more models wireup
This commit is contained in:
Dobromir Popov
@ -58,20 +58,23 @@ class TradingDecision:
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class TradingOrchestrator:
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"""
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Main orchestrator that coordinates multiple AI models for trading decisions
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Enhanced Trading Orchestrator with full ML and COB integration
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Coordinates CNN, DQN, and COB models for advanced trading decisions
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Features real-time COB (Change of Bid) integration for market microstructure data
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"""
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def __init__(self, data_provider: DataProvider = None):
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"""Initialize the orchestrator with COB integration"""
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def __init__(self, data_provider: DataProvider = None, enhanced_rl_training: bool = True, model_registry: Dict = None):
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"""Initialize the enhanced orchestrator with full ML capabilities"""
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self.config = get_config()
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self.data_provider = data_provider or DataProvider()
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self.model_registry = get_model_registry()
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self.model_registry = model_registry or get_model_registry()
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self.enhanced_rl_training = enhanced_rl_training
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# Configuration
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self.confidence_threshold = self.config.orchestrator.get('confidence_threshold', 0.5)
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self.decision_frequency = self.config.orchestrator.get('decision_frequency', 60)
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self.symbols = self.config.get('symbols', ['ETH/USDT']) # Default symbols to trade
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self.confidence_threshold = self.config.orchestrator.get('confidence_threshold', 0.20)
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self.confidence_threshold_close = self.config.orchestrator.get('confidence_threshold_close', 0.10)
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self.decision_frequency = self.config.orchestrator.get('decision_frequency', 30)
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self.symbols = self.config.get('symbols', ['ETH/USDT', 'BTC/USDT']) # Enhanced to support multiple symbols
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# Dynamic weights (will be adapted based on performance)
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self.model_weights = {} # {model_name: weight}
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@ -92,22 +95,85 @@ class TradingOrchestrator:
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self.latest_cob_state: Dict[str, Any] = {} # {symbol: np.ndarray} - DQN state features
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self.cob_feature_history: Dict[str, List] = {symbol: [] for symbol in self.symbols} # Rolling history for models
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logger.info("TradingOrchestrator initialized with modular model system")
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# Enhanced ML Models
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self.rl_agent = None # DQN Agent
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self.cnn_model = None # CNN Model for pattern recognition
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self.extrema_trainer = None # Extrema/pivot trainer
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self.latest_cnn_features: Dict[str, Any] = {} # CNN hidden features
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self.latest_cnn_predictions: Dict[str, Any] = {} # CNN predictions
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# Enhanced RL features
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self.sensitivity_learning_queue = [] # For outcome-based learning
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self.perfect_move_buffer = [] # Buffer for perfect move analysis
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self.position_status = {} # Current positions
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# Real-time processing
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self.realtime_processing = False
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self.realtime_tasks = []
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logger.info("Enhanced TradingOrchestrator initialized with full ML capabilities")
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logger.info(f"Enhanced RL training: {enhanced_rl_training}")
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logger.info(f"Confidence threshold: {self.confidence_threshold}")
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logger.info(f"Decision frequency: {self.decision_frequency}s")
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logger.info(f"Symbols: {self.symbols}")
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# Initialize COB integration
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# Initialize models and COB integration
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self._initialize_ml_models()
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self._initialize_cob_integration()
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def _initialize_ml_models(self):
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"""Initialize ML models for enhanced trading"""
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try:
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logger.info("Initializing ML models...")
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# Initialize DQN Agent
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try:
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from NN.models.dqn_agent import DQNAgent
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state_size = self.config.rl.get('state_size', 13800) # Enhanced with COB features
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action_size = self.config.rl.get('action_space', 3)
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self.rl_agent = DQNAgent(state_size=state_size, action_size=action_size)
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logger.info(f"DQN Agent initialized: {state_size} state features, {action_size} actions")
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except ImportError:
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logger.warning("DQN Agent not available")
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self.rl_agent = None
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# Initialize CNN Model
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try:
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from NN.models.enhanced_cnn import EnhancedCNN
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self.cnn_model = EnhancedCNN()
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logger.info("Enhanced CNN model initialized")
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except ImportError:
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try:
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from NN.models.cnn_model import CNNModel
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self.cnn_model = CNNModel()
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logger.info("Basic CNN model initialized")
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except ImportError:
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logger.warning("CNN model not available")
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self.cnn_model = None
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# Initialize Extrema Trainer
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try:
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from core.extrema_trainer import ExtremaTrainer
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self.extrema_trainer = ExtremaTrainer(
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data_provider=self.data_provider,
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symbols=self.symbols
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)
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logger.info("Extrema trainer initialized")
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except ImportError:
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logger.warning("Extrema trainer not available")
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self.extrema_trainer = None
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logger.info("ML models initialization completed")
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except Exception as e:
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logger.error(f"Error initializing ML models: {e}")
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def _initialize_cob_integration(self):
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"""Initialize real-time COB integration for market microstructure data"""
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try:
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if COB_INTEGRATION_AVAILABLE:
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# Initialize COB integration with our symbols
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self.cob_integration = COBIntegration(
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data_provider=self.data_provider,
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symbols=self.symbols
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)
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self.cob_integration = COBIntegration(data_provider=self.data_provider, symbols=self.symbols )
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# Register callbacks to receive real-time COB data
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self.cob_integration.add_cnn_callback(self._on_cob_cnn_features)
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@ -116,9 +182,8 @@ class TradingOrchestrator:
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logger.info("COB Integration initialized - real-time market microstructure data available")
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logger.info(f"COB symbols: {self.symbols}")
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# Start COB integration in background
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asyncio.create_task(self._start_cob_integration())
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# COB integration will be started manually via start_cob_integration()
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else:
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logger.warning("COB Integration not available - models will use basic price data only")
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@ -1177,4 +1242,165 @@ class TradingOrchestrator:
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except Exception as e:
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logger.warning(f"Error generating fallback prediction for {symbol}: {e}")
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return None
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# Enhanced Orchestrator Methods
