NOC_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\n",
    "\n",
    "import datetime as dt\n",
    "plt.style.use('fivethirtyeight')\n",
    "\n",
    "\n",
    "API_key = \"ed733879878dc4eefc0f1f08664391a3\"\n",
    "\n",
    "\n",
    "ticker = \"NOC\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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54.7483927060975}, {'date': '2015-12-16', 'open': 176.2286, 'high': 177.4386, 'low': 173.7486, 'close': 176.1886, 'volume': 1423400.0, 'rsi': 62.219091369912}, {'date': '2015-12-15', 'open': 174.6051, 'high': 176.3251, 'low': 174.1851, 'close': 174.4151, 'volume': 1935600.0, 'rsi': 54.86734448620113}, {'date': '2015-12-14', 'open': 173.2501, 'high': 175.1301, 'low': 171.8701, 'close': 173.7001, 'volume': 1257600.0, 'rsi': 51.438671503046294}, {'date': '2015-12-11', 'open': 172.3052, 'high': 173.8152, 'low': 171.7652, 'close': 172.7252, 'volume': 1594200.0, 'rsi': 46.44611868815356}, {'date': '2015-12-10', 'open': 173.3323, 'high': 174.8423, 'low': 173.2823, 'close': 174.0623, 'volume': 1568700.0, 'rsi': 53.197090890940096}, {'date': '2015-12-09', 'open': 173.8273, 'high': 175.2373, 'low': 171.7473, 'close': 173.2173, 'volume': 1386000.0, 'rsi': 48.979156119731186}, {'date': '2015-12-08', 'open': 173.1236, 'high': 175.4836, 'low': 172.5236, 'close': 174.5636, 'volume': 1213900.0, 'rsi': 56.24793082532742}, {'date': '2015-12-07', 'open': 174.5344, 'high': 175.0044, 'low': 173.3344, 'close': 174.5544, 'volume': 707700.0, 'rsi': 56.207960878640414}, {'date': '2015-12-04', 'open': 170.0473, 'high': 174.7673, 'low': 169.9573, 'close': 174.5173, 'volume': 891900.0, 'rsi': 56.06228049525807}, {'date': '2015-12-03', 'open': 172.8867, 'high': 173.2967, 'low': 168.9267, 'close': 169.9767, 'volume': 1186600.0, 'rsi': 30.650870222124414}, {'date': '2015-12-02', 'open': 173.6487, 'high': 174.5488, 'low': 172.1487, 'close': 172.4187, 'volume': 750100.0, 'rsi': 42.56715239161564}, {'date': '2015-12-01', 'open': 173.703, 'high': 175.983, 'low': 173.213, 'close': 173.923, 'volume': 1035900.0, 'rsi': 54.263047175290986}, {'date': '2015-11-30', 'open': 175.6009, 'high': 175.6109, 'low': 172.6309, 'close': 173.0409, 'volume': 1534300.0, 'rsi': 46.5061384775043}, {'date': '2015-11-27', 'open': 174.6044, 'high': 175.4844, 'low': 173.1144, 'close': 174.9444, 'volume': 456300.0, 'rsi': 69.34811509433898}, {'date': '2015-11-25', 'open': 174.3401, 'high': 175.5301, 'low': 173.5401, 'close': 174.3501, 'volume': 886900.0, 'rsi': 64.44044870336042}, {'date': '2015-11-24', 'open': 172.6209, 'high': 174.4309, 'low': 172.0009, 'close': 173.8209, 'volume': 1025800.0, 'rsi': 59.206002697518805}, {'date': '2015-11-23', 'open': 175.3336, 'high': 175.8336, 'low': 173.6037, 'close': 173.6637, 'volume': 889200.0, 'rsi': 57.53482518146773}, {'date': '2015-11-20', 'open': 175.0692, 'high': 175.8592, 'low': 174.5492, 'close': 175.1892, 'volume': 1243100.0, 'rsi': 89.58883780552439}, {'date': '2015-11-19', 'open': 174.7212, 'high': 175.2412, 'low': 172.9812, 'close': 174.0612, 'volume': 1030200.0, 'rsi': 83.45442792501616}, {'date': '2015-11-18', 'open': 172.2603, 'high': 174.2903, 'low': 172.0903, 'close': 173.9503, 'volume': 845100.0, 'rsi': 82.54435888851691}, {'date': '2015-11-17', 'open': 173.9611, 'high': 173.9611, 'low': 171.0711, 'close': 172.1011, 'volume': 1325500.0, 'rsi': 0.0}, {'date': '2015-11-16', 'open': 165.8256, 'high': 172.6556, 'low': 165.3256, 'close': 172.5356, 'volume': 1780200.0, '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>2015-11-16</td>\n",
       "      <td>165.825600</td>\n",
       "      <td>172.655600</td>\n",
       "      <td>165.325600</td>\n",
       "      <td>172.535600</td>\n",
       "      <td>1780200.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1257</th>\n",
       "      <td>2015-11-17</td>\n",
       "      <td>173.961100</td>\n",
       "      <td>173.961100</td>\n",
       "      <td>171.071100</td>\n",
       "      <td>172.101100</td>\n",
       "      <td>1325500.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1256</th>\n",
       "      <td>2015-11-18</td>\n",
       "      <td>172.260300</td>\n",
       "      <td>174.290300</td>\n",
       "      <td>172.090300</td>\n",
       "      <td>173.950300</td>\n",
       "      <td>845100.0</td>\n",
       "      <td>82.544359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1255</th>\n",
       "      <td>2015-11-19</td>\n",
       "      <td>174.721200</td>\n",
       "      <td>175.241200</td>\n",
       "      <td>172.981200</td>\n",
       "      <td>174.061200</td>\n",
       "      <td>1030200.0</td>\n",
       "      <td>83.454428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1254</th>\n",
       "      <td>2015-11-20</td>\n",
       "      <td>175.069200</td>\n",
       "      <td>175.859200</td>\n",
       "      <td>174.549200</td>\n",
       "      <td>175.189200</td>\n",
       "      <td>1243100.0</td>\n",
       "      <td>89.588838</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>2020-11-09</td>\n",
       "      <td>315.000000</td>\n",
       "      <td>317.600006</td>\n",
       "      <td>300.070007</td>\n",
       "      <td>301.279999</td>\n",
       "      <td>1732000.0</td>\n",
       "      <td>44.775865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-11-10</td>\n",
       "      <td>302.730011</td>\n",
       "      <td>316.899994</td>\n",
       "      <td>301.480011</td>\n",
       "      <td>314.510010</td>\n",
       "      <td>948500.0</td>\n",
       "      <td>57.396475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-11-11</td>\n",
       "      <td>314.959991</td>\n",
       "      <td>314.970001</td>\n",
       "      <td>306.019989</td>\n",
       "      <td>309.959991</td>\n",
       "      <td>590700.0</td>\n",
       "      <td>52.786634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-11-12</td>\n",
       "      <td>308.000000</td>\n",
       "      <td>308.779999</td>\n",
       "      <td>302.339996</td>\n",
       "      <td>306.230011</td>\n",
       "      <td>737400.0</td>\n",
       "      <td>49.188210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-11-13</td>\n",
       "      <td>308.079987</td>\n",
       "      <td>313.119995</td>\n",
       "      <td>307.109985</td>\n",
       "      <td>311.709991</td>\n",
       "      <td>493400.0</td>\n",
       "      <td>54.276352</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  2015-11-16  165.825600  172.655600  165.325600  172.535600  1780200.0   \n",
       "1257  2015-11-17  173.961100  173.961100  171.071100  172.101100  1325500.0   \n",
       "1256  2015-11-18  172.260300  174.290300  172.090300  173.950300   845100.0   \n",
       "1255  2015-11-19  174.721200  175.241200  172.981200  174.061200  1030200.0   \n",
       "1254  2015-11-20  175.069200  175.859200  174.549200  175.189200  1243100.0   \n",
       "...          ...         ...         ...         ...         ...        ...   \n",
       "4     2020-11-09  315.000000  317.600006  300.070007  301.279999  1732000.0   \n",
       "3     2020-11-10  302.730011  316.899994  301.480011  314.510010   948500.0   \n",
       "2     2020-11-11  314.959991  314.970001  306.019989  309.959991   590700.0   \n",
       "1     2020-11-12  308.000000  308.779999  302.339996  306.230011   737400.0   \n",
       "0     2020-11-13  308.079987  313.119995  307.109985  311.709991   493400.0   \n",
       "\n",
       "            rsi  \n",
       "1258   0.000000  \n",
       "1257   0.000000  \n",
       "1256  82.544359  \n",
       "1255  83.454428  \n",
       "1254  89.588838  \n",
       "...         ...  \n",
       "4     44.775865  \n",
       "3     57.396475  \n",
       "2     52.786634  \n",
       "1     49.188210  \n",
       "0     54.276352  \n",
       "\n",
       "[1259 rows x 7 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#creating DataFrame\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": [
      "[{'date': '2020-11-13', 'open': 355.269989, 'high': 358.899994, 'low': 354.709991, 'close': 358.100006, 'volume': 62892200.0, 'rsi': 67.46742883715231}, {'date': '2020-11-12', 'open': 355.579987, 'high': 356.720001, 'low': 351.26001, 'close': 353.209991, 'volume': 67546200.0, 'rsi': 62.529124440155556}, {'date': '2020-11-11', 'open': 356.399994, 'high': 357.559998, 'low': 355.059998, 'close': 356.670013, 'volume': 58649000.0, 'rsi': 69.22032896995137}, {'date': '2020-11-10', 'open': 353.48999, 'high': 355.179993, 'low': 350.51001, 'close': 354.040009, 'volume': 85552000.0, 'rsi': 66.78911933645477}, {'date': '2020-11-09', 'open': 363.970001, 'high': 364.380005, 'low': 354.059998, 'close': 354.559998, 'volume': 172304200.0, 'rsi': 67.74123964999349}, {'date': '2020-11-06', 'open': 349.929993, 'high': 351.51001, 'low': 347.649994, 'close': 350.160004, 'volume': 74973000.0, 'rsi': 63.81258237762847}, {'date': '2020-11-05', 'open': 349.23999, 'high': 352.190002, 'low': 348.859985, 'close': 350.23999, 'volume': 82039700.0, 'rsi': 63.939983540911115}, {'date': '2020-11-04', 'open': 340.859985, 'high': 347.940002, 'low': 339.589996, 'close': 343.540009, 'volume': 126959700.0, 'rsi': 57.55091566954566}, {'date': '2020-11-03', 'open': 333.690002, 'high': 338.25, 'low': 330.290009, 'close': 336.029999, 'volume': 93294200.0, 'rsi': 48.31226525438109}, {'date': '2020-11-02', 'open': 330.200012, 'high': 332.359985, 'low': 327.23999, 'close': 330.200012, 'volume': 86068300.0, 'rsi': 39.04330046660495}, {'date': '2020-10-30', 'open': 328.279999, 'high': 329.690002, 'low': 322.600006, 'close': 326.540009, 'volume': 120287300.0, 'rsi': 32.170764529921186}, {'date': '2020-10-29', 'open': 326.910004, 'high': 333.399994, 'low': 325.089996, 'close': 329.980011, 'volume': 90597700.0, 'rsi': 35.56237610665694}, {'date': '2020-10-28', 'open': 332.100006, 'high': 338.25, 'low': 326.130005, 'close': 326.660004, 'volume': 127094300.0, 'rsi': 29.06680958627092}, {'date': '2020-10-27', 'open': 339.76001, 'high': 340.119995, 'low': 337.98999, 'close': 338.220001, 'volume': 65994100.0, 'rsi': 42.48863238798527}, {'date': '2020-10-26', 'open': 342.130005, 'high': 342.980011, 'low': 335.619995, 'close': 339.390015, 'volume': 91473000.0, 'rsi': 44.35426160891887}, {'date': '2020-10-23', 'open': 345.929993, 'high': 345.98999, 'low': 343.130005, 'close': 345.779999, 'volume': 49143900.0, 'rsi': 56.56176156849784}, {'date': '2020-10-22', 'open': 342.959991, 'high': 345.23999, 'low': 340.649994, 'close': 344.609985, 'volume': 55399300.0, 'rsi': 54.49801921245227}, {'date': '2020-10-21', 'open': 343.329987, 'high': 345.670013, 'low': 342.399994, 'close': 342.730011, 'volume': 63575000.0, 'rsi': 51.14118828924803}, {'date': '2020-10-20', 'open': 343.459991, 'high': 346.880005, 'low': 342.640015, 'close': 343.380005, 'volume': 60051900.0, 'rsi': 52.34277728136932}, {'date': '2020-10-19', 'open': 348.649994, 'high': 349.329987, 'low': 341.040009, 'close': 342.01001, 'volume': 68425600.0, 'rsi': 50.11963695917208}, {'date': '2020-10-16', 'open': 348.959991, 'high': 350.75, 'low': 347.100006, 'close': 347.290009, 'volume': 89501900.0, 'rsi': 59.79481698292683}, {'date': '2020-10-15', 'open': 343.709991, 'high': 348.019989, 'low': 343.130005, 'close': 347.5, 'volume': 60357700.0, 'rsi': 60.21085718530446}, {'date': '2020-10-14', 'open': 350.75, 'high': 351.929993, 'low': 347.140015, 'close': 347.929993, 'volume': 57958700.0, 'rsi': 60.99294327680183}, {'date': '2020-10-13', 'open': 352.279999, 'high': 352.470001, 'low': 349.089996, 'close': 350.130005, 'volume': 73255500.0, 'rsi': 64.87311244815479}, {'date': '2020-10-12', 'open': 349.589996, 'high': 354.019989, 'low': 349.059998, 'close': 352.429993, 'volume': 80388500.0, 'rsi': 69.00344351137286}, {'date': '2020-10-09', 'open': 345.559998, 'high': 347.350006, 'low': 344.890015, 'close': 346.850006, 'volume': 59528600.0, 'rsi': 63.99861148220089}, {'date': '2020-10-08', 'open': 342.850006, 'high': 343.850006, 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193.0995, 'low': 191.3396, 'close': 192.6396, 'volume': 77329400.0, 'rsi': 64.50481352461625}, {'date': '2016-04-26', 'open': 192.3639, 'high': 192.8439, 'low': 191.6839, 'close': 192.2439, 'volume': 75864200.0, 'rsi': 62.379365305814616}, {'date': '2016-04-25', 'open': 191.6087, 'high': 192.0087, 'low': 190.8887, 'close': 191.9587, 'volume': 66166500.0, 'rsi': 60.8590327299748}, {'date': '2016-04-22', 'open': 191.8699, 'high': 192.6099, 'low': 191.2299, 'close': 192.2899, 'volume': 99251700.0, 'rsi': 63.54291159273869}, {'date': '2016-04-21', 'open': 193.4399, 'high': 193.5699, 'low': 191.9699, 'close': 192.2899, 'volume': 85695000.0, 'rsi': 63.54291159273869}, {'date': '2016-04-20', 'open': 193.1797, 'high': 194.1497, 'low': 192.6197, 'close': 193.3297, 'volume': 81100300.0, 'rsi': 71.56907031745286}, {'date': '2016-04-19', 'open': 192.9857, 'high': 193.4457, 'low': 192.1857, 'close': 193.1457, 'volume': 88316100.0, 'rsi': 70.98539981818978}, {'date': '2016-04-18', 'open': 191.0784, 'high': 192.5784, 'low': 190.2984, 'close': 192.5384, 'volume': 82531000.0, 'rsi': 69.10111080252017}, {'date': '2016-04-15', 'open': 191.4249, 'high': 191.5849, 'low': 190.8149, 'close': 191.1949, 'volume': 75761600.0, 'rsi': 