AAII_VIX_LSTM.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "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": 2,
   "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>Date</th>\n",
       "      <th>Bullish</th>\n",
       "      <th>Neutral</th>\n",
       "      <th>Bearish</th>\n",
       "      <th>Total</th>\n",
       "      <th>Bullish 8-Week Mov Avg</th>\n",
       "      <th>Bull-Bear Spread</th>\n",
       "      <th>Bullish Average</th>\n",
       "      <th>Bullish Average + St. Dev</th>\n",
       "      <th>Bullish Average - St. Dev</th>\n",
       "      <th>S&amp;P 500 Weekly High</th>\n",
       "      <th>S&amp;P 500 Weekly Low</th>\n",
       "      <th>S&amp;P 500 Weekly Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-06-27</td>\n",
       "      <td>0.295890</td>\n",
       "      <td>0.383562</td>\n",
       "      <td>0.320548</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.288630</td>\n",
       "      <td>-0.024658</td>\n",
       "      <td>0.381644</td>\n",
       "      <td>0.482854</td>\n",
       "      <td>0.280434</td>\n",
       "      <td>2964.15</td>\n",
       "      <td>2911.43</td>\n",
       "      <td>2913.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-06-20</td>\n",
       "      <td>0.295082</td>\n",
       "      <td>0.383607</td>\n",
       "      <td>0.321311</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.300425</td>\n",
       "      <td>-0.026229</td>\n",
       "      <td>0.381644</td>\n",
       "      <td>0.482854</td>\n",
       "      <td>0.280434</td>\n",
       "      <td>2931.74</td>\n",
       "      <td>2874.68</td>\n",
       "      <td>2926.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-06-13</td>\n",
       "      <td>0.268398</td>\n",
       "      <td>0.389610</td>\n",
       "      <td>0.341991</td>\n",
       "      <td>0.999999</td>\n",
       "      <td>0.305443</td>\n",
       "      <td>-0.073593</td>\n",
       "      <td>0.381644</td>\n",
       "      <td>0.482854</td>\n",
       "      <td>0.280434</td>\n",
       "      <td>2910.61</td>\n",
       "      <td>2800.92</td>\n",
       "      <td>2879.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-06-06</td>\n",
       "      <td>0.225275</td>\n",
       "      <td>0.348901</td>\n",
       "      <td>0.425824</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.318847</td>\n",
       "      <td>-0.200549</td>\n",
       "      <td>0.381644</td>\n",
       "      <td>0.482854</td>\n",
       "      <td>0.280434</td>\n",
       "      <td>2827.28</td>\n",
       "      <td>2728.81</td>\n",
       "      <td>2826.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-05-30</td>\n",
       "      <td>0.247934</td>\n",
       "      <td>0.351240</td>\n",
       "      <td>0.400826</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.341048</td>\n",
       "      <td>-0.152892</td>\n",
       "      <td>0.381644</td>\n",
       "      <td>0.482854</td>\n",
       "      <td>0.280434</td>\n",
       "      <td>2868.88</td>\n",
       "      <td>2766.06</td>\n",
       "      <td>2783.02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date   Bullish   Neutral   Bearish     Total  Bullish 8-Week Mov Avg  \\\n",
       "0  2019-06-27  0.295890  0.383562  0.320548  1.000000                0.288630   \n",
       "1  2019-06-20  0.295082  0.383607  0.321311  1.000000                0.300425   \n",
       "2  2019-06-13  0.268398  0.389610  0.341991  0.999999                0.305443   \n",
       "3  2019-06-06  0.225275  0.348901  0.425824  1.000000                0.318847   \n",
       "4  2019-05-30  0.247934  0.351240  0.400826  1.000000                0.341048   \n",
       "\n",
       "   Bull-Bear Spread  Bullish Average  Bullish Average + St. Dev  \\\n",
       "0         -0.024658         0.381644                   0.482854   \n",
       "1         -0.026229         0.381644                   0.482854   \n",
       "2         -0.073593         0.381644                   0.482854   \n",
       "3         -0.200549         0.381644                   0.482854   \n",
       "4         -0.152892         0.381644                   0.482854   \n",
       "\n",
       "   Bullish Average - St. Dev  S&P 500 Weekly High  S&P 500 Weekly Low  \\\n",
       "0                   0.280434              2964.15             2911.43   \n",
       "1                   0.280434              2931.74             2874.68   \n",
       "2                   0.280434              2910.61             2800.92   \n",
       "3                   0.280434              2827.28             2728.81   \n",
       "4                   0.280434              2868.88             2766.06   \n",
       "\n",
       "   S&P 500 Weekly Close  \n",
       "0               2913.78  \n",
       "1               2926.46  \n",
       "2               2879.84  \n",
       "3               2826.15  \n",
       "4               2783.02  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#notclean = pd.read_csv(\"add file path\\AAII_Sentiment.csv\")\n",
    "notclean.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Bullish</th>\n",
       "      <th>Neutral</th>\n",
       "      <th>Bearish</th>\n",
       "      <th>S&amp;P 500 Weekly High</th>\n",
       "      <th>S&amp;P 500 Weekly Low</th>\n",
       "      <th>S&amp;P 500 Weekly Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-06-27</td>\n",
       "      <td>0.295890</td>\n",
       "      <td>0.383562</td>\n",
       "      <td>0.320548</td>\n",
       "      <td>2964.15</td>\n",
       "      <td>2911.43</td>\n",
       "      <td>2913.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-06-20</td>\n",
       "      <td>0.295082</td>\n",
       "      <td>0.383607</td>\n",
       "      <td>0.321311</td>\n",
       "      <td>2931.74</td>\n",
       "      <td>2874.68</td>\n",
       "      <td>2926.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-06-13</td>\n",
       "      <td>0.268398</td>\n",
       "      <td>0.389610</td>\n",
       "      <td>0.341991</td>\n",
       "      <td>2910.61</td>\n",
       "      <td>2800.92</td>\n",
       "      <td>2879.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-06-06</td>\n",
       "      <td>0.225275</td>\n",
       "      <td>0.348901</td>\n",
       "      <td>0.425824</td>\n",
       "      <td>2827.28</td>\n",
       "      <td>2728.81</td>\n",
       "      <td>2826.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-05-30</td>\n",
       "      <td>0.247934</td>\n",
       "      <td>0.351240</td>\n",
       "      <td>0.400826</td>\n",
       "      <td>2868.88</td>\n",
       "      <td>2766.06</td>\n",
       "      <td>2783.02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date   Bullish   Neutral   Bearish  S&P 500 Weekly High  \\\n",
       "0  2019-06-27  0.295890  0.383562  0.320548              2964.15   \n",
       "1  2019-06-20  0.295082  0.383607  0.321311              2931.74   \n",
       "2  2019-06-13  0.268398  0.389610  0.341991              2910.61   \n",
       "3  2019-06-06  0.225275  0.348901  0.425824              2827.28   \n",
       "4  2019-05-30  0.247934  0.351240  0.400826              2868.88   \n",
       "\n",
       "   S&P 500 Weekly Low  S&P 500 Weekly Close  \n",
       "0             2911.43               2913.78  \n",
       "1             2874.68               2926.46  \n",
       "2             2800.92               2879.84  \n",
       "3             2728.81               2826.15  \n",
       "4             2766.06               2783.02  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aaii_sentiment = notclean.drop(['Total','Bullish 8-Week Mov Avg', 'Bull-Bear Spread', 'Bullish Average', 'Bullish Average + St. Dev', 'Bullish Average - St. Dev'], axis=1)\n",
    "aaii_sentiment.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Bullish</th>\n",
       "      <th>Neutral</th>\n",
       "      <th>Bearish</th>\n",
       "      <th>S&amp;P 500 Weekly High</th>\n",
       "      <th>S&amp;P 500 Weekly Low</th>\n",
       "      <th>S&amp;P 500 Weekly Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1667</th>\n",
       "      <td>1987-06-26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1666</th>\n",
       "      <td>1987-07-17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>314.59</td>\n",
       "      <td>307.63</td>\n",
       "      <td>314.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1665</th>\n",
       "      <td>1987-07-24</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.14</td>\n",
       "      <td>311.39</td>\n",
       "      <td>307.81</td>\n",
       "      <td>309.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1664</th>\n",
       "      <td>1987-07-31</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.26</td>\n",
       "      <td>318.66</td>\n",
       "      <td>310.65</td>\n",
       "      <td>318.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1663</th>\n",
       "      <td>1987-08-07</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.29</td>\n",
       "      <td>323.00</td>\n",
       "      <td>316.23</td>\n",
       "      <td>323.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date  Bullish  Neutral  Bearish  S&P 500 Weekly High  \\\n",
       "1667  1987-06-26      NaN      NaN      NaN                  NaN   \n",
       "1666  1987-07-17      NaN      NaN      NaN               314.59   \n",
       "1665  1987-07-24     0.36     0.50     0.14               311.39   \n",
       "1664  1987-07-31     0.26     0.48     0.26               318.66   \n",
       "1663  1987-08-07     0.56     0.15     0.29               323.00   \n",
       "\n",
       "      S&P 500 Weekly Low  S&P 500 Weekly Close  \n",
       "1667                 NaN                   NaN  \n",
       "1666              307.63                314.59  \n",
       "1665              307.81                309.27  \n",
       "1664              310.65                318.66  \n",
       "1663              316.23                323.00  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aaii_sentiment = aaii_sentiment.sort_values(by='Date', ascending=True)\n",
    "aaii_sentiment.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Bullish</th>\n",
       "      <th>Neutral</th>\n",
       "      <th>Bearish</th>\n",
       "      <th>S&amp;P 500 Weekly High</th>\n",
       "      <th>S&amp;P 500 Weekly Low</th>\n",
       "      <th>S&amp;P 500 Weekly Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1987-06-26</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987-07-17</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>314.59</td>\n",
       "      <td>307.63</td>\n",
       "      <td>314.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987-07-24</th>\n",
       "      <td>0.36</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.14</td>\n",
       "      <td>311.39</td>\n",
       "      <td>307.81</td>\n",
       "      <td>309.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987-07-31</th>\n",
       "      <td>0.26</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.26</td>\n",
       "      <td>318.66</td>\n",
       "      <td>310.65</td>\n",
       "      <td>318.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987-08-07</th>\n",
       "      <td>0.56</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.29</td>\n",
       "      <td>323.00</td>\n",
       "      <td>316.23</td>\n",
       "      <td>323.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Bullish  Neutral  Bearish  S&P 500 Weekly High  \\\n",
       "Date                                                         \n",
       "1987-06-26      NaN      NaN      NaN                  NaN   \n",
       "1987-07-17      NaN      NaN      NaN               314.59   \n",
       "1987-07-24     0.36     0.50     0.14               311.39   \n",
       "1987-07-31     0.26     0.48     0.26               318.66   \n",
       "1987-08-07     0.56     0.15     0.29               323.00   \n",
       "\n",
       "            S&P 500 Weekly Low  S&P 500 Weekly Close  \n",
       "Date                                                  \n",
       "1987-06-26                 NaN                   NaN  \n",
       "1987-07-17              307.63                314.59  \n",
       "1987-07-24              307.81                309.27  \n",
       "1987-07-31              310.65                318.66  \n",
       "1987-08-07              316.23                323.00  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aaii_sentiment = aaii_sentiment.set_index(pd.DatetimeIndex(aaii_sentiment['Date']))\n",
    "aaii_sentiment = aaii_sentiment.drop(['Date'], axis=1)\n",
    "aaii_sentiment.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>VIX Open</th>\n",
       "      <th>VIX High</th>\n",
       "      <th>VIX Low</th>\n",
       "      <th>VIX Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/2/2004</td>\n",
       "      <td>17.96</td>\n",
       "      <td>18.68</td>\n",
       "      <td>17.54</td>\n",
       "      <td>18.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/5/2004</td>\n",
       "      <td>18.45</td>\n",
