feature_engineering.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Capstone project\n",
    "This is my submission for Udacity's *Machine Learning Engineer* Nanodegree.\n",
    "## Data exploration\n",
    "We'll start by exploring our data.<br>\n",
    "We'll be working on two financial time-series.<br>\n",
    "The firt one are the values of the shares of a ETF called \"iShares Ibovespa Fundo de Índice\" (ticker BOVA11). This ETF is indexed by IBOVESPA, which is Brazil's main financial index.<br>\n",
    "The second time-series prwsents the prices for WEG's ordinary stocks (ticker WEGE3). WEG is one of the world leaders in the manufacturing of electrical motors, and one of Brazil's biggest companies. <br>\n",
    "Both time series comprise data from January 1st, 2010 to October 14th, 2020."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.0.1'"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import relevant libraries\n",
    "import os\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly.graph_objects as go\n",
    "import numpy as np\n",
    "from datetime import datetime\n",
    "pd.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load data into pandas dataframes\n",
    "orig_data_folder = 'orig-data'\n",
    "bova_orig_path = os.path.join(orig_data_folder, \"BOVA11.csv\")\n",
    "wege_orig_path = os.path.join(orig_data_folder, \"WEGE3.csv\")\n",
    "\n",
    "bova_df = pd.read_csv(bova_orig_path)\n",
    "wege_df = pd.read_csv(wege_orig_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "def adjust_date(df):\n",
    "    datetimes = [datetime.strptime(date_str, '%d/%m/%Y') for date_str in df[\"Date\"].values]\n",
    "    return [date.strftime('%Y-%m-%d') for date in datetimes]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "wege_df[\"Date\"] = adjust_date(wege_df)\n",
    "bova_df[\"Date\"] = adjust_date(bova_df)\n",
    "wege_df.set_index(\"Date\", inplace=True)\n",
    "bova_df.set_index(\"Date\", inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below, we can see what kinds of data we have available. <br>\n",
    "Both datasets should have the same lenghts and columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume [BRL]</th>\n",
       "      <th>Volume [shares]</th>\n",
       "      <th>IFR (RSI) [9]</th>\n",
       "      <th>True Range [1]</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-10-16</th>\n",
       "      <td>95.12</td>\n",
       "      <td>95.45</td>\n",
       "      <td>94.54</td>\n",
       "      <td>94.54</td>\n",
       "      <td>9.673009e+08</td>\n",
       "      <td>10188511</td>\n",
       "      <td>54.42</td>\n",
       "      <td>0.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-15</th>\n",
       "      <td>94.36</td>\n",
       "      <td>95.80</td>\n",
       "      <td>94.05</td>\n",
       "      <td>95.34</td>\n",
       "      <td>9.081088e+08</td>\n",
       "      <td>9544784</td>\n",
       "      <td>59.67</td>\n",
       "      <td>1.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-14</th>\n",
       "      <td>95.01</td>\n",
       "      <td>95.86</td>\n",
       "      <td>95.01</td>\n",
       "      <td>95.59</td>\n",
       "      <td>1.092614e+09</td>\n",
       "      <td>11431930</td>\n",
       "      <td>61.32</td>\n",
       "      <td>1.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-13</th>\n",
       "      <td>94.10</td>\n",
       "      <td>95.35</td>\n",
       "      <td>93.65</td>\n",
       "      <td>94.80</td>\n",
       "      <td>9.415492e+08</td>\n",
       "      <td>9960394</td>\n",
       "      <td>58.07</td>\n",
       "      <td>1.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-09</th>\n",
       "      <td>94.13</td>\n",
       "      <td>94.99</td>\n",
       "      <td>93.50</td>\n",
       "      <td>93.65</td>\n",
       "      <td>8.611634e+08</td>\n",
       "      <td>9149800</td>\n",
       "      <td>52.96</td>\n",
       "      <td>1.49</td>\n",
       "    </tr>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-12-08</th>\n",
       "      <td>37.35</td>\n",
       "      <td>38.46</td>\n",
       "      <td>36.95</td>\n",
       "      <td>38.46</td>\n",
       "      <td>3.857295e+06</td>\n",
       "      <td>103100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-12-05</th>\n",
       "      <td>34.51</td>\n",
       "      <td>35.35</td>\n",
       "      <td>34.11</td>\n",
       "      <td>35.35</td>\n",
       "      <td>6.671196e+06</td>\n",
       "      <td>193700</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-12-04</th>\n",
       "      <td>35.80</td>\n",
       "      <td>36.00</td>\n",
       "      <td>35.15</td>\n",
       "      <td>35.70</td>\n",
       "      <td>4.554168e+06</td>\n",
       "      <td>128400</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-12-03</th>\n",
       "      <td>34.73</td>\n",
       "      <td>35.40</td>\n",
       "      <td>33.90</td>\n",
       "      <td>35.31</td>\n",
       "      <td>1.148272e+07</td>\n",
       "      <td>330100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-12-02</th>\n",
       "      <td>35.11</td>\n",
       "      <td>35.81</td>\n",
       "      <td>34.91</td>\n",
       "      <td>35.39</td>\n",
       "      <td>2.681997e+07</td>\n",
       "      <td>759900</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2936 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Open   High    Low  Close  Volume [BRL]  Volume [shares]  \\\n",
       "Date                                                                    \n",
       "2020-10-16  95.12  95.45  94.54  94.54  9.673009e+08         10188511   \n",
       "2020-10-15  94.36  95.80  94.05  95.34  9.081088e+08          9544784   \n",
       "2020-10-14  95.01  95.86  95.01  95.59  1.092614e+09         11431930   \n",
       "2020-10-13  94.10  95.35  93.65  94.80  9.415492e+08          9960394   \n",
       "2020-10-09  94.13  94.99  93.50  93.65  8.611634e+08          9149800   \n",
       "...           ...    ...    ...    ...           ...              ...   \n",
       "2008-12-08  37.35  38.46  36.95  38.46  3.857295e+06           103100   \n",
       "2008-12-05  34.51  35.35  34.11  35.35  6.671196e+06           193700   \n",
       "2008-12-04  35.80  36.00  35.15  35.70  4.554168e+06           128400   \n",
       "2008-12-03  34.73  35.40  33.90  35.31  1.148272e+07           330100   \n",
       "2008-12-02  35.11  35.81  34.91  35.39  2.681997e+07           759900   \n",
       "\n",
       "            IFR (RSI) [9]  True Range [1]  \n",
       "Date                                       \n",
       "2020-10-16          54.42            0.91  \n",
       "2020-10-15          59.67            1.75  \n",
       "2020-10-14          61.32            1.06  \n",
       "2020-10-13          58.07            1.70  \n",
       "2020-10-09          52.96            1.49  \n",
       "...                   ...             ...  \n",
       "2008-12-08            NaN            3.11  \n",
       "2008-12-05            NaN            1.59  \n",
       "2008-12-04            NaN            0.85  \n",
       "2008-12-03            NaN            1.50  \n",
       "2008-12-02            NaN             NaN  \n",
       "\n",
       "[2936 rows x 8 columns]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bova_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume [BRL]</th>\n",
       "      <th>Volume [shares]</th>\n",
       "      <th>IFR (RSI) [9]</th>\n",
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       "      <th>count</th>\n",
       "      <td>2936.000000</td>\n",
       "      <td>2936.000000</td>\n",
       "      <td>2936.000000</td>\n",
       "      <td>2936.000000</td>\n",
       "      <td>2.936000e+03</td>\n",
       "      <td>2.936000e+03</td>\n",
       "      <td>2928.000000</td>\n",
       "      <td>2935.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>64.107006</td>\n",
       "      <td>64.664939</td>\n",
       "      <td>63.452142</td>\n",
       "      <td>64.074029</td>\n",
       "      <td>1.979881e+08</td>\n",
       "      <td>2.727684e+06</td>\n",
       "      <td>52.383282</td>\n",
       "      <td>1.344508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>16.816599</td>\n",
       "      <td>16.888117</td>\n",
       "      <td>16.696268</td>\n",
       "      <td>16.793429</td>\n",
       "      <td>2.800640e+08</td>\n",
       "      <td>3.319674e+06</td>\n",
       "      <td>14.718881</td>\n",
       "      <td>0.875526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>34.510000</td>\n",
       "      <td>35.350000</td>\n",
       "      <td>33.900000</td>\n",
       "      <td>35.310000</td>\n",
       "      <td>1.816440e+05</td>\n",
       "      <td>4.400000e+03</td>\n",
       "      <td>9.910000</td>\n",
       "      <td>0.170000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>52.110000</td>\n",
       "      <td>52.520000</td>\n",
       "      <td>51.500000</td>\n",
       "      <td>52.017500</td>\n",
       "      <td>4.614790e+07</td>\n",
       "      <td>8.376700e+05</td>\n",
       "      <td>41.467500</td>\n",
       "      <td>0.860000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>60.140000</td>\n",
       "      <td>60.685000</td>\n",
       "      <td>59.610000</td>\n",
       "      <td>60.150000</td>\n",
       "      <td>1.036209e+08</td>\n",
       "      <td>1.851050e+06</td>\n",
       "      <td>52.650000</td>\n",
       "      <td>1.160000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>70.717500</td>\n",
       "      <td>71.300000</td>\n",
       "      <td>70.012500</td>\n",
       "      <td>70.720000</td>\n",
       "      <td>2.164058e+08</td>\n",
       "      <td>3.348998e+06</td>\n",
       "      <td>63.395000</td>\n",
       "      <td>1.580000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>115.210000</td>\n",
       "      <td>115.260000</td>\n",
       "      <td>113.710000</td>\n",
       "      <td>115.210000</td>\n",
       "      <td>3.295958e+09</td>\n",
       "      <td>4.589951e+07</td>\n",
       "      <td>88.730000</td>\n",
       "      <td>15.760000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Open         High          Low        Close  Volume [BRL]  \\\n",
       "count  2936.000000  2936.000000  2936.000000  2936.000000  2.936000e+03   \n",
       "mean     64.107006    64.664939    63.452142    64.074029  1.979881e+08   \n",
       "std      16.816599    16.888117    16.696268    16.793429  2.800640e+08   \n",
       "min      34.510000    35.350000    33.900000    35.310000  1.816440e+05   \n",
