p2_yahoo_stats_scrapper.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "faa7fe94",
   "metadata": {},
   "outputs": [],
   "source": [
    "from modules.utils import *\n",
    "from modules.YfScrapper import *\n",
    "pd.set_option('display.max_columns', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fe03a609",
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "import requests\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from bs4 import BeautifulSoup\n",
    "from datetime import datetime\n",
    "from io import StringIO\n",
    "from collections.abc import Iterable \n",
    "from modules.utils import logger\n",
    "\n",
    "class YfScrapper():\n",
    "    '''\n",
    "    Scrapper object to get data from Yahoo Finance, can contain multiple data for different tickers\n",
    "    '''    \n",
    "    def __init__(self):\n",
    "        '''\n",
    "        Sets the headers to be used\n",
    "        '''\n",
    "        self.headers = {\n",
    "            'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.3',\n",
    "            'Accept-Language': 'en-US,en;q=0.5',\n",
    "            'DNT': '1',\n",
    "            'Connection': 'keep-alive',\n",
    "            'Upgrade-Insecure-Requests': '1'\n",
    "            }\n",
    "        self.mapping_dict = {\n",
    "            'Market Cap (intraday)' : 'Market Cap (B)',\n",
    "            'Enterprise Value' : 'Enterprise Value (B)',\n",
    "            '52-Week Change' : '52 Week Change (%)',\n",
    "            'S&P500 52-Week Change': 'S&P500 52-Week Change (%)',\n",
    "            'Avg Vol 3 month' : 'Avg Vol 3 month (B)',\n",
    "            'Avg Vol (10 day)' : 'Avg Vol 10 day (B)',\n",
    "            'Shares Short' : 'Shares Short (M) (prior month)',\n",
    "            'Forward Annual Dividend Yield': 'Forward Annual Dividend Yield (%)',\n",
    "            'Trailing Annual Dividend Yield' : 'Trailing Annual Dividend Yield (%)',\n",
    "            'Payout Ratio' : 'Payout Ratio (%)',\n",
    "            'Last Split Factor' : 'Last Split Factor (x:1)',\n",
    "            'Profit Margin' : 'Profit Margin (%)',\n",
    "            'Operating Margin (ttm)' : 'Operating Margin (ttm) (%)',\n",
    "            'Return on Assets (ttm)' : 'Return on Assets (ttm) (%)',\n",
    "            'Return on Equity (ttm)' : 'Return on Equity (ttm) (%)',\n",
    "            'Revenue (ttm)' : 'Revenue (ttm) (B)',\n",
    "            'Quarterly Revenue Growth (yoy)' : 'Quarterly Revenue Growth (yoy) (%)',\n",
    "            'Gross Profit (ttm)' : 'Gross Profit (ttm) (B)',\n",
    "            'EBITDA' : 'EBITDA (B)',\n",
    "            'Net Income Avi to Common (ttm)' : 'Net Income Avi to Common (ttm) (B)',\n",
    "            'Quarterly Earnings Growth (yoy)' : 'Quarterly Earnings Growth (yoy) (%)',\n",
    "            'Total Cash (mrq)' :'Total Cash (mrq) (B)',\n",
    "            'Total Debt (mrq)' :'Total Debt (mrq) (B)',\n",
    "            'Operating Cash Flow (ttm)' : 'Operating Cash Flow (ttm) (B)',\n",
    "            'Levered Free Cash Flow (ttm)' : 'Levered Free Cash Flow (ttm) (B)'\n",
    "        }\n",
    "        self.tickers = {}\n",
    "        self.compiled_dataframes = None\n",
    "\n",
    "    def add_tickers(self, tickers):\n",
    "        '''\n",
    "        Adds a list of tickers to the class instance\n",
    "        Parameters:\n",
    "            tickers (list[str] or str) : the ticker symbols to be added\n",
    "\n",
    "        '''\n",
    "        if isinstance(tickers, str):\n",
    "            self.tickers.setdefault(ticker)\n",
    "        elif isinstance(tickers, Iterable):\n",
    "            count = 0\n",
    "            for ticker in tickers:\n",
    "                self.tickers.setdefault(ticker)\n",
    "                count +=1\n",
    "            logger.info(f'Added {count} tickers')\n",
    "        else:\n",
    "            raise TypeError('tickers must be str or iterable list of strings')\n",
    "    \n",
    "    def _mapper(self, row):\n",
    "            '''Helper function to map or return original values'''\n",
    "            return self.mapping_dict.get(row, row)\n",
    "\n",
    "    def get_ticker_stats(self, tickers, clean_df=True):  \n",
    "        '''\n",
    "        Function take takes in a list of tickers and scraps the yahoo stats into a dictionary.\n",
    "        Parameters:\n",
    "            tickers (str or iterable list of strings): \n",
    "                If 'all', will scrap for all the stored tickers, otherwise provide a list of tickers to scrap or a ticker\n",
    "            clean_df (bool):\n",
    "                option whether to clean the data\n",
    "        '''\n",
    "        if isinstance(tickers, str):\n",
    "            if tickers.upper() == 'ALL':\n",
    "                tickers = self.tickers\n",
    "            elif tickers.upper().startswith('S&P'):\n",
    "                tickers = self.get_SP500_data(tickers_only=True)\n",
    "            else:\n",
    "                tickers = list(tickers)\n",
    "        elif isinstance(tickers, Iterable):\n",
    "            pass\n",
    "        else:\n",
    "            raise TypeError('tickers must be str or iterable list of strings')\n",
    "        \n",
    "        for ticker in tickers:\n",
    "            url = f'https://finance.yahoo.com/quote/{ticker}/key-statistics?p={ticker}'\n",
    "            resp = requests.get(url, headers = self.headers)\n",
    "            logger.info(f'{ticker} status - {resp.status_code}')\n",
    "            soup = BeautifulSoup(resp.text, \"html.parser\")\n",
    "            name = soup.find(\"h1\").text\n",
    "\n",
    "            # Read the html using pandas to parse tables directly, then concatenate them\n",
    "            dfs = pd.read_html(StringIO(resp.text))\n",
    "            df = pd.concat([*dfs])\n",
    "            df.columns = ['metrics', ticker]\n",
    "            # Header cleaning\n",
    "            df['metrics'].replace(regex={r'[0-9]$': ''}, inplace = True) # Removes the annotations appearing at the end of rows\n",
    "            df['metrics'].replace(regex={r'(\\(.+,.+\\))': ''}, inplace = True) # This will specifically remove dates inside brackets, by checking for ','\n",
    "            ### UNRESOLVED, there are tow columns shares short, the latter is for prior month\n",
    "            df['metrics'] = df['metrics'].str.strip()\n",
    "            df['metrics'] = df['metrics'].apply(self._mapper)\n",
    "            df = df.T\n",
    "            df.columns = df.iloc[0,:] # Update the first row as the header\n",
    "            df.insert(0, 'Name', name)\n",
    "            df = df.drop('metrics') # Drop the first row\n",
    "            idx = df.columns.to_list().index('Shares Short (M) (prior month)')\n",
    "            updated_columns = df.columns.to_list()\n",
    "            updated_columns[idx] = 'Shares Short (M)'\n",
    "            df.columns = updated_columns\n",
    "            if clean_df:\n",
    "                df = self.clean_df(df)\n",
    "            logger.info(f'\\t{df.iloc[0,0]} : {df.iloc[0,1]}')\n",
    "            # Save to the object variable\n",
    "            self.tickers[ticker] = df\n",
    "\n",
    "    def clean_df(self, df):\n",
    "        '''\n",
    "        Function to cast and clean the dataframe via the following:\n",
    "        1. Format strings into numbers according (large number format)\n",
    "        2. Clean stocksplit ratios \n",
    "        3. Recast dates into datetime format\n",
    "\n",
    "        Parameters:\n",
    "            df (pd.DataFrame) - dataframe for cleaning\n",
    "        '''\n",
    "        for col in df.columns:\n",
    "            if col in ['Dividend Date', 'Ex-Dividend Date', 'Last Split Date', 'Fiscal Year Ends', 'Most Recent Quarter (mrq)']:\n",
    "                df[col] = df[col].apply(self._date_conversion)\n",
    "            elif col == 'Last Split Factor (x:1)':\n",
    "                df[col] = df[col].apply(self._stocksplits)\n",
    "            else:\n",
    "                df[col] = df[col].apply(self._num_reformat)\n",
    "        return df\n",
    "\n",
    "    def _num_reformat(self, x):\n",
    "        '''\n",
    "        Helper function to reformat large sums to be in Billions and removing % and commas\n",
    "        Casts numerical values into float type\n",
    "        '''\n",
    "        if isinstance(x, str):\n",
    "            x = re.sub(\"[,]\", \"\", x)\n",
    "            if x[-1] == 'T':\n",
    "                x = round(float(x[:-1])*1000,2)\n",
    "            elif x[-1] == 'B':\n",
    "                x = round(float(x[:-1]),2)\n",
    "            elif x[-1] == 'M':\n",
    "                x = round(float(x[:-1])*0.001,2)\n",
