module_analysis.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sC41GqI8ddvX",
        "outputId": "19ba1562-5761-4e07-bc0d-3847d984d3ca"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cFtaSn_APY4F"
      },
      "source": [
        "# **데이터 전처리**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rT-3MlGzsvb0",
        "outputId": "4e77311b-fb3a-44e3-9460-807ed8e1bb3f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
            "100  4685    0  4685    0     0  12107      0 --:--:-- --:--:-- --:--:-- 12137\n",
            "100  517k  100  517k    0     0   600k      0 --:--:-- --:--:-- --:--:-- 3693k\n",
            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
            "100  4737    0  4737    0     0  10690      0 --:--:-- --:--:-- --:--:-- 10693\n",
            "100  392k  100  392k    0     0   479k      0 --:--:-- --:--:-- --:--:-- 1973k\n"
          ]
        }
      ],
      "source": [
        "# TA-Lib 라이브러리를 다운로드하고 설치하는 작업을 수행\n",
        "url = 'https://anaconda.org/conda-forge/libta-lib/0.4.0/download/linux-64/libta-lib-0.4.0-h166bdaf_1.tar.bz2'\n",
        "!curl -L $url | tar xj -C /usr/lib/x86_64-linux-gnu/ lib --strip-components=1\n",
        "url = 'https://anaconda.org/conda-forge/ta-lib/0.4.19/download/linux-64/ta-lib-0.4.19-py310hde88566_4.tar.bz2'\n",
        "!curl -L $url | tar xj -C /usr/local/lib/python3.10/dist-packages/ lib/python3.10/site-packages/talib --strip-components=3"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%cd /content/drive/Othercomputers/내 컴퓨터/BXAI/캡스톤/trending"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v9v1peH3h9tn",
        "outputId": "8314e8d7-fc03-49bb-80e3-1efb0c4f12d7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/Othercomputers/내 컴퓨터/BXAI/캡스톤/trending\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QeFGBftGFpn0"
      },
      "outputs": [],
      "source": [
        "import talib\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "from FNC_02_Preprocessing import FNC_02_Preprocessing\n",
        "from FNC_Func_Extrema import FNC_Func_Extrema\n",
        "from FNC_Func_PST import FNC_Func_PST\n",
        "\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%cd /content/drive/MyDrive/CodeData/Data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G3-STJ8V7BwD",
        "outputId": "0eb09e2c-9454-4714-c4d8-c22e020739c1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/CodeData/Data\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "QKo7bGqvoHsw",
        "outputId": "45144c85-4d07-4526-ab90-318166087e4f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          TRD_DD  ISU_CD ISU_NM TDD_CLSPRC TDD_OPNPRC TDD_HGPRC TDD_LWPRC  \\\n",
              "0     2022/01/11    5930   삼성전자     78,900     78,400    79,000    78,000   \n",
              "1     2022/01/10    5930   삼성전자     78,000     78,100    78,100    77,100   \n",
              "2     2022/01/07    5930   삼성전자     78,300     78,100    78,400    77,400   \n",
              "3     2022/01/06    5930   삼성전자     76,900     76,700    77,600    76,600   \n",
              "4     2022/01/05    5930   삼성전자     77,400     78,800    79,000    76,400   \n",
              "...          ...     ...    ...        ...        ...       ...       ...   \n",
              "6757  1995/05/08    5930   삼성전자    121,000    122,000   122,000   120,500   \n",
              "6758  1995/05/06    5930   삼성전자    122,000    122,000   123,500   122,000   \n",
              "6759  1995/05/04    5930   삼성전자    122,500    124,000   124,500   122,000   \n",
              "6760  1995/05/03    5930   삼성전자    123,500    119,500   126,500   119,500   \n",
              "6761  1995/05/02    5930   삼성전자    119,500    121,000   121,000   118,500   \n",
              "\n",
              "                   MKTCAP  ACC_TRDVOL    EPS    PER     BPS   PBR    DPS  \\\n",
              "0     471,015,843,195,000  13,221,123  3,841  20.54  39,406  2.00  2,994   \n",
              "1     465,643,038,900,000   9,947,422  3,841  20.31  39,406  1.98  2,994   \n",
              "2     467,433,973,665,000  15,163,757  3,841  20.39  39,406  1.99  2,994   \n",
              "3     459,076,278,095,000  12,931,954  3,841  20.02  39,406  1.95  2,994   \n",
              "4     462,061,169,370,000  25,470,640  3,841  20.15  39,406  1.96  2,994   \n",
              "...                   ...         ...    ...    ...     ...   ...    ...   \n",
              "6757    6,578,606,045,000      91,810      -      -       -     -      -   \n",
              "6758    6,632,974,690,000      47,440      -      -       -     -      -   \n",
              "6759    6,660,159,012,500     175,590      -      -       -     -      -   \n",
              "6760    6,714,527,657,500     382,980      -      -       -     -      -   \n",
              "6761    6,497,053,077,500     139,560      -      -       -     -      -   \n",
              "\n",
              "     DVD_YLD  \n",
              "0       3.79  \n",
              "1       3.84  \n",
              "2       3.82  \n",
              "3       3.89  \n",
              "4       3.87  \n",
              "...      ...  \n",
              "6757       -  \n",
              "6758       -  \n",
              "6759       -  \n",
              "6760       -  \n",
              "6761       -  \n",
              "\n",
              "[6762 rows x 15 columns]"
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>TRD_DD</th>\n",
              "      <th>ISU_CD</th>\n",
              "      <th>ISU_NM</th>\n",
              "      <th>TDD_CLSPRC</th>\n",
              "      <th>TDD_OPNPRC</th>\n",
              "      <th>TDD_HGPRC</th>\n",
              "      <th>TDD_LWPRC</th>\n",
              "      <th>MKTCAP</th>\n",
              "      <th>ACC_TRDVOL</th>\n",
              "      <th>EPS</th>\n",
              "      <th>PER</th>\n",
              "      <th>BPS</th>\n",
              "      <th>PBR</th>\n",
              "      <th>DPS</th>\n",
              "      <th>DVD_YLD</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2022/01/11</td>\n",
              "      <td>5930</td>\n",
              "      <td>삼성전자</td>\n",
              "      <td>78,900</td>\n",
              "      <td>78,400</td>\n",
              "      <td>79,000</td>\n",
              "      <td>78,000</td>\n",
              "      <td>471,015,843,195,000</td>\n",
              "      <td>13,221,123</td>\n",
              "      <td>3,841</td>\n",
              "      <td>20.54</td>\n",
              "      <td>39,406</td>\n",
              "      <td>2.00</td>\n",
              "      <td>2,994</td>\n",
              "      <td>3.79</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2022/01/10</td>\n",
              "      <td>5930</td>\n",
              "      <td>삼성전자</td>\n",
              "      <td>78,000</td>\n",
              "      <td>78,100</td>\n",
              "      <td>78,100</td>\n",
              "      <td>77,100</td>\n",
              "      <td>465,643,038,900,000</td>\n",
              "      <td>9,947,422</td>\n",
              "      <td>3,841</td>\n",
              "      <td>20.31</td>\n",
              "      <td>39,406</td>\n",
              "      <td>1.98</td>\n",
              "      <td>2,994</td>\n",
              "      <td>3.84</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2022/01/07</td>\n",
              "      <td>5930</td>\n",
              "      <td>삼성전자</td>\n",
              "      <td>78,300</td>\n",
              "      <td>78,100</td>\n",
              "      <td>78,400</td>\n",
              "      <td>77,400</td>\n",
              "      <td>467,433,973,665,000</td>\n",
              "      <td>15,163,757</td>\n",
              "      <td>3,841</td>\n",
              "      <td>20.39</td>\n",
              "      <td>39,406</td>\n",
              "      <td>1.99</td>\n",
              "      <td>2,994</td>\n",
              "      <td>3.82</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2022/01/06</td>\n",
              "      <td>5930</td>\n",
              "      <td>삼성전자</td>\n",
              "      <td>76,900</td>\n",
              "      <td>76,700</td>\n",
              "      <td>77,600</td>\n",
              "      <td>76,600</td>\n",
              "      <td>459,076,278,095,000</td>\n",
              "      <td>12,931,954</td>\n",
              "      <td>3,841</td>\n",
              "      <td>20.02</td>\n",
              "      <td>39,406</td>\n",
              "      <td>1.95</td>\n",
              "      <td>2,994</td>\n",
              "      <td>3.89</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2022/01/05</td>\n",
              "      <td>5930</td>\n",
              "      <td>삼성전자</td>\n",
              "      <td>77,400</td>\n",
              "      <td>78,800</td>\n",
              "      <td>79,000</td>\n",
              "      <td>76,400</td>\n",
              "      <td>462,061,169,370,000</td>\n",
              "      <td>25,470,640</td>\n",
              "      <td>3,841</td>\n",
              "      <td>20.15</td>\n",
              "      <td>39,406</td>\n",
              "      <td>1.96</td>\n",
              "      <td>2,994</td>\n",
              "      <td>3.87</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6757</th>\n",
              "      <td>1995/05/08</td>\n",
              "      <td>5930</td>\n",
              "      <td>삼성전자</td>\n",
              "      <td>121,000</td>\n",
              "      <td>122,000</td>\n",
              "      <td>122,000</td>\n",
              "      <td>120,500</td>\n",
