checkpoint manager

core/cnn_dashboard_integration.py

@@ -0,0 +1,276 @@
+"""
+CNN Dashboard Integration
+
+This module integrates the EnhancedCNN model with the dashboard, providing real-time
+training and visualization of model predictions.
+"""
+
+import logging
+import threading
+import time
+from datetime import datetime
+from typing import Dict, List, Optional, Any, Tuple
+import os
+import json
+
+from .enhanced_cnn_adapter import EnhancedCNNAdapter
+from .data_models import BaseDataInput, ModelOutput, create_model_output
+from utils.training_integration import get_training_integration
+
+logger = logging.getLogger(__name__)
+
+class CNNDashboardIntegration:
+    """
+    Integrates the EnhancedCNN model with the dashboard
+    
+    This class:
+    1. Loads and initializes the CNN model
+    2. Processes real-time data for model inference
+    3. Manages continuous training of the model
+    4. Provides visualization data for the dashboard
+    """
+    
+    def __init__(self, data_provider=None, checkpoint_dir: str = "models/enhanced_cnn"):
+        """
+        Initialize the CNN dashboard integration
+        
+        Args:
+            data_provider: Data provider instance
+            checkpoint_dir: Directory to save checkpoints to
+        """
+        self.data_provider = data_provider
+        self.checkpoint_dir = checkpoint_dir
+        self.cnn_adapter = None
+        self.training_thread = None
+        self.training_active = False
+        self.training_interval = 60  # Train every 60 seconds
+        self.training_samples = []
+        self.max_training_samples = 1000
+        self.last_training_time = 0
+        self.last_predictions = {}
+        self.performance_metrics = {}
+        self.model_name = "enhanced_cnn_v1"
+        
+        # Create checkpoint directory if it doesn't exist
+        os.makedirs(checkpoint_dir, exist_ok=True)
+        
+        # Initialize CNN adapter
+        self._initialize_cnn_adapter()
+        
+        logger.info(f"CNNDashboardIntegration initialized with checkpoint_dir: {checkpoint_dir}")
+    
+    def _initialize_cnn_adapter(self):
+        """Initialize the CNN adapter"""
+        try:
+            # Import here to avoid circular imports
+            from .enhanced_cnn_adapter import EnhancedCNNAdapter
+            
+            # Create CNN adapter
+            self.cnn_adapter = EnhancedCNNAdapter(checkpoint_dir=self.checkpoint_dir)
+            
+            # Load best checkpoint if available
+            self.cnn_adapter.load_best_checkpoint()
+            
+            logger.info("CNN adapter initialized successfully")
+            
+        except Exception as e:
+            logger.error(f"Error initializing CNN adapter: {e}")
+            self.cnn_adapter = None
+    
+    def start_training_thread(self):
+        """Start the training thread"""
+        if self.training_thread is not None and self.training_thread.is_alive():
+            logger.info("Training thread already running")
+            return
+        
+        self.training_active = True
+        self.training_thread = threading.Thread(target=self._training_loop, daemon=True)
+        self.training_thread.start()
+        
+        logger.info("CNN training thread started")
+    
+    def stop_training_thread(self):
+        """Stop the training thread"""
+        self.training_active = False
+        if self.training_thread is not None:
+            self.training_thread.join(timeout=5)
+            self.training_thread = None
+        
+        logger.info("CNN training thread stopped")
+    
+    def _training_loop(self):
+        """Training loop for continuous model training"""
+        while self.training_active:
+            try:
+                # Check if it's time to train
+                current_time = time.time()
+                if current_time - self.last_training_time >= self.training_interval and len(self.training_samples) >= 10:
+                    logger.info(f"Training CNN model with {len(self.training_samples)} samples")
+                    
+                    # Train model
+                    if self.cnn_adapter is not None:
+                        metrics = self.cnn_adapter.train(epochs=1)
+                        
+                        # Update performance metrics
+                        self.performance_metrics = {
+                            'loss': metrics.get('loss', 0.0),
+                            'accuracy': metrics.get('accuracy', 0.0),
+                            'samples': metrics.get('samples', 0),
+                            'last_training': datetime.now().isoformat()
+                        }
+                        
+                        # Log training metrics
+                        logger.info(f"CNN training metrics: loss={metrics.get('loss', 0.0):.4f}, accuracy={metrics.get('accuracy', 0.0):.4f}")
+                        
+                        # Update last training time
+                        self.last_training_time = current_time
+                    
+                # Sleep to avoid high CPU usage
+                time.sleep(1)
+                
+            except Exception as e:
+                logger.error(f"Error in CNN training loop: {e}")
+                time.sleep(5)  # Sleep longer on error
+    
+    def process_data(self, symbol: str, base_data: BaseDataInput) -> Optional[ModelOutput]:
+        """
+        Process data for model inference and training
+        
+        Args:
+            symbol: Trading symbol
+            base_data: Standardized input data
+        
+        Returns:
+            Optional[ModelOutput]: Model output, or None if processing failed
+        """
+        try:
+            if self.cnn_adapter is None:
+                logger.warning("CNN adapter not initialized")
+                return None
+            
+            # Make prediction
+            model_output = self.cnn_adapter.predict(base_data)
+            
+            # Store prediction
+            self.last_predictions[symbol] = model_output
+            
+            # Store model output in data provider
+            if self.data_provider is not None:
+                self.data_provider.store_model_output(model_output)
+            
+            return model_output
+            
+        except Exception as e:
+            logger.error(f"Error processing data for CNN model: {e}")
+            return None
+    
+    def add_training_sample(self, base_data: BaseDataInput, actual_action: str, reward: float):
+        """
+        Add a training sample
+        
+        Args:
+            base_data: Standardized input data
+            actual_action: Actual action taken ('BUY', 'SELL', 'HOLD')
+            reward: Reward received for the action
+        """
+        try:
+            if self.cnn_adapter is None:
+                logger.warning("CNN adapter not initialized")
+                return
+            
+            # Add training sample to CNN adapter
+            self.cnn_adapter.add_training_sample(base_data, actual_action, reward)
+            
+            # Add to local training samples
+            self.training_samples.append((base_data.symbol, actual_action, reward))
+            
+            # Limit training samples
+            if len(self.training_samples) > self.max_training_samples:
+                self.training_samples = self.training_samples[-self.max_training_samples:]
+            
+            logger.debug(f"Added training sample for {base_data.symbol}, action: {actual_action}, reward: {reward:.4f}")
+            
+        except Exception as e:
+            logger.error(f"Error adding training sample: {e}")
+    
+    def get_performance_metrics(self) -> Dict[str, Any]:
+        """
+        Get performance metrics
+        
+        Returns:
+            Dict[str, Any]: Performance metrics
+        """
+        metrics = self.performance_metrics.copy()
+        
+        # Add additional metrics
+        metrics['training_samples'] = len(self.training_samples)
+        metrics['model_name'] = self.model_name
+        
+        # Add last prediction metrics
+        if self.last_predictions:
+            for symbol, prediction in self.last_predictions.items():
+                metrics[f'{symbol}_last_action'] = prediction.predictions.get('action', 'UNKNOWN')
+                metrics[f'{symbol}_last_confidence'] = prediction.confidence
+        
+        return metrics
+    
+    def get_visualization_data(self, symbol: str) -> Dict[str, Any]:
+        """
+        Get visualization data for the dashboard
+        
+        Args:
+            symbol: Trading symbol
+        
+        Returns:
+            Dict[str, Any]: Visualization data
+        """
+        data = {
+            'model_name': self.model_name,
+            'symbol': symbol,
+            'timestamp': datetime.now().isoformat(),
+            'performance_metrics': self.get_performance_metrics()
+        }
+        
+        # Add last prediction
+        if symbol in self.last_predictions:
+            prediction = self.last_predictions[symbol]
+            data['last_prediction'] = {
+                'action': prediction.predictions.get('action', 'UNKNOWN'),
+                'confidence': prediction.confidence,
+                'timestamp': prediction.timestamp.isoformat(),
+                'buy_probability': prediction.predictions.get('buy_probability', 0.0),
+                'sell_probability': prediction.predictions.get('sell_probability', 0.0),
+                'hold_probability': prediction.predictions.get('hold_probability', 0.0)
+            }
+        
+        # Add training samples summary
+        symbol_samples = [s for s in self.training_samples if s[0] == symbol]
+        data['training_samples'] = {
+            'total': len(symbol_samples),
+            'buy': len([s for s in symbol_samples if s[1] == 'BUY']),
+            'sell': len([s for s in symbol_samples if s[1] == 'SELL']),
+            'hold': len([s for s in symbol_samples if s[1] == 'HOLD']),
+            'avg_reward': sum(s[2] for s in symbol_samples) / len(symbol_samples) if symbol_samples else 0.0
+        }
+        
+        return data
+
+# Global CNN dashboard integration instance
+_cnn_dashboard_integration = None
+
+def get_cnn_dashboard_integration(data_provider=None) -> CNNDashboardIntegration:
+    """
+    Get the global CNN dashboard integration instance
+    
+    Args:
+        data_provider: Data provider instance
+    
+    Returns:
+        CNNDashboardIntegration: Global CNN dashboard integration instance
+    """
+    global _cnn_dashboard_integration
+    
+    if _cnn_dashboard_integration is None:
+        _cnn_dashboard_integration = CNNDashboardIntegration(data_provider=data_provider)
+    
+    return _cnn_dashboard_integration

core/data_provider.py

@@ -1467,12 +1467,10 @@ class DataProvider:
             # Update COB data cache for distribution
             binance_symbol = symbol.replace('/', '').upper()
             if binance_symbol not in self.cob_data_cache or self.cob_data_cache[binance_symbol] is None:
-                from collections import deque
                 self.cob_data_cache[binance_symbol] = deque(maxlen=300)
             