    "            elif x[-1] == 'k':\n",
    "                x = round(float(x[:-1])*0.000001,2)\n",
    "            elif x[-1] == '%':\n",
    "                x = round(float(x[:-1]),2)             \n",
    "            elif x == \"N/A\":\n",
    "                x = 0\n",
    "        return x\n",
    "\n",
    "    def _stocksplits(self, x):\n",
    "        '''\n",
    "        Changes split factors into x:1 whole ratios\n",
    "        '''\n",
    "        if isinstance(x, str):\n",
    "            x = x.split(':')\n",
    "            return round(int(x[0])/ int(x[1]),2)\n",
    "        else:\n",
    "            return x\n",
    "    \n",
    "    def _date_conversion(self, x):\n",
    "        '''\n",
    "        Parses dates into datetime format at the end of the dataframe\n",
    "        '''\n",
    "        if isinstance(x, str):\n",
    "            # Dec 30, 2022\n",
    "            return datetime.strptime(x, '%b %d, %Y').date()\n",
    "        else:\n",
    "            return x\n",
    "    \n",
    "    def compile_dataframes(self):\n",
    "        '''\n",
    "        Function to concatenate all the ticker dataframe together\n",
    "        '''\n",
    "        dfs = [ticker for ticker in self.tickers.values() if isinstance(ticker, pd.DataFrame)]\n",
    "        df = pd.concat([*dfs])\n",
    "        self.compiled_dataframes = df\n",
    "        return df\n",
    "        \n",
    "    def get_SP500_data(self, tickers_only: bool=False, url: str='https://www.slickcharts.com/sp500', tableclass: str =\"table-responsive\"):\n",
    "        '''\n",
    "        Function to scrap the latest S&P data from a website containing S&P data\n",
    "        Inputs:\n",
    "            tickers_only: boolean - whether to return the ticker symbols only, or the whole dataframe\n",
    "            url: string - website url\n",
    "            tableclass: string - tableclass containing the data\n",
    "        Returns:\n",
    "            pd.DataFrame or list\n",
    "        '''\n",
    "        resp = requests.get(url, headers = self.headers)\n",
    "        soup = BeautifulSoup(resp.text, \"html.parser\")\n",
    "        table = soup.find(class_ = tableclass)\n",
    "\n",
    "        table_head = table.find('thead')\n",
    "        header_list = [th.text.strip() for th in table_head.find_all('th')]\n",
    "\n",
    "        table_body = table.find('tbody')\n",
    "        rows = table_body.find_all('tr')\n",
    "        sp_data = []\n",
    "        for row in rows:\n",
    "            cols = row.find_all('td')\n",
    "            cols = [ele.text.strip() for ele in cols]\n",
    "            sp_data.append([ele for ele in cols if ele]) # Get rid of empty values\n",
    "\n",
    "        sp_df = pd.DataFrame(np.array(sp_data))\n",
    "        sp_df.columns = header_list\n",
    "        sp_df = sp_df.drop('#', axis=1)\n",
    "        sp_df['Symbol'].replace(regex={r'[\\.]': '-'}, inplace=True) #tickers need to have - instead of . for proper search on yahoo\n",
    "        logger.info(f'Number of S&P constituent data obtained: {len(sp_df)}')\n",
    "        if tickers_only:\n",
    "            return sp_df['Symbol'].to_list()\n",
    "        else:\n",
    "            return sp_df\n",
    "\n",
    "    def to_csv(self, filepath):\n",
    "        if self.compiled_dataframes:\n",
    "            self.compiled_dataframes.to_csv(filepath + '.csv')\n",
    "            logger.info(f'File {filepath} saved!')\n",
    "\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "44c56d39",
   "metadata": {},
   "source": [
    "<h2>Part 2 - S&P and portfolio web scraper</h2>\n",
    "<h3>Introduction</h3>\n",
    "<p>The Standard and Poor's 500, or simply the S&P 500, is a stock market index tracking the stock performance of 500 large companies listed on stock exchanges in the United States. Having consisting of 11 different sectors and over 500 different companies, the index can be used as a benchmark for a basic stock portfolio diversification in the US stock marker. It is one of the most commonly followed equity indices.</p>\n",
    "    \n",
    "<p>This script scraps data from the yahoo finance statistics page.</p>\n",
    "<img src=\"images/sample.JPG\">\n",
    "<p>Two sets of data are in focus</p>\n",
    "<ol>\n",
    "    <li>The S&P index constituents.</li>\n",
    "    <li>One's portfolio</li>\n",
    "    </ol>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "36658f62",
   "metadata": {},
   "source": [
    "### Option 1 - S&P data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2746e27b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2024-01-06 23:03:30,835 - root - INFO - Number of S&P constituent data obtained: 503\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>Symbol</th>\n",
       "      <th>Portfolio%</th>\n",
       "      <th>Price</th>\n",
       "      <th>Chg</th>\n",
       "      <th>% Chg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Microsoft Corp</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>6.95%</td>\n",
       "      <td>367.75</td>\n",
       "      <td>-0.19</td>\n",
       "      <td>(-0.05%)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apple Inc.</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>6.76%</td>\n",
       "      <td>181.18</td>\n",
       "      <td>-0.73</td>\n",
       "      <td>(-0.40%)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Amazon.com Inc</td>\n",
       "      <td>AMZN</td>\n",
       "      <td>3.34%</td>\n",
       "      <td>145.24</td>\n",
       "      <td>0.67</td>\n",
       "      <td>(0.46%)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Nvidia Corp</td>\n",
       "      <td>NVDA</td>\n",
       "      <td>3.01%</td>\n",
       "      <td>490.97</td>\n",
       "      <td>10.99</td>\n",
       "      <td>(2.29%)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Alphabet Inc. Class A</td>\n",
       "      <td>GOOGL</td>\n",
       "      <td>2.05%</td>\n",
       "      <td>135.73</td>\n",
       "      <td>-0.66</td>\n",
       "      <td>(-0.48%)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>Davita Inc.</td>\n",
       "      <td>DVA</td>\n",
       "      <td>0.01%</td>\n",
       "      <td>107.24</td>\n",
       "      <td>1.06</td>\n",
       "      <td>(1.00%)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>V.F. Corporation</td>\n",
       "      <td>VFC</td>\n",
       "      <td>0.01%</td>\n",
       "      <td>16.90</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>(-0.24%)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>Mohawk Industries, Inc.</td>\n",
       "      <td>MHK</td>\n",
       "      <td>0.01%</td>\n",
       "      <td>103.88</td>\n",
       "      <td>3.17</td>\n",
       "      <td>(3.15%)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>Fox Corporation Class B</td>\n",
       "      <td>FOX</td>\n",
       "      <td>0.01%</td>\n",
       "      <td>28.10</td>\n",
       "      <td>0.11</td>\n",
       "      <td>(0.39%)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>News Corporation Class B</td>\n",
       "      <td>NWS</td>\n",
       "      <td>0.01%</td>\n",
       "      <td>25.58</td>\n",
       "      <td>0.20</td>\n",
       "      <td>(0.79%)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>503 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Company Symbol Portfolio%   Price    Chg     % Chg\n",
       "0              Microsoft Corp   MSFT      6.95%  367.75  -0.19  (-0.05%)\n",
       "1                  Apple Inc.   AAPL      6.76%  181.18  -0.73  (-0.40%)\n",
       "2              Amazon.com Inc   AMZN      3.34%  145.24   0.67   (0.46%)\n",
       "3                 Nvidia Corp   NVDA      3.01%  490.97  10.99   (2.29%)\n",
       "4       Alphabet Inc. Class A  GOOGL      2.05%  135.73  -0.66  (-0.48%)\n",
       "..                        ...    ...        ...     ...    ...       ...\n",
       "498               Davita Inc.    DVA      0.01%  107.24   1.06   (1.00%)\n",
       "499          V.F. Corporation    VFC      0.01%   16.90  -0.04  (-0.24%)\n",
       "500   Mohawk Industries, Inc.    MHK      0.01%  103.88   3.17   (3.15%)\n",
       "501   Fox Corporation Class B    FOX      0.01%   28.10   0.11   (0.39%)\n",
       "502  News Corporation Class B    NWS      0.01%   25.58   0.20   (0.79%)\n",
       "\n",
       "[503 rows x 6 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scrapper = YfScrapper()\n",
    "sp_data = scrapper.get_SP500_data()\n",
    "sp_data"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "0dfff801",
   "metadata": {},
   "source": [
    "### Option 2 - Personal portfolio data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0e56e024",
   "metadata": {},
   "outputs": [],
   "source": [
