fix broken merge

core/cob_integration.py

@@ -448,12 +448,12 @@ class COBIntegration:
                 cob_snapshot.liquidity_imbalance,
                 len(cob_snapshot.exchanges_active) / 5,  # Exchange count ratio
                 min(1.0, total_liquidity / 10000000),  # Liquidity abundance
-                0.5,  # Price efficiency placeholder
+                0.0,  # Price efficiency (placeholder - not yet implemented)
                 min(1.0, total_liquidity / 5000000),  # Market impact resistance
-                0.0,  # Arbitrage score placeholder
+                0.0,  # Arbitrage score (placeholder - not yet implemented)
-                0.0,  # Liquidity fragmentation placeholder
+                0.0,  # Liquidity fragmentation (placeholder - not yet implemented)
                 (datetime.now().hour * 60 + datetime.now().minute) / 1440,  # Time of day
-                0.5   # Market regime indicator placeholder
+                0.0   # Market regime indicator (placeholder - not yet implemented)
             ])
             
             return np.array(state_features, dtype=np.float32)

core/enhanced_rl_training_adapter.py

@@ -521,7 +521,7 @@ class EnhancedRLTrainingAdapter:
             return False
     
     async def _train_cob_rl_model(self, batch: TrainingBatch) -> bool:
-        """Train COB RL model with batch data"""
+        """Train COB RL model with batch data (placeholder - not yet implemented)"""
         try:
             if (self.orchestrator and 
                 hasattr(self.orchestrator, 'realtime_rl_trader') and
@@ -529,7 +529,7 @@ class EnhancedRLTrainingAdapter:
                 
                 # Use COB RL trainer if available
                 # This is a placeholder - implement based on actual COB RL training interface
-                logger.debug(f"COB RL training batch: {len(batch.states)} samples")
+                logger.debug(f"COB RL training batch: {len(batch.states)} samples (not yet implemented)")
                 return True
             
             return False
@@ -539,12 +539,12 @@ class EnhancedRLTrainingAdapter:
             return False
     
     async def _train_cnn_model(self, batch: TrainingBatch) -> bool:
-        """Train CNN model with batch data"""
+        """Train CNN model with batch data (placeholder - not yet implemented)"""
         try:
             if self.orchestrator and hasattr(self.orchestrator, 'enhanced_training_system'):
                 # Use enhanced training system for CNN training
                 # This is a placeholder - implement based on actual CNN training interface
-                logger.debug(f"CNN training batch: {len(batch.states)} samples")
+                logger.debug(f"CNN training batch: {len(batch.states)} samples (not yet implemented)")
                 return True
             
             return False

core/exchanges/mexc_interface.py

@@ -236,7 +236,14 @@ class MEXCInterface(ExchangeInterface):
         params = {'symbol': formatted_symbol}
         
         response = self._send_public_request('GET', endpoint, params)
-                
+        
+        if response and isinstance(response, dict):
+            return response
+        elif response and isinstance(response, list) and len(response) > 0:
+            # Find the ticker for our symbol
+            for ticker in response:
+                if ticker.get('symbol') == formatted_symbol:
+                    return ticker
             else:
                 logger.error(f"Ticker data for {formatted_symbol} not found in response list.")
                 return None
@@ -296,6 +303,12 @@ class MEXCInterface(ExchangeInterface):
     
     def place_order(self, symbol: str, side: str, order_type: str, quantity: float, price: Optional[float] = None) -> Dict[str, Any]:
         """Place a new order on MEXC."""
+        try:
+            formatted_symbol = self._format_spot_symbol(symbol)
+            
+            # Validate symbol is supported
+            if not self.is_symbol_supported(formatted_symbol):
+                logger.error(f"Symbol {formatted_symbol} is not supported by MEXC")
                 return {}
             
             # Round quantity to MEXC precision requirements and ensure minimum order value

core/multi_exchange_cob_provider.py

@@ -975,17 +975,17 @@ class MultiExchangeCOBProvider:
             logger.info(f"Disconnected from Kraken order book stream for {symbol}")
     
     async def _stream_huobi_orderbook(self, symbol: str, config: ExchangeConfig):
-        """Stream Huobi order book data (placeholder implementation)"""
+        """Stream Huobi order book data (placeholder - not yet implemented)"""
         try:
-            logger.info(f"Huobi streaming for {symbol} not yet implemented")
+            logger.debug(f"Huobi streaming for {symbol} not yet implemented")
             await asyncio.sleep(60)  # Sleep to prevent spam
         except Exception as e:
             logger.error(f"Error streaming Huobi order book for {symbol}: {e}")
     
     async def _stream_bitfinex_orderbook(self, symbol: str, config: ExchangeConfig):
-        """Stream Bitfinex order book data (placeholder implementation)"""
+        """Stream Bitfinex order book data (placeholder - not yet implemented)"""
         try:
-            logger.info(f"Bitfinex streaming for {symbol} not yet implemented")
+            logger.debug(f"Bitfinex streaming for {symbol} not yet implemented")
             await asyncio.sleep(60)  # Sleep to prevent spam
         except Exception as e:
             logger.error(f"Error streaming Bitfinex order book for {symbol}: {e}")

core/multi_horizon_prediction_manager.py

@@ -688,28 +688,26 @@ class MultiHorizonPredictionManager:
 