             # Ensure the deque is properly initialized
             if not isinstance(self.cob_data_cache[binance_symbol], deque):
-                from collections import deque
                 self.cob_data_cache[binance_symbol] = deque(maxlen=300)
             
             self.cob_data_cache[binance_symbol].append({

core/enhanced_cnn_adapter.py

@@ -0,0 +1,430 @@
+"""
+Enhanced CNN Adapter for Standardized Input Format
+
+This module provides an adapter for the EnhancedCNN model to work with the standardized
+BaseDataInput format, enabling seamless integration with the multi-modal trading system.
+"""
+
+import torch
+import numpy as np
+import logging
+import os
+from datetime import datetime
+from typing import Dict, List, Optional, Tuple, Any, Union
+from threading import Lock
+
+from .data_models import BaseDataInput, ModelOutput, create_model_output
+from NN.models.enhanced_cnn import EnhancedCNN
+
+logger = logging.getLogger(__name__)
+
+class EnhancedCNNAdapter:
+    """
+    Adapter for EnhancedCNN model to work with standardized BaseDataInput format
+    
+    This adapter:
+    1. Converts BaseDataInput to the format expected by EnhancedCNN
+    2. Processes model outputs to create standardized ModelOutput
+    3. Manages model training with collected data
+    4. Handles checkpoint management
+    """
+    
+    def __init__(self, model_path: str = None, checkpoint_dir: str = "models/enhanced_cnn"):
+        """
+        Initialize the EnhancedCNN adapter
+        
+        Args:
+            model_path: Path to load model from, if None a new model is created
+            checkpoint_dir: Directory to save checkpoints to
+        """
+        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+        self.model = None
+        self.model_path = model_path
+        self.checkpoint_dir = checkpoint_dir
+        self.training_lock = Lock()
+        self.training_data = []
+        self.max_training_samples = 10000
+        self.batch_size = 32
+        self.learning_rate = 0.0001
+        self.model_name = "enhanced_cnn_v1"
+        
+        # Create checkpoint directory if it doesn't exist
+        os.makedirs(checkpoint_dir, exist_ok=True)
+        
+        # Initialize model
+        self._initialize_model()
+        
+        logger.info(f"EnhancedCNNAdapter initialized with device: {self.device}")
+    
+    def _initialize_model(self):
+        """Initialize the EnhancedCNN model"""
+        try:
+            # Calculate input shape based on BaseDataInput structure
+            # OHLCV: 300 frames x 4 timeframes x 5 features = 6000 features
+            # BTC OHLCV: 300 frames x 5 features = 1500 features
+            # COB: ±20 buckets x 4 metrics = 160 features
+            # MA: 4 timeframes x 10 buckets = 40 features
+            # Technical indicators: 100 features
+            # Last predictions: 50 features
+            # Total: 7850 features
+            input_shape = 7850
+            n_actions = 3  # BUY, SELL, HOLD
+            
+            # Create model
+            self.model = EnhancedCNN(input_shape=input_shape, n_actions=n_actions)
+            self.model.to(self.device)
+            
+            # Load model if path is provided
+            if self.model_path:
+                success = self.model.load(self.model_path)
+                if success:
+                    logger.info(f"Model loaded from {self.model_path}")
+                else:
+                    logger.warning(f"Failed to load model from {self.model_path}, using new model")
+            else:
+                logger.info("No model path provided, using new model")
+            
+        except Exception as e:
+            logger.error(f"Error initializing EnhancedCNN model: {e}")
+            raise
+    
+    def _convert_base_data_to_features(self, base_data: BaseDataInput) -> torch.Tensor:
+        """
+        Convert BaseDataInput to feature vector for EnhancedCNN
+        
+        Args:
+            base_data: Standardized input data
+        
+        Returns:
+            torch.Tensor: Feature vector for EnhancedCNN
+        """
+        try:
+            # Use the get_feature_vector method from BaseDataInput
+            features = base_data.get_feature_vector()
+            
+            # Convert to torch tensor
+            features_tensor = torch.tensor(features, dtype=torch.float32, device=self.device)
+            
+            return features_tensor
+            
+        except Exception as e:
+            logger.error(f"Error converting BaseDataInput to features: {e}")
+            # Return empty tensor with correct shape
+            return torch.zeros(7850, dtype=torch.float32, device=self.device)
+    
+    def predict(self, base_data: BaseDataInput) -> ModelOutput:
+        """
+        Make a prediction using the EnhancedCNN model
+        
+        Args:
+            base_data: Standardized input data
+        
+        Returns:
+            ModelOutput: Standardized model output
+        """
+        try:
+            # Convert BaseDataInput to features
+            features = self._convert_base_data_to_features(base_data)
+            
+            # Ensure features has batch dimension
+            if features.dim() == 1:
+                features = features.unsqueeze(0)
+            
+            # Set model to evaluation mode
+            self.model.eval()
+            
+            # Make prediction
+            with torch.no_grad():
+                q_values, extrema_pred, price_pred, features_refined, advanced_pred = self.model(features)
+                
+                # Get action and confidence
+                action_probs = torch.softmax(q_values, dim=1)
+                action_idx = torch.argmax(action_probs, dim=1).item()
+                confidence = float(action_probs[0, action_idx].item())
+                
+                # Map action index to action string
+                actions = ['BUY', 'SELL', 'HOLD']
+                action = actions[action_idx]
+                
+                # Create predictions dictionary
+                predictions = {
+                    'action': action,
+                    'buy_probability': float(action_probs[0, 0].item()),
+                    'sell_probability': float(action_probs[0, 1].item()),
+                    'hold_probability': float(action_probs[0, 2].item()),
+                    'extrema': extrema_pred.squeeze(0).cpu().numpy().tolist(),
+                    'price_prediction': price_pred.squeeze(0).cpu().numpy().tolist()
+                }
+                
+                # Create hidden states dictionary
+                hidden_states = {
+                    'features': features_refined.squeeze(0).cpu().numpy().tolist()
+                }
+                
+                # Create metadata dictionary
+                metadata = {
+                    'model_version': '1.0',
+                    'timestamp': datetime.now().isoformat(),
+                    'input_shape': features.shape
+                }
+                
+                # Create ModelOutput
+                model_output = ModelOutput(
+                    model_type='cnn',
+                    model_name=self.model_name,
+                    symbol=base_data.symbol,
+                    timestamp=datetime.now(),
+                    confidence=confidence,
+                    predictions=predictions,
+                    hidden_states=hidden_states,
+                    metadata=metadata
+                )
+                
+                return model_output
+                
+        except Exception as e:
+            logger.error(f"Error making prediction with EnhancedCNN: {e}")
+            # Return default ModelOutput
+            return create_model_output(
+                model_type='cnn',
+                model_name=self.model_name,
+                symbol=base_data.symbol,
+                action='HOLD',
+                confidence=0.0
+            )
+    
+    def add_training_sample(self, base_data: BaseDataInput, actual_action: str, reward: float):
+        """
+        Add a training sample to the training data
+        
+        Args:
+            base_data: Standardized input data
+            actual_action: Actual action taken ('BUY', 'SELL', 'HOLD')
+            reward: Reward received for the action
+        """
+        try:
+            # Convert BaseDataInput to features
+            features = self._convert_base_data_to_features(base_data)
+            
+            # Convert action to index
+            actions = ['BUY', 'SELL', 'HOLD']
+            action_idx = actions.index(actual_action)
+            
+            # Add to training data
+            with self.training_lock:
+                self.training_data.append((features, action_idx, reward))
+                
+                # Limit training data size
+                if len(self.training_data) > self.max_training_samples:
+                    # Sort by reward (highest first) and keep top samples
+                    self.training_data.sort(key=lambda x: x[2], reverse=True)
+                    self.training_data = self.training_data[:self.max_training_samples]
+            
+            logger.debug(f"Added training sample for {base_data.symbol}, action: {actual_action}, reward: {reward:.4f}")
+            
+        except Exception as e:
+            logger.error(f"Error adding training sample: {e}")
+    
+    def train(self, epochs: int = 1) -> Dict[str, float]:
+        """
+        Train the model with collected data
+        
+        Args:
+            epochs: Number of epochs to train for
+        
+        Returns:
+            Dict[str, float]: Training metrics
+        """
+        try:
+            with self.training_lock:
+                # Check if we have enough data
+                if len(self.training_data) < self.batch_size:
+                    logger.info(f"Not enough training data: {len(self.training_data)} samples, need at least {self.batch_size}")
+                    return {'loss': 0.0, 'accuracy': 0.0, 'samples': len(self.training_data)}
+                
+                # Set model to training mode
+                self.model.train()
+                
+                # Create optimizer
+                optimizer = torch.optim.Adam(self.model.parameters(), lr=self.learning_rate)
+                
+                # Training metrics
+                total_loss = 0.0
+                correct_predictions = 0
+                total_predictions = 0
+                
+                # Train for specified number of epochs
+                for epoch in range(epochs):
+                    # Shuffle training data
+                    np.random.shuffle(self.training_data)
+                    
+                    # Process in batches
+                    for i in range(0, len(self.training_data), self.batch_size):
+                        batch = self.training_data[i:i+self.batch_size]
+                        
+                        # Skip if batch is too small
+                        if len(batch) < 2:
+                            continue
+                        
+                        # Prepare batch
+                        features = torch.stack([sample[0] for sample in batch])
+                        actions = torch.tensor([sample[1] for sample in batch], dtype=torch.long, device=self.device)
+                        rewards = torch.tensor([sample[2] for sample in batch], dtype=torch.float32, device=self.device)
+                        
+                        # Zero gradients
+                        optimizer.zero_grad()
+                        
+                        # Forward pass
+                        q_values, _, _, _, _ = self.model(features)
+                        
+                        # Calculate loss (CrossEntropyLoss with reward weighting)
+                        # First, apply softmax to get probabilities
+                        probs = torch.softmax(q_values, dim=1)
+                        
+                        # Get probability of chosen action
+                        chosen_probs = probs[torch.arange(len(actions)), actions]
+                        
+                        # Calculate negative log likelihood loss
+                        nll_loss = -torch.log(chosen_probs + 1e-10)
+                        
+                        # Weight by reward (higher reward = higher weight)
+                        # Normalize rewards to [0, 1] range
+                        min_reward = rewards.min()
+                        max_reward = rewards.max()
+                        if max_reward > min_reward:
+                            normalized_rewards = (rewards - min_reward) / (max_reward - min_reward)
+                        else:
+                            normalized_rewards = torch.ones_like(rewards)
+                        
+                        # Apply reward weighting (higher reward = higher weight)
+                        weighted_loss = nll_loss * (normalized_rewards + 0.1)  # Add small constant to avoid zero weights
+                        
+                        # Mean loss
+                        loss = weighted_loss.mean()
+                        
+                        # Backward pass
+                        loss.backward()
+                        
+                        # Update weights
+                        optimizer.step()
+                        
+                        # Update metrics
+                        total_loss += loss.item()
+                        
+                        # Calculate accuracy
+                        predicted_actions = torch.argmax(q_values, dim=1)
+                        correct_predictions += (predicted_actions == actions).sum().item()
+                        total_predictions += len(actions)
+                
+                # Calculate final metrics
+                avg_loss = total_loss / (len(self.training_data) / self.batch_size)
+                accuracy = correct_predictions / total_predictions if total_predictions > 0 else 0.0
+                
+                # Save checkpoint
+                self._save_checkpoint(avg_loss, accuracy)
+                
+                logger.info(f"Training completed: loss={avg_loss:.4f}, accuracy={accuracy:.4f}, samples={len(self.training_data)}")
+                
+                return {
+                    'loss': avg_loss,
+                    'accuracy': accuracy,
+                    'samples': len(self.training_data)
+                }
+                
+        except Exception as e:
+            logger.error(f"Error training model: {e}")
+            return {'loss': 0.0, 'accuracy': 0.0, 'samples': 0, 'error': str(e)}
+    
+    def _save_checkpoint(self, loss: float, accuracy: float):
+        """
+        Save model checkpoint
+        
+        Args:
+            loss: Training loss
+            accuracy: Training accuracy
+        """
+        try:
+            # Import checkpoint manager
+            from utils.checkpoint_manager import CheckpointManager
+            
+            # Create checkpoint manager
+            checkpoint_manager = CheckpointManager(
+                checkpoint_dir=self.checkpoint_dir,
+                max_checkpoints=10,
+                metric_name="accuracy"
+            )
+            
+            # Create temporary model file
+            temp_path = os.path.join(self.checkpoint_dir, f"{self.model_name}_temp")
+            self.model.save(temp_path)
+            
+            # Create metrics
+            metrics = {
+                'loss': loss,
+                'accuracy': accuracy,
+                'samples': len(self.training_data)
+            }
+            
+            # Create metadata
+            metadata = {
+                'timestamp': datetime.now().isoformat(),
+                'model_name': self.model_name,
+                'input_shape': self.model.input_shape,
+                'n_actions': self.model.n_actions
+            }
+            
+            # Save checkpoint
+            checkpoint_path = checkpoint_manager.save_checkpoint(
+                model_name=self.model_name,
+                model_path=f"{temp_path}.pt",
+                metrics=metrics,
+                metadata=metadata
+            )
+            
+            # Delete temporary model file
+            if os.path.exists(f"{temp_path}.pt"):
+                os.remove(f"{temp_path}.pt")
+            
+            logger.info(f"Model checkpoint saved to {checkpoint_path}")
+            
+        except Exception as e:
+            logger.error(f"Error saving checkpoint: {e}")
+    
+    def load_best_checkpoint(self):
+        """Load the best checkpoint based on accuracy"""
+        try:
+            # Import checkpoint manager
+            from utils.checkpoint_manager import CheckpointManager
+            
+            # Create checkpoint manager
+            checkpoint_manager = CheckpointManager(
+                checkpoint_dir=self.checkpoint_dir,
+                max_checkpoints=10,
+                metric_name="accuracy"
+            )
+            
+            # Load best checkpoint
+            best_checkpoint_path, best_checkpoint_metadata = checkpoint_manager.load_best_checkpoint(self.model_name)
+            
+            if not best_checkpoint_path:
+                logger.info("No checkpoints found")
+                return False
+            
+            # Load model
+            success = self.model.load(best_checkpoint_path)
+            
+            if success:
+                logger.info(f"Loaded best checkpoint from {best_checkpoint_path}")
+                
+                # Log metrics
+                metrics = best_checkpoint_metadata.get('metrics', {})
+                logger.info(f"Checkpoint metrics: accuracy={metrics.get('accuracy', 0.0):.4f}, loss={metrics.get('loss', 0.0):.4f}")
+                
+                return True
+            else:
+                logger.warning(f"Failed to load best checkpoint from {best_checkpoint_path}")
+                return False
+            
+        except Exception as e:
+            logger.error(f"Error loading best checkpoint: {e}")
+            return False

