APPL_daily.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import requests\n",
    "import math\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, LSTM, Dropout\n",
    "from keras.optimizers import Adam\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "\n",
    "import datetime as dt\n",
    "plt.style.use('fivethirtyeight')\n",
    "\n",
    "\n",
    "API_key = \"ed733879878dc4eefc0f1f08664391a3\"\n",
    "\n",
    "\n",
    "ticker = \"AAPL\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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'high': 25.3825, 'low': 24.8025, 'close': 24.860001, 'volume': 207178000.0, 'rsi': 45.22180445757402}, {'date': '2016-01-22', 'open': 24.657499, 'high': 25.365, 'low': 24.592501, 'close': 25.355, 'volume': 263202000.0, 'rsi': 50.79396465944201}, {'date': '2016-01-21', 'open': 24.264999, 'high': 24.469999, 'low': 23.735001, 'close': 24.075001, 'volume': 208646000.0, 'rsi': 31.010233638224975}, {'date': '2016-01-20', 'open': 23.775, 'high': 24.547501, 'low': 23.355, 'close': 24.1975, 'volume': 289337600.0, 'rsi': 32.12264799634881}, {'date': '2016-01-19', 'open': 24.602501, 'high': 24.6625, 'low': 23.875, 'close': 24.165001, 'volume': 212350800.0, 'rsi': 31.53623676646648}, {'date': '2016-01-15', 'open': 24.049999, 'high': 24.4275, 'low': 23.84, 'close': 24.282499, 'volume': 319335600.0, 'rsi': 32.44840237010028}, {'date': '2016-01-14', 'open': 24.49, 'high': 25.120001, 'low': 23.934999, 'close': 24.879999, 'volume': 252680400.0, 'rsi': 37.39921191557912}, {'date': '2016-01-13', 'open': 25.08, 'high': 25.297501, 'low': 24.325001, 'close': 24.3475, 'volume': 249758400.0, 'rsi': 28.669920197095756}, {'date': '2016-01-12', 'open': 25.137501, 'high': 25.172501, 'low': 24.709999, 'close': 24.99, 'volume': 196616800.0, 'rsi': 33.78593779348601}, {'date': '2016-01-11', 'open': 24.7425, 'high': 24.764999, 'low': 24.334999, 'close': 24.6325, 'volume': 198957600.0, 'rsi': 27.288303788925205}, {'date': '2016-01-08', 'open': 24.637501, 'high': 24.7775, 'low': 24.190001, 'close': 24.24, 'volume': 283192000.0, 'rsi': 19.480832160896796}, {'date': '2016-01-07', 'open': 24.67, 'high': 25.032499, 'low': 24.1075, 'close': 24.112499, 'volume': 324377600.0, 'rsi': 16.871232299177244}, {'date': '2016-01-06', 'open': 25.139999, 'high': 25.592501, 'low': 24.967501, 'close': 25.174999, 'volume': 273829600.0, 'rsi': 22.289044572960417}, {'date': '2016-01-05', 'open': 26.4375, 'high': 26.4625, 'low': 25.602501, 'close': 25.6775, 'volume': 223164000.0, 'rsi': 25.818038327377884}, {'date': '2016-01-04', 'open': 25.6525, 'high': 26.342501, 'low': 25.5, 'close': 26.3375, 'volume': 270597600.0, 'rsi': 31.762686892099932}, {'date': '2015-12-31', 'open': 26.752501, 'high': 26.7575, 'low': 26.205, 'close': 26.315001, 'volume': 163649200.0, 'rsi': 31.277213430170992}, {'date': '2015-12-30', 'open': 27.145, 'high': 27.174999, 'low': 26.795, 'close': 26.83, 'volume': 100855200.0, 'rsi': 36.64860342205497}, {'date': '2015-12-29', 'open': 26.74, 'high': 27.3575, 'low': 26.715, 'close': 27.184999, 'volume': 123724800.0, 'rsi': 41.01884351611397}, {'date': '2015-12-28', 'open': 26.897499, 'high': 26.922501, 'low': 26.545, 'close': 26.705, 'volume': 106816800.0, 'rsi': 31.007146789209568}, {'date': '2015-12-24', 'open': 27.25, 'high': 27.25, 'low': 26.987499, 'close': 27.0075, 'volume': 54281600.0, 'rsi': 34.31044942915651}, {'date': '2015-12-23', 'open': 26.817499, 'high': 27.2125, 'low': 26.799999, 'close': 27.1525, 'volume': 130629600.0, 'rsi': 35.96329056812052}, {'date': '2015-12-22', 'open': 26.85, 'high': 26.93, 'low': 26.612499, 'close': 26.807501, 'volume': 131157600.0, 'rsi': 28.597668271166995}, {'date': '2015-12-21', 'open': 26.82, 'high': 26.842501, 'low': 26.3925, 'close': 26.8325, 'volume': 190362400.0, 'rsi': 28.813804892546912}, {'date': '2015-12-18', 'open': 27.227501, 'high': 27.379999, 'low': 26.452499, 'close': 26.5075, 'volume': 385813200.0, 'rsi': 21.908118450501092}, {'date': '2015-12-17', 'open': 28.004999, 'high': 28.0625, 'low': 27.245001, 'close': 27.245001, 'volume': 179091200.0, 'rsi': 27.321014053991966}, {'date': '2015-12-16', 'open': 27.7675, 'high': 27.997499, 'low': 27.200001, 'close': 27.834999, 'volume': 224954000.0, 'rsi': 33.23285363599024}, {'date': '2015-12-15', 'open': 27.985001, 'high': 28.200001, 'low': 27.5875, 'close': 27.622499, 'volume': 213292400.0, 'rsi': 28.196423581310523}, {'date': '2015-12-14', 'open': 28.045, 'high': 28.17, 'low': 27.4475, 'close': 28.120001, 'volume': 257274800.0, 'rsi': 33.524941445215326}, {'date': '2015-12-11', 'open': 28.797501, 'high': 28.8475, 'low': 28.2125, 'close': 28.295, 'volume': 187544800.0, 'rsi': 35.65826042833512}, {'date': '2015-12-10', 'open': 29.01, 'high': 29.235001, 'low': 28.877501, 'close': 29.0425, 'volume': 116850800.0, 'rsi': 47.206215797436506}, {'date': '2015-12-09', 'open': 29.41, 'high': 29.422501, 'low': 28.77, 'close': 28.905001, 'volume': 185445600.0, 'rsi': 44.215396698137006}, {'date': '2015-12-08', 'open': 29.379999, 'high': 29.65, 'low': 29.215, 'close': 29.557501, 'volume': 137238000.0, 'rsi': 58.32803384865375}, {'date': '2015-12-07', 'open': 29.745001, 'high': 29.965, 'low': 29.452499, 'close': 29.57, 'volume': 128336800.0, 'rsi': 58.650768515724224}, {'date': '2015-12-04', 'open': 28.8225, 'high': 29.8125, 'low': 28.7775, 'close': 29.7575, 'volume': 231108000.0, 'rsi': 63.38586951405589}, {'date': '2015-12-03', 'open': 29.137501, 'high': 29.1975, 'low': 28.555, 'close': 28.799999, 'volume': 166278000.0, 'rsi': 41.78502517998176}, {'date': '2015-12-02', 'open': 29.334999, 'high': 29.5275, 'low': 29.02, 'close': 29.07, 'volume': 133546400.0, 'rsi': 49.14288076292024}, {'date': '2015-12-01', 'open': 29.6875, 'high': 29.702499, 'low': 29.215, 'close': 29.334999, 'volume': 139409600.0, 'rsi': 58.19468825582928}, {'date': '2015-11-30', 'open': 29.497499, 'high': 29.852501, 'low': 29.4375, 'close': 29.575001, 'volume': 156721200.0, 'rsi': 68.47537851117958}, {'date': '2015-11-27', 'open': 29.5725, 'high': 29.602501, 'low': 29.4, 'close': 29.452499, 'volume': 52185600.0, 'rsi': 65.6910729640432}, {'date': '2015-11-25', 'open': 29.8025, 'high': 29.807501, 'low': 29.48, 'close': 29.5075, 'volume': 85553200.0, 'rsi': 68.12230414370057}, {'date': '2015-11-24', 'open': 29.3325, 'high': 29.8375, 'low': 29.280001, 'close': 29.719999, 'volume': 171212800.0, 'rsi': 78.18387811521008}, {'date': '2015-11-23', 'open': 29.817499, 'high': 29.932501, 'low': 29.334999, 'close': 29.4375, 'volume': 129930000.0, 'rsi': 73.50105553161163}, {'date': '2015-11-20', 'open': 29.799999, 'high': 29.98, 'low': 29.7125, 'close': 29.825001, 'volume': 137148400.0, 'rsi': 100.0}, {'date': '2015-11-19', 'open': 29.41, 'high': 29.9375, 'low': 29.190001, 'close': 29.695, 'volume': 173183200.0, 'rsi': 100.0}, {'date': '2015-11-18', 'open': 28.940001, 'high': 29.372499, 'low': 28.875, 'close': 29.3225, 'volume': 186698800.0, 'rsi': 100.0}, {'date': '2015-11-17', 'open': 28.73, 'high': 28.762501, 'low': 28.33, 'close': 28.422501, 'volume': 110467600.0, 'rsi': 0.0}]\n"
     ]
    }
   ],
   "source": [
    "# pulling the data for the company\n",
    "\n",
    "url2 = \"https://financialmodelingprep.com/api/v3/technical_indicator/daily/\"\n",
    "   \n",
    "query_url2 = url2 + ticker + \"?period=10&type=rsi\" + \"&apikey=\" + API_key\n",
    "\n",
    "indicator = requests.get(query_url2).json()\n",
    "\n",
    "print(indicator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>rsi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1258</th>\n",
       "      <td>2015-11-17</td>\n",
       "      <td>28.730000</td>\n",
       "      <td>28.762501</td>\n",
       "      <td>28.330000</td>\n",
       "      <td>28.422501</td>\n",
       "      <td>110467600.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1257</th>\n",
       "      <td>2015-11-18</td>\n",
       "      <td>28.940001</td>\n",
       "      <td>29.372499</td>\n",
       "      <td>28.875000</td>\n",
       "      <td>29.322500</td>\n",
       "      <td>186698800.0</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1256</th>\n",
       "      <td>2015-11-19</td>\n",
       "      <td>29.410000</td>\n",
       "      <td>29.937500</td>\n",
       "      <td>29.190001</td>\n",
       "      <td>29.695000</td>\n",
       "      <td>173183200.0</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1255</th>\n",
       "      <td>2015-11-20</td>\n",
       "      <td>29.799999</td>\n",
       "      <td>29.980000</td>\n",
       "      <td>29.712500</td>\n",
       "      <td>29.825001</td>\n",
       "      <td>137148400.0</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1254</th>\n",
       "      <td>2015-11-23</td>\n",
       "      <td>29.817499</td>\n",
       "      <td>29.932501</td>\n",
       "      <td>29.334999</td>\n",
       "      <td>29.437500</td>\n",
       "      <td>129930000.0</td>\n",
       "      <td>73.501056</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-11-10</td>\n",
       "      <td>115.550003</td>\n",
       "      <td>117.589996</td>\n",
       "      <td>114.129997</td>\n",
       "      <td>115.970001</td>\n",
       "      <td>138023400.0</td>\n",
       "      <td>51.550781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-11-11</td>\n",
       "      <td>117.190002</td>\n",
       "      <td>119.629997</td>\n",
       "      <td>116.440002</td>\n",
       "      <td>119.489998</td>\n",
       "      <td>112295000.0</td>\n",
       "      <td>58.683031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-11-12</td>\n",
       "      <td>119.620003</td>\n",
       "      <td>120.529999</td>\n",
       "      <td>118.570000</td>\n",
       "      <td>119.209999</td>\n",
       "      <td>103162300.0</td>\n",
       "      <td>57.929312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-11-13</td>\n",
       "      <td>119.440002</td>\n",
       "      <td>119.669998</td>\n",
       "      <td>117.870003</td>\n",
       "      <td>119.260002</td>\n",
       "      <td>81581900.0</td>\n",
       "      <td>58.036259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-11-16</td>\n",
       "      <td>118.919998</td>\n",
       "      <td>120.989998</td>\n",
       "      <td>118.150002</td>\n",
       "      <td>120.300003</td>\n",
       "      <td>91183018.0</td>\n",
       "      <td>60.364705</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1259 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            date        open        high         low       close       volume  \\\n",
       "1258  2015-11-17   28.730000   28.762501   28.330000   28.422501  110467600.0   \n",
       "1257  2015-11-18   28.940001   29.372499   28.875000   29.322500  186698800.0   \n",
       "1256  2015-11-19   29.410000   29.937500   29.190001   29.695000  173183200.0   \n",
       "1255  2015-11-20   29.799999   29.980000   29.712500   29.825001  137148400.0   \n",
       "1254  2015-11-23   29.817499   29.932501   29.334999   29.437500  129930000.0   \n",
       "...          ...         ...         ...         ...         ...          ...   \n",
       "4     2020-11-10  115.550003  117.589996  114.129997  115.970001  138023400.0   \n",
       "3     2020-11-11  117.190002  119.629997  116.440002  119.489998  112295000.0   \n",
       "2     2020-11-12  119.620003  120.529999  118.570000  119.209999  103162300.0   \n",
       "1     2020-11-13  119.440002  119.669998  117.870003  119.260002   81581900.0   \n",
       "0     2020-11-16  118.919998  120.989998  118.150002  120.300003   91183018.0   \n",
       "\n",
       "             rsi  \n",
       "1258    0.000000  \n",
       "1257  100.000000  \n",
       "1256  100.000000  \n",
       "1255  100.000000  \n",
       "1254   73.501056  \n",
       "...          ...  \n",
       "4      51.550781  \n",
       "3      58.683031  \n",
       "2      57.929312  \n",
       "1      58.036259  \n",
       "0      60.364705  \n",
       "\n",
       "[1259 rows x 7 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#creating DataFrame\n",
    "\n",
    "indicator_df = pd.DataFrame(indicator)\n",
    "indicator_df = indicator_df.sort_values(by=\"date\")\n",
    "indicator_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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53.064202556366205}, {'date': '2015-12-29', 'open': 188.976, 'high': 190.256, 'low': 188.936, 'close': 189.866, 'volume': 92640700.0, 'rsi': 58.041373629376665}, {'date': '2015-12-28', 'open': 187.5111, 'high': 187.9111, 'low': 186.5911, 'close': 187.8611, 'volume': 65899900.0, 'rsi': 52.00570728744745}, {'date': '2015-12-24', 'open': 188.3314, 'high': 188.9414, 'low': 188.0314, 'close': 188.2914, 'volume': 48539600.0, 'rsi': 53.492033754025236}, {'date': '2015-12-23', 'open': 187.2726, 'high': 188.6526, 'low': 187.1626, 'close': 188.6026, 'volume': 110987200.0, 'rsi': 54.50598755687945}, {'date': '2015-12-22', 'open': 185.5157, 'high': 186.6457, 'low': 184.3457, 'close': 186.2957, 'volume': 111026200.0, 'rsi': 47.919812190110775}, {'date': '2015-12-21', 'open': 184.3604, 'high': 184.8304, 'low': 183.0404, 'close': 184.6204, 'volume': 99094300.0, 'rsi': 42.47695077577374}, {'date': '2015-12-18', 'open': 185.8599, 'high': 186.0199, 'low': 182.9199, 'close': 183.1099, 'volume': 251393500.0, 'rsi': 37.14662184518251}, {'date': '2015-12-17', 'open': 189.9712, 'high': 190.0512, 'low': 186.4112, 'close': 186.4312, 'volume': 173092500.0, 'rsi': 45.48802417390748}, {'date': '2015-12-16', 'open': 187.656, 'high': 189.676, 'low': 186.086, 'close': 189.316, 'volume': 197017000.0, 'rsi': 55.17294362120209}, {'date': '2015-12-15', 'open': 186.2559, 'high': 187.6659, 'low': 184.4259, 'close': 186.5859, 'volume': 154069600.0, 'rsi': 45.24307936789043}, {'date': '2015-12-14', 'open': 183.8175, 'high': 184.7975, 'low': 181.6975, 'close': 184.6475, 'volume': 182385200.0, 'rsi': 36.21418542170636}, {'date': '2015-12-11', 'open': 185.1893, 'high': 185.9792, 'low': 183.3492, 'close': 183.7193, 'volume': 211173300.0, 'rsi': 31.334705992742535}, {'date': '2015-12-10', 'open': 186.9003, 'high': 188.9103, 'low': 186.6203, 'close': 187.3503, 'volume': 116128900.0, 'rsi': 42.88459797983063}, {'date': '2015-12-09', 'open': 187.718, 'high': 190.208, 'low': 185.708, 'close': 186.868, 'volume': 162401500.0, 'rsi': 40.25184368683397}, {'date': '2015-12-08', 'open': 187.8732, 'high': 189.6732, 'low': 187.1632, 'close': 188.3332, 'volume': 103372400.0, 'rsi': 46.056403188924286}, {'date': '2015-12-07', 'open': 190.4872, 'high': 190.9872, 'low': 188.4572, 'close': 189.6072, 'volume': 102027100.0, 'rsi': 51.91496601355815}, {'date': '2015-12-04', 'open': 186.7529, 'high': 191.1129, 'low': 186.7529, 'close': 190.7629, 'volume': 192913900.0, 'rsi': 57.93130934891498}, {'date': '2015-12-03', 'open': 190.3337, 'high': 190.6537, 'low': 186.2537, 'close': 187.1137, 'volume': 166224200.0, 'rsi': 37.27336587658895}, {'date': '2015-12-02', 'open': 191.861, 'high': 192.241, 'low': 189.471, 'close': 189.771, 'volume': 108441300.0, 'rsi': 54.96090392376639}, {'date': '2015-12-01', 'open': 190.4876, 'high': 191.8676, 'low': 190.1576, 'close': 191.7276, 'volume': 97858400.0, 'rsi': 80.17231637541819}, {'date': '2015-11-30', 'open': 190.9766, 'high': 191.1166, 'low': 189.7866, 'close': 189.9166, 'volume': 112822700.0, 'rsi': 67.91002644707987}, {'date': '2015-11-27', 'open': 190.5784, 'high': 190.9484, 'low': 190.0084, 'close': 190.7084, 'volume': 37317800.0, 'rsi': 89.75142933447563}, {'date': '2015-11-25', 'open': 190.67, 'high': 190.91, 'low': 190.1799, 'close': 190.49, 'volume': 51980100.0, 'rsi': 88.86217349675721}, {'date': '2015-11-24', 'open': 189.0372, 'high': 190.9972, 'low': 188.5772, 'close': 190.5172, 'volume': 98874400.0, 'rsi': 89.7349110038503}, {'date': '2015-11-23', 'open': 190.5725, 'high': 191.1725, 'low': 189.7125, 'close': 190.2625, 'volume': 64931200.0, 'rsi': 88.8086073135138}, {'date': '2015-11-20', 'open': 190.6208, 'high': 191.2908, 'low': 190.0308, 'close': 190.4808, 'volume': 94011500.0, 'rsi': 95.45287283003609}, {'date': '2015-11-19', 'open': 189.8292, 'high': 190.2892, 'low': 189.4392, 'close': 189.7892, 'volume': 88220500.0, 'rsi': 94.21983675802713}, {'date': '2015-11-18', 'open': 187.263, 'high': 190.123, 'low': 187.213, 'close': 189.953, 'volume': 121342500.0, 'rsi': 100.0}, {'date': '2015-11-17', 'open': 187.5063, 'high': 188.5563, 'low': 186.3963, 'close': 186.9863, 'volume': 121123700.0, 'rsi': 0.0}]\n"
