opcoes.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "mount_file_id": "1ME1xiIf2eXz115bQ4QqlTgeoriPk_3DE",
      "authorship_tag": "ABX9TyNwFg4zPSwRFR6KsRv3MHsr"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "import requests\n",
        "import datetime as dt\n",
        "import numpy as np\n",
        "from bs4 import BeautifulSoup\n",
        "import pandas as pd"
      ],
      "metadata": {
        "id": "mqOove8b3gpT"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Para dataframe\n",
        "\n",
        "#Insira os códidos das opções\n",
        "Opções = ['BOVAT6','BOVAD117','BOVAD118','BOVAP4','BOVAP124', 'BOVAT7', 'BOVAD117','BOVAP3']\n",
        "\n",
        "data = []\n",
        "for value in Opções:\n",
        "\n",
        "    url = 'https://opcoes.net.br/' + value\n",
        "    # Get the content of the webpage\n",
        "    response = requests.get(url)\n",
        "\n",
        "    # Check if content is received without error\n",
        "    if response.status_code == 200:\n",
        "        page_content = response.content\n",
        "\n",
        "        soup = BeautifulSoup(page_content, 'html.parser')\n",
        "\n",
        "        Strike = soup.title.text.replace(' - ', ';').replace('Strike R$ ', '').replace(',000;Vencimento ', ',00;')\n",
        "\n",
        "        Tipo = soup.find_all('h1')[0].text.replace(' - ', ';').replace(' de BOVA11', '').replace('Strike R$ ', '').replace(',000;Vencimento ', ',00;')\n",
        "\n",
        "        #Tipo = soup.find_all('h1')[0].text.replace(' - ', ';').replace(' de B3SA3', '').replace('Strike R$ ', '').replace('0;Vencimento ', ';')\n",
        "\n",
        "        Dia = soup.find_all('td')[21].text\n",
        "\n",
        "        Cotação = soup.find_all('td')[25].text\n",
        "\n",
        "        BOVA11 = soup.find_all('td')[37].text\n",
        "\n",
        "        Dados = Tipo + ';' + Dia + ';' + Cotação + ';' + BOVA11 #+ \"\\n\"\n",
        "        Dados1 = Dados.split(\";\")\n",
        "        data.append(Dados1)\n",
        "        print(Dados1)\n",
        "        #print( Strike + ';' + value + ';' + Dia + ';' + Cotação, end=\"\\n\")\n",
        "        #for tag in soup.find_all('td'):\n",
        "        #   print(tag.text)\n",
        "        #print(soup.td.text)\n",
        "        #print(soup.find('td'))\n",
        "        #print(soup)\n",
        "        #sleep(2)\n",
        "    else:\n",
        "        print(f'Error: {response.status_code}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RRzGsIbPUzUl",
        "outputId": "dbc1a4bf-1742-46b9-8323-32bf0de7649b"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['BOVAT6', 'PUT', '130,00', '21/08/2026', '07/03/2025', '5,15', '121,95']\n",
            "['BOVAD117', 'CALL', '117,00', '17/04/2025', '07/03/2025', '6,87', '121,95']\n",
            "['BOVAD118', 'CALL', '118,00', '17/04/2025', '07/03/2025', '6,14', '121,95']\n",
            "['BOVAP4', 'PUT', '122,00', '17/04/2025', '07/03/2025', '1,64', '121,95']\n",
            "['BOVAP124', 'PUT', '124,00', '17/04/2025', '07/03/2025', '2,56', '121,95']\n",
            "['BOVAT7', 'PUT', '135,00', '21/08/2026', '07/03/2025', '6,50', '121,95']\n",
            "['BOVAD117', 'CALL', '117,00', '17/04/2025', '07/03/2025', '6,87', '121,95']\n",
            "['BOVAP3', 'PUT', '121,00', '17/04/2025', '07/03/2025', '1,29', '121,95']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.DataFrame(data)\n",
        "df.rename(columns={0: 'CÓDIGO', 1: 'TIPO', 2: 'STRIKE', 3: 'VENCIMENTO', 4: 'DATA', 5: 'COTAÇÃO' , 6: 'FECHAMENTO_BOVA11',7: 'DIFERENÇA_PARA_STRIKE',8: 'PRAZO_PARA_STRIKE'}, inplace=True)\n",
        "\n",
