integrationg COB
This commit is contained in:
Dobromir Popov
@ -1387,4 +1387,246 @@ class WilliamsMarketStructure:
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except Exception as e:
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logger.error(f"Error calculating CNN ground truth: {e}", exc_info=True)
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return np.zeros(10, dtype=np.float32)
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return np.zeros(10, dtype=np.float32)
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def extract_pivot_features(df: pd.DataFrame) -> Optional[np.ndarray]:
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"""
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Extract pivot-based features for RL state building
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Args:
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df: Market data DataFrame with OHLCV columns
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Returns:
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numpy array with pivot features (1000 features)
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"""
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try:
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if df is None or df.empty or len(df) < 50:
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return None
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features = []
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# === PIVOT DETECTION FEATURES (200) ===
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highs = df['high'].values
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lows = df['low'].values
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closes = df['close'].values
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# Find pivot highs and lows
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pivot_high_indices = []
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pivot_low_indices = []
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window = 5
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for i in range(window, len(highs) - window):
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# Pivot high: current high is higher than surrounding highs
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if all(highs[i] > highs[j] for j in range(i-window, i)) and \
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all(highs[i] > highs[j] for j in range(i+1, i+window+1)):
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pivot_high_indices.append(i)
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# Pivot low: current low is lower than surrounding lows
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if all(lows[i] < lows[j] for j in range(i-window, i)) and \
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all(lows[i] < lows[j] for j in range(i+1, i+window+1)):
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pivot_low_indices.append(i)
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# Pivot high features (100 features)
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if pivot_high_indices:
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recent_pivot_highs = [highs[i] for i in pivot_high_indices[-100:]]
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features.extend(recent_pivot_highs)
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features.extend([0.0] * max(0, 100 - len(recent_pivot_highs)))
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else:
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features.extend([0.0] * 100)
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# Pivot low features (100 features)
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if pivot_low_indices:
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recent_pivot_lows = [lows[i] for i in pivot_low_indices[-100:]]
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features.extend(recent_pivot_lows)
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features.extend([0.0] * max(0, 100 - len(recent_pivot_lows)))
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else:
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features.extend([0.0] * 100)
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# === PIVOT DISTANCE FEATURES (200) ===
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current_price = closes[-1]
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# Distance to nearest pivot highs (100 features)
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if pivot_high_indices:
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distances_to_highs = [(current_price - highs[i]) / current_price for i in pivot_high_indices[-100:]]
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features.extend(distances_to_highs)
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features.extend([0.0] * max(0, 100 - len(distances_to_highs)))
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else:
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features.extend([0.0] * 100)
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# Distance to nearest pivot lows (100 features)
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if pivot_low_indices:
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distances_to_lows = [(current_price - lows[i]) / current_price for i in pivot_low_indices[-100:]]
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features.extend(distances_to_lows)
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features.extend([0.0] * max(0, 100 - len(distances_to_lows)))
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else:
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features.extend([0.0] * 100)
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# === MARKET STRUCTURE FEATURES (200) ===
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# Higher highs and higher lows detection
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structure_features = []
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if len(pivot_high_indices) >= 2:
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# Recent pivot high trend
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recent_highs = [highs[i] for i in pivot_high_indices[-5:]]
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high_trend = 1.0 if len(recent_highs) >= 2 and recent_highs[-1] > recent_highs[-2] else -1.0
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structure_features.append(high_trend)
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else:
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structure_features.append(0.0)
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if len(pivot_low_indices) >= 2:
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# Recent pivot low trend
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recent_lows = [lows[i] for i in pivot_low_indices[-5:]]
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low_trend = 1.0 if len(recent_lows) >= 2 and recent_lows[-1] > recent_lows[-2] else -1.0
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structure_features.append(low_trend)
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else:
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structure_features.append(0.0)
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# Swing strength
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if pivot_high_indices and pivot_low_indices:
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last_high = highs[pivot_high_indices[-1]] if pivot_high_indices else current_price
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last_low = lows[pivot_low_indices[-1]] if pivot_low_indices else current_price
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swing_range = (last_high - last_low) / current_price if current_price > 0 else 0
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structure_features.append(swing_range)
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else:
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structure_features.append(0.0)
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# Pad structure features to 200
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features.extend(structure_features)
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features.extend([0.0] * (200 - len(structure_features)))
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# === TREND AND MOMENTUM FEATURES (400) ===
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# Moving averages
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if len(closes) >= 50:
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sma_20 = np.mean(closes[-20:])
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sma_50 = np.mean(closes[-50:])
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features.extend([sma_20, sma_50, current_price - sma_20, current_price - sma_50])
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else:
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features.extend([0.0, 0.0, 0.0, 0.0])
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# Price momentum over different periods
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momentum_periods = [5, 10, 20, 30, 50]
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for period in momentum_periods:
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if len(closes) > period:
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momentum = (closes[-1] - closes[-period-1]) / closes[-period-1]
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features.append(momentum)
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else:
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features.append(0.0)
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# Volume analysis
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if 'volume' in df.columns and len(df['volume']) > 20:
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volume_sma = np.mean(df['volume'].values[-20:])
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current_volume = df['volume'].values[-1]
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volume_ratio = current_volume / volume_sma if volume_sma > 0 else 1.0
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features.append(volume_ratio)
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else:
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features.append(1.0)
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# Volatility features
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if len(closes) > 20:
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returns = np.diff(np.log(closes[-20:]))
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volatility = np.std(returns) * np.sqrt(1440) # Daily volatility
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features.append(volatility)
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else:
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features.append(0.02) # Default volatility
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# Pad to 400 features
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while len(features) < 800:
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features.append(0.0)
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# Ensure exactly 1000 features
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features = features[:1000]
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while len(features) < 1000:
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features.append(0.0)
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return np.array(features, dtype=np.float32)
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except Exception as e:
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logger.error(f"Error extracting pivot features: {e}")
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return None
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def analyze_pivot_context(market_data: Dict, trade_timestamp: datetime, trade_action: str) -> Optional[Dict]:
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"""
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Analyze pivot context around a specific trade for reward calculation
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Args:
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market_data: Market data context
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trade_timestamp: When the trade was made
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trade_action: BUY/SELL action
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Returns:
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Dictionary with pivot analysis results
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"""
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try:
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# Extract price data if available
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if 'ohlcv_data' not in market_data:
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return None
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df = market_data['ohlcv_data']
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if df is None or df.empty:
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return None
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# Find recent pivot points
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highs = df['high'].values
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lows = df['low'].values
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closes = df['close'].values
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if len(closes) < 20:
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return None
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current_price = closes[-1]
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# Find pivot points
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pivot_highs = []
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pivot_lows = []
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window = 3
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for i in range(window, len(highs) - window):
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# Pivot high
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if all(highs[i] >= highs[j] for j in range(i-window, i)) and \
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all(highs[i] >= highs[j] for j in range(i+1, i+window+1)):
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pivot_highs.append((i, highs[i]))
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# Pivot low
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if all(lows[i] <= lows[j] for j in range(i-window, i)) and \
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all(lows[i] <= lows[j] for j in range(i+1, i+window+1)):
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pivot_lows.append((i, lows[i]))
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analysis = {
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'near_pivot': False,
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'pivot_strength': 0.0,
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'pivot_break_direction': None,
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'against_pivot_structure': False
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}
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# Check if near significant pivot
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pivot_threshold = current_price * 0.005 # 0.5% threshold
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for idx, price in pivot_highs[-5:]: # Check last 5 pivot highs
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if abs(current_price - price) < pivot_threshold:
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analysis['near_pivot'] = True
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analysis['pivot_strength'] = min(1.0, (current_price - price) / pivot_threshold)
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# Check for breakout
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if current_price > price * 1.001: # 0.1% breakout
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analysis['pivot_break_direction'] = 'up'
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elif trade_action == 'SELL' and current_price < price:
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analysis['against_pivot_structure'] = True
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break
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for idx, price in pivot_lows[-5:]: # Check last 5 pivot lows
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if abs(current_price - price) < pivot_threshold:
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analysis['near_pivot'] = True
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analysis['pivot_strength'] = min(1.0, (price - current_price) / pivot_threshold)
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# Check for breakout
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if current_price < price * 0.999: # 0.1% breakdown
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analysis['pivot_break_direction'] = 'down'
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elif trade_action == 'BUY' and current_price > price:
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analysis['against_pivot_structure'] = True
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break
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return analysis
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except Exception as e:
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logger.error(f"Error analyzing pivot context: {e}")
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return None
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