integration of (legacy) training systems, initialize, train, show on the UI
This commit is contained in:
Dobromir Popov
@ -8,6 +8,7 @@ This is the core orchestrator that:
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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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7. Integrates EnhancedRealtimeTrainingSystem for continuous learning
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"""
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import asyncio
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@ -35,6 +36,14 @@ except ImportError:
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COBIntegration = None
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COBSnapshot = None
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# Import EnhancedRealtimeTrainingSystem
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try:
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from enhanced_realtime_training import EnhancedRealtimeTrainingSystem
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ENHANCED_TRAINING_AVAILABLE = True
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except ImportError:
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ENHANCED_TRAINING_AVAILABLE = False
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EnhancedRealtimeTrainingSystem = None
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logger = logging.getLogger(__name__)
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@dataclass
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@ -64,6 +73,7 @@ class TradingOrchestrator:
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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) data for market microstructure data
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Includes EnhancedRealtimeTrainingSystem for continuous learning
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"""
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def __init__(self, data_provider: Optional[DataProvider] = None, enhanced_rl_training: bool = True, model_registry: Optional[ModelRegistry] = None):
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@ -141,17 +151,24 @@ class TradingOrchestrator:
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self.realtime_processing: bool = False
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self.realtime_tasks: List[Any] = []
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# ENHANCED: Real-time Training System Integration
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self.enhanced_training_system: Optional[EnhancedRealtimeTrainingSystem] = None
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self.training_enabled: bool = enhanced_rl_training and ENHANCED_TRAINING_AVAILABLE
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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"Real-time training system available: {ENHANCED_TRAINING_AVAILABLE}")
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logger.info(f"Training enabled: {self.training_enabled}")
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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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logger.info("Universal Data Adapter integrated for centralized data flow")
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# Initialize models and COB integration
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# Initialize models, COB integration, and training system
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self._initialize_ml_models()
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self._initialize_cob_integration()
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self._initialize_decision_fusion() # Initialize fusion system
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self._initialize_enhanced_training_system() # Initialize real-time training
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def _initialize_ml_models(self):
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"""Initialize ML models for enhanced trading"""
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@ -2391,7 +2408,7 @@ class TradingOrchestrator:
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# ENHANCED: Decision Fusion Methods - Built into orchestrator (NO SEPARATE FILE NEEDED!)
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def _initialize_decision_fusion(self):
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"""Initialize the decision fusion neural network"""
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"""Initialize the decision fusion neural network for learning model effectiveness"""
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try:
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if not self.decision_fusion_enabled:
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return
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@ -2399,168 +2416,121 @@ class TradingOrchestrator:
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import torch
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import torch.nn as nn
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# Simple decision fusion network
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# Create decision fusion network
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class DecisionFusionNet(nn.Module):
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def __init__(self, input_size=32, hidden_size=64):
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super().__init__()
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self.fusion_layers = nn.Sequential(
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nn.Linear(input_size, hidden_size),
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nn.ReLU(),
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nn.Dropout(0.2),
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nn.Linear(hidden_size, hidden_size // 2),
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nn.ReLU(),
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nn.Linear(hidden_size // 2, 16)
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)
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self.action_head = nn.Linear(16, 3) # BUY, SELL, HOLD
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self.confidence_head = nn.Linear(16, 1)
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self.fc1 = nn.Linear(input_size, hidden_size)
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self.fc2 = nn.Linear(hidden_size, hidden_size)
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self.fc3 = nn.Linear(hidden_size, 3) # BUY, SELL, HOLD
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self.dropout = nn.Dropout(0.2)
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def forward(self, x):
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features = self.fusion_layers(x)
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action_logits = self.action_head(features)
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confidence = torch.sigmoid(self.confidence_head(features))
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return action_logits, confidence.squeeze()
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x = torch.relu(self.fc1(x))
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x = self.dropout(x)
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x = torch.relu(self.fc2(x))
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x = self.dropout(x)
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return torch.softmax(self.fc3(x), dim=1)
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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self.decision_fusion_network = DecisionFusionNet().to(device)
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self.fusion_optimizer = torch.optim.Adam(self.decision_fusion_network.parameters(), lr=0.001)
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self.fusion_device = device
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# Try to load existing checkpoint
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try:
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from utils.checkpoint_manager import load_best_checkpoint
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result = load_best_checkpoint("decision")
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if result:
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file_path, metadata = result
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checkpoint = torch.load(file_path, map_location=device)
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if 'model_state_dict' in checkpoint:
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self.decision_fusion_network.load_state_dict(checkpoint['model_state_dict'])
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self.model_states['decision']['checkpoint_loaded'] = True
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self.model_states['decision']['checkpoint_filename'] = metadata.checkpoint_id
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self.model_states['decision']['current_loss'] = metadata.loss or 0.0089
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self.model_states['decision']['best_loss'] = metadata.loss or 0.0065
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logger.info(f"Decision fusion checkpoint loaded: {metadata.checkpoint_id} (loss={metadata.loss:.4f})")
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except Exception as e:
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logger.debug(f"No decision fusion checkpoint found: {e}")
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logger.info("Decision fusion network initialized in orchestrator - TRAINING ON EVERY SIGNAL!")
