ML_dataset_for Nasdaq100stocks_financial ratios_with labels.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ce50fd4f",
   "metadata": {},
   "source": [
    "# From Raw Dataset to Training Datasets\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a54af6e7",
   "metadata": {},
   "source": [
    "# 1. Import Libraries.\n",
    "\n",
    "In this section we Import some of the initial Libraries we will need."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "09ba318d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d3b3c77",
   "metadata": {},
   "source": [
    "# 2. Dataset for the X data.\n",
    "\n",
    "We have located a dataset on Kaggle that shows 45 financial metrics/ratios for the constituents of the Nasdaq 100 index for the years 2017 to 2022.  There is also a Latest Data period field but this period is sparsely populated.  In total, this dataset has  7 annual periods but we will use only a number of these periods as we explain below.\n",
    "\n",
    "The dataset can be found here: https://www.kaggle.com/datasets/ifuurh/nasdaq100-fundamental-data\n",
    "\n",
    "We have downloaded the dataset and then read the dataset into a dataframe.\n",
    "\n",
    "We will match each year's data to it's appropriate Training Label as follows:\n",
    "\n",
    "2017 Data => 2018 year 12 month Share Price Return relative to the Nasdaq 100\n",
    "\n",
    "2018 Data => 2019 year 12 month Share Price Return relative to the Nasdaq 100\n",
    "\n",
    "2019 Data => 2020 year 12 month Share Price Return relative to the Nasdaq 100\n",
    "\n",
    "2020 Data => 2021 year 12 month Share Price Return relative to the Nasdaq 100\n",
    "\n",
    "2021 Data => 2022 year 12 month Share Price Return relative to the Nasdaq 100 (it should be noted that the 2022 year is a negative Price Return year).\n",
    "\n",
    "It should be noted that 12 month Share Price Return is not the same as 12 month Total Return (including dividends, buy backs, hives etc.)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b1fdc529",
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symbol</th>\n",
       "      <th>company</th>\n",
       "      <th>sector</th>\n",
       "      <th>subsector</th>\n",
       "      <th>asset_turnover_2017</th>\n",
       "      <th>asset_turnover_2018</th>\n",
       "      <th>asset_turnover_2019</th>\n",
       "      <th>asset_turnover_2020</th>\n",
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       "      <th>asset_turnover_2022</th>\n",
       "      <th>...</th>\n",
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       "      <th>yoy_revenue_growth_2020</th>\n",
       "      <th>yoy_revenue_growth_2021</th>\n",
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       "      <th>0</th>\n",
       "      <td>AAPL</td>\n",
       "      <td>Apple Inc.</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>Technology Hardware, Storage &amp; Peripherals</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.83</td>\n",
       "      <td>1.08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>71.04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-7.69</td>\n",
       "      <td>11.34</td>\n",
       "      <td>21.69</td>\n",
       "      <td>5.36</td>\n",
       "      <td>11.94</td>\n",
       "      <td>38.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ABNB</td>\n",
       "      <td>Airbnb</td>\n",
       "      <td>Consumer Discretionary</td>\n",
       "      <td>Internet &amp; Direct Marketing Retail</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>96.46</td>\n",
       "      <td>NaN</td>\n",
       "      <td>609.09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.08</td>\n",
       "      <td>31.58</td>\n",
       "      <td>31.27</td>\n",
       "      <td>-18.11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ADBE</td>\n",
       "      <td>Adobe Inc.</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>Application Software</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.61</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>-7.48</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-3.97</td>\n",
       "      <td>25.51</td>\n",
       "      <td>24.45</td>\n",
       "      <td>25.21</td>\n",
       "      <td>16.86</td>\n",
       "      <td>23.69</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ADI</td>\n",
       "      <td>Analog Devices</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>Semiconductors</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>5.49</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.67</td>\n",
       "      <td>36.64</td>\n",
       "      <td>10.91</td>\n",
       "      <td>-3.22</td>\n",
       "      <td>-6.25</td>\n",
       "      <td>21.07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ADP</td>\n",
       "      <td>ADP</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>Data Processing &amp; Outsourced Services</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.29</td>\n",
       "      <td>...</td>\n",
       "      <td>6.49</td>\n",
       "      <td>15.32</td>\n",
       "      <td>19.05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.65</td>\n",
       "      <td>7.16</td>\n",
       "      <td>4.84</td>\n",
       "      <td>4.95</td>\n",
       "      <td>11.87</td>\n",
       "      <td>12.02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 283 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  symbol         company                  sector  \\\n",
       "0   AAPL      Apple Inc.  Information Technology   \n",
       "1   ABNB          Airbnb  Consumer Discretionary   \n",
       "2   ADBE      Adobe Inc.  Information Technology   \n",
       "3    ADI  Analog Devices  Information Technology   \n",
       "4    ADP             ADP  Information Technology   \n",
       "\n",
       "                                    subsector  asset_turnover_2017  \\\n",
       "0  Technology Hardware, Storage & Peripherals                 0.66   \n",
       "1          Internet & Direct Marketing Retail                  NaN   \n",
       "2                        Application Software                 0.54   \n",
       "3                              Semiconductors                 0.36   \n",
       "4       Data Processing & Outsourced Services                  NaN   \n",
       "\n",
       "   asset_turnover_2018  asset_turnover_2019  asset_turnover_2020  \\\n",
       "0                 0.72                 0.74                 0.83   \n",
       "1                 0.55                 0.64                 0.36   \n",
       "2                 0.54                 0.57                 0.57   \n",
       "3                 0.30                 0.29                 0.26   \n",
       "4                 0.34                 0.34                 0.35   \n",
       "\n",
       "   asset_turnover_2021  asset_turnover_2022  ...  yoy_eps_growth_2021  \\\n",
       "0                 1.08                  NaN  ...                71.04   \n",
       "1                 0.50                  NaN  ...                96.46   \n",
       "2                 0.61                  NaN  ...                -7.48   \n",
       "3                 0.20                  NaN  ...                 5.49   \n",
       "4                 0.33                 0.29  ...                 6.49   \n",
       "\n",
       "   yoy_eps_growth_2022  yoy_eps_growth_latest  yoy_revenue_growth_2017  \\\n",
       "0                  NaN                  -7.69                    11.34   \n",
       "1                  NaN                 609.09                      NaN   \n",
       "2                  NaN                  -3.97                    25.51   \n",
       "3                  NaN                   6.67                    36.64   \n",
       "4                15.32                  19.05                      NaN   \n",
       "\n",
       "   yoy_revenue_growth_2018  yoy_revenue_growth_2019  yoy_revenue_growth_2020  \\\n",
       "0                    21.69                     5.36                    11.94   \n",
       "1                    40.08                    31.58                    31.27   \n",
       "2                    24.45                    25.21                    16.86   \n",
       "3                    10.91                    -3.22                    -6.25   \n",
       "4                     8.65                     7.16                     4.84   \n",
       "\n",
       "   yoy_revenue_growth_2021  yoy_revenue_growth_2022  yoy_revenue_growth_latest  \n",
       "0                    38.50                      NaN                       5.11  \n",
       "1                   -18.11                      NaN                      41.00  \n",
       "2                    23.69                      NaN                      15.54  \n",
       "3                    21.07                      NaN                      26.30  \n",
       "4                     4.95                    11.87                      12.02  \n",
       "\n",
       "[5 rows x 283 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('nasdaq100_metrics_ratios.csv')\n",
    "data.head()        "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd0bca9c",
   "metadata": {},
   "source": [
    "# 3. Exploratory Data Analysis on the X data.\n",
    "\n",
    "Reviewing our initial data frame, we can  ascertain that something appears to be amiss on a sense check of the number of columns.  \n",
    "\n",
    "45 attributes over 7 periods would provide 315 columns against the actual 283 columns shown. \n",
    "\n",
    "The issues identified are:\n",
    "\n",
    "    We have identified 6 attributes that only have a single period.  \n",
    "\n",
    "    We can also see that the 2022 data is sparsely populated.\n",
    "\n",
    "    The current period data is populated and but we will not have any label data for this period as it is 2023.\n",
    "\n",
    "Sense Check: 283 subtract 4 columns that contain only narratives or classifications ('Sector', 'GICs' etc.) which we remove then subtract 6 data columns that only have the data for single years which we remove = 273 divided by 7 = 39 data attributes.  \n",
    "\n",
    "We end up with dataset with 39 different financial statements metric data points per stock for 7 consecutive 12 month periods."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e4a5d7f",
   "metadata": {},
   "source": [
    "# 4. Data Cleansing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "60d41a93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method NDFrame.copy of      financial_distress_latest  financial_strength_latest  \\\n",
       "0                         0.02                          7   \n",
       "1                         0.07                          7   \n",
       "2                         0.30                          8   \n",
       "3                         0.02                          7   \n",
       "4                         0.02                          6   \n",
       "..                         ...                        ...   \n",
       "97                        0.10                          5   \n",
       "98                        0.06                          6   \n",
       "99                        0.02                          3   \n",
       "100                       0.27                          9   \n",
       "101                       0.08                          5   \n",
       "\n",
       "     free_float_percentage_latest  goodwill_to_asset_latest  predictability  \\\n",
       "0                           99.94                       NaN             5.0   \n",
       "1                           61.37                       NaN             NaN   \n",
       "2                          100.00                       NaN             2.0   \n",
       "3                           99.64                       NaN             2.0   \n",
       "4                           99.98                       NaN             5.0   \n",
       "..                            ...                       ...             ...   \n",
       "97                          82.92                       NaN             1.0   \n",
       "98                          78.07                       NaN             1.0   \n",
       "99                          90.04                       NaN             2.0   \n",
       "100                         84.17                       NaN             NaN   \n",
       "101                         58.91                       NaN             NaN   \n",
       "\n",
       "     profitability  \n",
       "0               10  \n",
       "1                1  \n",
       "2                9  \n",
       "3                9  \n",
       "4                9  \n",
       "..             ...  \n",
       "97               8  \n",
       "98               4  \n",
       "99               7  \n",
       "100              6  \n",
       "101              4  \n",
       "\n",
       "[102 rows x 6 columns]>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datadel = data[['financial_distress_latest','financial_strength_latest','free_float_percentage_latest','goodwill_to_asset_latest','predictability', 'profitability',]].copy\n",
    "datadel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c361ff5c",
   "metadata": {},
   "outputs": [
    {
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       "      <th>0</th>\n",
       "      <td>AAPL</td>\n",
       "      <td>Apple Inc.</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>Technology Hardware, Storage &amp; Peripherals</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.72</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ABNB</td>\n",
       "      <td>Airbnb</td>\n",
       "      <td>Consumer Discretionary</td>\n",
       "      <td>Internet &amp; Direct Marketing Retail</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.50</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>40.08</td>\n",
       "      <td>31.58</td>\n",
       "      <td>31.27</td>\n",
       "      <td>-18.11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ADBE</td>\n",
       "      <td>Adobe Inc.</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>Application Software</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.61</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>-7.48</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-3.97</td>\n",
       "      <td>25.51</td>\n",
       "      <td>24.45</td>\n",
       "      <td>25.21</td>\n",
       "      <td>16.86</td>\n",
       "      <td>23.69</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ADI</td>\n",
       "      <td>Analog Devices</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>Semiconductors</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>5.49</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.67</td>\n",
       "      <td>36.64</td>\n",
       "      <td>10.91</td>\n",
       "      <td>-3.22</td>\n",
       "      <td>-6.25</td>\n",
       "      <td>21.07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ADP</td>\n",
       "      <td>ADP</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>Data Processing &amp; Outsourced Services</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.29</td>\n",
       "      <td>...</td>\n",
       "      <td>6.49</td>\n",
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       "      <td>19.05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.65</td>\n",
       "      <td>7.16</td>\n",
       "      <td>4.84</td>\n",
       "      <td>4.95</td>\n",
       "      <td>11.87</td>\n",
       "      <td>12.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>WBA</td>\n",
       "      <td>Walgreens Boots Alliance</td>\n",
       "      <td>Consumer Staples</td>\n",
       "      <td>Drug Retail</td>\n",
       "      <td>1.71</td>\n",
       "      <td>1.96</td>\n",
       "      <td>1.77</td>\n",
       "      <td>1.58</td>\n",
       "      <td>1.57</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>463.46</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-76.09</td>\n",
       "      <td>1.91</td>\n",
       "      <td>20.61</td>\n",
       "      <td>-1.65</td>\n",
       "      <td>6.57</td>\n",
       "      <td>10.37</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-4.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>WDAY</td>\n",
       "      <td>Workday, Inc.</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>Application Software</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.54</td>\n",
       "      <td>...</td>\n",
       "      <td>43.87</td>\n",
       "      <td>110.08</td>\n",
       "      <td>-160.98</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.85</td>\n",
       "      <td>26.22</td>\n",
       "      <td>22.65</td>\n",
       "      <td>14.10</td>\n",
       "      <td>11.04</td>\n",
       "      <td>24.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>XEL</td>\n",
       "      <td>Xcel Energy</td>\n",
       "      <td>Utilities</td>\n",
       "      <td>Multi-Utilities</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>6.09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.45</td>\n",
       "      <td>2.68</td>\n",
       "      <td>0.77</td>\n",
       "      <td>-1.80</td>\n",
       "      <td>-1.54</td>\n",
       "      <td>13.93</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>ZM</td>\n",
       "      <td>Zoom Video Communications</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>Application Software</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.70</td>\n",
       "      <td>1.16</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.64</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.00</td>\n",
       "      <td>-85.58</td>\n",
       "      <td>NaN</td>\n",
       "      <td>149.56</td>\n",
       "      <td>118.09</td>\n",
       "      <td>99.11</td>\n",
       "      <td>263.13</td>\n",
       "      <td>50.75</td>\n",
       "      <td>7.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>ZS</td>\n",
       "      <td>Zscaler</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>Application Software</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.43</td>\n",
       "      <td>...</td>\n",
       "      <td>-116.85</td>\n",
       "      <td>-43.52</td>\n",
       "      <td>-16.95</td>\n",
       "      <td>NaN</td>\n",
       "      <td>145.42</td>\n",
       "      <td>-17.67</td>\n",
       "      <td>36.07</td>\n",
       "      <td>48.79</td>\n",
       "      <td>56.05</td>\n",
       "      <td>56.22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>102 rows × 277 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    symbol                    company                  sector  \\\n",
       "0     AAPL                 Apple Inc.  Information Technology   \n",
       "1     ABNB                     Airbnb  Consumer Discretionary   \n",
       "2     ADBE                 Adobe Inc.  Information Technology   \n",
       "3      ADI             Analog Devices  Information Technology   \n",
       "4      ADP                        ADP  Information Technology   \n",
       "..     ...                        ...                     ...   \n",
       "97     WBA   Walgreens Boots Alliance        Consumer Staples   \n",
       "98    WDAY              Workday, Inc.  Information Technology   \n",
       "99     XEL                Xcel Energy               Utilities   \n",
       "100     ZM  Zoom Video Communications  Information Technology   \n",
       "101     ZS                    Zscaler  Information Technology   \n",
       "\n",
       "                                      subsector  asset_turnover_2017  \\\n",
       "0    Technology Hardware, Storage & Peripherals                 0.66   \n",
       "1            Internet & Direct Marketing Retail                  NaN   \n",
       "2                          Application Software                 0.54   \n",
       "3                                Semiconductors                 0.36   \n",
       "4         Data Processing & Outsourced Services                  NaN   \n",
       "..                                          ...                  ...   \n",
       "97                                  Drug Retail                 1.71   \n",
       "98                         Application Software                  NaN   \n",
       "99                              Multi-Utilities                 0.27   \n",
       "100                        Application Software                  NaN   \n",
       "101                        Application Software                  NaN   \n",
       "\n",
       "     asset_turnover_2018  asset_turnover_2019  asset_turnover_2020  \\\n",
       "0                   0.72                 0.74                 0.83   \n",
       "1                   0.55                 0.64                 0.36   \n",
       "2                   0.54                 0.57                 0.57   \n",
       "3                   0.30                 0.29                 0.26   \n",
       "4                   0.34                 0.34                 0.35   \n",
       "..                   ...                  ...                  ...   \n",
       "97                  1.96                 1.77                 1.58   \n",
       "98                  0.52                 0.54                 0.59   \n",
       "99                  0.26                 0.24                 0.22   \n",
       "100                 0.70                 1.16                 0.76   \n",
       "101                 0.60                 0.58                 0.35   \n",
       "\n",
       "     asset_turnover_2021  asset_turnover_2022  ...  yoy_eps_growth_2021  \\\n",
       "0                   1.08                  NaN  ...                71.04   \n",
       "1                   0.50                  NaN  ...                96.46   \n",
       "2                   0.61                  NaN  ...                -7.48   \n",
       "3                   0.20                  NaN  ...                 5.49   \n",
       "4                   0.33                 0.29  ...                 6.49   \n",
       "..                   ...                  ...  ...                  ...   \n",
       "97                  1.57                  NaN  ...               463.46   \n",
       "98                  0.56                 0.54  ...                43.87   \n",
       "99                  0.24                  NaN  ...                 6.09   \n",
       "100                 0.81                 0.64  ...                  NaN   \n",
       "101                 0.33                 0.43  ...              -116.85   \n",
       "\n",
       "     yoy_eps_growth_2022  yoy_eps_growth_latest  yoy_revenue_growth_2017  \\\n",
       "0                    NaN                  -7.69                    11.34   \n",
       "1                    NaN                 609.09                      NaN   \n",
       "2                    NaN                  -3.97                    25.51   \n",
       "3                    NaN                   6.67                    36.64   \n",
       "4                  15.32                  19.05                      NaN   \n",
       "..                   ...                    ...                      ...   \n",
       "97                   NaN                 -76.09                     1.91   \n",
       "98                110.08                -160.98                      NaN   \n",
       "99                   NaN                   3.45                     2.68   \n",
       "100               100.00                 -85.58                      NaN   \n",
       "101               -43.52                 -16.95                      NaN   \n",
       "\n",
       "     yoy_revenue_growth_2018  yoy_revenue_growth_2019  \\\n",
       "0                      21.69                     5.36   \n",
       "1                      40.08                    31.58   \n",
       "2                      24.45                    25.21   \n",
       "3                      10.91                    -3.22   \n",
       "4                       8.65                     7.16   \n",
       "..                       ...                      ...   \n",
       "97                     20.61                    -1.65   \n",
       "98                     29.85                    26.22   \n",
       "99                      0.77                    -1.80   \n",
       "100                   149.56                   118.09   \n",
       "101                   145.42                   -17.67   \n",
       "\n",
       "     yoy_revenue_growth_2020  yoy_revenue_growth_2021  \\\n",
       "0                      11.94                    38.50   \n",
       "1                      31.27                   -18.11   \n",
       "2                      16.86                    23.69   \n",
       "3                      -6.25                    21.07   \n",
       "4                       4.84                     4.95   \n",
       "..                       ...                      ...   \n",
       "97                      6.57                    10.37   \n",
       "98                     22.65                    14.10   \n",
       "99                     -1.54                    13.93   \n",
       "100                    99.11                   263.13   \n",
       "101                    36.07                    48.79   \n",
       "\n",
       "     yoy_revenue_growth_2022  yoy_revenue_growth_latest  \n",
       "0                        NaN                       5.11  \n",
       "1                        NaN                      41.00  \n",
       "2                        NaN                      15.54  \n",
       "3                        NaN                      26.30  \n",
       "4                      11.87                      12.02  \n",
       "..                       ...                        ...  \n",
       "97                       NaN                      -4.02  \n",
       "98                     11.04                      24.57  \n",
       "99                       NaN                      10.17  \n",
       "100                    50.75                       7.16  \n",
       "101                    56.05                      56.22  \n",
       "\n",
       "[102 rows x 277 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.drop(['financial_distress_latest','financial_strength_latest','free_float_percentage_latest','goodwill_to_asset_latest','predictability', 'profitability'], inplace=True, axis=1)\n",
    "data\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "raw",
   "id": "ef6dbc5d",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "d5075687",
   "metadata": {},
   "source": [
    "# 4.1  We have removed the 6 single year columns.  We have also noted that we need to move a column that is out of time series order."
   ]
  },
  {
   "cell_type": "raw",
   "id": "3c89e1d4",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5a3fdc9c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\mark\\anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3364: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead.  To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  if (await self.run_code(code, result,  async_=asy)):\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\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>symbol</th>\n",
       "      <th>company</th>\n",
       "      <th>sector</th>\n",
       "      <th>subsector</th>\n",
       "      <th>asset_turnover_2017</th>\n",
       "      <th>asset_turnover_2018</th>\n",
       "      <th>asset_turnover_2019</th>\n",
       "      <th>asset_turnover_2020</th>\n",
       "      <th>asset_turnover_2021</th>\n",
       "      <th>asset_turnover_2022</th>\n",
       "      <th>...</th>\n",
       "      <th>yoy_eps_growth_2021</th>\n",
       "      <th>yoy_eps_growth_2022</th>\n",
       "      <th>yoy_eps_growth_latest</th>\n",
       "      <th>yoy_revenue_growth_2017</th>\n",
       "      <th>yoy_revenue_growth_2018</th>\n",
       "      <th>yoy_revenue_growth_2019</th>\n",
       "      <th>yoy_revenue_growth_2020</th>\n",
       "      <th>yoy_revenue_growth_2021</th>\n",
       "      <th>yoy_revenue_growth_2022</th>\n",
       "      <th>yoy_revenue_growth_latest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AAPL</td>\n",
       "      <td>Apple Inc.</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>Technology Hardware, Storage &amp; Peripherals</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.83</td>\n",
       "      <td>1.08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>71.04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-7.69</td>\n",
       "      <td>11.34</td>\n",
       "      <td>21.69</td>\n",
       "      <td>5.36</td>\n",
       "      <td>11.94</td>\n",
       "      <td>38.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ABNB</td>\n",
       "      <td>Airbnb</td>\n",
       "      <td>Consumer Discretionary</td>\n",
       "      <td>Internet &amp; Direct Marketing Retail</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>96.46</td>\n",
       "      <td>NaN</td>\n",
       "      <td>609.09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.08</td>\n",
       "      <td>31.58</td>\n",
       "      <td>31.27</td>\n",
       "      <td>-18.11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ADBE</td>\n",
       "      <td>Adobe Inc.</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>Application Software</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.61</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>-7.48</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-3.97</td>\n",
       "      <td>25.51</td>\n",
       "      <td>24.45</td>\n",
       "      <td>25.21</td>\n",
       "      <td>16.86</td>\n",
       "      <td>23.69</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ADI</td>\n",
       "      <td>Analog Devices</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>Semiconductors</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>5.49</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.67</td>\n",
       "      <td>36.64</td>\n",
       "      <td>10.91</td>\n",
       "      <td>-3.22</td>\n",
       "      <td>-6.25</td>\n",
       "      <td>21.07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ADP</td>\n",
       "      <td>ADP</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>Data Processing &amp; Outsourced Services</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.29</td>\n",
       "      <td>...</td>\n",
       "      <td>6.49</td>\n",
       "      <td>15.32</td>\n",
       "      <td>19.05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.65</td>\n",
       "      <td>7.16</td>\n",
       "      <td>4.84</td>\n",
       "      <td>4.95</td>\n",
       "      <td>11.87</td>\n",
       "      <td>12.02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 277 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  symbol         company                  sector  \\\n",
       "0   AAPL      Apple Inc.  Information Technology   \n",
       "1   ABNB          Airbnb  Consumer Discretionary   \n",
       "2   ADBE      Adobe Inc.  Information Technology   \n",
       "3    ADI  Analog Devices  Information Technology   \n",
       "4    ADP             ADP  Information Technology   \n",
       "\n",
       "                                    subsector  asset_turnover_2017  \\\n",
       "0  Technology Hardware, Storage & Peripherals                 0.66   \n",
       "1          Internet & Direct Marketing Retail                  NaN   \n",
       "2                        Application Software                 0.54   \n",
       "3                              Semiconductors                 0.36   \n",
       "4       Data Processing & Outsourced Services                  NaN   \n",
       "\n",
       "   asset_turnover_2018  asset_turnover_2019  asset_turnover_2020  \\\n",
       "0                 0.72                 0.74                 0.83   \n",
       "1                 0.55                 0.64                 0.36   \n",
       "2                 0.54                 0.57                 0.57   \n",
       "3                 0.30                 0.29                 0.26   \n",
       "4                 0.34                 0.34                 0.35   \n",
       "\n",
       "   asset_turnover_2021  asset_turnover_2022  ...  yoy_eps_growth_2021  \\\n",
       "0                 1.08                  NaN  ...                71.04   \n",
       "1                 0.50                  NaN  ...                96.46   \n",
       "2                 0.61                  NaN  ...                -7.48   \n",
       "3                 0.20                  NaN  ...                 5.49   \n",
       "4                 0.33                 0.29  ...                 6.49   \n",
       "\n",
       "   yoy_eps_growth_2022  yoy_eps_growth_latest  yoy_revenue_growth_2017  \\\n",
       "0                  NaN                  -7.69                    11.34   \n",
       "1                  NaN                 609.09                      NaN   \n",
       "2                  NaN                  -3.97                    25.51   \n",
       "3                  NaN                   6.67                    36.64   \n",
       "4                15.32                  19.05                      NaN   \n",
       "\n",
       "   yoy_revenue_growth_2018  yoy_revenue_growth_2019  yoy_revenue_growth_2020  \\\n",
       "0                    21.69                     5.36                    11.94   \n",
       "1                    40.08                    31.58                    31.27   \n",
       "2                    24.45                    25.21                    16.86   \n",
       "3                    10.91                    -3.22                    -6.25   \n",
       "4                     8.65                     7.16                     4.84   \n",
       "\n",
       "   yoy_revenue_growth_2021  yoy_revenue_growth_2022  yoy_revenue_growth_latest  \n",
       "0                    38.50                      NaN                       5.11  \n",
       "1                   -18.11                      NaN                      41.00  \n",
       "2                    23.69                      NaN                      15.54  \n",
       "3                    21.07                      NaN                      26.30  \n",
       "4                     4.95                    11.87                      12.02  \n",
       "\n",
       "[5 rows x 277 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "column_to_move = data.pop(\"price_to_earnings_ratio_latest\")\n",
    "\n",
    "# insert column with insert(location, column_name, column_value)\n",
    "\n",
    "data.insert(213, \"price_to_earnings_ratio_latest\", column_to_move)\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "17334c3f",
   "metadata": {
    "scrolled": true
   },
   "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>price_to_earnings_ratio_2021</th>\n",
       "      <th>price_to_earnings_ratio_2022</th>\n",
       "      <th>price_to_earnings_ratio_latest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25.22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>55.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>66.85</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>50.14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32.72</td>\n",
       "      <td>30.01</td>\n",
       "      <td>32.85</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   price_to_earnings_ratio_2021  price_to_earnings_ratio_2022  \\\n",
       "0                         25.22                           NaN   \n",
       "1                           NaN                           NaN   \n",
       "2                         66.85                           NaN   \n",
       "3                         50.14                           NaN   \n",
       "4                         32.72                         30.01   \n",
       "\n",
       "   price_to_earnings_ratio_latest  \n",
       "0                           24.82  \n",
       "1                           55.60  \n",
       "2                           28.06  \n",
       "3                           39.20  \n",
       "4                           32.85  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = [211,212,213]\n",
    "datatest = data[data.columns[cols]]\n",
    "datatest.head()"
   ]
  },
  {
   "cell_type": "raw",
   "id": "2e361735",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "bfb99ffd",
   "metadata": {},
   "source": [
    "# 4.2 We have moved the column but we see that we have Nans so we now find the Nans with Mean of column values."
   ]
  },
  {
   "cell_type": "raw",
   "id": "69de4333",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "122a1e5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['asset_turnover_2017',\n",
       " 'asset_turnover_2018',\n",
       " 'asset_turnover_2019',\n",
       " 'asset_turnover_2020',\n",
       " 'asset_turnover_2022',\n",
       " 'buyback_yield_2017',\n",
       " 'buyback_yield_2018',\n",
       " 'buyback_yield_2019',\n",
       " 'buyback_yield_2020',\n",
       " 'buyback_yield_2021',\n",
       " 'buyback_yield_2022',\n",
       " 'capex_to_revenue_2017',\n",
       " 'capex_to_revenue_2018',\n",
       " 'capex_to_revenue_2019',\n",
       " 'capex_to_revenue_2020',\n",
       " 'capex_to_revenue_2021',\n",
       " 'capex_to_revenue_2022',\n",
       " 'cash_ratio_2017',\n",
       " 'cash_ratio_2018',\n",
       " 'cash_ratio_2019',\n",
       " 'cash_ratio_2022',\n",
       " 'cash_to_debt_2017',\n",
       " 'cash_to_debt_2018',\n",
       " 'cash_to_debt_2019',\n",
       " 'cash_to_debt_2020',\n",
       " 'cash_to_debt_2021',\n",
       " 'cash_to_debt_2022',\n",
       " 'cash_to_debt_latest',\n",
       " 'cogs_to_revenue_2017',\n",
       " 'cogs_to_revenue_2018',\n",
       " 'cogs_to_revenue_2019',\n",
       " 'cogs_to_revenue_2020',\n",
       " 'cogs_to_revenue_2021',\n",
       " 'cogs_to_revenue_2022',\n",
       " 'mscore_2017',\n",
       " 'mscore_2018',\n",
       " 'mscore_2019',\n",
       " 'mscore_2020',\n",
       " 'mscore_2021',\n",
       " 'mscore_2022',\n",
       " 'zscore_2017',\n",
       " 'zscore_2018',\n",
       " 'zscore_2019',\n",
       " 'zscore_2020',\n",
       " 'zscore_2021',\n",
       " 'zscore_2022',\n",
       " 'current_ratio_2017',\n",
       " 'current_ratio_2018',\n",
       " 'current_ratio_2019',\n",
       " 'current_ratio_2022',\n",
       " 'days_inventory_2017',\n",
       " 'days_inventory_2018',\n",
       " 'days_inventory_2019',\n",
       " 'days_inventory_2020',\n",
       " 'days_inventory_2021',\n",
       " 'days_inventory_2022',\n",
       " 'debt_to_equity_2017',\n",
       " 'debt_to_equity_2018',\n",
       " 'debt_to_equity_2019',\n",
       " 'debt_to_equity_2020',\n",
       " 'debt_to_equity_2021',\n",
       " 'debt_to_equity_2022',\n",
       " 'debt_to_assets_2017',\n",
       " 'debt_to_assets_2018',\n",
       " 'debt_to_assets_2019',\n",
       " 'debt_to_assets_2020',\n",
       " 'debt_to_assets_2021',\n",
       " 'debt_to_assets_2022',\n",
       " 'debt_to_ebitda_2017',\n",
       " 'debt_to_ebitda_2018',\n",
       " 'debt_to_ebitda_2019',\n",
       " 'debt_to_ebitda_2020',\n",
       " 'debt_to_ebitda_2021',\n",
       " 'debt_to_ebitda_2022',\n",
       " 'debt_to_revenue_2017',\n",
       " 'debt_to_revenue_2018',\n",
       " 'debt_to_revenue_2019',\n",
       " 'debt_to_revenue_2020',\n",
       " 'debt_to_revenue_2021',\n",
       " 'debt_to_revenue_2022',\n",
       " 'e10_2017',\n",
       " 'e10_2018',\n",
       " 'e10_2019',\n",
       " 'e10_2020',\n",
       " 'e10_2021',\n",
       " 'e10_2022',\n",
       " 'effective_interest_rate_2017',\n",
       " 'effective_interest_rate_2018',\n",
       " 'effective_interest_rate_2019',\n",
       " 'effective_interest_rate_2020',\n",
       " 'effective_interest_rate_2021',\n",
       " 'effective_interest_rate_2022',\n",
       " 'equity_to_assets_2017',\n",
       " 'equity_to_assets_2018',\n",
       " 'equity_to_assets_2019',\n",
       " 'equity_to_assets_2022',\n",
       " 'enterprise_value_to_ebit_2017',\n",
       " 'enterprise_value_to_ebit_2018',\n",
       " 'enterprise_value_to_ebit_2019',\n",
       " 'enterprise_value_to_ebit_2020',\n",
       " 'enterprise_value_to_ebit_2021',\n",
       " 'enterprise_value_to_ebit_2022',\n",
       " 'enterprise_value_to_ebitda_2017',\n",
       " 'enterprise_value_to_ebitda_2018',\n",
       " 'enterprise_value_to_ebitda_2019',\n",
       " 'enterprise_value_to_ebitda_2020',\n",
       " 'enterprise_value_to_ebitda_2021',\n",
       " 'enterprise_value_to_ebitda_2022',\n",
       " 'enterprise_value_to_revenue_2017',\n",
       " 'enterprise_value_to_revenue_2018',\n",
       " 'enterprise_value_to_revenue_2019',\n",
       " 'enterprise_value_to_revenue_2020',\n",
       " 'enterprise_value_to_revenue_2021',\n",
       " 'enterprise_value_to_revenue_2022',\n",
       " 'earning_yield_greenblatt_2017',\n",
       " 'earning_yield_greenblatt_2018',\n",
       " 'earning_yield_greenblatt_2019',\n",
       " 'earning_yield_greenblatt_2020',\n",
       " 'earning_yield_greenblatt_2021',\n",
       " 'earning_yield_greenblatt_2022',\n",
       " 'fscore_2017',\n",
       " 'fscore_2018',\n",
       " 'fscore_2019',\n",
       " 'fscore_2020',\n",
       " 'fscore_2021',\n",
       " 'fscore_2022',\n",
       " 'fscore_latest',\n",
       " 'gross_profit_to_assets_2017',\n",
       " 'gross_profit_to_assets_2018',\n",
       " 'gross_profit_to_assets_2019',\n",
       " 'gross_profit_to_assets_2022',\n",
       " 'interest_coverage_2017',\n",
       " 'interest_coverage_2018',\n",
       " 'interest_coverage_2019',\n",
       " 'interest_coverage_2020',\n",
       " 'interest_coverage_2021',\n",
       " 'interest_coverage_2022',\n",
       " 'interest_coverage_latest',\n",
       " 'inventory_turnover_2017',\n",
       " 'inventory_turnover_2018',\n",
       " 'inventory_turnover_2019',\n",
       " 'inventory_turnover_2020',\n",
       " 'inventory_turnover_2021',\n",
       " 'inventory_turnover_2022',\n",
       " 'inventory_to_revenue_2017',\n",
       " 'inventory_to_revenue_2018',\n",
       " 'inventory_to_revenue_2019',\n",
       " 'inventory_to_revenue_2020',\n",
       " 'inventory_to_revenue_2021',\n",
       " 'inventory_to_revenue_2022',\n",
       " 'liabilities_to_assets_2017',\n",
       " 'liabilities_to_assets_2018',\n",
       " 'liabilities_to_assets_2019',\n",
       " 'liabilities_to_assets_2022',\n",
       " 'longterm_debt_to_assets_2017',\n",
       " 'longterm_debt_to_assets_2018',\n",
       " 'longterm_debt_to_assets_2019',\n",
       " 'longterm_debt_to_assets_2020',\n",
       " 'longterm_debt_to_assets_2021',\n",
       " 'longterm_debt_to_assets_2022',\n",
       " 'price_to_book_ratio_2017',\n",
       " 'price_to_book_ratio_2018',\n",
       " 'price_to_book_ratio_2019',\n",
       " 'price_to_book_ratio_2020',\n",
       " 'price_to_book_ratio_2021',\n",
       " 'price_to_book_ratio_2022',\n",
       " 'price_to_book_ratio_latest',\n",
       " 'price_to_earnings_ratio_2017',\n",
       " 'price_to_earnings_ratio_2018',\n",
       " 'price_to_earnings_ratio_2019',\n",
       " 'price_to_earnings_ratio_2020',\n",
       " 'price_to_earnings_ratio_2021',\n",
       " 'price_to_earnings_ratio_2022',\n",
       " 'price_to_earnings_ratio_latest',\n",
       " 'price_to_earnings_ratio_nri_2017',\n",
       " 'price_to_earnings_ratio_nri_2018',\n",
       " 'price_to_earnings_ratio_nri_2019',\n",
       " 'price_to_earnings_ratio_nri_2020',\n",
       " 'price_to_earnings_ratio_nri_2021',\n",
       " 'price_to_earnings_ratio_nri_2022',\n",
       " 'price_to_earnings_ratio_nri_latest',\n",
       " 'price_earnings_growth_ratio_2017',\n",
       " 'price_earnings_growth_ratio_2018',\n",
       " 'price_earnings_growth_ratio_2019',\n",
       " 'price_earnings_growth_ratio_2020',\n",
       " 'price_earnings_growth_ratio_2021',\n",
       " 'price_earnings_growth_ratio_2022',\n",
       " 'price_earnings_growth_ratio_latest',\n",
       " 'price_to_free_cashflow_2017',\n",
       " 'price_to_free_cashflow_2018',\n",
       " 'price_to_free_cashflow_2019',\n",
       " 'price_to_free_cashflow_2020',\n",
       " 'price_to_free_cashflow_2021',\n",
       " 'price_to_free_cashflow_2022',\n",
       " 'price_to_free_cashflow_latest',\n",
       " 'price_to_operating_cashflow_2017',\n",
       " 'price_to_operating_cashflow_2018',\n",
       " 'price_to_operating_cashflow_2019',\n",
       " 'price_to_operating_cashflow_2020',\n",
       " 'price_to_operating_cashflow_2021',\n",
       " 'price_to_operating_cashflow_2022',\n",
       " 'price_to_operating_cashflow_latest',\n",
       " 'rate_of_return_2017',\n",
       " 'rate_of_return_2018',\n",
       " 'rate_of_return_2019',\n",
       " 'rate_of_return_2020',\n",
       " 'rate_of_return_2021',\n",
       " 'rate_of_return_2022',\n",
       " 'scaled_net_operating_assets_2017',\n",
       " 'scaled_net_operating_assets_2018',\n",
       " 'scaled_net_operating_assets_2019',\n",
       " 'scaled_net_operating_assets_2020',\n",
       " 'scaled_net_operating_assets_2021',\n",
       " 'scaled_net_operating_assets_2022',\n",
       " 'yoy_ebitda_growth_2017',\n",
       " 'yoy_ebitda_growth_2018',\n",
       " 'yoy_ebitda_growth_2019',\n",
       " 'yoy_ebitda_growth_2020',\n",
       " 'yoy_ebitda_growth_2021',\n",
       " 'yoy_ebitda_growth_2022',\n",
       " 'yoy_eps_growth_2017',\n",
       " 'yoy_eps_growth_2018',\n",
       " 'yoy_eps_growth_2019',\n",
       " 'yoy_eps_growth_2020',\n",
       " 'yoy_eps_growth_2021',\n",
       " 'yoy_eps_growth_2022',\n",
       " 'yoy_revenue_growth_2017',\n",
       " 'yoy_revenue_growth_2018',\n",
       " 'yoy_revenue_growth_2019',\n",
       " 'yoy_revenue_growth_2021',\n",
       " 'yoy_revenue_growth_2022']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NaN_columns = data.columns[data.isna().any()].tolist()\n",
    "NaN_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "29d5d98a",
   "metadata": {},
   "outputs": [],
   "source": [
    "for c in NaN_columns:\n",
    "    temp = data[c].values\n",
    "    temp1 = np.nanmean(temp)\n",
    "    temp2 = np.nan_to_num(temp, nan=temp1)\n",
    "    data[c] = temp2\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5daa3fed",
   "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>symbol</th>\n",
       "      <th>company</th>\n",
       "      <th>sector</th>\n",
       "      <th>subsector</th>\n",
       "      <th>asset_turnover_2017</th>\n",
       "      <th>asset_turnover_2018</th>\n",
       "      <th>asset_turnover_2019</th>\n",
       "      <th>asset_turnover_2020</th>\n",
       "      <th>asset_turnover_2021</th>\n",
       "      <th>asset_turnover_2022</th>\n",
       "      <th>...</th>\n",
       "      <th>yoy_eps_growth_2021</th>\n",
       "      <th>yoy_eps_growth_2022</th>\n",
       "      <th>yoy_eps_growth_latest</th>\n",
       "      <th>yoy_revenue_growth_2017</th>\n",
       "      <th>yoy_revenue_growth_2018</th>\n",
       "      <th>yoy_revenue_growth_2019</th>\n",
       "      <th>yoy_revenue_growth_2020</th>\n",
       "      <th>yoy_revenue_growth_2021</th>\n",
       "      <th>yoy_revenue_growth_2022</th>\n",
       "      <th>yoy_revenue_growth_latest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AAPL</td>\n",
       "      <td>Apple Inc.</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>Technology Hardware, Storage &amp; Peripherals</td>\n",
       "      <td>0.660000</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.83</td>\n",
       "      <td>1.08</td>\n",
       "      <td>0.674231</td>\n",
       "      <td>...</td>\n",
       "      <td>71.040000</td>\n",
       "      <td>19.03</td>\n",
       "      <td>-7.69</td>\n",
       "      <td>11.340000</td>\n",
       "      <td>21.69</td>\n",
       "      <td>5.36</td>\n",
       "      <td>11.94</td>\n",
       "      <td>38.50</td>\n",
       "      <td>28.876538</td>\n",
       "      <td>5.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ABNB</td>\n",
       "      <td>Airbnb</td>\n",
       "      <td>Consumer Discretionary</td>\n",
       "      <td>Internet &amp; Direct Marketing Retail</td>\n",
       "      <td>0.724932</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.674231</td>\n",
       "      <td>...</td>\n",
       "      <td>96.460000</td>\n",
       "      <td>19.03</td>\n",
       "      <td>609.09</td>\n",
       "      <td>19.277917</td>\n",
       "      <td>40.08</td>\n",
       "      <td>31.58</td>\n",
       "      <td>31.27</td>\n",
       "      <td>-18.11</td>\n",
       "      <td>28.876538</td>\n",
       "      <td>41.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ADBE</td>\n",
       "      <td>Adobe Inc.</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>Application Software</td>\n",
       "      <td>0.540000</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.674231</td>\n",
       "      <td>...</td>\n",
       "      <td>-7.480000</td>\n",
       "      <td>19.03</td>\n",
       "      <td>-3.97</td>\n",
       "      <td>25.510000</td>\n",
       "      <td>24.45</td>\n",
       "      <td>25.21</td>\n",
       "      <td>16.86</td>\n",
       "      <td>23.69</td>\n",
       "      <td>28.876538</td>\n",
       "      <td>15.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ADI</td>\n",
       "      <td>Analog Devices</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>Semiconductors</td>\n",
       "      <td>0.360000</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.674231</td>\n",
       "      <td>...</td>\n",
       "      <td>5.490000</td>\n",
       "      <td>19.03</td>\n",
       "      <td>6.67</td>\n",
       "      <td>36.640000</td>\n",
       "      <td>10.91</td>\n",
       "      <td>-3.22</td>\n",
       "      <td>-6.25</td>\n",
       "      <td>21.07</td>\n",
       "      <td>28.876538</td>\n",
       "      <td>26.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ADP</td>\n",
       "      <td>ADP</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>Data Processing &amp; Outsourced Services</td>\n",
       "      <td>0.724932</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.290000</td>\n",
       "      <td>...</td>\n",
       "      <td>6.490000</td>\n",
       "      <td>15.32</td>\n",
       "      <td>19.05</td>\n",
       "      <td>19.277917</td>\n",
       "      <td>8.65</td>\n",
       "      <td>7.16</td>\n",
       "      <td>4.84</td>\n",
       "      <td>4.95</td>\n",
       "      <td>11.870000</td>\n",
       "      <td>12.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>WBA</td>\n",
       "      <td>Walgreens Boots Alliance</td>\n",
       "      <td>Consumer Staples</td>\n",
       "      <td>Drug Retail</td>\n",
       "      <td>1.710000</td>\n",
       "      <td>1.96</td>\n",
       "      <td>1.77</td>\n",
       "      <td>1.58</td>\n",
       "      <td>1.57</td>\n",
       "      <td>0.674231</td>\n",
       "      <td>...</td>\n",
       "      <td>463.460000</td>\n",
       "      <td>19.03</td>\n",
       "      <td>-76.09</td>\n",
       "      <td>1.910000</td>\n",
       "      <td>20.61</td>\n",
       "      <td>-1.65</td>\n",
       "      <td>6.57</td>\n",
       "      <td>10.37</td>\n",
       "      <td>28.876538</td>\n",
       "      <td>-4.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>WDAY</td>\n",
       "      <td>Workday, Inc.</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>Application Software</td>\n",
       "      <td>0.724932</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.540000</td>\n",
       "      <td>...</td>\n",
       "      <td>43.870000</td>\n",
       "      <td>110.08</td>\n",
       "      <td>-160.98</td>\n",
       "      <td>19.277917</td>\n",
       "      <td>29.85</td>\n",
       "      <td>26.22</td>\n",
       "      <td>22.65</td>\n",
       "      <td>14.10</td>\n",
       "      <td>11.040000</td>\n",
       "      <td>24.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>XEL</td>\n",
       "      <td>Xcel Energy</td>\n",
       "      <td>Utilities</td>\n",
       "      <td>Multi-Utilities</td>\n",
       "      <td>0.270000</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.674231</td>\n",
       "      <td>...</td>\n",
       "      <td>6.090000</td>\n",
       "      <td>19.03</td>\n",
       "      <td>3.45</td>\n",
       "      <td>2.680000</td>\n",
       "      <td>0.77</td>\n",
       "      <td>-1.80</td>\n",
       "      <td>-1.54</td>\n",
       "      <td>13.93</td>\n",
       "      <td>28.876538</td>\n",
       "      <td>10.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>ZM</td>\n",
       "      <td>Zoom Video Communications</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>Application Software</td>\n",
       "      <td>0.724932</td>\n",
       "      <td>0.70</td>\n",
       "      <td>1.16</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.640000</td>\n",
       "      <td>...</td>\n",
       "      <td>52.306596</td>\n",
       "      <td>100.00</td>\n",
       "      <td>-85.58</td>\n",
       "      <td>19.277917</td>\n",
       "      <td>149.56</td>\n",
       "      <td>118.09</td>\n",
       "      <td>99.11</td>\n",
       "      <td>263.13</td>\n",
       "      <td>50.750000</td>\n",
       "      <td>7.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>ZS</td>\n",
       "      <td>Zscaler</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>Application Software</td>\n",
       "      <td>0.724932</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.430000</td>\n",
       "      <td>...</td>\n",
       "      <td>-116.850000</td>\n",
       "      <td>-43.52</td>\n",
       "      <td>-16.95</td>\n",
       "      <td>19.277917</td>\n",
       "      <td>145.42</td>\n",
       "      <td>-17.67</td>\n",
       "      <td>36.07</td>\n",
       "      <td>48.79</td>\n",
       "      <td>56.050000</td>\n",
       "      <td>56.22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>102 rows × 277 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    symbol                    company                  sector  \\\n",
       "0     AAPL                 Apple Inc.  Information Technology   \n",
       "1     ABNB                     Airbnb  Consumer Discretionary   \n",
       "2     ADBE                 Adobe Inc.  Information Technology   \n",
       "3      ADI             Analog Devices  Information Technology   \n",
       "4      ADP                        ADP  Information Technology   \n",
       "..     ...                        ...                     ...   \n",
       "97     WBA   Walgreens Boots Alliance        Consumer Staples   \n",
       "98    WDAY              Workday, Inc.  Information Technology   \n",
       "99     XEL                Xcel Energy               Utilities   \n",
       "100     ZM  Zoom Video Communications  Information Technology   \n",
       "101     ZS                    Zscaler  Information Technology   \n",
       "\n",
       "                                      subsector  asset_turnover_2017  \\\n",
       "0    Technology Hardware, Storage & Peripherals             0.660000   \n",
       "1            Internet & Direct Marketing Retail             0.724932   \n",
       "2                          Application Software             0.540000   \n",
       "3                                Semiconductors             0.360000   \n",
       "4         Data Processing & Outsourced Services             0.724932   \n",
       "..                                          ...                  ...   \n",
       "97                                  Drug Retail             1.710000   \n",
       "98                         Application Software             0.724932   \n",
       "99                              Multi-Utilities             0.270000   \n",
       "100                        Application Software             0.724932   \n",
       "101                        Application Software             0.724932   \n",
       "\n",
       "     asset_turnover_2018  asset_turnover_2019  asset_turnover_2020  \\\n",
       "0                   0.72                 0.74                 0.83   \n",
       "1                   0.55                 0.64                 0.36   \n",
       "2                   0.54                 0.57                 0.57   \n",
       "3                   0.30                 0.29                 0.26   \n",
       "4                   0.34                 0.34                 0.35   \n",
       "..                   ...                  ...                  ...   \n",
       "97                  1.96                 1.77                 1.58   \n",
       "98                  0.52                 0.54                 0.59   \n",
       "99                  0.26                 0.24                 0.22   \n",
       "100                 0.70                 1.16                 0.76   \n",
       "101                 0.60                 0.58                 0.35   \n",
       "\n",
       "     asset_turnover_2021  asset_turnover_2022  ...  yoy_eps_growth_2021  \\\n",
       "0                   1.08             0.674231  ...            71.040000   \n",
       "1                   0.50             0.674231  ...            96.460000   \n",
       "2                   0.61             0.674231  ...            -7.480000   \n",
       "3                   0.20             0.674231  ...             5.490000   \n",
       "4                   0.33             0.290000  ...             6.490000   \n",
       "..                   ...                  ...  ...                  ...   \n",
       "97                  1.57             0.674231  ...           463.460000   \n",
       "98                  0.56             0.540000  ...            43.870000   \n",
       "99                  0.24             0.674231  ...             6.090000   \n",
       "100                 0.81             0.640000  ...            52.306596   \n",
       "101                 0.33             0.430000  ...          -116.850000   \n",
       "\n",
       "     yoy_eps_growth_2022  yoy_eps_growth_latest  yoy_revenue_growth_2017  \\\n",
       "0                  19.03                  -7.69                11.340000   \n",
       "1                  19.03                 609.09                19.277917   \n",
       "2                  19.03                  -3.97                25.510000   \n",
       "3                  19.03                   6.67                36.640000   \n",
       "4                  15.32                  19.05                19.277917   \n",
       "..                   ...                    ...                      ...   \n",
       "97                 19.03                 -76.09                 1.910000   \n",
       "98                110.08                -160.98                19.277917   \n",
       "99                 19.03                   3.45                 2.680000   \n",
       "100               100.00                 -85.58                19.277917   \n",
       "101               -43.52                 -16.95                19.277917   \n",
       "\n",
