Module 4 - Algo Trading using Classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Module 4 - Algo Trading using Classification\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Query 4.1 \n",
    "Import the csv file of the stock which contained the Bollinger columns as well.Create a new column 'Call' , whose entries are - \n",
    "\n",
    ">'Buy' if the stock price is below the lower Bollinger band.\n",
    "  Hold Buy/ Liquidate Short' if the stock price is between the lower and middle Bollinger band. \n",
    "  Hold Short/ Liquidate Buy' if the stock price is between the middle and upper Bollinger band.\n",
    "  Short' if the stock price is above the upper Bollinger band.\n",
    "\n",
    ">Now train a classification model with the 3 bollinger columns and the stock price as inputs and 'Calls' as output. Check the accuracy on a test set. (There are many classifier models to choose from, try each one out and compare the accuracy for each) \n",
    "\n",
    ">Import another stock data and create the bollinger columns. Using the already defined model, predict the daily calls for this new stock."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Symbol</th>\n",
       "      <th>Series</th>\n",
       "      <th>Date</th>\n",
       "      <th>Prev Close</th>\n",
       "      <th>Open Price</th>\n",
       "      <th>High Price</th>\n",
       "      <th>Low Price</th>\n",
       "      <th>Last Price</th>\n",
       "      <th>Close Price</th>\n",
       "      <th>Average Price</th>\n",
       "      <th>Total Traded Quantity</th>\n",
       "      <th>Turnover</th>\n",
       "      <th>No. of Trades</th>\n",
       "      <th>Deliverable Qty</th>\n",
       "      <th>% Dly Qt to Traded Qty</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>BAJAJELEC</td>\n",
       "      <td>EQ</td>\n",
       "      <td>15-May-2017</td>\n",
       "      <td>350.75</td>\n",
       "      <td>351.20</td>\n",
       "      <td>352.30</td>\n",
       "      <td>339.10</td>\n",
       "      <td>342.00</td>\n",
       "      <td>341.15</td>\n",
       "      <td>344.83</td>\n",
       "      <td>150265</td>\n",
       "      <td>51815366.05</td>\n",
       "      <td>2733</td>\n",
       "      <td>91705</td>\n",
       "      <td>61.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>BAJAJELEC</td>\n",
       "      <td>EQ</td>\n",
       "      <td>16-May-2017</td>\n",
       "      <td>341.15</td>\n",
       "      <td>342.90</td>\n",
       "      <td>350.50</td>\n",
       "      <td>341.65</td>\n",
       "      <td>346.60</td>\n",
       "      <td>347.00</td>\n",
       "      <td>346.14</td>\n",
       "      <td>118849</td>\n",
       "      <td>41138046.60</td>\n",
       "      <td>2671</td>\n",
       "      <td>53340</td>\n",
       "      <td>44.88</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>BAJAJELEC</td>\n",
       "      <td>EQ</td>\n",
       "      <td>17-May-2017</td>\n",
       "      <td>347.00</td>\n",
       "      <td>347.10</td>\n",
       "      <td>352.00</td>\n",
       "      <td>345.85</td>\n",
       "      <td>349.50</td>\n",
       "      <td>349.85</td>\n",
       "      <td>349.90</td>\n",
       "      <td>108592</td>\n",
       "      <td>37996454.75</td>\n",
       "      <td>2259</td>\n",
       "      <td>46366</td>\n",
       "      <td>42.70</td>\n",
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       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>BAJAJELEC</td>\n",
       "      <td>EQ</td>\n",
       "      <td>18-May-2017</td>\n",
       "      <td>349.85</td>\n",
       "      <td>346.95</td>\n",
       "      <td>346.95</td>\n",
       "      <td>331.50</td>\n",
       "      <td>336.00</td>\n",
       "      <td>334.10</td>\n",
       "      <td>339.31</td>\n",
       "      <td>158652</td>\n",
       "      <td>53831647.20</td>\n",
       "      <td>3454</td>\n",
       "      <td>91087</td>\n",
       "      <td>57.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>BAJAJELEC</td>\n",
       "      <td>EQ</td>\n",
       "      <td>19-May-2017</td>\n",
       "      <td>334.10</td>\n",
       "      <td>339.05</td>\n",
       "      <td>344.00</td>\n",
       "      <td>332.00</td>\n",
       "      <td>335.50</td>\n",
       "      <td>336.20</td>\n",
       "      <td>338.64</td>\n",
       "      <td>129244</td>\n",
       "      <td>43767310.50</td>\n",
       "      <td>3097</td>\n",
       "      <td>47468</td>\n",
       "      <td>36.73</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <td>489</td>\n",
       "      <td>BAJAJELEC</td>\n",
       "      <td>EQ</td>\n",
       "      <td>07-May-2019</td>\n",
       "      <td>557.85</td>\n",
       "      <td>561.00</td>\n",
       "      <td>565.00</td>\n",
       "      <td>552.05</td>\n",
       "      <td>558.20</td>\n",
       "      <td>557.75</td>\n",
       "      <td>557.86</td>\n",
       "      <td>68237</td>\n",
       "      <td>38066482.60</td>\n",
       "      <td>5173</td>\n",
       "      <td>21908</td>\n",
       "      <td>32.11</td>\n",
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       "    <tr>\n",
       "      <td>490</td>\n",
       "      <td>BAJAJELEC</td>\n",
       "      <td>EQ</td>\n",
       "      <td>08-May-2019</td>\n",
       "      <td>557.75</td>\n",
       "      <td>561.00</td>\n",
       "      <td>561.00</td>\n",
       "      <td>530.00</td>\n",
       "      <td>531.00</td>\n",
       "      <td>534.45</td>\n",
       "      <td>544.69</td>\n",
       "      <td>160037</td>\n",
       "      <td>87171061.90</td>\n",
       "      <td>11144</td>\n",
       "      <td>39062</td>\n",
       "      <td>24.41</td>\n",
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       "    <tr>\n",
       "      <td>491</td>\n",
       "      <td>BAJAJELEC</td>\n",
       "      <td>EQ</td>\n",
       "      <td>09-May-2019</td>\n",
       "      <td>534.45</td>\n",
       "      <td>531.00</td>\n",
       "      <td>535.40</td>\n",
       "      <td>525.50</td>\n",
       "      <td>530.70</td>\n",
       "      <td>531.85</td>\n",
       "      <td>530.82</td>\n",
       "      <td>81531</td>\n",
       "      <td>43278252.70</td>\n",
       "      <td>3990</td>\n",
       "      <td>32290</td>\n",
       "      <td>39.60</td>\n",
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       "    <tr>\n",
       "      <td>492</td>\n",
       "      <td>BAJAJELEC</td>\n",
       "      <td>EQ</td>\n",
       "      <td>10-May-2019</td>\n",
       "      <td>531.85</td>\n",
       "      <td>528.00</td>\n",
       "      <td>545.00</td>\n",
       "      <td>521.60</td>\n",
       "      <td>543.45</td>\n",
       "      <td>540.90</td>\n",
       "      <td>533.29</td>\n",
       "      <td>177917</td>\n",
       "      <td>94880470.80</td>\n",
       "      <td>7768</td>\n",
       "      <td>31813</td>\n",
       "      <td>17.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>493</td>\n",
       "      <td>BAJAJELEC</td>\n",
       "      <td>EQ</td>\n",
       "      <td>13-May-2019</td>\n",
       "      <td>540.90</td>\n",
       "      <td>539.90</td>\n",
       "      <td>543.85</td>\n",
       "      <td>531.05</td>\n",
       "      <td>538.60</td>\n",
       "      <td>541.55</td>\n",
       "      <td>536.97</td>\n",
       "      <td>98236</td>\n",
       "      <td>52749975.45</td>\n",
       "      <td>4292</td>\n",
       "      <td>39071</td>\n",
       "      <td>39.77</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>494 rows × 15 columns</p>\n",
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       "        Symbol Series         Date  Prev Close  Open Price  High Price  \\\n",
       "0    BAJAJELEC     EQ  15-May-2017      350.75      351.20      352.30   \n",
       "1    BAJAJELEC     EQ  16-May-2017      341.15      342.90      350.50   \n",
       "2    BAJAJELEC     EQ  17-May-2017      347.00      347.10      352.00   \n",
       "3    BAJAJELEC     EQ  18-May-2017      349.85      346.95      346.95   \n",
       "4    BAJAJELEC     EQ  19-May-2017      334.10      339.05      344.00   \n",
       "..         ...    ...          ...         ...         ...         ...   \n",
       "489  BAJAJELEC     EQ  07-May-2019      557.85      561.00      565.00   \n",
       "490  BAJAJELEC     EQ  08-May-2019      557.75      561.00      561.00   \n",
       "491  BAJAJELEC     EQ  09-May-2019      534.45      531.00      535.40   \n",
       "492  BAJAJELEC     EQ  10-May-2019      531.85      528.00      545.00   \n",
       "493  BAJAJELEC     EQ  13-May-2019      540.90      539.90      543.85   \n",
       "\n",
       "     Low Price  Last Price  Close Price  Average Price  Total Traded Quantity  \\\n",
       "0       339.10      342.00       341.15         344.83                 150265   \n",
       "1       341.65      346.60       347.00         346.14                 118849   \n",
       "2       345.85      349.50       349.85         349.90                 108592   \n",
       "3       331.50      336.00       334.10         339.31                 158652   \n",
       "4       332.00      335.50       336.20         338.64                 129244   \n",
       "..         ...         ...          ...            ...                    ...   \n",
       "489     552.05      558.20       557.75         557.86                  68237   \n",
