QuantCat 15M Trend Strategy [Crypto]**NOTE: IF YOU DO NOT OWN TRADINGVIEW PRO YOUR STRATEGIES WILL TAKE DATA FROM A SMALLER SAMPLE SIZE, AND THUS GIVE LESS ACCURATE BACKTESTING RESULTS TO THE ONES WE SHOW (LINKS WITH STEP 3) AND OPTIMISED OUR STRATEGIES WITH**
QuantCat 15M Crypto Trend Strategy
This strategy will allow you to absolutely smash the Crypto/USD pairs. We have optimised this strategy to work best for the Crypto/USD pairs on the 15 minute time frame. There are 4 pairs that we optimised the strategy to work for. There are a few main steps are as follows to use the strategy in it's most efficient manner-
1. Pyramiding orders needs to be set to 2 for maximum efficiency of the strategy due to aggressive and conservative entries, recommended % of equity per trade = 10-50%.
2. 15 Minute time frame needs to be applied (obvious, we know).
3. Pairs we used to optimise and backtest the strategy should ideally be used when using the strategy-
BTC/USD - prntscr.com
LTC/USD - prntscr.com
BCH/USD - prntscr.com
ETH/BTC - prntscr.com
XRP/BTC - prntscr.com
LTC/BTC - prntscr.com
We have a list of Visual and Technical features that are implemented into our strategy to make sure it is easy to use, consistent and giving it the highest chance of being profitable.
Visual Features
Colored candles- a visual to notification of an entry.
Stop-loss line- a horizontal red line to show the user where the stop-loss has been placed. Our testing proved a horizontal stop was more profitable than implementation of a trailing.
Price Bands- a dynamic price band to show the direction of the trend and support zones.
Technical Features
Trend Confirmation- Our algorithm fires entries only on trend confirmation and looks to trade the move for as long as it can, while also filtering out any false entries.
Aggressive and Conservative Entries- Two different types of entries have been incorporated in the strategy to maximise profit.
If you have discord alerts access, be sure to watch out in the discord for entries!
Search in scripts for "profit"
QuantCat 15M Crypto Trend StrategyQuantCat 15M Crypto Trend Strategy
This strategy will allow you to absolutely smash the Crypto/USD pairs. We have optimised this strategy to work best for the Crypto/USD pairs on the 15 minute time frame. There are 4 pairs that we optimised the strategy to work for. There are a few main steps are as follows to use the strategy in it's most efficient manner-
1. Pyramiding orders needs to be set to 2 for maximum efficiency of the strategy.
2. 15 Minute timeframe needs to be applied (obvious, we know)
3. Pairs we used to optimise and backtest the strategy should ideally be used when using the strategy-
BTC/USD
ETH/USD
LTC/USD
BCH/USD
We have a list of Visual and Technical features that are implemented into our strategy to make sure it is easy to use, consistent and giving it the highest chance of being profitable.
Visual Features
Colored candles- a visual to notification of an entry.
Stop-loss line- a horizontal red line to show the user where the stop-loss has been placed. Our testing proved a horizontal stop was more profitable than implementation of a trailing.
Price Bands- a dynamic price band to show the direction of the trend and support zones.
Technical Features
Trend Confirmation- Our algorithm fires entries only on trend confirmation and looks to trade the move for as long as it can, while also filtering out any false entries.
Aggressive and Conservative Entries- Two different types of entries have been incorporated in the strategy to maximise profit.
If you have discord alerts access, be sure to watch out in the discord for entries!
BitcoinNinjas Ninja Signals Buy/Sell Alert Trading Strategy v3.0Bitcoin Ninjas 'Ninja Signals' Buy/Sell Alerts & Backtesting TradingView Script v3.0
(for Cryptocurrencies, Forex, GunBot, ProfitTrailer, automatic trading software, and more)
This is version 3 of our popular Ninja Signals trading script, which is similar to version 2, but with a new hard-coded calibration (resolution) setting that automatically matches candle size (period) to ensure that no repainting occurs regardless of the number or type of indicators and filters traders apply when configuring the script.
'Ninja Signals' v3.0 (SCRIPT)
'Ninja Signals' v3.0 (STRATEGY)
'Ninja Signals' v2.0 (SCRIPT)
'Ninja Signals' v2.0 (STRATEGY)
'Ninja Signals' v1.0 (SCRIPT)
'Ninja Signals' v1.0 (STRATEGY)
-Allows users to easily set automated buy and sell alerts on TradingView for use with automatic and manual trading of cryptocurrencies, Forex securities, and more (alerts are compatible with automatic trading software such as GunBot, ProfitTrailer, and more).
-Synthesizes many powerful indicators [e.g. Relative Strength Index (RSI), stochastic RSI, Money Flow Index (MFI), Moving Average Convergence Divergence (MACD), etc.) into one super script to generate very precise buy and sell signals in almost any market condition.
-Buy arrows (blue) and sell arrows (red) can be changed or hidden for ease of viewing.
-No lag EMA trendline featuring trend-reversal color-coding (white uptrend, black downtrend).
-Adjustable ‘calibration’ setting allows users to customize the script to work for any currency or security available through TradingView, on any exchange, simply by adjusting a number.
-Complete with backtesting strategy version of script which allows users to test various buy and sell strategies based on the alerts the script generates (see info and screenshots below).
-Backtesting strategy incorporates a user-defined adjustable date range, so users can estimate the script’s performance over specific periods of time, such as the last day, week, or month.
-Backtesting strategy utilizes a minimum protective gain setting to help you never sell for a loss. Simply adjust your minimum profit (%) per trade, and the test results will update.
-Backtesting strategy allows for pyramid buying to test various average down / double up buying strategies. Simply adjust the number of pyramid buys and the quantity of each buy.
- Free 7-day trial available for TradingView users who join our free BitcoinNinjas community.
-Free 24/7 support via BitcoinNinjas Telegram GunBot support group with script purchase.
-Fully compatible with GunBot automatic trading software (TradingView plugin is required).
-Special discount available for traders who purchase GunBot automatic trading software and the GunBot TradingView plugin from BitcoinNinjas, allowing for fully automatic trading.
-Contact us via Email or Telegram for more information, to request additional / custom screenshots, or to start your free trial.
DISCLAIMER: By using our BitcoinNinjas ‘Ninja Signals’ planning script, you agree to the BitcoinNinjas 'Terms of Use', as presented on our website (www.BitcoinNinjas.org) and as stated here. No sharing, copying, reselling, modifying, or any other forms of use are authorized for our documents, script / strategy, and the information published with them. This informational planning script / strategy is strictly for individual use and educational purposes only. This is not financial or investment advice. Investments are always made at your own risk and are based on your personal judgement. Bitcoin Ninjas is not responsible for any losses you may incur. Please invest wisely.
Heikin Ashi Margin Scalping script for BitmexA script made for a lot of trades (scalping) and good solid profits on the Tested and it generates very attractive profits. Test it for yourself.
- Made for BITCOIN / US DOLLAR PERPETUAL INVERSE SWAP CONTRACT XBTUSD on Bitmex.
- Only 40% of these trades are profitable, but within this 40 percent, it catches all the spikes and lows. And therefore a lot more profit then loss. The most loss is generated at the moment BTC is falling still and there is not a lot of volatility.
- I use it to create alerts and sent these to my bot, so i don't have to do manual trading.
You can ask for access and try this strategy for yourself for 5 days. When you are convinced and want to have the script with alerts to be shared with you, I ask for a small donation of 0.02 BTC. This allows me to create other scripts and to improve this script.
I only sell this script to 50 people, so that it remains exclusive.
Has Indıcator Has indicator uses heiken ashi and macd (macd is custom)
works almost any cryptocurrency
1- 1min chart recommended
2- Use only USD pairs
3-Each trade is 10,000 USD
4-Comission is 0.13
Combined Strategy By CadotraderSCStrategy for the Iota / usd medium / long term. Use in 4h. For better results I recommend the following: Enter the capital that you will use in this strategy and limit to working only with that capital always, keeping a stop short, because no script is 100% profitable. As it is a medium / long term strategy he will not enter the bottom or the top, he of the entrance confirming the medium / long term trend. It is good that you keep in mind a satisfactory profit margin and when you hit that profit margin you can close the transaction and wait for the next signal to reenter the operation, further reducing the risk. Good profits.
Estratégia para o par Iota/usd de médio/longo prazo. Utilizar em 4h. Para melhor obtenção de resultados recomendo o seguinte: Insira o capital que você irá utilizar nessa estratégia e limite a trabalhar apenas com esse capital sempre, mantendo um stop curto, pois nenhum script é 100% rentável. Como é uma estratégia de médio/longo prazo ele não vai dar entrada no fundo ou no topo, ele da entrada confirmando a tendência de médio/longo prazo. É bacana você ter em mente uma margem de lucro satisfatória e quando bater essa margem de lucro você pode fechar a operação e aguardar o próximo sinal para entrar novamente na operação diminuindo ainda mais os riscos. Bons lucros.
BitcoinNinjas Ninja Signals Buy Sell Alert Trading Strategy v2.0Bitcoin Ninjas 'Ninja Signals' Buy/Sell Alerts & Backtesting TradingView Script v2.0
(for Cryptocurrencies, Forex, GunBot, ProfitTrailer, automatic trading software, and more)
'Ninja Signals' v2.0 (SCRIPT)
'Ninja Signals' v2.0 (STRATEGY)
'Ninja Signals' v1.0 (SCRIPT)
'Ninja Signals' v1.0 (STRATEGY)
-Allows users to easily set automated buy and sell alerts on TradingView for use with automatic and manual trading of cryptocurrencies, Forex securities, and more (alerts are compatible with automatic trading software such as GunBot, ProfitTrailer, and more).
-Synthesizes many powerful indicators [e.g. Relative Strength Index (RSI), stochastic RSI, Money Flow Index (MFI), Moving Average Convergence Divergence (MACD), etc.) into one super script to generate very precise buy and sell signals in almost any market condition.
-Buy arrows (blue) and sell arrows (red) can be changed or hidden for ease of viewing.
-No lag EMA trendline featuring trend-reversal color-coding (white uptrend, black downtrend).
-Adjustable ‘calibration’ setting allows users to customize the script to work for any currency or security available through TradingView, on any exchange, simply by adjusting a number.
-Complete with backtesting strategy version of script which allows users to test various buy and sell strategies based on the alerts the script generates (see info and screenshots below).
-Backtesting strategy incorporates a user-defined adjustable date range, so users can estimate the script’s performance over specific periods of time, such as the last day, week, or month.