     ]
    }
   ],
   "source": [
    "# pulling the market data (SPY)\n",
    "\n",
    "url3 = \"https://financialmodelingprep.com/api/v3/technical_indicator/daily/\"\n",
    "   \n",
    "query_url3 = url3 + \"SPY?period=10&type=rsi\" + \"&apikey=\" + API_key\n",
    "\n",
    "spy = requests.get(query_url3).json()\n",
    "\n",
    "print(spy)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>rsi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1258</th>\n",
       "      <td>2015-11-17</td>\n",
       "      <td>187.506300</td>\n",
       "      <td>188.556300</td>\n",
       "      <td>186.396300</td>\n",
       "      <td>186.986300</td>\n",
       "      <td>121123700.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1257</th>\n",
       "      <td>2015-11-18</td>\n",
       "      <td>187.263000</td>\n",
       "      <td>190.123000</td>\n",
       "      <td>187.213000</td>\n",
       "      <td>189.953000</td>\n",
       "      <td>121342500.0</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1256</th>\n",
       "      <td>2015-11-19</td>\n",
       "      <td>189.829200</td>\n",
       "      <td>190.289200</td>\n",
       "      <td>189.439200</td>\n",
       "      <td>189.789200</td>\n",
       "      <td>88220500.0</td>\n",
       "      <td>94.219837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1255</th>\n",
       "      <td>2015-11-20</td>\n",
       "      <td>190.620800</td>\n",
       "      <td>191.290800</td>\n",
       "      <td>190.030800</td>\n",
       "      <td>190.480800</td>\n",
       "      <td>94011500.0</td>\n",
       "      <td>95.452873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1254</th>\n",
       "      <td>2015-11-23</td>\n",
       "      <td>190.572500</td>\n",
       "      <td>191.172500</td>\n",
       "      <td>189.712500</td>\n",
       "      <td>190.262500</td>\n",
       "      <td>64931200.0</td>\n",
       "      <td>88.808607</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-11-10</td>\n",
       "      <td>353.489990</td>\n",
       "      <td>355.179993</td>\n",
       "      <td>350.510010</td>\n",
       "      <td>354.040009</td>\n",
       "      <td>85552000.0</td>\n",
       "      <td>66.789119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-11-11</td>\n",
       "      <td>356.399994</td>\n",
       "      <td>357.559998</td>\n",
       "      <td>355.059998</td>\n",
       "      <td>356.670013</td>\n",
       "      <td>58649000.0</td>\n",
       "      <td>69.220329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-11-12</td>\n",
       "      <td>355.579987</td>\n",
       "      <td>356.720001</td>\n",
       "      <td>351.260010</td>\n",
       "      <td>353.209991</td>\n",
       "      <td>67546200.0</td>\n",
       "      <td>62.529124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-11-13</td>\n",
       "      <td>355.269989</td>\n",
       "      <td>358.899994</td>\n",
       "      <td>354.709991</td>\n",
       "      <td>358.100006</td>\n",
       "      <td>62892200.0</td>\n",
       "      <td>67.467429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-11-16</td>\n",
       "      <td>360.980011</td>\n",
       "      <td>362.579987</td>\n",
       "      <td>359.589996</td>\n",
       "      <td>362.570007</td>\n",
       "      <td>65777372.0</td>\n",
       "      <td>71.308031</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1259 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            date        open        high         low       close       volume  \\\n",
       "1258  2015-11-17  187.506300  188.556300  186.396300  186.986300  121123700.0   \n",
       "1257  2015-11-18  187.263000  190.123000  187.213000  189.953000  121342500.0   \n",
       "1256  2015-11-19  189.829200  190.289200  189.439200  189.789200   88220500.0   \n",
       "1255  2015-11-20  190.620800  191.290800  190.030800  190.480800   94011500.0   \n",
       "1254  2015-11-23  190.572500  191.172500  189.712500  190.262500   64931200.0   \n",
       "...          ...         ...         ...         ...         ...          ...   \n",
       "4     2020-11-10  353.489990  355.179993  350.510010  354.040009   85552000.0   \n",
       "3     2020-11-11  356.399994  357.559998  355.059998  356.670013   58649000.0   \n",
       "2     2020-11-12  355.579987  356.720001  351.260010  353.209991   67546200.0   \n",
       "1     2020-11-13  355.269989  358.899994  354.709991  358.100006   62892200.0   \n",
       "0     2020-11-16  360.980011  362.579987  359.589996  362.570007   65777372.0   \n",
       "\n",
       "             rsi  \n",
       "1258    0.000000  \n",
       "1257  100.000000  \n",
       "1256   94.219837  \n",
       "1255   95.452873  \n",
       "1254   88.808607  \n",
       "...          ...  \n",
       "4      66.789119  \n",
       "3      69.220329  \n",
       "2      62.529124  \n",
       "1      67.467429  \n",
       "0      71.308031  \n",
       "\n",
       "[1259 rows x 7 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#creating DataFrame\n",
    "\n",
    "spy_df = pd.DataFrame(spy)\n",
    "spy_df = spy_df.sort_values(by='date')\n",
    "spy_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{'date': '2020-11-16', 'open': 12.55, 'high': 13.28, 'low': 12.54, 'close': 12.6, 'volume': 31756884.0, 'rsi': 31.359321008358837}, {'date': '2020-11-13', 'open': 13.66, 'high': 13.66, 'low': 12.73, 'close': 13.05, 'volume': 43994400.0, 'rsi': 32.79737897633361}, {'date': '2020-11-12', 'open': 13.3, 'high': 14.82, 'low': 13.09, 'close': 14.37, 'volume': 68251200.0, 'rsi': 37.314849922717535}, {'date': '2020-11-11', 'open': 13.18, 'high': 13.44, 'low': 12.64, 'close': 13.06, 'volume': 34220400.0, 'rsi': 28.52110338630687}, {'date': '2020-11-10', 'open': 13.63, 'high': 14.4, 'low': 13.45, 'close': 13.49, 'volume': 36231600.0, 'rsi': 29.754200343357567}, {'date': '2020-11-09', 'open': 13.14, 'high': 14.18, 'low': 12.18, 'close': 14.18, 'volume': 54605600.0, 'rsi': 31.73573955648732}, {'date': '2020-11-06', 'open': 16.01, 'high': 16.08, 'low': 14.63, 'close': 14.67, 'volume': 53209600.0, 'rsi': 33.1465959863587}, {'date': '2020-11-05', 'open': 15.89, 'high': 16.530001, 'low': 15.65, 'close': 16.209999, 'volume': 33461500.0, 'rsi': 37.91423499083043}, {'date': '2020-11-04', 'open': 18.0, 'high': 18.049999, 'low': 16.5, 'close': 16.67, 'volume': 55513700.0, 'rsi': 39.43924928849119}, {'date': '2020-11-03', 'open': 19.969999, 'high': 20.16, 'low': 18.790001, 'close': 19.18, 'volume': 50097700.0, 'rsi': 49.14720809767294}, {'date': '2020-11-02', 'open': 20.129999, 'high': 21.360001, 'low': 20.059999, 'close': 20.879999, 'volume': 51415700.0, 'rsi': 57.82319350006514}, {'date': '2020-10-30', 'open': 21.209999, 'high': 22.219999, 'low': 20.74, 'close': 21.58, 'volume': 58263200.0, 'rsi': 61.87079911806475}, {'date': '2020-10-29', 'open': 22.459999, 'high': 23.129999, 'low': 19.68, 'close': 20.68, 'volume': 56381100.0, 'rsi': 58.51014015616811}, {'date': '2020-10-28', 'open': 21.23, 'high': 23.200001, 'low': 20.85, 'close': 23.139999, 'volume': 94240700.0, 'rsi': 74.70853067518347}, {'date': '2020-10-27', 'open': 18.969999, 'high': 19.559999, 'low': 18.469999, 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'2016-01-22', 'open': 22825.0, 'high': 23340.0, 'low': 20875.0, 'close': 20930.0, 'volume': 41300.0, 'rsi': 53.10998376886699}, {'date': '2016-01-21', 'open': 24825.0, 'high': 27345.0, 'low': 23440.0, 'close': 25170.0, 'volume': 77200.0, 'rsi': 69.61963576695943}, {'date': '2016-01-20', 'open': 25695.0, 'high': 29500.0, 'low': 24275.0, 'close': 25320.0, 'volume': 92500.0, 'rsi': 70.31559098188826}, {'date': '2016-01-19', 'open': 22325.0, 'high': 26400.0, 'low': 22270.0, 'close': 24180.0, 'volume': 80700.0, 'rsi': 68.13691055891158}, {'date': '2016-01-15', 'open': 24600.0, 'high': 25695.0, 'low': 22900.0, 'close': 24075.0, 'volume': 72300.0, 'rsi': 67.94186758984137}, {'date': '2016-01-14', 'open': 21295.0, 'high': 23355.0, 'low': 19000.0, 'close': 20030.0, 'volume': 80500.0, 'rsi': 59.305026649462484}, {'date': '2016-01-13', 'open': 17360.0, 'high': 22450.0, 'low': 17270.0, 'close': 21730.0, 'volume': 92300.0, 'rsi': 66.03414500728888}, {'date': '2016-01-12', 'open': 18305.0, 'high': 20670.0, 'low': 17960.0, 'close': 18085.0, 'volume': 82800.0, 'rsi': 56.512212596939094}, {'date': '2016-01-11', 'open': 20860.0, 'high': 24720.0, 'low': 19560.0, 'close': 19950.0, 'volume': 107600.0, 'rsi': 64.88899875918557}, {'date': '2016-01-08', 'open': 17920.0, 'high': 21985.0, 'low': 17515.0, 'close': 21410.0, 'volume': 96600.0, 'rsi': 72.45604273286082}, {'date': '2016-01-07', 'open': 18115.0, 'high': 19930.0, 'low': 17270.0, 'close': 19420.0, 'volume': 94200.0, 'rsi': 67.85802707676145}, {'date': '2016-01-06', 'open': 17045.0, 'high': 17055.0, 'low': 15865.0, 'close': 15965.0, 'volume': 72400.0, 'rsi': 56.515279141045205}, {'date': '2016-01-05', 'open': 15325.0, 'high': 16295.0, 'low': 14765.0, 'close': 15065.0, 'volume': 63600.0, 'rsi': 52.593131025622185}, {'date': '2016-01-04', 'open': 16465.0, 'high': 17415.0, 'low': 15875.0, 'close': 15980.0, 'volume': 86300.0, 'rsi': 57.324047077932114}, {'date': '2015-12-31', 'open': 13935.0, 'high': 14250.0, 'low': 13445.0, 'close': 14175.0, 'volume': 54800.0, 'rsi': 49.21321170971923}, {'date': '2015-12-30', 'open': 13155.0, 'high': 13695.0, 'low': 13075.0, 'close': 13590.0, 'volume': 37300.0, 'rsi': 46.23247530752755}, {'date': '2015-12-29', 'open': 12900.0, 'high': 13125.0, 'low': 12685.0, 'close': 12855.0, 'volume': 37400.0, 'rsi': 42.41047892726107}, {'date': '2015-12-28', 'open': 14100.0, 'high': 14630.0, 'low': 13265.0, 'close': 13285.0, 'volume': 45800.0, 'rsi': 44.059528452835025}, {'date': '2015-12-24', 'open': 13350.0, 'high': 13700.0, 'low': 13220.0, 'close': 13610.0, 'volume': 17700.0, 'rsi': 45.25654267473225}, {'date': '2015-12-23', 'open': 13135.0, 'high': 13745.0, 'low': 12940.0, 'close': 13150.0, 'volume': 41600.0, 'rsi': 43.294063892808715}, {'date': '2015-12-22', 'open': 14305.0, 'high': 14725.0, 'low': 13510.0, 'close': 13825.0, 'volume': 46200.0, 'rsi': 45.44562288894556}, {'date': '2015-12-21', 'open': 15650.0, 'high': 16970.0, 'low': 15320.0, 'close': 15380.0, 'volume': 64400.0, 'rsi': 50.66611762625774}, {'date': '2015-12-18', 'open': 15580.0, 'high': 17260.0, 'low': 15240.0, 'close': 16975.0, 'volume': 93600.0, 'rsi': 56.67639668283627}, {'date': '2015-12-17', 'open': 13295.0, 'high': 14950.0, 'low': 13285.0, 'close': 14605.0, 'volume': 58100.0, 'rsi': 48.50777381164976}, {'date': '2015-12-16', 'open': 14460.0, 'high': 15195.0, 'low': 12950.0, 'close': 13465.0, 'volume': 57700.0, 'rsi': 43.931153179934725}, {'date': '2015-12-15', 'open': 15825.0, 'high': 16930.0, 'low': 15105.0, 'close': 15580.0, 'volume': 59700.0, 'rsi': 51.58696507418029}, {'date': '2015-12-14', 'open': 19545.0, 'high': 21545.0, 'low': 16625.0, 'close': 16945.0, 'volume': 96600.0, 'rsi': 57.396909056746686}, {'date': '2015-12-11', 'open': 16900.0, 'high': 20555.0, 'low': 16560.0, 'close': 19905.0, 'volume': 105800.0, 'rsi': 73.56719659248424}, {'date': '2015-12-10', 'open': 15060.0, 'high': 15635.0, 'low': 14205.0, 'close': 15285.0, 'volume': 63000.0, 'rsi': 56.255115003993026}, {'date': '2015-12-09', 'open': 14100.0, 'high': 15740.0, 'low': 13100.0, 'close': 14905.0, 'volume': 88000.0, 'rsi': 54.02616176196947}, {'date': '2015-12-08', 'open': 14055.0, 'high': 14405.0, 'low': 13175.0, 'close': 13775.0, 'volume': 68100.0, 'rsi': 46.76689349289309}, {'date': '2015-12-07', 'open': 12460.0, 'high': 13850.0, 'low': 12445.0, 'close': 12990.0, 'volume': 53200.0, 'rsi': 40.93594708244376}, {'date': '2015-12-04', 'open': 14245.0, 'high': 14500.0, 'low': 12375.0, 'close': 12385.0, 'volume': 67500.0, 'rsi': 36.07941566050987}, {'date': '2015-12-03', 'open': 12775.0, 'high': 15750.0, 'low': 12625.0, 'close': 15100.0, 'volume': 89600.0, 'rsi': 54.0186345682499}, {'date': '2015-12-02', 'open': 12335.0, 'high': 13400.0, 'low': 11915.0, 'close': 13230.0, 'volume': 57500.0, 'rsi': 33.53197907568285}, {'date': '2015-12-01', 'open': 13080.0, 'high': 13335.0, 'low': 12250.0, 'close': 12320.0, 'volume': 43000.0, 'rsi': 17.417350122750186}, {'date': '2015-11-30', 'open': 13395.0, 'high': 13750.0, 'low': 13235.0, 'close': 13435.0, 'volume': 37600.0, 'rsi': 23.773151259418928}, {'date': '2015-11-27', 'open': 13275.0, 'high': 13675.0, 'low': 13225.0, 'close': 13590.0, 'volume': 17000.0, 'rsi': 24.91043510908835}, {'date': '2015-11-25', 'open': 13700.0, 'high': 13945.0, 'low': 13225.0, 'close': 13375.0, 'volume': 30600.0, 'rsi': 20.14113650036909}, {'date': '2015-11-24', 'open': 14405.0, 'high': 14720.0, 'low': 13500.0, 'close': 13790.0, 'volume': 50100.0, 'rsi': 22.63910092341259}, {'date': '2015-11-23', 'open': 14250.0, 'high': 14540.0, 'low': 13505.0, 'close': 13670.0, 'volume': 41900.0, 'rsi': 20.058933592147525}, {'date': '2015-11-20', 'open': 14510.0, 'high': 14715.0, 'low': 14075.0, 'close': 14445.0, 'volume': 43700.0, 'rsi': 24.882707774798927}, {'date': '2015-11-19', 'open': 14740.0, 'high': 15830.0, 'low': 14600.0, 'close': 15450.0, 'volume': 40700.0, 'rsi': 34.59119496855346}, {'date': '2015-11-18', 'open': 15945.0, 'high': 16030.0, 'low': 14400.0, 'close': 14460.0, 'volume': 51100.0, 'rsi': 0.0}, {'date': '2015-11-17', 'open': 14560.0, 'high': 17125.0, 'low': 14430.0, 'close': 16540.0, 'volume': 71500.0, 'rsi': 0.0}]\n"
     ]
    }
   ],
   "source": [
    "# pulling VIX data\n",
    "\n",
    "url4 = \"https://financialmodelingprep.com/api/v3/technical_indicator/daily/\"\n",
    "   \n",
    "query_url4 = url4 + \"UVXY?period=10&type=rsi\" + \"&apikey=\" + API_key\n",
    "\n",
    "vix = requests.get(query_url4).json()\n",
    "\n",
    "print(vix)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>rsi</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1258</th>\n",
       "      <td>2015-11-17</td>\n",
       "      <td>14560.00</td>\n",
       "      <td>17125.00</td>\n",
       "      <td>14430.00</td>\n",
       "      <td>16540.00</td>\n",
       "      <td>71500.0</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>1257</th>\n",
       "      <td>2015-11-18</td>\n",
       "      <td>15945.00</td>\n",
       "      <td>16030.00</td>\n",
       "      <td>14400.00</td>\n",
       "      <td>14460.00</td>\n",
       "      <td>51100.0</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>1256</th>\n",
       "      <td>2015-11-19</td>\n",
       "      <td>14740.00</td>\n",
       "      <td>15830.00</td>\n",
       "      <td>14600.00</td>\n",
       "      <td>15450.00</td>\n",
       "      <td>40700.0</td>\n",
       "      <td>34.591195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1255</th>\n",
       "      <td>2015-11-20</td>\n",
       "      <td>14510.00</td>\n",
       "      <td>14715.00</td>\n",
       "      <td>14075.00</td>\n",
       "      <td>14445.00</td>\n",
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       "      <td>24.882708</td>\n",
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       "      <th>1254</th>\n",
       "      <td>2015-11-23</td>\n",
       "      <td>14250.00</td>\n",
       "      <td>14540.00</td>\n",
       "      <td>13505.00</td>\n",
       "      <td>13670.00</td>\n",
       "      <td>41900.0</td>\n",
       "      <td>20.058934</td>\n",
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       "      <th>4</th>\n",
       "      <td>2020-11-10</td>\n",
       "      <td>13.63</td>\n",
       "      <td>14.40</td>\n",
       "      <td>13.45</td>\n",
       "      <td>13.49</td>\n",
       "      <td>36231600.0</td>\n",
       "      <td>29.754200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-11-11</td>\n",
       "      <td>13.18</td>\n",
       "      <td>13.44</td>\n",
       "      <td>12.64</td>\n",
       "      <td>13.06</td>\n",
       "      <td>34220400.0</td>\n",
       "      <td>28.521103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-11-12</td>\n",
       "      <td>13.30</td>\n",
