Argentina Stock Index - Merval - IN USD.ipynb

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      "name": "Ciencia_de_Datos[Industrial-UTN].ipynb",
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      "cell_type": "markdown",
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        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/jidemaestri/Financial-Markets/blob/master/Argentina%20Stock%20Index%20-%20Merval%20-%20IN%20USD.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "id": "QT5MNCcOCBNS",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        }
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import os\n",
        "import requests\n",
        "import datetime as dt\n",
        "import pandas_datareader as wb"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v-OpIk_YIwvX"
      },
      "source": [
        "#from google.colab import drive\n",
        "#drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8lLLdo8Hig3K"
      },
      "source": [
        "indice_bursatil = pd.read_csv(\"indices-bursatiles-argentina-indices-cotizaciones.csv\", delimiter=\",\")\n",
        "\n",
        "#indice_bursatil was extracted from datos.gob.ar \n",
        "#for more info: https://datos.gob.ar/dataset/sspm-indices-bursatiles-argentina\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iW6IuDu85GzL"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o8MAj6v2kmWr"
      },
      "source": [
        "Índice MERVAL: Índice Bursátil Argentina\n",
        "\n",
        "\n",
        "> Bloque con sangría\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P8Xh2PipFBaz",
        "outputId": "bfec4b0a-60a2-4ad3-c0fb-f80838e3e108",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "indice_bursatil.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>indice_tiempo</th>\n",
              "      <th>bolsa_g</th>\n",
              "      <th>burcap</th>\n",
              "      <th>merval</th>\n",
              "      <th>merval_arg</th>\n",
              "      <th>merval_25</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2004-10-04</td>\n",
              "      <td>51553.00</td>\n",
              "      <td>2881.26</td>\n",
              "      <td>1167.09</td>\n",
              "      <td>1085.43</td>\n",
              "      <td>1151.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2004-10-05</td>\n",
              "      <td>50346.26</td>\n",
              "      <td>2830.79</td>\n",
              "      <td>1135.37</td>\n",
              "      <td>1054.10</td>\n",
              "      <td>1151.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2004-10-06</td>\n",
              "      <td>50553.64</td>\n",
              "      <td>2852.15</td>\n",
              "      <td>1128.00</td>\n",
              "      <td>1046.48</td>\n",
              "      <td>1113.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2004-10-07</td>\n",
              "      <td>50978.02</td>\n",
              "      <td>2868.16</td>\n",
              "      <td>1140.26</td>\n",
              "      <td>1059.00</td>\n",
              "      <td>1126.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2004-10-08</td>\n",
              "      <td>50763.08</td>\n",
              "      <td>2853.87</td>\n",
              "      <td>1128.74</td>\n",
              "      <td>1048.06</td>\n",
              "      <td>1115.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            ],
            "text/plain": [
              "  indice_tiempo   bolsa_g   burcap   merval  merval_arg  merval_25\n",
              "0    2004-10-04  51553.00  2881.26  1167.09     1085.43     1151.0\n",
              "1    2004-10-05  50346.26  2830.79  1135.37     1054.10     1151.0\n",
              "2    2004-10-06  50553.64  2852.15  1128.00     1046.48     1113.0\n",
              "3    2004-10-07  50978.02  2868.16  1140.26     1059.00     1126.0\n",
              "4    2004-10-08  50763.08  2853.87  1128.74     1048.06     1115.0"
            ]
          },
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          "height": 297
        }
      },
      "source": [
        "indice_bursatil.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>bolsa_g</th>\n",
              "      <th>burcap</th>\n",
              "      <th>merval</th>\n",
              "      <th>merval_arg</th>\n",
              "      <th>merval_25</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>3.904000e+03</td>\n",
              "      <td>3545.000000</td>\n",
              "      <td>3904.000000</td>\n",
              "      <td>3904.000000</td>\n",
              "      <td>3545.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>4.668402e+05</td>\n",
              "      <td>23504.097836</td>\n",
              "      <td>10449.357351</td>\n",
              "      <td>9417.586004</td>\n",
              "      <td>8372.645368</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>5.093040e+05</td>\n",
              "      <td>26233.515235</td>\n",
              "      <td>12057.938642</td>\n",
              "      <td>11110.245265</td>\n",
              "      <td>10149.826091</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>5.034626e+04</td>\n",
              "      <td>2814.380000</td>\n",
              "      <td>828.990000</td>\n",
              "      <td>591.870000</td>\n",
              "      <td>828.940000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.115118e+05</td>\n",
              "      <td>7172.360000</td>\n",
              "      <td>2076.897500</td>\n",
              "      <td>1513.107500</td>\n",
              "      <td>2003.120000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.828902e+05</td>\n",
              "      <td>9533.550000</td>\n",
              "      <td>3307.000000</td>\n",
              "      <td>2464.500000</td>\n",
              "      <td>2825.800000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>6.810196e+05</td>\n",
              "      <td>32254.870000</td>\n",
              "      <td>15836.047500</td>\n",
              "      <td>14760.732500</td>\n",
              "      <td>11622.540000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2.095585e+06</td>\n",
              "      <td>109493.300000</td>\n",
              "      <td>49449.860000</td>\n",
              "      <td>39634.670000</td>\n",
              "      <td>41313.960000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            bolsa_g         burcap        merval    merval_arg     merval_25\n",
              "count  3.904000e+03    3545.000000   3904.000000   3904.000000   3545.000000\n",
              "mean   4.668402e+05   23504.097836  10449.357351   9417.586004   8372.645368\n",
              "std    5.093040e+05   26233.515235  12057.938642  11110.245265  10149.826091\n",
              "min    5.034626e+04    2814.380000    828.990000    591.870000    828.940000\n",
              "25%    1.115118e+05    7172.360000   2076.897500   1513.107500   2003.120000\n",
              "50%    1.828902e+05    9533.550000   3307.000000   2464.500000   2825.800000\n",
              "75%    6.810196e+05   32254.870000  15836.047500  14760.732500  11622.540000\n",
              "max    2.095585e+06  109493.300000  49449.860000  39634.670000  41313.960000"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2nZjo6qVkvcs"
      },
      "source": [
        "merval = indice_bursatil[[\"indice_tiempo\",\"merval\"]]\n",
        "#Argentinian stock index, measured in ARS (Argentinian pesos)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bUGSNfFfk1fg",
        "outputId": "a130d1aa-177e-4e6f-bd37-8182d2cceb65",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        }
      },
      "source": [
        "merval.tail(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>indice_tiempo</th>\n",
              "      <th>merval</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>5770</th>\n",
              "      <td>2020-07-22</td>\n",
              "      <td>48634.09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5771</th>\n",
              "      <td>2020-07-23</td>\n",
              "      <td>48408.27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5772</th>\n",
              "      <td>2020-07-24</td>\n",