-Backtesting strategy utilizes a minimum protective gain setting to help you never sell for a loss. Simply adjust your minimum profit (%) per trade, and the test results will update.
-Backtesting strategy allows for pyramid buying to test various average down / double up buying strategies. Simply adjust the number of pyramid buys and the quantity of each buy.
- Free 7-day trial available for TradingView users who join our free BitcoinNinjas community.
-Free 24/7 support via BitcoinNinjas Telegram GunBot support group with script purchase.
-Fully compatible with GunBot automatic trading software (TradingView plugin is required).
-Special discount available for traders who purchase GunBot automatic trading software and the GunBot TradingView plugin from BitcoinNinjas, allowing for fully automatic trading.
-Contact us via Email or Telegram for more information, to request additional / custom screenshots, or to start your free trial.
DISCLAIMER: By using this BitcoinNinjas document or ‘Ninja Signals’ planning script, you agree to the BitcoinNinjas 'Terms of Use', as presented on our website (www.BitcoinNinjas.org) and as stated here. No sharing, copying, reselling, modifying, or any other forms of use are authorized for our documents, script / strategy, and the information published with them. This informational document and planning script / strategy is strictly for individual use and educational purposes only. This is not financial or investment advice. Investments are always made at your own risk and are based on your personal judgement. BitcoinNinjas is not responsible for any losses you may incur. Please invest wisely.
Bitcoin Ninjas 'Ninja Signals' Buy & Sell Alert Trading StrategyBitcoin Ninjas 'Ninja Signals' Buy/Sell Alerts & Backtesting TradingView Script
(for Cryptocurrencies, Forex, GunBot, ProfitTrailer, automatic trading software, and more)
Main Script:
-Allows users to easily set automated buy and sell alerts on TradingView for use with automatic and manual trading of cryptocurrencies, Forex securities, and more (alerts are compatible with automatic trading software such as GunBot, ProfitTrailer, and more).
-Synthesizes many powerful indicators [e.g. Relative Strength Index (RSI), stochastic RSI, Money Flow Index (MFI), Moving Average Convergence Divergence (MACD), etc.) into one super script to generate very precise buy and sell signals in almost any market condition.
-Buy arrows (blue) and sell arrows (red) can be changed or hidden for ease of viewing.
-No lag EMA trendline featuring trend-reversal color-coding (white uptrend, black downtrend).
-Adjustable ‘calibration’ setting allows users to customize the script to work for any currency or security available through TradingView, on any exchange, simply by adjusting a number.
-Complete with backtesting strategy version of script which allows users to test various buy and sell strategies based on the alerts the script generates (see info and screenshots below).
-Backtesting strategy incorporates a user-defined adjustable date range, so users can estimate the script’s performance over specific periods of time, such as the last day, week, or month.
-Backtesting strategy utilizes a minimum protective gain setting to help you never sell for a loss. Simply adjust your minimum profit (%) per trade, and the test results will update.
-Backtesting strategy allows for pyramid buying to test various average down / double up buying strategies. Simply adjust the number of pyramid buys and the quantity of each buy.
-Free 7-day trial available for TradingView users who join our free BitcoinNinjas community.
-Free 24/7 support via BitcoinNinjas Telegram GunBot support group with script purchase.
-Fully compatible with GunBot automatic trading software (TradingView plugin is required).
-Special discount available for traders who purchase GunBot automatic trading software and the GunBot TradingView plugin from BitcoinNinjas, allowing for fully automatic trading.
-Contact us via Email or Telegram for more information, to request additional / custom screenshots, or to start your free trial.
Screenshot (Script)
Screenshot (Backtesting Strategy)
Screenshot (Adjustable Calibration, Minimum Profit Per Trade, & Date Range)
Screenshot (Optional Pyramid / Average Down Buying)
Screenshot (Customizable Buy/Sell Arrows & Trend Reversal Line)
DISCLAIMER: By viewing and/or using this TradingView script, you agree to the BitcoinNinjas 'Terms of Use', as presented on our website ( BitcoinNinjas.org ) and as stated here. No sharing, copying, reselling, modifying, or any other forms of use, are authorized for this document. This document is strictly for individual use and informational purposes only. This is not financial or investment advice. Investments are always made at your own risk, and are based on yo
Gold/Silver 30m Only Strategy Buy/Sell SignalsIn my free time I felt like coding this strategy, and after backtesting it, it appears that the 30m time frame is the most profitable.
I only have been working on it for gold, but it should work similarly for silver as well.
This includes no pyramiding, and with pyramiding orders of 5, this strategy is upwards of 100% profitable.
Buy order - when price is above the 162 day EMA and RSI is less than 35
Sell order - when price is below the 162 day EMA and RSI is greater than 65
I will probably be adjusting it to increase the profitability and %success rate.
NIFTY_2min_FVG_sell_StrategyThis strategy is designed for scalping Nifty on a 2-minute chart, focusing exclusively on short entries. The script's purpose is to identify and act on specific bearish patterns based on volume analysis and price action.
Concept & Core Logic
The strategy operates on a two-stage confirmation process:
Volume Absorption: The initial condition seeks to identify potential bearish movements by detecting signs of buying pressure being absorbed by sellers. This suggests that a upward move may be losing momentum.
Fair Value Gap (FVG) Confirmation: After a volume absorption signal, the strategy waits for a Bearish Fair Value Gap (FVG) to appear. A short entry signal is generated only after a candle closes below the FVG zone, serving as confirmation of bearish intent.
Risk Management
The strategy employs a fixed take profit and stop loss for each trade, based on the Nifty underlying price:
Take Profit: The exit signal is triggered when a trade reaches a 30-point profit.
Stop Loss: The exit signal is triggered when a trade reaches a 35-point loss.
Intended Use
This tool is intended for traders who:
Utilize mechanical, rule-based systems for intraday trading and scalping.
Are interested in studying a structured approach that combines volume analysis with price action inefficiencies like Fair Value Gaps.
BRT Support MA [STRATEGY] v2BRT Support MA Strategy v2 - Dynamic Support Line Strategy
📊 Strategy Description
BRT Support MA Strategy v2 is an automated trading strategy based on the analysis of dynamic support and resistance levels using volatility calculations on higher timeframes. The strategy is designed to identify key trend reversal moments and enter positions with optimal risk-to-reward ratios.
🎯 Key Features
Unique strategy characteristics:
1. Multi-Timeframe Volatility Analysis - indicators are calculated on a user-selected timeframe, which allows filtering market noise and obtaining more reliable signals
2. Adaptive Hedging System - a unique algorithm for dynamic position volume calculation during reversals, which accounts for current drawdown and automatically adjusts order size for optimal risk management
3. Visual Trend Indication - dynamic color change of the main line (green = uptrend, red = downtrend) for quick assessment of current market conditions
4. Automatic Signal Markers - the strategy marks trend change moments on the chart with arrows for convenient analysis
5. Limit Orders - entries into positions occur via limit orders at key levels, ensuring better price execution
⚙️ Strategy Settings
Support MA Length - calculation period for the main support/resistance line
Support MA Timeframe - timeframe for indicator calculations (can be set higher than current for noise filtering)
TP (%) - take profit percentage from entry point
SL (%) - stop loss percentage from entry point
Hedge Multiplier - volume multiplier for hedging positions during reversals
📈 Operating Logic
The strategy analyzes the relationship between two dynamic levels calculated based on market volatility. When price breaks through the main support level in the direction of the trend:
Long positions are opened when the main indicator is in an uptrend and price breaks above it
Short positions are opened when the main indicator is in a downtrend and price breaks below it
When there is an open position and an opposite signal forms, the strategy automatically calculates the optimal volume for a hedging position based on the percentage price movement and the set take profit.
🎨 Visual Elements
Blue/Green/Red line - main dynamic support/resistance level (color changes depending on current trend)
Green arrows down ▼ - uptrend reversal signals
Red arrows up ▲ - downtrend reversal signals
TP and SL - displayed in data window for current open position
💡 Usage Recommendations
Test the strategy on historical data of different instruments before use
Optimize parameters for the specific trading instrument and timeframe
Configure TP/SL parameters according to your trading system and risk tolerance
Hedge Multiplier controls hedging system aggressiveness - start with conservative values
⚠️ DISCLAIMER
IMPORTANT! PLEASE READ BEFORE USE:
This script is provided for educational and research purposes only . It is intended for testing on historical data and studying algorithmic trading approaches.
The author is NOT responsible for:
Any financial losses incurred as a result of using this strategy
Trading results in real-time or on demo accounts
Losses arising from incorrect parameter configuration
Technical failures, slippage, and other market conditions
Trading involves a high level of risk and is not suitable for all investors. You can lose all of your invested capital. Do not invest money you cannot afford to lose.
Before starting real trading:
Conduct thorough testing on historical data
Ensure you fully understand the strategy's operating logic
Consult with a financial advisor
Consider broker commissions and slippage
Start with minimum volumes
Past performance does not guarantee future profitability. Use of the strategy is at your own risk.
© MaxBRFZCO | Version 2.0 | Pine Script v5
For questions and suggestions, please use comments under the publication
VWolf - Raptor ClawOVERVIEW
The 'VWolf - Raptor Claw' is a straightforward scalping strategy designed for high-frequency trades based on the Stochastic RSI indicator. It focuses exclusively on identifying potential trend reversals through stochastic cross signals in extreme zones, without the need for additional confirmations. This makes it highly responsive to market movements, capturing rapid price shifts while maintaining simplicity.
This strategy is best suited for highly liquid and volatile markets like forex, indices, and major cryptocurrencies, where quick momentum shifts are common. It is ideal for experienced scalpers who prioritize fast entries and exits, but it can also be adapted for swing trading in lower timeframes.
Entry Conditions:
Long Entry:Stochastic RSI crosses above the oversold threshold (typically 20), indicating a potential bullish reversal.
Short Entry:Stochastic RSI crosses below the overbought threshold (typically 80), indicating a potential bearish reversal.
Exit Conditions:
Stop Loss: Set at the minimum (for longs) or maximum (for shorts) within a configurable lookback window to reduce risk.
Take Profit: Defined by a risk-reward ratio (RRR) input to optimize potential gains relative to risk.
CONCLUSION
The 'VWolf - Raptor Claw' strategy is perfect for traders seeking a simple yet aggressive approach to the markets. It capitalizes on sharp momentum shifts in extreme zones, relying on precise stop loss and take profit settings to capture rapid profits while minimizing risk. This approach is highly effective in high-volatility environments where quick decision-making is essential.
