MindTree(Module 6).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Query 6.1\n",
    "Create a table/data frame with the closing prices of 30 different stocks, with 10 from each of the caps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pylab import plot,show\n",
    "from numpy import vstack,array\n",
    "from numpy.random import rand\n",
    "import numpy as np\n",
    "from scipy.cluster.vq import kmeans,vq\n",
    "import pandas as pd\n",
    "import pandas_datareader as dr\n",
    "from math import sqrt\n",
    "from sklearn.cluster import KMeans\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "gr = pd.DataFrame()\n",
    "airtel = pd.read_csv('C:/Users/rajat/OneDrive/Documents/GitHub/30 stocks/largecaps/bharti_airtel_stock_data.csv')\n",
    "gr['Airtel'] = airtel['Close Price']\n",
    "bhel = pd.read_csv('C:/Users/rajat/OneDrive/Documents/GitHub/30 stocks/largecaps/bhel_stock_data.csv')\n",
    "gr['Bhel'] = bhel['Close Price']\n",
    "cadila = pd.read_csv('C:/Users/rajat/OneDrive/Documents/GitHub/30 stocks/largecaps/cadila_stock_data.csv')\n",
    "gr['Cadila'] = cadila['Close Price']\n",
    "dlf = pd.read_csv('C:/Users/rajat/OneDrive/Documents/GitHub/30 stocks/largecaps/dlf_stock_data.csv')\n",
    "gr['DLF'] = dlf['Close Price']\n",
    "hdfc = pd.read_csv('C:/Users/rajat/OneDrive/Documents/GitHub/30 stocks/largecaps/hdfc_stock_data.csv')\n",
    "gr['Hdfc'] = hdfc['Close Price']\n",
    "hdnzinc = pd.read_csv('C:/Users/rajat/OneDrive/Documents/GitHub/30 stocks/largecaps/hdnzinc_stock_data.csv')\n",
    "gr['hdnzinc'] = hdnzinc['Close Price']\n",
    "IDEA = pd.read_csv('C:/Users/rajat/OneDrive/Documents/GitHub/30 stocks/largecaps/IDEA_stock_data.csv')\n",
    "gr['IDEA'] = IDEA['Close Price']\n",
    "msm = pd.read_csv('C:/Users/rajat/OneDrive/Documents/GitHub/30 stocks/largecaps/msm_stock_data.csv')\n",
    "gr['Msm'] = msm['Close Price']\n",
    "piramal = pd.read_csv('C:/Users/rajat/OneDrive/Documents/GitHub/30 stocks/largecaps/piramal_stock_data.csv')\n",
    "gr['Piramal'] = piramal['Close Price']\n",
    "ultracemo = pd.read_csv('C:/Users/rajat/OneDrive/Documents/GitHub/30 stocks/largecaps/ultracemo_stock_data.csv')\n",
    "gr['ultracemo'] = ultracemo['Close Price']\n",
    "boi = pd.read_csv('C:/Users/rajat/OneDrive/Documents/GitHub/30 stocks/midcaps/boi_stock_data.csv')\n",
    "gr['Boi'] = boi['Close Price']\n",
    "dishtv = pd.read_csv('C:/Users/rajat/OneDrive/Documents/GitHub/30 stocks/midcaps/dishtv_stock_data.csv')\n",
    "gr['Dishtv'] = dishtv['Close Price']\n",
    "escorts = pd.read_csv('C:/Users/rajat/OneDrive/Documents/GitHub/30 stocks/midcaps/escorts_stock_data.csv')\n",
    "gr['Escorts'] = escorts['Close Price']\n",
    "godrej = pd.read_csv('C:/Users/rajat/OneDrive/Documents/GitHub/30 stocks/midcaps/godrej_stock_data.csv')\n",
    "gr['godrej'] = godrej['Close Price']\n",
    "gujgas = pd.read_csv('C:/Users/rajat/OneDrive/Documents/GitHub/30 stocks/midcaps/gujgas_stock_data.csv')\n",
    "gr['Gujgas'] = gujgas['Close Price']\n",
    "pageinds = pd.read_csv('C:/Users/rajat/OneDrive/Documents/GitHub/30 stocks/midcaps/pageinds_stock_data.csv')\n",
    "gr['Pageinds'] = pageinds['Close Price']\n",
    "supreme = pd.read_csv('C:/Users/rajat/OneDrive/Documents/GitHub/30 stocks/midcaps/supreme_stock_data.csv')\n",
    "gr['Supreme'] = supreme['Close Price']\n",
    "tatacomm = pd.read_csv('C:/Users/rajat/OneDrive/Documents/GitHub/30 stocks/midcaps/tatacomm_stock_data.csv')\n",
    "gr['Tatacomm'] = tatacomm['Close Price']\n",
    "tatglobal = pd.read_csv('C:/Users/rajat/OneDrive/Documents/GitHub/30 stocks/midcaps/tatglobal_stock_data.csv')\n",
    "gr['Tatglobal'] = tatglobal['Close Price']\n",
    "ubl = pd.read_csv('C:/Users/rajat/OneDrive/Documents/GitHub/30 stocks/midcaps/ubl_stock_data.csv')\n",
    "gr['Ubl'] = ubl['Close Price']\n",
    "cafin = pd.read_csv('C:/Users/rajat/OneDrive/Documents/GitHub/30 stocks/smallcaps/canfin_stock_data.csv')\n",
    "gr['cafin'] = cafin['Close Price']\n",
    "capacite = pd.read_csv('C:/Users/rajat/OneDrive/Documents/GitHub/30 stocks/smallcaps/capacite_stock_data.csv')\n",
    "gr['Capacite'] = capacite['Close Price']\n",
    "db_corp = pd.read_csv('C:/Users/rajat/OneDrive/Documents/GitHub/30 stocks/smallcaps/db_corp_stock_data.csv')\n",
    "gr['db_corp'] = db_corp['Close Price']\n",
    "manapuram = pd.read_csv('C:/Users/rajat/OneDrive/Documents/GitHub/30 stocks/smallcaps/manapuram_stock_data.csv')\n",
    "gr['Manapuram'] = manapuram['Close Price']\n",
    "program = pd.read_csv('C:/Users/rajat/OneDrive/Documents/GitHub/30 stocks/smallcaps/progam_stock_data.csv')\n",
    "gr['Program'] = program['Close Price']\n",
    "ramcos = pd.read_csv('C:/Users/rajat/OneDrive/Documents/GitHub/30 stocks/smallcaps/ramcos_stock_data.csv')\n",
    "gr['Ramcos'] = ramcos['Close Price']\n",
    "sunteck = pd.read_csv('C:/Users/rajat/OneDrive/Documents/GitHub/30 stocks/smallcaps/sunteck_stock_data.csv')\n",
    "gr['Sunteck'] = sunteck['Close Price']\n",
    "time_techno = pd.read_csv('C:/Users/rajat/OneDrive/Documents/GitHub/30 stocks/smallcaps/time_techno_stock_data.csv')\n",
    "gr['Time_Techno'] = time_techno['Close Price']\n",
    "welcorp = pd.read_csv('C:/Users/rajat/OneDrive/Documents/GitHub/30 stocks/smallcaps/welcorp_stock_data.csv')\n",
    "gr['Welcorp'] = welcorp['Close Price']\n",
    "yes_bank = pd.read_csv('C:/Users/rajat/OneDrive/Documents/GitHub/30 stocks/smallcaps/yes_bank_stock_data.csv')\n",
    "gr['Yes_Bank'] = yes_bank['Close Price']\n",
    "gr['Date'] = bhel['Date']\n",
    "gr['Date'] = pd.to_datetime(gr['Date'])\n",
    "gr.set_index('Date',inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>Airtel</th>\n",
       "      <th>Bhel</th>\n",
       "      <th>Cadila</th>\n",
       "      <th>DLF</th>\n",
       "      <th>Hdfc</th>\n",
       "      <th>hdnzinc</th>\n",
       "      <th>IDEA</th>\n",
       "      <th>Msm</th>\n",
       "      <th>Piramal</th>\n",
       "      <th>ultracemo</th>\n",
       "      <th>...</th>\n",
       "      <th>cafin</th>\n",
       "      <th>Capacite</th>\n",
       "      <th>db_corp</th>\n",
       "      <th>Manapuram</th>\n",
       "      <th>Program</th>\n",
       "      <th>Ramcos</th>\n",
       "      <th>Sunteck</th>\n",
       "      <th>Time_Techno</th>\n",
       "      <th>Welcorp</th>\n",
       "      <th>Yes_Bank</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-07-16</th>\n",
       "      <td>347.40</td>\n",
       "      <td>65.00</td>\n",
       "      <td>349.75</td>\n",
       "      <td>178.05</td>\n",
       "      <td>459.85</td>\n",
       "      <td>268.55</td>\n",
       "      <td>52.45</td>\n",
       "      <td>283.75</td>\n",
       "      <td>2522.50</td>\n",
       "      <td>3864.00</td>\n",
       "      <td>...</td>\n",
       "      <td>324.20</td>\n",
       "      <td>270.15</td>\n",
       "      <td>275.10</td>\n",
       "      <td>98.90</td>\n",
       "      <td>10104.05</td>\n",
       "      <td>338.05</td>\n",
       "      <td>386.85</td>\n",
       "      <td>130.65</td>\n",
       "      <td>106.55</td>\n",
       "      <td>378.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-17</th>\n",
       "      <td>342.55</td>\n",
       "      <td>68.05</td>\n",
       "      <td>360.15</td>\n",
       "      <td>179.95</td>\n",
       "      <td>464.50</td>\n",
       "      <td>269.50</td>\n",
       "      <td>52.45</td>\n",
       "      <td>285.05</td>\n",
       "      <td>2531.40</td>\n",
       "      <td>3895.20</td>\n",
       "      <td>...</td>\n",
       "      <td>331.15</td>\n",
       "      <td>264.65</td>\n",
       "      <td>253.90</td>\n",
       "      <td>99.85</td>\n",
       "      <td>10452.50</td>\n",
       "      <td>335.50</td>\n",
       "      <td>389.40</td>\n",
       "      <td>134.10</td>\n",
       "      <td>106.40</td>\n",
       "      <td>380.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-18</th>\n",
       "      <td>336.80</td>\n",
       "      <td>66.55</td>\n",
       "      <td>354.25</td>\n",
       "      <td>170.70</td>\n",
       "      <td>473.90</td>\n",
       "      <td>269.35</td>\n",
       "      <td>50.00</td>\n",
       "      <td>283.85</td>\n",
       "      <td>2560.40</td>\n",
       "      <td>3849.35</td>\n",
       "      <td>...</td>\n",
       "      <td>329.65</td>\n",
       "      <td>260.80</td>\n",
       "      <td>253.70</td>\n",
       "      <td>100.15</td>\n",
       "      <td>10392.85</td>\n",
       "      <td>344.50</td>\n",
       "      <td>388.60</td>\n",
       "      <td>130.70</td>\n",
       "      <td>106.20</td>\n",
       "      <td>383.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-19</th>\n",
       "      <td>345.00</td>\n",
       "      <td>66.15</td>\n",
       "      <td>343.55</td>\n",
       "      <td>169.85</td>\n",
       "      <td>471.00</td>\n",
       "      <td>268.90</td>\n",
       "      <td>50.40</td>\n",
       "      <td>282.10</td>\n",
       "      <td>2525.15</td>\n",
       "      <td>3857.25</td>\n",
       "      <td>...</td>\n",
       "      <td>328.05</td>\n",
       "      <td>262.10</td>\n",
       "      <td>266.15</td>\n",
       "      <td>100.10</td>\n",
       "      <td>10337.20</td>\n",
       "      <td>343.85</td>\n",
       "      <td>392.70</td>\n",
       "      <td>126.85</td>\n",
       "      <td>104.65</td>\n",
       "      <td>392.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-20</th>\n",
       "      <td>345.40</td>\n",
       "      <td>67.50</td>\n",
       "      <td>347.95</td>\n",
       "      <td>170.40</td>\n",
       "      <td>489.60</td>\n",
       "      <td>265.25</td>\n",
       "      <td>51.15</td>\n",
       "      <td>298.45</td>\n",
       "      <td>2501.95</td>\n",
       "      <td>3871.70</td>\n",
       "      <td>...</td>\n",
       "      <td>325.80</td>\n",
       "      <td>256.40</td>\n",
       "      <td>263.25</td>\n",
       "      <td>100.65</td>\n",
       "      <td>10428.95</td>\n",
