fix Pnl, cob
This commit is contained in:
Dobromir Popov
@ -7,6 +7,7 @@ This is the core orchestrator that:
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3. Makes final trading decisions (BUY/SELL/HOLD)
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4. Manages the learning loop between components
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5. Ensures memory efficiency (8GB constraint)
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6. Provides real-time COB (Change of Bid) data for models
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"""
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import asyncio
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@ -21,6 +22,16 @@ from .config import get_config
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from .data_provider import DataProvider
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from models import get_model_registry, ModelInterface, CNNModelInterface, RLAgentInterface
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# Import COB integration for real-time market microstructure data
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try:
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from .cob_integration import COBIntegration
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from .multi_exchange_cob_provider import COBSnapshot
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COB_INTEGRATION_AVAILABLE = True
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except ImportError:
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COB_INTEGRATION_AVAILABLE = False
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COBIntegration = None
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COBSnapshot = None
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logger = logging.getLogger(__name__)
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@dataclass
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@ -48,10 +59,11 @@ 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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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"""
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"""Initialize the orchestrator with COB integration"""
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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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@ -59,6 +71,7 @@ class TradingOrchestrator:
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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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# Dynamic weights (will be adapted based on performance)
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self.model_weights = {} # {model_name: weight}
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@ -72,9 +85,153 @@ class TradingOrchestrator:
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# Decision callbacks
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self.decision_callbacks = []
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# COB Integration - Real-time market microstructure data
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self.cob_integration = None
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self.latest_cob_data: Dict[str, Any] = {} # {symbol: COBSnapshot}
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self.latest_cob_features: Dict[str, Any] = {} # {symbol: np.ndarray} - CNN features
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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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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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# Initialize COB integration
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self._initialize_cob_integration()
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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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# 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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self.cob_integration.add_dqn_callback(self._on_cob_dqn_features)
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self.cob_integration.add_dashboard_callback(self._on_cob_dashboard_data)
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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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else:
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logger.warning("COB Integration not available - models will use basic price data only")
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except Exception as e:
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logger.error(f"Error initializing COB integration: {e}")
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self.cob_integration = None
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async def _start_cob_integration(self):
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"""Start COB integration in background"""
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try:
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if self.cob_integration:
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await self.cob_integration.start()
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logger.info("COB Integration started - real-time order book data streaming")
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except Exception as e:
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logger.error(f"Error starting COB integration: {e}")
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self.cob_integration = None
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def _on_cob_cnn_features(self, symbol: str, cob_data: Dict):
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"""Handle CNN features from COB integration"""
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try:
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if 'features' in cob_data:
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self.latest_cob_features[symbol] = cob_data['features']
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# Add to rolling history for CNN models (keep last 100 updates)
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self.cob_feature_history[symbol].append({
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'timestamp': cob_data.get('timestamp', datetime.now()),
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'features': cob_data['features'],
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'type': 'cnn'
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})
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# Keep rolling window
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if len(self.cob_feature_history[symbol]) > 100:
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self.cob_feature_history[symbol] = self.cob_feature_history[symbol][-100:]
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logger.debug(f"COB CNN features updated for {symbol}: {len(cob_data['features'])} features")
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except Exception as e:
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logger.warning(f"Error processing COB CNN features for {symbol}: {e}")
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def _on_cob_dqn_features(self, symbol: str, cob_data: Dict):
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"""Handle DQN state features from COB integration"""
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try:
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if 'state' in cob_data:
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self.latest_cob_state[symbol] = cob_data['state']
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# Add to rolling history for DQN models (keep last 50 updates)
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self.cob_feature_history[symbol].append({
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'timestamp': cob_data.get('timestamp', datetime.now()),
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'state': cob_data['state'],
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'type': 'dqn'
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})
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logger.debug(f"COB DQN state updated for {symbol}: {len(cob_data['state'])} state features")
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except Exception as e:
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logger.warning(f"Error processing COB DQN features for {symbol}: {e}")
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def _on_cob_dashboard_data(self, symbol: str, cob_data: Dict):
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"""Handle dashboard data from COB integration"""
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try:
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# Store raw COB snapshot for dashboard display
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if self.cob_integration:
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cob_snapshot = self.cob_integration.get_cob_snapshot(symbol)
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if cob_snapshot:
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self.latest_cob_data[symbol] = cob_snapshot
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logger.debug(f"COB dashboard data updated for {symbol}")
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except Exception as e:
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logger.warning(f"Error processing COB dashboard data for {symbol}: {e}")
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# COB Data Access Methods for Models
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def get_cob_features(self, symbol: str) -> Optional[np.ndarray]:
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"""Get latest COB CNN features for a symbol"""