              "      <td>48605.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5773</th>\n",
              "      <td>2020-07-25</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5774</th>\n",
              "      <td>2020-07-26</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5775</th>\n",
              "      <td>2020-07-27</td>\n",
              "      <td>49449.86</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5776</th>\n",
              "      <td>2020-07-28</td>\n",
              "      <td>48934.44</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5777</th>\n",
              "      <td>2020-07-29</td>\n",
              "      <td>48884.46</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5778</th>\n",
              "      <td>2020-07-30</td>\n",
              "      <td>49405.75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5779</th>\n",
              "      <td>2020-07-31</td>\n",
              "      <td>49253.62</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     indice_tiempo    merval\n",
              "5770    2020-07-22  48634.09\n",
              "5771    2020-07-23  48408.27\n",
              "5772    2020-07-24  48605.06\n",
              "5773    2020-07-25       NaN\n",
              "5774    2020-07-26       NaN\n",
              "5775    2020-07-27  49449.86\n",
              "5776    2020-07-28  48934.44\n",
              "5777    2020-07-29  48884.46\n",
              "5778    2020-07-30  49405.75\n",
              "5779    2020-07-31  49253.62"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
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          "base_uri": "https://localhost:8080/",
          "height": 530
        }
      },
      "source": [
        "merval.indice_tiempo = pd.to_datetime(merval.indice_tiempo) #### indicar que columna queremos transformar #####\n",
        "merval['year'] = merval.indice_tiempo.dt.year #creamos una nueva columna \"year\" con .dt.year sobre la col \"indice_tiempo\"\n",
        "merval[\"month\"] = merval.indice_tiempo.dt.month\n",
        "merval.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/pandas/core/generic.py:5303: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self[name] = value\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
          ],
          "name": "stderr"
        },
        {
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          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>indice_tiempo</th>\n",
              "      <th>merval</th>\n",
              "      <th>year</th>\n",
              "      <th>month</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2004-10-04</td>\n",
              "      <td>1167.09</td>\n",
              "      <td>2004</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2004-10-05</td>\n",
              "      <td>1135.37</td>\n",
              "      <td>2004</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2004-10-06</td>\n",
              "      <td>1128.00</td>\n",
              "      <td>2004</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2004-10-07</td>\n",
              "      <td>1140.26</td>\n",
              "      <td>2004</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2004-10-08</td>\n",
              "      <td>1128.74</td>\n",
              "      <td>2004</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  indice_tiempo   merval  year  month\n",
              "0    2004-10-04  1167.09  2004     10\n",
              "1    2004-10-05  1135.37  2004     10\n",
              "2    2004-10-06  1128.00  2004     10\n",
              "3    2004-10-07  1140.26  2004     10\n",
              "4    2004-10-08  1128.74  2004     10"
            ]
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      "metadata": {
        "id": "bAOI-6cDl7zj",
        "outputId": "8312574c-c18c-4387-db9f-87a7864cc5e5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        }
      },
      "source": [
        "merval_by_year = merval.groupby([\"year\"])[\"merval\"].mean()\n",
        "merval_by_year.head(18)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "year\n",
              "2004     1248.644375\n",
              "2005     1492.634127\n",
              "2006     1743.976048\n",
              "2007     2155.937581\n",
              "2008     1771.014597\n",
              "2009     1628.339918\n",
              "2010     2543.173984\n",
              "2011     3106.676082\n",
              "2012     2490.404357\n",
              "2013     3979.724609\n",
              "2014     8088.324091\n",
              "2015    11061.224835\n",
              "2016    14468.665976\n",
              "2017    22694.177571\n",
              "2018    30355.156132\n",
              "2019    34402.599246\n",
              "2020    37724.875855\n",
              "Name: merval, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s8y9INtYol1I",
        "outputId": "d6ca6c13-80de-4d5e-e394-b4c5350d4561",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 311
        }
      },
      "source": [
        "ax = sns.set_context(\"talk\", font_scale=0.8)\n",
        "merval_by_year.plot(title = \"Índice Bursátil - MERVAL Argentina - in ARS\" )\n",
        "plt.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mRMIgzlFpwPb",
        "outputId": "1296a51f-7c6b-4b13-816f-b9801107c14a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 524
        }
      },
      "source": [
        "fig, ax = plt.subplots(figsize=(16,8))\n",
        "sns.barplot(x = \"year\", y = \"merval\", ax=ax, data=merval)\n",
        "plt.title(\"Merval - Argentina Stock Index - in ARS\")\n",
        "plt.plot()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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RO8Z7gduAD1bVZ9f/HkuSJEmS5qppvzXWXJJk6YIFCxZ4ayxJkiRJmntmza2xJEmSJEmaCwzTkiRJkiR1ZJiWJEmSJKmjabsAmSRJkiRpw1RVDA8Pr9Y2MDAwclHpjZJhWpIkSZI0puHhYRYtWrRa29DQEIODgzM0o5nnYd6SJEmSJHVkmJYkSZIkqSPDtCRJkiRJHRmmJUmSJEnqyDAtSZIkSVJHhmlJkiRJkjoyTEuSJEmS1JFhWpIkSZKkjgzTkiRJkiR1ZJiWJEmSJKkjw7QkSZIkSR0ZpiVJkiRJ6sgwLUmSJElSRwMzPQFJkiRJ0tS55e+/NukxhletXKPt1tO+wcC8+ZMa9yF/udektp9JrkxLkiRJktSRYVqSJEmSpI4M05IkSZIkdWSYliRJkiSpI8O0JEmSJEkdGaYlSZIkSerIMC1JkiRJUkeGaUmSJEmSOjJMS5IkSZLUkWFakiRJkqSODNOSJEmSJHVkmJYkSZIkqaOBmZ6AJEmSJGl2m595fGr/d6/RtjEzTEuSJEmSxpSEgcyf6WnMKhv3nxIkSZIkSVoHhmlJkiRJkjoyTEuSJEmS1JFhWpIkSZKkjgzTkiRJkiR1ZJiWJEmSJKkjw7QkSZIkSR0ZpiVJkiRJ6sgwLUmSJElSR4ZpSZIkSZI6MkxLkiRJktTRtIXpJO9P8sskdya5LcmlSXbtqVeSFUnu6nk8sac+L8nxSW5ua0NJduh7jRckWZxkeZLrkhzYV98myflJliW5PckZSTaZ+r2XJEmStLGqKu69997VHlU109PSJA1M42tdAPxjVd2R5AHAXwL/muRhVbWq7bNPVX1zLdsfDbwGeA7wG+Bk4OIku1bVqiSPAL7Yjvsp4EXABUmWVNUV7RifBlYCOwCbtf0/Arx1ve6pJEmSJLWGh4dZtGjRam1DQ0MMDg7O0Iy0PkxbmK6q63qehibUbgssAO6YwBBvBk6oqp8DJDkauBl4FvBt4FDgJ1X18bb/JUkuBt4EXNGG7b2BnavqDuCOJB8APpfknVV1d/8LJlk6zpwWTGDekiRJkqQ5ZlrPmU7y4jag3k2zsnxyG2xHnJfkd0l+lOSInu0W0KwmXznSVlVLgeuBkUPFd+mtt67sqy+vqmv76g8Edpr0zkmSJEmSNhrTeZg3VfUlYGGSrYFDgBt7yi8ALqdZsd4T+EySgao6Hdiy7dO/Ury0p7YlcM049dG2p6dP/3wXjrU/7R8GXJ2WJEmSpI3MjFzNu6puB04BPpHk8W3b16pqRVXdU1VDNCvXB7ebLGt/9gfXhT21ZetY7x1fkiRJkqRxzeStseYBg8Cj11JfRXNuNVV1J7AE2GOk2B76/Ujg6rZpcW+9tXtfffMkj+2rrwCuQ5IkSZKkCZrOW2O9Lcm27e8PBk4D/gD8W5InJ9kjyQOSDCR5IfB24LyeIc4A3pVkpySbAyfQhODL2vo5wJOSHJZkMMm+wH7AmQBV9SvgUuCkJFsl2Q44FvjkaBcfkyRJkiRpbaZzZXpP4Ookvwd+DDwU2KuqbgYeRnM7q9uB24APA++vqlN7tj+R5vZalwG3ADsC+4/cVquqbgD2B46iOWz7FOANPbfFAngdzUr0Eprzq68C3jEleytJkiRJmrOm89ZYLx2jdjFw8TjbrwLe0z7W1uerwJPGqN8GHDjuZCVJkiSp9duTfzap7YdXDa/RdvMp1zAwb/JxbNu3P37SY2jdzOQ505IkSZIkbZAM05IkSZIkdWSYliRJkiSpI8O0JEmSJEkdGaYlSZIkSerIMC1JkiRJUkeGaUmSJEmSOpq2+0xLkiRJ0sZofuZzzj4nr9GmDZthWpIkSZKmUBIGYvSaazzMW5IkSZKkjgzTkiRJkiR1ZJiWJEmSJKkjw7QkSZIkSR0ZpiVJkiRJ6sgwLUmSJElSR4ZpSZIkSZI6MkxLkiRJktSRYVqSJEmSpI4M05IkSZIkdWSYliRJkiSpI8O0JEmSJEkdGaYlSZIkSerIMC1JkiRJUkeGaUmSJEmSOjJMS5IkSZLUkWFakiRJkqSODNOSJEmSJHVkmJYkSZIkqSPDtCRJkiRJHRmmJUmSJEnqyDAtSZIkSVJHhmlJkiRJkjoyTEuSJEmS1JFhWpIkSZKkjgzTkiRJkiR1ZJiWJEmSJKmjgZmegCRJkiSNpaoYHh5erW1gYIAkMzQjyTAtSZIkaZYbHh5m0aJFq7UNDQ0xODg4QzOSPMxbkiRJkqTODNOSJEmSJHVkmJYkSZIkqSPDtCRJkiRJHXkBMkmSJElT6hf/cPOkth9eNbxG2/Wn38LAvMnFmUe/9aGT2l4bt2lbmU7y/iS/THJnktuSXJpk1576bkkuT7I8yY1J3ta3/WZJzkxye5JlSc5PsnVfn9ck+UWSFUkWJ9mzr75jkqEkdyW5OcmH4vX0JUmSJEkdTedh3hcAe1TVAuCPga8A/5pkXpItgSHgUmBr4EDgmCQH9Gz/UWB34AnADsAWwLkjxSTPAD4BHAUsAE4FLk6yfVufD1wC3AhsCzwHOAh4x1TtsCRJkiRpbpq2MF1V11XVHe