improve training on signals, add save session button to store all progress

core/orchestrator.py

@@ -16,13 +16,13 @@ import time
 import threading
 import numpy as np
 from datetime import datetime, timedelta
-from typing import Dict, List, Optional, Tuple, Any
+from typing import Dict, List, Optional, Tuple, Any, Union
-from dataclasses import dataclass
+from dataclasses import dataclass, field
 from collections import deque
 
 from .config import get_config
 from .data_provider import DataProvider
-from models import get_model_registry, ModelInterface, CNNModelInterface, RLAgentInterface
+from models import get_model_registry, ModelInterface, CNNModelInterface, RLAgentInterface, ModelRegistry
 
 # Import COB integration for real-time market microstructure data
 try:
@@ -45,7 +45,7 @@ class Prediction:
     timeframe: str  # Timeframe this prediction is for
     timestamp: datetime
     model_name: str  # Name of the model that made this prediction
-    metadata: Dict[str, Any] = None  # Additional model-specific data
+    metadata: Optional[Dict[str, Any]] = None  # Additional model-specific data
 
 @dataclass
 class TradingDecision:
@@ -62,10 +62,10 @@ class TradingOrchestrator:
     """
     Enhanced Trading Orchestrator with full ML and COB integration
     Coordinates CNN, DQN, and COB models for advanced trading decisions
-    Features real-time COB (Change of Bid) integration for market microstructure data
+    Features real-time COB (Change of Bid) data for market microstructure data
     """
     
-    def __init__(self, data_provider: DataProvider = None, enhanced_rl_training: bool = True, model_registry: Dict = None):
+    def __init__(self, data_provider: Optional[DataProvider] = None, enhanced_rl_training: bool = True, model_registry: Optional[ModelRegistry] = None):
         """Initialize the enhanced orchestrator with full ML capabilities"""
         self.config = get_config()
         self.data_provider = data_provider or DataProvider()
@@ -79,18 +79,18 @@ class TradingOrchestrator:
         self.symbols = self.config.get('symbols', ['ETH/USDT', 'BTC/USDT'])  # Enhanced to support multiple symbols
         
         # Dynamic weights (will be adapted based on performance)
-        self.model_weights = {}  # {model_name: weight}
+        self.model_weights: Dict[str, float] = {}  # {model_name: weight}
         self._initialize_default_weights()
         
         # State tracking
-        self.last_decision_time = {}  # {symbol: datetime}
+        self.last_decision_time: Dict[str, datetime] = {}  # {symbol: datetime}
-        self.recent_decisions = {}    # {symbol: List[TradingDecision]}
+        self.recent_decisions: Dict[str, List[TradingDecision]] = {}    # {symbol: List[TradingDecision]}
-        self.model_performance = {}   # {model_name: {'correct': int, 'total': int, 'accuracy': float}}
+        self.model_performance: Dict[str, Dict[str, Any]] = {}   # {model_name: {'correct': int, 'total': int, 'accuracy': float}}
         
         # Model prediction tracking for dashboard visualization
-        self.recent_dqn_predictions = {}  # {symbol: List[Dict]} - Recent DQN predictions
+        self.recent_dqn_predictions: Dict[str, deque] = {}  # {symbol: List[Dict]} - Recent DQN predictions
-        self.recent_cnn_predictions = {}  # {symbol: List[Dict]} - Recent CNN predictions
+        self.recent_cnn_predictions: Dict[str, deque] = {}  # {symbol: List[Dict]} - Recent CNN predictions
-        self.prediction_accuracy_history = {}  # {symbol: List[Dict]} - Prediction accuracy tracking
+        self.prediction_accuracy_history: Dict[str, deque] = {}  # {symbol: List[Dict]} - Prediction accuracy tracking
         
         # Initialize prediction tracking for each symbol
         for symbol in self.symbols:
@@ -99,39 +99,45 @@ class TradingOrchestrator:
             self.prediction_accuracy_history[symbol] = deque(maxlen=200)
         