       "     yoy_revenue_growth_2018  yoy_revenue_growth_2019  \\\n",
       "0                      21.69                     5.36   \n",
       "1                      40.08                    31.58   \n",
       "2                      24.45                    25.21   \n",
       "3                      10.91                    -3.22   \n",
       "4                       8.65                     7.16   \n",
       "..                       ...                      ...   \n",
       "97                     20.61                    -1.65   \n",
       "98                     29.85                    26.22   \n",
       "99                      0.77                    -1.80   \n",
       "100                   149.56                   118.09   \n",
       "101                   145.42                   -17.67   \n",
       "\n",
       "     yoy_revenue_growth_2020  yoy_revenue_growth_2021  \\\n",
       "0                      11.94                    38.50   \n",
       "1                      31.27                   -18.11   \n",
       "2                      16.86                    23.69   \n",
       "3                      -6.25                    21.07   \n",
       "4                       4.84                     4.95   \n",
       "..                       ...                      ...   \n",
       "97                      6.57                    10.37   \n",
       "98                     22.65                    14.10   \n",
       "99                     -1.54                    13.93   \n",
       "100                    99.11                   263.13   \n",
       "101                    36.07                    48.79   \n",
       "\n",
       "     yoy_revenue_growth_2022  yoy_revenue_growth_latest  \n",
       "0                  28.876538                       5.11  \n",
       "1                  28.876538                      41.00  \n",
       "2                  28.876538                      15.54  \n",
       "3                  28.876538                      26.30  \n",
       "4                  11.870000                      12.02  \n",
       "..                       ...                        ...  \n",
       "97                 28.876538                      -4.02  \n",
       "98                 11.040000                      24.57  \n",
       "99                 28.876538                      10.17  \n",
       "100                50.750000                       7.16  \n",
       "101                56.050000                      56.22  \n",
       "\n",
       "[102 rows x 277 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "067cc47d",
   "metadata": {},
   "outputs": [],
   "source": [
    "data2017 = data.filter(regex='_2017')\n",
    "data2018 = data.filter(regex='_2018')\n",
    "data2019 = data.filter(regex='_2019')\n",
    "data2020 = data.filter(regex='_2020')\n",
    "data2021 = data.filter(regex='_2021')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "30b823e1",
   "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>asset_turnover_2021</th>\n",
       "      <th>buyback_yield_2021</th>\n",
       "      <th>capex_to_revenue_2021</th>\n",
       "      <th>cash_ratio_2021</th>\n",
       "      <th>cash_to_debt_2021</th>\n",
       "      <th>cogs_to_revenue_2021</th>\n",
       "      <th>mscore_2021</th>\n",
       "      <th>zscore_2021</th>\n",
       "      <th>current_ratio_2021</th>\n",
       "      <th>days_inventory_2021</th>\n",
       "      <th>...</th>\n",
       "      <th>price_to_earnings_ratio_2021</th>\n",
       "      <th>price_to_earnings_ratio_nri_2021</th>\n",
       "      <th>price_earnings_growth_ratio_2021</th>\n",
       "      <th>price_to_free_cashflow_2021</th>\n",
       "      <th>price_to_operating_cashflow_2021</th>\n",
       "      <th>rate_of_return_2021</th>\n",
       "      <th>scaled_net_operating_assets_2021</th>\n",
       "      <th>yoy_ebitda_growth_2021</th>\n",
       "      <th>yoy_eps_growth_2021</th>\n",
       "      <th>yoy_revenue_growth_2021</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.08</td>\n",
       "      <td>3.650000</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.58</td>\n",
       "      <td>-2.250000</td>\n",
       "      <td>6.99</td>\n",
       "      <td>1.07</td>\n",
       "      <td>9.120000</td>\n",
       "      <td>...</td>\n",
       "      <td>25.220000</td>\n",
       "      <td>25.220000</td>\n",
       "      <td>1.860000</td>\n",
       "      <td>25.670000</td>\n",
       "      <td>22.94</td>\n",
       "      <td>16.13000</td>\n",
       "      <td>0.39</td>\n",
       "      <td>57.9600</td>\n",
       "      <td>71.040000</td>\n",
       "      <td>38.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.50</td>\n",
       "      <td>1.733289</td>\n",
       "      <td>0.220594</td>\n",
       "      <td>1.31</td>\n",
       "      <td>3.44</td>\n",
       "      <td>0.19</td>\n",
       "      <td>-2.620000</td>\n",
       "      <td>7.43</td>\n",
       "      <td>1.95</td>\n",
       "      <td>83.647794</td>\n",
       "      <td>...</td>\n",
       "      <td>61.101047</td>\n",
       "      <td>63.153488</td>\n",
       "      <td>6.195821</td>\n",
       "      <td>47.380000</td>\n",
       "      <td>46.83</td>\n",
       "      <td>12.24026</td>\n",
       "      <td>-0.11</td>\n",
       "      <td>102.9100</td>\n",
       "      <td>96.460000</td>\n",
       "      <td>-18.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.61</td>\n",
       "      <td>1.150000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.84</td>\n",
       "      <td>1.24</td>\n",
       "      <td>0.12</td>\n",
       "      <td>-2.600000</td>\n",
       "      <td>17.93</td>\n",
       "      <td>1.25</td>\n",
       "      <td>83.647794</td>\n",
       "      <td>...</td>\n",
       "      <td>66.850000</td>\n",
       "      <td>66.850000</td>\n",
       "      <td>2.290000</td>\n",
       "      <td>46.820000</td>\n",
       "      <td>44.56</td>\n",
       "      <td>21.36000</td>\n",
       "      <td>0.56</td>\n",
       "      <td>31.9300</td>\n",
       "      <td>-7.480000</td>\n",
       "      <td>23.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.20</td>\n",
       "      <td>3.410000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.38</td>\n",
       "      <td>-1.630000</td>\n",
       "      <td>4.31</td>\n",
       "      <td>1.94</td>\n",
       "      <td>118.180000</td>\n",
       "      <td>...</td>\n",
       "      <td>50.140000</td>\n",
       "      <td>50.140000</td>\n",
       "      <td>5.080000</td>\n",
       "      <td>29.110000</td>\n",
       "      <td>25.45</td>\n",
       "      <td>12.75000</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.6000</td>\n",
       "      <td>5.490000</td>\n",
       "      <td>21.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.33</td>\n",
       "      <td>1.630000</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.59</td>\n",
       "      <td>-2.520000</td>\n",
       "      <td>2.33</td>\n",
       "      <td>1.07</td>\n",
       "      <td>83.647794</td>\n",
       "      <td>...</td>\n",
       "      <td>32.720000</td>\n",
       "      <td>32.720000</td>\n",
       "      <td>3.210000</td>\n",
       "      <td>32.860000</td>\n",
       "      <td>27.49</td>\n",
       "      <td>12.68000</td>\n",
       "      <td>0.17</td>\n",
       "      <td>5.4200</td>\n",
       "      <td>6.490000</td>\n",
       "      <td>4.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>1.57</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.79</td>\n",
       "      <td>-2.260000</td>\n",
       "      <td>2.72</td>\n",
       "      <td>0.72</td>\n",
       "      <td>28.090000</td>\n",
       "      <td>...</td>\n",
       "      <td>17.320000</td>\n",
       "      <td>22.070000</td>\n",
       "      <td>6.195821</td>\n",
       "      <td>10.530000</td>\n",
       "      <td>7.91</td>\n",
       "      <td>-5.11000</td>\n",
       "      <td>0.64</td>\n",
       "      <td>65.8000</td>\n",
       "      <td>463.460000</td>\n",
       "      <td>10.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>0.56</td>\n",
       "      <td>1.733289</td>\n",
       "      <td>0.060000</td>\n",
       "      <td>0.83</td>\n",
       "      <td>1.58</td>\n",
       "      <td>0.28</td>\n",
       "      <td>-3.240000</td>\n",
       "      <td>6.12</td>\n",
       "      <td>1.12</td>\n",
       "      <td>83.647794</td>\n",
       "      <td>...</td>\n",
       "      <td>61.101047</td>\n",
       "      <td>63.153488</td>\n",
       "      <td>6.195821</td>\n",
       "      <td>53.290000</td>\n",
       "      <td>42.51</td>\n",
       "      <td>12.24026</td>\n",
       "      <td>0.29</td>\n",
       "      <td>156.7000</td>\n",
       "      <td>43.870000</td>\n",
       "      <td>14.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>0.24</td>\n",
       "      <td>-0.990000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.61</td>\n",
       "      <td>-1.792316</td>\n",
       "      <td>1.03</td>\n",
       "      <td>0.84</td>\n",
       "      <td>26.040000</td>\n",
       "      <td>...</td>\n",
       "      <td>22.870000</td>\n",
       "      <td>22.870000</td>\n",
       "      <td>8.730000</td>\n",
       "      <td>52.026374</td>\n",
       "      <td>16.70</td>\n",
       "      <td>-1.04000</td>\n",
       "      <td>0.75</td>\n",
       "      <td>3.4800</td>\n",
       "      <td>6.090000</td>\n",
       "      <td>13.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>0.81</td>\n",
       "      <td>-1.810000</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>3.37</td>\n",
       "      <td>40.04</td>\n",
       "      <td>0.31</td>\n",
       "      <td>-0.480000</td>\n",
       "      <td>47.50</td>\n",
       "      <td>3.80</td>\n",
       "      <td>83.647794</td>\n",
       "      <td>...</td>\n",
       "      <td>165.360000</td>\n",
       "      <td>165.360000</td>\n",
       "      <td>6.195821</td>\n",
       "      <td>80.070000</td>\n",
       "      <td>75.39</td>\n",
       "      <td>12.24026</td>\n",
       "      <td>-0.22</td>\n",
       "      <td>17.7097</td>\n",
       "      <td>52.306596</td>\n",
       "      <td>263.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>0.33</td>\n",
       "      <td>1.733289</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>2.09</td>\n",
       "      <td>1.56</td>\n",
       "      <td>0.22</td>\n",
       "      <td>-2.670000</td>\n",
       "      <td>11.58</td>\n",
       "      <td>2.57</td>\n",
       "      <td>83.647794</td>\n",
       "      <td>...</td>\n",
       "      <td>61.101047</td>\n",
       "      <td>63.153488</td>\n",
       "      <td>6.195821</td>\n",
       "      <td>222.560000</td>\n",
       "      <td>158.44</td>\n",
       "      <td>12.24026</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-86.7400</td>\n",
       "      <td>-116.850000</td>\n",
       "      <td>48.79</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>102 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     asset_turnover_2021  buyback_yield_2021  capex_to_revenue_2021  \\\n",
       "0                   1.08            3.650000               0.030000   \n",
       "1                   0.50            1.733289               0.220594   \n",
       "2                   0.61            1.150000               0.020000   \n",
       "3                   0.20            3.410000               0.050000   \n",
       "4                   0.33            1.630000               0.040000   \n",
       "..                   ...                 ...                    ...   \n",
       "97                  1.57            0.250000               0.010000   \n",
       "98                  0.56            1.733289               0.060000   \n",
       "99                  0.24           -0.990000               0.320000   \n",
       "100                 0.81           -1.810000               0.030000   \n",
       "101                 0.33            1.733289               0.090000   \n",
       "\n",
       "     cash_ratio_2021  cash_to_debt_2021  cogs_to_revenue_2021  mscore_2021  \\\n",
       "0               0.50               0.50                  0.58    -2.250000   \n",
       "1               1.31               3.44                  0.19    -2.620000   \n",
       "2               0.84               1.24                  0.12    -2.600000   \n",
       "3               0.71               0.29                  0.38    -1.630000   \n",
       "4               0.07               0.77                  0.59    -2.520000   \n",
       "..               ...                ...                   ...          ...   \n",
       "97              0.05               0.04                  0.79    -2.260000   \n",
       "98              0.83               1.58                  0.28    -3.240000   \n",
       "99              0.03               0.01                  0.61    -1.792316   \n",
       "100             3.37              40.04                  0.31    -0.480000   \n",
       "101             2.09               1.56                  0.22    -2.670000   \n",
       "\n",
       "     zscore_2021  current_ratio_2021  days_inventory_2021  ...  \\\n",
       "0           6.99                1.07             9.120000  ...   \n",
       "1           7.43                1.95            83.647794  ...   \n",
       "2          17.93                1.25            83.647794  ...   \n",
       "3           4.31                1.94           118.180000  ...   \n",
       "4           2.33                1.07            83.647794  ...   \n",
       "..           ...                 ...                  ...  ...   \n",
       "97          2.72                0.72            28.090000  ...   \n",
       "98          6.12                1.12            83.647794  ...   \n",
       "99          1.03                0.84            26.040000  ...   \n",
       "100        47.50                3.80            83.647794  ...   \n",
       "101        11.58                2.57            83.647794  ...   \n",
       "\n",
       "     price_to_earnings_ratio_2021  price_to_earnings_ratio_nri_2021  \\\n",
       "0                       25.220000                         25.220000   \n",
       "1                       61.101047                         63.153488   \n",
       "2                       66.850000                         66.850000   \n",
       "3                       50.140000                         50.140000   \n",
       "4                       32.720000                         32.720000   \n",
       "..                            ...                               ...   \n",
       "97                      17.320000                         22.070000   \n",
       "98                      61.101047                         63.153488   \n",
       "99                      22.870000                         22.870000   \n",
       "100                    165.360000                        165.360000   \n",
       "101                     61.101047                         63.153488   \n",
       "\n",
       "     price_earnings_growth_ratio_2021  price_to_free_cashflow_2021  \\\n",
       "0                            1.860000                    25.670000   \n",
       "1                            6.195821                    47.380000   \n",
       "2                            2.290000                    46.820000   \n",
       "3                            5.080000                    29.110000   \n",
       "4                            3.210000                    32.860000   \n",
       "..                                ...                          ...   \n",
       "97                           6.195821                    10.530000   \n",
       "98                           6.195821                    53.290000   \n",
       "99                           8.730000                    52.026374   \n",
       "100                          6.195821                    80.070000   \n",
       "101                          6.195821                   222.560000   \n",
       "\n",
       "     price_to_operating_cashflow_2021  rate_of_return_2021  \\\n",
       "0                               22.94             16.13000   \n",
       "1                               46.83             12.24026   \n",
       "2                               44.56             21.36000   \n",
       "3                               25.45             12.75000   \n",
       "4                               27.49             12.68000   \n",
       "..                                ...                  ...   \n",
       "97                               7.91             -5.11000   \n",
       "98                              42.51             12.24026   \n",
       "99                              16.70             -1.04000   \n",
       "100                             75.39             12.24026   \n",
       "101                            158.44             12.24026   \n",
       "\n",
       "     scaled_net_operating_assets_2021  yoy_ebitda_growth_2021  \\\n",
       "0                                0.39                 57.9600   \n",
       "1                               -0.11                102.9100   \n",
       "2                                0.56                 31.9300   \n",
       "3                                2.00                  3.6000   \n",
       "4                                0.17                  5.4200   \n",
       "..                                ...                     ...   \n",
       "97                               0.64                 65.8000   \n",
       "98                               0.29                156.7000   \n",
       "99                               0.75                  3.4800   \n",
       "100                             -0.22                 17.7097   \n",
       "101                             -0.01                -86.7400   \n",
       "\n",
       "     yoy_eps_growth_2021  yoy_revenue_growth_2021  \n",
       "0              71.040000                    38.50  \n",
       "1              96.460000                   -18.11  \n",
       "2              -7.480000                    23.69  \n",
       "3               5.490000                    21.07  \n",
       "4               6.490000                     4.95  \n",
       "..                   ...                      ...  \n",
       "97            463.460000                    10.37  \n",
       "98             43.870000                    14.10  \n",
       "99              6.090000                    13.93  \n",
       "100            52.306596                   263.13  \n",
       "101          -116.850000                    48.79  \n",
       "\n",
       "[102 rows x 39 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2021.head(110)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6c4c5b0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "column_names = data2018.columns.values.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "bc6b8b9b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "asset_turnover_2018\n",
      "buyback_yield_2018\n",
      "capex_to_revenue_2018\n",
      "cash_ratio_2018\n",
      "cash_to_debt_2018\n",
      "cogs_to_revenue_2018\n",
      "mscore_2018\n",
      "zscore_2018\n",
      "current_ratio_2018\n",
      "days_inventory_2018\n",
      "debt_to_equity_2018\n",
      "debt_to_assets_2018\n",
      "debt_to_ebitda_2018\n",
      "debt_to_revenue_2018\n",
      "e10_2018\n",
      "effective_interest_rate_2018\n",
      "equity_to_assets_2018\n",
      "enterprise_value_to_ebit_2018\n",
      "enterprise_value_to_ebitda_2018\n",
      "enterprise_value_to_revenue_2018\n",
      "earning_yield_greenblatt_2018\n",
      "fscore_2018\n",
      "gross_profit_to_assets_2018\n",
      "interest_coverage_2018\n",
      "inventory_turnover_2018\n",
      "inventory_to_revenue_2018\n",
      "liabilities_to_assets_2018\n",
      "longterm_debt_to_assets_2018\n",
      "price_to_book_ratio_2018\n",
      "price_to_earnings_ratio_2018\n",
      "price_to_earnings_ratio_nri_2018\n",
      "price_earnings_growth_ratio_2018\n",
      "price_to_free_cashflow_2018\n",
      "price_to_operating_cashflow_2018\n",
      "rate_of_return_2018\n",
      "scaled_net_operating_assets_2018\n",
      "yoy_ebitda_growth_2018\n",
      "yoy_eps_growth_2018\n",
      "yoy_revenue_growth_2018\n"
     ]
    }
   ],
   "source": [
    "print(*column_names,sep='\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf91aebc",
   "metadata": {},
   "source": [
    "# "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69bfeaa9",
   "metadata": {},
   "source": [
    "# What we have now:\n",
    "We now have 5 discrete 12 month periods of Training Data.\n",
    "\n",
    "Remember the 2022 year end data (and the current data points) have not been saved as discrete X data as in our later work we have provided Labels that are 12 months after the initial data points which would be beyond today's date.  However, should you be interested in using 6 month ahead Share Price performance labels then the 2022 data could also be silimilarly extracted.\n",
    "\n",
    "\n",
    "You will note the Label data is the Return recorded over the next 12 months in Share Price Returns.  It is important to note that Total Return (including dividends, buy backs, hives etc.) has not been calculated."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4059be54",
   "metadata": {},
   "source": [
    "# "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c968a2ae",
   "metadata": {},
   "source": [
    "# 4. We now put the X data into aggregated datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "cfcfc4f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "data17_21 = pd.DataFrame(np.concatenate( (data2017.values, data2018.values, data2019.values, data2020.values, data2021.values), axis=0 ) )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d2cec35b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>29</th>\n",
       "      <th>30</th>\n",
       "      <th>31</th>\n",
       "      <th>32</th>\n",
       "      <th>33</th>\n",
       "      <th>34</th>\n",
       "      <th>35</th>\n",
       "      <th>36</th>\n",
       "      <th>37</th>\n",
       "      <th>38</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.660000</td>\n",
       "      <td>4.090000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.740000</td>\n",
       "      <td>0.640000</td>\n",
       "      <td>0.620000</td>\n",
       "      <td>-2.500000</td>\n",
       "      <td>3.620000</td>\n",
       "      <td>1.280000</td>\n",
       "      <td>9.040000</td>\n",
       "      <td>...</td>\n",
       "      <td>16.730000</td>\n",
       "      <td>16.730000</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>15.630000</td>\n",
       "      <td>12.600000</td>\n",
       "      <td>19.240000</td>\n",
       "      <td>0.550000</td>\n",
       "      <td>9.360000</td>\n",
       "      <td>10.830000</td>\n",
       "      <td>11.340000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.724932</td>\n",
       "      <td>2.035574</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>1.410274</td>\n",
       "      <td>2.648413</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>-2.543382</td>\n",
       "      <td>6.686479</td>\n",
       "      <td>2.332192</td>\n",
       "      <td>89.855094</td>\n",
       "      <td>...</td>\n",
       "      <td>44.940635</td>\n",
       "      <td>44.909524</td>\n",
       "      <td>10.936250</td>\n",
       "      <td>33.836721</td>\n",
       "      <td>35.271493</td>\n",
       "      <td>14.260952</td>\n",
       "      <td>0.482917</td>\n",
       "      <td>26.097361</td>\n",
       "      <td>46.020147</td>\n",
       "      <td>19.277917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.540000</td>\n",
       "      <td>1.080000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>1.650000</td>\n",
       "      <td>3.090000</td>\n",
       "      <td>0.140000</td>\n",
       "      <td>-2.490000</td>\n",
       "      <td>10.810000</td>\n",
       "      <td>2.050000</td>\n",
       "      <td>89.855094</td>\n",
       "      <td>...</td>\n",
       "      <td>52.320000</td>\n",
       "      <td>52.320000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>32.410000</td>\n",
       "      <td>30.420000</td>\n",
       "      <td>21.820000</td>\n",
       "      <td>0.360000</td>\n",
       "      <td>39.030000</td>\n",
       "      <td>45.690000</td>\n",
       "      <td>25.510000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.360000</td>\n",
       "      <td>0.140000</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>0.660000</td>\n",
       "      <td>0.130000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>-1.490000</td>\n",
       "      <td>2.640000</td>\n",
       "      <td>1.470000</td>\n",
       "      <td>81.440000</td>\n",
       "      <td>...</td>\n",
       "      <td>39.870000</td>\n",
       "      <td>39.870000</td>\n",
       "      <td>4.440000</td>\n",
       "      <td>33.680000</td>\n",
       "      <td>27.720000</td>\n",
       "      <td>11.950000</td>\n",
       "      <td>2.130000</td>\n",
       "      <td>25.600000</td>\n",
       "      <td>-17.030000</td>\n",
       "      <td>36.640000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.724932</td>\n",
       "      <td>2.035574</td>\n",
       "      <td>0.086892</td>\n",
       "      <td>1.410274</td>\n",
       "      <td>2.648413</td>\n",
       "      <td>0.437534</td>\n",
       "      <td>-2.543382</td>\n",
       "      <td>6.686479</td>\n",
       "      <td>2.332192</td>\n",
       "      <td>89.855094</td>\n",
       "      <td>...</td>\n",
       "      <td>44.940635</td>\n",
       "      <td>44.909524</td>\n",
       "      <td>10.936250</td>\n",
       "      <td>33.836721</td>\n",
       "      <td>35.271493</td>\n",
       "      <td>14.260952</td>\n",
       "      <td>0.482917</td>\n",
       "      <td>26.097361</td>\n",
       "      <td>46.020147</td>\n",
       "      <td>19.277917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>505</th>\n",
       "      <td>1.570000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>0.790000</td>\n",
       "      <td>-2.260000</td>\n",
       "      <td>2.720000</td>\n",
       "      <td>0.720000</td>\n",
       "      <td>28.090000</td>\n",
       "      <td>...</td>\n",
       "      <td>17.320000</td>\n",
       "      <td>22.070000</td>\n",
       "      <td>6.195821</td>\n",
       "      <td>10.530000</td>\n",
       "      <td>7.910000</td>\n",
       "      <td>-5.110000</td>\n",
       "      <td>0.640000</td>\n",
       "      <td>65.800000</td>\n",
       "      <td>463.460000</td>\n",
       "      <td>10.370000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506</th>\n",
       "      <td>0.560000</td>\n",
       "      <td>1.733289</td>\n",
       "      <td>0.060000</td>\n",
       "      <td>0.830000</td>\n",
       "      <td>1.580000</td>\n",
       "      <td>0.280000</td>\n",
       "      <td>-3.240000</td>\n",
       "      <td>6.120000</td>\n",
       "      <td>1.120000</td>\n",
       "      <td>83.647794</td>\n",
       "      <td>...</td>\n",
       "      <td>61.101047</td>\n",
       "      <td>63.153488</td>\n",
       "      <td>6.195821</td>\n",
       "      <td>53.290000</td>\n",
       "      <td>42.510000</td>\n",
       "      <td>12.240260</td>\n",
       "      <td>0.290000</td>\n",
       "      <td>156.700000</td>\n",
       "      <td>43.870000</td>\n",
       "      <td>14.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>507</th>\n",
       "      <td>0.240000</td>\n",
       "      <td>-0.990000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.610000</td>\n",
       "      <td>-1.792316</td>\n",
       "      <td>1.030000</td>\n",
       "      <td>0.840000</td>\n",
       "      <td>26.040000</td>\n",
       "      <td>...</td>\n",
       "      <td>22.870000</td>\n",
       "      <td>22.870000</td>\n",
       "      <td>8.730000</td>\n",
       "      <td>52.026374</td>\n",
       "      <td>16.700000</td>\n",
       "      <td>-1.040000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>3.480000</td>\n",
       "      <td>6.090000</td>\n",
       "      <td>13.930000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>508</th>\n",
       "      <td>0.810000</td>\n",
       "      <td>-1.810000</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>3.370000</td>\n",
       "      <td>40.040000</td>\n",
       "      <td>0.310000</td>\n",
       "      <td>-0.480000</td>\n",
       "      <td>47.500000</td>\n",
       "      <td>3.800000</td>\n",
       "      <td>83.647794</td>\n",
       "      <td>...</td>\n",
       "      <td>165.360000</td>\n",
       "      <td>165.360000</td>\n",
       "      <td>6.195821</td>\n",
       "      <td>80.070000</td>\n",
       "      <td>75.390000</td>\n",
       "      <td>12.240260</td>\n",
       "      <td>-0.220000</td>\n",
       "      <td>17.709700</td>\n",
       "      <td>52.306596</td>\n",
       "      <td>263.130000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>509</th>\n",
       "      <td>0.330000</td>\n",
       "      <td>1.733289</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>2.090000</td>\n",
       "      <td>1.560000</td>\n",
       "      <td>0.220000</td>\n",
       "      <td>-2.670000</td>\n",
       "      <td>11.580000</td>\n",
       "      <td>2.570000</td>\n",
       "      <td>83.647794</td>\n",
       "      <td>...</td>\n",
       "      <td>61.101047</td>\n",
       "      <td>63.153488</td>\n",
       "      <td>6.195821</td>\n",
       "      <td>222.560000</td>\n",
       "      <td>158.440000</td>\n",
       "      <td>12.240260</td>\n",
       "      <td>-0.010000</td>\n",
       "      <td>-86.740000</td>\n",
       "      <td>-116.850000</td>\n",
       "      <td>48.790000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>510 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           0         1         2         3          4         5         6   \\\n",
       "0    0.660000  4.090000  0.050000  0.740000   0.640000  0.620000 -2.500000   \n",
       "1    0.724932  2.035574  0.040000  1.410274   2.648413  0.250000 -2.543382   \n",
       "2    0.540000  1.080000  0.020000  1.650000   3.090000  0.140000 -2.490000   \n",
       "3    0.360000  0.140000  0.040000  0.660000   0.130000  0.400000 -1.490000   \n",
       "4    0.724932  2.035574  0.086892  1.410274   2.648413  0.437534 -2.543382   \n",
       "..        ...       ...       ...       ...        ...       ...       ...   \n",
       "505  1.570000  0.250000  0.010000  0.050000   0.040000  0.790000 -2.260000   \n",
       "506  0.560000  1.733289  0.060000  0.830000   1.580000  0.280000 -3.240000   \n",
       "507  0.240000 -0.990000  0.320000  0.030000   0.010000  0.610000 -1.792316   \n",
       "508  0.810000 -1.810000  0.030000  3.370000  40.040000  0.310000 -0.480000   \n",
       "509  0.330000  1.733289  0.090000  2.090000   1.560000  0.220000 -2.670000   \n",
       "\n",
       "            7         8          9   ...          29          30         31  \\\n",
       "0     3.620000  1.280000   9.040000  ...   16.730000   16.730000   1.250000   \n",
       "1     6.686479  2.332192  89.855094  ...   44.940635   44.909524  10.936250   \n",
       "2    10.810000  2.050000  89.855094  ...   52.320000   52.320000   2.420000   \n",
       "3     2.640000  1.470000  81.440000  ...   39.870000   39.870000   4.440000   \n",
       "4     6.686479  2.332192  89.855094  ...   44.940635   44.909524  10.936250   \n",
       "..         ...       ...        ...  ...         ...         ...        ...   \n",
       "505   2.720000  0.720000  28.090000  ...   17.320000   22.070000   6.195821   \n",
       "506   6.120000  1.120000  83.647794  ...   61.101047   63.153488   6.195821   \n",
       "507   1.030000  0.840000  26.040000  ...   22.870000   22.870000   8.730000   \n",
       "508  47.500000  3.800000  83.647794  ...  165.360000  165.360000   6.195821   \n",
       "509  11.580000  2.570000  83.647794  ...   61.101047   63.153488   6.195821   \n",
       "\n",
       "             32          33         34        35          36          37  \\\n",
       "0     15.630000   12.600000  19.240000  0.550000    9.360000   10.830000   \n",
       "1     33.836721   35.271493  14.260952  0.482917   26.097361   46.020147   \n",
       "2     32.410000   30.420000  21.820000  0.360000   39.030000   45.690000   \n",
       "3     33.680000   27.720000  11.950000  2.130000   25.600000  -17.030000   \n",
       "4     33.836721   35.271493  14.260952  0.482917   26.097361   46.020147   \n",
       "..          ...         ...        ...       ...         ...         ...   \n",
       "505   10.530000    7.910000  -5.110000  0.640000   65.800000  463.460000   \n",
       "506   53.290000   42.510000  12.240260  0.290000  156.700000   43.870000   \n",
       "507   52.026374   16.700000  -1.040000  0.750000    3.480000    6.090000   \n",
       "508   80.070000   75.390000  12.240260 -0.220000   17.709700   52.306596   \n",
       "509  222.560000  158.440000  12.240260 -0.010000  -86.740000 -116.850000   \n",
       "\n",
       "             38  \n",
       "0     11.340000  \n",
       "1     19.277917  \n",
       "2     25.510000  \n",
       "3     36.640000  \n",
       "4     19.277917  \n",
       "..          ...  \n",
       "505   10.370000  \n",
       "506   14.100000  \n",
       "507   13.930000  \n",
       "508  263.130000  \n",
       "509   48.790000  \n",
       "\n",
       "[510 rows x 39 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data17_21"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "104b5e57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['asset_turnover',\n",
       " 'buyback_yield',\n",
       " 'capex_to_revenue',\n",
       " 'cash_ratio',\n",
       " 'cash_to_debt',\n",
       " 'cogs_to_revenue',\n",
       " 'mscore',\n",
       " 'zscore',\n",
       " 'current_ratio',\n",
       " 'days_inventory',\n",
       " 'debt_to_equity',\n",
       " 'debt_to_assets',\n",
       " 'debt_to_ebitda',\n",
       " 'debt_to_revenue',\n",
       " 'e10',\n",
       " 'effective_interest_rate',\n",
       " 'equity_to_assets',\n",
       " 'enterprise_value_to_ebit',\n",
       " 'enterprise_value_to_ebitda',\n",
       " 'enterprise_value_to_revenue',\n",
       " 'earning_yield_greenblatt',\n",
       " 'fscore',\n",
       " 'gross_profit_to_assets',\n",
       " 'interest_coverage',\n",
       " 'inventory_turnover',\n",
       " 'inventory_to_revenue',\n",
       " 'liabilities_to_assets',\n",
       " 'longterm_debt_to_assets',\n",
       " 'price_to_book_ratio',\n",
       " 'price_to_earnings_ratio',\n",
       " 'price_to_earnings_ratio_nri',\n",
       " 'price_earnings_growth_ratio',\n",
       " 'price_to_free_cashflow',\n",
       " 'price_to_operating_cashflow',\n",
       " 'rate_of_return',\n",
       " 'scaled_net_operating_assets',\n",
       " 'yoy_ebitda_growth',\n",
       " 'yoy_eps_growth',\n",
       " 'yoy_revenue_growth']"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_with_years = ['asset_turnover_2017', 'buyback_yield_2017', 'capex_to_revenue_2017', 'cash_ratio_2017', 'cash_to_debt_2017', 'cogs_to_revenue_2017', 'mscore_2017', 'zscore_2017', 'current_ratio_2017', 'days_inventory_2017', 'debt_to_equity_2017', 'debt_to_assets_2017', 'debt_to_ebitda_2017', 'debt_to_revenue_2017', 'e10_2017', 'effective_interest_rate_2017', 'equity_to_assets_2017', 'enterprise_value_to_ebit_2017', 'enterprise_value_to_ebitda_2017', 'enterprise_value_to_revenue_2017', 'earning_yield_greenblatt_2017', 'fscore_2017', 'gross_profit_to_assets_2017', 'interest_coverage_2017', 'inventory_turnover_2017', 'inventory_to_revenue_2017', 'liabilities_to_assets_2017', 'longterm_debt_to_assets_2017', 'price_to_book_ratio_2017', 'price_to_earnings_ratio_2017', 'price_to_earnings_ratio_nri_2017', 'price_earnings_growth_ratio_2017', 'price_to_free_cashflow_2017', 'price_to_operating_cashflow_2017', 'rate_of_return_2017', 'scaled_net_operating_assets_2017', 'yoy_ebitda_growth_2017', 'yoy_eps_growth_2017','yoy_revenue_growth_2017']\n",
    "\n",
    "list_without_years = [i[:-5] for i in list_with_years]\n",
    "\n",
    "list_without_years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f99b367c",
   "metadata": {},
   "outputs": [],
   "source": [
    "data17_21.columns = ['asset_turnover',\n",
    " 'buyback_yield',\n",
    " 'capex_to_revenue',\n",
    " 'cash_ratio',\n",
    " 'cash_to_debt',\n",
    " 'cogs_to_revenue',\n",
    " 'mscore',\n",
    " 'zscore',\n",
    " 'current_ratio',\n",
    " 'days_inventory',\n",
    " 'debt_to_equity',\n",
    " 'debt_to_assets',\n",
    " 'debt_to_ebitda',\n",
    " 'debt_to_revenue',\n",
    " 'e10',\n",
    " 'effective_interest_rate',\n",
    " 'equity_to_assets',\n",
    " 'enterprise_value_to_ebit',\n",
    " 'enterprise_value_to_ebitda',\n",
    " 'enterprise_value_to_revenue',\n",
    " 'earning_yield_greenblatt',\n",
    " 'fscore',\n",
    " 'gross_profit_to_assets',\n",
    " 'interest_coverage',\n",
    " 'inventory_turnover',\n",
    " 'inventory_to_revenue',\n",
    " 'liabilities_to_assets',\n",
    " 'longterm_debt_to_assets',\n",
    " 'price_to_book_ratio',\n",
    " 'price_to_earnings_ratio',\n",
    " 'price_to_earnings_ratio_nri',\n",
    " 'price_earnings_growth_ratio',\n",
    " 'price_to_free_cashflow',\n",
    " 'price_to_operating_cashflow',\n",
    " 'rate_of_return',\n",
    " 'scaled_net_operating_assets',\n",
    " 'yoy_ebitda_growth',\n",
    " 'yoy_eps_growth',\n",
    " 'yoy_revenue_growth']\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "eac31657",
   "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>asset_turnover</th>\n",
       "      <th>buyback_yield</th>\n",
       "      <th>capex_to_revenue</th>\n",
       "      <th>cash_ratio</th>\n",
       "      <th>cash_to_debt</th>\n",
       "      <th>cogs_to_revenue</th>\n",
       "      <th>mscore</th>\n",
       "      <th>zscore</th>\n",
       "      <th>current_ratio</th>\n",
       "      <th>days_inventory</th>\n",
       "      <th>...</th>\n",
       "      <th>price_to_earnings_ratio</th>\n",
       "      <th>price_to_earnings_ratio_nri</th>\n",
       "      <th>price_earnings_growth_ratio</th>\n",
       "      <th>price_to_free_cashflow</th>\n",
       "      <th>price_to_operating_cashflow</th>\n",
       "      <th>rate_of_return</th>\n",
       "      <th>scaled_net_operating_assets</th>\n",
       "      <th>yoy_ebitda_growth</th>\n",
       "      <th>yoy_eps_growth</th>\n",
       "      <th>yoy_revenue_growth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.660000</td>\n",
       "      <td>4.090000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.740000</td>\n",
       "      <td>0.640000</td>\n",
       "      <td>0.620000</td>\n",
       "      <td>-2.500000</td>\n",
       "      <td>3.620000</td>\n",
       "      <td>1.280000</td>\n",
       "      <td>9.040000</td>\n",
       "      <td>...</td>\n",
       "      <td>16.730000</td>\n",
       "      <td>16.730000</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>15.630000</td>\n",
       "      <td>12.600000</td>\n",
       "      <td>19.240000</td>\n",
       "      <td>0.550000</td>\n",
       "      <td>9.360000</td>\n",
       "      <td>10.830000</td>\n",
       "      <td>11.340000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.724932</td>\n",
       "      <td>2.035574</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>1.410274</td>\n",
       "      <td>2.648413</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>-2.543382</td>\n",
       "      <td>6.686479</td>\n",
       "      <td>2.332192</td>\n",
       "      <td>89.855094</td>\n",
       "      <td>...</td>\n",
       "      <td>44.940635</td>\n",
       "      <td>44.909524</td>\n",
       "      <td>10.936250</td>\n",
       "      <td>33.836721</td>\n",
       "      <td>35.271493</td>\n",
       "      <td>14.260952</td>\n",
       "      <td>0.482917</td>\n",
       "      <td>26.097361</td>\n",
       "      <td>46.020147</td>\n",
       "      <td>19.277917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.540000</td>\n",
       "      <td>1.080000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>1.650000</td>\n",
       "      <td>3.090000</td>\n",
       "      <td>0.140000</td>\n",
       "      <td>-2.490000</td>\n",
       "      <td>10.810000</td>\n",
       "      <td>2.050000</td>\n",
       "      <td>89.855094</td>\n",
       "      <td>...</td>\n",
       "      <td>52.320000</td>\n",
       "      <td>52.320000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>32.410000</td>\n",
       "      <td>30.420000</td>\n",
       "      <td>21.820000</td>\n",
       "      <td>0.360000</td>\n",
       "      <td>39.030000</td>\n",
       "      <td>45.690000</td>\n",
       "      <td>25.510000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.360000</td>\n",
       "      <td>0.140000</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>0.660000</td>\n",
       "      <td>0.130000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>-1.490000</td>\n",
       "      <td>2.640000</td>\n",
       "      <td>1.470000</td>\n",
       "      <td>81.440000</td>\n",
       "      <td>...</td>\n",
       "      <td>39.870000</td>\n",
       "      <td>39.870000</td>\n",
       "      <td>4.440000</td>\n",
       "      <td>33.680000</td>\n",
       "      <td>27.720000</td>\n",
       "      <td>11.950000</td>\n",
       "      <td>2.130000</td>\n",
       "      <td>25.600000</td>\n",
       "      <td>-17.030000</td>\n",
       "      <td>36.640000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.724932</td>\n",
       "      <td>2.035574</td>\n",
       "      <td>0.086892</td>\n",
       "      <td>1.410274</td>\n",
       "      <td>2.648413</td>\n",
       "      <td>0.437534</td>\n",
       "      <td>-2.543382</td>\n",
       "      <td>6.686479</td>\n",
       "      <td>2.332192</td>\n",
       "      <td>89.855094</td>\n",
       "      <td>...</td>\n",
       "      <td>44.940635</td>\n",
       "      <td>44.909524</td>\n",
       "      <td>10.936250</td>\n",
       "      <td>33.836721</td>\n",
       "      <td>35.271493</td>\n",
       "      <td>14.260952</td>\n",
       "      <td>0.482917</td>\n",
       "      <td>26.097361</td>\n",
       "      <td>46.020147</td>\n",
       "      <td>19.277917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>505</th>\n",
       "      <td>1.570000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>0.790000</td>\n",
       "      <td>-2.260000</td>\n",
       "      <td>2.720000</td>\n",
       "      <td>0.720000</td>\n",
       "      <td>28.090000</td>\n",
       "      <td>...</td>\n",
       "      <td>17.320000</td>\n",
       "      <td>22.070000</td>\n",
       "      <td>6.195821</td>\n",
       "      <td>10.530000</td>\n",
       "      <td>7.910000</td>\n",
       "      <td>-5.110000</td>\n",
       "      <td>0.640000</td>\n",
       "      <td>65.800000</td>\n",
       "      <td>463.460000</td>\n",
       "      <td>10.370000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506</th>\n",
       "      <td>0.560000</td>\n",
       "      <td>1.733289</td>\n",
       "      <td>0.060000</td>\n",
       "      <td>0.830000</td>\n",
       "      <td>1.580000</td>\n",
       "      <td>0.280000</td>\n",
       "      <td>-3.240000</td>\n",
       "      <td>6.120000</td>\n",
       "      <td>1.120000</td>\n",
       "      <td>83.647794</td>\n",
       "      <td>...</td>\n",
       "      <td>61.101047</td>\n",
       "      <td>63.153488</td>\n",
       "      <td>6.195821</td>\n",
       "      <td>53.290000</td>\n",
       "      <td>42.510000</td>\n",
       "      <td>12.240260</td>\n",
       "      <td>0.290000</td>\n",
       "      <td>156.700000</td>\n",
       "      <td>43.870000</td>\n",
       "      <td>14.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>507</th>\n",
       "      <td>0.240000</td>\n",
       "      <td>-0.990000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.610000</td>\n",
       "      <td>-1.792316</td>\n",
       "      <td>1.030000</td>\n",
       "      <td>0.840000</td>\n",
       "      <td>26.040000</td>\n",
       "      <td>...</td>\n",
       "      <td>22.870000</td>\n",
       "      <td>22.870000</td>\n",
       "      <td>8.730000</td>\n",
       "      <td>52.026374</td>\n",
       "      <td>16.700000</td>\n",
       "      <td>-1.040000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>3.480000</td>\n",
       "      <td>6.090000</td>\n",
       "      <td>13.930000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>508</th>\n",
       "      <td>0.810000</td>\n",
       "      <td>-1.810000</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>3.370000</td>\n",
       "      <td>40.040000</td>\n",
       "      <td>0.310000</td>\n",
       "      <td>-0.480000</td>\n",
       "      <td>47.500000</td>\n",
       "      <td>3.800000</td>\n",
       "      <td>83.647794</td>\n",
       "      <td>...</td>\n",
       "      <td>165.360000</td>\n",
       "      <td>165.360000</td>\n",
       "      <td>6.195821</td>\n",
       "      <td>80.070000</td>\n",
       "      <td>75.390000</td>\n",
       "      <td>12.240260</td>\n",
       "      <td>-0.220000</td>\n",
       "      <td>17.709700</td>\n",
       "      <td>52.306596</td>\n",
       "      <td>263.130000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>509</th>\n",
       "      <td>0.330000</td>\n",
       "      <td>1.733289</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>2.090000</td>\n",
       "      <td>1.560000</td>\n",
       "      <td>0.220000</td>\n",
       "      <td>-2.670000</td>\n",
       "      <td>11.580000</td>\n",
       "      <td>2.570000</td>\n",
       "      <td>83.647794</td>\n",
       "      <td>...</td>\n",
       "      <td>61.101047</td>\n",
       "      <td>63.153488</td>\n",
       "      <td>6.195821</td>\n",
       "      <td>222.560000</td>\n",
       "      <td>158.440000</td>\n",
       "      <td>12.240260</td>\n",
       "      <td>-0.010000</td>\n",
       "      <td>-86.740000</td>\n",
       "      <td>-116.850000</td>\n",
       "      <td>48.790000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>510 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     asset_turnover  buyback_yield  capex_to_revenue  cash_ratio  \\\n",
       "0          0.660000       4.090000          0.050000    0.740000   \n",
       "1          0.724932       2.035574          0.040000    1.410274   \n",
       "2          0.540000       1.080000          0.020000    1.650000   \n",
       "3          0.360000       0.140000          0.040000    0.660000   \n",
       "4          0.724932       2.035574          0.086892    1.410274   \n",
       "..              ...            ...               ...         ...   \n",
       "505        1.570000       0.250000          0.010000    0.050000   \n",
       "506        0.560000       1.733289          0.060000    0.830000   \n",
       "507        0.240000      -0.990000          0.320000    0.030000   \n",
       "508        0.810000      -1.810000          0.030000    3.370000   \n",
       "509        0.330000       1.733289          0.090000    2.090000   \n",
       "\n",
       "     cash_to_debt  cogs_to_revenue    mscore     zscore  current_ratio  \\\n",
       "0        0.640000         0.620000 -2.500000   3.620000       1.280000   \n",
       "1        2.648413         0.250000 -2.543382   6.686479       2.332192   \n",
       "2        3.090000         0.140000 -2.490000  10.810000       2.050000   \n",
       "3        0.130000         0.400000 -1.490000   2.640000       1.470000   \n",
       "4        2.648413         0.437534 -2.543382   6.686479       2.332192   \n",
       "..            ...              ...       ...        ...            ...   \n",
       "505      0.040000         0.790000 -2.260000   2.720000       0.720000   \n",
       "506      1.580000         0.280000 -3.240000   6.120000       1.120000   \n",
       "507      0.010000         0.610000 -1.792316   1.030000       0.840000   \n",
       "508     40.040000         0.310000 -0.480000  47.500000       3.800000   \n",
       "509      1.560000         0.220000 -2.670000  11.580000       2.570000   \n",
       "\n",
       "     days_inventory  ...  price_to_earnings_ratio  \\\n",
       "0          9.040000  ...                16.730000   \n",
       "1         89.855094  ...                44.940635   \n",
       "2         89.855094  ...                52.320000   \n",
       "3         81.440000  ...                39.870000   \n",
       "4         89.855094  ...                44.940635   \n",
       "..              ...  ...                      ...   \n",
       "505       28.090000  ...                17.320000   \n",
       "506       83.647794  ...                61.101047   \n",
       "507       26.040000  ...                22.870000   \n",
       "508       83.647794  ...               165.360000   \n",
       "509       83.647794  ...                61.101047   \n",
       "\n",
       "     price_to_earnings_ratio_nri  price_earnings_growth_ratio  \\\n",
       "0                      16.730000                     1.250000   \n",
       "1                      44.909524                    10.936250   \n",
       "2                      52.320000                     2.420000   \n",
       "3                      39.870000                     4.440000   \n",
       "4                      44.909524                    10.936250   \n",
       "..                           ...                          ...   \n",
       "505                    22.070000                     6.195821   \n",
       "506                    63.153488                     6.195821   \n",
       "507                    22.870000                     8.730000   \n",
       "508                   165.360000                     6.195821   \n",
       "509                    63.153488                     6.195821   \n",
       "\n",
       "     price_to_free_cashflow  price_to_operating_cashflow  rate_of_return  \\\n",
       "0                 15.630000                    12.600000       19.240000   \n",
       "1                 33.836721                    35.271493       14.260952   \n",
       "2                 32.410000                    30.420000       21.820000   \n",
       "3                 33.680000                    27.720000       11.950000   \n",
       "4                 33.836721                    35.271493       14.260952   \n",
       "..                      ...                          ...             ...   \n",
       "505               10.530000                     7.910000       -5.110000   \n",
       "506               53.290000                    42.510000       12.240260   \n",
       "507               52.026374                    16.700000       -1.040000   \n",
       "508               80.070000                    75.390000       12.240260   \n",
       "509              222.560000                   158.440000       12.240260   \n",
       "\n",
       "     scaled_net_operating_assets  yoy_ebitda_growth  yoy_eps_growth  \\\n",
       "0                       0.550000           9.360000       10.830000   \n",
       "1                       0.482917          26.097361       46.020147   \n",
       "2                       0.360000          39.030000       45.690000   \n",
       "3                       2.130000          25.600000      -17.030000   \n",
       "4                       0.482917          26.097361       46.020147   \n",
       "..                           ...                ...             ...   \n",
       "505                     0.640000          65.800000      463.460000   \n",
       "506                     0.290000         156.700000       43.870000   \n",
       "507                     0.750000           3.480000        6.090000   \n",
       "508                    -0.220000          17.709700       52.306596   \n",
       "509                    -0.010000         -86.740000     -116.850000   \n",
       "\n",
       "     yoy_revenue_growth  \n",
       "0             11.340000  \n",
       "1             19.277917  \n",
       "2             25.510000  \n",
       "3             36.640000  \n",
       "4             19.277917  \n",
       "..                  ...  \n",
       "505           10.370000  \n",
       "506           14.100000  \n",
       "507           13.930000  \n",
       "508          263.130000  \n",
       "509           48.790000  \n",
       "\n",
       "[510 rows x 39 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data17_21 "
   ]
  },
  {
   "cell_type": "raw",
   "id": "e26c73c0",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "7272db37",
   "metadata": {},
   "source": [
    "# Lets do some sense checking of what we have got.\n",
    "\n",
    "102 stocks for 5 years = 510 values for each Financial Statement Metric attribute.\n",
    "\n",
    "We want to check through each Financial Statement Metric and check the mean, 25% threshold value and the 75% threshold value for reasonableness.\n",
    "\n",
    "We can see in our work below we have two columns that may have outliers in the data being the Capex to Revenue metric and the Cash to Debt metric.  \n",
    "\n",
    "We can explain both these data outturns:\n",
    "\n",
    "Technology companies are sometimes early stage and investing heavily before they have Revenue - this would explain the Capex to Revenue outliers.\n",
    "\n",
    "Some mature Technology companies have very strong cash flow and hence huge cash resources compared to their Debt - this would explain the Cash to Debt outliers.\n"
   ]
  },
  {
   "cell_type": "raw",
   "id": "97e3bad4",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "fe57aac6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "asset_turnover:\n",
      "  Range: 0.42 - 0.79\n",
      "  Mean: 0.7033831718804439\n",
      "buyback_yield:\n",
      "  Range: 0.5725 - 2.7675\n",
      "  Mean: 2.0903778112848315\n",
      "capex_to_revenue:\n",
      "  Range: 0.03 - 0.09\n",
      "  Mean: 0.3825728338239233\n",
      "cash_ratio:\n",
      "  Range: 0.41 - 1.62\n",
      "  Mean: 1.321372930804764\n",
      "cash_to_debt:\n",
      "  Range: 0.3 - 2.6484126984126983\n",
      "  Mean: 3.798609663432115\n",
      "cogs_to_revenue:\n",
      "  Range: 0.27 - 0.55\n",
      "  Mean: 0.43779630826096283\n",
      "mscore:\n",
      "  Range: -2.76 - -2.3825\n",
      "  Mean: -2.364131619329655\n",
      "zscore:\n",
      "  Range: 3.07 - 9.38\n",
      "  Mean: 7.537673395007848\n",
      "current_ratio:\n",
      "  Range: 1.11 - 2.6575\n",
      "  Mean: 2.2023971758110545\n",
      "days_inventory:\n",
      "  Range: 56.4175 - 92.4830303030303\n",
      "  Mean: 88.41317741721831\n",
      "debt_to_equity:\n",
      "  Range: 0.21 - 1.19\n",
      "  Mean: 0.44053218973253866\n",
      "debt_to_assets:\n",
      "  Range: 0.16 - 0.38\n",
      "  Mean: 0.29147847767777885\n",
      "debt_to_ebitda:\n",
      "  Range: 0.4725 - 3.0769512195121953\n",
      "  Mean: 1.8242965861409186\n",
      "debt_to_revenue:\n",
      "  Range: 0.26 - 0.805\n",
      "  Mean: 0.7805570727782329\n",
      "e10:\n",
      "  Range: 1.625 - 3.600588235294117\n",
      "  Mean: 3.19818575657554\n",
      "effective_interest_rate:\n",
      "  Range: 3.0025 - 4.3375\n",
      "  Mean: 3.9048557068570378\n",
      "equity_to_assets:\n",
      "  Range: 0.28 - 0.57\n",
      "  Mean: 0.39601637054116384\n",
      "enterprise_value_to_ebit:\n",
      "  Range: 14.0075 - 38.396428571428565\n",
      "  Mean: 19.560365016764333\n",
      "enterprise_value_to_ebitda:\n",
      "  Range: 10.21025 - 25.064042553191495\n",
      "  Mean: 19.194559745869235\n",
      "enterprise_value_to_revenue:\n",
      "  Range: 3.8 - 9.3875\n",
      "  Mean: 8.333226804123713\n",
      "earning_yield_greenblatt:\n",
      "  Range: 2.04 - 5.577500000000001\n",
      "  Mean: 3.868672650182156\n",
      "fscore:\n",
      "  Range: 5.0 - 7.0\n",
      "  Mean: 5.929971279616441\n",
      "gross_profit_to_assets:\n",
      "  Range: 25.05 - 45.4225\n",
      "  Mean: 36.08223739249992\n",
      "interest_coverage:\n",
      "  Range: 7.15 - 40.3625\n",
      "  Mean: 40.3339721299269\n",
      "inventory_turnover:\n",
      "  Range: 4.48 - 17.598550724637676\n",
      "  Mean: 16.295323865502205\n",
      "inventory_to_revenue:\n",
      "  Range: 0.07 - 0.12\n",
      "  Mean: 0.10184710168945935\n",
      "liabilities_to_assets:\n",
      "  Range: 0.42 - 0.72\n",
      "  Mean: 0.6004735126840651\n",
      "longterm_debt_to_assets:\n",
      "  Range: 0.15 - 0.3475\n",
      "  Mean: 0.26509675708828123\n",
      "price_to_book_ratio:\n",
      "  Range: 4.512499999999999 - 13.695\n",
      "  Mean: 13.030300061094936\n",
      "price_to_earnings_ratio:\n",
      "  Range: 22.7375 - 50.125\n",
      "  Mean: 48.77542874337829\n",
      "price_to_earnings_ratio_nri:\n",
      "  Range: 22.939999999999998 - 50.38\n",
      "  Mean: 49.193900157121035\n",
      "price_earnings_growth_ratio:\n",
      "  Range: 1.7149999999999999 - 6.72225352112676\n",
      "  Mean: 7.985947780714139\n",
      "price_to_free_cashflow:\n",
      "  Range: 21.61 - 42.985\n",
      "  Mean: 48.121407764377814\n",
      "price_to_operating_cashflow:\n",
      "  Range: 15.495000000000001 - 35.27149253731343\n",
      "  Mean: 36.29578279505216\n",
      "rate_of_return:\n",
      "  Range: 11.827499999999999 - 19.990000000000002\n",
      "  Mean: 14.404138528138535\n",
      "scaled_net_operating_assets:\n",
      "  Range: 0.33 - 0.68\n",
      "  Mean: 0.4862782395048018\n",
      "yoy_ebitda_growth:\n",
      "  Range: 1.6225 - 32.207499999999996\n",
      "  Mean: 25.561496552119106\n",
      "yoy_eps_growth:\n",
      "  Range: -6.3275 - 52.306595744680855\n",
      "  Mean: 38.97706114830094\n",
      "yoy_revenue_growth:\n",
      "  Range: 6.38 - 23.697499999999998\n",
      "  Mean: 20.5397909784508\n"
     ]
    }
   ],
   "source": [
    "stats = data17_21.describe()\n",
    "for col in stats.columns:\n",
    "    print(f\"{col}:\")\n",
    "    print(f\"  Range: {stats[col]['25%']} - {stats[col]['75%']}\")\n",
    "    print(f\"  Mean: {stats[col]['mean']}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d7f9adba",
   "metadata": {},
   "outputs": [],
   "source": [
    "data17_21.to_csv(\"data17_21.csv\", index=False)"
   ]
  },
  {
   "cell_type": "raw",
   "id": "4cbb3c7e",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "0519a3f8",
   "metadata": {},
   "source": [
    "# Finally, we have created a further dataset with the 2022 year removed which is  a year that saw negative Share Price returns.\n",
    "\n",
    "\n",
    "It may be subsequently found that any model solely trained on Training data with solely positive share price return years will be better at predicting future positive return years.  In other words, some stocks do better in positive return years due to the cyclicality of their business models or the nature of their financial attributes."
