fiis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 0.0 IMPORTS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import pandas           as pd\n",
    "import pandas_ta        as pd_ta\n",
    "import numpy            as np\n",
    "import seaborn          as sns\n",
    "import yfinance         as yfin #workaround as pandas_datareader is not working on yahoo today\n",
    "yfin.pdr_override()\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from datetime                   import date, timedelta\n",
    "from bs4                        import BeautifulSoup\n",
    "from pandas_datareader          import data as pdr\n",
    "from sklearn.linear_model       import LinearRegression \n",
    "from sklearn.model_selection    import train_test_split\n",
    "from sklearn.metrics            import mean_squared_error, r2_score, mean_absolute_error"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Caso use Anaconda e enfrente o problema SSL ao rodar o código, a solução pode ser encontrada abaixo:\n",
    "\n",
    "https://github.com/conda/conda/issues/8273"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1.0 Fundos de Investimento Imobiliário (FII)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.1 Carregando dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(250, 26)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = \"https://www.fundsexplorer.com.br/ranking\"\n",
    "r = requests.get(url)\n",
    "df_list = pd.read_html(r.text)\n",
    "df = df_list[0]\n",
    "df.shape\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.2 Dimensões de dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total de linhas: 250\n",
      "Total de colunas: 26\n"
     ]
    }
   ],
   "source": [
    "print(f'Total de linhas: {df.shape[0]}')\n",
    "print(f'Total de colunas: {df.shape[1]}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.3 Tipo dos dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Códigodo fundo              object\n",
       "Setor                       object\n",
       "Preço Atual                 object\n",
       "Liquidez Diária            float64\n",
       "Dividendo                   object\n",
       "DividendYield               object\n",
       "DY (3M)Acumulado            object\n",
       "DY (6M)Acumulado            object\n",
       "DY (12M)Acumulado           object\n",
       "DY (3M)Média                object\n",
       "DY (6M)Média                object\n",
       "DY (12M)Média               object\n",
       "DY Ano                      object\n",
       "Variação Preço              object\n",
       "Rentab.Período              object\n",
       "Rentab.Acumulada            object\n",
       "PatrimônioLíq.              object\n",
       "VPA                         object\n",
       "P/VPA                      float64\n",
       "DYPatrimonial               object\n",
       "VariaçãoPatrimonial         object\n",
       "Rentab. Patr.no Período     object\n",
       "Rentab. Patr.Acumulada      object\n",
       "VacânciaFísica              object\n",
       "VacânciaFinanceira          object\n",
       "QuantidadeAtivos             int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.4 Verificando e subtituindo NA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Códigodo fundo               0\n",
       "Setor                        0\n",
       "Preço Atual                  7\n",
       "Liquidez Diária              7\n",
       "Dividendo                    0\n",
       "DividendYield               12\n",
       "DY (3M)Acumulado            12\n",
       "DY (6M)Acumulado            12\n",
       "DY (12M)Acumulado           12\n",
       "DY (3M)Média                12\n",
       "DY (6M)Média                12\n",
       "DY (12M)Média               12\n",
       "DY Ano                      23\n",
       "Variação Preço              12\n",
       "Rentab.Período              12\n",
       "Rentab.Acumulada            12\n",
       "PatrimônioLíq.               0\n",
       "VPA                          0\n",
       "P/VPA                        7\n",
       "DYPatrimonial               47\n",
       "VariaçãoPatrimonial         47\n",
       "Rentab. Patr.no Período     47\n",
       "Rentab. Patr.Acumulada      47\n",
       "VacânciaFísica             130\n",
       "VacânciaFinanceira         224\n",
