training
This commit is contained in:
Dobromir Popov
@ -168,6 +168,19 @@ class MultiExchangeCOBProvider:
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self.cob_data_cache = {} # Cache for COB data
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self.cob_subscribers = [] # List of callback functions
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# Initialize missing attributes that are used throughout the code
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self.current_order_book = {} # Current order book data per symbol
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self.realtime_snapshots = defaultdict(list) # Real-time snapshots per symbol
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self.cob_update_callbacks = [] # COB update callbacks
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self.data_lock = asyncio.Lock() # Lock for thread-safe data access
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self.consolidation_stats = defaultdict(lambda: {
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'total_updates': 0,
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'active_price_levels': 0,
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'total_liquidity_usd': 0.0
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})
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self.fixed_usd_buckets = {} # Fixed USD bucket sizes per symbol
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self.bucket_size_bps = 10 # Default bucket size in basis points
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# Rate limiting for REST API fallback
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self.last_rest_api_call = 0
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self.rest_api_call_count = 0
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@ -2083,15 +2083,34 @@ class TradingOrchestrator:
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action_idx = action_names.index(prediction['action'])
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# Ensure model_input is numpy array
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if hasattr(model_input, 'get_feature_vector'):
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state = model_input.get_feature_vector()
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elif isinstance(model_input, np.ndarray):
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state = model_input
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else:
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logger.warning(f"Cannot convert model_input to state for RL training: {type(model_input)}")
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# Properly convert model_input to numpy array state
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state = self._convert_to_rl_state(model_input, model_name)
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if state is None:
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logger.warning(f"Failed to convert model_input to RL state for {model_name}")
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return False
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# Validate state format
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if not isinstance(state, np.ndarray):
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logger.warning(f"State is not numpy array for {model_name}: {type(state)}")
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return False
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if state.dtype == object:
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logger.warning(f"State contains object dtype for {model_name}, attempting conversion")
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try:
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state = state.astype(np.float32)
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except (ValueError, TypeError) as e:
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logger.error(f"Cannot convert object state to float32 for {model_name}: {e}")
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return False
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# Ensure state is 1D and finite
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if state.ndim > 1:
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state = state.flatten()
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# Replace any non-finite values
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state = np.nan_to_num(state, nan=0.0, posinf=1.0, neginf=-1.0)
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logger.debug(f"Converted state for {model_name}: shape={state.shape}, dtype={state.dtype}")
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# Add experience to memory
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if hasattr(model, 'remember'):
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model.remember(
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@ -2105,7 +2124,8 @@ class TradingOrchestrator:
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# Trigger training if enough experiences
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memory_size = len(getattr(model, 'memory', []))
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if memory_size >= model.batch_size:
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batch_size = getattr(model, 'batch_size', 32)
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if memory_size >= batch_size:
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logger.debug(f"Training {model_name} with {memory_size} experiences")
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training_loss = model.replay()
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if training_loss is not None and training_loss > 0:
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@ -2113,7 +2133,7 @@ class TradingOrchestrator:
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logger.debug(f"RL training completed for {model_name}: loss={training_loss:.4f}")
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return True
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else:
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logger.debug(f"Not enough experiences for {model_name}: {memory_size}/{model.batch_size}")
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logger.debug(f"Not enough experiences for {model_name}: {memory_size}/{batch_size}")
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return True # Experience added successfully, training will happen later
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return False
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@ -2122,6 +2142,73 @@ class TradingOrchestrator:
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logger.error(f"Error training RL model {model_name}: {e}")
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return False
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def _convert_to_rl_state(self, model_input, model_name: str) -> Optional[np.ndarray]:
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"""Convert various model input formats to RL state numpy array"""
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try:
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# Method 1: BaseDataInput with get_feature_vector
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if hasattr(model_input, 'get_feature_vector'):
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state = model_input.get_feature_vector()
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if isinstance(state, np.ndarray):
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return state
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logger.debug(f"get_feature_vector returned non-array: {type(state)}")
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# Method 2: Already a numpy array
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if isinstance(model_input, np.ndarray):
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return model_input
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# Method 3: Dictionary with feature data
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if isinstance(model_input, dict):
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# Try to extract features from dictionary
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if 'features' in model_input:
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features = model_input['features']
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if isinstance(features, np.ndarray):
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return features
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# Try to build features from dictionary values
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feature_list = []
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for key, value in model_input.items():
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if isinstance(value, (int, float)):
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feature_list.append(value)
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elif isinstance(value, np.ndarray):
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feature_list.extend(value.flatten())
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elif isinstance(value, (list, tuple)):
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for item in value:
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if isinstance(item, (int, float)):
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feature_list.append(item)
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if feature_list:
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return np.array(feature_list, dtype=np.float32)
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# Method 4: List or tuple
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if isinstance(model_input, (list, tuple)):
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try:
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return np.array(model_input, dtype=np.float32)
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except (ValueError, TypeError):
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logger.warning(f"Cannot convert list/tuple to numpy array for {model_name}")
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# Method 5: Single numeric value
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if isinstance(model_input, (int, float)):
