Analisando_preços_das_Ações_da_Petrobrás_2019_2020.ipynb

{
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  "metadata": {
    "colab": {
      "name": "Analisando preços das Ações da Petrobrás_2019/2020.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "RwASDuwDXRfv",
        "outputId": "a4b6f132-0b27-493a-93fc-4dd26930047f",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 72
        }
      },
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-10c542d5-f317-4b0a-8973-98d9f4ff987f\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-10c542d5-f317-4b0a-8973-98d9f4ff987f\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving PETR4.SA.csv to PETR4.SA.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d2VVxpr0Xbxk"
      },
      "source": [
        "import plotly\n",
        "import plotly.offline as py\n",
        "import pandas as pd\n",
        "import io"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C_O0c_T2Z2u4",
        "outputId": "5d516ae3-5ff3-4e92-c71c-27f2753ee0d5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        }
      },
      "source": [
        "# Habilita o modo offline\n",
        "from plotly.offline import plot, iplot\n",
        "plotly.offline.init_notebook_mode(connected=True)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "        <script type=\"text/javascript\">\n",
              "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
              "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
              "        if (typeof require !== 'undefined') {\n",
              "        require.undef(\"plotly\");\n",
              "        requirejs.config({\n",
              "            paths: {\n",
              "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
              "            }\n",
              "        });\n",
              "        require(['plotly'], function(Plotly) {\n",
              "            window._Plotly = Plotly;\n",
              "        });\n",
              "        }\n",
              "        </script>\n",
              "        "
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gcGeORYlbYfI"
      },
      "source": [
        "df = pd.read_csv(io.BytesIO(uploaded['PETR4.SA.csv']))"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pb_yGMAGaxRF",
        "outputId": "0c64a112-f77e-485e-e072-64cef63006ba",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 5,
      "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>Date</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",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2015-09-28</td>\n",
              "      <td>6.74</td>\n",
              "      <td>6.75</td>\n",
              "      <td>6.44</td>\n",
              "      <td>6.44</td>\n",
              "      <td>5.974405</td>\n",
              "      <td>37466900.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2015-09-29</td>\n",
              "      <td>6.54</td>\n",
              "      <td>6.79</td>\n",
              "      <td>6.50</td>\n",
              "      <td>6.59</td>\n",
              "      <td>6.113561</td>\n",
              "      <td>52387600.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2015-09-30</td>\n",
              "      <td>7.25</td>\n",
              "      <td>7.26</td>\n",
              "      <td>6.97</td>\n",
              "      <td>7.24</td>\n",
              "      <td>6.716568</td>\n",
              "      <td>107779500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2015-10-01</td>\n",
              "      <td>7.31</td>\n",
              "      <td>7.46</td>\n",
              "      <td>6.99</td>\n",
              "      <td>7.02</td>\n",
              "      <td>6.512473</td>\n",
              "      <td>69296500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2015-10-02</td>\n",
              "      <td>6.99</td>\n",
              "      <td>7.83</td>\n",
              "      <td>6.90</td>\n",
              "      <td>7.77</td>\n",
              "      <td>7.208250</td>\n",
              "      <td>88891400.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         Date  Open  High   Low  Close  Adj Close       Volume\n",
              "0  2015-09-28  6.74  6.75  6.44   6.44   5.974405   37466900.0\n",
              "1  2015-09-29  6.54  6.79  6.50   6.59   6.113561   52387600.0\n",
              "2  2015-09-30  7.25  7.26  6.97   7.24   6.716568  107779500.0\n",
              "3  2015-10-01  7.31  7.46  6.99   7.02   6.512473   69296500.0\n",
              "4  2015-10-02  6.99  7.83  6.90   7.77   7.208250   88891400.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UEVVpj_QbtVS"
      },
      "source": [
