cob integration (wip)

core/enhanced_orchestrator.py

@@ -8,6 +8,7 @@ This enhanced orchestrator implements:
 4. Perfect move marking for CNN backpropagation training
 5. Market environment adaptation through RL evaluation
 6. Universal data format compliance (5 timeseries streams)
+7. Consolidated Order Book (COB) integration for real-time market microstructure
 """
 
 import asyncio
@@ -32,6 +33,7 @@ from .trading_action import TradingAction
 from .negative_case_trainer import NegativeCaseTrainer
 from .trading_executor import TradingExecutor
 from .cnn_monitor import log_cnn_prediction, start_cnn_training_session
+from .cob_integration import COBIntegration
 # Enhanced pivot RL trainer functionality integrated into orchestrator
 
 logger = logging.getLogger(__name__)
@@ -71,7 +73,7 @@ class TradingAction:
 
 @dataclass
 class MarketState:
-    """Complete market state for RL evaluation with comprehensive data"""
+    """Complete market state for RL evaluation with comprehensive data including COB"""
     symbol: str
     timestamp: datetime
     prices: Dict[str, float]  # {timeframe: current_price}
@@ -90,6 +92,14 @@ class MarketState:
     cnn_predictions: Optional[Dict[str, np.ndarray]] = None  # CNN predictions by timeframe
     pivot_points: Optional[Dict[str, Any]] = None  # Williams market structure data
     market_microstructure: Dict[str, Any] = field(default_factory=dict)  # Tick-level patterns
+    
+    # COB (Consolidated Order Book) data for market microstructure analysis
+    cob_features: Optional[np.ndarray] = None  # COB CNN features (200 dimensions)
+    cob_state: Optional[np.ndarray] = None  # COB DQN state features (50 dimensions)
+    order_book_imbalance: float = 0.0  # Bid/ask imbalance ratio
+    liquidity_depth: float = 0.0  # Total liquidity within 1% of mid price
+    exchange_diversity: float = 0.0  # Number of exchanges contributing to liquidity
+    market_impact_estimate: float = 0.0  # Estimated market impact for standard trade size
 
 @dataclass
 class PerfectMove:
@@ -136,7 +146,7 @@ class EnhancedTradingOrchestrator:
                  symbols: List[str] = None,
                  enhanced_rl_training: bool = True,
                  model_registry: Dict = None):
-        """Initialize the enhanced orchestrator with 2-action system"""
+        """Initialize the enhanced orchestrator with 2-action system and COB integration"""
         self.config = get_config()
         self.data_provider = data_provider or DataProvider()
         self.model_registry = model_registry or get_model_registry()
@@ -155,6 +165,22 @@ class EnhancedTradingOrchestrator:
         if self.enhanced_rl_training:
             logger.info("Enhanced RL training enabled")
         
+        # Initialize COB Integration for real-time market microstructure
+        self.cob_integration = COBIntegration(
+            data_provider=self.data_provider,
+            symbols=self.symbols
+        )
+        # Register COB callbacks for CNN and RL models
+        self.cob_integration.add_cnn_callback(self._on_cob_cnn_features)
+        self.cob_integration.add_dqn_callback(self._on_cob_dqn_state)
+        
+        # COB feature storage for model integration
+        self.latest_cob_features: Dict[str, np.ndarray] = {}
+        self.latest_cob_state: Dict[str, np.ndarray] = {}
+        self.cob_feature_history: Dict[str, deque] = {symbol: deque(maxlen=100) for symbol in self.symbols}
+        
+        logger.info("COB Integration initialized for real-time market microstructure")
+        
         # Position tracking for 2-action system
         self.current_positions = {}  # symbol -> {'side': 'LONG'|'SHORT'|'FLAT', 'entry_price': float, 'timestamp': datetime}
         self.last_signals = {}  # symbol -> {'action': 'BUY'|'SELL', 'timestamp': datetime, 'confidence': float}
@@ -414,6 +440,46 @@ class EnhancedTradingOrchestrator:
                 # Analyze market microstructure
                 market_microstructure = self._analyze_market_microstructure(raw_ticks)
                 
+                # Get COB (Consolidated Order Book) data if available
+                cob_features = self.latest_cob_features.get(symbol)
+                cob_state = self.latest_cob_state.get(symbol)
+                
+                # Get COB snapshot for additional metrics
+                cob_snapshot = None
+                order_book_imbalance = 0.0
+                liquidity_depth = 0.0
+                exchange_diversity = 0.0
+                market_impact_estimate = 0.0
+                
+                try:
+                    if self.cob_integration:
+                        cob_snapshot = self.cob_integration.get_cob_snapshot(symbol)
+                        if cob_snapshot:
+                            # Calculate order book imbalance
+                            bid_liquidity = sum(level.total_volume_usd for level in cob_snapshot.consolidated_bids[:10])
+                            ask_liquidity = sum(level.total_volume_usd for level in cob_snapshot.consolidated_asks[:10])
+                            if ask_liquidity > 0:
+                                order_book_imbalance = (bid_liquidity - ask_liquidity) / (bid_liquidity + ask_liquidity)
+                            
+                            # Calculate liquidity depth (within 1% of mid price)
+                            mid_price = cob_snapshot.volume_weighted_mid
+                            price_range = mid_price * 0.01  # 1%
+                            depth_bids = [l for l in cob_snapshot.consolidated_bids if l.price >= mid_price - price_range]
+                            depth_asks = [l for l in cob_snapshot.consolidated_asks if l.price <= mid_price + price_range]
+                            liquidity_depth = sum(l.total_volume_usd for l in depth_bids + depth_asks)
+                            
+                            # Calculate exchange diversity
+                            all_exchanges = set()
+                            for level in cob_snapshot.consolidated_bids[:20] + cob_snapshot.consolidated_asks[:20]:
+                                all_exchanges.update(level.exchange_breakdown.keys())
+                            exchange_diversity = len(all_exchanges)
+                            
+                            # Estimate market impact for 10k USD trade
+                            market_impact_estimate = self._estimate_market_impact(cob_snapshot, 10000)
+                            
+                except Exception as e:
+                    logger.warning(f"Error calculating COB metrics for {symbol}: {e}")
+                
                 # Create comprehensive market state
                 market_state = MarketState(
                     symbol=symbol,
@@ -431,17 +497,51 @@ class EnhancedTradingOrchestrator:
                     cnn_hidden_features=cnn_hidden_features,
                     cnn_predictions=cnn_predictions,
                     pivot_points=pivot_points,
-                    market_microstructure=market_microstructure
+                    market_microstructure=market_microstructure,
+                    # COB data integration
+                    cob_features=cob_features,
+                    cob_state=cob_state,
+                    order_book_imbalance=order_book_imbalance,
+                    liquidity_depth=liquidity_depth,
+                    exchange_diversity=exchange_diversity,
+                    market_impact_estimate=market_impact_estimate
                 )
                 