3TACtpQu0C4OXt8+Oq6u6q+h7wMeAt0KxKA4cAH6iqm9px3gm8eCQsA0cCX6yqS6rqnqr6OPBT4NC2/mzgUcDRVXVXVf0cOHHkNUaTZOlYj3bukiRJkqSNzLRegCzJi9sQejdwMnByG4x3Aa6qqlU93a8ERg4D3wnYtG0DoKquBZb39Nmltz7KGLsA11fV0r76ju3KuCRJkiRJEzKtFyCrqi8BC9tznQ+hOeQaYEtgaV/3pW07PT/H6zNa/THj1Edqy0aZ78K17Qs0K9e4Oi1JkiRJG50ZuZp3Vd2e5BTgjiT/ThNk+y+lt5D7A+7IzwXAbWP06Q+2E6n3ji9JkiRplpmf+fzDn5+7Rps0k2byPtPzgEHg0cBiYLckvfPZHbi6/f06mkPD9xgpJnks8MB2W9qfe7C63jEWA49KsqCvfkNVGaYlSZKkWSoJA/MGVnt4Ux7NtOm8Ndbbkmzb/v5g4DTgD8C/ARfSrJK/L8kmSZ4KHAGcDlBVK4BzgGOTbJdkK+Ak4MtVtaR9ibOA/ZPsm2QwyWHAE9vtAL4D/BI4IcnmSXYCjh55DUmSJEmSJmo6V6b3BK5O8nvgxzSHde9VVTe3K8OLgH1pzmP+PPDBqvpsz/ZH0awyXwMsAVYAB48Uq+py4HDgFJrDto8C9hsJ21W1EngJsCNwC3AZcB7w4anaYUmSJEnS3DRt50xX1UvHqV8FPH2M+gqa218dOUaf82gC8trqNwB7jztZSZIkSZLGMJPnTEuSJEmStEEyTEuSJEmS1JFhWpIkSZKkjgzTkiRJkiR1ZJiWJEmSJKkjw7QkSZIkSR0ZpiVJkiRJ6sgwLUmSJElSR4ZpSZIkSZI6MkxLkiRJktSRYVqSJEmSpI4M05IkSZIkdWSYliRJkiSpI8O0JEmSJEkdGaYlSZIkSerIMC1JkiRJUkeGaUmSJEmSOjJMS5IkSZLUkWFakiRJkqSODNOSJEmSJHVkmJYkSZIkqSPDtCRJkiRJHRmmJUmSJEnqyDAtSZIkSVJHhmlJkiRJkjoyTEuSJEmS1JFhWpIkSZKkjgzTkiRJkiR1ZJiWJEmSJKkjw7QkSZIkSR0ZpiVJkiRJ6sgwLUmSJElSR4ZpSZIkSZI6MkxLkiRJktSRYVqSJEmSpI4M05IkSZIkdWSYliRJkiSpI8O0JEmSJEkdGaYlSZIkSerIMC1JkiRJUkeGaUmSJEmSOjJMS5IkSZLUkWFakiRJkqSOpi1MJzkhyc+SLEtyU5KPJ9mmr08lWZHkrp7HE3vq85Icn+TmtjaUZIe+MV6QZHGS5UmuS3JgX32bJOe387g9yRlJNpnavZckSZIkzSXTuTK9EngdsA2wK/AnwNmj9Nunqrboefykp3Y08BrgOcC2wI3AxUnmASR5BPBF4FRgIfB24OwkT+sZ49PAFsAOwBOAPYCPrJ9dlCRJkiRtDKYtTFfVe6vqqqq6t6puoQm8z+04zJuBE6vq51V1F024fgzwrLZ+KPCTqvp4Vd1TVZcAFwNvgvvC9t7AO6vqjqq6CfgAcFiSTSe3h5IkSZKkjcVMnjO9F7B4lPbzkvwuyY+SHDHSmGQBzWrylSNtVbUUuJ5mpRtgl95668q++vKqurav/kBgp/6JJFk61gNY0GWHJUmSJElzw8BMvGh7HvMbWXNl+gXA5TSHhO8JfCbJQFWdDmzZ9lnat83SntqWwDXj1Efbnp4+kiRJkiSNadrDdJJXA6cD+1fVj3prVfW1nqdDSU4GDm77L2vb+1eDF/bUlq1jnZ4+vfNZ2N/Wty+uTkuSJEnSRmhaD/NOcjhwGvCSqvrGBDZZBQSgqu4EltBcMGxkvAXAI4Gr26bFvfXW7n31zZM8tq++Ariu085IkiRJkjZa03lrrLcBJwAvqqrvjlJ/cpI9kjwgyUCSF9Jcjfu8nm5nAO9KslOSzdvxrgMua+vnAE9KcliSwST7AvsBZwJU1a+AS4GTkmyVZDvgWOCTVXX3VOy3JEmSJGnumc7DvE8BhoFvJult37mqbgQeBpxIc8usYZpV6PdX1Rk9fU+kOaz6MmDz9uf+VbUKoKpuSLI/cDLNCvivgTdU1RU9Y7yurS2hOTf7AuAd63dXJUmSJElz2bSF6arKOPWLaW5jNVafVcB72sfa+nwVeNIY9duAA8ecrCRJkiRJY5jJW2NJkiRJkrRBMkxLkiRJktSRYVqSJEmSpI4M05IkSZIkdWSYliRJkiSpI8O0JEmSJEkdGaYlSZIkSerIMC1JkiRJUkeGaUmSJEmSOjJMS5IkSZLUkWFakiRJkqSOBmZ6ApIkSZKmVlUxPDy8WtvAwABJZmhG0obPMC1JkiTNccPDwyxatGi1tqGhIQYHB2doRtKGz8O8JUmSJEnqyDAtSZIkSVJHhmlJkiRJkjoyTEuSJEmS1JFhWpIkSZKkjryatyRJkjSLff3Tt056jJUrh9do++Z5tzF//uTiwJ4HPXhS20sbMlemJUmSJEnqyDAtSZIkSVJHhmlJkiRJkjoyTEuSJEmS1NGYVxxIctZEB6qqIyc/HUmSJEmSZr/xLt/36AmOU5OdiCRJkqSpMW/efP7mkH9ao03SuhszTFfV86drIpIkSZKmRpJJ3wZL0uo8Z1qSJEmSpI46/XkqyaOAVwI7AA/orVXVG9bjvCRJkiRJmrUmHKaT7A18Afh3YGdgMfCnNKvbP5iS2UmSJEmSNAt1Ocz7OODEqtoV+APwKmB74NvAhVMwN0mSJEmSZqUuYfpxwLnt78PAZlX1e+B/AUev74lJkiRJkjRbdQnTy7n/sPDfAo9ofx8GHroe5yRJkiRJ0qzW5QJkPwSeSnPO9DeA45M8HDgIuGoK5iZJkiRJ0qzUZWX6fcCS9vf/CfwaOAnYDHjTep6XJEmSJEmz1oRXpqvqqp7fbwNePCUzkiRJkiRplpvwynSSY5M8cionI0mSJEnShqDLYd6vBK5L8t0kRyZZMFWTkiRJkiRpNptwmK6qxwFPp7nY2N8C/5XkgiQvTtIllEuSJEmStEHrFIKr6vtV9VZgO+C1wHzgc8BvpmBukiRJkiTNSl1ujXWfqhpO8h3g4cBj24ckSZIkSRuFTivTSQaTvCLJv9CsRr8XGAJ2m4rJSZIkSZI0G014ZTrJmcABNPeV/iLwcuDSqlo5RXOTJEmSJGlW6nKY9+OBvwEuqKo7p2g+kiRJkiTNehM6zDvJIPCfwNcN0pIkSZKkjd2EwnRV3QvsA6xa1xdKckKSnyVZluSmJB9Psk1fn92SXJ5keZIbk7ytr75ZkjOT3N6Oc36Srfv6vCbJL5KsSLI4yZ599R2TDCW5K8nNST6UJOu6X5IkSZKkjU+XC5B9iSZQr6uVwOuAbYBdgT8Bzh4pJtmS5mJmlwJbAwcCxyQ5oGeMjwK7A08AdgC2AM7tGeMZwCeAo4AFwKnAxUm2b+vzgUuAG4FtgecABwHvmMR+SZIkSZI2Ml3Omf4e8MEkuwI/AH7fW6yqz4y1cVW9t+fpLUlOBXq3eTlN4D6uqlYB30vyMeAtwOeSbAYcAry8qm4CSPJO4Jok21fVjcCRwPlUrDsAACAASURBVBer6pJ2zI8nORI4FDgWeDbwKOCZVXUX8PMkJwLvAj7cP+ckS8d5TxaMU5ckSZIkzUFdwvQp7c83to9exerBeCL2Ahb3PN8FuKoN0iOuBI5of98J2LRta1606toky2lWum9sx+ifx5VtfeQ1rq+qpX31HZNsWVXLOu6DJEmSJGkjNOEwXVWd7kk9liQH0gTy5/Y0bwn0rwQvbdvp+Tlen9HqjxmnPlJbLUxX1cK17gT3rVy7Oi1JkiRJG5n1FpAnKsmrgTOB/avqRz2lZawZTBdyf8Ad+Tlen3Wp944vSZIkSdKYOoXpJIcluaq9kvaObdvRSV4xwe0PB04DXlJV3+grLwZ2S9I7p92Bq9vfrwPuBvboGe+xwAO5/3Dxxb31UcZYDDwqyYK++g0e4i1JkiRJmqgJh+n2Ql4fAS4EBoGR20ndCrx1Atu/DTgBeFFVfXeULhfSHHb+viSbJHkqzfnSpwNU1QrgHODYJNsl2Qo4CfhyVS1pxzgL2D/JvkkGkxwGPLHdDuA7wC+BE5JsnmQn4OiR15AkSZIkaSK6rEz/JfCmqjoOGO5p/yHw+AlsfwrNIdbfbO/xPPLYHqBdGV4E7EtzHvPngQ9W1Wd7xjiKZpX5GmAJsAI4eKRYVZcDh7evtaztv99I2K6qlcBLgB2BW4DLgPMY5UrekiRJkiStTZereT8K+P4o7b/n/guArVVVZQJ9rgKePkZ9Bc3tr44co895NAF5bfUbgL3Hm4skSZIkSWvTZWX6v2gCdb+nA/+xfqYjSZIkSdLs1yVMnwt8pD3PuIDNkuxLcx70J6ZicpIkSZIkzUZdDvP+EPAI4Fqai4/9uP35CZoLk0mSJEmStFGYcJiuqmHg0CTH0Nx+ah7ww6r65RTNTZIkSZKkWanLyjRJ9gb2Ah5CE6ZfkjTXFauq16/32UmSJEmSNAtNOEwn+RDwXprDu39Lc960JEmSJEkbnS4r00cCh1bVuVM1GUmSJEmSNgRdrua9Crh8qiYiSZIkSdKGokuYPg1441RNRJIkSZKkDUWXw7yPA76UZDHNedP39har6g3rc2KSJEmSJM1WXcL0scAi4BpgO7wAmSRJkiRpI9UlTL8VeENVnT1Fc5EkSZIkaYPQ5Zzpe4DLpmoikiRJkiRtKLqE6bOAw6dqIpIkSZIkbSi6HOa9HfCKJHsDi1nzAmRHrs+JSZIkSZI0W3UJ048Erm5/f0RfzYuRSZIkSZI2GhMO01X1/KmciCRJkiRJG4ou50xLkiRJkiQM05IkSZIkdWaYliRJkiSpI8O0JEmSJEkdGaYlSZIkSerIMC1JkiRJUkeGaUmSJEmSOjJMS5IkSZLUkWFakiRJkqSODNOSJEmSJHVkmJYkSZIkqSPDtCRJkiRJHRmmJUmSJEnqyDAtSZIkSVJHhmlJkiRJkjoyTEuSJEmS1JFhWpIkSZKkjgzTkiRJkiR1ZJiWJEmSJKkjw7QkSZIkSR0ZpiVJkiRJ6sgwLUmSJElSR4ZpSZIkSZI6MkxLkiRJktSRYVqSJEmSpI4M05IkSZIkdTStYTrJq5N8J8myJDVKvZKsSHJXz+OJPfV5SY5PcnNbG0qyQ98YL0iyOMnyJNclObCvvk2S89s53J7kjCSbTN1eS5IkSZLmmulemb4DOA346zH67FNVW/Q8ftJTOxp4DfAcYFvgRuDiJPMAkjwC+CJwKrAQeDtwdpKn9YzxaWALYAfgCcAewEcmv2uSJEmSpI3FwHS+WFVdCpDkees4xJuBE6rq5+04RwM3A88Cvg0cCvykqj7e9r8kycXAm4Ar2rC9N7BzVd0B3JHkA8Dnkryzqu7ufbEkS8eZz4J13A9JkiRJ0gZsNp4zfV6S3yX5UZIjRhqTLKBZTb5ypK2qlgLXA7u2Tbv01ltX9tWXV9W1ffUHAjut172QJEnSnFBV3Hvvvas9qtY4Y1HSRmZaV6Yn4AXA5cBKYE/gM0kGqup0YMu2T/9q8dKe2pbANePUR9uenj73qaqFY022Xbl2dVqSJGkOGx4eZtGiRau1DQ0NMTg4OEMzkjQbzKqV6ar6WlWtqKp7qmoIOBk4uC0va3/2h9eFPbVl61jvHV+SJEmSpDHNqjA9ilVAAKrqTmAJzQXDgPsO/X4kcHXbtLi33tq9r755ksf21VcA163vyUuSJEmS5qbpvjXW/CSbAg9on2/aPuYleXKSPZI8IMlAkhfSXI37vJ4hzgDelWSnJJsDJ9CE4Mva+jnAk5IclmQwyb7AfsCZAFX1K+BS4KQkWyXZDjgW+GT/xcckSZIkSVqb6T5n+mDgkz3PV7Q/nw88CDgR+BNgmGYV+v1VdUZP/xNpDtO+DNi8/bl/Va0CqKobkuxPc3j4acCvgTdU1RU9Y7yurS2hOTf7AuAd63EfJUmSJElz3HTfGuts4Owxulw8zvargPe0j7X1+SrwpDHqtwEHjvU6kiRJmjvOuvCWSW2/cuXwGm3/919uZf78yf9T+siXP2TSY0iaGbP9nGlJkiRJkmYdw7QkSZIkSR0ZpiVJkiRJ6sgwLUmSJElSR9N9NW9JkiRpgzJv3nwOePOn1miTtHEzTEuSJEljSLJertwtaW7xMG9JkiRJkjoyTEuSJEmS1JFhWpIkSZKkjgzTkiRJkiR1ZJiWJEmSJKkjw7QkSZIkSR0ZpiVJkiRJ6sgwLUmSJElSR4ZpSZIkSZI6MkxLkiRJktSRYVqSJEmSpI4M05IkSZIkdWSYliRJkiSpI8O0JEmSJEkdGaYlSZIkSerIMC1JkiRJUkeGaUmSJEmSOjJMS5IkSZLUkWFakiRJkqSODNOSJEmSJHVkmJYkSZIkqSPDtCRJkiRJHRmmJUmSJEnqyDAtSZIkSVJHAzM9AUmSJM1tVcXw8PBqbQMDAySZoRlJ0uQZpiVJkjSlhoeHWbRo0WptQ0NDDA4OztCMJGnyPMxbkiRJkqSODNOSJEmSJHVkmJYkSZIkqSPPmZYkSdKYXvX56ya1fa0cXqPtoIt+QeZP7p+i579ip0ltL0mT4cq0JEmSJEkdGaYlSZIkSerIMC1JkiRJUkeeMy1JkqSpNW8+27zp79dok6QNmWFakiRJUyoJTPJiY5I023iYtyRJkiRJHRmmJUmSJEnqaFrDdJJXJ/lOkmVJapT6bkkuT7I8yY1J3tZX3yzJmUlub8c4P8nWfX1ek+QXSVYkWZxkz776jkmGktyV5OYkH0qSqdljSZIkSdJcNN0r03cApwF/3V9IsiUwBFwKbA0cCByT5ICebh8FdgeeAOwAbAGc2zPGM4BPAEcBC4BTgYuTbN/W5wOXADcC2wLPAQ4C3rE+d1KSJEmSNLdNa5iuqkur6jzgP0YpvxxYCRxXVXdX1feAjwFvgWZVGjgE+EBV3VRVdwDvBF48EpaBI4EvVtUlVXVPVX0c+ClwaFt/NvAo4Oiququqfg6cOPIakiRJkiRNxGw6Z3oX4KqqWtXTdiWwa/v7TsCmbRsAVXUtsLynzy699VHG2AW4vqqW9tV3bFfGV5Nk6VgPmtVvSZIkSdJGZjaF6S2BpX1tS9t2en6O12dd6r3jS5IkSZI0ptl0w79lwEP72ha27fT8XADcNkaf/tXiidR7x79PVS3sb+vl6rQkSZIkbZxm08r0YmC3JL1z2h24uv39OuBuYI+RYpLHAg9stx0ZYw9W1zvGYuBRSRb01W+oqjXCtCRJkiRJo5nuW2PNT7Ip8ID2+abtYx5wIc1K+fuSbJLkqcARwOkAVbUCOAc4Nsl2SbYCTgK+XFVL2pc4C9g/yb5JBpMcBjyx3Q7gO8AvgROSbJ5kJ+DokdeQJEmSJGkipntl+mBgBc3tr2h/XwE8p10ZXgTsS3Me8+eBD1bVZ3u2P4pmlfkaYEm77cEjxaq6HDgcOIXmsO2jgP1GwnZVrQReAuwI3AJcBpwHfHgK9lWSJEmSNEdN6znTVXU2cPYY9auAp49RX0Fz+6sjx+hzHk1AXlv9BmDv8WcrSZIkSdLoZtM505IkSZIkbRAM05IkSZIkdWSYliRJkiSpI8O0JEmSJEkdGaYlSZIkSerIMC1JkiRJUkeGaUmSJEmSOjJMS5IkSZLUkWFakiRJkqSOBmZ6ApIkSRpbVTE8PLxa28DAAElmaEaSJMO0JEnSLDc8PMyiRYtWaxsaGmJwcHCGZiRJ8jBvSZIkSZI6MkxLkiRJktSRYVqSJEmSpI48Z1qSJGmKvezz35jU9rVy5Rptr7zoW2T+/EmNe9Ernj+p7SVpY+bKtCRJkiRJHRmmJUmSJEnqyDAtSZIkSVJHhmlJkiRJkjryAmSSJEmz3bx5POhN71mjTZI0cwzTkiRJs1wSmOSVuyVJ65d/0pQkSZIkqSPDtCRJkiRJHRmmJUmSJEnqyDAtSZIkSVJHhmlJkiRJkjoyTEuSJEmS1JFhWpIkSZKkjgzTkiRJkiR1ZJiWJEmSJKkjw7QkSZIkSR0ZpiVJkiRJ6sgwLUmSJElSR4ZpSZIkSZI6MkxLkiRJktSRYVqSJEmSpI4M05IkSZIkdWSYliRJkiSpI8O0JEmSJEkdGaYlSZIkSerIMC1JkiRJUkeGaUmSJEmSOjJMS5IkSZLUkWFakiRJkqSOZlWYTnJMkpVJ7up5nNdT3y3J5UmWJ7kxydv6tt8syZlJbk+yLMn5Sbbu6/OaJL9IsiLJ4iR7Ttf+SZIkSZLmhlkVplvfqaoteh6vAUiyJTAEXApsDRwIHJPkgJ5tPwrsDjwB2AHYAjh3pJjkGcAngKOABcCpwMVJtp/63ZIkSZIkzRWzMUyvzcuBlcBxVXV3VX0P+BjwFmhWpYFDgA9U1U1VdQfwTuDFPWH5SOCLVXVJVd1TVR8HfgocOtoLJlk61oMmkEuSJEmSNjKzMUzvkeTWJEuSfCbJjm37LsBVVbWqp++VwK7t7zsBm7ZtAFTVtcDynj679NZHGUOSJEmSpHHNtjD9OeDxwEOApwPDwFeTbAFsCSzt67+0bafn53h9xqqvpqoWjvUA7uy2e5IkSZKkuWBgpifQq6p+2vP0piSH0wTWZwDLgIf2bbKwbafn5wLgtjH69B+a3VuXJEmSJGlcs21lul+1jwCLgd2S9M55d+Dq9vfrgLuBPUaKSR4LPLDdlvbnHqyudwxJkiRJksY1q8J0klcleXD7+0NoLjB2K3A5cCHNSvr7kmyS5KnAEcDpAFW1AjgHODbJdkm2Ak4CvlxVS9qXOAvYP8m+SQaTHAY8sd1OkiRJkqQJmVVhGjgIuCbJcuAqmguKvaCq/ruqlgGLgH1pznP+PPDBqvpsz/ZH0awyXwMsAVYAB48Uq+py4HDgFJpDu48C9usJ25IkSZIkjWu2nTO9/zj1q2guTLa2+gqa218dOUaf84Dz1nWOkiRJkiTNqjAtSZI0FaqK4eHh1doGBgZIMkMzkiRt6AzTkiRpzhseHmbRokWrtQ0NDTE4ODhDM5Ikbehm2znTkiRJkiTNeoZpSZIkSZI68jBvSZI0q+33uc9PeoxauXKNtpddeBGZP39S4158wCsmtb0kacPlyrQkSZIkSR0ZpiVJkiRJ6sjDvCVJ0tw3bx6bHfmmNdokSVpXhmlJkjTnJYFJnh8tSVIv/yQrSZIkSVJHrkxLkjTFqorh4eHV2gYGBprVUkmStEEyTEuSNMWGh4dZtGjRam1DQ0MMDg7O0IwkSdJkeZi3JEmSJEkduTItSdI49r3ohEltXytXrdG2/xc+TOZP7m/aX37Zuye1vSRJWneuTEuSJEmS1JFhWpIkSZKkjjzMW5I0623wV8OeFx7wFy9Yo21DscG//5L0/9u79yhJyvKO499nZmcvbGRBQFEwICoRjOLGRYkBQVCJK8hNAx5NQgREEVAOygkqyRoVxaOJolEwgGtEEdToBhUvaIjZiAQiQgRERC5BCYjAwi6XXdgnf7w10PTOsNMzXd1d29/POX1muvrt6t9b3dVVT91aqoHFtCRp4DX9atgRAaPNLTybPv0lSaqDh3lLkiRJktQh90xLkmr1ymWvm/E48uFcZ9i+5/85McO9vRfsd86Mni9JkoaXxbQkSRu4fb565oyenw8/vM6w/b/2WWJ0dEbjBfjGQYfNeBySJPWDh3lLkiRJktQh90xLkgbfCIy9dYt1hqlHRkaYe+TB6wyTJGmYWUxLelzfOnNxvyNMavFh3+p3BPVIuRp2v1MMrzL9fQMkSWplMS1J6+Fv7EqSJKmdxbQkrUfTf2PXjQGSJEndZzEtSRu4pm8MkCRJGkQW05I2eGd97hUzev7DE/zG8dLPL2Z0hr9x/Ma//O6Mni9JkqT+sZiWanb5afv2O8KkFr75/H5HkCRJkhrJYlqSBtz7z917Rs9fO8Ge9Q9++VWMzHDPOsB7Dv7OjMchSZLURBbTGni3furd/Y4woacc9YF+R1CPjIzAIa8fWWeYJEmShpfFtCStR0T4E7uSJEl6DItpSdrAxQjsftjoOsMkSZI0fRbTG7jffvrsfkeY1BZveUO/I0hDISII96xLkiR1lfsmJEmSJEnqkMW0JEmSJEkdspiWJEmSJKlDFtOSJEmSJHXIYlqSJEmSpA5ZTEuSJEmS1CGLaUmSJEmSOmQxLUmSJElShyymJUmSJEnq0FAW0xExEhEnR8RtEbEyIr4dEdv0O5ckSZIkqRmGspgGTgBeB7wE2BK4GTg/IoZ1ekiSJEmSOhCZ2e8MPRcRNwKnZOanq/ubALcBL8/MH7a0u3s9o1oAsGDBgpqSzlyuXtPvCJOK2WNTaperH6g5yfTE7LlTavfw6lU1J5m+0dnz19vmoQHOP2sK+QFWD2gfZk8x/4NrBjM/wJyx9fdh1Zr7epBkeuaPbTSldqvWPFhzkumZPzZnSu1WrVldc5Lpmz82e71tVq0Z3GXZ/LGpLctWrXmo5iTTM39s1pTa3bdmbc1JpmejsantB1m9ZnDXd2ePxXrbPDTA+WdNIT/A2tWD2YeR2VPLnw8O5jwAEHPWPx/k6sH8DgKI2VP7HuqXFStWAGRmrjOhh66YjogFwN3ACzPz0pbhVwGnZ+apLcOmUkwncE8dWQfQ+FaDFX1NMX3m77+m98H8/df0Ppi//5reB/P3V9PzQ/P7YP7+2xD60ImNgbWZuU7VP9ibAeqxcfW3vVC+u+UxADJzk54kaojxjQtNnS7m77+m98H8/df0Ppi//5reB/P3V9PzQ/P7YP7+2xD60C3DeI7w+F7k9mOzN2F49jBLkiRJkmZg6IrpzFwB3AQsGh9WHfr9DOCn/colSZIkSWqOoSumK6cB74yI7SNiPnAK8AtgeX9jSZIkSZKaYBjPmQb4MOUw7+XA/OrvqzNzcC/TJ0mSJEkaGENZTFdF84nVTZIkSZKkjgzrYd6SJEmSJE2bxbQkSZIkSR2KzOx3BkmSJEmSGsU905IkSZIkdchiWpIkSZKkDllMS5IkSZLUIYtpSZIkSZI6ZDEtSZIkSVKHLKaHTEScEhFXRcQ9EfGbiDgzIjZra7MwIn4UEfdFxM0RcWzb4/Mi4vSIuLMaz7kR8cRJXu9tEZERsaQp+SNijyrzypbbLd3I36s+VG22jYjzIuKuqs3lEfHUJuSPiNPapv/K6j35eEPyj0bEyRFxU0TcGxHXRsSRM83ew/wjEXFiRPyqmvaXRcRu3cjfxT4cGxGXVI/fOMnrvCwirqja/CIi/qwp+SNiq4hYVn2GMiIO7Ub2HuZfHBE/iIg7onwHLY+I3RvWh50j4tKW+eTqiHhLU/K3td2v+hwtbUr+KMuwjIhV8dhlwYKm9KFqs1lEnBERt0dZHlwdEQubkD8i3hXrLovXRsSyJuSv2hwXEddV0/6GiDgpImKm+Xvch8Mj4ppq+l8dEQcMQv6ImBNlXWJ8+t4cER+NiHlt46hlWTwwMtPbEN2Ak4GFwBjwJOC7wPktj28M3Ab8LTAX2AW4E3hNS5vTgMuApwKbAt8EvjHBa/0B8CvgSmBJU/IDe5RZo7nvAbA5cAvwoerxEeAPgY2bkH+C13wekMCiJuQHjqnG8Zzq/u7A/cBeDcl/PHA9ZR6eBbwNWAk8bYDmgdcABwHvBm6c4DW2Be4DDgNmA/tU91/UkPxPAd4K/Anwv8Ch3Zj2Pcz/euAAYEH1GToaWNWwz9DmwHbASHX/ecDtwOIm5G/rxw3AcmBpg6b/tpTv/W279dnvQx/mAlcA/wxsCQTwDODJTcg/wWtuATzQOo5Bzg/sS1n27lZN++dS5uHDGvQZOqh6zs6UdbmDgDUMwPoQMB/4APBsYBR4OnA58Im2+biWZfGg3PoewFufPwDlQ31Py/1Dgd9QrTxUw04BflD9P6/6Ynply+M7UBZ4v98ybBS4BNgfuIguFdO9yE/NxXSP+nAycGlT80/wGqcBlzQlP/Bx4Mttr3MZcHxD8l8CvKPtdW4BThqE96DtuYcy8QrIkvbPDHAucFYT8re1uZEuFtO9zt/S9g7gwCb2gUdXxG8DjmtSfuCrwNuBpXSpmO5FfmoupnvUhzcBvwZmNzH/BO3+uurPrCbkB46jbV0I+AotxV4D+nAu8Mm2YcuBMwcpf8vjRwNXttxfQo+Wxf26eZi39qJsNR23E3B5Zq5tGXYZ8Pzq/+0pW6cuG38wM6+hbGV6fstzTgSuz8yv1xG6RV35qQ5XuS0ivh9dPDxxAnX0YU/ghiiHid5ZHRZ0TIPyPyIiNqbs5fpUd2M/oo78nwF2iIidohwyvRdlD9cFDck/QikeWgVlC3YdOu3DVOxESx+nOY6pqiN/L9WePyKeD2wC/M90x7EetfUhIm6i7I27krJB4AszyDmZWvJHxBsoe5xOnXHCx1fnZ+g/o5wu8KNuHd46iTr6sCdwLXBG1YfrIuK9ETFr5nHXUet8HBEjwJHAZzLzoWmnnFwd+c8B5kXES6tl8UJgV2DGh6lPoo4+9HJ53I38E42jV8vivqhjZlZDVOcsHE45BHXcxsDdbU3vrobT8nfSNtVK05uob8Wb6nVqyQ/8nDKTX0XZi3c48J2I2CUzf9qd9EWNfdicckjQ6ymHEC0CLoiIOzLznO6krzV/q78AHqRsyeyqGvPfAFwI/ISyZ2UtcGxmXt2d5EWN+ZcBR0XEN4FfUg5bfwrwhO4kf9Q0+zAVGwPt07vTcaxXjfl7ohf5I2JLyt6gj2TmddMZx3rGX2sfMnObiJhdjX9X4N5pRp1QXfkjYivKXqQ9MnNtl04Tneh16pr+dwAvBv6bcrTbAcA5EXFgZn5r+onXVWMfNgdeCrwDOAJ4JvANysaZD043b7sefQ8tBrambCzuqhrz/xb4EmVD9ixKYXpyZl44/bQTq7EPy4BPRMTZwKWUw7x3oZxG2TXdyB8R76ScmrSobRy1L4v7yT3TQyoiDgFOB16dmT9peegeyjlurTaphtPyd8I2ETFGOTfo7Zn5u+6mflRd+QEy8/8y84rMfCgz783Mf6AcUtPVCybU2QfKyt6PM/NLmbkmMy+m7E3ZvyH5W70F+GxmPjCzxI9Vc/5/pKwEbk85F2kRcEJEHN6d9LXnP4XyeTkfuJVyPtSFlJXbrplBH6aiG+N4XDXnr10v8lcF3UXAtylHLHVVr96DzFydmd+jnDO6ZDrjmEjN+c8EPlrHBoxxdebPzJWZeXE17e/PzC8CZwNvmGnuVjW/B/cCv87Mj2bmg5l5FeUoq0FYFnfqKODrmXnrNJ8/oZrzvwd4I/BCyvm6zwJeERHvn37iddU8H5wNvBc4i3K+98HAF+ni8rgb+SPiHZTrreyZmTdPZxxNZTE9hCLiMMqX+T6Z+W9tD18BLKwO5xn3AmB8j+wvKFtUH9nqFBHPBjaqnrsV5byyz1SHNN1B2Up1QkRc2YD8k1nLuofZTFsP+jC+R7RV+/1p69V7EBF7UM7n/XS3svco/yLg85l5fRZXAl8H9mtC/moDzHsy8xmZuRnlHKjnAO2v1a8+TMUVPHbr+HTGMake5K9VL/JHxHbAf1AuaHN0ZnbtO6gafz/egzHKhflmrAf59wbe3bIsPgQ4pLo/OpPs0LfpP0jL4qn4yQTDBmVZ3MnrbEf5PHX1dKse5F8EfC0zr8zMtZl5PWWDTFeWxdCb9yAzP5aZO2TmEzPzQMp6UVeWx93IH+UXe94O7J6ZP5tgHLUtiwfCdE+29tbMG3AsZWvWhFcB5NEr950EzKFszfsd8NqWNqcB/0U57HNTyt6rb1aPjVIOA2q9XQz8PbDloOevHt+b6gqulALjGKriownvQfX4zsBqyuFAI9X9O1vHMcj5W9qdB1zQpHmgevxTwI+Bbar7z6EckrWkIfmfTLnibFCuQPs5yrmucwfoPZhFOff7COCm6v+5QFSPP51yHvhfUQqgxZSrSXfjat6156/ajA+7qWo3FxhrQn7K0Qy3AH/XrXm3D304gEevdDsbOJBy8b43NSR/+7L4vOq2dUPy7wbsWLWbTdkjdz9l71lTPkNPo/wSwtuqtttTfinhnU3I39Luw8DVDZyHT6CcqrRjdX8byrJvaYP68ATKOsQIZXn9IcpFwbYYkPwfoZzatt0k46htWTwot74H8NbjN7xsEV1Tfbm33lqvxL2QUgDfT/lJlmPbxjGPcs7MXZTDNM4Dnvg4r3kR3ftprNrzV18aN1cz+28pW//2bNp7QFkRvLrqx8+BIxuWf0vKBoF9mzYPAL8HfLJ67krKAvIjdOGKrj3Kv5By0ZxVlAXtZ+nCgrvLfVhSjaf9tm1Lm5dRLhp1P3AdcHDD8k/0+NIm5K8+MznBa7yrKe8B5dofP6/GexflJ1/e3JT8E7zm0m58fno4/Q+nFJ6rKBuDf0wXNgj3YT7ejXLBpVWUouMkYLRB+edQ1oWO6da07+FnaBR4H2Vj9r2UK5H/E7BJg/qwFWU5thJYQbk6/4SFa6/zUzZOJOW6No8ZR9vr1LIsHpTb+FYPSZIkSZI0RZ4zomGhtQAAAq5JREFULUmSJElShyymJUmSJEnqkMW0JEmSJEkdspiWJEmSJKlDFtOSJEmSJHXIYlqSJEmSpA5ZTEuSJEmS1CGLaUmSJEmSOmQxLUmSeiIixvqdQZKkbrGYliRpyETEoRFxd0Rs1Db8byLiuiieGRFfrdrdFRHfjYjntrTdNCLOjoibI+L+iLg2Io6PiGhpszQiLoyIYyLiRuDBiJjXu55KklQfi2lJkobPuUACrx0fEBEjwBuBM4AnAcuB24HdgF2Aa4GLImKL6ilzgJ8B+wM7Au8D3gsc2vZaLwT2BPYDdgJW19EhSZJ6LTKz3xkkSVKPRcSpwB9l5q7V/b2B84GtgaOAP83MXVraB/BL4BOZ+bFJxvlxYMfMfHl1fyml2N46M1fW2B1JknpuVr8DSJKkvjgd+FlE7JCZ1wBHAP+ambdHxM7ACyKivQCeBzwLHtmTfQJwCKUAnwuMATe1PecaC2lJ0obIYlqSpCGUmVdFxHLgiIj4EPBqYJ/q4RHg+8DREzx1RfX3eOBE4DjgcuDe6v9XtbVf1eXokiQNBItpSZKG1+nAx4A7gV8D36uGX0Y59/mWzHxgkue+BPh2Zp41PiAinlVfVEmSBosXIJMkaXh9pfp7EnBGPnohlU8Co8CyiNgtIraNiF0j4gMR8eKqzbXAHhHx0ojYPiLeD7yot/ElSeofi2lJkoZUtdf585T1gbNaht8G/DFwB/AvlML5C8A2wK1Vs/cB/w4sAy4GNgVO7VV2SZL6zat5S5I0xCLiPGAsMw/odxZJkprEc6YlSRpCEbEp5TegDwD26nMcSZIax2JakqThdDmwGfDhzPxhv8NIktQ0HuYtSZIkSVKHvACZJEmSJEkdspiWJEmSJKlDFtOSJEmSJHXIYlqSJEmSpA5ZTEuSJEmS1KH/B8EQRaQ4JxaFAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SaaWq7tBqyM2"
      },
      "source": [
        "Dólar CCL - FX: USD to ARS Exchange Rate \n",
        "\n",
        " Esta operatoria financiera también conocida como contado con liquidez, CCL o contado con liqui, se realiza a través de la bolsa local, y es una alternativa para que las grandes empresas puedan hacerse de dólares americanos. Es una medida más realista del valor del dólar libre.\n",
        "\n",
        "This financial operation, also known as cash with liquidity, CCL, is carried out through the local stock exchange, and is an alternative for big companies to obtain US dollars. It is a more realistic measure of the value of the free dollar."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zm2ks9ERrb2G"
      },
      "source": [
        "tickers=[\"GGAL\", \"GGAL.BA\"]\n",
        "CCL_data = pd.DataFrame()\n",
        "for t in tickers:\n",
        "    CCL_data[t]= wb.DataReader(t, data_source=\"yahoo\", start=\"01-01-2003\")[(\"Adj Close\")]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4z0Olf2pssUj",
        "outputId": "dbf7b314-b8bd-40b9-875e-8eea647292e6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        }
