indicators/bollinger_bands.py

# indicators/bollinger_bands.py

import pandas as pd

def calculate_bollinger_bands(data, period=21, std_multiplier=1.7):
    """
    Calculates Bollinger Bands for a given period and standard deviation multiplier.

    Parameters:
    - data: DataFrame containing stock prices with a 'Close' column (DataFrame).
    - period: The period over which to calculate the SMA and standard deviation (int).
    - std_multiplier: The multiplier for the standard deviation to calculate the upper and lower bands (float).

    Returns:
    - A DataFrame with columns 'BB_Middle', 'BB_Upper', 'BB_Lower'.
    """
    # Calculate the middle band (SMA)
    data['BB_Middle'] = data['Close'].rolling(window=period, min_periods=1).mean()
    
    # Calculate the standard deviation
    std_dev = data['Close'].rolling(window=period, min_periods=1).std()
    
    # Calculate the upper and lower bands
    data['BB_Upper'] = data['BB_Middle'] + (std_multiplier * std_dev)
    data['BB_Lower'] = data['BB_Middle'] - (std_multiplier * std_dev)
    
    return data[['BB_Middle', 'BB_Upper', 'BB_Lower']]

# Example usage
if __name__ == "__main__":
    # Assuming 'data' is a DataFrame that contains stock price data including a 'Close' column.
    # For the sake of example, let's create a dummy DataFrame.
    dates = pd.date_range(start="2023-01-01", end="2023-02-28", freq='D')
    prices = pd.Series([100 + i * 0.5 for i in range(len(dates))], index=dates)
    data = pd.DataFrame(prices, columns=['Close'])
    
    # Calculate Bollinger Bands
    bollinger_bands = calculate_bollinger_bands(data)
    print(bollinger_bands.head())  # Display the first few rows to verify the calculations