        "Visualizando os Renderizadores"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j57l6CI4bzRc",
        "outputId": "5f990713-e62c-4c22-a253-3467f4d67488",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        }
      },
      "source": [
        "import plotly.io as pio\n",
        "pio.renderers"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Renderers configuration\n",
              "-----------------------\n",
              "    Default renderer: 'plotly_mimetype+notebook_connected'\n",
              "    Available renderers:\n",
              "        ['plotly_mimetype', 'jupyterlab', 'nteract', 'vscode',\n",
              "         'notebook', 'notebook_connected', 'kaggle', 'azure', 'colab',\n",
              "         'cocalc', 'databricks', 'json', 'png', 'jpeg', 'jpg', 'svg',\n",
              "         'pdf', 'browser', 'firefox', 'chrome', 'chromium', 'iframe',\n",
              "         'iframe_connected', 'sphinx_gallery']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xM6ONPewcFQd"
      },
      "source": [
        "pio.renderers.default = \"colab\""
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8JdDqrZXcRDB"
      },
      "source": [
        "**Plotando preços de fechamento da ação AAPL(Apple)**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9fZrZisecYKN",
        "outputId": "586300d9-9477-4f9d-9b47-5faa1c84036b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        }
      },
      "source": [
        "# import chart_studio.plotly as py\n",
        "import plotly.graph_objs as go\n",
        "\n",
        "data = [go.Scatter(\n",
        "          x = df.Date,\n",
        "          y = df['Close'])]\n",
        "\n",
        "py.iplot(data)\n"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
              "<body>\n",
              "    <div>\n",
              "            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>\n",
              "                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
              "        <script src=\"https://cdn.plot.ly/plotly-latest.min.js\"></script>    \n",
              "            <div id=\"8021c04d-52bc-4539-8721-c56d78301459\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>\n",
              "            <script type=\"text/javascript\">\n",
              "                \n",
              "                    window.PLOTLYENV=window.PLOTLYENV || {};\n",
              "                    \n",
              "                if (document.getElementById(\"8021c04d-52bc-4539-8721-c56d78301459\")) {\n",
              "                    Plotly.newPlot(\n",
              "                        '8021c04d-52bc-4539-8721-c56d78301459',\n",
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      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "Customizando Faixs de Eixos (Range)"
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        "Close = go.Scatter(\n",
        "                x = df.Date,\n",
        "                y = df.Close,\n",
        "                name = \"Petr4 High\",\n",
        "                line = dict (color = '#17BECF'),\n",
        "                opacity = 0.8)\n",
        "\n",
        "data = [Close]\n",
        "\n",
        "layout = dict (\n",
        "    title = \"Faixa de Datas Customizada\",\n",
        "    title_x = 0.5,\n",
        "    xaxis = dict(\n",
        "        range = ['2019-01-01', '2020-09-24'])\n",
        ")\n",
        "\n",
        "fig = dict (data = data, layout = layout)\n",
        "py.iplot(fig, filename = \"Definir intervalo manualmente\")"
      ],
      "execution_count": 9,
      "outputs": [
        {
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hD5RTBKTgxsh"
      },
      "source": [
        "**Rangeslider e Rangeselector**"
      ]
    },
    {
      "cell_type": "code",
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        "id": "z1RWh3cIg6P-",
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      "source": [
        "Close = go.Scatter(\n",
        "                x = df.Date,\n",
        "                y = df.Close,\n",
        "                name = \"Petr4 High\",\n",
        "                line = dict (color = '#17BECF'),\n",
        "                opacity = 0.8)\n",
        "\n",
        "data = [Close]\n",
        "\n",
        "layout = dict (\n",
        "    title = \"Série com Rangeslider e Botões\",\n",
        "    title_x = 0.5,\n",
        "    xaxis = dict (\n",
        "        rangeselector=dict (\n",
        "            buttons = list ([\n",
        "                dict (count= 1,\n",
        "                      label = '1 mês',\n",
        "                      step = 'month',\n",