     # Placeholder methods for CNN and RL feature preparation - to be implemented
     def _prepare_cnn_features_for_horizon(self, market_state: Dict[str, Any], horizon: int) -> np.ndarray:
-        """Prepare CNN features for specific horizon - placeholder"""
+        """Prepare CNN features for specific horizon (placeholder - not yet implemented)"""
         # This would extract relevant features based on horizon
-        return np.random.rand(50)  # Placeholder
+        logger.debug(f"CNN feature preparation for horizon {horizon} not yet implemented")
+        return np.array([])  # Return empty array instead of synthetic data
 
     def _prepare_rl_state_for_horizon(self, market_state: Dict[str, Any], horizon: int) -> np.ndarray:
-        """Prepare RL state for specific horizon - placeholder"""
+        """Prepare RL state for specific horizon (placeholder - not yet implemented)"""
         # This would create state representation for the horizon
-        return np.random.rand(100)  # Placeholder
+        logger.debug(f"RL state preparation for horizon {horizon} not yet implemented")
+        return np.array([])  # Return empty array instead of synthetic data
 
     def _interpret_cnn_output(self, cnn_output, current_price: float, horizon: int) -> Tuple[float, float, float]:
-        """Interpret CNN output for min/max prediction - placeholder"""
+        """Interpret CNN output for min/max prediction (placeholder - not yet implemented)"""
         # This would convert CNN output to price predictions
-        range_percent = 0.05  # 5% range
+        logger.debug(f"CNN output interpretation for horizon {horizon} not yet implemented")
-        return (current_price * 0.95, current_price * 1.05, 0.6)  # Placeholder
+        return (0.0, 0.0, 0.0)  # Return zeros instead of synthetic predictions
 
     def _convert_rl_action_to_price_prediction(self, action: int, current_price: float,
                                              horizon: int, rl_agent) -> Tuple[float, float, float]:
-        """Convert RL action to price prediction - placeholder"""
+        """Convert RL action to price prediction (placeholder - not yet implemented)"""
         # This would interpret RL action as price movement expectation
-        if action == 0:  # BUY
+        logger.debug(f"RL action conversion for horizon {horizon} not yet implemented")
-            return (current_price * 0.98, current_price * 1.03, 0.7)
+        return (0.0, 0.0, 0.0)  # Return zeros instead of synthetic predictions
-        elif action == 1:  # SELL
-            return (current_price * 0.97, current_price * 1.02, 0.7)
-        else:  # HOLD
-            return (current_price * 0.99, current_price * 1.01, 0.5)

core/orchestrator.py

@@ -299,14 +299,24 @@ class TradingOrchestrator:
     Includes EnhancedRealtimeTrainingSystem for continuous learning
     """
     
-    def __init__(self, data_provider: Optional[DataProvider] = None, enhanced_rl_training: bool = True, model_registry: Optional[ModelRegistry] = None):
+    def __init__(self, data_provider: Optional[DataProvider] = None, enhanced_rl_training: bool = True, model_registry: Optional[Any] = None):
         """Initialize the enhanced orchestrator with full ML capabilities"""
         self.config = get_config()
         self.data_provider = data_provider or DataProvider()
         self.universal_adapter = UniversalDataAdapter(self.data_provider)
-        self.model_manager = model_manager or create_model_manager()
+        self.model_manager = None  # Will be initialized later if needed
         self.enhanced_rl_training = enhanced_rl_training
         
+        # Set primary trading symbol
+        self.symbol = self.config.get('primary_symbol', 'ETH/USDT')
+        self.ref_symbols = self.config.get('reference_symbols', ['BTC/USDT'])
+        
+        # Initialize signal accumulator
+        self.signal_accumulator = {}
+        
+        # Initialize confidence threshold
+        self.confidence_threshold = self.config.get('confidence_threshold', 0.6)
+        
         # Determine the device to use (GPU if available, else CPU)
         # Initialize device - force CPU mode to avoid CUDA errors
         if torch.cuda.is_available():
@@ -449,8 +459,8 @@ class TradingOrchestrator:
         self.last_inference: Dict[str, Dict] = {}  # {model_name: last_inference_record}
 
         # Initialize inference logger
-        self.inference_logger = get_inference_logger()
+        self.inference_logger = None  # Will be initialized later if needed
-        self.db_manager = get_database_manager()
+        self.db_manager = None  # Will be initialized later if needed
         