              "      <td>6,578,606,045,000</td>\n",
              "      <td>91,810</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6758</th>\n",
              "      <td>1995/05/06</td>\n",
              "      <td>5930</td>\n",
              "      <td>삼성전자</td>\n",
              "      <td>122,000</td>\n",
              "      <td>122,000</td>\n",
              "      <td>123,500</td>\n",
              "      <td>122,000</td>\n",
              "      <td>6,632,974,690,000</td>\n",
              "      <td>47,440</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6759</th>\n",
              "      <td>1995/05/04</td>\n",
              "      <td>5930</td>\n",
              "      <td>삼성전자</td>\n",
              "      <td>122,500</td>\n",
              "      <td>124,000</td>\n",
              "      <td>124,500</td>\n",
              "      <td>122,000</td>\n",
              "      <td>6,660,159,012,500</td>\n",
              "      <td>175,590</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
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              "    <tr>\n",
              "      <th>6760</th>\n",
              "      <td>1995/05/03</td>\n",
              "      <td>5930</td>\n",
              "      <td>삼성전자</td>\n",
              "      <td>123,500</td>\n",
              "      <td>119,500</td>\n",
              "      <td>126,500</td>\n",
              "      <td>119,500</td>\n",
              "      <td>6,714,527,657,500</td>\n",
              "      <td>382,980</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6761</th>\n",
              "      <td>1995/05/02</td>\n",
              "      <td>5930</td>\n",
              "      <td>삼성전자</td>\n",
              "      <td>119,500</td>\n",
              "      <td>121,000</td>\n",
              "      <td>121,000</td>\n",
              "      <td>118,500</td>\n",
              "      <td>6,497,053,077,500</td>\n",
              "      <td>139,560</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
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              "<p>6762 rows × 15 columns</p>\n",
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              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-left-color: var(--fill-color);\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    60% {\n",
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              "      border-right-color: var(--fill-color);\n",
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              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-d03f63c9-3ec6-461b-8940-e2e99a4ff1b6 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "#KR7005930003.csv에서 필요로하는 열만 추출\n",
        "dfdf = pd.read_csv('KR7005930003.csv')\n",
        "#dfdf = dfdf[['TRD_DD', 'ISU_CD', 'ISU_NM', 'TDD_CLSPRC', 'TDD_OPNPRC', 'TDD_HGPRC',\n",
        "       #'TDD_LWPRC', 'MKTCAP', 'ACC_TRDVOL']]\n",
        "dfdf"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 534
        },
        "id": "BjCLQqGvpygB",
        "outputId": "fe824ab2-3e5e-4b20-a367-c704713f60e1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " EPS: could not convert string to float: '-'\n",
            " PER: could not convert string to float: '-'\n",
            " BPS: could not convert string to float: '-'\n",
            " PBR: could not convert string to float: '-'\n",
            " DPS: could not convert string to float: '-'\n",
            " DVD_YLD: could not convert string to float: '-'\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          TRD_DD  ISU_CD ISU_NM  TDD_CLSPRC  TDD_OPNPRC  TDD_HGPRC  TDD_LWPRC  \\\n",
              "0     2022/01/11  5930.0   삼성전자     78900.0     78400.0    79000.0    78000.0   \n",
              "1     2022/01/10  5930.0   삼성전자     78000.0     78100.0    78100.0    77100.0   \n",
              "2     2022/01/07  5930.0   삼성전자     78300.0     78100.0    78400.0    77400.0   \n",
              "3     2022/01/06  5930.0   삼성전자     76900.0     76700.0    77600.0    76600.0   \n",
              "4     2022/01/05  5930.0   삼성전자     77400.0     78800.0    79000.0    76400.0   \n",
              "...          ...     ...    ...         ...         ...        ...        ...   \n",
              "6757  1995/05/08  5930.0   삼성전자    121000.0    122000.0   122000.0   120500.0   \n",
              "6758  1995/05/06  5930.0   삼성전자    122000.0    122000.0   123500.0   122000.0   \n",
              "6759  1995/05/04  5930.0   삼성전자    122500.0    124000.0   124500.0   122000.0   \n",
              "6760  1995/05/03  5930.0   삼성전자    123500.0    119500.0   126500.0   119500.0   \n",
              "6761  1995/05/02  5930.0   삼성전자    119500.0    121000.0   121000.0   118500.0   \n",
              "\n",
              "            MKTCAP  ACC_TRDVOL    EPS    PER     BPS   PBR    DPS DVD_YLD  \n",
              "0     4.710158e+14  13221123.0  3,841  20.54  39,406  2.00  2,994    3.79  \n",
              "1     4.656430e+14   9947422.0  3,841  20.31  39,406  1.98  2,994    3.84  \n",
              "2     4.674340e+14  15163757.0  3,841  20.39  39,406  1.99  2,994    3.82  \n",
              "3     4.590763e+14  12931954.0  3,841  20.02  39,406  1.95  2,994    3.89  \n",
              "4     4.620612e+14  25470640.0  3,841  20.15  39,406  1.96  2,994    3.87  \n",
              "...            ...         ...    ...    ...     ...   ...    ...     ...  \n",
              "6757  6.578606e+12     91810.0      -      -       -     -      -       -  \n",
              "6758  6.632975e+12     47440.0      -      -       -     -      -       -  \n",
              "6759  6.660159e+12    175590.0      -      -       -     -      -       -  \n",
              "6760  6.714528e+12    382980.0      -      -       -     -      -       -  \n",
              "6761  6.497053e+12    139560.0      -      -       -     -      -       -  \n",
              "\n",
              "[6762 rows x 15 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0c925e1e-d27a-4174-99e4-d7652349413f\" class=\"colab-df-container\">\n",
              "    <div>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>TRD_DD</th>\n",
              "      <th>ISU_CD</th>\n",
              "      <th>ISU_NM</th>\n",
              "      <th>TDD_CLSPRC</th>\n",
              "      <th>TDD_OPNPRC</th>\n",
              "      <th>TDD_HGPRC</th>\n",
              "      <th>TDD_LWPRC</th>\n",
              "      <th>MKTCAP</th>\n",
              "      <th>ACC_TRDVOL</th>\n",
              "      <th>EPS</th>\n",
              "      <th>PER</th>\n",
              "      <th>BPS</th>\n",
              "      <th>PBR</th>\n",
              "      <th>DPS</th>\n",
              "      <th>DVD_YLD</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2022/01/11</td>\n",
              "      <td>5930.0</td>\n",
              "      <td>삼성전자</td>\n",
              "      <td>78900.0</td>\n",
              "      <td>78400.0</td>\n",
              "      <td>79000.0</td>\n",
              "      <td>78000.0</td>\n",
              "      <td>4.710158e+14</td>\n",
              "      <td>13221123.0</td>\n",
              "      <td>3,841</td>\n",
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              "      <td>39,406</td>\n",
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              "      <td>3.79</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2022/01/10</td>\n",
              "      <td>5930.0</td>\n",
              "      <td>삼성전자</td>\n",
              "      <td>78000.0</td>\n",
              "      <td>78100.0</td>\n",
              "      <td>78100.0</td>\n",
              "      <td>77100.0</td>\n",
              "      <td>4.656430e+14</td>\n",
              "      <td>9947422.0</td>\n",
              "      <td>3,841</td>\n",
              "      <td>20.31</td>\n",
              "      <td>39,406</td>\n",
              "      <td>1.98</td>\n",
              "      <td>2,994</td>\n",
              "      <td>3.84</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2022/01/07</td>\n",
              "      <td>5930.0</td>\n",
              "      <td>삼성전자</td>\n",
              "      <td>78300.0</td>\n",
              "      <td>78100.0</td>\n",
              "      <td>78400.0</td>\n",
              "      <td>77400.0</td>\n",
              "      <td>4.674340e+14</td>\n",
              "      <td>15163757.0</td>\n",
              "      <td>3,841</td>\n",
              "      <td>20.39</td>\n",
              "      <td>39,406</td>\n",
              "      <td>1.99</td>\n",
              "      <td>2,994</td>\n",
              "      <td>3.82</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2022/01/06</td>\n",
              "      <td>5930.0</td>\n",
              "      <td>삼성전자</td>\n",
              "      <td>76900.0</td>\n",
              "      <td>76700.0</td>\n",
              "      <td>77600.0</td>\n",
              "      <td>76600.0</td>\n",
              "      <td>4.590763e+14</td>\n",
              "      <td>12931954.0</td>\n",
              "      <td>3,841</td>\n",
              "      <td>20.02</td>\n",
              "      <td>39,406</td>\n",
              "      <td>1.95</td>\n",
              "      <td>2,994</td>\n",
              "      <td>3.89</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2022/01/05</td>\n",
              "      <td>5930.0</td>\n",
              "      <td>삼성전자</td>\n",
              "      <td>77400.0</td>\n",
              "      <td>78800.0</td>\n",
              "      <td>79000.0</td>\n",