64.51247564008995}, {'date': '2016-04-14', 'open': 191.4665, 'high': 191.9965, 'low': 190.9965, 'close': 191.4065, 'volume': 65212900.0, 'rsi': 65.89969313113689}, {'date': '2016-04-13', 'open': 190.3974, 'high': 191.4974, 'low': 190.2374, 'close': 191.3974, 'volume': 96336400.0, 'rsi': 65.8712885114776}, {'date': '2016-04-12', 'open': 187.7834, 'high': 189.8134, 'low': 187.2634, 'close': 189.4834, 'volume': 115350600.0, 'rsi': 59.482537188160634}, {'date': '2016-04-11', 'open': 188.965, 'high': 189.785, 'low': 187.625, 'close': 187.735, 'volume': 83757500.0, 'rsi': 52.11270154037478}, {'date': '2016-04-08', 'open': 189.0167, 'high': 189.5267, 'low': 187.5467, 'close': 188.1767, 'volume': 95040600.0, 'rsi': 54.360881985686454}, {'date': '2016-04-07', 'open': 188.8606, 'high': 189.2806, 'low': 186.8106, 'close': 187.6706, 'volume': 113859000.0, 'rsi': 52.23597490542301}, {'date': '2016-04-06', 'open': 187.7134, 'high': 190.0134, 'low': 187.5034, 'close': 189.9434, 'volume': 91839800.0, 'rsi': 64.34416610621328}, {'date': '2016-04-05', 'open': 188.3715, 'high': 189.9615, 'low': 187.5915, 'close': 187.8915, 'volume': 99662200.0, 'rsi': 56.070375231802856}, {'date': '2016-04-04', 'open': 190.3671, 'high': 190.6071, 'low': 189.427, 'close': 189.787, 'volume': 63497000.0, 'rsi': 69.47339166029194}, {'date': '2016-04-01', 'open': 187.8335, 'high': 190.6235, 'low': 187.4635, 'close': 190.4035, 'volume': 114423500.0, 'rsi': 74.700278657191}, {'date': '2016-03-31', 'open': 189.5053, 'high': 190.0053, 'low': 188.9253, 'close': 189.1153, 'volume': 94584100.0, 'rsi': 70.5307561193118}, {'date': '2016-03-30', 'open': 189.8554, 'high': 190.4254, 'low': 189.1454, 'close': 189.5754, 'volume': 86365300.0, 'rsi': 74.4762352532386}, {'date': '2016-03-29', 'open': 186.3872, 'high': 188.8772, 'low': 186.0272, 'close': 188.7472, 'volume': 92922900.0, 'rsi': 71.93263824316183}, {'date': '2016-03-28', 'open': 187.3873, 'high': 187.6373, 'low': 186.4873, 'close': 187.0173, 'volume': 62408200.0, 'rsi': 65.46233147582771}, {'date': '2016-03-24', 'open': 185.7869, 'high': 186.9469, 'low': 185.5269, 'close': 186.9069, 'volume': 84360900.0, 'rsi': 64.99888915365453}, {'date': '2016-03-23', 'open': 187.8897, 'high': 188.1097, 'low': 186.7897, 'close': 186.9897, 'volume': 81052500.0, 'rsi': 65.59299511416809}, {'date': '2016-03-22', 'open': 187.4319, 'high': 188.9019, 'low': 187.2419, 'close': 188.2319, 'volume': 97471900.0, 'rsi': 74.82772541685623}, {'date': '2016-03-21', 'open': 187.7332, 'high': 188.6031, 'low': 187.4632, 'close': 188.3331, 'volume': 72926700.0, 'rsi': 75.60821522777742}, {'date': '2016-03-18', 'open': 187.8563, 'high': 188.4663, 'low': 187.4863, 'close': 188.0663, 'volume': 138372400.0, 'rsi': 74.98923103185209}, {'date': '2016-03-17', 'open': 185.9402, 'high': 187.9302, 'low': 185.4702, 'close': 187.3302, 'volume': 134278500.0, 'rsi': 73.30724293847562}, {'date': '2016-03-16', 'open': 184.4092, 'high': 186.6292, 'low': 184.3592, 'close': 186.1492, 'volume': 129303200.0, 'rsi': 70.43640478614276}, {'date': '2016-03-15', 'open': 184.2681, 'high': 185.4381, 'low': 183.9581, 'close': 185.0781, 'volume': 93169100.0, 'rsi': 67.59129101634898}, {'date': '2016-03-14', 'open': 185.0402, 'high': 185.9202, 'low': 184.6502, 'close': 185.3802, 'volume': 73612000.0, 'rsi': 69.28382525686538}, {'date': '2016-03-11', 'open': 184.1183, 'high': 185.6683, 'low': 182.3783, 'close': 185.6183, 'volume': 137964500.0, 'rsi': 70.53671581483204}, {'date': '2016-03-10', 'open': 183.0905, 'high': 184.2005, 'low': 180.5105, 'close': 182.6705, 'volume': 156838700.0, 'rsi': 63.101981539993034}, {'date': '2016-03-09', 'open': 182.504, 'high': 182.934, 'low': 181.574, 'close': 182.524, 'volume': 94801200.0, 'rsi': 62.6807711484567}, {'date': '2016-03-08', 'open': 182.5469, 'high': 183.1469, 'low': 181.4369, 'close': 181.6269, 'volume': 123974900.0, 'rsi': 60.175269947397204}, {'date': '2016-03-07', 'open': 182.3817, 'high': 184.1117, 'low': 182.2917, 'close': 183.6317, 'volume': 100219000.0, 'rsi': 69.5693087257048}, {'date': '2016-03-04', 'open': 183.0653, 'high': 184.4053, 'low': 182.0853, 'close': 183.4853, 'volume': 129293600.0, 'rsi': 69.25385364681503}, {'date': '2016-03-03', 'open': 181.9002, 'high': 182.9102, 'low': 181.2202, 'close': 182.8902, 'volume': 95172200.0, 'rsi': 68.04186510352166}, {'date': '2016-03-02', 'open': 180.9162, 'high': 182.2361, 'low': 180.4261, 'close': 182.1761, 'volume': 102415000.0, 'rsi': 66.6208636838545}, {'date': '2016-03-01', 'open': 178.2614, 'high': 181.4614, 'low': 177.7014, 'close': 181.3614, 'volume': 141799700.0, 'rsi': 65.0240131959949}, {'date': '2016-02-29', 'open': 178.746, 'high': 179.866, 'low': 176.966, 'close': 177.196, 'volume': 125918100.0, 'rsi': 55.15118328184923}, {'date': '2016-02-26', 'open': 180.0767, 'high': 180.1867, 'low': 178.4067, 'close': 178.5967, 'volume': 129833700.0, 'rsi': 60.302755100221}, {'date': '2016-02-25', 'open': 177.1987, 'high': 179.0187, 'low': 176.2987, 'close': 179.0087, 'volume': 110728300.0, 'rsi': 61.83169593312303}, {'date': '2016-02-24', 'open': 174.2965, 'high': 177.1965, 'low': 172.9865, 'close': 176.8665, 'volume': 150812200.0, 'rsi': 56.69348311379518}, {'date': '2016-02-23', 'open': 177.7409, 'high': 178.0609, 'low': 175.9209, 'close': 176.0609, 'volume': 111455300.0, 'rsi': 54.62611834490777}, {'date': '2016-02-22', 'open': 177.4029, 'high': 178.4829, 'low': 177.3229, 'close': 178.3129, 'volume': 103640300.0, 'rsi': 62.0824290102789}, {'date': '2016-02-19', 'open': 174.9379, 'high': 175.9479, 'low': 174.2179, 'close': 175.7679, 'volume': 114793000.0, 'rsi': 55.969666255038895}, {'date': '2016-02-18', 'open': 176.9603, 'high': 177.0303, 'low': 175.4803, 'close': 175.8503, 'volume': 102343000.0, 'rsi': 56.23383208843649}, {'date': '2016-02-17', 'open': 174.8535, 'high': 177.0135, 'low': 174.7035, 'close': 176.5735, 'volume': 136009500.0, 'rsi': 58.41152266469515}, {'date': '2016-02-16', 'open': 172.7256, 'high': 173.7656, 'low': 171.5856, 'close': 173.7356, 'volume': 120250700.0, 'rsi': 51.82244849246999}, {'date': '2016-02-12', 'open': 169.1819, 'high': 170.8719, 'low': 168.1819, 'close': 170.8519, 'volume': 127632400.0, 'rsi': 43.659049033590534}, {'date': '2016-02-11', 'open': 166.8806, 'high': 168.6407, 'low': 165.6306, 'close': 167.4007, 'volume': 219058900.0, 'rsi': 31.08048389142854}, {'date': '2016-02-10', 'open': 170.7469, 'high': 172.6769, 'low': 169.4569, 'close': 169.6069, 'volume': 148214100.0, 'rsi': 35.66103987117468}, {'date': '2016-02-09', 'open': 167.6834, 'high': 171.2634, 'low': 167.5234, 'close': 169.7534, 'volume': 184513100.0, 'rsi': 35.97792494907918}, {'date': '2016-02-08', 'open': 170.0942, 'high': 170.4442, 'low': 167.1242, 'close': 169.7442, 'volume': 191526700.0, 'rsi': 35.945755145184776}, {'date': '2016-02-05', 'open': 175.1003, 'high': 175.7803, 'low': 171.3103, 'close': 172.0603, 'volume': 180788300.0, 'rsi': 40.56393369927376}, {'date': '2016-02-04', 'open': 174.5117, 'high': 176.5517, 'low': 173.7617, 'close': 175.4017, 'volume': 139531800.0, 'rsi': 48.685424141097876}, {'date': '2016-02-03', 'open': 175.2371, 'high': 175.6071, 'low': 170.9271, 'close': 175.1271, 'volume': 205054900.0, 'rsi': 47.91411172497871}, {'date': '2016-02-02', 'open': 175.8835, 'high': 175.8934, 'low': 173.4634, 'close': 174.0835, 'volume': 182564900.0, 'rsi': 45.091129552278076}, {'date': '2016-02-01', 'open': 176.1584, 'high': 178.2084, 'low': 175.4684, 'close': 177.2784, 'volume': 136061600.0, 'rsi': 53.006749664354416}, {'date': '2016-01-29', 'open': 173.6425, 'high': 177.5025, 'low': 173.5025, 'close': 177.3425, 'volume': 210529300.0, 'rsi': 53.17530678440811}, {'date': '2016-01-28', 'open': 173.9723, 'high': 174.2122, 'low': 171.1723, 'close': 173.1223, 'volume': 143798800.0, 'rsi': 42.304080363083166}, {'date': '2016-01-27', 'open': 173.6751, 'high': 175.6551, 'low': 171.1551, 'close': 172.2251, 'volume': 185681700.0, 'rsi': 39.62194003551417}, {'date': '2016-01-26', 'open': 172.3401, 'high': 174.4501, 'low': 171.9401, 'close': 174.1201, 'volume': 141036800.0, 'rsi': 43.46268439103469}, {'date': '2016-01-25', 'open': 174.0565, 'high': 174.2865, 'low': 171.5465, 'close': 171.7765, 'volume': 130371700.0, 'rsi': 36.624905220816245}, {'date': '2016-01-22', 'open': 173.673, 'high': 174.653, 'low': 172.7731, 'close': 174.4131, 'volume': 168319600.0, 'rsi': 41.73573237114169}, {'date': '2016-01-21', 'open': 170.4268, 'high': 173.0868, 'low': 168.8568, 'close': 170.9068, 'volume': 195772900.0, 'rsi': 30.053430096934438}, {'date': '2016-01-20', 'open': 169.3348, 'high': 171.8048, 'low': 165.3248, 'close': 169.9548, 'volume': 286547800.0, 'rsi': 26.449798376157435}, {'date': '2016-01-19', 'open': 174.061, 'high': 174.211, 'low': 170.301, 'close': 172.161, 'volume': 195244400.0, 'rsi': 29.634116134969876}, {'date': '2016-01-15', 'open': 170.8922, 'high': 172.8822, 'low': 169.6422, 'close': 171.9322, 'volume': 324846400.0, 'rsi': 28.83443363452545}, {'date': '2016-01-14', 'open': 173.3239, 'high': 177.0339, 'low': 171.4339, 'close': 175.7039, 'volume': 240795600.0, 'rsi': 34.68213038455326}, {'date': '2016-01-13', 'open': 178.4859, 'high': 178.8959, 'low': 172.4159, 'close': 172.8659, 'volume': 221168900.0, 'rsi': 24.283345537070154}, {'date': '2016-01-12', 'open': 177.4476, 'high': 178.1776, 'low': 174.7676, 'close': 177.2876, 'volume': 172330500.0, 'rsi': 31.262311064441583}, {'date': '2016-01-11', 'open': 176.7686, 'high': 177.1686, 'low': 173.5786, 'close': 175.8686, 'volume': 187941300.0, 'rsi': 25.040062118679803}, {'date': '2016-01-08', 'open': 178.9647, 'high': 179.6247, 'low': 175.3547, 'close': 175.6947, 'volume': 209817200.0, 'rsi': 24.284101392820727}, {'date': '2016-01-07', 'open': 178.9246, 'high': 181.0346, 'low': 177.1846, 'close': 177.6446, 'volume': 213436100.0, 'rsi': 27.03554355276031}, {'date': '2016-01-06', 'open': 181.5313, 'high': 183.2513, 'low': 180.7913, 'close': 182.0113, 'volume': 152112600.0, 'rsi': 35.03651636700285}, {'date': '2016-01-05', 'open': 184.3766, 'high': 184.8766, 'low': 183.0266, 'close': 184.3366, 'volume': 110845800.0, 'rsi': 40.82713151182661}, {'date': '2016-01-04', 'open': 183.4953, 'high': 184.0353, 'low': 181.5953, 'close': 184.0253, 'volume': 222353500.0, 'rsi': 39.62485430460416}, {'date': '2015-12-31', 'open': 187.8944, 'high': 188.6544, 'low': 186.6344, 'close': 186.6344, 'volume': 114877900.0, 'rsi': 46.79709779353446}, {'date': '2015-12-30', 'open': 189.7002, 'high': 189.8002, 'low': 188.3502, 'close': 188.5202, 'volume': 63317700.0, 'rsi': 53.04247286538042}, {'date': '2015-12-29', 'open': 188.976, 'high': 190.256, 'low': 188.936, 'close': 189.866, 'volume': 92640700.0, 'rsi': 58.01537748442856}, {'date': '2015-12-28', 'open': 187.5111, 'high': 187.9111, 'low': 186.5911, 'close': 187.8611, 'volume': 65899900.0, 'rsi': 51.97906554478308}, {'date': '2015-12-24', 'open': 188.3314, 'high': 188.9414, 'low': 188.0314, 'close': 188.2914, 'volume': 48539600.0, 'rsi': 53.4638478170792}, {'date': '2015-12-23', 'open': 187.2726, 'high': 188.6526, 'low': 187.1626, 'close': 188.6026, 'volume': 110987200.0, 'rsi': 54.47672324315811}, {'date': '2015-12-22', 'open': 185.5157, 'high': 186.6457, 'low': 184.3457, 'close': 186.2957, 'volume': 111026200.0, 'rsi': 47.89036161481112}, {'date': '2015-12-21', 'open': 184.3604, 'high': 184.8304, 'low': 183.0404, 'close': 184.6204, 'volume': 99094300.0, 'rsi': 42.4481188618357}, {'date': '2015-12-18', 'open': 185.8599, 'high': 186.0199, 'low': 182.9199, 'close': 183.1099, 'volume': 251393500.0, 'rsi': 37.119073286971194}, {'date': '2015-12-17', 'open': 189.9712, 'high': 190.0512, 'low': 186.4112, 'close': 186.4312, 'volume': 173092500.0, 'rsi': 45.44672113055174}, {'date': '2015-12-16', 'open': 187.656, 'high': 189.676, 'low': 186.086, 'close': 189.316, 'volume': 197017000.0, 'rsi': 55.11219222457305}, {'date': '2015-12-15', 'open': 186.2559, 'high': 187.6659, 'low': 184.4259, 'close': 186.5859, 'volume': 154069600.0, 'rsi': 45.18224133738099}, {'date': '2015-12-14', 'open': 183.8175, 'high': 184.7975, 'low': 181.6975, 'close': 184.6475, 'volume': 182385200.0, 'rsi': 36.15747139074645}, {'date': '2015-12-11', 'open': 185.1893, 'high': 185.9792, 'low': 183.3492, 'close': 183.7193, 'volume': 211173300.0, 'rsi': 31.281885971543545}, {'date': '2015-12-10', 'open': 186.9003, 'high': 188.9103, 'low': 186.6203, 'close': 187.3503, 'volume': 116128900.0, 'rsi': 42.78572440173268}, {'date': '2015-12-09', 'open': 187.718, 'high': 190.208, 'low': 185.708, 'close': 186.868, 'volume': 162401500.0, 'rsi': 40.15477261990339}, {'date': '2015-12-08', 'open': 187.8732, 'high': 189.6732, 'low': 187.1632, 'close': 188.3332, 'volume': 103372400.0, 'rsi': 45.92936120180407}, {'date': '2015-12-07', 'open': 190.4872, 'high': 190.9872, 'low': 188.4572, 'close': 189.6072, 'volume': 102027100.0, 'rsi': 51.75360447319504}, {'date': '2015-12-04', 'open': 186.7529, 'high': 191.1129, 'low': 186.7529, 'close': 190.7629, 'volume': 192913900.0, 'rsi': 57.730453184198396}, {'date': '2015-12-03', 'open': 190.3337, 'high': 190.6537, 'low': 186.2537, 'close': 187.1137, 'volume': 166224200.0, 'rsi': 37.081001502731446}, {'date': '2015-12-02', 'open': 191.861, 'high': 192.241, 'low': 189.471, 'close': 189.771, 'volume': 108441300.0, 'rsi': 54.543676496000785}, {'date': '2015-12-01', 'open': 190.4876, 'high': 191.8676, 'low': 190.1576, 'close': 191.7276, 'volume': 97858400.0, 'rsi': 79.2875995698239}, {'date': '2015-11-30', 'open': 190.9766, 'high': 191.1166, 'low': 189.7866, 'close': 189.9166, 'volume': 112822700.0, 'rsi': 66.70538641847972}, {'date': '2015-11-27', 'open': 190.5784, 'high': 190.9484, 'low': 190.0084, 'close': 190.7084, 'volume': 37317800.0, 'rsi': 87.65923728503151}, {'date': '2015-11-25', 'open': 190.67, 'high': 190.91, 'low': 190.1799, 'close': 190.49, 'volume': 51980100.0, 'rsi': 86.61551682651526}, {'date': '2015-11-24', 'open': 189.0372, 'high': 190.9972, 'low': 188.5772, 'close': 190.5172, 'volume': 98874400.0, 'rsi': 87.44447640785015}, {'date': '2015-11-23', 'open': 190.5725, 'high': 191.1725, 'low': 189.7125, 'close': 190.2625, 'volume': 64931200.0, 'rsi': 86.34294395546617}, {'date': '2015-11-20', 'open': 190.6208, 'high': 191.2908, 'low': 190.0308, 'close': 190.4808, 'volume': 94011500.0, 'rsi': 92.61037262436611}, {'date': '2015-11-19', 'open': 189.8292, 'high': 190.2892, 'low': 189.4392, 'close': 189.7892, 'volume': 88220500.0, 'rsi': 90.68178692057282}, {'date': '2015-11-18', 'open': 187.263, 'high': 190.123, 'low': 187.213, 'close': 189.953, 'volume': 121342500.0, 'rsi': 96.02369277079187}, {'date': '2015-11-17', 'open': 187.5063, 'high': 188.5563, 'low': 186.3963, 'close': 186.9863, 'volume': 121123700.0, 'rsi': 0.0}, {'date': '2015-11-16', 'open': 183.8228, 'high': 187.1928, 'low': 183.6828, 'close': 187.1228, 'volume': 117645200.0, '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>2015-11-16</td>\n",
