LM_10X_LSTM.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "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": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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": 7,
     "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": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <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",
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       "      <td>63255</td>\n",
       "      <td>25233</td>\n",
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       "    <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",
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       "      <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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      ],
      "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": 8,
     "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": 9,
   "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": 9,
     "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": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>FYE</th>\n",
       "      <th>FORM_TYPE</th>\n",
       "      <th>N_Words</th>\n",
       "      <th>N_Unique_Words</th>\n",
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       "      <th>N_Tables</th>\n",
       "      <th>N_Exhibits</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FILING_DATE</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",
       "    </tr>\n",
       "    <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": 10,
     "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": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1950-01-03</td>\n",
       "      <td>16.66</td>\n",
       "      <td>16.66</td>\n",
       "      <td>16.66</td>\n",
       "      <td>16.66</td>\n",
       "      <td>16.66</td>\n",
       "      <td>1260000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1950-01-04</td>\n",
       "      <td>16.85</td>\n",
       "      <td>16.85</td>\n",
       "      <td>16.85</td>\n",
       "      <td>16.85</td>\n",
       "      <td>16.85</td>\n",
       "      <td>1890000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1950-01-05</td>\n",
       "      <td>16.93</td>\n",
       "      <td>16.93</td>\n",
       "      <td>16.93</td>\n",
       "      <td>16.93</td>\n",
       "      <td>16.93</td>\n",
       "      <td>2550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1950-01-06</td>\n",
       "      <td>16.98</td>\n",
       "      <td>16.98</td>\n",
       "      <td>16.98</td>\n",
       "      <td>16.98</td>\n",
       "      <td>16.98</td>\n",
       "      <td>2010000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1950-01-09</td>\n",
       "      <td>17.08</td>\n",
       "      <td>17.08</td>\n",
       "      <td>17.08</td>\n",
       "      <td>17.08</td>\n",
       "      <td>17.08</td>\n",
       "      <td>2520000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date   Open   High    Low  Close  Adj Close   Volume\n",
       "0  1950-01-03  16.66  16.66  16.66  16.66      16.66  1260000\n",
       "1  1950-01-04  16.85  16.85  16.85  16.85      16.85  1890000\n",
       "2  1950-01-05  16.93  16.93  16.93  16.93      16.93  2550000\n",
       "3  1950-01-06  16.98  16.98  16.98  16.98      16.98  2010000\n",
       "4  1950-01-09  17.08  17.08  17.08  17.08      17.08  2520000"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"Z:\\Dokumente\\Studium\\Bachelorthesis\\S&P_500_Historical_Data.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1950-01-03</th>\n",
       "      <td>16.66</td>\n",
       "      <td>16.66</td>\n",
       "      <td>16.66</td>\n",
       "      <td>16.66</td>\n",
       "      <td>16.66</td>\n",
       "      <td>1260000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-01-04</th>\n",
       "      <td>16.85</td>\n",
       "      <td>16.85</td>\n",
       "      <td>16.85</td>\n",
       "      <td>16.85</td>\n",
       "      <td>16.85</td>\n",
       "      <td>1890000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-01-05</th>\n",
       "      <td>16.93</td>\n",
       "      <td>16.93</td>\n",
       "      <td>16.93</td>\n",
       "      <td>16.93</td>\n",
       "      <td>16.93</td>\n",
       "      <td>2550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-01-06</th>\n",
       "      <td>16.98</td>\n",
       "      <td>16.98</td>\n",
       "      <td>16.98</td>\n",
       "      <td>16.98</td>\n",
       "      <td>16.98</td>\n",
       "      <td>2010000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-01-09</th>\n",
       "      <td>17.08</td>\n",
       "      <td>17.08</td>\n",
       "      <td>17.08</td>\n",
       "      <td>17.08</td>\n",
       "      <td>17.08</td>\n",
       "      <td>2520000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Open   High    Low  Close  Adj Close   Volume\n",
       "Date                                                      \n",
       "1950-01-03  16.66  16.66  16.66  16.66      16.66  1260000\n",
       "1950-01-04  16.85  16.85  16.85  16.85      16.85  1890000\n",
       "1950-01-05  16.93  16.93  16.93  16.93      16.93  2550000\n",
       "1950-01-06  16.98  16.98  16.98  16.98      16.98  2010000\n",
       "1950-01-09  17.08  17.08  17.08  17.08      17.08  2520000"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index = pd.to_datetime(df.index)\n",
    "df = df.set_index('Date')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>N_Unique_Words</th>\n",
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       "      <th>NetFileSize</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1993-08-13</th>\n",
       "      <td>448.970001</td>\n",
       "      <td>450.250000</td>\n",
       "      <td>448.970001</td>\n",
       "      <td>450.140015</td>\n",
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       "      <td>19930630</td>\n",
       "      <td>10-Q</td>\n",
       "      <td>3037</td>\n",
       "      <td>700</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>448.970001</td>\n",
       "      <td>450.250000</td>\n",
       "      <td>448.970001</td>\n",
       "      <td>450.140015</td>\n",
       "      <td>450.140015</td>\n",
       "      <td>214370000</td>\n",
       "      <td>19930630</td>\n",
       "      <td>10-Q</td>\n",
       "      <td>4102</td>\n",
       "      <td>823</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>460.709991</td>\n",
       "      <td>461.130005</td>\n",
       "      <td>459.079987</td>\n",
       "      <td>459.179993</td>\n",
       "      <td>459.179993</td>\n",
       "      <td>255210000</td>\n",
       "      <td>19921231</td>\n",
       "      <td>10-K-A</td>\n",
       "      <td>7278</td>\n",
       "      <td>1184</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>460.399994</td>\n",
       "      <td>463.720001</td>\n",
       "      <td>459.570007</td>\n",
       "      <td>463.720001</td>\n",
       "      <td>463.720001</td>\n",
       "      <td>283450000</td>\n",
       "      <td>19930930</td>\n",
       "      <td>10-Q</td>\n",
       "      <td>3768</td>\n",
       "      <td>739</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-11-12</th>\n",
       "      <td>462.640015</td>\n",
       "      <td>465.839996</td>\n",
       "      <td>462.640015</td>\n",
       "      <td>465.390015</td>\n",
       "      <td>465.390015</td>\n",
       "      <td>326240000</td>\n",
       "      <td>19930930</td>\n",
       "      <td>10-Q</td>\n",
       "      <td>3822</td>\n",
       "      <td>897</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 × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Open        High         Low       Close   Adj Close  \\\n",
       "1993-08-13  448.970001  450.250000  448.970001  450.140015  450.140015   \n",
       "1993-08-13  448.970001  450.250000  448.970001  450.140015  450.140015   \n",
       "1993-10-07  460.709991  461.130005  459.079987  459.179993  459.179993   \n",
       "1993-11-10  460.399994  463.720001  459.570007  463.720001  463.720001   \n",
       "1993-11-12  462.640015  465.839996  462.640015  465.390015  465.390015   \n",
       "\n",
       "               Volume       FYE FORM_TYPE  N_Words  N_Unique_Words  ...  \\\n",
       "1993-08-13  214370000  19930630      10-Q     3037             700  ...   \n",
       "1993-08-13  214370000  19930630      10-Q     4102             823  ...   \n",
       "1993-10-07  255210000  19921231    10-K-A     7278            1184  ...   \n",
       "1993-11-10  283450000  19930930      10-Q     3768             739  ...   \n",
       "1993-11-12  326240000  19930930      10-Q     3822             897  ...   \n",
       "\n",
       "            N_Litigious  N_ModalWeak  N_Modal_Moderate  N_Modal_Strong  \\\n",
       "1993-08-13           40            6                 5               9   \n",
       "1993-08-13           20            7                 8               9   \n",
       "1993-10-07          104           15                15              31   \n",
       "1993-11-10           50            4                 7              16   \n",
       "1993-11-12           60            5                12              19   \n",
       "\n",
       "            N_Constraining  N_Negation  GrossFileSize  NetFileSize  N_Tables  \\\n",
       "1993-08-13               6           1          63255        25233         7   \n",
       "1993-08-13               7           3          57283        47739         2   \n",
       "1993-10-07              26           5         114136        56958        22   \n",
       "1993-11-10               3           1          72062        31256         8   \n",
       "1993-11-12              32           2          40392        39056         0   \n",
       "\n",
       "            N_Exhibits  \n",
       "1993-08-13           0  \n",
       "1993-08-13           3  \n",
       "1993-10-07           0  \n",
       "1993-11-10           0  \n",
       "1993-11-12           0  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm_10X_snp500 = pd.merge(df, lm_10X, how='inner', left_index=True, right_index=True)\n",
    "lm_10X_snp500.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>FYE</th>\n",
       "      <th>FORM_TYPE</th>\n",
       "      <th>N_Words</th>\n",
       "      <th>N_Unique_Words</th>\n",
       "      <th>...</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>1993-08-13</th>\n",
       "      <td>448.970001</td>\n",
       "      <td>450.250000</td>\n",
       "      <td>448.970001</td>\n",
       "      <td>450.140015</td>\n",
       "      <td>450.140015</td>\n",
       "      <td>214370000</td>\n",
       "      <td>19930630</td>\n",
       "      <td>10-Q</td>\n",
       "      <td>3037</td>\n",
       "      <td>700</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>448.970001</td>\n",
       "      <td>450.250000</td>\n",
       "      <td>448.970001</td>\n",
       "      <td>450.140015</td>\n",
       "      <td>450.140015</td>\n",
       "      <td>214370000</td>\n",
       "      <td>19930630</td>\n",
       "      <td>10-Q</td>\n",
       "      <td>4102</td>\n",
       "      <td>823</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>460.709991</td>\n",
       "      <td>461.130005</td>\n",
       "      <td>459.079987</td>\n",
       "      <td>459.179993</td>\n",
       "      <td>459.179993</td>\n",
       "      <td>255210000</td>\n",
       "      <td>19921231</td>\n",
       "      <td>10-K-A</td>\n",
       "      <td>7278</td>\n",
       "      <td>1184</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>460.399994</td>\n",
       "      <td>463.720001</td>\n",
       "      <td>459.570007</td>\n",
       "      <td>463.720001</td>\n",
       "      <td>463.720001</td>\n",
       "      <td>283450000</td>\n",
       "      <td>19930930</td>\n",
       "      <td>10-Q</td>\n",
       "      <td>3768</td>\n",
       "      <td>739</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-11-12</th>\n",
       "      <td>462.640015</td>\n",
       "      <td>465.839996</td>\n",
       "      <td>462.640015</td>\n",
       "      <td>465.390015</td>\n",
       "      <td>465.390015</td>\n",
       "      <td>326240000</td>\n",
       "      <td>19930930</td>\n",
       "      <td>10-Q</td>\n",
       "      <td>3822</td>\n",
       "      <td>897</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",
       "    <tr>\n",
       "      <th>1993-11-12</th>\n",
       "      <td>462.640015</td>\n",
       "      <td>465.839996</td>\n",
       "      <td>462.640015</td>\n",
       "      <td>465.390015</td>\n",
       "      <td>465.390015</td>\n",
       "      <td>326240000</td>\n",
       "      <td>19930930</td>\n",
       "      <td>10-Q</td>\n",
       "      <td>3450</td>\n",
       "      <td>848</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-11-12</th>\n",
       "      <td>462.640015</td>\n",
       "      <td>465.839996</td>\n",
       "      <td>462.640015</td>\n",
       "      <td>465.390015</td>\n",
       "      <td>465.390015</td>\n",
       "      <td>326240000</td>\n",
       "      <td>19930930</td>\n",
       "      <td>10-Q</td>\n",
       "      <td>3767</td>\n",
       "      <td>791</td>\n",
       "      <td>...</td>\n",
       "      <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>463.059998</td>\n",
       "      <td>464.829987</td>\n",
       "      <td>461.829987</td>\n",
       "      <td>461.899994</td>\n",
       "      <td>461.899994</td>\n",
       "      <td>272710000</td>\n",
       "      <td>19930831</td>\n",
       "      <td>10-K</td>\n",
       "      <td>18063</td>\n",
       "      <td>1942</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>463.929993</td>\n",
       "      <td>465.709991</td>\n",
       "      <td>462.709991</td>\n",
       "      <td>465.700012</td>\n",
       "      <td>465.700012</td>\n",
       "      <td>256580000</td>\n",
       "      <td>19930930</td>\n",
       "      <td>10-K</td>\n",
       "      <td>12826</td>\n",
       "      <td>1473</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",
       "      <td>166496</td>\n",
       "      <td>117190</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-12-14</th>\n",
       "      <td>465.730011</td>\n",
       "      <td>466.119995</td>\n",
       "      <td>462.459991</td>\n",
       "      <td>463.059998</td>\n",
       "      <td>463.059998</td>\n",
       "      <td>275050000</td>\n",
       "      <td>19931030</td>\n",
       "      <td>10-Q</td>\n",
       "      <td>1741</td>\n",
       "      <td>463</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>465.079987</td>\n",
       "      <td>467.380005</td>\n",
       "      <td>465.079987</td>\n",
       "      <td>467.320007</td>\n",
       "      <td>467.320007</td>\n",
       "      <td>272440000</td>\n",
       "      <td>19921231</td>\n",
       "      <td>10-K-A</td>\n",
       "      <td>3961</td>\n",
       "      <td>1043</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>465.079987</td>\n",
       "      <td>467.380005</td>\n",
       "      <td>465.079987</td>\n",
       "      <td>467.320007</td>\n",
       "      <td>467.320007</td>\n",
       "      <td>272440000</td>\n",
       "      <td>19930930</td>\n",
       "      <td>10-K</td>\n",
       "      <td>17366</td>\n",
       "      <td>1939</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>467.299988</td>\n",
       "      <td>468.970001</td>\n",
       "      <td>467.299988</td>\n",
       "      <td>467.380005</td>\n",
       "      <td>467.380005</td>\n",
       "      <td>227240000</td>\n",
       "      <td>19930930</td>\n",
       "      <td>10-K</td>\n",
       "      <td>29469</td>\n",
       "      <td>2559</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>465.440002</td>\n",
       "      <td>466.890015</td>\n",
       "      <td>464.440002</td>\n",
       "      <td>466.890015</td>\n",
       "      <td>466.890015</td>\n",
       "      <td>326600000</td>\n",
       "      <td>19921231</td>\n",
       "      <td>10-K-A</td>\n",
       "      <td>12415</td>\n",
       "      <td>1378</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>465.440002</td>\n",
       "      <td>466.890015</td>\n",
       "      <td>464.440002</td>\n",
       "      <td>466.890015</td>\n",
       "      <td>466.890015</td>\n",
       "      <td>326600000</td>\n",
       "      <td>19930930</td>\n",
       "      <td>10-Q-A</td>\n",
       "      <td>3212</td>\n",
       "      <td>554</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>465.440002</td>\n",
       "      <td>466.890015</td>\n",
       "      <td>464.440002</td>\n",
       "      <td>466.890015</td>\n",
       "      <td>466.890015</td>\n",