    "input_tickers = pd.read_csv('portfolio_tickers.csv') #input file with a list of portfolio tickers\n",
    "tickers = input_tickers['Tickers'].tolist()\n",
    "scrapper.add_tickers(tickers)\n",
    "scrapper.get_ticker_stats('all', clean_df=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d09ee9d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "scrapper.get_ticker_stats('S&P500')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f95750cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Market Cap (B)</th>\n",
       "      <th>Enterprise Value (B)</th>\n",
       "      <th>Trailing P/E</th>\n",
       "      <th>Forward P/E</th>\n",
       "      <th>PEG Ratio (5 yr expected)</th>\n",
       "      <th>Price/Sales (ttm)</th>\n",
       "      <th>Price/Book (mrq)</th>\n",
       "      <th>Enterprise Value/Revenue</th>\n",
       "      <th>Enterprise Value/EBITDA</th>\n",
       "      <th>Beta (5Y Monthly)</th>\n",
       "      <th>52 Week Change (%)</th>\n",
       "      <th>S&amp;P500 52-Week Change (%)</th>\n",
       "      <th>52 Week High</th>\n",
       "      <th>52 Week Low</th>\n",
       "      <th>50-Day Moving Average</th>\n",
       "      <th>200-Day Moving Average</th>\n",
       "      <th>Avg Vol (3 month)</th>\n",
       "      <th>Avg Vol 10 day (B)</th>\n",
       "      <th>Shares Outstanding</th>\n",
       "      <th>Implied Shares Outstanding</th>\n",
       "      <th>Float</th>\n",
       "      <th>% Held by Insiders</th>\n",
       "      <th>% Held by Institutions</th>\n",
       "      <th>Shares Short (M)</th>\n",
       "      <th>Short Ratio</th>\n",
       "      <th>Short % of Float</th>\n",
       "      <th>Short % of Shares Outstanding</th>\n",
       "      <th>Shares Short (M) (prior month)</th>\n",
       "      <th>Forward Annual Dividend Rate</th>\n",
       "      <th>Forward Annual Dividend Yield (%)</th>\n",
       "      <th>Trailing Annual Dividend Rate</th>\n",
       "      <th>Trailing Annual Dividend Yield (%)</th>\n",
       "      <th>5 Year Average Dividend Yield</th>\n",
       "      <th>Payout Ratio (%)</th>\n",
       "      <th>Dividend Date</th>\n",
       "      <th>Ex-Dividend Date</th>\n",
       "      <th>Last Split Factor (x:1)</th>\n",
       "      <th>Last Split Date</th>\n",
       "      <th>Fiscal Year Ends</th>\n",
       "      <th>Most Recent Quarter (mrq)</th>\n",
       "      <th>Profit Margin (%)</th>\n",
       "      <th>Operating Margin (ttm) (%)</th>\n",
       "      <th>Return on Assets (ttm) (%)</th>\n",
       "      <th>Return on Equity (ttm) (%)</th>\n",
       "      <th>Revenue (ttm) (B)</th>\n",
       "      <th>Revenue Per Share (ttm)</th>\n",
       "      <th>Quarterly Revenue Growth (yoy) (%)</th>\n",
       "      <th>Gross Profit (ttm) (B)</th>\n",
       "      <th>EBITDA (B)</th>\n",
       "      <th>Net Income Avi to Common (ttm) (B)</th>\n",
       "      <th>Diluted EPS (ttm)</th>\n",
       "      <th>Quarterly Earnings Growth (yoy) (%)</th>\n",
       "      <th>Total Cash (mrq) (B)</th>\n",
       "      <th>Total Cash Per Share (mrq)</th>\n",
       "      <th>Total Debt (mrq) (B)</th>\n",
       "      <th>Total Debt/Equity (mrq)</th>\n",
       "      <th>Current Ratio (mrq)</th>\n",
       "      <th>Book Value Per Share (mrq)</th>\n",
       "      <th>Operating Cash Flow (ttm) (B)</th>\n",
       "      <th>Levered Free Cash Flow (ttm) (B)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MSFT</th>\n",
       "      <td>Microsoft Corporation (MSFT)</td>\n",
       "      <td>2730.00</td>\n",
       "      <td>2670.00</td>\n",
       "      <td>35.63</td>\n",
       "      <td>32.89</td>\n",
       "      <td>2.19</td>\n",
       "      <td>12.58</td>\n",
       "      <td>12.38</td>\n",
       "      <td>12.25</td>\n",
       "      <td>23.88</td>\n",
       "      <td>0.88</td>\n",
       "      <td>61.92</td>\n",
       "      <td>20.69</td>\n",
       "      <td>384.30</td>\n",
       "      <td>226.41</td>\n",
       "      <td>366.76</td>\n",
       "      <td>331.92</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.02</td>\n",
       "      <td>7.43</td>\n",
       "      <td>7.43</td>\n",
       "      <td>7.43</td>\n",
       "      <td>0.05</td>\n",
       "      <td>73.01</td>\n",
       "      <td>0.05</td>\n",
       "      <td>1.77</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.05</td>\n",
       "      <td>3</td>\n",
       "      <td>0.82</td>\n",
       "      <td>2.79</td>\n",
       "      <td>0.76</td>\n",
       "      <td>1.00</td>\n",
       "      <td>26.36</td>\n",
       "      <td>2024-03-13</td>\n",
       "      <td>2024-02-13</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2003-02-17</td>\n",
       "      <td>2023-06-29</td>\n",
       "      <td>2023-09-29</td>\n",
       "      <td>35.31</td>\n",
       "      <td>47.59</td>\n",
       "      <td>14.72</td>\n",
       "      <td>39.11</td>\n",
       "      <td>218.31</td>\n",
       "      <td>29.35</td>\n",
       "      <td>12.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>109.48</td>\n",
       "      <td>77.10</td>\n",
       "      <td>10.33</td>\n",
       "      <td>27.0</td>\n",
       "      <td>143.95</td>\n",
       "      <td>19.37</td>\n",
       "      <td>105.68</td>\n",
       "      <td>47.88</td>\n",
       "      <td>1.66</td>\n",
       "      <td>29.70</td>\n",
       "      <td>94.97</td>\n",
       "      <td>51.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAPL</th>\n",
       "      <td>Apple Inc. (AAPL)</td>\n",
       "      <td>2820.00</td>\n",
       "      <td>2870.00</td>\n",
       "      <td>29.56</td>\n",
       "      <td>27.47</td>\n",
       "      <td>2.17</td>\n",
       "      <td>7.47</td>\n",
       "      <td>45.34</td>\n",
       "      <td>7.48</td>\n",
       "      <td>22.20</td>\n",
       "      <td>1.29</td>\n",
       "      <td>39.21</td>\n",
       "      <td>20.69</td>\n",
       "      <td>199.62</td>\n",
       "      <td>128.12</td>\n",
       "      <td>187.40</td>\n",
       "      <td>180.02</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.05</td>\n",
       "      <td>15.55</td>\n",
       "      <td>15.75</td>\n",
       "      <td>15.54</td>\n",
       "      <td>0.07</td>\n",
       "      <td>61.50</td>\n",
       "      <td>0.12</td>\n",
       "      <td>2.29</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.80</td>\n",
       "      <td>15.33</td>\n",
       "      <td>2023-11-15</td>\n",
       "      <td>2023-11-09</td>\n",
       "      <td>4.00</td>\n",
       "      <td>2020-08-30</td>\n",
       "      <td>2023-09-29</td>\n",
       "      <td>2023-09-29</td>\n",
       "      <td>25.31</td>\n",
       "      <td>30.13</td>\n",
       "      <td>20.26</td>\n",
       "      <td>171.95</td>\n",
       "      <td>383.29</td>\n",
       "      <td>24.34</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>170.78</td>\n",
       "      <td>125.82</td>\n",
       "      <td>97.00</td>\n",
       "      <td>6.12</td>\n",
       "      <td>10.8</td>\n",
       "      <td>61.55</td>\n",
       "      <td>3.96</td>\n",
       "      <td>123.93</td>\n",
       "      <td>199.42</td>\n",
       "      <td>0.99</td>\n",
       "      <td>4.00</td>\n",
       "      <td>110.54</td>\n",
       "      <td>82.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMZN</th>\n",
       "      <td>Amazon.com Inc. (AMZN)</td>\n",
       "      <td>1500.00</td>\n",
       "      <td>1570.00</td>\n",
       "      <td>76.04</td>\n",
       "      <td>37.74</td>\n",
       "      <td>2.36</td>\n",
       "      <td>2.72</td>\n",
       "      <td>8.20</td>\n",
       "      <td>2.84</td>\n",
       "      <td>21.38</td>\n",
       "      <td>1.16</td>\n",
       "      <td>66.25</td>\n",
       "      <td>20.69</td>\n",
       "      <td>155.63</td>\n",
       "      <td>87.08</td>\n",
       "      <td>144.64</td>\n",
       "      <td>128.69</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.04</td>\n",
       "      <td>10.33</td>\n",
       "      <td>10.33</td>\n",
       "      <td>9.06</td>\n",
       "      <td>9.62</td>\n",
       "      <td>61.80</td>\n",
       "      <td>0.09</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1.15</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.00</td>\n",
       "      <td>2022-06-05</td>\n",
       "      <td>2022-12-30</td>\n",
       "      <td>2023-09-29</td>\n",
       "      <td>3.62</td>\n",
       "      <td>7.82</td>\n",
       "      <td>3.61</td>\n",
       "      <td>12.53</td>\n",
       "      <td>554.03</td>\n",
       "      <td>53.95</td>\n",
       "      <td>12.6</td>\n",
       "      <td>225.15</td>\n",
       "      <td>73.98</td>\n",
       "      <td>20.08</td>\n",
       "      <td>1.92</td>\n",
       "      <td>244.0</td>\n",
       "      <td>64.17</td>\n",
       "      <td>6.21</td>\n",
       "      <td>166.06</td>\n",
       "      <td>90.76</td>\n",
       "      <td>0.99</td>\n",
       "      <td>17.71</td>\n",
       "      <td>71.65</td>\n",
       "      <td>36.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NVDA</th>\n",
       "      <td>NVIDIA Corporation (NVDA)</td>\n",
       "      <td>1210.00</td>\n",
       "      <td>1210.00</td>\n",
       "      <td>64.77</td>\n",
       "      <td>24.81</td>\n",
       "      <td>0.49</td>\n",
       "      <td>27.24</td>\n",
       "      <td>36.46</td>\n",
       "      <td>26.87</td>\n",
       "      <td>52.86</td>\n",
       "      <td>1.64</td>\n",
       "      <td>214.16</td>\n",
       "      <td>20.69</td>\n",
       "      <td>505.48</td>\n",
       "      <td>151.41</td>\n",
       "      <td>471.39</td>\n",
       "      <td>410.45</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.03</td>\n",
       "      <td>2.47</td>\n",
       "      <td>2.53</td>\n",
       "      <td>2.37</td>\n",
       "      <td>4.04</td>\n",
       "      <td>68.41</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.08</td>\n",
       "      <td>1.04</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.15</td>\n",
       "      <td>2.11</td>\n",
       "      <td>2023-12-27</td>\n",
       "      <td>2023-12-04</td>\n",
       "      <td>4.00</td>\n",
       "      <td>2021-07-19</td>\n",
       "      <td>2023-01-28</td>\n",
       "      <td>2023-10-28</td>\n",
       "      <td>42.10</td>\n",
       "      <td>57.49</td>\n",
       "      <td>27.23</td>\n",
       "      <td>69.17</td>\n",
       "      <td>44.87</td>\n",
       "      <td>18.18</td>\n",
       "      <td>205.5</td>\n",
       "      <td>15.36</td>\n",
       "      <td>22.16</td>\n",
       "      <td>18.89</td>\n",
       "      <td>7.56</td>\n",
       "      <td>1259.3</td>\n",
       "      <td>18.28</td>\n",
       "      <td>7.4</td>\n",
       "      <td>11.03</td>\n",
       "      <td>33.15</td>\n",
       "      <td>3.59</td>\n",
       "      <td>13.49</td>\n",
       "      <td>18.84</td>\n",
       "      <td>14.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GOOGL</th>\n",
       "      <td>Alphabet Inc. (GOOGL)</td>\n",
       "      <td>1710.00</td>\n",
       "      <td>1620.00</td>\n",
       "      <td>26.00</td>\n",
       "      <td>20.37</td>\n",
       "      <td>1.28</td>\n",
       "      <td>5.85</td>\n",
       "      <td>6.22</td>\n",
       "      <td>5.44</td>\n",
       "      <td>17.32</td>\n",
       "      <td>1.05</td>\n",
       "      <td>54.20</td>\n",
       "      <td>20.69</td>\n",
       "      <td>142.68</td>\n",
       "      <td>85.83</td>\n",
       "      <td>133.61</td>\n",
       "      <td>125.62</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.02</td>\n",
       "      <td>5.92</td>\n",
       "      <td>12.59</td>\n",
       "      <td>10.79</td>\n",
       "      <td>0.28</td>\n",
       "      <td>79.70</td>\n",
       "      <td>0.05</td>\n",
       "      <td>1.66</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.00</td>\n",
       "      <td>2022-07-17</td>\n",
       "      <td>2022-12-30</td>\n",
       "      <td>2023-09-29</td>\n",
       "      <td>22.46</td>\n",
       "      <td>27.96</td>\n",
       "      <td>13.49</td>\n",
       "      <td>25.33</td>\n",
       "      <td>297.13</td>\n",
       "      <td>23.34</td>\n",
       "      <td>11.0</td>\n",
       "      <td>156.63</td>\n",
       "      <td>96.07</td>\n",
       "      <td>66.73</td>\n",
       "      <td>5.23</td>\n",
       "      <td>41.5</td>\n",
       "      <td>119.94</td>\n",
       "      <td>9.58</td>\n",
       "      <td>30.45</td>\n",
       "      <td>11.14</td>\n",
       "      <td>2.04</td>\n",
       "      <td>21.78</td>\n",
       "      <td>106.44</td>\n",
       "      <td>70.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FMC</th>\n",
       "      <td>FMC Corporation (FMC)</td>\n",
       "      <td>7.58</td>\n",
       "      <td>11.37</td>\n",
       "      <td>12.93</td>\n",
       "      <td>14.06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.54</td>\n",
       "      <td>2.30</td>\n",
       "      <td>2.29</td>\n",
       "      <td>10.36</td>\n",
       "      <td>0.89</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>133.37</td>\n",
       "      <td>49.49</td>\n",
       "      <td>56.36</td>\n",
       "      <td>86.4</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.78</td>\n",
       "      <td>92.72</td>\n",
       "      <td>0.01</td>\n",
       "      <td>3.15</td>\n",
       "      <td>5.02</td>\n",
       "      <td>4.43</td>\n",
       "      <td>0.01</td>\n",
       "      <td>2.32</td>\n",
       "      <td>3.82</td>\n",
       "      <td>2.32</td>\n",
       "      <td>3.85</td>\n",
       "      <td>1.89</td>\n",
       "      <td>49.36</td>\n",
       "      <td>2024-01-17</td>\n",
       "      <td>2023-12-27</td>\n",
       "      <td>1.15</td>\n",
       "      <td>2019-03-03</td>\n",
       "      <td>2022-12-30</td>\n",
       "      <td>2023-09-29</td>\n",
       "      <td>10.01</td>\n",
       "      <td>13.14</td>\n",
       "      <td>5.61</td>\n",
       "      <td>18.45</td>\n",
       "      <td>4.96</td>\n",
       "      <td>39.64</td>\n",
       "      <td>-28.7</td>\n",
       "      <td>2.33</td>\n",
       "      <td>1.16</td>\n",
       "      <td>0.59</td>\n",
       "      <td>4.70</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.32</td>\n",
       "      <td>2.6</td>\n",
       "      <td>4.26</td>\n",
       "      <td>128.63</td>\n",
       "      <td>1.54</td>\n",
       "      <td>26.38</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>-0.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XRAY</th>\n",
       "      <td>DENTSPLY SIRONA Inc. (XRAY)</td>\n",
       "      <td>7.50</td>\n",
       "      <td>9.31</td>\n",
       "      <td>126.19</td>\n",
       "      <td>17.06</td>\n",
       "      <td>2.21</td>\n",
       "      <td>1.92</td>\n",
       "      <td>2.22</td>\n",
       "      <td>2.37</td>\n",
       "      <td>49.00</td>\n",
       "      <td>1.04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43.24</td>\n",
       "      <td>26.27</td>\n",
       "      <td>31.88</td>\n",
       "      <td>36.52</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.51</td>\n",
       "      <td>99.16</td>\n",
       "      <td>0.01</td>\n",
       "      <td>3.32</td>\n",
       "      <td>6.94</td>\n",
       "      <td>6.09</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.58</td>\n",
       "      <td>0.55</td>\n",
       "      <td>1.53</td>\n",
       "      <td>0.99</td>\n",
       "      <td>28.83</td>\n",
       "      <td>2024-01-11</td>\n",
       "      <td>2023-12-27</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2006-07-17</td>\n",
       "      <td>2022-12-30</td>\n",
       "      <td>2023-09-29</td>\n",
       "      <td>-5.44</td>\n",
       "      <td>8.24</td>\n",
       "      <td>2.46</td>\n",
       "      <td>-6.27</td>\n",
       "      <td>3.94</td>\n",
       "      <td>18.45</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.13</td>\n",
       "      <td>0.63</td>\n",
       "      <td>-0.21</td>\n",
       "      <td>-1.01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.31</td>\n",
       "      <td>1.46</td>\n",
       "      <td>2.16</td>\n",
       "      <td>64.08</td>\n",
       "      <td>1.57</td>\n",
       "      <td>15.94</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FRT</th>\n",
       "      <td>Federal Realty Investment Trust (FRT)</td>\n",
       "      <td>8.32</td>\n",
       "      <td>12.98</td>\n",
       "      <td>29.64</td>\n",
       "      <td>36.36</td>\n",
       "      <td>7.33</td>\n",
       "      <td>7.40</td>\n",
       "      <td>3.06</td>\n",
       "      <td>11.58</td>\n",
       "      <td>16.65</td>\n",
       "      <td>1.26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>115.08</td>\n",
       "      <td>85.27</td>\n",
       "      <td>97.07</td>\n",
       "      <td>95.69</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.08</td>\n",
       "      <td>1.01</td>\n",
       "      <td>98.62</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.16</td>\n",
       "      <td>2.77</td>\n",
       "      <td>1.96</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.36</td>\n",
       "      <td>4.28</td>\n",
       "      <td>4.33</td>\n",
       "      <td>4.25</td>\n",
       "      <td>4.05</td>\n",
       "      <td>125.87</td>\n",
       "      <td>2024-01-15</td>\n",
       "      <td>2023-12-28</td>\n",
       "      <td>1.50</td>\n",
       "      <td>1985-11-05</td>\n",
       "      <td>2022-12-30</td>\n",
       "      <td>2023-09-29</td>\n",
       "      <td>25.67</td>\n",
       "      <td>35.22</td>\n",
       "      <td>2.96</td>\n",
       "      <td>9.45</td>\n",
       "      <td>1.12</td>\n",
       "      <td>13.85</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.28</td>\n",
       "      <td>3.44</td>\n",
       "      <td>-63.5</td>\n",
       "      <td>0.11</td>\n",
       "      <td>1.29</td>\n",
       "      <td>4.59</td>\n",
       "      <td>146.44</td>\n",
       "      <td>0.20</td>\n",
       "      <td>33.35</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FOXA</th>\n",
       "      <td>Fox Corporation (FOXA)</td>\n",
       "      <td>14.07</td>\n",
       "      <td>18.43</td>\n",
       "      <td>14.70</td>\n",
       "      <td>9.10</td>\n",
       "      <td>16.85</td>\n",
       "      <td>1.04</td>\n",
       "      <td>1.40</td>\n",
       "      <td>1.23</td>\n",
       "      <td>8.33</td>\n",
       "      <td>0.82</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.26</td>\n",
       "      <td>28.67</td>\n",
       "      <td>30.03</td>\n",
       "      <td>31.96</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.38</td>\n",
       "      <td>1.00</td>\n",
       "      <td>110.08</td>\n",
       "      <td>0.02</td>\n",
       "      <td>3.87</td>\n",
       "      <td>11.31</td>\n",
       "      <td>4.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.52</td>\n",
       "      <td>1.73</td>\n",
       "      <td>0.51</td>\n",
       "      <td>1.70</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.88</td>\n",
       "      <td>2023-09-26</td>\n",
       "      <td>2023-08-28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2023-06-29</td>\n",
       "      <td>2023-09-29</td>\n",
       "      <td>6.97</td>\n",
       "      <td>23.98</td>\n",
       "      <td>7.12</td>\n",
       "      <td>9.40</td>\n",
       "      <td>14.93</td>\n",
       "      <td>29.01</td>\n",
       "      <td>0.5</td>\n",
       "      <td>5.22</td>\n",
       "      <td>2.94</td>\n",
       "      <td>1.04</td>\n",
       "      <td>2.05</td>\n",
       "      <td>-32.7</td>\n",
       "      <td>3.83</td>\n",
       "      <td>7.93</td>\n",
       "      <td>8.19</td>\n",
       "      <td>76.68</td>\n",
       "      <td>2.02</td>\n",
       "      <td>21.44</td>\n",
       "      <td>1.53</td>\n",
       "      <td>1.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NCLH</th>\n",
       "      <td>Norwegian Cruise Line Holdings Ltd. (NCLH)</td>\n",
       "      <td>7.57</td>\n",
       "      <td>20.77</td>\n",
       "      <td>3.81</td>\n",
       "      <td>12.36</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.99</td>\n",
       "      <td>17.26</td>\n",
       "      <td>2.57</td>\n",
       "      <td>15.23</td>\n",
       "      <td>2.62</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.75</td>\n",
       "      <td>12.15</td>\n",
       "      <td>16.41</td>\n",
       "      <td>16.47</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.43</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.68</td>\n",
       "      <td>73.33</td>\n",
       "      <td>0.05</td>\n",
       "      <td>3.25</td>\n",
       "      <td>13.54</td>\n",
       "      <td>11.96</td>\n",
       "      <td>0.06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2022-12-30</td>\n",
       "      <td>2023-09-29</td>\n",
       "      <td>-2.60</td>\n",
       "      <td>20.63</td>\n",
       "      <td>1.72</td>\n",
       "      <td>-50.05</td>\n",
       "      <td>8.08</td>\n",
       "      <td>19.09</td>\n",
       "      <td>57.0</td>\n",
       "      <td>0.58</td>\n",
       "      <td>1.39</td>\n",
       "      <td>-0.21</td>\n",
       "      <td>-0.53</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.68</td>\n",
       "      <td>1.6</td>\n",
       "      <td>14.49</td>\n",
       "      <td>3303.97</td>\n",
       "      <td>0.30</td>\n",
       "      <td>1.03</td>\n",
       "      <td>1.98</td>\n",
       "      <td>-0.82</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>490 rows × 61 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             Name  Market Cap (B)  \\\n",
       "MSFT                 Microsoft Corporation (MSFT)         2730.00   \n",
       "AAPL                            Apple Inc. (AAPL)         2820.00   \n",
       "AMZN                       Amazon.com Inc. (AMZN)         1500.00   \n",
       "NVDA                    NVIDIA Corporation (NVDA)         1210.00   \n",
       "GOOGL                       Alphabet Inc. (GOOGL)         1710.00   \n",
       "...                                           ...             ...   \n",
       "FMC                         FMC Corporation (FMC)            7.58   \n",
       "XRAY                  DENTSPLY SIRONA Inc. (XRAY)            7.50   \n",
       "FRT         Federal Realty Investment Trust (FRT)            8.32   \n",
       "FOXA                       Fox Corporation (FOXA)           14.07   \n",
       "NCLH   Norwegian Cruise Line Holdings Ltd. (NCLH)            7.57   \n",
       "\n",
       "       Enterprise Value (B) Trailing P/E Forward P/E  \\\n",
       "MSFT                2670.00        35.63       32.89   \n",
       "AAPL                2870.00        29.56       27.47   \n",
       "AMZN                1570.00        76.04       37.74   \n",
       "NVDA                1210.00        64.77       24.81   \n",
       "GOOGL               1620.00        26.00       20.37   \n",
       "...                     ...          ...         ...   \n",
       "FMC                   11.37        12.93       14.06   \n",
       "XRAY                   9.31       126.19       17.06   \n",
       "FRT                   12.98        29.64       36.36   \n",
       "FOXA                  18.43        14.70        9.10   \n",
       "NCLH                  20.77         3.81       12.36   \n",
       "\n",
       "      PEG Ratio (5 yr expected) Price/Sales (ttm) Price/Book (mrq)  \\\n",
       "MSFT                       2.19             12.58            12.38   \n",
       "AAPL                       2.17              7.47            45.34   \n",
       "AMZN                       2.36              2.72             8.20   \n",
       "NVDA                       0.49             27.24            36.46   \n",
       "GOOGL                      1.28              5.85             6.22   \n",
       "...                         ...               ...              ...   \n",
       "FMC                         NaN              1.54             2.30   \n",
       "XRAY                       2.21              1.92             2.22   \n",
       "FRT                        7.33              7.40             3.06   \n",
       "FOXA                      16.85              1.04             1.40   \n",
       "NCLH                        NaN              0.99            17.26   \n",
       "\n",
       "      Enterprise Value/Revenue Enterprise Value/EBITDA Beta (5Y Monthly)  \\\n",
       "MSFT                     12.25                   23.88              0.88   \n",
       "AAPL                      7.48                   22.20              1.29   \n",
       "AMZN                      2.84                   21.38              1.16   \n",
       "NVDA                     26.87                   52.86              1.64   \n",
       "GOOGL                     5.44                   17.32              1.05   \n",
       "...                        ...                     ...               ...   \n",
       "FMC                       2.29                   10.36              0.89   \n",
       "XRAY                      2.37                   49.00              1.04   \n",
       "FRT                      11.58                   16.65              1.26   \n",
       "FOXA                      1.23                    8.33              0.82   \n",
       "NCLH                      2.57                   15.23              2.62   \n",
       "\n",
       "       52 Week Change (%)  S&P500 52-Week Change (%) 52 Week High 52 Week Low  \\\n",
       "MSFT                61.92                      20.69       384.30      226.41   \n",
       "AAPL                39.21                      20.69       199.62      128.12   \n",
       "AMZN                66.25                      20.69       155.63       87.08   \n",
       "NVDA               214.16                      20.69       505.48      151.41   \n",
       "GOOGL               54.20                      20.69       142.68       85.83   \n",
       "...                   ...                        ...          ...         ...   \n",
       "FMC                   NaN                        NaN       133.37       49.49   \n",
       "XRAY                  NaN                        NaN        43.24       26.27   \n",
       "FRT                   NaN                        NaN       115.08       85.27   \n",