       "      <td>183.822800</td>\n",
       "      <td>187.192800</td>\n",
       "      <td>183.682800</td>\n",
       "      <td>187.122800</td>\n",
       "      <td>117645200.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1257</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>1256</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>96.023693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1255</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>90.681787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1254</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>92.610373</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>2020-11-09</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</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>2</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>1</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>0</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",
       "  </tbody>\n",
       "</table>\n",
       "<p>1259 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            date        open        high         low       close       volume  \\\n",
       "1258  2015-11-16  183.822800  187.192800  183.682800  187.122800  117645200.0   \n",
       "1257  2015-11-17  187.506300  188.556300  186.396300  186.986300  121123700.0   \n",
       "1256  2015-11-18  187.263000  190.123000  187.213000  189.953000  121342500.0   \n",
       "1255  2015-11-19  189.829200  190.289200  189.439200  189.789200   88220500.0   \n",
       "1254  2015-11-20  190.620800  191.290800  190.030800  190.480800   94011500.0   \n",
       "...          ...         ...         ...         ...         ...          ...   \n",
       "4     2020-11-09  363.970001  364.380005  354.059998  354.559998  172304200.0   \n",
       "3     2020-11-10  353.489990  355.179993  350.510010  354.040009   85552000.0   \n",
       "2     2020-11-11  356.399994  357.559998  355.059998  356.670013   58649000.0   \n",
       "1     2020-11-12  355.579987  356.720001  351.260010  353.209991   67546200.0   \n",
       "0     2020-11-13  355.269989  358.899994  354.709991  358.100006   62892200.0   \n",
       "\n",
       "            rsi  \n",
       "1258   0.000000  \n",
       "1257   0.000000  \n",
       "1256  96.023693  \n",
       "1255  90.681787  \n",
       "1254  92.610373  \n",
       "...         ...  \n",
       "4     67.741240  \n",
       "3     66.789119  \n",
       "2     69.220329  \n",
       "1     62.529124  \n",
       "0     67.467429  \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': '2020-11-13', '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, 'close': 19.07, 'volume': 37536700.0, 'rsi': 56.970256374373335}, {'date': '2020-10-26', 'open': 17.309999, 'high': 19.4, 'low': 17.049999, 'close': 18.99, 'volume': 62410700.0, 'rsi': 56.429668419547426}, {'date': '2020-10-23', 'open': 16.6, 'high': 17.27, 'low': 16.540001, 'close': 16.719999, 'volume': 20279500.0, 'rsi': 35.84759526049243}, {'date': '2020-10-22', 'open': 17.23, 'high': 17.469999, 'low': 16.440001, 'close': 16.68, 'volume': 24574800.0, 'rsi': 35.363375492075335}, {'date': '2020-10-21', 'open': 17.77, 'high': 18.120001, 'low': 17.110001, 'close': 17.27, 'volume': 31359100.0, 'rsi': 39.301446044088074}, {'date': '2020-10-20', 'open': 17.75, 'high': 18.08, 'low': 17.51, 'close': 17.9, 'volume': 28551600.0, 'rsi': 44.01151354739944}, {'date': '2020-10-19', 'open': 16.700001, 'high': 18.1, 'low': 16.639999, 'close': 17.93, 'volume': 35907000.0, 'rsi': 44.23873246733617}, {'date': '2020-10-16', 'open': 16.620001, 'high': 16.83, 'low': 16.25, 'close': 16.790001, 'volume': 21694100.0, 'rsi': 32.28214968059894}, {'date': '2020-10-15', 'open': 17.52, 'high': 17.75, 'low': 16.58, 'close': 16.67, 'volume': 33842000.0, 'rsi': 30.877999233415697}, {'date': '2020-10-14', 'open': 16.48, 'high': 16.84, 'low': 16.059999, 'close': 16.440001, 'volume': 32299300.0, 'rsi': 28.31393553530011}, {'date': '2020-10-13', 'open': 16.67, 'high': 17.200001, 'low': 16.49, 'close': 16.67, 'volume': 27907200.0, 'rsi': 29.29185221409708}, {'date': '2020-10-12', 'open': 16.42, 'high': 16.860001, 'low': 16.299999, 'close': 16.389999, 'volume': 22250900.0, 'rsi': 26.510852342811148}, {'date': '2020-10-09', 'open': 17.440001, 'high': 17.49, 'low': 16.860001, 'close': 16.889999, 'volume': 33258100.0, 'rsi': 28.299668845291283}, {'date': '2020-10-08', 'open': 19.129999, 'high': 19.290001, 'low': 18.24, 'close': 18.33, 'volume': 33402400.0, 'rsi': 34.29826378554552}, {'date': '2020-10-07', 'open': 19.85, 'high': 19.99, 'low': 19.26, 'close': 19.459999, 'volume': 24140300.0, 'rsi': 40.3367300910522}, {'date': '2020-10-06', 'open': 19.77, 'high': 20.73, 'low': 19.23, 'close': 20.41, 'volume': 40618200.0, 'rsi': 46.53586050378626}, {'date': '2020-10-05', 'open': 20.719999, 'high': 21.040001, 'low': 19.799999, 'close': 20.030001, 'volume': 20667700.0, 'rsi': 43.404654050136806}, {'date': '2020-10-02', 'open': 21.65, 'high': 21.76, 'low': 20.450001, 'close': 21.030001, 'volume': 43572200.0, 'rsi': 50.39496183986413}, {'date': '2020-10-01', 'open': 19.639999, 'high': 20.57, 'low': 19.620001, 'close': 20.110001, 'volume': 23409000.0, 'rsi': 42.76237120711476}, {'date': '2020-09-30', 'open': 19.629999, 'high': 20.280001, 'low': 19.25, 'close': 19.92, 'volume': 31236100.0, 'rsi': 41.07721559104851}, {'date': '2020-09-29', 'open': 20.700001, 'high': 20.709999, 'low': 19.57, 'close': 20.030001, 'volume': 29955500.0, 'rsi': 41.71718113115574}, {'date': '2020-09-28', 'open': 20.51, 'high': 20.889999, 'low': 20.42, 'close': 20.65, 'volume': 18547600.0, 'rsi': 45.29700755575197}, {'date': '2020-09-25', 'open': 21.58, 'high': 21.84, 'low': 20.58, 'close': 20.780001, 'volume': 27147200.0, 'rsi': 46.0426062291364}, {'date': '2020-09-24', 'open': 22.18, 'high': 22.59, 'low': 21.01, 'close': 21.6, 'volume': 38373000.0, 'rsi': 50.788405097720066}, {'date': '2020-09-23', 'open': 20.440001, 'high': 22.219999, 'low': 20.299999, 'close': 22.209999, 'volume': 37766500.0, 'rsi': 54.553075679951924}, {'date': '2020-09-22', 'open': 20.26, 'high': 21.379999, 'low': 20.26, 'close': 20.549999, 'volume': 26687500.0, 'rsi': 44.472310944786855}, {'date': '2020-09-21', 'open': 20.57, 'high': 22.09, 'low': 20.17, 'close': 20.299999, 'volume': 54634400.0, 'rsi': 42.751114466009035}, {'date': '2020-09-18', 'open': 19.18, 'high': 20.1, 'low': 18.549999, 'close': 19.41, 'volume': 45470800.0, 'rsi': 36.438536465851335}, {'date': '2020-09-17', 'open': 21.01, 'high': 21.1, 'low': 19.27, 'close': 19.450001, 'volume': 47315900.0, 'rsi': 36.60179124351816}, {'date': '2020-09-16', 'open': 19.98, 'high': 20.110001, 'low': 19.280001, 'close': 20.059999, 'volume': 42531200.0, 'rsi': 38.99989802845466}, {'date': '2020-09-15', 'open': 19.959999, 'high': 20.74, 'low': 19.790001, 'close': 20.190001, 'volume': 31805700.0, 'rsi': 39.4962458587972}, {'date': '2020-09-14', 'open': 20.389999, 'high': 20.700001, 'low': 19.799999, 'close': 20.23, 'volume': 32343200.0, 'rsi': 39.635932006553546}, {'date': '2020-09-11', 'open': 21.860001, 'high': 22.85, 'low': 20.76, 'close': 20.799999, 'volume': 59861300.0, 'rsi': 41.519208374608}, {'date': '2020-09-10', 'open': 22.120001, 'high': 23.6, 'low': 21.83, 'close': 22.780001, 'volume': 54363400.0, 'rsi': 48.76267666833728}, {'date': '2020-09-09', 'open': 23.34, 'high': 23.719999, 'low': 21.799999, 'close': 22.440001, 'volume': 45746300.0, 'rsi': 47.342932928611496}, {'date': '2020-09-08', 'open': 28.200001, 'high': 28.469999, 'low': 24.15, 'close': 24.32, 'volume': 56407200.0, 'rsi': 54.915430276745795}, {'date': '2020-09-04', 'open': 27.92, 'high': 32.18, 'low': 24.57, 'close': 25.299999, 'volume': 129374600.0, 'rsi': 59.370618619062}, {'date': '2020-09-03', 'open': 24.51, 'high': 30.280001, 'low': 23.23, 'close': 28.9, 'volume': 135998900.0, 'rsi': 81.13178757802834}, {'date': '2020-09-02', 'open': 23.15, 'high': 24.389999, 'low': 23.02, 'close': 24.08, 'volume': 42972600.0, 'rsi': 66.20600515136864}, {'date': '2020-09-01', 'open': 22.67, 'high': 23.440001, 'low': 22.4, 'close': 23.24, 'volume': 34285700.0, 'rsi': 61.41911712612264}, {'date': '2020-08-31', 'open': 21.93, 'high': 22.950001, 'low': 21.219999, 'close': 22.65, 'volume': 41189100.0, 'rsi': 57.62473182013785}, {'date': '2020-08-28', 'open': 21.639999, 'high': 22.98, 'low': 20.6, 'close': 21.110001, 'volume': 50204700.0, 'rsi': 44.89301938571566}, {'date': '2020-08-27', 'open': 20.0, 'high': 23.690001, 'low': 19.700001, 'close': 21.23, 'volume': 73365600.0, 'rsi': 45.85929593901726}, {'date': '2020-08-26', 'open': 19.469999, 'high': 20.48, 'low': 18.75, 'close': 20.309999, 'volume': 29100800.0, 'rsi': 36.41599864803702}, {'date': '2020-08-25', 'open': 19.950001, 'high': 20.790001, 'low': 19.459999, 'close': 19.66, 'volume': 27572400.0, 'rsi': 28.484261998639308}, {'date': '2020-08-24', 'open': 19.09, 'high': 20.129999, 'low': 19.040001, 'close': 19.799999, 'volume': 24983000.0, 'rsi': 29.190109547585024}, {'date': '2020-08-21', 'open': 20.32, 'high': 20.450001, 'low': 19.780001, 'close': 19.92, 'volume': 27482600.0, 'rsi': 29.758997420572033}, {'date': '2020-08-20', 'open': 21.290001, 'high': 21.42, 'low': 19.67, 'close': 19.82, 'volume': 32646500.0, 'rsi': 28.717078106608206}, {'date': '2020-08-19', 'open': 19.34, 'high': 20.32, 'low': 19.15, 'close': 20.139999, 'volume': 35061700.0, 'rsi': 29.998629205459306}, {'date': '2020-08-18', 'open': 19.639999, 'high': 20.33, 'low': 19.32, 'close': 19.58, 'volume': 22852700.0, 'rsi': 24.70644934712029}, {'date': '2020-08-17', 'open': 20.299999, 'high': 20.540001, 'low': 19.709999, 'close': 19.91, 'volume': 19971500.0, 'rsi': 25.73844917406423}, {'date': '2020-08-14', 'open': 21.309999, 'high': 21.58, 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23440.0, 'close': 25170.0, 'volume': 77200.0, 'rsi': 69.65372590596782}, {'date': '2016-01-20', 'open': 25695.0, 'high': 29500.0, 'low': 24275.0, 'close': 25320.0, 'volume': 92500.0, 'rsi': 70.34923278119686}, {'date': '2016-01-19', 'open': 22325.0, 'high': 26400.0, 'low': 22270.0, 'close': 24180.0, 'volume': 80700.0, 'rsi': 68.17566862009919}, {'date': '2016-01-15', 'open': 24600.0, 'high': 25695.0, 'low': 22900.0, 'close': 24075.0, 'volume': 72300.0, 'rsi': 67.98110130924415}, {'date': '2016-01-14', 'open': 21295.0, 'high': 23355.0, 'low': 19000.0, 'close': 20030.0, 'volume': 80500.0, 'rsi': 59.36822728016576}, {'date': '2016-01-13', 'open': 17360.0, 'high': 22450.0, 'low': 17270.0, 'close': 21730.0, 'volume': 92300.0, 'rsi': 66.09287010194952}, {'date': '2016-01-12', 'open': 18305.0, 'high': 20670.0, 'low': 17960.0, 'close': 18085.0, 'volume': 82800.0, 'rsi': 56.60843203140629}, {'date': '2016-01-11', 'open': 20860.0, 'high': 24720.0, 'low': 19560.0, 'close': 19950.0, 'volume': 107600.0, 'rsi': 64.97817004988458}, {'date': '2016-01-08', 'open': 17920.0, 'high': 21985.0, 'low': 17515.0, 'close': 21410.0, 'volume': 96600.0, 'rsi': 72.53413051851591}, {'date': '2016-01-07', 'open': 18115.0, 'high': 19930.0, 'low': 17270.0, 'close': 19420.0, 'volume': 94200.0, 'rsi': 67.96431159578508}, {'date': '2016-01-06', 'open': 17045.0, 'high': 17055.0, 'low': 15865.0, 'close': 15965.0, 'volume': 72400.0, 'rsi': 56.70958765434175}, {'date': '2016-01-05', 'open': 15325.0, 'high': 16295.0, 'low': 14765.0, 'close': 15065.0, 'volume': 63600.0, 'rsi': 52.82397896441313}, {'date': '2016-01-04', 'open': 16465.0, 'high': 17415.0, 'low': 15875.0, 'close': 15980.0, 'volume': 86300.0, 'rsi': 57.55045185290795}, {'date': '2015-12-31', 'open': 13935.0, 'high': 14250.0, 'low': 13445.0, 'close': 14175.0, 'volume': 54800.0, 'rsi': 