                 market_states[symbol] = market_state
-                logger.debug(f"Created comprehensive market state for {symbol} with {len(raw_ticks)} ticks")
+                logger.debug(f"Created comprehensive market state for {symbol} with COB integration")
                 
             except Exception as e:
                 logger.error(f"Error creating market state for {symbol}: {e}")
                 
         return market_states
     
+    def _estimate_market_impact(self, cob_snapshot, trade_size_usd: float) -> float:
+        """Estimate market impact for a given trade size"""
+        try:
+            # Simple market impact estimation based on order book depth
+            cumulative_volume = 0
+            weighted_price = 0
+            mid_price = cob_snapshot.volume_weighted_mid
+            
+            # For buy orders, walk through asks
+            for level in cob_snapshot.consolidated_asks:
+                if cumulative_volume >= trade_size_usd:
+                    break
+                volume_needed = min(level.total_volume_usd, trade_size_usd - cumulative_volume)
+                weighted_price += level.price * volume_needed
+                cumulative_volume += volume_needed
+            
+            if cumulative_volume > 0:
+                avg_execution_price = weighted_price / cumulative_volume
+                impact = (avg_execution_price - mid_price) / mid_price
+                return abs(impact)
+            
+            return 0.0
+            
+        except Exception as e:
+            logger.warning(f"Error estimating market impact: {e}")
+            return 0.0
+    
     def _get_recent_tick_data_for_rl(self, symbol: str, seconds: int = 300) -> List[Dict[str, Any]]:
         """Get recent tick data for RL state building"""
         try:
@@ -600,18 +700,130 @@ class EnhancedTradingOrchestrator:
                                     hidden_features[model_name] = model_hidden
                                 if model_pred is not None:
                                     predictions[model_name] = model_pred
-                        
                         except Exception as e:
-                            logger.warning(f"Error getting features from CNN model {model_name}: {e}")
+                            logger.warning(f"Error extracting CNN features from {model_name}: {e}")
+                    
+                    return (hidden_features if hidden_features else None, 
+                           predictions if predictions else None)
                     
-                    return hidden_features if hidden_features else None, predictions if predictions else None
-            
             return None, None
             
         except Exception as e:
             logger.warning(f"Error getting CNN features for {symbol}: {e}")
             return None, None
     
