My Quantopian Notes

While working on Quantopian.com, I have had to do a fair bit of learning in various areas

1. Learn Statistics
2. Operating on Data in Pandas
3. Trading Models

This is my attempt at documenting some of my learnings for my own and others.

Fundamentals Data Operations

Calculate Z-Score for specific fundamental fields in the Quantopian fundamentals DataFrame.

from scipy import stats

#Get rows you want

d=fund_df.loc[['pe_ratio','ev_to_ebitda']]

#Transpose it

d=d.T

#Drop NANs

d=d.dropna()

#Apply stats.zscore on that data

d=d.apply(stats.zscore)

#transpose it back

d=d.T

#rename the columns as needed

zscore=d.rename({'ev_to_ebitda':'ev_to_ebitda_zscore','pe_ratio':'pe_ratio_zscore'})

Calculate Z-Score for specific fundamental fields in the Quantopian fundamentals DataFrame, but do it group-wise by Morning Start Sector Code

from scipy import stats

#Transpose it

d=fund_df.T

#Drop NANs

d=d.dropna()

#Groupby Morning Star Code

d=d.groupby('morningstar_sector_code')

#Get rows you want

d=d[['pe_ratio','ev_to_ebitda']]

#Transform with stats.zscore on that grouped data

d=d.transform(stats.zscore)

#transpose it back

d=d.T

#rename the columns as needed

gzscore=d.rename({'ev_to_ebitda':'ev_to_ebitda_gzscore','pe_ratio':'pe_ratio_gzscore'})

Add these rows to back to fundamentals data

fund_df = pandas.concat([fund_df,zscore,gzscore]) 

Copyright (c) Sarvi Shanmugham