LM_10X_VIX_LSTM.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "setup complete\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import re\n",
    "from matplotlib import pyplot\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import os # accessing directory structure\n",
    "#print(os.listdir(\"../input\"))\n",
    "#set seed\n",
    "np.random.seed(12345) # receive the same random numbers each execution\n",
    "print('setup complete')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Pre-processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CIK</th>\n",
       "      <th>FILING_DATE</th>\n",
       "      <th>FYE</th>\n",
       "      <th>FORM_TYPE</th>\n",
       "      <th>FILE_NAME</th>\n",
       "      <th>SIC</th>\n",
       "      <th>FFInd</th>\n",
       "      <th>N_Words</th>\n",
       "      <th>N_Unique_Words</th>\n",
       "      <th>N_Negative</th>\n",
       "      <th>...</th>\n",
       "      <th>N_Constraining</th>\n",
       "      <th>N_Negation</th>\n",
       "      <th>GrossFileSize</th>\n",
       "      <th>NetFileSize</th>\n",
       "      <th>NonTextDocTypeChars</th>\n",
       "      <th>HTMLChars</th>\n",
       "      <th>XBRLChars</th>\n",
       "      <th>XMLChars</th>\n",
       "      <th>N_Tables</th>\n",
       "      <th>N_Exhibits</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>60512</td>\n",
       "      <td>19930813</td>\n",
       "      <td>19930630</td>\n",
       "      <td>10-Q</td>\n",
       "      <td>D:/Edgar/10-X_C\\1993\\QTR3\\19930813_10-Q_edgar_...</td>\n",
       "      <td>1311</td>\n",
       "      <td>30</td>\n",
       "      <td>3037</td>\n",
       "      <td>700</td>\n",
       "      <td>68</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>63255</td>\n",
       "      <td>25233</td>\n",
       "      <td>0</td>\n",
       "      <td>186</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>66740</td>\n",
       "      <td>19930813</td>\n",
       "      <td>19930630</td>\n",
       "      <td>10-Q</td>\n",
       "      <td>D:/Edgar/10-X_C\\1993\\QTR3\\19930813_10-Q_edgar_...</td>\n",
       "      <td>2670</td>\n",
       "      <td>38</td>\n",
       "      <td>4102</td>\n",
       "      <td>823</td>\n",
       "      <td>53</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>57283</td>\n",
       "      <td>47739</td>\n",
       "      <td>0</td>\n",
       "      <td>317</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60512</td>\n",
       "      <td>19931007</td>\n",
       "      <td>19921231</td>\n",
       "      <td>10-K-A</td>\n",
       "      <td>D:/Edgar/10-X_C\\1993\\QTR4\\19931007_10-K-A_edga...</td>\n",
       "      <td>1311</td>\n",
       "      <td>30</td>\n",
       "      <td>7278</td>\n",
       "      <td>1184</td>\n",
       "      <td>157</td>\n",
       "      <td>...</td>\n",
       "      <td>26</td>\n",
       "      <td>5</td>\n",
       "      <td>114136</td>\n",
       "      <td>56958</td>\n",
       "      <td>0</td>\n",
       "      <td>276</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>60512</td>\n",
       "      <td>19931110</td>\n",
       "      <td>19930930</td>\n",
       "      <td>10-Q</td>\n",
       "      <td>D:/Edgar/10-X_C\\1993\\QTR4\\19931110_10-Q_edgar_...</td>\n",
       "      <td>1311</td>\n",
       "      <td>30</td>\n",
       "      <td>3768</td>\n",
       "      <td>739</td>\n",
       "      <td>69</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>72062</td>\n",
       "      <td>31256</td>\n",
       "      <td>0</td>\n",
       "      <td>201</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11860</td>\n",
       "      <td>19931112</td>\n",
       "      <td>19930930</td>\n",
       "      <td>10-Q</td>\n",
       "      <td>D:/Edgar/10-X_C\\1993\\QTR4\\19931112_10-Q_edgar_...</td>\n",
       "      <td>3312</td>\n",
       "      <td>19</td>\n",
       "      <td>3822</td>\n",
       "      <td>897</td>\n",
       "      <td>77</td>\n",
       "      <td>...</td>\n",
       "      <td>32</td>\n",
       "      <td>2</td>\n",
       "      <td>40392</td>\n",
       "      <td>39056</td>\n",
       "      <td>0</td>\n",
       "      <td>201</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     CIK  FILING_DATE       FYE FORM_TYPE  \\\n",
       "0  60512     19930813  19930630      10-Q   \n",
       "1  66740     19930813  19930630      10-Q   \n",
       "2  60512     19931007  19921231    10-K-A   \n",
       "3  60512     19931110  19930930      10-Q   \n",
       "4  11860     19931112  19930930      10-Q   \n",
       "\n",
       "                                           FILE_NAME   SIC  FFInd  N_Words  \\\n",
       "0  D:/Edgar/10-X_C\\1993\\QTR3\\19930813_10-Q_edgar_...  1311     30     3037   \n",
       "1  D:/Edgar/10-X_C\\1993\\QTR3\\19930813_10-Q_edgar_...  2670     38     4102   \n",
       "2  D:/Edgar/10-X_C\\1993\\QTR4\\19931007_10-K-A_edga...  1311     30     7278   \n",
       "3  D:/Edgar/10-X_C\\1993\\QTR4\\19931110_10-Q_edgar_...  1311     30     3768   \n",
       "4  D:/Edgar/10-X_C\\1993\\QTR4\\19931112_10-Q_edgar_...  3312     19     3822   \n",
       "\n",
       "   N_Unique_Words  N_Negative  ...  N_Constraining  N_Negation  GrossFileSize  \\\n",
       "0             700          68  ...               6           1          63255   \n",
       "1             823          53  ...               7           3          57283   \n",
       "2            1184         157  ...              26           5         114136   \n",
       "3             739          69  ...               3           1          72062   \n",
       "4             897          77  ...              32           2          40392   \n",
       "\n",
       "   NetFileSize  NonTextDocTypeChars  HTMLChars  XBRLChars  XMLChars  N_Tables  \\\n",
       "0        25233                    0        186          0         0         7   \n",
       "1        47739                    0        317          0         0         2   \n",
       "2        56958                    0        276          0         0        22   \n",
       "3        31256                    0        201          0         0         8   \n",
       "4        39056                    0        201          0         0         0   \n",
       "\n",
       "   N_Exhibits  \n",
       "0           0  \n",
       "1           3  \n",
       "2           0  \n",
       "3           0  \n",
       "4           0  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#notclean = pd.read_csv(\"add file path\\LM_10X_Summaries_2018.csv\")\n",
    "notclean.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>FILING_DATE</th>\n",
       "      <th>FYE</th>\n",
       "      <th>FORM_TYPE</th>\n",
       "      <th>N_Words</th>\n",
       "      <th>N_Unique_Words</th>\n",
       "      <th>N_Negative</th>\n",
       "      <th>N_Positive</th>\n",
       "      <th>N_Uncertainty</th>\n",
       "      <th>N_Litigious</th>\n",
       "      <th>N_ModalWeak</th>\n",
       "      <th>N_Modal_Moderate</th>\n",
       "      <th>N_Modal_Strong</th>\n",
       "      <th>N_Constraining</th>\n",
       "      <th>N_Negation</th>\n",
       "      <th>GrossFileSize</th>\n",
       "      <th>NetFileSize</th>\n",
       "      <th>N_Tables</th>\n",
       "      <th>N_Exhibits</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19930813</td>\n",
       "      <td>19930630</td>\n",
       "      <td>10-Q</td>\n",
       "      <td>3037</td>\n",
       "      <td>700</td>\n",
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       "      <td>1</td>\n",
       "      <td>63255</td>\n",
       "      <td>25233</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19930813</td>\n",
       "      <td>19930630</td>\n",
       "      <td>10-Q</td>\n",
       "      <td>4102</td>\n",
       "      <td>823</td>\n",
       "      <td>53</td>\n",
       "      <td>24</td>\n",
       "      <td>16</td>\n",
       "      <td>20</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>57283</td>\n",
       "      <td>47739</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19931007</td>\n",
       "      <td>19921231</td>\n",
       "      <td>10-K-A</td>\n",
       "      <td>7278</td>\n",
       "      <td>1184</td>\n",
       "      <td>157</td>\n",
       "      <td>41</td>\n",
       "      <td>67</td>\n",
       "      <td>104</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>31</td>\n",
       "      <td>26</td>\n",
       "      <td>5</td>\n",
       "      <td>114136</td>\n",
       "      <td>56958</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19931110</td>\n",
       "      <td>19930930</td>\n",
       "      <td>10-Q</td>\n",
       "      <td>3768</td>\n",
       "      <td>739</td>\n",
       "      <td>69</td>\n",
       "      <td>13</td>\n",
       "      <td>23</td>\n",
       "      <td>50</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>72062</td>\n",
       "      <td>31256</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19931112</td>\n",
       "      <td>19930930</td>\n",
       "      <td>10-Q</td>\n",
       "      <td>3822</td>\n",
       "      <td>897</td>\n",
       "      <td>77</td>\n",
       "      <td>39</td>\n",
       "      <td>23</td>\n",
       "      <td>60</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>19</td>\n",
       "      <td>32</td>\n",
       "      <td>2</td>\n",
       "      <td>40392</td>\n",
       "      <td>39056</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "   FILING_DATE       FYE FORM_TYPE  N_Words  N_Unique_Words  N_Negative  \\\n",
       "0     19930813  19930630      10-Q     3037             700          68   \n",
       "1     19930813  19930630      10-Q     4102             823          53   \n",
       "2     19931007  19921231    10-K-A     7278            1184         157   \n",
       "3     19931110  19930930      10-Q     3768             739          69   \n",
       "4     19931112  19930930      10-Q     3822             897          77   \n",
       "\n",
       "   N_Positive  N_Uncertainty  N_Litigious  N_ModalWeak  N_Modal_Moderate  \\\n",
       "0           6             23           40            6                 5   \n",
       "1          24             16           20            7                 8   \n",
       "2          41             67          104           15                15   \n",
       "3          13             23           50            4                 7   \n",
       "4          39             23           60            5                12   \n",
       "\n",
       "   N_Modal_Strong  N_Constraining  N_Negation  GrossFileSize  NetFileSize  \\\n",
       "0               9               6           1          63255        25233   \n",
       "1               9               7           3          57283        47739   \n",
       "2              31              26           5         114136        56958   \n",
       "3              16               3           1          72062        31256   \n",
       "4              19              32           2          40392        39056   \n",
       "\n",
       "   N_Tables  N_Exhibits  \n",
       "0         7           0  \n",
       "1         2           3  \n",
       "2        22           0  \n",
       "3         8           0  \n",
       "4         0           0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm_10X = notclean.drop(['CIK','FILE_NAME', 'SIC', 'FFInd', 'NonTextDocTypeChars', 'HTMLChars', 'XBRLChars', 'XMLChars'], axis=1)\n",
    "lm_10X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FILING_DATE</th>\n",
       "      <th>FYE</th>\n",
       "      <th>FORM_TYPE</th>\n",
       "      <th>N_Words</th>\n",
       "      <th>N_Unique_Words</th>\n",
       "      <th>N_Negative</th>\n",
       "      <th>N_Positive</th>\n",
       "      <th>N_Uncertainty</th>\n",
       "      <th>N_Litigious</th>\n",
       "      <th>N_ModalWeak</th>\n",
       "      <th>N_Modal_Moderate</th>\n",
       "      <th>N_Modal_Strong</th>\n",
       "      <th>N_Constraining</th>\n",
       "      <th>N_Negation</th>\n",
       "      <th>GrossFileSize</th>\n",
       "      <th>NetFileSize</th>\n",
       "      <th>N_Tables</th>\n",
       "      <th>N_Exhibits</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1993-08-13</td>\n",
       "      <td>19930630</td>\n",
       "      <td>10-Q</td>\n",
       "      <td>3037</td>\n",
       "      <td>700</td>\n",
       "      <td>68</td>\n",
       "      <td>6</td>\n",
       "      <td>23</td>\n",
       "      <td>40</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>63255</td>\n",
       "      <td>25233</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1993-08-13</td>\n",
       "      <td>19930630</td>\n",
       "      <td>10-Q</td>\n",
       "      <td>4102</td>\n",
       "      <td>823</td>\n",
       "      <td>53</td>\n",
       "      <td>24</td>\n",
       "      <td>16</td>\n",
       "      <td>20</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>57283</td>\n",
       "      <td>47739</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1993-10-07</td>\n",
       "      <td>19921231</td>\n",
       "      <td>10-K-A</td>\n",
       "      <td>7278</td>\n",
       "      <td>1184</td>\n",
       "      <td>157</td>\n",
       "      <td>41</td>\n",
       "      <td>67</td>\n",
       "      <td>104</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>31</td>\n",
       "      <td>26</td>\n",
       "      <td>5</td>\n",
       "      <td>114136</td>\n",
       "      <td>56958</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1993-11-10</td>\n",
       "      <td>19930930</td>\n",
       "      <td>10-Q</td>\n",
       "      <td>3768</td>\n",
       "      <td>739</td>\n",
       "      <td>69</td>\n",
       "      <td>13</td>\n",
       "      <td>23</td>\n",
       "      <td>50</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>72062</td>\n",
       "      <td>31256</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1993-11-12</td>\n",
       "      <td>19930930</td>\n",
       "      <td>10-Q</td>\n",
       "      <td>3822</td>\n",
       "      <td>897</td>\n",
       "      <td>77</td>\n",
       "      <td>39</td>\n",
       "      <td>23</td>\n",
       "      <td>60</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>19</td>\n",
       "      <td>32</td>\n",
       "      <td>2</td>\n",
       "      <td>40392</td>\n",
       "      <td>39056</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  FILING_DATE       FYE FORM_TYPE  N_Words  N_Unique_Words  N_Negative  \\\n",
       "0  1993-08-13  19930630      10-Q     3037             700          68   \n",
       "1  1993-08-13  19930630      10-Q     4102             823          53   \n",
       "2  1993-10-07  19921231    10-K-A     7278            1184         157   \n",
       "3  1993-11-10  19930930      10-Q     3768             739          69   \n",
       "4  1993-11-12  19930930      10-Q     3822             897          77   \n",
       "\n",
       "   N_Positive  N_Uncertainty  N_Litigious  N_ModalWeak  N_Modal_Moderate  \\\n",
       "0           6             23           40            6                 5   \n",
       "1          24             16           20            7                 8   \n",
       "2          41             67          104           15                15   \n",
       "3          13             23           50            4                 7   \n",
       "4          39             23           60            5                12   \n",
       "\n",
       "   N_Modal_Strong  N_Constraining  N_Negation  GrossFileSize  NetFileSize  \\\n",
       "0               9               6           1          63255        25233   \n",
       "1               9               7           3          57283        47739   \n",
       "2              31              26           5         114136        56958   \n",
       "3              16               3           1          72062        31256   \n",
       "4              19              32           2          40392        39056   \n",
       "\n",
       "   N_Tables  N_Exhibits  \n",
       "0         7           0  \n",
       "1         2           3  \n",
       "2        22           0  \n",
       "3         8           0  \n",
       "4         0           0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm_10X['FILING_DATE'] = pd.to_datetime(lm_10X['FILING_DATE'], format='%Y%m%d')\n",
    "#lm_10X['FYE'] = pd.to_datetime(lm_10X['FYE'], format='%Y%m%d')\n",
    "lm_10X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FYE</th>\n",
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       "      <th>N_Tables</th>\n",
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       "    <tr>\n",
       "      <th>FILING_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",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1993-08-13</th>\n",
       "      <td>19930630</td>\n",
       "      <td>10-Q</td>\n",
       "      <td>3037</td>\n",
       "      <td>700</td>\n",
       "      <td>68</td>\n",
       "      <td>6</td>\n",
       "      <td>23</td>\n",
       "      <td>40</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>63255</td>\n",
       "      <td>25233</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-08-13</th>\n",
       "      <td>19930630</td>\n",
       "      <td>10-Q</td>\n",
       "      <td>4102</td>\n",
       "      <td>823</td>\n",
       "      <td>53</td>\n",
       "      <td>24</td>\n",
       "      <td>16</td>\n",
       "      <td>20</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>57283</td>\n",
       "      <td>47739</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-10-07</th>\n",
       "      <td>19921231</td>\n",
       "      <td>10-K-A</td>\n",
       "      <td>7278</td>\n",
       "      <td>1184</td>\n",
       "      <td>157</td>\n",
       "      <td>41</td>\n",
       "      <td>67</td>\n",
       "      <td>104</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>31</td>\n",
       "      <td>26</td>\n",
       "      <td>5</td>\n",
       "      <td>114136</td>\n",
       "      <td>56958</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-11-10</th>\n",
       "      <td>19930930</td>\n",
       "      <td>10-Q</td>\n",
       "      <td>3768</td>\n",
       "      <td>739</td>\n",
       "      <td>69</td>\n",
       "      <td>13</td>\n",
       "      <td>23</td>\n",
       "      <td>50</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>72062</td>\n",
       "      <td>31256</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1993-11-12</th>\n",
       "      <td>19930930</td>\n",
       "      <td>10-Q</td>\n",
       "      <td>3822</td>\n",
       "      <td>897</td>\n",
       "      <td>77</td>\n",
       "      <td>39</td>\n",
       "      <td>23</td>\n",
       "      <td>60</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>19</td>\n",
       "      <td>32</td>\n",
       "      <td>2</td>\n",
       "      <td>40392</td>\n",
       "      <td>39056</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  FYE FORM_TYPE  N_Words  N_Unique_Words  N_Negative  \\\n",
       "FILING_DATE                                                            \n",
       "1993-08-13   19930630      10-Q     3037             700          68   \n",
       "1993-08-13   19930630      10-Q     4102             823          53   \n",
       "1993-10-07   19921231    10-K-A     7278            1184         157   \n",
       "1993-11-10   19930930      10-Q     3768             739          69   \n",
       "1993-11-12   19930930      10-Q     3822             897          77   \n",
       "\n",
       "             N_Positive  N_Uncertainty  N_Litigious  N_ModalWeak  \\\n",
       "FILING_DATE                                                        \n",
       "1993-08-13            6             23           40            6   \n",
       "1993-08-13           24             16           20            7   \n",
       "1993-10-07           41             67          104           15   \n",
       "1993-11-10           13             23           50            4   \n",
       "1993-11-12           39             23           60            5   \n",
       "\n",
       "             N_Modal_Moderate  N_Modal_Strong  N_Constraining  N_Negation  \\\n",
       "FILING_DATE                                                                 \n",
       "1993-08-13                  5               9               6           1   \n",
       "1993-08-13                  8               9               7           3   \n",
       "1993-10-07                 15              31              26           5   \n",
       "1993-11-10                  7              16               3           1   \n",
       "1993-11-12                 12              19              32           2   \n",
       "\n",
       "             GrossFileSize  NetFileSize  N_Tables  N_Exhibits  \n",
       "FILING_DATE                                                    \n",
       "1993-08-13           63255        25233         7           0  \n",
       "1993-08-13           57283        47739         2           3  \n",
       "1993-10-07          114136        56958        22           0  \n",
       "1993-11-10           72062        31256         8           0  \n",
       "1993-11-12           40392        39056         0           0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm_10X = lm_10X.set_index(pd.DatetimeIndex(lm_10X['FILING_DATE']))\n",
    "lm_10X = lm_10X.drop(['FILING_DATE'], axis=1)\n",
    "lm_10X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>VIX Open</th>\n",
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       "      <th>VIX Low</th>\n",
       "      <th>VIX Close</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/2/2004</td>\n",
       "      <td>17.96</td>\n",
       "      <td>18.68</td>\n",
       "      <td>17.54</td>\n",
       "      <td>18.22</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/5/2004</td>\n",
       "      <td>18.45</td>\n",
       "      <td>18.49</td>\n",
       "      <td>17.44</td>\n",
       "      <td>17.49</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/6/2004</td>\n",
       "      <td>17.66</td>\n",
       "      <td>17.67</td>\n",
       "      <td>16.19</td>\n",
       "      <td>16.73</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/7/2004</td>\n",
       "      <td>16.72</td>\n",
       "      <td>16.75</td>\n",
       "      <td>15.50</td>\n",
       "      <td>15.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1/8/2004</td>\n",
       "      <td>15.42</td>\n",
       "      <td>15.68</td>\n",
       "      <td>15.32</td>\n",
       "      <td>15.61</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Date  VIX Open  VIX High  VIX Low  VIX Close\n",
       "0  1/2/2004     17.96     18.68    17.54      18.22\n",