49.533530896005544}, {'date': '2015-12-30', 'open': 13155.0, 'high': 13695.0, 'low': 13075.0, 'close': 13590.0, 'volume': 37300.0, 'rsi': 46.59136485175899}, {'date': '2015-12-29', 'open': 12900.0, 'high': 13125.0, 'low': 12685.0, 'close': 12855.0, 'volume': 37400.0, 'rsi': 42.822009060956205}, {'date': '2015-12-28', 'open': 14100.0, 'high': 14630.0, 'low': 13265.0, 'close': 13285.0, 'volume': 45800.0, 'rsi': 44.474702608443835}, {'date': '2015-12-24', 'open': 13350.0, 'high': 13700.0, 'low': 13220.0, 'close': 13610.0, 'volume': 17700.0, 'rsi': 45.673786942490736}, {'date': '2015-12-23', 'open': 13135.0, 'high': 13745.0, 'low': 12940.0, 'close': 13150.0, 'volume': 41600.0, 'rsi': 43.74163736403264}, {'date': '2015-12-22', 'open': 14305.0, 'high': 14725.0, 'low': 13510.0, 'close': 13825.0, 'volume': 46200.0, 'rsi': 45.89743597253613}, {'date': '2015-12-21', 'open': 15650.0, 'high': 16970.0, 'low': 15320.0, 'close': 15380.0, 'volume': 64400.0, 'rsi': 51.12119692959693}, {'date': '2015-12-18', 'open': 15580.0, 'high': 17260.0, 'low': 15240.0, 'close': 16975.0, 'volume': 93600.0, 'rsi': 57.12295275185591}, {'date': '2015-12-17', 'open': 13295.0, 'high': 14950.0, 'low': 13285.0, 'close': 14605.0, 'volume': 58100.0, 'rsi': 49.13737708366982}, {'date': '2015-12-16', 'open': 14460.0, 'high': 15195.0, 'low': 12950.0, 'close': 13465.0, 'volume': 57700.0, 'rsi': 44.67683778262528}, {'date': '2015-12-15', 'open': 15825.0, 'high': 16930.0, 'low': 15105.0, 'close': 15580.0, 'volume': 59700.0, 'rsi': 52.34128893794303}, {'date': '2015-12-14', 'open': 19545.0, 'high': 21545.0, 'low': 16625.0, 'close': 16945.0, 'volume': 96600.0, 'rsi': 58.13417426992335}, {'date': '2015-12-11', 'open': 16900.0, 'high': 20555.0, 'low': 16560.0, 'close': 19905.0, 'volume': 105800.0, 'rsi': 74.15065424262804}, {'date': '2015-12-10', 'open': 15060.0, 'high': 15635.0, 'low': 14205.0, 'close': 15285.0, 'volume': 63000.0, 'rsi': 57.830344688453344}, {'date': '2015-12-09', 'open': 14100.0, 'high': 15740.0, 'low': 13100.0, 'close': 14905.0, 'volume': 88000.0, 'rsi': 55.76282151477585}, {'date': '2015-12-08', 'open': 14055.0, 'high': 14405.0, 'low': 13175.0, 'close': 13775.0, 'volume': 68100.0, 'rsi': 49.08148368445612}, {'date': '2015-12-07', 'open': 12460.0, 'high': 13850.0, 'low': 12445.0, 'close': 12990.0, 'volume': 53200.0, 'rsi': 43.7718639049701}, {'date': '2015-12-04', 'open': 14245.0, 'high': 14500.0, 'low': 12375.0, 'close': 12385.0, 'volume': 67500.0, 'rsi': 39.38781000232763}, {'date': '2015-12-03', 'open': 12775.0, 'high': 15750.0, 'low': 12625.0, 'close': 15100.0, 'volume': 89600.0, 'rsi': 57.492455434144965}, {'date': '2015-12-02', 'open': 12335.0, 'high': 13400.0, 'low': 11915.0, 'close': 13230.0, 'volume': 57500.0, 'rsi': 40.554474364261424}, {'date': '2015-12-01', 'open': 13080.0, 'high': 13335.0, 'low': 12250.0, 'close': 12320.0, 'volume': 43000.0, 'rsi': 27.98697142297783}, {'date': '2015-11-30', 'open': 13395.0, 'high': 13750.0, 'low': 13235.0, 'close': 13435.0, 'volume': 37600.0, 'rsi': 36.49526150617564}, {'date': '2015-11-27', 'open': 13275.0, 'high': 13675.0, 'low': 13225.0, 'close': 13590.0, 'volume': 17000.0, 'rsi': 37.93825051888655}, {'date': '2015-11-25', 'open': 13700.0, 'high': 13945.0, 'low': 13225.0, 'close': 13375.0, 'volume': 30600.0, 'rsi': 34.71581838370824}, {'date': '2015-11-24', 'open': 14405.0, 'high': 14720.0, 'low': 13500.0, 'close': 13790.0, 'volume': 50100.0, 'rsi': 38.15768408252454}, {'date': '2015-11-23', 'open': 14250.0, 'high': 14540.0, 'low': 13505.0, 'close': 13670.0, 'volume': 41900.0, 'rsi': 36.519810470371795}, {'date': '2015-11-20', 'open': 14510.0, 'high': 14715.0, 'low': 14075.0, 'close': 14445.0, 'volume': 43700.0, 'rsi': 43.16467411285926}, {'date': '2015-11-19', 'open': 14740.0, 'high': 15830.0, 'low': 14600.0, 'close': 15450.0, 'volume': 40700.0, 'rsi': 54.80225988700565}, {'date': '2015-11-18', 'open': 15945.0, 'high': 16030.0, 'low': 14400.0, 'close': 14460.0, 'volume': 51100.0, 'rsi': 40.60536836093661}, {'date': '2015-11-17', 'open': 14560.0, 'high': 17125.0, 'low': 14430.0, 'close': 16540.0, 'volume': 71500.0, 'rsi': 100.0}, {'date': '2015-11-16', 'open': 18625.0, 'high': 18900.0, 'low': 14840.0, 'close': 14960.0, 'volume': 65800.0, '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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       "    }\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>2015-11-16</td>\n",
       "      <td>18625.00</td>\n",
       "      <td>18900.00</td>\n",
       "      <td>14840.00</td>\n",
       "      <td>14960.00</td>\n",
       "      <td>65800.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1257</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>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1256</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>40.605368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1255</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>54.802260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1254</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",
       "      <td>43700.0</td>\n",
       "      <td>43.164674</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>2020-11-09</td>\n",
       "      <td>13.14</td>\n",
       "      <td>14.18</td>\n",
       "      <td>12.18</td>\n",
       "      <td>14.18</td>\n",
       "      <td>54605600.0</td>\n",
       "      <td>31.735740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</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>2</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>1</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>0</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",
       "  </tbody>\n",
       "</table>\n",
       "<p>1259 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            date      open      high       low     close      volume  \\\n",
       "1258  2015-11-16  18625.00  18900.00  14840.00  14960.00     65800.0   \n",
       "1257  2015-11-17  14560.00  17125.00  14430.00  16540.00     71500.0   \n",
       "1256  2015-11-18  15945.00  16030.00  14400.00  14460.00     51100.0   \n",
       "1255  2015-11-19  14740.00  15830.00  14600.00  15450.00     40700.0   \n",
       "1254  2015-11-20  14510.00  14715.00  14075.00  14445.00     43700.0   \n",
       "...          ...       ...       ...       ...       ...         ...   \n",
       "4     2020-11-09     13.14     14.18     12.18     14.18  54605600.0   \n",
       "3     2020-11-10     13.63     14.40     13.45     13.49  36231600.0   \n",
       "2     2020-11-11     13.18     13.44     12.64     13.06  34220400.0   \n",
       "1     2020-11-12     13.30     14.82     13.09     14.37  68251200.0   \n",
       "0     2020-11-13     13.66     13.66     12.73     13.05  43994400.0   \n",
       "\n",
       "             rsi  \n",
       "1258    0.000000  \n",
       "1257  100.000000  \n",
       "1256   40.605368  \n",
       "1255   54.802260  \n",
       "1254   43.164674  \n",
       "...          ...  \n",
       "4      31.735740  \n",
       "3      29.754200  \n",
       "2      28.521103  \n",
       "1      37.314850  \n",
       "0      32.797379  \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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       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>2015-11-16</td>\n",
       "      <td>165.825600</td>\n",
       "      <td>172.655600</td>\n",
       "      <td>165.325600</td>\n",
       "      <td>172.535600</td>\n",
       "      <td>1780200.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>183.822800</td>\n",
       "      <td>187.192800</td>\n",
       "      <td>183.682800</td>\n",
       "      <td>187.122800</td>\n",
       "      <td>117645200.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-11-17</td>\n",
       "      <td>173.961100</td>\n",
       "      <td>173.961100</td>\n",
       "      <td>171.071100</td>\n",
       "      <td>172.101100</td>\n",
       "      <td>1325500.0</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>2</th>\n",
       "      <td>2015-11-18</td>\n",
       "      <td>172.260300</td>\n",
       "      <td>174.290300</td>\n",
       "      <td>172.090300</td>\n",
       "      <td>173.950300</td>\n",
       "      <td>845100.0</td>\n",
       "      <td>82.544359</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>96.023693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-11-19</td>\n",
       "      <td>174.721200</td>\n",
       "      <td>175.241200</td>\n",
       "      <td>172.981200</td>\n",
       "      <td>174.061200</td>\n",
       "      <td>1030200.0</td>\n",
       "      <td>83.454428</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>90.681787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-11-20</td>\n",
       "      <td>175.069200</td>\n",
       "      <td>175.859200</td>\n",
       "      <td>174.549200</td>\n",
       "      <td>175.189200</td>\n",
       "      <td>1243100.0</td>\n",
       "      <td>89.588838</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>92.610373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1254</th>\n",
       "      <td>2020-11-09</td>\n",
       "      <td>315.000000</td>\n",
       "      <td>317.600006</td>\n",
       "      <td>300.070007</td>\n",
       "      <td>301.279999</td>\n",
       "      <td>1732000.0</td>\n",
       "      <td>44.775865</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1255</th>\n",
       "      <td>2020-11-10</td>\n",
       "      <td>302.730011</td>\n",
       "      <td>316.899994</td>\n",
       "      <td>301.480011</td>\n",
       "      <td>314.510010</td>\n",
       "      <td>948500.0</td>\n",
       "      <td>57.396475</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>1256</th>\n",
       "      <td>2020-11-11</td>\n",
       "      <td>314.959991</td>\n",
       "      <td>314.970001</td>\n",
       "      <td>306.019989</td>\n",
       "      <td>309.959991</td>\n",
       "      <td>590700.0</td>\n",
       "      <td>52.786634</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>1257</th>\n",
       "      <td>2020-11-12</td>\n",
       "      <td>308.000000</td>\n",
       "      <td>308.779999</td>\n",
       "      <td>302.339996</td>\n",
       "      <td>306.230011</td>\n",
       "      <td>737400.0</td>\n",
       "      <td>49.188210</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>1258</th>\n",
       "      <td>2020-11-13</td>\n",
       "      <td>308.079987</td>\n",
       "      <td>313.119995</td>\n",
       "      <td>307.109985</td>\n",
       "      <td>311.709991</td>\n",
       "      <td>493400.0</td>\n",
       "      <td>54.276352</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",
       "  </tbody>\n",
       "</table>\n",
       "<p>1259 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            date        open        high         low       close     volume  \\\n",
       "0     2015-11-16  165.825600  172.655600  165.325600  172.535600  1780200.0   \n",
       "1     2015-11-17  173.961100  173.961100  171.071100  172.101100  1325500.0   \n",
       "2     2015-11-18  172.260300  174.290300  172.090300  173.950300   845100.0   \n",
       "3     2015-11-19  174.721200  175.241200  172.981200  174.061200  1030200.0   \n",
       "4     2015-11-20  175.069200  175.859200  174.549200  175.189200  1243100.0   \n",
       "...          ...         ...         ...         ...         ...        ...   \n",
       "1254  2020-11-09  315.000000  317.600006  300.070007  301.279999  1732000.0   \n",
       "1255  2020-11-10  302.730011  316.899994  301.480011  314.510010   948500.0   \n",
       "1256  2020-11-11  314.959991  314.970001  306.019989  309.959991   590700.0   \n",
       "1257  2020-11-12  308.000000  308.779999  302.339996  306.230011   737400.0   \n",
       "1258  2020-11-13  308.079987  313.119995  307.109985  311.709991   493400.0   \n",
       "\n",
       "            rsi    open_SPY    high_SPY     low_SPY   close_SPY   volume_SPY  \\\n",
       "0      0.000000  183.822800  187.192800  183.682800  187.122800  117645200.0   \n",
       "1      0.000000  187.506300  188.556300  186.396300  186.986300  121123700.0   \n",
       "2     82.544359  187.263000  190.123000  187.213000  189.953000  121342500.0   \n",
       "3     83.454428  189.829200  190.289200  189.439200  189.789200   88220500.0   \n",
       "4     89.588838  190.620800  191.290800  190.030800  190.480800   94011500.0   \n",
       "...         ...         ...         ...         ...         ...          ...   \n",
       "1254  44.775865  363.970001  364.380005  354.059998  354.559998  172304200.0   \n",
       "1255  57.396475  353.489990  355.179993  350.510010  354.040009   85552000.0   \n",
       "1256  52.786634  356.399994  357.559998  355.059998  356.670013   58649000.0   \n",
       "1257  49.188210  355.579987  356.720001  351.260010  353.209991   67546200.0   \n",
       "1258  54.276352  355.269989  358.899994  354.709991  358.100006   62892200.0   \n",
       "\n",
       "        rsi_SPY  \n",
       "0      0.000000  \n",
       "1      0.000000  \n",
       "2     96.023693  \n",
       "3     90.681787  \n",
       "4     92.610373  \n",
