data_exploration.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import bot_functions as bf\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from tda import auth, client\n",
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.float_format', lambda x: '%.4f' % x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 1\n",
    "%aimport bot_functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Enter the directory containing the .json file with your API key\n",
    "keys = bf.get_keys(\"/your/API/key/file/location.json\")\n",
    "api_key = keys[\"consumer_key\"]\n",
    "\n",
    "# Enter the directory containing the path to your access token\n",
    "# One will be created here if you don't already have one\n",
    "token_path = \"/your/access/token/folder\"\n",
    "\n",
    "# Establish your redirect URL, don't change unless you have a remotely hosted app\n",
    "redirect_url = \"https://localhost\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Enter the path to your previously scraped watchlist\n",
    "path =  'C:\\your\\scraped\\database\\path'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Establish a tda api client to pass to functions\n",
    "c = auth.easy_client(api_key, \n",
    "                     redirect_url, \n",
    "                     token_path\n",
    "                    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "        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>Symbol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AAL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Symbol\n",
       "0      A\n",
       "1    AAL"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Retrieve the SP500 tickers from the contained .csv file\n",
    "sp500_tickers = pd.read_csv('sp500.csv', index_col='Unnamed: 0')\n",
    "sp500_tickers.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Accumulate your scraped data into one big dataframe for comparisons\n",
    "sp500 = bf.build_big_df(sp500_tickers['Symbol'], path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of growing stocks: 156\n",
      "Number of shrinking stocks: 321\n"
     ]
    }
   ],
   "source": [
    "# Find only stocks that analysts predict will grow over 1yr and 2yr periods\n",
    "growing = sp500[(sp500['Growth 1yr Consensus Est'] > 0)\n",
    "               &sp500['Growth 2yr Consensus Est'] > 0]\n",
    "print(\"Number of growing stocks:\", len(growing))\n",
    "\n",
    "# Find only stocks that analysts predict will shrink over 1yr period\n",
    "shrinking = sp500[sp500['Growth 1yr Consensus Est'] < 0]\n",
    "print(\"Number of shrinking stocks:\", len(shrinking))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a watchlist for growing stocks (possible longs)\n",
    "value_watch = list(growing.index)\n",
    "# Create a watchlist for falling stocks (possible shorts)\n",
    "short_watch = list(shrinking.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Additional filtering of watchlist by analyst reports (just one example):\n",
    "theStreet_buy = sp500[sp500['theStreet'] == 'buy'].index\n",
    "value_watch = [x for x in value_watch if x in theStreet_buy]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\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>Est High</th>\n",
       "      <th>Est Low</th>\n",
       "      <th>Intrinsic: FCF</th>\n",
       "      <th>Intrinsic: PB</th>\n",
       "      <th>Intrinsic: PE</th>\n",
       "      <th>Intrinsic: PR</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AAPL</th>\n",
       "      <td>388.279176</td>\n",
       "      <td>176.232710</td>\n",
       "      <td>206.351235</td>\n",
       "      <td>176.232710</td>\n",
       "      <td>388.279176</td>\n",
       "      <td>302.888120</td>\n",
       "      <td>290.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACN</th>\n",
       "      <td>710.231301</td>\n",
       "      <td>237.259534</td>\n",
       "      <td>237.259534</td>\n",
       "      <td>673.510882</td>\n",
       "      <td>334.181422</td>\n",
       "      <td>710.231301</td>\n",
       "      <td>180.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADBE</th>\n",
       "      <td>929.802896</td>\n",
       "      <td>368.916524</td>\n",
       "      <td>929.802896</td>\n",