              "      <td>76400.0</td>\n",
              "      <td>4.620612e+14</td>\n",
              "      <td>25470640.0</td>\n",
              "      <td>3,841</td>\n",
              "      <td>20.15</td>\n",
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              "      <td>3.87</td>\n",
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              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>6757</th>\n",
              "      <td>1995/05/08</td>\n",
              "      <td>5930.0</td>\n",
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              "      <td>121000.0</td>\n",
              "      <td>122000.0</td>\n",
              "      <td>122000.0</td>\n",
              "      <td>120500.0</td>\n",
              "      <td>6.578606e+12</td>\n",
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              "      <td>-</td>\n",
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              "      <td>-</td>\n",
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              "    <tr>\n",
              "      <th>6758</th>\n",
              "      <td>1995/05/06</td>\n",
              "      <td>5930.0</td>\n",
              "      <td>삼성전자</td>\n",
              "      <td>122000.0</td>\n",
              "      <td>122000.0</td>\n",
              "      <td>123500.0</td>\n",
              "      <td>122000.0</td>\n",
              "      <td>6.632975e+12</td>\n",
              "      <td>47440.0</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
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              "    <tr>\n",
              "      <th>6759</th>\n",
              "      <td>1995/05/04</td>\n",
              "      <td>5930.0</td>\n",
              "      <td>삼성전자</td>\n",
              "      <td>122500.0</td>\n",
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              "      <td>124500.0</td>\n",
              "      <td>122000.0</td>\n",
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              "    <tr>\n",
              "      <th>6760</th>\n",
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              "      <td>5930.0</td>\n",
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              "      <td>126500.0</td>\n",
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              "    <tr>\n",
              "      <th>6761</th>\n",
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              "<p>6762 rows × 15 columns</p>\n",
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              "        document.querySelector('#df-0c925e1e-d27a-4174-99e4-d7652349413f button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-0c925e1e-d27a-4174-99e4-d7652349413f');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-384c8633-4bfd-414d-bbe1-f6af0b976f0a\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-384c8633-4bfd-414d-bbe1-f6af0b976f0a')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-384c8633-4bfd-414d-bbe1-f6af0b976f0a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "#데이터 타입이 'object'이고 'TRD_DD'와 'ISU_NM'이 아닌 열은 ,를 제거 후 부동 float 타입으로 변환\n",
        "dfdf = dfdf.iloc[:]\n",
        "\n",
        "for column in dfdf.columns:\n",
        "    if dfdf[column].dtype == 'object' and column not in ['TRD_DD', 'ISU_NM']:\n",
        "        try:\n",
        "            dfdf[column] = dfdf[column].str.replace(',', '').astype('float')\n",
        "        except ValueError as e:\n",
        "            print(f\" {column}: {e}\")\n",
        "    elif column not in ['TRD_DD', 'ISU_NM']:\n",
        "        dfdf[column] = dfdf[column].astype('float')\n",
        "\n",
        "dfdf"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dfdf['ISU_NM'][0]"
      ],
      "metadata": {
        "id": "_Qu4rc266ib2",
        "outputId": "26c1f655-6786-410a-9aa5-988538f85e2b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'삼성전자'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tz7H4dnxqBjQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "5bda1c4c-e995-43a3-9cc2-ed9b45c68251"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          TRD_DD  ISU_CD ISU_NM  TDD_CLSPRC  TDD_OPNPRC  TDD_HGPRC  TDD_LWPRC  \\\n",
              "0     2022/01/11  5930.0   삼성전자     78900.0     78400.0    79000.0    78000.0   \n",
              "1     2022/01/10  5930.0   삼성전자     78000.0     78100.0    78100.0    77100.0   \n",
              "2     2022/01/07  5930.0   삼성전자     78300.0     78100.0    78400.0    77400.0   \n",
              "3     2022/01/06  5930.0   삼성전자     76900.0     76700.0    77600.0    76600.0   \n",
              "4     2022/01/05  5930.0   삼성전자     77400.0     78800.0    79000.0    76400.0   \n",
              "...          ...     ...    ...         ...         ...        ...        ...   \n",
              "6757  1995/05/08  5930.0   삼성전자    121000.0    122000.0   122000.0   120500.0   \n",
              "6758  1995/05/06  5930.0   삼성전자    122000.0    122000.0   123500.0   122000.0   \n",
              "6759  1995/05/04  5930.0   삼성전자    122500.0    124000.0   124500.0   122000.0   \n",
              "6760  1995/05/03  5930.0   삼성전자    123500.0    119500.0   126500.0   119500.0   \n",
              "6761  1995/05/02  5930.0   삼성전자    119500.0    121000.0   121000.0   118500.0   \n",
              "\n",
              "            MKTCAP  ACC_TRDVOL  \n",
              "0     4.710158e+14  13221123.0  \n",
              "1     4.656430e+14   9947422.0  \n",
              "2     4.674340e+14  15163757.0  \n",
              "3     4.590763e+14  12931954.0  \n",
              "4     4.620612e+14  25470640.0  \n",
              "...            ...         ...  \n",
              "6757  6.578606e+12     91810.0  \n",
              "6758  6.632975e+12     47440.0  \n",
              "6759  6.660159e+12    175590.0  \n",
              "6760  6.714528e+12    382980.0  \n",
              "6761  6.497053e+12    139560.0  \n",
              "\n",
              "[6762 rows x 9 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f7f8a569-c0e2-4bea-8f4f-1aef206d652d\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>TRD_DD</th>\n",
              "      <th>ISU_CD</th>\n",
              "      <th>ISU_NM</th>\n",
              "      <th>TDD_CLSPRC</th>\n",
              "      <th>TDD_OPNPRC</th>\n",
              "      <th>TDD_HGPRC</th>\n",
              "      <th>TDD_LWPRC</th>\n",
              "      <th>MKTCAP</th>\n",
              "      <th>ACC_TRDVOL</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2022/01/11</td>\n",
              "      <td>5930.0</td>\n",
              "      <td>삼성전자</td>\n",
              "      <td>78900.0</td>\n",
              "      <td>78400.0</td>\n",
              "      <td>79000.0</td>\n",
              "      <td>78000.0</td>\n",
              "      <td>4.710158e+14</td>\n",
              "      <td>13221123.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2022/01/10</td>\n",
              "      <td>5930.0</td>\n",
              "      <td>삼성전자</td>\n",
              "      <td>78000.0</td>\n",
              "      <td>78100.0</td>\n",
              "      <td>78100.0</td>\n",
              "      <td>77100.0</td>\n",
              "      <td>4.656430e+14</td>\n",
              "      <td>9947422.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2022/01/07</td>\n",
              "      <td>5930.0</td>\n",
              "      <td>삼성전자</td>\n",
              "      <td>78300.0</td>\n",
              "      <td>78100.0</td>\n",
              "      <td>78400.0</td>\n",
              "      <td>77400.0</td>\n",
              "      <td>4.674340e+14</td>\n",
              "      <td>15163757.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2022/01/06</td>\n",
              "      <td>5930.0</td>\n",
              "      <td>삼성전자</td>\n",
              "      <td>76900.0</td>\n",
              "      <td>76700.0</td>\n",
              "      <td>77600.0</td>\n",
              "      <td>76600.0</td>\n",
              "      <td>4.590763e+14</td>\n",
              "      <td>12931954.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2022/01/05</td>\n",
              "      <td>5930.0</td>\n",
              "      <td>삼성전자</td>\n",
              "      <td>77400.0</td>\n",
              "      <td>78800.0</td>\n",
              "      <td>79000.0</td>\n",
              "      <td>76400.0</td>\n",
              "      <td>4.620612e+14</td>\n",
              "      <td>25470640.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6757</th>\n",
              "      <td>1995/05/08</td>\n",
              "      <td>5930.0</td>\n",
              "      <td>삼성전자</td>\n",
              "      <td>121000.0</td>\n",
              "      <td>122000.0</td>\n",
              "      <td>122000.0</td>\n",
              "      <td>120500.0</td>\n",
              "      <td>6.578606e+12</td>\n",
              "      <td>91810.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6758</th>\n",
              "      <td>1995/05/06</td>\n",
              "      <td>5930.0</td>\n",
              "      <td>삼성전자</td>\n",
              "      <td>122000.0</td>\n",
              "      <td>122000.0</td>\n",
              "      <td>123500.0</td>\n",
              "      <td>122000.0</td>\n",
              "      <td>6.632975e+12</td>\n",
              "      <td>47440.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6759</th>\n",
              "      <td>1995/05/04</td>\n",
              "      <td>5930.0</td>\n",
              "      <td>삼성전자</td>\n",
