Downloading Stock Data Using Python Code:¶

In this document, we will apply what we learned in the last lecture by obtaining a list of US stock data using the package yfinance. Then we obtain some preliminary statistics on this data.

Loading the necessary packages¶

Always make it a habit to load the packages you need in the first cell:

In [ ]:
# This package is used for downloading stock data from Yahoo Finance
# we will import it in our project and give it a nickname (yf)
import yfinance as yf
# This package is important so we can use tables and datasets 
# we will nickname it (pd)
import pandas as pd
# load the package (datetime) and refer it as the nickname (dt)
import datetime as dt
# The following package allows us to draw figures and charts
import matplotlib as mpl
import matplotlib.pyplot as plt
# Numpy is a package that helps python use math to deal with numbers
import numpy as np
In [ ]:
# define two variables, which are dates, so we can pass them in the `yf.downlaod` command later
start_date = dt.date(1999,12,31)
end_date = dt.date(2023,12,31)
# Note that if you want to add days, weeks, or months, you can use the command dt.timedelta().
# for example:
add_days = dt.timedelta(days = 100)
new_start_date = start_date + add_days
new_end_date = end_date + add_days
print(new_start_date, new_end_date)

2000-04-09 2024-04-09

Obtaining stock data for multiple companies in one comaand¶

By passing a list of tickers, the function can obtain data of multiple companies instead of running the function .download each time.

In [ ]:
# The following are a list of tickers
ticker_list = ["BA", "AMZN", "CVX", "DIS", "GOLD", "HSY", "INTC", "K", "KO", "M", "MMM", "MSFT", "NVDA"]
# Instead of using a ticker, now we will pass the list as our input
main_data = yf.download(ticker_list, start = start_date, end = end_date, interval = "1mo")

[*********************100%***********************]  13 of 13 completed
In [ ]:
# LEts have a look at the dataset
main_data.head()
Out[ ]:
Adj Close ... Volume
AMZN BA CVX DIS GOLD HSY INTC K KO M ... DIS GOLD HSY INTC K KO M MMM MSFT NVDA
Date
2000-01-01 3.228125 28.723930 17.139805 28.004887 11.627259 12.283645 28.394407 10.309633 14.823807 11.775007 ... 247072276 30307800 27437600 1406932200 20080045 224495200 54676200 68507000 1274875200 494145600
2000-02-01 3.443750 23.842466 15.307978 26.221441 11.582878 12.699118 32.430264 10.761341 12.549431 10.378275 ... 144199796 41421300 26855600 965312200 23470157 189933800 54626000 66870400 1334487600 916084800
2000-03-01 3.350000 24.490906 19.106281 31.812773 11.139091 14.168968 37.876537 11.076875 12.113902 11.951815 ... 185073291 34786700 30930800 1131350200 22992184 303736800 50651600 87122200 2028187600 1755216000
2000-04-01 2.759375 25.705317 17.594824 33.644440 11.937909 13.224373 36.405254 10.431618 12.240291 9.618027 ... 111137455 35090400 19653800 1209578800 18671158 240129800 44807000 68960000 2258146600 648172800
2000-05-01 2.415625 25.300518 19.106281 32.535797 12.869864 15.077240 35.795193 13.066406 13.826995 10.891005 ... 110547998 39834900 17981400 1145907800 23087501 229033600 53502600 51990800 1344430800 869558400

5 rows × 78 columns

In [ ]:
# Mostly we are interested in the big column "Adj Close" 
# So I will crete a new dataset, which is a slice of "main_data" 

