Update

2026-02-13 08:03:25 +01:00
parent 09c2f54c71
commit 98a87a69ca
134 changed files with 20003 additions and 253 deletions
@@ -0,0 +1,115 @@
+"""
+Retrain XAUUSD ONNX Model with Improved Settings
+
+This script retrains the model with:
+- Price change percentage prediction (instead of absolute price)
+- More training epochs
+- Better model architecture
+- Improved data preprocessing
+"""
+
+import os
+import sys
+from datetime import datetime
+import MetaTrader5 as mt5
+
+# Add paths
+current_dir = os.path.dirname(os.path.abspath(__file__))
+sys.path.insert(0, current_dir)
+
+from train_onnx_model import ONNXModelTrainer
+
+
+def main():
+    """Retrain XAUUSD model with improved settings."""
+    print("="*60)
+    print("Retraining XAUUSD ONNX Model (Improved)")
+    print("="*60)
+    
+    symbol = 'XAUUSD'
+    timeframe_str = 'H1'
+    lookback = 60
+    epochs = 50  # More epochs for better training
+    
+    # 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)
+    
+    print(f"\nConfiguration:")
+    print(f"  Symbol: {symbol}")
+    print(f"  Timeframe: {timeframe_str}")
+    print(f"  Lookback: {lookback} bars")
+    print(f"  Epochs: {epochs}")
+    print(f"  Prediction: Price change percentage (improved)")
+    print("\nThis will take 10-20 minutes...\n")
+    
+    trainer = ONNXModelTrainer(
+        symbol=symbol,
+        timeframe=timeframe,
+        lookback=lookback
+    )
+    
+    try:
+        # Train model
+        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{'='*60}")
+        print("Retraining Completed Successfully!")
+        print(f"{'='*60}")
+        print(f"\nModel: {model_path}")
+        print(f"Scaler: {scaler_path}")
+        print("\nNext steps:")
+        print("  1. Run: python quick_backtest.py")
+        print("  2. Or: python optimize_onnx_params.py 2 30")
+        
+    except Exception as e:
+        print(f"\nERROR: Training failed: {e}")
+        import traceback
+        traceback.print_exc()
+        trainer.cleanup()
+        return
+    
+    finally:
+        trainer.cleanup()
+
+
+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\nTraining interrupted by user")
+    finally:
+        mt5.shutdown()