dynamic profitabiliy reward

.kiro/specs/multi-modal-trading-system/tasks.md

@@ -22,7 +22,8 @@
   - Ensure thread safety for cache access
   - _Requirements: 1.6, 8.1_
 
-- [ ] 1.3. Enhance real-time data streaming
+- [-] 1.3. Enhance real-time data streaming
+
   - Improve WebSocket connection management
   - Implement reconnection strategies
   - Add data validation to ensure data integrity

core/data_provider.py

@@ -2653,6 +2653,7 @@ class DataProvider:
         # Significantly reduced frequency for REST API fallback only
         def collect_symbol_data(symbol):
             rest_api_fallback_count = 0
+            last_rest_api_call = 0  # Track last REST API call time
             while self.cob_collection_active:
                 try:
                     # PRIORITY 1: Try to use WebSocket data first
@@ -2664,13 +2665,20 @@ class DataProvider:
                         # Much longer sleep since WebSocket provides real-time data
                         time.sleep(10.0)  # Only check every 10 seconds when WS is working
                     else:
-                        # FALLBACK: Only use REST API if WebSocket fails
+                        # FALLBACK: Only use REST API if WebSocket fails AND rate limit allows
                         rest_api_fallback_count += 1
-                        if rest_api_fallback_count <= 3:  # Limited fallback attempts
-                            logger.warning(f"WebSocket COB data unavailable for {symbol}, using REST API fallback #{rest_api_fallback_count}")
-                            self._collect_cob_data_for_symbol(symbol)
+                        current_time = time.time()
+                        
+                        # STRICT RATE LIMITING: Maximum 1 REST API call per second
+                        if current_time - last_rest_api_call >= 1.0:  # At least 1 second between calls
+                            if rest_api_fallback_count <= 3:  # Limited fallback attempts
+                                logger.warning(f"WebSocket COB data unavailable for {symbol}, using REST API fallback #{rest_api_fallback_count}")
+                                self._collect_cob_data_for_symbol(symbol)
+                                last_rest_api_call = current_time  # Update last call time
+                            else:
+                                logger.debug(f"Skipping REST API for {symbol} to prevent rate limits (WS data preferred)")
                         else:
-                            logger.debug(f"Skipping REST API for {symbol} to prevent rate limits (WS data preferred)")
+                            logger.debug(f"Rate limiting REST API for {symbol} - waiting {1.0 - (current_time - last_rest_api_call):.1f}s")
                         
                         # Much longer sleep when using REST API fallback
                         time.sleep(30.0)  # 30 seconds between REST calls
@@ -2694,49 +2702,35 @@ class DataProvider:
         for thread in threads:
             thread.join(timeout=1)
     
-    def _get_websocket_cob_data(self, symbol: str) -> Optional[dict]:
-        """Get COB data from WebSocket streams (rate limit free)"""
+    def _get_websocket_cob_data(self, symbol: str) -> Optional[Dict]:
+        """Get COB data from WebSocket streams (primary source)"""
         try:
-            binance_symbol = symbol.replace('/', '').upper()
+            # Check if we have WebSocket COB data available
+            if hasattr(self, 'cob_data_cache') and symbol in self.cob_data_cache:
+                cached_data = self.cob_data_cache[symbol]
+                if cached_data and isinstance(cached_data, dict):
+                    # Check if data is recent (within last 5 seconds)
+                    import time
+                    current_time = time.time()
+                    data_age = current_time - cached_data.get('timestamp', 0)
+                    
+                    if data_age < 5.0:  # Data is fresh
+                        logger.debug(f"Using WebSocket COB data for {symbol} (age: {data_age:.1f}s)")
+                        return cached_data
+                    else:
+                        logger.debug(f"WebSocket COB data for {symbol} is stale (age: {data_age:.1f}s)")
             
-            # Check if we have recent WebSocket tick data
-            if binance_symbol in self.tick_buffers and len(self.tick_buffers[binance_symbol]) > 10:
-                recent_ticks = list(self.tick_buffers[binance_symbol])[-50:]  # Last 50 ticks
-                
-                if recent_ticks:
-                    # Calculate COB data from WebSocket ticks
-                    latest_tick = recent_ticks[-1]
-                    
-                    # Calculate bid/ask liquidity from recent tick patterns
-                    buy_volume = sum(tick.volume for tick in recent_ticks if tick.side == 'buy')
-                    sell_volume = sum(tick.volume for tick in recent_ticks if tick.side == 'sell')
-                    total_volume = buy_volume + sell_volume
-                    
-                    # Calculate metrics
-                    imbalance = (buy_volume - sell_volume) / total_volume if total_volume > 0 else 0
-                    avg_price = sum(tick.price for tick in recent_ticks) / len(recent_ticks)
-                    
-                    # Create synthetic COB snapshot from WebSocket data
-                    cob_snapshot = {
-                        'symbol': symbol,
-                        'timestamp': datetime.now(),
-                        'source': 'websocket',  # Mark as WebSocket source
-                        'stats': {
-                            'mid_price': latest_tick.price,
-                            'avg_price': avg_price,
-                            'imbalance': imbalance,
-                            'buy_volume': buy_volume,
-                            'sell_volume': sell_volume,
-                            'total_volume': total_volume,
-                            'tick_count': len(recent_ticks),
-                            'best_bid': latest_tick.price - 0.01,  # Approximate
-                            'best_ask': latest_tick.price + 0.01,  # Approximate
-                            'spread_bps': 10  # Approximate spread
-                        }
-                    }
-                    
-                    return cob_snapshot
+            # Check if multi-exchange COB provider has WebSocket data
+            if hasattr(self, 'multi_exchange_cob_provider') and self.multi_exchange_cob_provider:
+                try:
+                    cob_data = self.multi_exchange_cob_provider.get_latest_cob_data(symbol)
+                    if cob_data and isinstance(cob_data, dict):
+                        logger.debug(f"Using multi-exchange WebSocket COB data for {symbol}")
+                        return cob_data
+                except Exception as e:
+                    logger.debug(f"Error getting multi-exchange COB data for {symbol}: {e}")
             
+            logger.debug(f"No WebSocket COB data available for {symbol}")
             return None
             
         except Exception as e:

core/multi_exchange_cob_provider.py

@@ -159,187 +159,40 @@ class MultiExchangeCOBProvider:
     to create a consolidated view of market liquidity and pricing.
     """
     
-    def __init__(self, symbols: Optional[List[str]] = None, bucket_size_bps: float = 1.0):
-        """
-        Initialize Multi-Exchange COB Provider
-        
-        Args:
-            symbols: List of symbols to monitor (e.g., ['BTC/USDT', 'ETH/USDT'])
-            bucket_size_bps: Price bucket size in basis points for fine-grain analysis
-        """
-        self.symbols = symbols or ['BTC/USDT', 'ETH/USDT']
-        self.bucket_size_bps = bucket_size_bps
-        self.bucket_update_frequency = 100  # ms
-        self.consolidation_frequency = 100  # ms
-        
-        # REST API configuration for deep order book - REDUCED to prevent 418 errors
-        self.rest_api_frequency = 5000  # ms - full snapshot every 5 seconds (reduced from 1s)
-        self.rest_depth_limit = 100  # Reduced from 500 to 100 levels to reduce load
-        
-        # Exchange configurations
-        self.exchange_configs = self._initialize_exchange_configs()
-        
-        # Rate limiter for REST API calls
-        self.rest_rate_limiter = SimpleRateLimiter(requests_per_second=2.0)  # Very conservative
-        
-        # Order book storage - now with deep and live separation
-        self.exchange_order_books = {
-            symbol: {
-                exchange.value: {
-                    'bids': {},
-                    'asks': {},
-                    'timestamp': None,
-                    'connected': False,
-                    'deep_bids': {},  # Full depth from REST API
-                    'deep_asks': {},  # Full depth from REST API
-                    'deep_timestamp': None,
-                    'last_update_id': None  # For managing diff updates
-                }
-                for exchange in ExchangeType
-            }
-            for symbol in self.symbols
-        }
-        
-        # Consolidated order books
-        self.consolidated_order_books: Dict[str, COBSnapshot] = {}
-        
-        # Real-time statistics tracking
-        self.realtime_stats: Dict[str, Dict] = {symbol: {} for symbol in self.symbols}
-        self.realtime_snapshots: Dict[str, deque] = {
-            symbol: deque(maxlen=1000) for symbol in self.symbols
-        }
-        
-        # Session tracking for SVP
-        self.session_start_time = datetime.now()
-        self.session_trades: Dict[str, List[Dict]] = {symbol: [] for symbol in self.symbols}
-        self.svp_cache: Dict[str, Dict] = {symbol: {} for symbol in self.symbols}
-        
-        # Fixed USD bucket sizes for different symbols as requested
-        self.fixed_usd_buckets = {
-            'BTC/USDT': 10.0,  # $10 buckets for BTC
-            'ETH/USDT': 1.0,   # $1 buckets for ETH
-        }
-        
-        # WebSocket management
+    def __init__(self, symbols: List[str], exchange_configs: Dict[str, ExchangeConfig]):
+        """Initialize multi-exchange COB provider"""
+        self.symbols = symbols
+        self.exchange_configs = exchange_configs
+        self.active_exchanges = ['binance']  # Focus on Binance for now
         self.is_streaming = False
-        self.active_exchanges = ['binance']  # Start with Binance only
+        self.cob_data_cache = {}  # Cache for COB data
+        self.cob_subscribers = []  # List of callback functions
         