       "      <td>326600000</td>\n",
       "      <td>19931127</td>\n",
       "      <td>10-Q</td>\n",
       "      <td>1794</td>\n",
       "      <td>464</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>466.890015</td>\n",
       "      <td>467.820007</td>\n",
       "      <td>465.920013</td>\n",
       "      <td>467.549988</td>\n",
       "      <td>467.549988</td>\n",
       "      <td>400030000</td>\n",
       "      <td>19931127</td>\n",
       "      <td>10-Q</td>\n",
       "      <td>1295</td>\n",
       "      <td>374</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>466.890015</td>\n",
       "      <td>467.820007</td>\n",
       "      <td>465.920013</td>\n",
       "      <td>467.549988</td>\n",
       "      <td>467.549988</td>\n",
       "      <td>400030000</td>\n",
       "      <td>19931127</td>\n",
       "      <td>10-Q</td>\n",
       "      <td>1821</td>\n",
       "      <td>485</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>466.890015</td>\n",
       "      <td>467.820007</td>\n",
       "      <td>465.920013</td>\n",
       "      <td>467.549988</td>\n",
       "      <td>467.549988</td>\n",
       "      <td>400030000</td>\n",
       "      <td>19931121</td>\n",
       "      <td>10-Q</td>\n",
       "      <td>4448</td>\n",
       "      <td>811</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>467.549988</td>\n",
       "      <td>469.000000</td>\n",
       "      <td>467.019989</td>\n",
       "      <td>467.119995</td>\n",
       "      <td>467.119995</td>\n",
       "      <td>365960000</td>\n",
       "      <td>19930625</td>\n",
       "      <td>10-K-A</td>\n",
       "      <td>2739</td>\n",
       "      <td>612</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>467.549988</td>\n",
       "      <td>469.000000</td>\n",
       "      <td>467.019989</td>\n",
       "      <td>467.119995</td>\n",
       "      <td>467.119995</td>\n",
       "      <td>365960000</td>\n",
       "      <td>19930930</td>\n",
       "      <td>10-K</td>\n",
       "      <td>28446</td>\n",
       "      <td>2460</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>467.549988</td>\n",
       "      <td>469.000000</td>\n",
       "      <td>467.019989</td>\n",
       "      <td>467.119995</td>\n",
       "      <td>467.119995</td>\n",
       "      <td>365960000</td>\n",
       "      <td>19931126</td>\n",
       "      <td>10-Q</td>\n",
       "      <td>2425</td>\n",
       "      <td>617</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>467.549988</td>\n",
       "      <td>469.000000</td>\n",
       "      <td>467.019989</td>\n",
       "      <td>467.119995</td>\n",
       "      <td>467.119995</td>\n",
       "      <td>365960000</td>\n",
       "      <td>19931130</td>\n",
       "      <td>10-Q</td>\n",
       "      <td>2424</td>\n",
       "      <td>596</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>467.549988</td>\n",
       "      <td>469.000000</td>\n",
       "      <td>467.019989</td>\n",
       "      <td>467.119995</td>\n",
       "      <td>467.119995</td>\n",
       "      <td>365960000</td>\n",
       "      <td>19931127</td>\n",
       "      <td>10-Q</td>\n",
       "      <td>2022</td>\n",
       "      <td>504</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>467.089996</td>\n",
       "      <td>470.260010</td>\n",
       "      <td>467.029999</td>\n",
       "      <td>469.899994</td>\n",
       "      <td>469.899994</td>\n",
       "      <td>324920000</td>\n",
       "      <td>19930930</td>\n",
       "      <td>10-K-A</td>\n",
       "      <td>419</td>\n",
       "      <td>164</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>467.089996</td>\n",
       "      <td>470.260010</td>\n",
       "      <td>467.029999</td>\n",
       "      <td>469.899994</td>\n",
       "      <td>469.899994</td>\n",
       "      <td>324920000</td>\n",
       "      <td>19931130</td>\n",
       "      <td>10-Q</td>\n",
       "      <td>2105</td>\n",
       "      <td>530</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>467.089996</td>\n",
       "      <td>470.260010</td>\n",
       "      <td>467.029999</td>\n",
       "      <td>469.899994</td>\n",
       "      <td>469.899994</td>\n",
       "      <td>324920000</td>\n",
       "      <td>19931126</td>\n",
       "      <td>10-Q</td>\n",
       "      <td>963</td>\n",
       "      <td>294</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>467.089996</td>\n",
       "      <td>470.260010</td>\n",
       "      <td>467.029999</td>\n",
       "      <td>469.899994</td>\n",
       "      <td>469.899994</td>\n",
       "      <td>324920000</td>\n",
       "      <td>19931126</td>\n",
       "      <td>10-Q</td>\n",
       "      <td>2080</td>\n",
       "      <td>511</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>469.899994</td>\n",
       "      <td>475.269989</td>\n",
       "      <td>469.549988</td>\n",
       "      <td>475.269989</td>\n",
       "      <td>475.269989</td>\n",
       "      <td>319490000</td>\n",
       "      <td>19930930</td>\n",
       "      <td>10-K-A</td>\n",
       "      <td>5027</td>\n",
       "      <td>794</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>469.899994</td>\n",
       "      <td>475.269989</td>\n",
       "      <td>469.549988</td>\n",
       "      <td>475.269989</td>\n",
       "      <td>475.269989</td>\n",
       "      <td>319490000</td>\n",
       "      <td>19930930</td>\n",
       "      <td>10-Q-A</td>\n",
       "      <td>1851</td>\n",
       "      <td>416</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>2498.770020</td>\n",
       "      <td>2520.270020</td>\n",
       "      <td>2472.889893</td>\n",
       "      <td>2485.739990</td>\n",
       "      <td>2485.739990</td>\n",
       "      <td>3702620000</td>\n",
       "      <td>20180930</td>\n",
       "      <td>10-K</td>\n",
       "      <td>15958</td>\n",
       "      <td>1922</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>2498.770020</td>\n",
       "      <td>2520.270020</td>\n",
       "      <td>2472.889893</td>\n",
       "      <td>2485.739990</td>\n",
       "      <td>2485.739990</td>\n",
       "      <td>3702620000</td>\n",
       "      <td>20180930</td>\n",
       "      <td>10-K</td>\n",
       "      <td>16764</td>\n",
       "      <td>1758</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>2498.770020</td>\n",
       "      <td>2520.270020</td>\n",
       "      <td>2472.889893</td>\n",
       "      <td>2485.739990</td>\n",
       "      <td>2485.739990</td>\n",
       "      <td>3702620000</td>\n",
       "      <td>20180930</td>\n",
       "      <td>10-K</td>\n",
       "      <td>16814</td>\n",
       "      <td>2127</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>2498.770020</td>\n",
       "      <td>2520.270020</td>\n",
       "      <td>2472.889893</td>\n",
       "      <td>2485.739990</td>\n",
       "      <td>2485.739990</td>\n",
       "      <td>3702620000</td>\n",
       "      <td>20180630</td>\n",
       "      <td>10-K</td>\n",
       "      <td>29427</td>\n",
       "      <td>2773</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>2498.770020</td>\n",
       "      <td>2520.270020</td>\n",
       "      <td>2472.889893</td>\n",
       "      <td>2485.739990</td>\n",
       "      <td>2485.739990</td>\n",
       "      <td>3702620000</td>\n",
       "      <td>20181031</td>\n",
       "      <td>10-K</td>\n",
       "      <td>17858</td>\n",
       "      <td>1924</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>2498.770020</td>\n",
       "      <td>2520.270020</td>\n",
       "      <td>2472.889893</td>\n",
       "      <td>2485.739990</td>\n",
       "      <td>2485.739990</td>\n",
       "      <td>3702620000</td>\n",
       "      <td>20180930</td>\n",
       "      <td>10-K</td>\n",
       "      <td>24203</td>\n",
       "      <td>2137</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>2498.770020</td>\n",
       "      <td>2520.270020</td>\n",
       "      <td>2472.889893</td>\n",
       "      <td>2485.739990</td>\n",
       "      <td>2485.739990</td>\n",
       "      <td>3702620000</td>\n",
       "      <td>20180930</td>\n",
       "      <td>10-Q-A</td>\n",
       "      <td>1119</td>\n",
       "      <td>300</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>2498.770020</td>\n",
       "      <td>2520.270020</td>\n",
       "      <td>2472.889893</td>\n",
       "      <td>2485.739990</td>\n",
       "      <td>2485.739990</td>\n",
       "      <td>3702620000</td>\n",
       "      <td>20180930</td>\n",
       "      <td>10-Q</td>\n",
       "      <td>17341</td>\n",
       "      <td>1800</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>2498.770020</td>\n",
       "      <td>2520.270020</td>\n",
       "      <td>2472.889893</td>\n",
       "      <td>2485.739990</td>\n",
       "      <td>2485.739990</td>\n",
       "      <td>3702620000</td>\n",
       "      <td>20171231</td>\n",
       "      <td>10-Q</td>\n",
       "      <td>16224</td>\n",
       "      <td>1745</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>2498.770020</td>\n",
       "      <td>2520.270020</td>\n",
       "      <td>2472.889893</td>\n",
       "      <td>2485.739990</td>\n",
       "      <td>2485.739990</td>\n",
       "      <td>3702620000</td>\n",
       "      <td>20181031</td>\n",
       "      <td>10-Q</td>\n",
       "      <td>4541</td>\n",
       "      <td>1000</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>2498.770020</td>\n",
       "      <td>2520.270020</td>\n",
       "      <td>2472.889893</td>\n",
       "      <td>2485.739990</td>\n",
       "      <td>2485.739990</td>\n",
       "      <td>3702620000</td>\n",
       "      <td>20180930</td>\n",
       "      <td>10-Q</td>\n",
       "      <td>3590</td>\n",
       "      <td>804</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>2498.770020</td>\n",
       "      <td>2520.270020</td>\n",
       "      <td>2472.889893</td>\n",
       "      <td>2485.739990</td>\n",
       "      <td>2485.739990</td>\n",
       "      <td>3702620000</td>\n",
       "      <td>20181130</td>\n",
       "      <td>10-Q</td>\n",
       "      <td>10427</td>\n",
       "      <td>1499</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>2498.939941</td>\n",
       "      <td>2509.239990</td>\n",
       "      <td>2482.820068</td>\n",
       "      <td>2506.850098</td>\n",
       "      <td>2506.850098</td>\n",
       "      <td>3442870000</td>\n",
       "      <td>20180630</td>\n",
       "      <td>10-K-A</td>\n",
       "      <td>6800</td>\n",
       "      <td>1034</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>2498.939941</td>\n",
       "      <td>2509.239990</td>\n",
       "      <td>2482.820068</td>\n",
       "      <td>2506.850098</td>\n",
       "      <td>2506.850098</td>\n",
       "      <td>3442870000</td>\n",
       "      <td>20180930</td>\n",
       "      <td>10-K-A</td>\n",
       "      <td>398</td>\n",
       "      <td>144</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>2498.939941</td>\n",
       "      <td>2509.239990</td>\n",
       "      <td>2482.820068</td>\n",
       "      <td>2506.850098</td>\n",
       "      <td>2506.850098</td>\n",
       "      <td>3442870000</td>\n",
       "      <td>20180930</td>\n",
       "      <td>10-K</td>\n",
       "      <td>24891</td>\n",
       "      <td>2105</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>2498.939941</td>\n",
       "      <td>2509.239990</td>\n",
       "      <td>2482.820068</td>\n",
       "      <td>2506.850098</td>\n",
       "      <td>2506.850098</td>\n",
       "      <td>3442870000</td>\n",
       "      <td>20180930</td>\n",
       "      <td>10-K</td>\n",
       "      <td>32515</td>\n",
       "      <td>2714</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>2498.939941</td>\n",
       "      <td>2509.239990</td>\n",
       "      <td>2482.820068</td>\n",
       "      <td>2506.850098</td>\n",
       "      <td>2506.850098</td>\n",
       "      <td>3442870000</td>\n",
       "      <td>20180930</td>\n",
       "      <td>10-K</td>\n",
       "      <td>29142</td>\n",
       "      <td>2688</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>2498.939941</td>\n",
       "      <td>2509.239990</td>\n",
       "      <td>2482.820068</td>\n",
       "      <td>2506.850098</td>\n",
       "      <td>2506.850098</td>\n",
       "      <td>3442870000</td>\n",
       "      <td>20180930</td>\n",
       "      <td>10-K</td>\n",
       "      <td>17337</td>\n",
       "      <td>2249</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>2498.939941</td>\n",
       "      <td>2509.239990</td>\n",
       "      <td>2482.820068</td>\n",
       "      <td>2506.850098</td>\n",
       "      <td>2506.850098</td>\n",
       "      <td>3442870000</td>\n",
       "      <td>20180930</td>\n",
       "      <td>10-K</td>\n",
       "      <td>20448</td>\n",
       "      <td>2083</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>2498.939941</td>\n",
       "      <td>2509.239990</td>\n",
       "      <td>2482.820068</td>\n",
       "      <td>2506.850098</td>\n",
       "      <td>2506.850098</td>\n",
       "      <td>3442870000</td>\n",
       "      <td>20180930</td>\n",
       "      <td>10-K</td>\n",
       "      <td>10679</td>\n",
       "      <td>1504</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>2498.939941</td>\n",
       "      <td>2509.239990</td>\n",
       "      <td>2482.820068</td>\n",
       "      <td>2506.850098</td>\n",
       "      <td>2506.850098</td>\n",
       "      <td>3442870000</td>\n",
       "      <td>20180930</td>\n",
       "      <td>10-K</td>\n",
       "      <td>61971</td>\n",
       "      <td>3473</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>2498.939941</td>\n",
       "      <td>2509.239990</td>\n",
       "      <td>2482.820068</td>\n",
       "      <td>2506.850098</td>\n",
       "      <td>2506.850098</td>\n",
       "      <td>3442870000</td>\n",
       "      <td>20181031</td>\n",
       "      <td>10-K</td>\n",
       "      <td>31030</td>\n",
       "      <td>2726</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>2498.939941</td>\n",
       "      <td>2509.239990</td>\n",
       "      <td>2482.820068</td>\n",
       "      <td>2506.850098</td>\n",
       "      <td>2506.850098</td>\n",
       "      <td>3442870000</td>\n",
       "      <td>20180930</td>\n",
       "      <td>10-K</td>\n",
       "      <td>39547</td>\n",
       "      <td>3064</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>2498.939941</td>\n",
       "      <td>2509.239990</td>\n",
       "      <td>2482.820068</td>\n",
       "      <td>2506.850098</td>\n",
       "      <td>2506.850098</td>\n",
       "      <td>3442870000</td>\n",
       "      <td>20180930</td>\n",
       "      <td>10-Q-A</td>\n",
       "      <td>1157</td>\n",
       "      <td>334</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>2498.939941</td>\n",
       "      <td>2509.239990</td>\n",
       "      <td>2482.820068</td>\n",
       "      <td>2506.850098</td>\n",
       "      <td>2506.850098</td>\n",
       "      <td>3442870000</td>\n",
       "      <td>20180630</td>\n",
       "      <td>10-Q-A</td>\n",
       "      <td>18375</td>\n",
       "      <td>1892</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>2498.939941</td>\n",
       "      <td>2509.239990</td>\n",
       "      <td>2482.820068</td>\n",
       "      <td>2506.850098</td>\n",
       "      <td>2506.850098</td>\n",
       "      <td>3442870000</td>\n",
       "      <td>20180930</td>\n",
       "      <td>10-Q</td>\n",
       "      <td>4072</td>\n",
       "      <td>806</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>2498.939941</td>\n",
       "      <td>2509.239990</td>\n",
       "      <td>2482.820068</td>\n",
       "      <td>2506.850098</td>\n",
       "      <td>2506.850098</td>\n",
       "      <td>3442870000</td>\n",
       "      <td>20180930</td>\n",
       "      <td>10-Q</td>\n",
       "      <td>6403</td>\n",
       "      <td>1039</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>2498.939941</td>\n",
       "      <td>2509.239990</td>\n",
       "      <td>2482.820068</td>\n",
       "      <td>2506.850098</td>\n",
       "      <td>2506.850098</td>\n",
       "      <td>3442870000</td>\n",
       "      <td>20180930</td>\n",
       "      <td>10-Q</td>\n",
       "      <td>15359</td>\n",
       "      <td>1908</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>2498.939941</td>\n",
       "      <td>2509.239990</td>\n",
       "      <td>2482.820068</td>\n",
       "      <td>2506.850098</td>\n",
       "      <td>2506.850098</td>\n",
       "      <td>3442870000</td>\n",
       "      <td>20180930</td>\n",