       "25%      52.110000    52.520000    51.500000    52.017500  4.614790e+07   \n",
       "50%      60.140000    60.685000    59.610000    60.150000  1.036209e+08   \n",
       "75%      70.717500    71.300000    70.012500    70.720000  2.164058e+08   \n",
       "max     115.210000   115.260000   113.710000   115.210000  3.295958e+09   \n",
       "\n",
       "       Volume [shares]  IFR (RSI) [9]  True Range [1]  \n",
       "count     2.936000e+03    2928.000000     2935.000000  \n",
       "mean      2.727684e+06      52.383282        1.344508  \n",
       "std       3.319674e+06      14.718881        0.875526  \n",
       "min       4.400000e+03       9.910000        0.170000  \n",
       "25%       8.376700e+05      41.467500        0.860000  \n",
       "50%       1.851050e+06      52.650000        1.160000  \n",
       "75%       3.348998e+06      63.395000        1.580000  \n",
       "max       4.589951e+07      88.730000       15.760000  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bova_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2020-10-16</th>\n",
       "      <td>81.06</td>\n",
       "      <td>83.36</td>\n",
       "      <td>80.91</td>\n",
       "      <td>82.05</td>\n",
       "      <td>525025471.0</td>\n",
       "      <td>6355600</td>\n",
       "      <td>2.59</td>\n",
       "      <td>89.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-15</th>\n",
       "      <td>79.64</td>\n",
       "      <td>81.28</td>\n",
       "      <td>78.10</td>\n",
       "      <td>80.77</td>\n",
       "      <td>490407232.0</td>\n",
       "      <td>6096300</td>\n",
       "      <td>3.18</td>\n",
       "      <td>88.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-14</th>\n",
       "      <td>79.26</td>\n",
       "      <td>80.98</td>\n",
       "      <td>79.26</td>\n",
       "      <td>80.58</td>\n",
       "      <td>527948834.0</td>\n",
       "      <td>6579000</td>\n",
       "      <td>1.83</td>\n",
       "      <td>88.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-13</th>\n",
       "      <td>77.88</td>\n",
       "      <td>79.47</td>\n",
       "      <td>77.35</td>\n",
       "      <td>79.15</td>\n",
       "      <td>473625847.0</td>\n",
       "      <td>6030100</td>\n",
       "      <td>2.57</td>\n",
       "      <td>86.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-09</th>\n",
       "      <td>74.94</td>\n",
       "      <td>77.18</td>\n",
       "      <td>74.55</td>\n",
       "      <td>76.90</td>\n",
       "      <td>438389500.0</td>\n",
       "      <td>5745100</td>\n",
       "      <td>2.63</td>\n",
       "      <td>84.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994-07-08</th>\n",
       "      <td>0.04</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.04</td>\n",
       "      <td>152830.0</td>\n",
       "      <td>1666340</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994-07-07</th>\n",
       "      <td>0.04</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.04</td>\n",
       "      <td>393366.0</td>\n",
       "      <td>4495400</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994-07-06</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.04</td>\n",
       "      <td>6195.0</td>\n",
       "      <td>74360</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994-07-05</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>1925.0</td>\n",
       "      <td>23660</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994-07-04</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>267.0</td>\n",
       "      <td>3380</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5660 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Open   High    Low  Close  Volume [BRL]  Volume [shares]  \\\n",
       "Date                                                                    \n",
       "2020-10-16  81.06  83.36  80.91  82.05   525025471.0          6355600   \n",
       "2020-10-15  79.64  81.28  78.10  80.77   490407232.0          6096300   \n",
       "2020-10-14  79.26  80.98  79.26  80.58   527948834.0          6579000   \n",
       "2020-10-13  77.88  79.47  77.35  79.15   473625847.0          6030100   \n",
       "2020-10-09  74.94  77.18  74.55  76.90   438389500.0          5745100   \n",
       "...           ...    ...    ...    ...           ...              ...   \n",
       "1994-07-08   0.04   0.04   0.04   0.04      152830.0          1666340   \n",
       "1994-07-07   0.04   0.04   0.04   0.04      393366.0          4495400   \n",
       "1994-07-06   0.03   0.04   0.03   0.04        6195.0            74360   \n",
       "1994-07-05   0.03   0.03   0.03   0.03        1925.0            23660   \n",
       "1994-07-04   0.03   0.03   0.03   0.03         267.0             3380   \n",
       "\n",
       "            IFR (RSI) [9]  True Range [1]  \n",
       "Date                                       \n",
       "2020-10-16           2.59           89.56  \n",
       "2020-10-15           3.18           88.37  \n",
       "2020-10-14           1.83           88.19  \n",
       "2020-10-13           2.57           86.84  \n",
       "2020-10-09           2.63           84.34  \n",
       "...                   ...             ...  \n",
       "1994-07-08           0.00             NaN  \n",
       "1994-07-07           0.00             NaN  \n",
       "1994-07-06           0.00             NaN  \n",
       "1994-07-05           0.00             NaN  \n",
       "1994-07-04            NaN             NaN  \n",
       "\n",
       "[5660 rows x 8 columns]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wege_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume [BRL]</th>\n",
       "      <th>Volume [shares]</th>\n",
       "      <th>IFR (RSI) [9]</th>\n",
       "      <th>True Range [1]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5660.000000</td>\n",
       "      <td>5660.000000</td>\n",
       "      <td>5660.000000</td>\n",
       "      <td>5660.000000</td>\n",
       "      <td>5.660000e+03</td>\n",
       "      <td>5.660000e+03</td>\n",
       "      <td>5659.000000</td>\n",
       "      <td>5652.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.189864</td>\n",
       "      <td>7.308733</td>\n",
       "      <td>7.080466</td>\n",
       "      <td>7.190986</td>\n",
       "      <td>2.421622e+07</td>\n",
       "      <td>1.696467e+06</td>\n",
       "      <td>0.239435</td>\n",
       "      <td>54.764418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>10.367506</td>\n",
       "      <td>10.575458</td>\n",
       "      <td>10.195435</td>\n",
       "      <td>10.388577</td>\n",
       "      <td>6.375940e+07</td>\n",
       "      <td>2.378154e+06</td>\n",
       "      <td>0.494385</td>\n",
       "      <td>15.186430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.030000</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>2.670000e+02</td>\n",
       "      <td>3.380000e+03</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.820000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.745000</td>\n",
       "      <td>0.757500</td>\n",
       "      <td>0.745000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>3.395825e+05</td>\n",
       "      <td>3.627585e+05</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>44.115000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.170000</td>\n",
       "      <td>4.230000</td>\n",
       "      <td>4.100000</td>\n",
       "      <td>4.160000</td>\n",
       "      <td>4.826972e+06</td>\n",
       "      <td>1.147172e+06</td>\n",
       "      <td>0.110000</td>\n",
       "      <td>54.555000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>10.120000</td>\n",
       "      <td>10.302500</td>\n",
       "      <td>9.942500</td>\n",
       "      <td>10.092500</td>\n",
       "      <td>2.168816e+07</td>\n",
       "      <td>2.295858e+06</td>\n",
       "      <td>0.260000</td>\n",
       "      <td>65.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>81.060000</td>\n",
       "      <td>83.360000</td>\n",
       "      <td>80.910000</td>\n",
       "      <td>82.050000</td>\n",
       "      <td>1.172040e+09</td>\n",
       "      <td>6.801033e+07</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>95.270000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Open         High          Low        Close  Volume [BRL]  \\\n",
       "count  5660.000000  5660.000000  5660.000000  5660.000000  5.660000e+03   \n",
       "mean      7.189864     7.308733     7.080466     7.190986  2.421622e+07   \n",
       "std      10.367506    10.575458    10.195435    10.388577  6.375940e+07   \n",
       "min       0.030000     0.030000     0.030000     0.030000  2.670000e+02   \n",
       "25%       0.745000     0.757500     0.745000     0.750000  3.395825e+05   \n",
       "50%       4.170000     4.230000     4.100000     4.160000  4.826972e+06   \n",
       "75%      10.120000    10.302500     9.942500    10.092500  2.168816e+07   \n",
       "max      81.060000    83.360000    80.910000    82.050000  1.172040e+09   \n",
       "\n",
       "       Volume [shares]  IFR (RSI) [9]  True Range [1]  \n",
       "count     5.660000e+03    5659.000000     5652.000000  \n",
       "mean      1.696467e+06       0.239435       54.764418  \n",
       "std       2.378154e+06       0.494385       15.186430  \n",
       "min       3.380000e+03       0.000000        8.820000  \n",
       "25%       3.627585e+05       0.010000       44.115000  \n",
       "50%       1.147172e+06       0.110000       54.555000  \n",
       "75%       2.295858e+06       0.260000       65.400000  \n",
       "max       6.801033e+07       9.000000       95.270000  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wege_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check how many entries we have for each dataframe and the first entry's date:\n",
    "(keep in mind these dates are in the format dd/mm/yyyy) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We have 2936 entries for BOVA11 since the date of 2008-12-02\n",
      "We have 5660 entries for WEGE3 since the date of 1994-07-04\n"
     ]
    }
   ],
   "source": [
    "print(f'We have {len(bova_df)} entries for BOVA11 since the date of {bova_df.index[-1]}')\n",
    "print(f'We have {len(wege_df)} entries for WEGE3 since the date of {wege_df.index[-1]}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume [BRL]</th>\n",
       "      <th>Volume [shares]</th>\n",
       "      <th>IFR (RSI) [9]</th>\n",
       "      <th>True Range [1]</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1994-07-04</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>267.0</td>\n",