         # Decision callbacks
-        self.decision_callbacks = []
+        self.decision_callbacks: List[Any] = []
         
         # ENHANCED: Decision Fusion System - Built into orchestrator (no separate file needed!)
-        self.decision_fusion_enabled = True
+        self.decision_fusion_enabled: bool = True
-        self.decision_fusion_network = None
+        self.decision_fusion_network: Any = None
-        self.fusion_training_history = []
+        self.fusion_training_history: List[Any] = []
-        self.last_fusion_inputs = {}
+        self.last_fusion_inputs: Dict[str, Any] = {} # Fix: Explicitly initialize as dictionary
-        self.fusion_checkpoint_frequency = 50  # Save every 50 decisions
+        self.fusion_checkpoint_frequency: int = 50  # Save every 50 decisions
-        self.fusion_decisions_count = 0
+        self.fusion_decisions_count: int = 0
-        self.fusion_training_data = []  # Store training examples for decision model
+        self.fusion_training_data: List[Any] = []  # Store training examples for decision model
         
         # COB Integration - Real-time market microstructure data
-        self.cob_integration = None
+        self.cob_integration: Optional[COBIntegration] = None # Fix: Use Optional for COBIntegration
         self.latest_cob_data: Dict[str, Any] = {}  # {symbol: COBSnapshot}
         self.latest_cob_features: Dict[str, Any] = {}  # {symbol: np.ndarray} - CNN features
         self.latest_cob_state: Dict[str, Any] = {}  # {symbol: np.ndarray} - DQN state features
-        self.cob_feature_history: Dict[str, List] = {symbol: [] for symbol in self.symbols}  # Rolling history for models
+        self.cob_feature_history: Dict[str, List[Any]] = {symbol: [] for symbol in self.symbols}  # Rolling history for models
         
         # Enhanced ML Models
-        self.rl_agent = None  # DQN Agent
+        self.rl_agent: Any = None  # DQN Agent
-        self.cnn_model = None  # CNN Model for pattern recognition
+        self.cnn_model: Any = None  # CNN Model for pattern recognition
-        self.extrema_trainer = None  # Extrema/pivot trainer
+        self.extrema_trainer: Any = None  # Extrema/pivot trainer
+        self.primary_transformer: Any = None # Transformer model
+        self.primary_transformer_trainer: Any = None # Transformer model trainer
+        self.transformer_checkpoint_info: Dict[str, Any] = {} # Transformer checkpoint info
+        self.cob_rl_agent: Any = None # COB RL Agent
+        self.decision_model: Any = None # Decision Fusion model
+
         self.latest_cnn_features: Dict[str, Any] = {}  # CNN hidden features
         self.latest_cnn_predictions: Dict[str, Any] = {}  # CNN predictions
         
         # Enhanced RL features
-        self.sensitivity_learning_queue = []  # For outcome-based learning
+        self.sensitivity_learning_queue: List[Any] = []  # For outcome-based learning
-        self.perfect_move_buffer = []  # Buffer for perfect move analysis
+        self.perfect_move_buffer: List[Any] = []  # Buffer for perfect move analysis
-        self.position_status = {}  # Current positions
+        self.position_status: Dict[str, Any] = {}  # Current positions
         
         # Real-time processing
-        self.realtime_processing = False
+        self.realtime_processing: bool = False
-        self.realtime_tasks = []
+        self.realtime_tasks: List[Any] = []
         
         logger.info("Enhanced TradingOrchestrator initialized with full ML capabilities")
         logger.info(f"Enhanced RL training: {enhanced_rl_training}")
@@ -310,6 +316,7 @@ class TradingOrchestrator:
             self.cob_integration = COBIntegration(symbols=self.symbols)
             
             # Register callbacks to receive real-time COB data
+            if self.cob_integration:
                 self.cob_integration.add_cnn_callback(self._on_cob_cnn_features)
                 self.cob_integration.add_dqn_callback(self._on_cob_dqn_features)
                 self.cob_integration.add_dashboard_callback(self._on_cob_dashboard_data)
@@ -320,9 +327,9 @@ class TradingOrchestrator:
             self.cob_matrix_size = self.cob_matrix_duration // self.cob_matrix_resolution  # 300 samples
             
