selection_criteria.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing libraries\n",
    "from pathlib import Path \n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Sector</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Symbol</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MMM</th>\n",
       "      <td>3M Company</td>\n",
       "      <td>Industrials</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AOS</th>\n",
       "      <td>A.O. Smith Corp</td>\n",
       "      <td>Industrials</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABT</th>\n",
       "      <td>Abbott Laboratories</td>\n",
       "      <td>Health Care</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABBV</th>\n",
       "      <td>AbbVie Inc.</td>\n",
       "      <td>Health Care</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACN</th>\n",
       "      <td>Accenture plc</td>\n",
       "      <td>Information Technology</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XYL</th>\n",
       "      <td>Xylem Inc.</td>\n",
       "      <td>Industrials</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YUM</th>\n",
       "      <td>Yum! Brands Inc</td>\n",
       "      <td>Consumer Discretionary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZBH</th>\n",
       "      <td>Zimmer Biomet Holdings</td>\n",
       "      <td>Health Care</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZION</th>\n",
       "      <td>Zions Bancorp</td>\n",
       "      <td>Financials</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZTS</th>\n",
       "      <td>Zoetis</td>\n",
       "      <td>Health Care</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>505 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Name                  Sector\n",
       "Symbol                                                \n",
       "MMM                 3M Company             Industrials\n",
       "AOS            A.O. Smith Corp             Industrials\n",
       "ABT        Abbott Laboratories             Health Care\n",
       "ABBV               AbbVie Inc.             Health Care\n",
       "ACN              Accenture plc  Information Technology\n",
       "...                        ...                     ...\n",
       "XYL                 Xylem Inc.             Industrials\n",
       "YUM            Yum! Brands Inc  Consumer Discretionary\n",
       "ZBH     Zimmer Biomet Holdings             Health Care\n",
       "ZION             Zions Bancorp              Financials\n",
       "ZTS                     Zoetis             Health Care\n",
       "\n",
       "[505 rows x 2 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# importing S&P 500 data\n",
    "sp500_path = Path('S&P500.csv')\n",
    "sp500_df = pd.read_csv(sp500_path, index_col = 'Symbol')\n",
    "sp500_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sharpe Ratio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stock</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>DXC</th>\n",
       "      <td>0.665931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BBY</th>\n",
       "      <td>0.823915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMG</th>\n",
       "      <td>1.097986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CNP</th>\n",
       "      <td>1.361230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WMT</th>\n",
       "      <td>1.271096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MCO</th>\n",
       "      <td>0.927984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HII</th>\n",
       "      <td>1.154338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RL</th>\n",
       "      <td>1.685028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LNT</th>\n",
       "      <td>1.366718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AXP</th>\n",
       "      <td>1.214408</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Sharpe Ratio\n",
       "Stock              \n",
       "DXC        0.665931\n",
       "BBY        0.823915\n",
       "AMG        1.097986\n",
       "CNP        1.361230\n",
       "WMT        1.271096\n",
       "...             ...\n",
       "MCO        0.927984\n",
       "HII        1.154338\n",
       "RL         1.685028\n",
       "LNT        1.366718\n",
       "AXP        1.214408\n",
       "\n",
       "[100 rows x 1 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# importing sharpe ratio data\n",
    "sharpe_path = Path('sharpe_ratios.csv')\n",
    "sharpe_df = pd.read_csv(sharpe_path, index_col = 'Stock')\n",
    "sharpe_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FFM Returns</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stock</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ADBE</th>\n",
       "      <td>0.329761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSFT</th>\n",
       "      <td>0.322501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CTAS</th>\n",
       "      <td>0.285973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BBY</th>\n",
       "      <td>0.264073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SNPS</th>\n",
       "      <td>0.258979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COG</th>\n",
       "      <td>-0.050017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRCL</th>\n",
       "      <td>-0.085881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>-0.126598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MYL</th>\n",
       "      <td>-0.130829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMG</th>\n",
       "      <td>-0.138798</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       FFM Returns\n",
       "Stock             \n",
       "ADBE      0.329761\n",
       "MSFT      0.322501\n",
       "CTAS      0.285973\n",
       "BBY       0.264073\n",
       "SNPS      0.258979\n",
       "...            ...\n",
       "COG      -0.050017\n",
       "SRCL     -0.085881\n",
       "M        -0.126598\n",
       "MYL      -0.130829\n",
       "AMG      -0.138798\n",
       "\n",
       "[100 rows x 1 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# importing sharpe ratio data\n",
    "ffm_path = Path('ffmreturns.csv')\n",
    "ffm_df = pd.read_csv(ffm_path, index_col = 'Stock')\n",
    "ffm_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pe</th>\n",
       "      <th>eps</th>\n",
       "      <th>beta</th>\n",
       "      <th>marketcap</th>\n",
       "      <th>shares</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stock</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>DXC</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-6.26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7819681142</td>\n",
       "      <td>253721000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BBY</th>\n",
       "      <td>15.98</td>\n",
       "      <td>5.64</td>\n",
       "      <td>1.12</td>\n",
       "      <td>23344272933</td>\n",
       "      <td>258777000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMG</th>\n",
       "      <td>267.18</td>\n",
       "      <td>0.31</td>\n",
       "      <td>1.69</td>\n",
       "      <td>4084648875</td>\n",
       "      <td>49272000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CNP</th>\n",
       "      <td>21.58</td>\n",
       "      <td>1.26</td>\n",
       "      <td>0.44</td>\n",
       "      <td>13650929559</td>\n",
       "      <td>502242000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WMT</th>\n",
       "      <td>23.55</td>\n",
       "      <td>5.00</td>\n",
       "      <td>0.36</td>\n",
       "      <td>334474559018</td>\n",
       "      <td>2837175000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MCO</th>\n",
       "      <td>37.43</td>\n",
       "      <td>7.42</td>\n",
       "      <td>1.21</td>\n",
       "      <td>52148688479</td>\n",
       "      <td>187700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HII</th>\n",
       "      <td>18.57</td>\n",
       "      <td>13.26</td>\n",
       "      <td>1.24</td>\n",
       "      <td>10035519873</td>\n",
       "      <td>40760000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RL</th>\n",
       "      <td>14.16</td>\n",
       "      <td>8.61</td>\n",
       "      <td>0.90</td>\n",
       "      <td>8984857815</td>\n",
       "      <td>48862000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LNT</th>\n",
       "      <td>26.79</td>\n",
       "      <td>2.23</td>\n",
       "      <td>0.23</td>\n",
       "      <td>14358652779</td>\n",
       "      <td>244628000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AXP</th>\n",
       "      <td>17.01</td>\n",
       "      <td>7.99</td>\n",
       "      <td>0.99</td>\n",
       "      <td>111178449600</td>\n",
       "      <td>808041000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           pe    eps  beta     marketcap      shares\n",
       "Stock                                               \n",
       "DXC       NaN  -6.26   NaN    7819681142   253721000\n",
       "BBY     15.98   5.64  1.12   23344272933   258777000\n",
       "AMG    267.18   0.31  1.69    4084648875    49272000\n",
       "CNP     21.58   1.26  0.44   13650929559   502242000\n",
       "WMT     23.55   5.00  0.36  334474559018  2837175000\n",
       "...       ...    ...   ...           ...         ...\n",
       "MCO     37.43   7.42  1.21   52148688479   187700000\n",
       "HII     18.57  13.26  1.24   10035519873    40760000\n",
       "RL      14.16   8.61  0.90    8984857815    48862000\n",
       "LNT     26.79   2.23  0.23   14358652779   244628000\n",
       "AXP     17.01   7.99  0.99  111178449600   808041000\n",
       "\n",
       "[100 rows x 5 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# importing stock data\n",
    "stock_data_path = Path('stock_data.csv')\n",
    "stock_data_df = pd.read_csv(stock_data_path, index_col = 'Stock')\n",
    "stock_data_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "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>ticker</th>\n",
       "      <th>name</th>\n",
       "      <th>sector</th>\n",
       "      <th>sharpe ratio</th>\n",
       "      <th>FFM returns</th>\n",
       "      <th>P/E ratio</th>\n",
       "      <th>EPS</th>\n",
       "      <th>beta</th>\n",
       "      <th>mkt cap</th>\n",
       "      <th>shares</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ACN</td>\n",
       "      <td>Accenture plc</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>1.113858</td>\n",
       "      <td>0.225818</td>\n",
       "      <td>28.34</td>\n",
       "      <td>7.49</td>\n",
       "      <td>1.03</td>\n",
       "      <td>139515576338</td>\n",
       "      <td>656946000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ADBE</td>\n",
       "      <td>Adobe Systems Inc</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>0.616060</td>\n",
       "      <td>0.329761</td>\n",
