20210904_Obtendo_Dividendos.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "20210904 - Obtendo Dividendos.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DMK4x49VPras",
        "outputId": "b55a5eb1-6cc5-4374-b4b9-b2638d424b80"
      },
      "source": [
        "!pip install yfinance"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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 21.3 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: 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: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.20->yfinance) (2021.5.30)\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=3d9ce4d1d853b203dfcd04b0c54ff1b71bf9df84a95794b1b1383c38a69b1057\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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pJt8NOUzQFQU"
      },
      "source": [
        "import yfinance as yf"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R17Uq2SXQGcf"
      },
      "source": [
        "weg = yf.Ticker(\"WEGE3.SA\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rodrFPRzSnhr"
      },
      "source": [
        "data = weg.history(period=\"max\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "CZoiAH9FSrrH",
        "outputId": "a490c40c-84d3-4b08-bba3-7785cf0318ee"
      },
      "source": [
        "data.Close.plot()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0d67b97a90>"
            ]
          },
          "metadata": {},
          "execution_count": 5
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "6jG_a__NStDv",
        "outputId": "63eaccb4-2c83-4e7b-a557-5cc99900a514"
      },
      "source": [
        "data.Dividends.plot()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0d67ef5610>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dxYaNUBUSvuo",
        "outputId": "43c68dfe-a26b-41d0-c664-a7f108b12e08"
      },
      "source": [
        "data.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3691, 7)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g-7XgC-1S6xA"
      },
      "source": [
        "import pandas as pd"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "nWMelJ26SyPt",
        "outputId": "6b8ca4ff-23b6-4182-dd69-b97b95838627"
      },
      "source": [
        "pd.DataFrame(data.Dividends)"
      ],
      "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>Dividends</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2000-01-03</th>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-01-04</th>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-01-05</th>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-01-06</th>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-01-07</th>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-08-30</th>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-08-31</th>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-09-01</th>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-09-02</th>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-09-03</th>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3691 rows × 1 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            Dividends\n",
              "Date                 \n",
              "2000-01-03        0.0\n",
              "2000-01-04        0.0\n",
              "2000-01-05        0.0\n",
              "2000-01-06        0.0\n",
              "2000-01-07        0.0\n",
              "...               ...\n",
              "2021-08-30        0.0\n",
              "2021-08-31        0.0\n",
              "2021-09-01        0.0\n",
              "2021-09-02        0.0\n",
              "2021-09-03        0.0\n",
              "\n",
              "[3691 rows x 1 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xzHKA9gkNTUz",
        "outputId": "b43268b6-0189-4b62-975e-6b204aea190b"
      },
      "source": [
        "data.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Open', 'High', 'Low', 'Close', 'Volume', 'Dividends', 'Stock Splits'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ISU7LDVEUvi7"
      },
      "source": [
        "pd.DataFrame(data.Dividends).to_csv('Dividendos.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "id": "NoQbHnY7S1uh",
        "outputId": "26f0205f-173a-4cce-d3ba-5b51fc1ba7fc"
      },
      "source": [
        "data.Dividends.iloc[3440:3689].plot(figsize = (12,8))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0d67edc810>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AviXBh9BXTHB"
      },
      "source": [
        "Comparar com esse link: https://ri.weg.net/nossas-acoes/dividendos-e-jcp/"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G45ty-v1UgHZ"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}