How to compute volatility (standard deviation) in rolling window in Pandas

Question

I have a time series "Ser" and I want to compute volatilities (standard deviations) with a rolling window. My current code correctly does it in this form:

w = 10
for timestep in range(length):
    subSer = Ser[timestep:timestep + w]
    mean_i = np.mean(subSer)
    vol_i = (np.sum((subSer - mean_i)**2) / len(subSer))**0.5
    volList.append(w_i)

This seems to me very inefficient. Does Pandas have built-in functionality for doing something like this?

Answer 1

Typically, [finance-type] people quote volatility in annualized terms of percent changes in price.

Assuming you have daily prices in a dataframe df and there are 252 trading days in a year, something like the following is probably what you want:

df.pct_change().rolling(window_size).std()*(252**0.5)

Answer 2

It looks like you are looking for Series.rolling. You can apply the std calculations to the resulting object:

roller = Ser.rolling(w)
volList = roller.std(ddof=0)

If you don't plan on using the rolling window object again, you can write a one-liner:

volList = Ser.rolling(w).std(ddof=0)

Keep in mind that ddof=0 is necessary in this case because the normalization of the standard deviation is by len(Ser)-ddof, and that ddof defaults to 1 in pandas.