        "                      stepmode = 'backward'),\n",
        "                dict (count=6,\n",
        "                      label = '6 meses',\n",
        "                      step = 'month',\n",
        "                      stepmode = 'backward'),\n",
        "                dict (step = 'all')\n",
        "            ])\n",
        "        ),\n",
        "        rangeslider = dict (\n",
        "            visible = True\n",
        "        ),\n",
        "        type = 'date'\n",
        "    )\n",
        ")\n",
        "\n",
        "fig = dict (data = data, layout = layout)\n",
        "py.iplot(fig)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gb56C3sf2Oi0"
      },
      "source": [
        "Trabalhando com Candlesticks"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TDNVbMe-2Xgv",
        "outputId": "879d5e84-f079-4763-f338-c742f94f4a97",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        }
      },
      "source": [
        "trace = go.Candlestick (x=df['Date'],\n",
        "                        open = df['Open'],\n",
        "                        high=df['High'],\n",
        "                        low=df['Low'],\n",
        "                        close=df['Close'])\n",
        "\n",
        "data = [trace]\n",
        "\n",
        "layout = {\n",
        "    'title': 'Petr4 - Preços no Período',\n",
        "    'title_x': 0.5,\n",
        "    'yaxis': {'title':'Petr4 - Preço'},\n",
        "    'annotations': [{\n",
        "        'x': '2020-02-27',\n",
        "        'y': 35,\n",
        "        'xref': 'x',\n",
        "        'yref': 'y',\n",
        "        'showarrow': True,\n",
        "        'font': dict (\n",
        "            family=\"Corrier New, monospace\",\n",
        "            size=12\n",
        "        ),\n",
        "        'text': 'Crise da Covid-19 no Brasil',\n",
        "        'align': \"center\",\n",
        "        'arrowhead': 2,\n",
        "        'arrowsize': 1,\n",
        "        'arrowwidth': 2,\n",
        "        'bordercolor': \"#c7c7c7\",\n",
        "        'borderwidth': 2,\n",
        "        'borderpad': 4\n",
        "    }]\n",
        "}\n",
        "\n",
        "#py.iplot(data, filename=  'Simple_Candlestick')\n",
        "fig = dict (data=data, layout=layout)\n",
        "py.iplot(fig)\n"
      ],
      "execution_count": 17,
      "outputs": [
        {
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          },
          "metadata": {
            "tags": []
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        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SCZmWksh8Wmr"
      },
      "source": [
        "**Trabalhando com Médias**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7qjxkTCJ3UYT"
      },
      "source": [
        "# Média Simples dos últimos 07 dias\n",
        "df['Média_07_dias'] = df.Close.rolling(window=7).mean()\n",
        "\n",
        "# Média Simples dos últimos 15 dias\n",
        "df['Média_15_dias'] = df.Close.rolling(window=15).mean()\n",
        "\n",
        "# Média Simples dos últimos 30 dias\n",
        "df['Média_30_dias'] = df.Close.rolling(window=30).mean()"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ap0bpwY-ABn_",
        "outputId": "da45c87c-54e1-42c5-d832-00fed0a3ee06",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "# Visualizando colunas criadas\n",
        "df.head(31)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>2015-09-28</td>\n",
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              "      <td>2015-09-29</td>\n",
              "      <td>6.54</td>\n",
              "      <td>6.79</td>\n",
              "      <td>6.50</td>\n",
              "      <td>6.59</td>\n",
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              "      <td>2015-09-30</td>\n",
              "      <td>7.25</td>\n",
              "      <td>7.26</td>\n",
              "      <td>6.97</td>\n",
              "      <td>7.24</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>2015-10-01</td>\n",
              "      <td>7.31</td>\n",
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              "      <td>6.99</td>\n",
              "      <td>7.02</td>\n",
              "      <td>6.512473</td>\n",
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              "      <td>6.99</td>\n",
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              "      <td>2015-10-05</td>\n",
              "      <td>7.90</td>\n",
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              "      <td>7.75</td>\n",
              "      <td>7.82</td>\n",
              "      <td>7.254635</td>\n",
