references

core/orchestrator.py

@@ -2503,3 +2503,157 @@ class TradingOrchestrator:
         except Exception as e:
             logger.error(f"Error checking signal confirmation for {symbol}: {e}")
             return None   
+ def _initialize_checkpoint_manager(self):
+        """Initialize the checkpoint manager for model persistence"""
+        try:
+            from utils.checkpoint_manager import get_checkpoint_manager
+            self.checkpoint_manager = get_checkpoint_manager()
+            
+            # Initialize model states dictionary to track performance
+            self.model_states = {
+                'dqn': {'initial_loss': None, 'current_loss': None, 'best_loss': float('inf'), 'checkpoint_loaded': False},
+                'cnn': {'initial_loss': None, 'current_loss': None, 'best_loss': float('inf'), 'checkpoint_loaded': False},
+                'cob_rl': {'initial_loss': None, 'current_loss': None, 'best_loss': float('inf'), 'checkpoint_loaded': False},
+                'extrema': {'initial_loss': None, 'current_loss': None, 'best_loss': float('inf'), 'checkpoint_loaded': False}
+            }
+            
+            logger.info("Checkpoint manager initialized for model persistence")
+        except Exception as e:
+            logger.error(f"Error initializing checkpoint manager: {e}")
+            self.checkpoint_manager = None    de
+f _save_training_checkpoints(self, models_trained: List[str], performance_score: float):
+        """Save checkpoints for trained models if performance improved
+        
+        This is CRITICAL for preserving training progress across restarts.
+        """
+        try:
+            if not self.checkpoint_manager:
+                return
+            
+            # Increment training counter
+            self.training_iterations += 1
+            
+            # Save checkpoints for each trained model
+            for model_name in models_trained:
+                try:
+                    model_obj = None
+                    current_loss = None
+                    model_type = model_name
+                    
+                    # Get model object and calculate current performance
+                    if model_name == 'dqn' and self.rl_agent:
+                        model_obj = self.rl_agent
+                        # Use current loss from model state or estimate from performance
+                        current_loss = self.model_states['dqn'].get('current_loss')
+                        if current_loss is None:
+                            # Estimate loss from performance score (inverse relationship)
+                            current_loss = max(0.001, 1.0 - performance_score)
+                        
+                        # Update model state tracking
+                        self.model_states['dqn']['current_loss'] = current_loss
+                        
+                        # If this is the first loss value, set it as initial and best
+                        if self.model_states['dqn']['initial_loss'] is None:
+                            self.model_states['dqn']['initial_loss'] = current_loss
+                        if self.model_states['dqn']['best_loss'] is None or current_loss < self.model_states['dqn']['best_loss']:
+                            self.model_states['dqn']['best_loss'] = current_loss
+                    
+                    elif model_name == 'cnn' and self.cnn_model:
+                        model_obj = self.cnn_model
+                        # Use current loss from model state or estimate from performance
+                        current_loss = self.model_states['cnn'].get('current_loss')
+                        if current_loss is None:
+                            # Estimate loss from performance score (inverse relationship)
+                            current_loss = max(0.001, 1.0 - performance_score)
+                        
+                        # Update model state tracking
+                        self.model_states['cnn']['current_loss'] = current_loss
+                        
+                        # If this is the first loss value, set it as initial and best
+                        if self.model_states['cnn']['initial_loss'] is None:
+                            self.model_states['cnn']['initial_loss'] = current_loss
+                        if self.model_states['cnn']['best_loss'] is None or current_loss < self.model_states['cnn']['best_loss']:
+                            self.model_states['cnn']['best_loss'] = current_loss
+                    
+                    elif model_name == 'cob_rl' and self.cob_rl_agent:
+                        model_obj = self.cob_rl_agent
+                        # Use current loss from model state or estimate from performance
+                        current_loss = self.model_states['cob_rl'].get('current_loss')
+                        if current_loss is None:
+                            # Estimate loss from performance score (inverse relationship)
+                            current_loss = max(0.001, 1.0 - performance_score)
+                        
+                        # Update model state tracking
+                        self.model_states['cob_rl']['current_loss'] = current_loss
+                        
+                        # If this is the first loss value, set it as initial and best
+                        if self.model_states['cob_rl']['initial_loss'] is None:
+                            self.model_states['cob_rl']['initial_loss'] = current_loss
+                        if self.model_states['cob_rl']['best_loss'] is None or current_loss < self.model_states['cob_rl']['best_loss']:
+                            self.model_states['cob_rl']['best_loss'] = current_loss
+                    
+                    elif model_name == 'extrema' and hasattr(self, 'extrema_trainer') and self.extrema_trainer:
+                        model_obj = self.extrema_trainer
+                        # Use current loss from model state or estimate from performance
+                        current_loss = self.model_states['extrema'].get('current_loss')
+                        if current_loss is None:
+                            # Estimate loss from performance score (inverse relationship)
+                            current_loss = max(0.001, 1.0 - performance_score)
+                        
+                        # Update model state tracking
+                        self.model_states['extrema']['current_loss'] = current_loss
+                        
+                        # If this is the first loss value, set it as initial and best
+                        if self.model_states['extrema']['initial_loss'] is None:
+                            self.model_states['extrema']['initial_loss'] = current_loss
+                        if self.model_states['extrema']['best_loss'] is None or current_loss < self.model_states['extrema']['best_loss']:
+                            self.model_states['extrema']['best_loss'] = current_loss
+                    
+                    # Skip if we couldn't get a model object
+                    if model_obj is None:
+                        continue
+                    
+                    # Prepare performance metrics for checkpoint
+                    performance_metrics = {
+                        'loss': current_loss,
+                        'accuracy': performance_score,  # Use confidence as a proxy for accuracy
+                    }
+                    
+                    # Prepare training metadata
+                    training_metadata = {
+                        'training_iteration': self.training_iterations,
+                        'timestamp': datetime.now().isoformat()
+                    }
+                    
+                    # Save checkpoint using checkpoint manager
+                    from utils.checkpoint_manager import save_checkpoint
+                    checkpoint_metadata = save_checkpoint(
+                        model=model_obj,
+                        model_name=model_name,
+                        model_type=model_type,
+                        performance_metrics=performance_metrics,
+                        training_metadata=training_metadata
+                    )
+                    
+                    if checkpoint_metadata:
+                        logger.info(f"Saved checkpoint for {model_name}: {checkpoint_metadata.checkpoint_id} (loss={current_loss:.4f})")
+                    
+                    # Also save periodically based on training iterations
+                    if self.training_iterations % 100 == 0:
+                        # Force save every 100 training iterations regardless of performance
+                        checkpoint_metadata = save_checkpoint(
+                            model=model_obj,
+                            model_name=model_name,
+                            model_type=model_type,
+                            performance_metrics=performance_metrics,
+                            training_metadata=training_metadata,
+                            force_save=True
+                        )
+                        if checkpoint_metadata:
+                            logger.info(f"Periodic checkpoint saved for {model_name}: {checkpoint_metadata.checkpoint_id}")
+                
+                except Exception as e:
+                    logger.error(f"Error saving checkpoint for {model_name}: {e}")
+        
+        except Exception as e:
+            logger.error(f"Error in _save_training_checkpoints: {e}")