       "      <td>63.17</td>\n",
       "      <td>6.01</td>\n",
       "      <td>1.09</td>\n",
       "      <td>185174936969</td>\n",
       "      <td>487726000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AES</td>\n",
       "      <td>AES Corp</td>\n",
       "      <td>Utilities</td>\n",
       "      <td>1.182641</td>\n",
       "      <td>0.162654</td>\n",
       "      <td>27.44</td>\n",
       "      <td>0.76</td>\n",
       "      <td>1.05</td>\n",
       "      <td>13895280692</td>\n",
       "      <td>663893000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AMG</td>\n",
       "      <td>Affiliated Managers Group Inc</td>\n",
       "      <td>Financials</td>\n",
       "      <td>1.097986</td>\n",
       "      <td>-0.138798</td>\n",
       "      <td>267.18</td>\n",
       "      <td>0.31</td>\n",
       "      <td>1.69</td>\n",
       "      <td>4084648875</td>\n",
       "      <td>49272000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>APD</td>\n",
       "      <td>Air Products &amp; Chemicals Inc</td>\n",
       "      <td>Materials</td>\n",
       "      <td>1.224824</td>\n",
       "      <td>0.153161</td>\n",
       "      <td>30.09</td>\n",
       "      <td>8.51</td>\n",
       "      <td>0.86</td>\n",
       "      <td>56495776935</td>\n",
       "      <td>220678000</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>TMO</td>\n",
       "      <td>Thermo Fisher Scientific</td>\n",
       "      <td>Health Care</td>\n",
       "      <td>0.957237</td>\n",
       "      <td>0.223396</td>\n",
       "      <td>36.98</td>\n",
       "      <td>9.17</td>\n",
       "      <td>1.14</td>\n",
       "      <td>136012138268</td>\n",
       "      <td>400991000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>UNH</td>\n",
       "      <td>United Health Group Inc.</td>\n",
       "      <td>Health Care</td>\n",
       "      <td>0.892893</td>\n",
       "      <td>0.243094</td>\n",
       "      <td>20.86</td>\n",
       "      <td>14.33</td>\n",
       "      <td>0.69</td>\n",
       "      <td>283068652543</td>\n",
       "      <td>947415000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>VFC</td>\n",
       "      <td>V.F. Corp.</td>\n",
       "      <td>Consumer Discretionary</td>\n",
       "      <td>1.323241</td>\n",
       "      <td>0.129111</td>\n",
       "      <td>25.99</td>\n",
       "      <td>3.22</td>\n",
       "      <td>1.20</td>\n",
       "      <td>33022293377</td>\n",
       "      <td>394720000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>WMT</td>\n",
       "      <td>Wal-Mart Stores</td>\n",
       "      <td>Consumer Staples</td>\n",
       "      <td>1.271096</td>\n",
       "      <td>0.125055</td>\n",
       "      <td>23.55</td>\n",
       "      <td>5.00</td>\n",
       "      <td>0.36</td>\n",
       "      <td>334474559018</td>\n",
       "      <td>2837175000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>WBA</td>\n",
       "      <td>Walgreens Boots Alliance</td>\n",
       "      <td>Consumer Staples</td>\n",
       "      <td>2.224201</td>\n",
       "      <td>-0.007405</td>\n",
       "      <td>13.00</td>\n",
       "      <td>4.07</td>\n",
       "      <td>0.92</td>\n",
       "      <td>46817805348</td>\n",
       "      <td>885862000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>92 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   ticker                           name                  sector  \\\n",
       "0     ACN                  Accenture plc  Information Technology   \n",
       "1    ADBE              Adobe Systems Inc  Information Technology   \n",
       "2     AES                       AES Corp               Utilities   \n",
       "3     AMG  Affiliated Managers Group Inc              Financials   \n",
       "4     APD   Air Products & Chemicals Inc               Materials   \n",
       "..    ...                            ...                     ...   \n",
       "87    TMO       Thermo Fisher Scientific             Health Care   \n",
       "88    UNH       United Health Group Inc.             Health Care   \n",
       "89    VFC                     V.F. Corp.  Consumer Discretionary   \n",
       "90    WMT                Wal-Mart Stores        Consumer Staples   \n",
       "91    WBA       Walgreens Boots Alliance        Consumer Staples   \n",
       "\n",
       "    sharpe ratio  FFM returns  P/E ratio    EPS  beta       mkt cap  \\\n",
       "0       1.113858     0.225818      28.34   7.49  1.03  139515576338   \n",
       "1       0.616060     0.329761      63.17   6.01  1.09  185174936969   \n",
       "2       1.182641     0.162654      27.44   0.76  1.05   13895280692   \n",
       "3       1.097986    -0.138798     267.18   0.31  1.69    4084648875   \n",
       "4       1.224824     0.153161      30.09   8.51  0.86   56495776935   \n",
       "..           ...          ...        ...    ...   ...           ...   \n",
       "87      0.957237     0.223396      36.98   9.17  1.14  136012138268   \n",
       "88      0.892893     0.243094      20.86  14.33  0.69  283068652543   \n",
       "89      1.323241     0.129111      25.99   3.22  1.20   33022293377   \n",
       "90      1.271096     0.125055      23.55   5.00  0.36  334474559018   \n",
       "91      2.224201    -0.007405      13.00   4.07  0.92   46817805348   \n",
       "\n",
       "        shares  \n",
       "0    656946000  \n",
       "1    487726000  \n",
       "2    663893000  \n",
       "3     49272000  \n",
       "4    220678000  \n",
       "..         ...  \n",
       "87   400991000  \n",
       "88   947415000  \n",
       "89   394720000  \n",
       "90  2837175000  \n",
       "91   885862000  \n",
       "\n",
       "[92 rows x 10 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# concatenating the datasets and dropping nulls\n",
    "merged_df = pd.concat([sp500_df,sharpe_df,ffm_df,stock_data_df], axis='columns', join='inner')\n",
    "merged_df.dropna(inplace=True)\n",
    "merged_df.reset_index(inplace=True)\n",
    "merged_df.columns = ['ticker','name','sector','sharpe ratio','FFM returns','P/E ratio','EPS','beta','mkt cap','shares']\n",
    "merged_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Sharpe Ratio')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1800x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting sharpe ratios\n",
    "merged_df.plot(x='ticker',y='sharpe ratio', kind='bar', figsize=(25,10))\n",
    "plt.suptitle('Sharpe Ratios in Investment Universe', fontsize=30)\n",
    "plt.axhline(2, color='g')\n",
    "plt.text(0,2.1,'Ideal Min. Sharpe Ratio',rotation=0,fontsize=20)\n",
    "plt.axhline(1.5, color='y')\n",
    "plt.text(0,1.6,'Lower Band Sharpe Ratio',rotation=0,fontsize=20)\n",
    "plt.xlabel('Company Ticker',fontsize=20)\n",
    "plt.ylabel('Sharpe Ratio',fontsize=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 100)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting P/E Ratios\n",
    "merged_df.plot(x='ticker',y='P/E ratio', kind='bar', figsize=(25,10))\n",
    "plt.suptitle('P/E Ratios in Investment Universe', fontsize=30)\n",
    "plt.axhline(9, color='g')\n",
    "plt.text(0,10,'Ideal P/E Ratio',rotation=0,fontsize=20)\n",
    "plt.axhline(25, color='y')\n",
    "plt.text(0,26,'Ideal Max. P/E Ratio',rotation=0,fontsize=20)\n",
    "plt.xlabel('Company Ticker', fontsize=20)\n",
    "plt.ylabel('P/E Ratio',fontsize=20)\n",
    "plt.ylim(0,100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'FFM Returns')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting returns\n",
    "merged_df.plot(x='ticker',y='FFM returns', kind='bar', figsize=(25,10))\n",
    "plt.suptitle('FFM Returns in Investment Universe', fontsize=30)\n",
    "plt.xlabel('Company Ticker',fontsize=20)\n",
    "plt.ylabel('FFM Returns',fontsize=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# printing the dataset to csv \n",
    "output = 'selection_data.csv'\n",
    "\n",
    "with open(output, 'w') as file:\n",
    "    file.write(merged_df.to_csv(index=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  after removing the cwd from sys.path.\n",
      "/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n",
      "/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:12: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  if sys.path[0] == '':\n",
      "/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  app.launch_new_instance()\n",
      "/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:20: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:24: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:28: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:32: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:36: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:40: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"
     ]
    }
   ],
   "source": [
    "# creating datasets for each sector\n",
    "cd_mask = merged_df['sector'] == 'Consumer Discretionary'\n",
    "cd_df = merged_df.loc[cd_mask]\n",
    "cd_df.sort_values(by = 'sharpe ratio', ascending = False, inplace = True)\n",
    "\n",
    "cs_mask = merged_df['sector'] == 'Consumer Staples'\n",
    "cs_df = merged_df.loc[cs_mask]\n",
    "cs_df.sort_values(by = 'sharpe ratio', ascending = False, inplace = True)\n",
    "\n",
    "energy_mask = merged_df['sector'] == 'Energy'\n",
    "energy_df = merged_df.loc[energy_mask]\n",
    "energy_df.sort_values(by = 'sharpe ratio', ascending = False, inplace = True)\n",
    "\n",
    "financials_mask = merged_df['sector'] == 'Financials'\n",
    "financials_df = merged_df.loc[financials_mask]\n",
    "financials_df.sort_values(by = 'sharpe ratio', ascending = False, inplace = True)\n",
    "\n",
    "hc_mask = merged_df['sector'] == 'Health Care'\n",
    "hc_df = merged_df.loc[hc_mask]\n",
    "hc_df.sort_values(by = 'sharpe ratio', ascending = False, inplace = True)\n",
    "\n",
    "industrials_mask = merged_df['sector'] == 'Industrials'\n",
    "industrials_df = merged_df.loc[industrials_mask]\n",
    "industrials_df.sort_values(by = 'sharpe ratio', ascending = False, inplace = True)\n",
    "\n",
    "it_mask = merged_df['sector'] == 'Information Technology'\n",
    "it_df = merged_df.loc[it_mask]\n",
    "it_df.sort_values(by = 'sharpe ratio', ascending = False, inplace = True)\n",
    "\n",
    "materials_mask = merged_df['sector'] == 'Materials'\n",
    "materials_df = merged_df.loc[materials_mask]\n",
    "materials_df.sort_values(by = 'sharpe ratio', ascending = False, inplace = True)\n",
    "\n",
    "re_mask = merged_df['sector'] == 'Real Estate'\n",
    "re_df = merged_df.loc[re_mask]\n",
    "re_df.sort_values(by = 'sharpe ratio', ascending = False, inplace = True)\n",
    "\n",
    "utilities_mask = merged_df['sector'] == 'Utilities'\n",
    "utilities_df = merged_df.loc[utilities_mask]\n",