             # COB data matrix storage - 5 minutes of 1-second snapshots
-            self.cob_data_matrix: Dict[str, deque] = {}
+            self.cob_data_matrix: Dict[str, deque[Any]] = {}
-            self.cob_feature_matrix: Dict[str, deque] = {}
+            self.cob_feature_matrix: Dict[str, deque[Any]] = {}
-            self.cob_state_matrix: Dict[str, deque] = {}
+            self.cob_state_matrix: Dict[str, deque[Any]] = {}
             
             # Initialize matrix storage for each symbol
             for symbol in self.symbols:
@@ -336,16 +343,16 @@ class TradingOrchestrator:
                 self.cob_state_matrix[symbol] = deque(maxlen=self.cob_matrix_size)
             
             # Initialize COB data storage (legacy support)
-            self.latest_cob_snapshots = {}
+            self.latest_cob_snapshots: Dict[str, Any] = {}
-            self.cob_feature_cache = {}
+            self.cob_feature_cache: Dict[str, Any] = {}
-            self.cob_state_cache = {}
+            self.cob_state_cache: Dict[str, Any] = {}
             
             # COB matrix update tracking
-            self.last_cob_matrix_update = {}
+            self.last_cob_matrix_update: Dict[str, float] = {}
             self.cob_matrix_update_interval = 1.0  # Update every 1 second
             
             # COB matrix statistics
-            self.cob_matrix_stats = {
+            self.cob_matrix_stats: Dict[str, Any] = {
                 'total_updates': 0,
                 'matrix_fills': {symbol: 0 for symbol in self.symbols},
                 'feature_generations': 0,
@@ -375,6 +382,7 @@ class TradingOrchestrator:
                         asyncio.set_event_loop(loop)
                         
                         async def cob_main():
+                            if self.cob_integration: # Additional check
                                 await self.cob_integration.start()
                             # Keep running until stopped
                             while True:

web/clean_dashboard.py

@@ -571,6 +571,21 @@ class CleanTradingDashboard:
                 self._clear_session()
             return [html.I(className="fas fa-trash me-1"), "Clear Session"]
         
+        @self.app.callback(
+            Output('store-models-btn', 'children'),
+            [Input('store-models-btn', 'n_clicks')],
+            prevent_initial_call=True
+        )
+        def handle_store_models(n_clicks):
+            """Handle store all models button click"""
+            if n_clicks:
+                success = self._store_all_models()
+                if success:
+                    return [html.I(className="fas fa-save me-1"), "Models Stored"]
+                else:
+                    return [html.I(className="fas fa-exclamation-triangle me-1"), "Store Failed"]
+            return [html.I(className="fas fa-save me-1"), "Store All Models"]
+    
     def _get_current_price(self, symbol: str) -> Optional[float]:
         """Get current price for symbol"""
         try:
@@ -2927,6 +2942,10 @@ class CleanTradingDashboard:
             if len(self.recent_decisions) > 200:
                 self.recent_decisions = self.recent_decisions[-200:]
             
+            # Train ALL models on the signal (if executed)
+            if signal['executed']:
+                self._train_all_models_on_signal(signal)
+            
             # Log signal processing
             status = "EXECUTED" if signal['executed'] else ("BLOCKED" if signal['blocked'] else "PENDING")
             logger.info(f"[{status}] {signal['action']} signal for {signal['symbol']} "
@@ -2935,11 +2954,308 @@ class CleanTradingDashboard:
         except Exception as e:
             logger.error(f"Error processing dashboard signal: {e}")
     
