Machine Learning RSI Bands V4 (Alpha)

yesterday

Machine Learning RSI Bands V4 (Alpha) – Strategy Overview

Description

This strategy, Machine Learning RSI Bands V4 (Alpha), is a hybrid machine learning-driven approach to trend detection and trade execution, primarily leveraging RSI-based signals enhanced with pattern-matching and historical context analysis via a trained machine learning model. The core objective is to identify optimal long entries while filtering noise through historical pattern similarity scoring.

Core Features & Logic

✅ RSI-Based Entry Signals
The strategy relies on Relative Strength Index (RSI) with a short length of 4, using customized lower-band values (lb) to determine oversold conditions where market entries are favorable.

✅ Machine Learning-Driven Condition Matching
The script calls the external ML-based function fxe.cnd_400x8, which searches for historically similar RSI conditions and evaluates their effectiveness in generating future moves.
This function returns 10 similarity-based voting outputs (o1 to o10).

✅ Voting System for Trade Confirmation
Averaging function (avg_no_na) determines how consistent the machine learning votes are.
The votes threshold (vn) is set via input, ensuring that only statistically significant historical patterns trigger a trade.

✅ Backtesting & Strategy Tracking
Backtesting duration (btw) allows simulation of realistic trade duration.
Success Rate & Profit Factor Table is dynamically generated to measure historical performance.

✅ Custom Trade Execution & Alerts
Long trades are executed when RSI crosses above the lower band (lb) AND the vote count (votes) exceeds the threshold (vn).
Alerts for entries and exits trigger real-time notifications for automated execution.

✅ Start Date Filtering for Robust Strategy Testing
Trades only trigger after a defined timestamp (startTimestamp), ensuring the strategy has sufficient backloaded data for ML model accuracy.
Trade Execution Logic

RSI Band Crossover + Historical Match Confirmation
If RSI crosses above the lb AND the vote-based ML confirmation exceeds the threshold (vn), a long trade is triggered.

Backtesting Duration Controls Trade Holding Period
Trades automatically close after btw bars, simulating a fixed holding period for performance assessment.

Alerts & Strategy Integration
Alerts (alertcondition(long, 'long')) are created for automated trade notifications.
The strategy tester dynamically records performance, showing profit factor & success rate.

📊 Custom Table Displaying Strategy Performance:
Success Rate (%): Measures how often trades resulted in profit.
Profit Factor: Ratio of total profits vs. total losses over 1000 bars.
Strategy Start Recommendation: Suggests an optimal historical start date for testing.

📈 Plot Components for Visual Analysis:
RSI Indicator (Stepline Format) for a cleaner visualization of market conditions.
Lower Band (lb) Plot to identify key oversold levels.
Long Trade Markers to easily identify executed trades.

Final Thoughts
🚀 This AI-driven RSI strategy enhances traditional momentum indicators by incorporating historical pattern-matching techniques to increase entry signal accuracy.
🔍 Ideal for traders looking to automate market entries while ensuring historical pattern consistency before making decisions.
⚡ Future versions could incorporate Stop-Loss & Take-Profit (SL/TP) logic and additional feature enhancements to further refine trade execution.
🔥 For those looking to trade using machine learning, this strategy is a solid foundation for intelligent, data-backed decision-making!

yesterday

Release Notes

Fixed Input Titles