       "490     530.00      531.00       534.45         544.69                 160037   \n",
       "491     525.50      530.70       531.85         530.82                  81531   \n",
       "492     521.60      543.45       540.90         533.29                 177917   \n",
       "493     531.05      538.60       541.55         536.97                  98236   \n",
       "\n",
       "        Turnover  No. of Trades  Deliverable Qty  % Dly Qt to Traded Qty  \n",
       "0    51815366.05           2733            91705                   61.03  \n",
       "1    41138046.60           2671            53340                   44.88  \n",
       "2    37996454.75           2259            46366                   42.70  \n",
       "3    53831647.20           3454            91087                   57.41  \n",
       "4    43767310.50           3097            47468                   36.73  \n",
       "..           ...            ...              ...                     ...  \n",
       "489  38066482.60           5173            21908                   32.11  \n",
       "490  87171061.90          11144            39062                   24.41  \n",
       "491  43278252.70           3990            32290                   39.60  \n",
       "492  94880470.80           7768            31813                   17.88  \n",
       "493  52749975.45           4292            39071                   39.77  \n",
       "\n",
       "[494 rows x 15 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(r\"BAJAJELEC.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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       "      <td>57</td>\n",
       "      <td>BAJAJELEC</td>\n",
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       "      <td>03-Aug-2017</td>\n",
       "      <td>329.00</td>\n",
       "      <td>329.90</td>\n",
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       "      <td>326.20</td>\n",
       "      <td>327.00</td>\n",
       "      <td>328.25</td>\n",
       "      <td>330.82</td>\n",
       "      <td>444329</td>\n",
       "      <td>1.469917e+08</td>\n",
       "      <td>8318</td>\n",
       "      <td>105055</td>\n",
       "      <td>23.64</td>\n",
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       "      <td>4.821331</td>\n",
       "      <td>343.367663</td>\n",
       "      <td>324.082337</td>\n",
       "      <td>333.725000</td>\n",
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       "      <td>BAJAJELEC</td>\n",
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       "      <td>04-Aug-2017</td>\n",
       "      <td>328.25</td>\n",
       "      <td>325.00</td>\n",
       "      <td>330.70</td>\n",
       "      <td>321.50</td>\n",
       "      <td>325.70</td>\n",
       "      <td>325.90</td>\n",
       "      <td>326.63</td>\n",
       "      <td>77798</td>\n",
       "      <td>2.541108e+07</td>\n",
       "      <td>1989</td>\n",
       "      <td>26446</td>\n",
       "      <td>33.99</td>\n",
       "      <td>332.946429</td>\n",
       "      <td>5.006059</td>\n",
       "      <td>342.958548</td>\n",
       "      <td>322.934310</td>\n",
       "      <td>332.946429</td>\n",
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       "      <td>59</td>\n",
       "      <td>BAJAJELEC</td>\n",
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       "      <td>07-Aug-2017</td>\n",
       "      <td>325.90</td>\n",
       "      <td>330.70</td>\n",
       "      <td>334.75</td>\n",
       "      <td>328.15</td>\n",
       "      <td>332.45</td>\n",
       "      <td>331.95</td>\n",
       "      <td>331.77</td>\n",
       "      <td>81490</td>\n",
       "      <td>2.703600e+07</td>\n",
       "      <td>1650</td>\n",
       "      <td>50666</td>\n",
       "      <td>62.17</td>\n",
       "      <td>332.942857</td>\n",
       "      <td>4.976055</td>\n",
       "      <td>342.894967</td>\n",
       "      <td>322.990747</td>\n",
       "      <td>332.942857</td>\n",
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       "    <tr>\n",
       "      <td>60</td>\n",
       "      <td>BAJAJELEC</td>\n",
       "      <td>EQ</td>\n",
       "      <td>08-Aug-2017</td>\n",
       "      <td>331.95</td>\n",
       "      <td>332.05</td>\n",
       "      <td>338.50</td>\n",
       "      <td>326.00</td>\n",
       "      <td>331.00</td>\n",
       "      <td>329.85</td>\n",
       "      <td>333.63</td>\n",
       "      <td>162378</td>\n",
       "      <td>5.417373e+07</td>\n",
       "      <td>2871</td>\n",
       "      <td>64647</td>\n",
       "      <td>39.81</td>\n",
       "      <td>332.710714</td>\n",
       "      <td>4.863400</td>\n",
       "      <td>342.437514</td>\n",
       "      <td>322.983915</td>\n",
       "      <td>332.710714</td>\n",
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       "      <td>61</td>\n",
       "      <td>BAJAJELEC</td>\n",
       "      <td>EQ</td>\n",
       "      <td>09-Aug-2017</td>\n",
       "      <td>329.85</td>\n",
       "      <td>329.85</td>\n",
       "      <td>330.70</td>\n",
       "      <td>316.70</td>\n",
       "      <td>317.90</td>\n",
       "      <td>319.95</td>\n",
       "      <td>324.83</td>\n",
       "      <td>67710</td>\n",
       "      <td>2.199391e+07</td>\n",
       "      <td>2595</td>\n",
       "      <td>48574</td>\n",
       "      <td>71.74</td>\n",
       "      <td>331.607143</td>\n",
       "      <td>5.684267</td>\n",
       "      <td>342.975677</td>\n",
       "      <td>320.238608</td>\n",
       "      <td>331.607143</td>\n",
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       "      <td>489</td>\n",
       "      <td>BAJAJELEC</td>\n",
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       "      <td>07-May-2019</td>\n",
       "      <td>557.85</td>\n",
       "      <td>561.00</td>\n",
       "      <td>565.00</td>\n",
       "      <td>552.05</td>\n",
       "      <td>558.20</td>\n",
       "      <td>557.75</td>\n",
       "      <td>557.86</td>\n",
       "      <td>68237</td>\n",
       "      <td>3.806648e+07</td>\n",
       "      <td>5173</td>\n",
       "      <td>21908</td>\n",
       "      <td>32.11</td>\n",
       "      <td>565.246429</td>\n",
       "      <td>12.436750</td>\n",
       "      <td>590.119929</td>\n",
       "      <td>540.372928</td>\n",
       "      <td>565.246429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>490</td>\n",
       "      <td>BAJAJELEC</td>\n",
       "      <td>EQ</td>\n",
       "      <td>08-May-2019</td>\n",
       "      <td>557.75</td>\n",
       "      <td>561.00</td>\n",
       "      <td>561.00</td>\n",
       "      <td>530.00</td>\n",
       "      <td>531.00</td>\n",
       "      <td>534.45</td>\n",
       "      <td>544.69</td>\n",
       "      <td>160037</td>\n",
       "      <td>8.717106e+07</td>\n",
       "      <td>11144</td>\n",
       "      <td>39062</td>\n",
       "      <td>24.41</td>\n",
       "      <td>563.689286</td>\n",
       "      <td>12.789401</td>\n",
       "      <td>589.268089</td>\n",
       "      <td>538.110483</td>\n",
       "      <td>563.689286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>491</td>\n",
       "      <td>BAJAJELEC</td>\n",
       "      <td>EQ</td>\n",
       "      <td>09-May-2019</td>\n",
       "      <td>534.45</td>\n",
       "      <td>531.00</td>\n",
       "      <td>535.40</td>\n",
       "      <td>525.50</td>\n",
       "      <td>530.70</td>\n",
       "      <td>531.85</td>\n",
       "      <td>530.82</td>\n",
       "      <td>81531</td>\n",
       "      <td>4.327825e+07</td>\n",
       "      <td>3990</td>\n",
       "      <td>32290</td>\n",
       "      <td>39.60</td>\n",
       "      <td>560.842857</td>\n",
       "      <td>13.783679</td>\n",
       "      <td>588.410215</td>\n",
       "      <td>533.275499</td>\n",
       "      <td>560.842857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>492</td>\n",
       "      <td>BAJAJELEC</td>\n",
       "      <td>EQ</td>\n",
       "      <td>10-May-2019</td>\n",
       "      <td>531.85</td>\n",
       "      <td>528.00</td>\n",
       "      <td>545.00</td>\n",
       "      <td>521.60</td>\n",
       "      <td>543.45</td>\n",
       "      <td>540.90</td>\n",
       "      <td>533.29</td>\n",
       "      <td>177917</td>\n",
       "      <td>9.488047e+07</td>\n",
       "      <td>7768</td>\n",
       "      <td>31813</td>\n",
       "      <td>17.88</td>\n",
       "      <td>558.167857</td>\n",
       "      <td>13.089338</td>\n",
       "      <td>584.346532</td>\n",
       "      <td>531.989182</td>\n",
       "      <td>558.167857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>493</td>\n",
       "      <td>BAJAJELEC</td>\n",
       "      <td>EQ</td>\n",
       "      <td>13-May-2019</td>\n",
       "      <td>540.90</td>\n",
       "      <td>539.90</td>\n",
       "      <td>543.85</td>\n",
       "      <td>531.05</td>\n",
       "      <td>538.60</td>\n",
       "      <td>541.55</td>\n",
       "      <td>536.97</td>\n",
       "      <td>98236</td>\n",
       "      <td>5.274998e+07</td>\n",
       "      <td>4292</td>\n",
       "      <td>39071</td>\n",
       "      <td>39.77</td>\n",
       "      <td>556.360714</td>\n",
       "      <td>13.052239</td>\n",
       "      <td>582.465193</td>\n",
       "      <td>530.256236</td>\n",
