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import pandas as pd
def calculate_sma(data, window):
"""
Calculate the Simple Moving Average (SMA) for the given data.
Parameters:
- data (pd.Series): The stock data (typically closing prices).
- window (int): The period over which to calculate the SMA.
Returns:
- pd.Series: The calculated SMA values.
"""
return data.rolling(window=window, min_periods=1).mean()
def calculate_bollinger_bands(data, window=21, std_multiplier=1.7):
"""
Calculate Bollinger Bands for the given stock data.
Parameters:
- data (pd.DataFrame): The stock data, expected to have a 'Close' column.
- window (int): The SMA period for the middle band. Defaults to 21.
- std_multiplier (float): The standard deviation multiplier for the upper and lower bands. Defaults to 1.7.
Returns:
- pd.DataFrame: The input data frame with added columns for the Bollinger Bands ('BB_Middle', 'BB_Upper', 'BB_Lower').
"""
if 'Close' not in data.columns:
raise ValueError("Data frame must contain a 'Close' column.")
# Calculate the middle band (SMA)
data['BB_Middle'] = calculate_sma(data['Close'], window)
# Calculate the standard deviation
std_dev = data['Close'].rolling(window=window).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
if __name__ == "__main__":
# Example usage
# Generate a sample DataFrame with 'Close' prices
import numpy as np
dates = pd.date_range(start='2023-01-01', periods=100, freq='D')
close_prices = pd.Series((100 + np.random.randn(100).cumsum()), index=dates)
sample_data = pd.DataFrame({'Close': close_prices})
# Calculate Bollinger Bands
bb_data = calculate_bollinger_bands(sample_data)
print(bb_data.head()) # Print the first few rows to verify
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