cob ma data for models

core/orchestrator.py

@@ -220,6 +220,7 @@ class TradingOrchestrator:
         # Load historical data for models and RL training
         self._load_historical_data_for_models()
     
+    # SINGLE-USE FUNCTION - Called only once in codebase
     def _initialize_ml_models(self):
         """Initialize ML models for enhanced trading"""
         try:
@@ -592,6 +593,7 @@ class TradingOrchestrator:
                                 logger.error(f"Error in extrema trainer prediction: {e}")
                                 return None
 
+                        # UNUSED FUNCTION - Not called anywhere in codebase
                         def get_memory_usage(self) -> float:
                             return 30.0  # MB
 
@@ -623,6 +625,7 @@ class TradingOrchestrator:
                                 logger.error(f"Error in transformer prediction: {e}")
                                 return None
 
+                        # UNUSED FUNCTION - Not called anywhere in codebase
                         def get_memory_usage(self) -> float:
                             return 60.0  # MB estimate for transformer
 
@@ -649,6 +652,7 @@ class TradingOrchestrator:
                                 logger.error(f"Error in decision model prediction: {e}")
                                 return None
 
+                        # UNUSED FUNCTION - Not called anywhere in codebase
                         def get_memory_usage(self) -> float:
                             return 40.0  # MB estimate for decision model
 
@@ -666,6 +670,7 @@ class TradingOrchestrator:
         except Exception as e:
             logger.error(f"Error initializing ML models: {e}")
 
+    # UNUSED FUNCTION - Not called anywhere in codebase
     def update_model_loss(self, model_name: str, current_loss: float, best_loss: float = None):
         """Update model loss and potentially best loss"""
         if model_name in self.model_states:
@@ -676,6 +681,7 @@ class TradingOrchestrator:
                 self.model_states[model_name]['best_loss'] = current_loss
             logger.debug(f"Updated {model_name} loss: current={current_loss:.4f}, best={self.model_states[model_name]['best_loss']:.4f}")
 
+    # UNUSED FUNCTION - Not called anywhere in codebase
     def checkpoint_saved(self, model_name: str, checkpoint_data: Dict[str, Any]):
         """Callback when a model checkpoint is saved"""
         if model_name in self.model_states:
@@ -689,6 +695,7 @@ class TradingOrchestrator:
                     self.model_states[model_name]['best_loss'] = saved_loss
                     logger.info(f"New best loss for {model_name}: {saved_loss:.4f}")
 
+    # UNUSED FUNCTION - Not called anywhere in codebase
     def get_recent_predictions(self, limit: int = 10) -> List[Dict[str, Any]]:
         """Get recent predictions from all models for data streaming"""
         try:
@@ -728,6 +735,7 @@ class TradingOrchestrator:
             logger.debug(f"Error getting recent predictions: {e}")
             return []
 
+    # UNUSED FUNCTION - Not called anywhere in codebase
     def _save_orchestrator_state(self):
         """Save the current state of the orchestrator, including model states."""
         state = {
@@ -742,6 +750,7 @@ class TradingOrchestrator:
             json.dump(state, f, indent=4)
         logger.info(f"Orchestrator state saved to {save_path}")
 
+    # UNUSED FUNCTION - Not called anywhere in codebase
     def _load_orchestrator_state(self):
         """Load the orchestrator state from a saved file."""
         save_path = os.path.join(self.config.paths.get('checkpoint_dir', './models/saved'), 'orchestrator_state.json')
@@ -777,6 +786,7 @@ class TradingOrchestrator:
         self.trade_loop_task = asyncio.create_task(self._trading_decision_loop())
         logger.info("Continuous trading loop initiated.")
 
+    # UNUSED FUNCTION - Not called anywhere in codebase
     def _initialize_cob_integration(self):
         """Initialize COB integration for real-time market microstructure data"""
         if COB_INTEGRATION_AVAILABLE:
@@ -807,12 +817,14 @@ class TradingOrchestrator:
         else:
             logger.warning("COB Integration not initialized. Cannot start streaming.")
 