       "      <td>18.49</td>\n",
       "      <td>17.44</td>\n",
       "      <td>17.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/6/2004</td>\n",
       "      <td>17.66</td>\n",
       "      <td>17.67</td>\n",
       "      <td>16.19</td>\n",
       "      <td>16.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/7/2004</td>\n",
       "      <td>16.72</td>\n",
       "      <td>16.75</td>\n",
       "      <td>15.50</td>\n",
       "      <td>15.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1/8/2004</td>\n",
       "      <td>15.42</td>\n",
       "      <td>15.68</td>\n",
       "      <td>15.32</td>\n",
       "      <td>15.61</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Date  VIX Open  VIX High  VIX Low  VIX Close\n",
       "0  1/2/2004     17.96     18.68    17.54      18.22\n",
       "1  1/5/2004     18.45     18.49    17.44      17.49\n",
       "2  1/6/2004     17.66     17.67    16.19      16.73\n",
       "3  1/7/2004     16.72     16.75    15.50      15.50\n",
       "4  1/8/2004     15.42     15.68    15.32      15.61"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfn = pd.read_csv(\"Z:\\Dokumente\\Studium\\Bachelorthesis\\VIX_2004-present.csv\")\n",
    "dfn.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th>0</th>\n",
       "      <td>01/02/1990</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>17.24</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01/03/1990</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.19</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01/04/1990</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.22</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01/05/1990</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01/08/1990</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.26</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date  VIX Open  VIX High  VIX Low  VIX Close\n",
       "0  01/02/1990       NaN       NaN      NaN      17.24\n",
       "1  01/03/1990       NaN       NaN      NaN      18.19\n",
       "2  01/04/1990       NaN       NaN      NaN      19.22\n",
       "3  01/05/1990       NaN       NaN      NaN      20.11\n",
       "4  01/08/1990       NaN       NaN      NaN      20.26"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfo = pd.read_csv(\"Z:\\Dokumente\\Studium\\Bachelorthesis\\VIX_1990-2003.csv\")\n",
    "dfo.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "frames = [dfo, dfn]\n",
    "vix = pd.concat(frames)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>2</th>\n",
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       "      <th>4</th>\n",
       "      <td>01/08/1990</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>01/09/1990</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.20</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>01/10/1990</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>01/11/1990</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>01/12/1990</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>01/15/90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>01/16/90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>01/17/90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>01/18/90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.34</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>01/19/90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>01/22/90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>01/23/90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>01/24/90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>01/25/90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>01/26/90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>01/29/90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>01/30/90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>01/31/90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>02/01/1990</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.87</td>\n",
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       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>02/02/1990</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.32</td>\n",
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       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>02/05/1990</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>02/06/1990</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>02/07/1990</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>02/08/1990</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>02/09/1990</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>02/12/1990</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3875</th>\n",
       "      <td>05/28/2019</td>\n",
       "      <td>16.55</td>\n",
       "      <td>17.70</td>\n",
       "      <td>15.90</td>\n",
       "      <td>17.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3876</th>\n",
       "      <td>05/29/2019</td>\n",
       "      <td>18.55</td>\n",
       "      <td>19.04</td>\n",
       "      <td>17.62</td>\n",
       "      <td>17.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3877</th>\n",
       "      <td>05/30/2019</td>\n",
       "      <td>17.47</td>\n",
       "      <td>18.11</td>\n",
       "      <td>16.72</td>\n",
       "      <td>17.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3878</th>\n",
       "      <td>05/31/2019</td>\n",
       "      <td>19.05</td>\n",
       "      <td>19.72</td>\n",
       "      <td>18.01</td>\n",
       "      <td>18.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3879</th>\n",
       "      <td>06/03/2019</td>\n",
       "      <td>19.41</td>\n",
       "      <td>19.75</td>\n",
       "      <td>18.16</td>\n",
       "      <td>18.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3880</th>\n",
       "      <td>06/04/2019</td>\n",
       "      <td>18.74</td>\n",
       "      <td>18.74</td>\n",
       "      <td>16.97</td>\n",
       "      <td>16.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3881</th>\n",
       "      <td>06/05/2019</td>\n",
       "      <td>17.06</td>\n",
       "      <td>17.49</td>\n",
       "      <td>16.04</td>\n",
       "      <td>16.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3882</th>\n",
       "      <td>06/06/2019</td>\n",
       "      <td>16.25</td>\n",
       "      <td>16.54</td>\n",
       "      <td>15.36</td>\n",
       "      <td>15.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3883</th>\n",
       "      <td>06/07/2019</td>\n",
       "      <td>15.65</td>\n",
       "      <td>16.44</td>\n",
       "      <td>15.33</td>\n",
       "      <td>16.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3884</th>\n",
       "      <td>06/10/2019</td>\n",
       "      <td>16.30</td>\n",
       "      <td>16.47</td>\n",
       "      <td>15.84</td>\n",
       "      <td>15.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3885</th>\n",
       "      <td>06/11/2019</td>\n",
       "      <td>15.84</td>\n",
       "      <td>16.70</td>\n",
       "      <td>15.50</td>\n",
       "      <td>15.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3886</th>\n",
       "      <td>06/12/2019</td>\n",
       "      <td>16.26</td>\n",
       "      <td>16.43</td>\n",
       "      <td>15.78</td>\n",
       "      <td>15.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3887</th>\n",
       "      <td>06/13/2019</td>\n",
       "      <td>16.16</td>\n",
       "      <td>16.21</td>\n",
       "      <td>15.61</td>\n",
       "      <td>15.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3888</th>\n",
       "      <td>06/14/2019</td>\n",
       "      <td>16.00</td>\n",
       "      <td>16.40</td>\n",
       "      <td>15.21</td>\n",
       "      <td>15.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3889</th>\n",
       "      <td>06/17/2019</td>\n",
       "      <td>15.65</td>\n",
       "      <td>15.76</td>\n",
       "      <td>15.24</td>\n",
       "      <td>15.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3890</th>\n",
       "      <td>06/18/2019</td>\n",
       "      <td>14.89</td>\n",
       "      <td>15.54</td>\n",
       "      <td>14.62</td>\n",
       "      <td>15.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3891</th>\n",
       "      <td>06/19/2019</td>\n",
       "      <td>15.05</td>\n",
       "      <td>15.71</td>\n",
       "      <td>14.15</td>\n",
       "      <td>14.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3892</th>\n",
       "      <td>06/20/2019</td>\n",
       "      <td>14.04</td>\n",
       "      <td>16.03</td>\n",
       "      <td>13.19</td>\n",
       "      <td>14.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3893</th>\n",
       "      <td>06/21/2019</td>\n",
       "      <td>14.74</td>\n",
       "      <td>15.48</td>\n",
       "      <td>14.42</td>\n",
       "      <td>15.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3894</th>\n",
       "      <td>06/24/2019</td>\n",
       "      <td>15.46</td>\n",
       "      <td>15.56</td>\n",
       "      <td>15.23</td>\n",
       "      <td>15.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3895</th>\n",
       "      <td>06/25/2019</td>\n",
       "      <td>15.43</td>\n",
       "      <td>16.68</td>\n",
       "      <td>15.10</td>\n",
       "      <td>16.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3896</th>\n",
       "      <td>06/26/2019</td>\n",
       "      <td>16.10</td>\n",
       "      <td>16.60</td>\n",
       "      <td>15.47</td>\n",
       "      <td>16.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3897</th>\n",
       "      <td>06/27/2019</td>\n",
       "      <td>15.66</td>\n",
       "      <td>16.40</td>\n",
       "      <td>15.66</td>\n",
       "      <td>15.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3898</th>\n",
       "      <td>06/28/2019</td>\n",
       "      <td>15.70</td>\n",
       "      <td>16.13</td>\n",
       "      <td>15.08</td>\n",
       "      <td>15.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3899</th>\n",
       "      <td>07/01/2019</td>\n",
       "      <td>13.85</td>\n",
       "      <td>14.64</td>\n",
       "      <td>13.80</td>\n",
       "      <td>14.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3900</th>\n",
       "      <td>07/02/2019</td>\n",
       "      <td>14.16</td>\n",
       "      <td>14.30</td>\n",
       "      <td>12.90</td>\n",
       "      <td>12.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3901</th>\n",
       "      <td>07/03/2019</td>\n",
       "      <td>13.18</td>\n",
       "      <td>13.19</td>\n",
       "      <td>12.56</td>\n",
       "      <td>12.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3902</th>\n",
       "      <td>07/05/2019</td>\n",
       "      <td>13.00</td>\n",
       "      <td>14.47</td>\n",
       "      <td>12.04</td>\n",
       "      <td>13.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3903</th>\n",
       "      <td>07/08/2019</td>\n",
       "      <td>13.73</td>\n",
       "      <td>14.44</td>\n",
       "      <td>13.64</td>\n",
       "      <td>13.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3904</th>\n",
       "      <td>07/09/2019</td>\n",
       "      <td>14.47</td>\n",
       "      <td>14.71</td>\n",
       "      <td>13.99</td>\n",
       "      <td>14.09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7437 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date  VIX Open  VIX High  VIX Low  VIX Close\n",
       "0     01/02/1990       NaN       NaN      NaN      17.24\n",
       "1     01/03/1990       NaN       NaN      NaN      18.19\n",
       "2     01/04/1990       NaN       NaN      NaN      19.22\n",
       "3     01/05/1990       NaN       NaN      NaN      20.11\n",
       "4     01/08/1990       NaN       NaN      NaN      20.26\n",
       "5     01/09/1990       NaN       NaN      NaN      22.20\n",
       "6     01/10/1990       NaN       NaN      NaN      22.44\n",
       "7     01/11/1990       NaN       NaN      NaN      20.05\n",
       "8     01/12/1990       NaN       NaN      NaN      24.64\n",
       "9       01/15/90       NaN       NaN      NaN      26.34\n",
       "10      01/16/90       NaN       NaN      NaN      24.18\n",
       "11      01/17/90       NaN       NaN      NaN      24.16\n",
       "12      01/18/90       NaN       NaN      NaN      24.34\n",
       "13      01/19/90       NaN       NaN      NaN      22.50\n",
       "14      01/22/90       NaN       NaN      NaN      26.70\n",
       "15      01/23/90       NaN       NaN      NaN      24.72\n",
       "16      01/24/90       NaN       NaN      NaN      25.39\n",
       "17      01/25/90       NaN       NaN      NaN      25.63\n",
       "18      01/26/90       NaN       NaN      NaN      26.28\n",
       "19      01/29/90       NaN       NaN      NaN      26.44\n",
       "20      01/30/90       NaN       NaN      NaN      27.25\n",
       "21      01/31/90       NaN       NaN      NaN      25.36\n",
       "22    02/01/1990       NaN       NaN      NaN      24.87\n",
       "23    02/02/1990       NaN       NaN      NaN      24.32\n",
       "24    02/05/1990       NaN       NaN      NaN      24.54\n",
