Carteira_Formula_Mágica_Joel_Greenblatt.ipynb

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  "metadata": {
    "colab": {
      "name": "Carteira com a 'formula mágica'",
      "provenance": [],
      "collapsed_sections": [
        "rtbULQbQ2FcB"
      ],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/michelmb/InvestmentManagement/blob/master/Carteira_Formula_M%C3%A1gica_Joel_Greenblatt.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v-5O89Tc16pw",
        "colab_type": "text"
      },
      "source": [
        "## Como montar uma carteira de investimentos utilizando a formula mágica de Joel Greenblatt"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rtbULQbQ2FcB",
        "colab_type": "text"
      },
      "source": [
        "### Importar as bibliotecas que serão utilizadas"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OD9FFdklZ0IH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from datetime import datetime"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g-hl4OxF2cfl",
        "colab_type": "text"
      },
      "source": [
        "### Obter dados do site [Fundamentus](https://www.fundamentus.com.br/resultado.php?segmento=)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MIxYQijo2QAt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "733386f2-eb33-4645-b328-5e3768fbf23d"
      },
      "source": [
        "# função para converter valores percentuais para valores decimais\n",
        "def convert_perc(value):\n",
        "    return pd.to_numeric(value.replace('%','').replace('.', '').replace(',', '.'))\n",
        "\n",
        "# mapeando colunas que possuem valores percentuais\n",
        "convs = {5: convert_perc, 12: convert_perc, 13: convert_perc, 15: convert_perc, 16: convert_perc, 20: convert_perc}\n",
        "\n",
        "# lendo tabela de dados\n",
        "fundamentus = pd.read_html('https://www.fundamentus.com.br/resultado.php?segmento=',\n",
        "                           decimal=',', thousands='.', index_col='Papel', converters=convs)[0]\n",
        "\n",
        "# Inspecionando valores do dataset\n",
        "fundamentus.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .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>Cotação</th>\n",
              "      <th>P/L</th>\n",
              "      <th>P/VP</th>\n",
              "      <th>PSR</th>\n",
              "      <th>Div.Yield</th>\n",
              "      <th>P/Ativo</th>\n",
              "      <th>P/Cap.Giro</th>\n",
              "      <th>P/EBIT</th>\n",
              "      <th>P/Ativ Circ.Liq</th>\n",
              "      <th>EV/EBIT</th>\n",
              "      <th>EV/EBITDA</th>\n",
              "      <th>Mrg Ebit</th>\n",
              "      <th>Mrg. Líq.</th>\n",
              "      <th>Liq. Corr.</th>\n",
              "      <th>ROIC</th>\n",
              "      <th>ROE</th>\n",
              "      <th>Liq.2meses</th>\n",
              "      <th>Patrim. Líq</th>\n",
              "      <th>Dív.Brut/ Patrim.</th>\n",
              "      <th>Cresc. Rec.5a</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Papel</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>VNET3</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>12.99</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9.257250e+09</td>\n",
              "      <td>0.00</td>\n",
              "      <td>-2.71</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CFLU4</th>\n",
              "      <td>1000.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.88</td>\n",
              "      <td>10.72</td>\n",
              "      <td>1.1</td>\n",
              "      <td>17.68</td>\n",
              "      <td>32.15</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.035100e+07</td>\n",
              "      <td>0.06</td>\n",
              "      <td>8.14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CLAN3</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>-1.05</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.012240e+09</td>\n",
              "      <td>0.00</td>\n",
              "      <td>-63.96</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CLSC6</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>22.88</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.551160e+09</td>\n",
              "      <td>0.00</td>\n",
              "      <td>6.66</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CSTB3</th>\n",
              "      <td>150.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>40.85</td>\n",
              "      <td>28.98</td>\n",
              "      <td>2.6</td>\n",
              "      <td>22.40</td>\n",
              "      <td>20.11</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.420670e+09</td>\n",
              "      <td>0.14</td>\n",
              "      <td>31.91</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Cotação  P/L  P/VP  ...   Patrim. Líq  Dív.Brut/ Patrim.  Cresc. Rec.5a\n",
              "Papel                      ...                                                \n",
              "VNET3      0.0  0.0   0.0  ...  9.257250e+09               0.00          -2.71\n",
              "CFLU4   1000.0  0.0   0.0  ...  6.035100e+07               0.06           8.14\n",
              "CLAN3      0.0  0.0   0.0  ...  1.012240e+09               0.00         -63.96\n",
              "CLSC6      0.0  0.0   0.0  ...  1.551160e+09               0.00           6.66\n",
              "CSTB3    150.0  0.0   0.0  ...  8.420670e+09               0.14          31.91\n",
              "\n",
              "[5 rows x 20 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 267
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "STq_UQv64xq8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 480
        },
        "outputId": "94e8f945-b665-4f62-8082-bb4c9e1ba5b8"
      },
      "source": [
        "# Inspecionando valores do dataset\n",