       "      <td>344.85</td>\n",
       "      <td>388.65</td>\n",
       "      <td>124.55</td>\n",
       "      <td>108.05</td>\n",
       "      <td>386.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-23</th>\n",
       "      <td>358.95</td>\n",
       "      <td>69.50</td>\n",
       "      <td>359.45</td>\n",
       "      <td>177.65</td>\n",
       "      <td>482.95</td>\n",
       "      <td>267.70</td>\n",
       "      <td>52.55</td>\n",
       "      <td>293.30</td>\n",
       "      <td>2551.75</td>\n",
       "      <td>4009.65</td>\n",
       "      <td>...</td>\n",
       "      <td>330.90</td>\n",
       "      <td>252.35</td>\n",
       "      <td>262.70</td>\n",
       "      <td>101.75</td>\n",
       "      <td>10527.95</td>\n",
       "      <td>347.10</td>\n",
       "      <td>388.65</td>\n",
       "      <td>125.60</td>\n",
       "      <td>109.75</td>\n",
       "      <td>386.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-24</th>\n",
       "      <td>356.80</td>\n",
       "      <td>73.45</td>\n",
       "      <td>372.45</td>\n",
       "      <td>189.10</td>\n",
       "      <td>480.15</td>\n",
       "      <td>272.60</td>\n",
       "      <td>56.40</td>\n",
       "      <td>297.95</td>\n",
       "      <td>107400.00</td>\n",
       "      <td>4136.80</td>\n",
       "      <td>...</td>\n",
       "      <td>345.30</td>\n",
       "      <td>265.15</td>\n",
       "      <td>263.45</td>\n",
       "      <td>103.00</td>\n",
       "      <td>10534.25</td>\n",
       "      <td>360.00</td>\n",
       "      <td>400.35</td>\n",
       "      <td>132.35</td>\n",
       "      <td>115.05</td>\n",
       "      <td>384.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-25</th>\n",
       "      <td>352.30</td>\n",
       "      <td>71.65</td>\n",
       "      <td>364.95</td>\n",
       "      <td>183.50</td>\n",
       "      <td>494.35</td>\n",
       "      <td>281.85</td>\n",
       "      <td>54.90</td>\n",
       "      <td>299.20</td>\n",
       "      <td>2721.40</td>\n",
       "      <td>4044.85</td>\n",
       "      <td>...</td>\n",
       "      <td>355.70</td>\n",
       "      <td>261.20</td>\n",
       "      <td>267.35</td>\n",
       "      <td>101.65</td>\n",
       "      <td>10560.90</td>\n",
       "      <td>358.45</td>\n",
       "      <td>400.00</td>\n",
       "      <td>136.80</td>\n",
       "      <td>111.75</td>\n",
       "      <td>382.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-26</th>\n",
       "      <td>357.65</td>\n",
       "      <td>70.70</td>\n",
       "      <td>371.10</td>\n",
       "      <td>190.20</td>\n",
       "      <td>489.40</td>\n",
       "      <td>272.10</td>\n",
       "      <td>56.80</td>\n",
       "      <td>309.05</td>\n",
       "      <td>2700.05</td>\n",
       "      <td>4142.80</td>\n",
       "      <td>...</td>\n",
       "      <td>352.70</td>\n",
       "      <td>259.80</td>\n",
       "      <td>258.05</td>\n",
       "      <td>103.50</td>\n",
       "      <td>10566.05</td>\n",
       "      <td>361.20</td>\n",
       "      <td>405.15</td>\n",
       "      <td>136.95</td>\n",
       "      <td>127.80</td>\n",
       "      <td>369.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-27</th>\n",
       "      <td>364.70</td>\n",
       "      <td>71.15</td>\n",
       "      <td>373.55</td>\n",
       "      <td>190.50</td>\n",
       "      <td>508.40</td>\n",
       "      <td>273.85</td>\n",
       "      <td>56.35</td>\n",
       "      <td>318.70</td>\n",
       "      <td>2684.25</td>\n",
       "      <td>4124.65</td>\n",
       "      <td>...</td>\n",
       "      <td>369.15</td>\n",
       "      <td>262.60</td>\n",
       "      <td>249.75</td>\n",
       "      <td>104.05</td>\n",
       "      <td>10556.60</td>\n",
       "      <td>358.95</td>\n",
       "      <td>407.90</td>\n",
       "      <td>138.00</td>\n",
       "      <td>123.10</td>\n",
       "      <td>370.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-30</th>\n",
       "      <td>383.25</td>\n",
       "      <td>73.30</td>\n",
       "      <td>382.60</td>\n",
       "      <td>188.90</td>\n",
       "      <td>495.20</td>\n",
       "      <td>274.95</td>\n",
       "      <td>58.40</td>\n",
       "      <td>324.90</td>\n",
       "      <td>2653.60</td>\n",
       "      <td>4243.35</td>\n",
       "      <td>...</td>\n",
       "      <td>363.65</td>\n",
       "      <td>259.25</td>\n",
       "      <td>245.95</td>\n",
       "      <td>105.05</td>\n",
       "      <td>10499.55</td>\n",
       "      <td>361.60</td>\n",
       "      <td>409.05</td>\n",
       "      <td>138.25</td>\n",
       "      <td>121.80</td>\n",
       "      <td>369.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-31</th>\n",
       "      <td>390.60</td>\n",
       "      <td>74.05</td>\n",
       "      <td>381.45</td>\n",
       "      <td>196.30</td>\n",
       "      <td>502.70</td>\n",
       "      <td>280.05</td>\n",
       "      <td>54.85</td>\n",
       "      <td>322.10</td>\n",
       "      <td>109800.00</td>\n",
       "      <td>4193.80</td>\n",
       "      <td>...</td>\n",
       "      <td>334.65</td>\n",
       "      <td>261.75</td>\n",
       "      <td>235.50</td>\n",
       "      <td>105.40</td>\n",
       "      <td>10339.00</td>\n",
       "      <td>362.30</td>\n",
       "      <td>411.50</td>\n",
       "      <td>139.95</td>\n",
       "      <td>125.10</td>\n",
       "      <td>367.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-01</th>\n",
       "      <td>384.50</td>\n",
       "      <td>73.55</td>\n",
       "      <td>377.50</td>\n",
       "      <td>193.35</td>\n",
       "      <td>495.75</td>\n",
       "      <td>277.70</td>\n",
       "      <td>53.55</td>\n",
       "      <td>314.60</td>\n",
       "      <td>2668.65</td>\n",
       "      <td>4193.05</td>\n",
       "      <td>...</td>\n",
       "      <td>331.95</td>\n",
       "      <td>281.35</td>\n",
       "      <td>237.00</td>\n",
       "      <td>105.35</td>\n",
       "      <td>10121.10</td>\n",
       "      <td>364.95</td>\n",
       "      <td>410.80</td>\n",
       "      <td>142.05</td>\n",
       "      <td>125.85</td>\n",
       "      <td>365.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-02</th>\n",
       "      <td>373.00</td>\n",
       "      <td>73.70</td>\n",
       "      <td>376.10</td>\n",
       "      <td>187.75</td>\n",
       "      <td>493.10</td>\n",
       "      <td>280.80</td>\n",
       "      <td>54.40</td>\n",
       "      <td>304.05</td>\n",
       "      <td>111000.00</td>\n",
       "      <td>4183.50</td>\n",
       "      <td>...</td>\n",
       "      <td>332.05</td>\n",
       "      <td>286.10</td>\n",
       "      <td>234.90</td>\n",
       "      <td>109.45</td>\n",
       "      <td>10279.85</td>\n",
       "      <td>382.15</td>\n",
       "      <td>407.40</td>\n",
       "      <td>143.95</td>\n",
       "      <td>125.60</td>\n",
       "      <td>361.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-03</th>\n",
       "      <td>374.50</td>\n",
       "      <td>74.00</td>\n",
       "      <td>377.30</td>\n",
       "      <td>188.90</td>\n",
       "      <td>486.40</td>\n",
       "      <td>282.30</td>\n",
       "      <td>55.20</td>\n",
       "      <td>307.80</td>\n",
       "      <td>2679.80</td>\n",
       "      <td>4167.75</td>\n",
       "      <td>...</td>\n",
       "      <td>335.50</td>\n",
       "      <td>288.35</td>\n",
       "      <td>235.20</td>\n",
       "      <td>112.60</td>\n",
       "      <td>10414.25</td>\n",
       "      <td>401.85</td>\n",
       "      <td>417.55</td>\n",
       "      <td>140.20</td>\n",
       "      <td>125.85</td>\n",
       "      <td>373.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-06</th>\n",
       "      <td>385.75</td>\n",
       "      <td>75.05</td>\n",
       "      <td>380.30</td>\n",
       "      <td>189.35</td>\n",
       "      <td>485.40</td>\n",
       "      <td>285.30</td>\n",
       "      <td>54.90</td>\n",
       "      <td>314.20</td>\n",
       "      <td>2806.30</td>\n",
       "      <td>4177.80</td>\n",
       "      <td>...</td>\n",
       "      <td>333.05</td>\n",
       "      <td>286.75</td>\n",
       "      <td>235.20</td>\n",
       "      <td>112.00</td>\n",
       "      <td>10730.10</td>\n",
       "      <td>399.65</td>\n",
       "      <td>435.70</td>\n",
       "      <td>151.15</td>\n",
       "      <td>125.80</td>\n",
       "      <td>377.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-07</th>\n",
       "      <td>381.90</td>\n",
       "      <td>73.45</td>\n",
       "      <td>373.85</td>\n",
       "      <td>186.80</td>\n",
       "      <td>478.10</td>\n",
       "      <td>285.75</td>\n",
       "      <td>55.00</td>\n",
       "      <td>304.60</td>\n",
       "      <td>120000.00</td>\n",
       "      <td>4206.70</td>\n",
       "      <td>...</td>\n",
       "      <td>330.10</td>\n",
       "      <td>283.50</td>\n",
       "      <td>242.85</td>\n",
       "      <td>111.05</td>\n",
       "      <td>10793.90</td>\n",
       "      <td>383.65</td>\n",
       "      <td>437.55</td>\n",
       "      <td>157.55</td>\n",
       "      <td>124.55</td>\n",
       "      <td>381.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-08</th>\n",
       "      <td>385.30</td>\n",
       "      <td>73.55</td>\n",
       "      <td>375.70</td>\n",
       "      <td>188.95</td>\n",
       "      <td>468.00</td>\n",
       "      <td>286.05</td>\n",
       "      <td>55.35</td>\n",
       "      <td>305.40</td>\n",
       "      <td>2850.85</td>\n",
       "      <td>4244.10</td>\n",
       "      <td>...</td>\n",
       "      <td>326.45</td>\n",
       "      <td>283.95</td>\n",
       "      <td>247.70</td>\n",
       "      <td>111.05</td>\n",
       "      <td>10625.30</td>\n",
       "      <td>377.60</td>\n",
       "      <td>435.00</td>\n",
       "      <td>160.85</td>\n",
