zhutoutoutousan
160 lines
4.5 KiB
Python
160 lines
4.5 KiB
Python
"""
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Complete Example Workflow for ONNX + MT5
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This script demonstrates the complete workflow:
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1. Train an ONNX model
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2. Test predictions
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3. Show how to use in MT5
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Run this to see the full process in action.
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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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# Import our modules
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from train_onnx_model import ONNXModelTrainer
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from predict_with_onnx import ONNXPredictor
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def main():
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"""Complete workflow example."""
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print("="*60)
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print("ONNX + MetaTrader 5 - Complete Workflow Example")
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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 = 20 # Reduced for quick demo
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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 output 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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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: Test Predictions
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print("\n" + "="*60)
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print("STEP 2: Testing Predictions")
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print("="*60)
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predictor = ONNXPredictor(model_path)
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try:
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# Get current price
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symbol_info = mt5.symbol_info(symbol)
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if symbol_info:
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current_price = symbol_info.bid
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print(f"\nCurrent {symbol} price: {current_price:.5f}")
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else:
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print(f"\nWarning: Could not get current price for {symbol}")
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current_price = 0
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# Make predictions
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print(f"\nMaking predictions...")
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predictions = predictor.predict_batch(symbol, timeframe, n_predictions=3)
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print("\nPredictions:")
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for i, pred in enumerate(predictions, 1):
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if current_price > 0:
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change = pred - current_price
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change_pct = (change / current_price) * 100
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print(f" {i}. {pred:.5f} (change: {change:+.5f}, {change_pct:+.2f}%)")
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else:
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print(f" {i}. {pred:.5f}")
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except Exception as e:
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print(f"\n✗ Prediction failed: {e}")
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import traceback
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traceback.print_exc()
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finally:
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predictor.cleanup()
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# Step 3: Instructions for MT5
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print("\n" + "="*60)
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print("STEP 3: Using in MetaTrader 5")
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print("="*60)
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print(f"\nTo use this model in MetaTrader 5:")
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print(f"\n1. Copy the model file to MT5's Files folder:")
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print(f" {model_path}")
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print(f" → <MT5 Data Folder>\\MQL5\\Files\\models\\{os.path.basename(model_path)}")
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print(f"\n2. Open ONNX_EA.mq5 in MetaEditor")
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print(f"\n3. Set EA parameters:")
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print(f" - Model Path: models\\{os.path.basename(model_path)}")
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print(f" - Lookback: {lookback}")
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print(f" - Your trading parameters")
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print(f"\n4. Compile and attach to chart")
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print(f"\n5. Monitor performance")
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print("\n" + "="*60)
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print("Workflow completed!")
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print("="*60 + "\n")
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if __name__ == '__main__':
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# Check MT5 connection first
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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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