stocks valuation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1312a0b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import yfinance as yf\n",
    "import pandas as pd\n",
    "import datetime\n",
    "from statistics import mean\n",
    "import matplotlib.pyplot as plt #pyplot package under the matplotlib package\n",
    "import seaborn as sns\n",
    "import math\n",
    "\n",
    "plt.style.use(\"ggplot\")\n",
    " \n",
    "\n",
    "import warnings\n",
    "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
    "pd.options.mode.chained_assignment = None  # default='warn'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "afec5190",
   "metadata": {},
   "outputs": [],
   "source": [
    "ticker = yf.Ticker(\"tsla\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c113a8f3",
   "metadata": {},
   "outputs": [
    {
     "ename": "ConnectionError",
     "evalue": "HTTPSConnectionPool(host='finance.yahoo.com', port=443): Max retries exceeded with url: /quote/TSLA (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x000001414E058340>: Failed to establish a new connection: [WinError 10060] A connection attempt failed because the connected party did not properly respond after a period of time, or established connection failed because connected host has failed to respond'))",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTimeoutError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\urllib3\\connection.py\u001b[0m in \u001b[0;36m_new_conn\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    168\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 169\u001b[1;33m             conn = connection.create_connection(\n\u001b[0m\u001b[0;32m    170\u001b[0m                 \u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_dns_host\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mport\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mextra_kw\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\urllib3\\util\\connection.py\u001b[0m in \u001b[0;36mcreate_connection\u001b[1;34m(address, timeout, source_address, socket_options)\u001b[0m\n\u001b[0;32m     95\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0merr\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 96\u001b[1;33m         \u001b[1;32mraise\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     97\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\urllib3\\util\\connection.py\u001b[0m in \u001b[0;36mcreate_connection\u001b[1;34m(address, timeout, source_address, socket_options)\u001b[0m\n\u001b[0;32m     85\u001b[0m                 \u001b[0msock\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msource_address\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 86\u001b[1;33m             \u001b[0msock\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msa\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     87\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0msock\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTimeoutError\u001b[0m: [WinError 10060] A connection attempt failed because the connected party did not properly respond after a period of time, or established connection failed because connected host has failed to respond",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mNewConnectionError\u001b[0m                        Traceback (most recent call last)",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\urllib3\\connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[1;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[0;32m    698\u001b[0m             \u001b[1;31m# Make the request on the httplib connection object.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 699\u001b[1;33m             httplib_response = self._make_request(\n\u001b[0m\u001b[0;32m    700\u001b[0m                 \u001b[0mconn\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\urllib3\\connectionpool.py\u001b[0m in \u001b[0;36m_make_request\u001b[1;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[0;32m    381\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 382\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_validate_conn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mconn\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    383\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mSocketTimeout\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mBaseSSLError\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\urllib3\\connectionpool.py\u001b[0m in \u001b[0;36m_validate_conn\u001b[1;34m(self, conn)\u001b[0m\n\u001b[0;32m   1009\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mconn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"sock\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m  \u001b[1;31m# AppEngine might not have  `.sock`\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1010\u001b[1;33m             \u001b[0mconn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1011\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\urllib3\\connection.py\u001b[0m in \u001b[0;36mconnect\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    352\u001b[0m         \u001b[1;31m# Add certificate verification\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 353\u001b[1;33m         \u001b[0mconn\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_new_conn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    354\u001b[0m         \u001b[0mhostname\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhost\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\urllib3\\connection.py\u001b[0m in \u001b[0;36m_new_conn\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    180\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mSocketError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 181\u001b[1;33m             raise NewConnectionError(\n\u001b[0m\u001b[0;32m    182\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"Failed to establish a new connection: %s\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNewConnectionError\u001b[0m: <urllib3.connection.HTTPSConnection object at 0x000001414E058340>: Failed to establish a new connection: [WinError 10060] A connection attempt failed because the connected party did not properly respond after a period of time, or established connection failed because connected host has failed