       "      <td>127.15</td>\n",
       "      <td>382.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-09</th>\n",
       "      <td>367.05</td>\n",
       "      <td>74.50</td>\n",
       "      <td>378.55</td>\n",
       "      <td>200.60</td>\n",
       "      <td>465.95</td>\n",
       "      <td>286.70</td>\n",
       "      <td>56.35</td>\n",
       "      <td>304.10</td>\n",
       "      <td>2794.35</td>\n",
       "      <td>4310.80</td>\n",
       "      <td>...</td>\n",
       "      <td>326.25</td>\n",
       "      <td>283.95</td>\n",
       "      <td>248.35</td>\n",
       "      <td>112.65</td>\n",
       "      <td>10486.90</td>\n",
       "      <td>382.65</td>\n",
       "      <td>452.85</td>\n",
       "      <td>157.00</td>\n",
       "      <td>129.45</td>\n",
       "      <td>381.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-10</th>\n",
       "      <td>369.80</td>\n",
       "      <td>73.20</td>\n",
       "      <td>377.15</td>\n",
       "      <td>196.55</td>\n",
       "      <td>464.90</td>\n",
       "      <td>286.95</td>\n",
       "      <td>54.40</td>\n",
       "      <td>301.35</td>\n",
       "      <td>2803.80</td>\n",
       "      <td>4246.20</td>\n",
       "      <td>...</td>\n",
       "      <td>320.25</td>\n",
       "      <td>280.70</td>\n",
       "      <td>254.65</td>\n",
       "      <td>107.25</td>\n",
       "      <td>10675.70</td>\n",
       "      <td>385.60</td>\n",
       "      <td>456.95</td>\n",
       "      <td>150.95</td>\n",
       "      <td>127.75</td>\n",
       "      <td>382.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-13</th>\n",
       "      <td>370.15</td>\n",
       "      <td>72.60</td>\n",
       "      <td>354.35</td>\n",
       "      <td>194.15</td>\n",
       "      <td>464.10</td>\n",
       "      <td>286.65</td>\n",
       "      <td>52.50</td>\n",
       "      <td>291.55</td>\n",
       "      <td>2906.10</td>\n",
       "      <td>4194.20</td>\n",
       "      <td>...</td>\n",
       "      <td>312.20</td>\n",
       "      <td>267.90</td>\n",
       "      <td>246.35</td>\n",
       "      <td>105.15</td>\n",
       "      <td>10798.10</td>\n",
       "      <td>381.60</td>\n",
       "      <td>460.50</td>\n",
       "      <td>145.90</td>\n",
       "      <td>126.10</td>\n",
       "      <td>370.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-14</th>\n",
       "      <td>367.00</td>\n",
       "      <td>72.25</td>\n",
       "      <td>340.00</td>\n",
       "      <td>202.80</td>\n",
       "      <td>468.10</td>\n",
       "      <td>285.15</td>\n",
       "      <td>52.00</td>\n",
       "      <td>297.95</td>\n",
       "      <td>2876.25</td>\n",
       "      <td>4299.10</td>\n",
       "      <td>...</td>\n",
       "      <td>314.70</td>\n",
       "      <td>267.20</td>\n",
       "      <td>246.25</td>\n",
       "      <td>103.70</td>\n",
       "      <td>10797.95</td>\n",
       "      <td>381.55</td>\n",
       "      <td>463.70</td>\n",
       "      <td>145.00</td>\n",
       "      <td>127.00</td>\n",
       "      <td>382.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-16</th>\n",
       "      <td>372.00</td>\n",
       "      <td>72.60</td>\n",
       "      <td>340.20</td>\n",
       "      <td>206.70</td>\n",
       "      <td>461.20</td>\n",
       "      <td>276.05</td>\n",
       "      <td>50.60</td>\n",
       "      <td>302.10</td>\n",
       "      <td>2872.30</td>\n",
       "      <td>4213.50</td>\n",
       "      <td>...</td>\n",
       "      <td>313.15</td>\n",
       "      <td>266.25</td>\n",
       "      <td>246.50</td>\n",
       "      <td>102.60</td>\n",
       "      <td>10571.85</td>\n",
       "      <td>379.60</td>\n",
       "      <td>462.20</td>\n",
       "      <td>143.30</td>\n",
       "      <td>126.35</td>\n",
       "      <td>378.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-17</th>\n",
       "      <td>371.40</td>\n",
       "      <td>73.85</td>\n",
       "      <td>357.25</td>\n",
       "      <td>210.15</td>\n",
       "      <td>461.30</td>\n",
       "      <td>277.35</td>\n",
       "      <td>50.90</td>\n",
       "      <td>311.90</td>\n",
       "      <td>111530.00</td>\n",
       "      <td>4285.70</td>\n",
       "      <td>...</td>\n",
       "      <td>323.80</td>\n",
       "      <td>267.45</td>\n",
       "      <td>249.70</td>\n",
       "      <td>100.85</td>\n",
       "      <td>10463.25</td>\n",
       "      <td>380.00</td>\n",
       "      <td>476.60</td>\n",
       "      <td>144.10</td>\n",
       "      <td>126.30</td>\n",
       "      <td>393.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-20</th>\n",
       "      <td>370.55</td>\n",
       "      <td>75.00</td>\n",
       "      <td>377.45</td>\n",
       "      <td>214.85</td>\n",
       "      <td>462.45</td>\n",
       "      <td>280.25</td>\n",
       "      <td>53.40</td>\n",
       "      <td>315.65</td>\n",
       "      <td>2809.55</td>\n",
       "      <td>4300.70</td>\n",
       "      <td>...</td>\n",
       "      <td>326.35</td>\n",
       "      <td>265.90</td>\n",
       "      <td>251.50</td>\n",
       "      <td>100.05</td>\n",
       "      <td>10549.15</td>\n",
       "      <td>376.10</td>\n",
       "      <td>477.90</td>\n",
       "      <td>142.95</td>\n",
       "      <td>140.80</td>\n",
       "      <td>394.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-21</th>\n",
       "      <td>368.25</td>\n",
       "      <td>75.50</td>\n",
       "      <td>387.45</td>\n",
       "      <td>208.20</td>\n",
       "      <td>462.30</td>\n",
       "      <td>284.40</td>\n",
       "      <td>52.90</td>\n",
       "      <td>311.75</td>\n",
       "      <td>111550.00</td>\n",
       "      <td>4350.20</td>\n",
       "      <td>...</td>\n",
       "      <td>339.60</td>\n",
       "      <td>269.55</td>\n",
       "      <td>248.15</td>\n",
       "      <td>102.95</td>\n",
       "      <td>10681.30</td>\n",
       "      <td>369.15</td>\n",
       "      <td>487.75</td>\n",
       "      <td>142.80</td>\n",
       "      <td>148.95</td>\n",
       "      <td>391.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-23</th>\n",
       "      <td>369.90</td>\n",
       "      <td>73.90</td>\n",
       "      <td>391.45</td>\n",
       "      <td>211.90</td>\n",
       "      <td>467.20</td>\n",
       "      <td>286.20</td>\n",
       "      <td>52.80</td>\n",
       "      <td>301.90</td>\n",
       "      <td>2777.70</td>\n",
       "      <td>4347.25</td>\n",
       "      <td>...</td>\n",
       "      <td>330.15</td>\n",
       "      <td>266.85</td>\n",
       "      <td>248.20</td>\n",
       "      <td>100.20</td>\n",
       "      <td>10376.65</td>\n",
       "      <td>370.35</td>\n",
       "      <td>501.45</td>\n",
       "      <td>147.05</td>\n",
       "      <td>156.10</td>\n",
       "      <td>388.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-24</th>\n",
       "      <td>368.95</td>\n",
       "      <td>80.20</td>\n",
       "      <td>398.45</td>\n",
       "      <td>212.95</td>\n",
       "      <td>462.15</td>\n",
       "      <td>292.30</td>\n",
       "      <td>52.15</td>\n",
       "      <td>300.15</td>\n",
       "      <td>111530.00</td>\n",
       "      <td>4316.00</td>\n",
       "      <td>...</td>\n",
       "      <td>319.50</td>\n",
       "      <td>264.60</td>\n",
       "      <td>250.55</td>\n",
       "      <td>100.10</td>\n",
       "      <td>10074.65</td>\n",
       "      <td>364.15</td>\n",
       "      <td>520.65</td>\n",
       "      <td>151.40</td>\n",
       "      <td>162.25</td>\n",
       "      <td>374.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-27</th>\n",
       "      <td>379.15</td>\n",
       "      <td>79.95</td>\n",
       "      <td>396.15</td>\n",
       "      <td>213.65</td>\n",
       "      <td>460.80</td>\n",
       "      <td>299.40</td>\n",
       "      <td>51.40</td>\n",
       "      <td>304.30</td>\n",
       "      <td>2833.75</td>\n",
       "      <td>4370.80</td>\n",
       "      <td>...</td>\n",
       "      <td>320.95</td>\n",
       "      <td>264.80</td>\n",
       "      <td>248.95</td>\n",
       "      <td>101.25</td>\n",
       "      <td>10051.10</td>\n",
       "      <td>364.65</td>\n",
       "      <td>512.10</td>\n",
       "      <td>154.90</td>\n",
       "      <td>163.45</td>\n",
       "      <td>383.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-28</th>\n",
       "      <td>376.80</td>\n",
       "      <td>77.70</td>\n",
       "      <td>397.35</td>\n",
       "      <td>213.35</td>\n",
       "      <td>461.45</td>\n",
       "      <td>297.90</td>\n",
       "      <td>51.10</td>\n",
       "      <td>309.15</td>\n",
       "      <td>107200.00</td>\n",
       "      <td>4427.65</td>\n",
       "      <td>...</td>\n",
       "      <td>319.50</td>\n",
       "      <td>263.75</td>\n",
       "      <td>235.05</td>\n",
       "      <td>100.10</td>\n",
       "      <td>10057.15</td>\n",
       "      <td>368.15</td>\n",
       "      <td>513.65</td>\n",
       "      <td>152.10</td>\n",
       "      <td>166.70</td>\n",
       "      <td>370.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-04</th>\n",
       "      <td>352.75</td>\n",
       "      <td>70.90</td>\n",
       "      <td>257.30</td>\n",
       "      <td>196.15</td>\n",
       "      <td>442.70</td>\n",
       "      <td>248.55</td>\n",
       "      <td>14.00</td>\n",
       "      <td>114.85</td>\n",
       "      <td>2651.55</td>\n",
       "      <td>4760.10</td>\n",
       "      <td>...</td>\n",
       "      <td>364.85</td>\n",
       "      <td>269.40</td>\n",
       "      <td>196.30</td>\n",
       "      <td>127.50</td>\n",
       "      <td>10869.20</td>\n",
       "      <td>230.25</td>\n",
       "      <td>500.60</td>\n",
       "      <td>97.65</td>\n",
       "      <td>142.40</td>\n",
       "      <td>149.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-06</th>\n",
