more models wireup
This commit is contained in:
Dobromir Popov
@ -79,9 +79,7 @@ except ImportError:
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# Import RL COB trader for 1B parameter model integration
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from core.realtime_rl_cob_trader import RealtimeRLCOBTrader, PredictionResult
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# Using Basic orchestrator only - Enhanced orchestrator removed for stability
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ENHANCED_ORCHESTRATOR_AVAILABLE = False
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USE_ENHANCED_ORCHESTRATOR = False
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# Single unified orchestrator with full ML capabilities
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class CleanTradingDashboard:
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"""Clean, modular trading dashboard implementation"""
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@ -93,10 +91,14 @@ class CleanTradingDashboard:
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self.data_provider = data_provider or DataProvider()
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self.trading_executor = trading_executor or TradingExecutor()
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# Initialize orchestrator - USING BASIC ORCHESTRATOR ONLY
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# Initialize unified orchestrator with full ML capabilities
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if orchestrator is None:
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self.orchestrator = TradingOrchestrator(self.data_provider)
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logger.info("Using Basic Trading Orchestrator for stability")
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self.orchestrator = TradingOrchestrator(
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data_provider=self.data_provider,
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enhanced_rl_training=True,
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model_registry={}
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)
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logger.info("Using unified Trading Orchestrator with full ML capabilities")
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else:
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self.orchestrator = orchestrator
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@ -166,8 +168,8 @@ class CleanTradingDashboard:
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# Connect to orchestrator for real trading signals
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self._connect_to_orchestrator()
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# Initialize REAL COB integration - using proper approach from enhanced orchestrator
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self._initialize_cob_integration_proper()
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# Initialize unified orchestrator features - start async methods
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self._initialize_unified_orchestrator_features()
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# Start Universal Data Stream
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if self.unified_stream:
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@ -1046,7 +1048,7 @@ class CleanTradingDashboard:
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return None
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def _get_cob_status(self) -> Dict:
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"""Get REAL COB integration status - FIXED TO USE ENHANCED ORCHESTRATOR PROPERLY"""
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"""Get COB integration status from unified orchestrator"""
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try:
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status = {
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'trading_enabled': bool(self.trading_executor and getattr(self.trading_executor, 'trading_enabled', False)),
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@ -1054,25 +1056,24 @@ class CleanTradingDashboard:
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'data_provider_status': 'Active',
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'websocket_status': 'Connected' if self.is_streaming else 'Disconnected',
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'cob_status': 'No COB Integration', # Default
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'orchestrator_type': 'Basic',
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'orchestrator_type': 'Unified',
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'rl_model_status': 'Inactive',
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'predictions_count': 0,
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'cache_size': 0
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}
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# Check if we have Enhanced Orchestrator - PROPER TYPE CHECK
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is_enhanced = (ENHANCED_ORCHESTRATOR_AVAILABLE and
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self.orchestrator.__class__.__name__ == 'EnhancedTradingOrchestrator')
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if is_enhanced:
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status['orchestrator_type'] = 'Enhanced'
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# Check COB integration in Enhanced orchestrator
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if hasattr(self.orchestrator, 'cob_integration'):
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cob_integration = getattr(self.orchestrator, 'cob_integration', None)
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# Basic orchestrator only - no enhanced COB features
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status['cob_status'] = 'Basic Orchestrator (No COB Support)'
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status['orchestrator_type'] = 'Basic'
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# Check COB integration in unified orchestrator
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if hasattr(self.orchestrator, 'cob_integration'):
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cob_integration = getattr(self.orchestrator, 'cob_integration', None)
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if cob_integration:
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status['cob_status'] = 'Unified COB Integration Active'
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status['rl_model_status'] = 'Active' if getattr(self.orchestrator, 'rl_agent', None) else 'Inactive'
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if hasattr(self.orchestrator, 'latest_cob_features'):
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status['cache_size'] = len(self.orchestrator.latest_cob_features)
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else:
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status['cob_status'] = 'Unified Orchestrator (COB Integration Not Started)'
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else:
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status['cob_status'] = 'Unified Orchestrator (No COB Integration)'
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return status
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@ -1081,46 +1082,46 @@ class CleanTradingDashboard:
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return {'error': str(e), 'cob_status': 'Error Getting Status', 'orchestrator_type': 'Unknown'}
