profitable-expert-advisor/ai/dummy

ONNX Models with MetaTrader 5

A complete framework for training and using ONNX machine learning models in MetaTrader 5 for algorithmic trading.

Based on the MQL5 ONNX documentation.

Overview

This framework allows you to:

1. Train neural network models in Python using MetaTrader 5 historical data
2. Export models to ONNX format for use in MQL5
3. Use ONNX models in Expert Advisors for real-time trading predictions
4. Test predictions using Python scripts

Features

• 🧠 LSTM Neural Networks for price prediction
• 📊 Technical Indicators as features (RSI, EMA, ATR, etc.)
• 🔄 ONNX Export for MQL5 integration
• 📈 Real-time Prediction in Expert Advisors
• 🎯 Flexible Configuration for different symbols and timeframes

Installation

1. Install Python Dependencies

cd ai
pip install -r requirements.txt

2. Install MetaTrader 5

• Download and install MetaTrader 5
• Create a demo or live account
• Enable Python integration in MT5 settings:
  • Tools → Options → Expert Advisors
  • Check "Allow DLL imports"
  • Check "Integration with Python" (if available)

3. Configure MetaEditor (Optional)

If you want to run Python scripts from MetaEditor:

• MetaEditor → Tools → Options → Compiler
• Set Python executable path
• Or click "Install" to download Python

Quick Start

Step 1: Train an ONNX Model

Train a model for price prediction:

python train_onnx_model.py --symbol XAUUSD --timeframe H1 --lookback 60 --epochs 50

This will:

• Fetch 2 years of historical data from MT5
• Prepare features (OHLCV + technical indicators)
• Train an LSTM neural network
• Export the model to models/XAUUSD_H1_model.onnx

Step 2: Test the Model

Make predictions using the trained model:

python predict_with_onnx.py --model models/XAUUSD_H1_model.onnx --symbol XAUUSD --timeframe H1

Step 3: Use in Expert Advisor

For Strategy Tester:

1. Copy the ONNX model to Tester Files folder:

   <MT5 Data Folder>\Tester\Files\XAUUSD_H1_model.onnx
   

   Or use the full path shown in error messages if file not found.

2. Compile ONNX_EA.mq5 in MetaEditor (F7)

3. Open Strategy Tester (View → Strategy Tester or Ctrl+R)

4. Configure:

   • Expert Advisor: ONNX_EA
   • Symbol: XAUUSD (or your symbol)
   • Period: H1 (or your timeframe)
   • Inputs:
     • InpModelPath: XAUUSD_H1_model.onnx (just filename)
     • Adjust other parameters as needed

5. Click Start

For Live/Demo Trading:

1. Copy the ONNX model to MT5's Files folder:

   <MT5 Data Folder>\MQL5\Files\XAUUSD_H1_model.onnx
   

   To find your Data Folder: Tools → Options → Expert Advisors → Data Folder

2. Compile ONNX_EA.mq5 in MetaEditor (F7)

3. Open a chart (e.g., XAUUSD H1)

4. Drag ONNX_EA from Navigator (Ctrl+N) onto the chart

5. Configure inputs:

   • InpModelPath: XAUUSD_H1_model.onnx (just filename)
   • Adjust trading parameters

6. Click OK and enable AutoTrading if needed

Detailed Usage

Training Models

Basic Training

python train_onnx_model.py \
    --symbol XAUUSD \
    --timeframe H1 \
    --lookback 60 \
    --epochs 50 \
    --batch-size 32

Advanced Options

python train_onnx_model.py \
    --symbol EURUSD \
    --timeframe M15 \
    --lookback 100 \
    --epochs 100 \
    --batch-size 64 \
    --output custom_models

Parameters:

• --symbol: Trading symbol (XAUUSD, EURUSD, BTCUSD, etc.)
• --timeframe: M1, M5, M15, M30, H1, H4, D1
• --lookback: Number of bars to use for prediction (default: 60)
• --epochs: Training epochs (default: 50)
• --batch-size: Batch size (default: 32)
• --output: Output directory (default: models)

Making Predictions

Single Prediction

python predict_with_onnx.py \
    --model models/XAUUSD_H1_model.onnx \
    --symbol XAUUSD \
    --timeframe H1

Multiple Predictions

python predict_with_onnx.py \
    --model models/XAUUSD_H1_model.onnx \
    --symbol XAUUSD \
    --timeframe H1 \
    --predictions 5