       "      <td>3380</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994-07-05</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>1925.0</td>\n",
       "      <td>23660</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994-07-06</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.04</td>\n",
       "      <td>6195.0</td>\n",
       "      <td>74360</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994-07-07</th>\n",
       "      <td>0.04</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.04</td>\n",
       "      <td>393366.0</td>\n",
       "      <td>4495400</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994-07-08</th>\n",
       "      <td>0.04</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.04</td>\n",
       "      <td>152830.0</td>\n",
       "      <td>1666340</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Open  High   Low  Close  Volume [BRL]  Volume [shares]  \\\n",
       "Date                                                                 \n",
       "1994-07-04  0.03  0.03  0.03   0.03         267.0             3380   \n",
       "1994-07-05  0.03  0.03  0.03   0.03        1925.0            23660   \n",
       "1994-07-06  0.03  0.04  0.03   0.04        6195.0            74360   \n",
       "1994-07-07  0.04  0.04  0.04   0.04      393366.0          4495400   \n",
       "1994-07-08  0.04  0.04  0.04   0.04      152830.0          1666340   \n",
       "\n",
       "            IFR (RSI) [9]  True Range [1]  \n",
       "Date                                       \n",
       "1994-07-04            NaN             NaN  \n",
       "1994-07-05            0.0             NaN  \n",
       "1994-07-06            0.0             NaN  \n",
       "1994-07-07            0.0             NaN  \n",
       "1994-07-08            0.0             NaN  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wege_df = wege_df.iloc[::-1]\n",
    "# wege_df.reset_index(drop=True, inplace=True) \n",
    "wege_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume [BRL]</th>\n",
       "      <th>Volume [shares]</th>\n",
       "      <th>IFR (RSI) [9]</th>\n",
       "      <th>True Range [1]</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2008-12-02</th>\n",
       "      <td>35.11</td>\n",
       "      <td>35.81</td>\n",
       "      <td>34.91</td>\n",
       "      <td>35.39</td>\n",
       "      <td>26819970.0</td>\n",
       "      <td>759900</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-12-03</th>\n",
       "      <td>34.73</td>\n",
       "      <td>35.40</td>\n",
       "      <td>33.90</td>\n",
       "      <td>35.31</td>\n",
       "      <td>11482717.0</td>\n",
       "      <td>330100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-12-04</th>\n",
       "      <td>35.80</td>\n",
       "      <td>36.00</td>\n",
       "      <td>35.15</td>\n",
       "      <td>35.70</td>\n",
       "      <td>4554168.0</td>\n",
       "      <td>128400</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-12-05</th>\n",
       "      <td>34.51</td>\n",
       "      <td>35.35</td>\n",
       "      <td>34.11</td>\n",
       "      <td>35.35</td>\n",
       "      <td>6671196.0</td>\n",
       "      <td>193700</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-12-08</th>\n",
       "      <td>37.35</td>\n",
       "      <td>38.46</td>\n",
       "      <td>36.95</td>\n",
       "      <td>38.46</td>\n",
       "      <td>3857295.0</td>\n",
       "      <td>103100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Open   High    Low  Close  Volume [BRL]  Volume [shares]  \\\n",
       "Date                                                                    \n",
       "2008-12-02  35.11  35.81  34.91  35.39    26819970.0           759900   \n",
       "2008-12-03  34.73  35.40  33.90  35.31    11482717.0           330100   \n",
       "2008-12-04  35.80  36.00  35.15  35.70     4554168.0           128400   \n",
       "2008-12-05  34.51  35.35  34.11  35.35     6671196.0           193700   \n",
       "2008-12-08  37.35  38.46  36.95  38.46     3857295.0           103100   \n",
       "\n",
       "            IFR (RSI) [9]  True Range [1]  \n",
       "Date                                       \n",
       "2008-12-02            NaN             NaN  \n",
       "2008-12-03            NaN            1.50  \n",
       "2008-12-04            NaN            0.85  \n",
       "2008-12-05            NaN            1.59  \n",
       "2008-12-08            NaN            3.11  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bova_df = bova_df.iloc[::-1]\n",
    "# bova_df.reset_index(drop=True, inplace=True)\n",
    "bova_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because our dataset show the most recent data first, we should reverse de dataframes for plotting in a usual way."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x504 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax1 = wege_df[[\"Open\", \"High\", \"Low\", \"Close\"]].plot(figsize=(20,7), title=\"WEGE3 OCHL Price [BRL]\", logy=True, grid=True)\n",
    "plt.savefig(\"WEGE3_OCHL.png\")\n",
    "ax2 = wege_df[[\"Volume [BRL]\", \"Volume [shares]\", \"IFR (RSI) [9]\", \"True Range [1]\"]].plot(figsize=(20,7), subplots=True, sharex=ax1, grid=True, title=\"WEGE3 Technical Indicators\")\n",
    "plt.savefig(\"WEGE3_TA.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2004\n",
      "3656\n"
     ]
    }
   ],
   "source": [
    "print(len(wege_df[wege_df[\"Open\"] < wege_df[\"Close\"]]))\n",
    "print(len(wege_df[wege_df[\"Open\"] >= wege_df[\"Close\"]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x504 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax1 = bova_df[[\"Open\", \"High\", \"Low\", \"Close\"]].plot(figsize=(20,7), title=\"BOVA11 OCHL Prices [BRL]\", logy=True, grid=True)\n",
    "plt.savefig(\"BOVA11_OCHL.png\")\n",
    "ax2 = bova_df[[\"Volume [BRL]\", \"Volume [shares]\", \"IFR (RSI) [9]\", \"True Range [1]\"]].plot(figsize=(20,7), subplots=True, sharex=ax1, grid=True, title=\"BOVA11 Technical Indicators\")\n",
    "plt.savefig(\"BOVA11_TA.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1432\n",
      "1504\n"
     ]
    }
   ],
   "source": [
    "print(len(bova_df[bova_df[\"Open\"] < bova_df[\"Close\"]]))\n",
    "print(len(bova_df[bova_df[\"Open\"] >= bova_df[\"Close\"]]))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Moving averages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculateMovingAverages(values, periods=21, exponential=False):\n",
    "    '''\n",
    "    \n",
    "    '''\n",
    "    moving_avgs = np.zeros(len(values))\n",
    "    if not exponential:\n",
    "        for i, value in enumerate(values):\n",
    "            #just so we don't have empty entries in our df\n",
    "            if i < periods: \n",
    "                moving_avgs[i] = sum(values[:i + 1])/(i + 1)\n",
    "            else:\n",
    "                moving_avgs[i] = sum(values[(i + 1 -periods):i + 1]) / periods\n",
    "    else:\n",
    "        K = 2 / (periods + 1)\n",
    "        for i, value in enumerate(values):\n",
    "            #just so we don't have empty entries in our df\n",
    "            if i == 0:\n",
    "                moving_avgs[i] - values[i]\n",
    "            elif i < periods:\n",
    "                moving_avgs[i] = (values[i] * (2 / (i + 1))) + (moving_avgs[i - 1] * (1 - (2 / (i + 1))))\n",
    "            else:\n",
    "                moving_avgs[i] = (values[i] * K) + (moving_avgs[i - 1] * (1 - K))\n",
    "    return moving_avgs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "wege_df[\"Moving Average A [21]\"] = calculateMovingAverages(wege_df[\"Close\"].values)\n",
    "bova_df[\"Moving Average A [21]\"] = calculateMovingAverages(bova_df[\"Close\"].values)\n",
    "wege_df[\"Moving Average E [9]\"] = calculateMovingAverages(wege_df[\"Close\"].values, periods=9, exponential=True)\n",
    "bova_df[\"Moving Average E [9]\"] = calculateMovingAverages(bova_df[\"Close\"].values, periods=9, exponential=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7ffbb09290>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "wege_df.head(10)\n",
    "wege_df[[\"Close\", \"Moving Average A [21]\", \"Moving Average E [9]\"]][-50:].plot(logy=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume [BRL]</th>\n",
       "      <th>Volume [shares]</th>\n",
       "      <th>IFR (RSI) [9]</th>\n",
       "      <th>True Range [1]</th>\n",
       "      <th>Moving Average A [21]</th>\n",
       "      <th>Moving Average E [9]</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2008-12-02</th>\n",
       "      <td>35.11</td>\n",
       "      <td>35.81</td>\n",
       "      <td>34.91</td>\n",
       "      <td>35.39</td>\n",
       "      <td>26819970.0</td>\n",
       "      <td>759900</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35.390000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-12-03</th>\n",
       "      <td>34.73</td>\n",
       "      <td>35.40</td>\n",
       "      <td>33.90</td>\n",
       "      <td>35.31</td>\n",
       "      <td>11482717.0</td>\n",
       "      <td>330100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.50</td>\n",
       "      <td>35.350000</td>\n",
       "      <td>35.310000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-12-04</th>\n",
       "      <td>35.80</td>\n",
       "      <td>36.00</td>\n",
       "      <td>35.15</td>\n",
       "      <td>35.70</td>\n",
       "      <td>4554168.0</td>\n",
       "      <td>128400</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.85</td>\n",
       "      <td>35.466667</td>\n",
       "      <td>35.570000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-12-05</th>\n",
       "      <td>34.51</td>\n",
       "      <td>35.35</td>\n",
       "      <td>34.11</td>\n",
       "      <td>35.35</td>\n",
       "      <td>6671196.0</td>\n",
       "      <td>193700</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.59</td>\n",
       "      <td>35.437500</td>\n",
       "      <td>35.460000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-12-08</th>\n",
       "      <td>37.35</td>\n",
       "      <td>38.46</td>\n",
       "      <td>36.95</td>\n",
       "      <td>38.46</td>\n",
       "      <td>3857295.0</td>\n",
       "      <td>103100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.11</td>\n",
       "      <td>36.042000</td>\n",
       "      <td>36.660000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-12-09</th>\n",
       "      <td>38.58</td>\n",
       "      <td>38.82</td>\n",