       "1  1/5/2004     18.45     18.49    17.44      17.49\n",
       "2  1/6/2004     17.66     17.67    16.19      16.73\n",
       "3  1/7/2004     16.72     16.75    15.50      15.50\n",
       "4  1/8/2004     15.42     15.68    15.32      15.61"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfn = pd.read_csv(\"Z:\\Dokumente\\Studium\\Bachelorthesis\\VIX_2004-present.csv\")\n",
    "dfn.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>4</th>\n",
       "      <td>01/08/1990</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date  VIX Open  VIX High  VIX Low  VIX Close\n",
       "0  01/02/1990       NaN       NaN      NaN      17.24\n",
       "1  01/03/1990       NaN       NaN      NaN      18.19\n",
       "2  01/04/1990       NaN       NaN      NaN      19.22\n",
       "3  01/05/1990       NaN       NaN      NaN      20.11\n",
       "4  01/08/1990       NaN       NaN      NaN      20.26"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfo = pd.read_csv(\"Z:\\Dokumente\\Studium\\Bachelorthesis\\VIX_1990-2003.csv\")\n",
    "dfo.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "frames = [dfo, dfn]\n",
    "vix = pd.concat(frames)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>01/12/1990</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>01/15/90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>01/16/90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>01/17/90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>01/18/90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>01/19/90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>01/22/90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>01/23/90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>01/24/90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>01/25/90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>01/26/90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>01/29/90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>01/30/90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>01/31/90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>02/01/1990</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>02/02/1990</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>02/05/1990</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>02/06/1990</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>02/07/1990</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>02/08/1990</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>02/09/1990</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>02/12/1990</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3875</th>\n",
       "      <td>05/28/2019</td>\n",
       "      <td>16.55</td>\n",
       "      <td>17.70</td>\n",
       "      <td>15.90</td>\n",
       "      <td>17.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3876</th>\n",
       "      <td>05/29/2019</td>\n",
       "      <td>18.55</td>\n",
       "      <td>19.04</td>\n",
       "      <td>17.62</td>\n",
       "      <td>17.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3877</th>\n",
       "      <td>05/30/2019</td>\n",
       "      <td>17.47</td>\n",
       "      <td>18.11</td>\n",
       "      <td>16.72</td>\n",
       "      <td>17.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3878</th>\n",
       "      <td>05/31/2019</td>\n",
       "      <td>19.05</td>\n",
       "      <td>19.72</td>\n",
       "      <td>18.01</td>\n",
       "      <td>18.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3879</th>\n",
       "      <td>06/03/2019</td>\n",
       "      <td>19.41</td>\n",
       "      <td>19.75</td>\n",
       "      <td>18.16</td>\n",
       "      <td>18.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3880</th>\n",
       "      <td>06/04/2019</td>\n",
       "      <td>18.74</td>\n",
       "      <td>18.74</td>\n",
       "      <td>16.97</td>\n",
       "      <td>16.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3881</th>\n",
       "      <td>06/05/2019</td>\n",
       "      <td>17.06</td>\n",
       "      <td>17.49</td>\n",
       "      <td>16.04</td>\n",
       "      <td>16.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3882</th>\n",
       "      <td>06/06/2019</td>\n",
       "      <td>16.25</td>\n",
       "      <td>16.54</td>\n",
       "      <td>15.36</td>\n",
       "      <td>15.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3883</th>\n",
       "      <td>06/07/2019</td>\n",
       "      <td>15.65</td>\n",
       "      <td>16.44</td>\n",
       "      <td>15.33</td>\n",
       "      <td>16.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3884</th>\n",
       "      <td>06/10/2019</td>\n",
       "      <td>16.30</td>\n",
       "      <td>16.47</td>\n",
       "      <td>15.84</td>\n",
       "      <td>15.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3885</th>\n",
       "      <td>06/11/2019</td>\n",
       "      <td>15.84</td>\n",
       "      <td>16.70</td>\n",
       "      <td>15.50</td>\n",
       "      <td>15.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3886</th>\n",
       "      <td>06/12/2019</td>\n",
       "      <td>16.26</td>\n",
       "      <td>16.43</td>\n",
       "      <td>15.78</td>\n",
       "      <td>15.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3887</th>\n",
       "      <td>06/13/2019</td>\n",
       "      <td>16.16</td>\n",
       "      <td>16.21</td>\n",
       "      <td>15.61</td>\n",
       "      <td>15.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3888</th>\n",
       "      <td>06/14/2019</td>\n",
       "      <td>16.00</td>\n",
       "      <td>16.40</td>\n",
       "      <td>15.21</td>\n",
       "      <td>15.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3889</th>\n",
       "      <td>06/17/2019</td>\n",
       "      <td>15.65</td>\n",
       "      <td>15.76</td>\n",
       "      <td>15.24</td>\n",
       "      <td>15.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3890</th>\n",
       "      <td>06/18/2019</td>\n",
       "      <td>14.89</td>\n",
       "      <td>15.54</td>\n",
       "      <td>14.62</td>\n",
       "      <td>15.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3891</th>\n",
       "      <td>06/19/2019</td>\n",
       "      <td>15.05</td>\n",
       "      <td>15.71</td>\n",
       "      <td>14.15</td>\n",
       "      <td>14.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3892</th>\n",
       "      <td>06/20/2019</td>\n",
       "      <td>14.04</td>\n",
       "      <td>16.03</td>\n",
       "      <td>13.19</td>\n",
       "      <td>14.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3893</th>\n",
       "      <td>06/21/2019</td>\n",
       "      <td>14.74</td>\n",
       "      <td>15.48</td>\n",
       "      <td>14.42</td>\n",
       "      <td>15.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3894</th>\n",
       "      <td>06/24/2019</td>\n",
       "      <td>15.46</td>\n",
       "      <td>15.56</td>\n",
       "      <td>15.23</td>\n",
       "      <td>15.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3895</th>\n",
       "      <td>06/25/2019</td>\n",
       "      <td>15.43</td>\n",
       "      <td>16.68</td>\n",
       "      <td>15.10</td>\n",
       "      <td>16.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3896</th>\n",
       "      <td>06/26/2019</td>\n",
       "      <td>16.10</td>\n",
       "      <td>16.60</td>\n",
       "      <td>15.47</td>\n",
       "      <td>16.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3897</th>\n",
       "      <td>06/27/2019</td>\n",
       "      <td>15.66</td>\n",
       "      <td>16.40</td>\n",
       "      <td>15.66</td>\n",
       "      <td>15.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3898</th>\n",
       "      <td>06/28/2019</td>\n",
       "      <td>15.70</td>\n",
       "      <td>16.13</td>\n",
       "      <td>15.08</td>\n",
       "      <td>15.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3899</th>\n",
       "      <td>07/01/2019</td>\n",
       "      <td>13.85</td>\n",
       "      <td>14.64</td>\n",
       "      <td>13.80</td>\n",
       "      <td>14.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3900</th>\n",
       "      <td>07/02/2019</td>\n",
       "      <td>14.16</td>\n",
       "      <td>14.30</td>\n",
       "      <td>12.90</td>\n",
       "      <td>12.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3901</th>\n",
       "      <td>07/03/2019</td>\n",
       "      <td>13.18</td>\n",
       "      <td>13.19</td>\n",
       "      <td>12.56</td>\n",
       "      <td>12.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3902</th>\n",
       "      <td>07/05/2019</td>\n",
       "      <td>13.00</td>\n",
       "      <td>14.47</td>\n",
       "      <td>12.04</td>\n",
       "      <td>13.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3903</th>\n",
       "      <td>07/08/2019</td>\n",
       "      <td>13.73</td>\n",
       "      <td>14.44</td>\n",
       "      <td>13.64</td>\n",
       "      <td>13.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3904</th>\n",
       "      <td>07/09/2019</td>\n",
       "      <td>14.47</td>\n",
       "      <td>14.71</td>\n",
       "      <td>13.99</td>\n",
       "      <td>14.09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7437 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date  VIX Open  VIX High  VIX Low  VIX Close\n",
       "0     01/02/1990       NaN       NaN      NaN      17.24\n",
       "1     01/03/1990       NaN       NaN      NaN      18.19\n",
       "2     01/04/1990       NaN       NaN      NaN      19.22\n",
       "3     01/05/1990       NaN       NaN      NaN      20.11\n",
       "4     01/08/1990       NaN       NaN      NaN      20.26\n",
       "5     01/09/1990       NaN       NaN      NaN      22.20\n",
       "6     01/10/1990       NaN       NaN      NaN      22.44\n",
       "7     01/11/1990       NaN       NaN      NaN      20.05\n",
       "8     01/12/1990       NaN       NaN      NaN      24.64\n",
       "9       01/15/90       NaN       NaN      NaN      26.34\n",
       "10      01/16/90       NaN       NaN      NaN      24.18\n",
       "11      01/17/90       NaN       NaN      NaN      24.16\n",
       "12      01/18/90       NaN       NaN      NaN      24.34\n",
       "13      01/19/90       NaN       NaN      NaN      22.50\n",
       "14      01/22/90       NaN       NaN      NaN      26.70\n",
       "15      01/23/90       NaN       NaN      NaN      24.72\n",
       "16      01/24/90       NaN       NaN      NaN      25.39\n",
       "17      01/25/90       NaN       NaN      NaN      25.63\n",
       "18      01/26/90       NaN       NaN      NaN      26.28\n",
       "19      01/29/90       NaN       NaN      NaN      26.44\n",
       "20      01/30/90       NaN       NaN      NaN      27.25\n",
       "21      01/31/90       NaN       NaN      NaN      25.36\n",
       "22    02/01/1990       NaN       NaN      NaN      24.87\n",
       "23    02/02/1990       NaN       NaN      NaN      24.32\n",
       "24    02/05/1990       NaN       NaN      NaN      24.54\n",
       "25    02/06/1990       NaN       NaN      NaN      24.69\n",
       "26    02/07/1990       NaN       NaN      NaN      24.29\n",
       "27    02/08/1990       NaN       NaN      NaN      23.77\n",
       "28    02/09/1990       NaN       NaN      NaN      23.69\n",
       "29    02/12/1990       NaN       NaN      NaN      24.38\n",
       "...          ...       ...       ...      ...        ...\n",
       "3875  05/28/2019     16.55     17.70    15.90      17.50\n",
       "3876  05/29/2019     18.55     19.04    17.62      17.90\n",
       "3877  05/30/2019     17.47     18.11    16.72      17.30\n",
       "3878  05/31/2019     19.05     19.72    18.01      18.71\n",
       "3879  06/03/2019     19.41     19.75    18.16      18.86\n",
       "3880  06/04/2019     18.74     18.74    16.97      16.97\n",
       "3881  06/05/2019     17.06     17.49    16.04      16.09\n",
       "3882  06/06/2019     16.25     16.54    15.36      15.93\n",
       "3883  06/07/2019     15.65     16.44    15.33      16.30\n",
       "3884  06/10/2019     16.30     16.47    15.84      15.94\n",
       "3885  06/11/2019     15.84     16.70    15.50      15.99\n",
       "3886  06/12/2019     16.26     16.43    15.78      15.91\n",
       "3887  06/13/2019     16.16     16.21    15.61      15.82\n",
       "3888  06/14/2019     16.00     16.40    15.21      15.28\n",
       "3889  06/17/2019     15.65     15.76    15.24      15.35\n",
       "3890  06/18/2019     14.89     15.54    14.62      15.15\n",
       "3891  06/19/2019     15.05     15.71    14.15      14.33\n",
       "3892  06/20/2019     14.04     16.03    13.19      14.75\n",
       "3893  06/21/2019     14.74     15.48    14.42      15.40\n",
       "3894  06/24/2019     15.46     15.56    15.23      15.26\n",
       "3895  06/25/2019     15.43     16.68    15.10      16.28\n",
       "3896  06/26/2019     16.10     16.60    15.47      16.21\n",
       "3897  06/27/2019     15.66     16.40    15.66      15.82\n",
       "3898  06/28/2019     15.70     16.13    15.08      15.08\n",
       "3899  07/01/2019     13.85     14.64    13.80      14.06\n",
       "3900  07/02/2019     14.16     14.30    12.90      12.93\n",
       "3901  07/03/2019     13.18     13.19    12.56      12.57\n",
       "3902  07/05/2019     13.00     14.47    12.04      13.28\n",
       "3903  07/08/2019     13.73     14.44    13.64      13.96\n",
       "3904  07/09/2019     14.47     14.71    13.99      14.09\n",
       "\n",
       "[7437 rows x 5 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VIX Open</th>\n",
       "      <th>VIX High</th>\n",
       "      <th>VIX Low</th>\n",
       "      <th>VIX Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>01/02/1990</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/03/1990</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/04/1990</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/05/1990</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/08/1990</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            VIX Open  VIX High  VIX Low  VIX Close\n",
       "Date                                              \n",
       "01/02/1990       NaN       NaN      NaN      17.24\n",
       "01/03/1990       NaN       NaN      NaN      18.19\n",
       "01/04/1990       NaN       NaN      NaN      19.22\n",
       "01/05/1990       NaN       NaN      NaN      20.11\n",
       "01/08/1990       NaN       NaN      NaN      20.26"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vix.index = pd.to_datetime(vix.index)\n",
    "vix = vix.set_index('Date')\n",
    "vix.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>VIX Open</th>\n",
       "      <th>VIX High</th>\n",
       "      <th>VIX Low</th>\n",
       "      <th>VIX Close</th>\n",
       "      <th>FYE</th>\n",
       "      <th>FORM_TYPE</th>\n",
       "      <th>N_Words</th>\n",
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       "      <th>N_Negative</th>\n",
       "      <th>N_Positive</th>\n",
       "      <th>...</th>\n",
       "      <th>N_Litigious</th>\n",
       "      <th>N_ModalWeak</th>\n",
       "      <th>N_Modal_Moderate</th>\n",
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       "      <th>N_Negation</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1993-08-13</th>\n",
       "      <td>12.80</td>\n",
       "      <td>13.25</td>\n",
       "      <td>12.17</td>\n",
       "      <td>12.19</td>\n",
       "      <td>19930630</td>\n",
       "      <td>10-Q</td>\n",
       "      <td>3037</td>\n",
       "      <td>700</td>\n",
       "      <td>68</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>40</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>63255</td>\n",
       "      <td>25233</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-08-13</th>\n",
       "      <td>12.80</td>\n",
       "      <td>13.25</td>\n",
       "      <td>12.17</td>\n",
       "      <td>12.19</td>\n",
       "      <td>19930630</td>\n",
       "      <td>10-Q</td>\n",
       "      <td>4102</td>\n",
       "      <td>823</td>\n",
       "      <td>53</td>\n",
       "      <td>24</td>\n",
       "      <td>...</td>\n",
       "      <td>20</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>57283</td>\n",
       "      <td>47739</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-10-07</th>\n",
       "      <td>12.65</td>\n",
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       "      <td>12.44</td>\n",
       "      <td>13.14</td>\n",
       "      <td>19921231</td>\n",
       "      <td>10-K-A</td>\n",
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       "      <td>1184</td>\n",
       "      <td>157</td>\n",
       "      <td>41</td>\n",
       "      <td>...</td>\n",
       "      <td>104</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>31</td>\n",
       "      <td>26</td>\n",
       "      <td>5</td>\n",
       "      <td>114136</td>\n",
       "      <td>56958</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-11-10</th>\n",
       "      <td>13.18</td>\n",
       "      <td>14.34</td>\n",
       "      <td>12.99</td>\n",
       "      <td>13.72</td>\n",
       "      <td>19930930</td>\n",
       "      <td>10-Q</td>\n",
       "      <td>3768</td>\n",
       "      <td>739</td>\n",
       "      <td>69</td>\n",
       "      <td>13</td>\n",
       "      <td>...</td>\n",
       "      <td>50</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
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       "      <td>31256</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-11-12</th>\n",
       "      <td>14.47</td>\n",
       "      <td>14.61</td>\n",
       "      <td>12.99</td>\n",
       "      <td>13.33</td>\n",
       "      <td>19930930</td>\n",
       "      <td>10-Q</td>\n",
       "      <td>3822</td>\n",
       "      <td>897</td>\n",
       "      <td>77</td>\n",
       "      <td>39</td>\n",
       "      <td>...</td>\n",
       "      <td>60</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>19</td>\n",
       "      <td>32</td>\n",
       "      <td>2</td>\n",
       "      <td>40392</td>\n",
       "      <td>39056</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            VIX Open  VIX High  VIX Low  VIX Close       FYE FORM_TYPE  \\\n",
       "1993-08-13     12.80     13.25    12.17      12.19  19930630      10-Q   \n",
       "1993-08-13     12.80     13.25    12.17      12.19  19930630      10-Q   \n",
       "1993-10-07     12.65     13.56    12.44      13.14  19921231    10-K-A   \n",
       "1993-11-10     13.18     14.34    12.99      13.72  19930930      10-Q   \n",
       "1993-11-12     14.47     14.61    12.99      13.33  19930930      10-Q   \n",
       "\n",
       "            N_Words  N_Unique_Words  N_Negative  N_Positive  ...  N_Litigious  \\\n",
       "1993-08-13     3037             700          68           6  ...           40   \n",
       "1993-08-13     4102             823          53          24  ...           20   \n",
       "1993-10-07     7278            1184         157          41  ...          104   \n",
       "1993-11-10     3768             739          69          13  ...           50   \n",
       "1993-11-12     3822             897          77          39  ...           60   \n",
       "\n",
       "            N_ModalWeak  N_Modal_Moderate  N_Modal_Strong  N_Constraining  \\\n",
       "1993-08-13            6                 5               9               6   \n",
       "1993-08-13            7                 8               9               7   \n",
       "1993-10-07           15                15              31              26   \n",
       "1993-11-10            4                 7              16               3   \n",
       "1993-11-12            5                12              19              32   \n",
       "\n",
       "            N_Negation  GrossFileSize  NetFileSize  N_Tables  N_Exhibits  \n",
       "1993-08-13           1          63255        25233         7           0  \n",
       "1993-08-13           3          57283        47739         2           3  \n",
       "1993-10-07           5         114136        56958        22           0  \n",
       "1993-11-10           1          72062        31256         8           0  \n",
       "1993-11-12           2          40392        39056         0           0  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm_10X_vix = pd.merge(vix, lm_10X, how='inner', left_index=True, right_index=True)\n",
    "lm_10X_vix.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>VIX Open</th>\n",
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       "      <th>1993-08-13</th>\n",
       "      <td>12.80</td>\n",
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       "      <th>1993-08-13</th>\n",
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       "      <th>1993-10-07</th>\n",
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       "      <td>12.44</td>\n",
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       "      <td>...</td>\n",
       "      <td>104</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>31</td>\n",
       "      <td>26</td>\n",
       "      <td>5</td>\n",
       "      <td>114136</td>\n",
       "      <td>56958</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-11-10</th>\n",
       "      <td>13.18</td>\n",
       "      <td>14.34</td>\n",
       "      <td>12.99</td>\n",
       "      <td>13.72</td>\n",
       "      <td>19930930</td>\n",
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       "      <td>3768</td>\n",
       "      <td>739</td>\n",
       "      <td>69</td>\n",
       "      <td>13</td>\n",
       "      <td>...</td>\n",
       "      <td>50</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>72062</td>\n",
       "      <td>31256</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1993-11-12</th>\n",
       "      <td>14.47</td>\n",
       "      <td>14.61</td>\n",
       "      <td>12.99</td>\n",
       "      <td>13.33</td>\n",
       "      <td>19930930</td>\n",
       "      <td>10-Q</td>\n",
       "      <td>3822</td>\n",
       "      <td>897</td>\n",
       "      <td>77</td>\n",
       "      <td>39</td>\n",
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       "      <td>60</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>19</td>\n",
       "      <td>32</td>\n",
       "      <td>2</td>\n",
       "      <td>40392</td>\n",
       "      <td>39056</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-11-12</th>\n",
       "      <td>14.47</td>\n",
       "      <td>14.61</td>\n",
       "      <td>12.99</td>\n",
       "      <td>13.33</td>\n",
       "      <td>19930930</td>\n",
       "      <td>10-Q</td>\n",
       "      <td>3450</td>\n",
       "      <td>848</td>\n",
       "      <td>67</td>\n",
