ES_TA_Capítulo_03_Pre_Procesado_de_Datos.ipynb

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      "name": "ES - TA Capítulo 03: Pre Procesado de Datos.ipynb",
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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/joanby/trading-algoritmico-a-z-con-python/blob/main/ES_TA_Cap%C3%ADtulo_03_Pre_Procesado_de_Datos.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<p><img alt=\"Colaboratory logo\" height=\"45px\" src=\"https://www.quantreo.com/wp-content/uploads/2021/10/Original-on-Transparent.png\" align=\"left\" hspace=\"10px\" vspace=\"0px\">\n",
        "<img alt=\"Colaboratory logo\" height=\"45px\" src=\"https://static-881c.kxcdn.com/wp-content/uploads/2016/03/frogamesLogoFull4.png\" align=\"left\" hspace=\"10px\" vspace=\"0px\"></p>\n",
        "\n",
        "# Preprocesado de Datos\n",
        "\n",
        "\n",
        "<br>\n",
        "\n",
        "\n",
        "💰 Únete a la comunidad de [Discord](https://discord.gg/z3dx5XpkX4)\n",
        "\n",
        "📚 Puedes leer nuestro libro en [Amazon](https://www.amazon.es/Python-para-finanzas-trading-algor%C3%ADtmico-ebook/dp/B0BT4ZS9Q3/)\n",
        "\n",
        "🖥️ El canal de [YouTube de Quantreo's](https://www.youtube.com/channel/UCp7jckfiEglNf_Gj62VR0pw) (en inglés) y el de [Frogames](https://www.youtube.com/channel/UCMUxXNYrVCv6-bQakhomvBg) en español\n"
      ],
      "metadata": {
        "id": "1ZsY5xy5sSf2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install yfinance"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SELynRZH3hfA",
        "outputId": "5dcd53df-2d2a-45d4-f23e-c20994552d8c"
      },
      "execution_count": 1,
      "outputs": [
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          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting yfinance\n",
            "  Downloading yfinance-0.1.72-py2.py3-none-any.whl (27 kB)\n",
            "Collecting requests>=2.26\n",
            "  Downloading requests-2.28.0-py3-none-any.whl (62 kB)\n",
            "\u001b[K     |████████████████████████████████| 62 kB 1.2 MB/s \n",
            "\u001b[?25hRequirement 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",
            "Requirement already satisfied: numpy>=1.15 in /usr/local/lib/python3.7/dist-packages (from yfinance) (1.21.6)\n",
            "Collecting lxml>=4.5.1\n",
            "  Downloading lxml-4.9.0-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 9.7 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) (2022.1)\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: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.26->yfinance) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.26->yfinance) (2022.5.18.1)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.26->yfinance) (2.10)\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 2.28.0 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-4.9.0 requests-2.28.0 yfinance-0.1.72\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "vjIltp4XqmxX"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import yfinance as yf\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Manejar datos que proceden de MT5 (manualmente)\n"
      ],
      "metadata": {
        "id": "ojd6NbD3Us06"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Importar los datos en bruto\n",
        "df = pd.read_csv(\"/content/EURUSD_D1.csv\")\n",
        "df"
      ],
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          "height": 424
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        "outputId": "b0d1c048-a699-45a0-b7f5-7e1ab17f22ba"
      },
      "execution_count": 3,
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      "cell_type": "code",
      "source": [
        "# Añadir delimitador\n",
        "df = pd.read_csv(\"/content/EURUSD_D1.csv\", delimiter=\"\\t\")\n",
        "df"
      ],
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              "          <DATE>   <OPEN>   <HIGH>    <LOW>  <CLOSE>  <TICKVOL>  <VOL>  \\\n",
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              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>2861</th>\n",
              "      <td>2022.01.10</td>\n",
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              "      <td>1.12853</td>\n",
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              "      <td>2022.01.11</td>\n",
              "      <td>1.13230</td>\n",
              "      <td>1.13753</td>\n",
              "      <td>1.13131</td>\n",
              "      <td>1.13666</td>\n",
              "      <td>49386</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>2863</th>\n",
              "      <td>2022.01.12</td>\n",
              "      <td>1.13655</td>\n",
              "      <td>1.14528</td>\n",
              "      <td>1.13545</td>\n",
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              "    <tr>\n",
              "      <th>2864</th>\n",
              "      <td>2022.01.13</td>\n",
              "      <td>1.14402</td>\n",
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              "      <td>1.14357</td>\n",
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              "      <td>53078</td>\n",
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              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>2865</th>\n",
              "      <td>2022.01.14</td>\n",
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              "      <td>1.13987</td>\n",
              "      <td>1.14151</td>\n",
              "      <td>59882</td>\n",
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              "      <td>0</td>\n",
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              "<p>2866 rows × 8 columns</p>\n",
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    {
      "cell_type": "code",
      "source": [
        "# Función de preprocesado\n",
        "\n",
        "def preprocessing(name):\n",
        "  \n",
        "  # Importar los datos\n",
        "  df = pd.read_csv(name, delimiter=\"\\t\", index_col=\"<DATE>\", parse_dates=True).dropna()\n",