core/model_output_manager.py

@@ -1,34 +1,31 @@
 """
 Model Output Manager
 
-This module provides extensible model output storage and management for the multi-modal trading system.
+This module provides a centralized storage and management system for model outputs,
-Supports CNN, RL, LSTM, Transformer, and future model types with cross-model feeding capabilities.
+enabling cross-model feeding and evaluation.
 """
 
-import logging
+import os
 import json
-import pickle
+import logging
-from datetime import datetime, timedelta
+import time
-from typing import Dict, List, Optional, Any, Union
+from datetime import datetime
-from collections import deque, defaultdict
+from typing import Dict, List, Optional, Any
 from threading import Lock
-from pathlib import Path
 
-from .data_models import ModelOutput, create_model_output
+from .data_models import ModelOutput
 
 logger = logging.getLogger(__name__)
 
 class ModelOutputManager:
     """
-    Extensible model output storage and management system
+    Centralized storage and management system for model outputs
     
-    Features:
+    This class:
-    - Standardized ModelOutput storage for all model types
+    1. Stores model outputs for all models
-    - Cross-model feeding with hidden states
+    2. Provides access to current and historical outputs
-    - Historical output tracking
+    3. Handles persistence of outputs to disk
-    - Metadata management
+    4. Supports evaluation of model performance
-    - Persistence and recovery
-    - Performance analytics
     """
     
     def __init__(self, cache_dir: str = "cache/model_outputs", max_history: int = 1000):
@@ -36,279 +33,226 @@ class ModelOutputManager:
         Initialize the model output manager
         
         Args:
-            cache_dir: Directory for persistent storage
+            cache_dir: Directory to store model outputs
-            max_history: Maximum number of outputs to keep in memory per model
+            max_history: Maximum number of historical outputs to keep per model
         """
-        self.cache_dir = Path(cache_dir)
+        self.cache_dir = cache_dir
-        self.cache_dir.mkdir(parents=True, exist_ok=True)
         self.max_history = max_history
+        self.outputs_lock = Lock()
         
-        # In-memory storage
+        # Current outputs for each model and symbol
-        self.current_outputs: Dict[str, Dict[str, ModelOutput]] = defaultdict(dict)  # {symbol: {model_name: ModelOutput}}
+        # {symbol: {model_name: ModelOutput}}
-        self.output_history: Dict[str, Dict[str, deque]] = defaultdict(lambda: defaultdict(lambda: deque(maxlen=max_history)))  # {symbol: {model_name: deque}}
+        self.current_outputs: Dict[str, Dict[str, ModelOutput]] = {}
-        self.cross_model_states: Dict[str, Dict[str, Dict[str, Any]]] = defaultdict(lambda: defaultdict(dict))  # {symbol: {model_name: hidden_states}}
         
-        # Metadata tracking
+        # Historical outputs for each model and symbol
-        self.model_metadata: Dict[str, Dict[str, Any]] = defaultdict(dict)  # {model_name: metadata}
+        # {symbol: {model_name: List[ModelOutput]}}
-        self.performance_stats: Dict[str, Dict[str, Any]] = defaultdict(lambda: defaultdict(dict))  # {symbol: {model_name: stats}}
+        self.historical_outputs: Dict[str, Dict[str, List[ModelOutput]]] = {}
         
-        # Thread safety
+        # Performance metrics for each model and symbol
-        self.storage_lock = Lock()
+        # {symbol: {model_name: Dict[str, float]}}
+        self.performance_metrics: Dict[str, Dict[str, Dict[str, float]]] = {}
         
-        # Supported model types
+        # Create cache directory if it doesn't exist
-        self.supported_model_types = {
+        os.makedirs(cache_dir, exist_ok=True)
-            'cnn', 'rl', 'lstm', 'transformer', 'orchestrator', 
-            'ensemble', 'hybrid', 'custom'  # Extensible for future types
-        }
         
-        logger.info(f"ModelOutputManager initialized with cache dir: {self.cache_dir}")
+        logger.info(f"ModelOutputManager initialized with cache_dir: {cache_dir}")
-        logger.info(f"Supported model types: {self.supported_model_types}")
     
     def store_output(self, model_output: ModelOutput) -> bool:
         """
-        Store model output with full extensibility support
+        Store a model output
         
         Args:
-            model_output: ModelOutput from any model type
+            model_output: Model output to store
         
         Returns:
-            bool: True if stored successfully, False otherwise
+            bool: True if successful, False otherwise
         """
         try:
-            with self.storage_lock:
+            symbol = model_output.symbol
-                symbol = model_output.symbol
+            model_name = model_output.model_name
-                model_name = model_output.model_name
+            
-                model_type = model_output.model_type
+            with self.outputs_lock:
-                
+                # Initialize dictionaries if they don't exist
-                # Validate model type (extensible)
+                if symbol not in self.current_outputs:
-                if model_type not in self.supported_model_types:
+                    self.current_outputs[symbol] = {}
-                    logger.warning(f"Unknown model type '{model_type}' - adding to supported types")
+                if symbol not in self.historical_outputs:
-                    self.supported_model_types.add(model_type)
+                    self.historical_outputs[symbol] = {}
+                if model_name not in self.historical_outputs[symbol]:
+                    self.historical_outputs[symbol][model_name] = []
                 
                 # Store current output
                 self.current_outputs[symbol][model_name] = model_output
                 
-                # Add to history
+                # Add to historical outputs
-                self.output_history[symbol][model_name].append(model_output)
+                self.historical_outputs[symbol][model_name].append(model_output)
-                
-                # Store cross-model states if available
-                if model_output.hidden_states:
-                    self.cross_model_states[symbol][model_name] = model_output.hidden_states
-                
-                # Update model metadata
-                self._update_model_metadata(model_name, model_type, model_output.metadata)
-                
-                # Update performance statistics
-                self._update_performance_stats(symbol, model_name, model_output)
-                
-                # Persist to disk (async to avoid blocking)
-                self._persist_output_async(model_output)
-                
-                logger.debug(f"Stored output from {model_name} ({model_type}) for {symbol}")
-                return True
                 
+                # Limit historical outputs
+                if len(self.historical_outputs[symbol][model_name]) > self.max_history:
+                    self.historical_outputs[symbol][model_name] = self.historical_outputs[symbol][model_name][-self.max_history:]
+            
+            # Persist output to disk
+            self._persist_output(model_output)
+            
+            return True
+            
         except Exception as e:
             logger.error(f"Error storing model output: {e}")
             return False
     
     def get_current_output(self, symbol: str, model_name: str) -> Optional[ModelOutput]:
         """
-        Get the current (latest) output from a specific model
+        Get the current output for a model and symbol
         
         Args:
-            symbol: Trading symbol
+            symbol: Symbol to get output for
-            model_name: Name of the model
+            model_name: Model name to get output for
         
         Returns:
-            ModelOutput: Latest output from the model, or None if not available
+            ModelOutput: Current output, or None if not available
         """
         try:
-            return self.current_outputs.get(symbol, {}).get(model_name)
+            with self.outputs_lock:
+                if symbol in self.current_outputs and model_name in self.current_outputs[symbol]:
+                    return self.current_outputs[symbol][model_name]
+            return None
+            
         except Exception as e:
-            logger.error(f"Error getting current output for {model_name}: {e}")
+            logger.error(f"Error getting current output: {e}")
             return None
     
     def get_all_current_outputs(self, symbol: str) -> Dict[str, ModelOutput]:
         """
-        Get all current outputs for a symbol (for cross-model feeding)
+        Get all current outputs for a symbol
         
         Args:
-            symbol: Trading symbol
+            symbol: Symbol to get outputs for
         
         Returns:
-            Dict[str, ModelOutput]: Dictionary of current outputs by model name
+            Dict[str, ModelOutput]: Dictionary of model name to output
         """
         try:
-            return dict(self.current_outputs.get(symbol, {}))
+            with self.outputs_lock:
+                if symbol in self.current_outputs:
+                    return self.current_outputs[symbol].copy()
+            return {}
+            
         except Exception as e:
-            logger.error(f"Error getting all current outputs for {symbol}: {e}")
+            logger.error(f"Error getting all current outputs: {e}")
             return {}
     
-    def get_output_history(self, symbol: str, model_name: str, count: int = 10) -> List[ModelOutput]:
+    def get_historical_outputs(self, symbol: str, model_name: str, limit: int = None) -> List[ModelOutput]:
         """
-        Get historical outputs from a model
+        Get historical outputs for a model and symbol
         
         Args:
-            symbol: Trading symbol
+            symbol: Symbol to get outputs for
-            model_name: Name of the model
+            model_name: Model name to get outputs for
-            count: Number of historical outputs to retrieve
+            limit: Maximum number of outputs to return, None for all
         
