load market data for training/inference

NN/models/advanced_transformer_trading.py

@@ -971,20 +971,20 @@ class TradingTransformerTrainer:
         
         # Add confidence loss if available
         if 'confidence' in outputs and 'trade_success' in batch:
-            # Ensure both tensors have compatible shapes for BCELoss
-            # BCELoss requires both inputs to have the same shape
-            confidence_pred = outputs['confidence']  # Keep as [batch_size, 1]
+            # Both tensors should have shape [batch_size, 1]
+            # confidence: [batch_size, 1] from confidence_head
+            # trade_success: [batch_size, 1] from batch preparation
+            confidence_pred = outputs['confidence']
             trade_target = batch['trade_success'].float()
             
-            # Reshape target to match prediction shape [batch_size, 1]
-            if trade_target.dim() == 1:
-                trade_target = trade_target.unsqueeze(-1)
-            
-            # Ensure both have same shape
+            # Verify shapes match (should both be [batch_size, 1])
             if confidence_pred.shape != trade_target.shape:
-                # If shapes still don't match, squeeze both to 1D
-                confidence_pred = confidence_pred.view(-1)
-                trade_target = trade_target.view(-1)
+                logger.warning(f"Shape mismatch: confidence {confidence_pred.shape} vs target {trade_target.shape}")
+                # Reshape to match if needed
+                if trade_target.dim() == 1:
+                    trade_target = trade_target.unsqueeze(-1)
+                if confidence_pred.dim() == 1:
+                    confidence_pred = confidence_pred.unsqueeze(-1)
             
             confidence_loss = self.confidence_criterion(confidence_pred, trade_target)
             # Use addition instead of += to avoid inplace operation