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              "      <td>2015-10-06</td>\n",
              "      <td>7.86</td>\n",
              "      <td>8.34</td>\n",
              "      <td>7.84</td>\n",
              "      <td>8.19</td>\n",
              "      <td>7.597885</td>\n",
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              "      <td>2015-10-07</td>\n",
              "      <td>8.51</td>\n",
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              "      <td>8.27</td>\n",
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              "      <td>8.46</td>\n",
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              "      <td>8.28</td>\n",
              "      <td>8.75</td>\n",
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              "      <td>2015-10-09</td>\n",
              "      <td>9.02</td>\n",
              "      <td>9.07</td>\n",
              "      <td>8.58</td>\n",
              "      <td>8.80</td>\n",
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              "      <td>8.117143</td>\n",
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              "      <th>10</th>\n",
              "      <td>2015-10-13</td>\n",
              "      <td>8.36</td>\n",
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              "      <td>8.07</td>\n",
              "      <td>8.13</td>\n",
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              "      <td>8.275714</td>\n",
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              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>2015-10-14</td>\n",
              "      <td>8.07</td>\n",
              "      <td>8.23</td>\n",
              "      <td>7.89</td>\n",
              "      <td>7.96</td>\n",
              "      <td>7.384513</td>\n",
              "      <td>75339100.0</td>\n",
              "      <td>8.302857</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>2015-10-15</td>\n",
              "      <td>8.00</td>\n",
              "      <td>8.07</td>\n",
              "      <td>7.67</td>\n",
              "      <td>8.00</td>\n",
              "      <td>7.421620</td>\n",
              "      <td>61444100.0</td>\n",
              "      <td>8.328571</td>\n",
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              "      <th>13</th>\n",
              "      <td>2015-10-16</td>\n",
              "      <td>7.99</td>\n",
              "      <td>8.08</td>\n",
              "      <td>7.81</td>\n",
              "      <td>7.95</td>\n",
              "      <td>7.375236</td>\n",
              "      <td>42963800.0</td>\n",
              "      <td>8.294286</td>\n",
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              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>2015-10-19</td>\n",
              "      <td>7.90</td>\n",
              "      <td>7.96</td>\n",
              "      <td>7.76</td>\n",
              "      <td>7.95</td>\n",
              "      <td>7.375236</td>\n",
              "      <td>40096200.0</td>\n",
              "      <td>8.220000</td>\n",
              "      <td>7.805333</td>\n",
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              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>2015-10-20</td>\n",
              "      <td>8.00</td>\n",
              "      <td>8.19</td>\n",
              "      <td>7.97</td>\n",
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              "      <th>16</th>\n",
              "      <td>2015-10-21</td>\n",
              "      <td>7.95</td>\n",
              "      <td>8.06</td>\n",
              "      <td>7.71</td>\n",
              "      <td>7.75</td>\n",
              "      <td>7.189695</td>\n",
              "      <td>45394400.0</td>\n",
              "      <td>7.970000</td>\n",
              "      <td>7.990000</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>2015-10-22</td>\n",
              "      <td>7.82</td>\n",
              "      <td>8.11</td>\n",
              "      <td>7.81</td>\n",
              "      <td>8.02</td>\n",
              "      <td>7.440176</td>\n",
              "      <td>46174500.0</td>\n",
              "      <td>7.954286</td>\n",
              "      <td>8.042000</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>2015-10-23</td>\n",
              "      <td>8.26</td>\n",
              "      <td>8.28</td>\n",
              "      <td>7.94</td>\n",
              "      <td>7.99</td>\n",
              "      <td>7.412344</td>\n",
              "      <td>45715300.0</td>\n",
              "      <td>7.958571</td>\n",
              "      <td>8.106667</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>2015-10-26</td>\n",
