Momentum Basic Strategy - Analysis Brazilian Stock Market .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hello"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This code is particularly original, with no instructions from books or classes. You may find it curious because of this, but I put all of my quantitative research into practice, and Storytelling can be found at the URL below.\n",
    "\n",
    "https://public.tableau.com/app/profile/atos.reis/viz/MomentumStrategy-BrazilianStockMarketV2/Histria1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Set Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-11T23:03:41.973632Z",
     "start_time": "2022-03-11T23:03:40.888094Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import datetime\n",
    "import seaborn as sns\n",
    "pd.set_option('display.float_format', lambda x: '%.2f' % x)\n",
    "import pandas.tseries.offsets\n",
    "from pandas.tseries.offsets import CustomBusinessMonthEnd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-11T23:03:41.989640Z",
     "start_time": "2022-03-11T23:03:41.974637Z"
    }
   },
   "outputs": [],
   "source": [
    "def enable_plotly_in_cell():\n",
    "  import IPython\n",
    "  from plotly.offline import init_notebook_mode\n",
    "  display(IPython.core.display.HTML('''<script src=\"/static/components/requirejs/require.js\"></script>'''))\n",
    "  init_notebook_mode(connected=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-11T23:03:42.389730Z",
     "start_time": "2022-03-11T23:03:41.991641Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Atos\\AppData\\Local\\Temp/ipykernel_14168/1090634594.py:2: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only\n",
      "  df = df.drop('Unnamed: 0', 1)\n"
     ]
    },
    {
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       "      <th>date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>vol</th>\n",
       "      <th>ticker</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>28/10/2016</td>\n",
       "      <td>19.03</td>\n",
       "      <td>19.49</td>\n",
       "      <td>18.66</td>\n",
       "      <td>18.94</td>\n",
       "      <td>6342600.00</td>\n",
       "      <td>AALR3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>31/10/2016</td>\n",
       "      <td>18.93</td>\n",
       "      <td>18.94</td>\n",
       "      <td>17.27</td>\n",
       "      <td>17.81</td>\n",
       "      <td>2523300.00</td>\n",
       "      <td>AALR3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01/11/2016</td>\n",
       "      <td>17.81</td>\n",
       "      <td>18.13</td>\n",
       "      <td>16.92</td>\n",
       "      <td>17.65</td>\n",
       "      <td>996200.00</td>\n",
       "      <td>AALR3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>03/11/2016</td>\n",
       "      <td>17.75</td>\n",
       "      <td>17.99</td>\n",
       "      <td>17.07</td>\n",
       "      <td>17.74</td>\n",
       "      <td>621000.00</td>\n",
       "      <td>AALR3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>04/11/2016</td>\n",
       "      <td>17.70</td>\n",
       "      <td>17.87</td>\n",
       "      <td>17.47</td>\n",
       "      <td>17.51</td>\n",
       "      <td>389800.00</td>\n",
       "      <td>AALR3</td>\n",
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       "      <th>...</th>\n",
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       "      <th>464085</th>\n",
       "      <td>23/12/2021</td>\n",
       "      <td>20.75</td>\n",
       "      <td>20.75</td>\n",
       "      <td>20.25</td>\n",
       "      <td>20.33</td>\n",
       "      <td>1731000.00</td>\n",
       "      <td>YDUQ3</td>\n",
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       "      <th>464086</th>\n",
       "      <td>27/12/2021</td>\n",
       "      <td>20.45</td>\n",
       "      <td>20.82</td>\n",
       "      <td>20.05</td>\n",
       "      <td>20.07</td>\n",
       "      <td>1809000.00</td>\n",
       "      <td>YDUQ3</td>\n",
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       "      <th>464087</th>\n",
       "      <td>28/12/2021</td>\n",
       "      <td>20.15</td>\n",
       "      <td>20.89</td>\n",
       "      <td>20.01</td>\n",
       "      <td>20.86</td>\n",
       "      <td>1710500.00</td>\n",
       "      <td>YDUQ3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>464088</th>\n",
       "      <td>29/12/2021</td>\n",
       "      <td>20.86</td>\n",
       "      <td>21.18</td>\n",
       "      <td>20.26</td>\n",
       "      <td>20.32</td>\n",
       "      <td>1342300.00</td>\n",
       "      <td>YDUQ3</td>\n",
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       "    <tr>\n",
       "      <th>464089</th>\n",
       "      <td>30/12/2021</td>\n",
       "      <td>20.54</td>\n",
       "      <td>20.80</td>\n",
       "      <td>20.17</td>\n",
       "      <td>20.56</td>\n",
       "      <td>2053000.00</td>\n",
       "      <td>YDUQ3</td>\n",
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       "              date  open  high   low  close        vol ticker\n",
       "0       28/10/2016 19.03 19.49 18.66  18.94 6342600.00  AALR3\n",
       "1       31/10/2016 18.93 18.94 17.27  17.81 2523300.00  AALR3\n",
       "2       01/11/2016 17.81 18.13 16.92  17.65  996200.00  AALR3\n",
       "3       03/11/2016 17.75 17.99 17.07  17.74  621000.00  AALR3\n",
       "4       04/11/2016 17.70 17.87 17.47  17.51  389800.00  AALR3\n",
       "...            ...   ...   ...   ...    ...        ...    ...\n",
       "464085  23/12/2021 20.75 20.75 20.25  20.33 1731000.00  YDUQ3\n",
       "464086  27/12/2021 20.45 20.82 20.05  20.07 1809000.00  YDUQ3\n",
       "464087  28/12/2021 20.15 20.89 20.01  20.86 1710500.00  YDUQ3\n",
       "464088  29/12/2021 20.86 21.18 20.26  20.32 1342300.00  YDUQ3\n",
       "464089  30/12/2021 20.54 20.80 20.17  20.56 2053000.00  YDUQ3\n",
       "\n",
       "[464090 rows x 7 columns]"
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     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df = pd.read_csv('C:/')\n",
    "df = df.drop('Unnamed: 0', 1)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
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     "start_time": "2022-03-11T23:03:42.391732Z"
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   "outputs": [
    {
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       "      <th>0</th>\n",
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       "      <td>19.03</td>\n",
       "      <td>19.49</td>\n",
       "      <td>18.66</td>\n",
       "      <td>18.94</td>\n",
       "      <td>6342600.00</td>\n",
       "      <td>AALR3</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>31/10/2016</td>\n",
       "      <td>18.93</td>\n",
       "      <td>18.94</td>\n",
       "      <td>17.27</td>\n",
       "      <td>17.81</td>\n",
       "      <td>2523300.00</td>\n",
       "      <td>AALR3</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01/11/2016</td>\n",
       "      <td>17.81</td>\n",
       "      <td>18.13</td>\n",
       "      <td>16.92</td>\n",
       "      <td>17.65</td>\n",
       "      <td>996200.00</td>\n",
       "      <td>AALR3</td>\n",
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       "      <td>17.87</td>\n",
       "      <td>17.47</td>\n",
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       "      <td>20.15</td>\n",
       "      <td>20.89</td>\n",
       "      <td>20.01</td>\n",
       "      <td>20.86</td>\n",
       "      <td>1710500.00</td>\n",
       "      <td>YDUQ3</td>\n",
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       "    <tr>\n",
       "      <th>464088</th>\n",
       "      <td>29/12/2021</td>\n",
       "      <td>20.86</td>\n",
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       "      <td>20.32</td>\n",
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       "      <td>YDUQ3</td>\n",
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       "      <td>30/12/2021</td>\n",
       "      <td>20.54</td>\n",
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      "text/plain": [
       "          datetime  open  high   low  close     volume ticker\n",
       "0       28/10/2016 19.03 19.49 18.66  18.94 6342600.00  AALR3\n",
       "1       31/10/2016 18.93 18.94 17.27  17.81 2523300.00  AALR3\n",
       "2       01/11/2016 17.81 18.13 16.92  17.65  996200.00  AALR3\n",
       "3       03/11/2016 17.75 17.99 17.07  17.74  621000.00  AALR3\n",
       "4       04/11/2016 17.70 17.87 17.47  17.51  389800.00  AALR3\n",
       "...            ...   ...   ...   ...    ...        ...    ...\n",
       "464085  23/12/2021 20.75 20.75 20.25  20.33 1731000.00  YDUQ3\n",
       "464086  27/12/2021 20.45 20.82 20.05  20.07 1809000.00  YDUQ3\n",
       "464087  28/12/2021 20.15 20.89 20.01  20.86 1710500.00  YDUQ3\n",
       "464088  29/12/2021 20.86 21.18 20.26  20.32 1342300.00  YDUQ3\n",
       "464089  30/12/2021 20.54 20.80 20.17  20.56 2053000.00  YDUQ3\n",
       "\n",
       "[464090 rows x 7 columns]"
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     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.rename(columns={'date': 'datetime', 'vol': 'volume'}, inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-11T23:03:42.932853Z",
     "start_time": "2022-03-11T23:03:42.422738Z"
    }
   },
   "outputs": [],
   "source": [
    "# # object to datatype \n",
    "df['datetime'] = pd.to_datetime(df['datetime'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-11T23:03:42.947857Z",
     "start_time": "2022-03-11T23:03:42.933854Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime    datetime64[ns]\n",
       "open               float64\n",
       "high               float64\n",
       "low                float64\n",
       "close              float64\n",
       "volume             float64\n",
       "ticker              object\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-11T23:03:43.043884Z",
     "start_time": "2022-03-11T23:03:42.948857Z"
    }
   },
   "outputs": [],
   "source": [
    "# #slice by year\n",
    "df['datetime'] = df.loc[df['datetime'] > pd.to_datetime('2017-01-01', format='%Y-%m-%d'), 'datetime'].sort_values(ascending=True)\n",
    "df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-11T23:03:43.059889Z",
     "start_time": "2022-03-11T23:03:43.044886Z"
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   "outputs": [
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       "      <th>43</th>\n",
       "      <td>2017-02-01</td>\n",
       "      <td>14.42</td>\n",
       "      <td>14.68</td>\n",
       "      <td>14.20</td>\n",
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       "      <th>44</th>\n",
       "      <td>2017-03-01</td>\n",
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       "      <td>14.37</td>\n",
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       "      <th>45</th>\n",
       "      <td>2017-04-01</td>\n",
       "      <td>14.70</td>\n",
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       "      <th>46</th>\n",
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       "      <td>15.24</td>\n",
       "      <td>14.73</td>\n",
       "      <td>15.24</td>\n",
       "      <td>151000.00</td>\n",
       "      <td>AALR3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>464085</th>\n",
       "      <td>2021-12-23</td>\n",
       "      <td>20.75</td>\n",
       "      <td>20.75</td>\n",
       "      <td>20.25</td>\n",
       "      <td>20.33</td>\n",
       "      <td>1731000.00</td>\n",
       "      <td>YDUQ3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>464086</th>\n",
       "      <td>2021-12-27</td>\n",
       "      <td>20.45</td>\n",
       "      <td>20.82</td>\n",
       "      <td>20.05</td>\n",
       "      <td>20.07</td>\n",
       "      <td>1809000.00</td>\n",
       "      <td>YDUQ3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>464087</th>\n",
       "      <td>2021-12-28</td>\n",
       "      <td>20.15</td>\n",
       "      <td>20.89</td>\n",
       "      <td>20.01</td>\n",
       "      <td>20.86</td>\n",
       "      <td>1710500.00</td>\n",
       "      <td>YDUQ3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>464088</th>\n",
       "      <td>2021-12-29</td>\n",
       "      <td>20.86</td>\n",
       "      <td>21.18</td>\n",
       "      <td>20.26</td>\n",
       "      <td>20.32</td>\n",
       "      <td>1342300.00</td>\n",
       "      <td>YDUQ3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>464089</th>\n",
       "      <td>2021-12-30</td>\n",
       "      <td>20.54</td>\n",
       "      <td>20.80</td>\n",
       "      <td>20.17</td>\n",
       "      <td>20.56</td>\n",
       "      <td>2053000.00</td>\n",
       "      <td>YDUQ3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>187870 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         datetime  open  high   low  close     volume ticker\n",
       "43     2017-02-01 14.42 14.68 14.20  14.40   35900.00  AALR3\n",
       "44     2017-03-01 14.54 14.85 14.37  14.70  357200.00  AALR3\n",
       "45     2017-04-01 14.70 14.83 14.61  14.70  117100.00  AALR3\n",
       "46     2017-05-01 14.70 15.14 14.70  15.14  124300.00  AALR3\n",
       "47     2017-06-01 15.07 15.24 14.73  15.24  151000.00  AALR3\n",
       "...           ...   ...   ...   ...    ...        ...    ...\n",
       "464085 2021-12-23 20.75 20.75 20.25  20.33 1731000.00  YDUQ3\n",
       "464086 2021-12-27 20.45 20.82 20.05  20.07 1809000.00  YDUQ3\n",
       "464087 2021-12-28 20.15 20.89 20.01  20.86 1710500.00  YDUQ3\n",
       "464088 2021-12-29 20.86 21.18 20.26  20.32 1342300.00  YDUQ3\n",
       "464089 2021-12-30 20.54 20.80 20.17  20.56 2053000.00  YDUQ3\n",
       "\n",
       "[187870 rows x 7 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Select 40 higher volume stocks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-11T23:03:43.107899Z",
     "start_time": "2022-03-11T23:03:43.062889Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['VVAR3', 'PETR4', 'MGLU3', 'COGN3', 'IRBR3', 'CIEL3', 'HAPV3',\n",
       "       'VALE3', 'PRIO3', 'LAME4', 'CPLE6', 'ABEV3', 'PETR3', 'BBDC4',\n",
       "       'BRML3', 'EMBR3', 'CCRO3', 'ITSA4', 'ITUB4', 'BIDI4', 'BBAS3',\n",
       "       'CSAN3', 'BPAC11', 'JBSS3', 'LWSA3', 'SAPR4', 'USIM5', 'BRDT3',\n",
       "       'BRFS3', 'DMMO3', 'BRAP4', 'NTCO3', 'LAME3', 'AZUL4', 'CMIG4',\n",
       "       'SULA11', 'TOTS3', 'NEOE3', 'CSNA3', 'ENEV3'], dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = df[['ticker','volume']].sort_values('volume', ascending=False)\n",
    "list_40 = df1.ticker.drop_duplicates(keep='first').head(40)\n",
    "list_40.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-11T23:03:43.155910Z",
     "start_time": "2022-03-11T23:03:43.108899Z"
    }
   },
   "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>datetime</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>ticker</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>2017-02-01</td>\n",
       "      <td>14.42</td>\n",
       "      <td>14.68</td>\n",
       "      <td>14.20</td>\n",
       "      <td>14.40</td>\n",
       "      <td>35900.00</td>\n",
       "      <td>AALR3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>2017-03-01</td>\n",
       "      <td>14.54</td>\n",
       "      <td>14.85</td>\n",
       "      <td>14.37</td>\n",
       "      <td>14.70</td>\n",
       "      <td>357200.00</td>\n",
       "      <td>AALR3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>2017-04-01</td>\n",
       "      <td>14.70</td>\n",
       "      <td>14.83</td>\n",
       "      <td>14.61</td>\n",
       "      <td>14.70</td>\n",
       "      <td>117100.00</td>\n",
       "      <td>AALR3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>2017-05-01</td>\n",
       "      <td>14.70</td>\n",
       "      <td>15.14</td>\n",
       "      <td>14.70</td>\n",
       "      <td>15.14</td>\n",
       "      <td>124300.00</td>\n",
       "      <td>AALR3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>2017-06-01</td>\n",
       "      <td>15.07</td>\n",
       "      <td>15.24</td>\n",
       "      <td>14.73</td>\n",
       "      <td>15.24</td>\n",
       "      <td>151000.00</td>\n",
       "      <td>AALR3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>464085</th>\n",
       "      <td>2021-12-23</td>\n",
       "      <td>20.75</td>\n",
       "      <td>20.75</td>\n",
       "      <td>20.25</td>\n",
       "      <td>20.33</td>\n",
       "      <td>1731000.00</td>\n",
       "      <td>YDUQ3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>464086</th>\n",
       "      <td>2021-12-27</td>\n",
       "      <td>20.45</td>\n",
       "      <td>20.82</td>\n",
       "      <td>20.05</td>\n",
       "      <td>20.07</td>\n",
       "      <td>1809000.00</td>\n",
       "      <td>YDUQ3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>464087</th>\n",
       "      <td>2021-12-28</td>\n",
       "      <td>20.15</td>\n",
       "      <td>20.89</td>\n",
       "      <td>20.01</td>\n",
       "      <td>20.86</td>\n",
       "      <td>1710500.00</td>\n",
       "      <td>YDUQ3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>464088</th>\n",
       "      <td>2021-12-29</td>\n",
       "      <td>20.86</td>\n",
       "      <td>21.18</td>\n",
       "      <td>20.26</td>\n",
       "      <td>20.32</td>\n",
       "      <td>1342300.00</td>\n",
       "      <td>YDUQ3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>464089</th>\n",
       "      <td>2021-12-30</td>\n",
       "      <td>20.54</td>\n",
       "      <td>20.80</td>\n",
       "      <td>20.17</td>\n",
       "      <td>20.56</td>\n",
       "      <td>2053000.00</td>\n",
       "      <td>YDUQ3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>187870 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         datetime  open  high   low  close     volume ticker\n",
       "43     2017-02-01 14.42 14.68 14.20  14.40   35900.00  AALR3\n",
       "44     2017-03-01 14.54 14.85 14.37  14.70  357200.00  AALR3\n",
       "45     2017-04-01 14.70 14.83 14.61  14.70  117100.00  AALR3\n",
       "46     2017-05-01 14.70 15.14 14.70  15.14  124300.00  AALR3\n",
       "47     2017-06-01 15.07 15.24 14.73  15.24  151000.00  AALR3\n",
       "...           ...   ...   ...   ...    ...        ...    ...\n",
       "464085 2021-12-23 20.75 20.75 20.25  20.33 1731000.00  YDUQ3\n",
       "464086 2021-12-27 20.45 20.82 20.05  20.07 1809000.00  YDUQ3\n",
       "464087 2021-12-28 20.15 20.89 20.01  20.86 1710500.00  YDUQ3\n",
       "464088 2021-12-29 20.86 21.18 20.26  20.32 1342300.00  YDUQ3\n",
       "464089 2021-12-30 20.54 20.80 20.17  20.56 2053000.00  YDUQ3\n",
       "\n",
       "[187870 rows x 7 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.dropna()\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create new DF with selected stocks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-11T23:03:43.187120Z",
     "start_time": "2022-03-11T23:03:43.156910Z"
    }
   },
   "outputs": [],
   "source": [
    "df = df[df.ticker.isin(['VVAR3', 'PETR4', 'MGLU3', 'COGN3', 'IRBR3', 'CIEL3', 'HAPV3',\n",
    "       'VALE3', 'PRIO3', 'LAME4', 'CPLE6', 'ABEV3', 'PETR3', 'BBDC4',\n",
    "       'BRML3', 'EMBR3', 'CCRO3', 'ITSA4', 'ITUB4', 'BIDI4', 'BBAS3',\n",
    "       'CSAN3', 'BPAC11', 'JBSS3', 'LWSA3', 'SAPR4', 'USIM5', 'BRDT3',\n",
    "       'BRFS3', 'DMMO3', 'BRAP4', 'NTCO3', 'LAME3', 'AZUL4', 'CMIG4',\n",
    "       'SULA11', 'TOTS3', 'NEOE3', 'CSNA3', 'ENEV3'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-11T23:03:43.202382Z",
     "start_time": "2022-03-11T23:03:43.188123Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "40"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.ticker.unique().size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-11T23:03:43.218389Z",
     "start_time": "2022-03-11T23:03:43.203385Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['ABEV3', 'AZUL4', 'BBAS3', 'BBDC4', 'BIDI4', 'BPAC11', 'BRAP4',\n",
       "       'BRDT3', 'BRFS3', 'BRML3', 'CCRO3', 'CIEL3', 'CMIG4', 'COGN3',\n",
       "       'CPLE6', 'CSAN3', 'CSNA3', 'DMMO3', 'EMBR3', 'ENEV3', 'HAPV3',\n",
       "       'IRBR3', 'ITSA4', 'ITUB4', 'JBSS3', 'LAME3', 'LAME4', 'LWSA3',\n",
       "       'MGLU3', 'NEOE3', 'NTCO3', 'PETR3', 'PETR4', 'PRIO3', 'SAPR4',\n",
       "       'SULA11', 'TOTS3', 'USIM5', 'VALE3', 'VVAR3'], dtype=object)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.ticker.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-11T23:03:43.234393Z",
     "start_time": "2022-03-11T23:03:43.219389Z"
    }
   },
   "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",
       "        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>datetime</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>ticker</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7272</th>\n",
       "      <td>2017-02-01</td>\n",
       "      <td>14.23</td>\n",
       "      <td>14.51</td>\n",
       "      <td>14.16</td>\n",
       "      <td>14.20</td>\n",
       "      <td>4265400.00</td>\n",
       "      <td>ABEV3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7273</th>\n",
       "      <td>2017-03-01</td>\n",
       "      <td>14.37</td>\n",
       "      <td>14.52</td>\n",
       "      <td>14.32</td>\n",
       "      <td>14.49</td>\n",
       "      <td>9535500.00</td>\n",
       "      <td>ABEV3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7274</th>\n",
       "      <td>2017-04-01</td>\n",
       "      <td>14.51</td>\n",
       "      <td>14.51</td>\n",
       "      <td>14.31</td>\n",
       "      <td>14.38</td>\n",
       "      <td>9426500.00</td>\n",
       "      <td>ABEV3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7275</th>\n",
       "      <td>2017-05-01</td>\n",
       "      <td>14.38</td>\n",
       "      <td>14.59</td>\n",
       "      <td>14.35</td>\n",
       "      <td>14.45</td>\n",
       "      <td>11555800.00</td>\n",
       "      <td>ABEV3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7276</th>\n",
       "      <td>2017-06-01</td>\n",
       "      <td>14.45</td>\n",
       "      <td>14.49</td>\n",
       "      <td>14.25</td>\n",
       "      <td>14.33</td>\n",
       "      <td>7863200.00</td>\n",
       "      <td>ABEV3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>455429</th>\n",
       "      <td>2021-09-08</td>\n",
       "      <td>12.28</td>\n",
       "      <td>12.75</td>\n",
       "      <td>12.26</td>\n",
       "      <td>12.71</td>\n",
       "      <td>26796400.00</td>\n",
       "      <td>VVAR3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>455430</th>\n",
       "      <td>2021-10-08</td>\n",
       "      <td>12.76</td>\n",
       "      <td>13.06</td>\n",
       "      <td>12.69</td>\n",
       "      <td>12.80</td>\n",
       "      <td>28060000.00</td>\n",
       "      <td>VVAR3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>455431</th>\n",
       "      <td>2021-11-08</td>\n",
       "      <td>12.88</td>\n",
       "      <td>13.02</td>\n",
       "      <td>12.45</td>\n",
       "      <td>13.02</td>\n",
       "      <td>30918700.00</td>\n",
       "      <td>VVAR3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>455432</th>\n",
       "      <td>2021-12-08</td>\n",
       "      <td>12.50</td>\n",
       "      <td>12.52</td>\n",
       "      <td>11.91</td>\n",
       "      <td>12.07</td>\n",
       "      <td>100321100.00</td>\n",
       "      <td>VVAR3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>455433</th>\n",
       "      <td>2021-08-13</td>\n",
       "      <td>12.18</td>\n",
       "      <td>12.23</td>\n",
       "      <td>11.25</td>\n",
       "      <td>11.30</td>\n",
       "      <td>64987000.00</td>\n",
       "      <td>VVAR3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>46542 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         datetime  open  high   low  close       volume ticker\n",
       "7272   2017-02-01 14.23 14.51 14.16  14.20   4265400.00  ABEV3\n",
       "7273   2017-03-01 14.37 14.52 14.32  14.49   9535500.00  ABEV3\n",
       "7274   2017-04-01 14.51 14.51 14.31  14.38   9426500.00  ABEV3\n",
       "7275   2017-05-01 14.38 14.59 14.35  14.45  11555800.00  ABEV3\n",
       "7276   2017-06-01 14.45 14.49 14.25  14.33   7863200.00  ABEV3\n",
       "...           ...   ...   ...   ...    ...          ...    ...\n",
       "455429 2021-09-08 12.28 12.75 12.26  12.71  26796400.00  VVAR3\n",
       "455430 2021-10-08 12.76 13.06 12.69  12.80  28060000.00  VVAR3\n",
       "455431 2021-11-08 12.88 13.02 12.45  13.02  30918700.00  VVAR3\n",
       "455432 2021-12-08 12.50 12.52 11.91  12.07 100321100.00  VVAR3\n",
       "455433 2021-08-13 12.18 12.23 11.25  11.30  64987000.00  VVAR3\n",
       "\n",
       "[46542 rows x 7 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reshape DF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-11T23:03:43.250395Z",
     "start_time": "2022-03-11T23:03:43.236394Z"
    }
   },
   "outputs": [],
   "source": [
    "df.set_index(['datetime', 'ticker'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-11T23:03:43.266400Z",
     "start_time": "2022-03-11T23:03:43.251396Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th>ticker</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-02-01</th>\n",
       "      <th>ABEV3</th>\n",
       "      <td>14.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-01</th>\n",
       "      <th>ABEV3</th>\n",
       "      <td>14.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-01</th>\n",
       "      <th>ABEV3</th>\n",
       "      <td>14.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-01</th>\n",
       "      <th>ABEV3</th>\n",
       "      <td>14.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-01</th>\n",
       "      <th>ABEV3</th>\n",
       "      <td>14.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-08</th>\n",
       "      <th>VVAR3</th>\n",
       "      <td>12.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-08</th>\n",
       "      <th>VVAR3</th>\n",
       "      <td>12.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-08</th>\n",
       "      <th>VVAR3</th>\n",
       "      <td>13.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-08</th>\n",
       "      <th>VVAR3</th>\n",
       "      <td>12.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-13</th>\n",
       "      <th>VVAR3</th>\n",
       "      <td>11.30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>46542 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   close\n",
       "datetime   ticker       \n",
       "2017-02-01 ABEV3   14.20\n",
       "2017-03-01 ABEV3   14.49\n",
       "2017-04-01 ABEV3   14.38\n",
       "2017-05-01 ABEV3   14.45\n",
       "2017-06-01 ABEV3   14.33\n",
       "...                  ...\n",
       "2021-09-08 VVAR3   12.71\n",
       "2021-10-08 VVAR3   12.80\n",
       "2021-11-08 VVAR3   13.02\n",
       "2021-12-08 VVAR3   12.07\n",
       "2021-08-13 VVAR3   11.30\n",
       "\n",
       "[46542 rows x 1 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.loc[:, ['close']]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-11T23:03:43.314410Z",
     "start_time": "2022-03-11T23:03:43.267400Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>ticker</th>\n",
       "      <th>close</th>\n",
       "      <th>log_ret</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-03-01</td>\n",
       "      <td>ABEV3</td>\n",
       "      <td>14.49</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-04-01</td>\n",
       "      <td>ABEV3</td>\n",
       "      <td>14.38</td>\n",
       "      <td>-0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-05-01</td>\n",
       "      <td>ABEV3</td>\n",
       "      <td>14.45</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-06-01</td>\n",
       "      <td>ABEV3</td>\n",
       "      <td>14.33</td>\n",
       "      <td>-0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2017-09-01</td>\n",
       "      <td>ABEV3</td>\n",
       "      <td>14.39</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46537</th>\n",
       "      <td>2021-09-08</td>\n",
       "      <td>VVAR3</td>\n",
       "      <td>12.71</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46538</th>\n",
       "      <td>2021-10-08</td>\n",
       "      <td>VVAR3</td>\n",
       "      <td>12.80</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46539</th>\n",
       "      <td>2021-11-08</td>\n",
       "      <td>VVAR3</td>\n",
       "      <td>13.02</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46540</th>\n",
       "      <td>2021-12-08</td>\n",
       "      <td>VVAR3</td>\n",
       "      <td>12.07</td>\n",
       "      <td>-0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46541</th>\n",
       "      <td>2021-08-13</td>\n",
       "      <td>VVAR3</td>\n",
       "      <td>11.30</td>\n",
       "      <td>-0.07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>46541 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        datetime ticker  close  log_ret\n",
       "1     2017-03-01  ABEV3  14.49     0.02\n",
       "2     2017-04-01  ABEV3  14.38    -0.01\n",
       "3     2017-05-01  ABEV3  14.45     0.00\n",
       "4     2017-06-01  ABEV3  14.33    -0.01\n",
       "5     2017-09-01  ABEV3  14.39     0.00\n",
       "...          ...    ...    ...      ...\n",
       "46537 2021-09-08  VVAR3  12.71     0.03\n",
       "46538 2021-10-08  VVAR3  12.80     0.01\n",
       "46539 2021-11-08  VVAR3  13.02     0.02\n",
       "46540 2021-12-08  VVAR3  12.07    -0.08\n",
       "46541 2021-08-13  VVAR3  11.30    -0.07\n",
       "\n",
       "[46541 rows x 4 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create log_return to identify momentum\n",
    "df.reset_index(inplace=True)\n",
    "df['log_ret'] =  np.log(df.close) - np.log(df.close.shift(1))\n",
    "df.dropna(inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-11T23:03:43.650491Z",
     "start_time": "2022-03-11T23:03:43.315411Z"
    }
   },
   "outputs": [],
   "source": [
    "#set index\n",
    "df['datetime'] = df['datetime'].astype('string')\n",
    "df.set_index(['datetime'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-11T23:03:43.666495Z",
     "start_time": "2022-03-11T23:03:43.651491Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\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>ticker</th>\n",
       "      <th>close</th>\n",
       "      <th>log_ret</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-03-01</th>\n",
       "      <td>ABEV3</td>\n",
       "      <td>14.49</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-01</th>\n",
       "      <td>ABEV3</td>\n",
       "      <td>14.38</td>\n",
       "      <td>-0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-01</th>\n",
       "      <td>ABEV3</td>\n",
       "      <td>14.45</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-01</th>\n",
       "      <td>ABEV3</td>\n",
       "      <td>14.33</td>\n",
       "      <td>-0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09-01</th>\n",
       "      <td>ABEV3</td>\n",
       "      <td>14.39</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-08</th>\n",
       "      <td>VVAR3</td>\n",
