trying to fix training

NN/realtime-main.py

@@ -151,35 +151,59 @@ def main():
     logger.info("Neural Network Trading System finished successfully")
 
 def train(data_interface, model, args):
-    """Train the model using the data interface"""
+    """Enhanced training with performance tracking"""
+    from torch.utils.tensorboard import SummaryWriter
+    
     logger.info("Starting training mode...")
+    writer = SummaryWriter(log_dir=f"runs/{args.model_type}_{datetime.now().strftime('%Y%m%d_%H%M%S')}")
     
     try:
-        # Prepare training data
-        logger.info("Preparing training data...")
-        X_train, y_train, X_val, y_val = data_interface.prepare_training_data()
+        best_val_acc = 0
         
-        # Train the model
-        logger.info("Training model...")
-        model.train(
-            X_train, y_train,
-            X_val, y_val,
-            batch_size=args.batch_size,
-            epochs=args.epochs
-        )
-        
-        # Save the model
+        for epoch in range(args.epochs):
+            # Refresh data every few epochs
+            if epoch % 3 == 0:
+                X_train, y_train, X_val, y_val = data_interface.prepare_training_data(refresh=True)
+            else:
+                X_train, y_train, X_val, y_val = data_interface.prepare_training_data()
+            
+            # Train for one epoch
+            train_loss, train_acc = model.train_epoch(
+                X_train, y_train,
+                batch_size=args.batch_size
+            )
+            
+            # Validate
+            val_loss, val_acc = model.evaluate(X_val, y_val)
+            
+            # Log metrics
+            writer.add_scalar('Loss/Train', train_loss, epoch)
+            writer.add_scalar('Accuracy/Train', train_acc, epoch)
+            writer.add_scalar('Loss/Validation', val_loss, epoch) 
+            writer.add_scalar('Accuracy/Validation', val_acc, epoch)
+            
+            # Save best model
+            if val_acc > best_val_acc:
+                best_val_acc = val_acc
+                model_path = os.path.join(
+                    'models',
+                    f"{args.model_type}_best_{args.symbol.replace('/', '_')}.pt"
+                )
+                model.save(model_path)
+                logger.info(f"New best model saved with val_acc: {val_acc:.2f}")
+                
+            logger.info(f"Epoch {epoch+1}/{args.epochs} - "
+                       f"Train Loss: {train_loss:.4f}, Acc: {train_acc:.2f} - "
+                       f"Val Loss: {val_loss:.4f}, Acc: {val_acc:.2f}")
+            
+        # Save final model
         model_path = os.path.join(
-            'models', 
-            f"{args.model_type}_{args.symbol.replace('/', '_')}_{datetime.now().strftime('%Y%m%d_%H%M%S')}"
+            'models',
+            f"{args.model_type}_final_{args.symbol.replace('/', '_')}_{datetime.now().strftime('%Y%m%d_%H%M%S')}.pt"
         )
-        logger.info(f"Saving model to {model_path}...")
         model.save(model_path)
         
-        # Evaluate the model
-        logger.info("Evaluating model...")
-        metrics = model.evaluate(X_val, y_val)
-        logger.info(f"Evaluation metrics: {metrics}")
+        logger.info(f"Training Complete - Best Val Accuracy: {best_val_acc:.2f}")
         
     except Exception as e:
         logger.error(f"Error in training mode: {str(e)}")