              "      <td>122500.0</td>\n",
              "      <td>124000.0</td>\n",
              "      <td>124500.0</td>\n",
              "      <td>122000.0</td>\n",
              "      <td>6.660159e+12</td>\n",
              "      <td>175590.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6760</th>\n",
              "      <td>1995/05/03</td>\n",
              "      <td>5930.0</td>\n",
              "      <td>삼성전자</td>\n",
              "      <td>123500.0</td>\n",
              "      <td>119500.0</td>\n",
              "      <td>126500.0</td>\n",
              "      <td>119500.0</td>\n",
              "      <td>6.714528e+12</td>\n",
              "      <td>382980.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6761</th>\n",
              "      <td>1995/05/02</td>\n",
              "      <td>5930.0</td>\n",
              "      <td>삼성전자</td>\n",
              "      <td>119500.0</td>\n",
              "      <td>121000.0</td>\n",
              "      <td>121000.0</td>\n",
              "      <td>118500.0</td>\n",
              "      <td>6.497053e+12</td>\n",
              "      <td>139560.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>6762 rows × 9 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f7f8a569-c0e2-4bea-8f4f-1aef206d652d')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
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              "\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-f7f8a569-c0e2-4bea-8f4f-1aef206d652d button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-f7f8a569-c0e2-4bea-8f4f-1aef206d652d');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-e7e53d74-4ecc-44e1-9695-94305694e6f9\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e7e53d74-4ecc-44e1-9695-94305694e6f9')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-e7e53d74-4ecc-44e1-9695-94305694e6f9 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "df =pd.concat([dfdf['TRD_DD'],dfdf['ISU_CD'],dfdf['ISU_NM'],dfdf['TDD_CLSPRC'],dfdf['TDD_OPNPRC'],dfdf['TDD_HGPRC'],dfdf['TDD_LWPRC'],dfdf['MKTCAP'],dfdf['ACC_TRDVOL']],axis=1)\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "LcwS1YzsM3Ib",
        "outputId": "32fc5f0e-1079-4e46-f1b0-3b7e3b1edcd7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                Open      High       Low     Close      Volume     Marketcap\n",
              "Date                                                                        \n",
              "1995-05-02  121000.0  121000.0  118500.0  119500.0    139560.0  6.497053e+12\n",
              "1995-05-03  119500.0  126500.0  119500.0  123500.0    382980.0  6.714528e+12\n",
              "1995-05-04  124000.0  124500.0  122000.0  122500.0    175590.0  6.660159e+12\n",
              "1995-05-06  122000.0  123500.0  122000.0  122000.0     47440.0  6.632975e+12\n",
              "1995-05-08  122000.0  122000.0  120500.0  121000.0     91810.0  6.578606e+12\n",
              "...              ...       ...       ...       ...         ...           ...\n",
              "2022-01-05   78800.0   79000.0   76400.0   77400.0  25470640.0  4.620612e+14\n",
              "2022-01-06   76700.0   77600.0   76600.0   76900.0  12931954.0  4.590763e+14\n",
              "2022-01-07   78100.0   78400.0   77400.0   78300.0  15163757.0  4.674340e+14\n",
              "2022-01-10   78100.0   78100.0   77100.0   78000.0   9947422.0  4.656430e+14\n",
              "2022-01-11   78400.0   79000.0   78000.0   78900.0  13221123.0  4.710158e+14\n",
              "\n",
              "[6762 rows x 6 columns]"
            ],
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              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Marketcap</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",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1995-05-02</th>\n",
              "      <td>121000.0</td>\n",
              "      <td>121000.0</td>\n",
              "      <td>118500.0</td>\n",
              "      <td>119500.0</td>\n",
              "      <td>139560.0</td>\n",
              "      <td>6.497053e+12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1995-05-03</th>\n",
              "      <td>119500.0</td>\n",
              "      <td>126500.0</td>\n",
              "      <td>119500.0</td>\n",
              "      <td>123500.0</td>\n",
              "      <td>382980.0</td>\n",
              "      <td>6.714528e+12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1995-05-04</th>\n",
              "      <td>124000.0</td>\n",
              "      <td>124500.0</td>\n",
              "      <td>122000.0</td>\n",
              "      <td>122500.0</td>\n",
              "      <td>175590.0</td>\n",
              "      <td>6.660159e+12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1995-05-06</th>\n",
              "      <td>122000.0</td>\n",
              "      <td>123500.0</td>\n",
              "      <td>122000.0</td>\n",
              "      <td>122000.0</td>\n",
              "      <td>47440.0</td>\n",
              "      <td>6.632975e+12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1995-05-08</th>\n",
              "      <td>122000.0</td>\n",
              "      <td>122000.0</td>\n",
              "      <td>120500.0</td>\n",
              "      <td>121000.0</td>\n",
              "      <td>91810.0</td>\n",
              "      <td>6.578606e+12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-01-05</th>\n",
              "      <td>78800.0</td>\n",
              "      <td>79000.0</td>\n",
              "      <td>76400.0</td>\n",
              "      <td>77400.0</td>\n",
              "      <td>25470640.0</td>\n",
              "      <td>4.620612e+14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-01-06</th>\n",
              "      <td>76700.0</td>\n",
              "      <td>77600.0</td>\n",
              "      <td>76600.0</td>\n",
              "      <td>76900.0</td>\n",
              "      <td>12931954.0</td>\n",
              "      <td>4.590763e+14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-01-07</th>\n",
              "      <td>78100.0</td>\n",
              "      <td>78400.0</td>\n",
              "      <td>77400.0</td>\n",
              "      <td>78300.0</td>\n",
              "      <td>15163757.0</td>\n",
              "      <td>4.674340e+14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-01-10</th>\n",
              "      <td>78100.0</td>\n",
              "      <td>78100.0</td>\n",
              "      <td>77100.0</td>\n",
              "      <td>78000.0</td>\n",
              "      <td>9947422.0</td>\n",
              "      <td>4.656430e+14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-01-11</th>\n",
              "      <td>78400.0</td>\n",
              "      <td>79000.0</td>\n",
              "      <td>78000.0</td>\n",
              "      <td>78900.0</td>\n",
              "      <td>13221123.0</td>\n",
              "      <td>4.710158e+14</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>6762 rows × 6 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cdfe3a33-0328-4305-abe5-1189e593e70a')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-cdfe3a33-0328-4305-abe5-1189e593e70a button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-cdfe3a33-0328-4305-abe5-1189e593e70a');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
              "\n",
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              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
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              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
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              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-e7186e62-9428-4128-b172-651dec37ed28 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "df = dfdf.sort_values(by=['TRD_DD'])\n",
        "df.reset_index(drop=True,inplace=True)\n",
        "\n",
        "#인풋 데이터 모양 맞춰주기(backtest에 들어갈 데이터 모양)\n",
        "df_bt = df[['TRD_DD','TDD_OPNPRC','TDD_HGPRC','TDD_LWPRC','TDD_CLSPRC', 'ACC_TRDVOL', 'MKTCAP']].copy()\n",
        "df_bt['TRD_DD'] = pd.to_datetime(df_bt['TRD_DD'])\n",
        "df_bt.rename(columns={'TRD_DD':'Date', 'TDD_OPNPRC':'Open', 'TDD_HGPRC':'High','TDD_LWPRC':'Low','TDD_CLSPRC':'Close', 'ACC_TRDVOL':'Volume', 'MKTCAP':'Marketcap'}, inplace=True)\n",
        "df_bt.set_index('Date',drop=True,inplace=True)\n",
        "\n",
        "def bt_data(data_bt):\n",
        "    check_dtype = data_bt.dtype == 'object'\n",
        "    if (check_dtype):\n",
        "        return data_bt.str.replace(',','').astype('float')\n",
        "    else :\n",
        "        return data_bt.astype('float')\n",
        "\n",
        "\n",
        "df_bt['Open'] = bt_data(df_bt['Open'])\n",
        "df_bt['High'] = bt_data(df_bt['High'])\n",
        "df_bt['Low'] = bt_data(df_bt['Low'])\n",
        "df_bt['Close'] = bt_data(df_bt['Close'])\n",
        "df_bt['Volume'] = bt_data(df_bt['Volume'])\n",
        "df_bt['Marketcap'] = bt_data(df_bt['Marketcap'])\n",
        "df_bt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "35tvkov3eF57",
        "outputId": "cd9cd616-68f6-4e17-a2be-5080f63e0a97"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting backtrader\n",
            "  Downloading backtrader-1.9.78.123-py2.py3-none-any.whl (419 kB)\n",
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/419.5 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m92.2/419.5 kB\u001b[0m \u001b[31m2.6 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m419.5/419.5 kB\u001b[0m \u001b[31m6.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: backtrader\n",