       "      <td>368.916524</td>\n",
       "      <td>883.165041</td>\n",
       "      <td>629.068205</td>\n",
       "      <td>343.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADP</th>\n",
       "      <td>369.227971</td>\n",
       "      <td>151.332297</td>\n",
       "      <td>151.332297</td>\n",
       "      <td>187.843174</td>\n",
       "      <td>306.180690</td>\n",
       "      <td>369.227971</td>\n",
       "      <td>141.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AIZ</th>\n",
       "      <td>94523.980213</td>\n",
       "      <td>171.050791</td>\n",
       "      <td>94523.980213</td>\n",
       "      <td>197.615236</td>\n",
       "      <td>171.050791</td>\n",
       "      <td>562.337398</td>\n",
       "      <td>102.32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Est High     Est Low  Intrinsic: FCF  Intrinsic: PB  Intrinsic: PE  \\\n",
       "AAPL    388.279176  176.232710      206.351235     176.232710     388.279176   \n",
       "ACN     710.231301  237.259534      237.259534     673.510882     334.181422   \n",
       "ADBE    929.802896  368.916524      929.802896     368.916524     883.165041   \n",
       "ADP     369.227971  151.332297      151.332297     187.843174     306.180690   \n",
       "AIZ   94523.980213  171.050791    94523.980213     197.615236     171.050791   \n",
       "\n",
       "      Intrinsic: PR   Price  \n",
       "AAPL     302.888120  290.40  \n",
       "ACN      710.231301  180.09  \n",
       "ADBE     629.068205  343.62  \n",
       "ADP      369.227971  141.06  \n",
       "AIZ      562.337398  102.32  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create intrinsic value estimate ranges for growing stocks\n",
    "value_estimates = bf.get_intrinsic_range(c, value_watch, path)\n",
    "value_estimates.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of undervalued companies: 42\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Est High</th>\n",
       "      <th>Est Low</th>\n",
       "      <th>Intrinsic: FCF</th>\n",
       "      <th>Intrinsic: PB</th>\n",
       "      <th>Intrinsic: PE</th>\n",
       "      <th>Intrinsic: PR</th>\n",
       "      <th>Price</th>\n",
       "      <th>Margin of Safety</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AKAM</th>\n",
       "      <td>356.082025</td>\n",
       "      <td>150.758060</td>\n",
       "      <td>150.758060</td>\n",
       "      <td>356.082025</td>\n",
       "      <td>229.424892</td>\n",
       "      <td>219.740708</td>\n",
       "      <td>95.84</td>\n",
       "      <td>0.573018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANTM</th>\n",
       "      <td>4838.433653</td>\n",
       "      <td>424.852642</td>\n",
       "      <td>424.852642</td>\n",
       "      <td>838.977299</td>\n",
       "      <td>942.273477</td>\n",
       "      <td>4838.433653</td>\n",
       "      <td>269.57</td>\n",
       "      <td>0.576038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KR</th>\n",
       "      <td>94.507409</td>\n",
       "      <td>50.766023</td>\n",
       "      <td>59.068767</td>\n",
       "      <td>65.894517</td>\n",
       "      <td>50.766023</td>\n",
       "      <td>94.507409</td>\n",
       "      <td>32.18</td>\n",
       "      <td>0.577564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SNPS</th>\n",
       "      <td>392.716517</td>\n",
       "      <td>238.569259</td>\n",
       "      <td>290.001856</td>\n",
       "      <td>392.716517</td>\n",
       "      <td>238.569259</td>\n",
       "      <td>362.746374</td>\n",
       "      <td>149.64</td>\n",
       "      <td>0.594288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMAT</th>\n",
       "      <td>163.356130</td>\n",
       "      <td>76.600440</td>\n",
       "      <td>163.356130</td>\n",
       "      <td>76.600440</td>\n",
       "      <td>94.176569</td>\n",
       "      <td>130.478541</td>\n",
       "      <td>47.10</td>\n",
       "      <td>0.626336</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Est High     Est Low  Intrinsic: FCF  Intrinsic: PB  Intrinsic: PE  \\\n",
       "AKAM   356.082025  150.758060      150.758060     356.082025     229.424892   \n",
       "ANTM  4838.433653  424.852642      424.852642     838.977299     942.273477   \n",