       "      <td>29</td>\n",
       "      <td>...</td>\n",
       "      <td>43</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>23</td>\n",
       "      <td>3</td>\n",
       "      <td>51625</td>\n",
       "      <td>28738</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1993-11-12</th>\n",
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       "      <td>12.99</td>\n",
       "      <td>13.33</td>\n",
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       "      <td>3767</td>\n",
       "      <td>791</td>\n",
       "      <td>49</td>\n",
       "      <td>28</td>\n",
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       "      <td>31</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>51346</td>\n",
       "      <td>41630</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-11-29</th>\n",
       "      <td>13.39</td>\n",
       "      <td>14.65</td>\n",
       "      <td>13.39</td>\n",
       "      <td>14.12</td>\n",
       "      <td>19930831</td>\n",
       "      <td>10-K</td>\n",
       "      <td>18063</td>\n",
       "      <td>1942</td>\n",
       "      <td>196</td>\n",
       "      <td>103</td>\n",
       "      <td>...</td>\n",
       "      <td>281</td>\n",
       "      <td>53</td>\n",
       "      <td>42</td>\n",
       "      <td>48</td>\n",
       "      <td>88</td>\n",
       "      <td>14</td>\n",
       "      <td>278174</td>\n",
       "      <td>159453</td>\n",
       "      <td>35</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-12-13</th>\n",
       "      <td>9.70</td>\n",
       "      <td>11.51</td>\n",
       "      <td>9.65</td>\n",
       "      <td>10.98</td>\n",
       "      <td>19930930</td>\n",
       "      <td>10-K</td>\n",
       "      <td>12826</td>\n",
       "      <td>1473</td>\n",
       "      <td>61</td>\n",
       "      <td>68</td>\n",
       "      <td>...</td>\n",
       "      <td>132</td>\n",
       "      <td>20</td>\n",
       "      <td>34</td>\n",
       "      <td>65</td>\n",
       "      <td>91</td>\n",
       "      <td>2</td>\n",
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       "      <td>117190</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-12-14</th>\n",
       "      <td>10.71</td>\n",
       "      <td>11.23</td>\n",
       "      <td>10.64</td>\n",
       "      <td>11.13</td>\n",
       "      <td>19931030</td>\n",
       "      <td>10-Q</td>\n",
       "      <td>1741</td>\n",
       "      <td>463</td>\n",
       "      <td>35</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>28487</td>\n",
       "      <td>15442</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-12-22</th>\n",
       "      <td>10.03</td>\n",
       "      <td>10.03</td>\n",
       "      <td>9.28</td>\n",
       "      <td>9.31</td>\n",
       "      <td>19921231</td>\n",
       "      <td>10-K-A</td>\n",
       "      <td>3961</td>\n",
       "      <td>1043</td>\n",
       "      <td>40</td>\n",
       "      <td>22</td>\n",
       "      <td>...</td>\n",
       "      <td>41</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>39588</td>\n",
       "      <td>37928</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-12-22</th>\n",
       "      <td>10.03</td>\n",
       "      <td>10.03</td>\n",
       "      <td>9.28</td>\n",
       "      <td>9.31</td>\n",
       "      <td>19930930</td>\n",
       "      <td>10-K</td>\n",
       "      <td>17366</td>\n",
       "      <td>1939</td>\n",
       "      <td>119</td>\n",
       "      <td>119</td>\n",
       "      <td>...</td>\n",
       "      <td>117</td>\n",
       "      <td>13</td>\n",
       "      <td>41</td>\n",
       "      <td>19</td>\n",
       "      <td>63</td>\n",
       "      <td>7</td>\n",
       "      <td>249191</td>\n",
       "      <td>145992</td>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-12-23</th>\n",
       "      <td>9.50</td>\n",
       "      <td>9.55</td>\n",
       "      <td>9.17</td>\n",
       "      <td>9.48</td>\n",
       "      <td>19930930</td>\n",
       "      <td>10-K</td>\n",
       "      <td>29469</td>\n",
       "      <td>2559</td>\n",
       "      <td>385</td>\n",
       "      <td>201</td>\n",
       "      <td>...</td>\n",
       "      <td>663</td>\n",
       "      <td>109</td>\n",
       "      <td>32</td>\n",
       "      <td>50</td>\n",
       "      <td>156</td>\n",
       "      <td>22</td>\n",
       "      <td>272459</td>\n",
       "      <td>267989</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994-01-04</th>\n",
       "      <td>12.87</td>\n",
       "      <td>12.87</td>\n",
       "      <td>11.81</td>\n",
       "      <td>11.91</td>\n",
       "      <td>19921231</td>\n",
       "      <td>10-K-A</td>\n",
       "      <td>12415</td>\n",
       "      <td>1378</td>\n",
       "      <td>143</td>\n",
       "      <td>122</td>\n",
       "      <td>...</td>\n",
       "      <td>64</td>\n",
       "      <td>19</td>\n",
       "      <td>13</td>\n",
       "      <td>17</td>\n",
       "      <td>60</td>\n",
       "      <td>2</td>\n",
       "      <td>142125</td>\n",
       "      <td>135660</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994-01-04</th>\n",
       "      <td>12.87</td>\n",
       "      <td>12.87</td>\n",
       "      <td>11.81</td>\n",
       "      <td>11.91</td>\n",
       "      <td>19930930</td>\n",
       "      <td>10-Q-A</td>\n",
       "      <td>3212</td>\n",
       "      <td>554</td>\n",
       "      <td>24</td>\n",
       "      <td>13</td>\n",
       "      <td>...</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>42771</td>\n",
       "      <td>39727</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994-01-04</th>\n",
       "      <td>12.87</td>\n",
       "      <td>12.87</td>\n",
       "      <td>11.81</td>\n",
       "      <td>11.91</td>\n",
       "      <td>19931127</td>\n",
       "      <td>10-Q</td>\n",
       "      <td>1794</td>\n",
       "      <td>464</td>\n",
       "      <td>20</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>37094</td>\n",
       "      <td>15718</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994-01-05</th>\n",
       "      <td>12.87</td>\n",
       "      <td>11.47</td>\n",
       "      <td>10.37</td>\n",
       "      <td>10.94</td>\n",
       "      <td>19931127</td>\n",
       "      <td>10-Q</td>\n",
       "      <td>1295</td>\n",
       "      <td>374</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>34027</td>\n",
       "      <td>13201</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994-01-05</th>\n",
       "      <td>12.87</td>\n",
       "      <td>11.47</td>\n",
       "      <td>10.37</td>\n",
       "      <td>10.94</td>\n",
       "      <td>19931127</td>\n",
       "      <td>10-Q</td>\n",
       "      <td>1821</td>\n",
       "      <td>485</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>36908</td>\n",
       "      <td>19026</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994-01-05</th>\n",
       "      <td>12.87</td>\n",
       "      <td>11.47</td>\n",
       "      <td>10.37</td>\n",
       "      <td>10.94</td>\n",
       "      <td>19931121</td>\n",
       "      <td>10-Q</td>\n",
       "      <td>4448</td>\n",
       "      <td>811</td>\n",
       "      <td>92</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>56</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>64490</td>\n",
       "      <td>40774</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994-01-06</th>\n",
       "      <td>11.47</td>\n",
       "      <td>11.74</td>\n",
       "      <td>10.34</td>\n",
       "      <td>11.27</td>\n",
       "      <td>19930625</td>\n",
       "      <td>10-K-A</td>\n",
       "      <td>2739</td>\n",
       "      <td>612</td>\n",
       "      <td>21</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>27</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>35381</td>\n",
       "      <td>23171</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994-01-06</th>\n",
       "      <td>11.47</td>\n",
       "      <td>11.74</td>\n",
       "      <td>10.34</td>\n",
       "      <td>11.27</td>\n",
       "      <td>19930930</td>\n",
       "      <td>10-K</td>\n",
       "      <td>28446</td>\n",
       "      <td>2460</td>\n",
       "      <td>252</td>\n",
       "      <td>125</td>\n",
       "      <td>...</td>\n",
       "      <td>418</td>\n",
       "      <td>105</td>\n",
       "      <td>58</td>\n",
       "      <td>82</td>\n",
       "      <td>124</td>\n",
       "      <td>10</td>\n",
       "      <td>307627</td>\n",
       "      <td>245092</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994-01-06</th>\n",
       "      <td>11.47</td>\n",
       "      <td>11.74</td>\n",
       "      <td>10.34</td>\n",
       "      <td>11.27</td>\n",
       "      <td>19931126</td>\n",
       "      <td>10-Q</td>\n",
       "      <td>2425</td>\n",
       "      <td>617</td>\n",
       "      <td>39</td>\n",
       "      <td>15</td>\n",
       "      <td>...</td>\n",
       "      <td>33</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>37766</td>\n",
       "      <td>21502</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994-01-06</th>\n",
       "      <td>11.47</td>\n",
       "      <td>11.74</td>\n",
       "      <td>10.34</td>\n",
       "      <td>11.27</td>\n",
       "      <td>19931130</td>\n",
       "      <td>10-Q</td>\n",
       "      <td>2424</td>\n",
       "      <td>596</td>\n",
       "      <td>8</td>\n",
       "      <td>19</td>\n",
       "      <td>...</td>\n",
       "      <td>13</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>36160</td>\n",
       "      <td>20715</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994-01-06</th>\n",
       "      <td>11.47</td>\n",
       "      <td>11.74</td>\n",
       "      <td>10.34</td>\n",
       "      <td>11.27</td>\n",
       "      <td>19931127</td>\n",
       "      <td>10-Q</td>\n",
       "      <td>2022</td>\n",
       "      <td>504</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>...</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>41020</td>\n",
       "      <td>18431</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994-01-07</th>\n",
       "      <td>11.47</td>\n",
       "      <td>12.14</td>\n",
       "      <td>10.00</td>\n",
       "      <td>10.96</td>\n",
       "      <td>19930930</td>\n",
       "      <td>10-K-A</td>\n",
       "      <td>419</td>\n",
       "      <td>164</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>10600</td>\n",
       "      <td>5701</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994-01-07</th>\n",
       "      <td>11.47</td>\n",
       "      <td>12.14</td>\n",
       "      <td>10.00</td>\n",
       "      <td>10.96</td>\n",
       "      <td>19931130</td>\n",
       "      <td>10-Q</td>\n",
       "      <td>2105</td>\n",
       "      <td>530</td>\n",
       "      <td>17</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>26</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>26581</td>\n",
       "      <td>25674</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994-01-07</th>\n",
       "      <td>11.47</td>\n",
       "      <td>12.14</td>\n",
       "      <td>10.00</td>\n",
       "      <td>10.96</td>\n",
       "      <td>19931126</td>\n",
       "      <td>10-Q</td>\n",
       "      <td>963</td>\n",
       "      <td>294</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>27550</td>\n",
       "      <td>9539</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994-01-07</th>\n",
       "      <td>11.47</td>\n",
       "      <td>12.14</td>\n",
       "      <td>10.00</td>\n",
       "      <td>10.96</td>\n",
       "      <td>19931126</td>\n",
       "      <td>10-Q</td>\n",
       "      <td>2080</td>\n",
       "      <td>511</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>28468</td>\n",
       "      <td>23478</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994-01-10</th>\n",
       "      <td>10.64</td>\n",
       "      <td>11.30</td>\n",
       "      <td>10.28</td>\n",
       "      <td>10.74</td>\n",
       "      <td>19930930</td>\n",
       "      <td>10-K-A</td>\n",
       "      <td>5027</td>\n",
       "      <td>794</td>\n",
       "      <td>37</td>\n",
       "      <td>14</td>\n",
       "      <td>...</td>\n",
       "      <td>231</td>\n",
       "      <td>50</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>43215</td>\n",
       "      <td>41547</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994-01-10</th>\n",
       "      <td>10.64</td>\n",
       "      <td>11.30</td>\n",
       "      <td>10.28</td>\n",
       "      <td>10.74</td>\n",
       "      <td>19930930</td>\n",
       "      <td>10-Q-A</td>\n",
       "      <td>1851</td>\n",
       "      <td>416</td>\n",
       "      <td>16</td>\n",
       "      <td>22</td>\n",
       "      <td>...</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>155906</td>\n",
       "      <td>20252</td>\n",
       "      <td>28</td>\n",
       "      <td>0</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>2018-12-28</th>\n",
       "      <td>29.97</td>\n",
       "      <td>31.05</td>\n",
       "      <td>27.43</td>\n",
       "      <td>28.34</td>\n",
       "      <td>20180930</td>\n",
       "      <td>10-K</td>\n",
       "      <td>15958</td>\n",
       "      <td>1922</td>\n",
       "      <td>201</td>\n",
       "      <td>87</td>\n",
       "      <td>...</td>\n",
       "      <td>137</td>\n",
       "      <td>57</td>\n",
       "      <td>54</td>\n",
       "      <td>58</td>\n",
       "      <td>115</td>\n",
       "      <td>12</td>\n",
       "      <td>2560244</td>\n",
       "      <td>117555</td>\n",
       "      <td>61</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-28</th>\n",
       "      <td>29.97</td>\n",
       "      <td>31.05</td>\n",
       "      <td>27.43</td>\n",
       "      <td>28.34</td>\n",
       "      <td>20180930</td>\n",
       "      <td>10-K</td>\n",
       "      <td>16764</td>\n",
       "      <td>1758</td>\n",
       "      <td>254</td>\n",
       "      <td>140</td>\n",
       "      <td>...</td>\n",
       "      <td>110</td>\n",
       "      <td>150</td>\n",
       "      <td>64</td>\n",
       "      <td>94</td>\n",
       "      <td>119</td>\n",
       "      <td>14</td>\n",
       "      <td>1723886</td>\n",
       "      <td>126710</td>\n",
       "      <td>34</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-28</th>\n",
       "      <td>29.97</td>\n",
       "      <td>31.05</td>\n",
       "      <td>27.43</td>\n",
       "      <td>28.34</td>\n",
       "      <td>20180930</td>\n",
       "      <td>10-K</td>\n",
       "      <td>16814</td>\n",
       "      <td>2127</td>\n",
       "      <td>328</td>\n",
       "      <td>134</td>\n",
       "      <td>...</td>\n",
       "      <td>133</td>\n",
       "      <td>189</td>\n",
       "      <td>64</td>\n",
       "      <td>93</td>\n",
       "      <td>132</td>\n",
       "      <td>14</td>\n",
       "      <td>1542111</td>\n",
       "      <td>121936</td>\n",
       "      <td>15</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-28</th>\n",
       "      <td>29.97</td>\n",
       "      <td>31.05</td>\n",
       "      <td>27.43</td>\n",
       "      <td>28.34</td>\n",
       "      <td>20180630</td>\n",
       "      <td>10-K</td>\n",
       "      <td>29427</td>\n",
       "      <td>2773</td>\n",
       "      <td>642</td>\n",
       "      <td>198</td>\n",
       "      <td>...</td>\n",
       "      <td>569</td>\n",
       "      <td>234</td>\n",
       "      <td>104</td>\n",
       "      <td>148</td>\n",
       "      <td>264</td>\n",
       "      <td>31</td>\n",
       "      <td>6049527</td>\n",
       "      <td>206478</td>\n",
       "      <td>56</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-28</th>\n",
       "      <td>29.97</td>\n",
       "      <td>31.05</td>\n",
       "      <td>27.43</td>\n",
       "      <td>28.34</td>\n",
       "      <td>20181031</td>\n",
       "      <td>10-K</td>\n",
       "      <td>17858</td>\n",
       "      <td>1924</td>\n",
       "      <td>218</td>\n",
       "      <td>59</td>\n",
       "      <td>...</td>\n",
       "      <td>116</td>\n",
       "      <td>72</td>\n",
       "      <td>100</td>\n",
       "      <td>59</td>\n",
       "      <td>91</td>\n",
       "      <td>13</td>\n",
       "      <td>220686</td>\n",
       "      <td>132285</td>\n",
       "      <td>31</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-28</th>\n",
       "      <td>29.97</td>\n",
       "      <td>31.05</td>\n",
       "      <td>27.43</td>\n",
       "      <td>28.34</td>\n",
       "      <td>20180930</td>\n",
       "      <td>10-K</td>\n",
       "      <td>24203</td>\n",
       "      <td>2137</td>\n",
       "      <td>274</td>\n",
       "      <td>132</td>\n",
       "      <td>...</td>\n",
       "      <td>109</td>\n",
       "      <td>41</td>\n",
       "      <td>88</td>\n",
       "      <td>91</td>\n",
       "      <td>182</td>\n",
       "      <td>7</td>\n",
       "      <td>1727342</td>\n",
       "      <td>191651</td>\n",
       "      <td>98</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-28</th>\n",
       "      <td>29.97</td>\n",
       "      <td>31.05</td>\n",
       "      <td>27.43</td>\n",
       "      <td>28.34</td>\n",
       "      <td>20180930</td>\n",
       "      <td>10-Q-A</td>\n",
       "      <td>1119</td>\n",
       "      <td>300</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>4613628</td>\n",
       "      <td>10389</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-28</th>\n",
       "      <td>29.97</td>\n",
       "      <td>31.05</td>\n",
       "      <td>27.43</td>\n",
       "      <td>28.34</td>\n",
       "      <td>20180930</td>\n",
       "      <td>10-Q</td>\n",
       "      <td>17341</td>\n",
       "      <td>1800</td>\n",
       "      <td>239</td>\n",
       "      <td>114</td>\n",
       "      <td>...</td>\n",
       "      <td>197</td>\n",
       "      <td>38</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>71</td>\n",
       "      <td>11</td>\n",
       "      <td>5177207</td>\n",
       "      <td>134480</td>\n",
       "      <td>22</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-28</th>\n",
       "      <td>29.97</td>\n",
       "      <td>31.05</td>\n",
       "      <td>27.43</td>\n",
       "      <td>28.34</td>\n",
       "      <td>20171231</td>\n",
       "      <td>10-Q</td>\n",
       "      <td>16224</td>\n",
       "      <td>1745</td>\n",
       "      <td>386</td>\n",
       "      <td>103</td>\n",
       "      <td>...</td>\n",
       "      <td>326</td>\n",
       "      <td>59</td>\n",
       "      <td>49</td>\n",
       "      <td>53</td>\n",
       "      <td>118</td>\n",
       "      <td>8</td>\n",
       "      <td>2915584</td>\n",
       "      <td>122833</td>\n",
       "      <td>45</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-28</th>\n",
       "      <td>29.97</td>\n",
       "      <td>31.05</td>\n",
       "      <td>27.43</td>\n",
       "      <td>28.34</td>\n",
       "      <td>20181031</td>\n",
       "      <td>10-Q</td>\n",
       "      <td>4541</td>\n",
       "      <td>1000</td>\n",
       "      <td>57</td>\n",
       "      <td>24</td>\n",
       "      <td>...</td>\n",
       "      <td>34</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>17</td>\n",
       "      <td>38</td>\n",
       "      <td>4</td>\n",
       "      <td>710478</td>\n",
       "      <td>36346</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-28</th>\n",
       "      <td>29.97</td>\n",
       "      <td>31.05</td>\n",
       "      <td>27.43</td>\n",
       "      <td>28.34</td>\n",
       "      <td>20180930</td>\n",
       "      <td>10-Q</td>\n",
       "      <td>3590</td>\n",
       "      <td>804</td>\n",
       "      <td>36</td>\n",
       "      <td>35</td>\n",
       "      <td>...</td>\n",
       "      <td>24</td>\n",
       "      <td>11</td>\n",
       "      <td>18</td>\n",
       "      <td>9</td>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "      <td>142206</td>\n",
       "      <td>27790</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-28</th>\n",
       "      <td>29.97</td>\n",
       "      <td>31.05</td>\n",
       "      <td>27.43</td>\n",
       "      <td>28.34</td>\n",
       "      <td>20181130</td>\n",
       "      <td>10-Q</td>\n",
       "      <td>10427</td>\n",
       "      <td>1499</td>\n",
       "      <td>71</td>\n",
       "      <td>53</td>\n",
       "      <td>...</td>\n",
       "      <td>86</td>\n",
       "      <td>24</td>\n",
       "      <td>34</td>\n",
       "      <td>13</td>\n",
       "      <td>44</td>\n",
       "      <td>5</td>\n",
       "      <td>4844151</td>\n",
       "      <td>78991</td>\n",
       "      <td>26</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-31</th>\n",
       "      <td>27.59</td>\n",
       "      <td>27.64</td>\n",
       "      <td>25.33</td>\n",
       "      <td>25.42</td>\n",
       "      <td>20180630</td>\n",
       "      <td>10-K-A</td>\n",
       "      <td>6800</td>\n",
       "      <td>1034</td>\n",
       "      <td>58</td>\n",
       "      <td>47</td>\n",
       "      <td>...</td>\n",
       "      <td>89</td>\n",
       "      <td>4</td>\n",
       "      <td>18</td>\n",
       "      <td>14</td>\n",
       "      <td>25</td>\n",
       "      <td>3</td>\n",
       "      <td>257945</td>\n",
       "      <td>54351</td>\n",
       "      <td>16</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-31</th>\n",
       "      <td>27.59</td>\n",
       "      <td>27.64</td>\n",
       "      <td>25.33</td>\n",
       "      <td>25.42</td>\n",
       "      <td>20180930</td>\n",
       "      <td>10-K-A</td>\n",
       "      <td>398</td>\n",
       "      <td>144</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>5251231</td>\n",
       "      <td>3572</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-31</th>\n",
       "      <td>27.59</td>\n",
       "      <td>27.64</td>\n",
       "      <td>25.33</td>\n",
       "      <td>25.42</td>\n",
       "      <td>20180930</td>\n",
       "      <td>10-K</td>\n",
       "      <td>24891</td>\n",
       "      <td>2105</td>\n",
       "      <td>397</td>\n",
       "      <td>135</td>\n",
       "      <td>...</td>\n",
       "      <td>407</td>\n",
       "      <td>158</td>\n",
       "      <td>87</td>\n",
       "      <td>69</td>\n",
       "      <td>118</td>\n",
       "      <td>16</td>\n",
       "      <td>21324002</td>\n",
       "      <td>178865</td>\n",
       "      <td>82</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-31</th>\n",
       "      <td>27.59</td>\n",
       "      <td>27.64</td>\n",
       "      <td>25.33</td>\n",
       "      <td>25.42</td>\n",
       "      <td>20180930</td>\n",