+    def _get_latest_price_from_universal(self, symbol: str, timeframe: str, universal_stream: UniversalDataStream) -> Optional[float]:
+        """Get latest price for symbol and timeframe from universal data stream"""
+        try:
+            if symbol == 'ETH/USDT':
+                if timeframe == '1s' and len(universal_stream.eth_ticks) > 0:
+                    # Get latest tick price (close price is at index 4)
+                    return float(universal_stream.eth_ticks[-1, 4])  # close price
+                elif timeframe == '1m' and len(universal_stream.eth_1m) > 0:
+                    return float(universal_stream.eth_1m[-1, 4])  # close price
+                elif timeframe == '1h' and len(universal_stream.eth_1h) > 0:
+                    return float(universal_stream.eth_1h[-1, 4])  # close price
+                elif timeframe == '1d' and len(universal_stream.eth_1d) > 0:
+                    return float(universal_stream.eth_1d[-1, 4])  # close price
+            elif symbol == 'BTC/USDT':
+                if timeframe == '1s' and len(universal_stream.btc_ticks) > 0:
+                    return float(universal_stream.btc_ticks[-1, 4])  # close price
+            
+            # Fallback to data provider
+            return self._get_latest_price_fallback(symbol, timeframe)
+            
+        except Exception as e:
+            logger.warning(f"Error getting latest price for {symbol} {timeframe}: {e}")
+            return self._get_latest_price_fallback(symbol, timeframe)
+    
+    def _get_latest_price_fallback(self, symbol: str, timeframe: str) -> Optional[float]:
+        """Fallback method to get latest price from data provider"""
+        try:
+            df = self.data_provider.get_historical_data(symbol, timeframe, limit=1)
+            if df is not None and not df.empty:
+                return float(df['close'].iloc[-1])
+            return None
+        except Exception as e:
+            logger.warning(f"Error in price fallback for {symbol} {timeframe}: {e}")
+            return None
+    
+    def _calculate_volatility_from_universal(self, symbol: str, universal_stream: UniversalDataStream) -> float:
+        """Calculate volatility from universal data stream"""
+        try:
+            if symbol == 'ETH/USDT' and len(universal_stream.eth_1m) > 1:
+                # Calculate volatility from 1m candles
+                closes = universal_stream.eth_1m[:, 4]  # close prices
+                if len(closes) > 1:
+                    returns = np.diff(np.log(closes))
+                    return float(np.std(returns) * np.sqrt(1440))  # Daily volatility
+            elif symbol == 'BTC/USDT' and len(universal_stream.btc_ticks) > 1:
+                # Calculate volatility from tick data
+                closes = universal_stream.btc_ticks[:, 4]  # close prices
+                if len(closes) > 1:
+                    returns = np.diff(np.log(closes))
+                    return float(np.std(returns) * np.sqrt(86400))  # Daily volatility
+            return 0.0
+        except Exception as e:
+            logger.warning(f"Error calculating volatility for {symbol}: {e}")
+            return 0.0
+    
+    def _calculate_volume_from_universal(self, symbol: str, universal_stream: UniversalDataStream) -> float:
+        """Calculate volume from universal data stream"""
+        try:
+            if symbol == 'ETH/USDT' and len(universal_stream.eth_1m) > 0:
+                # Get latest volume from 1m candles
+                volumes = universal_stream.eth_1m[:, 5]  # volume
+                return float(np.mean(volumes[-10:]))  # Average of last 10 candles
+            elif symbol == 'BTC/USDT' and len(universal_stream.btc_ticks) > 0:
+                # Calculate volume from tick data
+                volumes = universal_stream.btc_ticks[:, 5]  # volume
+                return float(np.sum(volumes[-100:]))  # Sum of last 100 ticks
+            return 0.0
+        except Exception as e:
+            logger.warning(f"Error calculating volume for {symbol}: {e}")
+            return 0.0
+    
+    def _calculate_trend_strength_from_universal(self, symbol: str, universal_stream: UniversalDataStream) -> float:
+        """Calculate trend strength from universal data stream"""
+        try:
+            if symbol == 'ETH/USDT' and len(universal_stream.eth_1m) > 20:
+                # Calculate trend strength using 20-period moving average
+                closes = universal_stream.eth_1m[-20:, 4]  # last 20 closes
+                if len(closes) >= 20:
+                    sma = np.mean(closes)
+                    current_price = closes[-1]
+                    return float((current_price - sma) / sma)  # Relative trend strength
+            elif symbol == 'BTC/USDT' and len(universal_stream.btc_ticks) > 100:
+                # Calculate trend from tick data
+                closes = universal_stream.btc_ticks[-100:, 4]  # last 100 ticks
+                if len(closes) >= 100:
+                    start_price = closes[0]
+                    end_price = closes[-1]
+                    return float((end_price - start_price) / start_price)
+            return 0.0
+        except Exception as e:
+            logger.warning(f"Error calculating trend strength for {symbol}: {e}")
+            return 0.0
+    
+    def _determine_market_regime(self, symbol: str, universal_stream: UniversalDataStream) -> str:
+        """Determine market regime from universal data stream"""
+        try:
+            # Calculate volatility and trend strength
+            volatility = self._calculate_volatility_from_universal(symbol, universal_stream)
+            trend_strength = abs(self._calculate_trend_strength_from_universal(symbol, universal_stream))
+            
+            # Classify market regime
+            if volatility > 0.05:  # High volatility threshold
+                return 'volatile'
+            elif trend_strength > 0.02:  # Strong trend threshold
+                return 'trending'
+            else:
+                return 'ranging'
+                
+        except Exception as e:
+            logger.warning(f"Error determining market regime for {symbol}: {e}")
+            return 'unknown'
+    
     def _extract_cnn_features(self, model, feature_matrix: np.ndarray) -> Tuple[Optional[np.ndarray], Optional[np.ndarray]]:
         """Extract hidden features and predictions from CNN model"""
         try:
@@ -2535,4 +2747,45 @@ class EnhancedTradingOrchestrator:
                 'positions': {sym: pos for sym, pos in self.current_positions.items()},
                 'total_positions': len(self.current_positions),
                 'last_signals': self.last_signals
-            }
+            }
+
+    def _on_cob_cnn_features(self, symbol: str, cob_data: Dict):
+        """Handle COB features for CNN model integration"""
+        try:
+            if 'features' in cob_data:
+                features = cob_data['features']
+                self.latest_cob_features[symbol] = features
+                self.cob_feature_history[symbol].append({
+                    'timestamp': cob_data.get('timestamp', datetime.now()),
+                    'features': features
+                })
+                logger.debug(f"COB CNN features updated for {symbol}: {features.shape}")
+        except Exception as e:
+            logger.error(f"Error processing COB CNN features for {symbol}: {e}")
+    
+    def _on_cob_dqn_state(self, symbol: str, cob_data: Dict):
+        """Handle COB state features for DQN model integration"""
+        try:
+            if 'state' in cob_data:
+                state = cob_data['state']
+                self.latest_cob_state[symbol] = state
+                logger.debug(f"COB DQN state updated for {symbol}: {state.shape}")
+        except Exception as e:
+            logger.error(f"Error processing COB DQN state for {symbol}: {e}")
+    
+    async def start_cob_integration(self):
+        """Start COB integration for real-time data feed"""
+        try:
+            logger.info("Starting COB integration for real-time market microstructure...")
+            await self.cob_integration.start()
+            logger.info("COB integration started successfully")
+        except Exception as e:
+            logger.error(f"Error starting COB integration: {e}")
+    
+    async def stop_cob_integration(self):
+        """Stop COB integration"""
+        try:
+            await self.cob_integration.stop()
+            logger.info("COB integration stopped")
+        except Exception as e:
+            logger.error(f"Error stopping COB integration: {e}")

main.py

@@ -1,9 +1,10 @@
 #!/usr/bin/env python3
 """
-Streamlined Trading System - Web Dashboard Only
+Streamlined Trading System - Web Dashboard + Training
 
-Simplified entry point with only the web dashboard mode:
+Integrated system with both training loop and web dashboard:
-- Streamlined Flow: Data -> Indicators/Pivots -> CNN -> RL -> Orchestrator -> Execution
+- Training Pipeline: Data -> COB -> Indicators -> CNN -> RL -> Orchestrator -> Execution
+- Web Dashboard: Real-time monitoring and control interface
 - 2-Action System: BUY/SELL with intelligent position management
 - Always invested approach with smart risk/reward setup detection
 
@@ -11,6 +12,11 @@ Usage:
     python main.py [--symbol ETH/USDT] [--port 8050]
 """
 
+import os
+# Fix OpenMP library conflicts before importing other modules
+os.environ['KMP_DUPLICATE_LIB_OK'] = 'TRUE'
+os.environ['OMP_NUM_THREADS'] = '4'
+
 import asyncio
 import argparse
 import logging
@@ -28,7 +34,7 @@ from core.data_provider import DataProvider
 
 logger = logging.getLogger(__name__)
 
-def run_web_dashboard():
+async def run_web_dashboard():
     """Run the streamlined web dashboard with 2-action system and always-invested approach"""
     try:
         logger.info("Starting Streamlined Trading Dashboard...")
@@ -60,9 +66,9 @@ def run_web_dashboard():
         
         # Load model registry for integrated pipeline
         try:
-            from core.model_registry import get_model_registry
+            from models import get_model_registry
-            model_registry = get_model_registry()
+            model_registry = {}  # Use simple dict for now
-            logger.info("[MODELS] Model registry loaded for integrated training")
+            logger.info("[MODELS] Model registry initialized for training")
         except ImportError:
             model_registry = {}
             logger.warning("Model registry not available, using empty registry")
@@ -77,56 +83,139 @@ def run_web_dashboard():
         logger.info("Enhanced Trading Orchestrator with 2-Action System initialized")
         logger.info("Always Invested: Learning to spot high risk/reward setups")
         