       "KR      94.507409   50.766023       59.068767      65.894517      50.766023   \n",
       "SNPS   392.716517  238.569259      290.001856     392.716517     238.569259   \n",
       "AMAT   163.356130   76.600440      163.356130      76.600440      94.176569   \n",
       "\n",
       "      Intrinsic: PR   Price  Margin of Safety  \n",
       "AKAM     219.740708   95.84          0.573018  \n",
       "ANTM    4838.433653  269.57          0.576038  \n",
       "KR        94.507409   32.18          0.577564  \n",
       "SNPS     362.746374  149.64          0.594288  \n",
       "AMAT     130.478541   47.10          0.626336  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Ticker')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Inspect only those stocks which are selling beneath the lowest estimate from above\n",
    "inspect = value_estimates[(value_estimates['Price'] < value_estimates['Est Low'])].copy()\n",
    "# Calculate Margin of Safety between current price and your lowest estimate\n",
    "inspect['Margin of Safety'] = inspect['Est Low']/ inspect['Price'] - 1\n",
    "inspect.sort_values(by='Margin of Safety', inplace=True)\n",
    "print(\"Number of undervalued companies:\", len(inspect))\n",
    "\n",
    "# View your dataframe\n",
    "display(inspect.iloc[25:30])\n",
    "\n",
    "# Visually inspect the results\n",
    "inspect.reset_index().plot(x='index', \n",
    "                           y='Margin of Safety',\n",
    "                           figsize=(12,6))\n",
    "plt.title('Undervalued Securities')\n",
    "plt.xlabel('Ticker')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No P/E information available for BIIB, skipping\n",
      "No Price/Sales data for BIIB, skipping\n",
      "Price/Book info not available for BIIB, skipping\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>Est High</th>\n",
       "      <th>Est Low</th>\n",
       "      <th>Intrinsic: FCF</th>\n",
       "      <th>Intrinsic: PB</th>\n",
       "      <th>Intrinsic: PE</th>\n",
       "      <th>Intrinsic: PR</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>125.206421</td>\n",
       "      <td>50.678006</td>\n",
       "      <td>50.678006</td>\n",
       "      <td>101.395002</td>\n",
       "      <td>125.206421</td>\n",
       "      <td>121.937435</td>\n",
       "      <td>74.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAL</th>\n",
       "      <td>244.065317</td>\n",
       "      <td>-0.005624</td>\n",
       "      <td>244.065317</td>\n",
       "      <td>-0.005624</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>38.983465</td>\n",
       "      <td>10.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAP</th>\n",
       "      <td>23187.356987</td>\n",
       "      <td>206.558226</td>\n",
       "      <td>206.558226</td>\n",
       "      <td>1248.449558</td>\n",
       "      <td>214.500118</td>\n",
       "      <td>23187.356987</td>\n",
       "      <td>116.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABC</th>\n",
       "      <td>11115.185429</td>\n",
       "      <td>74.238579</td>\n",
       "      <td>74.238579</td>\n",
       "      <td>649.816066</td>\n",
       "      <td>95.365044</td>\n",
       "      <td>11115.185429</td>\n",
       "      <td>86.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABMD</th>\n",
       "      <td>1001.984259</td>\n",
       "      <td>472.476332</td>\n",
       "      <td>1001.984259</td>\n",
       "      <td>712.275704</td>\n",
       "      <td>506.722456</td>\n",
       "      <td>472.476332</td>\n",
       "      <td>187.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Est High     Est Low  Intrinsic: FCF  Intrinsic: PB  Intrinsic: PE  \\\n",
       "A       125.206421   50.678006       50.678006     101.395002     125.206421   \n",
       "AAL     244.065317   -0.005624      244.065317      -0.005624      -0.000000   \n",
       "AAP   23187.356987  206.558226      206.558226    1248.449558     214.500118   \n",
       "ABC   11115.185429   74.238579       74.238579     649.816066      95.365044   \n",
       "ABMD   1001.984259  472.476332     1001.984259     712.275704     506.722456   \n",
       "\n",
       "      Intrinsic: PR   Price  \n",
       "A        121.937435   74.00  \n",
