Solution.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pytz\n",
    "import datetime\n",
    "import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>timeStamp</th>\n",
       "      <th>TSLA_open</th>\n",
       "      <th>AAPL_open</th>\n",
       "      <th>NVDA_open</th>\n",
       "      <th>ORCL_open</th>\n",
       "      <th>SPY_open</th>\n",
       "      <th>MSFT_open</th>\n",
       "      <th>C_open</th>\n",
       "      <th>FB_open</th>\n",
       "      <th>QQQ_open</th>\n",
       "      <th>...</th>\n",
       "      <th>INTC_open</th>\n",
       "      <th>PFE_open</th>\n",
       "      <th>BAC_open</th>\n",
       "      <th>NFLX_open</th>\n",
       "      <th>MU_open</th>\n",
       "      <th>XOM_open</th>\n",
       "      <th>GILD_open</th>\n",
       "      <th>SNAP_open</th>\n",
       "      <th>GE_open</th>\n",
       "      <th>IWM_open</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-08-14 09:30:00-04:00</td>\n",
       "      <td>332.9980</td>\n",
       "      <td>114.8288</td>\n",
       "      <td>461.2000</td>\n",
       "      <td>54.030</td>\n",
       "      <td>336.410</td>\n",
       "      <td>208.76</td>\n",
       "      <td>52.450</td>\n",
       "      <td>262.3050</td>\n",
       "      <td>273.01</td>\n",
       "      <td>...</td>\n",
       "      <td>48.730</td>\n",
       "      <td>38.0100</td>\n",
       "      <td>26.0600</td>\n",
       "      <td>482.820</td>\n",
       "      <td>45.600</td>\n",
       "      <td>42.6000</td>\n",
       "      <td>68.090</td>\n",
       "      <td>21.9000</td>\n",
       "      <td>6.5400</td>\n",
       "      <td>156.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-08-14 09:31:00-04:00</td>\n",
       "      <td>330.6161</td>\n",
       "      <td>114.7500</td>\n",
       "      <td>461.2800</td>\n",
       "      <td>53.865</td>\n",
       "      <td>336.420</td>\n",
       "      <td>208.93</td>\n",
       "      <td>52.565</td>\n",
       "      <td>261.9600</td>\n",
       "      <td>273.03</td>\n",
       "      <td>...</td>\n",
       "      <td>48.800</td>\n",
       "      <td>38.0600</td>\n",
       "      <td>26.1200</td>\n",
       "      <td>483.250</td>\n",
       "      <td>45.550</td>\n",
       "      <td>42.6608</td>\n",
       "      <td>68.170</td>\n",
       "      <td>21.9901</td>\n",
       "      <td>6.5300</td>\n",
       "      <td>156.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-08-14 09:32:00-04:00</td>\n",
       "      <td>331.2920</td>\n",
       "      <td>114.6325</td>\n",
       "      <td>461.8016</td>\n",
       "      <td>53.810</td>\n",
       "      <td>336.160</td>\n",
       "      <td>208.40</td>\n",
       "      <td>52.700</td>\n",
       "      <td>262.1600</td>\n",
       "      <td>272.64</td>\n",
       "      <td>...</td>\n",
       "      <td>48.780</td>\n",
       "      <td>37.9900</td>\n",
       "      <td>26.1973</td>\n",
       "      <td>482.760</td>\n",
       "      <td>45.495</td>\n",
       "      <td>42.7500</td>\n",
       "      <td>68.120</td>\n",
       "      <td>21.9900</td>\n",
       "      <td>6.5500</td>\n",
       "      <td>156.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-08-14 09:33:00-04:00</td>\n",
       "      <td>332.9500</td>\n",
       "      <td>114.6575</td>\n",
       "      <td>463.7550</td>\n",
       "      <td>53.890</td>\n",
       "      <td>336.415</td>\n",
       "      <td>208.92</td>\n",
       "      <td>52.560</td>\n",
       "      <td>262.3400</td>\n",
       "      <td>273.04</td>\n",
       "      <td>...</td>\n",
       "      <td>48.870</td>\n",
       "      <td>37.9618</td>\n",
       "      <td>26.1950</td>\n",
       "      <td>484.705</td>\n",
       "      <td>45.470</td>\n",
       "      <td>42.9100</td>\n",
       "      <td>68.255</td>\n",
       "      <td>22.0250</td>\n",
       "      <td>6.5499</td>\n",
       "      <td>156.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-08-14 09:34:00-04:00</td>\n",
       "      <td>331.6272</td>\n",
       "      <td>114.4225</td>\n",
       "      <td>462.0636</td>\n",
       "      <td>53.960</td>\n",
       "      <td>336.130</td>\n",
       "      <td>208.93</td>\n",
       "      <td>52.470</td>\n",
       "      <td>261.8229</td>\n",
       "      <td>272.80</td>\n",
       "      <td>...</td>\n",
       "      <td>48.795</td>\n",
       "      <td>38.0000</td>\n",
       "      <td>26.1300</td>\n",
       "      <td>485.080</td>\n",
       "      <td>45.340</td>\n",
       "      <td>42.8300</td>\n",
       "      <td>68.200</td>\n",
       "      <td>22.0150</td>\n",
       "      <td>6.5450</td>\n",
       "      <td>156.42</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   timeStamp  TSLA_open  AAPL_open  NVDA_open  ORCL_open  \\\n",
       "0  2020-08-14 09:30:00-04:00   332.9980   114.8288   461.2000     54.030   \n",
       "1  2020-08-14 09:31:00-04:00   330.6161   114.7500   461.2800     53.865   \n",
       "2  2020-08-14 09:32:00-04:00   331.2920   114.6325   461.8016     53.810   \n",
       "3  2020-08-14 09:33:00-04:00   332.9500   114.6575   463.7550     53.890   \n",
       "4  2020-08-14 09:34:00-04:00   331.6272   114.4225   462.0636     53.960   \n",
       "\n",
       "   SPY_open  MSFT_open  C_open   FB_open  QQQ_open  ...  INTC_open  PFE_open  \\\n",
       "0   336.410     208.76  52.450  262.3050    273.01  ...     48.730   38.0100   \n",
       "1   336.420     208.93  52.565  261.9600    273.03  ...     48.800   38.0600   \n",
       "2   336.160     208.40  52.700  262.1600    272.64  ...     48.780   37.9900   \n",
       "3   336.415     208.92  52.560  262.3400    273.04  ...     48.870   37.9618   \n",
       "4   336.130     208.93  52.470  261.8229    272.80  ...     48.795   38.0000   \n",
       "\n",
       "   BAC_open  NFLX_open  MU_open  XOM_open  GILD_open  SNAP_open  GE_open  \\\n",