       "      <td>556.360714</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>437 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Symbol Series         Date  Prev Close  Open Price  High Price  \\\n",
       "57   BAJAJELEC     EQ  03-Aug-2017      329.00      329.90      336.00   \n",
       "58   BAJAJELEC     EQ  04-Aug-2017      328.25      325.00      330.70   \n",
       "59   BAJAJELEC     EQ  07-Aug-2017      325.90      330.70      334.75   \n",
       "60   BAJAJELEC     EQ  08-Aug-2017      331.95      332.05      338.50   \n",
       "61   BAJAJELEC     EQ  09-Aug-2017      329.85      329.85      330.70   \n",
       "..         ...    ...          ...         ...         ...         ...   \n",
       "489  BAJAJELEC     EQ  07-May-2019      557.85      561.00      565.00   \n",
       "490  BAJAJELEC     EQ  08-May-2019      557.75      561.00      561.00   \n",
       "491  BAJAJELEC     EQ  09-May-2019      534.45      531.00      535.40   \n",
       "492  BAJAJELEC     EQ  10-May-2019      531.85      528.00      545.00   \n",
       "493  BAJAJELEC     EQ  13-May-2019      540.90      539.90      543.85   \n",
       "\n",
       "     Low Price  Last Price  Close Price  Average Price  Total Traded Quantity  \\\n",
       "57      326.20      327.00       328.25         330.82                 444329   \n",
       "58      321.50      325.70       325.90         326.63                  77798   \n",
       "59      328.15      332.45       331.95         331.77                  81490   \n",
       "60      326.00      331.00       329.85         333.63                 162378   \n",
       "61      316.70      317.90       319.95         324.83                  67710   \n",
       "..         ...         ...          ...            ...                    ...   \n",
       "489     552.05      558.20       557.75         557.86                  68237   \n",
       "490     530.00      531.00       534.45         544.69                 160037   \n",
       "491     525.50      530.70       531.85         530.82                  81531   \n",
       "492     521.60      543.45       540.90         533.29                 177917   \n",
       "493     531.05      538.60       541.55         536.97                  98236   \n",
       "\n",
       "         Turnover  No. of Trades  Deliverable Qty  % Dly Qt to Traded Qty  \\\n",
       "57   1.469917e+08           8318           105055                   23.64   \n",
       "58   2.541108e+07           1989            26446                   33.99   \n",
       "59   2.703600e+07           1650            50666                   62.17   \n",
       "60   5.417373e+07           2871            64647                   39.81   \n",
       "61   2.199391e+07           2595            48574                   71.74   \n",
       "..            ...            ...              ...                     ...   \n",
       "489  3.806648e+07           5173            21908                   32.11   \n",
       "490  8.717106e+07          11144            39062                   24.41   \n",
       "491  4.327825e+07           3990            32290                   39.60   \n",
       "492  9.488047e+07           7768            31813                   17.88   \n",
       "493  5.274998e+07           4292            39071                   39.77   \n",
       "\n",
       "      14 Day MA  30 Day STD  Upper Band  Lower Band    Mid Band  \n",
       "57   333.725000    4.821331  343.367663  324.082337  333.725000  \n",
       "58   332.946429    5.006059  342.958548  322.934310  332.946429  \n",
       "59   332.942857    4.976055  342.894967  322.990747  332.942857  \n",
       "60   332.710714    4.863400  342.437514  322.983915  332.710714  \n",
       "61   331.607143    5.684267  342.975677  320.238608  331.607143  \n",
       "..          ...         ...         ...         ...         ...  \n",
       "489  565.246429   12.436750  590.119929  540.372928  565.246429  \n",
       "490  563.689286   12.789401  589.268089  538.110483  563.689286  \n",
       "491  560.842857   13.783679  588.410215  533.275499  560.842857  \n",
       "492  558.167857   13.089338  584.346532  531.989182  558.167857  \n",
       "493  556.360714   13.052239  582.465193  530.256236  556.360714  \n",
       "\n",
       "[437 rows x 20 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['14 Day MA'] = df['Close Price'].rolling(window=14).mean()\n",
    "df['30 Day STD'] = df['Close Price'].rolling(window=20).std()\n",
    "df['Upper Band'] = df['14 Day MA'] + (df['30 Day STD'] * 2)\n",
    "df['Lower Band'] = df['14 Day MA'] - (df['30 Day STD'] * 2)\n",
    "df = df.dropna()\n",
    "df['Mid Band'] = (df['Upper Band']+df['Lower Band'])/2\n",
    "df"
   ]
  },
  {
   "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>Symbol</th>\n",
       "      <th>Series</th>\n",
       "      <th>Date</th>\n",
       "      <th>Prev Close</th>\n",
       "      <th>Open Price</th>\n",
       "      <th>High Price</th>\n",
       "      <th>Low Price</th>\n",
       "      <th>Last Price</th>\n",
       "      <th>Close Price</th>\n",
       "      <th>Average Price</th>\n",
       "      <th>...</th>\n",
       "      <th>Turnover</th>\n",
       "      <th>No. of Trades</th>\n",
       "      <th>Deliverable Qty</th>\n",
       "      <th>% Dly Qt to Traded Qty</th>\n",
       "      <th>14 Day MA</th>\n",
       "      <th>30 Day STD</th>\n",
       "      <th>Upper Band</th>\n",
       "      <th>Lower Band</th>\n",
       "      <th>Mid Band</th>\n",
       "      <th>Call</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>57</td>\n",
       "      <td>BAJAJELEC</td>\n",
       "      <td>EQ</td>\n",
       "      <td>03-Aug-2017</td>\n",
       "      <td>329.00</td>\n",
       "      <td>329.90</td>\n",
       "      <td>336.00</td>\n",
       "      <td>326.20</td>\n",
       "      <td>327.00</td>\n",
       "      <td>328.25</td>\n",
       "      <td>330.82</td>\n",
       "      <td>...</td>\n",
       "      <td>1.469917e+08</td>\n",
       "      <td>8318</td>\n",
       "      <td>105055</td>\n",
       "      <td>23.64</td>\n",
       "      <td>333.725000</td>\n",
       "      <td>4.821331</td>\n",
       "      <td>343.367663</td>\n",
       "      <td>324.082337</td>\n",
       "      <td>333.725000</td>\n",
       "      <td>Hold Buy/ Liquidate Short</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>58</td>\n",
       "      <td>BAJAJELEC</td>\n",
       "      <td>EQ</td>\n",
       "      <td>04-Aug-2017</td>\n",
       "      <td>328.25</td>\n",
       "      <td>325.00</td>\n",
       "      <td>330.70</td>\n",
       "      <td>321.50</td>\n",
       "      <td>325.70</td>\n",
       "      <td>325.90</td>\n",
       "      <td>326.63</td>\n",
       "      <td>...</td>\n",
       "      <td>2.541108e+07</td>\n",
       "      <td>1989</td>\n",
       "      <td>26446</td>\n",
       "      <td>33.99</td>\n",
       "      <td>332.946429</td>\n",
       "      <td>5.006059</td>\n",
       "      <td>342.958548</td>\n",
       "      <td>322.934310</td>\n",
       "      <td>332.946429</td>\n",
       "      <td>Hold Buy/ Liquidate Short</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>59</td>\n",
       "      <td>BAJAJELEC</td>\n",
       "      <td>EQ</td>\n",
       "      <td>07-Aug-2017</td>\n",
       "      <td>325.90</td>\n",
       "      <td>330.70</td>\n",
       "      <td>334.75</td>\n",
       "      <td>328.15</td>\n",
       "      <td>332.45</td>\n",
       "      <td>331.95</td>\n",
       "      <td>331.77</td>\n",
       "      <td>...</td>\n",
       "      <td>2.703600e+07</td>\n",
       "      <td>1650</td>\n",
       "      <td>50666</td>\n",
       "      <td>62.17</td>\n",
       "      <td>332.942857</td>\n",
       "      <td>4.976055</td>\n",
       "      <td>342.894967</td>\n",
       "      <td>322.990747</td>\n",
       "      <td>332.942857</td>\n",
       "      <td>Hold Buy/ Liquidate Short</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>60</td>\n",
       "      <td>BAJAJELEC</td>\n",
       "      <td>EQ</td>\n",
       "      <td>08-Aug-2017</td>\n",
       "      <td>331.95</td>\n",
       "      <td>332.05</td>\n",
       "      <td>338.50</td>\n",
       "      <td>326.00</td>\n",
       "      <td>331.00</td>\n",
       "      <td>329.85</td>\n",
       "      <td>333.63</td>\n",
       "      <td>...</td>\n",
       "      <td>5.417373e+07</td>\n",
       "      <td>2871</td>\n",
       "      <td>64647</td>\n",
       "      <td>39.81</td>\n",
       "      <td>332.710714</td>\n",
       "      <td>4.863400</td>\n",
       "      <td>342.437514</td>\n",
       "      <td>322.983915</td>\n",
       "      <td>332.710714</td>\n",
       "      <td>Hold Buy/ Liquidate Short</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>61</td>\n",
       "      <td>BAJAJELEC</td>\n",
       "      <td>EQ</td>\n",
       "      <td>09-Aug-2017</td>\n",
       "      <td>329.85</td>\n",
       "      <td>329.85</td>\n",
       "      <td>330.70</td>\n",
       "      <td>316.70</td>\n",
       "      <td>317.90</td>\n",
       "      <td>319.95</td>\n",
       "      <td>324.83</td>\n",
       "      <td>...</td>\n",
       "      <td>2.199391e+07</td>\n",
       "      <td>2595</td>\n",
       "      <td>48574</td>\n",
       "      <td>71.74</td>\n",
       "      <td>331.607143</td>\n",
       "      <td>5.684267</td>\n",