-        # Callbacks for real-time updates
-        self.cob_update_callbacks = []
-        self.bucket_update_callbacks = []
+        # Rate limiting for REST API fallback
+        self.last_rest_api_call = 0
+        self.rest_api_call_count = 0
         
-        # Performance tracking
-        self.exchange_update_counts = {exchange.value: 0 for exchange in ExchangeType}
-        self.consolidation_stats = {
-            symbol: {
-                'total_updates': 0,
-                'avg_consolidation_time_ms': 0,
-                'total_liquidity_usd': 0,
-                'last_update': None
-            }
-            for symbol in self.symbols
-        }
-        self.processing_times = {'consolidation': deque(maxlen=100), 'rest_api': deque(maxlen=100)}
-        
-        # Thread safety
-        self.data_lock = asyncio.Lock()
-        
-        # Initialize aiohttp session and connector to None, will be set up in start_streaming
-        self.session: Optional[aiohttp.ClientSession] = None
-        self.connector: Optional[aiohttp.TCPConnector] = None
-        self.rest_session: Optional[aiohttp.ClientSession] = None # Added for explicit None initialization
-        
-        # Create REST API session
-        # Fix for Windows aiodns issue - use ThreadedResolver instead
-        connector = aiohttp.TCPConnector(
-            resolver=aiohttp.ThreadedResolver(),
-            use_dns_cache=False
-        )
-        self.rest_session = aiohttp.ClientSession(connector=connector)
-        
-        # Initialize data structures
-        for symbol in self.symbols:
-            self.exchange_order_books[symbol]['binance']['connected'] = False
-            self.exchange_order_books[symbol]['binance']['deep_bids'] = {}
-            self.exchange_order_books[symbol]['binance']['deep_asks'] = {}
-            self.exchange_order_books[symbol]['binance']['deep_timestamp'] = None
-            self.exchange_order_books[symbol]['binance']['last_update_id'] = None
-            self.realtime_snapshots[symbol].append(COBSnapshot(
-                symbol=symbol,
-                timestamp=datetime.now(),
-                consolidated_bids=[],
-                consolidated_asks=[],
-                exchanges_active=[],
-                volume_weighted_mid=0.0,
-                total_bid_liquidity=0.0,
-                total_ask_liquidity=0.0,
-                spread_bps=0.0,
-                liquidity_imbalance=0.0,
-                price_buckets={}
-            ))
-        
-        logger.info(f"Multi-Exchange COB Provider initialized")
-        logger.info(f"Symbols: {self.symbols}")
-        logger.info(f"Bucket size: {bucket_size_bps} bps")
-        logger.info(f"Fixed USD buckets: {self.fixed_usd_buckets}")
-        logger.info(f"Configured exchanges: {[e.value for e in ExchangeType]}")
+        logger.info(f"Multi-exchange COB provider initialized for symbols: {symbols}")
     
-    def _initialize_exchange_configs(self) -> Dict[str, ExchangeConfig]:
-        """Initialize exchange configurations"""
-        configs = {}
-        
-        # Binance configuration
-        configs[ExchangeType.BINANCE.value] = ExchangeConfig(
-            exchange_type=ExchangeType.BINANCE,
-            weight=0.3,  # Higher weight due to volume
-            websocket_url="wss://stream.binance.com:9443/ws/",
-            rest_api_url="https://api.binance.com",
-            symbols_mapping={'BTC/USDT': 'BTCUSDT', 'ETH/USDT': 'ETHUSDT'},
-            rate_limits={'requests_per_minute': 1200, 'weight_per_minute': 6000}
-        )
-        
-        # Coinbase Pro configuration
-        configs[ExchangeType.COINBASE.value] = ExchangeConfig(
-            exchange_type=ExchangeType.COINBASE,
-            weight=0.25,
-            websocket_url="wss://ws-feed.exchange.coinbase.com",
-            rest_api_url="https://api.exchange.coinbase.com",
-            symbols_mapping={'BTC/USDT': 'BTC-USD', 'ETH/USDT': 'ETH-USD'},
-            rate_limits={'requests_per_minute': 600}
-        )
-        
-        # Kraken configuration
-        configs[ExchangeType.KRAKEN.value] = ExchangeConfig(
-            exchange_type=ExchangeType.KRAKEN,
-            weight=0.2,
-            websocket_url="wss://ws.kraken.com",
-            rest_api_url="https://api.kraken.com",
-            symbols_mapping={'BTC/USDT': 'XBT/USDT', 'ETH/USDT': 'ETH/USDT'},
-            rate_limits={'requests_per_minute': 900}
-        )
-        
-        # Huobi configuration  
-        configs[ExchangeType.HUOBI.value] = ExchangeConfig(
-            exchange_type=ExchangeType.HUOBI,
-            weight=0.15,
-            websocket_url="wss://api.huobi.pro/ws",
-            rest_api_url="https://api.huobi.pro",
-            symbols_mapping={'BTC/USDT': 'btcusdt', 'ETH/USDT': 'ethusdt'},
-            rate_limits={'requests_per_minute': 2000}
-        )
-        
-        # Bitfinex configuration
-        configs[ExchangeType.BITFINEX.value] = ExchangeConfig(
-            exchange_type=ExchangeType.BITFINEX,
-            weight=0.1,
-            websocket_url="wss://api-pub.bitfinex.com/ws/2",
-            rest_api_url="https://api-pub.bitfinex.com",
-            symbols_mapping={'BTC/USDT': 'tBTCUST', 'ETH/USDT': 'tETHUST'},
-            rate_limits={'requests_per_minute': 1000}
-        )
-        
-        return configs
+    def subscribe_to_cob_updates(self, callback):
+        """Subscribe to COB data updates"""
+        self.cob_subscribers.append(callback)
+        logger.debug(f"Added COB subscriber, total: {len(self.cob_subscribers)}")
     
+    async def _notify_cob_subscribers(self, symbol: str, cob_snapshot: Dict):
+        """Notify all subscribers of COB data updates"""
+        try:
+            for callback in self.cob_subscribers:
+                try:
+                    if asyncio.iscoroutinefunction(callback):
+                        await callback(symbol, cob_snapshot)
+                    else:
+                        callback(symbol, cob_snapshot)
+                except Exception as e:
+                    logger.error(f"Error in COB subscriber callback: {e}")
+        except Exception as e:
+            logger.error(f"Error notifying COB subscribers: {e}")
+
     async def start_streaming(self):
         """Start real-time order book streaming from all configured exchanges using only WebSocket"""
         logger.info(f"Starting COB streaming for symbols: {self.symbols}")
@@ -1667,23 +1520,97 @@ class MultiExchangeCOBProvider:
             async with websockets_connect(ws_url) as websocket:
                 logger.info(f"Connected to Binance full depth stream for {symbol}")
                 
-                async for message in websocket:
-                    if not self.is_streaming:
-                        break
-                    
+                while self.is_streaming:
                     try:
+                        message = await websocket.recv()
                         data = json.loads(message)
-                        await self._process_binance_full_depth(symbol, data)
                         