         Returns:
-            List[ModelOutput]: List of historical outputs (most recent first)
+            List[ModelOutput]: List of historical outputs
         """
         try:
-            history = self.output_history.get(symbol, {}).get(model_name, deque())
+            with self.outputs_lock:
-            return list(history)[-count:][::-1]  # Most recent first
+                if symbol in self.historical_outputs and model_name in self.historical_outputs[symbol]:
+                    outputs = self.historical_outputs[symbol][model_name]
+                    if limit is not None:
+                        outputs = outputs[-limit:]
+                    return outputs.copy()
+            return []
+            
         except Exception as e:
-            logger.error(f"Error getting output history for {model_name}: {e}")
+            logger.error(f"Error getting historical outputs: {e}")
             return []
     
-    def get_cross_model_states(self, symbol: str, requesting_model: str) -> Dict[str, Dict[str, Any]]:
+    def evaluate_model_performance(self, symbol: str, model_name: str) -> Dict[str, float]:
         """
-        Get hidden states from other models for cross-model feeding
+        Evaluate model performance based on historical outputs
         
         Args:
-            symbol: Trading symbol
+            symbol: Symbol to evaluate
-            requesting_model: Name of the model requesting the states
+            model_name: Model name to evaluate
         
         Returns:
-            Dict[str, Dict[str, Any]]: Hidden states from other models
+            Dict[str, float]: Performance metrics
         """
         try:
-            all_states = self.cross_model_states.get(symbol, {})
+            # Get historical outputs
-            # Return states from all models except the requesting one
+            outputs = self.get_historical_outputs(symbol, model_name)
-            return {model_name: states for model_name, states in all_states.items() 
-                   if model_name != requesting_model}
-        except Exception as e:
-            logger.error(f"Error getting cross-model states for {requesting_model}: {e}")
-            return {}
-    
-    def get_model_types_active(self, symbol: str) -> List[str]:
-        """
-        Get list of active model types for a symbol
-        
-        Args:
-            symbol: Trading symbol
-        
-        Returns:
-            List[str]: List of active model types
-        """
-        try:
-            current_outputs = self.current_outputs.get(symbol, {})
-            return [output.model_type for output in current_outputs.values()]
-        except Exception as e:
-            logger.error(f"Error getting active model types for {symbol}: {e}")
-            return []
-    
-    def get_consensus_prediction(self, symbol: str, confidence_threshold: float = 0.5) -> Optional[Dict[str, Any]]:
-        """
-        Get consensus prediction from all active models
-        
-        Args:
-            symbol: Trading symbol
-            confidence_threshold: Minimum confidence threshold for inclusion
-        
-        Returns:
-            Dict containing consensus prediction or None
-        """
-        try:
-            current_outputs = self.current_outputs.get(symbol, {})
-            if not current_outputs:
-                return None
             
-            # Filter by confidence threshold
+            if not outputs:
-            high_confidence_outputs = [
+                return {'accuracy': 0.0, 'confidence': 0.0, 'samples': 0}
-                output for output in current_outputs.values() 
-                if output.confidence >= confidence_threshold
-            ]
             
-            if not high_confidence_outputs:
+            # Calculate metrics
-                return None
+            total_outputs = len(outputs)
+            total_confidence = sum(output.confidence for output in outputs)
+            avg_confidence = total_confidence / total_outputs if total_outputs > 0 else 0.0
             
-            # Calculate consensus
+            # For now, we don't have ground truth to calculate accuracy
-            buy_votes = sum(1 for output in high_confidence_outputs 
+            # In the future, we can add this by comparing predictions to actual market movements
-                          if output.predictions.get('action') == 'BUY')
-            sell_votes = sum(1 for output in high_confidence_outputs 
-                           if output.predictions.get('action') == 'SELL')
-            hold_votes = sum(1 for output in high_confidence_outputs 
-                           if output.predictions.get('action') == 'HOLD')
             
-            total_votes = len(high_confidence_outputs)
+            metrics = {
-            avg_confidence = sum(output.confidence for output in high_confidence_outputs) / total_votes
-            
-            # Determine consensus action
-            if buy_votes > sell_votes and buy_votes > hold_votes:
-                consensus_action = 'BUY'
-            elif sell_votes > buy_votes and sell_votes > hold_votes:
-                consensus_action = 'SELL'
-            else:
-                consensus_action = 'HOLD'
-            
-            return {
-                'action': consensus_action,
                 'confidence': avg_confidence,
-                'votes': {'BUY': buy_votes, 'SELL': sell_votes, 'HOLD': hold_votes},
+                'samples': total_outputs,
-                'total_models': total_votes,
+                'last_update': datetime.now().isoformat()
-                'model_types': [output.model_type for output in high_confidence_outputs]
             }
             
-        except Exception as e:
+            # Store metrics
-            logger.error(f"Error calculating consensus prediction for {symbol}: {e}")
+            with self.outputs_lock:
-            return None
+                if symbol not in self.performance_metrics:
-    
+                    self.performance_metrics[symbol] = {}
-    def _update_model_metadata(self, model_name: str, model_type: str, metadata: Dict[str, Any]):
+                self.performance_metrics[symbol][model_name] = metrics
-        """Update metadata for a model"""
-        try:
-            if model_name not in self.model_metadata:
-                self.model_metadata[model_name] = {
-                    'model_type': model_type,
-                    'first_seen': datetime.now(),
-                    'total_predictions': 0,
-                    'custom_metadata': {}
-                }
             
-            self.model_metadata[model_name]['total_predictions'] += 1
+            return metrics
-            self.model_metadata[model_name]['last_seen'] = datetime.now()
-            
-            # Merge custom metadata
-            if metadata:
-                self.model_metadata[model_name]['custom_metadata'].update(metadata)
-                
-        except Exception as e:
-            logger.error(f"Error updating model metadata: {e}")
-    
-    def _update_performance_stats(self, symbol: str, model_name: str, model_output: ModelOutput):
-        """Update performance statistics for a model"""
-        try:
-            stats = self.performance_stats[symbol][model_name]
-            
-            if 'prediction_count' not in stats:
-                stats['prediction_count'] = 0
-                stats['confidence_sum'] = 0.0
-                stats['action_counts'] = {'BUY': 0, 'SELL': 0, 'HOLD': 0}
-                stats['first_prediction'] = model_output.timestamp
-            
-            stats['prediction_count'] += 1
-            stats['confidence_sum'] += model_output.confidence
-            stats['avg_confidence'] = stats['confidence_sum'] / stats['prediction_count']
-            stats['last_prediction'] = model_output.timestamp
-            
-            action = model_output.predictions.get('action', 'HOLD')
-            if action in stats['action_counts']:
-                stats['action_counts'][action] += 1
             
         except Exception as e:
-            logger.error(f"Error updating performance stats: {e}")
+            logger.error(f"Error evaluating model performance: {e}")
+            return {'error': str(e)}
     
-    def _persist_output_async(self, model_output: ModelOutput):
+    def get_performance_metrics(self, symbol: str, model_name: str) -> Dict[str, float]:
-        """Persist model output to disk (simplified version)"""
+        """
+        Get performance metrics for a model and symbol
+        
+        Args:
+            symbol: Symbol to get metrics for
+            model_name: Model name to get metrics for
+        
+        Returns:
+            Dict[str, float]: Performance metrics
+        """
         try:
-            # Create filename based on model and timestamp
+            with self.outputs_lock:
-            timestamp_str = model_output.timestamp.strftime("%Y%m%d_%H%M%S")
+                if symbol in self.performance_metrics and model_name in self.performance_metrics[symbol]:
-            filename = f"{model_output.model_name}_{model_output.symbol.replace('/', '_')}_{timestamp_str}.json"
+                    return self.performance_metrics[symbol][model_name].copy()
-            filepath = self.cache_dir / filename
             
-            # Convert to JSON-serializable format
+            # If no metrics are available, calculate them
+            return self.evaluate_model_performance(symbol, model_name)
+            
+        except Exception as e:
+            logger.error(f"Error getting performance metrics: {e}")
+            return {'error': str(e)}
+    
+    def _persist_output(self, model_output: ModelOutput) -> bool:
+        """
+        Persist a model output to disk
+        
+        Args:
+            model_output: Model output to persist
+        
+        Returns:
+            bool: True if successful, False otherwise
+        """
+        try:
+            # Create directory if it doesn't exist
+            symbol_dir = os.path.join(self.cache_dir, model_output.symbol.replace('/', '_'))
+            os.makedirs(symbol_dir, exist_ok=True)
+            
+            # Create filename with timestamp
+            timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+            filename = f"{model_output.model_name}_{model_output.symbol.replace('/', '_')}_{timestamp}.json"
+            filepath = os.path.join(self.cache_dir, filename)
+            
+            # Convert ModelOutput to dictionary
             output_dict = {
                 'model_type': model_output.model_type,
                 'model_name': model_output.model_name,
@@ -319,77 +263,120 @@ class ModelOutputManager:
                 'metadata': model_output.metadata
             }
             
-            # Save to file (in a real implementation, this would be async)
+            # Don't store hidden states in file (too large)
+            
+            # Write to file
             with open(filepath, 'w') as f:
                 json.dump(output_dict, f, indent=2)
-                
+            
+            return True
+            
         except Exception as e:
             logger.error(f"Error persisting model output: {e}")
+            return False
     
-    def get_performance_summary(self, symbol: str) -> Dict[str, Any]:
+    def load_outputs_from_disk(self, symbol: str = None, model_name: str = None) -> int:
         """
-        Get performance summary for all models for a symbol
+        Load model outputs from disk
         
         Args:
-            symbol: Trading symbol
+            symbol: Symbol to load outputs for, None for all
+            model_name: Model name to load outputs for, None for all
         
         Returns:
-            Dict containing performance summary
+            int: Number of outputs loaded
         """
         try:
-            summary = {
+            # Find all output files
-                'symbol': symbol,
+            import glob
-                'active_models': len(self.current_outputs.get(symbol, {})),
-                'model_stats': {}
-            }
             
-            for model_name, stats in self.performance_stats.get(symbol, {}).items():
+            if symbol and model_name:
-                summary['model_stats'][model_name] = {
+                pattern = os.path.join(self.cache_dir, f"{model_name}_{symbol.replace('/', '_')}*.json")
-                    'predictions': stats.get('prediction_count', 0),
+            elif symbol:
-                    'avg_confidence': round(stats.get('avg_confidence', 0.0), 3),
+                pattern = os.path.join(self.cache_dir, f"*_{symbol.replace('/', '_')}*.json")
-                    'action_distribution': stats.get('action_counts', {}),
+            elif model_name:
-                    'model_type': self.model_metadata.get(model_name, {}).get('model_type', 'unknown')
+                pattern = os.path.join(self.cache_dir, f"{model_name}_*.json")
-                }
+            else:
+                pattern = os.path.join(self.cache_dir, "*.json")
             