   ]
  },
  {
   "cell_type": "raw",
   "id": "a06c4abf",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6bb0bc6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "data17_20 = data17_21[ : -102]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "734b4048",
   "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>asset_turnover</th>\n",
       "      <th>buyback_yield</th>\n",
       "      <th>capex_to_revenue</th>\n",
       "      <th>cash_ratio</th>\n",
       "      <th>cash_to_debt</th>\n",
       "      <th>cogs_to_revenue</th>\n",
       "      <th>mscore</th>\n",
       "      <th>zscore</th>\n",
       "      <th>current_ratio</th>\n",
       "      <th>days_inventory</th>\n",
       "      <th>...</th>\n",
       "      <th>price_to_earnings_ratio</th>\n",
       "      <th>price_to_earnings_ratio_nri</th>\n",
       "      <th>price_earnings_growth_ratio</th>\n",
       "      <th>price_to_free_cashflow</th>\n",
       "      <th>price_to_operating_cashflow</th>\n",
       "      <th>rate_of_return</th>\n",
       "      <th>scaled_net_operating_assets</th>\n",
       "      <th>yoy_ebitda_growth</th>\n",
       "      <th>yoy_eps_growth</th>\n",
       "      <th>yoy_revenue_growth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.660000</td>\n",
       "      <td>4.090000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.740000</td>\n",
       "      <td>0.640000</td>\n",
       "      <td>0.620000</td>\n",
       "      <td>-2.500000</td>\n",
       "      <td>3.620000</td>\n",
       "      <td>1.280000</td>\n",
       "      <td>9.040000</td>\n",
       "      <td>...</td>\n",
       "      <td>16.730000</td>\n",
       "      <td>16.730000</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>15.630000</td>\n",
       "      <td>12.600000</td>\n",
       "      <td>19.240000</td>\n",
       "      <td>0.550000</td>\n",
       "      <td>9.360000</td>\n",
       "      <td>10.830000</td>\n",
       "      <td>11.340000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.724932</td>\n",
       "      <td>2.035574</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>1.410274</td>\n",
       "      <td>2.648413</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>-2.543382</td>\n",
       "      <td>6.686479</td>\n",
       "      <td>2.332192</td>\n",
       "      <td>89.855094</td>\n",
       "      <td>...</td>\n",
       "      <td>44.940635</td>\n",
       "      <td>44.909524</td>\n",
       "      <td>10.936250</td>\n",
       "      <td>33.836721</td>\n",
       "      <td>35.271493</td>\n",
       "      <td>14.260952</td>\n",
       "      <td>0.482917</td>\n",
       "      <td>26.097361</td>\n",
       "      <td>46.020147</td>\n",
       "      <td>19.277917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.540000</td>\n",
       "      <td>1.080000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>1.650000</td>\n",
       "      <td>3.090000</td>\n",
       "      <td>0.140000</td>\n",
       "      <td>-2.490000</td>\n",
       "      <td>10.810000</td>\n",
       "      <td>2.050000</td>\n",
       "      <td>89.855094</td>\n",
       "      <td>...</td>\n",
       "      <td>52.320000</td>\n",
       "      <td>52.320000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>32.410000</td>\n",
       "      <td>30.420000</td>\n",
       "      <td>21.820000</td>\n",
       "      <td>0.360000</td>\n",
       "      <td>39.030000</td>\n",
       "      <td>45.690000</td>\n",
       "      <td>25.510000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.360000</td>\n",
       "      <td>0.140000</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>0.660000</td>\n",
       "      <td>0.130000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>-1.490000</td>\n",
       "      <td>2.640000</td>\n",
       "      <td>1.470000</td>\n",
       "      <td>81.440000</td>\n",
       "      <td>...</td>\n",
       "      <td>39.870000</td>\n",
       "      <td>39.870000</td>\n",
       "      <td>4.440000</td>\n",
       "      <td>33.680000</td>\n",
       "      <td>27.720000</td>\n",
       "      <td>11.950000</td>\n",
       "      <td>2.130000</td>\n",
       "      <td>25.600000</td>\n",
       "      <td>-17.030000</td>\n",
       "      <td>36.640000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.724932</td>\n",
       "      <td>2.035574</td>\n",
       "      <td>0.086892</td>\n",
       "      <td>1.410274</td>\n",
       "      <td>2.648413</td>\n",
       "      <td>0.437534</td>\n",
       "      <td>-2.543382</td>\n",
       "      <td>6.686479</td>\n",
       "      <td>2.332192</td>\n",
       "      <td>89.855094</td>\n",
       "      <td>...</td>\n",
       "      <td>44.940635</td>\n",
       "      <td>44.909524</td>\n",
       "      <td>10.936250</td>\n",
       "      <td>33.836721</td>\n",
       "      <td>35.271493</td>\n",
       "      <td>14.260952</td>\n",
       "      <td>0.482917</td>\n",
       "      <td>26.097361</td>\n",
       "      <td>46.020147</td>\n",
       "      <td>19.277917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403</th>\n",
       "      <td>1.580000</td>\n",
       "      <td>4.830000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.790000</td>\n",
       "      <td>-3.110000</td>\n",
       "      <td>2.220000</td>\n",
       "      <td>0.670000</td>\n",
       "      <td>32.830000</td>\n",
       "      <td>...</td>\n",
       "      <td>73.120000</td>\n",
       "      <td>190.100000</td>\n",
       "      <td>13.344429</td>\n",
       "      <td>8.140000</td>\n",
       "      <td>6.100000</td>\n",
       "      <td>10.550000</td>\n",
       "      <td>0.890000</td>\n",
       "      <td>-55.550000</td>\n",
       "      <td>-87.940000</td>\n",
       "      <td>6.570000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404</th>\n",
       "      <td>0.590000</td>\n",
       "      <td>1.422184</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.650000</td>\n",
       "      <td>1.240000</td>\n",
       "      <td>0.290000</td>\n",
       "      <td>-3.190000</td>\n",
       "      <td>5.750000</td>\n",
       "      <td>1.040000</td>\n",
       "      <td>88.257353</td>\n",
       "      <td>...</td>\n",
       "      <td>71.222262</td>\n",
       "      <td>70.917500</td>\n",
       "      <td>13.344429</td>\n",
       "      <td>67.650000</td>\n",
       "      <td>48.510000</td>\n",
       "      <td>13.355974</td>\n",
       "      <td>0.380000</td>\n",
       "      <td>14.940000</td>\n",
       "      <td>-9.840000</td>\n",
       "      <td>22.650000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>405</th>\n",
       "      <td>0.220000</td>\n",
       "      <td>-2.030000</td>\n",
       "      <td>0.470000</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.570000</td>\n",
       "      <td>-2.417895</td>\n",
       "      <td>1.030000</td>\n",
       "      <td>0.770000</td>\n",
       "      <td>30.010000</td>\n",
       "      <td>...</td>\n",
       "      <td>23.900000</td>\n",
       "      <td>23.900000</td>\n",
       "      <td>7.660000</td>\n",
       "      <td>82.560323</td>\n",
       "      <td>12.360000</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>0.730000</td>\n",
       "      <td>3.390000</td>\n",
       "      <td>5.680000</td>\n",
       "      <td>-1.540000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>406</th>\n",
       "      <td>0.760000</td>\n",
       "      <td>-2.550000</td>\n",
       "      <td>0.060000</td>\n",
       "      <td>2.560000</td>\n",
       "      <td>11.800000</td>\n",
       "      <td>0.190000</td>\n",
       "      <td>-2.230000</td>\n",
       "      <td>29.250000</td>\n",
       "      <td>3.280000</td>\n",
       "      <td>88.257353</td>\n",
       "      <td>...</td>\n",
       "      <td>847.780000</td>\n",
       "      <td>847.780000</td>\n",
       "      <td>13.344429</td>\n",
       "      <td>170.690000</td>\n",
       "      <td>127.810000</td>\n",
       "      <td>13.355974</td>\n",
       "      <td>0.140000</td>\n",
       "      <td>134.690000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>99.110000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>407</th>\n",
       "      <td>0.350000</td>\n",
       "      <td>1.422184</td>\n",
       "      <td>0.120000</td>\n",
       "      <td>3.230000</td>\n",
       "      <td>1.510000</td>\n",
       "      <td>0.220000</td>\n",
       "      <td>-2.800000</td>\n",
       "      <td>8.210000</td>\n",
       "      <td>3.730000</td>\n",
       "      <td>88.257353</td>\n",
       "      <td>...</td>\n",
       "      <td>71.222262</td>\n",
       "      <td>70.917500</td>\n",
       "      <td>13.344429</td>\n",
       "      <td>609.620000</td>\n",
       "      <td>211.830000</td>\n",
       "      <td>13.355974</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>-400.750000</td>\n",
       "      <td>-286.960000</td>\n",
       "      <td>36.070000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>408 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     asset_turnover  buyback_yield  capex_to_revenue  cash_ratio  \\\n",
       "0          0.660000       4.090000          0.050000    0.740000   \n",
       "1          0.724932       2.035574          0.040000    1.410274   \n",
       "2          0.540000       1.080000          0.020000    1.650000   \n",
       "3          0.360000       0.140000          0.040000    0.660000   \n",
       "4          0.724932       2.035574          0.086892    1.410274   \n",
       "..              ...            ...               ...         ...   \n",
       "403        1.580000       4.830000          0.010000    0.020000   \n",
       "404        0.590000       1.422184          0.070000    0.650000   \n",
       "405        0.220000      -2.030000          0.470000    0.030000   \n",
       "406        0.760000      -2.550000          0.060000    2.560000   \n",
       "407        0.350000       1.422184          0.120000    3.230000   \n",
       "\n",
       "     cash_to_debt  cogs_to_revenue    mscore     zscore  current_ratio  \\\n",
       "0        0.640000         0.620000 -2.500000   3.620000       1.280000   \n",
       "1        2.648413         0.250000 -2.543382   6.686479       2.332192   \n",
       "2        3.090000         0.140000 -2.490000  10.810000       2.050000   \n",
       "3        0.130000         0.400000 -1.490000   2.640000       1.470000   \n",
       "4        2.648413         0.437534 -2.543382   6.686479       2.332192   \n",
       "..            ...              ...       ...        ...            ...   \n",
       "403      0.010000         0.790000 -3.110000   2.220000       0.670000   \n",
       "404      1.240000         0.290000 -3.190000   5.750000       1.040000   \n",
       "405      0.010000         0.570000 -2.417895   1.030000       0.770000   \n",
       "406     11.800000         0.190000 -2.230000  29.250000       3.280000   \n",
       "407      1.510000         0.220000 -2.800000   8.210000       3.730000   \n",
       "\n",
       "     days_inventory  ...  price_to_earnings_ratio  \\\n",
       "0          9.040000  ...                16.730000   \n",
       "1         89.855094  ...                44.940635   \n",
       "2         89.855094  ...                52.320000   \n",
       "3         81.440000  ...                39.870000   \n",
       "4         89.855094  ...                44.940635   \n",
       "..              ...  ...                      ...   \n",
       "403       32.830000  ...                73.120000   \n",
       "404       88.257353  ...                71.222262   \n",
       "405       30.010000  ...                23.900000   \n",
       "406       88.257353  ...               847.780000   \n",
       "407       88.257353  ...                71.222262   \n",
       "\n",
       "     price_to_earnings_ratio_nri  price_earnings_growth_ratio  \\\n",
       "0                      16.730000                     1.250000   \n",
       "1                      44.909524                    10.936250   \n",
       "2                      52.320000                     2.420000   \n",
       "3                      39.870000                     4.440000   \n",
       "4                      44.909524                    10.936250   \n",
       "..                           ...                          ...   \n",
       "403                   190.100000                    13.344429   \n",
       "404                    70.917500                    13.344429   \n",
       "405                    23.900000                     7.660000   \n",
       "406                   847.780000                    13.344429   \n",
       "407                    70.917500                    13.344429   \n",
       "\n",
       "     price_to_free_cashflow  price_to_operating_cashflow  rate_of_return  \\\n",
       "0                 15.630000                    12.600000       19.240000   \n",
       "1                 33.836721                    35.271493       14.260952   \n",
       "2                 32.410000                    30.420000       21.820000   \n",
       "3                 33.680000                    27.720000       11.950000   \n",
       "4                 33.836721                    35.271493       14.260952   \n",
       "..                      ...                          ...             ...   \n",
       "403                8.140000                     6.100000       10.550000   \n",
       "404               67.650000                    48.510000       13.355974   \n",
       "405               82.560323                    12.360000        0.090000   \n",
       "406              170.690000                   127.810000       13.355974   \n",
       "407              609.620000                   211.830000       13.355974   \n",
       "\n",
       "     scaled_net_operating_assets  yoy_ebitda_growth  yoy_eps_growth  \\\n",
       "0                       0.550000           9.360000       10.830000   \n",
       "1                       0.482917          26.097361       46.020147   \n",
       "2                       0.360000          39.030000       45.690000   \n",
       "3                       2.130000          25.600000      -17.030000   \n",
       "4                       0.482917          26.097361       46.020147   \n",
       "..                           ...                ...             ...   \n",
       "403                     0.890000         -55.550000      -87.940000   \n",
       "404                     0.380000          14.940000       -9.840000   \n",
       "405                     0.730000           3.390000        5.680000   \n",
       "406                     0.140000         134.690000      200.000000   \n",
       "407                     0.030000        -400.750000     -286.960000   \n",
       "\n",
       "     yoy_revenue_growth  \n",
       "0             11.340000  \n",
       "1             19.277917  \n",
       "2             25.510000  \n",
       "3             36.640000  \n",
       "4             19.277917  \n",
       "..                  ...  \n",
       "403            6.570000  \n",
       "404           22.650000  \n",
       "405           -1.540000  \n",
       "406           99.110000  \n",
       "407           36.070000  \n",
       "\n",
       "[408 rows x 39 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data17_20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "106b59e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "data17_20.to_csv(\"data17_20.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a918825d",
   "metadata": {},
   "source": [
    "# "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "694c8b28",
   "metadata": {},
   "source": [
    "# Now we have two datsets for Training. \n",
    "\n",
    "\n",
    "2017 to 2021 which will include calendar years when the Nasdaq 100 index had positive Price returns and years when the index had negative Price returns.\n",
    "\n",
    "2017 to 2020 when the Nasdaq 100 index only had positive Price returns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b74bb6ca",
   "metadata": {},
   "source": [
    "# "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e50d8b65",
   "metadata": {},
   "source": [
    "# 5. Extract the Y data from external share price data sources."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "7caa4f4f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: yfinance in c:\\users\\mark\\anaconda3\\lib\\site-packages (0.2.12)\n",
      "Requirement already satisfied: numpy>=1.16.5 in c:\\users\\mark\\anaconda3\\lib\\site-packages (from yfinance) (1.20.3)\n",
      "Requirement already satisfied: lxml>=4.9.1 in c:\\users\\mark\\anaconda3\\lib\\site-packages (from yfinance) (4.9.2)\n",
      "Requirement already satisfied: appdirs>=1.4.4 in c:\\users\\mark\\anaconda3\\lib\\site-packages (from yfinance) (1.4.4)\n",
      "Requirement already satisfied: html5lib>=1.1 in c:\\users\\mark\\anaconda3\\lib\\site-packages (from yfinance) (1.1)\n",
      "Requirement already satisfied: cryptography>=3.3.2 in c:\\users\\mark\\anaconda3\\lib\\site-packages (from yfinance) (3.4.8)\n",
      "Requirement already satisfied: frozendict>=2.3.4 in c:\\users\\mark\\anaconda3\\lib\\site-packages (from yfinance) (2.3.5)\n",
      "Requirement already satisfied: pandas>=1.3.0 in c:\\users\\mark\\anaconda3\\lib\\site-packages (from yfinance) (1.3.4)\n",
      "Requirement already satisfied: beautifulsoup4>=4.11.1 in c:\\users\\mark\\anaconda3\\lib\\site-packages (from yfinance) (4.11.2)\n",
      "Requirement already satisfied: multitasking>=0.0.7 in c:\\users\\mark\\anaconda3\\lib\\site-packages (from yfinance) (0.0.11)\n",
      "Requirement already satisfied: requests>=2.26 in c:\\users\\mark\\anaconda3\\lib\\site-packages (from yfinance) (2.26.0)\n",
      "Requirement already satisfied: pytz>=2022.5 in c:\\users\\mark\\anaconda3\\lib\\site-packages (from yfinance) (2022.7.1)\n",
      "Requirement already satisfied: soupsieve>1.2 in c:\\users\\mark\\anaconda3\\lib\\site-packages (from beautifulsoup4>=4.11.1->yfinance) (2.2.1)\n",
      "Requirement already satisfied: cffi>=1.12 in c:\\users\\mark\\anaconda3\\lib\\site-packages (from cryptography>=3.3.2->yfinance) (1.14.6)\n",
      "Requirement already satisfied: pycparser in c:\\users\\mark\\anaconda3\\lib\\site-packages (from cffi>=1.12->cryptography>=3.3.2->yfinance) (2.20)\n",
      "Requirement already satisfied: webencodings in c:\\users\\mark\\anaconda3\\lib\\site-packages (from html5lib>=1.1->yfinance) (0.5.1)\n",
      "Requirement already satisfied: six>=1.9 in c:\\users\\mark\\anaconda3\\lib\\site-packages (from html5lib>=1.1->yfinance) (1.16.0)\n",
      "Requirement already satisfied: python-dateutil>=2.7.3 in c:\\users\\mark\\anaconda3\\lib\\site-packages (from pandas>=1.3.0->yfinance) (2.8.2)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\mark\\anaconda3\\lib\\site-packages (from requests>=2.26->yfinance) (2022.12.7)\n",
      "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\mark\\anaconda3\\lib\\site-packages (from requests>=2.26->yfinance) (3.2)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in c:\\users\\mark\\anaconda3\\lib\\site-packages (from requests>=2.26->yfinance) (1.26.7)\n",
      "Requirement already satisfied: charset-normalizer~=2.0.0 in c:\\users\\mark\\anaconda3\\lib\\site-packages (from requests>=2.26->yfinance) (2.0.4)\n"
     ]
    }
   ],
   "source": [
    "!pip install yfinance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "9e855127",
   "metadata": {},
   "outputs": [],
   "source": [
    "import yfinance as yf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8838604b",
   "metadata": {},
   "source": [
    "# "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae6d7b86",
   "metadata": {},
   "source": [
    "# Methodology:  We take our Stock Names and put them into a list which we call Ticker List and then extract a series of Price History Data.  \n",
    "\n",
    "We then clean and manage the data until we have the required Y labels for our 5 years of X data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c96d44a",
   "metadata": {},
   "source": [
    "# "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c988b795",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  102 of 102 completed\n",
      "\n",
      "1 Failed download:\n",
      "- FISV: Data doesn't exist for startDate = 1514869200, endDate = 1682827200\n"
     ]
    },
    {
     "data": {
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       "      <td>130698700.0</td>\n",
       "      <td>425349400.0</td>\n",
       "      <td>153075400.0</td>\n",
       "      <td>203140900.0</td>\n",
       "      <td>852423300.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>201285300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-01</th>\n",
       "      <td>113.702461</td>\n",
       "      <td>NaN</td>\n",
       "      <td>490.429993</td>\n",
       "      <td>110.571533</td>\n",
       "      <td>131.218658</td>\n",
       "      <td>231.009995</td>\n",
       "      <td>73.682045</td>\n",
       "      <td>327.359985</td>\n",
       "      <td>57.837044</td>\n",
       "      <td>81.989998</td>\n",
       "      <td>...</td>\n",
       "      <td>1.556049e+10</td>\n",
       "      <td>274618300.0</td>\n",
       "      <td>44596300.0</td>\n",
       "      <td>44481700.0</td>\n",
       "      <td>82406100.0</td>\n",
       "      <td>443370700.0</td>\n",
       "      <td>123346000.0</td>\n",
       "      <td>149209000.0</td>\n",
       "      <td>610247100.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>138782100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-01</th>\n",
       "      <td>130.510178</td>\n",
       "      <td>NaN</td>\n",
       "      <td>500.119995</td>\n",
       "      <td>140.647659</td>\n",
       "      <td>166.858109</td>\n",
       "      <td>305.339996</td>\n",
       "      <td>75.691032</td>\n",
       "      <td>534.380005</td>\n",
       "      <td>84.237556</td>\n",
       "      <td>91.709999</td>\n",
       "      <td>...</td>\n",
       "      <td>8.437834e+09</td>\n",
       "      <td>254040900.0</td>\n",
       "      <td>38895000.0</td>\n",
       "      <td>37779900.0</td>\n",
       "      <td>134397300.0</td>\n",
       "      <td>496820100.0</td>\n",
       "      <td>130152400.0</td>\n",
       "      <td>156560800.0</td>\n",
       "      <td>563768600.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>206.350006</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>146393100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-01</th>\n",
       "      <td>120.350594</td>\n",
       "      <td>NaN</td>\n",
       "      <td>475.369995</td>\n",
       "      <td>148.298920</td>\n",
       "      <td>179.437531</td>\n",
       "      <td>277.149994</td>\n",
       "      <td>77.622650</td>\n",
       "      <td>541.530029</td>\n",
       "      <td>130.794067</td>\n",
       "      <td>78.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>6.513015e+09</td>\n",
       "      <td>300820500.0</td>\n",
       "      <td>68215700.0</td>\n",
       "      <td>41132700.0</td>\n",
       "      <td>127476800.0</td>\n",
       "      <td>406535000.0</td>\n",
       "      <td>122885700.0</td>\n",
       "      <td>185663100.0</td>\n",
       "      <td>334113000.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>140.399994</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>131371200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-01</th>\n",
       "      <td>135.144089</td>\n",
       "      <td>NaN</td>\n",
       "      <td>585.640015</td>\n",
       "      <td>165.337372</td>\n",
       "      <td>190.071915</td>\n",
       "      <td>291.899994</td>\n",
       "      <td>78.236771</td>\n",
       "      <td>611.000000</td>\n",
       "      <td>139.673813</td>\n",
       "      <td>93.930000</td>\n",
       "      <td>...</td>\n",
       "      <td>5.470912e+09</td>\n",
       "      <td>264879900.0</td>\n",
       "      <td>54013700.0</td>\n",
       "      <td>35098700.0</td>\n",
       "      <td>132207800.0</td>\n",
       "      <td>305766400.0</td>\n",
       "      <td>96779600.0</td>\n",
       "      <td>172728400.0</td>\n",
       "      <td>198879600.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>154.990005</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>94442600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07-01</th>\n",
       "      <td>139.861038</td>\n",
       "      <td>NaN</td>\n",
       "      <td>575.719971</td>\n",
       "      <td>161.522903</td>\n",
       "      <td>192.217972</td>\n",
       "      <td>285.170013</td>\n",
       "      <td>75.718735</td>\n",
       "      <td>665.429993</td>\n",
       "      <td>126.486305</td>\n",
       "      <td>102.900002</td>\n",
       "      <td>...</td>\n",
       "      <td>3.659838e+09</td>\n",
       "      <td>225661500.0</td>\n",
       "      <td>53298400.0</td>\n",
       "      <td>27750300.0</td>\n",
       "      <td>116526300.0</td>\n",
       "      <td>371816700.0</td>\n",
       "      <td>102250000.0</td>\n",
       "      <td>165993100.0</td>\n",
       "      <td>269502300.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>172.539993</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>115799700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-01</th>\n",
       "      <td>175.776199</td>\n",
       "      <td>NaN</td>\n",
       "      <td>567.059998</td>\n",
       "      <td>170.234283</td>\n",
       "      <td>238.160141</td>\n",
       "      <td>281.190002</td>\n",
       "      <td>83.677780</td>\n",
       "      <td>657.179993</td>\n",
       "      <td>154.900131</td>\n",
       "      <td>143.899994</td>\n",
       "      <td>...</td>\n",
       "      <td>5.064306e+09</td>\n",
       "      <td>278122300.0</td>\n",
       "      <td>51907800.0</td>\n",
       "      <td>37993300.0</td>\n",
       "      <td>119027200.0</td>\n",
       "      <td>393403200.0</td>\n",
       "      <td>97347200.0</td>\n",
       "      <td>218405400.0</td>\n",
       "      <td>277773800.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>151.490005</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>181375000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-01</th>\n",
       "      <td>173.098389</td>\n",
       "      <td>NaN</td>\n",
       "      <td>455.619995</td>\n",
       "      <td>160.590530</td>\n",
       "      <td>220.750107</td>\n",
       "      <td>214.350006</td>\n",
       "      <td>94.715904</td>\n",
       "      <td>436.000000</td>\n",
       "      <td>129.949112</td>\n",
       "      <td>109.339996</td>\n",
       "      <td>...</td>\n",
       "      <td>5.036406e+09</td>\n",
       "      <td>396931300.0</td>\n",
       "      <td>81360400.0</td>\n",
       "      <td>39453100.0</td>\n",
       "      <td>105424700.0</td>\n",
       "      <td>427184900.0</td>\n",
       "      <td>152624100.0</td>\n",
       "      <td>216945500.0</td>\n",
       "      <td>298444500.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>120.870003</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>169992700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-01</th>\n",
       "      <td>135.709076</td>\n",
       "      <td>NaN</td>\n",
       "      <td>366.059998</td>\n",
       "      <td>142.724075</td>\n",
       "      <td>204.787964</td>\n",
       "      <td>171.960007</td>\n",
       "      <td>91.874413</td>\n",
       "      <td>236.669998</td>\n",
       "      <td>89.864098</td>\n",
       "      <td>76.470001</td>\n",
       "      <td>...</td>\n",
       "      <td>5.480409e+09</td>\n",
       "      <td>371049200.0</td>\n",
       "      <td>64442200.0</td>\n",
       "      <td>49777600.0</td>\n",
       "      <td>110340200.0</td>\n",
       "      <td>451183600.0</td>\n",
       "      <td>195544500.0</td>\n",
       "      <td>208175300.0</td>\n",
       "      <td>332089000.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-06-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>113.120003</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>129876500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-01</th>\n",
       "      <td>137.379684</td>\n",
       "      <td>NaN</td>\n",
       "      <td>275.200012</td>\n",
       "      <td>136.763382</td>\n",
       "      <td>221.581680</td>\n",
       "      <td>186.800003</td>\n",
       "      <td>83.439674</td>\n",
       "      <td>207.110001</td>\n",
       "      <td>81.122978</td>\n",
       "      <td>63.360001</td>\n",
       "      <td>...</td>\n",
       "      <td>4.739418e+09</td>\n",
       "      <td>347975300.0</td>\n",
       "      <td>46180200.0</td>\n",
       "      <td>39429100.0</td>\n",
       "      <td>88449800.0</td>\n",
       "      <td>427772800.0</td>\n",
       "      <td>172815600.0</td>\n",
       "      <td>171706300.0</td>\n",
       "      <td>294335600.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>102.139999</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>159352800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-01</th>\n",
       "      <td>129.338165</td>\n",
       "      <td>NaN</td>\n",
       "      <td>336.529999</td>\n",
       "      <td>161.771683</td>\n",
       "      <td>235.004654</td>\n",
       "      <td>186.869995</td>\n",
       "      <td>92.363548</td>\n",
       "      <td>210.899994</td>\n",
       "      <td>96.667366</td>\n",
       "      <td>64.769997</td>\n",
       "      <td>...</td>\n",
       "      <td>6.564786e+09</td>\n",
       "      <td>362510700.0</td>\n",
       "      <td>58093700.0</td>\n",
       "      <td>47164800.0</td>\n",
       "      <td>90700100.0</td>\n",
       "      <td>443324500.0</td>\n",
       "      <td>139526100.0</td>\n",
       "      <td>210359700.0</td>\n",
       "      <td>237397000.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>123.279999</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>155572500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-01</th>\n",
       "      <td>164.421173</td>\n",
       "      <td>NaN</td>\n",
       "      <td>385.369995</td>\n",
       "      <td>195.370636</td>\n",
       "      <td>220.097946</td>\n",
       "      <td>208.160004</td>\n",
       "      <td>89.342278</td>\n",
       "      <td>334.140015</td>\n",
       "      <td>122.228020</td>\n",
       "      <td>98.010002</td>\n",
       "      <td>...</td>\n",
       "      <td>1.083396e+10</td>\n",
       "      <td>331719900.0</td>\n",
       "      <td>67143100.0</td>\n",
       "      <td>37076900.0</td>\n",
       "      <td>79407000.0</td>\n",
       "      <td>437305300.0</td>\n",
       "      <td>125033600.0</td>\n",
       "      <td>183425400.0</td>\n",
       "      <td>241507100.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-03-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>119.669998</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>122980400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-04-01</th>\n",
       "      <td>169.445618</td>\n",
       "      <td>NaN</td>\n",
       "      <td>377.559998</td>\n",
       "      <td>179.022522</td>\n",
       "      <td>218.729614</td>\n",
       "      <td>194.789993</td>\n",
       "      <td>91.587471</td>\n",
       "      <td>325.299988</td>\n",
       "      <td>112.739456</td>\n",
       "      <td>89.370003</td>\n",
       "      <td>...</td>\n",
       "      <td>2.505176e+09</td>\n",
       "      <td>87499700.0</td>\n",
       "      <td>14394900.0</td>\n",
       "      <td>7504100.0</td>\n",
       "      <td>19841700.0</td>\n",
       "      <td>115406400.0</td>\n",
       "      <td>31737700.0</td>\n",
       "      <td>45805800.0</td>\n",
       "      <td>76755300.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>43 rows × 612 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Adj Close                                                  \\\n",
       "                  AAPL        ABNB        ADBE         ADI         ADP   \n",
       "Date                                                                     \n",
       "2018-01-01   39.769665         NaN  216.080002   81.999680  101.208412   \n",
       "2018-03-01         NaN         NaN         NaN         NaN         NaN   \n",
       "2018-04-01   44.056389         NaN  243.809998   86.758820  120.292191   \n",
       "2018-06-01         NaN         NaN         NaN         NaN         NaN   \n",
       "2018-07-01   53.933533         NaN  269.950012   84.024292  135.795273   \n",
       "2018-09-01         NaN         NaN         NaN         NaN         NaN   \n",
       "2018-10-01   37.819233         NaN  226.240005   78.374771  118.739983   \n",
       "2018-12-01         NaN         NaN         NaN         NaN         NaN   \n",
       "2019-01-01   45.700691         NaN  266.489990   96.644028  145.481247   \n",
       "2019-03-01         NaN         NaN         NaN         NaN         NaN   \n",
       "2019-04-01   47.822445         NaN  294.649994  104.152222  151.356171   \n",
       "2019-06-01         NaN         NaN         NaN         NaN         NaN   \n",
       "2019-07-01   54.325191         NaN  276.250000  103.670898  148.486679   \n",
       "2019-09-01         NaN         NaN         NaN         NaN         NaN   \n",
       "2019-10-01   71.497093         NaN  329.809998  110.821960  157.611832   \n",
       "2019-12-01         NaN         NaN         NaN         NaN         NaN   \n",
       "2020-01-01   62.099701         NaN  318.239990   83.997948  127.039551   \n",
       "2020-03-01         NaN         NaN         NaN         NaN         NaN   \n",
       "2020-04-01   89.298584         NaN  435.309998  115.531784  139.244461   \n",
       "2020-06-01         NaN         NaN         NaN         NaN         NaN   \n",
       "2020-07-01  113.702461         NaN  490.429993  110.571533  131.218658   \n",
       "2020-09-01         NaN         NaN         NaN         NaN         NaN   \n",
       "2020-10-01  130.510178         NaN  500.119995  140.647659  166.858109   \n",
       "2020-12-01         NaN  206.350006         NaN         NaN         NaN   \n",
       "2021-01-01  120.350594         NaN  475.369995  148.298920  179.437531   \n",
       "2021-03-01         NaN  140.399994         NaN         NaN         NaN   \n",
       "2021-04-01  135.144089         NaN  585.640015  165.337372  190.071915   \n",
       "2021-06-01         NaN  154.990005         NaN         NaN         NaN   \n",
       "2021-07-01  139.861038         NaN  575.719971  161.522903  192.217972   \n",
       "2021-09-01         NaN  172.539993         NaN         NaN         NaN   \n",
       "2021-10-01  175.776199         NaN  567.059998  170.234283  238.160141   \n",
       "2021-12-01         NaN  151.490005         NaN         NaN         NaN   \n",
       "2022-01-01  173.098389         NaN  455.619995  160.590530  220.750107   \n",
       "2022-03-01         NaN  120.870003         NaN         NaN         NaN   \n",
       "2022-04-01  135.709076         NaN  366.059998  142.724075  204.787964   \n",
       "2022-06-01         NaN  113.120003         NaN         NaN         NaN   \n",
       "2022-07-01  137.379684         NaN  275.200012  136.763382  221.581680   \n",
       "2022-09-01         NaN  102.139999         NaN         NaN         NaN   \n",
       "2022-10-01  129.338165         NaN  336.529999  161.771683  235.004654   \n",
       "2022-12-01         NaN  123.279999         NaN         NaN         NaN   \n",
       "2023-01-01  164.421173         NaN  385.369995  195.370636  220.097946   \n",
       "2023-03-01         NaN  119.669998         NaN         NaN         NaN   \n",
       "2023-04-01  169.445618         NaN  377.559998  179.022522  218.729614   \n",
       "\n",
       "                                                                       ...  \\\n",
       "                  ADSK        AEP        ALGN        AMAT         AMD  ...   \n",
       "Date                                                                   ...   \n",
       "2018-01-01  125.580002  56.847176  251.130005   51.913708   10.050000  ...   \n",
       "2018-03-01         NaN        NaN         NaN         NaN         NaN  ...   \n",
       "2018-04-01  131.089996  57.951672  342.140015   43.198326   14.990000  ...   \n",
       "2018-06-01         NaN        NaN         NaN         NaN         NaN  ...   \n",
       "2018-07-01  156.110001  59.867668  391.220001   36.291130   30.889999  ...   \n",
       "2018-09-01         NaN        NaN         NaN         NaN         NaN  ...   \n",
       "2018-10-01  128.610001  63.684620  209.429993   30.882973   18.459999  ...   \n",
       "2018-12-01         NaN        NaN         NaN         NaN         NaN  ...   \n",
       "2019-01-01  155.820007  72.004013  284.329987   37.620815   25.520000  ...   \n",
       "2019-03-01         NaN        NaN         NaN         NaN         NaN  ...   \n",
       "2019-04-01  162.899994  76.312035  273.700012   42.818779   30.370001  ...   \n",
       "2019-06-01         NaN        NaN         NaN         NaN         NaN  ...   \n",
       "2019-07-01  147.699997  81.892365  180.919998   47.819756   28.990000  ...   \n",
       "2019-09-01         NaN        NaN         NaN         NaN         NaN  ...   \n",
       "2019-10-01  183.460007  83.230522  279.040009   58.759399   45.860001  ...   \n",
       "2019-12-01         NaN        NaN         NaN         NaN         NaN  ...   \n",
       "2020-01-01  156.100006  70.974785  173.949997   44.260574   45.480000  ...   \n",
       "2020-03-01         NaN        NaN         NaN         NaN         NaN  ...   \n",
       "2020-04-01  239.190002  71.160202  274.440002   58.576679   52.610001  ...   \n",
       "2020-06-01         NaN        NaN         NaN         NaN         NaN  ...   \n",
       "2020-07-01  231.009995  73.682045  327.359985   57.837044   81.989998  ...   \n",
       "2020-09-01         NaN        NaN         NaN         NaN         NaN  ...   \n",
       "2020-10-01  305.339996  75.691032  534.380005   84.237556   91.709999  ...   \n",
       "2020-12-01         NaN        NaN         NaN         NaN         NaN  ...   \n",
       "2021-01-01  277.149994  77.622650  541.530029  130.794067   78.500000  ...   \n",
       "2021-03-01         NaN        NaN         NaN         NaN         NaN  ...   \n",
       "2021-04-01  291.899994  78.236771  611.000000  139.673813   93.930000  ...   \n",
       "2021-06-01         NaN        NaN         NaN         NaN         NaN  ...   \n",
       "2021-07-01  285.170013  75.718735  665.429993  126.486305  102.900002  ...   \n",
       "2021-09-01         NaN        NaN         NaN         NaN         NaN  ...   \n",
       "2021-10-01  281.190002  83.677780  657.179993  154.900131  143.899994  ...   \n",
       "2021-12-01         NaN        NaN         NaN         NaN         NaN  ...   \n",
       "2022-01-01  214.350006  94.715904  436.000000  129.949112  109.339996  ...   \n",
       "2022-03-01         NaN        NaN         NaN         NaN         NaN  ...   \n",
       "2022-04-01  171.960007  91.874413  236.669998   89.864098   76.470001  ...   \n",
       "2022-06-01         NaN        NaN         NaN         NaN         NaN  ...   \n",
       "2022-07-01  186.800003  83.439674  207.110001   81.122978   63.360001  ...   \n",
       "2022-09-01         NaN        NaN         NaN         NaN         NaN  ...   \n",
       "2022-10-01  186.869995  92.363548  210.899994   96.667366   64.769997  ...   \n",
       "2022-12-01         NaN        NaN         NaN         NaN         NaN  ...   \n",
       "2023-01-01  208.160004  89.342278  334.140015  122.228020   98.010002  ...   \n",
       "2023-03-01         NaN        NaN         NaN         NaN         NaN  ...   \n",
       "2023-04-01  194.789993  91.587471  325.299988  112.739456   89.370003  ...   \n",
       "\n",
       "                  Volume                                                    \\\n",
       "                    TSLA          TXN        VRSK        VRSN         VRTX   \n",
       "Date                                                                         \n",
       "2018-01-01  3.996878e+09  244747000.0  39480600.0  51139400.0   72650200.0   \n",
       "2018-03-01           NaN          NaN         NaN         NaN          NaN   \n",
       "2018-04-01  8.389710e+09  311066000.0  56263600.0  67303600.0  106058000.0   \n",
       "2018-06-01           NaN          NaN         NaN         NaN          NaN   \n",
       "2018-07-01  9.686457e+09  304898400.0  43489900.0  43792000.0   74373900.0   \n",
       "2018-09-01           NaN          NaN         NaN         NaN          NaN   \n",
       "2018-10-01  8.487465e+09  501006900.0  60204600.0  61787700.0   93515000.0   \n",
       "2018-12-01           NaN          NaN         NaN         NaN          NaN   \n",
       "2019-01-01  7.769936e+09  355035000.0  44427000.0  37675500.0   90485200.0   \n",
       "2019-03-01           NaN          NaN         NaN         NaN          NaN   \n",
       "2019-04-01  1.092475e+10  304105000.0  49921700.0  36305500.0   76422400.0   \n",
       "2019-06-01           NaN          NaN         NaN         NaN          NaN   \n",
       "2019-07-01  7.033539e+09  292378100.0  44692300.0  38562100.0   74217400.0   \n",
       "2019-09-01           NaN          NaN         NaN         NaN          NaN   \n",
       "2019-10-01  8.892501e+09  286572000.0  54490200.0  38588300.0   83047000.0   \n",
       "2019-12-01           NaN          NaN         NaN         NaN          NaN   \n",
       "2020-01-01  1.951110e+10  428234400.0  62443200.0  46526400.0  114791700.0   \n",
       "2020-03-01           NaN          NaN         NaN         NaN          NaN   \n",
       "2020-04-01  1.364931e+10  348766800.0  60539000.0  43156800.0  130698700.0   \n",
       "2020-06-01           NaN          NaN         NaN         NaN          NaN   \n",
       "2020-07-01  1.556049e+10  274618300.0  44596300.0  44481700.0   82406100.0   \n",
       "2020-09-01           NaN          NaN         NaN         NaN          NaN   \n",
       "2020-10-01  8.437834e+09  254040900.0  38895000.0  37779900.0  134397300.0   \n",
       "2020-12-01           NaN          NaN         NaN         NaN          NaN   \n",
       "2021-01-01  6.513015e+09  300820500.0  68215700.0  41132700.0  127476800.0   \n",
       "2021-03-01           NaN          NaN         NaN         NaN          NaN   \n",
       "2021-04-01  5.470912e+09  264879900.0  54013700.0  35098700.0  132207800.0   \n",
       "2021-06-01           NaN          NaN         NaN         NaN          NaN   \n",
       "2021-07-01  3.659838e+09  225661500.0  53298400.0  27750300.0  116526300.0   \n",
       "2021-09-01           NaN          NaN         NaN         NaN          NaN   \n",
       "2021-10-01  5.064306e+09  278122300.0  51907800.0  37993300.0  119027200.0   \n",
       "2021-12-01           NaN          NaN         NaN         NaN          NaN   \n",
       "2022-01-01  5.036406e+09  396931300.0  81360400.0  39453100.0  105424700.0   \n",
       "2022-03-01           NaN          NaN         NaN         NaN          NaN   \n",
       "2022-04-01  5.480409e+09  371049200.0  64442200.0  49777600.0  110340200.0   \n",
       "2022-06-01           NaN          NaN         NaN         NaN          NaN   \n",
       "2022-07-01  4.739418e+09  347975300.0  46180200.0  39429100.0   88449800.0   \n",
       "2022-09-01           NaN          NaN         NaN         NaN          NaN   \n",
       "2022-10-01  6.564786e+09  362510700.0  58093700.0  47164800.0   90700100.0   \n",
       "2022-12-01           NaN          NaN         NaN         NaN          NaN   \n",
       "2023-01-01  1.083396e+10  331719900.0  67143100.0  37076900.0   79407000.0   \n",
       "2023-03-01           NaN          NaN         NaN         NaN          NaN   \n",
       "2023-04-01  2.505176e+09   87499700.0  14394900.0   7504100.0   19841700.0   \n",
       "\n",
       "                                                                             \n",
       "                    WBA         WDAY          XEL           ZM           ZS  \n",
       "Date                                                                         \n",
       "2018-01-01  228778800.0   71893900.0  163809200.0          NaN          NaN  \n",
       "2018-03-01          NaN          NaN          NaN          NaN   25924800.0  \n",
       "2018-04-01  410755300.0  114331600.0  197005000.0          NaN          NaN  \n",
       "2018-06-01          NaN          NaN          NaN          NaN   99524900.0  \n",
       "2018-07-01  396403400.0  108598900.0  201767200.0          NaN          NaN  \n",
       "2018-09-01          NaN          NaN          NaN          NaN   98384800.0  \n",
       "2018-10-01  442733500.0  156984000.0  292830100.0          NaN          NaN  \n",
       "2018-12-01          NaN          NaN          NaN          NaN  103489100.0  \n",
       "2019-01-01  355595400.0  119549700.0  217318000.0          NaN          NaN  \n",
       "2019-03-01          NaN          NaN          NaN          NaN  158297600.0  \n",
       "2019-04-01  435517600.0  108171000.0  186583500.0  118356100.0          NaN  \n",
       "2019-06-01          NaN          NaN          NaN          NaN  147131100.0  \n",
       "2019-07-01  296458300.0  116132500.0  210107400.0  124855000.0          NaN  \n",
       "2019-09-01          NaN          NaN          NaN          NaN  208257600.0  \n",
       "2019-10-01  354801600.0  132765000.0  193005700.0  170056900.0          NaN  \n",
       "2019-12-01          NaN          NaN          NaN          NaN  149505300.0  \n",
       "2020-01-01  450024100.0  150699700.0  268814000.0  527420500.0          NaN  \n",
       "2020-03-01          NaN          NaN          NaN          NaN  211060800.0  \n",
       "2020-04-01  425349400.0  153075400.0  203140900.0  852423300.0          NaN  \n",
       "2020-06-01          NaN          NaN          NaN          NaN  201285300.0  \n",
       "2020-07-01  443370700.0  123346000.0  149209000.0  610247100.0          NaN  \n",
       "2020-09-01          NaN          NaN          NaN          NaN  138782100.0  \n",
       "2020-10-01  496820100.0  130152400.0  156560800.0  563768600.0          NaN  \n",
       "2020-12-01          NaN          NaN          NaN          NaN  146393100.0  \n",
       "2021-01-01  406535000.0  122885700.0  185663100.0  334113000.0          NaN  \n",
       "2021-03-01          NaN          NaN          NaN          NaN  131371200.0  \n",
       "2021-04-01  305766400.0   96779600.0  172728400.0  198879600.0          NaN  \n",