+    # UNUSED FUNCTION - Not called anywhere in codebase
     def _start_cob_matrix_worker(self):
         """Start a background worker to continuously update COB matrices for models"""
         if not self.cob_integration:
             logger.warning("COB Integration not available, cannot start COB matrix worker.")
             return
 
+        # UNUSED FUNCTION - Not called anywhere in codebase
         def matrix_worker():
             logger.info("COB Matrix Worker started.")
             while self.realtime_processing:
@@ -851,6 +863,7 @@ class TradingOrchestrator:
         matrix_thread = threading.Thread(target=matrix_worker, daemon=True)
         matrix_thread.start()
 
+    # UNUSED FUNCTION - Not called anywhere in codebase
     def _update_cob_matrix_for_symbol(self, symbol: str):
         """Updates the COB matrix and features for a specific symbol."""
         if not self.cob_integration:
@@ -967,6 +980,7 @@ class TradingOrchestrator:
             logger.error(f"Error generating COB DQN features for {symbol}: {e}")
             return None
 
+    # UNUSED FUNCTION - Not called anywhere in codebase
     def _on_cob_cnn_features(self, symbol: str, cob_data: Dict):
         """Callback for when new COB CNN features are available"""
         if not self.realtime_processing:
@@ -984,6 +998,7 @@ class TradingOrchestrator:
         except Exception as e:
             logger.error(f"Error in _on_cob_cnn_features for {symbol}: {e}")
 
+    # UNUSED FUNCTION - Not called anywhere in codebase
     def _on_cob_dqn_features(self, symbol: str, cob_data: Dict):
         """Callback for when new COB DQN features are available"""
         if not self.realtime_processing:
@@ -1001,6 +1016,7 @@ class TradingOrchestrator:
         except Exception as e:
             logger.error(f"Error in _on_cob_dqn_features for {symbol}: {e}")
 
+    # UNUSED FUNCTION - Not called anywhere in codebase
     def _on_cob_dashboard_data(self, symbol: str, cob_data: Dict):
         """Callback for when new COB data is available for the dashboard"""
         if not self.realtime_processing:
@@ -1013,20 +1029,24 @@ class TradingOrchestrator:
         except Exception as e:
             logger.error(f"Error in _on_cob_dashboard_data for {symbol}: {e}")
 
+    # UNUSED FUNCTION - Not called anywhere in codebase
     def get_cob_features(self, symbol: str) -> Optional[np.ndarray]:
         """Get the latest COB features for CNN model"""
         return self.latest_cob_features.get(symbol)
 
+    # UNUSED FUNCTION - Not called anywhere in codebase
     def get_cob_state(self, symbol: str) -> Optional[np.ndarray]:
         """Get the latest COB state for DQN model"""
         return self.latest_cob_state.get(symbol)
 
+    # SINGLE-USE FUNCTION - Called only once in codebase
     def get_cob_snapshot(self, symbol: str) -> Optional[COBSnapshot]:
         """Get the latest raw COB snapshot for a symbol"""
         if self.cob_integration:
             return self.cob_integration.get_latest_cob_snapshot(symbol)
         return None
 
+    # SINGLE-USE FUNCTION - Called only once in codebase
     def get_cob_feature_matrix(self, symbol: str, sequence_length: int = 60) -> Optional[np.ndarray]:
         """Get a sequence of COB CNN features for sequence models"""
         if symbol not in self.cob_feature_history or not self.cob_feature_history[symbol]:
@@ -1059,6 +1079,7 @@ class TradingOrchestrator:
     
     # Weight normalization removed - handled by ModelManager
     
+    # UNUSED FUNCTION - Not called anywhere in codebase
     def add_decision_callback(self, callback):
         """Add a callback function to be called when decisions are made"""
         self.decision_callbacks.append(callback)
@@ -1322,6 +1343,7 @@ class TradingOrchestrator:
             logger.debug(f"Error building RL state for {symbol}: {e}")
             return None
     
