Performing a normality test with scikits-statsmodels

The scikits-statsmodels package has lots of 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).

How to do it...

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 zero and one. The complete code for this tutorial is as follows:

import datetime
import numpy
from matplotlib import finance
from statsmodels.stats.adnorm import normal_ad
import sys

#1. Download price data

# 2011 to 2012
start = datetime.datetime(2011, 01, 01)
end = datetime.datetime(2012, 01, 01)

print "Retrieving data for", sys.argv[1]
quotes = finance.quotes_historical_yahoo(sys.argv[1], start, end, asobject=True)

close = numpy...