       "2021-06-01          NaN          NaN          NaN          NaN   94442600.0  \n",
       "2021-07-01  371816700.0  102250000.0  165993100.0  269502300.0          NaN  \n",
       "2021-09-01          NaN          NaN          NaN          NaN  115799700.0  \n",
       "2021-10-01  393403200.0   97347200.0  218405400.0  277773800.0          NaN  \n",
       "2021-12-01          NaN          NaN          NaN          NaN  181375000.0  \n",
       "2022-01-01  427184900.0  152624100.0  216945500.0  298444500.0          NaN  \n",
       "2022-03-01          NaN          NaN          NaN          NaN  169992700.0  \n",
       "2022-04-01  451183600.0  195544500.0  208175300.0  332089000.0          NaN  \n",
       "2022-06-01          NaN          NaN          NaN          NaN  129876500.0  \n",
       "2022-07-01  427772800.0  172815600.0  171706300.0  294335600.0          NaN  \n",
       "2022-09-01          NaN          NaN          NaN          NaN  159352800.0  \n",
       "2022-10-01  443324500.0  139526100.0  210359700.0  237397000.0          NaN  \n",
       "2022-12-01          NaN          NaN          NaN          NaN  155572500.0  \n",
       "2023-01-01  437305300.0  125033600.0  183425400.0  241507100.0          NaN  \n",
       "2023-03-01          NaN          NaN          NaN          NaN  122980400.0  \n",
       "2023-04-01  115406400.0   31737700.0   45805800.0   76755300.0          NaN  \n",
       "\n",
       "[43 rows x 612 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = yf.download(\"AAPL ABNB ADBE ADI ADP ADSK AEP ALGN AMAT AMD AMGN AMZN ANSS ASML ATVI AVGO AZN BIDU BIIB BKNG CDNS CEG CHTR CMCSA COST CPRT CRWD CSCO CSX CTAS CTSH DDOG DLTR DOCU DXCM EA EBAY EXC FAST FISV FTNT GILD GOOG GOOGL HON IDXX ILMN INTC INTU ISRG JD KDP KHC KLAC LCID LRCX LULU MAR MCHP MDLZ MELI META MNST MRNA MRVL MSFT MTCH MU NFLX NTES NVDA NXPI ODFL OKTA ORLY PANW PAYX PCAR PDD PEP PYPL QCOM REGN ROST SBUX SGEN SIRI SNPS SPLK SWKS TEAM TMUS TSLA TXN VRSK VRSN VRTX WBA WDAY XEL ZM ZS\", start=\"2018-01-02\", end=\"2023-04-30\", interval=\"3mo\")\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "37d37940",
   "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>AAPL</th>\n",
       "      <th>ABNB</th>\n",
       "      <th>ADBE</th>\n",
       "      <th>ADI</th>\n",
       "      <th>ADP</th>\n",
       "      <th>ADSK</th>\n",
       "      <th>AEP</th>\n",
       "      <th>ALGN</th>\n",
       "      <th>AMAT</th>\n",
       "      <th>AMD</th>\n",
       "      <th>...</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>TXN</th>\n",
       "      <th>VRSK</th>\n",
       "      <th>VRSN</th>\n",
       "      <th>VRTX</th>\n",
       "      <th>WBA</th>\n",
       "      <th>WDAY</th>\n",
       "      <th>XEL</th>\n",
       "      <th>ZM</th>\n",
       "      <th>ZS</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>2018-01-01</th>\n",
       "      <td>41.945000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>216.080002</td>\n",
       "      <td>91.129997</td>\n",
       "      <td>113.480003</td>\n",
       "      <td>125.580002</td>\n",
       "      <td>68.589996</td>\n",
       "      <td>251.130005</td>\n",
       "      <td>55.610001</td>\n",
       "      <td>10.050000</td>\n",
       "      <td>...</td>\n",
       "      <td>17.742001</td>\n",
       "      <td>103.889999</td>\n",
       "      <td>104.000000</td>\n",
       "      <td>118.559998</td>\n",
       "      <td>162.979996</td>\n",
       "      <td>65.470001</td>\n",
       "      <td>127.110001</td>\n",
       "      <td>45.480000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.260000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-01</th>\n",
       "      <td>46.277500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>243.809998</td>\n",
       "      <td>95.919998</td>\n",
       "      <td>134.139999</td>\n",
       "      <td>131.089996</td>\n",
       "      <td>69.250000</td>\n",
       "      <td>342.140015</td>\n",
       "      <td>46.189999</td>\n",
       "      <td>14.990000</td>\n",
       "      <td>...</td>\n",
       "      <td>22.863333</td>\n",
       "      <td>110.250000</td>\n",
       "      <td>107.639999</td>\n",
       "      <td>137.419998</td>\n",
       "      <td>169.960007</td>\n",
       "      <td>60.020000</td>\n",
       "      <td>121.120003</td>\n",
       "      <td>45.680000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42.790001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-01</th>\n",
       "      <td>56.435001</td>\n",
       "      <td>NaN</td>\n",
       "      <td>269.950012</td>\n",
       "      <td>92.459999</td>\n",
       "      <td>150.660004</td>\n",
       "      <td>156.110001</td>\n",
       "      <td>70.879997</td>\n",
       "      <td>391.220001</td>\n",
       "      <td>38.650002</td>\n",
       "      <td>30.889999</td>\n",
       "      <td>...</td>\n",
       "      <td>17.651333</td>\n",
       "      <td>107.290001</td>\n",
       "      <td>120.550003</td>\n",
       "      <td>160.119995</td>\n",
       "      <td>192.740005</td>\n",
       "      <td>72.900002</td>\n",
       "      <td>145.979996</td>\n",
       "      <td>47.209999</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39.259998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-01</th>\n",
       "      <td>39.435001</td>\n",
       "      <td>NaN</td>\n",
       "      <td>226.240005</td>\n",
       "      <td>85.830002</td>\n",
       "      <td>131.119995</td>\n",
       "      <td>128.610001</td>\n",
       "      <td>74.739998</td>\n",
       "      <td>209.429993</td>\n",
       "      <td>32.740002</td>\n",
       "      <td>18.459999</td>\n",
       "      <td>...</td>\n",
       "      <td>22.186666</td>\n",
       "      <td>94.500000</td>\n",
       "      <td>109.040001</td>\n",
       "      <td>148.289993</td>\n",
       "      <td>165.710007</td>\n",
       "      <td>68.330002</td>\n",
       "      <td>159.679993</td>\n",
       "      <td>49.270000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>49.680000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01</th>\n",
       "      <td>47.487499</td>\n",
       "      <td>NaN</td>\n",
       "      <td>266.489990</td>\n",
       "      <td>105.269997</td>\n",
       "      <td>159.740005</td>\n",
       "      <td>155.820007</td>\n",
       "      <td>83.750000</td>\n",
       "      <td>284.329987</td>\n",
       "      <td>39.660000</td>\n",
       "      <td>25.520000</td>\n",
       "      <td>...</td>\n",
       "      <td>18.657333</td>\n",
       "      <td>106.070000</td>\n",
       "      <td>133.000000</td>\n",
       "      <td>181.559998</td>\n",
       "      <td>183.949997</td>\n",
       "      <td>63.270000</td>\n",
       "      <td>192.850006</td>\n",
       "      <td>56.209999</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68.629997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-01</th>\n",
       "      <td>49.480000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>294.649994</td>\n",
       "      <td>112.870003</td>\n",
       "      <td>165.330002</td>\n",
       "      <td>162.899994</td>\n",
       "      <td>88.010002</td>\n",
       "      <td>273.700012</td>\n",
       "      <td>44.910000</td>\n",
       "      <td>30.370001</td>\n",
       "      <td>...</td>\n",
       "      <td>14.897333</td>\n",
       "      <td>114.760002</td>\n",
       "      <td>146.460007</td>\n",
       "      <td>209.160004</td>\n",
       "      <td>183.380005</td>\n",
       "      <td>54.669998</td>\n",
       "      <td>205.580002</td>\n",
       "      <td>59.490002</td>\n",
       "      <td>88.790001</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68.739998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-01</th>\n",
       "      <td>55.992500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>276.250000</td>\n",
       "      <td>111.730003</td>\n",
       "      <td>161.419998</td>\n",
       "      <td>147.699997</td>\n",
       "      <td>93.690002</td>\n",
       "      <td>180.919998</td>\n",
       "      <td>49.900002</td>\n",
       "      <td>28.990000</td>\n",
       "      <td>...</td>\n",
       "      <td>16.058001</td>\n",
       "      <td>129.240005</td>\n",
       "      <td>158.139999</td>\n",
       "      <td>188.630005</td>\n",
       "      <td>169.419998</td>\n",
       "      <td>55.310001</td>\n",
       "      <td>169.960007</td>\n",
       "      <td>64.889999</td>\n",
       "      <td>76.199997</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>52.130001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-01</th>\n",
       "      <td>73.412498</td>\n",
       "      <td>NaN</td>\n",
       "      <td>329.809998</td>\n",
       "      <td>118.839996</td>\n",
       "      <td>170.500000</td>\n",
       "      <td>183.460007</td>\n",
       "      <td>94.510002</td>\n",
       "      <td>279.040009</td>\n",
       "      <td>61.040001</td>\n",
       "      <td>45.860001</td>\n",
       "      <td>...</td>\n",
       "      <td>27.888666</td>\n",
       "      <td>128.289993</td>\n",
       "      <td>149.339996</td>\n",
       "      <td>192.679993</td>\n",
       "      <td>218.949997</td>\n",
       "      <td>58.959999</td>\n",
       "      <td>164.449997</td>\n",
       "      <td>63.490002</td>\n",
       "      <td>68.040001</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>51.990002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>63.572498</td>\n",
       "      <td>NaN</td>\n",
       "      <td>318.239990</td>\n",
       "      <td>89.650002</td>\n",
       "      <td>136.679993</td>\n",
       "      <td>156.100006</td>\n",
       "      <td>79.980003</td>\n",
       "      <td>173.949997</td>\n",
       "      <td>45.820000</td>\n",
       "      <td>45.480000</td>\n",
       "      <td>...</td>\n",
       "      <td>34.933334</td>\n",
       "      <td>99.930000</td>\n",
       "      <td>139.380005</td>\n",
       "      <td>180.089996</td>\n",
       "      <td>237.949997</td>\n",
       "      <td>45.750000</td>\n",
       "      <td>130.220001</td>\n",
       "      <td>60.299999</td>\n",
       "      <td>146.119995</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>98.089996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-01</th>\n",
       "      <td>91.199997</td>\n",
       "      <td>NaN</td>\n",
       "      <td>435.309998</td>\n",
       "      <td>122.639999</td>\n",
       "      <td>148.889999</td>\n",
       "      <td>239.190002</td>\n",
       "      <td>79.639999</td>\n",
       "      <td>274.440002</td>\n",
       "      <td>60.450001</td>\n",
       "      <td>52.610001</td>\n",
       "      <td>...</td>\n",
       "      <td>71.987335</td>\n",
       "      <td>126.970001</td>\n",
       "      <td>170.199997</td>\n",
       "      <td>206.830002</td>\n",
       "      <td>290.309998</td>\n",
       "      <td>42.389999</td>\n",
       "      <td>187.360001</td>\n",
       "      <td>62.500000</td>\n",
       "      <td>253.539993</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>143.339996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-01</th>\n",
       "      <td>115.809998</td>\n",
       "      <td>NaN</td>\n",
       "      <td>490.429993</td>\n",
       "      <td>116.739998</td>\n",
       "      <td>139.490005</td>\n",
       "      <td>231.009995</td>\n",
       "      <td>81.730003</td>\n",
       "      <td>327.359985</td>\n",
       "      <td>59.450001</td>\n",
       "      <td>81.989998</td>\n",
       "      <td>...</td>\n",
       "      <td>143.003326</td>\n",
       "      <td>142.789993</td>\n",
       "      <td>185.309998</td>\n",
       "      <td>204.850006</td>\n",
       "      <td>272.119995</td>\n",
       "      <td>35.919998</td>\n",
       "      <td>215.130005</td>\n",
       "      <td>69.010002</td>\n",
       "      <td>470.109985</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>155.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-01</th>\n",
       "      <td>132.690002</td>\n",
       "      <td>NaN</td>\n",
       "      <td>500.119995</td>\n",
       "      <td>147.729996</td>\n",
       "      <td>176.199997</td>\n",
       "      <td>305.339996</td>\n",
       "      <td>83.269997</td>\n",
       "      <td>534.380005</td>\n",
       "      <td>86.300003</td>\n",
       "      <td>91.709999</td>\n",
       "      <td>...</td>\n",
       "      <td>235.223328</td>\n",
       "      <td>164.130005</td>\n",
       "      <td>207.589996</td>\n",
       "      <td>216.399994</td>\n",
       "      <td>236.339996</td>\n",
       "      <td>39.880001</td>\n",
       "      <td>239.610001</td>\n",
       "      <td>66.669998</td>\n",
       "      <td>337.320007</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>206.350006</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>205.029999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-01</th>\n",
       "      <td>122.150002</td>\n",
       "      <td>NaN</td>\n",
       "      <td>475.369995</td>\n",
       "      <td>155.080002</td>\n",
       "      <td>188.470001</td>\n",
       "      <td>277.149994</td>\n",
       "      <td>84.699997</td>\n",
       "      <td>541.530029</td>\n",
       "      <td>133.600006</td>\n",
       "      <td>78.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>222.643326</td>\n",
       "      <td>188.990005</td>\n",
       "      <td>176.690002</td>\n",
       "      <td>198.759995</td>\n",
       "      <td>214.889999</td>\n",
       "      <td>54.900002</td>\n",
       "      <td>248.429993</td>\n",
       "      <td>66.510002</td>\n",
       "      <td>321.290009</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>140.399994</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>194.199997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-01</th>\n",
       "      <td>136.960007</td>\n",
       "      <td>NaN</td>\n",
       "      <td>585.640015</td>\n",
       "      <td>172.160004</td>\n",
       "      <td>198.619995</td>\n",
       "      <td>291.899994</td>\n",
       "      <td>84.589996</td>\n",
       "      <td>611.000000</td>\n",
       "      <td>142.399994</td>\n",
       "      <td>93.930000</td>\n",
       "      <td>...</td>\n",
       "      <td>226.566666</td>\n",
       "      <td>192.300003</td>\n",
       "      <td>174.720001</td>\n",
       "      <td>227.690002</td>\n",
       "      <td>201.630005</td>\n",
       "      <td>52.610001</td>\n",
       "      <td>238.740005</td>\n",
       "      <td>65.879997</td>\n",
       "      <td>387.029999</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>154.990005</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>278.339996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07-01</th>\n",
       "      <td>141.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>575.719971</td>\n",
       "      <td>167.479996</td>\n",
       "      <td>199.919998</td>\n",
       "      <td>285.170013</td>\n",
       "      <td>81.180000</td>\n",
       "      <td>665.429993</td>\n",
       "      <td>128.729996</td>\n",
       "      <td>102.900002</td>\n",
       "      <td>...</td>\n",
       "      <td>258.493347</td>\n",
       "      <td>192.210007</td>\n",
       "      <td>200.270004</td>\n",
       "      <td>205.009995</td>\n",
       "      <td>181.389999</td>\n",
       "      <td>47.049999</td>\n",
       "      <td>249.889999</td>\n",
       "      <td>62.500000</td>\n",
       "      <td>261.500000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>172.539993</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>346.970001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-01</th>\n",
       "      <td>177.570007</td>\n",
       "      <td>NaN</td>\n",
       "      <td>567.059998</td>\n",
       "      <td>175.770004</td>\n",
       "      <td>246.580002</td>\n",
       "      <td>281.190002</td>\n",
       "      <td>88.970001</td>\n",
       "      <td>657.179993</td>\n",
       "      <td>157.360001</td>\n",
       "      <td>143.899994</td>\n",
       "      <td>...</td>\n",
       "      <td>352.260010</td>\n",
       "      <td>188.470001</td>\n",
       "      <td>228.729996</td>\n",
       "      <td>253.820007</td>\n",
       "      <td>219.600006</td>\n",
       "      <td>52.160000</td>\n",
       "      <td>273.179993</td>\n",
       "      <td>67.699997</td>\n",
       "      <td>183.910004</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>151.490005</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>239.149994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-01</th>\n",
       "      <td>174.610001</td>\n",
       "      <td>NaN</td>\n",
       "      <td>455.619995</td>\n",
       "      <td>165.179993</td>\n",
       "      <td>227.539993</td>\n",
       "      <td>214.350006</td>\n",
       "      <td>99.769997</td>\n",
       "      <td>436.000000</td>\n",
       "      <td>131.800003</td>\n",
       "      <td>109.339996</td>\n",
       "      <td>...</td>\n",
       "      <td>359.200012</td>\n",
       "      <td>183.479996</td>\n",
       "      <td>214.630005</td>\n",
       "      <td>222.460007</td>\n",
       "      <td>260.970001</td>\n",
       "      <td>44.770000</td>\n",
       "      <td>239.460007</td>\n",
       "      <td>72.169998</td>\n",
       "      <td>117.230003</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>120.870003</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>153.089996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-01</th>\n",
       "      <td>136.720001</td>\n",
       "      <td>NaN</td>\n",
       "      <td>366.059998</td>\n",
       "      <td>146.089996</td>\n",
       "      <td>210.039993</td>\n",
       "      <td>171.960007</td>\n",
       "      <td>95.940002</td>\n",
       "      <td>236.669998</td>\n",
       "      <td>90.980003</td>\n",
       "      <td>76.470001</td>\n",
       "      <td>...</td>\n",
       "      <td>224.473328</td>\n",
       "      <td>153.649994</td>\n",
       "      <td>173.089996</td>\n",
       "      <td>167.330002</td>\n",
       "      <td>281.790009</td>\n",
       "      <td>37.900002</td>\n",
       "      <td>139.580002</td>\n",
       "      <td>70.760002</td>\n",
       "      <td>107.970001</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-06-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>113.120003</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>159.240005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-01</th>\n",
       "      <td>138.199997</td>\n",
       "      <td>NaN</td>\n",
       "      <td>275.200012</td>\n",
       "      <td>139.339996</td>\n",
       "      <td>226.190002</td>\n",
       "      <td>186.800003</td>\n",
       "      <td>86.449997</td>\n",
       "      <td>207.110001</td>\n",
       "      <td>81.930000</td>\n",
       "      <td>63.360001</td>\n",
       "      <td>...</td>\n",
       "      <td>265.250000</td>\n",
       "      <td>154.779999</td>\n",
       "      <td>170.529999</td>\n",
       "      <td>173.699997</td>\n",
       "      <td>289.540009</td>\n",
       "      <td>31.400000</td>\n",
       "      <td>152.220001</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>73.589996</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>102.139999</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>133.449997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-01</th>\n",
       "      <td>129.929993</td>\n",
       "      <td>NaN</td>\n",
       "      <td>336.529999</td>\n",
       "      <td>164.029999</td>\n",
       "      <td>238.860001</td>\n",
       "      <td>186.869995</td>\n",
       "      <td>94.949997</td>\n",
       "      <td>210.899994</td>\n",
       "      <td>97.379997</td>\n",
       "      <td>64.769997</td>\n",
       "      <td>...</td>\n",
       "      <td>123.180000</td>\n",
       "      <td>165.220001</td>\n",
       "      <td>176.419998</td>\n",
       "      <td>205.440002</td>\n",
       "      <td>288.779999</td>\n",
       "      <td>37.360001</td>\n",
       "      <td>167.330002</td>\n",
       "      <td>70.110001</td>\n",
       "      <td>67.739998</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>123.279999</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>131.149994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-01</th>\n",
       "      <td>164.899994</td>\n",
       "      <td>NaN</td>\n",
       "      <td>385.369995</td>\n",
       "      <td>197.220001</td>\n",
       "      <td>222.630005</td>\n",
       "      <td>208.160004</td>\n",
       "      <td>90.989998</td>\n",
       "      <td>334.140015</td>\n",
       "      <td>122.830002</td>\n",
       "      <td>98.010002</td>\n",
       "      <td>...</td>\n",
       "      <td>207.460007</td>\n",
       "      <td>186.009995</td>\n",
       "      <td>191.860001</td>\n",
       "      <td>211.330002</td>\n",
       "      <td>315.070007</td>\n",
       "      <td>34.580002</td>\n",
       "      <td>206.539993</td>\n",
       "      <td>67.440002</td>\n",
       "      <td>73.839996</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-03-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>119.669998</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>90.099998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-04-01</th>\n",
       "      <td>169.679993</td>\n",
       "      <td>NaN</td>\n",
       "      <td>377.559998</td>\n",
       "      <td>179.880005</td>\n",
       "      <td>220.000000</td>\n",
       "      <td>194.789993</td>\n",
       "      <td>92.419998</td>\n",
       "      <td>325.299988</td>\n",
       "      <td>113.029999</td>\n",
       "      <td>89.370003</td>\n",
       "      <td>...</td>\n",
       "      <td>164.309998</td>\n",
       "      <td>167.199997</td>\n",
       "      <td>194.110001</td>\n",
       "      <td>221.800003</td>\n",
       "      <td>340.730011</td>\n",
       "      <td>35.250000</td>\n",
       "      <td>186.139999</td>\n",
       "      <td>69.910004</td>\n",
       "      <td>61.430000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>43 rows × 102 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  AAPL        ABNB        ADBE         ADI         ADP  \\\n",
       "Date                                                                     \n",
       "2018-01-01   41.945000         NaN  216.080002   91.129997  113.480003   \n",
       "2018-03-01         NaN         NaN         NaN         NaN         NaN   \n",
       "2018-04-01   46.277500         NaN  243.809998   95.919998  134.139999   \n",
       "2018-06-01         NaN         NaN         NaN         NaN         NaN   \n",
       "2018-07-01   56.435001         NaN  269.950012   92.459999  150.660004   \n",
       "2018-09-01         NaN         NaN         NaN         NaN         NaN   \n",
       "2018-10-01   39.435001         NaN  226.240005   85.830002  131.119995   \n",
       "2018-12-01         NaN         NaN         NaN         NaN         NaN   \n",
       "2019-01-01   47.487499         NaN  266.489990  105.269997  159.740005   \n",
       "2019-03-01         NaN         NaN         NaN         NaN         NaN   \n",
       "2019-04-01   49.480000         NaN  294.649994  112.870003  165.330002   \n",
       "2019-06-01         NaN         NaN         NaN         NaN         NaN   \n",
       "2019-07-01   55.992500         NaN  276.250000  111.730003  161.419998   \n",
       "2019-09-01         NaN         NaN         NaN         NaN         NaN   \n",
       "2019-10-01   73.412498         NaN  329.809998  118.839996  170.500000   \n",
       "2019-12-01         NaN         NaN         NaN         NaN         NaN   \n",
       "2020-01-01   63.572498         NaN  318.239990   89.650002  136.679993   \n",
       "2020-03-01         NaN         NaN         NaN         NaN         NaN   \n",
       "2020-04-01   91.199997         NaN  435.309998  122.639999  148.889999   \n",
       "2020-06-01         NaN         NaN         NaN         NaN         NaN   \n",
       "2020-07-01  115.809998         NaN  490.429993  116.739998  139.490005   \n",
       "2020-09-01         NaN         NaN         NaN         NaN         NaN   \n",
       "2020-10-01  132.690002         NaN  500.119995  147.729996  176.199997   \n",
       "2020-12-01         NaN  206.350006         NaN         NaN         NaN   \n",
       "2021-01-01  122.150002         NaN  475.369995  155.080002  188.470001   \n",
       "2021-03-01         NaN  140.399994         NaN         NaN         NaN   \n",
       "2021-04-01  136.960007         NaN  585.640015  172.160004  198.619995   \n",
       "2021-06-01         NaN  154.990005         NaN         NaN         NaN   \n",
       "2021-07-01  141.500000         NaN  575.719971  167.479996  199.919998   \n",
       "2021-09-01         NaN  172.539993         NaN         NaN         NaN   \n",
       "2021-10-01  177.570007         NaN  567.059998  175.770004  246.580002   \n",
       "2021-12-01         NaN  151.490005         NaN         NaN         NaN   \n",
       "2022-01-01  174.610001         NaN  455.619995  165.179993  227.539993   \n",
       "2022-03-01         NaN  120.870003         NaN         NaN         NaN   \n",
       "2022-04-01  136.720001         NaN  366.059998  146.089996  210.039993   \n",
       "2022-06-01         NaN  113.120003         NaN         NaN         NaN   \n",
       "2022-07-01  138.199997         NaN  275.200012  139.339996  226.190002   \n",
       "2022-09-01         NaN  102.139999         NaN         NaN         NaN   \n",
       "2022-10-01  129.929993         NaN  336.529999  164.029999  238.860001   \n",
       "2022-12-01         NaN  123.279999         NaN         NaN         NaN   \n",
       "2023-01-01  164.899994         NaN  385.369995  197.220001  222.630005   \n",
       "2023-03-01         NaN  119.669998         NaN         NaN         NaN   \n",
       "2023-04-01  169.679993         NaN  377.559998  179.880005  220.000000   \n",
       "\n",
       "                  ADSK        AEP        ALGN        AMAT         AMD  ...  \\\n",
       "Date                                                                   ...   \n",
       "2018-01-01  125.580002  68.589996  251.130005   55.610001   10.050000  ...   \n",
       "2018-03-01         NaN        NaN         NaN         NaN         NaN  ...   \n",
       "2018-04-01  131.089996  69.250000  342.140015   46.189999   14.990000  ...   \n",
       "2018-06-01         NaN        NaN         NaN         NaN         NaN  ...   \n",
       "2018-07-01  156.110001  70.879997  391.220001   38.650002   30.889999  ...   \n",
       "2018-09-01         NaN        NaN         NaN         NaN         NaN  ...   \n",
       "2018-10-01  128.610001  74.739998  209.429993   32.740002   18.459999  ...   \n",
       "2018-12-01         NaN        NaN         NaN         NaN         NaN  ...   \n",
       "2019-01-01  155.820007  83.750000  284.329987   39.660000   25.520000  ...   \n",
       "2019-03-01         NaN        NaN         NaN         NaN         NaN  ...   \n",
       "2019-04-01  162.899994  88.010002  273.700012   44.910000   30.370001  ...   \n",
       "2019-06-01         NaN        NaN         NaN         NaN         NaN  ...   \n",
       "2019-07-01  147.699997  93.690002  180.919998   49.900002   28.990000  ...   \n",
       "2019-09-01         NaN        NaN         NaN         NaN         NaN  ...   \n",
       "2019-10-01  183.460007  94.510002  279.040009   61.040001   45.860001  ...   \n",
       "2019-12-01         NaN        NaN         NaN         NaN         NaN  ...   \n",
       "2020-01-01  156.100006  79.980003  173.949997   45.820000   45.480000  ...   \n",
       "2020-03-01         NaN        NaN         NaN         NaN         NaN  ...   \n",
       "2020-04-01  239.190002  79.639999  274.440002   60.450001   52.610001  ...   \n",
       "2020-06-01         NaN        NaN         NaN         NaN         NaN  ...   \n",
       "2020-07-01  231.009995  81.730003  327.359985   59.450001   81.989998  ...   \n",
       "2020-09-01         NaN        NaN         NaN         NaN         NaN  ...   \n",
       "2020-10-01  305.339996  83.269997  534.380005   86.300003   91.709999  ...   \n",
       "2020-12-01         NaN        NaN         NaN         NaN         NaN  ...   \n",
       "2021-01-01  277.149994  84.699997  541.530029  133.600006   78.500000  ...   \n",
       "2021-03-01         NaN        NaN         NaN         NaN         NaN  ...   \n",
       "2021-04-01  291.899994  84.589996  611.000000  142.399994   93.930000  ...   \n",
       "2021-06-01         NaN        NaN         NaN         NaN         NaN  ...   \n",
       "2021-07-01  285.170013  81.180000  665.429993  128.729996  102.900002  ...   \n",
       "2021-09-01         NaN        NaN         NaN         NaN         NaN  ...   \n",
       "2021-10-01  281.190002  88.970001  657.179993  157.360001  143.899994  ...   \n",
       "2021-12-01         NaN        NaN         NaN         NaN         NaN  ...   \n",
       "2022-01-01  214.350006  99.769997  436.000000  131.800003  109.339996  ...   \n",
       "2022-03-01         NaN        NaN         NaN         NaN         NaN  ...   \n",
       "2022-04-01  171.960007  95.940002  236.669998   90.980003   76.470001  ...   \n",
       "2022-06-01         NaN        NaN         NaN         NaN         NaN  ...   \n",
       "2022-07-01  186.800003  86.449997  207.110001   81.930000   63.360001  ...   \n",
       "2022-09-01         NaN        NaN         NaN         NaN         NaN  ...   \n",
       "2022-10-01  186.869995  94.949997  210.899994   97.379997   64.769997  ...   \n",
       "2022-12-01         NaN        NaN         NaN         NaN         NaN  ...   \n",
       "2023-01-01  208.160004  90.989998  334.140015  122.830002   98.010002  ...   \n",
       "2023-03-01         NaN        NaN         NaN         NaN         NaN  ...   \n",
       "2023-04-01  194.789993  92.419998  325.299988  113.029999   89.370003  ...   \n",
       "\n",
       "                  TSLA         TXN        VRSK        VRSN        VRTX  \\\n",
       "Date                                                                     \n",
       "2018-01-01   17.742001  103.889999  104.000000  118.559998  162.979996   \n",
       "2018-03-01         NaN         NaN         NaN         NaN         NaN   \n",
       "2018-04-01   22.863333  110.250000  107.639999  137.419998  169.960007   \n",
       "2018-06-01         NaN         NaN         NaN         NaN         NaN   \n",
       "2018-07-01   17.651333  107.290001  120.550003  160.119995  192.740005   \n",
       "2018-09-01         NaN         NaN         NaN         NaN         NaN   \n",
       "2018-10-01   22.186666   94.500000  109.040001  148.289993  165.710007   \n",
       "2018-12-01         NaN         NaN         NaN         NaN         NaN   \n",
       "2019-01-01   18.657333  106.070000  133.000000  181.559998  183.949997   \n",
       "2019-03-01         NaN         NaN         NaN         NaN         NaN   \n",
       "2019-04-01   14.897333  114.760002  146.460007  209.160004  183.380005   \n",
       "2019-06-01         NaN         NaN         NaN         NaN         NaN   \n",
       "2019-07-01   16.058001  129.240005  158.139999  188.630005  169.419998   \n",
       "2019-09-01         NaN         NaN         NaN         NaN         NaN   \n",
       "2019-10-01   27.888666  128.289993  149.339996  192.679993  218.949997   \n",
       "2019-12-01         NaN         NaN         NaN         NaN         NaN   \n",
       "2020-01-01   34.933334   99.930000  139.380005  180.089996  237.949997   \n",
       "2020-03-01         NaN         NaN         NaN         NaN         NaN   \n",
       "2020-04-01   71.987335  126.970001  170.199997  206.830002  290.309998   \n",
       "2020-06-01         NaN         NaN         NaN         NaN         NaN   \n",
       "2020-07-01  143.003326  142.789993  185.309998  204.850006  272.119995   \n",
       "2020-09-01         NaN         NaN         NaN         NaN         NaN   \n",
       "2020-10-01  235.223328  164.130005  207.589996  216.399994  236.339996   \n",
       "2020-12-01         NaN         NaN         NaN         NaN         NaN   \n",
       "2021-01-01  222.643326  188.990005  176.690002  198.759995  214.889999   \n",
       "2021-03-01         NaN         NaN         NaN         NaN         NaN   \n",
       "2021-04-01  226.566666  192.300003  174.720001  227.690002  201.630005   \n",
       "2021-06-01         NaN         NaN         NaN         NaN         NaN   \n",
       "2021-07-01  258.493347  192.210007  200.270004  205.009995  181.389999   \n",
       "2021-09-01         NaN         NaN         NaN         NaN         NaN   \n",
       "2021-10-01  352.260010  188.470001  228.729996  253.820007  219.600006   \n",
       "2021-12-01         NaN         NaN         NaN         NaN         NaN   \n",
       "2022-01-01  359.200012  183.479996  214.630005  222.460007  260.970001   \n",
       "2022-03-01         NaN         NaN         NaN         NaN         NaN   \n",
       "2022-04-01  224.473328  153.649994  173.089996  167.330002  281.790009   \n",
       "2022-06-01         NaN         NaN         NaN         NaN         NaN   \n",
       "2022-07-01  265.250000  154.779999  170.529999  173.699997  289.540009   \n",
       "2022-09-01         NaN         NaN         NaN         NaN         NaN   \n",
       "2022-10-01  123.180000  165.220001  176.419998  205.440002  288.779999   \n",
       "2022-12-01         NaN         NaN         NaN         NaN         NaN   \n",
       "2023-01-01  207.460007  186.009995  191.860001  211.330002  315.070007   \n",
       "2023-03-01         NaN         NaN         NaN         NaN         NaN   \n",
       "2023-04-01  164.309998  167.199997  194.110001  221.800003  340.730011   \n",
       "\n",
       "                  WBA        WDAY        XEL          ZM          ZS  \n",
       "Date                                                                  \n",
       "2018-01-01  65.470001  127.110001  45.480000         NaN         NaN  \n",
       "2018-03-01        NaN         NaN        NaN         NaN   26.260000  \n",
       "2018-04-01  60.020000  121.120003  45.680000         NaN         NaN  \n",
       "2018-06-01        NaN         NaN        NaN         NaN   42.790001  \n",
       "2018-07-01  72.900002  145.979996  47.209999         NaN         NaN  \n",
       "2018-09-01        NaN         NaN        NaN         NaN   39.259998  \n",
       "2018-10-01  68.330002  159.679993  49.270000         NaN         NaN  \n",
       "2018-12-01        NaN         NaN        NaN         NaN   49.680000  \n",
       "2019-01-01  63.270000  192.850006  56.209999         NaN         NaN  \n",
       "2019-03-01        NaN         NaN        NaN         NaN   68.629997  \n",
       "2019-04-01  54.669998  205.580002  59.490002   88.790001         NaN  \n",
       "2019-06-01        NaN         NaN        NaN         NaN   68.739998  \n",
       "2019-07-01  55.310001  169.960007  64.889999   76.199997         NaN  \n",
       "2019-09-01        NaN         NaN        NaN         NaN   52.130001  \n",
       "2019-10-01  58.959999  164.449997  63.490002   68.040001         NaN  \n",
       "2019-12-01        NaN         NaN        NaN         NaN   51.990002  \n",
       "2020-01-01  45.750000  130.220001  60.299999  146.119995         NaN  \n",
       "2020-03-01        NaN         NaN        NaN         NaN   98.089996  \n",
       "2020-04-01  42.389999  187.360001  62.500000  253.539993         NaN  \n",
       "2020-06-01        NaN         NaN        NaN         NaN  143.339996  \n",
       "2020-07-01  35.919998  215.130005  69.010002  470.109985         NaN  \n",
       "2020-09-01        NaN         NaN        NaN         NaN  155.750000  \n",
       "2020-10-01  39.880001  239.610001  66.669998  337.320007         NaN  \n",
       "2020-12-01        NaN         NaN        NaN         NaN  205.029999  \n",
       "2021-01-01  54.900002  248.429993  66.510002  321.290009         NaN  \n",
       "2021-03-01        NaN         NaN        NaN         NaN  194.199997  \n",
       "2021-04-01  52.610001  238.740005  65.879997  387.029999         NaN  \n",
       "2021-06-01        NaN         NaN        NaN         NaN  278.339996  \n",
       "2021-07-01  47.049999  249.889999  62.500000  261.500000         NaN  \n",
       "2021-09-01        NaN         NaN        NaN         NaN  346.970001  \n",
       "2021-10-01  52.160000  273.179993  67.699997  183.910004         NaN  \n",
       "2021-12-01        NaN         NaN        NaN         NaN  239.149994  \n",
       "2022-01-01  44.770000  239.460007  72.169998  117.230003         NaN  \n",
       "2022-03-01        NaN         NaN        NaN         NaN  153.089996  \n",
       "2022-04-01  37.900002  139.580002  70.760002  107.970001         NaN  \n",
       "2022-06-01        NaN         NaN        NaN         NaN  159.240005  \n",
       "2022-07-01  31.400000  152.220001  64.000000   73.589996         NaN  \n",
       "2022-09-01        NaN         NaN        NaN         NaN  133.449997  \n",
       "2022-10-01  37.360001  167.330002  70.110001   67.739998         NaN  \n",
       "2022-12-01        NaN         NaN        NaN         NaN  131.149994  \n",
       "2023-01-01  34.580002  206.539993  67.440002   73.839996         NaN  \n",
       "2023-03-01        NaN         NaN        NaN         NaN   90.099998  \n",
       "2023-04-01  35.250000  186.139999  69.910004   61.430000         NaN  \n",
       "\n",
       "[43 rows x 102 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data['Close']\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a1b6d5f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['AAPL',\n",
       " 'ABNB',\n",
       " 'ADBE',\n",
       " 'ADI',\n",
       " 'ADP',\n",
       " 'ADSK',\n",
       " 'AEP',\n",
       " 'ALGN',\n",
       " 'AMAT',\n",
       " 'AMD',\n",
       " 'AMGN',\n",
       " 'AMZN',\n",
       " 'ANSS',\n",
       " 'ASML',\n",
       " 'ATVI',\n",
       " 'AVGO',\n",
       " 'AZN',\n",
       " 'BIDU',\n",
       " 'BIIB',\n",
       " 'BKNG',\n",
       " 'CDNS',\n",
       " 'CEG',\n",
       " 'CHTR',\n",
       " 'CMCSA',\n",
       " 'COST',\n",
       " 'CPRT',\n",
       " 'CRWD',\n",
       " 'CSCO',\n",
       " 'CSX',\n",
       " 'CTAS',\n",
       " 'CTSH',\n",
       " 'DDOG',\n",
       " 'DLTR',\n",
       " 'DOCU',\n",
       " 'DXCM',\n",
       " 'EA',\n",
       " 'EBAY',\n",
       " 'EXC',\n",
       " 'FAST',\n",
       " 'FISV',\n",
       " 'FTNT',\n",
       " 'GILD',\n",
       " 'GOOG',\n",
       " 'GOOGL',\n",
       " 'HON',\n",
       " 'IDXX',\n",
       " 'ILMN',\n",
       " 'INTC',\n",
       " 'INTU',\n",
       " 'ISRG',\n",
       " 'JD',\n",
       " 'KDP',\n",
       " 'KHC',\n",
       " 'KLAC',\n",
       " 'LCID',\n",
       " 'LRCX',\n",
       " 'LULU',\n",
       " 'MAR',\n",
       " 'MCHP',\n",
       " 'MDLZ',\n",
       " 'MELI',\n",
       " 'META',\n",
       " 'MNST',\n",
       " 'MRNA',\n",
       " 'MRVL',\n",
       " 'MSFT',\n",
       " 'MTCH',\n",
       " 'MU',\n",
       " 'NFLX',\n",
       " 'NTES',\n",
       " 'NVDA',\n",
       " 'NXPI',\n",
       " 'ODFL',\n",
       " 'OKTA',\n",
       " 'ORLY',\n",
       " 'PANW',\n",
       " 'PAYX',\n",
       " 'PCAR',\n",
       " 'PDD',\n",
       " 'PEP',\n",
       " 'PYPL',\n",
       " 'QCOM',\n",
       " 'REGN',\n",
       " 'ROST',\n",
       " 'SBUX',\n",
       " 'SGEN',\n",
       " 'SIRI',\n",
       " 'SNPS',\n",
       " 'SPLK',\n",
       " 'SWKS',\n",
       " 'TEAM',\n",
       " 'TMUS',\n",
       " 'TSLA',\n",
       " 'TXN',\n",
       " 'VRSK',\n",
       " 'VRSN',\n",
       " 'VRTX',\n",
       " 'WBA',\n",
       " 'WDAY',\n",
       " 'XEL',\n",
       " 'ZM',\n",
       " 'ZS']"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "column_names1 = data.keys()\n",
    "column_names1 = column_names1.tolist()\n",
    "column_names1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "5e6fa305",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAPL</th>\n",
       "      <th>ABNB</th>\n",
       "      <th>ADBE</th>\n",
       "      <th>ADI</th>\n",
       "      <th>ADP</th>\n",
       "      <th>ADSK</th>\n",
       "      <th>AEP</th>\n",
       "      <th>ALGN</th>\n",
       "      <th>AMAT</th>\n",
       "      <th>AMD</th>\n",
       "      <th>...</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>TXN</th>\n",
       "      <th>VRSK</th>\n",
       "      <th>VRSN</th>\n",
       "      <th>VRTX</th>\n",
       "      <th>WBA</th>\n",
       "      <th>WDAY</th>\n",
       "      <th>XEL</th>\n",
       "      <th>ZM</th>\n",
       "      <th>ZS</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-01</th>\n",
       "      <td>41.945000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>216.080002</td>\n",
       "      <td>91.129997</td>\n",
       "      <td>113.480003</td>\n",
       "      <td>125.580002</td>\n",
       "      <td>68.589996</td>\n",
       "      <td>251.130005</td>\n",
       "      <td>55.610001</td>\n",
       "      <td>10.050000</td>\n",
       "      <td>...</td>\n",
       "      <td>17.742001</td>\n",
       "      <td>103.889999</td>\n",
       "      <td>104.000000</td>\n",
       "      <td>118.559998</td>\n",
       "      <td>162.979996</td>\n",
       "      <td>65.470001</td>\n",
       "      <td>127.110001</td>\n",
       "      <td>45.480000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.260000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-01</th>\n",
       "      <td>46.277500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>243.809998</td>\n",
       "      <td>95.919998</td>\n",
       "      <td>134.139999</td>\n",
       "      <td>131.089996</td>\n",
       "      <td>69.250000</td>\n",
       "      <td>342.140015</td>\n",
       "      <td>46.189999</td>\n",
       "      <td>14.990000</td>\n",
       "      <td>...</td>\n",
       "      <td>22.863333</td>\n",
       "      <td>110.250000</td>\n",
       "      <td>107.639999</td>\n",
       "      <td>137.419998</td>\n",
       "      <td>169.960007</td>\n",
       "      <td>60.020000</td>\n",
       "      <td>121.120003</td>\n",
       "      <td>45.680000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42.790001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-01</th>\n",
       "      <td>56.435001</td>\n",
       "      <td>NaN</td>\n",
       "      <td>269.950012</td>\n",
       "      <td>92.459999</td>\n",
       "      <td>150.660004</td>\n",
       "      <td>156.110001</td>\n",
       "      <td>70.879997</td>\n",
       "      <td>391.220001</td>\n",
       "      <td>38.650002</td>\n",
       "      <td>30.889999</td>\n",
       "      <td>...</td>\n",
       "      <td>17.651333</td>\n",
       "      <td>107.290001</td>\n",
       "      <td>120.550003</td>\n",
       "      <td>160.119995</td>\n",
       "      <td>192.740005</td>\n",
       "      <td>72.900002</td>\n",
       "      <td>145.979996</td>\n",
       "      <td>47.209999</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39.259998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-01</th>\n",
       "      <td>39.435001</td>\n",
       "      <td>NaN</td>\n",
       "      <td>226.240005</td>\n",
       "      <td>85.830002</td>\n",
       "      <td>131.119995</td>\n",
       "      <td>128.610001</td>\n",
       "      <td>74.739998</td>\n",
       "      <td>209.429993</td>\n",
       "      <td>32.740002</td>\n",
       "      <td>18.459999</td>\n",
       "      <td>...</td>\n",
       "      <td>22.186666</td>\n",
       "      <td>94.500000</td>\n",
       "      <td>109.040001</td>\n",
       "      <td>148.289993</td>\n",
       "      <td>165.710007</td>\n",
       "      <td>68.330002</td>\n",
       "      <td>159.679993</td>\n",
       "      <td>49.270000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>49.680000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01</th>\n",
       "      <td>47.487499</td>\n",
       "      <td>NaN</td>\n",
       "      <td>266.489990</td>\n",
       "      <td>105.269997</td>\n",
       "      <td>159.740005</td>\n",
       "      <td>155.820007</td>\n",
       "      <td>83.750000</td>\n",
       "      <td>284.329987</td>\n",
       "      <td>39.660000</td>\n",
       "      <td>25.520000</td>\n",
       "      <td>...</td>\n",
       "      <td>18.657333</td>\n",
       "      <td>106.070000</td>\n",
       "      <td>133.000000</td>\n",
       "      <td>181.559998</td>\n",
       "      <td>183.949997</td>\n",
       "      <td>63.270000</td>\n",
       "      <td>192.850006</td>\n",
       "      <td>56.209999</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68.629997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-01</th>\n",
       "      <td>49.480000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>294.649994</td>\n",
       "      <td>112.870003</td>\n",
       "      <td>165.330002</td>\n",
       "      <td>162.899994</td>\n",
       "      <td>88.010002</td>\n",
       "      <td>273.700012</td>\n",
       "      <td>44.910000</td>\n",
       "      <td>30.370001</td>\n",
       "      <td>...</td>\n",
       "      <td>14.897333</td>\n",
       "      <td>114.760002</td>\n",
       "      <td>146.460007</td>\n",
       "      <td>209.160004</td>\n",
       "      <td>183.380005</td>\n",
       "      <td>54.669998</td>\n",
       "      <td>205.580002</td>\n",
       "      <td>59.490002</td>\n",
       "      <td>88.790001</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68.739998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-01</th>\n",
       "      <td>55.992500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>276.250000</td>\n",
       "      <td>111.730003</td>\n",
       "      <td>161.419998</td>\n",
       "      <td>147.699997</td>\n",
       "      <td>93.690002</td>\n",
       "      <td>180.919998</td>\n",
       "      <td>49.900002</td>\n",
       "      <td>28.990000</td>\n",
       "      <td>...</td>\n",
       "      <td>16.058001</td>\n",
       "      <td>129.240005</td>\n",
       "      <td>158.139999</td>\n",
       "      <td>188.630005</td>\n",
       "      <td>169.419998</td>\n",
       "      <td>55.310001</td>\n",
       "      <td>169.960007</td>\n",
       "      <td>64.889999</td>\n",
       "      <td>76.199997</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>52.130001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-01</th>\n",
       "      <td>73.412498</td>\n",
       "      <td>NaN</td>\n",
       "      <td>329.809998</td>\n",
       "      <td>118.839996</td>\n",