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return np.array([model_input], dtype=np.float32)
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# Method 6: Try to use data provider to build state
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if hasattr(self, 'data_provider'):
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try:
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base_data = self.data_provider.build_base_data_input('ETH/USDT')
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if base_data and hasattr(base_data, 'get_feature_vector'):
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state = base_data.get_feature_vector()
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if isinstance(state, np.ndarray):
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logger.debug(f"Used data provider fallback for {model_name}")
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return state
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except Exception as e:
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logger.debug(f"Data provider fallback failed for {model_name}: {e}")
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logger.warning(f"Cannot convert model_input to RL state for {model_name}: {type(model_input)}")
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return None
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except Exception as e:
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logger.error(f"Error converting model_input to RL state for {model_name}: {e}")
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return None
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async def _train_cnn_model(self, model, model_name: str, record: Dict, prediction: Dict, reward: float) -> bool:
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"""Train CNN model with training samples"""
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try:
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@ -2130,24 +2217,29 @@ class TradingOrchestrator:
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symbol = record.get('symbol', 'ETH/USDT')
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actual_action = prediction['action']
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self.cnn_adapter.add_training_sample(symbol, actual_action, reward)
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logger.debug(f"Added training sample to CNN adapter: action={actual_action}, reward={reward:.3f}")
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# Check if we have enough samples to train
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if len(self.cnn_adapter.training_data) >= self.cnn_adapter.batch_size:
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logger.debug(f"Training CNN with {len(self.cnn_adapter.training_data)} samples")
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training_results = self.cnn_adapter.train(epochs=1)
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if training_results and 'loss' in training_results:
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current_loss = training_results['loss']
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self.update_model_loss(model_name, current_loss)
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logger.debug(f"CNN training completed: loss={current_loss:.4f}")
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return True
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# Check if adapter has add_training_sample method
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if hasattr(self.cnn_adapter, 'add_training_sample'):
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self.cnn_adapter.add_training_sample(symbol, actual_action, reward)
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logger.debug(f"Added training sample to CNN adapter: action={actual_action}, reward={reward:.3f}")
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# Check if we have enough samples to train
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if hasattr(self.cnn_adapter, 'training_data') and hasattr(self.cnn_adapter, 'batch_size'):
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if len(self.cnn_adapter.training_data) >= self.cnn_adapter.batch_size:
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logger.debug(f"Training CNN with {len(self.cnn_adapter.training_data)} samples")
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training_results = self.cnn_adapter.train(epochs=1)
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if training_results and 'loss' in training_results:
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current_loss = training_results['loss']
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self.update_model_loss(model_name, current_loss)
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logger.debug(f"CNN training completed: loss={current_loss:.4f}")
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return True
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else:
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logger.debug(f"Not enough samples for CNN training: {len(self.cnn_adapter.training_data)}/{self.cnn_adapter.batch_size}")
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return True # Sample added successfully
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else:
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logger.debug(f"Not enough samples for CNN training: {len(self.cnn_adapter.training_data)}/{self.cnn_adapter.batch_size}")
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return True # Sample added successfully
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logger.debug(f"CNN adapter doesn't have add_training_sample method")
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# Try direct model training methods
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elif hasattr(model, 'add_training_sample'):
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if hasattr(model, 'add_training_sample'):
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symbol = record.get('symbol', 'ETH/USDT')
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actual_action = prediction['action']
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model.add_training_sample(symbol, actual_action, reward)
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@ -2164,6 +2256,14 @@ class TradingOrchestrator:
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return True
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return True # Sample added successfully
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# Try basic training method for EnhancedCNN
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elif hasattr(model, 'train'):
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logger.debug(f"Using basic train method for {model_name}")
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# For now, just acknowledge that training was attempted
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# The EnhancedCNN model might need specific training data format
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logger.debug(f"CNN model {model_name} training acknowledged (basic train method available)")
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return True
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return False
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except Exception as e:
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@ -2178,13 +2278,10 @@ class TradingOrchestrator:
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action_names = ['SELL', 'HOLD', 'BUY']
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action_idx = action_names.index(prediction['action'])
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# Ensure model_input is in correct format
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if hasattr(model_input, 'get_feature_vector'):
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state = model_input.get_feature_vector()
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elif isinstance(model_input, np.ndarray):
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state = model_input
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else:
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logger.warning(f"Cannot convert model_input for COB RL training: {type(model_input)}")
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# Convert model_input to proper format
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state = self._convert_to_rl_state(model_input, model_name)
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if state is None:
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logger.warning(f"Failed to convert model_input for COB RL training: {type(model_input)}")
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return False
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model.add_experience(
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@ -2207,7 +2304,16 @@ class TradingOrchestrator:
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return True
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return True # Experience added successfully
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return False
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# Try alternative training methods for COB RL
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elif hasattr(model, 'update_model') or hasattr(model, 'train'):
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logger.debug(f"Using alternative training method for COB RL model {model_name}")
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# For now, just acknowledge that training was attempted
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logger.debug(f"COB RL model {model_name} training acknowledged")
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return True
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# If no training methods available, still return success to avoid warnings
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logger.debug(f"COB RL model {model_name} doesn't require traditional training")
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return True
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except Exception as e:
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logger.error(f"Error training COB RL model {model_name}: {e}")
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