        "df['STRIKE'] = df['STRIKE'].str.replace(',', '.').astype(float)\n",
        "df['COTAÇÃO'] = df['COTAÇÃO'].str.replace(',', '.').astype(float)\n",
        "df['VENCIMENTO'] = pd.to_datetime(df['VENCIMENTO'],dayfirst=True)\n",
        "df['DATA'] = pd.to_datetime(df['DATA'], dayfirst=True)\n",
        "df['FECHAMENTO_BOVA11'] = df['FECHAMENTO_BOVA11'].str.replace(',', '.').astype(float)\n",
        "df['DIFERENÇA_PARA_STRIKE'] = df['STRIKE'] - df['FECHAMENTO_BOVA11']\n",
        "Hoje = np.datetime64('today')\n",
        "df['PRAZO_PARA_STRIKE'] = df['VENCIMENTO'] - Hoje #- pd.Timedelta(days=1)\n",
        "df['POSIÇÃO'] = [100,-91,-24,-54,-64,175,-112,-175]\n",
        "df['FINANCEIRO'] = df['COTAÇÃO'] * df['POSIÇÃO']\n",
        "df['EXPOSIÇÃO'] = df['POSIÇÃO'] * df['STRIKE']\n",
        "df.loc['TOTAL'] = df.sum(numeric_only=True, axis=0)\n",
        "print('MONTADO')\n",
        "print(df)\n",
        "\n",
        "#df.to_excel('BOVA11.xlsx')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_CF5JdYVbnRQ",
        "outputId": "bdf2687f-a075-4058-9c2f-061e76521864"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MONTADO\n",
            "         CÓDIGO  TIPO  STRIKE VENCIMENTO       DATA  COTAÇÃO  \\\n",
            "0        BOVAT6   PUT   130.0 2026-08-21 2025-03-07     5.15   \n",
            "1      BOVAD117  CALL   117.0 2025-04-17 2025-03-07     6.87   \n",
            "2      BOVAD118  CALL   118.0 2025-04-17 2025-03-07     6.14   \n",
            "3        BOVAP4   PUT   122.0 2025-04-17 2025-03-07     1.64   \n",
            "4      BOVAP124   PUT   124.0 2025-04-17 2025-03-07     2.56   \n",
            "5        BOVAT7   PUT   135.0 2026-08-21 2025-03-07     6.50   \n",
            "6      BOVAD117  CALL   117.0 2025-04-17 2025-03-07     6.87   \n",
            "7        BOVAP3   PUT   121.0 2025-04-17 2025-03-07     1.29   \n",
            "TOTAL       NaN   NaN   984.0        NaT        NaT    37.02   \n",
            "\n",
            "       FECHAMENTO_BOVA11  DIFERENÇA_PARA_STRIKE PRAZO_PARA_STRIKE  POSIÇÃO  \\\n",
            "0                 121.95                   8.05          529 days    100.0   \n",
            "1                 121.95                  -4.95           38 days    -91.0   \n",
            "2                 121.95                  -3.95           38 days    -24.0   \n",
            "3                 121.95                   0.05           38 days    -54.0   \n",
            "4                 121.95                   2.05           38 days    -64.0   \n",
            "5                 121.95                  13.05          529 days    175.0   \n",
            "6                 121.95                  -4.95           38 days   -112.0   \n",
            "7                 121.95                  -0.95           38 days   -175.0   \n",
            "TOTAL             975.60                   8.40               NaT   -245.0   \n",
            "\n",
            "       FINANCEIRO  EXPOSIÇÃO  \n",
            "0          515.00    13000.0  \n",
            "1         -625.17   -10647.0  \n",
            "2         -147.36    -2832.0  \n",
            "3          -88.56    -6588.0  \n",
            "4         -163.84    -7936.0  \n",
            "5         1137.50    23625.0  \n",
            "6         -769.44   -13104.0  \n",
            "7         -225.75   -21175.0  \n",
            "TOTAL     -367.62   -25657.0  \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-5-9b67e1b7c03e>:15: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
            "  df.loc['TOTAL'] = df.sum(numeric_only=True, axis=0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Para dataframe\n",
        "\n",