       "      <td>10-K</td>\n",
       "      <td>32515</td>\n",
       "      <td>2714</td>\n",
       "      <td>507</td>\n",
       "      <td>170</td>\n",
       "      <td>...</td>\n",
       "      <td>436</td>\n",
       "      <td>267</td>\n",
       "      <td>111</td>\n",
       "      <td>120</td>\n",
       "      <td>363</td>\n",
       "      <td>21</td>\n",
       "      <td>7863936</td>\n",
       "      <td>241475</td>\n",
       "      <td>48</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-31</th>\n",
       "      <td>27.59</td>\n",
       "      <td>27.64</td>\n",
       "      <td>25.33</td>\n",
       "      <td>25.42</td>\n",
       "      <td>20180930</td>\n",
       "      <td>10-K</td>\n",
       "      <td>29142</td>\n",
       "      <td>2688</td>\n",
       "      <td>525</td>\n",
       "      <td>239</td>\n",
       "      <td>...</td>\n",
       "      <td>274</td>\n",
       "      <td>244</td>\n",
       "      <td>107</td>\n",
       "      <td>163</td>\n",
       "      <td>268</td>\n",
       "      <td>23</td>\n",
       "      <td>4711653</td>\n",
       "      <td>213064</td>\n",
       "      <td>31</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-31</th>\n",
       "      <td>27.59</td>\n",
       "      <td>27.64</td>\n",
       "      <td>25.33</td>\n",
       "      <td>25.42</td>\n",
       "      <td>20180930</td>\n",
       "      <td>10-K</td>\n",
       "      <td>17337</td>\n",
       "      <td>2249</td>\n",
       "      <td>199</td>\n",
       "      <td>67</td>\n",
       "      <td>...</td>\n",
       "      <td>112</td>\n",
       "      <td>45</td>\n",
       "      <td>62</td>\n",
       "      <td>79</td>\n",
       "      <td>84</td>\n",
       "      <td>14</td>\n",
       "      <td>3760744</td>\n",
       "      <td>133599</td>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-31</th>\n",
       "      <td>27.59</td>\n",
       "      <td>27.64</td>\n",
       "      <td>25.33</td>\n",
       "      <td>25.42</td>\n",
       "      <td>20180930</td>\n",
       "      <td>10-K</td>\n",
       "      <td>20448</td>\n",
       "      <td>2083</td>\n",
       "      <td>330</td>\n",
       "      <td>140</td>\n",
       "      <td>...</td>\n",
       "      <td>291</td>\n",
       "      <td>115</td>\n",
       "      <td>79</td>\n",
       "      <td>117</td>\n",
       "      <td>147</td>\n",
       "      <td>15</td>\n",
       "      <td>2536971</td>\n",
       "      <td>152534</td>\n",
       "      <td>16</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-31</th>\n",
       "      <td>27.59</td>\n",
       "      <td>27.64</td>\n",
       "      <td>25.33</td>\n",
       "      <td>25.42</td>\n",
       "      <td>20180930</td>\n",
       "      <td>10-K</td>\n",
       "      <td>10679</td>\n",
       "      <td>1504</td>\n",
       "      <td>126</td>\n",
       "      <td>59</td>\n",
       "      <td>...</td>\n",
       "      <td>87</td>\n",
       "      <td>27</td>\n",
       "      <td>32</td>\n",
       "      <td>49</td>\n",
       "      <td>53</td>\n",
       "      <td>11</td>\n",
       "      <td>1325731</td>\n",
       "      <td>83282</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-31</th>\n",
       "      <td>27.59</td>\n",
       "      <td>27.64</td>\n",
       "      <td>25.33</td>\n",
       "      <td>25.42</td>\n",
       "      <td>20180930</td>\n",
       "      <td>10-K</td>\n",
       "      <td>61971</td>\n",
       "      <td>3473</td>\n",
       "      <td>1521</td>\n",
       "      <td>352</td>\n",
       "      <td>...</td>\n",
       "      <td>862</td>\n",
       "      <td>557</td>\n",
       "      <td>188</td>\n",
       "      <td>309</td>\n",
       "      <td>477</td>\n",
       "      <td>40</td>\n",
       "      <td>6940663</td>\n",
       "      <td>448617</td>\n",
       "      <td>27</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-31</th>\n",
       "      <td>27.59</td>\n",
       "      <td>27.64</td>\n",
       "      <td>25.33</td>\n",
       "      <td>25.42</td>\n",
       "      <td>20181031</td>\n",
       "      <td>10-K</td>\n",
       "      <td>31030</td>\n",
       "      <td>2726</td>\n",
       "      <td>683</td>\n",
       "      <td>205</td>\n",
       "      <td>...</td>\n",
       "      <td>326</td>\n",
       "      <td>355</td>\n",
       "      <td>85</td>\n",
       "      <td>112</td>\n",
       "      <td>266</td>\n",
       "      <td>21</td>\n",
       "      <td>2035116</td>\n",
       "      <td>227041</td>\n",
       "      <td>92</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-31</th>\n",
       "      <td>27.59</td>\n",
       "      <td>27.64</td>\n",
       "      <td>25.33</td>\n",
       "      <td>25.42</td>\n",
       "      <td>20180930</td>\n",
       "      <td>10-K</td>\n",
       "      <td>39547</td>\n",
       "      <td>3064</td>\n",
       "      <td>623</td>\n",
       "      <td>283</td>\n",
       "      <td>...</td>\n",
       "      <td>331</td>\n",
       "      <td>199</td>\n",
       "      <td>120</td>\n",
       "      <td>113</td>\n",
       "      <td>278</td>\n",
       "      <td>22</td>\n",
       "      <td>9056058</td>\n",
       "      <td>304574</td>\n",
       "      <td>183</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-31</th>\n",
       "      <td>27.59</td>\n",
       "      <td>27.64</td>\n",
       "      <td>25.33</td>\n",
       "      <td>25.42</td>\n",
       "      <td>20180930</td>\n",
       "      <td>10-Q-A</td>\n",
       "      <td>1157</td>\n",
       "      <td>334</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>390270</td>\n",
       "      <td>9957</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-31</th>\n",
       "      <td>27.59</td>\n",
       "      <td>27.64</td>\n",
       "      <td>25.33</td>\n",
       "      <td>25.42</td>\n",
       "      <td>20180630</td>\n",
       "      <td>10-Q-A</td>\n",
       "      <td>18375</td>\n",
       "      <td>1892</td>\n",
       "      <td>222</td>\n",
       "      <td>111</td>\n",
       "      <td>...</td>\n",
       "      <td>117</td>\n",
       "      <td>63</td>\n",
       "      <td>92</td>\n",
       "      <td>83</td>\n",
       "      <td>76</td>\n",
       "      <td>12</td>\n",
       "      <td>5759717</td>\n",
       "      <td>137309</td>\n",
       "      <td>72</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-31</th>\n",
       "      <td>27.59</td>\n",
       "      <td>27.64</td>\n",
       "      <td>25.33</td>\n",
       "      <td>25.42</td>\n",
       "      <td>20180930</td>\n",
       "      <td>10-Q</td>\n",
       "      <td>4072</td>\n",
       "      <td>806</td>\n",
       "      <td>51</td>\n",
       "      <td>14</td>\n",
       "      <td>...</td>\n",
       "      <td>23</td>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "      <td>13</td>\n",
       "      <td>26</td>\n",
       "      <td>3</td>\n",
       "      <td>1106870</td>\n",
       "      <td>33975</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-31</th>\n",
       "      <td>27.59</td>\n",
       "      <td>27.64</td>\n",
       "      <td>25.33</td>\n",
       "      <td>25.42</td>\n",
       "      <td>20180930</td>\n",
       "      <td>10-Q</td>\n",
       "      <td>6403</td>\n",
       "      <td>1039</td>\n",
       "      <td>119</td>\n",
       "      <td>32</td>\n",
       "      <td>...</td>\n",
       "      <td>45</td>\n",
       "      <td>33</td>\n",
       "      <td>25</td>\n",
       "      <td>24</td>\n",
       "      <td>40</td>\n",
       "      <td>9</td>\n",
       "      <td>1092655</td>\n",
       "      <td>50263</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-31</th>\n",
       "      <td>27.59</td>\n",
       "      <td>27.64</td>\n",
       "      <td>25.33</td>\n",
       "      <td>25.42</td>\n",
       "      <td>20180930</td>\n",
       "      <td>10-Q</td>\n",
       "      <td>15359</td>\n",
       "      <td>1908</td>\n",
       "      <td>236</td>\n",
       "      <td>97</td>\n",
       "      <td>...</td>\n",
       "      <td>301</td>\n",
       "      <td>56</td>\n",
       "      <td>44</td>\n",
       "      <td>63</td>\n",
       "      <td>103</td>\n",
       "      <td>10</td>\n",
       "      <td>2175983</td>\n",
       "      <td>114219</td>\n",
       "      <td>15</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-31</th>\n",
       "      <td>27.59</td>\n",
       "      <td>27.64</td>\n",
       "      <td>25.33</td>\n",
       "      <td>25.42</td>\n",
       "      <td>20180930</td>\n",
       "      <td>10-Q</td>\n",
       "      <td>5021</td>\n",
       "      <td>868</td>\n",
       "      <td>78</td>\n",
       "      <td>25</td>\n",
       "      <td>...</td>\n",
       "      <td>32</td>\n",
       "      <td>20</td>\n",
       "      <td>27</td>\n",
       "      <td>16</td>\n",
       "      <td>33</td>\n",
       "      <td>4</td>\n",
       "      <td>814265</td>\n",
       "      <td>42154</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-31</th>\n",
       "      <td>27.59</td>\n",
       "      <td>27.64</td>\n",
       "      <td>25.33</td>\n",
       "      <td>25.42</td>\n",
       "      <td>20180930</td>\n",
       "      <td>10-Q</td>\n",
       "      <td>5085</td>\n",
       "      <td>823</td>\n",
       "      <td>73</td>\n",
       "      <td>19</td>\n",
       "      <td>...</td>\n",
       "      <td>36</td>\n",
       "      <td>21</td>\n",
       "      <td>31</td>\n",
       "      <td>15</td>\n",
       "      <td>30</td>\n",
       "      <td>4</td>\n",
       "      <td>1041360</td>\n",
       "      <td>41757</td>\n",
       "      <td>16</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1022399 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            VIX Open  VIX High  VIX Low  VIX Close       FYE FORM_TYPE  \\\n",
       "1993-08-13     12.80     13.25    12.17      12.19  19930630      10-Q   \n",
       "1993-08-13     12.80     13.25    12.17      12.19  19930630      10-Q   \n",
       "1993-10-07     12.65     13.56    12.44      13.14  19921231    10-K-A   \n",
       "1993-11-10     13.18     14.34    12.99      13.72  19930930      10-Q   \n",
       "1993-11-12     14.47     14.61    12.99      13.33  19930930      10-Q   \n",
       "1993-11-12     14.47     14.61    12.99      13.33  19930930      10-Q   \n",
       "1993-11-12     14.47     14.61    12.99      13.33  19930930      10-Q   \n",
       "1993-11-29     13.39     14.65    13.39      14.12  19930831      10-K   \n",
       "1993-12-13      9.70     11.51     9.65      10.98  19930930      10-K   \n",
       "1993-12-14     10.71     11.23    10.64      11.13  19931030      10-Q   \n",
       "1993-12-22     10.03     10.03     9.28       9.31  19921231    10-K-A   \n",
       "1993-12-22     10.03     10.03     9.28       9.31  19930930      10-K   \n",
       "1993-12-23      9.50      9.55     9.17       9.48  19930930      10-K   \n",
       "1994-01-04     12.87     12.87    11.81      11.91  19921231    10-K-A   \n",
       "1994-01-04     12.87     12.87    11.81      11.91  19930930    10-Q-A   \n",
       "1994-01-04     12.87     12.87    11.81      11.91  19931127      10-Q   \n",
       "1994-01-05     12.87     11.47    10.37      10.94  19931127      10-Q   \n",
       "1994-01-05     12.87     11.47    10.37      10.94  19931127      10-Q   \n",
       "1994-01-05     12.87     11.47    10.37      10.94  19931121      10-Q   \n",
       "1994-01-06     11.47     11.74    10.34      11.27  19930625    10-K-A   \n",
       "1994-01-06     11.47     11.74    10.34      11.27  19930930      10-K   \n",
       "1994-01-06     11.47     11.74    10.34      11.27  19931126      10-Q   \n",
       "1994-01-06     11.47     11.74    10.34      11.27  19931130      10-Q   \n",
       "1994-01-06     11.47     11.74    10.34      11.27  19931127      10-Q   \n",
       "1994-01-07     11.47     12.14    10.00      10.96  19930930    10-K-A   \n",
       "1994-01-07     11.47     12.14    10.00      10.96  19931130      10-Q   \n",
       "1994-01-07     11.47     12.14    10.00      10.96  19931126      10-Q   \n",
       "1994-01-07     11.47     12.14    10.00      10.96  19931126      10-Q   \n",
       "1994-01-10     10.64     11.30    10.28      10.74  19930930    10-K-A   \n",
       "1994-01-10     10.64     11.30    10.28      10.74  19930930    10-Q-A   \n",
       "...              ...       ...      ...        ...       ...       ...   \n",
       "2018-12-28     29.97     31.05    27.43      28.34  20180930      10-K   \n",
       "2018-12-28     29.97     31.05    27.43      28.34  20180930      10-K   \n",
       "2018-12-28     29.97     31.05    27.43      28.34  20180930      10-K   \n",
       "2018-12-28     29.97     31.05    27.43      28.34  20180630      10-K   \n",
       "2018-12-28     29.97     31.05    27.43      28.34  20181031      10-K   \n",
       "2018-12-28     29.97     31.05    27.43      28.34  20180930      10-K   \n",
       "2018-12-28     29.97     31.05    27.43      28.34  20180930    10-Q-A   \n",
       "2018-12-28     29.97     31.05    27.43      28.34  20180930      10-Q   \n",
       "2018-12-28     29.97     31.05    27.43      28.34  20171231      10-Q   \n",
       "2018-12-28     29.97     31.05    27.43      28.34  20181031      10-Q   \n",
       "2018-12-28     29.97     31.05    27.43      28.34  20180930      10-Q   \n",
       "2018-12-28     29.97     31.05    27.43      28.34  20181130      10-Q   \n",
       "2018-12-31     27.59     27.64    25.33      25.42  20180630    10-K-A   \n",
       "2018-12-31     27.59     27.64    25.33      25.42  20180930    10-K-A   \n",
       "2018-12-31     27.59     27.64    25.33      25.42  20180930      10-K   \n",
       "2018-12-31     27.59     27.64    25.33      25.42  20180930      10-K   \n",
       "2018-12-31     27.59     27.64    25.33      25.42  20180930      10-K   \n",
       "2018-12-31     27.59     27.64    25.33      25.42  20180930      10-K   \n",
       "2018-12-31     27.59     27.64    25.33      25.42  20180930      10-K   \n",
       "2018-12-31     27.59     27.64    25.33      25.42  20180930      10-K   \n",
       "2018-12-31     27.59     27.64    25.33      25.42  20180930      10-K   \n",
       "2018-12-31     27.59     27.64    25.33      25.42  20181031      10-K   \n",
       "2018-12-31     27.59     27.64    25.33      25.42  20180930      10-K   \n",
       "2018-12-31     27.59     27.64    25.33      25.42  20180930    10-Q-A   \n",
       "2018-12-31     27.59     27.64    25.33      25.42  20180630    10-Q-A   \n",
       "2018-12-31     27.59     27.64    25.33      25.42  20180930      10-Q   \n",
       "2018-12-31     27.59     27.64    25.33      25.42  20180930      10-Q   \n",
       "2018-12-31     27.59     27.64    25.33      25.42  20180930      10-Q   \n",
       "2018-12-31     27.59     27.64    25.33      25.42  20180930      10-Q   \n",
       "2018-12-31     27.59     27.64    25.33      25.42  20180930      10-Q   \n",
       "\n",
       "            N_Words  N_Unique_Words  N_Negative  N_Positive  ...  N_Litigious  \\\n",
       "1993-08-13     3037             700          68           6  ...           40   \n",
       "1993-08-13     4102             823          53          24  ...           20   \n",
       "1993-10-07     7278            1184         157          41  ...          104   \n",
       "1993-11-10     3768             739          69          13  ...           50   \n",
       "1993-11-12     3822             897          77          39  ...           60   \n",
       "1993-11-12     3450             848          67          29  ...           43   \n",
       "1993-11-12     3767             791          49          28  ...           31   \n",
       "1993-11-29    18063            1942         196         103  ...          281   \n",
       "1993-12-13    12826            1473          61          68  ...          132   \n",
       "1993-12-14     1741             463          35          11  ...           15   \n",
       "1993-12-22     3961            1043          40          22  ...           41   \n",
       "1993-12-22    17366            1939         119         119  ...          117   \n",
       "1993-12-23    29469            2559         385         201  ...          663   \n",
       "1994-01-04    12415            1378         143         122  ...           64   \n",
       "1994-01-04     3212             554          24          13  ...           12   \n",
       "1994-01-04     1794             464          20          12  ...           14   \n",
       "1994-01-05     1295             374           7           2  ...            8   \n",
       "1994-01-05     1821             485          11          12  ...           13   \n",
       "1994-01-05     4448             811          92          12  ...           56   \n",
       "1994-01-06     2739             612          21          10  ...           27   \n",
       "1994-01-06    28446            2460         252         125  ...          418   \n",
       "1994-01-06     2425             617          39          15  ...           33   \n",
       "1994-01-06     2424             596           8          19  ...           13   \n",
       "1994-01-06     2022             504           1          16  ...            9   \n",
       "1994-01-07      419             164           1           1  ...           12   \n",
       "1994-01-07     2105             530          17           8  ...           26   \n",
       "1994-01-07      963             294           2           3  ...            7   \n",
       "1994-01-07     2080             511          12          12  ...            6   \n",
       "1994-01-10     5027             794          37          14  ...          231   \n",
       "1994-01-10     1851             416          16          22  ...           13   \n",
       "...             ...             ...         ...         ...  ...          ...   \n",
       "2018-12-28    15958            1922         201          87  ...          137   \n",
       "2018-12-28    16764            1758         254         140  ...          110   \n",
       "2018-12-28    16814            2127         328         134  ...          133   \n",
       "2018-12-28    29427            2773         642         198  ...          569   \n",
       "2018-12-28    17858            1924         218          59  ...          116   \n",
       "2018-12-28    24203            2137         274         132  ...          109   \n",
       "2018-12-28     1119             300           8           1  ...           15   \n",
       "2018-12-28    17341            1800         239         114  ...          197   \n",
       "2018-12-28    16224            1745         386         103  ...          326   \n",
       "2018-12-28     4541            1000          57          24  ...           34   \n",
       "2018-12-28     3590             804          36          35  ...           24   \n",
       "2018-12-28    10427            1499          71          53  ...           86   \n",
       "2018-12-31     6800            1034          58          47  ...           89   \n",
       "2018-12-31      398             144           2           1  ...            4   \n",
       "2018-12-31    24891            2105         397         135  ...          407   \n",
       "2018-12-31    32515            2714         507         170  ...          436   \n",
       "2018-12-31    29142            2688         525         239  ...          274   \n",
       "2018-12-31    17337            2249         199          67  ...          112   \n",
       "2018-12-31    20448            2083         330         140  ...          291   \n",
       "2018-12-31    10679            1504         126          59  ...           87   \n",
       "2018-12-31    61971            3473        1521         352  ...          862   \n",
       "2018-12-31    31030            2726         683         205  ...          326   \n",
       "2018-12-31    39547            3064         623         283  ...          331   \n",
       "2018-12-31     1157             334           8           6  ...           11   \n",
       "2018-12-31    18375            1892         222         111  ...          117   \n",
       "2018-12-31     4072             806          51          14  ...           23   \n",
       "2018-12-31     6403            1039         119          32  ...           45   \n",
       "2018-12-31    15359            1908         236          97  ...          301   \n",
       "2018-12-31     5021             868          78          25  ...           32   \n",
       "2018-12-31     5085             823          73          19  ...           36   \n",
       "\n",
       "            N_ModalWeak  N_Modal_Moderate  N_Modal_Strong  N_Constraining  \\\n",
       "1993-08-13            6                 5               9               6   \n",
       "1993-08-13            7                 8               9               7   \n",
       "1993-10-07           15                15              31              26   \n",
       "1993-11-10            4                 7              16               3   \n",
       "1993-11-12            5                12              19              32   \n",
       "1993-11-12            9                 8               4              23   \n",
       "1993-11-12            8                10               7               8   \n",
       "1993-11-29           53                42              48              88   \n",
       "1993-12-13           20                34              65              91   \n",
       "1993-12-14            2                 8               5               9   \n",
       "1993-12-22            7                 6               6              29   \n",
       "1993-12-22           13                41              19              63   \n",
       "1993-12-23          109                32              50             156   \n",
       "1994-01-04           19                13              17              60   \n",
       "1994-01-04            1                 2               7              15   \n",
       "1994-01-04            1                 1               5               5   \n",
       "1994-01-05            2                 2               0              18   \n",
       "1994-01-05            0                 6               0               7   \n",
       "1994-01-05            2                12              14              18   \n",
       "1994-01-06            5                 4              11               6   \n",
       "1994-01-06          105                58              82             124   \n",
       "1994-01-06            6                 6              12               7   \n",
       "1994-01-06            8                 5               5              20   \n",