+        # Start COB integration for real-time market microstructure
+        try:
+            # Create and start COB integration task
+            cob_task = asyncio.create_task(orchestrator.start_cob_integration())
+            logger.info("COB Integration startup task created")
+        except Exception as e:
+            logger.warning(f"COB Integration startup failed (will retry): {e}")
+        
         # Create trading executor for live execution
         trading_executor = TradingExecutor()
         
-        # Import and create streamlined dashboard
+        # Start the training and monitoring loop
-        from web.dashboard import TradingDashboard
+        logger.info(f"Starting Enhanced Training Pipeline")
-        dashboard = TradingDashboard(
-            data_provider=data_provider, 
-            orchestrator=orchestrator,
-            trading_executor=trading_executor
-        )
-        
-        # Start the integrated dashboard
-        port = config.get('web', {}).get('port', 8050)
-        host = config.get('web', {}).get('host', '127.0.0.1')
-        
-        logger.info(f"Starting Streamlined Dashboard at http://{host}:{port}")
         logger.info("Live Data Processing: ENABLED")
+        logger.info("COB Integration: ENABLED (Real-time market microstructure)")
         logger.info("Integrated CNN Training: ENABLED") 
         logger.info("Integrated RL Training: ENABLED")
         logger.info("Real-time Indicators & Pivots: ENABLED")
         logger.info("Live Trading Execution: ENABLED")
         logger.info("2-Action System: BUY/SELL with position intelligence")
         logger.info("Always Invested: Different thresholds for entry/exit")
-        logger.info("Pipeline: Data -> Indicators -> CNN -> RL -> Orchestrator -> Execution")
+        logger.info("Pipeline: Data -> COB -> Indicators -> CNN -> RL -> Orchestrator -> Execution")
-        logger.info(f"Dashboard optimized: 300ms updates for sub-1s responsiveness")
+        logger.info("Starting training loop...")
         
-        dashboard.run(host=host, port=port, debug=False)
+        # Start the training loop
+        await start_training_loop(orchestrator, trading_executor)
         
     except Exception as e:
         logger.error(f"Error in streamlined dashboard: {e}")
-        logger.error("Dashboard stopped - trying minimal fallback")
+        logger.error("Training stopped")
+        import traceback
+        logger.error(traceback.format_exc())
+
+def start_web_ui():
+    """Start the main TradingDashboard UI in a separate thread"""
+    try:
+        logger.info("=" * 50)
+        logger.info("Starting Main Trading Dashboard UI...")
+        logger.info("Trading Dashboard: http://127.0.0.1:8051")
+        logger.info("=" * 50)
         
-        try:
+        # Import and create the main TradingDashboard (simplified approach)
-            # Minimal fallback dashboard
+        from web.dashboard import TradingDashboard
-            from web.dashboard import TradingDashboard
+        from core.data_provider import DataProvider
-            from core.data_provider import DataProvider
+        from core.orchestrator import TradingOrchestrator
+        from core.trading_executor import TradingExecutor
+        
+        # Initialize components for the dashboard
+        config = get_config()
+        data_provider = DataProvider()
+        
+        # Create orchestrator for the dashboard (standard version for UI compatibility)
+        dashboard_orchestrator = TradingOrchestrator(data_provider=data_provider)
+        
+        trading_executor = TradingExecutor()
+        
+        # Create the main trading dashboard
+        dashboard = TradingDashboard(
+            data_provider=data_provider,
+            orchestrator=dashboard_orchestrator,
+            trading_executor=trading_executor
+        )
+        
+        logger.info("Main TradingDashboard created successfully")
+        logger.info("Features: Live trading, RL training monitoring, Position management")
+        
+        # Run the dashboard server (simplified - no async loop)
+        dashboard.app.run(host='127.0.0.1', port=8051, debug=False, use_reloader=False)
+        
+    except Exception as e:
+        logger.error(f"Error starting main trading dashboard UI: {e}")
+        import traceback
+        logger.error(traceback.format_exc())
+
+async def start_training_loop(orchestrator, trading_executor):
+    """Start the main training and monitoring loop"""
+    logger.info("=" * 70)
+    logger.info("STARTING ENHANCED TRAINING LOOP WITH COB INTEGRATION")
+    logger.info("=" * 70)
+    
+    try:
+        # Start real-time processing
+        await orchestrator.start_realtime_processing()
+        
+        # Main training loop
+        iteration = 0
+        while True:
+            iteration += 1
             
-            data_provider = DataProvider()
+            logger.info(f"Training iteration {iteration}")
-            dashboard = TradingDashboard(data_provider)
+            
-            logger.info("Using minimal fallback dashboard")
+            # Make coordinated decisions (this triggers CNN and RL training)
-            dashboard.run(host='127.0.0.1', port=8050, debug=False)
+            decisions = await orchestrator.make_coordinated_decisions()
-        except Exception as fallback_error:
+            
-            logger.error(f"Fallback dashboard failed: {fallback_error}")
+            # Log decisions and performance
-            logger.error(f"Fatal error: {e}")
+            for symbol, decision in decisions.items():
-            import traceback
+                if decision:
-            logger.error(traceback.format_exc())
+                    logger.info(f"{symbol}: {decision.action} (confidence: {decision.confidence:.3f})")
+                    
+                    # Execute if confidence is high enough
+                    if decision.confidence > 0.7:
+                        logger.info(f"Executing {symbol}: {decision.action}")
+                        # trading_executor.execute_action(decision)
+            
+            # Log performance metrics every 10 iterations
+            if iteration % 10 == 0:
+                metrics = orchestrator.get_performance_metrics()
+                logger.info(f"Performance metrics: {metrics}")
+                
+                # Log COB integration status
+                for symbol in orchestrator.symbols:
+                    cob_features = orchestrator.latest_cob_features.get(symbol)
+                    cob_state = orchestrator.latest_cob_state.get(symbol)
+                    if cob_features is not None:
+                        logger.info(f"{symbol} COB: CNN features {cob_features.shape}, DQN state {cob_state.shape if cob_state is not None else 'None'}")
+            
+            # Sleep between iterations
+            await asyncio.sleep(5)  # 5 second intervals
+            
+    except KeyboardInterrupt:
+        logger.info("Training interrupted by user")
+    except Exception as e:
+        logger.error(f"Error in training loop: {e}")
+        import traceback
+        logger.error(traceback.format_exc())
+    finally:
+        await orchestrator.stop_realtime_processing()
+        await orchestrator.stop_cob_integration()
+        logger.info("Training loop stopped")
 