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self.decision_fusion_network = DecisionFusionNet()
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logger.info("Decision fusion network initialized")
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except Exception as e:
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logger.error(f"Error initializing decision fusion: {e}")
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logger.warning(f"Decision fusion initialization failed: {e}")
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self.decision_fusion_enabled = False
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def train_fusion_on_every_signal(self, decision: TradingDecision, market_outcome: Dict):
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"""Train the decision fusion network on EVERY signal/action - COMPREHENSIVE TRAINING"""
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def _initialize_enhanced_training_system(self):
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"""Initialize the enhanced real-time training system"""
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try:
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if not self.decision_fusion_enabled or not self.decision_fusion_network:
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if not self.training_enabled:
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logger.info("Enhanced training system disabled")
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return
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symbol = decision.symbol
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if symbol not in self.last_fusion_inputs:
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if not ENHANCED_TRAINING_AVAILABLE:
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logger.warning("EnhancedRealtimeTrainingSystem not available - training disabled")
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self.training_enabled = False
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return
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import torch
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import torch.nn as nn
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# Get the features used for this decision
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fusion_input = self.last_fusion_inputs[symbol]
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features = fusion_input['features'].to(self.fusion_device)
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# Create training target based on outcome
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actual_outcome = market_outcome.get('price_change', 0)
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pnl = market_outcome.get('pnl', 0)
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# Convert decision and outcome to training labels
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action_target = {'BUY': 0, 'SELL': 1, 'HOLD': 2}[decision.action]
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# Enhanced reward based on actual market movement
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if decision.action == 'BUY' and actual_outcome > 0:
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confidence_target = min(0.95, 0.5 + abs(actual_outcome) * 10) # Higher confidence for good predictions
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elif decision.action == 'SELL' and actual_outcome < 0:
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confidence_target = min(0.95, 0.5 + abs(actual_outcome) * 10)
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elif decision.action == 'HOLD':
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confidence_target = 0.5 # Neutral confidence for hold
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else:
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confidence_target = max(0.05, 0.5 - abs(actual_outcome) * 10) # Lower confidence for bad predictions
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# Train the network
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self.decision_fusion_network.train()
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self.fusion_optimizer.zero_grad()
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action_logits, predicted_confidence = self.decision_fusion_network(features)
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# Calculate losses
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action_loss = nn.CrossEntropyLoss()(action_logits, torch.tensor([action_target], device=self.fusion_device))
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confidence_loss = nn.MSELoss()(predicted_confidence, torch.tensor([confidence_target], device=self.fusion_device))
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total_loss = action_loss + confidence_loss
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total_loss.backward()
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self.fusion_optimizer.step()
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# Update model state with REAL loss values
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self.model_states['decision']['current_loss'] = total_loss.item()
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if self.model_states['decision']['best_loss'] is None or total_loss.item() < self.model_states['decision']['best_loss']:
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self.model_states['decision']['best_loss'] = total_loss.item()
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# Store training example
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self.fusion_training_data.append({
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'features': features.cpu().numpy(),
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'action_target': action_target,
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'confidence_target': confidence_target,
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'loss': total_loss.item(),
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'timestamp': datetime.now()
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})
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# Save checkpoint periodically
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if self.fusion_decisions_count % self.fusion_checkpoint_frequency == 0:
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self._save_fusion_checkpoint()
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logger.debug(f"🧠 Fusion training: action_loss={action_loss.item():.4f}, conf_loss={confidence_loss.item():.4f}, total={total_loss.item():.4f}")
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except Exception as e:
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logger.error(f"Error training fusion network: {e}")
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def _save_fusion_checkpoint(self):
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"""Save decision fusion checkpoint with real performance data"""
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try:
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if not self.decision_fusion_network:
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return
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from utils.checkpoint_manager import save_checkpoint
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# Prepare checkpoint data
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checkpoint_data = {
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'model_state_dict': self.decision_fusion_network.state_dict(),
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'optimizer_state_dict': self.fusion_optimizer.state_dict(),
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'fusion_decisions_count': self.fusion_decisions_count,
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'training_history': self.fusion_training_history[-100:], # Last 100 entries
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}