+    # SINGLE-USE FUNCTION - Called only once in codebase
     def _get_cob_state(self, symbol: str) -> Optional[np.ndarray]:
         """Build COB state vector for COB RL agent"""
         try:
@@ -1478,6 +1500,7 @@ class TradingOrchestrator:
             logger.error(f"Error creating RL state for {symbol}: {e}")
             return None
     
+    # SINGLE-USE FUNCTION - Called only once in codebase
     def _combine_predictions(self, symbol: str, price: float, 
                            predictions: List[Prediction],
                            timestamp: datetime) -> TradingDecision:
@@ -1593,6 +1616,7 @@ class TradingOrchestrator:
                 current_position_pnl=0.0
             )
     
+    # SINGLE-USE FUNCTION - Called only once in codebase
     def _get_timeframe_weight(self, timeframe: str) -> float:
         """Get importance weight for a timeframe"""
         # Higher timeframes get more weight in decision making
@@ -1605,12 +1629,14 @@ class TradingOrchestrator:
     # Model performance and weight adaptation removed - handled by ModelManager
     # Use self.model_manager for all model performance tracking
     
+    # UNUSED FUNCTION - Not called anywhere in codebase
     def get_recent_decisions(self, symbol: str, limit: int = 10) -> List[TradingDecision]:
         """Get recent decisions for a symbol"""
         if symbol in self.recent_decisions:
             return self.recent_decisions[symbol][-limit:]
         return []
     
+    # UNUSED FUNCTION - Not called anywhere in codebase
     def get_performance_metrics(self) -> Dict[str, Any]:
         """Get performance metrics for the orchestrator"""
         return {
@@ -1625,6 +1651,7 @@ class TradingOrchestrator:
             }
         }
     
+    # UNUSED FUNCTION - Not called anywhere in codebase
     def get_model_states(self) -> Dict[str, Dict]:
         """Get current model states with REAL checkpoint data - SSOT for dashboard"""
         try:
@@ -1749,6 +1776,7 @@ class TradingOrchestrator:
                 'extrema_trainer': {'initial_loss': None, 'current_loss': None, 'best_loss': None, 'checkpoint_loaded': False}
             }
     
+    # SINGLE-USE FUNCTION - Called only once in codebase
     def _initialize_decision_fusion(self):
         """Initialize the decision fusion neural network for learning model effectiveness"""
         try:
@@ -1767,6 +1795,7 @@ class TradingOrchestrator:
                     self.fc3 = nn.Linear(hidden_size, 3)  # BUY, SELL, HOLD
                     self.dropout = nn.Dropout(0.2)
                     
+                # UNUSED FUNCTION - Not called anywhere in codebase
                 def forward(self, x):
                     x = torch.relu(self.fc1(x))
                     x = self.dropout(x)
@@ -1781,6 +1810,7 @@ class TradingOrchestrator:
             logger.warning(f"Decision fusion initialization failed: {e}")
             self.decision_fusion_enabled = False
     
+    # SINGLE-USE FUNCTION - Called only once in codebase
     def _initialize_enhanced_training_system(self):
         """Initialize the enhanced real-time training system"""
         try:
@@ -1825,6 +1855,7 @@ class TradingOrchestrator:
             self.training_enabled = False
             self.enhanced_training_system = None
 
+    # SINGLE-USE FUNCTION - Called only once in codebase
     def start_enhanced_training(self):
         """Start the enhanced real-time training system"""
         try:
@@ -1845,6 +1876,7 @@ class TradingOrchestrator:
             logger.error(f"Error starting enhanced training: {e}")
             return False
 
+    # UNUSED FUNCTION - Not called anywhere in codebase
     def stop_enhanced_training(self):
         """Stop the enhanced real-time training system"""
         try:
@@ -1858,6 +1890,7 @@ class TradingOrchestrator:
             logger.error(f"Error stopping enhanced training: {e}")
             return False
 
+    # UNUSED FUNCTION - Not called anywhere in codebase
     def get_enhanced_training_stats(self) -> Dict[str, Any]:
         """Get enhanced training system statistics with orchestrator integration"""
         try:
@@ -1954,6 +1987,7 @@ class TradingOrchestrator:
                 'error': str(e)
             }
 
