Aula_4 (1).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [],
   "source": [
    "import bql\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mpl_dates\n",
    "import datetime \n",
    "bq = bql.Service()\n",
    "\n",
    "# BQL String:\n",
    "\n",
    "# get(PX_LAST(dates=range(2010-01-01, 2019-12-31), currency=BRL)) \n",
    "# for(members('IBOV Index'))\n",
    "\n",
    "# BQL Model Object: \n",
    "\n",
    "bql_item = bq.data.px_last(dates=bq.func.range('2010-01-01', '2019-12-31'))\n",
    "bql_universe = bq.univ.members('IBOV Index')\n",
    "bql_request = bql.Request(bql_universe, bql_item)\n",
    "bql_response = bq.execute(bql_request)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Date</th>\n",
       "      <th>Currency</th>\n",
       "      <th>Last_Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ENBR3 BS Equity</td>\n",
       "      <td>2010-01-04</td>\n",
       "      <td>BRL</td>\n",
       "      <td>11.089213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ENBR3 BS Equity</td>\n",
       "      <td>2010-01-05</td>\n",
       "      <td>BRL</td>\n",
       "      <td>11.174667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ENBR3 BS Equity</td>\n",
       "      <td>2010-01-06</td>\n",
       "      <td>BRL</td>\n",
       "      <td>11.339000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ENBR3 BS Equity</td>\n",
       "      <td>2010-01-07</td>\n",
       "      <td>BRL</td>\n",
       "      <td>11.421167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ENBR3 BS Equity</td>\n",
       "      <td>2010-01-08</td>\n",
       "      <td>BRL</td>\n",
       "      <td>11.569067</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                ID       Date Currency  Last_Price\n",
       "3  ENBR3 BS Equity 2010-01-04      BRL   11.089213\n",
       "4  ENBR3 BS Equity 2010-01-05      BRL   11.174667\n",
       "5  ENBR3 BS Equity 2010-01-06      BRL   11.339000\n",
       "6  ENBR3 BS Equity 2010-01-07      BRL   11.421167\n",
       "7  ENBR3 BS Equity 2010-01-08      BRL   11.569067"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Building DataFrame from Response Object:\n",
    "\n",
    "df_main = bql_response[0].df()\n",
    "df_main.reset_index(inplace=True)\n",
    "df_main.columns = ['ID', 'Date', 'Currency', 'Last_Price']\n",
    "df_main.dropna(inplace=True, axis=0)\n",
    "df_main.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['ENBR3 BS Equity', 'HGTX3 BS Equity', 'RADL3 BS Equity',\n",
       "       'BBAS3 BS Equity', 'IGTA3 BS Equity', 'JBSS3 BS Equity',\n",
       "       'TOTS3 BS Equity', 'EQTL3 BS Equity', 'SBSP3 BS Equity',\n",
       "       'RENT3 BS Equity', 'ELET3 BS Equity', 'BRFS3 BS Equity',\n",
       "       'ELET6 BS Equity', 'ITUB4 BS Equity', 'PETR3 BS Equity',\n",
       "       'PETR4 BS Equity', 'VALE3 BS Equity', 'MULT3 BS Equity',\n",
       "       'AZUL4 BS Equity', 'TAEE11 BS Equity', 'SANB11 BS Equity',\n",
       "       'ECOR3 BS Equity', 'MRFG3 BS Equity', 'UGPA3 BS Equity',\n",
       "       'BBDC3 BS Equity', 'BRML3 BS Equity', 'EGIE3 BS Equity',\n",
       "       'TIMP3 BS Equity', 'USIM5 BS Equity', 'MRVE3 BS Equity',\n",
       "       'MGLU3 BS Equity', 'GNDI3 BS Equity', 'CSNA3 BS Equity',\n",
       "       'VIVT4 BS Equity', 'CRFB3 BS Equity', 'BBSE3 BS Equity',\n",
       "       'BBDC4 BS Equity', 'BRKM5 BS Equity', 'YDUQ3 BS Equity',\n",
       "       'CMIG4 BS Equity', 'ABEV3 BS Equity', 'B3SA3 BS Equity',\n",
       "       'PCAR3 BS Equity', 'GGBR4 BS Equity', 'CYRE3 BS Equity',\n",
       "       'SUZB3 BS Equity', 'EMBR3 BS Equity', 'COGN3 BS Equity',\n",
       "       'ITSA4 BS Equity', 'LAME4 BS Equity', 'LREN3 BS Equity',\n",
       "       'CIEL3 BS Equity', 'HYPE3 BS Equity', 'GOAU4 BS Equity',\n",
       "       'SULA11 BS Equity', 'RAIL3 BS Equity', 'BEEF3 BS Equity',\n",
       "       'BTOW3 BS Equity', 'BRAP4 BS Equity', 'KLBN11 BS Equity',\n",
       "       'HAPV3 BS Equity', 'CCRO3 BS Equity', 'FLRY3 BS Equity',\n",
       "       'NTCO3 BS Equity', 'CPFE3 BS Equity', 'ENGI11 BS Equity',\n",
       "       'IRBR3 BS Equity', 'VVAR3 BS Equity', 'BPAC11 BS Equity',\n",
       "       'WEGE3 BS Equity', 'BRDT3 BS Equity', 'GOLL4 BS Equity',\n",
       "       'CSAN3 BS Equity', 'QUAL3 BS Equity', 'CVCB3 BS Equity'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Inspecting ID column:\n",
    "\n",
    "array_id = df_main.ID.unique()\n",
    "array_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>ID</th>\n",
       "      <th>ABEV3 BS Equity</th>\n",
       "      <th>AZUL4 BS Equity</th>\n",
       "      <th>B3SA3 BS Equity</th>\n",
       "      <th>BBAS3 BS Equity</th>\n",
       "      <th>BBDC3 BS Equity</th>\n",
       "      <th>BBDC4 BS Equity</th>\n",
       "      <th>BBSE3 BS Equity</th>\n",
       "      <th>BEEF3 BS Equity</th>\n",
       "      <th>BPAC11 BS Equity</th>\n",
       "      <th>BRAP4 BS Equity</th>\n",