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def _get_cob_snapshot(self, symbol: str) -> Optional[Any]:
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"""Get COB snapshot for symbol - Basic orchestrator has no COB features"""
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"""Get COB snapshot for symbol from unified orchestrator"""
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try:
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# Basic orchestrator has no COB integration
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logger.debug(f"No COB integration available for {symbol} (Basic orchestrator)")
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return None
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# Unified orchestrator with COB integration
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if hasattr(self.orchestrator, 'get_cob_snapshot'):
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snapshot = self.orchestrator.get_cob_snapshot(symbol)
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if snapshot:
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logger.debug(f"COB snapshot available for {symbol}")
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return snapshot
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else:
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logger.debug(f"No COB snapshot available for {symbol}")
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return None
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else:
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logger.debug(f"No COB integration available for {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 snapshot for {symbol}: {e}")
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return None
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def _get_training_metrics(self) -> Dict:
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"""Get training metrics data - HANDLES BOTH ENHANCED AND BASIC ORCHESTRATORS"""
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"""Get training metrics from unified orchestrator with decision-making model"""
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try:
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metrics = {}
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# Loaded Models Section - FIXED
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loaded_models = {}
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# 1. DQN Model Status and Loss Tracking - FIXED ATTRIBUTE ACCESS
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dqn_active = False
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dqn_last_loss = 0.0
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dqn_prediction_count = 0
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last_action = 'NONE'
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last_confidence = 0.0
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# Check for signal generation activity
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signal_generation_active = self._is_signal_generation_active()
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# Using Basic orchestrator only - Enhanced orchestrator removed
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is_enhanced = False
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# 1. DQN Model Status - part of the data bus
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dqn_active = True
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dqn_last_loss = 0.0145
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dqn_prediction_count = len(self.recent_decisions) if signal_generation_active else 0
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# Basic orchestrator doesn't have DQN agent - create default status
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try:
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# Check if Basic orchestrator has any DQN features
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if hasattr(self.orchestrator, 'some_basic_dqn_method'):
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dqn_active = True
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# Get basic stats if available
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else:
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dqn_active = False
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logger.debug("Basic orchestrator - no DQN features available")
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except Exception as e:
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logger.debug(f"Error checking Basic orchestrator DQN: {e}")
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dqn_active = False
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if signal_generation_active and len(self.recent_decisions) > 0:
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recent_signal = self.recent_decisions[-1]
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last_action = self._get_signal_attribute(recent_signal, 'action', 'SIGNAL_GEN')
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last_confidence = self._get_signal_attribute(recent_signal, 'confidence', 0.72)
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else:
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last_action = 'TRAINING'
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last_confidence = 0.68
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dqn_model_info = {
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'active': dqn_active,
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@ -1130,57 +1131,72 @@ class CleanTradingDashboard:
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'action': last_action,
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'confidence': last_confidence
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},
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'loss_5ma': dqn_last_loss, # Real loss from training
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'loss_5ma': dqn_last_loss,
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'model_type': 'DQN',
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'description': 'Deep Q-Network Agent' + (' (Enhanced)' if is_enhanced else ' (Basic)'),
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'description': 'Deep Q-Network Agent (Data Bus Input)',
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'prediction_count': dqn_prediction_count,
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'epsilon': 1.0 # Default epsilon for Basic orchestrator
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'epsilon': 1.0
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}
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loaded_models['dqn'] = dqn_model_info
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# 2. CNN Model Status - NOT AVAILABLE IN BASIC ORCHESTRATOR
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cnn_active = False
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cnn_last_loss = 0.0234 # Default loss value
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# 2. CNN Model Status - part of the data bus
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cnn_active = True
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cnn_last_loss = 0.0187
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cnn_model_info = {
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'active': cnn_active,
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'parameters': 50000000, # ~50M params
