profitable-expert-advisor/ai/rsi-divergence

RSI Divergence ONNX Trading System for BTCUSD

A complete AI-powered trading system that uses machine learning to identify genuine RSI (Relative Strength Index) divergences and execute trades on MetaTrader 5.

Overview

This system trains a neural network to classify RSI divergences into 5 categories:

• NONE (0): No divergence detected
• REGULAR_BULLISH (1): Price makes lower low, RSI makes higher low (reversal signal)
• REGULAR_BEARISH (2): Price makes higher high, RSI makes lower high (reversal signal)
• HIDDEN_BULLISH (3): Price makes higher low, RSI makes lower low (continuation signal)
• HIDDEN_BEARISH (4): Price makes lower high, RSI makes higher high (continuation signal)

The trained model is exported to ONNX format and used in a MetaTrader 5 Expert Advisor for live trading.

Features

• Advanced Divergence Detection: Identifies both regular and hidden RSI divergences
• Machine Learning Classification: Uses LSTM neural network to learn genuine divergence patterns
• ONNX Integration: Model runs efficiently in MetaTrader 5 using ONNX Runtime
• Comprehensive Backtesting: Test model performance on historical data
• Risk Management: Built-in stop loss, take profit, trailing stop, and position time limits

Project Structure

ai/rsi-divergence/
├── rsi_divergence_detector.py    # Core divergence detection module
├── collect_btcusd_data.py        # Data collection and labeling script
├── train_onnx_model.py           # Model training script
├── backtest_model.py              # Backtesting script
├── RSIDivergence_EA.mq5          # MetaTrader 5 Expert Advisor
├── requirements.txt               # Python dependencies
└── README.md                      # This file

Installation

1. Install Python Dependencies

cd ai/rsi-divergence
pip install -r requirements.txt

2. Setup MetaTrader 5

1. Install MetaTrader 5
2. Enable automated trading in MT5 settings
3. Copy RSIDivergence_EA.mq5 to MT5_Data_Folder/MQL5/Experts/
4. Compile the EA in MetaEditor

Usage

Step 1: Collect and Label Data

Collect BTCUSD historical data and label it with RSI divergence signals:

python collect_btcusd_data.py \
    --symbol BTCUSD \
    --timeframe H1 \
    --days 365 \
    --rsi-period 14 \
    --output data \
    --min-strength 0.15

This will:

• Fetch BTCUSD data from MetaTrader 5
• Calculate RSI and other technical indicators
• Detect and label RSI divergences
• Save labeled data to data/BTCUSD_H1_labeled.csv

Step 2: Train the Model

Train the neural network to classify divergences:

python train_onnx_model.py \
    --data data/BTCUSD_H1_labeled.csv \
    --lookback 60 \
    --epochs 50 \
    --batch-size 32 \
    --output models

This will:

• Load labeled data
• Train an LSTM-based classification model
• Export model to ONNX format
• Save scaler and feature list for inference

Output files:

• models/BTCUSD_H1_rsi_divergence_model.onnx - ONNX model
• models/BTCUSD_H1_rsi_divergence_scaler.pkl - Feature scaler
• models/BTCUSD_H1_rsi_divergence_features.pkl - Feature list

Step 3: Backtest the Model

Test the trained model on historical data:

python backtest_model.py \
    --model models/BTCUSD_H1_rsi_divergence_model.onnx \
    --scaler models/BTCUSD_H1_rsi_divergence_scaler.pkl \
    --features models/BTCUSD_H1_rsi_divergence_features.pkl \
    --symbol BTCUSD \
    --timeframe H1 \
    --days 90 \
    --balance 10000 \
    --lot-size 0.01 \
    --min-confidence 0.7

This will:

• Load the trained model
• Run backtest on historical data
• Generate performance metrics
• Save trade history to CSV

Step 4: Deploy to MetaTrader 5

1. Copy Model Files:

   • Copy BTCUSD_H1_rsi_divergence_model.onnx to MT5_Data_Folder/MQL5/Files/models/
   • Create the models folder if it doesn't exist