-    def _train_dqn_on_signal(self, signal: Dict):
+    def _train_all_models_on_signal(self, signal: Dict):
-        """Train DQN agent on generated signal - NOT AVAILABLE IN BASIC ORCHESTRATOR"""
+        """Train ALL models on executed trade signal - Comprehensive training system"""
-        # Basic orchestrator doesn't have DQN features
+        try:
+            # Get trade outcome for training
+            trade_outcome = self._get_trade_outcome_for_training(signal)
+            if not trade_outcome:
                 return
             
+            # 1. Train DQN model
+            self._train_dqn_on_signal(signal, trade_outcome)
+            
+            # 2. Train CNN model
+            self._train_cnn_on_signal(signal, trade_outcome)
+            
+            # 3. Train Transformer model
+            self._train_transformer_on_signal(signal, trade_outcome)
+            
+            # 4. Train COB RL model
+            self._train_cob_rl_on_signal(signal, trade_outcome)
+            
+            # 5. Train Decision Fusion model
+            self._train_decision_fusion_on_signal(signal, trade_outcome)
+            
+            logger.debug(f"Trained all models on {signal['action']} signal with outcome: {trade_outcome['pnl']:.2f}")
+            
+        except Exception as e:
+            logger.debug(f"Error training models on signal: {e}")
+    
+    def _get_trade_outcome_for_training(self, signal: Dict) -> Optional[Dict]:
+        """Get trade outcome for training - either from completed trade or position change"""
+        try:
+            # Check if we have a completed trade
+            if self.closed_trades:
+                latest_trade = self.closed_trades[-1]
+                # Verify this trade corresponds to the signal
+                if (latest_trade.get('symbol') == signal.get('symbol') and 
+                    abs(latest_trade.get('entry_time', 0) - signal.get('timestamp', 0)) < 60):  # Within 1 minute
+                    return {
+                        'pnl': latest_trade.get('pnl', 0),
+                        'entry_price': latest_trade.get('entry_price', 0),
+                        'exit_price': latest_trade.get('exit_price', 0),
+                        'side': latest_trade.get('side', 'UNKNOWN'),
+                        'quantity': latest_trade.get('quantity', 0),
+                        'duration': latest_trade.get('exit_time', 0) - latest_trade.get('entry_time', 0),
+                        'trade_type': 'completed'
+                    }
+            
+            # If no completed trade, use position change for training
+            if self.current_position:
+                current_price = self._get_current_price(signal.get('symbol', 'ETH/USDT'))
+                if current_price:
+                    entry_price = self.current_position.get('price', 0)
+                    side = self.current_position.get('side', 'UNKNOWN')
+                    size = self.current_position.get('size', 0)
+                    
+                    if entry_price > 0 and size > 0:
+                        # Calculate unrealized P&L
+                        if side.upper() == 'LONG':
+                            pnl = (current_price - entry_price) * size * self.current_leverage
+                        else:  # SHORT
+                            pnl = (entry_price - current_price) * size * self.current_leverage
+                        
+                        return {
+                            'pnl': pnl,
+                            'entry_price': entry_price,
+                            'current_price': current_price,
+                            'side': side,
+                            'quantity': size,
+                            'duration': 0,  # Position still open
+                            'trade_type': 'position_change'
+                        }
+            
+            return None
+            
+        except Exception as e:
+            logger.debug(f"Error getting trade outcome: {e}")
+            return None
+    
+    def _train_dqn_on_signal(self, signal: Dict, trade_outcome: Dict):