FOR MORE INFORMATION VISIT vwolftrading.com
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.
Adaptive Averaging Concept [NeuraAlgo]Adaptive Averaging Concept
A Quant-Engineered Dynamic Position Sizing & Optimization Framework
Adaptive Averaging Concept™ is a next-generation, research-driven trading framework that combines multistage entries, ATR-based intelligent scaling, real-time sentiment filtering, and a fully automated optimization engine.
It is designed for traders who want precision execution, adaptive risk control, and an architecture capable of learning from market structure.
🔹 Core Concept
Unlike traditional averaging or DCA methods, this engine uses Adaptive Averaging — a controlled, mathematically tuned accumulation system that adjusts entries based on volatility, trend conditions, and signal confidence.
Each additional entry intelligently recalculates average price and updates a volatility-sensitive dynamic Take Profit.
🔹 Main Features
1. Intelligent Multi-Stage Entry System
Initial entries triggered by SMA crossover, rising volume, or Always-On mode
Secondary entries triggered only when price retraces by a volatility-adjusted threshold
Every added position recalculates:
Total quantities
Capital distribution
Average price
Adaptive Take Profit (ATR-based)
2. Adaptive Risk & Position Management
ATR-driven take-profit using Exit Sensitivity
ATR-driven add-entry logic using Exit Tuner
Dynamic or Fixed lot sizing
Capital-per-entry control
Automatic minimum lot protection
3. High-Level Market Filters
Trend Filter
A volatility-normalized EMA slope filter that identifies:
1.Bullish trend
2.Bearish trend
3.Neutral trend
Sentiment Cloud Filter
A structural sentiment engine analyzing:
1.Micro-gaps
2.Bull and bear pressure
3.Range compression
4.Market regime bias
Trades only execute when filters align with your directional bias.
4. NeuraAlgo Optimization Engine
The strategy includes a built-in optimizer allowing you to test & tune with no loops and no external computation.
You can automatically optimize:
Smooth Period (ATR)
Exit Sensitivity
Exit Tuner
SMA Period
Trend Filter Length
Trend Filter Smooth
Sentiment Cloud Period
Optimization Goals:
Maximize Winrate
Maximize Net Profits
This allows the strategy to self-configure based on live market conditions.
Here, the optimization is finally complete.
🔹 Summary
Adaptive Averaging Concept™ is not a simple indicator or basic DCA script.
It is a complete quant-grade execution engine capable of dynamically adjusting its behavior to volatility, price structure, trend strength, and sentiment.
Engineered for traders who demand:
High-precision entry logic
Adaptive position sizing
Volatility-calibrated exits
Smart accumulation
Built-in optimization
Professional-grade backtesting
It is a powerful framework suitable for swing traders, intraday traders, and automated system developers.
DynamicQuant Lite Strategy v1.1.1🚀 DynamicQuant Pro - Adaptive Channel-Based Trading Strategy
📊 Strategy Overview
DynamicQuant Pro is an adaptive trading strategy based on price channel breakouts. It offers both trend-following and mean-reversion modes to adapt to various market conditions.
⚡ Core Features
🎯 Entry System
- Channel Breakout Based: Uses upper/lower band breakouts as entry signals
- Multi-Layer Filtering: Triple-filter system combining volume, momentum, and volatility indicators to eliminate false signals
- Smart Entry Control: Entry restriction zones and minimum bar spacing to prevent excessive positions
- Multi-Stage Position Building: Up to 5-stage scaling to optimize average entry price
🔄 Exit System (4 Modes)
- Band Mode: Exit based on channel centerline
- Split Mode: Individual exit per entry price
- Trailing Mode: Dynamic trailing exit
- Position Mode: Unified exit based on average price
🛡️ Risk Management
- Advanced Stop Loss: Intelligent exit system with recovery failure detection and time-based stops
- Multi-Level Take Profit: Flexible exit strategies including weighted partial exits and ladder profits
- Profit Protection: Safety mechanism preventing exits at loss levels
- Leverage-Based Margin Management: Margin calculation matching real exchange systems
✨ Key Strengths
⚡ Real-Time Exits: Tick-by-tick monitoring for immediate exits when targets are reached (no waiting for bar close)
📈 Detailed Visualization: Real-time PnL, entry prices, targets, stops - all displayed on chart
📊 Backtest Performance Table: Detailed statistics including win rate, profit factor, Long/Short performance
🎛️ Flexible Configuration: 30+ parameters to customize to your trading style
👥 Ideal For
✅ Traders seeking systematic risk management
✅ Traders looking for adaptable strategies across market conditions
✅ Traders preferring backtest-based strategy optimization
✅ Traders interested in scaling entry/exit strategies
⚠️ Disclaimer
This strategy is for educational and informational purposes only. Past performance does not guarantee future results. Trading involves substantial risk of loss. Always conduct your own research and risk assessment before trading with real capital.
Superior-Range Bound Renko - Strategy - 11-29-25 - SignalLynxSuperior-Range Bound Renko Strategy with Advanced Risk Management Template
Signal Lynx | Free Scripts supporting Automation for the Night-Shift Nation 🌙
1. Overview
Welcome to Superior-Range Bound Renko (RBR) — a volatility-aware, structure-respecting swing-trading system built on top of a full Risk Management (RM) Template from Signal Lynx.
Instead of relying on static lookbacks (like “14-period RSI”) or plain MA crosses, Superior RBR:
Adapts its range definition to market volatility in real time
Emulates Renko Bricks on a standard, time-based chart (no Renko chart type required)
Uses a stack of Laguerre Filters to detect genuine impulse vs. noise
Adds an Adaptive SuperTrend powered by a small k-means-style clustering routine on volatility
Under the hood, this script also includes the full Signal Lynx Risk Management Engine:
A state machine that separates “Signal” from “Execution”
Layered exit tools: Stop Loss, Trailing Stop, Staged Take Profit, Advanced Adaptive Trailing Stop (AATS), and an RSI-style stop (RSIS)
Designed for non-repainting behavior on closed candles by basing execution-critical logic on previous-bar data
We are publishing this as an open-source template so traders and developers can leverage a professional-grade RM engine while integrating their own signal logic if they wish.
2. Quick Action Guide (TL;DR)
Best Timeframe:
4 Hours (H4) and above. This is a high-conviction swing-trading system, not a scalper.
Best Assets:
Volatile instruments that still respect market structure:
Bitcoin, Ethereum, Gold (XAUUSD), high-volatility Forex pairs (e.g., GBPJPY), indices with clean ranges.
Strategy Type:
Volatility-Adaptive Trend Following + Impulse Detection.
It hunts for genuine expansion out of ranges, not tiny mean-reversion nibbles.
Key Feature:
Renko Emulation on time-based candles.
We mathematically model Renko Bricks and overlay them on your standard chart to define:
“Equilibrium” zones (inside the brick structure)
“Breakout / impulse” zones (when price AND the impulse line depart from the bricks)
Repainting:
Designed to be non-repainting on closed candles.
All RM execution logic uses confirmed historical data (no future bars, no security() lookahead). Intrabar flicker during formation is allowed, but once a bar closes the engine’s decisions are stable.
Core Toggles & Filters:
Enable Longs and Shorts independently
Optional Weekend filter (block trades on Saturday/Sunday)
Per-module toggles: Stop Loss, Trailing Stop, Staged Take Profits, AATS, RSIS
3. Detailed Report: How It Works
A. The Strategy Logic: Superior RBR
Superior RBR builds its entry signal from multiple mathematical layers working together.
1) Adaptive Lookback (Volatility Normalization)
Instead of a fixed 100-bar or 200-bar range, the script:
Computes ATR-based volatility over a user-defined period.
Normalizes that volatility relative to its recent min/max.
Maps the normalized value into a dynamic lookback window between a minimum and maximum (e.g., 4 to 100 bars).
High Volatility:
The lookback shrinks, so the system reacts faster to explosive moves.
Low Volatility:
The lookback expands, so the system sees a “bigger picture” and filters out chop.
All the core “Range High/Low” and “Range Close High/Low” boundaries are built on top of this adaptive window.
2) Range Construction & Quick Ranges
The engine constructs several nested ranges:
Outer Range:
rangeHighFinal – dynamic highest high
rangeLowFinal – dynamic lowest low
Inner Close Range:
rangeCloseHighFinal – highest close
rangeCloseLowFinal – lowest close
Quick Ranges:
“Half-length” variants of those, used to detect more responsive changes in structure and volatility.
These ranges define:
The macro box price is trading inside
Shorter-term “pressure zones” where price is coiling before expansion
3) Renko Emulation (The Bricks)
Rather than using the Renko chart type (which discards time), this script emulates Renko behavior on your normal candles:
A “brick size” is defined either:
As a standard percentage move, or
As a volatility-driven (ATR) brick, optionally inhibited by a minimum standard size
The engine tracks a base value and derives:
brickUpper – top of the emulated brick
brickLower – bottom of the emulated brick
When price moves sufficiently beyond those levels, the brick “shifts”, and the directional memory (renkoDir) updates:
renkoDir = +2 when bricks are advancing upward
renkoDir = -2 when bricks are stepping downward
You can think of this as a synthetic Renko tape overlaid on time-based candles:
Inside the brick: equilibrium / consolidation
Breaking away from the brick: momentum / expansion
4) Impulse Tracking with Laguerre Filters
The script uses multiple Laguerre Filters to smooth price and brick-derived data without traditional lag.
Key filters include:
LagF_1 / LagF_W: Based on brick upper/lower baselines
LagF_Q: Based on HLCC4 (high + low + 2×close)/4
LagF_Y / LagF_P: Complex averages combining brick structures and range averages
LagF_V (Primary Impulse Line):
A smooth, high-level impulse line derived from a blend of the above plus the outer ranges
Conceptually:
When the impulse line pushes away from the brick structure and continues in one direction, an impulse move is underway.
When its direction flips and begins to roll over, the impulse is fading, hinting at mean reversion back into the range.
5) Fib-Based Structure & Swaps
The system also layers in Fib levels derived from the adaptive ranges:
Standard levels (12%, 23.6%, 38.2%, 50%, 61%, 76.8%, 88%) from the main range
A secondary “swap” set derived from close-range dynamics (fib12Swap, fib23Swap, etc.)
These Fibs are used to:
Bucket price into structural zones (below 12, between 23–38, etc.)
