improve orchestrator

core/orchestrator.py

@@ -302,10 +302,14 @@ class TradingOrchestrator:
             # CRITICAL: Register models with the model registry
             logger.info("Registering models with model registry...")
             
+            # Import model interfaces
+            from models import CNNModelInterface, RLAgentInterface, ModelInterface
+            
             # Register RL Agent
             if self.rl_agent:
                 try:
-                    self.register_model(self.rl_agent, weight=0.3)
+                    rl_interface = RLAgentInterface(self.rl_agent, name="dqn_agent")
+                    self.register_model(rl_interface, weight=0.3)
                     logger.info("RL Agent registered successfully")
                 except Exception as e:
                     logger.error(f"Failed to register RL Agent: {e}")
@@ -313,15 +317,35 @@ class TradingOrchestrator:
             # Register CNN Model
             if self.cnn_model:
                 try:
-                    self.register_model(self.cnn_model, weight=0.7)
+                    cnn_interface = CNNModelInterface(self.cnn_model, name="enhanced_cnn")
+                    self.register_model(cnn_interface, weight=0.7)
                     logger.info("CNN Model registered successfully")
                 except Exception as e:
                     logger.error(f"Failed to register CNN Model: {e}")
             
-            # Register Extrema Trainer
+            # Register Extrema Trainer (as generic ModelInterface)
             if self.extrema_trainer:
                 try:
-                    self.register_model(self.extrema_trainer, weight=0.2)
+                    # Create a simple wrapper for extrema trainer
+                    class ExtremaTrainerInterface(ModelInterface):
+                        def __init__(self, model, name: str):
+                            super().__init__(name)
+                            self.model = model
+                        
+                        def predict(self, data):
+                            try:
+                                if hasattr(self.model, 'predict'):
+                                    return self.model.predict(data)
+                                return None
+                            except Exception as e:
+                                logger.error(f"Error in extrema trainer prediction: {e}")
+                                return None
+                        
+                        def get_memory_usage(self) -> float:
+                            return 30.0  # MB
+                    
+                    extrema_interface = ExtremaTrainerInterface(self.extrema_trainer, name="extrema_trainer")
+                    self.register_model(extrema_interface, weight=0.2)
                     logger.info("Extrema Trainer registered successfully")
                 except Exception as e:
                     logger.error(f"Failed to register Extrema Trainer: {e}")