Expert Advisor Configuration

The ONNX_EA.mq5 Expert Advisor includes:

ONNX Model Settings:

• InpModelPath: Path to ONNX model file
• InpLookback: Lookback period (must match training)
• InpUsePrediction: Enable/disable model predictions

Trading Settings:

• InpLotSize: Position size
• InpMagicNumber: Magic number for trades
• InpStopLoss: Stop loss in pips
• InpTakeProfit: Take profit in pips

Prediction Settings:

• InpPredictionThreshold: Minimum prediction change to trade (0.01% = 0.0001)
• InpUseConfidence: Enable confidence filtering
• InpMinConfidence: Minimum confidence level (0.0-1.0)

Model Architecture

The default model uses:

• Input: 60 bars × 12 features
• Architecture:
  • LSTM(128) → Dropout(0.2)
  • LSTM(64) → Dropout(0.2)
  • LSTM(32) → Dropout(0.2)
  • Dense(16, ReLU)
  • Dense(1) - Price prediction
• Features:
  • OHLC prices
  • Tick volume
  • RSI (14)
  • EMA(20), EMA(50)
  • ATR(14)
  • Price changes
  • High/Low ratio
  • Volume ratios

Customization

Modify Features

Edit train_onnx_model.py to add/remove features:

def prepare_features(self, df: pd.DataFrame) -> pd.DataFrame:
    feature_df = df[['open', 'high', 'low', 'close', 'tick_volume']].copy()
    
    # Add your custom indicators
    feature_df['custom_indicator'] = your_calculation(df)
    
    return feature_df

Change Model Architecture

Modify build_model() in train_onnx_model.py:

def build_model(self, input_shape: tuple) -> keras.Model:
    model = keras.Sequential([
        layers.LSTM(256, return_sequences=True, input_shape=input_shape),
        # Add your layers here
        layers.Dense(1)
    ])
    return model

Adjust Expert Advisor Logic

Edit ONNX_EA.mq5 to customize trading logic:

• Entry conditions
• Exit conditions
• Position management
• Risk management

File Structure

ai/
├── requirements.txt           # Python dependencies
├── train_onnx_model.py       # Model training script
├── predict_with_onnx.py       # Prediction testing script
├── ONNX_EA.mq5               # MQL5 Expert Advisor
├── README.md                 # This file
└── models/                   # Trained ONNX models (created after training)

Troubleshooting

MT5 Connection Issues

Error: "MT5 initialization failed"

• Ensure MetaTrader 5 is installed and running
• Log into a demo or live account
• Check that the symbol exists in MT5

Model Loading Issues

Error: "Failed to load ONNX model"

• Verify the model file path is correct
• Ensure the model file is in MT5's Files folder
• Check that the model was exported correctly

Prediction Issues

Error: "Failed to prepare input data"

• Ensure enough historical data is available
• Check that lookback period matches training
• Verify indicators can be calculated

Shape Mismatch Errors

If you get shape mismatch errors:

1. Check that InpLookback in EA matches training --lookback
2. Verify feature count matches (default: 12 features)
3. Ensure input normalization matches training

Best Practices

1. Data Quality: Use high-quality historical data
2. Feature Engineering: Experiment with different indicators
3. Model Validation: Always validate on out-of-sample data
4. Risk Management: Use stop loss and position sizing
5. Backtesting: Test thoroughly before live trading
6. Monitoring: Monitor model performance regularly

Example Workflow

1. Train Model:

   python train_onnx_model.py --symbol XAUUSD --timeframe H1
   

2. Test Predictions:

   python predict_with_onnx.py --model models/XAUUSD_H1_model.onnx --symbol XAUUSD
   

3. Backtest in MT5:

   • Use Strategy Tester with ONNX_EA.mq5
   • Test on historical data
   • Analyze results

4. Optimize Parameters:

   • Adjust prediction threshold
   • Tune confidence levels
   • Optimize stop loss/take profit

5. Deploy:

   • Start with small position sizes
   • Monitor performance
   • Adjust as needed

References

• MQL5 ONNX Documentation
• ONNX Model Zoo
• MetaTrader 5 Python Module
• TensorFlow to ONNX

Disclaimer

Trading involves substantial risk of loss. This framework is provided for educational purposes only. Always test thoroughly on a demo account before using with real money. Past performance does not guarantee future results.

License

This framework is provided for educational and research purposes.