       "      <td>10-Q</td>\n",
       "      <td>5021</td>\n",
       "      <td>868</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>2498.939941</td>\n",
       "      <td>2509.239990</td>\n",
       "      <td>2482.820068</td>\n",
       "      <td>2506.850098</td>\n",
       "      <td>2506.850098</td>\n",
       "      <td>3442870000</td>\n",
       "      <td>20180930</td>\n",
       "      <td>10-Q</td>\n",
       "      <td>5085</td>\n",
       "      <td>823</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>1022593 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Open         High          Low        Close    Adj Close  \\\n",
       "1993-08-13   448.970001   450.250000   448.970001   450.140015   450.140015   \n",
       "1993-08-13   448.970001   450.250000   448.970001   450.140015   450.140015   \n",
       "1993-10-07   460.709991   461.130005   459.079987   459.179993   459.179993   \n",
       "1993-11-10   460.399994   463.720001   459.570007   463.720001   463.720001   \n",
       "1993-11-12   462.640015   465.839996   462.640015   465.390015   465.390015   \n",
       "1993-11-12   462.640015   465.839996   462.640015   465.390015   465.390015   \n",
       "1993-11-12   462.640015   465.839996   462.640015   465.390015   465.390015   \n",
       "1993-11-29   463.059998   464.829987   461.829987   461.899994   461.899994   \n",
       "1993-12-13   463.929993   465.709991   462.709991   465.700012   465.700012   \n",
       "1993-12-14   465.730011   466.119995   462.459991   463.059998   463.059998   \n",
       "1993-12-22   465.079987   467.380005   465.079987   467.320007   467.320007   \n",
       "1993-12-22   465.079987   467.380005   465.079987   467.320007   467.320007   \n",
       "1993-12-23   467.299988   468.970001   467.299988   467.380005   467.380005   \n",
       "1994-01-04   465.440002   466.890015   464.440002   466.890015   466.890015   \n",
       "1994-01-04   465.440002   466.890015   464.440002   466.890015   466.890015   \n",
       "1994-01-04   465.440002   466.890015   464.440002   466.890015   466.890015   \n",
       "1994-01-05   466.890015   467.820007   465.920013   467.549988   467.549988   \n",
       "1994-01-05   466.890015   467.820007   465.920013   467.549988   467.549988   \n",
       "1994-01-05   466.890015   467.820007   465.920013   467.549988   467.549988   \n",
       "1994-01-06   467.549988   469.000000   467.019989   467.119995   467.119995   \n",
       "1994-01-06   467.549988   469.000000   467.019989   467.119995   467.119995   \n",
       "1994-01-06   467.549988   469.000000   467.019989   467.119995   467.119995   \n",
       "1994-01-06   467.549988   469.000000   467.019989   467.119995   467.119995   \n",
       "1994-01-06   467.549988   469.000000   467.019989   467.119995   467.119995   \n",
       "1994-01-07   467.089996   470.260010   467.029999   469.899994   469.899994   \n",
       "1994-01-07   467.089996   470.260010   467.029999   469.899994   469.899994   \n",
       "1994-01-07   467.089996   470.260010   467.029999   469.899994   469.899994   \n",
       "1994-01-07   467.089996   470.260010   467.029999   469.899994   469.899994   \n",
       "1994-01-10   469.899994   475.269989   469.549988   475.269989   475.269989   \n",
       "1994-01-10   469.899994   475.269989   469.549988   475.269989   475.269989   \n",
       "...                 ...          ...          ...          ...          ...   \n",
       "2018-12-28  2498.770020  2520.270020  2472.889893  2485.739990  2485.739990   \n",
       "2018-12-28  2498.770020  2520.270020  2472.889893  2485.739990  2485.739990   \n",
       "2018-12-28  2498.770020  2520.270020  2472.889893  2485.739990  2485.739990   \n",
       "2018-12-28  2498.770020  2520.270020  2472.889893  2485.739990  2485.739990   \n",
       "2018-12-28  2498.770020  2520.270020  2472.889893  2485.739990  2485.739990   \n",
       "2018-12-28  2498.770020  2520.270020  2472.889893  2485.739990  2485.739990   \n",
       "2018-12-28  2498.770020  2520.270020  2472.889893  2485.739990  2485.739990   \n",
       "2018-12-28  2498.770020  2520.270020  2472.889893  2485.739990  2485.739990   \n",
       "2018-12-28  2498.770020  2520.270020  2472.889893  2485.739990  2485.739990   \n",
       "2018-12-28  2498.770020  2520.270020  2472.889893  2485.739990  2485.739990   \n",
       "2018-12-28  2498.770020  2520.270020  2472.889893  2485.739990  2485.739990   \n",
       "2018-12-28  2498.770020  2520.270020  2472.889893  2485.739990  2485.739990   \n",
       "2018-12-31  2498.939941  2509.239990  2482.820068  2506.850098  2506.850098   \n",
       "2018-12-31  2498.939941  2509.239990  2482.820068  2506.850098  2506.850098   \n",
       "2018-12-31  2498.939941  2509.239990  2482.820068  2506.850098  2506.850098   \n",
       "2018-12-31  2498.939941  2509.239990  2482.820068  2506.850098  2506.850098   \n",
       "2018-12-31  2498.939941  2509.239990  2482.820068  2506.850098  2506.850098   \n",
       "2018-12-31  2498.939941  2509.239990  2482.820068  2506.850098  2506.850098   \n",
       "2018-12-31  2498.939941  2509.239990  2482.820068  2506.850098  2506.850098   \n",
       "2018-12-31  2498.939941  2509.239990  2482.820068  2506.850098  2506.850098   \n",
       "2018-12-31  2498.939941  2509.239990  2482.820068  2506.850098  2506.850098   \n",
       "2018-12-31  2498.939941  2509.239990  2482.820068  2506.850098  2506.850098   \n",
       "2018-12-31  2498.939941  2509.239990  2482.820068  2506.850098  2506.850098   \n",
       "2018-12-31  2498.939941  2509.239990  2482.820068  2506.850098  2506.850098   \n",
       "2018-12-31  2498.939941  2509.239990  2482.820068  2506.850098  2506.850098   \n",
       "2018-12-31  2498.939941  2509.239990  2482.820068  2506.850098  2506.850098   \n",
       "2018-12-31  2498.939941  2509.239990  2482.820068  2506.850098  2506.850098   \n",
       "2018-12-31  2498.939941  2509.239990  2482.820068  2506.850098  2506.850098   \n",
       "2018-12-31  2498.939941  2509.239990  2482.820068  2506.850098  2506.850098   \n",
       "2018-12-31  2498.939941  2509.239990  2482.820068  2506.850098  2506.850098   \n",
       "\n",
       "                Volume       FYE FORM_TYPE  N_Words  N_Unique_Words  ...  \\\n",
       "1993-08-13   214370000  19930630      10-Q     3037             700  ...   \n",
       "1993-08-13   214370000  19930630      10-Q     4102             823  ...   \n",
       "1993-10-07   255210000  19921231    10-K-A     7278            1184  ...   \n",
       "1993-11-10   283450000  19930930      10-Q     3768             739  ...   \n",
       "1993-11-12   326240000  19930930      10-Q     3822             897  ...   \n",
       "1993-11-12   326240000  19930930      10-Q     3450             848  ...   \n",
       "1993-11-12   326240000  19930930      10-Q     3767             791  ...   \n",
       "1993-11-29   272710000  19930831      10-K    18063            1942  ...   \n",
       "1993-12-13   256580000  19930930      10-K    12826            1473  ...   \n",
       "1993-12-14   275050000  19931030      10-Q     1741             463  ...   \n",
       "1993-12-22   272440000  19921231    10-K-A     3961            1043  ...   \n",
       "1993-12-22   272440000  19930930      10-K    17366            1939  ...   \n",
       "1993-12-23   227240000  19930930      10-K    29469            2559  ...   \n",
       "1994-01-04   326600000  19921231    10-K-A    12415            1378  ...   \n",
       "1994-01-04   326600000  19930930    10-Q-A     3212             554  ...   \n",
       "1994-01-04   326600000  19931127      10-Q     1794             464  ...   \n",
       "1994-01-05   400030000  19931127      10-Q     1295             374  ...   \n",
       "1994-01-05   400030000  19931127      10-Q     1821             485  ...   \n",
       "1994-01-05   400030000  19931121      10-Q     4448             811  ...   \n",
       "1994-01-06   365960000  19930625    10-K-A     2739             612  ...   \n",
       "1994-01-06   365960000  19930930      10-K    28446            2460  ...   \n",
       "1994-01-06   365960000  19931126      10-Q     2425             617  ...   \n",
       "1994-01-06   365960000  19931130      10-Q     2424             596  ...   \n",
       "1994-01-06   365960000  19931127      10-Q     2022             504  ...   \n",
       "1994-01-07   324920000  19930930    10-K-A      419             164  ...   \n",
       "1994-01-07   324920000  19931130      10-Q     2105             530  ...   \n",
       "1994-01-07   324920000  19931126      10-Q      963             294  ...   \n",
       "1994-01-07   324920000  19931126      10-Q     2080             511  ...   \n",
       "1994-01-10   319490000  19930930    10-K-A     5027             794  ...   \n",
       "1994-01-10   319490000  19930930    10-Q-A     1851             416  ...   \n",
       "...                ...       ...       ...      ...             ...  ...   \n",
       "2018-12-28  3702620000  20180930      10-K    15958            1922  ...   \n",
       "2018-12-28  3702620000  20180930      10-K    16764            1758  ...   \n",
       "2018-12-28  3702620000  20180930      10-K    16814            2127  ...   \n",
       "2018-12-28  3702620000  20180630      10-K    29427            2773  ...   \n",
       "2018-12-28  3702620000  20181031      10-K    17858            1924  ...   \n",
       "2018-12-28  3702620000  20180930      10-K    24203            2137  ...   \n",
       "2018-12-28  3702620000  20180930    10-Q-A     1119             300  ...   \n",
       "2018-12-28  3702620000  20180930      10-Q    17341            1800  ...   \n",
       "2018-12-28  3702620000  20171231      10-Q    16224            1745  ...   \n",
       "2018-12-28  3702620000  20181031      10-Q     4541            1000  ...   \n",
       "2018-12-28  3702620000  20180930      10-Q     3590             804  ...   \n",
       "2018-12-28  3702620000  20181130      10-Q    10427            1499  ...   \n",
       "2018-12-31  3442870000  20180630    10-K-A     6800            1034  ...   \n",
       "2018-12-31  3442870000  20180930    10-K-A      398             144  ...   \n",
       "2018-12-31  3442870000  20180930      10-K    24891            2105  ...   \n",
       "2018-12-31  3442870000  20180930      10-K    32515            2714  ...   \n",
       "2018-12-31  3442870000  20180930      10-K    29142            2688  ...   \n",
       "2018-12-31  3442870000  20180930      10-K    17337            2249  ...   \n",
       "2018-12-31  3442870000  20180930      10-K    20448            2083  ...   \n",
       "2018-12-31  3442870000  20180930      10-K    10679            1504  ...   \n",
       "2018-12-31  3442870000  20180930      10-K    61971            3473  ...   \n",
       "2018-12-31  3442870000  20181031      10-K    31030            2726  ...   \n",
       "2018-12-31  3442870000  20180930      10-K    39547            3064  ...   \n",
       "2018-12-31  3442870000  20180930    10-Q-A     1157             334  ...   \n",
       "2018-12-31  3442870000  20180630    10-Q-A    18375            1892  ...   \n",
       "2018-12-31  3442870000  20180930      10-Q     4072             806  ...   \n",
       "2018-12-31  3442870000  20180930      10-Q     6403            1039  ...   \n",
       "2018-12-31  3442870000  20180930      10-Q    15359            1908  ...   \n",
       "2018-12-31  3442870000  20180930      10-Q     5021             868  ...   \n",
       "2018-12-31  3442870000  20180930      10-Q     5085             823  ...   \n",
       "\n",
       "            N_Litigious  N_ModalWeak  N_Modal_Moderate  N_Modal_Strong  \\\n",
       "1993-08-13           40            6                 5               9   \n",
       "1993-08-13           20            7                 8               9   \n",
       "1993-10-07          104           15                15              31   \n",
       "1993-11-10           50            4                 7              16   \n",
       "1993-11-12           60            5                12              19   \n",
       "1993-11-12           43            9                 8               4   \n",
       "1993-11-12           31            8                10               7   \n",
       "1993-11-29          281           53                42              48   \n",
       "1993-12-13          132           20                34              65   \n",
       "1993-12-14           15            2                 8               5   \n",
       "1993-12-22           41            7                 6               6   \n",
       "1993-12-22          117           13                41              19   \n",
       "1993-12-23          663          109                32              50   \n",
       "1994-01-04           64           19                13              17   \n",
       "1994-01-04           12            1                 2               7   \n",
       "1994-01-04           14            1                 1               5   \n",
       "1994-01-05            8            2                 2               0   \n",
       "1994-01-05           13            0                 6               0   \n",
       "1994-01-05           56            2                12              14   \n",
       "1994-01-06           27            5                 4              11   \n",
       "1994-01-06          418          105                58              82   \n",
       "1994-01-06           33            6                 6              12   \n",
       "1994-01-06           13            8                 5               5   \n",
       "1994-01-06            9            0                 2               2   \n",
       "1994-01-07           12            0                 0               2   \n",
       "1994-01-07           26            2                 1               3   \n",
       "1994-01-07            7            1                 0               1   \n",
       "1994-01-07            6            2                 5               6   \n",
       "1994-01-10          231           50                 3               8   \n",
       "1994-01-10           13            4                 0               4   \n",
       "...                 ...          ...               ...             ...   \n",
       "2018-12-28          137           57                54              58   \n",
       "2018-12-28          110          150                64              94   \n",
       "2018-12-28          133          189                64              93   \n",
       "2018-12-28          569          234               104             148   \n",
       "2018-12-28          116           72               100              59   \n",
       "2018-12-28          109           41                88              91   \n",
       "2018-12-28           15            1                 2               1   \n",
       "2018-12-28          197           38                50              50   \n",
       "2018-12-28          326           59                49              53   \n",
       "2018-12-28           34           18                22              17   \n",
       "2018-12-28           24           11                18               9   \n",
       "2018-12-28           86           24                34              13   \n",
       "2018-12-31           89            4                18              14   \n",
       "2018-12-31            4            0                 0               2   \n",
       "2018-12-31          407          158                87              69   \n",
       "2018-12-31          436          267               111             120   \n",
       "2018-12-31          274          244               107             163   \n",
       "2018-12-31          112           45                62              79   \n",
       "2018-12-31          291          115                79             117   \n",
       "2018-12-31           87           27                32              49   \n",
       "2018-12-31          862          557               188             309   \n",
       "2018-12-31          326          355                85             112   \n",
       "2018-12-31          331          199               120             113   \n",
       "2018-12-31           11            2                 3               1   \n",
       "2018-12-31          117           63                92              83   \n",
       "2018-12-31           23           12                15              13   \n",
       "2018-12-31           45           33                25              24   \n",
       "2018-12-31          301           56                44              63   \n",
       "2018-12-31           32           20                27              16   \n",