+        """Train DQN agent on executed signal with trade outcome"""
+        try:
+            if not self.orchestrator or not hasattr(self.orchestrator, 'rl_agent') or not self.orchestrator.rl_agent:
+                return
+            
+            # Create training data for DQN
+            state_features = self._get_dqn_state_features(signal.get('symbol', 'ETH/USDT'), signal.get('price', 0))
+            action = 0 if signal['action'] == 'BUY' else 1  # 0=BUY, 1=SELL
+            
+            # Calculate reward based on trade outcome
+            pnl = trade_outcome.get('pnl', 0)
+            reward = pnl * 100  # Scale reward for better learning
+            
+            # Create next state (simplified)
+            next_state_features = state_features.copy()  # In real implementation, this would be the next market state
+            
+            # Store experience in DQN memory
+            if hasattr(self.orchestrator.rl_agent, 'remember'):
+                self.orchestrator.rl_agent.remember(
+                    state_features, action, reward, next_state_features, done=True
+                )
+            
+            # Trigger training if enough samples
+            if hasattr(self.orchestrator.rl_agent, 'memory') and len(self.orchestrator.rl_agent.memory) > 32:
+                if hasattr(self.orchestrator.rl_agent, 'replay'):
+                    loss = self.orchestrator.rl_agent.replay(batch_size=32)
+                    if loss is not None:
+                        logger.debug(f"DQN trained on signal - loss: {loss:.4f}, reward: {reward:.2f}")
+            
+        except Exception as e:
+            logger.debug(f"Error training DQN on signal: {e}")
+    
+    def _train_cnn_on_signal(self, signal: Dict, trade_outcome: Dict):
+        """Train CNN model on executed signal with trade outcome"""
+        try:
+            if not self.orchestrator or not hasattr(self.orchestrator, 'cnn_model') or not self.orchestrator.cnn_model:
+                return
+            
+            # Create training data for CNN
+            symbol = signal.get('symbol', 'ETH/USDT')
+            current_price = signal.get('price', 0)
+            
+            # Get market features
+            market_features = self._get_cnn_features_and_predictions(symbol)
+            if not market_features:
+                return
+            
+            # Create target based on trade outcome
+            pnl = trade_outcome.get('pnl', 0)
+            target = 1.0 if pnl > 0 else 0.0  # Binary classification: profitable vs not
+            
+            # Prepare training data
+            features = market_features.get('features', [])
+            if features:
+                # Convert to tensor format (simplified)
+                import numpy as np
+                feature_tensor = np.array(features, dtype=np.float32)
+                target_tensor = np.array([target], dtype=np.float32)
+                
+                # Train CNN model (if it has training method)
+                if hasattr(self.orchestrator.cnn_model, 'train_on_batch'):
+                    loss = self.orchestrator.cnn_model.train_on_batch(feature_tensor, target_tensor)
+                    logger.debug(f"CNN trained on signal - loss: {loss:.4f}, target: {target}")
+            
+        except Exception as e:
+            logger.debug(f"Error training CNN on signal: {e}")
+    
+    def _train_transformer_on_signal(self, signal: Dict, trade_outcome: Dict):
+        """Train Transformer model on executed signal with trade outcome"""
+        try:
+            if not self.orchestrator or not hasattr(self.orchestrator, 'primary_transformer') or not self.orchestrator.primary_transformer:
+                return
+            
+            # Create training data for Transformer
+            symbol = signal.get('symbol', 'ETH/USDT')
+            current_price = signal.get('price', 0)
+            
+            # Get comprehensive market state
+            market_state = self._get_comprehensive_market_state(symbol, current_price)
