filter anotations by symbol

ANNOTATE/core/annotation_manager.py

@@ -338,8 +338,17 @@ class AnnotationManager:
         logger.info(f"Generated lightweight test case: {test_case['test_case_id']} (OHLCV data will be fetched dynamically)")
         return test_case
     
-    def get_all_test_cases(self) -> List[Dict]:
-        """Load all test cases from disk"""
+    def get_all_test_cases(self, symbol: Optional[str] = None) -> List[Dict]:
+        """
+        Load all test cases from disk
+        
+        Args:
+            symbol: Optional symbol filter (e.g., 'ETH/USDT'). If provided, only returns
+                   test cases for that symbol. Critical for avoiding cross-symbol training.
+        
+        Returns:
+            List of test case dictionaries
+        """
         test_cases = []
         
         if not self.test_cases_dir.exists():
@@ -349,11 +358,22 @@ class AnnotationManager:
             try:
                 with open(test_case_file, 'r') as f:
                     test_case = json.load(f)
+                    
+                    # CRITICAL: Filter by symbol to avoid training on wrong symbol
+                    if symbol:
+                        test_case_symbol = test_case.get('symbol', '')
+                        if test_case_symbol != symbol:
+                            logger.debug(f"Skipping {test_case_file.name}: symbol {test_case_symbol} != {symbol}")
+                            continue
+                    
                     test_cases.append(test_case)
             except Exception as e:
                 logger.error(f"Error loading test case {test_case_file}: {e}")
         
-        logger.info(f"Loaded {len(test_cases)} test cases from disk")
+        if symbol:
+            logger.info(f"Loaded {len(test_cases)} test cases for symbol {symbol}")
+        else:
+            logger.info(f"Loaded {len(test_cases)} test cases (all symbols)")
         return test_cases
     
     def _calculate_holding_period(self, annotation: TradeAnnotation) -> float:

ANNOTATE/core/real_training_adapter.py

@@ -1740,13 +1740,17 @@ class RealTrainingAdapter:
         logger.info(f"Using orchestrator's TradingTransformerTrainer")
         logger.info(f"   Trainer type: {type(trainer).__name__}")
         
+        # Import torch at function level (not inside try block)
+        import torch
+        import gc
+        
         # Load best checkpoint if available to continue training
         try:
             checkpoint_dir = "models/checkpoints/transformer"
             best_checkpoint_path = self._find_best_checkpoint(checkpoint_dir, metric='accuracy')
             
             if best_checkpoint_path and os.path.exists(best_checkpoint_path):
-                checkpoint = torch.load(best_checkpoint_path)
+                checkpoint = torch.load(best_checkpoint_path, map_location='cpu')
                 trainer.model.load_state_dict(checkpoint['model_state_dict'])
                 trainer.optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
                 trainer.scheduler.load_state_dict(checkpoint['scheduler_state_dict'])