{
 "cells": [
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "# Estratégia Magic Formula de Joel Greenblatt\n",
    "\n",
    "A Magic Formula é uma estratégia de investimento que classifica ações com base em dois critérios principais:\n",
    "\n",
    "- **Qualidade**: medida pelo Retorno sobre o Capital Investido (ROIC)\n",
    "- **Valor**: medida pelo Rendimento de Lucros (Earnings Yield)"
   ],
   "id": "f0f9bbafd34ec9e"
  },
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2025-04-13T20:27:04.058395Z",
     "start_time": "2025-04-13T20:26:52.206786Z"
    }
   },
   "source": "!pip install pandas numpy requests beautifulsoup4",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/usr/bin/bash: warning: setlocale: LC_ALL: cannot change locale (en_US.UTF-8): No such file or directory\r\n",
      "Collecting pandas\r\n",
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      "  Using cached numpy-2.2.4-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (62 kB)\r\n",
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      "Requirement already satisfied: six>=1.5 in /home/geron/Projects/python-for-finances/.venv/lib/python3.13/site-packages (from python-dateutil>=2.8.2->pandas) (1.17.0)\r\n",
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      "Using cached tzdata-2025.2-py2.py3-none-any.whl (347 kB)\r\n",
      "Installing collected packages: pytz, peewee, multitasking, tzdata, numpy, frozendict, pandas, yfinance\r\n",
      "Successfully installed frozendict-2.4.6 multitasking-0.0.11 numpy-2.2.4 pandas-2.2.3 peewee-3.17.9 pytz-2025.2 tzdata-2025.2 yfinance-0.2.55\r\n"
     ]
    }
   ],
   "execution_count": 1
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-13T20:27:40.137379Z",
     "start_time": "2025-04-13T20:27:39.725248Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import requests\n",
    "from bs4 import BeautifulSoup"
   ],
   "id": "737ea007e532eace",
   "outputs": [],
   "execution_count": 2
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-13T20:55:39.461968Z",
     "start_time": "2025-04-13T20:55:39.453184Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def obter_acoes_b3():\n",
    "    url = \"https://www.fundamentus.com.br/resultado.php\"\n",
    "    headers = {\n",
    "        'User-Agent': 'Mozilla/5.0 (X11; Linux x86_64; rv:136.0) Gecko/20100101 Firefox/136.0'\n",
    "    }\n",
    "\n",
    "    response = requests.get(url, headers=headers)\n",
    "    soup = BeautifulSoup(response.text, 'html.parser')\n",
    "\n",
    "    tabela = soup.find('table', {'id': 'resultado'})\n",
    "    dados = []\n",
    "\n",
    "    for linha in tabela.find_all('tr')[1:]:\n",
    "        colunas = linha.find_all('td')\n",
    "        ticker = colunas[0].text.strip()\n",
    "\n",
    "        # Extrair métricas fundamentalistas\n",
    "        ev_ebit = colunas[10].text.strip().replace('.', '').replace(',', '.')\n",
    "        roic = colunas[16].text.strip().replace('.', '').replace(',', '.')\n",
    "        liquidez = colunas[18].text.strip().replace('.', '').replace(',', '.')\n",
    "        divida_pl = colunas[19].text.strip().replace('.', '').replace(',', '.')\n",
    "\n",
    "        # Converter para float, tratando valores inválidos\n",
    "        try:\n",
    "            ev_ebit = float(ev_ebit) if ev_ebit != '-' else np.nan\n",
    "            roic = float(roic[:-1]) if '%' in roic else float(roic)\n",
    "            liquidez = float(liquidez)\n",
    "            divida_pl = float(divida_pl)\n",
    "\n",
    "            # Calcular Earnings Yield (inverso do EV/EBIT)\n",
    "            earnings_yield = 1 / ev_ebit if ev_ebit > 0 else np.nan\n",
    "\n",
    "            dados.append({\n",
    "                'Ticker': ticker,\n",
    "                'ROIC': roic,\n",
    "                'EV/EBIT': ev_ebit,\n",
    "                'Earnings Yield': earnings_yield,\n",
    "                'Liquidez Media': liquidez,\n",
    "                'Divida Bruta/PL': divida_pl\n",
    "            })\n",
    "        except (ValueError, ZeroDivisionError):\n",
    "            continue\n",
    "\n",
    "    return pd.DataFrame(dados)"