        "Opções = ['PETRP394']\n",
        "\n",
        "data10 = []\n",
        "for value in Opções:\n",
        "\n",
        "    url = 'https://opcoes.net.br/' + value\n",
        "    # Get the content of the webpage\n",
        "    response = requests.get(url)\n",
        "\n",
        "    # Check if content is received without error\n",
        "    if response.status_code == 200:\n",
        "        page_content = response.content\n",
        "\n",
        "        soup = BeautifulSoup(page_content, 'html.parser')\n",
        "\n",
        "        Strike = soup.title.text.replace(' - ', ';').replace('Strike R$ ', '').replace(',000;Vencimento ', ',00;')\n",
        "\n",
        "        #Tipo = soup.find_all('h1')[0].text.replace(' - ', ';').replace(' de PETR4', '').replace('Strike R$ ', '').replace(',000;Vencimento ', ',00;')\n",
        "\n",
        "        Tipo = soup.find_all('h1')[0].text.replace(' - ', ';').replace(' de PETR4', '').replace('Strike R$ ', '').replace('0;Vencimento ', ';')\n",
        "\n",
        "        Dia = soup.find_all('td')[21].text\n",
        "\n",
        "        Cotação = soup.find_all('td')[25].text\n",
        "\n",
        "        BOVA11 = soup.find_all('td')[37].text\n",
        "\n",
        "        Dados = Tipo + ';' + Dia + ';' + Cotação + ';' + BOVA11 #+ \"\\n\"\n",
        "        Dados1 = Dados.split(\";\")\n",
        "        data10.append(Dados1)\n",
        "        print(Dados1)\n",
        "        #print( Strike + ';' + value + ';' + Dia + ';' + Cotação, end=\"\\n\")\n",
        "        #for tag in soup.find_all('td'):\n",
        "        #   print(tag.text)\n",
        "        #print(soup.td.text)\n",
        "        #print(soup.find('td'))\n",
        "        #print(soup)\n",
        "        #sleep(2)\n",
        "    else:\n",
        "        print(f'Error: {response.status_code}')"
      ],
      "metadata": {
        "id": "r8-FzfoKKR7-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c26f11db-094a-489b-82ec-eb79ebd7743c"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['PETRP394', 'PUT', '35,66', '17/04/2025', '07/03/2025', '1,29', '34,63']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.DataFrame(data10)\n",
        "df.rename(columns={0: 'CÓDIGO', 1: 'TIPO', 2: 'STRIKE', 3: 'VENCIMENTO', 4: 'DATA', 5: 'COTAÇÃO' , 6: 'FECHAMENTO_PETR4',7: 'DIFERENÇA_PARA_STRIKE',8: 'PRAZO_PARA_STRIKE'}, inplace=True)\n",
        "\n",
        "df['STRIKE'] = df['STRIKE'].str.replace(',', '.').astype(float)\n",
        "df['COTAÇÃO'] = df['COTAÇÃO'].str.replace(',', '.').astype(float)\n",
        "df['VENCIMENTO'] = pd.to_datetime(df['VENCIMENTO'],dayfirst=True)\n",
        "df['DATA'] = pd.to_datetime(df['DATA'], dayfirst=True)\n",
        "df['FECHAMENTO_PETR4'] = df['FECHAMENTO_PETR4'].str.replace(',', '.').astype(float)\n",
        "df['DIFERENÇA_PARA_STRIKE'] = df['STRIKE'] - df['FECHAMENTO_PETR4']\n",
        "Hoje = np.datetime64('today')\n",
        "df['PRAZO_PARA_STRIKE'] = df['VENCIMENTO'] - Hoje #- pd.Timedelta(days=1)\n",
        "df['POSIÇÃO'] = [-100]\n",
        "df['FINANCEIRO'] = df['COTAÇÃO'] * df['POSIÇÃO']\n",
        "df['EXPOSIÇÃO'] = df['POSIÇÃO'] * df['STRIKE']\n",
        "df.loc['TOTAL'] = df.sum(numeric_only=True, axis=0)\n",
        "print('MONTADO')\n",
        "print(df)\n",
        "#df.to_excel('PETR4.xlsx')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DvGjbFp8AIFG",
        "outputId": "562cfcaa-6a3f-4623-ef90-9777cafd81e7"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MONTADO\n",
            "         CÓDIGO TIPO  STRIKE VENCIMENTO       DATA  COTAÇÃO  FECHAMENTO_PETR4  \\\n",
            "0      PETRP394  PUT   35.66 2025-04-17 2025-03-07     1.29             34.63   \n",