       "      <td>12.71</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-08</th>\n",
       "      <td>VVAR3</td>\n",
       "      <td>12.80</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-08</th>\n",
       "      <td>VVAR3</td>\n",
       "      <td>13.02</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-08</th>\n",
       "      <td>VVAR3</td>\n",
       "      <td>12.07</td>\n",
       "      <td>-0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-13</th>\n",
       "      <td>VVAR3</td>\n",
       "      <td>11.30</td>\n",
       "      <td>-0.07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>46541 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           ticker  close  log_ret\n",
       "datetime                         \n",
       "2017-03-01  ABEV3  14.49     0.02\n",
       "2017-04-01  ABEV3  14.38    -0.01\n",
       "2017-05-01  ABEV3  14.45     0.00\n",
       "2017-06-01  ABEV3  14.33    -0.01\n",
       "2017-09-01  ABEV3  14.39     0.00\n",
       "...           ...    ...      ...\n",
       "2021-09-08  VVAR3  12.71     0.03\n",
       "2021-10-08  VVAR3  12.80     0.01\n",
       "2021-11-08  VVAR3  13.02     0.02\n",
       "2021-12-08  VVAR3  12.07    -0.08\n",
       "2021-08-13  VVAR3  11.30    -0.07\n",
       "\n",
       "[46541 rows x 3 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-11T23:03:43.682498Z",
     "start_time": "2022-03-11T23:03:43.667495Z"
    }
   },
   "outputs": [],
   "source": [
    "#df.loc[df['datetime']== '2021-12-30']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-11T23:03:43.698502Z",
     "start_time": "2022-03-11T23:03:43.683499Z"
    }
   },
   "outputs": [],
   "source": [
    "df = df.loc[['2017-01-31',\n",
    " '2017-02-24',       \n",
    " '2017-03-31',\n",
    " '2017-04-28',\n",
    " '2017-05-31',\n",
    " '2017-06-30',\n",
    " '2017-07-31',\n",
    " '2017-08-31',       \n",
    " '2017-09-29',\n",
    " '2017-10-31',\n",
    " '2017-11-30',\n",
    " '2017-12-28',\n",
    " '2017-01-31',\n",
    " '2017-07-31',\n",
    " '2018-01-31',       \n",
    " '2018-02-28',\n",
    " '2018-03-29',\n",
    " '2018-04-30',\n",
    " '2018-05-30',\n",
    " '2018-06-29',\n",
    " '2018-07-31',\n",
    " '2018-08-31',\n",
    " '2018-09-28',\n",
    " '2018-10-31',\n",
    " '2018-11-30',\n",
    " '2018-12-28',\n",
    " '2019-01-31',\n",
    " '2019-02-28',\n",
    " '2019-03-29',\n",
    " '2019-04-30',\n",
    " '2019-05-31',\n",
    " '2019-06-28',\n",
    " '2019-07-31',\n",
    " '2019-08-30',\n",
    " '2019-09-30',\n",
    " '2019-10-31',\n",
    " '2019-11-29',\n",
    " '2019-12-30',\n",
    " '2020-01-31',\n",
    " '2020-02-28',\n",
    " '2020-03-31',\n",
    " '2020-04-30',\n",
    " '2020-05-29',\n",
    " '2020-06-30',\n",
    " '2020-07-31',\n",
    " '2020-08-31',\n",
    " '2020-09-30',\n",
    " '2020-10-30',\n",
    " '2020-11-30',\n",
    " '2020-12-30',\n",
    " '2021-01-29',\n",
    " '2021-02-26',\n",
    " '2021-03-31',\n",
    " '2021-04-30',\n",
    " '2021-05-31',\n",
    " '2021-06-30',\n",
    " '2021-07-30',\n",
    " '2021-08-31',\n",
    " '2021-09-30',\n",
    " '2021-10-29',\n",
    " '2021-11-30',\n",
    " '2021-12-30',]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-11T23:03:43.714506Z",
     "start_time": "2022-03-11T23:03:43.699502Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ticker</th>\n",
       "      <th>close</th>\n",
       "      <th>log_ret</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01-31</th>\n",
       "      <td>ABEV3</td>\n",
       "      <td>15.04</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-31</th>\n",
       "      <td>BBAS3</td>\n",
       "      <td>25.32</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-31</th>\n",
       "      <td>BBDC4</td>\n",
       "      <td>15.59</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-31</th>\n",
       "      <td>BRAP4</td>\n",
       "      <td>5.90</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-31</th>\n",
       "      <td>BRFS3</td>\n",
       "      <td>44.46</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-30</th>\n",
       "      <td>SAPR4</td>\n",
       "      <td>3.87</td>\n",
       "      <td>-0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-30</th>\n",
       "      <td>SULA11</td>\n",
       "      <td>27.49</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-30</th>\n",
       "      <td>TOTS3</td>\n",
       "      <td>28.64</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-30</th>\n",
       "      <td>USIM5</td>\n",
       "      <td>15.16</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-30</th>\n",
       "      <td>VALE3</td>\n",
       "      <td>77.96</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2327 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            ticker  close  log_ret\n",
       "datetime                          \n",
       "2017-01-31   ABEV3  15.04     0.01\n",
       "2017-01-31   BBAS3  25.32     0.02\n",
       "2017-01-31   BBDC4  15.59     0.01\n",
       "2017-01-31   BRAP4   5.90     0.01\n",
       "2017-01-31   BRFS3  44.46     0.01\n",
       "...            ...    ...      ...\n",
       "2021-12-30   SAPR4   3.87    -0.00\n",
       "2021-12-30  SULA11  27.49     0.07\n",
       "2021-12-30   TOTS3  28.64     0.03\n",
       "2021-12-30   USIM5  15.16     0.02\n",
       "2021-12-30   VALE3  77.96     0.01\n",
       "\n",
       "[2327 rows x 3 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-04T21:33:11.519571Z",
     "start_time": "2022-01-04T21:33:10.966425Z"
    }
   },
   "source": [
    "# Create pivot table for close and log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-11T23:03:43.778520Z",
     "start_time": "2022-03-11T23:03:43.715506Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>ticker</th>\n",
       "      <th>ABEV3</th>\n",
       "      <th>AZUL4</th>\n",
       "      <th>BBAS3</th>\n",
       "      <th>BBDC4</th>\n",
       "      <th>BIDI4</th>\n",
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       "      <th>SULA11</th>\n",
       "      <th>TOTS3</th>\n",
       "      <th>USIM5</th>\n",
       "      <th>VALE3</th>\n",
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       "      <th>datetime</th>\n",
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       "      <th>2017-01-31</th>\n",
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       "      <th>2017-03-31</th>\n",
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       "      <td>0.02</td>\n",
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       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
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       "    <tr>\n",
       "      <th>2017-04-28</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
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       "      <td>0.01</td>\n",
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       "      <td>0.01</td>\n",
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       "      <td>0.01</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.02</td>\n",
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       "    <tr>\n",
       "      <th>2017-05-31</th>\n",
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       "      <td>-0.03</td>\n",
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       "      <td>-0.01</td>\n",
       "      <td>-0.03</td>\n",
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       "      <td>0.01</td>\n",
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       "    <tr>\n",
       "      <th>2017-06-30</th>\n",
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       "      <td>0.01</td>\n",
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       "      <td>0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.05</td>\n",
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       "    <tr>\n",
       "      <th>2017-07-31</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.01</td>\n",
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       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
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       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.03</td>\n",
       "      <td>-0.04</td>\n",
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       "    <tr>\n",
       "      <th>2017-08-31</th>\n",
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       "      <td>0.01</td>\n",
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       "      <th>2017-09-29</th>\n",
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       "      <td>0.03</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
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       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
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       "      <td>0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.07</td>\n",
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       "    <tr>\n",
       "      <th>2017-10-31</th>\n",
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       "      <td>-0.04</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>...</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.04</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.03</td>\n",
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       "    <tr>\n",
       "      <th>2017-11-30</th>\n",
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       "      <td>-0.03</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.03</td>\n",
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       "    <tr>\n",
       "      <th>2017-12-28</th>\n",
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       "      <td>0.02</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>...</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
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       "    <tr>\n",
       "      <th>2018-01-31</th>\n",
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       "      <td>0.02</td>\n",
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       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.04</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-28</th>\n",
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       "      <td>-0.01</td>\n",
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       "      <td>-0.02</td>\n",
       "      <td>0.04</td>\n",
       "      <td>-0.01</td>\n",
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       "      <td>-0.02</td>\n",
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       "      <td>0.03</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.05</td>\n",
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       "    <tr>\n",
       "      <th>2018-03-29</th>\n",
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       "      <td>0.03</td>\n",
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       "      <td>-0.02</td>\n",
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       "      <td>0.02</td>\n",
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       "    <tr>\n",
       "      <th>2018-04-30</th>\n",
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       "      <td>-0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-30</th>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.06</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.03</td>\n",
       "      <td>...</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-29</th>\n",
       "      <td>-0.00</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-31</th>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-31</th>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-28</th>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.03</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-31</th>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.04</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.05</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.02</td>\n",
       "      <td>...</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.05</td>\n",
       "      <td>-0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-30</th>\n",
       "      <td>0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-28</th>\n",
       "      <td>0.02</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>...</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.05</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-31</th>\n",
       "      <td>-0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-28</th>\n",
       "      <td>-0.06</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-29</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.04</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>...</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-30</th>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>-0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-31</th>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.03</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-28</th>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-31</th>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>...</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-30</th>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>0.04</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-30</th>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-31</th>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-29</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.06</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-30</th>\n",
       "      <td>-0.03</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-31</th>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.03</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-28</th>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>-0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-31</th>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.08</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.08</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.04</td>\n",
       "      <td>-0.08</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.04</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>-0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-30</th>\n",
       "      <td>-0.03</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.08</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-29</th>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.04</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.06</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.06</td>\n",
       "      <td>-0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-30</th>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-31</th>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-31</th>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-30</th>\n",
       "      <td>0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-30</th>\n",
       "      <td>-0.06</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-30</th>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.03</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.03</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-30</th>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.04</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>...</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.03</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-29</th>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-26</th>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-31</th>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-30</th>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>...</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-31</th>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>...</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-30</th>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.06</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07-30</th>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>-0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-31</th>\n",
       "      <td>-0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-30</th>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>...</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.09</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-29</th>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>-0.08</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-30</th>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.03</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-30</th>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>...</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.03</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>60 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "ticker      ABEV3  AZUL4  BBAS3  BBDC4  BIDI4  BPAC11  BRAP4  BRDT3  BRFS3  \\\n",
       "datetime                                                                     \n",
       "2017-01-31   0.01   0.00   0.02   0.01   0.00    0.00   0.01   0.00   0.01   \n",
       "2017-02-24  -0.02   0.00  -0.01  -0.01   0.00    0.01   0.01   0.00   0.00   \n",
       "2017-03-31  -0.00   0.00  -0.01  -0.01   0.00    0.00  -0.00   0.00   0.00   \n",
       "2017-04-28   0.00   0.01   0.01  -0.00   0.00    0.00   0.04   0.00   0.01   \n",
       "2017-05-31  -0.00  -0.05  -0.02  -0.02   0.00   -0.08  -0.04   0.00  -0.04   \n",
       "2017-06-30   0.00   0.01   0.01   0.02   0.00    0.03  -0.00   0.00   0.01   \n",
       "2017-07-31   0.00   0.03   0.01   0.02   0.00    0.00   0.04   0.00  -0.00   \n",
       "2017-08-31  -0.00   0.02  -0.02  -0.00   0.00   -0.01   0.02   0.00   0.02   \n",
       "2017-09-29  -0.00   0.03   0.01   0.01   0.00   -0.00   0.02   0.00   0.00   \n",
       "2017-10-31  -0.00  -0.04  -0.02  -0.01   0.00    0.01   0.01   0.00   0.01   \n",
       "2017-11-30   0.00  -0.03  -0.04  -0.01   0.00   -0.01  -0.01   0.00  -0.02   \n",
       "2017-12-28  -0.01   0.02  -0.01   0.01   0.00   -0.00   0.00   0.02  -0.01   \n",
       "2018-01-31   0.00   0.02   0.02   0.01   0.00    0.00   0.01   0.04  -0.03   \n",
       "2018-02-28  -0.01  -0.00  -0.02  -0.03   0.00   -0.01  -0.04  -0.02   0.04   \n",
       "2018-03-29   0.01   0.01   0.02   0.03   0.00    0.01   0.02   0.01   0.03   \n",
       "2018-04-30   0.01   0.03  -0.02  -0.02   0.00   -0.01   0.01   0.01  -0.04   \n",
       "2018-05-30   0.00  -0.02   0.06  -0.01  -0.05    0.00   0.01   0.03   0.01   \n",
       "2018-06-29  -0.00  -0.01   0.02   0.02   0.01    0.00   0.01   0.00   0.01   \n",
       "2018-07-31  -0.02  -0.01  -0.01  -0.02  -0.01   -0.03   0.02  -0.06  -0.01   \n",
       "2018-08-31  -0.01   0.03   0.01   0.01   0.06    0.02  -0.02   0.05   0.02   \n",
       "2018-09-28  -0.01  -0.02  -0.03  -0.02  -0.02   -0.02   0.00   0.00  -0.01   \n",
       "2018-10-31   0.02  -0.01  -0.01  -0.01   0.04   -0.01   0.05  -0.01   0.03   \n",
       "2018-11-30   0.02   0.01  -0.01  -0.01   0.00    0.00   0.01  -0.00  -0.01   \n",
       "2018-12-28   0.02   0.03   0.03   0.03  -0.01    0.00   0.04   0.04   0.01   \n",
       "2019-01-31  -0.03   0.03   0.02   0.06   0.03    0.01  -0.02   0.00  -0.01   \n",
       "2019-02-28  -0.06  -0.01  -0.03  -0.03  -0.01   -0.00  -0.00  -0.01  -0.05   \n",
       "2019-03-29   0.00   0.03   0.01   0.02   0.02    0.05   0.04  -0.04   0.00   \n",
       "2019-04-30   0.01   0.01   0.01   0.00   0.01    0.02   0.00   0.04   0.02   \n",
       "2019-05-31   0.01  -0.00  -0.00  -0.00  -0.03   -0.03  -0.01   0.03  -0.05   \n",
       "2019-06-28  -0.01   0.02   0.00  -0.00   0.00    0.02   0.00   0.00   0.01   \n",
       "2019-07-31  -0.01  -0.02  -0.01  -0.03   0.05    0.00  -0.01   0.01  -0.01   \n",
       "2019-08-30   0.01  -0.03   0.03   0.02  -0.03    0.04  -0.00   0.00  -0.02   \n",
       "2019-09-30  -0.01   0.00   0.00  -0.02  -0.01    0.00   0.01   0.00  -0.01   \n",
       "2019-10-31  -0.01  -0.02  -0.03  -0.04  -0.02    0.01  -0.02  -0.02  -0.00   \n",
       "2019-11-29   0.00   0.01   0.00   0.01   0.03    0.03   0.00   0.01   0.01   \n",
       "2019-12-30  -0.03   0.01  -0.00  -0.00   0.04    0.00  -0.01  -0.01  -0.00   \n",
       "2020-01-31  -0.03  -0.01  -0.03  -0.02  -0.02   -0.01  -0.02  -0.01  -0.00   \n",
       "2020-02-28   0.00  -0.02   0.02   0.02   0.05    0.03   0.01  -0.00  -0.02   \n",
       "2020-03-31  -0.05  -0.08  -0.04  -0.04  -0.02   -0.08   0.02  -0.02   0.04   \n",
       "2020-04-30  -0.03   0.02  -0.04  -0.08  -0.04   -0.03  -0.05  -0.05  -0.05   \n",
       "2020-05-29  -0.03  -0.03   0.00  -0.01   0.01   -0.01   0.04  -0.02  -0.00   \n",
       "2020-06-30  -0.01  -0.03  -0.04  -0.03  -0.05    0.01   0.01   0.00   0.02   \n",
       "2020-07-31  -0.05  -0.03  -0.04  -0.04   0.01   -0.01   0.00  -0.01  -0.02   \n",
       "2020-08-31  -0.03  -0.03  -0.02  -0.03   0.00   -0.03  -0.02  -0.03  -0.02   \n",
       "2020-09-30   0.02   0.02  -0.01   0.01   0.03    0.02   0.01   0.00   0.01   \n",
       "2020-10-30  -0.06  -0.04  -0.04  -0.03  -0.07   -0.03  -0.01  -0.02  -0.01   \n",
       "2020-11-30  -0.02   0.03  -0.02  -0.01   0.03   -0.02  -0.03  -0.03   0.01   \n",
       "2020-12-30  -0.02   0.04  -0.01  -0.01  -0.03   -0.02  -0.01   0.00  -0.02   \n",
       "2021-01-29  -0.03  -0.03  -0.02  -0.03  -0.01   -0.03  -0.05  -0.02  -0.02   \n",
       "2021-02-26  -0.01  -0.04  -0.05  -0.02   0.00   -0.01  -0.01  -0.02  -0.07   \n",
       "2021-03-31  -0.02  -0.03  -0.01  -0.02   0.02   -0.00   0.00  -0.01  -0.01   \n",
       "2021-04-30  -0.02  -0.04   0.00   0.00   0.00    0.01  -0.02  -0.01  -0.02   \n",
       "2021-05-31   0.01   0.00  -0.00  -0.00   0.02    0.01   0.02   0.01   0.00   \n",
       "2021-06-30  -0.01  -0.01  -0.01  -0.01   0.06   -0.01  -0.00   0.01  -0.00   \n",
       "2021-07-30  -0.03  -0.03  -0.02  -0.02  -0.05   -0.05  -0.06  -0.04  -0.02   \n",
       "2021-08-31  -0.00  -0.00   0.01   0.01  -0.04   -0.02  -0.01   0.01  -0.01   \n",
       "2021-09-30  -0.01  -0.02  -0.02   0.00  -0.06   -0.03   0.02  -0.01   0.00   \n",
       "2021-10-29   0.02  -0.04  -0.01  -0.03  -0.10   -0.00  -0.03   0.00   0.02   \n",
       "2021-11-30  -0.02  -0.00   0.03  -0.02  -0.04   -0.01   0.01   0.00  -0.03   \n",
       "2021-12-30  -0.00   0.02  -0.00  -0.00   0.03    0.01   0.02   0.00   0.01   \n",
       "\n",
       "ticker      BRML3  ...  NTCO3  PETR3  PETR4  PRIO3  SAPR4  SULA11  TOTS3  \\\n",
       "datetime           ...                                                     \n",
       "2017-01-31   0.07  ...  -0.02   0.00   0.01   0.00   0.03   -0.02  -0.01   \n",
       "2017-02-24  -0.01  ...   0.02  -0.03  -0.02   0.00   0.03   -0.03   0.01   \n",
       "2017-03-31   0.02  ...   0.01   0.00   0.00   0.00   0.04   -0.00   0.02   \n",
       "2017-04-28   0.01  ...   0.00   0.01   0.02   0.04   0.01   -0.00   0.01   \n",
       "2017-05-31  -0.01  ...  -0.03  -0.04  -0.03   0.03   0.01   -0.02  -0.01   \n",
       "2017-06-30   0.03  ...  -0.00   0.01   0.01   0.00   0.02    0.01   0.02   \n",
       "2017-07-31   0.01  ...   0.01   0.01   0.01   0.01   0.00   -0.01  -0.01   \n",
       "2017-08-31   0.00  ...  -0.02   0.01   0.01   0.01   0.01    0.01  -0.01   \n",
       "2017-09-29   0.01  ...   0.01  -0.01  -0.00  -0.03   0.02    0.02   0.01   \n",
       "2017-10-31  -0.02  ...   0.01   0.00   0.00   0.04  -0.00   -0.00   0.01   \n",
       "2017-11-30  -0.01  ...  -0.02   0.00   0.00   0.02  -0.04   -0.03  -0.04   \n",
       "2017-12-28   0.02  ...   0.01   0.01   0.00   0.01   0.02   -0.01  -0.00   \n",
       "2018-01-31  -0.01  ...  -0.02   0.01   0.01   0.01  -0.01   -0.01  -0.00   \n",
       "2018-02-28  -0.01  ...  -0.02   0.00  -0.00  -0.03   0.01    0.03  -0.03   \n",
       "2018-03-29   0.01  ...   0.01   0.01   0.01   0.00  -0.01   -0.02   0.00   \n",
       "2018-04-30  -0.01  ...  -0.04   0.00   0.01   0.01   0.01   -0.02  -0.00   \n",
       "2018-05-30   0.03  ...   0.02  -0.00  -0.02   0.01  -0.01    0.02   0.01   \n",
       "2018-06-29  -0.01  ...  -0.02   0.03   0.03   0.00   0.03    0.02  -0.02   \n",
       "2018-07-31  -0.01  ...  -0.01  -0.01  -0.01   0.01  -0.02    0.01  -0.05   \n",
       "2018-08-31  -0.01  ...  -0.00   0.02   0.02   0.01   0.00    0.00   0.01   \n",
       "2018-09-28  -0.02  ...   0.00  -0.01  -0.02   0.03  -0.01    0.01  -0.00   \n",
       "2018-10-31   0.02  ...   0.01  -0.01  -0.01   0.02   0.01    0.01  -0.02   \n",
       "2018-11-30   0.00  ...   0.01   0.01   0.01  -0.03  -0.01   -0.00  -0.01   \n",
       "2018-12-28   0.01  ...   0.02   0.04   0.05  -0.01   0.03    0.03   0.01   \n",
       "2019-01-31   0.00  ...  -0.02   0.00  -0.00  -0.03   0.00    0.01   0.01   \n",
       "2019-02-28  -0.01  ...   0.00  -0.03  -0.00  -0.01  -0.03   -0.03   0.02   \n",
       "2019-03-29   0.01  ...   0.02   0.01   0.00  -0.01   0.02    0.03   0.00   \n",
       "2019-04-30  -0.00  ...  -0.00  -0.01  -0.01   0.01   0.00    0.00   0.01   \n",
       "2019-05-31  -0.00  ...  -0.01  -0.02  -0.02  -0.02   0.03    0.03   0.00   \n",
       "2019-06-28   0.02  ...   0.00   0.00   0.01   0.02   0.02    0.03   0.01   \n",
       "2019-07-31  -0.01  ...   0.02   0.00  -0.01  -0.01   0.00   -0.01   0.00   \n",
       "2019-08-30  -0.02  ...  -0.01   0.01   0.00   0.05   0.10    0.01   0.01   \n",
       "2019-09-30   0.00  ...  -0.03  -0.01  -0.00   0.02   0.02    0.01  -0.00   \n",
       "2019-10-31   0.00  ...   0.00   0.00   0.01   0.00   0.01   -0.02  -0.02   \n",
       "2019-11-29  -0.01  ...  -0.00  -0.01  -0.01   0.06  -0.00   -0.00  -0.01   \n",
       "2019-12-30   0.00  ...  -0.01  -0.01  -0.01  -0.03   0.01    0.01  -0.00   \n",
       "2020-01-31  -0.02  ...  -0.03  -0.02  -0.02   0.01  -0.00   -0.01   0.03   \n",
       "2020-02-28   0.00  ...  -0.00   0.01   0.00  -0.01  -0.02    0.01  -0.04   \n",
       "2020-03-31  -0.08  ...  -0.06   0.05   0.04  -0.02  -0.03   -0.06  -0.01   \n",
       "2020-04-30  -0.06  ...  -0.02  -0.02  -0.01  -0.05  -0.05   -0.06   0.01   \n",
       "2020-05-29  -0.01  ...  -0.01   0.01   0.03   0.06  -0.01    0.02  -0.02   \n",
       "2020-06-30  -0.01  ...  -0.00  -0.01  -0.01   0.01   0.01   -0.02   0.03   \n",
       "2020-07-31  -0.04  ...  -0.03  -0.03  -0.03  -0.00  -0.00   -0.00  -0.01   \n",
       "2020-08-31  -0.02  ...  -0.02  -0.03  -0.03  -0.03  -0.03   -0.04   0.00   \n",
       "2020-09-30   0.00  ...   0.03   0.01   0.02   0.02  -0.01    0.00  -0.01   \n",
       "2020-10-30  -0.03  ...  -0.04  -0.02  -0.02  -0.05  -0.03   -0.00  -0.05   \n",
       "2020-11-30  -0.04  ...  -0.02  -0.01  -0.02  -0.03  -0.03   -0.02  -0.03   \n",
       "2020-12-30  -0.02  ...   0.01   0.01   0.00   0.03  -0.05   -0.00   0.02   \n",
       "2021-01-29  -0.03  ...  -0.02  -0.05  -0.04  -0.02  -0.02   -0.04  -0.04   \n",
       "2021-02-26  -0.04  ...  -0.04  -0.03  -0.04  -0.00  -0.03   -0.05  -0.01   \n",
       "2021-03-31  -0.01  ...  -0.01   0.01   0.01   0.00  -0.01   -0.03  -0.00   \n",
       "2021-04-30  -0.02  ...   0.03   0.00   0.00  -0.01  -0.01   -0.02  -0.00   \n",
       "2021-05-31  -0.01  ...   0.01  -0.00  -0.00   0.00  -0.01   -0.00   0.03   \n",
       "2021-06-30   0.00  ...   0.01   0.02   0.01   0.03   0.01   -0.02   0.01   \n",
       "2021-07-30  -0.04  ...  -0.03  -0.03  -0.03  -0.03  -0.00   -0.03  -0.00   \n",
       "2021-08-31  -0.01  ...  -0.02  -0.03  -0.04  -0.01  -0.02   -0.01  -0.01   \n",
       "2021-09-30  -0.01  ...   0.01  -0.00  -0.01   0.09  -0.02   -0.01   0.02   \n",
       "2021-10-29   0.01  ...  -0.00  -0.07  -0.06  -0.00  -0.02    0.01  -0.00   \n",
       "2021-11-30  -0.01  ...  -0.01  -0.00  -0.00  -0.03   0.01   -0.02  -0.01   \n",
       "2021-12-30   0.02  ...   0.02  -0.01  -0.00   0.03  -0.00    0.07   0.03   \n",
       "\n",
       "ticker      USIM5  VALE3  VVAR3  \n",
       "datetime                         \n",
       "2017-01-31   0.04  -0.00   0.02  \n",
       "2017-02-24  -0.02  -0.01   0.02  \n",
       "2017-03-31   0.01  -0.01  -0.01  \n",
       "2017-04-28   0.03   0.02   0.04  \n",
       "2017-05-31  -0.03  -0.05   0.01  \n",
       "2017-06-30   0.02   0.00   0.05  \n",
       "2017-07-31   0.01   0.03  -0.04  \n",
       "2017-08-31   0.05   0.02   0.00  \n",
       "2017-09-29   0.02   0.00   0.07  \n",
       "2017-10-31   0.01  -0.01   0.03  \n",
       "2017-11-30  -0.04  -0.02  -0.03  \n",
       "2017-12-28   0.00   0.01   0.01  \n",
       "2018-01-31   0.02  -0.00   0.01  \n",
       "2018-02-28  -0.03  -0.05   0.01  \n",
       "2018-03-29   0.02   0.02  -0.01  \n",
       "2018-04-30   0.02   0.01  -0.02  \n",
       "2018-05-30  -0.04   0.01   0.04  \n",
       "2018-06-29  -0.01   0.01   0.01  \n",
       "2018-07-31  -0.03   0.01   0.01  \n",
       "2018-08-31  -0.00  -0.02  -0.05  \n",
       "2018-09-28  -0.01   0.01  -0.10  \n",
       "2018-10-31  -0.01   0.05  -0.01  \n",
       "2018-11-30   0.04   0.02   0.03  \n",
       "2018-12-28   0.02   0.03   0.02  \n",
       "2019-01-31  -0.01  -0.02   0.04  \n",
       "2019-02-28  -0.02   0.01  -0.01  \n",
       "2019-03-29   0.04   0.03   0.01  \n",
       "2019-04-30   0.00  -0.00  -0.00  \n",
       "2019-05-31  -0.02  -0.02  -0.02  \n",
       "2019-06-28   0.03   0.00   0.02  \n",
       "2019-07-31  -0.01  -0.00   0.01  \n",
       "2019-08-30   0.03   0.01   0.06  \n",
       "2019-09-30  -0.02   0.00   0.01  \n",
       "2019-10-31  -0.01  -0.03   0.00  \n",
       "2019-11-29   0.01  -0.01   0.03  \n",
       "2019-12-30   0.01  -0.01  -0.03  \n",
       "2020-01-31  -0.01  -0.02  -0.05  \n",
       "2020-02-28  -0.00  -0.00  -0.02  \n",
       "2020-03-31   0.03   0.03  -0.03  \n",
       "2020-04-30  -0.05  -0.04   0.00  \n",
       "2020-05-29   0.02   0.06  -0.01  \n",
       "2020-06-30   0.02   0.01  -0.00  \n",
       "2020-07-31  -0.01  -0.01  -0.03  \n",
       "2020-08-31   0.01  -0.02   0.01  \n",
       "2020-09-30   0.02   0.01   0.02  \n",
       "2020-10-30  -0.03  -0.02  -0.06  \n",
       "2020-11-30  -0.01  -0.01  -0.04  \n",
       "2020-12-30  -0.03   0.00  -0.03  \n",
       "2021-01-29  -0.05  -0.04  -0.04  \n",
       "2021-02-26  -0.01  -0.01  -0.06  \n",
       "2021-03-31   0.01   0.01  -0.02  \n",
       "2021-04-30  -0.01  -0.03  -0.02  \n",
       "2021-05-31   0.01   0.03   0.02  \n",
       "2021-06-30  -0.00   0.01  -0.01  \n",
       "2021-07-30  -0.01  -0.06  -0.05  \n",
       "2021-08-31  -0.01  -0.01   0.00  \n",
       "2021-09-30   0.03   0.01   0.00  \n",
       "2021-10-29  -0.08  -0.03   0.00  \n",
       "2021-11-30  -0.01   0.01   0.00  \n",
       "2021-12-30   0.02   0.01   0.00  \n",
       "\n",
       "[60 rows x 40 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create pivot table for log_return\n",
    "\n",
    "df_log =  df.pivot_table(values='log_ret', index='datetime', columns='ticker', fill_value= 0)\n",
    "df_log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-11T23:03:43.842535Z",
     "start_time": "2022-03-11T23:03:43.779521Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>ticker</th>\n",
       "      <th>ABEV3</th>\n",
       "      <th>AZUL4</th>\n",
       "      <th>BBAS3</th>\n",
       "      <th>BBDC4</th>\n",
       "      <th>BIDI4</th>\n",
       "      <th>BPAC11</th>\n",
       "      <th>BRAP4</th>\n",
       "      <th>BRDT3</th>\n",
       "      <th>BRFS3</th>\n",
       "      <th>BRML3</th>\n",