      },
      "source": [
        "CCL_data"
      ],
      "execution_count": null,
      "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>GGAL</th>\n",
              "      <th>GGAL.BA</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2003-01-02</th>\n",
              "      <td>1.952801</td>\n",
              "      <td>0.666931</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2003-01-03</th>\n",
              "      <td>2.076637</td>\n",
              "      <td>0.702539</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2003-01-06</th>\n",
              "      <td>2.400516</td>\n",
              "      <td>0.765094</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2003-01-07</th>\n",
              "      <td>2.381465</td>\n",
              "      <td>0.765094</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2003-01-08</th>\n",
              "      <td>2.381465</td>\n",
              "      <td>0.784342</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-21</th>\n",
              "      <td>7.600000</td>\n",
              "      <td>105.699997</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-22</th>\n",
              "      <td>7.250000</td>\n",
              "      <td>104.449997</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-23</th>\n",
              "      <td>6.860000</td>\n",
              "      <td>103.199997</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-24</th>\n",
              "      <td>7.630000</td>\n",
              "      <td>110.849998</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-25</th>\n",
              "      <td>7.400000</td>\n",
              "      <td>107.500000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4465 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                GGAL     GGAL.BA\n",
              "Date                            \n",
              "2003-01-02  1.952801    0.666931\n",
              "2003-01-03  2.076637    0.702539\n",
              "2003-01-06  2.400516    0.765094\n",
              "2003-01-07  2.381465    0.765094\n",
              "2003-01-08  2.381465    0.784342\n",
              "...              ...         ...\n",
              "2020-09-21  7.600000  105.699997\n",
              "2020-09-22  7.250000  104.449997\n",
              "2020-09-23  6.860000  103.199997\n",
              "2020-09-24  7.630000  110.849998\n",
              "2020-09-25  7.400000  107.500000\n",
              "\n",
              "[4465 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IT5TcFXrs2VJ"
      },
      "source": [
        "GGAL_CCL = CCL_data.dropna()\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "slrLdoaKs9zT",
        "outputId": "ee36e268-0260-4a1b-8fe4-1614c059ae16",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "GGAL_CCL.describe()"
      ],
      "execution_count": null,
      "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>GGAL</th>\n",
              "      <th>GGAL.BA</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>4261.000000</td>\n",
              "      <td>4261.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>14.026634</td>\n",
              "      <td>27.109764</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>13.218953</td>\n",
              "      <td>40.675900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.389264</td>\n",
              "      <td>0.550483</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>5.667885</td>\n",
              "      <td>2.107617</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>8.125557</td>\n",
              "      <td>3.524767</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>17.230000</td>\n",
              "      <td>39.750168</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>71.444939</td>\n",
              "      <td>174.550003</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              GGAL      GGAL.BA\n",
              "count  4261.000000  4261.000000\n",
              "mean     14.026634    27.109764\n",
              "std      13.218953    40.675900\n",
              "min       1.389264     0.550483\n",
              "25%       5.667885     2.107617\n",
              "50%       8.125557     3.524767\n",
              "75%      17.230000    39.750168\n",
              "max      71.444939   174.550003"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j5CY272ItbFs",
        "outputId": "3c7d68d4-4c38-4d2b-d9a8-24f44907d70e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 572
        }
      },
      "source": [
        "GGAL_CCL[\"Dólar CCL\"] = ((GGAL_CCL[\"GGAL.BA\"]*10)/(GGAL_CCL[\"GGAL\"]))\n",
        "GGAL_CCL"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        },
        {
          "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>GGAL</th>\n",
              "      <th>GGAL.BA</th>\n",
              "      <th>Dólar CCL</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2003-01-02</th>\n",
              "      <td>1.952801</td>\n",
              "      <td>0.666931</td>\n",
              "      <td>3.415254</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2003-01-03</th>\n",
              "      <td>2.076637</td>\n",
              "      <td>0.702539</td>\n",
              "      <td>3.383061</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2003-01-06</th>\n",
              "      <td>2.400516</td>\n",
              "      <td>0.765094</td>\n",
              "      <td>3.187206</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2003-01-07</th>\n",
              "      <td>2.381465</td>\n",
              "      <td>0.765094</td>\n",
              "      <td>3.212703</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2003-01-08</th>\n",
              "      <td>2.381465</td>\n",
              "      <td>0.784342</td>\n",
              "      <td>3.293526</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-21</th>\n",
              "      <td>7.600000</td>\n",
              "      <td>105.699997</td>\n",
              "      <td>139.078945</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-22</th>\n",
              "      <td>7.250000</td>\n",
              "      <td>104.449997</td>\n",
              "      <td>144.068961</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-23</th>\n",
              "      <td>6.860000</td>\n",
              "      <td>103.199997</td>\n",
              "      <td>150.437310</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-24</th>\n",
              "      <td>7.630000</td>\n",
              "      <td>110.849998</td>\n",
              "      <td>145.281778</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-25</th>\n",
              "      <td>7.400000</td>\n",
              "      <td>107.500000</td>\n",
              "      <td>145.270268</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4261 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                GGAL     GGAL.BA   Dólar CCL\n",
              "Date                                        \n",
              "2003-01-02  1.952801    0.666931    3.415254\n",
              "2003-01-03  2.076637    0.702539    3.383061\n",
              "2003-01-06  2.400516    0.765094    3.187206\n",
              "2003-01-07  2.381465    0.765094    3.212703\n",
              "2003-01-08  2.381465    0.784342    3.293526\n",
              "...              ...         ...         ...\n",
              "2020-09-21  7.600000  105.699997  139.078945\n",
              "2020-09-22  7.250000  104.449997  144.068961\n",
              "2020-09-23  6.860000  103.199997  150.437310\n",
              "2020-09-24  7.630000  110.849998  145.281778\n",
              "2020-09-25  7.400000  107.500000  145.270268\n",
              "\n",
              "[4261 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ag3tqoUMtj4b",
        "outputId": "c77bdc8c-2905-498f-9f26-cf37169db8f0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        }
      },
      "source": [
        "GGAL_CCL[\"Dólar CCL\"].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    4261.000000\n",
              "mean       14.574563\n",
              "std        23.364835\n",
              "min         2.667486\n",
              "25%         3.111753\n",
              "50%         4.613799\n",