       "2018-12-31           36           21                31              15   \n",
       "\n",
       "            N_Constraining  N_Negation  GrossFileSize  NetFileSize  N_Tables  \\\n",
       "1993-08-13               6           1          63255        25233         7   \n",
       "1993-08-13               7           3          57283        47739         2   \n",
       "1993-10-07              26           5         114136        56958        22   \n",
       "1993-11-10               3           1          72062        31256         8   \n",
       "1993-11-12              32           2          40392        39056         0   \n",
       "1993-11-12              23           3          51625        28738        11   \n",
       "1993-11-12               8           1          51346        41630         2   \n",
       "1993-11-29              88          14         278174       159453        35   \n",
       "1993-12-13              91           2         166496       117190        12   \n",
       "1993-12-14               9           1          28487        15442         6   \n",
       "1993-12-22              29           1          39588        37928         0   \n",
       "1993-12-22              63           7         249191       145992        63   \n",
       "1993-12-23             156          22         272459       267989         0   \n",
       "1994-01-04              60           2         142125       135660         1   \n",
       "1994-01-04              15           0          42771        39727         0   \n",
       "1994-01-04               5           1          37094        15718         7   \n",
       "1994-01-05              18           0          34027        13201        10   \n",
       "1994-01-05               7           0          36908        19026         8   \n",
       "1994-01-05              18           1          64490        40774         8   \n",
       "1994-01-06               6           1          35381        23171         7   \n",
       "1994-01-06             124          10         307627       245092        34   \n",
       "1994-01-06               7           1          37766        21502         4   \n",
       "1994-01-06              20           0          36160        20715         6   \n",
       "1994-01-06               8           1          41020        18431         6   \n",
       "1994-01-07               4           0          10600         5701         1   \n",
       "1994-01-07               9           0          26581        25674         0   \n",
       "1994-01-07               9           0          27550         9539         4   \n",
       "1994-01-07              15           0          28468        23478         2   \n",
       "1994-01-10              19           1          43215        41547         0   \n",
       "1994-01-10               4           1         155906        20252        28   \n",
       "...                    ...         ...            ...          ...       ...   \n",
       "2018-12-28             115          12        2560244       117555        61   \n",
       "2018-12-28             119          14        1723886       126710        34   \n",
       "2018-12-28             132          14        1542111       121936        15   \n",
       "2018-12-28             264          31        6049527       206478        56   \n",
       "2018-12-28              91          13         220686       132285        31   \n",
       "2018-12-28             182           7        1727342       191651        98   \n",
       "2018-12-28               7           1        4613628        10389         4   \n",
       "2018-12-28              71          11        5177207       134480        22   \n",
       "2018-12-28             118           8        2915584       122833        45   \n",
       "2018-12-28              38           4         710478        36346         6   \n",
       "2018-12-28              20           5         142206        27790        20   \n",
       "2018-12-28              44           5        4844151        78991        26   \n",
       "2018-12-31              25           3         257945        54351        16   \n",
       "2018-12-31               6           0        5251231         3572         3   \n",
       "2018-12-31             118          16       21324002       178865        82   \n",
       "2018-12-31             363          21        7863936       241475        48   \n",
       "2018-12-31             268          23        4711653       213064        31   \n",
       "2018-12-31              84          14        3760744       133599        20   \n",
       "2018-12-31             147          15        2536971       152534        16   \n",
       "2018-12-31              53          11        1325731        83282        14   \n",
       "2018-12-31             477          40        6940663       448617        27   \n",
       "2018-12-31             266          21        2035116       227041        92   \n",
       "2018-12-31             278          22        9056058       304574       183   \n",
       "2018-12-31               8           1         390270         9957         6   \n",
       "2018-12-31              76          12        5759717       137309        72   \n",
       "2018-12-31              26           3        1106870        33975        11   \n",
       "2018-12-31              40           9        1092655        50263        10   \n",
       "2018-12-31             103          10        2175983       114219        15   \n",
       "2018-12-31              33           4         814265        42154        10   \n",
       "2018-12-31              30           4        1041360        41757        16   \n",
       "\n",
       "            N_Exhibits  \n",
       "1993-08-13           0  \n",
       "1993-08-13           3  \n",
       "1993-10-07           0  \n",
       "1993-11-10           0  \n",
       "1993-11-12           0  \n",
       "1993-11-12           0  \n",
       "1993-11-12           3  \n",
       "1993-11-29           2  \n",
       "1993-12-13           1  \n",
       "1993-12-14           0  \n",
       "1993-12-22           0  \n",
       "1993-12-22           1  \n",
       "1993-12-23           8  \n",
       "1994-01-04           1  \n",
       "1994-01-04           0  \n",
       "1994-01-04           1  \n",
       "1994-01-05           0  \n",
       "1994-01-05           2  \n",
       "1994-01-05           0  \n",
       "1994-01-06           0  \n",
       "1994-01-06           0  \n",
       "1994-01-06           0  \n",
       "1994-01-06           0  \n",
       "1994-01-06           1  \n",
       "1994-01-07           0  \n",
       "1994-01-07           0  \n",
       "1994-01-07           0  \n",
       "1994-01-07           1  \n",
       "1994-01-10           1  \n",
       "1994-01-10           0  \n",
       "...                ...  \n",
       "2018-12-28           9  \n",
       "2018-12-28           8  \n",
       "2018-12-28           8  \n",
       "2018-12-28          19  \n",
       "2018-12-28           3  \n",
       "2018-12-28           6  \n",
       "2018-12-28           8  \n",
       "2018-12-28          10  \n",
       "2018-12-28          10  \n",
       "2018-12-28           8  \n",
       "2018-12-28           2  \n",
       "2018-12-28          10  \n",
       "2018-12-31           5  \n",
       "2018-12-31           6  \n",
       "2018-12-31          13  \n",
       "2018-12-31          13  \n",
       "2018-12-31          11  \n",
       "2018-12-31           8  \n",
       "2018-12-31          12  \n",
       "2018-12-31           8  \n",
       "2018-12-31           9  \n",
       "2018-12-31          11  \n",
       "2018-12-31          12  \n",
       "2018-12-31           8  \n",
       "2018-12-31           9  \n",
       "2018-12-31           8  \n",
       "2018-12-31          10  \n",
       "2018-12-31          11  \n",
       "2018-12-31          10  \n",
       "2018-12-31          10  \n",
       "\n",
       "[1022593 rows x 23 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm_10X_snp500"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</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>448.970001</td>\n",
       "      <td>450.250000</td>\n",
       "      <td>448.970001</td>\n",
       "      <td>450.140015</td>\n",
       "      <td>450.140015</td>\n",
       "      <td>214370000</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>448.970001</td>\n",
       "      <td>450.250000</td>\n",
       "      <td>448.970001</td>\n",
       "      <td>450.140015</td>\n",
       "      <td>450.140015</td>\n",
       "      <td>214370000</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>460.709991</td>\n",
       "      <td>461.130005</td>\n",
       "      <td>459.079987</td>\n",
       "      <td>459.179993</td>\n",
       "      <td>459.179993</td>\n",
       "      <td>255210000</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>460.399994</td>\n",
       "      <td>463.720001</td>\n",
       "      <td>459.570007</td>\n",
       "      <td>463.720001</td>\n",
       "      <td>463.720001</td>\n",
       "      <td>283450000</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>462.640015</td>\n",
       "      <td>465.839996</td>\n",
       "      <td>462.640015</td>\n",
       "      <td>465.390015</td>\n",
       "      <td>465.390015</td>\n",
       "      <td>326240000</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": [
       "                  Open        High         Low       Close   Adj Close  \\\n",
       "1993-08-13  448.970001  450.250000  448.970001  450.140015  450.140015   \n",
       "1993-08-13  448.970001  450.250000  448.970001  450.140015  450.140015   \n",
       "1993-10-07  460.709991  461.130005  459.079987  459.179993  459.179993   \n",
       "1993-11-10  460.399994  463.720001  459.570007  463.720001  463.720001   \n",
       "1993-11-12  462.640015  465.839996  462.640015  465.390015  465.390015   \n",
       "\n",
       "               Volume  N_Words  N_Unique_Words  N_Negative  N_Positive  \\\n",
       "1993-08-13  214370000     3037             700          68           6   \n",
       "1993-08-13  214370000     4102             823          53          24   \n",
       "1993-10-07  255210000     7278            1184         157          41   \n",
       "1993-11-10  283450000     3768             739          69          13   \n",
       "1993-11-12  326240000     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": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm_10X_snp500 = lm_10X_snp500.drop(['FYE','FORM_TYPE','GrossFileSize', 'NetFileSize', 'N_Tables', 'N_Exhibits'], axis=1)\n",
    "lm_10X_snp500.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 1022593 entries, 1993-08-13 to 2018-12-31\n",
      "Data columns (total 17 columns):\n",
      "Open                1022593 non-null float64\n",
      "High                1022593 non-null float64\n",
      "Low                 1022593 non-null float64\n",
      "Close               1022593 non-null float64\n",
      "Adj Close           1022593 non-null float64\n",
      "Volume              1022593 non-null int64\n",
      "N_Words             1022593 non-null int64\n",
      "N_Unique_Words      1022593 non-null int64\n",
      "N_Negative          1022593 non-null int64\n",
      "N_Positive          1022593 non-null int64\n",
      "N_Uncertainty       1022593 non-null int64\n",
      "N_Litigious         1022593 non-null int64\n",
      "N_ModalWeak         1022593 non-null int64\n",
      "N_Modal_Moderate    1022593 non-null int64\n",
      "N_Modal_Strong      1022593 non-null int64\n",
      "N_Constraining      1022593 non-null int64\n",
      "N_Negation          1022593 non-null int64\n",
      "dtypes: float64(5), int64(12)\n",
      "memory usage: 140.4 MB\n"
     ]
    }
   ],
   "source": [
    "lm_10X_snp500.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 17 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "values = lm_10X_snp500.values\n",
    "groups = [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]\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_snp500.columns[group], y=.5, loc='right')\n",
    "    i += 1\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</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>1.022593e+06</td>\n",
       "      <td>1.022593e+06</td>\n",
       "      <td>1.022593e+06</td>\n",
       "      <td>1.022593e+06</td>\n",
       "      <td>1.022593e+06</td>\n",
       "      <td>1.022593e+06</td>\n",
       "      <td>1.022593e+06</td>\n",
       "      <td>1.022593e+06</td>\n",
       "      <td>1.022593e+06</td>\n",
       "      <td>1.022593e+06</td>\n",
       "      <td>1.022593e+06</td>\n",
       "      <td>1.022593e+06</td>\n",
       "      <td>1.022593e+06</td>\n",
       "      <td>1.022593e+06</td>\n",
       "      <td>1.022593e+06</td>\n",
       "      <td>1.022593e+06</td>\n",
       "      <td>1.022593e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.311059e+03</td>\n",
       "      <td>1.319625e+03</td>\n",
       "      <td>1.302412e+03</td>\n",
       "      <td>1.311721e+03</td>\n",
       "      <td>1.311721e+03</td>\n",
       "      <td>2.368351e+09</td>\n",
       "      <td>1.813307e+04</td>\n",
       "      <td>1.535070e+03</td>\n",
       "      <td>2.994437e+02</td>\n",
       "      <td>1.109818e+02</td>\n",
       "      <td>2.014528e+02</td>\n",
       "      <td>3.312975e+02</td>\n",
       "      <td>8.423879e+01</td>\n",
       "      <td>4.940433e+01</td>\n",
       "      <td>5.629233e+01</td>\n",
       "      <td>1.411584e+02</td>\n",
       "      <td>1.522095e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.680510e+02</td>\n",
       "      <td>4.689824e+02</td>\n",
       "      <td>4.667529e+02</td>\n",
       "      <td>4.680317e+02</td>\n",
       "      <td>4.680317e+02</td>\n",
       "      <td>1.704995e+09</td>\n",
       "      <td>2.612568e+04</td>\n",
       "      <td>9.048268e+02</td>\n",
       "      <td>4.846705e+02</td>\n",
       "      <td>1.650993e+02</td>\n",
       "      <td>3.328384e+02</td>\n",
       "      <td>8.453337e+02</td>\n",
       "      <td>1.412381e+02</td>\n",
       "      <td>6.107526e+01</td>\n",
       "      <td>9.162540e+01</td>\n",
       "      <td>2.672834e+02</td>\n",
       "      <td>3.170178e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.391400e+02</td>\n",
       "      <td>4.447200e+02</td>\n",
       "      <td>4.358600e+02</td>\n",
       "      <td>4.389200e+02</td>\n",
       "      <td>4.389200e+02</td>\n",
       "      <td>1.499000e+07</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>1.058560e+03</td>\n",
       "      <td>1.067580e+03</td>\n",
       "      <td>1.052960e+03</td>\n",
       "      <td>1.060870e+03</td>\n",
       "      <td>1.060870e+03</td>\n",
       "      <td>9.243000e+08</td>\n",
       "      <td>4.507000e+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>1.234720e+03</td>\n",
       "      <td>1.239030e+03</td>\n",
       "      <td>1.226200e+03</td>\n",
       "      <td>1.233760e+03</td>\n",
       "      <td>1.233760e+03</td>\n",
       "      <td>1.816900e+09</td>\n",
       "      <td>1.018900e+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>1.425280e+03</td>\n",
       "      <td>1.440160e+03</td>\n",
       "      <td>1.416540e+03</td>\n",
       "      <td>1.427840e+03</td>\n",
       "      <td>1.427840e+03</td>\n",
       "      <td>3.635170e+09</td>\n",
       "      <td>2.141600e+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>2.936760e+03</td>\n",
       "      <td>2.940910e+03</td>\n",
       "      <td>2.927110e+03</td>\n",
       "      <td>2.930750e+03</td>\n",
       "      <td>2.930750e+03</td>\n",
       "      <td>1.145623e+10</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": [
       "               Open          High           Low         Close     Adj Close  \\\n",
       "count  1.022593e+06  1.022593e+06  1.022593e+06  1.022593e+06  1.022593e+06   \n",
       "mean   1.311059e+03  1.319625e+03  1.302412e+03  1.311721e+03  1.311721e+03   \n",
       "std    4.680510e+02  4.689824e+02  4.667529e+02  4.680317e+02  4.680317e+02   \n",
       "min    4.391400e+02  4.447200e+02  4.358600e+02  4.389200e+02  4.389200e+02   \n",
       "25%    1.058560e+03  1.067580e+03  1.052960e+03  1.060870e+03  1.060870e+03   \n",
       "50%    1.234720e+03  1.239030e+03  1.226200e+03  1.233760e+03  1.233760e+03   \n",
       "75%    1.425280e+03  1.440160e+03  1.416540e+03  1.427840e+03  1.427840e+03   \n",
       "max    2.936760e+03  2.940910e+03  2.927110e+03  2.930750e+03  2.930750e+03   \n",
       "\n",