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       "      <th>NTCO3</th>\n",
       "      <th>PETR3</th>\n",
       "      <th>PETR4</th>\n",
       "      <th>PRIO3</th>\n",
       "      <th>SAPR4</th>\n",
       "      <th>SULA11</th>\n",
       "      <th>TOTS3</th>\n",
       "      <th>USIM5</th>\n",
       "      <th>VALE3</th>\n",
       "      <th>VVAR3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</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>2017-01-31</th>\n",
       "      <td>15.04</td>\n",
       "      <td>0.00</td>\n",
       "      <td>25.32</td>\n",
       "      <td>15.59</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.90</td>\n",
       "      <td>0.00</td>\n",
       "      <td>44.46</td>\n",
       "      <td>11.82</td>\n",
       "      <td>...</td>\n",
       "      <td>12.25</td>\n",
       "      <td>12.85</td>\n",
       "      <td>11.51</td>\n",
       "      <td>0.52</td>\n",
       "      <td>3.81</td>\n",
       "      <td>13.24</td>\n",
       "      <td>8.29</td>\n",
       "      <td>4.73</td>\n",
       "      <td>24.33</td>\n",
       "      <td>3.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-24</th>\n",
       "      <td>15.60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>26.94</td>\n",
       "      <td>15.95</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.96</td>\n",
       "      <td>6.65</td>\n",
       "      <td>0.00</td>\n",
       "      <td>40.62</td>\n",
       "      <td>12.02</td>\n",
       "      <td>...</td>\n",
       "      <td>12.42</td>\n",
       "      <td>12.67</td>\n",
       "      <td>11.64</td>\n",
       "      <td>0.76</td>\n",
       "      <td>3.89</td>\n",
       "      <td>13.83</td>\n",
       "      <td>8.05</td>\n",
       "      <td>4.44</td>\n",
       "      <td>24.74</td>\n",
       "      <td>4.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-31</th>\n",
       "      <td>15.95</td>\n",
       "      <td>0.00</td>\n",
       "      <td>27.57</td>\n",
       "      <td>15.46</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.13</td>\n",
       "      <td>6.21</td>\n",
       "      <td>0.00</td>\n",
       "      <td>38.61</td>\n",
       "      <td>11.67</td>\n",
       "      <td>...</td>\n",
       "      <td>14.14</td>\n",
       "      <td>12.03</td>\n",
       "      <td>11.11</td>\n",
       "      <td>0.94</td>\n",
       "      <td>2.97</td>\n",
       "      <td>13.93</td>\n",
       "      <td>8.82</td>\n",
       "      <td>4.00</td>\n",
       "      <td>22.52</td>\n",
       "      <td>3.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-28</th>\n",
       "      <td>15.96</td>\n",
       "      <td>23.95</td>\n",
       "      <td>26.82</td>\n",
       "      <td>15.96</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.77</td>\n",
       "      <td>0.00</td>\n",
       "      <td>39.79</td>\n",
       "      <td>11.32</td>\n",
       "      <td>...</td>\n",
       "      <td>14.75</td>\n",
       "      <td>11.40</td>\n",
       "      <td>10.71</td>\n",
       "      <td>0.87</td>\n",
       "      <td>2.78</td>\n",
       "      <td>14.01</td>\n",
       "      <td>8.85</td>\n",
       "      <td>3.84</td>\n",
       "      <td>21.35</td>\n",
       "      <td>4.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-31</th>\n",
       "      <td>16.36</td>\n",
       "      <td>23.58</td>\n",
       "      <td>23.27</td>\n",
       "      <td>14.47</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.08</td>\n",
       "      <td>5.56</td>\n",
       "      <td>0.00</td>\n",
       "      <td>43.32</td>\n",
       "      <td>11.16</td>\n",
       "      <td>...</td>\n",
       "      <td>15.88</td>\n",
       "      <td>10.81</td>\n",
       "      <td>9.93</td>\n",
       "      <td>0.74</td>\n",
       "      <td>2.82</td>\n",
       "      <td>14.90</td>\n",
       "      <td>9.60</td>\n",
       "      <td>3.58</td>\n",
       "      <td>21.12</td>\n",
       "      <td>3.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-30</th>\n",
       "      <td>16.15</td>\n",
       "      <td>23.44</td>\n",
       "      <td>22.06</td>\n",
       "      <td>14.81</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.14</td>\n",
       "      <td>5.99</td>\n",
       "      <td>0.00</td>\n",
       "      <td>39.20</td>\n",
       "      <td>11.13</td>\n",
       "      <td>...</td>\n",
       "      <td>12.53</td>\n",
       "      <td>10.48</td>\n",
       "      <td>9.48</td>\n",
       "      <td>0.74</td>\n",
       "      <td>2.95</td>\n",
       "      <td>14.77</td>\n",
       "      <td>9.60</td>\n",
       "      <td>4.15</td>\n",
       "      <td>22.54</td>\n",
       "      <td>4.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-31</th>\n",
       "      <td>16.89</td>\n",
       "      <td>27.29</td>\n",
       "      <td>23.63</td>\n",
       "      <td>16.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.71</td>\n",
       "      <td>0.00</td>\n",
       "      <td>36.92</td>\n",
       "      <td>12.30</td>\n",
       "      <td>...</td>\n",
       "      <td>12.21</td>\n",
       "      <td>10.96</td>\n",
       "      <td>10.19</td>\n",
       "      <td>0.75</td>\n",
       "      <td>2.94</td>\n",
       "      <td>14.64</td>\n",
       "      <td>9.55</td>\n",
       "      <td>4.66</td>\n",
       "      <td>24.33</td>\n",
       "      <td>5.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-31</th>\n",
       "      <td>17.52</td>\n",
       "      <td>26.54</td>\n",
       "      <td>25.42</td>\n",
       "      <td>17.80</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.69</td>\n",
       "      <td>7.82</td>\n",
       "      <td>0.00</td>\n",
       "      <td>42.52</td>\n",
       "      <td>12.67</td>\n",
       "      <td>...</td>\n",
       "      <td>14.38</td>\n",
       "      <td>11.09</td>\n",
       "      <td>10.46</td>\n",
       "      <td>0.88</td>\n",
       "      <td>2.90</td>\n",
       "      <td>15.00</td>\n",
       "      <td>9.94</td>\n",
       "      <td>6.21</td>\n",
       "      <td>27.27</td>\n",
       "      <td>6.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09-29</th>\n",
       "      <td>18.56</td>\n",
       "      <td>29.19</td>\n",
       "      <td>28.96</td>\n",
       "      <td>18.59</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.19</td>\n",
       "      <td>7.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>45.67</td>\n",
       "      <td>13.11</td>\n",
       "      <td>...</td>\n",
       "      <td>15.21</td>\n",
       "      <td>12.55</td>\n",
       "      <td>11.73</td>\n",
       "      <td>0.93</td>\n",
       "      <td>3.00</td>\n",
       "      <td>14.93</td>\n",
       "      <td>10.02</td>\n",
       "      <td>7.01</td>\n",
       "      <td>24.77</td>\n",
       "      <td>7.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-31</th>\n",
       "      <td>18.43</td>\n",
       "      <td>27.39</td>\n",
       "      <td>28.57</td>\n",
       "      <td>18.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.90</td>\n",
       "      <td>6.98</td>\n",
       "      <td>0.00</td>\n",
       "      <td>44.25</td>\n",
       "      <td>11.81</td>\n",
       "      <td>...</td>\n",
       "      <td>15.10</td>\n",
       "      <td>13.83</td>\n",
       "      <td>12.86</td>\n",
       "      <td>1.14</td>\n",
       "      <td>3.01</td>\n",
       "      <td>14.96</td>\n",
       "      <td>10.43</td>\n",
       "      <td>7.94</td>\n",
       "      <td>24.95</td>\n",
       "      <td>7.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-30</th>\n",
       "      <td>18.13</td>\n",
       "      <td>26.86</td>\n",
       "      <td>24.98</td>\n",
       "      <td>17.34</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.23</td>\n",
       "      <td>7.36</td>\n",
       "      <td>0.00</td>\n",
       "      <td>38.48</td>\n",
       "      <td>11.26</td>\n",
       "      <td>...</td>\n",
       "      <td>14.22</td>\n",
       "      <td>12.66</td>\n",
       "      <td>11.79</td>\n",
       "      <td>1.28</td>\n",
       "      <td>2.94</td>\n",
       "      <td>14.93</td>\n",
       "      <td>9.69</td>\n",
       "      <td>7.72</td>\n",
       "      <td>27.32</td>\n",
       "      <td>7.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-28</th>\n",
       "      <td>19.01</td>\n",
       "      <td>26.89</td>\n",
       "      <td>26.61</td>\n",
       "      <td>18.36</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.22</td>\n",
       "      <td>8.59</td>\n",
       "      <td>12.95</td>\n",
       "      <td>36.60</td>\n",
       "      <td>11.86</td>\n",
       "      <td>...</td>\n",
       "      <td>16.20</td>\n",
       "      <td>13.43</td>\n",
       "      <td>12.34</td>\n",
       "      <td>1.64</td>\n",
       "      <td>3.25</td>\n",
       "      <td>15.57</td>\n",
       "      <td>9.60</td>\n",
       "      <td>8.20</td>\n",
       "      <td>31.58</td>\n",
       "      <td>7.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-31</th>\n",
       "      <td>19.61</td>\n",
       "      <td>31.04</td>\n",
       "      <td>33.19</td>\n",
       "      <td>22.12</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.85</td>\n",
       "      <td>9.36</td>\n",
       "      <td>15.90</td>\n",
       "      <td>35.35</td>\n",
       "      <td>11.97</td>\n",
       "      <td>...</td>\n",
       "      <td>17.11</td>\n",
       "      <td>16.91</td>\n",
       "      <td>15.10</td>\n",
       "      <td>1.77</td>\n",
       "      <td>3.01</td>\n",
       "      <td>17.22</td>\n",
       "      <td>10.43</td>\n",
       "      <td>10.69</td>\n",
       "      <td>32.51</td>\n",
       "      <td>8.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-28</th>\n",
       "      <td>19.71</td>\n",
       "      <td>33.62</td>\n",
       "      <td>34.96</td>\n",
       "      <td>21.05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.29</td>\n",
       "      <td>10.22</td>\n",
       "      <td>17.10</td>\n",
       "      <td>29.92</td>\n",
       "      <td>11.11</td>\n",
       "      <td>...</td>\n",
       "      <td>16.82</td>\n",
       "      <td>18.38</td>\n",
       "      <td>16.45</td>\n",
       "      <td>1.45</td>\n",
       "      <td>2.94</td>\n",
       "      <td>18.11</td>\n",
       "      <td>9.80</td>\n",
       "      <td>11.11</td>\n",
       "      <td>35.34</td>\n",
       "      <td>7.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-29</th>\n",
       "      <td>21.57</td>\n",
       "      <td>38.18</td>\n",
       "      <td>34.54</td>\n",
       "      <td>21.45</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.28</td>\n",
       "      <td>9.42</td>\n",
       "      <td>17.88</td>\n",
       "      <td>22.83</td>\n",
       "      <td>10.85</td>\n",
       "      <td>...</td>\n",
       "      <td>15.83</td>\n",
       "      <td>18.55</td>\n",
       "      <td>16.41</td>\n",
       "      <td>1.18</td>\n",
       "      <td>3.08</td>\n",
       "      <td>18.62</td>\n",
       "      <td>9.25</td>\n",
       "      <td>9.85</td>\n",
       "      <td>33.43</td>\n",
       "      <td>8.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-30</th>\n",
       "      <td>20.91</td>\n",
       "      <td>36.39</td>\n",
       "      <td>30.90</td>\n",
       "      <td>20.66</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.13</td>\n",
       "      <td>11.03</td>\n",
       "      <td>18.09</td>\n",
       "      <td>24.80</td>\n",
       "      <td>10.17</td>\n",
       "      <td>...</td>\n",
       "      <td>15.93</td>\n",
       "      <td>19.56</td>\n",
       "      <td>17.61</td>\n",
       "      <td>1.45</td>\n",
       "      <td>3.04</td>\n",
       "      <td>18.48</td>\n",
       "      <td>10.28</td>\n",
       "      <td>9.87</td>\n",
       "      <td>38.54</td>\n",
       "      <td>8.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-30</th>\n",
       "      <td>17.64</td>\n",
       "      <td>26.50</td>\n",
       "      <td>25.71</td>\n",
       "      <td>17.45</td>\n",
       "      <td>0.95</td>\n",
       "      <td>4.46</td>\n",
       "      <td>9.66</td>\n",
       "      <td>14.34</td>\n",
       "      <td>21.42</td>\n",
       "      <td>9.60</td>\n",
       "      <td>...</td>\n",
       "      <td>16.68</td>\n",
       "      <td>17.65</td>\n",
       "      <td>14.57</td>\n",
       "      <td>1.43</td>\n",
       "      <td>2.53</td>\n",
       "      <td>16.74</td>\n",
       "      <td>9.01</td>\n",
       "      <td>7.47</td>\n",
       "      <td>40.09</td>\n",
       "      <td>6.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-29</th>\n",
       "      <td>16.25</td>\n",
       "      <td>21.32</td>\n",
       "      <td>24.32</td>\n",
       "      <td>16.10</td>\n",
       "      <td>0.78</td>\n",
       "      <td>4.24</td>\n",
       "      <td>9.06</td>\n",
       "      <td>14.41</td>\n",
       "      <td>18.00</td>\n",
       "      <td>9.04</td>\n",
       "      <td>...</td>\n",
       "      <td>14.97</td>\n",
       "      <td>15.44</td>\n",
       "      <td>13.20</td>\n",
       "      <td>1.40</td>\n",
       "      <td>2.55</td>\n",
       "      <td>15.63</td>\n",
       "      <td>8.74</td>\n",
       "      <td>6.63</td>\n",
       "      <td>39.27</td>\n",
       "      <td>5.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-31</th>\n",
       "      <td>17.54</td>\n",
       "      <td>24.12</td>\n",
       "      <td>27.59</td>\n",
       "      <td>18.39</td>\n",
       "      <td>0.93</td>\n",
       "      <td>4.54</td>\n",
       "      <td>10.10</td>\n",
       "      <td>15.35</td>\n",
       "      <td>22.65</td>\n",
       "      <td>9.27</td>\n",
       "      <td>...</td>\n",
       "      <td>14.39</td>\n",
       "      <td>17.45</td>\n",
       "      <td>15.14</td>\n",
       "      <td>1.60</td>\n",
       "      <td>2.44</td>\n",
       "      <td>18.87</td>\n",
       "      <td>9.47</td>\n",
       "      <td>8.03</td>\n",
       "      <td>43.43</td>\n",
       "      <td>7.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-31</th>\n",
       "      <td>17.04</td>\n",
       "      <td>22.99</td>\n",
       "      <td>25.75</td>\n",
       "      <td>17.10</td>\n",
       "      <td>1.47</td>\n",
       "      <td>4.84</td>\n",
       "      <td>9.63</td>\n",
       "      <td>15.58</td>\n",
       "      <td>20.13</td>\n",
       "      <td>8.52</td>\n",
       "      <td>...</td>\n",
       "      <td>14.34</td>\n",
       "      <td>17.59</td>\n",
       "      <td>14.82</td>\n",
       "      <td>1.89</td>\n",
       "      <td>2.50</td>\n",
       "      <td>19.64</td>\n",
       "      <td>8.62</td>\n",
       "      <td>7.31</td>\n",
       "      <td>43.52</td>\n",
       "      <td>5.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-28</th>\n",
       "      <td>16.58</td>\n",
       "      <td>24.04</td>\n",
       "      <td>25.26</td>\n",
       "      <td>17.29</td>\n",
       "      <td>1.66</td>\n",
       "      <td>5.03</td>\n",
       "      <td>11.49</td>\n",
       "      <td>15.36</td>\n",
       "      <td>21.97</td>\n",
       "      <td>8.96</td>\n",
       "      <td>...</td>\n",
       "      <td>14.04</td>\n",
       "      <td>19.30</td>\n",
       "      <td>16.23</td>\n",
       "      <td>2.22</td>\n",
       "      <td>2.44</td>\n",
       "      <td>22.23</td>\n",
       "      <td>8.12</td>\n",
       "      <td>7.53</td>\n",
       "      <td>48.55</td>\n",
       "      <td>5.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-31</th>\n",
       "      <td>14.85</td>\n",
       "      <td>30.59</td>\n",
       "      <td>36.67</td>\n",
       "      <td>20.69</td>\n",
       "      <td>2.29</td>\n",
       "      <td>4.67</td>\n",
       "      <td>10.72</td>\n",
       "      <td>18.89</td>\n",
       "      <td>21.90</td>\n",
       "      <td>11.83</td>\n",
       "      <td>...</td>\n",
       "      <td>16.12</td>\n",
       "      <td>24.18</td>\n",
       "      <td>21.25</td>\n",
       "      <td>2.38</td>\n",
       "      <td>2.96</td>\n",
       "      <td>21.20</td>\n",
       "      <td>8.11</td>\n",
       "      <td>9.31</td>\n",
       "      <td>46.06</td>\n",
       "      <td>5.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-30</th>\n",
       "      <td>15.38</td>\n",
       "      <td>34.74</td>\n",
       "      <td>38.59</td>\n",
       "      <td>23.21</td>\n",
       "      <td>2.16</td>\n",
       "      <td>5.23</td>\n",
       "      <td>9.85</td>\n",
       "      <td>19.26</td>\n",
       "      <td>22.17</td>\n",
       "      <td>12.19</td>\n",
       "      <td>...</td>\n",
       "      <td>20.23</td>\n",
       "      <td>22.50</td>\n",
       "      <td>19.66</td>\n",
       "      <td>1.94</td>\n",
       "      <td>3.07</td>\n",
       "      <td>23.00</td>\n",
       "      <td>9.37</td>\n",
       "      <td>8.47</td>\n",
       "      <td>42.88</td>\n",
       "      <td>5.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-28</th>\n",
       "      <td>14.22</td>\n",
       "      <td>36.00</td>\n",
       "      <td>40.30</td>\n",
       "      <td>23.73</td>\n",
       "      <td>2.12</td>\n",
       "      <td>5.56</td>\n",
       "      <td>9.83</td>\n",
       "      <td>20.60</td>\n",
       "      <td>21.93</td>\n",
       "      <td>12.26</td>\n",
       "      <td>...</td>\n",
       "      <td>22.26</td>\n",
       "      <td>20.35</td>\n",
       "      <td>18.01</td>\n",
       "      <td>1.98</td>\n",
       "      <td>3.14</td>\n",
       "      <td>24.45</td>\n",
       "      <td>8.78</td>\n",
       "      <td>8.35</td>\n",
       "      <td>41.42</td>\n",
       "      <td>4.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-31</th>\n",
       "      <td>16.19</td>\n",
       "      <td>37.11</td>\n",
       "      <td>44.96</td>\n",
       "      <td>27.80</td>\n",
       "      <td>2.30</td>\n",
       "      <td>7.14</td>\n",
       "      <td>8.88</td>\n",
       "      <td>21.36</td>\n",
       "      <td>23.54</td>\n",
       "      <td>13.65</td>\n",
       "      <td>...</td>\n",
       "      <td>23.57</td>\n",
       "      <td>23.68</td>\n",
       "      <td>20.31</td>\n",
       "      <td>2.30</td>\n",
       "      <td>4.07</td>\n",
       "      <td>27.78</td>\n",
       "      <td>10.78</td>\n",
       "      <td>8.94</td>\n",
       "      <td>36.96</td>\n",
       "      <td>6.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-28</th>\n",
       "      <td>15.93</td>\n",
       "      <td>37.65</td>\n",
       "      <td>44.28</td>\n",
       "      <td>26.59</td>\n",
       "      <td>2.10</td>\n",
       "      <td>8.24</td>\n",
       "      <td>9.09</td>\n",
       "      <td>19.60</td>\n",
       "      <td>20.57</td>\n",
       "      <td>12.56</td>\n",
       "      <td>...</td>\n",
       "      <td>24.11</td>\n",
       "      <td>23.93</td>\n",
       "      <td>21.49</td>\n",
       "      <td>3.02</td>\n",
       "      <td>4.13</td>\n",
       "      <td>26.31</td>\n",
       "      <td>11.81</td>\n",
       "      <td>8.81</td>\n",
       "      <td>38.26</td>\n",
       "      <td>4.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-29</th>\n",
       "      <td>15.49</td>\n",
       "      <td>38.27</td>\n",
       "      <td>42.74</td>\n",
       "      <td>26.40</td>\n",
       "      <td>2.91</td>\n",
       "      <td>8.73</td>\n",
       "      <td>10.06</td>\n",
       "      <td>18.56</td>\n",
       "      <td>22.65</td>\n",
       "      <td>11.84</td>\n",
       "      <td>...</td>\n",
       "      <td>22.62</td>\n",
       "      <td>24.96</td>\n",
       "      <td>22.28</td>\n",
       "      <td>3.78</td>\n",
       "      <td>4.19</td>\n",
       "      <td>25.97</td>\n",
       "      <td>12.77</td>\n",
       "      <td>9.15</td>\n",
       "      <td>41.37</td>\n",
       "      <td>4.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-30</th>\n",
       "      <td>17.00</td>\n",
       "      <td>34.00</td>\n",
       "      <td>43.58</td>\n",
       "      <td>26.26</td>\n",
       "      <td>3.57</td>\n",
       "      <td>9.98</td>\n",
       "      <td>10.06</td>\n",
       "      <td>20.89</td>\n",
       "      <td>31.05</td>\n",
       "      <td>11.59</td>\n",
       "      <td>...</td>\n",
       "      <td>26.03</td>\n",
       "      <td>24.01</td>\n",
       "      <td>21.54</td>\n",
       "      <td>3.69</td>\n",
       "      <td>4.36</td>\n",
       "      <td>27.19</td>\n",
       "      <td>14.44</td>\n",
       "      <td>7.93</td>\n",
       "      <td>40.69</td>\n",
       "      <td>4.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-31</th>\n",
       "      <td>16.16</td>\n",
       "      <td>39.20</td>\n",
       "      <td>45.82</td>\n",
       "      <td>27.32</td>\n",
       "      <td>3.70</td>\n",
       "      <td>11.27</td>\n",
       "      <td>9.85</td>\n",
       "      <td>22.52</td>\n",
       "      <td>27.70</td>\n",
       "      <td>12.15</td>\n",
       "      <td>...</td>\n",
       "      <td>29.89</td>\n",
       "      <td>22.69</td>\n",
       "      <td>20.37</td>\n",
       "      <td>3.47</td>\n",
       "      <td>4.14</td>\n",
       "      <td>30.43</td>\n",
       "      <td>12.80</td>\n",
       "      <td>7.53</td>\n",
       "      <td>39.80</td>\n",
       "      <td>4.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-28</th>\n",
       "      <td>16.47</td>\n",
       "      <td>43.29</td>\n",
       "      <td>47.79</td>\n",
       "      <td>27.88</td>\n",
       "      <td>3.36</td>\n",
       "      <td>12.27</td>\n",
       "      <td>10.54</td>\n",
       "      <td>22.04</td>\n",
       "      <td>29.52</td>\n",
       "      <td>13.59</td>\n",
       "      <td>...</td>\n",
       "      <td>28.15</td>\n",
       "      <td>24.13</td>\n",
       "      <td>21.85</td>\n",
       "      <td>3.15</td>\n",
       "      <td>4.50</td>\n",
       "      <td>32.70</td>\n",
       "      <td>14.25</td>\n",
       "      <td>8.21</td>\n",
       "      <td>42.09</td>\n",
       "      <td>5.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-31</th>\n",
       "      <td>18.53</td>\n",
       "      <td>51.78</td>\n",
       "      <td>43.61</td>\n",
       "      <td>25.60</td>\n",
       "      <td>5.45</td>\n",
       "      <td>14.42</td>\n",
       "      <td>10.14</td>\n",
       "      <td>23.45</td>\n",
       "      <td>33.41</td>\n",
       "      <td>14.24</td>\n",
       "      <td>...</td>\n",
       "      <td>30.48</td>\n",
       "      <td>23.14</td>\n",
       "      <td>20.79</td>\n",
       "      <td>3.52</td>\n",
       "      <td>4.97</td>\n",
       "      <td>36.63</td>\n",
       "      <td>15.72</td>\n",
       "      <td>8.05</td>\n",
       "      <td>40.46</td>\n",
       "      <td>7.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-30</th>\n",
       "      <td>17.23</td>\n",
       "      <td>47.10</td>\n",
       "      <td>41.30</td>\n",
       "      <td>24.46</td>\n",
       "      <td>6.57</td>\n",
       "      <td>14.27</td>\n",
       "      <td>9.12</td>\n",
       "      <td>25.39</td>\n",
       "      <td>38.14</td>\n",
       "      <td>13.48</td>\n",
       "      <td>...</td>\n",
       "      <td>32.87</td>\n",
       "      <td>22.86</td>\n",
       "      <td>20.46</td>\n",
       "      <td>3.38</td>\n",
       "      <td>5.47</td>\n",
       "      <td>42.23</td>\n",
       "      <td>17.51</td>\n",
       "      <td>7.27</td>\n",
       "      <td>37.01</td>\n",
       "      <td>7.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-30</th>\n",
       "      <td>17.72</td>\n",
       "      <td>49.71</td>\n",
       "      <td>40.80</td>\n",
       "      <td>25.20</td>\n",
       "      <td>6.25</td>\n",
       "      <td>14.26</td>\n",
       "      <td>10.12</td>\n",
       "      <td>24.27</td>\n",
       "      <td>38.20</td>\n",
       "      <td>14.41</td>\n",
       "      <td>...</td>\n",
       "      <td>33.74</td>\n",
       "      <td>24.38</td>\n",
       "      <td>22.11</td>\n",
       "      <td>3.48</td>\n",
       "      <td>5.14</td>\n",
       "      <td>41.71</td>\n",
       "      <td>18.77</td>\n",
       "      <td>7.18</td>\n",
       "      <td>38.78</td>\n",
       "      <td>7.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-31</th>\n",
       "      <td>16.00</td>\n",
       "      <td>52.52</td>\n",
       "      <td>43.18</td>\n",
       "      <td>26.94</td>\n",
       "      <td>5.68</td>\n",
       "      <td>15.82</td>\n",
       "      <td>10.27</td>\n",
       "      <td>24.93</td>\n",
       "      <td>35.51</td>\n",
       "      <td>15.35</td>\n",
       "      <td>...</td>\n",
       "      <td>31.05</td>\n",
       "      <td>26.44</td>\n",
       "      <td>24.39</td>\n",
       "      <td>3.70</td>\n",
       "      <td>5.67</td>\n",
       "      <td>42.22</td>\n",
       "      <td>20.24</td>\n",
       "      <td>6.68</td>\n",
       "      <td>38.34</td>\n",
       "      <td>7.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-29</th>\n",
       "      <td>16.62</td>\n",
       "      <td>53.10</td>\n",
       "      <td>43.67</td>\n",
       "      <td>25.58</td>\n",
       "      <td>4.99</td>\n",
       "      <td>17.11</td>\n",
       "      <td>10.97</td>\n",
       "      <td>24.90</td>\n",
       "      <td>36.60</td>\n",
       "      <td>15.57</td>\n",
       "      <td>...</td>\n",
       "      <td>33.12</td>\n",
       "      <td>25.36</td>\n",
       "      <td>23.52</td>\n",
       "      <td>4.65</td>\n",
       "      <td>5.78</td>\n",
       "      <td>46.50</td>\n",
       "      <td>21.15</td>\n",
       "      <td>7.83</td>\n",
       "      <td>40.59</td>\n",
       "      <td>8.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-30</th>\n",
       "      <td>17.57</td>\n",
       "      <td>58.28</td>\n",
       "      <td>48.39</td>\n",
       "      <td>28.10</td>\n",
       "      <td>5.20</td>\n",
       "      <td>18.54</td>\n",
       "      <td>12.50</td>\n",
       "      <td>26.70</td>\n",
       "      <td>35.20</td>\n",
       "      <td>18.06</td>\n",
       "      <td>...</td>\n",
       "      <td>38.67</td>\n",
       "      <td>26.01</td>\n",
       "      <td>24.64</td>\n",
       "      <td>6.61</td>\n",
       "      <td>6.58</td>\n",
       "      <td>52.62</td>\n",
       "      <td>21.17</td>\n",
       "      <td>8.75</td>\n",
       "      <td>44.32</td>\n",
       "      <td>11.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-31</th>\n",
       "      <td>16.80</td>\n",
       "      <td>59.26</td>\n",
       "      <td>44.47</td>\n",
       "      <td>25.56</td>\n",
       "      <td>5.29</td>\n",
       "      <td>18.39</td>\n",
       "      <td>12.08</td>\n",
       "      <td>25.57</td>\n",
       "      <td>30.55</td>\n",
       "      <td>18.40</td>\n",
       "      <td>...</td>\n",
       "      <td>47.58</td>\n",
       "      <td>24.67</td>\n",
       "      <td>23.26</td>\n",
       "      <td>9.59</td>\n",
       "      <td>6.31</td>\n",
       "      <td>55.32</td>\n",
       "      <td>24.48</td>\n",
       "      <td>8.86</td>\n",
       "      <td>41.80</td>\n",
       "      <td>14.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-28</th>\n",
       "      <td>13.68</td>\n",
       "      <td>44.44</td>\n",
       "      <td>43.26</td>\n",
       "      <td>23.79</td>\n",
       "      <td>5.08</td>\n",
       "      <td>16.57</td>\n",
       "      <td>10.90</td>\n",
       "      <td>23.18</td>\n",
       "      <td>27.04</td>\n",
       "      <td>16.34</td>\n",
       "      <td>...</td>\n",
       "      <td>45.51</td>\n",
       "      <td>22.07</td>\n",
       "      <td>20.72</td>\n",
       "      <td>7.88</td>\n",
       "      <td>6.27</td>\n",
       "      <td>47.47</td>\n",
       "      <td>23.28</td>\n",
       "      <td>7.51</td>\n",
       "      <td>36.84</td>\n",
       "      <td>13.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-31</th>\n",
       "      <td>11.22</td>\n",
       "      <td>17.55</td>\n",
       "      <td>25.86</td>\n",
       "      <td>16.19</td>\n",
       "      <td>3.51</td>\n",
       "      <td>8.15</td>\n",
       "      <td>9.52</td>\n",
       "      <td>13.77</td>\n",
       "      <td>15.09</td>\n",
       "      <td>9.98</td>\n",
       "      <td>...</td>\n",
       "      <td>25.74</td>\n",
       "      <td>11.49</td>\n",
       "      <td>11.44</td>\n",
       "      <td>3.34</td>\n",
       "      <td>4.57</td>\n",
       "      <td>29.85</td>\n",
       "      <td>15.31</td>\n",
       "      <td>4.52</td>\n",
       "      <td>35.94</td>\n",
       "      <td>5.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-30</th>\n",
       "      <td>10.67</td>\n",
       "      <td>17.40</td>\n",
       "      <td>26.42</td>\n",
       "      <td>16.43</td>\n",
       "      <td>3.10</td>\n",
       "      <td>10.37</td>\n",
       "      <td>9.75</td>\n",
       "      <td>17.34</td>\n",
       "      <td>19.39</td>\n",
       "      <td>10.04</td>\n",
       "      <td>...</td>\n",
       "      <td>35.52</td>\n",
       "      <td>15.16</td>\n",
       "      <td>14.76</td>\n",
       "      <td>4.09</td>\n",
       "      <td>4.73</td>\n",
       "      <td>39.52</td>\n",
       "      <td>19.44</td>\n",
       "      <td>4.46</td>\n",
       "      <td>37.30</td>\n",
       "      <td>9.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-29</th>\n",
       "      <td>11.75</td>\n",
       "      <td>14.28</td>\n",
       "      <td>28.59</td>\n",
       "      <td>16.28</td>\n",
       "      <td>3.85</td>\n",
       "      <td>11.98</td>\n",
       "      <td>11.51</td>\n",
       "      <td>19.22</td>\n",
       "      <td>23.13</td>\n",
       "      <td>9.81</td>\n",
       "      <td>...</td>\n",
       "      <td>37.18</td>\n",
       "      <td>16.94</td>\n",
       "      <td>16.63</td>\n",
       "      <td>6.20</td>\n",
       "      <td>5.05</td>\n",
       "      <td>39.61</td>\n",
       "      <td>19.83</td>\n",
       "      <td>5.71</td>\n",
       "      <td>44.07</td>\n",
       "      <td>12.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-30</th>\n",
       "      <td>13.31</td>\n",
       "      <td>20.11</td>\n",
       "      <td>29.81</td>\n",
       "      <td>17.79</td>\n",
       "      <td>4.78</td>\n",
       "      <td>18.76</td>\n",
       "      <td>11.92</td>\n",
       "      <td>19.14</td>\n",
       "      <td>21.26</td>\n",
       "      <td>10.08</td>\n",
       "      <td>...</td>\n",
       "      <td>39.90</td>\n",
       "      <td>18.16</td>\n",
       "      <td>17.62</td>\n",
       "      <td>7.08</td>\n",
       "      <td>5.96</td>\n",
       "      <td>39.96</td>\n",
       "      <td>22.83</td>\n",
       "      <td>6.75</td>\n",
       "      <td>46.50</td>\n",
       "      <td>15.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-31</th>\n",
       "      <td>13.08</td>\n",
       "      <td>20.31</td>\n",
       "      <td>31.13</td>\n",
       "      <td>19.30</td>\n",
       "      <td>6.86</td>\n",
       "      <td>21.31</td>\n",
       "      <td>14.05</td>\n",
       "      <td>20.16</td>\n",
       "      <td>20.70</td>\n",
       "      <td>9.90</td>\n",
       "      <td>...</td>\n",