    "utilities_df.sort_values(by = 'sharpe ratio', ascending = False, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>ticker</th>\n",
       "      <th>name</th>\n",
       "      <th>sector</th>\n",
       "      <th>sharpe ratio</th>\n",
       "      <th>FFM returns</th>\n",
       "      <th>P/E ratio</th>\n",
       "      <th>EPS</th>\n",
       "      <th>beta</th>\n",
       "      <th>mkt cap</th>\n",
       "      <th>shares</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>GPC</td>\n",
       "      <td>Genuine Parts</td>\n",
       "      <td>Consumer Discretionary</td>\n",
       "      <td>2.924464</td>\n",
       "      <td>0.074228</td>\n",
       "      <td>17.71</td>\n",
       "      <td>5.45</td>\n",
       "      <td>0.89</td>\n",
       "      <td>14016415887</td>\n",
       "      <td>145293000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>EXPE</td>\n",
       "      <td>Expedia Inc.</td>\n",
       "      <td>Consumer Discretionary</td>\n",
       "      <td>2.407311</td>\n",
       "      <td>0.086286</td>\n",
       "      <td>32.58</td>\n",
       "      <td>3.77</td>\n",
       "      <td>1.01</td>\n",
       "      <td>17792025802</td>\n",
       "      <td>139363000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>DISCK</td>\n",
       "      <td>Discovery Communications-C</td>\n",
       "      <td>Consumer Discretionary</td>\n",
       "      <td>2.309659</td>\n",
       "      <td>0.061552</td>\n",
       "      <td>11.10</td>\n",
       "      <td>2.59</td>\n",
       "      <td>1.45</td>\n",
       "      <td>15392873644</td>\n",
       "      <td>360664000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>LKQ</td>\n",
       "      <td>LKQ Corporation</td>\n",
       "      <td>Consumer Discretionary</td>\n",
       "      <td>2.216066</td>\n",
       "      <td>0.121772</td>\n",
       "      <td>23.73</td>\n",
       "      <td>1.40</td>\n",
       "      <td>1.30</td>\n",
       "      <td>10174538633</td>\n",
       "      <td>306462000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>GM</td>\n",
       "      <td>General Motors</td>\n",
       "      <td>Consumer Discretionary</td>\n",
       "      <td>1.903721</td>\n",
       "      <td>0.084495</td>\n",
       "      <td>7.60</td>\n",
       "      <td>4.57</td>\n",
       "      <td>1.38</td>\n",
       "      <td>49672072361</td>\n",
       "      <td>1429002000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>KMX</td>\n",
       "      <td>Carmax Inc</td>\n",
       "      <td>Consumer Discretionary</td>\n",
       "      <td>1.703158</td>\n",
       "      <td>0.099285</td>\n",
       "      <td>19.16</td>\n",
       "      <td>5.16</td>\n",
       "      <td>1.10</td>\n",
       "      <td>16144071451</td>\n",
       "      <td>163385000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>RL</td>\n",
       "      <td>Polo Ralph Lauren Corp.</td>\n",
       "      <td>Consumer Discretionary</td>\n",
       "      <td>1.685028</td>\n",
       "      <td>0.046267</td>\n",
       "      <td>14.16</td>\n",
       "      <td>8.61</td>\n",
       "      <td>0.90</td>\n",
       "      <td>8984857815</td>\n",
       "      <td>48862000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>BWA</td>\n",
       "      <td>BorgWarner</td>\n",
       "      <td>Consumer Discretionary</td>\n",
       "      <td>1.682271</td>\n",
       "      <td>-0.008937</td>\n",
       "      <td>9.20</td>\n",
       "      <td>3.71</td>\n",
       "      <td>1.91</td>\n",
       "      <td>7048901814</td>\n",
       "      <td>206410000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>SBUX</td>\n",
       "      <td>Starbucks Corp.</td>\n",
       "      <td>Consumer Discretionary</td>\n",
       "      <td>1.388488</td>\n",
       "      <td>0.177745</td>\n",
       "      <td>29.15</td>\n",
       "      <td>3.06</td>\n",
       "      <td>0.56</td>\n",
       "      <td>104787934567</td>\n",
       "      <td>1173700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>RCL</td>\n",
       "      <td>Royal Caribbean Cruises Ltd</td>\n",
       "      <td>Consumer Discretionary</td>\n",
       "      <td>1.347063</td>\n",
       "      <td>0.195659</td>\n",
       "      <td>12.55</td>\n",
       "      <td>9.02</td>\n",
       "      <td>1.44</td>\n",
       "      <td>23627921924</td>\n",
       "      <td>208801000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>TGT</td>\n",
       "      <td>Target Corp.</td>\n",
       "      <td>Consumer Discretionary</td>\n",
       "      <td>1.340882</td>\n",
       "      <td>0.185070</td>\n",
       "      <td>18.64</td>\n",
       "      <td>6.26</td>\n",
       "      <td>0.56</td>\n",
       "      <td>59100734918</td>\n",
       "      <td>506737000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>VFC</td>\n",
       "      <td>V.F. Corp.</td>\n",
       "      <td>Consumer Discretionary</td>\n",
       "      <td>1.323241</td>\n",
       "      <td>0.129111</td>\n",
       "      <td>25.99</td>\n",
       "      <td>3.22</td>\n",
       "      <td>1.20</td>\n",
       "      <td>33022293377</td>\n",
       "      <td>394720000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>MHK</td>\n",
       "      <td>Mohawk Industries</td>\n",
       "      <td>Consumer Discretionary</td>\n",
       "      <td>1.255168</td>\n",
       "      <td>-0.016397</td>\n",
       "      <td>13.40</td>\n",
       "      <td>10.30</td>\n",
       "      <td>1.36</td>\n",
       "      <td>9882403253</td>\n",
       "      <td>71622000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>PHM</td>\n",
       "      <td>Pulte Homes Inc.</td>\n",
       "      <td>Consumer Discretionary</td>\n",
       "      <td>1.125225</td>\n",
       "      <td>0.165132</td>\n",
       "      <td>12.77</td>\n",
       "      <td>3.66</td>\n",
       "      <td>0.56</td>\n",
       "      <td>12605132379</td>\n",
       "      <td>269975000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>HLT</td>\n",
       "      <td>Hilton Worldwide Holdings Inc</td>\n",
       "      <td>Consumer Discretionary</td>\n",
       "      <td>1.059899</td>\n",
       "      <td>0.171047</td>\n",
       "      <td>37.27</td>\n",
       "      <td>3.04</td>\n",
       "      <td>1.14</td>\n",
       "      <td>31415437971</td>\n",
       "      <td>277448000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>M</td>\n",
       "      <td>Macy's Inc.</td>\n",
       "      <td>Consumer Discretionary</td>\n",
       "      <td>0.826483</td>\n",
       "      <td>-0.126598</td>\n",
       "      <td>5.39</td>\n",
       "      <td>3.09</td>\n",
       "      <td>0.66</td>\n",
       "      <td>5150446573</td>\n",
       "      <td>308965000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>BBY</td>\n",
       "      <td>Best Buy Co. Inc.</td>\n",
       "      <td>Consumer Discretionary</td>\n",
       "      <td>0.823915</td>\n",
       "      <td>0.264073</td>\n",
       "      <td>15.98</td>\n",
       "      <td>5.64</td>\n",
       "      <td>1.12</td>\n",
       "      <td>23344272933</td>\n",
       "      <td>258777000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ticker                           name                  sector  \\\n",
       "44    GPC                  Genuine Parts  Consumer Discretionary   \n",
       "33   EXPE                   Expedia Inc.  Consumer Discretionary   \n",
       "30  DISCK     Discovery Communications-C  Consumer Discretionary   \n",
       "54    LKQ                LKQ Corporation  Consumer Discretionary   \n",
       "43     GM                 General Motors  Consumer Discretionary   \n",
       "22    KMX                     Carmax Inc  Consumer Discretionary   \n",
       "72     RL        Polo Ralph Lauren Corp.  Consumer Discretionary   \n",
       "17    BWA                     BorgWarner  Consumer Discretionary   \n",
       "81   SBUX                Starbucks Corp.  Consumer Discretionary   \n",
       "78    RCL    Royal Caribbean Cruises Ltd  Consumer Discretionary   \n",
       "84    TGT                   Target Corp.  Consumer Discretionary   \n",
       "89    VFC                     V.F. Corp.  Consumer Discretionary   \n",
       "61    MHK              Mohawk Industries  Consumer Discretionary   \n",
       "74    PHM               Pulte Homes Inc.  Consumer Discretionary   \n",
       "47    HLT  Hilton Worldwide Holdings Inc  Consumer Discretionary   \n",
       "56      M                    Macy's Inc.  Consumer Discretionary   \n",
       "15    BBY              Best Buy Co. Inc.  Consumer Discretionary   \n",
       "\n",
       "    sharpe ratio  FFM returns  P/E ratio    EPS  beta       mkt cap  \\\n",
       "44      2.924464     0.074228      17.71   5.45  0.89   14016415887   \n",
       "33      2.407311     0.086286      32.58   3.77  1.01   17792025802   \n",
       "30      2.309659     0.061552      11.10   2.59  1.45   15392873644   \n",
       "54      2.216066     0.121772      23.73   1.40  1.30   10174538633   \n",
       "43      1.903721     0.084495       7.60   4.57  1.38   49672072361   \n",
       "22      1.703158     0.099285      19.16   5.16  1.10   16144071451   \n",
       "72      1.685028     0.046267      14.16   8.61  0.90    8984857815   \n",
       "17      1.682271    -0.008937       9.20   3.71  1.91    7048901814   \n",
       "81      1.388488     0.177745      29.15   3.06  0.56  104787934567   \n",
       "78      1.347063     0.195659      12.55   9.02  1.44   23627921924   \n",
       "84      1.340882     0.185070      18.64   6.26  0.56   59100734918   \n",
       "89      1.323241     0.129111      25.99   3.22  1.20   33022293377   \n",
       "61      1.255168    -0.016397      13.40  10.30  1.36    9882403253   \n",
       "74      1.125225     0.165132      12.77   3.66  0.56   12605132379   \n",
       "47      1.059899     0.171047      37.27   3.04  1.14   31415437971   \n",
       "56      0.826483    -0.126598       5.39   3.09  0.66    5150446573   \n",
       "15      0.823915     0.264073      15.98   5.64  1.12   23344272933   \n",
       "\n",
       "        shares  \n",
       "44   145293000  \n",
       "33   139363000  \n",
       "30   360664000  \n",
       "54   306462000  \n",
       "43  1429002000  \n",
       "22   163385000  \n",
       "72    48862000  \n",
       "17   206410000  \n",
       "81  1173700000  \n",
       "78   208801000  \n",
       "84   506737000  \n",
       "89   394720000  \n",
       "61    71622000  \n",
       "74   269975000  \n",
       "47   277448000  \n",
       "56   308965000  \n",
       "15   258777000  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cd_df\n",
    "\n",
    "# GPC - high sharpe, decent p/e ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>ticker</th>\n",
       "      <th>name</th>\n",
       "      <th>sector</th>\n",
       "      <th>sharpe ratio</th>\n",
       "      <th>FFM returns</th>\n",
       "      <th>P/E ratio</th>\n",
