Menu
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.
686
cumprod() is used to find the cumulative product of the values seen so far over any axis. Each cell is populated with the cumulative product of the values seen so far. Example #1: Use cumprod() function to find the cumulative product of the values seen so far along the index axis.
The cumsum() function is used to get cumulative sum over a DataFrame or Series axis. Returns a DataFrame or Series of the same size containing the cumulative sum. The index or the name of the axis. 0 is equivalent to None or 'index'.
In Python, we can calculate the moving average using . rolling() method. This method provides rolling windows over the data, and we can use the mean function over these windows to calculate moving averages. The size of the window is passed as a parameter in the function .
rolling() function is a very useful function. It Provides rolling window calculations over the underlying data in the given Series object. Syntax: Series.rolling(window, min_periods=None, center=False, win_type=None, on=None, axis=0, closed=None)
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
If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
Donate Us With