       "      <td>170.500000</td>\n",
       "      <td>183.460007</td>\n",
       "      <td>94.510002</td>\n",
       "      <td>279.040009</td>\n",
       "      <td>61.040001</td>\n",
       "      <td>45.860001</td>\n",
       "      <td>...</td>\n",
       "      <td>27.888666</td>\n",
       "      <td>128.289993</td>\n",
       "      <td>149.339996</td>\n",
       "      <td>192.679993</td>\n",
       "      <td>218.949997</td>\n",
       "      <td>58.959999</td>\n",
       "      <td>164.449997</td>\n",
       "      <td>63.490002</td>\n",
       "      <td>68.040001</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>51.990002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>63.572498</td>\n",
       "      <td>NaN</td>\n",
       "      <td>318.239990</td>\n",
       "      <td>89.650002</td>\n",
       "      <td>136.679993</td>\n",
       "      <td>156.100006</td>\n",
       "      <td>79.980003</td>\n",
       "      <td>173.949997</td>\n",
       "      <td>45.820000</td>\n",
       "      <td>45.480000</td>\n",
       "      <td>...</td>\n",
       "      <td>34.933334</td>\n",
       "      <td>99.930000</td>\n",
       "      <td>139.380005</td>\n",
       "      <td>180.089996</td>\n",
       "      <td>237.949997</td>\n",
       "      <td>45.750000</td>\n",
       "      <td>130.220001</td>\n",
       "      <td>60.299999</td>\n",
       "      <td>146.119995</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>98.089996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-01</th>\n",
       "      <td>91.199997</td>\n",
       "      <td>NaN</td>\n",
       "      <td>435.309998</td>\n",
       "      <td>122.639999</td>\n",
       "      <td>148.889999</td>\n",
       "      <td>239.190002</td>\n",
       "      <td>79.639999</td>\n",
       "      <td>274.440002</td>\n",
       "      <td>60.450001</td>\n",
       "      <td>52.610001</td>\n",
       "      <td>...</td>\n",
       "      <td>71.987335</td>\n",
       "      <td>126.970001</td>\n",
       "      <td>170.199997</td>\n",
       "      <td>206.830002</td>\n",
       "      <td>290.309998</td>\n",
       "      <td>42.389999</td>\n",
       "      <td>187.360001</td>\n",
       "      <td>62.500000</td>\n",
       "      <td>253.539993</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>143.339996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-01</th>\n",
       "      <td>115.809998</td>\n",
       "      <td>NaN</td>\n",
       "      <td>490.429993</td>\n",
       "      <td>116.739998</td>\n",
       "      <td>139.490005</td>\n",
       "      <td>231.009995</td>\n",
       "      <td>81.730003</td>\n",
       "      <td>327.359985</td>\n",
       "      <td>59.450001</td>\n",
       "      <td>81.989998</td>\n",
       "      <td>...</td>\n",
       "      <td>143.003326</td>\n",
       "      <td>142.789993</td>\n",
       "      <td>185.309998</td>\n",
       "      <td>204.850006</td>\n",
       "      <td>272.119995</td>\n",
       "      <td>35.919998</td>\n",
       "      <td>215.130005</td>\n",
       "      <td>69.010002</td>\n",
       "      <td>470.109985</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>155.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-01</th>\n",
       "      <td>132.690002</td>\n",
       "      <td>NaN</td>\n",
       "      <td>500.119995</td>\n",
       "      <td>147.729996</td>\n",
       "      <td>176.199997</td>\n",
       "      <td>305.339996</td>\n",
       "      <td>83.269997</td>\n",
       "      <td>534.380005</td>\n",
       "      <td>86.300003</td>\n",
       "      <td>91.709999</td>\n",
       "      <td>...</td>\n",
       "      <td>235.223328</td>\n",
       "      <td>164.130005</td>\n",
       "      <td>207.589996</td>\n",
       "      <td>216.399994</td>\n",
       "      <td>236.339996</td>\n",
       "      <td>39.880001</td>\n",
       "      <td>239.610001</td>\n",
       "      <td>66.669998</td>\n",
       "      <td>337.320007</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>206.350006</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>205.029999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-01</th>\n",
       "      <td>122.150002</td>\n",
       "      <td>NaN</td>\n",
       "      <td>475.369995</td>\n",
       "      <td>155.080002</td>\n",
       "      <td>188.470001</td>\n",
       "      <td>277.149994</td>\n",
       "      <td>84.699997</td>\n",
       "      <td>541.530029</td>\n",
       "      <td>133.600006</td>\n",
       "      <td>78.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>222.643326</td>\n",
       "      <td>188.990005</td>\n",
       "      <td>176.690002</td>\n",
       "      <td>198.759995</td>\n",
       "      <td>214.889999</td>\n",
       "      <td>54.900002</td>\n",
       "      <td>248.429993</td>\n",
       "      <td>66.510002</td>\n",
       "      <td>321.290009</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>140.399994</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>194.199997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-01</th>\n",
       "      <td>136.960007</td>\n",
       "      <td>NaN</td>\n",
       "      <td>585.640015</td>\n",
       "      <td>172.160004</td>\n",
       "      <td>198.619995</td>\n",
       "      <td>291.899994</td>\n",
       "      <td>84.589996</td>\n",
       "      <td>611.000000</td>\n",
       "      <td>142.399994</td>\n",
       "      <td>93.930000</td>\n",
       "      <td>...</td>\n",
       "      <td>226.566666</td>\n",
       "      <td>192.300003</td>\n",
       "      <td>174.720001</td>\n",
       "      <td>227.690002</td>\n",
       "      <td>201.630005</td>\n",
       "      <td>52.610001</td>\n",
       "      <td>238.740005</td>\n",
       "      <td>65.879997</td>\n",
       "      <td>387.029999</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>154.990005</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>278.339996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07-01</th>\n",
       "      <td>141.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>575.719971</td>\n",
       "      <td>167.479996</td>\n",
       "      <td>199.919998</td>\n",
       "      <td>285.170013</td>\n",
       "      <td>81.180000</td>\n",
       "      <td>665.429993</td>\n",
       "      <td>128.729996</td>\n",
       "      <td>102.900002</td>\n",
       "      <td>...</td>\n",
       "      <td>258.493347</td>\n",
       "      <td>192.210007</td>\n",
       "      <td>200.270004</td>\n",
       "      <td>205.009995</td>\n",
       "      <td>181.389999</td>\n",
       "      <td>47.049999</td>\n",
       "      <td>249.889999</td>\n",
       "      <td>62.500000</td>\n",
       "      <td>261.500000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>172.539993</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>346.970001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-01</th>\n",
       "      <td>177.570007</td>\n",
       "      <td>NaN</td>\n",
       "      <td>567.059998</td>\n",
       "      <td>175.770004</td>\n",
       "      <td>246.580002</td>\n",
       "      <td>281.190002</td>\n",
       "      <td>88.970001</td>\n",
       "      <td>657.179993</td>\n",
       "      <td>157.360001</td>\n",
       "      <td>143.899994</td>\n",
       "      <td>...</td>\n",
       "      <td>352.260010</td>\n",
       "      <td>188.470001</td>\n",
       "      <td>228.729996</td>\n",
       "      <td>253.820007</td>\n",
       "      <td>219.600006</td>\n",
       "      <td>52.160000</td>\n",
       "      <td>273.179993</td>\n",
       "      <td>67.699997</td>\n",
       "      <td>183.910004</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>151.490005</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>239.149994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-01</th>\n",
       "      <td>174.610001</td>\n",
       "      <td>NaN</td>\n",
       "      <td>455.619995</td>\n",
       "      <td>165.179993</td>\n",
       "      <td>227.539993</td>\n",
       "      <td>214.350006</td>\n",
       "      <td>99.769997</td>\n",
       "      <td>436.000000</td>\n",
       "      <td>131.800003</td>\n",
       "      <td>109.339996</td>\n",
       "      <td>...</td>\n",
       "      <td>359.200012</td>\n",
       "      <td>183.479996</td>\n",
       "      <td>214.630005</td>\n",
       "      <td>222.460007</td>\n",
       "      <td>260.970001</td>\n",
       "      <td>44.770000</td>\n",
       "      <td>239.460007</td>\n",
       "      <td>72.169998</td>\n",
       "      <td>117.230003</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>120.870003</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>153.089996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-01</th>\n",
       "      <td>136.720001</td>\n",
       "      <td>NaN</td>\n",
       "      <td>366.059998</td>\n",
       "      <td>146.089996</td>\n",
       "      <td>210.039993</td>\n",
       "      <td>171.960007</td>\n",
       "      <td>95.940002</td>\n",
       "      <td>236.669998</td>\n",
       "      <td>90.980003</td>\n",
       "      <td>76.470001</td>\n",
       "      <td>...</td>\n",
       "      <td>224.473328</td>\n",
       "      <td>153.649994</td>\n",
       "      <td>173.089996</td>\n",
       "      <td>167.330002</td>\n",
       "      <td>281.790009</td>\n",
       "      <td>37.900002</td>\n",
       "      <td>139.580002</td>\n",
       "      <td>70.760002</td>\n",
       "      <td>107.970001</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-06-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>113.120003</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>159.240005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-01</th>\n",
       "      <td>138.199997</td>\n",
       "      <td>NaN</td>\n",
       "      <td>275.200012</td>\n",
       "      <td>139.339996</td>\n",
       "      <td>226.190002</td>\n",
       "      <td>186.800003</td>\n",
       "      <td>86.449997</td>\n",
       "      <td>207.110001</td>\n",
       "      <td>81.930000</td>\n",
       "      <td>63.360001</td>\n",
       "      <td>...</td>\n",
       "      <td>265.250000</td>\n",
       "      <td>154.779999</td>\n",
       "      <td>170.529999</td>\n",
       "      <td>173.699997</td>\n",
       "      <td>289.540009</td>\n",
       "      <td>31.400000</td>\n",
       "      <td>152.220001</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>73.589996</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>102.139999</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>133.449997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-01</th>\n",
       "      <td>129.929993</td>\n",
       "      <td>NaN</td>\n",
       "      <td>336.529999</td>\n",
       "      <td>164.029999</td>\n",
       "      <td>238.860001</td>\n",
       "      <td>186.869995</td>\n",
       "      <td>94.949997</td>\n",
       "      <td>210.899994</td>\n",
       "      <td>97.379997</td>\n",
       "      <td>64.769997</td>\n",
       "      <td>...</td>\n",
       "      <td>123.180000</td>\n",
       "      <td>165.220001</td>\n",
       "      <td>176.419998</td>\n",
       "      <td>205.440002</td>\n",
       "      <td>288.779999</td>\n",
       "      <td>37.360001</td>\n",
       "      <td>167.330002</td>\n",
       "      <td>70.110001</td>\n",
       "      <td>67.739998</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>123.279999</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>131.149994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-01</th>\n",
       "      <td>164.899994</td>\n",
       "      <td>NaN</td>\n",
       "      <td>385.369995</td>\n",
       "      <td>197.220001</td>\n",
       "      <td>222.630005</td>\n",
       "      <td>208.160004</td>\n",
       "      <td>90.989998</td>\n",
       "      <td>334.140015</td>\n",
       "      <td>122.830002</td>\n",
       "      <td>98.010002</td>\n",
       "      <td>...</td>\n",
       "      <td>207.460007</td>\n",
       "      <td>186.009995</td>\n",
       "      <td>191.860001</td>\n",
       "      <td>211.330002</td>\n",
       "      <td>315.070007</td>\n",
       "      <td>34.580002</td>\n",
       "      <td>206.539993</td>\n",
       "      <td>67.440002</td>\n",
       "      <td>73.839996</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-03-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>119.669998</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>90.099998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-04-01</th>\n",
       "      <td>169.679993</td>\n",
       "      <td>NaN</td>\n",
       "      <td>377.559998</td>\n",
       "      <td>179.880005</td>\n",
       "      <td>220.000000</td>\n",
       "      <td>194.789993</td>\n",
       "      <td>92.419998</td>\n",
       "      <td>325.299988</td>\n",
       "      <td>113.029999</td>\n",
       "      <td>89.370003</td>\n",
       "      <td>...</td>\n",
       "      <td>164.309998</td>\n",
       "      <td>167.199997</td>\n",
       "      <td>194.110001</td>\n",
       "      <td>221.800003</td>\n",
       "      <td>340.730011</td>\n",
       "      <td>35.250000</td>\n",
       "      <td>186.139999</td>\n",
       "      <td>69.910004</td>\n",
       "      <td>61.430000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>43 rows × 102 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  AAPL        ABNB        ADBE         ADI         ADP  \\\n",
       "Date                                                                     \n",
       "2018-01-01   41.945000         NaN  216.080002   91.129997  113.480003   \n",
       "2018-03-01         NaN         NaN         NaN         NaN         NaN   \n",
       "2018-04-01   46.277500         NaN  243.809998   95.919998  134.139999   \n",
       "2018-06-01         NaN         NaN         NaN         NaN         NaN   \n",
       "2018-07-01   56.435001         NaN  269.950012   92.459999  150.660004   \n",
       "2018-09-01         NaN         NaN         NaN         NaN         NaN   \n",
       "2018-10-01   39.435001         NaN  226.240005   85.830002  131.119995   \n",
       "2018-12-01         NaN         NaN         NaN         NaN         NaN   \n",
       "2019-01-01   47.487499         NaN  266.489990  105.269997  159.740005   \n",
       "2019-03-01         NaN         NaN         NaN         NaN         NaN   \n",
       "2019-04-01   49.480000         NaN  294.649994  112.870003  165.330002   \n",
       "2019-06-01         NaN         NaN         NaN         NaN         NaN   \n",
       "2019-07-01   55.992500         NaN  276.250000  111.730003  161.419998   \n",
       "2019-09-01         NaN         NaN         NaN         NaN         NaN   \n",
       "2019-10-01   73.412498         NaN  329.809998  118.839996  170.500000   \n",
       "2019-12-01         NaN         NaN         NaN         NaN         NaN   \n",
       "2020-01-01   63.572498         NaN  318.239990   89.650002  136.679993   \n",
       "2020-03-01         NaN         NaN         NaN         NaN         NaN   \n",
       "2020-04-01   91.199997         NaN  435.309998  122.639999  148.889999   \n",
       "2020-06-01         NaN         NaN         NaN         NaN         NaN   \n",
       "2020-07-01  115.809998         NaN  490.429993  116.739998  139.490005   \n",
       "2020-09-01         NaN         NaN         NaN         NaN         NaN   \n",
       "2020-10-01  132.690002         NaN  500.119995  147.729996  176.199997   \n",
       "2020-12-01         NaN  206.350006         NaN         NaN         NaN   \n",
       "2021-01-01  122.150002         NaN  475.369995  155.080002  188.470001   \n",
       "2021-03-01         NaN  140.399994         NaN         NaN         NaN   \n",
       "2021-04-01  136.960007         NaN  585.640015  172.160004  198.619995   \n",
       "2021-06-01         NaN  154.990005         NaN         NaN         NaN   \n",
       "2021-07-01  141.500000         NaN  575.719971  167.479996  199.919998   \n",
       "2021-09-01         NaN  172.539993         NaN         NaN         NaN   \n",
       "2021-10-01  177.570007         NaN  567.059998  175.770004  246.580002   \n",
       "2021-12-01         NaN  151.490005         NaN         NaN         NaN   \n",
       "2022-01-01  174.610001         NaN  455.619995  165.179993  227.539993   \n",
       "2022-03-01         NaN  120.870003         NaN         NaN         NaN   \n",
       "2022-04-01  136.720001         NaN  366.059998  146.089996  210.039993   \n",
       "2022-06-01         NaN  113.120003         NaN         NaN         NaN   \n",
       "2022-07-01  138.199997         NaN  275.200012  139.339996  226.190002   \n",
       "2022-09-01         NaN  102.139999         NaN         NaN         NaN   \n",
       "2022-10-01  129.929993         NaN  336.529999  164.029999  238.860001   \n",
       "2022-12-01         NaN  123.279999         NaN         NaN         NaN   \n",
       "2023-01-01  164.899994         NaN  385.369995  197.220001  222.630005   \n",
       "2023-03-01         NaN  119.669998         NaN         NaN         NaN   \n",
       "2023-04-01  169.679993         NaN  377.559998  179.880005  220.000000   \n",
       "\n",
       "                  ADSK        AEP        ALGN        AMAT         AMD  ...  \\\n",
       "Date                                                                   ...   \n",
       "2018-01-01  125.580002  68.589996  251.130005   55.610001   10.050000  ...   \n",
       "2018-03-01         NaN        NaN         NaN         NaN         NaN  ...   \n",
       "2018-04-01  131.089996  69.250000  342.140015   46.189999   14.990000  ...   \n",
       "2018-06-01         NaN        NaN         NaN         NaN         NaN  ...   \n",
       "2018-07-01  156.110001  70.879997  391.220001   38.650002   30.889999  ...   \n",
       "2018-09-01         NaN        NaN         NaN         NaN         NaN  ...   \n",
       "2018-10-01  128.610001  74.739998  209.429993   32.740002   18.459999  ...   \n",
       "2018-12-01         NaN        NaN         NaN         NaN         NaN  ...   \n",
       "2019-01-01  155.820007  83.750000  284.329987   39.660000   25.520000  ...   \n",
       "2019-03-01         NaN        NaN         NaN         NaN         NaN  ...   \n",
       "2019-04-01  162.899994  88.010002  273.700012   44.910000   30.370001  ...   \n",
       "2019-06-01         NaN        NaN         NaN         NaN         NaN  ...   \n",
       "2019-07-01  147.699997  93.690002  180.919998   49.900002   28.990000  ...   \n",
       "2019-09-01         NaN        NaN         NaN         NaN         NaN  ...   \n",
       "2019-10-01  183.460007  94.510002  279.040009   61.040001   45.860001  ...   \n",
       "2019-12-01         NaN        NaN         NaN         NaN         NaN  ...   \n",
       "2020-01-01  156.100006  79.980003  173.949997   45.820000   45.480000  ...   \n",
       "2020-03-01         NaN        NaN         NaN         NaN         NaN  ...   \n",
       "2020-04-01  239.190002  79.639999  274.440002   60.450001   52.610001  ...   \n",
       "2020-06-01         NaN        NaN         NaN         NaN         NaN  ...   \n",
       "2020-07-01  231.009995  81.730003  327.359985   59.450001   81.989998  ...   \n",
       "2020-09-01         NaN        NaN         NaN         NaN         NaN  ...   \n",
       "2020-10-01  305.339996  83.269997  534.380005   86.300003   91.709999  ...   \n",
       "2020-12-01         NaN        NaN         NaN         NaN         NaN  ...   \n",
       "2021-01-01  277.149994  84.699997  541.530029  133.600006   78.500000  ...   \n",
       "2021-03-01         NaN        NaN         NaN         NaN         NaN  ...   \n",
       "2021-04-01  291.899994  84.589996  611.000000  142.399994   93.930000  ...   \n",
       "2021-06-01         NaN        NaN         NaN         NaN         NaN  ...   \n",
       "2021-07-01  285.170013  81.180000  665.429993  128.729996  102.900002  ...   \n",
       "2021-09-01         NaN        NaN         NaN         NaN         NaN  ...   \n",
       "2021-10-01  281.190002  88.970001  657.179993  157.360001  143.899994  ...   \n",
       "2021-12-01         NaN        NaN         NaN         NaN         NaN  ...   \n",
       "2022-01-01  214.350006  99.769997  436.000000  131.800003  109.339996  ...   \n",
       "2022-03-01         NaN        NaN         NaN         NaN         NaN  ...   \n",
       "2022-04-01  171.960007  95.940002  236.669998   90.980003   76.470001  ...   \n",
       "2022-06-01         NaN        NaN         NaN         NaN         NaN  ...   \n",
       "2022-07-01  186.800003  86.449997  207.110001   81.930000   63.360001  ...   \n",
       "2022-09-01         NaN        NaN         NaN         NaN         NaN  ...   \n",
       "2022-10-01  186.869995  94.949997  210.899994   97.379997   64.769997  ...   \n",
       "2022-12-01         NaN        NaN         NaN         NaN         NaN  ...   \n",
       "2023-01-01  208.160004  90.989998  334.140015  122.830002   98.010002  ...   \n",
       "2023-03-01         NaN        NaN         NaN         NaN         NaN  ...   \n",
       "2023-04-01  194.789993  92.419998  325.299988  113.029999   89.370003  ...   \n",
       "\n",
       "                  TSLA         TXN        VRSK        VRSN        VRTX  \\\n",
       "Date                                                                     \n",
       "2018-01-01   17.742001  103.889999  104.000000  118.559998  162.979996   \n",
       "2018-03-01         NaN         NaN         NaN         NaN         NaN   \n",
       "2018-04-01   22.863333  110.250000  107.639999  137.419998  169.960007   \n",
       "2018-06-01         NaN         NaN         NaN         NaN         NaN   \n",
       "2018-07-01   17.651333  107.290001  120.550003  160.119995  192.740005   \n",
       "2018-09-01         NaN         NaN         NaN         NaN         NaN   \n",
       "2018-10-01   22.186666   94.500000  109.040001  148.289993  165.710007   \n",
       "2018-12-01         NaN         NaN         NaN         NaN         NaN   \n",
       "2019-01-01   18.657333  106.070000  133.000000  181.559998  183.949997   \n",
       "2019-03-01         NaN         NaN         NaN         NaN         NaN   \n",
       "2019-04-01   14.897333  114.760002  146.460007  209.160004  183.380005   \n",
       "2019-06-01         NaN         NaN         NaN         NaN         NaN   \n",
       "2019-07-01   16.058001  129.240005  158.139999  188.630005  169.419998   \n",
       "2019-09-01         NaN         NaN         NaN         NaN         NaN   \n",
       "2019-10-01   27.888666  128.289993  149.339996  192.679993  218.949997   \n",
       "2019-12-01         NaN         NaN         NaN         NaN         NaN   \n",
       "2020-01-01   34.933334   99.930000  139.380005  180.089996  237.949997   \n",
       "2020-03-01         NaN         NaN         NaN         NaN         NaN   \n",
       "2020-04-01   71.987335  126.970001  170.199997  206.830002  290.309998   \n",
       "2020-06-01         NaN         NaN         NaN         NaN         NaN   \n",
       "2020-07-01  143.003326  142.789993  185.309998  204.850006  272.119995   \n",
       "2020-09-01         NaN         NaN         NaN         NaN         NaN   \n",
       "2020-10-01  235.223328  164.130005  207.589996  216.399994  236.339996   \n",
       "2020-12-01         NaN         NaN         NaN         NaN         NaN   \n",
       "2021-01-01  222.643326  188.990005  176.690002  198.759995  214.889999   \n",
       "2021-03-01         NaN         NaN         NaN         NaN         NaN   \n",
       "2021-04-01  226.566666  192.300003  174.720001  227.690002  201.630005   \n",
       "2021-06-01         NaN         NaN         NaN         NaN         NaN   \n",
       "2021-07-01  258.493347  192.210007  200.270004  205.009995  181.389999   \n",
       "2021-09-01         NaN         NaN         NaN         NaN         NaN   \n",
       "2021-10-01  352.260010  188.470001  228.729996  253.820007  219.600006   \n",
       "2021-12-01         NaN         NaN         NaN         NaN         NaN   \n",
       "2022-01-01  359.200012  183.479996  214.630005  222.460007  260.970001   \n",
       "2022-03-01         NaN         NaN         NaN         NaN         NaN   \n",
       "2022-04-01  224.473328  153.649994  173.089996  167.330002  281.790009   \n",
       "2022-06-01         NaN         NaN         NaN         NaN         NaN   \n",
       "2022-07-01  265.250000  154.779999  170.529999  173.699997  289.540009   \n",
       "2022-09-01         NaN         NaN         NaN         NaN         NaN   \n",
       "2022-10-01  123.180000  165.220001  176.419998  205.440002  288.779999   \n",
       "2022-12-01         NaN         NaN         NaN         NaN         NaN   \n",
       "2023-01-01  207.460007  186.009995  191.860001  211.330002  315.070007   \n",
       "2023-03-01         NaN         NaN         NaN         NaN         NaN   \n",
       "2023-04-01  164.309998  167.199997  194.110001  221.800003  340.730011   \n",
       "\n",
       "                  WBA        WDAY        XEL          ZM          ZS  \n",
       "Date                                                                  \n",
       "2018-01-01  65.470001  127.110001  45.480000         NaN         NaN  \n",
       "2018-03-01        NaN         NaN        NaN         NaN   26.260000  \n",
       "2018-04-01  60.020000  121.120003  45.680000         NaN         NaN  \n",
       "2018-06-01        NaN         NaN        NaN         NaN   42.790001  \n",
       "2018-07-01  72.900002  145.979996  47.209999         NaN         NaN  \n",
       "2018-09-01        NaN         NaN        NaN         NaN   39.259998  \n",
       "2018-10-01  68.330002  159.679993  49.270000         NaN         NaN  \n",
       "2018-12-01        NaN         NaN        NaN         NaN   49.680000  \n",
       "2019-01-01  63.270000  192.850006  56.209999         NaN         NaN  \n",
       "2019-03-01        NaN         NaN        NaN         NaN   68.629997  \n",
       "2019-04-01  54.669998  205.580002  59.490002   88.790001         NaN  \n",
       "2019-06-01        NaN         NaN        NaN         NaN   68.739998  \n",
       "2019-07-01  55.310001  169.960007  64.889999   76.199997         NaN  \n",
       "2019-09-01        NaN         NaN        NaN         NaN   52.130001  \n",
       "2019-10-01  58.959999  164.449997  63.490002   68.040001         NaN  \n",
       "2019-12-01        NaN         NaN        NaN         NaN   51.990002  \n",
       "2020-01-01  45.750000  130.220001  60.299999  146.119995         NaN  \n",
       "2020-03-01        NaN         NaN        NaN         NaN   98.089996  \n",
       "2020-04-01  42.389999  187.360001  62.500000  253.539993         NaN  \n",
       "2020-06-01        NaN         NaN        NaN         NaN  143.339996  \n",
       "2020-07-01  35.919998  215.130005  69.010002  470.109985         NaN  \n",
       "2020-09-01        NaN         NaN        NaN         NaN  155.750000  \n",
       "2020-10-01  39.880001  239.610001  66.669998  337.320007         NaN  \n",
       "2020-12-01        NaN         NaN        NaN         NaN  205.029999  \n",
       "2021-01-01  54.900002  248.429993  66.510002  321.290009         NaN  \n",
       "2021-03-01        NaN         NaN        NaN         NaN  194.199997  \n",
       "2021-04-01  52.610001  238.740005  65.879997  387.029999         NaN  \n",
       "2021-06-01        NaN         NaN        NaN         NaN  278.339996  \n",
       "2021-07-01  47.049999  249.889999  62.500000  261.500000         NaN  \n",
       "2021-09-01        NaN         NaN        NaN         NaN  346.970001  \n",
       "2021-10-01  52.160000  273.179993  67.699997  183.910004         NaN  \n",
       "2021-12-01        NaN         NaN        NaN         NaN  239.149994  \n",
       "2022-01-01  44.770000  239.460007  72.169998  117.230003         NaN  \n",
       "2022-03-01        NaN         NaN        NaN         NaN  153.089996  \n",
       "2022-04-01  37.900002  139.580002  70.760002  107.970001         NaN  \n",
       "2022-06-01        NaN         NaN        NaN         NaN  159.240005  \n",
       "2022-07-01  31.400000  152.220001  64.000000   73.589996         NaN  \n",
       "2022-09-01        NaN         NaN        NaN         NaN  133.449997  \n",
       "2022-10-01  37.360001  167.330002  70.110001   67.739998         NaN  \n",
       "2022-12-01        NaN         NaN        NaN         NaN  131.149994  \n",
       "2023-01-01  34.580002  206.539993  67.440002   73.839996         NaN  \n",
       "2023-03-01        NaN         NaN        NaN         NaN   90.099998  \n",
       "2023-04-01  35.250000  186.139999  69.910004   61.430000         NaN  \n",
       "\n",
       "[43 rows x 102 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_price = data.iloc[0:103]\n",
    "\n",
    "data_price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "605e14ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(43, 102)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_price.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "132cd6c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Date</th>\n",
       "      <th>2018-01-01</th>\n",
       "      <th>2018-03-01</th>\n",
       "      <th>2018-04-01</th>\n",
       "      <th>2018-06-01</th>\n",
       "      <th>2018-07-01</th>\n",
       "      <th>2018-09-01</th>\n",
       "      <th>2018-10-01</th>\n",
       "      <th>2018-12-01</th>\n",
       "      <th>2019-01-01</th>\n",
       "      <th>2019-03-01</th>\n",
       "      <th>...</th>\n",
       "      <th>2022-03-01</th>\n",
       "      <th>2022-04-01</th>\n",
       "      <th>2022-06-01</th>\n",
       "      <th>2022-07-01</th>\n",
       "      <th>2022-09-01</th>\n",
       "      <th>2022-10-01</th>\n",
       "      <th>2022-12-01</th>\n",
       "      <th>2023-01-01</th>\n",
       "      <th>2023-03-01</th>\n",
       "      <th>2023-04-01</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AAPL</th>\n",
       "      <td>41.945000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>46.277500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>56.435001</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39.435001</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.487499</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>136.720001</td>\n",
       "      <td>NaN</td>\n",
       "      <td>138.199997</td>\n",
       "      <td>NaN</td>\n",
       "      <td>129.929993</td>\n",
       "      <td>NaN</td>\n",
       "      <td>164.899994</td>\n",
       "      <td>NaN</td>\n",
       "      <td>169.679993</td>\n",
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       "    <tr>\n",
       "      <th>ABNB</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>120.870003</td>\n",
       "      <td>NaN</td>\n",
       "      <td>113.120003</td>\n",
       "      <td>NaN</td>\n",
       "      <td>102.139999</td>\n",
       "      <td>NaN</td>\n",
       "      <td>123.279999</td>\n",
       "      <td>NaN</td>\n",
       "      <td>119.669998</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>ADBE</th>\n",
       "      <td>216.080002</td>\n",
       "      <td>NaN</td>\n",
       "      <td>243.809998</td>\n",
       "      <td>NaN</td>\n",
       "      <td>269.950012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>226.240005</td>\n",
       "      <td>NaN</td>\n",
       "      <td>266.489990</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>366.059998</td>\n",
       "      <td>NaN</td>\n",
       "      <td>275.200012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>336.529999</td>\n",
       "      <td>NaN</td>\n",
       "      <td>385.369995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>377.559998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADI</th>\n",
       "      <td>91.129997</td>\n",
       "      <td>NaN</td>\n",
       "      <td>95.919998</td>\n",
       "      <td>NaN</td>\n",
       "      <td>92.459999</td>\n",
       "      <td>NaN</td>\n",
       "      <td>85.830002</td>\n",
       "      <td>NaN</td>\n",
       "      <td>105.269997</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>146.089996</td>\n",
       "      <td>NaN</td>\n",
       "      <td>139.339996</td>\n",
       "      <td>NaN</td>\n",
       "      <td>164.029999</td>\n",
       "      <td>NaN</td>\n",
       "      <td>197.220001</td>\n",
       "      <td>NaN</td>\n",
       "      <td>179.880005</td>\n",
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       "    <tr>\n",
       "      <th>ADP</th>\n",
       "      <td>113.480003</td>\n",
       "      <td>NaN</td>\n",
       "      <td>134.139999</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.660004</td>\n",
       "      <td>NaN</td>\n",
       "      <td>131.119995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>159.740005</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>210.039993</td>\n",
       "      <td>NaN</td>\n",
       "      <td>226.190002</td>\n",
       "      <td>NaN</td>\n",
       "      <td>238.860001</td>\n",
       "      <td>NaN</td>\n",
       "      <td>222.630005</td>\n",
       "      <td>NaN</td>\n",
       "      <td>220.000000</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>WBA</th>\n",
       "      <td>65.470001</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60.020000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>72.900002</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68.330002</td>\n",
       "      <td>NaN</td>\n",
       "      <td>63.270000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.900002</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31.400000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.360001</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.580002</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WDAY</th>\n",
       "      <td>127.110001</td>\n",
       "      <td>NaN</td>\n",
       "      <td>121.120003</td>\n",
       "      <td>NaN</td>\n",
       "      <td>145.979996</td>\n",
       "      <td>NaN</td>\n",
       "      <td>159.679993</td>\n",
       "      <td>NaN</td>\n",
       "      <td>192.850006</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>139.580002</td>\n",
       "      <td>NaN</td>\n",
       "      <td>152.220001</td>\n",
       "      <td>NaN</td>\n",
       "      <td>167.330002</td>\n",
       "      <td>NaN</td>\n",
       "      <td>206.539993</td>\n",
       "      <td>NaN</td>\n",
       "      <td>186.139999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XEL</th>\n",
       "      <td>45.480000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>45.680000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.209999</td>\n",
       "      <td>NaN</td>\n",
       "      <td>49.270000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>56.209999</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.760002</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.110001</td>\n",
       "      <td>NaN</td>\n",
       "      <td>67.440002</td>\n",
       "      <td>NaN</td>\n",
       "      <td>69.910004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZM</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>107.970001</td>\n",
       "      <td>NaN</td>\n",
       "      <td>73.589996</td>\n",
       "      <td>NaN</td>\n",
       "      <td>67.739998</td>\n",
       "      <td>NaN</td>\n",
       "      <td>73.839996</td>\n",
       "      <td>NaN</td>\n",
       "      <td>61.430000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZS</th>\n",
       "      <td>NaN</td>\n",
       "      <td>26.26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42.790001</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39.259998</td>\n",
       "      <td>NaN</td>\n",
       "      <td>49.68</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68.629997</td>\n",
       "      <td>...</td>\n",
       "      <td>153.089996</td>\n",
       "      <td>NaN</td>\n",
       "      <td>159.240005</td>\n",
       "      <td>NaN</td>\n",
       "      <td>133.449997</td>\n",
       "      <td>NaN</td>\n",
       "      <td>131.149994</td>\n",
       "      <td>NaN</td>\n",
       "      <td>90.099998</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>102 rows × 43 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Date  2018-01-01  2018-03-01  2018-04-01  2018-06-01  2018-07-01  2018-09-01  \\\n",
       "AAPL   41.945000         NaN   46.277500         NaN   56.435001         NaN   \n",
       "ABNB         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "ADBE  216.080002         NaN  243.809998         NaN  269.950012         NaN   \n",
       "ADI    91.129997         NaN   95.919998         NaN   92.459999         NaN   \n",
       "ADP   113.480003         NaN  134.139999         NaN  150.660004         NaN   \n",
       "...          ...         ...         ...         ...         ...         ...   \n",
       "WBA    65.470001         NaN   60.020000         NaN   72.900002         NaN   \n",
       "WDAY  127.110001         NaN  121.120003         NaN  145.979996         NaN   \n",
       "XEL    45.480000         NaN   45.680000         NaN   47.209999         NaN   \n",
       "ZM           NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "ZS           NaN       26.26         NaN   42.790001         NaN   39.259998   \n",
       "\n",
       "Date  2018-10-01  2018-12-01  2019-01-01  2019-03-01  ...  2022-03-01  \\\n",
       "AAPL   39.435001         NaN   47.487499         NaN  ...         NaN   \n",
       "ABNB         NaN         NaN         NaN         NaN  ...  120.870003   \n",
       "ADBE  226.240005         NaN  266.489990         NaN  ...         NaN   \n",
       "ADI    85.830002         NaN  105.269997         NaN  ...         NaN   \n",
       "ADP   131.119995         NaN  159.740005         NaN  ...         NaN   \n",
       "...          ...         ...         ...         ...  ...         ...   \n",
       "WBA    68.330002         NaN   63.270000         NaN  ...         NaN   \n",
       "WDAY  159.679993         NaN  192.850006         NaN  ...         NaN   \n",
       "XEL    49.270000         NaN   56.209999         NaN  ...         NaN   \n",
       "ZM           NaN         NaN         NaN         NaN  ...         NaN   \n",
       "ZS           NaN       49.68         NaN   68.629997  ...  153.089996   \n",
       "\n",
       "Date  2022-04-01  2022-06-01  2022-07-01  2022-09-01  2022-10-01  2022-12-01  \\\n",
       "AAPL  136.720001         NaN  138.199997         NaN  129.929993         NaN   \n",
       "ABNB         NaN  113.120003         NaN  102.139999         NaN  123.279999   \n",
       "ADBE  366.059998         NaN  275.200012         NaN  336.529999         NaN   \n",
       "ADI   146.089996         NaN  139.339996         NaN  164.029999         NaN   \n",
       "ADP   210.039993         NaN  226.190002         NaN  238.860001         NaN   \n",
       "...          ...         ...         ...         ...         ...         ...   \n",
       "WBA    37.900002         NaN   31.400000         NaN   37.360001         NaN   \n",
       "WDAY  139.580002         NaN  152.220001         NaN  167.330002         NaN   \n",
       "XEL    70.760002         NaN   64.000000         NaN   70.110001         NaN   \n",
       "ZM    107.970001         NaN   73.589996         NaN   67.739998         NaN   \n",
       "ZS           NaN  159.240005         NaN  133.449997         NaN  131.149994   \n",
       "\n",
       "Date  2023-01-01  2023-03-01  2023-04-01  \n",
       "AAPL  164.899994         NaN  169.679993  \n",
       "ABNB         NaN  119.669998         NaN  \n",
       "ADBE  385.369995         NaN  377.559998  \n",
       "ADI   197.220001         NaN  179.880005  \n",
       "ADP   222.630005         NaN  220.000000  \n",
       "...          ...         ...         ...  \n",
       "WBA    34.580002         NaN   35.250000  \n",
       "WDAY  206.539993         NaN  186.139999  \n",
       "XEL    67.440002         NaN   69.910004  \n",
       "ZM     73.839996         NaN   61.430000  \n",
       "ZS           NaN   90.099998         NaN  \n",
       "\n",
       "[102 rows x 43 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_tran = data_price.T\n",
    "data_tran"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "aaf6509d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Date</th>\n",
       "      <th>2018-01-01</th>\n",
       "      <th>2019-01-01</th>\n",
       "      <th>2020-01-01</th>\n",
       "      <th>2021-01-01</th>\n",
       "      <th>2022-01-01</th>\n",
       "      <th>2023-01-01</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AAPL</th>\n",
       "      <td>41.945000</td>\n",
       "      <td>47.487499</td>\n",
       "      <td>63.572498</td>\n",
       "      <td>122.150002</td>\n",
       "      <td>174.610001</td>\n",
       "      <td>164.899994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABNB</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADBE</th>\n",
       "      <td>216.080002</td>\n",
       "      <td>266.489990</td>\n",
       "      <td>318.239990</td>\n",
       "      <td>475.369995</td>\n",
       "      <td>455.619995</td>\n",
       "      <td>385.369995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADI</th>\n",
       "      <td>91.129997</td>\n",
       "      <td>105.269997</td>\n",
       "      <td>89.650002</td>\n",
       "      <td>155.080002</td>\n",
       "      <td>165.179993</td>\n",
       "      <td>197.220001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADP</th>\n",
       "      <td>113.480003</td>\n",
       "      <td>159.740005</td>\n",
       "      <td>136.679993</td>\n",
       "      <td>188.470001</td>\n",
       "      <td>227.539993</td>\n",
       "      <td>222.630005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WBA</th>\n",
       "      <td>65.470001</td>\n",
       "      <td>63.270000</td>\n",
       "      <td>45.750000</td>\n",
       "      <td>54.900002</td>\n",
       "      <td>44.770000</td>\n",
       "      <td>34.580002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WDAY</th>\n",
       "      <td>127.110001</td>\n",
       "      <td>192.850006</td>\n",
       "      <td>130.220001</td>\n",
       "      <td>248.429993</td>\n",
       "      <td>239.460007</td>\n",
       "      <td>206.539993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XEL</th>\n",
       "      <td>45.480000</td>\n",
       "      <td>56.209999</td>\n",
       "      <td>60.299999</td>\n",
       "      <td>66.510002</td>\n",
       "      <td>72.169998</td>\n",
       "      <td>67.440002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZM</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>146.119995</td>\n",
       "      <td>321.290009</td>\n",
       "      <td>117.230003</td>\n",
       "      <td>73.839996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZS</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>102 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Date  2018-01-01  2019-01-01  2020-01-01  2021-01-01  2022-01-01  2023-01-01\n",
       "AAPL   41.945000   47.487499   63.572498  122.150002  174.610001  164.899994\n",
       "ABNB         NaN         NaN         NaN         NaN         NaN         NaN\n",
       "ADBE  216.080002  266.489990  318.239990  475.369995  455.619995  385.369995\n",
       "ADI    91.129997  105.269997   89.650002  155.080002  165.179993  197.220001\n",
       "ADP   113.480003  159.740005  136.679993  188.470001  227.539993  222.630005\n",
       "...          ...         ...         ...         ...         ...         ...\n",
       "WBA    65.470001   63.270000   45.750000   54.900002   44.770000   34.580002\n",
       "WDAY  127.110001  192.850006  130.220001  248.429993  239.460007  206.539993\n",
       "XEL    45.480000   56.209999   60.299999   66.510002   72.169998   67.440002\n",
       "ZM           NaN         NaN  146.119995  321.290009  117.230003   73.839996\n",
       "ZS           NaN         NaN         NaN         NaN         NaN         NaN\n",
       "\n",
       "[102 rows x 6 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_transposed = data_tran.loc[:,['2018-01-01', '2019-01-01', '2020-01-01', '2021-01-01', '2022-01-01', '2023-01-01']]\n",
    "data_transposed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e3790c57",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "b901b26a",
   "metadata": {},
   "source": [
    "# "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f7a07d1",
   "metadata": {},
   "source": [
    "# Methodology: At this junction we undertake some manual work in Excel. \n",
    "\n",
    "We replace NANs with actual data that we can source from (www.Nasdaq.com) and we hardcode into the dataset.\n",
    "\n",
    "We also calculate annual returns.   \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5bf361af",
   "metadata": {},
   "source": [
    "# "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "c501b758",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_transposed.iloc[1,3] = 146.8\n",
    "data_transposed.iloc[1,4] = 166.49\n",
    "data_transposed.iloc[1,5] = 85.65\n",
    "\n",
    "data_transposed.iloc[26,2] = 49.87\n",
    "data_transposed.iloc[26,3] = 211.82\n",
    "data_transposed.iloc[26,4] = 204.75\n",
    "data_transposed.iloc[26,5] = 105.29\n",
    "\n",
    "data_transposed.iloc[31,2] = 37.78\n",
    "data_transposed.iloc[31,3] = 98.44\n",
    "data_transposed.iloc[31,4] = 178.11\n",
    "data_transposed.iloc[31,5] = 73.5\n",
    "\n",
    "data_transposed.iloc[33,1] = 29.695\n",
    "\n",
    "data_transposed.iloc[54,3] = 10.01\n",
    "data_transposed.iloc[54,4] = 38.05\n",
    "data_transposed.iloc[54,5] = 6.83\n",
    "\n",
    "data_transposed.iloc[63,1] = 15.27\n",
    "data_transposed.iloc[63,2] = 19.56\n",
    "data_transposed.iloc[63,3] = 104.47\n",
    "data_transposed.iloc[63,4] = 253.98\n",
    "data_transposed.iloc[63,5] = 179.62\n",
    "\n",
    "data_transposed.iloc[78,0] = 68.08\n",
    "\n",
    "data_transposed.iloc[101,1] = 39.21\n",
    "data_transposed.iloc[101,2] = 46.5\n",
    "data_transposed.iloc[101,3] = 199.71\n",
    "data_transposed.iloc[101,4] = 321.33\n",
    "data_transposed.iloc[101,5] = 111.9\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "raw",
   "id": "1da00cce",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "bda48c14",
   "metadata": {},
   "source": [
    "# Methodology: So we have ended up with some NANs again where we have divided a Share Price by a non-existent Share Price.  \n",
    "\n",
    "In these circumstances, we put the return for that period as 0."