        "fundamentus.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 887 entries, VNET3 to BRAP4\n",
            "Data columns (total 20 columns):\n",
            " #   Column             Non-Null Count  Dtype  \n",
            "---  ------             --------------  -----  \n",
            " 0   Cotação            887 non-null    float64\n",
            " 1   P/L                887 non-null    float64\n",
            " 2   P/VP               887 non-null    float64\n",
            " 3   PSR                887 non-null    float64\n",
            " 4   Div.Yield          887 non-null    float64\n",
            " 5   P/Ativo            887 non-null    float64\n",
            " 6   P/Cap.Giro         887 non-null    float64\n",
            " 7   P/EBIT             887 non-null    float64\n",
            " 8   P/Ativ Circ.Liq    887 non-null    float64\n",
            " 9   EV/EBIT            887 non-null    float64\n",
            " 10  EV/EBITDA          887 non-null    float64\n",
            " 11  Mrg Ebit           887 non-null    float64\n",
            " 12  Mrg. Líq.          887 non-null    float64\n",
            " 13  Liq. Corr.         887 non-null    float64\n",
            " 14  ROIC               887 non-null    float64\n",
            " 15  ROE                887 non-null    float64\n",
            " 16  Liq.2meses         887 non-null    float64\n",
            " 17  Patrim. Líq        887 non-null    float64\n",
            " 18  Dív.Brut/ Patrim.  887 non-null    float64\n",
            " 19  Cresc. Rec.5a      887 non-null    float64\n",
            "dtypes: float64(20)\n",
            "memory usage: 145.5+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8Sn9EU_f5Dl7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 332
        },
        "outputId": "c1bfebb9-fa79-403f-b600-ef779b3fe8b8"
      },
      "source": [
        "fundamentus.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Cotação</th>\n",
              "      <th>P/L</th>\n",
              "      <th>P/VP</th>\n",
              "      <th>PSR</th>\n",
              "      <th>Div.Yield</th>\n",
              "      <th>P/Ativo</th>\n",
              "      <th>P/Cap.Giro</th>\n",
              "      <th>P/EBIT</th>\n",
              "      <th>P/Ativ Circ.Liq</th>\n",
              "      <th>EV/EBIT</th>\n",
              "      <th>EV/EBITDA</th>\n",
              "      <th>Mrg Ebit</th>\n",
              "      <th>Mrg. Líq.</th>\n",
              "      <th>Liq. Corr.</th>\n",
              "      <th>ROIC</th>\n",
              "      <th>ROE</th>\n",
              "      <th>Liq.2meses</th>\n",
              "      <th>Patrim. Líq</th>\n",
              "      <th>Dív.Brut/ Patrim.</th>\n",
              "      <th>Cresc. Rec.5a</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>887.000000</td>\n",
              "      <td>887.000000</td>\n",
              "      <td>887.000000</td>\n",
              "      <td>887.000000</td>\n",
              "      <td>887.000000</td>\n",
              "      <td>887.000000</td>\n",
              "      <td>887.000000</td>\n",
              "      <td>887.000000</td>\n",
              "      <td>887.000000</td>\n",
              "      <td>887.000000</td>\n",
              "      <td>887.000000</td>\n",
              "      <td>887.000000</td>\n",
              "      <td>887.000000</td>\n",
              "      <td>887.000000</td>\n",
              "      <td>887.000000</td>\n",
              "      <td>8.870000e+02</td>\n",
              "      <td>8.870000e+02</td>\n",
              "      <td>8.870000e+02</td>\n",
              "      <td>887.000000</td>\n",
              "      <td>887.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>66.267554</td>\n",
              "      <td>-8.833540</td>\n",
              "      <td>1600.415017</td>\n",
              "      <td>12.631807</td>\n",
              "      <td>2.726043</td>\n",
              "      <td>9.627042</td>\n",
              "      <td>-2.643145</td>\n",
              "      <td>-16.920913</td>\n",
              "      <td>-1.718816</td>\n",
              "      <td>-580.149030</td>\n",
              "      <td>83.869696</td>\n",
              "      <td>-107.328151</td>\n",
              "      <td>-718.526505</td>\n",
              "      <td>1.516945</td>\n",
              "      <td>20.598997</td>\n",
              "      <td>2.980048e+03</td>\n",
              "      <td>2.806045e+07</td>\n",
              "      <td>4.907485e+09</td>\n",
              "      <td>48.673123</td>\n",
              "      <td>21.098782</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>504.684593</td>\n",
              "      <td>625.473121</td>\n",
              "      <td>33615.944005</td>\n",
              "      <td>322.526890</td>\n",
              "      <td>42.444865</td>\n",
              "      <td>133.078125</td>\n",
              "      <td>93.094474</td>\n",
              "      <td>335.621395</td>\n",
              "      <td>62.775703</td>\n",
              "      <td>12938.614446</td>\n",
              "      <td>1405.814364</td>\n",
              "      <td>1277.478474</td>\n",
              "      <td>10731.490521</td>\n",
              "      <td>2.266408</td>\n",
              "      <td>294.833581</td>\n",
              "      <td>6.470692e+04</td>\n",
              "      <td>1.225289e+08</td>\n",
              "      <td>1.853814e+10</td>\n",
              "      <td>1007.374315</td>\n",
              "      <td>306.342682</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>-14786.900000</td>\n",
              "      <td>-2060.540000</td>\n",
              "      <td>-7656.880000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-2300.530000</td>\n",
              "      <td>-7214.410000</td>\n",
              "      <td>-1452.250000</td>\n",
              "      <td>-272246.000000</td>\n",