       "             Volume       N_Words  N_Unique_Words    N_Negative    N_Positive  \\\n",
       "count  1.022593e+06  1.022593e+06    1.022593e+06  1.022593e+06  1.022593e+06   \n",
       "mean   2.368351e+09  1.813307e+04    1.535070e+03  2.994437e+02  1.109818e+02   \n",
       "std    1.704995e+09  2.612568e+04    9.048268e+02  4.846705e+02  1.650993e+02   \n",
       "min    1.499000e+07  0.000000e+00    0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "25%    9.243000e+08  4.507000e+03    8.420000e+02  5.400000e+01  2.000000e+01   \n",
       "50%    1.816900e+09  1.018900e+04    1.352000e+03  1.360000e+02  5.400000e+01   \n",
       "75%    3.635170e+09  2.141600e+04    2.042000e+03  3.450000e+02  1.340000e+02   \n",
       "max    1.145623e+10  1.407044e+06    8.316000e+03  2.900000e+04  9.674000e+03   \n",
       "\n",
       "       N_Uncertainty   N_Litigious   N_ModalWeak  N_Modal_Moderate  \\\n",
       "count   1.022593e+06  1.022593e+06  1.022593e+06      1.022593e+06   \n",
       "mean    2.014528e+02  3.312975e+02  8.423879e+01      4.940433e+01   \n",
       "std     3.328384e+02  8.453337e+02  1.412381e+02      6.107526e+01   \n",
       "min     0.000000e+00  0.000000e+00  0.000000e+00      0.000000e+00   \n",
       "25%     4.200000e+01  3.000000e+01  1.100000e+01      1.200000e+01   \n",
       "50%     1.090000e+02  8.500000e+01  3.000000e+01      3.000000e+01   \n",
       "75%     2.500000e+02  2.730000e+02  9.400000e+01      6.300000e+01   \n",
       "max     2.016710e+05  5.640400e+04  6.347000e+03      3.545000e+03   \n",
       "\n",
       "       N_Modal_Strong  N_Constraining    N_Negation  \n",
       "count    1.022593e+06    1.022593e+06  1.022593e+06  \n",
       "mean     5.629233e+01    1.411584e+02  1.522095e+01  \n",
       "std      9.162540e+01    2.672834e+02  3.170178e+01  \n",
       "min      0.000000e+00    0.000000e+00  0.000000e+00  \n",
       "25%      1.100000e+01    2.400000e+01  3.000000e+00  \n",
       "50%      2.800000e+01    6.100000e+01  7.000000e+00  \n",
       "75%      6.800000e+01    1.510000e+02  1.500000e+01  \n",
       "max      9.901000e+03    2.052100e+04  2.891000e+03  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm_10X_snp500.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</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>Open</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.999823</td>\n",
       "      <td>0.999746</td>\n",
       "      <td>0.999533</td>\n",
       "      <td>0.999533</td>\n",
       "      <td>0.383286</td>\n",
       "      <td>0.143294</td>\n",
       "      <td>0.237492</td>\n",
       "      <td>0.143271</td>\n",
       "      <td>0.158248</td>\n",
       "      <td>0.184003</td>\n",
       "      <td>0.035384</td>\n",
       "      <td>0.195451</td>\n",
       "      <td>0.230907</td>\n",
       "      <td>0.129868</td>\n",
       "      <td>0.125991</td>\n",
       "      <td>0.072890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>High</th>\n",
       "      <td>0.999823</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.999706</td>\n",
       "      <td>0.999788</td>\n",
       "      <td>0.999788</td>\n",
       "      <td>0.384779</td>\n",
       "      <td>0.143018</td>\n",
       "      <td>0.237159</td>\n",
       "      <td>0.143037</td>\n",
       "      <td>0.158004</td>\n",
       "      <td>0.183783</td>\n",
       "      <td>0.035260</td>\n",
       "      <td>0.195252</td>\n",
       "      <td>0.230543</td>\n",
       "      <td>0.129695</td>\n",
       "      <td>0.125778</td>\n",
       "      <td>0.072731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Low</th>\n",
       "      <td>0.999746</td>\n",
       "      <td>0.999706</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.999797</td>\n",
       "      <td>0.999797</td>\n",
       "      <td>0.378891</td>\n",
       "      <td>0.142765</td>\n",
       "      <td>0.236629</td>\n",
       "      <td>0.142832</td>\n",
       "      <td>0.157679</td>\n",
       "      <td>0.183549</td>\n",
       "      <td>0.035045</td>\n",
       "      <td>0.195039</td>\n",
       "      <td>0.230317</td>\n",
       "      <td>0.129338</td>\n",
       "      <td>0.125621</td>\n",
       "      <td>0.072513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Close</th>\n",
       "      <td>0.999533</td>\n",
       "      <td>0.999788</td>\n",
       "      <td>0.999797</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.380887</td>\n",
       "      <td>0.142458</td>\n",
       "      <td>0.236268</td>\n",
       "      <td>0.142575</td>\n",
       "      <td>0.157399</td>\n",
       "      <td>0.183302</td>\n",
       "      <td>0.034914</td>\n",
       "      <td>0.194775</td>\n",
       "      <td>0.229903</td>\n",
       "      <td>0.129111</td>\n",
       "      <td>0.125379</td>\n",
       "      <td>0.072335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adj Close</th>\n",
       "      <td>0.999533</td>\n",
       "      <td>0.999788</td>\n",
       "      <td>0.999797</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.380887</td>\n",
       "      <td>0.142458</td>\n",
       "      <td>0.236268</td>\n",
       "      <td>0.142575</td>\n",
       "      <td>0.157399</td>\n",
       "      <td>0.183302</td>\n",
       "      <td>0.034914</td>\n",
       "      <td>0.194775</td>\n",
       "      <td>0.229903</td>\n",
       "      <td>0.129111</td>\n",
       "      <td>0.125379</td>\n",
       "      <td>0.072335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Volume</th>\n",
       "      <td>0.383286</td>\n",
       "      <td>0.384779</td>\n",
       "      <td>0.378891</td>\n",
       "      <td>0.380887</td>\n",
       "      <td>0.380887</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.160323</td>\n",
       "      <td>0.278415</td>\n",
       "      <td>0.160811</td>\n",
       "      <td>0.178483</td>\n",
       "      <td>0.205240</td>\n",
       "      <td>0.025869</td>\n",
       "      <td>0.193806</td>\n",
       "      <td>0.279386</td>\n",
       "      <td>0.146907</td>\n",
       "      <td>0.133423</td>\n",
       "      <td>0.075854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N_Words</th>\n",
       "      <td>0.143294</td>\n",
       "      <td>0.143018</td>\n",
       "      <td>0.142765</td>\n",
       "      <td>0.142458</td>\n",
       "      <td>0.142458</td>\n",
       "      <td>0.160323</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.809166</td>\n",
       "      <td>0.959051</td>\n",
       "      <td>0.939099</td>\n",
       "      <td>0.722660</td>\n",
       "      <td>0.914602</td>\n",
       "      <td>0.891023</td>\n",
       "      <td>0.878831</td>\n",
       "      <td>0.849359</td>\n",
       "      <td>0.956559</td>\n",
       "      <td>0.889842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N_Unique_Words</th>\n",
       "      <td>0.237492</td>\n",
       "      <td>0.237159</td>\n",
       "      <td>0.236629</td>\n",
       "      <td>0.236268</td>\n",
       "      <td>0.236268</td>\n",
       "      <td>0.278415</td>\n",
       "      <td>0.809166</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.792838</td>\n",
       "      <td>0.830631</td>\n",
       "      <td>0.687661</td>\n",
       "      <td>0.616014</td>\n",
       "      <td>0.801095</td>\n",
       "      <td>0.849348</td>\n",
       "      <td>0.733284</td>\n",
       "      <td>0.716759</td>\n",
       "      <td>0.647483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N_Negative</th>\n",
       "      <td>0.143271</td>\n",
       "      <td>0.143037</td>\n",
       "      <td>0.142832</td>\n",
       "      <td>0.142575</td>\n",
       "      <td>0.142575</td>\n",
       "      <td>0.160811</td>\n",
       "      <td>0.959051</td>\n",
       "      <td>0.792838</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.916033</td>\n",
       "      <td>0.711416</td>\n",
       "      <td>0.894612</td>\n",
       "      <td>0.887906</td>\n",
       "      <td>0.859551</td>\n",
       "      <td>0.835781</td>\n",
       "      <td>0.926877</td>\n",
       "      <td>0.905439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N_Positive</th>\n",
       "      <td>0.158248</td>\n",
       "      <td>0.158004</td>\n",
       "      <td>0.157679</td>\n",
       "      <td>0.157399</td>\n",
       "      <td>0.157399</td>\n",
       "      <td>0.178483</td>\n",
       "      <td>0.939099</td>\n",
       "      <td>0.830631</td>\n",
       "      <td>0.916033</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.727986</td>\n",
       "      <td>0.804773</td>\n",
       "      <td>0.896281</td>\n",
       "      <td>0.891289</td>\n",
       "      <td>0.842779</td>\n",
       "      <td>0.871109</td>\n",
       "      <td>0.795195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N_Uncertainty</th>\n",
       "      <td>0.184003</td>\n",
       "      <td>0.183783</td>\n",
       "      <td>0.183549</td>\n",
       "      <td>0.183302</td>\n",
       "      <td>0.183302</td>\n",
       "      <td>0.205240</td>\n",
       "      <td>0.722660</td>\n",
       "      <td>0.687661</td>\n",
       "      <td>0.711416</td>\n",
       "      <td>0.727986</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.554900</td>\n",
       "      <td>0.757021</td>\n",
       "      <td>0.755853</td>\n",
       "      <td>0.640032</td>\n",
       "      <td>0.661054</td>\n",
       "      <td>0.559404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N_Litigious</th>\n",
       "      <td>0.035384</td>\n",
       "      <td>0.035260</td>\n",
       "      <td>0.035045</td>\n",
       "      <td>0.034914</td>\n",
       "      <td>0.034914</td>\n",
       "      <td>0.025869</td>\n",
       "      <td>0.914602</td>\n",
       "      <td>0.616014</td>\n",
       "      <td>0.894612</td>\n",
       "      <td>0.804773</td>\n",
       "      <td>0.554900</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.757049</td>\n",
       "      <td>0.663866</td>\n",
       "      <td>0.730032</td>\n",
       "      <td>0.920265</td>\n",
       "      <td>0.937902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N_ModalWeak</th>\n",
       "      <td>0.195451</td>\n",
       "      <td>0.195252</td>\n",
       "      <td>0.195039</td>\n",
       "      <td>0.194775</td>\n",
       "      <td>0.194775</td>\n",
       "      <td>0.193806</td>\n",
       "      <td>0.891023</td>\n",
       "      <td>0.801095</td>\n",
       "      <td>0.887906</td>\n",
       "      <td>0.896281</td>\n",
       "      <td>0.757021</td>\n",
       "      <td>0.757049</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.878896</td>\n",
       "      <td>0.816612</td>\n",
       "      <td>0.846535</td>\n",
       "      <td>0.750891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N_Modal_Moderate</th>\n",
       "      <td>0.230907</td>\n",
       "      <td>0.230543</td>\n",
       "      <td>0.230317</td>\n",
       "      <td>0.229903</td>\n",
       "      <td>0.229903</td>\n",
       "      <td>0.279386</td>\n",
       "      <td>0.878831</td>\n",
       "      <td>0.849348</td>\n",
       "      <td>0.859551</td>\n",
       "      <td>0.891289</td>\n",
       "      <td>0.755853</td>\n",
       "      <td>0.663866</td>\n",
       "      <td>0.878896</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.801344</td>\n",
       "      <td>0.802961</td>\n",
       "      <td>0.683610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N_Modal_Strong</th>\n",
       "      <td>0.129868</td>\n",
       "      <td>0.129695</td>\n",
       "      <td>0.129338</td>\n",
       "      <td>0.129111</td>\n",
       "      <td>0.129111</td>\n",
       "      <td>0.146907</td>\n",
       "      <td>0.849359</td>\n",
       "      <td>0.733284</td>\n",
       "      <td>0.835781</td>\n",
       "      <td>0.842779</td>\n",
       "      <td>0.640032</td>\n",
       "      <td>0.730032</td>\n",
       "      <td>0.816612</td>\n",
       "      <td>0.801344</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.791211</td>\n",
       "      <td>0.750632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N_Constraining</th>\n",
       "      <td>0.125991</td>\n",
       "      <td>0.125778</td>\n",
       "      <td>0.125621</td>\n",
       "      <td>0.125379</td>\n",
       "      <td>0.125379</td>\n",
       "      <td>0.133423</td>\n",
       "      <td>0.956559</td>\n",
       "      <td>0.716759</td>\n",
       "      <td>0.926877</td>\n",
       "      <td>0.871109</td>\n",
       "      <td>0.661054</td>\n",
       "      <td>0.920265</td>\n",
       "      <td>0.846535</td>\n",
       "      <td>0.802961</td>\n",
       "      <td>0.791211</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.908709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N_Negation</th>\n",
       "      <td>0.072890</td>\n",
       "      <td>0.072731</td>\n",
       "      <td>0.072513</td>\n",
       "      <td>0.072335</td>\n",
       "      <td>0.072335</td>\n",
       "      <td>0.075854</td>\n",
       "      <td>0.889842</td>\n",
       "      <td>0.647483</td>\n",
       "      <td>0.905439</td>\n",
       "      <td>0.795195</td>\n",
       "      <td>0.559404</td>\n",
       "      <td>0.937902</td>\n",
       "      <td>0.750891</td>\n",
       "      <td>0.683610</td>\n",
       "      <td>0.750632</td>\n",
       "      <td>0.908709</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Open      High       Low     Close  Adj Close    Volume  \\\n",
       "Open              1.000000  0.999823  0.999746  0.999533   0.999533  0.383286   \n",
       "High              0.999823  1.000000  0.999706  0.999788   0.999788  0.384779   \n",
       "Low               0.999746  0.999706  1.000000  0.999797   0.999797  0.378891   \n",
       "Close             0.999533  0.999788  0.999797  1.000000   1.000000  0.380887   \n",
       "Adj Close         0.999533  0.999788  0.999797  1.000000   1.000000  0.380887   \n",
       "Volume            0.383286  0.384779  0.378891  0.380887   0.380887  1.000000   \n",
       "N_Words           0.143294  0.143018  0.142765  0.142458   0.142458  0.160323   \n",
       "N_Unique_Words    0.237492  0.237159  0.236629  0.236268   0.236268  0.278415   \n",
       "N_Negative        0.143271  0.143037  0.142832  0.142575   0.142575  0.160811   \n",
       "N_Positive        0.158248  0.158004  0.157679  0.157399   0.157399  0.178483   \n",
       "N_Uncertainty     0.184003  0.183783  0.183549  0.183302   0.183302  0.205240   \n",
       "N_Litigious       0.035384  0.035260  0.035045  0.034914   0.034914  0.025869   \n",
       "N_ModalWeak       0.195451  0.195252  0.195039  0.194775   0.194775  0.193806   \n",
       "N_Modal_Moderate  0.230907  0.230543  0.230317  0.229903   0.229903  0.279386   \n",
       "N_Modal_Strong    0.129868  0.129695  0.129338  0.129111   0.129111  0.146907   \n",
       "N_Constraining    0.125991  0.125778  0.125621  0.125379   0.125379  0.133423   \n",
       "N_Negation        0.072890  0.072731  0.072513  0.072335   0.072335  0.075854   \n",
       "\n",
       "                   N_Words  N_Unique_Words  N_Negative  N_Positive  \\\n",
       "Open              0.143294        0.237492    0.143271    0.158248   \n",
       "High              0.143018        0.237159    0.143037    0.158004   \n",
       "Low               0.142765        0.236629    0.142832    0.157679   \n",
       "Close             0.142458        0.236268    0.142575    0.157399   \n",
       "Adj Close         0.142458        0.236268    0.142575    0.157399   \n",
       "Volume            0.160323        0.278415    0.160811    0.178483   \n",
       "N_Words           1.000000        0.809166    0.959051    0.939099   \n",
       "N_Unique_Words    0.809166        1.000000    0.792838    0.830631   \n",
       "N_Negative        0.959051        0.792838    1.000000    0.916033   \n",
       "N_Positive        0.939099        0.830631    0.916033    1.000000   \n",
       "N_Uncertainty     0.722660        0.687661    0.711416    0.727986   \n",
       "N_Litigious       0.914602        0.616014    0.894612    0.804773   \n",
       "N_ModalWeak       0.891023        0.801095    0.887906    0.896281   \n",
       "N_Modal_Moderate  0.878831        0.849348    0.859551    0.891289   \n",
       "N_Modal_Strong    0.849359        0.733284    0.835781    0.842779   \n",
       "N_Constraining    0.956559        0.716759    0.926877    0.871109   \n",
       "N_Negation        0.889842        0.647483    0.905439    0.795195   \n",
       "\n",
       "                  N_Uncertainty  N_Litigious  N_ModalWeak  N_Modal_Moderate  \\\n",
       "Open                   0.184003     0.035384     0.195451          0.230907   \n",