       "...         ...  \n",
       "1254  67.741240  \n",
       "1255  66.789119  \n",
       "1256  69.220329  \n",
       "1257  62.529124  \n",
       "1258  67.467429  \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": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>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",
       "      <th>open_VIX</th>\n",
       "      <th>high_VIX</th>\n",
       "      <th>low_VIX</th>\n",
       "      <th>close_VIX</th>\n",
       "      <th>volume_VIX</th>\n",
       "      <th>rsi_VIX</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1199</th>\n",
       "      <td>2020-08-21</td>\n",
       "      <td>337.299988</td>\n",
       "      <td>339.130005</td>\n",
       "      <td>336.440002</td>\n",
       "      <td>337.859985</td>\n",
       "      <td>440800.0</td>\n",
       "      <td>63.589715</td>\n",
       "      <td>337.920013</td>\n",
       "      <td>339.720001</td>\n",
       "      <td>337.549988</td>\n",
       "      <td>339.480011</td>\n",
       "      <td>55106600.0</td>\n",
       "      <td>71.305039</td>\n",
       "      <td>20.320000</td>\n",
       "      <td>20.450001</td>\n",
       "      <td>19.780001</td>\n",
       "      <td>19.920000</td>\n",
       "      <td>27482600.0</td>\n",
       "      <td>29.758997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1200</th>\n",
       "      <td>2020-08-24</td>\n",
       "      <td>338.700012</td>\n",
       "      <td>344.510010</td>\n",
       "      <td>338.410004</td>\n",
       "      <td>344.420013</td>\n",
       "      <td>416700.0</td>\n",
       "      <td>71.057744</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1201</th>\n",
       "      <td>2020-08-25</td>\n",
       "      <td>345.000000</td>\n",
       "      <td>345.070007</td>\n",
       "      <td>341.450012</td>\n",
       "      <td>343.000000</td>\n",
       "      <td>454600.0</td>\n",
       "      <td>67.717143</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1202</th>\n",
       "      <td>2020-08-26</td>\n",
       "      <td>341.500000</td>\n",
       "      <td>342.880005</td>\n",
       "      <td>338.019989</td>\n",
       "      <td>341.410004</td>\n",
       "      <td>607200.0</td>\n",
       "      <td>63.975290</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>1203</th>\n",
       "      <td>2020-08-27</td>\n",
       "      <td>343.459991</td>\n",
       "      <td>349.230011</td>\n",
       "      <td>342.059998</td>\n",
       "      <td>348.989990</td>\n",
       "      <td>669200.0</td>\n",
       "      <td>72.132127</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>1204</th>\n",
       "      <td>2020-08-28</td>\n",
       "      <td>347.779999</td>\n",
       "      <td>348.549988</td>\n",
       "      <td>342.269989</td>\n",
       "      <td>344.750000</td>\n",
       "      <td>513500.0</td>\n",
       "      <td>63.233510</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>1205</th>\n",
       "      <td>2020-08-31</td>\n",
       "      <td>343.200012</td>\n",
       "      <td>344.459991</td>\n",
       "      <td>341.570007</td>\n",
       "      <td>342.609985</td>\n",
       "      <td>558000.0</td>\n",
       "      <td>59.141862</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>1206</th>\n",
       "      <td>2020-09-01</td>\n",
       "      <td>340.160004</td>\n",
       "      <td>342.869995</td>\n",
       "      <td>337.109985</td>\n",
       "      <td>340.380005</td>\n",
       "      <td>538800.0</td>\n",
       "      <td>55.019826</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>1207</th>\n",
       "      <td>2020-09-02</td>\n",
       "      <td>338.820007</td>\n",
       "      <td>350.250000</td>\n",
       "      <td>338.019989</td>\n",
       "      <td>348.359985</td>\n",
       "      <td>660600.0</td>\n",
       "      <td>64.780122</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>1208</th>\n",
       "      <td>2020-09-03</td>\n",
       "      <td>350.649994</td>\n",
       "      <td>352.119995</td>\n",
       "      <td>339.920013</td>\n",
       "      <td>342.230011</td>\n",
       "      <td>791700.0</td>\n",
       "      <td>54.657246</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>1209</th>\n",
       "      <td>2020-09-04</td>\n",
       "      <td>343.989990</td>\n",
       "      <td>347.059998</td>\n",
       "      <td>337.339996</td>\n",
       "      <td>339.029999</td>\n",
       "      <td>584400.0</td>\n",
       "      <td>50.114907</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>1210</th>\n",
       "      <td>2020-09-08</td>\n",
       "      <td>339.890015</td>\n",
       "      <td>339.890015</td>\n",
       "      <td>334.209991</td>\n",
       "      <td>334.559998</td>\n",
       "      <td>661800.0</td>\n",
       "      <td>44.389276</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>1211</th>\n",
       "      <td>2020-09-09</td>\n",
       "      <td>342.679993</td>\n",
       "      <td>342.760010</td>\n",
       "      <td>336.660004</td>\n",
       "      <td>339.410004</td>\n",
       "      <td>608800.0</td>\n",
       "      <td>51.121607</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>1212</th>\n",
       "      <td>2020-09-10</td>\n",
       "      <td>339.769989</td>\n",
       "      <td>341.040009</td>\n",
       "      <td>329.410004</td>\n",
       "      <td>330.019989</td>\n",
       "      <td>558100.0</td>\n",
       "      <td>40.558912</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>1213</th>\n",
       "      <td>2020-09-11</td>\n",
       "      <td>333.059998</td>\n",
       "      <td>340.170013</td>\n",
       "      <td>331.700012</td>\n",
       "      <td>338.880005</td>\n",
       "      <td>489300.0</td>\n",
       "      <td>51.142385</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>1214</th>\n",
       "      <td>2020-09-14</td>\n",
       "      <td>340.750000</td>\n",
       "      <td>341.660004</td>\n",
       "      <td>337.660004</td>\n",
       "      <td>338.450012</td>\n",
       "      <td>552900.0</td>\n",
       "      <td>50.656026</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>1215</th>\n",
       "      <td>2020-09-15</td>\n",
       "      <td>337.549988</td>\n",
       "      <td>339.950012</td>\n",
       "      <td>334.730011</td>\n",
       "      <td>338.019989</td>\n",
       "      <td>523500.0</td>\n",
       "      <td>50.126327</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>1216</th>\n",
       "      <td>2020-09-16</td>\n",
       "      <td>337.250000</td>\n",
       "      <td>346.890015</td>\n",
       "      <td>335.739990</td>\n",
       "      <td>343.470001</td>\n",
       "      <td>750000.0</td>\n",
       "      <td>56.527716</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>1217</th>\n",
       "      <td>2020-09-17</td>\n",
       "      <td>341.130005</td>\n",
       "      <td>345.470001</td>\n",
       "      <td>338.079987</td>\n",
       "      <td>343.839996</td>\n",
       "      <td>361300.0</td>\n",
       "      <td>56.944573</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>1218</th>\n",
       "      <td>2020-09-18</td>\n",
       "      <td>341.160004</td>\n",
       "      <td>345.890015</td>\n",
       "      <td>341.160004</td>\n",
       "      <td>341.500000</td>\n",
       "      <td>689900.0</td>\n",
       "      <td>53.349704</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>1219</th>\n",
       "      <td>2020-09-21</td>\n",
       "      <td>337.929993</td>\n",
       "      <td>338.559998</td>\n",
       "      <td>324.410004</td>\n",
       "      <td>325.890015</td>\n",
       "      <td>714900.0</td>\n",
       "      <td>36.343637</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>1220</th>\n",
       "      <td>2020-09-22</td>\n",
       "      <td>327.320007</td>\n",
       "      <td>333.459991</td>\n",
       "      <td>326.910004</td>\n",
       "      <td>332.799988</td>\n",
       "      <td>651900.0</td>\n",
       "      <td>44.971288</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>1221</th>\n",
       "      <td>2020-09-23</td>\n",
       "      <td>334.450012</td>\n",
       "      <td>337.329987</td>\n",
       "      <td>326.519989</td>\n",
       "      <td>328.059998</td>\n",
       "      <td>790800.0</td>\n",
       "      <td>40.760628</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>1222</th>\n",
       "      <td>2020-09-24</td>\n",
       "      <td>327.480011</td>\n",
       "      <td>328.700012</td>\n",
       "      <td>321.170013</td>\n",
       "      <td>324.720001</td>\n",
       "      <td>574900.0</td>\n",
       "      <td>37.976699</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>1223</th>\n",
       "      <td>2020-09-25</td>\n",
       "      <td>324.130005</td>\n",
       "      <td>329.579987</td>\n",
       "      <td>322.940002</td>\n",
       "      <td>328.000000</td>\n",
       "      <td>646300.0</td>\n",
       "      <td>42.278405</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>1224</th>\n",
       "      <td>2020-09-28</td>\n",
       "      <td>330.540009</td>\n",
       "      <td>333.559998</td>\n",
       "      <td>328.720001</td>\n",
       "      <td>329.519989</td>\n",
       "      <td>566900.0</td>\n",
       "      <td>44.268664</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>1225</th>\n",
       "      <td>2020-09-29</td>\n",
       "      <td>328.779999</td>\n",
       "      <td>330.420013</td>\n",
       "      <td>324.429993</td>\n",
       "      <td>325.279999</td>\n",
       "      <td>483200.0</td>\n",
       "      <td>39.994480</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>1226</th>\n",
       "      <td>2020-09-30</td>\n",
       "      <td>326.200012</td>\n",
       "      <td>326.730011</td>\n",
       "      <td>312.250000</td>\n",
       "      <td>315.489990</td>\n",
       "      <td>1179400.0</td>\n",
       "      <td>32.054469</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>1227</th>\n",
       "      <td>2020-10-01</td>\n",
       "      <td>317.350006</td>\n",
       "      <td>321.049988</td>\n",
       "      <td>309.359985</td>\n",
       "      <td>311.809998</td>\n",
       "      <td>1687900.0</td>\n",
       "      <td>29.600123</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>1228</th>\n",
       "      <td>2020-10-02</td>\n",
       "      <td>309.109985</td>\n",
       "      <td>315.140015</td>\n",
       "      <td>307.119995</td>\n",
       "      <td>313.950012</td>\n",
       "      <td>1380900.0</td>\n",
       "      <td>32.918875</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>1229</th>\n",
       "      <td>2020-10-05</td>\n",
       "      <td>315.850006</td>\n",
       "      <td>317.230011</td>\n",
       "      <td>311.299988</td>\n",
       "      <td>312.179993</td>\n",
       "      <td>881700.0</td>\n",
       "      <td>31.551940</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>1230</th>\n",
       "      <td>2020-10-06</td>\n",
       "      <td>320.329987</td>\n",
       "      <td>320.329987</td>\n",
       "      <td>308.890015</td>\n",
       "      <td>308.970001</td>\n",
       "      <td>1149200.0</td>\n",
       "      <td>29.115734</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>1231</th>\n",
       "      <td>2020-10-07</td>\n",
       "      <td>310.399994</td>\n",
       "      <td>315.570007</td>\n",
       "      <td>310.399994</td>\n",
       "      <td>313.890015</td>\n",
       "      <td>1019500.0</td>\n",
       "      <td>37.353420</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>1232</th>\n",
       "      <td>2020-10-08</td>\n",
       "      <td>314.220001</td>\n",
       "      <td>319.820007</td>\n",
       "      <td>314.220001</td>\n",
       "      <td>319.679993</td>\n",
       "      <td>596800.0</td>\n",
       "      <td>45.617304</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>1233</th>\n",
       "      <td>2020-10-09</td>\n",
       "      <td>320.000000</td>\n",
       "      <td>322.470001</td>\n",
       "      <td>313.630005</td>\n",
       "      <td>315.279999</td>\n",
       "      <td>1210200.0</td>\n",
       "      <td>41.045524</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>1234</th>\n",
       "      <td>2020-10-12</td>\n",
       "      <td>314.519989</td>\n",
       "      <td>318.809998</td>\n",
       "      <td>313.220001</td>\n",
       "      <td>316.589996</td>\n",
       "      <td>834600.0</td>\n",
       "      <td>42.937359</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>1235</th>\n",
       "      <td>2020-10-13</td>\n",
       "      <td>315.100006</td>\n",
       "      <td>318.429993</td>\n",
       "      <td>311.859985</td>\n",
       "      <td>312.600006</td>\n",
       "      <td>636400.0</td>\n",
       "      <td>38.731194</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>1236</th>\n",
       "      <td>2020-10-14</td>\n",
       "      <td>311.429993</td>\n",
       "      <td>317.600006</td>\n",
       "      <td>311.399994</td>\n",
       "      <td>314.890015</td>\n",
       "      <td>1119900.0</td>\n",
       "      <td>42.333631</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>1237</th>\n",