       "      <td>47.04</td>\n",
       "      <td>18.64</td>\n",
       "      <td>18.15</td>\n",
       "      <td>7.92</td>\n",
       "      <td>5.83</td>\n",
       "      <td>45.25</td>\n",
       "      <td>25.47</td>\n",
       "      <td>7.55</td>\n",
       "      <td>50.48</td>\n",
       "      <td>19.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-31</th>\n",
       "      <td>11.59</td>\n",
       "      <td>22.21</td>\n",
       "      <td>30.62</td>\n",
       "      <td>17.85</td>\n",
       "      <td>7.46</td>\n",
       "      <td>19.81</td>\n",
       "      <td>14.36</td>\n",
       "      <td>19.38</td>\n",
       "      <td>19.59</td>\n",
       "      <td>9.28</td>\n",
       "      <td>...</td>\n",
       "      <td>49.34</td>\n",
       "      <td>18.32</td>\n",
       "      <td>17.90</td>\n",
       "      <td>8.38</td>\n",
       "      <td>5.09</td>\n",
       "      <td>38.54</td>\n",
       "      <td>28.52</td>\n",
       "      <td>9.42</td>\n",
       "      <td>49.62</td>\n",
       "      <td>20.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-30</th>\n",
       "      <td>11.80</td>\n",
       "      <td>24.38</td>\n",
       "      <td>27.85</td>\n",
       "      <td>16.72</td>\n",
       "      <td>6.22</td>\n",
       "      <td>17.97</td>\n",
       "      <td>14.65</td>\n",
       "      <td>18.30</td>\n",
       "      <td>18.38</td>\n",
       "      <td>8.38</td>\n",
       "      <td>...</td>\n",
       "      <td>51.13</td>\n",
       "      <td>16.23</td>\n",
       "      <td>16.04</td>\n",
       "      <td>6.98</td>\n",
       "      <td>4.90</td>\n",
       "      <td>35.37</td>\n",
       "      <td>26.78</td>\n",
       "      <td>9.31</td>\n",
       "      <td>51.07</td>\n",
       "      <td>17.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-30</th>\n",
       "      <td>11.46</td>\n",
       "      <td>22.55</td>\n",
       "      <td>28.02</td>\n",
       "      <td>17.38</td>\n",
       "      <td>5.50</td>\n",
       "      <td>17.88</td>\n",
       "      <td>15.26</td>\n",
       "      <td>17.42</td>\n",
       "      <td>16.78</td>\n",
       "      <td>8.16</td>\n",
       "      <td>...</td>\n",
       "      <td>46.10</td>\n",
       "      <td>15.60</td>\n",
       "      <td>15.49</td>\n",
       "      <td>6.26</td>\n",
       "      <td>4.58</td>\n",
       "      <td>35.84</td>\n",
       "      <td>26.68</td>\n",
       "      <td>10.12</td>\n",
       "      <td>52.31</td>\n",
       "      <td>17.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-30</th>\n",
       "      <td>13.16</td>\n",
       "      <td>38.02</td>\n",
       "      <td>32.00</td>\n",
       "      <td>20.97</td>\n",
       "      <td>8.13</td>\n",
       "      <td>19.54</td>\n",
       "      <td>19.72</td>\n",
       "      <td>18.64</td>\n",
       "      <td>22.03</td>\n",
       "      <td>9.79</td>\n",
       "      <td>...</td>\n",
       "      <td>50.41</td>\n",
       "      <td>20.98</td>\n",
       "      <td>20.36</td>\n",
       "      <td>10.03</td>\n",
       "      <td>4.80</td>\n",
       "      <td>39.95</td>\n",
       "      <td>26.43</td>\n",
       "      <td>12.67</td>\n",
       "      <td>66.40</td>\n",
       "      <td>17.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-30</th>\n",
       "      <td>15.06</td>\n",
       "      <td>39.30</td>\n",
       "      <td>36.41</td>\n",
       "      <td>23.73</td>\n",
       "      <td>10.96</td>\n",
       "      <td>23.18</td>\n",
       "      <td>21.63</td>\n",
       "      <td>20.43</td>\n",
       "      <td>22.04</td>\n",
       "      <td>9.90</td>\n",
       "      <td>...</td>\n",
       "      <td>52.50</td>\n",
       "      <td>23.68</td>\n",
       "      <td>23.17</td>\n",
       "      <td>14.04</td>\n",
       "      <td>4.91</td>\n",
       "      <td>41.38</td>\n",
       "      <td>28.47</td>\n",
       "      <td>13.56</td>\n",
       "      <td>74.44</td>\n",
       "      <td>16.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-29</th>\n",
       "      <td>14.61</td>\n",
       "      <td>40.34</td>\n",
       "      <td>31.77</td>\n",
       "      <td>21.65</td>\n",
       "      <td>14.51</td>\n",
       "      <td>23.58</td>\n",
       "      <td>21.22</td>\n",
       "      <td>21.47</td>\n",
       "      <td>21.22</td>\n",
       "      <td>9.55</td>\n",
       "      <td>...</td>\n",
       "      <td>49.06</td>\n",
       "      <td>22.44</td>\n",
       "      <td>21.82</td>\n",
       "      <td>14.10</td>\n",
       "      <td>4.22</td>\n",
       "      <td>37.36</td>\n",
       "      <td>28.15</td>\n",
       "      <td>12.25</td>\n",
       "      <td>74.87</td>\n",
       "      <td>14.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-26</th>\n",
       "      <td>13.55</td>\n",
       "      <td>40.60</td>\n",
       "      <td>26.66</td>\n",
       "      <td>20.15</td>\n",
       "      <td>16.99</td>\n",
       "      <td>25.22</td>\n",
       "      <td>21.34</td>\n",
       "      <td>18.39</td>\n",
       "      <td>21.64</td>\n",
       "      <td>8.35</td>\n",
       "      <td>...</td>\n",
       "      <td>46.17</td>\n",
       "      <td>18.18</td>\n",
       "      <td>18.19</td>\n",
       "      <td>16.72</td>\n",
       "      <td>3.88</td>\n",
       "      <td>30.92</td>\n",
       "      <td>31.10</td>\n",
       "      <td>15.07</td>\n",
       "      <td>80.46</td>\n",
       "      <td>11.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-31</th>\n",
       "      <td>14.78</td>\n",
       "      <td>37.85</td>\n",
       "      <td>29.06</td>\n",
       "      <td>23.47</td>\n",
       "      <td>17.36</td>\n",
       "      <td>24.08</td>\n",
       "      <td>23.19</td>\n",
       "      <td>20.38</td>\n",
       "      <td>25.22</td>\n",
       "      <td>10.12</td>\n",
       "      <td>...</td>\n",
       "      <td>48.14</td>\n",
       "      <td>19.65</td>\n",
       "      <td>19.71</td>\n",
       "      <td>18.46</td>\n",
       "      <td>4.33</td>\n",
       "      <td>33.92</td>\n",
       "      <td>28.67</td>\n",
       "      <td>15.87</td>\n",
       "      <td>87.04</td>\n",
       "      <td>12.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-30</th>\n",
       "      <td>14.44</td>\n",
       "      <td>38.50</td>\n",
       "      <td>28.29</td>\n",
       "      <td>23.02</td>\n",
       "      <td>25.95</td>\n",
       "      <td>26.74</td>\n",
       "      <td>25.34</td>\n",
       "      <td>22.26</td>\n",
       "      <td>20.79</td>\n",
       "      <td>9.52</td>\n",
       "      <td>...</td>\n",
       "      <td>48.74</td>\n",
       "      <td>19.61</td>\n",
       "      <td>19.96</td>\n",
       "      <td>18.32</td>\n",
       "      <td>4.17</td>\n",
       "      <td>32.47</td>\n",
       "      <td>30.91</td>\n",
       "      <td>20.96</td>\n",
       "      <td>96.86</td>\n",
       "      <td>11.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-31</th>\n",
       "      <td>17.35</td>\n",
       "      <td>43.00</td>\n",
       "      <td>32.32</td>\n",
       "      <td>25.57</td>\n",
       "      <td>22.73</td>\n",
       "      <td>30.84</td>\n",
       "      <td>25.41</td>\n",
       "      <td>25.52</td>\n",
       "      <td>25.76</td>\n",
       "      <td>11.18</td>\n",
       "      <td>...</td>\n",
       "      <td>51.55</td>\n",
       "      <td>22.62</td>\n",
       "      <td>22.71</td>\n",
       "      <td>19.73</td>\n",
       "      <td>3.98</td>\n",
       "      <td>34.42</td>\n",
       "      <td>34.26</td>\n",
       "      <td>18.55</td>\n",
       "      <td>101.97</td>\n",
       "      <td>12.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-30</th>\n",
       "      <td>16.50</td>\n",
       "      <td>43.89</td>\n",
       "      <td>31.13</td>\n",
       "      <td>24.86</td>\n",
       "      <td>26.15</td>\n",
       "      <td>30.29</td>\n",
       "      <td>26.53</td>\n",
       "      <td>26.39</td>\n",
       "      <td>27.33</td>\n",
       "      <td>10.14</td>\n",
       "      <td>...</td>\n",
       "      <td>56.74</td>\n",
       "      <td>25.71</td>\n",
       "      <td>24.87</td>\n",
       "      <td>19.49</td>\n",
       "      <td>4.10</td>\n",
       "      <td>34.52</td>\n",
       "      <td>37.42</td>\n",
       "      <td>17.84</td>\n",
       "      <td>102.59</td>\n",
       "      <td>15.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07-30</th>\n",
       "      <td>16.09</td>\n",
       "      <td>38.90</td>\n",
       "      <td>30.65</td>\n",
       "      <td>23.87</td>\n",
       "      <td>23.87</td>\n",
       "      <td>29.04</td>\n",
       "      <td>27.00</td>\n",
       "      <td>28.02</td>\n",
       "      <td>25.62</td>\n",
       "      <td>10.03</td>\n",
       "      <td>...</td>\n",
       "      <td>53.75</td>\n",
       "      <td>23.34</td>\n",
       "      <td>22.74</td>\n",
       "      <td>17.85</td>\n",
       "      <td>3.96</td>\n",
       "      <td>29.71</td>\n",
       "      <td>35.15</td>\n",
       "      <td>19.21</td>\n",
       "      <td>98.53</td>\n",
       "      <td>12.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-31</th>\n",
       "      <td>16.53</td>\n",
       "      <td>37.73</td>\n",
       "      <td>29.78</td>\n",
       "      <td>22.80</td>\n",
       "      <td>21.95</td>\n",
       "      <td>28.19</td>\n",
       "      <td>23.05</td>\n",
       "      <td>26.89</td>\n",
       "      <td>23.42</td>\n",
       "      <td>9.03</td>\n",
       "      <td>...</td>\n",
       "      <td>51.62</td>\n",
       "      <td>25.07</td>\n",
       "      <td>24.35</td>\n",
       "      <td>19.17</td>\n",
       "      <td>3.91</td>\n",
       "      <td>29.13</td>\n",
       "      <td>39.45</td>\n",
       "      <td>17.27</td>\n",
       "      <td>89.39</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-30</th>\n",
       "      <td>14.79</td>\n",
       "      <td>36.41</td>\n",
       "      <td>28.43</td>\n",
       "      <td>20.53</td>\n",
       "      <td>15.68</td>\n",
       "      <td>25.09</td>\n",
       "      <td>21.55</td>\n",
       "      <td>23.49</td>\n",
       "      <td>27.09</td>\n",
       "      <td>8.08</td>\n",
       "      <td>...</td>\n",
       "      <td>45.57</td>\n",
       "      <td>25.28</td>\n",
       "      <td>24.38</td>\n",
       "      <td>25.02</td>\n",
       "      <td>3.85</td>\n",
       "      <td>25.29</td>\n",
       "      <td>35.96</td>\n",
       "      <td>15.95</td>\n",
       "      <td>76.24</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-29</th>\n",
       "      <td>16.43</td>\n",
       "      <td>24.87</td>\n",
       "      <td>28.04</td>\n",
       "      <td>19.63</td>\n",
       "      <td>12.23</td>\n",
       "      <td>22.49</td>\n",
       "      <td>20.06</td>\n",
       "      <td>0.00</td>\n",
       "      <td>23.23</td>\n",
       "      <td>7.17</td>\n",
       "      <td>...</td>\n",
       "      <td>38.90</td>\n",
       "      <td>24.85</td>\n",
       "      <td>24.40</td>\n",
       "      <td>23.47</td>\n",
       "      <td>3.72</td>\n",
       "      <td>25.78</td>\n",
       "      <td>32.63</td>\n",
       "      <td>13.11</td>\n",
       "      <td>71.61</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-30</th>\n",
       "      <td>15.53</td>\n",
       "      <td>23.38</td>\n",
       "      <td>31.69</td>\n",
       "      <td>19.65</td>\n",
       "      <td>11.95</td>\n",
       "      <td>20.89</td>\n",
       "      <td>20.51</td>\n",
       "      <td>0.00</td>\n",
       "      <td>19.75</td>\n",
       "      <td>7.90</td>\n",
       "      <td>...</td>\n",
       "      <td>26.69</td>\n",
       "      <td>27.07</td>\n",
       "      <td>26.35</td>\n",
       "      <td>20.29</td>\n",
       "      <td>3.80</td>\n",
       "      <td>24.78</td>\n",
       "      <td>31.57</td>\n",
       "      <td>13.45</td>\n",
       "      <td>69.95</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-30</th>\n",
       "      <td>15.42</td>\n",
       "      <td>24.36</td>\n",
       "      <td>28.85</td>\n",
       "      <td>19.21</td>\n",
       "      <td>9.54</td>\n",
       "      <td>21.00</td>\n",
       "      <td>24.99</td>\n",
       "      <td>0.00</td>\n",
       "      <td>22.52</td>\n",
       "      <td>8.31</td>\n",
       "      <td>...</td>\n",
       "      <td>25.43</td>\n",
       "      <td>30.70</td>\n",
       "      <td>28.45</td>\n",
       "      <td>20.67</td>\n",
       "      <td>3.87</td>\n",
       "      <td>27.49</td>\n",
       "      <td>28.64</td>\n",
       "      <td>15.16</td>\n",
       "      <td>77.96</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>60 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "ticker      ABEV3  AZUL4  BBAS3  BBDC4  BIDI4  BPAC11  BRAP4  BRDT3  BRFS3  \\\n",
       "datetime                                                                     \n",
       "2017-01-31  15.04   0.00  25.32  15.59   0.00    0.00   5.90   0.00  44.46   \n",
       "2017-02-24  15.60   0.00  26.94  15.95   0.00    3.96   6.65   0.00  40.62   \n",
       "2017-03-31  15.95   0.00  27.57  15.46   0.00    4.13   6.21   0.00  38.61   \n",
       "2017-04-28  15.96  23.95  26.82  15.96   0.00    0.00   5.77   0.00  39.79   \n",
       "2017-05-31  16.36  23.58  23.27  14.47   0.00    3.08   5.56   0.00  43.32   \n",
       "2017-06-30  16.15  23.44  22.06  14.81   0.00    3.14   5.99   0.00  39.20   \n",
       "2017-07-31  16.89  27.29  23.63  16.00   0.00    0.00   6.71   0.00  36.92   \n",
       "2017-08-31  17.52  26.54  25.42  17.80   0.00    3.69   7.82   0.00  42.52   \n",
       "2017-09-29  18.56  29.19  28.96  18.59   0.00    4.19   7.01   0.00  45.67   \n",
       "2017-10-31  18.43  27.39  28.57  18.40   0.00    4.90   6.98   0.00  44.25   \n",
       "2017-11-30  18.13  26.86  24.98  17.34   0.00    4.23   7.36   0.00  38.48   \n",
       "2017-12-28  19.01  26.89  26.61  18.36   0.00    4.22   8.59  12.95  36.60   \n",
       "2018-01-31  19.61  31.04  33.19  22.12   0.00    4.85   9.36  15.90  35.35   \n",
       "2018-02-28  19.71  33.62  34.96  21.05   0.00    5.29  10.22  17.10  29.92   \n",
       "2018-03-29  21.57  38.18  34.54  21.45   0.00    5.28   9.42  17.88  22.83   \n",
       "2018-04-30  20.91  36.39  30.90  20.66   0.00    5.13  11.03  18.09  24.80   \n",
       "2018-05-30  17.64  26.50  25.71  17.45   0.95    4.46   9.66  14.34  21.42   \n",
       "2018-06-29  16.25  21.32  24.32  16.10   0.78    4.24   9.06  14.41  18.00   \n",
       "2018-07-31  17.54  24.12  27.59  18.39   0.93    4.54  10.10  15.35  22.65   \n",
       "2018-08-31  17.04  22.99  25.75  17.10   1.47    4.84   9.63  15.58  20.13   \n",
       "2018-09-28  16.58  24.04  25.26  17.29   1.66    5.03  11.49  15.36  21.97   \n",
       "2018-10-31  14.85  30.59  36.67  20.69   2.29    4.67  10.72  18.89  21.90   \n",
       "2018-11-30  15.38  34.74  38.59  23.21   2.16    5.23   9.85  19.26  22.17   \n",
       "2018-12-28  14.22  36.00  40.30  23.73   2.12    5.56   9.83  20.60  21.93   \n",
       "2019-01-31  16.19  37.11  44.96  27.80   2.30    7.14   8.88  21.36  23.54   \n",
       "2019-02-28  15.93  37.65  44.28  26.59   2.10    8.24   9.09  19.60  20.57   \n",
       "2019-03-29  15.49  38.27  42.74  26.40   2.91    8.73  10.06  18.56  22.65   \n",
       "2019-04-30  17.00  34.00  43.58  26.26   3.57    9.98  10.06  20.89  31.05   \n",
       "2019-05-31  16.16  39.20  45.82  27.32   3.70   11.27   9.85  22.52  27.70   \n",
       "2019-06-28  16.47  43.29  47.79  27.88   3.36   12.27  10.54  22.04  29.52   \n",
       "2019-07-31  18.53  51.78  43.61  25.60   5.45   14.42  10.14  23.45  33.41   \n",
       "2019-08-30  17.23  47.10  41.30  24.46   6.57   14.27   9.12  25.39  38.14   \n",
       "2019-09-30  17.72  49.71  40.80  25.20   6.25   14.26  10.12  24.27  38.20   \n",
       "2019-10-31  16.00  52.52  43.18  26.94   5.68   15.82  10.27  24.93  35.51   \n",
       "2019-11-29  16.62  53.10  43.67  25.58   4.99   17.11  10.97  24.90  36.60   \n",
       "2019-12-30  17.57  58.28  48.39  28.10   5.20   18.54  12.50  26.70  35.20   \n",
       "2020-01-31  16.80  59.26  44.47  25.56   5.29   18.39  12.08  25.57  30.55   \n",
       "2020-02-28  13.68  44.44  43.26  23.79   5.08   16.57  10.90  23.18  27.04   \n",
       "2020-03-31  11.22  17.55  25.86  16.19   3.51    8.15   9.52  13.77  15.09   \n",
       "2020-04-30  10.67  17.40  26.42  16.43   3.10   10.37   9.75  17.34  19.39   \n",
       "2020-05-29  11.75  14.28  28.59  16.28   3.85   11.98  11.51  19.22  23.13   \n",
       "2020-06-30  13.31  20.11  29.81  17.79   4.78   18.76  11.92  19.14  21.26   \n",
       "2020-07-31  13.08  20.31  31.13  19.30   6.86   21.31  14.05  20.16  20.70   \n",
       "2020-08-31  11.59  22.21  30.62  17.85   7.46   19.81  14.36  19.38  19.59   \n",
       "2020-09-30  11.80  24.38  27.85  16.72   6.22   17.97  14.65  18.30  18.38   \n",
       "2020-10-30  11.46  22.55  28.02  17.38   5.50   17.88  15.26  17.42  16.78   \n",
       "2020-11-30  13.16  38.02  32.00  20.97   8.13   19.54  19.72  18.64  22.03   \n",
       "2020-12-30  15.06  39.30  36.41  23.73  10.96   23.18  21.63  20.43  22.04   \n",
       "2021-01-29  14.61  40.34  31.77  21.65  14.51   23.58  21.22  21.47  21.22   \n",
       "2021-02-26  13.55  40.60  26.66  20.15  16.99   25.22  21.34  18.39  21.64   \n",
       "2021-03-31  14.78  37.85  29.06  23.47  17.36   24.08  23.19  20.38  25.22   \n",
       "2021-04-30  14.44  38.50  28.29  23.02  25.95   26.74  25.34  22.26  20.79   \n",
       "2021-05-31  17.35  43.00  32.32  25.57  22.73   30.84  25.41  25.52  25.76   \n",
       "2021-06-30  16.50  43.89  31.13  24.86  26.15   30.29  26.53  26.39  27.33   \n",
       "2021-07-30  16.09  38.90  30.65  23.87  23.87   29.04  27.00  28.02  25.62   \n",
       "2021-08-31  16.53  37.73  29.78  22.80  21.95   28.19  23.05  26.89  23.42   \n",
       "2021-09-30  14.79  36.41  28.43  20.53  15.68   25.09  21.55  23.49  27.09   \n",
       "2021-10-29  16.43  24.87  28.04  19.63  12.23   22.49  20.06   0.00  23.23   \n",
       "2021-11-30  15.53  23.38  31.69  19.65  11.95   20.89  20.51   0.00  19.75   \n",
       "2021-12-30  15.42  24.36  28.85  19.21   9.54   21.00  24.99   0.00  22.52   \n",
       "\n",
       "ticker      BRML3  ...  NTCO3  PETR3  PETR4  PRIO3  SAPR4  SULA11  TOTS3  \\\n",
       "datetime           ...                                                     \n",
       "2017-01-31  11.82  ...  12.25  12.85  11.51   0.52   3.81   13.24   8.29   \n",
       "2017-02-24  12.02  ...  12.42  12.67  11.64   0.76   3.89   13.83   8.05   \n",
       "2017-03-31  11.67  ...  14.14  12.03  11.11   0.94   2.97   13.93   8.82   \n",
       "2017-04-28  11.32  ...  14.75  11.40  10.71   0.87   2.78   14.01   8.85   \n",
       "2017-05-31  11.16  ...  15.88  10.81   9.93   0.74   2.82   14.90   9.60   \n",
       "2017-06-30  11.13  ...  12.53  10.48   9.48   0.74   2.95   14.77   9.60   \n",
       "2017-07-31  12.30  ...  12.21  10.96  10.19   0.75   2.94   14.64   9.55   \n",
       "2017-08-31  12.67  ...  14.38  11.09  10.46   0.88   2.90   15.00   9.94   \n",
       "2017-09-29  13.11  ...  15.21  12.55  11.73   0.93   3.00   14.93  10.02   \n",
       "2017-10-31  11.81  ...  15.10  13.83  12.86   1.14   3.01   14.96  10.43   \n",
       "2017-11-30  11.26  ...  14.22  12.66  11.79   1.28   2.94   14.93   9.69   \n",
       "2017-12-28  11.86  ...  16.20  13.43  12.34   1.64   3.25   15.57   9.60   \n",
       "2018-01-31  11.97  ...  17.11  16.91  15.10   1.77   3.01   17.22  10.43   \n",
       "2018-02-28  11.11  ...  16.82  18.38  16.45   1.45   2.94   18.11   9.80   \n",
       "2018-03-29  10.85  ...  15.83  18.55  16.41   1.18   3.08   18.62   9.25   \n",
       "2018-04-30  10.17  ...  15.93  19.56  17.61   1.45   3.04   18.48  10.28   \n",
       "2018-05-30   9.60  ...  16.68  17.65  14.57   1.43   2.53   16.74   9.01   \n",
       "2018-06-29   9.04  ...  14.97  15.44  13.20   1.40   2.55   15.63   8.74   \n",
       "2018-07-31   9.27  ...  14.39  17.45  15.14   1.60   2.44   18.87   9.47   \n",
       "2018-08-31   8.52  ...  14.34  17.59  14.82   1.89   2.50   19.64   8.62   \n",
       "2018-09-28   8.96  ...  14.04  19.30  16.23   2.22   2.44   22.23   8.12   \n",
       "2018-10-31  11.83  ...  16.12  24.18  21.25   2.38   2.96   21.20   8.11   \n",
       "2018-11-30  12.19  ...  20.23  22.50  19.66   1.94   3.07   23.00   9.37   \n",
       "2018-12-28  12.26  ...  22.26  20.35  18.01   1.98   3.14   24.45   8.78   \n",
       "2019-01-31  13.65  ...  23.57  23.68  20.31   2.30   4.07   27.78  10.78   \n",
       "2019-02-28  12.56  ...  24.11  23.93  21.49   3.02   4.13   26.31  11.81   \n",
       "2019-03-29  11.84  ...  22.62  24.96  22.28   3.78   4.19   25.97  12.77   \n",
       "2019-04-30  11.59  ...  26.03  24.01  21.54   3.69   4.36   27.19  14.44   \n",
       "2019-05-31  12.15  ...  29.89  22.69  20.37   3.47   4.14   30.43  12.80   \n",
       "2019-06-28  13.59  ...  28.15  24.13  21.85   3.15   4.50   32.70  14.25   \n",
       "2019-07-31  14.24  ...  30.48  23.14  20.79   3.52   4.97   36.63  15.72   \n",
       "2019-08-30  13.48  ...  32.87  22.86  20.46   3.38   5.47   42.23  17.51   \n",
       "2019-09-30  14.41  ...  33.74  24.38  22.11   3.48   5.14   41.71  18.77   \n",
       "2019-10-31  15.35  ...  31.05  26.44  24.39   3.70   5.67   42.22  20.24   \n",
       "2019-11-29  15.57  ...  33.12  25.36  23.52   4.65   5.78   46.50  21.15   \n",
       "2019-12-30  18.06  ...  38.67  26.01  24.64   6.61   6.58   52.62  21.17   \n",
       "2020-01-31  18.40  ...  47.58  24.67  23.26   9.59   6.31   55.32  24.48   \n",
       "2020-02-28  16.34  ...  45.51  22.07  20.72   7.88   6.27   47.47  23.28   \n",
       "2020-03-31   9.98  ...  25.74  11.49  11.44   3.34   4.57   29.85  15.31   \n",
       "2020-04-30  10.04  ...  35.52  15.16  14.76   4.09   4.73   39.52  19.44   \n",
       "2020-05-29   9.81  ...  37.18  16.94  16.63   6.20   5.05   39.61  19.83   \n",
       "2020-06-30  10.08  ...  39.90  18.16  17.62   7.08   5.96   39.96  22.83   \n",
       "2020-07-31   9.90  ...  47.04  18.64  18.15   7.92   5.83   45.25  25.47   \n",
       "2020-08-31   9.28  ...  49.34  18.32  17.90   8.38   5.09   38.54  28.52   \n",
       "2020-09-30   8.38  ...  51.13  16.23  16.04   6.98   4.90   35.37  26.78   \n",
       "2020-10-30   8.16  ...  46.10  15.60  15.49   6.26   4.58   35.84  26.68   \n",
       "2020-11-30   9.79  ...  50.41  20.98  20.36  10.03   4.80   39.95  26.43   \n",
       "2020-12-30   9.90  ...  52.50  23.68  23.17  14.04   4.91   41.38  28.47   \n",
       "2021-01-29   9.55  ...  49.06  22.44  21.82  14.10   4.22   37.36  28.15   \n",
       "2021-02-26   8.35  ...  46.17  18.18  18.19  16.72   3.88   30.92  31.10   \n",
       "2021-03-31  10.12  ...  48.14  19.65  19.71  18.46   4.33   33.92  28.67   \n",
       "2021-04-30   9.52  ...  48.74  19.61  19.96  18.32   4.17   32.47  30.91   \n",
       "2021-05-31  11.18  ...  51.55  22.62  22.71  19.73   3.98   34.42  34.26   \n",
       "2021-06-30  10.14  ...  56.74  25.71  24.87  19.49   4.10   34.52  37.42   \n",
       "2021-07-30  10.03  ...  53.75  23.34  22.74  17.85   3.96   29.71  35.15   \n",
       "2021-08-31   9.03  ...  51.62  25.07  24.35  19.17   3.91   29.13  39.45   \n",
       "2021-09-30   8.08  ...  45.57  25.28  24.38  25.02   3.85   25.29  35.96   \n",
       "2021-10-29   7.17  ...  38.90  24.85  24.40  23.47   3.72   25.78  32.63   \n",
       "2021-11-30   7.90  ...  26.69  27.07  26.35  20.29   3.80   24.78  31.57   \n",
       "2021-12-30   8.31  ...  25.43  30.70  28.45  20.67   3.87   27.49  28.64   \n",
       "\n",
       "ticker      USIM5  VALE3  VVAR3  \n",
       "datetime                         \n",
       "2017-01-31   4.73  24.33   3.24  \n",
       "2017-02-24   4.44  24.74   4.02  \n",
       "2017-03-31   4.00  22.52   3.62  \n",
       "2017-04-28   3.84  21.35   4.48  \n",
       "2017-05-31   3.58  21.12   3.67  \n",
       "2017-06-30   4.15  22.54   4.00  \n",
       "2017-07-31   4.66  24.33   5.83  \n",
       "2017-08-31   6.21  27.27   6.67  \n",
       "2017-09-29   7.01  24.77   7.81  \n",
       "2017-10-31   7.94  24.95   7.57  \n",
       "2017-11-30   7.72  27.32   7.33  \n",
       "2017-12-28   8.20  31.58   7.92  \n",
       "2018-01-31  10.69  32.51   8.09  \n",
       "2018-02-28  11.11  35.34   7.87  \n",
       "2018-03-29   9.85  33.43   8.14  \n",
       "2018-04-30   9.87  38.54   8.11  \n",
       "2018-05-30   7.47  40.09   6.50  \n",
       "2018-06-29   6.63  39.27   5.89  \n",
       "2018-07-31   8.03  43.43   7.15  \n",
       "2018-08-31   7.31  43.52   5.70  \n",
       "2018-09-28   7.53  48.55   5.06  \n",
       "2018-10-31   9.31  46.06   5.60  \n",
       "2018-11-30   8.47  42.88   5.18  \n",
       "2018-12-28   8.35  41.42   4.39  \n",
       "2019-01-31   8.94  36.96   6.00  \n",
       "2019-02-28   8.81  38.26   4.58  \n",
       "2019-03-29   9.15  41.37   4.22  \n",
       "2019-04-30   7.93  40.69   4.10  \n",
       "2019-05-31   7.53  39.80   4.70  \n",
       "2019-06-28   8.21  42.09   5.07  \n",
       "2019-07-31   8.05  40.46   7.72  \n",
       "2019-08-30   7.27  37.01   7.73  \n",
       "2019-09-30   7.18  38.78   7.92  \n",
       "2019-10-31   6.68  38.34   7.42  \n",
       "2019-11-29   7.83  40.59   8.81  \n",
       "2019-12-30   8.75  44.32  11.17  \n",
       "2020-01-31   8.86  41.80  14.00  \n",
       "2020-02-28   7.51  36.84  13.81  \n",
       "2020-03-31   4.52  35.94   5.28  \n",
       "2020-04-30   4.46  37.30   9.18  \n",
       "2020-05-29   5.71  44.07  12.40  \n",
       "2020-06-30   6.75  46.50  15.31  \n",
       "2020-07-31   7.55  50.48  19.52  \n",
       "2020-08-31   9.42  49.62  20.50  \n",
       "2020-09-30   9.31  51.07  17.35  \n",
       "2020-10-30  10.12  52.31  17.16  \n",
       "2020-11-30  12.67  66.40  17.75  \n",
       "2020-12-30  13.56  74.44  16.16  \n",
       "2021-01-29  12.25  74.87  14.69  \n",
       "2021-02-26  15.07  80.46  11.87  \n",
       "2021-03-31  15.87  87.04  12.07  \n",
       "2021-04-30  20.96  96.86  11.83  \n",
       "2021-05-31  18.55 101.97  12.98  \n",
       "2021-06-30  17.84 102.59  15.79  \n",
       "2021-07-30  19.21  98.53  12.59  \n",
       "2021-08-31  17.27  89.39   0.00  \n",
       "2021-09-30  15.95  76.24   0.00  \n",
       "2021-10-29  13.11  71.61   0.00  \n",
       "2021-11-30  13.45  69.95   0.00  \n",
       "2021-12-30  15.16  77.96   0.00  \n",
       "\n",
       "[60 rows x 40 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create pivot table for close price \n",
    "\n",
    "df_close = df.pivot_table(values='close', index=['datetime'], columns='ticker', fill_value= 0)\n",
    "df_close"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-04T21:38:47.219355Z",
     "start_time": "2022-01-04T21:38:43.558Z"
    }
   },
   "source": [
    "# DEF Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-11T23:03:43.858538Z",
     "start_time": "2022-03-11T23:03:43.843535Z"
    }
   },
   "outputs": [],
   "source": [
    "#Var\n",
    "\n",
    "invest = 20000\n",
    "x = df_log\n",
    "y = df_close\n",
    "tickers = ['VVAR3', 'PETR4', 'MGLU3', 'COGN3', 'IRBR3', 'CIEL3', 'HAPV3',\n",
    "       'VALE3', 'PRIO3', 'LAME4', 'CPLE6', 'ABEV3', 'PETR3', 'BBDC4',\n",
    "       'BRML3', 'EMBR3', 'CCRO3', 'ITSA4', 'ITUB4', 'BIDI4', 'BBAS3',\n",
    "       'CSAN3', 'BPAC11', 'JBSS3', 'LWSA3', 'SAPR4', 'USIM5', 'BRDT3',\n",
    "       'BRFS3', 'DMMO3', 'BRAP4', 'NTCO3', 'LAME3', 'AZUL4', 'CMIG4',\n",
    "       'SULA11', 'TOTS3', 'NEOE3', 'CSNA3', 'ENEV3']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-11T23:03:43.874542Z",
     "start_time": "2022-03-11T23:03:43.859538Z"
    }
   },
   "outputs": [],
   "source": [
    "# select 4 long momentum\n",
    "def nlarg(i):\n",
    "    return x.loc[x.index[i], tickers].sort_values(ascending=False).nlargest(4)\n",
    "\n",
    "# select 4 short momentum\n",
    "def nsmall(i):\n",
    "    return x.loc[x.index[i], tickers].sort_values(ascending=False).nsmallest(4)\n",
    "\n",
    "# get list of long/short stocks \n",
    "def get_tickers(list):\n",
    "    return list.index.tolist()\n",
    "\n",
    "# select long close price\n",
    "def lclose_price(i): # i = data (0 = jan, 1=fev...) - tickerlist - X =close i= data \n",
    "    return y.loc[y.index[i], get_tickers(nlarg(i))]\n",
    "\n",
    "# select short close price \n",
    "def sclose_price(i): # i = data\n",
    "    return y.loc[y.index[i], get_tickers(nsmall(i))]\n",
    "\n",
    "# count long stocks bought\n",
    "def l_qtd_stocks(invest, i):\n",
    "    return invest/lclose_price(i)   \n",
    "\n",
    "# count short stocks sold\n",
    "def s_qtd_stocks(invest, i): #i = 0 (primeiro mês)\n",
    "    return invest/sclose_price(i)\n",
    "\n",
    "# select long stocks sold\n",
    "def lclose_price_shift(i): \n",
    "    return y.loc[y.index[i+1], get_tickers(nlarg(i))]\n",
    "\n",
    "# select short stocks bought \n",
    "def sclose_price_shift(i): #n = mês que foi vendido\n",
    "    return y.loc[y.index[i+1], get_tickers(nsmall(i))]\n",
    "\n",
    "# compute long stocks profit\n",
    "def profit_long(invest, i): # n = mês referência do que foi comprado i = mês de referência da venda\n",
    "    return (l_qtd_stocks(invest, i) * lclose_price_shift(i) - invest)\n",
    "\n",
    "# compute short stocks profit\n",
    "def profit_short(invest, i): # n = mês referência do que foi comprado i = mês de referência da venda\n",
    "    return invest - (s_qtd_stocks(invest, i) * sclose_price_shift(i))\n",
    "\n",
    "# compute long total result\n",
    "def total_profit_long(invest, i):\n",
    "    return profit_long(invest , i).sum()\n",
    "\n",
    "# compute short total result \n",
    "def total_profit_short(invest, i):\n",
    "    return profit_short(invest , i).sum()\n",
    "\n",
    "# compute total PL\n",
    "def pl_total(invest, i):\n",
    "    return total_profit_long(invest, i) + total_profit_short(invest, i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create the final DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-11T23:03:44.066514Z",
     "start_time": "2022-03-11T23:03:44.051510Z"
    }
   },
   "outputs": [],
   "source": [
    "df.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-11T23:03:44.082518Z",
     "start_time": "2022-03-11T23:03:44.067514Z"
    }
   },
   "outputs": [],
   "source": [
    "df['datetime'] = df['datetime'].astype('string')\n",
    "df_date = df['datetime'].drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-11T23:03:44.844703Z",
     "start_time": "2022-03-11T23:03:44.083518Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\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>data</th>\n",
       "      <th>tickers</th>\n",
       "      <th>close_begin</th>\n",
       "      <th>close_last</th>\n",
       "      <th>result</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-01-31</td>\n",
       "      <td>MGLU3</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.79</td>\n",
       "      <td>12244.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-01-31</td>\n",
       "      <td>BRML3</td>\n",
       "      <td>11.82</td>\n",
       "      <td>12.02</td>\n",
