zhutoutoutousan
165 lines
4.6 KiB
Python
165 lines
4.6 KiB
Python
"""
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Train ONNX Model for XAUUSD and Backtest
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This script:
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1. Trains an ONNX model for XAUUSD
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2. Runs backtest using the trained model
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3. Generates performance report
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"""
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import os
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import sys
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from datetime import datetime, timedelta
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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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backtest_dir = os.path.join(os.path.dirname(current_dir), 'backtesting', 'MT5')
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sys.path.insert(0, current_dir)
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sys.path.insert(0, backtest_dir)
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from train_onnx_model import ONNXModelTrainer
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from backtest_engine import BacktestEngine
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from onnx_backtest_strategy import ONNXBacktestStrategy
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from performance_analyzer import PerformanceAnalyzer
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def main():
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"""Main function to train model and run backtest."""
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print("="*60)
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print("XAUUSD ONNX Model Training and Backtesting")
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print("="*60)
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# Configuration
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symbol = 'XAUUSD'
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timeframe_str = 'H1'
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lookback = 60
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epochs = 30 # Reduced for faster training
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initial_balance = 10000.0
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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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# Step 1: Train Model
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print("\n" + "="*60)
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print("STEP 1: Training ONNX Model")
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print("="*60)
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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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print(f"\nTraining model for {symbol} on {timeframe_str} timeframe...")
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print(f"Lookback: {lookback} bars")
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print(f"Epochs: {epochs}")
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print("\nThis may take several minutes...\n")
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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✓ Model saved to: {model_path}")
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except Exception as e:
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print(f"\n✗ 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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# Step 2: Run Backtest
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print("\n" + "="*60)
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print("STEP 2: Running Backtest")
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print("="*60)
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# Backtest date range (last 6 months for testing)
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end_date = datetime.now()
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start_date = end_date - timedelta(days=180)
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# Create strategy
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strategy = ONNXBacktestStrategy(
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symbol=symbol,
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timeframe=timeframe,
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model_path=model_path,
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scaler_path=scaler_path,
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initial_balance=initial_balance,
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prediction_threshold=0.0001, # 0.01% minimum change
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min_confidence=0.3, # 30% minimum confidence
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lot_size=0.1,
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stop_loss_pips=50,
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take_profit_pips=100
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)
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# Run backtest
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try:
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print(f"\nRunning backtest from {start_date.date()} to {end_date.date()}...")
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engine = BacktestEngine(strategy, start_date, end_date)
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results = engine.run()
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# Analyze results
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print("\n" + "="*60)
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print("STEP 3: Performance Analysis")
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print("="*60)
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analyzer = PerformanceAnalyzer(results)
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analyzer.generate_report('onnx_backtest_results')
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print("\n" + "="*60)
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print("Training and Backtesting Completed!")
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print("="*60)
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print(f"\nModel: {model_path}")
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print(f"Scaler: {scaler_path}")
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print(f"Results: onnx_backtest_results/")
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except Exception as e:
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print(f"\n✗ Backtest failed: {e}")
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import traceback
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traceback.print_exc()
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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\nInterrupted by user")
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finally:
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mt5.shutdown()
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