Detect breakouts when price and Laguerre move beyond key Fib thresholds
Drive zSwap logic (where a secondary Fib set becomes the active structure once certain conditions are met)
6) Adaptive SuperTrend with K-Means-Style Volatility Clustering
Under the hood, the script uses a small k-means-style clustering routine on ATR:
ATR is measured over a fixed period
The range of ATR values is split into Low, Medium, High volatility centroids
Current ATR is assigned to the nearest centroid (cluster)
From that, a SuperTrend variant (STK) is computed with dynamic sensitivity:
In quiet markets, SuperTrend can afford to be tighter
In wild markets, it widens appropriately to avoid constant whipsaw
This SuperTrend-based oscillator (LagF_K and its signals) is then combined with the brick and Laguerre stack to confirm valid trend regimes.
7) Final Baseline Signals (+2 / -2)
The “brain” of Superior RBR lives in the Baseline & Signal Generation block:
Two composite signals are built: B1 and B2:
They combine:
Fib breakouts
Renko direction (renkoDir)
Expansion direction (expansionQuickDir)
Multiple Laguerre alignments (LagF_Q, LagF_W, LagF_Y, LagF_Z, LagF_P, LagF_V)
They also factor in whether Fib structures are expanding or contracting.
A user toggle selects the “Baseline” signal:
finalSig = B2 (default) or B1 (alternate baseline)
finalSig is then filtered through the RM state machine and only when everything aligns, we emit:
+2 = Long / Buy signal
-2 = Short / Sell signal
0 = No new trade
Those +2 / -2 values are what feed the Risk Management Engine.
B. The Risk Management (RM) Engine
This script features the Signal Lynx Risk Management Engine, a proprietary state machine built to separate Signal from Execution.
Instead of firing orders directly on indicator conditions, we:
Convert the raw signal into a clean integer (Fin = +2 / -2 / 0)
Feed it into a Trade State Machine that understands:
Are we flat?
Are we in a long or short?
Are we in a closing sequence?
Should we permit re-entry now or wait?
Logic Injection / Template Concept:
The RM engine expects a simple integer:
+2 → Buy
-2 → Sell
Everything else (0) is “no new trade”
This makes the script a template:
You can remove the Superior RBR block
Drop in your own logic (RSI, MACD, price action, etc.)
As long as you output +2 or -2 into the same signal channel, the RM engine can drive all exits and state transitions.
Aggressive vs Conservative Modes:
The input AgressiveRM (Aggressive RM) governs how we interpret signals:
Conservative Mode (Aggressive RM = false):
Uses a more filtered internal signal (AF) to open trades
Effectively waits for a clean trend flip / confirmation before new entries
Minimizes whipsaw at the cost of fewer trades
Aggressive Mode (Aggressive RM = true):
Reacts directly to the fresh alert (AO) pulses
Allows faster re-entries in the same direction after RM-based exits
Still respects your pyramiding setting; this script ships with pyramiding = 0 by default, so it will not stack multiple positions unless you change that parameter in the strategy() call.
The state machine enforces discipline on top of your signal logic, reducing double-fires and signal spam.
C. Advanced Exit Protocols (Layered Defense)
The exit side is where this template really shines. Instead of a single “take profit or stop loss,” it uses multiple, cooperating layers.
1) Hard Stop Loss
A classic percentage-based Stop Loss (SL) relative to the entry price.
Acts as a final “catastrophic protection” layer for unexpected moves.
2) Standard Trailing Stop
A percentage-based Trailing Stop (TS) that:
Activates only after price has moved a certain percentage in your favor (tsActivation)
Then trails price by a configurable percentage (ts)
This is a straightforward, battle-tested trailing mechanism.
3) Staged Take Profits (Three Levels)
The script supports three staged Take Profit levels (TP1, TP2, TP3):
Each stage has:
Activation percentage (how far price must move in your favor)
Trailing amount for that stage
Position percentage to close
Example setup:
TP1:
Activate at +10%
Trailing 5%
Close 10% of the position
TP2:
Activate at +20%
Trailing 10%
Close another 10%
TP3:
Activate at +30%
Trailing 5%
Close the remaining 80% (“runner”)
You can tailor these quantities for partial scaling out vs. letting a core position ride.
4) Advanced Adaptive Trailing Stop (AATS)
AATS is a sophisticated volatility- and structure-aware stop:
Uses Hirashima Sugita style levels (HSRS) to model “floors” and “ceilings” of price:
Dungeon → Lower floors → Mid → Upper floors → Penthouse
These levels classify where current price sits within a long-term distribution.
Combines HSRS with Bollinger-style envelopes and EMAs to determine:
Is price extended far into the upper structure?
Is it compressed near the lower ranges?
From this, it computes an adaptive factor that controls how tight or loose the trailing level (aATS / bATS) should be:
High Volatility / Penthouse areas:
Stop loosens to avoid getting wicked out by inevitable spikes.
Low Volatility / compressed structure:
Stop tightens to lock in and protect profit.
AATS is designed to be the “smart last line” that responds to context instead of a single fixed percentage.
5) RSI-Style Stop (RSIS)
On top of AATS, the script includes a RSI-like regime filter:
A McGinley Dynamic mean of price plus ATR bands creates a dynamic channel.
Crosses above the top band and below the lower band change a directional state.
When enabled (UseRSIS):
RSIS can confirm or veto AATS closes:
For longs: A shift to bearish RSIS can force exits sooner.
For shorts: A shift to bullish RSIS can do the same.
This extra layer helps avoid over-reactive stops in strong trends while still respecting a regime change when it happens.
D. Repainting Protection
Many strategies look incredible in the Strategy Tester but fail in live trading because they rely on intrabar values or future-knowledge functions.
This template is built with closed-candle realism in mind:
The Risk Management logic explicitly uses previous bar data (open , high , low , close ) for the key decisions on:
Trailing stop updates
TP triggers
SL hits
RM state transitions
No security() lookahead or future-bar access is used.
This means:
Backtest behavior is designed to match what you can actually get with TradingView alerts and live automation.
Signals may “flicker” intrabar while the candle is forming (as with any strategy), but on closed candles, the RM decisions are stable and non-repainting.
4. For Developers & Modders
We strongly encourage you to mod this script.
To plug your own strategy into the RM engine:
Look for the section titled:
// BASELINE & SIGNAL GENERATION
You will see composite logic building B1 and B2, and then selecting:
baseSig = B2
altSig = B1
finalSig = sigSwap ? baseSig : altSig
You can replace the content used to generate baseSig / altSig with your own logic, for example:
RSI crosses
MACD histogram flips
Candle pattern detectors
External condition flags
Requirements are simple:
Your final logic must output:
2 → Buy signal
-2 → Sell signal
0 → No new trade
That output flows into the RM engine via finalSig → AlertOpen → state machine → Fin.
Once you wire your signals into finalSig, the entire Risk Management system (Stops, TPs, AATS, RSIS, re-entry logic, weekend filters, long/short toggles) becomes available for your custom strategy without re-inventing the wheel.
This makes Superior RBR not just a strategy, but a reference architecture for serious Pine dev work.
5. About Signal Lynx
Automation for the Night-Shift Nation 🌙
Signal Lynx focuses on helping traders and developers bridge the gap between indicator logic and real-world automation. The same RM engine you see here powers multiple internal systems and templates, including other public scripts like the Super-AO Strategy with Advanced Risk Management.
We provide this code open source under the Mozilla Public License 2.0 (MPL-2.0) to:
Demonstrate how Adaptive Logic and structured Risk Management can outperform static, one-layer indicators
Give Pine Script users a battle-tested RM backbone they can reuse, remix, and extend
If you are looking to automate your TradingView strategies, route signals to exchanges, or simply want safer, smarter strategy structures, please keep Signal Lynx in your search.
License: Mozilla Public License 2.0 (Open Source).
If you make beneficial modifications, please consider releasing them back to the community so everyone can benefit.
Super-AO with Risk Management Strategy Template - 11-29-25Super-AO Strategy with Advanced Risk Management Template
Signal Lynx | Free Scripts supporting Automation for the Night-Shift Nation 🌙
1. Overview
Welcome to the Super-AO Strategy. This is more than just a buy/sell indicator; it is a complete, open-source Risk Management (RM) Template designed for the Pine Script community.
At its core, this script implements a robust swing-trading strategy combining the SuperTrend (for macro direction) and the Awesome Oscillator (for momentum). However, the real power lies under the hood: a custom-built Risk Management Engine that handles trade states, prevents repainting, and manages complex exit conditions like Staged Take Profits and Advanced Adaptive Trailing Stops (AATS).
We are releasing this code to help traders transition from simple indicators to professional-grade strategy structures.
2. Quick Action Guide (TL;DR)
Best Timeframe: 4 Hours (H4) and above. Designed for Swing Trading.
Best Assets: "Well-behaved" assets with clear liquidity (Major Forex pairs, BTC, ETH, Indices).
Strategy Type: Trend Following + Momentum Confirmation.
Key Feature: The Risk Management Engine is modular. You can strip out the "Super-AO" logic and insert your own strategy logic into the template easily.
Repainting: Strictly Non-Repainting. The engine calculates logic based on confirmed candle closes.
3. Detailed Report: How It Works
A. The Strategy Logic: Super-AO
The entry logic is based on the convergence of two classic indicators:
SuperTrend: Determines the overall trend bias (Green/Red).
Awesome Oscillator (AO): Measures market momentum.
The Signal:
LONG (+2): SuperTrend is Green AND AO is above the Zero Line AND AO is Rising.
SHORT (-2): SuperTrend is Red AND AO is below the Zero Line AND AO is Falling.
By requiring momentum to agree with the trend, this system filters out many false signals found in ranging markets.
B. The Risk Management (RM) Engine
This script features a proprietary State Machine designed by Signal Lynx. Unlike standard strategies that simply fire orders, this engine separates the Signal from the Execution.
Logic Injection: The engine listens for a specific integer signal: +2 (Buy) or -2 (Sell). This makes the code a Template. You can delete the Super-AO section, write your own logic, and simply pass a +2 or -2 to the RM_EngineInput variable. The engine handles the rest.
Trade States: The engine tracks the state of the trade (Entry, In-Trade, Exiting) to prevent signal spamming.
Aggressive vs. Conservative:
Conservative Mode: Waits for a full trend reversal before taking a new trade.
Aggressive Mode: Allows for re-entries if the trend is strong and valid conditions present themselves again (Pyramiding Type 1).
C. Advanced Exit Protocols
The strategy does not rely on a single exit point. It employs a "Layered Defense" approach:
Hard Stop Loss: A fixed percentage safety net.
Staged Take Profits (Scaling Out): The script allows you to set 3 distinct Take Profit levels. For example, you can close 10% of your position at TP1, 10% at TP2, and let the remaining 80% ride the trend.