       "      <td>2020-10-15</td>\n",
       "      <td>313.529999</td>\n",
       "      <td>316.260010</td>\n",
       "      <td>312.239990</td>\n",
       "      <td>314.820007</td>\n",
       "      <td>854000.0</td>\n",
       "      <td>42.249251</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>1238</th>\n",
       "      <td>2020-10-16</td>\n",
       "      <td>315.970001</td>\n",
       "      <td>319.730011</td>\n",
       "      <td>315.559998</td>\n",
       "      <td>316.239990</td>\n",
       "      <td>1393700.0</td>\n",
       "      <td>44.731958</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>1239</th>\n",
       "      <td>2020-10-19</td>\n",
       "      <td>316.040009</td>\n",
       "      <td>318.890015</td>\n",
       "      <td>310.820007</td>\n",
       "      <td>311.329987</td>\n",
       "      <td>775300.0</td>\n",
       "      <td>38.391018</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>1240</th>\n",
       "      <td>2020-10-20</td>\n",
       "      <td>313.739990</td>\n",
       "      <td>313.769989</td>\n",
       "      <td>308.279999</td>\n",
       "      <td>309.049988</td>\n",
       "      <td>786300.0</td>\n",
       "      <td>35.774522</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>1241</th>\n",
       "      <td>2020-10-21</td>\n",
       "      <td>308.040009</td>\n",
       "      <td>309.839996</td>\n",
       "      <td>305.910004</td>\n",
       "      <td>308.369995</td>\n",
       "      <td>830000.0</td>\n",
       "      <td>34.984404</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>1242</th>\n",
       "      <td>2020-10-22</td>\n",
       "      <td>309.260010</td>\n",
       "      <td>314.690002</td>\n",
       "      <td>302.989990</td>\n",
       "      <td>302.989990</td>\n",
       "      <td>1388000.0</td>\n",
       "      <td>29.296308</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>1243</th>\n",
       "      <td>2020-10-23</td>\n",
       "      <td>305.160004</td>\n",
       "      <td>309.559998</td>\n",
       "      <td>304.000000</td>\n",
       "      <td>308.429993</td>\n",
       "      <td>1113300.0</td>\n",
       "      <td>40.216871</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>1244</th>\n",
       "      <td>2020-10-26</td>\n",
       "      <td>305.399994</td>\n",
       "      <td>312.350006</td>\n",
       "      <td>301.700012</td>\n",
       "      <td>309.839996</td>\n",
       "      <td>1312700.0</td>\n",
       "      <td>42.762875</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>1245</th>\n",
       "      <td>2020-10-27</td>\n",
       "      <td>308.670013</td>\n",
       "      <td>309.010010</td>\n",
       "      <td>302.070007</td>\n",
       "      <td>302.119995</td>\n",
       "      <td>1081500.0</td>\n",
       "      <td>33.963566</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>1246</th>\n",
       "      <td>2020-10-28</td>\n",
       "      <td>299.579987</td>\n",
       "      <td>304.250000</td>\n",
       "      <td>293.540009</td>\n",
       "      <td>293.660004</td>\n",
       "      <td>1221300.0</td>\n",
       "      <td>27.158936</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>1247</th>\n",
       "      <td>2020-10-29</td>\n",
       "      <td>292.540009</td>\n",
       "      <td>297.690002</td>\n",
       "      <td>291.250000</td>\n",
       "      <td>292.390015</td>\n",
       "      <td>1260400.0</td>\n",
       "      <td>26.280692</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>1248</th>\n",
       "      <td>2020-10-30</td>\n",
       "      <td>290.869995</td>\n",
       "      <td>293.920013</td>\n",
       "      <td>285.130005</td>\n",
       "      <td>289.820007</td>\n",
       "      <td>1227100.0</td>\n",
       "      <td>24.499342</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>1249</th>\n",
       "      <td>2020-11-02</td>\n",
       "      <td>294.480011</td>\n",
       "      <td>298.980011</td>\n",
       "      <td>292.750000</td>\n",
       "      <td>296.630005</td>\n",
       "      <td>852100.0</td>\n",
       "      <td>37.059922</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>1250</th>\n",
       "      <td>2020-11-03</td>\n",
       "      <td>299.309998</td>\n",
       "      <td>307.119995</td>\n",
       "      <td>299.309998</td>\n",
       "      <td>304.820007</td>\n",
       "      <td>864900.0</td>\n",
       "      <td>48.507151</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>1251</th>\n",
       "      <td>2020-11-04</td>\n",
       "      <td>310.000000</td>\n",
       "      <td>324.260010</td>\n",
       "      <td>309.000000</td>\n",
       "      <td>315.640015</td>\n",
       "      <td>1684000.0</td>\n",
       "      <td>59.357715</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>1252</th>\n",
       "      <td>2020-11-05</td>\n",
       "      <td>316.500000</td>\n",
       "      <td>317.279999</td>\n",
       "      <td>306.059998</td>\n",
       "      <td>307.399994</td>\n",
       "      <td>1410300.0</td>\n",
       "      <td>50.375508</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>1253</th>\n",
       "      <td>2020-11-06</td>\n",
       "      <td>305.739990</td>\n",
       "      <td>305.940002</td>\n",
       "      <td>296.630005</td>\n",
       "      <td>301.359985</td>\n",
       "      <td>946700.0</td>\n",
       "      <td>44.848155</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>1254</th>\n",
       "      <td>2020-11-09</td>\n",
       "      <td>315.000000</td>\n",
       "      <td>317.600006</td>\n",
       "      <td>300.070007</td>\n",
       "      <td>301.279999</td>\n",
       "      <td>1732000.0</td>\n",
       "      <td>44.775865</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>1255</th>\n",
       "      <td>2020-11-10</td>\n",
       "      <td>302.730011</td>\n",
       "      <td>316.899994</td>\n",
       "      <td>301.480011</td>\n",
       "      <td>314.510010</td>\n",
       "      <td>948500.0</td>\n",
       "      <td>57.396475</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>1256</th>\n",
       "      <td>2020-11-11</td>\n",
       "      <td>314.959991</td>\n",
       "      <td>314.970001</td>\n",
       "      <td>306.019989</td>\n",
       "      <td>309.959991</td>\n",
       "      <td>590700.0</td>\n",
       "      <td>52.786634</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>1257</th>\n",
       "      <td>2020-11-12</td>\n",
       "      <td>308.000000</td>\n",
       "      <td>308.779999</td>\n",
       "      <td>302.339996</td>\n",
       "      <td>306.230011</td>\n",
       "      <td>737400.0</td>\n",
       "      <td>49.188210</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>1258</th>\n",
       "      <td>2020-11-13</td>\n",
       "      <td>308.079987</td>\n",
       "      <td>313.119995</td>\n",
       "      <td>307.109985</td>\n",
       "      <td>311.709991</td>\n",
       "      <td>493400.0</td>\n",
       "      <td>54.276352</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            date        open        high         low       close     volume  \\\n",
       "1199  2020-08-21  337.299988  339.130005  336.440002  337.859985   440800.0   \n",
       "1200  2020-08-24  338.700012  344.510010  338.410004  344.420013   416700.0   \n",
       "1201  2020-08-25  345.000000  345.070007  341.450012  343.000000   454600.0   \n",
       "1202  2020-08-26  341.500000  342.880005  338.019989  341.410004   607200.0   \n",
       "1203  2020-08-27  343.459991  349.230011  342.059998  348.989990   669200.0   \n",
       "1204  2020-08-28  347.779999  348.549988  342.269989  344.750000   513500.0   \n",
       "1205  2020-08-31  343.200012  344.459991  341.570007  342.609985   558000.0   \n",
       "1206  2020-09-01  340.160004  342.869995  337.109985  340.380005   538800.0   \n",
       "1207  2020-09-02  338.820007  350.250000  338.019989  348.359985   660600.0   \n",
       "1208  2020-09-03  350.649994  352.119995  339.920013  342.230011   791700.0   \n",
       "1209  2020-09-04  343.989990  347.059998  337.339996  339.029999   584400.0   \n",
       "1210  2020-09-08  339.890015  339.890015  334.209991  334.559998   661800.0   \n",
       "1211  2020-09-09  342.679993  342.760010  336.660004  339.410004   608800.0   \n",
       "1212  2020-09-10  339.769989  341.040009  329.410004  330.019989   558100.0   \n",
       "1213  2020-09-11  333.059998  340.170013  331.700012  338.880005   489300.0   \n",
       "1214  2020-09-14  340.750000  341.660004  337.660004  338.450012   552900.0   \n",
       "1215  2020-09-15  337.549988  339.950012  334.730011  338.019989   523500.0   \n",
       "1216  2020-09-16  337.250000  346.890015  335.739990  343.470001   750000.0   \n",
       "1217  2020-09-17  341.130005  345.470001  338.079987  343.839996   361300.0   \n",
       "1218  2020-09-18  341.160004  345.890015  341.160004  341.500000   689900.0   \n",
       "1219  2020-09-21  337.929993  338.559998  324.410004  325.890015   714900.0   \n",
       "1220  2020-09-22  327.320007  333.459991  326.910004  332.799988   651900.0   \n",
       "1221  2020-09-23  334.450012  337.329987  326.519989  328.059998   790800.0   \n",
       "1222  2020-09-24  327.480011  328.700012  321.170013  324.720001   574900.0   \n",
       "1223  2020-09-25  324.130005  329.579987  322.940002  328.000000   646300.0   \n",
       "1224  2020-09-28  330.540009  333.559998  328.720001  329.519989   566900.0   \n",
       "1225  2020-09-29  328.779999  330.420013  324.429993  325.279999   483200.0   \n",
       "1226  2020-09-30  326.200012  326.730011  312.250000  315.489990  1179400.0   \n",
       "1227  2020-10-01  317.350006  321.049988  309.359985  311.809998  1687900.0   \n",
       "1228  2020-10-02  309.109985  315.140015  307.119995  313.950012  1380900.0   \n",
       "1229  2020-10-05  315.850006  317.230011  311.299988  312.179993   881700.0   \n",
       "1230  2020-10-06  320.329987  320.329987  308.890015  308.970001  1149200.0   \n",
       "1231  2020-10-07  310.399994  315.570007  310.399994  313.890015  1019500.0   \n",
       "1232  2020-10-08  314.220001  319.820007  314.220001  319.679993   596800.0   \n",
       "1233  2020-10-09  320.000000  322.470001  313.630005  315.279999  1210200.0   \n",
       "1234  2020-10-12  314.519989  318.809998  313.220001  316.589996   834600.0   \n",
       "1235  2020-10-13  315.100006  318.429993  311.859985  312.600006   636400.0   \n",
       "1236  2020-10-14  311.429993  317.600006  311.399994  314.890015  1119900.0   \n",
       "1237  2020-10-15  313.529999  316.260010  312.239990  314.820007   854000.0   \n",
       "1238  2020-10-16  315.970001  319.730011  315.559998  316.239990  1393700.0   \n",
       "1239  2020-10-19  316.040009  318.890015  310.820007  311.329987   775300.0   \n",
       "1240  2020-10-20  313.739990  313.769989  308.279999  309.049988   786300.0   \n",
       "1241  2020-10-21  308.040009  309.839996  305.910004  308.369995   830000.0   \n",
       "1242  2020-10-22  309.260010  314.690002  302.989990  302.989990  1388000.0   \n",
       "1243  2020-10-23  305.160004  309.559998  304.000000  308.429993  1113300.0   \n",
       "1244  2020-10-26  305.399994  312.350006  301.700012  309.839996  1312700.0   \n",
       "1245  2020-10-27  308.670013  309.010010  302.070007  302.119995  1081500.0   \n",
       "1246  2020-10-28  299.579987  304.250000  293.540009  293.660004  1221300.0   \n",
       "1247  2020-10-29  292.540009  297.690002  291.250000  292.390015  1260400.0   \n",
       "1248  2020-10-30  290.869995  293.920013  285.130005  289.820007  1227100.0   \n",
       "1249  2020-11-02  294.480011  298.980011  292.750000  296.630005   852100.0   \n",
       "1250  2020-11-03  299.309998  307.119995  299.309998  304.820007   864900.0   \n",
       "1251  2020-11-04  310.000000  324.260010  309.000000  315.640015  1684000.0   \n",
       "1252  2020-11-05  316.500000  317.279999  306.059998  307.399994  1410300.0   \n",
       "1253  2020-11-06  305.739990  305.940002  296.630005  301.359985   946700.0   \n",
       "1254  2020-11-09  315.000000  317.600006  300.070007  301.279999  1732000.0   \n",
       "1255  2020-11-10  302.730011  316.899994  301.480011  314.510010   948500.0   \n",
       "1256  2020-11-11  314.959991  314.970001  306.019989  309.959991   590700.0   \n",
       "1257  2020-11-12  308.000000  308.779999  302.339996  306.230011   737400.0   \n",
       "1258  2020-11-13  308.079987  313.119995  307.109985  311.709991   493400.0   \n",
       "\n",
       "            rsi    open_SPY    high_SPY     low_SPY   close_SPY   volume_SPY  \\\n",
       "1199  63.589715  337.920013  339.720001  337.549988  339.480011   55106600.0   \n",
       "1200  71.057744  342.119995  343.000000  339.450012  342.920013   48588700.0   \n",
       "1201  67.717143  343.529999  344.209991  342.269989  344.119995   38463400.0   \n",
       "1202  63.975290  344.760010  347.859985  344.170013  347.570007   50790200.0   \n",
       "1203  72.132127  348.510010  349.899994  346.529999  348.329987   58034100.0   \n",