 async def main():
-    """Main entry point with streamlined web-only operation"""
+    """Main entry point with both training loop and web dashboard"""
-    parser = argparse.ArgumentParser(description='Streamlined Trading System - 2-Action Web Dashboard')
+    parser = argparse.ArgumentParser(description='Streamlined Trading System - Training + Web Dashboard')
     parser.add_argument('--symbol', type=str, default='ETH/USDT',
                        help='Primary trading symbol (default: ETH/USDT)')
     parser.add_argument('--port', type=int, default=8050,
@@ -141,16 +230,26 @@ async def main():
     
     try:
         logger.info("=" * 70)
-        logger.info("STREAMLINED TRADING SYSTEM - 2-ACTION WEB DASHBOARD")
+        logger.info("STREAMLINED TRADING SYSTEM - TRAINING + MAIN DASHBOARD")
         logger.info(f"Primary Symbol: {args.symbol}")
-        logger.info(f"Web Port: {args.port}")
+        logger.info(f"Training Port: {args.port}")
+        logger.info(f"Main Trading Dashboard: http://127.0.0.1:8051")
         logger.info("2-Action System: BUY/SELL with intelligent position management")
         logger.info("Always Invested: Learning to spot high risk/reward setups")
-        logger.info("Flow: Data -> Indicators -> CNN -> RL -> Orchestrator -> Execution")
+        logger.info("Flow: Data -> COB -> Indicators -> CNN -> RL -> Orchestrator -> Execution")
+        logger.info("Main Dashboard: Live trading, RL monitoring, Position management")
         logger.info("=" * 70)
         
-        # Run the web dashboard
+        # Start main trading dashboard UI in a separate thread
-        run_web_dashboard()
+        web_thread = Thread(target=start_web_ui, daemon=True)
+        web_thread.start()
+        logger.info("Main trading dashboard UI thread started")
+        
+        # Give web UI time to start
+        await asyncio.sleep(2)
+        
+        # Run the training loop (this will run indefinitely)
+        await run_web_dashboard()
         
         logger.info("[SUCCESS] Operation completed successfully!")
         

run_enhanced_cob_training.py

@@ -0,0 +1,233 @@
+#!/usr/bin/env python3
+"""
+Enhanced COB + ML Training Pipeline
+
+Runs the complete pipeline:
+Data -> COB Integration -> CNN Features -> RL States -> Model Training -> Trading Decisions
+
+Real-time training with COB market microstructure integration.
+"""
+
+import asyncio
+import logging
+import sys
+from pathlib import Path
+import time
+from datetime import datetime
+
+# Add project root to path
+project_root = Path(__file__).parent
+sys.path.insert(0, str(project_root))
+
+from core.config import setup_logging, get_config
+from core.data_provider import DataProvider
+from core.enhanced_orchestrator import EnhancedTradingOrchestrator
+from core.trading_executor import TradingExecutor
+
+# Setup logging
+setup_logging()
+logger = logging.getLogger(__name__)
+
+class EnhancedCOBTrainer:
+    """Enhanced COB + ML Training Pipeline"""
+    
+    def __init__(self):
+        self.config = get_config()
+        self.symbols = ['BTC/USDT', 'ETH/USDT']
+        self.data_provider = DataProvider()
+        self.orchestrator = None
+        self.trading_executor = None
+        self.running = False
+        
+    async def start_training(self):
+        """Start the enhanced training pipeline"""
+        logger.info("=" * 80)
+        logger.info("ENHANCED COB + ML TRAINING PIPELINE")
+        logger.info("=" * 80)
+        logger.info("Pipeline: Data -> COB -> CNN Features -> RL States -> Model Training")
+        logger.info(f"Symbols: {self.symbols}")
+        logger.info(f"Start time: {datetime.now()}")
+        logger.info("=" * 80)
+        
+        try:
+            # Initialize components
+            await self._initialize_components()
+            
+            # Start training loop
+            await self._run_training_loop()
+            
+        except KeyboardInterrupt:
+            logger.info("Training interrupted by user")
+        except Exception as e:
+            logger.error(f"Training error: {e}")
+            import traceback
+            logger.error(traceback.format_exc())
+        finally:
+            await self._cleanup()
+    
+    async def _initialize_components(self):
+        """Initialize all training components"""
+        logger.info("1. Initializing Enhanced Trading Orchestrator...")
+        
+        self.orchestrator = EnhancedTradingOrchestrator(
+            data_provider=self.data_provider,
+            symbols=self.symbols,
+            enhanced_rl_training=True,
+            model_registry={}
+        )
+        
+        logger.info("2. Starting COB Integration...")
+        await self.orchestrator.start_cob_integration()
+        
+        logger.info("3. Starting Real-time Processing...")
+        await self.orchestrator.start_realtime_processing()
+        
+        logger.info("4. Initializing Trading Executor...")
+        self.trading_executor = TradingExecutor()
+        
+        logger.info("✅ All components initialized successfully")
+        
+        # Wait for initial data collection
+        logger.info("Collecting initial data...")
+        await asyncio.sleep(10)
+    
+    async def _run_training_loop(self):
+        """Main training loop with monitoring"""
+        logger.info("Starting main training loop...")
+        self.running = True
+        iteration = 0
+        
+        while self.running:
+            iteration += 1
+            start_time = time.time()
+            
+            try:
+                # Make coordinated decisions (triggers CNN and RL training)
+                decisions = await self.orchestrator.make_coordinated_decisions()
+                
+                # Process decisions
+                active_decisions = 0
+                for symbol, decision in decisions.items():
+                    if decision and decision.action != 'HOLD':
+                        active_decisions += 1
+                        logger.info(f"🎯 {symbol}: {decision.action} "
+                                  f"(confidence: {decision.confidence:.3f})")
+                
+                # Monitor every 5 iterations
+                if iteration % 5 == 0:
+                    await self._log_training_status(iteration, active_decisions)
+                
+                # Detailed monitoring every 20 iterations
+                if iteration % 20 == 0:
+                    await self._detailed_monitoring(iteration)
+                
+                # Sleep to maintain 5-second intervals
+                elapsed = time.time() - start_time
+                sleep_time = max(0, 5.0 - elapsed)
+                await asyncio.sleep(sleep_time)
+                
+            except Exception as e:
+                logger.error(f"Error in training iteration {iteration}: {e}")
+                await asyncio.sleep(5)
+    
+    async def _log_training_status(self, iteration, active_decisions):
+        """Log current training status"""
+        logger.info(f"📊 Iteration {iteration} - Active decisions: {active_decisions}")
+        
+        # Log COB integration status
+        for symbol in self.symbols:
+            cob_features = self.orchestrator.latest_cob_features.get(symbol)
+            cob_state = self.orchestrator.latest_cob_state.get(symbol)
+            
+            if cob_features is not None:
+                logger.info(f"   {symbol}: COB CNN features: {cob_features.shape}")
+            if cob_state is not None:
+                logger.info(f"   {symbol}: COB RL state: {cob_state.shape}")
+    
+    async def _detailed_monitoring(self, iteration):
+        """Detailed monitoring and metrics"""
+        logger.info("=" * 60)
+        logger.info(f"DETAILED MONITORING - Iteration {iteration}")
+        logger.info("=" * 60)
+        
+        # Performance metrics
+        try:
+            metrics = self.orchestrator.get_performance_metrics()
+            logger.info(f"📈 Performance Metrics:")
+            for key, value in metrics.items():
+                logger.info(f"   {key}: {value}")
+        except Exception as e:
+            logger.warning(f"Could not get performance metrics: {e}")
+        
+        # COB integration status
+        logger.info("🔄 COB Integration Status:")
+        for symbol in self.symbols:
+            try:
+                # Check COB features
+                cob_features = self.orchestrator.latest_cob_features.get(symbol)
+                cob_state = self.orchestrator.latest_cob_state.get(symbol)
+                history_len = len(self.orchestrator.cob_feature_history[symbol])
+                
+                logger.info(f"   {symbol}:")
+                logger.info(f"     CNN Features: {cob_features.shape if cob_features is not None else 'None'}")
+                logger.info(f"     RL State: {cob_state.shape if cob_state is not None else 'None'}")
+                logger.info(f"     History Length: {history_len}")
+                
+                # Get COB snapshot if available
+                if self.orchestrator.cob_integration:
+                    snapshot = self.orchestrator.cob_integration.get_cob_snapshot(symbol)
+                    if snapshot:
+                        logger.info(f"     Order Book: {len(snapshot.consolidated_bids)} bids, "
+                                  f"{len(snapshot.consolidated_asks)} asks")
+                        logger.info(f"     Mid Price: ${snapshot.volume_weighted_mid:.2f}")
+                        
+            except Exception as e:
+                logger.warning(f"Error checking {symbol} status: {e}")
+        
+        # Model training status
+        logger.info("🧠 Model Training Status:")
+        # Add model-specific status here when available
+        
+        # Position status
+        try:
+            positions = self.orchestrator.get_position_status()
+            logger.info(f"💼 Positions: {positions}")
+        except Exception as e:
+            logger.warning(f"Could not get position status: {e}")
+        
+        logger.info("=" * 60)
+    
+    async def _cleanup(self):
+        """Cleanup resources"""
+        logger.info("Cleaning up resources...")
+        
+        if self.orchestrator:
+            try:
+                await self.orchestrator.stop_realtime_processing()
+                logger.info("✅ Real-time processing stopped")
+            except Exception as e:
+                logger.warning(f"Error stopping real-time processing: {e}")
+            
+            try:
+                await self.orchestrator.stop_cob_integration()
+                logger.info("✅ COB integration stopped")
+            except Exception as e:
+                logger.warning(f"Error stopping COB integration: {e}")
+        
+        self.running = False
+        logger.info("🏁 Training pipeline stopped")
+
+async def main():
+    """Main entry point"""
+    trainer = EnhancedCOBTrainer()
+    await trainer.start_training()
+
+if __name__ == "__main__":
+    try:
+        asyncio.run(main())
+    except KeyboardInterrupt:
+        print("\nTraining interrupted by user")
+    except Exception as e:
+        print(f"Training failed: {e}")
+        import traceback
+        traceback.print_exc() 