       "25    02/06/1990       NaN       NaN      NaN      24.69\n",
       "26    02/07/1990       NaN       NaN      NaN      24.29\n",
       "27    02/08/1990       NaN       NaN      NaN      23.77\n",
       "28    02/09/1990       NaN       NaN      NaN      23.69\n",
       "29    02/12/1990       NaN       NaN      NaN      24.38\n",
       "...          ...       ...       ...      ...        ...\n",
       "3875  05/28/2019     16.55     17.70    15.90      17.50\n",
       "3876  05/29/2019     18.55     19.04    17.62      17.90\n",
       "3877  05/30/2019     17.47     18.11    16.72      17.30\n",
       "3878  05/31/2019     19.05     19.72    18.01      18.71\n",
       "3879  06/03/2019     19.41     19.75    18.16      18.86\n",
       "3880  06/04/2019     18.74     18.74    16.97      16.97\n",
       "3881  06/05/2019     17.06     17.49    16.04      16.09\n",
       "3882  06/06/2019     16.25     16.54    15.36      15.93\n",
       "3883  06/07/2019     15.65     16.44    15.33      16.30\n",
       "3884  06/10/2019     16.30     16.47    15.84      15.94\n",
       "3885  06/11/2019     15.84     16.70    15.50      15.99\n",
       "3886  06/12/2019     16.26     16.43    15.78      15.91\n",
       "3887  06/13/2019     16.16     16.21    15.61      15.82\n",
       "3888  06/14/2019     16.00     16.40    15.21      15.28\n",
       "3889  06/17/2019     15.65     15.76    15.24      15.35\n",
       "3890  06/18/2019     14.89     15.54    14.62      15.15\n",
       "3891  06/19/2019     15.05     15.71    14.15      14.33\n",
       "3892  06/20/2019     14.04     16.03    13.19      14.75\n",
       "3893  06/21/2019     14.74     15.48    14.42      15.40\n",
       "3894  06/24/2019     15.46     15.56    15.23      15.26\n",
       "3895  06/25/2019     15.43     16.68    15.10      16.28\n",
       "3896  06/26/2019     16.10     16.60    15.47      16.21\n",
       "3897  06/27/2019     15.66     16.40    15.66      15.82\n",
       "3898  06/28/2019     15.70     16.13    15.08      15.08\n",
       "3899  07/01/2019     13.85     14.64    13.80      14.06\n",
       "3900  07/02/2019     14.16     14.30    12.90      12.93\n",
       "3901  07/03/2019     13.18     13.19    12.56      12.57\n",
       "3902  07/05/2019     13.00     14.47    12.04      13.28\n",
       "3903  07/08/2019     13.73     14.44    13.64      13.96\n",
       "3904  07/09/2019     14.47     14.71    13.99      14.09\n",
       "\n",
       "[7437 rows x 5 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VIX Open</th>\n",
       "      <th>VIX High</th>\n",
       "      <th>VIX Low</th>\n",
       "      <th>VIX Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>01/02/1990</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/03/1990</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/04/1990</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/05/1990</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/08/1990</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            VIX Open  VIX High  VIX Low  VIX Close\n",
       "Date                                              \n",
       "01/02/1990       NaN       NaN      NaN      17.24\n",
       "01/03/1990       NaN       NaN      NaN      18.19\n",
       "01/04/1990       NaN       NaN      NaN      19.22\n",
       "01/05/1990       NaN       NaN      NaN      20.11\n",
       "01/08/1990       NaN       NaN      NaN      20.26"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vix.index = pd.to_datetime(vix.index)\n",
    "vix = vix.set_index('Date')\n",
    "vix.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Bullish</th>\n",
       "      <th>Neutral</th>\n",
       "      <th>Bearish</th>\n",
       "      <th>S&amp;P 500 Weekly High</th>\n",
       "      <th>S&amp;P 500 Weekly Low</th>\n",
       "      <th>S&amp;P 500 Weekly Close</th>\n",
       "      <th>VIX Open</th>\n",
       "      <th>VIX High</th>\n",
       "      <th>VIX Low</th>\n",
       "      <th>VIX Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1990-01-05</th>\n",
       "      <td>0.40</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.30</td>\n",
       "      <td>359.69</td>\n",
       "      <td>352.20</td>\n",
       "      <td>352.20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-12</th>\n",
       "      <td>0.45</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.17</td>\n",
       "      <td>353.79</td>\n",
       "      <td>339.93</td>\n",
       "      <td>339.93</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-19</th>\n",
       "      <td>0.31</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.20</td>\n",
       "      <td>340.75</td>\n",
       "      <td>337.00</td>\n",
       "      <td>339.15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-26</th>\n",
       "      <td>0.24</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.38</td>\n",
       "      <td>331.61</td>\n",
       "      <td>325.80</td>\n",
       "      <td>325.80</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-02-02</th>\n",
       "      <td>0.15</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.48</td>\n",
       "      <td>330.92</td>\n",
       "      <td>322.98</td>\n",
       "      <td>330.92</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Bullish  Neutral  Bearish  S&P 500 Weekly High  \\\n",
       "Date                                                         \n",
       "1990-01-05     0.40     0.30     0.30               359.69   \n",
       "1990-01-12     0.45     0.38     0.17               353.79   \n",
       "1990-01-19     0.31     0.49     0.20               340.75   \n",
       "1990-01-26     0.24     0.38     0.38               331.61   \n",
       "1990-02-02     0.15     0.37     0.48               330.92   \n",
       "\n",
       "            S&P 500 Weekly Low  S&P 500 Weekly Close  VIX Open  VIX High  \\\n",
       "Date                                                                       \n",
       "1990-01-05              352.20                352.20       NaN       NaN   \n",
       "1990-01-12              339.93                339.93       NaN       NaN   \n",
       "1990-01-19              337.00                339.15       NaN       NaN   \n",
       "1990-01-26              325.80                325.80       NaN       NaN   \n",
       "1990-02-02              322.98                330.92       NaN       NaN   \n",
       "\n",
       "            VIX Low  VIX Close  \n",
       "Date                            \n",
       "1990-01-05      NaN      20.11  \n",
       "1990-01-12      NaN      24.64  \n",
       "1990-01-19      NaN      22.50  \n",
       "1990-01-26      NaN      26.28  \n",
       "1990-02-02      NaN      24.32  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aaii_vix = pd.merge(aaii_sentiment, vix, how='inner', left_index=True, right_index=True)\n",
    "aaii_vix.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>Bullish</th>\n",
       "      <th>Neutral</th>\n",
       "      <th>Bearish</th>\n",
       "      <th>S&amp;P 500 Weekly High</th>\n",
       "      <th>S&amp;P 500 Weekly Low</th>\n",
       "      <th>S&amp;P 500 Weekly Close</th>\n",
       "      <th>VIX Open</th>\n",
       "      <th>VIX High</th>\n",
       "      <th>VIX Low</th>\n",
       "      <th>VIX Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1990-01-05</th>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>359.69</td>\n",
       "      <td>352.20</td>\n",
       "      <td>352.20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-12</th>\n",
       "      <td>0.450000</td>\n",
       "      <td>0.380000</td>\n",
       "      <td>0.170000</td>\n",
       "      <td>353.79</td>\n",
       "      <td>339.93</td>\n",
       "      <td>339.93</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-19</th>\n",
       "      <td>0.310000</td>\n",
       "      <td>0.490000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>340.75</td>\n",
       "      <td>337.00</td>\n",
       "      <td>339.15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-26</th>\n",
       "      <td>0.240000</td>\n",
       "      <td>0.380000</td>\n",
       "      <td>0.380000</td>\n",
       "      <td>331.61</td>\n",
       "      <td>325.80</td>\n",
       "      <td>325.80</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-02-02</th>\n",
       "      <td>0.150000</td>\n",
       "      <td>0.370000</td>\n",
       "      <td>0.480000</td>\n",
       "      <td>330.92</td>\n",
       "      <td>322.98</td>\n",
       "      <td>330.92</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-02-09</th>\n",
       "      <td>0.190000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.510000</td>\n",
       "      <td>333.75</td>\n",
       "      <td>329.66</td>\n",
       "      <td>333.62</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-02-16</th>\n",
       "      <td>0.230000</td>\n",
       "      <td>0.430000</td>\n",
       "      <td>0.340000</td>\n",
       "      <td>334.89</td>\n",
       "      <td>330.08</td>\n",
       "      <td>332.72</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-02-23</th>\n",
       "      <td>0.240000</td>\n",
       "      <td>0.380000</td>\n",
       "      <td>0.380000</td>\n",
       "      <td>327.99</td>\n",
       "      <td>324.15</td>\n",
       "      <td>324.15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-03-02</th>\n",
       "      <td>0.210000</td>\n",
       "      <td>0.370000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>335.54</td>\n",
       "      <td>328.67</td>\n",
       "      <td>335.54</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-03-09</th>\n",
       "      <td>0.220000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.360000</td>\n",
       "      <td>340.27</td>\n",
       "      <td>333.74</td>\n",
       "      <td>337.93</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-03-16</th>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>341.91</td>\n",
       "      <td>336.00</td>\n",
       "      <td>341.91</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-03-23</th>\n",
       "      <td>0.370000</td>\n",
       "      <td>0.390000</td>\n",
       "      <td>0.240000</td>\n",
       "      <td>343.53</td>\n",
       "      <td>335.65</td>\n",
       "      <td>337.22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-03-30</th>\n",
       "      <td>0.340000</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>0.310000</td>\n",
       "      <td>342.00</td>\n",
       "      <td>337.63</td>\n",
       "      <td>339.94</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-04-06</th>\n",
       "      <td>0.380000</td>\n",
       "      <td>0.370000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>343.64</td>\n",
       "      <td>338.70</td>\n",
       "      <td>340.08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-04-12</th>\n",
       "      <td>0.270000</td>\n",
       "      <td>0.560000</td>\n",
       "      <td>0.170000</td>\n",
       "      <td>344.34</td>\n",
       "      <td>341.37</td>\n",
       "      <td>344.34</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-04-20</th>\n",
       "      <td>0.340000</td>\n",
       "      <td>0.430000</td>\n",
       "      <td>0.230000</td>\n",
       "      <td>344.74</td>\n",
       "      <td>335.12</td>\n",
       "      <td>335.12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-04-27</th>\n",
       "      <td>0.280000</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>0.370000</td>\n",
       "      <td>332.92</td>\n",
       "      <td>329.11</td>\n",
       "      <td>329.11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-05-04</th>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>0.450000</td>\n",
       "      <td>338.39</td>\n",
       "      <td>330.80</td>\n",
       "      <td>338.39</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-05-11</th>\n",
       "      <td>0.280000</td>\n",
       "      <td>0.380000</td>\n",
       "      <td>0.340000</td>\n",
       "      <td>352.00</td>\n",
       "      <td>340.53</td>\n",
       "      <td>352.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-05-18</th>\n",
       "      <td>0.370000</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>0.280000</td>\n",
       "      <td>354.75</td>\n",
       "      <td>354.00</td>\n",
       "      <td>354.64</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-05-25</th>\n",
       "      <td>0.390000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>0.290000</td>\n",
       "      <td>359.29</td>\n",
       "      <td>354.58</td>\n",
       "      <td>354.58</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-06-01</th>\n",
       "      <td>0.370000</td>\n",
       "      <td>0.280000</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>363.16</td>\n",
       "      <td>360.65</td>\n",
       "      <td>363.16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-06-08</th>\n",
       "      <td>0.350000</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>367.40</td>\n",