       "      <th>EPS</th>\n",
       "      <th>beta</th>\n",
       "      <th>mkt cap</th>\n",
       "      <th>shares</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>GIS</td>\n",
       "      <td>General Mills</td>\n",
       "      <td>Consumer Staples</td>\n",
       "      <td>2.641281</td>\n",
       "      <td>0.063147</td>\n",
       "      <td>15.34</td>\n",
       "      <td>3.48</td>\n",
       "      <td>0.73</td>\n",
       "      <td>32242795100</td>\n",
       "      <td>604817000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>WBA</td>\n",
       "      <td>Walgreens Boots Alliance</td>\n",
       "      <td>Consumer Staples</td>\n",
       "      <td>2.224201</td>\n",
       "      <td>-0.007405</td>\n",
       "      <td>13.00</td>\n",
       "      <td>4.07</td>\n",
       "      <td>0.92</td>\n",
       "      <td>46817805348</td>\n",
       "      <td>885862000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>KO</td>\n",
       "      <td>Coca-Cola Company (The)</td>\n",
       "      <td>Consumer Staples</td>\n",
       "      <td>2.197158</td>\n",
       "      <td>0.103820</td>\n",
       "      <td>28.99</td>\n",
       "      <td>2.07</td>\n",
       "      <td>0.41</td>\n",
       "      <td>256586003265</td>\n",
       "      <td>4280000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>PEP</td>\n",
       "      <td>PepsiCo Inc.</td>\n",
       "      <td>Consumer Staples</td>\n",
       "      <td>1.951250</td>\n",
       "      <td>0.117935</td>\n",
       "      <td>28.31</td>\n",
       "      <td>5.19</td>\n",
       "      <td>0.53</td>\n",
       "      <td>204968008309</td>\n",
       "      <td>1389545000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>CVS</td>\n",
       "      <td>CVS Health</td>\n",
       "      <td>Consumer Staples</td>\n",
       "      <td>1.707963</td>\n",
       "      <td>-0.010387</td>\n",
       "      <td>14.05</td>\n",
       "      <td>5.08</td>\n",
       "      <td>0.93</td>\n",
       "      <td>92849804253</td>\n",
       "      <td>1300964000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>HSY</td>\n",
       "      <td>The Hershey Company</td>\n",
       "      <td>Consumer Staples</td>\n",
       "      <td>1.469682</td>\n",
       "      <td>0.125085</td>\n",
       "      <td>29.33</td>\n",
       "      <td>5.47</td>\n",
       "      <td>0.02</td>\n",
       "      <td>23779704176</td>\n",
       "      <td>148308000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>WMT</td>\n",
       "      <td>Wal-Mart Stores</td>\n",
       "      <td>Consumer Staples</td>\n",
       "      <td>1.271096</td>\n",
       "      <td>0.125055</td>\n",
       "      <td>23.55</td>\n",
       "      <td>5.00</td>\n",
       "      <td>0.36</td>\n",
       "      <td>334474559018</td>\n",
       "      <td>2837175000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>COST</td>\n",
       "      <td>Costco Wholesale Corp.</td>\n",
       "      <td>Consumer Staples</td>\n",
       "      <td>1.060644</td>\n",
       "      <td>0.192130</td>\n",
       "      <td>37.75</td>\n",
       "      <td>8.43</td>\n",
       "      <td>0.90</td>\n",
       "      <td>140615987901</td>\n",
       "      <td>441758000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ticker                      name            sector  sharpe ratio  \\\n",
       "42    GIS             General Mills  Consumer Staples      2.641281   \n",
       "91    WBA  Walgreens Boots Alliance  Consumer Staples      2.224201   \n",
       "26     KO   Coca-Cola Company (The)  Consumer Staples      2.197158   \n",
       "71    PEP              PepsiCo Inc.  Consumer Staples      1.951250   \n",
       "29    CVS                CVS Health  Consumer Staples      1.707963   \n",
       "86    HSY       The Hershey Company  Consumer Staples      1.469682   \n",
       "90    WMT           Wal-Mart Stores  Consumer Staples      1.271096   \n",
       "27   COST    Costco Wholesale Corp.  Consumer Staples      1.060644   \n",
       "\n",
       "    FFM returns  P/E ratio   EPS  beta       mkt cap      shares  \n",
       "42     0.063147      15.34  3.48  0.73   32242795100   604817000  \n",
       "91    -0.007405      13.00  4.07  0.92   46817805348   885862000  \n",
       "26     0.103820      28.99  2.07  0.41  256586003265  4280000000  \n",
       "71     0.117935      28.31  5.19  0.53  204968008309  1389545000  \n",
       "29    -0.010387      14.05  5.08  0.93   92849804253  1300964000  \n",
       "86     0.125085      29.33  5.47  0.02   23779704176   148308000  \n",
       "90     0.125055      23.55  5.00  0.36  334474559018  2837175000  \n",
       "27     0.192130      37.75  8.43  0.90  140615987901   441758000  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cs_df\n",
    "\n",
    "# GIS - high sharpe, decent p/e"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>ticker</th>\n",
       "      <th>name</th>\n",
       "      <th>sector</th>\n",
       "      <th>sharpe ratio</th>\n",
       "      <th>FFM returns</th>\n",
       "      <th>P/E ratio</th>\n",
       "      <th>EPS</th>\n",
       "      <th>beta</th>\n",
       "      <th>mkt cap</th>\n",
       "      <th>shares</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>OXY</td>\n",
       "      <td>Occidental Petroleum</td>\n",
       "      <td>Energy</td>\n",
       "      <td>2.054771</td>\n",
       "      <td>-0.042821</td>\n",
       "      <td>29.53</td>\n",
       "      <td>1.41</td>\n",
       "      <td>0.90</td>\n",
       "      <td>37161985836</td>\n",
       "      <td>893317000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>COG</td>\n",
       "      <td>Cabot Oil &amp; Gas</td>\n",
       "      <td>Energy</td>\n",
       "      <td>1.952983</td>\n",
       "      <td>-0.050017</td>\n",
       "      <td>7.80</td>\n",
       "      <td>1.90</td>\n",
       "      <td>0.47</td>\n",
       "      <td>6037284157</td>\n",
       "      <td>407925000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>KMI</td>\n",
       "      <td>Kinder Morgan</td>\n",
       "      <td>Energy</td>\n",
       "      <td>1.038470</td>\n",
       "      <td>-0.038511</td>\n",
       "      <td>22.87</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.81</td>\n",
       "      <td>49831386000</td>\n",
       "      <td>2265063000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>OKE</td>\n",
       "      <td>ONEOK</td>\n",
       "      <td>Energy</td>\n",
       "      <td>0.917326</td>\n",
       "      <td>0.230148</td>\n",
       "      <td>25.56</td>\n",
       "      <td>3.01</td>\n",
       "      <td>1.11</td>\n",
       "      <td>31799253894</td>\n",
       "      <td>413085000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ticker                  name  sector  sharpe ratio  FFM returns  P/E ratio  \\\n",
       "68    OXY  Occidental Petroleum  Energy      2.054771    -0.042821      29.53   \n",
       "20    COG       Cabot Oil & Gas  Energy      1.952983    -0.050017       7.80   \n",
       "53    KMI         Kinder Morgan  Energy      1.038470    -0.038511      22.87   \n",
       "69    OKE                 ONEOK  Energy      0.917326     0.230148      25.56   \n",
       "\n",
       "     EPS  beta      mkt cap      shares  \n",
       "68  1.41  0.90  37161985836   893317000  \n",
       "20  1.90  0.47   6037284157   407925000  \n",
       "53  0.96  0.81  49831386000  2265063000  \n",
       "69  3.01  1.11  31799253894   413085000  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "energy_df\n",
    "\n",
    "# OKE - only one with positive returns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>ticker</th>\n",
       "      <th>name</th>\n",
       "      <th>sector</th>\n",
       "      <th>sharpe ratio</th>\n",
       "      <th>FFM returns</th>\n",
       "      <th>P/E ratio</th>\n",
       "      <th>EPS</th>\n",
       "      <th>beta</th>\n",
       "      <th>mkt cap</th>\n",
       "      <th>shares</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>SYF</td>\n",
       "      <td>Synchrony Financial</td>\n",
       "      <td>Financials</td>\n",
       "      <td>2.493336</td>\n",
       "      <td>0.091836</td>\n",
       "      <td>6.07</td>\n",
       "      <td>5.56</td>\n",
       "      <td>1.21</td>\n",
       "      <td>20704848750</td>\n",
       "      <td>613477000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>HRB</td>\n",
       "      <td>Block H&amp;R</td>\n",
       "      <td>Financials</td>\n",
       "      <td>2.210675</td>\n",
       "      <td>0.001104</td>\n",
       "      <td>11.75</td>\n",
       "      <td>1.90</td>\n",
       "      <td>0.23</td>\n",
       "      <td>4369605316</td>\n",
       "      <td>195246000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>COF</td>\n",
       "      <td>Capital One Financial</td>\n",
       "      <td>Financials</td>\n",
       "      <td>2.034764</td>\n",
       "      <td>0.116104</td>\n",
       "      <td>9.31</td>\n",
       "      <td>11.02</td>\n",
       "      <td>1.25</td>\n",
       "      <td>46865423721</td>\n",
       "      <td>456600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>GS</td>\n",
       "      <td>Goldman Sachs Group</td>\n",
       "      <td>Financials</td>\n",
       "      <td>1.828821</td>\n",
       "      <td>0.099915</td>\n",
       "      <td>11.27</td>\n",
       "      <td>21.03</td>\n",
       "      <td>1.32</td>\n",
       "      <td>83946946608</td>\n",
       "      <td>354087000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>RJF</td>\n",
       "      <td>Raymond James Financial Inc.</td>\n",
       "      <td>Financials</td>\n",
       "      <td>1.281138</td>\n",
       "      <td>0.155046</td>\n",
       "      <td>13.44</td>\n",
       "      <td>7.36</td>\n",
       "      <td>1.50</td>\n",
       "      <td>13782151374</td>\n",
       "      <td>139284000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>AXP</td>\n",
       "      <td>American Express Co</td>\n",
       "      <td>Financials</td>\n",
       "      <td>1.214408</td>\n",
       "      <td>0.132252</td>\n",
       "      <td>17.01</td>\n",
       "      <td>7.99</td>\n",
       "      <td>0.99</td>\n",
       "      <td>111178449600</td>\n",
       "      <td>808041000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>CINF</td>\n",
       "      <td>Cincinnati Financial</td>\n",
       "      <td>Financials</td>\n",
       "      <td>1.121374</td>\n",
       "      <td>0.199838</td>\n",
       "      <td>9.52</td>\n",
       "      <td>12.10</td>\n",
       "      <td>0.57</td>\n",
       "      <td>18822318390</td>\n",
       "      <td>163374000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>AON</td>\n",
       "      <td>Aon plc</td>\n",
       "      <td>Financials</td>\n",
       "      <td>1.104319</td>\n",
       "      <td>0.183167</td>\n",
