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| import pandas as pd | |
| def calculate_bollinger_bands(prices, ma_length=20, std_dev_multiplier=2): | |
| """ | |
| Calculates the Bollinger Bands for a given set of stock prices. | |
| Parameters: | |
| - prices (pd.Series): A pandas Series containing the stock's closing prices. | |
| - ma_length (int): The length of the moving average. Defaults to 20. | |
| - std_dev_multiplier (int): The number of standard deviations to calculate the upper and lower bands. Defaults to 2. | |
| Returns: | |
| - pd.DataFrame: A DataFrame containing the Moving Average, Upper Band, and Lower Band. | |
| """ | |
| # Calculate the Moving Average | |
| ma = prices.rolling(window=ma_length).mean() | |
| # Calculate the standard deviation | |
| std_dev = prices.rolling(window=ma_length).std() | |
| # Calculate the Upper and Lower Bollinger Bands | |
| upper_band = ma + (std_dev_multiplier * std_dev) | |
| lower_band = ma - (std_dev_multiplier * std_dev) | |
| bollinger_bands_df = pd.DataFrame(data={ | |
| 'Moving_Average': ma, | |
| 'Upper_Band': upper_band, | |
| 'Lower_Band': lower_band | |
| }) | |
| return bollinger_bands_df | |
| if __name__ == "__main__": | |
| # Example usage | |
| data = {'Close': [22, 24, 23, 25, 26, 28, 27, 29, 30, 32, 31, 33]} | |
| prices = pd.Series(data['Close']) | |
| # User-defined parameters for Bollinger Bands | |
| ma_length = 20 # Moving Average length | |
| std_dev_multiplier = 2 # Standard Deviation Multiplier | |
| # Calculate Bollinger Bands | |
| bollinger_bands_df = calculate_bollinger_bands(prices, ma_length, std_dev_multiplier) | |
| print(bollinger_bands_df) | |