+    # UNUSED FUNCTION - Not called anywhere in codebase
     def set_training_dashboard(self, dashboard):
         """Set the dashboard reference for the training system"""
         try:
@@ -1972,6 +2006,7 @@ class TradingOrchestrator:
             logger.error(f"Error getting universal data stream: {e}")
             return None
     
+    # UNUSED FUNCTION - Not called anywhere in codebase
     def get_universal_data_for_model(self, model_type: str = 'cnn') -> Optional[Dict[str, Any]]:
         """Get formatted universal data for specific model types"""
         try:
@@ -2014,6 +2049,7 @@ class TradingOrchestrator:
         except Exception:
             return False
     
+    # SINGLE-USE FUNCTION - Called only once in codebase
     def _calculate_aggressiveness_thresholds(self, current_pnl: float, symbol: str) -> tuple:
         """Calculate confidence thresholds based on aggressiveness settings"""
         # Base thresholds
@@ -2036,6 +2072,7 @@ class TradingOrchestrator:
         
         return entry_threshold, exit_threshold
     
+    # SINGLE-USE FUNCTION - Called only once in codebase
     def _apply_pnl_feedback(self, action: str, confidence: float, current_pnl: float, 
                            symbol: str, reasoning: dict) -> tuple:
         """Apply P&L-based feedback to decision making"""
@@ -2069,6 +2106,7 @@ class TradingOrchestrator:
             logger.debug(f"Error applying P&L feedback: {e}")
             return action, confidence
     
+    # SINGLE-USE FUNCTION - Called only once in codebase
     def _calculate_dynamic_entry_aggressiveness(self, symbol: str) -> float:
         """Calculate dynamic entry aggressiveness based on recent performance"""
         try:
@@ -2097,6 +2135,7 @@ class TradingOrchestrator:
             logger.debug(f"Error calculating dynamic entry aggressiveness: {e}")
             return 0.5
     
+    # SINGLE-USE FUNCTION - Called only once in codebase
     def _calculate_dynamic_exit_aggressiveness(self, symbol: str, current_pnl: float) -> float:
         """Calculate dynamic exit aggressiveness based on P&L and market conditions"""
         try:
@@ -2119,11 +2158,13 @@ class TradingOrchestrator:
             logger.debug(f"Error calculating dynamic exit aggressiveness: {e}")
             return 0.5
     
+    # UNUSED FUNCTION - Not called anywhere in codebase
     def set_trading_executor(self, trading_executor):
         """Set the trading executor for position tracking"""
         self.trading_executor = trading_executor
         logger.info("Trading executor set for position tracking and P&L feedback")
 
+    # SINGLE-USE FUNCTION - Called only once in codebase
     def _get_current_price(self, symbol: str) -> float:
         """Get current price for symbol"""
         try:
@@ -2169,6 +2210,7 @@ class TradingOrchestrator:
             else:
                 return 1000.0
 
+    # SINGLE-USE FUNCTION - Called only once in codebase
     def _generate_fallback_prediction(self, symbol: str) -> Dict[str, Any]:
         """Generate fallback prediction when models fail"""
         try:
@@ -2189,6 +2231,7 @@ class TradingOrchestrator:
                 'model': 'fallback'
             }
 
+    # UNUSED FUNCTION - Not called anywhere in codebase
     def capture_dqn_prediction(self, symbol: str, action_idx: int, confidence: float, price: float, q_values: List[float] = None):
         """Capture DQN prediction for dashboard visualization"""
         try:
@@ -2205,6 +2248,7 @@ class TradingOrchestrator:
         except Exception as e:
             logger.debug(f"Error capturing DQN prediction: {e}")
 
+    # UNUSED FUNCTION - Not called anywhere in codebase
     def capture_cnn_prediction(self, symbol: str, direction: int, confidence: float, current_price: float, predicted_price: float):
         """Capture CNN prediction for dashboard visualization"""
         try:
@@ -2270,6 +2314,7 @@ class TradingOrchestrator:
             logger.warning(f"Data stream monitor initialization failed: {e}")
             self.data_stream_monitor = None
 