              "      <td>-7466.350000</td>\n",
              "      <td>-20051.700000</td>\n",
              "      <td>-219100.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-139.550000</td>\n",
              "      <td>-1.337780e+04</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>-1.225680e+10</td>\n",
              "      <td>-56.900000</td>\n",
              "      <td>-97.740000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>5.180000</td>\n",
              "      <td>-0.380000</td>\n",
              "      <td>0.010000</td>\n",
              "      <td>0.015500</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.073000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-1.805000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-2.795000</td>\n",
              "      <td>0.340000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>6.375950e+07</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-0.595000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>14.540000</td>\n",
              "      <td>4.720000</td>\n",
              "      <td>1.170000</td>\n",
              "      <td>0.685000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.410000</td>\n",
              "      <td>0.680000</td>\n",
              "      <td>3.740000</td>\n",
              "      <td>-0.190000</td>\n",
              "      <td>6.680000</td>\n",
              "      <td>5.300000</td>\n",
              "      <td>6.080000</td>\n",
              "      <td>1.540000</td>\n",
              "      <td>1.200000</td>\n",
              "      <td>4.900000</td>\n",
              "      <td>8.890000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>8.586920e+08</td>\n",
              "      <td>0.230000</td>\n",
              "      <td>5.480000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>33.425000</td>\n",
              "      <td>16.565000</td>\n",
              "      <td>2.310000</td>\n",
              "      <td>2.009500</td>\n",
              "      <td>0.625000</td>\n",
              "      <td>0.918500</td>\n",
              "      <td>5.295000</td>\n",
              "      <td>11.225000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>14.840000</td>\n",
              "      <td>10.925000</td>\n",
              "      <td>14.990000</td>\n",
              "      <td>9.920000</td>\n",
              "      <td>1.930000</td>\n",
              "      <td>11.175000</td>\n",
              "      <td>1.793000e+01</td>\n",
              "      <td>6.733885e+05</td>\n",
              "      <td>3.105800e+09</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>12.915000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>10081.900000</td>\n",
              "      <td>2220.880000</td>\n",
              "      <td>723708.000000</td>\n",
              "      <td>2659.020000</td>\n",
              "      <td>1254.590000</td>\n",
              "      <td>3275.470000</td>\n",
              "      <td>457.140000</td>\n",
              "      <td>2948.170000</td>\n",
              "      <td>910.250000</td>\n",
              "      <td>19665.000000</td>\n",
              "      <td>21412.600000</td>\n",
              "      <td>18112.100000</td>\n",
              "      <td>15969.500000</td>\n",
              "      <td>28.580000</td>\n",
              "      <td>6170.970000</td>\n",
              "      <td>1.363200e+06</td>\n",
              "      <td>1.592310e+09</td>\n",
              "      <td>2.318290e+11</td>\n",
              "      <td>21227.000000</td>\n",
              "      <td>6354.070000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            Cotação           P/L  ...  Dív.Brut/ Patrim.  Cresc. Rec.5a\n",
              "count    887.000000    887.000000  ...         887.000000     887.000000\n",
              "mean      66.267554     -8.833540  ...          48.673123      21.098782\n",
              "std      504.684593    625.473121  ...        1007.374315     306.342682\n",
              "min        0.000000 -14786.900000  ...         -56.900000     -97.740000\n",
              "25%        5.180000     -0.380000  ...           0.000000      -0.595000\n",
              "50%       14.540000      4.720000  ...           0.230000       5.480000\n",
              "75%       33.425000     16.565000  ...           1.000000      12.915000\n",
              "max    10081.900000   2220.880000  ...       21227.000000    6354.070000\n",
              "\n",
              "[8 rows x 20 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 257
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IWl-KLUu4dcK",
        "colab_type": "text"
      },
      "source": [
        "### Filtrando empresas: possui EV maior que 50M"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K_FZrNqK29Vf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 332
        },
        "outputId": "fdec9c41-3aed-4aff-84db-974cd0ed7e5b"
      },
      "source": [
        "fundamentus = fundamentus[fundamentus['Patrim. Líq'] > 50000000]\n",
        "fundamentus.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Cotação</th>\n",
              "      <th>P/L</th>\n",
              "      <th>P/VP</th>\n",
              "      <th>PSR</th>\n",
              "      <th>Div.Yield</th>\n",
              "      <th>P/Ativo</th>\n",
              "      <th>P/Cap.Giro</th>\n",
              "      <th>P/EBIT</th>\n",
              "      <th>P/Ativ Circ.Liq</th>\n",
              "      <th>EV/EBIT</th>\n",
              "      <th>EV/EBITDA</th>\n",
              "      <th>Mrg Ebit</th>\n",
              "      <th>Mrg. Líq.</th>\n",
              "      <th>Liq. Corr.</th>\n",
              "      <th>ROIC</th>\n",
              "      <th>ROE</th>\n",
              "      <th>Liq.2meses</th>\n",
              "      <th>Patrim. Líq</th>\n",
              "      <th>Dív.Brut/ Patrim.</th>\n",