   ],
   "id": "5a0af3f299da432",
   "outputs": [],
   "execution_count": 33
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-13T20:55:40.849860Z",
     "start_time": "2025-04-13T20:55:40.092717Z"
    }
   },
   "cell_type": "code",
   "source": [
    "acoes_df = obter_acoes_b3()\n",
    "acoes_df"
   ],
   "id": "eee764781bd7c385",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "     Ticker    ROIC  EV/EBIT  Earnings Yield  Liquidez Media  Divida Bruta/PL\n",
       "0     PORP4   -2.08     0.00             NaN    2.239900e+07             0.00\n",
       "1     POPR4   19.93     0.00             NaN    5.458030e+08             0.82\n",
       "2     MNSA3  145.70     0.00             NaN   -9.105000e+06            -6.52\n",
       "3     CFLU4   32.15     0.00             NaN    6.035100e+07             0.06\n",
       "4     CSTB4   20.11     0.00             NaN    8.420670e+09             0.14\n",
       "..      ...     ...      ...             ...             ...              ...\n",
       "986   UBBR4    0.33     0.00             NaN    1.031720e+10             0.00\n",
       "987   VSTE3    0.06    25.50        0.039216    1.043280e+09             0.33\n",
       "988  UBBR11    0.33     0.00             NaN    1.031720e+10             0.00\n",
       "989   UBBR3    0.33     0.00             NaN    1.031720e+10             0.00\n",
       "990   LJQQ3    0.03     4.86        0.205761    5.459970e+08             0.98\n",
       "\n",
       "[991 rows x 6 columns]"
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       "      <td>PORP4</td>\n",
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       "      <th>1</th>\n",
       "      <td>POPR4</td>\n",
       "      <td>19.93</td>\n",
       "      <td>0.00</td>\n",
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       "      <td>5.458030e+08</td>\n",
       "      <td>0.82</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>-6.52</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CFLU4</td>\n",
       "      <td>32.15</td>\n",
       "      <td>0.00</td>\n",
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       "      <td>0.06</td>\n",
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     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 34
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-13T20:55:41.458678Z",
     "start_time": "2025-04-13T20:55:41.454935Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def filtrar_acoes(df):\n",
    "    \"\"\"Filtra ações conforme recomendação de Greenblatt\"\"\"\n",
    "\n",
    "    # Remover empresas com baixa liquidez (menor que 1M)\n",
    "    df_filtrado = df[df['Liquidez Media'] > 1_000_000]\n",
    "\n",
    "    # Remover empresas com dívida muito alta\n",
    "    df_filtrado = df_filtrado[(df_filtrado['Divida Bruta/PL'] > 0) & (df_filtrado['Divida Bruta/PL'] < 4)]\n",
    "\n",
    "    return df_filtrado"
   ],
   "id": "f91f75a047ff57bf",
   "outputs": [],
   "execution_count": 35
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-13T20:55:42.184602Z",
     "start_time": "2025-04-13T20:55:42.181491Z"
    }
   },
   "cell_type": "code",
   "source": "acoes_df = filtrar_acoes(acoes_df)",
   "id": "17276b69637e2cbe",
   "outputs": [],
   "execution_count": 36
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-13T20:55:42.796804Z",
     "start_time": "2025-04-13T20:55:42.793964Z"
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   },
   "cell_type": "code",
   "source": [
    "def magic_formula(df):\n",
    "    # Remover dados inválidos\n",
    "    df = df[df['Earnings Yield'].notna()].copy()\n",
    "\n",
    "    # Classificar as ações (ranking)\n",
    "    df['ROIC_Rank'] = df['ROIC'].rank(ascending=False)\n",
    "    df['EY_Rank'] = df['Earnings Yield'].rank(ascending=False)\n",
    "\n",
    "    # Calcular o ranking combinado\n",
    "    df['Magic_Rank'] = df['ROIC_Rank'] + df['EY_Rank']\n",
    "\n",
    "    # Ordenar pelo ranking combinado (do menor para o maior)\n",
    "    df_result = df.sort_values('Magic_Rank')\n",
    "\n",
    "    return df_result"
   ],
   "id": "32611e00190bf868",
   "outputs": [],