       "0   26.0600    482.820   45.600   42.6000     68.090    21.9000   6.5400   \n",
       "1   26.1200    483.250   45.550   42.6608     68.170    21.9901   6.5300   \n",
       "2   26.1973    482.760   45.495   42.7500     68.120    21.9900   6.5500   \n",
       "3   26.1950    484.705   45.470   42.9100     68.255    22.0250   6.5499   \n",
       "4   26.1300    485.080   45.340   42.8300     68.200    22.0150   6.5450   \n",
       "\n",
       "   IWM_open  \n",
       "0    156.28  \n",
       "1    156.47  \n",
       "2    156.50  \n",
       "3    156.67  \n",
       "4    156.42  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('minuteData_NbTicksMin25_BusDay100_2020-12-31.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(data['timeStamp'][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['timeStamp'] = pd.to_datetime(data['timeStamp'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timeStamp</th>\n",
       "      <th>TSLA_open</th>\n",
       "      <th>AAPL_open</th>\n",
       "      <th>NVDA_open</th>\n",
       "      <th>ORCL_open</th>\n",
       "      <th>SPY_open</th>\n",
       "      <th>MSFT_open</th>\n",
       "      <th>C_open</th>\n",
       "      <th>FB_open</th>\n",
       "      <th>QQQ_open</th>\n",
       "      <th>...</th>\n",
       "      <th>INTC_open</th>\n",
       "      <th>PFE_open</th>\n",
       "      <th>BAC_open</th>\n",
       "      <th>NFLX_open</th>\n",
       "      <th>MU_open</th>\n",
       "      <th>XOM_open</th>\n",
       "      <th>GILD_open</th>\n",
       "      <th>SNAP_open</th>\n",
       "      <th>GE_open</th>\n",
       "      <th>IWM_open</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-08-14 09:30:00-04:00</td>\n",
       "      <td>332.9980</td>\n",
       "      <td>114.8288</td>\n",
       "      <td>461.2000</td>\n",
       "      <td>54.030</td>\n",
       "      <td>336.410</td>\n",
       "      <td>208.76</td>\n",
       "      <td>52.450</td>\n",
       "      <td>262.3050</td>\n",
       "      <td>273.01</td>\n",
       "      <td>...</td>\n",
       "      <td>48.730</td>\n",
       "      <td>38.0100</td>\n",
       "      <td>26.0600</td>\n",
       "      <td>482.820</td>\n",
       "      <td>45.600</td>\n",
       "      <td>42.6000</td>\n",
       "      <td>68.090</td>\n",
       "      <td>21.9000</td>\n",
       "      <td>6.5400</td>\n",
       "      <td>156.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-08-14 09:31:00-04:00</td>\n",
       "      <td>330.6161</td>\n",
       "      <td>114.7500</td>\n",
       "      <td>461.2800</td>\n",
       "      <td>53.865</td>\n",
       "      <td>336.420</td>\n",
       "      <td>208.93</td>\n",
       "      <td>52.565</td>\n",
       "      <td>261.9600</td>\n",
       "      <td>273.03</td>\n",
       "      <td>...</td>\n",
       "      <td>48.800</td>\n",
       "      <td>38.0600</td>\n",
       "      <td>26.1200</td>\n",
       "      <td>483.250</td>\n",
       "      <td>45.550</td>\n",
       "      <td>42.6608</td>\n",
       "      <td>68.170</td>\n",
       "      <td>21.9901</td>\n",
       "      <td>6.5300</td>\n",
       "      <td>156.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-08-14 09:32:00-04:00</td>\n",
       "      <td>331.2920</td>\n",
       "      <td>114.6325</td>\n",
       "      <td>461.8016</td>\n",
       "      <td>53.810</td>\n",
       "      <td>336.160</td>\n",
       "      <td>208.40</td>\n",
       "      <td>52.700</td>\n",
       "      <td>262.1600</td>\n",
       "      <td>272.64</td>\n",
       "      <td>...</td>\n",
       "      <td>48.780</td>\n",
       "      <td>37.9900</td>\n",
       "      <td>26.1973</td>\n",
       "      <td>482.760</td>\n",
       "      <td>45.495</td>\n",
       "      <td>42.7500</td>\n",
       "      <td>68.120</td>\n",
       "      <td>21.9900</td>\n",
       "      <td>6.5500</td>\n",
       "      <td>156.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-08-14 09:33:00-04:00</td>\n",
       "      <td>332.9500</td>\n",
       "      <td>114.6575</td>\n",
       "      <td>463.7550</td>\n",
       "      <td>53.890</td>\n",
       "      <td>336.415</td>\n",
       "      <td>208.92</td>\n",
       "      <td>52.560</td>\n",
       "      <td>262.3400</td>\n",
       "      <td>273.04</td>\n",
       "      <td>...</td>\n",
       "      <td>48.870</td>\n",
       "      <td>37.9618</td>\n",
       "      <td>26.1950</td>\n",
       "      <td>484.705</td>\n",
       "      <td>45.470</td>\n",
       "      <td>42.9100</td>\n",
       "      <td>68.255</td>\n",
       "      <td>22.0250</td>\n",
       "      <td>6.5499</td>\n",
       "      <td>156.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-08-14 09:34:00-04:00</td>\n",
       "      <td>331.6272</td>\n",
       "      <td>114.4225</td>\n",
       "      <td>462.0636</td>\n",
       "      <td>53.960</td>\n",
       "      <td>336.130</td>\n",
       "      <td>208.93</td>\n",
       "      <td>52.470</td>\n",
       "      <td>261.8229</td>\n",
       "      <td>272.80</td>\n",
       "      <td>...</td>\n",
       "      <td>48.795</td>\n",
       "      <td>38.0000</td>\n",
       "      <td>26.1300</td>\n",
       "      <td>485.080</td>\n",
       "      <td>45.340</td>\n",
       "      <td>42.8300</td>\n",
       "      <td>68.200</td>\n",
       "      <td>22.0150</td>\n",
       "      <td>6.5450</td>\n",
       "      <td>156.42</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   timeStamp  TSLA_open  AAPL_open  NVDA_open  ORCL_open  \\\n",
       "0  2020-08-14 09:30:00-04:00   332.9980   114.8288   461.2000     54.030   \n",
       "1  2020-08-14 09:31:00-04:00   330.6161   114.7500   461.2800     53.865   \n",
       "2  2020-08-14 09:32:00-04:00   331.2920   114.6325   461.8016     53.810   \n",
       "3  2020-08-14 09:33:00-04:00   332.9500   114.6575   463.7550     53.890   \n",