+    # UNUSED FUNCTION - Not called anywhere in codebase
     def start_data_stream(self) -> bool:
         """Start data streaming if not already active."""
         try:
@@ -2282,6 +2327,7 @@ class TradingOrchestrator:
             logger.error(f"Failed to start data stream: {e}")
             return False
 
+    # UNUSED FUNCTION - Not called anywhere in codebase
     def stop_data_stream(self) -> bool:
         """Stop data streaming if active."""
         try:
@@ -2292,6 +2338,7 @@ class TradingOrchestrator:
             logger.error(f"Failed to stop data stream: {e}")
             return False
 
+    # SINGLE-USE FUNCTION - Called only once in codebase
     def get_data_stream_status(self) -> Dict[str, any]:
         """Return current data stream status and buffer sizes."""
         status = {
@@ -2310,6 +2357,7 @@ class TradingOrchestrator:
             pass
         return status
 
+    # UNUSED FUNCTION - Not called anywhere in codebase
     def save_data_snapshot(self, filepath: str = None) -> str:
         """Save a snapshot of current data stream buffers to a file.
         
@@ -2337,6 +2385,7 @@ class TradingOrchestrator:
             logger.error(f"Failed to save data snapshot: {e}")
             raise
 
+    # UNUSED FUNCTION - Not called anywhere in codebase
     def get_stream_summary(self) -> Dict[str, any]:
         """Get a summary of current data stream activity."""
         status = self.get_data_stream_status()
@@ -2360,6 +2409,7 @@ class TradingOrchestrator:
         
         return summary
 
+    # UNUSED FUNCTION - Not called anywhere in codebase
     def get_cob_data(self, symbol: str, limit: int = 300) -> List:
         """Get COB data for a symbol with specified limit."""
         try:
@@ -2370,6 +2420,7 @@ class TradingOrchestrator:
             logger.error(f"Error getting COB data: {e}")
             return []
 
+    # SINGLE-USE FUNCTION - Called only once in codebase
     def _load_historical_data_for_models(self):
         """Load 300 historical candles for all required timeframes and symbols for model training"""
         logger.info("Loading 300 historical candles for model training and RL context...")
@@ -2425,6 +2476,7 @@ class TradingOrchestrator:
         except Exception as e:
             logger.error(f"Error in historical data loading: {e}")
 
+    # SINGLE-USE FUNCTION - Called only once in codebase
     def _initialize_models_with_historical_data(self, symbols_timeframes: List[Tuple[str, str]]):
         """Initialize all NN models with historical data using data provider's normalized methods"""
         try:
@@ -2458,6 +2510,7 @@ class TradingOrchestrator:
         except Exception as e:
             logger.error(f"Error initializing models with historical data: {e}")
 
+    # SINGLE-USE FUNCTION - Called only once in codebase
     def _initialize_cnn_with_provider_data(self):
         """Initialize CNN using data provider's normalized feature extraction"""
         try:
@@ -2488,6 +2541,7 @@ class TradingOrchestrator:
         except Exception as e:
             logger.error(f"Error initializing CNN with provider data: {e}")
 
+    # SINGLE-USE FUNCTION - Called only once in codebase
     def _initialize_dqn_with_provider_data(self, symbols_timeframes: List[Tuple[str, str]]):
         """Initialize DQN using data provider's normalized state vector creation"""
         try:
@@ -2505,6 +2559,7 @@ class TradingOrchestrator:
         except Exception as e:
             logger.error(f"Error initializing DQN with provider data: {e}")
 
+    # SINGLE-USE FUNCTION - Called only once in codebase
     def _initialize_transformer_with_provider_data(self, symbols_timeframes: List[Tuple[str, str]]):
         """Initialize Transformer using data provider's normalized sequence creation"""
         try:
@@ -2522,6 +2577,7 @@ class TradingOrchestrator:
         except Exception as e:
             logger.error(f"Error initializing Transformer with provider data: {e}")
 