       "FOXA                  NaN                        NaN        37.26       28.67   \n",
       "NCLH                  NaN                        NaN        22.75       12.15   \n",
       "\n",
       "      50-Day Moving Average 200-Day Moving Average  Avg Vol (3 month)  \\\n",
       "MSFT                 366.76                 331.92               0.03   \n",
       "AAPL                 187.40                 180.02               0.05   \n",
       "AMZN                 144.64                 128.69               0.05   \n",
       "NVDA                 471.39                 410.45               0.04   \n",
       "GOOGL                133.61                 125.62               0.03   \n",
       "...                     ...                    ...                ...   \n",
       "FMC                   56.36                   86.4               0.00   \n",
       "XRAY                  31.88                  36.52               0.00   \n",
       "FRT                   97.07                  95.69               0.00   \n",
       "FOXA                  30.03                  31.96               0.00   \n",
       "NCLH                  16.41                  16.47               0.02   \n",
       "\n",
       "       Avg Vol 10 day (B)  Shares Outstanding  Implied Shares Outstanding  \\\n",
       "MSFT                 0.02                7.43                        7.43   \n",
       "AAPL                 0.05               15.55                       15.75   \n",
       "AMZN                 0.04               10.33                       10.33   \n",
       "NVDA                 0.03                2.47                        2.53   \n",
       "GOOGL                0.02                5.92                       12.59   \n",
       "...                   ...                 ...                         ...   \n",
       "FMC                  0.00                0.12                         NaN   \n",
       "XRAY                 0.00                0.21                         NaN   \n",
       "FRT                  0.00                0.08                         NaN   \n",
       "FOXA                 0.00                0.25                         NaN   \n",
       "NCLH                 0.01                0.43                         NaN   \n",
       "\n",
       "       Float  % Held by Insiders  % Held by Institutions  Shares Short (M)  \\\n",
       "MSFT    7.43                0.05                   73.01              0.05   \n",
       "AAPL   15.54                0.07                   61.50              0.12   \n",
       "AMZN    9.06                9.62                   61.80              0.09   \n",
       "NVDA    2.37                4.04                   68.41              0.03   \n",
       "GOOGL  10.79                0.28                   79.70              0.05   \n",
       "...      ...                 ...                     ...               ...   \n",
       "FMC     0.12                0.78                   92.72              0.01   \n",
       "XRAY    0.21                0.51                   99.16              0.01   \n",
       "FRT     0.08                1.01                   98.62              0.00   \n",
       "FOXA    0.38                1.00                  110.08              0.02   \n",
       "NCLH    0.42                0.68                   73.33              0.05   \n",
       "\n",
       "      Short Ratio  Short % of Float  Short % of Shares Outstanding  \\\n",
       "MSFT         1.77              0.72                           0.72   \n",
       "AAPL         2.29              0.77                           0.77   \n",
       "AMZN         1.75              1.15                           0.87   \n",
       "NVDA          0.6              1.08                           1.04   \n",
       "GOOGL        1.66              0.84                           0.40   \n",
       "...           ...               ...                            ...   \n",
       "FMC          3.15              5.02                           4.43   \n",
       "XRAY         3.32              6.94                           6.09   \n",
       "FRT          2.16              2.77                           1.96   \n",
       "FOXA         3.87             11.31                           4.01   \n",
       "NCLH         3.25             13.54                          11.96   \n",
       "\n",
       "       Shares Short (M) (prior month) Forward Annual Dividend Rate  \\\n",
       "MSFT                             0.05                            3   \n",
       "AAPL                             0.11                         0.96   \n",
       "AMZN                             0.09                          NaN   \n",
       "NVDA                             0.03                         0.16   \n",
       "GOOGL                            0.05                          NaN   \n",
       "...                               ...                          ...   \n",
       "FMC                              0.01                         2.32   \n",
       "XRAY                             0.01                         0.56   \n",
       "FRT                              0.00                         4.36   \n",
       "FOXA                             0.02                         0.52   \n",
       "NCLH                             0.06                          NaN   \n",
       "\n",
       "       Forward Annual Dividend Yield (%) Trailing Annual Dividend Rate  \\\n",
       "MSFT                                0.82                          2.79   \n",
       "AAPL                                0.53                          0.94   \n",
       "AMZN                                 NaN                          0.00   \n",
       "NVDA                                0.03                          0.16   \n",
       "GOOGL                                NaN                          0.00   \n",
       "...                                  ...                           ...   \n",
       "FMC                                 3.82                          2.32   \n",
       "XRAY                                1.58                          0.55   \n",
       "FRT                                 4.28                          4.33   \n",
       "FOXA                                1.73                          0.51   \n",
       "NCLH                                 NaN                          0.00   \n",
       "\n",
       "       Trailing Annual Dividend Yield (%) 5 Year Average Dividend Yield  \\\n",
       "MSFT                                 0.76                          1.00   \n",
       "AAPL                                 0.52                          0.80   \n",
       "AMZN                                 0.00                           NaN   \n",
       "NVDA                                 0.03                          0.15   \n",
       "GOOGL                                0.00                           NaN   \n",
       "...                                   ...                           ...   \n",
       "FMC                                  3.85                          1.89   \n",
       "XRAY                                 1.53                          0.99   \n",
       "FRT                                  4.25                          4.05   \n",
       "FOXA                                 1.70                           NaN   \n",
       "NCLH                                 0.00                           NaN   \n",
       "\n",
       "       Payout Ratio (%) Dividend Date Ex-Dividend Date  \\\n",
       "MSFT              26.36    2024-03-13       2024-02-13   \n",
       "AAPL              15.33    2023-11-15       2023-11-09   \n",
       "AMZN               0.00           NaN              NaN   \n",
       "NVDA               2.11    2023-12-27       2023-12-04   \n",
       "GOOGL              0.00           NaN              NaN   \n",
       "...                 ...           ...              ...   \n",
       "FMC               49.36    2024-01-17       2023-12-27   \n",
       "XRAY              28.83    2024-01-11       2023-12-27   \n",
       "FRT              125.87    2024-01-15       2023-12-28   \n",
       "FOXA              24.88    2023-09-26       2023-08-28   \n",
       "NCLH               0.00           NaN              NaN   \n",
       "\n",
       "       Last Split Factor (x:1) Last Split Date Fiscal Year Ends  \\\n",
       "MSFT                      2.00      2003-02-17       2023-06-29   \n",
       "AAPL                      4.00      2020-08-30       2023-09-29   \n",
       "AMZN                     20.00      2022-06-05       2022-12-30   \n",
       "NVDA                      4.00      2021-07-19       2023-01-28   \n",
       "GOOGL                    20.00      2022-07-17       2022-12-30   \n",
