pivots

core/data_provider.py

@@ -34,6 +34,7 @@ from collections import deque
 from .config import get_config
 from .tick_aggregator import RealTimeTickAggregator, RawTick, OHLCVBar
 from .cnn_monitor import log_cnn_prediction
+from .williams_market_structure import WilliamsMarketStructure, PivotPoint, TrendLevel
 
 logger = logging.getLogger(__name__)
 
@@ -182,6 +183,16 @@ class DataProvider:
             '1h': 3600, '4h': 14400, '1d': 86400
         }
         
+        # Williams Market Structure integration
+        self.williams_structure: Dict[str, WilliamsMarketStructure] = {}
+        for symbol in self.symbols:
+            self.williams_structure[symbol] = WilliamsMarketStructure(min_pivot_distance=3)
+        
+        # Pivot point caching
+        self.pivot_points_cache: Dict[str, Dict[int, TrendLevel]] = {}  # {symbol: {level: TrendLevel}}
+        self.last_pivot_calculation: Dict[str, datetime] = {}
+        self.pivot_calculation_interval = timedelta(minutes=5)  # Recalculate every 5 minutes
+        
         # Load existing pivot bounds from cache
         self._load_all_pivot_bounds()
         
@@ -189,6 +200,7 @@ class DataProvider:
         logger.info(f"Timeframes: {self.timeframes}")
         logger.info("Centralized data distribution enabled")
         logger.info("Pivot-based normalization system enabled")
+        logger.info("Williams Market Structure integration enabled")
         
         # Rate limiting
         self.last_request_time = {}
@@ -1613,6 +1625,151 @@ class DataProvider:
             logger.error(f"Error getting current price for {symbol}: {e}")
             return None
     
+    def calculate_williams_pivot_points(self, symbol: str, force_recalculate: bool = False) -> Dict[int, TrendLevel]:
+        """
+        Calculate Williams Market Structure pivot points for a symbol
+        
+        Args:
+            symbol: Trading symbol (e.g., 'ETH/USDT')
+            force_recalculate: Force recalculation even if cache is fresh
+            
+        Returns:
+            Dictionary of trend levels with pivot points
+        """
+        try:
+            # Check if we need to recalculate
+            now = datetime.now()
+            if (not force_recalculate and 
+                symbol in self.last_pivot_calculation and 
+                now - self.last_pivot_calculation[symbol] < self.pivot_calculation_interval):
+                # Return cached results
+                return self.pivot_points_cache.get(symbol, {})
+            
+            # Get 1s OHLCV data for Williams Market Structure calculation
+            df_1s = self.get_historical_data(symbol, '1s', limit=1000)
+            if df_1s is None or len(df_1s) < 50:
+                logger.warning(f"Insufficient 1s data for Williams pivot calculation: {symbol}")
+                return {}
+            
+            # Convert DataFrame to numpy array for Williams calculation
+            # Format: [timestamp_ms, open, high, low, close, volume]
+            ohlcv_array = np.column_stack([
+                df_1s.index.astype(np.int64) // 10**6,  # Convert to milliseconds
+                df_1s['open'].values,
+                df_1s['high'].values,
+                df_1s['low'].values,
+                df_1s['close'].values,
+                df_1s['volume'].values
+            ])
+            
+            # Calculate recursive pivot points using Williams Market Structure
+            williams = self.williams_structure[symbol]
+            pivot_levels = williams.calculate_recursive_pivot_points(ohlcv_array)
+            
+            # Cache the results
+            self.pivot_points_cache[symbol] = pivot_levels
+            self.last_pivot_calculation[symbol] = now
+            
+            logger.debug(f"Calculated Williams pivot points for {symbol}: {len(pivot_levels)} levels")
+            return pivot_levels
+            
+        except Exception as e:
+            logger.error(f"Error calculating Williams pivot points for {symbol}: {e}")
+            return {}
+    
+    def get_pivot_features_for_ml(self, symbol: str) -> np.ndarray:
+        """
+        Get pivot point features for machine learning models
+        
+        Returns a 250-element feature vector containing:
+        - Recent pivot points (price, strength, type) for each level
+        - Trend direction and strength for each level
+        - Time since last pivot for each level
+        """
+        try:
+            # Ensure we have fresh pivot points
+            pivot_levels = self.calculate_williams_pivot_points(symbol)
+            
+            if not pivot_levels:
+                logger.warning(f"No pivot points available for {symbol}")
+                return np.zeros(250, dtype=np.float32)
+            
+            # Use Williams Market Structure to extract ML features
+            williams = self.williams_structure[symbol]
+            features = williams.get_pivot_features_for_ml(symbol)
+            
+            return features
+            
+        except Exception as e:
+            logger.error(f"Error getting pivot features for ML: {e}")
+            return np.zeros(250, dtype=np.float32)
+    
+    def get_market_structure_summary(self, symbol: str) -> Dict[str, Any]:
+        """
+        Get current market structure summary for dashboard display
+        
+        Returns:
+            Dictionary containing market structure information
+        """
+        try:
+            # Ensure we have fresh pivot points
+            pivot_levels = self.calculate_williams_pivot_points(symbol)
+            
+            if not pivot_levels:
+                return {
+                    'symbol': symbol,
+                    'levels': {},
+                    'overall_trend': 'sideways',
+                    'overall_strength': 0.0,
+                    'last_update': datetime.now().isoformat(),
+                    'error': 'No pivot points available'
+                }
+            
+            # Use Williams Market Structure to get summary
+            williams = self.williams_structure[symbol]
+            structure = williams.get_current_market_structure()
+            structure['symbol'] = symbol
+            
+            return structure
+            
+        except Exception as e:
+            logger.error(f"Error getting market structure summary for {symbol}: {e}")
+            return {
+                'symbol': symbol,
+                'levels': {},
+                'overall_trend': 'sideways',
+                'overall_strength': 0.0,
+                'last_update': datetime.now().isoformat(),
+                'error': str(e)
+            }
+    
+    def get_recent_pivot_points(self, symbol: str, level: int = 1, count: int = 10) -> List[PivotPoint]:
+        """
+        Get recent pivot points for a specific level
+        
+        Args:
+            symbol: Trading symbol
+            level: Pivot level (1-5)
+            count: Number of recent pivots to return
+            
+        Returns:
+            List of recent pivot points
+        """
+        try:
+            pivot_levels = self.calculate_williams_pivot_points(symbol)
+            
+            if level not in pivot_levels:
+                return []
+            
+            trend_level = pivot_levels[level]
+            recent_pivots = trend_level.pivot_points[-count:] if len(trend_level.pivot_points) >= count else trend_level.pivot_points
+            
+            return recent_pivots
+            
+        except Exception as e:
+            logger.error(f"Error getting recent pivot points for {symbol} level {level}: {e}")
+            return []
+    
     def get_price_at_index(self, symbol: str, index: int, timeframe: str = '1m') -> Optional[float]:
         """Get price at specific index for backtesting"""
         try: