StockMarketDataAnalysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "hsY0PBEjV9NR"
   },
   "source": [
    "Code used to download daily stock price using python package yfinance. \n",
    "A file was created as a csv to store downloaded price on the initial run.\n",
    "After initial run, only new data are downloaded, then append to the existing data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 357
    },
    "colab_type": "code",
    "id": "eXr7hwMOJGz1",
    "outputId": "4501b1e4-e009-4226-933e-ea139d1571bf"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: yfinance in c:\\users\\jcw\\appdata\\roaming\\python\\python37\\site-packages (0.1.54)\n",
      "Requirement already satisfied: pandas>=0.24 in c:\\users\\jcw\\anaconda3\\lib\\site-packages (from yfinance) (1.0.1)\n",
      "Requirement already satisfied: numpy>=1.15 in c:\\users\\jcw\\anaconda3\\lib\\site-packages (from yfinance) (1.18.1)\n",
      "Requirement already satisfied: requests>=2.20 in c:\\users\\jcw\\appdata\\roaming\\python\\python37\\site-packages (from yfinance) (2.23.0)\n",
      "Requirement already satisfied: multitasking>=0.0.7 in c:\\users\\jcw\\appdata\\roaming\\python\\python37\\site-packages (from yfinance) (0.0.9)\n",
      "Requirement already satisfied: pytz>=2017.2 in c:\\users\\jcw\\anaconda3\\lib\\site-packages (from pandas>=0.24->yfinance) (2019.3)\n",
      "Requirement already satisfied: python-dateutil>=2.6.1 in c:\\users\\jcw\\anaconda3\\lib\\site-packages (from pandas>=0.24->yfinance) (2.8.1)\n",
      "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in c:\\users\\jcw\\anaconda3\\lib\\site-packages (from requests>=2.20->yfinance) (1.25.8)\n",
      "Requirement already satisfied: chardet<4,>=3.0.2 in c:\\users\\jcw\\anaconda3\\lib\\site-packages (from requests>=2.20->yfinance) (3.0.4)\n",
      "Requirement already satisfied: idna<3,>=2.5 in c:\\users\\jcw\\anaconda3\\lib\\site-packages (from requests>=2.20->yfinance) (2.8)\n",
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      "Requirement already satisfied: six>=1.5 in c:\\users\\jcw\\anaconda3\\lib\\site-packages (from python-dateutil>=2.6.1->pandas>=0.24->yfinance) (1.14.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install yfinance\n",
    "import yfinance as yf\n",
    "import numpy as np\n",
    "import datetime as dt\n",
    "import bs4 as bs\n",
    "from datetime import datetime\n",
    "\n",
    "import requests\n",
    "import pandas as pd\n",
    "from pandas import DataFrame\n",
    "import pandas_datareader.data as web\n",
    "import pandas_datareader as web\n",
    "from concurrent import futures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    ticker                security                industry  \\\n",
      "0      MMM              3M Company             Industrials   \n",
      "1      ABT     Abbott Laboratories             Health Care   \n",
      "2     ABBV             AbbVie Inc.             Health Care   \n",
      "3     ABMD             ABIOMED Inc             Health Care   \n",
      "4      ACN           Accenture plc  Information Technology   \n",
      "..     ...                     ...                     ...   \n",
      "500    YUM         Yum! Brands Inc  Consumer Discretionary   \n",
      "501   ZBRA      Zebra Technologies  Information Technology   \n",
      "502    ZBH  Zimmer Biomet Holdings             Health Care   \n",
      "503   ZION           Zions Bancorp              Financials   \n",
      "504    ZTS                  Zoetis             Health Care   \n",
      "\n",
      "                            subindustry              headquarters       added  \\\n",
      "0              Industrial Conglomerates       St. Paul, Minnesota  1976-08-09   \n",
      "1                 Health Care Equipment   North Chicago, Illinois  1964-03-31   \n",
      "2                       Pharmaceuticals   North Chicago, Illinois  2012-12-31   \n",
      "3                 Health Care Equipment    Danvers, Massachusetts  2018-05-31   \n",
      "4        IT Consulting & Other Services           Dublin, Ireland  2011-07-06   \n",
      "..                                  ...                       ...         ...   \n",
      "500                         Restaurants      Louisville, Kentucky  1997-10-06   \n",
      "501  Electronic Equipment & Instruments    Lincolnshire, Illinois  2019-12-23   \n",
      "502               Health Care Equipment           Warsaw, Indiana  2001-08-07   \n",
      "503                      Regional Banks      Salt Lake City, Utah  2001-06-22   \n",
      "504                     Pharmaceuticals  Florham Park, New Jersey  2013-06-21   \n",
      "\n",
      "         founded  \n",
      "0           1902  \n",
      "1           1888  \n",
      "2    2013 (1888)  \n",
      "3           1981  \n",
      "4           1989  \n",
      "..           ...  \n",
      "500         1997  \n",
      "501         1969  \n",
      "502         1927  \n",
      "503         1873  \n",
      "504         1952  \n",
      "\n",
      "[505 rows x 7 columns]\n"
     ]
    }
   ],
   "source": [
    "# Get Company Information (i.e. Sector, Industry)\n",
    "def save_sp500_tickers():\n",
    "    resp = requests.get('http://en.wikipedia.org/wiki/List_of_S%26P_500_companies')\n",
    "    soup = bs.BeautifulSoup(resp.text, 'lxml')\n",
    "    table = soup.find('table', {'class': 'wikitable sortable'})\n",
    "\n",
    "    tickers = []\n",
    "    securities = []\n",
    "    industries = []\n",
    "    subindustries = []\n",
    "    headquarters = []\n",
    "    added = []\n",
    "    founded = []\n",
    "\n",
    "    for row in table.findAll('tr')[1:]:\n",
    "        ticker = row.findAll('td')[0].text\n",
    "        security = row.findAll('td')[1].text\n",
    "        industry = row.findAll('td')[3].text\n",
    "        subindustry = row.findAll('td')[4].text\n",
    "        headquarter = row.findAll('td')[5].text\n",
    "        add = row.findAll('td')[6].text\n",
    "        found = row.findAll('td')[8].text\n",
    "  \n",
    "        tickers.append(ticker)\n",
    "        securities.append(security)\n",
    "        industries.append(industry)\n",
    "        subindustries.append(subindustry)\n",
    "        headquarters.append(headquarter)\n",
    "        added.append(add)\n",
    "        founded.append(found)\n",
    "\n",
    "        tickers = list(map(lambda s: s.strip(), tickers))\n",
    "        securities = list(map(lambda s: s.strip(), securities))\n",
    "        industries = list(map(lambda s: s.strip(), industries))\n",
    "        subindustries = list(map(lambda s: s.strip(), subindustries))\n",
    "        headquarters = list(map(lambda s: s.strip(), headquarters))\n",
    "        added = list(map(lambda s: s.strip(), added))\n",
    "        founded = list(map(lambda s: s.strip(), founded))\n",
    "\n",
    "        tickerdf = pd.DataFrame(tickers, columns = ['ticker'])\n",
    "        securitydf = pd.DataFrame(securities,columns = ['security'])\n",
    "        sectordf = pd.DataFrame(industries, columns = ['industry'])\n",
    "        subsectordf = pd.DataFrame(subindustries, columns =['subindustry'])\n",
    "        headquarterdf = pd.DataFrame(headquarters, columns = ['headquarters'])\n",
    "        adddf = pd.DataFrame(added, columns = ['added'])\n",
    "        founddf = pd.DataFrame(founded, columns = ['founded'])\n",
    "\n",
    "        tickerandsecurityandsectorandsubsectorandheadquarterandaddandfound = pd.concat([tickerdf, securitydf, sectordf, subsectordf, headquarterdf, adddf, founddf], axis = 1).reindex(tickerdf.index)\n",
    "        data = tickerandsecurityandsectorandsubsectorandheadquarterandaddandfound\n",
    "    \n",
    "    print(data)\n",
    "    df = data\n",
    "    df.to_csv('c://Users/JCW/Desktop/covidStocks/SP500CompanyInfo.csv')\n",
    "    data.to_pickle(r'sp500_data.pickle')\n",
    "save_sp500_tickers()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\"\\ntickers = {\\'Abbott Laboratories\\': \\'ABT\\', \\'Abbvie\\': \\'ABBV\\', \\'Activision\\': \\'ATVI\\', \\'Adobe\\': \\'ADBE\\', \\'Advanced Micro Devices\\': \\'AMD\\', \\'Akamai\\': \\'AKAM\\', \\'Alphabet\\': \\'GOOGL\\', \\'Amazon\\': \\'AMZN\\', \\'American Tower\\': \\'AMT\\', \\'Apple\\': \\'AAPL\\', \\'Barrick Gold\\': \\'GOLD\\', \\'Baxter\\': \\'BAX\\', \\'Beyond Meat\\': \\'BYND\\', \\'BioNTech\\': \\'BNTX\\', \\'Campell Soup\\': \\'CPB\\', \\'Centene\\': \\'CNC\\', \\'Chegg\\': \\'CHGG\\', \\n\\'Chewy\\': \\'CHWY\\', \\'Chipotle\\': \\'CMG\\', \\'Citrix\\': \\'CTXS\\', \\'Clorox\\': \\'CLX\\', \\'Cloudflare\\': \\'NET\\', \\'Colgate\\': \\'CL \\', \\'Conagra\\': \\'CAG\\', \\'CoreSite\\': \\'COR\\', \\'Costco\\': \\'COST\\', \\'Coupa\\': \\'COUP\\', \\'Crown Castle\\': \\'CCI\\', \\'Danaher\\': \\'DHR\\', \\'Datadog\\': \\'DDOG\\', \\'DexCom\\': \\'DXCM\\', \\'DocuSign\\': \\'DOCU\\', \\n\\'Dollar General\\': \\'DG\\', \\'Dominion Energy\\': \\'D\\', \\'Domino’s\\': \\'DPZ\\', \\'Electronic Arts\\': \\'EA\\', \\'Eli Lilly\\': \\'LLY\\', \\'Emergent BioSolutions\\': \\'EBS\\', \\'Equinix\\': \\'EQIX\\', \\'Etsy\\': \\'ETSY\\', \\'Everbridge\\': \\'EVBG\\', \\'Fastly\\': \\'FSLY\\', \\'General Mills\\': \\'GIS\\', \\'Gilead Sciences\\': \\'GILD\\', \\'GlaxoSmithKline\\': \\'GSK\\', \\'Home Depot\\': \\'HD\\', \\'Hormel\\': \\'HRL\\', \\'JM Smucker\\': \\'SJM\\', \\'Johnson\\': \\'JNJ\\', \\'Kellogg\\': \\'K\\', \\'Kroger\\': \\'KR\\', \\'Livongo Health\\': \\'LVGO\\', \\'Logitech\\': \\'LOGI\\', \\'Marketaxess\\': \\'MKTX\\', \\n\\'Marvell\\': \\'MRVL\\', \\'Masimo\\': \\'MASI\\', \\'McCormick\\': \\'MKC\\', \\'Microsoft\\': \\'MSFT\\', \\'Moderna\\': \\'MRNA\\', \\'Mondelez\\': \\'MDLZ\\', \\'Netflix\\': \\'NFLX\\', \\'Newmont\\': \\'NEM\\', \\'NVIDIA\\': \\'NVDA\\', \\'Okta\\': \\'OKTA\\', \\'Palo Alto Networks\\': \\'PANW\\', \\'PayPal\\': \\'PYPL\\', \\'Peloton\\': \\'PTON\\', \\'Pepsi\\': \\'PEP\\', \\'Perrigo\\': \\'PRGO\\', \\'Pfizer\\': \\'PFE\\', \\'Prologis\\': \\'PLD\\', \\'Regeneron Pharmaceuticals\\': \\'REGN\\', \\'Resmed\\': \\'RMD\\', \\'RingCentral\\': \\'RNG\\', \\'Salesforce\\': \\'CRM\\', \\'Sanofi\\': \\'SNY\\', \\'Shopify\\': \\'SHOP\\', \\'Slack\\': \\'WORK\\', \\'Splunk\\': \\'SPLK\\', \\'Spotify\\': \\'SPOT\\', \\'Square\\': \\'SQ\\', \\'Take-Two Interactive Software\\': \\'TTWO\\', \\n\\'Target\\': \\'TGT\\', \\'Thermo Fisher Scientific\\': \\'TMO\\', \\'Tradeweb\\': \\'TW\\', \\'Trade Desk\\': \\'TTD\\', \\'UnitedHealth\\': \\'UNH\\', \\'Veeva Systems\\': \\'VEEV\\', \\'VMWare\\': \\'VMW\\', \\'Verizon\\': \\'VZ\\', \\'Wix.Com\\': \\'WIX\\', \\'Walmart\\': \\'WMT\\', \\'Wingstop\\': \\'WING\\', \\'Zoetis\\': \\'ZTS\\', \\'Zoom\\': \\'ZM\\', }\\n\\ndf_stocks=pd.DataFrame()\\nfor key, value in tickers.items():\\n    download=yf.download(value)\\n    download = pd.DataFrame(download)\\n    download[\\'name\\'] = key\\n    df_stocks=df_stocks.append(download)\\ndf_stocks.to_excel(\\'c:/Users/JCW/Desktop/covidStocks/stockData.xlsx\\', sheet_name=\\'stockData\\')\\n'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\"\n",
    "tickers = {'Abbott Laboratories': 'ABT', 'Abbvie': 'ABBV', 'Activision': 'ATVI', 'Adobe': 'ADBE', 'Advanced Micro Devices': 'AMD', 'Akamai': 'AKAM', 'Alphabet': 'GOOGL', 'Amazon': 'AMZN', 'American Tower': 'AMT', 'Apple': 'AAPL', 'Barrick Gold': 'GOLD', 'Baxter': 'BAX', 'Beyond Meat': 'BYND', 'BioNTech': 'BNTX', 'Campell Soup': 'CPB', 'Centene': 'CNC', 'Chegg': 'CHGG', \n",
    "'Chewy': 'CHWY', 'Chipotle': 'CMG', 'Citrix': 'CTXS', 'Clorox': 'CLX', 'Cloudflare': 'NET', 'Colgate': 'CL ', 'Conagra': 'CAG', 'CoreSite': 'COR', 'Costco': 'COST', 'Coupa': 'COUP', 'Crown Castle': 'CCI', 'Danaher': 'DHR', 'Datadog': 'DDOG', 'DexCom': 'DXCM', 'DocuSign': 'DOCU', \n",
    "'Dollar General': 'DG', 'Dominion Energy': 'D', 'Domino’s': 'DPZ', 'Electronic Arts': 'EA', 'Eli Lilly': 'LLY', 'Emergent BioSolutions': 'EBS', 'Equinix': 'EQIX', 'Etsy': 'ETSY', 'Everbridge': 'EVBG', 'Fastly': 'FSLY', 'General Mills': 'GIS', 'Gilead Sciences': 'GILD', 'GlaxoSmithKline': 'GSK', 'Home Depot': 'HD', 'Hormel': 'HRL', 'JM Smucker': 'SJM', 'Johnson': 'JNJ', 'Kellogg': 'K', 'Kroger': 'KR', 'Livongo Health': 'LVGO', 'Logitech': 'LOGI', 'Marketaxess': 'MKTX', \n",
    "'Marvell': 'MRVL', 'Masimo': 'MASI', 'McCormick': 'MKC', 'Microsoft': 'MSFT', 'Moderna': 'MRNA', 'Mondelez': 'MDLZ', 'Netflix': 'NFLX', 'Newmont': 'NEM', 'NVIDIA': 'NVDA', 'Okta': 'OKTA', 'Palo Alto Networks': 'PANW', 'PayPal': 'PYPL', 'Peloton': 'PTON', 'Pepsi': 'PEP', 'Perrigo': 'PRGO', 'Pfizer': 'PFE', 'Prologis': 'PLD', 'Regeneron Pharmaceuticals': 'REGN', 'Resmed': 'RMD', 'RingCentral': 'RNG', 'Salesforce': 'CRM', 'Sanofi': 'SNY', 'Shopify': 'SHOP', 'Slack': 'WORK', 'Splunk': 'SPLK', 'Spotify': 'SPOT', 'Square': 'SQ', 'Take-Two Interactive Software': 'TTWO', \n",
    "'Target': 'TGT', 'Thermo Fisher Scientific': 'TMO', 'Tradeweb': 'TW', 'Trade Desk': 'TTD', 'UnitedHealth': 'UNH', 'Veeva Systems': 'VEEV', 'VMWare': 'VMW', 'Verizon': 'VZ', 'Wix.Com': 'WIX', 'Walmart': 'WMT', 'Wingstop': 'WING', 'Zoetis': 'ZTS', 'Zoom': 'ZM', }\n",
    "\n",
    "df_stocks=pd.DataFrame()\n",
    "for key, value in tickers.items():\n",
    "    download=yf.download(value)\n",
    "    download = pd.DataFrame(download)\n",
    "    download['name'] = key\n",
    "    df_stocks=df_stocks.append(download)\n",
    "df_stocks.to_excel('c:/Users/JCW/Desktop/covidStocks/stockData.xlsx', sheet_name='stockData')\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {},
    "colab_type": "code",
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      "\n",
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    }
   ],
   "source": [
    "### Uncomment this code when running the code first time to download the history data and create a dataset in google drive. Skip this step if history data is downloaded and file is created. \n",
    "# Full SP500 & Mad Money \"COVID-19\" Index\n",
    "tickers = {'SP 500': '^GSPC', 'Tesla': 'TSLA', 'Shell':'RDS-A', 'BP':'BP', 'Gold':'GC=F', 'MYR':'MYR=X', '10y Tresury':'^TNX', 'Brent':'BZ=F', 'Copper':'HG=F', 'Masimo': 'MASI', 'GlaxoSmithKline': 'GSK', 'Barrick Gold': 'GOLD', 'Beyond Meat': 'BYND', 'BioNTech': 'BNTX', 'Chegg': 'CHGG', 'Chewy': 'CHWY', 'Cloudflare': 'NET', 'CoreSite': 'COR', 'Coupa': 'COUP', 'Datadog': 'DDOG', 'DocuSign': 'DOCU', 'Emergent BioSolutions': 'EBS', 'Etsy': 'ETSY', 'Everbridge': 'EVBG', 'Fastly': 'FSLY', 'GlaxoSmithKline': 'GSK', 'Livongo Health': 'LVGO', 'Logitech': 'LOGI', 'Marketaxess': 'MKTX', 'Marvell': 'MRVL', 'Masimo': 'MASI', 'McCormick': 'MKC', 'Moderna': 'MRNA', 'Newmont': 'NEM', 'Okta': 'OKTA', 'Palo Alto Networks': 'PANW', 'Peloton': 'PTON', 'Prologis': 'PLD', 'RingCentral': 'RNG', 'Salesforce': 'CRM', 'Sanofi': 'SNY', 'Shopify': 'SHOP', 'Slack': 'WORK', 'Splunk': 'SPLK', 'Spotify': 'SPOT', 'Square': 'SQ', 'Take-Two Interactive Software': 'TTWO', 'Tradeweb': 'TW', 'Trade Desk': 'TTD', 'Veeva Systems': 'VEEV', 'VMWare': 'VMW', 'Wix.Com': 'WIX', 'Wingstop': 'WING', 'Zoom': 'ZM',\n",
    "'3M Company': 'MMM', 'Abbott Laboratories': 'ABT', 'AbbVie Inc': 'ABBV', 'ABIOMED Inc': 'ABMD', 'Accenture PLC': 'ACN', 'Activision Blizzard': 'ATVI', 'Adobe Inc': 'ADBE', 'Advanced Micro Devices Inc': 'AMD', 'Advance Auto Parts': 'AAP', 'AES Corp': 'AES', 'AFLAC Inc': 'AFL', 'Agilent Technologies Inc': 'A', 'Air Products & Chemicals Inc': 'APD', 'Akamai Technologies Inc': 'AKAM', 'Alaska Air Group Inc': 'ALK', 'Albemarle Corp': 'ALB', 'Alexandria Real Estate Equities': 'ARE', 'Alexion Pharmaceuticals': 'ALXN', 'Align Technology': 'ALGN', 'Allegion': 'ALLE', 'Alliance Data Systems': 'ADS', 'Alliant Energy Corp': 'LNT', 'Allstate Corp': 'ALL', 'Alphabet Inc. (Class A)': 'GOOGL', 'Alphabet Inc (Class C)': 'GOOG', 'Altria Group Inc': 'MO', 'Amazon.com Inc': 'AMZN', 'Amcor PLC': 'AMCR', 'Ameren Corp': 'AEE', 'American Airlines Group': 'AAL', 'American Electric Power': 'AEP', 'American Express Co': 'AXP', 'American International Group': 'AIG', 'American Tower Corp.': 'AMT', 'American Water Works Company Inc': 'AWK', 'Ameriprise Financial': 'AMP', 'AmerisourceBergen Corp': 'ABC', 'AMETEK Inc': 'AME', 'Amgen Inc': 'AMGN', 'Amphenol Corp': 'APH', 'Analog Devices, Inc.': 'ADI', 'ANSYS': 'ANSS', 'Anthem': 'ANTM', 'Aon PLC': 'AON', 'AO Smith Corp': 'AOS', 'Apache Corp': 'APA', 'Apartment Investment & Management': 'AIV', 'Apple Inc': 'AAPL', 'Applied Materials Inc': 'AMAT', 'Aptiv PLC': 'APTV', 'Archer Daniels Midland Co': 'ADM', 'Arista Networks': 'ANET', 'Arthur J Gallagher & Co': 'AJG', 'Assurant': 'AIZ', 'AT&T Inc': 'T', 'Atmos Energy': 'ATO', 'Autodesk Inc': 'ADSK', 'Automatic Data Processing': 'ADP', 'AutoZone Inc': 'AZO', 'AvalonBay Communities': 'AVB', 'Avery Dennison Corp': 'AVY', 'Baker Hughes Co': 'BKR', 'Ball Corp': 'BLL', 'Bank of America Corp': 'BAC', 'The Bank of New York Mellon': 'BK', 'Baxter International Inc': 'BAX', 'Becton Dickinson': 'BDX', 'Berkshire Hathaway': 'BRK', 'Best Buy Co Inc': 'BBY', 'Biogen Inc': 'BIIB', 'BlackRock': 'BLK', 'Boeing Company': 'BA', 'Booking Holdings Inc': 'BKNG', 'BorgWarner': 'BWA', 'Boston Properties': 'BXP',\n",
    "'Boston Scientific': 'BSX', 'Bristol Myers Squibb': 'BMY', 'Broadcom Inc': 'AVGO', 'Broadridge Financial Solutions': 'BR', 'Brown Forman Corp': 'BF', 'C H Robinson Worldwide': 'CHRW', 'Cabot Oil & Gas': 'COG', 'Cadence Design Systems': 'CDNS', 'Campbell Soup': 'CPB', 'Capital One Financial': 'COF', 'Cardinal Health Inc.': 'CAH', 'Carmax Inc': 'KMX', 'Carnival Corp': 'CCL', 'Carrier Global': 'CARR', 'Caterpillar Inc': 'CAT', 'Cboe Global Markets': 'CBOE', 'CBRE Group': 'CBRE', 'CDW': 'CDW', 'Celanese': 'CE', 'Centene Corp': 'CNC', 'CenterPoint Energy': 'CNP', 'CenturyLink Inc': 'CTL', 'Cerner': 'CERN', 'CF Industries Holdings Inc': 'CF', 'Charles Schwab Corp': 'SCHW', 'Charter Communications': 'CHTR', 'Chevron Corp': 'CVX', 'Chipotle Mexican Grill': 'CMG', 'Chubb Ltd': 'CB', 'Church & Dwight': 'CHD', 'CIGNA Corp': 'CI', 'Cincinnati Financial': 'CINF', 'Cintas Corp': 'CTAS', 'Cisco Systems': 'CSCO', 'Citigroup Inc': 'C', 'Citizens Financial Group': 'CFG', 'Citrix Systems': 'CTXS', 'The Clorox Co': 'CLX', 'CME Group Inc': 'CME', 'CMS Energy': 'CMS', 'Coca Cola Co': 'KO', 'Cognizant Technology Solutions': 'CTSH', 'Colgate Palmolive': 'CL', 'Comcast Corp': 'CMCSA', 'Comerica Inc': 'CMA', 'Conagra Brands': 'CAG', 'Concho Resources': 'CXO', 'ConocoPhillips': 'COP', 'Consolidated Edison': 'ED', 'Entergy Corp': 'ETR', 'The Cooper Cos': 'COO', 'Copart Inc': 'CPRT', 'Corning Inc': 'GLW', 'Corteva': 'CTVA', 'Costco Wholesale Corp': 'COST', 'Coty Inc': 'COTY', 'Crown Castle International Corp': 'CCI', 'CSX Corp': 'CSX', 'Cummins Inc': 'CMI', 'CVS Health': 'CVS', 'D R Horton': 'DHI', 'Danaher Corp': 'DHR', 'Darden Restaurants': 'DRI', 'DaVita Inc': 'DVA', 'Deere & Co': 'DE', 'Delta Air Lines Inc': 'DAL',\n",
    "'Dentsply Sirona': 'XRAY', 'Devon Energy': 'DVN', 'DexCom': 'DXCM', 'Diamondback Energy': 'FANG', 'Digital Realty Trust Inc': 'DLR', 'Discover Financial Services': 'DFS', 'Discovery Inc (Class A)': 'DISCA', 'Discovery Inc (Class C)': 'DISCK', 'Dish Network': 'DISH', 'Dollar General': 'DG', 'Dollar Tree': 'DLTR', 'Dominion Energy': 'D', 'Dominos Pizza': 'DPZ', 'Dover Corp': 'DOV', 'Dow Inc': 'DOW', 'DTE Energy Co': 'DTE', 'Duke Energy': 'DUK', 'Duke Realty Corp': 'DRE', 'DuPont de Nemours Inc': 'DD', 'DXC Technology': 'DXC', 'ETrade': 'ETFC', 'Eastman Chemical': 'EMN', 'Eaton Corp': 'ETN', 'eBay Inc': 'EBAY', 'Ecolab Inc': 'ECL', 'Edison International': 'EIX', 'Edwards Lifesciences': 'EW', 'Electronic Arts': 'EA', 'Emerson Electric Co': 'EMR', 'Entergy Corp.': 'ETR', 'EOG Resources': 'EOG', 'Equifax Inc': 'EFX', 'Equinix': 'EQIX', 'Equity Residential': 'EQR', 'Essex Property Trust Inc': 'ESS', 'Estée Lauder Companies': 'EL', 'Evergy': 'EVRG', 'Eversource Energy': 'ES', 'Everest Re Group Ltd': 'RE', 'Exelon Corp': 'EXC', 'Expedia Group': 'EXPE', 'Expeditors': 'EXPD', 'Extra Space Storage': 'EXR', 'Exxon Mobil Corp': 'XOM', 'F5 Networks': 'FFIV', 'Facebook Inc': 'FB', 'Fastenal Co': 'FAST', 'Federal Realty Investment Trust': 'FRT', 'FedEx Corp': 'FDX', 'Fidelity National Information Services': 'FIS', 'Fifth Third Bancorp': 'FITB', 'FirstEnergy Corp': 'FE', 'First Republic Bank': 'FRC', 'Fiserv Inc': 'FISV', 'FleetCor Technologies Inc': 'FLT', 'FLIR Systems': 'FLIR', 'Flowserve Corp': 'FLS', 'FMC Corp': 'FMC', 'Ford Motor Co': 'F', 'Fortinet': 'FTNT', 'Fortive Corp': 'FTV', 'Fortune Brands Home & Security': 'FBHS', 'Fox Corp (Class A)': 'FOXA', 'Fox Corp (Class B)': 'FOX', 'Franklin Resources': 'BEN', 'Freeport McMoRan Inc': 'FCX', 'Gap Inc': 'GPS', 'Garmin Ltd': 'GRMN', 'Gartner Inc': 'IT', 'General Dynamics': 'GD', 'General Electric': 'GE', 'General Mills': 'GIS', 'General Motors': 'GM', 'Genuine Parts': 'GPC', 'Gilead Sciences': 'GILD', 'Globe Life Inc': 'GL', 'Global Payments Inc': 'GPN', 'Goldman Sachs Group': 'GS', 'Grainger (W W) Inc': 'GWW', 'H&R Block': 'HRB', 'Halliburton Co': 'HAL', 'Hanesbrands Inc': 'HBI', 'Harley Davidson': 'HOG', 'Hartford Financial Services Group': 'HIG', 'Hasbro Inc': 'HAS', 'HCA Healthcare': 'HCA', 'Healthpeak Properties': 'PEAK','Henry Schein': 'HSIC', 'The Hershey Co': 'HSY', 'Hess Corp': 'HES', 'Hewlett Packard Enterprise': 'HPE', 'Hilton Worldwide Holdings Inc': 'HLT', 'HollyFrontier Corp': 'HFC', 'Hologic': 'HOLX', 'Home Depot': 'HD', 'Honeywell International Inc': 'HON', 'Hormel Foods Corp': 'HRL', 'Host Hotels & Resorts': 'HST', 'Howmet Aerospace': 'HWM', 'HP Inc': 'HPQ', 'Humana Inc': 'HUM', 'Huntington Bancshares': 'HBAN', 'Huntington Ingalls Industries': 'HII', 'IDEX Corp': 'IEX', 'IDEXX Laboratories': 'IDXX', 'IHS Markit Ltd': 'INFO', 'Illinois Tool Works': 'ITW', 'Illumina Inc': 'ILMN', 'Incyte': 'INCY', 'Ingersoll Rand': 'IR', 'Intel Corp': 'INTC', 'Intercontinental Exchange': 'ICE', 'International Business Machines': 'IBM', 'International Paper': 'IP', 'Interpublic Group': 'IPG', 'Intl Flavors & Fragrances': 'IFF', 'Intuit Inc': 'INTU', 'Intuitive Surgical Inc': 'ISRG', 'Invesco Ltd': 'IVZ', 'IPG Photonics Corp': 'IPGP', 'IQVIA Holdings Inc': 'IQV', 'Iron Mountain Incorporated': 'IRM', 'Jack Henry & Associates': 'JKHY', 'Jacobs Engineering Group': 'J', 'J B Hunt Transport Services': 'JBHT', 'JM Smucker': 'SJM', 'Johnson & Johnson': 'JNJ', 'Johnson Controls International': 'JCI', 'JPMorgan Chase & Co': 'JPM', 'Juniper Networks': 'JNPR', 'Kansas City Southern': 'KSU', 'Kellogg Co': 'K', 'KeyCorp': 'KEY', 'Keysight Technologies': 'KEYS', 'Kimberly Clark': 'KMB', 'Kimco Realty': 'KIM', 'Kinder Morgan': 'KMI', 'KLA Corp': 'KLAC', 'Kohls Corp': 'KSS', 'Kraft Heinz Co': 'KHC', 'Kroger Co': 'KR', 'L Brands Inc': 'LB',\n",
