Jensen_alpha.py

import numpy as np
import pandas as pd
import statsmodels.regression.linear_model as lm
import statsmodels.tools.tools as ct

# Jensen’s alpha’s performance metric data
returns = pd.read_csv(‘Data_file_link’, index_col=’Date’, parse_dates=True)

# Jensen’s alpha’s performance metric calculation
returns.loc[:CT’] = ct.add_constant(returns)

# Using risk free rate of ticker and risk free rate of market for calculating Jensen’s alpha
alphaj = lm.OLS(returns[‘TICKER-RF’], returns[[‘CT’,  ‘MKT-RF’]], hasconst=bool).fit()
print (‘Jensen Alpha Linear Regression Summary’)
print(alphaj.summary())