       "      <td>352.40</td>\n",
       "      <td>68.55</td>\n",
       "      <td>245.70</td>\n",
       "      <td>195.45</td>\n",
       "      <td>442.25</td>\n",
       "      <td>246.65</td>\n",
       "      <td>13.85</td>\n",
       "      <td>114.10</td>\n",
       "      <td>2643.90</td>\n",
       "      <td>4762.30</td>\n",
       "      <td>...</td>\n",
       "      <td>353.60</td>\n",
       "      <td>268.55</td>\n",
       "      <td>196.25</td>\n",
       "      <td>127.90</td>\n",
       "      <td>10735.60</td>\n",
       "      <td>233.85</td>\n",
       "      <td>493.60</td>\n",
       "      <td>96.70</td>\n",
       "      <td>139.10</td>\n",
       "      <td>152.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-07</th>\n",
       "      <td>353.80</td>\n",
       "      <td>68.50</td>\n",
       "      <td>242.70</td>\n",
       "      <td>190.40</td>\n",
       "      <td>451.40</td>\n",
       "      <td>243.40</td>\n",
       "      <td>13.45</td>\n",
       "      <td>114.55</td>\n",
       "      <td>2639.40</td>\n",
       "      <td>4692.00</td>\n",
       "      <td>...</td>\n",
       "      <td>354.10</td>\n",
       "      <td>270.90</td>\n",
       "      <td>195.45</td>\n",
       "      <td>138.20</td>\n",
       "      <td>10873.05</td>\n",
       "      <td>230.00</td>\n",
       "      <td>487.20</td>\n",
       "      <td>96.35</td>\n",
       "      <td>139.15</td>\n",
       "      <td>143.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-10</th>\n",
       "      <td>357.10</td>\n",
       "      <td>68.20</td>\n",
       "      <td>247.60</td>\n",
       "      <td>190.05</td>\n",
       "      <td>450.15</td>\n",
       "      <td>239.00</td>\n",
       "      <td>13.40</td>\n",
       "      <td>119.05</td>\n",
       "      <td>2649.85</td>\n",
       "      <td>4571.20</td>\n",
       "      <td>...</td>\n",
       "      <td>349.80</td>\n",
       "      <td>271.00</td>\n",
       "      <td>194.35</td>\n",
       "      <td>135.50</td>\n",
       "      <td>10944.40</td>\n",
       "      <td>221.35</td>\n",
       "      <td>474.80</td>\n",
       "      <td>96.15</td>\n",
       "      <td>141.15</td>\n",
       "      <td>139.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-11</th>\n",
       "      <td>363.45</td>\n",
       "      <td>69.70</td>\n",
       "      <td>250.80</td>\n",
       "      <td>189.25</td>\n",
       "      <td>445.30</td>\n",
       "      <td>241.80</td>\n",
       "      <td>12.80</td>\n",
       "      <td>118.30</td>\n",
       "      <td>2607.20</td>\n",
       "      <td>4572.85</td>\n",
       "      <td>...</td>\n",
       "      <td>346.60</td>\n",
       "      <td>270.00</td>\n",
       "      <td>197.95</td>\n",
       "      <td>132.60</td>\n",
       "      <td>10961.65</td>\n",
       "      <td>229.95</td>\n",
       "      <td>471.50</td>\n",
       "      <td>96.30</td>\n",
       "      <td>139.30</td>\n",
       "      <td>135.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-12</th>\n",
       "      <td>363.25</td>\n",
       "      <td>68.20</td>\n",
       "      <td>246.20</td>\n",
       "      <td>191.55</td>\n",
       "      <td>440.95</td>\n",
       "      <td>241.85</td>\n",
       "      <td>13.15</td>\n",
       "      <td>122.35</td>\n",
       "      <td>2600.70</td>\n",
       "      <td>4597.85</td>\n",
       "      <td>...</td>\n",
       "      <td>345.80</td>\n",
       "      <td>269.05</td>\n",
       "      <td>197.95</td>\n",
       "      <td>131.40</td>\n",
       "      <td>11099.05</td>\n",
       "      <td>224.75</td>\n",
       "      <td>473.15</td>\n",
       "      <td>95.55</td>\n",
       "      <td>139.80</td>\n",
       "      <td>139.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-13</th>\n",
       "      <td>359.90</td>\n",
       "      <td>68.70</td>\n",
       "      <td>245.80</td>\n",
       "      <td>184.55</td>\n",
       "      <td>452.00</td>\n",
       "      <td>241.85</td>\n",
       "      <td>12.85</td>\n",
       "      <td>124.80</td>\n",
       "      <td>2600.70</td>\n",
       "      <td>4584.75</td>\n",
       "      <td>...</td>\n",
       "      <td>350.80</td>\n",
       "      <td>270.15</td>\n",
       "      <td>197.65</td>\n",
       "      <td>135.90</td>\n",
       "      <td>11041.85</td>\n",
       "      <td>224.65</td>\n",
       "      <td>473.15</td>\n",
       "      <td>95.10</td>\n",
       "      <td>140.35</td>\n",
       "      <td>134.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-14</th>\n",
       "      <td>363.75</td>\n",
       "      <td>68.65</td>\n",
       "      <td>245.60</td>\n",
       "      <td>184.95</td>\n",
       "      <td>448.15</td>\n",
       "      <td>239.85</td>\n",
       "      <td>12.90</td>\n",
       "      <td>123.25</td>\n",
       "      <td>2573.50</td>\n",
       "      <td>4569.10</td>\n",
       "      <td>...</td>\n",
       "      <td>345.55</td>\n",
       "      <td>269.20</td>\n",
       "      <td>196.95</td>\n",
       "      <td>134.80</td>\n",
       "      <td>11045.45</td>\n",
       "      <td>221.95</td>\n",
       "      <td>466.45</td>\n",
       "      <td>94.75</td>\n",
       "      <td>140.05</td>\n",
       "      <td>117.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-17</th>\n",
       "      <td>353.30</td>\n",
       "      <td>67.65</td>\n",
       "      <td>242.90</td>\n",
       "      <td>177.85</td>\n",
       "      <td>443.35</td>\n",
       "      <td>236.75</td>\n",
       "      <td>12.50</td>\n",
       "      <td>123.00</td>\n",
       "      <td>2628.05</td>\n",
       "      <td>4570.20</td>\n",
       "      <td>...</td>\n",
       "      <td>337.00</td>\n",
       "      <td>268.95</td>\n",
       "      <td>193.00</td>\n",
       "      <td>134.50</td>\n",
       "      <td>10817.95</td>\n",
       "      <td>219.40</td>\n",
       "      <td>449.40</td>\n",
       "      <td>95.80</td>\n",
       "      <td>138.75</td>\n",
       "      <td>115.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-18</th>\n",
       "      <td>344.00</td>\n",
       "      <td>68.60</td>\n",
       "      <td>233.95</td>\n",
       "      <td>172.65</td>\n",
       "      <td>441.70</td>\n",
       "      <td>234.75</td>\n",
       "      <td>12.10</td>\n",
       "      <td>124.40</td>\n",
       "      <td>106002.00</td>\n",
       "      <td>4501.75</td>\n",
       "      <td>...</td>\n",
       "      <td>343.80</td>\n",
       "      <td>268.95</td>\n",
       "      <td>191.35</td>\n",
       "      <td>138.95</td>\n",
       "      <td>10520.50</td>\n",
       "      <td>212.45</td>\n",
       "      <td>446.55</td>\n",
       "      <td>95.90</td>\n",
       "      <td>137.70</td>\n",
       "      <td>116.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-19</th>\n",
       "      <td>345.15</td>\n",
       "      <td>67.65</td>\n",
       "      <td>237.90</td>\n",
       "      <td>173.70</td>\n",
       "      <td>437.15</td>\n",
       "      <td>231.60</td>\n",
       "      <td>11.95</td>\n",
       "      <td>125.30</td>\n",
       "      <td>2631.30</td>\n",
       "      <td>4455.30</td>\n",
       "      <td>...</td>\n",
       "      <td>347.80</td>\n",
       "      <td>268.80</td>\n",
       "      <td>190.35</td>\n",
       "      <td>137.75</td>\n",
       "      <td>10684.55</td>\n",
       "      <td>205.05</td>\n",
       "      <td>443.05</td>\n",
       "      <td>93.30</td>\n",
       "      <td>136.30</td>\n",
       "      <td>109.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-20</th>\n",
       "      <td>341.10</td>\n",
       "      <td>70.80</td>\n",
       "      <td>242.55</td>\n",
       "      <td>176.40</td>\n",
       "      <td>440.45</td>\n",
       "      <td>231.90</td>\n",
       "      <td>11.40</td>\n",
       "      <td>127.85</td>\n",
       "      <td>112000.00</td>\n",
       "      <td>4511.45</td>\n",
       "      <td>...</td>\n",
       "      <td>355.40</td>\n",
       "      <td>269.35</td>\n",
       "      <td>193.00</td>\n",
       "      <td>1511.00</td>\n",
       "      <td>10787.60</td>\n",
       "      <td>210.85</td>\n",
       "      <td>440.30</td>\n",
       "      <td>94.45</td>\n",
       "      <td>137.20</td>\n",
       "      <td>103.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-21</th>\n",
       "      <td>348.55</td>\n",
       "      <td>71.40</td>\n",
       "      <td>235.05</td>\n",
       "      <td>181.35</td>\n",
       "      <td>438.15</td>\n",
       "      <td>233.85</td>\n",
       "      <td>11.90</td>\n",
       "      <td>126.50</td>\n",
       "      <td>2693.65</td>\n",
       "      <td>4485.00</td>\n",
       "      <td>...</td>\n",
       "      <td>348.85</td>\n",
       "      <td>270.10</td>\n",
       "      <td>193.15</td>\n",
       "      <td>132.10</td>\n",
       "      <td>10765.25</td>\n",
       "      <td>209.40</td>\n",
       "      <td>431.95</td>\n",
       "      <td>94.20</td>\n",
       "      <td>140.05</td>\n",
       "      <td>114.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-24</th>\n",
       "      <td>343.80</td>\n",
       "      <td>73.70</td>\n",
       "      <td>229.05</td>\n",
       "      <td>177.55</td>\n",
       "      <td>440.10</td>\n",
       "      <td>230.75</td>\n",
       "      <td>12.15</td>\n",
       "      <td>121.15</td>\n",
       "      <td>2763.25</td>\n",
       "      <td>4576.05</td>\n",
       "      <td>...</td>\n",
       "      <td>348.60</td>\n",
       "      <td>270.30</td>\n",
       "      <td>193.20</td>\n",
       "      <td>138.10</td>\n",
       "      <td>10717.95</td>\n",
       "      <td>213.65</td>\n",
       "      <td>419.25</td>\n",
       "      <td>95.05</td>\n",
       "      <td>139.95</td>\n",
       "      <td>109.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-25</th>\n",
       "      <td>345.10</td>\n",
       "      <td>73.90</td>\n",
       "      <td>230.40</td>\n",