       "1204  63.233510  349.440002  350.720001  348.149994  350.579987   48588900.0   \n",
       "1205  59.141862  350.350006  351.299988  349.059998  349.309998   66099200.0   \n",
       "1206  55.019826  350.209991  352.709991  349.239990  352.600006   54999300.0   \n",
       "1207  64.780122  354.670013  358.750000  353.429993  357.700012   69540000.0   \n",
       "1208  54.657246  355.869995  356.380005  342.589996  345.390015  148011100.0   \n",
       "1209  50.114907  346.130005  347.829987  334.869995  342.570007  139156300.0   \n",
       "1210  44.389276  336.709991  342.640015  332.880005  333.209991  114465300.0   \n",
       "1211  51.121607  337.549988  342.459991  336.609985  339.790009   91462300.0   \n",
       "1212  40.558912  341.820007  342.529999  332.850006  333.890015   90569500.0   \n",
       "1213  51.142385  335.820007  336.970001  331.000000  334.059998   84680200.0   \n",
       "1214  50.656026  337.489990  340.380005  334.220001  338.459991   65605700.0   \n",
       "1215  50.126327  341.119995  342.019989  338.470001  340.170013   52920900.0   \n",
       "1216  56.527716  341.510010  343.059998  338.519989  338.820007   82096000.0   \n",
       "1217  56.944573  333.559998  337.700012  332.989990  335.839996   91523300.0   \n",
       "1218  53.349704  335.369995  335.489990  327.970001  330.649994  105877900.0   \n",
       "1219  36.343637  325.700012  327.130005  321.730011  326.970001   99450800.0   \n",
       "1220  44.971288  328.570007  330.899994  325.859985  330.299988   63612100.0   \n",
       "1221  40.760628  330.899994  331.200012  322.100006  322.640015   93112200.0   \n",
       "1222  37.976699  321.220001  326.799988  319.799988  323.500000   76681300.0   \n",
       "1223  42.278405  322.579987  329.579987  321.640015  328.730011   71069400.0   \n",
       "1224  44.268664  333.220001  334.959991  332.149994  334.190002   64584600.0   \n",
       "1225  39.994480  333.970001  334.769989  331.619995  332.369995   51304000.0   \n",
       "1226  32.054469  333.089996  338.290009  332.880005  334.890015  103653800.0   \n",
       "1227  29.600123  337.690002  338.739990  335.010010  337.040009   88698700.0   \n",
       "1228  32.918875  331.700012  337.010010  331.190002  333.839996   89431100.0   \n",
       "1229  31.551940  336.059998  339.959991  336.010010  339.760010   45713100.0   \n",
       "1230  29.115734  339.910004  342.170013  334.380005  334.929993   90128900.0   \n",
       "1231  37.353420  338.119995  341.630005  338.089996  340.760010   56999600.0   \n",
       "1232  45.617304  342.850006  343.850006  341.859985  343.779999   45242500.0   \n",
       "1233  41.045524  345.559998  347.350006  344.890015  346.850006   59528600.0   \n",
       "1234  42.937359  349.589996  354.019989  349.059998  352.429993   80388500.0   \n",
       "1235  38.731194  352.279999  352.470001  349.089996  350.130005   73255500.0   \n",
       "1236  42.333631  350.750000  351.929993  347.140015  347.929993   57958700.0   \n",
       "1237  42.249251  343.709991  348.019989  343.130005  347.500000   60357700.0   \n",
       "1238  44.731958  348.959991  350.750000  347.100006  347.290009   89501900.0   \n",
       "1239  38.391018  348.649994  349.329987  341.040009  342.010010   68425600.0   \n",
       "1240  35.774522  343.459991  346.880005  342.640015  343.380005   60051900.0   \n",
       "1241  34.984404  343.329987  345.670013  342.399994  342.730011   63575000.0   \n",
       "1242  29.296308  342.959991  345.239990  340.649994  344.609985   55399300.0   \n",
       "1243  40.216871  345.929993  345.989990  343.130005  345.779999   49143900.0   \n",
       "1244  42.762875  342.130005  342.980011  335.619995  339.390015   91473000.0   \n",
       "1245  33.963566  339.760010  340.119995  337.989990  338.220001   65994100.0   \n",
       "1246  27.158936  332.100006  338.250000  326.130005  326.660004  127094300.0   \n",
       "1247  26.280692  326.910004  333.399994  325.089996  329.980011   90597700.0   \n",
       "1248  24.499342  328.279999  329.690002  322.600006  326.540009  120287300.0   \n",
       "1249  37.059922  330.200012  332.359985  327.239990  330.200012   86068300.0   \n",
       "1250  48.507151  333.690002  338.250000  330.290009  336.029999   93294200.0   \n",
       "1251  59.357715  340.859985  347.940002  339.589996  343.540009  126959700.0   \n",
       "1252  50.375508  349.239990  352.190002  348.859985  350.239990   82039700.0   \n",
       "1253  44.848155  349.929993  351.510010  347.649994  350.160004   74973000.0   \n",
       "1254  44.775865  363.970001  364.380005  354.059998  354.559998  172304200.0   \n",
       "1255  57.396475  353.489990  355.179993  350.510010  354.040009   85552000.0   \n",
       "1256  52.786634  356.399994  357.559998  355.059998  356.670013   58649000.0   \n",
       "1257  49.188210  355.579987  356.720001  351.260010  353.209991   67546200.0   \n",
       "1258  54.276352  355.269989  358.899994  354.709991  358.100006   62892200.0   \n",
       "\n",
       "        rsi_SPY   open_VIX   high_VIX    low_VIX  close_VIX   volume_VIX  \\\n",
       "1199  71.305039  20.320000  20.450001  19.780001  19.920000   27482600.0   \n",
       "1200  76.812765  19.090000  20.129999  19.040001  19.799999   24983000.0   \n",
       "1201  78.418321  19.950001  20.790001  19.459999  19.660000   27572400.0   \n",
       "1202  82.327443  19.469999  20.480000  18.750000  20.309999   29100800.0   \n",
       "1203  83.077671  20.000000  23.690001  19.700001  21.230000   73365600.0   \n",
       "1204  85.151254  21.639999  22.980000  20.600000  21.110001   50204700.0   \n",
       "1205  79.074487  21.930000  22.950001  21.219999  22.650000   41189100.0   \n",
       "1206  82.640434  22.670000  23.440001  22.400000  23.240000   34285700.0   \n",
       "1207  86.579538  23.150000  24.389999  23.020000  24.080000   42972600.0   \n",
       "1208  53.824254  24.510000  30.280001  23.230000  28.900000  135998900.0   \n",
       "1209  49.096384  27.920000  32.180000  24.570000  25.299999  129374600.0   \n",
       "1210  37.083403  28.200001  28.469999  24.150000  24.320000   56407200.0   \n",
       "1211  47.178685  23.340000  23.719999  21.799999  22.440001   45746300.0   \n",
       "1212  40.676249  22.120001  23.600000  21.830000  22.780001   54363400.0   \n",
       "1213  40.936839  21.860001  22.850000  20.760000  20.799999   59861300.0   \n",
       "1214  47.561776  20.389999  20.700001  19.799999  20.230000   32343200.0   \n",
       "1215  49.984356  19.959999  20.740000  19.790001  20.190001   31805700.0   \n",
       "1216  48.037637  19.980000  20.110001  19.280001  20.059999   42531200.0   \n",
       "1217  43.849037  21.010000  21.100000  19.270000  19.450001   47315900.0   \n",
       "1218  37.518508  19.180000  20.100000  18.549999  19.410000   45470800.0   \n",
       "1219  33.686925  20.570000  22.090000  20.170000  20.299999   54634400.0   \n",
       "1220  39.861903  20.260000  21.379999  20.260000  20.549999   26687500.0   \n",
       "1221  32.198608  20.440001  22.219999  20.299999  22.209999   37766500.0   \n",
       "1222  33.786515  22.180000  22.590000  21.010000  21.600000   38373000.0   \n",
       "1223  42.833382  21.580000  21.840000  20.580000  20.780001   27147200.0   \n",
       "1224  50.654146  20.510000  20.889999  20.420000  20.650000   18547600.0   \n",
       "1225  48.211311  20.700001  20.709999  19.570000  20.030001   29955500.0   \n",
       "1226  51.788319  19.629999  20.280001  19.250000  19.920000   31236100.0   \n",
       "1227  54.750999  19.639999  20.570000  19.620001  20.110001   23409000.0   \n",
       "1228  49.700173  21.650000  21.760000  20.450001  21.030001   43572200.0   \n",
       "1229  57.717964  20.719999  21.040001  19.799999  20.030001   20667700.0   \n",
       "1230  50.430669  19.770000  20.730000  19.230000  20.410000   40618200.0   \n",
       "1231  57.608777  19.850000  19.990000  19.260000  19.459999   24140300.0   \n",
       "1232  60.870133  19.129999  19.290001  18.240000  18.330000   33402400.0   \n",
       "1233  63.998611  17.440001  17.490000  16.860001  16.889999   33258100.0   \n",
       "1234  69.003444  16.420000  16.860001  16.299999  16.389999   22250900.0   \n",
       "1235  64.873112  16.670000  17.200001  16.490000  16.670000   27907200.0   \n",
       "1236  60.992943  16.480000  16.840000  16.059999  16.440001   32299300.0   \n",
       "1237  60.210857  17.520000  17.750000  16.580000  16.670000   33842000.0   \n",
       "1238  59.794817  16.620001  16.830000  16.250000  16.790001   21694100.0   \n",
       "1239  50.119637  16.700001  18.100000  16.639999  17.930000   35907000.0   \n",
       "1240  52.342777  17.750000  18.080000  17.510000  17.900000   28551600.0   \n",
       "1241  51.141188  17.770000  18.120001  17.110001  17.270000   31359100.0   \n",
       "1242  54.498019  17.230000  17.469999  16.440001  16.680000   24574800.0   \n",
       "1243  56.561762  16.600000  17.270000  16.540001  16.719999   20279500.0   \n",
       "1244  44.354262  17.309999  19.400000  17.049999  18.990000   62410700.0   \n",
       "1245  42.488632  18.969999  19.559999  18.469999  19.070000   37536700.0   \n",
       "1246  29.066810  21.230000  23.200001  20.850000  23.139999   94240700.0   \n",
       "1247  35.562376  22.459999  23.129999  19.680000  20.680000   56381100.0   \n",
       "1248  32.170765  21.209999  22.219999  20.740000  21.580000   58263200.0   \n",
       "1249  39.043300  20.129999  21.360001  20.059999  20.879999   51415700.0   \n",
       "1250  48.312265  19.969999  20.160000  18.790001  19.180000   50097700.0   \n",
       "1251  57.550916  18.000000  18.049999  16.500000  16.670000   55513700.0   \n",
       "1252  63.939984  15.890000  16.530001  15.650000  16.209999   33461500.0   \n",
       "1253  63.812582  16.010000  16.080000  14.630000  14.670000   53209600.0   \n",
       "1254  67.741240  13.140000  14.180000  12.180000  14.180000   54605600.0   \n",
       "1255  66.789119  13.630000  14.400000  13.450000  13.490000   36231600.0   \n",
       "1256  69.220329  13.180000  13.440000  12.640000  13.060000   34220400.0   \n",
       "1257  62.529124  13.300000  14.820000  13.090000  14.370000   68251200.0   \n",
       "1258  67.467429  13.660000  13.660000  12.730000  13.050000   43994400.0   \n",
       "\n",
       "        rsi_VIX  \n",
       "1199  29.758997  \n",
       "1200  29.190110  \n",
       "1201  28.484262  \n",
       "1202  36.415999  \n",
       "1203  45.859296  \n",
       "1204  44.893019  \n",
       "1205  57.624732  \n",
       "1206  61.419117  \n",
       "1207  66.206005  \n",
       "1208  81.131788  \n",
       "1209  59.370619  \n",
       "1210  54.915430  \n",
       "1211  47.342933  \n",
       "1212  48.762677  \n",
       "1213  41.519208  \n",
       "1214  39.635932  \n",
       "1215  39.496246  \n",
       "1216  38.999898  \n",
       "1217  36.601791  \n",
       "1218  36.438536  \n",
       "1219  42.751114  \n",
       "1220  44.472311  \n",
       "1221  54.553076  \n",
       "1222  50.788405  \n",
       "1223  46.042606  \n",
       "1224  45.297008  \n",
       "1225  41.717181  \n",
       "1226  41.077216  \n",
       "1227  42.762371  \n",
       "1228  50.394962  \n",
       "1229  43.404654  \n",
       "1230  46.535861  \n",
       "1231  40.336730  \n",
       "1232  34.298264  \n",
       "1233  28.299669  \n",
       "1234  26.510852  \n",
       "1235  29.291852  \n",
       "1236  28.313936  \n",
       "1237  30.877999  \n",
       "1238  32.282150  \n",
       "1239  44.238732  \n",
       "1240  44.011514  \n",
       "1241  39.301446  \n",
       "1242  35.363375  \n",
       "1243  35.847595  \n",
       "1244  56.429668  \n",
       "1245  56.970256  \n",
       "1246  74.708531  \n",
       "1247  58.510140  \n",
       "1248  61.870799  \n",
       "1249  57.823194  \n",
       "1250  49.147208  \n",
       "1251  39.439249  \n",
       "1252  37.914235  \n",
       "1253  33.146596  \n",
       "1254  31.735740  \n",
       "1255  29.754200  \n",
       "1256  28.521103  \n",
       "1257  37.314850  \n",
       "1258  32.797379  "
      ]
     },
     "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",
    "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([[1.65825600e+02, 1.72655600e+02, 1.65325600e+02, ...,\n",
       "        1.49600000e+04, 6.58000000e+04, 0.00000000e+00],\n",
       "       [1.73961100e+02, 1.73961100e+02, 1.71071100e+02, ...,\n",
       "        1.65400000e+04, 7.15000000e+04, 1.00000000e+02],\n",
       "       [1.72260300e+02, 1.74290300e+02, 1.72090300e+02, ...,\n",
       "        1.44600000e+04, 5.11000000e+04, 4.06053684e+01],\n",
       "       ...,\n",
       "       [3.14959991e+02, 3.14970001e+02, 3.06019989e+02, ...,\n",
       "        1.30600000e+01, 3.42204000e+07, 2.85211034e+01],\n",
       "       [3.08000000e+02, 3.08779999e+02, 3.02339996e+02, ...,\n",
       "        1.43700000e+01, 6.82512000e+07, 3.73148499e+01],\n",
       "       [3.08079987e+02, 3.13119995e+02, 3.07109985e+02, ...,\n",
       "        1.30500000e+01, 4.39944000e+07, 3.27973790e+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([[-1.92870639],\n",
