wip dqn state

core/orchestrator.py

@@ -3660,9 +3660,24 @@ class TradingOrchestrator:
                 # Check if dictionary is empty - this is the main issue!
                 if not model_input:
                     logger.warning(
-                        f"Empty dictionary passed as model_input for {model_name}, using data provider fallback"
+                        f"Empty dictionary passed as model_input for {model_name}, using build_base_data_input fallback"
                     )
-                    # Use data provider to build proper state as fallback
+                    # Use the same data source as the new training system
+                    try:
+                        # Try to get symbol from the record context or use default
+                        symbol = "ETH/USDT"  # Default symbol
+                        base_data = self.build_base_data_input(symbol)
+                        if base_data and hasattr(base_data, "get_feature_vector"):
+                            state = base_data.get_feature_vector()
+                            if isinstance(state, np.ndarray) and state.size > 0:
+                                logger.info(
+                                    f"Generated fresh state for {model_name} from build_base_data_input: shape={state.shape}"
+                                )
+                                return state
+                    except Exception as e:
+                        logger.debug(f"build_base_data_input fallback failed for {model_name}: {e}")
+                    
+                    # Fallback to data provider method
                     return self._generate_fresh_state_fallback(model_name)
 
                 # Try to extract features from dictionary
@@ -3719,7 +3734,23 @@ class TradingOrchestrator:
     def _generate_fresh_state_fallback(self, model_name: str) -> np.ndarray:
         """Generate a fresh state from current market data when model_input is empty/invalid"""
         try:
-            # Try to use data provider to build fresh state
+            # Try to use build_base_data_input first (same as new training system)
+            try:
+                symbol = "ETH/USDT"  # Default symbol
+                base_data = self.build_base_data_input(symbol)
+                if base_data and hasattr(base_data, "get_feature_vector"):
+                    state = base_data.get_feature_vector()
+                    if isinstance(state, np.ndarray) and state.size > 0:
+                        logger.info(
+                            f"Generated fresh state for {model_name} from build_base_data_input: shape={state.shape}"
+                        )
+                        return state
+            except Exception as e:
+                logger.debug(
+                    f"build_base_data_input fresh state generation failed for {model_name}: {e}"
+                )
+
+            # Fallback to data provider method
             if hasattr(self, "data_provider") and self.data_provider:
                 try:
                     # Build fresh BaseDataInput with current market data