              "75%        14.568177\n",
              "max       150.437310\n",
              "Name: Dólar CCL, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2rdPwIs4uZiK",
        "outputId": "6f0d310b-50e4-4d9e-9145-688fa1d3cf41",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "GGAL_CCL.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['GGAL', 'GGAL.BA', 'Dólar CCL'], dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mk6z_y_GtAE3",
        "outputId": "d393373d-09ab-4fc2-bfdf-5a6000dfabce",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 848
        }
      },
      "source": [
        "plt.figure(figsize=(16,8))\n",
        "sns.kdeplot(GGAL_CCL['Dólar CCL'], color= \"Green\", shade = True)\n",
        "plt.xlabel(\"Dólar CCL\",size = 20)\n",
        "plt.ylabel(\"Distribution\",size = 10)\n",
        "plt.title('Exchange Rate - USD/ARS - \"Dólar CCL',size = 20)\n",
        "#plt.imsave('KDE',format='png') # Guardar la imagen\n",
        "plt.show()\n",
        "\n",
        "g = sns.catplot(y=\"Dólar CCL\",\n",
        "                data=GGAL_CCL, \n",
        "                kind=\"violin\", # box , violin\n",
        "                height = 5,aspect=3 ,palette = \"muted\")\n",
        "g.despine(left=True)\n",
        "g.set_xticklabels(rotation=90)\n",
        "g = g.set_ylabels(\"USD/ARS\")\n",
        "plt.title('Distribution USD/ARS',size = 20)\n",
        "plt.show()\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eggssXJ9wEsz",
        "outputId": "dee46a68-10ff-494e-e0c1-599ce8623c22",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 456
        }
      },
      "source": [
        "GGAL_CCL[\"Dólar CCL\"].plot(figsize=(18,8), color=\"g\")\n",
        "plt.title(\"DÓLAR CCL\")\n",
        "plt.ylabel(\"Pesos ARS\")\n",
        "plt.plot(label=\"Fecha\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1296x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M2mfB93UvSg5"
      },
      "source": [
        "Most of the time, the monetary exchange rate was in the range of [10-25] ARS per US dollar. Due to the fact that the great leap occurred last year, values that emerged from the set are shown.\n",
        "\n",
        "La mayor parte del tiempo, el tipo de cambio monetario se encontró en la franja de [10-25] ARS por Dólar amerícano. Debido a que el gran salto se dio el último año se muestran valores que se desprendieron del conjunto."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AubUkSqEvRyq"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jXN3ss-mKF5u",
        "outputId": "bb61aff0-2674-4596-9419-9149fa2042f1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "merval.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5780, 4)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7ZPQxyYOOJio",
        "outputId": "ebd5e7a9-ceb8-4504-bf43-d03c8feade10",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        }
      },
      "source": [
        "GGAL_CCL.index "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatetimeIndex(['2003-01-02', '2003-01-03', '2003-01-06', '2003-01-07',\n",
              "               '2003-01-08', '2003-01-09', '2003-01-10', '2003-01-13',\n",
              "               '2003-01-14', '2003-01-15',\n",
              "               ...\n",
              "               '2020-09-14', '2020-09-15', '2020-09-16', '2020-09-17',\n",
              "               '2020-09-18', '2020-09-21', '2020-09-22', '2020-09-23',\n",
              "               '2020-09-24', '2020-09-25'],\n",
              "              dtype='datetime64[ns]', name='Date', length=4261, freq=None)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0kls3xZoKIhj",
        "outputId": "2b27d1a8-f927-4a85-acd5-891bb3e2544b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 139
        }
      },
      "source": [
        "GGAL_CCL[\"indice_tiempo\"]=GGAL_CCL.index"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rxrbJr0XL0Pp",
        "outputId": "47f76458-b68e-4188-b89f-50617815a65c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        }
      },
      "source": [
        "GGAL_CCL"
      ],
      "execution_count": null,
      "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>GGAL</th>\n",
              "      <th>GGAL.BA</th>\n",
              "      <th>Dólar CCL</th>\n",
              "      <th>indice_tiempo</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2003-01-02</th>\n",
              "      <td>1.952801</td>\n",
              "      <td>0.666931</td>\n",
              "      <td>3.415254</td>\n",
              "      <td>2003-01-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2003-01-03</th>\n",
              "      <td>2.076637</td>\n",
              "      <td>0.702539</td>\n",
              "      <td>3.383061</td>\n",
              "      <td>2003-01-03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2003-01-06</th>\n",
              "      <td>2.400516</td>\n",
              "      <td>0.765094</td>\n",
              "      <td>3.187206</td>\n",
              "      <td>2003-01-06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2003-01-07</th>\n",
              "      <td>2.381465</td>\n",
              "      <td>0.765094</td>\n",
              "      <td>3.212703</td>\n",
              "      <td>2003-01-07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2003-01-08</th>\n",
              "      <td>2.381465</td>\n",
              "      <td>0.784342</td>\n",
              "      <td>3.293526</td>\n",
              "      <td>2003-01-08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-21</th>\n",
              "      <td>7.600000</td>\n",
              "      <td>105.699997</td>\n",
              "      <td>139.078945</td>\n",
              "      <td>2020-09-21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-22</th>\n",
              "      <td>7.250000</td>\n",
              "      <td>104.449997</td>\n",
              "      <td>144.068961</td>\n",
              "      <td>2020-09-22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-23</th>\n",
              "      <td>6.860000</td>\n",
              "      <td>103.199997</td>\n",
              "      <td>150.437310</td>\n",
              "      <td>2020-09-23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-24</th>\n",
              "      <td>7.630000</td>\n",
              "      <td>110.849998</td>\n",
              "      <td>145.281778</td>\n",
              "      <td>2020-09-24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-25</th>\n",
              "      <td>7.400000</td>\n",
              "      <td>107.500000</td>\n",
              "      <td>145.270268</td>\n",
              "      <td>2020-09-25</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4261 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                GGAL     GGAL.BA   Dólar CCL indice_tiempo\n",
              "Date                                                      \n",
              "2003-01-02  1.952801    0.666931    3.415254    2003-01-02\n",
              "2003-01-03  2.076637    0.702539    3.383061    2003-01-03\n",
              "2003-01-06  2.400516    0.765094    3.187206    2003-01-06\n",
              "2003-01-07  2.381465    0.765094    3.212703    2003-01-07\n",
              "2003-01-08  2.381465    0.784342    3.293526    2003-01-08\n",
              "...              ...         ...         ...           ...\n",
              "2020-09-21  7.600000  105.699997  139.078945    2020-09-21\n",
              "2020-09-22  7.250000  104.449997  144.068961    2020-09-22\n",
              "2020-09-23  6.860000  103.199997  150.437310    2020-09-23\n",
              "2020-09-24  7.630000  110.849998  145.281778    2020-09-24\n",
              "2020-09-25  7.400000  107.500000  145.270268    2020-09-25\n",
              "\n",
              "[4261 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YiBl46ZOQp_C",
        "outputId": "a998ff0f-8d6f-497f-c05f-035776a85392",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 674
        }
      },
      "source": [
        "GGAL_CCL.indice_tiempo = pd.to_datetime(GGAL_CCL.indice_tiempo) #### indicar que columna queremos transformar #####\n",
        "GGAL_CCL['year'] = GGAL_CCL.indice_tiempo.dt.year #creamos una nueva columna \"year\" con .dt.year sobre la col \"indice_tiempo\"\n",