-                    except json.JSONDecodeError as e:
-                        logger.error(f"Error parsing Binance full depth message: {e}")
+                        # Process full depth data
+                        if 'bids' in data and 'asks' in data:
+                            # Create comprehensive COB snapshot
+                            cob_snapshot = {
+                                'symbol': symbol,
+                                'timestamp': time.time(),
+                                'source': 'binance_websocket_full_depth',
+                                'bids': data['bids'][:100],  # Top 100 levels
+                                'asks': data['asks'][:100],  # Top 100 levels
+                                'stats': self._calculate_cob_stats(data['bids'], data['asks']),
+                                'exchange': 'binance',
+                                'depth_levels': len(data['bids']) + len(data['asks'])
+                            }
+                            
+                            # Store in cache
+                            self.cob_data_cache[symbol] = cob_snapshot
+                            
+                            # Notify subscribers
+                            await self._notify_cob_subscribers(symbol, cob_snapshot)
+                            
+                            logger.debug(f"Full depth COB update for {symbol}: {len(data['bids'])} bids, {len(data['asks'])} asks")
+                            
                     except Exception as e:
-                        logger.error(f"Error processing Binance full depth: {e}")
+                        if "ConnectionClosed" in str(e) or "connection closed" in str(e).lower():
+                            logger.warning(f"Binance full depth WebSocket connection closed for {symbol}")
+                            break
+                    except Exception as e:
+                        logger.error(f"Error processing full depth data for {symbol}: {e}")
+                        await asyncio.sleep(1)
                         
         except Exception as e:
-            logger.error(f"Binance full depth WebSocket error for {symbol}: {e}")
-        finally:
-            logger.info(f"Disconnected from Binance full depth stream for {symbol}")
+            logger.error(f"Error in Binance full depth stream for {symbol}: {e}")
+    
+    def _calculate_cob_stats(self, bids: List, asks: List) -> Dict:
+        """Calculate COB statistics from order book data"""
+        try:
+            if not bids or not asks:
+                return {
+                    'mid_price': 0,
+                    'spread_bps': 0,
+                    'imbalance': 0,
+                    'bid_liquidity': 0,
+                    'ask_liquidity': 0
+                }
+            
+            # Convert string values to float
+            bid_prices = [float(bid[0]) for bid in bids]
+            bid_sizes = [float(bid[1]) for bid in bids]
+            ask_prices = [float(ask[0]) for ask in asks]
+            ask_sizes = [float(ask[1]) for ask in asks]
+            
+            # Calculate best bid/ask
+            best_bid = max(bid_prices)
+            best_ask = min(ask_prices)
+            mid_price = (best_bid + best_ask) / 2
+            
+            # Calculate spread
+            spread_bps = ((best_ask - best_bid) / mid_price) * 10000 if mid_price > 0 else 0
+            
+            # Calculate liquidity
+            bid_liquidity = sum(bid_sizes[:20])  # Top 20 levels
+            ask_liquidity = sum(ask_sizes[:20])  # Top 20 levels
+            total_liquidity = bid_liquidity + ask_liquidity
+            
+            # Calculate imbalance
+            imbalance = (bid_liquidity - ask_liquidity) / total_liquidity if total_liquidity > 0 else 0
+            
+            return {
+                'mid_price': mid_price,
+                'spread_bps': spread_bps,
+                'imbalance': imbalance,
+                'bid_liquidity': bid_liquidity,
+                'ask_liquidity': ask_liquidity,
+                'best_bid': best_bid,
+                'best_ask': best_ask
+            }
+            
+        except Exception as e:
+            logger.error(f"Error calculating COB stats: {e}")
+            return {
+                'mid_price': 0,
+                'spread_bps': 0,
+                'imbalance': 0,
+                'bid_liquidity': 0,
+                'ask_liquidity': 0
+            }
 
     async def _stream_binance_book_ticker(self, symbol: str):
         """Stream best bid/ask prices from Binance WebSocket"""
@@ -1909,4 +1836,14 @@ class MultiExchangeCOBProvider:
                     })
                 
         except Exception as e:
-            logger.error(f"Error adding aggregate trade to analysis for {symbol}: {e}") 
+            logger.error(f"Error adding aggregate trade to analysis for {symbol}: {e}") 
+
+    def get_latest_cob_data(self, symbol: str) -> Optional[Dict]:
+        """Get latest COB data for a symbol from cache"""
+        try:
+            if symbol in self.cob_data_cache:
+                return self.cob_data_cache[symbol]
+            return None
+        except Exception as e:
+            logger.error(f"Error getting latest COB data for {symbol}: {e}")
+            return None

core/orchestrator.py

@@ -1985,6 +1985,53 @@ class TradingOrchestrator:
         self.trading_executor = trading_executor
         logger.info("Trading executor set for position tracking and P&L feedback")
     
+    def get_profitability_reward_multiplier(self) -> float:
+        """Get the current profitability reward multiplier from trading executor
+        
+        Returns:
+            float: Current profitability reward multiplier (0.0 to 2.0)
+        """
+        try:
+            if self.trading_executor and hasattr(self.trading_executor, 'get_profitability_reward_multiplier'):
+                multiplier = self.trading_executor.get_profitability_reward_multiplier()
+                logger.debug(f"Current profitability reward multiplier: {multiplier:.2f}")
+                return multiplier
+            return 0.0
+        except Exception as e:
+            logger.error(f"Error getting profitability reward multiplier: {e}")
+            return 0.0
+    
+    def calculate_enhanced_reward(self, base_pnl: float, confidence: float = 1.0) -> float:
+        """Calculate enhanced reward with profitability multiplier
+        
+        Args:
+            base_pnl: Base P&L from the trade
+            confidence: Confidence level of the prediction (0.0 to 1.0)
+            
+        Returns:
+            float: Enhanced reward with profitability multiplier applied
+        """
+        try:
+            # Get the dynamic profitability multiplier
+            profitability_multiplier = self.get_profitability_reward_multiplier()
+            
+            # Base reward is the P&L
+            base_reward = base_pnl
+            
+            # Apply profitability multiplier only to positive P&L (profitable trades)
+            if base_pnl > 0 and profitability_multiplier > 0:
+                # Enhance profitable trades with the multiplier
+                enhanced_reward = base_pnl * (1.0 + profitability_multiplier)
+                logger.debug(f"Enhanced reward: ${base_pnl:.2f} → ${enhanced_reward:.2f} (multiplier: {profitability_multiplier:.2f})")
+                return enhanced_reward
+            else:
+                # No enhancement for losing trades or when multiplier is 0
+                return base_reward
+                
+        except Exception as e:
+            logger.error(f"Error calculating enhanced reward: {e}")
+            return base_pnl
+    
     def _check_signal_confirmation(self, symbol: str, signal_data: Dict) -> Optional[str]:
         """Check if we have enough signal confirmations for trend confirmation with rate limiting"""
         try:

core/trading_executor.py

@@ -176,13 +176,25 @@ class TradingExecutor:
         self.simulation_balance = self.trading_config.get('simulation_account_usd', 100.0)
         self.simulation_positions = {}  # symbol -> position data with real entry prices
         
-        # Trading fees configuration (0.1% for both open and close)
+        # Trading fees configuration (0.1% for both open and close - REVERTED TO NORMAL)
         self.trading_fees = {
             'open_fee_percent': 0.001,  # 0.1% fee when opening position
             'close_fee_percent': 0.001,  # 0.1% fee when closing position
             'total_round_trip_fee': 0.002  # 0.2% total for round trip
         }
         
+        # Dynamic profitability reward parameter - starts at 0, adjusts based on success rate
+        self.profitability_reward_multiplier = 0.0  # Starts at 0, can be increased
+        self.min_profitability_multiplier = 0.0     # Minimum value
+        self.max_profitability_multiplier = 2.0     # Maximum 2x multiplier
+        self.profitability_adjustment_step = 0.1    # Adjust by 0.1 each time
+        
+        # Success rate tracking for profitability adjustment
+        self.recent_trades_window = 20              # Look at last 20 trades
+        self.success_rate_increase_threshold = 0.60 # Increase multiplier if >60% success
+        self.success_rate_decrease_threshold = 0.51 # Decrease multiplier if <51% success
+        self.last_profitability_adjustment = datetime.now()
+        
         logger.info(f"TradingExecutor initialized - Trading: {self.trading_enabled}, Mode: {self.trading_mode}")
         logger.info(f"Simulation balance: ${self.simulation_balance:.2f}")
 
@@ -622,6 +634,83 @@ class TradingExecutor:
             logger.error(f"Error cancelling open orders for {symbol}: {e}")
             return 0
     
