Computes the cumulative gains and losses for a timeseries dataset

cumulative_gain_loss.py

import numpy as np

def cumulative_gain_loss(x: np.ndarray, y: np.ndarray):
    
    """
    Computes the cumulative gains and losses for a timeseries dataset
    
    Parameters
    ------------
    x: numpy.ndarray 
        time dimension
    y: numpy.ndarray
        variable of interest
    Returns
    -----------
    x_gains: numpy.ndarray
        x coordinates of positive changes (gains) in y
    gains: numpy.ndarray
        cumulative sum of positive changes (gains) in y
    x_losses: numpy.ndarray
        x coordinates of negative changes (losses) in y
    losses: numpy.ndarray
        cumulative sum of negative changes (losses) in y
    """
    
    # calculate the difference in y values between each time step x
    delta_y = np.diff(y)
    
    # calculate the cumulative sum for all delta_y where the change was positive
    gains = np.cumsum(delta_y[delta_y > 0])
    # get the time steps (x coordinates) when those positive changes happened
    x_gains = x[1:][delta_y > 0]
    
    # calculate the cumulative sum for all delta_y where the change was negative
    losses = np.cumsum(delta_y[delta_y < 0])
    # get the time steps (x coordinates) when those negative changes happened
    x_losses = x[1:][delta_y < 0]
    
    return x_gains, gains, x_losses, losses