3-Data_Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns; sns.set()\n",
    "from sklearn import preprocessing\n",
    "from numpy import log\n",
    "\n",
    "## Statistical libs \n",
    "from statsmodels.tsa.stattools import adfuller"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"Data//features_data.csv\",sep=\";\",index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Close</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Vol</th>\n",
       "      <th>Var</th>\n",
       "      <th>Open_Dollar</th>\n",
       "      <th>Open_IBOVESPA</th>\n",
       "      <th>DayofWeek</th>\n",
       "      <th>Weekofyear</th>\n",
       "      <th>Quarter</th>\n",
       "      <th>Month</th>\n",
       "      <th>News_N</th>\n",
       "      <th>News_NN</th>\n",
       "      <th>News_P</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-05-20</td>\n",
       "      <td>21.60</td>\n",
       "      <td>21.93</td>\n",
       "      <td>21.95</td>\n",
       "      <td>21.36</td>\n",
       "      <td>41170000.0</td>\n",
       "      <td>-0.41</td>\n",
       "      <td>5.7559</td>\n",
       "      <td>80746.81</td>\n",
       "      <td>2</td>\n",
       "      <td>21</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-05-19</td>\n",
       "      <td>21.69</td>\n",
       "      <td>22.63</td>\n",
       "      <td>22.72</td>\n",
       "      <td>21.61</td>\n",
       "      <td>40390000.0</td>\n",
       "      <td>-4.32</td>\n",
       "      <td>5.7192</td>\n",
       "      <td>81196.69</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-05-18</td>\n",
       "      <td>22.67</td>\n",
       "      <td>22.45</td>\n",
       "      <td>22.94</td>\n",
       "      <td>22.01</td>\n",
       "      <td>49340000.0</td>\n",
       "      <td>4.81</td>\n",
       "      <td>5.8565</td>\n",
       "      <td>77575.71</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-05-15</td>\n",
       "      <td>21.63</td>\n",
       "      <td>22.50</td>\n",
       "      <td>22.77</td>\n",
       "      <td>21.63</td>\n",
       "      <td>37620000.0</td>\n",
       "      <td>-4.08</td>\n",
       "      <td>5.8110</td>\n",
       "      <td>79010.59</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-05-14</td>\n",
       "      <td>22.55</td>\n",
       "      <td>21.31</td>\n",
       "      <td>22.59</td>\n",
       "      <td>20.73</td>\n",
       "      <td>61540000.0</td>\n",
       "      <td>4.40</td>\n",
       "      <td>5.9248</td>\n",
       "      <td>77770.48</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "         Date  Close   Open   High    Low         Vol   Var  Open_Dollar  \\\n",
       "0  2020-05-20  21.60  21.93  21.95  21.36  41170000.0 -0.41       5.7559   \n",
       "1  2020-05-19  21.69  22.63  22.72  21.61  40390000.0 -4.32       5.7192   \n",
       "2  2020-05-18  22.67  22.45  22.94  22.01  49340000.0  4.81       5.8565   \n",
       "3  2020-05-15  21.63  22.50  22.77  21.63  37620000.0 -4.08       5.8110   \n",
       "4  2020-05-14  22.55  21.31  22.59  20.73  61540000.0  4.40       5.9248   \n",
       "\n",
       "   Open_IBOVESPA  DayofWeek  Weekofyear  Quarter  Month  News_N  News_NN  \\\n",
       "0       80746.81          2          21        2      5       0        0   \n",
       "1       81196.69          1          21        2      5       0        0   \n",
       "2       77575.71          0          21        2      5       0        0   \n",
       "3       79010.59          4          20        2      5       0        0   \n",
       "4       77770.48          3          20        2      5       0        0   \n",
       "\n",
       "   News_P  \n",
       "0       1  \n",
       "1       1  \n",
       "2       1  \n",
       "3       1  \n",
       "4       1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature scaling\n",
    "\n",
    "Remember, our target variable is the Close, that's the price that the share will be in final of the day. So we'll scale all the independent variables to avoid the larger scale resources impacting more that the others."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = df['Close']\n",
    "lag_col = pd.concat([s.shift(), s.shift(2),s.shift(3)], axis=1)\n",
    "lag_col.columns = ['lag_1','lag_2','lag_3']\n",
    "df = pd.concat([df, lag_col], axis=1, sort=False).dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Close</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Vol</th>\n",
       "      <th>Var</th>\n",
       "      <th>Open_Dollar</th>\n",
       "      <th>Open_IBOVESPA</th>\n",
       "      <th>DayofWeek</th>\n",
       "      <th>Weekofyear</th>\n",
       "      <th>Quarter</th>\n",
       "      <th>Month</th>\n",
       "      <th>News_N</th>\n",
       "      <th>News_NN</th>\n",
       "      <th>News_P</th>\n",
       "      <th>lag_1</th>\n",
       "      <th>lag_2</th>\n",