stock_prices = main_data['Adj Close']
stock_prices.head(n = 7)
Out[ ]:
AMZN BA CVX DIS GOLD HSY INTC K KO M MMM MSFT NVDA
Date
2000-01-01 3.228125 28.723930 17.139805 28.004887 11.627259 12.283645 28.394407 10.309633 14.823807 11.775007 23.799902 30.283478 0.708314
2000-02-01 3.443750 23.842466 15.307978 26.221441 11.582878 12.699118 32.430264 10.761341 12.549431 10.378275 22.417660 27.653498 1.223127
2000-03-01 3.350000 24.490906 19.106281 31.812773 11.139091 14.168968 37.876537 11.076875 12.113902 11.951815 22.658825 32.874763 1.614611
2000-04-01 2.759375 25.705317 17.594824 33.644440 11.937909 13.224373 36.405254 10.431618 12.240291 9.618027 22.163111 21.581335 1.703300
2000-05-01 2.415625 25.300518 19.106281 32.535797 12.869864 15.077240 35.795193 13.066406 13.826995 10.891005 21.939230 19.357439 2.181084
2000-06-01 1.815625 27.180988 17.653696 29.932936 12.981661 14.168630 38.388420 12.911996 14.879401 9.547308 21.373287 24.752775 2.429532
2000-07-01 1.506250 31.731459 16.440578 29.740133 11.385468 13.511317 38.334579 11.257315 15.934709 6.806873 23.191954 21.600664 2.293363

Descriptive Statistics for Stock Returns¶

In order to find the descriptive stats for the list of stocks we picked, we need to calculate each period's return. Luckily, pandas has a useful function pct_change. We can apply it to our whole dataset stock_prices. Becuase we are apllying it to the whole table, the function automatically measures the % change for each column:

In [ ]:
# Take % changes of each row for the table "stock_prices" 
# Save the results in a new variable "stock_returns"
stock_returns = stock_prices.pct_change()

# Lets see the new table
stock_returns.head()
Out[ ]:
AMZN BA CVX DIS GOLD HSY INTC K KO M MMM MSFT NVDA
Date
2000-01-01 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
2000-02-01 0.066796 -0.169944 -0.106876 -0.063683 -0.003817 0.033823 0.142136 0.043814 -0.153427 -0.118618 -0.058078 -0.086845 0.726813
2000-03-01 -0.027223 0.027197 0.248126 0.213235 -0.038314 0.115744 0.167938 0.029321 -0.034705 0.151619 0.010758 0.188810 0.320068
2000-04-01 -0.176306 0.049586 -0.079108 0.057576 0.071713 -0.066666 -0.038844 -0.058253 0.010433 -0.195266 -0.021877 -0.343529 0.054929
2000-05-01 -0.124575 -0.015748 0.085904 -0.032952 0.078067 0.140110 -0.016758 0.252577 0.129630 0.132353 -0.010102 -0.103047 0.280505
In [ ]:
# Now we can find the descriptive stats for the returns by using pandas handy tool "describe"
return_stats = stock_returns.describe()

return_stats
Out[ ]:
AMZN BA CVX DIS GOLD HSY INTC K KO M MMM MSFT NVDA
count 287.000000 287.000000 287.000000 287.000000 287.000000 287.000000 287.000000 287.000000 287.000000 287.000000 287.000000 287.000000 287.000000
mean 0.021791 0.012425 0.009857 0.006993 0.008019 0.011105 0.006905 0.007126 0.006123 0.010586 0.007103 0.012066 0.037451
std 0.129926 0.095598 0.069003 0.076331 0.114357 0.057571 0.097305 0.049604 0.050752 0.129830 0.060703 0.081098 0.174042
min -0.411552 -0.454664 -0.214625 -0.267794 -0.381056 -0.177236 -0.444733 -0.138635 -0.172743 -0.628874 -0.157037 -0.343529 -0.486551
25% -0.048718 -0.042805 -0.029320 -0.034121 -0.065293 -0.025150 -0.047196 -0.020944 -0.024000 -0.061762 -0.028618 -0.037006 -0.054362
50% 0.021677 0.015899 0.008623 0.005748 0.007592 0.008660 0.007672 0.009468 0.008546 0.011221 0.010560 0.017244 0.030835
75% 0.087617 0.070994 0.046279 0.050940 0.072783 0.044363 0.062263 0.035984 0.038312 0.077279 0.039402 0.055028 0.122156
max 0.621776 0.459312 0.269666 0.248734 0.426362 0.275953 0.337428 0.252577 0.141928 0.644122 0.213949 0.407781 0.826239

The new table return_stats is like any other pandas table. I can add, subtract, or delete any row or column. So, I would like to measure the Sharpe Ratio for each stock. Recall, the Sharpe Ratio is: $$Sharpe_{x} = \frac{Expected \,\, Return_{x} - Risk-free}{Stock \,\, Risk} = \frac{E(R_{x})- R_{f}}{\sigma_{x}}$$