run_main_dashboard.py

@@ -0,0 +1,79 @@
+#!/usr/bin/env python3
+"""
+Run Main Trading Dashboard
+
+Dedicated script to run the main TradingDashboard with all trading controls,
+RL training monitoring, and position management features.
+
+Usage:
+    python run_main_dashboard.py
+"""
+
+import sys
+import logging
+from pathlib import Path
+
+# Add project root to path
+project_root = Path(__file__).parent
+sys.path.insert(0, str(project_root))
+
+from core.config import setup_logging, get_config
+from core.data_provider import DataProvider
+from core.orchestrator import TradingOrchestrator
+from core.trading_executor import TradingExecutor
+from web.dashboard import TradingDashboard
+
+def main():
+    """Run the main TradingDashboard"""
+    # Setup logging
+    setup_logging()
+    logger = logging.getLogger(__name__)
+    
+    try:
+        logger.info("=" * 60)
+        logger.info("STARTING MAIN TRADING DASHBOARD")
+        logger.info("=" * 60)
+        logger.info("Features:")
+        logger.info("- Live trading with BUY/SELL controls")
+        logger.info("- Real-time RL training monitoring")
+        logger.info("- Position management & P&L tracking")
+        logger.info("- Performance metrics & trade history")
+        logger.info("- Model accuracy & confidence tracking")
+        logger.info("=" * 60)
+        
+        # Get configuration
+        config = get_config()
+        
+        # Initialize components
+        data_provider = DataProvider()
+        orchestrator = TradingOrchestrator(data_provider=data_provider)
+        trading_executor = TradingExecutor()
+        
+        # Create the main trading dashboard
+        dashboard = TradingDashboard(
+            data_provider=data_provider,
+            orchestrator=orchestrator,
+            trading_executor=trading_executor
+        )
+        
+        logger.info("TradingDashboard created successfully")
+        logger.info("Starting web server at http://127.0.0.1:8051")
+        logger.info("Open your browser to access the trading interface")
+        
+        # Run the dashboard
+        dashboard.app.run(
+            host='127.0.0.1',
+            port=8051,
+            debug=False,
+            use_reloader=False
+        )
+        
+    except KeyboardInterrupt:
+        logger.info("Dashboard shutdown requested by user")
+    except Exception as e:
+        logger.error(f"Error running main trading dashboard: {e}")
+        import traceback
+        logger.error(traceback.format_exc())
+
+if __name__ == "__main__":
+    main() 

test_dashboard_simple.py

@@ -1,55 +1,103 @@
 #!/usr/bin/env python3
 """
-Simple test for the scalping dashboard with dynamic throttling
+Simple Dashboard Test - Isolate dashboard startup issues
 """
-import requests
-import time
 
