wip cnn training and cob
This commit is contained in:
Dobromir Popov
@ -46,7 +46,7 @@ class EnhancedCNNAdapter:
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self.max_training_samples = 10000
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self.batch_size = 32
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self.learning_rate = 0.0001
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self.model_name = "enhanced_cnn_v1"
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self.model_name = "enhanced_cnn"
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# Enhanced metrics tracking
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self.last_inference_time = None
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@ -72,6 +72,30 @@ class EnhancedCNNAdapter:
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logger.info(f"EnhancedCNNAdapter initialized on {self.device}")
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def _initialize_model(self):
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"""Initialize the EnhancedCNN model"""
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try:
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# Calculate input shape based on BaseDataInput structure
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# OHLCV: 300 frames x 4 timeframes x 5 features = 6000 features
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# BTC OHLCV: 300 frames x 5 features = 1500 features
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# COB: ±20 buckets x 4 metrics = 160 features
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# MA: 4 timeframes x 10 buckets = 40 features
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# Technical indicators: 100 features
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# Last predictions: 50 features
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# Total: 7850 features
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input_shape = 7850
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n_actions = 3 # BUY, SELL, HOLD
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# Create model
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self.model = EnhancedCNN(input_shape=input_shape, n_actions=n_actions)
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self.model.to(self.device)
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logger.info(f"EnhancedCNN model initialized with input_shape={input_shape}, n_actions={n_actions}")
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except Exception as e:
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logger.error(f"Error initializing EnhancedCNN model: {e}")
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raise
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def _load_checkpoint(self, checkpoint_path: str) -> bool:
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"""Load model from checkpoint path"""
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try:
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@ -98,6 +122,45 @@ class EnhancedCNNAdapter:
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logger.error(f"Error loading best checkpoint: {e}")
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return False
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def load_best_checkpoint(self) -> bool:
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"""Load the best checkpoint based on accuracy"""
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try:
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# Import checkpoint manager
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from utils.checkpoint_manager import CheckpointManager
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# Create checkpoint manager
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checkpoint_manager = CheckpointManager(
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checkpoint_dir=self.checkpoint_dir,
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max_checkpoints=10,
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metric_name="accuracy"
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)
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# Load best checkpoint
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best_checkpoint_path, best_checkpoint_metadata = checkpoint_manager.load_best_checkpoint(self.model_name)
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if not best_checkpoint_path:
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logger.info(f"No checkpoints found for {self.model_name} - starting in COLD START mode")
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return False
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# Load model
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success = self.model.load(best_checkpoint_path)
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if success:
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logger.info(f"Loaded best checkpoint from {best_checkpoint_path}")
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# Log metrics
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metrics = best_checkpoint_metadata.get('metrics', {})
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logger.info(f"Checkpoint metrics: accuracy={metrics.get('accuracy', 0.0):.4f}, loss={metrics.get('loss', 0.0):.4f}")
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return True
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else:
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logger.warning(f"Failed to load best checkpoint from {best_checkpoint_path}")
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return False
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except Exception as e:
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logger.error(f"Error loading best checkpoint: {e}")
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return False
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def _create_default_output(self, symbol: str) -> ModelOutput:
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@ -124,37 +187,7 @@ class EnhancedCNNAdapter:
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return processed_states
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def _initialize_model(self):
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"""Initialize the EnhancedCNN model"""
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try:
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# Calculate input shape based on BaseDataInput structure
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# OHLCV: 300 frames x 4 timeframes x 5 features = 6000 features
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# BTC OHLCV: 300 frames x 5 features = 1500 features
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# COB: ±20 buckets x 4 metrics = 160 features
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# MA: 4 timeframes x 10 buckets = 40 features
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# Technical indicators: 100 features
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# Last predictions: 50 features
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# Total: 7850 features
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input_shape = 7850
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n_actions = 3 # BUY, SELL, HOLD
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# Create model
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self.model = EnhancedCNN(input_shape=input_shape, n_actions=n_actions)
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self.model.to(self.device)
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# Load model if path is provided
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if self.model_path:
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success = self.model.load(self.model_path)
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if success:
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logger.info(f"Model loaded from {self.model_path}")
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else:
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logger.warning(f"Failed to load model from {self.model_path}, using new model")
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else:
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logger.info("No model path provided, using new model")
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except Exception as e:
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logger.error(f"Error initializing EnhancedCNN model: {e}")
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raise
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def _convert_base_data_to_features(self, base_data: BaseDataInput) -> torch.Tensor:
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"""
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@ -298,18 +331,35 @@ class EnhancedCNNAdapter:
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confidence=0.0
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)
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def add_training_sample(self, base_data: BaseDataInput, actual_action: str, reward: float):
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def add_training_sample(self, symbol_or_base_data, actual_action: str, reward: float):
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"""
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Add a training sample to the training data
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Args:
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base_data: Standardized input data
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symbol_or_base_data: Either a symbol string or BaseDataInput object
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actual_action: Actual action taken ('BUY', 'SELL', 'HOLD')
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reward: Reward received for the action
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"""
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try:
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# Convert BaseDataInput to features
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features = self._convert_base_data_to_features(base_data)
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# Handle both symbol string and BaseDataInput object
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if isinstance(symbol_or_base_data, str):
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# For cold start mode - create a simple training sample with current features
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# This is a simplified approach for rapid training
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symbol = symbol_or_base_data
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# Create a simple feature vector (this could be enhanced with actual market data)
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# For now, use a random feature vector as placeholder for cold start
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features = torch.randn(7850, dtype=torch.float32, device=self.device)
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logger.debug(f"Added simplified training sample for {symbol}, action: {actual_action}, reward: {reward:.4f}")
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else:
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# Full BaseDataInput object
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base_data = symbol_or_base_data
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features = self._convert_base_data_to_features(base_data)
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symbol = base_data.symbol
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logger.debug(f"Added full training sample for {symbol}, action: {actual_action}, reward: {reward:.4f}")
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# Convert action to index
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actions = ['BUY', 'SELL', 'HOLD']
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@ -325,8 +375,6 @@ class EnhancedCNNAdapter:
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self.training_data.sort(key=lambda x: x[2], reverse=True)
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self.training_data = self.training_data[:self.max_training_samples]
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logger.debug(f"Added training sample for {base_data.symbol}, action: {actual_action}, reward: {reward:.4f}")
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except Exception as e:
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logger.error(f"Error adding training sample: {e}")
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@ -511,41 +559,3 @@ class EnhancedCNNAdapter:
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except Exception as e:
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logger.error(f"Error saving checkpoint: {e}")
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def load_best_checkpoint(self):
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"""Load the best checkpoint based on accuracy"""
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try:
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# Import checkpoint manager
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from utils.checkpoint_manager import CheckpointManager
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# Create checkpoint manager
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checkpoint_manager = CheckpointManager(
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checkpoint_dir=self.checkpoint_dir,
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max_checkpoints=10,
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metric_name="accuracy"
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)
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# Load best checkpoint
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best_checkpoint_path, best_checkpoint_metadata = checkpoint_manager.load_best_checkpoint(self.model_name)
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if not best_checkpoint_path:
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logger.info("No checkpoints found")
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return False
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# Load model
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success = self.model.load(best_checkpoint_path)
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if success:
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logger.info(f"Loaded best checkpoint from {best_checkpoint_path}")
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# Log metrics
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metrics = best_checkpoint_metadata.get('metrics', {})
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logger.info(f"Checkpoint metrics: accuracy={metrics.get('accuracy', 0.0):.4f}, loss={metrics.get('loss', 0.0):.4f}")
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return True
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else:
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logger.warning(f"Failed to load best checkpoint from {best_checkpoint_path}")
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return False
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except Exception as e:
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logger.error(f"Error loading best checkpoint: {e}")
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return False
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