2. Attach EA to Chart:

   • Open BTCUSD chart in MT5
   • Drag RSIDivergence_EA from Navigator to chart
   • Configure parameters:
     • InpModelPath: Path to ONNX model (e.g., models\\BTCUSD_H1_rsi_divergence_model.onnx)
     • InpMinConfidence: Minimum confidence threshold (0.7 recommended)
     • InpLotSize: Position size
     • InpStopLoss: Stop loss in pips
     • InpTakeProfit: Take profit in pips

3. Enable AutoTrading:

   • Click "AutoTrading" button in MT5 toolbar
   • EA will start analyzing and trading automatically

Parameters

Data Collection Parameters

• --symbol: Trading symbol (default: BTCUSD)
• --timeframe: Timeframe (M1, M5, M15, M30, H1, H4, D1)
• --days: Number of days of historical data
• --rsi-period: RSI calculation period (default: 14)
• --min-strength: Minimum divergence strength (0-1)

Training Parameters

• --data: Path to labeled CSV file
• --lookback: Number of bars to look back (default: 60)
• --epochs: Training epochs (default: 50)
• --batch-size: Batch size (default: 32)

EA Parameters

ONNX Model Settings:

• InpModelPath: Path to ONNX model file
• InpLookback: Lookback period (must match training)
• InpMinConfidence: Minimum confidence to trade (0-1)

Trading Settings:

• InpLotSize: Position size
• InpMagicNumber: Unique identifier for EA trades
• InpStopLoss: Stop loss in pips (0 = disabled)
• InpTakeProfit: Take profit in pips (0 = disabled)
• InpMaxBarsInTrade: Maximum bars to hold position (0 = disabled)

Divergence Filter:

• InpUseRegularBullish: Enable regular bullish divergence trades
• InpUseRegularBearish: Enable regular bearish divergence trades
• InpUseHiddenBullish: Enable hidden bullish divergence trades
• InpUseHiddenBearish: Enable hidden bearish divergence trades

Risk Management:

• InpUseTrailingStop: Enable trailing stop
• InpTrailingStopPips: Trailing stop distance in pips
• InpTrailingStepPips: Trailing stop step in pips

Understanding RSI Divergences

Regular Divergences (Reversal Signals)

• Bullish: Price makes lower low, RSI makes higher low → Potential upward reversal
• Bearish: Price makes higher high, RSI makes lower high → Potential downward reversal

Hidden Divergences (Continuation Signals)

• Bullish: Price makes higher low, RSI makes lower low → Trend continuation upward
• Bearish: Price makes lower high, RSI makes higher high → Trend continuation downward

Performance Optimization

1. Data Quality: Use more historical data (1-2 years) for better training
2. Feature Engineering: Experiment with additional technical indicators
3. Model Tuning: Adjust LSTM architecture, dropout rates, learning rate
4. Confidence Threshold: Higher threshold = fewer but higher quality trades
5. Risk Management: Always use stop loss and position sizing

Troubleshooting

Model Not Loading in MT5

• Check model file path is correct
• Ensure model file is in MQL5/Files/models/ folder
• Verify ONNX model version compatibility (opset 13)

No Trades Executed

• Check confidence threshold (try lowering InpMinConfidence)
• Verify divergence types are enabled
• Check that sufficient historical data is available

Poor Backtest Results

• Collect more training data
• Adjust divergence detection parameters
• Retrain with different model architecture
• Test on different timeframes

Notes

• Model Compatibility: ONNX model uses opset 13 for MT5 compatibility
• Feature Normalization: Features are normalized using MinMaxScaler - ensure same normalization in EA
• Timeframe: Model trained on H1 timeframe - retrain for other timeframes
• Symbol: Model trained on BTCUSD - retrain for other symbols

License

This project is provided as-is for educational and research purposes.

References

• MetaTrader 5 ONNX Documentation
• RSI Divergence Trading Strategies
• ONNX Runtime