Update

ai/dummy/train_and_backtest_xauusd.py

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+"""
+Train ONNX Model for XAUUSD and Backtest
+
+This script:
+1. Trains an ONNX model for XAUUSD
+2. Runs backtest using the trained model
+3. Generates performance report
+"""
+
+import os
+import sys
+from datetime import datetime, timedelta
+import MetaTrader5 as mt5
+
+# Add paths
+current_dir = os.path.dirname(os.path.abspath(__file__))
+backtest_dir = os.path.join(os.path.dirname(current_dir), 'backtesting', 'MT5')
+sys.path.insert(0, current_dir)
+sys.path.insert(0, backtest_dir)
+
+from train_onnx_model import ONNXModelTrainer
+from backtest_engine import BacktestEngine
+from onnx_backtest_strategy import ONNXBacktestStrategy
+from performance_analyzer import PerformanceAnalyzer
+
+
+def main():
+    """Main function to train model and run backtest."""
+    print("="*60)
+    print("XAUUSD ONNX Model Training and Backtesting")
+    print("="*60)
+    
+    # Configuration
+    symbol = 'XAUUSD'
+    timeframe_str = 'H1'
+    lookback = 60
+    epochs = 30  # Reduced for faster training
+    initial_balance = 10000.0
+    
+    # Convert timeframe
+    timeframe_map = {
+        'M1': mt5.TIMEFRAME_M1,
+        'M5': mt5.TIMEFRAME_M5,
+        'M15': mt5.TIMEFRAME_M15,
+        'M30': mt5.TIMEFRAME_M30,
+        'H1': mt5.TIMEFRAME_H1,
+        'H4': mt5.TIMEFRAME_H4,
+        'D1': mt5.TIMEFRAME_D1
+    }
+    timeframe = timeframe_map[timeframe_str]
+    
+    # Create models directory
+    models_dir = 'models'
+    os.makedirs(models_dir, exist_ok=True)
+    
+    # Step 1: Train Model
+    print("\n" + "="*60)
+    print("STEP 1: Training ONNX Model")
+    print("="*60)
+    
+    trainer = ONNXModelTrainer(
+        symbol=symbol,
+        timeframe=timeframe,
+        lookback=lookback
+    )
+    
+    try:
+        print(f"\nTraining model for {symbol} on {timeframe_str} timeframe...")
+        print(f"Lookback: {lookback} bars")
+        print(f"Epochs: {epochs}")
+        print("\nThis may take several minutes...\n")
+        
+        trainer.train(epochs=epochs, batch_size=32, verbose=1)
+        
+        # Export model
+        model_name = f"{symbol}_{timeframe_str}_model.onnx"
+        model_path = os.path.join(models_dir, model_name)
+        
+        print(f"\nExporting model to ONNX format...")
+        trainer.export_to_onnx(model_path)
+        
+        # Save scaler
+        scaler_name = f"{symbol}_{timeframe_str}_scaler.pkl"
+        scaler_path = os.path.join(models_dir, scaler_name)
+        import pickle
+        with open(scaler_path, 'wb') as f:
+            pickle.dump(trainer.scaler, f)
+        print(f"✓ Scaler saved to: {scaler_path}")
+        
+        print(f"\n✓ Model saved to: {model_path}")
+        
+    except Exception as e:
+        print(f"\n✗ Training failed: {e}")
+        import traceback
+        traceback.print_exc()
+        trainer.cleanup()
+        return
+    
+    finally:
+        trainer.cleanup()
+    
+    # Step 2: Run Backtest
+    print("\n" + "="*60)
+    print("STEP 2: Running Backtest")
+    print("="*60)
+    
+    # Backtest date range (last 6 months for testing)
+    end_date = datetime.now()
+    start_date = end_date - timedelta(days=180)
+    
+    # Create strategy
+    strategy = ONNXBacktestStrategy(
+        symbol=symbol,
+        timeframe=timeframe,
+        model_path=model_path,
+        scaler_path=scaler_path,
+        initial_balance=initial_balance,
+        prediction_threshold=0.0001,  # 0.01% minimum change
+        min_confidence=0.3,  # 30% minimum confidence
+        lot_size=0.1,
+        stop_loss_pips=50,
+        take_profit_pips=100
+    )
+    
+    # Run backtest
+    try:
+        print(f"\nRunning backtest from {start_date.date()} to {end_date.date()}...")
+        engine = BacktestEngine(strategy, start_date, end_date)
+        results = engine.run()
+        
+        # Analyze results
+        print("\n" + "="*60)
+        print("STEP 3: Performance Analysis")
+        print("="*60)
+        
+        analyzer = PerformanceAnalyzer(results)
+        analyzer.generate_report('onnx_backtest_results')
+        
+        print("\n" + "="*60)
+        print("Training and Backtesting Completed!")
+        print("="*60)
+        print(f"\nModel: {model_path}")
+        print(f"Scaler: {scaler_path}")
+        print(f"Results: onnx_backtest_results/")
+        
+    except Exception as e:
+        print(f"\n✗ Backtest failed: {e}")
+        import traceback
+        traceback.print_exc()
+
+
+if __name__ == '__main__':
+    # Check MT5 connection
+    if not mt5.initialize():
+        print("ERROR: Failed to initialize MT5")
+        print("Make sure MetaTrader 5 is running and you're logged in.")
+        sys.exit(1)
+    
+    try:
+        main()
+    except KeyboardInterrupt:
+        print("\n\nInterrupted by user")
+    finally:
+        mt5.shutdown()