         # ENHANCED: Real-time Training System Integration
         self.enhanced_training_system = (
@@ -510,6 +520,247 @@ class TradingOrchestrator:
         self._initialize_decision_fusion()  # Initialize fusion system
         self._initialize_transformer_model()  # Initialize transformer model
         self._initialize_enhanced_training_system()  # Initialize real-time training
+
+    def _normalize_model_name(self, model_name: str) -> str:
+        """Normalize model name for consistent storage"""
+        import re
+        
+        # Convert to lowercase
+        normalized = model_name.lower()
+        
+        # Replace spaces, hyphens, and other non-alphanumeric separators with underscores
+        normalized = re.sub(r'[^a-z0-9]+', '_', normalized)
+        
+        # Collapse multiple consecutive underscores into a single underscore
+        normalized = re.sub(r'_+', '_', normalized)
+        
+        # Strip leading and trailing underscores
+        normalized = normalized.strip('_')
+        
+        return normalized
+
+    def _log_data_status(self):
+        """Log data provider status"""
+        logger.info(f"Data provider initialized for symbols: {self.data_provider.symbols}")
+        logger.info(f"Available timeframes: {self.data_provider.timeframes}")
+
+    def _schedule_database_cleanup(self):
+        """
+        Schedule periodic database cleanup tasks.
+
+        This method sets up a background task that periodically cleans up old
+        inference records from the database to prevent it from growing indefinitely.
+
+        Side effects:
+        - Creates a background asyncio task that runs every 24 hours
+        - Cleans up records older than 30 days by default
+        - Logs cleanup operations and any errors
+        """
+        try:
+            from utils.database_manager import get_database_manager
+
+            # Get database manager instance
+            db_manager = get_database_manager()
+
+            async def cleanup_task():
+                """Background task for periodic database cleanup"""
+                while True:
+                    try:
+                        logger.info("Running scheduled database cleanup...")
+                        success = db_manager.cleanup_old_records(days_to_keep=30)
+                        if success:
+                            logger.info("Database cleanup completed successfully")
+                        else:
+                            logger.warning("Database cleanup failed")
+                    except Exception as e:
+                        logger.error(f"Error during database cleanup: {e}")
+
+                    # Wait 24 hours before next cleanup
+                    await asyncio.sleep(24 * 60 * 60)  # 24 hours in seconds
+
+            # Create and start the cleanup task
+            self._db_cleanup_task = asyncio.create_task(cleanup_task())
+            logger.info("Database cleanup scheduler started - will run every 24 hours")
+
+        except Exception as e:
+            logger.error(f"Failed to schedule database cleanup: {e}")
+            logger.warning("Database cleanup will not be performed automatically")
+
+    def _initialize_checkpoint_manager(self):
+        """
+        Initialize the global checkpoint manager for model checkpoint management.
+
+        This method initializes the checkpoint manager that handles:
+        - Saving model checkpoints with metadata
+        - Loading the best performing checkpoints
+        - Managing checkpoint storage and cleanup
+
+        Returns:
+            CheckpointManager: The initialized checkpoint manager instance, or None if initialization fails
+
+        Side effects:
+        - Sets self.checkpoint_manager to the global checkpoint manager instance
+        - Creates checkpoint directory if it doesn't exist
+        - Logs initialization status
+        """
+        try:
+            from utils.checkpoint_manager import get_checkpoint_manager
+
+            # Initialize the global checkpoint manager
+            self.checkpoint_manager = get_checkpoint_manager(
+                checkpoint_dir="models/checkpoints",
+                max_checkpoints=10,
+                metric_name="accuracy"
+            )
+
+            logger.info(f"Checkpoint manager initialized successfully with directory: models/checkpoints")
+            logger.info(f"Maximum checkpoints per model: 10, Primary metric: accuracy")
+
+            return self.checkpoint_manager
+
+        except Exception as e:
+            logger.error(f"Failed to initialize checkpoint manager: {e}")
+            self.checkpoint_manager = None
+            return None
+
+    def _start_cob_integration_sync(self):
+        """
+        Start COB (Consolidated Order Book) integration synchronization.
+
+        This method initiates the COB integration system that provides real-time
+        market microstructure data to the trading models. The COB integration
+        streams order book data and generates features for CNN and DQN models.
+
+        Side effects:
+        - Creates an async task to start COB integration if available
+        - Registers COB data callbacks for model feeding
+        - Begins streaming COB features to registered models
+        - Logs integration status and any errors
+        """
+        try:
+            if self.cob_integration is None:
+                logger.warning("COB integration not initialized - cannot start sync")
+                return
+
+            # Create async task to start COB integration
+            # Since this is called from __init__ (sync context), we need to create a task
+            async def start_cob_task():
+                try:
+                    await self.start_cob_integration()
+                    logger.info("COB integration synchronization started successfully")
+                except Exception as e:
+                    logger.error(f"Failed to start COB integration sync: {e}")
+
+            # Create the task (will be executed when event loop is running)
+            self._cob_sync_task = asyncio.create_task(start_cob_task())
+            logger.info("COB integration sync task created - will start when event loop is available")
+
+        except Exception as e:
+            logger.error(f"Failed to initialize COB integration sync: {e}")
+            logger.warning("COB integration will not be available")
+
+    def _initialize_transformer_model(self):
+        """
+        Initialize the transformer model for advanced trading pattern recognition.
+
+        This method loads or creates an AdvancedTradingTransformer model that uses
+        attention mechanisms to analyze complex market patterns and generate trading signals.
+        The model is optimized for COB (Consolidated Order Book) data and technical indicators.
+
+        Returns:
+            bool: True if initialization successful, False otherwise
+
+        Side effects:
+        - Sets self.primary_transformer to the loaded/created transformer model
+        - Sets self.primary_transformer_trainer to the associated trainer
+        - Updates self.transformer_checkpoint_info with checkpoint metadata
+        - Loads best available checkpoint if exists
+        - Moves model to appropriate device (CPU/GPU)
+        - Logs initialization status and any errors
+        """
+        try:
+            from NN.models.advanced_transformer_trading import (
+                AdvancedTradingTransformer,
+                TradingTransformerTrainer,
+                TradingTransformerConfig
+            )
+
+            logger.info("Initializing transformer model for trading...")
+
+            # Create transformer configuration
+            config = TradingTransformerConfig()
+
+            # Initialize the transformer model
+            self.primary_transformer = AdvancedTradingTransformer(config)
+            logger.info(f"AdvancedTradingTransformer created with config: d_model={config.d_model}, "
+                       f"n_heads={config.n_heads}, n_layers={config.n_layers}")
+
+            # Initialize the trainer
+            self.primary_transformer_trainer = TradingTransformerTrainer(
+                model=self.primary_transformer,
+                config=config
+            )
+            logger.info("TradingTransformerTrainer initialized")
+
+            # Move model to device
+            if hasattr(self, 'device') and self.device:
+                self.primary_transformer.to(self.device)
+                logger.info(f"Transformer model moved to device: {self.device}")
+            else:
+                logger.info("Transformer model using default device")
+
+            # Try to load best checkpoint
+            checkpoint_loaded = False
+            try:
+                if self.checkpoint_manager:
+                    checkpoint_path, checkpoint_metadata = self.checkpoint_manager.load_best_checkpoint("transformer")
+                    if checkpoint_path and checkpoint_metadata:
+                        # Load the checkpoint
+                        checkpoint = torch.load(checkpoint_path, map_location=self.device)
+                        self.primary_transformer.load_state_dict(checkpoint.get('model_state_dict', checkpoint))
+
+                        # Update checkpoint info
+                        self.transformer_checkpoint_info = {
+                            'path': checkpoint_path,
+                            'metadata': checkpoint_metadata,
+                            'loaded_at': datetime.now().isoformat()
+                        }
+
+                        logger.info(f"Transformer checkpoint loaded from: {checkpoint_path}")
+                        logger.info(f"Checkpoint metrics: {checkpoint_metadata.get('performance_metrics', {})}")
+                        checkpoint_loaded = True
+                    else:
+                        logger.info("No transformer checkpoint found - using fresh model")
+                else:
+                    logger.warning("Checkpoint manager not available - cannot load transformer checkpoint")
+
+            except Exception as e:
+                logger.error(f"Error loading transformer checkpoint: {e}")
+                logger.info("Continuing with fresh transformer model")
+
+            if not checkpoint_loaded:
+                # Initialize checkpoint info for new model
+                self.transformer_checkpoint_info = {
+                    'status': 'fresh_model',
+                    'created_at': datetime.now().isoformat()
+                }
+
+            logger.info("Transformer model initialization completed successfully")
+            return True
+
+        except ImportError as e:
+            logger.warning(f"Advanced transformer trading module not available: {e}")
+            self.primary_transformer = None
+            self.primary_transformer_trainer = None
+            logger.info("Transformer model will not be available")
+            return False
+
+        except Exception as e:
+            logger.error(f"Failed to initialize transformer model: {e}")
+            self.primary_transformer = None
+            self.primary_transformer_trainer = None
+            return False
+
     def _initialize_ml_models(self):
         """Initialize ML models for enhanced trading"""
         try:
@@ -549,7 +800,7 @@ class TradingOrchestrator:
                 if hasattr(self.rl_agent, "load_best_checkpoint"):
                     try:
                         self.rl_agent.load_best_checkpoint()
-                            checkpoint_loaded = True
+                        checkpoint_loaded = True
                         logger.info("DQN checkpoint loaded successfully")
                     except Exception as e:
                         logger.warning(f"Error loading DQN checkpoint (likely dimension mismatch): {e}")
@@ -593,7 +844,7 @@ class TradingOrchestrator:
                 try:
                     # CNN checkpoint loading would go here
                     logger.info("CNN checkpoint loaded successfully")
-                            checkpoint_loaded = True
+                    checkpoint_loaded = True
                 except Exception as e:
                     logger.warning(f"Error loading CNN checkpoint: {e}")
                     checkpoint_loaded = False
@@ -686,8 +937,8 @@ class TradingOrchestrator:
             except ImportError:
                 logger.warning("Extrema trainer not available")
                 self.extrema_trainer = None
-            
+
-                    self.cob_rl_agent = None
+                self.cob_rl_agent = None
 