to respond",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mMaxRetryError\u001b[0m                             Traceback (most recent call last)",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\requests\\adapters.py\u001b[0m in \u001b[0;36msend\u001b[1;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[0;32m    488\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mchunked\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 489\u001b[1;33m                 resp = conn.urlopen(\n\u001b[0m\u001b[0;32m    490\u001b[0m                     \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrequest\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\urllib3\\connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[1;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[0;32m    754\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 755\u001b[1;33m             retries = retries.increment(\n\u001b[0m\u001b[0;32m    756\u001b[0m                 \u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merror\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_pool\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_stacktrace\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexc_info\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\urllib3\\util\\retry.py\u001b[0m in \u001b[0;36mincrement\u001b[1;34m(self, method, url, response, error, _pool, _stacktrace)\u001b[0m\n\u001b[0;32m    573\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mnew_retry\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_exhausted\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 574\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mMaxRetryError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_pool\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merror\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mResponseError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcause\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    575\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mMaxRetryError\u001b[0m: HTTPSConnectionPool(host='finance.yahoo.com', port=443): Max retries exceeded with url: /quote/TSLA (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x000001414E058340>: Failed to establish a new connection: [WinError 10060] A connection attempt failed because the connected party did not properly respond after a period of time, or established connection failed because connected host has failed to respond'))",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mConnectionError\u001b[0m                           Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-5-20643c0676d1>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mticker\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"longBusinessSummary\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[1;31m# ticker.info\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\yfinance\\ticker.py\u001b[0m in \u001b[0;36minfo\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    140\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    141\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0minfo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[0mdict\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 142\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_info\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    143\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    144\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\yfinance\\base.py\u001b[0m in \u001b[0;36mget_info\u001b[1;34m(self, proxy)\u001b[0m\n\u001b[0;32m   1266\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mget_info\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mproxy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[0mdict\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1267\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_quote\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mproxy\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mproxy\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1268\u001b[1;33m         \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_quote\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1269\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1270\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\yfinance\\scrapers\\quote.py\u001b[0m in \u001b[0;36minfo\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m     93\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0minfo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[0mdict\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     94\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_info\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 95\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_scrape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mproxy\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     96\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_scrape_complementary\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mproxy\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     97\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\yfinance\\scrapers\\quote.py\u001b[0m in \u001b[0;36m_scrape\u001b[1;34m(self, proxy)\u001b[0m\n\u001b[0;32m    122\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    123\u001b[0m         \u001b[1;31m# get info and sustainability\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 124\u001b[1;33m         \u001b[0mjson_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_json_data_stores\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mproxy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mproxy\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    125\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    126\u001b[0m             \u001b[0mquote_summary_store\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mjson_data\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'QuoteSummaryStore'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\yfinance\\data.py\u001b[0m in \u001b[0;36mwrapped\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     39\u001b[0m         \u001b[0margs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtuple\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mtuple\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marg\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marg\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32melse\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     40\u001b[0m         \u001b[0mkwargs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m{\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mtuple\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mv\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mv\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32melse\u001b[0m \u001b[0mv\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mv\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 41\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     42\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     43\u001b[0m     \u001b[1;31m# copy over the lru_cache extra methods to this wrapper to be able to access them\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\yfinance\\data.py\u001b[0m in \u001b[0;36mget_json_data_stores\u001b[1;34m(self, sub_page, proxy)\u001b[0m\n\u001b[0;32m    274\u001b[0m             \u001b[0mticker_url\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"{}/{}\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_SCRAPE_URL_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mticker\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    275\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 276\u001b[1;33m         \u001b[0mresponse\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mticker_url\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mproxy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mproxy\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    277\u001b[0m         \u001b[0mhtml\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mresponse\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtext\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    278\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\yfinance\\data.py\u001b[0m in \u001b[0;36mget\u001b[1;34m(self, url, user_agent_headers, params, proxy, timeout)\u001b[0m\n\u001b[0;32m    182\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0muser_agent_headers\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mproxy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m30\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    183\u001b[0m         \u001b[0mproxy\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_proxy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mproxy\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 184\u001b[1;33m         response = self._session.get(\n\u001b[0m\u001b[0;32m    185\u001b[0m             \u001b[0murl\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    186\u001b[0m             \u001b[0mparams\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mparams\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\requests\\api.py\u001b[0m in \u001b[0;36mget\u001b[1;34m(url, params, **kwargs)\u001b[0m\n\u001b[0;32m     71\u001b[0m     \"\"\"\n\u001b[0;32m     72\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 73\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mrequest\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"get\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mparams\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     74\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     75\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\requests\\api.py\u001b[0m in \u001b[0;36mrequest\u001b[1;34m(method, url, **kwargs)\u001b[0m\n\u001b[0;32m     57\u001b[0m     \u001b[1;31m# cases, and look like a memory leak in others.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     58\u001b[0m     \u001b[1;32mwith\u001b[0m \u001b[0msessions\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSession\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0msession\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 59\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0msession\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     60\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     61\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\requests\\sessions.py\u001b[0m in \u001b[0;36mrequest\u001b[1;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[0;32m    585\u001b[0m         }\n\u001b[0;32m    586\u001b[0m         \u001b[0msend_kwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msettings\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 587\u001b[1;33m         \u001b[0mresp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mprep\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0msend_kwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    588\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    589\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mresp\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\requests\\sessions.py\u001b[0m in \u001b[0;36msend\u001b[1;34m(self, request, **kwargs)\u001b[0m\n\u001b[0;32m    699\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    700\u001b[0m         \u001b[1;31m# Send the request\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 701\u001b[1;33m         \u001b[0mr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0madapter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrequest\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    702\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    703\u001b[0m         \u001b[1;31m# Total elapsed time of the request (approximately)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\requests\\adapters.py\u001b[0m in \u001b[0;36msend\u001b[1;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[0;32m    563\u001b[0m                 \u001b[1;32mraise\u001b[0m \u001b[0mSSLError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrequest\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrequest\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    564\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 565\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mConnectionError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrequest\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrequest\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    566\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    567\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mClosedPoolError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mConnectionError\u001b[0m: HTTPSConnectionPool(host='finance.yahoo.com', port=443): Max retries exceeded with url: /quote/TSLA (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x000001414E058340>: Failed to establish a new connection: [WinError 10060] A connection attempt failed because the connected party did not properly respond after a period of time, or established connection failed because connected host has failed to respond'))"