       "      <td>14.82</td>\n",
       "      <td>13.09</td>\n",
       "      <td>14.37</td>\n",
       "      <td>68251200.0</td>\n",
       "      <td>37.314850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-11-13</td>\n",
       "      <td>13.66</td>\n",
       "      <td>13.66</td>\n",
       "      <td>12.73</td>\n",
       "      <td>13.05</td>\n",
       "      <td>43994400.0</td>\n",
       "      <td>32.797379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-11-16</td>\n",
       "      <td>12.55</td>\n",
       "      <td>13.28</td>\n",
       "      <td>12.54</td>\n",
       "      <td>12.60</td>\n",
       "      <td>31756884.0</td>\n",
       "      <td>31.359321</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1259 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            date      open      high       low     close      volume  \\\n",
       "1258  2015-11-17  14560.00  17125.00  14430.00  16540.00     71500.0   \n",
       "1257  2015-11-18  15945.00  16030.00  14400.00  14460.00     51100.0   \n",
       "1256  2015-11-19  14740.00  15830.00  14600.00  15450.00     40700.0   \n",
       "1255  2015-11-20  14510.00  14715.00  14075.00  14445.00     43700.0   \n",
       "1254  2015-11-23  14250.00  14540.00  13505.00  13670.00     41900.0   \n",
       "...          ...       ...       ...       ...       ...         ...   \n",
       "4     2020-11-10     13.63     14.40     13.45     13.49  36231600.0   \n",
       "3     2020-11-11     13.18     13.44     12.64     13.06  34220400.0   \n",
       "2     2020-11-12     13.30     14.82     13.09     14.37  68251200.0   \n",
       "1     2020-11-13     13.66     13.66     12.73     13.05  43994400.0   \n",
       "0     2020-11-16     12.55     13.28     12.54     12.60  31756884.0   \n",
       "\n",
       "            rsi  \n",
       "1258   0.000000  \n",
       "1257   0.000000  \n",
       "1256  34.591195  \n",
       "1255  24.882708  \n",
       "1254  20.058934  \n",
       "...         ...  \n",
       "4     29.754200  \n",
       "3     28.521103  \n",
       "2     37.314850  \n",
       "1     32.797379  \n",
       "0     31.359321  \n",
       "\n",
       "[1259 rows x 7 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#creating DataFrame\n",
    "\n",
    "vix_df = pd.DataFrame(vix)\n",
    "vix_df = vix_df.sort_values(by='date')\n",
    "vix_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
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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>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>rsi</th>\n",
       "      <th>open_SPY</th>\n",
       "      <th>high_SPY</th>\n",
       "      <th>low_SPY</th>\n",
       "      <th>close_SPY</th>\n",
       "      <th>volume_SPY</th>\n",
       "      <th>rsi_SPY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-11-17</td>\n",
       "      <td>28.730000</td>\n",
       "      <td>28.762501</td>\n",
       "      <td>28.330000</td>\n",
       "      <td>28.422501</td>\n",
       "      <td>110467600.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>187.506300</td>\n",
       "      <td>188.556300</td>\n",
       "      <td>186.396300</td>\n",
       "      <td>186.986300</td>\n",
       "      <td>121123700.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-11-18</td>\n",
       "      <td>28.940001</td>\n",
       "      <td>29.372499</td>\n",
       "      <td>28.875000</td>\n",
       "      <td>29.322500</td>\n",
       "      <td>186698800.0</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>187.263000</td>\n",
       "      <td>190.123000</td>\n",
       "      <td>187.213000</td>\n",
       "      <td>189.953000</td>\n",
       "      <td>121342500.0</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-11-19</td>\n",
       "      <td>29.410000</td>\n",
       "      <td>29.937500</td>\n",
       "      <td>29.190001</td>\n",
       "      <td>29.695000</td>\n",
       "      <td>173183200.0</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>189.829200</td>\n",
       "      <td>190.289200</td>\n",
       "      <td>189.439200</td>\n",
       "      <td>189.789200</td>\n",
       "      <td>88220500.0</td>\n",
       "      <td>94.219837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-11-20</td>\n",
       "      <td>29.799999</td>\n",
       "      <td>29.980000</td>\n",
       "      <td>29.712500</td>\n",
       "      <td>29.825001</td>\n",
       "      <td>137148400.0</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>190.620800</td>\n",
       "      <td>191.290800</td>\n",
       "      <td>190.030800</td>\n",
       "      <td>190.480800</td>\n",
       "      <td>94011500.0</td>\n",
       "      <td>95.452873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-11-23</td>\n",
       "      <td>29.817499</td>\n",
       "      <td>29.932501</td>\n",
       "      <td>29.334999</td>\n",
       "      <td>29.437500</td>\n",
       "      <td>129930000.0</td>\n",
       "      <td>73.501056</td>\n",
       "      <td>190.572500</td>\n",
       "      <td>191.172500</td>\n",
       "      <td>189.712500</td>\n",
       "      <td>190.262500</td>\n",
       "      <td>64931200.0</td>\n",
       "      <td>88.808607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1254</th>\n",
       "      <td>2020-11-10</td>\n",
       "      <td>115.550003</td>\n",
       "      <td>117.589996</td>\n",
       "      <td>114.129997</td>\n",
       "      <td>115.970001</td>\n",
       "      <td>138023400.0</td>\n",
       "      <td>51.550781</td>\n",
       "      <td>353.489990</td>\n",
       "      <td>355.179993</td>\n",
       "      <td>350.510010</td>\n",
       "      <td>354.040009</td>\n",
       "      <td>85552000.0</td>\n",
       "      <td>66.789119</td>\n",
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       "    <tr>\n",
       "      <th>1255</th>\n",
       "      <td>2020-11-11</td>\n",
       "      <td>117.190002</td>\n",
       "      <td>119.629997</td>\n",
       "      <td>116.440002</td>\n",
       "      <td>119.489998</td>\n",
       "      <td>112295000.0</td>\n",
       "      <td>58.683031</td>\n",
       "      <td>356.399994</td>\n",
       "      <td>357.559998</td>\n",
       "      <td>355.059998</td>\n",
       "      <td>356.670013</td>\n",
       "      <td>58649000.0</td>\n",
       "      <td>69.220329</td>\n",
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       "    <tr>\n",
       "      <th>1256</th>\n",
       "      <td>2020-11-12</td>\n",
       "      <td>119.620003</td>\n",
       "      <td>120.529999</td>\n",
       "      <td>118.570000</td>\n",
       "      <td>119.209999</td>\n",
       "      <td>103162300.0</td>\n",
       "      <td>57.929312</td>\n",
       "      <td>355.579987</td>\n",
       "      <td>356.720001</td>\n",
       "      <td>351.260010</td>\n",
       "      <td>353.209991</td>\n",
       "      <td>67546200.0</td>\n",
       "      <td>62.529124</td>\n",
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       "    <tr>\n",
       "      <th>1257</th>\n",
       "      <td>2020-11-13</td>\n",
       "      <td>119.440002</td>\n",
       "      <td>119.669998</td>\n",
       "      <td>117.870003</td>\n",
       "      <td>119.260002</td>\n",
       "      <td>81581900.0</td>\n",
       "      <td>58.036259</td>\n",
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       "      <td>358.899994</td>\n",
       "      <td>354.709991</td>\n",
       "      <td>358.100006</td>\n",
       "      <td>62892200.0</td>\n",
       "      <td>67.467429</td>\n",
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       "    <tr>\n",
       "      <th>1258</th>\n",
       "      <td>2020-11-16</td>\n",
       "      <td>118.919998</td>\n",
       "      <td>120.989998</td>\n",
       "      <td>118.150002</td>\n",
       "      <td>120.300003</td>\n",
       "      <td>91183018.0</td>\n",
       "      <td>60.364705</td>\n",
       "      <td>360.980011</td>\n",
       "      <td>362.579987</td>\n",
       "      <td>359.589996</td>\n",
       "      <td>362.570007</td>\n",
       "      <td>65777372.0</td>\n",
       "      <td>71.308031</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1259 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            date        open        high         low       close       volume  \\\n",
       "0     2015-11-17   28.730000   28.762501   28.330000   28.422501  110467600.0   \n",
       "1     2015-11-18   28.940001   29.372499   28.875000   29.322500  186698800.0   \n",
       "2     2015-11-19   29.410000   29.937500   29.190001   29.695000  173183200.0   \n",
       "3     2015-11-20   29.799999   29.980000   29.712500   29.825001  137148400.0   \n",
       "4     2015-11-23   29.817499   29.932501   29.334999   29.437500  129930000.0   \n",
       "...          ...         ...         ...         ...         ...          ...   \n",
       "1254  2020-11-10  115.550003  117.589996  114.129997  115.970001  138023400.0   \n",
       "1255  2020-11-11  117.190002  119.629997  116.440002  119.489998  112295000.0   \n",
       "1256  2020-11-12  119.620003  120.529999  118.570000  119.209999  103162300.0   \n",
       "1257  2020-11-13  119.440002  119.669998  117.870003  119.260002   81581900.0   \n",
       "1258  2020-11-16  118.919998  120.989998  118.150002  120.300003   91183018.0   \n",
       "\n",
       "             rsi    open_SPY    high_SPY     low_SPY   close_SPY   volume_SPY  \\\n",
       "0       0.000000  187.506300  188.556300  186.396300  186.986300  121123700.0   \n",
       "1     100.000000  187.263000  190.123000  187.213000  189.953000  121342500.0   \n",
       "2     100.000000  189.829200  190.289200  189.439200  189.789200   88220500.0   \n",
       "3     100.000000  190.620800  191.290800  190.030800  190.480800   94011500.0   \n",
       "4      73.501056  190.572500  191.172500  189.712500  190.262500   64931200.0   \n",
       "...          ...         ...         ...         ...         ...          ...   \n",
       "1254   51.550781  353.489990  355.179993  350.510010  354.040009   85552000.0   \n",
       "1255   58.683031  356.399994  357.559998  355.059998  356.670013   58649000.0   \n",
       "1256   57.929312  355.579987  356.720001  351.260010  353.209991   67546200.0   \n",
       "1257   58.036259  355.269989  358.899994  354.709991  358.100006   62892200.0   \n",
       "1258   60.364705  360.980011  362.579987  359.589996  362.570007   65777372.0   \n",
       "\n",
       "         rsi_SPY  \n",
       "0       0.000000  \n",
       "1     100.000000  \n",
       "2      94.219837  \n",
       "3      95.452873  \n",
       "4      88.808607  \n",
       "...          ...  \n",
       "1254   66.789119  \n",
       "1255   69.220329  \n",
       "1256   62.529124  \n",
       "1257   67.467429  \n",
       "1258   71.308031  \n",
       "\n",
       "[1259 rows x 13 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#merging data\n",
    "\n",
    "merge_df = pd.merge(indicator_df,spy_df, on=\"date\", how=\"left\", suffixes=(\"\",\"_SPY\"))\n",
    "#merge_df = merge_df[merge_df[\"date\"] > \"2018-12-31\"]\n",
    "merge_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>high_SPY</th>\n",
       "      <th>low_SPY</th>\n",
       "      <th>close_SPY</th>\n",
       "      <th>volume_SPY</th>\n",
       "      <th>rsi_SPY</th>\n",
       "      <th>open_VIX</th>\n",
       "      <th>high_VIX</th>\n",
       "      <th>low_VIX</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1199</th>\n",
       "      <td>2020-08-24</td>\n",
       "      <td>128.697495</td>\n",
       "      <td>128.785004</td>\n",
       "      <td>123.937500</td>\n",
       "      <td>125.857498</td>\n",
       "      <td>345937600.0</td>\n",
       "      <td>84.121261</td>\n",
       "      <td>342.119995</td>\n",
       "      <td>343.000000</td>\n",
       "      <td>339.450012</td>\n",
       "      <td>342.920013</td>\n",
       "      <td>48588700.0</td>\n",
       "      <td>76.812765</td>\n",
       "      <td>19.090000</td>\n",
       "      <td>20.129999</td>\n",
       "      <td>19.040001</td>\n",
       "      <td>19.799999</td>\n",
       "      <td>24983000.0</td>\n",
       "      <td>29.190110</td>\n",
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       "    <tr>\n",
       "      <th>1200</th>\n",
       "      <td>2020-08-25</td>\n",
       "      <td>124.697502</td>\n",
       "      <td>125.180000</td>\n",
       "      <td>123.052498</td>\n",
       "      <td>124.824997</td>\n",
       "      <td>211495600.0</td>\n",
       "      <td>79.832588</td>\n",
       "      <td>343.529999</td>\n",
       "      <td>344.209991</td>\n",
       "      <td>342.269989</td>\n",
       "      <td>344.119995</td>\n",
       "      <td>38463400.0</td>\n",
       "      <td>78.418321</td>\n",
       "      <td>19.950001</td>\n",
       "      <td>20.790001</td>\n",
       "      <td>19.459999</td>\n",
       "      <td>19.660000</td>\n",
       "      <td>27572400.0</td>\n",
       "      <td>28.484262</td>\n",
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       "    <tr>\n",
       "      <th>1201</th>\n",
       "      <td>2020-08-26</td>\n",
       "      <td>126.180000</td>\n",
       "      <td>126.992500</td>\n",
       "      <td>125.082497</td>\n",
       "      <td>126.522499</td>\n",
       "      <td>163022400.0</td>\n",
       "      <td>81.550783</td>\n",
       "      <td>344.760010</td>\n",
       "      <td>347.859985</td>\n",
       "      <td>344.170013</td>\n",
       "      <td>347.570007</td>\n",
       "      <td>50790200.0</td>\n",
       "      <td>82.327443</td>\n",
       "      <td>19.469999</td>\n",
       "      <td>20.480000</td>\n",
       "      <td>18.750000</td>\n",
       "      <td>20.309999</td>\n",
       "      <td>29100800.0</td>\n",
       "      <td>36.415999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1202</th>\n",
       "      <td>2020-08-27</td>\n",
       "      <td>127.142502</td>\n",
       "      <td>127.485001</td>\n",
       "      <td>123.832497</td>\n",
       "      <td>125.010002</td>\n",
       "      <td>155552400.0</td>\n",
       "      <td>75.207351</td>\n",
       "      <td>348.510010</td>\n",
       "      <td>349.899994</td>\n",
       "      <td>346.529999</td>\n",
       "      <td>348.329987</td>\n",
       "      <td>58034100.0</td>\n",
       "      <td>83.077671</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>23.690001</td>\n",
       "      <td>19.700001</td>\n",
       "      <td>21.230000</td>\n",
       "      <td>73365600.0</td>\n",
       "      <td>45.859296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1203</th>\n",
       "      <td>2020-08-28</td>\n",
       "      <td>126.012497</td>\n",
       "      <td>126.442497</td>\n",
       "      <td>124.577499</td>\n",
       "      <td>124.807503</td>\n",
       "      <td>187630000.0</td>\n",
       "      <td>74.347059</td>\n",
       "      <td>349.440002</td>\n",
       "      <td>350.720001</td>\n",
       "      <td>348.149994</td>\n",
       "      <td>350.579987</td>\n",
       "      <td>48588900.0</td>\n",
       "      <td>85.151254</td>\n",
       "      <td>21.639999</td>\n",
       "      <td>22.980000</td>\n",
       "      <td>20.600000</td>\n",
       "      <td>21.110001</td>\n",
       "      <td>50204700.0</td>\n",
       "      <td>44.893019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1204</th>\n",
       "      <td>2020-08-31</td>\n",
       "      <td>127.580002</td>\n",
       "      <td>131.000000</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>129.039993</td>\n",
       "      <td>225702700.0</td>\n",
       "      <td>79.731471</td>\n",
       "      <td>350.350006</td>\n",
       "      <td>351.299988</td>\n",
       "      <td>349.059998</td>\n",
       "      <td>349.309998</td>\n",
       "      <td>66099200.0</td>\n",
       "      <td>79.074487</td>\n",
       "      <td>21.930000</td>\n",
       "      <td>22.950001</td>\n",
       "      <td>21.219999</td>\n",
       "      <td>22.650000</td>\n",
       "      <td>41189100.0</td>\n",