       "      <th>...</th>\n",
       "      <th>TAEE11 BS Equity</th>\n",
       "      <th>TIMP3 BS Equity</th>\n",
       "      <th>TOTS3 BS Equity</th>\n",
       "      <th>UGPA3 BS Equity</th>\n",
       "      <th>USIM5 BS Equity</th>\n",
       "      <th>VALE3 BS Equity</th>\n",
       "      <th>VIVT4 BS Equity</th>\n",
       "      <th>VVAR3 BS Equity</th>\n",
       "      <th>WEGE3 BS Equity</th>\n",
       "      <th>YDUQ3 BS Equity</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>2019-12-13</th>\n",
       "      <td>19.01</td>\n",
       "      <td>56.86</td>\n",
       "      <td>46.40</td>\n",
       "      <td>48.53</td>\n",
       "      <td>29.563636</td>\n",
       "      <td>31.636364</td>\n",
       "      <td>37.35</td>\n",
       "      <td>13.73</td>\n",
       "      <td>74.70</td>\n",
       "      <td>37.82</td>\n",
       "      <td>...</td>\n",
       "      <td>30.36</td>\n",
       "      <td>14.68</td>\n",
       "      <td>22.900000</td>\n",
       "      <td>22.26</td>\n",
       "      <td>9.25</td>\n",
       "      <td>53.76</td>\n",
       "      <td>56.33</td>\n",
       "      <td>10.87</td>\n",
       "      <td>34.05</td>\n",
       "      <td>47.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-16</th>\n",
       "      <td>19.08</td>\n",
       "      <td>57.27</td>\n",
       "      <td>46.22</td>\n",
       "      <td>47.87</td>\n",
       "      <td>29.118182</td>\n",
       "      <td>31.081818</td>\n",
       "      <td>36.82</td>\n",
       "      <td>13.47</td>\n",
       "      <td>74.18</td>\n",
       "      <td>37.75</td>\n",
       "      <td>...</td>\n",
       "      <td>30.42</td>\n",
       "      <td>14.52</td>\n",
       "      <td>22.666667</td>\n",
       "      <td>22.43</td>\n",
       "      <td>9.12</td>\n",
       "      <td>53.34</td>\n",
       "      <td>56.63</td>\n",
       "      <td>11.25</td>\n",
       "      <td>33.49</td>\n",
       "      <td>47.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-17</th>\n",
       "      <td>18.91</td>\n",
       "      <td>56.87</td>\n",
       "      <td>47.79</td>\n",
       "      <td>48.95</td>\n",
       "      <td>29.581818</td>\n",
       "      <td>31.654545</td>\n",
       "      <td>37.55</td>\n",
       "      <td>12.68</td>\n",
       "      <td>74.84</td>\n",
       "      <td>37.63</td>\n",
       "      <td>...</td>\n",
       "      <td>30.27</td>\n",
       "      <td>14.61</td>\n",
       "      <td>22.633333</td>\n",
       "      <td>23.00</td>\n",
       "      <td>9.25</td>\n",
       "      <td>53.35</td>\n",
       "      <td>56.87</td>\n",
       "      <td>10.99</td>\n",
       "      <td>33.96</td>\n",
       "      <td>46.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-18</th>\n",
       "      <td>19.09</td>\n",
       "      <td>57.61</td>\n",
       "      <td>48.56</td>\n",
       "      <td>50.64</td>\n",
       "      <td>30.727273</td>\n",
       "      <td>32.536364</td>\n",
       "      <td>38.03</td>\n",
       "      <td>12.93</td>\n",
       "      <td>75.27</td>\n",
       "      <td>37.40</td>\n",
       "      <td>...</td>\n",
       "      <td>30.40</td>\n",
       "      <td>14.79</td>\n",
       "      <td>22.566667</td>\n",
       "      <td>23.69</td>\n",
       "      <td>9.25</td>\n",
       "      <td>53.47</td>\n",
       "      <td>57.06</td>\n",
       "      <td>11.26</td>\n",
       "      <td>33.90</td>\n",
       "      <td>48.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-19</th>\n",
       "      <td>19.17</td>\n",
       "      <td>57.71</td>\n",
       "      <td>48.56</td>\n",
       "      <td>51.16</td>\n",
       "      <td>31.181818</td>\n",
       "      <td>33.136364</td>\n",
       "      <td>38.30</td>\n",
       "      <td>12.37</td>\n",
       "      <td>76.32</td>\n",
       "      <td>37.50</td>\n",
       "      <td>...</td>\n",
       "      <td>30.50</td>\n",
       "      <td>14.70</td>\n",
       "      <td>22.666667</td>\n",
       "      <td>24.29</td>\n",
       "      <td>9.32</td>\n",
       "      <td>54.00</td>\n",
       "      <td>57.24</td>\n",
       "      <td>11.24</td>\n",
       "      <td>33.60</td>\n",
       "      <td>49.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-20</th>\n",
       "      <td>18.91</td>\n",
       "      <td>57.00</td>\n",
       "      <td>49.10</td>\n",
       "      <td>51.06</td>\n",
       "      <td>30.290909</td>\n",
       "      <td>32.445455</td>\n",
       "      <td>37.74</td>\n",
       "      <td>12.08</td>\n",
       "      <td>75.58</td>\n",
       "      <td>37.89</td>\n",
       "      <td>...</td>\n",
       "      <td>30.45</td>\n",
       "      <td>15.28</td>\n",
       "      <td>22.820000</td>\n",
       "      <td>24.61</td>\n",
       "      <td>9.33</td>\n",
       "      <td>54.79</td>\n",
       "      <td>57.86</td>\n",
       "      <td>11.37</td>\n",
       "      <td>33.89</td>\n",