       "4  2020-08-14 09:34:00-04:00   331.6272   114.4225   462.0636     53.960   \n",
       "\n",
       "   SPY_open  MSFT_open  C_open   FB_open  QQQ_open  ...  INTC_open  PFE_open  \\\n",
       "0   336.410     208.76  52.450  262.3050    273.01  ...     48.730   38.0100   \n",
       "1   336.420     208.93  52.565  261.9600    273.03  ...     48.800   38.0600   \n",
       "2   336.160     208.40  52.700  262.1600    272.64  ...     48.780   37.9900   \n",
       "3   336.415     208.92  52.560  262.3400    273.04  ...     48.870   37.9618   \n",
       "4   336.130     208.93  52.470  261.8229    272.80  ...     48.795   38.0000   \n",
       "\n",
       "   BAC_open  NFLX_open  MU_open  XOM_open  GILD_open  SNAP_open  GE_open  \\\n",
       "0   26.0600    482.820   45.600   42.6000     68.090    21.9000   6.5400   \n",
       "1   26.1200    483.250   45.550   42.6608     68.170    21.9901   6.5300   \n",
       "2   26.1973    482.760   45.495   42.7500     68.120    21.9900   6.5500   \n",
       "3   26.1950    484.705   45.470   42.9100     68.255    22.0250   6.5499   \n",
       "4   26.1300    485.080   45.340   42.8300     68.200    22.0150   6.5450   \n",
       "\n",
       "   IWM_open  \n",
       "0    156.28  \n",
       "1    156.47  \n",
       "2    156.50  \n",
       "3    156.67  \n",
       "4    156.42  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8491678319831647"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['TSLA_open', 'AAPL_open', 'NVDA_open', 'ORCL_open', 'SPY_open',\n",
       "       'MSFT_open', 'C_open', 'FB_open', 'QQQ_open', 'AMZN_open', 'WFC_open',\n",
       "       'SQQQ_open', 'WMT_open', 'UBER_open', 'BA_open', 'INTC_open',\n",
       "       'PFE_open', 'BAC_open', 'NFLX_open', 'MU_open', 'XOM_open', 'GILD_open',\n",
       "       'SNAP_open', 'GE_open', 'IWM_open'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = cols.drop(\"timeStamp\")\n",
    "cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['TSLA',\n",
       " 'AAPL',\n",
       " 'NVDA',\n",
       " 'ORCL',\n",
       " 'SPY',\n",
       " 'MSFT',\n",
       " 'C',\n",
       " 'FB',\n",
       " 'QQQ',\n",
       " 'AMZN',\n",
       " 'WFC',\n",
       " 'SQQQ',\n",
       " 'WMT',\n",
       " 'UBER',\n",
       " 'BA',\n",
       " 'INTC',\n",
       " 'PFE',\n",
       " 'BAC',\n",
       " 'NFLX',\n",
       " 'MU',\n",
       " 'XOM',\n",
       " 'GILD',\n",
       " 'SNAP',\n",
       " 'GE',\n",
       " 'IWM']"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = [x[:-5] for x in cols]\n",
    "cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "log_returns = pd.DataFrame(columns = [x+'_log_return' for x in cols])\n",
    "vol_5 = pd.DataFrame(columns = [x+'_vol_5' for x in cols])\n",
    "vol_30 = pd.DataFrame(columns = [x+'_vol_30' for x in cols])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "  0%|          | 0/37470 [00:00<?, ?it/s]\u001b[A\n",
      "  0%|          | 1/37470 [00:00<3:04:06,  3.39it/s]\u001b[A\n",
      "  0%|          | 6/37470 [00:00<2:12:48,  4.70it/s]\u001b[A\n",
      "  0%|          | 10/37470 [00:00<1:37:40,  6.39it/s]\u001b[A\n",
      "  0%|          | 13/37470 [00:00<1:15:20,  8.29it/s]\u001b[A\n",
      "  0%|          | 17/37470 [00:00<57:25, 10.87it/s]  \u001b[A\n",
      "  0%|          | 21/37470 [00:00<45:00, 13.87it/s]\u001b[A\n",
      "  0%|          | 27/37470 [00:00<34:58, 17.84it/s]\u001b[A\n",
      "  0%|          | 31/37470 [00:01<30:36, 20.38it/s]\u001b[A\n",
      "  0%|          | 35/37470 [00:01<27:18, 22.85it/s]\u001b[A\n",
      "  0%|          | 39/37470 [00:01<26:41, 23.37it/s]\u001b[A\n",
      "  0%|          | 43/37470 [00:01<24:44, 25.21it/s]\u001b[A\n",
      "  0%|          | 47/37470 [00:01<22:53, 27.25it/s]\u001b[A\n",
      "  0%|          | 51/37470 [00:01<22:04, 28.25it/s]\u001b[A\n",
      "  0%|          | 55/37470 [00:01<21:04, 29.58it/s]\u001b[A\n",
      "  0%|          | 59/37470 [00:01<20:20, 30.64it/s]\u001b[A\n",
      "  0%|          | 63/37470 [00:02<19:42, 31.63it/s]\u001b[A\n",
      "  0%|          | 67/37470 [00:02<19:57, 31.23it/s]\u001b[A\n",
      "  0%|          | 71/37470 [00:02<22:34, 27.61it/s]\u001b[A\n",
      "  0%|          | 74/37470 [00:02<24:06, 25.86it/s]\u001b[A\n",
      "  0%|          | 77/37470 [00:02<25:44, 24.22it/s]\u001b[A\n",
      "  0%|          | 80/37470 [00:02<24:45, 25.17it/s]\u001b[A\n",
      "  0%|          | 83/37470 [00:02<24:36, 25.31it/s]\u001b[A\n",
      "  0%|          | 86/37470 [00:03<24:15, 25.68it/s]\u001b[A\n",
      "  0%|          | 89/37470 [00:03<25:05, 24.83it/s]\u001b[A\n",
      "  0%|          | 92/37470 [00:03<25:43, 24.22it/s]\u001b[A\n",
      "  0%|          | 95/37470 [00:03<26:01, 23.93it/s]\u001b[A\n",
      "  0%|          | 99/37470 [00:03<23:22, 26.65it/s]\u001b[A\n",
      "  0%|          | 102/37470 [00:03<23:24, 26.61it/s]\u001b[A\n",
      "  0%|          | 105/37470 [00:03<22:40, 27.46it/s]\u001b[A\n",
      "  0%|          | 108/37470 [00:03<22:25, 27.77it/s]\u001b[A\n",
      "  0%|          | 111/37470 [00:03<22:53, 27.19it/s]\u001b[A\n",
      "  0%|          | 114/37470 [00:04<26:01, 23.92it/s]\u001b[A\n",
      "  0%|          | 117/37470 [00:04<24:34, 25.33it/s]\u001b[A\n",
      "  0%|          | 120/37470 [00:04<23:37, 26.36it/s]\u001b[A\n",
      "  0%|          | 123/37470 [00:04<24:36, 25.29it/s]\u001b[A\n",
      "  0%|          | 126/37470 [00:04<25:32, 24.37it/s]\u001b[A\n",
      "  0%|          | 129/37470 [00:04<26:58, 23.06it/s]\u001b[A\n",
      "  0%|          | 132/37470 [00:04<26:15, 23.70it/s]\u001b[A\n",
      "  0%|          | 135/37470 [00:04<25:21, 24.54it/s]\u001b[A\n",
      "  0%|          | 138/37470 [00:05<26:44, 23.27it/s]\u001b[A\n",
      "  0%|          | 141/37470 [00:05<26:25, 23.55it/s]\u001b[A\n",
      "  0%|          | 144/37470 [00:05<25:10, 24.72it/s]\u001b[A\n",