       "      <td>342.975677</td>\n",
       "      <td>320.238608</td>\n",
       "      <td>331.607143</td>\n",
       "      <td>Buy</td>\n",
       "    </tr>\n",
       "    <tr>\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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>489</td>\n",
       "      <td>BAJAJELEC</td>\n",
       "      <td>EQ</td>\n",
       "      <td>07-May-2019</td>\n",
       "      <td>557.85</td>\n",
       "      <td>561.00</td>\n",
       "      <td>565.00</td>\n",
       "      <td>552.05</td>\n",
       "      <td>558.20</td>\n",
       "      <td>557.75</td>\n",
       "      <td>557.86</td>\n",
       "      <td>...</td>\n",
       "      <td>3.806648e+07</td>\n",
       "      <td>5173</td>\n",
       "      <td>21908</td>\n",
       "      <td>32.11</td>\n",
       "      <td>565.246429</td>\n",
       "      <td>12.436750</td>\n",
       "      <td>590.119929</td>\n",
       "      <td>540.372928</td>\n",
       "      <td>565.246429</td>\n",
       "      <td>Hold Buy/ Liquidate Short</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>490</td>\n",
       "      <td>BAJAJELEC</td>\n",
       "      <td>EQ</td>\n",
       "      <td>08-May-2019</td>\n",
       "      <td>557.75</td>\n",
       "      <td>561.00</td>\n",
       "      <td>561.00</td>\n",
       "      <td>530.00</td>\n",
       "      <td>531.00</td>\n",
       "      <td>534.45</td>\n",
       "      <td>544.69</td>\n",
       "      <td>...</td>\n",
       "      <td>8.717106e+07</td>\n",
       "      <td>11144</td>\n",
       "      <td>39062</td>\n",
       "      <td>24.41</td>\n",
       "      <td>563.689286</td>\n",
       "      <td>12.789401</td>\n",
       "      <td>589.268089</td>\n",
       "      <td>538.110483</td>\n",
       "      <td>563.689286</td>\n",
       "      <td>Buy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>491</td>\n",
       "      <td>BAJAJELEC</td>\n",
       "      <td>EQ</td>\n",
       "      <td>09-May-2019</td>\n",
       "      <td>534.45</td>\n",
       "      <td>531.00</td>\n",
       "      <td>535.40</td>\n",
       "      <td>525.50</td>\n",
       "      <td>530.70</td>\n",
       "      <td>531.85</td>\n",
       "      <td>530.82</td>\n",
       "      <td>...</td>\n",
       "      <td>4.327825e+07</td>\n",
       "      <td>3990</td>\n",
       "      <td>32290</td>\n",
       "      <td>39.60</td>\n",
       "      <td>560.842857</td>\n",
       "      <td>13.783679</td>\n",
       "      <td>588.410215</td>\n",
       "      <td>533.275499</td>\n",
       "      <td>560.842857</td>\n",
       "      <td>Buy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>492</td>\n",
       "      <td>BAJAJELEC</td>\n",
       "      <td>EQ</td>\n",
       "      <td>10-May-2019</td>\n",
       "      <td>531.85</td>\n",
       "      <td>528.00</td>\n",
       "      <td>545.00</td>\n",
       "      <td>521.60</td>\n",
       "      <td>543.45</td>\n",
       "      <td>540.90</td>\n",
       "      <td>533.29</td>\n",
       "      <td>...</td>\n",
       "      <td>9.488047e+07</td>\n",
       "      <td>7768</td>\n",
       "      <td>31813</td>\n",
       "      <td>17.88</td>\n",
       "      <td>558.167857</td>\n",
       "      <td>13.089338</td>\n",
       "      <td>584.346532</td>\n",
       "      <td>531.989182</td>\n",
       "      <td>558.167857</td>\n",
       "      <td>Hold Buy/ Liquidate Short</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>493</td>\n",
       "      <td>BAJAJELEC</td>\n",
       "      <td>EQ</td>\n",
       "      <td>13-May-2019</td>\n",
       "      <td>540.90</td>\n",
       "      <td>539.90</td>\n",
       "      <td>543.85</td>\n",
       "      <td>531.05</td>\n",
       "      <td>538.60</td>\n",
       "      <td>541.55</td>\n",
       "      <td>536.97</td>\n",
       "      <td>...</td>\n",
       "      <td>5.274998e+07</td>\n",
       "      <td>4292</td>\n",
       "      <td>39071</td>\n",
       "      <td>39.77</td>\n",
       "      <td>556.360714</td>\n",
       "      <td>13.052239</td>\n",
       "      <td>582.465193</td>\n",
       "      <td>530.256236</td>\n",
       "      <td>556.360714</td>\n",
       "      <td>Hold Buy/ Liquidate Short</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>437 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Symbol Series         Date  Prev Close  Open Price  High Price  \\\n",
       "57   BAJAJELEC     EQ  03-Aug-2017      329.00      329.90      336.00   \n",
       "58   BAJAJELEC     EQ  04-Aug-2017      328.25      325.00      330.70   \n",
       "59   BAJAJELEC     EQ  07-Aug-2017      325.90      330.70      334.75   \n",
       "60   BAJAJELEC     EQ  08-Aug-2017      331.95      332.05      338.50   \n",
       "61   BAJAJELEC     EQ  09-Aug-2017      329.85      329.85      330.70   \n",
       "..         ...    ...          ...         ...         ...         ...   \n",
       "489  BAJAJELEC     EQ  07-May-2019      557.85      561.00      565.00   \n",
       "490  BAJAJELEC     EQ  08-May-2019      557.75      561.00      561.00   \n",
       "491  BAJAJELEC     EQ  09-May-2019      534.45      531.00      535.40   \n",
       "492  BAJAJELEC     EQ  10-May-2019      531.85      528.00      545.00   \n",
       "493  BAJAJELEC     EQ  13-May-2019      540.90      539.90      543.85   \n",
       "\n",
       "     Low Price  Last Price  Close Price  Average Price  ...      Turnover  \\\n",
       "57      326.20      327.00       328.25         330.82  ...  1.469917e+08   \n",
       "58      321.50      325.70       325.90         326.63  ...  2.541108e+07   \n",
       "59      328.15      332.45       331.95         331.77  ...  2.703600e+07   \n",
       "60      326.00      331.00       329.85         333.63  ...  5.417373e+07   \n",
       "61      316.70      317.90       319.95         324.83  ...  2.199391e+07   \n",
       "..         ...         ...          ...            ...  ...           ...   \n",
       "489     552.05      558.20       557.75         557.86  ...  3.806648e+07   \n",
       "490     530.00      531.00       534.45         544.69  ...  8.717106e+07   \n",
       "491     525.50      530.70       531.85         530.82  ...  4.327825e+07   \n",
       "492     521.60      543.45       540.90         533.29  ...  9.488047e+07   \n",
       "493     531.05      538.60       541.55         536.97  ...  5.274998e+07   \n",
       "\n",
       "     No. of Trades  Deliverable Qty  % Dly Qt to Traded Qty   14 Day MA  \\\n",
       "57            8318           105055                   23.64  333.725000   \n",
       "58            1989            26446                   33.99  332.946429   \n",
       "59            1650            50666                   62.17  332.942857   \n",
       "60            2871            64647                   39.81  332.710714   \n",
       "61            2595            48574                   71.74  331.607143   \n",
       "..             ...              ...                     ...         ...   \n",
       "489           5173            21908                   32.11  565.246429   \n",
       "490          11144            39062                   24.41  563.689286   \n",
       "491           3990            32290                   39.60  560.842857   \n",
       "492           7768            31813                   17.88  558.167857   \n",
       "493           4292            39071                   39.77  556.360714   \n",
       "\n",
       "     30 Day STD  Upper Band  Lower Band    Mid Band                       Call  \n",
       "57     4.821331  343.367663  324.082337  333.725000  Hold Buy/ Liquidate Short  \n",
       "58     5.006059  342.958548  322.934310  332.946429  Hold Buy/ Liquidate Short  \n",
       "59     4.976055  342.894967  322.990747  332.942857  Hold Buy/ Liquidate Short  \n",
       "60     4.863400  342.437514  322.983915  332.710714  Hold Buy/ Liquidate Short  \n",
       "61     5.684267  342.975677  320.238608  331.607143                        Buy  \n",
       "..          ...         ...         ...         ...                        ...  \n",
       "489   12.436750  590.119929  540.372928  565.246429  Hold Buy/ Liquidate Short  \n",
       "490   12.789401  589.268089  538.110483  563.689286                        Buy  \n",
       "491   13.783679  588.410215  533.275499  560.842857                        Buy  \n",
       "492   13.089338  584.346532  531.989182  558.167857  Hold Buy/ Liquidate Short  \n",
       "493   13.052239  582.465193  530.256236  556.360714  Hold Buy/ Liquidate Short  \n",
       "\n",
       "[437 rows x 21 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def select_buy(df):\n",
    "    if df['Close Price'] < df['Lower Band']:\n",
    "        return \"Buy\"\n",
    "    if df['Close Price'] > df['Lower Band'] and df['Close Price'] < df['Mid Band']:\n",
    "        return \"Hold Buy/ Liquidate Short\"\n",
    "    if df['Close Price'] > df['Mid Band'] and df['Close Price'] < df['Upper Band']:\n",
    "        return \"Hold Short/ Liquidate Buy\"\n",
    "    if df['Close Price'] > df['Upper Band']:\n",
    "        return \"Short\"\n",
    "df = df.assign(Call = df.apply(select_buy, axis=1))\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import preprocessing\n",
    "le = preprocessing.LabelEncoder()\n",