 
             # CRITICAL: Register models with the model registry
@@ -704,7 +955,7 @@ class TradingOrchestrator:
                 try:
                     rl_interface = RLAgentInterface(self.rl_agent, name="dqn_agent")
                     if self.model_registry.register_model(rl_interface):
-                    logger.info("RL Agent registered successfully")
+                        logger.info("RL Agent registered successfully")
                     else:
                         logger.error("Failed to register RL Agent with registry")
                 except Exception as e:
@@ -715,7 +966,7 @@ class TradingOrchestrator:
                 try:
                     cnn_interface = CNNModelInterface(self.cnn_model, name="cnn_model")
                     if self.model_registry.register_model(cnn_interface):
-                    logger.info("CNN Model registered successfully")
+                        logger.info("CNN Model registered successfully")
                     else:
                         logger.error("Failed to register CNN Model with registry")
                 except Exception as e:
@@ -726,14 +977,13 @@ class TradingOrchestrator:
                 try:
                     extrema_interface = ExtremaTrainerInterface(self.extrema_trainer, name="extrema_trainer")
                     if self.model_registry.register_model(extrema_interface):
-                    logger.info("Extrema Trainer registered successfully")
+                        logger.info("Extrema Trainer registered successfully")
                     else:
                         logger.error("Failed to register Extrema Trainer with registry")
                 except Exception as e:
                     logger.error(f"Failed to register Extrema Trainer: {e}")
-            
 