    "'L3Harris Technologies': 'LHX', 'Laboratory Corp of America Holding': 'LH', 'Lam Research': 'LRCX', 'Lamb Weston Holdings Inc': 'LW', 'Las Vegas Sands': 'LVS', 'Leggett & Platt': 'LEG', 'Leidos Holdings': 'LDOS', 'Lennar Corp': 'LEN', 'Lilly (Eli) & Co': 'LLY', 'Lincoln National': 'LNC', 'Linde PLC': 'LIN', 'Live Nation Entertainment': 'LYV', 'LKQ Corp': 'LKQ', 'Lockheed Martin Corp': 'LMT', 'Loews Corp': 'L', 'Lowes Cos': 'LOW', 'LyondellBasell': 'LYB', 'M&T Bank Corp': 'MTB', 'Marathon Oil Corp': 'MRO', 'Marathon Petroleum': 'MPC', 'MarketAxess': 'MKTX', 'Marriott International': 'MAR', 'Marsh & McLennan': 'MMC', 'Martin Marietta Materials': 'MLM', 'Masco Corp': 'MAS', 'Mastercard Inc': 'MA', 'McCormick & Co': 'MKC', 'Maxim Integrated Products Inc': 'MXIM', 'McDonalds Corp': 'MCD', 'McKesson Corp': 'MCK', 'Medtronic PLC': 'MDT', 'Merck & Co': 'MRK', 'MetLife Inc': 'MET', 'Mettler Toledo': 'MTD', 'MGM Resorts International': 'MGM', 'Microchip Technology': 'MCHP', 'Micron Technology': 'MU', 'Microsoft Corp': 'MSFT', 'Mid America Apartments': 'MAA', 'Mohawk Industries': 'MHK','Molson Coors Brewing Co': 'TAP', 'Mondelez International': 'MDLZ', 'Monster Beverage': 'MNST', 'Moodys Corp': 'MCO', 'Morgan Stanley': 'MS', 'The Mosaic Co': 'MOS', 'Motorola Solutions Inc': 'MSI', 'MSCI Inc': 'MSCI', 'Mylan N V': 'MYL', 'Nasdaq Inc': 'NDAQ', 'National Oilwell Varco Inc': 'NOV', 'NetApp': 'NTAP', 'Netflix Inc': 'NFLX', 'Newell Brands': 'NWL', 'Newmont Corp': 'NEM', 'News Corp (Class A)': 'NWSA', 'News Corp (Class B)': 'NWS', 'NextEra Energy': 'NEE', 'Nielsen Holdings': 'NLSN', 'Nike': 'NKE', 'NiSource Inc': 'NI', 'Noble Energy Inc': 'NBL', 'Nordstrom': 'JWN', 'Norfolk Southern Corp': 'NSC', 'Northern Trust Corp': 'NTRS', 'Northrop Grumman': 'NOC', 'NortonLifeLock': 'NLOK', 'Norwegian Cruise Line Holdings': 'NCLH', 'NRG Energy': 'NRG', 'Nucor Corp': 'NUE', 'Nvidia Corp': 'NVDA', 'NVR Inc': 'NVR', 'OReilly Automotive': 'ORLY', 'Occidental Petroleum': 'OXY', 'Old Dominion Freight Line': 'ODFL', 'Omnicom Group': 'OMC', 'ONEOK': 'OKE', 'Oracle Corp': 'ORCL', 'Otis Worldwide': 'OTIS', 'PACCAR Inc': 'PCAR', 'Packaging Corp of America': 'PKG', 'Parker Hannifin': 'PH', 'Paychex Inc': 'PAYX', 'Paycom': 'PAYC', 'PayPal': 'PYPL', 'Pentair PLC': 'PNR', 'Peoples United Financial': 'PBCT', 'PepsiCo Inc': 'PEP', 'PerkinElmer': 'PKI', 'Perrigo': 'PRGO', 'Pfizer Inc': 'PFE', 'Philip Morris International': 'PM', 'Phillips 66': 'PSX', 'Pinnacle West Capital': 'PNW', 'Pioneer Natural Resources': 'PXD', 'PNC Financial Services': 'PNC', 'PPG Industries': 'PPG', 'PPL Corp': 'PPL', 'Principal Financial Group': 'PFG', 'Procter & Gamble': 'PG', 'Progressive Corp': 'PGR', 'Prologis': 'PLD', 'Prudential Financial': 'PRU', 'Public Services Enterprise Inc': 'PEG', 'Public Storage': 'PSA', 'PulteGroup': 'PHM', 'PVH Corp': 'PVH', 'Qorvo': 'QRVO', 'Quanta Services Inc': 'PWR', 'QUALCOMM Inc': 'QCOM', 'Quest Diagnostics': 'DGX', 'Ralph Lauren Corp': 'RL', 'Raymond James Financial Inc': 'RJF', 'Raytheon Technologies': 'RTX', 'Realty Income Corp': 'O', 'Regency Centers Corp': 'REG', 'Regeneron Pharmaceuticals': 'REGN', 'Regions Financial Corp': 'RF', 'Republic Services Inc': 'RSG', 'ResMed': 'RMD',\n",
    "'Robert Half International': 'RHI', 'Rockwell Automation Inc': 'ROK', 'Rollins Inc': 'ROL', 'Roper Technologies': 'ROP', 'Ross Stores': 'ROST', 'Royal Caribbean Cruises Ltd': 'RCL', 'S&P Global Inc': 'SPGI', 'Salesforce.com': 'CRM', 'SBA Communications': 'SBAC', 'Schlumberger Ltd': 'SLB', 'Seagate Technology': 'STX', 'Sealed Air': 'SEE', 'Sempra Energy': 'SRE', 'ServiceNow': 'NOW', 'Sherwin Williams': 'SHW', 'Simon Property Group Inc': 'SPG', 'Skyworks Solutions': 'SWKS', 'SL Green Realty': 'SLG', 'Snap on': 'SNA', 'Southern Co': 'SO', 'Southwest Airlines': 'LUV', 'Stanley Black & Decker': 'SWK', 'Starbucks Corp': 'SBUX', 'State Street Corp': 'STT', 'STERIS PLC': 'STE', 'Stryker Corp': 'SYK', 'SVB Financial': 'SIVB', 'Synchrony Financial': 'SYF', 'Synopsys Inc': 'SNPS', 'Sysco Corp': 'SYY', 'T Mobile US': 'TMUS', 'T Rowe Price Group': 'TROW', 'Take Two Interactive': 'TTWO', 'Tapestry Inc': 'TPR', 'Target Corp': 'TGT', 'TE Connectivity Ltd': 'TEL', 'TechnipFMC': 'FTI', 'Teleflex': 'TFX', 'Texas Instruments': 'TXN', 'Textron Inc': 'TXT', 'Thermo Fisher Scientific': 'TMO', 'Tiffany & Co': 'TIF', 'TJX Companies Inc': 'TJX', 'Tractor Supply Company': 'TSCO', 'Trane Technologies PLC': 'TT', 'TransDigm Group': 'TDG', 'The Travelers Companies Inc': 'TRV', 'Truist Financial': 'TFC', 'Twitter Inc': 'TWTR', 'Tyson Foods': 'TSN', 'UDR Inc': 'UDR', 'Ulta Beauty': 'ULTA', 'US Bancorp': 'USB', 'Under Armour (Class A)': 'UAA', 'Under Armour (Class C)': 'UA', 'Union Pacific Corp': 'UNP', 'United Airlines Holdings': 'UAL', 'United Health Group Inc': 'UNH', 'United Parcel Service': 'UPS', 'United Rentals Inc': 'URI', 'Universal Health Services Inc': 'UHS', 'Unum Group': 'UNM', 'V F Corp': 'VFC', 'Valero Energy': 'VLO', 'Varian Medical Systems': 'VAR', 'Ventas Inc': 'VTR', 'Verisign Inc': 'VRSN', 'Verisk Analytics': 'VRSK', 'Verizon Communications': 'VZ', 'Vertex Pharmaceuticals Inc': 'VRTX', 'ViacomCBS': 'VIAC', 'Visa Inc': 'V', 'Vornado Realty Trust': 'VNO', 'Vulcan Materials': 'VMC',\n",
