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

Building on top of Divakar's great answer, here is an implementation which corresponds to the adjust=True flag of the pandas function, i.e. using weights rather than recursion.

def numpy_ewma(data, window):
    alpha = 2 /(window + 1.0)
    scale = 1/(1-alpha)
    n = data.shape[0]
    scale_arr = (1-alpha)**(-1*np.arange(n))
    weights = (1-alpha)**np.arange(n)
    pw0 = (1-alpha)**(n-1)
    mult = data*pw0*scale_arr
    cumsums = mult.cumsum()
    out = cumsums*scale_arr[::-1] / weights.cumsum()

    return out