There are several ways to get the list of securities which are normally distributed. Since the pipeline output is a pandas dataframe, I prefer using pandas methods rather than loops and lists. A few lines of code can do it.
# Assume log_returns is a pipeline output dataframe with our returns
# The following filters for just those securities which have non-nan data for all days
complete_groups = log_returns.dropna().groupby(level=1).filter(complete_data)
# Apply a function to each security to find the p-values. Group by level 1 which are the securities.
p_values = complete_groups.groupby(level=1).daily_returns.agg(get_pvalue).to_frame('p_value')
# Finally compare these p-values to our significance_level to find all securities which are likely normally distributed
normally distributed_securities = p_values.query('p_value >= @significance_level')
The resulting dataframe 'normally distributed_securities' are our securities which likely have returns which are normally distributed.
The index contains the security objects and the column is the associated p_value.
Check out the attached notebook. Several cells have been added to the bottom with the above code along with the associated helper functions and updated pipeline definition for log returns.
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