       "...                        ...             ...              ...   \n",
       "FMC                       1.15      2019-03-03       2022-12-30   \n",
       "XRAY                      2.00      2006-07-17       2022-12-30   \n",
       "FRT                       1.50      1985-11-05       2022-12-30   \n",
       "FOXA                       NaN             NaN       2023-06-29   \n",
       "NCLH                       NaN             NaN       2022-12-30   \n",
       "\n",
       "      Most Recent Quarter (mrq)  Profit Margin (%)  \\\n",
       "MSFT                 2023-09-29              35.31   \n",
       "AAPL                 2023-09-29              25.31   \n",
       "AMZN                 2023-09-29               3.62   \n",
       "NVDA                 2023-10-28              42.10   \n",
       "GOOGL                2023-09-29              22.46   \n",
       "...                         ...                ...   \n",
       "FMC                  2023-09-29              10.01   \n",
       "XRAY                 2023-09-29              -5.44   \n",
       "FRT                  2023-09-29              25.67   \n",
       "FOXA                 2023-09-29               6.97   \n",
       "NCLH                 2023-09-29              -2.60   \n",
       "\n",
       "       Operating Margin (ttm) (%)  Return on Assets (ttm) (%)  \\\n",
       "MSFT                        47.59                       14.72   \n",
       "AAPL                        30.13                       20.26   \n",
       "AMZN                         7.82                        3.61   \n",
       "NVDA                        57.49                       27.23   \n",
       "GOOGL                       27.96                       13.49   \n",
       "...                           ...                         ...   \n",
       "FMC                         13.14                        5.61   \n",
       "XRAY                         8.24                        2.46   \n",
       "FRT                         35.22                        2.96   \n",
       "FOXA                        23.98                        7.12   \n",
       "NCLH                        20.63                        1.72   \n",
       "\n",
       "       Return on Equity (ttm) (%)  Revenue (ttm) (B) Revenue Per Share (ttm)  \\\n",
       "MSFT                        39.11             218.31                   29.35   \n",
       "AAPL                       171.95             383.29                   24.34   \n",
       "AMZN                        12.53             554.03                   53.95   \n",
       "NVDA                        69.17              44.87                   18.18   \n",
       "GOOGL                       25.33             297.13                   23.34   \n",
       "...                           ...                ...                     ...   \n",
       "FMC                         18.45               4.96                   39.64   \n",
       "XRAY                        -6.27               3.94                   18.45   \n",
       "FRT                          9.45               1.12                   13.85   \n",
       "FOXA                         9.40              14.93                   29.01   \n",
       "NCLH                       -50.05               8.08                   19.09   \n",
       "\n",
       "       Quarterly Revenue Growth (yoy) (%)  Gross Profit (ttm) (B)  EBITDA (B)  \\\n",
       "MSFT                                 12.8                     NaN      109.48   \n",
       "AAPL                                 -0.7                  170.78      125.82   \n",
       "AMZN                                 12.6                  225.15       73.98   \n",
       "NVDA                                205.5                   15.36       22.16   \n",
       "GOOGL                                11.0                  156.63       96.07   \n",
       "...                                   ...                     ...         ...   \n",
       "FMC                                 -28.7                    2.33        1.16   \n",
       "XRAY                                  0.0                    2.13        0.63   \n",
       "FRT                                   4.6                    0.73        0.70   \n",
       "FOXA                                  0.5                    5.22        2.94   \n",
       "NCLH                                 57.0                    0.58        1.39   \n",
       "\n",
       "       Net Income Avi to Common (ttm) (B) Diluted EPS (ttm)  \\\n",
       "MSFT                                77.10             10.33   \n",
       "AAPL                                97.00              6.12   \n",
       "AMZN                                20.08              1.92   \n",
       "NVDA                                18.89              7.56   \n",
       "GOOGL                               66.73              5.23   \n",
       "...                                   ...               ...   \n",
       "FMC                                  0.59              4.70   \n",
       "XRAY                                -0.21             -1.01   \n",
       "FRT                                  0.28              3.44   \n",
       "FOXA                                 1.04              2.05   \n",
       "NCLH                                -0.21             -0.53   \n",
       "\n",
       "       Quarterly Earnings Growth (yoy) (%)  Total Cash (mrq) (B)  \\\n",
       "MSFT                                  27.0                143.95   \n",
       "AAPL                                  10.8                 61.55   \n",
       "AMZN                                 244.0                 64.17   \n",
       "NVDA                                1259.3                 18.28   \n",
       "GOOGL                                 41.5                119.94   \n",
       "...                                    ...                   ...   \n",
       "FMC                                    NaN                  0.32   \n",
       "XRAY                                   NaN                  0.31   \n",
       "FRT                                  -63.5                  0.11   \n",
       "FOXA                                 -32.7                  3.83   \n",
       "NCLH                                   NaN                  0.68   \n",
       "\n",
       "      Total Cash Per Share (mrq)  Total Debt (mrq) (B)  \\\n",
       "MSFT                       19.37                105.68   \n",
       "AAPL                        3.96                123.93   \n",
       "AMZN                        6.21                166.06   \n",
       "NVDA                         7.4                 11.03   \n",
       "GOOGL                       9.58                 30.45   \n",
       "...                          ...                   ...   \n",
       "FMC                          2.6                  4.26   \n",
       "XRAY                        1.46                  2.16   \n",
       "FRT                         1.29                  4.59   \n",
       "FOXA                        7.93                  8.19   \n",
       "NCLH                         1.6                 14.49   \n",
       "\n",
       "      Total Debt/Equity (mrq) Current Ratio (mrq) Book Value Per Share (mrq)  \\\n",
       "MSFT                    47.88                1.66                      29.70   \n",
       "AAPL                   199.42                0.99                       4.00   \n",
       "AMZN                    90.76                0.99                      17.71   \n",
       "NVDA                    33.15                3.59                      13.49   \n",
       "GOOGL                   11.14                2.04                      21.78   \n",
       "...                       ...                 ...                        ...   \n",
       "FMC                    128.63                1.54                      26.38   \n",
       "XRAY                    64.08                1.57                      15.94   \n",
       "FRT                    146.44                0.20                      33.35   \n",
       "FOXA                    76.68                2.02                      21.44   \n",
       "NCLH                  3303.97                0.30                       1.03   \n",
       "\n",
       "       Operating Cash Flow (ttm) (B)  Levered Free Cash Flow (ttm) (B)  \n",
       "MSFT                           94.97                             51.01  \n",
       "AAPL                          110.54                             82.18  \n",
       "AMZN                           71.65                             36.39  \n",
       "NVDA                           18.84                             14.11  \n",
       "GOOGL                         106.44                             70.76  \n",
       "...                              ...                               ...  \n",