       "      <td>37.00</td>\n",
       "      <td>6.37</td>\n",
       "      <td>0.83</td>\n",
       "      <td>54701475530</td>\n",
       "      <td>234137000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AMG</td>\n",
       "      <td>Affiliated Managers Group Inc</td>\n",
       "      <td>Financials</td>\n",
       "      <td>1.097986</td>\n",
       "      <td>-0.138798</td>\n",
       "      <td>267.18</td>\n",
       "      <td>0.31</td>\n",
       "      <td>1.69</td>\n",
       "      <td>4084648875</td>\n",
       "      <td>49272000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>AJG</td>\n",
       "      <td>Arthur J. Gallagher &amp; Co.</td>\n",
       "      <td>Financials</td>\n",
       "      <td>0.967112</td>\n",
       "      <td>0.195583</td>\n",
       "      <td>31.03</td>\n",
       "      <td>3.52</td>\n",
       "      <td>0.76</td>\n",
       "      <td>20549043209</td>\n",
       "      <td>188247000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>MCO</td>\n",
       "      <td>Moody's Corp</td>\n",
       "      <td>Financials</td>\n",
       "      <td>0.927984</td>\n",
       "      <td>0.234858</td>\n",
       "      <td>37.43</td>\n",
       "      <td>7.42</td>\n",
       "      <td>1.21</td>\n",
       "      <td>52148688479</td>\n",
       "      <td>187700000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ticker                           name      sector  sharpe ratio  \\\n",
       "82    SYF            Synchrony Financial  Financials      2.493336   \n",
       "16    HRB                      Block H&R  Financials      2.210675   \n",
       "21    COF          Capital One Financial  Financials      2.034764   \n",
       "45     GS            Goldman Sachs Group  Financials      1.828821   \n",
       "75    RJF   Raymond James Financial Inc.  Financials      1.281138   \n",
       "8     AXP            American Express Co  Financials      1.214408   \n",
       "24   CINF           Cincinnati Financial  Financials      1.121374   \n",
       "11    AON                        Aon plc  Financials      1.104319   \n",
       "3     AMG  Affiliated Managers Group Inc  Financials      1.097986   \n",
       "13    AJG      Arthur J. Gallagher & Co.  Financials      0.967112   \n",
       "62    MCO                   Moody's Corp  Financials      0.927984   \n",
       "\n",
       "    FFM returns  P/E ratio    EPS  beta       mkt cap     shares  \n",
       "82     0.091836       6.07   5.56  1.21   20704848750  613477000  \n",
       "16     0.001104      11.75   1.90  0.23    4369605316  195246000  \n",
       "21     0.116104       9.31  11.02  1.25   46865423721  456600000  \n",
       "45     0.099915      11.27  21.03  1.32   83946946608  354087000  \n",
       "75     0.155046      13.44   7.36  1.50   13782151374  139284000  \n",
       "8      0.132252      17.01   7.99  0.99  111178449600  808041000  \n",
       "24     0.199838       9.52  12.10  0.57   18822318390  163374000  \n",
       "11     0.183167      37.00   6.37  0.83   54701475530  234137000  \n",
       "3     -0.138798     267.18   0.31  1.69    4084648875   49272000  \n",
       "13     0.195583      31.03   3.52  0.76   20549043209  188247000  \n",
       "62     0.234858      37.43   7.42  1.21   52148688479  187700000  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "financials_df\n",
    "\n",
    "# SYF - good sharpe, good p/e, nice eps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ticker</th>\n",
       "      <th>name</th>\n",
       "      <th>sector</th>\n",
       "      <th>sharpe ratio</th>\n",
       "      <th>FFM returns</th>\n",
       "      <th>P/E ratio</th>\n",
       "      <th>EPS</th>\n",
       "      <th>beta</th>\n",
       "      <th>mkt cap</th>\n",
       "      <th>shares</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>ABC</td>\n",
       "      <td>AmerisourceBergen Corp</td>\n",
       "      <td>Health Care</td>\n",
       "      <td>2.824204</td>\n",
       "      <td>0.018487</td>\n",
       "      <td>30.62</td>\n",
       "      <td>3.08</td>\n",
       "      <td>1.01</td>\n",
       "      <td>19427969245</td>\n",
       "      <td>205892000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>BMY</td>\n",
       "      <td>Bristol-Myers Squibb</td>\n",
       "      <td>Health Care</td>\n",
       "      <td>2.688515</td>\n",
       "      <td>0.083177</td>\n",
       "      <td>33.05</td>\n",
       "      <td>2.01</td>\n",
       "      <td>0.76</td>\n",
       "      <td>155607591281</td>\n",
       "      <td>2344185000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>HOLX</td>\n",
       "      <td>Hologic</td>\n",
       "      <td>Health Care</td>\n",
       "      <td>2.425412</td>\n",
       "      <td>0.129968</td>\n",
       "      <td>173.01</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.85</td>\n",
       "      <td>13960272160</td>\n",
       "      <td>263302000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>MDT</td>\n",
       "      <td>Medtronic plc</td>\n",
       "      <td>Health Care</td>\n",
       "      <td>1.917921</td>\n",
       "      <td>0.124213</td>\n",
       "      <td>33.97</td>\n",
       "      <td>3.45</td>\n",
       "      <td>0.60</td>\n",
       "      <td>157266553194</td>\n",
       "      <td>1340378000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>AMGN</td>\n",
       "      <td>Amgen Inc</td>\n",
       "      <td>Health Care</td>\n",
       "      <td>1.861649</td>\n",
       "      <td>0.151749</td>\n",
       "      <td>17.36</td>\n",
       "      <td>12.88</td>\n",
       "      <td>1.12</td>\n",
       "      <td>131810065840</td>\n",
       "      <td>589807000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>MRK</td>\n",
       "      <td>Merck &amp; Co.</td>\n",
       "      <td>Health Care</td>\n",
       "      <td>1.368058</td>\n",
       "      <td>0.147016</td>\n",
       "      <td>21.52</td>\n",
       "      <td>3.84</td>\n",
       "      <td>0.57</td>\n",
       "      <td>210425581484</td>\n",
       "      <td>2545984000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>INCY</td>\n",
       "      <td>Incyte</td>\n",
       "      <td>Health Care</td>\n",
       "      <td>1.268258</td>\n",
       "      <td>0.076793</td>\n",
       "      <td>38.21</td>\n",
       "      <td>2.05</td>\n",
       "      <td>1.06</td>\n",
       "      <td>17006076538</td>\n",
       "      <td>216776000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>MTD</td>\n",
       "      <td>Mettler Toledo</td>\n",
       "      <td>Health Care</td>\n",
       "      <td>0.969242</td>\n",
       "      <td>0.232895</td>\n",
       "      <td>33.97</td>\n",
       "      <td>22.47</td>\n",
       "      <td>1.39</td>\n",
       "      <td>18357249893</td>\n",
       "      <td>24053000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>TMO</td>\n",
       "      <td>Thermo Fisher Scientific</td>\n",
       "      <td>Health Care</td>\n",
       "      <td>0.957237</td>\n",
       "      <td>0.223396</td>\n",
       "      <td>36.98</td>\n",
       "      <td>9.17</td>\n",
       "      <td>1.14</td>\n",
       "      <td>136012138268</td>\n",
       "      <td>400991000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>BSX</td>\n",
       "      <td>Boston Scientific</td>\n",
       "      <td>Health Care</td>\n",
       "      <td>0.933787</td>\n",
       "      <td>0.233861</td>\n",
       "      <td>12.70</td>\n",
       "      <td>3.33</td>\n",
       "      <td>0.87</td>\n",
       "      <td>58972695354</td>\n",
       "      <td>1393824000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>MYL</td>\n",
       "      <td>Mylan N.V.</td>\n",
       "      <td>Health Care</td>\n",
       "      <td>0.893280</td>\n",
       "      <td>-0.130829</td>\n",
       "      <td>239.58</td>\n",
       "      <td>0.09</td>\n",
       "      <td>1.72</td>\n",
       "      <td>11303303743</td>\n",
       "      <td>516177000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>UNH</td>\n",
       "      <td>United Health Group Inc.</td>\n",
       "      <td>Health Care</td>\n",
       "      <td>0.892893</td>\n",
       "      <td>0.243094</td>\n",
       "      <td>20.86</td>\n",
       "      <td>14.33</td>\n",
       "      <td>0.69</td>\n",
       "      <td>283068652543</td>\n",
       "      <td>947415000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>RMD</td>\n",
       "      <td>ResMed</td>\n",
       "      <td>Health Care</td>\n",
       "      <td>0.834963</td>\n",
       "      <td>0.236317</td>\n",
       "      <td>56.31</td>\n",
       "      <td>3.14</td>\n",
       "      <td>0.48</td>\n",
       "      <td>25539363082</td>\n",
       "      <td>144617000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ticker                      name       sector  sharpe ratio  FFM returns  \\\n",
       "9     ABC    AmerisourceBergen Corp  Health Care      2.824204     0.018487   \n",
       "19    BMY      Bristol-Myers Squibb  Health Care      2.688515     0.083177   \n",
       "48   HOLX                   Hologic  Health Care      2.425412     0.129968   \n",
       "57    MDT             Medtronic plc  Health Care      1.917921     0.124213   \n",
       "10   AMGN                 Amgen Inc  Health Care      1.861649     0.151749   \n",
       "58    MRK               Merck & Co.  Health Care      1.368058     0.147016   \n",
       "51   INCY                    Incyte  Health Care      1.268258     0.076793   \n",
       "59    MTD            Mettler Toledo  Health Care      0.969242     0.232895   \n",
       "87    TMO  Thermo Fisher Scientific  Health Care      0.957237     0.223396   \n",
       "18    BSX         Boston Scientific  Health Care      0.933787     0.233861   \n",
       "64    MYL                Mylan N.V.  Health Care      0.893280    -0.130829   \n",
       "88    UNH  United Health Group Inc.  Health Care      0.892893     0.243094   \n",
       "77    RMD                    ResMed  Health Care      0.834963     0.236317   \n",
       "\n",
       "    P/E ratio    EPS  beta       mkt cap      shares  \n",
       "9       30.62   3.08  1.01   19427969245   205892000  \n",
       "19      33.05   2.01  0.76  155607591281  2344185000  \n",
       "48     173.01   0.31  0.85   13960272160   263302000  \n",
       "57      33.97   3.45  0.60  157266553194  1340378000  \n",
       "10      17.36  12.88  1.12  131810065840   589807000  \n",
       "58      21.52   3.84  0.57  210425581484  2545984000  \n",
       "51      38.21   2.05  1.06   17006076538   216776000  \n",
       "59      33.97  22.47  1.39   18357249893    24053000  \n",
       "87      36.98   9.17  1.14  136012138268   400991000  \n",
       "18      12.70   3.33  0.87   58972695354  1393824000  \n",
       "64     239.58   0.09  1.72   11303303743   516177000  \n",