              "      <td>8.05</td>\n",
              "      <td>8.13</td>\n",
              "      <td>7.80</td>\n",
              "      <td>7.85</td>\n",
              "      <td>7.282466</td>\n",
              "      <td>35069500.0</td>\n",
              "      <td>7.937143</td>\n",
              "      <td>8.112000</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>2015-10-27</td>\n",
              "      <td>7.86</td>\n",
              "      <td>7.86</td>\n",
              "      <td>7.55</td>\n",
              "      <td>7.60</td>\n",
              "      <td>7.050539</td>\n",
              "      <td>44085700.0</td>\n",
              "      <td>7.887143</td>\n",
              "      <td>8.097333</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>2015-10-28</td>\n",
              "      <td>7.87</td>\n",
              "      <td>7.95</td>\n",
              "      <td>7.86</td>\n",
              "      <td>7.95</td>\n",
              "      <td>7.375236</td>\n",
              "      <td>4630900.0</td>\n",
              "      <td>7.887143</td>\n",
              "      <td>8.081333</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>2015-10-29</td>\n",
              "      <td>7.57</td>\n",
              "      <td>7.93</td>\n",
              "      <td>7.57</td>\n",
              "      <td>7.61</td>\n",
              "      <td>7.059817</td>\n",
              "      <td>41336300.0</td>\n",
              "      <td>7.824286</td>\n",
              "      <td>8.024000</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>2015-10-30</td>\n",
              "      <td>7.60</td>\n",
              "      <td>7.78</td>\n",
              "      <td>7.54</td>\n",
              "      <td>7.71</td>\n",
              "      <td>7.152587</td>\n",
              "      <td>36951300.0</td>\n",
              "      <td>7.818571</td>\n",
              "      <td>7.954667</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>2015-11-03</td>\n",
              "      <td>7.84</td>\n",
              "      <td>8.48</td>\n",
              "      <td>7.77</td>\n",
              "      <td>8.48</td>\n",
              "      <td>7.866919</td>\n",
              "      <td>60306200.0</td>\n",
              "      <td>7.884286</td>\n",
              "      <td>7.933333</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>2015-11-04</td>\n",
              "      <td>8.58</td>\n",
              "      <td>8.65</td>\n",
              "      <td>8.02</td>\n",
              "      <td>8.08</td>\n",
              "      <td>7.495837</td>\n",
              "      <td>55661500.0</td>\n",
              "      <td>7.897143</td>\n",
              "      <td>7.930000</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>2015-11-05</td>\n",
              "      <td>8.08</td>\n",
              "      <td>8.17</td>\n",
              "      <td>7.87</td>\n",
              "      <td>8.11</td>\n",
              "      <td>7.523668</td>\n",
              "      <td>92690300.0</td>\n",
              "      <td>7.934286</td>\n",
              "      <td>7.940000</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>2015-11-06</td>\n",
              "      <td>8.04</td>\n",
              "      <td>8.08</td>\n",
              "      <td>7.73</td>\n",
              "      <td>7.82</td>\n",
              "      <td>7.254635</td>\n",
              "      <td>39680600.0</td>\n",
              "      <td>7.965714</td>\n",
              "      <td>7.928000</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>2015-11-09</td>\n",
              "      <td>7.83</td>\n",
              "      <td>7.93</td>\n",
              "      <td>7.59</td>\n",
              "      <td>7.91</td>\n",
              "      <td>7.338127</td>\n",
              "      <td>36340400.0</td>\n",
              "      <td>7.960000</td>\n",
              "      <td>7.925333</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>2015-11-10</td>\n",
              "      <td>7.60</td>\n",
              "      <td>7.69</td>\n",
              "      <td>7.51</td>\n",
              "      <td>7.61</td>\n",
              "      <td>7.059817</td>\n",
              "      <td>34554300.0</td>\n",
              "      <td>7.960000</td>\n",
              "      <td>7.902667</td>\n",
              "      <td>7.854000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>2015-11-11</td>\n",
              "      <td>7.71</td>\n",
              "      <td>7.83</td>\n",
              "      <td>7.64</td>\n",
              "      <td>7.72</td>\n",
              "      <td>7.161864</td>\n",
              "      <td>44596400.0</td>\n",
              "      <td>7.961429</td>\n",
              "      <td>7.880667</td>\n",