       "      <td>57.624732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1205</th>\n",
       "      <td>2020-09-01</td>\n",
       "      <td>132.759995</td>\n",
       "      <td>134.800003</td>\n",
       "      <td>130.529999</td>\n",
       "      <td>134.179993</td>\n",
       "      <td>152470100.0</td>\n",
       "      <td>84.204961</td>\n",
       "      <td>350.209991</td>\n",
       "      <td>352.709991</td>\n",
       "      <td>349.239990</td>\n",
       "      <td>352.600006</td>\n",
       "      <td>54999300.0</td>\n",
       "      <td>82.640434</td>\n",
       "      <td>22.670000</td>\n",
       "      <td>23.440001</td>\n",
       "      <td>22.400000</td>\n",
       "      <td>23.240000</td>\n",
       "      <td>34285700.0</td>\n",
       "      <td>61.419117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1206</th>\n",
       "      <td>2020-09-02</td>\n",
       "      <td>137.589996</td>\n",
       "      <td>137.979996</td>\n",
       "      <td>127.000000</td>\n",
       "      <td>131.399994</td>\n",
       "      <td>200119000.0</td>\n",
       "      <td>74.344202</td>\n",
       "      <td>354.670013</td>\n",
       "      <td>358.750000</td>\n",
       "      <td>353.429993</td>\n",
       "      <td>357.700012</td>\n",
       "      <td>69540000.0</td>\n",
       "      <td>86.579538</td>\n",
       "      <td>23.150000</td>\n",
       "      <td>24.389999</td>\n",
       "      <td>23.020000</td>\n",
       "      <td>24.080000</td>\n",
       "      <td>42972600.0</td>\n",
       "      <td>66.206005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1207</th>\n",
       "      <td>2020-09-03</td>\n",
       "      <td>126.910004</td>\n",
       "      <td>128.839996</td>\n",
       "      <td>120.500000</td>\n",
       "      <td>120.879997</td>\n",
       "      <td>257599600.0</td>\n",
       "      <td>49.815837</td>\n",
       "      <td>355.869995</td>\n",
       "      <td>356.380005</td>\n",
       "      <td>342.589996</td>\n",
       "      <td>345.390015</td>\n",
       "      <td>148011100.0</td>\n",
       "      <td>53.824254</td>\n",
       "      <td>24.510000</td>\n",
       "      <td>30.280001</td>\n",
       "      <td>23.230000</td>\n",
       "      <td>28.900000</td>\n",
       "      <td>135998900.0</td>\n",
       "      <td>81.131788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1208</th>\n",
       "      <td>2020-09-04</td>\n",
       "      <td>120.070000</td>\n",
       "      <td>123.699997</td>\n",
       "      <td>110.889999</td>\n",
       "      <td>120.959999</td>\n",
       "      <td>332607200.0</td>\n",
       "      <td>49.955353</td>\n",
       "      <td>346.130005</td>\n",
       "      <td>347.829987</td>\n",
       "      <td>334.869995</td>\n",
       "      <td>342.570007</td>\n",
       "      <td>139156300.0</td>\n",
       "      <td>49.096384</td>\n",
       "      <td>27.920000</td>\n",
       "      <td>32.180000</td>\n",
       "      <td>24.570000</td>\n",
       "      <td>25.299999</td>\n",
       "      <td>129374600.0</td>\n",
       "      <td>59.370619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1209</th>\n",
       "      <td>2020-09-08</td>\n",
       "      <td>113.949997</td>\n",
       "      <td>118.989998</td>\n",
       "      <td>112.680000</td>\n",
       "      <td>112.820000</td>\n",
       "      <td>231366600.0</td>\n",
       "      <td>38.009267</td>\n",
       "      <td>336.709991</td>\n",
       "      <td>342.640015</td>\n",
       "      <td>332.880005</td>\n",
       "      <td>333.209991</td>\n",
       "      <td>114465300.0</td>\n",
       "      <td>37.083403</td>\n",
       "      <td>28.200001</td>\n",
       "      <td>28.469999</td>\n",
       "      <td>24.150000</td>\n",
       "      <td>24.320000</td>\n",
       "      <td>56407200.0</td>\n",
       "      <td>54.915430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1210</th>\n",
       "      <td>2020-09-09</td>\n",
       "      <td>117.260002</td>\n",
       "      <td>119.139999</td>\n",
       "      <td>115.260002</td>\n",
       "      <td>117.320000</td>\n",
       "      <td>176940500.0</td>\n",
       "      <td>45.948794</td>\n",
       "      <td>337.549988</td>\n",
       "      <td>342.459991</td>\n",
       "      <td>336.609985</td>\n",
       "      <td>339.790009</td>\n",
       "      <td>91462300.0</td>\n",
       "      <td>47.178685</td>\n",
       "      <td>23.340000</td>\n",
       "      <td>23.719999</td>\n",
       "      <td>21.799999</td>\n",
       "      <td>22.440001</td>\n",
       "      <td>45746300.0</td>\n",
       "      <td>47.342933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1211</th>\n",
       "      <td>2020-09-10</td>\n",
       "      <td>120.360001</td>\n",
       "      <td>120.500000</td>\n",
       "      <td>112.500000</td>\n",
       "      <td>113.489998</td>\n",
       "      <td>182274400.0</td>\n",
       "      <td>40.984765</td>\n",
       "      <td>341.820007</td>\n",
       "      <td>342.529999</td>\n",
       "      <td>332.850006</td>\n",
       "      <td>333.890015</td>\n",
       "      <td>90569500.0</td>\n",
       "      <td>40.676249</td>\n",
       "      <td>22.120001</td>\n",
       "      <td>23.600000</td>\n",
       "      <td>21.830000</td>\n",
       "      <td>22.780001</td>\n",
       "      <td>54363400.0</td>\n",
       "      <td>48.762677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1212</th>\n",
       "      <td>2020-09-11</td>\n",
       "      <td>114.570000</td>\n",
       "      <td>115.230003</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>112.000000</td>\n",
       "      <td>180860300.0</td>\n",
       "      <td>39.156222</td>\n",
       "      <td>335.820007</td>\n",
       "      <td>336.970001</td>\n",
       "      <td>331.000000</td>\n",
       "      <td>334.059998</td>\n",
       "      <td>84680200.0</td>\n",
       "      <td>40.936839</td>\n",
       "      <td>21.860001</td>\n",
       "      <td>22.850000</td>\n",
       "      <td>20.760000</td>\n",
       "      <td>20.799999</td>\n",
       "      <td>59861300.0</td>\n",
       "      <td>41.519208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1213</th>\n",
       "      <td>2020-09-14</td>\n",
       "      <td>114.720001</td>\n",
       "      <td>115.930000</td>\n",
       "      <td>112.800003</td>\n",
       "      <td>115.360001</td>\n",
       "      <td>140150100.0</td>\n",
       "      <td>45.273923</td>\n",
       "      <td>337.489990</td>\n",
       "      <td>340.380005</td>\n",
       "      <td>334.220001</td>\n",
       "      <td>338.459991</td>\n",
       "      <td>65605700.0</td>\n",
       "      <td>47.561776</td>\n",
       "      <td>20.389999</td>\n",
       "      <td>20.700001</td>\n",
       "      <td>19.799999</td>\n",
       "      <td>20.230000</td>\n",
       "      <td>32343200.0</td>\n",
       "      <td>39.635932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1214</th>\n",
       "      <td>2020-09-15</td>\n",
       "      <td>118.330002</td>\n",
       "      <td>118.830002</td>\n",
       "      <td>113.610001</td>\n",
       "      <td>115.540001</td>\n",
       "      <td>184642000.0</td>\n",
       "      <td>45.599509</td>\n",
       "      <td>341.119995</td>\n",
       "      <td>342.019989</td>\n",
       "      <td>338.470001</td>\n",
       "      <td>340.170013</td>\n",
       "      <td>52920900.0</td>\n",
       "      <td>49.984356</td>\n",
       "      <td>19.959999</td>\n",
       "      <td>20.740000</td>\n",
       "      <td>19.790001</td>\n",
       "      <td>20.190001</td>\n",
       "      <td>31805700.0</td>\n",
       "      <td>39.496246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1215</th>\n",
       "      <td>2020-09-16</td>\n",
       "      <td>115.230003</td>\n",
       "      <td>116.000000</td>\n",
       "      <td>112.040001</td>\n",
       "      <td>112.129997</td>\n",
       "      <td>154679000.0</td>\n",
       "      <td>40.524584</td>\n",
       "      <td>341.510010</td>\n",
       "      <td>343.059998</td>\n",
       "      <td>338.519989</td>\n",
       "      <td>338.820007</td>\n",
       "      <td>82096000.0</td>\n",
       "      <td>48.037637</td>\n",
       "      <td>19.980000</td>\n",
       "      <td>20.110001</td>\n",
       "      <td>19.280001</td>\n",
       "      <td>20.059999</td>\n",
       "      <td>42531200.0</td>\n",
       "      <td>38.999898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1216</th>\n",
       "      <td>2020-09-17</td>\n",
       "      <td>109.720001</td>\n",
       "      <td>112.199997</td>\n",
       "      <td>108.709999</td>\n",
       "      <td>110.339996</td>\n",
       "      <td>178011000.0</td>\n",
       "      <td>38.054395</td>\n",
       "      <td>333.559998</td>\n",
       "      <td>337.700012</td>\n",
       "      <td>332.989990</td>\n",
       "      <td>335.839996</td>\n",
       "      <td>91523300.0</td>\n",
       "      <td>43.849037</td>\n",
       "      <td>21.010000</td>\n",
       "      <td>21.100000</td>\n",
       "      <td>19.270000</td>\n",
       "      <td>19.450001</td>\n",
       "      <td>47315900.0</td>\n",
       "      <td>36.601791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1217</th>\n",
       "      <td>2020-09-18</td>\n",
       "      <td>110.400002</td>\n",
       "      <td>110.879997</td>\n",
       "      <td>106.089996</td>\n",
       "      <td>106.839996</td>\n",
       "      <td>287104900.0</td>\n",
       "      <td>33.604215</td>\n",
       "      <td>335.369995</td>\n",
       "      <td>335.489990</td>\n",
       "      <td>327.970001</td>\n",
       "      <td>330.649994</td>\n",
       "      <td>105877900.0</td>\n",
       "      <td>37.518508</td>\n",
       "      <td>19.180000</td>\n",
       "      <td>20.100000</td>\n",
       "      <td>18.549999</td>\n",
       "      <td>19.410000</td>\n",
       "      <td>45470800.0</td>\n",
       "      <td>36.438536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1218</th>\n",
       "      <td>2020-09-21</td>\n",
       "      <td>104.540001</td>\n",
       "      <td>110.190002</td>\n",
       "      <td>103.099998</td>\n",
       "      <td>110.080002</td>\n",
       "      <td>195713800.0</td>\n",
       "      <td>40.733080</td>\n",
       "      <td>325.700012</td>\n",
       "      <td>327.130005</td>\n",
       "      <td>321.730011</td>\n",
       "      <td>326.970001</td>\n",
       "      <td>99450800.0</td>\n",
       "      <td>33.686925</td>\n",
       "      <td>20.570000</td>\n",
       "      <td>22.090000</td>\n",
       "      <td>20.170000</td>\n",
       "      <td>20.299999</td>\n",
       "      <td>54634400.0</td>\n",
       "      <td>42.751114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1219</th>\n",
       "      <td>2020-09-22</td>\n",
       "      <td>112.680000</td>\n",
       "      <td>112.860001</td>\n",
       "      <td>109.160004</td>\n",
       "      <td>111.809998</td>\n",
       "      <td>183055400.0</td>\n",
       "      <td>44.282276</td>\n",
       "      <td>328.570007</td>\n",
       "      <td>330.899994</td>\n",
       "      <td>325.859985</td>\n",
       "      <td>330.299988</td>\n",
       "      <td>63612100.0</td>\n",
       "      <td>39.861903</td>\n",
       "      <td>20.260000</td>\n",
       "      <td>21.379999</td>\n",
       "      <td>20.260000</td>\n",
       "      <td>20.549999</td>\n",
       "      <td>26687500.0</td>\n",
       "      <td>44.472311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1220</th>\n",
       "      <td>2020-09-23</td>\n",
       "      <td>111.620003</td>\n",
       "      <td>112.110001</td>\n",
       "      <td>106.769997</td>\n",
       "      <td>107.120003</td>\n",
       "      <td>150718700.0</td>\n",
       "      <td>37.515086</td>\n",
       "      <td>330.899994</td>\n",
       "      <td>331.200012</td>\n",
       "      <td>322.100006</td>\n",
       "      <td>322.640015</td>\n",
       "      <td>93112200.0</td>\n",
       "      <td>32.198608</td>\n",
       "      <td>20.440001</td>\n",
       "      <td>22.219999</td>\n",
       "      <td>20.299999</td>\n",
       "      <td>22.209999</td>\n",
       "      <td>37766500.0</td>\n",
       "      <td>54.553076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1221</th>\n",
       "      <td>2020-09-24</td>\n",
       "      <td>105.169998</td>\n",
       "      <td>110.250000</td>\n",
       "      <td>105.000000</td>\n",
       "      <td>108.220001</td>\n",
       "      <td>167743300.0</td>\n",
       "      <td>39.908239</td>\n",
       "      <td>321.220001</td>\n",
       "      <td>326.799988</td>\n",
       "      <td>319.799988</td>\n",
       "      <td>323.500000</td>\n",
       "      <td>76681300.0</td>\n",
       "      <td>33.786515</td>\n",
       "      <td>22.180000</td>\n",
       "      <td>22.590000</td>\n",
       "      <td>21.010000</td>\n",
       "      <td>21.600000</td>\n",
       "      <td>38373000.0</td>\n",
       "      <td>50.788405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1222</th>\n",
       "      <td>2020-09-25</td>\n",
       "      <td>108.430000</td>\n",
       "      <td>112.440002</td>\n",
       "      <td>107.669998</td>\n",
       "      <td>112.279999</td>\n",
       "      <td>149981400.0</td>\n",
       "      <td>48.065471</td>\n",
       "      <td>322.579987</td>\n",
       "      <td>329.579987</td>\n",
       "      <td>321.640015</td>\n",
       "      <td>328.730011</td>\n",
       "      <td>71069400.0</td>\n",
       "      <td>42.833382</td>\n",
       "      <td>21.580000</td>\n",
       "      <td>21.840000</td>\n",
       "      <td>20.580000</td>\n",
       "      <td>20.780001</td>\n",
       "      <td>27147200.0</td>\n",
       "      <td>46.042606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1223</th>\n",
       "      <td>2020-09-28</td>\n",
       "      <td>115.010002</td>\n",
       "      <td>115.320000</td>\n",
       "      <td>112.779999</td>\n",
       "      <td>114.959999</td>\n",
       "      <td>137672400.0</td>\n",
       "      <td>52.767992</td>\n",
       "      <td>333.220001</td>\n",
       "      <td>334.959991</td>\n",
       "      <td>332.149994</td>\n",
       "      <td>334.190002</td>\n",
       "      <td>64584600.0</td>\n",
       "      <td>50.654146</td>\n",
       "      <td>20.510000</td>\n",
       "      <td>20.889999</td>\n",
       "      <td>20.420000</td>\n",
       "      <td>20.650000</td>\n",
       "      <td>18547600.0</td>\n",
       "      <td>45.297008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1224</th>\n",
       "      <td>2020-09-29</td>\n",
       "      <td>114.550003</td>\n",
       "      <td>115.309998</td>\n",
       "      <td>113.570000</td>\n",
       "      <td>114.089996</td>\n",
       "      <td>99382200.0</td>\n",
       "      <td>51.099089</td>\n",
       "      <td>333.970001</td>\n",
       "      <td>334.769989</td>\n",
       "      <td>331.619995</td>\n",
       "      <td>332.369995</td>\n",
       "      <td>51304000.0</td>\n",
       "      <td>48.211311</td>\n",
       "      <td>20.700001</td>\n",
       "      <td>20.709999</td>\n",
       "      <td>19.570000</td>\n",
       "      <td>20.030001</td>\n",
       "      <td>29955500.0</td>\n",
       "      <td>41.717181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1225</th>\n",
       "      <td>2020-09-30</td>\n",
       "      <td>113.790001</td>\n",
       "      <td>117.260002</td>\n",
       "      <td>113.620003</td>\n",
       "      <td>115.809998</td>\n",
       "      <td>142675200.0</td>\n",
       "      <td>54.275763</td>\n",
       "      <td>333.089996</td>\n",
       "      <td>338.290009</td>\n",
       "      <td>332.880005</td>\n",
       "      <td>334.890015</td>\n",
       "      <td>103653800.0</td>\n",
       "      <td>51.788319</td>\n",
       "      <td>19.629999</td>\n",
       "      <td>20.280001</td>\n",
       "      <td>19.250000</td>\n",
       "      <td>19.920000</td>\n",
       "      <td>31236100.0</td>\n",
       "      <td>41.077216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1226</th>\n",