       "1994-01-06            0                 2               2               8   \n",
       "1994-01-07            0                 0               2               4   \n",
       "1994-01-07            2                 1               3               9   \n",
       "1994-01-07            1                 0               1               9   \n",
       "1994-01-07            2                 5               6              15   \n",
       "1994-01-10           50                 3               8              19   \n",
       "1994-01-10            4                 0               4               4   \n",
       "...                 ...               ...             ...             ...   \n",
       "2018-12-28           57                54              58             115   \n",
       "2018-12-28          150                64              94             119   \n",
       "2018-12-28          189                64              93             132   \n",
       "2018-12-28          234               104             148             264   \n",
       "2018-12-28           72               100              59              91   \n",
       "2018-12-28           41                88              91             182   \n",
       "2018-12-28            1                 2               1               7   \n",
       "2018-12-28           38                50              50              71   \n",
       "2018-12-28           59                49              53             118   \n",
       "2018-12-28           18                22              17              38   \n",
       "2018-12-28           11                18               9              20   \n",
       "2018-12-28           24                34              13              44   \n",
       "2018-12-31            4                18              14              25   \n",
       "2018-12-31            0                 0               2               6   \n",
       "2018-12-31          158                87              69             118   \n",
       "2018-12-31          267               111             120             363   \n",
       "2018-12-31          244               107             163             268   \n",
       "2018-12-31           45                62              79              84   \n",
       "2018-12-31          115                79             117             147   \n",
       "2018-12-31           27                32              49              53   \n",
       "2018-12-31          557               188             309             477   \n",
       "2018-12-31          355                85             112             266   \n",
       "2018-12-31          199               120             113             278   \n",
       "2018-12-31            2                 3               1               8   \n",
       "2018-12-31           63                92              83              76   \n",
       "2018-12-31           12                15              13              26   \n",
       "2018-12-31           33                25              24              40   \n",
       "2018-12-31           56                44              63             103   \n",
       "2018-12-31           20                27              16              33   \n",
       "2018-12-31           21                31              15              30   \n",
       "\n",
       "            N_Negation  GrossFileSize  NetFileSize  N_Tables  N_Exhibits  \n",
       "1993-08-13           1          63255        25233         7           0  \n",
       "1993-08-13           3          57283        47739         2           3  \n",
       "1993-10-07           5         114136        56958        22           0  \n",
       "1993-11-10           1          72062        31256         8           0  \n",
       "1993-11-12           2          40392        39056         0           0  \n",
       "1993-11-12           3          51625        28738        11           0  \n",
       "1993-11-12           1          51346        41630         2           3  \n",
       "1993-11-29          14         278174       159453        35           2  \n",
       "1993-12-13           2         166496       117190        12           1  \n",
       "1993-12-14           1          28487        15442         6           0  \n",
       "1993-12-22           1          39588        37928         0           0  \n",
       "1993-12-22           7         249191       145992        63           1  \n",
       "1993-12-23          22         272459       267989         0           8  \n",
       "1994-01-04           2         142125       135660         1           1  \n",
       "1994-01-04           0          42771        39727         0           0  \n",
       "1994-01-04           1          37094        15718         7           1  \n",
       "1994-01-05           0          34027        13201        10           0  \n",
       "1994-01-05           0          36908        19026         8           2  \n",
       "1994-01-05           1          64490        40774         8           0  \n",
       "1994-01-06           1          35381        23171         7           0  \n",
       "1994-01-06          10         307627       245092        34           0  \n",
       "1994-01-06           1          37766        21502         4           0  \n",
       "1994-01-06           0          36160        20715         6           0  \n",
       "1994-01-06           1          41020        18431         6           1  \n",
       "1994-01-07           0          10600         5701         1           0  \n",
       "1994-01-07           0          26581        25674         0           0  \n",
       "1994-01-07           0          27550         9539         4           0  \n",
       "1994-01-07           0          28468        23478         2           1  \n",
       "1994-01-10           1          43215        41547         0           1  \n",
       "1994-01-10           1         155906        20252        28           0  \n",
       "...                ...            ...          ...       ...         ...  \n",
       "2018-12-28          12        2560244       117555        61           9  \n",
       "2018-12-28          14        1723886       126710        34           8  \n",
       "2018-12-28          14        1542111       121936        15           8  \n",
       "2018-12-28          31        6049527       206478        56          19  \n",
       "2018-12-28          13         220686       132285        31           3  \n",
       "2018-12-28           7        1727342       191651        98           6  \n",
       "2018-12-28           1        4613628        10389         4           8  \n",
       "2018-12-28          11        5177207       134480        22          10  \n",
       "2018-12-28           8        2915584       122833        45          10  \n",
       "2018-12-28           4         710478        36346         6           8  \n",
       "2018-12-28           5         142206        27790        20           2  \n",
       "2018-12-28           5        4844151        78991        26          10  \n",
       "2018-12-31           3         257945        54351        16           5  \n",
       "2018-12-31           0        5251231         3572         3           6  \n",
       "2018-12-31          16       21324002       178865        82          13  \n",
       "2018-12-31          21        7863936       241475        48          13  \n",
       "2018-12-31          23        4711653       213064        31          11  \n",
       "2018-12-31          14        3760744       133599        20           8  \n",
       "2018-12-31          15        2536971       152534        16          12  \n",
       "2018-12-31          11        1325731        83282        14           8  \n",
       "2018-12-31          40        6940663       448617        27           9  \n",
       "2018-12-31          21        2035116       227041        92          11  \n",
       "2018-12-31          22        9056058       304574       183          12  \n",
       "2018-12-31           1         390270         9957         6           8  \n",
       "2018-12-31          12        5759717       137309        72           9  \n",
       "2018-12-31           3        1106870        33975        11           8  \n",
       "2018-12-31           9        1092655        50263        10          10  \n",
       "2018-12-31          10        2175983       114219        15          11  \n",
       "2018-12-31           4         814265        42154        10          10  \n",
       "2018-12-31           4        1041360        41757        16          10  \n",
       "\n",
       "[1022399 rows x 21 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm_10X_vix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>VIX Open</th>\n",
       "      <th>VIX High</th>\n",
       "      <th>VIX Low</th>\n",
       "      <th>VIX Close</th>\n",
       "      <th>N_Words</th>\n",
       "      <th>N_Unique_Words</th>\n",
       "      <th>N_Negative</th>\n",
       "      <th>N_Positive</th>\n",
       "      <th>N_Uncertainty</th>\n",
       "      <th>N_Litigious</th>\n",
       "      <th>N_ModalWeak</th>\n",
       "      <th>N_Modal_Moderate</th>\n",
       "      <th>N_Modal_Strong</th>\n",
       "      <th>N_Constraining</th>\n",
       "      <th>N_Negation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1993-08-13</th>\n",
       "      <td>12.80</td>\n",
       "      <td>13.25</td>\n",
       "      <td>12.17</td>\n",
       "      <td>12.19</td>\n",
       "      <td>3037</td>\n",
       "      <td>700</td>\n",
       "      <td>68</td>\n",
       "      <td>6</td>\n",
       "      <td>23</td>\n",
       "      <td>40</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-08-13</th>\n",
       "      <td>12.80</td>\n",
       "      <td>13.25</td>\n",
       "      <td>12.17</td>\n",
       "      <td>12.19</td>\n",
       "      <td>4102</td>\n",
       "      <td>823</td>\n",
       "      <td>53</td>\n",
       "      <td>24</td>\n",
       "      <td>16</td>\n",
       "      <td>20</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-10-07</th>\n",
       "      <td>12.65</td>\n",
       "      <td>13.56</td>\n",
       "      <td>12.44</td>\n",
       "      <td>13.14</td>\n",
       "      <td>7278</td>\n",
       "      <td>1184</td>\n",
       "      <td>157</td>\n",
       "      <td>41</td>\n",
       "      <td>67</td>\n",
       "      <td>104</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>31</td>\n",
       "      <td>26</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-11-10</th>\n",
       "      <td>13.18</td>\n",
       "      <td>14.34</td>\n",
       "      <td>12.99</td>\n",
       "      <td>13.72</td>\n",
       "      <td>3768</td>\n",
       "      <td>739</td>\n",
       "      <td>69</td>\n",
       "      <td>13</td>\n",
       "      <td>23</td>\n",
       "      <td>50</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-11-12</th>\n",
       "      <td>14.47</td>\n",
       "      <td>14.61</td>\n",
       "      <td>12.99</td>\n",
       "      <td>13.33</td>\n",
       "      <td>3822</td>\n",
       "      <td>897</td>\n",
       "      <td>77</td>\n",
       "      <td>39</td>\n",
       "      <td>23</td>\n",
       "      <td>60</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>19</td>\n",
       "      <td>32</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            VIX Open  VIX High  VIX Low  VIX Close  N_Words  N_Unique_Words  \\\n",
       "1993-08-13     12.80     13.25    12.17      12.19     3037             700   \n",
       "1993-08-13     12.80     13.25    12.17      12.19     4102             823   \n",
       "1993-10-07     12.65     13.56    12.44      13.14     7278            1184   \n",
       "1993-11-10     13.18     14.34    12.99      13.72     3768             739   \n",
       "1993-11-12     14.47     14.61    12.99      13.33     3822             897   \n",
       "\n",
       "            N_Negative  N_Positive  N_Uncertainty  N_Litigious  N_ModalWeak  \\\n",
       "1993-08-13          68           6             23           40            6   \n",
       "1993-08-13          53          24             16           20            7   \n",
       "1993-10-07         157          41             67          104           15   \n",
       "1993-11-10          69          13             23           50            4   \n",
       "1993-11-12          77          39             23           60            5   \n",
       "\n",
       "            N_Modal_Moderate  N_Modal_Strong  N_Constraining  N_Negation  \n",
       "1993-08-13                 5               9               6           1  \n",
       "1993-08-13                 8               9               7           3  \n",
       "1993-10-07                15              31              26           5  \n",
       "1993-11-10                 7              16               3           1  \n",
       "1993-11-12                12              19              32           2  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm_10X_vix = lm_10X_vix.drop(['FYE','FORM_TYPE','GrossFileSize', 'NetFileSize', 'N_Tables', 'N_Exhibits'], axis=1)\n",
    "lm_10X_vix.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 1022399 entries, 1993-08-13 to 2018-12-31\n",
      "Data columns (total 15 columns):\n",
      "VIX Open            1022399 non-null float64\n",
      "VIX High            1022399 non-null float64\n",
      "VIX Low             1022399 non-null float64\n",
      "VIX Close           1022399 non-null float64\n",
      "N_Words             1022399 non-null int64\n",
      "N_Unique_Words      1022399 non-null int64\n",
      "N_Negative          1022399 non-null int64\n",
      "N_Positive          1022399 non-null int64\n",
      "N_Uncertainty       1022399 non-null int64\n",
      "N_Litigious         1022399 non-null int64\n",
      "N_ModalWeak         1022399 non-null int64\n",
      "N_Modal_Moderate    1022399 non-null int64\n",
      "N_Modal_Strong      1022399 non-null int64\n",
      "N_Constraining      1022399 non-null int64\n",
      "N_Negation          1022399 non-null int64\n",
      "dtypes: float64(4), int64(11)\n",
      "memory usage: 124.8 MB\n"
     ]
    }
   ],
   "source": [
    "lm_10X_vix.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 15 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "values = lm_10X_vix.values\n",
    "groups = [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14]\n",
    "i = 1\n",
    "pyplot.figure()\n",
    "for group in groups:\n",
    "    pyplot.subplot(len(groups), 1,i)\n",
    "    pyplot.plot(values[:,group])\n",
    "    pyplot.title(lm_10X_vix.columns[group], y=.5, loc='right')\n",
    "    i += 1\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>VIX Open</th>\n",
       "      <th>VIX High</th>\n",
       "      <th>VIX Low</th>\n",
       "      <th>VIX Close</th>\n",
       "      <th>N_Words</th>\n",
       "      <th>N_Unique_Words</th>\n",
       "      <th>N_Negative</th>\n",
       "      <th>N_Positive</th>\n",
       "      <th>N_Uncertainty</th>\n",
       "      <th>N_Litigious</th>\n",
       "      <th>N_ModalWeak</th>\n",
       "      <th>N_Modal_Moderate</th>\n",
       "      <th>N_Modal_Strong</th>\n",
       "      <th>N_Constraining</th>\n",
       "      <th>N_Negation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.022399e+06</td>\n",
       "      <td>1.022399e+06</td>\n",
       "      <td>1.022399e+06</td>\n",
       "      <td>1.022399e+06</td>\n",
       "      <td>1.022399e+06</td>\n",
       "      <td>1.022399e+06</td>\n",
       "      <td>1.022399e+06</td>\n",
       "      <td>1.022399e+06</td>\n",
       "      <td>1.022399e+06</td>\n",
       "      <td>1.022399e+06</td>\n",
       "      <td>1.022399e+06</td>\n",
       "      <td>1.022399e+06</td>\n",
       "      <td>1.022399e+06</td>\n",
       "      <td>1.022399e+06</td>\n",
       "      <td>1.022399e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.065154e+01</td>\n",
       "      <td>2.144118e+01</td>\n",
       "      <td>1.987366e+01</td>\n",
       "      <td>2.056038e+01</td>\n",
       "      <td>1.813373e+04</td>\n",
       "      <td>1.535124e+03</td>\n",
       "      <td>2.994578e+02</td>\n",
       "      <td>1.109865e+02</td>\n",
       "      <td>2.014694e+02</td>\n",
       "      <td>3.312831e+02</td>\n",
       "      <td>8.424439e+01</td>\n",
       "      <td>4.940847e+01</td>\n",
       "      <td>5.629474e+01</td>\n",
       "      <td>1.411634e+02</td>\n",
       "      <td>1.522111e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.183454e+00</td>\n",
       "      <td>8.626059e+00</td>\n",
       "      <td>7.747308e+00</td>\n",
       "      <td>8.197397e+00</td>\n",
       "      <td>2.612524e+04</td>\n",
       "      <td>9.048182e+02</td>\n",
       "      <td>4.846639e+02</td>\n",
       "      <td>1.651005e+02</td>\n",
       "      <td>3.328560e+02</td>\n",
       "      <td>8.452458e+02</td>\n",
       "      <td>1.412373e+02</td>\n",
       "      <td>6.107776e+01</td>\n",
       "      <td>9.162679e+01</td>\n",
       "      <td>2.672695e+02</td>\n",
       "      <td>3.170101e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>9.010000e+00</td>\n",
       "      <td>9.310000e+00</td>\n",
       "      <td>8.560000e+00</td>\n",
       "      <td>9.140000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.454000e+01</td>\n",
       "      <td>1.511000e+01</td>\n",
       "      <td>1.387000e+01</td>\n",
       "      <td>1.441000e+01</td>\n",
       "      <td>4.508000e+03</td>\n",
       "      <td>8.420000e+02</td>\n",
       "      <td>5.400000e+01</td>\n",
       "      <td>2.000000e+01</td>\n",
       "      <td>4.200000e+01</td>\n",
       "      <td>3.000000e+01</td>\n",
       "      <td>1.100000e+01</td>\n",
       "      <td>1.200000e+01</td>\n",
       "      <td>1.100000e+01</td>\n",
       "      <td>2.400000e+01</td>\n",
       "      <td>3.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.926000e+01</td>\n",
       "      <td>1.980000e+01</td>\n",
       "      <td>1.850000e+01</td>\n",
       "      <td>1.919000e+01</td>\n",
       "      <td>1.019000e+04</td>\n",
       "      <td>1.352000e+03</td>\n",
       "      <td>1.360000e+02</td>\n",
       "      <td>5.400000e+01</td>\n",
       "      <td>1.090000e+02</td>\n",
       "      <td>8.500000e+01</td>\n",
       "      <td>3.000000e+01</td>\n",
       "      <td>3.000000e+01</td>\n",
       "      <td>2.800000e+01</td>\n",
       "      <td>6.100000e+01</td>\n",
       "      <td>7.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.457000e+01</td>\n",
       "      <td>2.554000e+01</td>\n",
       "      <td>2.383000e+01</td>\n",
       "      <td>2.452000e+01</td>\n",
       "      <td>2.141700e+04</td>\n",
       "      <td>2.042000e+03</td>\n",
       "      <td>3.450000e+02</td>\n",
       "      <td>1.340000e+02</td>\n",
       "      <td>2.500000e+02</td>\n",
       "      <td>2.730000e+02</td>\n",
       "      <td>9.400000e+01</td>\n",
       "      <td>6.300000e+01</td>\n",
       "      <td>6.800000e+01</td>\n",
       "      <td>1.510000e+02</td>\n",
       "      <td>1.500000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>8.074000e+01</td>\n",
       "      <td>8.953000e+01</td>\n",
       "      <td>7.276000e+01</td>\n",
       "      <td>8.086000e+01</td>\n",
       "      <td>1.407044e+06</td>\n",
       "      <td>8.316000e+03</td>\n",
       "      <td>2.900000e+04</td>\n",
       "      <td>9.674000e+03</td>\n",
       "      <td>2.016710e+05</td>\n",
       "      <td>5.640400e+04</td>\n",
       "      <td>6.347000e+03</td>\n",
       "      <td>3.545000e+03</td>\n",
       "      <td>9.901000e+03</td>\n",
       "      <td>2.052100e+04</td>\n",
       "      <td>2.891000e+03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           VIX Open      VIX High       VIX Low     VIX Close       N_Words  \\\n",
       "count  1.022399e+06  1.022399e+06  1.022399e+06  1.022399e+06  1.022399e+06   \n",
       "mean   2.065154e+01  2.144118e+01  1.987366e+01  2.056038e+01  1.813373e+04   \n",
       "std    8.183454e+00  8.626059e+00  7.747308e+00  8.197397e+00  2.612524e+04   \n",
       "min    9.010000e+00  9.310000e+00  8.560000e+00  9.140000e+00  0.000000e+00   \n",
       "25%    1.454000e+01  1.511000e+01  1.387000e+01  1.441000e+01  4.508000e+03   \n",
       "50%    1.926000e+01  1.980000e+01  1.850000e+01  1.919000e+01  1.019000e+04   \n",
       "75%    2.457000e+01  2.554000e+01  2.383000e+01  2.452000e+01  2.141700e+04   \n",
       "max    8.074000e+01  8.953000e+01  7.276000e+01  8.086000e+01  1.407044e+06   \n",
       "\n",
       "       N_Unique_Words    N_Negative    N_Positive  N_Uncertainty  \\\n",
       "count    1.022399e+06  1.022399e+06  1.022399e+06   1.022399e+06   \n",
       "mean     1.535124e+03  2.994578e+02  1.109865e+02   2.014694e+02   \n",
       "std      9.048182e+02  4.846639e+02  1.651005e+02   3.328560e+02   \n",
       "min      0.000000e+00  0.000000e+00  0.000000e+00   0.000000e+00   \n",
       "25%      8.420000e+02  5.400000e+01  2.000000e+01   4.200000e+01   \n",
       "50%      1.352000e+03  1.360000e+02  5.400000e+01   1.090000e+02   \n",
       "75%      2.042000e+03  3.450000e+02  1.340000e+02   2.500000e+02   \n",
       "max      8.316000e+03  2.900000e+04  9.674000e+03   2.016710e+05   \n",
       "\n",
       "        N_Litigious   N_ModalWeak  N_Modal_Moderate  N_Modal_Strong  \\\n",
       "count  1.022399e+06  1.022399e+06      1.022399e+06    1.022399e+06   \n",
       "mean   3.312831e+02  8.424439e+01      4.940847e+01    5.629474e+01   \n",
       "std    8.452458e+02  1.412373e+02      6.107776e+01    9.162679e+01   \n",
       "min    0.000000e+00  0.000000e+00      0.000000e+00    0.000000e+00   \n",
       "25%    3.000000e+01  1.100000e+01      1.200000e+01    1.100000e+01   \n",
       "50%    8.500000e+01  3.000000e+01      3.000000e+01    2.800000e+01   \n",
       "75%    2.730000e+02  9.400000e+01      6.300000e+01    6.800000e+01   \n",
       "max    5.640400e+04  6.347000e+03      3.545000e+03    9.901000e+03   \n",
       "\n",
       "       N_Constraining    N_Negation  \n",
       "count    1.022399e+06  1.022399e+06  \n",
       "mean     1.411634e+02  1.522111e+01  \n",
       "std      2.672695e+02  3.170101e+01  \n",
       "min      0.000000e+00  0.000000e+00  \n",
       "25%      2.400000e+01  3.000000e+00  \n",
       "50%      6.100000e+01  7.000000e+00  \n",
       "75%      1.510000e+02  1.500000e+01  \n",
       "max      2.052100e+04  2.891000e+03  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm_10X_vix.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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>VIX Open</th>\n",
