import pandas as pd

class BollingerBands:
    """
    Calculates Bollinger Bands for given financial data.
    """
    def __init__(self, period: int = 20, std_dev_multiplier: float = 2.0):
        """
        Initializes the BollingerBands calculator.

        Args:
            period (int): The period for the moving average and standard deviation.
            std_dev_multiplier (float): The number of standard deviations for the upper and lower bands.
        """
        if period <= 0:
            raise ValueError("Period must be a positive integer.")
        if std_dev_multiplier <= 0:
            raise ValueError("Standard deviation multiplier must be positive.")
            
        self.period = period
        self.std_dev_multiplier = std_dev_multiplier

    def calculate(self, data_df: pd.DataFrame, price_column: str = 'close') -> pd.DataFrame:
        """
        Calculates Bollinger Bands and adds them to the DataFrame.

        Args:
            data_df (pd.DataFrame): DataFrame with price data. Must include the price_column.
            price_column (str): The name of the column containing the price data (e.g., 'close').

        Returns:
            pd.DataFrame: The original DataFrame with added columns:
                          'SMA' (Simple Moving Average),
                          'UpperBand',
                          'LowerBand'.
        """
        if price_column not in data_df.columns:
            raise ValueError(f"Price column '{price_column}' not found in DataFrame.")

        # Calculate SMA
        data_df['SMA'] = data_df[price_column].rolling(window=self.period).mean()

        # Calculate Standard Deviation
        std_dev = data_df[price_column].rolling(window=self.period).std()

        # Calculate Upper and Lower Bands
        data_df['UpperBand'] = data_df['SMA'] + (self.std_dev_multiplier * std_dev)
        data_df['LowerBand'] = data_df['SMA'] - (self.std_dev_multiplier * std_dev)

        return data_df