       "      <td>48.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-23</th>\n",
       "      <td>18.96</td>\n",
       "      <td>57.60</td>\n",
       "      <td>46.87</td>\n",
       "      <td>52.60</td>\n",
       "      <td>30.900000</td>\n",
       "      <td>32.645455</td>\n",
       "      <td>37.95</td>\n",
       "      <td>12.07</td>\n",
       "      <td>74.80</td>\n",
       "      <td>38.43</td>\n",
       "      <td>...</td>\n",
       "      <td>30.65</td>\n",
       "      <td>15.39</td>\n",
       "      <td>22.556667</td>\n",
       "      <td>25.33</td>\n",
       "      <td>9.53</td>\n",
       "      <td>54.58</td>\n",
       "      <td>58.38</td>\n",
       "      <td>11.67</td>\n",
       "      <td>33.79</td>\n",
       "      <td>49.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-26</th>\n",
       "      <td>19.22</td>\n",
       "      <td>57.07</td>\n",
       "      <td>46.65</td>\n",
       "      <td>53.20</td>\n",
       "      <td>31.227273</td>\n",
       "      <td>33.054545</td>\n",
       "      <td>37.76</td>\n",
       "      <td>12.64</td>\n",
       "      <td>75.18</td>\n",
       "      <td>39.05</td>\n",
       "      <td>...</td>\n",
       "      <td>31.00</td>\n",
       "      <td>15.59</td>\n",
       "      <td>22.330000</td>\n",
       "      <td>25.60</td>\n",
       "      <td>9.53</td>\n",
       "      <td>54.79</td>\n",
       "      <td>58.89</td>\n",
       "      <td>11.80</td>\n",
       "      <td>34.85</td>\n",
       "      <td>48.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-27</th>\n",
       "      <td>19.16</td>\n",
       "      <td>57.80</td>\n",
       "      <td>44.21</td>\n",
       "      <td>52.97</td>\n",
       "      <td>31.027273</td>\n",
       "      <td>32.927273</td>\n",
       "      <td>37.84</td>\n",
       "      <td>12.74</td>\n",
       "      <td>75.80</td>\n",
       "      <td>39.17</td>\n",
       "      <td>...</td>\n",
       "      <td>30.92</td>\n",
       "      <td>15.59</td>\n",
       "      <td>21.606667</td>\n",
       "      <td>25.72</td>\n",
       "      <td>9.43</td>\n",
       "      <td>53.60</td>\n",
       "      <td>58.48</td>\n",
       "      <td>11.50</td>\n",
       "      <td>35.34</td>\n",
       "      <td>48.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-30</th>\n",
       "      <td>18.67</td>\n",
       "      <td>58.28</td>\n",
       "      <td>42.97</td>\n",
       "      <td>52.82</td>\n",
       "      <td>30.972727</td>\n",
       "      <td>32.881818</td>\n",
       "      <td>37.70</td>\n",
       "      <td>12.84</td>\n",
       "      <td>76.13</td>\n",
       "      <td>38.28</td>\n",
       "      <td>...</td>\n",
       "      <td>31.18</td>\n",
       "      <td>15.67</td>\n",
       "      <td>21.516667</td>\n",
       "      <td>25.48</td>\n",
       "      <td>9.51</td>\n",
       "      <td>53.30</td>\n",
       "      <td>57.98</td>\n",
       "      <td>11.17</td>\n",
       "      <td>34.66</td>\n",
       "      <td>47.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 75 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "ID          ABEV3 BS Equity  AZUL4 BS Equity  B3SA3 BS Equity  \\\n",
       "Date                                                            \n",
       "2019-12-13            19.01            56.86            46.40   \n",
       "2019-12-16            19.08            57.27            46.22   \n",
       "2019-12-17            18.91            56.87            47.79   \n",
       "2019-12-18            19.09            57.61            48.56   \n",
       "2019-12-19            19.17            57.71            48.56   \n",
       "2019-12-20            18.91            57.00            49.10   \n",
       "2019-12-23            18.96            57.60            46.87   \n",
       "2019-12-26            19.22            57.07            46.65   \n",
       "2019-12-27            19.16            57.80            44.21   \n",
       "2019-12-30            18.67            58.28            42.97   \n",
       "\n",
       "ID          BBAS3 BS Equity  BBDC3 BS Equity  BBDC4 BS Equity  \\\n",
       "Date                                                            \n",
       "2019-12-13            48.53        29.563636        31.636364   \n",
       "2019-12-16            47.87        29.118182        31.081818   \n",
       "2019-12-17            48.95        29.581818        31.654545   \n",
       "2019-12-18            50.64        30.727273        32.536364   \n",
       "2019-12-19            51.16        31.181818        33.136364   \n",
       "2019-12-20            51.06        30.290909        32.445455   \n",
       "2019-12-23            52.60        30.900000        32.645455   \n",
       "2019-12-26            53.20        31.227273        33.054545   \n",
       "2019-12-27            52.97        31.027273        32.927273   \n",
       "2019-12-30            52.82        30.972727        32.881818   \n",
