https://mocca96.medium.com/risk-factor-regressions-d1fc45a9abc6

Risk_Factor_Regression.py

import pandas as pd
import numpy as np 
import scipy as sp
import statsmodels.formula.api as sm 
from statsmodels.iolib.summary2 import summary_col
import datetime as dt
from datetime import datetime
from datetime import datetime, timedelta 
import matplotlib.pyplot as plt
import matplotlib
matplotlib.rcParams['figure.figsize'] = (16.0, 8.0)

class Stock:
    def __init__(self, ticker, start=None, end=None):

        self.ticker = ticker

        try:
            self._ticker = yf.Ticker(self.ticker)

            if not (start or end):
                self.df = self.df_ = self._ticker.history(period='max', auto_adjust=True)
                
            else:
                self.df = self.df_ = self._ticker.history(start=start, end=end, auto_adjust=True)

        except Exception as err:
            print(err)
            
            
def adj_data(sym):
    df = Stock(sym).df
    ohlc = df[['Close']]
    ohlc =ohlc.rename(columns={'Close': sym})
    ohlc.name = sym 
    return ohlc       
 
def to_return(prices,retType='simple'):
    if retType == 'simple':
        ret = (prices/prices.shift(1))-1
    else:
        ret = np.log(prices/prices.shift(1))
    return ret 
    
def factor_regression(df_stk):
    stkName = df_stk.name
    import pandas_datareader.data as web 
    df_factors = web.DataReader('F-F_Research_Data_5_Factors_2x3_daily', 'famafrench')[0]
    df_factors.rename(columns={'Mkt-RF': 'MKT'}, inplace=True)
    df_factors['MKT'] = df_factors['MKT']/100
    df_factors['SMB'] = df_factors['SMB']/100
    df_factors['HML'] = df_factors['HML']/100
    df_factors['RMW'] = df_factors['RMW']/100
    df_factors['CMA'] = df_factors['CMA']/100
    df_stock_factor = pd.merge(df_stk,df_factors,left_index=True,right_index=True)
    df_stock_factor['XsRet'] = df_stock_factor[stkName] - df_stock_factor['RF'] 
    
    CAPM = sm.ols(formula = 'XsRet ~ MKT', data=df_stock_factor).fit(cov_type='HAC',cov_kwds={'maxlags':1})
    FF3 = sm.ols( formula = 'XsRet ~ MKT + SMB + HML', data=df_stock_factor).fit(cov_type='HAC',cov_kwds={'maxlags':1})
    FF5 = sm.ols( formula = 'XsRet ~ MKT + SMB + HML + RMW + CMA', data=df_stock_factor).fit(cov_type='HAC',cov_kwds={'maxlags':1})
    
    CAPMtstat = CAPM.tvalues
    FF3tstat = FF3.tvalues
    FF5tstat = FF5.tvalues
    
    CAPMcoeff = CAPM.params
    FF3coeff = FF3.params
    FF5coeff = FF5.params
    
    CAPMpredi = CAPM.predict()
    FF3predi = FF3.predict()
    FF5predi = FF5.predict()
    
    CAPMresid = CAPM.resid
    FF3resid = FF3.resid
    FF5resid = FF5.resid

    results_df = pd.DataFrame({'CAPMcoeff':CAPMcoeff,'CAPMtstat':CAPMtstat,
                               'FF3coeff':FF3coeff, 'FF3tstat':FF3tstat,
                               'FF5coeff':FF5coeff, 'FF5tstat':FF5tstat},
    index = ['Intercept', 'MKT', 'SMB', 'HML', 'RMW', 'CMA'])
    
    df_stock_factor['CAPM'] = CAPMpredi
    df_stock_factor['CAPMResiduals'] = CAPMresid
    # Fama-French 3 Factor Model (FF3)
    df_stock_factor['FF3'] = FF3predi
    df_stock_factor['FF3Residuals'] = FF3resid 
    # Fama-French 5 Factor Model (FF5)
    df_stock_factor['FF5'] = FF5predi
    df_stock_factor['FF5Residuals'] = FF5resid
    
    dfoutput = summary_col([CAPM, FF3, FF5],stars=True,float_format='%0.4f',
                  model_names=['CAPM','FF3','FF5'],
                  info_dict={'N':lambda x: "{0:d}".format(int(x.nobs)),
                             'Adjusted R2':lambda x: "{:.4f}".format(x.rsquared_adj)}, 
                             regressor_order = ['Intercept', 'MKT', 'SMB', 'HML', 'RMW', 'CMA'])
    
    results_df.to_csv('stats/stats_'+stkName+'.csv')
    df_stock_factor.to_csv('apm/'+stkName+'.csv')

    return print(dfoutput)

df = adj_data("AMD")
sym = df.name
data = to_return(df,retType='simple')
data.name = sym
data.dropna(inplace=True)
print('==============================================')
print(sym+' - Risk Factor Regression')
print('==============================================')
factor_regression(data)
print('                                              ')