     ]
    }
   ],
   "source": [
    "ticker.info[\"longBusinessSummary\"]\n",
    "# ticker.info"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bd4267f",
   "metadata": {},
   "source": [
    "# set user input parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8b57ce42",
   "metadata": {},
   "outputs": [],
   "source": [
    "risk_free_rate = 0.0302"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5c308521",
   "metadata": {},
   "outputs": [],
   "source": [
    "market_return_rate = 0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0e8a2b0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "cap_growth = 1.15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "edc44b33",
   "metadata": {},
   "outputs": [],
   "source": [
    "current_year = \"2022\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e12337e9",
   "metadata": {},
   "source": [
    "# Custom functions "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "939ea9cb",
   "metadata": {},
   "source": [
    "### Dataframe functions  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "50d02486",
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_dataframe(frame):\n",
    "    frame = frame.T.rename_axis('year').reset_index()\n",
    "    frame[\"year\"] = pd.DatetimeIndex(frame[\"year\"]).year\n",
    "    frame = frame.sort_values(by=['year'], ascending=False)\n",
    "    frame.fillna(0, inplace=True)\n",
    "    return frame"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ad7382e",
   "metadata": {},
   "source": [
    "### Valuation calculation functions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e85bc3b",
   "metadata": {},
   "source": [
    "#### growth calculators"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a20428e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# gets average growth from the past few years. \n",
    "# function ingests a list of values\n",
    "# function returns a 1 value\n",
    "\n",
    "def get_avg_growth(history):\n",
    "    growth_history = []\n",
    "    \n",
    "    #calculate the growth between each year. add to the list\n",
    "    for i in range(0,(len(ocf_history)-1)):\n",
    "        growth = (history[i] - history[i+1]) / abs(history[i+1])    \n",
    "        growth_history.append(growth)\n",
    "    \n",
    "    #get the average growth\n",
    "    average_growth_rate = 1 + mean(growth_history)\n",
    "    return average_growth_rate\n",
    "\n",
    "\n",
    "# gets cagr growth from the past few years. \n",
    "# function ingests a list of values\n",
    "# function returns a 1 value\n",
    "\n",
    "def get_cagr_growth(history):\n",
    "    #calculate CAGR\n",
    "    base = history[0] / history[-1]\n",
    "    power = 1/(len(history)-1)\n",
    "    average_growth_rate = math.pow(base, power)\n",
    "    return average_growth_rate\n",
    "\n",
    "\n",
    "# gets minimum growth rate from the past few years. If it is less than 1, assume no growth\n",
    "# function ingests a list of values\n",
    "# function returns a 1 value\n",
    "def get_min_growth(history):\n",
    "    growth_history = []\n",
    "    \n",
    "    #calculate the growth between each year. add to the list\n",
    "    for i in range(0,(len(history)-1)):\n",
    "        growth = (history[i] - history[i+1]) / abs(history[i+1])    \n",
    "        growth_history.append(growth)\n",
    "\n",
    "    #get the minimum growth\n",
    "    if min(growth_history) >= 0: \n",
    "        min_growth_rate = 1 + min(growth_history)\n",
    "    else: \n",
    "        min_growth_rate = 1\n",
    "    \n",
    "    return min_growth_rate"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "679acd7e",
   "metadata": {},
   "source": [
    "#### future and present values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a411b7d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# get list of future values via the growth rates  \n",
    "# function ingests a list of past values, average growth rate, and capped average growth rate\n",
    "# function returns a list of 10 future value\n",
    "\n",
    "def get_future_values(history,average_growth_rate,average_growth_rate_cap):\n",
    "    future_value = []\n",
    "    start_value = history[0]\n",
    "    for i in range(1, 11):\n",
    "        #for year 1 - 3, grow by average growth rate\n",
    "        if i <= 3:\n",
    "            value =  start_value * average_growth_rate\n",
    "            future_value.append(value)\n",
    "            start_value = value\n",
    "        #for year 4-10, grow by cap growth rate\n",
    "        elif i > 3:\n",
    "            value =  start_value * average_growth_rate_cap\n",
    "            future_value.append(value)\n",
    "            start_value = value\n",
    "    return future_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "66774a9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# converts future values into present value and calculate value per share\n",
    "# function ingests a list of future values values\n",
    "# function returns get 1 number : value per share\n",
    "\n",
    "def get_present_values_per_share(future_value):\n",
    "    present_value = 0 \n",
    "\n",
    "    for i in range(0, len(future_value)):\n",
    "        value = future_value[i] / pow(wacc, (i+1))\n",
    "        present_value += value\n",
    "    \n",
    "    #calculate present value per share\n",
    "    fair_value = present_value/shares\n",
    "    \n",
    "    return fair_value"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f29d448a",
   "metadata": {},
   "source": [
    "#### returning valuations  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "abc88bca",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_DCF_value(history):\n",