       "      <td>358.71</td>\n",
       "      <td>358.71</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-06-15</th>\n",
       "      <td>0.430000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>366.25</td>\n",
       "      <td>361.63</td>\n",
       "      <td>362.91</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-06-22</th>\n",
       "      <td>0.340000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>0.340000</td>\n",
       "      <td>360.47</td>\n",
       "      <td>355.43</td>\n",
       "      <td>355.43</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-06-29</th>\n",
       "      <td>0.310000</td>\n",
       "      <td>0.310000</td>\n",
       "      <td>0.380000</td>\n",
       "      <td>358.02</td>\n",
       "      <td>352.06</td>\n",
       "      <td>358.02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-07-06</th>\n",
       "      <td>0.280000</td>\n",
       "      <td>0.340000</td>\n",
       "      <td>0.380000</td>\n",
       "      <td>360.16</td>\n",
       "      <td>355.68</td>\n",
       "      <td>358.42</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-07-13</th>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>367.31</td>\n",
       "      <td>356.49</td>\n",
       "      <td>367.31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-07-20</th>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>368.95</td>\n",
       "      <td>361.61</td>\n",
       "      <td>361.61</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-07-27</th>\n",
       "      <td>0.520000</td>\n",
       "      <td>0.240000</td>\n",
       "      <td>0.240000</td>\n",
       "      <td>357.09</td>\n",
       "      <td>353.44</td>\n",
       "      <td>353.44</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-06</th>\n",
       "      <td>0.379433</td>\n",
       "      <td>0.315603</td>\n",
       "      <td>0.304965</td>\n",
       "      <td>2800.18</td>\n",
       "      <td>2655.89</td>\n",
       "      <td>2700.06</td>\n",
       "      <td>23.53</td>\n",
       "      <td>25.94</td>\n",
       "      <td>20.94</td>\n",
       "      <td>21.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-13</th>\n",
       "      <td>0.209040</td>\n",
       "      <td>0.302260</td>\n",
       "      <td>0.488701</td>\n",
       "      <td>2785.93</td>\n",
       "      <td>2583.23</td>\n",
       "      <td>2651.07</td>\n",
       "      <td>21.00</td>\n",
       "      <td>21.57</td>\n",
       "      <td>20.34</td>\n",
       "      <td>20.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-20</th>\n",
       "      <td>0.248649</td>\n",
       "      <td>0.278378</td>\n",
       "      <td>0.472973</td>\n",
       "      <td>2685.44</td>\n",
       "      <td>2488.96</td>\n",
       "      <td>2506.96</td>\n",
       "      <td>26.09</td>\n",
       "      <td>30.30</td>\n",
       "      <td>24.68</td>\n",
       "      <td>28.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-27</th>\n",
       "      <td>0.315476</td>\n",
       "      <td>0.181548</td>\n",
       "      <td>0.502976</td>\n",
       "      <td>2585.29</td>\n",
       "      <td>2346.58</td>\n",
       "      <td>2467.70</td>\n",
       "      <td>31.20</td>\n",
       "      <td>33.80</td>\n",
       "      <td>29.66</td>\n",
       "      <td>29.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-03</th>\n",
       "      <td>0.330189</td>\n",
       "      <td>0.242138</td>\n",
       "      <td>0.427673</td>\n",
       "      <td>2520.27</td>\n",
       "      <td>2346.58</td>\n",
       "      <td>2510.03</td>\n",
       "      <td>25.68</td>\n",
       "      <td>26.60</td>\n",
       "      <td>24.05</td>\n",
       "      <td>25.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-10</th>\n",
       "      <td>0.384615</td>\n",
       "      <td>0.321678</td>\n",
       "      <td>0.293706</td>\n",
       "      <td>2595.32</td>\n",
       "      <td>2443.96</td>\n",
       "      <td>2584.96</td>\n",
       "      <td>20.98</td>\n",
       "      <td>21.32</td>\n",
       "      <td>19.29</td>\n",
       "      <td>19.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-17</th>\n",
       "      <td>0.335347</td>\n",
       "      <td>0.302115</td>\n",
       "      <td>0.362538</td>\n",
       "      <td>2625.76</td>\n",
       "      <td>2562.02</td>\n",
       "      <td>2616.10</td>\n",
       "      <td>19.51</td>\n",
       "      <td>19.72</td>\n",
       "      <td>17.85</td>\n",
       "      <td>18.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-24</th>\n",
       "      <td>0.376590</td>\n",
       "      <td>0.300254</td>\n",
       "      <td>0.323155</td>\n",
       "      <td>2675.47</td>\n",
       "      <td>2585.10</td>\n",
       "      <td>2638.70</td>\n",
       "      <td>19.69</td>\n",
       "      <td>20.25</td>\n",
       "      <td>18.63</td>\n",
       "      <td>18.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-31</th>\n",
       "      <td>0.317568</td>\n",
       "      <td>0.364865</td>\n",
       "      <td>0.317568</td>\n",
       "      <td>2690.44</td>\n",
       "      <td>2612.86</td>\n",
       "      <td>2681.05</td>\n",
       "      <td>17.39</td>\n",
       "      <td>17.72</td>\n",
       "      <td>16.54</td>\n",
       "      <td>16.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-07</th>\n",
       "      <td>0.398734</td>\n",
       "      <td>0.373418</td>\n",
       "      <td>0.227848</td>\n",
       "      <td>2738.98</td>\n",
       "      <td>2648.34</td>\n",
       "      <td>2731.61</td>\n",
       "      <td>15.66</td>\n",
       "      <td>17.89</td>\n",
       "      <td>15.51</td>\n",
       "      <td>16.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-14</th>\n",
       "      <td>0.351032</td>\n",
       "      <td>0.398230</td>\n",
       "      <td>0.250737</td>\n",
       "      <td>2761.85</td>\n",
       "      <td>2681.83</td>\n",
       "      <td>2753.03</td>\n",
       "      <td>15.61</td>\n",
       "      <td>17.27</td>\n",
       "      <td>15.30</td>\n",
       "      <td>16.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-21</th>\n",
       "      <td>0.393189</td>\n",
       "      <td>0.352941</td>\n",
       "      <td>0.253870</td>\n",
       "      <td>2787.33</td>\n",
       "      <td>2703.79</td>\n",
       "      <td>2779.76</td>\n",
       "      <td>14.00</td>\n",
       "      <td>15.17</td>\n",
       "      <td>13.85</td>\n",
       "      <td>14.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-28</th>\n",
       "      <td>0.416327</td>\n",
       "      <td>0.383673</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>2795.76</td>\n",
       "      <td>2764.55</td>\n",
       "      <td>2792.38</td>\n",
       "      <td>15.17</td>\n",
       "      <td>15.24</td>\n",
       "      <td>14.39</td>\n",
       "      <td>14.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-07</th>\n",
       "      <td>0.373860</td>\n",
       "      <td>0.358663</td>\n",
       "      <td>0.267477</td>\n",
       "      <td>2816.88</td>\n",
       "      <td>2767.66</td>\n",
       "      <td>2771.45</td>\n",
       "      <td>16.33</td>\n",
       "      <td>17.81</td>\n",
       "      <td>15.54</td>\n",
       "      <td>16.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-14</th>\n",
       "      <td>0.324232</td>\n",
       "      <td>0.365188</td>\n",
       "      <td>0.310580</td>\n",
       "      <td>2821.24</td>\n",
       "      <td>2722.27</td>\n",
       "      <td>2810.92</td>\n",
       "      <td>13.35</td>\n",
       "      <td>13.84</td>\n",
       "      <td>13.16</td>\n",
       "      <td>13.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-21</th>\n",
       "      <td>0.373016</td>\n",
       "      <td>0.392857</td>\n",
       "      <td>0.234127</td>\n",
       "      <td>2852.42</td>\n",
       "      <td>2799.78</td>\n",
       "      <td>2824.23</td>\n",
       "      <td>14.11</td>\n",
       "      <td>14.56</td>\n",
       "      <td>13.26</td>\n",
       "      <td>13.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-28</th>\n",
       "      <td>0.332000</td>\n",
       "      <td>0.396000</td>\n",
       "      <td>0.272000</td>\n",
       "      <td>2860.31</td>\n",
       "      <td>2785.02</td>\n",
       "      <td>2805.37</td>\n",
       "      <td>15.38</td>\n",
       "      <td>15.62</td>\n",
       "      <td>14.41</td>\n",
       "      <td>14.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-04</th>\n",
       "      <td>0.350211</td>\n",
       "      <td>0.367089</td>\n",
       "      <td>0.282700</td>\n",
       "      <td>2885.25</td>\n",
       "      <td>2787.72</td>\n",
       "      <td>2873.40</td>\n",
       "      <td>13.84</td>\n",
       "      <td>14.03</td>\n",
       "      <td>13.40</td>\n",
       "      <td>13.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-11</th>\n",
       "      <td>0.402878</td>\n",
       "      <td>0.393285</td>\n",
       "      <td>0.203837</td>\n",
       "      <td>2895.95</td>\n",
       "      <td>2865.17</td>\n",
       "      <td>2888.21</td>\n",
       "      <td>13.37</td>\n",
       "      <td>13.58</td>\n",
       "      <td>12.91</td>\n",
       "      <td>13.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-18</th>\n",
       "      <td>0.375635</td>\n",
       "      <td>0.406091</td>\n",
       "      <td>0.218274</td>\n",
       "      <td>2918.00</td>\n",
       "      <td>2879.13</td>\n",
       "      <td>2900.45</td>\n",
       "      <td>12.80</td>\n",
       "      <td>13.12</td>\n",
       "      <td>12.02</td>\n",
       "      <td>12.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-25</th>\n",
       "      <td>0.335227</td>\n",
       "      <td>0.463068</td>\n",
       "      <td>0.201705</td>\n",
       "      <td>2936.83</td>\n",
       "      <td>2891.90</td>\n",
       "      <td>2927.25</td>\n",
       "      <td>13.29</td>\n",
       "      <td>14.30</td>\n",
       "      <td>12.81</td>\n",
       "      <td>13.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-02</th>\n",
       "      <td>0.390244</td>\n",
       "      <td>0.396341</td>\n",
       "      <td>0.213415</td>\n",
       "      <td>2954.13</td>\n",
       "      <td>2912.84</td>\n",
       "      <td>2923.73</td>\n",
       "      <td>14.35</td>\n",
       "      <td>15.92</td>\n",
       "      <td>13.80</td>\n",
       "      <td>14.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-09</th>\n",
       "      <td>0.431159</td>\n",
       "      <td>0.336957</td>\n",
       "      <td>0.231884</td>\n",
       "      <td>2954.13</td>\n",
       "      <td>2862.60</td>\n",
       "      <td>2879.42</td>\n",
       "      <td>21.40</td>\n",
       "      <td>23.38</td>\n",
       "      <td>18.87</td>\n",
       "      <td>19.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-16</th>\n",
       "      <td>0.298246</td>\n",
       "      <td>0.308772</td>\n",
       "      <td>0.392982</td>\n",
       "      <td>2897.96</td>\n",
       "      <td>2801.43</td>\n",
       "      <td>2850.96</td>\n",
       "      <td>16.69</td>\n",
       "      <td>17.13</td>\n",
       "      <td>15.16</td>\n",
       "      <td>15.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-23</th>\n",
       "      <td>0.247059</td>\n",
       "      <td>0.392157</td>\n",
       "      <td>0.360784</td>\n",
       "      <td>2892.15</td>\n",
       "      <td>2815.08</td>\n",
       "      <td>2856.27</td>\n",
       "      <td>15.93</td>\n",
       "      <td>18.05</td>\n",
       "      <td>15.28</td>\n",
       "      <td>16.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30</th>\n",
       "      <td>0.247934</td>\n",
       "      <td>0.351240</td>\n",
       "      <td>0.400826</td>\n",
       "      <td>2868.88</td>\n",
       "      <td>2766.06</td>\n",
       "      <td>2783.02</td>\n",
       "      <td>17.47</td>\n",
       "      <td>18.11</td>\n",
       "      <td>16.72</td>\n",
       "      <td>17.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-06</th>\n",
       "      <td>0.225275</td>\n",
       "      <td>0.348901</td>\n",
       "      <td>0.425824</td>\n",
       "      <td>2827.28</td>\n",
       "      <td>2728.81</td>\n",
       "      <td>2826.15</td>\n",
       "      <td>16.25</td>\n",
       "      <td>16.54</td>\n",
       "      <td>15.36</td>\n",
       "      <td>15.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-13</th>\n",
       "      <td>0.268398</td>\n",
       "      <td>0.389610</td>\n",
       "      <td>0.341991</td>\n",
       "      <td>2910.61</td>\n",
       "      <td>2800.92</td>\n",
       "      <td>2879.84</td>\n",
       "      <td>16.16</td>\n",
       "      <td>16.21</td>\n",
       "      <td>15.61</td>\n",
       "      <td>15.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-20</th>\n",
       "      <td>0.295082</td>\n",
       "      <td>0.383607</td>\n",
       "      <td>0.321311</td>\n",
       "      <td>2931.74</td>\n",
       "      <td>2874.68</td>\n",
       "      <td>2926.46</td>\n",
       "      <td>14.04</td>\n",
       "      <td>16.03</td>\n",