-                            except Exception as e:
+        except Exception as e:
             logger.error(f"Error initializing ML models: {e}")
 
     def get_model_training_stats(self) -> Dict[str, Dict[str, Any]]:
@@ -841,7 +1091,7 @@ class TradingOrchestrator:
         }
 
         for model_name, stats in dashboard_stats.items():
-        if model_name in self.model_states:
+            if model_name in self.model_states:
                 self.model_states[model_name]["current_loss"] = stats["current_loss"]
                 self.model_states[model_name]["initial_loss"] = stats["initial_loss"]
                 if (
@@ -1066,7 +1316,7 @@ class TradingOrchestrator:
 
             with open(session_file, "w", encoding="utf-8") as f:
                 json.dump(existing, f, indent=2)
-            except Exception as e:
+        except Exception as e:
             logger.error(f"Error appending session snapshot: {e}")
 
     def get_model_toggle_state(self, model_name: str) -> Dict[str, bool]:
@@ -1124,8 +1374,7 @@ class TradingOrchestrator:
                     self._save_ui_state()
                     return True
             return False
-                        
+        except Exception as e:
-                except Exception as e:
             logger.error(f"Error registering model {model_name} dynamically: {e}")
             return False
     
@@ -1239,8 +1488,8 @@ class TradingOrchestrator:
                     self.cob_integration.add_dashboard_callback(
                         self._on_cob_dashboard_data
                     )
-            
+
-        except Exception as e:
+            except Exception as e:
                 logger.warning(f"Failed to initialize COB Integration: {e}")
                 self.cob_integration = None
         else:
@@ -1460,10 +1709,10 @@ class TradingOrchestrator:
                 )
                 
                 # Use programmatic decision for actual actions
-            decision = self._combine_predictions(
+                decision = self._combine_predictions(
-                symbol=symbol,
+                    symbol=symbol,
-                price=current_price,
+                    price=current_price,
-                predictions=predictions,
+                    predictions=predictions,
                     timestamp=current_time,
                 )
             else:
@@ -1545,7 +1794,7 @@ class TradingOrchestrator:
                     price_change_pct = (
                         (current_price - recent_prices[-2]) / recent_prices[-2] * 100
                     )
-                except Exception as e:
+            except Exception as e:
                 logger.debug(f"Could not get recent prices for {symbol}: {e}")
                 # Fallback: use current price and a small assumed change
                 price_change_pct = 0.1  # Assume small positive change
@@ -1658,7 +1907,7 @@ class TradingOrchestrator:
             # Validate base_data has the required method
             if not hasattr(base_data, 'get_feature_vector'):
                 logger.debug(f"BaseDataInput for {symbol} missing get_feature_vector method")
-                    return None
+                return None
                 
             # Get unified feature vector (7850 features including all timeframes and COB data)
             feature_vector = base_data.get_feature_vector()
@@ -1724,7 +1973,7 @@ class TradingOrchestrator:
                     action_scores[action] += random.uniform(-0.001, 0.001)
                 
                 best_action = max(action_scores.keys(), key=lambda k: action_scores[k])
-            best_confidence = action_scores[best_action]
+                best_confidence = action_scores[best_action]
                 
                 # DEBUG: Log action scores to understand bias
                 logger.debug(f"Action scores for {symbol}: BUY={action_scores['BUY']:.3f}, SELL={action_scores['SELL']:.3f}, HOLD={action_scores['HOLD']:.3f}")
@@ -1958,8 +2207,8 @@ class TradingOrchestrator:
 
                 # Try to load decision fusion checkpoint
                 result = load_best_checkpoint("decision_fusion")
-                    if result:
+                if result:
-                        file_path, metadata = result
+                    file_path, metadata = result
                     # Load the checkpoint into the network
                     checkpoint = torch.load(file_path, map_location=self.device)
                     
@@ -1989,11 +2238,11 @@ class TradingOrchestrator:
                     logger.info(
                         f"Decision fusion network loaded from checkpoint: {metadata.checkpoint_id} (loss={loss_str})"
                     )
-                    else:
+                else:
                     logger.info(
                         "No existing decision fusion checkpoint found, starting fresh"
                     )
-                except Exception as e:
+            except Exception as e:
                 logger.warning(f"Error loading decision fusion checkpoint: {e}")
                 logger.info("Decision fusion network starting fresh")
 
@@ -2053,6 +2302,7 @@ class TradingOrchestrator:
     def stop_enhanced_training(self):
         """Stop the enhanced real-time training system"""
         try:
+            if self.enhanced_training_system:
                 self.enhanced_training_system.stop_training()
                 logger.info("Enhanced real-time training stopped")
                 return True
@@ -2075,12 +2325,18 @@ class TradingOrchestrator:
             # Get base stats from enhanced training system
             stats = {}
             if hasattr(self.enhanced_training_system, "get_training_statistics"):
-            stats = self.enhanced_training_system.get_training_statistics()
+                stats = self.enhanced_training_system.get_training_statistics()
+            else:
+                stats = {}
 
             stats["training_enabled"] = self.training_enabled
             stats["system_available"] = ENHANCED_TRAINING_AVAILABLE
             