       "      <td>2020-10-01</td>\n",
       "      <td>117.639999</td>\n",
       "      <td>117.720001</td>\n",
       "      <td>115.830002</td>\n",
       "      <td>116.790001</td>\n",
       "      <td>116120400.0</td>\n",
       "      <td>56.081919</td>\n",
       "      <td>337.690002</td>\n",
       "      <td>338.739990</td>\n",
       "      <td>335.010010</td>\n",
       "      <td>337.040009</td>\n",
       "      <td>88698700.0</td>\n",
       "      <td>54.750999</td>\n",
       "      <td>19.639999</td>\n",
       "      <td>20.570000</td>\n",
       "      <td>19.620001</td>\n",
       "      <td>20.110001</td>\n",
       "      <td>23409000.0</td>\n",
       "      <td>42.762371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1227</th>\n",
       "      <td>2020-10-02</td>\n",
       "      <td>112.889999</td>\n",
       "      <td>115.370003</td>\n",
       "      <td>112.220001</td>\n",
       "      <td>113.019997</td>\n",
       "      <td>144712000.0</td>\n",
       "      <td>47.980752</td>\n",
       "      <td>331.700012</td>\n",
       "      <td>337.010010</td>\n",
       "      <td>331.190002</td>\n",
       "      <td>333.839996</td>\n",
       "      <td>89431100.0</td>\n",
       "      <td>49.700173</td>\n",
       "      <td>21.650000</td>\n",
       "      <td>21.760000</td>\n",
       "      <td>20.450001</td>\n",
       "      <td>21.030001</td>\n",
       "      <td>43572200.0</td>\n",
       "      <td>50.394962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1228</th>\n",
       "      <td>2020-10-05</td>\n",
       "      <td>113.910004</td>\n",
       "      <td>116.650002</td>\n",
       "      <td>113.550003</td>\n",
       "      <td>116.500000</td>\n",
       "      <td>106243800.0</td>\n",
       "      <td>54.693233</td>\n",
       "      <td>336.059998</td>\n",
       "      <td>339.959991</td>\n",
       "      <td>336.010010</td>\n",
       "      <td>339.760010</td>\n",
       "      <td>45713100.0</td>\n",
       "      <td>57.717964</td>\n",
       "      <td>20.719999</td>\n",
       "      <td>21.040001</td>\n",
       "      <td>19.799999</td>\n",
       "      <td>20.030001</td>\n",
       "      <td>20667700.0</td>\n",
       "      <td>43.404654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1229</th>\n",
       "      <td>2020-10-06</td>\n",
       "      <td>115.699997</td>\n",
       "      <td>116.120003</td>\n",
       "      <td>112.250000</td>\n",
       "      <td>113.160004</td>\n",
       "      <td>161498200.0</td>\n",
       "      <td>48.077410</td>\n",
       "      <td>339.910004</td>\n",
       "      <td>342.170013</td>\n",
       "      <td>334.380005</td>\n",
       "      <td>334.929993</td>\n",
       "      <td>90128900.0</td>\n",
       "      <td>50.430669</td>\n",
       "      <td>19.770000</td>\n",
       "      <td>20.730000</td>\n",
       "      <td>19.230000</td>\n",
       "      <td>20.410000</td>\n",
       "      <td>40618200.0</td>\n",
       "      <td>46.535861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1230</th>\n",
       "      <td>2020-10-07</td>\n",
       "      <td>114.620003</td>\n",
       "      <td>115.550003</td>\n",
       "      <td>114.129997</td>\n",
       "      <td>115.080002</td>\n",
       "      <td>96849000.0</td>\n",
       "      <td>51.801309</td>\n",
       "      <td>338.119995</td>\n",
       "      <td>341.630005</td>\n",
       "      <td>338.089996</td>\n",
       "      <td>340.760010</td>\n",
       "      <td>56999600.0</td>\n",
       "      <td>57.608777</td>\n",
       "      <td>19.850000</td>\n",
       "      <td>19.990000</td>\n",
       "      <td>19.260000</td>\n",
       "      <td>19.459999</td>\n",
       "      <td>24140300.0</td>\n",
       "      <td>40.336730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1231</th>\n",
       "      <td>2020-10-08</td>\n",
       "      <td>116.250000</td>\n",
       "      <td>116.400002</td>\n",
       "      <td>114.589996</td>\n",
       "      <td>114.970001</td>\n",
       "      <td>83477200.0</td>\n",
       "      <td>51.565881</td>\n",
       "      <td>342.850006</td>\n",
       "      <td>343.850006</td>\n",
       "      <td>341.859985</td>\n",
       "      <td>343.779999</td>\n",
       "      <td>45242500.0</td>\n",
       "      <td>60.870133</td>\n",
       "      <td>19.129999</td>\n",
       "      <td>19.290001</td>\n",
       "      <td>18.240000</td>\n",
       "      <td>18.330000</td>\n",
       "      <td>33402400.0</td>\n",
       "      <td>34.298264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1232</th>\n",
       "      <td>2020-10-09</td>\n",
       "      <td>115.279999</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>114.919998</td>\n",
       "      <td>116.970001</td>\n",
       "      <td>100506900.0</td>\n",
       "      <td>55.638842</td>\n",
       "      <td>345.559998</td>\n",
       "      <td>347.350006</td>\n",
       "      <td>344.890015</td>\n",
       "      <td>346.850006</td>\n",
       "      <td>59528600.0</td>\n",
       "      <td>63.998611</td>\n",
       "      <td>17.440001</td>\n",
       "      <td>17.490000</td>\n",
       "      <td>16.860001</td>\n",
       "      <td>16.889999</td>\n",
       "      <td>33258100.0</td>\n",
       "      <td>28.299669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1233</th>\n",
       "      <td>2020-10-12</td>\n",
       "      <td>120.059998</td>\n",
       "      <td>125.180000</td>\n",
       "      <td>119.279999</td>\n",
       "      <td>124.400002</td>\n",
       "      <td>240226800.0</td>\n",
       "      <td>67.069546</td>\n",
       "      <td>349.589996</td>\n",
       "      <td>354.019989</td>\n",
       "      <td>349.059998</td>\n",
       "      <td>352.429993</td>\n",
       "      <td>80388500.0</td>\n",
       "      <td>69.003444</td>\n",
       "      <td>16.420000</td>\n",
       "      <td>16.860001</td>\n",
       "      <td>16.299999</td>\n",
       "      <td>16.389999</td>\n",
       "      <td>22250900.0</td>\n",
       "      <td>26.510852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1234</th>\n",
       "      <td>2020-10-13</td>\n",
       "      <td>125.269997</td>\n",
       "      <td>125.389999</td>\n",
       "      <td>119.650002</td>\n",
       "      <td>121.099998</td>\n",
       "      <td>262330500.0</td>\n",
       "      <td>59.503087</td>\n",
       "      <td>352.279999</td>\n",
       "      <td>352.470001</td>\n",
       "      <td>349.089996</td>\n",
       "      <td>350.130005</td>\n",
       "      <td>73255500.0</td>\n",
       "      <td>64.873112</td>\n",
       "      <td>16.670000</td>\n",
       "      <td>17.200001</td>\n",
       "      <td>16.490000</td>\n",
       "      <td>16.670000</td>\n",
       "      <td>27907200.0</td>\n",
       "      <td>29.291852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1235</th>\n",
       "      <td>2020-10-14</td>\n",
       "      <td>121.000000</td>\n",
       "      <td>123.029999</td>\n",
       "      <td>119.620003</td>\n",
       "      <td>121.190002</td>\n",
       "      <td>151062300.0</td>\n",
       "      <td>59.641066</td>\n",
       "      <td>350.750000</td>\n",
       "      <td>351.929993</td>\n",
       "      <td>347.140015</td>\n",
       "      <td>347.929993</td>\n",
       "      <td>57958700.0</td>\n",
       "      <td>60.992943</td>\n",
       "      <td>16.480000</td>\n",
       "      <td>16.840000</td>\n",
       "      <td>16.059999</td>\n",
       "      <td>16.440001</td>\n",
       "      <td>32299300.0</td>\n",
       "      <td>28.313936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1236</th>\n",
       "      <td>2020-10-15</td>\n",
       "      <td>118.720001</td>\n",
       "      <td>121.199997</td>\n",
       "      <td>118.150002</td>\n",
       "      <td>120.709999</td>\n",
       "      <td>112559200.0</td>\n",
       "      <td>58.460761</td>\n",
       "      <td>343.709991</td>\n",
       "      <td>348.019989</td>\n",
       "      <td>343.130005</td>\n",
       "      <td>347.500000</td>\n",
       "      <td>60357700.0</td>\n",
       "      <td>60.210857</td>\n",
       "      <td>17.520000</td>\n",
       "      <td>17.750000</td>\n",
       "      <td>16.580000</td>\n",
       "      <td>16.670000</td>\n",
       "      <td>33842000.0</td>\n",
       "      <td>30.877999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1237</th>\n",
       "      <td>2020-10-16</td>\n",
       "      <td>121.279999</td>\n",
       "      <td>121.550003</td>\n",
       "      <td>118.809998</td>\n",
       "      <td>119.019997</td>\n",
       "      <td>115393800.0</td>\n",
       "      <td>54.259990</td>\n",
       "      <td>348.959991</td>\n",
       "      <td>350.750000</td>\n",
       "      <td>347.100006</td>\n",
       "      <td>347.290009</td>\n",
       "      <td>89501900.0</td>\n",
       "      <td>59.794817</td>\n",
       "      <td>16.620001</td>\n",
       "      <td>16.830000</td>\n",
       "      <td>16.250000</td>\n",
       "      <td>16.790001</td>\n",
       "      <td>21694100.0</td>\n",
       "      <td>32.282150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1238</th>\n",
       "      <td>2020-10-19</td>\n",
       "      <td>119.959999</td>\n",
       "      <td>120.419998</td>\n",
       "      <td>115.660004</td>\n",
       "      <td>115.980003</td>\n",
       "      <td>120639300.0</td>\n",
       "      <td>47.445921</td>\n",
       "      <td>348.649994</td>\n",
       "      <td>349.329987</td>\n",
       "      <td>341.040009</td>\n",
       "      <td>342.010010</td>\n",
       "      <td>68425600.0</td>\n",
       "      <td>50.119637</td>\n",
       "      <td>16.700001</td>\n",
       "      <td>18.100000</td>\n",
       "      <td>16.639999</td>\n",
       "      <td>17.930000</td>\n",
       "      <td>35907000.0</td>\n",
       "      <td>44.238732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1239</th>\n",
       "      <td>2020-10-20</td>\n",
       "      <td>116.199997</td>\n",
       "      <td>118.980003</td>\n",
       "      <td>115.629997</td>\n",
       "      <td>117.510002</td>\n",
       "      <td>124423700.0</td>\n",
       "      <td>50.894446</td>\n",
       "      <td>343.459991</td>\n",
       "      <td>346.880005</td>\n",
       "      <td>342.640015</td>\n",
       "      <td>343.380005</td>\n",
       "      <td>60051900.0</td>\n",
       "      <td>52.342777</td>\n",
       "      <td>17.750000</td>\n",
       "      <td>18.080000</td>\n",
       "      <td>17.510000</td>\n",
       "      <td>17.900000</td>\n",
       "      <td>28551600.0</td>\n",
       "      <td>44.011514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1240</th>\n",
       "      <td>2020-10-21</td>\n",
       "      <td>116.669998</td>\n",
       "      <td>118.709999</td>\n",
       "      <td>116.449997</td>\n",
       "      <td>116.870003</td>\n",
       "      <td>89946000.0</td>\n",
       "      <td>49.388200</td>\n",
       "      <td>343.329987</td>\n",
       "      <td>345.670013</td>\n",
       "      <td>342.399994</td>\n",
       "      <td>342.730011</td>\n",
       "      <td>63575000.0</td>\n",
       "      <td>51.141188</td>\n",
       "      <td>17.770000</td>\n",
       "      <td>18.120001</td>\n",
       "      <td>17.110001</td>\n",
       "      <td>17.270000</td>\n",
       "      <td>31359100.0</td>\n",
       "      <td>39.301446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1241</th>\n",
       "      <td>2020-10-22</td>\n",
       "      <td>117.449997</td>\n",
       "      <td>118.040001</td>\n",
       "      <td>114.589996</td>\n",
       "      <td>115.750000</td>\n",
       "      <td>101988000.0</td>\n",
       "      <td>46.700714</td>\n",
       "      <td>342.959991</td>\n",
       "      <td>345.239990</td>\n",
       "      <td>340.649994</td>\n",
       "      <td>344.609985</td>\n",
       "      <td>55399300.0</td>\n",
       "      <td>54.498019</td>\n",
       "      <td>17.230000</td>\n",
       "      <td>17.469999</td>\n",
       "      <td>16.440001</td>\n",
       "      <td>16.680000</td>\n",
       "      <td>24574800.0</td>\n",
       "      <td>35.363375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1242</th>\n",
       "      <td>2020-10-23</td>\n",
       "      <td>116.389999</td>\n",
       "      <td>116.550003</td>\n",
       "      <td>114.279999</td>\n",
       "      <td>115.040001</td>\n",
       "      <td>82572600.0</td>\n",
       "      <td>44.976830</td>\n",
       "      <td>345.929993</td>\n",
       "      <td>345.989990</td>\n",
       "      <td>343.130005</td>\n",
       "      <td>345.779999</td>\n",
       "      <td>49143900.0</td>\n",
       "      <td>56.561762</td>\n",
       "      <td>16.600000</td>\n",
       "      <td>17.270000</td>\n",
       "      <td>16.540001</td>\n",
       "      <td>16.719999</td>\n",
       "      <td>20279500.0</td>\n",
       "      <td>35.847595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1243</th>\n",
       "      <td>2020-10-26</td>\n",
       "      <td>114.010002</td>\n",
       "      <td>116.550003</td>\n",
       "      <td>112.879997</td>\n",
       "      <td>115.050003</td>\n",
       "      <td>111850700.0</td>\n",
       "      <td>45.008604</td>\n",
       "      <td>342.130005</td>\n",
       "      <td>342.980011</td>\n",
       "      <td>335.619995</td>\n",
       "      <td>339.390015</td>\n",
       "      <td>91473000.0</td>\n",
       "      <td>44.354262</td>\n",
       "      <td>17.309999</td>\n",
       "      <td>19.400000</td>\n",
       "      <td>17.049999</td>\n",
       "      <td>18.990000</td>\n",
       "      <td>62410700.0</td>\n",
       "      <td>56.429668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1244</th>\n",
       "      <td>2020-10-27</td>\n",
       "      <td>115.489998</td>\n",
       "      <td>117.279999</td>\n",
       "      <td>114.540001</td>\n",
       "      <td>116.599998</td>\n",
       "      <td>92276800.0</td>\n",
       "      <td>49.981941</td>\n",
       "      <td>339.760010</td>\n",
       "      <td>340.119995</td>\n",
       "      <td>337.989990</td>\n",
       "      <td>338.220001</td>\n",
       "      <td>65994100.0</td>\n",
       "      <td>42.488632</td>\n",
       "      <td>18.969999</td>\n",
       "      <td>19.559999</td>\n",
       "      <td>18.469999</td>\n",
       "      <td>19.070000</td>\n",
       "      <td>37536700.0</td>\n",
       "      <td>56.970256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1245</th>\n",
       "      <td>2020-10-28</td>\n",
       "      <td>115.050003</td>\n",
       "      <td>115.430000</td>\n",
       "      <td>111.099998</td>\n",
       "      <td>111.199997</td>\n",
       "      <td>143937800.0</td>\n",
       "      <td>37.021313</td>\n",
       "      <td>332.100006</td>\n",
       "      <td>338.250000</td>\n",
       "      <td>326.130005</td>\n",
       "      <td>326.660004</td>\n",
       "      <td>127094300.0</td>\n",
       "      <td>29.066810</td>\n",
       "      <td>21.230000</td>\n",
       "      <td>23.200001</td>\n",
       "      <td>20.850000</td>\n",
       "      <td>23.139999</td>\n",
       "      <td>94240700.0</td>\n",
       "      <td>74.708531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1246</th>\n",
       "      <td>2020-10-29</td>\n",
       "      <td>112.370003</td>\n",
       "      <td>116.930000</td>\n",
       "      <td>112.199997</td>\n",
       "      <td>115.320000</td>\n",
       "      <td>146129200.0</td>\n",
       "      <td>48.370656</td>\n",
       "      <td>326.910004</td>\n",
       "      <td>333.399994</td>\n",
       "      <td>325.089996</td>\n",
       "      <td>329.980011</td>\n",
       "      <td>90597700.0</td>\n",
       "      <td>35.562376</td>\n",
       "      <td>22.459999</td>\n",
       "      <td>23.129999</td>\n",
       "      <td>19.680000</td>\n",
       "      <td>20.680000</td>\n",
       "      <td>56381100.0</td>\n",
       "      <td>58.510140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1247</th>\n",
       "      <td>2020-10-30</td>\n",