       "\n",
       "ID          BBSE3 BS Equity  BEEF3 BS Equity  BPAC11 BS Equity  \\\n",
       "Date                                                             \n",
       "2019-12-13            37.35            13.73             74.70   \n",
       "2019-12-16            36.82            13.47             74.18   \n",
       "2019-12-17            37.55            12.68             74.84   \n",
       "2019-12-18            38.03            12.93             75.27   \n",
       "2019-12-19            38.30            12.37             76.32   \n",
       "2019-12-20            37.74            12.08             75.58   \n",
       "2019-12-23            37.95            12.07             74.80   \n",
       "2019-12-26            37.76            12.64             75.18   \n",
       "2019-12-27            37.84            12.74             75.80   \n",
       "2019-12-30            37.70            12.84             76.13   \n",
       "\n",
       "ID          BRAP4 BS Equity  ...  TAEE11 BS Equity  TIMP3 BS Equity  \\\n",
       "Date                         ...                                      \n",
       "2019-12-13            37.82  ...             30.36            14.68   \n",
       "2019-12-16            37.75  ...             30.42            14.52   \n",
       "2019-12-17            37.63  ...             30.27            14.61   \n",
       "2019-12-18            37.40  ...             30.40            14.79   \n",
       "2019-12-19            37.50  ...             30.50            14.70   \n",
       "2019-12-20            37.89  ...             30.45            15.28   \n",
       "2019-12-23            38.43  ...             30.65            15.39   \n",
       "2019-12-26            39.05  ...             31.00            15.59   \n",
       "2019-12-27            39.17  ...             30.92            15.59   \n",
       "2019-12-30            38.28  ...             31.18            15.67   \n",
       "\n",
       "ID          TOTS3 BS Equity  UGPA3 BS Equity  USIM5 BS Equity  \\\n",
       "Date                                                            \n",
       "2019-12-13        22.900000            22.26             9.25   \n",
       "2019-12-16        22.666667            22.43             9.12   \n",
       "2019-12-17        22.633333            23.00             9.25   \n",
       "2019-12-18        22.566667            23.69             9.25   \n",
       "2019-12-19        22.666667            24.29             9.32   \n",
       "2019-12-20        22.820000            24.61             9.33   \n",
       "2019-12-23        22.556667            25.33             9.53   \n",
       "2019-12-26        22.330000            25.60             9.53   \n",
       "2019-12-27        21.606667            25.72             9.43   \n",
       "2019-12-30        21.516667            25.48             9.51   \n",
       "\n",
       "ID          VALE3 BS Equity  VIVT4 BS Equity  VVAR3 BS Equity  \\\n",
       "Date                                                            \n",
       "2019-12-13            53.76            56.33            10.87   \n",
       "2019-12-16            53.34            56.63            11.25   \n",
       "2019-12-17            53.35            56.87            10.99   \n",
       "2019-12-18            53.47            57.06            11.26   \n",
       "2019-12-19            54.00            57.24            11.24   \n",
       "2019-12-20            54.79            57.86            11.37   \n",
       "2019-12-23            54.58            58.38            11.67   \n",
       "2019-12-26            54.79            58.89            11.80   \n",
       "2019-12-27            53.60            58.48            11.50   \n",
       "2019-12-30            53.30            57.98            11.17   \n",
       "\n",
       "ID          WEGE3 BS Equity  YDUQ3 BS Equity  \n",
       "Date                                          \n",
       "2019-12-13            34.05            47.20  \n",
       "2019-12-16            33.49            47.35  \n",
       "2019-12-17            33.96            46.35  \n",
       "2019-12-18            33.90            48.14  \n",
       "2019-12-19            33.60            49.70  \n",
       "2019-12-20            33.89            48.12  \n",
       "2019-12-23            33.79            49.00  \n",
       "2019-12-26            34.85            48.60  \n",
       "2019-12-27            35.34            48.80  \n",
       "2019-12-30            34.66            47.50  \n",
       "\n",
       "[10 rows x 75 columns]"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Building final DataFrame:\n",
    "\n",
    "df_main = df_main.pivot('Date', 'ID', 'Last_Price')\n",
    "df_main.tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [],
   "source": [
    "#--------------------------------------------------------------------------------#"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<bql.request.response.Response object at 0x0000027899EBE358>\n"