       "      <td>175.55</td>\n",
       "      <td>437.50</td>\n",
       "      <td>227.95</td>\n",
       "      <td>12.10</td>\n",
       "      <td>119.45</td>\n",
       "      <td>2693.55</td>\n",
       "      <td>4595.15</td>\n",
       "      <td>...</td>\n",
       "      <td>356.05</td>\n",
       "      <td>271.15</td>\n",
       "      <td>195.15</td>\n",
       "      <td>139.60</td>\n",
       "      <td>10754.50</td>\n",
       "      <td>212.25</td>\n",
       "      <td>427.60</td>\n",
       "      <td>93.85</td>\n",
       "      <td>139.00</td>\n",
       "      <td>111.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-26</th>\n",
       "      <td>348.60</td>\n",
       "      <td>75.20</td>\n",
       "      <td>238.90</td>\n",
       "      <td>176.25</td>\n",
       "      <td>441.75</td>\n",
       "      <td>230.60</td>\n",
       "      <td>12.10</td>\n",
       "      <td>118.80</td>\n",
       "      <td>2639.10</td>\n",
       "      <td>4574.80</td>\n",
       "      <td>...</td>\n",
       "      <td>355.25</td>\n",
       "      <td>271.05</td>\n",
       "      <td>188.15</td>\n",
       "      <td>139.75</td>\n",
       "      <td>10818.35</td>\n",
       "      <td>209.35</td>\n",
       "      <td>424.40</td>\n",
       "      <td>95.60</td>\n",
       "      <td>140.60</td>\n",
       "      <td>110.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-27</th>\n",
       "      <td>345.55</td>\n",
       "      <td>74.70</td>\n",
       "      <td>237.85</td>\n",
       "      <td>184.60</td>\n",
       "      <td>455.15</td>\n",
       "      <td>240.90</td>\n",
       "      <td>12.35</td>\n",
       "      <td>121.45</td>\n",
       "      <td>2604.70</td>\n",
       "      <td>4596.05</td>\n",
       "      <td>...</td>\n",
       "      <td>347.45</td>\n",
       "      <td>271.20</td>\n",
       "      <td>192.50</td>\n",
       "      <td>140.20</td>\n",
       "      <td>10866.40</td>\n",
       "      <td>212.25</td>\n",
       "      <td>433.90</td>\n",
       "      <td>94.35</td>\n",
       "      <td>140.00</td>\n",
       "      <td>113.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-28</th>\n",
       "      <td>348.65</td>\n",
       "      <td>73.15</td>\n",
       "      <td>241.70</td>\n",
       "      <td>185.60</td>\n",
       "      <td>464.05</td>\n",
       "      <td>244.60</td>\n",
       "      <td>12.35</td>\n",
       "      <td>121.95</td>\n",
       "      <td>2615.10</td>\n",
       "      <td>4597.40</td>\n",
       "      <td>...</td>\n",
       "      <td>356.45</td>\n",
       "      <td>271.40</td>\n",
       "      <td>192.20</td>\n",
       "      <td>142.00</td>\n",
       "      <td>10714.85</td>\n",
       "      <td>211.25</td>\n",
       "      <td>435.25</td>\n",
       "      <td>94.60</td>\n",
       "      <td>140.45</td>\n",
       "      <td>112.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-01</th>\n",
       "      <td>346.65</td>\n",
       "      <td>73.05</td>\n",
       "      <td>240.35</td>\n",
       "      <td>188.55</td>\n",
       "      <td>469.50</td>\n",
       "      <td>244.10</td>\n",
       "      <td>12.15</td>\n",
       "      <td>126.00</td>\n",
       "      <td>2613.90</td>\n",
       "      <td>4599.15</td>\n",
       "      <td>...</td>\n",
       "      <td>355.80</td>\n",
       "      <td>271.25</td>\n",
       "      <td>191.75</td>\n",
       "      <td>140.20</td>\n",
       "      <td>10771.60</td>\n",
       "      <td>210.50</td>\n",
       "      <td>461.40</td>\n",
       "      <td>95.40</td>\n",
       "      <td>147.05</td>\n",
       "      <td>108.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-02</th>\n",
       "      <td>348.50</td>\n",
       "      <td>72.05</td>\n",
       "      <td>235.90</td>\n",
       "      <td>190.80</td>\n",
       "      <td>469.80</td>\n",
       "      <td>244.55</td>\n",
       "      <td>12.00</td>\n",
       "      <td>126.35</td>\n",
       "      <td>105555.00</td>\n",
       "      <td>4555.85</td>\n",
       "      <td>...</td>\n",
       "      <td>358.30</td>\n",
       "      <td>270.05</td>\n",
       "      <td>191.30</td>\n",
       "      <td>142.00</td>\n",
       "      <td>11047.15</td>\n",
       "      <td>223.90</td>\n",
       "      <td>462.55</td>\n",
       "      <td>92.20</td>\n",
       "      <td>144.35</td>\n",
       "      <td>109.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-03</th>\n",
       "      <td>353.60</td>\n",
       "      <td>72.95</td>\n",
       "      <td>237.10</td>\n",
       "      <td>192.50</td>\n",
       "      <td>479.25</td>\n",
       "      <td>242.20</td>\n",
       "      <td>11.70</td>\n",
       "      <td>126.65</td>\n",
       "      <td>2642.60</td>\n",
       "      <td>4526.65</td>\n",
       "      <td>...</td>\n",
       "      <td>357.95</td>\n",
       "      <td>271.05</td>\n",
       "      <td>195.55</td>\n",
       "      <td>135.45</td>\n",
       "      <td>10929.65</td>\n",
       "      <td>221.40</td>\n",
       "      <td>469.00</td>\n",
       "      <td>93.75</td>\n",
       "      <td>143.45</td>\n",
       "      <td>100.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-04</th>\n",
       "      <td>353.20</td>\n",
       "      <td>73.10</td>\n",
       "      <td>236.75</td>\n",
       "      <td>193.65</td>\n",
       "      <td>489.10</td>\n",
       "      <td>240.85</td>\n",
       "      <td>11.95</td>\n",
       "      <td>126.85</td>\n",
       "      <td>2636.25</td>\n",
       "      <td>4544.10</td>\n",
       "      <td>...</td>\n",
       "      <td>357.95</td>\n",
       "      <td>271.00</td>\n",
       "      <td>192.25</td>\n",
       "      <td>136.70</td>\n",
       "      <td>10986.80</td>\n",
       "      <td>220.95</td>\n",
       "      <td>476.20</td>\n",
       "      <td>94.05</td>\n",
       "      <td>146.15</td>\n",
       "      <td>99.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-05</th>\n",
       "      <td>362.75</td>\n",
       "      <td>70.15</td>\n",
       "      <td>228.70</td>\n",
       "      <td>194.60</td>\n",
       "      <td>473.75</td>\n",
       "      <td>239.85</td>\n",
       "      <td>12.35</td>\n",
       "      <td>122.90</td>\n",
       "      <td>108000.00</td>\n",
       "      <td>4561.15</td>\n",
       "      <td>...</td>\n",
       "      <td>360.45</td>\n",
       "      <td>273.70</td>\n",
       "      <td>194.90</td>\n",
       "      <td>131.45</td>\n",
       "      <td>10887.70</td>\n",
       "      <td>214.05</td>\n",
       "      <td>456.05</td>\n",
       "      <td>94.00</td>\n",
       "      <td>144.30</td>\n",
       "      <td>96.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-08</th>\n",
       "      <td>364.80</td>\n",
       "      <td>67.30</td>\n",
       "      <td>224.65</td>\n",
       "      <td>186.95</td>\n",
       "      <td>462.75</td>\n",
       "      <td>234.65</td>\n",
       "      <td>12.00</td>\n",
       "      <td>118.10</td>\n",
       "      <td>2683.90</td>\n",
       "      <td>4645.35</td>\n",
       "      <td>...</td>\n",
       "      <td>362.25</td>\n",
       "      <td>271.00</td>\n",
       "      <td>189.55</td>\n",
       "      <td>131.30</td>\n",
       "      <td>10900.10</td>\n",
       "      <td>211.75</td>\n",
       "      <td>438.40</td>\n",
       "      <td>90.75</td>\n",
       "      <td>140.40</td>\n",
       "      <td>88.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-09</th>\n",
       "      <td>356.40</td>\n",
       "      <td>67.20</td>\n",
       "      <td>230.75</td>\n",
       "      <td>182.40</td>\n",
       "      <td>455.75</td>\n",
       "      <td>229.25</td>\n",
       "      <td>11.45</td>\n",
       "      <td>118.20</td>\n",
       "      <td>107000.00</td>\n",
       "      <td>4522.05</td>\n",
       "      <td>...</td>\n",
       "      <td>366.00</td>\n",
       "      <td>271.45</td>\n",
       "      <td>187.95</td>\n",
       "      <td>141.25</td>\n",
       "      <td>10962.80</td>\n",
       "      <td>210.45</td>\n",
       "      <td>457.15</td>\n",
       "      <td>91.20</td>\n",
       "      <td>139.70</td>\n",
       "      <td>93.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-10</th>\n",
       "      <td>362.30</td>\n",
       "      <td>66.75</td>\n",
       "      <td>228.30</td>\n",
       "      <td>183.65</td>\n",
       "      <td>455.75</td>\n",
       "      <td>230.45</td>\n",
       "      <td>11.35</td>\n",
       "      <td>118.15</td>\n",
       "      <td>2667.15</td>\n",
       "      <td>4392.35</td>\n",
       "      <td>...</td>\n",
       "      <td>365.80</td>\n",
       "      <td>271.20</td>\n",
       "      <td>190.55</td>\n",
       "      <td>141.70</td>\n",
       "      <td>10921.90</td>\n",
       "      <td>209.45</td>\n",
       "      <td>453.15</td>\n",
       "      <td>90.45</td>\n",
       "      <td>137.20</td>\n",
       "      <td>91.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-11</th>\n",
       "      <td>358.55</td>\n",
       "      <td>65.05</td>\n",
       "      <td>234.35</td>\n",
       "      <td>177.70</td>\n",
       "      <td>467.80</td>\n",
       "      <td>223.10</td>\n",
       "      <td>11.25</td>\n",
       "      <td>120.20</td>\n",
       "      <td>2671.70</td>\n",
       "      <td>4498.70</td>\n",
       "      <td>...</td>\n",
       "      <td>374.00</td>\n",
       "      <td>271.75</td>\n",
       "      <td>188.15</td>\n",
       "      <td>139.85</td>\n",
       "      <td>10901.35</td>\n",
       "      <td>209.90</td>\n",
       "      <td>446.80</td>\n",
       "      <td>90.70</td>\n",
       "      <td>136.85</td>\n",
       "      <td>93.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-12</th>\n",
       "      <td>360.75</td>\n",
       "      <td>64.80</td>\n",
       "      <td>236.20</td>\n",
       "      <td>186.00</td>\n",
       "      <td>481.75</td>\n",
       "      <td>228.05</td>\n",