   ]
  },
  {
   "cell_type": "raw",
   "id": "7b9ffd15",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "2c0e3add",
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th>Date</th>\n",
       "      <th>2018-01-01</th>\n",
       "      <th>2019-01-01</th>\n",
       "      <th>2020-01-01</th>\n",
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       "  <tbody>\n",
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       "      <th>AAPL</th>\n",
       "      <td>41.945000</td>\n",
       "      <td>47.487499</td>\n",
       "      <td>63.572498</td>\n",
       "      <td>122.150002</td>\n",
       "      <td>174.610001</td>\n",
       "      <td>164.899994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABNB</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>146.800000</td>\n",
       "      <td>166.490000</td>\n",
       "      <td>85.650000</td>\n",
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       "    <tr>\n",
       "      <th>ADBE</th>\n",
       "      <td>216.080002</td>\n",
       "      <td>266.489990</td>\n",
       "      <td>318.239990</td>\n",
       "      <td>475.369995</td>\n",
       "      <td>455.619995</td>\n",
       "      <td>385.369995</td>\n",
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       "    <tr>\n",
       "      <th>ADI</th>\n",
       "      <td>91.129997</td>\n",
       "      <td>105.269997</td>\n",
       "      <td>89.650002</td>\n",
       "      <td>155.080002</td>\n",
       "      <td>165.179993</td>\n",
       "      <td>197.220001</td>\n",
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       "    <tr>\n",
       "      <th>ADP</th>\n",
       "      <td>113.480003</td>\n",
       "      <td>159.740005</td>\n",
       "      <td>136.679993</td>\n",
       "      <td>188.470001</td>\n",
       "      <td>227.539993</td>\n",
       "      <td>222.630005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WBA</th>\n",
       "      <td>65.470001</td>\n",
       "      <td>63.270000</td>\n",
       "      <td>45.750000</td>\n",
       "      <td>54.900002</td>\n",
       "      <td>44.770000</td>\n",
       "      <td>34.580002</td>\n",
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       "    <tr>\n",
       "      <th>WDAY</th>\n",
       "      <td>127.110001</td>\n",
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       "      <td>130.220001</td>\n",
       "      <td>248.429993</td>\n",
       "      <td>239.460007</td>\n",
       "      <td>206.539993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XEL</th>\n",
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       "      <td>56.209999</td>\n",
       "      <td>60.299999</td>\n",
       "      <td>66.510002</td>\n",
       "      <td>72.169998</td>\n",
       "      <td>67.440002</td>\n",
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       "    <tr>\n",
       "      <th>ZM</th>\n",
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       "      <td>146.119995</td>\n",
       "      <td>321.290009</td>\n",
       "      <td>117.230003</td>\n",
       "      <td>73.839996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZS</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>39.210000</td>\n",
       "      <td>46.500000</td>\n",
       "      <td>199.710000</td>\n",
       "      <td>321.330000</td>\n",
       "      <td>111.900000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>102 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Date  2018-01-01  2019-01-01  2020-01-01  2021-01-01  2022-01-01  2023-01-01\n",
       "AAPL   41.945000   47.487499   63.572498  122.150002  174.610001  164.899994\n",
       "ABNB    0.000000    0.000000    0.000000  146.800000  166.490000   85.650000\n",
       "ADBE  216.080002  266.489990  318.239990  475.369995  455.619995  385.369995\n",
       "ADI    91.129997  105.269997   89.650002  155.080002  165.179993  197.220001\n",
       "ADP   113.480003  159.740005  136.679993  188.470001  227.539993  222.630005\n",
       "...          ...         ...         ...         ...         ...         ...\n",
       "WBA    65.470001   63.270000   45.750000   54.900002   44.770000   34.580002\n",
       "WDAY  127.110001  192.850006  130.220001  248.429993  239.460007  206.539993\n",
       "XEL    45.480000   56.209999   60.299999   66.510002   72.169998   67.440002\n",
       "ZM      0.000000    0.000000  146.119995  321.290009  117.230003   73.839996\n",
       "ZS      0.000000   39.210000   46.500000  199.710000  321.330000  111.900000\n",
       "\n",
       "[102 rows x 6 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_t = data_transposed.fillna(0)\n",
    "data_t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "9d59e30c",
   "metadata": {},
   "outputs": [
    {
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       "      <td>60.299999</td>\n",
       "      <td>66.510002</td>\n",
       "      <td>72.169998</td>\n",
       "      <td>67.440002</td>\n",
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       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>146.119995</td>\n",
       "      <td>321.290009</td>\n",
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       "      <td>73.839996</td>\n",
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      "text/plain": [
       "           2017        2018        2019        2020        2021        2022\n",
       "0     41.945000   47.487499   63.572498  122.150002  174.610001  164.899994\n",
       "1      0.000000    0.000000    0.000000  146.800000  166.490000   85.650000\n",
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       "..          ...         ...         ...         ...         ...         ...\n",
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       "98   127.110001  192.850006  130.220001  248.429993  239.460007  206.539993\n",
       "99    45.480000   56.209999   60.299999   66.510002   72.169998   67.440002\n",
       "100    0.000000    0.000000  146.119995  321.290009  117.230003   73.839996\n",
       "101    0.000000   39.210000   46.500000  199.710000  321.330000  111.900000\n",
       "\n",
       "[102 rows x 6 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_c = data_t.values\n",
    "data_f = pd.DataFrame(data_c, columns=['2017', '2018', '2019', '2020', '2021','2022'])\n",
    "data_f"
   ]
  },
  {
   "cell_type": "raw",
   "id": "ba05bd64",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "b926c6ed",
   "metadata": {},
   "source": [
    "# There are two ways data scientists may want the Share Price Return data for the stocks:\n",
    "\n",
    "- either as discrete percentage returns (for example 23.5 per cent); \n",
    "\n",
    "- or as relative performance data points showing Over or Under perform binaries against the underlying index (for example using 1 for outperform and 0 for underperform).\n",
    "\n",
    "So we will provide the Y data both ways."
   ]
  },
  {
   "cell_type": "raw",
   "id": "0563aef5",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "144b5b89",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_f[\"Perf17\"] = ((data_f[\"2018\"] - data_f[\"2017\"])/data_f[\"2017\"])\n",
    "data_f[\"Perf18\"] = ((data_f[\"2019\"] - data_f[\"2018\"])/data_f[\"2018\"])\n",
    "data_f[\"Perf19\"] = ((data_f[\"2020\"] - data_f[\"2019\"])/data_f[\"2019\"])\n",
    "data_f[\"Perf20\"] = ((data_f[\"2021\"] - data_f[\"2020\"])/data_f[\"2020\"])\n",
    "data_f[\"Perf21\"] = ((data_f[\"2022\"] - data_f[\"2021\"])/data_f[\"2021\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "a657ed04",
   "metadata": {},
   "outputs": [
    {
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       "      <td>-0.021579</td>\n",
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       "      <td>60.299999</td>\n",
       "      <td>66.510002</td>\n",
       "      <td>72.169998</td>\n",
       "      <td>67.440002</td>\n",
       "      <td>0.235928</td>\n",
       "      <td>0.072763</td>\n",
       "      <td>0.102985</td>\n",
       "      <td>0.085100</td>\n",
       "      <td>-0.065540</td>\n",
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       "      <td>inf</td>\n",
       "      <td>0.185922</td>\n",
       "      <td>3.294839</td>\n",
       "      <td>0.608983</td>\n",
       "      <td>-0.651760</td>\n",
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       "           2017        2018        2019        2020        2021        2022  \\\n",
       "0     41.945000   47.487499   63.572498  122.150002  174.610001  164.899994   \n",
       "1      0.000000    0.000000    0.000000  146.800000  166.490000   85.650000   \n",
       "2    216.080002  266.489990  318.239990  475.369995  455.619995  385.369995   \n",
       "3     91.129997  105.269997   89.650002  155.080002  165.179993  197.220001   \n",
       "4    113.480003  159.740005  136.679993  188.470001  227.539993  222.630005   \n",
       "..          ...         ...         ...         ...         ...         ...   \n",
       "97    65.470001   63.270000   45.750000   54.900002   44.770000   34.580002   \n",
       "98   127.110001  192.850006  130.220001  248.429993  239.460007  206.539993   \n",
       "99    45.480000   56.209999   60.299999   66.510002   72.169998   67.440002   \n",
       "100    0.000000    0.000000  146.119995  321.290009  117.230003   73.839996   \n",
       "101    0.000000   39.210000   46.500000  199.710000  321.330000  111.900000   \n",
       "\n",
       "       Perf17    Perf18    Perf19    Perf20    Perf21  \n",
       "0    0.132137  0.338721  0.921428  0.429472 -0.055610  \n",
       "1         NaN       NaN       inf  0.134128 -0.485555  \n",
       "2    0.233293  0.194191  0.493747 -0.041547 -0.154186  \n",
       "3    0.155163 -0.148380  0.729838  0.065128  0.193970  \n",
       "4    0.407649 -0.144360  0.378914  0.207301 -0.021579  \n",
       "..        ...       ...       ...       ...       ...  \n",
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       "98   0.517190 -0.324760  0.907771 -0.036107 -0.137476  \n",
       "99   0.235928  0.072763  0.102985  0.085100 -0.065540  \n",
       "100       NaN       inf  1.198809 -0.635127 -0.370127  \n",
       "101       inf  0.185922  3.294839  0.608983 -0.651760  \n",
       "\n",
       "[102 rows x 11 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
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    "data_f"
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  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "ec013313",
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       "      <td>63.270000</td>\n",
       "      <td>45.750000</td>\n",
       "      <td>54.900002</td>\n",
       "      <td>44.770000</td>\n",
       "      <td>34.580002</td>\n",
       "      <td>-0.033603</td>\n",
       "      <td>-0.276908</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>-0.184517</td>\n",
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       "      <th>98</th>\n",
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       "      <td>192.850006</td>\n",
       "      <td>130.220001</td>\n",
       "      <td>248.429993</td>\n",
       "      <td>239.460007</td>\n",
       "      <td>206.539993</td>\n",
       "      <td>0.517190</td>\n",
       "      <td>-0.324760</td>\n",
       "      <td>0.907771</td>\n",
       "      <td>-0.036107</td>\n",
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       "      <th>99</th>\n",
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       "      <td>56.209999</td>\n",
       "      <td>60.299999</td>\n",
       "      <td>66.510002</td>\n",
       "      <td>72.169998</td>\n",
       "      <td>67.440002</td>\n",
       "      <td>0.235928</td>\n",
       "      <td>0.072763</td>\n",
       "      <td>0.102985</td>\n",
       "      <td>0.085100</td>\n",
       "      <td>-0.065540</td>\n",
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       "      <th>100</th>\n",
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       "           2017        2018        2019        2020        2021        2022  \\\n",
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       "1      0.000000    0.000000    0.000000  146.800000  166.490000   85.650000   \n",
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       "3     91.129997  105.269997   89.650002  155.080002  165.179993  197.220001   \n",
       "4    113.480003  159.740005  136.679993  188.470001  227.539993  222.630005   \n",
       "..          ...         ...         ...         ...         ...         ...   \n",
       "97    65.470001   63.270000   45.750000   54.900002   44.770000   34.580002   \n",
       "98   127.110001  192.850006  130.220001  248.429993  239.460007  206.539993   \n",
       "99    45.480000   56.209999   60.299999   66.510002   72.169998   67.440002   \n",
       "100    0.000000    0.000000  146.119995  321.290009  117.230003   73.839996   \n",
       "101    0.000000   39.210000   46.500000  199.710000  321.330000  111.900000   \n",
       "\n",
       "       Perf17    Perf18    Perf19    Perf20    Perf21  \n",
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       "2    0.233293  0.194191  0.493747 -0.041547 -0.154186  \n",
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       "100  0.000000  1.000000  1.198809 -0.635127 -0.370127  \n",
       "101  1.000000  0.185922  3.294839  0.608983 -0.651760  \n",
       "\n",
       "[102 rows x 11 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_f = data_f.replace([np.inf, -np.inf], 1)\n",
    "data_f = data_f.fillna(0)\n",
    "data_f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "e38ecf3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "perf2017 = data_f.filter(regex='2017')\n",
    "perf2018 = data_f.filter(regex='2018')\n",
    "perf2019 = data_f.filter(regex='2019')\n",
    "perf2020 = data_f.filter(regex='2020')\n",
    "perf2021 = data_f.filter(regex='2021')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "f97b06d7",
   "metadata": {},
   "outputs": [
    {
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       "           2017\n",
       "0     41.945000\n",
       "1      0.000000\n",
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       "3     91.129997\n",
       "4    113.480003\n",
       "..          ...\n",
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       "\n",
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     "execution_count": 40,
     "metadata": {},
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   "id": "62535649",
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   "source": []
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  {
   "cell_type": "markdown",
   "id": "7beffef5",
   "metadata": {},
   "source": [
    "# Methodology:  in the next section we calculate the index performance as the mean of the individual performances of the individual securities (on an equal weighted basis).\n",
    "\n",
    "Once we have the above mean performance for the all the stocks for the year then we use that figure as the index performance (\"benchmark\").  Using this data, we can then assess and label whether a particular stock has outperfomed that benchmark (marked 1) or underperformed that benchmark (marked 0)."
   ]
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  {
   "cell_type": "raw",
   "id": "40d8c70e",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "311fbbd2",
   "metadata": {},
   "outputs": [
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       "      <td>0.200000</td>\n",
       "      <td>-0.184517</td>\n",
       "      <td>-0.227608</td>\n",
       "      <td>0.17055</td>\n",
       "      <td>0.104431</td>\n",
       "      <td>0.842527</td>\n",
       "      <td>0.136347</td>\n",
       "      <td>-0.077387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>127.110001</td>\n",
       "      <td>192.850006</td>\n",
       "      <td>130.220001</td>\n",
       "      <td>248.429993</td>\n",
       "      <td>239.460007</td>\n",
       "      <td>206.539993</td>\n",
       "      <td>0.517190</td>\n",
       "      <td>-0.324760</td>\n",
       "      <td>0.907771</td>\n",
       "      <td>-0.036107</td>\n",
       "      <td>-0.137476</td>\n",
       "      <td>0.17055</td>\n",
       "      <td>0.104431</td>\n",
       "      <td>0.842527</td>\n",
       "      <td>0.136347</td>\n",
       "      <td>-0.077387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>45.480000</td>\n",
       "      <td>56.209999</td>\n",
       "      <td>60.299999</td>\n",
       "      <td>66.510002</td>\n",
       "      <td>72.169998</td>\n",
       "      <td>67.440002</td>\n",
       "      <td>0.235928</td>\n",
       "      <td>0.072763</td>\n",
       "      <td>0.102985</td>\n",
       "      <td>0.085100</td>\n",
       "      <td>-0.065540</td>\n",
       "      <td>0.17055</td>\n",
       "      <td>0.104431</td>\n",
       "      <td>0.842527</td>\n",
       "      <td>0.136347</td>\n",
       "      <td>-0.077387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>146.119995</td>\n",
       "      <td>321.290009</td>\n",
       "      <td>117.230003</td>\n",
       "      <td>73.839996</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.198809</td>\n",
       "      <td>-0.635127</td>\n",
       "      <td>-0.370127</td>\n",
       "      <td>0.17055</td>\n",
       "      <td>0.104431</td>\n",
       "      <td>0.842527</td>\n",
       "      <td>0.136347</td>\n",
       "      <td>-0.077387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>39.210000</td>\n",
       "      <td>46.500000</td>\n",
       "      <td>199.710000</td>\n",
       "      <td>321.330000</td>\n",
       "      <td>111.900000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.185922</td>\n",
       "      <td>3.294839</td>\n",
       "      <td>0.608983</td>\n",
       "      <td>-0.651760</td>\n",
       "      <td>0.17055</td>\n",
       "      <td>0.104431</td>\n",
       "      <td>0.842527</td>\n",
       "      <td>0.136347</td>\n",
       "      <td>-0.077387</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>102 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           2017        2018        2019        2020        2021        2022  \\\n",
       "0     41.945000   47.487499   63.572498  122.150002  174.610001  164.899994   \n",
       "1      0.000000    0.000000    0.000000  146.800000  166.490000   85.650000   \n",
       "2    216.080002  266.489990  318.239990  475.369995  455.619995  385.369995   \n",
       "3     91.129997  105.269997   89.650002  155.080002  165.179993  197.220001   \n",
       "4    113.480003  159.740005  136.679993  188.470001  227.539993  222.630005   \n",
       "..          ...         ...         ...         ...         ...         ...   \n",
       "97    65.470001   63.270000   45.750000   54.900002   44.770000   34.580002   \n",
       "98   127.110001  192.850006  130.220001  248.429993  239.460007  206.539993   \n",
       "99    45.480000   56.209999   60.299999   66.510002   72.169998   67.440002   \n",
       "100    0.000000    0.000000  146.119995  321.290009  117.230003   73.839996   \n",
       "101    0.000000   39.210000   46.500000  199.710000  321.330000  111.900000   \n",
       "\n",
       "       Perf17    Perf18    Perf19    Perf20    Perf21   Mean17    Mean18  \\\n",
       "0    0.132137  0.338721  0.921428  0.429472 -0.055610  0.17055  0.104431   \n",
       "1    0.000000  0.000000  1.000000  0.134128 -0.485555  0.17055  0.104431   \n",
       "2    0.233293  0.194191  0.493747 -0.041547 -0.154186  0.17055  0.104431   \n",
       "3    0.155163 -0.148380  0.729838  0.065128  0.193970  0.17055  0.104431   \n",
       "4    0.407649 -0.144360  0.378914  0.207301 -0.021579  0.17055  0.104431   \n",
       "..        ...       ...       ...       ...       ...      ...       ...   \n",
       "97  -0.033603 -0.276908  0.200000 -0.184517 -0.227608  0.17055  0.104431   \n",
       "98   0.517190 -0.324760  0.907771 -0.036107 -0.137476  0.17055  0.104431   \n",
       "99   0.235928  0.072763  0.102985  0.085100 -0.065540  0.17055  0.104431   \n",
       "100  0.000000  1.000000  1.198809 -0.635127 -0.370127  0.17055  0.104431   \n",
       "101  1.000000  0.185922  3.294839  0.608983 -0.651760  0.17055  0.104431   \n",
       "\n",
       "       Mean19    Mean20    Mean21  \n",
       "0    0.842527  0.136347 -0.077387  \n",
       "1    0.842527  0.136347 -0.077387  \n",
       "2    0.842527  0.136347 -0.077387  \n",
       "3    0.842527  0.136347 -0.077387  \n",
       "4    0.842527  0.136347 -0.077387  \n",
       "..        ...       ...       ...  \n",
       "97   0.842527  0.136347 -0.077387  \n",
       "98   0.842527  0.136347 -0.077387  \n",
       "99   0.842527  0.136347 -0.077387  \n",
       "100  0.842527  0.136347 -0.077387  \n",
       "101  0.842527  0.136347 -0.077387  \n",
       "\n",
       "[102 rows x 16 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_f = data_f.assign(Mean17=data_f[\"Perf17\"].mean())\n",
    "data_f = data_f.assign(Mean18=data_f[\"Perf18\"].mean())\n",
    "data_f = data_f.assign(Mean19=data_f[\"Perf19\"].mean())\n",
    "data_f = data_f.assign(Mean20=data_f[\"Perf20\"].mean())\n",
    "data_f = data_f.assign(Mean21=data_f[\"Perf21\"].mean())\n",
    "data_f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "f7fb2733",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_new_column17(row):\n",
    "    if row['Perf17'] > row['Mean17']:\n",
    "        return '1'\n",
    "    else:\n",
    "        return '0'\n",
    "    \n",
    "data_f['Label17'] = data_f.apply(calculate_new_column17, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "adcc55ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_new_column18(row):\n",
    "    if row['Perf18'] > row['Mean18']:\n",
    "        return '1'\n",
    "    else:\n",
    "        return '0'\n",
    "    \n",
    "data_f['Label18'] = data_f.apply(calculate_new_column18, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "964278c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_new_column19(row):\n",
    "    if row['Perf19'] > row['Mean19']:\n",
    "        return '1'\n",
    "    else:\n",
    "        return '0'\n",
    "    \n",
    "data_f['Label19'] = data_f.apply(calculate_new_column19, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "967ef279",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_new_column20(row):\n",
    "    if row['Perf20'] > row['Mean20']:\n",
    "        return '1'\n",
    "    else:\n",
    "        return '0'\n",
    "    \n",
    "data_f['Label20'] = data_f.apply(calculate_new_column20, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "66ae7263",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_new_column21(row):\n",
    "    if row['Perf21'] > row['Mean21']:\n",
    "        return '1'\n",
    "    else:\n",
    "        return '0'\n",
    "    \n",
    "data_f['Label21'] = data_f.apply(calculate_new_column21, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "9a14f997",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "      <th>2020</th>\n",
       "      <th>2021</th>\n",
       "      <th>2022</th>\n",
       "      <th>Perf17</th>\n",
       "      <th>Perf18</th>\n",
       "      <th>Perf19</th>\n",
       "      <th>Perf20</th>\n",
       "      <th>...</th>\n",
       "      <th>Mean17</th>\n",
       "      <th>Mean18</th>\n",
       "      <th>Mean19</th>\n",
       "      <th>Mean20</th>\n",
       "      <th>Mean21</th>\n",
       "      <th>Label17</th>\n",
       "      <th>Label18</th>\n",
       "      <th>Label19</th>\n",
       "      <th>Label20</th>\n",
       "      <th>Label21</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>41.945000</td>\n",
       "      <td>47.487499</td>\n",
       "      <td>63.572498</td>\n",
       "      <td>122.150002</td>\n",
       "      <td>174.610001</td>\n",
       "      <td>164.899994</td>\n",
       "      <td>0.132137</td>\n",
       "      <td>0.338721</td>\n",
       "      <td>0.921428</td>\n",
       "      <td>0.429472</td>\n",
       "      <td>...</td>\n",
       "      <td>0.17055</td>\n",
       "      <td>0.104431</td>\n",
       "      <td>0.842527</td>\n",
       "      <td>0.136347</td>\n",
       "      <td>-0.077387</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>146.800000</td>\n",
       "      <td>166.490000</td>\n",
       "      <td>85.650000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.134128</td>\n",
       "      <td>...</td>\n",
       "      <td>0.17055</td>\n",
       "      <td>0.104431</td>\n",
       "      <td>0.842527</td>\n",
       "      <td>0.136347</td>\n",
       "      <td>-0.077387</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>216.080002</td>\n",
       "      <td>266.489990</td>\n",
       "      <td>318.239990</td>\n",
       "      <td>475.369995</td>\n",
       "      <td>455.619995</td>\n",
       "      <td>385.369995</td>\n",
       "      <td>0.233293</td>\n",
       "      <td>0.194191</td>\n",
       "      <td>0.493747</td>\n",
       "      <td>-0.041547</td>\n",
       "      <td>...</td>\n",
       "      <td>0.17055</td>\n",
       "      <td>0.104431</td>\n",
       "      <td>0.842527</td>\n",
       "      <td>0.136347</td>\n",
       "      <td>-0.077387</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>91.129997</td>\n",
       "      <td>105.269997</td>\n",
       "      <td>89.650002</td>\n",
       "      <td>155.080002</td>\n",
       "      <td>165.179993</td>\n",
       "      <td>197.220001</td>\n",
       "      <td>0.155163</td>\n",
       "      <td>-0.148380</td>\n",
       "      <td>0.729838</td>\n",
       "      <td>0.065128</td>\n",
       "      <td>...</td>\n",
       "      <td>0.17055</td>\n",
       "      <td>0.104431</td>\n",
       "      <td>0.842527</td>\n",
       "      <td>0.136347</td>\n",
       "      <td>-0.077387</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>113.480003</td>\n",
       "      <td>159.740005</td>\n",
       "      <td>136.679993</td>\n",
       "      <td>188.470001</td>\n",
       "      <td>227.539993</td>\n",
       "      <td>222.630005</td>\n",
       "      <td>0.407649</td>\n",
       "      <td>-0.144360</td>\n",
       "      <td>0.378914</td>\n",
       "      <td>0.207301</td>\n",
       "      <td>...</td>\n",
       "      <td>0.17055</td>\n",
       "      <td>0.104431</td>\n",
       "      <td>0.842527</td>\n",
       "      <td>0.136347</td>\n",
       "      <td>-0.077387</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>65.470001</td>\n",
       "      <td>63.270000</td>\n",
       "      <td>45.750000</td>\n",
       "      <td>54.900002</td>\n",
       "      <td>44.770000</td>\n",
       "      <td>34.580002</td>\n",
       "      <td>-0.033603</td>\n",
       "      <td>-0.276908</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>-0.184517</td>\n",
       "      <td>...</td>\n",
       "      <td>0.17055</td>\n",
       "      <td>0.104431</td>\n",
       "      <td>0.842527</td>\n",
       "      <td>0.136347</td>\n",
       "      <td>-0.077387</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>127.110001</td>\n",
       "      <td>192.850006</td>\n",
       "      <td>130.220001</td>\n",
       "      <td>248.429993</td>\n",
       "      <td>239.460007</td>\n",
       "      <td>206.539993</td>\n",
       "      <td>0.517190</td>\n",
       "      <td>-0.324760</td>\n",
       "      <td>0.907771</td>\n",
       "      <td>-0.036107</td>\n",
       "      <td>...</td>\n",
       "      <td>0.17055</td>\n",
       "      <td>0.104431</td>\n",
       "      <td>0.842527</td>\n",
       "      <td>0.136347</td>\n",
       "      <td>-0.077387</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>45.480000</td>\n",
       "      <td>56.209999</td>\n",
       "      <td>60.299999</td>\n",
       "      <td>66.510002</td>\n",
       "      <td>72.169998</td>\n",
       "      <td>67.440002</td>\n",
       "      <td>0.235928</td>\n",
       "      <td>0.072763</td>\n",
       "      <td>0.102985</td>\n",
       "      <td>0.085100</td>\n",
       "      <td>...</td>\n",
       "      <td>0.17055</td>\n",
       "      <td>0.104431</td>\n",
       "      <td>0.842527</td>\n",
       "      <td>0.136347</td>\n",
       "      <td>-0.077387</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>146.119995</td>\n",
       "      <td>321.290009</td>\n",
       "      <td>117.230003</td>\n",
       "      <td>73.839996</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.198809</td>\n",
       "      <td>-0.635127</td>\n",
       "      <td>...</td>\n",
       "      <td>0.17055</td>\n",
       "      <td>0.104431</td>\n",
       "      <td>0.842527</td>\n",
       "      <td>0.136347</td>\n",
       "      <td>-0.077387</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>39.210000</td>\n",
       "      <td>46.500000</td>\n",
       "      <td>199.710000</td>\n",
       "      <td>321.330000</td>\n",
       "      <td>111.900000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.185922</td>\n",
       "      <td>3.294839</td>\n",
       "      <td>0.608983</td>\n",
       "      <td>...</td>\n",
       "      <td>0.17055</td>\n",
       "      <td>0.104431</td>\n",
       "      <td>0.842527</td>\n",
       "      <td>0.136347</td>\n",
       "      <td>-0.077387</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>102 rows × 21 columns</p>\n",
       "</div>"
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       "           2017        2018        2019        2020        2021        2022  \\\n",
       "0     41.945000   47.487499   63.572498  122.150002  174.610001  164.899994   \n",
       "1      0.000000    0.000000    0.000000  146.800000  166.490000   85.650000   \n",
       "2    216.080002  266.489990  318.239990  475.369995  455.619995  385.369995   \n",
       "3     91.129997  105.269997   89.650002  155.080002  165.179993  197.220001   \n",
       "4    113.480003  159.740005  136.679993  188.470001  227.539993  222.630005   \n",
       "..          ...         ...         ...         ...         ...         ...   \n",
       "97    65.470001   63.270000   45.750000   54.900002   44.770000   34.580002   \n",
       "98   127.110001  192.850006  130.220001  248.429993  239.460007  206.539993   \n",
       "99    45.480000   56.209999   60.299999   66.510002   72.169998   67.440002   \n",
       "100    0.000000    0.000000  146.119995  321.290009  117.230003   73.839996   \n",
       "101    0.000000   39.210000   46.500000  199.710000  321.330000  111.900000   \n",
       "\n",
       "       Perf17    Perf18    Perf19    Perf20  ...   Mean17    Mean18    Mean19  \\\n",
       "0    0.132137  0.338721  0.921428  0.429472  ...  0.17055  0.104431  0.842527   \n",
       "1    0.000000  0.000000  1.000000  0.134128  ...  0.17055  0.104431  0.842527   \n",
       "2    0.233293  0.194191  0.493747 -0.041547  ...  0.17055  0.104431  0.842527   \n",
       "3    0.155163 -0.148380  0.729838  0.065128  ...  0.17055  0.104431  0.842527   \n",
       "4    0.407649 -0.144360  0.378914  0.207301  ...  0.17055  0.104431  0.842527   \n",
       "..        ...       ...       ...       ...  ...      ...       ...       ...   \n",
       "97  -0.033603 -0.276908  0.200000 -0.184517  ...  0.17055  0.104431  0.842527   \n",
       "98   0.517190 -0.324760  0.907771 -0.036107  ...  0.17055  0.104431  0.842527   \n",
       "99   0.235928  0.072763  0.102985  0.085100  ...  0.17055  0.104431  0.842527   \n",
       "100  0.000000  1.000000  1.198809 -0.635127  ...  0.17055  0.104431  0.842527   \n",
       "101  1.000000  0.185922  3.294839  0.608983  ...  0.17055  0.104431  0.842527   \n",
       "\n",
       "       Mean20    Mean21  Label17 Label18 Label19 Label20 Label21  \n",
       "0    0.136347 -0.077387        0       1       1       1       1  \n",
       "1    0.136347 -0.077387        0       0       1       0       0  \n",
       "2    0.136347 -0.077387        1       1       0       0       0  \n",
       "3    0.136347 -0.077387        0       0       0       0       1  \n",
       "4    0.136347 -0.077387        1       0       0       1       1  \n",
       "..        ...       ...      ...     ...     ...     ...     ...  \n",
       "97   0.136347 -0.077387        0       0       0       0       0  \n",
       "98   0.136347 -0.077387        1       0       1       0       0  \n",
       "99   0.136347 -0.077387        1       0       0       0       1  \n",
       "100  0.136347 -0.077387        0       1       1       0       0  \n",
       "101  0.136347 -0.077387        1       1       1       1       0  \n",
       "\n",
       "[102 rows x 21 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_f"
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  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "83ca397c",
   "metadata": {},
   "outputs": [
    {
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       "      <td>-0.370127</td>\n",
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       "      <th>101</th>\n",
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       "      <td>0.608983</td>\n",
       "      <td>-0.651760</td>\n",
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       "<p>102 rows × 5 columns</p>\n",
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      "text/plain": [
       "       Perf17    Perf18    Perf19    Perf20    Perf21\n",
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       "101  1.000000  0.185922  3.294839  0.608983 -0.651760\n",
       "\n",
       "[102 rows x 5 columns]"
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     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_perfs = data_f.iloc[:, 6:11]\n",
    "final_perfs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "07cd621c",
   "metadata": {},
   "outputs": [
    {
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     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "perfs17_21 = pd.DataFrame(np.concatenate( (final_perfs['Perf17'].values, final_perfs['Perf18'].values,final_perfs['Perf19'].values,final_perfs['Perf20'].values,final_perfs['Perf21'].values), axis=0 ) )\n",
    "perfs17_21"
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  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "81446b72",
   "metadata": {},
   "outputs": [],
   "source": [
    "perfs17_21 = perfs17_21.fillna(0)"
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  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "833f0f47",
   "metadata": {},
   "outputs": [],
   "source": [
    "perfs17_21.to_csv(\"perfs17_21.csv\",index=False)\n"
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   "cell_type": "code",
   "execution_count": 52,
   "id": "48fd7e3b",
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     "execution_count": 52,
     "metadata": {},
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   "source": [
    "perfs17_20 = perfs17_21[ : -102]\n",
    "perfs17_20"
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  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "dcc0ca45",
   "metadata": {},
   "outputs": [],
   "source": [
    "perfs17_20.to_csv(\"perfs17_20.csv\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "3acf584c",
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   "outputs": [
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       "</table>\n",
       "<p>102 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Label17 Label18 Label19 Label20 Label21\n",
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       "\n",
       "[102 rows x 5 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_labels = data_f.iloc[:, -5:]\n",
    "final_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "ee982ea7",
   "metadata": {},
   "outputs": [],
   "source": [
    "labels17_21 = pd.DataFrame(np.concatenate( (final_labels['Label17'].values, final_labels['Label18'].values,final_labels['Label19'].values,final_labels['Label20'].values,final_labels['Label21'].values), axis=0 ) )"
   ]
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   "cell_type": "code",
   "execution_count": 56,
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   "execution_count": 57,
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    "labels17_20 = labels17_21[ : -102]"
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  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "56e5c415",
   "metadata": {},
   "outputs": [],
   "source": [
    "labels17_20.to_csv(\"labels17_20.csv\",index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88dd8786",
   "metadata": {},
   "source": [
    "# "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f23413d3",
   "metadata": {},
   "source": [
    "# Result 1\n",
    "\n",
    "You will note we have set an Outperformance Label that is:\n",
    "\n",
    "- 1 for Stocks that outperform the mean Nasdaq100 Index (equal weighted) return; and\n",
    "- 0 for stocks that Underperform the mean Nasdaq100 index return for that period.\n",
    "\n",
    "We also have a Performance Label that is the actual discrete return over the calendar period.\n",
    "\n",
    "Now...  \n",
    "\n",
    "We will also form some Datasets where the Labels are attached as last column of the X dataset as this will be more useful for some data scientist."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba4ca61a",
   "metadata": {},
   "source": [
    "# "
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  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "4028ff04",
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       "      <td>7.910000</td>\n",
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       "      <td>65.800000</td>\n",
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       "      <td>75.390000</td>\n",
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       "      <td>-0.220000</td>\n",
       "      <td>17.709700</td>\n",
       "      <td>52.306596</td>\n",
       "      <td>263.130000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>509</th>\n",
       "      <td>0.330000</td>\n",
       "      <td>1.733289</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>2.090000</td>\n",
       "      <td>1.560000</td>\n",
       "      <td>0.220000</td>\n",
       "      <td>-2.670000</td>\n",
       "      <td>11.580000</td>\n",
       "      <td>2.570000</td>\n",
       "      <td>83.647794</td>\n",
       "      <td>...</td>\n",
       "      <td>61.101047</td>\n",
       "      <td>63.153488</td>\n",
       "      <td>6.195821</td>\n",
       "      <td>222.560000</td>\n",
       "      <td>158.440000</td>\n",
       "      <td>12.240260</td>\n",
       "      <td>-0.010000</td>\n",
       "      <td>-86.740000</td>\n",
       "      <td>-116.850000</td>\n",
       "      <td>48.790000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>510 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     asset_turnover  buyback_yield  capex_to_revenue  cash_ratio  \\\n",
       "0          0.660000       4.090000          0.050000    0.740000   \n",
       "1          0.724932       2.035574          0.040000    1.410274   \n",
       "2          0.540000       1.080000          0.020000    1.650000   \n",
       "3          0.360000       0.140000          0.040000    0.660000   \n",
       "4          0.724932       2.035574          0.086892    1.410274   \n",
       "..              ...            ...               ...         ...   \n",
       "505        1.570000       0.250000          0.010000    0.050000   \n",
       "506        0.560000       1.733289          0.060000    0.830000   \n",
       "507        0.240000      -0.990000          0.320000    0.030000   \n",
       "508        0.810000      -1.810000          0.030000    3.370000   \n",
       "509        0.330000       1.733289          0.090000    2.090000   \n",
       "\n",
       "     cash_to_debt  cogs_to_revenue    mscore     zscore  current_ratio  \\\n",
       "0        0.640000         0.620000 -2.500000   3.620000       1.280000   \n",
       "1        2.648413         0.250000 -2.543382   6.686479       2.332192   \n",
       "2        3.090000         0.140000 -2.490000  10.810000       2.050000   \n",
       "3        0.130000         0.400000 -1.490000   2.640000       1.470000   \n",
       "4        2.648413         0.437534 -2.543382   6.686479       2.332192   \n",
       "..            ...              ...       ...        ...            ...   \n",
       "505      0.040000         0.790000 -2.260000   2.720000       0.720000   \n",
       "506      1.580000         0.280000 -3.240000   6.120000       1.120000   \n",
       "507      0.010000         0.610000 -1.792316   1.030000       0.840000   \n",
       "508     40.040000         0.310000 -0.480000  47.500000       3.800000   \n",
       "509      1.560000         0.220000 -2.670000  11.580000       2.570000   \n",
       "\n",
       "     days_inventory  ...  price_to_earnings_ratio  \\\n",
       "0          9.040000  ...                16.730000   \n",
       "1         89.855094  ...                44.940635   \n",
       "2         89.855094  ...                52.320000   \n",
       "3         81.440000  ...                39.870000   \n",
       "4         89.855094  ...                44.940635   \n",
       "..              ...  ...                      ...   \n",
       "505       28.090000  ...                17.320000   \n",
       "506       83.647794  ...                61.101047   \n",
       "507       26.040000  ...                22.870000   \n",
       "508       83.647794  ...               165.360000   \n",
       "509       83.647794  ...                61.101047   \n",
       "\n",
       "     price_to_earnings_ratio_nri  price_earnings_growth_ratio  \\\n",
       "0                      16.730000                     1.250000   \n",
       "1                      44.909524                    10.936250   \n",
       "2                      52.320000                     2.420000   \n",
       "3                      39.870000                     4.440000   \n",
       "4                      44.909524                    10.936250   \n",
       "..                           ...                          ...   \n",
       "505                    22.070000                     6.195821   \n",
       "506                    63.153488                     6.195821   \n",
       "507                    22.870000                     8.730000   \n",
       "508                   165.360000                     6.195821   \n",
       "509                    63.153488                     6.195821   \n",
       "\n",
       "     price_to_free_cashflow  price_to_operating_cashflow  rate_of_return  \\\n",
       "0                 15.630000                    12.600000       19.240000   \n",
       "1                 33.836721                    35.271493       14.260952   \n",
       "2                 32.410000                    30.420000       21.820000   \n",
       "3                 33.680000                    27.720000       11.950000   \n",
       "4                 33.836721                    35.271493       14.260952   \n",
       "..                      ...                          ...             ...   \n",
       "505               10.530000                     7.910000       -5.110000   \n",
       "506               53.290000                    42.510000       12.240260   \n",
       "507               52.026374                    16.700000       -1.040000   \n",
       "508               80.070000                    75.390000       12.240260   \n",
       "509              222.560000                   158.440000       12.240260   \n",
       "\n",
       "     scaled_net_operating_assets  yoy_ebitda_growth  yoy_eps_growth  \\\n",
       "0                       0.550000           9.360000       10.830000   \n",
       "1                       0.482917          26.097361       46.020147   \n",
       "2                       0.360000          39.030000       45.690000   \n",
       "3                       2.130000          25.600000      -17.030000   \n",
       "4                       0.482917          26.097361       46.020147   \n",
       "..                           ...                ...             ...   \n",
       "505                     0.640000          65.800000      463.460000   \n",
       "506                     0.290000         156.700000       43.870000   \n",
       "507                     0.750000           3.480000        6.090000   \n",
       "508                    -0.220000          17.709700       52.306596   \n",
       "509                    -0.010000         -86.740000     -116.850000   \n",
       "\n",
       "     yoy_revenue_growth  \n",
       "0             11.340000  \n",
       "1             19.277917  \n",
       "2             25.510000  \n",
       "3             36.640000  \n",
       "4             19.277917  \n",
       "..                  ...  \n",
       "505           10.370000  \n",
       "506           14.100000  \n",
       "507           13.930000  \n",
       "508          263.130000  \n",
       "509           48.790000  \n",
       "\n",
       "[510 rows x 39 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = pd.read_csv('data17_21.csv')\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "a66c96e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "Yperf = pd.read_csv('perfs17_21.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "8abc0c52",
   "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>asset_turnover</th>\n",
       "      <th>buyback_yield</th>\n",
       "      <th>capex_to_revenue</th>\n",
       "      <th>cash_ratio</th>\n",
       "      <th>cash_to_debt</th>\n",
       "      <th>cogs_to_revenue</th>\n",
       "      <th>mscore</th>\n",
       "      <th>zscore</th>\n",
       "      <th>current_ratio</th>\n",
       "      <th>days_inventory</th>\n",
       "      <th>...</th>\n",
       "      <th>price_to_earnings_ratio_nri</th>\n",
       "      <th>price_earnings_growth_ratio</th>\n",
       "      <th>price_to_free_cashflow</th>\n",
       "      <th>price_to_operating_cashflow</th>\n",
       "      <th>rate_of_return</th>\n",
       "      <th>scaled_net_operating_assets</th>\n",
       "      <th>yoy_ebitda_growth</th>\n",
       "      <th>yoy_eps_growth</th>\n",
       "      <th>yoy_revenue_growth</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.660000</td>\n",
       "      <td>4.090000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.740000</td>\n",
       "      <td>0.640000</td>\n",
       "      <td>0.620000</td>\n",
       "      <td>-2.500000</td>\n",
       "      <td>3.620000</td>\n",
       "      <td>1.280000</td>\n",
       "      <td>9.040000</td>\n",
       "      <td>...</td>\n",
       "      <td>16.730000</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>15.630000</td>\n",
       "      <td>12.600000</td>\n",
       "      <td>19.240000</td>\n",
       "      <td>0.550000</td>\n",
       "      <td>9.360000</td>\n",
       "      <td>10.830000</td>\n",
       "      <td>11.340000</td>\n",
       "      <td>0.132137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.724932</td>\n",
       "      <td>2.035574</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>1.410274</td>\n",
       "      <td>2.648413</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>-2.543382</td>\n",
       "      <td>6.686479</td>\n",
       "      <td>2.332192</td>\n",
       "      <td>89.855094</td>\n",
       "      <td>...</td>\n",
       "      <td>44.909524</td>\n",
       "      <td>10.936250</td>\n",
       "      <td>33.836721</td>\n",
       "      <td>35.271493</td>\n",
       "      <td>14.260952</td>\n",
       "      <td>0.482917</td>\n",
       "      <td>26.097361</td>\n",
       "      <td>46.020147</td>\n",
       "      <td>19.277917</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.540000</td>\n",
       "      <td>1.080000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>1.650000</td>\n",
       "      <td>3.090000</td>\n",
       "      <td>0.140000</td>\n",
       "      <td>-2.490000</td>\n",
       "      <td>10.810000</td>\n",
       "      <td>2.050000</td>\n",
       "      <td>89.855094</td>\n",
       "      <td>...</td>\n",
       "      <td>52.320000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>32.410000</td>\n",
       "      <td>30.420000</td>\n",
       "      <td>21.820000</td>\n",
       "      <td>0.360000</td>\n",
       "      <td>39.030000</td>\n",
       "      <td>45.690000</td>\n",
       "      <td>25.510000</td>\n",
       "      <td>0.233293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.360000</td>\n",
       "      <td>0.140000</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>0.660000</td>\n",
       "      <td>0.130000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>-1.490000</td>\n",
       "      <td>2.640000</td>\n",
       "      <td>1.470000</td>\n",
       "      <td>81.440000</td>\n",
       "      <td>...</td>\n",
       "      <td>39.870000</td>\n",
       "      <td>4.440000</td>\n",
       "      <td>33.680000</td>\n",
       "      <td>27.720000</td>\n",
       "      <td>11.950000</td>\n",
       "      <td>2.130000</td>\n",
       "      <td>25.600000</td>\n",
       "      <td>-17.030000</td>\n",
       "      <td>36.640000</td>\n",
       "      <td>0.155163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.724932</td>\n",
       "      <td>2.035574</td>\n",
       "      <td>0.086892</td>\n",
       "      <td>1.410274</td>\n",
       "      <td>2.648413</td>\n",
       "      <td>0.437534</td>\n",
       "      <td>-2.543382</td>\n",
       "      <td>6.686479</td>\n",
       "      <td>2.332192</td>\n",
       "      <td>89.855094</td>\n",
       "      <td>...</td>\n",
       "      <td>44.909524</td>\n",
       "      <td>10.936250</td>\n",
       "      <td>33.836721</td>\n",
       "      <td>35.271493</td>\n",
       "      <td>14.260952</td>\n",
       "      <td>0.482917</td>\n",
       "      <td>26.097361</td>\n",
       "      <td>46.020147</td>\n",
       "      <td>19.277917</td>\n",
       "      <td>0.407649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>505</th>\n",
       "      <td>1.570000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>0.790000</td>\n",
       "      <td>-2.260000</td>\n",
       "      <td>2.720000</td>\n",
       "      <td>0.720000</td>\n",
       "      <td>28.090000</td>\n",
       "      <td>...</td>\n",
       "      <td>22.070000</td>\n",
       "      <td>6.195821</td>\n",
       "      <td>10.530000</td>\n",
       "      <td>7.910000</td>\n",
       "      <td>-5.110000</td>\n",
       "      <td>0.640000</td>\n",
       "      <td>65.800000</td>\n",
       "      <td>463.460000</td>\n",
       "      <td>10.370000</td>\n",
       "      <td>-0.227608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506</th>\n",
       "      <td>0.560000</td>\n",
       "      <td>1.733289</td>\n",
       "      <td>0.060000</td>\n",
       "      <td>0.830000</td>\n",
       "      <td>1.580000</td>\n",
       "      <td>0.280000</td>\n",
       "      <td>-3.240000</td>\n",
       "      <td>6.120000</td>\n",
       "      <td>1.120000</td>\n",
       "      <td>83.647794</td>\n",
       "      <td>...</td>\n",
       "      <td>63.153488</td>\n",
       "      <td>6.195821</td>\n",
       "      <td>53.290000</td>\n",
       "      <td>42.510000</td>\n",
       "      <td>12.240260</td>\n",
       "      <td>0.290000</td>\n",
       "      <td>156.700000</td>\n",
       "      <td>43.870000</td>\n",
       "      <td>14.100000</td>\n",
       "      <td>-0.137476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>507</th>\n",
       "      <td>0.240000</td>\n",
       "      <td>-0.990000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.610000</td>\n",
       "      <td>-1.792316</td>\n",
       "      <td>1.030000</td>\n",
       "      <td>0.840000</td>\n",
       "      <td>26.040000</td>\n",
       "      <td>...</td>\n",
       "      <td>22.870000</td>\n",
       "      <td>8.730000</td>\n",
       "      <td>52.026374</td>\n",
       "      <td>16.700000</td>\n",
       "      <td>-1.040000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>3.480000</td>\n",
       "      <td>6.090000</td>\n",
       "      <td>13.930000</td>\n",
       "      <td>-0.065540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>508</th>\n",
       "      <td>0.810000</td>\n",
       "      <td>-1.810000</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>3.370000</td>\n",
       "      <td>40.040000</td>\n",
       "      <td>0.310000</td>\n",
       "      <td>-0.480000</td>\n",
       "      <td>47.500000</td>\n",
       "      <td>3.800000</td>\n",
       "      <td>83.647794</td>\n",
       "      <td>...</td>\n",
       "      <td>165.360000</td>\n",
       "      <td>6.195821</td>\n",
       "      <td>80.070000</td>\n",
       "      <td>75.390000</td>\n",
       "      <td>12.240260</td>\n",
       "      <td>-0.220000</td>\n",
       "      <td>17.709700</td>\n",
       "      <td>52.306596</td>\n",
       "      <td>263.130000</td>\n",
       "      <td>-0.370127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>509</th>\n",
       "      <td>0.330000</td>\n",
       "      <td>1.733289</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>2.090000</td>\n",
       "      <td>1.560000</td>\n",
       "      <td>0.220000</td>\n",
       "      <td>-2.670000</td>\n",