       "      <td>338.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-01-31</td>\n",
       "      <td>CMIG4</td>\n",
       "      <td>5.82</td>\n",
       "      <td>6.80</td>\n",
       "      <td>3367.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-01-31</td>\n",
       "      <td>USIM5</td>\n",
       "      <td>4.73</td>\n",
       "      <td>4.44</td>\n",
       "      <td>-1226.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-01-31</td>\n",
       "      <td>COGN3</td>\n",
       "      <td>12.55</td>\n",
       "      <td>12.70</td>\n",
       "      <td>-239.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>467</th>\n",
       "      <td>2021-11-30</td>\n",
       "      <td>IRBR3</td>\n",
       "      <td>4.31</td>\n",
       "      <td>4.02</td>\n",
       "      <td>-1345.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>468</th>\n",
       "      <td>2021-11-30</td>\n",
       "      <td>LWSA3</td>\n",
       "      <td>13.19</td>\n",
       "      <td>13.16</td>\n",
       "      <td>45.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>469</th>\n",
       "      <td>2021-11-30</td>\n",
       "      <td>DMMO3</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.52</td>\n",
       "      <td>2950.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>470</th>\n",
       "      <td>2021-11-30</td>\n",
       "      <td>BIDI4</td>\n",
       "      <td>11.95</td>\n",
       "      <td>9.54</td>\n",
       "      <td>4033.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>471</th>\n",
       "      <td>2021-11-30</td>\n",
       "      <td>HAPV3</td>\n",
       "      <td>10.91</td>\n",
       "      <td>10.38</td>\n",
       "      <td>971.59</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>472 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           data tickers  close_begin  close_last   result\n",
       "0    2017-01-31   MGLU3         0.49        0.79 12244.90\n",
       "1    2017-01-31   BRML3        11.82       12.02   338.41\n",
       "2    2017-01-31   CMIG4         5.82        6.80  3367.70\n",
       "3    2017-01-31   USIM5         4.73        4.44 -1226.22\n",
       "4    2017-01-31   COGN3        12.55       12.70  -239.04\n",
       "..          ...     ...          ...         ...      ...\n",
       "467  2021-11-30   IRBR3         4.31        4.02 -1345.71\n",
       "468  2021-11-30   LWSA3        13.19       13.16    45.49\n",
       "469  2021-11-30   DMMO3         0.61        0.52  2950.82\n",
       "470  2021-11-30   BIDI4        11.95        9.54  4033.47\n",
       "471  2021-11-30   HAPV3        10.91       10.38   971.59\n",
       "\n",
       "[472 rows x 5 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final = pd.DataFrame()\n",
    "i = 0\n",
    "\n",
    "for ultimo_dia in df_date[:-1]:\n",
    "    \n",
    "    df_dia = pd.DataFrame()\n",
    "    invest = 20000\n",
    "    dia = []\n",
    "\n",
    "    for dia_atual in range(8):\n",
    "        dia.append(ultimo_dia)\n",
    "    \n",
    "\n",
    "    #Colunas\n",
    "\n",
    "    df_dia['data'] = dia \n",
    "    df_dia['tickers'] = get_tickers(nlarg(i))+get_tickers(nsmall(i))\n",
    "    df_dia['close_begin'] = lclose_price(i).tolist()+ sclose_price(i).tolist()\n",
    "    df_dia['close_last'] = lclose_price_shift(i).tolist()+ sclose_price_shift(i).tolist()\n",
    "    df_dia['result'] =  profit_long(invest, i).tolist() + profit_short(invest, i).tolist()\n",
    "    df_final = df_final.append(df_dia, ignore_index=True)    \n",
    "    i = i+1\n",
    "    \n",
    "  \n",
    "df_final   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-11T23:03:44.876696Z",
     "start_time": "2022-03-11T23:03:44.846691Z"
    }
   },
   "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",
       "        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></th>\n",
       "      <th>close_begin</th>\n",
       "      <th>close_last</th>\n",
       "      <th>result</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>data</th>\n",
       "      <th>tickers</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2017-01-31</th>\n",
       "      <th>MGLU3</th>\n",
       "      <td>0.49</td>\n",
       "      <td>0.79</td>\n",
       "      <td>12244.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BRML3</th>\n",
       "      <td>11.82</td>\n",
       "      <td>12.02</td>\n",
       "      <td>338.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CMIG4</th>\n",
       "      <td>5.82</td>\n",
       "      <td>6.80</td>\n",
       "      <td>3367.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>USIM5</th>\n",
       "      <td>4.73</td>\n",
       "      <td>4.44</td>\n",
       "      <td>-1226.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COGN3</th>\n",
       "      <td>12.55</td>\n",
       "      <td>12.70</td>\n",
       "      <td>-239.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2021-11-30</th>\n",
       "      <th>IRBR3</th>\n",
       "      <td>4.31</td>\n",
       "      <td>4.02</td>\n",
       "      <td>-1345.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LWSA3</th>\n",
       "      <td>13.19</td>\n",
       "      <td>13.16</td>\n",
       "      <td>45.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DMMO3</th>\n",
       "      <td>0.61</td>\n",
       "      <td>0.52</td>\n",
       "      <td>2950.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BIDI4</th>\n",
       "      <td>11.95</td>\n",
       "      <td>9.54</td>\n",
       "      <td>4033.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HAPV3</th>\n",
       "      <td>10.91</td>\n",
       "      <td>10.38</td>\n",
       "      <td>971.59</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>472 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    close_begin  close_last   result\n",
       "data       tickers                                  \n",
       "2017-01-31 MGLU3           0.49        0.79 12244.90\n",
       "           BRML3          11.82       12.02   338.41\n",
       "           CMIG4           5.82        6.80  3367.70\n",
       "           USIM5           4.73        4.44 -1226.22\n",
       "           COGN3          12.55       12.70  -239.04\n",
       "...                         ...         ...      ...\n",
       "2021-11-30 IRBR3           4.31        4.02 -1345.71\n",
       "           LWSA3          13.19       13.16    45.49\n",
       "           DMMO3           0.61        0.52  2950.82\n",
       "           BIDI4          11.95        9.54  4033.47\n",
       "           HAPV3          10.91       10.38   971.59\n",
       "\n",
       "[472 rows x 3 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final.set_index(['data','tickers'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-11T23:03:44.892700Z",
     "start_time": "2022-03-11T23:03:44.877698Z"
    }
   },
   "outputs": [],
   "source": [
    "# df_final.to_csv('b3_2017-21_df_final_vlast.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Storytelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-11T23:03:44.908705Z",
     "start_time": "2022-03-11T23:03:44.893701Z"
    }
   },
   "outputs": [],
   "source": [
    "df2 = df_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-11T23:03:44.924718Z",
     "start_time": "2022-03-11T23:03:44.909710Z"
    }
   },
   "outputs": [],
   "source": [
    "invest2 = 160000\n",
    "df2['return_pct'] = (df2.result / invest2) * 100 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-11T23:03:44.940722Z",
     "start_time": "2022-03-11T23:03:44.926720Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>data</th>\n",
       "      <th>tickers</th>\n",
       "      <th>close_begin</th>\n",
       "      <th>close_last</th>\n",
       "      <th>result</th>\n",
       "      <th>return_pct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-01-31</td>\n",
       "      <td>MGLU3</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.79</td>\n",
       "      <td>12244.90</td>\n",
       "      <td>7.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-01-31</td>\n",
       "      <td>BRML3</td>\n",
       "      <td>11.82</td>\n",
       "      <td>12.02</td>\n",
       "      <td>338.41</td>\n",
       "      <td>0.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-01-31</td>\n",
       "      <td>CMIG4</td>\n",
       "      <td>5.82</td>\n",
       "      <td>6.80</td>\n",
       "      <td>3367.70</td>\n",
       "      <td>2.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-01-31</td>\n",
       "      <td>USIM5</td>\n",
       "      <td>4.73</td>\n",
       "      <td>4.44</td>\n",
       "      <td>-1226.22</td>\n",
       "      <td>-0.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-01-31</td>\n",
       "      <td>COGN3</td>\n",
       "      <td>12.55</td>\n",
       "      <td>12.70</td>\n",
       "      <td>-239.04</td>\n",
       "      <td>-0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>467</th>\n",
       "      <td>2021-11-30</td>\n",
       "      <td>IRBR3</td>\n",
       "      <td>4.31</td>\n",
       "      <td>4.02</td>\n",
       "      <td>-1345.71</td>\n",
       "      <td>-0.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>468</th>\n",
       "      <td>2021-11-30</td>\n",
       "      <td>LWSA3</td>\n",
       "      <td>13.19</td>\n",
       "      <td>13.16</td>\n",
       "      <td>45.49</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>469</th>\n",
       "      <td>2021-11-30</td>\n",
       "      <td>DMMO3</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.52</td>\n",
       "      <td>2950.82</td>\n",
       "      <td>1.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>470</th>\n",
       "      <td>2021-11-30</td>\n",
       "      <td>BIDI4</td>\n",
       "      <td>11.95</td>\n",
       "      <td>9.54</td>\n",
       "      <td>4033.47</td>\n",
       "      <td>2.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>471</th>\n",
       "      <td>2021-11-30</td>\n",
       "      <td>HAPV3</td>\n",
       "      <td>10.91</td>\n",
       "      <td>10.38</td>\n",
       "      <td>971.59</td>\n",
       "      <td>0.61</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>472 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           data tickers  close_begin  close_last   result  return_pct\n",
       "0    2017-01-31   MGLU3         0.49        0.79 12244.90        7.65\n",
       "1    2017-01-31   BRML3        11.82       12.02   338.41        0.21\n",
       "2    2017-01-31   CMIG4         5.82        6.80  3367.70        2.10\n",
       "3    2017-01-31   USIM5         4.73        4.44 -1226.22       -0.77\n",
       "4    2017-01-31   COGN3        12.55       12.70  -239.04       -0.15\n",
       "..          ...     ...          ...         ...      ...         ...\n",
       "467  2021-11-30   IRBR3         4.31        4.02 -1345.71       -0.84\n",
       "468  2021-11-30   LWSA3        13.19       13.16    45.49        0.03\n",
       "469  2021-11-30   DMMO3         0.61        0.52  2950.82        1.84\n",
       "470  2021-11-30   BIDI4        11.95        9.54  4033.47        2.52\n",
       "471  2021-11-30   HAPV3        10.91       10.38   971.59        0.61\n",
       "\n",
       "[472 rows x 6 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### What's the mensal result?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-11T23:03:44.956726Z",
     "start_time": "2022-03-11T23:03:44.941723Z"
    }
   },
   "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>data</th>\n",
       "      <th>result</th>\n",
       "      <th>return_pct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-01-31</td>\n",
       "      <td>12928.61</td>\n",
       "      <td>8.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-02-24</td>\n",
       "      <td>-1417.92</td>\n",
       "      <td>-0.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-03-31</td>\n",
       "      <td>-15607.89</td>\n",
       "      <td>-9.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-04-28</td>\n",
       "      <td>-1517.99</td>\n",
       "      <td>-0.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-05-31</td>\n",
       "      <td>-5452.50</td>\n",
       "      <td>-3.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2017-06-30</td>\n",
       "      <td>-9151.96</td>\n",
       "      <td>-5.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2017-07-31</td>\n",
       "      <td>1176.25</td>\n",
       "      <td>0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2017-08-31</td>\n",
       "      <td>-360.23</td>\n",
       "      <td>-0.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2017-09-29</td>\n",
       "      <td>-10861.24</td>\n",
       "      <td>-6.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2017-10-31</td>\n",
       "      <td>1628.07</td>\n",
       "      <td>1.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2017-11-30</td>\n",
       "      <td>4635.15</td>\n",
       "      <td>2.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2017-12-28</td>\n",
       "      <td>6269.91</td>\n",
       "      <td>3.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2018-01-31</td>\n",
       "      <td>7523.19</td>\n",
       "      <td>4.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2018-02-28</td>\n",
       "      <td>2932.29</td>\n",
       "      <td>1.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2018-03-29</td>\n",
       "      <td>-95.34</td>\n",
       "      <td>-0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2018-04-30</td>\n",
       "      <td>-4152.62</td>\n",
       "      <td>-2.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2018-05-30</td>\n",
       "      <td>5706.53</td>\n",
       "      <td>3.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2018-06-29</td>\n",
       "      <td>3753.22</td>\n",
       "      <td>2.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2018-07-31</td>\n",
       "      <td>1008.74</td>\n",
       "      <td>0.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2018-08-31</td>\n",
       "      <td>-5128.39</td>\n",
       "      <td>-3.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2018-09-28</td>\n",
       "      <td>-1341.48</td>\n",
       "      <td>-0.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2018-10-31</td>\n",
       "      <td>-16827.72</td>\n",
       "      <td>-10.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2018-11-30</td>\n",
       "      <td>-8662.59</td>\n",
       "      <td>-5.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2018-12-28</td>\n",
       "      <td>-2209.41</td>\n",
       "      <td>-1.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2019-01-31</td>\n",
       "      <td>-12264.07</td>\n",
       "      <td>-7.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2019-02-28</td>\n",
       "      <td>3422.40</td>\n",
       "      <td>2.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2019-03-29</td>\n",
       "      <td>600.89</td>\n",
       "      <td>0.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2019-04-30</td>\n",
       "      <td>12652.73</td>\n",
       "      <td>7.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2019-05-31</td>\n",
       "      <td>1596.68</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2019-06-28</td>\n",
       "      <td>3787.57</td>\n",
       "      <td>2.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2019-07-31</td>\n",
       "      <td>2063.67</td>\n",
       "      <td>1.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2019-08-30</td>\n",
       "      <td>-1573.90</td>\n",
       "      <td>-0.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2019-09-30</td>\n",
       "      <td>2750.68</td>\n",
       "      <td>1.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2019-10-31</td>\n",
       "      <td>11032.62</td>\n",
       "      <td>6.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2019-11-29</td>\n",
       "      <td>11989.87</td>\n",
       "      <td>7.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2019-12-30</td>\n",
       "      <td>-4760.82</td>\n",
       "      <td>-2.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>2020-01-31</td>\n",
       "      <td>-2432.67</td>\n",
       "      <td>-1.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2020-02-28</td>\n",
       "      <td>15309.43</td>\n",
       "      <td>9.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2020-03-31</td>\n",
       "      <td>4557.77</td>\n",
       "      <td>2.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>2020-04-30</td>\n",
       "      <td>-3982.59</td>\n",
       "      <td>-2.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>2020-05-29</td>\n",
       "      <td>-12765.66</td>\n",
       "      <td>-7.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>2404.94</td>\n",
       "      <td>1.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>2020-07-31</td>\n",
       "      <td>13543.83</td>\n",
       "      <td>8.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>2020-08-31</td>\n",
       "      <td>-3238.65</td>\n",
       "      <td>-2.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>2020-09-30</td>\n",
       "      <td>13780.55</td>\n",
       "      <td>8.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>2020-10-30</td>\n",
       "      <td>7723.71</td>\n",
       "      <td>4.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>2020-11-30</td>\n",
       "      <td>6637.25</td>\n",
       "      <td>4.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>2020-12-30</td>\n",
       "      <td>6758.86</td>\n",
       "      <td>4.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>2021-01-29</td>\n",
       "      <td>-2617.05</td>\n",
       "      <td>-1.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>2021-02-26</td>\n",
       "      <td>-10047.82</td>\n",
       "      <td>-6.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>2021-03-31</td>\n",
       "      <td>5472.98</td>\n",
       "      <td>3.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>2021-04-30</td>\n",
       "      <td>4101.97</td>\n",
       "      <td>2.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>2021-05-31</td>\n",
       "      <td>3308.67</td>\n",
       "      <td>2.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>2021-06-30</td>\n",
       "      <td>8012.15</td>\n",
       "      <td>5.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>2021-07-30</td>\n",
       "      <td>4913.61</td>\n",
       "      <td>3.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>2021-08-31</td>\n",
       "      <td>12572.00</td>\n",
       "      <td>7.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>2021-09-30</td>\n",
       "      <td>-6843.71</td>\n",
       "      <td>-4.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>2021-10-29</td>\n",
       "      <td>-2377.01</td>\n",
       "      <td>-1.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>2021-11-30</td>\n",
       "      <td>3655.40</td>\n",
       "      <td>2.28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          data    result  return_pct\n",
       "0   2017-01-31  12928.61        8.08\n",
       "1   2017-02-24  -1417.92       -0.89\n",
       "2   2017-03-31 -15607.89       -9.75\n",
       "3   2017-04-28  -1517.99       -0.95\n",
       "4   2017-05-31  -5452.50       -3.41\n",
       "5   2017-06-30  -9151.96       -5.72\n",
       "6   2017-07-31   1176.25        0.74\n",
       "7   2017-08-31   -360.23       -0.23\n",
       "8   2017-09-29 -10861.24       -6.79\n",
       "9   2017-10-31   1628.07        1.02\n",
       "10  2017-11-30   4635.15        2.90\n",
       "11  2017-12-28   6269.91        3.92\n",
       "12  2018-01-31   7523.19        4.70\n",
       "13  2018-02-28   2932.29        1.83\n",
       "14  2018-03-29    -95.34       -0.06\n",
       "15  2018-04-30  -4152.62       -2.60\n",
       "16  2018-05-30   5706.53        3.57\n",
       "17  2018-06-29   3753.22        2.35\n",
       "18  2018-07-31   1008.74        0.63\n",
       "19  2018-08-31  -5128.39       -3.21\n",
       "20  2018-09-28  -1341.48       -0.84\n",
       "21  2018-10-31 -16827.72      -10.52\n",
       "22  2018-11-30  -8662.59       -5.41\n",
       "23  2018-12-28  -2209.41       -1.38\n",
       "24  2019-01-31 -12264.07       -7.67\n",
       "25  2019-02-28   3422.40        2.14\n",
       "26  2019-03-29    600.89        0.38\n",
       "27  2019-04-30  12652.73        7.91\n",
       "28  2019-05-31   1596.68        1.00\n",
       "29  2019-06-28   3787.57        2.37\n",
       "30  2019-07-31   2063.67        1.29\n",
       "31  2019-08-30  -1573.90       -0.98\n",
       "32  2019-09-30   2750.68        1.72\n",
       "33  2019-10-31  11032.62        6.90\n",
       "34  2019-11-29  11989.87        7.49\n",
       "35  2019-12-30  -4760.82       -2.98\n",
       "36  2020-01-31  -2432.67       -1.52\n",
       "37  2020-02-28  15309.43        9.57\n",
       "38  2020-03-31   4557.77        2.85\n",
       "39  2020-04-30  -3982.59       -2.49\n",
       "40  2020-05-29 -12765.66       -7.98\n",
       "41  2020-06-30   2404.94        1.50\n",
       "42  2020-07-31  13543.83        8.46\n",
       "43  2020-08-31  -3238.65       -2.02\n",
       "44  2020-09-30  13780.55        8.61\n",
       "45  2020-10-30   7723.71        4.83\n",
       "46  2020-11-30   6637.25        4.15\n",
       "47  2020-12-30   6758.86        4.22\n",
       "48  2021-01-29  -2617.05       -1.64\n",
       "49  2021-02-26 -10047.82       -6.28\n",
       "50  2021-03-31   5472.98        3.42\n",
       "51  2021-04-30   4101.97        2.56\n",
       "52  2021-05-31   3308.67        2.07\n",
       "53  2021-06-30   8012.15        5.01\n",
       "54  2021-07-30   4913.61        3.07\n",
       "55  2021-08-31  12572.00        7.86\n",
       "56  2021-09-30  -6843.71       -4.28\n",
       "57  2021-10-29  -2377.01       -1.49\n",
       "58  2021-11-30   3655.40        2.28"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mensal_result = df2.groupby('data').sum().drop(labels=['close_begin', 'close_last'], axis=1)\n",
    "mensal_result.reset_index(inplace=True)\n",
    "mensal_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-11T23:03:46.519097Z",
     "start_time": "2022-03-11T23:03:44.957726Z"
    }
   },
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 2592x1296 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Bars\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "#plt.figure(figsize=(36, 18))\n",
    "plt.gcf().set_size_inches(36, 18)\n",
    "sns.set_theme(style=\"darkgrid\")\n",
    "plot = sns.barplot(x='data', y='result', data = mensal_result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Best and worst month?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-11T23:03:46.535084Z",
     "start_time": "2022-03-11T23:03:46.521081Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data</th>\n",
       "      <th>result</th>\n",
       "      <th>return_pct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2020-02-28</td>\n",
       "      <td>15309.43</td>\n",
       "      <td>9.57</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          data   result  return_pct\n",
       "37  2020-02-28 15309.43        9.57"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_result = mensal_result.sort_values(by='result', ascending=False).head(1)\n",
    "best_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-11T23:03:46.551085Z",
     "start_time": "2022-03-11T23:03:46.537083Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data</th>\n",
       "      <th>result</th>\n",
       "      <th>return_pct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2018-10-31</td>\n",
       "      <td>-16827.72</td>\n",
       "      <td>-10.52</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          data    result  return_pct\n",
       "21  2018-10-31 -16827.72      -10.52"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "worse_result = mensal_result.sort_values(by='result', ascending=False).tail(1)\n",
    "worse_result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Whats the mean return?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-11T23:03:46.567089Z",
     "start_time": "2022-03-11T23:03:46.552089Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Atos\\AppData\\Local\\Temp/ipykernel_14168/1660535763.py:1: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  mensal_result.mean()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "result       1093.58\n",
       "return_pct      0.68\n",
       "dtype: float64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mensal_result.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Most negotiated stocks?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-11T23:03:46.583093Z",
     "start_time": "2022-03-11T23:03:46.568090Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VVAR3     24\n",
       "BIDI4     23\n",
       "DMMO3     23\n",
       "MGLU3     20\n",
       "IRBR3     20\n",
       "CIEL3     19\n",
       "PRIO3     18\n",
       "SULA11    16\n",
       "CSNA3     16\n",
       "AZUL4     16\n",
       "COGN3     14\n",
       "SAPR4     14\n",
       "JBSS3     13\n",
       "USIM5     13\n",
       "ENEV3     13\n",
       "VALE3     12\n",
       "BRAP4     12\n",
       "TOTS3     11\n",
       "CCRO3     11\n",
       "CMIG4     11\n",
       "BRFS3     11\n",
       "LAME4     11\n",
       "NTCO3     11\n",
       "CPLE6     11\n",
       "BRDT3     10\n",
       "LAME3     10\n",
       "BPAC11     8\n",
       "BBDC4      8\n",
       "CSAN3      8\n",
       "ITSA4      8\n",
       "BRML3      7\n",
       "EMBR3      7\n",
       "PETR4      7\n",
       "ABEV3      7\n",
       "LWSA3      7\n",
       "HAPV3      6\n",
       "PETR3      5\n",
       "BBAS3      5\n",
       "ITUB4      5\n",
       "NEOE3      1\n",
       "Name: tickers, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.reset_index().tickers.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### PL Curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-11T23:03:46.599096Z",
     "start_time": "2022-03-11T23:03:46.584093Z"
    }
   },
   "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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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data</th>\n",
       "      <th>tickers</th>\n",
       "      <th>close_begin</th>\n",
       "      <th>close_last</th>\n",
       "      <th>result</th>\n",
       "      <th>return_pct</th>\n",
       "      <th>Equity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-01-31</td>\n",
       "      <td>MGLU3</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.79</td>\n",
       "      <td>12244.90</td>\n",
       "      <td>7.65</td>\n",
       "      <td>172244.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-01-31</td>\n",
       "      <td>BRML3</td>\n",
       "      <td>11.82</td>\n",
       "      <td>12.02</td>\n",
       "      <td>338.41</td>\n",
       "      <td>0.21</td>\n",
       "      <td>172583.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-01-31</td>\n",
       "      <td>CMIG4</td>\n",
       "      <td>5.82</td>\n",
       "      <td>6.80</td>\n",
       "      <td>3367.70</td>\n",
       "      <td>2.10</td>\n",
       "      <td>175951.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-01-31</td>\n",
       "      <td>USIM5</td>\n",
       "      <td>4.73</td>\n",
       "      <td>4.44</td>\n",
       "      <td>-1226.22</td>\n",
       "      <td>-0.77</td>\n",
       "      <td>174724.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-01-31</td>\n",
       "      <td>COGN3</td>\n",
       "      <td>12.55</td>\n",
       "      <td>12.70</td>\n",
       "      <td>-239.04</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>174485.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>467</th>\n",
       "      <td>2021-11-30</td>\n",
       "      <td>IRBR3</td>\n",
       "      <td>4.31</td>\n",
       "      <td>4.02</td>\n",
       "      <td>-1345.71</td>\n",
       "      <td>-0.84</td>\n",
       "      <td>216519.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>468</th>\n",
       "      <td>2021-11-30</td>\n",
       "      <td>LWSA3</td>\n",
       "      <td>13.19</td>\n",
       "      <td>13.16</td>\n",
       "      <td>45.49</td>\n",
       "      <td>0.03</td>\n",
       "      <td>216565.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>469</th>\n",
       "      <td>2021-11-30</td>\n",
       "      <td>DMMO3</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.52</td>\n",
       "      <td>2950.82</td>\n",
       "      <td>1.84</td>\n",
       "      <td>219515.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>470</th>\n",
       "      <td>2021-11-30</td>\n",
       "      <td>BIDI4</td>\n",
       "      <td>11.95</td>\n",
       "      <td>9.54</td>\n",
       "      <td>4033.47</td>\n",
       "      <td>2.52</td>\n",
       "      <td>223549.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>471</th>\n",
       "      <td>2021-11-30</td>\n",
       "      <td>HAPV3</td>\n",
       "      <td>10.91</td>\n",
       "      <td>10.38</td>\n",
       "      <td>971.59</td>\n",
       "      <td>0.61</td>\n",
       "      <td>224520.96</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>472 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           data tickers  close_begin  close_last   result  return_pct  \\\n",
       "0    2017-01-31   MGLU3         0.49        0.79 12244.90        7.65   \n",
       "1    2017-01-31   BRML3        11.82       12.02   338.41        0.21   \n",
       "2    2017-01-31   CMIG4         5.82        6.80  3367.70        2.10   \n",
       "3    2017-01-31   USIM5         4.73        4.44 -1226.22       -0.77   \n",
       "4    2017-01-31   COGN3        12.55       12.70  -239.04       -0.15   \n",
       "..          ...     ...          ...         ...      ...         ...   \n",
       "467  2021-11-30   IRBR3         4.31        4.02 -1345.71       -0.84   \n",
       "468  2021-11-30   LWSA3        13.19       13.16    45.49        0.03   \n",
       "469  2021-11-30   DMMO3         0.61        0.52  2950.82        1.84   \n",
       "470  2021-11-30   BIDI4        11.95        9.54  4033.47        2.52   \n",
       "471  2021-11-30   HAPV3        10.91       10.38   971.59        0.61   \n",
       "\n",
       "       Equity  \n",
       "0   172244.90  \n",
       "1   172583.31  \n",
       "2   175951.01  \n",
       "3   174724.79  \n",
       "4   174485.75  \n",
       "..        ...  \n",
       "467 216519.59  \n",
       "468 216565.08  \n",
       "469 219515.90  \n",
       "470 223549.37  \n",
       "471 224520.96  \n",
       "\n",
       "[472 rows x 7 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "capital = 160000 \n",
    "df2['Equity'] = capital + df2.result.cumsum()\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-11T23:04:59.065161Z",
     "start_time": "2022-03-11T23:04:56.373547Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,6))\n",
    "sns.set_theme(style=\"darkgrid\")\n",
    "df2.reset_index() \n",
    "sns.lineplot(x='data', y='Equity', data=df2);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### What's the worst drawdown?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-11T23:05:01.454954Z",
