SPREAD_PETR,_BBDC_e_CMIG.ipynb

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    "colab": {
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Trading-com-Dados/tutoriais_publicos/blob/main/SPREAD_PETR%2C_BBDC_e_CMIG.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Instalando e importando libs**"
      ],
      "metadata": {
        "id": "-Xxu6AE5GfM6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install investpy\n",
        "import investpy as inv\n",
        "import plotly.express as px"
      ],
      "metadata": {
        "id": "Qnr7UVw-CoTX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f22aa721-d371-49fb-938d-0870c12f716a"
      },
      "execution_count": 1,
      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting investpy\n",
            "  Downloading investpy-1.0.8.tar.gz (4.4 MB)\n",
            "\u001b[K     |████████████████████████████████| 4.4 MB 4.3 MB/s \n",
            "\u001b[?25hCollecting Unidecode>=1.1.1\n",
            "  Downloading Unidecode-1.3.4-py3-none-any.whl (235 kB)\n",
            "\u001b[K     |████████████████████████████████| 235 kB 58.0 MB/s \n",
            "\u001b[?25hRequirement already satisfied: setuptools>=41.2.0 in /usr/local/lib/python3.7/dist-packages (from investpy) (57.4.0)\n",
            "Requirement already satisfied: numpy>=1.17.2 in /usr/local/lib/python3.7/dist-packages (from investpy) (1.21.6)\n",
            "Requirement already satisfied: pandas>=0.25.1 in /usr/local/lib/python3.7/dist-packages (from investpy) (1.3.5)\n",
            "Requirement already satisfied: lxml>=4.4.1 in /usr/local/lib/python3.7/dist-packages (from investpy) (4.9.1)\n",
            "Requirement already satisfied: requests>=2.22.0 in /usr/local/lib/python3.7/dist-packages (from investpy) (2.23.0)\n",
            "Requirement already satisfied: pytz>=2019.3 in /usr/local/lib/python3.7/dist-packages (from investpy) (2022.2.1)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.25.1->investpy) (2.8.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas>=0.25.1->investpy) (1.15.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.22.0->investpy) (2022.6.15)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.22.0->investpy) (2.10)\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>=2.22.0->investpy) (1.24.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests>=2.22.0->investpy) (3.0.4)\n",
            "Building wheels for collected packages: investpy\n",
            "  Building wheel for investpy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for investpy: filename=investpy-1.0.8-py3-none-any.whl size=4481592 sha256=25a2b1373a97f94b45793381a36208cec8fba34acb9ef153c316fcc19e920d22\n",
            "  Stored in directory: /root/.cache/pip/wheels/96/a8/a5/0d33c72eaf00b41df7b9dc1e15d2b7c7154b3f1379ed350211\n",
            "Successfully built investpy\n",
            "Installing collected packages: Unidecode, investpy\n",
            "Successfully installed Unidecode-1.3.4 investpy-1.0.8\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Obtendo dados dos ativos**"
      ],
      "metadata": {
        "id": "7E8hOpExGX4a"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Primeiro para PETR3 & PETR4"
      ],
      "metadata": {
        "id": "7fUpWRgAGlMP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "petr4 = inv.get_stock_historical_data('PETR4', country='brazil', \n",
        "                                      from_date='01/01/2000', \n",
        "                                      to_date='09/09/2022')\n",
        "\n",
        "petr3 = inv.get_stock_historical_data('PETR3', country='brazil', \n",
        "                                      from_date='01/01/2000', \n",
        "                                      to_date='09/09/2022')"
      ],
      "metadata": {
        "id": "iCOXPOXTGh6m"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "long_short = petr4.Close/petr3.Close"
      ],
      "metadata": {
        "id": "Odmj2EpBJmYw"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "long_short.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZXo-EO9jJrjT",
        "outputId": "cf34f119-f434-48ef-b805-fe86ea2290a6"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Date\n",
              "2000-01-03    1.082294\n",
              "2000-01-04    1.076517\n",
              "2000-01-05    1.057441\n",
              "2000-01-06    1.063158\n",
              "2000-01-07    1.060052\n",
              "Name: Close, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig = px.line(x=long_short.index, y=long_short)\n",
        "\n",
        "fig.add_hline(y=long_short.mean(), line_width=5, line_color=\"green\")\n",