       "AAL       38.983465   10.76  \n",
       "AAP    23187.356987  116.50  \n",
       "ABC    11115.185429   86.38  \n",
       "ABMD     472.476332  187.00  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate intrinsic value ranges for shrinking stocks\n",
    "short_estimates = bf.get_intrinsic_range(c, short_watch, path)\n",
    "short_estimates.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of overvalued compaines: 5\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Est High</th>\n",
       "      <th>Est Low</th>\n",
       "      <th>Intrinsic: FCF</th>\n",
       "      <th>Intrinsic: PB</th>\n",
       "      <th>Intrinsic: PE</th>\n",
       "      <th>Intrinsic: PR</th>\n",
       "      <th>Price</th>\n",
       "      <th>Margin of Safety</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>UA</th>\n",
       "      <td>-65.635445</td>\n",
       "      <td>-65.635445</td>\n",
       "      <td>-65.635445</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.90</td>\n",
       "      <td>-1.135597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PNR</th>\n",
       "      <td>32.419875</td>\n",
       "      <td>13.824568</td>\n",
       "      <td>13.824568</td>\n",
       "      <td>32.419875</td>\n",
       "      <td>18.727949</td>\n",
       "      <td>17.426305</td>\n",
       "      <td>33.43</td>\n",
       "      <td>0.031158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MMM</th>\n",
       "      <td>130.146211</td>\n",
       "      <td>65.940746</td>\n",
       "      <td>130.146211</td>\n",
       "      <td>65.940746</td>\n",
       "      <td>119.695668</td>\n",
       "      <td>121.229061</td>\n",
       "      <td>148.60</td>\n",
       "      <td>0.141793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ROK</th>\n",
       "      <td>151.808844</td>\n",
       "      <td>7.718489</td>\n",
       "      <td>151.808844</td>\n",
       "      <td>7.718489</td>\n",
       "      <td>121.942999</td>\n",
       "      <td>118.161492</td>\n",
       "      <td>183.19</td>\n",
       "      <td>0.206715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SLB</th>\n",
       "      <td>11.940383</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>7.498281</td>\n",
       "      <td>11.940383</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>10.070008</td>\n",
       "      <td>15.75</td>\n",
       "      <td>0.319053</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Est High    Est Low  Intrinsic: FCF  Intrinsic: PB  Intrinsic: PE  \\\n",
       "UA   -65.635445 -65.635445      -65.635445            NaN            NaN   \n",
       "PNR   32.419875  13.824568       13.824568      32.419875      18.727949   \n",
       "MMM  130.146211  65.940746      130.146211      65.940746     119.695668   \n",
       "ROK  151.808844   7.718489      151.808844       7.718489     121.942999   \n",
       "SLB   11.940383  -0.000000        7.498281      11.940383      -0.000000   \n",
       "\n",
       "     Intrinsic: PR   Price  Margin of Safety  \n",
       "UA             NaN    8.90         -1.135597  \n",
       "PNR      17.426305   33.43          0.031158  \n",
       "MMM     121.229061  148.60          0.141793  \n",
       "ROK     118.161492  183.19          0.206715  \n",
       "SLB      10.070008   15.75          0.319053  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1863b83f048>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Inspect only stocks selling above highest intrinsic value estimate\n",
    "# Note that since these estimates can be very large, this will return a shorter list,\n",
    "# and that a bit more complex querying may serve those looking for more shortable stocks\n",
    "inspect2 = short_estimates[(short_estimates['Price'] > short_estimates['Est High'])].copy()\n",
    "inspect2['Margin of Safety'] = inspect2['Price']/ inspect2['Est High'] - 1\n",
    "inspect2.sort_values(by='Margin of Safety', inplace=True)\n",
    "print(\"Number of overvalued compaines:\", len(inspect2))\n",
    "display(inspect2)\n",
    "inspect2.reset_index().plot(x='index', \n",
    "                           y='Margin of Safety',\n",
    "                           figsize=(12,6))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}