      "  0%|          | 147/37470 [00:05<24:10, 25.73it/s]\u001b[A\n",
      "  0%|          | 150/37470 [00:05<23:29, 26.48it/s]\u001b[A\n",
      "  0%|          | 153/37470 [00:05<24:35, 25.29it/s]\u001b[A\n",
      "  0%|          | 156/37470 [00:05<24:44, 25.14it/s]\u001b[A\n",
      "  0%|          | 159/37470 [00:05<25:12, 24.66it/s]\u001b[A\n",
      "  0%|          | 162/37470 [00:06<24:21, 25.52it/s]\u001b[A\n",
      "  0%|          | 165/37470 [00:06<23:30, 26.44it/s]\u001b[A\n",
      "  0%|          | 169/37470 [00:06<22:03, 28.18it/s]\u001b[A\n",
      "  0%|          | 173/37470 [00:06<20:48, 29.88it/s]\u001b[A\n",
      "  0%|          | 177/37470 [00:06<20:06, 30.90it/s]\u001b[A\n",
      "  0%|          | 181/37470 [00:06<19:49, 31.34it/s]\u001b[A\n",
      "  0%|          | 185/37470 [00:06<19:31, 31.83it/s]\u001b[A\n",
      "  1%|          | 189/37470 [00:06<19:14, 32.28it/s]\u001b[A\n",
      "  1%|          | 193/37470 [00:07<20:32, 30.24it/s]\u001b[A\n",
      "  1%|          | 197/37470 [00:07<21:50, 28.45it/s]\u001b[A\n",
      "  1%|          | 200/37470 [00:07<22:31, 27.57it/s]\u001b[A\n",
      "  1%|          | 203/37470 [00:07<22:06, 28.09it/s]\u001b[A\n",
      "  1%|          | 206/37470 [00:07<23:51, 26.04it/s]\u001b[A\n",
      "  1%|          | 209/37470 [00:07<25:45, 24.11it/s]\u001b[A\n",
      "  1%|          | 212/37470 [00:07<25:21, 24.48it/s]\u001b[A\n",
      "  1%|          | 215/37470 [00:07<25:43, 24.14it/s]\u001b[A\n",
      "  1%|          | 218/37470 [00:08<25:54, 23.97it/s]\u001b[A\n",
      "  1%|          | 221/37470 [00:08<25:56, 23.94it/s]\u001b[A\n",
      "  1%|          | 224/37470 [00:08<25:59, 23.88it/s]\u001b[A\n",
      "  1%|          | 227/37470 [00:08<26:10, 23.72it/s]\u001b[A\n",
      "  1%|          | 230/37470 [00:08<25:42, 24.15it/s]\u001b[A\n",
      "  1%|          | 233/37470 [00:08<26:04, 23.80it/s]\u001b[A\n",
      "  1%|          | 236/37470 [00:08<27:06, 22.89it/s]\u001b[A\n",
      "  1%|          | 239/37470 [00:08<25:41, 24.15it/s]\u001b[A\n",
      "  1%|          | 242/37470 [00:09<24:34, 25.25it/s]\u001b[A\n",
      "  1%|          | 245/37470 [00:09<24:56, 24.88it/s]\u001b[A\n",
      "  1%|          | 249/37470 [00:09<23:09, 26.78it/s]\u001b[A\n",
      "  1%|          | 252/37470 [00:09<22:39, 27.38it/s]\u001b[A\n",
      "  1%|          | 256/37470 [00:09<21:40, 28.62it/s]\u001b[A\n",
      "  1%|          | 259/37470 [00:09<26:43, 23.21it/s]\u001b[A\n",
      "  1%|          | 262/37470 [00:09<26:32, 23.37it/s]\u001b[A\n",
      "  1%|          | 265/37470 [00:09<25:43, 24.10it/s]\u001b[A\n",
      "  1%|          | 268/37470 [00:10<26:06, 23.75it/s]\u001b[A\n",
      "  1%|          | 271/37470 [00:10<24:35, 25.21it/s]\u001b[A\n",
      "  1%|          | 274/37470 [00:10<26:39, 23.26it/s]\u001b[A\n",
      "  1%|          | 277/37470 [00:10<27:39, 22.41it/s]\u001b[A\n",
      "  1%|          | 280/37470 [00:10<28:12, 21.97it/s]\u001b[A\n",
      "  1%|          | 283/37470 [00:10<30:04, 20.60it/s]\u001b[A\n",
      "  1%|          | 286/37470 [00:10<30:16, 20.47it/s]\u001b[A\n",
      "  1%|          | 289/37470 [00:11<29:42, 20.86it/s]\u001b[A\n",
      "  1%|          | 292/37470 [00:11<29:43, 20.84it/s]\u001b[A\n",
      "  1%|          | 295/37470 [00:11<29:44, 20.83it/s]\u001b[A\n",
      "  1%|          | 298/37470 [00:11<29:02, 21.33it/s]\u001b[A\n",
      "  1%|          | 301/37470 [00:11<28:07, 22.02it/s]\u001b[A\n",
      "  1%|          | 304/37470 [00:11<29:04, 21.30it/s]\u001b[A\n",
      "  1%|          | 307/37470 [00:11<30:20, 20.42it/s]\u001b[A\n",
      "  1%|          | 310/37470 [00:12<30:32, 20.28it/s]\u001b[A\n",
      "  1%|          | 313/37470 [00:12<29:38, 20.89it/s]\u001b[A\n",
      "  1%|          | 316/37470 [00:12<38:33, 16.06it/s]\u001b[A\n",
      "  1%|          | 318/37470 [00:12<37:48, 16.38it/s]\u001b[A\n",
      "  1%|          | 320/37470 [00:12<37:17, 16.60it/s]\u001b[A\n",
      "  1%|          | 322/37470 [00:12<42:47, 14.47it/s]\u001b[A\n",
      "  1%|          | 324/37470 [00:13<46:43, 13.25it/s]\u001b[A\n",
      "  1%|          | 327/37470 [00:13<40:42, 15.21it/s]\u001b[A\n",
      "  1%|          | 330/37470 [00:13<37:11, 16.65it/s]\u001b[A\n",
      "  1%|          | 333/37470 [00:13<32:58, 18.77it/s]\u001b[A\n",
      "  1%|          | 336/37470 [00:13<31:39, 19.55it/s]\u001b[A"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-35-942d50cea9d4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     14\u001b[0m         \u001b[0mlog_returns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlog_returns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlog_return\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m>\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m         \u001b[0mvol_5\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvol_5\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mv_5\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     17\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m>\u001b[0m\u001b[0;36m28\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m         \u001b[0mvol_30\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvol_30\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mv_30\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.7/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m__setitem__\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m    668\u001b[0m             \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_if_callable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    669\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_setitem_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 670\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setitem_with_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    671\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    