       "QuantidadeAtivos             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verificar quantas colunas possuem dados nulos\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.replace(np.nan,0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.5 Selecionando colunas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "df = df.drop(columns=['DYPatrimonial','VariaçãoPatrimonial','Rentab. Patr.no Período',\n",
    "                    'Rentab. Patr.Acumulada','VacânciaFinanceira','DY Ano','DividendYield','DY (3M)Média','DY (6M)Média',\n",
    "                    'DY (3M)Acumulado',\t'DY (6M)Acumulado','DY (12M)Acumulado','DY (12M)Média'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfColunas = []\n",
    "naoNumColunas = ['Códigodo fundo','Setor']\n",
    "for coluna in df:\n",
    "    if coluna in naoNumColunas:\n",
    "        pass\n",
    "    else:\n",
    "        dfColunas.append(coluna)       "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.6 Tratamento de dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "for colunas in dfColunas:\n",
    "    df[colunas] = [str(x).replace(\"R$ \",\"\").replace(\"%\",\"\").replace(\".\",\"\").replace(\",\",\".\") for x in df[colunas]]\n",
    "    df[colunas] = [pd.to_numeric(x) for x in df[colunas]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ajustando VPA para as cadas decimais corretas\n",
    "df['P/VPA'] = df['P/VPA'] / 1000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.7 Filtrando variáveis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def filtroDF(quantidadeAtivos=9, liquidezDiaria=1000,PVPA=1.2,VacânciaFísica=10):\n",
    "# Filtrando para tirar FII's com alto risco\n",
    "        # Com mais de 5 ativos. Casos menores do que esse aumentam o risco pela baixa pulverização\n",
    "        dataFrame = df[(df['QuantidadeAtivos'] > quantidadeAtivos) & \n",
    "        # Liquidez diária superior a 1000\n",
    "                (df['Liquidez Diária'] > liquidezDiaria) &\n",
    "        # P/VPA até Máximo 1,2 (20% a mais do que o patrimonio)\n",
    "                (df['P/VPA'] < PVPA) &\n",
    "        # Vacância inferior a 10% do território\n",
    "                (df['VacânciaFísica'] < VacânciaFísica)]\n",
    "        return dataFrame\n",
    "        \n",
    "df = filtroDF()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['KNRI11', 'HGBS11', 'VILG11', 'HGLG11', 'BTLG11', 'XPML11']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testing = []\n",
    "listingCarlos = ['BTLG11','HGRY11','VILG11','HSML11','HGBS11','XPML11','KNRI11','HGRE11','HGLG11']\n",
    "for i in df['Códigodo fundo']:\n",
    "    if i in listingCarlos:\n",
    "        testing.append(i)\n",
    "testing        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>Códigodo fundo</th>\n",
       "      <th>Setor</th>\n",
       "      <th>Preço Atual</th>\n",
       "      <th>Liquidez Diária</th>\n",
       "      <th>Dividendo</th>\n",
       "      <th>Variação Preço</th>\n",
       "      <th>Rentab.Período</th>\n",
       "      <th>Rentab.Acumulada</th>\n",
       "      <th>PatrimônioLíq.</th>\n",
       "      <th>VPA</th>\n",
       "      <th>P/VPA</th>\n",
       "      <th>VacânciaFísica</th>\n",
       "      <th>QuantidadeAtivos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>KNRI11</td>\n",
       "      <td>Híbrido</td>\n",
       "      <td>142.35</td>\n",
       "      <td>249070</td>\n",
       "      <td>0.77</td>\n",
       "      <td>3.40</td>\n",
       "      <td>3.94</td>\n",
       "      <td>-3.99</td>\n",
       "      <td>3,800,803,257.11</td>\n",
       "      <td>157.34</td>\n",
       "      <td>0.90</td>\n",
       "      <td>3.35</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>HGLG11</td>\n",
       "      <td>Logística</td>\n",
       "      <td>167.51</td>\n",
       "      <td>433700</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1.35</td>\n",
       "      <td>2.02</td>\n",
       "      <td>-2.84</td>\n",
       "      <td>3,151,358,842.72</td>\n",
       "      <td>147.86</td>\n",
       "      <td>1.13</td>\n",
       "      <td>6.30</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>HGRU11</td>\n",
       "      <td>Híbrido</td>\n",
       "      <td>114.00</td>\n",
       "      <td>375980</td>\n",
       "      <td>0.72</td>\n",
       "      <td>-3.13</td>\n",
       "      <td>-2.51</td>\n",