       "      <td>37.54</td>\n",
       "      <td>37.54</td>\n",
       "      <td>10449386.0</td>\n",
       "      <td>274500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.28</td>\n",
       "      <td>36.291667</td>\n",
       "      <td>36.953333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-12-10</th>\n",
       "      <td>38.95</td>\n",
       "      <td>39.68</td>\n",
       "      <td>38.61</td>\n",
       "      <td>39.03</td>\n",
       "      <td>9367989.0</td>\n",
       "      <td>241100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.14</td>\n",
       "      <td>36.682857</td>\n",
       "      <td>37.546667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-12-11</th>\n",
       "      <td>39.37</td>\n",
       "      <td>39.83</td>\n",
       "      <td>38.66</td>\n",
       "      <td>38.89</td>\n",
       "      <td>6451271.0</td>\n",
       "      <td>164200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.17</td>\n",
       "      <td>36.958750</td>\n",
       "      <td>37.882500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-12-12</th>\n",
       "      <td>37.35</td>\n",
       "      <td>39.28</td>\n",
       "      <td>37.00</td>\n",
       "      <td>39.11</td>\n",
       "      <td>2449122.0</td>\n",
       "      <td>64400</td>\n",
       "      <td>77.76</td>\n",
       "      <td>2.28</td>\n",
       "      <td>37.197778</td>\n",
       "      <td>38.155278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-12-15</th>\n",
       "      <td>39.31</td>\n",
       "      <td>39.73</td>\n",
       "      <td>38.37</td>\n",
       "      <td>38.57</td>\n",
       "      <td>3122589.0</td>\n",
       "      <td>79900</td>\n",
       "      <td>71.96</td>\n",
       "      <td>1.36</td>\n",
       "      <td>37.335000</td>\n",
       "      <td>38.238222</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Open   High    Low  Close  Volume [BRL]  Volume [shares]  \\\n",
       "Date                                                                    \n",
       "2008-12-02  35.11  35.81  34.91  35.39    26819970.0           759900   \n",
       "2008-12-03  34.73  35.40  33.90  35.31    11482717.0           330100   \n",
       "2008-12-04  35.80  36.00  35.15  35.70     4554168.0           128400   \n",
       "2008-12-05  34.51  35.35  34.11  35.35     6671196.0           193700   \n",
       "2008-12-08  37.35  38.46  36.95  38.46     3857295.0           103100   \n",
       "2008-12-09  38.58  38.82  37.54  37.54    10449386.0           274500   \n",
       "2008-12-10  38.95  39.68  38.61  39.03     9367989.0           241100   \n",
       "2008-12-11  39.37  39.83  38.66  38.89     6451271.0           164200   \n",
       "2008-12-12  37.35  39.28  37.00  39.11     2449122.0            64400   \n",
       "2008-12-15  39.31  39.73  38.37  38.57     3122589.0            79900   \n",
       "\n",
       "            IFR (RSI) [9]  True Range [1]  Moving Average A [21]  \\\n",
       "Date                                                               \n",
       "2008-12-02            NaN             NaN              35.390000   \n",
       "2008-12-03            NaN            1.50              35.350000   \n",
       "2008-12-04            NaN            0.85              35.466667   \n",
       "2008-12-05            NaN            1.59              35.437500   \n",
       "2008-12-08            NaN            3.11              36.042000   \n",
       "2008-12-09            NaN            1.28              36.291667   \n",
       "2008-12-10            NaN            2.14              36.682857   \n",
       "2008-12-11            NaN            1.17              36.958750   \n",
       "2008-12-12          77.76            2.28              37.197778   \n",
       "2008-12-15          71.96            1.36              37.335000   \n",
       "\n",
       "            Moving Average E [9]  \n",
       "Date                              \n",
       "2008-12-02              0.000000  \n",
       "2008-12-03             35.310000  \n",
       "2008-12-04             35.570000  \n",
       "2008-12-05             35.460000  \n",
       "2008-12-08             36.660000  \n",
       "2008-12-09             36.953333  \n",
       "2008-12-10             37.546667  \n",
       "2008-12-11             37.882500  \n",
       "2008-12-12             38.155278  \n",
       "2008-12-15             38.238222  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bova_df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Local Peaks and Troughs\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def FindLocalPeaks(df):\n",
    "    max_values = df[\"High\"].values\n",
    "    results = np.zeros(len(max_values))\n",
    "    for i, price in enumerate(max_values):\n",
    "        if i > 0 and i < len(max_values) - 1:\n",
    "            price_previous = max_values[i - 1]\n",
    "            price_current = price\n",
    "            price_next = max_values[i + 1]\n",
    "            if((price > price_previous) and (price > price_next)):\n",
    "                results[i] = 1\n",
    "    df[\"IsLocalPeak\"] = results\n",
    "       \n",
    "def FindLocalTroughs(df):\n",
    "    min_values = df[\"Low\"].values\n",
    "    results = np.zeros(len(min_values))\n",
    "    for i, price in enumerate(min_values):\n",
    "        if i > 0 and i < len(min_values) - 1:\n",
    "            price_previous = min_values[i - 1]\n",
    "            price_current = price\n",
    "            price_next = min_values[i + 1]\n",
    "            if((price < price_previous) and (price < price_next)):\n",
    "                results[i] = 1\n",
    "    df[\"IsLocalTrough\"] = results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume [BRL]</th>\n",
       "      <th>Volume [shares]</th>\n",
       "      <th>IFR (RSI) [9]</th>\n",
       "      <th>True Range [1]</th>\n",
       "      <th>Moving Average A [21]</th>\n",
       "      <th>Moving Average E [9]</th>\n",
       "      <th>IsLocalPeak</th>\n",
       "      <th>IsLocalTrough</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-05-27</th>\n",
       "      <td>40.72</td>\n",
       "      <td>40.86</td>\n",
       "      <td>39.48</td>\n",
       "      <td>40.46</td>\n",
       "      <td>194857220.0</td>\n",
       "      <td>4847700</td>\n",
       "      <td>1.38</td>\n",
       "      <td>68.03</td>\n",
       "      <td>38.500476</td>\n",
       "      <td>38.531814</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-28</th>\n",
       "      <td>40.25</td>\n",
       "      <td>41.03</td>\n",
       "      <td>40.03</td>\n",
       "      <td>40.56</td>\n",
       "      <td>187897946.0</td>\n",
       "      <td>4619700</td>\n",
       "      <td>1.00</td>\n",
       "      <td>68.56</td>\n",
       "      <td>38.457619</td>\n",
       "      <td>38.937451</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-29</th>\n",
       "      <td>40.08</td>\n",
       "      <td>41.70</td>\n",
       "      <td>39.49</td>\n",
       "      <td>41.70</td>\n",
       "      <td>362255443.0</td>\n",
       "      <td>8774100</td>\n",
       "      <td>2.21</td>\n",
       "      <td>74.05</td>\n",
       "      <td>38.477619</td>\n",
       "      <td>39.489961</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-01</th>\n",
       "      <td>41.76</td>\n",
       "      <td>42.87</td>\n",
       "      <td>41.68</td>\n",
       "      <td>42.03</td>\n",
       "      <td>233660323.0</td>\n",
       "      <td>5511600</td>\n",
       "      <td>1.19</td>\n",
       "      <td>75.44</td>\n",
       "      <td>38.582857</td>\n",
       "      <td>39.997969</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-02</th>\n",
       "      <td>42.29</td>\n",
       "      <td>42.67</td>\n",
       "      <td>41.68</td>\n",
       "      <td>42.29</td>\n",
       "      <td>139793807.0</td>\n",
       "      <td>3305300</td>\n",
       "      <td>0.99</td>\n",
       "      <td>76.55</td>\n",
       "      <td>38.705238</td>\n",
       "      <td>40.456375</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-09</th>\n",
       "      <td>74.94</td>\n",
       "      <td>77.18</td>\n",
       "      <td>74.55</td>\n",
       "      <td>76.90</td>\n",
       "      <td>438389500.0</td>\n",
       "      <td>5745100</td>\n",
       "      <td>2.63</td>\n",
       "      <td>84.34</td>\n",
       "      <td>66.217619</td>\n",
       "      <td>71.205888</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-13</th>\n",
       "      <td>77.88</td>\n",
       "      <td>79.47</td>\n",
       "      <td>77.35</td>\n",
       "      <td>79.15</td>\n",
       "      <td>473625847.0</td>\n",
       "      <td>6030100</td>\n",
       "      <td>2.57</td>\n",
       "      <td>86.84</td>\n",
       "      <td>67.031429</td>\n",
       "      <td>72.794711</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-14</th>\n",
       "      <td>79.26</td>\n",
       "      <td>80.98</td>\n",
       "      <td>79.26</td>\n",
       "      <td>80.58</td>\n",
       "      <td>527948834.0</td>\n",
       "      <td>6579000</td>\n",
       "      <td>1.83</td>\n",
       "      <td>88.19</td>\n",
       "      <td>67.854286</td>\n",
       "      <td>74.351768</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-15</th>\n",
       "      <td>79.64</td>\n",
       "      <td>81.28</td>\n",
       "      <td>78.10</td>\n",
       "      <td>80.77</td>\n",
       "      <td>490407232.0</td>\n",
       "      <td>6096300</td>\n",
       "      <td>3.18</td>\n",
       "      <td>88.37</td>\n",
       "      <td>68.650476</td>\n",
       "      <td>75.635415</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-16</th>\n",
       "      <td>81.06</td>\n",
       "      <td>83.36</td>\n",
       "      <td>80.91</td>\n",
       "      <td>82.05</td>\n",
       "      <td>525025471.0</td>\n",
       "      <td>6355600</td>\n",
       "      <td>2.59</td>\n",
       "      <td>89.56</td>\n",
       "      <td>69.561905</td>\n",
       "      <td>76.918332</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Open   High    Low  Close  Volume [BRL]  Volume [shares]  \\\n",
       "Date                                                                    \n",
       "2020-05-27  40.72  40.86  39.48  40.46   194857220.0          4847700   \n",
       "2020-05-28  40.25  41.03  40.03  40.56   187897946.0          4619700   \n",
       "2020-05-29  40.08  41.70  39.49  41.70   362255443.0          8774100   \n",
       "2020-06-01  41.76  42.87  41.68  42.03   233660323.0          5511600   \n",