       "      <td>13.19</td>\n",
       "      <td>14.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-27</th>\n",
       "      <td>0.295890</td>\n",
       "      <td>0.383562</td>\n",
       "      <td>0.320548</td>\n",
       "      <td>2964.15</td>\n",
       "      <td>2911.43</td>\n",
       "      <td>2913.78</td>\n",
       "      <td>15.66</td>\n",
       "      <td>16.40</td>\n",
       "      <td>15.66</td>\n",
       "      <td>15.82</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1509 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Bullish   Neutral   Bearish  S&P 500 Weekly High  \\\n",
       "Date                                                            \n",
       "1990-01-05  0.400000  0.300000  0.300000               359.69   \n",
       "1990-01-12  0.450000  0.380000  0.170000               353.79   \n",
       "1990-01-19  0.310000  0.490000  0.200000               340.75   \n",
       "1990-01-26  0.240000  0.380000  0.380000               331.61   \n",
       "1990-02-02  0.150000  0.370000  0.480000               330.92   \n",
       "1990-02-09  0.190000  0.300000  0.510000               333.75   \n",
       "1990-02-16  0.230000  0.430000  0.340000               334.89   \n",
       "1990-02-23  0.240000  0.380000  0.380000               327.99   \n",
       "1990-03-02  0.210000  0.370000  0.420000               335.54   \n",
       "1990-03-09  0.220000  0.420000  0.360000               340.27   \n",
       "1990-03-16  0.250000  0.350000  0.400000               341.91   \n",
       "1990-03-23  0.370000  0.390000  0.240000               343.53   \n",
       "1990-03-30  0.340000  0.350000  0.310000               342.00   \n",
       "1990-04-06  0.380000  0.370000  0.250000               343.64   \n",
       "1990-04-12  0.270000  0.560000  0.170000               344.34   \n",
       "1990-04-20  0.340000  0.430000  0.230000               344.74   \n",
       "1990-04-27  0.280000  0.350000  0.370000               332.92   \n",
       "1990-05-04  0.200000  0.350000  0.450000               338.39   \n",
       "1990-05-11  0.280000  0.380000  0.340000               352.00   \n",
       "1990-05-18  0.370000  0.350000  0.280000               354.75   \n",
       "1990-05-25  0.390000  0.320000  0.290000               359.29   \n",
       "1990-06-01  0.370000  0.280000  0.350000               363.16   \n",
       "1990-06-08  0.350000  0.350000  0.300000               367.40   \n",
       "1990-06-15  0.430000  0.320000  0.250000               366.25   \n",
       "1990-06-22  0.340000  0.320000  0.340000               360.47   \n",
       "1990-06-29  0.310000  0.310000  0.380000               358.02   \n",
       "1990-07-06  0.280000  0.340000  0.380000               360.16   \n",
       "1990-07-13  0.400000  0.300000  0.300000               367.31   \n",
       "1990-07-20  0.400000  0.300000  0.300000               368.95   \n",
       "1990-07-27  0.520000  0.240000  0.240000               357.09   \n",
       "...              ...       ...       ...                  ...   \n",
       "2018-12-06  0.379433  0.315603  0.304965              2800.18   \n",
       "2018-12-13  0.209040  0.302260  0.488701              2785.93   \n",
       "2018-12-20  0.248649  0.278378  0.472973              2685.44   \n",
       "2018-12-27  0.315476  0.181548  0.502976              2585.29   \n",
       "2019-01-03  0.330189  0.242138  0.427673              2520.27   \n",
       "2019-01-10  0.384615  0.321678  0.293706              2595.32   \n",
       "2019-01-17  0.335347  0.302115  0.362538              2625.76   \n",
       "2019-01-24  0.376590  0.300254  0.323155              2675.47   \n",
       "2019-01-31  0.317568  0.364865  0.317568              2690.44   \n",
       "2019-02-07  0.398734  0.373418  0.227848              2738.98   \n",
       "2019-02-14  0.351032  0.398230  0.250737              2761.85   \n",
       "2019-02-21  0.393189  0.352941  0.253870              2787.33   \n",
       "2019-02-28  0.416327  0.383673  0.200000              2795.76   \n",
       "2019-03-07  0.373860  0.358663  0.267477              2816.88   \n",
       "2019-03-14  0.324232  0.365188  0.310580              2821.24   \n",
       "2019-03-21  0.373016  0.392857  0.234127              2852.42   \n",
       "2019-03-28  0.332000  0.396000  0.272000              2860.31   \n",
       "2019-04-04  0.350211  0.367089  0.282700              2885.25   \n",
       "2019-04-11  0.402878  0.393285  0.203837              2895.95   \n",
       "2019-04-18  0.375635  0.406091  0.218274              2918.00   \n",
       "2019-04-25  0.335227  0.463068  0.201705              2936.83   \n",
       "2019-05-02  0.390244  0.396341  0.213415              2954.13   \n",
       "2019-05-09  0.431159  0.336957  0.231884              2954.13   \n",
       "2019-05-16  0.298246  0.308772  0.392982              2897.96   \n",
       "2019-05-23  0.247059  0.392157  0.360784              2892.15   \n",
       "2019-05-30  0.247934  0.351240  0.400826              2868.88   \n",
       "2019-06-06  0.225275  0.348901  0.425824              2827.28   \n",
       "2019-06-13  0.268398  0.389610  0.341991              2910.61   \n",
       "2019-06-20  0.295082  0.383607  0.321311              2931.74   \n",
       "2019-06-27  0.295890  0.383562  0.320548              2964.15   \n",
       "\n",
       "            S&P 500 Weekly Low  S&P 500 Weekly Close  VIX Open  VIX High  \\\n",
       "Date                                                                       \n",
       "1990-01-05              352.20                352.20       NaN       NaN   \n",
       "1990-01-12              339.93                339.93       NaN       NaN   \n",
       "1990-01-19              337.00                339.15       NaN       NaN   \n",
       "1990-01-26              325.80                325.80       NaN       NaN   \n",
       "1990-02-02              322.98                330.92       NaN       NaN   \n",
       "1990-02-09              329.66                333.62       NaN       NaN   \n",
       "1990-02-16              330.08                332.72       NaN       NaN   \n",
       "1990-02-23              324.15                324.15       NaN       NaN   \n",
       "1990-03-02              328.67                335.54       NaN       NaN   \n",
       "1990-03-09              333.74                337.93       NaN       NaN   \n",
       "1990-03-16              336.00                341.91       NaN       NaN   \n",
       "1990-03-23              335.65                337.22       NaN       NaN   \n",
       "1990-03-30              337.63                339.94       NaN       NaN   \n",
       "1990-04-06              338.70                340.08       NaN       NaN   \n",
       "1990-04-12              341.37                344.34       NaN       NaN   \n",
       "1990-04-20              335.12                335.12       NaN       NaN   \n",
       "1990-04-27              329.11                329.11       NaN       NaN   \n",
       "1990-05-04              330.80                338.39       NaN       NaN   \n",
       "1990-05-11              340.53                352.00       NaN       NaN   \n",
       "1990-05-18              354.00                354.64       NaN       NaN   \n",
       "1990-05-25              354.58                354.58       NaN       NaN   \n",
       "1990-06-01              360.65                363.16       NaN       NaN   \n",
       "1990-06-08              358.71                358.71       NaN       NaN   \n",
       "1990-06-15              361.63                362.91       NaN       NaN   \n",
       "1990-06-22              355.43                355.43       NaN       NaN   \n",
       "1990-06-29              352.06                358.02       NaN       NaN   \n",
       "1990-07-06              355.68                358.42       NaN       NaN   \n",
       "1990-07-13              356.49                367.31       NaN       NaN   \n",
       "1990-07-20              361.61                361.61       NaN       NaN   \n",
       "1990-07-27              353.44                353.44       NaN       NaN   \n",
       "...                        ...                   ...       ...       ...   \n",
       "2018-12-06             2655.89               2700.06     23.53     25.94   \n",
       "2018-12-13             2583.23               2651.07     21.00     21.57   \n",
       "2018-12-20             2488.96               2506.96     26.09     30.30   \n",
       "2018-12-27             2346.58               2467.70     31.20     33.80   \n",
       "2019-01-03             2346.58               2510.03     25.68     26.60   \n",
       "2019-01-10             2443.96               2584.96     20.98     21.32   \n",
       "2019-01-17             2562.02               2616.10     19.51     19.72   \n",
       "2019-01-24             2585.10               2638.70     19.69     20.25   \n",
       "2019-01-31             2612.86               2681.05     17.39     17.72   \n",
       "2019-02-07             2648.34               2731.61     15.66     17.89   \n",
       "2019-02-14             2681.83               2753.03     15.61     17.27   \n",
       "2019-02-21             2703.79               2779.76     14.00     15.17   \n",
       "2019-02-28             2764.55               2792.38     15.17     15.24   \n",
       "2019-03-07             2767.66               2771.45     16.33     17.81   \n",
       "2019-03-14             2722.27               2810.92     13.35     13.84   \n",
       "2019-03-21             2799.78               2824.23     14.11     14.56   \n",
       "2019-03-28             2785.02               2805.37     15.38     15.62   \n",
       "2019-04-04             2787.72               2873.40     13.84     14.03   \n",
       "2019-04-11             2865.17               2888.21     13.37     13.58   \n",
       "2019-04-18             2879.13               2900.45     12.80     13.12   \n",
       "2019-04-25             2891.90               2927.25     13.29     14.30   \n",
       "2019-05-02             2912.84               2923.73     14.35     15.92   \n",
       "2019-05-09             2862.60               2879.42     21.40     23.38   \n",
       "2019-05-16             2801.43               2850.96     16.69     17.13   \n",
       "2019-05-23             2815.08               2856.27     15.93     18.05   \n",
       "2019-05-30             2766.06               2783.02     17.47     18.11   \n",
       "2019-06-06             2728.81               2826.15     16.25     16.54   \n",
       "2019-06-13             2800.92               2879.84     16.16     16.21   \n",
       "2019-06-20             2874.68               2926.46     14.04     16.03   \n",
       "2019-06-27             2911.43               2913.78     15.66     16.40   \n",
       "\n",
       "            VIX Low  VIX Close  \n",
       "Date                            \n",
       "1990-01-05      NaN      20.11  \n",
       "1990-01-12      NaN      24.64  \n",
       "1990-01-19      NaN      22.50  \n",
       "1990-01-26      NaN      26.28  \n",
       "1990-02-02      NaN      24.32  \n",
       "1990-02-09      NaN      23.69  \n",
       "1990-02-16      NaN      20.78  \n",
       "1990-02-23      NaN      23.69  \n",
       "1990-03-02      NaN      21.34  \n",
       "1990-03-09      NaN      20.30  \n",
       "1990-03-16      NaN      17.62  \n",
       "1990-03-23      NaN      20.46  \n",
       "1990-03-30      NaN      19.73  \n",
       "1990-04-06      NaN      21.76  \n",
       "1990-04-12      NaN      19.80  \n",
       "1990-04-20      NaN      23.33  \n",
       "1990-04-27      NaN      22.19  \n",
       "1990-05-04      NaN      17.28  \n",
       "1990-05-11      NaN      19.81  \n",
       "1990-05-18      NaN      17.20  \n",
       "1990-05-25      NaN      17.32  \n",
       "1990-06-01      NaN      17.09  \n",
       "1990-06-08      NaN      17.58  \n",
       "1990-06-15      NaN      15.90  \n",
       "1990-06-22      NaN      19.36  \n",
       "1990-06-29      NaN      15.50  \n",
       "1990-07-06      NaN      16.41  \n",
       "1990-07-13      NaN      17.09  \n",
       "1990-07-20      NaN      15.63  \n",
       "1990-07-27      NaN      21.12  \n",
       "...             ...        ...  \n",
       "2018-12-06    20.94      21.19  \n",
       "2018-12-13    20.34      20.65  \n",
       "2018-12-20    24.68      28.38  \n",
       "2018-12-27    29.66      29.96  \n",
       "2019-01-03    24.05      25.45  \n",
       "2019-01-10    19.29      19.50  \n",
       "2019-01-17    17.85      18.06  \n",
       "2019-01-24    18.63      18.89  \n",
       "2019-01-31    16.54      16.57  \n",
       "2019-02-07    15.51      16.37  \n",
       "2019-02-14    15.30      16.22  \n",
       "2019-02-21    13.85      14.46  \n",
       "2019-02-28    14.39      14.78  \n",
       "2019-03-07    15.54      16.59  \n",
       "2019-03-14    13.16      13.50  \n",
       "2019-03-21    13.26      13.63  \n",
       "2019-03-28    14.41      14.43  \n",