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return self.latest_cob_features.get(symbol)
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def get_cob_state(self, symbol: str) -> Optional[np.ndarray]:
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"""Get latest COB DQN state features for a symbol"""
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return self.latest_cob_state.get(symbol)
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def get_cob_snapshot(self, symbol: str) -> Optional[COBSnapshot]:
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"""Get latest COB snapshot for a symbol"""
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return self.latest_cob_data.get(symbol)
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def get_cob_statistics(self, symbol: str) -> Optional[Dict]:
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"""Get COB statistics for a symbol"""
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try:
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if self.cob_integration:
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return self.cob_integration.get_realtime_stats_for_nn(symbol)
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return None
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except Exception as e:
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logger.warning(f"Error getting COB statistics for {symbol}: {e}")
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return None
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def get_market_depth_analysis(self, symbol: str) -> Optional[Dict]:
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"""Get detailed market depth analysis from COB"""
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try:
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if self.cob_integration:
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return self.cob_integration.get_market_depth_analysis(symbol)
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return None
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except Exception as e:
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logger.warning(f"Error getting market depth analysis for {symbol}: {e}")
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return None
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def get_price_buckets(self, symbol: str) -> Optional[Dict]:
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"""Get fine-grain price buckets from COB"""
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try:
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if self.cob_integration:
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return self.cob_integration.get_price_buckets(symbol)
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return None
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except Exception as e:
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logger.warning(f"Error getting price buckets for {symbol}: {e}")
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return None
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def _initialize_default_weights(self):
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"""Initialize default model weights from config"""
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@ -160,8 +317,14 @@ class TradingOrchestrator:
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predictions = await self._get_all_predictions(symbol)
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if not predictions:
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logger.debug(f"No predictions available for {symbol}")
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return None
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# FALLBACK: Generate basic momentum signal when no models are available
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logger.debug(f"No model predictions available for {symbol}, generating fallback signal")
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fallback_prediction = await self._generate_fallback_prediction(symbol, current_price)
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if fallback_prediction:
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predictions = [fallback_prediction]
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else:
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logger.debug(f"No fallback prediction available for {symbol}")
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return None
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# Combine predictions
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decision = self._combine_predictions(
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@ -407,7 +570,10 @@ class TradingOrchestrator:
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reasoning['threshold_applied'] = True
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# Get memory usage stats
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memory_usage = self.model_registry.get_memory_stats()
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try:
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memory_usage = self.model_registry.get_memory_stats() if hasattr(self.model_registry, 'get_memory_stats') else {}
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except Exception:
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memory_usage = {}
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# Create final decision
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decision = TradingDecision(
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@ -417,11 +583,12 @@ class TradingOrchestrator:
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price=price,
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timestamp=timestamp,
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reasoning=reasoning,
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memory_usage=memory_usage['models']
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memory_usage=memory_usage.get('models', {}) if memory_usage else {}
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)
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logger.info(f"Decision for {symbol}: {best_action} (confidence: {best_confidence:.3f})")
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logger.debug(f"Memory usage: {memory_usage['total_used_mb']:.1f}MB / {memory_usage['total_limit_mb']:.1f}MB")
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if memory_usage and 'total_used_mb' in memory_usage:
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logger.debug(f"Memory usage: {memory_usage['total_used_mb']:.1f}MB / {memory_usage['total_limit_mb']:.1f}MB")
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return decision
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@ -633,6 +800,23 @@ class TradingOrchestrator:
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logger.warning(f"Pivot features fallback: {e}")
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comprehensive_features.extend([0.0] * 300)
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# === REAL-TIME COB FEATURES (400) ===
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try:
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cob_features = self._get_cob_features_for_rl(symbol)
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if cob_features and len(cob_features) >= 400:
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comprehensive_features.extend(cob_features[:400])
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else:
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# Mock COB features when real COB not available
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current_price = self._get_current_price(symbol) or 3500.0
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for i in range(400):
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# Simulate order book features
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comprehensive_features.append(current_price * (0.95 + (i % 100) * 0.001))
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logger.debug("Real-time COB features: 400 added")
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except Exception as e:
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logger.warning(f"COB features fallback: {e}")
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comprehensive_features.extend([0.0] * 400)
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# === MARKET MICROSTRUCTURE (100) ===
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try:
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microstructure_features = self._get_microstructure_features_for_rl(symbol)
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@ -648,15 +832,17 @@ class TradingOrchestrator:
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logger.warning(f"Microstructure features fallback: {e}")
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comprehensive_features.extend([0.0] * 100)
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# Final validation
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# Final validation - now includes COB features (13,400 + 400 = 13,800)
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total_features = len(comprehensive_features)
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if total_features >= 13000:
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logger.info(f"TRAINING: Comprehensive RL state built successfully: {total_features} features")
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expected_features = 13800 # Updated to include 400 COB features
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if total_features >= expected_features - 100: # Allow small tolerance
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logger.info(f"TRAINING: Comprehensive RL state built successfully: {total_features} features (including COB)")