       "      <td>-7.36</td>\n",
       "      <td>2,163,602,469.79</td>\n",
       "      <td>117.55</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.00</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>HGBS11</td>\n",
       "      <td>Shoppings</td>\n",
       "      <td>189.32</td>\n",
       "      <td>196470</td>\n",
       "      <td>0.85</td>\n",
       "      <td>-3.29</td>\n",
       "      <td>-2.84</td>\n",
       "      <td>-13.48</td>\n",
       "      <td>2,143,980,697.65</td>\n",
       "      <td>214.40</td>\n",
       "      <td>0.88</td>\n",
       "      <td>7.10</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>XPML11</td>\n",
       "      <td>Shoppings</td>\n",
       "      <td>101.81</td>\n",
       "      <td>699970</td>\n",
       "      <td>0.57</td>\n",
       "      <td>-1.27</td>\n",
       "      <td>-0.72</td>\n",
       "      <td>-7.28</td>\n",
       "      <td>1,900,090,443.88</td>\n",
       "      <td>104.36</td>\n",
       "      <td>0.98</td>\n",
       "      <td>4.60</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>VILG11</td>\n",
       "      <td>Logística</td>\n",
       "      <td>102.58</td>\n",
       "      <td>394390</td>\n",
       "      <td>0.64</td>\n",
       "      <td>-0.83</td>\n",
       "      <td>-0.23</td>\n",
       "      <td>-8.67</td>\n",
       "      <td>1,708,615,548.48</td>\n",
       "      <td>113.93</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.00</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>VISC11</td>\n",
       "      <td>Shoppings</td>\n",
       "      <td>105.35</td>\n",
       "      <td>263140</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.59</td>\n",
       "      <td>1.16</td>\n",
       "      <td>-8.90</td>\n",
       "      <td>1,702,561,351.78</td>\n",
       "      <td>119.30</td>\n",
       "      <td>0.88</td>\n",
       "      <td>8.10</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>BRCO11</td>\n",
       "      <td>Logística</td>\n",
       "      <td>99.20</td>\n",
       "      <td>436720</td>\n",
       "      <td>0.57</td>\n",
       "      <td>-3.48</td>\n",
       "      <td>-2.92</td>\n",
       "      <td>-9.47</td>\n",
       "      <td>1,603,955,235.27</td>\n",
       "      <td>108.53</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.00</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>BBPO11</td>\n",
       "      <td>Lajes Corporativas</td>\n",
       "      <td>92.00</td>\n",
       "      <td>263620</td>\n",
       "      <td>0.82</td>\n",
       "      <td>-15.18</td>\n",
       "      <td>-14.46</td>\n",
       "      <td>-7.83</td>\n",
       "      <td>1,526,344,263.71</td>\n",
       "      <td>95.88</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.00</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>BTLG11</td>\n",
       "      <td>Logística</td>\n",
       "      <td>107.10</td>\n",
       "      <td>396070</td>\n",
       "      <td>0.72</td>\n",
       "      <td>1.37</td>\n",
       "      <td>2.05</td>\n",
       "      <td>-4.50</td>\n",
       "      <td>1,516,402,298.06</td>\n",
       "      <td>99.57</td>\n",
       "      <td>1.08</td>\n",
       "      <td>0.00</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>LVBI11</td>\n",
       "      <td>Logística</td>\n",
       "      <td>100.97</td>\n",
       "      <td>158800</td>\n",
       "      <td>0.65</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>0.49</td>\n",
       "      <td>-10.24</td>\n",
       "      <td>1,350,783,017.73</td>\n",
       "      <td>114.71</td>\n",
       "      <td>0.88</td>\n",
       "      <td>3.20</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>RBVA11</td>\n",
       "      <td>Outros</td>\n",
       "      <td>103.30</td>\n",
       "      <td>160410</td>\n",
       "      <td>0.87</td>\n",
       "      <td>-2.60</td>\n",
       "      <td>-1.82</td>\n",
       "      <td>1.98</td>\n",
       "      <td>1,313,544,384.58</td>\n",
       "      <td>113.35</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.70</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>RZTR11</td>\n",
       "      <td>Híbrido</td>\n",
       "      <td>99.98</td>\n",
       "      <td>413960</td>\n",
       "      <td>0.95</td>\n",
       "      <td>2.65</td>\n",
       "      <td>3.62</td>\n",
       "      <td>-0.23</td>\n",