        "\n",
        "  # Eliminar las últimas dos columnas\n",
        "  df = df.iloc[:,:-2]\n",
        "\n",
        "  # Renombrar \n",
        "  df.columns = [\"open\", \"high\", \"low\", \"close\", \"volume\"]\n",
        "  df.index.name = \"time\"\n",
        "\n",
        "  return df"
      ],
      "metadata": {
        "id": "3nkP0bhO4pMq"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = preprocessing(\"/content/EURUSD_D1.csv\")\n",
        "df"
      ],
      "metadata": {
        "id": "vIxzE2pJ-l0a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "outputId": "1f7ebea8-8544-400b-8c6c-10140570feb2"
      },
      "execution_count": 6,
      "outputs": [
        {
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              "               open     high      low    close  volume\n",
              "time                                                  \n",
              "2011-01-03  1.33443  1.33945  1.32501  1.33499   63214\n",
              "2011-01-04  1.33507  1.34330  1.32919  1.33104   80673\n",
              "2011-01-05  1.33099  1.33247  1.31255  1.31485   83725\n",
              "2011-01-06  1.31495  1.31699  1.29834  1.30022   73723\n",
              "2011-01-07  1.30018  1.30206  1.29044  1.29055   82379\n",
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              "2022-01-11  1.13230  1.13753  1.13131  1.13666   49386\n",
              "2022-01-12  1.13655  1.14528  1.13545  1.14411   51872\n",
              "2022-01-13  1.14402  1.14817  1.14357  1.14539   53078\n",
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              "            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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              "  "
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          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Manejar los datos que proceden de Yfinance\n"
      ],
      "metadata": {
        "id": "d5kArxfa4And"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "yf.download(\"EURUSD=X\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 473
        },
        "id": "3zel86pF1CIu",
        "outputId": "738636f0-298e-43d3-af9a-08349e325c1b"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r[*********************100%***********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "execute_result",
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            "text/plain": [
              "                Open      High       Low     Close  Adj Close  Volume\n",
              "Date                                                                 \n",
              "2003-12-01  1.203398  1.204007  1.194401  1.196501   1.196501       0\n",
              "2003-12-02  1.196101  1.210903  1.194600  1.208897   1.208897       0\n",
              "2003-12-03  1.209000  1.213003  1.207700  1.212298   1.212298       0\n",
              "2003-12-04  1.212004  1.214403  1.204398  1.208094   1.208094       0\n",
              "2003-12-05  1.207802  1.219096  1.206593  1.218695   1.218695       0\n",
              "...              ...       ...       ...       ...        ...     ...\n",
              "2022-06-10  1.061921  1.064215  1.050773  1.061921   1.061921       0\n",
              "2022-06-13  1.048999  1.049759  1.041905  1.048999   1.048999       0\n",
              "2022-06-14  1.041515  1.048240  1.039825  1.041515   1.041515       0\n",
              "2022-06-15  1.044059  1.050718  1.038627  1.044059   1.044059       0\n",
              "2022-06-16  1.044714  1.060220  1.038314  1.060108   1.060108       0\n",
              "\n",
              "[4810 rows x 6 columns]"
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              "      <th>Volume</th>\n",
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              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2003-12-01</th>\n",
              "      <td>1.203398</td>\n",
              "      <td>1.204007</td>\n",
              "      <td>1.194401</td>\n",
              "      <td>1.196501</td>\n",
              "      <td>1.196501</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2003-12-02</th>\n",
              "      <td>1.196101</td>\n",
              "      <td>1.210903</td>\n",
              "      <td>1.194600</td>\n",
              "      <td>1.208897</td>\n",
              "      <td>1.208897</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2003-12-03</th>\n",
              "      <td>1.209000</td>\n",
              "      <td>1.213003</td>\n",
              "      <td>1.207700</td>\n",
              "      <td>1.212298</td>\n",
              "      <td>1.212298</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2003-12-04</th>\n",
              "      <td>1.212004</td>\n",
              "      <td>1.214403</td>\n",
              "      <td>1.204398</td>\n",
              "      <td>1.208094</td>\n",
              "      <td>1.208094</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2003-12-05</th>\n",
              "      <td>1.207802</td>\n",
              "      <td>1.219096</td>\n",
              "      <td>1.206593</td>\n",
              "      <td>1.218695</td>\n",
              "      <td>1.218695</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-06-10</th>\n",
              "      <td>1.061921</td>\n",
              "      <td>1.064215</td>\n",
              "      <td>1.050773</td>\n",
              "      <td>1.061921</td>\n",
              "      <td>1.061921</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-06-13</th>\n",
              "      <td>1.048999</td>\n",
              "      <td>1.049759</td>\n",
              "      <td>1.041905</td>\n",
              "      <td>1.048999</td>\n",
              "      <td>1.048999</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-06-14</th>\n",
              "      <td>1.041515</td>\n",
              "      <td>1.048240</td>\n",
              "      <td>1.039825</td>\n",
              "      <td>1.041515</td>\n",
              "      <td>1.041515</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-06-15</th>\n",