       "88      20.86  14.33  0.69  283068652543   947415000  \n",
       "77      56.31   3.14  0.48   25539363082   144617000  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hc_df\n",
    "\n",
    "# AMGN - not highest sharpe, but decent p/e and very good eps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ticker</th>\n",
       "      <th>name</th>\n",
       "      <th>sector</th>\n",
       "      <th>sharpe ratio</th>\n",
       "      <th>FFM returns</th>\n",
       "      <th>P/E ratio</th>\n",
       "      <th>EPS</th>\n",
       "      <th>beta</th>\n",
       "      <th>mkt cap</th>\n",
       "      <th>shares</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>LUV</td>\n",
       "      <td>Southwest Airlines</td>\n",
       "      <td>Industrials</td>\n",
       "      <td>1.726576</td>\n",
       "      <td>0.107833</td>\n",
       "      <td>13.77</td>\n",
       "      <td>4.21</td>\n",
       "      <td>1.47</td>\n",
       "      <td>29987649751</td>\n",
       "      <td>517296000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>ETN</td>\n",
       "      <td>Eaton Corporation</td>\n",
       "      <td>Industrials</td>\n",
       "      <td>1.604766</td>\n",
       "      <td>0.128998</td>\n",
       "      <td>19.71</td>\n",
       "      <td>5.25</td>\n",
       "      <td>1.42</td>\n",
       "      <td>42807571261</td>\n",
       "      <td>413400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>FDX</td>\n",
       "      <td>FedEx Corporation</td>\n",
       "      <td>Industrials</td>\n",
       "      <td>1.509820</td>\n",
       "      <td>0.024254</td>\n",
       "      <td>583.74</td>\n",
       "      <td>0.27</td>\n",
       "      <td>1.71</td>\n",
       "      <td>41418694505</td>\n",
       "      <td>261119000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>GWW</td>\n",
       "      <td>Grainger (W.W.) Inc.</td>\n",
       "      <td>Industrials</td>\n",
       "      <td>1.358110</td>\n",
       "      <td>0.143754</td>\n",
       "      <td>17.94</td>\n",
       "      <td>17.15</td>\n",
       "      <td>0.97</td>\n",
       "      <td>16517112872</td>\n",
       "      <td>53688000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>RTN</td>\n",
       "      <td>Raytheon Co.</td>\n",
       "      <td>Industrials</td>\n",
       "      <td>1.158338</td>\n",
       "      <td>0.194796</td>\n",
       "      <td>19.05</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.85</td>\n",
       "      <td>63256224340</td>\n",
       "      <td>278441000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>HII</td>\n",
       "      <td>Huntington Ingalls Industries</td>\n",
       "      <td>Industrials</td>\n",
       "      <td>1.154338</td>\n",
       "      <td>0.172310</td>\n",
       "      <td>18.57</td>\n",
       "      <td>13.26</td>\n",
       "      <td>1.24</td>\n",
       "      <td>10035519873</td>\n",
       "      <td>40760000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>CTAS</td>\n",
       "      <td>Cintas Corporation</td>\n",
       "      <td>Industrials</td>\n",
       "      <td>0.703186</td>\n",
       "      <td>0.285973</td>\n",
       "      <td>35.38</td>\n",
       "      <td>8.51</td>\n",
       "      <td>0.99</td>\n",
       "      <td>31216601386</td>\n",
       "      <td>103751000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ticker                           name       sector  sharpe ratio  \\\n",
       "80    LUV             Southwest Airlines  Industrials      1.726576   \n",
       "31    ETN              Eaton Corporation  Industrials      1.604766   \n",
       "36    FDX              FedEx Corporation  Industrials      1.509820   \n",
       "46    GWW           Grainger (W.W.) Inc.  Industrials      1.358110   \n",
       "76    RTN                   Raytheon Co.  Industrials      1.158338   \n",
       "50    HII  Huntington Ingalls Industries  Industrials      1.154338   \n",
       "25   CTAS             Cintas Corporation  Industrials      0.703186   \n",
       "\n",
       "    FFM returns  P/E ratio    EPS  beta      mkt cap     shares  \n",
       "80     0.107833      13.77   4.21  1.47  29987649751  517296000  \n",
       "31     0.128998      19.71   5.25  1.42  42807571261  413400000  \n",
       "36     0.024254     583.74   0.27  1.71  41418694505  261119000  \n",
       "46     0.143754      17.94  17.15  0.97  16517112872   53688000  \n",
       "76     0.194796      19.05  11.93  0.85  63256224340  278441000  \n",
       "50     0.172310      18.57  13.26  1.24  10035519873   40760000  \n",
       "25     0.285973      35.38   8.51  0.99  31216601386  103751000  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "industrials_df\n",
    "\n",
    "# LUV - decent sharpe, decent p/e"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ticker</th>\n",
       "      <th>name</th>\n",
       "      <th>sector</th>\n",
       "      <th>sharpe ratio</th>\n",
       "      <th>FFM returns</th>\n",
       "      <th>P/E ratio</th>\n",
       "      <th>EPS</th>\n",
       "      <th>beta</th>\n",
       "      <th>mkt cap</th>\n",
       "      <th>shares</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>ORCL</td>\n",
       "      <td>Oracle Corp.</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>1.949619</td>\n",
       "      <td>0.077350</td>\n",
       "      <td>17.61</td>\n",
       "      <td>3.15</td>\n",
       "      <td>1.11</td>\n",
       "      <td>177928293945</td>\n",
       "      <td>3207649000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>FFIV</td>\n",
       "      <td>F5 Networks</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>1.701382</td>\n",
       "      <td>0.064989</td>\n",
       "      <td>19.49</td>\n",
       "      <td>6.54</td>\n",
       "      <td>0.98</td>\n",
       "      <td>7753118169</td>\n",
       "      <td>60804000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>IT</td>\n",
       "      <td>Gartner Inc</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>1.337328</td>\n",
       "      <td>0.165214</td>\n",
       "      <td>59.34</td>\n",
       "      <td>2.57</td>\n",
       "      <td>1.21</td>\n",
       "      <td>13633532057</td>\n",
       "      <td>89453000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>GOOGL</td>\n",
       "      <td>Alphabet Inc Class A</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>1.198026</td>\n",
       "      <td>0.214640</td>\n",
       "      <td>30.89</td>\n",
       "      <td>49.16</td>\n",
       "      <td>1.02</td>\n",
       "      <td>1044236513876</td>\n",
       "      <td>299895000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ACN</td>\n",
       "      <td>Accenture plc</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>1.113858</td>\n",
       "      <td>0.225818</td>\n",
       "      <td>28.34</td>\n",
       "      <td>7.49</td>\n",
       "      <td>1.03</td>\n",
       "      <td>139515576338</td>\n",
       "      <td>656946000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>FB</td>\n",
       "      <td>Facebook, Inc.</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>1.105767</td>\n",
       "      <td>0.238789</td>\n",
       "      <td>33.32</td>\n",
       "      <td>6.43</td>\n",
       "      <td>1.05</td>\n",
       "      <td>610509360122</td>\n",
       "      <td>2405746000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>ADP</td>\n",
       "      <td>Automatic Data Processing</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>1.004579</td>\n",
       "      <td>0.183269</td>\n",
       "      <td>32.04</td>\n",
       "      <td>5.66</td>\n",
       "      <td>0.87</td>\n",
       "      <td>78255412500</td>\n",
       "      <td>431754000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>FISV</td>\n",
       "      <td>Fiserv Inc</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>0.948895</td>\n",
       "      <td>0.249903</td>\n",
       "      <td>71.58</td>\n",
       "      <td>1.71</td>\n",
       "      <td>0.80</td>\n",
       "      <td>83164756192</td>\n",
       "      <td>679895000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>AAPL</td>\n",
       "      <td>Apple Inc.</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>0.919063</td>\n",
       "      <td>0.236049</td>\n",
       "      <td>25.66</td>\n",
       "      <td>12.66</td>\n",
       "      <td>1.29</td>\n",
       "      <td>1421812279411</td>\n",
       "      <td>4375480000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>TXN</td>\n",
       "      <td>Texas Instruments</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>0.886247</td>\n",
       "      <td>0.224912</td>\n",
       "      <td>25.24</td>\n",
       "      <td>5.24</td>\n",
       "      <td>1.24</td>\n",
       "      <td>123223956977</td>\n",
       "      <td>932032000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>CRM</td>\n",
       "      <td>Salesforce.com</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>0.885841</td>\n",
       "      <td>0.207470</td>\n",
       "      <td>200.68</td>\n",
       "      <td>0.95</td>\n",
       "      <td>1.19</td>\n",
       "      <td>168485647293</td>\n",
       "      <td>887000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>SNPS</td>\n",
       "      <td>Synopsys Inc.</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>0.801366</td>\n",
       "      <td>0.258979</td>\n",
       "      <td>47.31</td>\n",
       "      <td>3.45</td>\n",
       "      <td>1.18</td>\n",
       "      <td>24589893168</td>\n",
       "      <td>150535000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>MSI</td>\n",
       "      <td>Motorola Solutions Inc.</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>0.785703</td>\n",
       "      <td>0.231468</td>\n",
       "      <td>37.31</td>\n",
       "      <td>4.94</td>\n",
       "      <td>0.50</td>\n",
       "      <td>31596072387</td>\n",
       "      <td>171336000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>Microsoft Corp.</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>0.688151</td>\n",
       "      <td>0.322501</td>\n",
       "      <td>32.28</td>\n",
       "      <td>5.74</td>\n",
       "      <td>1.15</td>\n",
       "      <td>1409780857873</td>\n",
       "      <td>7606047000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ADBE</td>\n",
       "      <td>Adobe Systems Inc</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>0.616060</td>\n",
       "      <td>0.329761</td>\n",
       "      <td>63.17</td>\n",
       "      <td>6.01</td>\n",
       "      <td>1.09</td>\n",
       "      <td>185174936969</td>\n",
       "      <td>487726000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ticker                       name                  sector  sharpe ratio  \\\n",
       "70   ORCL               Oracle Corp.  Information Technology      1.949619   \n",