       "      <th>lag_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-05-15</td>\n",
       "      <td>21.63</td>\n",
       "      <td>22.50</td>\n",
       "      <td>22.77</td>\n",
       "      <td>21.63</td>\n",
       "      <td>37620000.0</td>\n",
       "      <td>-4.08</td>\n",
       "      <td>5.8110</td>\n",
       "      <td>79010.59</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>22.67</td>\n",
       "      <td>21.69</td>\n",
       "      <td>21.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-05-14</td>\n",
       "      <td>22.55</td>\n",
       "      <td>21.31</td>\n",
       "      <td>22.59</td>\n",
       "      <td>20.73</td>\n",
       "      <td>61540000.0</td>\n",
       "      <td>4.40</td>\n",
       "      <td>5.9248</td>\n",
       "      <td>77770.48</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>21.63</td>\n",
       "      <td>22.67</td>\n",
       "      <td>21.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2020-05-13</td>\n",
       "      <td>21.60</td>\n",
       "      <td>21.74</td>\n",
       "      <td>21.94</td>\n",
       "      <td>21.20</td>\n",
       "      <td>34530000.0</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>5.8863</td>\n",
       "      <td>77876.88</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>22.55</td>\n",
       "      <td>21.63</td>\n",
       "      <td>22.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2020-05-12</td>\n",
       "      <td>21.61</td>\n",
       "      <td>22.51</td>\n",
       "      <td>22.66</td>\n",
       "      <td>21.59</td>\n",
       "      <td>42900000.0</td>\n",
       "      <td>-3.44</td>\n",
       "      <td>5.8190</td>\n",
       "      <td>79064.60</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21.60</td>\n",
       "      <td>22.55</td>\n",
       "      <td>21.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2020-05-11</td>\n",
       "      <td>22.38</td>\n",
       "      <td>22.17</td>\n",
       "      <td>23.03</td>\n",
       "      <td>22.17</td>\n",
       "      <td>37900000.0</td>\n",
       "      <td>0.27</td>\n",
       "      <td>5.7825</td>\n",
       "      <td>80263.42</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21.61</td>\n",
       "      <td>21.60</td>\n",
       "      <td>22.55</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date  Close   Open   High    Low         Vol   Var  Open_Dollar  \\\n",
       "3  2020-05-15  21.63  22.50  22.77  21.63  37620000.0 -4.08       5.8110   \n",
       "4  2020-05-14  22.55  21.31  22.59  20.73  61540000.0  4.40       5.9248   \n",
       "5  2020-05-13  21.60  21.74  21.94  21.20  34530000.0 -0.05       5.8863   \n",
       "6  2020-05-12  21.61  22.51  22.66  21.59  42900000.0 -3.44       5.8190   \n",
       "7  2020-05-11  22.38  22.17  23.03  22.17  37900000.0  0.27       5.7825   \n",
       "\n",
       "   Open_IBOVESPA  DayofWeek  Weekofyear  Quarter  Month  News_N  News_NN  \\\n",
       "3       79010.59          4          20        2      5       0        0   \n",
       "4       77770.48          3          20        2      5       0        0   \n",
       "5       77876.88          2          20        2      5       1        0   \n",
       "6       79064.60          1          20        2      5       1        0   \n",
       "7       80263.42          0          20        2      5       1        0   \n",
       "\n",
       "   News_P  lag_1  lag_2  lag_3  \n",
       "3       1  22.67  21.69  21.60  \n",
       "4       1  21.63  22.67  21.69  \n",
       "5       0  22.55  21.63  22.67  \n",
       "6       0  21.60  22.55  21.63  \n",
       "7       0  21.61  21.60  22.55  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['lag_1']= preprocessing.scale(df['lag_1'])\n",
    "df['lag_2']= preprocessing.scale(df['lag_2'])\n",
    "df['lag_3']= preprocessing.scale(df['lag_3'])\n",
    "df['Open']= preprocessing.scale(df['Open'])\n",
    "df['High']= preprocessing.scale(df['High'])\n",
    "df['Low']= preprocessing.scale(df['Low'])\n",
    "df['Vol']= preprocessing.scale(df['Vol'])\n",
    "df['Var']= preprocessing.scale(df['Var'])\n",
    "df['Open_Dollar']= preprocessing.scale(df['Open_Dollar'])\n",
    "df['Open_IBOVESPA']= preprocessing.scale(df['Open_IBOVESPA'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"Data//Normalized_data.csv\", sep=';')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Close</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Vol</th>\n",
       "      <th>Var</th>\n",
       "      <th>Open_Dollar</th>\n",
       "      <th>Open_IBOVESPA</th>\n",
       "      <th>DayofWeek</th>\n",
       "      <th>Weekofyear</th>\n",
       "      <th>Quarter</th>\n",
       "      <th>Month</th>\n",
       "      <th>News_N</th>\n",
       "      <th>News_NN</th>\n",
       "      <th>News_P</th>\n",
       "      <th>lag_1</th>\n",
       "      <th>lag_2</th>\n",
       "      <th>lag_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3742</th>\n",
