tter pivots
This commit is contained in:
Dobromir Popov
146
web/dashboard.py
146
web/dashboard.py
@ -321,6 +321,19 @@ class TradingDashboard:
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logger.info("Trading Dashboard initialized with enhanced RL training integration")
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logger.info(f"Enhanced RL enabled: {self.enhanced_rl_training_enabled}")
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logger.info(f"Stream consumer ID: {self.stream_consumer_id}")
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# Initialize Williams Market Structure once
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try:
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from training.williams_market_structure import WilliamsMarketStructure
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self.williams_structure = WilliamsMarketStructure(
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swing_strengths=[2, 3, 5], # Simplified for better performance
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enable_cnn_feature=False, # Disable CNN until TensorFlow available
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training_data_provider=None
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)
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logger.info("Williams Market Structure initialized for dashboard")
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except ImportError:
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self.williams_structure = None
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logger.warning("Williams Market Structure not available")
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def _to_local_timezone(self, dt: datetime) -> datetime:
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"""Convert datetime to configured local timezone"""
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@ -4532,32 +4545,49 @@ class TradingDashboard:
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logger.error(f"Error stopping streaming: {e}")
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def _get_williams_pivot_features(self, df: pd.DataFrame) -> Optional[List[float]]:
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"""Calculate Williams Market Structure pivot points features"""
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"""Get Williams Market Structure pivot features for RL training"""
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try:
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# Import Williams Market Structure
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try:
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from training.williams_market_structure import WilliamsMarketStructure
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except ImportError:
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# Use reused Williams instance
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if not self.williams_structure:
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logger.warning("Williams Market Structure not available")
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return None
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# Convert DataFrame to numpy array for Williams calculation
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if len(df) < 50:
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if len(df) < 20: # Reduced from 50 to match Williams minimum requirement
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logger.debug(f"[WILLIAMS] Insufficient data for pivot calculation: {len(df)} bars (need 20+)")
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return None
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ohlcv_array = np.array([
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[self._to_local_timezone(df.index[i]).timestamp() if hasattr(df.index[i], 'timestamp') else time.time(),
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df['open'].iloc[i], df['high'].iloc[i], df['low'].iloc[i],
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df['close'].iloc[i], df['volume'].iloc[i]]
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for i in range(len(df))
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])
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try:
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ohlcv_array = np.array([
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[self._to_local_timezone(df.index[i]).timestamp() if hasattr(df.index[i], 'timestamp') else time.time(),
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df['open'].iloc[i], df['high'].iloc[i], df['low'].iloc[i],
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df['close'].iloc[i], df['volume'].iloc[i]]
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for i in range(len(df))
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])
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logger.debug(f"[WILLIAMS] Prepared OHLCV array: {ohlcv_array.shape}, price range: {ohlcv_array[:, 4].min():.2f} - {ohlcv_array[:, 4].max():.2f}")
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except Exception as e:
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logger.warning(f"[WILLIAMS] Error preparing OHLCV array: {e}")
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return None
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# Calculate Williams pivot points
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williams = WilliamsMarketStructure()
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structure_levels = williams.calculate_recursive_pivot_points(ohlcv_array)
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# Calculate Williams pivot points with reused instance
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try:
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structure_levels = self.williams_structure.calculate_recursive_pivot_points(ohlcv_array)
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# Add diagnostics for debugging
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total_pivots = sum(len(level.swing_points) for level in structure_levels.values())
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if total_pivots == 0:
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logger.debug(f"[WILLIAMS] No pivot points detected in {len(ohlcv_array)} bars")
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else:
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logger.debug(f"[WILLIAMS] Successfully detected {total_pivots} pivot points across {len([l for l in structure_levels.values() if len(l.swing_points) > 0])} levels")
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except Exception as e:
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logger.warning(f"[WILLIAMS] Error in pivot calculation: {e}")
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return None
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# Extract features (250 features total)
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pivot_features = williams.extract_features_for_rl(structure_levels)
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pivot_features = self.williams_structure.extract_features_for_rl(structure_levels)
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logger.debug(f"[PIVOT] Calculated {len(pivot_features)} Williams pivot features")
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return pivot_features
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@ -4795,40 +4825,66 @@ class TradingDashboard:
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logger.warning("Williams Market Structure not available for chart")
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return None
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# Need at least 50 bars for meaningful pivot calculation
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if len(df) < 50:
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logger.debug(f"[WILLIAMS] Insufficient data for pivot calculation: {len(df)} bars")
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# Reduced requirement to match Williams minimum
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if len(df) < 20:
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logger.debug(f"[WILLIAMS_CHART] Insufficient data for pivot calculation: {len(df)} bars (need 20+)")
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return None