+    def _calculate_recent_success_rate(self) -> float:
+        """Calculate success rate of recent closed trades
+        
+        Returns:
+            float: Success rate (0.0 to 1.0) of recent trades
+        """
+        try:
+            if len(self.trade_records) < 5:  # Need at least 5 trades
+                return 0.0
+            
+            # Get recent trades (up to the window size)
+            recent_trades = self.trade_records[-self.recent_trades_window:]
+            
+            if not recent_trades:
+                return 0.0
+            
+            # Count winning trades (net PnL > 0)
+            winning_trades = sum(1 for trade in recent_trades if trade.net_pnl > 0)
+            success_rate = winning_trades / len(recent_trades)
+            
+            logger.debug(f"Recent success rate: {success_rate:.2%} ({winning_trades}/{len(recent_trades)} trades)")
+            return success_rate
+            
+        except Exception as e:
+            logger.error(f"Error calculating success rate: {e}")
+            return 0.0
+    
+    def _adjust_profitability_reward_multiplier(self):
+        """Adjust profitability reward multiplier based on recent success rate"""
+        try:
+            # Only adjust every 5 minutes to avoid too frequent changes
+            current_time = datetime.now()
+            time_since_last_adjustment = (current_time - self.last_profitability_adjustment).total_seconds()
+            
+            if time_since_last_adjustment < 300:  # 5 minutes
+                return
+            
+            success_rate = self._calculate_recent_success_rate()
+            
+            # Only adjust if we have enough trades
+            if len(self.trade_records) < 10:
+                return
+            
+            old_multiplier = self.profitability_reward_multiplier
+            
+            # Increase multiplier if success rate > 60%
+            if success_rate > self.success_rate_increase_threshold:
+                self.profitability_reward_multiplier = min(
+                    self.max_profitability_multiplier,
+                    self.profitability_reward_multiplier + self.profitability_adjustment_step
+                )
+                logger.info(f"🎯 SUCCESS RATE HIGH ({success_rate:.1%}) - Increased profitability multiplier: {old_multiplier:.1f} → {self.profitability_reward_multiplier:.1f}")
+            
+            # Decrease multiplier if success rate < 51%
+            elif success_rate < self.success_rate_decrease_threshold:
+                self.profitability_reward_multiplier = max(
+                    self.min_profitability_multiplier,
+                    self.profitability_reward_multiplier - self.profitability_adjustment_step
+                )
+                logger.info(f"⚠️ SUCCESS RATE LOW ({success_rate:.1%}) - Decreased profitability multiplier: {old_multiplier:.1f} → {self.profitability_reward_multiplier:.1f}")
+            
+            else:
+                logger.debug(f"Success rate {success_rate:.1%} in acceptable range - keeping multiplier at {self.profitability_reward_multiplier:.1f}")
+            
+            self.last_profitability_adjustment = current_time
+            
+        except Exception as e:
+            logger.error(f"Error adjusting profitability reward multiplier: {e}")
+    
+    def get_profitability_reward_multiplier(self) -> float:
+        """Get current profitability reward multiplier
+        
+        Returns:
+            float: Current profitability reward multiplier
+        """
+        return self.profitability_reward_multiplier
+    
     def _can_reenable_live_trading(self) -> bool:
         """Check if trading performance has improved enough to re-enable live trading
         
@@ -1198,7 +1287,11 @@ class TradingExecutor:
             )
             
             self.trade_history.append(trade_record)
+            self.trade_records.append(trade_record)  # Add to trade records for success rate tracking
             self.daily_loss += max(0, -pnl)  # Add to daily loss if negative
+            
+            # Adjust profitability reward multiplier based on recent performance
+            self._adjust_profitability_reward_multiplier()
 
             # Update consecutive losses
             if pnl < -0.001:  # A losing trade
@@ -1289,8 +1382,12 @@ class TradingExecutor:
                 )
                 
                 self.trade_history.append(trade_record)
+                self.trade_records.append(trade_record)  # Add to trade records for success rate tracking
                 self.daily_loss += max(0, -(pnl - fees))  # Add to daily loss if negative
                 
+                # Adjust profitability reward multiplier based on recent performance
+                self._adjust_profitability_reward_multiplier()
+                
                 # Update consecutive losses
                 if pnl < -0.001:  # A losing trade
                     self.consecutive_losses += 1
@@ -1356,7 +1453,11 @@ class TradingExecutor:
             )
             
             self.trade_history.append(trade_record)
+            self.trade_records.append(trade_record)  # Add to trade records for success rate tracking
             self.daily_loss += max(0, -pnl)  # Add to daily loss if negative
+            
+            # Adjust profitability reward multiplier based on recent performance
+            self._adjust_profitability_reward_multiplier()
 
             # Update consecutive losses
             if pnl < -0.001:  # A losing trade
@@ -1428,8 +1529,12 @@ class TradingExecutor:
                 )
                 
                 self.trade_history.append(trade_record)
+                self.trade_records.append(trade_record)  # Add to trade records for success rate tracking
                 self.daily_loss += max(0, -(pnl - fees))  # Add to daily loss if negative
                 
+                # Adjust profitability reward multiplier based on recent performance
+                self._adjust_profitability_reward_multiplier()
+                
                 # Update consecutive losses
                 if pnl < -0.001:  # A losing trade
                     self.consecutive_losses += 1

run_simple_dashboard.py

@@ -0,0 +1,218 @@
+#!/usr/bin/env python3
+"""
+Simple Dashboard Runner - Fixed version for testing
+"""
+
+import os
+import sys
+import logging
+import time
+import threading
+from pathlib import Path
+
+# Fix OpenMP library conflicts
+os.environ['KMP_DUPLICATE_LIB_OK'] = 'TRUE'
+os.environ['OMP_NUM_THREADS'] = '4'
+
+# Fix matplotlib backend
+import matplotlib
+matplotlib.use('Agg')
+
+# Add project root to path
+project_root = Path(__file__).parent
+sys.path.insert(0, str(project_root))
+
+# Setup logging
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+)
+logger = logging.getLogger(__name__)
+
+def create_simple_dashboard():
+    """Create a simple working dashboard"""
+    try:
+        import dash
+        from dash import html, dcc, Input, Output
+        import plotly.graph_objs as go
+        import pandas as pd
+        import numpy as np
+        from datetime import datetime, timedelta
+        
+        # Create Dash app
+        app = dash.Dash(__name__)
+        
+        # Simple layout
+        app.layout = html.Div([
+            html.H1("Trading System Dashboard", style={'textAlign': 'center', 'color': '#2c3e50'}),
+            
+            html.Div([
+                html.Div([
+                    html.H3("System Status", style={'color': '#27ae60'}),
+                    html.P(id='system-status', children="System: RUNNING", style={'fontSize': '18px'}),
+                    html.P(id='current-time', children=f"Time: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}"),
+                ], style={'width': '48%', 'display': 'inline-block', 'padding': '20px'}),
+                
+                html.Div([
+                    html.H3("Trading Stats", style={'color': '#3498db'}),
+                    html.P("Total Trades: 0"),
+                    html.P("Success Rate: 0%"),
+                    html.P("Current PnL: $0.00"),
+                ], style={'width': '48%', 'display': 'inline-block', 'padding': '20px'}),
+            ]),
+            
+            html.Div([
+                dcc.Graph(id='price-chart'),
+            ], style={'padding': '20px'}),
+            
+            html.Div([
+                dcc.Graph(id='performance-chart'),
+            ], style={'padding': '20px'}),
+            
+            # Auto-refresh component
+            dcc.Interval(
+                id='interval-component',
+                interval=5000,  # Update every 5 seconds
+                n_intervals=0
+            )
+        ])
+        
+        # Callback for updating time
+        @app.callback(
+            Output('current-time', 'children'),
+            Input('interval-component', 'n_intervals')
+        )
+        def update_time(n):
+            return f"Time: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}"
+        
+        # Callback for price chart
+        @app.callback(
+            Output('price-chart', 'figure'),
+            Input('interval-component', 'n_intervals')
+        )
+        def update_price_chart(n):
+            # Generate sample data
+            dates = pd.date_range(start=datetime.now() - timedelta(hours=24), 
+                                end=datetime.now(), freq='1H')
+            prices = 3000 + np.cumsum(np.random.randn(len(dates)) * 10)
+            
+            fig = go.Figure()
+            fig.add_trace(go.Scatter(
+                x=dates,
+                y=prices,
+                mode='lines',
+                name='ETH/USDT',
+                line=dict(color='#3498db', width=2)
+            ))
+            
+            fig.update_layout(
+                title='ETH/USDT Price Chart (24H)',
+                xaxis_title='Time',
+                yaxis_title='Price (USD)',
+                template='plotly_white',
+                height=400
+            )
+            
+            return fig
+        
+        # Callback for performance chart
+        @app.callback(
+            Output('performance-chart', 'figure'),
+            Input('interval-component', 'n_intervals')
+        )
+        def update_performance_chart(n):
+            # Generate sample performance data
+            dates = pd.date_range(start=datetime.now() - timedelta(days=7), 
+                                end=datetime.now(), freq='1D')
+            performance = np.cumsum(np.random.randn(len(dates)) * 0.02) * 100
+            
+            fig = go.Figure()
+            fig.add_trace(go.Scatter(
+                x=dates,
+                y=performance,
+                mode='lines+markers',
+                name='Portfolio Performance',
+                line=dict(color='#27ae60', width=3),
+                marker=dict(size=6)
+            ))
+            
+            fig.update_layout(
+                title='Portfolio Performance (7 Days)',
+                xaxis_title='Date',
+                yaxis_title='Performance (%)',
+                template='plotly_white',
+                height=400
+            )
+            
+            return fig
+        
+        return app
+        
+    except Exception as e:
+        logger.error(f"Error creating dashboard: {e}")
+        import traceback
+        logger.error(traceback.format_exc())
+        return None
+
+def test_data_provider():
+    """Test data provider in background"""
+    try:
+        from core.data_provider import DataProvider
+        from core.api_rate_limiter import get_rate_limiter
+        
+        logger.info("Testing data provider...")
+        
+        # Create data provider
+        data_provider = DataProvider(
+            symbols=['ETH/USDT'],
+            timeframes=['1m', '5m']
+        )
+        
+        # Test getting data
+        df = data_provider.get_historical_data('ETH/USDT', '1m', limit=10)
+        if df is not None and len(df) > 0:
+            logger.info(f"✓ Data provider working: {len(df)} candles retrieved")
+        else:
+            logger.warning("⚠ Data provider returned no data (rate limiting)")
+        
+        # Test rate limiter status
+        rate_limiter = get_rate_limiter()
+        status = rate_limiter.get_all_endpoint_status()
+        logger.info(f"Rate limiter status: {status}")
+        
+    except Exception as e:
+        logger.error(f"Data provider test error: {e}")
+
+def main():
+    """Main function"""
+    logger.info("=" * 60)
+    logger.info("SIMPLE DASHBOARD RUNNER - TESTING SYSTEM")
+    logger.info("=" * 60)
+    
+    # Test data provider in background
+    data_thread = threading.Thread(target=test_data_provider, daemon=True)
+    data_thread.start()
+    
+    # Create and run dashboard
+    app = create_simple_dashboard()
+    if app is None:
+        logger.error("Failed to create dashboard")
+        return
+    
+    try:
+        logger.info("Starting dashboard server...")
+        logger.info("Dashboard URL: http://127.0.0.1:8050")
+        logger.info("Press Ctrl+C to stop")
+        
+        # Run the dashboard
+        app.run(debug=False, host='127.0.0.1', port=8050, use_reloader=False)
+        
+    except KeyboardInterrupt:
+        logger.info("Dashboard stopped by user")
+    except Exception as e:
+        logger.error(f"Dashboard error: {e}")
+        import traceback
+        logger.error(traceback.format_exc())
+
+if __name__ == "__main__":
+    main()