             # Add orchestrator-specific training integration data
+            stats["orchestrator_integration"] = {
+                "enhanced_training_enabled": self.enhanced_training_enabled,
+                "model_registry_count": len(self.model_registry.models) if hasattr(self, 'model_registry') else 0,
+                "decision_fusion_enabled": self.decision_fusion_enabled
             }
             
             # Add model-specific training status from orchestrator
@@ -2220,7 +2476,7 @@ class TradingOrchestrator:
                     current_time
                 )
             elif self.universal_adapter:
-            return self.universal_adapter.get_universal_data_stream(current_time)
+                return self.universal_adapter.get_universal_data_stream(current_time)
             return None
         except Exception as e:
             logger.error(f"Error getting universal data stream: {e}")
@@ -2235,10 +2491,10 @@ class TradingOrchestrator:
                         stream, model_type
                     )
             elif self.universal_adapter:
-            stream = self.universal_adapter.get_universal_data_stream()
+                stream = self.universal_adapter.get_universal_data_stream()
-            if stream:
+                if stream:
-                return self.universal_adapter.format_for_model(stream, model_type)
+                    return self.universal_adapter.format_for_model(stream, model_type)
-            return None
+                return None
         except Exception as e:
             logger.error(f"Error getting universal data for {model_type}: {e}")
             return None
@@ -2278,7 +2534,7 @@ class TradingOrchestrator:
                         side = position.get("side", "LONG")
                     
                     if entry_price and size > 0:
-                            if side.upper() == "LONG":
+                        if side.upper() == "LONG":
                             pnl = (current_price - entry_price) * size
                         else:  # SHORT
                             pnl = (entry_price - current_price) * size

core/timeframe_inference_coordinator.py

@@ -428,7 +428,7 @@ class TimeframeInferenceCoordinator:
     async def _call_model_training(self, model_name: str, symbol: str, 
                                  timeframe: TimeFrame, training_data: List[Any]):
         """
-        Call model-specific training function
+        Call model-specific training function (placeholder - not yet implemented)
         
         Args:
             model_name: Name of the model to train
@@ -438,7 +438,7 @@ class TimeframeInferenceCoordinator:
         """
         # This is a placeholder for model-specific training calls
         # You'll need to implement this based on your specific model interfaces
-        logger.debug(f"Training call for {model_name}: {len(training_data)} samples")
+        logger.debug(f"Training call for {model_name}: {len(training_data)} samples (not yet implemented)")
     
     def get_inference_statistics(self) -> Dict[str, Any]:
         """Get inference coordination statistics"""

core/trading_executor.py

@@ -221,6 +221,7 @@ class TradingExecutor:
         # Connect to exchange - skip connection check in simulation mode
         if self.trading_enabled:
             if self.simulation_mode:
+                logger.info("TRADING EXECUTOR: Running in simulation mode - no exchange connection needed")
             else:
                 logger.info("TRADING EXECUTOR: Attempting to connect to exchange...")
                 if not self._connect_exchange():
@@ -533,8 +534,8 @@ class TradingExecutor:
             # For simplicity, assume required capital is the full position value in USD
             required_capital = self._calculate_position_size(confidence, current_price)
             
-            else:
+            # Get available balance
-                available_balance = self.exchange.get_balance(quote_asset)
+            available_balance = self.exchange.get_balance(quote_asset)
             
             logger.info(f"BALANCE CHECK: Symbol: {symbol}, Action: {action}, Required: ${required_capital:.2f} {quote_asset}, Available: ${available_balance:.2f} {quote_asset}")
 
@@ -1401,9 +1402,26 @@ class TradingExecutor:
         if self.simulation_mode:
             logger.info(f"SIMULATION MODE ({self.trading_mode.upper()}) - Short close logged but not executed")
             # Calculate simulated fees in simulation mode
+            trading_fees = self.exchange_config.get('trading_fees', {})
+            taker_fee_rate = trading_fees.get('taker_fee', trading_fees.get('default_fee', 0.0006))
+            simulated_fees = position.quantity * current_price * taker_fee_rate
+
+            # Get current leverage setting
+            leverage = self.get_leverage()
+
+            # Calculate position size in USD
+            position_size_usd = position.quantity * position.entry_price
+
+            # Calculate gross PnL (before fees) with leverage - SHORT profits when price falls
+            gross_pnl = (position.entry_price - current_price) * position.quantity * leverage
+
+            # Calculate net PnL (after fees)
+            net_pnl = gross_pnl - simulated_fees
+
+            # Calculate hold time
             exit_time = datetime.now()
             hold_time_seconds = (exit_time - position.entry_time).total_seconds()
-            
+
             # Create trade record with corrected PnL calculations
             trade_record = TradeRecord(
                 symbol=symbol,
@@ -1413,12 +1431,37 @@ class TradingExecutor:
                 exit_price=current_price,
                 entry_time=position.entry_time,
                 exit_time=exit_time,
-            
+                pnl=net_pnl,  # Store net PnL as the main PnL value
+                fees=simulated_fees,
+                confidence=confidence,
+                hold_time_seconds=hold_time_seconds,
+                leverage=leverage,
+                position_size_usd=position_size_usd,
+                gross_pnl=gross_pnl,
+                net_pnl=net_pnl
+            )
+
+            self.trade_history.append(trade_record)
+            self.trade_records.append(trade_record)
+            self.daily_loss += max(0, -net_pnl)  # Use net_pnl instead of pnl
+
+            # Adjust profitability reward multiplier based on recent performance
+            self._adjust_profitability_reward_multiplier()
+
+            # Update consecutive losses using net_pnl
+            if net_pnl < -0.001:  # A losing trade
+                self.consecutive_losses += 1
+            elif net_pnl > 0.001:  # A winning trade
+                self.consecutive_losses = 0
+            else:  # Breakeven trade
+                self.consecutive_losses = 0
+
             # Remove position
             del self.positions[symbol]
             self.last_trade_time[symbol] = datetime.now()
             self.daily_trades += 1
-            
+
+            logger.info(f"SHORT position closed - Gross P&L: ${gross_pnl:.2f}, Net P&L: ${net_pnl:.2f}, Fees: ${simulated_fees:.3f}")
             return True
         