    "train_X = df[['Upper Band','Lower Band','Mid Band','Close Price']]\n",
    "transfomed_label = le.fit_transform(df[['Call']])\n",
    "train_Y = transfomed_label.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9771"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression  \n",
    "LR = LogisticRegression(random_state=0, solver='lbfgs', multi_class='ovr').fit(train_X, train_Y.ravel())\n",
    "LR.predict(train_X) \n",
    "print(\"Logistic Regression\")\n",
    "round(LR.score(train_X,train_Y), 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Support Vector Machines\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9085"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import svm\n",
    "SVM = svm.LinearSVC()\n",
    "SVM.fit(train_X, train_Y)\n",
    "SVM.predict(train_X)\n",
    "print(\"Support Vector Machines\")\n",
    "round(SVM.score(train_X,train_Y), 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forests\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.6888"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "RF = RandomForestClassifier(n_estimators=100, max_depth=2, random_state=0)  \n",
    "RF.fit(train_X, train_Y)  \n",
    "RF.predict(train_X) \n",
    "print(\"Random Forests\")\n",
    "round(RF.score(train_X,train_Y), 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Neural Networks\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.5011"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neural_network import MLPClassifier\n",
    "NN = MLPClassifier(solver='lbfgs', alpha=1e-5, hidden_layer_sizes=(5, 2), random_state=1)  \n",
    "NN.fit(train_X, train_Y)  \n",
    "NN.predict(train_X)\n",
    "print(\"Neural Networks\")\n",
    "round(NN.score(train_X, train_Y), 4) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Query4.2 \n",
    "Now, we'll again utilize classification to make a trade call, and measure the efficiency of our trading algorithm over the past two years. For this assignment , we will use RandomForest classifier.\n",
    "\n",
    ">Import the stock data file of your choice\n",
    " Define 4 new columns , whose values are:\n",
    " % change between Open and Close price for the day\n",
    " % change between Low and High price for the day\n",
    " 5 day rolling mean of the day to day % change in Close Price\n",
    " 5 day rolling std of the day to day % change in Close Price\n",
    "    \n",
    ">Create a new column 'Action' whose values are:\n",
    " 1 if next day's price(Close) is greater than present day's.\n",
    " (-1) if next day's price(Close) is less than present day's.\n",
    " i.e. Action [ i ] = 1 if Close[ i+1 ] > Close[ i ]\n",
    " i.e. Action [ i ] = (-1) if Close[ i+1 ] < Close[ i ]\n",
    "    \n",
    ">Construct a classification model with the 4 new inputs and 'Action' as target\n",
    "    \n",
    ">Check the accuracy of this model , also , plot the net cumulative returns (in %) if we were to follow this algorithmic model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "        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>Symbol</th>\n",
       "      <th>Series</th>\n",
       "      <th>Date</th>\n",
       "      <th>Prev Close</th>\n",
       "      <th>Open Price</th>\n",
       "      <th>High Price</th>\n",
       "      <th>Low Price</th>\n",
       "      <th>Last Price</th>\n",
       "      <th>Close Price</th>\n",
       "      <th>Average Price</th>\n",
       "      <th>Total Traded Quantity</th>\n",
       "      <th>Turnover</th>\n",
       "      <th>No. of Trades</th>\n",
       "      <th>Deliverable Qty</th>\n",
       "      <th>% Dly Qt to Traded Qty</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>RCOM</td>\n",
       "      <td>EQ</td>\n",
       "      <td>15-May-2017</td>\n",
       "      <td>32.45</td>\n",
       "      <td>32.50</td>\n",
       "      <td>32.60</td>\n",
       "      <td>31.70</td>\n",
       "      <td>32.00</td>\n",
       "      <td>32.00</td>\n",
       "      <td>31.99</td>\n",
       "      <td>7126316</td>\n",
       "      <td>2.279973e+08</td>\n",
       "      <td>11883</td>\n",
       "      <td>1908042</td>\n",
       "      <td>26.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>RCOM</td>\n",
       "      <td>EQ</td>\n",
       "      <td>16-May-2017</td>\n",
       "      <td>32.00</td>\n",
       "      <td>32.05</td>\n",
       "      <td>32.35</td>\n",
       "      <td>31.85</td>\n",
       "      <td>32.00</td>\n",
       "      <td>32.10</td>\n",
       "      <td>32.12</td>\n",
       "      <td>7274881</td>\n",
       "      <td>2.336877e+08</td>\n",
       "      <td>9976</td>\n",
       "      <td>2211910</td>\n",
       "      <td>30.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>RCOM</td>\n",
       "      <td>EQ</td>\n",
       "      <td>17-May-2017</td>\n",
       "      <td>32.10</td>\n",
       "      <td>32.10</td>\n",
       "      <td>32.30</td>\n",
       "      <td>31.70</td>\n",
       "      <td>31.90</td>\n",
       "      <td>31.95</td>\n",
       "      <td>31.97</td>\n",
       "      <td>6245114</td>\n",
       "      <td>1.996360e+08</td>\n",
       "      <td>8838</td>\n",
       "      <td>1399815</td>\n",
       "      <td>22.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>RCOM</td>\n",
       "      <td>EQ</td>\n",
       "      <td>18-May-2017</td>\n",
       "      <td>31.95</td>\n",
       "      <td>31.60</td>\n",
       "      <td>31.90</td>\n",
       "      <td>31.00</td>\n",
       "      <td>31.05</td>\n",
       "      <td>31.05</td>\n",
       "      <td>31.34</td>\n",
       "      <td>11189938</td>\n",
       "      <td>3.506776e+08</td>\n",
       "      <td>17051</td>\n",
       "      <td>3629175</td>\n",
       "      <td>32.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>RCOM</td>\n",
       "      <td>EQ</td>\n",
       "      <td>19-May-2017</td>\n",
       "      <td>31.05</td>\n",
       "      <td>31.20</td>\n",
       "      <td>31.45</td>\n",
       "      <td>30.25</td>\n",
       "      <td>30.55</td>\n",
       "      <td>30.55</td>\n",
       "      <td>30.80</td>\n",
       "      <td>11898780</td>\n",
       "      <td>3.664547e+08</td>\n",
       "      <td>18065</td>\n",
       "      <td>3309529</td>\n",
       "      <td>27.81</td>\n",
       "    </tr>\n",
       "    <tr>\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",
       "      <td>489</td>\n",
       "      <td>RCOM</td>\n",
       "      <td>EQ</td>\n",
       "      <td>07-May-2019</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.10</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.10</td>\n",
       "      <td>2.10</td>\n",
       "      <td>2.06</td>\n",
       "      <td>64199212</td>\n",
       "      <td>1.325275e+08</td>\n",
       "      <td>9103</td>\n",
       "      <td>34677474</td>\n",
       "      <td>54.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>490</td>\n",
       "      <td>RCOM</td>\n",
       "      <td>EQ</td>\n",
       "      <td>08-May-2019</td>\n",
       "      <td>2.10</td>\n",
       "      <td>2.10</td>\n",
       "      <td>2.20</td>\n",
       "      <td>2.10</td>\n",
       "      <td>2.20</td>\n",
       "      <td>2.20</td>\n",
       "      <td>2.18</td>\n",
       "      <td>27455636</td>\n",
       "      <td>5.972756e+07</td>\n",
       "      <td>4208</td>\n",
       "      <td>14957834</td>\n",
       "      <td>54.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>491</td>\n",
       "      <td>RCOM</td>\n",
       "      <td>EQ</td>\n",
       "      <td>09-May-2019</td>\n",
       "      <td>2.20</td>\n",
       "      <td>2.30</td>\n",
       "      <td>2.30</td>\n",
       "      <td>2.30</td>\n",
       "      <td>2.30</td>\n",
       "      <td>2.30</td>\n",
       "      <td>2.30</td>\n",
       "      <td>7854126</td>\n",
       "      <td>1.806449e+07</td>\n",
       "      <td>2385</td>\n",
       "      <td>7711773</td>\n",
       "      <td>98.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>492</td>\n",
       "      <td>RCOM</td>\n",
       "      <td>EQ</td>\n",
       "      <td>10-May-2019</td>\n",
       "      <td>2.30</td>\n",
       "      <td>2.40</td>\n",
       "      <td>2.40</td>\n",
       "      <td>2.40</td>\n",
       "      <td>2.40</td>\n",
       "      <td>2.40</td>\n",
       "      <td>2.40</td>\n",
       "      <td>3584592</td>\n",
       "      <td>8.603021e+06</td>\n",
       "      <td>1560</td>\n",
       "      <td>3584592</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>493</td>\n",
       "      <td>RCOM</td>\n",
       "      <td>EQ</td>\n",
       "      <td>13-May-2019</td>\n",
       "      <td>2.40</td>\n",
       "      <td>2.45</td>\n",
       "      <td>2.50</td>\n",
       "      <td>2.30</td>\n",
       "      <td>2.30</td>\n",
       "      <td>2.30</td>\n",
       "      <td>2.40</td>\n",
       "      <td>42329516</td>\n",
       "      <td>1.014503e+08</td>\n",
       "      <td>9541</td>\n",
       "      <td>26309575</td>\n",
       "      <td>62.15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>494 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Symbol Series         Date  Prev Close  Open Price  High Price  Low Price  \\\n",
       "0     RCOM     EQ  15-May-2017       32.45       32.50       32.60      31.70   \n",
       "1     RCOM     EQ  16-May-2017       32.00       32.05       32.35      31.85   \n",