       "      <td>11.65</td>\n",
       "      <td>118.55</td>\n",
       "      <td>107517.24</td>\n",
       "      <td>4480.70</td>\n",
       "      <td>...</td>\n",
       "      <td>372.85</td>\n",
       "      <td>271.55</td>\n",
       "      <td>186.00</td>\n",
       "      <td>138.20</td>\n",
       "      <td>10861.55</td>\n",
       "      <td>209.05</td>\n",
       "      <td>454.85</td>\n",
       "      <td>91.45</td>\n",
       "      <td>138.35</td>\n",
       "      <td>92.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-15</th>\n",
       "      <td>356.10</td>\n",
       "      <td>64.25</td>\n",
       "      <td>238.30</td>\n",
       "      <td>186.15</td>\n",
       "      <td>491.10</td>\n",
       "      <td>227.85</td>\n",
       "      <td>11.70</td>\n",
       "      <td>118.50</td>\n",
       "      <td>2688.90</td>\n",
       "      <td>4539.10</td>\n",
       "      <td>...</td>\n",
       "      <td>370.10</td>\n",
       "      <td>271.70</td>\n",
       "      <td>182.60</td>\n",
       "      <td>135.90</td>\n",
       "      <td>10951.10</td>\n",
       "      <td>208.00</td>\n",
       "      <td>443.05</td>\n",
       "      <td>89.30</td>\n",
       "      <td>136.50</td>\n",
       "      <td>94.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaT</th>\n",
       "      <td>350.60</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>186.05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>228.35</td>\n",
       "      <td>11.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2672.95</td>\n",
       "      <td>4565.85</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>136.65</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>93.20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>246 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Airtel   Bhel  Cadila     DLF    Hdfc  hdnzinc   IDEA     Msm  \\\n",
       "Date                                                                        \n",
       "2018-07-16  347.40  65.00  349.75  178.05  459.85   268.55  52.45  283.75   \n",
       "2018-07-17  342.55  68.05  360.15  179.95  464.50   269.50  52.45  285.05   \n",
       "2018-07-18  336.80  66.55  354.25  170.70  473.90   269.35  50.00  283.85   \n",
       "2018-07-19  345.00  66.15  343.55  169.85  471.00   268.90  50.40  282.10   \n",
       "2018-07-20  345.40  67.50  347.95  170.40  489.60   265.25  51.15  298.45   \n",
       "2018-07-23  358.95  69.50  359.45  177.65  482.95   267.70  52.55  293.30   \n",
       "2018-07-24  356.80  73.45  372.45  189.10  480.15   272.60  56.40  297.95   \n",
       "2018-07-25  352.30  71.65  364.95  183.50  494.35   281.85  54.90  299.20   \n",
       "2018-07-26  357.65  70.70  371.10  190.20  489.40   272.10  56.80  309.05   \n",
       "2018-07-27  364.70  71.15  373.55  190.50  508.40   273.85  56.35  318.70   \n",
       "2018-07-30  383.25  73.30  382.60  188.90  495.20   274.95  58.40  324.90   \n",
       "2018-07-31  390.60  74.05  381.45  196.30  502.70   280.05  54.85  322.10   \n",
       "2018-08-01  384.50  73.55  377.50  193.35  495.75   277.70  53.55  314.60   \n",
       "2018-08-02  373.00  73.70  376.10  187.75  493.10   280.80  54.40  304.05   \n",
       "2018-08-03  374.50  74.00  377.30  188.90  486.40   282.30  55.20  307.80   \n",
       "2018-08-06  385.75  75.05  380.30  189.35  485.40   285.30  54.90  314.20   \n",
       "2018-08-07  381.90  73.45  373.85  186.80  478.10   285.75  55.00  304.60   \n",
       "2018-08-08  385.30  73.55  375.70  188.95  468.00   286.05  55.35  305.40   \n",
       "2018-08-09  367.05  74.50  378.55  200.60  465.95   286.70  56.35  304.10   \n",
       "2018-08-10  369.80  73.20  377.15  196.55  464.90   286.95  54.40  301.35   \n",
       "2018-08-13  370.15  72.60  354.35  194.15  464.10   286.65  52.50  291.55   \n",
       "2018-08-14  367.00  72.25  340.00  202.80  468.10   285.15  52.00  297.95   \n",
       "2018-08-16  372.00  72.60  340.20  206.70  461.20   276.05  50.60  302.10   \n",
       "2018-08-17  371.40  73.85  357.25  210.15  461.30   277.35  50.90  311.90   \n",
       "2018-08-20  370.55  75.00  377.45  214.85  462.45   280.25  53.40  315.65   \n",
       "2018-08-21  368.25  75.50  387.45  208.20  462.30   284.40  52.90  311.75   \n",
       "2018-08-23  369.90  73.90  391.45  211.90  467.20   286.20  52.80  301.90   \n",
       "2018-08-24  368.95  80.20  398.45  212.95  462.15   292.30  52.15  300.15   \n",
       "2018-08-27  379.15  79.95  396.15  213.65  460.80   299.40  51.40  304.30   \n",
       "2018-08-28  376.80  77.70  397.35  213.35  461.45   297.90  51.10  309.15   \n",
       "...            ...    ...     ...     ...     ...      ...    ...     ...   \n",
       "2019-06-04  352.75  70.90  257.30  196.15  442.70   248.55  14.00  114.85   \n",
       "2019-06-06  352.40  68.55  245.70  195.45  442.25   246.65  13.85  114.10   \n",
       "2019-06-07  353.80  68.50  242.70  190.40  451.40   243.40  13.45  114.55   \n",
       "2019-06-10  357.10  68.20  247.60  190.05  450.15   239.00  13.40  119.05   \n",
       "2019-06-11  363.45  69.70  250.80  189.25  445.30   241.80  12.80  118.30   \n",
       "2019-06-12  363.25  68.20  246.20  191.55  440.95   241.85  13.15  122.35   \n",
       "2019-06-13  359.90  68.70  245.80  184.55  452.00   241.85  12.85  124.80   \n",
       "2019-06-14  363.75  68.65  245.60  184.95  448.15   239.85  12.90  123.25   \n",
       "2019-06-17  353.30  67.65  242.90  177.85  443.35   236.75  12.50  123.00   \n",
       "2019-06-18  344.00  68.60  233.95  172.65  441.70   234.75  12.10  124.40   \n",
       "2019-06-19  345.15  67.65  237.90  173.70  437.15   231.60  11.95  125.30   \n",
       "2019-06-20  341.10  70.80  242.55  176.40  440.45   231.90  11.40  127.85   \n",
       "2019-06-21  348.55  71.40  235.05  181.35  438.15   233.85  11.90  126.50   \n",
       "2019-06-24  343.80  73.70  229.05  177.55  440.10   230.75  12.15  121.15   \n",
       "2019-06-25  345.10  73.90  230.40  175.55  437.50   227.95  12.10  119.45   \n",
       "2019-06-26  348.60  75.20  238.90  176.25  441.75   230.60  12.10  118.80   \n",
       "2019-06-27  345.55  74.70  237.85  184.60  455.15   240.90  12.35  121.45   \n",
       "2019-06-28  348.65  73.15  241.70  185.60  464.05   244.60  12.35  121.95   \n",
       "2019-07-01  346.65  73.05  240.35  188.55  469.50   244.10  12.15  126.00   \n",
       "2019-07-02  348.50  72.05  235.90  190.80  469.80   244.55  12.00  126.35   \n",
       "2019-07-03  353.60  72.95  237.10  192.50  479.25   242.20  11.70  126.65   \n",
       "2019-07-04  353.20  73.10  236.75  193.65  489.10   240.85  11.95  126.85   \n",
       "2019-07-05  362.75  70.15  228.70  194.60  473.75   239.85  12.35  122.90   \n",
       "2019-07-08  364.80  67.30  224.65  186.95  462.75   234.65  12.00  118.10   \n",
       "2019-07-09  356.40  67.20  230.75  182.40  455.75   229.25  11.45  118.20   \n",
       "2019-07-10  362.30  66.75  228.30  183.65  455.75   230.45  11.35  118.15   \n",
       "2019-07-11  358.55  65.05  234.35  177.70  467.80   223.10  11.25  120.20   \n",
       "2019-07-12  360.75  64.80  236.20  186.00  481.75   228.05  11.65  118.55   \n",
       "2019-07-15  356.10  64.25  238.30  186.15  491.10   227.85  11.70  118.50   \n",
       "NaT         350.60    NaN     NaN  186.05     NaN   228.35  11.75     NaN   \n",
       "\n",
       "              Piramal  ultracemo  ...   cafin  Capacite  db_corp  Manapuram  \\\n",
       "Date                              ...                                         \n",
       "2018-07-16    2522.50    3864.00  ...  324.20    270.15   275.10      98.90   \n",
       "2018-07-17    2531.40    3895.20  ...  331.15    264.65   253.90      99.85   \n",
       "2018-07-18    2560.40    3849.35  ...  329.65    260.80   253.70     100.15   \n",
       "2018-07-19    2525.15    3857.25  ...  328.05    262.10   266.15     100.10   \n",
       "2018-07-20    2501.95    3871.70  ...  325.80    256.40   263.25     100.65   \n",
       "2018-07-23    2551.75    4009.65  ...  330.90    252.35   262.70     101.75   \n",
       "2018-07-24  107400.00    4136.80  ...  345.30    265.15   263.45     103.00   \n",
       "2018-07-25    2721.40    4044.85  ...  355.70    261.20   267.35     101.65   \n",
       "2018-07-26    2700.05    4142.80  ...  352.70    259.80   258.05     103.50   \n",
       "2018-07-27    2684.25    4124.65  ...  369.15    262.60   249.75     104.05   \n",
       "2018-07-30    2653.60    4243.35  ...  363.65    259.25   245.95     105.05   \n",
       "2018-07-31  109800.00    4193.80  ...  334.65    261.75   235.50     105.40   \n",
       "2018-08-01    2668.65    4193.05  ...  331.95    281.35   237.00     105.35   \n",
       "2018-08-02  111000.00    4183.50  ...  332.05    286.10   234.90     109.45   \n",
       "2018-08-03    2679.80    4167.75  ...  335.50    288.35   235.20     112.60   \n",
       "2018-08-06    2806.30    4177.80  ...  333.05    286.75   235.20     112.00   \n",
       "2018-08-07  120000.00    4206.70  ...  330.10    283.50   242.85     111.05   \n",
       "2018-08-08    2850.85    4244.10  ...  326.45    283.95   247.70     111.05   \n",
       "2018-08-09    2794.35    4310.80  ...  326.25    283.95   248.35     112.65   \n",
       "2018-08-10    2803.80    4246.20  ...  320.25    280.70   254.65     107.25   \n",
       "2018-08-13    2906.10    4194.20  ...  312.20    267.90   246.35     105.15   \n",
       "2018-08-14    2876.25    4299.10  ...  314.70    267.20   246.25     103.70   \n",
       "2018-08-16    2872.30    4213.50  ...  313.15    266.25   246.50     102.60   \n",
       "2018-08-17  111530.00    4285.70  ...  323.80    267.45   249.70     100.85   \n",
       "2018-08-20    2809.55    4300.70  ...  326.35    265.90   251.50     100.05   \n",
       "2018-08-21  111550.00    4350.20  ...  339.60    269.55   248.15     102.95   \n",
       "2018-08-23    2777.70    4347.25  ...  330.15    266.85   248.20     100.20   \n",