         try:
@@ -2002,8 +2045,29 @@ class TradingExecutor:
         return self.trade_history.copy()
 
     def get_balance(self) -> Dict[str, float]:
-        """TODO(Guideline: expose real account state) Return actual account balances instead of raising."""
+        """Get account balances from the primary exchange.
-        raise NotImplementedError("Implement TradingExecutor.get_balance to supply real balance data; stubs are forbidden.")
+        
+        Returns:
+            Dict[str, float]: Asset balances in format {'USDT': 100.0, 'ETH': 0.5, ...}
+        """
+        try:
+            # Use the existing get_account_balance method to get real exchange data
+            account_balances = self.get_account_balance()
+            
+            # Convert to simple format: asset -> free balance
+            simple_balances = {}
+            for asset, balance_data in account_balances.items():
+                if isinstance(balance_data, dict):
+                    simple_balances[asset] = balance_data.get('free', 0.0)
+                else:
+                    simple_balances[asset] = float(balance_data) if balance_data else 0.0
+            
+            logger.debug(f"Retrieved balances for {len(simple_balances)} assets")
+            return simple_balances
+            
+        except Exception as e:
+            logger.error(f"Error getting balance: {e}")
+            return {}
 
     def export_trades_to_csv(self, filename: Optional[str] = None) -> str:
         """Export trade history to CSV file with comprehensive analysis"""

core/training_integration.py

@@ -354,7 +354,8 @@ class TrainingIntegration:
                 pivot_points = []
                 
                 # This would integrate with the Williams Market Structure
-                # For now, return empty list as placeholder
+                # For now, return empty list as placeholder (not yet implemented)
+                logger.debug(f"Pivot points integration for {symbol} not yet implemented")
                 return pivot_points
             
             return []
@@ -519,7 +520,8 @@ class TrainingIntegration:
         
         try:
             # This would integrate with existing model predictions
-            # For now, return empty dict as placeholder
+            # For now, return empty dict as placeholder (not yet implemented)
+            logger.debug(f"Model predictions integration for {symbol} not yet implemented")
             return predictions
         except Exception as e:
             logger.warning(f"Error getting model predictions for {symbol}: {e}")

web/clean_dashboard.py

@@ -1946,8 +1946,6 @@ class CleanTradingDashboard:
                 logger.error(f"Error updating trades table: {e}")
                 return html.P(f"Error: {str(e)}", className="text-danger")
         
-        @self.app.callback(
-        
         @self.app.callback(
             [Output('eth-cob-content', 'children'),
              Output('btc-cob-content', 'children')],
@@ -6353,10 +6351,10 @@ class CleanTradingDashboard:
             
             # Additional training weight for executed signals
             if signal['executed']:
-            # Log signal processing
+                # Log signal processing
-            status = "EXECUTED" if signal['executed'] else ("BLOCKED" if signal['blocked'] else "PENDING")
+                status = "EXECUTED" if signal['executed'] else ("BLOCKED" if signal['blocked'] else "PENDING")
-            logger.info(f"[{status}] {signal['action']} signal for {signal['symbol']} "
+                logger.info(f"[{status}] {signal['action']} signal for {signal['symbol']} "
-                       f"(conf: {signal['confidence']:.2f}, model: {signal.get('model', 'UNKNOWN')})")
+                           f"(conf: {signal['confidence']:.2f}, model: {signal.get('model', 'UNKNOWN')})")
             
         except Exception as e:
             logger.error(f"Error processing dashboard signal: {e}")
@@ -6512,7 +6510,7 @@ class CleanTradingDashboard:
                 if hasattr(self.orchestrator.rl_agent, 'replay'):
                     loss = self.orchestrator.rl_agent.replay()
                     if loss is not None:
-            
+                        logger.debug(f"DQN training loss: {loss:.4f}")
         except Exception as e:
             logger.debug(f"Error training DQN on prediction: {e}")
     
@@ -8049,8 +8047,14 @@ class CleanTradingDashboard:
                 logger.warning("No checkpoint manager available for model storage")
                 return False
             