              "      <td>1.044059</td>\n",
              "      <td>1.050718</td>\n",
              "      <td>1.038627</td>\n",
              "      <td>1.044059</td>\n",
              "      <td>1.044059</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-06-16</th>\n",
              "      <td>1.044714</td>\n",
              "      <td>1.060220</td>\n",
              "      <td>1.038314</td>\n",
              "      <td>1.060108</td>\n",
              "      <td>1.060108</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4810 rows × 6 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0f7fa314-8918-4fcd-b7a4-7e7a0ceb9cec')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0f7fa314-8918-4fcd-b7a4-7e7a0ceb9cec button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-0f7fa314-8918-4fcd-b7a4-7e7a0ceb9cec');\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",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def preprocessing_yf(symbol):\n",
        "  \n",
        "  #Importar los datos\n",
        "  df = yf.download(symbol).dropna()\n",
        "\n",
        "  #Renombrar\n",
        "  df.columns = [\"open\", \"high\", \"low\", \"close\", \"adj close\", \"volume\"]\n",
        "  df.index.name = \"time\"\n",
        "\n",
        "  # Eliminar la columna adj close\n",
        "  del df[\"adj close\"]\n",
        "\n",
        "  return df"
      ],
      "metadata": {
        "id": "CAfQp8Jc4D5M"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = preprocessing_yf(\"EURUSD=X\")\n",
        "df"
      ],
      "metadata": {
        "id": "vnBDvdnP1EfF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 473
        },
        "outputId": "fdf1e36f-66a0-47e0-ee01-a2a85d71ecec"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r[*********************100%***********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                open      high       low     close  volume\n",
              "time                                                      \n",
              "2003-12-01  1.203398  1.204007  1.194401  1.196501       0\n",
              "2003-12-02  1.196101  1.210903  1.194600  1.208897       0\n",
              "2003-12-03  1.209000  1.213003  1.207700  1.212298       0\n",
              "2003-12-04  1.212004  1.214403  1.204398  1.208094       0\n",
              "2003-12-05  1.207802  1.219096  1.206593  1.218695       0\n",
              "...              ...       ...       ...       ...     ...\n",
              "2022-06-10  1.061921  1.064215  1.050773  1.061921       0\n",
              "2022-06-13  1.048999  1.049759  1.041905  1.048999       0\n",
              "2022-06-14  1.041515  1.048240  1.039825  1.041515       0\n",
              "2022-06-15  1.044059  1.050718  1.038627  1.044059       0\n",
              "2022-06-16  1.044714  1.060220  1.038314  1.059434       0\n",
              "\n",
              "[4810 rows x 5 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f6a0b7e2-a2f6-4630-ac62-12f63b1a80b5\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
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              "<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>volume</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>time</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2003-12-01</th>\n",
              "      <td>1.203398</td>\n",
              "      <td>1.204007</td>\n",
              "      <td>1.194401</td>\n",
              "      <td>1.196501</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2003-12-02</th>\n",
              "      <td>1.196101</td>\n",
              "      <td>1.210903</td>\n",
              "      <td>1.194600</td>\n",
              "      <td>1.208897</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2003-12-03</th>\n",
              "      <td>1.209000</td>\n",
              "      <td>1.213003</td>\n",
              "      <td>1.207700</td>\n",
              "      <td>1.212298</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2003-12-04</th>\n",
              "      <td>1.212004</td>\n",
              "      <td>1.214403</td>\n",
              "      <td>1.204398</td>\n",
              "      <td>1.208094</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2003-12-05</th>\n",
              "      <td>1.207802</td>\n",
              "      <td>1.219096</td>\n",
              "      <td>1.206593</td>\n",
              "      <td>1.218695</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-06-10</th>\n",
              "      <td>1.061921</td>\n",
              "      <td>1.064215</td>\n",
              "      <td>1.050773</td>\n",
              "      <td>1.061921</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-06-13</th>\n",
              "      <td>1.048999</td>\n",
              "      <td>1.049759</td>\n",
              "      <td>1.041905</td>\n",
              "      <td>1.048999</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-06-14</th>\n",
              "      <td>1.041515</td>\n",
              "      <td>1.048240</td>\n",
              "      <td>1.039825</td>\n",
              "      <td>1.041515</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-06-15</th>\n",
              "      <td>1.044059</td>\n",
              "      <td>1.050718</td>\n",
              "      <td>1.038627</td>\n",
              "      <td>1.044059</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-06-16</th>\n",
              "      <td>1.044714</td>\n",
              "      <td>1.060220</td>\n",
              "      <td>1.038314</td>\n",
              "      <td>1.059434</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4810 rows × 5 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f6a0b7e2-a2f6-4630-ac62-12f63b1a80b5')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f6a0b7e2-a2f6-4630-ac62-12f63b1a80b5 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f6a0b7e2-a2f6-4630-ac62-12f63b1a80b5');\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",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    }
  ]
}