       "2018-08-24  111530.00    4316.00  ...  319.50    264.60   250.55     100.10   \n",
       "2018-08-27    2833.75    4370.80  ...  320.95    264.80   248.95     101.25   \n",
       "2018-08-28  107200.00    4427.65  ...  319.50    263.75   235.05     100.10   \n",
       "...               ...        ...  ...     ...       ...      ...        ...   \n",
       "2019-06-04    2651.55    4760.10  ...  364.85    269.40   196.30     127.50   \n",
       "2019-06-06    2643.90    4762.30  ...  353.60    268.55   196.25     127.90   \n",
       "2019-06-07    2639.40    4692.00  ...  354.10    270.90   195.45     138.20   \n",
       "2019-06-10    2649.85    4571.20  ...  349.80    271.00   194.35     135.50   \n",
       "2019-06-11    2607.20    4572.85  ...  346.60    270.00   197.95     132.60   \n",
       "2019-06-12    2600.70    4597.85  ...  345.80    269.05   197.95     131.40   \n",
       "2019-06-13    2600.70    4584.75  ...  350.80    270.15   197.65     135.90   \n",
       "2019-06-14    2573.50    4569.10  ...  345.55    269.20   196.95     134.80   \n",
       "2019-06-17    2628.05    4570.20  ...  337.00    268.95   193.00     134.50   \n",
       "2019-06-18  106002.00    4501.75  ...  343.80    268.95   191.35     138.95   \n",
       "2019-06-19    2631.30    4455.30  ...  347.80    268.80   190.35     137.75   \n",
       "2019-06-20  112000.00    4511.45  ...  355.40    269.35   193.00    1511.00   \n",
       "2019-06-21    2693.65    4485.00  ...  348.85    270.10   193.15     132.10   \n",
       "2019-06-24    2763.25    4576.05  ...  348.60    270.30   193.20     138.10   \n",
       "2019-06-25    2693.55    4595.15  ...  356.05    271.15   195.15     139.60   \n",
       "2019-06-26    2639.10    4574.80  ...  355.25    271.05   188.15     139.75   \n",
       "2019-06-27    2604.70    4596.05  ...  347.45    271.20   192.50     140.20   \n",
       "2019-06-28    2615.10    4597.40  ...  356.45    271.40   192.20     142.00   \n",
       "2019-07-01    2613.90    4599.15  ...  355.80    271.25   191.75     140.20   \n",
       "2019-07-02  105555.00    4555.85  ...  358.30    270.05   191.30     142.00   \n",
       "2019-07-03    2642.60    4526.65  ...  357.95    271.05   195.55     135.45   \n",
       "2019-07-04    2636.25    4544.10  ...  357.95    271.00   192.25     136.70   \n",
       "2019-07-05  108000.00    4561.15  ...  360.45    273.70   194.90     131.45   \n",
       "2019-07-08    2683.90    4645.35  ...  362.25    271.00   189.55     131.30   \n",
       "2019-07-09  107000.00    4522.05  ...  366.00    271.45   187.95     141.25   \n",
       "2019-07-10    2667.15    4392.35  ...  365.80    271.20   190.55     141.70   \n",
       "2019-07-11    2671.70    4498.70  ...  374.00    271.75   188.15     139.85   \n",
       "2019-07-12  107517.24    4480.70  ...  372.85    271.55   186.00     138.20   \n",
       "2019-07-15    2688.90    4539.10  ...  370.10    271.70   182.60     135.90   \n",
       "NaT           2672.95    4565.85  ...     NaN       NaN      NaN     136.65   \n",
       "\n",
       "             Program  Ramcos  Sunteck  Time_Techno  Welcorp  Yes_Bank  \n",
       "Date                                                                   \n",
       "2018-07-16  10104.05  338.05   386.85       130.65   106.55    378.50  \n",
       "2018-07-17  10452.50  335.50   389.40       134.10   106.40    380.80  \n",
       "2018-07-18  10392.85  344.50   388.60       130.70   106.20    383.65  \n",
       "2018-07-19  10337.20  343.85   392.70       126.85   104.65    392.30  \n",
       "2018-07-20  10428.95  344.85   388.65       124.55   108.05    386.50  \n",
       "2018-07-23  10527.95  347.10   388.65       125.60   109.75    386.60  \n",
       "2018-07-24  10534.25  360.00   400.35       132.35   115.05    384.35  \n",
       "2018-07-25  10560.90  358.45   400.00       136.80   111.75    382.90  \n",
       "2018-07-26  10566.05  361.20   405.15       136.95   127.80    369.75  \n",
       "2018-07-27  10556.60  358.95   407.90       138.00   123.10    370.00  \n",
       "2018-07-30  10499.55  361.60   409.05       138.25   121.80    369.30  \n",
       "2018-07-31  10339.00  362.30   411.50       139.95   125.10    367.95  \n",
       "2018-08-01  10121.10  364.95   410.80       142.05   125.85    365.85  \n",
       "2018-08-02  10279.85  382.15   407.40       143.95   125.60    361.80  \n",
       "2018-08-03  10414.25  401.85   417.55       140.20   125.85    373.20  \n",
       "2018-08-06  10730.10  399.65   435.70       151.15   125.80    377.90  \n",
       "2018-08-07  10793.90  383.65   437.55       157.55   124.55    381.65  \n",
       "2018-08-08  10625.30  377.60   435.00       160.85   127.15    382.75  \n",
       "2018-08-09  10486.90  382.65   452.85       157.00   129.45    381.05  \n",
       "2018-08-10  10675.70  385.60   456.95       150.95   127.75    382.85  \n",
       "2018-08-13  10798.10  381.60   460.50       145.90   126.10    370.70  \n",
       "2018-08-14  10797.95  381.55   463.70       145.00   127.00    382.50  \n",
       "2018-08-16  10571.85  379.60   462.20       143.30   126.35    378.45  \n",
       "2018-08-17  10463.25  380.00   476.60       144.10   126.30    393.20  \n",
       "2018-08-20  10549.15  376.10   477.90       142.95   140.80    394.00  \n",
       "2018-08-21  10681.30  369.15   487.75       142.80   148.95    391.35  \n",
       "2018-08-23  10376.65  370.35   501.45       147.05   156.10    388.60  \n",
       "2018-08-24  10074.65  364.15   520.65       151.40   162.25    374.20  \n",
       "2018-08-27  10051.10  364.65   512.10       154.90   163.45    383.00  \n",
       "2018-08-28  10057.15  368.15   513.65       152.10   166.70    370.60  \n",
       "...              ...     ...      ...          ...      ...       ...  \n",
       "2019-06-04  10869.20  230.25   500.60        97.65   142.40    149.45  \n",
       "2019-06-06  10735.60  233.85   493.60        96.70   139.10    152.80  \n",
       "2019-06-07  10873.05  230.00   487.20        96.35   139.15    143.40  \n",
       "2019-06-10  10944.40  221.35   474.80        96.15   141.15    139.90  \n",
       "2019-06-11  10961.65  229.95   471.50        96.30   139.30    135.90  \n",
       "2019-06-12  11099.05  224.75   473.15        95.55   139.80    139.55  \n",
       "2019-06-13  11041.85  224.65   473.15        95.10   140.35    134.75  \n",
       "2019-06-14  11045.45  221.95   466.45        94.75   140.05    117.10  \n",
       "2019-06-17  10817.95  219.40   449.40        95.80   138.75    115.05  \n",
       "2019-06-18  10520.50  212.45   446.55        95.90   137.70    116.15  \n",
       "2019-06-19  10684.55  205.05   443.05        93.30   136.30    109.30  \n",
       "2019-06-20  10787.60  210.85   440.30        94.45   137.20    103.20  \n",
       "2019-06-21  10765.25  209.40   431.95        94.20   140.05    114.55  \n",
       "2019-06-24  10717.95  213.65   419.25        95.05   139.95    109.60  \n",
       "2019-06-25  10754.50  212.25   427.60        93.85   139.00    111.95  \n",
       "2019-06-26  10818.35  209.35   424.40        95.60   140.60    110.05  \n",
       "2019-06-27  10866.40  212.25   433.90        94.35   140.00    113.05  \n",
       "2019-06-28  10714.85  211.25   435.25        94.60   140.45    112.40  \n",
       "2019-07-01  10771.60  210.50   461.40        95.40   147.05    108.75  \n",
       "2019-07-02  11047.15  223.90   462.55        92.20   144.35    109.15  \n",
       "2019-07-03  10929.65  221.40   469.00        93.75   143.45    100.80  \n",
       "2019-07-04  10986.80  220.95   476.20        94.05   146.15     99.75  \n",
       "2019-07-05  10887.70  214.05   456.05        94.00   144.30     96.25  \n",
       "2019-07-08  10900.10  211.75   438.40        90.75   140.40     88.15  \n",
       "2019-07-09  10962.80  210.45   457.15        91.20   139.70     93.15  \n",
       "2019-07-10  10921.90  209.45   453.15        90.45   137.20     91.35  \n",
       "2019-07-11  10901.35  209.90   446.80        90.70   136.85     93.00  \n",
       "2019-07-12  10861.55  209.05   454.85        91.45   138.35     92.45  \n",
       "2019-07-15  10951.10  208.00   443.05        89.30   136.50     94.20  \n",
       "NaT              NaN     NaN      NaN          NaN      NaN     93.20  \n",
       "\n",
       "[246 rows x 30 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Query 6.2\n",
    "Calculate average annual percentage return and volatility of all 30 stocks over a theoretical one year period"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Returns</th>\n",
       "      <th>Volatility</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Airtel</th>\n",
       "      <td>0.091128</td>\n",
       "      <td>0.364091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bhel</th>\n",
       "      <td>0.061414</td>\n",
       "      <td>0.383614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cadila</th>\n",
       "      <td>-0.347095</td>\n",
       "      <td>0.312668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DLF</th>\n",
       "      <td>0.149078</td>\n",
       "      <td>0.453534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hdfc</th>\n",
       "      <td>0.102871</td>\n",
       "      <td>0.265441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hdnzinc</th>\n",
       "      <td>-0.138794</td>\n",
       "      <td>0.248340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IDEA</th>\n",
       "      <td>-1.314304</td>\n",
       "      <td>0.644506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Msm</th>\n",
       "      <td>-0.757928</td>\n",
       "      <td>0.514570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Piramal</th>\n",
       "      <td>2340.595623</td>\n",
       "      <td>270.736837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ultracemo</th>\n",
       "      <td>0.204864</td>\n",
       "      <td>0.277846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Boi</th>\n",