       "      <td>111.059998</td>\n",
       "      <td>111.989998</td>\n",
       "      <td>107.720001</td>\n",
       "      <td>108.860001</td>\n",
       "      <td>190272600.0</td>\n",
       "      <td>36.812981</td>\n",
       "      <td>328.279999</td>\n",
       "      <td>329.690002</td>\n",
       "      <td>322.600006</td>\n",
       "      <td>326.540009</td>\n",
       "      <td>120287300.0</td>\n",
       "      <td>32.170765</td>\n",
       "      <td>21.209999</td>\n",
       "      <td>22.219999</td>\n",
       "      <td>20.740000</td>\n",
       "      <td>21.580000</td>\n",
       "      <td>58263200.0</td>\n",
       "      <td>61.870799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1248</th>\n",
       "      <td>2020-11-02</td>\n",
       "      <td>109.110001</td>\n",
       "      <td>110.680000</td>\n",
       "      <td>107.320000</td>\n",
       "      <td>108.769997</td>\n",
       "      <td>122866900.0</td>\n",
       "      <td>36.677314</td>\n",
       "      <td>330.200012</td>\n",
       "      <td>332.359985</td>\n",
       "      <td>327.239990</td>\n",
       "      <td>330.200012</td>\n",
       "      <td>86068300.0</td>\n",
       "      <td>39.043300</td>\n",
       "      <td>20.129999</td>\n",
       "      <td>21.360001</td>\n",
       "      <td>20.059999</td>\n",
       "      <td>20.879999</td>\n",
       "      <td>51415700.0</td>\n",
       "      <td>57.823194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1249</th>\n",
       "      <td>2020-11-03</td>\n",
       "      <td>109.660004</td>\n",
       "      <td>111.489998</td>\n",
       "      <td>108.730003</td>\n",
       "      <td>110.440002</td>\n",
       "      <td>107624400.0</td>\n",
       "      <td>41.148693</td>\n",
       "      <td>333.690002</td>\n",
       "      <td>338.250000</td>\n",
       "      <td>330.290009</td>\n",
       "      <td>336.029999</td>\n",
       "      <td>93294200.0</td>\n",
       "      <td>48.312265</td>\n",
       "      <td>19.969999</td>\n",
       "      <td>20.160000</td>\n",
       "      <td>18.790001</td>\n",
       "      <td>19.180000</td>\n",
       "      <td>50097700.0</td>\n",
       "      <td>49.147208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1250</th>\n",
       "      <td>2020-11-04</td>\n",
       "      <td>114.139999</td>\n",
       "      <td>115.589996</td>\n",
       "      <td>112.349998</td>\n",
       "      <td>114.949997</td>\n",
       "      <td>138235500.0</td>\n",
       "      <td>51.438164</td>\n",
       "      <td>340.859985</td>\n",
       "      <td>347.940002</td>\n",
       "      <td>339.589996</td>\n",
       "      <td>343.540009</td>\n",
       "      <td>126959700.0</td>\n",
       "      <td>57.550916</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>18.049999</td>\n",
       "      <td>16.500000</td>\n",
       "      <td>16.670000</td>\n",
       "      <td>55513700.0</td>\n",
       "      <td>39.439249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1251</th>\n",
       "      <td>2020-11-05</td>\n",
       "      <td>117.949997</td>\n",
       "      <td>119.620003</td>\n",
       "      <td>116.870003</td>\n",
       "      <td>119.029999</td>\n",
       "      <td>126387100.0</td>\n",
       "      <td>58.696905</td>\n",
       "      <td>349.239990</td>\n",
       "      <td>352.190002</td>\n",
       "      <td>348.859985</td>\n",
       "      <td>350.239990</td>\n",
       "      <td>82039700.0</td>\n",
       "      <td>63.939984</td>\n",
       "      <td>15.890000</td>\n",
       "      <td>16.530001</td>\n",
       "      <td>15.650000</td>\n",
       "      <td>16.209999</td>\n",
       "      <td>33461500.0</td>\n",
       "      <td>37.914235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1252</th>\n",
       "      <td>2020-11-06</td>\n",
       "      <td>118.320000</td>\n",
       "      <td>119.199997</td>\n",
       "      <td>116.129997</td>\n",
       "      <td>118.690002</td>\n",
       "      <td>114457900.0</td>\n",
       "      <td>57.895625</td>\n",
       "      <td>349.929993</td>\n",
       "      <td>351.510010</td>\n",
       "      <td>347.649994</td>\n",
       "      <td>350.160004</td>\n",
       "      <td>74973000.0</td>\n",
       "      <td>63.812582</td>\n",
       "      <td>16.010000</td>\n",
       "      <td>16.080000</td>\n",
       "      <td>14.630000</td>\n",
       "      <td>14.670000</td>\n",
       "      <td>53209600.0</td>\n",
       "      <td>33.146596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1253</th>\n",
       "      <td>2020-11-09</td>\n",
       "      <td>120.500000</td>\n",
       "      <td>121.989998</td>\n",
       "      <td>116.050003</td>\n",
       "      <td>116.320000</td>\n",
       "      <td>154515300.0</td>\n",
       "      <td>52.359619</td>\n",
       "      <td>363.970001</td>\n",
       "      <td>364.380005</td>\n",
       "      <td>354.059998</td>\n",
       "      <td>354.559998</td>\n",
       "      <td>172304200.0</td>\n",
       "      <td>67.741240</td>\n",
       "      <td>13.140000</td>\n",
       "      <td>14.180000</td>\n",
       "      <td>12.180000</td>\n",
       "      <td>14.180000</td>\n",
       "      <td>54605600.0</td>\n",
       "      <td>31.735740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1254</th>\n",
       "      <td>2020-11-10</td>\n",
       "      <td>115.550003</td>\n",
       "      <td>117.589996</td>\n",
       "      <td>114.129997</td>\n",
       "      <td>115.970001</td>\n",
       "      <td>138023400.0</td>\n",
       "      <td>51.550781</td>\n",
       "      <td>353.489990</td>\n",
       "      <td>355.179993</td>\n",
       "      <td>350.510010</td>\n",
       "      <td>354.040009</td>\n",
       "      <td>85552000.0</td>\n",
       "      <td>66.789119</td>\n",
       "      <td>13.630000</td>\n",
       "      <td>14.400000</td>\n",
       "      <td>13.450000</td>\n",
       "      <td>13.490000</td>\n",
       "      <td>36231600.0</td>\n",
       "      <td>29.754200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1255</th>\n",
       "      <td>2020-11-11</td>\n",
       "      <td>117.190002</td>\n",
       "      <td>119.629997</td>\n",
       "      <td>116.440002</td>\n",
       "      <td>119.489998</td>\n",
       "      <td>112295000.0</td>\n",
       "      <td>58.683031</td>\n",
       "      <td>356.399994</td>\n",
       "      <td>357.559998</td>\n",
       "      <td>355.059998</td>\n",
       "      <td>356.670013</td>\n",
       "      <td>58649000.0</td>\n",
       "      <td>69.220329</td>\n",
       "      <td>13.180000</td>\n",
       "      <td>13.440000</td>\n",
       "      <td>12.640000</td>\n",
       "      <td>13.060000</td>\n",
       "      <td>34220400.0</td>\n",
       "      <td>28.521103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1256</th>\n",
       "      <td>2020-11-12</td>\n",
       "      <td>119.620003</td>\n",
       "      <td>120.529999</td>\n",
       "      <td>118.570000</td>\n",
       "      <td>119.209999</td>\n",
       "      <td>103162300.0</td>\n",
       "      <td>57.929312</td>\n",
       "      <td>355.579987</td>\n",
       "      <td>356.720001</td>\n",
       "      <td>351.260010</td>\n",
       "      <td>353.209991</td>\n",
       "      <td>67546200.0</td>\n",
       "      <td>62.529124</td>\n",
       "      <td>13.300000</td>\n",
       "      <td>14.820000</td>\n",
       "      <td>13.090000</td>\n",
       "      <td>14.370000</td>\n",
       "      <td>68251200.0</td>\n",
       "      <td>37.314850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1257</th>\n",
       "      <td>2020-11-13</td>\n",
       "      <td>119.440002</td>\n",
       "      <td>119.669998</td>\n",
       "      <td>117.870003</td>\n",
       "      <td>119.260002</td>\n",
       "      <td>81581900.0</td>\n",
       "      <td>58.036259</td>\n",
       "      <td>355.269989</td>\n",
       "      <td>358.899994</td>\n",
       "      <td>354.709991</td>\n",
       "      <td>358.100006</td>\n",
       "      <td>62892200.0</td>\n",
       "      <td>67.467429</td>\n",
       "      <td>13.660000</td>\n",
       "      <td>13.660000</td>\n",
       "      <td>12.730000</td>\n",
       "      <td>13.050000</td>\n",
       "      <td>43994400.0</td>\n",
       "      <td>32.797379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1258</th>\n",
       "      <td>2020-11-16</td>\n",
       "      <td>118.919998</td>\n",
       "      <td>120.989998</td>\n",
       "      <td>118.150002</td>\n",
       "      <td>120.300003</td>\n",
       "      <td>91183018.0</td>\n",
       "      <td>60.364705</td>\n",
       "      <td>360.980011</td>\n",
       "      <td>362.579987</td>\n",
       "      <td>359.589996</td>\n",
       "      <td>362.570007</td>\n",
       "      <td>65777372.0</td>\n",
       "      <td>71.308031</td>\n",
       "      <td>12.550000</td>\n",
       "      <td>13.280000</td>\n",
       "      <td>12.540000</td>\n",
       "      <td>12.600000</td>\n",
       "      <td>31756884.0</td>\n",
       "      <td>31.359321</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            date        open        high         low       close       volume  \\\n",
       "1199  2020-08-24  128.697495  128.785004  123.937500  125.857498  345937600.0   \n",
       "1200  2020-08-25  124.697502  125.180000  123.052498  124.824997  211495600.0   \n",
       "1201  2020-08-26  126.180000  126.992500  125.082497  126.522499  163022400.0   \n",
       "1202  2020-08-27  127.142502  127.485001  123.832497  125.010002  155552400.0   \n",
       "1203  2020-08-28  126.012497  126.442497  124.577499  124.807503  187630000.0   \n",
       "1204  2020-08-31  127.580002  131.000000  126.000000  129.039993  225702700.0   \n",
       "1205  2020-09-01  132.759995  134.800003  130.529999  134.179993  152470100.0   \n",
       "1206  2020-09-02  137.589996  137.979996  127.000000  131.399994  200119000.0   \n",
       "1207  2020-09-03  126.910004  128.839996  120.500000  120.879997  257599600.0   \n",
       "1208  2020-09-04  120.070000  123.699997  110.889999  120.959999  332607200.0   \n",
       "1209  2020-09-08  113.949997  118.989998  112.680000  112.820000  231366600.0   \n",
       "1210  2020-09-09  117.260002  119.139999  115.260002  117.320000  176940500.0   \n",
       "1211  2020-09-10  120.360001  120.500000  112.500000  113.489998  182274400.0   \n",
       "1212  2020-09-11  114.570000  115.230003  110.000000  112.000000  180860300.0   \n",
       "1213  2020-09-14  114.720001  115.930000  112.800003  115.360001  140150100.0   \n",
       "1214  2020-09-15  118.330002  118.830002  113.610001  115.540001  184642000.0   \n",
       "1215  2020-09-16  115.230003  116.000000  112.040001  112.129997  154679000.0   \n",
       "1216  2020-09-17  109.720001  112.199997  108.709999  110.339996  178011000.0   \n",
       "1217  2020-09-18  110.400002  110.879997  106.089996  106.839996  287104900.0   \n",
       "1218  2020-09-21  104.540001  110.190002  103.099998  110.080002  195713800.0   \n",
       "1219  2020-09-22  112.680000  112.860001  109.160004  111.809998  183055400.0   \n",
       "1220  2020-09-23  111.620003  112.110001  106.769997  107.120003  150718700.0   \n",
       "1221  2020-09-24  105.169998  110.250000  105.000000  108.220001  167743300.0   \n",
       "1222  2020-09-25  108.430000  112.440002  107.669998  112.279999  149981400.0   \n",
       "1223  2020-09-28  115.010002  115.320000  112.779999  114.959999  137672400.0   \n",
       "1224  2020-09-29  114.550003  115.309998  113.570000  114.089996   99382200.0   \n",
       "1225  2020-09-30  113.790001  117.260002  113.620003  115.809998  142675200.0   \n",
       "1226  2020-10-01  117.639999  117.720001  115.830002  116.790001  116120400.0   \n",
       "1227  2020-10-02  112.889999  115.370003  112.220001  113.019997  144712000.0   \n",
       "1228  2020-10-05  113.910004  116.650002  113.550003  116.500000  106243800.0   \n",
       "1229  2020-10-06  115.699997  116.120003  112.250000  113.160004  161498200.0   \n",
       "1230  2020-10-07  114.620003  115.550003  114.129997  115.080002   96849000.0   \n",
       "1231  2020-10-08  116.250000  116.400002  114.589996  114.970001   83477200.0   \n",
       "1232  2020-10-09  115.279999  117.000000  114.919998  116.970001  100506900.0   \n",
       "1233  2020-10-12  120.059998  125.180000  119.279999  124.400002  240226800.0   \n",
       "1234  2020-10-13  125.269997  125.389999  119.650002  121.099998  262330500.0   \n",
       "1235  2020-10-14  121.000000  123.029999  119.620003  121.190002  151062300.0   \n",
       "1236  2020-10-15  118.720001  121.199997  118.150002  120.709999  112559200.0   \n",
       "1237  2020-10-16  121.279999  121.550003  118.809998  119.019997  115393800.0   \n",
       "1238  2020-10-19  119.959999  120.419998  115.660004  115.980003  120639300.0   \n",
       "1239  2020-10-20  116.199997  118.980003  115.629997  117.510002  124423700.0   \n",
       "1240  2020-10-21  116.669998  118.709999  116.449997  116.870003   89946000.0   \n",
       "1241  2020-10-22  117.449997  118.040001  114.589996  115.750000  101988000.0   \n",
       "1242  2020-10-23  116.389999  116.550003  114.279999  115.040001   82572600.0   \n",
       "1243  2020-10-26  114.010002  116.550003  112.879997  115.050003  111850700.0   \n",
       "1244  2020-10-27  115.489998  117.279999  114.540001  116.599998   92276800.0   \n",
       "1245  2020-10-28  115.050003  115.430000  111.099998  111.199997  143937800.0   \n",
       "1246  2020-10-29  112.370003  116.930000  112.199997  115.320000  146129200.0   \n",
       "1247  2020-10-30  111.059998  111.989998  107.720001  108.860001  190272600.0   \n",
       "1248  2020-11-02  109.110001  110.680000  107.320000  108.769997  122866900.0   \n",
       "1249  2020-11-03  109.660004  111.489998  108.730003  110.440002  107624400.0   \n",
       "1250  2020-11-04  114.139999  115.589996  112.349998  114.949997  138235500.0   \n",
       "1251  2020-11-05  117.949997  119.620003  116.870003  119.029999  126387100.0   \n",
       "1252  2020-11-06  118.320000  119.199997  116.129997  118.690002  114457900.0   \n",
       "1253  2020-11-09  120.500000  121.989998  116.050003  116.320000  154515300.0   \n",
       "1254  2020-11-10  115.550003  117.589996  114.129997  115.970001  138023400.0   \n",
       "1255  2020-11-11  117.190002  119.629997  116.440002  119.489998  112295000.0   \n",
       "1256  2020-11-12  119.620003  120.529999  118.570000  119.209999  103162300.0   \n",
       "1257  2020-11-13  119.440002  119.669998  117.870003  119.260002   81581900.0   \n",
       "1258  2020-11-16  118.919998  120.989998  118.150002  120.300003   91183018.0   \n",
       "\n",
       "            rsi    open_SPY    high_SPY     low_SPY   close_SPY   volume_SPY  \\\n",
       "1199  84.121261  342.119995  343.000000  339.450012  342.920013   48588700.0   \n",
       "1200  79.832588  343.529999  344.209991  342.269989  344.119995   38463400.0   \n",
       "1201  81.550783  344.760010  347.859985  344.170013  347.570007   50790200.0   \n",
       "1202  75.207351  348.510010  349.899994  346.529999  348.329987   58034100.0   \n",
       "1203  74.347059  349.440002  350.720001  348.149994  350.579987   48588900.0   \n",
       "1204  79.731471  350.350006  351.299988  349.059998  349.309998   66099200.0   \n",
       "1205  84.204961  350.209991  352.709991  349.239990  352.600006   54999300.0   \n",
       "1206  74.344202  354.670013  358.750000  353.429993  357.700012   69540000.0   \n",
       "1207  49.815837  355.869995  356.380005  342.589996  345.390015  148011100.0   \n",
       "1208  49.955353  346.130005  347.829987  334.869995  342.570007  139156300.0   \n",
       "1209  38.009267  336.709991  342.640015  332.880005  333.209991  114465300.0   \n",
       "1210  45.948794  337.549988  342.459991  336.609985  339.790009   91462300.0   \n",
       "1211  40.984765  341.820007  342.529999  332.850006  333.890015   90569500.0   \n",
       "1212  39.156222  335.820007  336.970001  331.000000  334.059998   84680200.0   \n",