       "      <td>11.580000</td>\n",
       "      <td>2.570000</td>\n",
       "      <td>83.647794</td>\n",
       "      <td>...</td>\n",
       "      <td>63.153488</td>\n",
       "      <td>6.195821</td>\n",
       "      <td>222.560000</td>\n",
       "      <td>158.440000</td>\n",
       "      <td>12.240260</td>\n",
       "      <td>-0.010000</td>\n",
       "      <td>-86.740000</td>\n",
       "      <td>-116.850000</td>\n",
       "      <td>48.790000</td>\n",
       "      <td>-0.651760</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>510 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     asset_turnover  buyback_yield  capex_to_revenue  cash_ratio  \\\n",
       "0          0.660000       4.090000          0.050000    0.740000   \n",
       "1          0.724932       2.035574          0.040000    1.410274   \n",
       "2          0.540000       1.080000          0.020000    1.650000   \n",
       "3          0.360000       0.140000          0.040000    0.660000   \n",
       "4          0.724932       2.035574          0.086892    1.410274   \n",
       "..              ...            ...               ...         ...   \n",
       "505        1.570000       0.250000          0.010000    0.050000   \n",
       "506        0.560000       1.733289          0.060000    0.830000   \n",
       "507        0.240000      -0.990000          0.320000    0.030000   \n",
       "508        0.810000      -1.810000          0.030000    3.370000   \n",
       "509        0.330000       1.733289          0.090000    2.090000   \n",
       "\n",
       "     cash_to_debt  cogs_to_revenue    mscore     zscore  current_ratio  \\\n",
       "0        0.640000         0.620000 -2.500000   3.620000       1.280000   \n",
       "1        2.648413         0.250000 -2.543382   6.686479       2.332192   \n",
       "2        3.090000         0.140000 -2.490000  10.810000       2.050000   \n",
       "3        0.130000         0.400000 -1.490000   2.640000       1.470000   \n",
       "4        2.648413         0.437534 -2.543382   6.686479       2.332192   \n",
       "..            ...              ...       ...        ...            ...   \n",
       "505      0.040000         0.790000 -2.260000   2.720000       0.720000   \n",
       "506      1.580000         0.280000 -3.240000   6.120000       1.120000   \n",
       "507      0.010000         0.610000 -1.792316   1.030000       0.840000   \n",
       "508     40.040000         0.310000 -0.480000  47.500000       3.800000   \n",
       "509      1.560000         0.220000 -2.670000  11.580000       2.570000   \n",
       "\n",
       "     days_inventory  ...  price_to_earnings_ratio_nri  \\\n",
       "0          9.040000  ...                    16.730000   \n",
       "1         89.855094  ...                    44.909524   \n",
       "2         89.855094  ...                    52.320000   \n",
       "3         81.440000  ...                    39.870000   \n",
       "4         89.855094  ...                    44.909524   \n",
       "..              ...  ...                          ...   \n",
       "505       28.090000  ...                    22.070000   \n",
       "506       83.647794  ...                    63.153488   \n",
       "507       26.040000  ...                    22.870000   \n",
       "508       83.647794  ...                   165.360000   \n",
       "509       83.647794  ...                    63.153488   \n",
       "\n",
       "     price_earnings_growth_ratio  price_to_free_cashflow  \\\n",
       "0                       1.250000               15.630000   \n",
       "1                      10.936250               33.836721   \n",
       "2                       2.420000               32.410000   \n",
       "3                       4.440000               33.680000   \n",
       "4                      10.936250               33.836721   \n",
       "..                           ...                     ...   \n",
       "505                     6.195821               10.530000   \n",
       "506                     6.195821               53.290000   \n",
       "507                     8.730000               52.026374   \n",
       "508                     6.195821               80.070000   \n",
       "509                     6.195821              222.560000   \n",
       "\n",
       "     price_to_operating_cashflow  rate_of_return  scaled_net_operating_assets  \\\n",
       "0                      12.600000       19.240000                     0.550000   \n",
       "1                      35.271493       14.260952                     0.482917   \n",
       "2                      30.420000       21.820000                     0.360000   \n",
       "3                      27.720000       11.950000                     2.130000   \n",
       "4                      35.271493       14.260952                     0.482917   \n",
       "..                           ...             ...                          ...   \n",
       "505                     7.910000       -5.110000                     0.640000   \n",
       "506                    42.510000       12.240260                     0.290000   \n",
       "507                    16.700000       -1.040000                     0.750000   \n",
       "508                    75.390000       12.240260                    -0.220000   \n",
       "509                   158.440000       12.240260                    -0.010000   \n",
       "\n",
       "     yoy_ebitda_growth  yoy_eps_growth  yoy_revenue_growth         0  \n",
       "0             9.360000       10.830000           11.340000  0.132137  \n",
       "1            26.097361       46.020147           19.277917  0.000000  \n",
       "2            39.030000       45.690000           25.510000  0.233293  \n",
       "3            25.600000      -17.030000           36.640000  0.155163  \n",
       "4            26.097361       46.020147           19.277917  0.407649  \n",
       "..                 ...             ...                 ...       ...  \n",
       "505          65.800000      463.460000           10.370000 -0.227608  \n",
       "506         156.700000       43.870000           14.100000 -0.137476  \n",
       "507           3.480000        6.090000           13.930000 -0.065540  \n",
       "508          17.709700       52.306596          263.130000 -0.370127  \n",
       "509         -86.740000     -116.850000           48.790000 -0.651760  \n",
       "\n",
       "[510 rows x 40 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_Yperf = pd.concat([X, Yperf], axis=\"columns\")\n",
    "X_Yperf.drop([41])\n",
    "X_Yperf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "1def1e0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_Yperf.to_csv(\"X_Yperf_17_21.csv\",index=False)"
   ]
  },
  {
   "cell_type": "raw",
   "id": "06aaf928",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "2a960813",
   "metadata": {},
   "outputs": [],
   "source": [
    "Y1perf = pd.read_csv('perfs17_20.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "d67461d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = pd.read_csv('data17_20.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "23dfcac5",
   "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>asset_turnover</th>\n",
       "      <th>buyback_yield</th>\n",
       "      <th>capex_to_revenue</th>\n",
       "      <th>cash_ratio</th>\n",
       "      <th>cash_to_debt</th>\n",
       "      <th>cogs_to_revenue</th>\n",
       "      <th>mscore</th>\n",
       "      <th>zscore</th>\n",
       "      <th>current_ratio</th>\n",
       "      <th>days_inventory</th>\n",
       "      <th>...</th>\n",
       "      <th>price_to_earnings_ratio_nri</th>\n",
       "      <th>price_earnings_growth_ratio</th>\n",
       "      <th>price_to_free_cashflow</th>\n",
       "      <th>price_to_operating_cashflow</th>\n",
       "      <th>rate_of_return</th>\n",
       "      <th>scaled_net_operating_assets</th>\n",
       "      <th>yoy_ebitda_growth</th>\n",
       "      <th>yoy_eps_growth</th>\n",
       "      <th>yoy_revenue_growth</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.660000</td>\n",
       "      <td>4.090000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.740000</td>\n",
       "      <td>0.640000</td>\n",
       "      <td>0.620000</td>\n",
       "      <td>-2.500000</td>\n",
       "      <td>3.620000</td>\n",
       "      <td>1.280000</td>\n",
       "      <td>9.040000</td>\n",
       "      <td>...</td>\n",
       "      <td>16.730000</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>15.630000</td>\n",
       "      <td>12.600000</td>\n",
       "      <td>19.240000</td>\n",
       "      <td>0.550000</td>\n",
       "      <td>9.360000</td>\n",
       "      <td>10.830000</td>\n",
       "      <td>11.340000</td>\n",
       "      <td>0.132137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.724932</td>\n",
       "      <td>2.035574</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>1.410274</td>\n",
       "      <td>2.648413</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>-2.543382</td>\n",
       "      <td>6.686479</td>\n",
       "      <td>2.332192</td>\n",
       "      <td>89.855094</td>\n",
       "      <td>...</td>\n",
       "      <td>44.909524</td>\n",
       "      <td>10.936250</td>\n",
       "      <td>33.836721</td>\n",
       "      <td>35.271493</td>\n",
       "      <td>14.260952</td>\n",
       "      <td>0.482917</td>\n",
       "      <td>26.097361</td>\n",
       "      <td>46.020147</td>\n",
       "      <td>19.277917</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.540000</td>\n",
       "      <td>1.080000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>1.650000</td>\n",
       "      <td>3.090000</td>\n",
       "      <td>0.140000</td>\n",
       "      <td>-2.490000</td>\n",
       "      <td>10.810000</td>\n",
       "      <td>2.050000</td>\n",
       "      <td>89.855094</td>\n",
       "      <td>...</td>\n",
       "      <td>52.320000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>32.410000</td>\n",
       "      <td>30.420000</td>\n",
       "      <td>21.820000</td>\n",
       "      <td>0.360000</td>\n",
       "      <td>39.030000</td>\n",
       "      <td>45.690000</td>\n",
       "      <td>25.510000</td>\n",
       "      <td>0.233293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.360000</td>\n",
       "      <td>0.140000</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>0.660000</td>\n",
       "      <td>0.130000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>-1.490000</td>\n",
       "      <td>2.640000</td>\n",
       "      <td>1.470000</td>\n",
       "      <td>81.440000</td>\n",
       "      <td>...</td>\n",
       "      <td>39.870000</td>\n",
       "      <td>4.440000</td>\n",
       "      <td>33.680000</td>\n",
       "      <td>27.720000</td>\n",
       "      <td>11.950000</td>\n",
       "      <td>2.130000</td>\n",
       "      <td>25.600000</td>\n",
       "      <td>-17.030000</td>\n",
       "      <td>36.640000</td>\n",
       "      <td>0.155163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.724932</td>\n",
       "      <td>2.035574</td>\n",
       "      <td>0.086892</td>\n",
       "      <td>1.410274</td>\n",
       "      <td>2.648413</td>\n",
       "      <td>0.437534</td>\n",
       "      <td>-2.543382</td>\n",
       "      <td>6.686479</td>\n",
       "      <td>2.332192</td>\n",
       "      <td>89.855094</td>\n",
       "      <td>...</td>\n",
       "      <td>44.909524</td>\n",
       "      <td>10.936250</td>\n",
       "      <td>33.836721</td>\n",
       "      <td>35.271493</td>\n",
       "      <td>14.260952</td>\n",
       "      <td>0.482917</td>\n",
       "      <td>26.097361</td>\n",
       "      <td>46.020147</td>\n",
       "      <td>19.277917</td>\n",
       "      <td>0.407649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403</th>\n",
       "      <td>1.580000</td>\n",
       "      <td>4.830000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.790000</td>\n",
       "      <td>-3.110000</td>\n",
       "      <td>2.220000</td>\n",
       "      <td>0.670000</td>\n",
       "      <td>32.830000</td>\n",
       "      <td>...</td>\n",
       "      <td>190.100000</td>\n",
       "      <td>13.344429</td>\n",
       "      <td>8.140000</td>\n",
       "      <td>6.100000</td>\n",
       "      <td>10.550000</td>\n",
       "      <td>0.890000</td>\n",
       "      <td>-55.550000</td>\n",
       "      <td>-87.940000</td>\n",
       "      <td>6.570000</td>\n",
       "      <td>-0.184517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404</th>\n",
       "      <td>0.590000</td>\n",
       "      <td>1.422184</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.650000</td>\n",
       "      <td>1.240000</td>\n",
       "      <td>0.290000</td>\n",
       "      <td>-3.190000</td>\n",
       "      <td>5.750000</td>\n",
       "      <td>1.040000</td>\n",
       "      <td>88.257353</td>\n",
       "      <td>...</td>\n",
       "      <td>70.917500</td>\n",
       "      <td>13.344429</td>\n",
       "      <td>67.650000</td>\n",
       "      <td>48.510000</td>\n",
       "      <td>13.355974</td>\n",
       "      <td>0.380000</td>\n",
       "      <td>14.940000</td>\n",
       "      <td>-9.840000</td>\n",
       "      <td>22.650000</td>\n",
       "      <td>-0.036107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>405</th>\n",
       "      <td>0.220000</td>\n",
       "      <td>-2.030000</td>\n",
       "      <td>0.470000</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.570000</td>\n",
       "      <td>-2.417895</td>\n",
       "      <td>1.030000</td>\n",
       "      <td>0.770000</td>\n",
       "      <td>30.010000</td>\n",
       "      <td>...</td>\n",
       "      <td>23.900000</td>\n",
       "      <td>7.660000</td>\n",
       "      <td>82.560323</td>\n",
       "      <td>12.360000</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>0.730000</td>\n",
       "      <td>3.390000</td>\n",
       "      <td>5.680000</td>\n",
       "      <td>-1.540000</td>\n",
       "      <td>0.085100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>406</th>\n",
       "      <td>0.760000</td>\n",
       "      <td>-2.550000</td>\n",
       "      <td>0.060000</td>\n",
       "      <td>2.560000</td>\n",
       "      <td>11.800000</td>\n",
       "      <td>0.190000</td>\n",
       "      <td>-2.230000</td>\n",
       "      <td>29.250000</td>\n",
       "      <td>3.280000</td>\n",
       "      <td>88.257353</td>\n",
       "      <td>...</td>\n",
       "      <td>847.780000</td>\n",
       "      <td>13.344429</td>\n",
       "      <td>170.690000</td>\n",
       "      <td>127.810000</td>\n",
       "      <td>13.355974</td>\n",
       "      <td>0.140000</td>\n",
       "      <td>134.690000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>99.110000</td>\n",
       "      <td>-0.635127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>407</th>\n",
       "      <td>0.350000</td>\n",
       "      <td>1.422184</td>\n",
       "      <td>0.120000</td>\n",
       "      <td>3.230000</td>\n",
       "      <td>1.510000</td>\n",
       "      <td>0.220000</td>\n",
       "      <td>-2.800000</td>\n",
       "      <td>8.210000</td>\n",
       "      <td>3.730000</td>\n",
       "      <td>88.257353</td>\n",
       "      <td>...</td>\n",
       "      <td>70.917500</td>\n",
       "      <td>13.344429</td>\n",
       "      <td>609.620000</td>\n",
       "      <td>211.830000</td>\n",
       "      <td>13.355974</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>-400.750000</td>\n",
       "      <td>-286.960000</td>\n",
       "      <td>36.070000</td>\n",
       "      <td>0.608983</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>408 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     asset_turnover  buyback_yield  capex_to_revenue  cash_ratio  \\\n",
       "0          0.660000       4.090000          0.050000    0.740000   \n",
       "1          0.724932       2.035574          0.040000    1.410274   \n",
       "2          0.540000       1.080000          0.020000    1.650000   \n",
       "3          0.360000       0.140000          0.040000    0.660000   \n",
       "4          0.724932       2.035574          0.086892    1.410274   \n",
       "..              ...            ...               ...         ...   \n",
       "403        1.580000       4.830000          0.010000    0.020000   \n",
       "404        0.590000       1.422184          0.070000    0.650000   \n",
       "405        0.220000      -2.030000          0.470000    0.030000   \n",
       "406        0.760000      -2.550000          0.060000    2.560000   \n",
       "407        0.350000       1.422184          0.120000    3.230000   \n",
       "\n",
       "     cash_to_debt  cogs_to_revenue    mscore     zscore  current_ratio  \\\n",
       "0        0.640000         0.620000 -2.500000   3.620000       1.280000   \n",
       "1        2.648413         0.250000 -2.543382   6.686479       2.332192   \n",
       "2        3.090000         0.140000 -2.490000  10.810000       2.050000   \n",
       "3        0.130000         0.400000 -1.490000   2.640000       1.470000   \n",
       "4        2.648413         0.437534 -2.543382   6.686479       2.332192   \n",
       "..            ...              ...       ...        ...            ...   \n",
       "403      0.010000         0.790000 -3.110000   2.220000       0.670000   \n",
       "404      1.240000         0.290000 -3.190000   5.750000       1.040000   \n",
       "405      0.010000         0.570000 -2.417895   1.030000       0.770000   \n",
       "406     11.800000         0.190000 -2.230000  29.250000       3.280000   \n",
       "407      1.510000         0.220000 -2.800000   8.210000       3.730000   \n",
       "\n",
       "     days_inventory  ...  price_to_earnings_ratio_nri  \\\n",
       "0          9.040000  ...                    16.730000   \n",
       "1         89.855094  ...                    44.909524   \n",
       "2         89.855094  ...                    52.320000   \n",
       "3         81.440000  ...                    39.870000   \n",
       "4         89.855094  ...                    44.909524   \n",
       "..              ...  ...                          ...   \n",
       "403       32.830000  ...                   190.100000   \n",
       "404       88.257353  ...                    70.917500   \n",
       "405       30.010000  ...                    23.900000   \n",
       "406       88.257353  ...                   847.780000   \n",
       "407       88.257353  ...                    70.917500   \n",
       "\n",
       "     price_earnings_growth_ratio  price_to_free_cashflow  \\\n",
       "0                       1.250000               15.630000   \n",
       "1                      10.936250               33.836721   \n",
       "2                       2.420000               32.410000   \n",
       "3                       4.440000               33.680000   \n",
       "4                      10.936250               33.836721   \n",
       "..                           ...                     ...   \n",
       "403                    13.344429                8.140000   \n",
       "404                    13.344429               67.650000   \n",
       "405                     7.660000               82.560323   \n",
       "406                    13.344429              170.690000   \n",
       "407                    13.344429              609.620000   \n",
       "\n",
       "     price_to_operating_cashflow  rate_of_return  scaled_net_operating_assets  \\\n",
       "0                      12.600000       19.240000                     0.550000   \n",
       "1                      35.271493       14.260952                     0.482917   \n",
       "2                      30.420000       21.820000                     0.360000   \n",
       "3                      27.720000       11.950000                     2.130000   \n",
       "4                      35.271493       14.260952                     0.482917   \n",
       "..                           ...             ...                          ...   \n",
       "403                     6.100000       10.550000                     0.890000   \n",
       "404                    48.510000       13.355974                     0.380000   \n",
       "405                    12.360000        0.090000                     0.730000   \n",
       "406                   127.810000       13.355974                     0.140000   \n",
       "407                   211.830000       13.355974                     0.030000   \n",
       "\n",
       "     yoy_ebitda_growth  yoy_eps_growth  yoy_revenue_growth         0  \n",
       "0             9.360000       10.830000           11.340000  0.132137  \n",
       "1            26.097361       46.020147           19.277917  0.000000  \n",
       "2            39.030000       45.690000           25.510000  0.233293  \n",
       "3            25.600000      -17.030000           36.640000  0.155163  \n",
       "4            26.097361       46.020147           19.277917  0.407649  \n",
       "..                 ...             ...                 ...       ...  \n",
       "403         -55.550000      -87.940000            6.570000 -0.184517  \n",
       "404          14.940000       -9.840000           22.650000 -0.036107  \n",
       "405           3.390000        5.680000           -1.540000  0.085100  \n",
       "406         134.690000      200.000000           99.110000 -0.635127  \n",
       "407        -400.750000     -286.960000           36.070000  0.608983  \n",
       "\n",
       "[408 rows x 40 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_Y1perf = pd.concat([X, Y1perf], axis=\"columns\")\n",
    "X_Y1perf.drop([41])\n",
    "X_Y1perf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "f2b9ee84",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_Y1perf.to_csv(\"X_Yperf_17_20.csv\",index=False)"
   ]
  },
  {
   "cell_type": "raw",
   "id": "af34cb79",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "d2f54e36",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = pd.read_csv('data17_21.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "9f154aca",
   "metadata": {},
   "outputs": [],
   "source": [
    "Ylabels = pd.read_csv('labels17_21.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "abb38e2a",
   "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>asset_turnover</th>\n",
       "      <th>buyback_yield</th>\n",
       "      <th>capex_to_revenue</th>\n",
       "      <th>cash_ratio</th>\n",
       "      <th>cash_to_debt</th>\n",
       "      <th>cogs_to_revenue</th>\n",
       "      <th>mscore</th>\n",
       "      <th>zscore</th>\n",
       "      <th>current_ratio</th>\n",
       "      <th>days_inventory</th>\n",
       "      <th>...</th>\n",
       "      <th>price_to_earnings_ratio_nri</th>\n",
       "      <th>price_earnings_growth_ratio</th>\n",
       "      <th>price_to_free_cashflow</th>\n",
       "      <th>price_to_operating_cashflow</th>\n",
       "      <th>rate_of_return</th>\n",
       "      <th>scaled_net_operating_assets</th>\n",
       "      <th>yoy_ebitda_growth</th>\n",
       "      <th>yoy_eps_growth</th>\n",
       "      <th>yoy_revenue_growth</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.660000</td>\n",
       "      <td>4.090000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.740000</td>\n",
       "      <td>0.640000</td>\n",
       "      <td>0.620000</td>\n",
       "      <td>-2.500000</td>\n",
       "      <td>3.620000</td>\n",
       "      <td>1.280000</td>\n",
       "      <td>9.040000</td>\n",
       "      <td>...</td>\n",
       "      <td>16.730000</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>15.630000</td>\n",
       "      <td>12.600000</td>\n",
       "      <td>19.240000</td>\n",
       "      <td>0.550000</td>\n",
       "      <td>9.360000</td>\n",
       "      <td>10.830000</td>\n",
       "      <td>11.340000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.724932</td>\n",
       "      <td>2.035574</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>1.410274</td>\n",
       "      <td>2.648413</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>-2.543382</td>\n",
       "      <td>6.686479</td>\n",
       "      <td>2.332192</td>\n",
       "      <td>89.855094</td>\n",
       "      <td>...</td>\n",
       "      <td>44.909524</td>\n",
       "      <td>10.936250</td>\n",
       "      <td>33.836721</td>\n",
       "      <td>35.271493</td>\n",
       "      <td>14.260952</td>\n",
       "      <td>0.482917</td>\n",
       "      <td>26.097361</td>\n",
       "      <td>46.020147</td>\n",
       "      <td>19.277917</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.540000</td>\n",
       "      <td>1.080000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>1.650000</td>\n",
       "      <td>3.090000</td>\n",
       "      <td>0.140000</td>\n",
       "      <td>-2.490000</td>\n",
       "      <td>10.810000</td>\n",
       "      <td>2.050000</td>\n",
       "      <td>89.855094</td>\n",
       "      <td>...</td>\n",
       "      <td>52.320000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>32.410000</td>\n",
       "      <td>30.420000</td>\n",
       "      <td>21.820000</td>\n",
       "      <td>0.360000</td>\n",
       "      <td>39.030000</td>\n",
       "      <td>45.690000</td>\n",
       "      <td>25.510000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.360000</td>\n",
       "      <td>0.140000</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>0.660000</td>\n",
       "      <td>0.130000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>-1.490000</td>\n",
       "      <td>2.640000</td>\n",
       "      <td>1.470000</td>\n",
       "      <td>81.440000</td>\n",
       "      <td>...</td>\n",
       "      <td>39.870000</td>\n",
       "      <td>4.440000</td>\n",
       "      <td>33.680000</td>\n",
       "      <td>27.720000</td>\n",
       "      <td>11.950000</td>\n",
       "      <td>2.130000</td>\n",
       "      <td>25.600000</td>\n",
       "      <td>-17.030000</td>\n",
       "      <td>36.640000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.724932</td>\n",
       "      <td>2.035574</td>\n",
       "      <td>0.086892</td>\n",
       "      <td>1.410274</td>\n",
       "      <td>2.648413</td>\n",
       "      <td>0.437534</td>\n",
       "      <td>-2.543382</td>\n",
       "      <td>6.686479</td>\n",
       "      <td>2.332192</td>\n",
       "      <td>89.855094</td>\n",
       "      <td>...</td>\n",
       "      <td>44.909524</td>\n",
       "      <td>10.936250</td>\n",
       "      <td>33.836721</td>\n",
       "      <td>35.271493</td>\n",
       "      <td>14.260952</td>\n",
       "      <td>0.482917</td>\n",
       "      <td>26.097361</td>\n",
       "      <td>46.020147</td>\n",
       "      <td>19.277917</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>505</th>\n",
       "      <td>1.570000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>0.790000</td>\n",
       "      <td>-2.260000</td>\n",
       "      <td>2.720000</td>\n",
       "      <td>0.720000</td>\n",
       "      <td>28.090000</td>\n",
       "      <td>...</td>\n",
       "      <td>22.070000</td>\n",
       "      <td>6.195821</td>\n",
       "      <td>10.530000</td>\n",
       "      <td>7.910000</td>\n",
       "      <td>-5.110000</td>\n",
       "      <td>0.640000</td>\n",
       "      <td>65.800000</td>\n",
       "      <td>463.460000</td>\n",
       "      <td>10.370000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506</th>\n",
       "      <td>0.560000</td>\n",
       "      <td>1.733289</td>\n",
       "      <td>0.060000</td>\n",
       "      <td>0.830000</td>\n",
       "      <td>1.580000</td>\n",
       "      <td>0.280000</td>\n",
       "      <td>-3.240000</td>\n",
       "      <td>6.120000</td>\n",
       "      <td>1.120000</td>\n",
       "      <td>83.647794</td>\n",
       "      <td>...</td>\n",
       "      <td>63.153488</td>\n",
       "      <td>6.195821</td>\n",
       "      <td>53.290000</td>\n",
       "      <td>42.510000</td>\n",
       "      <td>12.240260</td>\n",
       "      <td>0.290000</td>\n",
       "      <td>156.700000</td>\n",
       "      <td>43.870000</td>\n",
       "      <td>14.100000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>507</th>\n",
       "      <td>0.240000</td>\n",
       "      <td>-0.990000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.610000</td>\n",
       "      <td>-1.792316</td>\n",
       "      <td>1.030000</td>\n",
       "      <td>0.840000</td>\n",
       "      <td>26.040000</td>\n",
       "      <td>...</td>\n",
       "      <td>22.870000</td>\n",
       "      <td>8.730000</td>\n",
       "      <td>52.026374</td>\n",
       "      <td>16.700000</td>\n",
       "      <td>-1.040000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>3.480000</td>\n",
       "      <td>6.090000</td>\n",
       "      <td>13.930000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>508</th>\n",
       "      <td>0.810000</td>\n",
       "      <td>-1.810000</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>3.370000</td>\n",
       "      <td>40.040000</td>\n",
       "      <td>0.310000</td>\n",
       "      <td>-0.480000</td>\n",
       "      <td>47.500000</td>\n",
       "      <td>3.800000</td>\n",
       "      <td>83.647794</td>\n",
       "      <td>...</td>\n",
       "      <td>165.360000</td>\n",
       "      <td>6.195821</td>\n",
       "      <td>80.070000</td>\n",
       "      <td>75.390000</td>\n",
       "      <td>12.240260</td>\n",
       "      <td>-0.220000</td>\n",
       "      <td>17.709700</td>\n",
       "      <td>52.306596</td>\n",
       "      <td>263.130000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>509</th>\n",
       "      <td>0.330000</td>\n",
       "      <td>1.733289</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>2.090000</td>\n",
       "      <td>1.560000</td>\n",
       "      <td>0.220000</td>\n",
       "      <td>-2.670000</td>\n",
       "      <td>11.580000</td>\n",
       "      <td>2.570000</td>\n",
       "      <td>83.647794</td>\n",
       "      <td>...</td>\n",
       "      <td>63.153488</td>\n",
       "      <td>6.195821</td>\n",
       "      <td>222.560000</td>\n",
       "      <td>158.440000</td>\n",
       "      <td>12.240260</td>\n",
       "      <td>-0.010000</td>\n",
       "      <td>-86.740000</td>\n",
       "      <td>-116.850000</td>\n",
       "      <td>48.790000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>510 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     asset_turnover  buyback_yield  capex_to_revenue  cash_ratio  \\\n",
       "0          0.660000       4.090000          0.050000    0.740000   \n",
       "1          0.724932       2.035574          0.040000    1.410274   \n",
       "2          0.540000       1.080000          0.020000    1.650000   \n",
       "3          0.360000       0.140000          0.040000    0.660000   \n",
       "4          0.724932       2.035574          0.086892    1.410274   \n",
       "..              ...            ...               ...         ...   \n",
       "505        1.570000       0.250000          0.010000    0.050000   \n",
       "506        0.560000       1.733289          0.060000    0.830000   \n",
       "507        0.240000      -0.990000          0.320000    0.030000   \n",
       "508        0.810000      -1.810000          0.030000    3.370000   \n",
       "509        0.330000       1.733289          0.090000    2.090000   \n",
       "\n",
       "     cash_to_debt  cogs_to_revenue    mscore     zscore  current_ratio  \\\n",
       "0        0.640000         0.620000 -2.500000   3.620000       1.280000   \n",
       "1        2.648413         0.250000 -2.543382   6.686479       2.332192   \n",
       "2        3.090000         0.140000 -2.490000  10.810000       2.050000   \n",
       "3        0.130000         0.400000 -1.490000   2.640000       1.470000   \n",
       "4        2.648413         0.437534 -2.543382   6.686479       2.332192   \n",
       "..            ...              ...       ...        ...            ...   \n",
       "505      0.040000         0.790000 -2.260000   2.720000       0.720000   \n",
       "506      1.580000         0.280000 -3.240000   6.120000       1.120000   \n",
       "507      0.010000         0.610000 -1.792316   1.030000       0.840000   \n",
       "508     40.040000         0.310000 -0.480000  47.500000       3.800000   \n",
       "509      1.560000         0.220000 -2.670000  11.580000       2.570000   \n",
       "\n",
       "     days_inventory  ...  price_to_earnings_ratio_nri  \\\n",
       "0          9.040000  ...                    16.730000   \n",
       "1         89.855094  ...                    44.909524   \n",
       "2         89.855094  ...                    52.320000   \n",
       "3         81.440000  ...                    39.870000   \n",
       "4         89.855094  ...                    44.909524   \n",
       "..              ...  ...                          ...   \n",
       "505       28.090000  ...                    22.070000   \n",
       "506       83.647794  ...                    63.153488   \n",
       "507       26.040000  ...                    22.870000   \n",
       "508       83.647794  ...                   165.360000   \n",
       "509       83.647794  ...                    63.153488   \n",
       "\n",
       "     price_earnings_growth_ratio  price_to_free_cashflow  \\\n",
       "0                       1.250000               15.630000   \n",
       "1                      10.936250               33.836721   \n",
       "2                       2.420000               32.410000   \n",
       "3                       4.440000               33.680000   \n",
       "4                      10.936250               33.836721   \n",
       "..                           ...                     ...   \n",
       "505                     6.195821               10.530000   \n",
       "506                     6.195821               53.290000   \n",
       "507                     8.730000               52.026374   \n",
       "508                     6.195821               80.070000   \n",
       "509                     6.195821              222.560000   \n",
       "\n",
       "     price_to_operating_cashflow  rate_of_return  scaled_net_operating_assets  \\\n",
       "0                      12.600000       19.240000                     0.550000   \n",
       "1                      35.271493       14.260952                     0.482917   \n",
       "2                      30.420000       21.820000                     0.360000   \n",
       "3                      27.720000       11.950000                     2.130000   \n",
       "4                      35.271493       14.260952                     0.482917   \n",
       "..                           ...             ...                          ...   \n",
       "505                     7.910000       -5.110000                     0.640000   \n",
       "506                    42.510000       12.240260                     0.290000   \n",
       "507                    16.700000       -1.040000                     0.750000   \n",
       "508                    75.390000       12.240260                    -0.220000   \n",
       "509                   158.440000       12.240260                    -0.010000   \n",
       "\n",
       "     yoy_ebitda_growth  yoy_eps_growth  yoy_revenue_growth  0  \n",
       "0             9.360000       10.830000           11.340000  0  \n",
       "1            26.097361       46.020147           19.277917  0  \n",
       "2            39.030000       45.690000           25.510000  1  \n",
       "3            25.600000      -17.030000           36.640000  0  \n",
       "4            26.097361       46.020147           19.277917  1  \n",
       "..                 ...             ...                 ... ..  \n",
       "505          65.800000      463.460000           10.370000  0  \n",
       "506         156.700000       43.870000           14.100000  0  \n",
       "507           3.480000        6.090000           13.930000  1  \n",
       "508          17.709700       52.306596          263.130000  0  \n",
       "509         -86.740000     -116.850000           48.790000  0  \n",
       "\n",
       "[510 rows x 40 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_Ylabels = pd.concat([X, Ylabels], axis=\"columns\")\n",
    "X_Ylabels.drop([41])\n",
    "X_Ylabels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "ea117564",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_Ylabels.to_csv(\"X_Ylabels_17_21.csv\",index=False)"
   ]
  },
  {
   "cell_type": "raw",
   "id": "c6128381",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "2afda9d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>asset_turnover</th>\n",
       "      <th>buyback_yield</th>\n",
       "      <th>capex_to_revenue</th>\n",
       "      <th>cash_ratio</th>\n",
       "      <th>cash_to_debt</th>\n",
       "      <th>cogs_to_revenue</th>\n",
       "      <th>mscore</th>\n",
       "      <th>zscore</th>\n",
       "      <th>current_ratio</th>\n",
       "      <th>days_inventory</th>\n",
       "      <th>...</th>\n",
       "      <th>price_to_earnings_ratio</th>\n",
       "      <th>price_to_earnings_ratio_nri</th>\n",
       "      <th>price_earnings_growth_ratio</th>\n",
       "      <th>price_to_free_cashflow</th>\n",
       "      <th>price_to_operating_cashflow</th>\n",
       "      <th>rate_of_return</th>\n",
       "      <th>scaled_net_operating_assets</th>\n",
       "      <th>yoy_ebitda_growth</th>\n",
       "      <th>yoy_eps_growth</th>\n",
       "      <th>yoy_revenue_growth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.660000</td>\n",
       "      <td>4.090000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.740000</td>\n",
       "      <td>0.640000</td>\n",
       "      <td>0.620000</td>\n",
       "      <td>-2.500000</td>\n",
       "      <td>3.620000</td>\n",
       "      <td>1.280000</td>\n",
       "      <td>9.040000</td>\n",
       "      <td>...</td>\n",
       "      <td>16.730000</td>\n",
       "      <td>16.730000</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>15.630000</td>\n",
       "      <td>12.600000</td>\n",
       "      <td>19.240000</td>\n",
       "      <td>0.550000</td>\n",
       "      <td>9.360000</td>\n",
       "      <td>10.830000</td>\n",
       "      <td>11.340000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.724932</td>\n",
       "      <td>2.035574</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>1.410274</td>\n",
       "      <td>2.648413</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>-2.543382</td>\n",
       "      <td>6.686479</td>\n",
       "      <td>2.332192</td>\n",
       "      <td>89.855094</td>\n",
       "      <td>...</td>\n",
       "      <td>44.940635</td>\n",
       "      <td>44.909524</td>\n",
       "      <td>10.936250</td>\n",
       "      <td>33.836721</td>\n",
       "      <td>35.271493</td>\n",
       "      <td>14.260952</td>\n",
       "      <td>0.482917</td>\n",
       "      <td>26.097361</td>\n",
       "      <td>46.020147</td>\n",
       "      <td>19.277917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.540000</td>\n",
       "      <td>1.080000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>1.650000</td>\n",
       "      <td>3.090000</td>\n",
       "      <td>0.140000</td>\n",
       "      <td>-2.490000</td>\n",
       "      <td>10.810000</td>\n",
       "      <td>2.050000</td>\n",
       "      <td>89.855094</td>\n",
       "      <td>...</td>\n",
       "      <td>52.320000</td>\n",
       "      <td>52.320000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>32.410000</td>\n",
       "      <td>30.420000</td>\n",
       "      <td>21.820000</td>\n",
       "      <td>0.360000</td>\n",
       "      <td>39.030000</td>\n",
       "      <td>45.690000</td>\n",
       "      <td>25.510000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.360000</td>\n",
       "      <td>0.140000</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>0.660000</td>\n",
       "      <td>0.130000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>-1.490000</td>\n",
       "      <td>2.640000</td>\n",
       "      <td>1.470000</td>\n",
       "      <td>81.440000</td>\n",
       "      <td>...</td>\n",
       "      <td>39.870000</td>\n",
       "      <td>39.870000</td>\n",
       "      <td>4.440000</td>\n",
       "      <td>33.680000</td>\n",
       "      <td>27.720000</td>\n",
       "      <td>11.950000</td>\n",
       "      <td>2.130000</td>\n",
       "      <td>25.600000</td>\n",
       "      <td>-17.030000</td>\n",
       "      <td>36.640000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.724932</td>\n",
       "      <td>2.035574</td>\n",
       "      <td>0.086892</td>\n",
       "      <td>1.410274</td>\n",
       "      <td>2.648413</td>\n",
       "      <td>0.437534</td>\n",
       "      <td>-2.543382</td>\n",
       "      <td>6.686479</td>\n",
       "      <td>2.332192</td>\n",
       "      <td>89.855094</td>\n",
       "      <td>...</td>\n",
       "      <td>44.940635</td>\n",
       "      <td>44.909524</td>\n",
       "      <td>10.936250</td>\n",
       "      <td>33.836721</td>\n",
       "      <td>35.271493</td>\n",
       "      <td>14.260952</td>\n",
       "      <td>0.482917</td>\n",
       "      <td>26.097361</td>\n",
       "      <td>46.020147</td>\n",
       "      <td>19.277917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403</th>\n",
       "      <td>1.580000</td>\n",
       "      <td>4.830000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.790000</td>\n",
       "      <td>-3.110000</td>\n",
       "      <td>2.220000</td>\n",
       "      <td>0.670000</td>\n",
       "      <td>32.830000</td>\n",
       "      <td>...</td>\n",
       "      <td>73.120000</td>\n",
       "      <td>190.100000</td>\n",
       "      <td>13.344429</td>\n",
       "      <td>8.140000</td>\n",
       "      <td>6.100000</td>\n",
       "      <td>10.550000</td>\n",
       "      <td>0.890000</td>\n",
       "      <td>-55.550000</td>\n",
       "      <td>-87.940000</td>\n",
       "      <td>6.570000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404</th>\n",
       "      <td>0.590000</td>\n",
       "      <td>1.422184</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.650000</td>\n",
       "      <td>1.240000</td>\n",
       "      <td>0.290000</td>\n",
       "      <td>-3.190000</td>\n",
       "      <td>5.750000</td>\n",
       "      <td>1.040000</td>\n",
       "      <td>88.257353</td>\n",
       "      <td>...</td>\n",
       "      <td>71.222262</td>\n",
       "      <td>70.917500</td>\n",
       "      <td>13.344429</td>\n",
       "      <td>67.650000</td>\n",
       "      <td>48.510000</td>\n",
       "      <td>13.355974</td>\n",
       "      <td>0.380000</td>\n",
       "      <td>14.940000</td>\n",
       "      <td>-9.840000</td>\n",
       "      <td>22.650000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>405</th>\n",
       "      <td>0.220000</td>\n",
       "      <td>-2.030000</td>\n",
       "      <td>0.470000</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.570000</td>\n",
       "      <td>-2.417895</td>\n",
       "      <td>1.030000</td>\n",
       "      <td>0.770000</td>\n",
       "      <td>30.010000</td>\n",
       "      <td>...</td>\n",
       "      <td>23.900000</td>\n",
       "      <td>23.900000</td>\n",
       "      <td>7.660000</td>\n",
       "      <td>82.560323</td>\n",
       "      <td>12.360000</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>0.730000</td>\n",
       "      <td>3.390000</td>\n",
       "      <td>5.680000</td>\n",
       "      <td>-1.540000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>406</th>\n",
       "      <td>0.760000</td>\n",
       "      <td>-2.550000</td>\n",
       "      <td>0.060000</td>\n",
       "      <td>2.560000</td>\n",
       "      <td>11.800000</td>\n",
       "      <td>0.190000</td>\n",
       "      <td>-2.230000</td>\n",
       "      <td>29.250000</td>\n",
       "      <td>3.280000</td>\n",
       "      <td>88.257353</td>\n",
       "      <td>...</td>\n",
       "      <td>847.780000</td>\n",
       "      <td>847.780000</td>\n",
       "      <td>13.344429</td>\n",
       "      <td>170.690000</td>\n",
       "      <td>127.810000</td>\n",
       "      <td>13.355974</td>\n",
       "      <td>0.140000</td>\n",
       "      <td>134.690000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>99.110000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>407</th>\n",
       "      <td>0.350000</td>\n",
       "      <td>1.422184</td>\n",
       "      <td>0.120000</td>\n",
       "      <td>3.230000</td>\n",
       "      <td>1.510000</td>\n",
       "      <td>0.220000</td>\n",
       "      <td>-2.800000</td>\n",
       "      <td>8.210000</td>\n",
       "      <td>3.730000</td>\n",
       "      <td>88.257353</td>\n",
       "      <td>...</td>\n",
       "      <td>71.222262</td>\n",
       "      <td>70.917500</td>\n",
       "      <td>13.344429</td>\n",
       "      <td>609.620000</td>\n",
       "      <td>211.830000</td>\n",
       "      <td>13.355974</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>-400.750000</td>\n",
       "      <td>-286.960000</td>\n",
       "      <td>36.070000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>408 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     asset_turnover  buyback_yield  capex_to_revenue  cash_ratio  \\\n",
       "0          0.660000       4.090000          0.050000    0.740000   \n",
       "1          0.724932       2.035574          0.040000    1.410274   \n",
       "2          0.540000       1.080000          0.020000    1.650000   \n",
       "3          0.360000       0.140000          0.040000    0.660000   \n",
       "4          0.724932       2.035574          0.086892    1.410274   \n",
       "..              ...            ...               ...         ...   \n",
       "403        1.580000       4.830000          0.010000    0.020000   \n",
       "404        0.590000       1.422184          0.070000    0.650000   \n",
       "405        0.220000      -2.030000          0.470000    0.030000   \n",
       "406        0.760000      -2.550000          0.060000    2.560000   \n",
       "407        0.350000       1.422184          0.120000    3.230000   \n",
       "\n",
       "     cash_to_debt  cogs_to_revenue    mscore     zscore  current_ratio  \\\n",
       "0        0.640000         0.620000 -2.500000   3.620000       1.280000   \n",
       "1        2.648413         0.250000 -2.543382   6.686479       2.332192   \n",
       "2        3.090000         0.140000 -2.490000  10.810000       2.050000   \n",
       "3        0.130000         0.400000 -1.490000   2.640000       1.470000   \n",
       "4        2.648413         0.437534 -2.543382   6.686479       2.332192   \n",
       "..            ...              ...       ...        ...            ...   \n",
       "403      0.010000         0.790000 -3.110000   2.220000       0.670000   \n",
       "404      1.240000         0.290000 -3.190000   5.750000       1.040000   \n",
       "405      0.010000         0.570000 -2.417895   1.030000       0.770000   \n",
       "406     11.800000         0.190000 -2.230000  29.250000       3.280000   \n",
       "407      1.510000         0.220000 -2.800000   8.210000       3.730000   \n",
       "\n",
       "     days_inventory  ...  price_to_earnings_ratio  \\\n",
       "0          9.040000  ...                16.730000   \n",
       "1         89.855094  ...                44.940635   \n",
       "2         89.855094  ...                52.320000   \n",
       "3         81.440000  ...                39.870000   \n",
       "4         89.855094  ...                44.940635   \n",
       "..              ...  ...                      ...   \n",
       "403       32.830000  ...                73.120000   \n",
       "404       88.257353  ...                71.222262   \n",
       "405       30.010000  ...                23.900000   \n",
       "406       88.257353  ...               847.780000   \n",
       "407       88.257353  ...                71.222262   \n",
       "\n",
       "     price_to_earnings_ratio_nri  price_earnings_growth_ratio  \\\n",
       "0                      16.730000                     1.250000   \n",
       "1                      44.909524                    10.936250   \n",
       "2                      52.320000                     2.420000   \n",
       "3                      39.870000                     4.440000   \n",
       "4                      44.909524                    10.936250   \n",
       "..                           ...                          ...   \n",
       "403                   190.100000                    13.344429   \n",
       "404                    70.917500                    13.344429   \n",
       "405                    23.900000                     7.660000   \n",
       "406                   847.780000                    13.344429   \n",
       "407                    70.917500                    13.344429   \n",
       "\n",
       "     price_to_free_cashflow  price_to_operating_cashflow  rate_of_return  \\\n",
       "0                 15.630000                    12.600000       19.240000   \n",
       "1                 33.836721                    35.271493       14.260952   \n",
       "2                 32.410000                    30.420000       21.820000   \n",
       "3                 33.680000                    27.720000       11.950000   \n",
       "4                 33.836721                    35.271493       14.260952   \n",
       "..                      ...                          ...             ...   \n",
       "403                8.140000                     6.100000       10.550000   \n",
       "404               67.650000                    48.510000       13.355974   \n",
       "405               82.560323                    12.360000        0.090000   \n",
       "406              170.690000                   127.810000       13.355974   \n",
       "407              609.620000                   211.830000       13.355974   \n",
       "\n",
       "     scaled_net_operating_assets  yoy_ebitda_growth  yoy_eps_growth  \\\n",
       "0                       0.550000           9.360000       10.830000   \n",
       "1                       0.482917          26.097361       46.020147   \n",
       "2                       0.360000          39.030000       45.690000   \n",
       "3                       2.130000          25.600000      -17.030000   \n",
       "4                       0.482917          26.097361       46.020147   \n",
       "..                           ...                ...             ...   \n",
       "403                     0.890000         -55.550000      -87.940000   \n",
       "404                     0.380000          14.940000       -9.840000   \n",
       "405                     0.730000           3.390000        5.680000   \n",
       "406                     0.140000         134.690000      200.000000   \n",
       "407                     0.030000        -400.750000     -286.960000   \n",
       "\n",
       "     yoy_revenue_growth  \n",
       "0             11.340000  \n",
       "1             19.277917  \n",
       "2             25.510000  \n",
       "3             36.640000  \n",
       "4             19.277917  \n",
       "..                  ...  \n",
       "403            6.570000  \n",
       "404           22.650000  \n",
       "405           -1.540000  \n",
       "406           99.110000  \n",
       "407           36.070000  \n",
       "\n",
       "[408 rows x 39 columns]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X1 = pd.read_csv('data17_20.csv')\n",
    "X1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "23c9a93e",
   "metadata": {},
   "outputs": [],
   "source": [
    "Y1labels = pd.read_csv('labels17_20.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "c0d09428",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>asset_turnover</th>\n",
       "      <th>buyback_yield</th>\n",
       "      <th>capex_to_revenue</th>\n",
       "      <th>cash_ratio</th>\n",
       "      <th>cash_to_debt</th>\n",
       "      <th>cogs_to_revenue</th>\n",
       "      <th>mscore</th>\n",
       "      <th>zscore</th>\n",
       "      <th>current_ratio</th>\n",
       "      <th>days_inventory</th>\n",
       "      <th>...</th>\n",
       "      <th>price_to_earnings_ratio_nri</th>\n",
       "      <th>price_earnings_growth_ratio</th>\n",
       "      <th>price_to_free_cashflow</th>\n",
       "      <th>price_to_operating_cashflow</th>\n",
       "      <th>rate_of_return</th>\n",
       "      <th>scaled_net_operating_assets</th>\n",
       "      <th>yoy_ebitda_growth</th>\n",
       "      <th>yoy_eps_growth</th>\n",
       "      <th>yoy_revenue_growth</th>\n",
       "      <th>0</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.660000</td>\n",
       "      <td>4.090000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.740000</td>\n",
       "      <td>0.640000</td>\n",