       "      <td>0.194045</td>\n",
       "      <td>0.473889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dishtv</th>\n",
       "      <td>-0.480459</td>\n",
       "      <td>0.728412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Escorts</th>\n",
       "      <td>-0.442481</td>\n",
       "      <td>0.393127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>godrej</th>\n",
       "      <td>-0.139062</td>\n",
       "      <td>0.278744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gujgas</th>\n",
       "      <td>-0.724707</td>\n",
       "      <td>0.860907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pageinds</th>\n",
       "      <td>-0.250851</td>\n",
       "      <td>0.378052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Supreme</th>\n",
       "      <td>-0.047810</td>\n",
       "      <td>0.285237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tatacomm</th>\n",
       "      <td>-0.096821</td>\n",
       "      <td>0.291973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tatglobal</th>\n",
       "      <td>0.111737</td>\n",
       "      <td>0.321566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ubl</th>\n",
       "      <td>0.225874</td>\n",
       "      <td>0.302653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cafin</th>\n",
       "      <td>0.248780</td>\n",
       "      <td>0.477593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Capacite</th>\n",
       "      <td>0.065185</td>\n",
       "      <td>0.348851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>db_corp</th>\n",
       "      <td>-0.379854</td>\n",
       "      <td>0.294190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Manapuram</th>\n",
       "      <td>127.470044</td>\n",
       "      <td>42.801235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Program</th>\n",
       "      <td>0.102394</td>\n",
       "      <td>0.196956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ramcos</th>\n",
       "      <td>-0.447532</td>\n",
       "      <td>0.328845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sunteck</th>\n",
       "      <td>0.199797</td>\n",
       "      <td>0.346400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time_Techno</th>\n",
       "      <td>-0.299798</td>\n",
       "      <td>0.434606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Welcorp</th>\n",
       "      <td>0.395526</td>\n",
       "      <td>0.532596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes_Bank</th>\n",
       "      <td>-1.125970</td>\n",
       "      <td>0.765806</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Returns  Volatility\n",
       "Airtel          0.091128    0.364091\n",
       "Bhel            0.061414    0.383614\n",
       "Cadila         -0.347095    0.312668\n",
       "DLF             0.149078    0.453534\n",
       "Hdfc            0.102871    0.265441\n",
       "hdnzinc        -0.138794    0.248340\n",
       "IDEA           -1.314304    0.644506\n",
       "Msm            -0.757928    0.514570\n",
       "Piramal      2340.595623  270.736837\n",
       "ultracemo       0.204864    0.277846\n",
       "Boi             0.194045    0.473889\n",
       "Dishtv         -0.480459    0.728412\n",
       "Escorts        -0.442481    0.393127\n",
       "godrej         -0.139062    0.278744\n",
       "Gujgas         -0.724707    0.860907\n",
       "Pageinds       -0.250851    0.378052\n",
       "Supreme        -0.047810    0.285237\n",
       "Tatacomm       -0.096821    0.291973\n",
       "Tatglobal       0.111737    0.321566\n",
       "Ubl             0.225874    0.302653\n",
       "cafin           0.248780    0.477593\n",
       "Capacite        0.065185    0.348851\n",
       "db_corp        -0.379854    0.294190\n",
       "Manapuram     127.470044   42.801235\n",
       "Program         0.102394    0.196956\n",
       "Ramcos         -0.447532    0.328845\n",
       "Sunteck         0.199797    0.346400\n",
       "Time_Techno    -0.299798    0.434606\n",
       "Welcorp         0.395526    0.532596\n",
       "Yes_Bank       -1.125970    0.765806"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Calculate average annual percentage return and volatilities over a theoretical one year period\n",
    "gr = gr.dropna()\n",
    "returns = gr.pct_change().mean() * 252\n",
    "returns = pd.DataFrame(returns)\n",
    "returns.columns = ['Returns']\n",
    "returns['Volatility'] = gr.pct_change().std() * sqrt(252)\n",
    "returns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Query 6.3 \n",
    "Cluster the 30 stocks according to their mean annual Volatilities and Returns using K-means clustering. Identify the optimum number of clusters using the Elbow curve method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "from sklearn import metrics\n",
    "from scipy.spatial.distance import cdist\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "data = np.asarray([np.asarray(returns['Returns']),np.asarray(returns['Volatility'])]).T\n",
    "X = data\n",
    "distortions = []\n",
    "K = range(1,10)\n",
    "for k in K:\n",
    "    kmeanModel = KMeans(n_clusters=k).fit(X)\n",
    "    kmeanModel.fit(X)\n",
    "    distortions.append(sum(np.min(cdist(X, kmeanModel.cluster_centers_, 'euclidean'), axis=1)) / X.shape[0])\n",
    "\n",
    "# Plot the elbow\n",
    "plt.plot(K, distortions, 'bx-')\n",
    "plt.xlabel('k')\n",
    "plt.ylabel('Distortion')\n",
    "plt.title('The Elbow Method showing the optimal k')\n",
    "plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Query 6.4\n",
    "Prepare a separate Data frame to show which stocks belong to the same cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1f8c593edd8>]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# computing K-Means with K = 3 (3 clusters)\n",
    "centroids,_ = kmeans(X,3)\n",
    "# assign each sample to a cluster\n",
    "idx,_ = vq(data,centroids)\n",
    "# some plotting using numpy's logical indexing\n",
    "plot(data[idx==0,0],data[idx==0,1],'ob',\n",
    "     data[idx==1,0],data[idx==1,1],'oy',\n",
    "     data[idx==2,0],data[idx==2,1],'or',\n",
    "     data[idx==3,0],data[idx==3,1],'og',\n",
    "     data[idx==4,0],data[idx==4,1],'om')\n",
    "plot(centroids[:,0],centroids[:,1],'sg',markersize=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Returns       Piramal\n",
      "Volatility    Piramal\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "#identify the outlier\n",
    "print(returns.idxmax())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop the relevant stock from our data\n",
    "returns.drop('Piramal',inplace=True)\n",
    "#recreate data to feed into the algorithm\n",
    "data = np.asarray([np.asarray(returns['Returns']),np.asarray(returns['Volatility'])]).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# computing K-Means with K = 3 (3 clusters)\n",
    "centroids,_ = kmeans(data,3)\n",
    "# assign each sample to a cluster\n",
    "idx,_ = vq(data,centroids)\n",
    "# some plotting using numpy's logical indexing\n",
    "plot(data[idx==0,0],data[idx==0,1],'ob',\n",
    "     data[idx==1,0],data[idx==1,1],'oy',\n",
    "     data[idx==2,0],data[idx==2,1],'or',\n",
    "     data[idx==3,0],data[idx==3,1],'og',\n",
    "     data[idx==4,0],data[idx==4,1],'om')\n",
    "plot(centroids[:,0],centroids[:,1],'sg',markersize=8)\n",
    "show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1f8c5a16b70>]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "centroids,_ = kmeans(X,3)\n",
    "# assign each sample to a cluster\n",
    "idx,_ = vq(data,centroids)\n",
    "# some plotting using numpy's logical indexing\n",
    "plot(data[idx==0,0],data[idx==0,1],'ob',\n",
    "     data[idx==1,0],data[idx==1,1],'oy',\n",
    "     data[idx==2,0],data[idx==2,1],'or',\n",
    "     data[idx==3,0],data[idx==3,1],'og',\n",
    "     data[idx==4,0],data[idx==4,1],'om')\n",
    "plot(centroids[:,0],centroids[:,1],'sg',markersize=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Returns       Manapuram\n",
      "Volatility    Manapuram\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "#identify the outlier\n",
    "print(returns.idxmax())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop the relevant stock from our data\n",
    "returns.drop('Manapuram',inplace=True)\n",
    "#recreate data to feed into the algorithm\n",
    "data = np.asarray([np.asarray(returns['Returns']),np.asarray(returns['Volatility'])]).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# computing K-Means with K = 3 (3 clusters)\n",
    "centroids,_ = kmeans(data,3)\n",
    "# assign each sample to a cluster\n",
    "idx,_ = vq(data,centroids)\n",
    "# some plotting using numpy's logical indexing\n",
    "plot(data[idx==0,0],data[idx==0,1],'ob',\n",
    "     data[idx==1,0],data[idx==1,1],'oy',\n",
    "     data[idx==2,0],data[idx==2,1],'or',\n",
    "     data[idx==3,0],data[idx==3,1],'og',\n",
    "     data[idx==4,0],data[idx==4,1],'om')\n",
    "plot(centroids[:,0],centroids[:,1],'sg',markersize=8)\n",
    "show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Airtel', 2)\n",
      "('Bhel', 2)\n",
      "('Cadila', 0)\n",
      "('DLF', 2)\n",
      "('Hdfc', 2)\n",
      "('hdnzinc', 0)\n",
      "('IDEA', 1)\n",
      "('Msm', 1)\n",
      "('ultracemo', 2)\n",
      "('Boi', 2)\n",
      "('Dishtv', 0)\n",
      "('Escorts', 0)\n",
      "('godrej', 0)\n",
      "('Gujgas', 1)\n",
      "('Pageinds', 0)\n",
      "('Supreme', 2)\n",
      "('Tatacomm', 2)\n",
      "('Tatglobal', 2)\n",
      "('Ubl', 2)\n",
      "('cafin', 2)\n",
      "('Capacite', 2)\n",
      "('db_corp', 0)\n",
      "('Program', 2)\n",
      "('Ramcos', 0)\n",
      "('Sunteck', 2)\n",
      "('Time_Techno', 0)\n",
      "('Welcorp', 2)\n",
      "('Yes_Bank', 1)\n"
     ]
    }
   ],
   "source": [
    "details = [(name,cluster) for name, cluster in zip(returns.index,idx)]\n",
    "for detail in details:\n",
    "    print(detail)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}