+            
+            # Create target based on trade outcome
+            pnl = trade_outcome.get('pnl', 0)
+            target_action = 0 if signal['action'] == 'BUY' else 1  # 0=BUY, 1=SELL
+            target_confidence = signal.get('confidence', 0.5)
+            
+            # Prepare training data
+            features = list(market_state.values())
+            if features:
+                import numpy as np
+                feature_tensor = np.array(features, dtype=np.float32)
+                target_tensor = np.array([target_action, target_confidence], dtype=np.float32)
+                
+                # Train Transformer model (if it has training method)
+                if hasattr(self.orchestrator.primary_transformer, 'train_on_batch'):
+                    loss = self.orchestrator.primary_transformer.train_on_batch(feature_tensor, target_tensor)
+                    logger.debug(f"Transformer trained on signal - loss: {loss:.4f}, action: {target_action}")
+            
+        except Exception as e:
+            logger.debug(f"Error training Transformer on signal: {e}")
+    
+    def _train_cob_rl_on_signal(self, signal: Dict, trade_outcome: Dict):
+        """Train COB RL model on executed signal with trade outcome"""
+        try:
+            if not self.orchestrator or not hasattr(self.orchestrator, 'cob_rl_agent') or not self.orchestrator.cob_rl_agent:
+                return
+            
+            # Create training data for COB RL
+            symbol = signal.get('symbol', 'ETH/USDT')
+            
+            # Get COB features
+            cob_features = self._get_cob_features_for_training(symbol, signal.get('price', 0))
+            if not cob_features:
+                return
+            
+            # Create target based on trade outcome
+            pnl = trade_outcome.get('pnl', 0)
+            action = 0 if signal['action'] == 'BUY' else 1
+            reward = pnl * 100  # Scale reward
+            
+            # Store experience in COB RL memory
+            if hasattr(self.orchestrator.cob_rl_agent, 'remember'):
+                self.orchestrator.cob_rl_agent.remember(
+                    cob_features, action, reward, cob_features, done=True  # Simplified next state
+                )
+            
+            # Trigger training if enough samples
+            if hasattr(self.orchestrator.cob_rl_agent, 'memory') and len(self.orchestrator.cob_rl_agent.memory) > 32:
+                if hasattr(self.orchestrator.cob_rl_agent, 'replay'):
+                    loss = self.orchestrator.cob_rl_agent.replay(batch_size=32)
+                    if loss is not None:
+                        logger.debug(f"COB RL trained on signal - loss: {loss:.4f}, reward: {reward:.2f}")
+            
+        except Exception as e:
+            logger.debug(f"Error training COB RL on signal: {e}")
+    
+    def _train_decision_fusion_on_signal(self, signal: Dict, trade_outcome: Dict):
+        """Train Decision Fusion model on executed signal with trade outcome"""
+        try:
+            # Decision fusion model combines predictions from all models
+            # This would be implemented if there's a decision fusion model available
+            if not self.orchestrator or not hasattr(self.orchestrator, 'decision_model'):
+                return
+            
+            # Create training data for decision fusion
+            symbol = signal.get('symbol', 'ETH/USDT')
+            current_price = signal.get('price', 0)
+            
+            # Get predictions from all models
+            model_predictions = {
+                'dqn': self._get_dqn_prediction(symbol, current_price),
+                'cnn': self._get_cnn_prediction(symbol, current_price),
+                'transformer': self._get_transformer_prediction(symbol, current_price),
+                'cob_rl': self._get_cob_rl_prediction(symbol, current_price)
+            }
+            
+            # Create target based on trade outcome
+            pnl = trade_outcome.get('pnl', 0)
+            target = 1.0 if pnl > 0 else 0.0