-            return summary
+            output_files = glob.glob(pattern)
+            
+            if not output_files:
+                logger.info(f"No output files found for pattern: {pattern}")
+                return 0
+            
+            # Load each file
+            loaded_count = 0
+            for filepath in output_files:
+                try:
+                    with open(filepath, 'r') as f:
+                        output_dict = json.load(f)
+                    
+                    # Create ModelOutput
+                    model_output = ModelOutput(
+                        model_type=output_dict['model_type'],
+                        model_name=output_dict['model_name'],
+                        symbol=output_dict['symbol'],
+                        timestamp=datetime.fromisoformat(output_dict['timestamp']),
+                        confidence=output_dict['confidence'],
+                        predictions=output_dict['predictions'],
+                        hidden_states={},  # Don't load hidden states from disk
+                        metadata=output_dict.get('metadata', {})
+                    )
+                    
+                    # Store output
+                    self.store_output(model_output)
+                    loaded_count += 1
+                    
+                except Exception as e:
+                    logger.error(f"Error loading output file {filepath}: {e}")
+            
+            logger.info(f"Loaded {loaded_count} model outputs from disk")
+            return loaded_count
             
         except Exception as e:
-            logger.error(f"Error getting performance summary: {e}")
+            logger.error(f"Error loading outputs from disk: {e}")
-            return {'symbol': symbol, 'error': str(e)}
+            return 0
     
-    def cleanup_old_outputs(self, max_age_hours: int = 24):
+    def cleanup_old_outputs(self, max_age_days: int = 30) -> int:
         """
-        Clean up old outputs to manage memory usage
+        Clean up old output files
         
         Args:
-            max_age_hours: Maximum age of outputs to keep in hours
+            max_age_days: Maximum age of files to keep in days
+        
+        Returns:
+            int: Number of files deleted
         """
         try:
-            cutoff_time = datetime.now() - timedelta(hours=max_age_hours)
+            # Find all output files
+            import glob
+            output_files = glob.glob(os.path.join(self.cache_dir, "*.json"))
             
-            with self.storage_lock:
+            if not output_files:
-                for symbol in self.output_history:
+                return 0
-                    for model_name in self.output_history[symbol]:
-                        history = self.output_history[symbol][model_name]
-                        # Remove old outputs
-                        while history and history[0].timestamp < cutoff_time:
-                            history.popleft()
             
-            logger.info(f"Cleaned up outputs older than {max_age_hours} hours")
+            # Calculate cutoff time
+            cutoff_time = time.time() - (max_age_days * 24 * 60 * 60)
+            
+            # Delete old files
+            deleted_count = 0
+            for filepath in output_files:
+                try:
+                    # Get file modification time
+                    mtime = os.path.getmtime(filepath)
+                    
+                    # Delete if older than cutoff
+                    if mtime < cutoff_time:
+                        os.remove(filepath)
+                        deleted_count += 1
+                        
+                except Exception as e:
+                    logger.error(f"Error deleting file {filepath}: {e}")
+            
+            logger.info(f"Deleted {deleted_count} old model output files")
+            return deleted_count
             
         except Exception as e:
             logger.error(f"Error cleaning up old outputs: {e}")
-    
+            return 0
-    def add_custom_model_type(self, model_type: str):
-        """
-        Add support for a new custom model type
-        
-        Args:
-            model_type: Name of the new model type
-        """
-        self.supported_model_types.add(model_type)
-        logger.info(f"Added support for custom model type: {model_type}")
-    
-    def get_supported_model_types(self) -> List[str]:
-        """Get list of all supported model types"""
-        return list(self.supported_model_types)

test_continuous_cnn_training.py

@@ -0,0 +1,155 @@
+"""
+Test Continuous CNN Training
+
+This script demonstrates how the CNN model can be trained with each new inference result
+using collected data, implementing a continuous learning loop.
+"""
+
+import logging
+import time
+from datetime import datetime
+import random
+import os
+
+from core.standardized_data_provider import StandardizedDataProvider
+from core.enhanced_cnn_adapter import EnhancedCNNAdapter
+from core.data_models import create_model_output
+
+# Configure logging
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
+def simulate_market_feedback(action, symbol):
+    """
+    Simulate market feedback for a given action
+    
+    In a real system, this would be replaced with actual market performance data
+    
+    Args:
+        action: Trading action ('BUY', 'SELL', 'HOLD')
+        symbol: Trading symbol
+    
+    Returns:
+        tuple: (actual_action, reward)
+    """
+    # Simulate market movement (random for demonstration)
+    market_direction = random.choice(['up', 'down', 'sideways'])
+    
+    # Determine actual best action based on market direction
+    if market_direction == 'up':
+        best_action = 'BUY'
+    elif market_direction == 'down':
+        best_action = 'SELL'
+    else:
+        best_action = 'HOLD'
+    
+    # Calculate reward based on whether the action matched the best action
+    if action == best_action:
+        reward = random.uniform(0.01, 0.1)  # Positive reward for correct action
+    else:
+        reward = random.uniform(-0.1, -0.01)  # Negative reward for incorrect action
+    
+    logger.info(f"Market went {market_direction}, best action was {best_action}, model chose {action}, reward: {reward:.4f}")
+    
+    return best_action, reward
+
+def test_continuous_training():
+    """Test continuous training of the CNN model with new inference results"""
+    try:
+        # Initialize data provider
+        symbols = ['ETH/USDT', 'BTC/USDT']
+        timeframes = ['1s', '1m', '1h', '1d']
+        data_provider = StandardizedDataProvider(symbols=symbols, timeframes=timeframes)
+        
+        # Initialize CNN adapter
+        checkpoint_dir = "models/enhanced_cnn"
+        os.makedirs(checkpoint_dir, exist_ok=True)
+        cnn_adapter = EnhancedCNNAdapter(checkpoint_dir=checkpoint_dir)
+        
+        # Load best checkpoint if available
+        cnn_adapter.load_best_checkpoint()
+        
+        # Continuous learning loop
+        num_iterations = 10
+        training_frequency = 3  # Train every N iterations
+        samples_collected = 0
+        
+        logger.info(f"Starting continuous learning loop with {num_iterations} iterations")
+        
+        for i in range(num_iterations):
+            logger.info(f"\nIteration {i+1}/{num_iterations}")
+            
+            # Get standardized input data
+            symbol = random.choice(symbols)
+            logger.info(f"Getting data for {symbol}...")
+            base_data = data_provider.get_base_data_input(symbol)
+            
+            if base_data is None:
+                logger.warning(f"Failed to get base data input for {symbol}, skipping iteration")
+                continue
+            
+            # Make prediction
+            logger.info(f"Making prediction for {symbol}...")
+            model_output = cnn_adapter.predict(base_data)
+            
+            # Log prediction
+            action = model_output.predictions['action']
+            confidence = model_output.confidence
+            logger.info(f"Prediction: {action} with confidence {confidence:.4f}")
+            
+            # Store model output
+            data_provider.store_model_output(model_output)
+            
+            # Simulate market feedback
+            best_action, reward = simulate_market_feedback(action, symbol)
+            
+            # Add training sample
+            logger.info(f"Adding training sample: action={best_action}, reward={reward:.4f}")
+            cnn_adapter.add_training_sample(base_data, best_action, reward)
+            samples_collected += 1
+            
+            # Train model periodically
+            if (i + 1) % training_frequency == 0 and samples_collected >= 3:
+                logger.info(f"Training model with {samples_collected} samples...")
+                metrics = cnn_adapter.train(epochs=1)
+                
+                # Log training metrics
+                logger.info(f"Training metrics: loss={metrics.get('loss', 0.0):.4f}, accuracy={metrics.get('accuracy', 0.0):.4f}")
+            
+            # Simulate time passing
+            time.sleep(1)
+        
+        logger.info("\nContinuous learning loop completed")
+        
+        # Final evaluation
+        logger.info("Performing final evaluation...")
+        
+        # Get data for evaluation
+        symbol = 'ETH/USDT'
+        base_data = data_provider.get_base_data_input(symbol)
+        
+        if base_data is not None:
+            # Make prediction
+            model_output = cnn_adapter.predict(base_data)
+            
+            # Log prediction
+            action = model_output.predictions['action']
+            confidence = model_output.confidence
+            logger.info(f"Final prediction for {symbol}: {action} with confidence {confidence:.4f}")
+            
+            # Get model output manager
+            output_manager = data_provider.get_model_output_manager()
+            
+            # Evaluate model performance
+            metrics = output_manager.evaluate_model_performance(symbol, cnn_adapter.model_name)
+            logger.info(f"Performance metrics: {metrics}")
+        else:
+            logger.warning(f"Failed to get base data input for final evaluation")
+        
+        logger.info("Test completed successfully")
+        
+    except Exception as e:
+        logger.error(f"Error in test: {e}", exc_info=True)
+
+if __name__ == "__main__":
+    test_continuous_training()

test_enhanced_cnn_adapter.py

@@ -0,0 +1,87 @@
+"""
+Test Enhanced CNN Adapter
+
+This script tests the EnhancedCNNAdapter with standardized input format.
+"""
+
+import logging
+import time
+from datetime import datetime
+
+from core.standardized_data_provider import StandardizedDataProvider
+from core.enhanced_cnn_adapter import EnhancedCNNAdapter
+from core.data_models import create_model_output
+
+# Configure logging
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
+def test_cnn_adapter():
+    """Test the EnhancedCNNAdapter with standardized input format"""
+    try:
+        # Initialize data provider
+        symbols = ['ETH/USDT', 'BTC/USDT']
+        timeframes = ['1s', '1m', '1h', '1d']
+        data_provider = StandardizedDataProvider(symbols=symbols, timeframes=timeframes)
+        
+        # Initialize CNN adapter
+        cnn_adapter = EnhancedCNNAdapter(checkpoint_dir="models/enhanced_cnn")
+        
+        # Load best checkpoint if available
+        cnn_adapter.load_best_checkpoint()
+        
+        # Get standardized input data
+        logger.info("Getting standardized input data...")
+        base_data = data_provider.get_base_data_input('ETH/USDT')
+        
+        if base_data is None:
+            logger.error("Failed to get base data input")
+            return
+        
+        # Make prediction
+        logger.info("Making prediction...")
+        model_output = cnn_adapter.predict(base_data)
+        
+        # Log prediction
+        logger.info(f"Prediction: {model_output.predictions['action']} with confidence {model_output.confidence:.4f}")
+        
+        # Store model output
+        data_provider.store_model_output(model_output)
+        
+        # Add training sample (simulated)
+        logger.info("Adding training sample...")
+        cnn_adapter.add_training_sample(base_data, 'BUY', 0.05)
+        
+        # Train model
+        logger.info("Training model...")
+        metrics = cnn_adapter.train(epochs=1)
+        
+        # Log training metrics
+        logger.info(f"Training metrics: {metrics}")
+        
+        # Make another prediction
+        logger.info("Making another prediction...")
+        model_output = cnn_adapter.predict(base_data)
+        
+        # Log prediction
+        logger.info(f"Prediction: {model_output.predictions['action']} with confidence {model_output.confidence:.4f}")
+        
+        # Test model output manager
+        logger.info("Testing model output manager...")
+        output_manager = data_provider.get_model_output_manager()
+        
+        # Get current outputs
+        current_outputs = output_manager.get_all_current_outputs('ETH/USDT')
+        logger.info(f"Current outputs: {len(current_outputs)} models")
+        
+        # Evaluate model performance
+        metrics = output_manager.evaluate_model_performance('ETH/USDT', 'enhanced_cnn_v1')
+        logger.info(f"Performance metrics: {metrics}")
+        
+        logger.info("Test completed successfully")
+        
+    except Exception as e:
+        logger.error(f"Error in test: {e}", exc_info=True)
+
+if __name__ == "__main__":
+    test_cnn_adapter()

utils/__init__.py

@@ -0,0 +1,3 @@
+"""
+Utils package for the multi-modal trading system
+"""

utils/checkpoint_manager.py

@@ -1,466 +1,408 @@
-#!/usr/bin/env python3
+"""
-"""
+Checkpoint Manager
-Checkpoint Management System for W&B Training
+
+This module provides functionality for managing model checkpoints, including:
+- Saving checkpoints with metadata
+- Loading the best checkpoint based on performance metrics
+- Cleaning up old or underperforming checkpoints
 """
 
 import os
 import json
+import glob
 import logging
-from datetime import datetime, timedelta
+import shutil
-from pathlib import Path
-from typing import Dict, List, Optional, Tuple, Any
-from dataclasses import dataclass, asdict
-from collections import defaultdict
 import torch
-import random
+from datetime import datetime
-
+from typing import Dict, List, Optional, Any, Tuple
-try:
-    import wandb
-    WANDB_AVAILABLE = True
-except ImportError:
-    WANDB_AVAILABLE = False
 
 logger = logging.getLogger(__name__)
 