       "34   FFIV                F5 Networks  Information Technology      1.701382   \n",
       "41     IT                Gartner Inc  Information Technology      1.337328   \n",
       "7   GOOGL       Alphabet Inc Class A  Information Technology      1.198026   \n",
       "0     ACN              Accenture plc  Information Technology      1.113858   \n",
       "35     FB             Facebook, Inc.  Information Technology      1.105767   \n",
       "14    ADP  Automatic Data Processing  Information Technology      1.004579   \n",
       "38   FISV                 Fiserv Inc  Information Technology      0.948895   \n",
       "12   AAPL                 Apple Inc.  Information Technology      0.919063   \n",
       "85    TXN          Texas Instruments  Information Technology      0.886247   \n",
       "79    CRM             Salesforce.com  Information Technology      0.885841   \n",
       "83   SNPS              Synopsys Inc.  Information Technology      0.801366   \n",
       "63    MSI    Motorola Solutions Inc.  Information Technology      0.785703   \n",
       "60   MSFT            Microsoft Corp.  Information Technology      0.688151   \n",
       "1    ADBE          Adobe Systems Inc  Information Technology      0.616060   \n",
       "\n",
       "    FFM returns  P/E ratio    EPS  beta        mkt cap      shares  \n",
       "70     0.077350      17.61   3.15  1.11   177928293945  3207649000  \n",
       "34     0.064989      19.49   6.54  0.98     7753118169    60804000  \n",
       "41     0.165214      59.34   2.57  1.21    13633532057    89453000  \n",
       "7      0.214640      30.89  49.16  1.02  1044236513876   299895000  \n",
       "0      0.225818      28.34   7.49  1.03   139515576338   656946000  \n",
       "35     0.238789      33.32   6.43  1.05   610509360122  2405746000  \n",
       "14     0.183269      32.04   5.66  0.87    78255412500   431754000  \n",
       "38     0.249903      71.58   1.71  0.80    83164756192   679895000  \n",
       "12     0.236049      25.66  12.66  1.29  1421812279411  4375480000  \n",
       "85     0.224912      25.24   5.24  1.24   123223956977   932032000  \n",
       "79     0.207470     200.68   0.95  1.19   168485647293   887000000  \n",
       "83     0.258979      47.31   3.45  1.18    24589893168   150535000  \n",
       "63     0.231468      37.31   4.94  0.50    31596072387   171336000  \n",
       "60     0.322501      32.28   5.74  1.15  1409780857873  7606047000  \n",
       "1      0.329761      63.17   6.01  1.09   185174936969   487726000  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "it_df\n",
    "\n",
    "#GOOGL - a bit out of whack with sharpe and p/e constraints... but just look at that eps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>ticker</th>\n",
       "      <th>name</th>\n",
       "      <th>sector</th>\n",
       "      <th>sharpe ratio</th>\n",
       "      <th>FFM returns</th>\n",
       "      <th>P/E ratio</th>\n",
       "      <th>EPS</th>\n",
       "      <th>beta</th>\n",
       "      <th>mkt cap</th>\n",
       "      <th>shares</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>LYB</td>\n",
       "      <td>LyondellBasell</td>\n",
       "      <td>Materials</td>\n",
       "      <td>2.072393</td>\n",
       "      <td>0.103512</td>\n",
       "      <td>8.65</td>\n",
       "      <td>9.55</td>\n",
       "      <td>1.45</td>\n",
       "      <td>27505799491</td>\n",
       "      <td>333000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>NEM</td>\n",
       "      <td>Newmont Mining Corporation</td>\n",
       "      <td>Materials</td>\n",
       "      <td>1.248661</td>\n",
       "      <td>0.189686</td>\n",
       "      <td>15.83</td>\n",
       "      <td>2.78</td>\n",
       "      <td>0.16</td>\n",
       "      <td>36130286851</td>\n",
       "      <td>819839000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>APD</td>\n",
       "      <td>Air Products &amp; Chemicals Inc</td>\n",
       "      <td>Materials</td>\n",
       "      <td>1.224824</td>\n",
       "      <td>0.153161</td>\n",
       "      <td>30.09</td>\n",
       "      <td>8.51</td>\n",
       "      <td>0.86</td>\n",
       "      <td>56495776935</td>\n",
       "      <td>220678000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>FCX</td>\n",
       "      <td>Freeport-McMoRan Inc.</td>\n",
       "      <td>Materials</td>\n",
       "      <td>1.031316</td>\n",
       "      <td>0.038646</td>\n",
       "      <td>78.35</td>\n",
       "      <td>0.16</td>\n",
       "      <td>2.42</td>\n",
       "      <td>17759187027</td>\n",
       "      <td>1450914000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ALB</td>\n",
       "      <td>Albemarle Corp</td>\n",
       "      <td>Materials</td>\n",
       "      <td>0.990292</td>\n",
       "      <td>0.132207</td>\n",
       "      <td>16.67</td>\n",
       "      <td>5.38</td>\n",
       "      <td>1.57</td>\n",
       "      <td>9504798055</td>\n",
       "      <td>106033000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>FMC</td>\n",
       "      <td>FMC Corporation</td>\n",
       "      <td>Materials</td>\n",
       "      <td>0.889526</td>\n",
       "      <td>0.193867</td>\n",
       "      <td>27.41</td>\n",
       "      <td>3.85</td>\n",
       "      <td>1.54</td>\n",
       "      <td>13670494208</td>\n",
       "      <td>129615000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ticker                          name     sector  sharpe ratio  FFM returns  \\\n",
       "55    LYB                LyondellBasell  Materials      2.072393     0.103512   \n",
       "65    NEM    Newmont Mining Corporation  Materials      1.248661     0.189686   \n",
       "4     APD  Air Products & Chemicals Inc  Materials      1.224824     0.153161   \n",
       "40    FCX         Freeport-McMoRan Inc.  Materials      1.031316     0.038646   \n",
       "5     ALB                Albemarle Corp  Materials      0.990292     0.132207   \n",
       "39    FMC               FMC Corporation  Materials      0.889526     0.193867   \n",
       "\n",
       "    P/E ratio   EPS  beta      mkt cap      shares  \n",
       "55       8.65  9.55  1.45  27505799491   333000000  \n",
       "65      15.83  2.78  0.16  36130286851   819839000  \n",
       "4       30.09  8.51  0.86  56495776935   220678000  \n",
       "40      78.35  0.16  2.42  17759187027  1450914000  \n",
       "5       16.67  5.38  1.57   9504798055   106033000  \n",
       "39      27.41  3.85  1.54  13670494208   129615000  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "materials_df\n",
    "\n",
    "# LYB - good sharpe, good p/e, good eps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>ticker</th>\n",
       "      <th>name</th>\n",
       "      <th>sector</th>\n",
       "      <th>sharpe ratio</th>\n",
       "      <th>FFM returns</th>\n",
       "      <th>P/E ratio</th>\n",
       "      <th>EPS</th>\n",
       "      <th>beta</th>\n",
       "      <th>mkt cap</th>\n",
       "      <th>shares</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>HST</td>\n",
       "      <td>Host Hotels &amp; Resorts</td>\n",
       "      <td>Real Estate</td>\n",
       "      <td>2.308137</td>\n",
       "      <td>0.052833</td>\n",
       "      <td>10.89</td>\n",
       "      <td>1.55</td>\n",
       "      <td>1.17</td>\n",
       "      <td>12127479870</td>\n",
       "      <td>717178000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>IRM</td>\n",
       "      <td>Iron Mountain Incorporated</td>\n",
       "      <td>Real Estate</td>\n",
       "      <td>2.233518</td>\n",
       "      <td>0.073061</td>\n",
       "      <td>35.67</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.53</td>\n",
       "      <td>9521121824</td>\n",
       "      <td>287300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>EQR</td>\n",
       "      <td>Equity Residential</td>\n",
       "      <td>Real Estate</td>\n",
       "      <td>2.158996</td>\n",
       "      <td>0.089495</td>\n",
       "      <td>34.53</td>\n",
       "      <td>2.50</td>\n",
       "      <td>0.45</td>\n",
       "      <td>32134657027</td>\n",
       "      <td>371671000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>CCI</td>\n",
       "      <td>Crown Castle International Corp.</td>\n",
       "      <td>Real Estate</td>\n",
       "      <td>1.281977</td>\n",
       "      <td>0.163975</td>\n",
       "      <td>83.64</td>\n",
       "      <td>1.98</td>\n",
       "      <td>0.30</td>\n",
       "      <td>69005081855</td>\n",
       "      <td>415768000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ticker                              name       sector  sharpe ratio  \\\n",
       "49    HST             Host Hotels & Resorts  Real Estate      2.308137   \n",
       "52    IRM        Iron Mountain Incorporated  Real Estate      2.233518   \n",
       "32    EQR                Equity Residential  Real Estate      2.158996   \n",
       "28    CCI  Crown Castle International Corp.  Real Estate      1.281977   \n",
       "\n",
       "    FFM returns  P/E ratio   EPS  beta      mkt cap     shares  \n",
       "49     0.052833      10.89  1.55  1.17  12127479870  717178000  \n",
       "52     0.073061      35.67  0.93  0.53   9521121824  287300000  \n",
       "32     0.089495      34.53  2.50  0.45  32134657027  371671000  \n",
       "28     0.163975      83.64  1.98  0.30  69005081855  415768000  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "re_df\n",
    "\n",
    "# HST - good sharpe, fairly good p/e"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ticker</th>\n",
       "      <th>name</th>\n",
       "      <th>sector</th>\n",
       "      <th>sharpe ratio</th>\n",
       "      <th>FFM returns</th>\n",
       "      <th>P/E ratio</th>\n",
       "      <th>EPS</th>\n",
       "      <th>beta</th>\n",
       "      <th>mkt cap</th>\n",
       "      <th>shares</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>PPL</td>\n",
       "      <td>PPL Corp.</td>\n",
       "      <td>Utilities</td>\n",
       "      <td>2.755488</td>\n",
       "      <td>0.098239</td>\n",
       "      <td>14.38</td>\n",
       "      <td>2.46</td>\n",
       "      <td>0.51</td>\n",
       "      <td>25573676437</td>\n",
       "      <td>723033000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>FE</td>\n",
       "      <td>FirstEnergy Corp</td>\n",
       "      <td>Utilities</td>\n",
       "      <td>1.510567</td>\n",
       "      <td>0.129910</td>\n",
       "      <td>31.45</td>\n",
       "      <td>1.66</td>\n",
       "      <td>0.20</td>\n",
       "      <td>28241486749</td>\n",
       "      <td>540714000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>LNT</td>\n",
       "      <td>Alliant Energy Corp</td>\n",
       "      <td>Utilities</td>\n",
       "      <td>1.366718</td>\n",
       "      <td>0.166244</td>\n",