       "FMC                            -0.06                             -0.21  \n",
       "XRAY                            0.36                              0.27  \n",
       "FRT                             0.55                              0.57  \n",
       "FOXA                            1.53                              1.66  \n",
       "NCLH                            1.98                             -0.82  \n",
       "\n",
       "[490 rows x 61 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df = scrapper.compile_data_to_df()\n",
    "final_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed80f75a",
   "metadata": {},
   "source": [
    "# OLD CODE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "16df3416",
   "metadata": {},
   "outputs": [],
   "source": [
    "# from modules.custom_functions import *\n",
    "\n",
    "# OLD CODE\n",
    "# scrapped_data = {}\n",
    "\n",
    "# try:\n",
    "#     missed_tickers = scrap_ticker(tickers, data_dict=scrapped_data, sleeptime=2, batch_interval=50)\n",
    "#     if missed_tickers:\n",
    "#         missed_tickers = rescrap_missed(missed_tickers, data_dict=scrapped_data, max_tries=2)\n",
    "# except Exception as e:\n",
    "#     print(e)\n",
    "# finally:\n",
    "#     final_df, ticker_df = clean_df(scrapped_data, metrics, display_progress=False, market_indices=market_indices)\n",
    "#     final_df.reset_index(drop=True, inplace=True)\n",
    "# final_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "3af391f6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved to file: s&p_2023-04-16.csv\n"
     ]
    }
   ],
   "source": [
    "# filename = input(\"Enter filename to save as: \")\n",
    "# if filename == '':\n",
    "#     filename = 'portfolio'\n",
    "# current_date = date.today().isoformat()\n",
    "# final_df.to_csv(f'data/{filename}_{current_date}.csv')\n",
    "# print(f'Saved to file: {filename}_{current_date}.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5a3396c3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Connected to database\n",
      "1062: Duplicate entry 'TSLA' for key 'PRIMARY'\n",
      "1062: Duplicate entry 'NVDA' for key 'PRIMARY'\n",
      "1062: Duplicate entry 'GOOG' for key 'PRIMARY'\n",
      "1062: Duplicate entry 'AMZN' for key 'PRIMARY'\n",
      "1062: Duplicate entry 'IDXX' for key 'PRIMARY'\n",
      "1062: Duplicate entry 'CDNS' for key 'PRIMARY'\n",
      "1062: Duplicate entry 'MRNA' for key 'PRIMARY'\n",
      "Completed\n"
     ]
    }
   ],
   "source": [
    "# import mysql.connector\n",
    "# import json\n",
    "\n",
    "# def get_value_from_json(json_file, key, sub_key=None):\n",
    "#    '''\n",
    "#    Function to read the json file for our app secret key\n",
    "#    '''\n",
    "#    try:\n",
    "#        with open(json_file) as f:\n",
    "#            data = json.load(f)\n",
    "#            if sub_key:\n",
    "#                return data[key][sub_key]\n",
    "#            else:\n",
    "#                return data[key]\n",
    "#    except Exception as e:\n",
    "#        print(\"Error: \", e)\n",
    "\n",
    "# config = get_value_from_json(\"data/secrets.json\", \"mysql_connector\")\n",
    "\n",
    "# # Connect to server on localhost\n",
    "# try:\n",
    "#     cnx = mysql.connector.connect(**config)\n",
    "#     print('Connected to database')\n",
    "#     cur = cnx.cursor()\n",
    "#     insert_query = \"INSERT IGNORE INTO companies (`ticker`, `name`, `index`) VALUES (%s, %s, %s)\"\n",
    "#     for index, row in ticker_df.iterrows():\n",
    "#         try:\n",
    "#             data = (row['ticker'], row['name'].strip(), row['index'])\n",
    "#             #print(f'Inserting {data}', end = ' ')\n",
    "#             cur.execute(insert_query, data)\n",
    "#             cnx.commit()\n",
    "#             #print('Done')\n",
    "#         except mysql.connector.Error as err:\n",
    "#             print(err)\n",
    "# except mysql.connector.Error as err:\n",
    "#     print(err)\n",
    "# finally:\n",
    "#     print('Completed')\n",
    "#     cur.close()\n",
    "#     cnx.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "9bef1d2b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Connected to database\n",
      "1062: Duplicate entry '2023-04-16-AAPL' for key 'PRIMARY'\n",
      "1062: Duplicate entry '2023-04-16-MSFT' for key 'PRIMARY'\n",
      "1062: Duplicate entry '2023-04-16-AMZN' for key 'PRIMARY'\n",
      "1062: Duplicate entry '2023-04-16-NVDA' for key 'PRIMARY'\n",
      "1062: Duplicate entry '2023-04-16-GOOG' for key 'PRIMARY'\n",
      "1062: Duplicate entry '2023-04-16-TSLA' for key 'PRIMARY'\n",
      "1062: Duplicate entry '2023-04-16-CDNS' for key 'PRIMARY'\n",
      "1062: Duplicate entry '2023-04-16-MRNA' for key 'PRIMARY'\n",
      "1062: Duplicate entry '2023-04-16-IDXX' for key 'PRIMARY'\n",
      "Completed\n"
     ]
    }
   ],
   "source": [
    "# try:\n",
    "#     cnx = mysql.connector.connect(**config)\n",
    "#     print('Connected to database')\n",
    "#     cur = cnx.cursor()\n",
    "#     insert_query = '''INSERT IGNORE INTO statistics (Ticker, `Market Cap (B)`, `Enterprise Value (B)`, `Trailing P/E`,\n",
    "#        `Forward P/E`, `PEG Ratio (5 yr expected)`, `Price/Sales (ttm)`,\n",
    "#        `Price/Book (mrq)`, `Enterprise Value/Revenue`,\n",
    "#        `Enterprise Value/EBITDA`, `Beta (5Y Monthly)`, `52 Week Change (%)`,\n",
    "#        `S&P500 52-Week Change (%)`, `52 Week High`, `52 Week Low`,\n",
    "#        `50-Day Moving Average`, `200-Day Moving Average`,\n",
    "#        `Avg Vol 3 month (M)`, `Avg Vol 10 day (M)`, `Shares Outstanding`,\n",
    "#        `Implied Shares Outstanding`, `Float`, `% Held by Insiders`,\n",
    "#        `% Held by Institutions`, `Shares Short (M)`, `Short Ratio (M)`,\n",
    "#        `Short % of Float`, `Short % of Shares Outstanding`, `Shares Short`,\n",
    "#        `Forward Annual Dividend Rate`, `Forward Annual Dividend Yield (%)`,\n",
    "#        `Trailing Annual Dividend Rate`, `Trailing Annual Dividend Yield (%)`,\n",
    "#        `5 Year Average Dividend Yield`, `Payout Ratio (%)`,\n",
    "#        `Last Split Factor (x:1)`, `Profit Margin (%)`,\n",
    "#        `Operating Margin (ttm) (%)`, `Return on Assets (ttm) (%)`,\n",
    "#        `Return on Equity (ttm) (%)`, `Revenue (ttm) (B)`,\n",
    "#        `Revenue Per Share (ttm)`, `Quarterly Revenue Growth (yoy) (%)`,\n",
    "#        `Gross Profit (ttm) (B)`, `EBITDA (B)`,\n",
    "#        `Net Income Avi to Common (ttm) (B)`, `Diluted EPS (ttm)`,\n",
    "#        `Quarterly Earnings Growth (yoy) (%)`, `Total Cash (mrq) (B)`,\n",
    "#        `Total Cash Per Share (mrq)`, `Total Debt (mrq) (B)`,\n",
    "#        `Total Debt/Equity (mrq)`, `Current Ratio (mrq)`,\n",
    "#        `Book Value Per Share (mrq)`, `Operating Cash Flow (ttm) (B)`,\n",
    "#        `Levered Free Cash Flow (ttm) (B)`, `Dividend Date`, `Ex-Dividend Date`,\n",
    "#        `Last Split Date`, `Fiscal Year Ends`, `Most Recent Quarter (mrq)`) \n",
    "#        VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s,\n",
    "#        %s, %s, %s, %s, %s, %s, %s, %s, %s, %s,\n",
    "#        %s, %s, %s, %s, %s, %s, %s, %s, %s, %s,\n",
    "#        %s, %s, %s, %s, %s, %s, %s, %s, %s, %s,\n",
    "#        %s, %s, %s, %s, %s, %s, %s, %s, %s, %s,\n",
    "#        %s, %s, %s, %s, %s, %s, %s, %s, %s, %s,\n",
    "#        %s)'''\n",
    "#     for index, row in final_df.iterrows():\n",
    "#         try:\n",
    "#             data = (row[0], row[1], row[2], row[3], row[4], row[5], row[6], row[7], row[8], row[9], \n",
    "#                 row[10], row[11], row[12], row[13], row[14], row[15], row[16], row[17], row[18], \n",
    "#                 row[19], row[20], row[21], row[22], row[23], row[24], row[25], row[26], row[27], \n",
    "#                 row[28], row[29], row[30], row[31], row[32], row[33], row[34], row[35], row[36], \n",
    "#                 row[37], row[38], row[39], row[40], row[41], row[42], row[43], row[44], row[45], \n",
    "#                 row[46], row[47], row[48], row[49], row[50], row[51], row[52], row[53], row[54], \n",
    "#                 row[55], row[56], row[57], row[58], row[59], row[60])\n",
    "#             #print(f'Inserting {data}', end = ' ')\n",
    "#             cur.execute(insert_query, data)\n",
    "#             cnx.commit()\n",
    "#             #print('Done')\n",
    "#         except mysql.connector.Error as err:\n",
    "#             print(err)\n",
    "\n",
    "# except mysql.connector.Error as err:\n",
    "#     print(err)\n",
    "# finally:\n",
    "#     print('Completed')\n",
    "#     cur.close()\n",
    "#     cnx.close()"
   ]
  }
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