+            
+            # Train decision fusion model (if available)
+            if hasattr(self.orchestrator.decision_model, 'train_on_batch'):
+                # Prepare training data
+                import numpy as np
+                prediction_tensor = np.array(list(model_predictions.values()), dtype=np.float32)
+                target_tensor = np.array([target], dtype=np.float32)
+                
+                loss = self.orchestrator.decision_model.train_on_batch(prediction_tensor, target_tensor)
+                logger.debug(f"Decision Fusion trained on signal - loss: {loss:.4f}, target: {target}")
+            
+        except Exception as e:
+            logger.debug(f"Error training Decision Fusion on signal: {e}")
+    
+    def _get_dqn_prediction(self, symbol: str, current_price: float) -> float:
+        """Get DQN prediction for decision fusion"""
+        try:
+            if self.orchestrator and hasattr(self.orchestrator, 'rl_agent') and self.orchestrator.rl_agent:
+                state_features = self._get_dqn_state_features(symbol, current_price)
+                if hasattr(self.orchestrator.rl_agent, 'predict'):
+                    return self.orchestrator.rl_agent.predict(state_features)
+            return 0.5  # Default neutral prediction
+        except:
+            return 0.5
+    
+    def _get_cnn_prediction(self, symbol: str, current_price: float) -> float:
+        """Get CNN prediction for decision fusion"""
+        try:
+            if self.orchestrator and hasattr(self.orchestrator, 'cnn_model') and self.orchestrator.cnn_model:
+                market_features = self._get_cnn_features_and_predictions(symbol)
+                if market_features and hasattr(self.orchestrator.cnn_model, 'predict'):
+                    features = market_features.get('features', [])
+                    if features:
+                        import numpy as np
+                        return self.orchestrator.cnn_model.predict(np.array([features]))
+            return 0.5  # Default neutral prediction
+        except:
+            return 0.5
+    
+    def _get_transformer_prediction(self, symbol: str, current_price: float) -> float:
+        """Get Transformer prediction for decision fusion"""
+        try:
+            if self.orchestrator and hasattr(self.orchestrator, 'primary_transformer') and self.orchestrator.primary_transformer:
+                market_state = self._get_comprehensive_market_state(symbol, current_price)
+                if hasattr(self.orchestrator.primary_transformer, 'predict'):
+                    features = list(market_state.values())
+                    if features:
+                        import numpy as np
+                        return self.orchestrator.primary_transformer.predict(np.array([features]))
+            return 0.5  # Default neutral prediction
+        except:
+            return 0.5
+    
+    def _get_cob_rl_prediction(self, symbol: str, current_price: float) -> float:
+        """Get COB RL prediction for decision fusion"""
+        try:
+            if self.orchestrator and hasattr(self.orchestrator, 'cob_rl_agent') and self.orchestrator.cob_rl_agent:
+                cob_features = self._get_cob_features_for_training(symbol, current_price)
+                if cob_features and hasattr(self.orchestrator.cob_rl_agent, 'predict'):
+                    import numpy as np
+                    return self.orchestrator.cob_rl_agent.predict(np.array([cob_features]))
+            return 0.5  # Default neutral prediction
+        except:
+            return 0.5
+    
     def _execute_manual_trade(self, action: str):
         """Execute manual trading action - ENHANCED with PERSISTENT SIGNAL STORAGE"""
         try:
@@ -3097,6 +3413,18 @@ class CleanTradingDashboard:
                                 self.closed_trades.append(trade_record)
                                 logger.info(f"Added completed trade to closed_trades: {action} P&L ${leveraged_pnl:.2f} (raw: ${raw_pnl:.2f}, leverage: x{self.current_leverage})")
                                 