       "      <td>26.79</td>\n",
       "      <td>2.23</td>\n",
       "      <td>0.23</td>\n",
       "      <td>14358652779</td>\n",
       "      <td>244628000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>CNP</td>\n",
       "      <td>CenterPoint Energy</td>\n",
       "      <td>Utilities</td>\n",
       "      <td>1.361230</td>\n",
       "      <td>0.121502</td>\n",
       "      <td>21.58</td>\n",
       "      <td>1.26</td>\n",
       "      <td>0.44</td>\n",
       "      <td>13650929559</td>\n",
       "      <td>502242000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AES</td>\n",
       "      <td>AES Corp</td>\n",
       "      <td>Utilities</td>\n",
       "      <td>1.182641</td>\n",
       "      <td>0.162654</td>\n",
       "      <td>27.44</td>\n",
       "      <td>0.76</td>\n",
       "      <td>1.05</td>\n",
       "      <td>13895280692</td>\n",
       "      <td>663893000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>NEE</td>\n",
       "      <td>NextEra Energy</td>\n",
       "      <td>Utilities</td>\n",
       "      <td>0.928683</td>\n",
       "      <td>0.227986</td>\n",
       "      <td>34.73</td>\n",
       "      <td>8.02</td>\n",
       "      <td>0.15</td>\n",
       "      <td>136133886150</td>\n",
       "      <td>488776000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>NRG</td>\n",
       "      <td>NRG Energy</td>\n",
       "      <td>Utilities</td>\n",
       "      <td>0.692217</td>\n",
       "      <td>0.208670</td>\n",
       "      <td>10.31</td>\n",
       "      <td>3.89</td>\n",
       "      <td>0.71</td>\n",
       "      <td>10078855985</td>\n",
       "      <td>251594000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ticker                 name     sector  sharpe ratio  FFM returns  \\\n",
       "73    PPL            PPL Corp.  Utilities      2.755488     0.098239   \n",
       "37     FE     FirstEnergy Corp  Utilities      1.510567     0.129910   \n",
       "6     LNT  Alliant Energy Corp  Utilities      1.366718     0.166244   \n",
       "23    CNP   CenterPoint Energy  Utilities      1.361230     0.121502   \n",
       "2     AES             AES Corp  Utilities      1.182641     0.162654   \n",
       "66    NEE       NextEra Energy  Utilities      0.928683     0.227986   \n",
       "67    NRG           NRG Energy  Utilities      0.692217     0.208670   \n",
       "\n",
       "    P/E ratio   EPS  beta       mkt cap     shares  \n",
       "73      14.38  2.46  0.51   25573676437  723033000  \n",
       "37      31.45  1.66  0.20   28241486749  540714000  \n",
       "6       26.79  2.23  0.23   14358652779  244628000  \n",
       "23      21.58  1.26  0.44   13650929559  502242000  \n",
       "2       27.44  0.76  1.05   13895280692  663893000  \n",
       "66      34.73  8.02  0.15  136133886150  488776000  \n",
       "67      10.31  3.89  0.71   10078855985  251594000  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "utilities_df\n",
    "\n",
    "# PPL - good sharpe, decent p/e"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>ticker</th>\n",
       "      <th>name</th>\n",
       "      <th>sector</th>\n",
       "      <th>sharpe ratio</th>\n",
       "      <th>FFM returns</th>\n",
       "      <th>P/E ratio</th>\n",
       "      <th>EPS</th>\n",
       "      <th>beta</th>\n",
       "      <th>mkt cap</th>\n",
       "      <th>shares</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GPC</td>\n",
       "      <td>Genuine Parts</td>\n",
       "      <td>Consumer Discretionary</td>\n",
       "      <td>2.924464</td>\n",
       "      <td>0.074228</td>\n",
       "      <td>17.71</td>\n",
       "      <td>5.45</td>\n",
       "      <td>0.89</td>\n",
       "      <td>14016415887</td>\n",
       "      <td>145293000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GIS</td>\n",
       "      <td>General Mills</td>\n",
       "      <td>Consumer Staples</td>\n",
       "      <td>2.641281</td>\n",
       "      <td>0.063147</td>\n",
       "      <td>15.34</td>\n",
       "      <td>3.48</td>\n",
       "      <td>0.73</td>\n",
       "      <td>32242795100</td>\n",
       "      <td>604817000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>OKE</td>\n",
       "      <td>ONEOK</td>\n",
       "      <td>Energy</td>\n",
       "      <td>0.917326</td>\n",
       "      <td>0.230148</td>\n",
       "      <td>25.56</td>\n",
       "      <td>3.01</td>\n",
       "      <td>1.11</td>\n",
       "      <td>31799253894</td>\n",
       "      <td>413085000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SYF</td>\n",
       "      <td>Synchrony Financial</td>\n",
       "      <td>Financials</td>\n",
       "      <td>2.493336</td>\n",
       "      <td>0.091836</td>\n",
       "      <td>6.07</td>\n",
       "      <td>5.56</td>\n",
       "      <td>1.21</td>\n",
       "      <td>20704848750</td>\n",
       "      <td>613477000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AMGN</td>\n",
       "      <td>Amgen Inc</td>\n",
       "      <td>Health Care</td>\n",
       "      <td>1.861649</td>\n",
       "      <td>0.151749</td>\n",
       "      <td>17.36</td>\n",
       "      <td>12.88</td>\n",
       "      <td>1.12</td>\n",
       "      <td>131810065840</td>\n",
       "      <td>589807000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>LUV</td>\n",
       "      <td>Southwest Airlines</td>\n",
       "      <td>Industrials</td>\n",
       "      <td>1.726576</td>\n",
       "      <td>0.107833</td>\n",
       "      <td>13.77</td>\n",
       "      <td>4.21</td>\n",
       "      <td>1.47</td>\n",
       "      <td>29987649751</td>\n",
       "      <td>517296000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>GOOGL</td>\n",
       "      <td>Alphabet Inc Class A</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>1.198026</td>\n",
       "      <td>0.214640</td>\n",
       "      <td>30.89</td>\n",
       "      <td>49.16</td>\n",
       "      <td>1.02</td>\n",
       "      <td>1044236513876</td>\n",
       "      <td>299895000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>LYB</td>\n",
       "      <td>LyondellBasell</td>\n",
       "      <td>Materials</td>\n",
       "      <td>2.072393</td>\n",
       "      <td>0.103512</td>\n",
       "      <td>8.65</td>\n",
       "      <td>9.55</td>\n",
       "      <td>1.45</td>\n",
       "      <td>27505799491</td>\n",
       "      <td>333000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>HST</td>\n",
       "      <td>Host Hotels &amp; Resorts</td>\n",
       "      <td>Real Estate</td>\n",
       "      <td>2.308137</td>\n",
       "      <td>0.052833</td>\n",
       "      <td>10.89</td>\n",
       "      <td>1.55</td>\n",
       "      <td>1.17</td>\n",
       "      <td>12127479870</td>\n",
       "      <td>717178000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>PPL</td>\n",
       "      <td>PPL Corp.</td>\n",
       "      <td>Utilities</td>\n",
       "      <td>2.755488</td>\n",
       "      <td>0.098239</td>\n",
       "      <td>14.38</td>\n",
       "      <td>2.46</td>\n",
       "      <td>0.51</td>\n",
       "      <td>25573676437</td>\n",
       "      <td>723033000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  ticker                   name                  sector  sharpe ratio  \\\n",
       "0    GPC          Genuine Parts  Consumer Discretionary      2.924464   \n",
       "1    GIS          General Mills        Consumer Staples      2.641281   \n",
       "2    OKE                  ONEOK                  Energy      0.917326   \n",
       "3    SYF    Synchrony Financial              Financials      2.493336   \n",
       "4   AMGN              Amgen Inc             Health Care      1.861649   \n",
       "5    LUV     Southwest Airlines             Industrials      1.726576   \n",
       "6  GOOGL   Alphabet Inc Class A  Information Technology      1.198026   \n",
       "7    LYB         LyondellBasell               Materials      2.072393   \n",
       "8    HST  Host Hotels & Resorts             Real Estate      2.308137   \n",
       "9    PPL              PPL Corp.               Utilities      2.755488   \n",
       "\n",
       "   FFM returns  P/E ratio    EPS  beta        mkt cap     shares  \n",
       "0     0.074228      17.71   5.45  0.89    14016415887  145293000  \n",
       "1     0.063147      15.34   3.48  0.73    32242795100  604817000  \n",
       "2     0.230148      25.56   3.01  1.11    31799253894  413085000  \n",
       "3     0.091836       6.07   5.56  1.21    20704848750  613477000  \n",
       "4     0.151749      17.36  12.88  1.12   131810065840  589807000  \n",
       "5     0.107833      13.77   4.21  1.47    29987649751  517296000  \n",
       "6     0.214640      30.89  49.16  1.02  1044236513876  299895000  \n",
       "7     0.103512       8.65   9.55  1.45    27505799491  333000000  \n",
       "8     0.052833      10.89   1.55  1.17    12127479870  717178000  \n",
       "9     0.098239      14.38   2.46  0.51    25573676437  723033000  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creating portfolio of best pick from each sector\n",
    "portfolio_df = merged_df.loc[[44,42,69,82,10,80,7,55,49,73]]\n",
    "portfolio_df.sort_values(by = 'sector', inplace=True)\n",
    "portfolio_df.reset_index(inplace=True)\n",
    "portfolio_df.drop(['index'], axis=1, inplace=True)\n",
    "portfolio_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Sharpe Ratio')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting Sharpe Ratios for portfolio\n",
    "portfolio_df.plot(x='ticker',y='sharpe ratio', kind='bar', figsize=(25,10))\n",
    "plt.suptitle('Portfolio Sharpe Ratios', fontsize=30)\n",
    "plt.axhline(2, color='g')\n",
    "plt.text(0,2.1,'Ideal Min. Sharpe Ratio',rotation=0,fontsize=20)\n",
    "plt.axhline(1.5, color='y')\n",
    "plt.text(0,1.6,'Lower Band Sharpe Ratio',rotation=0,fontsize=20)\n",
    "plt.xlabel('Company Ticker', fontsize=20)\n",
    "plt.ylabel('Sharpe Ratio',fontsize=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'P/E Ratio')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting P/E Ratios for portfolio\n",
    "portfolio_df.plot(x='ticker',y='P/E ratio', kind='bar', figsize=(25,10))\n",
    "plt.suptitle('Portfolio P/E Ratios', fontsize=30)\n",
    "plt.axhline(25, color='y')\n",
    "plt.text(0,26,'Ideal Maximum P/E Ratio',rotation=0,fontsize=20)\n",
    "plt.axhline(9, color='g')\n",
    "plt.text(0,10,'Ideal P/E Ratio',rotation=0,fontsize=20)\n",
    "plt.xlabel('Company Ticker', fontsize=20)\n",
    "plt.ylabel('P/E Ratio',fontsize=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# printing the dataset to csv \n",
    "output = 'portfolio.csv'\n",
    "\n",
    "with open(output, 'w') as file:\n",
    "    file.write(portfolio_df.to_csv(index=False))"
   ]
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}