              "      <th>Cresc. Rec.5a</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>678.000000</td>\n",
              "      <td>678.000000</td>\n",
              "      <td>678.000000</td>\n",
              "      <td>678.000000</td>\n",
              "      <td>678.000000</td>\n",
              "      <td>678.000000</td>\n",
              "      <td>678.000000</td>\n",
              "      <td>678.000000</td>\n",
              "      <td>678.000000</td>\n",
              "      <td>678.000000</td>\n",
              "      <td>678.000000</td>\n",
              "      <td>678.000000</td>\n",
              "      <td>678.000000</td>\n",
              "      <td>678.000000</td>\n",
              "      <td>678.000000</td>\n",
              "      <td>678.000000</td>\n",
              "      <td>6.780000e+02</td>\n",
              "      <td>6.780000e+02</td>\n",
              "      <td>678.000000</td>\n",
              "      <td>678.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>79.332729</td>\n",
              "      <td>13.058201</td>\n",
              "      <td>3.008569</td>\n",
              "      <td>7.910611</td>\n",
              "      <td>3.505354</td>\n",
              "      <td>0.857171</td>\n",
              "      <td>0.786106</td>\n",
              "      <td>-8.610782</td>\n",
              "      <td>0.719027</td>\n",
              "      <td>-7.730339</td>\n",
              "      <td>-6.689292</td>\n",
              "      <td>-1.861844</td>\n",
              "      <td>58.934941</td>\n",
              "      <td>1.730708</td>\n",
              "      <td>8.316254</td>\n",
              "      <td>-2.005900</td>\n",
              "      <td>3.402053e+07</td>\n",
              "      <td>6.647143e+09</td>\n",
              "      <td>1.322743</td>\n",
              "      <td>11.664174</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>574.593391</td>\n",
              "      <td>433.783772</td>\n",
              "      <td>6.789947</td>\n",
              "      <td>350.883992</td>\n",
              "      <td>48.524563</td>\n",
              "      <td>1.352567</td>\n",
              "      <td>58.251647</td>\n",
              "      <td>345.151070</td>\n",
              "      <td>44.384438</td>\n",
              "      <td>349.089461</td>\n",
              "      <td>310.131735</td>\n",
              "      <td>744.094090</td>\n",
              "      <td>1423.100467</td>\n",
              "      <td>2.076325</td>\n",
              "      <td>22.822568</td>\n",
              "      <td>73.440107</td>\n",
              "      <td>1.358924e+08</td>\n",
              "      <td>2.086903e+10</td>\n",
              "      <td>3.984618</td>\n",
              "      <td>58.814377</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>-9868.830000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-7656.880000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-593.560000</td>\n",
              "      <td>-7214.410000</td>\n",
              "      <td>-337.190000</td>\n",
              "      <td>-7466.350000</td>\n",
              "      <td>-7466.350000</td>\n",
              "      <td>-2737.880000</td>\n",
              "      <td>-13821.200000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-30.210000</td>\n",
              "      <td>-874.440000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>5.189200e+07</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-77.350000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>7.192500</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.780000</td>\n",
              "      <td>0.174500</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.161250</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-2.417500</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.392500</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.900000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.050000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>5.248408e+08</td>\n",
              "      <td>0.040000</td>\n",
              "      <td>0.275000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>18.490000</td>\n",
              "      <td>9.230000</td>\n",
              "      <td>1.460000</td>\n",
              "      <td>0.850000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.492500</td>\n",
              "      <td>2.190000</td>\n",
              "      <td>6.220000</td>\n",
              "      <td>-0.460000</td>\n",
              "      <td>8.775000</td>\n",
              "      <td>6.585000</td>\n",
              "      <td>8.735000</td>\n",
              "      <td>4.470000</td>\n",
              "      <td>1.370000</td>\n",
              "      <td>6.230000</td>\n",
              "      <td>8.990000</td>\n",
              "      <td>2.159000e+01</td>\n",
              "      <td>1.487405e+09</td>\n",
              "      <td>0.420000</td>\n",
              "      <td>8.125000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>36.965000</td>\n",
              "      <td>20.475000</td>\n",
              "      <td>2.652500</td>\n",
              "      <td>2.090750</td>\n",
              "      <td>1.452500</td>\n",
              "      <td>0.970000</td>\n",
              "      <td>6.787500</td>\n",
              "      <td>13.645000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>17.430000</td>\n",
              "      <td>11.985000</td>\n",
              "      <td>17.157500</td>\n",
              "      <td>11.512500</td>\n",
              "      <td>2.090000</td>\n",
              "      <td>11.750000</td>\n",
              "      <td>16.867500</td>\n",
              "      <td>2.797775e+06</td>\n",
              "      <td>4.790400e+09</td>\n",
              "      <td>1.210000</td>\n",
              "      <td>13.635000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>10081.900000</td>\n",
