples.
You enter the position only when all three layers break through.
The initial losscut ratio setpoint is 3%.
If you don't get losscut and get on the trend safely, you liquidate your position when L4 breaks down.
advantages
- This strategy is profitable for almost every time frame. However, the longer the candle period you using in the chart, the more you have to increase the losscut ratio.
And if the candle period is shortened, you have to reduce the losscut ratio.
- One of the factors behind the strategy's bottom line is that it only enters at critical points and does not have a large number of entries.
- It was intended to make clear visual effects as simple as possible to understand the current trend.
- Korean Description -
전략 : 카퍼헤드
이것은 4LBS채널 3개를 결합하여 만든 전략으로, 가장 변동성이 극단적이 되는 지점들, 진입하자마자 수익이 나는 지점들을 캐치하는 것을 목표로 만든 전략입니다.
최소의 진입횟수, 그리고 급소를 놓치지 않고 공략하는 것이 목적입니다.
** 특징 **
- 각각의 채널은 피보나치 배수비의 기간을 사용한 4LBS의 L1입니다.
- 모든 3개의 레이어가 돌파될때에만 포지션을 진입합니다.
- 최초의 로스컷 설정점은 3%입니다.
- 로스컷당하지 않고 무사히 추세가 나오면, L4가 돌파될때 익절합니다.
** 장점 **
- 거의 모든 타임프레임에서 수익이 납니다. 그러나 기간이 길어질수록 손절폭을 늘려주어야 합니다. 그리고 캔들 기간이 짧아지면 손절폭도 따라서 조금씩 줄여주어야 합니다.
- 중요한 급소에서만 진입하고 진입횟수가 많지 않은것이 이 전략의 수익의 요인 중 하나입니다.
- 최대한 간명하게 현재추세를 알아볼 수 있도록, 명확한 시각적 효과를 살리려고 의도하였습니다.
** 수익률 **
The default setting of number of operating contracts is set to 10 contract operation. This may be a dramatic example, but This a deep backtesting result of how much the return would be from January 2019 when trading Bitcoin 10 contract.
기본설정은 10계약 운용으로 설정되어 있습니다. 이것은 좀 극적인 예일 수 있겠으나, 비트코인 10계약을 운용시 지난 2019년 1월부터 얼마나 수익률이 나오는지 딥 백테스팅한 것입니다.
지난 4월부터 로스컷 2.5% 설정하고 5개월동안의 운용할 시에 수익은 58561 달러, 1계약 운용시에는 5800여 달러입니다.
Entry Examples(during last 5 month)
지난 5달간의 진입 예시들
Click Draw TrendLine [UhoKang] v2
This is an indicator that directly draws a trend line by clicking on the candle.
Click to Create Trend lines !!
Create a trend line by connecting A, B, and C with three vertices.
1. Draw Bear Trend line
Click pivot high : First pivothigh of the downtrend line
Click pivot low : pivotlow of the downtrend line
Click pivot high : Second pivothigh of the downtrend line
2. Draw Bull Trend line
Click pivot low : First pivotlow of the uptrend line
Click pivot high : pivothigh of the uptrend line
Click pivot low : Second pivotlow of the uptrend line
Modefiy Trendlines
1. Edit Bear Trend line
Drag Red , ,
2. Edit Bull Trend line
Drag Green , ,
Heikin Ashi SupertrendAbout this Strategy
This supertrend strategy uses the Heikin Ashi candles to generate the supertrend but enters and exits trades using normal candle close prices. If you use the standard built in Supertrend indicator on Heikin Ashi candles, it will produce very unrealistic backtesting results because it uses the Heikin Ashi prices instead of the real prices. However, by signaling the supertrend reversals using Heikin Ashi while using standard candle close prices for the entries and exits, it corrects the backtesting errors and gives you a more realistic equity curve. You should set the chart to use standard candles and then hide them (the strategy creates the candles).
This strategy includes:
Plotting of Heikin Ashi candles
Heikin Ashi Supertrend
Long and Short Entry Signals
Move stop loss after trade is X% in profit
Profit Target
Stop Loss
Built in Alertatron automation
Alertatron Trade Automation Integration
For Alertatron integration, be sure to configure the strategy settings and "Enable Webhook Messages" before creating an alert with {{strategy.order.alert_message}} in the body of your alert message. Be sure to enable webhooks and point it to your Incoming Alertatron webhook URL.
Notes
While this strategy does pretty well during trending markets, It's worth noting that the Buy and Hold ROI is much better during peak times of the bull market
Not financial advice. Do not risk more than you can afford to lose.