       "      <td>1,087,621,455.10</td>\n",
       "      <td>98.44</td>\n",
       "      <td>1.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>RBRP11</td>\n",
       "      <td>Outros</td>\n",
       "      <td>75.91</td>\n",
       "      <td>168590</td>\n",
       "      <td>0.45</td>\n",
       "      <td>-2.84</td>\n",
       "      <td>-2.25</td>\n",
       "      <td>-20.84</td>\n",
       "      <td>1,039,528,286.23</td>\n",
       "      <td>85.35</td>\n",
       "      <td>0.89</td>\n",
       "      <td>8.00</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>GGRC11</td>\n",
       "      <td>Logística</td>\n",
       "      <td>115.90</td>\n",
       "      <td>93560</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.68</td>\n",
       "      <td>1.37</td>\n",
       "      <td>-12.36</td>\n",
       "      <td>1,039,367,829.17</td>\n",
       "      <td>133.97</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.00</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>GSFI11</td>\n",
       "      <td>Shoppings</td>\n",
       "      <td>4.46</td>\n",
       "      <td>44130</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>907,019,849.19</td>\n",
       "      <td>13.24</td>\n",
       "      <td>0.34</td>\n",
       "      <td>9.50</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>ALZR11</td>\n",
       "      <td>Logística</td>\n",
       "      <td>115.30</td>\n",
       "      <td>94310</td>\n",
       "      <td>0.62</td>\n",
       "      <td>-0.34</td>\n",
       "      <td>0.19</td>\n",
       "      <td>-7.58</td>\n",
       "      <td>642,993,788.51</td>\n",
       "      <td>108.58</td>\n",
       "      <td>1.06</td>\n",
       "      <td>0.00</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>TRXF11</td>\n",
       "      <td>Outros</td>\n",
       "      <td>101.54</td>\n",
       "      <td>134080</td>\n",
       "      <td>0.72</td>\n",
       "      <td>-0.50</td>\n",
       "      <td>0.21</td>\n",
       "      <td>1.12</td>\n",
       "      <td>578,875,374.53</td>\n",
       "      <td>102.61</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.00</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>QAGR11</td>\n",
       "      <td>Outros</td>\n",
       "      <td>51.70</td>\n",
       "      <td>71520</td>\n",
       "      <td>0.38</td>\n",
       "      <td>-0.44</td>\n",
       "      <td>0.31</td>\n",
       "      <td>-1.59</td>\n",
       "      <td>301,127,070.71</td>\n",
       "      <td>59.75</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.00</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>BBRC11</td>\n",
       "      <td>Outros</td>\n",
       "      <td>100.93</td>\n",
       "      <td>9260</td>\n",
       "      <td>0.85</td>\n",
       "      <td>-4.15</td>\n",
       "      <td>-3.38</td>\n",
       "      <td>-10.35</td>\n",
       "      <td>180,306,764.82</td>\n",
       "      <td>113.40</td>\n",
       "      <td>0.89</td>\n",
       "      <td>5.40</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>BNFS11</td>\n",
       "      <td>Outros</td>\n",
       "      <td>122.98</td>\n",
       "      <td>1580</td>\n",
       "      <td>1.01</td>\n",
       "      <td>-3.81</td>\n",
       "      <td>-3.01</td>\n",
       "      <td>3.20</td>\n",
       "      <td>72,884,567.41</td>\n",
       "      <td>104.12</td>\n",
       "      <td>1.18</td>\n",
       "      <td>0.00</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Códigodo fundo               Setor  Preço Atual  Liquidez Diária  \\\n",
       "84          KNRI11             Híbrido       142.35           249070   \n",
       "163         HGLG11           Logística       167.51           433700   \n",
       "113         HGRU11             Híbrido       114.00           375980   \n",
       "87          HGBS11           Shoppings       189.32           196470   \n",
       "247         XPML11           Shoppings       101.81           699970   \n",
       "110         VILG11           Logística       102.58           394390   \n",
       "103         VISC11           Shoppings       105.35           263140   \n",
       "102         BRCO11           Logística        99.20           436720   \n",
       "77          BBPO11  Lajes Corporativas        92.00           263620   \n",
       "242         BTLG11           Logística       107.10           396070   \n",