test_bybit_eth_futures.py

@@ -25,7 +25,7 @@ except ImportError:
                     key, value = line.strip().split('=', 1)
                     os.environ[key] = value
 
-from NN.exchanges.bybit_interface import BybitInterface
+from core.exchanges.bybit_interface import BybitInterface
 
 # Configure logging
 logging.basicConfig(
@@ -104,7 +104,7 @@ class BybitEthFuturesTest:
         # First test simple connectivity without auth
         print("Testing basic API connectivity...")
         try:
-            from NN.exchanges.bybit_rest_client import BybitRestClient
+            from core.exchanges.bybit_rest_client import BybitRestClient
             client = BybitRestClient(
                 api_key="dummy", 
                 api_secret="dummy", 

test_bybit_eth_futures_fixed.py

@@ -24,7 +24,7 @@ except ImportError:
                     key, value = line.strip().split('=', 1)
                     os.environ[key] = value
 
-from NN.exchanges.bybit_interface import BybitInterface
+from core.exchanges.bybit_interface import BybitInterface
 
 # Configure logging
 logging.basicConfig(

test_bybit_eth_live.py

@@ -23,7 +23,7 @@ except ImportError:
                     key, value = line.strip().split('=', 1)
                     os.environ[key] = value
 
-from NN.exchanges.bybit_interface import BybitInterface
+from core.exchanges.bybit_interface import BybitInterface
 
 # Configure logging
 logging.basicConfig(

test_bybit_public_api.py

@@ -11,7 +11,7 @@ import logging
 # Add the project root to the path
 sys.path.append(os.path.dirname(os.path.abspath(__file__)))
 
-from NN.exchanges.bybit_rest_client import BybitRestClient
+from core.exchanges.bybit_rest_client import BybitRestClient
 
 # Configure logging
 logging.basicConfig(

test_deribit_integration.py

@@ -15,8 +15,8 @@ load_dotenv()
 sys.path.append(os.path.join(os.path.dirname(__file__), 'NN'))
 sys.path.append(os.path.join(os.path.dirname(__file__), 'core'))
 
-from NN.exchanges.exchange_factory import ExchangeFactory
+from core.exchanges.exchange_factory import ExchangeFactory
-from NN.exchanges.deribit_interface import DeribitInterface
+from core.exchanges.deribit_interface import DeribitInterface
 from core.config import get_config
 
 # Setup logging