       "      <th>VIX High</th>\n",
       "      <th>VIX Low</th>\n",
       "      <th>VIX Close</th>\n",
       "      <th>N_Words</th>\n",
       "      <th>N_Unique_Words</th>\n",
       "      <th>N_Negative</th>\n",
       "      <th>N_Positive</th>\n",
       "      <th>N_Uncertainty</th>\n",
       "      <th>N_Litigious</th>\n",
       "      <th>N_ModalWeak</th>\n",
       "      <th>N_Modal_Moderate</th>\n",
       "      <th>N_Modal_Strong</th>\n",
       "      <th>N_Constraining</th>\n",
       "      <th>N_Negation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>VIX Open</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.991336</td>\n",
       "      <td>0.994522</td>\n",
       "      <td>0.986040</td>\n",
       "      <td>-0.043669</td>\n",
       "      <td>-0.065789</td>\n",
       "      <td>-0.042979</td>\n",
       "      <td>-0.042657</td>\n",
       "      <td>-0.051631</td>\n",
       "      <td>-0.011700</td>\n",
       "      <td>-0.055741</td>\n",
       "      <td>-0.067118</td>\n",
       "      <td>-0.030289</td>\n",
       "      <td>-0.038700</td>\n",
       "      <td>-0.021881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VIX High</th>\n",
       "      <td>0.991336</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.989762</td>\n",
       "      <td>0.993968</td>\n",
       "      <td>-0.037282</td>\n",
       "      <td>-0.055887</td>\n",
       "      <td>-0.037024</td>\n",
       "      <td>-0.036013</td>\n",
       "      <td>-0.044579</td>\n",
       "      <td>-0.009395</td>\n",
       "      <td>-0.048903</td>\n",
       "      <td>-0.057644</td>\n",
       "      <td>-0.025107</td>\n",
       "      <td>-0.033418</td>\n",
       "      <td>-0.018408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VIX Low</th>\n",
       "      <td>0.994522</td>\n",
       "      <td>0.989762</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.992550</td>\n",
       "      <td>-0.047530</td>\n",
       "      <td>-0.072431</td>\n",
       "      <td>-0.047106</td>\n",
       "      <td>-0.047104</td>\n",
       "      <td>-0.057159</td>\n",
       "      <td>-0.011712</td>\n",
       "      <td>-0.061077</td>\n",
       "      <td>-0.074567</td>\n",
       "      <td>-0.033585</td>\n",
       "      <td>-0.042036</td>\n",
       "      <td>-0.023286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VIX Close</th>\n",
       "      <td>0.986040</td>\n",
       "      <td>0.993968</td>\n",
       "      <td>0.992550</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.039824</td>\n",
       "      <td>-0.060518</td>\n",
       "      <td>-0.039810</td>\n",
       "      <td>-0.038917</td>\n",
       "      <td>-0.048585</td>\n",
       "      <td>-0.009013</td>\n",
       "      <td>-0.052649</td>\n",
       "      <td>-0.063015</td>\n",
       "      <td>-0.027060</td>\n",
       "      <td>-0.035707</td>\n",
       "      <td>-0.019094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N_Words</th>\n",
       "      <td>-0.043669</td>\n",
       "      <td>-0.037282</td>\n",
       "      <td>-0.047530</td>\n",
       "      <td>-0.039824</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.809176</td>\n",
       "      <td>0.959046</td>\n",
       "      <td>0.939095</td>\n",
       "      <td>0.722641</td>\n",
       "      <td>0.914601</td>\n",
       "      <td>0.891006</td>\n",
       "      <td>0.878841</td>\n",
       "      <td>0.849370</td>\n",
       "      <td>0.956559</td>\n",
       "      <td>0.889830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N_Unique_Words</th>\n",
       "      <td>-0.065789</td>\n",
       "      <td>-0.055887</td>\n",
       "      <td>-0.072431</td>\n",
       "      <td>-0.060518</td>\n",
       "      <td>0.809176</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.792853</td>\n",
       "      <td>0.830639</td>\n",
       "      <td>0.687657</td>\n",
       "      <td>0.616032</td>\n",
       "      <td>0.801120</td>\n",
       "      <td>0.849352</td>\n",
       "      <td>0.733281</td>\n",
       "      <td>0.716785</td>\n",
       "      <td>0.647478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N_Negative</th>\n",
       "      <td>-0.042979</td>\n",
       "      <td>-0.037024</td>\n",
       "      <td>-0.047106</td>\n",
       "      <td>-0.039810</td>\n",
       "      <td>0.959046</td>\n",
       "      <td>0.792853</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.916026</td>\n",
       "      <td>0.711398</td>\n",
       "      <td>0.894608</td>\n",
       "      <td>0.887893</td>\n",
       "      <td>0.859560</td>\n",
       "      <td>0.835784</td>\n",
       "      <td>0.926877</td>\n",
       "      <td>0.905426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N_Positive</th>\n",
       "      <td>-0.042657</td>\n",
       "      <td>-0.036013</td>\n",
       "      <td>-0.047104</td>\n",
       "      <td>-0.038917</td>\n",
       "      <td>0.939095</td>\n",
       "      <td>0.830639</td>\n",
       "      <td>0.916026</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.727968</td>\n",
       "      <td>0.804764</td>\n",
       "      <td>0.896274</td>\n",
       "      <td>0.891294</td>\n",
       "      <td>0.842786</td>\n",
       "      <td>0.871108</td>\n",
       "      <td>0.795171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N_Uncertainty</th>\n",
       "      <td>-0.051631</td>\n",
       "      <td>-0.044579</td>\n",
       "      <td>-0.057159</td>\n",
       "      <td>-0.048585</td>\n",
       "      <td>0.722641</td>\n",
       "      <td>0.687657</td>\n",
       "      <td>0.711398</td>\n",
       "      <td>0.727968</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.554878</td>\n",
       "      <td>0.757004</td>\n",
       "      <td>0.755838</td>\n",
       "      <td>0.640024</td>\n",
       "      <td>0.661037</td>\n",
       "      <td>0.559372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N_Litigious</th>\n",
       "      <td>-0.011700</td>\n",
       "      <td>-0.009395</td>\n",
       "      <td>-0.011712</td>\n",
       "      <td>-0.009013</td>\n",
       "      <td>0.914601</td>\n",
       "      <td>0.616032</td>\n",
       "      <td>0.894608</td>\n",
       "      <td>0.804764</td>\n",
       "      <td>0.554878</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.757007</td>\n",
       "      <td>0.663887</td>\n",
       "      <td>0.730060</td>\n",
       "      <td>0.920254</td>\n",
       "      <td>0.937907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N_ModalWeak</th>\n",
       "      <td>-0.055741</td>\n",
       "      <td>-0.048903</td>\n",
       "      <td>-0.061077</td>\n",
       "      <td>-0.052649</td>\n",
       "      <td>0.891006</td>\n",
       "      <td>0.801120</td>\n",
       "      <td>0.887893</td>\n",
       "      <td>0.896274</td>\n",
       "      <td>0.757004</td>\n",
       "      <td>0.757007</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.878909</td>\n",
       "      <td>0.816628</td>\n",
       "      <td>0.846511</td>\n",
       "      <td>0.750854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N_Modal_Moderate</th>\n",
       "      <td>-0.067118</td>\n",
       "      <td>-0.057644</td>\n",
       "      <td>-0.074567</td>\n",
       "      <td>-0.063015</td>\n",
       "      <td>0.878841</td>\n",
       "      <td>0.849352</td>\n",
       "      <td>0.859560</td>\n",
       "      <td>0.891294</td>\n",
       "      <td>0.755838</td>\n",
       "      <td>0.663887</td>\n",
       "      <td>0.878909</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.801340</td>\n",
       "      <td>0.802987</td>\n",
       "      <td>0.683604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N_Modal_Strong</th>\n",
       "      <td>-0.030289</td>\n",
       "      <td>-0.025107</td>\n",
       "      <td>-0.033585</td>\n",
       "      <td>-0.027060</td>\n",
       "      <td>0.849370</td>\n",
       "      <td>0.733281</td>\n",
       "      <td>0.835784</td>\n",
       "      <td>0.842786</td>\n",
       "      <td>0.640024</td>\n",
       "      <td>0.730060</td>\n",
       "      <td>0.816628</td>\n",
       "      <td>0.801340</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.791243</td>\n",
       "      <td>0.750631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N_Constraining</th>\n",
       "      <td>-0.038700</td>\n",
       "      <td>-0.033418</td>\n",
       "      <td>-0.042036</td>\n",
       "      <td>-0.035707</td>\n",
       "      <td>0.956559</td>\n",
       "      <td>0.716785</td>\n",
       "      <td>0.926877</td>\n",
       "      <td>0.871108</td>\n",
       "      <td>0.661037</td>\n",
       "      <td>0.920254</td>\n",
       "      <td>0.846511</td>\n",
       "      <td>0.802987</td>\n",
       "      <td>0.791243</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.908707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N_Negation</th>\n",
       "      <td>-0.021881</td>\n",
       "      <td>-0.018408</td>\n",
       "      <td>-0.023286</td>\n",
       "      <td>-0.019094</td>\n",
       "      <td>0.889830</td>\n",
       "      <td>0.647478</td>\n",
       "      <td>0.905426</td>\n",
       "      <td>0.795171</td>\n",
       "      <td>0.559372</td>\n",
       "      <td>0.937907</td>\n",
       "      <td>0.750854</td>\n",
       "      <td>0.683604</td>\n",
       "      <td>0.750631</td>\n",
       "      <td>0.908707</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  VIX Open  VIX High   VIX Low  VIX Close   N_Words  \\\n",
       "VIX Open          1.000000  0.991336  0.994522   0.986040 -0.043669   \n",
       "VIX High          0.991336  1.000000  0.989762   0.993968 -0.037282   \n",
       "VIX Low           0.994522  0.989762  1.000000   0.992550 -0.047530   \n",
       "VIX Close         0.986040  0.993968  0.992550   1.000000 -0.039824   \n",
       "N_Words          -0.043669 -0.037282 -0.047530  -0.039824  1.000000   \n",
       "N_Unique_Words   -0.065789 -0.055887 -0.072431  -0.060518  0.809176   \n",
       "N_Negative       -0.042979 -0.037024 -0.047106  -0.039810  0.959046   \n",
       "N_Positive       -0.042657 -0.036013 -0.047104  -0.038917  0.939095   \n",
       "N_Uncertainty    -0.051631 -0.044579 -0.057159  -0.048585  0.722641   \n",
       "N_Litigious      -0.011700 -0.009395 -0.011712  -0.009013  0.914601   \n",
       "N_ModalWeak      -0.055741 -0.048903 -0.061077  -0.052649  0.891006   \n",
       "N_Modal_Moderate -0.067118 -0.057644 -0.074567  -0.063015  0.878841   \n",
       "N_Modal_Strong   -0.030289 -0.025107 -0.033585  -0.027060  0.849370   \n",
       "N_Constraining   -0.038700 -0.033418 -0.042036  -0.035707  0.956559   \n",
       "N_Negation       -0.021881 -0.018408 -0.023286  -0.019094  0.889830   \n",
       "\n",
       "                  N_Unique_Words  N_Negative  N_Positive  N_Uncertainty  \\\n",
       "VIX Open               -0.065789   -0.042979   -0.042657      -0.051631   \n",
       "VIX High               -0.055887   -0.037024   -0.036013      -0.044579   \n",
       "VIX Low                -0.072431   -0.047106   -0.047104      -0.057159   \n",
       "VIX Close              -0.060518   -0.039810   -0.038917      -0.048585   \n",
       "N_Words                 0.809176    0.959046    0.939095       0.722641   \n",
       "N_Unique_Words          1.000000    0.792853    0.830639       0.687657   \n",
       "N_Negative              0.792853    1.000000    0.916026       0.711398   \n",
       "N_Positive              0.830639    0.916026    1.000000       0.727968   \n",
       "N_Uncertainty           0.687657    0.711398    0.727968       1.000000   \n",
       "N_Litigious             0.616032    0.894608    0.804764       0.554878   \n",
       "N_ModalWeak             0.801120    0.887893    0.896274       0.757004   \n",
       "N_Modal_Moderate        0.849352    0.859560    0.891294       0.755838   \n",
       "N_Modal_Strong          0.733281    0.835784    0.842786       0.640024   \n",
       "N_Constraining          0.716785    0.926877    0.871108       0.661037   \n",
       "N_Negation              0.647478    0.905426    0.795171       0.559372   \n",
       "\n",
       "                  N_Litigious  N_ModalWeak  N_Modal_Moderate  N_Modal_Strong  \\\n",
       "VIX Open            -0.011700    -0.055741         -0.067118       -0.030289   \n",
       "VIX High            -0.009395    -0.048903         -0.057644       -0.025107   \n",
       "VIX Low             -0.011712    -0.061077         -0.074567       -0.033585   \n",
       "VIX Close           -0.009013    -0.052649         -0.063015       -0.027060   \n",
       "N_Words              0.914601     0.891006          0.878841        0.849370   \n",
       "N_Unique_Words       0.616032     0.801120          0.849352        0.733281   \n",
       "N_Negative           0.894608     0.887893          0.859560        0.835784   \n",
       "N_Positive           0.804764     0.896274          0.891294        0.842786   \n",
       "N_Uncertainty        0.554878     0.757004          0.755838        0.640024   \n",
       "N_Litigious          1.000000     0.757007          0.663887        0.730060   \n",
       "N_ModalWeak          0.757007     1.000000          0.878909        0.816628   \n",
       "N_Modal_Moderate     0.663887     0.878909          1.000000        0.801340   \n",
       "N_Modal_Strong       0.730060     0.816628          0.801340        1.000000   \n",
       "N_Constraining       0.920254     0.846511          0.802987        0.791243   \n",
       "N_Negation           0.937907     0.750854          0.683604        0.750631   \n",
       "\n",
       "                  N_Constraining  N_Negation  \n",
       "VIX Open               -0.038700   -0.021881  \n",
       "VIX High               -0.033418   -0.018408  \n",
       "VIX Low                -0.042036   -0.023286  \n",
       "VIX Close              -0.035707   -0.019094  \n",
       "N_Words                 0.956559    0.889830  \n",
       "N_Unique_Words          0.716785    0.647478  \n",
       "N_Negative              0.926877    0.905426  \n",
       "N_Positive              0.871108    0.795171  \n",
       "N_Uncertainty           0.661037    0.559372  \n",
       "N_Litigious             0.920254    0.937907  \n",
       "N_ModalWeak             0.846511    0.750854  \n",
       "N_Modal_Moderate        0.802987    0.683604  \n",
       "N_Modal_Strong          0.791243    0.750631  \n",
       "N_Constraining          1.000000    0.908707  \n",
       "N_Negation              0.908707    1.000000  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cor = lm_10X_vix.corr()\n",
    "cor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x648 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "sns.set(style=\"white\")\n",
    "f, ax = plt.subplots(figsize=(11, 9))\n",
    "\n",
    "cmap = sns.diverging_palette(220, 10, as_cmap=True)\n",
    "ax = sns.heatmap(cor, cmap=cmap, vmax=1, center=0,\n",
    "                square=True, linewidths=.5, cbar_kws={\"shrink\": .7})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Dennis Thumm\\Anaconda3\\lib\\site-packages\\pandas\\plotting\\_converter.py:129: FutureWarning: Using an implicitly registered datetime converter for a matplotlib plotting method. The converter was registered by pandas on import. Future versions of pandas will require you to explicitly register matplotlib converters.\n",
      "\n",
      "To register the converters:\n",
      "\t>>> from pandas.plotting import register_matplotlib_converters\n",
      "\t>>> register_matplotlib_converters()\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AW7ZsQVBQEDw8PBAYGIiUlBQAQFJSEoKCgiyOhYiIpCX6TqZdu3b47rvvUFVVBa1WCx8fH6t22KNHD7zzzjsYOXIkamtr8eKLL2LEiBF4+OGH8d5776G6uhrBwcEICQkBACQkJCAmJgbl5eV48sknMWrUKABAXFwcoqKisGTJEnTu3Blz5861Kh4iIpKO6CTTp08f/O///i+GDx+OwMBAm3Y6dOhQDB06tNGyXr16YevWrU3Wfeyxx7Bhw4Ymy7t06YJVq1bZFAcREUlLdHHZ7t278fTTT2POnDkICQnBihUrUFxcLGVsRETk4kQnmQ4dOmDEiBFYv3495s+fj507dyI4OFjK2IiIyMVZ1Ab55MmT2Lx5s76X/YIFC6SKi4iIWgDRSSYsLAxVVVUIDw/Hxo0b0alTJynjIiKiFkB0komKisKLL74oZSxERNTCmE0yy5cvx//93/9hz5492Lt3b5PnY2JiJAmMiIhcn9kk06FDBwDAPffcI3kwRNQylVdXoKC8EI/e+0e5QyEHM5tkXn/9dQCAr68vRo4c2ei5ZcuWSRMVEbUo8fvm49LNq0h8bYncoZCDmU0yP/74I9RqNb799ltUV1frl9fW1mLt2rUYPXq0pAESkeu7dPOq3CGQTMwmGXd3d5w9exZqtRpnz94ZBdXNzQ1RUVGSBkdELU+NpgbF6lsI8PEzvzK5PLNJZtiwYRg2bBh27dqFvn37OiImIof4NT8HgIC/3t9d7lBalS/SluO3ghysHf4V2ihkmQGeHEh067L09HT88ssvTZ5n6zJyVbMP1s3synqCpkrVZfB084C3h7fdt33s+sm6PwQAnAaqxWPrMiJq4p0tk3FfO18sDpspan2doEOO8gyeCnhc4sjI1YhuXTZ+/Hj9MqVSiStXrtg8GjMROa+iSvED4G4/swerjm/E5L+NRWCXHhJGRa5GdIHojz/+iI8++gjFxcUIDw9HdHQ0vvjiCyljI3Iqn+6bj9kHvpI7DKekLK+bwfZG5U2ZIyFnIzrJrF+/HlOnTkVqaip69+6N7du34/Dhw1LGRuRUTijP4NeCHLnDIHIpopOMQqHAfffdh/T0dPTq1Qvu7u7Q6XRSxkZEJJus/BOIWP8e1LVquUNxaaKTjKenJ5YvX46jR4/ixRdfxJo1a9C2bVspYyMikk3iiW2o1WmQX1YodyguTXSSmTlzJvLy8vDZZ5/h7rvvRlZWFmbMmCFlbERE5OJEJ5mHH34Y06ZNQ9u2bZGWlob4+Hj86U9/kjI2InIxAgS5QyAnI3o+mezsbLz77ru47777oNVqoVQqsXTpUvz1r3+VMj4il3T4cgYO5B3B1KDx5lduCdipslm7zx/CUwGPw6/9vXKH4nCik8ycOXOQkJCAF154AQCQnp6O2bNnIzExUbLgiFzVgvRv5A6BnESNpgb/zVwNv3a++Epk59aWRHRxWUVFhT7BAECvXr1QVVUlSVBE1vjutw1IObtH7jCIGtHdLkIsrS6XORJ5WNSE+dq1a/rHV69ehZubmyRBEVlj+9nd+Pa39XKHQeaw2qZVEV1c9u9//xuvvfYaevXqBYVCgUOHDiEuLk7K2IioJWM9TqsgOsn07dsXDz/8MH755RfodDqMGTOGrcuIiKhZopLM6tWrcfHiRbzwwgtNpmAmIlLwtsQ0oXWXD5qtk5k1axa2bdsGLy8vzJ07F99++60DwiJqGcqrK/DLlV/lDoOcgaJ1JmKzSSYtLQ0//PADIiMjsXLlSmzbts0RcRG1CF+kLcPctOUo5ujE1EqZTTLu7u5wd68rVevUqRNqa2slD4qopSisuAEA0Og0TZ7bc+Ew1p2Q56KtsqYKGp1Wln27ntZd3GUriyfYZrNlIvtYmvEDNp5KkWXfb22eiC9/YYfRZrXO0i27M1vxr1arcerUKQi3K68MHz/55JPSRkhEkpCirkho5ZXc1JTZJFNdXd1o6mXgzlTMCoUCu3fvtmrHc+bMQUlJCWbPno3c3FxER0ejoqICgYGBiI+Ph7u7O/Lz8xEZGYkbN27gj3/8IxISEtC+fXuUlpZi0qRJuHLlCnx9fTF//nz4+flZFQcR2Y6ty0xr7WnXbHHZnj17TP6rTzDJyckW7TQ9PR2bN2/WP46MjERsbCx27twJQRD046HFx8dj5MiRSE1NRbdu3bB48WIAwPz58xEYGIgdO3Zg2LBhmDmz9Y0HROSKWvOdTmtNwxbXyRizYsUK0evevHkT8+bNw9ixYwEA165dg1qtRs+ePQEA4eHhSE1NRW1tLTIyMtC/f/9GywFg3759CAsLAwAMGjQIBw4cYIMEImpxjhWcxLKM1XKHYRO7JBlLrk5iY2Px4Ycf4q677gIAFBYWNirq8vPzg1KpRElJCXx8fPQt2+qXG77G3d0dPj4+KC4utsdbIbJJa75Sd4SiipZ7nOt0Onx/bCNKqm7pl/3nwCLsunBIxqhsZ5ckoxDZyWj9+vXo3LkzevXqpV+m0+kavV4QBCgUCv3/YvYjCALatLHLWyEiidkysdnkn/5jx0icS07hGSSf2YX/Zvwgdyh2JXrsMntISUmBSqXCkCFDcOvWLVRWVkKhUEClUunXKSoqgr+/P3x9fVFWVgatVgs3NzeoVCr4+/sDAPz9/VFUVISAgABoNBpUVFSgY8eOjnwrRC6nVF0GKBS4y8tHsn1IPTNmeU2FpNuXk07QAQC0t/9vKRx6+b9y5UokJydjy5YteP/999G7d2/MmjULXl5eyMrKAgBs2bIFQUFB8PDwQGBgIFJS6voRJCUlISgoCAAQHByMpKQkAHWJKzAwEB4eHo58K0Qu550tk/FOUqQ0G2+ttdoitPYpqR1eJ2NMQkICZs2ahZCQEFRWVmLUqFEAgLi4OCQmJmLgwIHIzMzEBx98AACYMGECjh07htDQUKxZswaxsbE2vwciSTh5Hc3wdeNQUVNp83Ya1iM0xx4n3BVZazF83Tibt+NorbWZt9nisqlTp5p8TqFQ4D//+Y++pZclwsPDER4eDgB47LHHsGHDhibrdOnSBatWrWqyvGPHjli6dKnF+6TW62zRBcTs/hzxvSficb9HHR+AEw+OeL1chT/5PmjTNo5c/c1O0Zi38/f9DtuXI9UnYOf9pVjHbJJ59NGmB2RJSQm+++47dOnSBQDwr3/9y/6RUat39OoxXC0tQPgTA2zeVrYyt+7/66clTTLZylz0CHhCsu1Ty1V/02uqgVPymV3Yff4w5g10rckizSaZt99+u9HjtLQ0TJkyBWFhYYiJiZEsMKKEw/8FALskGUdpyU1s7e3H7C2I6PGK3GHg6NVjSDj8XywYGI/OHfybPG+vAs8qjdqm139/bKOdInEs0a3LNBoNvvjiC2zevBnx8fH6TpJEUjtbdAEBPn64y7uD3KGQFUzV2W45/RMevfePyC9T4uXH5TufZFw7DgA4U3S+UZKxWx2Kc1fLSU5UksnLy8PEiRPRvn17JCUlISAgQOq4iPRidn+OAB8/fBk6Xe5QzGrl5xOjond9ZvK5+rtVOZMMScts67KNGzdi+PDh6NevH1atWsUEQ7K4Xq4yv1Iz6q+m5ap/zy+9LroFVkvze3HenQctPAtvOrUD525ctPLV9R9Oy6r6N3snEx0djTZt2mDZsmVYvny5fnl9j/xff+XUsgSkntuHNgoFXnokWO5QjLpzbpP6ADZ+Fv3PgUUAgMTXljR5bk12Em6qS/Huc6MkjYyaV1VrW50JAKw9sRVrT2w1+j2b0zJTjIgkY+1Q/tS6fPPrOgBw2iQjRua1bHx2aAnmDojFA3d1tmob1nSLScrdCQBMMjLJK7kCAFj5WyIG/PkfMkdjPbWmGutzkvFmz1flDqURs0mmvpkyUUtXP4nX+RuXmiSZypoqu+6rVstRw52Fra2+nMXXmT/iwKUjeOb+7njC/89yh6PHUSXJrr79NRGfHTLeUXbr6Z+x4eR2B0dUz7aObvUV1PbyddZau27PWfxefEnuEJyOo4aVqaitG7mhsta+F0S2YpIhq5Wqy6A2KMdOObcXmbebhBr64fgmJOZYNsGdvZjr6GbO5VvXxO3H4IRi6vSSqzpnVRzO7tClo3KH4BCVNVVNfvu2Eu78SG3aztXS63aIxn6YZMhq72yZjMidnJWUXJs19xlvbZ6IMVtND7llSyQKAKqKG1bfkazJTmqyrFpTg1vqUluCs5pDh/qnlkdZUSR3CE6mhbfRtZGzj0isE3RooxB37S1lXc6/k+07mkrsngRcLLliVas3W/FORkafHVyCpU40QVGu6hyy8k/IHYZLM2xdZqq3u7GlPxzfhLUntoje1/Yzu0XXcR2/fkr0dsXIvHYcJ5Sn7bpNZ/DT7wfMruOKs59evN2CTg5MMjLKzM/GnguH5Q5DL27PXMw5uNjoc4Ig4OffD0pWqagTdNh7IQ0arQYA7DL8fEOCTL0QdBA/AdXW0z9j06lU0et/d2yDqDquGm0tZu5fKHq7Ynx2aCk+3bfArtu0p5tGOr7+/PtBzD7wVbOvK6osabLMMKk4Y33aheJLKK68KXcYRrG4jETJVZ3D8qw1OFN0HuNfeMvu20+7nIklGatQVFmMYd0GQaPTWPT6zGvZZtYw37pMP9S6Ew/Lb42WPJukKaO3RjVZtjxrjUXbMDV2mUantWg7YosI71wGWf77i/p5tsWvcRTeyZAoak0NAKCsplyS7ZffvnMprbZu+58dsr2s2R6FIM5e50BGWFj8JdV3bK/WZc6GSUYml25elXT7giBg+LpxRluaWLnF2/875gAwdhhfuZWP4evG4fJNcc2J7clY8stRnsGC9BXWbdAFy/VbKn4T0mKSkcm1UqWk29cJdXUBW0//bPFrd5zdq399PYfXaBg5CdfPvph+xfLx8mztJ2PM9H3zcfhyJkrVZXbbpiSc6CzqRKGQgzDJyMZ5D7eVvyVi93nnaZBgD1IWY63L2XZnP054h8IivKbOFl3AtJ/noMZgeB8x9VdO+BU7NSYZmUh94Nt651Gibtw6p6WWF9uDzoqzDk/88vrm13X4vTgPV27lN/ouJGntKfKrFkQ0TjFGpxPfglEOTDIykfxq6PYOtIK9foC2jf1l3d5aACd4I7YktPLq1tcyzTxpv1RLW5eduXFeokjsg0lGNlLfydi2fcOfuWHzSnuPSqyfVOz29l31St8wbsM7Qlu9kxSJ6J/nWPYiGz7KfXnp1r/YBdhavPntr4l2isR6zl58xyQjE6l/GLZu3rCC3LC4LL/Mvg0X9CdniW+Vmr1KNEh0zqi0uhznivMcNlXA98c2OmQ/jibmOy5Wm+/cmHJurz3CadGYZGQi+ZW6xFnM1e407ky/bPrkcqczpgPiEbHOsYKT2H7G+KSB9ROdWSPl7B6rX9simfgyTE2XLX0/GUk2LxsmGTvacXYvfnWSsb/sfRgYVkpaWsxQfbszpwU7dEnmPpfRW6aI/iz+c2ARvju2wehzlRYMM294Uvz2t/WiX2t3drz40el0OHr1mHUvbnAid7oLJicLx1ZMMna08rdEzDYx9pfrMX45ZW1R0jQLh71wugPfTm6qS5F8ZpfN27Hk82lJn2TD398Pxzch4fB/cabI+opva35nluZJS/fR0n77TDIykbo/hd0r/g1u5S3d/pXSApvisZUzHbbrcrZBVXHD4tc1ukt29tpeKxRX3cRvBTkAgH9umoj9F39pdn1VZTEA08VazZGi3s1ex7Q1DeKdGZOMTFy1Tkbf+kvyhgv2L/ADpK/Ul/JjaXiXLOV+rt4yfkGg0WlRUFYo2X6jd32GWbdHSa6orcJXR79rupKdvz57XuytkqmRhLPNhGmISaYFEgQBeTaO79WkdVmT05p9T3OGTZid/OLM6W06tQPD142DVqe1+Ipg46kUo8u//S0RE1LijA6jbw83jAyzb8heFwkNt2L5BY3x9c/euGh1PNbFUUfMHDhyYpKRyeKj30u27T0XDiN2T4L+sT2u1u6Ulimg1lRjuw0tlIavG4cc5Rmjz8nZsObOp1QXxdGrxzB83TgUV1kyT4djsqO5E9LaE1sB1M3LY6+7wpOFZwHcGTG77u+W11nztOp3WfZrbeMyy36fjsck0wLlGYzwnHpun9FITySaAAAZP0lEQVT1Cq2oF4Cibg7x+sEqrbXvohN28tM3c657eCDvCADgnAVXqA6rKhG5H8OBTu2t3MLJ5ZyxUvuWwQCnsXu+aHZ9Me9g38V0q5OVmO2XVpfj9xt5AJw/0TPJ2En29Vy5Q9AzPJB/udp01OLTqvMYnxwjqs/EmaLz+hY8Cijs3tvfGHufjASDBOLMrperGj2e9vMcLDrybaNlxj6f8poKJOXubHznqlBIelpfmy1+umh7EPP1iSl2a/hDkCIRLz76vT5ZSfH5f7zrc0zbZeHIDzKRJcksWrQIoaGhCA0NxWeffQYASEtLQ1hYGF566SXMmzdPv25ubi7Cw8PRv39/REdHQ6OpmzExPz8fERERCAkJwbhx41BRIW82n7H/S1n334iIX3V9j33Du556Dcu+P96dgB23eza7ythliTnbGt3BGRaF2aLMyonVxMozmI/99+I8/V1VPWNJ5ptfE7EmOwnHG1zwHBV5x6muVSPfigpkawYHtYmIq4RrBu8jscEo2Yasa8Isf6OdgnLpGmDYm8OTTFpaGg4dOoTNmzcjKSkJJ0+eRHJyMqZNm4bFixcjJSUFOTk52L9/PwAgMjISsbGx2Lmz7gotMbFurKD4+HiMHDkSqamp6NatGxYvbin9U2xnj7sAV5+CeMPJFHzz67omy6ss6MRoyr+SIhG96zOjzzmsOMjIbqpq6+4wa3V3hpwpq64QddKadXAxPtgR3+w6lbe33/A9agTLpiKWw4aTjRsyXL55zaYGBKa+Y0vviDQ6LabvnY+zRRf0WwZgt9ttjU6LGks7QUvA4UnGz88PUVFR8PT0hIeHB/70pz8hLy8PDz74ILp27Qp3d3eEhYUhNTUV165dg1qtRs+ePQEA4eHhSE1NRW1tLTIyMtC/f/9Gy6lO0yst0z/a88WXkKs6J37jEiUfqU/NRRV1fSpMtZwCgKzbfTTEsKSexlKl1dZNgmbLibPhb8DUdoz1Rymypl7PBvZoXTZp5wz93/a4KamvX/y9OM+i110vL0RO4RksObqqLhaRR8GVW/mi1hu5fjze2DjBopik4PAk8+ijj+qTRl5eHnbs2AGFQgE/Pz/9Ov7+/lAqlSgsLGy03M/PD0qlEiUlJfDx8YG7u3uj5VTHkuPmyq18xO2ZK