S&P 500 analysis using beautifulsoup and pandas

Published

August 1, 2020

To extract stock information

To extract stock information we will use yfinance module which is a convenient way to download data from Yahoo Finance. The official API for Yahoo Finance was decommissioned some time back. More details about this module can be found here.

from requests import get 
import numpy as np 
import pandas as pd 
from bs4 import BeautifulSoup
import time as time 
from tqdm import tqdm
import yfinance as yf

from IPython.core.display import clear_output
#----- PLOTTING PARAMS ----# 
import matplotlib.pyplot as plt
from matplotlib.pyplot import cm
import seaborn as sns
sns.set(style="whitegrid")
sns.color_palette("husl")
%config InlineBackend.figure_format = 'retina'
%config InlineBackend.print_figure_kwargs={'facecolor' : "w"}

plot_params = {
'font.size' : 30,
'axes.titlesize' : 24,
'axes.labelsize' : 20,
'axes.labelweight' : 'bold',
'lines.linewidth' : 3,
'lines.markersize' : 10,
'xtick.labelsize' : 16,
'ytick.labelsize' : 16,
}
 
plt.rcParams.update(plot_params)

1. Generate list of S&P 500 companies

Parse wikipedia to generate a list

wiki_url = 'https://en.wikipedia.org/wiki/List_of_S%26P_500_companies'
response = get(wiki_url)
html_soup = BeautifulSoup(response.text, 'html.parser')
tab = html_soup.find("table",{"class":"wikitable sortable"})
column_headings = [entry.text.strip() for entry in tab.findAll('th')]
print(column_headings)
['Symbol', 'Security', 'SEC filings', 'GICS Sector', 'GICS Sub-Industry', 'Headquarters Location', 'Date first added', 'CIK', 'Founded']
SP_500_dict = {keys:[] for keys in column_headings}
for i, name in enumerate(SP_500_dict.keys()):
    print(i, name)
0 Symbol
1 Security
2 SEC filings
3 GICS Sector
4 GICS Sub-Industry
5 Headquarters Location
6 Date first added
7 CIK
8 Founded

Populate each row entry as per company data

for row_entry in tab.findAll('tr')[1:]:
    row_elements = row_entry.findAll('td')
    for key, _elements in zip(SP_500_dict.keys(), row_elements):
        SP_500_dict[key].append(_elements.text.strip())
SP_500_df = pd.DataFrame(SP_500_dict, columns=SP_500_dict.keys())
SP_500_df
Symbol Security SEC filings GICS Sector GICS Sub-Industry Headquarters Location Date first added CIK Founded
0 MMM 3M Company reports Industrials Industrial Conglomerates St. Paul, Minnesota 1976-08-09 0000066740 1902
1 ABT Abbott Laboratories reports Health Care Health Care Equipment North Chicago, Illinois 1964-03-31 0000001800 1888
2 ABBV AbbVie Inc. reports Health Care Pharmaceuticals North Chicago, Illinois 2012-12-31 0001551152 2013 (1888)
3 ABMD Abiomed reports Health Care Health Care Equipment Danvers, Massachusetts 2018-05-31 0000815094 1981
4 ACN Accenture reports Information Technology IT Consulting & Other Services Dublin, Ireland 2011-07-06 0001467373 1989
... ... ... ... ... ... ... ... ... ...
500 YUM Yum! Brands Inc reports Consumer Discretionary Restaurants Louisville, Kentucky 1997-10-06 0001041061 1997
501 ZBRA Zebra Technologies reports Information Technology Electronic Equipment & Instruments Lincolnshire, Illinois 2019-12-23 0000877212 1969
502 ZBH Zimmer Biomet reports Health Care Health Care Equipment Warsaw, Indiana 2001-08-07 0001136869 1927
503 ZION Zions Bancorp reports Financials Regional Banks Salt Lake City, Utah 2001-06-22 0000109380 1873
504 ZTS Zoetis reports Health Care Pharmaceuticals Parsippany, New Jersey 2013-06-21 0001555280 1952

505 rows × 9 columns

SP_500_df['GICS Sector'].value_counts()
Information Technology    75
Industrials               74
Financials                65
Consumer Discretionary    63
Health Care               62
Consumer Staples          32
Real Estate               29
Materials                 28
Utilities                 28
Communication Services    26
Energy                    23
Name: GICS Sector, dtype: int64

Visualize distribution of the companies as per sectors

fig, ax = plt.subplots(1,1, figsize=(10,10))
SP_500_df['GICS Sector'].value_counts().plot.pie(y='GICS Sector', autopct='%1.1f%%', fontsize=20, ax = ax, colormap='tab20')
plt.axis('off')