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# Ensure timezone consistency for the chart data
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df = self._ensure_timezone_consistency(df)
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# Convert DataFrame to numpy array for Williams calculation with proper timezone handling
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ohlcv_array = []
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for i in range(len(df)):
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timestamp = df.index[i]
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try:
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ohlcv_array = []
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for i in range(len(df)):
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timestamp = df.index[i]
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# Convert timestamp to local timezone and then to Unix timestamp
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if hasattr(timestamp, 'timestamp'):
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local_time = self._to_local_timezone(timestamp)
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unix_timestamp = local_time.timestamp()
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else:
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unix_timestamp = time.time()
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ohlcv_array.append([
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unix_timestamp,
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df['open'].iloc[i],
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df['high'].iloc[i],
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df['low'].iloc[i],
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df['close'].iloc[i],
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df['volume'].iloc[i]
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])
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# Convert timestamp to local timezone and then to Unix timestamp
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if hasattr(timestamp, 'timestamp'):
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local_time = self._to_local_timezone(timestamp)
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unix_timestamp = local_time.timestamp()
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ohlcv_array = np.array(ohlcv_array)
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logger.debug(f"[WILLIAMS_CHART] Prepared OHLCV array: {ohlcv_array.shape}, price range: {ohlcv_array[:, 4].min():.2f} - {ohlcv_array[:, 4].max():.2f}")
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except Exception as e:
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logger.warning(f"[WILLIAMS_CHART] Error preparing OHLCV array: {e}")
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return None
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# Calculate Williams pivot points with proper configuration
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try:
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williams = WilliamsMarketStructure(
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swing_strengths=[2, 3, 5], # Start with simpler strengths
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enable_cnn_feature=False, # Disable CNN for chart display
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training_data_provider=None # No training data provider needed for chart
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)
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structure_levels = williams.calculate_recursive_pivot_points(ohlcv_array)
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# Add diagnostics for debugging
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total_pivots_detected = sum(len(level.swing_points) for level in structure_levels.values())
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if total_pivots_detected == 0:
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logger.warning(f"[WILLIAMS_CHART] No pivot points detected in {len(ohlcv_array)} bars for chart display")
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price_volatility = np.std(ohlcv_array[:, 4]) / np.mean(ohlcv_array[:, 4]) if np.mean(ohlcv_array[:, 4]) > 0 else 0.0
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logger.debug(f"[WILLIAMS_CHART] Data diagnostics: volatility={price_volatility:.4f}, time_span={ohlcv_array[-1, 0] - ohlcv_array[0, 0]:.0f}s")
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return None
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else:
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unix_timestamp = time.time()
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ohlcv_array.append([
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unix_timestamp,
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df['open'].iloc[i],
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df['high'].iloc[i],
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df['low'].iloc[i],
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df['close'].iloc[i],
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df['volume'].iloc[i]
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])
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ohlcv_array = np.array(ohlcv_array)
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# Calculate Williams pivot points
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williams = WilliamsMarketStructure()
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structure_levels = williams.calculate_recursive_pivot_points(ohlcv_array)
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logger.debug(f"[WILLIAMS_CHART] Successfully detected {total_pivots_detected} pivot points for chart")
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except Exception as e:
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logger.warning(f"[WILLIAMS_CHART] Error in pivot calculation: {e}")
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return None
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# Extract pivot points for chart display
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chart_pivots = {}
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@ -4846,7 +4902,7 @@ class TradingDashboard:
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# Log swing point details for validation
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highs = [s for s in swing_points if s.swing_type.name == 'SWING_HIGH']
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lows = [s for s in swing_points if s.swing_type.name == 'SWING_LOW']
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logger.debug(f"[WILLIAMS] Level {level}: {len(highs)} highs, {len(lows)} lows, total: {len(swing_points)}")
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logger.debug(f"[WILLIAMS_CHART] Level {level}: {len(highs)} highs, {len(lows)} lows, total: {len(swing_points)}")
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# Convert swing points to chart format
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chart_pivots[f'level_{level}'] = {
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@ -4858,11 +4914,11 @@ class TradingDashboard:
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}
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total_pivots += len(swing_points)
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logger.info(f"[WILLIAMS] Calculated {total_pivots} total pivot points across {len(chart_pivots)} levels")
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logger.info(f"[WILLIAMS_CHART] Calculated {total_pivots} total pivot points across {len(chart_pivots)} levels")
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return chart_pivots
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except Exception as e:
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logger.warning(f"Error calculating Williams pivot points for chart: {e}")
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logger.warning(f"Error calculating Williams pivot points: {e}")
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return None
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def _add_williams_pivot_points_to_chart(self, fig, pivot_points: Dict, row: int = 1):
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