     "start_time": "2022-03-11T23:05:01.443952Z"
    }
   },
   "outputs": [],
   "source": [
    "df2.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-11T23:05:08.423152Z",
     "start_time": "2022-03-11T23:05:08.412140Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "199   112510.62\n",
       "Name: Equity, dtype: float64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_dd = df2.Equity.sort_values(ascending=False).tail(1)\n",
    "max_dd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-11T23:05:13.923697Z",
     "start_time": "2022-03-11T23:05:13.909695Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29.68 %\n"
     ]
    }
   ],
   "source": [
    "max_dd2 = (160000 - 112510.62) / 160000 * 100\n",
    "print('{:0.2f}'.format(max_dd2),'%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-11T23:05:19.229964Z",
     "start_time": "2022-03-11T23:05:19.212961Z"
    }
   },
   "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>data</th>\n",
       "      <th>tickers</th>\n",
       "      <th>close_begin</th>\n",
       "      <th>close_last</th>\n",
       "      <th>result</th>\n",
       "      <th>return_pct</th>\n",
       "      <th>Equity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>2019-01-31</td>\n",
       "      <td>NTCO3</td>\n",
       "      <td>23.57</td>\n",
       "      <td>24.11</td>\n",
       "      <td>-458.21</td>\n",
       "      <td>-0.29</td>\n",
       "      <td>112510.62</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           data tickers  close_begin  close_last  result  return_pct    Equity\n",
       "199  2019-01-31   NTCO3        23.57       24.11 -458.21       -0.29 112510.62"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.sort_values(by='Equity', ascending=True).head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### What's the highest PL moment?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-11T23:05:22.920310Z",
     "start_time": "2022-03-11T23:05:22.900306Z"
    }
   },
   "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>data</th>\n",
       "      <th>tickers</th>\n",
       "      <th>close_begin</th>\n",
       "      <th>close_last</th>\n",
       "      <th>result</th>\n",
       "      <th>return_pct</th>\n",
       "      <th>Equity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>447</th>\n",
       "      <td>2021-08-31</td>\n",
       "      <td>MGLU3</td>\n",
       "      <td>18.24</td>\n",
       "      <td>14.34</td>\n",
       "      <td>4276.32</td>\n",
       "      <td>2.67</td>\n",
       "      <td>230086.28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           data tickers  close_begin  close_last  result  return_pct    Equity\n",
       "447  2021-08-31   MGLU3        18.24       14.34 4276.32        2.67 230086.28"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.sort_values(by='Equity', ascending=False).head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-11T23:05:26.576331Z",
     "start_time": "2022-03-11T23:05:26.564332Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "43.80 %\n"
     ]
    }
   ],
   "source": [
    "top_pl = (230086.28 - 160000) / 160000 * 100\n",
    "print('{:0.2f}'.format(top_pl),'%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### What's the Sharpe-Ratio?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-11T23:05:50.561982Z",
     "start_time": "2022-03-11T23:05:50.540977Z"
    }
   },
   "outputs": [],
   "source": [
    "selic = pd.read_csv('C:/')\n",
    "selic.drop(0, axis=0, inplace= True)\n",
    "selic.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-11T23:06:05.706631Z",
     "start_time": "2022-03-11T23:06:05.684636Z"
    }
   },
   "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>data</th>\n",
       "      <th>result</th>\n",
       "      <th>return_pct</th>\n",
       "      <th>Equity</th>\n",
       "      <th>selic_M</th>\n",
       "      <th>return_selic</th>\n",
       "      <th>return_pct_selic</th>\n",
       "      <th>Equity_selic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-01-31</td>\n",
       "      <td>12928.61</td>\n",
       "      <td>8.08</td>\n",
       "      <td>172928.61</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1384.13</td>\n",
       "      <td>0.87</td>\n",
       "      <td>161384.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-02-24</td>\n",
       "      <td>-1417.92</td>\n",
       "      <td>-0.89</td>\n",
       "      <td>171510.68</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1683.29</td>\n",
       "      <td>1.05</td>\n",
       "      <td>163067.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-03-31</td>\n",
       "      <td>-15607.89</td>\n",
       "      <td>-9.75</td>\n",
       "      <td>155902.79</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1258.53</td>\n",
       "      <td>0.79</td>\n",
       "      <td>164325.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-04-28</td>\n",
       "      <td>-1517.99</td>\n",
       "      <td>-0.95</td>\n",
       "      <td>154384.80</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1483.41</td>\n",
       "      <td>0.93</td>\n",
       "      <td>165809.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-05-31</td>\n",
       "      <td>-5452.50</td>\n",
       "      <td>-3.41</td>\n",
       "      <td>148932.30</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1294.19</td>\n",
       "      <td>0.81</td>\n",
       "      <td>167103.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2017-06-30</td>\n",
       "      <td>-9151.96</td>\n",
       "      <td>-5.72</td>\n",
       "      <td>139780.34</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1276.68</td>\n",
       "      <td>0.80</td>\n",
       "      <td>168380.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2017-07-31</td>\n",
       "      <td>1176.25</td>\n",
       "      <td>0.74</td>\n",
       "      <td>140956.59</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1283.66</td>\n",
       "      <td>0.80</td>\n",
       "      <td>169663.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2017-08-31</td>\n",
       "      <td>-360.23</td>\n",
       "      <td>-0.23</td>\n",
       "      <td>140596.36</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1021.54</td>\n",
       "      <td>0.64</td>\n",
       "      <td>170685.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2017-09-29</td>\n",
       "      <td>-10861.24</td>\n",
       "      <td>-6.79</td>\n",
       "      <td>129735.12</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1030.29</td>\n",
       "      <td>0.64</td>\n",
       "      <td>171715.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2017-10-31</td>\n",
       "      <td>1628.07</td>\n",
       "      <td>1.02</td>\n",
       "      <td>131363.20</td>\n",
       "      <td>0.01</td>\n",
       "      <td>909.10</td>\n",
       "      <td>0.57</td>\n",
       "      <td>172624.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2017-11-30</td>\n",
       "      <td>4635.15</td>\n",
       "      <td>2.90</td>\n",
       "      <td>135998.35</td>\n",
       "      <td>0.01</td>\n",
       "      <td>861.44</td>\n",
       "      <td>0.54</td>\n",
       "      <td>173486.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2017-12-28</td>\n",
       "      <td>6269.91</td>\n",
       "      <td>3.92</td>\n",
       "      <td>142268.26</td>\n",
       "      <td>0.01</td>\n",
       "      <td>934.73</td>\n",
       "      <td>0.58</td>\n",
       "      <td>174420.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2018-01-31</td>\n",
       "      <td>7523.19</td>\n",
       "      <td>4.70</td>\n",
       "      <td>149791.45</td>\n",
       "      <td>0.00</td>\n",
       "      <td>744.96</td>\n",
       "      <td>0.47</td>\n",
       "      <td>175165.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2018-02-28</td>\n",
       "      <td>2932.29</td>\n",
       "      <td>1.83</td>\n",
       "      <td>152723.74</td>\n",
       "      <td>0.01</td>\n",
       "      <td>851.75</td>\n",
       "      <td>0.53</td>\n",
       "      <td>176017.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2018-03-29</td>\n",
       "      <td>-95.34</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>152628.40</td>\n",
       "      <td>0.01</td>\n",
       "      <td>829.27</td>\n",
       "      <td>0.52</td>\n",
       "      <td>176846.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2018-04-30</td>\n",
       "      <td>-4152.62</td>\n",
       "      <td>-2.60</td>\n",
       "      <td>148475.78</td>\n",
       "      <td>0.01</td>\n",
       "      <td>829.27</td>\n",
       "      <td>0.52</td>\n",
       "      <td>177676.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2018-05-30</td>\n",
       "      <td>5706.53</td>\n",
       "      <td>3.57</td>\n",
       "      <td>154182.31</td>\n",
       "      <td>0.01</td>\n",
       "      <td>829.27</td>\n",
       "      <td>0.52</td>\n",
       "      <td>178505.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2018-06-29</td>\n",
       "      <td>3753.22</td>\n",
       "      <td>2.35</td>\n",
       "      <td>157935.54</td>\n",
       "      <td>0.01</td>\n",
       "      <td>868.87</td>\n",
       "      <td>0.54</td>\n",
       "      <td>179374.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2018-07-31</td>\n",
       "      <td>1008.74</td>\n",
       "      <td>0.63</td>\n",
       "      <td>158944.28</td>\n",
       "      <td>0.01</td>\n",
       "      <td>908.47</td>\n",
       "      <td>0.57</td>\n",
       "      <td>180282.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2018-08-31</td>\n",
       "      <td>-5128.39</td>\n",
       "      <td>-3.21</td>\n",
       "      <td>153815.89</td>\n",
       "      <td>0.00</td>\n",
       "      <td>750.11</td>\n",
       "      <td>0.47</td>\n",
       "      <td>181032.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2018-09-28</td>\n",
       "      <td>-1341.48</td>\n",
       "      <td>-0.84</td>\n",
       "      <td>152474.41</td>\n",
       "      <td>0.01</td>\n",
       "      <td>868.87</td>\n",
       "      <td>0.54</td>\n",
       "      <td>181901.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2018-10-31</td>\n",
       "      <td>-16827.72</td>\n",
       "      <td>-10.52</td>\n",
       "      <td>135646.69</td>\n",
       "      <td>0.00</td>\n",
       "      <td>789.68</td>\n",
       "      <td>0.49</td>\n",
       "      <td>182691.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2018-11-30</td>\n",
       "      <td>-8662.59</td>\n",
       "      <td>-5.41</td>\n",
       "      <td>126984.10</td>\n",
       "      <td>0.00</td>\n",
       "      <td>789.68</td>\n",
       "      <td>0.49</td>\n",
       "      <td>183481.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2018-12-28</td>\n",
       "      <td>-2209.41</td>\n",
       "      <td>-1.38</td>\n",
       "      <td>124774.69</td>\n",
       "      <td>0.01</td>\n",
       "      <td>868.87</td>\n",
       "      <td>0.54</td>\n",
       "      <td>184350.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2019-01-31</td>\n",
       "      <td>-12264.07</td>\n",
       "      <td>-7.67</td>\n",
       "      <td>112510.62</td>\n",
       "      <td>0.00</td>\n",
       "      <td>789.68</td>\n",
       "      <td>0.49</td>\n",
       "      <td>185139.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2019-02-28</td>\n",
       "      <td>3422.40</td>\n",
       "      <td>2.14</td>\n",
       "      <td>115933.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>750.11</td>\n",
       "      <td>0.47</td>\n",
       "      <td>185889.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2019-03-29</td>\n",
       "      <td>600.89</td>\n",
       "      <td>0.38</td>\n",
       "      <td>116533.91</td>\n",
       "      <td>0.01</td>\n",
       "      <td>829.27</td>\n",
       "      <td>0.52</td>\n",
       "      <td>186719.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2019-04-30</td>\n",
       "      <td>12652.73</td>\n",
       "      <td>7.91</td>\n",
       "      <td>129186.63</td>\n",
       "      <td>0.01</td>\n",
       "      <td>868.87</td>\n",
       "      <td>0.54</td>\n",
       "      <td>187588.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2019-05-31</td>\n",
       "      <td>1596.68</td>\n",
       "      <td>1.00</td>\n",
       "      <td>130783.31</td>\n",
       "      <td>0.00</td>\n",
       "      <td>750.11</td>\n",
       "      <td>0.47</td>\n",
       "      <td>188338.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2019-06-28</td>\n",
       "      <td>3787.57</td>\n",
       "      <td>2.37</td>\n",
       "      <td>134570.88</td>\n",
       "      <td>0.01</td>\n",
       "      <td>908.47</td>\n",
       "      <td>0.57</td>\n",
       "      <td>189246.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2019-07-31</td>\n",
       "      <td>2063.67</td>\n",
       "      <td>1.29</td>\n",
       "      <td>136634.56</td>\n",
       "      <td>0.01</td>\n",
       "      <td>802.75</td>\n",
       "      <td>0.50</td>\n",
       "      <td>190049.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2019-08-30</td>\n",
       "      <td>-1573.90</td>\n",
       "      <td>-0.98</td>\n",
       "      <td>135060.66</td>\n",
       "      <td>0.00</td>\n",
       "      <td>742.02</td>\n",
       "      <td>0.46</td>\n",
       "      <td>190791.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2019-09-30</td>\n",
       "      <td>2750.68</td>\n",
       "      <td>1.72</td>\n",
       "      <td>137811.33</td>\n",
       "      <td>0.00</td>\n",
       "      <td>766.82</td>\n",
       "      <td>0.48</td>\n",
       "      <td>191558.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2019-10-31</td>\n",
       "      <td>11032.62</td>\n",
       "      <td>6.90</td>\n",
       "      <td>148843.95</td>\n",
       "      <td>0.00</td>\n",
       "      <td>608.62</td>\n",
       "      <td>0.38</td>\n",
       "      <td>192166.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2019-11-29</td>\n",
       "      <td>11989.87</td>\n",
       "      <td>7.49</td>\n",
       "      <td>160833.82</td>\n",
       "      <td>0.00</td>\n",
       "      <td>599.53</td>\n",
       "      <td>0.37</td>\n",
       "      <td>192766.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2019-12-30</td>\n",
       "      <td>-4760.82</td>\n",
       "      <td>-2.98</td>\n",
       "      <td>156073.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>602.61</td>\n",
       "      <td>0.38</td>\n",
       "      <td>193368.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>2020-01-31</td>\n",
       "      <td>-2432.67</td>\n",
       "      <td>-1.52</td>\n",
       "      <td>153640.34</td>\n",
       "      <td>0.00</td>\n",
       "      <td>469.97</td>\n",
       "      <td>0.29</td>\n",
       "      <td>193838.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2020-02-28</td>\n",
       "      <td>15309.43</td>\n",
       "      <td>9.57</td>\n",
       "      <td>168949.77</td>\n",
       "      <td>0.00</td>\n",
       "      <td>541.39</td>\n",
       "      <td>0.34</td>\n",
       "      <td>194380.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2020-03-31</td>\n",
       "      <td>4557.77</td>\n",
       "      <td>2.85</td>\n",
       "      <td>173507.53</td>\n",
       "      <td>0.00</td>\n",
       "      <td>455.88</td>\n",
       "      <td>0.28</td>\n",
       "      <td>194836.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>2020-04-30</td>\n",
       "      <td>-3982.59</td>\n",
       "      <td>-2.49</td>\n",
       "      <td>169524.94</td>\n",
       "      <td>0.00</td>\n",
       "      <td>377.30</td>\n",
       "      <td>0.24</td>\n",
       "      <td>195213.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>2020-05-29</td>\n",
       "      <td>-12765.66</td>\n",
       "      <td>-7.98</td>\n",
       "      <td>156759.28</td>\n",
       "      <td>0.00</td>\n",
       "      <td>339.73</td>\n",
       "      <td>0.21</td>\n",
       "      <td>195553.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>2404.94</td>\n",
       "      <td>1.50</td>\n",
       "      <td>159164.22</td>\n",
       "      <td>0.00</td>\n",
       "      <td>310.95</td>\n",
       "      <td>0.19</td>\n",
       "      <td>195864.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>2020-07-31</td>\n",
       "      <td>13543.83</td>\n",
       "      <td>8.46</td>\n",
       "      <td>172708.05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>255.82</td>\n",
       "      <td>0.16</td>\n",
       "      <td>196119.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>2020-08-31</td>\n",
       "      <td>-3238.65</td>\n",
       "      <td>-2.02</td>\n",
       "      <td>169469.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>251.15</td>\n",
       "      <td>0.16</td>\n",
       "      <td>196371.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>2020-09-30</td>\n",
       "      <td>13780.55</td>\n",
       "      <td>8.61</td>\n",
       "      <td>183249.95</td>\n",
       "      <td>0.00</td>\n",
       "      <td>251.15</td>\n",
       "      <td>0.16</td>\n",
       "      <td>196622.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>2020-10-30</td>\n",
       "      <td>7723.71</td>\n",
       "      <td>4.83</td>\n",
       "      <td>190973.66</td>\n",
       "      <td>0.00</td>\n",
       "      <td>239.18</td>\n",
       "      <td>0.15</td>\n",
       "      <td>196861.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>2020-11-30</td>\n",
       "      <td>6637.25</td>\n",
       "      <td>4.15</td>\n",
       "      <td>197610.91</td>\n",
       "      <td>0.00</td>\n",
       "      <td>263.12</td>\n",
       "      <td>0.16</td>\n",
       "      <td>197124.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>2020-12-30</td>\n",
       "      <td>6758.86</td>\n",
       "      <td>4.22</td>\n",
       "      <td>204369.77</td>\n",
       "      <td>0.00</td>\n",
       "      <td>239.18</td>\n",
       "      <td>0.15</td>\n",
       "      <td>197363.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>2021-01-29</td>\n",
       "      <td>-2617.05</td>\n",
       "      <td>-1.64</td>\n",
       "      <td>201752.72</td>\n",
       "      <td>0.00</td>\n",
       "      <td>215.24</td>\n",
       "      <td>0.13</td>\n",
       "      <td>197578.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>2021-02-26</td>\n",
       "      <td>-10047.82</td>\n",
       "      <td>-6.28</td>\n",
       "      <td>191704.90</td>\n",
       "      <td>0.00</td>\n",
       "      <td>321.73</td>\n",
       "      <td>0.20</td>\n",
       "      <td>197900.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>2021-03-31</td>\n",
       "      <td>5472.98</td>\n",
       "      <td>3.42</td>\n",
       "      <td>197177.88</td>\n",
       "      <td>0.00</td>\n",
       "      <td>332.46</td>\n",
       "      <td>0.21</td>\n",
       "      <td>198233.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>2021-04-30</td>\n",
       "      <td>4101.97</td>\n",
       "      <td>2.56</td>\n",
       "      <td>201279.85</td>\n",
       "      <td>0.00</td>\n",
       "      <td>432.52</td>\n",
       "      <td>0.27</td>\n",
       "      <td>198665.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>2021-05-31</td>\n",
       "      <td>3308.67</td>\n",
       "      <td>2.07</td>\n",
       "      <td>204588.51</td>\n",
       "      <td>0.00</td>\n",
       "      <td>492.45</td>\n",
       "      <td>0.31</td>\n",
       "      <td>199158.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>2021-06-30</td>\n",
       "      <td>8012.15</td>\n",
       "      <td>5.01</td>\n",
       "      <td>212600.66</td>\n",
       "      <td>0.00</td>\n",
       "      <td>568.99</td>\n",
       "      <td>0.36</td>\n",
       "      <td>199727.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>2021-07-30</td>\n",
       "      <td>4913.61</td>\n",
       "      <td>3.07</td>\n",
       "      <td>217514.27</td>\n",
       "      <td>0.00</td>\n",
       "      <td>684.72</td>\n",
       "      <td>0.43</td>\n",
       "      <td>200411.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>2021-08-31</td>\n",
       "      <td>12572.00</td>\n",
       "      <td>7.86</td>\n",
       "      <td>230086.28</td>\n",
       "      <td>0.00</td>\n",
       "      <td>707.20</td>\n",
       "      <td>0.44</td>\n",
       "      <td>201119.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>2021-09-30</td>\n",
       "      <td>-6843.71</td>\n",
       "      <td>-4.28</td>\n",
       "      <td>223242.57</td>\n",
       "      <td>0.00</td>\n",
       "      <td>777.59</td>\n",
       "      <td>0.49</td>\n",
       "      <td>201896.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>2021-10-29</td>\n",
       "      <td>-2377.01</td>\n",
       "      <td>-1.49</td>\n",
       "      <td>220865.56</td>\n",
       "      <td>0.01</td>\n",
       "      <td>938.80</td>\n",
       "      <td>0.59</td>\n",
       "      <td>202835.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>2021-11-30</td>\n",
       "      <td>3655.40</td>\n",
       "      <td>2.28</td>\n",
       "      <td>224520.96</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1230.53</td>\n",
       "      <td>0.77</td>\n",
       "      <td>204065.96</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          data    result  return_pct    Equity  selic_M  return_selic  \\\n",
       "0   2017-01-31  12928.61        8.08 172928.61     0.01       1384.13   \n",
       "1   2017-02-24  -1417.92       -0.89 171510.68     0.01       1683.29   \n",
       "2   2017-03-31 -15607.89       -9.75 155902.79     0.01       1258.53   \n",
       "3   2017-04-28  -1517.99       -0.95 154384.80     0.01       1483.41   \n",
       "4   2017-05-31  -5452.50       -3.41 148932.30     0.01       1294.19   \n",
       "5   2017-06-30  -9151.96       -5.72 139780.34     0.01       1276.68   \n",
       "6   2017-07-31   1176.25        0.74 140956.59     0.01       1283.66   \n",
       "7   2017-08-31   -360.23       -0.23 140596.36     0.01       1021.54   \n",
       "8   2017-09-29 -10861.24       -6.79 129735.12     0.01       1030.29   \n",
       "9   2017-10-31   1628.07        1.02 131363.20     0.01        909.10   \n",
       "10  2017-11-30   4635.15        2.90 135998.35     0.01        861.44   \n",
       "11  2017-12-28   6269.91        3.92 142268.26     0.01        934.73   \n",
       "12  2018-01-31   7523.19        4.70 149791.45     0.00        744.96   \n",
       "13  2018-02-28   2932.29        1.83 152723.74     0.01        851.75   \n",
       "14  2018-03-29    -95.34       -0.06 152628.40     0.01        829.27   \n",
       "15  2018-04-30  -4152.62       -2.60 148475.78     0.01        829.27   \n",
       "16  2018-05-30   5706.53        3.57 154182.31     0.01        829.27   \n",
       "17  2018-06-29   3753.22        2.35 157935.54     0.01        868.87   \n",
       "18  2018-07-31   1008.74        0.63 158944.28     0.01        908.47   \n",
       "19  2018-08-31  -5128.39       -3.21 153815.89     0.00        750.11   \n",
       "20  2018-09-28  -1341.48       -0.84 152474.41     0.01        868.87   \n",
       "21  2018-10-31 -16827.72      -10.52 135646.69     0.00        789.68   \n",
       "22  2018-11-30  -8662.59       -5.41 126984.10     0.00        789.68   \n",
       "23  2018-12-28  -2209.41       -1.38 124774.69     0.01        868.87   \n",
       "24  2019-01-31 -12264.07       -7.67 112510.62     0.00        789.68   \n",
       "25  2019-02-28   3422.40        2.14 115933.02     0.00        750.11   \n",
       "26  2019-03-29    600.89        0.38 116533.91     0.01        829.27   \n",
       "27  2019-04-30  12652.73        7.91 129186.63     0.01        868.87   \n",
       "28  2019-05-31   1596.68        1.00 130783.31     0.00        750.11   \n",
       "29  2019-06-28   3787.57        2.37 134570.88     0.01        908.47   \n",
       "30  2019-07-31   2063.67        1.29 136634.56     0.01        802.75   \n",
       "31  2019-08-30  -1573.90       -0.98 135060.66     0.00        742.02   \n",
       "32  2019-09-30   2750.68        1.72 137811.33     0.00        766.82   \n",
       "33  2019-10-31  11032.62        6.90 148843.95     0.00        608.62   \n",
       "34  2019-11-29  11989.87        7.49 160833.82     0.00        599.53   \n",
       "35  2019-12-30  -4760.82       -2.98 156073.01     0.00        602.61   \n",
       "36  2020-01-31  -2432.67       -1.52 153640.34     0.00        469.97   \n",
       "37  2020-02-28  15309.43        9.57 168949.77     0.00        541.39   \n",
       "38  2020-03-31   4557.77        2.85 173507.53     0.00        455.88   \n",
       "39  2020-04-30  -3982.59       -2.49 169524.94     0.00        377.30   \n",
       "40  2020-05-29 -12765.66       -7.98 156759.28     0.00        339.73   \n",
       "41  2020-06-30   2404.94        1.50 159164.22     0.00        310.95   \n",
       "42  2020-07-31  13543.83        8.46 172708.05     0.00        255.82   \n",
       "43  2020-08-31  -3238.65       -2.02 169469.40     0.00        251.15   \n",
       "44  2020-09-30  13780.55        8.61 183249.95     0.00        251.15   \n",
       "45  2020-10-30   7723.71        4.83 190973.66     0.00        239.18   \n",
       "46  2020-11-30   6637.25        4.15 197610.91     0.00        263.12   \n",
       "47  2020-12-30   6758.86        4.22 204369.77     0.00        239.18   \n",
       "48  2021-01-29  -2617.05       -1.64 201752.72     0.00        215.24   \n",
       "49  2021-02-26 -10047.82       -6.28 191704.90     0.00        321.73   \n",
       "50  2021-03-31   5472.98        3.42 197177.88     0.00        332.46   \n",
       "51  2021-04-30   4101.97        2.56 201279.85     0.00        432.52   \n",
       "52  2021-05-31   3308.67        2.07 204588.51     0.00        492.45   \n",
       "53  2021-06-30   8012.15        5.01 212600.66     0.00        568.99   \n",
       "54  2021-07-30   4913.61        3.07 217514.27     0.00        684.72   \n",
       "55  2021-08-31  12572.00        7.86 230086.28     0.00        707.20   \n",
       "56  2021-09-30  -6843.71       -4.28 223242.57     0.00        777.59   \n",
       "57  2021-10-29  -2377.01       -1.49 220865.56     0.01        938.80   \n",
       "58  2021-11-30   3655.40        2.28 224520.96     0.01       1230.53   \n",
       "\n",
       "    return_pct_selic  Equity_selic  \n",
       "0               0.87     161384.13  \n",
       "1               1.05     163067.42  \n",
       "2               0.79     164325.95  \n",
       "3               0.93     165809.36  \n",
       "4               0.81     167103.56  \n",
       "5               0.80     168380.23  \n",
       "6               0.80     169663.89  \n",
       "7               0.64     170685.43  \n",
       "8               0.64     171715.72  \n",
       "9               0.57     172624.82  \n",
       "10              0.54     173486.26  \n",
       "11              0.58     174420.99  \n",
       "12              0.47     175165.95  \n",
       "13              0.53     176017.70  \n",
       "14              0.52     176846.97  \n",
       "15              0.52     177676.25  \n",
       "16              0.52     178505.52  \n",
       "17              0.54     179374.39  \n",
       "18              0.57     180282.86  \n",
       "19              0.47     181032.97  \n",
       "20              0.54     181901.84  \n",
       "21              0.49     182691.52  \n",
       "22              0.49     183481.20  \n",
       "23              0.54     184350.07  \n",
       "24              0.49     185139.76  \n",
       "25              0.47     185889.87  \n",
       "26              0.52     186719.14  \n",
       "27              0.54     187588.00  \n",
       "28              0.47     188338.11  \n",
       "29              0.57     189246.59  \n",
       "30              0.50     190049.34  \n",
       "31              0.46     190791.35  \n",
       "32              0.48     191558.18  \n",
       "33              0.38     192166.79  \n",
       "34              0.37     192766.32  \n",
       "35              0.38     193368.93  \n",
       "36              0.29     193838.90  \n",
       "37              0.34     194380.29  \n",
       "38              0.28     194836.17  \n",
       "39              0.24     195213.47  \n",
       "40              0.21     195553.20  \n",
       "41              0.19     195864.15  \n",
       "42              0.16     196119.97  \n",
       "43              0.16     196371.12  \n",
       "44              0.16     196622.27  \n",
       "45              0.15     196861.44  \n",
       "46              0.16     197124.56  \n",
       "47              0.15     197363.74  \n",
       "48              0.13     197578.98  \n",
       "49              0.20     197900.71  \n",
       "50              0.21     198233.16  \n",
       "51              0.27     198665.68  \n",
       "52              0.31     199158.13  \n",
       "53              0.36     199727.12  \n",
       "54              0.43     200411.84  \n",
       "55              0.44     201119.04  \n",
       "56              0.49     201896.63  \n",
       "57              0.59     202835.43  \n",
       "58              0.77     204065.96  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "capital = 160000 \n",
    "mensal_result['Equity'] = capital + mensal_result.result.cumsum()\n",
    "mensal_result['selic_M'] = selic['selic_mensal']\n",
    "mensal_result['return_selic'] = capital * mensal_result['selic_M']\n",
    "mensal_result['return_pct_selic'] = (mensal_result.return_selic / capital) * 100 \n",
    "mensal_result['Equity_selic'] = capital + mensal_result.return_selic.cumsum()\n",
    "mensal_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-11T23:06:19.565556Z",
     "start_time": "2022-03-11T23:06:18.284267Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,6))\n",
    "sns.set_theme(style=\"darkgrid\")\n",
    "sns.lineplot(x='data', y='Equity', data=mensal_result);\n",
    "sns.lineplot(x='data', y='Equity_selic', data=mensal_result);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-11T23:06:23.755973Z",
     "start_time": "2022-03-11T23:06:23.739970Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.04623786883307228"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sharp_ratio = (mensal_result['return_pct'].mean()-mensal_result['return_pct_selic'].mean()) / mensal_result['return_pct'].std()\n",
    "sharp_ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-11T23:06:25.518479Z",
     "start_time": "2022-03-11T23:06:25.508464Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3551598095776596"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 59 meses\n",
    "a_sharp =  (59**0.5) * sharp_ratio\n",
    "a_sharp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### What's the Value at Risk (VAR)?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-11T23:06:28.577623Z",
     "start_time": "2022-03-11T23:06:28.563621Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-7.70 %\n"
