The `statsmodels`

package has many statistical tests. We will see an example of such a test—the
**Anderson-Darling test** for normality (http://en.wikipedia.org/wiki/Anderson%E2%80%93Darling_test).

We will download price data as in the previous recipe, but this time for a single stock. Again, we will calculate the log returns of the close price of this stock, and use that as an input for the normality test function.

This function returns a tuple containing a second element—a **p-value** between 0 and 1. The complete code for this tutorial is as follows:

from __future__ import print_function import datetime import numpy as np from matplotlib import finance from statsmodels.stats.adnorm import normal_ad ...

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