Trailing Stop: A standard percentage-based trailer.
Advanced Adaptive Trailing Stop (AATS): This is a highly sophisticated volatility stop. It calculates market structure using Hirashima Sugita (HSRS) levels and Bollinger Bands to determine the "floor" and "ceiling" of price action.
If volatility is high: The stop loosens to prevent wicking out.
If volatility is low: The stop tightens to protect profit.
D. Repainting Protection
Many Pine Script strategies look great in backtesting but fail in live trading because they rely on "real-time" price data that disappears when the candle closes.
This Risk Management engine explicitly pulls data from the previous candle close (close , high , low ) for its calculations. This ensures that the backtest results you see match the reality of live execution.
4. For Developers & Modders
We encourage you to tear this code apart!
Look for the section titled // Super-AO Strategy Logic.
Replace that block with your own RSI, MACD, or Price Action logic.
Ensure your logic outputs a 2 for Buy and -2 for Sell.
Connect it to RM_EngineInput.
You now have a fully functioning Risk Management system for your custom strategy.
5. About Signal Lynx
Automation for the Night-Shift Nation 🌙
This code has been in action since 2022 and is a known performer in PineScript v5. We provide this open source to help the community build better, safer automated systems.
If you are looking to automate your strategies, please take a look at Signal Lynx in your search.
License: Mozilla Public License 2.0 (Open Source). If you make beneficial modifications, please release them back to the community!
Retracement Strategy [OmegaTools]Retracement Strategy is a systematic trend–retracement framework designed to identify directional opportunities after a confirmed momentum shift, and to manage exits using either trend reversals or overextension conditions. It is built around a smoothed RSI regime filter and a simple, price-based retracement trigger, making it applicable across a wide range of markets and timeframes while remaining transparent and easy to interpret.
The strategy begins by defining the underlying trend through a two-stage RSI signal. A standard RSI is computed over the user-defined Length input, then smoothed with a short moving average to reduce noise. Two symmetric thresholds are derived from the Threshold parameter: an upper band at 100 minus the threshold and a lower band at the threshold itself. When the smoothed RSI crosses above the upper band, the environment is classified as bullish and the internal trend state is set to uptrend. When the smoothed RSI crosses below the lower band, the environment is classified as bearish and the trend state becomes downtrend. When RSI moves back into the central zone between the two bands, the trend is considered neutral. In addition to the current trend, the strategy tracks the last non-neutral trend direction, which is used to detect genuine trend changes rather than transient oscillations.
Once a trend is established, the strategy looks for retracement entries in the direction of that trend. For long setups in an uptrend, it computes the lowest low over the previous Length minus one bars, excluding the current bar. A long signal is generated when price dips below this recent low while the trend state remains bullish. Symmetrically, for short setups in a downtrend, it computes the highest high over the previous Length minus one bars and enters short when price spikes above this recent high while the trend state remains bearish. This logic is designed to capture pullbacks against the prevailing RSI-defined trend, entering when the market tests or slightly violates recent extremes, rather than chasing breakouts. The candles are visually coloured to reflect the detected trend, highlighting bullish and bearish environments while keeping neutral phases distinguishable on the chart. An ATR-based measure is used solely to position the “UP” and “DN” labels on the chart for clearer visualisation of entry points; it does not directly influence position sizing or stop calculation in this implementation.
Take profit and stop loss behaviour are fully parameterized through the “Take Profit” and “Stop Loss” inputs, each offering three modes: None, Trend Change and Extension. When “Trend Change” is selected for the take profit, the strategy will only exit profitable positions when a confirmed trend reversal occurs. For a long position, this means that the strategy will close the trade when the trend state flips from uptrend to downtrend, and the last recorded trend direction validates that this is a genuine reversal rather than a neutral fluctuation; the same logic applies symmetrically for short positions. When “Extension” is selected as the take profit mode, the strategy closes profitable long trades when the smoothed RSI reaches or exceeds the upper threshold, interpreted as an overbought extension within the bullish regime, and closes profitable short trades when the smoothed RSI falls to or below the lower threshold, interpreted as an oversold extension within the bearish regime. When “None” is chosen, the strategy does not apply any explicit take profit logic, leaving trades to be managed by the stop loss settings or by user discretion in backtesting.
The stop loss parameter works in a parallel way. With “Trend Change” selected as stop loss, any open long position is closed when the trend flips from uptrend to downtrend, regardless of whether the trade is currently in profit or loss, and any open short is closed when the trend flips from downtrend to uptrend. This turns the RSI trend regime into a hard invalidation rule: once the underlying momentum structure reverses, the position is exited. With “Extension” selected for stop loss, long positions are closed when RSI falls back below the upper band and moves towards the opposite side of the range, while short positions are closed when RSI rises above the lower band and moves towards the upper side. In practice, this acts as a dynamic exit based on the oscillator moving out of a favourable context for the existing trade. Selecting “None” for stop loss disables these automatic exits, leaving only the take profit logic, if any, to manage the position. Because take profit and stop loss configuration are independent, the user can construct different profiles, such as pure trend-change exits on both sides, pure overextension exits, or a mix (for example, take profit on overextension and stop loss on trend reversal).
This strategy is designed as an analytical and backtesting framework rather than a finished plug-and-play trading system. It does not include position sizing, risk-per-trade controls, multi-timeframe confirmation, volatility filters or instrument-specific fine-tuning. Its primary purpose is to provide a clear, rule-based structure for testing retracement logic within RSI-defined trends, and to allow users to explore how different exit regimes (trend-change based versus extension based) affect performance on their instruments and timeframes of interest.
Nothing in this script or its description should be interpreted as financial advice, investment recommendation or solicitation to buy or sell any financial instrument. Past performance on backtests does not guarantee future results. The behaviour of this strategy can vary significantly across symbols, timeframes and market conditions, and correlations, volatility and liquidity can change without warning. Before considering any live application, users should thoroughly backtest and forward test the strategy on their own data, adjust parameters to their risk profile and instrument characteristics, and integrate proper money management and trade management rules. Use of this script is entirely at the user’s own risk.
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.
smart honey 2.0The smart honey 2.0 is a long-only trading strategy based on averaging entries.
At "Entry" you can set to enter a trade at a specified averaging level. The best backtest result at "only 4th averaging".
"Tp" is take profit.
"Sensitivity" controls the frequency of trades - lower sensitivity means fewer, but higher-quality trades.
Settings recommendations
For 1m-5m timeframes, use low sensitivity and take profit values. For higher timeframes, increase the take profit value.
For example, a profitable setting for many coins on a 5-minute timeframe is
Tp = 1.5%
Sensitivity = 2.7
Entry = only 4th averaging
The strategy features a "Blue line" showing liquidity clusters influenced by Sensitivity. Price often bounces off this line.
You can also set alerts for lists of coins, receiving notifications at each new candle about active positions
BTC Risk Metric DCA Adapter (3Commas Webhook Strategy)Risk Metric DCA Adapter (3Commas Webhook Strategy) - WORK IN PROGRESS
This Pine Script strategy, originally inspired by the Risk Metric Indicator, is fundamentally engineered as an Adapter to interface with external trading bots like 3Commas via Webhooks. It calculates a dynamic market risk score and translates that score into specific dollar-cost averaging (DCA) entry levels and tiered profit-taking exits.
Key Features & Logic
Risk Metric Calculation (Credit to The Trading Parrot):
The strategy incorporates a complex, multi-timeframe Risk Metric calculation based on daily and weekly moving averages (SMA) and standard deviation (StDev). This metric aims to quantify the current market overextension or compression relative to long-term historical data. The resulting score dictates the level of conviction for a new trade.
Tiered DCA Entry Sizing:
The strategy defines three distinct Buy Levels (L1, L2, L3) corresponding to increasingly favorable (lower) Risk Metric scores.
L1 (Base): Risk is moderate, initiating the minimum defined trade amount.
L2 (Scaled): Risk is low, initiating L1 amount + L2 amount.
L3 (Aggressive): Risk is very low, initiating L1 + L2 + L3 amounts.
Tiered Profit-Taking Exits:
The strategy implements a staggered, partial profit-taking approach based on the Risk Metric rising:
Sell L1 & L2: Closes a percentage of the current position when the Risk Metric reaches defined high thresholds, locking in partial profits.
Sell L3 (Full Exit): Closes the remaining position when the Risk Metric reaches the highest defined threshold.
The Adapter Function (Webhook Integration)
This script is unique because it uses the Pine Script strategy() function to trigger Order Fills, which are necessary to access powerful placeholders in the TradingView alert system.
Trigger Type: The alert must be set to trigger on Any order fill.
Dynamic Webhook Data: Instead of using fixed alert() commands, the strategy generates dynamic labels (e.g., BUY_ENTRY_L3_USD_1000 or SELL_L1_PCT_25) using the strategy.entry and strategy.close commands.
Data Transfer: The alert message then uses the placeholder {{strategy.order.comment}} to pass these dynamic labels to the 3Commas bot, allowing the bot to execute the precise action (e.g., start_deal_with_volume_in_quote_currency or close_deal_at_market_percentage).
Full Strategy Webhook payload
{
"secret": "YOUR_3COMMAS_SECRET_KEY",
"max_lag": "300",
"timestamp": "{{timenow}}",
"trigger_price": "{{close}}",
"tv_exchange": "{{exchange}}",
"tv_instrument": "{{ticker}}",
"action": "{{strategy.order.action}}",
"bot_uuid": "YOUR_BOT_UUID",
"strategy_info": {
"market_position": "{{strategy.market_position}}",
"market_position_size": "{{strategy.market_position_size}}",
"prev_market_position": "{{strategy.prev_market_position}}",
"prev_market_position_size": "{{strategy.prev_market_position_size}}"
},
"order": {
"amount": "{{strategy.order.contracts}}",
"currency_type": "base",
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Disclaimer: This script is an adapter tool and does not guarantee profit. Trading requires manual configuration of risk settings, bot parameters, and adherence to platform-specific setup instructions.
Reversal Point Dynamics - Machine Learning⇋ Reversal Point Dynamics - Machine Learning
RPD Machine Learning: Self-Adaptive Multi-Armed Bandit Trading System
RPD Machine Learning is an advanced algorithmic trading system that implements genuine machine learning through contextual multi-armed bandits, reinforcement learning, and online adaptation. Unlike traditional indicators that use fixed rules, RPD learns from every trade outcome , automatically discovers which strategies work in current market conditions, and continuously adapts without manual intervention .
