104 lines
3.3 KiB
Python
104 lines
3.3 KiB
Python
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"""
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Test with very low thresholds to see if we can get any trades
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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, backtest_dir)
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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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"""Test with very low thresholds."""
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print("="*60)
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print("Testing with VERY LOW Thresholds")
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print("="*60)
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symbol = 'XAUUSD'
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timeframe = mt5.TIMEFRAME_H1
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model_path = 'models/XAUUSD_H1_model.onnx'
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scaler_path = 'models/XAUUSD_H1_scaler.pkl'
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initial_balance = 10000.0
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if not os.path.exists(model_path):
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print(f"ERROR: Model not found: {model_path}")
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return
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end_date = datetime.now()
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start_date = end_date - timedelta(days=180)
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print(f"\nModel: {model_path}")
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print(f"Date Range: {start_date.date()} to {end_date.date()}")
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print(f"\nVERY RELAXED Parameters:")
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print(" Prediction Threshold: 0.00001 (0.001%)")
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print(" Min Confidence: 0.05 (5%)")
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print(" Stop Loss: 50 pips")
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print(" Take Profit: 100 pips")
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print(" Lot Size: 0.1\n")
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if not mt5.initialize():
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print("ERROR: Failed to initialize MT5")
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return
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try:
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# Create strategy with VERY low thresholds
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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=0.00001, # Very low: 0.001%
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min_confidence=0.05, # Very low: 5%
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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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print("Running backtest...\n")
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engine = BacktestEngine(strategy, start_date, end_date)
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results = engine.run()
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analyzer = PerformanceAnalyzer(results)
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metrics = analyzer.metrics
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print("\n" + "="*60)
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print("Results")
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print("="*60)
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print(f"Total Trades: {metrics.get('total_trades', 0)}")
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print(f"Final Balance: ${metrics.get('final_balance', initial_balance):,.2f}")
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print(f"Total Return: {metrics.get('total_return_pct', 0):.2f}%")
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if metrics.get('total_trades', 0) == 0:
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print("\n" + "="*60)
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print("STILL NO TRADES!")
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print("="*60)
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print("This suggests the model predictions may be:")
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print(" 1. Too small in magnitude")
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print(" 2. Not meeting even very low thresholds")
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print(" 3. Or there's an issue with the prediction logic")
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print("\nNext steps:")
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print(" - Check model predictions directly")
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print(" - Verify feature preparation matches training")
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print(" - Consider retraining with different architecture")
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except Exception as e:
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print(f"\nERROR: {e}")
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import traceback
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traceback.print_exc()
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finally:
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mt5.shutdown()
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if __name__ == '__main__':
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main()
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