     ]
    }
   ],
   "source": [
    "var_level = 95\n",
    "var_95 = np.percentile(mensal_result.return_pct, 100 - var_level)\n",
    "print('{:0.2f}'.format(var_95),'%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### What's the Conditional Value at Risk (CVAR)?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-11T23:06:29.878329Z",
     "start_time": "2022-03-11T23:06:29.860325Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.05 %\n"
     ]
    }
   ],
   "source": [
    "cvar_95 = (mensal_result.return_pct <= var_95).mean()\n",
    "print('{:0.2f}'.format(cvar_95),'%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### What's the systematic sensitivity (IBOVESPA)?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-11T23:06:33.575882Z",
     "start_time": "2022-03-11T23:06:33.555881Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Atos\\AppData\\Local\\Temp/ipykernel_14168/3662612548.py:3: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only\n",
      "  ibov.drop(['data', 'close'], 1, inplace=True)\n"
     ]
    },
    {
     "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>index</th>\n",
       "      <th>return_ibov</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-02-24</td>\n",
       "      <td>3.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-03-31</td>\n",
       "      <td>-2.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-04-28</td>\n",
       "      <td>0.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-05-31</td>\n",
       "      <td>-4.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-06-30</td>\n",
       "      <td>0.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2017-07-31</td>\n",
       "      <td>4.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2017-08-31</td>\n",
       "      <td>7.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2017-09-29</td>\n",
       "      <td>4.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2017-10-31</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2017-11-30</td>\n",
       "      <td>-3.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2017-12-28</td>\n",
       "      <td>6.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2018-01-31</td>\n",
       "      <td>11.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2018-02-28</td>\n",
       "      <td>0.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2018-03-29</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2018-04-30</td>\n",
       "      <td>0.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2018-05-30</td>\n",
       "      <td>-10.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2018-06-29</td>\n",
       "      <td>-5.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2018-07-31</td>\n",
       "      <td>8.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2018-08-31</td>\n",
       "      <td>-3.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2018-09-28</td>\n",
       "      <td>3.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2018-10-31</td>\n",
       "      <td>10.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2018-11-30</td>\n",
       "      <td>2.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2018-12-28</td>\n",
       "      <td>-1.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2019-01-31</td>\n",
       "      <td>10.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2019-02-28</td>\n",
       "      <td>-1.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2019-03-29</td>\n",
       "      <td>-0.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2019-04-30</td>\n",
       "      <td>0.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2019-05-31</td>\n",
       "      <td>0.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2019-06-28</td>\n",
       "      <td>4.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2019-07-31</td>\n",
       "      <td>0.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2019-08-30</td>\n",
       "      <td>-0.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2019-09-30</td>\n",
       "      <td>3.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2019-10-31</td>\n",
       "      <td>2.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2019-11-29</td>\n",
       "      <td>0.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2019-12-30</td>\n",
       "      <td>6.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2020-01-31</td>\n",
       "      <td>-1.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>2020-02-28</td>\n",
       "      <td>-8.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2020-03-31</td>\n",
       "      <td>-29.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2020-04-30</td>\n",
       "      <td>10.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>2020-05-29</td>\n",
       "      <td>8.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>8.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>2020-07-31</td>\n",
       "      <td>8.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>2020-08-31</td>\n",
       "      <td>-3.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>2020-09-30</td>\n",
       "      <td>-4.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>2020-10-30</td>\n",
       "      <td>-0.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>2020-11-30</td>\n",
       "      <td>15.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>2020-12-30</td>\n",
       "      <td>9.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>2021-01-29</td>\n",
       "      <td>-3.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>2021-02-26</td>\n",
       "      <td>-4.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>2021-03-31</td>\n",
       "      <td>6.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>2021-04-30</td>\n",
       "      <td>1.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>2021-05-31</td>\n",
       "      <td>6.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>2021-06-30</td>\n",
       "      <td>0.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>2021-07-30</td>\n",
       "      <td>-3.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>2021-08-31</td>\n",
       "      <td>-2.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>2021-09-30</td>\n",
       "      <td>-6.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>2021-10-29</td>\n",
       "      <td>-6.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>2021-11-30</td>\n",
       "      <td>-1.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>2021-12-30</td>\n",
       "      <td>2.85</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         index  return_ibov\n",
       "0   2017-02-24         3.08\n",
       "1   2017-03-31        -2.52\n",
       "2   2017-04-28         0.65\n",
       "3   2017-05-31        -4.12\n",
       "4   2017-06-30         0.30\n",
       "5   2017-07-31         4.80\n",
       "6   2017-08-31         7.46\n",
       "7   2017-09-29         4.88\n",
       "8   2017-10-31         0.02\n",
       "9   2017-11-30        -3.15\n",
       "10  2017-12-28         6.16\n",
       "11  2018-01-31        11.14\n",
       "12  2018-02-28         0.52\n",
       "13  2018-03-29         0.01\n",
       "14  2018-04-30         0.88\n",
       "15  2018-05-30       -10.87\n",
       "16  2018-06-29        -5.20\n",
       "17  2018-07-31         8.88\n",
       "18  2018-08-31        -3.21\n",
       "19  2018-09-28         3.48\n",
       "20  2018-10-31        10.19\n",
       "21  2018-11-30         2.38\n",
       "22  2018-12-28        -1.81\n",
       "23  2019-01-31        10.82\n",
       "24  2019-02-28        -1.86\n",
       "25  2019-03-29        -0.18\n",
       "26  2019-04-30         0.98\n",
       "27  2019-05-31         0.70\n",
       "28  2019-06-28         4.06\n",
       "29  2019-07-31         0.84\n",
       "30  2019-08-30        -0.67\n",
       "31  2019-09-30         3.57\n",
       "32  2019-10-31         2.36\n",
       "33  2019-11-29         0.95\n",
       "34  2019-12-30         6.85\n",
       "35  2020-01-31        -1.63\n",
       "36  2020-02-28        -8.43\n",
       "37  2020-03-31       -29.90\n",
       "38  2020-04-30        10.25\n",
       "39  2020-05-29         8.57\n",
       "40  2020-06-30         8.76\n",
       "41  2020-07-31         8.27\n",
       "42  2020-08-31        -3.44\n",
       "43  2020-09-30        -4.80\n",
       "44  2020-10-30        -0.69\n",
       "45  2020-11-30        15.90\n",
       "46  2020-12-30         9.30\n",
       "47  2021-01-29        -3.32\n",
       "48  2021-02-26        -4.37\n",
       "49  2021-03-31         6.00\n",
       "50  2021-04-30         1.94\n",
       "51  2021-05-31         6.16\n",
       "52  2021-06-30         0.46\n",
       "53  2021-07-30        -3.94\n",
       "54  2021-08-31        -2.48\n",
       "55  2021-09-30        -6.57\n",
       "56  2021-10-29        -6.74\n",
       "57  2021-11-30        -1.53\n",
       "58  2021-12-30         2.85"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ibov = pd.read_csv('C:/')\n",
    "ibov.reset_index(inplace=True)\n",
    "ibov.drop(['data', 'close'], 1, inplace=True)\n",
    "ibov"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-11T23:06:42.633081Z",
     "start_time": "2022-03-11T23:06:42.453239Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data</th>\n",
       "      <th>result</th>\n",
       "      <th>return_pct</th>\n",
       "      <th>Equity</th>\n",
       "      <th>selic_M</th>\n",
       "      <th>return_selic</th>\n",
       "      <th>return_pct_selic</th>\n",
       "      <th>Equity_selic</th>\n",
       "      <th>return_pct_ibov</th>\n",
       "      <th>return_ibov</th>\n",
       "      <th>Equity_ibov</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-01-31</td>\n",
       "      <td>12928.61</td>\n",
       "      <td>8.08</td>\n",
       "      <td>172928.61</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1384.13</td>\n",
       "      <td>0.87</td>\n",
       "      <td>161384.13</td>\n",
       "      <td>0.03</td>\n",
       "      <td>4926.66</td>\n",
       "      <td>164926.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-02-24</td>\n",
       "      <td>-1417.92</td>\n",
       "      <td>-0.89</td>\n",
       "      <td>171510.68</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1683.29</td>\n",
       "      <td>1.05</td>\n",
       "      <td>163067.42</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-4027.57</td>\n",
       "      <td>160899.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-03-31</td>\n",
       "      <td>-15607.89</td>\n",
       "      <td>-9.75</td>\n",
       "      <td>155902.79</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1258.53</td>\n",
       "      <td>0.79</td>\n",
       "      <td>164325.95</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1032.08</td>\n",
       "      <td>161931.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-04-28</td>\n",
       "      <td>-1517.99</td>\n",
       "      <td>-0.95</td>\n",
       "      <td>154384.80</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1483.41</td>\n",
       "      <td>0.93</td>\n",
       "      <td>165809.36</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-6585.04</td>\n",
       "      <td>155346.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-05-31</td>\n",
       "      <td>-5452.50</td>\n",
       "      <td>-3.41</td>\n",
       "      <td>148932.30</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1294.19</td>\n",
       "      <td>0.81</td>\n",
       "      <td>167103.56</td>\n",
       "      <td>0.00</td>\n",
       "      <td>480.93</td>\n",
       "      <td>155827.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2017-06-30</td>\n",
       "      <td>-9151.96</td>\n",
       "      <td>-5.72</td>\n",
       "      <td>139780.34</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1276.68</td>\n",
       "      <td>0.80</td>\n",
       "      <td>168380.23</td>\n",
       "      <td>0.05</td>\n",
       "      <td>7683.03</td>\n",
       "      <td>163510.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2017-07-31</td>\n",
       "      <td>1176.25</td>\n",
       "      <td>0.74</td>\n",
       "      <td>140956.59</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1283.66</td>\n",
       "      <td>0.80</td>\n",
       "      <td>169663.89</td>\n",
       "      <td>0.07</td>\n",
       "      <td>11928.79</td>\n",
       "      <td>175438.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2017-08-31</td>\n",
       "      <td>-360.23</td>\n",
       "      <td>-0.23</td>\n",
       "      <td>140596.36</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1021.54</td>\n",
       "      <td>0.64</td>\n",
       "      <td>170685.43</td>\n",
       "      <td>0.05</td>\n",
       "      <td>7811.84</td>\n",
       "      <td>183250.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2017-09-29</td>\n",
       "      <td>-10861.24</td>\n",
       "      <td>-6.79</td>\n",
       "      <td>129735.12</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1030.29</td>\n",
       "      <td>0.64</td>\n",
       "      <td>171715.72</td>\n",
       "      <td>0.00</td>\n",
       "      <td>32.28</td>\n",
       "      <td>183283.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2017-10-31</td>\n",
       "      <td>1628.07</td>\n",
       "      <td>1.02</td>\n",
       "      <td>131363.20</td>\n",
       "      <td>0.01</td>\n",
       "      <td>909.10</td>\n",
       "      <td>0.57</td>\n",
       "      <td>172624.82</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-5033.07</td>\n",
       "      <td>178249.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2017-11-30</td>\n",
       "      <td>4635.15</td>\n",
       "      <td>2.90</td>\n",
       "      <td>135998.35</td>\n",
       "      <td>0.01</td>\n",
       "      <td>861.44</td>\n",
       "      <td>0.54</td>\n",
       "      <td>173486.26</td>\n",
       "      <td>0.06</td>\n",
       "      <td>9850.84</td>\n",
       "      <td>188100.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2017-12-28</td>\n",
       "      <td>6269.91</td>\n",
       "      <td>3.92</td>\n",
       "      <td>142268.26</td>\n",
       "      <td>0.01</td>\n",
       "      <td>934.73</td>\n",
       "      <td>0.58</td>\n",
       "      <td>174420.99</td>\n",
       "      <td>0.11</td>\n",
       "      <td>17822.80</td>\n",
       "      <td>205923.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2018-01-31</td>\n",
       "      <td>7523.19</td>\n",
       "      <td>4.70</td>\n",
       "      <td>149791.45</td>\n",
       "      <td>0.00</td>\n",
       "      <td>744.96</td>\n",
       "      <td>0.47</td>\n",
       "      <td>175165.95</td>\n",
       "      <td>0.01</td>\n",
       "      <td>830.78</td>\n",
       "      <td>206754.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2018-02-28</td>\n",
       "      <td>2932.29</td>\n",
       "      <td>1.83</td>\n",
       "      <td>152723.74</td>\n",
       "      <td>0.01</td>\n",
       "      <td>851.75</td>\n",
       "      <td>0.53</td>\n",
       "      <td>176017.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>22.42</td>\n",
       "      <td>206776.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2018-03-29</td>\n",
       "      <td>-95.34</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>152628.40</td>\n",
       "      <td>0.01</td>\n",
       "      <td>829.27</td>\n",
       "      <td>0.52</td>\n",
       "      <td>176846.97</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1405.61</td>\n",
       "      <td>208182.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2018-04-30</td>\n",
       "      <td>-4152.62</td>\n",
       "      <td>-2.60</td>\n",
       "      <td>148475.78</td>\n",
       "      <td>0.01</td>\n",
       "      <td>829.27</td>\n",
       "      <td>0.52</td>\n",
       "      <td>177676.25</td>\n",
       "      <td>-0.11</td>\n",
       "      <td>-17394.11</td>\n",
       "      <td>190788.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2018-05-30</td>\n",
       "      <td>5706.53</td>\n",
       "      <td>3.57</td>\n",
       "      <td>154182.31</td>\n",
       "      <td>0.01</td>\n",
       "      <td>829.27</td>\n",
       "      <td>0.52</td>\n",
       "      <td>178505.52</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-8319.80</td>\n",
       "      <td>182468.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2018-06-29</td>\n",
       "      <td>3753.22</td>\n",
       "      <td>2.35</td>\n",
       "      <td>157935.54</td>\n",
       "      <td>0.01</td>\n",
       "      <td>868.87</td>\n",
       "      <td>0.54</td>\n",
       "      <td>179374.39</td>\n",
       "      <td>0.09</td>\n",
       "      <td>14200.54</td>\n",
       "      <td>196669.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2018-07-31</td>\n",
       "      <td>1008.74</td>\n",
       "      <td>0.63</td>\n",
       "      <td>158944.28</td>\n",
       "      <td>0.01</td>\n",
       "      <td>908.47</td>\n",
       "      <td>0.57</td>\n",
       "      <td>180282.86</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-5135.87</td>\n",
       "      <td>191533.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2018-08-31</td>\n",
       "      <td>-5128.39</td>\n",
       "      <td>-3.21</td>\n",
       "      <td>153815.89</td>\n",
       "      <td>0.00</td>\n",
       "      <td>750.11</td>\n",
       "      <td>0.47</td>\n",
       "      <td>181032.97</td>\n",
       "      <td>0.03</td>\n",
       "      <td>5560.74</td>\n",
       "      <td>197093.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2018-09-28</td>\n",
       "      <td>-1341.48</td>\n",
       "      <td>-0.84</td>\n",
       "      <td>152474.41</td>\n",
       "      <td>0.01</td>\n",
       "      <td>868.87</td>\n",
       "      <td>0.54</td>\n",
       "      <td>181901.84</td>\n",
       "      <td>0.10</td>\n",
       "      <td>16296.19</td>\n",
       "      <td>213390.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2018-10-31</td>\n",
       "      <td>-16827.72</td>\n",
       "      <td>-10.52</td>\n",
       "      <td>135646.69</td>\n",
       "      <td>0.00</td>\n",
       "      <td>789.68</td>\n",
       "      <td>0.49</td>\n",
       "      <td>182691.52</td>\n",
       "      <td>0.02</td>\n",
       "      <td>3807.63</td>\n",
       "      <td>217197.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2018-11-30</td>\n",
       "      <td>-8662.59</td>\n",
       "      <td>-5.41</td>\n",
       "      <td>126984.10</td>\n",
       "      <td>0.00</td>\n",
       "      <td>789.68</td>\n",
       "      <td>0.49</td>\n",
       "      <td>183481.20</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-2890.17</td>\n",
       "      <td>214307.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2018-12-28</td>\n",
       "      <td>-2209.41</td>\n",
       "      <td>-1.38</td>\n",
       "      <td>124774.69</td>\n",
       "      <td>0.01</td>\n",
       "      <td>868.87</td>\n",
       "      <td>0.54</td>\n",
       "      <td>184350.07</td>\n",
       "      <td>0.11</td>\n",
       "      <td>17306.68</td>\n",
       "      <td>231614.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2019-01-31</td>\n",
       "      <td>-12264.07</td>\n",
       "      <td>-7.67</td>\n",
       "      <td>112510.62</td>\n",
       "      <td>0.00</td>\n",
       "      <td>789.68</td>\n",
       "      <td>0.49</td>\n",
       "      <td>185139.76</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-2972.51</td>\n",
       "      <td>228641.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2019-02-28</td>\n",
       "      <td>3422.40</td>\n",
       "      <td>2.14</td>\n",
       "      <td>115933.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>750.11</td>\n",
       "      <td>0.47</td>\n",
       "      <td>185889.87</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>-284.21</td>\n",
       "      <td>228357.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2019-03-29</td>\n",
       "      <td>600.89</td>\n",
       "      <td>0.38</td>\n",
       "      <td>116533.91</td>\n",
       "      <td>0.01</td>\n",
       "      <td>829.27</td>\n",
       "      <td>0.52</td>\n",
       "      <td>186719.14</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1574.22</td>\n",
       "      <td>229931.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2019-04-30</td>\n",
       "      <td>12652.73</td>\n",
       "      <td>7.91</td>\n",
       "      <td>129186.63</td>\n",
       "      <td>0.01</td>\n",
       "      <td>868.87</td>\n",
       "      <td>0.54</td>\n",
       "      <td>187588.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1124.18</td>\n",
       "      <td>231055.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2019-05-31</td>\n",
       "      <td>1596.68</td>\n",
       "      <td>1.00</td>\n",
       "      <td>130783.31</td>\n",
       "      <td>0.00</td>\n",
       "      <td>750.11</td>\n",
       "      <td>0.47</td>\n",
       "      <td>188338.11</td>\n",
       "      <td>0.04</td>\n",
       "      <td>6491.79</td>\n",
       "      <td>237547.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2019-06-28</td>\n",
       "      <td>3787.57</td>\n",
       "      <td>2.37</td>\n",
       "      <td>134570.88</td>\n",
       "      <td>0.01</td>\n",
       "      <td>908.47</td>\n",
       "      <td>0.57</td>\n",
       "      <td>189246.59</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1338.94</td>\n",
       "      <td>238886.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2019-07-31</td>\n",
       "      <td>2063.67</td>\n",
       "      <td>1.29</td>\n",
       "      <td>136634.56</td>\n",
       "      <td>0.01</td>\n",
       "      <td>802.75</td>\n",
       "      <td>0.50</td>\n",
       "      <td>190049.34</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-1064.74</td>\n",
       "      <td>237821.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2019-08-30</td>\n",
       "      <td>-1573.90</td>\n",
       "      <td>-0.98</td>\n",
       "      <td>135060.66</td>\n",
       "      <td>0.00</td>\n",
       "      <td>742.02</td>\n",
       "      <td>0.46</td>\n",
       "      <td>190791.35</td>\n",
       "      <td>0.04</td>\n",
       "      <td>5712.32</td>\n",
       "      <td>243534.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2019-09-30</td>\n",
       "      <td>2750.68</td>\n",
       "      <td>1.72</td>\n",
       "      <td>137811.33</td>\n",
       "      <td>0.00</td>\n",
       "      <td>766.82</td>\n",
       "      <td>0.48</td>\n",
       "      <td>191558.18</td>\n",
       "      <td>0.02</td>\n",
       "      <td>3779.85</td>\n",
       "      <td>247314.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2019-10-31</td>\n",
       "      <td>11032.62</td>\n",
       "      <td>6.90</td>\n",
       "      <td>148843.95</td>\n",
       "      <td>0.00</td>\n",
       "      <td>608.62</td>\n",
       "      <td>0.38</td>\n",
       "      <td>192166.79</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1512.33</td>\n",
       "      <td>248826.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2019-11-29</td>\n",
       "      <td>11989.87</td>\n",
       "      <td>7.49</td>\n",
       "      <td>160833.82</td>\n",
       "      <td>0.00</td>\n",
       "      <td>599.53</td>\n",
       "      <td>0.37</td>\n",
       "      <td>192766.32</td>\n",
       "      <td>0.07</td>\n",
       "      <td>10957.16</td>\n",
       "      <td>259783.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2019-12-30</td>\n",
       "      <td>-4760.82</td>\n",
       "      <td>-2.98</td>\n",
       "      <td>156073.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>602.61</td>\n",
       "      <td>0.38</td>\n",
       "      <td>193368.93</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-2607.66</td>\n",
       "      <td>257175.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>2020-01-31</td>\n",
       "      <td>-2432.67</td>\n",
       "      <td>-1.52</td>\n",
       "      <td>153640.34</td>\n",
       "      <td>0.00</td>\n",
       "      <td>469.97</td>\n",
       "      <td>0.29</td>\n",
       "      <td>193838.90</td>\n",
       "      <td>-0.08</td>\n",
       "      <td>-13486.57</td>\n",
       "      <td>243689.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2020-02-28</td>\n",
       "      <td>15309.43</td>\n",
       "      <td>9.57</td>\n",
       "      <td>168949.77</td>\n",
       "      <td>0.00</td>\n",
       "      <td>541.39</td>\n",
       "      <td>0.34</td>\n",
       "      <td>194380.29</td>\n",
       "      <td>-0.30</td>\n",
       "      <td>-47846.93</td>\n",
       "      <td>195842.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2020-03-31</td>\n",
       "      <td>4557.77</td>\n",
       "      <td>2.85</td>\n",
       "      <td>173507.53</td>\n",
       "      <td>0.00</td>\n",
       "      <td>455.88</td>\n",
       "      <td>0.28</td>\n",
       "      <td>194836.17</td>\n",
       "      <td>0.10</td>\n",
       "      <td>16403.52</td>\n",
       "      <td>212245.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>2020-04-30</td>\n",
       "      <td>-3982.59</td>\n",
       "      <td>-2.49</td>\n",
       "      <td>169524.94</td>\n",
       "      <td>0.00</td>\n",
       "      <td>377.30</td>\n",
       "      <td>0.24</td>\n",
       "      <td>195213.47</td>\n",
       "      <td>0.09</td>\n",
       "      <td>13706.72</td>\n",
       "      <td>225952.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>2020-05-29</td>\n",
       "      <td>-12765.66</td>\n",
       "      <td>-7.98</td>\n",
       "      <td>156759.28</td>\n",
       "      <td>0.00</td>\n",
       "      <td>339.73</td>\n",
       "      <td>0.21</td>\n",
       "      <td>195553.20</td>\n",
       "      <td>0.09</td>\n",
       "      <td>14010.07</td>\n",
       "      <td>239962.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>2404.94</td>\n",
       "      <td>1.50</td>\n",
       "      <td>159164.22</td>\n",
       "      <td>0.00</td>\n",
       "      <td>310.95</td>\n",
       "      <td>0.19</td>\n",
       "      <td>195864.15</td>\n",
       "      <td>0.08</td>\n",
       "      <td>13224.10</td>\n",
       "      <td>253186.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>2020-07-31</td>\n",
       "      <td>13543.83</td>\n",
       "      <td>8.46</td>\n",
       "      <td>172708.05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>255.82</td>\n",
       "      <td>0.16</td>\n",
       "      <td>196119.97</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-5508.52</td>\n",
       "      <td>247678.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>2020-08-31</td>\n",
       "      <td>-3238.65</td>\n",
       "      <td>-2.02</td>\n",
       "      <td>169469.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>251.15</td>\n",
       "      <td>0.16</td>\n",
       "      <td>196371.12</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-7673.64</td>\n",
       "      <td>240004.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>2020-09-30</td>\n",
       "      <td>13780.55</td>\n",
       "      <td>8.61</td>\n",
       "      <td>183249.95</td>\n",
       "      <td>0.00</td>\n",
       "      <td>251.15</td>\n",
       "      <td>0.16</td>\n",
       "      <td>196622.27</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-1100.98</td>\n",
       "      <td>238903.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>2020-10-30</td>\n",
       "      <td>7723.71</td>\n",
       "      <td>4.83</td>\n",
       "      <td>190973.66</td>\n",
       "      <td>0.00</td>\n",
       "      <td>239.18</td>\n",
       "      <td>0.15</td>\n",
       "      <td>196861.44</td>\n",
       "      <td>0.16</td>\n",
       "      <td>25444.24</td>\n",
       "      <td>264347.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>2020-11-30</td>\n",
       "      <td>6637.25</td>\n",
       "      <td>4.15</td>\n",
       "      <td>197610.91</td>\n",
       "      <td>0.00</td>\n",
       "      <td>263.12</td>\n",
       "      <td>0.16</td>\n",
       "      <td>197124.56</td>\n",
       "      <td>0.09</td>\n",
       "      <td>14875.36</td>\n",
       "      <td>279223.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>2020-12-30</td>\n",
       "      <td>6758.86</td>\n",
       "      <td>4.22</td>\n",
       "      <td>204369.77</td>\n",
       "      <td>0.00</td>\n",
       "      <td>239.18</td>\n",
       "      <td>0.15</td>\n",
       "      <td>197363.74</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-5309.74</td>\n",
       "      <td>273913.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>2021-01-29</td>\n",
       "      <td>-2617.05</td>\n",
       "      <td>-1.64</td>\n",
       "      <td>201752.72</td>\n",
       "      <td>0.00</td>\n",
       "      <td>215.24</td>\n",
       "      <td>0.13</td>\n",
       "      <td>197578.98</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-6997.46</td>\n",
       "      <td>266916.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>2021-02-26</td>\n",
       "      <td>-10047.82</td>\n",
       "      <td>-6.28</td>\n",
       "      <td>191704.90</td>\n",
       "      <td>0.00</td>\n",
       "      <td>321.73</td>\n",
       "      <td>0.20</td>\n",
       "      <td>197900.71</td>\n",
       "      <td>0.06</td>\n",
       "      <td>9594.82</td>\n",
       "      <td>276510.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>2021-03-31</td>\n",
       "      <td>5472.98</td>\n",
       "      <td>3.42</td>\n",
       "      <td>197177.88</td>\n",
       "      <td>0.00</td>\n",
       "      <td>332.46</td>\n",
       "      <td>0.21</td>\n",
       "      <td>198233.16</td>\n",
       "      <td>0.02</td>\n",
       "      <td>3100.47</td>\n",
       "      <td>279611.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>2021-04-30</td>\n",
       "      <td>4101.97</td>\n",
       "      <td>2.56</td>\n",
       "      <td>201279.85</td>\n",
       "      <td>0.00</td>\n",
       "      <td>432.52</td>\n",
       "      <td>0.27</td>\n",
       "      <td>198665.68</td>\n",
       "      <td>0.06</td>\n",
       "      <td>9853.35</td>\n",
       "      <td>289464.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>2021-05-31</td>\n",
       "      <td>3308.67</td>\n",
       "      <td>2.07</td>\n",
       "      <td>204588.51</td>\n",
       "      <td>0.00</td>\n",
       "      <td>492.45</td>\n",
       "      <td>0.31</td>\n",
       "      <td>199158.13</td>\n",
       "      <td>0.00</td>\n",
       "      <td>742.77</td>\n",
       "      <td>290207.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>2021-06-30</td>\n",
       "      <td>8012.15</td>\n",
       "      <td>5.01</td>\n",
       "      <td>212600.66</td>\n",
       "      <td>0.00</td>\n",
       "      <td>568.99</td>\n",
       "      <td>0.36</td>\n",
       "      <td>199727.12</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-6310.16</td>\n",
       "      <td>283897.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>2021-07-30</td>\n",
       "      <td>4913.61</td>\n",
       "      <td>3.07</td>\n",
       "      <td>217514.27</td>\n",
