API_+_PDF.ipynb

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      "source": [
        "\"\"\"\n",
        "Install Yahoo! Finance's API to get finance market data\n",
        "NASDAQ (USA Stock Exchange)\n",
        "\"\"\"\n",
        "!pip install yfinance --upgrade --no-cache-dir"
      ]
    },
    {
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      "execution_count": 2,
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        "id": "vQWhARhpfGeF"
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      "source": [
        "# NECESSARY LIBRARIES\n",
        "\n",
        "# >> Data Analysis Packages\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import pandas_datareader.data as web\n",
        "\n",
        "# >> Graphics Package\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import plotly.graph_objects as go\n",
        "import plotly.express as px\n",
        "from plotly.subplots import make_subplots\n",
        "\n",
        "# >> Data Function\n",
        "from datetime import datetime\n",
        "\n",
        "# >> YFinance\n",
        "import yfinance as yf\n",
        "yf.pdr_override()\n",
        "\n",
        "# >> Warnings\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N-YIDPEv05TU"
      },
      "source": [
        "# **API Yahoo Finance**\n",
        "Analyzing market stocks from EA Games within 1 (one) year."
      ]
    },
    {
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      "execution_count": 3,
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      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "\r[*********************100%***********************]  1 of 1 completed\n"
          ]
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            "text/plain": [
              "(252, 6)"
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          "metadata": {},
          "execution_count": 3
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      ],
      "source": [
        "# Collecting data from API\n",
        "Data = web.get_data_yahoo('EA', period='1y')\n",
        "\n",
        "# Shape of the data collected\n",
        "Data.shape"
      ]
    },
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              "                  Open        High         Low       Close   Adj Close  \\\n",
              "Date                                                                     \n",
              "2022-02-15  134.399994  135.460007  133.360001  134.500000  133.742142   \n",
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              "2022-02-18  130.009995  131.169998  128.919998  129.330002  128.601273   \n",
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        "# Verifying the first 5 (five) registries\n",
        "Data.head()"
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              "                  Open        High         Low       Close   Adj Close  \\\n",
              "Date                                                                     \n",
              "2023-02-09  113.459999  113.949997  111.389999  111.790001  111.790001   \n",
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              "2023-02-10  2904800  \n",
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          "metadata": {},
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      ],
      "source": [
        "# Last 5 (five) registries\n",
        "Data.tail()"
      ]
    },
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              "             Open        High         Low       Close   Adj Close  \\\n",
              "count  252.000000  252.000000  252.000000  252.000000  252.000000   \n",
              "mean   125.916468  127.355833  124.503968  125.937341  125.627858   \n",
              "std      5.592924    5.636486    5.615912    5.644152    5.568976   \n",
              "min    111.279999  112.900002  109.239998  111.599998  111.120789   \n",
              "25%    122.879997  124.135000  121.257500  122.782503  122.497101   \n",
              "50%    126.064999  127.639999  124.544998  126.185001  125.978382   \n",
              "75%    129.297497  130.600006  127.792500  129.169998  128.884411   \n",
              "max    141.460007  142.789993  140.160004  141.500000  140.892410   \n",
              "\n",
              "             Volume  \n",
              "count  2.520000e+02  \n",
              "mean   2.185958e+06  \n",
              "std    1.370385e+06  \n",
              "min    7.813000e+05  \n",
              "25%    1.498000e+06  \n",
              "50%    1.828450e+06  \n",
              "75%    2.379950e+06  \n",
              "max    1.449230e+07  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b33aaf04-d3d7-4ea2-aa81-7104912ede22\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "</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",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>252.000000</td>\n",
              "      <td>252.000000</td>\n",
              "      <td>252.000000</td>\n",
              "      <td>252.000000</td>\n",
              "      <td>252.000000</td>\n",
              "      <td>2.520000e+02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>125.916468</td>\n",
              "      <td>127.355833</td>\n",
              "      <td>124.503968</td>\n",
              "      <td>125.937341</td>\n",
              "      <td>125.627858</td>\n",
              "      <td>2.185958e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>5.592924</td>\n",