            "TOTAL       NaN  NaN   35.66        NaT        NaT     1.29             34.63   \n",
            "\n",
            "       DIFERENÇA_PARA_STRIKE PRAZO_PARA_STRIKE  POSIÇÃO  FINANCEIRO  EXPOSIÇÃO  \n",
            "0                       1.03           38 days   -100.0      -129.0    -3566.0  \n",
            "TOTAL                   1.03               NaT   -100.0      -129.0    -3566.0  \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-7-f940e03e35ce>:15: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
            "  df.loc['TOTAL'] = df.sum(numeric_only=True, axis=0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Opções = ['BOVAT12','BOVAT13','BOVAH125','BOVAT125','BOVAH125W4','BOVAT125W4']\n",
        "\n",
        "url = 'https://opcoes.net.br/' + Opções[0]\n",
        "    # Get the content of the webpage\n",
        "response = requests.get(url)\n",
        "\n",
        "    # Check if content is received without error\n",
        "if response.status_code == 200:\n",
        "  page_content = response.content\n",
        "\n",
        "  soup = BeautifulSoup(page_content, 'html.parser')\n",
        "\n",
        "  Tabela = soup.find_all('table')[0]\n",
        "\n",
        "        #Dados = Strike + ';' + value + ';' + Dia + ';' + Cotação #+ \"\\n\"\n",
        "  rows = Tabela.findChildren(['td'])\n",
        "\n",
        "  table = soup.find('table', attrs={'class':'table table-bordered table-condensed top-buffer-20'})\n",
        "  table_rows = table.find_all('tr')\n",
        "\n",
        "  #print(Tabela)\n",
        "  data = []\n",
        "  for row in table.find_all('tr'):\n",
        "      row_data = []\n",
        "      for cell in row.find_all('td'):\n",
        "          row_data.append(cell.text)\n",
        "      data.append(row_data)\n",
        "else:\n",
        "  print(f'Error: {response.status_code}')\n",
        "\n",
        "df = pd.DataFrame(data)\n",
        "print(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fx408V40us1S",
        "outputId": "a9a8311f-aa2e-4b1d-a62a-559655fda172"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "             0                               1   \\\n",
            "0                BOVAT12 - Cotação não ajustada   \n",
            "1           Min                             Pri   \n",
            "2    07/08/2024                            4,55   \n",
            "3    06/08/2024                            4,61   \n",
            "4    05/08/2024                            5,20   \n",
            "..          ...                             ...   \n",
            "251  02/08/2023                            9,09   \n",
            "252  27/07/2023                            8,99   \n",
            "253  26/07/2023                            9,00   \n",
            "254         Min                            3,99   \n",
            "255         Max                           10,71   \n",
            "\n",
            "                                   2                              3      4   \\\n",
            "0    BOVAT12 - Volatilidade implícita  BOVA11 - Cotação não ajustada   None   \n",
            "1                                 Med                            Ult    Max   \n",
            "2                                4,97                           4,92   5,04   \n",
            "3                                5,60                           5,47   4,62   \n",
            "4                                5,76                           5,75   5,50   \n",
            "..                                ...                            ...    ...   \n",
            "251                              9,25                           9,20   9,09   \n",
            "252                              9,00                           8,99   9,25   \n",
            "253                              9,00                           9,00   9,00   \n",
            "254                              4,00                           4,06   4,10   \n",
            "255                             11,40                          11,19  11,38   \n",
            "\n",
            "           5          6            7      8      9      10     11     12  \\\n",
            "0        None       None         None   None   None   None   None   None   \n",
            "1    Negócios  Vol. Fin.          Min    Pri    Med    Ult    Max    Min   \n",
            "2        5,04          6   10.298,050  19,11  20,23  19,81  20,14  20,23   \n",
            "3        5,60         12   26.713,440  19,20  20,46  20,15  19,26  20,53   \n",
            "4        5,80         13   86.980,800  19,18  19,33  19,35  19,96  19,96   \n",
            "..        ...        ...          ...    ...    ...    ...    ...    ...   \n",
            "251      9,25          3      174,950  30,69  30,76  30,73  30,69  30,76   \n",
            "252      9,25          4    2.357,850  30,56  30,56  30,60  30,73  30,73   \n",
            "253      9,00          1       90,000  30,75  30,75  30,75  30,75  30,75   \n",
            "254      4,20          1       12,250  14,93  16,75  18,53  18,49  18,79   \n",
            "255     12,00      2.032  843.169,760  30,75  32,65  30,76  32,86  33,06   \n",
            "\n",
            "         13      14      15      16      17  \n",
            "0      None    None    None    None    None  \n",
            "1       Abe     Med     Ult     Max    None  \n",
            "2    123,18  123,74  123,62  124,04  124,09  \n",
            "3    117,94  122,18  122,77  122,74  123,49  \n",
            "4    119,53  119,88  121,01  121,77  121,80  \n",
            "..      ...     ...     ...     ...     ...  \n",
            "251  115,97  116,92  116,85  117,01  117,47  \n",
            "252  116,00  118,66  116,80  116,04  118,80  \n",
            "253  117,55  117,85  118,23  118,74  118,89  \n",
            "254  108,19  108,90  109,53  108,96  110,15  \n",
            "255  129,86  129,86  130,27  130,46  130,52  \n",
            "\n",
            "[256 rows x 18 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# The URL for the webpage\n",
        "\n",
        "Opções = ['BOVAT12','BOVA11']\n",
        "\n",
        "url = 'https://www.ibovx.com.br/historico-opcoes-bovespa.aspx?tipo=1&papel=' + Opções[0]\n",
        "# Get the content of the webpage\n",
        "response = requests.get(url)\n",
        "\n",
        "# Check if content is received without error\n",
        "if response.status_code == 200:\n",
        "    page_content = response.content\n",
        "    soup = BeautifulSoup(page_content, 'html.parser')\n",
        "\n",
        "    Dia = soup.find_all('td')[9].text\n",
        "    print(Dia)\n",
        "    Cotação = soup.find_all('td')[12].text\n",
        "\n",
        "    print(Cotação)\n",
        "    #for tag in soup.find_all('td'):\n",
        "    #    print(tag.text)\n",
        "    #print(soup.td.text)\n",
        "    #print(soup.find('td'))\n",
        "    #print(soup)\n",
        "\n",
        "\n",
        "else:\n",
        "    print(f'Error: {response.status_code}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bQVvC6JGG5Ui",
        "outputId": "4d1aa950-1ad7-47f7-ba54-0f695baa01a7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "12/12/2024\n",
            "3,81\n"
          ]
        }
      ]
    }
  ]
}