Core Innovation: The system deploys six distinct trading policies (ranging from aggressive trend-following to conservative range-bound strategies) and uses LinUCB contextual bandit algorithms with Random Fourier Features to learn which policy performs best in each market regime. After the initial learning phase (50-100 trades), the system achieves autonomous adaptation , automatically shifting between policies as market conditions evolve.
Target Users: Quantitative traders, algorithmic trading developers, systematic traders, and data-driven investors who want a system that adapts over time . Suitable for stocks, futures, forex, and cryptocurrency on any liquid instrument with >100k daily volume.
The Problem This System Solves
Traditional Technical Analysis Limitations
Most trading systems suffer from three fundamental challenges :
Fixed Parameters: Static settings (like "buy when RSI < 30") work well in backtests but may struggle when markets change character. What worked in low-volatility environments may not work in high-volatility regimes.
Strategy Degradation: Manual optimization (curve-fitting) produces systems that perform well on historical data but may underperform in live trading. The system never adapts to new market conditions.
Cognitive Overload: Running multiple strategies simultaneously forces traders to manually decide which one to trust. This leads to hesitation, late entries, and inconsistent execution.
How RPD Machine Learning Addresses These Challenges
Automated Strategy Selection: Instead of requiring you to choose between trend-following and mean-reversion strategies, RPD runs all six policies simultaneously and uses machine learning to automatically select the best one for current conditions. The decision happens algorithmically, removing human hesitation.
Continuous Learning: After every trade, the system updates its understanding of which policies are working. If the market shifts from trending to ranging, RPD automatically detects this through changing performance patterns and adjusts selection accordingly.
Context-Aware Decisions: Unlike simple voting systems that treat all conditions equally, RPD analyzes market context (ADX regime, entropy levels, volatility state, volume patterns, time of day, historical performance) and learns which combinations of context features correlate with policy success.
Machine Learning Architecture: What Makes This "Real" ML
Component 1: Contextual Multi-Armed Bandits (LinUCB)
What Is a Multi-Armed Bandit Problem?
Imagine facing six slot machines, each with unknown payout rates. The exploration-exploitation dilemma asks: Should you keep pulling the machine that's worked well (exploitation) or try others that might be better (exploration)? RPD solves this for trading policies.
Academic Foundation:
RPD implements Linear Upper Confidence Bound (LinUCB) from the research paper "A Contextual-Bandit Approach to Personalized News Article Recommendation" (Li et al., 2010, WWW Conference). This algorithm is used in content recommendation and ad placement systems.
How It Works:
Each policy (AggressiveTrend, ConservativeRange, VolatilityBreakout, etc.) is treated as an "arm." The system maintains:
Reward History: Tracks wins/losses for each policy
Contextual Features: Current market state (8-10 features including ADX, entropy, volatility, volume)
Uncertainty Estimates: Confidence in each policy's performance
UCB Formula: predicted_reward + α × uncertainty
The system selects the policy with highest UCB score , balancing proven performance (predicted_reward) with potential for discovery (uncertainty bonus). Initially, all policies have high uncertainty, so the system explores broadly. After 50-100 trades, uncertainty decreases, and the system focuses on known-performing policies.
Why This Matters:
Traditional systems pick strategies based on historical backtests or user preference. RPD learns from actual outcomes in your specific market, on your timeframe, with your execution characteristics.
Component 2: Random Fourier Features (RFF)
The Non-Linearity Challenge:
Market relationships are often non-linear. High ADX may indicate favorable conditions when volatility is normal, but unfavorable when volatility spikes. Simple linear models struggle to capture these interactions.
Academic Foundation:
RPD implements Random Fourier Features from "Random Features for Large-Scale Kernel Machines" (Rahimi & Recht, 2007, NIPS). This technique approximates kernel methods (like Support Vector Machines) while maintaining computational efficiency for real-time trading.
How It Works:
The system transforms base features (ADX, entropy, volatility, etc.) into a higher-dimensional space using random projections and cosine transformations:
Input: 8 base features
Projection: Through random Gaussian weights
Transformation: cos(W×features + b)
Output: 16 RFF dimensions
This allows the bandit to learn non-linear relationships between market context and policy success. For example: "AggressiveTrend performs well when ADX >25 AND entropy <0.6 AND hour >9" becomes naturally encoded in the RFF space.
Why This Matters:
Without RFF, the system could only learn "this policy has X% historical performance." With RFF, it learns "this policy performs differently in these specific contexts" - enabling more nuanced selection.
Component 3: Reinforcement Learning Stack
Beyond bandits, RPD implements a complete RL framework :
Q-Learning: Value-based RL that learns state-action values. Maps 54 discrete market states (trend×volatility×RSI×volume combinations) to 5 actions (4 policies + no-trade). Updates via Bellman equation after each trade. Converges toward optimal policy after 100-200 trades.
TD(λ) with Eligibility Traces: Extension of Q-Learning that propagates credit backwards through time . When a trade produces an outcome, TD(λ) updates not just the final state-action but all states visited during the trade, weighted by eligibility decay (λ=0.90). This accelerates learning from multi-bar trades.
Policy Gradient (REINFORCE): Learns a stochastic policy directly from 12 continuous market features without discretization. Uses gradient ascent to increase probability of actions that led to positive outcomes. Includes baseline (average reward) for variance reduction.
Meta-Learning: The system learns how to learn by adapting its own learning rates based on feature stability and correlation with outcomes. If a feature (like volume ratio) consistently correlates with success, its learning rate increases. If unstable, rate decreases.
Why This Matters:
Q-Learning provides fast discrete decisions. Policy Gradient handles continuous features. TD(λ) accelerates learning. Meta-learning optimizes the optimization. Together, they create a robust, multi-approach learning system that adapts more quickly than any single algorithm.
Component 4: Policy Momentum Tracking (v2 Feature)
The Recency Challenge:
Standard bandits treat all historical data equally. If a policy performed well historically but struggles in current conditions due to regime shift, the system may be slow to adapt because historical success outweighs recent underperformance.
RPD's Solution:
Each policy maintains a ring buffer of the last 10 outcomes. The system calculates:
Momentum: recent_win_rate - global_win_rate (range: -1 to +1)
Confidence: consistency of recent results (1 - variance)
Policies with positive momentum (recent outperformance) get an exploration bonus. Policies with negative momentum and high confidence (consistent recent underperformance) receive a selection penalty.
Effect: When markets shift, the system detects the shift more quickly through momentum tracking, enabling faster adaptation than standard bandits.
Signal Generation: The Core Algorithm
Multi-Timeframe Fractal Detection
RPD identifies reversal points using three complementary methods :
1. Quantum State Analysis:
Divides price range into discrete states (default: 6 levels)
Peak signals require price in top states (≥ state 5)
Valley signals require price in bottom states (≤ state 1)
Prevents mid-range signals that may struggle in strong trends
2. Fractal Geometry:
Identifies swing highs/lows using configurable fractal strength
Confirms local extremum with neighboring bars
Validates reversal only if price crosses prior extreme
3. Multi-Timeframe Confirmation:
Analyzes higher timeframe (4× default) for alignment
MTF confirmation adds probability bonus
Designed to reduce false signals while preserving valid setups
Probability Scoring System
Each signal receives a dynamic probability score (40-99%) based on:
Base Components:
Trend Strength: EMA(velocity) / ATR × 30 points
Entropy Quality: (1 - entropy) × 10 points
Starting baseline: 40 points
Enhancement Bonuses:
Divergence Detection: +20 points (price/momentum divergence)
RSI Extremes: +8 points (RSI >65 for peaks, <40 for valleys)
Volume Confirmation: +5 points (volume >1.2× average)
Adaptive Momentum: +10 points (strong directional velocity)
MTF Alignment: +12 points (higher timeframe confirms)
Range Factor: (high-low)/ATR × 3 - 1.5 points (volatility adjustment)
Regime Bonus: +8 points (trending ADX >25 with directional agreement)
Penalties:
High Entropy: -5 points (entropy >0.85, chaotic price action)
Consolidation Regime: -10 points (ADX <20, no directional conviction)
Final Score: Clamped to 40-99% range, classified as ELITE (>85%), STRONG (75-85%), GOOD (65-75%), or FAIR (<65%)
Entropy-Based Quality Filter
What Is Entropy?
Entropy measures randomness in price changes . Low entropy indicates orderly, directional moves. High entropy indicates chaotic, unpredictable conditions.
Calculation:
Count up/down price changes over adaptive period
Calculate probability: p = ups / total_changes
Shannon entropy: -p×log(p) - (1-p)×log(1-p)
Normalized to 0-1 range
Application:
Entropy <0.5: Highly ordered (ELITE signals possible)
Entropy 0.5-0.75: Mixed (GOOD signals)
Entropy >0.85: Chaotic (signals blocked or heavily penalized)
Why This Matters:
Prevents trading during choppy, news-driven conditions where technical patterns may be less reliable. Automatically raises quality bar when market is unpredictable.
Regime Detection & Market Microstructure - ADX-Based Regime Classification
RPD uses Wilder's Average Directional Index to classify markets:
Bull Trend: ADX >25, +DI > -DI (directional conviction bullish)
Bear Trend: ADX >25, +DI < -DI (directional conviction bearish)
Consolidation: ADX <20 (no directional conviction)
Transitional: ADX 20-25 (forming direction, ambiguous)
Filter Logic:
Blocks all signals during Transitional regime (avoids trading during uncertain conditions)
Blocks Consolidation signals unless ADX ≥ Min Trend Strength
Adds probability bonus during strong trends (ADX >30)
Effect: Designed to reduce signal frequency while focusing on higher-quality setups.
Divergence Detection
Bearish Divergence:
Price makes higher high
Velocity (price momentum) makes lower high
Indicates weakening upward pressure → SHORT signal quality boost
Bullish Divergence:
Price makes lower low
Velocity makes higher low
Indicates weakening downward pressure → LONG signal quality boost
Bonus: Adds probability points and additional acceleration factor. Divergence signals have historically shown higher success rates in testing.