+    # SINGLE-USE FUNCTION - Called only once in codebase
     def _initialize_decision_with_provider_data(self, symbol_features: Dict[str, Dict[str, pd.DataFrame]]):
         """Initialize Decision Fusion using data provider's feature aggregation"""
         try:
@@ -2551,6 +2607,7 @@ class TradingOrchestrator:
         except Exception as e:
             logger.error(f"Error initializing Decision Fusion with provider data: {e}")
 
+    # UNUSED FUNCTION - Not called anywhere in codebase
     def get_ohlcv_data(self, symbol: str, timeframe: str, limit: int = 300) -> List:
         """Get OHLCV data for a symbol with specified timeframe and limit."""
         try:

web/clean_dashboard.py

@@ -166,8 +166,14 @@ class CleanTradingDashboard:
         self.cob_update_count = 0
         self.last_cob_broadcast: dict = {}  # Rate limiting for UI updates
         self.cob_data_history: Dict[str, deque] = {
-            'ETH/USDT': deque(maxlen=61),  # Store ~60 seconds of 1s snapshots
+            'ETH/USDT': deque(maxlen=120),  # Store ~120 seconds of 1s snapshots for MA calculations
-            'BTC/USDT': deque(maxlen=61)
+            'BTC/USDT': deque(maxlen=120)
+        }
+
+        # COB imbalance moving averages for different timeframes
+        self.cob_imbalance_ma: Dict[str, Dict[str, float]] = {
+            'ETH/USDT': {},
+            'BTC/USDT': {}
         }
         
         # Initialize timezone
@@ -314,9 +320,14 @@ class CleanTradingDashboard:
                 
                 # Get COB data from orchestrator
                 cob_data = self._get_cob_data_with_buckets(symbol, limit)
+
+                # Add COB imbalance moving averages
+                cob_imbalance_mas = self.cob_imbalance_ma.get(symbol, {})
+
                 return jsonify({
                     'symbol': symbol,
                     'data': cob_data,
+                    'cob_imbalance_ma': cob_imbalance_mas,
                     'timestamp': datetime.now().isoformat()
                 })
             except Exception as e:
@@ -1183,8 +1194,12 @@ class CleanTradingDashboard:
                 # Determine COB data source mode
                 cob_mode = self._get_cob_mode()
 
-                eth_components = self.component_manager.format_cob_data(eth_snapshot, 'ETH/USDT', eth_imbalance_stats, cob_mode)
+                # Get COB imbalance moving averages
-                btc_components = self.component_manager.format_cob_data(btc_snapshot, 'BTC/USDT', btc_imbalance_stats, cob_mode)
+                eth_ma_data = self.cob_imbalance_ma.get('ETH/USDT', {})
+                btc_ma_data = self.cob_imbalance_ma.get('BTC/USDT', {})
+
+                eth_components = self.component_manager.format_cob_data(eth_snapshot, 'ETH/USDT', eth_imbalance_stats, cob_mode, eth_ma_data)
+                btc_components = self.component_manager.format_cob_data(btc_snapshot, 'BTC/USDT', btc_imbalance_stats, cob_mode, btc_ma_data)
 
                 return eth_components, btc_components
                 
@@ -5170,10 +5185,11 @@ class CleanTradingDashboard:
                         }
                     }
                     
-                    # Store in history (keep last 15 seconds)
+                    # Store in history (keep last 120 seconds for MA calculations)
                     self.cob_data_history[symbol].append(cob_snapshot)
-                    if len(self.cob_data_history[symbol]) > 15:  # Keep 15 seconds
+
-                        self.cob_data_history[symbol] = self.cob_data_history[symbol][-15:]
+                    # Calculate COB imbalance moving averages for different timeframes
+                    self._calculate_cob_imbalance_mas(symbol)
                     