   "execution_count": 37
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-13T20:55:43.475901Z",
     "start_time": "2025-04-13T20:55:43.454407Z"
    }
   },
   "cell_type": "code",
   "source": [
    "resultado = magic_formula(acoes_df)\n",
    "\n",
    "# Selecionar as 15 melhores ações\n",
    "top_acoes = resultado.head(15)\n",
    "top_acoes"
   ],
   "id": "625cd15e9bf429eb",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "     Ticker   ROIC  EV/EBIT  Earnings Yield  Liquidez Media  Divida Bruta/PL  \\\n",
       "453   AHEB3  61.47     2.17        0.460829    2.444130e+08             0.02   \n",
       "455   AHEB5  61.47     2.48        0.403226    2.444130e+08             0.02   \n",
       "459   AHEB6  61.47     2.80        0.357143    2.444130e+08             0.02   \n",
       "367   CSPC4  45.33     1.43        0.699301    2.126670e+09             1.48   \n",
       "371   CSPC3  45.33     1.43        0.699301    2.126670e+09             1.48   \n",
       "433   SYNE3  50.87     2.41        0.414938    1.073780e+09             0.76   \n",
       "435   CPFG4  36.99     0.95        1.052632    3.614430e+09             0.42   \n",
       "437   CPFG3  36.99     0.97        1.030928    3.614430e+09             0.42   \n",
       "396  PALF11  46.56     2.84        0.352113    3.055110e+09             3.00   \n",
       "403   PALF5  46.56     2.87        0.348432    3.055110e+09             3.00   \n",
       "405   PALF3  46.56     2.88        0.347222    3.055110e+09             3.00   \n",
       "380   ENER3  51.72     3.09        0.323625    1.167120e+09             3.89   \n",
       "381   ENER6  51.72     3.09        0.323625    1.167120e+09             3.89   \n",
       "379   ENER5  51.72     3.09        0.323625    1.167120e+09             3.89   \n",
       "382  TEFC11  46.72     3.45        0.289855    4.585600e+10             2.95   \n",
       "\n",
       "     ROIC_Rank  EY_Rank  Magic_Rank  \n",
       "453        7.0     25.0        32.0  \n",
       "455        7.0     30.0        37.0  \n",
       "459        7.0     32.0        39.0  \n",
       "367       23.5     16.5        40.0  \n",
       "371       23.5     16.5        40.0  \n",
       "433       16.0     28.0        44.0  \n",
       "435       37.5      7.0        44.5  \n",
       "437       37.5      8.0        45.5  \n",
       "396       19.0     33.0        52.0  \n",
       "403       19.0     34.0        53.0  \n",
       "405       19.0     35.0        54.0  \n",
       "380       11.0     44.0        55.0  \n",
       "381       11.0     44.0        55.0  \n",
       "379       11.0     44.0        55.0  \n",
       "382       17.0     52.0        69.0  "
      ],
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       "    <tr>\n",
       "      <th>453</th>\n",
       "      <td>AHEB3</td>\n",
       "      <td>61.47</td>\n",
       "      <td>2.17</td>\n",
       "      <td>0.460829</td>\n",
       "      <td>2.444130e+08</td>\n",
       "      <td>0.02</td>\n",
       "      <td>7.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>455</th>\n",
       "      <td>AHEB5</td>\n",
       "      <td>61.47</td>\n",
       "      <td>2.48</td>\n",
       "      <td>0.403226</td>\n",
       "      <td>2.444130e+08</td>\n",
       "      <td>0.02</td>\n",
       "      <td>7.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>459</th>\n",
       "      <td>AHEB6</td>\n",
       "      <td>61.47</td>\n",
       "      <td>2.80</td>\n",
       "      <td>0.357143</td>\n",
       "      <td>2.444130e+08</td>\n",
       "      <td>0.02</td>\n",
       "      <td>7.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>39.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>367</th>\n",
       "      <td>CSPC4</td>\n",
       "      <td>45.33</td>\n",
       "      <td>1.43</td>\n",
       "      <td>0.699301</td>\n",
       "      <td>2.126670e+09</td>\n",
       "      <td>1.48</td>\n",
       "      <td>23.5</td>\n",
       "      <td>16.5</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>371</th>\n",
       "      <td>CSPC3</td>\n",
       "      <td>45.33</td>\n",
       "      <td>1.43</td>\n",