3p9e6UYAQKUjeoeGl/F2XsASLWm8R3MnavHutZu18sKUa2ptsOebD9rqUXE0dwJyfAiQ8oE7ujBRO21N3tGbUlL0foWegD0FfeGSsy0Fvso9VPR+3MGslX8nzt3Dm+//TYmT56Mrl27NiqeEQQBCoUCOp3O6PL6/xty9eKdhrKv5+p7PjeUJnKqX8MTkDWfjKmThwIKuxyhBy8dxXvbY/HT7/uN7tdUizhD+aXXmx0L7ONdn0MQmp6SK2obt4rKaXDwA7fHxbpm+bhYYhKEPRhLHLrbS52lBZdOp8Mpg8/VWai19b8ZB9TJNFg/fu+d+mZTyel88SWLY3Jm7nLsNCsrC++//z6mTZuG0NBQHD16FCrVnatalUoFf39/BAQENFpeVFQEf39/+Pr6oqysDFqtFm5ubvr1W4r6RgSJry3RL7ulLsX89BVNlhtlcBDYNQHbOZlvPLUDak11kwpkjc58Wb9Wp8UHO+LxdOdujZY3PAmcuXHB4pOuAgqknLOuH9CGkyl45v6nrHptwwisUT/szBeHl93ZkkJh9v3bVJFtJNTY3QmY3mcSvv51LXadP9h4XzbGUKvTWBigcZdMNHgRw9jnqdEaj6u48ibatGndjXgd/u4LCgrw73//GwkJCQgNDQUA9OjRAxcvXsSlS5eg1WqRnJyMoKAgdOnSBV5eXsjKygIAbNmyBUFBQfDw8EBgYCBSUurK15OSkhAUFOTot+JQpk66xlo62eM61lE3hiVVt/DD8c1Wde7U3v5McpSnGy1fl7O10eOs/BM4fv2U/vGBvCP64UuMOVN0AVk2jKZt65XobwU5Jk9aenZs4WTvUoDTt5u7K52oBdRNdanVrxUgQKvT6iv2jSXET020Lh27bSo2n2paX1zfiKLRfpzjBtTuHJ5kVqxYgerqasyePRtDhgzBkCFDsGnTJsyePRvvvfceBg4ciIcffhghISEAgISEBMyaNQshISGorKzEqFGjAABxcXFITEzEwIEDkZmZiQ8++MDRb8UkMUVapljSB+V88SX8KykSB/OONlpun+KSuhOPYQW3slyFgtLG9V+GrXesUV/MdPTqbxAEASeU5vsd6d+lwUlyk8FB/fmhpY0eG/Y5MbTj3N5GZeeWsrWe4mThWazJTmp2HcOiPFN3Ar8VnLT72UvMXUeNtha5qqajIxs7udrbvrymA2tOMlGPkWpQXGvKiPXj8dlB0yUIzTWeyTbyW35r00Qja0qTZfJKrL9rsweHF5fFxMQgJibG6HNbt25tsuyxxx7Dhg1N59To0qULVq1aZff47OGdLZPNF2mZ8KGZZqQN1d/y5xSewd8feu7OE3b6rWZcO97kBG2sVVVz/RDEqj8tqyqLcfz6KVGjEdQn0zYSVz7XaJpvhGD4mdjjzqCwsvn3X2pwB3vw0lGj61k6DL4gCKiwMBEYa6Txxob3ja5rbmRleygwMmur4edVL+1ypujt/mpB68OGzDZttuHnImYcuck/zdT/vejIt3j3uVFoo3Dc/UXrLix0QqbmhDd2d2LqitJeFb9SjrjbnLJqcR1rDaeEtkVlrenhUU4YFMcZunyr8WCkBy8dMbGmeJbcDdVoa5vE0JAlvwYBgtFGJw01bGKtgALLM38Uvf2W0D7H0uNLyuNoqoW9/g/kHUGRmBER7IhJxkU0V0TRZO6XJq3Lmh7Zhk16m25TvrPBjSpxB4HhbJ1SOXLNsnHacu0wwKIln/8bG95vtg7H3q3NrhkUl5pLwg0prDjlSN7D3ojm5vuxdzj1IxRYs11RQ+gYcPSRzSQjsc2nUnGqUFxnR8MK7Ib2XkzT/13fa73hb1In6DB83bi6ud8Nfqw5hWfwy5XG45eZO3BtveK8YGHld8OTqrn6CD39jYy0h409RgmwlKXvSUxrPFGM/CyGrxsHne5OkU9VgwsUe12MNJcI5RgAcm7a8maelSbpOarpuaO7ezDJSOzHE1vwyV7znR2LK2/icjM9eRsmqv+36cMmA3Iqy4sA1J2gjf1Ymz9o7H+1GGXhdMvWnKvu3Mm0gDIYA5a+I6lPHA1/U036Almwa1NhNldXk3J2r9Hlp25XthdVFmP4unE4VnDK6HrWMFWPkl+mtDkZXJV5llhHHy+y9JOhxqpq1Ri7bWqz6xj+rE+pzsGvna/+ccMKXlMJY/i6cehyVwBm92u6r/QrhiM1K4zs1XLlIutXxPzw61vebTyVgvQrWZjRN7JuuV1aLDlXorI0aTS7vgVfo7gTqPW/C1Pf85IM0414BBg/4dc336+fLGzPxcPo2fkJq2NrqE2D6++GRVIfpHyCu7062LTtiTumN1lWXHUTx67bL0k2x9F3MkwyTsBUccycg4sx5e/vAgAEI1dW9Yf6notp2NOgOK25E8W10uvYcHI7Onrf1Wj5/PSvGz0urS6Dj2d7MeGblJS7U3zRlwjJZ3fr/84vU9r17svZ7oYOW9DqCUCzrYUsufI2taa9rr6tOcGJHunZjjfjDeM0bKJ/y8qx5ZoTs+tzFFUW2327xjj6t87iMiegM3Gl1rBDoJG2ZSa3Z+5YqxExLlhS7k6z65hjSYKx5mdvzwI+R/TfkJKYeYFscaPScbMvNhwmKL/0utkmwPUnTXvWadg2PbPlHJVgAMffs/NORgZ7L6Q1XiCqhEJo7mGz6xraczENj933sIidOo6lfTMA2LWZT3OjM7d0DU/iWfnZIl5hMG6gBactMes2bNllbvqBhtEUVRRjacYPzbYME83F21p/k9V0mot6LC5rBQzLn8VcKVUaNDludhReM9uq1lQ3mtjK5HacfJwLnQOuMFsCc99jw7lxxPwuNp3aYXUsW07/ZPVrTUm7Ujfs1PmSSzhfYp/BJRt18HXy48CY1N/3mXxO4cCOmACTjM0EQbBq3K2G1p/cbnYd43NIGP/xm+sDI1bDOhCn5IIHvxy2nv5Z9LqGA1raW7GZYeydhbON6l5RY7qzsKWkHiHDEJOMjU4oT+P7Y02HvbFE/aRHliivrjBZ9PBbwUmb4ql3y4ZBBR2hxk4j8rZ01kxZYAl792OZmNq09ZWjNUwyV2RucgyYnibdKg7On0wyNrLX0OOW2nXhkCz7dSbjk42PgUeNSTnHjQDBYXPoOJZz3cnYE1uXuZg2TnZbTSSF2QdNT43QEjnbca2114gOYI9/l2PpWEy2DCFPRI7hbP2mjl61X5Ene/y7mOaueBrOI1+v4fSrYgxfN87imIgc5ULJZblDkISzVfzbtQ8Q72RcS3NJpuFowmoZBlkkIutYMrK0I4ge9UAE1sm4mObanBc36CWdmJOMfCOTKRERmbPbjg19lOUqu21LDBaX2cjUVYFOp2s0sN6ZovPO3++EiFq8o9eO4Q8duzhsf7yTsZGpgQmn/Dyr0XAd54rzHBQREZFpLC5zMabqZC7dvGrXclQiIntgxb+Lae4LMzd6LBFRS8ckY6Pm5vH46fwBB0ZCROR8mGRs5OwjFRMRNeTouZOYZGx04NIRuUMgIhLtuoObMDPJ2MjU1MlERMQkQ0TUuji4hJ9JxkaOmP+biMhepJ5fyBCTjI1Y8U9EZBqTjI2YZIiITGOSsRE7XBIRmcYkY6MKB7c5JyJyJUwyNjp+/ZTcIRAROS2XTjLbtm3DwIED8dJLL2H16tUO33+Npsbh+yQiciUuO5+MUqnEvHnzsGnTJnh6euL111/H888/j0ceecRhMcxN/9ph+yIickUum2TS0tLwwgsvoGPHjgCA/v37IzU1FePHj5dsnzXaWqw9sRVniy7g7I0Lku2HiKilcNnissLCQvj5+ekf+/v7Q6mUdnrjG5UlSD6zCzcqS3CP992S7ouIqCVw2TsZnU7XaC4XQRAkn4yncwd/fBHyMR64qzMAQK2pRml1Gd7bHivpfomI7GVGn0iH7s9lk0xAQAAyMzP1j1UqFfz9/SXfb9e779f/3dbDG209vJH42hLJ90tE5Ipctrjsf/7nf5Ceno7i4mJUVVXhp59+QlBQkNxhERFRAy57J9OpUyd8+OGHGDVqFGprazF06FA89dRTcodFREQNuGySAYCwsDCEhYXJHQYREZngssVlRETk/JhkiIhIMkwyREQkGSYZIiKSjEtX/FtDq9UCAK5fvy5zJERErqP+nFl/DhWr1SUZlUoFAIiIiJA5EiIi16NSqfDggw+KXl8htLL5g9VqNXJycuDn5wc3Nze5wyEicglarRYqlQrdunWDt7e36Ne1uiRDRESOw4p/IiKSDJMMERFJhkmGiIgkwyRDRESSYZIhIiLJMMkQEZFkmGSIiEgyTDJERCQZJhmiZlRWVsodgk1KSkrkDoFaOSYZJ1deXo4TJ06gvLxc7lDMqqqqQkJCAvr27Yvu3bujR48e6NevHz799FOUlZXJHZ5VXGmMu4KCAkRGRiI2NhZXrlxBWFgYBg4ciH79+uH06dNyh2dWQUEB3n33XYSHh2Px4sWNBmIcM2aMjJGJc+3aNUycOBGXL19GQUEB3nzzTTz99NN44403cPnyZbnDM0uq45dJxskcPnwYwcHBOH/+PI4cOYKQkBBMnz4dISEh2L9/v9zhNWvSpElo164dfvjhBxw7dgy//vorVq1aBT8/P0ycOFHu8Mzq3r07Hn/8cTz++ON47LHH8PjjjyM3N1f/t7ObMmUKevTogQceeACvvfYa3nvvPaSnp2P27NmYPn263OGZNW3aNPTp0wfTp09HdnY2xo4dC41GAwBQKpUyR2deZGQknn/+eQQEBGD69OkYPHgw0tPTMXLkSEyZMkXu8MyS7PgVyKkMGjRIOHfunCAIgvDaa68Jp0+fFgRBEC5fviwMGjRIztDMGjBggMnnQkNDHRiJdU6ePCmMGDFC2Llzp37ZkCFDZIzIMg1j/fvf/97oucGDBzs6HIu9/PLL+r91Op3w4YcfCh988IEgCK7xPTSM/9VXX230nLMfu4Ig3fHLOxkn4+7ujkceeQQA4Obmhr/85S8AgK5du0Jw8rFMfX19sWPHDuh0Ov0yQRCwfft23HPPPTJGJs4TTzyBb775BmlpaZg6dSoqKiqgUCjkDks0Hx8frF27Fl9//TW0Wi327t0LAMjKyoKXl5fM0Znn7u6Oc+fOAQAUCgXmzJmD4uJixMbGWjyHiRz8/f2RmJgIAAgMDNSXPBw8eBAdO3aUMzRRpDp+OQqzk5kxYwZu3ryJ0aNH4+jRo1AqlQgNDUVKSgqUSiXmzJkjd4gmFRQUID4+HhkZGejQoQMAoKysDM8++yxiY2Nx//33yxyheLt378bSpUtx69Yt/PTTT3KHI8q1a9ewYMEC6HQ6TJgwAVOmTMGFCxfg7e2NhQsXonv37nKH2KysrCxERkbiww8/RFhYGIC6hheTJk3C3r17kZubK3OEzVOpVJg8eTLOnDmDgIAA5ObmwsfHBwEBAVi4cCEeeughuUNsluHxq1AoUFZWhsDAQJuOXyYZJ1NbW4vly5djx44duHz5MrRaLe677z707t0bEydOhI+Pj9whmqXRaFBSUgKdTod7770X7u6uOTdeUVER9u3bh6FDh8oditWKi4vh6+srdxgWqampgaenZ6Nlubm5LlEvBtS16Lty5Qo0Gg38/PzQtWtXuUOyiL2PXyYZJ1VbW4uSkhJ4enq6xK02AOh0OiQmJiI1NRXXr19HmzZt4O/vj+DgYLzxxhvw8PCQO8Rm1ce/Y8cOKJVKffxBQUF48803XSZ+V/38SV5lZWVYuHAhCgoK0LdvXwwZMkT/3Mcff4xPP/3Uqu0yyTiZGzduICYmBocOHYJGo0HHjh2h0+kQEhKCKVOmoF27dnKHaNLHH38MnU6HV155Bf7+/hAEASqVClu3bkVlZSUSEhLkDrFZjF9eSUlJzT7/8ssvOygS67h6/OPHj8ef//xn/OUvf8GyZcvwxBNP6BPLK6+8gs2bN1u1Xdcsx2jBoqOjMWTIEMybNw/JyckoLy/H4MGD8c033yA6Ohrz5s2TO0STMjIykJqa2mjZgw8+iMDAQAwcOFCmqMRj/PJKT0/HTz/9hJCQEKPPO/tJ2tXjv3r1KhYtWgQACA4OxpgxYzB79mxERUXZ1OiIScbJFBQUYMCAAQCAoUOHIjw8HG+99RYmTZrk9CeK9u3bIzs7G0899VSj5b/99hvat28vU1TiMX55zZkzB7du3cIzzzzjkvVgrh4/UNd4wc/PD97e3li0aBEiIiKwdOlSm1pZMsk4GQ8PD2RkZODZZ59FWlqa/uRw4sQJeHt7yxxd82bMmIHJkyejuroafn5+UCgUKCwshJeXl9MX1QCM3xlMnz4d27ZtkzsMq7ly/OPHj0d4eDji4uLQt29fdOjQAV9//TXGjh2LM2fOWL1d1sk4mezsbEyYMAFqtRrt2rXDl19+CXd3d0ydOhXx8fFO3wwVAPLz81FYWAidToeAgACXaroMMH5qvcrLy6HVanH33Xfrl+l0OuzZswd9+/a1aptMMk7KFZueAnUdz4y1bnrppZfkDk0Uxi8vY/EHBQWhf//+cocmSkuM39bfD5OMk9HpdPj++++xe/duqFQqeHh44A9/+ANCQ0Odvk5mwYIFyM7OxuDBgxu1bkpOTsYjjzzi9OM3MX55MX55SRa/1QPSkCRmzpwpfPLJJ8K+ffuEqVOnCt99952wY8cOISIiQli0aJHc4TXrpZdeErRabZPlGo1GCAkJkSEiyzB+eTF+eUkVP8cuczK//PIL4uLiEBwcjBkzZmDHjh0ICQnBN9984/QVil5eXrh+/XqT5fn5+U16cDsjxi8vxi8vqeJn6zIno9VqcePGDdx7771QqVRQq9UA6kYAcPbhWaKiohAREYGHHnqoUeumvLw8zJo1S+7wzGL88mL88pIqftbJOJlNmzZhwYIFePrpp3H8+HF89NFH6N69O9566y2MHz8er776qtwhNmv79u24ePEi3Nzc8MADDyAgIAA9evTA5s2b8dprr8kdnlmMX16MX15SxM/iMicTHh6Ob7/9FiEhIVi5ciUGDRqEzp07Y/PmzU6fYBISErBhwwaUlJRg1apV0Ol0ePbZZ+Hp6Ym1a9fKHZ5ZjF9ejF9eUsXv3OUvrVB+fj68vLz0vbbz8/P1z1VWVjp1n4f9+/dj8+bNcHd3x5tvvom3334bnp6eGDBggNPPhQMwfrkxfnlJFT+TjJMZM2YM8vLy9E0IG1IoFNi9e7dMkZknCIJ++ImHHnoI//3vf/HPf/4Tvr6+LjH5F+OXF+OXl2TxW90ujSRRVlYmhIWFCZmZmXKHYrGFCxcKI0aMEI4fP65flpmZKbzwwgvCX//6VxkjE4fxy4vxy0uq+N0++eSTT+yTB8kePD098eSTT2LTpk3o3bu33OFY5LnnnsP999+Pe+65Rz9awf3334/Q0FCo1WoEBQXJHGHzGL+8GL+8pIqfrcuIiEgybF1GRESSYZIhIiLJMMkQEZFkmGSIiEgyTDJERCSZ/w+/A0eAfReu7gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(lm_10X_vix.index, lm_10X_vix['VIX Close'], 'black')\n",
    "plt.plot(lm_10X_vix.index, lm_10X_vix['VIX High'], 'green')\n",
    "plt.plot(lm_10X_vix.index, lm_10X_vix['VIX Low'], 'red')\n",
    "plt.plot(lm_10X_vix.index, lm_10X_vix['VIX Open'], 'yellow')\n",
    "plt.title('VIX Rate')\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.ylabel('Rate');\n",
    "plt.show();\n",
    "\n",
    "plt.plot(lm_10X_vix.index, lm_10X_vix['N_Words'], 'blue')\n",
    "plt.title('N_Words')\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.ylabel('N_Words');\n",
    "plt.show();\n",
    "\n",
    "plt.plot(lm_10X_vix.index, lm_10X_vix['N_Unique_Words'], 'y')\n",
    "plt.title('N_Unique_Words')\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.ylabel('N_Unique_Words');\n",
    "plt.show();\n",
    "\n",
    "plt.plot(lm_10X_vix.index, lm_10X_vix['N_Negative'], 'r')\n",
    "plt.title('N_Negative')\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.ylabel('N_Negative');\n",
    "plt.show();\n",
    "\n",
    "plt.plot(lm_10X_vix.index, lm_10X_vix['N_Positive'], 'g')\n",
    "plt.title('N_Positive')\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.ylabel('N_Positive');\n",
    "plt.show();\n",
    "\n",
    "plt.plot(lm_10X_vix.index, lm_10X_vix['N_Uncertainty'], 'r')\n",
    "plt.title('N_Uncertainty')\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.ylabel('N_Uncertainty');\n",
    "plt.show();\n",
    "\n",
    "plt.plot(lm_10X_vix.index, lm_10X_vix['N_Litigious'], 'r')\n",
    "plt.title('N_Litigious')\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.ylabel('N_Litigious');\n",
    "plt.show();\n",
    "\n",
    "plt.plot(lm_10X_vix.index, lm_10X_vix['N_ModalWeak'], 'r')\n",
    "plt.title('N_ModalWeak')\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.ylabel('N_ModalWeak');\n",
    "plt.show();\n",
    "\n",
    "plt.plot(lm_10X_vix.index, lm_10X_vix['N_Modal_Moderate'], 'blue')\n",
    "plt.title('N_Modal_Moderate')\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.ylabel('N_Modal_Moderate');\n",
    "plt.show();\n",
    "\n",
    "plt.plot(lm_10X_vix.index, lm_10X_vix['N_Modal_Strong'], 'g')\n",
    "plt.title('N_Modal_Strong')\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.ylabel('N_Modal_Strong');\n",
    "plt.show();\n",
    "\n",
    "plt.plot(lm_10X_vix.index, lm_10X_vix['N_Constraining'], 'crimson')\n",
    "plt.title('N_Constraining')\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.ylabel('N_Constraining');\n",
    "plt.show();\n",
    "\n",
    "plt.plot(lm_10X_vix.index, lm_10X_vix['N_Negation'], 'tomato')\n",
    "plt.title('N_Negation')\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.ylabel('N_Negation');\n",
    "plt.show();\n",
    "#plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 1022399 entries, 1993-08-13 to 2018-12-31\n",
      "Data columns (total 15 columns):\n",
      "VIX Open            1022399 non-null float64\n",
      "VIX High            1022399 non-null float64\n",
      "VIX Low             1022399 non-null float64\n",
      "VIX Close           1022399 non-null float64\n",
      "N_Words             1022399 non-null int64\n",
      "N_Unique_Words      1022399 non-null int64\n",
      "N_Negative          1022399 non-null int64\n",
      "N_Positive          1022399 non-null int64\n",
      "N_Uncertainty       1022399 non-null int64\n",
      "N_Litigious         1022399 non-null int64\n",
      "N_ModalWeak         1022399 non-null int64\n",
      "N_Modal_Moderate    1022399 non-null int64\n",
      "N_Modal_Strong      1022399 non-null int64\n",
      "N_Constraining      1022399 non-null int64\n",
      "N_Negation          1022399 non-null int64\n",
      "dtypes: float64(4), int64(11)\n",
      "memory usage: 124.8 MB\n"
     ]
    }
   ],
   "source": [
    "#LSTM Prep\n",
    "df = lm_10X_vix\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>VIX Close</th>\n",
       "      <th>N_Words</th>\n",
       "      <th>N_Unique_Words</th>\n",
       "      <th>N_Negative</th>\n",
       "      <th>N_Positive</th>\n",
       "      <th>N_Uncertainty</th>\n",
       "      <th>N_Litigious</th>\n",
       "      <th>N_ModalWeak</th>\n",
       "      <th>N_Modal_Moderate</th>\n",
       "      <th>N_Modal_Strong</th>\n",
       "      <th>N_Constraining</th>\n",
       "      <th>N_Negation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1993-08-13</th>\n",
       "      <td>12.19</td>\n",
       "      <td>3037</td>\n",
       "      <td>700</td>\n",
       "      <td>68</td>\n",
       "      <td>6</td>\n",
       "      <td>23</td>\n",
       "      <td>40</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-08-13</th>\n",
       "      <td>12.19</td>\n",
       "      <td>4102</td>\n",
       "      <td>823</td>\n",
       "      <td>53</td>\n",
       "      <td>24</td>\n",
       "      <td>16</td>\n",
       "      <td>20</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-10-07</th>\n",
       "      <td>13.14</td>\n",
       "      <td>7278</td>\n",
       "      <td>1184</td>\n",
       "      <td>157</td>\n",
       "      <td>41</td>\n",
       "      <td>67</td>\n",
       "      <td>104</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>31</td>\n",
       "      <td>26</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-11-10</th>\n",
       "      <td>13.72</td>\n",
       "      <td>3768</td>\n",
       "      <td>739</td>\n",
       "      <td>69</td>\n",
       "      <td>13</td>\n",
       "      <td>23</td>\n",
       "      <td>50</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-11-12</th>\n",
       "      <td>13.33</td>\n",
       "      <td>3822</td>\n",
       "      <td>897</td>\n",
       "      <td>77</td>\n",
       "      <td>39</td>\n",
       "      <td>23</td>\n",
       "      <td>60</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>19</td>\n",
       "      <td>32</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            VIX Close  N_Words  N_Unique_Words  N_Negative  N_Positive  \\\n",
       "1993-08-13      12.19     3037             700          68           6   \n",
       "1993-08-13      12.19     4102             823          53          24   \n",
       "1993-10-07      13.14     7278            1184         157          41   \n",
       "1993-11-10      13.72     3768             739          69          13   \n",
       "1993-11-12      13.33     3822             897          77          39   \n",
       "\n",
       "            N_Uncertainty  N_Litigious  N_ModalWeak  N_Modal_Moderate  \\\n",
       "1993-08-13             23           40            6                 5   \n",
       "1993-08-13             16           20            7                 8   \n",
       "1993-10-07             67          104           15                15   \n",
       "1993-11-10             23           50            4                 7   \n",
       "1993-11-12             23           60            5                12   \n",
       "\n",
       "            N_Modal_Strong  N_Constraining  N_Negation  \n",
       "1993-08-13               9               6           1  \n",
       "1993-08-13               9               7           3  \n",
       "1993-10-07              31              26           5  \n",
       "1993-11-10              16               3           1  \n",
       "1993-11-12              19              32           2  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop(['VIX Open', 'VIX High', 'VIX Low'], axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x648 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cor = df.corr()\n",
    "import matplotlib.pyplot as plt\n",
    "sns.set(style=\"white\")\n",
    "f, ax = plt.subplots(figsize=(11, 9))\n",
    "\n",
    "cmap = sns.diverging_palette(220, 10, as_cmap=True)\n",
    "ax = sns.heatmap(cor, cmap=cmap, vmax=1, center=0,\n",
    "                square=True, linewidths=.5, cbar_kws={\"shrink\":.7})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VIX Open               19.26\n",
       "VIX High               19.80\n",
       "VIX Low                18.50\n",
       "VIX Close              19.19\n",
       "N_Words             10190.00\n",
       "N_Unique_Words       1352.00\n",
       "N_Negative            136.00\n",
       "N_Positive             54.00\n",
       "N_Uncertainty         109.00\n",
       "N_Litigious            85.00\n",
       "N_ModalWeak            30.00\n",
       "N_Modal_Moderate       30.00\n",
       "N_Modal_Strong         28.00\n",
       "N_Constraining         61.00\n",
       "N_Negation              7.00\n",
       "dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm_10X_vix.median()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LSTM Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from math import sqrt\n",
    "from numpy import concatenate\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from matplotlib import pyplot\n",
    "\n",
    "from pandas import read_csv\n",
    "from pandas import DataFrame\n",
    "from pandas import concat\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers import LSTM\n",
    "\n",
    "#convert series to supervised learning\n",
    "def series_to_supervised(data, n_in=1, n_out=1, dropnan=True):\n",
    "    n_vars = 1 if type(data) is list else data.shape[1]\n",
    "    df = DataFrame(data)\n",
    "    cols, names = list(), list()\n",
    "    # input sequence (t-n, ... t-1)\n",
    "    for i in range(n_in, 0, -1):\n",
    "        cols.append(df.shift(i))\n",
    "        names += [('var%d(t-%d)' % (j+1, i)) for j in range(n_vars)]\n",
    "    # forecast sequence (t, t+1, ... t+n)\n",
    "    for i in range(0, n_out):\n",
    "        cols.append(df.shift(-i))\n",
    "        if i == 0:\n",
    "            names += [('var%d(t)' % (j+1)) for j in range(n_vars)]\n",
    "        else:\n",
    "            names += [('vars%d(t+%d)' % (j+1, i)) for j in range(n_vars)]\n",
    "    # put it all together\n",
    "    agg = concat(cols, axis=1)\n",
    "    agg.columns = names\n",
    "    # drop rows with NaN values\n",
    "    if dropnan:\n",
    "        agg.dropna(inplace=True)\n",
    "    return agg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "def mean_absolute_percentage_error(y_true, y_pred):\n",
    "    y_true, y_pred = np.array(y_true), np.array(y_pred)\n",
    "    return np.mean(np.abs((y_true-y_pred)/y_true))*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>VIX Close</th>\n",
       "      <th>N_Words</th>\n",
       "      <th>N_Unique_Words</th>\n",
       "      <th>N_Negative</th>\n",
       "      <th>N_Positive</th>\n",
       "      <th>N_Uncertainty</th>\n",
       "      <th>N_Litigious</th>\n",
       "      <th>N_ModalWeak</th>\n",
       "      <th>N_Modal_Moderate</th>\n",
       "      <th>N_Modal_Strong</th>\n",
       "      <th>N_Constraining</th>\n",
       "      <th>N_Negation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1993-08-13</th>\n",
       "      <td>12.19</td>\n",
       "      <td>3037</td>\n",
       "      <td>700</td>\n",
       "      <td>68</td>\n",
       "      <td>6</td>\n",
       "      <td>23</td>\n",
       "      <td>40</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-08-13</th>\n",
       "      <td>12.19</td>\n",
       "      <td>4102</td>\n",
       "      <td>823</td>\n",
       "      <td>53</td>\n",
       "      <td>24</td>\n",
       "      <td>16</td>\n",
       "      <td>20</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-10-07</th>\n",
       "      <td>13.14</td>\n",
       "      <td>7278</td>\n",
       "      <td>1184</td>\n",
       "      <td>157</td>\n",
       "      <td>41</td>\n",
       "      <td>67</td>\n",
       "      <td>104</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>31</td>\n",
       "      <td>26</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-11-10</th>\n",
       "      <td>13.72</td>\n",
       "      <td>3768</td>\n",
       "      <td>739</td>\n",
       "      <td>69</td>\n",
       "      <td>13</td>\n",
       "      <td>23</td>\n",
       "      <td>50</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-11-12</th>\n",
       "      <td>13.33</td>\n",
       "      <td>3822</td>\n",
       "      <td>897</td>\n",
       "      <td>77</td>\n",
       "      <td>39</td>\n",
       "      <td>23</td>\n",
       "      <td>60</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>19</td>\n",
       "      <td>32</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            VIX Close  N_Words  N_Unique_Words  N_Negative  N_Positive  \\\n",
       "1993-08-13      12.19     3037             700          68           6   \n",
       "1993-08-13      12.19     4102             823          53          24   \n",
       "1993-10-07      13.14     7278            1184         157          41   \n",
       "1993-11-10      13.72     3768             739          69          13   \n",
       "1993-11-12      13.33     3822             897          77          39   \n",
       "\n",
       "            N_Uncertainty  N_Litigious  N_ModalWeak  N_Modal_Moderate  \\\n",
       "1993-08-13             23           40            6                 5   \n",
       "1993-08-13             16           20            7                 8   \n",
       "1993-10-07             67          104           15                15   \n",
       "1993-11-10             23           50            4                 7   \n",
       "1993-11-12             23           60            5                12   \n",
       "\n",
       "            N_Modal_Strong  N_Constraining  N_Negation  \n",
       "1993-08-13               9               6           1  \n",
       "1993-08-13               9               7           3  \n",
       "1993-10-07              31              26           5  \n",
       "1993-11-10              16               3           1  \n",
       "1993-11-12              19              32           2  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "values = df.values\n",
    "cols = df.columns.tolist()\n",
    "cols = cols[-1:] + cols[:-1]\n",
    "df = df[cols]\n",
    "df = df[['VIX Close', 'N_Words', 'N_Unique_Words', 'N_Negative', 'N_Positive', 'N_Uncertainty', 'N_Litigious', 'N_ModalWeak', 'N_Modal_Moderate', 'N_Modal_Strong', 'N_Constraining', 'N_Negation']]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "scaled = scaler.fit_transform(df.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_weeks = 12 #adding 12 weeks (= one quarter) lags creating number of observations\n",
    "n_features = 12 #features in the dataset\n",
    "n_obs = n_weeks*n_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>var1(t-12)</th>\n",
       "      <th>var2(t-12)</th>\n",
       "      <th>var3(t-12)</th>\n",
       "      <th>var4(t-12)</th>\n",
       "      <th>var5(t-12)</th>\n",
       "      <th>var6(t-12)</th>\n",
       "      <th>var7(t-12)</th>\n",
       "      <th>var8(t-12)</th>\n",
       "      <th>var9(t-12)</th>\n",
       "      <th>var10(t-12)</th>\n",
       "      <th>...</th>\n",
       "      <th>var3(t)</th>\n",
       "      <th>var4(t)</th>\n",
       "      <th>var5(t)</th>\n",
       "      <th>var6(t)</th>\n",
       "      <th>var7(t)</th>\n",
       "      <th>var8(t)</th>\n",
       "      <th>var9(t)</th>\n",
       "      <th>var10(t)</th>\n",
       "      <th>var11(t)</th>\n",
       "      <th>var12(t)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.042526</td>\n",
