a924328c90
- Introduced a new Strategy class to encapsulate trading strategies, including the Market Regime Strategy that adapts to different market conditions. - Refactored BollingerBands and RSI classes to accept configuration parameters for improved flexibility and maintainability. - Updated test_bbrsi.py to utilize the new strategy implementation and adjusted date ranges for testing. - Enhanced documentation to include details about the new Strategy class and its components.
76 lines
3.4 KiB
Python
76 lines
3.4 KiB
Python
import pandas as pd
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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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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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# Calculate SMA
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data_df['SMA'] = data_df[price_column].rolling(window=self.config['bb_period']).mean()
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# Calculate Standard Deviation
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std_dev = data_df[price_column].rolling(window=self.config['bb_period']).std()
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# Calculate Upper and Lower Bands
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data_df['UpperBand'] = data_df['SMA'] + (2.0* std_dev)
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data_df['LowerBand'] = data_df['SMA'] - (2.0* std_dev)
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# Calculate the width of the Bollinger Bands
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data_df['BBWidth'] = (data_df['UpperBand'] - data_df['LowerBand']) / data_df['SMA']
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# Calculate the market regime
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# 1 = sideways, 0 = trending
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data_df['MarketRegime'] = (data_df['BBWidth'] < self.config['bb_width']).astype(int)
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if data_df['MarketRegime'].sum() > 0:
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data_df['UpperBand'] = data_df['SMA'] + (self.config['trending']['bb_std_dev_multiplier'] * std_dev)
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data_df['LowerBand'] = data_df['SMA'] - (self.config['trending']['bb_std_dev_multiplier'] * std_dev)
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else:
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data_df['UpperBand'] = data_df['SMA'] + (self.config['sideways']['bb_std_dev_multiplier'] * std_dev)
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data_df['LowerBand'] = data_df['SMA'] - (self.config['sideways']['bb_std_dev_multiplier'] * std_dev)
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else:
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data_df['SMA'] = data_df[price_column].rolling(window=14).mean()
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# Calculate Standard Deviation
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std_dev = data_df[price_column].rolling(window=14).std()
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# Calculate Upper and Lower Bands
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data_df['UpperBand'] = data_df['SMA'] + 1.5* std_dev
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data_df['LowerBand'] = data_df['SMA'] - 1.5* std_dev
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return data_df
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