672\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_validate_key\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.7/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_setitem_with_indexer\u001b[0;34m(self, indexer, value)\u001b[0m\n\u001b[1;32m    872\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    873\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mmissing\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 874\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setitem_with_indexer_missing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    875\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    876\u001b[0m         \u001b[0;31m# set\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.7/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_setitem_with_indexer_missing\u001b[0;34m(self, indexer, value)\u001b[0m\n\u001b[1;32m   1120\u001b[0m                 \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1121\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1122\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1123\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_update_cacher\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclear\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1124\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.7/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__setattr__\u001b[0;34m(self, name, value)\u001b[0m\n\u001b[1;32m   5274\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5275\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5276\u001b[0;31m     \u001b[0;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5277\u001b[0m         \"\"\"After regular attribute access, try setting the name\n\u001b[1;32m   5278\u001b[0m         \u001b[0mThis\u001b[0m \u001b[0mallows\u001b[0m \u001b[0msimpler\u001b[0m \u001b[0maccess\u001b[0m \u001b[0mto\u001b[0m \u001b[0mcolumns\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0minteractive\u001b[0m \u001b[0muse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "for index, row in tqdm.tqdm(data.iterrows(), total = data.shape[0]):\n",
    "    log_return = []\n",
    "    v_5 = []\n",
    "    v_30 = []\n",
    "    for c in cols:\n",
    "        if index > 0:\n",
    "            log_return.append(np.log(data[c+'_open'][index]/data[c+'_open'][index-1]))\n",
    "        if index > 3:\n",
    "            v_5.append(np.std(data[c+'_open'][index-4:index+1]))\n",
    "        if index > 28:\n",
    "            v_30.append(np.std(data[c+'_open'][index-29:index+1])) \n",
    "            \n",
    "    if index>0:\n",
    "        log_returns.loc[len(log_returns.index)] = log_return\n",
    "    if index>3:\n",
    "        vol_5.loc[len(vol_5.index)] = v_5\n",
    "    if index>28:\n",
    "        vol_30.loc[len(vol_30.index)] = v_30\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>TSLA_log_return</th>\n",
       "      <th>AAPL_log_return</th>\n",
       "      <th>NVDA_log_return</th>\n",
       "      <th>ORCL_log_return</th>\n",
       "      <th>SPY_log_return</th>\n",
       "      <th>MSFT_log_return</th>\n",
       "      <th>C_log_return</th>\n",
       "      <th>FB_log_return</th>\n",
       "      <th>QQQ_log_return</th>\n",
       "      <th>AMZN_log_return</th>\n",
       "      <th>...</th>\n",
       "      <th>INTC_log_return</th>\n",
       "      <th>PFE_log_return</th>\n",
       "      <th>BAC_log_return</th>\n",
       "      <th>NFLX_log_return</th>\n",
       "      <th>MU_log_return</th>\n",
       "      <th>XOM_log_return</th>\n",
       "      <th>GILD_log_return</th>\n",
       "      <th>SNAP_log_return</th>\n",
       "      <th>GE_log_return</th>\n",
       "      <th>IWM_log_return</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.007179</td>\n",
       "      <td>-0.000686</td>\n",
       "      <td>0.000173</td>\n",
       "      <td>-0.003059</td>\n",
       "      <td>0.000030</td>\n",
       "      <td>0.000814</td>\n",
       "      <td>0.002190</td>\n",
       "      <td>-0.001316</td>\n",
       "      <td>0.000073</td>\n",
       "      <td>-0.000371</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001435</td>\n",
       "      <td>0.001315</td>\n",
       "      <td>0.002300</td>\n",
       "      <td>0.000890</td>\n",
       "      <td>-0.001097</td>\n",
       "      <td>0.001426</td>\n",
       "      <td>0.001174</td>\n",
       "      <td>0.004106</td>\n",
       "      <td>-0.001530</td>\n",
       "      <td>0.001215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.002042</td>\n",
       "      <td>-0.001024</td>\n",
       "      <td>0.001130</td>\n",
       "      <td>-0.001022</td>\n",
       "      <td>-0.000773</td>\n",
       "      <td>-0.002540</td>\n",
       "      <td>0.002565</td>\n",
       "      <td>0.000763</td>\n",
       "      <td>-0.001429</td>\n",
       "      <td>-0.003007</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000410</td>\n",
       "      <td>-0.001841</td>\n",
       "      <td>0.002955</td>\n",
       "      <td>-0.001014</td>\n",
       "      <td>-0.001208</td>\n",
       "      <td>0.002089</td>\n",
       "      <td>-0.000734</td>\n",
       "      <td>-0.000005</td>\n",
       "      <td>0.003058</td>\n",
       "      <td>0.000192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.004992</td>\n",
       "      <td>0.000218</td>\n",
       "      <td>0.004221</td>\n",
       "      <td>0.001486</td>\n",
       "      <td>0.000758</td>\n",
       "      <td>0.002492</td>\n",
       "      <td>-0.002660</td>\n",
       "      <td>0.000686</td>\n",
       "      <td>0.001466</td>\n",
       "      <td>0.001511</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001843</td>\n",
       "      <td>-0.000743</td>\n",
       "      <td>-0.000088</td>\n",
       "      <td>0.004021</td>\n",
       "      <td>-0.000550</td>\n",
       "      <td>0.003736</td>\n",