    "    \n",
    "    #find average growth rate \n",
    "    average_growth_rate = get_avg_growth(history)\n",
    "    \n",
    "    #for year 4-10, if average growth in year 1-3 is more than 15%, cap it at 15%\n",
    "    if average_growth_rate > cap_growth: \n",
    "        average_growth_rate_cap = cap_growth\n",
    "    else: \n",
    "        average_growth_rate_cap = average_growth_rate\n",
    "\n",
    "    # get next 10 years of future value\n",
    "    future_value = get_future_values(history,average_growth_rate,average_growth_rate_cap)\n",
    "\n",
    "    # bring each future value back to present and get per share value\n",
    "    fair_value = get_present_values_per_share(future_value)\n",
    "    \n",
    "    return fair_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "82eddf16",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_cagr_DCF_value(history):\n",
    "\n",
    "    average_growth_rate = get_cagr_growth(history)\n",
    "\n",
    "    #for year 4-10, if average growth in year 1-3 is more than 15%, cap it at 15%\n",
    "    if average_growth_rate > cap_growth: \n",
    "        average_growth_rate_cap = cap_growth\n",
    "    else: \n",
    "        average_growth_rate_cap = average_growth_rate\n",
    "        \n",
    "    # get next 10 years of future value\n",
    "    future_value = get_future_values(history,average_growth_rate,average_growth_rate_cap)\n",
    "\n",
    "    # bring each future value back to present and get per share value\n",
    "    fair_value = get_present_values_per_share(future_value)\n",
    "    \n",
    "    return fair_value\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "190f3e47",
   "metadata": {},
   "outputs": [],
   "source": [
    "def perpetual_growth_valuation(history):\n",
    "    \n",
    "    min_growth_rate = get_min_growth(history)\n",
    "\n",
    "    #bring to current year and then project for next year. assuming we missed this year's payment\n",
    "    next_payment = history[0] * min_growth_rate * min_growth_rate \n",
    "\n",
    "    #apply formula\n",
    "    valuation = next_payment / (wacc - min_growth_rate)\n",
    "    return valuation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31347623",
   "metadata": {},
   "source": [
    "#### reporting functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "38677208",
   "metadata": {},
   "outputs": [],
   "source": [
    "def DCF_valuation_report(history):\n",
    "    print(\"based on average year-on-year growth, intrinsic value is: {}\".format(get_DCF_value(history)))\n",
    "    try:\n",
    "        print(\"based on CAGR, intrinsic value is: {}\".format(get_cagr_DCF_value(history)))\n",
    "    except ValueError: \n",
    "        print(\"CAGR growth calculation impossible as first year is negative value\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "58624fab",
   "metadata": {},
   "outputs": [],
   "source": [
    "def DDM_valuation_report(history):\n",
    "    print(\"based on average year-on-year growth, intrinsic value is: {}\".format(get_DCF_value(history)))\n",
    "    try:\n",
    "        print(\"based on CAGR, intrinsic value is: {}\".format(get_cagr_DCF_value(history)))\n",
    "    except ValueError: \n",
    "        print(\"CAGR growth calculation impossible as first year is negative value\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b800dfa2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_value(name, function, history, color):\n",
    "    try:\n",
    "        plt.barh([name], function(history), color=color)\n",
    "    except ValueError: \n",
    "        pass"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ada79d4",
   "metadata": {},
   "source": [
    "# Download fundementals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e9b23b55",
   "metadata": {},
   "outputs": [],
   "source": [
    "shares = ticker.info[\"sharesOutstanding\"]\n",
    "market_cap = ticker.fast_info[\"market_cap\"]\n",
    "beta = ticker.info[\"beta\"]\n",
    "current_price = ticker.fast_info[\"last_price\"]\n",
    "book_value = ticker.info[\"bookValue\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3af3b48",
   "metadata": {},
   "source": [
    "# download and clean statements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3cad70d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "## income statement \n",
    "IS = ticker.financials\n",
    "IS = clean_dataframe(IS)\n",
    "\n",
    "## balance sheet\n",
    "BS = ticker.balance_sheet\n",
    "BS = clean_dataframe(BS)\n",
    "\n",
    "## cashflow statement\n",
    "CF = ticker.cashflow\n",
    "CF = clean_dataframe(CF)\n",
    "\n",
    "## combined  dataset\n",
    "df = pd.merge(IS, BS, on=\"year\")\n",
    "df = df.merge(CF, on= \"year\")\n",
    "df[\"year\"] = df['year'].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ec1991fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "#create total debt column\n",
    "if \"Total Debt\" not in df.columns:\n",
    "    print(\"there is no total debt column. Will have to manually combine short term and long term debt\")\n",
    "    debt_types = [\"Current Debt And Capital Lease Obligation\",\"Long Term Debt And Capital Lease Obligation\"] \n",
    "    df[\"total_debt\"] = 0\n",
    "\n",
    "    for i in debt_types:\n",
    "        if (i in df.columns) == True:\n",
    "            print(i)\n",
    "            df[\"total_debt\"]  += df[i]\n",
    "            \n",
    "else: \n",
    "    df[\"total_debt\"] = df[\"Total Debt\"]\n",
    "    df[\"total_debt_bil\"] = df[\"Total Debt\"] / 1000000000\n",
    "    \n",
    "print(\"debt for each year is: {}\".format(list(df[\"total_debt_bil\"])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5c807b1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#free cash flow\n",
    "df[\"FCF\"]= df['Operating Cash Flow'] + df['Capital Expenditure']\n",
    "print(\"FCF for each year is: {}\".format(list(df[\"FCF\"])))\n",
    "\n",
    "#Return on Equity\n",
    "df[\"ROE\"] = df[\"Net Income\"] / df[\"Stockholders Equity\"]\n",
    "print(\"ROE for each year is: {}\".format(list(df[\"ROE\"])))\n",
    "\n",
    "#current ratio\n",