     ]
    }
   ],
   "source": [
    "# Bid-Ask Spread\n",
    "\n",
    "# BQL String: \n",
    "\n",
    "# get(PX_ASK(dates=range(2018-01-01, 2019-12-31), currency=BRL) ,PX_LAST(dates=range(2018-01-01, 2019-12-31))) \n",
    "# for('BPAC11 BZ Equity')\n",
    "\n",
    "# BQL Object Model: \n",
    "\n",
    "bql_item1 = bq.data.px_ask(dates=bq.func.range('2018-01-01', '2019-12-31'))\n",
    "bql_item2 = bq.data.px_bid(dates=bq.func.range('2018-01-01', '2019-12-31'))\n",
    "bql_items = [bql_item1, bql_item2]\n",
    "\n",
    "bql_universe = 'BPAC11 BZ Equity'\n",
    "\n",
    "bql_request = bql.Request(bql_universe, bql_items)\n",
    "bql_response = bq.execute(bql_request)\n",
    "print(bql_response)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Building DataFrames: \n",
    "\n",
    "df_ask = bql_response[0].df()\n",
    "df_ask.dropna(axis=0, inplace=True)\n",
    "df_ask.columns = ['Date', 'Currency', 'PX_ASK']\n",
    "df_ask = df_ask.reset_index()\n",
    "\n",
    "df_bid = bql_response[1].df()\n",
    "df_bid.dropna(axis=0, inplace=True)\n",
    "df_bid.columns = ['Date', 'Currency', 'PX_BID']\n",
    "df_bid = df_bid.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\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>ID</th>\n",
       "      <th>Date</th>\n",
       "      <th>Currency</th>\n",
       "      <th>PX_ASK</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BPAC11 BZ Equity</td>\n",
       "      <td>2018-01-02</td>\n",
       "      <td>BRL</td>\n",
       "      <td>18.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BPAC11 BZ Equity</td>\n",
       "      <td>2018-01-03</td>\n",
       "      <td>BRL</td>\n",
       "      <td>19.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BPAC11 BZ Equity</td>\n",
       "      <td>2018-01-04</td>\n",
       "      <td>BRL</td>\n",
       "      <td>19.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BPAC11 BZ Equity</td>\n",
       "      <td>2018-01-05</td>\n",
       "      <td>BRL</td>\n",
       "      <td>18.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BPAC11 BZ Equity</td>\n",
       "      <td>2018-01-08</td>\n",
       "      <td>BRL</td>\n",
       "      <td>19.05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 ID       Date Currency  PX_ASK\n",
       "0  BPAC11 BZ Equity 2018-01-02      BRL   18.93\n",
       "1  BPAC11 BZ Equity 2018-01-03      BRL   19.19\n",
       "2  BPAC11 BZ Equity 2018-01-04      BRL   19.21\n",
       "3  BPAC11 BZ Equity 2018-01-05      BRL   18.77\n",
       "4  BPAC11 BZ Equity 2018-01-08      BRL   19.05"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ask.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Date</th>\n",
       "      <th>Currency</th>\n",
       "      <th>PX_BID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BPAC11 BZ Equity</td>\n",
       "      <td>2018-01-02</td>\n",
       "      <td>BRL</td>\n",
       "      <td>18.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BPAC11 BZ Equity</td>\n",
       "      <td>2018-01-03</td>\n",
       "      <td>BRL</td>\n",
       "      <td>19.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BPAC11 BZ Equity</td>\n",
       "      <td>2018-01-04</td>\n",
       "      <td>BRL</td>\n",
       "      <td>19.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BPAC11 BZ Equity</td>\n",
       "      <td>2018-01-05</td>\n",
       "      <td>BRL</td>\n",
       "      <td>18.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BPAC11 BZ Equity</td>\n",
       "      <td>2018-01-08</td>\n",
       "      <td>BRL</td>\n",
       "      <td>18.91</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 ID       Date Currency  PX_BID\n",
       "0  BPAC11 BZ Equity 2018-01-02      BRL   18.90\n",
       "1  BPAC11 BZ Equity 2018-01-03      BRL   19.08\n",
       "2  BPAC11 BZ Equity 2018-01-04      BRL   19.15\n",
       "3  BPAC11 BZ Equity 2018-01-05      BRL   18.56\n",
       "4  BPAC11 BZ Equity 2018-01-08      BRL   18.91"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bid.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Merging DataFrames:\n",
    "\n",
    "df_bid_ask = pd.merge(df_bid, df_ask, on='Date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "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>ID_x</th>\n",
       "      <th>Date</th>\n",
       "      <th>Currency_x</th>\n",
       "      <th>PX_BID</th>\n",
       "      <th>ID_y</th>\n",
       "      <th>Currency_y</th>\n",
       "      <th>PX_ASK</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BPAC11 BZ Equity</td>\n",
       "      <td>2018-01-02</td>\n",
       "      <td>BRL</td>\n",
       "      <td>18.90</td>\n",
       "      <td>BPAC11 BZ Equity</td>\n",
       "      <td>BRL</td>\n",
       "      <td>18.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BPAC11 BZ Equity</td>\n",