              "      <td>5.636486</td>\n",
              "      <td>5.615912</td>\n",
              "      <td>5.644152</td>\n",
              "      <td>5.568976</td>\n",
              "      <td>1.370385e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>111.279999</td>\n",
              "      <td>112.900002</td>\n",
              "      <td>109.239998</td>\n",
              "      <td>111.599998</td>\n",
              "      <td>111.120789</td>\n",
              "      <td>7.813000e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>122.879997</td>\n",
              "      <td>124.135000</td>\n",
              "      <td>121.257500</td>\n",
              "      <td>122.782503</td>\n",
              "      <td>122.497101</td>\n",
              "      <td>1.498000e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>126.064999</td>\n",
              "      <td>127.639999</td>\n",
              "      <td>124.544998</td>\n",
              "      <td>126.185001</td>\n",
              "      <td>125.978382</td>\n",
              "      <td>1.828450e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>129.297497</td>\n",
              "      <td>130.600006</td>\n",
              "      <td>127.792500</td>\n",
              "      <td>129.169998</td>\n",
              "      <td>128.884411</td>\n",
              "      <td>2.379950e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>141.460007</td>\n",
              "      <td>142.789993</td>\n",
              "      <td>140.160004</td>\n",
              "      <td>141.500000</td>\n",
              "      <td>140.892410</td>\n",
              "      <td>1.449230e+07</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b33aaf04-d3d7-4ea2-aa81-7104912ede22')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "      \n",
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              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      border-radius: 50%;\n",
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              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "\n",
              "      <script>\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b33aaf04-d3d7-4ea2-aa81-7104912ede22');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "# Descriptive statistics\n",
        "Data.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ahG1LxTH3z_l",
        "outputId": "4a2fb1f9-d697-4fd3-98e4-4e6c3302a5ea"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "DatetimeIndex: 252 entries, 2022-02-15 to 2023-02-15\n",
            "Data columns (total 6 columns):\n",
            " #   Column     Non-Null Count  Dtype  \n",
            "---  ------     --------------  -----  \n",
            " 0   Open       252 non-null    float64\n",
            " 1   High       252 non-null    float64\n",
            " 2   Low        252 non-null    float64\n",
            " 3   Close      252 non-null    float64\n",
            " 4   Adj Close  252 non-null    float64\n",
            " 5   Volume     252 non-null    int64  \n",
            "dtypes: float64(5), int64(1)\n",
            "memory usage: 13.8 KB\n"
          ]
        }
      ],
      "source": [
        "# Information about the dataset\n",
        "Data.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zb4ahnRi8Wmb"
      },
      "source": [
        "# **Moving Average**\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KyXNBMtwkNQ9"
      },
      "source": [
        "### Understanding how the moving average works, before analyzing the data obtained."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j2BZIDG78xPl",
        "outputId": "ee75cec0-4b41-4892-fa34-4432fcf4479c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean: 14\n",
            "Mean: 14.0 \n"
          ]
        }
      ],
      "source": [
        "# Simple Average\n",
        "listAvg = [11, 12, 13, 15, 16, 17]\n",
        "\n",
        "from statistics import mean\n",
        "print(f'Mean: { mean(listAvg) }')\n",
        "print(f'Mean: { (11+12+13+15+16+17) / len(listAvg) } ')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-JsZVqoN38-B",
        "outputId": "adb1c28c-80e4-47ed-d594-cd3afdeec4b3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1ª Day: 11.2\n",
            "2ª Day: 8.8\n",
            "3ª Day: 6.2\n"
          ]
        }
      ],
      "source": [
        "# Calculating the Moving Average\n",
        "\n",
        "days = 5\n",
        "\n",
        "for Interval in range(1,4):\n",
        "  print(f'{Interval}ª Day: {sum(listAvg[Interval:days])/5}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "id": "thKAKqOd9v5X",
        "outputId": "4706235a-5886-40cf-8ce7-8db8d456dfa4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Closing Price')"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Plot\n",
        "plt.plot(Data['Close'])\n",
        "plt.title('Closing Price')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YU5hMP453xME"
      },
      "source": [
        "### Moving Average with Rolling Function.\n",
        "The function rolling() from Pandas, allows you to calculate statistics on sliding windows of data.<br>\n",
        "It is very useful for calculating moving averages, standard deviations, summations, minimums and maximums, among other statistics."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Import window module\n",
        "from pandas.core import window\n",
        "\n",
        "# Set period in days\n",
        "Period = 5\n",