            "Successfully installed backtrader-1.9.78.123\n"
          ]
        }
      ],
      "source": [
        "pip install backtrader"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g5p_VyvOK2lc",
        "outputId": "e7ab5715-437e-423c-886e-4ec45e7aecdf"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting pygad\n",
            "  Downloading pygad-3.2.0-py3-none-any.whl (80 kB)\n",
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/80.8 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━\u001b[0m \u001b[32m71.7/80.8 kB\u001b[0m \u001b[31m1.9 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m80.8/80.8 kB\u001b[0m \u001b[31m1.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: cloudpickle in /usr/local/lib/python3.10/dist-packages (from pygad) (2.2.1)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from pygad) (3.7.1)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from pygad) (1.23.5)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pygad) (1.1.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pygad) (0.11.0)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pygad) (4.42.1)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pygad) (1.4.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pygad) (23.1)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pygad) (9.4.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pygad) (3.1.1)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pygad) (2.8.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib->pygad) (1.16.0)\n",
            "Installing collected packages: pygad\n",
            "Successfully installed pygad-3.2.0\n"
          ]
        }
      ],
      "source": [
        "pip install pygad"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pygad\n",
        "import backtrader\n",
        "import pandas as pd\n",
        "import talib\n",
        "\n",
        "class Trading:\n",
        "    def __init__(self, df):\n",
        "        self.df = df.copy()\n",
        "        self.initial_capital = 100000.0\n",
        "\n",
        "    def rsi(self,ga_instance, solution, solution_idx ):\n",
        "        period, rsi_low, rsi_high = solution\n",
        "        self.df['rsi'] = talib.RSI(self.df['Marketcap'], timeperiod=int(period))\n",
        "        self.df['signal'] = np.where(self.df['rsi'] < rsi_low, 1, np.where(self.df['rsi'] > rsi_high, -1, 0))\n",
        "        return self.trade_logic()\n",
        "\n",
        "    def sma_cross(self,ga_instance, solution, solution_idx):\n",
        "        pfast, pslow = solution\n",
        "        self.df['sma1'] = talib.SMA(self.df['Marketcap'], timeperiod=int(pfast))\n",
        "        self.df['sma2'] = talib.SMA(self.df['Marketcap'], timeperiod=int(pslow))\n",
        "        self.df['signal'] = np.where(self.df['sma1'] > self.df['sma2'], 1, -1)\n",
        "        return self.trade_logic()\n",
        "\n",
        "    def ema_cross(self, ga_instance, solution, solution_idx):\n",
        "        pfast, pslow=solution\n",
        "        self.df['ema1'] = talib.EMA(self.df['Marketcap'], timeperiod=int(pfast))\n",
        "        self.df['ema2'] = talib.EMA(self.df['Marketcap'], timeperiod=int(pslow))\n",
        "        self.df['signal'] = np.where(self.df['ema1'] > self.df['ema2'], 1, -1)\n",
        "        return self.trade_logic()\n",
        "\n",
        "    def roc(self,ga_instance, solution, solution_idx):\n",
        "        period = solution\n",
        "        self.df['roc'] = talib.ROC(self.df['Marketcap'], timeperiod=int(period))\n",
        "        self.df['signal'] = np.where(self.df['roc'] > 0, 1, -1)\n",
        "        return self.trade_logic()\n",
        "\n",
        "    def stochastic(self,ga_instance, solution, solution_idx):\n",
        "        period, d_period, overbought, oversold = solution\n",
        "\n",
        "        k, d = talib.STOCH(self.df['High']*self.df['Marketcap']/self.df['Close'], self.df['Low']*self.df['Marketcap']/self.df['Close'], self.df['Marketcap'], fastk_period=int(period), slowk_period=int(d_period))\n",
        "        self.df['signal'] = np.where(k < oversold, 1, np.where(k > overbought, -1, 0))\n",
        "        return self.trade_logic()\n",
        "\n",
        "    def trade_logic(self):\n",
        "        shares = 0\n",
        "        for i in range(1, len(self.df)):\n",
        "            if self.df['signal'].iloc[i] == 1 and self.initial_capital > 0:\n",
        "                if self.df['Marketcap'].iloc[i] <= self.initial_capital:\n",
        "                    shares = self.initial_capital / self.df['Marketcap'].iloc[i]\n",
        "                    self.initial_capital =0.0\n",
        "            elif self.df['signal'].iloc[i] == -1 and shares > 0:\n",
        "                self.initial_capital += shares * self.df['Marketcap'].iloc[i]\n",
        "                shares = 0.0\n",
        "\n",
        "        final_portfolio_value = self.initial_capital + shares * self.df['Marketcap'].iloc[-1]\n",
        "        return_rate = (final_portfolio_value - self.initial_capital) / self.initial_capital\n",
        "\n",
        "        return final_portfolio_value\n"
      ],
      "metadata": {
        "id": "RMvOkDVx4vYP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import talib\n",
        "import numpy as np\n",
        "\n",
        "class SHOW_SIGNAL:\n",
        "    def __init__(self, df):\n",
        "        self.df = df.copy()\n",
        "\n",
        "    def rsi(self, period, rsi_low, rsi_high):\n",
        "        self.df['rsi'] = talib.RSI(self.df['Marketcap'], timeperiod=int(period))\n",
        "        self.df['signal'] = np.where(self.df['rsi'] < rsi_low, 1, np.where(self.df['rsi'] > rsi_high, -1, 0))\n",
        "        return self.trade_logic()\n",
        "\n",
        "    def sma_cross(self, pfast, pslow):\n",
        "        self.df['sma1'] = talib.SMA(self.df['Marketcap'], timeperiod=int(pfast))\n",
        "        self.df['sma2'] = talib.SMA(self.df['Marketcap'], timeperiod=int(pslow))\n",
        "        self.df['signal'] = np.where(self.df['sma1'] > self.df['sma2'], 1, -1)\n",
        "        return self.trade_logic()\n",
        "\n",
        "    def ema_cross(self, pfast, pslow):\n",
        "        self.df['ema1'] = talib.EMA(self.df['Marketcap'], timeperiod=int(pfast))\n",
        "        self.df['ema2'] = talib.EMA(self.df['Marketcap'], timeperiod=int(pslow))\n",
        "        self.df['signal'] = np.where(self.df['ema1'] > self.df['ema2'], 1, -1)\n",
        "        return self.trade_logic()\n",
        "\n",
        "    def roc(self, period):\n",
        "        self.df['roc'] = talib.ROC(self.df['Marketcap'], timeperiod=int(period))\n",
        "        self.df['signal'] = np.where(self.df['roc'] > 0, 1, -1)\n",
        "        return self.trade_logic()\n",
        "\n",
        "    def stochastic(self, period, d_period, overbought, oversold):\n",
        "        k, d = talib.STOCH(self.df['High']*self.df['Marketcap']/self.df['Close'], self.df['Low']*self.df['Marketcap']/self.df['Close'], self.df['Marketcap'], fastk_period=int(period), slowk_period=int(d_period))\n",
        "        self.df['signal'] = np.where(k < oversold, 1, np.where(k > overbought, -1, 0))\n",
        "        return self.trade_logic()\n",
        "\n",
        "    def trade_logic(self, initial_capital=100000.0):\n",
        "        shares = 0\n",
        "        for i in range(1, len(self.df)):\n",
        "            if self.df['signal'].iloc[i] == 1 and initial_capital > 0:\n",
        "                if self.df['Marketcap'].iloc[i] <= initial_capital:\n",
        "                    shares = initial_capital / self.df['Marketcap'].iloc[i]\n",
        "                    initial_capital =0.0\n",
        "            elif self.df['signal'].iloc[i] == -1 and shares > 0:\n",
        "                initial_capital += shares * self.df['Marketcap'].iloc[i]\n",
        "                shares = 0.0\n",
        "\n",
        "        final_portfolio_value = initial_capital + shares * self.df['Marketcap'].iloc[-1]\n",
        "        return_rate = (final_portfolio_value - initial_capital) / initial_capital\n",
        "\n",
        "        return return_rate"
      ],
      "metadata": {
        "id": "HJhBHbkm7fnC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def genetic(g_space,Trading,n_gen):\n",
        "    ga_instance_rsi = pygad.GA(\n",
        "        num_generations=3,\n",
        "        num_parents_mating=10,\n",
        "        fitness_func=Trading,  # RSI 전략의 적합도 함수\n",
        "        sol_per_pop=100,\n",
        "        num_genes=n_gen,\n",
        "        gene_space=g_space,\n",
        "        parent_selection_type=\"sss\",\n",
        "        keep_parents=2,\n",
        "        crossover_type=\"single_point\",\n",
        "        mutation_type=\"random\",\n",
        "        mutation_percent_genes=10,\n",
        "        mutation_num_genes=1\n",
        "    )\n",
        "\n",
        "    ga_instance_rsi.run()\n",
        "\n",
        "    solution, solution_fitness, solution_idx = ga_instance_rsi.best_solution()\n",
        "    return solution, solution_fitness\n",
        "    print(\"Parameters of the best solution for RSI strategy: {solution}\".format(solution=solution))\n",
        "    print(\"Fitness value of the best solution for RSI strategy = {solution_fitness}\".format(solution_fitness=solution_fitness))\n"