       "      <td>0.699301</td>\n",
       "      <td>2.126670e+09</td>\n",
       "      <td>1.48</td>\n",
       "      <td>23.5</td>\n",
       "      <td>16.5</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>433</th>\n",
       "      <td>SYNE3</td>\n",
       "      <td>50.87</td>\n",
       "      <td>2.41</td>\n",
       "      <td>0.414938</td>\n",
       "      <td>1.073780e+09</td>\n",
       "      <td>0.76</td>\n",
       "      <td>16.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>435</th>\n",
       "      <td>CPFG4</td>\n",
       "      <td>36.99</td>\n",
       "      <td>0.95</td>\n",
       "      <td>1.052632</td>\n",
       "      <td>3.614430e+09</td>\n",
       "      <td>0.42</td>\n",
       "      <td>37.5</td>\n",
       "      <td>7.0</td>\n",
       "      <td>44.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>437</th>\n",
       "      <td>CPFG3</td>\n",
       "      <td>36.99</td>\n",
       "      <td>0.97</td>\n",
       "      <td>1.030928</td>\n",
       "      <td>3.614430e+09</td>\n",
       "      <td>0.42</td>\n",
       "      <td>37.5</td>\n",
       "      <td>8.0</td>\n",
       "      <td>45.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>PALF11</td>\n",
       "      <td>46.56</td>\n",
       "      <td>2.84</td>\n",
       "      <td>0.352113</td>\n",
       "      <td>3.055110e+09</td>\n",
       "      <td>3.00</td>\n",
       "      <td>19.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>52.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403</th>\n",
       "      <td>PALF5</td>\n",
       "      <td>46.56</td>\n",
       "      <td>2.87</td>\n",
       "      <td>0.348432</td>\n",
       "      <td>3.055110e+09</td>\n",
       "      <td>3.00</td>\n",
       "      <td>19.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>53.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>405</th>\n",
       "      <td>PALF3</td>\n",
       "      <td>46.56</td>\n",
       "      <td>2.88</td>\n",
       "      <td>0.347222</td>\n",
       "      <td>3.055110e+09</td>\n",
       "      <td>3.00</td>\n",
       "      <td>19.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>54.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>380</th>\n",
       "      <td>ENER3</td>\n",
       "      <td>51.72</td>\n",
       "      <td>3.09</td>\n",
       "      <td>0.323625</td>\n",
       "      <td>1.167120e+09</td>\n",
       "      <td>3.89</td>\n",
       "      <td>11.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>381</th>\n",
       "      <td>ENER6</td>\n",
       "      <td>51.72</td>\n",
       "      <td>3.09</td>\n",
       "      <td>0.323625</td>\n",
       "      <td>1.167120e+09</td>\n",
       "      <td>3.89</td>\n",
       "      <td>11.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>379</th>\n",
       "      <td>ENER5</td>\n",
       "      <td>51.72</td>\n",
       "      <td>3.09</td>\n",
       "      <td>0.323625</td>\n",
       "      <td>1.167120e+09</td>\n",
       "      <td>3.89</td>\n",
       "      <td>11.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>382</th>\n",
       "      <td>TEFC11</td>\n",
       "      <td>46.72</td>\n",
       "      <td>3.45</td>\n",
       "      <td>0.289855</td>\n",
       "      <td>4.585600e+10</td>\n",
       "      <td>2.95</td>\n",
       "      <td>17.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>69.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 38
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-13T20:55:47.816488Z",
     "start_time": "2025-04-13T20:55:47.812631Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Salvar resultados em um CSV\n",
    "top_acoes.to_csv('magic_formula_b3_resultado.csv', index=False)\n",
    "print(\"\\nResultados salvos em 'magic_formula_b3_resultado.csv'\")"
   ],
   "id": "605685dca910a47c",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Resultados salvos em 'magic_formula_b3_resultado.csv'\n"
     ]
    }
   ],
   "execution_count": 39
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "",
   "id": "cdcab25ed5f48c3b"
  }
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