new__training
This commit is contained in:
Dobromir Popov
190
main_clean.py
190
main_clean.py
@ -83,7 +83,7 @@ def run_data_test():
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raise
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def run_cnn_training():
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"""Train CNN models only"""
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"""Train CNN models only with comprehensive pipeline"""
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try:
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logger.info("Starting CNN Training Mode...")
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@ -92,85 +92,185 @@ def run_cnn_training():
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symbols=['ETH/USDT', 'BTC/USDT'],
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timeframes=['1s', '1m', '5m', '1h', '4h']
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)
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orchestrator = TradingOrchestrator(data_provider)
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logger.info("Creating CNN training data...")
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# Import and create CNN trainer
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from training.cnn_trainer import CNNTrainer
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trainer = CNNTrainer(data_provider)
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# Prepare multi-timeframe, multi-symbol feature matrices
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# Configure training
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trainer.num_samples = 20000 # Training samples
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trainer.batch_size = 64
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trainer.num_epochs = 100
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trainer.patience = 15
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# Train the model
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symbols = ['ETH/USDT', 'BTC/USDT']
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timeframes = ['1m', '5m', '1h', '4h']
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save_path = 'models/cnn/scalping_cnn_trained.pt'
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for symbol in symbols:
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logger.info(f"Preparing CNN data for {symbol}...")
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feature_matrix = data_provider.get_feature_matrix(
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symbol, timeframes, window_size=50
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)
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if feature_matrix is not None:
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logger.info(f"CNN training data ready for {symbol}: {feature_matrix.shape}")
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# Here you would integrate with your CNN training module
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# Example: cnn_model.train(feature_matrix, labels)
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else:
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logger.warning(f"Could not prepare CNN data for {symbol}")
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logger.info(f"Training CNN for symbols: {symbols}")
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logger.info(f"Will save to: {save_path}")
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logger.info("CNN training preparation completed!")
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logger.info("Note: Integrate this with your actual CNN training module")
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results = trainer.train(symbols, save_path)
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# Log results
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logger.info("CNN Training Results:")
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logger.info(f" Best validation accuracy: {results['best_val_accuracy']:.4f}")
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logger.info(f" Best validation loss: {results['best_val_loss']:.4f}")
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logger.info(f" Total epochs: {results['total_epochs']}")
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logger.info(f" Training time: {results['total_time']:.2f} seconds")
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# Plot training history
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try:
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plot_path = 'models/cnn/training_history.png'
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trainer.plot_training_history(plot_path)
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logger.info(f"Training plots saved to: {plot_path}")
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except Exception as e:
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logger.warning(f"Could not save training plots: {e}")
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# Evaluate on test data
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try:
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logger.info("Evaluating CNN on test data...")
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test_symbols = ['ETH/USDT'] # Use subset for testing
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eval_results = trainer.evaluate_model(test_symbols)
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logger.info("CNN Evaluation Results:")
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logger.info(f" Test accuracy: {eval_results['test_accuracy']:.4f}")
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logger.info(f" Test loss: {eval_results['test_loss']:.4f}")
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logger.info(f" Average confidence: {eval_results['avg_confidence']:.4f}")
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except Exception as e:
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logger.warning(f"Could not run evaluation: {e}")
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logger.info("CNN training completed successfully!")
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except Exception as e:
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logger.error(f"Error in CNN training: {e}")
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import traceback
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logger.error(traceback.format_exc())
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raise
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def run_rl_training():
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"""Train RL agents only"""
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"""Train RL agents only with comprehensive pipeline"""
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try:
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logger.info("Starting RL Training Mode...")
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# Initialize components for RL
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data_provider = DataProvider(
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symbols=['ETH/USDT'],
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timeframes=['1s', '1m', '5m'] # Focus on short timeframes for RL
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timeframes=['1s', '1m', '5m', '1h'] # Focus on scalping timeframes
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)
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orchestrator = TradingOrchestrator(data_provider)
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logger.info("Setting up RL environment...")