              "      <td>2220.880000</td>\n",
              "      <td>77.260000</td>\n",
              "      <td>2659.020000</td>\n",
              "      <td>1254.590000</td>\n",
              "      <td>14.086000</td>\n",
              "      <td>457.140000</td>\n",
              "      <td>2948.170000</td>\n",
              "      <td>910.250000</td>\n",
              "      <td>2484.320000</td>\n",
              "      <td>1640.310000</td>\n",
              "      <td>18112.100000</td>\n",
              "      <td>15969.500000</td>\n",
              "      <td>28.580000</td>\n",
              "      <td>523.010000</td>\n",
              "      <td>202.510000</td>\n",
              "      <td>1.592310e+09</td>\n",
              "      <td>2.318290e+11</td>\n",
              "      <td>57.640000</td>\n",
              "      <td>1014.420000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            Cotação          P/L  ...  Dív.Brut/ Patrim.  Cresc. Rec.5a\n",
              "count    678.000000   678.000000  ...         678.000000     678.000000\n",
              "mean      79.332729    13.058201  ...           1.322743      11.664174\n",
              "std      574.593391   433.783772  ...           3.984618      58.814377\n",
              "min        0.000000 -9868.830000  ...           0.000000     -77.350000\n",
              "25%        7.192500     0.000000  ...           0.040000       0.275000\n",
              "50%       18.490000     9.230000  ...           0.420000       8.125000\n",
              "75%       36.965000    20.475000  ...           1.210000      13.635000\n",
              "max    10081.900000  2220.880000  ...          57.640000    1014.420000\n",
              "\n",
              "[8 rows x 20 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 258
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pjGDZeIX6TRl",
        "colab_type": "text"
      },
      "source": [
        "### Filtrando empresas: exclui empresas *utility* e financeiras\n",
        "\n",
        "Este dataset não possui informações dos setores da empresas :(\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GCR-IXDV9u4S",
        "colab_type": "text"
      },
      "source": [
        "### Filtros adicionais\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "28gVrp5N93TB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 332
        },
        "outputId": "cf3f7271-ac29-473b-bc70-39b7d2448c2d"
      },
      "source": [
        "# empresas que possuem alguma liquidez\n",
        "fundamentus = fundamentus[fundamentus['Liq.2meses'] > 1000000]\n",
        "\n",
        "# com retornos positivos\n",
        "fundamentus = fundamentus[fundamentus['ROIC'] > 0]\n",
        "fundamentus = fundamentus[fundamentus['EV/EBIT'] > 0]\n",
        "\n",
        "fundamentus.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Cotação</th>\n",
              "      <th>P/L</th>\n",
              "      <th>P/VP</th>\n",
              "      <th>PSR</th>\n",
              "      <th>Div.Yield</th>\n",
              "      <th>P/Ativo</th>\n",
              "      <th>P/Cap.Giro</th>\n",
              "      <th>P/EBIT</th>\n",
              "      <th>P/Ativ Circ.Liq</th>\n",
              "      <th>EV/EBIT</th>\n",
              "      <th>EV/EBITDA</th>\n",
              "      <th>Mrg Ebit</th>\n",
              "      <th>Mrg. Líq.</th>\n",
              "      <th>Liq. Corr.</th>\n",
              "      <th>ROIC</th>\n",
              "      <th>ROE</th>\n",
              "      <th>Liq.2meses</th>\n",
              "      <th>Patrim. Líq</th>\n",
              "      <th>Dív.Brut/ Patrim.</th>\n",
              "      <th>Cresc. Rec.5a</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>159.000000</td>\n",
              "      <td>159.000000</td>\n",
              "      <td>159.000000</td>\n",
              "      <td>159.000000</td>\n",
              "      <td>159.000000</td>\n",
              "      <td>159.000000</td>\n",
              "      <td>159.000000</td>\n",
              "      <td>159.000000</td>\n",
              "      <td>159.000000</td>\n",
              "      <td>159.000000</td>\n",
              "      <td>159.000000</td>\n",
              "      <td>159.000000</td>\n",
              "      <td>159.000000</td>\n",
              "      <td>159.000000</td>\n",
              "      <td>159.000000</td>\n",
              "      <td>159.000000</td>\n",
              "      <td>1.590000e+02</td>\n",
              "      <td>1.590000e+02</td>\n",
              "      <td>159.000000</td>\n",
              "      <td>159.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>24.972013</td>\n",
              "      <td>-47.436918</td>\n",
              "      <td>3.156352</td>\n",
              "      <td>2.929541</td>\n",
              "      <td>2.085723</td>\n",
              "      <td>1.054956</td>\n",
              "      <td>2.510440</td>\n",
              "      <td>41.761761</td>\n",
              "      <td>1.041132</td>\n",
              "      <td>41.366918</td>\n",
              "      <td>13.771132</td>\n",
              "      <td>19.658176</td>\n",
              "      <td>12.927862</td>\n",
              "      <td>2.050943</td>\n",
              "      <td>10.285723</td>\n",
              "      <td>4.893019</td>\n",
              "      <td>1.178176e+08</td>\n",
              "      <td>1.210994e+10</td>\n",
              "      <td>1.561509</td>\n",
              "      <td>9.128679</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>19.268802</td>\n",
              "      <td>791.539411</td>\n",
              "      <td>3.976697</td>\n",
              "      <td>3.705779</td>\n",
              "      <td>2.347198</td>\n",
              "      <td>1.074248</td>\n",
              "      <td>43.815346</td>\n",
              "      <td>237.430989</td>\n",
              "      <td>27.925784</td>\n",