                     # Update latest data
                     self.latest_cob_data[symbol] = cob_snapshot
@@ -5189,7 +5205,41 @@ class CleanTradingDashboard:
                                 
         except Exception as e:
             logger.debug(f"Error collecting COB data for {symbol}: {e}")
-    
+
+    def _calculate_cob_imbalance_mas(self, symbol: str):
+        """Calculate COB imbalance moving averages for different timeframes"""
+        try:
+            history = self.cob_data_history[symbol]
+            if len(history) < 2:
+                return
+
+            # Extract imbalance values from history
+            imbalances = [snapshot['stats']['imbalance'] for snapshot in history if 'stats' in snapshot and 'imbalance' in snapshot['stats']]
+
+            if not imbalances:
+                return
+
+            # Calculate moving averages for different timeframes
+            timeframes = {
+                '10s': min(10, len(imbalances)),  # 10 second MA
+                '30s': min(30, len(imbalances)),  # 30 second MA
+                '60s': min(60, len(imbalances)),  # 60 second MA
+            }
+
+            for timeframe, periods in timeframes.items():
+                if len(imbalances) >= periods:
+                    # Calculate simple moving average
+                    ma_value = sum(imbalances[-periods:]) / periods
+                    self.cob_imbalance_ma[symbol][timeframe] = ma_value
+                else:
+                    # If not enough data, use current imbalance
+                    self.cob_imbalance_ma[symbol][timeframe] = imbalances[-1]
+
+            logger.debug(f"COB imbalance MAs for {symbol}: {self.cob_imbalance_ma[symbol]}")
+
+        except Exception as e:
+            logger.debug(f"Error calculating COB imbalance MAs for {symbol}: {e}")
+
     def _generate_bucketed_cob_data(self, symbol: str, cob_snapshot: dict):
         """Generate bucketed COB data for model feeding"""
         try:

web/component_manager.py

@@ -272,7 +272,7 @@ class DashboardComponentManager:
             logger.error(f"Error formatting system status: {e}")
             return [html.P(f"Error: {str(e)}", className="text-danger small")]
     
-    def format_cob_data(self, cob_snapshot, symbol, cumulative_imbalance_stats=None, cob_mode="Unknown"):
+    def format_cob_data(self, cob_snapshot, symbol, cumulative_imbalance_stats=None, cob_mode="Unknown", imbalance_ma_data=None):
         """Format COB data into a split view with summary, imbalance stats, and a compact ladder."""
         try:
             if not cob_snapshot:
@@ -317,7 +317,7 @@ class DashboardComponentManager:
             }
 
             # --- Left Panel: Overview and Stats ---
-            overview_panel = self._create_cob_overview_panel(symbol, stats, cumulative_imbalance_stats, cob_mode)
+            overview_panel = self._create_cob_overview_panel(symbol, stats, cumulative_imbalance_stats, cob_mode, imbalance_ma_data)
 
             # --- Right Panel: Compact Ladder ---
             ladder_panel = self._create_cob_ladder_panel(bids, asks, mid_price, symbol)
@@ -331,7 +331,7 @@ class DashboardComponentManager:
             logger.error(f"Error formatting split COB data: {e}")
             return html.P(f"Error: {str(e)}", className="text-danger small")
 
-    def _create_cob_overview_panel(self, symbol, stats, cumulative_imbalance_stats, cob_mode="Unknown"):
+    def _create_cob_overview_panel(self, symbol, stats, cumulative_imbalance_stats, cob_mode="Unknown", imbalance_ma_data=None):
         """Creates the left panel with summary and imbalance stats."""
         mid_price = stats.get('mid_price', 0)
         spread_bps = stats.get('spread_bps', 0)
@@ -373,6 +373,20 @@ class DashboardComponentManager:
             
             html.Div(imbalance_stats_display),
 
+            # COB Imbalance Moving Averages
+            ma_display = []
+            if imbalance_ma_data:
+                ma_display.append(html.H6("Imbalance MAs", className="mt-3 mb-2 small text-muted text-uppercase"))
+                for timeframe, ma_value in imbalance_ma_data.items():
+                    ma_color = "text-success" if ma_value > 0 else "text-danger"
+                    ma_text = f"MA {timeframe}: {ma_value:.3f}"
+                    ma_display.append(html.Div([
+                        html.Strong(f"{timeframe}: ", className="small"),
+                        html.Span(ma_text, className=f"small {ma_color}")
+                    ], className="mb-1"))
+
+            html.Div(ma_display),
+
             html.Hr(className="my-2"),
             
             html.Table([