Hierarchical Policy System - The Six Trading Policies
1. AggressiveTrend (Policy 0):
Probability Threshold: 60% (trades more frequently)
Entropy Threshold: 0.70 (tolerates moderate chaos)
Stop Multiplier: 2.5× ATR (wider stops for trends)
Target Multiplier: 5.0R (larger targets)
Entry Mode: Pyramid (scales into winners)
Best For: Strong trending markets, breakouts, momentum continuation
2. ConservativeRange (Policy 1):
Probability Threshold: 75% (more selective)
Entropy Threshold: 0.60 (requires order)
Stop Multiplier: 1.8× ATR (tighter stops)
Target Multiplier: 3.0R (modest targets)
Entry Mode: Single (one-shot entries)
Best For: Range-bound markets, low volatility, mean reversion
3. VolatilityBreakout (Policy 2):
Probability Threshold: 65% (moderate)
Entropy Threshold: 0.80 (accepts high entropy)
Stop Multiplier: 3.0× ATR (wider stops)
Target Multiplier: 6.0R (larger targets)
Entry Mode: Tiered (splits entry)
Best For: Compression breakouts, post-consolidation moves, gap opens
4. EntropyScalp (Policy 3):
Probability Threshold: 80% (very selective)
Entropy Threshold: 0.40 (requires extreme order)
Stop Multiplier: 1.5× ATR (tightest stops)
Target Multiplier: 2.5R (quick targets)
Entry Mode: Single
Best For: Low-volatility grinding moves, tight ranges, highly predictable patterns
5. DivergenceHunter (Policy 4):
Probability Threshold: 70% (quality-focused)
Entropy Threshold: 0.65 (balanced)
Stop Multiplier: 2.2× ATR (moderate stops)
Target Multiplier: 4.5R (balanced targets)
Entry Mode: Tiered
Best For: Divergence-confirmed reversals, exhaustion moves, trend climax
6. AdaptiveBlend (Policy 5):
Probability Threshold: 68% (balanced)
Entropy Threshold: 0.75 (balanced)
Stop Multiplier: 2.0× ATR (standard)
Target Multiplier: 4.0R (standard)
Entry Mode: Single
Best For: Mixed conditions, general trading, fallback when no clear regime
Policy Clustering (Advanced/Extreme Modes)
Policies are grouped into three clusters based on regime affinity:
Cluster 1 (Trending): AggressiveTrend, DivergenceHunter
High regime affinity (0.8): Performs well when ADX >25
Moderate vol affinity (0.6): Works in various volatility
Cluster 2 (Ranging): ConservativeRange, AdaptiveBlend
Low regime affinity (0.3): Better suited for ADX <20
Low vol affinity (0.4): Optimized for calm markets
Cluster 3 (Breakout): VolatilityBreakout
Moderate regime affinity (0.6): Works in multiple regimes
High vol affinity (0.9): Requires high volatility for optimal characteristics
Hierarchical Selection Process:
Calculate cluster scores based on current regime and volatility
Select best-matching cluster
Run UCB selection within chosen cluster
Apply momentum boost/penalty
This two-stage process reduces learning time - instead of choosing among 6 policies from scratch, system first narrows to 1-2 policies per cluster, then optimizes within cluster.
Risk Management & Position Sizing
Dynamic Kelly Criterion Sizing (Optional)
Traditional Fixed Sizing Challenge:
Using the same position size for all signal probabilities may be suboptimal. Higher-probability signals could justify larger positions, lower-probability signals smaller positions.
Kelly Formula:
f = (p × b - q) / b
Where:
p = win probability (from signal score)
q = loss probability (1 - p)
b = win/loss ratio (average_win / average_loss)
f = fraction of capital to risk
RPD Implementation:
Uses Fractional Kelly (1/4 Kelly default) for safety. Full Kelly is theoretically optimal but can recommend large position sizes. Fractional Kelly reduces volatility while maintaining adaptive sizing benefits.
Enhancements:
Probability Bonus: Normalize(prob, 65, 95) × 0.5 multiplier
Divergence Bonus: Additional sizing on divergence signals
Regime Bonus: Additional sizing during strong trends (ADX >30)
Momentum Adjustment: Hot policies receive sizing boost, cold policies receive reduction
Safety Rails:
Minimum: 1 contract (floor)
Maximum: User-defined cap (default 10 contracts)
Portfolio Heat: Max total risk across all positions (default 4% equity)
Multi-Mode Stop Loss System
ATR Mode (Default):
Stop = entry ± (ATR × base_mult × policy_mult)
Consistent risk sizing
Ignores market structure
Best for: Futures, forex, algorithmic trading
Structural Mode:
Finds swing low (long) or high (short) over last 20 bars
Identifies fractal pivots within lookback
Places stop below/above structure + buffer (0.1× ATR)
Best for: Stocks, instruments that respect structure
Hybrid Mode (Intelligent):
Attempts structural stop first
Falls back to ATR if:
Structural level is invalid (beyond entry)
Structural stop >2× ATR away (too wide)
Best for: Mixed instruments, adaptability
Dynamic Adjustments:
Breakeven: Move stop to entry + 1 tick after 1.0R profit
Trailing: Trail stop 0.8R behind price after 1.5R profit
Timeout: Force close after 30 bars (optional)
Tiered Entry System
Challenge: Equal sizing on all signals may not optimize capital allocation relative to signal quality.
Solution:
Tier 1 (40% of size): Enters immediately on all signals
Tier 2 (60% of size): Enters only if probability ≥ Tier 2 trigger (default 75%)
Example:
Calculated optimal size: 10 contracts
Signal probability: 72%
Tier 2 trigger: 75%
Result: Enter 4 contracts only (Tier 1)
Same signal at 80% probability
Result: Enter 10 contracts (4 Tier 1 + 6 Tier 2)
Effect: Automatically scales size to signal quality, optimizing capital allocation.
Performance Optimization & Learning Curve
Warmup Phase (First 50 Trades)
Purpose: Ensure all policies get tested before system focuses on preferred strategies.
Modifications During Warmup:
Probability thresholds reduced 20% (65% becomes 52%)
Entropy thresholds increased 20% (more permissive)
Exploration rate stays high (30%)
Confidence width (α) doubled (more exploration)
Why This Matters:
Without warmup, system might commit to early-performing policy without testing alternatives. Warmup forces thorough exploration before focusing on best-performing strategies.
Curriculum Learning
Phase 1 (Trades 1-50): Exploration
Warmup active
All policies tested
High exploration (30%)
Learning fundamental patterns
Phase 2 (Trades 50-100): Refinement
Warmup ended, thresholds normalize
Exploration decaying (30% → 15%)
Policy preferences emerging
Meta-learning optimizing
Phase 3 (Trades 100-200): Specialization
Exploration low (15% → 8%)
Clear policy preferences established
Momentum tracking fully active
System focusing on learned patterns
Phase 4 (Trades 200+): Maturity
Exploration minimal (8% → 5%)
Regime-policy relationships learned
Auto-adaptation to market shifts
Stable performance expected
Convergence Indicators
System is learning well when:
Policy switch rate decreasing over time (initially ~50%, should drop to <20%)
Exploration rate decaying smoothly (30% → 5%)
One or two policies emerge with >50% selection frequency
Performance metrics stabilizing over time
Consistent behavior in similar market conditions
System may need adjustment when:
Policy switch rate >40% after 100 trades (excessive exploration)
Exploration rate not decaying (parameter issue)
All policies showing similar selection (not differentiating)
Performance declining despite relaxed thresholds (underlying signal issue)
Highly erratic behavior after learning phase
Advanced Features
Attention Mechanism (Extreme Mode)
Challenge: Not all features are equally important. Trading hour might matter more than price-volume correlation, but standard approaches treat them equally.
Solution:
Each RFF dimension has an importance weight . After each trade:
Calculate correlation: sign(feature - 0.5) × sign(reward)
Update importance: importance += correlation × 0.01
Clamp to range
Effect: Important features get amplified in RFF transformation, less important features get suppressed. System learns which features correlate with successful outcomes.
Temporal Context (Extreme Mode)
Challenge: Current market state alone may be incomplete. Historical context (was volatility rising or falling?) provides additional information.
Solution:
Includes 3-period historical context with exponential decay (0.85):
Current features (weight 1.0)
1 bar ago (weight 0.85)
2 bars ago (weight 0.72)
Effect: Captures momentum and acceleration of market features. System learns patterns like "rising volatility with falling entropy" that may precede significant moves.
Transfer Learning via Episodic Memory
Short-Term Memory (STM):
Last 20 trades
Fast adaptation to immediate regime
High learning rate
Long-Term Memory (LTM):
Condensed historical patterns
Preserved knowledge from past regimes
Low learning rate
Transfer Mechanism:
When STM fills (20 trades), patterns consolidated into LTM . When similar regime recurs later, LTM provides faster adaptation than starting from scratch.
Practical Implementation Guide - Recommended Settings by Instrument
Futures (ES, NQ, CL):
Adaptive Period: 20-25
ML Mode: Advanced
RFF Dimensions: 16
Policies: 6
Base Risk: 1.5%
Stop Mode: ATR or Hybrid
Timeframe: 5-15 min
Forex Majors (EURUSD, GBPUSD):
Adaptive Period: 25-30
ML Mode: Advanced
RFF Dimensions: 16
Policies: 6
Base Risk: 1.0-1.5%
Stop Mode: ATR
Timeframe: 5-30 min
Cryptocurrency (BTC, ETH):
Adaptive Period: 20-25
ML Mode: Extreme (handles non-stationarity)
RFF Dimensions: 32 (captures complexity)
Policies: 6
Base Risk: 1.0% (volatility consideration)
Stop Mode: Hybrid
Timeframe: 15 min - 4 hr
Stocks (Large Cap):
Adaptive Period: 25-30
ML Mode: Advanced
RFF Dimensions: 16
Policies: 5-6
Base Risk: 1.5-2.0%
Stop Mode: Structural or Hybrid
Timeframe: 15 min - Daily
Scaling Strategy
Phase 1 (Testing - First 50 Trades):
Max Contracts: 1-2
Goal: Validate system on your instrument
Monitor: Performance stabilization, learning progress
Phase 2 (Validation - Trades 50-100):
Max Contracts: 2-3
Goal: Confirm learning convergence
Monitor: Policy stability, exploration decay
Phase 3 (Scaling - Trades 100-200):
Max Contracts: 3-5
Enable: Kelly sizing (1/4 Kelly)
Goal: Optimize capital efficiency
Monitor: Risk-adjusted returns
Phase 4 (Full Deployment - Trades 200+):
Max Contracts: 5-10
Enable: Full momentum tracking
Goal: Sustained consistent performance
Monitor: Ongoing adaptation quality
Limitations & Disclaimers
Statistical Limitations
Learning Sample Size: System requires minimum 50-100 trades for basic convergence, 200+ trades for robust learning. Early performance (first 50 trades) may not reflect mature system behavior.
Non-Stationarity Risk: Markets change over time. A system trained on one market regime may need time to adapt when conditions shift (typically 30-50 trades for adjustment).