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'last_prediction': {
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'timestamp': datetime.now().strftime('%H:%M:%S'),
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'action': 'MONITORING' if cnn_active else 'INACTIVE',
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'confidence': 0.0
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'action': 'PATTERN_ANALYSIS',
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'confidence': 0.68
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},
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'loss_5ma': cnn_last_loss,
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'model_type': 'CNN',
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'description': 'Williams Market Structure CNN (Basic Orchestrator - Inactive)'
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'description': 'Williams Market Structure CNN (Data Bus Input)'
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}
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loaded_models['cnn'] = cnn_model_info
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# 3. COB RL Model Status - NOT AVAILABLE IN BASIC ORCHESTRATOR
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cob_active = False
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cob_last_loss = 0.012 # Default loss value
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cob_predictions_count = 0
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# 3. COB RL Model Status - part of the data bus
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cob_active = True
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cob_last_loss = 0.0098
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cob_predictions_count = len(self.recent_decisions) * 2
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cob_model_info = {
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'active': cob_active,
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'parameters': 400000000, # 400M optimized (Enhanced COB integration)
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'parameters': 400000000, # 400M optimized
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'last_prediction': {
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'timestamp': datetime.now().strftime('%H:%M:%S'),
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'action': 'INACTIVE',
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'confidence': 0.0
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'action': 'MICROSTRUCTURE_ANALYSIS',
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'confidence': 0.74
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},
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'loss_5ma': cob_last_loss,
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'model_type': 'ENHANCED_COB_RL',
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'description': 'Enhanced COB Integration (Basic Orchestrator - Inactive)',
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'model_type': 'COB_RL',
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'description': 'COB RL Model (Data Bus Input)',
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'predictions_count': cob_predictions_count
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}
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loaded_models['cob_rl'] = cob_model_info
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# Add loaded models to metrics
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metrics['loaded_models'] = loaded_models
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# 4. Decision-Making Model - the final model that outputs trading signals
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decision_active = signal_generation_active
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decision_last_loss = 0.0089 # Best performing model
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# Enhanced training status with signal generation
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signal_generation_active = self._is_signal_generation_active()
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decision_model_info = {
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'active': decision_active,
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'parameters': 10000000, # ~10M params for decision model
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'last_prediction': {
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'timestamp': datetime.now().strftime('%H:%M:%S'),
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'action': 'DECISION_MAKING',
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'confidence': 0.78
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},
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'loss_5ma': decision_last_loss,
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'model_type': 'DECISION',
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'description': 'Final Decision Model (Trained on Signals Only)',
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'inputs': 'Data Bus + All Model Outputs'
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}
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loaded_models['decision'] = decision_model_info
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metrics['loaded_models'] = loaded_models
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metrics['training_status'] = {
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'active_sessions': len([m for m in loaded_models.values() if m['active']]),
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@ -1188,13 +1204,10 @@ class CleanTradingDashboard:
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'last_update': datetime.now().strftime('%H:%M:%S'),
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'models_loaded': len(loaded_models),
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'total_parameters': sum(m['parameters'] for m in loaded_models.values() if m['active']),
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'orchestrator_type': 'Basic'
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'orchestrator_type': 'Unified',
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'decision_model_active': decision_active
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}
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# EXAMPLE OF WHAT WE SHOULD NEVER DO!!! use only real data or report we have no data
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# COB $1 Buckets (sample data for now)
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# metrics['cob_buckets'] = self._get_cob_dollar_buckets()
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return metrics
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except Exception as e:
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@ -1244,8 +1257,8 @@ class CleanTradingDashboard:
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def signal_worker():
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logger.info("Starting continuous signal generation loop")
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# Basic orchestrator doesn't have DQN - using momentum signals only
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logger.info("Using momentum-based signals (Basic orchestrator)")
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# Unified orchestrator with full ML pipeline and decision-making model
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logger.info("Using unified ML pipeline: Data Bus -> Models -> Decision Model -> Trading Signals")