       "2     RCOM     EQ  17-May-2017       32.10       32.10       32.30      31.70   \n",
       "3     RCOM     EQ  18-May-2017       31.95       31.60       31.90      31.00   \n",
       "4     RCOM     EQ  19-May-2017       31.05       31.20       31.45      30.25   \n",
       "..     ...    ...          ...         ...         ...         ...        ...   \n",
       "489   RCOM     EQ  07-May-2019        2.00        2.00        2.10       2.00   \n",
       "490   RCOM     EQ  08-May-2019        2.10        2.10        2.20       2.10   \n",
       "491   RCOM     EQ  09-May-2019        2.20        2.30        2.30       2.30   \n",
       "492   RCOM     EQ  10-May-2019        2.30        2.40        2.40       2.40   \n",
       "493   RCOM     EQ  13-May-2019        2.40        2.45        2.50       2.30   \n",
       "\n",
       "     Last Price  Close Price  Average Price  Total Traded Quantity  \\\n",
       "0         32.00        32.00          31.99                7126316   \n",
       "1         32.00        32.10          32.12                7274881   \n",
       "2         31.90        31.95          31.97                6245114   \n",
       "3         31.05        31.05          31.34               11189938   \n",
       "4         30.55        30.55          30.80               11898780   \n",
       "..          ...          ...            ...                    ...   \n",
       "489        2.10         2.10           2.06               64199212   \n",
       "490        2.20         2.20           2.18               27455636   \n",
       "491        2.30         2.30           2.30                7854126   \n",
       "492        2.40         2.40           2.40                3584592   \n",
       "493        2.30         2.30           2.40               42329516   \n",
       "\n",
       "         Turnover  No. of Trades  Deliverable Qty  % Dly Qt to Traded Qty  \n",
       "0    2.279973e+08          11883          1908042                   26.77  \n",
       "1    2.336877e+08           9976          2211910                   30.40  \n",
       "2    1.996360e+08           8838          1399815                   22.41  \n",
       "3    3.506776e+08          17051          3629175                   32.43  \n",
       "4    3.664547e+08          18065          3309529                   27.81  \n",
       "..            ...            ...              ...                     ...  \n",
       "489  1.325275e+08           9103         34677474                   54.02  \n",
       "490  5.972756e+07           4208         14957834                   54.48  \n",
       "491  1.806449e+07           2385          7711773                   98.19  \n",
       "492  8.603021e+06           1560          3584592                  100.00  \n",
       "493  1.014503e+08           9541         26309575                   62.15  \n",
       "\n",
       "[494 rows x 15 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2 = pd.read_csv(r\"RCOM.csv\")\n",
    "df_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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>Symbol</th>\n",
       "      <th>Series</th>\n",
       "      <th>Date</th>\n",
       "      <th>Prev Close</th>\n",
       "      <th>Open Price</th>\n",
       "      <th>High Price</th>\n",
       "      <th>Low Price</th>\n",
       "      <th>Last Price</th>\n",
       "      <th>Close Price</th>\n",
       "      <th>Average Price</th>\n",
       "      <th>Total Traded Quantity</th>\n",
       "      <th>Turnover</th>\n",
       "      <th>No. of Trades</th>\n",
       "      <th>Deliverable Qty</th>\n",
       "      <th>% Dly Qt to Traded Qty</th>\n",
       "      <th>%chg op_cl</th>\n",
       "      <th>%chg lw_hg</th>\n",
       "      <th>%chg 5dymean</th>\n",
       "      <th>%chg 5dystd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>RCOM</td>\n",
       "      <td>EQ</td>\n",
       "      <td>22-May-2017</td>\n",
       "      <td>30.55</td>\n",
       "      <td>30.75</td>\n",
       "      <td>30.80</td>\n",
       "      <td>29.70</td>\n",
       "      <td>29.90</td>\n",
       "      <td>29.80</td>\n",
       "      <td>30.08</td>\n",
       "      <td>10150535</td>\n",
       "      <td>3.053526e+08</td>\n",
       "      <td>19810</td>\n",
       "      <td>3463929</td>\n",
       "      <td>34.13</td>\n",
       "      <td>-3.187919</td>\n",
       "      <td>-3.367003</td>\n",
       "      <td>-0.014074</td>\n",
       "      <td>0.013198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>RCOM</td>\n",
       "      <td>EQ</td>\n",
       "      <td>23-May-2017</td>\n",
       "      <td>29.80</td>\n",
       "      <td>29.70</td>\n",
       "      <td>29.80</td>\n",
       "      <td>27.20</td>\n",
       "      <td>27.95</td>\n",
       "      <td>28.00</td>\n",
       "      <td>28.16</td>\n",
       "      <td>25930820</td>\n",
       "      <td>7.300927e+08</td>\n",
       "      <td>42741</td>\n",
       "      <td>5472973</td>\n",
       "      <td>21.11</td>\n",
       "      <td>-6.071429</td>\n",
       "      <td>-6.617647</td>\n",
       "      <td>-0.026780</td>\n",
       "      <td>0.020857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>RCOM</td>\n",
       "      <td>EQ</td>\n",
       "      <td>24-May-2017</td>\n",
       "      <td>28.00</td>\n",
       "      <td>28.00</td>\n",
       "      <td>28.20</td>\n",
       "      <td>25.30</td>\n",
       "      <td>25.90</td>\n",
       "      <td>25.90</td>\n",
       "      <td>26.34</td>\n",
       "      <td>46243616</td>\n",
       "      <td>1.218051e+09</td>\n",
       "      <td>60442</td>\n",
       "      <td>12211614</td>\n",
       "      <td>26.41</td>\n",
       "      <td>-8.108108</td>\n",
       "      <td>-9.090909</td>\n",
       "      <td>-0.040845</td>\n",
       "      <td>0.025433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>RCOM</td>\n",
       "      <td>EQ</td>\n",
       "      <td>25-May-2017</td>\n",
       "      <td>25.90</td>\n",
       "      <td>25.90</td>\n",
       "      <td>26.55</td>\n",
       "      <td>25.15</td>\n",
       "      <td>26.20</td>\n",
       "      <td>25.95</td>\n",
       "      <td>25.90</td>\n",
       "      <td>28257448</td>\n",
       "      <td>7.318248e+08</td>\n",
       "      <td>35034</td>\n",
       "      <td>6872713</td>\n",
       "      <td>24.32</td>\n",
       "      <td>0.192678</td>\n",
       "      <td>-2.385686</td>\n",
       "      <td>-0.034825</td>\n",
       "      <td>0.031919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>RCOM</td>\n",
       "      <td>EQ</td>\n",
       "      <td>26-May-2017</td>\n",
       "      <td>25.95</td>\n",
       "      <td>26.00</td>\n",
       "      <td>26.30</td>\n",
       "      <td>25.35</td>\n",
       "      <td>25.95</td>\n",
       "      <td>25.75</td>\n",
       "      <td>25.93</td>\n",
       "      <td>14024830</td>\n",
       "      <td>3.636344e+08</td>\n",
       "      <td>20142</td>\n",
       "      <td>4643580</td>\n",
       "      <td>33.11</td>\n",
       "      <td>-0.970874</td>\n",
       "      <td>-2.169625</td>\n",
       "      <td>-0.033146</td>\n",
       "      <td>0.033339</td>\n",
       "    </tr>\n",
       "    <tr>\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",
       "      <td>489</td>\n",
       "      <td>RCOM</td>\n",
       "      <td>EQ</td>\n",
       "      <td>07-May-2019</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.10</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.10</td>\n",
       "      <td>2.10</td>\n",
       "      <td>2.06</td>\n",
       "      <td>64199212</td>\n",
       "      <td>1.325275e+08</td>\n",
       "      <td>9103</td>\n",
       "      <td>34677474</td>\n",
       "      <td>54.02</td>\n",
       "      <td>4.761905</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.020534</td>\n",
       "      <td>0.028128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>490</td>\n",
       "      <td>RCOM</td>\n",
       "      <td>EQ</td>\n",
       "      <td>08-May-2019</td>\n",
       "      <td>2.10</td>\n",
       "      <td>2.10</td>\n",
       "      <td>2.20</td>\n",
       "      <td>2.10</td>\n",
       "      <td>2.20</td>\n",
       "      <td>2.20</td>\n",
       "      <td>2.18</td>\n",
       "      <td>27455636</td>\n",
       "      <td>5.972756e+07</td>\n",
       "      <td>4208</td>\n",
       "      <td>14957834</td>\n",
       "      <td>54.48</td>\n",
       "      <td>4.545455</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.035321</td>\n",
       "      <td>0.012352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>491</td>\n",
       "      <td>RCOM</td>\n",
       "      <td>EQ</td>\n",
       "      <td>09-May-2019</td>\n",
       "      <td>2.20</td>\n",
       "      <td>2.30</td>\n",
       "      <td>2.30</td>\n",
       "      <td>2.30</td>\n",
       "      <td>2.30</td>\n",
       "      <td>2.30</td>\n",
       "      <td>2.30</td>\n",
       "      <td>7854126</td>\n",
       "      <td>1.806449e+07</td>\n",
       "      <td>2385</td>\n",
       "      <td>7711773</td>\n",
       "      <td>98.19</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.039006</td>\n",
       "      <td>0.012003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>492</td>\n",
       "      <td>RCOM</td>\n",
       "      <td>EQ</td>\n",
       "      <td>10-May-2019</td>\n",