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# Calculate REAL performance metrics from actual training
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recent_losses = [entry['loss'] for entry in self.fusion_training_data[-50:]]
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avg_loss = sum(recent_losses) / len(recent_losses) if recent_losses else self.model_states['decision']['current_loss']
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performance_metrics = {
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'loss': avg_loss,
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'decisions_count': self.fusion_decisions_count,
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'model_parameters': sum(p.numel() for p in self.decision_fusion_network.parameters())
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}
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metadata = save_checkpoint(
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model=checkpoint_data,
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model_name="decision",
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model_type="decision_fusion",
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performance_metrics=performance_metrics,
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training_metadata={'decisions_trained': self.fusion_decisions_count}
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# Initialize the enhanced training system
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self.enhanced_training_system = EnhancedRealtimeTrainingSystem(
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orchestrator=self,
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data_provider=self.data_provider,
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dashboard=None # Will be set by dashboard when available
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)
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if metadata:
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self.model_states['decision']['checkpoint_filename'] = metadata.checkpoint_id
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logger.info(f"🧠 Decision fusion checkpoint saved: {metadata.checkpoint_id} (loss={avg_loss:.4f})")
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logger.info("Enhanced real-time training system initialized")
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logger.info(" - Real-time model training: ENABLED")
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logger.info(" - Comprehensive feature extraction: ENABLED")
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logger.info(" - Enhanced reward calculation: ENABLED")
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logger.info(" - Forward-looking predictions: ENABLED")
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except Exception as e:
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logger.error(f"Error saving fusion checkpoint: {e}")
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logger.error(f"Error initializing enhanced training system: {e}")
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self.training_enabled = False
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self.enhanced_training_system = None
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def start_enhanced_training(self):
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"""Start the enhanced real-time training system"""
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try:
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if not self.training_enabled or not self.enhanced_training_system:
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logger.warning("Enhanced training system not available")
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return False
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self.enhanced_training_system.start_training()
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logger.info("Enhanced real-time training started")
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return True
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except Exception as e:
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logger.error(f"Error starting enhanced training: {e}")
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return False
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def stop_enhanced_training(self):
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"""Stop the enhanced real-time training system"""
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try:
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if self.enhanced_training_system:
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self.enhanced_training_system.stop_training()
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logger.info("Enhanced real-time training stopped")
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return True
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return False
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except Exception as e:
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logger.error(f"Error stopping enhanced training: {e}")
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return False
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def get_enhanced_training_stats(self) -> Dict[str, Any]:
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"""Get enhanced training system statistics"""
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try:
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if not self.enhanced_training_system:
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return {
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'training_enabled': False,
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'system_available': ENHANCED_TRAINING_AVAILABLE,
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'error': 'Training system not initialized'
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}
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stats = self.enhanced_training_system.get_training_statistics()
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stats['training_enabled'] = self.training_enabled
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stats['system_available'] = ENHANCED_TRAINING_AVAILABLE
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return stats
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except Exception as e:
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logger.error(f"Error getting training stats: {e}")
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return {
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'training_enabled': self.training_enabled,
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'system_available': ENHANCED_TRAINING_AVAILABLE,
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'error': str(e)
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}
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def set_training_dashboard(self, dashboard):
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"""Set the dashboard reference for the training system"""
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try:
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if self.enhanced_training_system:
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self.enhanced_training_system.dashboard = dashboard
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logger.info("Dashboard reference set for enhanced training system")
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except Exception as e:
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logger.error(f"Error setting training dashboard: {e}")
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def get_universal_data_stream(self, current_time: datetime = None) -> Optional[UniversalDataStream]:
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"""Get universal data stream for external consumers like dashboard"""
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try:
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