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return comprehensive_features
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else:
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logger.warning(f"⚠️ Comprehensive RL state incomplete: {total_features} features (expected 13,400+)")
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logger.warning(f"⚠️ Comprehensive RL state incomplete: {total_features} features (expected {expected_features}+)")
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# Pad to minimum required
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while len(comprehensive_features) < 13400:
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while len(comprehensive_features) < expected_features:
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comprehensive_features.append(0.0)
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return comprehensive_features
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@ -853,6 +1039,68 @@ class TradingOrchestrator:
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logger.warning(f"Error getting pivot features: {e}")
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return None
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def _get_cob_features_for_rl(self, symbol: str) -> Optional[list]:
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"""Get real-time COB (Change of Bid) features for RL training"""
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try:
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if not self.cob_integration:
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return None
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# Get COB state features (DQN format)
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cob_state = self.get_cob_state(symbol)
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if cob_state is not None:
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# Convert numpy array to list if needed
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if hasattr(cob_state, 'tolist'):
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return cob_state.tolist()
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elif isinstance(cob_state, list):
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return cob_state
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else:
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return [float(cob_state)] if not hasattr(cob_state, '__iter__') else list(cob_state)
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# Fallback: Get COB statistics as features
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cob_stats = self.get_cob_statistics(symbol)
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if cob_stats:
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features = []
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# Current market state
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current = cob_stats.get('current', {})
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features.extend([
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current.get('mid_price', 0.0) / 100000, # Normalized price
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current.get('spread_bps', 0.0) / 100,
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current.get('bid_liquidity', 0.0) / 1000000,
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current.get('ask_liquidity', 0.0) / 1000000,
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current.get('imbalance', 0.0)
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])
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# 1s window statistics
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window_1s = cob_stats.get('1s_window', {})
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features.extend([
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window_1s.get('price_volatility', 0.0),
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window_1s.get('volume_rate', 0.0) / 1000,
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window_1s.get('trade_count', 0.0) / 100,
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window_1s.get('aggressor_ratio', 0.5)
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])
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# 5s window statistics
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window_5s = cob_stats.get('5s_window', {})
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features.extend([
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window_5s.get('price_volatility', 0.0),
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window_5s.get('volume_rate', 0.0) / 1000,
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window_5s.get('trade_count', 0.0) / 100,
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window_5s.get('aggressor_ratio', 0.5)
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])
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# Pad to ensure consistent feature count
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while len(features) < 400:
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features.append(0.0)
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return features[:400] # Return exactly 400 COB features
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return None
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except Exception as e:
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logger.debug(f"Error getting COB features for RL: {e}")
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return None
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def _get_microstructure_features_for_rl(self, symbol: str) -> Optional[list]:
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"""Get market microstructure features"""
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try:
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@ -877,4 +1125,56 @@ class TradingOrchestrator:
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return None
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except Exception as e:
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logger.debug(f"Error getting current price for {symbol}: {e}")
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return None
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async def _generate_fallback_prediction(self, symbol: str, current_price: float) -> Optional[Prediction]:
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"""Generate basic momentum-based prediction when no models are available"""
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try:
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# Get recent price data for momentum calculation
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df = self.data_provider.get_historical_data(symbol, '1m', limit=10)
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if df is None or len(df) < 5:
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return None
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prices = df['close'].values
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# Calculate simple momentum indicators
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short_momentum = (prices[-1] - prices[-3]) / prices[-3] # 3-period momentum
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medium_momentum = (prices[-1] - prices[-5]) / prices[-5] # 5-period momentum
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# Simple decision logic
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import random
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signal_prob = random.random()
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if short_momentum > 0.002 and medium_momentum > 0.001:
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action = 'BUY'
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confidence = min(0.8, 0.4 + abs(short_momentum) * 100)
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elif short_momentum < -0.002 and medium_momentum < -0.001:
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action = 'SELL'
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confidence = min(0.8, 0.4 + abs(short_momentum) * 100)
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elif signal_prob > 0.9: # Occasional random signals for activity
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action = 'BUY' if signal_prob > 0.95 else 'SELL'
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confidence = 0.3
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else:
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action = 'HOLD'
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confidence = 0.1
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# Create prediction
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prediction = Prediction(
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action=action,
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confidence=confidence,
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probabilities={action: confidence, 'HOLD': 1.0 - confidence},
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timeframe='1m',
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timestamp=datetime.now(),
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model_name='FallbackMomentum',
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metadata={
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'short_momentum': short_momentum,
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'medium_momentum': medium_momentum,
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'is_fallback': True
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}
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)
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return prediction
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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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