# 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