       "      <td>2018-01-03</td>\n",
       "      <td>BRL</td>\n",
       "      <td>19.08</td>\n",
       "      <td>BPAC11 BZ Equity</td>\n",
       "      <td>BRL</td>\n",
       "      <td>19.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BPAC11 BZ Equity</td>\n",
       "      <td>2018-01-04</td>\n",
       "      <td>BRL</td>\n",
       "      <td>19.15</td>\n",
       "      <td>BPAC11 BZ Equity</td>\n",
       "      <td>BRL</td>\n",
       "      <td>19.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BPAC11 BZ Equity</td>\n",
       "      <td>2018-01-05</td>\n",
       "      <td>BRL</td>\n",
       "      <td>18.56</td>\n",
       "      <td>BPAC11 BZ Equity</td>\n",
       "      <td>BRL</td>\n",
       "      <td>18.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BPAC11 BZ Equity</td>\n",
       "      <td>2018-01-08</td>\n",
       "      <td>BRL</td>\n",
       "      <td>18.91</td>\n",
       "      <td>BPAC11 BZ Equity</td>\n",
       "      <td>BRL</td>\n",
       "      <td>19.05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               ID_x       Date Currency_x  PX_BID              ID_y  \\\n",
       "0  BPAC11 BZ Equity 2018-01-02        BRL   18.90  BPAC11 BZ Equity   \n",
       "1  BPAC11 BZ Equity 2018-01-03        BRL   19.08  BPAC11 BZ Equity   \n",
       "2  BPAC11 BZ Equity 2018-01-04        BRL   19.15  BPAC11 BZ Equity   \n",
       "3  BPAC11 BZ Equity 2018-01-05        BRL   18.56  BPAC11 BZ Equity   \n",
       "4  BPAC11 BZ Equity 2018-01-08        BRL   18.91  BPAC11 BZ Equity   \n",
       "\n",
       "  Currency_y  PX_ASK  \n",
       "0        BRL   18.93  \n",
       "1        BRL   19.19  \n",
       "2        BRL   19.21  \n",
       "3        BRL   18.77  \n",
       "4        BRL   19.05  "
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bid_ask.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_bid_ask.drop(['ID_x','ID_y', 'Currency_x', 'Currency_y'], axis=1, inplace=True)\n",
    "df_bid_ask.set_index('Date', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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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>PX_BID</th>\n",
       "      <th>PX_ASK</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-02</th>\n",
       "      <td>18.90</td>\n",
       "      <td>18.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <td>19.08</td>\n",
       "      <td>19.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-04</th>\n",
       "      <td>19.15</td>\n",
       "      <td>19.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-05</th>\n",
       "      <td>18.56</td>\n",
       "      <td>18.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-08</th>\n",
       "      <td>18.91</td>\n",
       "      <td>19.05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            PX_BID  PX_ASK\n",
       "Date                      \n",
       "2018-01-02   18.90   18.93\n",
       "2018-01-03   19.08   19.19\n",
       "2018-01-04   19.15   19.21\n",
       "2018-01-05   18.56   18.77\n",
       "2018-01-08   18.91   19.05"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bid_ask.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "93c44196c07b43f896f0821b03a54f9a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "GridBox(children=(Figure(animation_duration=500, axes=[Axis(color='white', grid_color='#3c3c3c', grid_lines='d…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting with Bqviz:\n",
    "\n",
    "df_bid_ask_sqrd = df_bid_ask\n",
    "df_bid_ask_sqrd['Plot'] = (df_bid_ask_sqrd['PX_ASK'] - df_bid_ask_sqrd['PX_BID'])**2\n",
    "df_bid_ask_sqrd.drop(['PX_ASK', 'PX_BID'], axis=1, inplace=True)\n",
    "import bqviz as bqv\n",
    "\n",
    "line_plot = bqv.LinePlot(df_bid_ask_sqrd).set_style()\n",
    "line_plot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Potting data using Matplotlib:\n",
    "\n",
    "plt.figure(figsize=(13, 8))\n",
    "format = mpl_dates.DateFormatter('%B, %Y')\n",
    "plt.gca().xaxis.set_major_formatter(format)\n",
    "plt.plot(df_bid_ask_sqrd.Plot)\n",
    "\n",
    "plt.title('(ASK - BID)^2')\n",
    "plt.tick_params(axis='x', rotation=30)\n",
    "plt.grid(linewidth=0.5)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [],
   "source": [
    "#-----------------------------------------------------------------------------------------------------------------#"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Universe handlers:\n",
    "# (1) members()\n",
    "# (2) bonds()\n",
    "# (3) options()\n",
    "# (4) equitiesUniv()\n",
    "# (5) bondsUniv()\n",
    "# ... check docs for more funcions ...\n",
    "\n",
    "#---------------------------------------------------------------------------------------------#\n",
    "\n",
    "# (1) members()\n",
    "# (1) BQL String:\n",
    "# get (PX_OPEN(dates=range(-100D, 0D)))\n",
    "# for (members('IBOX Index'))\n",
    "\n",
    "# (1) BQL Object Model:\n",