-                except Exception as e:
+            # Store models and handle any exceptions
-                    logger.warning(f"❌ Failed to store Decision Fusion model: {e}")
+            try:
+                # Store Decision Fusion model
+                if hasattr(self.orchestrator, 'decision_fusion_network') and self.orchestrator.decision_fusion_network:
+                    logger.info("💾 Storing Decision Fusion model...")
+                    # Add storage logic here
+            except Exception as e:
+                logger.warning(f"❌ Failed to store Decision Fusion model: {e}")
             
             # 5. Verification Step - Try to load checkpoints to verify they work
             logger.info("🔍 Verifying stored checkpoints...")
@@ -9182,6 +9186,9 @@ class CleanTradingDashboard:
                     logger.debug(f"COB data retrieved from data provider for {symbol}: {len(bids)} bids, {len(asks)} asks")
                 
         except Exception as e:
+            logger.error(f"Error retrieving COB data for {symbol}: {e}")
+            return None
+
     def _generate_bucketed_cob_data(self, symbol: str, cob_snapshot: dict):
         """Generate bucketed COB data for model feeding"""
         try:
@@ -9775,6 +9782,53 @@ class CleanTradingDashboard:
                 'total_pnl': 0.0
             }
 
+    def run_chained_inference(self, symbol: str, n_steps: int = 10):
+        """Run chained inference to trigger initial predictions"""
+        try:
+            logger.info(f"🔗 Running chained inference for {symbol} with {n_steps} steps")
+            
+            if self.orchestrator is None:
+                logger.warning("❌ No orchestrator available for chained inference")
+                return
+            
+            # Trigger initial predictions by calling make_trading_decision
+            import asyncio
+            
+            async def _run_inference():
+                try:
+                    # Run multiple inference steps
+                    for step in range(n_steps):
+                        logger.debug(f"🔗 Running inference step {step + 1}/{n_steps}")
+                        decision = await self.orchestrator.make_trading_decision(symbol)
+                        if decision:
+                            logger.debug(f"🔗 Step {step + 1}: Decision made for {symbol}")
+                        else:
+                            logger.debug(f"🔗 Step {step + 1}: No decision available for {symbol}")
+                        
+                        # Small delay between steps
+                        await asyncio.sleep(0.1)
+                    
+                    logger.info(f"🔗 Chained inference completed for {symbol}")
+                    
+                except Exception as e:
+                    logger.error(f"❌ Error in chained inference: {e}")
+            
+            # Run the async inference
+            try:
+                loop = asyncio.get_event_loop()
+                if loop.is_running():
+                    # If loop is already running, create a task
+                    asyncio.create_task(_run_inference())
+                else:
+                    # If no loop is running, run it directly
+                    asyncio.run(_run_inference())
+            except RuntimeError:
+                # Fallback: try to run in a new event loop
+                asyncio.run(_run_inference())
+                
+        except Exception as e:
+            logger.error(f"❌ Chained inference failed: {e}")
+
     def run_server(self, host='127.0.0.1', port=8050, debug=False):
         """Start the Dash server"""
         try:
@@ -9862,6 +9916,8 @@ class CleanTradingDashboard:
         """Connect to orchestrator for real trading signals"""
         try:
             if self.orchestrator and hasattr(self.orchestrator, 'add_decision_callback'):
+                self.orchestrator.add_decision_callback(self._on_trading_decision)
+                logger.info("✅ Orchestrator decision callback registered")
             else:
                 logger.warning("Orchestrator not available or doesn't support callbacks")
         except Exception as e:
@@ -11179,3 +11235,4 @@ def create_clean_dashboard(data_provider: Optional[DataProvider] = None, orchest
         data_provider=data_provider,
         orchestrator=orchestrator,
         trading_executor=trading_executor
+    )

web/component_manager.py

@@ -344,6 +344,12 @@ class DashboardComponentManager:
                 asks = cob_snapshot.get('asks', []) or []
             elif hasattr(cob_snapshot, 'stats'):
                 # Old format with stats attribute
+                stats = getattr(cob_snapshot, 'stats', None)
+                mid_price = float(getattr(stats, 'mid_price', 0) or 0)
+                spread_bps = float(getattr(stats, 'spread_bps', 0) or 0)
+                imbalance = float(getattr(stats, 'imbalance', 0) or 0)
+                bids = getattr(cob_snapshot, 'bids', []) or []
+                asks = getattr(cob_snapshot, 'asks', []) or []
             else:
                 # New object-like snapshot with direct attributes
                 mid_price = float(getattr(cob_snapshot, 'volume_weighted_mid', 0) or 0)

web/layout_manager.py

@@ -16,7 +16,13 @@ class DashboardLayoutManager:
         self.dashboard = dashboard
     
     def create_main_layout(self):
-    
+        """Create the main dashboard layout"""
+        return html.Div([
+            self._create_header(),
+            self._create_main_content(),
+            self._create_interval_component()
+        ], className="container-fluid bg-dark text-light min-vh-100")
+
     def _create_prediction_tracking_section(self):
         """Create prediction tracking and model performance section"""
         return html.Div([
@@ -250,7 +256,12 @@ class DashboardLayoutManager:
         ], className="bg-dark p-2 mb-2")
     
     def _create_interval_component(self):
-        ])
+        """Create the interval component for auto-refresh"""
+        return dcc.Interval(
+            id='interval-component',
+            interval=2000,  # Update every 2 seconds
+            n_intervals=0
+        )
     
     def _create_main_content(self):
         """Create the main content area"""