       "High                   0.183783     0.035260     0.195252          0.230543   \n",
       "Low                    0.183549     0.035045     0.195039          0.230317   \n",
       "Close                  0.183302     0.034914     0.194775          0.229903   \n",
       "Adj Close              0.183302     0.034914     0.194775          0.229903   \n",
       "Volume                 0.205240     0.025869     0.193806          0.279386   \n",
       "N_Words                0.722660     0.914602     0.891023          0.878831   \n",
       "N_Unique_Words         0.687661     0.616014     0.801095          0.849348   \n",
       "N_Negative             0.711416     0.894612     0.887906          0.859551   \n",
       "N_Positive             0.727986     0.804773     0.896281          0.891289   \n",
       "N_Uncertainty          1.000000     0.554900     0.757021          0.755853   \n",
       "N_Litigious            0.554900     1.000000     0.757049          0.663866   \n",
       "N_ModalWeak            0.757021     0.757049     1.000000          0.878896   \n",
       "N_Modal_Moderate       0.755853     0.663866     0.878896          1.000000   \n",
       "N_Modal_Strong         0.640032     0.730032     0.816612          0.801344   \n",
       "N_Constraining         0.661054     0.920265     0.846535          0.802961   \n",
       "N_Negation             0.559404     0.937902     0.750891          0.683610   \n",
       "\n",
       "                  N_Modal_Strong  N_Constraining  N_Negation  \n",
       "Open                    0.129868        0.125991    0.072890  \n",
       "High                    0.129695        0.125778    0.072731  \n",
       "Low                     0.129338        0.125621    0.072513  \n",
       "Close                   0.129111        0.125379    0.072335  \n",
       "Adj Close               0.129111        0.125379    0.072335  \n",
       "Volume                  0.146907        0.133423    0.075854  \n",
       "N_Words                 0.849359        0.956559    0.889842  \n",
       "N_Unique_Words          0.733284        0.716759    0.647483  \n",
       "N_Negative              0.835781        0.926877    0.905439  \n",
       "N_Positive              0.842779        0.871109    0.795195  \n",
       "N_Uncertainty           0.640032        0.661054    0.559404  \n",
       "N_Litigious             0.730032        0.920265    0.937902  \n",
       "N_ModalWeak             0.816612        0.846535    0.750891  \n",
       "N_Modal_Moderate        0.801344        0.802961    0.683610  \n",
       "N_Modal_Strong          1.000000        0.791211    0.750632  \n",
       "N_Constraining          0.791211        1.000000    0.908709  \n",
       "N_Negation              0.750632        0.908709    1.000000  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cor = lm_10X_snp500.corr()\n",
    "cor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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": 17,
   "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_snp500.index, lm_10X_snp500['Close'], 'black')\n",
    "plt.plot(lm_10X_snp500.index, lm_10X_snp500['High'], 'green')\n",
    "plt.plot(lm_10X_snp500.index, lm_10X_snp500['Low'], 'red')\n",
    "plt.plot(lm_10X_snp500.index, lm_10X_snp500['Open'], 'yellow')\n",
    "plt.title('S&P 500 Rate')\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.ylabel('Rate');\n",
    "plt.show();\n",
    "\n",
    "plt.plot(lm_10X_snp500.index, lm_10X_snp500['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_snp500.index, lm_10X_snp500['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_snp500.index, lm_10X_snp500['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_snp500.index, lm_10X_snp500['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_snp500.index, lm_10X_snp500['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_snp500.index, lm_10X_snp500['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_snp500.index, lm_10X_snp500['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_snp500.index, lm_10X_snp500['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_snp500.index, lm_10X_snp500['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_snp500.index, lm_10X_snp500['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_snp500.index, lm_10X_snp500['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": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 1022593 entries, 1993-08-13 to 2018-12-31\n",
      "Data columns (total 17 columns):\n",
      "Open                1022593 non-null float64\n",
      "High                1022593 non-null float64\n",
      "Low                 1022593 non-null float64\n",
      "Close               1022593 non-null float64\n",
      "Adj Close           1022593 non-null float64\n",
      "Volume              1022593 non-null int64\n",
      "N_Words             1022593 non-null int64\n",
      "N_Unique_Words      1022593 non-null int64\n",
      "N_Negative          1022593 non-null int64\n",
      "N_Positive          1022593 non-null int64\n",
      "N_Uncertainty       1022593 non-null int64\n",
      "N_Litigious         1022593 non-null int64\n",
      "N_ModalWeak         1022593 non-null int64\n",
      "N_Modal_Moderate    1022593 non-null int64\n",
      "N_Modal_Strong      1022593 non-null int64\n",
      "N_Constraining      1022593 non-null int64\n",
      "N_Negation          1022593 non-null int64\n",
      "dtypes: float64(5), int64(12)\n",
      "memory usage: 140.4 MB\n"
     ]
    }
   ],
   "source": [
    "#LSTM Prep\n",
    "df = lm_10X_snp500\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>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>450.140015</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>450.140015</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>459.179993</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>463.720001</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>465.390015</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": [
       "                 Close  N_Words  N_Unique_Words  N_Negative  N_Positive  \\\n",
       "1993-08-13  450.140015     3037             700          68           6   \n",
       "1993-08-13  450.140015     4102             823          53          24   \n",
       "1993-10-07  459.179993     7278            1184         157          41   \n",
       "1993-11-10  463.720001     3768             739          69          13   \n",
       "1993-11-12  465.390015     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": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop(['Open', 'High', 'Low', 'Adj Close', 'Volume',], axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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": [
       "Open                1.234720e+03\n",
       "High                1.239030e+03\n",
       "Low                 1.226200e+03\n",
       "Close               1.233760e+03\n",
       "Adj Close           1.233760e+03\n",
       "Volume              1.816900e+09\n",
       "N_Words             1.018900e+04\n",
       "N_Unique_Words      1.352000e+03\n",
       "N_Negative          1.360000e+02\n",
       "N_Positive          5.400000e+01\n",
       "N_Uncertainty       1.090000e+02\n",
       "N_Litigious         8.500000e+01\n",
       "N_ModalWeak         3.000000e+01\n",
       "N_Modal_Moderate    3.000000e+01\n",
       "N_Modal_Strong      2.800000e+01\n",
       "N_Constraining      6.100000e+01\n",
       "N_Negation          7.000000e+00\n",
       "dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm_10X_snp500.median()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LSTM Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "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": 38,
   "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": 39,
   "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>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>450.140015</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>450.140015</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>459.179993</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>463.720001</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>465.390015</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": [
       "                 Close  N_Words  N_Unique_Words  N_Negative  N_Positive  \\\n",
       "1993-08-13  450.140015     3037             700          68           6   \n",
       "1993-08-13  450.140015     4102             823          53          24   \n",
       "1993-10-07  459.179993     7278            1184         157          41   \n",
       "1993-11-10  463.720001     3768             739          69          13   \n",
       "1993-11-12  465.390015     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": 39,
     "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[['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": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "scaled = scaler.fit_transform(df.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>0.004503</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>0.004503</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>0.008131</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>0.009953</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>0.010623</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    0.004503    0.002158    0.084175    0.002345    0.000620    0.000114   \n",
       "13    0.004503    0.002915    0.098966    0.001828    0.002481    0.000079   \n",
       "14    0.008131    0.005173    0.142376    0.005414    0.004238    0.000332   \n",
       "15    0.009953    0.002678    0.088865    0.002379    0.001344    0.000114   \n",
       "16    0.010623    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": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reframed = series_to_supervised(scaled, n_weeks, 1)\n",
    "reframed.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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    0.004503    0.002158    0.084175    0.002345    0.000620    0.000114   \n",
      "13    0.004503    0.002915    0.098966    0.001828    0.002481    0.000079   \n",
      "14    0.008131    0.005173    0.142376    0.005414    0.004238    0.000332   \n",
      "15    0.009953    0.002678    0.088865    0.002379    0.001344    0.000114   \n",
      "16    0.010623    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": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(715815, 156)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "values = reframed.values\n",
    "n_train_days = 715815 #1022593*.7\n",
    "train = values[:n_train_days, :]\n",
    "test = values[n_train_days:, :]\n",
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(715815, 12, 12) (715815,) (306766, 12, 12) (306766,)\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": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 715815 samples, validate on 306766 samples\n",
      "Epoch 1/50\n",
      " - 12s - loss: 0.0948 - mean_squared_error: 0.0127 - mean_absolute_percentage_error: 456.2778 - val_loss: 0.1581 - val_mean_squared_error: 0.0443 - val_mean_absolute_percentage_error: 23.9939\n",
      "Epoch 2/50\n",
      " - 9s - loss: 0.0484 - mean_squared_error: 0.0036 - mean_absolute_percentage_error: 1593.8782 - val_loss: 0.1425 - val_mean_squared_error: 0.0367 - val_mean_absolute_percentage_error: 21.5292\n",
      "Epoch 3/50\n",
      " - 9s - loss: 0.0430 - mean_squared_error: 0.0028 - mean_absolute_percentage_error: 1431.0975 - val_loss: 0.1262 - val_mean_squared_error: 0.0295 - val_mean_absolute_percentage_error: 18.9850\n",
      "Epoch 4/50\n",
      " - 9s - loss: 0.0371 - mean_squared_error: 0.0021 - mean_absolute_percentage_error: 1278.0664 - val_loss: 0.1130 - val_mean_squared_error: 0.0243 - val_mean_absolute_percentage_error: 16.8416\n",
      "Epoch 5/50\n",
      " - 9s - loss: 0.0309 - mean_squared_error: 0.0015 - mean_absolute_percentage_error: 1081.2473 - val_loss: 0.0975 - val_mean_squared_error: 0.0188 - val_mean_absolute_percentage_error: 14.3924\n",
      "Epoch 6/50\n",
      " - 9s - loss: 0.0246 - mean_squared_error: 9.2836e-04 - mean_absolute_percentage_error: 888.8838 - val_loss: 0.0816 - val_mean_squared_error: 0.0140 - val_mean_absolute_percentage_error: 11.8556\n",
      "Epoch 7/50\n",
      " - 9s - loss: 0.0178 - mean_squared_error: 4.8816e-04 - mean_absolute_percentage_error: 675.2840 - val_loss: 0.0647 - val_mean_squared_error: 0.0097 - val_mean_absolute_percentage_error: 9.1745\n",
      "Epoch 8/50\n",
      " - 9s - loss: 0.0105 - mean_squared_error: 1.8032e-04 - mean_absolute_percentage_error: 444.8078 - val_loss: 0.0480 - val_mean_squared_error: 0.0062 - val_mean_absolute_percentage_error: 6.6933\n",
      "Epoch 9/50\n",
      " - 9s - loss: 0.0048 - mean_squared_error: 5.5269e-05 - mean_absolute_percentage_error: 219.6301 - val_loss: 0.0430 - val_mean_squared_error: 0.0053 - val_mean_absolute_percentage_error: 5.8769\n",
      "Epoch 10/50\n",
      " - 9s - loss: 0.0064 - mean_squared_error: 7.3066e-05 - mean_absolute_percentage_error: 130.2352 - val_loss: 0.0464 - val_mean_squared_error: 0.0060 - val_mean_absolute_percentage_error: 6.2361\n",
      "Epoch 11/50\n",
      " - 9s - loss: 0.0048 - mean_squared_error: 4.9258e-05 - mean_absolute_percentage_error: 121.3384 - val_loss: 0.0457 - val_mean_squared_error: 0.0059 - val_mean_absolute_percentage_error: 6.1558\n",
      "Epoch 12/50\n",
      " - 9s - loss: 0.0042 - mean_squared_error: 4.1450e-05 - mean_absolute_percentage_error: 169.1360 - val_loss: 0.0425 - val_mean_squared_error: 0.0052 - val_mean_absolute_percentage_error: 5.7801\n",
      "Epoch 13/50\n",
      " - 10s - loss: 0.0056 - mean_squared_error: 5.5082e-05 - mean_absolute_percentage_error: 148.3381 - val_loss: 0.0427 - val_mean_squared_error: 0.0053 - val_mean_absolute_percentage_error: 5.6745\n",
      "Epoch 14/50\n",
      " - 9s - loss: 0.0056 - mean_squared_error: 5.2277e-05 - mean_absolute_percentage_error: 112.1699 - val_loss: 0.0438 - val_mean_squared_error: 0.0055 - val_mean_absolute_percentage_error: 5.8213\n",
      "Epoch 15/50\n",
      " - 9s - loss: 0.0046 - mean_squared_error: 3.8764e-05 - mean_absolute_percentage_error: 157.1675 - val_loss: 0.0398 - val_mean_squared_error: 0.0048 - val_mean_absolute_percentage_error: 5.3960\n",
      "Epoch 16/50\n",
      " - 9s - loss: 0.0068 - mean_squared_error: 6.8691e-05 - mean_absolute_percentage_error: 135.6369 - val_loss: 0.0484 - val_mean_squared_error: 0.0058 - val_mean_absolute_percentage_error: 6.8706\n",
      "Epoch 17/50\n",
      " - 9s - loss: 0.0042 - mean_squared_error: 3.0930e-05 - mean_absolute_percentage_error: 82.8730 - val_loss: 0.0469 - val_mean_squared_error: 0.0060 - val_mean_absolute_percentage_error: 6.2744\n",
      "Epoch 18/50\n",
      " - 9s - loss: 0.0041 - mean_squared_error: 3.3834e-05 - mean_absolute_percentage_error: 219.6015 - val_loss: 0.0486 - val_mean_squared_error: 0.0064 - val_mean_absolute_percentage_error: 6.4949\n",
      "Epoch 19/50\n",
      " - 9s - loss: 0.0041 - mean_squared_error: 3.2369e-05 - mean_absolute_percentage_error: 200.1732 - val_loss: 0.0512 - val_mean_squared_error: 0.0069 - val_mean_absolute_percentage_error: 6.8762\n",
      "Epoch 20/50\n",
      " - 9s - loss: 0.0053 - mean_squared_error: 4.7063e-05 - mean_absolute_percentage_error: 214.0286 - val_loss: 0.0517 - val_mean_squared_error: 0.0069 - val_mean_absolute_percentage_error: 7.0162\n",
      "Epoch 21/50\n",
      " - 9s - loss: 0.0050 - mean_squared_error: 3.8447e-05 - mean_absolute_percentage_error: 173.2763 - val_loss: 0.0546 - val_mean_squared_error: 0.0076 - val_mean_absolute_percentage_error: 7.3624\n",
      "Epoch 22/50\n",
      " - 9s - loss: 0.0076 - mean_squared_error: 7.9160e-05 - mean_absolute_percentage_error: 225.6995 - val_loss: 0.0453 - val_mean_squared_error: 0.0057 - val_mean_absolute_percentage_error: 6.2578\n",
      "Epoch 23/50\n",
      " - 9s - loss: 0.0035 - mean_squared_error: 2.3068e-05 - mean_absolute_percentage_error: 172.9763 - val_loss: 0.0407 - val_mean_squared_error: 0.0049 - val_mean_absolute_percentage_error: 5.6184\n",
      "Epoch 24/50\n",
      " - 9s - loss: 0.0070 - mean_squared_error: 6.8614e-05 - mean_absolute_percentage_error: 99.8242 - val_loss: 0.0502 - val_mean_squared_error: 0.0061 - val_mean_absolute_percentage_error: 7.1827\n",
      "Epoch 25/50\n",
      " - 9s - loss: 0.0040 - mean_squared_error: 2.5328e-05 - mean_absolute_percentage_error: 32.8189 - val_loss: 0.0475 - val_mean_squared_error: 0.0062 - val_mean_absolute_percentage_error: 6.3891\n",
      "Epoch 26/50\n",
      " - 8s - loss: 0.0034 - mean_squared_error: 2.1080e-05 - mean_absolute_percentage_error: 195.1460 - val_loss: 0.0485 - val_mean_squared_error: 0.0065 - val_mean_absolute_percentage_error: 6.4340\n",