              "      <td>201.300217</td>\n",
              "      <td>15.782498</td>\n",
              "      <td>16.493398</td>\n",
              "      <td>28.108071</td>\n",
              "      <td>1.236850</td>\n",
              "      <td>9.364386</td>\n",
              "      <td>36.872517</td>\n",
              "      <td>2.273631e+08</td>\n",
              "      <td>3.153443e+10</td>\n",
              "      <td>2.952979</td>\n",
              "      <td>13.264960</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>2.730000</td>\n",
              "      <td>-9868.830000</td>\n",
              "      <td>0.270000</td>\n",
              "      <td>0.132000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.105000</td>\n",
              "      <td>-472.820000</td>\n",
              "      <td>1.170000</td>\n",
              "      <td>-71.650000</td>\n",
              "      <td>0.420000</td>\n",
              "      <td>0.410000</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>-54.970000</td>\n",
              "      <td>0.570000</td>\n",
              "      <td>0.210000</td>\n",
              "      <td>-330.120000</td>\n",
              "      <td>1.057760e+06</td>\n",
              "      <td>8.222000e+07</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-27.720000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>11.175000</td>\n",
              "      <td>7.965000</td>\n",
              "      <td>1.175000</td>\n",
              "      <td>0.876500</td>\n",
              "      <td>0.220000</td>\n",
              "      <td>0.438500</td>\n",
              "      <td>2.035000</td>\n",
              "      <td>6.515000</td>\n",
              "      <td>-4.200000</td>\n",
              "      <td>9.385000</td>\n",
              "      <td>5.840000</td>\n",
              "      <td>8.445000</td>\n",
              "      <td>1.930000</td>\n",
              "      <td>1.290000</td>\n",
              "      <td>5.520000</td>\n",
              "      <td>3.020000</td>\n",
              "      <td>1.344340e+07</td>\n",
              "      <td>1.339555e+09</td>\n",
              "      <td>0.315000</td>\n",
              "      <td>1.540000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>19.680000</td>\n",
              "      <td>15.600000</td>\n",
              "      <td>1.890000</td>\n",
              "      <td>1.811000</td>\n",
              "      <td>1.480000</td>\n",
              "      <td>0.730000</td>\n",
              "      <td>5.060000</td>\n",
              "      <td>11.470000</td>\n",
              "      <td>-1.700000</td>\n",
              "      <td>13.840000</td>\n",
              "      <td>9.250000</td>\n",
              "      <td>15.470000</td>\n",
              "      <td>8.480000</td>\n",
              "      <td>1.720000</td>\n",
              "      <td>8.710000</td>\n",
              "      <td>10.450000</td>\n",
              "      <td>4.292920e+07</td>\n",
              "      <td>3.275420e+09</td>\n",
              "      <td>0.730000</td>\n",
              "      <td>7.720000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>32.525000</td>\n",
              "      <td>28.025000</td>\n",
              "      <td>3.310000</td>\n",
              "      <td>3.119000</td>\n",
              "      <td>3.190000</td>\n",
              "      <td>1.194000</td>\n",
              "      <td>11.005000</td>\n",
              "      <td>18.100000</td>\n",
              "      <td>-0.450000</td>\n",
              "      <td>22.480000</td>\n",
              "      <td>14.120000</td>\n",
              "      <td>25.730000</td>\n",
              "      <td>14.405000</td>\n",
              "      <td>2.350000</td>\n",
              "      <td>11.980000</td>\n",
              "      <td>15.220000</td>\n",
              "      <td>1.316160e+08</td>\n",
              "      <td>8.680915e+09</td>\n",
              "      <td>1.310000</td>\n",
              "      <td>14.455000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>114.620000</td>\n",
              "      <td>420.260000</td>\n",
              "      <td>38.740000</td>\n",
              "      <td>25.909000</td>\n",
              "      <td>14.450000</td>\n",
              "      <td>6.605000</td>\n",
              "      <td>61.450000</td>\n",
              "      <td>2948.170000</td>\n",
              "      <td>286.830000</td>\n",
              "      <td>2484.320000</td>\n",
              "      <td>101.730000</td>\n",
              "      <td>80.060000</td>\n",
              "      <td>178.720000</td>\n",
              "      <td>9.030000</td>\n",
              "      <td>77.520000</td>\n",
              "      <td>88.920000</td>\n",
              "      <td>1.592310e+09</td>\n",
              "      <td>2.318290e+11</td>\n",
              "      <td>20.220000</td>\n",
              "      <td>71.840000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          Cotação          P/L  ...  Dív.Brut/ Patrim.  Cresc. Rec.5a\n",
              "count  159.000000   159.000000  ...         159.000000     159.000000\n",
              "mean    24.972013   -47.436918  ...           1.561509       9.128679\n",
              "std     19.268802   791.539411  ...           2.952979      13.264960\n",
              "min      2.730000 -9868.830000  ...           0.000000     -27.720000\n",
              "25%     11.175000     7.965000  ...           0.315000       1.540000\n",
              "50%     19.680000    15.600000  ...           0.730000       7.720000\n",
              "75%     32.525000    28.025000  ...           1.310000      14.455000\n",
              "max    114.620000   420.260000  ...          20.220000      71.840000\n",
              "\n",
              "[8 rows x 20 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 259
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qkD_9TJRC_Io",