-@dataclass
+# Global checkpoint manager instance
-class CheckpointMetadata:
+_checkpoint_manager_instance = None
-    checkpoint_id: str
+
-    model_name: str
+def get_checkpoint_manager(checkpoint_dir: str = "models/checkpoints", max_checkpoints: int = 10, metric_name: str = "accuracy") -> 'CheckpointManager':
-    model_type: str
+    """
-    file_path: str
+    Get the global checkpoint manager instance
-    created_at: datetime
-    file_size_mb: float
-    performance_score: float
-    accuracy: Optional[float] = None
-    loss: Optional[float] = None
-    val_accuracy: Optional[float] = None
-    val_loss: Optional[float] = None
-    reward: Optional[float] = None
-    pnl: Optional[float] = None
-    epoch: Optional[int] = None
-    training_time_hours: Optional[float] = None
-    total_parameters: Optional[int] = None
-    wandb_run_id: Optional[str] = None
-    wandb_artifact_name: Optional[str] = None
     
-    def to_dict(self) -> Dict[str, Any]:
+    Args:
-        data = asdict(self)
+        checkpoint_dir: Directory to store checkpoints
-        data['created_at'] = self.created_at.isoformat()
+        max_checkpoints: Maximum number of checkpoints to keep
-        return data
+        metric_name: Metric to use for ranking checkpoints
     
-    @classmethod
+    Returns:
-    def from_dict(cls, data: Dict[str, Any]) -> 'CheckpointMetadata':
+        CheckpointManager: Global checkpoint manager instance
-        data['created_at'] = datetime.fromisoformat(data['created_at'])
+    """
-        return cls(**data)
+    global _checkpoint_manager_instance
+    
+    if _checkpoint_manager_instance is None:
+        _checkpoint_manager_instance = CheckpointManager(
+            checkpoint_dir=checkpoint_dir,
+            max_checkpoints=max_checkpoints,
+            metric_name=metric_name
+        )
+    
+    return _checkpoint_manager_instance
+
+def save_checkpoint(model, model_name: str, model_type: str, performance_metrics: Dict[str, float], training_metadata: Dict[str, Any] = None, checkpoint_dir: str = "models/checkpoints") -> Any:
+    """
+    Save a checkpoint with metadata
+    
+    Args:
+        model: The model to save
+        model_name: Name of the model
+        model_type: Type of the model ('cnn', 'rl', etc.)
+        performance_metrics: Performance metrics
+        training_metadata: Additional training metadata
+        checkpoint_dir: Directory to store checkpoints
+    
+    Returns:
+        Any: Checkpoint metadata
+    """
+    try:
+        # Create checkpoint directory
+        os.makedirs(checkpoint_dir, exist_ok=True)
+        
+        # Create timestamp
+        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+        
+        # Create checkpoint path
+        model_dir = os.path.join(checkpoint_dir, model_name)
+        os.makedirs(model_dir, exist_ok=True)
+        checkpoint_path = os.path.join(model_dir, f"{model_name}_{timestamp}")
+        
+        # Save model
+        if hasattr(model, 'save'):
+            # Use model's save method if available
+            model.save(checkpoint_path)
+        else:
+            # Otherwise, save state_dict
+            torch_path = f"{checkpoint_path}.pt"
+            torch.save({
+                'model_state_dict': model.state_dict() if hasattr(model, 'state_dict') else None,
+                'model_name': model_name,
+                'model_type': model_type,
+                'timestamp': timestamp
+            }, torch_path)
+        
+        # Create metadata
+        checkpoint_metadata = {
+            'model_name': model_name,
+            'model_type': model_type,
+            'timestamp': timestamp,
+            'performance_metrics': performance_metrics,
+            'training_metadata': training_metadata or {},
+            'checkpoint_id': f"{model_name}_{timestamp}"
+        }
+        
+        # Add performance score for sorting
+        primary_metric = 'accuracy' if 'accuracy' in performance_metrics else 'reward'
+        checkpoint_metadata['performance_score'] = performance_metrics.get(primary_metric, 0.0)
+        checkpoint_metadata['created_at'] = timestamp
+        
+        # Save metadata
+        with open(f"{checkpoint_path}_metadata.json", 'w') as f:
+            json.dump(checkpoint_metadata, f, indent=2)
+        
+        # Get checkpoint manager and clean up old checkpoints
+        checkpoint_manager = get_checkpoint_manager(checkpoint_dir=checkpoint_dir)
+        checkpoint_manager._cleanup_checkpoints(model_name)
+        
+        # Return metadata as an object
+        class CheckpointMetadata:
+            def __init__(self, metadata):
+                for key, value in metadata.items():
+                    setattr(self, key, value)
+        
+        return CheckpointMetadata(checkpoint_metadata)
+        
+    except Exception as e:
+        logger.error(f"Error saving checkpoint: {e}")
+        return None
+
+def load_best_checkpoint(model_name: str, checkpoint_dir: str = "models/checkpoints") -> Optional[Tuple[str, Any]]:
+    """
+    Load the best checkpoint based on performance metrics
+    
+    Args:
+        model_name: Name of the model
+        checkpoint_dir: Directory to store checkpoints
+    
+    Returns:
+        Optional[Tuple[str, Any]]: Path to the best checkpoint and its metadata, or None if not found
+    """
+    try:
+        checkpoint_manager = get_checkpoint_manager(checkpoint_dir=checkpoint_dir)
+        checkpoint_path, checkpoint_metadata = checkpoint_manager.load_best_checkpoint(model_name)
+        
+        if not checkpoint_path:
+            return None
+        
+        # Convert metadata to object
+        class CheckpointMetadata:
+            def __init__(self, metadata):
+                for key, value in metadata.items():
+                    setattr(self, key, value)
+                
+                # Add performance score if not present
+                if not hasattr(self, 'performance_score'):
+                    metrics = getattr(self, 'metrics', {})
+                    primary_metric = 'accuracy' if 'accuracy' in metrics else 'reward'
+                    self.performance_score = metrics.get(primary_metric, 0.0)
+                
+                # Add created_at if not present
+                if not hasattr(self, 'created_at'):
+                    self.created_at = getattr(self, 'timestamp', 'unknown')
+        
+        return f"{checkpoint_path}.pt", CheckpointMetadata(checkpoint_metadata)
+        
+    except Exception as e:
+        logger.error(f"Error loading best checkpoint: {e}")
+        return None
 
 class CheckpointManager:
-    def __init__(self, 
+    """
-                 base_checkpoint_dir: str = "NN/models/saved",
+    Manages model checkpoints with performance-based optimization
-                 max_checkpoints_per_model: int = 5,
-                 metadata_file: str = "checkpoint_metadata.json",
-                 enable_wandb: bool = True):
-        self.base_dir = Path(base_checkpoint_dir)
-        self.base_dir.mkdir(parents=True, exist_ok=True)
-        
-        self.max_checkpoints = max_checkpoints_per_model
-        self.metadata_file = self.base_dir / metadata_file
-        self.enable_wandb = enable_wandb and WANDB_AVAILABLE
-        
-        self.checkpoints: Dict[str, List[CheckpointMetadata]] = defaultdict(list)
-        self._load_metadata()
-        
-        logger.info(f"Checkpoint Manager initialized - Max checkpoints per model: {self.max_checkpoints}")
     
-    def save_checkpoint(self, model, model_name: str, model_type: str, 
+    This class:
-                       performance_metrics: Dict[str, float],
+    1. Saves checkpoints with metadata
-                       training_metadata: Optional[Dict[str, Any]] = None,
+    2. Loads the best checkpoint based on performance metrics
-                       force_save: bool = False) -> Optional[CheckpointMetadata]:
+    3. Cleans up old or underperforming checkpoints
+    """
+    
+    def __init__(self, checkpoint_dir: str, max_checkpoints: int = 10, metric_name: str = "accuracy"):
+        """
+        Initialize the checkpoint manager
+        
+        Args:
+            checkpoint_dir: Directory to store checkpoints
+            max_checkpoints: Maximum number of checkpoints to keep
+            metric_name: Metric to use for ranking checkpoints
+        """
+        self.checkpoint_dir = checkpoint_dir
+        self.max_checkpoints = max_checkpoints
+        self.metric_name = metric_name
+        
+        # Create checkpoint directory if it doesn't exist
+        os.makedirs(checkpoint_dir, exist_ok=True)
+        
+        logger.info(f"CheckpointManager initialized with checkpoint_dir: {checkpoint_dir}")
+    
+    def save_checkpoint(self, model_name: str, model_path: str, metrics: Dict[str, float], metadata: Dict[str, Any] = None) -> str:
+        """
+        Save a checkpoint with metadata
+        
+        Args:
+            model_name: Name of the model
+            model_path: Path to the model file
+            metrics: Performance metrics
+            metadata: Additional metadata
+        
+        Returns:
+            str: Path to the saved checkpoint
+        """
         try:
-            timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')
+            # Create timestamp
-            checkpoint_id = f"{model_name}_{timestamp}"
+            timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
             
-            model_dir = self.base_dir / model_name
+            # Create checkpoint directory
-            model_dir.mkdir(exist_ok=True)
+            checkpoint_dir = os.path.join(self.checkpoint_dir, model_name)
+            os.makedirs(checkpoint_dir, exist_ok=True)
             
-            checkpoint_path = model_dir / f"{checkpoint_id}.pt"
+            # Create checkpoint path
+            checkpoint_path = os.path.join(checkpoint_dir, f"{model_name}_{timestamp}")
             
-            performance_score = self._calculate_performance_score(performance_metrics)
+            # Copy model file to checkpoint path
+            shutil.copy2(model_path, f"{checkpoint_path}.pt")
             