              "      <td>7.896667</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          Date  Open  High  ...  Média_07_dias  Média_15_dias  Média_30_dias\n",
              "0   2015-09-28  6.74  6.75  ...            NaN            NaN            NaN\n",
              "1   2015-09-29  6.54  6.79  ...            NaN            NaN            NaN\n",
              "2   2015-09-30  7.25  7.26  ...            NaN            NaN            NaN\n",
              "3   2015-10-01  7.31  7.46  ...            NaN            NaN            NaN\n",
              "4   2015-10-02  6.99  7.83  ...            NaN            NaN            NaN\n",
              "5   2015-10-05  7.90  8.14  ...            NaN            NaN            NaN\n",
              "6   2015-10-06  7.86  8.34  ...       7.295714            NaN            NaN\n",
              "7   2015-10-07  8.51  8.90  ...       7.585714            NaN            NaN\n",
              "8   2015-10-08  8.46  8.84  ...       7.894286            NaN            NaN\n",
              "9   2015-10-09  9.02  9.07  ...       8.117143            NaN            NaN\n",
              "10  2015-10-13  8.36  8.47  ...       8.275714            NaN            NaN\n",
              "11  2015-10-14  8.07  8.23  ...       8.302857            NaN            NaN\n",
              "12  2015-10-15  8.00  8.07  ...       8.328571            NaN            NaN\n",
              "13  2015-10-16  7.99  8.08  ...       8.294286            NaN            NaN\n",
              "14  2015-10-19  7.90  7.96  ...       8.220000       7.805333            NaN\n",
              "15  2015-10-20  8.00  8.19  ...       8.120000       7.912667            NaN\n",
              "16  2015-10-21  7.95  8.06  ...       7.970000       7.990000            NaN\n",
              "17  2015-10-22  7.82  8.11  ...       7.954286       8.042000            NaN\n",
              "18  2015-10-23  8.26  8.28  ...       7.958571       8.106667            NaN\n",
              "19  2015-10-26  8.05  8.13  ...       7.937143       8.112000            NaN\n",
              "20  2015-10-27  7.86  7.86  ...       7.887143       8.097333            NaN\n",
              "21  2015-10-28  7.87  7.95  ...       7.887143       8.081333            NaN\n",
              "22  2015-10-29  7.57  7.93  ...       7.824286       8.024000            NaN\n",
              "23  2015-10-30  7.60  7.78  ...       7.818571       7.954667            NaN\n",
              "24  2015-11-03  7.84  8.48  ...       7.884286       7.933333            NaN\n",
              "25  2015-11-04  8.58  8.65  ...       7.897143       7.930000            NaN\n",
              "26  2015-11-05  8.08  8.17  ...       7.934286       7.940000            NaN\n",
              "27  2015-11-06  8.04  8.08  ...       7.965714       7.928000            NaN\n",
              "28  2015-11-09  7.83  7.93  ...       7.960000       7.925333            NaN\n",
              "29  2015-11-10  7.60  7.69  ...       7.960000       7.902667       7.854000\n",
              "30  2015-11-11  7.71  7.83  ...       7.961429       7.880667       7.896667\n",
              "\n",
              "[31 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GqiEwjG6BCgS"
      },
      "source": [
        "Plotar as médias e os preços"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pGOBp5gEAI7W",
        "outputId": "f16b605d-5691-4068-bad8-376b0fcce96d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        }
      },
      "source": [
        "close = go.Scatter(\n",
        "    x=df.Date,\n",
        "    y=df.Close,\n",
        "    name=\"Petr4 Close\",\n",
        "    line=dict (color = '#330000'),\n",
        "    opacity=0.8)\n",
        "\n",
        "Média_07_dias = go.Scatter(\n",
        "    x=df.Date,\n",
        "    y=df['Média_07_dias'],\n",
        "    name=\"Média últimos 07 dias\",\n",
        "    line=dict (color = '#FF00FF'),\n",
        "    opacity=0.8)\n",
        "\n",
        "Média_15_dias = go.Scatter(\n",
        "    x=df.Date,\n",
        "    y=df['Média_15_dias'],\n",
        "    name=\"Média últimos 15 dias\",\n",
        "    line=dict (color = '#FFD500'),\n",
        "    opacity=0.8)\n",
        "\n",
        "Média_30_dias = go.Scatter(\n",
        "    x=df.Date,\n",
        "    y=df['Média_30_dias'],\n",
        "    name=\"Média últimos 30 dias\",\n",
        "    line=dict (color = '#1c10a3'),\n",
        "    opacity=0.8)\n",
        "\n",
        "data = [close, Média_07_dias, Média_15_dias, Média_30_dias]\n",
        "py.iplot(data)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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