    "'W R Berkley Corp': 'WRB', 'Wabtec Corp': 'WAB', 'Walmart': 'WMT', 'Walgreens Boots Alliance': 'WBA', 'The Walt Disney Co': 'DIS', 'Waste Management Inc': 'WM', 'Waters Corp': 'WAT', 'WEC Energy Group': 'WEC', 'Wells Fargo': 'WFC', 'Welltower Inc': 'WELL', 'West Pharmaceutical Services': 'WST', 'Western Digital': 'WDC', 'Western Union Co': 'WU', 'WestRock': 'WRK', 'Weyerhaeuser': 'WY', 'Whirlpool Corp': 'WHR', 'Williams Cos': 'WMB', 'Willis Towers Watson': 'WLTW', 'Wynn Resorts Ltd': 'WYNN', 'Xcel Energy Inc': 'XEL', 'Xerox': 'XRX', 'Xilinx': 'XLNX', 'Xylem Inc': 'XYL', 'Yum Brands Inc': 'YUM', 'Zebra Technologies': 'ZBRA', 'Zimmer Biomet Holdings': 'ZBH', 'Zions Bancorp': 'ZION', 'Zoetis': 'ZTS'}\n",
    "           \n",
    "start = dt.datetime(2018,1,1)\n",
    "end = dt.datetime.now()\n",
    "df_stocks = pd.DataFrame()\n",
    "for key, value in tickers.items():\n",
    "    download = yf.download(value, start, end)\n",
    "    download = pd.DataFrame(download)\n",
    "    download['Ticker'] = value\n",
    "    df_stocks = df_stocks.append(download)\n",
    "df_stocks.to_csv('c:/Users/JCW/Desktop/covidStocks/getSP500DataNow.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "^GSPC nan\n",
      "TSLA 262818086912.0\n",
      "RDS-A 121536331776.0\n",
      "BP 76360335360.0\n",
      "GC=F nan\n",
      "^TNX nan\n",
      "BZ=F nan\n",
      "HG=F nan\n",
      "MASI 12633781248.0\n",
      "GSK 102656507904.0\n",
      "GOLD 50392072192.0\n",
      "BYND 7785188352.0\n",
      "BNTX 19584086016.0\n",
      "CHGG 9078088704.0\n",
      "CHWY 18455834624.0\n",
      "NET 10846663680.0\n",
      "COR 4723900416.0\n",
      "COUP 19592955904.0\n",
      "DDOG 25399314432.0\n",
      "DOCU 35041042432.0\n",
      "EBS 4711686656.0\n",
      "ETSY 12056497152.0\n",
      "EVBG 4576842752.0\n",
      "FSLY 8132160512.0\n",
      "GSK 10674768896.0\n",
      "LVGO 11898882048.0\n",
      "LOGI 19175358464.0\n",
      "MKTX 23278848000.0\n",
      "MRVL 25776670720.0\n",
      "MASI 27177308160.0\n",
      "MKC 53572550656.0\n",
      "MRNA 25624594432.0\n",
      "NEM 23515494400.0\n",
      "OKTA 17509402624.0\n",
      "PANW 73791733760.0\n",
      "PTON 23579549696.0\n",
      "PLD 169829498880.0\n",
      "RNG 129589035008.0\n",
      "CRM 110877982720.0\n",
      "SNY 16753103872.0\n",
      "SHOP 31705485312.0\n",
      "WORK 49973547008.0\n",
      "SPLK 53341605888.0\n",
      "SPOT 17777387520.0\n",
      "SQ 12308746240.0\n",
      "TTWO 19624101888.0\n",
      "TW 37829537792.0\n",
      "TTD 59010621440.0\n",
      "VEEV 13882388480.0\n",
      "VMW 4047304960.0\n",
      "WIX 69555314688.0\n",
      "WING 91939332096.0\n",
      "ZM 175027699712.0\n",
      "MMM 171141808128.0\n",
      "ABT 13195218944.0\n",
      "ABBV 141078773760.0\n",
      "ABMD 61631094784.0\n",
      "ACN 206404648960.0\n",
      "ATVI 81280589824.0\n",
      "ADBE 10176534528.0\n",
      "AMD 10239567872.0\n",
      "AAP 26382768128.0\n",
      "AES 29275146240.0\n",
      "AFL 64383647744.0\n",
      "A 18132496384.0\n",
      "APD 4297830400.0\n",
      "AKAM 9254005760.0\n",
      "ALK 21256683520.0\n",
      "ALB 22411732992.0\n",
      "ARE 23482402816.0\n",
      "ALXN 9316084736.0\n",
      "ALGN 2115159424.0\n",
      "ALLE 13253652480.0\n",
      "ADS 29639985152.0\n",
      "LNT 1029176819712.0\n",
      "ALL 1032403156992.0\n",
      "GOOGL 77772783616.0\n",
      "GOOG 1500772040704.0\n",
      "MO 16799273984.0\n",
      "AMZN 19632773120.0\n",
      "AMCR 5787373056.0\n",
      "AEE 43636084736.0\n",
      "AAL 76737986560.0\n",
      "AEP 26967021568.0\n",
      "AXP 113632616448.0\n",
      "AIG 26161446912.0\n",
      "AMT 18994042880.0\n",
      "AWK 21105096704.0\n",
      "AMP 21314232320.0\n",
      "ABC 145873485824.0\n",
      "AME 30888376320.0\n",
      "AMGN 41400328192.0\n",
      "APH 25123106816.0\n",
      "ADI 68194852864.0\n",
      "ANSS 47966482432.0\n",
      "ANTM 8268093440.0\n",
      "AON 5359449088.0\n",
      "AOS 5307001856.0\n",
      "APA 1605695832064.0\n",
      "AIV 55556956160.0\n",
      "AAPL 22209636352.0\n",
      "AMAT 23380826112.0\n",
      "APTV 17927581696.0\n",
      "ADM 19707310080.0\n",
      "ANET 6370269184.0\n",
      "AJG 210686246912.0\n",
      "AIZ 12598135808.0\n",
      "T 51046596608.0\n",
      "ATO 62502031360.0\n",
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    },
    {
     "data": {
      "text/plain": [