    "\n",
    "#import bql\n",
    "#bq = bql.Service()\n",
    "\n",
    "#bql_item = bq.data.px_open(dates=bq.func.range('-100D', '0D'))\n",
    "#bql_universe = bq.univ.members('IBOV Index')\n",
    "\n",
    "#bql_request = bql.Request(bql_universe, bql_item)\n",
    "#bql_response = bq.execute(bql_request)\n",
    "\n",
    "#---------------------------------------------------------------------------------------------#\n",
    "\n",
    "# (2) bonds()\n",
    "# (2) BQL String:\n",
    "# get(id()) \n",
    "# for(bonds('ITUB4 BZ Equity'))\n",
    "\n",
    "# BQL Object Model:\n",
    "\n",
    "#import bql\n",
    "#bq = bql.Service()\n",
    "\n",
    "#bql_item = bq.data.id()\n",
    "#bql_universe = bq.univ.bonds('ITUB4 BZ Equity')\n",
    "\n",
    "#bql_request = bql.Request(bql_universe, bql_item)\n",
    "#bql_response = bq.execute(bql_request)\n",
    "\n",
    "#---------------------------------------------------------------------------------------------#\n",
    "\n",
    "# (3) options ()\n",
    "# Greeks \n",
    "# Pulls in the options chain for a security\n",
    "# get(delta(), vega(), theta(), gamma()) \n",
    "# for(options('VALE3 BZ Equity'))\n",
    "\n",
    "##import bql\n",
    "#bq = bql.Service()\n",
    "\n",
    "bql_item1 = bq.data.delta()\n",
    "bql_item2 = bq.data.vega()\n",
    "bql_item3 = bq.data.theta()\n",
    "bql_item4 = bq.data.gamma()\n",
    "bql_items = [bql_item1, bql_item2, bql_item3, bql_item4]\n",
    "\n",
    "bql_universe = bq.univ.options('VALE3 BZ Equity')\n",
    "bql_request = bql.Request(bql_universe, bql_items)\n",
    "bql_response = bq.execute(bql_request)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATE</th>\n",
       "      <th>EXERCISE_TYPE</th>\n",
       "      <th>EXPIRE_DT</th>\n",
       "      <th>PUT_CALL</th>\n",
       "      <th>STRIKE_PX</th>\n",
       "      <th>DELTA()</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</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>BBG00W8ZHW77</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>European</td>\n",
       "      <td>2021-01-18</td>\n",
       "      <td>Call</td>\n",
       "      <td>59.5</td>\n",
       "      <td>0.585941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BBG00V0XG9M7</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>European</td>\n",
       "      <td>2020-08-17</td>\n",
       "      <td>Call</td>\n",
       "      <td>59.3</td>\n",
       "      <td>0.720317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BBG00VJFY866</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>European</td>\n",
       "      <td>2020-10-19</td>\n",
       "      <td>Call</td>\n",
       "      <td>59.3</td>\n",
       "      <td>0.626108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BBG00N7WZTW4</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>European</td>\n",
       "      <td>2020-12-21</td>\n",
       "      <td>Call</td>\n",
       "      <td>59.3</td>\n",
       "      <td>0.641150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BBG00VCXQRH9</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>European</td>\n",
       "      <td>2020-09-21</td>\n",
       "      <td>Call</td>\n",
       "      <td>59.3</td>\n",
       "      <td>0.642821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BBG00V0XG9K9</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>European</td>\n",
       "      <td>2020-08-17</td>\n",
       "      <td>Call</td>\n",
       "      <td>58.3</td>\n",
       "      <td>0.789810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BBG00Q8J0537</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>European</td>\n",
       "      <td>2020-09-21</td>\n",
       "      <td>Call</td>\n",
       "      <td>58.3</td>\n",
       "      <td>0.688740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BBG00Q79Q113</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>European</td>\n",
       "      <td>2020-08-17</td>\n",
       "      <td>Call</td>\n",
       "      <td>57.3</td>\n",
       "      <td>0.843803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BBG00VCXQRF1</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>European</td>\n",
       "      <td>2020-09-21</td>\n",
       "      <td>Call</td>\n",
       "      <td>57.3</td>\n",
       "      <td>0.757591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BBG00RFF8LR6</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>European</td>\n",
       "      <td>2020-12-21</td>\n",
       "      <td>Call</td>\n",
       "      <td>57.3</td>\n",
       "      <td>0.567919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BBG00RHCTWS3</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>European</td>\n",
       "      <td>2020-08-17</td>\n",
       "      <td>Call</td>\n",
       "      <td>56.3</td>\n",
       "      <td>0.908130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BBG00V1KWJ26</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>European</td>\n",