-def test_dashboard():
+import os
-    """Test dashboard basic functionality"""
+# Fix OpenMP library conflicts before importing other modules
-    base_url = "http://127.0.0.1:8051"
+os.environ['KMP_DUPLICATE_LIB_OK'] = 'TRUE'
-    
+os.environ['OMP_NUM_THREADS'] = '4'
-    print("Testing Scalping Dashboard with Dynamic Throttling...")
+
-    
+import sys
+import logging
+from pathlib import Path
+
+# Add project root to path
+project_root = Path(__file__).parent
+sys.path.insert(0, str(project_root))
+
+# Setup basic logging
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+)
+logger = logging.getLogger(__name__)
+
+def test_dashboard_startup():
+    """Test dashboard creation and startup"""
     try:
-        # Test main page
+        logger.info("=" * 50)
-        response = requests.get(base_url, timeout=5)
+        logger.info("TESTING DASHBOARD STARTUP")
-        print(f"Main page: {response.status_code}")
+        logger.info("=" * 50)
+        
+        # Test imports first
+        logger.info("Step 1: Testing imports...")
+        from core.config import get_config, setup_logging
+        from core.data_provider import DataProvider
+        from core.orchestrator import TradingOrchestrator
+        from core.trading_executor import TradingExecutor
+        logger.info("✓ Core imports successful")
+        
+        from web.dashboard import TradingDashboard
+        logger.info("✓ Dashboard import successful")
+        
+        # Test configuration
+        logger.info("Step 2: Testing configuration...")
+        setup_logging()
+        config = get_config()
+        logger.info("✓ Configuration loaded")
+        
+        # Test core component creation
+        logger.info("Step 3: Testing core component creation...")
+        data_provider = DataProvider()
+        logger.info("✓ DataProvider created")
+        
+        orchestrator = TradingOrchestrator(data_provider=data_provider)
+        logger.info("✓ TradingOrchestrator created")
+        
+        trading_executor = TradingExecutor()
+        logger.info("✓ TradingExecutor created")
+        
+        # Test dashboard creation
+        logger.info("Step 4: Testing dashboard creation...")
+        dashboard = TradingDashboard(
+            data_provider=data_provider,
+            orchestrator=orchestrator,
+            trading_executor=trading_executor
+        )
+        logger.info("✓ TradingDashboard created successfully")
+        
+        # Test dashboard startup
+        logger.info("Step 5: Testing dashboard server startup...")
+        logger.info("Dashboard will start on http://127.0.0.1:8052")
+        logger.info("Press Ctrl+C to stop the test")
+        
+        # Run the dashboard
+        dashboard.app.run(
+            host='127.0.0.1', 
+            port=8052, 
+            debug=False, 
+            use_reloader=False
+        )
         
-        if response.status_code == 200:
-            print("✅ Dashboard is running successfully!")
-            print("✅ Unicode encoding issues fixed")
-            print("✅ Dynamic throttling implemented")
-            print("✅ Charts should now display properly")
-            
-            print("\nDynamic Throttling Features:")
-            print("• Adaptive update frequency (500ms - 2000ms)")
-            print("• Performance-based throttling (0-5 levels)")
-            print("• Automatic optimization based on callback duration")
-            print("• Fallback to last known state when throttled")
-            print("• Real-time performance monitoring")
-            
-            return True
-        else:
-            print(f"❌ Dashboard returned status {response.status_code}")
-            return False
-            
-    except requests.exceptions.ConnectionError:
-        print("❌ Cannot connect to dashboard")
-        return False
     except Exception as e:
-        print(f"❌ Error: {e}")
+        logger.error(f"❌ Dashboard test failed: {e}")
+        import traceback
+        logger.error(traceback.format_exc())
         return False
+    
+    return True
 
 if __name__ == "__main__":
-    success = test_dashboard()
+    try:
-    if success:
+        success = test_dashboard_startup()
-        print("\n🎉 SCALPING DASHBOARD FIXED!")
+        if success:
-        print("The dashboard now has:")
+            logger.info("✓ Dashboard test completed successfully")
-        print("1. Fixed Unicode encoding issues")
+        else:
-        print("2. Proper Dash callback structure")
+            logger.error("❌ Dashboard test failed")
-        print("3. Dynamic throttling for optimal performance")
+            sys.exit(1)
-        print("4. Adaptive update frequency")
+    except KeyboardInterrupt:
-        print("5. Performance monitoring and optimization")
+        logger.info("Dashboard test interrupted by user")
-    else:
+    except Exception as e:
-        print("\n❌ Dashboard still has issues") 
+        logger.error(f"Fatal error in dashboard test: {e}")
+        sys.exit(1) 

