stability

enhanced_realtime_training.py

@@ -114,6 +114,34 @@ class EnhancedRealtimeTrainingSystem:
         
         logger.info("Enhanced Real-time Training System initialized")
     
+    def _get_dqn_state_features(self, symbol: str) -> Optional[np.ndarray]:
+        """Get DQN state features from orchestrator"""
+        try:
+            if not self.orchestrator:
+                return None
+            
+            # Get DQN state from orchestrator if available
+            if hasattr(self.orchestrator, 'build_comprehensive_rl_state'):
+                rl_state = self.orchestrator.build_comprehensive_rl_state(symbol)
+                if rl_state and 'state_vector' in rl_state:
+                    return np.array(rl_state['state_vector'], dtype=np.float32)
+            
+            # Fallback: create basic state from available data
+            if len(self.real_time_data['ohlcv_1m']) > 0:
+                latest_bar = self.real_time_data['ohlcv_1m'][-1]
+                basic_state = [
+                    latest_bar.get('close', 0) / 10000.0,  # Normalized price
+                    latest_bar.get('volume', 0) / 1000000.0,  # Normalized volume
+                    0.0, 0.0, 0.0  # Placeholder features
+                ]
+                return np.array(basic_state, dtype=np.float32)
+            
+            return None
+            
+        except Exception as e:
+            logger.debug(f"Error getting DQN state features for {symbol}: {e}")
+            return None
+    
     def start_training(self):
         """Start the enhanced real-time training system"""
         if self.is_training:
@@ -1885,7 +1913,7 @@ class EnhancedRealtimeTrainingSystem:
                 and self.orchestrator.rl_agent):
                 
                 # Use RL agent to make prediction
-                current_state = self._get_dqn_state(symbol)
+                current_state = self._get_dqn_state_features(symbol)
                 if current_state is None:
                     return
                 action = self.orchestrator.rl_agent.act(current_state, explore=False)