Predictive Hacks

How to Apply a Rolling Weighted Moving Average in Pandas

Pandas has built-in functions for rolling windows that enable us to get the moving average or even an exponential moving average. However, if we want to set custom weights to our observations there is not any built-in function. Below we provide an example of how we can apply a weighted moving average with a rolling window.

Assume that we have the following data frame and we want to get a moving average with a rolling window of 4 observations where the most recent observations will have more weight than the older ones.

import pandas as pd
import numpy as np

df = pd.DataFrame({'X':range(100,30, -5)})

We need to define the weights and to make sure that they add up to 1. Our weights can be [0.1, 0.2, 0.3, 0.4].

weights = np.array([0.1, 0.2, 0.3, 0.4])
df['MA'] = df['X'].rolling(4).apply(lambda x: np.sum(weights*x))
df

Note that the first three observations are NaN since our rolling window was set to 4. The 4th observation (index=3), is equal to 90 since 100*0.1 + 95*0.2 + 90*0.3 + 85*0.4=90

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