test_enhanced_cob_integration.py

@@ -0,0 +1,201 @@
+#!/usr/bin/env python3
+"""
+Test Enhanced COB Integration with RL and CNN Models
+
+This script tests the integration of Consolidated Order Book (COB) data
+with the real-time RL and CNN training pipeline.
+"""
+
+import asyncio
+import logging
+import sys
+from pathlib import Path
+import numpy as np
+import time
+from datetime import datetime
+
+# Add project root to path
+project_root = Path(__file__).parent
+sys.path.insert(0, str(project_root))
+
+from core.config import setup_logging
+from core.data_provider import DataProvider
+from core.enhanced_orchestrator import EnhancedTradingOrchestrator
+from core.cob_integration import COBIntegration
+
+# Setup logging
+setup_logging()
+logger = logging.getLogger(__name__)
+
+class COBMLIntegrationTester:
+    """Test COB integration with ML models"""
+    
+    def __init__(self):
+        self.symbols = ['BTC/USDT', 'ETH/USDT']
+        self.data_provider = DataProvider()
+        self.test_results = {}
+        
+    async def test_cob_ml_integration(self):
+        """Test full COB integration with ML pipeline"""
+        logger.info("=" * 60)
+        logger.info("TESTING COB INTEGRATION WITH RL AND CNN MODELS")
+        logger.info("=" * 60)
+        
+        try:
+            # Initialize enhanced orchestrator with COB integration
+            logger.info("1. Initializing Enhanced Trading Orchestrator with COB...")
+            orchestrator = EnhancedTradingOrchestrator(
+                data_provider=self.data_provider,
+                symbols=self.symbols,
+                enhanced_rl_training=True,
+                model_registry={}
+            )
+            
+            # Start COB integration
+            logger.info("2. Starting COB Integration...")
+            await orchestrator.start_cob_integration()
+            await asyncio.sleep(5)  # Allow startup and data collection
+            
+            # Test COB feature generation
+            logger.info("3. Testing COB feature generation...")
+            await self._test_cob_features(orchestrator)
+            
+            # Test market state with COB data
+            logger.info("4. Testing market state with COB data...")
+            await self._test_market_state_cob(orchestrator)
+            
+            # Test real-time COB callbacks
+            logger.info("5. Testing real-time COB callbacks...")
+            await self._test_realtime_callbacks(orchestrator)
+            
+            # Stop COB integration
+            await orchestrator.stop_cob_integration()
+            
+            # Print results
+            self._print_test_results()
+            
+        except Exception as e:
+            logger.error(f"Error in COB ML integration test: {e}")
+            import traceback
+            logger.error(traceback.format_exc())
+    
+    async def _test_cob_features(self, orchestrator):
+        """Test COB feature availability"""
+        try:
+            for symbol in self.symbols:
+                # Check if COB features are available
+                cob_features = orchestrator.latest_cob_features.get(symbol)
+                cob_state = orchestrator.latest_cob_state.get(symbol)
+                
+                if cob_features is not None:
+                    logger.info(f"✅ {symbol}: COB CNN features available - shape: {cob_features.shape}")
+                    self.test_results[f'{symbol}_cob_cnn_features'] = True
+                else:
+                    logger.warning(f"⚠️ {symbol}: COB CNN features not available")
+                    self.test_results[f'{symbol}_cob_cnn_features'] = False
+                
+                if cob_state is not None:
+                    logger.info(f"✅ {symbol}: COB DQN state available - shape: {cob_state.shape}")
+                    self.test_results[f'{symbol}_cob_dqn_state'] = True
+                else:
+                    logger.warning(f"⚠️ {symbol}: COB DQN state not available")
+                    self.test_results[f'{symbol}_cob_dqn_state'] = False
+                    
+        except Exception as e:
+            logger.error(f"Error testing COB features: {e}")
+    
+    async def _test_market_state_cob(self, orchestrator):
+        """Test market state includes COB data"""
+        try:
+            # Generate market states with COB data
+            from core.universal_data_adapter import UniversalDataAdapter
+            adapter = UniversalDataAdapter(self.data_provider)
+            universal_stream = await adapter.get_universal_stream(['BTC/USDT', 'ETH/USDT'])
+            
+            market_states = await orchestrator._get_all_market_states_universal(universal_stream)
+            
+            for symbol in self.symbols:
+                if symbol in market_states:
+                    state = market_states[symbol]
+                    
+                    # Check COB integration in market state
+                    tests = [
+                        ('cob_features', state.cob_features is not None),
+                        ('cob_state', state.cob_state is not None),
+                        ('order_book_imbalance', hasattr(state, 'order_book_imbalance')),
+                        ('liquidity_depth', hasattr(state, 'liquidity_depth')),
+                        ('exchange_diversity', hasattr(state, 'exchange_diversity')),
+                        ('market_impact_estimate', hasattr(state, 'market_impact_estimate'))
+                    ]
+                    
+                    for test_name, passed in tests:
+                        status = "✅" if passed else "❌"
+                        logger.info(f"{status} {symbol}: {test_name} - {passed}")
+                        self.test_results[f'{symbol}_market_state_{test_name}'] = passed
+                        
+                    # Log COB metrics if available
+                    if hasattr(state, 'order_book_imbalance'):
+                        logger.info(f"📊 {symbol} COB Metrics:")
+                        logger.info(f"   Order Book Imbalance: {state.order_book_imbalance:.4f}")
+                        logger.info(f"   Liquidity Depth: ${state.liquidity_depth:,.0f}")
+                        logger.info(f"   Exchange Diversity: {state.exchange_diversity}")
+                        logger.info(f"   Market Impact (10k): {state.market_impact_estimate:.4f}%")
+                        
+        except Exception as e:
+            logger.error(f"Error testing market state COB: {e}")
+    
+    async def _test_realtime_callbacks(self, orchestrator):
+        """Test real-time COB callbacks"""
+        try:
+            # Monitor COB callbacks for 10 seconds
+            initial_features = {s: len(orchestrator.cob_feature_history[s]) for s in self.symbols}
+            
+            logger.info("Monitoring COB callbacks for 10 seconds...")
+            await asyncio.sleep(10)
+            
+            final_features = {s: len(orchestrator.cob_feature_history[s]) for s in self.symbols}
+            
+            for symbol in self.symbols:
+                updates = final_features[symbol] - initial_features[symbol]
+                if updates > 0:
+                    logger.info(f"✅ {symbol}: Received {updates} COB feature updates")
+                    self.test_results[f'{symbol}_realtime_callbacks'] = True
+                else:
+                    logger.warning(f"⚠️ {symbol}: No COB feature updates received")
+                    self.test_results[f'{symbol}_realtime_callbacks'] = False
+                    
+        except Exception as e:
+            logger.error(f"Error testing realtime callbacks: {e}")
+    
+    def _print_test_results(self):
+        """Print comprehensive test results"""
+        logger.info("=" * 60)
+        logger.info("COB ML INTEGRATION TEST RESULTS")
+        logger.info("=" * 60)
+        
+        passed = sum(1 for result in self.test_results.values() if result)
+        total = len(self.test_results)
+        
+        logger.info(f"Overall: {passed}/{total} tests passed ({passed/total*100:.1f}%)")
+        logger.info("")
+        
+        for test_name, result in self.test_results.items():
+            status = "✅ PASS" if result else "❌ FAIL"
+            logger.info(f"{status}: {test_name}")
+        
+        logger.info("=" * 60)
+        
+        if passed == total:
+            logger.info("🎉 ALL TESTS PASSED - COB ML INTEGRATION WORKING!")
+        elif passed > total * 0.8:
+            logger.info("⚠️ MOSTLY WORKING - Some minor issues detected")
+        else:
+            logger.warning("🚨 INTEGRATION ISSUES - Significant problems detected")
+
+async def main():
+    """Run COB ML integration tests"""
+    tester = COBMLIntegrationTester()
+    await tester.test_cob_ml_integration()
+
+if __name__ == "__main__":
+    asyncio.run(main())