        "colab_type": "text"
      },
      "source": [
        "### Criando os rankings"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4Gu81Q06C-rf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "fundamentus['roic_rank'] = fundamentus['ROIC'].rank(method='dense', ascending=True)\n",
        "fundamentus['evebit_rank'] = fundamentus['EV/EBIT'].rank(method='dense', ascending=False)\n",
        "fundamentus['rank'] = fundamentus['roic_rank'] + fundamentus['evebit_rank']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yPykKxHWESMi",
        "colab_type": "text"
      },
      "source": [
        "### Carteira da 'Formula Mágica'\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N2YYv4zD_BkS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "outputId": "e7020990-027e-442e-af3b-fe6794696c7e"
      },
      "source": [
        "fundamentus['rank'].nlargest(20).sort_values()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Papel\n",
              "RAPT4     235.0\n",
              "CSMG3     235.0\n",
              "CIEL3     239.0\n",
              "PETR3     241.0\n",
              "PETR4     243.0\n",
              "ENAT3     249.0\n",
              "TGMA3     250.0\n",
              "WSON33    251.0\n",
              "CVCB3     253.0\n",
              "BEEF3     261.0\n",
              "LEVE3     265.0\n",
              "EQTL3     265.0\n",
              "ALUP11    265.0\n",
              "SAPR3     272.0\n",
              "SAPR4     274.0\n",
              "VALE3     275.0\n",
              "SAPR11    275.0\n",
              "SMLS3     288.0\n",
              "WIZS3     290.0\n",
              "PSSA3     292.0\n",
              "Name: rank, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 261
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MOuNcuvdGAKX",
        "colab_type": "text"
      },
      "source": [
        "### Como avaliar esta carteira?\n",
        "\n",
        "- Back test\n",
        "- Avaliação de portfolio\n",
        "\n",
        "\n",
        "### Bibliotecas que podem ser uteis\n",
        "\n",
        "- https://github.com/mementum/backtrader\n",
        "- https://github.com/quantopian/zipline\n",
        "- https://github.com/quantopian/pyfolio\n",
        "- https://github.com/robertmartin8/PyPortfolioOpt\n",
        "\n",
        "#### Falta de dados para realizar estas analises :(\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aEMNFtWHRSVT",
        "colab_type": "text"
      },
      "source": [
        "### Avaliação de Portfolio com froteira eficiente de Markowitz"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mC4eiSpGMIeu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from pandas_datareader import data as pdr\n",
        "import yfinance as yf\n",
        "yf.pdr_override()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CRa03_dTPqLO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356
        },
        "outputId": "b6e50ff7-6d67-4a3e-ee86-4883edb2b7cc"
      },
      "source": [
        "rank = fundamentus['rank'].nlargest(20).sort_values()\n",
        "rank = rank.reset_index()\n",
        "rank['SA'] = '.SA'\n",
        "df = pdr.DataReader(set(rank.Papel.str.cat(rank['SA']).values), data_source='yahoo', start='2017-01-01', end='2020-06-30')['Adj Close'].dropna()\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[*********************100%***********************]  20 of 20 completed\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "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>ALUP11.SA</th>\n",
              "      <th>BEEF3.SA</th>\n",
              "      <th>CIEL3.SA</th>\n",
              "      <th>CSMG3.SA</th>\n",
              "      <th>CVCB3.SA</th>\n",
              "      <th>ENAT3.SA</th>\n",
              "      <th>EQTL3.SA</th>\n",
              "      <th>LEVE3.SA</th>\n",
              "      <th>PETR3.SA</th>\n",
              "      <th>PETR4.SA</th>\n",
              "      <th>PSSA3.SA</th>\n",
              "      <th>RAPT4.SA</th>\n",
              "      <th>SAPR11.SA</th>\n",
              "      <th>SAPR3.SA</th>\n",
              "      <th>SAPR4.SA</th>\n",
              "      <th>SMLS3.SA</th>\n",
              "      <th>TGMA3.SA</th>\n",
              "      <th>VALE3.SA</th>\n",
              "      <th>WIZS3.SA</th>\n",
              "      <th>WSON33.SA</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2017-12-26</th>\n",
              "      <td>17.653847</td>\n",
              "      <td>10.2164</td>\n",
              "      <td>19.614975</td>\n",
              "      <td>36.297695</td>\n",
              "      <td>46.192177</td>\n",
              "      <td>6.452562</td>\n",
              "      <td>12.625571</td>\n",
              "      <td>20.255964</td>\n",
              "      <td>15.818643</td>\n",
              "      <td>14.815705</td>\n",
              "      <td>30.003777</td>\n",
              "      <td>6.715320</td>\n",
              "      <td>55.196495</td>\n",
              "      <td>12.065593</td>\n",
              "      <td>10.549649</td>\n",
              "      <td>49.955273</td>\n",
              "      <td>17.362810</td>\n",
              "      <td>37.309837</td>\n",
              "      <td>8.770071</td>\n",
              "      <td>32.638378</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-27</th>\n",
              "      <td>17.800961</td>\n",
              "      <td>10.5901</td>\n",
              "      <td>19.715609</td>\n",
              "      <td>37.029858</td>\n",
              "      <td>47.202969</td>\n",
              "      <td>6.440743</td>\n",
              "      <td>12.775181</td>\n",
              "      <td>20.332531</td>\n",
              "      <td>15.875477</td>\n",
              "      <td>14.889920</td>\n",
              "      <td>30.221560</td>\n",
              "      <td>6.753260</td>\n",
              "      <td>55.215294</td>\n",
