Create indicators/bollinger_bands.py

indicators/bollinger_bands.py

@@ -0,0 +1,39 @@
+# 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