       "      <td>0.00</td>\n",
       "      <td>684.72</td>\n",
       "      <td>0.43</td>\n",
       "      <td>200411.84</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-3966.82</td>\n",
       "      <td>279930.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>2021-08-31</td>\n",
       "      <td>12572.00</td>\n",
       "      <td>7.86</td>\n",
       "      <td>230086.28</td>\n",
       "      <td>0.00</td>\n",
       "      <td>707.20</td>\n",
       "      <td>0.44</td>\n",
       "      <td>201119.04</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>-10509.33</td>\n",
       "      <td>269421.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>2021-09-30</td>\n",
       "      <td>-6843.71</td>\n",
       "      <td>-4.28</td>\n",
       "      <td>223242.57</td>\n",
       "      <td>0.00</td>\n",
       "      <td>777.59</td>\n",
       "      <td>0.49</td>\n",
       "      <td>201896.63</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>-10781.69</td>\n",
       "      <td>258639.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>2021-10-29</td>\n",
       "      <td>-2377.01</td>\n",
       "      <td>-1.49</td>\n",
       "      <td>220865.56</td>\n",
       "      <td>0.01</td>\n",
       "      <td>938.80</td>\n",
       "      <td>0.59</td>\n",
       "      <td>202835.43</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-2450.63</td>\n",
       "      <td>256188.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>2021-11-30</td>\n",
       "      <td>3655.40</td>\n",
       "      <td>2.28</td>\n",
       "      <td>224520.96</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1230.53</td>\n",
       "      <td>0.77</td>\n",
       "      <td>204065.96</td>\n",
       "      <td>0.03</td>\n",
       "      <td>4563.77</td>\n",
       "      <td>260752.61</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          data    result  return_pct    Equity  selic_M  return_selic  \\\n",
       "0   2017-01-31  12928.61        8.08 172928.61     0.01       1384.13   \n",
       "1   2017-02-24  -1417.92       -0.89 171510.68     0.01       1683.29   \n",
       "2   2017-03-31 -15607.89       -9.75 155902.79     0.01       1258.53   \n",
       "3   2017-04-28  -1517.99       -0.95 154384.80     0.01       1483.41   \n",
       "4   2017-05-31  -5452.50       -3.41 148932.30     0.01       1294.19   \n",
       "5   2017-06-30  -9151.96       -5.72 139780.34     0.01       1276.68   \n",
       "6   2017-07-31   1176.25        0.74 140956.59     0.01       1283.66   \n",
       "7   2017-08-31   -360.23       -0.23 140596.36     0.01       1021.54   \n",
       "8   2017-09-29 -10861.24       -6.79 129735.12     0.01       1030.29   \n",
       "9   2017-10-31   1628.07        1.02 131363.20     0.01        909.10   \n",
       "10  2017-11-30   4635.15        2.90 135998.35     0.01        861.44   \n",
       "11  2017-12-28   6269.91        3.92 142268.26     0.01        934.73   \n",
       "12  2018-01-31   7523.19        4.70 149791.45     0.00        744.96   \n",
       "13  2018-02-28   2932.29        1.83 152723.74     0.01        851.75   \n",
       "14  2018-03-29    -95.34       -0.06 152628.40     0.01        829.27   \n",
       "15  2018-04-30  -4152.62       -2.60 148475.78     0.01        829.27   \n",
       "16  2018-05-30   5706.53        3.57 154182.31     0.01        829.27   \n",
       "17  2018-06-29   3753.22        2.35 157935.54     0.01        868.87   \n",
       "18  2018-07-31   1008.74        0.63 158944.28     0.01        908.47   \n",
       "19  2018-08-31  -5128.39       -3.21 153815.89     0.00        750.11   \n",
       "20  2018-09-28  -1341.48       -0.84 152474.41     0.01        868.87   \n",
       "21  2018-10-31 -16827.72      -10.52 135646.69     0.00        789.68   \n",
       "22  2018-11-30  -8662.59       -5.41 126984.10     0.00        789.68   \n",
       "23  2018-12-28  -2209.41       -1.38 124774.69     0.01        868.87   \n",
       "24  2019-01-31 -12264.07       -7.67 112510.62     0.00        789.68   \n",
       "25  2019-02-28   3422.40        2.14 115933.02     0.00        750.11   \n",
       "26  2019-03-29    600.89        0.38 116533.91     0.01        829.27   \n",
       "27  2019-04-30  12652.73        7.91 129186.63     0.01        868.87   \n",
       "28  2019-05-31   1596.68        1.00 130783.31     0.00        750.11   \n",
       "29  2019-06-28   3787.57        2.37 134570.88     0.01        908.47   \n",
       "30  2019-07-31   2063.67        1.29 136634.56     0.01        802.75   \n",
       "31  2019-08-30  -1573.90       -0.98 135060.66     0.00        742.02   \n",
       "32  2019-09-30   2750.68        1.72 137811.33     0.00        766.82   \n",
       "33  2019-10-31  11032.62        6.90 148843.95     0.00        608.62   \n",
       "34  2019-11-29  11989.87        7.49 160833.82     0.00        599.53   \n",
       "35  2019-12-30  -4760.82       -2.98 156073.01     0.00        602.61   \n",
       "36  2020-01-31  -2432.67       -1.52 153640.34     0.00        469.97   \n",
       "37  2020-02-28  15309.43        9.57 168949.77     0.00        541.39   \n",
       "38  2020-03-31   4557.77        2.85 173507.53     0.00        455.88   \n",
       "39  2020-04-30  -3982.59       -2.49 169524.94     0.00        377.30   \n",
       "40  2020-05-29 -12765.66       -7.98 156759.28     0.00        339.73   \n",
       "41  2020-06-30   2404.94        1.50 159164.22     0.00        310.95   \n",
       "42  2020-07-31  13543.83        8.46 172708.05     0.00        255.82   \n",
       "43  2020-08-31  -3238.65       -2.02 169469.40     0.00        251.15   \n",
       "44  2020-09-30  13780.55        8.61 183249.95     0.00        251.15   \n",
       "45  2020-10-30   7723.71        4.83 190973.66     0.00        239.18   \n",
       "46  2020-11-30   6637.25        4.15 197610.91     0.00        263.12   \n",
       "47  2020-12-30   6758.86        4.22 204369.77     0.00        239.18   \n",
       "48  2021-01-29  -2617.05       -1.64 201752.72     0.00        215.24   \n",
       "49  2021-02-26 -10047.82       -6.28 191704.90     0.00        321.73   \n",
       "50  2021-03-31   5472.98        3.42 197177.88     0.00        332.46   \n",
       "51  2021-04-30   4101.97        2.56 201279.85     0.00        432.52   \n",
       "52  2021-05-31   3308.67        2.07 204588.51     0.00        492.45   \n",
       "53  2021-06-30   8012.15        5.01 212600.66     0.00        568.99   \n",
       "54  2021-07-30   4913.61        3.07 217514.27     0.00        684.72   \n",
       "55  2021-08-31  12572.00        7.86 230086.28     0.00        707.20   \n",
       "56  2021-09-30  -6843.71       -4.28 223242.57     0.00        777.59   \n",
       "57  2021-10-29  -2377.01       -1.49 220865.56     0.01        938.80   \n",
       "58  2021-11-30   3655.40        2.28 224520.96     0.01       1230.53   \n",
       "\n",
       "    return_pct_selic  Equity_selic  return_pct_ibov  return_ibov  Equity_ibov  \n",
       "0               0.87     161384.13             0.03      4926.66    164926.66  \n",
       "1               1.05     163067.42            -0.03     -4027.57    160899.09  \n",
       "2               0.79     164325.95             0.01      1032.08    161931.17  \n",
       "3               0.93     165809.36            -0.04     -6585.04    155346.13  \n",
       "4               0.81     167103.56             0.00       480.93    155827.06  \n",
       "5               0.80     168380.23             0.05      7683.03    163510.09  \n",
       "6               0.80     169663.89             0.07     11928.79    175438.89  \n",
       "7               0.64     170685.43             0.05      7811.84    183250.73  \n",
       "8               0.64     171715.72             0.00        32.28    183283.01  \n",
       "9               0.57     172624.82            -0.03     -5033.07    178249.94  \n",
       "10              0.54     173486.26             0.06      9850.84    188100.77  \n",
       "11              0.58     174420.99             0.11     17822.80    205923.57  \n",
       "12              0.47     175165.95             0.01       830.78    206754.36  \n",
       "13              0.53     176017.70             0.00        22.42    206776.78  \n",
       "14              0.52     176846.97             0.01      1405.61    208182.38  \n",
       "15              0.52     177676.25            -0.11    -17394.11    190788.27  \n",
       "16              0.52     178505.52            -0.05     -8319.80    182468.48  \n",
       "17              0.54     179374.39             0.09     14200.54    196669.02  \n",
       "18              0.57     180282.86            -0.03     -5135.87    191533.15  \n",
       "19              0.47     181032.97             0.03      5560.74    197093.90  \n",
       "20              0.54     181901.84             0.10     16296.19    213390.08  \n",
       "21              0.49     182691.52             0.02      3807.63    217197.72  \n",
       "22              0.49     183481.20            -0.02     -2890.17    214307.55  \n",
       "23              0.54     184350.07             0.11     17306.68    231614.23  \n",
       "24              0.49     185139.76            -0.02     -2972.51    228641.72  \n",
       "25              0.47     185889.87            -0.00      -284.21    228357.50  \n",
       "26              0.52     186719.14             0.01      1574.22    229931.72  \n",
       "27              0.54     187588.00             0.01      1124.18    231055.90  \n",
       "28              0.47     188338.11             0.04      6491.79    237547.69  \n",
       "29              0.57     189246.59             0.01      1338.94    238886.63  \n",
       "30              0.50     190049.34            -0.01     -1064.74    237821.89  \n",
       "31              0.46     190791.35             0.04      5712.32    243534.22  \n",
       "32              0.48     191558.18             0.02      3779.85    247314.07  \n",
       "33              0.38     192166.79             0.01      1512.33    248826.40  \n",
       "34              0.37     192766.32             0.07     10957.16    259783.56  \n",
       "35              0.38     193368.93            -0.02     -2607.66    257175.91  \n",
       "36              0.29     193838.90            -0.08    -13486.57    243689.34  \n",
       "37              0.34     194380.29            -0.30    -47846.93    195842.41  \n",
       "38              0.28     194836.17             0.10     16403.52    212245.92  \n",
       "39              0.24     195213.47             0.09     13706.72    225952.65  \n",
       "40              0.21     195553.20             0.09     14010.07    239962.72  \n",
       "41              0.19     195864.15             0.08     13224.10    253186.82  \n",
       "42              0.16     196119.97            -0.03     -5508.52    247678.29  \n",
       "43              0.16     196371.12            -0.05     -7673.64    240004.65  \n",
       "44              0.16     196622.27            -0.01     -1100.98    238903.67  \n",
       "45              0.15     196861.44             0.16     25444.24    264347.91  \n",
       "46              0.16     197124.56             0.09     14875.36    279223.27  \n",
       "47              0.15     197363.74            -0.03     -5309.74    273913.53  \n",
       "48              0.13     197578.98            -0.04     -6997.46    266916.07  \n",
       "49              0.20     197900.71             0.06      9594.82    276510.89  \n",
       "50              0.21     198233.16             0.02      3100.47    279611.36  \n",
       "51              0.27     198665.68             0.06      9853.35    289464.71  \n",
       "52              0.31     199158.13             0.00       742.77    290207.47  \n",
       "53              0.36     199727.12            -0.04     -6310.16    283897.31  \n",
       "54              0.43     200411.84            -0.02     -3966.82    279930.49  \n",
       "55              0.44     201119.04            -0.07    -10509.33    269421.16  \n",
       "56              0.49     201896.63            -0.07    -10781.69    258639.47  \n",
       "57              0.59     202835.43            -0.02     -2450.63    256188.84  \n",
       "58              0.77     204065.96             0.03      4563.77    260752.61  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mensal_result['return_pct_ibov'] = ibov['return_ibov'] / 100\n",
    "mensal_result['return_ibov'] =  capital * mensal_result['return_pct_ibov']\n",
    "mensal_result['Equity_ibov'] = capital + mensal_result.return_ibov.cumsum()\n",
    "mensal_result.to_csv(\"Momentum_final.csv\")\n",
    "mensal_result.to_excel(\"Momentum_final.xlsx\")\n",
    "mensal_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-11T23:07:21.913839Z",
     "start_time": "2022-03-11T23:07:20.572535Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,6))\n",
    "sns.set_theme(style=\"darkgrid\")\n",
    "sns.lineplot(x='data', y='Equity', data=mensal_result);\n",
    "sns.lineplot(x='data', y='Equity_selic', data=mensal_result);\n",
    "sns.lineplot(x='data', y='Equity_ibov', data=mensal_result);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-11T23:07:30.432082Z",
     "start_time": "2022-03-11T23:07:30.110003Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<AxesSubplot:>, <AxesSubplot:>], dtype=object)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1584x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr_ibov_estr = pd.DataFrame()\n",
    "corr_ibov_estr['date'] = mensal_result['data']\n",
    "corr_ibov_estr['return_estrt'] = mensal_result['result']\n",
    "corr_ibov_estr['return_Ibov'] = mensal_result['return_ibov']\n",
    "corr_ibov_estr.plot(subplots=True, figsize=(22,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-11T23:07:33.683447Z",
     "start_time": "2022-03-11T23:07:33.676444Z"
    }
   },
   "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>return_estrt</th>\n",
       "      <th>return_Ibov</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>return_estrt</th>\n",
       "      <td>1.00</td>\n",
       "      <td>-0.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>return_Ibov</th>\n",
       "      <td>-0.11</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              return_estrt  return_Ibov\n",
       "return_estrt          1.00        -0.11\n",
       "return_Ibov          -0.11         1.00"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_ibov_estr.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-11T23:07:36.358544Z",
     "start_time": "2022-03-11T23:07:36.204519Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(corr_ibov_estr.corr(), annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-11T23:08:06.844115Z",
     "start_time": "2022-03-11T23:08:06.660085Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1584x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr_ibov_estr['return_estrt'].rolling(12).corr(corr_ibov_estr['return_Ibov']).plot(figsize=(22,8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DF com info adicional"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Best Results "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-11T23:08:17.825289Z",
     "start_time": "2022-03-11T23:08:17.820300Z"
    }
   },
   "outputs": [],
   "source": [
    "momentum_best_month = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-11T23:08:26.001011Z",
     "start_time": "2022-03-11T23:08:25.981016Z"
    }
   },
   "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>data</th>\n",
       "      <th>result</th>\n",
       "      <th>return_pct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2020-02-28</td>\n",
       "      <td>15309.43</td>\n",
       "      <td>9.57</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          data   result  return_pct\n",
       "37  2020-02-28 15309.43        9.57"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_result_momentum = mensal_result.sort_values(by='result', ascending=False).head(1)\n",
    "best_result_momentum = best_result_momentum.iloc[:, 0:3]\n",
    "best_result_momentum "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-11T23:08:28.766775Z",
     "start_time": "2022-03-11T23:08:28.756773Z"
    }
   },
   "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>data</th>\n",
       "      <th>return_ibov</th>\n",
       "      <th>return_pct_ibov</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>2020-10-30</td>\n",
       "      <td>25444.24</td>\n",
       "      <td>0.16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          data  return_ibov  return_pct_ibov\n",
       "45  2020-10-30     25444.24             0.16"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_result_ibov = mensal_result.sort_values(by='return_ibov', ascending=False).head(1)\n",
    "best_result_ibov = best_result_ibov.iloc[:, [0,9,8]]\n",
    "best_result_ibov"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-11T23:08:29.780410Z",
     "start_time": "2022-03-11T23:08:29.772408Z"
    }
   },
   "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>data</th>\n",
       "      <th>return_selic</th>\n",
       "      <th>return_pct_selic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-02-24</td>\n",
       "      <td>1683.29</td>\n",
       "      <td>1.05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         data  return_selic  return_pct_selic\n",
       "1  2017-02-24       1683.29              1.05"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_result_selic = mensal_result.sort_values(by='return_selic', ascending=False).head(1)\n",
    "best_result_selic = best_result_selic.iloc[:, [0,5,6]]\n",
    "best_result_selic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-11T23:08:31.754632Z",
     "start_time": "2022-03-11T23:08:31.740629Z"
    }
   },
   "outputs": [],
   "source": [
    "momentum_best_month = pd.concat([best_result_momentum, best_result_ibov, best_result_selic])\n",
    "#momentum_best_month.to_excel(\"best_months.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-11T23:08:36.822734Z",
     "start_time": "2022-03-11T23:08:36.803730Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data</th>\n",
       "      <th>result</th>\n",
       "      <th>return_pct</th>\n",
       "      <th>Equity</th>\n",
       "      <th>selic_M</th>\n",
       "      <th>return_selic</th>\n",
       "      <th>return_pct_selic</th>\n",
       "      <th>Equity_selic</th>\n",
       "      <th>return_pct_ibov</th>\n",
       "      <th>return_ibov</th>\n",
       "      <th>Equity_ibov</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-02-24</td>\n",
       "      <td>-1417.92</td>\n",
       "      <td>-0.89</td>\n",
       "      <td>171510.68</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1683.29</td>\n",
       "      <td>1.05</td>\n",
       "      <td>163067.42</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-4027.57</td>\n",
       "      <td>160899.09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "         data   result  return_pct    Equity  selic_M  return_selic  \\\n",
       "1  2017-02-24 -1417.92       -0.89 171510.68     0.01       1683.29   \n",
       "\n",
       "   return_pct_selic  Equity_selic  return_pct_ibov  return_ibov  Equity_ibov  \n",
       "1              1.05     163067.42            -0.03     -4027.57    160899.09  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mensal_result.sort_values(by='return_selic', ascending=False).head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Worst results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-11T23:08:49.995891Z",
     "start_time": "2022-03-11T23:08:49.987893Z"
    }
   },
   "outputs": [],
   "source": [
    "momentum_worst_month = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-11T23:08:57.488021Z",
     "start_time": "2022-03-11T23:08:57.477031Z"
    }
   },
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>data</th>\n",
       "      <th>result</th>\n",
       "      <th>return_pct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2018-10-31</td>\n",
       "      <td>-16827.72</td>\n",
       "      <td>-10.52</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          data    result  return_pct\n",
       "21  2018-10-31 -16827.72      -10.52"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "worst_result_momentum = mensal_result.sort_values(by='result', ascending=False).tail(1)\n",
    "worst_result_momentum = worst_result_momentum.iloc[:, 0:3]\n",
    "worst_result_momentum "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-11T23:09:02.189637Z",
     "start_time": "2022-03-11T23:09:02.180645Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data</th>\n",
       "      <th>return_ibov</th>\n",
       "      <th>return_pct_ibov</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2020-02-28</td>\n",
       "      <td>-47846.93</td>\n",
       "      <td>-29.90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          data  return_ibov  return_pct_ibov\n",
       "37  2020-02-28    -47846.93           -29.90"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "worst_result_ibov = mensal_result.sort_values(by='return_ibov', ascending=False).tail(1)\n",
    "worst_result_ibov['return_pct_ibov'] = worst_result_ibov['return_pct_ibov'] * 100\n",
    "worst_result_ibov = worst_result_ibov.iloc[:, [0,9,8]]\n",
    "worst_result_ibov"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-11T23:09:04.991752Z",
     "start_time": "2022-03-11T23:09:04.980751Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data</th>\n",
       "      <th>return_selic</th>\n",
       "      <th>return_pct_selic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>2021-01-29</td>\n",
       "      <td>215.24</td>\n",
       "      <td>0.13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          data  return_selic  return_pct_selic\n",
       "48  2021-01-29        215.24              0.13"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "worst_result_selic = mensal_result.sort_values(by='return_selic', ascending=False).tail(1)\n",
    "worst_result_selic = worst_result_selic.iloc[:, [0,5,6]]\n",
    "worst_result_selic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-11T23:09:16.621390Z",
     "start_time": "2022-03-11T23:09:16.605386Z"
    }
   },
   "outputs": [],
   "source": [
    "momentum_worst_month = pd.concat([worst_result_momentum, worst_result_ibov, worst_result_selic])\n",
    "# momentum_worst_month.to_excel(\"worst_months.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-11T23:09:17.750026Z",
     "start_time": "2022-03-11T23:09:17.732025Z"
    }
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   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>data</th>\n",
       "      <th>result</th>\n",
       "      <th>return_pct</th>\n",
       "      <th>return_ibov</th>\n",
       "      <th>return_pct_ibov</th>\n",
       "      <th>return_selic</th>\n",
       "      <th>return_pct_selic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2018-10-31</td>\n",
       "      <td>-16827.72</td>\n",
       "      <td>-10.52</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2020-02-28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-47846.93</td>\n",
       "      <td>-29.90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>2021-01-29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>215.24</td>\n",
       "      <td>0.13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          data    result  return_pct  return_ibov  return_pct_ibov  \\\n",
       "21  2018-10-31 -16827.72      -10.52          NaN              NaN   \n",
       "37  2020-02-28       NaN         NaN    -47846.93           -29.90   \n",
       "48  2021-01-29       NaN         NaN          NaN              NaN   \n",
       "\n",
       "    return_selic  return_pct_selic  \n",
       "21           NaN               NaN  \n",
       "37           NaN               NaN  \n",
       "48        215.24              0.13  "
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "momentum_worst_month"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Most negotiated stocks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-11T23:09:21.813650Z",
     "start_time": "2022-03-11T23:09:21.796646Z"
    }
   },
   "outputs": [],
   "source": [
    "most_stocks = pd.DataFrame()\n",
    "most_stocks['most negotiated stocks'] = df2.reset_index().tickers.value_counts()\n",
    "# most_stocks.to_excel(\"most_stocks.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-11T23:09:22.576997Z",
     "start_time": "2022-03-11T23:09:22.564995Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>most negotiated stocks</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>VVAR3</th>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BIDI4</th>\n",
       "      <td>23</td>\n",
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       "      <td>20</td>\n",
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       "      <td>20</td>\n",
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       "      <td>19</td>\n",
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       "      <td>18</td>\n",
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       "      <td>16</td>\n",
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       "      <th>AZUL4</th>\n",
       "      <td>16</td>\n",
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       "    <tr>\n",
       "      <th>COGN3</th>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SAPR4</th>\n",
       "      <td>14</td>\n",
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       "    <tr>\n",
       "      <th>JBSS3</th>\n",
       "      <td>13</td>\n",
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       "    <tr>\n",
       "      <th>ENEV3</th>\n",
       "      <td>13</td>\n",
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       "    <tr>\n",
       "      <th>USIM5</th>\n",
       "      <td>13</td>\n",
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       "    <tr>\n",
       "      <th>BRAP4</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VALE3</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOTS3</th>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CCRO3</th>\n",
       "      <td>11</td>\n",
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       "    <tr>\n",
       "      <th>CMIG4</th>\n",
       "      <td>11</td>\n",
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       "    <tr>\n",
       "      <th>BRFS3</th>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LAME4</th>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NTCO3</th>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CPLE6</th>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LAME3</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BRDT3</th>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BPAC11</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BBDC4</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CSAN3</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ITSA4</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BRML3</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EMBR3</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PETR4</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABEV3</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LWSA3</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HAPV3</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PETR3</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BBAS3</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ITUB4</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NEOE3</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        most negotiated stocks\n",
       "VVAR3                       24\n",
       "BIDI4                       23\n",
       "DMMO3                       23\n",
       "MGLU3                       20\n",
       "IRBR3                       20\n",
       "CIEL3                       19\n",
       "PRIO3                       18\n",
       "SULA11                      16\n",
       "CSNA3                       16\n",
       "AZUL4                       16\n",
       "COGN3                       14\n",
       "SAPR4                       14\n",
       "JBSS3                       13\n",
       "ENEV3                       13\n",
       "USIM5                       13\n",
       "BRAP4                       12\n",
       "VALE3                       12\n",
       "TOTS3                       11\n",
       "CCRO3                       11\n",
       "CMIG4                       11\n",
       "BRFS3                       11\n",
       "LAME4                       11\n",
       "NTCO3                       11\n",
       "CPLE6                       11\n",
       "LAME3                       10\n",
       "BRDT3                        9\n",
       "BPAC11                       8\n",
       "BBDC4                        8\n",
       "CSAN3                        8\n",
       "ITSA4                        8\n",
       "BRML3                        7\n",
       "EMBR3                        7\n",
       "PETR4                        7\n",
       "ABEV3                        7\n",
       "LWSA3                        7\n",
       "HAPV3                        6\n",
       "PETR3                        5\n",
       "BBAS3                        5\n",
       "ITUB4                        5\n",
       "NEOE3                        1"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "most_stocks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Final Details"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-11T23:09:38.298375Z",
     "start_time": "2022-03-11T23:09:37.580019Z"
    }
   },
   "outputs": [],
   "source": [
    "details = pd.read_excel('C:/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-11T23:09:39.016544Z",
     "start_time": "2022-03-11T23:09:39.001540Z"
    }
   },
   "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>Maximum Drawdown</th>\n",
       "      <th>Highest Equity Moment</th>\n",
       "      <th>Sharpe-Ratio</th>\n",
       "      <th>Value at Risk (VAR)</th>\n",
       "      <th>Conditional Value at Risk (CVAR)</th>\n",
       "      <th>Correlation Ibovespa</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>29.68</td>\n",
       "      <td>43.80</td>\n",
       "      <td>0.05</td>\n",
       "      <td>-7.70</td>\n",
       "      <td>0.05</td>\n",
       "      <td>-0.11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Maximum Drawdown  Highest Equity Moment  Sharpe-Ratio  Value at Risk (VAR)  \\\n",
       "0             29.68                  43.80          0.05                -7.70   \n",
       "\n",
       "   Conditional Value at Risk (CVAR)  Correlation Ibovespa  \n",
       "0                              0.05                 -0.11  "
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "details"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-11T23:03:47.168385Z",
     "start_time": "2022-03-11T23:03:47.168385Z"
    }
   },
   "outputs": [],
   "source": [
    "# details.to_excel(\"details.xlsx\")"
   ]
  }
 ],
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     "varRefreshCmd": "cat(var_dic_list()) "
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