       "      <td>2005-04-11</td>\n",
       "      <td>4.34</td>\n",
       "      <td>-1.244518</td>\n",
       "      <td>-1.249674</td>\n",
       "      <td>-1.236755</td>\n",
       "      <td>0.175913</td>\n",
       "      <td>0.380657</td>\n",
       "      <td>0.003181</td>\n",
       "      <td>-1.860300</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.245974</td>\n",
       "      <td>-1.246672</td>\n",
       "      <td>-1.259534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3743</th>\n",
       "      <td>2005-04-08</td>\n",
       "      <td>4.30</td>\n",
       "      <td>-1.243303</td>\n",
       "      <td>-1.253282</td>\n",
       "      <td>-1.237984</td>\n",
       "      <td>0.612276</td>\n",
       "      <td>-0.333290</td>\n",
       "      <td>0.013730</td>\n",
       "      <td>-1.837179</td>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.242325</td>\n",
       "      <td>-1.246672</td>\n",
       "      <td>-1.247369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3744</th>\n",
       "      <td>2005-04-07</td>\n",
       "      <td>4.33</td>\n",
       "      <td>-1.244518</td>\n",
       "      <td>-1.252080</td>\n",
       "      <td>-1.236755</td>\n",
       "      <td>2.178052</td>\n",
       "      <td>0.173524</td>\n",
       "      <td>0.013495</td>\n",
       "      <td>-1.870653</td>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.247190</td>\n",
       "      <td>-1.243022</td>\n",
       "      <td>-1.247369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3745</th>\n",
       "      <td>2005-04-06</td>\n",
       "      <td>4.31</td>\n",
       "      <td>-1.243303</td>\n",
       "      <td>-1.248472</td>\n",
       "      <td>-1.236755</td>\n",
       "      <td>2.175839</td>\n",
       "      <td>-0.130565</td>\n",
       "      <td>0.052995</td>\n",
       "      <td>-1.851831</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.243541</td>\n",
       "      <td>-1.247888</td>\n",
       "      <td>-1.243719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3746</th>\n",
       "      <td>2005-04-05</td>\n",
       "      <td>4.32</td>\n",
       "      <td>-1.244518</td>\n",
       "      <td>-1.244864</td>\n",
       "      <td>-1.236755</td>\n",
       "      <td>2.285151</td>\n",
       "      <td>-0.029202</td>\n",
       "      <td>0.061785</td>\n",
       "      <td>-1.831555</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.245974</td>\n",
       "      <td>-1.244239</td>\n",
       "      <td>-1.248585</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date  Close      Open      High       Low       Vol       Var  \\\n",
       "3742  2005-04-11   4.34 -1.244518 -1.249674 -1.236755  0.175913  0.380657   \n",
       "3743  2005-04-08   4.30 -1.243303 -1.253282 -1.237984  0.612276 -0.333290   \n",
       "3744  2005-04-07   4.33 -1.244518 -1.252080 -1.236755  2.178052  0.173524   \n",
       "3745  2005-04-06   4.31 -1.243303 -1.248472 -1.236755  2.175839 -0.130565   \n",
       "3746  2005-04-05   4.32 -1.244518 -1.244864 -1.236755  2.285151 -0.029202   \n",
       "\n",
       "      Open_Dollar  Open_IBOVESPA  DayofWeek  Weekofyear  Quarter  Month  \\\n",
       "3742     0.003181      -1.860300          0          15        2      4   \n",
       "3743     0.013730      -1.837179          4          14        2      4   \n",
       "3744     0.013495      -1.870653          3          14        2      4   \n",
       "3745     0.052995      -1.851831          2          14        2      4   \n",
       "3746     0.061785      -1.831555          1          14        2      4   \n",
       "\n",
       "      News_N  News_NN  News_P     lag_1     lag_2     lag_3  \n",
       "3742       0        0       1 -1.245974 -1.246672 -1.259534  \n",
       "3743       0        0       1 -1.242325 -1.246672 -1.247369  \n",
       "3744       0        0       1 -1.247190 -1.243022 -1.247369  \n",
       "3745       0        0       1 -1.243541 -1.247888 -1.243719  \n",
       "3746       0        0       1 -1.245974 -1.244239 -1.248585  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Correlation\n",
    "\n",
    "It's a important step to identify the features that has a great impact in our target variable, and eliminating the variables with a low correlation. In the Deep Learning, it's similar to L1 regularization that avoid the features with low impact in our model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "Matrix_corr = df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>Close</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Vol</th>\n",
       "      <th>Var</th>\n",
       "      <th>Open_Dollar</th>\n",
       "      <th>Open_IBOVESPA</th>\n",
       "      <th>DayofWeek</th>\n",
       "      <th>Weekofyear</th>\n",
       "      <th>Quarter</th>\n",
       "      <th>Month</th>\n",
       "      <th>News_N</th>\n",
       "      <th>News_NN</th>\n",
       "      <th>News_P</th>\n",
       "      <th>lag_1</th>\n",
       "      <th>lag_2</th>\n",
       "      <th>lag_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Close</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.999431</td>\n",
       "      <td>0.999741</td>\n",
       "      <td>0.999740</td>\n",
       "      <td>0.060209</td>\n",
       "      <td>0.003757</td>\n",
       "      <td>0.777244</td>\n",
       "      <td>0.862222</td>\n",
       "      <td>0.000654</td>\n",
       "      <td>-0.023384</td>\n",
       "      <td>-0.021621</td>\n",
       "      <td>-0.025061</td>\n",
       "      <td>0.338865</td>\n",
       "      <td>0.073264</td>\n",
       "      <td>-0.345495</td>\n",
       "      <td>0.999188</td>\n",
       "      <td>0.998422</td>\n",
       "      <td>0.997680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Open</th>\n",
       "      <td>0.999431</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.999732</td>\n",
       "      <td>0.999712</td>\n",
       "      <td>0.059886</td>\n",
       "      <td>-0.018614</td>\n",
       "      <td>0.777601</td>\n",
       "      <td>0.863125</td>\n",
       "      <td>-0.000003</td>\n",
       "      <td>-0.023241</td>\n",
       "      <td>-0.021534</td>\n",
       "      <td>-0.024832</td>\n",
       "      <td>0.338847</td>\n",
       "      <td>0.073748</td>\n",
       "      <td>-0.345631</td>\n",
       "      <td>0.998591</td>\n",
       "      <td>0.997904</td>\n",
       "      <td>0.997203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>High</th>\n",
       "      <td>0.999741</td>\n",
       "      <td>0.999732</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.999653</td>\n",
       "      <td>0.062757</td>\n",
       "      <td>-0.008207</td>\n",
       "      <td>0.780184</td>\n",
       "      <td>0.862325</td>\n",
       "      <td>0.000446</td>\n",
       "      <td>-0.023473</td>\n",
       "      <td>-0.021831</td>\n",
       "      <td>-0.025135</td>\n",
       "      <td>0.338986</td>\n",
       "      <td>0.074354</td>\n",
       "      <td>-0.345955</td>\n",
       "      <td>0.998941</td>\n",
       "      <td>0.998190</td>\n",
       "      <td>0.997463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Low</th>\n",
       "      <td>0.999740</td>\n",
       "      <td>0.999712</td>\n",
       "      <td>0.999653</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.057441</td>\n",
       "      <td>-0.007572</td>\n",
       "      <td>0.775282</td>\n",
       "      <td>0.862972</td>\n",
       "      <td>-0.000300</td>\n",
       "      <td>-0.022878</td>\n",
       "      <td>-0.020967</td>\n",
       "      <td>-0.024453</td>\n",
       "      <td>0.338512</td>\n",
       "      <td>0.072449</td>\n",
       "      <td>-0.344900</td>\n",
       "      <td>0.998973</td>\n",
       "      <td>0.998255</td>\n",
       "      <td>0.997553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vol</th>\n",
       "      <td>0.060209</td>\n",
       "      <td>0.059886</td>\n",
       "      <td>0.062757</td>\n",
       "      <td>0.057441</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.008024</td>\n",
       "      <td>0.191600</td>\n",
       "      <td>-0.038853</td>\n",
       "      <td>0.033644</td>\n",
       "      <td>-0.016688</td>\n",
       "      <td>-0.022193</td>\n",
       "      <td>-0.017174</td>\n",
       "      <td>0.042911</td>\n",
       "      <td>0.046039</td>\n",
       "      <td>-0.055405</td>\n",
       "      <td>0.059882</td>\n",
       "      <td>0.059609</td>\n",
       "      <td>0.059180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Var</th>\n",
       "      <td>0.003757</td>\n",
       "      <td>-0.018614</td>\n",
       "      <td>-0.008207</td>\n",
       "      <td>-0.007572</td>\n",
       "      <td>0.008024</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.004770</td>\n",
       "      <td>-0.047548</td>\n",
       "      <td>0.001534</td>\n",
       "      <td>0.011459</td>\n",
       "      <td>0.015673</td>\n",
       "      <td>0.010868</td>\n",
       "      <td>-0.012519</td>\n",
       "      <td>0.007338</td>\n",
       "      <td>0.009580</td>\n",
       "      <td>0.003464</td>\n",
       "      <td>0.002956</td>\n",
       "      <td>0.001118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Open_Dollar</th>\n",
       "      <td>0.777244</td>\n",
       "      <td>0.777601</td>\n",
       "      <td>0.780184</td>\n",
       "      <td>0.775282</td>\n",
       "      <td>0.191600</td>\n",
       "      <td>-0.004770</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.468717</td>\n",
       "      <td>-0.001316</td>\n",
       "      <td>-0.019838</td>\n",
       "      <td>-0.023090</td>\n",
       "      <td>-0.022277</td>\n",
       "      <td>0.310513</td>\n",
       "      <td>0.144166</td>\n",
       "      <td>-0.341011</td>\n",
       "      <td>0.777265</td>\n",
       "      <td>0.777123</td>\n",
       "      <td>0.776958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Open_IBOVESPA</th>\n",
       "      <td>0.862222</td>\n",
       "      <td>0.863125</td>\n",
       "      <td>0.862325</td>\n",
       "      <td>0.862972</td>\n",
       "      <td>-0.038853</td>\n",
       "      <td>-0.047548</td>\n",
       "      <td>0.468717</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.002019</td>\n",
       "      <td>-0.038659</td>\n",
       "      <td>-0.036810</td>\n",
       "      <td>-0.041266</td>\n",
       "      <td>0.365490</td>\n",
       "      <td>0.076671</td>\n",
       "      <td>-0.371896</td>\n",
       "      <td>0.860894</td>\n",
       "      <td>0.859539</td>\n",
       "      <td>0.858231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DayofWeek</th>\n",
       "      <td>0.000654</td>\n",
       "      <td>-0.000003</td>\n",
       "      <td>0.000446</td>\n",
       "      <td>-0.000300</td>\n",
       "      <td>0.033644</td>\n",
       "      <td>0.001534</td>\n",
       "      <td>-0.001316</td>\n",
       "      <td>0.002019</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.014529</td>\n",
       "      <td>-0.009518</td>\n",
       "      <td>-0.007394</td>\n",
       "      <td>0.004121</td>\n",
       "      <td>0.007487</td>\n",
       "      <td>-0.006293</td>\n",
       "      <td>-0.000569</td>\n",
       "      <td>-0.000302</td>\n",
       "      <td>0.000505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Weekofyear</th>\n",
       "      <td>-0.023384</td>\n",
       "      <td>-0.023241</td>\n",
       "      <td>-0.023473</td>\n",
       "      <td>-0.022878</td>\n",
       "      <td>-0.016688</td>\n",
       "      <td>0.011459</td>\n",
       "      <td>-0.019838</td>\n",
       "      <td>-0.038659</td>\n",
       "      <td>-0.014529</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.960022</td>\n",
       "      <td>0.987253</td>\n",
       "      <td>0.030426</td>\n",
       "      <td>-0.018023</td>\n",
       "      <td>-0.023222</td>\n",
       "      <td>-0.022560</td>\n",
       "      <td>-0.021372</td>\n",
       "      <td>-0.020189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Quarter</th>\n",
       "      <td>-0.021621</td>\n",
       "      <td>-0.021534</td>\n",
       "      <td>-0.021831</td>\n",
       "      <td>-0.020967</td>\n",
       "      <td>-0.022193</td>\n",
       "      <td>0.015673</td>\n",
       "      <td>-0.023090</td>\n",
       "      <td>-0.036810</td>\n",
       "      <td>-0.009518</td>\n",
       "      <td>0.960022</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.970582</td>\n",
       "      <td>0.020602</td>\n",
       "      <td>-0.009266</td>\n",
       "      <td>-0.016656</td>\n",
       "      <td>-0.020488</td>\n",
       "      <td>-0.019197</td>\n",
       "      <td>-0.017897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <td>-0.025061</td>\n",
       "      <td>-0.024832</td>\n",
       "      <td>-0.025135</td>\n",
       "      <td>-0.024453</td>\n",
       "      <td>-0.017174</td>\n",
       "      <td>0.010868</td>\n",
       "      <td>-0.022277</td>\n",
       "      <td>-0.041266</td>\n",
       "      <td>-0.007394</td>\n",
       "      <td>0.987253</td>\n",
       "      <td>0.970582</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.030775</td>\n",
       "      <td>-0.024068</td>\n",
       "      <td>-0.021637</td>\n",
       "      <td>-0.024159</td>\n",
       "      <td>-0.023033</td>\n",
       "      <td>-0.021890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>News_N</th>\n",
       "      <td>0.338865</td>\n",
       "      <td>0.338847</td>\n",
       "      <td>0.338986</td>\n",
       "      <td>0.338512</td>\n",
       "      <td>0.042911</td>\n",
       "      <td>-0.012519</td>\n",
       "      <td>0.310513</td>\n",
       "      <td>0.365490</td>\n",
       "      <td>0.004121</td>\n",
       "      <td>0.030426</td>\n",
       "      <td>0.020602</td>\n",
       "      <td>0.030775</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.008214</td>\n",
       "      <td>-0.948415</td>\n",
       "      <td>0.339017</td>\n",
       "      <td>0.339039</td>\n",
       "      <td>0.340551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>News_NN</th>\n",
       "      <td>0.073264</td>\n",
       "      <td>0.073748</td>\n",
       "      <td>0.074354</td>\n",
       "      <td>0.072449</td>\n",
       "      <td>0.046039</td>\n",
       "      <td>0.007338</td>\n",
       "      <td>0.144166</td>\n",
       "      <td>0.076671</td>\n",
       "      <td>0.007487</td>\n",
       "      <td>-0.018023</td>\n",
       "      <td>-0.009266</td>\n",
       "      <td>-0.024068</td>\n",
       "      <td>-0.008214</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.309231</td>\n",
       "      <td>0.072332</td>\n",
       "      <td>0.072430</td>\n",
       "      <td>0.073014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>News_P</th>\n",
       "      <td>-0.345495</td>\n",
       "      <td>-0.345631</td>\n",
       "      <td>-0.345955</td>\n",
       "      <td>-0.344900</td>\n",
       "      <td>-0.055405</td>\n",
       "      <td>0.009580</td>\n",
       "      <td>-0.341011</td>\n",
       "      <td>-0.371896</td>\n",
       "      <td>-0.006293</td>\n",
       "      <td>-0.023222</td>\n",
       "      <td>-0.016656</td>\n",
       "      <td>-0.021637</td>\n",
       "      <td>-0.948415</td>\n",
       "      <td>-0.309231</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.345344</td>\n",
       "      <td>-0.345396</td>\n",
       "      <td>-0.347019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lag_1</th>\n",
       "      <td>0.999188</td>\n",
       "      <td>0.998591</td>\n",
       "      <td>0.998941</td>\n",
       "      <td>0.998973</td>\n",
       "      <td>0.059882</td>\n",
       "      <td>0.003464</td>\n",
       "      <td>0.777265</td>\n",
       "      <td>0.860894</td>\n",
       "      <td>-0.000569</td>\n",
       "      <td>-0.022560</td>\n",
       "      <td>-0.020488</td>\n",
       "      <td>-0.024159</td>\n",
       "      <td>0.339017</td>\n",
       "      <td>0.072332</td>\n",
       "      <td>-0.345344</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.999186</td>\n",
       "      <td>0.998420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lag_2</th>\n",
       "      <td>0.998422</td>\n",
       "      <td>0.997904</td>\n",
       "      <td>0.998190</td>\n",
       "      <td>0.998255</td>\n",
       "      <td>0.059609</td>\n",
       "      <td>0.002956</td>\n",
       "      <td>0.777123</td>\n",
       "      <td>0.859539</td>\n",
       "      <td>-0.000302</td>\n",
       "      <td>-0.021372</td>\n",
       "      <td>-0.019197</td>\n",
       "      <td>-0.023033</td>\n",
       "      <td>0.339039</td>\n",
       "      <td>0.072430</td>\n",
       "      <td>-0.345396</td>\n",
       "      <td>0.999186</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.999186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lag_3</th>\n",
       "      <td>0.997680</td>\n",
       "      <td>0.997203</td>\n",
       "      <td>0.997463</td>\n",
       "      <td>0.997553</td>\n",
       "      <td>0.059180</td>\n",
       "      <td>0.001118</td>\n",
       "      <td>0.776958</td>\n",
       "      <td>0.858231</td>\n",
       "      <td>0.000505</td>\n",
       "      <td>-0.020189</td>\n",
       "      <td>-0.017897</td>\n",
       "      <td>-0.021890</td>\n",
       "      <td>0.340551</td>\n",
       "      <td>0.073014</td>\n",
       "      <td>-0.347019</td>\n",
       "      <td>0.998420</td>\n",
       "      <td>0.999186</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Close      Open      High       Low       Vol       Var  \\\n",
       "Close          1.000000  0.999431  0.999741  0.999740  0.060209  0.003757   \n",
       "Open           0.999431  1.000000  0.999732  0.999712  0.059886 -0.018614   \n",
       "High           0.999741  0.999732  1.000000  0.999653  0.062757 -0.008207   \n",
       "Low            0.999740  0.999712  0.999653  1.000000  0.057441 -0.007572   \n",
       "Vol            0.060209  0.059886  0.062757  0.057441  1.000000  0.008024   \n",
       "Var            0.003757 -0.018614 -0.008207 -0.007572  0.008024  1.000000   \n",
       "Open_Dollar    0.777244  0.777601  0.780184  0.775282  0.191600 -0.004770   \n",
       "Open_IBOVESPA  0.862222  0.863125  0.862325  0.862972 -0.038853 -0.047548   \n",
       "DayofWeek      0.000654 -0.000003  0.000446 -0.000300  0.033644  0.001534   \n",
       "Weekofyear    -0.023384 -0.023241 -0.023473 -0.022878 -0.016688  0.011459   \n",
       "Quarter       -0.021621 -0.021534 -0.021831 -0.020967 -0.022193  0.015673   \n",
       "Month         -0.025061 -0.024832 -0.025135 -0.024453 -0.017174  0.010868   \n",
       "News_N         0.338865  0.338847  0.338986  0.338512  0.042911 -0.012519   \n",
       "News_NN        0.073264  0.073748  0.074354  0.072449  0.046039  0.007338   \n",
       "News_P        -0.345495 -0.345631 -0.345955 -0.344900 -0.055405  0.009580   \n",
       "lag_1          0.999188  0.998591  0.998941  0.998973  0.059882  0.003464   \n",
       "lag_2          0.998422  0.997904  0.998190  0.998255  0.059609  0.002956   \n",
       "lag_3          0.997680  0.997203  0.997463  0.997553  0.059180  0.001118   \n",
       "\n",
       "               Open_Dollar  Open_IBOVESPA  DayofWeek  Weekofyear   Quarter  \\\n",
       "Close             0.777244       0.862222   0.000654   -0.023384 -0.021621   \n",
       "Open              0.777601       0.863125  -0.000003   -0.023241 -0.021534   \n",
       "High              0.780184       0.862325   0.000446   -0.023473 -0.021831   \n",
       "Low               0.775282       0.862972  -0.000300   -0.022878 -0.020967   \n",
       "Vol               0.191600      -0.038853   0.033644   -0.016688 -0.022193   \n",
       "Var              -0.004770      -0.047548   0.001534    0.011459  0.015673   \n",
       "Open_Dollar       1.000000       0.468717  -0.001316   -0.019838 -0.023090   \n",
       "Open_IBOVESPA     0.468717       1.000000   0.002019   -0.038659 -0.036810   \n",
       "DayofWeek        -0.001316       0.002019   1.000000   -0.014529 -0.009518   \n",
       "Weekofyear       -0.019838      -0.038659  -0.014529    1.000000  0.960022   \n",
       "Quarter          -0.023090      -0.036810  -0.009518    0.960022  1.000000   \n",
       "Month            -0.022277      -0.041266  -0.007394    0.987253  0.970582   \n",
       "News_N            0.310513       0.365490   0.004121    0.030426  0.020602   \n",
       "News_NN           0.144166       0.076671   0.007487   -0.018023 -0.009266   \n",
       "News_P           -0.341011      -0.371896  -0.006293   -0.023222 -0.016656   \n",
       "lag_1             0.777265       0.860894  -0.000569   -0.022560 -0.020488   \n",
       "lag_2             0.777123       0.859539  -0.000302   -0.021372 -0.019197   \n",
       "lag_3             0.776958       0.858231   0.000505   -0.020189 -0.017897   \n",
       "\n",
       "                  Month    News_N   News_NN    News_P     lag_1     lag_2  \\\n",
       "Close         -0.025061  0.338865  0.073264 -0.345495  0.999188  0.998422   \n",
       "Open          -0.024832  0.338847  0.073748 -0.345631  0.998591  0.997904   \n",
       "High          -0.025135  0.338986  0.074354 -0.345955  0.998941  0.998190   \n",
       "Low           -0.024453  0.338512  0.072449 -0.344900  0.998973  0.998255   \n",
       "Vol           -0.017174  0.042911  0.046039 -0.055405  0.059882  0.059609   \n",
       "Var            0.010868 -0.012519  0.007338  0.009580  0.003464  0.002956   \n",
       "Open_Dollar   -0.022277  0.310513  0.144166 -0.341011  0.777265  0.777123   \n",
       "Open_IBOVESPA -0.041266  0.365490  0.076671 -0.371896  0.860894  0.859539   \n",
       "DayofWeek     -0.007394  0.004121  0.007487 -0.006293 -0.000569 -0.000302   \n",
       "Weekofyear     0.987253  0.030426 -0.018023 -0.023222 -0.022560 -0.021372   \n",
       "Quarter        0.970582  0.020602 -0.009266 -0.016656 -0.020488 -0.019197   \n",
       "Month          1.000000  0.030775 -0.024068 -0.021637 -0.024159 -0.023033   \n",
       "News_N         0.030775  1.000000 -0.008214 -0.948415  0.339017  0.339039   \n",
       "News_NN       -0.024068 -0.008214  1.000000 -0.309231  0.072332  0.072430   \n",
       "News_P        -0.021637 -0.948415 -0.309231  1.000000 -0.345344 -0.345396   \n",
       "lag_1         -0.024159  0.339017  0.072332 -0.345344  1.000000  0.999186   \n",
       "lag_2         -0.023033  0.339039  0.072430 -0.345396  0.999186  1.000000   \n",
       "lag_3         -0.021890  0.340551  0.073014 -0.347019  0.998420  0.999186   \n",
       "\n",
       "                  lag_3  \n",
       "Close          0.997680  \n",
       "Open           0.997203  \n",
       "High           0.997463  \n",
       "Low            0.997553  \n",
       "Vol            0.059180  \n",
       "Var            0.001118  \n",
       "Open_Dollar    0.776958  \n",
       "Open_IBOVESPA  0.858231  \n",
       "DayofWeek      0.000505  \n",
       "Weekofyear    -0.020189  \n",
       "Quarter       -0.017897  \n",
       "Month         -0.021890  \n",
       "News_N         0.340551  \n",
       "News_NN        0.073014  \n",
       "News_P        -0.347019  \n",
       "lag_1          0.998420  \n",
       "lag_2          0.999186  \n",
       "lag_3          1.000000  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Matrix_corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.heatmap(Matrix_corr, vmin=-1, vmax=1, cmap='YlGnBu', robust=True)\n",
    "ax.set_xticklabels(ax.get_xticklabels(),rotation=90,horizontalalignment='right');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The features that have a significant impact in our model is: Open, Open_Dollar, Open_IBOVESPA and a minima relevance is the news (News_N, News_NN, News_P), considering that we can get it in 8am, where the stock market openning, so that we can make a forecast earlier in the day, due that the news doesn't seem impact a lot."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Statistical Study"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's study the Time Series a little bit more closely and identify if it is stationary or non-stationary, to verify it is used a hypothesis test using the Statistical modeling methods called Adfuller, if p-value > 0.05 it fails to reject the null hypothesis (H0), the data has a unit root and is non-stationarity. Why is it necessary? well, if garanting the non-stationary of the time series we can add the statistical forecast model such as AR, MA, ARIMA, SARIMA and among others variations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ADF Statistic: -1.268720\n",