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# Import and create RL trainer
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from training.rl_trainer import RLTrainer
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trainer = RLTrainer(data_provider)
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# Get scalping data for RL training
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scalping_data = data_provider.get_latest_candles('ETH/USDT', '1s', limit=1000)
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# Configure training
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trainer.num_episodes = 1000
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trainer.max_steps_per_episode = 1000
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trainer.evaluation_frequency = 50
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trainer.save_frequency = 100
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if not scalping_data.empty:
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logger.info(f"RL training data ready: {len(scalping_data)} 1s candles")
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logger.info(f"Price range: ${scalping_data['close'].min():.2f} - ${scalping_data['close'].max():.2f}")
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# Train the agent
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save_path = 'models/rl/scalping_agent_trained.pt'
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logger.info(f"Training RL agent for scalping")
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logger.info(f"Will save to: {save_path}")
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results = trainer.train(save_path)
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# Log results
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logger.info("RL Training Results:")
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logger.info(f" Best reward: {results['best_reward']:.4f}")
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logger.info(f" Best balance: ${results['best_balance']:.2f}")
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logger.info(f" Total episodes: {results['total_episodes']}")
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logger.info(f" Training time: {results['total_time']:.2f} seconds")
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logger.info(f" Final epsilon: {results['agent_config']['epsilon_final']:.4f}")
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# Final evaluation results
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final_eval = results['final_evaluation']
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logger.info("Final Evaluation:")
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logger.info(f" Win rate: {final_eval['win_rate']:.2%}")
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logger.info(f" Average PnL: {final_eval['avg_pnl_percentage']:.2f}%")
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logger.info(f" Average trades: {final_eval['avg_trades']:.1f}")
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# Plot training progress
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try:
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plot_path = 'models/rl/training_progress.png'
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trainer.plot_training_progress(plot_path)
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logger.info(f"Training plots saved to: {plot_path}")
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except Exception as e:
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logger.warning(f"Could not save training plots: {e}")
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# Backtest the trained agent
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try:
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logger.info("Backtesting trained agent...")
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backtest_results = trainer.backtest_agent(save_path, test_episodes=50)
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# Here you would integrate with your RL training module
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# Example: rl_agent.train(environment_data=scalping_data)
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else:
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logger.warning("No scalping data available for RL training")
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analysis = backtest_results['analysis']
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logger.info("Backtest Results:")
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logger.info(f" Win rate: {analysis['win_rate']:.2%}")
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logger.info(f" Average PnL: {analysis['avg_pnl']:.2f}%")
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logger.info(f" Sharpe ratio: {analysis['sharpe_ratio']:.4f}")
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logger.info(f" Max drawdown: {analysis['max_drawdown']:.2f}%")
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except Exception as e:
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logger.warning(f"Could not run backtest: {e}")
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logger.info("RL training preparation completed!")
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logger.info("Note: Integrate this with your actual RL training module")
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logger.info("RL training completed successfully!")
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except Exception as e:
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logger.error(f"Error in RL training: {e}")
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import traceback
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logger.error(traceback.format_exc())
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raise
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def run_combined_training():
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"""Train both CNN and RL models"""
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"""Train both CNN and RL models with hybrid approach"""
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try:
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logger.info("Starting Combined Training Mode...")
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logger.info("Starting Hybrid CNN + RL Training Mode...")
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# Run CNN training first
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logger.info("Phase 1: CNN Training")
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run_cnn_training()
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# Initialize data provider
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data_provider = DataProvider(
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symbols=['ETH/USDT', 'BTC/USDT'],
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timeframes=['1s', '1m', '5m', '1h', '4h']
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)
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# Then RL training
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logger.info("Phase 2: RL Training")
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run_rl_training()
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# Import and create hybrid trainer
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from training.rl_trainer import HybridTrainer
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trainer = HybridTrainer(data_provider)
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logger.info("Combined training completed!")
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# Define save paths
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cnn_save_path = 'models/cnn/hybrid_cnn_trained.pt'
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rl_save_path = 'models/rl/hybrid_rl_trained.pt'
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# Train hybrid system
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symbols = ['ETH/USDT', 'BTC/USDT']
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logger.info(f"Training hybrid system for symbols: {symbols}")
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results = trainer.train_hybrid(symbols, cnn_save_path, rl_save_path)
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# Log results
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cnn_results = results['cnn_results']
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rl_results = results['rl_results']
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logger.info("Hybrid Training Results:")
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logger.info("CNN Phase:")
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logger.info(f" Best accuracy: {cnn_results['best_val_accuracy']:.4f}")
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logger.info(f" Training time: {cnn_results['total_time']:.2f}s")
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logger.info("RL Phase:")
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logger.info(f" Best reward: {rl_results['best_reward']:.4f}")
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logger.info(f" Final balance: ${rl_results['best_balance']:.2f}")
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logger.info(f" Training time: {rl_results['total_time']:.2f}s")
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logger.info(f"Total training time: {results['total_time']:.2f}s")
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logger.info("Hybrid training completed successfully!")
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except Exception as e:
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logger.error(f"Error in combined training: {e}")
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logger.error(f"Error in hybrid training: {e}")
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import traceback
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logger.error(traceback.format_exc())
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raise
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def run_live_trading():
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