       "2020-06-02  42.29  42.67  41.68  42.29   139793807.0          3305300   \n",
       "...           ...    ...    ...    ...           ...              ...   \n",
       "2020-10-09  74.94  77.18  74.55  76.90   438389500.0          5745100   \n",
       "2020-10-13  77.88  79.47  77.35  79.15   473625847.0          6030100   \n",
       "2020-10-14  79.26  80.98  79.26  80.58   527948834.0          6579000   \n",
       "2020-10-15  79.64  81.28  78.10  80.77   490407232.0          6096300   \n",
       "2020-10-16  81.06  83.36  80.91  82.05   525025471.0          6355600   \n",
       "\n",
       "            IFR (RSI) [9]  True Range [1]  Moving Average A [21]  \\\n",
       "Date                                                               \n",
       "2020-05-27           1.38           68.03              38.500476   \n",
       "2020-05-28           1.00           68.56              38.457619   \n",
       "2020-05-29           2.21           74.05              38.477619   \n",
       "2020-06-01           1.19           75.44              38.582857   \n",
       "2020-06-02           0.99           76.55              38.705238   \n",
       "...                   ...             ...                    ...   \n",
       "2020-10-09           2.63           84.34              66.217619   \n",
       "2020-10-13           2.57           86.84              67.031429   \n",
       "2020-10-14           1.83           88.19              67.854286   \n",
       "2020-10-15           3.18           88.37              68.650476   \n",
       "2020-10-16           2.59           89.56              69.561905   \n",
       "\n",
       "            Moving Average E [9]  IsLocalPeak  IsLocalTrough  \n",
       "Date                                                          \n",
       "2020-05-27             38.531814          0.0            0.0  \n",
       "2020-05-28             38.937451          0.0            0.0  \n",
       "2020-05-29             39.489961          0.0            1.0  \n",
       "2020-06-01             39.997969          1.0            0.0  \n",
       "2020-06-02             40.456375          0.0            0.0  \n",
       "...                          ...          ...            ...  \n",
       "2020-10-09             71.205888          0.0            0.0  \n",
       "2020-10-13             72.794711          0.0            0.0  \n",
       "2020-10-14             74.351768          0.0            0.0  \n",
       "2020-10-15             75.635415          0.0            1.0  \n",
       "2020-10-16             76.918332          0.0            0.0  \n",
       "\n",
       "[100 rows x 12 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FindLocalPeaks(wege_df)\n",
    "FindLocalTroughs(wege_df)\n",
    "wege_df.tail(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
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       "      <th>IsLocalPeak</th>\n",
       "      <th>IsLocalTrough</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-05-27</th>\n",
       "      <td>83.44</td>\n",
       "      <td>85.00</td>\n",
       "      <td>82.71</td>\n",
       "      <td>84.68</td>\n",
       "      <td>8.240108e+08</td>\n",
       "      <td>9844800</td>\n",
       "      <td>75.13</td>\n",
       "      <td>2.55</td>\n",
       "      <td>78.010952</td>\n",
       "      <td>80.627227</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2020-05-28</th>\n",
       "      <td>84.23</td>\n",
       "      <td>84.86</td>\n",
       "      <td>83.47</td>\n",
       "      <td>83.65</td>\n",
       "      <td>1.079753e+09</td>\n",
       "      <td>12827420</td>\n",
       "      <td>68.83</td>\n",
       "      <td>1.39</td>\n",
       "      <td>78.265714</td>\n",
       "      <td>81.231781</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-29</th>\n",
       "      <td>83.59</td>\n",
       "      <td>84.18</td>\n",
       "      <td>82.15</td>\n",
       "      <td>84.15</td>\n",
       "      <td>1.076640e+09</td>\n",
       "      <td>12938580</td>\n",
       "      <td>70.19</td>\n",
       "      <td>2.03</td>\n",
       "      <td>78.454762</td>\n",
       "      <td>81.815425</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-01</th>\n",
       "      <td>83.60</td>\n",
       "      <td>85.74</td>\n",
       "      <td>83.48</td>\n",
       "      <td>85.33</td>\n",
       "      <td>6.296574e+08</td>\n",
       "      <td>7400780</td>\n",
       "      <td>73.30</td>\n",
       "      <td>2.26</td>\n",
       "      <td>78.841429</td>\n",
       "      <td>82.518340</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-02</th>\n",
       "      <td>86.00</td>\n",
       "      <td>87.78</td>\n",
       "      <td>85.99</td>\n",
       "      <td>87.78</td>\n",
       "      <td>8.950469e+08</td>\n",
       "      <td>10279210</td>\n",
       "      <td>78.53</td>\n",
       "      <td>2.45</td>\n",
       "      <td>79.397619</td>\n",
       "      <td>83.570672</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-09</th>\n",
       "      <td>94.13</td>\n",
       "      <td>94.99</td>\n",
       "      <td>93.50</td>\n",
       "      <td>93.65</td>\n",
       "      <td>8.611634e+08</td>\n",
       "      <td>9149800</td>\n",
       "      <td>52.96</td>\n",
       "      <td>1.49</td>\n",
       "      <td>93.292381</td>\n",
       "      <td>92.710995</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-13</th>\n",
       "      <td>94.10</td>\n",
       "      <td>95.35</td>\n",
       "      <td>93.65</td>\n",
       "      <td>94.80</td>\n",
       "      <td>9.415492e+08</td>\n",
       "      <td>9960394</td>\n",
       "      <td>58.07</td>\n",
       "      <td>1.70</td>\n",
       "      <td>93.307619</td>\n",
       "      <td>93.128796</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-14</th>\n",
       "      <td>95.01</td>\n",
       "      <td>95.86</td>\n",
       "      <td>95.01</td>\n",
       "      <td>95.59</td>\n",
       "      <td>1.092614e+09</td>\n",
       "      <td>11431930</td>\n",
       "      <td>61.32</td>\n",
       "      <td>1.06</td>\n",
       "      <td>93.263810</td>\n",
       "      <td>93.621037</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-15</th>\n",
       "      <td>94.36</td>\n",
       "      <td>95.80</td>\n",
       "      <td>94.05</td>\n",
       "      <td>95.34</td>\n",
       "      <td>9.081088e+08</td>\n",
       "      <td>9544784</td>\n",
       "      <td>59.67</td>\n",
       "      <td>1.75</td>\n",
       "      <td>93.210476</td>\n",
       "      <td>93.964830</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-16</th>\n",
       "      <td>95.12</td>\n",
       "      <td>95.45</td>\n",
       "      <td>94.54</td>\n",
       "      <td>94.54</td>\n",
       "      <td>9.673009e+08</td>\n",
       "      <td>10188511</td>\n",
       "      <td>54.42</td>\n",
       "      <td>0.91</td>\n",
       "      <td>93.145238</td>\n",
       "      <td>94.079864</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Open   High    Low  Close  Volume [BRL]  Volume [shares]  \\\n",
       "Date                                                                    \n",
       "2020-05-27  83.44  85.00  82.71  84.68  8.240108e+08          9844800   \n",
       "2020-05-28  84.23  84.86  83.47  83.65  1.079753e+09         12827420   \n",
       "2020-05-29  83.59  84.18  82.15  84.15  1.076640e+09         12938580   \n",
       "2020-06-01  83.60  85.74  83.48  85.33  6.296574e+08          7400780   \n",
       "2020-06-02  86.00  87.78  85.99  87.78  8.950469e+08         10279210   \n",
       "...           ...    ...    ...    ...           ...              ...   \n",
       "2020-10-09  94.13  94.99  93.50  93.65  8.611634e+08          9149800   \n",
       "2020-10-13  94.10  95.35  93.65  94.80  9.415492e+08          9960394   \n",
       "2020-10-14  95.01  95.86  95.01  95.59  1.092614e+09         11431930   \n",
       "2020-10-15  94.36  95.80  94.05  95.34  9.081088e+08          9544784   \n",
       "2020-10-16  95.12  95.45  94.54  94.54  9.673009e+08         10188511   \n",
       "\n",
       "            IFR (RSI) [9]  True Range [1]  Moving Average A [21]  \\\n",
       "Date                                                               \n",
       "2020-05-27          75.13            2.55              78.010952   \n",
       "2020-05-28          68.83            1.39              78.265714   \n",
       "2020-05-29          70.19            2.03              78.454762   \n",
       "2020-06-01          73.30            2.26              78.841429   \n",
       "2020-06-02          78.53            2.45              79.397619   \n",
       "...                   ...             ...                    ...   \n",
       "2020-10-09          52.96            1.49              93.292381   \n",
       "2020-10-13          58.07            1.70              93.307619   \n",
       "2020-10-14          61.32            1.06              93.263810   \n",
       "2020-10-15          59.67            1.75              93.210476   \n",
       "2020-10-16          54.42            0.91              93.145238   \n",
       "\n",
       "            Moving Average E [9]  IsLocalPeak  IsLocalTrough  \n",
       "Date                                                          \n",
       "2020-05-27             80.627227          1.0            0.0  \n",
       "2020-05-28             81.231781          0.0            0.0  \n",
       "2020-05-29             81.815425          0.0            1.0  \n",
       "2020-06-01             82.518340          0.0            0.0  \n",
       "2020-06-02             83.570672          0.0            0.0  \n",
       "...                          ...          ...            ...  \n",
       "2020-10-09             92.710995          0.0            0.0  \n",
       "2020-10-13             93.128796          0.0            0.0  \n",
       "2020-10-14             93.621037          1.0            0.0  \n",
       "2020-10-15             93.964830          0.0            1.0  \n",
       "2020-10-16             94.079864          0.0            0.0  \n",
       "\n",
       "[100 rows x 12 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FindLocalPeaks(bova_df)\n",
    "FindLocalTroughs(bova_df)\n",
    "bova_df.tail(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f800009f4d0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "colorsPeak = [\"blue\" if x == 1 else \"black\" for x in bova_df[\"IsLocalPeak\"][-50:-1]]\n",
    "plt.scatter(range(0,49),bova_df[\"High\"].values[-50:-1] ,color=colorsPeak)\n",