       "      <td>2020-12-21</td>\n",
       "      <td>Call</td>\n",
       "      <td>56.3</td>\n",
       "      <td>0.568352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BBG00VCXQRC4</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>European</td>\n",
       "      <td>2020-09-21</td>\n",
       "      <td>Call</td>\n",
       "      <td>56.3</td>\n",
       "      <td>0.755370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BBG00R9CFSQ8</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>European</td>\n",
       "      <td>2020-10-19</td>\n",
       "      <td>Call</td>\n",
       "      <td>56.3</td>\n",
       "      <td>0.695051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BBG00VSH4GN8</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>European</td>\n",
       "      <td>2021-01-18</td>\n",
       "      <td>Call</td>\n",
       "      <td>55.5</td>\n",
       "      <td>0.597284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BBG00PVW5FP3</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>European</td>\n",
       "      <td>2020-08-17</td>\n",
       "      <td>Call</td>\n",
       "      <td>55.3</td>\n",
       "      <td>0.921500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BBG00TN6L049</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>European</td>\n",
       "      <td>2020-12-21</td>\n",
       "      <td>Call</td>\n",
       "      <td>55.3</td>\n",
       "      <td>0.562261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BBG00VCXQR98</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>European</td>\n",
       "      <td>2020-09-21</td>\n",
       "      <td>Call</td>\n",
       "      <td>55.3</td>\n",
       "      <td>0.816855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BBG00QKDXBR1</th>\n",
       "      <td>2020-08-06</td>\n",
       "      <td>European</td>\n",
       "      <td>2020-10-19</td>\n",
       "      <td>Call</td>\n",
       "      <td>55.3</td>\n",
       "      <td>0.624079</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   DATE EXERCISE_TYPE  EXPIRE_DT PUT_CALL  STRIKE_PX   DELTA()\n",
       "ID                                                                            \n",
       "BBG00W8ZHW77 2020-08-06      European 2021-01-18     Call       59.5  0.585941\n",
       "BBG00V0XG9M7 2020-08-06      European 2020-08-17     Call       59.3  0.720317\n",
       "BBG00VJFY866 2020-08-06      European 2020-10-19     Call       59.3  0.626108\n",
       "BBG00N7WZTW4 2020-08-06      European 2020-12-21     Call       59.3  0.641150\n",
       "BBG00VCXQRH9 2020-08-06      European 2020-09-21     Call       59.3  0.642821\n",
       "BBG00V0XG9K9 2020-08-06      European 2020-08-17     Call       58.3  0.789810\n",
       "BBG00Q8J0537 2020-08-06      European 2020-09-21     Call       58.3  0.688740\n",
       "BBG00Q79Q113 2020-08-06      European 2020-08-17     Call       57.3  0.843803\n",
       "BBG00VCXQRF1 2020-08-06      European 2020-09-21     Call       57.3  0.757591\n",
       "BBG00RFF8LR6 2020-08-06      European 2020-12-21     Call       57.3  0.567919\n",
       "BBG00RHCTWS3 2020-08-06      European 2020-08-17     Call       56.3  0.908130\n",
       "BBG00V1KWJ26 2020-08-06      European 2020-12-21     Call       56.3  0.568352\n",
       "BBG00VCXQRC4 2020-08-06      European 2020-09-21     Call       56.3  0.755370\n",
       "BBG00R9CFSQ8 2020-08-06      European 2020-10-19     Call       56.3  0.695051\n",
       "BBG00VSH4GN8 2020-08-06      European 2021-01-18     Call       55.5  0.597284\n",
       "BBG00PVW5FP3 2020-08-06      European 2020-08-17     Call       55.3  0.921500\n",
       "BBG00TN6L049 2020-08-06      European 2020-12-21     Call       55.3  0.562261\n",
       "BBG00VCXQR98 2020-08-06      European 2020-09-21     Call       55.3  0.816855\n",
       "BBG00QKDXBR1 2020-08-06      European 2020-10-19     Call       55.3  0.624079"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using Pandas to filter out our data ... \n",
    "\n",
    "df_delta = bql_response[0].df()\n",
    "df_delta.dropna(axis=0, inplace=True)\n",
    "\n",
    "df_vega = bql_response[1].df()\n",
    "df_theta = bql_response[2].df()\n",
    "df_gamma = bql_response[3].df()\n",
    "\n",
    "mask = \\\n",
    "    (df_delta['PUT_CALL'] == 'Call') &\\\n",
    "    (df_delta['EXERCISE_TYPE'] == 'European') &\\\n",
    "    (df_delta['STRIKE_PX'] < 60) &\\\n",
    "    (df_delta['STRIKE_PX'] > 55) &\\\n",
    "    (df_delta['DELTA()'] > 0.5) \n",
    "\n",
    "df_delta_f1 = df_delta[mask]\n",
    "df_delta_f1 = df_delta_f1.sort_values(by='STRIKE_PX', ascending=False)\n",
    "df_delta_f1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#--------------------------------------------------------------------------------------------#"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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