SP_500_df.loc[ SP_500_df['GICS Sector'] == 'Energy']
Symbol Security SEC filings GICS Sector GICS Sub-Industry Headquarters Location Date first added CIK Founded
44 APA APA Corporation reports Energy Oil & Gas Exploration & Production Houston, Texas 1997-07-28 0000006769 1954
59 BKR Baker Hughes Co reports Energy Oil & Gas Equipment & Services Houston, Texas 2017-07-07 0001701605 2017
80 COG Cabot Oil & Gas reports Energy Oil & Gas Exploration & Production Houston, Texas 2008-06-23 0000858470 1989
101 CVX Chevron Corp. reports Energy Integrated Oil & Gas San Ramon, California 1957-03-04 0000093410 1879
121 COP ConocoPhillips reports Energy Oil & Gas Exploration & Production Houston, Texas 1957-03-04 0001163165 2002
140 DVN Devon Energy reports Energy Oil & Gas Exploration & Production Oklahoma City, Oklahoma 2000-08-30 0001090012 1971
142 FANG Diamondback Energy reports Energy Oil & Gas Exploration & Production Midland, Texas 2018-12-03 0001539838 2007
169 EOG EOG Resources reports Energy Oil & Gas Exploration & Production Houston, Texas 2000-11-02 0000821189 1999
183 XOM Exxon Mobil Corp. reports Energy Integrated Oil & Gas Irving, Texas 1957-03-04 0000034088 1999
219 HAL Halliburton Co. reports Energy Oil & Gas Equipment & Services Houston, Texas 1957-03-04 0000045012 1919
227 HES Hess Corporation reports Energy Integrated Oil & Gas New York, New York 1984-05-31 0000004447 1919
230 HFC HollyFrontier Corp reports Energy Oil & Gas Refining & Marketing Dallas, Texas 2018-06-18 0000048039 1947
274 KMI Kinder Morgan reports Energy Oil & Gas Storage & Transportation Houston, Texas 2012-05-25 0001506307 1997
298 MRO Marathon Oil Corp. reports Energy Oil & Gas Exploration & Production Houston, Texas 1991-05-01 0000101778 1887
299 MPC Marathon Petroleum reports Energy Oil & Gas Refining & Marketing Findlay, Ohio 2011-07-01 0001510295 2009 (1887)
345 NOV NOV Inc. reports Energy Oil & Gas Equipment & Services Houston, Texas 2005-03-14 0001021860 1841
352 OXY Occidental Petroleum reports Energy Oil & Gas Exploration & Production Houston, Texas 1982-12-31 0000797468 1920
355 OKE Oneok reports Energy Oil & Gas Storage & Transportation Tulsa, Oklahoma 2010-03-15 0001039684 1906
372 PSX Phillips 66 reports Energy Oil & Gas Refining & Marketing Houston, Texas 2012-05-01 0001534701 2012 (1917)
374 PXD Pioneer Natural Resources reports Energy Oil & Gas Exploration & Production Irving, Texas 2008-09-24 0001038357 1997
411 SLB Schlumberger Ltd. reports Energy Oil & Gas Equipment & Services Curaçao, Kingdom of the Netherlands 1965-03-31 0000087347 1926
466 VLO Valero Energy reports Energy Oil & Gas Refining & Marketing San Antonio, Texas 0001035002 1980
494 WMB Williams Companies reports Energy Oil & Gas Storage & Transportation Tulsa, Oklahoma 1975-03-31 0000107263 1908

We can parse these tables and search companies based on the sector

SP_500_df.loc[ SP_500_df['GICS Sector'] == 'Information Technology']
Symbol Security SEC filings GICS Sector GICS Sub-Industry Headquarters Location Date first added CIK Founded
4 ACN Accenture reports Information Technology IT Consulting & Other Services Dublin, Ireland 2011-07-06 0001467373 1989
6 ADBE Adobe Inc. reports Information Technology Application Software San Jose, California 1997-05-05 0000796343 1982
7 AMD Advanced Micro Devices reports Information Technology Semiconductors Santa Clara, California 2017-03-20 0000002488 1969
13 AKAM Akamai Technologies reports Information Technology Internet Services & Infrastructure Cambridge, Massachusetts 2007-07-12 0001086222 1998
38 APH Amphenol Corp reports Information Technology Electronic Components Wallingford, Connecticut 2008-09-30 0000820313 1932
... ... ... ... ... ... ... ... ... ...
475 V Visa Inc. reports Information Technology Data Processing & Outsourced Services San Francisco, California 2009-12-21 0001403161 1958
489 WDC Western Digital reports Information Technology Technology Hardware, Storage & Peripherals San Jose, California 2009-07-01 0000106040 1970
490 WU Western Union Co reports Information Technology Data Processing & Outsourced Services Englewood, Colorado 2006-09-29 0001365135 1851
498 XLNX Xilinx reports Information Technology Semiconductors San Jose, California 1999-11-08 0000743988 1984
501 ZBRA Zebra Technologies reports Information Technology Electronic Equipment & Instruments Lincolnshire, Illinois 2019-12-23 0000877212 1969