              "      <td>11.875584</td>\n",
              "      <td>10.774111</td>\n",
              "      <td>49.654537</td>\n",
              "      <td>17.362810</td>\n",
              "      <td>37.319202</td>\n",
              "      <td>8.922328</td>\n",
              "      <td>33.054688</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-28</th>\n",
              "      <td>18.095192</td>\n",
              "      <td>10.4721</td>\n",
              "      <td>19.822845</td>\n",
              "      <td>37.374397</td>\n",
              "      <td>47.595509</td>\n",
              "      <td>6.352109</td>\n",
              "      <td>12.755752</td>\n",
              "      <td>20.307011</td>\n",
              "      <td>16.017559</td>\n",
              "      <td>14.936310</td>\n",
              "      <td>30.430965</td>\n",
              "      <td>6.800685</td>\n",
              "      <td>56.276936</td>\n",
              "      <td>11.695073</td>\n",
              "      <td>10.942455</td>\n",
              "      <td>50.723816</td>\n",
              "      <td>17.513102</td>\n",
              "      <td>37.693699</td>\n",
              "      <td>8.922328</td>\n",
              "      <td>33.304470</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-29</th>\n",
              "      <td>18.095192</td>\n",
              "      <td>10.4721</td>\n",
              "      <td>19.822845</td>\n",
              "      <td>37.374397</td>\n",
              "      <td>47.595509</td>\n",
              "      <td>6.352109</td>\n",
              "      <td>12.755752</td>\n",
              "      <td>20.307011</td>\n",
              "      <td>16.017559</td>\n",
              "      <td>14.936310</td>\n",
              "      <td>30.430965</td>\n",
              "      <td>6.800685</td>\n",
              "      <td>56.276936</td>\n",
              "      <td>11.695073</td>\n",
              "      <td>10.942455</td>\n",
              "      <td>50.723816</td>\n",
              "      <td>17.513102</td>\n",
              "      <td>37.693699</td>\n",
              "      <td>8.922328</td>\n",
              "      <td>33.304470</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-02</th>\n",
              "      <td>18.006924</td>\n",
              "      <td>10.7081</td>\n",
              "      <td>20.303246</td>\n",
              "      <td>37.770618</td>\n",
              "      <td>48.949772</td>\n",
              "      <td>6.872096</td>\n",
              "      <td>12.769353</td>\n",
              "      <td>20.366560</td>\n",
              "      <td>16.415394</td>\n",
              "      <td>15.353781</td>\n",
              "      <td>30.489599</td>\n",
              "      <td>7.170597</td>\n",
              "      <td>55.921108</td>\n",
              "      <td>12.680605</td>\n",
              "      <td>10.667653</td>\n",
              "      <td>52.454708</td>\n",
              "      <td>17.681068</td>\n",
              "      <td>39.060638</td>\n",
              "      <td>9.249683</td>\n",
              "      <td>33.304470</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            ALUP11.SA  BEEF3.SA   CIEL3.SA  ...   VALE3.SA  WIZS3.SA  WSON33.SA\n",
              "Date                                        ...                                \n",
              "2017-12-26  17.653847   10.2164  19.614975  ...  37.309837  8.770071  32.638378\n",
              "2017-12-27  17.800961   10.5901  19.715609  ...  37.319202  8.922328  33.054688\n",
              "2017-12-28  18.095192   10.4721  19.822845  ...  37.693699  8.922328  33.304470\n",
              "2017-12-29  18.095192   10.4721  19.822845  ...  37.693699  8.922328  33.304470\n",
              "2018-01-02  18.006924   10.7081  20.303246  ...  39.060638  9.249683  33.304470\n",
              "\n",
              "[5 rows x 20 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 263
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VnT6-eepWKwT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 86
        },
        "outputId": "123239e0-b78b-40e5-928f-8966dcc402ae"
      },
      "source": [
        "from pypfopt import risk_models, expected_returns, plotting, CLA\n",
        "from matplotlib.ticker import FuncFormatter\n",
        "from matplotlib import pyplot as plt\n",
        "plt.rcParams['figure.figsize'] = [20, 10]\n",
        "\n",
        "mu = expected_returns.mean_historical_return(df)\n",
        "Sigma = risk_models.sample_cov(df)\n",
        "\n",
        "# Optimise for maximal Sharpe ratio\n",
        "ef = EfficientFrontier(mu, Sigma)\n",
        "raw_weights = ef.max_sharpe()\n",
        "cleaned_weights = ef.clean_weights()\n",
        "ef.portfolio_performance(verbose=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Expected annual return: 34.7%\n",
            "Annual volatility: 28.3%\n",
            "Sharpe Ratio: 1.16\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.34732491026932444, 0.2829032124118158, 1.1570208322443682)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 264
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "anIzucfRXs6k",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 502
        },
        "outputId": "c4f9d259-10fa-4bd4-ef48-89e4a084533f"
      },
      "source": [
        "cla = CLA(mu, Sigma)\n",
        "ax = plotting.plot_efficient_frontier(cla, showfig = False)\n",
        "ax.xaxis.set_major_formatter(FuncFormatter(lambda x, _: '{:.0%}'.format(x)))\n",
        "ax.yaxis.set_major_formatter(FuncFormatter(lambda y, _: '{:.0%}'.format(y)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YUk1-lEHWbQ5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 729
        },
        "outputId": "1bb116da-65c1-444e-bcfb-96c95fa03ce2"
      },
      "source": [
        "plotting.plot_covariance(Sigma);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}