       "      <td>0.001980</td>\n",
       "      <td>0.001590</td>\n",
       "      <td>-0.000015</td>\n",
       "      <td>0.001086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.003981</td>\n",
       "      <td>-0.002052</td>\n",
       "      <td>-0.003654</td>\n",
       "      <td>0.001298</td>\n",
       "      <td>-0.000848</td>\n",
       "      <td>0.000048</td>\n",
       "      <td>-0.001714</td>\n",
       "      <td>-0.001973</td>\n",
       "      <td>-0.000879</td>\n",
       "      <td>0.000548</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.001536</td>\n",
       "      <td>0.001006</td>\n",
       "      <td>-0.002484</td>\n",
       "      <td>0.000773</td>\n",
       "      <td>-0.002863</td>\n",
       "      <td>-0.001866</td>\n",
       "      <td>-0.000806</td>\n",
       "      <td>-0.000454</td>\n",
       "      <td>-0.000748</td>\n",
       "      <td>-0.001597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.006071</td>\n",
       "      <td>-0.002253</td>\n",
       "      <td>-0.003607</td>\n",
       "      <td>0.001481</td>\n",
       "      <td>-0.000268</td>\n",
       "      <td>-0.002204</td>\n",
       "      <td>0.001333</td>\n",
       "      <td>-0.002114</td>\n",
       "      <td>-0.001394</td>\n",
       "      <td>-0.002560</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000263</td>\n",
       "      <td>0.001530</td>\n",
       "      <td>-0.000144</td>\n",
       "      <td>0.000441</td>\n",
       "      <td>0.001633</td>\n",
       "      <td>0.000440</td>\n",
       "      <td>-0.005237</td>\n",
       "      <td>0.001527</td>\n",
       "      <td>-0.000703</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   TSLA_log_return  AAPL_log_return  NVDA_log_return  ORCL_log_return  \\\n",
       "0        -0.007179        -0.000686         0.000173        -0.003059   \n",
       "1         0.002042        -0.001024         0.001130        -0.001022   \n",
       "2         0.004992         0.000218         0.004221         0.001486   \n",
       "3        -0.003981        -0.002052        -0.003654         0.001298   \n",
       "4        -0.006071        -0.002253        -0.003607         0.001481   \n",
       "\n",
       "   SPY_log_return  MSFT_log_return  C_log_return  FB_log_return  \\\n",
       "0        0.000030         0.000814      0.002190      -0.001316   \n",
       "1       -0.000773        -0.002540      0.002565       0.000763   \n",
       "2        0.000758         0.002492     -0.002660       0.000686   \n",
       "3       -0.000848         0.000048     -0.001714      -0.001973   \n",
       "4       -0.000268        -0.002204      0.001333      -0.002114   \n",
       "\n",
       "   QQQ_log_return  AMZN_log_return  ...  INTC_log_return  PFE_log_return  \\\n",
       "0        0.000073        -0.000371  ...         0.001435        0.001315   \n",
       "1       -0.001429        -0.003007  ...        -0.000410       -0.001841   \n",
       "2        0.001466         0.001511  ...         0.001843       -0.000743   \n",
       "3       -0.000879         0.000548  ...        -0.001536        0.001006   \n",
       "4       -0.001394        -0.002560  ...         0.000000        0.000263   \n",
       "\n",
       "   BAC_log_return  NFLX_log_return  MU_log_return  XOM_log_return  \\\n",
       "0        0.002300         0.000890      -0.001097        0.001426   \n",
       "1        0.002955        -0.001014      -0.001208        0.002089   \n",
       "2       -0.000088         0.004021      -0.000550        0.003736   \n",
       "3       -0.002484         0.000773      -0.002863       -0.001866   \n",
       "4        0.001530        -0.000144       0.000441        0.001633   \n",
       "\n",
       "   GILD_log_return  SNAP_log_return  GE_log_return  IWM_log_return  \n",
       "0         0.001174         0.004106      -0.001530        0.001215  \n",
       "1        -0.000734        -0.000005       0.003058        0.000192  \n",
       "2         0.001980         0.001590      -0.000015        0.001086  \n",
       "3        -0.000806        -0.000454      -0.000748       -0.001597  \n",
       "4         0.000440        -0.005237       0.001527       -0.000703  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_returns.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>TSLA_vol_5</th>\n",
       "      <th>AAPL_vol_5</th>\n",
       "      <th>NVDA_vol_5</th>\n",
       "      <th>ORCL_vol_5</th>\n",
       "      <th>SPY_vol_5</th>\n",
       "      <th>MSFT_vol_5</th>\n",
       "      <th>C_vol_5</th>\n",
       "      <th>FB_vol_5</th>\n",
       "      <th>QQQ_vol_5</th>\n",
       "      <th>AMZN_vol_5</th>\n",
       "      <th>...</th>\n",
       "      <th>INTC_vol_5</th>\n",
       "      <th>PFE_vol_5</th>\n",
       "      <th>BAC_vol_5</th>\n",
       "      <th>NFLX_vol_5</th>\n",
       "      <th>MU_vol_5</th>\n",
       "      <th>XOM_vol_5</th>\n",
       "      <th>GILD_vol_5</th>\n",
       "      <th>SNAP_vol_5</th>\n",
       "      <th>GE_vol_5</th>\n",
       "      <th>IWM_vol_5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.938140</td>\n",
       "      <td>0.136928</td>\n",
       "      <td>0.925184</td>\n",
       "      <td>0.076577</td>\n",
       "      <td>0.132650</td>\n",
       "      <td>0.204490</td>\n",
       "      <td>0.088566</td>\n",
       "      <td>0.199030</td>\n",
       "      <td>0.158821</td>\n",
       "      <td>3.795636</td>\n",
       "      <td>...</td>\n",
       "      <td>0.044944</td>\n",
       "      <td>0.032138</td>\n",
       "      <td>0.051395</td>\n",
       "      <td>0.976963</td>\n",
       "      <td>0.087886</td>\n",
       "      <td>0.111764</td>\n",
       "      <td>0.058275</td>\n",
       "      <td>0.044207</td>\n",
       "      <td>0.007465</td>\n",
       "      <td>0.126079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.103502</td>\n",
