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


i have time series "ser" , want compute volatilities (standard deviations) rolling window. current code correctly in form:

w=10 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 me inefficient. pandas have built-in functionality doing this?

it looks looking series.rolling. can apply std calculations resulting object:

roller = ser.rolling(w) vollist = roller.std(ddof=0)

if don't plan on using rolling window object again, can write one-liner:

vollist = ser.rolling().std(ddof=0)

keep in mind ddof=0 necessary in case because normalization of standard deviation len(ser)-ddof, , ddof defaults 1 in pandas.


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