       "       [-1.78444906],\n",
       "       [-1.81460736],\n",
       "       ...,\n",
       "       [ 0.71571979],\n",
       "       [ 0.59230639],\n",
       "       [ 0.59372471]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Scaling data\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": [
    "# Creating a data structure with timestamps and 1 output\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 493ms/step - loss: 0.3701 - val_loss: 0.3541\n",
      "Epoch 2/50\n",
      "4/4 [==============================] - 0s 121ms/step - loss: 0.1369 - val_loss: 0.2986\n",
      "Epoch 3/50\n",
      "4/4 [==============================] - 1s 140ms/step - loss: 0.1310 - val_loss: 0.3936\n",
      "Epoch 4/50\n",
      "4/4 [==============================] - 1s 193ms/step - loss: 0.1107 - val_loss: 0.3323\n",
      "Epoch 5/50\n",
      "4/4 [==============================] - 1s 196ms/step - loss: 0.0965 - val_loss: 0.3110\n",
      "Epoch 6/50\n",
      "4/4 [==============================] - 0s 94ms/step - loss: 0.0750 - val_loss: 0.3131\n",
      "Epoch 7/50\n",
      "4/4 [==============================] - 1s 191ms/step - loss: 0.0632 - val_loss: 0.3427\n",
      "Epoch 8/50\n",
      "4/4 [==============================] - 1s 205ms/step - loss: 0.0557 - val_loss: 0.2630\n",
      "Epoch 9/50\n",
      "4/4 [==============================] - 1s 172ms/step - loss: 0.0562 - val_loss: 0.3773\n",
      "Epoch 10/50\n",
      "4/4 [==============================] - 1s 200ms/step - loss: 0.0563 - val_loss: 0.2940\n",
      "Epoch 11/50\n",
      "4/4 [==============================] - 0s 124ms/step - loss: 0.0492 - val_loss: 0.3004\n",
      "Epoch 12/50\n",
      "4/4 [==============================] - 1s 200ms/step - loss: 0.0436 - val_loss: 0.3267\n",
      "Epoch 13/50\n",
      "4/4 [==============================] - 1s 187ms/step - loss: 0.0444 - val_loss: 0.3267\n",
      "Epoch 14/50\n",
      "4/4 [==============================] - 1s 188ms/step - loss: 0.0428 - val_loss: 0.3791\n",
      "Epoch 15/50\n",
      "4/4 [==============================] - 1s 191ms/step - loss: 0.0419 - val_loss: 0.2982\n",
      "Epoch 16/50\n",
      "4/4 [==============================] - 1s 199ms/step - loss: 0.0360 - val_loss: 0.3813\n",
      "Epoch 17/50\n",
      "4/4 [==============================] - 1s 197ms/step - loss: 0.0365 - val_loss: 0.3783\n",
      "Epoch 18/50\n",
      "4/4 [==============================] - 1s 185ms/step - loss: 0.0397 - val_loss: 0.3733\n",
      "Epoch 19/50\n",
      "4/4 [==============================] - 1s 206ms/step - loss: 0.0407 - val_loss: 0.3609\n",
      "Epoch 20/50\n",
      "4/4 [==============================] - 1s 203ms/step - loss: 0.0387 - val_loss: 0.3149\n",
      "Epoch 21/50\n",
      "4/4 [==============================] - 1s 191ms/step - loss: 0.0326 - val_loss: 0.3852\n",
      "Epoch 22/50\n",
      "4/4 [==============================] - 1s 206ms/step - loss: 0.0357 - val_loss: 0.3248\n",
      "Epoch 23/50\n",
      "4/4 [==============================] - 1s 156ms/step - loss: 0.0358 - val_loss: 0.4064\n",
      "Epoch 24/50\n",
      "4/4 [==============================] - 1s 193ms/step - loss: 0.0388 - val_loss: 0.3255\n",
      "Epoch 25/50\n",
      "4/4 [==============================] - 1s 204ms/step - loss: 0.0378 - val_loss: 0.2569\n",
      "Epoch 26/50\n",
      "4/4 [==============================] - 1s 186ms/step - loss: 0.0370 - val_loss: 0.4453\n",
      "Epoch 27/50\n",
      "4/4 [==============================] - 1s 202ms/step - loss: 0.0353 - val_loss: 0.3169\n",
      "Epoch 28/50\n",
      "4/4 [==============================] - 0s 120ms/step - loss: 0.0369 - val_loss: 0.4068\n",
      "Epoch 29/50\n",
      "4/4 [==============================] - 1s 191ms/step - loss: 0.0370 - val_loss: 0.3236\n",
      "Epoch 30/50\n",
      "4/4 [==============================] - 1s 180ms/step - loss: 0.0368 - val_loss: 0.3393\n",
      "Epoch 31/50\n",
      "4/4 [==============================] - 1s 199ms/step - loss: 0.0331 - val_loss: 0.4310\n",
      "Epoch 32/50\n",
      "4/4 [==============================] - 1s 143ms/step - loss: 0.0337 - val_loss: 0.3900\n",
      "Epoch 33/50\n",
      "4/4 [==============================] - 1s 139ms/step - loss: 0.0326 - val_loss: 0.3918\n",
      "Epoch 34/50\n",
      "4/4 [==============================] - 0s 119ms/step - loss: 0.0358 - val_loss: 0.3952\n",
      "Epoch 35/50\n",
      "4/4 [==============================] - 1s 174ms/step - loss: 0.0322 - val_loss: 0.3557\n",
      "Epoch 36/50\n",
      "4/4 [==============================] - 1s 129ms/step - loss: 0.0352 - val_loss: 0.3631\n",
      "Epoch 37/50\n",
      "4/4 [==============================] - 1s 171ms/step - loss: 0.0304 - val_loss: 0.4551\n",
      "Epoch 38/50\n",
      "4/4 [==============================] - 1s 204ms/step - loss: 0.0314 - val_loss: 0.3448\n",
      "Epoch 39/50\n",
      "4/4 [==============================] - 1s 208ms/step - loss: 0.0331 - val_loss: 0.3684\n",
      "Epoch 40/50\n",
      "4/4 [==============================] - 1s 193ms/step - loss: 0.0309 - val_loss: 0.4208\n",
      "Epoch 41/50\n",
      "4/4 [==============================] - 0s 96ms/step - loss: 0.0323 - val_loss: 0.4523\n",
      "Epoch 42/50\n",
      "4/4 [==============================] - 0s 119ms/step - loss: 0.0311 - val_loss: 0.4212\n",
      "Epoch 43/50\n",
      "4/4 [==============================] - 0s 125ms/step - loss: 0.0293 - val_loss: 0.4319\n",
      "Epoch 44/50\n",
      "4/4 [==============================] - 1s 212ms/step - loss: 0.0320 - val_loss: 0.3884\n",
      "Epoch 45/50\n",
      "4/4 [==============================] - 1s 211ms/step - loss: 0.0313 - val_loss: 0.3759\n",
      "Epoch 46/50\n",
      "4/4 [==============================] - 1s 153ms/step - loss: 0.0274 - val_loss: 0.3729\n",
      "Epoch 47/50\n",
      "4/4 [==============================] - 0s 88ms/step - loss: 0.0292 - val_loss: 0.3496\n",
      "Epoch 48/50\n",
      "4/4 [==============================] - 1s 146ms/step - loss: 0.0286 - val_loss: 0.3599\n",
      "Epoch 49/50\n",
      "4/4 [==============================] - 1s 140ms/step - loss: 0.0319 - val_loss: 0.3612\n",
      "Epoch 50/50\n",
      "4/4 [==============================] - 1s 161ms/step - loss: 0.0267 - val_loss: 0.3746\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": [],
   "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": 18,
   "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": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-09-15</th>\n",
       "      <td>206.018082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-16</th>\n",
       "      <td>206.668243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-19</th>\n",
       "      <td>206.993774</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Open\n",
       "2016-09-15  206.018082\n",
       "2016-09-16  206.668243\n",
       "2016-09-19  206.993774"
      ]
     },
     "execution_count": 19,
     "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": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-11-13</th>\n",
       "      <td>20538.021484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-14</th>\n",
       "      <td>20535.126953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-15</th>\n",
       "      <td>20530.708984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-16</th>\n",
       "      <td>20522.789062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-17</th>\n",
       "      <td>20589.445312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-18</th>\n",
       "      <td>20539.412109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-19</th>\n",
       "      <td>20534.271484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-20</th>\n",
       "      <td>20517.902344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-21</th>\n",
       "      <td>20531.953125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-22</th>\n",
       "      <td>20681.544922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-23</th>\n",
       "      <td>20808.710938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-24</th>\n",
       "      <td>20847.785156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-25</th>\n",
       "      <td>20831.396484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-26</th>\n",
       "      <td>20836.546875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-27</th>\n",
       "      <td>20871.998047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-28</th>\n",
       "      <td>20805.076172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-29</th>\n",
       "      <td>20763.367188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-30</th>\n",
       "      <td>20775.728516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-01</th>\n",
       "      <td>20778.416016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-02</th>\n",
       "      <td>20813.076172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-03</th>\n",
       "      <td>20830.935547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-04</th>\n",
       "      <td>20795.828125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-05</th>\n",
       "      <td>20799.042969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-06</th>\n",
       "      <td>20789.279297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-07</th>\n",
       "      <td>20757.207031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-08</th>\n",
       "      <td>20706.154297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-09</th>\n",
       "      <td>20695.623047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-10</th>\n",
       "      <td>20663.710938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-11</th>\n",
       "      <td>20603.748047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-12</th>\n",
       "      <td>20538.085938</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Open\n",
       "2020-11-13  20538.021484\n",
       "2020-11-14  20535.126953\n",
       "2020-11-15  20530.708984\n",
       "2020-11-16  20522.789062\n",
       "2020-11-17  20589.445312\n",
       "2020-11-18  20539.412109\n",
       "2020-11-19  20534.271484\n",
       "2020-11-20  20517.902344\n",
       "2020-11-21  20531.953125\n",
       "2020-11-22  20681.544922\n",
       "2020-11-23  20808.710938\n",
       "2020-11-24  20847.785156\n",
       "2020-11-25  20831.396484\n",
       "2020-11-26  20836.546875\n",
       "2020-11-27  20871.998047\n",
       "2020-11-28  20805.076172\n",
       "2020-11-29  20763.367188\n",
       "2020-11-30  20775.728516\n",
       "2020-12-01  20778.416016\n",
       "2020-12-02  20813.076172\n",
       "2020-12-03  20830.935547\n",
       "2020-12-04  20795.828125\n",
       "2020-12-05  20799.042969\n",
       "2020-12-06  20789.279297\n",
       "2020-12-07  20757.207031\n",
       "2020-12-08  20706.154297\n",
       "2020-12-09  20695.623047\n",
       "2020-12-10  20663.710938\n",
       "2020-12-11  20603.748047\n",
       "2020-12-12  20538.085938"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-09-15</th>\n",
       "      <td>11893.140625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-16</th>\n",
       "      <td>11929.806641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-19</th>\n",
       "      <td>11948.165039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-20</th>\n",
       "      <td>11917.337891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-21</th>\n",
       "      <td>11931.878906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-09</th>\n",
       "      <td>20706.154297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-10</th>\n",
       "      <td>20695.623047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-11</th>\n",
       "      <td>20663.710938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-12</th>\n",
       "      <td>20603.748047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-13</th>\n",
       "      <td>20538.085938</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1050 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Open\n",
       "2016-09-15  11893.140625\n",
       "2016-09-16  11929.806641\n",
       "2016-09-19  11948.165039\n",
       "2016-09-20  11917.337891\n",
       "2016-09-21  11931.878906\n",
       "...                  ...\n",
       "2020-11-09  20706.154297\n",
       "2020-11-10  20695.623047\n",
       "2020-11-11  20663.710938\n",
       "2020-11-12  20603.748047\n",
       "2020-11-13  20538.085938\n",
       "\n",
       "[1050 rows x 1 columns]"
      ]
     },
     "execution_count": 21,
     "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": 22,
   "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(' NOC 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()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Open    0.001129\n",
       "dtype: float32"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse = np.sqrt (np.mean (PREDICTION_TRAIN -PREDICTIONS_FUTURE)**2)\n",
    "rmse"
   ]
  }
 ],
 "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
}