Overfitting Possibility: With 16-32 RFF dimensions and 6 policies, system has substantial parameter space. Small sample sizes (<200 trades) increase overfitting risk. Mitigated by regularization (λ) and fractional Kelly sizing.
Technical Limitations
Computational Complexity: Extreme mode with 32 RFF dimensions, 6 policies, and full RL stack requires significant computation. May perform slowly on lower-end systems or with many other indicators loaded.
Pine Script Constraints:
No true matrix inversion (uses diagonal approximation for LinUCB)
No cryptographic RNG (uses market data as entropy)
No proper random number generation for RFF (uses deterministic pseudo-random)
These approximations reduce mathematical precision compared to academic implementations but remain functional for trading applications.
Data Requirements: Needs clean OHLCV data. Missing bars, gaps, or low liquidity (<100k daily volume) can degrade signal quality.
Forward-Looking Bias Disclaimer
Reward Calculation Uses Future Data: The RL system evaluates trades using an 8-bar forward-looking window. This means when a position enters at bar 100, the reward calculation considers price movement through bar 108.
Why This is Disclosed:
Entry signals do NOT look ahead - decisions use only data up to entry bar
Forward data used for learning only, not signal generation
In live trading, system learns identically as bars unfold in real-time
Simulates natural learning process (outcomes are only known after trades complete)
Implication: Backtested metrics reflect this 8-bar evaluation window. Live performance may vary if:
- Positions held longer than 8 bars
- Slippage/commissions differ from backtest settings
- Market microstructure changes (wider spreads, different execution quality)
Risk Warnings
No Guarantee of Profit: All trading involves substantial risk of loss. Machine learning systems can fail if market structure fundamentally changes or during unprecedented events.
Maximum Drawdown: With 1.5% base risk and 4% max total risk, expect potential drawdowns. Historical drawdowns do not predict future drawdowns. Extreme market conditions can exceed expectations.
Black Swan Events: System has not been tested under: flash crashes, trading halts, circuit breakers, major geopolitical shocks, or other extreme events. Such events can exceed stop losses and cause significant losses.
Leverage Risk: Futures and forex involve leverage. Adverse moves combined with leverage can result in losses exceeding initial investment. Use appropriate position sizing for your risk tolerance.
System Failures: Code bugs, broker API failures, internet outages, or exchange issues can prevent proper execution. Always monitor automated systems and maintain appropriate safeguards.
Appropriate Use
This System Is:
✅ A machine learning framework for adaptive strategy selection
✅ A signal generation system with probabilistic scoring
✅ A risk management system with dynamic sizing
✅ A learning system designed to adapt over time
This System Is NOT:
❌ A price prediction system (does not forecast exact prices)
❌ A guarantee of profits (can and will experience losses)
❌ A replacement for due diligence (requires monitoring and understanding)
❌ Suitable for complete beginners (requires understanding of ML concepts, risk management, and trading fundamentals)
Recommended Use:
Paper trade for 100 signals before risking capital
Start with minimal position sizing (1-2 contracts) regardless of calculated size
Monitor learning progress via dashboard
Scale gradually over several months only after consistent results
Combine with fundamental analysis and broader market context
Set account-level risk limits (e.g., maximum drawdown threshold)
Never risk more than you can afford to lose
What Makes This System Different
RPD implements academically-derived machine learning algorithms rather than simple mathematical calculations or optimization:
✅ LinUCB Contextual Bandits - Algorithm from WWW 2010 conference (Li et al.)
✅ Random Fourier Features - Kernel approximation from NIPS 2007 (Rahimi & Recht)
✅ Q-Learning, TD(λ), REINFORCE - Standard RL algorithms from Sutton & Barto textbook
✅ Meta-Learning - Learning rate adaptation based on feature correlation
✅ Online Learning - Real-time updates from streaming data
✅ Hierarchical Policies - Two-stage selection with clustering
✅ Momentum Tracking - Recent performance analysis for faster adaptation
✅ Attention Mechanism - Feature importance weighting
✅ Transfer Learning - Episodic memory consolidation
Key Differentiators:
Actually learns from trade outcomes (not just parameter optimization)
Updates model parameters in real-time (true online learning)
Adapts to changing market regimes (not static rules)
Improves over time through reinforcement learning
Implements published ML algorithms with proper citations
Conclusion
RPD Machine Learning represents a different approach from traditional technical analysis to adaptive, self-learning systems . Instead of manually optimizing parameters (which can overfit to historical data), RPD learns behavior patterns from actual trading outcomes in your specific market.
The combination of contextual bandits, reinforcement learning, random fourier features, hierarchical policy selection, and momentum tracking creates a multi-algorithm learning system designed to handle non-stationary markets better than static approaches.
After the initial learning phase (50-100 trades), the system achieves autonomous adaptation - automatically discovering which strategies work in current conditions and shifting allocation without human intervention. This represents an approach where systems adapt over time rather than remaining static.
Use responsibly. Paper trade extensively. Scale gradually. Understand that past performance does not guarantee future results and all trading involves risk of loss.
Taking you to school. — Dskyz, Trade with insight. Trade with anticipation.
RastaRasta — Educational Strategy (Pine v5)
Momentum · Smoothing · Trend Study
Overview
The Rasta Strategy is a visual and educational framework designed to help traders study momentum transitions using the interaction between a fast-reacting EMA line and a slower smoothed reference line.
It is not a signal generator or profit system; it’s a learning tool for understanding how smoothing, crossovers, and filters interact under different market conditions.
The script displays:
A primary EMA line (the fast reactive wave).
A Smoothed line (using your chosen smoothing method).
Optional fog zones between them for quick visual context.
Optional DNA rungs connecting both lines to illustrate volatility compression and expansion.
Optional EMA 8 / EMA 21 trend filter to observe higher-time-frame alignment.
Core Idea
The Rasta model focuses on wave interaction. When the fast EMA crosses above the smoothed line, it reflects a shift in short-term momentum relative to background trend pressure. Cross-unders suggest weakening or reversal.
Rather than treating this as a trading “signal,” use it to observe structure, study trend alignment, and test how smoothing type affects reaction speed.
Smoothing Types Explained
The script lets you experiment with multiple smoothing techniques:
Type Description Use Case
SMA (Simple Moving Average) Arithmetic mean of the last n values. Smooth and steady, but slower. Trend-following studies; filters noise on higher time frames.
EMA (Exponential Moving Average) Weights recent data more. Responds faster to new price action. Momentum or reactive strategies; quick shifts and reversals.
RMA (Relative Moving Average) Used internally by RSI; smooths exponentially but slower than EMA. Momentum confirmation; balanced response.
WMA (Weighted Moving Average) Linear weights emphasizing the most recent data strongly. Intraday scalping; crisp but potentially noisy.
None Disables smoothing; uses the EMA line alone. Raw comparison baseline.
Each smoothing method changes how early or late the strategy reacts:
Faster smoothing (EMA/WMA) = more responsive, good for scalping.
Slower smoothing (SMA/RMA) = more stable, good for trend following.
Modes of Study
🔹 Scalper Mode
Use short EMA lengths (e.g., 3–5) and fast smoothing (EMA or WMA).
Focus on 1 min – 15 min charts.
Watch how quick crossovers appear near local tops/bottoms.
Fog and rung compression reveal volatility contraction before bursts.
Goal: study short-term rhythm and liquidity pulses.
🔹 Momentum Mode
Use moderate EMA (5–9) and RMA smoothing.
Ideal for 1 H–4 H charts.
Observe how the fog color aligns with trend shifts.
EMA 8 / 21 filter can act as macro bias; “Enter” labels will appear only in its direction when enabled.
Goal: study sustained motion between pullbacks and acceleration waves.
🔹 Trend-Follower Mode
Use longer EMA (13–21) with SMA smoothing.
Great for daily/weekly charts.
Focus on periods where fog stays unbroken for long stretches — these illustrate clear trend dominance.
Watch rung spacing: tight clusters often precede consolidations; wide rungs signal expanding volatility.
Goal: visualize slow-motion trend transitions and filter whipsaw conditions.
Components
EMA Line (Red): Fast-reacting short-term direction.
Smoothed Line (Yellow): Reference trend baseline.
Fog Zone: Green when EMA > Smoothed (up-momentum), red when below.
DNA Rungs: Thin connectors showing volatility structure.
EMA 8 / 21 Filter (optional):
When enabled, the strategy will only allow Enter events if EMA 8 > EMA 21.
Use this to study higher-trend gating effects.
Educational Applications
Momentum Visualization: Observe how the fast EMA “breathes” around the smoothed baseline.
Trend Transitions: Compare different smoothing types to see how early or late reversals are detected.
Noise Filtering: Experiment with fog opacity and smoothing lengths to understand trade-off between responsiveness and stability.
Risk Concept Simulation: Includes a simple fixed stop-loss parameter (default 13%) for educational demonstrations of position management in the Strategy Tester.
How to Use
Add to Chart → “Strategy.”
Works on any timeframe and instrument.
Adjust Parameters:
Length: base EMA speed.
Smoothing Type: choose SMA, EMA, RMA, or WMA.
Smoothing Length: controls delay and smoothness.
EMA 8 / 21 Filter: toggles trend gating.
Fog & Rungs: visual study options only.
Study Behavior:
Use Strategy Tester → List of Trades for entry/exit context.
Observe how different smoothing types affect early vs. late “Enter” points.
Compare trend periods vs. ranging periods to evaluate efficiency.
Combine with External Tools:
Overlay RSI, MACD, or Volume for deeper correlation analysis.
Use replay mode to visualize crossovers in live sequence.
Interpreting the Labels
Enter: Marks where fast EMA crosses above the smoothed line (or when filter flips positive).
Exit: Marks where fast EMA crosses back below.
These are purely analytical markers — they do not represent trade advice.
Educational Value
The Rasta framework helps learners explore:
Reaction time differences between moving-average algorithms.
Impact of smoothing on signal clarity.
Interaction of local and global trends.
Visualization of volatility contraction (tight DNA rungs) and expansion (wide fog zones).
It’s a sandbox for studying price structure, not a promise of profit.
Disclaimer
This script is provided for educational and research purposes only.
It does not constitute financial advice, trading signals, or performance guarantees. Past market behavior does not predict future outcomes.
Users are encouraged to experiment responsibly, record observations, and develop their own understanding of price behavior.
Author: Michael Culpepper (mikeyc747)
License: Educational / Open for study and modification with credit.
Philosophy:
“Learning the rhythm of the market is more valuable than chasing its profits.” — Rasta
