How to calculate rolling cumulative product on Pandas DataFrame

Question

I have a time series of returns, rolling beta, and rolling alpha in a pandas DataFrame. How can I calculate a rolling annualized alpha for the alpha column of the DataFrame? (I want to do the equivalent to =PRODUCT(1+[trailing 12 months])-1 in excel)

            SPX Index BBOEGEUS Index    Beta      Alpha
2006-07-31   0.005086    0.001910    1.177977   -0.004081
2006-08-31   0.021274    0.028854    1.167670    0.004012
2006-09-30   0.024566    0.009769    1.101618   -0.017293
2006-10-31   0.031508    0.030692    1.060355   -0.002717
2006-11-30   0.016467    0.031720    1.127585    0.013153

I was surprised to see that there was no “rolling” function built into pandas for this, but I was hoping somebody could help with a function that I can then apply to the df[‘Alpha’] column using pd.rolling_apply.

Thanks in advance for any help you have to offer.

Asked By: AP228

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Source

Answer 1

will this do?

import pandas as pd
import numpy as np

# your DataFrame; df = ...

pd.rolling_apply(df, 12, lambda x: np.prod(1 + x) - 1)

Answered By: herrfz

Answer 2

It will be a bit faster, if you move those +/-1 out of df, like this:

cumprod = (1.+df).rolling(window=12).agg(lambda x : x.prod()) -1

Answered By: Zellint

Answer 3

rolling_apply has been dropped in pandas and replaced by more versatile
window methods (e.g. rolling() etc.)

# Both agg and apply will give you the same answer
(1+df).rolling(window=12).agg(np.prod) - 1
# BUT apply(raw=True) will be much FASTER!
(1+df).rolling(window=12).apply(np.prod, raw=True) - 1

Answered By: YaOzI

Answer 4

rolling_apply is deprecated, so this works best:

(1 + df).cumprod() – 1

Answered By: develarist

Answer 5

You can do this by acumulating it all, and then getting the difference between the periods that you want:

(df+1).cumprod().pct_change(periods=12)

Answered By: Emerson Uehara

How to calculate rolling cumulative product on Pandas DataFrame

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