    "colorTrough = [\"red\" if x == 1 else \"grey\" for x in bova_df[\"IsLocalTrough\"][-50:-1]]\n",
    "plt.scatter(range(0,49),bova_df[\"Low\"].values[-50:-1] ,color=colorTrough)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f7ffbfc6d50>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "colorsPeak = [\"blue\" if x == 1 else \"black\" for x in wege_df[\"IsLocalPeak\"][-50:-1]]\n",
    "plt.scatter(range(0,49),wege_df[\"High\"].values[-50:-1] ,color=colorsPeak)\n",
    "colorTrough = [\"red\" if x == 1 else \"grey\" for x in wege_df[\"IsLocalTrough\"][-50:-1]]\n",
    "plt.scatter(range(0,49),wege_df[\"Low\"].values[-50:-1] ,color=colorTrough)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## First derivatives"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calc_derivatives(df, peaks=True):\n",
    "    column_values = \"High\" if peaks else \"Low\"\n",
    "    column_bools = \"IsLocalPeak\" if peaks else \"IsLocalTrough\"\n",
    "    values = df[column_values].values\n",
    "    booleans = df[column_bools].values == 1\n",
    "    derivatives = np.zeros(len(values))\n",
    "    isFirst = True\n",
    "    derivative = 0\n",
    "    for i, value in enumerate(values):\n",
    "        if booleans[i] and isFirst:\n",
    "            isFirst = False\n",
    "            previous_value = value\n",
    "            previous_index = i\n",
    "        elif not isFirst and booleans[i]:\n",
    "            derivative = (value - previous_value) / (i - previous_index)\n",
    "            previous_value = value\n",
    "            previous_index = i\n",
    "        derivatives[i] = derivative\n",
    "    return derivatives"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "wege_df[\"derivative_peaks\"] = calc_derivatives(wege_df, peaks=True)\n",
    "wege_df[\"derivative_troughs\"] = calc_derivatives(wege_df, peaks=False)\n",
    "\n",
    "bova_df[\"derivative_peaks\"] = calc_derivatives(bova_df, peaks=True)\n",
    "bova_df[\"derivative_troughs\"] = calc_derivatives(bova_df, peaks=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Candlestick Patterns\n",
    "### Bearish Harami\n",
    "#### KEY TAKEAWAYS\n",
    "A bearish harami is a candlestick chart indicator for reversal in a bull price movement.\n",
    "It is generally indicated by a small decrease in price (signified by a black candle) that can be contained within the given equity's upward price movement (signified by white candles) from the past day or two.\n",
    "Traders can use technical indicators, such as the relative strength index (RSI) and the stochastic oscillator with a bearish harami to increase the chance of a successful trade."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calc_harami_bear(df):\n",
    "    peak_derivatives = df[\"derivative_peaks\"].values\n",
    "    trough_derivatives = df[\"derivative_troughs\"].values\n",
    "    strenghts = np.zeros(len(df.index))\n",
    "    for i in range(1, len(df.index)):\n",
    "        strenght = 0\n",
    "        prev_open = df['Open'][i - 1]\n",
    "        prev_close = df['Close'][i - 1]\n",
    "        curr_open = df['Open'][i]\n",
    "        curr_close = df['Close'][i]\n",
    "        if peak_derivatives[i] > 0 and trough_derivatives[i] > 0:\n",
    "            if prev_open < prev_close: # previous candle is a bullish candle\n",
    "                if curr_open > curr_close: # current candle is a bearish candle \n",
    "                    if prev_close > curr_open and prev_open < curr_close: # current body is \"contained\" in previous body\n",
    "                        prev_body_size = prev_close - prev_open\n",
    "                        curr_body_size = curr_open - curr_close\n",
    "                        strenght = prev_body_size / curr_body_size\n",
    "        strenghts[i] = strenght\n",
    "    return strenghts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "bear_harami_colname = \"bear_harami\"\n",
    "wege_df[bear_harami_colname] = calc_harami_bear(wege_df)\n",
    "bova_df[bear_harami_colname] = calc_harami_bear(bova_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bullish Harami\n",
    "https://www.investopedia.com/terms/b/bullishengulfingpattern.asp#:~:text=A%20bullish%20engulfing%20pattern%20can,of%20the%20previous%20day's%20candlestick."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calc_harami_bull(df):\n",
    "    peak_derivatives = df[\"derivative_peaks\"].values\n",
    "    trough_derivatives = df[\"derivative_troughs\"].values\n",
    "    strenghts = np.zeros(len(df.index))\n",
    "    for i in range(1, len(df.index)):\n",
    "        strenght = 0\n",
    "        prev_open = df['Open'][i - 1]\n",
    "        prev_close = df['Close'][i - 1]\n",
    "        curr_open = df['Open'][i]\n",
    "        curr_close = df['Close'][i]\n",
    "        if peak_derivatives[i] < 0 and trough_derivatives[i] < 0:\n",
    "            if prev_open > prev_close: # previous candle is a bullish candle\n",
    "                if curr_open < curr_close: # current candle is a bearish candle \n",
    "                    if prev_close < curr_open and prev_open > curr_close: # current body is \"contained\" in previous body\n",
    "                        prev_body_size = - prev_close + prev_open\n",
    "                        curr_body_size = -curr_open + curr_close\n",
    "                        strenght = prev_body_size / curr_body_size\n",
    "        strenghts[i] = strenght\n",
    "    return strenghts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "bull_harami_colname = \"bull_harami\"\n",
    "wege_df[bull_harami_colname] = calc_harami_bull(wege_df)\n",
    "bova_df[bull_harami_colname] = calc_harami_bull(bova_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bearish Engulfing\n",
    "https://www.investopedia.com/terms/b/bearishengulfingp.asp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calc_bear_engulfing(df):\n",
    "    peak_derivatives = df[\"derivative_peaks\"].values\n",
    "    trough_derivatives = df[\"derivative_troughs\"].values\n",
    "    strenghts = np.zeros(len(df.index))\n",
    "    for i in range(2, len(df.index)):\n",
    "        strenght = 0\n",
    "        prev_open = df['Open'][i - 1]\n",
    "        prev_close = df['Close'][i - 1]\n",
    "        curr_open = df['Open'][i]\n",
    "        curr_close = df['Close'][i]\n",
    "        if peak_derivatives[i] > 0 and trough_derivatives[i] > 0:\n",
    "            if prev_open < prev_close: # previous candle is a bullish candle\n",
    "                if curr_open > curr_close: # current candle is a bearish candle \n",
    "                    if prev_close < curr_open and prev_open > curr_close: # previous body is \"contained\" in current body\n",
    "                        '''\n",
    "                        Ideally, both candles are of substantial size relative to the price bars around them.\n",
    "                        Two very small bars may create an engulfing pattern, but it is far less significant than if both candles are large.\n",
    "                        '''\n",
    "                        prev_body_size = prev_close - prev_open\n",
    "                        curr_body_size = curr_open - curr_close\n",
    "                        base_size = abs(df['Open'][i - 2] - df['Close'][i - 2]) + 0.01 # avoid division by zero\n",
    "                        strenght = (prev_body_size + curr_body_size) / base_size\n",
    "        strenghts[i] = strenght\n",
    "    return strenghts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "bear_engulfing_colname = \"bear_engulfing\"\n",
    "wege_df[bear_engulfing_colname] = calc_bear_engulfing(wege_df)\n",
    "bova_df[bear_engulfing_colname] = calc_bear_engulfing(bova_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bulish Engulfing\n",
    "https://www.investopedia.com/terms/b/bullishengulfingpattern.asp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calc_bull_engulfing(df):\n",
    "    peak_derivatives = df[\"derivative_peaks\"].values\n",
    "    trough_derivatives = df[\"derivative_troughs\"].values\n",
    "    strenghts = np.zeros(len(df.index))\n",
    "    for i in range(2, len(df.index)):\n",
    "        strenght = 0\n",
    "        prev_open = df['Open'][i - 1]\n",
    "        prev_close = df['Close'][i - 1]\n",
    "        curr_open = df['Open'][i]\n",
    "        curr_close = df['Close'][i]\n",
    "        if peak_derivatives[i] < 0 and trough_derivatives[i] < 0:\n",
    "            if prev_open > prev_close: # previous candle is a bullish candle\n",
    "                if curr_open < curr_close: # current candle is a bearish candle \n",
    "                    if prev_close > curr_open and prev_open < curr_close: # previous body is \"contained\" in current body\n",
    "                        '''\n",
    "                        Ideally, both candles are of substantial size relative to the price bars around them.\n",
    "                        Two very small bars may create an engulfing pattern, but it is far less significant than if both candles are large.\n",
    "                        '''\n",
    "                        prev_body_size = abs(prev_close - prev_open)\n",
    "                        curr_body_size = abs(curr_open - curr_close)\n",
    "                        base_size = abs(df['Open'][i - 2] - df['Close'][i - 2]) + 0.000001 # avoid division by zero\n",
    "                        strenght = (prev_body_size + curr_body_size) / base_size\n",
    "        strenghts[i] = strenght\n",
    "    return strenghts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "bull_engulfing_colname = \"bull_engulfing\"\n",
    "wege_df[bull_engulfing_colname] = calc_bull_engulfing(wege_df)\n",
    "bova_df[bull_engulfing_colname] = calc_bull_engulfing(bova_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hammer\n",
    "https://www.investopedia.com/terms/h/hammer.asp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "def detect_hammers(df):\n",
    "    peak_derivatives = df[\"derivative_peaks\"].values\n",
    "    trough_derivatives = df[\"derivative_troughs\"].values\n",
    "    hammer_detections = np.zeros(len(df.index))\n",
    "    for i in range(len(df.index)):\n",
    "        hammer_detected = 0\n",
    "        curr_open = df['Open'][i]\n",
    "        curr_close = df['Close'][i]\n",
    "        curr_max = df[\"High\"][i]\n",
    "        curr_min = df[\"Low\"][i]\n",
    "        if peak_derivatives[i] < 0 and trough_derivatives[i] < 0:\n",
    "            body_size = abs(curr_open - curr_close) + 0.000001 # avoid division by zero\n",