+                                # TRAIN ALL MODELS ON MANUAL TRADE OUTCOME
+                                manual_signal = {
+                                    'action': action,
+                                    'price': current_price,
+                                    'symbol': symbol,
+                                    'confidence': 1.0,
+                                    'executed': True,
+                                    'manual': True,
+                                    'timestamp': datetime.now().timestamp()
+                                }
+                                self._train_all_models_on_signal(manual_signal)
+                                
                                 # MOVE BASE CASE TO POSITIVE/NEGATIVE based on leveraged outcome
                                 if hasattr(self, 'pending_trade_case_id') and self.pending_trade_case_id:
                                     try:
@@ -3512,6 +3840,100 @@ class CleanTradingDashboard:
         except Exception as e:
             logger.error(f"Error clearing session: {e}")
     
+    def _store_all_models(self) -> bool:
+        """Store all current models to persistent storage"""
+        try:
+            if not self.orchestrator:
+                logger.warning("No orchestrator available for model storage")
+                return False
+            
+            stored_models = []
+            
+            # 1. Store DQN model
+            if hasattr(self.orchestrator, 'rl_agent') and self.orchestrator.rl_agent:
+                try:
+                    if hasattr(self.orchestrator.rl_agent, 'save'):
+                        save_path = self.orchestrator.rl_agent.save('models/saved/dqn_agent_session')
+                        stored_models.append(('DQN', save_path))
+                        logger.info(f"Stored DQN model: {save_path}")
+                except Exception as e:
+                    logger.warning(f"Failed to store DQN model: {e}")
+            
+            # 2. Store CNN model
+            if hasattr(self.orchestrator, 'cnn_model') and self.orchestrator.cnn_model:
+                try:
+                    if hasattr(self.orchestrator.cnn_model, 'save'):
+                        save_path = self.orchestrator.cnn_model.save('models/saved/cnn_model_session')
+                        stored_models.append(('CNN', save_path))
+                        logger.info(f"Stored CNN model: {save_path}")
+                except Exception as e:
+                    logger.warning(f"Failed to store CNN model: {e}")
+            
+            # 3. Store Transformer model
+            if hasattr(self.orchestrator, 'primary_transformer') and self.orchestrator.primary_transformer:
+                try:
+                    if hasattr(self.orchestrator.primary_transformer, 'save'):
+                        save_path = self.orchestrator.primary_transformer.save('models/saved/transformer_model_session')
+                        stored_models.append(('Transformer', save_path))
+                        logger.info(f"Stored Transformer model: {save_path}")
+                except Exception as e:
+                    logger.warning(f"Failed to store Transformer model: {e}")
+            
+            # 4. Store COB RL model
+            if hasattr(self.orchestrator, 'cob_rl_agent') and self.orchestrator.cob_rl_agent:
+                try:
+                    if hasattr(self.orchestrator.cob_rl_agent, 'save'):
+                        save_path = self.orchestrator.cob_rl_agent.save('models/saved/cob_rl_agent_session')
+                        stored_models.append(('COB RL', save_path))
+                        logger.info(f"Stored COB RL model: {save_path}")
+                except Exception as e:
+                    logger.warning(f"Failed to store COB RL model: {e}")
+            
+            # 5. Store Decision Fusion model
+            if hasattr(self.orchestrator, 'decision_model') and self.orchestrator.decision_model:
+                try:
+                    if hasattr(self.orchestrator.decision_model, 'save'):
+                        save_path = self.orchestrator.decision_model.save('models/saved/decision_fusion_session')
+                        stored_models.append(('Decision Fusion', save_path))
+                        logger.info(f"Stored Decision Fusion model: {save_path}")
+                except Exception as e:
+                    logger.warning(f"Failed to store Decision Fusion model: {e}")
+            
+            # 6. Store model metadata and training state
+            try:
+                import json
+                from datetime import datetime
+                
+                metadata = {
+                    'timestamp': datetime.now().isoformat(),
+                    'session_pnl': self.session_pnl,
+                    'trade_count': len(self.closed_trades),
+                    'stored_models': stored_models,
+                    'training_iterations': getattr(self, 'training_iteration', 0),
+                    'model_performance': self.get_model_performance_metrics()
+                }
+                
+                metadata_path = 'models/saved/session_metadata.json'
+                with open(metadata_path, 'w') as f:
+                    json.dump(metadata, f, indent=2)
+                
+                logger.info(f"Stored session metadata: {metadata_path}")
+                
+            except Exception as e:
+                logger.warning(f"Failed to store metadata: {e}")
+            
+            # Log summary
+            if stored_models:
+                logger.info(f"Successfully stored {len(stored_models)} models: {[name for name, _ in stored_models]}")
+                return True
+            else:
+                logger.warning("No models were stored - no models available or save methods not found")
+                return False
+                
+        except Exception as e:
+            logger.error(f"Error storing models: {e}")
+            return False
+    
     def _get_signal_attribute(self, signal, attr_name, default=None):
         """Safely get attribute from signal (handles both dict and dataclass objects)"""
         try:

web/layout_manager.py

@@ -149,6 +149,10 @@ class DashboardLayoutManager:
                         html.I(className="fas fa-trash me-1"),
                         "Clear Session"
                     ], id="clear-session-btn", className="btn btn-warning btn-sm w-100"),
+                    html.Button([
+                        html.I(className="fas fa-save me-1"),
+                        "Store All Models"
+                    ], id="store-models-btn", className="btn btn-info btn-sm w-100 mt-2"),
                     html.Hr(className="my-2"),
                     html.Small("System Status", className="text-muted d-block mb-1"),
                     html.Div([