    "df['current_ratio'] = df['Current Assets'] / df['Current Liabilities']\n",
    "# df['current_ratio'] = df['Cash Cash Equivalents And Federal Funds Sold'] / df['Payables And Accrued Expenses'] #for finanical company\n",
    "print(\"current_ratio for each year is: {}\".format(list(df[\"current_ratio\"])))\n",
    "\n",
    "#debt_to_equity\n",
    "df[\"debt_to_equity\"] = df[\"total_debt\"] / df['Stockholders Equity']\n",
    "print(\"debt_to_equity for each year is: {}\".format(list(df[\"debt_to_equity\"])))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b239b4a5",
   "metadata": {},
   "source": [
    "# Charts "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7d8f818f",
   "metadata": {},
   "outputs": [],
   "source": [
    "axis = list(df[\"year\"])\n",
    "axis = sorted(axis)\n",
    "axis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc38e13e",
   "metadata": {},
   "source": [
    "## cashflows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a1b359da",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, axes = plt.subplots(2, 3,figsize=(20,12))\n",
    "\n",
    "\n",
    "cashflow_df = df[[\"year\",\"Operating Cash Flow\",\"Investing Cash Flow\", \"Financing Cash Flow\"]]\n",
    "cashflow_df = cashflow_df.melt(id_vars=\"year\",var_name=\"cashflow\",value_name=\"amount\")\n",
    "sns.barplot(x= \"year\", y=\"amount\", data= cashflow_df, hue = \"cashflow\", palette = [\"orange\", \"blue\", \"red\"], order=axis, ax=axes[0,0])\n",
    "axes[0,0].set(title=\"Cashflows\")\n",
    "\n",
    "fcf_df = df[[\"year\",\"FCF\", \"total_debt\"]]\n",
    "fcf_df = fcf_df.melt(id_vars=\"year\",var_name=\"cashflow\",value_name=\"amount\")\n",
    "sns.barplot(x= \"year\", y=\"amount\", data=fcf_df, hue = \"cashflow\", palette = [\"green\", \"red\"], order=axis, ax=axes[0,1])\n",
    "axes[0,1].set(title=\"Free CF vs Debt\")\n",
    "\n",
    "cashflow_income= df[[\"year\",\"Operating Cash Flow\",'Net Income']]\n",
    "cashflow_income = cashflow_income.melt(id_vars=\"year\",var_name=\"source\",value_name=\"amount\")\n",
    "sns.barplot(x=\"year\", y=\"amount\", data=cashflow_income, hue = \"source\", palette = [\"orange\", \"tan\"], order=axis,ax=axes[0,2])\n",
    "axes[0,2].set(title=\"Operating CF vs Net Income\")\n",
    "\n",
    "\n",
    "#dividends already adjusted for stock split according to yahoo finance\n",
    "dividend_history = ticker.actions\n",
    "dividend_history = dividend_history.rename_axis('year').reset_index()\n",
    "dividend_history[\"year\"] = pd.DatetimeIndex(dividend_history[\"year\"]).year\n",
    "dividend_history = dividend_history[dividend_history[\"Stock Splits\"]==0]\n",
    "dividend_history = dividend_history[[\"year\", \"Dividends\"]]\n",
    "dividend_history = dividend_history.groupby(\"year\").sum()\n",
    "\n",
    "if len(dividend_history)>0:\n",
    "    sns.barplot(x = \"year\", y= \"Dividends\", data=dividend_history.reset_index(), color = \"skyblue\", ax=axes[1,0])\n",
    "    axes[1,0].tick_params(axis='x', rotation=45)\n",
    "    axes[1,0].set(title=\"Dividends\")\n",
    "else:\n",
    "    sns.barplot(x = axis, y=[0] * len(axis) ,color = \"skyblue\", ax=axes[1,0])\n",
    "    axes[1,0].tick_params(axis='x', rotation=45)   \n",
    "    axes[1,0].set(title=\"Dividends\")\n",
    "    \n",
    "# #to add FCF/Debt in 2nd graph\n",
    "\n",
    "# # add revenue vs gross profit vs net income"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e23dd7a6",
   "metadata": {},
   "source": [
    "## balancesheet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "37ad757a",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "fig2, axes = plt.subplots(2, 3,figsize=(20,12))\n",
    "\n",
    "balancesheet_prop= df[[\"year\",'Cash And Cash Equivalents', \"total_debt\", 'Total Assets']]\n",
    "balancesheet_prop = balancesheet_prop.melt(id_vars=\"year\",var_name=\"source\",value_name=\"amount\")\n",
    "sns.barplot(x =\"year\",y= \"amount\", data=balancesheet_prop, hue = \"source\", palette = [\"green\", \"red\", 'black', \"black\"], order=axis,ax=axes[0,0])\n",
    "axes[0,0].set(title=\"Cash, Debt and Total Asset\")\n",
    "\n",
    "balancesheet_df = BS[[\"year\",'Stockholders Equity','Current Liabilities','Total Liabilities Net Minority Interest']]\n",
    "# balancesheet_df = BS[[\"year\",'Stockholders Equity','Payables And Accrued Expenses','Total Liabilities Net Minority Interest']]\n",
    "balancesheet_df[\"Total_long term_liab\"] = balancesheet_df[\"Total Liabilities Net Minority Interest\"] - balancesheet_df[\"Current Liabilities\"] \n",
    "# balancesheet_df[\"Total_long term_liab\"] = balancesheet_df[\"Total Liabilities Net Minority Interest\"] - balancesheet_df[\"Payables And Accrued Expenses\"] \n",
    "balancesheet_df.drop(\"Total Liabilities Net Minority Interest\",axis=\"columns\", inplace =True)\n",
    "balancesheet_df = balancesheet_df.set_index(\"year\").sort_index()\n",
    "balancesheet_df = balancesheet_df.div(balancesheet_df.sum(axis=1), axis=0)\n",
    "balancesheet_df.plot(kind='bar', stacked=True, color=['blue', 'brown', 'red'], ax=axes[0,1])\n",
    "axes[0,1].set(title=\"Balancesheet Proportion\")\n",
    "\n",
    "sns.lineplot(x=df[\"year\"], y= df[\"ROE\"], ax=axes[0,2])\n",
    "axes[0,2].invert_xaxis()\n",
    "axes[0,2].set(title=\"ROE\")\n",
    "\n",
    "bs_ratio_df = df[[\"year\",\"current_ratio\", \"debt_to_equity\"]]\n",
    "bs_ratio_df = bs_ratio_df.melt(id_vars=\"year\",var_name=\"ratio\",value_name=\"amount\")\n",
    "sns.lineplot(x= bs_ratio_df[\"year\"], y= bs_ratio_df[\"amount\"],hue=bs_ratio_df[\"ratio\"], palette=[\"blue\", \"red\"], ax=axes[1,0])\n",
    "sns.lineplot(x= bs_ratio_df[\"year\"], y=1, color = \"black\", linestyle=\"--\", ax=axes[1,0])\n",
    "axes[1,0].invert_xaxis()\n",
    "axes[1,0].set(title=\"Current Ratio and Debt to Equity\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e10a6b6",
   "metadata": {},
   "source": [
    "# WACC calculations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4068f00f",
   "metadata": {},
   "source": [
    "## Extract lates year data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "05aa115d",
   "metadata": {},
   "outputs": [],
   "source": [
    "latest_year = df.iloc[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f478bd35",
   "metadata": {},
   "source": [