test_profitability_reward_system.py

@@ -0,0 +1,294 @@
+#!/usr/bin/env python3
+"""
+Test script for the dynamic profitability reward system
+
+This script tests:
+1. Fee reversion to normal 0.1% (0.001)
+2. Dynamic profitability reward multiplier adjustment
+3. Success rate calculation
+4. Integration with dashboard display
+"""
+
+import sys
+import os
+import time
+from datetime import datetime, timedelta
+
+# Add project root to path
+sys.path.append(os.path.dirname(os.path.abspath(__file__)))
+
+from core.trading_executor import TradingExecutor, TradeRecord
+from core.orchestrator import TradingOrchestrator
+from core.data_provider import DataProvider
+
+def test_fee_configuration():
+    """Test that fees are reverted to normal 0.1%"""
+    print("=" * 60)
+    print("🧪 TESTING FEE CONFIGURATION")
+    print("=" * 60)
+    
+    executor = TradingExecutor()
+    
+    # Check fee configuration
+    expected_open_fee = 0.001  # 0.1%
+    expected_close_fee = 0.001  # 0.1%
+    expected_total_fee = 0.002  # 0.2%
+    
+    actual_open_fee = executor.trading_fees['open_fee_percent']
+    actual_close_fee = executor.trading_fees['close_fee_percent']
+    actual_total_fee = executor.trading_fees['total_round_trip_fee']
+    
+    print(f"Expected Open Fee: {expected_open_fee} (0.1%)")
+    print(f"Actual Open Fee:   {actual_open_fee} (0.1%)")
+    print(f"✅ Open Fee: {'PASS' if actual_open_fee == expected_open_fee else 'FAIL'}")
+    print()
+    
+    print(f"Expected Close Fee: {expected_close_fee} (0.1%)")
+    print(f"Actual Close Fee:   {actual_close_fee} (0.1%)")
+    print(f"✅ Close Fee: {'PASS' if actual_close_fee == expected_close_fee else 'FAIL'}")
+    print()
+    
+    print(f"Expected Total Fee: {expected_total_fee} (0.2%)")
+    print(f"Actual Total Fee:   {actual_total_fee} (0.2%)")
+    print(f"✅ Total Fee: {'PASS' if actual_total_fee == expected_total_fee else 'FAIL'}")
+    print()
+    
+    return actual_open_fee == expected_open_fee and actual_close_fee == expected_close_fee
+
+def test_profitability_multiplier_initialization():
+    """Test profitability multiplier initialization"""
+    print("=" * 60)
+    print("🧪 TESTING PROFITABILITY MULTIPLIER INITIALIZATION")
+    print("=" * 60)
+    
+    executor = TradingExecutor()
+    
+    # Check initial values
+    initial_multiplier = executor.profitability_reward_multiplier
+    min_multiplier = executor.min_profitability_multiplier
+    max_multiplier = executor.max_profitability_multiplier
+    adjustment_step = executor.profitability_adjustment_step
+    
+    print(f"Initial Multiplier: {initial_multiplier} (should be 0.0)")
+    print(f"Min Multiplier:     {min_multiplier} (should be 0.0)")
+    print(f"Max Multiplier:     {max_multiplier} (should be 2.0)")
+    print(f"Adjustment Step:    {adjustment_step} (should be 0.1)")
+    print()
+    
+    # Check thresholds
+    increase_threshold = executor.success_rate_increase_threshold
+    decrease_threshold = executor.success_rate_decrease_threshold
+    trades_window = executor.recent_trades_window
+    
+    print(f"Increase Threshold: {increase_threshold:.1%} (should be 60%)")
+    print(f"Decrease Threshold: {decrease_threshold:.1%} (should be 51%)")
+    print(f"Trades Window:      {trades_window} (should be 20)")
+    print()
+    
+    # Test getter method
+    multiplier_from_getter = executor.get_profitability_reward_multiplier()
+    print(f"Multiplier via getter: {multiplier_from_getter}")
+    print(f"✅ Getter method: {'PASS' if multiplier_from_getter == initial_multiplier else 'FAIL'}")
+    
+    return (initial_multiplier == 0.0 and 
+            min_multiplier == 0.0 and 
+            max_multiplier == 2.0 and
+            adjustment_step == 0.1)
+
+def simulate_trades_and_test_adjustment(executor, winning_trades, total_trades):
+    """Simulate trades and test multiplier adjustment"""
+    print(f"📊 Simulating {winning_trades}/{total_trades} winning trades ({winning_trades/total_trades:.1%} success rate)")
+    
+    # Clear existing trade records
+    executor.trade_records = []
+    
+    # Create simulated trade records
+    base_time = datetime.now() - timedelta(hours=1)
+    
+    for i in range(total_trades):
+        # Create winning or losing trade based on ratio
+        is_winning = i < winning_trades
+        pnl = 10.0 if is_winning else -5.0  # $10 profit or $5 loss
+        
+        trade_record = TradeRecord(
+            symbol="ETH/USDT",
+            side="LONG",
+            quantity=0.01,
+            entry_price=3000.0,
+            exit_price=3010.0 if is_winning else 2995.0,
+            entry_time=base_time + timedelta(minutes=i*2),
+            exit_time=base_time + timedelta(minutes=i*2+1),
+            pnl=pnl,
+            fees=2.0,
+            confidence=0.8,
+            net_pnl=pnl - 2.0  # After fees
+        )
+        
+        executor.trade_records.append(trade_record)
+    
+    # Force adjustment by setting last adjustment time to past
+    executor.last_profitability_adjustment = datetime.now() - timedelta(minutes=10)
+    
+    # Get initial multiplier
+    initial_multiplier = executor.get_profitability_reward_multiplier()
+    
+    # Calculate success rate
+    success_rate = executor._calculate_recent_success_rate()
+    print(f"Calculated success rate: {success_rate:.1%}")
+    
+    # Trigger adjustment
+    executor._adjust_profitability_reward_multiplier()
+    
+    # Get new multiplier
+    new_multiplier = executor.get_profitability_reward_multiplier()
+    
+    print(f"Initial multiplier: {initial_multiplier:.1f}")
+    print(f"New multiplier:     {new_multiplier:.1f}")
+    
+    # Determine expected change
+    if success_rate > executor.success_rate_increase_threshold:
+        expected_change = "increase"
+        expected_new = min(executor.max_profitability_multiplier, initial_multiplier + executor.profitability_adjustment_step)
+    elif success_rate < executor.success_rate_decrease_threshold:
+        expected_change = "decrease"
+        expected_new = max(executor.min_profitability_multiplier, initial_multiplier - executor.profitability_adjustment_step)
+    else:
+        expected_change = "no change"
+        expected_new = initial_multiplier
+    
+    print(f"Expected change:    {expected_change}")
+    print(f"Expected new value: {expected_new:.1f}")
+    
+    success = abs(new_multiplier - expected_new) < 0.01
+    print(f"✅ Adjustment: {'PASS' if success else 'FAIL'}")
+    print()
+    
+    return success
+
+def test_orchestrator_integration():
+    """Test orchestrator integration with profitability multiplier"""
+    print("=" * 60)
+    print("🧪 TESTING ORCHESTRATOR INTEGRATION")
+    print("=" * 60)
+    
+    # Create components
+    data_provider = DataProvider()
+    executor = TradingExecutor()
+    orchestrator = TradingOrchestrator(data_provider=data_provider)
+    
+    # Connect executor to orchestrator
+    orchestrator.set_trading_executor(executor)
+    
+    # Set a test multiplier
+    executor.profitability_reward_multiplier = 1.5
+    
+    # Test getting multiplier through orchestrator
+    multiplier = orchestrator.get_profitability_reward_multiplier()
+    print(f"Multiplier via orchestrator: {multiplier}")
+    print(f"✅ Orchestrator getter: {'PASS' if multiplier == 1.5 else 'FAIL'}")
+    
+    # Test enhanced reward calculation
+    base_pnl = 100.0  # $100 profit
+    confidence = 0.8
+    
+    enhanced_reward = orchestrator.calculate_enhanced_reward(base_pnl, confidence)
+    expected_enhanced = base_pnl * (1.0 + 1.5)  # 100 * 2.5 = 250
+    
+    print(f"Base P&L:        ${base_pnl:.2f}")
+    print(f"Enhanced reward: ${enhanced_reward:.2f}")
+    print(f"Expected:        ${expected_enhanced:.2f}")
+    print(f"✅ Enhanced reward: {'PASS' if abs(enhanced_reward - expected_enhanced) < 0.01 else 'FAIL'}")
+    
+    # Test with losing trade (should not be enhanced)
+    losing_pnl = -50.0
+    enhanced_losing = orchestrator.calculate_enhanced_reward(losing_pnl, confidence)
+    print(f"Losing P&L:      ${losing_pnl:.2f}")
+    print(f"Enhanced losing: ${enhanced_losing:.2f}")
+    print(f"✅ No enhancement for losses: {'PASS' if enhanced_losing == losing_pnl else 'FAIL'}")
+    
+    return multiplier == 1.5 and abs(enhanced_reward - expected_enhanced) < 0.01
+
+def main():
+    """Run all tests"""
+    print("🚀 DYNAMIC PROFITABILITY REWARD SYSTEM TEST")
+    print("Testing fee reversion and dynamic reward adjustment")
+    print()
+    
+    all_tests_passed = True
+    
+    # Test 1: Fee configuration
+    try:
+        fee_test_passed = test_fee_configuration()
+        all_tests_passed = all_tests_passed and fee_test_passed
+    except Exception as e:
+        print(f"❌ Fee configuration test failed: {e}")
+        all_tests_passed = False
+    
+    # Test 2: Profitability multiplier initialization
+    try:
+        init_test_passed = test_profitability_multiplier_initialization()
+        all_tests_passed = all_tests_passed and init_test_passed
+    except Exception as e:
+        print(f"❌ Initialization test failed: {e}")
+        all_tests_passed = False
+    
+    # Test 3: Multiplier adjustment scenarios
+    print("=" * 60)
+    print("🧪 TESTING MULTIPLIER ADJUSTMENT SCENARIOS")
+    print("=" * 60)
+    
+    executor = TradingExecutor()
+    
+    try:
+        # Scenario 1: High success rate (should increase multiplier)
+        print("Scenario 1: High success rate (65% - should increase)")
+        high_success_test = simulate_trades_and_test_adjustment(executor, 13, 20)  # 65%
+        all_tests_passed = all_tests_passed and high_success_test
+        
+        # Scenario 2: Low success rate (should decrease multiplier)
+        print("Scenario 2: Low success rate (45% - should decrease)")
+        low_success_test = simulate_trades_and_test_adjustment(executor, 9, 20)  # 45%
+        all_tests_passed = all_tests_passed and low_success_test
+        
+        # Scenario 3: Medium success rate (should not change)
+        print("Scenario 3: Medium success rate (55% - should not change)")
+        medium_success_test = simulate_trades_and_test_adjustment(executor, 11, 20)  # 55%
+        all_tests_passed = all_tests_passed and medium_success_test
+        
+    except Exception as e:
+        print(f"❌ Adjustment scenario tests failed: {e}")
+        all_tests_passed = False
+    
+    # Test 4: Orchestrator integration
+    try:
+        orchestrator_test_passed = test_orchestrator_integration()
+        all_tests_passed = all_tests_passed and orchestrator_test_passed
+    except Exception as e:
+        print(f"❌ Orchestrator integration test failed: {e}")
+        all_tests_passed = False
+    
+    # Final results
+    print("=" * 60)
+    print("📋 TEST RESULTS SUMMARY")
+    print("=" * 60)
+    
+    if all_tests_passed:
+        print("🎉 ALL TESTS PASSED!")
+        print("✅ Fees reverted to normal 0.1%")
+        print("✅ Dynamic profitability multiplier working")
+        print("✅ Success rate calculation accurate")
+        print("✅ Orchestrator integration functional")
+        print()
+        print("🚀 System ready for trading with dynamic profitability rewards!")
+        print("📈 The model will learn to prioritize more profitable trades over time")
+        print("🎯 Success rate >60% → increase reward multiplier")
+        print("⚠️  Success rate <51% → decrease reward multiplier")
+    else:
+        print("❌ SOME TESTS FAILED!")
+        print("Please check the error messages above and fix issues before trading.")
+    
+    return all_tests_passed
+
+if __name__ == "__main__":
+    success = main()
+    sys.exit(0 if success else 1)

web/clean_dashboard.py

@@ -496,6 +496,7 @@ class CleanTradingDashboard:
              Output('current-position', 'children'),
              Output('trade-count', 'children'),
              Output('portfolio-value', 'children'),
+             Output('profitability-multiplier', 'children'),
              Output('mexc-status', 'children')],
             [Input('interval-component', 'n_intervals')]
         )
@@ -600,6 +601,20 @@ class CleanTradingDashboard:
                 portfolio_value = current_balance + total_session_pnl  # Live balance + unrealized P&L
                 portfolio_str = f"${portfolio_value:.2f}"
                 