    "            lower_shadow = abs(min(curr_open, curr_close) - curr_min) + 0.000001 # avoid division by zero\n",
    "            upper_shadow = abs(max(curr_open, curr_close) - curr_max) + 0.000001 # avoid division by zero\n",
    "            if lower_shadow / body_size >= 5 and body_size / upper_shadow < 1:\n",
    "                hammer_detected = 1\n",
    "        hammer_detections[i] = hammer_detected\n",
    "    return hammer_detections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "hammer_colname = \"hammer\"\n",
    "wege_df[hammer_colname] = detect_hammers(wege_df)\n",
    "bova_df[hammer_colname] = detect_hammers(bova_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Shooting Star"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "def detect_shooting_stars(df):\n",
    "    peak_derivatives = df[\"derivative_peaks\"].values\n",
    "    trough_derivatives = df[\"derivative_troughs\"].values\n",
    "    hammer_detections = np.zeros(len(df.index))\n",
    "    for i in range(len(df.index)):\n",
    "        detected = 0\n",
    "        curr_open = df['Open'][i]\n",
    "        curr_close = df['Close'][i]\n",
    "        curr_max = df[\"High\"][i]\n",
    "        curr_min = df[\"Low\"][i]\n",
    "        if peak_derivatives[i] > 0 and trough_derivatives[i] > 0:\n",
    "            body_size = abs(curr_open - curr_close) + 0.000001 # avoid division by zero\n",
    "            lower_shadow = abs(min(curr_open, curr_close) - curr_min) + 0.000001 # avoid division by zero\n",
    "            upper_shadow = abs(max(curr_open, curr_close) - curr_max) + 0.000001 # avoid division by zero\n",
    "            if upper_shadow / body_size >= 5 and body_size / lower_shadow < 1:\n",
    "                detected = 1\n",
    "        hammer_detections[i] = detected\n",
    "    return hammer_detections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "shootin_star_colname = \"shooting_star\"\n",
    "wege_df[shootin_star_colname] = detect_shooting_stars(wege_df)\n",
    "bova_df[shootin_star_colname] = detect_shooting_stars(bova_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Doji\n",
    "https://www.investopedia.com/terms/d/doji.asp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume [BRL]</th>\n",
       "      <th>Volume [shares]</th>\n",
       "      <th>IFR (RSI) [9]</th>\n",
       "      <th>True Range [1]</th>\n",
       "      <th>Moving Average A [21]</th>\n",
       "      <th>Moving Average E [9]</th>\n",
       "      <th>IsLocalPeak</th>\n",
       "      <th>IsLocalTrough</th>\n",
       "      <th>derivative_peaks</th>\n",
       "      <th>derivative_troughs</th>\n",
       "      <th>bear_harami</th>\n",
       "      <th>bull_harami</th>\n",
       "      <th>bear_engulfing</th>\n",
       "      <th>bull_engulfing</th>\n",
       "      <th>hammer</th>\n",
       "      <th>shooting_star</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5660.000000</td>\n",
       "      <td>5660.000000</td>\n",
       "      <td>5660.000000</td>\n",
       "      <td>5660.000000</td>\n",
       "      <td>5.660000e+03</td>\n",
       "      <td>5.660000e+03</td>\n",
       "      <td>5659.000000</td>\n",
       "      <td>5652.000000</td>\n",
       "      <td>5660.000000</td>\n",
       "      <td>5660.000000</td>\n",
       "      <td>5660.000000</td>\n",
       "      <td>5660.000000</td>\n",
       "      <td>5660.000000</td>\n",
       "      <td>5660.000000</td>\n",
       "      <td>5660.000000</td>\n",
       "      <td>5660.000000</td>\n",
       "      <td>5660.000000</td>\n",
       "      <td>5660.000000</td>\n",
       "      <td>5660.000000</td>\n",
       "      <td>5660.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.189864</td>\n",
       "      <td>7.308733</td>\n",
       "      <td>7.080466</td>\n",
       "      <td>7.190986</td>\n",
       "      <td>2.421622e+07</td>\n",
       "      <td>1.696467e+06</td>\n",
       "      <td>0.239435</td>\n",
       "      <td>54.764418</td>\n",
       "      <td>7.061291</td>\n",
       "      <td>7.136648</td>\n",
       "      <td>0.154240</td>\n",
       "      <td>0.159187</td>\n",
       "      <td>0.006111</td>\n",
       "      <td>0.003583</td>\n",
       "      <td>0.028129</td>\n",
       "      <td>0.043797</td>\n",
       "      <td>0.058802</td>\n",
       "      <td>0.026635</td>\n",
       "      <td>0.016078</td>\n",
       "      <td>0.021731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>10.367506</td>\n",
       "      <td>10.575458</td>\n",
       "      <td>10.195435</td>\n",
       "      <td>10.388577</td>\n",
       "      <td>6.375940e+07</td>\n",
       "      <td>2.378154e+06</td>\n",
       "      <td>0.494385</td>\n",
       "      <td>15.186430</td>\n",
       "      <td>9.984208</td>\n",
       "      <td>10.207024</td>\n",
       "      <td>0.361211</td>\n",
       "      <td>0.365883</td>\n",
       "      <td>0.134899</td>\n",
       "      <td>0.131706</td>\n",
       "      <td>0.448885</td>\n",
       "      <td>0.709980</td>\n",
       "      <td>1.141617</td>\n",
       "      <td>1.064473</td>\n",
       "      <td>0.125786</td>\n",
       "      <td>0.145818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.030000</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>2.670000e+02</td>\n",
       "      <td>3.380000e+03</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.820000</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.655000</td>\n",
       "      <td>-2.354000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.745000</td>\n",
       "      <td>0.757500</td>\n",
       "      <td>0.745000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>3.395825e+05</td>\n",
       "      <td>3.627585e+05</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>44.115000</td>\n",
       "      <td>0.681548</td>\n",
       "      <td>0.716882</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.010313</td>\n",
       "      <td>-0.007500</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.170000</td>\n",
       "      <td>4.230000</td>\n",
       "      <td>4.100000</td>\n",
       "      <td>4.160000</td>\n",
       "      <td>4.826972e+06</td>\n",
       "      <td>1.147172e+06</td>\n",
       "      <td>0.110000</td>\n",
       "      <td>54.555000</td>\n",
       "      <td>4.180952</td>\n",
       "      <td>4.175921</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>10.120000</td>\n",
       "      <td>10.302500</td>\n",
       "      <td>9.942500</td>\n",
       "      <td>10.092500</td>\n",
       "      <td>2.168816e+07</td>\n",
       "      <td>2.295858e+06</td>\n",
       "      <td>0.260000</td>\n",
       "      <td>65.400000</td>\n",
       "      <td>10.090000</td>\n",
       "      <td>10.141766</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.015000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>81.060000</td>\n",
       "      <td>83.360000</td>\n",
       "      <td>80.910000</td>\n",
       "      <td>82.050000</td>\n",
       "      <td>1.172040e+09</td>\n",
       "      <td>6.801033e+07</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>95.270000</td>\n",
       "      <td>69.561905</td>\n",
       "      <td>76.918332</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.735000</td>\n",
       "      <td>1.953333</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>76.992301</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Open         High          Low        Close  Volume [BRL]  \\\n",
       "count  5660.000000  5660.000000  5660.000000  5660.000000  5.660000e+03   \n",
       "mean      7.189864     7.308733     7.080466     7.190986  2.421622e+07   \n",
       "std      10.367506    10.575458    10.195435    10.388577  6.375940e+07   \n",
       "min       0.030000     0.030000     0.030000     0.030000  2.670000e+02   \n",
       "25%       0.745000     0.757500     0.745000     0.750000  3.395825e+05   \n",
       "50%       4.170000     4.230000     4.100000     4.160000  4.826972e+06   \n",
       "75%      10.120000    10.302500     9.942500    10.092500  2.168816e+07   \n",
       "max      81.060000    83.360000    80.910000    82.050000  1.172040e+09   \n",
       "\n",
       "       Volume [shares]  IFR (RSI) [9]  True Range [1]  Moving Average A [21]  \\\n",
       "count     5.660000e+03    5659.000000     5652.000000            5660.000000   \n",
       "mean      1.696467e+06       0.239435       54.764418               7.061291   \n",
       "std       2.378154e+06       0.494385       15.186430               9.984208   \n",
       "min       3.380000e+03       0.000000        8.820000               0.030000   \n",
       "25%       3.627585e+05       0.010000       44.115000               0.681548   \n",
       "50%       1.147172e+06       0.110000       54.555000               4.180952   \n",
       "75%       2.295858e+06       0.260000       65.400000              10.090000   \n",
       "max       6.801033e+07       9.000000       95.270000              69.561905   \n",
       "\n",
       "       Moving Average E [9]  IsLocalPeak  IsLocalTrough  derivative_peaks  \\\n",
       "count           5660.000000  5660.000000    5660.000000       5660.000000   \n",
       "mean               7.136648     0.154240       0.159187          0.006111   \n",
       "std               10.207024     0.361211       0.365883          0.134899   \n",
       "min                0.000000     0.000000       0.000000         -1.655000   \n",
       "25%                0.716882     0.000000       0.000000         -0.010313   \n",
       "50%                4.175921     0.000000       0.000000          0.000000   \n",
       "75%               10.141766     0.000000       0.000000          0.015000   \n",
       "max               76.918332     1.000000       1.000000          1.735000   \n",
       "\n",
       "       derivative_troughs  bear_harami  bull_harami  bear_engulfing  \\\n",
       "count         5660.000000  5660.000000  5660.000000     5660.000000   \n",
       "mean             0.003583     0.028129     0.043797        0.058802   \n",
       "std              0.131706     0.448885     0.709980        1.141617   \n",
       "min             -2.354000     0.000000     0.000000        0.000000   \n",
       "25%             -0.007500     0.000000     0.000000        0.000000   \n",
       "50%              0.000000     0.000000     0.000000        0.000000   \n",
       "75%              0.020000     0.000000     0.000000        0.000000   \n",
       "max              1.953333    14.000000    25.000000       63.000000   \n",