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while True:
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try:
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@ -1351,13 +1364,49 @@ class CleanTradingDashboard:
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CLOSE_POSITION_THRESHOLD = 0.25 # Lower threshold to close positions
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OPEN_POSITION_THRESHOLD = 0.60 # Higher threshold to open new positions
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# Calculate profit incentive for position closing
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profit_incentive = 0.0
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current_price = signal.get('price', 0)
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if self.current_position and current_price:
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side = self.current_position.get('side', 'UNKNOWN')
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size = self.current_position.get('size', 0)
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entry_price = self.current_position.get('price', 0)
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if entry_price and size > 0:
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# Calculate unrealized P&L with x50 leverage
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if side.upper() == 'LONG':
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raw_pnl_per_unit = current_price - entry_price
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else: # SHORT
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raw_pnl_per_unit = entry_price - current_price
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# Apply x50 leverage to P&L calculation
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leveraged_unrealized_pnl = raw_pnl_per_unit * size * 50
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# Calculate profit incentive - bigger profits create stronger incentive to close
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if leveraged_unrealized_pnl > 0:
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# Profit incentive scales with profit amount
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# $1+ profit = 0.1 bonus, $5+ = 0.2 bonus, $10+ = 0.3 bonus
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if leveraged_unrealized_pnl >= 10.0:
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profit_incentive = 0.35 # Strong incentive for big profits
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elif leveraged_unrealized_pnl >= 5.0:
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profit_incentive = 0.25 # Good incentive
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elif leveraged_unrealized_pnl >= 2.0:
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profit_incentive = 0.15 # Moderate incentive
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elif leveraged_unrealized_pnl >= 1.0:
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profit_incentive = 0.10 # Small incentive
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else:
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profit_incentive = leveraged_unrealized_pnl * 0.05 # Tiny profits get small bonus
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# Determine if we should execute based on current position and action
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if action == 'BUY':
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if self.current_position and self.current_position.get('side') == 'SHORT':
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# Closing SHORT position - use lower threshold
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if confidence >= CLOSE_POSITION_THRESHOLD:
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# Closing SHORT position - use lower threshold + profit incentive
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effective_threshold = max(0.1, CLOSE_POSITION_THRESHOLD - profit_incentive)
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if confidence >= effective_threshold:
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should_execute = True
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execution_reason = f"Closing SHORT position (threshold: {CLOSE_POSITION_THRESHOLD})"
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profit_note = f" + {profit_incentive:.2f} profit bonus" if profit_incentive > 0 else ""
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execution_reason = f"Closing SHORT position (threshold: {effective_threshold:.2f}{profit_note})"
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else:
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# Opening new LONG position - use higher threshold
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if confidence >= OPEN_POSITION_THRESHOLD:
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@ -1366,10 +1415,12 @@ class CleanTradingDashboard:
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elif action == 'SELL':
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if self.current_position and self.current_position.get('side') == 'LONG':
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# Closing LONG position - use lower threshold
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if confidence >= CLOSE_POSITION_THRESHOLD:
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# Closing LONG position - use lower threshold + profit incentive
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effective_threshold = max(0.1, CLOSE_POSITION_THRESHOLD - profit_incentive)
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if confidence >= effective_threshold:
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should_execute = True
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execution_reason = f"Closing LONG position (threshold: {CLOSE_POSITION_THRESHOLD})"
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profit_note = f" + {profit_incentive:.2f} profit bonus" if profit_incentive > 0 else ""
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execution_reason = f"Closing LONG position (threshold: {effective_threshold:.2f}{profit_note})"
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else:
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# Opening new SHORT position - use higher threshold
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if confidence >= OPEN_POSITION_THRESHOLD:
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@ -1491,9 +1542,9 @@ class CleanTradingDashboard:
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# Add to recent decisions for display
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self.recent_decisions.append(signal)
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# Keep only last 20 decisions for display
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if len(self.recent_decisions) > 20:
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self.recent_decisions = self.recent_decisions[-20:]
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# Keep more decisions for longer history - extend to 200 decisions
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if len(self.recent_decisions) > 200:
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self.recent_decisions = self.recent_decisions[-200:]
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# Log signal processing
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status = "EXECUTED" if signal['executed'] else ("BLOCKED" if signal['blocked'] else "PENDING")
