Released by: Mountasser Labchiri

N.B: Please consider changing “YourUserName” in the path with your laptop name before executing the scripts.

Amazon Stock Database that I used (Downloadable):

AMZN.csv

Microsoft Stock Database that I used (Downloadable):

MSFT.csv

#Imported libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt 
import tqdm as tqdm

                                                        **Amazon Stock daily return**

#Table of daily $AMZN stock return creation and adding return columns based on higher price 
return_quot_amzn=pd.read_csv("C:\\Users\\YourUserName\\Downloads\\AMZN.csv",index_col=0)
return_quot_amzn['daily_return'] = (return_quot_amzn['High']/ return_quot_amzn['High'].shift(1)) -1
return_quot_amzn.dropna(inplace = True)
return_quot_amzn.head(5)

Output: $AMZN Stock

                                                      **Microsoft Stock daily return**

#Table of daily $MSFT stock return creation and adding return columns based on higher price 
return_quot_MSFT=pd.read_csv("C:\\Users\\YourUserName\\Downloads\\MSFT.csv",index_col=0)
return_quot_MSFT['daily_return'] = (return_quot_MSFT['High']/ return_quot_MSFT['High'].shift(1)) -1
return_quot_MSFT.dropna(inplace = True)
return_quot_MSFT.head(5)

Output: $MSFT Stock

                                                    **Comparing Tables**

#Joining the two tables making use of return values
portfolio=pd.DataFrame().assign(Return_AMZN=return_quot_amzn['daily_return'],Return_MSFT=return_quot_MSFT['daily_return'])
portfolio.head(5)

Comparison Output

                                                       **Annual Return**