       "      <td>0.209687</td>\n",
       "      <td>1.104888</td>\n",
       "      <td>0.079725</td>\n",
       "      <td>0.155743</td>\n",
       "      <td>0.241909</td>\n",
       "      <td>0.074673</td>\n",
       "      <td>0.365263</td>\n",
       "      <td>0.236440</td>\n",
       "      <td>4.118024</td>\n",
       "      <td>...</td>\n",
       "      <td>0.031718</td>\n",
       "      <td>0.032138</td>\n",
       "      <td>0.032206</td>\n",
       "      <td>0.964782</td>\n",
       "      <td>0.080474</td>\n",
       "      <td>0.094192</td>\n",
       "      <td>0.047117</td>\n",
       "      <td>0.044207</td>\n",
       "      <td>0.008593</td>\n",
       "      <td>0.117405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.086445</td>\n",
       "      <td>0.186601</td>\n",
       "      <td>1.154128</td>\n",
       "      <td>0.081731</td>\n",
       "      <td>0.131286</td>\n",
       "      <td>0.256796</td>\n",
       "      <td>0.086163</td>\n",
       "      <td>0.368218</td>\n",
       "      <td>0.211811</td>\n",
       "      <td>3.001680</td>\n",
       "      <td>...</td>\n",
       "      <td>0.031718</td>\n",
       "      <td>0.022565</td>\n",
       "      <td>0.027596</td>\n",
       "      <td>1.018570</td>\n",
       "      <td>0.061612</td>\n",
       "      <td>0.061838</td>\n",
       "      <td>0.055281</td>\n",
       "      <td>0.070498</td>\n",
       "      <td>0.003737</td>\n",
       "      <td>0.129213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.168755</td>\n",
       "      <td>0.161923</td>\n",
       "      <td>1.267422</td>\n",
       "      <td>0.063056</td>\n",
       "      <td>0.142632</td>\n",
       "      <td>0.260875</td>\n",
       "      <td>0.043081</td>\n",
       "      <td>0.401022</td>\n",
       "      <td>0.212941</td>\n",
       "      <td>2.787959</td>\n",
       "      <td>...</td>\n",
       "      <td>0.029563</td>\n",
       "      <td>0.042128</td>\n",
       "      <td>0.023537</td>\n",
       "      <td>0.621292</td>\n",
       "      <td>0.066603</td>\n",
       "      <td>0.070711</td>\n",
       "      <td>0.048415</td>\n",
       "      <td>0.072553</td>\n",
       "      <td>0.008355</td>\n",
       "      <td>0.170341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.836973</td>\n",
       "      <td>0.089451</td>\n",
       "      <td>1.284964</td>\n",
       "      <td>0.058310</td>\n",
       "      <td>0.170529</td>\n",
       "      <td>0.301423</td>\n",
       "      <td>0.032619</td>\n",
       "      <td>0.378062</td>\n",
       "      <td>0.233889</td>\n",
       "      <td>2.624043</td>\n",
       "      <td>...</td>\n",
       "      <td>0.010992</td>\n",
       "      <td>0.036551</td>\n",
       "      <td>0.018868</td>\n",
       "      <td>0.705141</td>\n",
       "      <td>0.077720</td>\n",
       "      <td>0.059174</td>\n",
       "      <td>0.058515</td>\n",
       "      <td>0.061205</td>\n",
       "      <td>0.009517</td>\n",
       "      <td>0.103034</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   TSLA_vol_5  AAPL_vol_5  NVDA_vol_5  ORCL_vol_5  SPY_vol_5  MSFT_vol_5  \\\n",
       "0    0.938140    0.136928    0.925184    0.076577   0.132650    0.204490   \n",
       "1    1.103502    0.209687    1.104888    0.079725   0.155743    0.241909   \n",
       "2    1.086445    0.186601    1.154128    0.081731   0.131286    0.256796   \n",
       "3    1.168755    0.161923    1.267422    0.063056   0.142632    0.260875   \n",
       "4    0.836973    0.089451    1.284964    0.058310   0.170529    0.301423   \n",
       "\n",
       "    C_vol_5  FB_vol_5  QQQ_vol_5  AMZN_vol_5  ...  INTC_vol_5  PFE_vol_5  \\\n",
       "0  0.088566  0.199030   0.158821    3.795636  ...    0.044944   0.032138   \n",
       "1  0.074673  0.365263   0.236440    4.118024  ...    0.031718   0.032138   \n",
       "2  0.086163  0.368218   0.211811    3.001680  ...    0.031718   0.022565   \n",
       "3  0.043081  0.401022   0.212941    2.787959  ...    0.029563   0.042128   \n",
       "4  0.032619  0.378062   0.233889    2.624043  ...    0.010992   0.036551   \n",
       "\n",
       "   BAC_vol_5  NFLX_vol_5  MU_vol_5  XOM_vol_5  GILD_vol_5  SNAP_vol_5  \\\n",
       "0   0.051395    0.976963  0.087886   0.111764    0.058275    0.044207   \n",
       "1   0.032206    0.964782  0.080474   0.094192    0.047117    0.044207   \n",
       "2   0.027596    1.018570  0.061612   0.061838    0.055281    0.070498   \n",
       "3   0.023537    0.621292  0.066603   0.070711    0.048415    0.072553   \n",
       "4   0.018868    0.705141  0.077720   0.059174    0.058515    0.061205   \n",
       "\n",
       "   GE_vol_5  IWM_vol_5  \n",
       "0  0.007465   0.126079  \n",
       "1  0.008593   0.117405  \n",
       "2  0.003737   0.129213  \n",
       "3  0.008355   0.170341  \n",
       "4  0.009517   0.103034  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "  1%|          | 336/37470 [00:32<31:39, 19.55it/s]\u001b[A"
     ]
    }
   ],
   "source": [
    "vol_5.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>TSLA_vol_30</th>\n",
       "      <th>AAPL_vol_30</th>\n",
       "      <th>NVDA_vol_30</th>\n",
       "      <th>ORCL_vol_30</th>\n",
       "      <th>SPY_vol_30</th>\n",
       "      <th>MSFT_vol_30</th>\n",
       "      <th>C_vol_30</th>\n",
       "      <th>FB_vol_30</th>\n",
       "      <th>QQQ_vol_30</th>\n",
       "      <th>AMZN_vol_30</th>\n",
       "      <th>...</th>\n",
       "      <th>INTC_vol_30</th>\n",
       "      <th>PFE_vol_30</th>\n",
       "      <th>BAC_vol_30</th>\n",
       "      <th>NFLX_vol_30</th>\n",
       "      <th>MU_vol_30</th>\n",
       "      <th>XOM_vol_30</th>\n",
       "      <th>GILD_vol_30</th>\n",
       "      <th>SNAP_vol_30</th>\n",
       "      <th>GE_vol_30</th>\n",
       "      <th>IWM_vol_30</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.305705</td>\n",
