pandas_rolling_apply_on_two_series.py

import pandas as pd

def rolling_apply_function_of_two_series(func, series1, series2, window=12):
    '''
    Pandas has a DataFrame.rolling() method, allowing to apply a function of a series/array on a rolling window of the data frame.
    However, only functions of one argument (function of one array) are supported.
    
    This is a workaround: allowing to apply a function of two series on a rolling window.
    This assumes the two input series have the same index (or at least that the index of series1 overlaps the index of series2)
    :param func: A function of two series, returning a scalar
    :param series1: 
    :param series2:
    :param window: An integer defining the size of the window.
    :return: A series, where each element is the result of 'func' applied to a window of size 'window' of the previous rows of the input series
    '''
    # Align the series2 (typically a benchmark series) with the index of the series1 (typically the fund series)
    #  and put them in one data frame of 2 columns
    df = pd.concat([series1, series2.reindex(series1.index)], axis=1)
    # Add a third column serving as integers index
    df['integer_index'] = range(len(df))
    # Define a function that calculates the input 'function' over a window defined by an integer,
    # taking series1 and series2 values over that window as argument
    def func_on_window(ii, df):
        x_df = df.iloc[map(int, ii)]
        return func(x_df[x_df.columns[0]], x_df[x_df.columns[1]])
    # Apply this function on rolling window on the integers column
    return df['integer_index'].rolling(window=window, min_periods=window).apply(lambda x: func_on_window(x, df)).fillna(0.0)