       "2019-04-04    13.40      13.58  \n",
       "2019-04-11    12.91      13.02  \n",
       "2019-04-18    12.02      12.09  \n",
       "2019-04-25    12.81      13.25  \n",
       "2019-05-02    13.80      14.42  \n",
       "2019-05-09    18.87      19.10  \n",
       "2019-05-16    15.16      15.29  \n",
       "2019-05-23    15.28      16.92  \n",
       "2019-05-30    16.72      17.30  \n",
       "2019-06-06    15.36      15.93  \n",
       "2019-06-13    15.61      15.82  \n",
       "2019-06-20    13.19      14.75  \n",
       "2019-06-27    15.66      15.82  \n",
       "\n",
       "[1509 rows x 10 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aaii_vix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Bullish</th>\n",
       "      <th>Neutral</th>\n",
       "      <th>Bearish</th>\n",
       "      <th>VIX Open</th>\n",
       "      <th>VIX High</th>\n",
       "      <th>VIX Low</th>\n",
       "      <th>VIX Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1990-01-05</th>\n",
       "      <td>0.40</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-12</th>\n",
       "      <td>0.45</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-19</th>\n",
       "      <td>0.31</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-26</th>\n",
       "      <td>0.24</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.38</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-02-02</th>\n",
       "      <td>0.15</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.48</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Bullish  Neutral  Bearish  VIX Open  VIX High  VIX Low  VIX Close\n",
       "Date                                                                         \n",
       "1990-01-05     0.40     0.30     0.30       NaN       NaN      NaN      20.11\n",
       "1990-01-12     0.45     0.38     0.17       NaN       NaN      NaN      24.64\n",
       "1990-01-19     0.31     0.49     0.20       NaN       NaN      NaN      22.50\n",
       "1990-01-26     0.24     0.38     0.38       NaN       NaN      NaN      26.28\n",
       "1990-02-02     0.15     0.37     0.48       NaN       NaN      NaN      24.32"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aaii_vix = aaii_vix.drop(['S&P 500 Weekly High','S&P 500 Weekly Low','S&P 500 Weekly Close'], axis=1)\n",
    "aaii_vix.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 1509 entries, 1990-01-05 to 2019-06-27\n",
      "Data columns (total 7 columns):\n",
      "Bullish      1508 non-null float64\n",
      "Neutral      1508 non-null float64\n",
      "Bearish      1508 non-null float64\n",
      "VIX Open     1405 non-null float64\n",
      "VIX High     1405 non-null float64\n",
      "VIX Low      1405 non-null float64\n",
      "VIX Close    1507 non-null float64\n",
      "dtypes: float64(7)\n",
      "memory usage: 94.3 KB\n"
     ]
    }
   ],
   "source": [
    "aaii_vix.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 7 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "values = aaii_vix.values\n",
    "groups = [0,1,2,3,4,5,6]\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(aaii_vix.columns[group], y=.5, loc='right')\n",
    "    i += 1\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Bullish</th>\n",
       "      <th>Neutral</th>\n",
       "      <th>Bearish</th>\n",
       "      <th>VIX Open</th>\n",
       "      <th>VIX High</th>\n",
       "      <th>VIX Low</th>\n",
       "      <th>VIX Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1508.000000</td>\n",
       "      <td>1508.000000</td>\n",
       "      <td>1508.000000</td>\n",
       "      <td>1405.000000</td>\n",
       "      <td>1405.000000</td>\n",
       "      <td>1405.000000</td>\n",
       "      <td>1507.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.386187</td>\n",
       "      <td>0.308044</td>\n",
       "      <td>0.305768</td>\n",
       "      <td>19.040384</td>\n",
       "      <td>19.888861</td>\n",
       "      <td>18.349886</td>\n",
       "      <td>19.136928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.099412</td>\n",
       "      <td>0.079432</td>\n",
       "      <td>0.095417</td>\n",
       "      <td>7.875121</td>\n",
       "      <td>8.348454</td>\n",
       "      <td>7.516376</td>\n",
       "      <td>7.716715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.120000</td>\n",
       "      <td>0.076900</td>\n",
       "      <td>0.066700</td>\n",
       "      <td>9.010000</td>\n",
       "      <td>9.310000</td>\n",
       "      <td>8.920000</td>\n",
       "      <td>9.190000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.312500</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.239540</td>\n",
       "      <td>13.350000</td>\n",
       "      <td>13.990000</td>\n",
       "      <td>12.920000</td>\n",
       "      <td>13.610000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.380000</td>\n",
       "      <td>0.310000</td>\n",
       "      <td>0.294192</td>\n",
       "      <td>17.010000</td>\n",
       "      <td>17.800000</td>\n",
       "      <td>16.310000</td>\n",
       "      <td>17.110000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.449775</td>\n",
       "      <td>0.365633</td>\n",
       "      <td>0.360000</td>\n",
       "      <td>22.440000</td>\n",
       "      <td>23.290000</td>\n",
       "      <td>21.710000</td>\n",
       "      <td>22.480000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.560000</td>\n",
       "      <td>0.702700</td>\n",
       "      <td>74.260000</td>\n",
       "      <td>81.480000</td>\n",
       "      <td>72.760000</td>\n",
       "      <td>80.860000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Bullish      Neutral      Bearish     VIX Open     VIX High  \\\n",
       "count  1508.000000  1508.000000  1508.000000  1405.000000  1405.000000   \n",
       "mean      0.386187     0.308044     0.305768    19.040384    19.888861   \n",
       "std       0.099412     0.079432     0.095417     7.875121     8.348454   \n",
       "min       0.120000     0.076900     0.066700     9.010000     9.310000   \n",
       "25%       0.312500     0.250000     0.239540    13.350000    13.990000   \n",
       "50%       0.380000     0.310000     0.294192    17.010000    17.800000   \n",
       "75%       0.449775     0.365633     0.360000    22.440000    23.290000   \n",
       "max       0.750000     0.560000     0.702700    74.260000    81.480000   \n",
       "\n",
       "           VIX Low    VIX Close  \n",
       "count  1405.000000  1507.000000  \n",
       "mean     18.349886    19.136928  \n",
       "std       7.516376     7.716715  \n",
       "min       8.920000     9.190000  \n",
       "25%      12.920000    13.610000  \n",
       "50%      16.310000    17.110000  \n",
       "75%      21.710000    22.480000  \n",
       "max      72.760000    80.860000  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aaii_vix.describe()"
   ]
  },
  {
   "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>Bullish</th>\n",
       "      <th>Neutral</th>\n",
       "      <th>Bearish</th>\n",
       "      <th>VIX Open</th>\n",
       "      <th>VIX High</th>\n",
       "      <th>VIX Low</th>\n",
       "      <th>VIX Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bullish</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.448800</td>\n",
       "      <td>-0.668239</td>\n",
       "      <td>-0.069848</td>\n",
       "      <td>-0.084377</td>\n",
       "      <td>-0.060841</td>\n",
       "      <td>-0.102160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neutral</th>\n",
       "      <td>-0.448800</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.364908</td>\n",
       "      <td>-0.267171</td>\n",
       "      <td>-0.259363</td>\n",
       "      <td>-0.265383</td>\n",
       "      <td>-0.261568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bearish</th>\n",
       "      <td>-0.668239</td>\n",
       "      <td>-0.364908</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.302725</td>\n",
       "      <td>0.311282</td>\n",
       "      <td>0.291732</td>\n",
       "      <td>0.323708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VIX Open</th>\n",
       "      <td>-0.069848</td>\n",
       "      <td>-0.267171</td>\n",
       "      <td>0.302725</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.991120</td>\n",
       "      <td>0.996093</td>\n",
       "      <td>0.988270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VIX High</th>\n",
       "      <td>-0.084377</td>\n",
       "      <td>-0.259363</td>\n",
       "      <td>0.311282</td>\n",
       "      <td>0.991120</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.990589</td>\n",
       "      <td>0.994364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VIX Low</th>\n",
       "      <td>-0.060841</td>\n",
       "      <td>-0.265383</td>\n",
       "      <td>0.291732</td>\n",
       "      <td>0.996093</td>\n",
       "      <td>0.990589</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.993302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VIX Close</th>\n",
       "      <td>-0.102160</td>\n",
       "      <td>-0.261568</td>\n",
       "      <td>0.323708</td>\n",
       "      <td>0.988270</td>\n",
       "      <td>0.994364</td>\n",
       "      <td>0.993302</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Bullish   Neutral   Bearish  VIX Open  VIX High   VIX Low  \\\n",
       "Bullish    1.000000 -0.448800 -0.668239 -0.069848 -0.084377 -0.060841   \n",
       "Neutral   -0.448800  1.000000 -0.364908 -0.267171 -0.259363 -0.265383   \n",
       "Bearish   -0.668239 -0.364908  1.000000  0.302725  0.311282  0.291732   \n",
       "VIX Open  -0.069848 -0.267171  0.302725  1.000000  0.991120  0.996093   \n",
       "VIX High  -0.084377 -0.259363  0.311282  0.991120  1.000000  0.990589   \n",
       "VIX Low   -0.060841 -0.265383  0.291732  0.996093  0.990589  1.000000   \n",
       "VIX Close -0.102160 -0.261568  0.323708  0.988270  0.994364  0.993302   \n",
       "\n",
       "           VIX Close  \n",
       "Bullish    -0.102160  \n",
       "Neutral    -0.261568  \n",
       "Bearish     0.323708  \n",
       "VIX Open    0.988270  \n",
       "VIX High    0.994364  \n",
       "VIX Low     0.993302  \n",
       "VIX Close   1.000000  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cor = aaii_vix.corr()\n",
    "cor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x648 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "sns.set(style=\"white\")\n",
    "f, ax = plt.subplots(figsize=(11, 9))\n",
    "\n",
    "cmap = sns.diverging_palette(220, 10, as_cmap=True)\n",
    "ax = sns.heatmap(cor, cmap=cmap, vmax=1, center=0,\n",
    "                square=True, linewidths=.5, cbar_kws={\"shrink\": .7})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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"
    }
   ],
   "source": [
    "plt.plot(aaii_vix.index, aaii_vix['VIX Close'], 'black', label = 'VIX Close')\n",
    "plt.plot(aaii_vix.index, aaii_vix['VIX High'], 'green', label = 'VIX High')\n",
    "plt.plot(aaii_vix.index, aaii_vix['VIX Low'], 'red', label = 'VIX Low')\n",
    "plt.plot(aaii_vix.index, aaii_vix['VIX Open'], 'yellow', label = 'VIX Open')\n",
    "plt.title('VIX Weekly Rate')\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.ylabel('Rate');\n",
    "plt.show();\n",
    "\n",
    "plt.plot(aaii_vix.index, aaii_vix['Bullish'], 'g')\n",
    "plt.title('Bullish')\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.ylabel('Bullish (0-1)');\n",
    "plt.show();\n",
    "\n",
    "plt.plot(aaii_vix.index, aaii_vix['Neutral'], 'blue')\n",
    "plt.title('Neutral')\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.ylabel('Neutral (0-1)');\n",
    "plt.show();\n",
    "\n",
    "plt.plot(aaii_vix.index, aaii_vix['Bearish'], 'r')\n",
    "plt.title('Bearish')\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.ylabel('Bearish (0-1)');\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: 1509 entries, 1990-01-05 to 2019-06-27\n",
      "Data columns (total 7 columns):\n",
      "Bullish      1508 non-null float64\n",
      "Neutral      1508 non-null float64\n",
      "Bearish      1508 non-null float64\n",
      "VIX Open     1405 non-null float64\n",
      "VIX High     1405 non-null float64\n",
      "VIX Low      1405 non-null float64\n",
      "VIX Close    1507 non-null float64\n",
      "dtypes: float64(7)\n",
      "memory usage: 94.3 KB\n"
     ]
    }
   ],
   "source": [
    "#LSTM Prep\n",
    "df = aaii_vix\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>Bullish</th>\n",
       "      <th>Neutral</th>\n",
       "      <th>Bearish</th>\n",
       "      <th>VIX Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1990-01-05</th>\n",
       "      <td>0.40</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.30</td>\n",
       "      <td>20.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-12</th>\n",
       "      <td>0.45</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.17</td>\n",
       "      <td>24.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-19</th>\n",
