Create bollinger_bands.py

indicators/bollinger_bands.py

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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)