      "Epoch 27/50\n",
      " - 9s - loss: 0.0031 - mean_squared_error: 1.7639e-05 - mean_absolute_percentage_error: 177.1698 - val_loss: 0.0483 - val_mean_squared_error: 0.0064 - val_mean_absolute_percentage_error: 6.4738\n",
      "Epoch 28/50\n",
      " - 9s - loss: 0.0031 - mean_squared_error: 1.6833e-05 - mean_absolute_percentage_error: 178.2155 - val_loss: 0.0482 - val_mean_squared_error: 0.0064 - val_mean_absolute_percentage_error: 6.4459\n",
      "Epoch 29/50\n",
      " - 8s - loss: 0.0028 - mean_squared_error: 1.4101e-05 - mean_absolute_percentage_error: 163.8435 - val_loss: 0.0475 - val_mean_squared_error: 0.0063 - val_mean_absolute_percentage_error: 6.3569\n",
      "Epoch 30/50\n",
      " - 9s - loss: 0.0026 - mean_squared_error: 1.1672e-05 - mean_absolute_percentage_error: 149.4464 - val_loss: 0.0462 - val_mean_squared_error: 0.0061 - val_mean_absolute_percentage_error: 6.1374\n",
      "Epoch 31/50\n",
      " - 9s - loss: 0.0023 - mean_squared_error: 9.4959e-06 - mean_absolute_percentage_error: 125.0701 - val_loss: 0.0445 - val_mean_squared_error: 0.0057 - val_mean_absolute_percentage_error: 5.8860\n",
      "Epoch 32/50\n",
      " - 10s - loss: 0.0026 - mean_squared_error: 1.0340e-05 - mean_absolute_percentage_error: 109.4445 - val_loss: 0.0416 - val_mean_squared_error: 0.0051 - val_mean_absolute_percentage_error: 5.6149\n",
      "Epoch 33/50\n",
      " - 9s - loss: 0.0053 - mean_squared_error: 4.1127e-05 - mean_absolute_percentage_error: 100.2267 - val_loss: 0.0448 - val_mean_squared_error: 0.0055 - val_mean_absolute_percentage_error: 6.1325\n",
      "Epoch 34/50\n",
      " - 9s - loss: 0.0050 - mean_squared_error: 3.6524e-05 - mean_absolute_percentage_error: 37.5445 - val_loss: 0.0466 - val_mean_squared_error: 0.0061 - val_mean_absolute_percentage_error: 6.1334\n",
      "Epoch 35/50\n",
      " - 9s - loss: 0.0024 - mean_squared_error: 1.0906e-05 - mean_absolute_percentage_error: 167.6016 - val_loss: 0.0449 - val_mean_squared_error: 0.0058 - val_mean_absolute_percentage_error: 5.8903\n",
      "Epoch 36/50\n",
      " - 9s - loss: 0.0029 - mean_squared_error: 1.3298e-05 - mean_absolute_percentage_error: 139.3935 - val_loss: 0.0464 - val_mean_squared_error: 0.0060 - val_mean_absolute_percentage_error: 6.2033\n",
      "Epoch 37/50\n",
      " - 9s - loss: 0.0030 - mean_squared_error: 1.4508e-05 - mean_absolute_percentage_error: 169.4095 - val_loss: 0.0469 - val_mean_squared_error: 0.0061 - val_mean_absolute_percentage_error: 6.2516\n",
      "Epoch 38/50\n",
      " - 10s - loss: 0.0028 - mean_squared_error: 1.2952e-05 - mean_absolute_percentage_error: 159.9519 - val_loss: 0.0465 - val_mean_squared_error: 0.0060 - val_mean_absolute_percentage_error: 6.2032\n",
      "Epoch 39/50\n",
      " - 9s - loss: 0.0024 - mean_squared_error: 1.0260e-05 - mean_absolute_percentage_error: 145.2830 - val_loss: 0.0443 - val_mean_squared_error: 0.0056 - val_mean_absolute_percentage_error: 5.8966\n",
      "Epoch 40/50\n",
      " - 9s - loss: 0.0026 - mean_squared_error: 1.0261e-05 - mean_absolute_percentage_error: 114.1661 - val_loss: 0.0403 - val_mean_squared_error: 0.0048 - val_mean_absolute_percentage_error: 5.5315\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 41/50\n",
      " - 9s - loss: 0.0062 - mean_squared_error: 5.9574e-05 - mean_absolute_percentage_error: 92.0384 - val_loss: 0.0502 - val_mean_squared_error: 0.0058 - val_mean_absolute_percentage_error: 7.3721\n",
      "Epoch 42/50\n",
      " - 9s - loss: 0.0040 - mean_squared_error: 2.4383e-05 - mean_absolute_percentage_error: 23.7030 - val_loss: 0.0453 - val_mean_squared_error: 0.0057 - val_mean_absolute_percentage_error: 6.0718\n",
      "Epoch 43/50\n",
      " - 9s - loss: 0.0028 - mean_squared_error: 1.3283e-05 - mean_absolute_percentage_error: 174.2364 - val_loss: 0.0445 - val_mean_squared_error: 0.0056 - val_mean_absolute_percentage_error: 5.9089\n",
      "Epoch 44/50\n",
      " - 8s - loss: 0.0024 - mean_squared_error: 1.0255e-05 - mean_absolute_percentage_error: 144.1417 - val_loss: 0.0423 - val_mean_squared_error: 0.0052 - val_mean_absolute_percentage_error: 5.6516\n",
      "Epoch 45/50\n",
      " - 9s - loss: 0.0034 - mean_squared_error: 1.7221e-05 - mean_absolute_percentage_error: 118.0865 - val_loss: 0.0383 - val_mean_squared_error: 0.0044 - val_mean_absolute_percentage_error: 5.2652\n",
      "Epoch 46/50\n",
      " - 9s - loss: 0.0071 - mean_squared_error: 7.0749e-05 - mean_absolute_percentage_error: 87.9321 - val_loss: 0.0498 - val_mean_squared_error: 0.0056 - val_mean_absolute_percentage_error: 7.4738\n",
      "Epoch 47/50\n",
      " - 9s - loss: 0.0054 - mean_squared_error: 4.4544e-05 - mean_absolute_percentage_error: 48.7323 - val_loss: 0.0486 - val_mean_squared_error: 0.0064 - val_mean_absolute_percentage_error: 6.4531\n",
      "Epoch 48/50\n",
      " - 9s - loss: 0.0038 - mean_squared_error: 2.3146e-05 - mean_absolute_percentage_error: 197.1729 - val_loss: 0.0503 - val_mean_squared_error: 0.0065 - val_mean_absolute_percentage_error: 6.7941\n",
      "Epoch 49/50\n",
      " - 9s - loss: 0.0034 - mean_squared_error: 1.7386e-05 - mean_absolute_percentage_error: 147.9523 - val_loss: 0.0524 - val_mean_squared_error: 0.0071 - val_mean_absolute_percentage_error: 7.0123\n",
      "Epoch 50/50\n",
      " - 9s - loss: 0.0062 - mean_squared_error: 5.0053e-05 - mean_absolute_percentage_error: 207.2654 - val_loss: 0.0438 - val_mean_squared_error: 0.0054 - val_mean_absolute_percentage_error: 6.0192\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=21474, validation_data=(test_X, test_y), verbose=2, shuffle=False, validation_split=0.2)\n",
    "# plot history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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": 49,
   "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": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test MSE: 33308.127\n",
      "Test RMSE: 182.505\n",
      "Test MAE: 109.197\n",
      "Test MAPE: 4.767\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 for amount of 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": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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DBgygX79+rFmzhry8PHmSV0X++OMPMVpEEAShhrZs2aKTGaIydXqH8p///IevvvoKpVJJUlISxcXFZGVl0bRpU3k+g7+/PwcPHuTRRx9FpVLJk9ICAgJYuXIlQ4YM4fTp06xZs0YuHzVqVJUCiqOjI6D9pZSkhxAEQRAqduPGDUaOHCl/hlZVnT/yMjMzY+XKlXz22Wf07t2b5ORknUY6OTmRlJSkV+7o6EhSUhIZGRnY2trKE+JKyqui5DGXs7OzmKAkCIJQTdXtKjBKp/zUqVM5efIkiYmJREdH60x6kyRJzjNkqLzkZ1l1PWlOEARBqL46DSj//vsvFy9eBMDKygo/Pz+dfE0AKSkpODk54ezsrFOempqKk5MTDg4OZGdny7mDSuoLgiAI95Y6DShxcXGEhIRQUFBAQUEBR44cYdiwYVy7do2YmBiKi4vZt28fPj4+uLq6YmFhIacNDwsLw8fHBzMzMzp06MD+/fsB2LNnDz4+PnXZbEEQBKEG6rQPxdfXl3PnzjFo0CBMTU3x8/OjX79+ODg4MGXKFNRqNb6+vvTu3RvQJnQLCQkhJycHLy8vxowZA8DcuXOZOXMm69atw8XFxeC6EoIgCMLd9UCnXomLi6N79+4cOXJEdMoLgiBUUU0/O8VMeUEQBKFWiIAiCIJwnwpZ/yv+74Td7WbIREARBEG4T529kgqAurD8FTSNSQQUQRCE+9DV2Jvy63ulK1wEFEEQhPtQUkae/PoeiScioAiCINyXygQRcYciCIIg1JiZWenHd2GR5i62pJQIKIIgCEby1f6/GTC9dkZlmZqU5jQs1twbdyh1nm1YEARB0Npx5ModHR+XnM2vZxNo49GInLxCufxeeeQlAoogCMI9Tl1YjEpdxLJvznClzOiue40IKIIgCPe4d9eHczE6/W43o1KiD0UQBOEeVzaYDO72qN7+e+SJlwgogiAIxlZcXPNRWd07PqJXJgKKIAjCQ+pOUqW4N67HzDEddco0ksRHWyJ4fcmRuzqEWAQUQRAEI6vpKN+SkcLPtG3Cng8H4OFmD8C+E1EcPRNHfEoOG3afq6VW1qB9d+3KgiAID6mSYb7FxRo0lUSXskOCP3vXT35taqIgX1UEQHqWCktzUwCycgtIzsjj9N83arvZlRKjvARBEIwsX1XE9RvZfHPoEsUaiSWTni237pc//A1Aj46P0NDeSmefqan2nqC4WJInOp48n0h6lorLMRns/WhgHb0Dw+o8oKxevZoDBw4A2iWBO3XqpLOEb1JSEm3btmXDhg2sXr2aXbt2YWdnB8DQoUMZOXIkFy9eJDg4mNzcXDp06MC8efNQKkUsFATh/vTlD39z/K/4KtXd9fNVALLzCvT2lQSRYo2GojJ3OpdjMmqhldVXp5/K4eHhnDhxgt27d6NQKBg/fjzt2rUjLEybeiAlJYXhw4cza9YsACIjI1m2bBnt2rXTOU9QUBDvv/8+3t7ezJ49m+3btzNixIi6bLogCEKdibiUVO1jDKVXMTVVyPuKiyXcG9cjNin7jttXU3Xah+Lo6MjMmTMxNzfHzMwMDw8PEhIS5P1Lly5l2LBhNGvWDNAGlA0bNuDv78/8+fNRq9XEx8ejUqnw9vYGICAggIMHD9ZlswVBEOrU7bEhuUwq+vJkZKvK3XfxWjpFxRrMze5ut3idXr1ly5ZyIIiOjubAgQP4+vrK27///jtjxowBIDc3l9atWxMUFMTu3bvJyspi7dq1JCcn4+joKJ/T0dGRpKTqR3dBEIR7Rb66SGf7qx8uVnrMv3GZemWqW+fJydfm9XrUrX4ttK7mjBLOrly5wrhx45gxY4Z8N7Jt2zZGjBiBubk5ADY2NmzcuBEPDw+USiXjxo3j2LFjaDQaFIrSrJqSJOlsC4Ig3O+srfR7HzQaiejELHm71SP6wcJMaaqz3balo14dY6rzgBIREcHYsWN55513eOGFF+TyI0eO0LdvX3k7ISGBnTt3ytuSJKFUKnF2diYlJUUuT01NxcnJqa6bLQiCYDS3f0XOUxUy7v0fmRL6s1z2z3X9pJCODXRHfTnYWepsGzsLcZ0GlMTERCZNmkRoaCj9+vWTy9PT01GpVLi7u8tllpaWfPjhh8TGxiJJElu2bKFnz564urpiYWFBREQEAGFhYfj4+NRlswVBEIxqf3i0zvafl1NIyyy/z6TE+IFt5NcmCmhorxtQjL1MSp2O8tq0aRNqtZolS5bIZcOGDcPLywtnZ2edug4ODsyfP5+JEydSWFhI+/bteeWVVwAIDQ0lJCSEnJwcvLy85H4XQRCEB8Wr7/9IS/cGzHy5I3mqwsoPACzNSz/CP5zqg3NDG5392jsU43UR1GlACQkJISQkxOC+7du365X16tWLXr166ZV7enrqPA4TBEF4ULg3rkdhUTE30vJIzsgHYPcx7dyTj6f58tbyYwC81KOV3rFFZZJMNnawBrR9LSWPx4ydNFKkXhEEQTCC1Jv5BsvtbMz1Hk2pC4pRKKCFq71cNszvMb1jyy4DbG6m7aCfO76LXGbsPhQx3VwQBMEI4pNzdLaVpiYUFWuwMDeloEz2YVVBEckZ+fTu0gyFQkHo1K40qGeJ0lT/+7+JQj+g2NmYy2UaEVAEQRAePIrb4sGM0U/xx8VkAM5cSpbLp4YeBcD+VmB4rKlD+ecsE1DK3q2UEI+8BEEQHkC7j/6rs93liSZMGepNWqbuo7DEtFwAAgyszHg7EwNBpKwHatiwIAiCoBX5b6rB8ogydycluj3lhrWlWaXntLGs+CHTJSMniRQBRRAEwQhUBaX9JPVtLSqse/sExfKYGuhXKevva2lVOk9tEX0ogiAIRqY0rfhRVXWWCF4743lyy5m3UtXAVFvEHYogCIIRuDe2Ld0oJx9h25aNeK69G36dmlbjvPXwvK3j/q1h2iVAnB1sDB1SZ8QdiiAIghG4N65HXHIOklRuPOERZzsmDHrijq/l5qQNXsYeNizuUARBEOpIYVExKbdmv/99LV0exls2npTtWG/mYlcr1y0ZTiwmNgqCIDwATpyN54Ov/gBg7vjO3MxWl+4sc4vSuKENUfHatU6e8qydTOomckCpldNV/brGvZwgCMKDb+4nJ+VgAjDv09909tuXmc0+uk9r+bVJLa31VHIaMVNeEAThPnbk9HXOXNbOLXk94ElcGtkw95OT8v7nO7jT75nm8naH1o2ZP6EL566mUr9excOJq6pkwqN45CUIgnAfW7ntT/l1SeBYENiFdzec5KUerRhV5o6kRLvHnGj3WO0vHPhArYciCILwsCn5EO/zdDO5zLuVE3PHd8a7lXGW6DW5S53yog9FEAShFpWsmjgx4Emd8g6tGxvMGFwXSvpQjN0pL+5QBEEQalHzJvaYmCh0MgEb290aNlzn4XL16tX069ePfv36sXTpUgBmzZqFn58fAwcOZODAgRw+fBiA8PBw/P398fPzY/ny5fI5Ll68SEBAAL169SI4OJiioqK6brYgCEKNSJJEbn7VlvCtKyWd8sbuQ6nTgBIeHs6JEyfYvXs3e/bs4cKFCxw+fJjIyEi+/vprwsLCCAsLo2fPnqhUKmbPns3atWvZv38/kZGRHDumXfoyKCiIOXPmcOjQISRJMrh8sCAIwr0gK7eABvWMm0PrdqWPvB6gOxRHR0dmzpyJubk5ZmZmeHh4kJCQQEJCArNnz8bf35+VK1ei0Wg4d+4cTZs2xd3dHaVSib+/PwcPHiQ+Ph6VSoW3tzcAAQEBHDx4sC6bLQiCUGNXYm8Sn5JTeUUj2HLwklGvV6d9KC1btpRfR0dHc+DAAbZs2cLvv//O3LlzqVevHoGBgezcuRNra2scHUtHQDg5OZGUlERycrJOuaOjI0lJSXXZbEEQhBopKtbc7SYAYK7ULgecrzZu94BRhhxcuXKFcePGMWPGDFq0aMGaNWtwcnLCysqK0aNHc+zYMTQajU4nliRJKBSKcssFQRDupsIiDYVFxcTcyOL8Ve3iWapbH+BeLRrezaY9uKO8IiIimDp1KrNnz6Zfv35cvnyZ6OhoevXqBWgDhFKpxNnZmZSUFPm4lJQUnJyc9MpTU1Nxcqr9CUCCIAhVpdFIBPx3r05Z947udHvKHYDmTWonyeOdys4rMOr16vQOJTExkUmTJhEaGkq/fv0AbQBZtGgRmZmZFBYWsm3bNnr27Enbtm25du0aMTExFBcXs2/fPnx8fHB1dcXCwoKIiAgAwsLC8PHxqctmC4IgVCgpPU+v7MjpWELWhwOw78Q1YzdJR8lTnAa1lMqlqur0DmXTpk2o1WqWLFkilw0bNowJEyYwfPhwioqK8PPzo3///gAsWbKEKVOmoFar8fX1pXfv3gCEhoYSEhJCTk4OXl5ejBkzpi6bLQiCUKGrsTcBePH5lrzc73GOnYkjdEuEvN9MeW/MGTfyE6+6DSghISGEhIQY3Ddy5Ei9si5duvD999/rlXt6erJz585ab58gCEJNLP1am0m4dXPtSonmZroBZNX0bkZvU1l3q5f53gijgiAI9wlVmZFT5rfuRCzMdL+bG3stdz0lEeVBmtgoCILwoMkrE1AUtz65H2/hwJi+pVmErSyMn9VK0hST9r8vKM7NlNslGTmiiFxegiAI1aAqKA0oeWptihVLcyVDureiqEhDRtmVGY0o70oEmaf2UpSVhkXPyXelDSKgCIIgVFFhkYY9x/6Vt+1sdEdRDe/laewmlaG9G5GKS/OIPXDzUARBEO4HN9Jy2R8ezXC/x3QeWaVl5vPf1Sf0hgoP8GlxRxMYcy6cwKr5k5ha19acldLZjA/sxEZBEIT7wfb//cPh368Tm5TNqwO8aNLIloFB+qNOSwQ892iNr1WUlUbynuVYPvI4TUYvqPF5dBiMIg9QckhBEIT7haqgGIA/LiYx8YOfdPpKOj7emB2L+rH4jWfksob2VjW+lqTRnrsoM6WSmlWnUGg/zu9meipxhyIIwkPvaEQsv/wVr1M2f9MpAEb4PSb3jbTxaFQ7F6yLZ1LyUOHScxr7kZe4QxEE4aH30Tdn9MouRKVpX9TBt/2ydxO1d9KSj/MyfSi1d/YqEQFFEISHWka2CtAmdPxwSle9jvZvDumuKeLftQVdnnC5o2sWZWmzE9fJLYQkYXprxcaS9e2NRTzyEgThofa/368D0KWNC57NHPQSKtpY6n5MThj0xB1fM+HLYAA0quotxJV/7RyZv+/D0X+y/uiwMnco1pZmTB/5FE88WkuP6KpI3KEIgvBQS7mZD0CvLs0AeHtEe0b4PSbvn/lyxzq7tlRUml6+ODcTSdKQcmADUQsH69eVNCR+M4+8qxHELH+FvKtniFo4WK5b0hFf8hjNt72b0VPAiDsUQRAeaiW5uUo+fM2Upgzv5cnjLRry/fEonnjUsaLDa0VRTgbXV4yn/jODyT7zo8E6mvxcne0b2xbqVpA7Tu7eqpFVDigFBQXEx8djamqKi4sLZmZmddkuQRAEo/jnegbNXPQnF7Zt6UjblrUbTDSFajT5uo+5Mv84gKWb9o4o70qEocNI+2kzmSf3lHve4txMEre8B4Aq5kLtNLYGKg0oly5dYvXq1Rw/fhwLCwtMTU0pKCigW7duBAYG0qpVK2O0UxAEodbdzFYTn5KLg51xFqKKXjpCryzt0Ke4jvtQu1HOiLKKgglAzMoJd9y22lBhQFm7di1//PEHL774IgsXLsTe3h6AnJwcTpw4wcKFC+nYsSOTJ9+dRGSCIAh34r1PTwKQnnVnCR1z/zmNwlSJtUe7Gh0v599SlHZrRy0cjEOPsdTv5F/5CTRFOpsJX8/B6pE2NPAZWqP21FSFAaVVq1a88cYbeuW2trb07t2b3r1787///a/OGicIglCX/o3LBKBHx0fu6DxJO7Sr0rYI3qW3r6STvKLZ6+rEf2/V0S1P/98XFKbGVbs9qpgLqGIu3FsBpUePHpWeoLI6q1ev5sCBAwD4+voyY8YMtm3bxubNm1EoFLRp04Z58+Zhbm7O6tWr2bVrF3Z22ueZQ4cOZeTIkVy8eJHg4GByc3Pp0KED8+bNQ6kU4wkEQai53PzSrLwDfFrU2XVi17yBpiCfZm9/UW6dtB83AVCQfF1vX/Zf989PpMyKAAAgAElEQVSX9gqHDYeFhfHdd98BkJ+fz9SpU2nfvj0jRowgOjq60pOHh4dz4sQJdu/ezZ49e7hw4QKffPIJmzZtYuvWrXz//fdoNBq++eYbACIjI1m2bBlhYWGEhYXJywQHBQUxZ84cDh06hCRJbN++/Q7ftiAID7sjf5R+eDdvYl9p/dSDGw0O561MUWYymvzsKtUtm3q+PM1nVf3zL++q4U7+ulJuQDl9+jRr166lWbNmAHz66adoNBr27duHv78/wcHBlZ7c0dGRmTNnYm5ujpmZGR4eHhQUFDB37lxsbW1RKBS0atWKhIQEQBtQNmzYgL+/P/Pnz0etVhMfH49KpcLb2xuAgIAADh48WAtvXRCEh1lyen616mdF3NnnjkZdvesZ0uydr1CYmNIieJfBx2u3K0iJveNrVke5AWXNmjW4u7sTHh7O6tWrCQsLw9bWlu+++460tDSioqJYvXp1hSdv2bKlHAiio6M5cOAA/fv355lntBk709PT2bJlC927dyc3N5fWrVsTFBTE7t27ycrKYu3atSQnJ+PoWDp0z9HRkaSkpNp474IgPMScHGqeLRi0S+5GL3uZwsxkg/vTj20l9/IpeTth87t3dD0AE0ubatUvruKdUW0pN6C0a9cOT09PAgIC6Ny5M5IkMXXqVF544QX69OmDmZkZAQEBVbrIlStXGDduHDNmzJDveJKSknj55ZcZPHgwnTp1wsbGho0bN+Lh4YFSqWTcuHEcO3YMjUaj05l1N1MzC4Lw4LAwMwXA2rJm/bFJu0LR5OcQu3qi3r6C5BhunthB0s6lpWVJ12rW0Ao4DZqms9102uc621JhAcZU7m/yxRdf5KWXXiI1NZU///yTMWPG0KRJE3777Tc++ugjnnvuOZo0aVLpBSIiIpg6dSqzZ8+mX79+APz777+MHz+e0aNHM27cOAASEhIIDw/nxRdfBLSBQ6lU4uzsTEpK6ZoBqampODk53dGbFgRBKCrWjr5aE/R8zY7PSit3X9zGt2t0zts1n7mV/JgLFOdkYPvEc3r7Tes5yK9LHoHV8+4hd+Rn/bGfRr1erZW2VEW5dyiurq5s3boVT09Ppk2bxssvvwyAWq2mb9++zJkzp9KTJyYmMmnSJEJDQ+VgkpOTw6uvvsqbb74pBxMAS0tLPvzwQ2JjY5EkiS1bttCzZ09cXV2xsLAgIkLbuRQWFoaPj88dvWlBEIS//tE+qrI0N63R8eU9KVHfqNqdSNO3PsNl1Lxy9z8ydSMKUzOsW3hT78luBq9n0biZXpljP/07JmOp8F5PpVIxduxYnTJfX198fX3l7StXrtCyZUuDx2/atAm1Ws2SJUvksr59+5Kamsrnn3/O559rb8+ef/553nzzTebPn8/EiRMpLCykffv2vPLKKwCEhoYSEhJCTk4OXl5ejBkzpkZvVhAEocRvkTcAUJrWMEeuieHj4jdNr9Lhpjb2WNmUP7pMWebuo9wmWFjT7J2vkDTFBvcrlOZVakttqTCgfPLJJ9jZ2TF8+HA8PDx09kVFRbF582aysrL46KOPDB4fEhJCSEiIXnlgYKDB+r169aJXr1565Z6enuzcubOipgqCIOiIS84mO7cQz2YNKux3Na1pQFHUfrJ2lxFz0RTkY9W8bZWPMdRRb99lkDZdi5GXbKwwoCxdupQDBw4wZcoU1Go1TZs2RaPRcP36dSwtLZk0aZL8KEsQBOFuS8nI59Bv0fwWmUjMjdIRTruX+uvciZRkGAZQmiqQJImM41uxbeOLecPK+4YB1HGX9MqKVboZgRv4Difj2Lfytu0Tvlg4t8C61X/kMrfAFcRteBMAy2ZP1MqgIxMLbZCpyryW2lTp8IY+ffrQp08frly5QlRUFAqFgubNm5f7mEsQBMHY/vf7dVZs+5PHmjbgckyG3v7P9l7QWRirbB2FQkFRdgY3T+wk++zPNJ36SYXXUidGobRraHBfzEelj+ObvvMVppY2NHj2xQrPZ97IDbfAFWjUebU2gtXEwrpWzlNdVR4v17JlSxFEBEG4J63Y9idQGigG+XrQxNGWtTvPArD3lygmDHoClbqIkPXhXL6urffOyKcAKM7T5vQqzk4jauFgmoxdjKWrNpN6UVYahTdL577FfxaEhYtuF4AhptWYM2LeyK3KdavCxPIeDyiCIAj3g3atHHl1QBsAktPz2PnTFXnfuaupcjBp0sgGj6gd5Nk/R8YJ3T7ahC9m0XTaF+RF/UlK2Aq9a5Qkc7xXmZjf2aTNmhIBRRCE+56DnSVFxRr6PN2Mkb085fKG9qVL4MYmZZOr0vYprJ/ZHVdHW6IWruPG5RMGzxmzfGydtrkuVXdGfa1d965cVRCEB4pGI1FQaHjoqjGYmCj4z+POjOrdWqcf4krsTfn11bibfHvoMgDWFrXzXdrKo73BnFruEytOS1XXTC1ttT+rMPS4NlUpoKSkpDBhwgR69epFamoqr776KsnJhvPXCILwcIlLzmZg0PcMnrmPwTP38b/f9VOw1zWNRoOpqX6Htodr6TyPvb9EkZiWi6mJggZ2lnp1a6LeE9o5eQ7Pj9YpN3NwqZXz15TCTDv/RGFq3IdQVbravHnz6NGjB19//TX29vZ4enoSEhLCJ59UPBpCEIT7j0YjEXb8X06eT6Srtyv+XUvXClmz8yzXEjIZ3K0ll2PSSc7I55e/4uX9BYXFrNj2Jz3+c2cLVlXH/vBr5a64+PSTTdgYFglAVLy2431oj9pZtlxhZomt17MA2HceiHWrjpg3dK2Vc9+pkgmNUpFxhw1X6Q4lPj6eoUOHYmJigpmZGUFBQSQmJtZ12wRBuAv2/RrFZ3svcDE6nU/2nOf74//y7Y+XSb2Zz8GT0VyOyWDRF7+z6+erOsGkrBNn48nNL+TYmThy8wu5ma1GkiQ0GolXFvzI53sv6NTPyStAXckjs8wcNRqN/kS9dbvOAXDotxi9fY3qW7H3o4H4dWpK8a1jS/pVpGpO+rPvNEBnu/mMLfJrhUJxzwQTKB02XJ0JkrWhSncoCoUCjUYjb+fk5OhsC4LwYCgq1rBxT6ROWck3/G8O6U/kK1HP2pzsvNLMth989QctmtgTlZCJbzs3jv0Zx6g+nnT1diX1Zj7fHb3KqQs3CH7lP7g3rsfwdw/QzMWOVdO7GTx/QkoOgUuO4NPOlaBRHar9vpwblg6jLZ3gWPWAUtJP0rDHy8R9Or1OMgfXJhNzSx6ZsgHTClK71IUqBRQ/Pz+mT59OdnY2W7duZceOHfTp06eu2yYIghEt2HSK8/9qM3tbmJvySON6Op3ahvi2c8PaSklXb1dmr/1VZ19UgvYR07E/tWuiX4rO4PTf2vkcNlZmxKfkcCU2A3WB9s4kOjGr3OvcSMsD4Pif8QSN6sDNbDWXY9Lxfqxqmccfb146EbEkoGhum9XeePAMbDw7kbJvLdlnj5SWD5mpU89tfGiVrnm3Ke0aGf+aVan0+uuvs2fPHjQaDeHh4QwbNkxOMy8Iwv0vI0vF73/fkLfnvdaF3y/cqDSgjB/Yhvr1LIhNqnwhJ40kyRMPxw9ow4ptf7L82z9pVL90zkR6lgoHAx3m3/xYene09fBl4lNyOBoRx+Qh3pVeF6B1MwecHKxJTs+Tr5f2vy916th4dgLAsf8bNOozAW6NFlOY1Cwb8cOoSn0oRUVFmJiY8PHHH/Puu++Sm5tb7eePgiDcu8bMO6Sz3cDOgr+upBis+8UcP4Z0b8mSSc9Sv54FAE0cbXXqLJ/mS4N6FrRu5sDCiU8DuvmzOrVxxvzWAlduTqXHzv3kpN71VAVFOqlSthy8RE6etrP5s72RevUNMTFR8OnsHmx9vy9eLbR3Kznnfi63vsJUicLEVASTaqpSQHnvvfc4evSo9gATEyIiIli0aFFdtksQhLvIsb4Vw3oaHg3V0N6KMX0flz+YAUxNdIfsPupWn6/e683SKV158lFHmjrXkzvdG9pbUs/anOCx2gSJhUWl/bGGHnt9tCVCr+yPi9pHZ3mq0iDl3lgbmCRJMviFV6FQYGNlZvA93f5YS6iZKj3y+uuvv9i3bx8ADRs2ZMWKFQwcOLBOGyYIwp0rSI7BxNK23GSGAAmpOXplZkpTGtrrpu/o3tEdH+/yc041srckNVPFK/299PYpFAqKbgWOMX0fv3UN7ffZC1GlKx9aWejeEew5dlVetwSgeRM7riUY7mtxVaRRkBwjr5Zo28aHoqw0bJ/wxc67e7ntBrBp1bHC/ULVVCmgFBYWUlBQgLm5dmxzUVFRJUcIgnAvKPlwNTSb+5c/4zl7NYUjpw1PRFTm3MAEDRpM2LWkv/yIqkT6sa3YtOooJ0p8a1h7QjaE095Tv6NcoYCCQm1AUd6agNjAzkKvnslt2XY3fa87vHjyEG/2/hJF5zYuqAqK+HirNinkCoevoADiNm6T6+ZEHgdAdf0C9Z7wRWGqRFOoJnrpCJ1zmjkab87