        "fig.add_hline(y=(long_short.mean()-long_short.std()), line_width=3, \n",
        "              line_dash=\"dash\", line_color=\"orange\")\n",
        "fig.add_hline(y=(long_short.mean()+long_short.std()), line_width=3, \n",
        "              line_dash=\"dash\", line_color=\"orange\")\n",
        "fig.add_hline(y=(long_short.mean()-2*long_short.std()), line_width=5, \n",
        "              line_dash=\"dash\", line_color=\"red\")\n",
        "fig.add_hline(y=(long_short.mean()+2*long_short.std()), line_width=5, \n",
        "              line_dash=\"dash\", line_color=\"red\")\n",
        "\n",
        "fig.update_layout(xaxis_rangeslider_visible=False, \n",
        "                  title_text='Razão entre preço PETR4 e PETR3 (2000 e set/2022)'\n",
        "\n",
        ",template = 'simple_white',width=500,height=500)\n",
        "\n",
        "fig.show()"
      ],
      "metadata": {
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          "height": 517
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        "id": "DfsW18TrDZDo",
        "outputId": "28d9120f-f71d-4c30-b8c8-a1346062add5"
      },
      "execution_count": 5,
      "outputs": [
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          "metadata": {}
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bbdc4 = inv.get_stock_historical_data('BBDC4', country='brazil', from_date='01/01/2000', \n",
        "                                      to_date='09/09/2022')\n",
        "bbdc3 = inv.get_stock_historical_data('BBDC3', country='brazil', from_date='01/01/2000', \n",
        "                                      to_date='09/09/2022')\n",
        "\n",
        "long_short = bbdc4.Close/bbdc3.Close\n",
        "\n",
        "fig = px.line(x=long_short.index, y=long_short)\n",
        "\n",
        "fig.add_hline(y=long_short.mean(), line_width=5, line_color=\"green\")\n",
        "\n",
        "fig.add_hline(y=(long_short.mean()-long_short.std()), line_width=3,line_dash=\"dash\", line_color=\"orange\")\n",
        "\n",
        "fig.add_hline(y=(long_short.mean()+long_short.std()), line_width=3,line_dash=\"dash\", line_color=\"orange\")\n",
        "\n",
        "fig.add_hline(y=(long_short.mean()-2*long_short.std()), line_width=5, line_dash=\"dash\", line_color=\"red\")\n",
        "\n",
        "fig.add_hline(y=(long_short.mean()+2*long_short.std()), line_width=5, line_dash=\"dash\", line_color=\"red\")\n",
        "\n",
        "fig.update_layout(xaxis_rangeslider_visible=False, \n",
        "                  title_text='Razão entre preço BBDC4/BBDC3 (2000 e set/2022)',\n",
        "                  template = 'simple_white',width=700,height=300)\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "0caBnofbEAmA",
        "outputId": "d6bf1630-7af8-4b7f-8a9f-e8abf85f40c2"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {}
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "cmig4 = inv.get_stock_historical_data('CMIG4', country='brazil', from_date='01/01/2000', \n",
        "                                      to_date='09/09/2022')\n",
        "cmig3 = inv.get_stock_historical_data('CMIG3', country='brazil', from_date='01/01/2000', \n",
        "                                      to_date='09/09/2022')\n",
        "\n",
        "long_short = cmig4.Close/cmig3.Close\n",
        "\n",
        "fig = px.line(x=long_short.index, y=long_short)\n",
        "\n",
        "fig.add_hline(y=long_short.mean(), line_width=5, line_color=\"green\")\n",
        "fig.add_hline(y=(long_short.mean()-long_short.std()), line_width=3, \n",
        "              line_dash=\"dash\", line_color=\"orange\")\n",
        "fig.add_hline(y=(long_short.mean()+long_short.std()), line_width=3, \n",
        "              line_dash=\"dash\", line_color=\"orange\")\n",
        "fig.add_hline(y=(long_short.mean()-2*long_short.std()), line_width=5, \n",
        "              line_dash=\"dash\", line_color=\"red\")\n",
        "fig.add_hline(y=(long_short.mean()+2*long_short.std()), line_width=5, \n",
        "              line_dash=\"dash\", line_color=\"red\")\n",
        "\n",
        "fig.update_layout(xaxis_rangeslider_visible=False, \n",
        "                  title_text='Razão entre preço CMIG4/CMIG3 (2000 e set/2022)',\n",
        "                  template = 'simple_white',width=500,height=500)\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        },
        "id": "pJgtnSl4FdUg",
        "outputId": "ab83e62a-b627-4ef4-e317-3895573989d3"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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