       "1213  45.273923  337.489990  340.380005  334.220001  338.459991   65605700.0   \n",
       "1214  45.599509  341.119995  342.019989  338.470001  340.170013   52920900.0   \n",
       "1215  40.524584  341.510010  343.059998  338.519989  338.820007   82096000.0   \n",
       "1216  38.054395  333.559998  337.700012  332.989990  335.839996   91523300.0   \n",
       "1217  33.604215  335.369995  335.489990  327.970001  330.649994  105877900.0   \n",
       "1218  40.733080  325.700012  327.130005  321.730011  326.970001   99450800.0   \n",
       "1219  44.282276  328.570007  330.899994  325.859985  330.299988   63612100.0   \n",
       "1220  37.515086  330.899994  331.200012  322.100006  322.640015   93112200.0   \n",
       "1221  39.908239  321.220001  326.799988  319.799988  323.500000   76681300.0   \n",
       "1222  48.065471  322.579987  329.579987  321.640015  328.730011   71069400.0   \n",
       "1223  52.767992  333.220001  334.959991  332.149994  334.190002   64584600.0   \n",
       "1224  51.099089  333.970001  334.769989  331.619995  332.369995   51304000.0   \n",
       "1225  54.275763  333.089996  338.290009  332.880005  334.890015  103653800.0   \n",
       "1226  56.081919  337.690002  338.739990  335.010010  337.040009   88698700.0   \n",
       "1227  47.980752  331.700012  337.010010  331.190002  333.839996   89431100.0   \n",
       "1228  54.693233  336.059998  339.959991  336.010010  339.760010   45713100.0   \n",
       "1229  48.077410  339.910004  342.170013  334.380005  334.929993   90128900.0   \n",
       "1230  51.801309  338.119995  341.630005  338.089996  340.760010   56999600.0   \n",
       "1231  51.565881  342.850006  343.850006  341.859985  343.779999   45242500.0   \n",
       "1232  55.638842  345.559998  347.350006  344.890015  346.850006   59528600.0   \n",
       "1233  67.069546  349.589996  354.019989  349.059998  352.429993   80388500.0   \n",
       "1234  59.503087  352.279999  352.470001  349.089996  350.130005   73255500.0   \n",
       "1235  59.641066  350.750000  351.929993  347.140015  347.929993   57958700.0   \n",
       "1236  58.460761  343.709991  348.019989  343.130005  347.500000   60357700.0   \n",
       "1237  54.259990  348.959991  350.750000  347.100006  347.290009   89501900.0   \n",
       "1238  47.445921  348.649994  349.329987  341.040009  342.010010   68425600.0   \n",
       "1239  50.894446  343.459991  346.880005  342.640015  343.380005   60051900.0   \n",
       "1240  49.388200  343.329987  345.670013  342.399994  342.730011   63575000.0   \n",
       "1241  46.700714  342.959991  345.239990  340.649994  344.609985   55399300.0   \n",
       "1242  44.976830  345.929993  345.989990  343.130005  345.779999   49143900.0   \n",
       "1243  45.008604  342.130005  342.980011  335.619995  339.390015   91473000.0   \n",
       "1244  49.981941  339.760010  340.119995  337.989990  338.220001   65994100.0   \n",
       "1245  37.021313  332.100006  338.250000  326.130005  326.660004  127094300.0   \n",
       "1246  48.370656  326.910004  333.399994  325.089996  329.980011   90597700.0   \n",
       "1247  36.812981  328.279999  329.690002  322.600006  326.540009  120287300.0   \n",
       "1248  36.677314  330.200012  332.359985  327.239990  330.200012   86068300.0   \n",
       "1249  41.148693  333.690002  338.250000  330.290009  336.029999   93294200.0   \n",
       "1250  51.438164  340.859985  347.940002  339.589996  343.540009  126959700.0   \n",
       "1251  58.696905  349.239990  352.190002  348.859985  350.239990   82039700.0   \n",
       "1252  57.895625  349.929993  351.510010  347.649994  350.160004   74973000.0   \n",
       "1253  52.359619  363.970001  364.380005  354.059998  354.559998  172304200.0   \n",
       "1254  51.550781  353.489990  355.179993  350.510010  354.040009   85552000.0   \n",
       "1255  58.683031  356.399994  357.559998  355.059998  356.670013   58649000.0   \n",
       "1256  57.929312  355.579987  356.720001  351.260010  353.209991   67546200.0   \n",
       "1257  58.036259  355.269989  358.899994  354.709991  358.100006   62892200.0   \n",
       "1258  60.364705  360.980011  362.579987  359.589996  362.570007   65777372.0   \n",
       "\n",
       "        rsi_SPY   open_VIX   high_VIX    low_VIX  close_VIX   volume_VIX  \\\n",
       "1199  76.812765  19.090000  20.129999  19.040001  19.799999   24983000.0   \n",
       "1200  78.418321  19.950001  20.790001  19.459999  19.660000   27572400.0   \n",
       "1201  82.327443  19.469999  20.480000  18.750000  20.309999   29100800.0   \n",
       "1202  83.077671  20.000000  23.690001  19.700001  21.230000   73365600.0   \n",
       "1203  85.151254  21.639999  22.980000  20.600000  21.110001   50204700.0   \n",
       "1204  79.074487  21.930000  22.950001  21.219999  22.650000   41189100.0   \n",
       "1205  82.640434  22.670000  23.440001  22.400000  23.240000   34285700.0   \n",
       "1206  86.579538  23.150000  24.389999  23.020000  24.080000   42972600.0   \n",
       "1207  53.824254  24.510000  30.280001  23.230000  28.900000  135998900.0   \n",
       "1208  49.096384  27.920000  32.180000  24.570000  25.299999  129374600.0   \n",
       "1209  37.083403  28.200001  28.469999  24.150000  24.320000   56407200.0   \n",
       "1210  47.178685  23.340000  23.719999  21.799999  22.440001   45746300.0   \n",
       "1211  40.676249  22.120001  23.600000  21.830000  22.780001   54363400.0   \n",
       "1212  40.936839  21.860001  22.850000  20.760000  20.799999   59861300.0   \n",
       "1213  47.561776  20.389999  20.700001  19.799999  20.230000   32343200.0   \n",
       "1214  49.984356  19.959999  20.740000  19.790001  20.190001   31805700.0   \n",
       "1215  48.037637  19.980000  20.110001  19.280001  20.059999   42531200.0   \n",
       "1216  43.849037  21.010000  21.100000  19.270000  19.450001   47315900.0   \n",
       "1217  37.518508  19.180000  20.100000  18.549999  19.410000   45470800.0   \n",
       "1218  33.686925  20.570000  22.090000  20.170000  20.299999   54634400.0   \n",
       "1219  39.861903  20.260000  21.379999  20.260000  20.549999   26687500.0   \n",
       "1220  32.198608  20.440001  22.219999  20.299999  22.209999   37766500.0   \n",
       "1221  33.786515  22.180000  22.590000  21.010000  21.600000   38373000.0   \n",
       "1222  42.833382  21.580000  21.840000  20.580000  20.780001   27147200.0   \n",
       "1223  50.654146  20.510000  20.889999  20.420000  20.650000   18547600.0   \n",
       "1224  48.211311  20.700001  20.709999  19.570000  20.030001   29955500.0   \n",
       "1225  51.788319  19.629999  20.280001  19.250000  19.920000   31236100.0   \n",
       "1226  54.750999  19.639999  20.570000  19.620001  20.110001   23409000.0   \n",
       "1227  49.700173  21.650000  21.760000  20.450001  21.030001   43572200.0   \n",
       "1228  57.717964  20.719999  21.040001  19.799999  20.030001   20667700.0   \n",
       "1229  50.430669  19.770000  20.730000  19.230000  20.410000   40618200.0   \n",
       "1230  57.608777  19.850000  19.990000  19.260000  19.459999   24140300.0   \n",
       "1231  60.870133  19.129999  19.290001  18.240000  18.330000   33402400.0   \n",
       "1232  63.998611  17.440001  17.490000  16.860001  16.889999   33258100.0   \n",
       "1233  69.003444  16.420000  16.860001  16.299999  16.389999   22250900.0   \n",
       "1234  64.873112  16.670000  17.200001  16.490000  16.670000   27907200.0   \n",
       "1235  60.992943  16.480000  16.840000  16.059999  16.440001   32299300.0   \n",
       "1236  60.210857  17.520000  17.750000  16.580000  16.670000   33842000.0   \n",
       "1237  59.794817  16.620001  16.830000  16.250000  16.790001   21694100.0   \n",
       "1238  50.119637  16.700001  18.100000  16.639999  17.930000   35907000.0   \n",
       "1239  52.342777  17.750000  18.080000  17.510000  17.900000   28551600.0   \n",
       "1240  51.141188  17.770000  18.120001  17.110001  17.270000   31359100.0   \n",
       "1241  54.498019  17.230000  17.469999  16.440001  16.680000   24574800.0   \n",
       "1242  56.561762  16.600000  17.270000  16.540001  16.719999   20279500.0   \n",
       "1243  44.354262  17.309999  19.400000  17.049999  18.990000   62410700.0   \n",
       "1244  42.488632  18.969999  19.559999  18.469999  19.070000   37536700.0   \n",
       "1245  29.066810  21.230000  23.200001  20.850000  23.139999   94240700.0   \n",
       "1246  35.562376  22.459999  23.129999  19.680000  20.680000   56381100.0   \n",
       "1247  32.170765  21.209999  22.219999  20.740000  21.580000   58263200.0   \n",
       "1248  39.043300  20.129999  21.360001  20.059999  20.879999   51415700.0   \n",
       "1249  48.312265  19.969999  20.160000  18.790001  19.180000   50097700.0   \n",
       "1250  57.550916  18.000000  18.049999  16.500000  16.670000   55513700.0   \n",
       "1251  63.939984  15.890000  16.530001  15.650000  16.209999   33461500.0   \n",
       "1252  63.812582  16.010000  16.080000  14.630000  14.670000   53209600.0   \n",
       "1253  67.741240  13.140000  14.180000  12.180000  14.180000   54605600.0   \n",
       "1254  66.789119  13.630000  14.400000  13.450000  13.490000   36231600.0   \n",
       "1255  69.220329  13.180000  13.440000  12.640000  13.060000   34220400.0   \n",
       "1256  62.529124  13.300000  14.820000  13.090000  14.370000   68251200.0   \n",
       "1257  67.467429  13.660000  13.660000  12.730000  13.050000   43994400.0   \n",
       "1258  71.308031  12.550000  13.280000  12.540000  12.600000   31756884.0   \n",
       "\n",
       "        rsi_VIX  \n",
       "1199  29.190110  \n",
       "1200  28.484262  \n",
       "1201  36.415999  \n",
       "1202  45.859296  \n",
       "1203  44.893019  \n",
       "1204  57.624732  \n",
       "1205  61.419117  \n",
       "1206  66.206005  \n",
       "1207  81.131788  \n",
       "1208  59.370619  \n",
       "1209  54.915430  \n",
       "1210  47.342933  \n",
       "1211  48.762677  \n",
       "1212  41.519208  \n",
       "1213  39.635932  \n",
       "1214  39.496246  \n",
       "1215  38.999898  \n",
       "1216  36.601791  \n",
       "1217  36.438536  \n",
       "1218  42.751114  \n",
       "1219  44.472311  \n",
       "1220  54.553076  \n",
       "1221  50.788405  \n",
       "1222  46.042606  \n",
       "1223  45.297008  \n",
       "1224  41.717181  \n",
       "1225  41.077216  \n",
       "1226  42.762371  \n",
       "1227  50.394962  \n",
       "1228  43.404654  \n",
       "1229  46.535861  \n",
       "1230  40.336730  \n",
       "1231  34.298264  \n",
       "1232  28.299669  \n",
       "1233  26.510852  \n",
       "1234  29.291852  \n",
       "1235  28.313936  \n",
       "1236  30.877999  \n",
       "1237  32.282150  \n",
       "1238  44.238732  \n",
       "1239  44.011514  \n",
       "1240  39.301446  \n",
       "1241  35.363375  \n",
       "1242  35.847595  \n",
       "1243  56.429668  \n",
       "1244  56.970256  \n",
       "1245  74.708531  \n",
       "1246  58.510140  \n",
       "1247  61.870799  \n",
       "1248  57.823194  \n",
       "1249  49.147208  \n",
       "1250  39.439249  \n",
       "1251  37.914235  \n",
       "1252  33.146596  \n",
       "1253  31.735740  \n",
       "1254  29.754200  \n",
       "1255  28.521103  \n",
       "1256  37.314850  \n",
       "1257  32.797379  \n",
       "1258  31.359321  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#merging data\n",
    "\n",
    "merge_df2 = pd.merge(merge_df, vix_df, on = \"date\", how=\"left\", suffixes=(\"\", \"_VIX\"))\n",
    "merge_df2.tail(60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "traindata shape = (1259, 19)\n",
      "time stamps = 1259\n",
      "features = ['open', 'high', 'low', 'close', 'volume', 'rsi', 'open_SPY', 'high_SPY', 'low_SPY', 'close_SPY', 'volume_SPY', 'rsi_SPY', 'open_VIX', 'high_VIX', 'low_VIX', 'close_VIX', 'volume_VIX', 'rsi_VIX']\n"
     ]
    }
   ],
   "source": [
    "#splitting data to X values and dates\n",
    "\n",
    "dataset_train = merge_df2\n",
    "cols = list(dataset_train)[1:]\n",
    "\n",
    "datelist_train = list(dataset_train['date'])\n",
    "datelist_train = [dt.datetime.strptime(date, '%Y-%m-%d').date() for date in datelist_train]\n",
    "\n",
    "print(f\"traindata shape = {dataset_train.shape}\")\n",
    "print(f\"time stamps = {len(dataset_train)}\")\n",
    "print(f\"features = {cols}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2.87300000e+01, 2.87625010e+01, 2.83300000e+01, ...,\n",
       "        1.65400000e+04, 7.15000000e+04, 0.00000000e+00],\n",
       "       [2.89400010e+01, 2.93724990e+01, 2.88750000e+01, ...,\n",
       "        1.44600000e+04, 5.11000000e+04, 0.00000000e+00],\n",
       "       [2.94100000e+01, 2.99375000e+01, 2.91900010e+01, ...,\n",
       "        1.54500000e+04, 4.07000000e+04, 3.45911950e+01],\n",
       "       ...,\n",
       "       [1.19620003e+02, 1.20529999e+02, 1.18570000e+02, ...,\n",
       "        1.43700000e+01, 6.82512000e+07, 3.73148499e+01],\n",
       "       [1.19440002e+02, 1.19669998e+02, 1.17870003e+02, ...,\n",
       "        1.30500000e+01, 4.39944000e+07, 3.27973790e+01],\n",
       "       [1.18919998e+02, 1.20989998e+02, 1.18150002e+02, ...,\n",
       "        1.26000000e+01, 3.17568840e+07, 3.13593210e+01]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#changing X values to numpy\n",
    "\n",
    "dataset_train = dataset_train[cols]\n",
    "dataset_train = dataset_train.astype(float)\n",
    "training_set = dataset_train.to_numpy()\n",
    "training_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.87622334],\n",
       "       [-0.86729568],\n",
       "       [-0.84731485],\n",
       "       ...,\n",
       "       [ 2.98773692],\n",
       "       [ 2.98008463],\n",
       "       [ 2.95797796]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Scaling data\n",
    "\n",
    "sc = StandardScaler()\n",
    "training_set_scaled = sc.fit_transform(training_set)\n",
    "\n",
    "sc_predict = StandardScaler()\n",
    "sc_predict.fit_transform(training_set[:, 0:1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train shape == (1140, 90, 17).\n",
      "y_train shape == (1140, 1).\n"
     ]
    }
   ],
   "source": [
    "# seperating dataset into x_train and y_train\n",
    "# Creating a data structure with timestamps and 1 output\n",
    "\n",
    "\n",
    "X_train = []\n",
    "y_train = []\n",
    "\n",
    "n_future = 30   # Number of days we want top predict into the future\n",
    "n_past = 90     # Number of past days we want to use to predict the future\n",
    "\n",
    "for i in range(n_past, len(training_set_scaled) - n_future +1):\n",
    "    X_train.append(training_set_scaled[i - n_past:i, 0:dataset_train.shape[1] - 1])\n",
    "    y_train.append(training_set_scaled[i + n_future - 1:i + n_future, 0])\n",
    "\n",
    "X_train, y_train = np.array(X_train), np.array(y_train)\n",
    "\n",
    "print('X_train shape == {}.'.format(X_train.shape))\n",
    "print('y_train shape == {}.'.format(y_train.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initializing the Neural Network based on LSTM\n",
    "model = Sequential()\n",
    "# Adding 1st LSTM layer\n",
    "model.add(LSTM(units=64, return_sequences=True, input_shape=(n_past, dataset_train.shape[1]-1)))\n",
    "# Adding 2nd LSTM layer\n",
    "model.add(LSTM(units=10, return_sequences=False))\n",
    "# Adding Dropout\n",
    "model.add(Dropout(0.25))\n",
    "# Output layer\n",
    "model.add(Dense(units=1, activation='linear'))\n",
    "# Compiling the Neural Network\n",
    "model.compile(optimizer = Adam(learning_rate=0.01), loss='mean_squared_error')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lstm (LSTM)                  (None, 90, 64)            20992     \n",
      "_________________________________________________________________\n",
      "lstm_1 (LSTM)                (None, 10)                3000      \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 10)                0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 1)                 11        \n",
      "=================================================================\n",
      "Total params: 24,003\n",