       "      <td>0.31</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.20</td>\n",
       "      <td>22.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-26</th>\n",
       "      <td>0.24</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.38</td>\n",
       "      <td>26.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-02-02</th>\n",
       "      <td>0.15</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.48</td>\n",
       "      <td>24.32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Bullish  Neutral  Bearish  VIX Close\n",
       "Date                                            \n",
       "1990-01-05     0.40     0.30     0.30      20.11\n",
       "1990-01-12     0.45     0.38     0.17      24.64\n",
       "1990-01-19     0.31     0.49     0.20      22.50\n",
       "1990-01-26     0.24     0.38     0.38      26.28\n",
       "1990-02-02     0.15     0.37     0.48      24.32"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop(['VIX Open', 'VIX High', 'VIX Low'], axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VIX Close</th>\n",
       "      <th>Bullish</th>\n",
       "      <th>Neutral</th>\n",
       "      <th>Bearish</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1990-01-05</th>\n",
       "      <td>20.11</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-12</th>\n",
       "      <td>24.64</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-19</th>\n",
       "      <td>22.50</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-26</th>\n",
       "      <td>26.28</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-02-02</th>\n",
       "      <td>24.32</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.48</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            VIX Close  Bullish  Neutral  Bearish\n",
       "Date                                            \n",
       "1990-01-05      20.11     0.40     0.30     0.30\n",
       "1990-01-12      24.64     0.45     0.38     0.17\n",
       "1990-01-19      22.50     0.31     0.49     0.20\n",
       "1990-01-26      26.28     0.24     0.38     0.38\n",
       "1990-02-02      24.32     0.15     0.37     0.48"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[['VIX Close', 'Bullish', 'Neutral', 'Bearish']]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Bullish       0.380000\n",
       "Neutral       0.310000\n",
       "Bearish       0.294192\n",
       "VIX Open     17.010000\n",
       "VIX High     17.800000\n",
       "VIX Low      16.310000\n",
       "VIX Close    17.110000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aaii_vix.median()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LSTM Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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": 39,
   "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": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VIX Close</th>\n",
       "      <th>Bullish</th>\n",
       "      <th>Neutral</th>\n",
       "      <th>Bearish</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1990-01-05</th>\n",
       "      <td>20.11</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-12</th>\n",
       "      <td>24.64</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-19</th>\n",
       "      <td>22.50</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-26</th>\n",
       "      <td>26.28</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-02-02</th>\n",
       "      <td>24.32</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.48</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            VIX Close  Bullish  Neutral  Bearish\n",
       "Date                                            \n",
       "1990-01-05      20.11     0.40     0.30     0.30\n",
       "1990-01-12      24.64     0.45     0.38     0.17\n",
       "1990-01-19      22.50     0.31     0.49     0.20\n",
       "1990-01-26      26.28     0.24     0.38     0.38\n",
       "1990-02-02      24.32     0.15     0.37     0.48"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "values = df.values\n",
    "cols = df.columns.tolist()\n",
    "cols = cols[-1:] + cols[:-1]\n",
    "df = df[cols]\n",
    "df = df[['VIX Close', 'Bullish', 'Neutral', 'Bearish']]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "scaled = scaler.fit_transform(df.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_days = 7 #adding 7 days lags creating number of observations\n",
    "n_features = 4 #features in the dataset\n",
    "n_obs = n_days*n_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>var1(t-7)</th>\n",
       "      <th>var2(t-7)</th>\n",
       "      <th>var3(t-7)</th>\n",
       "      <th>var4(t-7)</th>\n",
       "      <th>var1(t-6)</th>\n",
       "      <th>var2(t-6)</th>\n",
       "      <th>var3(t-6)</th>\n",
       "      <th>var4(t-6)</th>\n",
       "      <th>var1(t-5)</th>\n",
       "      <th>var2(t-5)</th>\n",
       "      <th>...</th>\n",
       "      <th>var3(t-2)</th>\n",
       "      <th>var4(t-2)</th>\n",
       "      <th>var1(t-1)</th>\n",
       "      <th>var2(t-1)</th>\n",
       "      <th>var3(t-1)</th>\n",
       "      <th>var4(t-1)</th>\n",
       "      <th>var1(t)</th>\n",
       "      <th>var2(t)</th>\n",
       "      <th>var3(t)</th>\n",
       "      <th>var4(t)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.152365</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.461809</td>\n",
       "      <td>0.366824</td>\n",
       "      <td>0.215571</td>\n",
       "      <td>0.523810</td>\n",
       "      <td>0.627406</td>\n",
       "      <td>0.162421</td>\n",
       "      <td>0.185712</td>\n",
       "      <td>0.301587</td>\n",
       "      <td>...</td>\n",
       "      <td>0.461809</td>\n",
       "      <td>0.697013</td>\n",
       "      <td>0.161713</td>\n",
       "      <td>0.174603</td>\n",
       "      <td>0.730905</td>\n",
       "      <td>0.429717</td>\n",
       "      <td>0.202316</td>\n",
       "      <td>0.190476</td>\n",
       "      <td>0.627406</td>\n",
       "      <td>0.492610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.215571</td>\n",
       "      <td>0.523810</td>\n",
       "      <td>0.627406</td>\n",
       "      <td>0.162421</td>\n",
       "      <td>0.185712</td>\n",
       "      <td>0.301587</td>\n",
       "      <td>0.855102</td>\n",
       "      <td>0.209591</td>\n",
       "      <td>0.238454</td>\n",
       "      <td>0.190476</td>\n",
       "      <td>...</td>\n",
       "      <td>0.730905</td>\n",
       "      <td>0.429717</td>\n",
       "      <td>0.202316</td>\n",
       "      <td>0.190476</td>\n",
       "      <td>0.627406</td>\n",
       "      <td>0.492610</td>\n",
       "      <td>0.169527</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.606707</td>\n",
       "      <td>0.555503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.185712</td>\n",
       "      <td>0.301587</td>\n",
       "      <td>0.855102</td>\n",
       "      <td>0.209591</td>\n",
       "      <td>0.238454</td>\n",
       "      <td>0.190476</td>\n",
       "      <td>0.627406</td>\n",
       "      <td>0.492610</td>\n",
       "      <td>0.211106</td>\n",
       "      <td>0.047619</td>\n",
       "      <td>...</td>\n",
       "      <td>0.627406</td>\n",
       "      <td>0.492610</td>\n",
       "      <td>0.169527</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.606707</td>\n",
       "      <td>0.555503</td>\n",
       "      <td>0.155016</td>\n",
       "      <td>0.158730</td>\n",
       "      <td>0.710205</td>\n",
       "      <td>0.461164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.238454</td>\n",
       "      <td>0.190476</td>\n",
       "      <td>0.627406</td>\n",
       "      <td>0.492610</td>\n",
       "      <td>0.211106</td>\n",
       "      <td>0.047619</td>\n",
       "      <td>0.606707</td>\n",
       "      <td>0.649843</td>\n",
       "      <td>0.202316</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>...</td>\n",
       "      <td>0.606707</td>\n",
       "      <td>0.555503</td>\n",
       "      <td>0.155016</td>\n",
       "      <td>0.158730</td>\n",
       "      <td>0.710205</td>\n",
       "      <td>0.461164</td>\n",
       "      <td>0.117622</td>\n",
       "      <td>0.206349</td>\n",
       "      <td>0.565307</td>\n",
       "      <td>0.524057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.211106</td>\n",
       "      <td>0.047619</td>\n",
       "      <td>0.606707</td>\n",
       "      <td>0.649843</td>\n",
       "      <td>0.202316</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.461809</td>\n",
       "      <td>0.697013</td>\n",
       "      <td>0.161713</td>\n",
       "      <td>0.174603</td>\n",
       "      <td>...</td>\n",
       "      <td>0.710205</td>\n",
       "      <td>0.461164</td>\n",
       "      <td>0.117622</td>\n",
       "      <td>0.206349</td>\n",
       "      <td>0.565307</td>\n",
       "      <td>0.524057</td>\n",
       "      <td>0.157249</td>\n",
       "      <td>0.396825</td>\n",
       "      <td>0.648106</td>\n",
       "      <td>0.272484</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    var1(t-7)  var2(t-7)  var3(t-7)  var4(t-7)  var1(t-6)  var2(t-6)  \\\n",
       "7    0.152365   0.444444   0.461809   0.366824   0.215571   0.523810   \n",
       "8    0.215571   0.523810   0.627406   0.162421   0.185712   0.301587   \n",
       "9    0.185712   0.301587   0.855102   0.209591   0.238454   0.190476   \n",
       "10   0.238454   0.190476   0.627406   0.492610   0.211106   0.047619   \n",
       "11   0.211106   0.047619   0.606707   0.649843   0.202316   0.111111   \n",
       "\n",
       "    var3(t-6)  var4(t-6)  var1(t-5)  var2(t-5)  ...  var3(t-2)  var4(t-2)  \\\n",
       "7    0.627406   0.162421   0.185712   0.301587  ...   0.461809   0.697013   \n",
       "8    0.855102   0.209591   0.238454   0.190476  ...   0.730905   0.429717   \n",
       "9    0.627406   0.492610   0.211106   0.047619  ...   0.627406   0.492610   \n",
       "10   0.606707   0.649843   0.202316   0.111111  ...   0.606707   0.555503   \n",
       "11   0.461809   0.697013   0.161713   0.174603  ...   0.710205   0.461164   \n",
       "\n",
       "    var1(t-1)  var2(t-1)  var3(t-1)  var4(t-1)   var1(t)   var2(t)   var3(t)  \\\n",
       "7    0.161713   0.174603   0.730905   0.429717  0.202316  0.190476  0.627406   \n",
       "8    0.202316   0.190476   0.627406   0.492610  0.169527  0.142857  0.606707   \n",
       "9    0.169527   0.142857   0.606707   0.555503  0.155016  0.158730  0.710205   \n",
       "10   0.155016   0.158730   0.710205   0.461164  0.117622  0.206349  0.565307   \n",
       "11   0.117622   0.206349   0.565307   0.524057  0.157249  0.396825  0.648106   \n",
       "\n",
       "     var4(t)  \n",
       "7   0.492610  \n",
       "8   0.555503  \n",
       "9   0.461164  \n",
       "10  0.524057  \n",
       "11  0.272484  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reframed = series_to_supervised(scaled, n_days, 1)\n",
    "reframed.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    var1(t-7)  var2(t-7)  var3(t-7)  var4(t-7)  var1(t-6)  var2(t-6)  \\\n",
      "7    0.152365   0.444444   0.461809   0.366824   0.215571   0.523810   \n",
      "8    0.215571   0.523810   0.627406   0.162421   0.185712   0.301587   \n",
      "9    0.185712   0.301587   0.855102   0.209591   0.238454   0.190476   \n",
      "10   0.238454   0.190476   0.627406   0.492610   0.211106   0.047619   \n",
      "11   0.211106   0.047619   0.606707   0.649843   0.202316   0.111111   \n",
      "\n",
      "    var3(t-6)  var4(t-6)  var1(t-5)  var2(t-5)  ...  var3(t-2)  var4(t-2)  \\\n",
      "7    0.627406   0.162421   0.185712   0.301587  ...   0.461809   0.697013   \n",
      "8    0.855102   0.209591   0.238454   0.190476  ...   0.730905   0.429717   \n",
      "9    0.627406   0.492610   0.211106   0.047619  ...   0.627406   0.492610   \n",
      "10   0.606707   0.649843   0.202316   0.111111  ...   0.606707   0.555503   \n",
      "11   0.461809   0.697013   0.161713   0.174603  ...   0.710205   0.461164   \n",
      "\n",
      "    var1(t-1)  var2(t-1)  var3(t-1)  var4(t-1)   var1(t)   var2(t)   var3(t)  \\\n",
      "7    0.161713   0.174603   0.730905   0.429717  0.202316  0.190476  0.627406   \n",
      "8    0.202316   0.190476   0.627406   0.492610  0.169527  0.142857  0.606707   \n",
      "9    0.169527   0.142857   0.606707   0.555503  0.155016  0.158730  0.710205   \n",
      "10   0.155016   0.158730   0.710205   0.461164  0.117622  0.206349  0.565307   \n",
      "11   0.117622   0.206349   0.565307   0.524057  0.157249  0.396825  0.648106   \n",
      "\n",
      "     var4(t)  \n",
      "7   0.492610  \n",
      "8   0.555503  \n",
      "9   0.461164  \n",
      "10  0.524057  \n",
      "11  0.272484  \n",
      "\n",
      "[5 rows x 32 columns]\n"
     ]
    }
   ],
   "source": [
    "print(reframed.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1055, 32)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "values = reframed.values\n",
    "n_train_days = 1055 #1507*.7\n",
    "train = values[:n_train_days, :]\n",