       "      <td>2.30</td>\n",
       "      <td>2.40</td>\n",
       "      <td>2.40</td>\n",
       "      <td>2.40</td>\n",
       "      <td>2.40</td>\n",
       "      <td>2.40</td>\n",
       "      <td>2.40</td>\n",
       "      <td>3584592</td>\n",
       "      <td>8.603021e+06</td>\n",
       "      <td>1560</td>\n",
       "      <td>3584592</td>\n",
       "      <td>100.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.042439</td>\n",
       "      <td>0.009700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>493</td>\n",
       "      <td>RCOM</td>\n",
       "      <td>EQ</td>\n",
       "      <td>13-May-2019</td>\n",
       "      <td>2.40</td>\n",
       "      <td>2.45</td>\n",
       "      <td>2.50</td>\n",
       "      <td>2.30</td>\n",
       "      <td>2.30</td>\n",
       "      <td>2.30</td>\n",
       "      <td>2.40</td>\n",
       "      <td>42329516</td>\n",
       "      <td>1.014503e+08</td>\n",
       "      <td>9541</td>\n",
       "      <td>26309575</td>\n",
       "      <td>62.15</td>\n",
       "      <td>-6.521739</td>\n",
       "      <td>-8.695652</td>\n",
       "      <td>0.028977</td>\n",
       "      <td>0.039566</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>489 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Symbol Series         Date  Prev Close  Open Price  High Price  Low Price  \\\n",
       "5     RCOM     EQ  22-May-2017       30.55       30.75       30.80      29.70   \n",
       "6     RCOM     EQ  23-May-2017       29.80       29.70       29.80      27.20   \n",
       "7     RCOM     EQ  24-May-2017       28.00       28.00       28.20      25.30   \n",
       "8     RCOM     EQ  25-May-2017       25.90       25.90       26.55      25.15   \n",
       "9     RCOM     EQ  26-May-2017       25.95       26.00       26.30      25.35   \n",
       "..     ...    ...          ...         ...         ...         ...        ...   \n",
       "489   RCOM     EQ  07-May-2019        2.00        2.00        2.10       2.00   \n",
       "490   RCOM     EQ  08-May-2019        2.10        2.10        2.20       2.10   \n",
       "491   RCOM     EQ  09-May-2019        2.20        2.30        2.30       2.30   \n",
       "492   RCOM     EQ  10-May-2019        2.30        2.40        2.40       2.40   \n",
       "493   RCOM     EQ  13-May-2019        2.40        2.45        2.50       2.30   \n",
       "\n",
       "     Last Price  Close Price  Average Price  Total Traded Quantity  \\\n",
       "5         29.90        29.80          30.08               10150535   \n",
       "6         27.95        28.00          28.16               25930820   \n",
       "7         25.90        25.90          26.34               46243616   \n",
       "8         26.20        25.95          25.90               28257448   \n",
       "9         25.95        25.75          25.93               14024830   \n",
       "..          ...          ...            ...                    ...   \n",
       "489        2.10         2.10           2.06               64199212   \n",
       "490        2.20         2.20           2.18               27455636   \n",
       "491        2.30         2.30           2.30                7854126   \n",
       "492        2.40         2.40           2.40                3584592   \n",
       "493        2.30         2.30           2.40               42329516   \n",
       "\n",
       "         Turnover  No. of Trades  Deliverable Qty  % Dly Qt to Traded Qty  \\\n",
       "5    3.053526e+08          19810          3463929                   34.13   \n",
       "6    7.300927e+08          42741          5472973                   21.11   \n",
       "7    1.218051e+09          60442         12211614                   26.41   \n",
       "8    7.318248e+08          35034          6872713                   24.32   \n",
       "9    3.636344e+08          20142          4643580                   33.11   \n",
       "..            ...            ...              ...                     ...   \n",
       "489  1.325275e+08           9103         34677474                   54.02   \n",
       "490  5.972756e+07           4208         14957834                   54.48   \n",
       "491  1.806449e+07           2385          7711773                   98.19   \n",
       "492  8.603021e+06           1560          3584592                  100.00   \n",
       "493  1.014503e+08           9541         26309575                   62.15   \n",
       "\n",
       "     %chg op_cl  %chg lw_hg  %chg 5dymean  %chg 5dystd  \n",
       "5     -3.187919   -3.367003     -0.014074     0.013198  \n",
       "6     -6.071429   -6.617647     -0.026780     0.020857  \n",
       "7     -8.108108   -9.090909     -0.040845     0.025433  \n",
       "8      0.192678   -2.385686     -0.034825     0.031919  \n",
       "9     -0.970874   -2.169625     -0.033146     0.033339  \n",
       "..          ...         ...           ...          ...  \n",
       "489    4.761905    0.000000      0.020534     0.028128  \n",
       "490    4.545455    0.000000      0.035321     0.012352  \n",
       "491    0.000000    0.000000      0.039006     0.012003  \n",
       "492    0.000000    0.000000      0.042439     0.009700  \n",
       "493   -6.521739   -8.695652      0.028977     0.039566  \n",
       "\n",
       "[489 rows x 19 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2['%chg op_cl'] = ((df_2['Close Price'] - df_2['Open Price'])/(df_2['Close Price']))*100\n",
    "df_2['%chg lw_hg'] = ((df_2['Close Price'] - df_2['High Price'])/(df_2['Low Price']))*100\n",
    "df_2['%chg 5dymean'] = df_2['Close Price'].pct_change().dropna().rolling(5).mean()\n",
    "df_2['%chg 5dystd'] = df_2['Close Price'].pct_change().dropna().rolling(5).std()\n",
    "df_2 = df_2.dropna()\n",
    "df_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Symbol</th>\n",
       "      <th>Series</th>\n",
       "      <th>Date</th>\n",
       "      <th>Prev Close</th>\n",
       "      <th>Open Price</th>\n",
       "      <th>High Price</th>\n",
       "      <th>Low Price</th>\n",
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       "      <th>Close Price</th>\n",
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       "      <th>No. of Trades</th>\n",
       "      <th>Deliverable Qty</th>\n",
       "      <th>% Dly Qt to Traded Qty</th>\n",
       "      <th>%chg op_cl</th>\n",
       "      <th>%chg lw_hg</th>\n",
       "      <th>%chg 5dymean</th>\n",
       "      <th>%chg 5dystd</th>\n",
       "      <th>Action</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>RCOM</td>\n",
       "      <td>EQ</td>\n",
       "      <td>22-May-2017</td>\n",
       "      <td>30.55</td>\n",
       "      <td>30.75</td>\n",
       "      <td>30.80</td>\n",
       "      <td>29.70</td>\n",
       "      <td>29.90</td>\n",
       "      <td>29.80</td>\n",
       "      <td>30.08</td>\n",
       "      <td>10150535</td>\n",
       "      <td>3.053526e+08</td>\n",
       "      <td>19810</td>\n",
       "      <td>3463929</td>\n",
       "      <td>34.13</td>\n",
       "      <td>-3.187919</td>\n",
       "      <td>-3.367003</td>\n",
       "      <td>-0.014074</td>\n",
       "      <td>0.013198</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>RCOM</td>\n",
       "      <td>EQ</td>\n",
       "      <td>23-May-2017</td>\n",
       "      <td>29.80</td>\n",
       "      <td>29.70</td>\n",
       "      <td>29.80</td>\n",
       "      <td>27.20</td>\n",
       "      <td>27.95</td>\n",
       "      <td>28.00</td>\n",
       "      <td>28.16</td>\n",
       "      <td>25930820</td>\n",
       "      <td>7.300927e+08</td>\n",
       "      <td>42741</td>\n",
       "      <td>5472973</td>\n",
       "      <td>21.11</td>\n",
       "      <td>-6.071429</td>\n",
       "      <td>-6.617647</td>\n",
       "      <td>-0.026780</td>\n",
       "      <td>0.020857</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>RCOM</td>\n",
       "      <td>EQ</td>\n",
       "      <td>24-May-2017</td>\n",
       "      <td>28.00</td>\n",
       "      <td>28.00</td>\n",
       "      <td>28.20</td>\n",
       "      <td>25.30</td>\n",
       "      <td>25.90</td>\n",
       "      <td>25.90</td>\n",
       "      <td>26.34</td>\n",
       "      <td>46243616</td>\n",
       "      <td>1.218051e+09</td>\n",
       "      <td>60442</td>\n",
       "      <td>12211614</td>\n",
       "      <td>26.41</td>\n",
       "      <td>-8.108108</td>\n",
       "      <td>-9.090909</td>\n",
       "      <td>-0.040845</td>\n",
       "      <td>0.025433</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>RCOM</td>\n",
       "      <td>EQ</td>\n",
       "      <td>25-May-2017</td>\n",
       "      <td>25.90</td>\n",
       "      <td>25.90</td>\n",
       "      <td>26.55</td>\n",
       "      <td>25.15</td>\n",
       "      <td>26.20</td>\n",
       "      <td>25.95</td>\n",
       "      <td>25.90</td>\n",
       "      <td>28257448</td>\n",
       "      <td>7.318248e+08</td>\n",
       "      <td>35034</td>\n",
       "      <td>6872713</td>\n",
       "      <td>24.32</td>\n",
       "      <td>0.192678</td>\n",
       "      <td>-2.385686</td>\n",
       "      <td>-0.034825</td>\n",
       "      <td>0.031919</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>RCOM</td>\n",
       "      <td>EQ</td>\n",
       "      <td>26-May-2017</td>\n",