      "Trainable params: 24,003\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "4/4 [==============================] - 2s 405ms/step - loss: 0.1375 - val_loss: 3.4181\n",
      "Epoch 2/50\n",
      "4/4 [==============================] - 1s 127ms/step - loss: 0.0641 - val_loss: 2.0557\n",
      "Epoch 3/50\n",
      "4/4 [==============================] - 1s 137ms/step - loss: 0.0364 - val_loss: 1.6313\n",
      "Epoch 4/50\n",
      "4/4 [==============================] - 1s 131ms/step - loss: 0.0325 - val_loss: 1.6164\n",
      "Epoch 5/50\n",
      "4/4 [==============================] - 0s 124ms/step - loss: 0.0239 - val_loss: 1.6091\n",
      "Epoch 6/50\n",
      "4/4 [==============================] - 0s 122ms/step - loss: 0.0234 - val_loss: 1.9948\n",
      "Epoch 7/50\n",
      "4/4 [==============================] - 0s 124ms/step - loss: 0.0201 - val_loss: 2.3050\n",
      "Epoch 8/50\n",
      "4/4 [==============================] - 1s 126ms/step - loss: 0.0184 - val_loss: 2.5312\n",
      "Epoch 9/50\n",
      "4/4 [==============================] - 0s 122ms/step - loss: 0.0168 - val_loss: 2.5427\n",
      "Epoch 10/50\n",
      "4/4 [==============================] - 0s 123ms/step - loss: 0.0161 - val_loss: 2.6402\n",
      "Epoch 11/50\n",
      "4/4 [==============================] - 1s 134ms/step - loss: 0.0152 - val_loss: 2.5825\n",
      "Epoch 12/50\n",
      "4/4 [==============================] - 1s 156ms/step - loss: 0.0160 - val_loss: 2.8399\n",
      "Epoch 13/50\n",
      "4/4 [==============================] - 1s 183ms/step - loss: 0.0156 - val_loss: 2.6952\n",
      "Epoch 14/50\n",
      "4/4 [==============================] - 1s 140ms/step - loss: 0.0151 - val_loss: 2.4865\n",
      "Epoch 15/50\n",
      "4/4 [==============================] - 0s 124ms/step - loss: 0.0131 - val_loss: 2.3053\n",
      "Epoch 16/50\n",
      "4/4 [==============================] - 1s 130ms/step - loss: 0.0142 - val_loss: 2.4143\n",
      "Epoch 17/50\n",
      "4/4 [==============================] - 1s 129ms/step - loss: 0.0118 - val_loss: 2.6851\n",
      "Epoch 18/50\n",
      "4/4 [==============================] - 1s 137ms/step - loss: 0.0113 - val_loss: 2.6682\n",
      "Epoch 19/50\n",
      "4/4 [==============================] - 1s 143ms/step - loss: 0.0117 - val_loss: 2.5601\n",
      "Epoch 20/50\n",
      "4/4 [==============================] - 1s 136ms/step - loss: 0.0109 - val_loss: 2.5070\n",
      "Epoch 21/50\n",
      "4/4 [==============================] - 0s 125ms/step - loss: 0.0128 - val_loss: 2.8770\n",
      "Epoch 22/50\n",
      "4/4 [==============================] - 1s 129ms/step - loss: 0.0122 - val_loss: 2.4908\n",
      "Epoch 23/50\n",
      "4/4 [==============================] - 1s 125ms/step - loss: 0.0096 - val_loss: 2.2996\n",
      "Epoch 24/50\n",
      "4/4 [==============================] - 1s 133ms/step - loss: 0.0101 - val_loss: 2.3100\n",
      "Epoch 25/50\n",
      "4/4 [==============================] - 1s 135ms/step - loss: 0.0104 - val_loss: 2.2373\n",
      "Epoch 26/50\n",
      "4/4 [==============================] - 1s 128ms/step - loss: 0.0110 - val_loss: 2.5723\n",
      "Epoch 27/50\n",
      "4/4 [==============================] - 0s 124ms/step - loss: 0.0093 - val_loss: 2.5975\n",
      "Epoch 28/50\n",
      "4/4 [==============================] - 1s 125ms/step - loss: 0.0093 - val_loss: 2.2952\n",
      "Epoch 29/50\n",
      "4/4 [==============================] - 0s 124ms/step - loss: 0.0095 - val_loss: 2.2199\n",
      "Epoch 30/50\n",
      "4/4 [==============================] - 1s 141ms/step - loss: 0.0096 - val_loss: 2.2103\n",
      "Epoch 31/50\n",
      "4/4 [==============================] - 1s 144ms/step - loss: 0.0101 - val_loss: 2.2682\n",
      "Epoch 32/50\n",
      "4/4 [==============================] - 0s 122ms/step - loss: 0.0090 - val_loss: 2.4893\n",
      "Epoch 33/50\n",
      "4/4 [==============================] - 1s 131ms/step - loss: 0.0104 - val_loss: 2.3819\n",
      "Epoch 34/50\n",
      "4/4 [==============================] - 1s 130ms/step - loss: 0.0096 - val_loss: 2.2632\n",
      "Epoch 35/50\n",
      "4/4 [==============================] - 1s 126ms/step - loss: 0.0088 - val_loss: 2.4558\n",
      "Epoch 36/50\n",
      "4/4 [==============================] - 1s 134ms/step - loss: 0.0096 - val_loss: 2.6038\n",
      "Epoch 37/50\n",
      "4/4 [==============================] - 1s 127ms/step - loss: 0.0076 - val_loss: 2.4301\n",
      "Epoch 38/50\n",
      "4/4 [==============================] - 1s 135ms/step - loss: 0.0089 - val_loss: 2.6594\n",
      "Epoch 39/50\n",
      "4/4 [==============================] - 1s 127ms/step - loss: 0.0100 - val_loss: 2.6822\n",
      "Epoch 40/50\n",
      "4/4 [==============================] - 1s 126ms/step - loss: 0.0091 - val_loss: 2.5497\n",
      "Epoch 41/50\n",
      "4/4 [==============================] - 1s 138ms/step - loss: 0.0088 - val_loss: 2.2726\n",
      "Epoch 42/50\n",
      "4/4 [==============================] - 1s 146ms/step - loss: 0.0082 - val_loss: 2.2110\n",
      "Epoch 43/50\n",
      "4/4 [==============================] - 1s 185ms/step - loss: 0.0091 - val_loss: 2.3370\n",
      "Epoch 44/50\n",
      "4/4 [==============================] - 1s 150ms/step - loss: 0.0085 - val_loss: 2.1854\n",
      "Epoch 45/50\n",
      "4/4 [==============================] - 1s 131ms/step - loss: 0.0083 - val_loss: 2.1073\n",
      "Epoch 46/50\n",
      "4/4 [==============================] - 0s 122ms/step - loss: 0.0080 - val_loss: 2.3244\n",
      "Epoch 47/50\n",
      "4/4 [==============================] - 1s 133ms/step - loss: 0.0085 - val_loss: 2.2568\n",
      "Epoch 48/50\n",
      "4/4 [==============================] - 1s 129ms/step - loss: 0.0081 - val_loss: 2.3331\n",
      "Epoch 49/50\n",
      "4/4 [==============================] - 0s 125ms/step - loss: 0.0080 - val_loss: 2.4804\n",
      "Epoch 50/50\n",
      "4/4 [==============================] - 1s 128ms/step - loss: 0.0082 - val_loss: 2.3615\n"
     ]
    }
   ],
   "source": [
    "#training data\n",
    "history = model.fit(X_train, y_train, shuffle=True, epochs=50, validation_split=0.2, verbose=1, batch_size=256)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "36/36 [==============================] - 1s 16ms/step - loss: 0.4735\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.47349607944488525"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate list of sequence of days for predictions\n",
    "datelist_future = pd.date_range(datelist_train[-1], periods=n_future, freq='1d').tolist()\n",
    "\n",
    "# creating date information into timestamp format\n",
    "datelist_future_ = []\n",
    "for this_timestamp in datelist_future:\n",
    "    datelist_future_.append(this_timestamp.date())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions_future = model.predict(X_train[-n_future:])\n",
    "\n",
    "predictions_train = model.predict(X_train[n_past:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Open</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-09-16</th>\n",
       "      <td>27.188143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-19</th>\n",
       "      <td>27.332968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-20</th>\n",
       "      <td>27.433590</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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       "                 Open\n",
       "2016-09-16  27.188143\n",
       "2016-09-19  27.332968\n",
       "2016-09-20  27.433590"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Inverse the predictions to original measurements\n",
    "\n",
    "def datetime_to_timestamp(x):\n",
    "    return dt.datetime.strptime(x.strftime('%Y%m%d'), '%Y%m%d')\n",
    "\n",
    "\n",
    "y_pred_future = sc_predict.inverse_transform(predictions_future)\n",
    "y_pred_train = sc_predict.inverse_transform(predictions_train)\n",
    "\n",
    "PREDICTIONS_FUTURE = pd.DataFrame(y_pred_future, columns=['Open']).set_index(pd.Series(datelist_future))\n",
    "PREDICTION_TRAIN = pd.DataFrame(y_pred_train, columns=['Open']).set_index(pd.Series(datelist_train[2 * n_past + n_future -1:]))\n",
    "\n",
    "# Convert <datetime.date> to <Timestamp> for PREDCITION_TRAIN\n",
    "PREDICTION_TRAIN.index = PREDICTION_TRAIN.index.to_series().apply(datetime_to_timestamp)\n",
    "\n",
    "PREDICTION_TRAIN.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
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     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-11-16</th>\n",
       "      <td>2166.438477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-17</th>\n",
       "      <td>2199.120117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-18</th>\n",
       "      <td>2219.139893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-19</th>\n",
       "      <td>2247.871826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-20</th>\n",
       "      <td>2257.747314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-21</th>\n",
       "      <td>2257.200439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-22</th>\n",
       "      <td>2275.761719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-23</th>\n",
       "      <td>2275.683350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-24</th>\n",
       "      <td>2278.136719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-25</th>\n",
       "      <td>2242.721436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-26</th>\n",
       "      <td>2219.707520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-27</th>\n",
       "      <td>2206.063232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-28</th>\n",
       "      <td>2187.138672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-29</th>\n",
       "      <td>2168.362793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-30</th>\n",
       "      <td>2165.642822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-01</th>\n",
       "      <td>2153.167969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-02</th>\n",
       "      <td>2152.656250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-03</th>\n",
       "      <td>2139.322754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-04</th>\n",
       "      <td>2087.473633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-05</th>\n",
       "      <td>2085.895996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-06</th>\n",
       "      <td>2086.699463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-07</th>\n",
       "      <td>2087.245361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-08</th>\n",
       "      <td>2077.718262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-09</th>\n",
       "      <td>2078.814209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-10</th>\n",
       "      <td>2081.923584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-11</th>\n",
       "      <td>2093.557861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-12</th>\n",
       "      <td>2088.234619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-13</th>\n",
       "      <td>2091.509033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-14</th>\n",
       "      <td>2087.220947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-15</th>\n",
       "      <td>2088.074463</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
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       "                   Open\n",
       "2020-11-16  2166.438477\n",
       "2020-11-17  2199.120117\n",
       "2020-11-18  2219.139893\n",
       "2020-11-19  2247.871826\n",
       "2020-11-20  2257.747314\n",
       "2020-11-21  2257.200439\n",
       "2020-11-22  2275.761719\n",
       "2020-11-23  2275.683350\n",
       "2020-11-24  2278.136719\n",
       "2020-11-25  2242.721436\n",
       "2020-11-26  2219.707520\n",
       "2020-11-27  2206.063232\n",
       "2020-11-28  2187.138672\n",
       "2020-11-29  2168.362793\n",
       "2020-11-30  2165.642822\n",
       "2020-12-01  2153.167969\n",
       "2020-12-02  2152.656250\n",
       "2020-12-03  2139.322754\n",
       "2020-12-04  2087.473633\n",
       "2020-12-05  2085.895996\n",
       "2020-12-06  2086.699463\n",
       "2020-12-07  2087.245361\n",
       "2020-12-08  2077.718262\n",
       "2020-12-09  2078.814209\n",
       "2020-12-10  2081.923584\n",
       "2020-12-11  2093.557861\n",
       "2020-12-12  2088.234619\n",
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       "2020-12-14  2087.220947\n",
       "2020-12-15  2088.074463"
      ]
     },
     "execution_count": 21,
     "metadata": {},
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    }
   ],
   "source": [
    "y_pred_future = sc_predict.inverse_transform(PREDICTIONS_FUTURE)\n",
    "\n",
    "predictions_future = pd.DataFrame(y_pred_future, columns=['Open']).set_index(pd.Series(datelist_future))\n",
    "predictions_future"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-09-16</th>\n",
       "      <td>688.874023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-19</th>\n",
       "      <td>692.280640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-20</th>\n",
       "      <td>694.647522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-21</th>\n",
       "      <td>700.130554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-22</th>\n",
       "      <td>704.037537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-10</th>\n",
       "      <td>2093.557861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-11</th>\n",
       "      <td>2088.234619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-12</th>\n",
       "      <td>2091.509033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-13</th>\n",
       "      <td>2087.220947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-16</th>\n",
       "      <td>2088.074463</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1050 rows × 1 columns</p>\n",
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      ],
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       "                   Open\n",
       "2016-09-16   688.874023\n",
       "2016-09-19   692.280640\n",
       "2016-09-20   694.647522\n",
       "2016-09-21   700.130554\n",
       "2016-09-22   704.037537\n",
       "...                 ...\n",
       "2020-11-10  2093.557861\n",
       "2020-11-11  2088.234619\n",
       "2020-11-12  2091.509033\n",
       "2020-11-13  2087.220947\n",
       "2020-11-16  2088.074463\n",
       "\n",
       "[1050 rows x 1 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_train = sc_predict.inverse_transform(PREDICTION_TRAIN)\n",
    "\n",
    "predictions_train = pd.DataFrame(y_pred_train, columns=['Open']).set_index(pd.Series(datelist_train[2 * n_past + n_future -1:]))\n",
    "predictions_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set plot size \n",
    "from pylab import rcParams\n",
    "rcParams['figure.figsize'] = 14, 5\n",
    "\n",
    "# Plot parameters\n",
    "START_DATE_FOR_PLOTTING = '2017-01-01' #there is a little bit of discrepancy because stock market is not open on weekends\n",
    "length =  len(pd.DataFrame(datelist_train).loc[START_DATE_FOR_PLOTTING:])\n",
    "\n",
    "plt.plot(datelist_train[-length:], dataset_train.iloc[-length:]['open'], color='b', label='Actual Stock Price')\n",
    "plt.plot(PREDICTIONS_FUTURE.index, PREDICTIONS_FUTURE['Open'], color='r', label='Predicted Stock Price')\n",
    "plt.plot(PREDICTION_TRAIN.iloc[-length:].index, PREDICTION_TRAIN.iloc[-length:]['Open'], color='orange', label='Training predictions')\n",
    "\n",
    "plt.axvline(x = min(PREDICTIONS_FUTURE.index), color='green', linewidth=2, linestyle='--')\n",
    "\n",
    "plt.grid(which='major', color='#cccccc', alpha=0.5)\n",
    "\n",
    "plt.legend(shadow=True)\n",
    "plt.title('APPL Predictions and Actual Stock Prices', family='Arial', fontsize=12)\n",
    "plt.xlabel('Timeline', family='Arial', fontsize=10)\n",
    "plt.ylabel('Stock Price Value', family='Arial', fontsize=10)\n",
    "plt.xticks(rotation=45, fontsize=8)\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}