I have simplified the program for better understanding.

Step 1- Copy past the below code and run the program. This will download all the stock prices in your local machine with "xlsx" extension.

import datetime as dt

import pandas as pd

import pandas_datareader.data as web

import numpy as np

def stockdata(ticker):

start= dt.datetime(2000,1,1) ## Start Date Range

end=dt.datetime.now() ## Curret date as end date Range

Stock=web.DataReader(ticker, "yahoo", start, end)

name=str(ticker) + ".xlsx"

Stock.to_excel(name)

return ()

ticker= ["MU", "ASML","TSM","QCOM", "UMC", "^SOX", "INTC","^IXIC"]

for i in ticker:

stockdata(i)

Step 2- Consolidate the adj closed price column of each company from the individual excel file in one common excel file "StockData.xlsx" as shown in the article screenshot.

Step3:- Save a below code in another file and run it. This code will do the analysis.

import pandas as pd

import matplotlib.pyplot as plt

import seaborn as sns

from statsmodels.stats.outliers_influence import variance_inflation_factor

data=pd.read_excel("StockData.xlsx")

data.set_index("Date", inplace= True)

sns.heatmap(data.corr(), annot=True, cmap="YlGnBu")

plt.show()

X=data.drop(["ASML"], axis=1)

vif = pd.DataFrame()

vif["features"] = X.columns

vif["vif_Factor"] = [variance_inflation_factor(X.values, i) for i in range(X.shape[1])]

print(vif)

Save the StockData.xlsx and the second program ( step3) in the same file directory.

In case, any line of the code is not clear then do not hesitate to write back to me and I will be happy to explain.