    "## debt section "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0439d326",
   "metadata": {},
   "outputs": [],
   "source": [
    "possible_interest_expression = [\"Interest Expense\", \"Interest Expense Non Operating\", 'Net Non Operating Interest Income Expense']\n",
    "for i in possible_interest_expression:\n",
    "    if i in latest_year.index:\n",
    "        interest_expression = i\n",
    "        print(\"interest expression is expressed as \\'{}\\' in income statement\".format(interest_expression))\n",
    "        break\n",
    "\n",
    "#there by the end of the cycle, there are no interest expression found, we will just assume interest expense to be 0        \n",
    "if i not in latest_year.index: \n",
    "    print(\"none of the expresssion was found in income statement. interst expression assumes as 0\")\n",
    "    latest_year[i] = 0\n",
    "    \n",
    "print(\"Latest interest expenditure is: {}\".format(latest_year[interest_expression])) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3f3124f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "debt = latest_year[\"total_debt\"]\n",
    "\n",
    "total_wacc_value = debt + market_cap\n",
    "total_wacc_value\n",
    "\n",
    "debt_weight = debt/total_wacc_value\n",
    "\n",
    "if abs(latest_year[interest_expression]) != 0 and debt!=0:\n",
    "    debt_cost = abs(latest_year[interest_expression]) / debt\n",
    "elif abs(latest_year[interest_expression]) == 0 and debt==0:\n",
    "    debt_cost = 0\n",
    "elif abs(latest_year[interest_expression]) != 0 and debt==0:\n",
    "    debt_cost = 0\n",
    "else:\n",
    "    debt_cost = 0\n",
    "\n",
    "tax_rate = latest_year['Tax Provision'] / latest_year['Pretax Income']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6e2f5aa7",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"interest rate of debt of company is: {}%\".format(debt_cost*100))\n",
    "print(\"total debt of company is: {}\".format(debt))\n",
    "print(\"total maketcap of company is: {}\".format(market_cap))\n",
    "print(\"total weighted value of company is (debt + market cap): {}\".format(total_wacc_value))\n",
    "print(\"equity portion is: {}\".format(market_cap/total_wacc_value))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58a3c283",
   "metadata": {},
   "source": [
    "## equity section"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bb9f6a85",
   "metadata": {},
   "outputs": [],
   "source": [
    "if beta == None:\n",
    "    beta = 1\n",
    "beta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "40978872",
   "metadata": {},
   "outputs": [],
   "source": [
    "equity_weight =  market_cap/total_wacc_value\n",
    "\n",
    "equity_cost = risk_free_rate +  beta*(market_return_rate-risk_free_rate)\n",
    "equity_cost"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8c649e4",
   "metadata": {},
   "source": [
    "## WACC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2f79c2a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# tax_rate = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7036dc40",
   "metadata": {},
   "outputs": [],
   "source": [
    "wacc = ((equity_weight * equity_cost) + (debt_weight * debt_cost * (1-tax_rate))) + 1\n",
    "wacc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "213d0744",
   "metadata": {},
   "source": [
    "# Discounted Growth Model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "986763ff",
   "metadata": {},
   "source": [
    "## a) Cashflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a2a8b994",
   "metadata": {},
   "outputs": [],
   "source": [
    "# averge growth rate\n",
    "ocf_history = list(df['Operating Cash Flow'])\n",
    "DCF_valuation_report(ocf_history)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1823a090",
   "metadata": {},
   "source": [
    "## b) Income"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "45754730",
   "metadata": {},
   "outputs": [],
   "source": [
    "ic_history = list(df['Net Income Common Stockholders'])\n",
    "DCF_valuation_report(ic_history)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c74c5c8",
   "metadata": {},
   "source": [
    "## c) Free Cash Flow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "057f34f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "fcf_history = list(df[\"FCF\"])\n",
    "DCF_valuation_report(fcf_history)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20ad86bc",
   "metadata": {},
   "source": [
    "# Dividend Discount Model "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efa9b218",
   "metadata": {},
   "source": [
    "## a) perpetual dividend growth "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "18658cf4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#get the only completed years with all dividends paid out. Company with fiscal year at the start of year still haven't \n",
    "#completed dividend pay out for the year\n",
    "dividend_year = []\n",
    "if axis[-1] == current_year:\n",
    "    for i in range(0, len(axis)): \n",
    "        dividend_year.append(int(axis[i])-1)\n",
    "        print(dividend_year[i])\n",
    "else: \n",
    "    dividend_year = [int(year) for year in axis]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2544b6b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "if len(dividend_history)>0:\n",
    "    #filter the last 4 years of dividends payment\n",
    "    dividend_history = dividend_history[dividend_history.index.isin(dividend_year)]\n",
    "    dividend_history = dividend_history.sort_index(ascending=False)\n",
    "    dividend_history = list(dividend_history[\"Dividends\"])\n",
    "    DDM_valuation = perpetual_growth_valuation(dividend_history)\n",
    "else: \n",
    "    DDM_valuation = 0\n",
    " \n",
    "print(\"past 4 complete yearly dividend payout are: \", dividend_history)\n",
    "print(\"DDM valuations is: \", DDM_valuation)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "467b63ee",
   "metadata": {},
   "source": [
    "### b) perpetual FCF growth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b16a4669",
   "metadata": {},
   "outputs": [],
   "source": [
    "PFCF_valuation = perpetual_growth_valuation(fcf_history)/shares\n",