       "      <td>25.95</td>\n",
       "      <td>26.00</td>\n",
       "      <td>26.30</td>\n",
       "      <td>25.35</td>\n",
       "      <td>25.95</td>\n",
       "      <td>25.75</td>\n",
       "      <td>25.93</td>\n",
       "      <td>14024830</td>\n",
       "      <td>3.636344e+08</td>\n",
       "      <td>20142</td>\n",
       "      <td>4643580</td>\n",
       "      <td>33.11</td>\n",
       "      <td>-0.970874</td>\n",
       "      <td>-2.169625</td>\n",
       "      <td>-0.033146</td>\n",
       "      <td>0.033339</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\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",
       "      <td>478</td>\n",
       "      <td>RCOM</td>\n",
       "      <td>EQ</td>\n",
       "      <td>16-Apr-2019</td>\n",
       "      <td>2.60</td>\n",
       "      <td>2.50</td>\n",
       "      <td>2.50</td>\n",
       "      <td>2.50</td>\n",
       "      <td>2.50</td>\n",
       "      <td>2.50</td>\n",
       "      <td>2.50</td>\n",
       "      <td>4179555</td>\n",
       "      <td>1.044889e+07</td>\n",
       "      <td>4527</td>\n",
       "      <td>4179555</td>\n",
       "      <td>100.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.038975</td>\n",
       "      <td>0.005895</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>479</td>\n",
       "      <td>RCOM</td>\n",
       "      <td>EQ</td>\n",
       "      <td>18-Apr-2019</td>\n",
       "      <td>2.50</td>\n",
       "      <td>2.40</td>\n",
       "      <td>2.40</td>\n",
       "      <td>2.40</td>\n",
       "      <td>2.40</td>\n",
       "      <td>2.40</td>\n",
       "      <td>2.40</td>\n",
       "      <td>4123227</td>\n",
       "      <td>9.895745e+06</td>\n",
       "      <td>3720</td>\n",
       "      <td>4123227</td>\n",
       "      <td>100.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.037139</td>\n",
       "      <td>0.002181</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>480</td>\n",
       "      <td>RCOM</td>\n",
       "      <td>EQ</td>\n",
       "      <td>22-Apr-2019</td>\n",
       "      <td>2.40</td>\n",
       "      <td>2.30</td>\n",
       "      <td>2.30</td>\n",
       "      <td>2.30</td>\n",
       "      <td>2.30</td>\n",
       "      <td>2.30</td>\n",
       "      <td>2.30</td>\n",
       "      <td>4567152</td>\n",
       "      <td>1.050445e+07</td>\n",
       "      <td>3418</td>\n",
       "      <td>4567152</td>\n",
       "      <td>100.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.038576</td>\n",
       "      <td>0.002353</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>481</td>\n",
       "      <td>RCOM</td>\n",
       "      <td>EQ</td>\n",
       "      <td>23-Apr-2019</td>\n",
       "      <td>2.30</td>\n",
       "      <td>2.20</td>\n",
       "      <td>2.20</td>\n",
       "      <td>2.20</td>\n",
       "      <td>2.20</td>\n",
       "      <td>2.20</td>\n",
       "      <td>2.20</td>\n",
       "      <td>3027180</td>\n",
       "      <td>6.659796e+06</td>\n",
       "      <td>2983</td>\n",
       "      <td>3027180</td>\n",
       "      <td>100.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.040129</td>\n",
       "      <td>0.002547</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>482</td>\n",
       "      <td>RCOM</td>\n",
       "      <td>EQ</td>\n",
       "      <td>24-Apr-2019</td>\n",
       "      <td>2.20</td>\n",
       "      <td>2.10</td>\n",
       "      <td>2.10</td>\n",
       "      <td>2.10</td>\n",
       "      <td>2.10</td>\n",
       "      <td>2.10</td>\n",
       "      <td>2.10</td>\n",
       "      <td>2377164</td>\n",
       "      <td>4.992044e+06</td>\n",
       "      <td>2791</td>\n",
       "      <td>2377164</td>\n",
       "      <td>100.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.041812</td>\n",
       "      <td>0.002765</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>478 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Symbol Series         Date  Prev Close  Open Price  High Price  Low Price  \\\n",
       "5     RCOM     EQ  22-May-2017       30.55       30.75       30.80      29.70   \n",
       "6     RCOM     EQ  23-May-2017       29.80       29.70       29.80      27.20   \n",
       "7     RCOM     EQ  24-May-2017       28.00       28.00       28.20      25.30   \n",
       "8     RCOM     EQ  25-May-2017       25.90       25.90       26.55      25.15   \n",
       "9     RCOM     EQ  26-May-2017       25.95       26.00       26.30      25.35   \n",
       "..     ...    ...          ...         ...         ...         ...        ...   \n",
       "478   RCOM     EQ  16-Apr-2019        2.60        2.50        2.50       2.50   \n",
       "479   RCOM     EQ  18-Apr-2019        2.50        2.40        2.40       2.40   \n",
       "480   RCOM     EQ  22-Apr-2019        2.40        2.30        2.30       2.30   \n",
       "481   RCOM     EQ  23-Apr-2019        2.30        2.20        2.20       2.20   \n",
       "482   RCOM     EQ  24-Apr-2019        2.20        2.10        2.10       2.10   \n",
       "\n",
       "     Last Price  Close Price  Average Price  Total Traded Quantity  \\\n",
       "5         29.90        29.80          30.08               10150535   \n",
       "6         27.95        28.00          28.16               25930820   \n",
       "7         25.90        25.90          26.34               46243616   \n",
       "8         26.20        25.95          25.90               28257448   \n",
       "9         25.95        25.75          25.93               14024830   \n",
       "..          ...          ...            ...                    ...   \n",
       "478        2.50         2.50           2.50                4179555   \n",
       "479        2.40         2.40           2.40                4123227   \n",
       "480        2.30         2.30           2.30                4567152   \n",
       "481        2.20         2.20           2.20                3027180   \n",
       "482        2.10         2.10           2.10                2377164   \n",
       "\n",
       "         Turnover  No. of Trades  Deliverable Qty  % Dly Qt to Traded Qty  \\\n",
       "5    3.053526e+08          19810          3463929                   34.13   \n",
       "6    7.300927e+08          42741          5472973                   21.11   \n",
       "7    1.218051e+09          60442         12211614                   26.41   \n",
       "8    7.318248e+08          35034          6872713                   24.32   \n",
       "9    3.636344e+08          20142          4643580                   33.11   \n",
       "..            ...            ...              ...                     ...   \n",
       "478  1.044889e+07           4527          4179555                  100.00   \n",
       "479  9.895745e+06           3720          4123227                  100.00   \n",
       "480  1.050445e+07           3418          4567152                  100.00   \n",
       "481  6.659796e+06           2983          3027180                  100.00   \n",
       "482  4.992044e+06           2791          2377164                  100.00   \n",
       "\n",
       "     %chg op_cl  %chg lw_hg  %chg 5dymean  %chg 5dystd  Action  \n",
       "5     -3.187919   -3.367003     -0.014074     0.013198    -1.0  \n",
       "6     -6.071429   -6.617647     -0.026780     0.020857    -1.0  \n",
       "7     -8.108108   -9.090909     -0.040845     0.025433     1.0  \n",
       "8      0.192678   -2.385686     -0.034825     0.031919    -1.0  \n",
       "9     -0.970874   -2.169625     -0.033146     0.033339     1.0  \n",
       "..          ...         ...           ...          ...     ...  \n",
       "478    0.000000    0.000000     -0.038975     0.005895    -1.0  \n",
       "479    0.000000    0.000000     -0.037139     0.002181    -1.0  \n",
       "480    0.000000    0.000000     -0.038576     0.002353     1.0  \n",
       "481    0.000000    0.000000     -0.040129     0.002547     1.0  \n",
       "482    0.000000    0.000000     -0.041812     0.002765     1.0  \n",
       "\n",
       "[478 rows x 20 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = []\n",
    "val = []\n",
    "for value in df_2['Close Price'].iteritems():\n",
    "    arr.append(value[1])\n",
    "for i in range(0,483):\n",
    "    if arr[i+1] > arr[i]:\n",
    "        val.append(1)\n",
    "    else:\n",
    "        val.append(-1)\n",
    "df_2['Action'] = pd.DataFrame(val)\n",
    "df_2 = df_2.dropna()\n",
    "df_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forests\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.6611"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_X = df_2[['%chg op_cl','%chg lw_hg','%chg 5dymean','%chg 5dystd']]\n",
    "train_Y = df_2[['Action']]\n",
    "RF = RandomForestClassifier(n_estimators=100, max_depth=2, random_state=0)  \n",
    "RF.fit(train_X, train_Y)  \n",
    "RF.predict(train_X) \n",
    "print(\"Random Forests\")\n",
    "round(RF.score(train_X,train_Y), 4) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0xe934e48>]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_2['Net Cummulative Returns'] = (((df_2['Open Price'] - df_2['Close Price'])/(df_2['Open Price']))*100).cumsum()\n",
    "plt.figure(figsize=(20,10))\n",
    "plt.plot(df_2['Net Cummulative Returns'])"
   ]
  },
  {
   "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}