skewness_rolling

AssetList.skewness_rolling(window=60)

Compute rolling skewness of the return time series for each asset.

Skewness is a measure of the asymmetry of the probability distribution of a real-valued random variable about its mean. The skewness value can be positive, zero, negative, or undefined.

For normally distributed returns, the skewness should be about zero. A skewness value greater than zero means that there is more weight in the right tail of the distribution.

Parameters:

windowint, default 60

Rolling window size in months. This is the number of observations used for calculating the statistic. The window size should be at least 12 months.

Returns:

DataFrame

Rolling skewness time series for each asset.

See also

skewness

Compute skewness.

kurtosis

Calculate expanding Fisher (normalized) kurtosis.

kurtosis_rolling

Calculate rolling Fisher (normalized) kurtosis.

jarque_bera

Perform Jarque-Bera test for normality.

kstest

Perform Kolmogorov-Smirnov test for different types of distributions.

Examples

>>> import matplotlib.pyplot as plt

>>> al = ok.AssetList(["VFINX.US", "GC.COMM"], last_date="2021-01")
>>> al.names
{'VFINX.US': 'VANGUARD 500 INDEX FUND INVESTOR SHARES',
'GC.COMM': 'Gold'}
>>> al.skewness_rolling(window=12 * 5).plot()
>>> plt.show()