Let's apply this by first assuming the risk-free rate is 0.5%

In [ ]:
# first, let me flip this table (make the row a column, and a column a row)
return_stats_flipped = return_stats.transpose()
return_stats_flipped
Out[ ]:
count mean std min 25% 50% 75% max
AMZN 287.0 0.021791 0.129926 -0.411552 -0.048718 0.021677 0.087617 0.621776
BA 287.0 0.012425 0.095598 -0.454664 -0.042805 0.015899 0.070994 0.459312
CVX 287.0 0.009857 0.069003 -0.214625 -0.029320 0.008623 0.046279 0.269666
DIS 287.0 0.006993 0.076331 -0.267794 -0.034121 0.005748 0.050940 0.248734
GOLD 287.0 0.008019 0.114357 -0.381056 -0.065293 0.007592 0.072783 0.426362
HSY 287.0 0.011105 0.057571 -0.177236 -0.025150 0.008660 0.044363 0.275953
INTC 287.0 0.006905 0.097305 -0.444733 -0.047196 0.007672 0.062263 0.337428
K 287.0 0.007126 0.049604 -0.138635 -0.020944 0.009468 0.035984 0.252577
KO 287.0 0.006123 0.050752 -0.172743 -0.024000 0.008546 0.038312 0.141928
M 287.0 0.010586 0.129830 -0.628874 -0.061762 0.011221 0.077279 0.644122
MMM 287.0 0.007103 0.060703 -0.157037 -0.028618 0.010560 0.039402 0.213949
MSFT 287.0 0.012066 0.081098 -0.343529 -0.037006 0.017244 0.055028 0.407781
NVDA 287.0 0.037451 0.174042 -0.486551 -0.054362 0.030835 0.122156 0.826239
In [ ]:
# I will assume the risk free rate is 0.005
r_free = 0.005
return_stats_flipped['Sharpe'] = (return_stats_flipped['mean'] - r_free)/return_stats_flipped['std']

return_stats_flipped
Out[ ]:
count mean std min 25% 50% 75% max Sharpe
AMZN 287.0 0.021791 0.129926 -0.411552 -0.048718 0.021677 0.087617 0.621776 0.129233
BA 287.0 0.012425 0.095598 -0.454664 -0.042805 0.015899 0.070994 0.459312 0.077671
CVX 287.0 0.009857 0.069003 -0.214625 -0.029320 0.008623 0.046279 0.269666 0.070391
DIS 287.0 0.006993 0.076331 -0.267794 -0.034121 0.005748 0.050940 0.248734 0.026107
GOLD 287.0 0.008019 0.114357 -0.381056 -0.065293 0.007592 0.072783 0.426362 0.026397
HSY 287.0 0.011105 0.057571 -0.177236 -0.025150 0.008660 0.044363 0.275953 0.106048
INTC 287.0 0.006905 0.097305 -0.444733 -0.047196 0.007672 0.062263 0.337428 0.019574
K 287.0 0.007126 0.049604 -0.138635 -0.020944 0.009468 0.035984 0.252577 0.042864
KO 287.0 0.006123 0.050752 -0.172743 -0.024000 0.008546 0.038312 0.141928 0.022124
M 287.0 0.010586 0.129830 -0.628874 -0.061762 0.011221 0.077279 0.644122 0.043027
MMM 287.0 0.007103 0.060703 -0.157037 -0.028618 0.010560 0.039402 0.213949 0.034641
MSFT 287.0 0.012066 0.081098 -0.343529 -0.037006 0.017244 0.055028 0.407781 0.087124
NVDA 287.0 0.037451 0.174042 -0.486551 -0.054362 0.030835 0.122156 0.826239 0.186455

Exporting Datasets¶

The pandas package offers its users a way to export their tables and datasets to any type of file. It also offers options to import files into a new dataset. In the following code I will export the 2 tables I have created earlier (return_stats_flipped, stock_returns) to an Excel file. The produced Excel file will be located in the same folder where this Jupyter Notebook exists.

In [ ]:
stock_returns.to_excel("all_stock_returns.xlsx")
return_stats_flipped.to_excel("return_stats.xlsx")
In [ ]: