misc
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Dobromir Popov
@ -103,7 +103,12 @@ class WilliamsMarketStructure:
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def calculate_recursive_pivot_points(self, ohlcv_data: np.ndarray) -> Dict[str, MarketStructureLevel]:
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"""
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Calculate 5 levels of recursive pivot points
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Calculate 5 levels of recursive pivot points using TRUE recursion
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Level 1: Calculated from 1s OHLCV data
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Level 2: Calculated from Level 1 pivot points treated as individual price bars
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Level 3: Calculated from Level 2 pivot points treated as individual price bars
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etc.
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Args:
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ohlcv_data: OHLCV data array with columns [timestamp, open, high, low, close, volume]
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@ -116,13 +121,18 @@ class WilliamsMarketStructure:
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return self._create_empty_structure()
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levels = {}
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current_data = ohlcv_data.copy()
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current_price_points = ohlcv_data.copy() # Start with raw price data
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for level in range(self.max_levels):
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logger.debug(f"Analyzing level {level} with {len(current_data)} data points")
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logger.debug(f"Analyzing level {level} with {len(current_price_points)} data points")
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# Find swing points for this level
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swing_points = self._find_swing_points_multi_strength(current_data)
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if level == 0:
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# Level 0 (Level 1): Calculate from raw OHLCV data
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swing_points = self._find_swing_points_multi_strength(current_price_points)
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else:
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# Level 1+ (Level 2+): Calculate from previous level's pivot points
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# Treat pivot points as individual price bars
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swing_points = self._find_pivot_points_from_pivot_points(current_price_points, level)
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if len(swing_points) < self.min_swings_for_trend:
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logger.debug(f"Not enough swings at level {level}: {len(swing_points)}")
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@ -136,14 +146,14 @@ class WilliamsMarketStructure:
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# Find support/resistance levels
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support_levels, resistance_levels = self._find_support_resistance(
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swing_points, current_data
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swing_points, current_price_points if level == 0 else None
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)
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# Determine current market bias
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current_bias = self._determine_market_bias(swing_points, trend_analysis)
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# Detect structure breaks
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structure_breaks = self._detect_structure_breaks(swing_points, current_data)
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structure_breaks = self._detect_structure_breaks(swing_points, current_price_points if level == 0 else None)
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# Create level data
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levels[f'level_{level}'] = MarketStructureLevel(
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@ -156,11 +166,11 @@ class WilliamsMarketStructure:
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structure_breaks=structure_breaks
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)
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# Prepare data for next level (use swing points as input)
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# Prepare data for next level: convert swing points to "price points"
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if len(swing_points) >= 5:
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current_data = self._convert_swings_to_ohlcv(swing_points)
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if len(current_data) < 10:
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logger.debug(f"Insufficient converted data for level {level + 1}")
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current_price_points = self._convert_pivots_to_price_points(swing_points)
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if len(current_price_points) < 10:
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logger.debug(f"Insufficient pivot data for level {level + 1}")
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break
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else:
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logger.debug(f"Not enough swings to continue to level {level + 1}")
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@ -490,41 +500,89 @@ class WilliamsMarketStructure:
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return structure_breaks
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def _convert_swings_to_ohlcv(self, swing_points: List[SwingPoint]) -> np.ndarray:
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"""Convert swing points to OHLCV format for next level analysis"""
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def _find_pivot_points_from_pivot_points(self, pivot_array: np.ndarray, level: int) -> List[SwingPoint]:
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"""
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Find pivot points from previous level's pivot points
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For Level 2+: A Level N low pivot is when a Level N-1 pivot low is surrounded
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by higher Level N-1 pivot lows (and vice versa for highs)
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Args:
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pivot_array: Array of pivot points as [timestamp, price, price, price, price, 0] format
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level: Current level being calculated
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"""
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swings = []
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if len(pivot_array) < 5:
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return swings
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# Use configurable strength for higher levels (more conservative)
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strength = min(2 + level, 5) # Level 1: 3 bars, Level 2: 4 bars, Level 3+: 5 bars
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for i in range(strength, len(pivot_array) - strength):
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current_price = pivot_array[i, 1] # Use the price from pivot point
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current_timestamp = pivot_array[i, 0]
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# Check for swing high (pivot high surrounded by lower pivot highs)
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is_swing_high = True
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for j in range(i - strength, i + strength + 1):
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if j != i and pivot_array[j, 1] >= current_price:
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is_swing_high = False
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break
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if is_swing_high:
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swings.append(SwingPoint(
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timestamp=datetime.fromtimestamp(current_timestamp) if current_timestamp > 1e9 else datetime.now(),
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price=current_price,
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index=i,
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swing_type=SwingType.SWING_HIGH,
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strength=strength,
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volume=0.0 # Pivot points don't have volume
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))
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# Check for swing low (pivot low surrounded by higher pivot lows)
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is_swing_low = True
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for j in range(i - strength, i + strength + 1):
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if j != i and pivot_array[j, 1] <= current_price:
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is_swing_low = False
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break
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if is_swing_low:
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swings.append(SwingPoint(
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timestamp=datetime.fromtimestamp(current_timestamp) if current_timestamp > 1e9 else datetime.now(),
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price=current_price,
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index=i,
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swing_type=SwingType.SWING_LOW,
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strength=strength,
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volume=0.0 # Pivot points don't have volume
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))
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return swings
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def _convert_pivots_to_price_points(self, swing_points: List[SwingPoint]) -> np.ndarray:
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"""
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Convert swing points to price point array for next level calculation
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Each swing point becomes a "price bar" where OHLC = pivot price
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This allows the next level to treat pivot points as individual price data
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"""
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if len(swing_points) < 2:
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return np.array([])
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ohlcv_data = []
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price_points = []
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for i in range(len(swing_points) - 1):
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current_swing = swing_points[i]
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next_swing = swing_points[i + 1]
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# Create synthetic OHLCV bar from swing to swing
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if current_swing.swing_type == SwingType.SWING_HIGH:
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# From high to next point
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open_price = current_swing.price
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high_price = current_swing.price
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low_price = min(current_swing.price, next_swing.price)
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close_price = next_swing.price
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else:
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# From low to next point
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open_price = current_swing.price
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high_price = max(current_swing.price, next_swing.price)
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low_price = current_swing.price
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close_price = next_swing.price
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ohlcv_data.append([
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current_swing.timestamp.timestamp(),
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open_price,
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high_price,
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low_price,
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close_price,
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current_swing.volume
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for swing in swing_points:
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# Each pivot point becomes a price point where OHLC = pivot price
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price_points.append([
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swing.timestamp.timestamp(),
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swing.price, # Open = pivot price
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swing.price, # High = pivot price
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swing.price, # Low = pivot price
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swing.price, # Close = pivot price
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0.0 # Volume = 0 (not applicable for pivot points)
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])
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return np.array(ohlcv_data)
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return np.array(price_points)
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def _create_empty_structure(self) -> Dict[str, MarketStructureLevel]:
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"""Create empty structure when insufficient data"""
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