       "123         LVBI11           Logística       100.97           158800   \n",
       "96          RBVA11              Outros       103.30           160410   \n",
       "153         RZTR11             Híbrido        99.98           413960   \n",
       "204         RBRP11              Outros        75.91           168590   \n",
       "174         GGRC11           Logística       115.90            93560   \n",
       "10          GSFI11           Shoppings         4.46            44130   \n",
       "244         ALZR11           Logística       115.30            94310   \n",
       "58          TRXF11              Outros       101.54           134080   \n",
       "173         QAGR11              Outros        51.70            71520   \n",
       "76          BBRC11              Outros       100.93             9260   \n",
       "60          BNFS11              Outros       122.98             1580   \n",
       "\n",
       "     Dividendo  Variação Preço  Rentab.Período  Rentab.Acumulada  \\\n",
       "84        0.77            3.40            3.94             -3.99   \n",
       "163       1.10            1.35            2.02             -2.84   \n",
       "113       0.72           -3.13           -2.51             -7.36   \n",
       "87        0.85           -3.29           -2.84            -13.48   \n",
       "247       0.57           -1.27           -0.72             -7.28   \n",
       "110       0.64           -0.83           -0.23             -8.67   \n",
       "103       0.58            0.59            1.16             -8.90   \n",
       "102       0.57           -3.48           -2.92             -9.47   \n",
       "77        0.82          -15.18          -14.46             -7.83   \n",
       "242       0.72            1.37            2.05             -4.50   \n",
       "123       0.65           -0.15            0.49            -10.24   \n",
       "96        0.87           -2.60           -1.82              1.98   \n",
       "153       0.95            2.65            3.62             -0.23   \n",
       "204       0.45           -2.84           -2.25            -20.84   \n",
       "174       0.80            0.68            1.37            -12.36   \n",
       "10        0.00            0.00            0.03              0.03   \n",
       "244       0.62           -0.34            0.19             -7.58   \n",
       "58        0.72           -0.50            0.21              1.12   \n",
       "173       0.38           -0.44            0.31             -1.59   \n",
       "76        0.85           -4.15           -3.38            -10.35   \n",
       "60        1.01           -3.81           -3.01              3.20   \n",
       "\n",
       "      PatrimônioLíq.    VPA  P/VPA  VacânciaFísica  QuantidadeAtivos  \n",
       "84  3,800,803,257.11 157.34   0.90            3.35                20  \n",
       "163 3,151,358,842.72 147.86   1.13            6.30                17  \n",
       "113 2,163,602,469.79 117.55   0.97            0.00                16  \n",
       "87  2,143,980,697.65 214.40   0.88            7.10                17  \n",
       "247 1,900,090,443.88 104.36   0.98            4.60                13  \n",
       "110 1,708,615,548.48 113.93   0.90            0.00                15  \n",
       "103 1,702,561,351.78 119.30   0.88            8.10                15  \n",
       "102 1,603,955,235.27 108.53   0.91            0.00                10  \n",
       "77  1,526,344,263.71  95.88   0.96            0.00                64  \n",
       "242 1,516,402,298.06  99.57   1.08            0.00                16  \n",
       "123 1,350,783,017.73 114.71   0.88            3.20                10  \n",
       "96  1,313,544,384.58 113.35   0.91            0.70                72  \n",
       "153 1,087,621,455.10  98.44   1.02            0.00                10  \n",
       "204 1,039,528,286.23  85.35   0.89            8.00                17  \n",
       "174 1,039,367,829.17 133.97   0.87            0.00                15  \n",
       "10    907,019,849.19  13.24   0.34            9.50                10  \n",
       "244   642,993,788.51 108.58   1.06            0.00                13  \n",
       "58    578,875,374.53 102.61   0.99            0.00                44  \n",
       "173   301,127,070.71  59.75   0.87            0.00                12  \n",
