NumPy version of “Exponential weighted moving average”, equivalent to pandas.ewm().mean()

Question

How do I get the exponential weighted moving average in NumPy just like the following in pandas?

import pandas as pd
import pandas_datareader as pdr
from dat         


        

Answer 1

This answer may seem irrelevant. But, for those who also need to calculate the exponentially weighted variance (and also standard deviation) with NumPy, the following solution will be useful:

import numpy as np

def ew(a, alpha, winSize):
    _alpha = 1 - alpha
    ws = _alpha ** np.arange(winSize)
    w_sum = ws.sum()
    ew_mean = np.convolve(a, ws)[winSize - 1] / w_sum
    bias = (w_sum ** 2) / ((w_sum ** 2) - (ws ** 2).sum())
    ew_var = (np.convolve((a - ew_mean) ** 2, ws)[winSize - 1] / w_sum) * bias
    ew_std = np.sqrt(ew_var)
    return (ew_mean, ew_var, ew_std)