okama.AssetList.skewness

property AssetList.skewness

Compute expanding skewness of the return time series for each asset returns.

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.

Returns:

Dataframe

Expanding skewness time series for each asset.

See also

skewness_rolling

Compute rolling 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.plot()
>>> plt.show()