    "print(\"Assuming perpetual FCF growth at last 3 years' rate. Valuation is: {}\".format(PFCF_valuation))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "206879e1",
   "metadata": {},
   "source": [
    "# Valuation Comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4c4a791e",
   "metadata": {},
   "outputs": [],
   "source": [
    "ax = plt.figure(figsize=(12, 3))\n",
    "\n",
    "plt.barh(\"book value\", book_value, color=\"grey\")\n",
    "\n",
    "plt.barh(\"Perpetual FCF Growth\", PFCF_valuation, color=\"green\")\n",
    "# plt.barh(\"Dividend Discount Model\", DDM_valuation, color=\"skyblue\")\n",
    "\n",
    "plot_value(\"FCF Average Growth DCF\", get_DCF_value, fcf_history, \"green\")\n",
    "plot_value(\"FCF CAGR DCF\", get_cagr_DCF_value, fcf_history, \"green\")\n",
    "\n",
    "plot_value(\"Income Average Growth DCF\", get_DCF_value,ic_history, \"tan\")\n",
    "plot_value(\"Income CAGR DCF\", get_cagr_DCF_value,ic_history, \"tan\")\n",
    "\n",
    "plot_value(\"Cashflow Average Growth DCF\", get_DCF_value,ocf_history, \"orange\")\n",
    "plot_value(\"Cashflow CAGR DCF\", get_cagr_DCF_value,ocf_history, \"orange\")\n",
    "\n",
    "plt.barh(\"current price\", current_price, color=\"black\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c60a04d",
   "metadata": {},
   "source": [
    "# Analyst's recommendations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d1a13c1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "ticker.recommendations[\"To Grade\"]\\\n",
    "    .value_counts()\\\n",
    "    .head(8)\\\n",
    "    .plot(kind=\"barh\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ee919d7",
   "metadata": {},
   "source": [
    "# Other Matrices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "412b9ba8",
   "metadata": {},
   "outputs": [],
   "source": [
    "PE_ratio = ticker.info[\"trailingPE\"]\n",
    "PE_ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0bc35962",
   "metadata": {},
   "outputs": [],
   "source": [
    "ticker.info[\"trailingPegRatio\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8934fa8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "ticker.info[\"payoutRatio\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "690aeb01",
   "metadata": {},
   "source": [
    "# Development area"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6f9b547",
   "metadata": {},
   "source": [
    "## cashflows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cc29da4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# axis = list(df[\"year\"])\n",
    "# axis = sorted(axis)\n",
    "# axis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "028c4ab4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# cashflow_df = df[[\"year\",\"Total Cash From Operating Activities\",\"Total Cashflows From Investing Activities\", \"Total Cash From Financing Activities\"]]\n",
    "# cashflow_df = cashflow_df.melt(id_vars=\"year\",var_name=\"cashflow\",value_name=\"amount\")\n",
    "# sns.barplot(\"year\", \"amount\", data=cashflow_df, hue = \"cashflow\", palette = [\"orange\", \"blue\", \"red\"], order=axis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6ed1832a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# fcf_df = df[[\"year\",\"FCF\", \"total_debt\"]]\n",
    "# fcf_df = fcf_df.melt(id_vars=\"year\",var_name=\"cashflow\",value_name=\"amount\")\n",
    "# sns.barplot(\"year\", \"amount\", data=fcf_df, hue = \"cashflow\", palette = [\"green\", \"red\"], order=axis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3c811039",
   "metadata": {},
   "outputs": [],
   "source": [
    "# cashflow_income= df[[\"year\",\"Total Cash From Operating Activities\",'Net Income Applicable To Common Shares']]\n",
    "# cashflow_income = cashflow_income.melt(id_vars=\"year\",var_name=\"source\",value_name=\"amount\")\n",
    "# sns.barplot(\"year\", \"amount\", data=cashflow_income, hue = \"source\", palette = [\"orange\", \"tan\"], order=axis)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ae576ad",
   "metadata": {},
   "source": [
    "## balance sheet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c32f871d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# balancesheet_prop= df[[\"year\",'Cash', \"total_debt\", 'Total Assets']]\n",
    "# balancesheet_prop = balancesheet_prop.melt(id_vars=\"year\",var_name=\"source\",value_name=\"amount\")\n",
    "# sns.barplot(\"year\", \"amount\", data=balancesheet_prop, hue = \"source\", palette = [\"green\", \"red\", 'black', \"black\"], order=axis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8f5f06ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "# balancesheet_df = BS[[\"year\",'Total Stockholder Equity','Total Current Liabilities','Total Liab']]\n",
    "# balancesheet_df[\"Total_long term_liab\"] = balancesheet_df[\"Total Liab\"] - balancesheet_df[\"Total Current Liabilities\"] \n",
    "# balancesheet_df.drop(\"Total Liab\",axis=\"columns\", inplace =True)\n",
    "# balancesheet_df = balancesheet_df.set_index(\"year\").sort_index()\n",
    "# balancesheet_df = balancesheet_df.div(balancesheet_df.sum(axis=1), axis=0)\n",
    "# balancesheet_df.plot(kind='bar', stacked=True, color=['blue', 'brown', 'red'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "84b2c52e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# ax = sns.lineplot(df[\"year\"], df[\"ROE\"])\n",
    "# ax.invert_xaxis()\n",
    "# ax.set(title=\"ROE\")\n",
    "# ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a371a017",
   "metadata": {},
   "outputs": [],
   "source": [
    "# bs_ratio_df = df[[\"year\",\"current_ratio\", \"debt_to_equity\"]]\n",
    "# bs_ratio_df = bs_ratio_df.melt(id_vars=\"year\",var_name=\"ratio\",value_name=\"amount\")\n",
    "# ax = sns.lineplot(bs_ratio_df[\"year\"], bs_ratio_df[\"amount\"],hue=bs_ratio_df[\"ratio\"], palette=[\"blue\", \"red\"])\n",
    "# ax = sns.lineplot(bs_ratio_df[\"year\"], 1, color = \"black\", linestyle=\"--\")\n",
    "# ax.invert_xaxis()\n",
    "# ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "45b54605",
   "metadata": {},
   "outputs": [],
   "source": [
    "ticker.info.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c1daf4e8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0b418ffc",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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