       "      <td>0.002158</td>\n",
       "      <td>0.084175</td>\n",
       "      <td>0.002345</td>\n",
       "      <td>0.000620</td>\n",
       "      <td>0.000114</td>\n",
       "      <td>0.000709</td>\n",
       "      <td>0.000945</td>\n",
       "      <td>0.001410</td>\n",
       "      <td>0.000909</td>\n",
       "      <td>...</td>\n",
       "      <td>0.307720</td>\n",
       "      <td>0.013276</td>\n",
       "      <td>0.020777</td>\n",
       "      <td>0.001155</td>\n",
       "      <td>0.011754</td>\n",
       "      <td>0.017173</td>\n",
       "      <td>0.009027</td>\n",
       "      <td>0.005050</td>\n",
       "      <td>0.007602</td>\n",
       "      <td>0.007610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.042526</td>\n",
       "      <td>0.002915</td>\n",
       "      <td>0.098966</td>\n",
       "      <td>0.001828</td>\n",
       "      <td>0.002481</td>\n",
       "      <td>0.000079</td>\n",
       "      <td>0.000355</td>\n",
       "      <td>0.001103</td>\n",
       "      <td>0.002257</td>\n",
       "      <td>0.000909</td>\n",
       "      <td>...</td>\n",
       "      <td>0.165705</td>\n",
       "      <td>0.004931</td>\n",
       "      <td>0.012611</td>\n",
       "      <td>0.000431</td>\n",
       "      <td>0.001135</td>\n",
       "      <td>0.002994</td>\n",
       "      <td>0.003667</td>\n",
       "      <td>0.001717</td>\n",
       "      <td>0.002924</td>\n",
       "      <td>0.000692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.055772</td>\n",
       "      <td>0.005173</td>\n",
       "      <td>0.142376</td>\n",
       "      <td>0.005414</td>\n",
       "      <td>0.004238</td>\n",
       "      <td>0.000332</td>\n",
       "      <td>0.001844</td>\n",
       "      <td>0.002363</td>\n",
       "      <td>0.004231</td>\n",
       "      <td>0.003131</td>\n",
       "      <td>...</td>\n",
       "      <td>0.066619</td>\n",
       "      <td>0.000828</td>\n",
       "      <td>0.001344</td>\n",
       "      <td>0.000069</td>\n",
       "      <td>0.000213</td>\n",
       "      <td>0.000158</td>\n",
       "      <td>0.000564</td>\n",
       "      <td>0.000707</td>\n",
       "      <td>0.000731</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.063859</td>\n",
       "      <td>0.002678</td>\n",
       "      <td>0.088865</td>\n",
       "      <td>0.002379</td>\n",
       "      <td>0.001344</td>\n",
       "      <td>0.000114</td>\n",
       "      <td>0.000886</td>\n",
       "      <td>0.000630</td>\n",
       "      <td>0.001975</td>\n",
       "      <td>0.001616</td>\n",
       "      <td>...</td>\n",
       "      <td>0.055796</td>\n",
       "      <td>0.000690</td>\n",
       "      <td>0.001240</td>\n",
       "      <td>0.000069</td>\n",
       "      <td>0.000248</td>\n",
       "      <td>0.000158</td>\n",
       "      <td>0.000282</td>\n",
       "      <td>0.000505</td>\n",
       "      <td>0.000244</td>\n",
       "      <td>0.000346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.058422</td>\n",
       "      <td>0.002716</td>\n",
       "      <td>0.107864</td>\n",
       "      <td>0.002655</td>\n",
       "      <td>0.004031</td>\n",
       "      <td>0.000114</td>\n",
       "      <td>0.001064</td>\n",
       "      <td>0.000788</td>\n",
       "      <td>0.003385</td>\n",
       "      <td>0.001919</td>\n",
       "      <td>...</td>\n",
       "      <td>0.044974</td>\n",
       "      <td>0.000241</td>\n",
       "      <td>0.000207</td>\n",
       "      <td>0.000020</td>\n",
       "      <td>0.000142</td>\n",
       "      <td>0.000315</td>\n",
       "      <td>0.000564</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000877</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 156 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    var1(t-12)  var2(t-12)  var3(t-12)  var4(t-12)  var5(t-12)  var6(t-12)  \\\n",
       "12    0.042526    0.002158    0.084175    0.002345    0.000620    0.000114   \n",
       "13    0.042526    0.002915    0.098966    0.001828    0.002481    0.000079   \n",
       "14    0.055772    0.005173    0.142376    0.005414    0.004238    0.000332   \n",
       "15    0.063859    0.002678    0.088865    0.002379    0.001344    0.000114   \n",
       "16    0.058422    0.002716    0.107864    0.002655    0.004031    0.000114   \n",
       "\n",
       "    var7(t-12)  var8(t-12)  var9(t-12)  var10(t-12)  ...   var3(t)   var4(t)  \\\n",
       "12    0.000709    0.000945    0.001410     0.000909  ...  0.307720  0.013276   \n",
       "13    0.000355    0.001103    0.002257     0.000909  ...  0.165705  0.004931   \n",
       "14    0.001844    0.002363    0.004231     0.003131  ...  0.066619  0.000828   \n",
       "15    0.000886    0.000630    0.001975     0.001616  ...  0.055796  0.000690   \n",
       "16    0.001064    0.000788    0.003385     0.001919  ...  0.044974  0.000241   \n",
       "\n",
       "     var5(t)   var6(t)   var7(t)   var8(t)   var9(t)  var10(t)  var11(t)  \\\n",
       "12  0.020777  0.001155  0.011754  0.017173  0.009027  0.005050  0.007602   \n",
       "13  0.012611  0.000431  0.001135  0.002994  0.003667  0.001717  0.002924   \n",
       "14  0.001344  0.000069  0.000213  0.000158  0.000564  0.000707  0.000731   \n",
       "15  0.001240  0.000069  0.000248  0.000158  0.000282  0.000505  0.000244   \n",
       "16  0.000207  0.000020  0.000142  0.000315  0.000564  0.000000  0.000877   \n",
       "\n",
       "    var12(t)  \n",
       "12  0.007610  \n",
       "13  0.000692  \n",
       "14  0.000000  \n",
       "15  0.000346  \n",
       "16  0.000000  \n",
       "\n",
       "[5 rows x 156 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reframed = series_to_supervised(scaled, n_weeks, 1)\n",
    "reframed.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    var1(t-12)  var2(t-12)  var3(t-12)  var4(t-12)  var5(t-12)  var6(t-12)  \\\n",
      "12    0.042526    0.002158    0.084175    0.002345    0.000620    0.000114   \n",
      "13    0.042526    0.002915    0.098966    0.001828    0.002481    0.000079   \n",
      "14    0.055772    0.005173    0.142376    0.005414    0.004238    0.000332   \n",
      "15    0.063859    0.002678    0.088865    0.002379    0.001344    0.000114   \n",
      "16    0.058422    0.002716    0.107864    0.002655    0.004031    0.000114   \n",
      "\n",
      "    var7(t-12)  var8(t-12)  var9(t-12)  var10(t-12)  ...   var3(t)   var4(t)  \\\n",
      "12    0.000709    0.000945    0.001410     0.000909  ...  0.307720  0.013276   \n",
      "13    0.000355    0.001103    0.002257     0.000909  ...  0.165705  0.004931   \n",
      "14    0.001844    0.002363    0.004231     0.003131  ...  0.066619  0.000828   \n",
      "15    0.000886    0.000630    0.001975     0.001616  ...  0.055796  0.000690   \n",
      "16    0.001064    0.000788    0.003385     0.001919  ...  0.044974  0.000241   \n",
      "\n",
      "     var5(t)   var6(t)   var7(t)   var8(t)   var9(t)  var10(t)  var11(t)  \\\n",
      "12  0.020777  0.001155  0.011754  0.017173  0.009027  0.005050  0.007602   \n",
      "13  0.012611  0.000431  0.001135  0.002994  0.003667  0.001717  0.002924   \n",
      "14  0.001344  0.000069  0.000213  0.000158  0.000564  0.000707  0.000731   \n",
      "15  0.001240  0.000069  0.000248  0.000158  0.000282  0.000505  0.000244   \n",
      "16  0.000207  0.000020  0.000142  0.000315  0.000564  0.000000  0.000877   \n",
      "\n",
      "    var12(t)  \n",
      "12  0.007610  \n",
      "13  0.000692  \n",
      "14  0.000000  \n",
      "15  0.000346  \n",
      "16  0.000000  \n",
      "\n",
      "[5 rows x 156 columns]\n"
     ]
    }
   ],
   "source": [
    "print(reframed.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(715680, 156)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "values = reframed.values\n",
    "n_train_days = 715680 #1022399*.7\n",
    "train = values[:n_train_days, :]\n",
    "test = values[n_train_days:, :]\n",
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split into input and outputs\n",
    "train_X, train_y = train[:, :n_obs], train[:, -n_features]\n",
    "test_X, test_y = test[:, :n_obs], test[:, -n_features]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(715680, 12, 12) (715680,) (306707, 12, 12) (306707,)\n"
     ]
    }
   ],
   "source": [
    "# reshape input to be 3D [samples, timesteps, features]\n",
    "train_X = train_X.reshape((train_X.shape[0], n_weeks, n_features))\n",
    "test_X = test_X.reshape((test_X.shape[0], n_weeks, n_features))\n",
    "print(train_X.shape, train_y.shape, test_X.shape, test_y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\Dennis Thumm\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n",
      "WARNING:tensorflow:From C:\\Users\\Dennis Thumm\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n",
      "Train on 715680 samples, validate on 306707 samples\n",
      "Epoch 1/50\n",
      " - 7s - loss: 0.1552 - mean_squared_error: 0.0414 - mean_absolute_percentage_error: 138.8796 - val_loss: 0.1568 - val_mean_squared_error: 0.0319 - val_mean_absolute_percentage_error: 378174.4899\n",
      "Epoch 2/50\n",
      " - 5s - loss: 0.1263 - mean_squared_error: 0.0288 - mean_absolute_percentage_error: 132.8446 - val_loss: 0.1497 - val_mean_squared_error: 0.0286 - val_mean_absolute_percentage_error: 359366.7857\n",
      "Epoch 3/50\n",
      " - 5s - loss: 0.1161 - mean_squared_error: 0.0243 - mean_absolute_percentage_error: 123.1018 - val_loss: 0.1370 - val_mean_squared_error: 0.0240 - val_mean_absolute_percentage_error: 330694.1172\n",
      "Epoch 4/50\n",
      " - 5s - loss: 0.1075 - mean_squared_error: 0.0208 - mean_absolute_percentage_error: 114.1055 - val_loss: 0.1267 - val_mean_squared_error: 0.0204 - val_mean_absolute_percentage_error: 306785.8419\n",
      "Epoch 5/50\n",
      " - 6s - loss: 0.0998 - mean_squared_error: 0.0179 - mean_absolute_percentage_error: 106.3150 - val_loss: 0.1175 - val_mean_squared_error: 0.0175 - val_mean_absolute_percentage_error: 285259.8267\n",
      "Epoch 6/50\n",
      " - 6s - loss: 0.0926 - mean_squared_error: 0.0154 - mean_absolute_percentage_error: 99.1411 - val_loss: 0.1089 - val_mean_squared_error: 0.0150 - val_mean_absolute_percentage_error: 264902.1760\n",
      "Epoch 7/50\n",
      " - 5s - loss: 0.0858 - mean_squared_error: 0.0132 - mean_absolute_percentage_error: 92.2314 - val_loss: 0.1004 - val_mean_squared_error: 0.0128 - val_mean_absolute_percentage_error: 244729.1814\n",
      "Epoch 8/50\n",
      " - 5s - loss: 0.0790 - mean_squared_error: 0.0111 - mean_absolute_percentage_error: 85.2957 - val_loss: 0.0916 - val_mean_squared_error: 0.0106 - val_mean_absolute_percentage_error: 223882.1342\n",
      "Epoch 9/50\n",
      " - 6s - loss: 0.0719 - mean_squared_error: 0.0091 - mean_absolute_percentage_error: 78.0350 - val_loss: 0.0820 - val_mean_squared_error: 0.0085 - val_mean_absolute_percentage_error: 201212.2054\n",
      "Epoch 10/50\n",
      " - 5s - loss: 0.0643 - mean_squared_error: 0.0072 - mean_absolute_percentage_error: 70.0962 - val_loss: 0.0710 - val_mean_squared_error: 0.0064 - val_mean_absolute_percentage_error: 175292.6337\n",
      "Epoch 11/50\n",
      " - 5s - loss: 0.0556 - mean_squared_error: 0.0054 - mean_absolute_percentage_error: 60.9075 - val_loss: 0.0572 - val_mean_squared_error: 0.0042 - val_mean_absolute_percentage_error: 143711.7429\n",
      "Epoch 12/50\n",
      " - 5s - loss: 0.0453 - mean_squared_error: 0.0035 - mean_absolute_percentage_error: 49.5524 - val_loss: 0.0391 - val_mean_squared_error: 0.0020 - val_mean_absolute_percentage_error: 102868.4048\n",
      "Epoch 13/50\n",
      " - 6s - loss: 0.0325 - mean_squared_error: 0.0019 - mean_absolute_percentage_error: 33.1386 - val_loss: 0.0193 - val_mean_squared_error: 6.3519e-04 - val_mean_absolute_percentage_error: 50841.5855\n",
      "Epoch 14/50\n",
      " - 5s - loss: 0.0224 - mean_squared_error: 0.0010 - mean_absolute_percentage_error: 17.3154 - val_loss: 0.0176 - val_mean_squared_error: 6.5039e-04 - val_mean_absolute_percentage_error: 22699.5904\n",
      "Epoch 15/50\n",
      " - 5s - loss: 0.0180 - mean_squared_error: 7.0415e-04 - mean_absolute_percentage_error: 15.4172 - val_loss: 0.0132 - val_mean_squared_error: 4.3271e-04 - val_mean_absolute_percentage_error: 28515.6966\n",
      "Epoch 16/50\n",
      " - 6s - loss: 0.0153 - mean_squared_error: 5.2795e-04 - mean_absolute_percentage_error: 13.8812 - val_loss: 0.0129 - val_mean_squared_error: 4.4859e-04 - val_mean_absolute_percentage_error: 24241.4655\n",
      "Epoch 17/50\n",
      " - 6s - loss: 0.0140 - mean_squared_error: 4.5816e-04 - mean_absolute_percentage_error: 12.8908 - val_loss: 0.0132 - val_mean_squared_error: 5.0488e-04 - val_mean_absolute_percentage_error: 20024.9394\n",
      "Epoch 18/50\n",
      " - 6s - loss: 0.0133 - mean_squared_error: 4.3758e-04 - mean_absolute_percentage_error: 12.4417 - val_loss: 0.0132 - val_mean_squared_error: 5.1190e-04 - val_mean_absolute_percentage_error: 20125.9181\n",
      "Epoch 19/50\n",
      " - 6s - loss: 0.0129 - mean_squared_error: 4.1484e-04 - mean_absolute_percentage_error: 12.0332 - val_loss: 0.0132 - val_mean_squared_error: 5.2472e-04 - val_mean_absolute_percentage_error: 18523.5978\n",
      "Epoch 20/50\n",
      " - 7s - loss: 0.0125 - mean_squared_error: 3.9431e-04 - mean_absolute_percentage_error: 11.6667 - val_loss: 0.0130 - val_mean_squared_error: 5.1257e-04 - val_mean_absolute_percentage_error: 18040.2515\n",
      "Epoch 21/50\n",
      " - 6s - loss: 0.0121 - mean_squared_error: 3.7291e-04 - mean_absolute_percentage_error: 11.3060 - val_loss: 0.0128 - val_mean_squared_error: 4.9725e-04 - val_mean_absolute_percentage_error: 17395.3402\n",
      "Epoch 22/50\n",
      " - 6s - loss: 0.0118 - mean_squared_error: 3.5199e-04 - mean_absolute_percentage_error: 10.9756 - val_loss: 0.0125 - val_mean_squared_error: 4.7921e-04 - val_mean_absolute_percentage_error: 16716.2813\n",
      "Epoch 23/50\n",
      " - 5s - loss: 0.0114 - mean_squared_error: 3.3189e-04 - mean_absolute_percentage_error: 10.6445 - val_loss: 0.0122 - val_mean_squared_error: 4.5660e-04 - val_mean_absolute_percentage_error: 16236.9306\n",
      "Epoch 24/50\n",
      " - 6s - loss: 0.0111 - mean_squared_error: 3.1239e-04 - mean_absolute_percentage_error: 10.3078 - val_loss: 0.0119 - val_mean_squared_error: 4.3478e-04 - val_mean_absolute_percentage_error: 15670.9944\n",
      "Epoch 25/50\n",
      " - 6s - loss: 0.0108 - mean_squared_error: 2.9380e-04 - mean_absolute_percentage_error: 9.9796 - val_loss: 0.0116 - val_mean_squared_error: 4.1209e-04 - val_mean_absolute_percentage_error: 15160.9834\n",
      "Epoch 26/50\n",
      " - 6s - loss: 0.0104 - mean_squared_error: 2.7632e-04 - mean_absolute_percentage_error: 9.6399 - val_loss: 0.0113 - val_mean_squared_error: 3.8844e-04 - val_mean_absolute_percentage_error: 14735.6131\n",
      "Epoch 27/50\n",
      " - 6s - loss: 0.0101 - mean_squared_error: 2.5961e-04 - mean_absolute_percentage_error: 9.2979 - val_loss: 0.0109 - val_mean_squared_error: 3.6476e-04 - val_mean_absolute_percentage_error: 14340.6043\n",
      "Epoch 28/50\n",
      " - 6s - loss: 0.0098 - mean_squared_error: 2.4379e-04 - mean_absolute_percentage_error: 8.9577 - val_loss: 0.0106 - val_mean_squared_error: 3.4100e-04 - val_mean_absolute_percentage_error: 13988.9678\n",
      "Epoch 29/50\n",
      " - 6s - loss: 0.0095 - mean_squared_error: 2.2896e-04 - mean_absolute_percentage_error: 8.6072 - val_loss: 0.0102 - val_mean_squared_error: 3.1650e-04 - val_mean_absolute_percentage_error: 13737.6928\n",
      "Epoch 30/50\n",
      " - 6s - loss: 0.0092 - mean_squared_error: 2.1511e-04 - mean_absolute_percentage_error: 8.2573 - val_loss: 0.0099 - val_mean_squared_error: 2.9229e-04 - val_mean_absolute_percentage_error: 13536.3315\n",
      "Epoch 31/50\n",
      " - 5s - loss: 0.0090 - mean_squared_error: 2.0207e-04 - mean_absolute_percentage_error: 7.9043 - val_loss: 0.0095 - val_mean_squared_error: 2.6819e-04 - val_mean_absolute_percentage_error: 13442.8669\n",
      "Epoch 32/50\n",
      " - 5s - loss: 0.0087 - mean_squared_error: 1.8999e-04 - mean_absolute_percentage_error: 7.5536 - val_loss: 0.0091 - val_mean_squared_error: 2.4488e-04 - val_mean_absolute_percentage_error: 13443.0661\n",
      "Epoch 33/50\n",
      " - 6s - loss: 0.0084 - mean_squared_error: 1.7891e-04 - mean_absolute_percentage_error: 7.2112 - val_loss: 0.0088 - val_mean_squared_error: 2.2307e-04 - val_mean_absolute_percentage_error: 13489.0189\n",
      "Epoch 34/50\n",
      " - 6s - loss: 0.0082 - mean_squared_error: 1.6859e-04 - mean_absolute_percentage_error: 6.8976 - val_loss: 0.0085 - val_mean_squared_error: 2.0320e-04 - val_mean_absolute_percentage_error: 13530.7055\n",
      "Epoch 35/50\n",
      " - 5s - loss: 0.0079 - mean_squared_error: 1.5943e-04 - mean_absolute_percentage_error: 6.6013 - val_loss: 0.0083 - val_mean_squared_error: 1.8439e-04 - val_mean_absolute_percentage_error: 13759.0753\n",
      "Epoch 36/50\n",
      " - 5s - loss: 0.0077 - mean_squared_error: 1.5054e-04 - mean_absolute_percentage_error: 6.3184 - val_loss: 0.0080 - val_mean_squared_error: 1.6777e-04 - val_mean_absolute_percentage_error: 13986.5424\n",
      "Epoch 37/50\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " - 5s - loss: 0.0075 - mean_squared_error: 1.4282e-04 - mean_absolute_percentage_error: 6.0463 - val_loss: 0.0079 - val_mean_squared_error: 1.5251e-04 - val_mean_absolute_percentage_error: 14495.3244\n",
      "Epoch 38/50\n",
      " - 5s - loss: 0.0072 - mean_squared_error: 1.3500e-04 - mean_absolute_percentage_error: 5.7858 - val_loss: 0.0077 - val_mean_squared_error: 1.3928e-04 - val_mean_absolute_percentage_error: 15015.0122\n",
      "Epoch 39/50\n",
      " - 5s - loss: 0.0069 - mean_squared_error: 1.2816e-04 - mean_absolute_percentage_error: 5.5483 - val_loss: 0.0077 - val_mean_squared_error: 1.2827e-04 - val_mean_absolute_percentage_error: 15752.5092\n",
      "Epoch 40/50\n",
      " - 5s - loss: 0.0067 - mean_squared_error: 1.2068e-04 - mean_absolute_percentage_error: 5.3309 - val_loss: 0.0076 - val_mean_squared_error: 1.1918e-04 - val_mean_absolute_percentage_error: 16375.4688\n",
      "Epoch 41/50\n",
      " - 5s - loss: 0.0064 - mean_squared_error: 1.1430e-04 - mean_absolute_percentage_error: 5.1466 - val_loss: 0.0076 - val_mean_squared_error: 1.1215e-04 - val_mean_absolute_percentage_error: 16983.1858\n",
      "Epoch 42/50\n",
      " - 5s - loss: 0.0061 - mean_squared_error: 1.0685e-04 - mean_absolute_percentage_error: 4.9958 - val_loss: 0.0075 - val_mean_squared_error: 1.0508e-04 - val_mean_absolute_percentage_error: 17039.4402\n",
      "Epoch 43/50\n",
      " - 5s - loss: 0.0059 - mean_squared_error: 1.0205e-04 - mean_absolute_percentage_error: 4.8706 - val_loss: 0.0074 - val_mean_squared_error: 9.9416e-05 - val_mean_absolute_percentage_error: 17140.3229\n",
      "Epoch 44/50\n",
      " - 5s - loss: 0.0056 - mean_squared_error: 9.5440e-05 - mean_absolute_percentage_error: 4.7758 - val_loss: 0.0071 - val_mean_squared_error: 9.1379e-05 - val_mean_absolute_percentage_error: 16389.4215\n",
      "Epoch 45/50\n",
      " - 5s - loss: 0.0054 - mean_squared_error: 9.2965e-05 - mean_absolute_percentage_error: 4.6642 - val_loss: 0.0069 - val_mean_squared_error: 8.6211e-05 - val_mean_absolute_percentage_error: 16150.9372\n",
      "Epoch 46/50\n",
      " - 5s - loss: 0.0053 - mean_squared_error: 8.7309e-05 - mean_absolute_percentage_error: 4.5900 - val_loss: 0.0065 - val_mean_squared_error: 7.7847e-05 - val_mean_absolute_percentage_error: 15014.8946\n",
      "Epoch 47/50\n",
      " - 5s - loss: 0.0051 - mean_squared_error: 8.7192e-05 - mean_absolute_percentage_error: 4.4668 - val_loss: 0.0064 - val_mean_squared_error: 7.4475e-05 - val_mean_absolute_percentage_error: 15025.0458\n",
      "Epoch 48/50\n",
      " - 5s - loss: 0.0050 - mean_squared_error: 8.1530e-05 - mean_absolute_percentage_error: 4.4325 - val_loss: 0.0058 - val_mean_squared_error: 6.5696e-05 - val_mean_absolute_percentage_error: 13482.6077\n",
      "Epoch 49/50\n",
      " - 5s - loss: 0.0048 - mean_squared_error: 8.3892e-05 - mean_absolute_percentage_error: 4.2700 - val_loss: 0.0060 - val_mean_squared_error: 6.5413e-05 - val_mean_absolute_percentage_error: 13981.9456\n",
      "Epoch 50/50\n",
      " - 5s - loss: 0.0048 - mean_squared_error: 7.7706e-05 - mean_absolute_percentage_error: 4.3130 - val_loss: 0.0050 - val_mean_squared_error: 5.4974e-05 - val_mean_absolute_percentage_error: 11477.7656\n"
     ]
    }
   ],
   "source": [
    "# design network\n",
    "model = Sequential()\n",
    "model.add(LSTM(5, input_shape=(train_X.shape[1], train_X.shape[2])))# number of units?, stateful=True?\n",
    "model.add(Dense(1)) #single output value\n",
    "model.compile(loss='mae', optimizer='adam', metrics=['mse','mape']) #metrics=['mse', 'mape'] to further check optimal epoch size\n",
    "# fit network\n",
    "history = model.fit(train_X, train_y, epochs=50, batch_size=21470, validation_data=(test_X, test_y), verbose=2, shuffle=False, validation_split=0.2)\n",
    "# plot history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['loss'], label='train')\n",
    "plt.plot(history.history['val_loss'], label='test')\n",
    "plt.legend\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['mean_absolute_percentage_error'], label='train')\n",
    "plt.plot(history.history['val_mean_absolute_percentage_error'], label='test')\n",
    "plt.legend\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test MSE: 0.283\n",
      "Test RMSE: 0.532\n",
      "Test MAE: 0.360\n",
      "Test MAPE: 2.300\n"
     ]
    }
   ],
   "source": [
    "# make a prediction\n",
    "yhat = model.predict(test_X)\n",
    "test_X = test_X.reshape((test_X.shape[0], n_weeks * n_features,))\n",
    "# invert scaling for forecast\n",
    "inv_yhat = concatenate((yhat, test_X[:, -11:]), axis=1) #-11 für Anzahl der Features (12-11=1)\n",
    "inv_yhat = scaler.inverse_transform(inv_yhat)\n",
    "inv_yhat = inv_yhat[:,0]\n",
    "# invert scaling for actual\n",
    "test_y = test_y.reshape((len(test_y), 1))\n",
    "inv_y = concatenate((test_y, test_X[:, -11:]), axis=1)\n",
    "inv_y = scaler.inverse_transform(inv_y)\n",
    "inv_y = inv_y[:,0]\n",
    "# calculate RMSE\n",
    "mse = (mean_squared_error(inv_y, inv_yhat))\n",
    "print('Test MSE: %.3f' % mse)\n",
    "rmse = sqrt(mean_squared_error(inv_y, inv_yhat))\n",
    "print('Test RMSE: %.3f' % rmse)\n",
    "mae =(mean_absolute_error(inv_y, inv_yhat))\n",
    "print('Test MAE: %.3f' % mae)\n",
    "mape =(mean_absolute_percentage_error(inv_y, inv_yhat))\n",
    "print('Test MAPE: %.3f' % mape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(inv_y, label='Real')#blue\n",
    "plt.plot(inv_yhat, label='Predicted')#orange\n",
    "plt.title('Real (blue) vs Predicted (orange) Close')\n",
    "plt.ylabel('VIX Rate')\n",
    "plt.xlabel('Epochs')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}