       "      <td>0.620000</td>\n",
       "      <td>-2.500000</td>\n",
       "      <td>3.620000</td>\n",
       "      <td>1.280000</td>\n",
       "      <td>9.040000</td>\n",
       "      <td>...</td>\n",
       "      <td>16.730000</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>15.630000</td>\n",
       "      <td>12.600000</td>\n",
       "      <td>19.240000</td>\n",
       "      <td>0.550000</td>\n",
       "      <td>9.360000</td>\n",
       "      <td>10.830000</td>\n",
       "      <td>11.340000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.724932</td>\n",
       "      <td>2.035574</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>1.410274</td>\n",
       "      <td>2.648413</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>-2.543382</td>\n",
       "      <td>6.686479</td>\n",
       "      <td>2.332192</td>\n",
       "      <td>89.855094</td>\n",
       "      <td>...</td>\n",
       "      <td>44.909524</td>\n",
       "      <td>10.936250</td>\n",
       "      <td>33.836721</td>\n",
       "      <td>35.271493</td>\n",
       "      <td>14.260952</td>\n",
       "      <td>0.482917</td>\n",
       "      <td>26.097361</td>\n",
       "      <td>46.020147</td>\n",
       "      <td>19.277917</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.540000</td>\n",
       "      <td>1.080000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>1.650000</td>\n",
       "      <td>3.090000</td>\n",
       "      <td>0.140000</td>\n",
       "      <td>-2.490000</td>\n",
       "      <td>10.810000</td>\n",
       "      <td>2.050000</td>\n",
       "      <td>89.855094</td>\n",
       "      <td>...</td>\n",
       "      <td>52.320000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>32.410000</td>\n",
       "      <td>30.420000</td>\n",
       "      <td>21.820000</td>\n",
       "      <td>0.360000</td>\n",
       "      <td>39.030000</td>\n",
       "      <td>45.690000</td>\n",
       "      <td>25.510000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.360000</td>\n",
       "      <td>0.140000</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>0.660000</td>\n",
       "      <td>0.130000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>-1.490000</td>\n",
       "      <td>2.640000</td>\n",
       "      <td>1.470000</td>\n",
       "      <td>81.440000</td>\n",
       "      <td>...</td>\n",
       "      <td>39.870000</td>\n",
       "      <td>4.440000</td>\n",
       "      <td>33.680000</td>\n",
       "      <td>27.720000</td>\n",
       "      <td>11.950000</td>\n",
       "      <td>2.130000</td>\n",
       "      <td>25.600000</td>\n",
       "      <td>-17.030000</td>\n",
       "      <td>36.640000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.724932</td>\n",
       "      <td>2.035574</td>\n",
       "      <td>0.086892</td>\n",
       "      <td>1.410274</td>\n",
       "      <td>2.648413</td>\n",
       "      <td>0.437534</td>\n",
       "      <td>-2.543382</td>\n",
       "      <td>6.686479</td>\n",
       "      <td>2.332192</td>\n",
       "      <td>89.855094</td>\n",
       "      <td>...</td>\n",
       "      <td>44.909524</td>\n",
       "      <td>10.936250</td>\n",
       "      <td>33.836721</td>\n",
       "      <td>35.271493</td>\n",
       "      <td>14.260952</td>\n",
       "      <td>0.482917</td>\n",
       "      <td>26.097361</td>\n",
       "      <td>46.020147</td>\n",
       "      <td>19.277917</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403</th>\n",
       "      <td>1.580000</td>\n",
       "      <td>4.830000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.790000</td>\n",
       "      <td>-3.110000</td>\n",
       "      <td>2.220000</td>\n",
       "      <td>0.670000</td>\n",
       "      <td>32.830000</td>\n",
       "      <td>...</td>\n",
       "      <td>190.100000</td>\n",
       "      <td>13.344429</td>\n",
       "      <td>8.140000</td>\n",
       "      <td>6.100000</td>\n",
       "      <td>10.550000</td>\n",
       "      <td>0.890000</td>\n",
       "      <td>-55.550000</td>\n",
       "      <td>-87.940000</td>\n",
       "      <td>6.570000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404</th>\n",
       "      <td>0.590000</td>\n",
       "      <td>1.422184</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.650000</td>\n",
       "      <td>1.240000</td>\n",
       "      <td>0.290000</td>\n",
       "      <td>-3.190000</td>\n",
       "      <td>5.750000</td>\n",
       "      <td>1.040000</td>\n",
       "      <td>88.257353</td>\n",
       "      <td>...</td>\n",
       "      <td>70.917500</td>\n",
       "      <td>13.344429</td>\n",
       "      <td>67.650000</td>\n",
       "      <td>48.510000</td>\n",
       "      <td>13.355974</td>\n",
       "      <td>0.380000</td>\n",
       "      <td>14.940000</td>\n",
       "      <td>-9.840000</td>\n",
       "      <td>22.650000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>405</th>\n",
       "      <td>0.220000</td>\n",
       "      <td>-2.030000</td>\n",
       "      <td>0.470000</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.570000</td>\n",
       "      <td>-2.417895</td>\n",
       "      <td>1.030000</td>\n",
       "      <td>0.770000</td>\n",
       "      <td>30.010000</td>\n",
       "      <td>...</td>\n",
       "      <td>23.900000</td>\n",
       "      <td>7.660000</td>\n",
       "      <td>82.560323</td>\n",
       "      <td>12.360000</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>0.730000</td>\n",
       "      <td>3.390000</td>\n",
       "      <td>5.680000</td>\n",
       "      <td>-1.540000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>406</th>\n",
       "      <td>0.760000</td>\n",
       "      <td>-2.550000</td>\n",
       "      <td>0.060000</td>\n",
       "      <td>2.560000</td>\n",
       "      <td>11.800000</td>\n",
       "      <td>0.190000</td>\n",
       "      <td>-2.230000</td>\n",
       "      <td>29.250000</td>\n",
       "      <td>3.280000</td>\n",
       "      <td>88.257353</td>\n",
       "      <td>...</td>\n",
       "      <td>847.780000</td>\n",
       "      <td>13.344429</td>\n",
       "      <td>170.690000</td>\n",
       "      <td>127.810000</td>\n",
       "      <td>13.355974</td>\n",
       "      <td>0.140000</td>\n",
       "      <td>134.690000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>99.110000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>407</th>\n",
       "      <td>0.350000</td>\n",
       "      <td>1.422184</td>\n",
       "      <td>0.120000</td>\n",
       "      <td>3.230000</td>\n",
       "      <td>1.510000</td>\n",
       "      <td>0.220000</td>\n",
       "      <td>-2.800000</td>\n",
       "      <td>8.210000</td>\n",
       "      <td>3.730000</td>\n",
       "      <td>88.257353</td>\n",
       "      <td>...</td>\n",
       "      <td>70.917500</td>\n",
       "      <td>13.344429</td>\n",
       "      <td>609.620000</td>\n",
       "      <td>211.830000</td>\n",
       "      <td>13.355974</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>-400.750000</td>\n",
       "      <td>-286.960000</td>\n",
       "      <td>36.070000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>408 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     asset_turnover  buyback_yield  capex_to_revenue  cash_ratio  \\\n",
       "0          0.660000       4.090000          0.050000    0.740000   \n",
       "1          0.724932       2.035574          0.040000    1.410274   \n",
       "2          0.540000       1.080000          0.020000    1.650000   \n",
       "3          0.360000       0.140000          0.040000    0.660000   \n",
       "4          0.724932       2.035574          0.086892    1.410274   \n",
       "..              ...            ...               ...         ...   \n",
       "403        1.580000       4.830000          0.010000    0.020000   \n",
       "404        0.590000       1.422184          0.070000    0.650000   \n",
       "405        0.220000      -2.030000          0.470000    0.030000   \n",
       "406        0.760000      -2.550000          0.060000    2.560000   \n",
       "407        0.350000       1.422184          0.120000    3.230000   \n",
       "\n",
       "     cash_to_debt  cogs_to_revenue    mscore     zscore  current_ratio  \\\n",
       "0        0.640000         0.620000 -2.500000   3.620000       1.280000   \n",
       "1        2.648413         0.250000 -2.543382   6.686479       2.332192   \n",
       "2        3.090000         0.140000 -2.490000  10.810000       2.050000   \n",
       "3        0.130000         0.400000 -1.490000   2.640000       1.470000   \n",
       "4        2.648413         0.437534 -2.543382   6.686479       2.332192   \n",
       "..            ...              ...       ...        ...            ...   \n",
       "403      0.010000         0.790000 -3.110000   2.220000       0.670000   \n",
       "404      1.240000         0.290000 -3.190000   5.750000       1.040000   \n",
       "405      0.010000         0.570000 -2.417895   1.030000       0.770000   \n",
       "406     11.800000         0.190000 -2.230000  29.250000       3.280000   \n",
       "407      1.510000         0.220000 -2.800000   8.210000       3.730000   \n",
       "\n",
       "     days_inventory  ...  price_to_earnings_ratio_nri  \\\n",
       "0          9.040000  ...                    16.730000   \n",
       "1         89.855094  ...                    44.909524   \n",
       "2         89.855094  ...                    52.320000   \n",
       "3         81.440000  ...                    39.870000   \n",
       "4         89.855094  ...                    44.909524   \n",
       "..              ...  ...                          ...   \n",
       "403       32.830000  ...                   190.100000   \n",
       "404       88.257353  ...                    70.917500   \n",
       "405       30.010000  ...                    23.900000   \n",
       "406       88.257353  ...                   847.780000   \n",
       "407       88.257353  ...                    70.917500   \n",
       "\n",
       "     price_earnings_growth_ratio  price_to_free_cashflow  \\\n",
       "0                       1.250000               15.630000   \n",
       "1                      10.936250               33.836721   \n",
       "2                       2.420000               32.410000   \n",
       "3                       4.440000               33.680000   \n",
       "4                      10.936250               33.836721   \n",
       "..                           ...                     ...   \n",
       "403                    13.344429                8.140000   \n",
       "404                    13.344429               67.650000   \n",
       "405                     7.660000               82.560323   \n",
       "406                    13.344429              170.690000   \n",
       "407                    13.344429              609.620000   \n",
       "\n",
       "     price_to_operating_cashflow  rate_of_return  scaled_net_operating_assets  \\\n",
       "0                      12.600000       19.240000                     0.550000   \n",
       "1                      35.271493       14.260952                     0.482917   \n",
       "2                      30.420000       21.820000                     0.360000   \n",
       "3                      27.720000       11.950000                     2.130000   \n",
       "4                      35.271493       14.260952                     0.482917   \n",
       "..                           ...             ...                          ...   \n",
       "403                     6.100000       10.550000                     0.890000   \n",
       "404                    48.510000       13.355974                     0.380000   \n",
       "405                    12.360000        0.090000                     0.730000   \n",
       "406                   127.810000       13.355974                     0.140000   \n",
       "407                   211.830000       13.355974                     0.030000   \n",
       "\n",
       "     yoy_ebitda_growth  yoy_eps_growth  yoy_revenue_growth  0  \n",
       "0             9.360000       10.830000           11.340000  0  \n",
       "1            26.097361       46.020147           19.277917  0  \n",
       "2            39.030000       45.690000           25.510000  1  \n",
       "3            25.600000      -17.030000           36.640000  0  \n",
       "4            26.097361       46.020147           19.277917  1  \n",
       "..                 ...             ...                 ... ..  \n",
       "403         -55.550000      -87.940000            6.570000  0  \n",
       "404          14.940000       -9.840000           22.650000  0  \n",
       "405           3.390000        5.680000           -1.540000  0  \n",
       "406         134.690000      200.000000           99.110000  0  \n",
       "407        -400.750000     -286.960000           36.070000  1  \n",
       "\n",
       "[408 rows x 40 columns]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_Y1labels = pd.concat([X1, Y1labels], axis=\"columns\")\n",
    "X_Y1labels.drop([41])\n",
    "X_Y1labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "feec26f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_Y1labels.to_csv(\"X_Ylabels_17_20.csv\",index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72377114",
   "metadata": {},
   "source": [
    "# "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e7baf02",
   "metadata": {},
   "source": [
    "# Result 2:\n",
    "\n",
    "So we now have two full datasets for 2017 to 2021 with the Y labels attached at the end column.\n",
    "\n",
    "- labels 1 or 0: for binary outperformance against index.\n",
    "\n",
    "- perfs labels: for actual performance for the stock for that calendar year.\n",
    "\n",
    "\n",
    "And we also have a 2017 to 2020  dataset for:\n",
    "\n",
    "- labels 1 or 0: for binary outperformance against index.\n",
    "\n",
    "- perfs labels: for actual performance for the stock for that calendar year."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a48e6c0",
   "metadata": {},
   "source": [
    "# Saving the final datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "e9bcd48f",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_Ylabels.to_csv(\"X_Ylabels_17_21.csv\",index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "2f0e1529",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_Y1labels.to_csv(\"X_Ylabels_17_20.csv\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "3c41aa52",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_Yperf.to_csv(\"X_Yperf_17_21.csv\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "b45b4fe0",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_Y1perf.to_csv(\"X_Yperf_17_20.csv\",index=False)"
   ]
  },
  {
   "cell_type": "raw",
   "id": "46ad5ceb",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "1965005a",
   "metadata": {},
   "source": [
    "# Undertaking some Principal Component Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "e15eba66",
   "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>asset_turnover</th>\n",
       "      <th>buyback_yield</th>\n",
       "      <th>capex_to_revenue</th>\n",
       "      <th>cash_ratio</th>\n",
       "      <th>cash_to_debt</th>\n",
       "      <th>cogs_to_revenue</th>\n",
       "      <th>mscore</th>\n",
       "      <th>zscore</th>\n",
       "      <th>current_ratio</th>\n",
       "      <th>days_inventory</th>\n",
       "      <th>...</th>\n",
       "      <th>price_to_earnings_ratio</th>\n",
       "      <th>price_to_earnings_ratio_nri</th>\n",
       "      <th>price_earnings_growth_ratio</th>\n",
       "      <th>price_to_free_cashflow</th>\n",
       "      <th>price_to_operating_cashflow</th>\n",
       "      <th>rate_of_return</th>\n",
       "      <th>scaled_net_operating_assets</th>\n",
       "      <th>yoy_ebitda_growth</th>\n",
       "      <th>yoy_eps_growth</th>\n",
       "      <th>yoy_revenue_growth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.660000</td>\n",
       "      <td>4.090000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.740000</td>\n",
       "      <td>0.640000</td>\n",
       "      <td>0.620000</td>\n",
       "      <td>-2.500000</td>\n",
       "      <td>3.620000</td>\n",
       "      <td>1.280000</td>\n",
       "      <td>9.040000</td>\n",
       "      <td>...</td>\n",
       "      <td>16.730000</td>\n",
       "      <td>16.730000</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>15.630000</td>\n",
       "      <td>12.600000</td>\n",
       "      <td>19.240000</td>\n",
       "      <td>0.550000</td>\n",
       "      <td>9.360000</td>\n",
       "      <td>10.830000</td>\n",
       "      <td>11.340000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.724932</td>\n",
       "      <td>2.035574</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>1.410274</td>\n",
       "      <td>2.648413</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>-2.543382</td>\n",
       "      <td>6.686479</td>\n",
       "      <td>2.332192</td>\n",
       "      <td>89.855094</td>\n",
       "      <td>...</td>\n",
       "      <td>44.940635</td>\n",
       "      <td>44.909524</td>\n",
       "      <td>10.936250</td>\n",
       "      <td>33.836721</td>\n",
       "      <td>35.271493</td>\n",
       "      <td>14.260952</td>\n",
       "      <td>0.482917</td>\n",
       "      <td>26.097361</td>\n",
       "      <td>46.020147</td>\n",
       "      <td>19.277917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.540000</td>\n",
       "      <td>1.080000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>1.650000</td>\n",
       "      <td>3.090000</td>\n",
       "      <td>0.140000</td>\n",
       "      <td>-2.490000</td>\n",
       "      <td>10.810000</td>\n",
       "      <td>2.050000</td>\n",
       "      <td>89.855094</td>\n",
       "      <td>...</td>\n",
       "      <td>52.320000</td>\n",
       "      <td>52.320000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>32.410000</td>\n",
       "      <td>30.420000</td>\n",
       "      <td>21.820000</td>\n",
       "      <td>0.360000</td>\n",
       "      <td>39.030000</td>\n",
       "      <td>45.690000</td>\n",
       "      <td>25.510000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.360000</td>\n",
       "      <td>0.140000</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>0.660000</td>\n",
       "      <td>0.130000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>-1.490000</td>\n",
       "      <td>2.640000</td>\n",
       "      <td>1.470000</td>\n",
       "      <td>81.440000</td>\n",
       "      <td>...</td>\n",
       "      <td>39.870000</td>\n",
       "      <td>39.870000</td>\n",
       "      <td>4.440000</td>\n",
       "      <td>33.680000</td>\n",
       "      <td>27.720000</td>\n",
       "      <td>11.950000</td>\n",
       "      <td>2.130000</td>\n",
       "      <td>25.600000</td>\n",
       "      <td>-17.030000</td>\n",
       "      <td>36.640000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.724932</td>\n",
       "      <td>2.035574</td>\n",
       "      <td>0.086892</td>\n",
       "      <td>1.410274</td>\n",
       "      <td>2.648413</td>\n",
       "      <td>0.437534</td>\n",
       "      <td>-2.543382</td>\n",
       "      <td>6.686479</td>\n",
       "      <td>2.332192</td>\n",
       "      <td>89.855094</td>\n",
       "      <td>...</td>\n",
       "      <td>44.940635</td>\n",
       "      <td>44.909524</td>\n",
       "      <td>10.936250</td>\n",
       "      <td>33.836721</td>\n",
       "      <td>35.271493</td>\n",
       "      <td>14.260952</td>\n",
       "      <td>0.482917</td>\n",
       "      <td>26.097361</td>\n",
       "      <td>46.020147</td>\n",
       "      <td>19.277917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>505</th>\n",
       "      <td>1.570000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>0.790000</td>\n",
       "      <td>-2.260000</td>\n",
       "      <td>2.720000</td>\n",
       "      <td>0.720000</td>\n",
       "      <td>28.090000</td>\n",
       "      <td>...</td>\n",
       "      <td>17.320000</td>\n",
       "      <td>22.070000</td>\n",
       "      <td>6.195821</td>\n",
       "      <td>10.530000</td>\n",
       "      <td>7.910000</td>\n",
       "      <td>-5.110000</td>\n",
       "      <td>0.640000</td>\n",
       "      <td>65.800000</td>\n",
       "      <td>463.460000</td>\n",
       "      <td>10.370000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506</th>\n",
       "      <td>0.560000</td>\n",
       "      <td>1.733289</td>\n",
       "      <td>0.060000</td>\n",
       "      <td>0.830000</td>\n",
       "      <td>1.580000</td>\n",
       "      <td>0.280000</td>\n",
       "      <td>-3.240000</td>\n",
       "      <td>6.120000</td>\n",
       "      <td>1.120000</td>\n",
       "      <td>83.647794</td>\n",
       "      <td>...</td>\n",
       "      <td>61.101047</td>\n",
       "      <td>63.153488</td>\n",
       "      <td>6.195821</td>\n",
       "      <td>53.290000</td>\n",
       "      <td>42.510000</td>\n",
       "      <td>12.240260</td>\n",
       "      <td>0.290000</td>\n",
       "      <td>156.700000</td>\n",
       "      <td>43.870000</td>\n",
       "      <td>14.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>507</th>\n",
       "      <td>0.240000</td>\n",
       "      <td>-0.990000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.610000</td>\n",
       "      <td>-1.792316</td>\n",
       "      <td>1.030000</td>\n",
       "      <td>0.840000</td>\n",
       "      <td>26.040000</td>\n",
       "      <td>...</td>\n",
       "      <td>22.870000</td>\n",
       "      <td>22.870000</td>\n",
       "      <td>8.730000</td>\n",
       "      <td>52.026374</td>\n",
       "      <td>16.700000</td>\n",
       "      <td>-1.040000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>3.480000</td>\n",
       "      <td>6.090000</td>\n",
       "      <td>13.930000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>508</th>\n",
       "      <td>0.810000</td>\n",
       "      <td>-1.810000</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>3.370000</td>\n",
       "      <td>40.040000</td>\n",
       "      <td>0.310000</td>\n",
       "      <td>-0.480000</td>\n",
       "      <td>47.500000</td>\n",
       "      <td>3.800000</td>\n",
       "      <td>83.647794</td>\n",
       "      <td>...</td>\n",
       "      <td>165.360000</td>\n",
       "      <td>165.360000</td>\n",
       "      <td>6.195821</td>\n",
       "      <td>80.070000</td>\n",
       "      <td>75.390000</td>\n",
       "      <td>12.240260</td>\n",
       "      <td>-0.220000</td>\n",
       "      <td>17.709700</td>\n",
       "      <td>52.306596</td>\n",
       "      <td>263.130000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>509</th>\n",
       "      <td>0.330000</td>\n",
       "      <td>1.733289</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>2.090000</td>\n",
       "      <td>1.560000</td>\n",
       "      <td>0.220000</td>\n",
       "      <td>-2.670000</td>\n",
       "      <td>11.580000</td>\n",
       "      <td>2.570000</td>\n",
       "      <td>83.647794</td>\n",
       "      <td>...</td>\n",
       "      <td>61.101047</td>\n",
       "      <td>63.153488</td>\n",
       "      <td>6.195821</td>\n",
       "      <td>222.560000</td>\n",
       "      <td>158.440000</td>\n",
       "      <td>12.240260</td>\n",
       "      <td>-0.010000</td>\n",
       "      <td>-86.740000</td>\n",
       "      <td>-116.850000</td>\n",
       "      <td>48.790000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>510 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     asset_turnover  buyback_yield  capex_to_revenue  cash_ratio  \\\n",
       "0          0.660000       4.090000          0.050000    0.740000   \n",
       "1          0.724932       2.035574          0.040000    1.410274   \n",
       "2          0.540000       1.080000          0.020000    1.650000   \n",
       "3          0.360000       0.140000          0.040000    0.660000   \n",
       "4          0.724932       2.035574          0.086892    1.410274   \n",
       "..              ...            ...               ...         ...   \n",
       "505        1.570000       0.250000          0.010000    0.050000   \n",
       "506        0.560000       1.733289          0.060000    0.830000   \n",
       "507        0.240000      -0.990000          0.320000    0.030000   \n",
       "508        0.810000      -1.810000          0.030000    3.370000   \n",
       "509        0.330000       1.733289          0.090000    2.090000   \n",
       "\n",
       "     cash_to_debt  cogs_to_revenue    mscore     zscore  current_ratio  \\\n",
       "0        0.640000         0.620000 -2.500000   3.620000       1.280000   \n",
       "1        2.648413         0.250000 -2.543382   6.686479       2.332192   \n",
       "2        3.090000         0.140000 -2.490000  10.810000       2.050000   \n",
       "3        0.130000         0.400000 -1.490000   2.640000       1.470000   \n",
       "4        2.648413         0.437534 -2.543382   6.686479       2.332192   \n",
       "..            ...              ...       ...        ...            ...   \n",
       "505      0.040000         0.790000 -2.260000   2.720000       0.720000   \n",
       "506      1.580000         0.280000 -3.240000   6.120000       1.120000   \n",
       "507      0.010000         0.610000 -1.792316   1.030000       0.840000   \n",
       "508     40.040000         0.310000 -0.480000  47.500000       3.800000   \n",
       "509      1.560000         0.220000 -2.670000  11.580000       2.570000   \n",
       "\n",
       "     days_inventory  ...  price_to_earnings_ratio  \\\n",
       "0          9.040000  ...                16.730000   \n",
       "1         89.855094  ...                44.940635   \n",
       "2         89.855094  ...                52.320000   \n",
       "3         81.440000  ...                39.870000   \n",
       "4         89.855094  ...                44.940635   \n",
       "..              ...  ...                      ...   \n",
       "505       28.090000  ...                17.320000   \n",
       "506       83.647794  ...                61.101047   \n",
       "507       26.040000  ...                22.870000   \n",
       "508       83.647794  ...               165.360000   \n",
       "509       83.647794  ...                61.101047   \n",
       "\n",
       "     price_to_earnings_ratio_nri  price_earnings_growth_ratio  \\\n",
       "0                      16.730000                     1.250000   \n",
       "1                      44.909524                    10.936250   \n",
       "2                      52.320000                     2.420000   \n",
       "3                      39.870000                     4.440000   \n",
       "4                      44.909524                    10.936250   \n",
       "..                           ...                          ...   \n",
       "505                    22.070000                     6.195821   \n",
       "506                    63.153488                     6.195821   \n",
       "507                    22.870000                     8.730000   \n",
       "508                   165.360000                     6.195821   \n",
       "509                    63.153488                     6.195821   \n",
       "\n",
       "     price_to_free_cashflow  price_to_operating_cashflow  rate_of_return  \\\n",
       "0                 15.630000                    12.600000       19.240000   \n",
       "1                 33.836721                    35.271493       14.260952   \n",
       "2                 32.410000                    30.420000       21.820000   \n",
       "3                 33.680000                    27.720000       11.950000   \n",
       "4                 33.836721                    35.271493       14.260952   \n",
       "..                      ...                          ...             ...   \n",
       "505               10.530000                     7.910000       -5.110000   \n",
       "506               53.290000                    42.510000       12.240260   \n",
       "507               52.026374                    16.700000       -1.040000   \n",
       "508               80.070000                    75.390000       12.240260   \n",
       "509              222.560000                   158.440000       12.240260   \n",
       "\n",
       "     scaled_net_operating_assets  yoy_ebitda_growth  yoy_eps_growth  \\\n",
       "0                       0.550000           9.360000       10.830000   \n",
       "1                       0.482917          26.097361       46.020147   \n",
       "2                       0.360000          39.030000       45.690000   \n",
       "3                       2.130000          25.600000      -17.030000   \n",
       "4                       0.482917          26.097361       46.020147   \n",
       "..                           ...                ...             ...   \n",
       "505                     0.640000          65.800000      463.460000   \n",
       "506                     0.290000         156.700000       43.870000   \n",
       "507                     0.750000           3.480000        6.090000   \n",
       "508                    -0.220000          17.709700       52.306596   \n",
       "509                    -0.010000         -86.740000     -116.850000   \n",
       "\n",
       "     yoy_revenue_growth  \n",
       "0             11.340000  \n",
       "1             19.277917  \n",
       "2             25.510000  \n",
       "3             36.640000  \n",
       "4             19.277917  \n",
       "..                  ...  \n",
       "505           10.370000  \n",
       "506           14.100000  \n",
       "507           13.930000  \n",
       "508          263.130000  \n",
       "509           48.790000  \n",
       "\n",
       "[510 rows x 39 columns]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_Ydata = pd.read_csv(\"data17_21.csv\")\n",
    "X_Ydata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "c08a5c01",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# Assuming your dataset is stored in a variable named 'data'\n",
    "\n",
    "# Standardize the data\n",
    "scaler = StandardScaler()\n",
    "data_scaled = scaler.fit_transform(X_Ydata)\n",
    "\n",
    "# Create an instance of PCA\n",
    "pca = PCA()\n",
    "\n",
    "# Perform PCA on the standardized data\n",
    "pca.fit(data_scaled)\n",
    "\n",
    "# Determine the explained variance ratio\n",
    "explained_variance_ratio = pca.explained_variance_ratio_\n",
    "\n",
    "# Determine the cumulative explained variance\n",
    "cumulative_explained_variance = np.cumsum(explained_variance_ratio)\n",
    "\n",
    "# Choose the number of components that explain a desired amount of variance\n",
    "desired_variance = 0.9\n",
    "n_components = np.argmax(cumulative_explained_variance >= desired_variance) + 1\n",
    "\n",
    "# Perform PCA again with the chosen number of components\n",
    "pca_final = PCA(n_components=n_components)\n",
    "data_pca = pca_final.fit_transform(data_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "cf6e5bac",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
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       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
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       "      <th>9</th>\n",
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       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.001226</td>\n",
       "      <td>-1.059893</td>\n",
       "      <td>0.986660</td>\n",
       "      <td>0.161155</td>\n",
       "      <td>-0.997699</td>\n",
       "      <td>-0.300203</td>\n",
       "      <td>0.754488</td>\n",
       "      <td>0.103010</td>\n",
       "      <td>0.850884</td>\n",
       "      <td>-0.321918</td>\n",
       "      <td>...</td>\n",
       "      <td>0.150729</td>\n",
       "      <td>-0.532187</td>\n",
       "      <td>-0.066752</td>\n",
       "      <td>0.277873</td>\n",
       "      <td>-0.779989</td>\n",
       "      <td>-0.661683</td>\n",
       "      <td>1.014897</td>\n",
       "      <td>0.183478</td>\n",
       "      <td>-0.292203</td>\n",
       "      <td>-0.154482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.066134</td>\n",
       "      <td>-0.399447</td>\n",
       "      <td>-0.065152</td>\n",
       "      <td>0.038366</td>\n",
       "      <td>-0.072110</td>\n",
       "      <td>0.005930</td>\n",
       "      <td>-0.276317</td>\n",
       "      <td>-0.006671</td>\n",
       "      <td>-0.031683</td>\n",
       "      <td>0.173165</td>\n",
       "      <td>...</td>\n",
       "      <td>0.126239</td>\n",
       "      <td>0.108393</td>\n",
       "      <td>-0.019713</td>\n",
       "      <td>-0.251871</td>\n",
       "      <td>-0.044188</td>\n",
       "      <td>-0.083677</td>\n",
       "      <td>-0.112400</td>\n",
       "      <td>-0.119917</td>\n",
       "      <td>0.044716</td>\n",
       "      <td>0.174352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.496734</td>\n",
       "      <td>-0.827214</td>\n",
       "      <td>-0.889009</td>\n",
       "      <td>-0.148621</td>\n",
       "      <td>0.376227</td>\n",
       "      <td>0.216439</td>\n",
       "      <td>-0.041229</td>\n",
       "      <td>-0.288756</td>\n",
       "      <td>-0.184608</td>\n",
       "      <td>0.290511</td>\n",
       "      <td>...</td>\n",
       "      <td>0.182107</td>\n",
       "      <td>-0.599143</td>\n",
       "      <td>0.047240</td>\n",
       "      <td>0.378696</td>\n",
       "      <td>0.762712</td>\n",
       "      <td>0.564445</td>\n",
       "      <td>-0.905975</td>\n",
       "      <td>0.017726</td>\n",
       "      <td>-0.118805</td>\n",
       "      <td>-0.258859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.730326</td>\n",
       "      <td>-0.119637</td>\n",
       "      <td>0.469753</td>\n",
       "      <td>1.338862</td>\n",
       "      <td>-2.045690</td>\n",
       "      <td>-0.825665</td>\n",
       "      <td>-0.776626</td>\n",
       "      <td>-0.949277</td>\n",
       "      <td>-0.637562</td>\n",
       "      <td>-0.248656</td>\n",
       "      <td>...</td>\n",
       "      <td>1.353734</td>\n",
       "      <td>0.606229</td>\n",
       "      <td>-0.315826</td>\n",
       "      <td>0.957854</td>\n",
       "      <td>-1.064890</td>\n",
       "      <td>-0.579852</td>\n",
       "      <td>-0.083798</td>\n",
       "      <td>0.851264</td>\n",
       "      <td>0.254949</td>\n",
       "      <td>0.048241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.052995</td>\n",
       "      <td>-0.278251</td>\n",
       "      <td>0.168833</td>\n",
       "      <td>0.129490</td>\n",
       "      <td>-0.010581</td>\n",
       "      <td>-0.058356</td>\n",
       "      <td>-0.094532</td>\n",
       "      <td>0.082996</td>\n",
       "      <td>-0.124160</td>\n",
       "      <td>0.111113</td>\n",
       "      <td>...</td>\n",
       "      <td>0.110436</td>\n",
       "      <td>0.100084</td>\n",
       "      <td>0.011917</td>\n",
       "      <td>-0.186938</td>\n",
       "      <td>-0.073753</td>\n",
       "      <td>-0.147407</td>\n",
       "      <td>-0.071179</td>\n",
       "      <td>-0.092330</td>\n",
       "      <td>0.048253</td>\n",
       "      <td>0.132163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>505</th>\n",
       "      <td>-1.841088</td>\n",
       "      <td>-1.573852</td>\n",
       "      <td>0.925308</td>\n",
       "      <td>1.328420</td>\n",
       "      <td>1.061030</td>\n",
       "      <td>-0.223651</td>\n",
       "      <td>1.452882</td>\n",
       "      <td>0.416119</td>\n",
       "      <td>-2.119895</td>\n",
       "      <td>0.061617</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.982610</td>\n",
       "      <td>1.249325</td>\n",
       "      <td>-1.159531</td>\n",
       "      <td>-0.397603</td>\n",
       "      <td>-0.000348</td>\n",
       "      <td>0.783153</td>\n",
       "      <td>-0.502866</td>\n",
       "      <td>0.267833</td>\n",
       "      <td>0.211469</td>\n",
       "      <td>0.081019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506</th>\n",
       "      <td>-0.037251</td>\n",
       "      <td>-0.009749</td>\n",
       "      <td>-1.638666</td>\n",
       "      <td>1.794693</td>\n",
       "      <td>1.262925</td>\n",
       "      <td>0.563673</td>\n",
       "      <td>2.222921</td>\n",
       "      <td>-3.363063</td>\n",
       "      <td>2.155335</td>\n",
       "      <td>-1.604268</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.198499</td>\n",
       "      <td>-1.664605</td>\n",
       "      <td>-0.156089</td>\n",
       "      <td>-1.166241</td>\n",
       "      <td>-1.011592</td>\n",
       "      <td>1.171418</td>\n",
       "      <td>-0.649655</td>\n",
       "      <td>0.264665</td>\n",
       "      <td>0.050947</td>\n",
       "      <td>-0.730816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>507</th>\n",
       "      <td>-2.227106</td>\n",
       "      <td>0.061165</td>\n",
       "      <td>0.902742</td>\n",
       "      <td>1.236015</td>\n",
       "      <td>-1.689608</td>\n",
       "      <td>-0.641463</td>\n",
       "      <td>0.654132</td>\n",
       "      <td>-0.734312</td>\n",
       "      <td>-0.364306</td>\n",
       "      <td>0.620598</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.671169</td>\n",
       "      <td>0.676739</td>\n",
       "      <td>-0.559069</td>\n",
       "      <td>-0.189427</td>\n",
       "      <td>0.566458</td>\n",
       "      <td>0.000305</td>\n",
       "      <td>-0.343038</td>\n",
       "      <td>0.128095</td>\n",
       "      <td>0.046791</td>\n",
       "      <td>0.369321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>508</th>\n",
       "      <td>5.465644</td>\n",
       "      <td>2.399047</td>\n",
       "      <td>-4.590174</td>\n",
       "      <td>-0.264209</td>\n",
       "      <td>2.200516</td>\n",
       "      <td>0.958209</td>\n",
       "      <td>1.116369</td>\n",
       "      <td>0.113043</td>\n",
       "      <td>-0.117697</td>\n",
       "      <td>1.990719</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.637207</td>\n",
       "      <td>0.374868</td>\n",
       "      <td>-1.934445</td>\n",
       "      <td>2.333957</td>\n",
       "      <td>1.259215</td>\n",
       "      <td>-0.534707</td>\n",
       "      <td>0.026886</td>\n",
       "      <td>1.729385</td>\n",
       "      <td>-0.156133</td>\n",
       "      <td>2.207489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>509</th>\n",
       "      <td>-0.426534</td>\n",
       "      <td>4.589082</td>\n",
       "      <td>-2.229936</td>\n",
       "      <td>-3.740811</td>\n",
       "      <td>-1.880061</td>\n",
       "      <td>-0.337073</td>\n",
       "      <td>-0.390823</td>\n",
       "      <td>-0.452998</td>\n",
       "      <td>0.129323</td>\n",
       "      <td>-0.132252</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.450911</td>\n",
       "      <td>-0.547877</td>\n",
       "      <td>0.419722</td>\n",
       "      <td>0.383752</td>\n",
       "      <td>0.194830</td>\n",
       "      <td>0.277032</td>\n",
       "      <td>0.253158</td>\n",
       "      <td>0.772802</td>\n",
       "      <td>-0.209244</td>\n",
       "      <td>-0.303139</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>510 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           0         1         2         3         4         5         6   \\\n",
       "0   -1.001226 -1.059893  0.986660  0.161155 -0.997699 -0.300203  0.754488   \n",
       "1   -0.066134 -0.399447 -0.065152  0.038366 -0.072110  0.005930 -0.276317   \n",
       "2    1.496734 -0.827214 -0.889009 -0.148621  0.376227  0.216439 -0.041229   \n",
       "3   -0.730326 -0.119637  0.469753  1.338862 -2.045690 -0.825665 -0.776626   \n",
       "4   -0.052995 -0.278251  0.168833  0.129490 -0.010581 -0.058356 -0.094532   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "505 -1.841088 -1.573852  0.925308  1.328420  1.061030 -0.223651  1.452882   \n",
       "506 -0.037251 -0.009749 -1.638666  1.794693  1.262925  0.563673  2.222921   \n",
       "507 -2.227106  0.061165  0.902742  1.236015 -1.689608 -0.641463  0.654132   \n",
       "508  5.465644  2.399047 -4.590174 -0.264209  2.200516  0.958209  1.116369   \n",
       "509 -0.426534  4.589082 -2.229936 -3.740811 -1.880061 -0.337073 -0.390823   \n",
       "\n",
       "           7         8         9   ...        13        14        15  \\\n",
       "0    0.103010  0.850884 -0.321918  ...  0.150729 -0.532187 -0.066752   \n",
       "1   -0.006671 -0.031683  0.173165  ...  0.126239  0.108393 -0.019713   \n",
       "2   -0.288756 -0.184608  0.290511  ...  0.182107 -0.599143  0.047240   \n",
       "3   -0.949277 -0.637562 -0.248656  ...  1.353734  0.606229 -0.315826   \n",
       "4    0.082996 -0.124160  0.111113  ...  0.110436  0.100084  0.011917   \n",
       "..        ...       ...       ...  ...       ...       ...       ...   \n",
       "505  0.416119 -2.119895  0.061617  ... -1.982610  1.249325 -1.159531   \n",
       "506 -3.363063  2.155335 -1.604268  ... -1.198499 -1.664605 -0.156089   \n",
       "507 -0.734312 -0.364306  0.620598  ... -0.671169  0.676739 -0.559069   \n",
       "508  0.113043 -0.117697  1.990719  ... -0.637207  0.374868 -1.934445   \n",
       "509 -0.452998  0.129323 -0.132252  ... -0.450911 -0.547877  0.419722   \n",
       "\n",
       "           16        17        18        19        20        21        22  \n",
       "0    0.277873 -0.779989 -0.661683  1.014897  0.183478 -0.292203 -0.154482  \n",
       "1   -0.251871 -0.044188 -0.083677 -0.112400 -0.119917  0.044716  0.174352  \n",
       "2    0.378696  0.762712  0.564445 -0.905975  0.017726 -0.118805 -0.258859  \n",
       "3    0.957854 -1.064890 -0.579852 -0.083798  0.851264  0.254949  0.048241  \n",
       "4   -0.186938 -0.073753 -0.147407 -0.071179 -0.092330  0.048253  0.132163  \n",
       "..        ...       ...       ...       ...       ...       ...       ...  \n",
       "505 -0.397603 -0.000348  0.783153 -0.502866  0.267833  0.211469  0.081019  \n",
       "506 -1.166241 -1.011592  1.171418 -0.649655  0.264665  0.050947 -0.730816  \n",
       "507 -0.189427  0.566458  0.000305 -0.343038  0.128095  0.046791  0.369321  \n",
       "508  2.333957  1.259215 -0.534707  0.026886  1.729385 -0.156133  2.207489  \n",
       "509  0.383752  0.194830  0.277032  0.253158  0.772802 -0.209244 -0.303139  \n",
       "\n",
       "[510 rows x 23 columns]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_pca = pd.DataFrame(data_pca)\n",
    "data_pca"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "6e373599",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data_pca"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "7a643bc0",
   "metadata": {},
   "outputs": [],
   "source": [
    "Ylabels = pd.read_csv('labels17_21.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "eaa9c2b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>-0.066134</td>\n",
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       "      <td>-0.065152</td>\n",
       "      <td>0.038366</td>\n",
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       "      <td>-0.031683</td>\n",
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       "      <th>2</th>\n",
       "      <td>1.496734</td>\n",
       "      <td>-0.827214</td>\n",
       "      <td>-0.889009</td>\n",
       "      <td>-0.148621</td>\n",
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       "      <td>-0.041229</td>\n",
       "      <td>-0.288756</td>\n",
       "      <td>-0.184608</td>\n",
       "      <td>0.290511</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.599143</td>\n",
       "      <td>0.047240</td>\n",
       "      <td>0.378696</td>\n",
       "      <td>0.762712</td>\n",
       "      <td>0.564445</td>\n",
       "      <td>-0.905975</td>\n",
       "      <td>0.017726</td>\n",
       "      <td>-0.118805</td>\n",
       "      <td>-0.258859</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.730326</td>\n",
       "      <td>-0.119637</td>\n",
       "      <td>0.469753</td>\n",
       "      <td>1.338862</td>\n",
       "      <td>-2.045690</td>\n",
       "      <td>-0.825665</td>\n",
       "      <td>-0.776626</td>\n",
       "      <td>-0.949277</td>\n",
       "      <td>-0.637562</td>\n",
       "      <td>-0.248656</td>\n",
       "      <td>...</td>\n",
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       "      <td>-0.315826</td>\n",
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       "      <td>-0.579852</td>\n",
       "      <td>-0.083798</td>\n",
       "      <td>0.851264</td>\n",
       "      <td>0.254949</td>\n",
       "      <td>0.048241</td>\n",
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       "      <th>4</th>\n",
       "      <td>-0.052995</td>\n",
       "      <td>-0.278251</td>\n",
       "      <td>0.168833</td>\n",
       "      <td>0.129490</td>\n",
       "      <td>-0.010581</td>\n",
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       "      <td>-0.094532</td>\n",
       "      <td>0.082996</td>\n",
       "      <td>-0.124160</td>\n",
       "      <td>0.111113</td>\n",
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       "      <td>-0.073753</td>\n",
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       "      <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>505</th>\n",
       "      <td>-1.841088</td>\n",
       "      <td>-1.573852</td>\n",
       "      <td>0.925308</td>\n",
       "      <td>1.328420</td>\n",
       "      <td>1.061030</td>\n",
       "      <td>-0.223651</td>\n",
       "      <td>1.452882</td>\n",
       "      <td>0.416119</td>\n",
       "      <td>-2.119895</td>\n",
       "      <td>0.061617</td>\n",
       "      <td>...</td>\n",
       "      <td>1.249325</td>\n",
       "      <td>-1.159531</td>\n",
       "      <td>-0.397603</td>\n",
       "      <td>-0.000348</td>\n",
       "      <td>0.783153</td>\n",
       "      <td>-0.502866</td>\n",
       "      <td>0.267833</td>\n",
       "      <td>0.211469</td>\n",
       "      <td>0.081019</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506</th>\n",
       "      <td>-0.037251</td>\n",
       "      <td>-0.009749</td>\n",
       "      <td>-1.638666</td>\n",
       "      <td>1.794693</td>\n",
       "      <td>1.262925</td>\n",
       "      <td>0.563673</td>\n",
       "      <td>2.222921</td>\n",
       "      <td>-3.363063</td>\n",
       "      <td>2.155335</td>\n",
       "      <td>-1.604268</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.664605</td>\n",
       "      <td>-0.156089</td>\n",
       "      <td>-1.166241</td>\n",
       "      <td>-1.011592</td>\n",
       "      <td>1.171418</td>\n",
       "      <td>-0.649655</td>\n",
       "      <td>0.264665</td>\n",
       "      <td>0.050947</td>\n",
       "      <td>-0.730816</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>507</th>\n",
       "      <td>-2.227106</td>\n",
       "      <td>0.061165</td>\n",
       "      <td>0.902742</td>\n",
       "      <td>1.236015</td>\n",
       "      <td>-1.689608</td>\n",
       "      <td>-0.641463</td>\n",
       "      <td>0.654132</td>\n",
       "      <td>-0.734312</td>\n",
       "      <td>-0.364306</td>\n",
       "      <td>0.620598</td>\n",
       "      <td>...</td>\n",
       "      <td>0.676739</td>\n",
       "      <td>-0.559069</td>\n",
       "      <td>-0.189427</td>\n",
       "      <td>0.566458</td>\n",
       "      <td>0.000305</td>\n",
       "      <td>-0.343038</td>\n",
       "      <td>0.128095</td>\n",
       "      <td>0.046791</td>\n",
       "      <td>0.369321</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>508</th>\n",
       "      <td>5.465644</td>\n",
       "      <td>2.399047</td>\n",
       "      <td>-4.590174</td>\n",
       "      <td>-0.264209</td>\n",
       "      <td>2.200516</td>\n",
       "      <td>0.958209</td>\n",
       "      <td>1.116369</td>\n",
       "      <td>0.113043</td>\n",
       "      <td>-0.117697</td>\n",
       "      <td>1.990719</td>\n",
       "      <td>...</td>\n",
       "      <td>0.374868</td>\n",
       "      <td>-1.934445</td>\n",
       "      <td>2.333957</td>\n",
       "      <td>1.259215</td>\n",
       "      <td>-0.534707</td>\n",
       "      <td>0.026886</td>\n",
       "      <td>1.729385</td>\n",
       "      <td>-0.156133</td>\n",
       "      <td>2.207489</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>509</th>\n",
       "      <td>-0.426534</td>\n",
       "      <td>4.589082</td>\n",
       "      <td>-2.229936</td>\n",
       "      <td>-3.740811</td>\n",
       "      <td>-1.880061</td>\n",
       "      <td>-0.337073</td>\n",
       "      <td>-0.390823</td>\n",
       "      <td>-0.452998</td>\n",
       "      <td>0.129323</td>\n",
       "      <td>-0.132252</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.547877</td>\n",
       "      <td>0.419722</td>\n",
       "      <td>0.383752</td>\n",
       "      <td>0.194830</td>\n",
       "      <td>0.277032</td>\n",
       "      <td>0.253158</td>\n",
       "      <td>0.772802</td>\n",
       "      <td>-0.209244</td>\n",
       "      <td>-0.303139</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>510 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            0         1         2         3         4         5         6  \\\n",
       "0   -1.001226 -1.059893  0.986660  0.161155 -0.997699 -0.300203  0.754488   \n",
       "1   -0.066134 -0.399447 -0.065152  0.038366 -0.072110  0.005930 -0.276317   \n",
       "2    1.496734 -0.827214 -0.889009 -0.148621  0.376227  0.216439 -0.041229   \n",
       "3   -0.730326 -0.119637  0.469753  1.338862 -2.045690 -0.825665 -0.776626   \n",
       "4   -0.052995 -0.278251  0.168833  0.129490 -0.010581 -0.058356 -0.094532   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "505 -1.841088 -1.573852  0.925308  1.328420  1.061030 -0.223651  1.452882   \n",
       "506 -0.037251 -0.009749 -1.638666  1.794693  1.262925  0.563673  2.222921   \n",
       "507 -2.227106  0.061165  0.902742  1.236015 -1.689608 -0.641463  0.654132   \n",
       "508  5.465644  2.399047 -4.590174 -0.264209  2.200516  0.958209  1.116369   \n",
       "509 -0.426534  4.589082 -2.229936 -3.740811 -1.880061 -0.337073 -0.390823   \n",
       "\n",
       "            7         8         9  ...        14        15        16  \\\n",
       "0    0.103010  0.850884 -0.321918  ... -0.532187 -0.066752  0.277873   \n",
       "1   -0.006671 -0.031683  0.173165  ...  0.108393 -0.019713 -0.251871   \n",
       "2   -0.288756 -0.184608  0.290511  ... -0.599143  0.047240  0.378696   \n",
       "3   -0.949277 -0.637562 -0.248656  ...  0.606229 -0.315826  0.957854   \n",
       "4    0.082996 -0.124160  0.111113  ...  0.100084  0.011917 -0.186938   \n",
       "..        ...       ...       ...  ...       ...       ...       ...   \n",
       "505  0.416119 -2.119895  0.061617  ...  1.249325 -1.159531 -0.397603   \n",
       "506 -3.363063  2.155335 -1.604268  ... -1.664605 -0.156089 -1.166241   \n",
       "507 -0.734312 -0.364306  0.620598  ...  0.676739 -0.559069 -0.189427   \n",
       "508  0.113043 -0.117697  1.990719  ...  0.374868 -1.934445  2.333957   \n",
       "509 -0.452998  0.129323 -0.132252  ... -0.547877  0.419722  0.383752   \n",
       "\n",
       "           17        18        19        20        21        22  0  \n",
       "0   -0.779989 -0.661683  1.014897  0.183478 -0.292203 -0.154482  0  \n",
       "1   -0.044188 -0.083677 -0.112400 -0.119917  0.044716  0.174352  0  \n",
       "2    0.762712  0.564445 -0.905975  0.017726 -0.118805 -0.258859  1  \n",
       "3   -1.064890 -0.579852 -0.083798  0.851264  0.254949  0.048241  0  \n",
       "4   -0.073753 -0.147407 -0.071179 -0.092330  0.048253  0.132163  1  \n",
       "..        ...       ...       ...       ...       ...       ... ..  \n",
       "505 -0.000348  0.783153 -0.502866  0.267833  0.211469  0.081019  0  \n",
       "506 -1.011592  1.171418 -0.649655  0.264665  0.050947 -0.730816  0  \n",
       "507  0.566458  0.000305 -0.343038  0.128095  0.046791  0.369321  1  \n",
       "508  1.259215 -0.534707  0.026886  1.729385 -0.156133  2.207489  0  \n",
       "509  0.194830  0.277032  0.253158  0.772802 -0.209244 -0.303139  0  \n",
       "\n",
       "[510 rows x 24 columns]"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_Ylabels_17_21_pca = pd.concat([X, Ylabels], axis=\"columns\")\n",
    "X_Ylabels_17_21_pca.drop([41])\n",
    "X_Ylabels_17_21_pca"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "f3ba6e2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_Ylabels_17_21_pca.to_csv(\"X_Ylabels_17_21_pca.csv\",index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9aca0bab",
   "metadata": {},
   "source": [
    "# Now we have a post-PCA dataset for Classifier models using the 2017 to 2021 data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c27cb68f",
   "metadata": {},
   "source": [
    "# END.\n",
    "\n",
    "\n",
    "\n"
   ]
  }
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