    "test = values[n_train_days:, :]\n",
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1055, 7, 4) (1055,) (423, 7, 4) (423,)\n"
     ]
    }
   ],
   "source": [
    "# reshape input to be 3D [samples, timesteps, features]\n",
    "train_X = train_X.reshape((train_X.shape[0], n_days, n_features))\n",
    "test_X = test_X.reshape((test_X.shape[0], n_days, n_features))\n",
    "print(train_X.shape, train_y.shape, test_X.shape, test_y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 1055 samples, validate on 423 samples\n",
      "Epoch 1/50\n",
      " - 2s - loss: 0.0769 - mean_squared_error: 0.0122 - mean_absolute_percentage_error: 62.3416 - val_loss: 0.0843 - val_mean_squared_error: 0.0094 - val_mean_absolute_percentage_error: 352665.2913\n",
      "Epoch 2/50\n",
      " - 0s - loss: 0.0729 - mean_squared_error: 0.0100 - mean_absolute_percentage_error: 83.9510 - val_loss: 0.0804 - val_mean_squared_error: 0.0085 - val_mean_absolute_percentage_error: 335314.4641\n",
      "Epoch 3/50\n",
      " - 0s - loss: 0.0688 - mean_squared_error: 0.0091 - mean_absolute_percentage_error: 76.3772 - val_loss: 0.0747 - val_mean_squared_error: 0.0074 - val_mean_absolute_percentage_error: 311537.3496\n",
      "Epoch 4/50\n",
      " - 0s - loss: 0.0644 - mean_squared_error: 0.0080 - mean_absolute_percentage_error: 71.8985 - val_loss: 0.0723 - val_mean_squared_error: 0.0069 - val_mean_absolute_percentage_error: 297914.8350\n",
      "Epoch 5/50\n",
      " - 0s - loss: 0.0613 - mean_squared_error: 0.0073 - mean_absolute_percentage_error: 68.2808 - val_loss: 0.0679 - val_mean_squared_error: 0.0062 - val_mean_absolute_percentage_error: 277439.6411\n",
      "Epoch 6/50\n",
      " - 0s - loss: 0.0578 - mean_squared_error: 0.0065 - mean_absolute_percentage_error: 63.7690 - val_loss: 0.0647 - val_mean_squared_error: 0.0056 - val_mean_absolute_percentage_error: 260509.3593\n",
      "Epoch 7/50\n",
      " - 0s - loss: 0.0547 - mean_squared_error: 0.0059 - mean_absolute_percentage_error: 59.2672 - val_loss: 0.0617 - val_mean_squared_error: 0.0051 - val_mean_absolute_percentage_error: 243346.7421\n",
      "Epoch 8/50\n",
      " - 0s - loss: 0.0517 - mean_squared_error: 0.0054 - mean_absolute_percentage_error: 55.0873 - val_loss: 0.0573 - val_mean_squared_error: 0.0045 - val_mean_absolute_percentage_error: 221046.1361\n",
      "Epoch 9/50\n",
      " - 0s - loss: 0.0484 - mean_squared_error: 0.0049 - mean_absolute_percentage_error: 50.2072 - val_loss: 0.0541 - val_mean_squared_error: 0.0041 - val_mean_absolute_percentage_error: 201160.0178\n",
      "Epoch 10/50\n",
      " - 0s - loss: 0.0451 - mean_squared_error: 0.0043 - mean_absolute_percentage_error: 46.0945 - val_loss: 0.0512 - val_mean_squared_error: 0.0037 - val_mean_absolute_percentage_error: 181425.5477\n",
      "Epoch 11/50\n",
      " - 0s - loss: 0.0420 - mean_squared_error: 0.0039 - mean_absolute_percentage_error: 41.8053 - val_loss: 0.0489 - val_mean_squared_error: 0.0034 - val_mean_absolute_percentage_error: 162476.3361\n",
      "Epoch 12/50\n",
      " - 0s - loss: 0.0393 - mean_squared_error: 0.0035 - mean_absolute_percentage_error: 37.9158 - val_loss: 0.0461 - val_mean_squared_error: 0.0031 - val_mean_absolute_percentage_error: 142725.5754\n",
      "Epoch 13/50\n",
      " - 0s - loss: 0.0369 - mean_squared_error: 0.0032 - mean_absolute_percentage_error: 34.3813 - val_loss: 0.0440 - val_mean_squared_error: 0.0028 - val_mean_absolute_percentage_error: 127372.0799\n",
      "Epoch 14/50\n",
      " - 0s - loss: 0.0354 - mean_squared_error: 0.0030 - mean_absolute_percentage_error: 31.9731 - val_loss: 0.0414 - val_mean_squared_error: 0.0026 - val_mean_absolute_percentage_error: 111771.7516\n",
      "Epoch 15/50\n",
      " - 0s - loss: 0.0338 - mean_squared_error: 0.0028 - mean_absolute_percentage_error: 29.9253 - val_loss: 0.0382 - val_mean_squared_error: 0.0023 - val_mean_absolute_percentage_error: 95129.5328\n",
      "Epoch 16/50\n",
      " - 0s - loss: 0.0321 - mean_squared_error: 0.0027 - mean_absolute_percentage_error: 27.2678 - val_loss: 0.0348 - val_mean_squared_error: 0.0021 - val_mean_absolute_percentage_error: 76475.9393\n",
      "Epoch 17/50\n",
      " - 0s - loss: 0.0307 - mean_squared_error: 0.0026 - mean_absolute_percentage_error: 25.0285 - val_loss: 0.0322 - val_mean_squared_error: 0.0020 - val_mean_absolute_percentage_error: 59029.9796\n",
      "Epoch 18/50\n",
      " - 0s - loss: 0.0298 - mean_squared_error: 0.0025 - mean_absolute_percentage_error: 23.3781 - val_loss: 0.0310 - val_mean_squared_error: 0.0019 - val_mean_absolute_percentage_error: 48942.2304\n",
      "Epoch 19/50\n",
      " - 0s - loss: 0.0292 - mean_squared_error: 0.0024 - mean_absolute_percentage_error: 22.7349 - val_loss: 0.0296 - val_mean_squared_error: 0.0019 - val_mean_absolute_percentage_error: 36162.3408\n",
      "Epoch 20/50\n",
      " - 0s - loss: 0.0289 - mean_squared_error: 0.0024 - mean_absolute_percentage_error: 22.2866 - val_loss: 0.0291 - val_mean_squared_error: 0.0019 - val_mean_absolute_percentage_error: 28105.4305\n",
      "Epoch 21/50\n",
      " - 0s - loss: 0.0287 - mean_squared_error: 0.0023 - mean_absolute_percentage_error: 22.2546 - val_loss: 0.0288 - val_mean_squared_error: 0.0020 - val_mean_absolute_percentage_error: 20587.9733\n",
      "Epoch 22/50\n",
      " - 0s - loss: 0.0288 - mean_squared_error: 0.0023 - mean_absolute_percentage_error: 22.5418 - val_loss: 0.0287 - val_mean_squared_error: 0.0020 - val_mean_absolute_percentage_error: 17215.6122\n",
      "Epoch 23/50\n",
      " - 0s - loss: 0.0287 - mean_squared_error: 0.0023 - mean_absolute_percentage_error: 22.4259 - val_loss: 0.0286 - val_mean_squared_error: 0.0019 - val_mean_absolute_percentage_error: 17919.6540\n",
      "Epoch 24/50\n",
      " - 0s - loss: 0.0286 - mean_squared_error: 0.0023 - mean_absolute_percentage_error: 22.2319 - val_loss: 0.0285 - val_mean_squared_error: 0.0019 - val_mean_absolute_percentage_error: 21600.9149\n",
      "Epoch 25/50\n",
      " - 0s - loss: 0.0286 - mean_squared_error: 0.0023 - mean_absolute_percentage_error: 22.2624 - val_loss: 0.0285 - val_mean_squared_error: 0.0019 - val_mean_absolute_percentage_error: 24652.2557\n",
      "Epoch 26/50\n",
      " - 0s - loss: 0.0285 - mean_squared_error: 0.0023 - mean_absolute_percentage_error: 22.0724 - val_loss: 0.0284 - val_mean_squared_error: 0.0019 - val_mean_absolute_percentage_error: 25571.4425\n",
      "Epoch 27/50\n",
      " - 0s - loss: 0.0284 - mean_squared_error: 0.0023 - mean_absolute_percentage_error: 21.9808 - val_loss: 0.0285 - val_mean_squared_error: 0.0019 - val_mean_absolute_percentage_error: 27301.0355\n",
      "Epoch 28/50\n",
      " - 0s - loss: 0.0283 - mean_squared_error: 0.0023 - mean_absolute_percentage_error: 21.7544 - val_loss: 0.0288 - val_mean_squared_error: 0.0018 - val_mean_absolute_percentage_error: 32693.3848\n",
      "Epoch 29/50\n",
      " - 0s - loss: 0.0282 - mean_squared_error: 0.0022 - mean_absolute_percentage_error: 21.9651 - val_loss: 0.0290 - val_mean_squared_error: 0.0018 - val_mean_absolute_percentage_error: 34224.2152\n",
      "Epoch 30/50\n",
      " - 0s - loss: 0.0281 - mean_squared_error: 0.0022 - mean_absolute_percentage_error: 21.8615 - val_loss: 0.0290 - val_mean_squared_error: 0.0018 - val_mean_absolute_percentage_error: 34404.7941\n",
      "Epoch 31/50\n",
      " - 0s - loss: 0.0281 - mean_squared_error: 0.0022 - mean_absolute_percentage_error: 21.9577 - val_loss: 0.0287 - val_mean_squared_error: 0.0018 - val_mean_absolute_percentage_error: 31341.7915\n",
      "Epoch 32/50\n",
      " - 0s - loss: 0.0279 - mean_squared_error: 0.0022 - mean_absolute_percentage_error: 21.7878 - val_loss: 0.0284 - val_mean_squared_error: 0.0018 - val_mean_absolute_percentage_error: 28769.1915\n",
      "Epoch 33/50\n",
      " - 0s - loss: 0.0279 - mean_squared_error: 0.0022 - mean_absolute_percentage_error: 21.9030 - val_loss: 0.0281 - val_mean_squared_error: 0.0018 - val_mean_absolute_percentage_error: 24476.4848\n",
      "Epoch 34/50\n",
      " - 0s - loss: 0.0278 - mean_squared_error: 0.0022 - mean_absolute_percentage_error: 21.8699 - val_loss: 0.0279 - val_mean_squared_error: 0.0018 - val_mean_absolute_percentage_error: 18356.2208\n",
      "Epoch 35/50\n",
      " - 0s - loss: 0.0278 - mean_squared_error: 0.0022 - mean_absolute_percentage_error: 21.7790 - val_loss: 0.0278 - val_mean_squared_error: 0.0019 - val_mean_absolute_percentage_error: 14782.4482\n",
      "Epoch 36/50\n",
      " - 0s - loss: 0.0279 - mean_squared_error: 0.0022 - mean_absolute_percentage_error: 21.9036 - val_loss: 0.0279 - val_mean_squared_error: 0.0019 - val_mean_absolute_percentage_error: 10187.5630\n",
      "Epoch 37/50\n",
      " - 0s - loss: 0.0279 - mean_squared_error: 0.0022 - mean_absolute_percentage_error: 22.1937 - val_loss: 0.0278 - val_mean_squared_error: 0.0019 - val_mean_absolute_percentage_error: 11221.4919\n",
      "Epoch 38/50\n",
      " - 0s - loss: 0.0279 - mean_squared_error: 0.0021 - mean_absolute_percentage_error: 22.0403 - val_loss: 0.0278 - val_mean_squared_error: 0.0019 - val_mean_absolute_percentage_error: 11349.9307\n",
      "Epoch 39/50\n",
      " - 0s - loss: 0.0277 - mean_squared_error: 0.0021 - mean_absolute_percentage_error: 21.9992 - val_loss: 0.0278 - val_mean_squared_error: 0.0019 - val_mean_absolute_percentage_error: 9803.7375\n",
      "Epoch 40/50\n",
      " - 0s - loss: 0.0278 - mean_squared_error: 0.0021 - mean_absolute_percentage_error: 22.0061 - val_loss: 0.0277 - val_mean_squared_error: 0.0019 - val_mean_absolute_percentage_error: 12721.3070\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 41/50\n",
      " - 0s - loss: 0.0277 - mean_squared_error: 0.0021 - mean_absolute_percentage_error: 22.0802 - val_loss: 0.0277 - val_mean_squared_error: 0.0019 - val_mean_absolute_percentage_error: 12196.1294\n",
      "Epoch 42/50\n",
      " - 0s - loss: 0.0277 - mean_squared_error: 0.0021 - mean_absolute_percentage_error: 22.0129 - val_loss: 0.0276 - val_mean_squared_error: 0.0018 - val_mean_absolute_percentage_error: 13357.6504\n",
      "Epoch 43/50\n",
      " - 0s - loss: 0.0276 - mean_squared_error: 0.0021 - mean_absolute_percentage_error: 21.9475 - val_loss: 0.0276 - val_mean_squared_error: 0.0018 - val_mean_absolute_percentage_error: 14322.8504\n",
      "Epoch 44/50\n",
      " - 0s - loss: 0.0276 - mean_squared_error: 0.0021 - mean_absolute_percentage_error: 21.9389 - val_loss: 0.0276 - val_mean_squared_error: 0.0018 - val_mean_absolute_percentage_error: 12874.0101\n",
      "Epoch 45/50\n",
      " - 0s - loss: 0.0275 - mean_squared_error: 0.0021 - mean_absolute_percentage_error: 21.8516 - val_loss: 0.0275 - val_mean_squared_error: 0.0018 - val_mean_absolute_percentage_error: 15080.4059\n",
      "Epoch 46/50\n",
      " - 0s - loss: 0.0275 - mean_squared_error: 0.0021 - mean_absolute_percentage_error: 21.8131 - val_loss: 0.0275 - val_mean_squared_error: 0.0018 - val_mean_absolute_percentage_error: 16269.1303\n",
      "Epoch 47/50\n",
      " - 0s - loss: 0.0274 - mean_squared_error: 0.0021 - mean_absolute_percentage_error: 21.7132 - val_loss: 0.0275 - val_mean_squared_error: 0.0018 - val_mean_absolute_percentage_error: 17154.9338\n",
      "Epoch 48/50\n",
      " - 0s - loss: 0.0273 - mean_squared_error: 0.0020 - mean_absolute_percentage_error: 21.7569 - val_loss: 0.0274 - val_mean_squared_error: 0.0018 - val_mean_absolute_percentage_error: 16945.9515\n",
      "Epoch 49/50\n",
      " - 0s - loss: 0.0273 - mean_squared_error: 0.0020 - mean_absolute_percentage_error: 21.8083 - val_loss: 0.0274 - val_mean_squared_error: 0.0018 - val_mean_absolute_percentage_error: 15271.7274\n",
      "Epoch 50/50\n",
      " - 0s - loss: 0.0273 - mean_squared_error: 0.0020 - mean_absolute_percentage_error: 21.7473 - val_loss: 0.0274 - val_mean_squared_error: 0.0018 - val_mean_absolute_percentage_error: 17418.9583\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=32, validation_data=(test_X, test_y), verbose=2, shuffle=False, validation_split=0.2)\n",
    "# plot history, bacht_size=n_train_days*.03"
   ]
  },
  {
   "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['loss'], label='train')\n",
    "plt.plot(history.history['val_loss'], label='test')\n",
    "plt.legend\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test MSE: 9.109\n",
      "Test RMSE: 3.018\n",
      "Test MAE: 1.962\n",
      "Test MAPE: 10.973\n"
     ]
    }
   ],
   "source": [
    "# make a prediction\n",
    "yhat = model.predict(test_X)\n",
    "test_X = test_X.reshape((test_X.shape[0], n_days * n_features,))\n",
    "# invert scaling for forecast\n",
    "inv_yhat = concatenate((yhat, test_X[:, -3:]), axis=1) #-3 für Anzahl der Features (4-3=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[:, -3:]), 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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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(inv_y, label='Real')#blue\n",
    "plt.plot(inv_yhat, label='Predicted')#orange\n",
    "plt.title('Real (blue) vs Predicted (orange) Close')\n",
    "plt.ylabel('VIX Rate')\n",
    "plt.xlabel('Epochs')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}