        "mask = GGAL_CCL[\"year\"]>2003\n",
        "GGAL_CCL = GGAL_CCL[mask]\n",
        "GGAL_CCL"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/pandas/core/generic.py:5303: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self[name] = value\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n"
          ],
          "name": "stderr"
        },
        {
          "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>GGAL</th>\n",
              "      <th>GGAL.BA</th>\n",
              "      <th>Dólar CCL</th>\n",
              "      <th>indice_tiempo</th>\n",
              "      <th>year</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</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>2004-01-02</th>\n",
              "      <td>6.753832</td>\n",
              "      <td>2.021003</td>\n",
              "      <td>2.992379</td>\n",
              "      <td>2004-01-02</td>\n",
              "      <td>2004</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2004-01-05</th>\n",
              "      <td>7.030083</td>\n",
              "      <td>2.021003</td>\n",
              "      <td>2.874792</td>\n",
              "      <td>2004-01-05</td>\n",
              "      <td>2004</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2004-01-06</th>\n",
              "      <td>7.106289</td>\n",
              "      <td>2.069122</td>\n",
              "      <td>2.911677</td>\n",
              "      <td>2004-01-06</td>\n",
              "      <td>2004</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2004-01-07</th>\n",
              "      <td>7.096763</td>\n",
              "      <td>2.059498</td>\n",
              "      <td>2.902025</td>\n",
              "      <td>2004-01-07</td>\n",
              "      <td>2004</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2004-01-08</th>\n",
              "      <td>6.963401</td>\n",
              "      <td>2.059498</td>\n",
              "      <td>2.957604</td>\n",
              "      <td>2004-01-08</td>\n",
              "      <td>2004</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>2020-09-21</th>\n",
              "      <td>7.600000</td>\n",
              "      <td>105.699997</td>\n",
              "      <td>139.078945</td>\n",
              "      <td>2020-09-21</td>\n",
              "      <td>2020</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-22</th>\n",
              "      <td>7.250000</td>\n",
              "      <td>104.449997</td>\n",
              "      <td>144.068961</td>\n",
              "      <td>2020-09-22</td>\n",
              "      <td>2020</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-23</th>\n",
              "      <td>6.860000</td>\n",
              "      <td>103.199997</td>\n",
              "      <td>150.437310</td>\n",
              "      <td>2020-09-23</td>\n",
              "      <td>2020</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-24</th>\n",
              "      <td>7.630000</td>\n",
              "      <td>110.849998</td>\n",
              "      <td>145.281778</td>\n",
              "      <td>2020-09-24</td>\n",
              "      <td>2020</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-25</th>\n",
              "      <td>7.400000</td>\n",
              "      <td>107.500000</td>\n",
              "      <td>145.270268</td>\n",
              "      <td>2020-09-25</td>\n",
              "      <td>2020</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4009 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                GGAL     GGAL.BA   Dólar CCL indice_tiempo  year\n",
              "Date                                                            \n",
              "2004-01-02  6.753832    2.021003    2.992379    2004-01-02  2004\n",
              "2004-01-05  7.030083    2.021003    2.874792    2004-01-05  2004\n",
              "2004-01-06  7.106289    2.069122    2.911677    2004-01-06  2004\n",
              "2004-01-07  7.096763    2.059498    2.902025    2004-01-07  2004\n",
              "2004-01-08  6.963401    2.059498    2.957604    2004-01-08  2004\n",
              "...              ...         ...         ...           ...   ...\n",
              "2020-09-21  7.600000  105.699997  139.078945    2020-09-21  2020\n",
              "2020-09-22  7.250000  104.449997  144.068961    2020-09-22  2020\n",
              "2020-09-23  6.860000  103.199997  150.437310    2020-09-23  2020\n",
              "2020-09-24  7.630000  110.849998  145.281778    2020-09-24  2020\n",
              "2020-09-25  7.400000  107.500000  145.270268    2020-09-25  2020\n",
              "\n",
              "[4009 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bNpciSZ9ULsd",
        "outputId": "95e4fdf2-9039-4224-e9ea-2cded08910c6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "GGAL_CCL.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4009, 5)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ReJISupqVVgc",
        "outputId": "41df9ae3-54b4-4c63-b251-73d0d1a76b54",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        }
      },
      "source": [
        "merval_usd = merval.merge(GGAL_CCL, on=\"indice_tiempo\", how=\"inner\")\n",
        "merval_usd = merval_usd[[\"indice_tiempo\",\"merval\", \"GGAL\", \"Dólar CCL\"]]\n",
        "merval_usd[\"Merval_USD\"] = merval_usd[\"merval\"]/merval_usd[\"Dólar CCL\"]\n",
        "merval_usd"
      ],
      "execution_count": null,
      "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>indice_tiempo</th>\n",
              "      <th>merval</th>\n",
              "      <th>GGAL</th>\n",
              "      <th>Dólar CCL</th>\n",
              "      <th>Merval_USD</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2004-10-04</td>\n",
              "      <td>1167.09</td>\n",
              "      <td>6.591819</td>\n",
              "      <td>2.978328</td>\n",
              "      <td>391.860814</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2004-10-05</td>\n",
              "      <td>1135.37</td>\n",
              "      <td>6.246899</td>\n",
              "      <td>3.034935</td>\n",
              "      <td>374.100277</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2004-10-06</td>\n",
              "      <td>1128.00</td>\n",
              "      <td>6.371454</td>\n",
              "      <td>2.930292</td>\n",
              "      <td>384.944613</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2004-10-07</td>\n",
              "      <td>1140.26</td>\n",
              "      <td>6.333129</td>\n",
              "      <td>3.008808</td>\n",
              "      <td>378.973982</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2004-10-08</td>\n",
              "      <td>1128.74</td>\n",
              "      <td>6.285224</td>\n",
              "      <td>2.970494</td>\n",
              "      <td>379.983978</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>3777</th>\n",
              "      <td>2020-07-27</td>\n",
              "      <td>49449.86</td>\n",
              "      <td>12.770000</td>\n",
              "      <td>118.167571</td>\n",
              "      <td>418.472340</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3778</th>\n",
              "      <td>2020-07-28</td>\n",
              "      <td>48934.44</td>\n",
              "      <td>12.220000</td>\n",
              "      <td>119.558104</td>\n",
              "      <td>409.294213</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3779</th>\n",
              "      <td>2020-07-29</td>\n",
              "      <td>48884.46</td>\n",
              "      <td>12.320000</td>\n",
              "      <td>118.465910</td>\n",
              "      <td>412.645800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3780</th>\n",
              "      <td>2020-07-30</td>\n",
              "      <td>49405.75</td>\n",
              "      <td>12.120000</td>\n",
              "      <td>121.369643</td>\n",
              "      <td>407.068429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3781</th>\n",
              "      <td>2020-07-31</td>\n",
              "      <td>49253.62</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>124.458338</td>\n",
