zhutoutoutousan
116 lines
3.1 KiB
Python
116 lines
3.1 KiB
Python
"""
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Retrain XAUUSD ONNX Model with Improved Settings
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This script retrains the model with:
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- Price change percentage prediction (instead of absolute price)
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- More training epochs
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- Better model architecture
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- Improved data preprocessing
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"""
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import os
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import sys
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from datetime import datetime
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import MetaTrader5 as mt5
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# Add paths
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current_dir = os.path.dirname(os.path.abspath(__file__))
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sys.path.insert(0, current_dir)
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from train_onnx_model import ONNXModelTrainer
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def main():
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"""Retrain XAUUSD model with improved settings."""
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print("="*60)
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print("Retraining XAUUSD ONNX Model (Improved)")
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print("="*60)
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symbol = 'XAUUSD'
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timeframe_str = 'H1'
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lookback = 60
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epochs = 50 # More epochs for better training
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# Convert timeframe
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timeframe_map = {
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'M1': mt5.TIMEFRAME_M1,
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'M5': mt5.TIMEFRAME_M5,
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'M15': mt5.TIMEFRAME_M15,
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'M30': mt5.TIMEFRAME_M30,
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'H1': mt5.TIMEFRAME_H1,
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'H4': mt5.TIMEFRAME_H4,
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'D1': mt5.TIMEFRAME_D1
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}
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timeframe = timeframe_map[timeframe_str]
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# Create models directory
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models_dir = 'models'
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os.makedirs(models_dir, exist_ok=True)
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print(f"\nConfiguration:")
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print(f" Symbol: {symbol}")
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print(f" Timeframe: {timeframe_str}")
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print(f" Lookback: {lookback} bars")
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print(f" Epochs: {epochs}")
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print(f" Prediction: Price change percentage (improved)")
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print("\nThis will take 10-20 minutes...\n")
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trainer = ONNXModelTrainer(
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symbol=symbol,
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timeframe=timeframe,
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lookback=lookback
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)
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try:
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# Train model
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trainer.train(epochs=epochs, batch_size=32, verbose=1)
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# Export model
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model_name = f"{symbol}_{timeframe_str}_model.onnx"
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model_path = os.path.join(models_dir, model_name)
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print(f"\nExporting model to ONNX format...")
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trainer.export_to_onnx(model_path)
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# Save scaler
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scaler_name = f"{symbol}_{timeframe_str}_scaler.pkl"
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scaler_path = os.path.join(models_dir, scaler_name)
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import pickle
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with open(scaler_path, 'wb') as f:
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pickle.dump(trainer.scaler, f)
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print(f"Scaler saved to: {scaler_path}")
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print(f"\n{'='*60}")
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print("Retraining Completed Successfully!")
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print(f"{'='*60}")
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print(f"\nModel: {model_path}")
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print(f"Scaler: {scaler_path}")
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print("\nNext steps:")
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print(" 1. Run: python quick_backtest.py")
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print(" 2. Or: python optimize_onnx_params.py 2 30")
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except Exception as e:
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print(f"\nERROR: Training failed: {e}")
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import traceback
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traceback.print_exc()
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trainer.cleanup()
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return
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finally:
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trainer.cleanup()
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if __name__ == '__main__':
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# Check MT5 connection
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if not mt5.initialize():
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print("ERROR: Failed to initialize MT5")
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print("Make sure MetaTrader 5 is running and you're logged in.")
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sys.exit(1)
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try:
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main()
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except KeyboardInterrupt:
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print("\n\nTraining interrupted by user")
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finally:
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mt5.shutdown()
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