       "\n",
       "       bull_engulfing       hammer  shooting_star  \n",
       "count     5660.000000  5660.000000    5660.000000  \n",
       "mean         0.026635     0.016078       0.021731  \n",
       "std          1.064473     0.125786       0.145818  \n",
       "min          0.000000     0.000000       0.000000  \n",
       "25%          0.000000     0.000000       0.000000  \n",
       "50%          0.000000     0.000000       0.000000  \n",
       "75%          0.000000     0.000000       0.000000  \n",
       "max         76.992301     1.000000       1.000000  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wege_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume [BRL]</th>\n",
       "      <th>Volume [shares]</th>\n",
       "      <th>IFR (RSI) [9]</th>\n",
       "      <th>True Range [1]</th>\n",
       "      <th>Moving Average A [21]</th>\n",
       "      <th>Moving Average E [9]</th>\n",
       "      <th>IsLocalPeak</th>\n",
       "      <th>IsLocalTrough</th>\n",
       "      <th>derivative_peaks</th>\n",
       "      <th>derivative_troughs</th>\n",
       "      <th>bear_harami</th>\n",
       "      <th>bull_harami</th>\n",
       "      <th>bear_engulfing</th>\n",
       "      <th>bull_engulfing</th>\n",
       "      <th>hammer</th>\n",
       "      <th>shooting_star</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2936.000000</td>\n",
       "      <td>2936.000000</td>\n",
       "      <td>2936.000000</td>\n",
       "      <td>2936.000000</td>\n",
       "      <td>2.936000e+03</td>\n",
       "      <td>2.936000e+03</td>\n",
       "      <td>2928.000000</td>\n",
       "      <td>2935.000000</td>\n",
       "      <td>2936.000000</td>\n",
       "      <td>2936.000000</td>\n",
       "      <td>2936.000000</td>\n",
       "      <td>2936.000000</td>\n",
       "      <td>2936.000000</td>\n",
       "      <td>2936.000000</td>\n",
       "      <td>2936.000000</td>\n",
       "      <td>2936.000000</td>\n",
       "      <td>2936.000000</td>\n",
       "      <td>2936.000000</td>\n",
       "      <td>2936.000000</td>\n",
       "      <td>2936.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>64.107006</td>\n",
       "      <td>64.664939</td>\n",
       "      <td>63.452142</td>\n",
       "      <td>64.074029</td>\n",
       "      <td>1.979881e+08</td>\n",
       "      <td>2.727684e+06</td>\n",
       "      <td>52.383282</td>\n",
       "      <td>1.344508</td>\n",
       "      <td>63.880718</td>\n",
       "      <td>63.983791</td>\n",
       "      <td>0.223433</td>\n",
       "      <td>0.228202</td>\n",
       "      <td>-0.000669</td>\n",
       "      <td>-0.000243</td>\n",
       "      <td>0.121997</td>\n",
       "      <td>0.093470</td>\n",
       "      <td>0.104939</td>\n",
       "      <td>0.046488</td>\n",
       "      <td>0.021458</td>\n",
       "      <td>0.028951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>16.816599</td>\n",
       "      <td>16.888117</td>\n",
       "      <td>16.696268</td>\n",
       "      <td>16.793429</td>\n",
       "      <td>2.800640e+08</td>\n",
       "      <td>3.319674e+06</td>\n",
       "      <td>14.718881</td>\n",
       "      <td>0.875526</td>\n",
       "      <td>16.668438</td>\n",
       "      <td>16.748775</td>\n",
       "      <td>0.416617</td>\n",
       "      <td>0.419745</td>\n",
       "      <td>0.406351</td>\n",
       "      <td>0.485071</td>\n",
       "      <td>1.709891</td>\n",
       "      <td>1.120097</td>\n",
       "      <td>1.633899</td>\n",
       "      <td>1.003846</td>\n",
       "      <td>0.144929</td>\n",
       "      <td>0.167697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>34.510000</td>\n",
       "      <td>35.350000</td>\n",
       "      <td>33.900000</td>\n",
       "      <td>35.310000</td>\n",
       "      <td>1.816440e+05</td>\n",
       "      <td>4.400000e+03</td>\n",
       "      <td>9.910000</td>\n",
       "      <td>0.170000</td>\n",
       "      <td>35.350000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-3.292000</td>\n",
       "      <td>-5.466667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>52.110000</td>\n",
       "      <td>52.520000</td>\n",
       "      <td>51.500000</td>\n",
       "      <td>52.017500</td>\n",
       "      <td>4.614790e+07</td>\n",
       "      <td>8.376700e+05</td>\n",
       "      <td>41.467500</td>\n",
       "      <td>0.860000</td>\n",
       "      <td>51.992500</td>\n",
       "      <td>52.032207</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.250000</td>\n",
       "      <td>-0.242500</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>60.140000</td>\n",
       "      <td>60.685000</td>\n",
       "      <td>59.610000</td>\n",
       "      <td>60.150000</td>\n",
       "      <td>1.036209e+08</td>\n",
       "      <td>1.851050e+06</td>\n",
       "      <td>52.650000</td>\n",
       "      <td>1.160000</td>\n",
       "      <td>60.023095</td>\n",
       "      <td>60.131659</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.025833</td>\n",
       "      <td>0.013333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>70.717500</td>\n",
       "      <td>71.300000</td>\n",
       "      <td>70.012500</td>\n",
       "      <td>70.720000</td>\n",
       "      <td>2.164058e+08</td>\n",
       "      <td>3.348998e+06</td>\n",
       "      <td>63.395000</td>\n",
       "      <td>1.580000</td>\n",
       "      <td>70.753571</td>\n",
       "      <td>70.694999</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.247500</td>\n",
       "      <td>0.265000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>115.210000</td>\n",
       "      <td>115.260000</td>\n",
       "      <td>113.710000</td>\n",
       "      <td>115.210000</td>\n",
       "      <td>3.295958e+09</td>\n",
       "      <td>4.589951e+07</td>\n",
       "      <td>88.730000</td>\n",
       "      <td>15.760000</td>\n",
       "      <td>112.906667</td>\n",
       "      <td>113.664528</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.900000</td>\n",
       "      <td>2.673333</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>67.500000</td>\n",
       "      <td>46.331789</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Open         High          Low        Close  Volume [BRL]  \\\n",
       "count  2936.000000  2936.000000  2936.000000  2936.000000  2.936000e+03   \n",
       "mean     64.107006    64.664939    63.452142    64.074029  1.979881e+08   \n",
       "std      16.816599    16.888117    16.696268    16.793429  2.800640e+08   \n",
       "min      34.510000    35.350000    33.900000    35.310000  1.816440e+05   \n",
       "25%      52.110000    52.520000    51.500000    52.017500  4.614790e+07   \n",
       "50%      60.140000    60.685000    59.610000    60.150000  1.036209e+08   \n",
       "75%      70.717500    71.300000    70.012500    70.720000  2.164058e+08   \n",
       "max     115.210000   115.260000   113.710000   115.210000  3.295958e+09   \n",
       "\n",
       "       Volume [shares]  IFR (RSI) [9]  True Range [1]  Moving Average A [21]  \\\n",
       "count     2.936000e+03    2928.000000     2935.000000            2936.000000   \n",
       "mean      2.727684e+06      52.383282        1.344508              63.880718   \n",
       "std       3.319674e+06      14.718881        0.875526              16.668438   \n",
       "min       4.400000e+03       9.910000        0.170000              35.350000   \n",
       "25%       8.376700e+05      41.467500        0.860000              51.992500   \n",
       "50%       1.851050e+06      52.650000        1.160000              60.023095   \n",
       "75%       3.348998e+06      63.395000        1.580000              70.753571   \n",
       "max       4.589951e+07      88.730000       15.760000             112.906667   \n",
       "\n",
       "       Moving Average E [9]  IsLocalPeak  IsLocalTrough  derivative_peaks  \\\n",
       "count           2936.000000  2936.000000    2936.000000       2936.000000   \n",
       "mean              63.983791     0.223433       0.228202         -0.000669   \n",
       "std               16.748775     0.416617       0.419745          0.406351   \n",
       "min                0.000000     0.000000       0.000000         -3.292000   \n",
       "25%               52.032207     0.000000       0.000000         -0.250000   \n",
       "50%               60.131659     0.000000       0.000000          0.025833   \n",
       "75%               70.694999     0.000000       0.000000          0.247500   \n",
       "max              113.664528     1.000000       1.000000          1.900000   \n",
       "\n",
       "       derivative_troughs  bear_harami  bull_harami  bear_engulfing  \\\n",
       "count         2936.000000  2936.000000  2936.000000     2936.000000   \n",
       "mean            -0.000243     0.121997     0.093470        0.104939   \n",
       "std              0.485071     1.709891     1.120097        1.633899   \n",
       "min             -5.466667     0.000000     0.000000        0.000000   \n",
       "25%             -0.242500     0.000000     0.000000        0.000000   \n",
       "50%              0.013333     0.000000     0.000000        0.000000   \n",
       "75%              0.265000     0.000000     0.000000        0.000000   \n",
       "max              2.673333    47.000000    36.000000       67.500000   \n",
       "\n",
       "       bull_engulfing       hammer  shooting_star  \n",
       "count     2936.000000  2936.000000    2936.000000  \n",
       "mean         0.046488     0.021458       0.028951  \n",
       "std          1.003846     0.144929       0.167697  \n",
       "min          0.000000     0.000000       0.000000  \n",
       "25%          0.000000     0.000000       0.000000  \n",
       "50%          0.000000     0.000000       0.000000  \n",
       "75%          0.000000     0.000000       0.000000  \n",
       "max         46.331789     1.000000       1.000000  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bova_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save data locally"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "output_folder = \"engineered-data\"\n",
    "wege_output_path = os.path.join(output_folder, \"wege.csv\")\n",
    "bova_output_path = os.path.join(output_folder, \"bova.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "wege_df.to_csv(wege_output_path, header=True, index=True)\n",
    "bova_df.to_csv(bova_output_path, header=True, index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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