-            if not force_save and not self._should_save_checkpoint(model_name, performance_score):
+            # Create metadata
-                logger.debug(f"Skipping checkpoint save for {model_name} - performance not improved")
+            checkpoint_metadata = {
-                return None
+                'model_name': model_name,
-            
+                'timestamp': timestamp,
-            success = self._save_model_file(model, checkpoint_path, model_type)
+                'metrics': metrics,
-            if not success:
+                'metadata': metadata or {}
-                return None
-            
-            file_size_mb = checkpoint_path.stat().st_size / (1024 * 1024)
-            
-            metadata = CheckpointMetadata(
-                checkpoint_id=checkpoint_id,
-                model_name=model_name,
-                model_type=model_type,
-                file_path=str(checkpoint_path),
-                created_at=datetime.now(),
-                file_size_mb=file_size_mb,
-                performance_score=performance_score,
-                accuracy=performance_metrics.get('accuracy'),
-                loss=performance_metrics.get('loss'),
-                val_accuracy=performance_metrics.get('val_accuracy'),
-                val_loss=performance_metrics.get('val_loss'),
-                reward=performance_metrics.get('reward'),
-                pnl=performance_metrics.get('pnl'),
-                epoch=training_metadata.get('epoch') if training_metadata else None,
-                training_time_hours=training_metadata.get('training_time_hours') if training_metadata else None,
-                total_parameters=training_metadata.get('total_parameters') if training_metadata else None
-            )
-            
-            if self.enable_wandb and wandb.run is not None:
-                artifact_name = self._upload_to_wandb(checkpoint_path, metadata)
-                metadata.wandb_run_id = wandb.run.id
-                metadata.wandb_artifact_name = artifact_name
-            
-            self.checkpoints[model_name].append(metadata)
-            self._rotate_checkpoints(model_name)
-            self._save_metadata()
-            
-            logger.debug(f"Saved checkpoint: {checkpoint_id} (score: {performance_score:.4f})")
-            return metadata
-            
-        except Exception as e:
-            logger.error(f"Error saving checkpoint for {model_name}: {e}")
-            return None
-    
-    def load_best_checkpoint(self, model_name: str) -> Optional[Tuple[str, CheckpointMetadata]]:
-        try:
-            # First, try the standard checkpoint system
-            if model_name in self.checkpoints and self.checkpoints[model_name]:
-                # Filter out checkpoints with non-existent files
-                valid_checkpoints = [
-                    cp for cp in self.checkpoints[model_name] 
-                    if Path(cp.file_path).exists()
-                ]
-                
-                if valid_checkpoints:
-                    best_checkpoint = max(valid_checkpoints, key=lambda x: x.performance_score)
-                    logger.debug(f"Loading best checkpoint for {model_name}: {best_checkpoint.checkpoint_id}")
-                    return best_checkpoint.file_path, best_checkpoint
-                else:
-                    # Clean up invalid metadata entries
-                    invalid_count = len(self.checkpoints[model_name])
-                    logger.warning(f"Found {invalid_count} invalid checkpoint entries for {model_name}, cleaning up metadata")
-                    self.checkpoints[model_name] = []
-                    self._save_metadata()
-            
-            # Fallback: Look for existing saved models in the legacy format
-            logger.debug(f"No valid checkpoints found for model: {model_name}, attempting to find legacy saved models")
-            legacy_model_path = self._find_legacy_model(model_name)
-            
-            if legacy_model_path:
-                # Create checkpoint metadata for the legacy model using actual file data
-                legacy_metadata = self._create_legacy_metadata(model_name, legacy_model_path)
-                logger.debug(f"Found legacy model for {model_name}: {legacy_model_path}")
-                return str(legacy_model_path), legacy_metadata
-            
-            logger.warning(f"No checkpoints or legacy models found for: {model_name}")
-            return None
-            
-        except Exception as e:
-            logger.error(f"Error loading best checkpoint for {model_name}: {e}")
-            return None
-    
-    def _calculate_performance_score(self, metrics: Dict[str, float]) -> float:
-        """Calculate performance score with improved sensitivity for training models"""
-        score = 0.0
-        
-        # Prioritize loss reduction for active training models
-        if 'loss' in metrics:
-            # Invert loss so lower loss = higher score, with better scaling
-            loss_value = metrics['loss']
-            if loss_value > 0:
-                score += max(0, 100 / (1 + loss_value))  # More sensitive to loss changes
-            else:
-                score += 100  # Perfect loss
-        
-        # Add other metrics with appropriate weights
-        if 'accuracy' in metrics:
-            score += metrics['accuracy'] * 50  # Reduced weight to balance with loss
-        if 'val_accuracy' in metrics:
-            score += metrics['val_accuracy'] * 50
-        if 'val_loss' in metrics:
-            val_loss = metrics['val_loss']
-            if val_loss > 0:
-                score += max(0, 50 / (1 + val_loss))
-        if 'reward' in metrics:
-            score += metrics['reward'] * 10
-        if 'pnl' in metrics:
-            score += metrics['pnl'] * 5
-        if 'training_samples' in metrics:
-            # Bonus for processing more training samples
-            score += min(10, metrics['training_samples'] / 10)
-        
-        # Return actual calculated score - NO SYNTHETIC MINIMUM
-        return score
-    
-    def _should_save_checkpoint(self, model_name: str, performance_score: float) -> bool:
-        """Improved checkpoint saving logic with more frequent saves during training"""
-        if model_name not in self.checkpoints or not self.checkpoints[model_name]:
-            return True  # Always save first checkpoint
-        
-        # Allow more checkpoints during active training
-        if len(self.checkpoints[model_name]) < self.max_checkpoints:
-            return True
-        
-        # Get current best and worst scores
-        scores = [cp.performance_score for cp in self.checkpoints[model_name]]
-        best_score = max(scores)
-        worst_score = min(scores)
-        
-        # Save if better than worst (more frequent saves)
-        if performance_score > worst_score:
-            return True
-        
-        # For high-performing models (score > 100), be more sensitive to small improvements
-        if best_score > 100:
-            # Save if within 0.1% of best score (very sensitive for converged models)
-            if performance_score >= best_score * 0.999:
-                return True
-        else:
-            # Also save if we're within 10% of best score (capture near-optimal models)
-            if performance_score >= best_score * 0.9:
-                return True
-        
-        # Save more frequently during active training (every 5th attempt instead of 10th)
-        if random.random() < 0.2:  # 20% chance to save anyway
-            logger.debug(f"Saving checkpoint for {model_name} - periodic save during active training")
-            return True
-        
-        return False
-    
-    def _save_model_file(self, model, file_path: Path, model_type: str) -> bool:
-        try:
-            if hasattr(model, 'state_dict'):
-                torch.save({
-                    'model_state_dict': model.state_dict(),
-                    'model_type': model_type,
-                    'saved_at': datetime.now().isoformat()
-                }, file_path)
-            else:
-                torch.save(model, file_path)
-            return True
-        except Exception as e:
-            logger.error(f"Error saving model file {file_path}: {e}")
-            return False
-    
-    def _rotate_checkpoints(self, model_name: str):
-        checkpoint_list = self.checkpoints[model_name]
-        
-        if len(checkpoint_list) <= self.max_checkpoints:
-            return
-        
-        checkpoint_list.sort(key=lambda x: x.performance_score, reverse=True)
-        
-        to_remove = checkpoint_list[self.max_checkpoints:]
-        self.checkpoints[model_name] = checkpoint_list[:self.max_checkpoints]
-        
-        for checkpoint in to_remove:
-            try:
-                file_path = Path(checkpoint.file_path)
-                if file_path.exists():
-                    file_path.unlink()
-                logger.debug(f"Rotated out checkpoint: {checkpoint.checkpoint_id}")
-            except Exception as e:
-                logger.error(f"Error removing rotated checkpoint {checkpoint.checkpoint_id}: {e}")
-    
-    def _upload_to_wandb(self, file_path: Path, metadata: CheckpointMetadata) -> Optional[str]:
-        try:
-            if not self.enable_wandb or wandb.run is None:
-                return None
-            
-            artifact_name = f"{metadata.model_name}_checkpoint"
-            artifact = wandb.Artifact(artifact_name, type="model")
-            artifact.add_file(str(file_path))
-            wandb.log_artifact(artifact)
-            
-            return artifact_name
-        except Exception as e:
-            logger.error(f"Error uploading to W&B: {e}")
-            return None
-    
-    def _load_metadata(self):
-        try:
-            if self.metadata_file.exists():
-                with open(self.metadata_file, 'r') as f:
-                    data = json.load(f)
-                
-                for model_name, checkpoint_list in data.items():
-                    self.checkpoints[model_name] = [
-                        CheckpointMetadata.from_dict(cp_data)
-                        for cp_data in checkpoint_list
-                    ]
-                
-                logger.info(f"Loaded metadata for {len(self.checkpoints)} models")
-        except Exception as e:
-            logger.error(f"Error loading checkpoint metadata: {e}")
-    
-    def _save_metadata(self):
-        try:
-            data = {}
-            for model_name, checkpoint_list in self.checkpoints.items():
-                data[model_name] = [cp.to_dict() for cp in checkpoint_list]
-            
-            with open(self.metadata_file, 'w') as f:
-                json.dump(data, f, indent=2)
-        except Exception as e:
-            logger.error(f"Error saving checkpoint metadata: {e}")
-    
-    def get_checkpoint_stats(self):
-        """Get statistics about managed checkpoints"""
-        stats = {
-            'total_models': len(self.checkpoints),
-            'total_checkpoints': sum(len(checkpoints) for checkpoints in self.checkpoints.values()),
-            'total_size_mb': 0.0,
-            'models': {}
-        }
-        
-        for model_name, checkpoint_list in self.checkpoints.items():
-            if not checkpoint_list:
-                continue
-                
-            model_size = sum(cp.file_size_mb for cp in checkpoint_list)
-            best_checkpoint = max(checkpoint_list, key=lambda x: x.performance_score)
-            
-            stats['models'][model_name] = {
-                'checkpoint_count': len(checkpoint_list),
-                'total_size_mb': model_size,
-                'best_performance': best_checkpoint.performance_score,
-                'best_checkpoint_id': best_checkpoint.checkpoint_id,
-                'latest_checkpoint': max(checkpoint_list, key=lambda x: x.created_at).checkpoint_id
             }
             
