fx T checkpoint and model loss measure

NN/models/advanced_transformer_trading.py

@@ -1267,6 +1267,20 @@ class TradingTransformerTrainer:
             with torch.no_grad():
                 predictions = torch.argmax(outputs['action_logits'], dim=-1)
                 accuracy = (predictions == batch['actions']).float().mean()
+                
+                # Calculate candle prediction accuracy (price direction)
+                candle_accuracy = 0.0
+                if 'next_candles' in outputs and 'future_prices' in batch:
+                    # Use 1m timeframe prediction as primary
+                    if '1m' in outputs['next_candles']:
+                        predicted_candle = outputs['next_candles']['1m']  # [batch, 5]
+                        # Predicted close is the 4th value (index 3)
+                        predicted_close_change = predicted_candle[:, 3]  # Predicted close price change
+                        actual_close_change = batch['future_prices']  # Actual price change ratio
+                        
+                        # Check if direction matches (both positive or both negative)
+                        direction_match = (torch.sign(predicted_close_change) == torch.sign(actual_close_change)).float()
+                        candle_accuracy = direction_match.mean().item()
             
             # Extract values and delete tensors to free memory
             result = {
@@ -1274,6 +1288,7 @@ class TradingTransformerTrainer:
                 'action_loss': action_loss.item(),
                 'price_loss': price_loss.item(),
                 'accuracy': accuracy.item(),
+                'candle_accuracy': candle_accuracy,
                 'learning_rate': self.scheduler.get_last_lr()[0]
             }