Python_Grafico_Financeiro.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Python_Grafico_Financeiro.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyO37GYHJpoeKg9z+nxiw/8T",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/raulc27/python_testes/blob/master/Python_Grafico_Financeiro.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "24KSmRNSd_EQ",
        "outputId": "6115edf8-0c46-4e16-d726-f8a36befc0dc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting yfinance\n",
            "  Downloading yfinance-0.1.70-py2.py3-none-any.whl (26 kB)\n",
            "Requirement already satisfied: numpy>=1.15 in /usr/local/lib/python3.7/dist-packages (from yfinance) (1.21.5)\n",
            "Requirement already satisfied: pandas>=0.24.0 in /usr/local/lib/python3.7/dist-packages (from yfinance) (1.3.5)\n",
            "Requirement already satisfied: multitasking>=0.0.7 in /usr/local/lib/python3.7/dist-packages (from yfinance) (0.0.10)\n",
            "Collecting lxml>=4.5.1\n",
            "  Downloading lxml-4.8.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_24_x86_64.whl (6.4 MB)\n",
            "\u001b[K     |████████████████████████████████| 6.4 MB 7.6 MB/s \n",
            "\u001b[?25hCollecting requests>=2.26\n",
            "  Downloading requests-2.27.1-py2.py3-none-any.whl (63 kB)\n",
            "\u001b[K     |████████████████████████████████| 63 kB 1.9 MB/s \n",
            "\u001b[?25hRequirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24.0->yfinance) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24.0->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>=0.24.0->yfinance) (1.15.0)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.7/dist-packages (from requests>=2.26->yfinance) (2.0.12)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.26->yfinance) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.26->yfinance) (2021.10.8)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.26->yfinance) (1.24.3)\n",
            "Installing collected packages: requests, lxml, yfinance\n",
            "  Attempting uninstall: requests\n",
            "    Found existing installation: requests 2.23.0\n",
            "    Uninstalling requests-2.23.0:\n",
            "      Successfully uninstalled requests-2.23.0\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",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "google-colab 1.0.0 requires requests~=2.23.0, but you have requests 2.27.1 which is incompatible.\n",
            "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\u001b[0m\n",
            "Successfully installed lxml-4.8.0 requests-2.27.1 yfinance-0.1.70\n"
          ]
        }
      ],
      "source": [
        "!pip install yfinance\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import yfinance as yf\n",
        "import plotly.graph_objects as go\n",
        "from plotly.subplots import make_subplots"
      ],
      "metadata": {
        "id": "QgqAvYpQeIwa"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "weg = yf.download('WEGE3.SA', start='2021-09-01', end = '2022-03-10')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m-4Gy6-aeWR8",
        "outputId": "c43da5f3-a7ae-457b-85c7-5d09281cc0f9"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r[*********************100%***********************]  1 of 1 completed\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "weg['Adj Close'].plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "L0LGNQIweqz6",
        "outputId": "02edf9d6-0f0c-437e-b395-360901bcb228"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f18ca23da50>"
            ]
          },
          "metadata": {},
          "execution_count": 5
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Cria a figura\n",
        "fig = make_subplots(rows=1, cols=1)\n",
        "\n",
        "# Adiciona o gráfico de candlestick com os parametros OHLC no eixo y e no eixo x o index com as datas\n",
        "fig.add_trace(go.Candlestick(name='Variação da cotação de WEGE3', \n",
        "                             x=weg.index, \n",
        "                             open=weg['Open'],\n",
        "                             high=weg['High'],\n",
        "                             low=weg['Low'],\n",
        "                             close=weg['Close'],\n",
        "                             showlegend=True),\n",
        "                             row=1,\n",
        "                             col=1)\n",
        "\n",
        "# Personalizar o eixo Y\n",
        "fig.update_yaxes(title_text=\"<b>Preço WEGE3 (R$)\", row=1, col=1)\n",
        "\n",
        "# Personalizar aspectos da figura em geral\n",
        "fig.update_layout(xaxis_rangeslider_visible=False, width=1000, height=500)\n",
        "\n",
        "# Mostra a figura\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        },
        "id": "p8M751bsexP6",
        "outputId": "cb921e58-eab4-4128-bf24-0d1de77ca0e6"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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