20211003 - Post_Instagram_Como_obter_dados_de_ações.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "view-in-github"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Trading-com-Dados/tutoriais_publicos/blob/main/Post_Instagram_Como_obter_dados_de_a%C3%A7%C3%B5es.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Código criado no dia 03/10/2021 para demonstrar diferentes formas de obter dados de ações"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "A melhor solução para obter dados em tempo real é utilizar a API do Metatrader"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uqBGVbWvM_eZ",
        "outputId": "52c79ae8-2191-49a4-b81a-181edeacaa4c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: MetaTrader5 in c:\\programdata\\anaconda3\\lib\\site-packages (5.0.34)\n",
            "Requirement already satisfied: numpy>=1.7 in c:\\programdata\\anaconda3\\lib\\site-packages (from MetaTrader5) (1.19.2)\n"
          ]
        }
      ],
      "source": [
        "!pip install MetaTrader5"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0cCViuqcNIvl",
        "outputId": "a3b773c5-c58e-4af4-95c6-b2e0edcdaf59"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting yfinance\n",
            "  Downloading yfinance-0.1.63.tar.gz (26 kB)\n",
            "Requirement already satisfied: pandas>=0.24 in /usr/local/lib/python3.7/dist-packages (from yfinance) (1.1.5)\n",
            "Requirement already satisfied: numpy>=1.15 in /usr/local/lib/python3.7/dist-packages (from yfinance) (1.19.5)\n",
            "Requirement already satisfied: requests>=2.20 in /usr/local/lib/python3.7/dist-packages (from yfinance) (2.23.0)\n",
            "Requirement already satisfied: multitasking>=0.0.7 in /usr/local/lib/python3.7/dist-packages (from yfinance) (0.0.9)\n",
            "Collecting lxml>=4.5.1\n",
            "  Downloading lxml-4.6.3-cp37-cp37m-manylinux2014_x86_64.whl (6.3 MB)\n",
            "\u001b[K     |████████████████████████████████| 6.3 MB 39.3 MB/s \n",
            "\u001b[?25hRequirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24->yfinance) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24->yfinance) (2018.9)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas>=0.24->yfinance) (1.15.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.20->yfinance) (2021.5.30)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests>=2.20->yfinance) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.20->yfinance) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.20->yfinance) (2.10)\n",
            "Building wheels for collected packages: yfinance\n",
            "  Building wheel for yfinance (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for yfinance: filename=yfinance-0.1.63-py2.py3-none-any.whl size=23918 sha256=c7d9c5b83b20dd2b2bc5bcbf96e51e88f97745b22e50ee91b424074e3ed82499\n",
            "  Stored in directory: /root/.cache/pip/wheels/fe/87/8b/7ec24486e001d3926537f5f7801f57a74d181be25b11157983\n",
            "Successfully built yfinance\n",
            "Installing collected packages: lxml, yfinance\n",
            "  Attempting uninstall: lxml\n",
            "    Found existing installation: lxml 4.2.6\n",
            "    Uninstalling lxml-4.2.6:\n",
            "      Successfully uninstalled lxml-4.2.6\n",
            "Successfully installed lxml-4.6.3 yfinance-0.1.63\n"
          ]
        }
      ],
      "source": [
        "!pip install yfinance"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qmRg0c-bLiVo"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import yfinance as yf"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Lr55OjU0L83N",
        "outputId": "f5efa487-f87d-4375-da55-285b653e1e6b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\r[*********************100%***********************]  1 of 1 completed\n"
          ]
        }
      ],
      "source": [
        "weg = yf.download('WEGE3.SA', \n",
        "                  start = '2020-01-01', \n",
        "                  end = '2021-10-01')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "6wHFUXg5Mmvo",
        "outputId": "ec4a4a6f-3786-4cdc-af06-ebac54298eea"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
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              "\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>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-01-02</th>\n",
              "      <td>17.450001</td>\n",
              "      <td>17.735001</td>\n",
              "      <td>17.360001</td>\n",
              "      <td>17.620001</td>\n",
              "      <td>17.296631</td>\n",
              "      <td>5876000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-03</th>\n",
              "      <td>17.500000</td>\n",
              "      <td>17.625000</td>\n",
              "      <td>17.334999</td>\n",
              "      <td>17.395000</td>\n",
              "      <td>17.075758</td>\n",
              "      <td>7345800.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-06</th>\n",
              "      <td>17.350000</td>\n",
              "      <td>17.549999</td>\n",
              "      <td>17.195000</td>\n",
              "      <td>17.440001</td>\n",
              "      <td>17.119938</td>\n",
              "      <td>6366600.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-07</th>\n",
              "      <td>17.455000</td>\n",
              "      <td>17.584999</td>\n",
              "      <td>17.330000</td>\n",
              "      <td>17.575001</td>\n",
              "      <td>17.252457</td>\n",
              "      <td>6303600.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-08</th>\n",
              "      <td>17.600000</td>\n",
              "      <td>17.610001</td>\n",
              "      <td>16.910000</td>\n",
              "      <td>16.910000</td>\n",
              "      <td>16.599663</td>\n",
              "      <td>8153800.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 Open       High        Low      Close  Adj Close     Volume\n",
              "Date                                                                        \n",
              "2020-01-02  17.450001  17.735001  17.360001  17.620001  17.296631  5876000.0\n",
              "2020-01-03  17.500000  17.625000  17.334999  17.395000  17.075758  7345800.0\n",
              "2020-01-06  17.350000  17.549999  17.195000  17.440001  17.119938  6366600.0\n",
              "2020-01-07  17.455000  17.584999  17.330000  17.575001  17.252457  6303600.0\n",
              "2020-01-08  17.600000  17.610001  16.910000  16.910000  16.599663  8153800.0"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "weg.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 277
        },
        "id": "O4gYnSSOMnkI",
        "outputId": "0b1c0a9d-07a5-4226-a3b9-631b4869a6b9"
      },
      "outputs": [
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "weg['Adj Close'].plot();"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IyYATGP6MqTm"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "authorship_tag": "ABX9TyODjCr2RY/mvmumVQL4Oyll",
      "include_colab_link": true,
      "name": "Post Instagram - Como obter dados de ações.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.5"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}