        "Data['Close'].rolling( window= Period).mean().head(21)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7JiBsza9sNpe",
        "outputId": "95a579d6-3060-4a51-cf8f-3b57237a928f"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Date\n",
              "2022-02-15           NaN\n",
              "2022-02-16           NaN\n",
              "2022-02-17           NaN\n",
              "2022-02-18           NaN\n",
              "2022-02-22    130.921997\n",
              "2022-02-23    129.365997\n",
              "2022-02-24    128.207999\n",
              "2022-02-25    127.870001\n",
              "2022-02-28    128.022000\n",
              "2022-03-01    128.234000\n",
              "2022-03-02    128.741998\n",
              "2022-03-03    128.809998\n",
              "2022-03-04    128.365996\n",
              "2022-03-07    127.599997\n",
              "2022-03-08    126.503998\n",
              "2022-03-09    125.755998\n",
              "2022-03-10    124.923999\n",
              "2022-03-11    124.092000\n",
              "2022-03-14    122.665999\n",
              "2022-03-15    122.292000\n",
              "2022-03-16    121.948001\n",
              "Name: Close, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421
        },
        "id": "hXQT9NEg-vjJ",
        "outputId": "28f54cac-6e45-4544-d8f8-062f80e62484"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'Period')"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Import window module\n",
        "from pandas.core import window\n",
        "\n",
        "# Set period in days\n",
        "Period = 5\n",
        "Data['Close'].rolling( window= Period).mean()\n",
        "\n",
        "# Creating graph\n",
        "plt.figure( figsize=(12, 6))\n",
        "\n",
        "plt.plot(Data['Close']) # plot with all data\n",
        "plt.plot(Data['Close'].rolling( window= Period).mean()) # plot with 5 (five) days window\n",
        "plt.plot(Data['Close'].rolling( window= Period + 20).mean()) # plot with 25 (twenty five) days window\n",
        "plt.title('Closing Price')\n",
        "plt.legend(['Close', '5 day average', '20 day average'])\n",
        "plt.ylabel('Closing Price')\n",
        "plt.xlabel('Period')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CG6WM0B0A0Jh"
      },
      "source": [
        "#### Graphics using **plotly.express (px)** and **plotly.graph_objects (go)**\n",
        "These two modules makes interactive graphics."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "TE9pI41m4QIw",
        "outputId": "e595750d-4a26-46c6-e269-7d8aea58968e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          },
          "metadata": {}
        }
      ],
      "source": [
        "# First with plotly.express\n",
        "\n",
        "Period = 5 # Period in days\n",
        "Data['Moving Average'] = Data['Close'].rolling( window= Period).mean() # Create a new table called Moving Average\n",
        "\n",
        "Figure = px.line(\n",
        "    Data,\n",
        "    y='Close',\n",
        "    title='Close'\n",
        ")\n",
        "\n",
        "Figure.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "9UOdPq349HRk",
        "outputId": "49215638-ca18-4dee-e309-d123f21ba14a"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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Analysis\"},\"xaxis\":{\"title\":{\"text\":\"Period\"}},\"yaxis\":{\"title\":{\"text\":\"Closing Price\"}}},                        {\"responsive\": true}                    ).then(function(){\n",
              "                            \n",
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              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Second with plotly.graph_objects\n",
        "\n",
        "Figure_02 = go.Figure(\n",
        "    data=go.Scatter(\n",
        "        x=Data.index,\n",
        "        y=Data['Close'],\n",
        "        line=( dict(color='firebrick', width=3))\n",
        "    )\n",
        ")\n",
        "\n",
        "Figure_02.update_layout(\n",
        "    title='Closing Analysis',\n",
        "    xaxis_title='Period',\n",
        "    yaxis_title='Closing Price'\n",
        ")\n",
        "Figure_02.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6FUmTHk_BSOs"
      },
      "source": [
        "**Candlestick Charts**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "Ov4Fx8dR_oBW",
        "outputId": "6d36deb8-1905-418d-e4a2-91b87fecfe11"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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              "                            \n",
              "var gd = document.getElementById('ff56c85e-1a0d-4e4e-b94a-9557a209ac79');\n",
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              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
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              "    x.observe(outputEl, {childList: true});\n",
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              "\n",
              "                        })                };                            </script>        </div>\n",
              "</body>\n",
              "</html>"
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          },
          "metadata": {}
        }
      ],
      "source": [
        "# Making a Candlestick Chart using plotly.graph_objects\n",
        "Candlestick_Graph = go.Figure(\n",
        "    data=[\n",
        "        go.Candlestick(\n",
        "            x = Data.index,\n",
