misc

main_clean.py

@@ -82,72 +82,50 @@ def run_data_test():
         logger.error(traceback.format_exc())
         raise
 
-def run_cnn_training():
-    """Train CNN models only with comprehensive pipeline"""
+def run_cnn_training(config: Config, symbol: str):
+    """Run CNN training mode with TensorBoard monitoring"""
+    logger.info("Starting CNN Training Mode...")
+    
+    # Initialize data provider and trainer
+    data_provider = DataProvider(config)
+    trainer = CNNTrainer(config)
+    
+    # Use configured symbols or provided symbol
+    symbols = config.symbols if symbol == "ETH/USDT" else [symbol] + config.symbols
+    save_path = f"models/cnn/scalping_cnn_trained.pt"
+    
+    logger.info(f"Training CNN for symbols: {symbols}")
+    logger.info(f"Will save to: {save_path}")
+    logger.info(f"🔗 Monitor training: tensorboard --logdir=runs")
+    
     try:
-        logger.info("Starting CNN Training Mode...")
+        # Train model with TensorBoard logging
+        results = trainer.train(symbols, save_path=save_path)
         
-        # Initialize components
-        data_provider = DataProvider(
-            symbols=['ETH/USDT', 'BTC/USDT'], 
-            timeframes=['1s', '1m', '5m', '1h', '4h']
-        )
-        
-        # Import and create CNN trainer
-        from training.cnn_trainer import CNNTrainer
-        trainer = CNNTrainer(data_provider)
-        
-        # Configure training
-        trainer.num_samples = 20000  # Training samples
-        trainer.batch_size = 64
-        trainer.num_epochs = 100
-        trainer.patience = 15
-        
-        # Train the model
-        symbols = ['ETH/USDT', 'BTC/USDT']
-        save_path = 'models/cnn/scalping_cnn_trained.pt'
-        
-        logger.info(f"Training CNN for symbols: {symbols}")
-        logger.info(f"Will save to: {save_path}")
-        
-        results = trainer.train(symbols, save_path)
-        
-        # Log results
         logger.info("CNN Training Results:")
         logger.info(f"  Best validation accuracy: {results['best_val_accuracy']:.4f}")
         logger.info(f"  Best validation loss: {results['best_val_loss']:.4f}")
         logger.info(f"  Total epochs: {results['total_epochs']}")
-        logger.info(f"  Training time: {results['total_time']:.2f} seconds")
+        logger.info(f"  Training time: {results['training_time']:.2f} seconds")
+        logger.info(f"  TensorBoard logs: {results['tensorboard_dir']}")
         
-        # Plot training history
-        try:
-            plot_path = 'models/cnn/training_history.png'
-            trainer.plot_training_history(plot_path)
-            logger.info(f"Training plots saved to: {plot_path}")
-        except Exception as e:
-            logger.warning(f"Could not save training plots: {e}")
+        logger.info(f"📊 View training progress: tensorboard --logdir=runs")
+        logger.info("Evaluating CNN on test data...")
         
-        # Evaluate on test data
-        try:
-            logger.info("Evaluating CNN on test data...")
-            test_symbols = ['ETH/USDT']  # Use subset for testing
-            eval_results = trainer.evaluate_model(test_symbols)
-            
-            logger.info("CNN Evaluation Results:")
-            logger.info(f"  Test accuracy: {eval_results['test_accuracy']:.4f}")
-            logger.info(f"  Test loss: {eval_results['test_loss']:.4f}")
-            logger.info(f"  Average confidence: {eval_results['avg_confidence']:.4f}")
-            
-        except Exception as e:
-            logger.warning(f"Could not run evaluation: {e}")
+        # Quick evaluation on same symbols
+        test_results = trainer.evaluate(symbols[:1])  # Use first symbol for quick test
+        logger.info("CNN Evaluation Results:")
+        logger.info(f"  Test accuracy: {test_results['test_accuracy']:.4f}")
+        logger.info(f"  Test loss: {test_results['test_loss']:.4f}")
+        logger.info(f"  Average confidence: {test_results['avg_confidence']:.4f}")
         
         logger.info("CNN training completed successfully!")
         
     except Exception as e:
-        logger.error(f"Error in CNN training: {e}")
-        import traceback
-        logger.error(traceback.format_exc())
+        logger.error(f"CNN training failed: {e}")
         raise
+    finally:
+        trainer.close_tensorboard()
 
 def run_rl_training():
     """Train RL agents only with comprehensive pipeline"""
@@ -404,7 +382,7 @@ async def main():
         if args.mode == 'test':
             run_data_test()
         elif args.mode == 'cnn':
-            run_cnn_training()
+            run_cnn_training(get_config(), args.symbol)
         elif args.mode == 'rl':
             run_rl_training()
         elif args.mode == 'train':