       "\"\\n# More keys that can be used\\n      ['language', 'region', 'quoteType', 'triggerable', 'quoteSourceName',\\n       'currency', 'preMarketChange', 'preMarketChangePercent',\\n       'preMarketTime', 'preMarketPrice', 'regularMarketChange',\\n       'regularMarketChangePercent', 'regularMarketTime', 'regularMarketPrice',\\n       'regularMarketDayHigh', 'regularMarketDayRange', 'regularMarketDayLow',\\n       'regularMarketVolume', 'regularMarketPreviousClose', 'bid', 'ask',\\n       'bidSize', 'askSize', 'fullExchangeName', 'financialCurrency',\\n       'regularMarketOpen', 'averageDailyVolume3Month',\\n       'averageDailyVolume10Day', 'fiftyTwoWeekLowChange',\\n       'fiftyTwoWeekLowChangePercent', 'fiftyTwoWeekRange',\\n       'fiftyTwoWeekHighChange', 'fiftyTwoWeekHighChangePercent',\\n       'fiftyTwoWeekLow', 'fiftyTwoWeekHigh', 'dividendDate',\\n       'earningsTimestamp', 'earningsTimestampStart', 'earningsTimestampEnd',\\n       'trailingAnnualDividendRate', 'trailingPE',\\n       'trailingAnnualDividendYield', 'marketState', 'epsTrailingTwelveMonths',\\n       'epsForward', 'sharesOutstanding', 'bookValue', 'fiftyDayAverage',\\n       'fiftyDayAverageChange', 'fiftyDayAverageChangePercent',\\n       'twoHundredDayAverage', 'twoHundredDayAverageChange',\\n       'twoHundredDayAverageChangePercent', 'marketCap', 'forwardPE',\\n       'priceToBook', 'sourceInterval', 'exchangeDataDelayedBy', 'tradeable',\\n       'firstTradeDateMilliseconds', 'priceHint', 'exchange', 'shortName',\\n       'longName', 'messageBoardId', 'exchangeTimezoneName',\\n       'exchangeTimezoneShortName', 'gmtOffSetMilliseconds', 'market',\\n       'esgPopulated', 'price']\\n\""
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get Market Capitalization for each Company in the S&P 500, and other top performers FY2020.\n",
    "tickers = ['^GSPC', 'TSLA', 'RDS-A', 'BP', 'GC=F', '^TNX', 'BZ=F', 'HG=F', 'MASI', 'GSK', 'GOLD', 'BYND', 'BNTX', 'CHGG', 'CHWY', 'NET', 'COR', 'COUP', 'DDOG', 'DOCU', 'EBS', 'ETSY', 'EVBG', 'FSLY', 'GSK', 'LVGO', 'LOGI', 'MKTX', 'MRVL', 'MASI', 'MKC', 'MRNA', 'NEM', 'OKTA', 'PANW', 'PTON', 'PLD', 'RNG', 'CRM', 'SNY', 'SHOP', 'WORK', 'SPLK', 'SPOT', 'SQ', 'TTWO', 'TW', 'TTD', 'VEEV', 'VMW', 'WIX', 'WING', 'ZM', \n",
    "           'MMM','ABT','ABBV','ABMD','ACN','ATVI','ADBE','AMD','AAP','AES','AFL','A','APD','AKAM','ALK','ALB','ARE','ALXN','ALGN','ALLE','ADS','LNT','ALL','GOOGL','GOOG','MO','AMZN','AMCR','AEE','AAL','AEP','AXP','AIG','AMT','AWK','AMP','ABC','AME','AMGN','APH','ADI','ANSS','ANTM','AON','AOS','APA','AIV','AAPL','AMAT','APTV','ADM','ANET','AJG','AIZ','T','ATO','ADSK','ADP','AZO','AVB','AVY','BKR','BLL','BAC','BK','BAX','BDX',\n",
    "           'BBY','BIIB','BLK','BA','BKNG','BWA','BXP','BSX','BMY','AVGO','BR','CHRW','COG','CDNS','CPB','COF','CAH','KMX','CCL','CARR','CAT','CBOE','CBRE', 'CDW','CE','CNC','CNP','CTL','CERN', 'CF','SCHW', 'CHTR', 'CVX', 'CMG', 'CB','CHD','CI','CINF','CTAS','CSCO','C','CFG','CTXS','CLX','CME','CMS','KO','CTSH','CL','CMCSA','CMA','CAG','CXO','COP','ED','STZ','COO','CPRT','GLW','CTVA','COST','COTY','CCI','CSX', 'CMI','CVS','DHI',\n",
    "           'DHR','DRI','DVA','DE','DAL','XRAY','DVN','DXCM','FANG','DLR','DFS','DISCA','DISCK','DISH','DG','DLTR','D','DPZ','DOV','DOW','DTE','DUK','DRE','DD','DXC','ETFC','EMN','ETN','EBAY','ECL','EIX','EW','EA','EMR','ETR','EOG','EFX','EQIX','EQR','ESS','EL','EVRG','ES','RE','EXC','EXPE','EXPD','EXR','XOM','FFIV','FB','FAST','FRT','FDX','FIS','FITB','FE','FRC','FISV','FLT','FLIR','FLS','FMC','F','FTNT', 'FTV','FBHS','FOXA',\n",
    "           'FOX','BEN','FCX','GPS','GRMN','IT','GD','GE','GIS','GM','GPC','GILD','GL','GPN','GS','GWW','HRB','HAL','HBI','HOG','HIG','HAS','HCA','PEAK','HSIC','HSY','HES','HPE','HLT','HFC','HOLX','HD','HON','HRL','HST','HWM','HPQ','HUM','HBAN','HII','IEX','IDXX','INFO','ITW','ILMN','INCY','IR','INTC','ICE','IBM','IP','IPG','IFF','INTU','ISRG','IVZ','IPGP','IQV','IRM','JKHY','J','JBHT','SJM','JNJ','JCI', 'JPM','JNPR','KSU','K',\n",
    "           'KEY','KEYS','KMB','KIM','KMI','KLAC','KSS','KHC','KR','LB','LHX','LH','LRCX','LW','LVS','LEG','LDOS','LEN','LLY','LNC','LIN','LYV','LKQ','LMT','L','LOW','LYB','MTB','MRO','MPC','MKTX','MAR','MMC','MLM','MAS','MA','MKC','MXIM','MCD','MCK','MDT','MRK','MET','MTD','MGM','MCHP','MU','MSFT','MAA','MHK','TAP','MDLZ','MNST','MCO','MS','MOS','MSI','MSCI','MYL','NDAQ','NOV','NTAP','NFLX','NWL','NEM', 'NWSA','NWS','NEE','NLSN',\n",
    "           'NKE','NI','NBL','JWN','NSC','NTRS','NOC','NLOK','NCLH','NRG','NUE','NVDA','NVR','ORLY','OXY','ODFL','OMC','OKE','ORCL','OTIS','PCAR','PKG','PH','PAYX','PAYC','PYPL','PNR','PBCT','PEP','PKI','PRGO','PFE','PM','PSX','PNW','PXD','PNC','PPG','PPL','PFG','PG','PGR','PLD','PRU','PEG','PSA','PHM', 'PVH','QRVO','PWR','QCOM','DGX','RL','RJF','RTX','O',\t'REG', 'REGN','RF','RSG', 'RMD','RHI','ROK', 'ROL','ROP','ROST','RCL',\n",
    "           'SPGI','CRM','SBAC','SLB','STX','SEE','SRE','NOW','SHW','SPG','SWKS','SLG','SNA','SO','LUV','SWK','SBUX','STT','STE','SYK', 'SIVB','SYF','SNPS','SYY','TMUS','TROW','TTWO','TPR','TGT','TEL','FTI','TFX','TXN','TXT','TMO','TIF','TJX','TSCO','TT','TDG','TRV','TFC','TWTR','TSN','UDR','ULTA','USB','UAA','UA','UNP','UAL','UNH','UPS','URI','UHS','UNM','VFC','VLO','VAR','VTR','VRSN','VRSK','VZ','VRTX', 'VIAC','V','VNO','VMC',\n",
    "           'WRB','WAB','WMT','WBA','DIS','WM','WAT','WEC','WFC','WELL','WST','WDC','WU','WRK','WY','WHR','WMB','WLTW','WYNN','XEL','XRX','XLNX','XYL','YUM','ZBRA','ZBH','ZION','ZTS', 'AVGO', 'BA', 'BBY', 'BIIB', 'BKNG', 'BLK', 'BMY', 'BR', 'BSX', 'BWA', 'BXP', 'C', 'CAG', 'CAH', 'CARR', 'CAT', 'CB', 'CBRE', 'CCI', 'CCL', 'CDNS', 'CDW', 'CE', 'CERN', 'CF', 'CFG', 'CHD', 'CHRW', 'CHTR', 'CI', 'CINF', 'CL', 'CLX', 'CMA', 'CMCSA', \n",
    "           'CME', 'CMG', 'CMS', 'CNC', 'CNP', 'COF', 'COG', 'COO', 'COP', 'COST', 'COTY', 'CPB', 'CPRT', 'CRM', 'CSCO', 'CSX', 'CTAS', 'CTL', 'CTSH', 'CTVA', 'CTXS', 'CVX', 'CXO', 'GLW', 'KMX', 'MO', 'SHOP', 'STZ','BBY','AVGO', 'BA', 'BIIB', 'BKNG', 'BLK', 'BMY', 'BR', 'BSX', 'BWA', 'BXP', 'C', 'CAG', 'CAH', 'CARR', 'CAT', 'CB', 'CBOE', 'CBRE', 'ED', 'KO', 'SCHW']\n",
    "\n",
    "# Get market cap\n",
    "market_cap_data = web.get_quote_yahoo(tickers)['marketCap']\n",
    "for stock, marketCap in zip(tickers, market_cap_data):\n",