              "      <td>395.743834</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3782 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     indice_tiempo    merval       GGAL   Dólar CCL  Merval_USD\n",
              "0       2004-10-04   1167.09   6.591819    2.978328  391.860814\n",
              "1       2004-10-05   1135.37   6.246899    3.034935  374.100277\n",
              "2       2004-10-06   1128.00   6.371454    2.930292  384.944613\n",
              "3       2004-10-07   1140.26   6.333129    3.008808  378.973982\n",
              "4       2004-10-08   1128.74   6.285224    2.970494  379.983978\n",
              "...            ...       ...        ...         ...         ...\n",
              "3777    2020-07-27  49449.86  12.770000  118.167571  418.472340\n",
              "3778    2020-07-28  48934.44  12.220000  119.558104  409.294213\n",
              "3779    2020-07-29  48884.46  12.320000  118.465910  412.645800\n",
              "3780    2020-07-30  49405.75  12.120000  121.369643  407.068429\n",
              "3781    2020-07-31  49253.62  12.000000  124.458338  395.743834\n",
              "\n",
              "[3782 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XUeFjAB0VzME",
        "outputId": "5251984f-5cc6-401f-aec7-cf3d03eb2605",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        }
      },
      "source": [
        "merval_usd.corr = merval_usd.corr()\n",
        "merval_usd.corr"
      ],
      "execution_count": null,
      "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>merval</th>\n",
              "      <th>GGAL</th>\n",
              "      <th>Dólar CCL</th>\n",
              "      <th>Merval_USD</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>merval</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.689575</td>\n",
              "      <td>0.826707</td>\n",
              "      <td>0.482602</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GGAL</th>\n",
              "      <td>0.689575</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.229110</td>\n",
              "      <td>0.926326</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Dólar CCL</th>\n",
              "      <td>0.826707</td>\n",
              "      <td>0.229110</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.000730</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Merval_USD</th>\n",
              "      <td>0.482602</td>\n",
              "      <td>0.926326</td>\n",
              "      <td>-0.000730</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              merval      GGAL  Dólar CCL  Merval_USD\n",
              "merval      1.000000  0.689575   0.826707    0.482602\n",
              "GGAL        0.689575  1.000000   0.229110    0.926326\n",
              "Dólar CCL   0.826707  0.229110   1.000000   -0.000730\n",
              "Merval_USD  0.482602  0.926326  -0.000730    1.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i-OiT0aEWeRO",
        "outputId": "213aba98-06fc-465c-bd7e-4f356ec2c308",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 339
        }
      },
      "source": [
        "merval_usd.corr = sns.heatmap(merval_usd.corr, xticklabels=1, yticklabels=1)\n",
        "merval_usd.corr.set_xticklabels(merval_usd.corr.get_xticklabels(), rotation=40, ha=\"right\")\n",
        "plt.title(\"Correlación Merval - GGAL - Dólar - MERVAL\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k_jdNoF3XjhE",
        "outputId": "de8b1bb9-504b-4354-eaed-101269f80888",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        }
      },
      "source": [
        "merval_usd.dropna()"
      ],
      "execution_count": null,
      "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>indice_tiempo</th>\n",
              "      <th>merval</th>\n",
              "      <th>GGAL</th>\n",
              "      <th>Dólar CCL</th>\n",
              "      <th>Merval_USD</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2004-10-04</td>\n",
              "      <td>1167.09</td>\n",
              "      <td>6.591819</td>\n",
              "      <td>2.978328</td>\n",
              "      <td>391.860814</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2004-10-05</td>\n",
              "      <td>1135.37</td>\n",
              "      <td>6.246899</td>\n",
              "      <td>3.034935</td>\n",
              "      <td>374.100277</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2004-10-06</td>\n",
              "      <td>1128.00</td>\n",
              "      <td>6.371454</td>\n",
              "      <td>2.930292</td>\n",
              "      <td>384.944613</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2004-10-07</td>\n",
              "      <td>1140.26</td>\n",
              "      <td>6.333129</td>\n",
              "      <td>3.008808</td>\n",
              "      <td>378.973982</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2004-10-08</td>\n",
              "      <td>1128.74</td>\n",
              "      <td>6.285224</td>\n",
              "      <td>2.970494</td>\n",
              "      <td>379.983978</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>3777</th>\n",
              "      <td>2020-07-27</td>\n",
              "      <td>49449.86</td>\n",
              "      <td>12.770000</td>\n",
              "      <td>118.167571</td>\n",
              "      <td>418.472340</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3778</th>\n",
              "      <td>2020-07-28</td>\n",
              "      <td>48934.44</td>\n",
              "      <td>12.220000</td>\n",
              "      <td>119.558104</td>\n",
              "      <td>409.294213</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3779</th>\n",
              "      <td>2020-07-29</td>\n",
              "      <td>48884.46</td>\n",
              "      <td>12.320000</td>\n",
              "      <td>118.465910</td>\n",
              "      <td>412.645800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3780</th>\n",
              "      <td>2020-07-30</td>\n",
              "      <td>49405.75</td>\n",
              "      <td>12.120000</td>\n",
              "      <td>121.369643</td>\n",
              "      <td>407.068429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3781</th>\n",
              "      <td>2020-07-31</td>\n",
              "      <td>49253.62</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>124.458338</td>\n",
              "      <td>395.743834</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3743 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     indice_tiempo    merval       GGAL   Dólar CCL  Merval_USD\n",
              "0       2004-10-04   1167.09   6.591819    2.978328  391.860814\n",
              "1       2004-10-05   1135.37   6.246899    3.034935  374.100277\n",
              "2       2004-10-06   1128.00   6.371454    2.930292  384.944613\n",
              "3       2004-10-07   1140.26   6.333129    3.008808  378.973982\n",
              "4       2004-10-08   1128.74   6.285224    2.970494  379.983978\n",
              "...            ...       ...        ...         ...         ...\n",
              "3777    2020-07-27  49449.86  12.770000  118.167571  418.472340\n",
              "3778    2020-07-28  48934.44  12.220000  119.558104  409.294213\n",
              "3779    2020-07-29  48884.46  12.320000  118.465910  412.645800\n",
              "3780    2020-07-30  49405.75  12.120000  121.369643  407.068429\n",
              "3781    2020-07-31  49253.62  12.000000  124.458338  395.743834\n",
              "\n",
              "[3743 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5o2OsjpWWt0S",
        "outputId": "1fd9cddc-7d6e-4f91-c038-d7f619282b4a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        }
      },
      "source": [
        "fig, ax = plt.subplots(figsize=(16,8))\n",
        "\n",
        "x = merval_usd[\"indice_tiempo\"]\n",
        "y = merval_usd[\"Merval_USD\"]\n",
        "ax.set_xtickslabels(x)\n",
        "sns.barplot(x, y, palette=\"inferno\")\n",
        "ax.set_xlabel(\"Tiempo\", fontsize=10)\n",
        "plt.title(\"Merval in USD - Argentina Stock Index\")\n",
        "plt.plot()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NTyCkYQjM5E8"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}