       "76    180,306,764.82 113.40   0.89            5.40                20  \n",
       "60     72,884,567.41 104.12   1.18            0.00                18  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ordernar por critérios de maior importância (na minha opinião)\n",
    "df = df.sort_values(by=['PatrimônioLíq.'], ascending = False)\n",
    "\n",
    "# Formatar para remover Notação Científica do Patrimônio Líquido\n",
    "# pd.set_option('display.float_format', lambda x: '%.2f' % x)\n",
    "pd.options.display.float_format = '{:,.2f}'.format\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Logística             7\n",
       "Outros                6\n",
       "Shoppings             4\n",
       "Híbrido               3\n",
       "Lajes Corporativas    1\n",
       "Name: Setor, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Setor'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2.0 AÇÕES"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.1 Carregando dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n"
     ]
    }
   ],
   "source": [
    "# Cotação de cada empresa\n",
    "df_ibov = pdr.get_data_yahoo(\"PSSA3.SA\", start=\"2018-01-01\", end=(date.today() + timedelta(days=-1) ))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.2 Selecionando colunas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ibov = df_ibov[['Open','Close','Volume']]\n",
    "df1 = df_ibov.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Open      0\n",
       "Close     0\n",
       "Volume    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 287,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.3 Tratamento de Dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1['MA_200'] = df1['Close'].rolling( window = 200).mean()\n",
    "df1['EMA_8'] = df1.ta.ema( close='Close', length= 8, append = True)\n",
    "# df1['EMA_21'] = df1.ta.ema( close='Close', length= 8, append = True)\n",
    "df1['MA_200'] = df1['MA_200'].fillna(0)\n",
    "df1['Volume'] = df1['Volume'].astype(int)\n",
    "df1 = df1.reset_index()\n",
    "df1 = df1.iloc[200:]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date      datetime64[ns]\n",
       "Open             float64\n",
       "Close            float64\n",
       "Volume             int32\n",
       "MA_200           float64\n",
       "EMA_8            float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 289,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 290,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = df1.corr()\n",
    "sns.heatmap(corr, annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,7))\n",
    "plt.plot(df1['Date'], df1['Close'], label='Close')\n",
    "plt.plot(df1['Date'], df1['EMA_8'], 'r--', label='EMA 8')\n",
    "# plt.plot(df1['Date'], df1['EMA_21'], 'b--', label='EMA 21')\n",
    "plt.plot(df1['Date'], df1['MA_200'], 'g--', label='MA 200')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.2 Divisão do período treino e teste"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(588, 1) (147, 1) (588, 4) (147, 4)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split( df1[['Close']], df1[['Open','Volume','EMA_8','MA_200']], test_size= .2)\n",
    "print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "147"
      ]
     },
     "execution_count": 293,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.47950180004827136"
      ]
     },
     "execution_count": 294,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = LinearRegression().fit(X_train, y_train)\n",
    "y_pred = lr.predict(X_test)\n",
    "lr.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Coefficients: [[ 9.68941257e-01]\n",
      " [-2.30127837e+04]\n",
      " [ 9.28573976e-01]\n",
      " [ 1.07524185e-01]]\n",
      "Mean Absolute Error: 87682.12129530567\n",
      "Coefficient of Determination: 0.47950180004827136\n"
     ]
    }
   ],
   "source": [
    "print(\"Model Coefficients:\", lr.coef_)\n",
    "print(\"Mean Absolute Error:\", mean_absolute_error( y_test, y_pred ))\n",
    "print(\"Coefficient of Determination:\", r2_score( y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "0ae0c0bdb8941e8f0d5e6666d42361fe810b7b04c11a3cb8a647a432190f32a9"
  },
  "kernelspec": {
   "display_name": "Python 3.8.8 64-bit ('venvfii': venv)",
   "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": "3.8.8"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}