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async def start_cob_integration(self):
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"""Start COB integration manually"""
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try:
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if self.cob_integration:
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await self._start_cob_integration()
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logger.info("COB Integration started successfully")
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else:
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logger.warning("COB Integration not available")
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except Exception as e:
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logger.error(f"Error starting COB integration: {e}")
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async def stop_cob_integration(self):
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"""Stop COB integration"""
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try:
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if self.cob_integration:
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await self.cob_integration.stop()
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logger.info("COB Integration stopped")
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except Exception as e:
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logger.error(f"Error stopping COB integration: {e}")
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async def start_realtime_processing(self):
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"""Start real-time processing"""
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try:
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self.realtime_processing = True
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logger.info("Real-time processing started")
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# Start background tasks for real-time processing
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for symbol in self.symbols:
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task = asyncio.create_task(self._realtime_processing_loop(symbol))
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self.realtime_tasks.append(task)
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except Exception as e:
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logger.error(f"Error starting real-time processing: {e}")
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async def stop_realtime_processing(self):
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"""Stop real-time processing"""
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try:
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self.realtime_processing = False
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# Cancel all background tasks
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for task in self.realtime_tasks:
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task.cancel()
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self.realtime_tasks = []
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logger.info("Real-time processing stopped")
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except Exception as e:
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logger.error(f"Error stopping real-time processing: {e}")
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async def _realtime_processing_loop(self, symbol: str):
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"""Real-time processing loop for a symbol"""
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while self.realtime_processing:
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try:
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# Update CNN features
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await self._update_cnn_features(symbol)
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# Update RL state
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await self._update_rl_state(symbol)
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# Sleep between updates
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await asyncio.sleep(1)
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except asyncio.CancelledError:
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break
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except Exception as e:
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logger.warning(f"Error in real-time processing for {symbol}: {e}")
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await asyncio.sleep(5)
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async def _update_cnn_features(self, symbol: str):
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"""Update CNN features for a symbol"""
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try:
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if self.cnn_model and hasattr(self.cnn_model, 'extract_features'):
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# Get current market data
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df = self.data_provider.get_historical_data(symbol, '1m', limit=100)
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if df is not None and not df.empty:
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# Generate CNN features
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features = self.cnn_model.extract_features(df)
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if features is not None:
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self.latest_cnn_features[symbol] = features
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# Generate CNN predictions
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if hasattr(self.cnn_model, 'predict'):
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predictions = self.cnn_model.predict(df)
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if predictions is not None:
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self.latest_cnn_predictions[symbol] = predictions
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except Exception as e:
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logger.debug(f"Error updating CNN features for {symbol}: {e}")
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async def _update_rl_state(self, symbol: str):
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"""Update RL state for a symbol"""
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try:
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if self.rl_agent:
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# Build comprehensive RL state
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rl_state = self.build_comprehensive_rl_state(symbol)
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if rl_state and hasattr(self.rl_agent, 'remember'):
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# Store for training
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pass
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except Exception as e:
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logger.debug(f"Error updating RL state for {symbol}: {e}")
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async def make_coordinated_decisions(self) -> Dict[str, Any]:
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"""Make coordinated trading decisions for all symbols"""
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decisions = {}
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try:
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for symbol in self.symbols:
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decision = await self.make_trading_decision(symbol)
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decisions[symbol] = decision
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return decisions
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except Exception as e:
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logger.error(f"Error making coordinated decisions: {e}")
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return {}
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def get_position_status(self) -> Dict[str, Any]:
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"""Get current position status"""
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return self.position_status.copy()
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def cleanup_all_models(self):
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"""Cleanup all models"""
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try:
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if hasattr(self.model_registry, 'cleanup_all_models'):
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self.model_registry.cleanup_all_models()
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else:
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logger.debug("Model registry cleanup not available")
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except Exception as e:
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logger.error(f"Error cleaning up models: {e}")
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def _get_cnn_hidden_features_for_rl_enhanced(self, symbol: str) -> Optional[List[float]]:
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"""Get CNN hidden features for RL (enhanced version)"""
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try:
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cnn_features = self.latest_cnn_features.get(symbol)
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if cnn_features is not None:
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if hasattr(cnn_features, 'tolist'):
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return cnn_features.tolist()[:1000] # First 1000 features
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elif isinstance(cnn_features, list):
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return cnn_features[:1000]
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return None
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except Exception as e:
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logger.debug(f"Error getting CNN hidden features: {e}")
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return None
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def _get_pivot_analysis_features_for_rl_enhanced(self, symbol: str) -> Optional[List[float]]:
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"""Get pivot analysis features for RL (enhanced version)"""
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try:
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if self.extrema_trainer and hasattr(self.extrema_trainer, 'get_context_features_for_model'):
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pivot_features = self.extrema_trainer.get_context_features_for_model(symbol)
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if pivot_features is not None:
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if hasattr(pivot_features, 'tolist'):
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return pivot_features.tolist()[:300] # First 300 features
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elif isinstance(pivot_features, list):
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return pivot_features[:300]
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return None
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except Exception as e:
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logger.debug(f"Error getting pivot analysis features: {e}")
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return None
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