       "      <td>0.362550</td>\n",
       "      <td>1.778925</td>\n",
       "      <td>0.104934</td>\n",
       "      <td>0.212408</td>\n",
       "      <td>0.365969</td>\n",
       "      <td>0.060673</td>\n",
       "      <td>0.643585</td>\n",
       "      <td>0.430724</td>\n",
       "      <td>8.776870</td>\n",
       "      <td>...</td>\n",
       "      <td>0.062904</td>\n",
       "      <td>0.046381</td>\n",
       "      <td>0.036379</td>\n",
       "      <td>1.626095</td>\n",
       "      <td>0.135028</td>\n",
       "      <td>0.097937</td>\n",
       "      <td>0.076545</td>\n",
       "      <td>0.089688</td>\n",
       "      <td>0.013265</td>\n",
       "      <td>0.210908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.241806</td>\n",
       "      <td>0.341990</td>\n",
       "      <td>1.691428</td>\n",
       "      <td>0.106750</td>\n",
       "      <td>0.220761</td>\n",
       "      <td>0.369471</td>\n",
       "      <td>0.059500</td>\n",
       "      <td>0.631061</td>\n",
       "      <td>0.426226</td>\n",
       "      <td>8.516449</td>\n",
       "      <td>...</td>\n",
       "      <td>0.061138</td>\n",
       "      <td>0.049667</td>\n",
       "      <td>0.030824</td>\n",
       "      <td>1.578571</td>\n",
       "      <td>0.135235</td>\n",
       "      <td>0.090687</td>\n",
       "      <td>0.080504</td>\n",
       "      <td>0.089587</td>\n",
       "      <td>0.012936</td>\n",
       "      <td>0.229076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.260227</td>\n",
       "      <td>0.321087</td>\n",
       "      <td>1.619430</td>\n",
       "      <td>0.101066</td>\n",
       "      <td>0.236867</td>\n",
       "      <td>0.369196</td>\n",
       "      <td>0.058914</td>\n",
       "      <td>0.627104</td>\n",
       "      <td>0.421093</td>\n",
       "      <td>8.380171</td>\n",
       "      <td>...</td>\n",
       "      <td>0.065239</td>\n",
       "      <td>0.055480</td>\n",
       "      <td>0.029158</td>\n",
       "      <td>1.560141</td>\n",
       "      <td>0.135335</td>\n",
       "      <td>0.085950</td>\n",
       "      <td>0.086243</td>\n",
       "      <td>0.087067</td>\n",
       "      <td>0.011806</td>\n",
       "      <td>0.241550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.255219</td>\n",
       "      <td>0.304867</td>\n",
       "      <td>1.549451</td>\n",
       "      <td>0.086599</td>\n",
       "      <td>0.240861</td>\n",
       "      <td>0.364865</td>\n",
       "      <td>0.051651</td>\n",
       "      <td>0.616619</td>\n",
       "      <td>0.421253</td>\n",
       "      <td>8.697911</td>\n",
       "      <td>...</td>\n",
       "      <td>0.066915</td>\n",
       "      <td>0.058395</td>\n",
       "      <td>0.029483</td>\n",
       "      <td>1.472857</td>\n",
       "      <td>0.134078</td>\n",
       "      <td>0.085730</td>\n",
       "      <td>0.088502</td>\n",
       "      <td>0.084357</td>\n",
       "      <td>0.012697</td>\n",
       "      <td>0.252277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.181466</td>\n",
       "      <td>0.284351</td>\n",
       "      <td>1.551113</td>\n",
       "      <td>0.079208</td>\n",
       "      <td>0.249831</td>\n",
       "      <td>0.365820</td>\n",
       "      <td>0.053572</td>\n",
       "      <td>0.597901</td>\n",
       "      <td>0.415051</td>\n",
       "      <td>8.733007</td>\n",
       "      <td>...</td>\n",
       "      <td>0.071645</td>\n",
       "      <td>0.058821</td>\n",
       "      <td>0.029692</td>\n",
       "      <td>1.460369</td>\n",
       "      <td>0.132858</td>\n",
       "      <td>0.088217</td>\n",
       "      <td>0.090930</td>\n",
       "      <td>0.079366</td>\n",
       "      <td>0.014144</td>\n",
       "      <td>0.258733</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   TSLA_vol_30  AAPL_vol_30  NVDA_vol_30  ORCL_vol_30  SPY_vol_30  \\\n",
       "0     1.305705     0.362550     1.778925     0.104934    0.212408   \n",
       "1     1.241806     0.341990     1.691428     0.106750    0.220761   \n",
       "2     1.260227     0.321087     1.619430     0.101066    0.236867   \n",
       "3     1.255219     0.304867     1.549451     0.086599    0.240861   \n",
       "4     1.181466     0.284351     1.551113     0.079208    0.249831   \n",
       "\n",
       "   MSFT_vol_30  C_vol_30  FB_vol_30  QQQ_vol_30  AMZN_vol_30  ...  \\\n",
       "0     0.365969  0.060673   0.643585    0.430724     8.776870  ...   \n",
       "1     0.369471  0.059500   0.631061    0.426226     8.516449  ...   \n",
       "2     0.369196  0.058914   0.627104    0.421093     8.380171  ...   \n",
       "3     0.364865  0.051651   0.616619    0.421253     8.697911  ...   \n",
       "4     0.365820  0.053572   0.597901    0.415051     8.733007  ...   \n",
       "\n",
       "   INTC_vol_30  PFE_vol_30  BAC_vol_30  NFLX_vol_30  MU_vol_30  XOM_vol_30  \\\n",
       "0     0.062904    0.046381    0.036379     1.626095   0.135028    0.097937   \n",
       "1     0.061138    0.049667    0.030824     1.578571   0.135235    0.090687   \n",
       "2     0.065239    0.055480    0.029158     1.560141   0.135335    0.085950   \n",
       "3     0.066915    0.058395    0.029483     1.472857   0.134078    0.085730   \n",
       "4     0.071645    0.058821    0.029692     1.460369   0.132858    0.088217   \n",
       "\n",
       "   GILD_vol_30  SNAP_vol_30  GE_vol_30  IWM_vol_30  \n",
       "0     0.076545     0.089688   0.013265    0.210908  \n",
       "1     0.080504     0.089587   0.012936    0.229076  \n",
       "2     0.086243     0.087067   0.011806    0.241550  \n",
       "3     0.088502     0.084357   0.012697    0.252277  \n",
       "4     0.090930     0.079366   0.014144    0.258733  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vol_30.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}