+                # Profitability multiplier - get from trading executor
+                profitability_multiplier = 0.0
+                success_rate = 0.0
+                if self.trading_executor and hasattr(self.trading_executor, 'get_profitability_reward_multiplier'):
+                    profitability_multiplier = self.trading_executor.get_profitability_reward_multiplier()
+                    if hasattr(self.trading_executor, '_calculate_recent_success_rate'):
+                        success_rate = self.trading_executor._calculate_recent_success_rate()
+                
+                # Format profitability multiplier display
+                if profitability_multiplier > 0:
+                    multiplier_str = f"+{profitability_multiplier:.1f}x ({success_rate:.0%})"
+                else:
+                    multiplier_str = f"0.0x ({success_rate:.0%})" if success_rate > 0 else "0.0x"
+                
                 # MEXC status - enhanced with sync status
                 mexc_status = "SIM"
                 if self.trading_executor:
@@ -607,11 +622,11 @@ class CleanTradingDashboard:
                         if hasattr(self.trading_executor, 'simulation_mode') and not self.trading_executor.simulation_mode:
                             mexc_status = "LIVE+SYNC"  # Indicate live trading with position sync
                 
-                return price_str, session_pnl_str, position_str, trade_str, portfolio_str, mexc_status
+                return price_str, session_pnl_str, position_str, trade_str, portfolio_str, multiplier_str, mexc_status
                 
             except Exception as e:
                 logger.error(f"Error updating metrics: {e}")
-                return "Error", "$0.00", "Error", "0", "$100.00", "ERROR"
+                return "Error", "$0.00", "Error", "0", "$100.00", "0.0x", "ERROR"
         
         @self.app.callback(
             Output('recent-decisions', 'children'),
@@ -2311,49 +2326,56 @@ class CleanTradingDashboard:
                     cob_data = self.data_provider.get_latest_cob_data(symbol)
                     logger.debug(f"COB data type for {symbol}: {type(cob_data)}, data: {cob_data}")
                     
-                    if cob_data and isinstance(cob_data, dict) and 'stats' in cob_data:
-                        logger.debug(f"COB snapshot available for {symbol} from centralized data provider")
-                        
-                        # Create a snapshot object from the data provider's data
-                        class COBSnapshot:
-                            def __init__(self, data):
-                                # Convert list format [[price, qty], ...] to dictionary format
-                                raw_bids = data.get('bids', [])
-                                raw_asks = data.get('asks', [])
-                                
-                                # Convert to dictionary format expected by component manager
-                                self.consolidated_bids = []
-                                for bid in raw_bids:
-                                    if isinstance(bid, list) and len(bid) >= 2:
-                                        self.consolidated_bids.append({
-                                            'price': bid[0],
-                                            'size': bid[1],
-                                            'total_size': bid[1],
-                                            'total_volume_usd': bid[0] * bid[1]
-                                        })
-                                
-                                self.consolidated_asks = []
-                                for ask in raw_asks:
-                                    if isinstance(ask, list) and len(ask) >= 2:
-                                        self.consolidated_asks.append({
-                                            'price': ask[0],
-                                            'size': ask[1],
-                                            'total_size': ask[1],
-                                            'total_volume_usd': ask[0] * ask[1]
-                                        })
-                                
-                                self.stats = data.get('stats', {})
-                                # Add direct attributes for new format compatibility
-                                self.volume_weighted_mid = self.stats.get('mid_price', 0)
-                                self.spread_bps = self.stats.get('spread_bps', 0)
-                                self.liquidity_imbalance = self.stats.get('imbalance', 0)
-                                self.total_bid_liquidity = self.stats.get('bid_liquidity', 0)
-                                self.total_ask_liquidity = self.stats.get('ask_liquidity', 0)
-                                self.exchanges_active = ['Binance']  # Default for now
-                        
-                        return COBSnapshot(cob_data)
+                    if cob_data and isinstance(cob_data, dict):
+                        # Validate COB data structure
+                        if 'stats' in cob_data and cob_data['stats']:
+                            logger.debug(f"COB snapshot available for {symbol} from centralized data provider")
+                            
+                            # Create a snapshot object from the data provider's data
+                            class COBSnapshot:
+                                def __init__(self, data):
+                                    # Convert list format [[price, qty], ...] to dictionary format
+                                    raw_bids = data.get('bids', [])
+                                    raw_asks = data.get('asks', [])
+                                    
+                                    # Convert to dictionary format expected by component manager
+                                    self.consolidated_bids = []
+                                    for bid in raw_bids:
+                                        if isinstance(bid, list) and len(bid) >= 2:
+                                            self.consolidated_bids.append({
+                                                'price': bid[0],
+                                                'size': bid[1],
+                                                'total_size': bid[1],
+                                                'total_volume_usd': bid[0] * bid[1]
+                                            })
+                                    
+                                    self.consolidated_asks = []
+                                    for ask in raw_asks:
+                                        if isinstance(ask, list) and len(ask) >= 2:
+                                            self.consolidated_asks.append({
+                                                'price': ask[0],
+                                                'size': ask[1],
+                                                'total_size': ask[1],
+                                                'total_volume_usd': ask[0] * ask[1]
+                                            })
+                                    
+                                    self.stats = data.get('stats', {})
+                                    # Add direct attributes for new format compatibility
+                                    self.volume_weighted_mid = self.stats.get('mid_price', 0)
+                                    self.spread_bps = self.stats.get('spread_bps', 0)
+                                    self.liquidity_imbalance = self.stats.get('imbalance', 0)
+                                    self.total_bid_liquidity = self.stats.get('bid_liquidity', 0)
+                                    self.total_ask_liquidity = self.stats.get('ask_liquidity', 0)
+                                    self.exchanges_active = ['Binance']  # Default for now
+                            
+                            return COBSnapshot(cob_data)
+                        else:
+                            # Data exists but no stats - this is the "Invalid COB data" case
+                            logger.debug(f"COB data for {symbol} missing stats structure: {type(cob_data)}, keys: {list(cob_data.keys()) if isinstance(cob_data, dict) else 'not dict'}")
+                            return None
                     else:
-                        logger.warning(f"Invalid COB data for {symbol}: type={type(cob_data)}, has_stats={'stats' in cob_data if isinstance(cob_data, dict) else False}")
+                        logger.debug(f"No COB data available for {symbol} from data provider")
+                        return None
                 except Exception as e:
                     logger.error(f"Error getting COB data from data provider: {e}")
             
@@ -5358,6 +5380,18 @@ class CleanTradingDashboard:
                         
                         self.latest_cob_data[symbol] = cob_snapshot
                         
+                        # Store in history for moving average calculations
+                        if not hasattr(self, 'cob_data_history'):
+                            self.cob_data_history = {'ETH/USDT': deque(maxlen=61), 'BTC/USDT': deque(maxlen=61)}
+                        
+                        if symbol in self.cob_data_history:
+                            self.cob_data_history[symbol].append(cob_snapshot)
+                        
+                        # Update last update timestamp
+                        if not hasattr(self, 'cob_last_update'):
+                            self.cob_last_update = {}
+                        self.cob_last_update[symbol] = time.time()
+                        
                         # Update current price from COB data
                         if 'stats' in cob_snapshot and 'mid_price' in cob_snapshot['stats']:
                             self.current_prices[symbol] = cob_snapshot['stats']['mid_price']
@@ -6021,33 +6055,71 @@ class CleanTradingDashboard:
             raise
 
     def _calculate_cumulative_imbalance(self, symbol: str) -> Dict[str, float]:
-        """Calculate average imbalance over multiple time windows."""
+        """Calculate Moving Averages (MA) of imbalance over different periods."""
         stats = {}
-        now = time.time()
         history = self.cob_data_history.get(symbol)
 
         if not history:
             return {'1s': 0.0, '5s': 0.0, '15s': 0.0, '60s': 0.0}
 
-        periods = {'1s': 1, '5s': 5, '15s': 15, '60s': 60}
-        
-        for name, duration in periods.items():
-            recent_imbalances = []
-            for snap in history:
-                # Check if snap is a valid dict with timestamp and stats
-                if isinstance(snap, dict) and 'timestamp' in snap and (now - snap['timestamp'] <= duration) and 'stats' in snap and snap['stats']:
-                    imbalance = snap['stats'].get('imbalance')
-                    if imbalance is not None:
-                        recent_imbalances.append(imbalance)
+        # Convert history to list and get recent snapshots
+        history_list = list(history)
+        if not history_list:
+            return {'1s': 0.0, '5s': 0.0, '15s': 0.0, '60s': 0.0}
 
