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zhutoutoutousan
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"""
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Debug ONNX Model Predictions
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This script helps debug why the model isn't generating trades.
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It shows actual predictions and checks if they meet trading criteria.
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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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import numpy as np
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import pandas as pd
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import onnxruntime as ort
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import pickle
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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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def main():
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"""Debug ONNX predictions."""
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print("="*60)
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print("ONNX Model Prediction Debug")
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print("="*60)
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# Configuration
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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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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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# Initialize MT5
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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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# Load model and scaler
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session = ort.InferenceSession(model_path)
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input_name = session.get_inputs()[0].name
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output_name = session.get_outputs()[0].name
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with open(scaler_path, 'rb') as f:
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scaler = pickle.load(f)
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# Get recent data
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end_date = datetime.now()
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start_date = end_date - timedelta(days=10)
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rates = mt5.copy_rates_range(symbol, timeframe, start_date, end_date)
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if rates is None or len(rates) == 0:
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print("ERROR: No data available")
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return
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df = pd.DataFrame(rates)
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df['time'] = pd.to_datetime(df['time'], unit='s')
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print(f"\nLoaded {len(df)} bars")
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print(f"Date range: {df['time'].min()} to {df['time'].max()}")
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# Create strategy to get features
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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=10000.0,
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prediction_threshold=0.0001,
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min_confidence=0.3,
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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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# Simulate a few bars
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print("\n" + "="*60)
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print("Predictions Analysis")
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print("="*60)
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predictions_data = []
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for i in range(60, min(100, len(df))): # Start from bar 60 (need lookback)
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bar = df.iloc[i]
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bar_time = bar['time'] if isinstance(bar['time'], datetime) else datetime.fromtimestamp(bar['time'])
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current_price = bar['close']
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# Build historical buffer
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bar_data = {
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'time': bar_time,
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'open': float(bar['open']),
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'high': float(bar['high']),
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'low': float(bar['low']),
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'close': float(bar['close']),
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'tick_volume': int(bar['tick_volume']),
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'rsi': 50.0, # Simplified
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'ema': current_price, # Simplified
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'atr': 0.0 # Simplified
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}
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strategy.historical_bars.append(bar_data)
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if len(strategy.historical_bars) >= strategy.lookback:
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# Get prediction
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features = strategy.prepare_features()
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if features is not None:
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input_data = features.astype(np.float32)
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outputs = session.run([output_name], {input_name: input_data})
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predicted_price = float(outputs[0][0][0])
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# Calculate metrics
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price_change = predicted_price - current_price
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price_change_pct = (price_change / current_price) if current_price > 0 else 0.0
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confidence = min(abs(price_change_pct) / 0.01, 1.0)
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predictions_data.append({
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'time': bar_time,
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'current_price': current_price,
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'predicted_price': predicted_price,
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'price_change': price_change,
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'price_change_pct': price_change_pct * 100,
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'confidence': confidence,
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'meets_threshold': abs(price_change_pct) >= 0.0001,
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'meets_confidence': confidence >= 0.3,
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'would_trade': abs(price_change_pct) >= 0.0001 and confidence >= 0.3
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})
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# Display results
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if predictions_data:
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pred_df = pd.DataFrame(predictions_data)
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print(f"\nAnalyzed {len(pred_df)} predictions")
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print(f"\nPrediction Statistics:")
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print(f" Mean price change: {pred_df['price_change_pct'].mean():.4f}%")
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print(f" Std price change: {pred_df['price_change_pct'].std():.4f}%")
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print(f" Min price change: {pred_df['price_change_pct'].min():.4f}%")
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print(f" Max price change: {pred_df['price_change_pct'].max():.4f}%")
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print(f"\n Mean confidence: {pred_df['confidence'].mean():.4f}")
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print(f" Predictions meeting threshold: {pred_df['meets_threshold'].sum()}/{len(pred_df)}")
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print(f" Predictions meeting confidence: {pred_df['meets_confidence'].sum()}/{len(pred_df)}")
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print(f" Predictions that would trade: {pred_df['would_trade'].sum()}/{len(pred_df)}")
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print(f"\nSample predictions (first 10):")
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print(pred_df[['time', 'current_price', 'predicted_price', 'price_change_pct', 'confidence', 'would_trade']].head(10).to_string(index=False))
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if pred_df['would_trade'].sum() == 0:
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print("\n" + "="*60)
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print("RECOMMENDATIONS:")
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print("="*60)
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print("No trades would be generated. Try:")
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print(f" 1. Lower prediction_threshold (current: 0.0001)")
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print(f" Suggested: {pred_df['price_change_pct'].abs().quantile(0.1):.6f}")
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print(f" 2. Lower min_confidence (current: 0.3)")
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print(f" Suggested: {pred_df['confidence'].quantile(0.1):.2f}")
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print(f" 3. Check if model predictions are reasonable")
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else:
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print("No predictions generated (need more historical data)")
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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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