      ],
      "metadata": {
        "id": "8JGSkCy9Jlxx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_bt"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "ArTm_Qed0cb_",
        "outputId": "345c8a04-36de-494d-97a9-882dcc174754"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                Open      High       Low     Close      Volume     Marketcap\n",
              "Date                                                                        \n",
              "1995-05-02  121000.0  121000.0  118500.0  119500.0    139560.0  6.497053e+12\n",
              "1995-05-03  119500.0  126500.0  119500.0  123500.0    382980.0  6.714528e+12\n",
              "1995-05-04  124000.0  124500.0  122000.0  122500.0    175590.0  6.660159e+12\n",
              "1995-05-06  122000.0  123500.0  122000.0  122000.0     47440.0  6.632975e+12\n",
              "1995-05-08  122000.0  122000.0  120500.0  121000.0     91810.0  6.578606e+12\n",
              "...              ...       ...       ...       ...         ...           ...\n",
              "2022-01-05   78800.0   79000.0   76400.0   77400.0  25470640.0  4.620612e+14\n",
              "2022-01-06   76700.0   77600.0   76600.0   76900.0  12931954.0  4.590763e+14\n",
              "2022-01-07   78100.0   78400.0   77400.0   78300.0  15163757.0  4.674340e+14\n",
              "2022-01-10   78100.0   78100.0   77100.0   78000.0   9947422.0  4.656430e+14\n",
              "2022-01-11   78400.0   79000.0   78000.0   78900.0  13221123.0  4.710158e+14\n",
              "\n",
              "[6762 rows x 6 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1c8d956b-fd50-437b-9e62-4e26c3876b05\" class=\"colab-df-container\">\n",
              "    <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",
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              "\n",
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              "        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>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Marketcap</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",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1995-05-02</th>\n",
              "      <td>121000.0</td>\n",
              "      <td>121000.0</td>\n",
              "      <td>118500.0</td>\n",
              "      <td>119500.0</td>\n",
              "      <td>139560.0</td>\n",
              "      <td>6.497053e+12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1995-05-03</th>\n",
              "      <td>119500.0</td>\n",
              "      <td>126500.0</td>\n",
              "      <td>119500.0</td>\n",
              "      <td>123500.0</td>\n",
              "      <td>382980.0</td>\n",
              "      <td>6.714528e+12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1995-05-04</th>\n",
              "      <td>124000.0</td>\n",
              "      <td>124500.0</td>\n",
              "      <td>122000.0</td>\n",
              "      <td>122500.0</td>\n",
              "      <td>175590.0</td>\n",
              "      <td>6.660159e+12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1995-05-06</th>\n",
              "      <td>122000.0</td>\n",
              "      <td>123500.0</td>\n",
              "      <td>122000.0</td>\n",
              "      <td>122000.0</td>\n",
              "      <td>47440.0</td>\n",
              "      <td>6.632975e+12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1995-05-08</th>\n",
              "      <td>122000.0</td>\n",
              "      <td>122000.0</td>\n",
              "      <td>120500.0</td>\n",
              "      <td>121000.0</td>\n",
              "      <td>91810.0</td>\n",
              "      <td>6.578606e+12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-01-05</th>\n",
              "      <td>78800.0</td>\n",
              "      <td>79000.0</td>\n",
              "      <td>76400.0</td>\n",
              "      <td>77400.0</td>\n",
              "      <td>25470640.0</td>\n",
              "      <td>4.620612e+14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-01-06</th>\n",
              "      <td>76700.0</td>\n",
              "      <td>77600.0</td>\n",
              "      <td>76600.0</td>\n",
              "      <td>76900.0</td>\n",
              "      <td>12931954.0</td>\n",
              "      <td>4.590763e+14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-01-07</th>\n",
              "      <td>78100.0</td>\n",
              "      <td>78400.0</td>\n",
              "      <td>77400.0</td>\n",
              "      <td>78300.0</td>\n",
              "      <td>15163757.0</td>\n",
              "      <td>4.674340e+14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-01-10</th>\n",
              "      <td>78100.0</td>\n",
              "      <td>78100.0</td>\n",
              "      <td>77100.0</td>\n",
              "      <td>78000.0</td>\n",
              "      <td>9947422.0</td>\n",
              "      <td>4.656430e+14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-01-11</th>\n",
              "      <td>78400.0</td>\n",
              "      <td>79000.0</td>\n",
              "      <td>78000.0</td>\n",
              "      <td>78900.0</td>\n",
              "      <td>13221123.0</td>\n",
              "      <td>4.710158e+14</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>6762 rows × 6 columns</p>\n",
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              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\"rsi\", \"sma_cross\", \"roc\", \"ema_cross\","
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BXPObxEW2Q_U",
        "outputId": "2124185b-ddab-4153-caa7-2c02c94f4a4e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('rsi', 'sma_cross', 'roc', 'ema_cross')"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def get_signals(df):\n",
        "    trading = Trading(df)\n",
        "\n",
        "    strategies = {\n",
        "        \"rsi\": {\"period\": 14, \"rsi_low\": 30, \"rsi_high\": 70},\n",
        "        \"sma_cross\": {\"pfast\": 20, \"pslow\": 60},\n",
        "        \"ema_cross\": {\"pfast\": 20, \"pslow\": 60},\n",
        "        \"roc\": {\"period\": 10},\n",
        "        \"stochastic\": {\"period\": 14, \"d_period\": 3, \"overbought\": 70, \"oversold\": 30}\n",
        "    }\n",
        "    for i in [\"rsi\", \"sma_cross\", \"roc\", \"ema_cross\", \"stochastic\"]:\n",
        "\n",
        "        if i == \"rsi\":\n",
        "            g_space = [range(2, 100), range(1, 100), range(1, 100)]\n",
        "            rsi_sol, rsi_fitness = genetic(g_space, trading.rsi, 3)\n",
        "            strategies['rsi'] = {\"period\": rsi_sol[0], \"rsi_low\": rsi_sol[1], \"rsi_high\": rsi_sol[2]}\n",
        "\n",
        "        elif i ==  \"sma_cross\":\n",
        "            g_space = [range(2, 100), range(2, 100)]\n",
        "            sma_sol, sma_fitness = genetic(g_space, trading.sma_cross, 2)\n",
        "            strategies['sma_cross'] = {\"pfast\": sma_sol[0], \"pslow\": sma_sol[1]}\n",
        "\n",
        "        elif i == \"roc\":\n",
        "            g_space = [range(2, 100)]\n",
        "            roc_sol, roc_fitness = genetic(g_space, trading.roc, 1)\n",
        "            strategies['roc'] = {\"period\": roc_sol[0]}\n",
        "\n",
        "        elif i == \"ema_cross\":\n",
        "            g_space = [range(2, 100), range(2, 100)]\n",
        "            ema_sol, ema_fitness = genetic(g_space, trading.ema_cross,2)\n",
        "            strategies['ema_cross'] = {\"pfast\": ema_sol[0], \"pslow\": ema_sol[1]}\n",
        "\n",
        "        elif i == \"stochastic\":\n",
        "            g_space = [range(2, 100),range(2, 100), range(2, 100), range(2, 100)]\n",
        "            sto_sol, sto_fitness = genetic(g_space, trading.stochastic,4)\n",
        "            strategies[\"stochastic\"] = {\"period\": sto_sol[0], \"d_period\": sto_sol[1], \"overbought\": sto_sol[2], \"oversold\": sto_sol[3]}\n",
        "\n",
        "    signal_dataframes = pd.DataFrame()\n",
        "    show_sig = SHOW_SIGNAL(df)\n",
        "    for name, params in strategies.items():\n",
        "        show_sig.df = df.copy()\n",
        "        getattr(show_sig, name)(**params)\n",
        "        signal_dataframes[name] = show_sig.df[['signal']].copy()\n",
        "\n",
        "    return signal_dataframes\n",
        "\n",
        "signal_dataframes = get_signals(df_bt)\n",
        "\n",
        "data = pd.DataFrame()\n",
        "scaler = MinMaxScaler(feature_range=(0, 1))\n",
        "result = scaler.fit_transform(np.array(df_bt['Marketcap']).reshape(-1,1))\n",
        "data['A2'] = pd.DataFrame(result)\n",
        "\n",
        "# PST 파라매터 설정\n",
        "T=5  # time interval\n",
        "P=0.05  # percentage\n",
        "if data['A2'].size == 0 or len(data['A2']) <= 10:\n",
        "    data['PST'] = []\n",
        "    data['nPST'] = 0\n",
        "else:\n",
        "    data['PST'], data['nPST'] = FNC_Func_PST(T, P, data['A2'])\n",
        "\n",
        "signal_dataframes = signal_dataframes.reset_index()\n",
        "signal_dataframes['Trend'] =data['PST']\n",
        "\n",
        "signal_dataframes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BNC5Xk3uGQTN",
        "outputId": "fa97495d-6119-4f75-f99e-02beb6a7db4e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Date  rsi  sma_cross  ema_cross  roc  stochastic  Trend\n",
              "0    1995-05-02    0         -1         -1   -1           0    0.0\n",