-            if recent_imbalances:
-                stats[name] = sum(recent_imbalances) / len(recent_imbalances)
-            else:
-                stats[name] = 0.0
+        # Extract imbalance values from recent snapshots
+        imbalances = []
+        for snap in history_list:
+            if isinstance(snap, dict) and 'stats' in snap and snap['stats']:
+                imbalance = snap['stats'].get('imbalance')
+                if imbalance is not None:
+                    imbalances.append(imbalance)
+
+        if not imbalances:
+            return {'1s': 0.0, '5s': 0.0, '15s': 0.0, '60s': 0.0}
+
+        # Calculate Moving Averages over different periods
+        # MA periods: 1s=1 period, 5s=5 periods, 15s=15 periods, 60s=60 periods
+        ma_periods = {'1s': 1, '5s': 5, '15s': 15, '60s': 60}
         
-        # Debug logging to verify cumulative imbalance calculation
+        for name, period in ma_periods.items():
+            if len(imbalances) >= period:
+                # Calculate SMA over the last 'period' values
+                recent_imbalances = imbalances[-period:]
+                sma_value = sum(recent_imbalances) / len(recent_imbalances)
+                
+                # Also calculate EMA for better responsiveness
+                if period > 1:
+                    # EMA calculation with alpha = 2/(period+1)
+                    alpha = 2.0 / (period + 1)
+                    ema_value = recent_imbalances[0]  # Start with first value
+                    for value in recent_imbalances[1:]:
+                        ema_value = alpha * value + (1 - alpha) * ema_value
+                    # Use EMA for better responsiveness
+                    stats[name] = ema_value
+                else:
+                    # For 1s, use SMA (no EMA needed)
+                    stats[name] = sma_value
+            else:
+                # If not enough data, use available data
+                available_imbalances = imbalances[-min(period, len(imbalances)):]
+                if available_imbalances:
+                    if len(available_imbalances) > 1:
+                        # Calculate EMA for available data
+                        alpha = 2.0 / (len(available_imbalances) + 1)
+                        ema_value = available_imbalances[0]
+                        for value in available_imbalances[1:]:
+                            ema_value = alpha * value + (1 - alpha) * ema_value
+                        stats[name] = ema_value
+                    else:
+                        # Single value, use as is
+                        stats[name] = available_imbalances[0]
+                else:
+                    stats[name] = 0.0
+        
+        # Debug logging to verify MA calculation
         if any(value != 0.0 for value in stats.values()):
-            logger.debug(f"[CUMULATIVE-IMBALANCE] {symbol}: {stats}")
+            logger.debug(f"[MOVING-AVERAGE-IMBALANCE] {symbol}: {stats} (from {len(imbalances)} snapshots)")
         
         return stats
     

web/component_manager.py

@@ -412,10 +412,10 @@ class DashboardComponentManager:
             ]),
             
             html.Div([
-                self._create_timeframe_imbalance("1s", stats.get('imbalance_1s', imbalance)),
-                self._create_timeframe_imbalance("5s", stats.get('imbalance_5s', imbalance)),
-                self._create_timeframe_imbalance("15s", stats.get('imbalance_15s', imbalance)),
-                self._create_timeframe_imbalance("60s", stats.get('imbalance_60s', imbalance)),
+                self._create_timeframe_imbalance("1s", cumulative_imbalance_stats.get('1s', imbalance)),
+                self._create_timeframe_imbalance("5s", cumulative_imbalance_stats.get('5s', imbalance)),
+                self._create_timeframe_imbalance("15s", cumulative_imbalance_stats.get('15s', imbalance)),
+                self._create_timeframe_imbalance("60s", cumulative_imbalance_stats.get('60s', imbalance)),
             ], className="d-flex justify-content-between mb-2"),
             
             html.Div(imbalance_stats_display),

web/layout_manager.py

@@ -93,6 +93,7 @@ class DashboardLayoutManager:
             # ("leverage-info", "Leverage", "text-primary"),
             ("trade-count", "Trades", "text-warning"),
             ("portfolio-value", "Portfolio", "text-secondary"),
+            ("profitability-multiplier", "Profit Boost", "text-primary"),
             ("mexc-status", f"{exchange_name} API", "text-info")
         ]
         

web/templated_dashboard.py

@@ -986,33 +986,71 @@ class TemplatedTradingDashboard:
             logger.debug(f"TEMPLATED DASHBOARD: Error generating bucketed COB data: {e}")
 
     def _calculate_cumulative_imbalance(self, symbol: str) -> Dict[str, float]:
-        """Calculate average imbalance over multiple time windows."""
+        """Calculate Moving Averages (MA) of imbalance over different periods."""
         stats = {}
-        now = time.time()
         history = self.cob_data_history.get(symbol)
 
         if not history:
             return {'1s': 0.0, '5s': 0.0, '15s': 0.0, '60s': 0.0}
 
-        periods = {'1s': 1, '5s': 5, '15s': 15, '60s': 60}
-        
-        for name, duration in periods.items():
-            recent_imbalances = []
-            for snap in history:
-                # Check if snap is a valid dict with timestamp and stats
-                if isinstance(snap, dict) and 'timestamp' in snap and (now - snap['timestamp'] <= duration) and 'stats' in snap and snap['stats']:
-                    imbalance = snap['stats'].get('imbalance')
-                    if imbalance is not None:
-                        recent_imbalances.append(imbalance)
+        # Convert history to list and get recent snapshots
+        history_list = list(history)
+        if not history_list:
+            return {'1s': 0.0, '5s': 0.0, '15s': 0.0, '60s': 0.0}
 
-            if recent_imbalances:
-                stats[name] = sum(recent_imbalances) / len(recent_imbalances)
-            else:
-                stats[name] = 0.0
+        # Extract imbalance values from recent snapshots
+        imbalances = []
+        for snap in history_list:
+            if isinstance(snap, dict) and 'stats' in snap and snap['stats']:
+                imbalance = snap['stats'].get('imbalance')
+                if imbalance is not None:
+                    imbalances.append(imbalance)
+
+        if not imbalances:
+            return {'1s': 0.0, '5s': 0.0, '15s': 0.0, '60s': 0.0}
+
+        # Calculate Moving Averages over different periods
+        # MA periods: 1s=1 period, 5s=5 periods, 15s=15 periods, 60s=60 periods
+        ma_periods = {'1s': 1, '5s': 5, '15s': 15, '60s': 60}
         
-        # Debug logging to verify cumulative imbalance calculation
+        for name, period in ma_periods.items():
+            if len(imbalances) >= period:
+                # Calculate SMA over the last 'period' values
+                recent_imbalances = imbalances[-period:]
+                sma_value = sum(recent_imbalances) / len(recent_imbalances)
+                
+                # Also calculate EMA for better responsiveness
+                if period > 1:
+                    # EMA calculation with alpha = 2/(period+1)
+                    alpha = 2.0 / (period + 1)
+                    ema_value = recent_imbalances[0]  # Start with first value
+                    for value in recent_imbalances[1:]:
+                        ema_value = alpha * value + (1 - alpha) * ema_value
+                    # Use EMA for better responsiveness
+                    stats[name] = ema_value
+                else:
+                    # For 1s, use SMA (no EMA needed)
+                    stats[name] = sma_value
+            else:
+                # If not enough data, use available data
+                available_imbalances = imbalances[-min(period, len(imbalances)):]
+                if available_imbalances:
+                    if len(available_imbalances) > 1:
+                        # Calculate EMA for available data
+                        alpha = 2.0 / (len(available_imbalances) + 1)
+                        ema_value = available_imbalances[0]
+                        for value in available_imbalances[1:]:
+                            ema_value = alpha * value + (1 - alpha) * ema_value
+                        stats[name] = ema_value
+                    else:
+                        # Single value, use as is
+                        stats[name] = available_imbalances[0]
+                else:
+                    stats[name] = 0.0
+        
+        # Debug logging to verify MA calculation
         if any(value != 0.0 for value in stats.values()):
-            logger.debug(f"TEMPLATED DASHBOARD: [CUMULATIVE-IMBALANCE] {symbol}: {stats}")
+            logger.debug(f"TEMPLATED DASHBOARD: [MOVING-AVERAGE-IMBALANCE] {symbol}: {stats} (from {len(imbalances)} snapshots)")
         
         return stats