      "p-value: 0.643359\n"
     ]
    }
   ],
   "source": [
    "df = df.sort_index(ascending = False).reset_index(drop=True)\n",
    "result = adfuller(df['Close'])\n",
    "\n",
    "print('ADF Statistic: %f' % result[0])\n",
    "print('p-value: %f' % result[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The p-value is 0.636, therefore it rejects the null-hypothesis and considers the alternative hypothesys (H1), so it's a non-stacionary time serie, and now we can try differents kind of statistical forecast."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/anthony/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:4: FutureWarning: the 'freq' keyword is deprecated, use 'period' instead\n",
      "  after removing the cwd from sys.path.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Residual')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1440 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.pyplot import figure\n",
    "import statsmodels.api as sm\n",
    "\n",
    "decomposition=sm.tsa.seasonal_decompose(df['Close'],model='additive',freq=260)\n",
    "\n",
    "fig, axes = plt.subplots(4, 1, sharex=True)\n",
    "fig.set_size_inches(15,20)\n",
    "decomposition.observed.plot(ax=axes[0], legend=False, color='orange')\n",
    "axes[0].set_ylabel('Observed')\n",
    "decomposition.trend.plot(ax=axes[1], legend=False, color='darkblue')\n",
    "axes[1].set_ylabel('Trend')\n",
    "decomposition.seasonal.plot(ax=axes[2], legend=False, color='orange')\n",
    "axes[2].set_ylabel('Seasonal')\n",
    "decomposition.resid.plot(ax=axes[3], legend=False, color='darkblue')\n",
    "axes[3].set_ylabel('Residual')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the decompose with a frequency of 260, due the working days of the year, we can notice clearly that the time series has a trend and a seasonality, using it to a solid understanding it's possible extract some insights and can be helpfull to our model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def test_adf(series, title=''):\n",
    "    dfout={}\n",
    "    dftest=sm.tsa.adfuller(series.dropna(), autolag='AIC', regression='ct')\n",
    "    for key,val in dftest[4].items():\n",
    "        dfout[f'critical value ({key})']=val\n",
    "    if dftest[1]<=0.05:\n",
    "        print(\"Strong evidence against Null Hypothesis\")\n",
    "        print(\"Reject Null Hypothesis - Data is Stationary\")\n",
    "        print(\"Data is Stationary\", title)\n",
    "    else:\n",
    "        print(\"Strong evidence for  Null Hypothesis\")\n",
    "        print(\"Accept Null Hypothesis - Data is not Stationary\")\n",
    "        print(\"Data is NOT Stationary for\", title)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Strong evidence for  Null Hypothesis\n",
      "Accept Null Hypothesis - Data is not Stationary\n",
      "Data is NOT Stationary for Itub4 Time Series\n"
     ]
    }
   ],
   "source": [
    "y_test = df['Close']\n",
    "test_adf(y_test, \"Itub4 Time Series\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Strong evidence against Null Hypothesis\n",
      "Reject Null Hypothesis - Data is Stationary\n",
      "Data is Stationary Itub4 Time Series\n"
     ]
    }
   ],
   "source": [
    "test_adf(y_test.diff(), \"Itub4 Time Series\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from statsmodels.graphics.tsaplots import plot_acf\n",
    "plot_acf(df['Close'], lags=50, color= 'orange');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ACF suggests there is long memory present in the data set, so that Long-range dependence. Its a caracteristic of non-stacionary time series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from statsmodels.graphics.tsaplots import plot_pacf\n",
    "plot_pacf(df['Close'], lags=120,color= 'orange');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The PACF will identify the order of the Ar model, thus AR(2) because it has 2 significant spikes on the lags 1 and 2, but we will use a powerful tool called auto-arima in the next step, so don't worry about that."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "It will help to select our regression models that we need compare them and pick the best model. it was possible identify features that have a significant correlation that we can use as independent variables for our regression models. It was possible too notice that we can add statistical models (ARIMA,SARIMA...) to make a comparison to others kind of regressions, due the non-stationarity of the time series. The next step is pick the best model to make a forecast for our time series using metrics of regression such as R-squared, rmse and others."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}