Mg65KAeW5557j1VdfZeDAgSgUCvbt26ezaqMgCPeP3PxC5n36Gxej0wFo6lxPZ+0QkChIiYXdIbz+SEeaD5ygE0yK87MxMbPk5okd3DyxQw5WbVs5svcjw08uFChISNWOqopP0f50c6qnV8+6nEdSHVo3Zmj3VrR6pIGcIRighas9Cz47Vel7jg4dTfP/fkvW6R/09rkM018EUKiZKvWhzJgxg549e3LkyBGOHj1Kz549efvtt6t0gdWrV9OvXz/69evH0qVLAQgPD8ff3x8/Pz+WL18u17148SIBAQH06tWL4OBg+U4oISGBkSNH0rt3byZOnEhubq7BawmCULHiYg2TPvyJi9HpNG9ix+rp3Vgd9LzO7HE/y/PEffIWAI/lnMZ8y2vkXTtL1MLBFKRcJ2bZWBK3LtA5b97VCKIWDqYwPcHgdcsubnjpViAD7SOsssqu716y5jvA3PGdad1cPy9V8yb2BI2sfF6KVKSdI5P+8xa9fRU9DhSqp0oBxdTUlDFjxrBu3TpWrVrFiBEjMClnPeWywsPDOXHiBLt372bPnj1cuHCBffv2MXv2bNauXcv+/fuJjIzk2LFjAAQFBTFnzhwOHTqEJEls374d0KZ+GTFiBAcPHqRNmzasXbv2Dt6yIDx8bmareWfFMX78/TppmSoAlr/lS1MX7Qf6s21LVyl8ykJ/0t6Nb+YDkHflDwBUMaWPoqIWDubGNu0gHVXCVYqy0sj5u3ROiiRJmEqls+AHdm0ur4E+Zag3o/qUZgc2UShQxV9BlXCVM5e1o8y6PFFxXixNFdOLZFcwqkuoHRU+8ho+fDjffvst7dq1M7iS2JkzZyo8uaOjIzNnzpT7Xjw8PIiOjqZp06a4u7sD4O/vz8GDB3n00UdRqVR4e2vHlQcEBLBy5UqGDBnC6dOnWbNmjVw+atQogoKCqv9uBeEhNfq9gwD8c107r6R7R3edtdTLLo/rbJpZ/okqWUe97NohGnUeqfvXAzAJ2GrRmZPqVtTf+w7XvlPRZMxCWrp70tK9AX9eTuFCVBpNne1I+EI74mp3unb1wzdfalfhNRXxfxksb9jzFdIOf17atr36GYCdXqjakxahaioMKCtWaP9xfP755zg6Olb75GVXeIyOjubAgQOMGjVK51xOTk4kJSWRnJysU+7o6EhSUhIZGRnY2tqiVCp1ygVB0CVJEueuptK6mYNeB/rthnbXHRJspjShhTIJC0XFA27Sf9pc5faUBJMSw2x+QwFQqL1DSvgqGPc31mDWwJkZgx7htWU38GzmALeNETA01FdTqCbrjwPYd/LH8lfdJLVmDZvg/voqAJ2AcjunF97G9vFnqvx+hMpVGFCcnLSjNWbOnMnBgwdrfJErV64QGBjIjBkzMDU1JTo6Wt4nSZKcK6zsXVBJecnPsmpr3WVBeJAMmP49AH26NOONFytOCujSSHcBJisLJW/aHSqndu15yeY3ne3YtZNwm7Ccm19M40MHiJT070ayzx0l5Yd1oNEGuxbBu0jZu4rciyflrLolSgJUZQyNehPuXJX6UFxdXTlz5kyNEkJGREQwduxY3nnnHV544QWcnZ1JSSmdgZuSkoKTk5NeeWpqKk5OTjg4OJCdnU1xcbFOfUEQDDtwMrrSOrd/KVNmGe5MN4a4T6bJr29PJtzFswEpe1fJwQS0fTa5F7Uz6tN+3KRT//Zg8sjUT2u5tUJFqjRs+N9//2XEiBEolUrMzc3lu4bK+lASExOZNGkSy5cvp0uXLgC0bduWa9euERMTg5ubG/v27WPw4MG4urpiYWFBREQETz31FGFhYfj4+GBmZkaHDh3Yv38//v7+7NmzBx8fnzt/54LwACk2kNbdEG+zaAotG5Dxy3YK0xOxeawzORd/JffvXys/2AjMcktHdimQGJa+7o7Op6zXgBbBu4haOPhOmyZUQZUCypYt+kPtqmLTpk2o1WqWLFkilw0bNowlS5YwZcoU1Go1vr6+9O7dG4DQ0FBCQkLIycnBy8uLMWO0nXJz585l5syZrFu3DhcXF5YtW1aj9gjCg+rvMrPNDbFV5GNnouKVetrJfhnaH/Lkv9spzCyQCg0vWnVbTaxatKVB16EkfDm7Ok02qPWfpZl8n7e8AEUFFdSuukemfsr1leMB7WJYQt1QSJVkedyyZQvXrl2jc+fO9OjRw1jtqhVxcXF0796dI0eO4OZWfsoIQbjfDZ65T2dN928X9MHGyoxri7RZwYtNLTEtVlX5fM2mbyY6dLTBfe4TVxO7bjL1n3kRh+eGy+Vl7wLcJ64idt0UAOp3HcrNX7brnMN5WAg3tr5f5fZUlegbqR01/eys8A5l8eLFnD17lqeeeoply5YRFxfH2LFj77StgiDUspJgYmdjTlZuAbPX/UpKRj4Lb6Xjqk4wAe2Kfzaencm9pNuJbmrbADMHF4Mf3GaO7hSmxNKo30TMHJro1Lk9oJiYW2LRpCXqhCvVapdwb6swoISHh7N7926USiVjxozhjTfeEAFFEO5RDUxymFLvN0LznuVaSR+7VYWHVMi2jY8cUJoFfY2JecUnc3ttOYUpsZg7VSE3lokpTcYu4tqiITrFuRpzbEzKf8zVbMY3pB/9hvpdBqFR5RK34U15X0lOMeHuqXCUl1KplOd/NG7cmMJC4y54LwhC1bg52TKg/iUaFiTgZ3W+ysdZNXsCt8AVBj+MrVt2oP7TATSd9kWlwQRuratelWACKBQmKAxMklSUsyxvk7GLaT57ByZmFjTq+QpK2waYN3KjRfAuXEa+pz3WXH9hLsG4qjRsuISpqVhsRhDqWklakupIvZlPe/4GoJvlxaodZKLEZeR7mDdyw8Raf+1xhYkpDt1GYmqtn8Txjt1auKrptC9o+vYXmDV0BeBakeEpAZaurQwGIAATS+06KMp6IifX3VZhQFGpVPz9999cuHCBCxcu6G0LglC7cv7+lWuLh1KQpjsvJCMhnrQ03ZQoi774nRHv7udq3E0Uhfk6+1Y4fMWat58t9zouI9+j6VulczTqP/2C/LpRn8A7eQtVoriVC9DUuh6mVvVwGTkPgIuFrnp1K+tot3BujtPg6dple4W7qsI+FLVazeTJk3XKSrYVCgVHjhypu5YJwkOoZMJeQXI05g2bkPXnYUyt7MjYtZQrhY3R+AWx/cgVZo/tyMnziQBMW36MFQ5b9c5ldnwN5SVSsXRvjcK09H9/C6emAJhY2mDX3q9235Qhty2tW9KWF21+r9HpbD273HGThDtXYUD56aefjNUOQRDKuBafyUc7DjHHtDRPVUuzJN7cdQ6Atz82PH+krPyos+XuKxtMABQW1tTvOhTb1k/XsMXVo7dWeznplJq+9ZkRWiPUlmr1oQiCUMdufa5eS8iU08yXZakoYLD1Kcwp5MlHG/F8B/fauaxCgYPPS5g71s75KlK/61CUt6VIUZS3HIZpleZeC/cIEVAE4R6UfzWC/5hf1Sv/oMFWfCwvE9I5i5De9XjJ4hhzx1W+HnrZfghFw6a12tbqcvB5yUCCV8N3KAql4RUchXuTCP+CcA8IXvcr566msqLRaQA6WkTR0SKq3Pr2//xA4j/a5WwdLp6Qyy3cHkMdd1mnbrMZ3wDa0VAaVQ6N/cbUdvPv2O2P4EqY3JZNWLi3VXiHcvFi+cMPt27V7wQUBKFmzl1N1b7QVLweSWVcX16Ec5k10lsE78LEzAIAEwvtXBJTKzuDx95Nhu5ERBqV+0+FASUwMJB9+/bplKlUKoKCgsQyvIJQixRIKKj+8hCGWHsYXuGwKDPFYPm9SAST+1OFj7w2b97MpEmTuHDhAkFBQURHRzN16lQcHR3ZvXu3sdooCA+8RfW3YV1BypHqavbOV+WutS7d4V2QIJSnwjuUpk2bsnXrVuLj4xk+fDjDhg2jd+/efPbZZzRsKGalCkJNSZJE4U3t2h8bdp+rlWDiFli6nruJpQ1K2/oG6xVliCW0hbpRaae8paUlLi4unDp1CnNzc7p06SKW4BWEO5T1x37SfvwMi8Hz2XfiGj0dqn6s6/iPkIoKsXRtCUDmHwdBKsa8UcVpxi1cPFAn/ouJle2dNL1GWgTvQioupKLVMtzfWEPs2klGbJVQ2yoMKImJiUydOhUrKyt++OEHIiMjmTx5MoGBgSLrsCDcgfwYbeqidV8cwZwm1TrWonEznW37Dr2rdJzTC2+T/tNmrJq2qdb1aovC1KycwcFayvpiae/7XYWPvF544QWefvppvvjiCxo1asRzzz3Ht99+y65du5g6dWqVL5KTk0P//v2Ji4vj2LFjDBw4UP6vc+fOBAZqcwetXr2abt26yftKVoq8ePEiAQEB9OrVi+DgYIqKxDNg4f6iKSog84+DSJK2413SaH8+a3mZDx2+rfBYt9dXya/NKrkLqYhZA2caDw66Z+d2lJf8Ubh/VHiHsmTJEp577jmdsmbNmrF161ZmzZpVpQucPXuWkJAQoqOjAfD19cXX1xeAlJQUhg8fLp8rMjKSZcuW0a6d7iiVoKAg3n//fby9vZk9ezbbt29nxIgRVbq+INwLMo5tJfO3MNIObWR1Vk8m22nnm7Qyu2GwvkO3Udh36o/CVPvhb9m0DaqYSJyHzDRam+8Gt9dXYmpVB9mNBaOo8CvBc889xz///MOPP/5IUlJpR56NjQ0rV66s0gW2b9/O3LlzcXLSv51dunQpw4YNo1mzZoA2oGzYsAF/f3/mz5+PWq0mPj4elUqFt7c3AAEBARw8eLCq708Q7gnFeVny68l2hyutb+P1jBxMAJyH/JfGL87AzMGlTtp3rzBv6Iqp9b03T0aomgoDyq5duxg1ahQbN25kwIABnDhxoqLqBi1cuJAOHTrolUdHR/P7778zZox21m5ubi6tW7cmKCiI3bt3k5WVxdq1a0lOTsbR0VE+ztHRUSe4CcL9ICUjt8p1m8/eiZm97hcwEwtrbB7rVNvNEoRaVWFA2bx5M3v37mXHjh2sX7+eTz75pKLq1bJt2zZGjBiBubk2tYKNjQ0bN27Ew8MDpVLJuHHjOHbsGBqNRmdUmSRJYpSZcM/K+GUHyXtX65UXV2Oorvj3LdyvKu0Fa9y4MQDt2rUjIyOj1i585MgR+vbtK28nJCSwc+dOeVuSJJRKJc7OzqSklM7wTU1NNfj4TBDuBRnHt5Jz7me9ctucmHKPcR3/UV02SRCMpsKAcvs3pdpaAjg9PR2VSoW7e2mqbEtLSz788ENiY2ORJIktW7bQs2dPXF1dsbCwICIiAoCwsDB8fHxqpR2CYAwXotLK3dd89g4sGjcTqUaEB0K1sg3X1q14XFwczs666yE4ODgwf/58Jk6cSGFhIe3bt+eVV14BIDQ0lJCQEHJycvDy8pL7XQThXhJ9NdpgedSlq3jdem31eFfy//5F3ld2qKyFm2e5s9sF4X6gkCqYuvr4449jaWkpb6tUKiwtLeV+jDNnzhilkTUVFxdH9+7dOXLkCG5uNR+/LwiVkYoKufbBMHlb6fMKDeysyYo4RFKugvpZVwBwe3szqssnobgIC7fH9CYpCsK9oKafnRXeoRw+XPnwRkEQ0AkmAEXHP6ek56/sPYfSzAw77+5Ga5cgGFOFAcXV1RVJkiguLkapVJKTk0N4eDitWrWS544IwsNOKq565gZFLfVDCsK9qMJO+atXr9K9e3d++eUXVCoVQ4YMYfny5YwePZpff/3VWG0UhHuSplBN1MLBXNsYVOVjRHoR4UFW4b/upUuX8tZbb9GtWzd++OEHJEnihx9+YPv27axataqiQwXhgZCybw03ti3SK9eo84heeiv9T9p1I7dKEO5NlWYbHjBgAACnTp2iR48emJiY4OLiQk5OjlEaKAh3U/bZn/TKpKJCokNHG6zfLGgL6htRSAUqTKztSPj8v3XdREG4Z1R4h2JiUrr7zz//pGPHjvK2Wq2uu1YJwj1KU5BP9LKx5e43MbfE6pHHsX60PZZNHjVewwThHlDhHYq9vT2XLl0iJyeHlJQUOaCcOXNGnkEvCA+DlB/Wob4RRcGNqBqfw+31qiVUFYT7VYUB5e2332bs2LHk5OQwffp0rK2t2bRpE+vXr2fNmjXGaqMg3HXZf/2v0jqF1o30yiw7DUZ1SjsL3ryha623SxDuJRUGFG9vb44fP45KpcLOTptSul27duzYsUMMGxYeejaPP0P2pVOYaLTDhpUq/Vx3Lt2Hc+3ULhoPrdr6QYJwP6s09Yq5ubmcERigffv2ddogQbgfOPpPod6Tz5G0cxO2l/cDoNAU69VTKBQiT5fw0KhWLi9BeFjkXPiF5D0fG9xXNkB4Pt+PuFsBxbL/DKO0TRDuVWKWlSAYkPFr1e4qzB1Kk5zaNfeqoKYgPPhEQHmAaQrVaNR5d7sZ9x1JkihMiTW4r+jxPuUep7QwL3efIDwMREB5gMVtfLvcCXiCYUU5GVxb9KLBfYU2TrR6YbxeeVLjpwFQKopxOYQAAB4ZSURBVMUTZOHhJv4PeIAVZdy42024b0iShuwzh9EUlj9h16ZHoMHyTuOmoVJNQmkqvp8JDzcRUIS7Kv3oN5g7N8fWs8tdbUfyno/J/bvihKeWjVwMlpuYmGBtbWlwnyA8TERAEe6qm7c6v23v8tDayoIJgKWNtRFaIgj3L6MElJycHIYNG8b69etxc3Nj1qxZREREYGVlBcDkyZPp2bMn4eHhLF68GLVaTZ8+fZg2bRoAFy9eJDg4mNzcXDp06MC8efPE82qh1qQd/txgufPwOVg+0pp8dTHXo+OxrVfPyC0ThPtLnT/0PXv2LMOHDyc6Oloui4yM5OuvvyYsLIywsDB69uyJSqVi9uzZrF27lv379xMZGcmxY8cACAoKYs6cORw6dAhJkti+fXtdN1t4iGT+vs9guXWLtpgozbGxsaK1l0j0KAiVqfOAsn37dubOnYuTkxMA+fn5JCQkMHv2bPz9/Vm5ciUajYZz587RtGlT3N3dUSqV+Pv7c/DgQeLj41GpVHh7ewMQEBDAwYMH67rZgiAIQjXV+XOjhQsX6mynpqbSuXNn5s6dS7169QgMDGTnzp1YW1vj6Ogo13NyciIpKYnk5GSdckdHR5KSkuq62cJdkLx3NYWpcbi+suRuNwWnF96+200QhPuO0cc5uru7s2bNGpycnLCysmL06NEcO3YMjUaDQqGQ60mShEKhKLdcePDknPsZdcKVWj2nOvFfJEmq9nG2jz9Tq+0QhIeB0QPK5cuXOXTokLwtSRJKpRJnZ2dSUlLk8pSUFJycnPTKU1NT5cdnglCRG9sXE//ZDK4tepHM0/sN1inOz5ZfWzz3qrGaJggPJKMHFEmSWLRoEZmZmRQWFrJt2zZ69uxJ27ZtuXbtGjExMRQXF7Nv3z58fHxwdXXFwsKCiIgIAMLCwvDx8TF2s4X7UN6VP+TXaT9uMlgn+/wv8mvXZ/riPHwOzWfvqPO2CcKDyOhjbz09PZkwYQLDhw+nqKgIPz8/+vfvD8CSJUuYMmUKarUaX19fevfuDUBoaCghISHk5OTg5eXFmDFjjN1s4QGVflgbaP4uaEILtCO7BEGoGaMFlJ9++kl+PXLkSEaOHKlXp0uXLnz//fd65Z6enuzcubNO2yc8WKTiIr2yqIWDaT5rOwoTU719aRZuxmiWIDzQRPIhQUdBWjxRCweTe+m3u9YGVdwlinMza3x8xq+7uLbkJYP74jdNN1je3qn8HF6CIFSNmG4uAKBR51GQfJ2iLO0AiJyL4dh4djbe9cskZUz4Mlh+3WTM+1i6t670+NRDn2Lh3IL86PPkRB4vt15B8nX5ddaZH0vLlTbVbbIgCLcRAUUAIGlXKPnXztKo7+vaAklj1OtHLx1hsDzhqxC9JXQ1hWqil46gYa/XsHuqV7np5g3Ja/Gc/Dr1wIbSc+ZlVau9giDoE4+8BADUSde0L0rWRa/B3I26knrbCK2SD/+bv+6qNJgUWDTQ2baOOmqwXq6Fo8FyQRCqTgQUQYf6hjawSJq6v0Op6oTDrNP7KcpKleeMSLeCXnFOeqXHnmgymjkZg4lQN5PLohYORhX/j069fEsRUAThTomA8hCQJA03ti2iIDXO4P6ohYPlb/3Zf/2v5KA6bVNBSmy1HlVdXxXI9TVvAKUBpTy5pva8kz6SDzL96dChNZsWv4Tv5BCdOglfzJJff5zdl6f6+Fej9YIgGCL6UB4Cef+cJu9qBHlXI3hkyico7RoiFReVOxIKqPM+lPyYSIPl/3pP+n979x7WxJX3AfwbSAgoKrKCKCAV79VWbLHrDSxawy0RBa1WxAv62vVhW7u64AVX1gvUItVdvPRetRWrrFYR1oJd+9q+W6yrtMXFReuKWAENIBEIhRCS8/6RMhCTKJdJIPr7PE+fzpw5mfkdB/JjzsycgyE/7jW6jal+0S0YSSiVjkPxbPhSlH0Sjz69xDixLhzqJi1EQt3fTC4uTlCaiOXtbUu5eoSQjqPfoidA5ZmPueVfbnwPANDU3X/oZ1q/x1H5xfu4/53h+0EdpVWrcC/nQ4PyBqfBmBE67aGfbaqpNNodN2zxJtj27KNbEeh+rFsnCYFAgCqxu9F9UjIhhB/0m/QE0NRUcstMo4FKXoyfdxufH71Z/c18FCVGoCgxAjXf56Dq7EE0lPIzcKOpJ7psJH/QW99b85JBnZ93v2r0CsXBQQwbB90EWI5PTzG6/0HR23HFf0d7wyWEtBEllCeNtumh72k8jPx4Ms/B6Bs5THcF8Yk4EjurgzFn4SzUvvyuQb3Sj+P01r/rK4XYzha2Do546o+H0Heq8a48V+cekPl5I2/Ua1xZ3vitPLaAkCcb3UN5wpia7rYttKp6HiPRl984CN6/Lv9+eTCU9Wo8NaA3AOBvE97E0dP5SHY+YvSzs5dFcVMa2IgdHnms517wwadVsSgqvY8tz9CQK4Twha5QrFzdTxfxS1E+b/ur1dqb3MYa+U0o79ZO55ab/Fdyy/2cHLhkAgBzpw/HZ2+FG93HBdUQiEWGY3M9zBAPJyQsn4CDCUHwanUcQkjnUEKxcvK/bcfdz7Z0ej+ne7+Mez294SXTzQlSpDb/exlvJv6OW547ffhD64qEtvjc7XWD8v8OCIKNDU24Rkh3QAnlMVWZY3z+D1N+/9o8jH9jB3qPmgCxxyg4vPQq/nzf+FVBZ7Qes8vWtn0/fm8s8TMoWz73t52OiRDCD0ooj6maS8ZnKGwt97ltAKD3FrmNuAfcF2/D5Cnj8KfXQ9p93OYnw5imCer7coPtFVn675gMXJwEz5h9bdq30NYG4ugP9Mqc+5juoiOEWBbdlH9CyW0HYmHwKLxZvhbDPfsarTPMsy+KOrj/5pcm+/rNQ1//l1s2CPT/hrH3GNGu/fbt2wt3W6239/4JIcR86ArlCVXno/vCX7/4BURMG9bmz7V3nhLF/x3VW7+j6gEAeLu6/Vc/ANDDXoRDyskAgLKejx7WnhBiORZJKEqlElKpFCUlurGkjh49CqlUCplMhvXr16OxsREAsGfPHgQEBCAsLAxhYWFIS0sDABQWFiI8PByBgYGIj49HU5PhbHykhVrR8jd8XM0ieMcfh3f8cdiPn82Vjxo3ukP7vvWX6HZ/prkbrCgxAr3/mwMAmB/g1aHjA8CEWRGIq5qPyx5zO7wPQgj/zJ5Q8vPz8corr6C4uBgAcPPmTXz00Uc4cuQITp06Ba1Wi8OHDwMACgoKsHPnTmRkZCAjI4ObJjg2NhabNm1CTk4OGGNIT083d9hWrfzkX7jlrb9ruZE9ULKQW+7n3LbHZS+pBvMXWCteQ57q8GcDnvfAjMkjsCiErlAI6U7MnlDS09ORkJAAV1dXAICdnR0SEhLg6OgIgUCA4cOHo6ysDIAuobz33nuQyWTYsmULVCoVSktL0dDQAB8fHwBAeHg4srOzzR22VVBe+afRclVZyxApowY7G63T1nsPDROisapqUZvqMo26TfUAQOTU8ceSRUJbvBr+LBx72HV4H4QQ/pk9oSQmJsLX15dbd3d3x+TJuj7wqqoqpKWlYfr06airq8OoUaMQGxuLEydOoKamBvv27UN5eTlcXFq+fFxcXCCXGz491J001VTi3tmDbZ7vo70aSq6iKDEC5Sd3PbRe4ZBIo+VK0W/afKxFIU8j8+0wXHWZ8ci6jfJbbd6vgwM9nUXI46bLnvKSy+VYvnw5IiIi8Nvf6t4l+OCDlkdCo6OjsWHDBvj7+3PDagC6SZlar3dHzQMvigcMhePTuuRZ8+NZiN28IXbrfBeS8j+5BmVFiRHwjj8ObUMdVzZ6quHgig9Op9tWY0cPguqc8W2lB9ZD9cCEVa01MVtoIIBY0HLvy6mXuENxEEK6ry5JKDdu3MDy5csRFRWF6GjdTd6ysjLk5uZizhzdpEuMMQiFQri5uaGiooL7bGVlJdd91t01ym8CT09GUWIEV+a15hPY2vfs1H5rLv7daDljDI33yrj1gf06d5zWhGJ7qFqtaxrqcOvttnWF5XpEwb/0AG+xEEK6J4s/NqxUKrFs2TKsWrWKSyYAYG9vjx07duD27dtgjCEtLQ0zZsyAu7s7xGIx8vLyAAAZGRnw9/e3dNgdYqzLq61fwh1xM2kOyg6s49bteHxHw8aupYuqNv+rR7aDATg/4g3k9I7A7DkS3uIghHRfFr9COXbsGCorK7F//37s368b+XbatGlYtWoVtmzZgpUrV0KtVuO5557D0qVLAQApKSnYuHEjlEolRo8ejUWLzPelzKfq8yfhONpwuBCm1YA1NsCmk1cqlmQrbkkoD77tboxmwTuIHNxyJXnLYQAc6++YJTZCSPdgsYTy1VdfAQCWLFmCJUuWGK0TGBiIwMBAg/KRI0fi2LFj5gzPbEo/XGNQVvW/h1D93Sn0Hh+KfpL2vdfReiZFAKgO3wMci0UfG/MNLQ8ADl5j8PA5HlsoBE54frB+t2QP9yHAf3UJ5a1qGd7jOT5CSNejN+W7QM0P/9D9/+Lf0aRUtOuz1Rf0p+Id6e0CRXASEhr/B3KN+YZitxeLoBK07UZ6XydHgzLx+JaBJnduNv70GSHEulFC4RkzMj2tQR3VL9xyeyetanjgaSoHsRDTfAfh081BcA5bzZVvreH3LXKBQIC+gSvaVFckEhmUeQwexC33dDDcTgixfpRQOqni7+9AXdVyb6Ay+0Oj9RoHvWC0vOTd14yWm1J3/XtuWfh0gN62AUNH4I9VC7CqKgpbVoe2a79tIbDV7yE9WjfBaD1jAz5290e9CSGdRwmlE37eF4PaH/+B2+/8niur/eGM0brDIuOMlgPA/fMn2/wSpIC1XAENmPay3rbePe1wPCUcmW/Pgmf/Xm3aX3vYCPXfTI+MWWq03m9mLOH92ISQ7o+Gr++A6kvZ0NbXoElx95F1B284xv11zgLjIMhJNqhT9dWnqPrqU+6lw5rvz6CpugIi5wHoMWw8bHsYJoeG2ckQ9TF8H8ecVwK2D3Rl9ftNHxj7F7ARGu/SetcmCorqOnxgdCshxNpRQumAeznGvxIZ0+qtuy3YpPcF79DDHg2/Ll9vGoBhQsPHaG/tfhWamkq9suZE06Rsec7Kqafl70M8mChEwpYL3H/iOUzB9w9+RE/y+llmiYsQ0j1Ql1c7/XI9z6AsT/UUai+fw82klhvhvcdL0WPwWL16QnHLU1LGkklRYoRBMgF0VywAoPjmCFdm1wXzStkK9f/+sLVp+fF5VDIhhDz+KKG0UVOtAqo7N3A3Pclg2/PiYlRk7tYr6ycxvL/Qy32I3npdwB9ROCHh0cf+9cqk9ocvuTLn/v3bEjavbMU99NZtbFquvq6Mj7d0OISQboa6vNqg+uJp3DvzUaf342Cv32X0zKSJeAZA0XePOH6VAvY387n12xgAb5HlB1cUOfbhlncLl2MXgLrATfjhUgGmjxsO1UWLh0QI6UYoobQBH8mkWenwl+H+06MnCBMOHImmsqsAAHblDO5eaXl6rM/ErrkXIXLsg37RuyDq44pdPXRDsTzjOxbP+Oq69g6rhqK/7X14d0l0hJCuRgnlIVR3i9r94iEADIjaanKb39x5aCifBHG/gSbr1Grt8XTUVtx+y/jLiU4jnm93THzpPWCQyW2nNP6oq2vCdAvGQwjpPiihmKCSF6P0o1iDcjWzQe7Q14ArOchTDcbqPl8Y1HEY9PRD923v6vnQ7QK34bonqBbsBQ7HGGx3czEc2qQ72L8pEBqteSYVI4R0f5RQTNDUVhmUXR33B/iM98FSF0dotX5YaiNAUaJhQumsa/V98SyAwU/1x9daVwyyKdfbbm/XPYcucRDTjxMhTzJ6ysuExlrDQRtDQqZg4K9XB62fcGpm5+aNXmOntftYh5ST9daVA3UzWAoEAowar+veahJ0zyRCCCHNKKGYUH1LfxBGUavRclu74697XHZ3jQQey3bARWrYRfUo8Yl/0FtfOGs8t9zXNwgCkRhPrUxt934JIcSSKKGYoG7SHzXYU2J8yPVJU8Yhf/JbWL82qsPHsrURILm6ZTDH1m+g2/XzwOC4w7Drax3THhNCnlzU6W0CE7T809gu3GOynkAgwOwXh3b6eG9uXADFbuNzxTf7z3Nr0cBs6LFcQki3ZPYrFKVSCalUipKSEgBAbm4uZDIZJBIJdu3axdUrLCxEeHg4AgMDER8fj6Ym3cyEZWVliIyMRFBQEFauXIm6ujpzhwwAGCJbzC17eQ0w+/H69rZ/ZB1p8AuYE+Jr9lgIIaQjzJpQ8vPz8corr6C4uBgA0NDQgA0bNmDfvn04ffo0CgoK8PXXXwMAYmNjsWnTJuTk5IAxhvR03ct/mzdvxoIFC5CdnY0xY8Zg37595gyZY2PnAOfpi9B/7jqLHA8A/lIThI/w8qMrEkJIN2TWhJKeno6EhAS4uur6/y9fvgwvLy94enpCKBRCJpMhOzsbpaWlaGhogI+PDwAgPDwc2dnZUKvVuHjxIjfPfHO5pThNCEPP4eMfXZEnO7ZF489r+Z1pkRBCLMWs91ASExP11svLy+Hi4sKtu7q6Qi6XG5S7uLhALpdDoVDA0dERwl9HuW0uf1yJRV0whDAhhPDEok95abVavflBGGMQCAQmy5v/3xpNJUsIId2TRROKm5sbKioquPWKigq4uroalFdWVsLV1RXOzs6ora2FRqPRq08IIaT7sWhCGTt2LG7evIlbt25Bo9EgKysL/v7+cHd3h1gsRl6ebvKqjIwM+Pv7QyQSwdfXF6dPnwYAnDx5Ev7+/pYMmRBCSBtZ9D0UsViM7du347XXXoNKpcLUqVMRFBQEAEhJScHGjRuhVCoxevRoLFq0CACQkJCAdevW4Z133sGAAQOwc+dOS4ZMCCGkjQSMscd2eNiSkhJMnz4dZ8+ehYeHR1eHQwghVqGj35009AohhBBePNZDrzTfzL97924XR0IIIdaj+Tuz+Tu0rR7rhNL85FhkpPGBHQkhhJhWUVEBLy+vNtd/rO+hNDQ0oKCgAC4uLrC1pZcGCSGkLTQaDSoqKjBmzBjY2z96nMFmj3VCIYQQYjl0U54QQggvKKEQQgjhBSUUQgghvKCEQgghhBeUUAghhPCCEgohhBBeUEIhhBDCC0ooJmRmZiIkJAQSiQRpaWldHQ4AICoqCqGhoQgLC0NYWBjy8/NNxpmbmwuZTAaJRIJdu3Zx5YWFhQgPD0dgYCDi4+PR1NQEACgrK0NkZCSCgoKwcuVK1NXV8Rq7UqmEVCpFSUkJr/HV1NRgxYoVCA4ORmRkJDc6QmNjI2JjYxEcHIzZs2fjxo0bZmnH+vXrIZFIuHPy5ZdfWqR9nbFnzx6EhoYiNDQUycnJFomX7/NhrA3WeC7++te/IiQkBKGhodi/f79F4jXX7wYAgBEDd+/eZQEBAUyhULC6ujomk8nY9evXuzQmrVbLpkyZwtRqNVdmKs76+no2depU9vPPPzO1Ws2io6PZuXPnGGOMhYaGsh9++IExxtj69etZWloaY4yxFStWsKysLMYYY3v27GHJycm8xf7jjz8yqVTKRo8ezW7fvs1rfJs3b2bvvfceY4yxEydOsFWrVjHGGPvwww/Zn/70J8YYY//617/Y3LlzeW8HY4xJpVIml8v16lmifR317bffsnnz5jGVSsUaGxvZokWLWGZmplWdD2NtOHPmjNWdiwsXLrD58+cztVrN6uvrWUBAACssLLSqc/EgukIxIjc3FxMmTICTkxN69OiBwMBAZGdnd2lMRUVFAIDo6GjMnDkThw4dMhnn5cuX4eXlBU9PTwiFQshkMmRnZ6O0tBQNDQ3w8fEBAISHhyM7OxtqtRoXL15EYGCgXjlf0tPTkZCQwM22yWd8586dg0wmAwBIpVJ88803UKvVOHfuHGbOnAkAGD9+PKqqqlBWVsZrO+rr61FWVoYNGzZAJpMhNTUVWq3WIu3rKBcXF6xbtw52dnYQiUQYMmQIiouLrep8GGtDWVmZ1Z2LF154AZ988gmEQiHu3bsHjUaDmpoaqzoXD6KEYkR5eTlcXFy4dVdXV8jl8i6MSHf5OnHiROzduxcHDhzAkSNHUFZWZjROU/E/WO7i4gK5XA6FQgFHR0cIhUK9cr4kJibC19eXW+czvtafEQqFcHR0RFVVldF9dXbU6QfbUVlZiQkTJiApKQnp6em4dOkSjh07ZpH2ddSwYcO4L6Xi4mJ88cUXEAgEVnU+jLXBz8/P6s4FAIhEIqSmpiI0NBQTJ0602t+NZpRQjNBqtRAIBNw6Y0xvvSuMGzcOycnJ6NWrF5ydnTFnzhykpqYajdNU/KbKjbXPnO01Z3yMMdjY2Bh8prmcT56enti7dy9cXV3h4OCAqKgofP311xZpX2ddv34d0dHRiIuLg6enp1Wej9Zt8Pb2ttpz8frrr+P8+fO4c+cOiouLrfJcNKOEYoSbm5veDbeKigqum6OrXLp0CefPn+fWGWNwd3c3Gqep+B8sr6yshKurK5ydnVFbW8vNfWDu9vIZn6urKyorKwEATU1NqKurg5OTE/r374/y8nKDffHp2rVryMnJ4dYZYxAKhRZpX2fk5eVhyZIlWLNmDWbPnm2V5+PBNljjubhx4wYKCwsBAA4ODpBIJLhw4YLVnYvWKKEYMWnSJJw/fx5VVVWor6/HmTNn4O/v36Ux1dbWIjk5GSqVCkqlEidOnMCOHTuMxjl27FjcvHkTt27dgkajQVZWFvz9/eHu7g6xWIy8vDwAQEZGBvz9/SESieDr64vTp08DAE6ePGnW9vIZ39SpU3Hy5EkAwOnTp+Hr6wuRSISpU6ciIyMDgC4Zi8ViDBw4kNd2MMaQlJSE6upqqNVqHD16FDNmzLBI+zrqzp07iImJQUpKCkJDQwFY3/kw1gZrPBclJSXYuHEjGhsb0djYiLNnz2L+/PlWdS4M8HZ7/zFz6tQpFhoayiQSCXv//fe7OhzGGGO7du1iQUFBTCKRsAMHDjDGTMeZm5vLZDIZk0gkLDExkWm1WsYYY4WFhSwiIoIFBgay1atXM5VKxRhjrKSkhC1cuJAFBwez6Ohodv/+fd7jDwgI4J6O4is+hULBXn31VRYSEsLmzZvH7b+hoYHFxcWxkJAQNmvWLFZQUGCWdhw6dIgFBwezGTNmsB07dnB1zN2+jtq6dSvz8fFhM2fO5P47fPiwVZ0PU22wtnPBGGOpqaksODiYSaVSlpqaapF4zfm7QfOhEEII4QV1eRFCCOEFJRRCCCG8oIRCCCGEF5RQCCGE8IISCiGEEF4IuzoAQqzNiBEjMHz4cIO3i/fu3QsPDw/ej3X+/Hk4Ozvzul9CzIESCiEdcPDgQfqSJ+QBlFAI4dGFCxeQkpKCgQMHoqioCPb29ti+fTuGDBmC2tpabN68GVevXoVAIICfnx9Wr14NoVCI/Px8bNu2DfX19RCJRIiLi8PEiRMBALt370Z+fj7u37+PZcuWcXNbrF27FgqFAoDureg33nijK5tOCN1DIaQjFi9ezE3kFBYWhpiYGG5bQUEBoqKikJmZifDwcMTGxgIAtm3bBicnJ2RmZuL48eO4du0aPv74Y6jVasTExCAmJgZZWVnYunUrkpKSoNVqAegGofz888+xZ88ebN++HWq1Gunp6fDw8MCJEyeQlpaGW7duoba2tkv+LQhpRlcohHTAw7q8Ro4cyQ1zHxERgS1btkChUOCbb77BZ599BoFAADs7O8yfPx8HDx7E5MmTYWNjgxdffBEAMGbMGGRmZnL7k0qlAIBRo0ahsbERSqUSfn5+WLFiBe7cuYNJkyZhzZo16NWrl3kbTcgj0BUKITyztbU1WvbgEORarRZNTU2wtbU1GHL8p59+4qZ4bZ7rorkOYwzPPvsszp49i3nz5qG0tBRz585FQUGBuZpESJtQQiGEZ1evXsXVq1cBAEePHsW4cePQu3dvTJkyBYcOHQJjDI2NjUhPT8ekSZPg7e0NgUCAb7/9FgBw5coVLF68mOvyMiYlJQX79u3DSy+9hPj4eAwdOhTXr1+3SPsIMYUGhySknUw9Nrx69WrY29tj7dq1GDlyJEpLS+Hs7IzExER4eHhAoVBg27ZtuHbtGtRqNfz8/BAXFwc7Ozv8+9//RlJSEn755ReIRCKsW7cOvr6+Bo8NN69rNBqsW7cOcrkcdnZ2GDFiBDZv3gw7O7uu+CchBAAlFEJ4deHCBWzduhVZWVldHQohFkddXoQQQnhBVyiEEEJ4QVcohBBCeEEJhRBCCC8ooRBCCOEFJRRCCCG8oIRCCCGEF5RQCCGE8OL/AXZz84f5mW5JAAAAAElFTkSuQmCC\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('SPX Price ($)')\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
}