7af8cdcb32
- Added validation to ensure the specified price column exists in the DataFrame for Bollinger Bands calculations. - Introduced a new method to ensure the DataFrame has a proper DatetimeIndex, improving time-series operations in strategy processing. - Updated strategy run method to call the new DatetimeIndex validation method before processing data. - Improved logging for better traceability of data transformations and potential issues.
146 lines
7.1 KiB
Python
146 lines
7.1 KiB
Python
import pandas as pd
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import numpy as np
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class BollingerBands:
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"""
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Calculates Bollinger Bands for given financial data.
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"""
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def __init__(self, config):
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"""
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Initializes the BollingerBands calculator.
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Args:
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period (int): The period for the moving average and standard deviation.
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std_dev_multiplier (float): The number of standard deviations for the upper and lower bands.
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bb_width (float): The width of the Bollinger Bands.
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"""
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if config['bb_period'] <= 0:
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raise ValueError("Period must be a positive integer.")
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if config['trending']['bb_std_dev_multiplier'] <= 0 or config['sideways']['bb_std_dev_multiplier'] <= 0:
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raise ValueError("Standard deviation multiplier must be positive.")
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if config['bb_width'] <= 0:
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raise ValueError("BB width must be positive.")
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self.config = config
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def calculate(self, data_df: pd.DataFrame, price_column: str = 'close', squeeze = False) -> pd.DataFrame:
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"""
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Calculates Bollinger Bands and adds them to the DataFrame.
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Args:
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data_df (pd.DataFrame): DataFrame with price data. Must include the price_column.
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price_column (str): The name of the column containing the price data (e.g., 'close').
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Returns:
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pd.DataFrame: The original DataFrame with added columns:
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'SMA' (Simple Moving Average),
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'UpperBand',
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'LowerBand'.
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"""
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# Work on a copy to avoid modifying the original DataFrame passed to the function
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data_df = data_df.copy()
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if price_column not in data_df.columns:
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raise ValueError(f"Price column '{price_column}' not found in DataFrame.")
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if not squeeze:
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period = self.config['bb_period']
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bb_width_threshold = self.config['bb_width']
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trending_std_multiplier = self.config['trending']['bb_std_dev_multiplier']
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sideways_std_multiplier = self.config['sideways']['bb_std_dev_multiplier']
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# Calculate SMA
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data_df['SMA'] = data_df[price_column].rolling(window=period).mean()
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# Calculate Standard Deviation
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std_dev = data_df[price_column].rolling(window=period).std()
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# Calculate reference Upper and Lower Bands for BBWidth calculation (e.g., using 2.0 std dev)
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# This ensures BBWidth is calculated based on a consistent band definition before applying adaptive multipliers.
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ref_upper_band = data_df['SMA'] + (2.0 * std_dev)
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ref_lower_band = data_df['SMA'] - (2.0 * std_dev)
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# Calculate the width of the Bollinger Bands
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# Avoid division by zero or NaN if SMA is zero or NaN by replacing with np.nan
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data_df['BBWidth'] = np.where(data_df['SMA'] != 0, (ref_upper_band - ref_lower_band) / data_df['SMA'], np.nan)
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# Calculate the market regime (1 = sideways, 0 = trending)
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# Handle NaN in BBWidth: if BBWidth is NaN, MarketRegime should also be NaN or a default (e.g. trending)
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data_df['MarketRegime'] = np.where(data_df['BBWidth'].isna(), np.nan,
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(data_df['BBWidth'] < bb_width_threshold).astype(float)) # Use float for NaN compatibility
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# Determine the std dev multiplier for each row based on its market regime
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conditions = [
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data_df['MarketRegime'] == 1, # Sideways market
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data_df['MarketRegime'] == 0 # Trending market
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]
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choices = [
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sideways_std_multiplier,
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trending_std_multiplier
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]
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# Default multiplier if MarketRegime is NaN (e.g., use trending or a neutral default like 2.0)
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# For now, let's use trending_std_multiplier as default if MarketRegime is NaN.
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# This can be adjusted based on desired behavior for periods where regime is undetermined.
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row_specific_std_multiplier = np.select(conditions, choices, default=trending_std_multiplier)
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# Calculate final Upper and Lower Bands using the row-specific multiplier
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data_df['UpperBand'] = data_df['SMA'] + (row_specific_std_multiplier * std_dev)
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data_df['LowerBand'] = data_df['SMA'] - (row_specific_std_multiplier * std_dev)
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else: # squeeze is True
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price_series = data_df[price_column]
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# Use the static method for the squeeze case with fixed parameters
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upper_band, sma, lower_band = self.calculate_custom_bands(
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price_series,
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window=14,
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num_std=1.5,
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min_periods=14 # Match typical squeeze behavior where bands appear after full period
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)
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data_df['SMA'] = sma
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data_df['UpperBand'] = upper_band
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data_df['LowerBand'] = lower_band
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# BBWidth and MarketRegime are not typically calculated/used in a simple squeeze context by this method
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# If needed, they could be added, but the current structure implies they are part of the non-squeeze path.
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data_df['BBWidth'] = np.nan
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data_df['MarketRegime'] = np.nan
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return data_df
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@staticmethod
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def calculate_custom_bands(price_series: pd.Series, window: int = 20, num_std: float = 2.0, min_periods: int = None) -> tuple[pd.Series, pd.Series, pd.Series]:
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"""
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Calculates Bollinger Bands with specified window and standard deviation multiplier.
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Args:
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price_series (pd.Series): Series of prices.
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window (int): The period for the moving average and standard deviation.
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num_std (float): The number of standard deviations for the upper and lower bands.
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min_periods (int, optional): Minimum number of observations in window required to have a value.
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Defaults to `window` if None.
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Returns:
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tuple[pd.Series, pd.Series, pd.Series]: Upper band, SMA, Lower band.
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"""
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if not isinstance(price_series, pd.Series):
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raise TypeError("price_series must be a pandas Series.")
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if not isinstance(window, int) or window <= 0:
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raise ValueError("window must be a positive integer.")
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if not isinstance(num_std, (int, float)) or num_std <= 0:
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raise ValueError("num_std must be a positive number.")
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if min_periods is not None and (not isinstance(min_periods, int) or min_periods <= 0):
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raise ValueError("min_periods must be a positive integer if provided.")
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actual_min_periods = window if min_periods is None else min_periods
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sma = price_series.rolling(window=window, min_periods=actual_min_periods).mean()
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std = price_series.rolling(window=window, min_periods=actual_min_periods).std()
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# Replace NaN std with 0 to avoid issues if sma is present but std is not (e.g. constant price in window)
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std = std.fillna(0)
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upper_band = sma + (std * num_std)
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lower_band = sma - (std * num_std)
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return upper_band, sma, lower_band
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