75 rows × 9 columns

Get total number of Shares

We will use yfinance to extact Tickr information for each SP500 company and use pandas datareader

yf_tickr = yf.Ticker('ADBE')
yf_tickr.info['sharesOutstanding'] #info has good summary info for the stock 
import yfinance as yf
START_DATE = "2020-01-01"
END_DATE = "2020-07-26"
yf_tickr = yf.Ticker('TSLA')
_shares_outstanding = yf_tickr.info['sharesOutstanding']
_previous_close = yf_tickr.info['previousClose']
print('Outstanding shares: {}'.format(_shares_outstanding))
print('Market Cap: {} Million USD'.format((_shares_outstanding * _previous_close)/10**6))
Outstanding shares: 959854016
Market Cap: 676447.51923584 Million USD
df_tckr = yf_tickr.history(start=START_DATE, end=END_DATE, interval="1wk", actions=False)
df_tckr['Market_Cap'] = df_tckr['Open'] * _shares_outstanding
df_tckr['YTD'] = (df_tckr['Open'] - df_tckr['Open'][0]) * 100 / df_tckr['Open'][0]
fig, ax = plt.subplots(1,1, figsize=(10,8))
df_tckr.plot(use_index=True, y="YTD",ax=ax, linewidth=4, grid=False, label='TSLA')
ax.set_xlabel('Date')
ax.set_ylabel('% YTD change (Weekly basis)')
Text(0, 0.5, '% YTD change (Weekly basis)')

Extend this to plotting for multiple companies

import time as time 
def plot_market_cap(tickr_list, START_DATE, END_DATE):
    
    total_data = {}
    for tickr in tickr_list:
        total_data[tickr] = {}
        print('Looking at: {}'.format(tickr))
        yf_tickr = yf.Ticker(tickr)
        #try:
        #    _shares_outstanding = yf_tickr.info['sharesOutstanding']
        #except(IndexError):
        #    print('Shares outstanding not found')
        #    _shares_outstanding = None
        
        df_tckr = yf_tickr.history(start=START_DATE, end=END_DATE, actions=False)
        df_tckr['YTD'] = (df_tckr['Open'] - df_tckr['Open'][0]) * 100 / df_tckr['Open'][0]
            
        total_data[tickr]['hist'] = df_tckr
        #total_data[tickr]['shares'] = _shares_outstanding
        time.sleep(np.random.randint(10))
        
    return total_data
tickr_list = ['AAPL', 'TSLA','FB','DAL','XOM']
data = plot_market_cap(tickr_list, START_DATE, END_DATE)
Looking at: AAPL
Looking at: TSLA
Looking at: FB
Looking at: DAL
Looking at: XOM
company_name = [SP_500_df[SP_500_df['Symbol'].str.contains(i)]['Security'].values[0] for i in tickr_list]
company_name
['Apple Inc.',
 'Tesla, Inc.',
 'Facebook, Inc.',
 'Delta Air Lines Inc.',
 'Exxon Mobil Corp.']
print(len(data['AAPL']['hist']['YTD']))
142
ytd_stat = pd.DataFrame()
for tickr in tickr_list: 
    ytd_stat[tickr] = data[tickr]['hist']['YTD'].values
ytd_stat['Date'] = data['AAPL']['hist'].index
ytd_stat
AAPL TSLA FB DAL XOM Date
0 0.000000 0.000000 0.000000 0.000000 0.000000 2020-01-02
1 0.307187 3.769137 0.222494 -2.426609 1.566061 2020-01-03
2 -0.827016 3.762073 -0.024185 -3.292044 0.113891 2020-01-06
3 1.215244 8.692576 2.935916 -1.730873 0.370157 2020-01-07
4 0.310568 11.590101 3.022975 -2.002382 -0.185078 2020-01-08
... ... ... ... ... ... ...
137 30.850611 257.835096 16.111244 -53.866312 -36.389268 2020-07-20
138 34.589490 286.320361 19.090690 -54.720640 -36.477584 2020-07-21
139 31.223803 276.678424 16.207978 -55.181977 -35.005460 2020-07-22
140 31.637726 295.512370 15.903267 -55.574967 -36.109559 2020-07-23
141 23.481414 233.571249 11.337365 -54.754814 -35.402933 2020-07-24

142 rows × 6 columns

Final plot for returns

fig, ax = plt.subplots(1,1,figsize=(15,10))
for i, tickr in enumerate(tickr_list):
    ax.plot(ytd_stat['Date'], ytd_stat[tickr], linewidth=5.0, label=company_name[i])
ax.set_ylabel('YTD %Return 2020')
ax.set_xlabel('Date')
ax.legend()