        "            open = Data['Open'],\n",
        "            high = Data['High'],\n",
        "            low = Data['Low'],\n",
        "            close = Data['Close'],\n",
        "            increasing_line_color='cyan',\n",
        "            decreasing_line_color='gray'\n",
        "        )\n",
        "    ]\n",
        ")\n",
        "\n",
        "Candlestick_Graph.update_layout(\n",
        "    xaxis_rangeslider_visible=False,\n",
        "    title='Closing Analysis',\n",
        "    xaxis_title='Period',\n",
        "    yaxis_title='Closing Price'\n",
        ")\n",
        "\n",
        "Candlestick_Graph.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W6tBgjL1F3vU"
      },
      "source": [
        "# Creating Analysis Report"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 817
        },
        "id": "05dGRrEFFKkK",
        "outputId": "1fbef8c4-a5e6-425f-8e8a-9bf29a35f7e2"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
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              "            Plotly.purge(gd);\n",
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              "</body>\n",
              "</html>"
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          },
          "metadata": {}
        }
      ],
      "source": [
        "# Create figure\n",
        "\n",
        "Figure = make_subplots(\n",
        "    rows=2,\n",
        "    cols=1,\n",
        "    specs=[\n",
        "        [{'type':'scatter'}],\n",
        "        [{'type':'scatter'}]\n",
        "    ],\n",
        "    vertical_spacing=0.075,\n",
        "    shared_xaxes=True,\n",
        "    subplot_titles=('Stock', 'Closing')\n",
        ")\n",
        "\n",
        "# Layout and Dimension\n",
        "Figure.update_layout(\n",
        "    width=1000,\n",
        "    height=800,\n",
        "    title_text='<b>Advanced Analytics</b> <b>Follow-up Petrobras</b>'\n",
        ")\n",
        "\n",
        "# Adding a graph on first position\n",
        "Figure.add_trace(\n",
        "  go.Candlestick(\n",
        "      x = Data.index,\n",
        "      open = Data['Open'],\n",
        "      high = Data['High'],\n",
        "      low = Data['Low'],\n",
        "      close = Data['Close'],\n",
        "      increasing_line_color='red',\n",
        "      decreasing_line_color='green'\n",
        "  ),\n",
        "  row = 1 , col = 1\n",
        ")\n",
        "\n",
        "Figure.add_trace(\n",
        "    go.Scatter(\n",
        "        x=Data.index,\n",
        "        y=Data['Moving Average'],\n",
        "        mode='lines',\n",
        "        name='Moving Average',\n",
        "        line=dict(color='yellow')\n",
        "    ),\n",
        "    row=1, col=1\n",
        ")\n",
        "\n",
        "Figure.update_layout(\n",
        "    xaxis_rangeslider_visible=False,\n",
        ")\n",
        "\n",
        "# Adding a graph on second position\n",
        "Figure.add_trace(\n",
        "    go.Scatter(\n",
        "        x=Data.index,\n",
        "        y=Data['Close'],\n",
        "        mode='lines',\n",
        "        name='Closing',\n",
        "        line=dict(color='green')\n",
        "    ),\n",
        "    row=2, col=1\n",
        ")\n",
        "\n",
        "Figure.add_trace(\n",
        "    go.Scatter(\n",
        "        x=Data.index,\n",
        "        y=Data['Moving Average'],\n",
        "        mode='lines',\n",
        "        name='Moving Average',\n",
        "        line=dict(color='red')\n",
        "    ),\n",
        "    row=2, col=1\n",
        ")\n",
        "\n",
        "Figure.update_layout(\n",
        "    legend=dict(\n",
        "        orientation='h',\n",
        "        yanchor='bottom',\n",
        "        y=1.02,\n",
        "        xanchor='right',\n",
        "        x=1,\n",
        "        font=dict( size=10) \n",
        "    )\n",
        ")\n",
        "\n",
        "Figure.add_annotation(\n",
        "    showarrow=False,\n",
        "    text='Análise feita no evento de Python (07/02/2023) <br> Professor: Odemir Depieri Jr',\n",
        "    font=dict(size=14),\n",
        "    xref='x domain',\n",
        "    x=1,\n",
        "    yref='y domain',\n",
        "    y=-1.4\n",
        ")\n",
        "\n",
        "Figure.add_annotation(\n",
        "    showarrow=False,\n",
        "    text='By: Társis Santos',\n",
        "    font=dict(size=14),\n",
        "    xref='x domain',\n",
        "    x=0,\n",
        "    yref='y domain',\n",
        "    y=-1.4\n",
        ")\n",
        "\n",
        "\n",
        "Figure.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KJCFB9DERnuB"
      },
      "source": [
        "# **Export report to PDF with Kaleido package**\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "MfdEfMIuHAL8"
      },
      "outputs": [],
      "source": [
        "# First install the package, after comment the line\n",
        "#!pip install -U kaleido"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Need to restart runtime environment after the installation, so can the PDF be exported. The report will be in the file area."
      ],
      "metadata": {
        "id": "O6rxoo9mxqSX"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "nVhqre1cSKI-"
      },
      "outputs": [],
      "source": [
        "Figure.write_image('Financial_Report.pdf')"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyPmaoj2REjo16XIk4+kP+Vt",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}