-            stats['total_size_mb'] += model_size
+            # Save metadata
-        
+            with open(f"{checkpoint_path}_metadata.json", 'w') as f:
-        return stats
+                json.dump(checkpoint_metadata, f, indent=2)
-    
+            
-    def _find_legacy_model(self, model_name: str) -> Optional[Path]:
+            logger.info(f"Saved checkpoint to {checkpoint_path}")
-        """Find legacy saved models based on model name patterns"""
+            
-        base_dir = Path(self.base_dir)
+            # Clean up old checkpoints
-        
+            self._cleanup_checkpoints(model_name)
-        # Define model name mappings and patterns for legacy files
+            
-        legacy_patterns = {
+            return checkpoint_path
-            'dqn_agent': [
-                'dqn_agent_best_policy.pt',
-                'enhanced_dqn_best_policy.pt',
-                'improved_dqn_agent_best_policy.pt',
-                'dqn_agent_final_policy.pt'
-            ],
-            'enhanced_cnn': [
-                'cnn_model_best.pt',
-                'optimized_short_term_model_best.pt',
-                'optimized_short_term_model_realtime_best.pt',
-                'optimized_short_term_model_ticks_best.pt'
-            ],
-            'extrema_trainer': [
-                'supervised_model_best.pt'
-            ],
-            'cob_rl': [
-                'best_rl_model.pth_policy.pt',
-                'rl_agent_best_policy.pt'
-            ],
-            'decision': [
-                # Decision models might be in subdirectories, but let's check main dir too
-                'decision_best.pt',
-                'decision_model_best.pt',
-                # Check for transformer models which might be used as decision models
-                'enhanced_dqn_best_policy.pt',
-                'improved_dqn_agent_best_policy.pt'
-            ]
-        }
-        
-        # Get patterns for this model name
-        patterns = legacy_patterns.get(model_name, [])
-        
-        # Also try generic patterns based on model name
-        patterns.extend([
-            f'{model_name}_best.pt',
-            f'{model_name}_best_policy.pt',
-            f'{model_name}_final.pt',
-            f'{model_name}_final_policy.pt'
-        ])
-        
-        # Search for the model files
-        for pattern in patterns:
-            candidate_path = base_dir / pattern
-            if candidate_path.exists():
-                logger.debug(f"Found legacy model file: {candidate_path}")
-                return candidate_path
-        
-        # Also check subdirectories
-        for subdir in base_dir.iterdir():
-            if subdir.is_dir() and subdir.name == model_name:
-                for pattern in patterns:
-                    candidate_path = subdir / pattern
-                    if candidate_path.exists():
-                        logger.debug(f"Found legacy model file in subdirectory: {candidate_path}")
-                        return candidate_path
-        
-        return None
-    
-    def _create_legacy_metadata(self, model_name: str, file_path: Path) -> CheckpointMetadata:
-        """Create metadata for legacy model files using only actual file information"""
-        try:
-            file_size_mb = file_path.stat().st_size / (1024 * 1024)
-            created_time = datetime.fromtimestamp(file_path.stat().st_mtime)
             
-            # NO SYNTHETIC DATA - use only actual file information
-            return CheckpointMetadata(
-                checkpoint_id=f"legacy_{model_name}_{int(created_time.timestamp())}",
-                model_name=model_name,
-                model_type=model_name,
-                file_path=str(file_path),
-                created_at=created_time,
-                file_size_mb=file_size_mb,
-                performance_score=0.0,  # Unknown performance - use 0, not synthetic values
-                accuracy=None,
-                loss=None,
-                val_accuracy=None,
-                val_loss=None,
-                reward=None,
-                pnl=None,
-                epoch=None,
-                training_time_hours=None,
-                total_parameters=None,
-                wandb_run_id=None,
-                wandb_artifact_name=None
-            )
         except Exception as e:
-            logger.error(f"Error creating legacy metadata for {model_name}: {e}")
+            logger.error(f"Error saving checkpoint: {e}")
-            # Return a basic metadata with minimal info - NO SYNTHETIC VALUES
+            return ""
-            return CheckpointMetadata(
+    
-                checkpoint_id=f"legacy_{model_name}",
+    def load_best_checkpoint(self, model_name: str) -> Tuple[str, Dict[str, Any]]:
-                model_name=model_name,
+        """
-                model_type=model_name,
+        Load the best checkpoint based on performance metrics
-                file_path=str(file_path),
+        
-                created_at=datetime.now(),
+        Args:
-                file_size_mb=0.0,
+            model_name: Name of the model
-                performance_score=0.0  # Unknown - use 0, not synthetic
+        
-            )
+        Returns:
-
+            Tuple[str, Dict[str, Any]]: Path to the best checkpoint and its metadata
-_checkpoint_manager = None
+        """
-
+        try:
-def get_checkpoint_manager() -> CheckpointManager:
+            # Find all checkpoint metadata files
-    global _checkpoint_manager
+            checkpoint_dir = os.path.join(self.checkpoint_dir, model_name)
-    if _checkpoint_manager is None:
+            metadata_files = glob.glob(os.path.join(checkpoint_dir, f"{model_name}_*_metadata.json"))
-        _checkpoint_manager = CheckpointManager()
+            
-    return _checkpoint_manager
+            if not metadata_files:
-
+                logger.info(f"No checkpoints found for {model_name}")
-def save_checkpoint(model, model_name: str, model_type: str,
+                return "", {}
-                   performance_metrics: Dict[str, float],
+            
-                   training_metadata: Optional[Dict[str, Any]] = None,
+            # Load metadata for each checkpoint
-                   force_save: bool = False) -> Optional[CheckpointMetadata]:
+            checkpoints = []
-    return get_checkpoint_manager().save_checkpoint(
+            for metadata_file in metadata_files:
-        model, model_name, model_type, performance_metrics, training_metadata, force_save
+                try:
-    )
+                    with open(metadata_file, 'r') as f:
-
+                        metadata = json.load(f)
-def load_best_checkpoint(model_name: str) -> Optional[Tuple[str, CheckpointMetadata]]:
+                    
-    return get_checkpoint_manager().load_best_checkpoint(model_name)
+                    # Get checkpoint path (remove _metadata.json)
+                    checkpoint_path = metadata_file[:-14]
+                    
+                    # Check if model file exists
+                    if not os.path.exists(f"{checkpoint_path}.pt"):
+                        logger.warning(f"Model file not found for checkpoint {checkpoint_path}")
+                        continue
+                    
+                    checkpoints.append((checkpoint_path, metadata))
+                    
+                except Exception as e:
+                    logger.error(f"Error loading checkpoint metadata {metadata_file}: {e}")
+            
+            if not checkpoints:
+                logger.info(f"No valid checkpoints found for {model_name}")
+                return "", {}
+            
+            # Sort by metric (highest first)
+            checkpoints.sort(key=lambda x: x[1].get('metrics', {}).get(self.metric_name, 0.0), reverse=True)
+            
+            # Return best checkpoint
+            best_checkpoint_path = checkpoints[0][0]
+            best_checkpoint_metadata = checkpoints[0][1]
+            
+            logger.info(f"Best checkpoint for {model_name}: {best_checkpoint_path}")
+            
+            return best_checkpoint_path, best_checkpoint_metadata
+            
+        except Exception as e:
+            logger.error(f"Error loading best checkpoint: {e}")
+            return "", {}
+    
+    def _cleanup_checkpoints(self, model_name: str) -> int:
+        """
+        Clean up old or underperforming checkpoints
+        
+        Args:
+            model_name: Name of the model
+        
+        Returns:
+            int: Number of checkpoints deleted
+        """
+        try:
+            # Find all checkpoint metadata files
+            checkpoint_dir = os.path.join(self.checkpoint_dir, model_name)
+            metadata_files = glob.glob(os.path.join(checkpoint_dir, f"{model_name}_*_metadata.json"))
+            
+            if not metadata_files or len(metadata_files) <= self.max_checkpoints:
+                return 0
+            
+            # Load metadata for each checkpoint
+            checkpoints = []
+            for metadata_file in metadata_files:
+                try:
+                    with open(metadata_file, 'r') as f:
+                        metadata = json.load(f)
+                    
+                    # Get checkpoint path (remove _metadata.json)
+                    checkpoint_path = metadata_file[:-14]
+                    
+                    checkpoints.append((checkpoint_path, metadata))
+                    
+                except Exception as e:
+                    logger.error(f"Error loading checkpoint metadata {metadata_file}: {e}")
+            
+            # Sort by metric (highest first)
+            checkpoints.sort(key=lambda x: x[1].get('metrics', {}).get(self.metric_name, 0.0), reverse=True)
+            
+            # Keep only the best checkpoints
+            checkpoints_to_delete = checkpoints[self.max_checkpoints:]
+            
+            # Delete checkpoints
+            deleted_count = 0
+            for checkpoint_path, _ in checkpoints_to_delete:
+                try:
+                    # Delete model file
+                    if os.path.exists(f"{checkpoint_path}.pt"):
+                        os.remove(f"{checkpoint_path}.pt")
+                    
+                    # Delete metadata file
+                    if os.path.exists(f"{checkpoint_path}_metadata.json"):
+                        os.remove(f"{checkpoint_path}_metadata.json")
+                    
+                    deleted_count += 1
+                    
+                except Exception as e:
+                    logger.error(f"Error deleting checkpoint {checkpoint_path}: {e}")
+            
+            logger.info(f"Deleted {deleted_count} old checkpoints for {model_name}")
+            
+            return deleted_count
+            
+        except Exception as e:
+            logger.error(f"Error cleaning up checkpoints: {e}")
+            return 0
+    
+    def get_all_checkpoints(self, model_name: str) -> List[Tuple[str, Dict[str, Any]]]:
+        """
+        Get all checkpoints for a model
+        
+        Args:
+            model_name: Name of the model
+        
+        Returns:
+            List[Tuple[str, Dict[str, Any]]]: List of checkpoint paths and metadata
+        """
+        try:
+            # Find all checkpoint metadata files
+            checkpoint_dir = os.path.join(self.checkpoint_dir, model_name)
+            metadata_files = glob.glob(os.path.join(checkpoint_dir, f"{model_name}_*_metadata.json"))
+            
+            if not metadata_files:
+                return []
+            
+            # Load metadata for each checkpoint
+            checkpoints = []
+            for metadata_file in metadata_files:
+                try:
+                    with open(metadata_file, 'r') as f:
+                        metadata = json.load(f)
+                    
+                    # Get checkpoint path (remove _metadata.json)
+                    checkpoint_path = metadata_file[:-14]
+                    
+                    # Check if model file exists
+                    if not os.path.exists(f"{checkpoint_path}.pt"):
+                        logger.warning(f"Model file not found for checkpoint {checkpoint_path}")
+                        continue
+                    
+                    checkpoints.append((checkpoint_path, metadata))
+                    
+                except Exception as e:
+                    logger.error(f"Error loading checkpoint metadata {metadata_file}: {e}")
+            
+            # Sort by timestamp (newest first)
+            checkpoints.sort(key=lambda x: x[1].get('timestamp', ''), reverse=True)
+            
+            return checkpoints
+            
+        except Exception as e:
+            logger.error(f"Error getting all checkpoints: {e}")
+            return []

utils/training_integration.py

@@ -9,7 +9,7 @@ from datetime import datetime
 from typing import Dict, Any, Optional
 from pathlib import Path
 
-from .checkpoint_manager import get_checkpoint_manager, save_checkpoint, load_best_checkpoint
+from .checkpoint_manager import get_checkpoint_manager, load_best_checkpoint
 
 logger = logging.getLogger(__name__)
 
@@ -78,7 +78,7 @@ class TrainingIntegration:
                 except Exception as e:
                     logger.warning(f"Error logging to W&B: {e}")
             
-            metadata = save_checkpoint(
+            metadata = self.checkpoint_manager.save_checkpoint(
                 model=cnn_model,
                 model_name=model_name,
                 model_type='cnn',
@@ -137,7 +137,7 @@ class TrainingIntegration:
                 except Exception as e:
                     logger.warning(f"Error logging to W&B: {e}")
             
-            metadata = save_checkpoint(
+            metadata = self.checkpoint_manager.save_checkpoint(
                 model=rl_agent,
                 model_name=model_name,
                 model_type='rl',
@@ -158,7 +158,7 @@ class TrainingIntegration:
     
     def load_best_model(self, model_name: str, model_class=None):
         try:
-            result = load_best_checkpoint(model_name)
+            result = self.checkpoint_manager.load_best_checkpoint(model_name)
             if not result:
                 logger.warning(f"No checkpoint found for model: {model_name}")
                 return None