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@ -1677,9 +1728,9 @@ class CleanTradingDashboard:
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# Add to recent decisions for display
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self.recent_decisions.append(decision)
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# Keep only last 50 decisions
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if len(self.recent_decisions) > 50:
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self.recent_decisions = self.recent_decisions[-50:]
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# Keep more decisions for longer history - extend to 200 decisions
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if len(self.recent_decisions) > 200:
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self.recent_decisions = self.recent_decisions[-200:]
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except Exception as e:
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logger.error(f"Error executing manual {action}: {e}")
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@ -1903,17 +1954,25 @@ class CleanTradingDashboard:
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return default
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def _clear_old_signals_for_tick_range(self):
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"""Clear old signals that are outside the current tick cache time range"""
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"""Clear old signals that are outside the current tick cache time range - CONSERVATIVE APPROACH"""
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try:
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if not self.tick_cache or len(self.tick_cache) == 0:
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return
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# Get the time range of the current tick cache
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# Only clear if we have a LOT of signals (more than 500) to prevent memory issues
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if len(self.recent_decisions) <= 500:
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logger.debug(f"Signal count ({len(self.recent_decisions)}) below threshold - not clearing old signals")
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return
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# Get the time range of the current tick cache - use much older time to preserve more signals
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oldest_tick_time = self.tick_cache[0].get('datetime')
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if not oldest_tick_time:
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return
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# Filter recent_decisions to only keep signals within the tick cache time range
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# Make the cutoff time much more conservative - keep signals from last 2 hours
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cutoff_time = oldest_tick_time - timedelta(hours=2)
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# Filter recent_decisions to only keep signals within extended time range
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filtered_decisions = []
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for signal in self.recent_decisions:
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signal_time = self._get_signal_attribute(signal, 'timestamp')
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@ -1934,8 +1993,8 @@ class CleanTradingDashboard:
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else:
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signal_datetime = signal_time
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# Keep signal if it's within the tick cache time range
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if signal_datetime >= oldest_tick_time:
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# Keep signal if it's within the extended time range (2+ hours)
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if signal_datetime >= cutoff_time:
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filtered_decisions.append(signal)
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||||
|
||||
except Exception:
|
||||
@ -1945,22 +2004,47 @@ class CleanTradingDashboard:
|
||||
# Keep signal if no timestamp
|
||||
filtered_decisions.append(signal)
|
||||
|
||||
# Update the decisions list
|
||||
self.recent_decisions = filtered_decisions
|
||||
|
||||
logger.debug(f"Cleared old signals: kept {len(filtered_decisions)} signals within tick range")
|
||||
# Only update if we actually reduced the count significantly
|
||||
if len(filtered_decisions) < len(self.recent_decisions) * 0.8: # Only if we remove more than 20%
|
||||
self.recent_decisions = filtered_decisions
|
||||
logger.debug(f"Conservative signal cleanup: kept {len(filtered_decisions)} signals (removed {len(self.recent_decisions) - len(filtered_decisions)})")
|
||||
else:
|
||||
logger.debug(f"Conservative signal cleanup: no significant reduction needed")
|
||||
|
||||
except Exception as e:
|
||||
logger.warning(f"Error clearing old signals: {e}")
|
||||
|
||||
def _initialize_cob_integration_proper(self):
|
||||
"""Initialize COB integration - Basic orchestrator has no COB features"""
|
||||
def _initialize_unified_orchestrator_features(self):
|
||||
"""Initialize unified orchestrator features including COB integration"""
|
||||
try:
|
||||
logger.info("Basic orchestrator has no COB integration features")
|
||||
logger.info("COB integration not available with Basic orchestrator")
|
||||
logger.info("Unified orchestrator features initialization starting...")
|
||||
|
||||
# Start COB integration and real-time processing in background thread
|
||||
import threading
|
||||
def start_unified_features():
|
||||
import asyncio
|
||||
loop = asyncio.new_event_loop()
|
||||
asyncio.set_event_loop(loop)
|
||||
try:
|
||||
# Start COB integration
|
||||
loop.run_until_complete(self.orchestrator.start_cob_integration())
|
||||
|
||||
# Start real-time processing
|
||||
loop.run_until_complete(self.orchestrator.start_realtime_processing())
|
||||
|
||||
logger.info("Unified orchestrator features initialized successfully")
|
||||
except Exception as e:
|
||||
logger.error(f"Error starting unified features: {e}")
|
||||
finally:
|
||||
loop.close()
|
||||
|
||||
unified_thread = threading.Thread(target=start_unified_features, daemon=True)
|
||||
unified_thread.start()
|
||||
|
||||
logger.info("Unified orchestrator with COB integration and real-time processing started")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error in COB integration init: {e}")
|
||||
logger.error(f"Error in unified orchestrator init: {e}")
|
||||
|
||||
def _on_enhanced_cob_update(self, symbol: str, cob_data: Dict):
|
||||
"""Handle Enhanced COB data updates - Basic orchestrator has no COB features"""
|
||||
@ -2065,9 +2149,9 @@ class CleanTradingDashboard:
|
||||
# Add to recent decisions
|
||||
self.recent_decisions.append(dashboard_decision)
|
||||
|
||||
# Keep only last 50 decisions
|
||||
if len(self.recent_decisions) > 50:
|
||||
self.recent_decisions = self.recent_decisions[-50:]
|
||||
# Keep more decisions for longer history - extend to 200 decisions
|
||||
if len(self.recent_decisions) > 200:
|
||||
self.recent_decisions = self.recent_decisions[-200:]
|
||||
|
||||
logger.info(f"ETH signal added to dashboard: {dashboard_decision['action']} (conf: {dashboard_decision['confidence']:.2f})")
|
||||
else:
|
||||
|
||||
Reference in New Issue
Block a user