    "    print(stock, marketCap)\n",
    "\n",
    "marketCapdf = pd.DataFrame(market_cap_data, columns = ['marketCap'])\n",
    "marketCapdf.to_csv('c:/Users/JCW/Desktop/covidStocks/marketCap.csv')\n",
    "#df2 = pd.DataFrame({'Ticker': market_cap.index, 'Market Cap': market_cap.values})\n",
    "#df2.to_csv(\"marketcap.csv\", index = False)\n",
    "# Get the P/E ratio directly\n",
    "#pe_data = web.get_quote_yahoo(tickers)['trailingPE']\n",
    "\n",
    "# print stock and p/e ratio\n",
    "#for stock, pe in zip(tickers, pe_data):\n",
    "#    print(stock, pe)\n",
    "    \n",
    "\"\"\"\n",
    "# More keys that can be used\n",
    "      ['language', 'region', 'quoteType', 'triggerable', 'quoteSourceName',\n",
    "       'currency', 'preMarketChange', 'preMarketChangePercent',\n",
    "       'preMarketTime', 'preMarketPrice', 'regularMarketChange',\n",
    "       'regularMarketChangePercent', 'regularMarketTime', 'regularMarketPrice',\n",
    "       'regularMarketDayHigh', 'regularMarketDayRange', 'regularMarketDayLow',\n",
    "       'regularMarketVolume', 'regularMarketPreviousClose', 'bid', 'ask',\n",
    "       'bidSize', 'askSize', 'fullExchangeName', 'financialCurrency',\n",
    "       'regularMarketOpen', 'averageDailyVolume3Month',\n",
    "       'averageDailyVolume10Day', 'fiftyTwoWeekLowChange',\n",
    "       'fiftyTwoWeekLowChangePercent', 'fiftyTwoWeekRange',\n",
    "       'fiftyTwoWeekHighChange', 'fiftyTwoWeekHighChangePercent',\n",
    "       'fiftyTwoWeekLow', 'fiftyTwoWeekHigh', 'dividendDate',\n",
    "       'earningsTimestamp', 'earningsTimestampStart', 'earningsTimestampEnd',\n",
    "       'trailingAnnualDividendRate', 'trailingPE',\n",
    "       'trailingAnnualDividendYield', 'marketState', 'epsTrailingTwelveMonths',\n",
    "       'epsForward', 'sharesOutstanding', 'bookValue', 'fiftyDayAverage',\n",
    "       'fiftyDayAverageChange', 'fiftyDayAverageChangePercent',\n",
    "       'twoHundredDayAverage', 'twoHundredDayAverageChange',\n",
    "       'twoHundredDayAverageChangePercent', 'marketCap', 'forwardPE',\n",
    "       'priceToBook', 'sourceInterval', 'exchangeDataDelayedBy', 'tradeable',\n",
    "       'firstTradeDateMilliseconds', 'priceHint', 'exchange', 'shortName',\n",
    "       'longName', 'messageBoardId', 'exchangeTimezoneName',\n",
    "       'exchangeTimezoneShortName', 'gmtOffSetMilliseconds', 'market',\n",
    "       'esgPopulated', 'price']\n",
    "\"\"\"\n",
    "\n",
    "#get market cap for all tickers\n",
    "#market_cap = web.get_quote_yahoo(tickers)['marketCap']\n",
    "#df = pd.DataFrame({'Ticker': market_cap.index, 'Market Cap': market_cap.values})\n",
    "#df.to_csv(\"marketcap.csv\", index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = pd.read_csv('c:/Users/JCW/Desktop/covidStocks/SP500CompanyInfo.csv')\n",
    "b = pd.read_csv('c:/Users/JCW/Desktop/covidStocks/marketCap.csv')\n",
    "merged = a.merge(b, on='ticker', how='outer')\n",
    "\n",
    "merged.to_csv(\"final_output.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "HLG_oBOVdUuN"
   },
   "source": [
    "Clean up downloaded data. \n",
    "Today's data isn't always correct, so code below will remove today's data.\n",
    "Sometimes, duplicate data are downloaded, code below will remove duplicates as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "t_716T0gFefV"
   },
   "outputs": [],
   "source": [
    "## Clean up downloaded data. \n",
    "if len(df_stocks)>0: \n",
    "    df_stocks=df_stocks.reset_index()\n",
    "    df_stocks=df_stocks[df_stocks['Date']<pd.to_datetime('today').floor('d')]\n",
    "if len(df_stocks)>0:\n",
    "    df_stocks['Total']=df_stocks['High']+df_stocks['Volume']\n",
    "    df_stocks=df_stocks[df_stocks['Total'] == df_stocks.groupby(['name', 'Date'])['Total'].transform('max')]\n",
    "    df_stocks=df_stocks.drop('Total', axis=1)\n",
    "    df_stocks=df_stocks.drop_duplicates()\n",
    "    df_stocks=df_stocks.set_index('Date')\n",
    "    stocks=stocks.set_index('Date')\n",
    "    stocks=stocks.append(df_stocks, sort=False, ignore_index=False)      \n",
    "    stocks.to_excel('c:/Users/JCW/Desktop/covidStocks/covidStocks2.xlsx', sheet_name='covidStocks')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "mPY9fNBDFit_",
    "outputId": "4e969161-24dd-4c6e-8f9c-a9673ff879be"
   },
   "outputs": [],
   "source": [
    "## Code below check if any tickers were missing from downloaded data. \n",
    "if len(df_stocks)<len(tickers):\n",
    "    missing_ticker=set(tickers.keys())-set(df_stocks['name'])\n",
    "    print('Missing:', missing_ticker)\n",
    "else:\n",
    "    print('All Tickers download')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "_lCzn-HgAELm"
   },
   "outputs": [],
   "source": [
    "### Code use for adding new ticker to the dataset\n",
    "# ticker = 'psxp'\n",
    "# name='Phillips66'  \n",
    "# df_new=yf.download(ticker )\n",
    "# df_new['name']=name\n",
    "# df_new=df_new.reset_index()\n",
    "# stocks=pd.read_excel('google_drive_path/file_name.xlsx', sheet_name='Sheet1')\n",
    "# new_ticker=stocks.append(df_new)\n",
    "# new_ticker=new_ticker.set_index('Date')\n",
    "# with pd.ExcelWriter('google_drive_path/file_name.xlsx'',engine='openpyxl') as writer:\n",
    "#     new_ticker.to_excel(writer)"
   ]
  }
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   "name": "Download Stock Price using yfinance.ipynb",
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