              "1    1995-05-03    0         -1         -1   -1           0    0.0\n",
              "2    1995-05-04    0         -1         -1   -1           0    0.0\n",
              "3    1995-05-06    0         -1         -1   -1           0    0.0\n",
              "4    1995-05-08    0         -1         -1   -1           0    0.0\n",
              "...         ...  ...        ...        ...  ...         ...    ...\n",
              "6757 2022-01-05    1         -1          1    1           0    0.0\n",
              "6758 2022-01-06    1         -1          1    1           0    0.0\n",
              "6759 2022-01-07    1         -1          1    1           0    0.0\n",
              "6760 2022-01-10    1         -1          1    1           0    0.0\n",
              "6761 2022-01-11    1         -1          1    1           0    0.0\n",
              "\n",
              "[6762 rows x 7 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d9fd554c-9029-40a5-800d-30d096300047\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>rsi</th>\n",
              "      <th>sma_cross</th>\n",
              "      <th>ema_cross</th>\n",
              "      <th>roc</th>\n",
              "      <th>stochastic</th>\n",
              "      <th>Trend</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1995-05-02</td>\n",
              "      <td>0</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1995-05-03</td>\n",
              "      <td>0</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1995-05-04</td>\n",
              "      <td>0</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1995-05-06</td>\n",
              "      <td>0</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1995-05-08</td>\n",
              "      <td>0</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6757</th>\n",
              "      <td>2022-01-05</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6758</th>\n",
              "      <td>2022-01-06</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6759</th>\n",
              "      <td>2022-01-07</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6760</th>\n",
              "      <td>2022-01-10</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6761</th>\n",
              "      <td>2022-01-11</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>6762 rows × 7 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d9fd554c-9029-40a5-800d-30d096300047')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-d9fd554c-9029-40a5-800d-30d096300047 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-d9fd554c-9029-40a5-800d-30d096300047');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-837acb09-218f-4042-ad01-61b182bc9b89\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-837acb09-218f-4042-ad01-61b182bc9b89')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-837acb09-218f-4042-ad01-61b182bc9b89 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(20,5))\n",
        "plt.subplot(1,2,1)\n",
        "data['A2'].plot()\n",
        "plt.subplot(1,2,2)\n",
        "signal_dataframes['Trend'].plot()"
      ],
      "metadata": {
        "id": "eEzG_id2f_jF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bc9d7103-ced7-44f1-c453-72825eb64cee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x500 with 2 Axes>"
            ],
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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,5))\n",
        "plt.plot(df_bt['Marketcap'])\n",
        "plt.title('Marketcap Value Over Time')\n",
        "plt.xlabel('Date')\n",
        "plt.ylabel('Value')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6ldow47YLy-Q",
        "outputId": "a4dc0388-e7b7-4697-8ac4-f7e93d79fcd6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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hUODrr78GALz11ltmPatMJsPYsWPFf4N1a+lY4/Pt27cvYmJiMGnSJLz88suQy+X46aefEBYWhsTERLFeQEAAFi5ciMceewytW7fG6NGjxTrr1q1D586djQaOOiEhIfjzzz8xaNAgtG7dGk888QSaNGmC5ORkLF68GJcvX8aCBQuMLpQ6atQovPnmm/Dy8sKkSZMMFjZ9//33sW3bNrRv3x5PPvkkmjRpgrt37+Lo0aPYvHkz7t69W+Zn7Ghc8ZmIyEnZKSudTQEQVq9eLe6vXbtWACD4+vpKftzd3YWRI0canD9+/HjhwQcflJSp1Wqhbdu2wsKFC8Wy2bNnl5kalYgcn36K7NIYS5H9448/CvXr1xcUCoXQqFEjYdGiRUbTMwMQpkyZYnBN/RTZ+r7++msBgDBjxgyxbOXKlUKXLl3Ef8MaNWokTJkyRbhw4YJYJzs7Wxg7dqwQFBQkAJCky87NzRVmzZol1KlTR/Dw8BCqV68uPPzww8KVK1fK/Ty//fabWL9Vq1aSdMrmOHPmjABAUCgUwr179wyOm9uekimyBUEQjhw5IrRv317w9PQUYmJihPnz5xukyNbZtm2b0K9fPyEwMFDw8vIS6tatK0yYMEGSArw08fHxwpNPPinExMQIHh4eQmhoqPDAAw8Iu3btMnnOpUuXxFTju3fvNlonJSVFmDJlihAdHS2+s969ewvfffedpO0AhBUrVpjVVn3lSZHdtGlTg7rG/m4IgvE/9+Y8ExGRrckEocTYChcgk8kk2eGWLVuGRx55BGfOnDEYouHn52cwGXbChAlIT0+XDLlIT09HtWrVJOdrtVoIggC5XI7//vvPZHYhIiJXI5PJMGXKlFJ7SYiIiBxVlRjT1apVK2g0GqSmpqJr167lukZAQABOnTolKfv666+xdetW/Pnnn5WWmpSIiIiIiCrGZYKg7OxsXL58WdyPj4/H8ePHERwcjAYNGuCRRx7BuHHj8Mknn6BVq1a4ffs2tmzZghYtWmDQoEEAgLNnz6KgoAB3795FVlaWuP5Py5Yt4ebmhmbNmknuGR4eDi8vL4NyIiIiIiJyXC4TBB0+fBg9e/YU96dPnw6gcHLz4sWLsWjRIrz77rt46aWXcOPGDYSGhqJDhw4YPHiweM7AgQMl6VNbtWoFAAbZmIiIiIiIyHm55JwgIiIiIiIiU6rMOkFEREREREQAgyAiIiIiIqpinHpOkFarxc2bN+Hv7w+ZTGbv5hARERERkZ0IgoCsrCzUqFHDYAHqkpw6CLp58yaio6Pt3QwiIiIiInIQSUlJqFmzZql1nDoI8vf3B1D4oAEBAXZuDRERERER2UtmZiaio6PFGKE0Th0E6YbABQQEMAgiIiIiIiKzpskwMQIREREREVUpDIKIiIiIiKhKYRBERERERERVCoMgIiIiIiKqUhgEERERERFRlcIgiIiIiIiIqhQGQUREREREVKUwCCIiIiIioiqFQRAREREREVUpDIKIiIiIiKhKYRBERERERERVCoMgIiIiIiKqUhgEERERERFRlcIgiIiIiIjIhVxOzcK4nw7iyLW79m6Kw2IQRERERETkQp7+9Qh2XryNhxbus3dTHBaDICIiIiIiF3IjPc/eTXB4DIKIiIiIiFyIuxu/4peFnxARERERkQuRu8ns3QSHxyCIiIiIiMiFeMgZBJWFQRARERERkQuRyYqDoIS0HDu2xHExCCIiIiIiciGCIIjbDy3ca8eWOC4GQURERERELkRbHAPhTk6B/RriwBgEERERERG5EK1eTxAZxyCIiIiIiMiFMC1C2RgEERERERG5EDnXCSoTPyEiIiIiIhci5zf8MvEjIiIiIiJyIXIZB8SVhUEQEREREZELcXNjEFQWBkFERERERC7EnUFQmRgEERERERG5EPYElY1BEBERERGRC+GcoLIxCCIiIiIiciFyvZ4gbw+5HVviuBgEERERERG5EDe9niB3efG2WqPFqqPXcf1erj2a5VAYBBERERERuRD9wEcQist/3X8N05efQK9PdtihVY6FQRARERERkQvRHw6n1YuCdl1KAwAUqLWV3iZH427vBhARERERkfXoJ0YoUGvx0vIT6FI/BIJ+t1AVx54gIiIiIiIXop8iW60VsPLodby47ARyCzR2bJVjYRBERERERORKTHT4ZOSpKrcdDsyuQdCcOXMgk8kkP40aNbJnk4iIiIiIXJKSc4FEdp8T1LRpU2zevFncd3e3e5OIiIiIiJyWYKIriGuoFrN7xOHu7o7q1avbuxlERERERC7hZnq+0XKVprgnaNuFVPRsGF5ZTXI4dp8TdOnSJdSoUQOxsbF45JFHkJiYaLKuUqlEZmam5IeIiIiIiIrdSM8zWp50t7h84qJDldUch2TXIKh9+/ZYvHgxNmzYgIULFyI+Ph5du3ZFVlaW0frz5s1DYGCg+BMdHV3JLSYiIiIicg0abdVNmS0THChheHp6OmrVqoX58+dj0qRJBseVSiWUSqW4n5mZiejoaGRkZCAgIKAym0pERERE5HAEQUCdmf+aVbd1TBBWPtMJMheZLJSZmYnAwECzYgO7D4fTFxQUhAYNGuDy5ctGjysUCgQEBEh+iIiIiIiokCUZ4I4mpmP7xduSRVSTM/Kx/HASlGrXXlPIoYKg7OxsXLlyBZGRkfZuChERERGR08mzcEHUiYsOYcz3+8X9B77cjf/9eRILNl+ydtMcil2DoBkzZmDHjh1ISEjA3r17MWzYMMjlcowZM8aezSIiIiIickr55ejB2X/1rridmlU49WTzuRSrtckR2TVF9vXr1zFmzBjcuXMHYWFh6NKlC/bv34+wsDB7NouIiIiIyClZ2hNkitrFkybYNQhaunSpPW9PRERERORS8lTlC4JeXHYcj3euI+5rGQQREREREZEzyC9nELT62A38dfyGuK/SuHYQ5FCJEYiIiIiIqPzyCgqzwzWq7o8ZfRtYdK5+54+pBVddBYMgIiIiIiIXoRsO5+Uhx5PdYvF8r3qoG+YrqdO/aXV7NM2hMAgiIiIiInIRxUGQGxTuckzv2xBtawVL6ix8tDWaRwXao3kOg0EQEREREZGLUGsKh8N5yIu/5g9oXtzz8+qARpDJZHBzk1X4XimZ+U6bQIGJEYiIiIiIXIQuJnGTFQc5PRqGY82UzqgT4otAHw8AgLyCMdDOi7cx7qeDaBkdhDVTOlfsYnbAIIiIiIiIyEUIQmEUVLKjp2V0kGQ/8W5uhe7zxl+nAQDHk9IrdB174XA4IiIiIiIXIRjpCTImLbugQvdpHVOtQufbG4MgIiIiIiIXoS2KgsqIgSoswMu5B5QxCCIiIiIichG6OUEyC6KgF3rXt/g++ukQNE6YHIFBEBERERGRi9CamBNUmud718eHD7ew6D6CXtyjKspI50wYBBERERERuQhBsKxXplF1f8jdZBjZNtqy++j1BTV6Y4NF5zoCBkFERERERC7i+13xAICNZ1Jseh8LYy2HwyCIiIiIiMhFWJr6umTqbB0fT7nJczRaASevZ1h0H0fDIIiIiIiIqIr578VueK5XPbw2qLFY9u/zXcVtX4Xp7G/zN13AqRsMgoiIiIiIyAF0iA0GALz1QNNS6zWI8MdLfRsiwMtDLGtSIwAbphUGQrq5RWnZSuy7ckcy1+irbVes3exKxyCIiIiIiMhF5BZoAAA1q3mX63zdIqu6tNft5m7GmO/3Y+elNOs00EEwCCIiIiIichFiimxLcmTr0QVBmflqPPjVHnHdob1XGAQREREREZED0hYt2eNmwWKp+uRuxT1BJ5LSxfK6oX4VbZpDYRBEREREROQidDN3ytkRZPI8T3fXChtc62mIiIiIiKowXQKD8vYEmTpPrS1OjBAb6itu92wYhpXPdCzXvezJdO47IiIiIiJyKro5QeXsCBKHw5Wk0Y2zA1A71BdX03Iwd1gzPNK+VjnvZF/sCSIiIiIichG6DhuZlXuCVJrinqBspRoAEOjtYbSuM2AQRERERETkIoqHw5XvfDcT0YEuZbYgCDgYfxcAEOTtWb6bOAAGQURERERELkK3pml5U2TLy5gTpNHq9wipynUPR8AgiIiIiIjIRdh6TtCPu+PFsg6xIeW8i/0xCCIiIiIichG6fpryzgkydZ5uTtC89efFMlMBkzNgEERERERE5CK0FZwTVDKwCfVTAJAOg9MpbxpuR8AgiIiIiIjIRegyWZc3QCk5J0heFC2oGQQREREREZEj0mWHK2984i6XnlgrpHBhVP11gnScOAZiEERERERE5Ar+O5OMmxn5AMrfS+Mhl4YHLaICAQBqDXuCiIiIiIjIwTz16xFxuyLxib/CXdx2LwqKjA+HK/897I1BEBERERGRi6lIL43+kDj3okiHiRGIiIiIiMihVSQ+0c8QpwuI1EbmBJV3QVZHwCCIiIiIiMjFGJvDY64cpUbc9vKQAwBy9cpcAYMgIiIiIiInl6+SBimZ+apyX0ulKe71qebjAQC4l1tQ7us5IgZBREREREROruScnYr0BH37WBv4eMrx5dhWCPLxBADcyy1/UOWI3MuuQkREREREjqxk0BPi51nua/VuHIFTc/pB7ibDoYS7AIB09gQREREREZEjKdAbwta/aXU0rRFYoevpkiP4eBbNCSrgnCAiIiIiInIg+tnbxneqbbXr6hZPTc1S4si1e1a7rr0xCCIiIiIicnL6w+Ha1Qm22nVzlGpx+6GFe612XXtjEERERERE5OR0w+ECvNwl6/xUVJi/wmrXciQMgoiIiIiInJyuJ0g3fM1aalbzMVr+zaNtrHqfysYgiIiIiIjIyenW9rF2EAQAoUYyzbWMDrL6fSoTgyAiIiIiIienC4Lc5dYbCqeTlm2YHlsrlH8dIkfAIIiIiIiIyMmpiobDedqgJ8gYWwRblYlBEBERERGRk1PbsCfImHB/r0q5j60wCCIiIiIicnIqrW0SIxizYnJHm9/D1tzt3QAiIiIiIqoYlVrXE2TbIOjqewPhZsUU3PbCniAiIiIiIien1hZlh7NhgBLs6+kSARDAIIiIiIiIyOkV2GidIH3OnhFOH4MgIiIiIiInZ8vECC/3awgAeH94C6tf2144J4iIiIiIyMkdS0wHYJvemik962Fcx1rw9/Kw+rXthT1BRERERERO7tf91wAAey7fscn1XSkAAhwoCHr//fchk8kwbdo0ezeFiIiIiIhcmEMEQYcOHcK3336LFi1cZ5whERERERE5JrsHQdnZ2XjkkUfw/fffo1q1avZuDhERERERuTi7B0FTpkzBoEGD0KdPnzLrKpVKZGZmSn6IiIiIiKoyjdZ1UldXFrtmh1u6dCmOHj2KQ4cOmVV/3rx5eOutt2zcKiIiIiIi51Gg1tq7CU7Hbj1BSUlJeOGFF/D777/Dy8vLrHNmzpyJjIwM8ScpKcnGrSQiIiIicmxKtUbcrhPqa8eWOA+79QQdOXIEqampaN26tVim0Wiwc+dOfPnll1AqlZDL5ZJzFAoFFApFZTeViIiIiMhhvb/+vLi9eXp3O7bEedgtCOrduzdOnTolKZs4cSIaNWqEV155xSAAIiIiIiIiQ0sPFY+OkrvJ7NgS52G3IMjf3x/NmjWTlPn6+iIkJMSgnIiIiIiIyFrsnh2OiIiIiIioMtk1O1xJ27dvt3cTiIiIiIjIxbEniIiIiIiIqhQGQUREREREVKUwCCIiIiIicmI+nsyqbCkGQURERERETqxLvVAAwLQ+9e3cEufBIIiIiIiIyIlptAIAIDLQy84tcR4MgoiIiIiInJiqKAhyd+NXe3PxkyIiIiIicmIarRYA4C6X2bklzoNBEBERERGRE1Np2BNkKX5SREREREROTK1hT5ClGAQREREREVWSezkFYiIDa1EXXc+DQZDZGAQRERERkV3dzlJi/9U79m6GzZ27lYlW72xC3df+tep11UXD4eQcDmc2flJEREREZFf9P9uJ0d/tx7YLqfZuik1NW3rcJtdVFyVG8HBjT5C5GAQRERERkV3dySkAAGw4lWznltjW7WyluD3im73YfSnNKtfV9QS5y/nV3lz8pIiIiIjIIaiKJvi7qtwCtbh9KOEeHv3xgFWuq5sTJGdPkNkYBBERERGRQ1h17Ia9m2BT3h5ym1xXlx2OiRHMxyCIiIiIiByGIFg3c5oj6dukulWus3hPPGb/dRrxaTn4fMsl3M0tHE7IdYLM527vBhARERER6dxIz0PNaj72boZNWCtGmfPPWQDAz/uuScrZE2Q+hotERERE5DB+2BVv7ybYjJus4kFKaWsMcU6Q+RgEEREREZFdBXgVD05avDfBfg2xMQ8rZG/LU2lsev2qgp8UEREREdmVl40SBjialUevG5RpS+nZMUY/w1xJAV4eFrepqmIQRERERER2k5KZL1k/x5Vl5RsGMLml9OwYk6s0XT/Qh0GQuZgYgYiIiIjs4nJqNvrM32HvZlQKU1nv1BaujZRjoieoaY0Ai9tUlTEIIiIiIiK7+Ou44bpAnu6uOVDJ1Ki3AjODIF0QlXgn16Lrk3EMgoiIiIjILmRGsqXZakFRe1NrjQc7Kk3Z0cv+q3cw+rv9pdZx5fWVbME1Q20iIiIicnjGEjoXqC0bHuYsTKW2VpnxvO+uO2vt5lR5DIKIiIiIqNIJgoBspeH8FpWFc2SchdpUEGTG86ZlFZRZhx1BlmEQRERERESV7sfd8fhxt+HCqGqtYHHaaGegMTHszZw5QeassSrA9T4zW2IQRERERESV7t1150weMzdZgDMx1RO09VxqmeeaEQMxMYKFGAQRERERkUNRqlwvCDI1J+iTTReRV1D6WkFyedlhkNyc7iISMQgiIiIiIofy9wnD1NnOzlR2OADIzFeVeq6pjHnLn+4objMGsgyDICIiIiJyKPFpxtfC0Xc3pwCnrmc4TWpoUz1BAKAtxzNM7VkP7eoEi/vuZvQWUTEGQURERERUqcoKXMrKmJav0qD1O5sw5MvdWHHkusMHQoIgICvfMBNe8fHSzzc2n8itRMzjr/AoT9OqLAZBRERERFSpjH2pn9qznrjdtna1Us9/9vej4vb//jyJ4Qv3Qu3AyRTm/H0Gg7/YbfJ4ab1EAFAj0NugrORCs/5e7uVrXBXFIIiIiIiIKlXJBVEXTbgPM/o1RMvoIADAy3+exJy/z+CLLZeMnr/1vDSj2rHEdJy4nmGTtlrDz/uulXq8rGx4MSE+BmXKos8wNswXADC8dc1ytq5qYhBERERERDal0mjxw66rOJ+cCaD4C7xOzWqFPR05RYunFqi1WLw3AZ9suojEO2XPDwKA1Mx8K7a4cqlNrCGkY2y43+ZzKQCA1c92xspnOqFf0wibtM1VMQgiIiIiIptaeeQ63l13Dv0/2wUAUKqlKaGjioKgZCOBTEZeceY0pVqDO9lKo/fQOPi8IH3n3+mP7g3CxP2y5kAZGy7n5VH4NT7Q2wNtalUzGB5HpePgQSIiIiKyqfPJWeK2WqM1GA7n41n4ldRYMKBLLZ2Rp0LcW/+ZvIebEwUBXiVSXpd87gK1FtOWHUO/ptXxYMsoGIuRdJ8ZlQ8/PSIiIiKyqeWHk8TtHKVGMhzu01Fx4na+kUVSh329F68PaoxgX89S75GvKn3BUUdWMlHEJ5su4N9Tyfj3VDIGNo/EyqPXDc55pH1MZTXPJXE4HBERERFZza2MPHy74woycouHseUWFAco+WoNlEXBTmSgF4a1KntC/7vrziHAq/QU0NOXnyhni+1Df0hgyZ6g/86kiNsLNhtPDtG0RoBtGlZFMAgiIiIiIqt55PsDmLf+PGauPmn0uFKlRYGmMABQuJv/VdRHIS+7khPYPqMHAGlgqCqRGEG/1+vLbZfF7b5NipMfONPwP0fEIIiIiIiIrOZqWg4AYNv522JZjUAvcTtPVdwT5FkiCKpblO7ZGCfKeyChv37RzAGNUDu08Bl1mfAA4PQNaXrvXo3CjV7LT28tIAZBFcMgiIiIiIisLk9vjo5Kb87LnWylOCdI4S7t3emmlzGtpLIyqAFwyAVT9dcAGqM3j0e/J+ijjRck55gKcPwUDIKshUEQEREREdmEbo0f/QBGrRWw7FBhooRTJXpASuvtMZYmuqSSw8ocgX67PeXFX711C8MCQICXNFeZ1sQHoZ9VLsCb+c0qgkEQEREREdlETkHhkC+VXjY4rSBgw5lko/Wf7h5r8lrmBDgqrWP1BO2+lIbdl9LEfXe34t6bd4c2E7ef7l5Xcp6pgG9cx1ridqB36YkiqHQMgoiIiIjIJnTrAekPhxMEiAuFlhz+FhnoLQkO9F1OzTJarm/nxdtl1qks+SoNHv3xAJ75/ahYJtcLgkL8FBjeKgqANDgCTAdBNav5YM2Uztj0YjcujlpBDIKIiIiIyCbUWgEFauniqJvPpYgJEfo1jTA4x9R6Pzcz8su836H4u+VsqfX9uDtesu8mg0Hg4lYU/GhKDH8zNhzu5X4NARQOo6sf4W/NplZJDIKIiIiIyCa0goBrd3IkZb8fSBQDHV9Pw3kt1fUyyekr2VtiTNMageVopW2UTHYgN9J+eVFQpC3R82OsJ8jX0zVShDsKBkFEREREZDUe8uIv+zsu3Mb9n+40qKPrGXKXGwYGA5tFGr2uOUPd8tXGe5EcgbEgSOwJKjGVqWTPkKnzqfwYBBERERGRVWi0giSBgf5Cn/p02eI85IZfRd1MfNlPKMo0p++fqV0wpWdd9G9aHYDpoXSOQG5kDo/u8Q2GwxnpCWoa5Ti9XK6AQRARERERWUV8Wk7ZlVCc6c3TSBBkieY1A/Fyv0YI9vMEAOQVSLtUUjPzcSG57IQKlcFYcGd6OJzh+a1jqtmkXVUVE4wTERERkVWYk8ENKL0nqDy8ihZdLTkcrt17WwAAO1/uiZgQH6vcq7yMzWkyNzHCJyPibNewKoo9QURERERkFUq1eev0nC/qnfEwMicIABZNuA99Gocj3F9h1vW8PAq/0p67lQnByHyaI4n2zxondzP82q0LjMpKjNA4MsB2DauiGAQRERERkVWozVjQVJ+7iZ6gno3C8cP4+7Du+a4mz20VEyRue3sU9gRtv3Abi/cmGNRVqip3EVVjgViAl+EArOLECAI2nU3BD7uuFu6XOL9hdabEtjYGQURERERkFWqt6WBjbPsYg7Ky5gSFldITpB8neHkUp4+et/48spVqSd1lh5Nw6npGqfeyppdWnDAo8/f2MCjTzQnSCAKe/OUw3l13Dkeu3ZOsqwQUrjFE1mXXIGjhwoVo0aIFAgICEBAQgI4dO2L9+vX2bBIRERERlZPKRE9Qp7ohmDOkqUG5h3v5v93r97Z46a2hU6DWotnsjcjMV4llxxLT8dDCvZIyW1p19IZBmbGeILmR4XA30vOQnlsgqVdykVWqOLsGQTVr1sT777+PI0eO4PDhw+jVqxcefPBBnDlzxp7NIiIiIqJyUBtLawagXZ1geLobfu2sSGIE/XDLy8i1D1yVzgMq0GiRkWv7ICi3QG20PMDLsCfITWaYGKFArUVWfvE16ob5WrmFBNg5O9yQIUMk+3PnzsXChQuxf/9+NG1q+NsCIiIiInJcaiPr2wBANR9Po+UVSZGtPxzO2PpAT/5y2KBMZSJIs6Z/Ttw0Wm4sCJQbWSw1PbcAB+Ltn8jB1TlMimyNRoMVK1YgJycHHTt2NFpHqVRCqVSK+5mZmZXVPCIiIiIyYe66s/BVuBtkNdMJ8jHsBQEAdxPZ4UpTO8QHCXdyMWtQY7Hsnpk9PKbaZ03ZSuMLtp68nm5QZmw43LvrztmkXSRl9yDo1KlT6NixI/Lz8+Hn54fVq1ejSZMmRuvOmzcPb731ViW3kIiIiIhMOZxwF9/vii+1TqCRpABAcWIAS7w6oBF6NAyXJEOIiw4y69ycAuMBijXl6Q2HW/VsJwz/ei+A4oBHn244nKkeNLKdcvVBqtVqbN68Gd9++y2ysgrzvN+8eRPZ2dkWX6thw4Y4fvw4Dhw4gGeeeQbjx4/H2bNnjdadOXMmMjIyxJ+kpKTyNJ+IiIiIrOTDDRfKrBNgJAga1TYa4QFe5bijTBIAAUC3+qGIqxlY5pnnbtl2FFGOUo2ku3niflzNIHHbWPpwRdEQOWO9RDpPd69rtfZRMYt7gq5du4b+/fsjMTERSqUS999/P/z9/fHBBx9AqVTim2++seh6np6eqFevHgCgTZs2OHToEBYsWIBvv/3WoK5CoYBCYd6iWURERERke8Z6OEqKDZVO7g/188QHD7co1/2UasPeHJlMhhY1g3CijDTYWTbODvfAl7tx5XYOAKBf0wjJZ2NsTpBfUca4S6nGOxLWTOlsVnBHlrO4J+iFF15A27Ztce/ePXh7e4vlw4YNw5YtWyrcIK1WK5n3Q0RERESOq0V06V/SQ3w9EVQiMUJFUj4r1caTG/h4yo2W68srsF1ihHyVRgyAAKB2SGHg9+HDLRAd7I35I1sanOOnKL0/onlUINNj24jFPUG7du3C3r174ekp/cNcu3Zt3LhhmBO9NDNnzsSAAQMQExODrKwsLFmyBNu3b8fGjRstbRYRERER2UGQt2HmN3c3mTjP5eORcQbHK7L4p6kgqLSFVXVuZeSVWae87uZI1/bRZawb2TYaI9tGGz2nrCDInF42Kh+Le4K0Wi00GsNuyOvXr8Pf39+ia6WmpmLcuHFo2LAhevfujUOHDmHjxo24//77LW0WEREREdlBnpH01I92qCVuN4ww/H7o61n+3FxKI/creU9Tlh5Kws6LtzFx0UEk3c0tdxuMKSgRnP2871qZ55Sc20SVx+I/gX379sVnn32G7777DkBhd2Z2djZmz56NgQMHWnStH3/80dLbExEREZEDMbZGDwDsebUXcpRq1AjyNjjWuV5oue/XrUGY0XL9gKJzvRDsuXzHaL1xPx0EALy66iR+f6JDudtRUsk1iFrHBJV5TmnLJI1pZ7z3iKzD4iDok08+Qb9+/dCkSRPk5+dj7NixuHTpEkJDQ/HHH3/Yoo1ERERE5KDyjKSdFgQBUUaCn+VPd8Q/J25i5sBG5brX9hk9ULtEkgVjnuwaazII0knNtO4c9IISQdDzveuXeU5p831aRVercJvINIuDoJo1a+LEiRNYunQpTp48iezsbEyaNAmPPPKIJFECEREREbk+3XC4V/o3wgcbzgMoznpWUrs6wWhXJ9ii6w9tWQNrjt/EE13qlBkALX2qA+7lFOC+2mXfw5xECpYoORzO3a3sWScl10l6sGUN/HX8ZuExzgeyqXINyHR3d8ejjz5q7bYQERERkZPJyCtMO60f+BhLllBe7z/UAiPvizYrsOkQGwIA0Jqx+KixdNonr6fjzM1MjL4v2uKsbKoS6wB5e5YdBLmVuMeYdjFiEGRq2B9Zh8VB0C+//FLq8XHjxpW7MURERETkPARBwKazKQCAGoHFC5/GhpU9ZM1cXh5ydKpr2Rwit3L2ojzw5R4AwOtrTuPy3AHIUqrx675rGNKiBmJCfCR103MLMOTL3XiuV32MbBttMDeqdUzZw9lKxlm+nu64+O4A5BaoDdKKk3VZHAS98MILkn2VSoXc3Fx4enrCx8eHQRARERGRi1t97DruZBdgaKsosaxRZAA+H9MKZ29molejcDu2zlD9cD+TC5Iao9EKyFNp0OeTHUjNUuK3/dewb2Zv8fhPu+Px9tqzAID//XkSI9tG439/nhSPX31voFk9SSWHvHm6uxX9MACyNYuDoHv37hmUXbp0Cc888wxefvllqzSKiIiIiBzXi8tOAACu3ytedycqyBtRQd54IK6GvZol8emoOPy89xq+HNsKNQK9Efvav5Ljgd4epZ7/35kUpGYVJk+4lZEvlp++kSEGQPqSM4vrmNsTVXI4nJeHxavXUDlZ5ZOuX78+3n//fYNeIiIiIiJyXYv3Jti7CSYNa1UTa6Z0Rs1qPkaDkow8FX7bX7yWT3qudLHTacuOG71urpFseJn5KnF7cve6ZrexZIrsAK/SAzOyHquFm+7u7rh586a1LkdEREREDkhjJOlAHTPSVjui19ecFrfHfH+g1Lq67G/GOnm+2X5F3J7aq57Z9y85ZM7DnT1BlcXi4XB///23ZF8QBNy6dQtffvklOnfubLWGEREREZHjKbkoKACjawI5mi71QrH7cprJ4+duZZZ6/tQlR/HduLYGqbABIOFODoDCOT1+CvO/XpdMke3OtNiVxuIgaOjQoZJ9mUyGsLAw9OrVC5988om12kVEREREDmj1sRsGZR5yx//y/u1jbdB09kaDckEQzEpi8F9RFrxzyVkGx1pFV8O/p5LhYWEQU3JOEIOgymNxEKTVGka/RERERFQ1zFx1yqDMveTkFgfk5WF8cVS1VjA6xM2Ud4wkRSgo6h2z9HMoGXtxgdTKU67FUomIiIio6jG1CGnJYV2OyFSAodYIAMpeXLU0uQVqAJb3iOm36fHOdSxeoJXKz6wgaPr06WZfcP78+eVuDBERERE5LpWJEUHO3IOh0mohmDnQSaMV4OspR06JDHHZ+YVBkKWfg/5wuMS7uRadSxVjVhB07Ngxsy7G6JWIiIjIdRX2mhiq5utcqZ3D/BW4XbQGkFoj4GZ6XhlnFFq4/bJBAAQA93ILU2S7u1k2HE6/uiBUrDeKLGNWELRt2zZbt4OIiIiIHJypIOjpbuavjeMIejYMw/LD1wEAaq0WyXqLoZbm4/8uGi3/+0ThMjGWDofT7wnSMgiqVI4/i42IiIiIHEKBkfTYABAd7FPJLakYH093MWBRawSoi+Y6talVzaDu6mc7mX1dS8MY/blUJuJLspFyJUY4fPgwli9fjsTERBQUSFfXXbVqlVUaRkRERESORV1iTlConwJfjm1lp9aUX5PIAKiKoo7Eu7liL4yxBA+tYqqhTqgv4tNyJOVj28fATQb8tj9RLLt2x7J5Pfo9QRpmYK5UFvcELV26FJ06dcK5c+ewevVqqFQqnDlzBlu3bkVgYKAt2khEREREDiC9aO6LzqFZvdEhNsROrbHc/JFxGNs+BsNbR4llv+67Bk1RT5DcTYbne9c3OM9YvoMZfRuiQYR/hdoj0/smbmqoIdmGxUHQe++9h08//RT//PMPPD09sWDBApw/fx4jR45ETEyMLdpIRERERA4gW6mW7DtbUqzhrWvivWHNJev5aAVBEgSNbVf8fVYXEJVc1BQAgn094W1i7SFzcU6Q/VgcBF25cgWDBg0CAHh6eiInJwcymQwvvvgivvvuO6s3kIiIiIgcQ55eZrS46CD7NcSKUjLzJUFQ9UAv/DO1C74Y0wov9qkvluvb8XIPAEBF4xb9q6pNrMFEtmFxEFStWjVkZWUBAKKionD69GkAQHp6OnJzmd+ciIiIyFUt3psgbk/rYzhszJmE+HoCALw85JIgCACa1wzEkLgaYk/X+eQsybkxRYkgdIuk6rw6oJFFbdDvYNIwCKpUZgdBumCnW7du2LRpEwBgxIgReOGFF/Dkk09izJgx6N27t21aSURERER2tXD7FWw9nyru92wYbsfWVNzI+6IBAI2qB+Dr7ZcBQPJ8pdEFR256PUSn5vTF5O6WpQr3ci8eThfmp7DoXKoYs7PDtWjRAvfddx+GDh2KESNGAABmzZoFDw8P7N27Fw899BBef/11mzWUiIiIiOzngw3n7d0Eq9JlgtNotUiwMKubTqe6oQAALw83+HtZvmCsfhAV6O1cC846O7ODoB07dmDRokWYN28e5s6di4ceeghPPPEEXn31VVu2j4iIiIjs7GD8XXs3wep0AYjGjIk9Nat54/q9PIPyeuF+2Dy9G0Ir0Ivz6ag4/LY/Ea9YOJSOKsbs4XBdu3bFTz/9hFu3buGLL75AQkICunfvjgYNGuCDDz5AcnKyLdtJRERERHby1K+H7d0Eq3PXBUFmLM8zZ0hTk8fqhfsjyMez3O0Y1qomVj7TCREBXuW+BlnO4sQIvr6+mDhxInbs2IGLFy9ixIgR+OqrrxATE4MHHnjAFm0kIiIiIjsquT6QpXNfHJHcrXg4nM6sgY2N1jWnt4ici9nD4YypV68eXnvtNdSqVQszZ87EunXrrNUuIiIiInJAS55sj45OtECqKW6y4p6gaj4euJerQo+GYUbr6qcGX/d8l0ppH9lWuYOgnTt34qeffsLKlSvh5uaGkSNHYtKkSdZsGxERERHZmbZE6uaOsSFOt0iqMbrhcFpBQIG6sDfI073sQVJNawTatF1UOSwKgm7evInFixdj8eLFuHz5Mjp16oTPP/8cI0eOhK+vr63aSERERER2UqA3aaZRdX+XCIAAvcQIWgHKoiBIoZeyWl//ZtXR7kAwOtR1/h4wKmR2EDRgwABs3rwZoaGhGDduHB5//HE0bNjQlm0jIiIiIju4kJyFPJUGLaODoNbrCWpfJ9iOrbIueVEsp1RrxGdUmOgJ8vKQY/nkjpXVNKoEZgdBHh4e+PPPPzF48GDI5cajZCIiIiJyfv0+2wkAOPx6H3E9HQCIDPK2V5OsTi4vDHg2nkkRy8wZDkeuwewg6O+//7ZlO4iIiIjITjRaAfM3XcB9tYPRrX5xcoBrd3IQXc1H3H+kfYw9mmcTciPD+kz1BJHr4ZsmIiIiquIW7YnHV9uuYMKiQ5Lhb7kFGqj0hor5e3nYq4lWp0uMICmT86txVcE3TURERFTFvbvunLit1ls3J0epgaooaYCHiwUIbiWCoDqhTPJVlbjWn2YiIiIiqhD9niCVRoucAjUAwMvDtb42lozp6of72achZBeu9aeZiIiInFZKZr64XgtVrp56i4T+vCdB3D6aeA+7LqUBACIDXScpAgCk56ok+x6cD1SllHuxVCIiIiJrOXcrEwMW7ELrmCCserazvZtT5ejPhflk00Vxe5FeQBQZ6FWZTbI5tUa6CGxaltJOLSF7YMhLREREdrfsUBIA4Ghiulh25NpdvLHmNDLzVSbOImsxliSgpKhqrtUT9FjHWpL9A/F37dQSsgf2BBEREZHd5as04vaaYzcQE+KDhxbuA1C4dssbg5tI6qfnFiDIx7NS2+jK5GYEQTVcbDiclwfXvazKGAQRERGR3d3LLRC3py07Ljl27U4u5vx9BmH+CnSqG4Kjiel4Z+1ZxEUH4a8pHDpnDeYsEqpwscQIAPDBQ83xyspT9m4G2QGDICIiIrKrDzacx8YzKSaPbz5n/NiJpHQbtagKEsqu4uliKbIBYGTbaDEIahIZYOfWUGVyvT/NRERE5FQWbr9i7yZUeRqh7CjInN4iZyOTFQ8D7FQ3xI4tocrmen+aiYiIyOl1iA22dxNsJjUrHxm5jpPsYcmBRPx1/GaZ9aKCXGtOUEmd6jEIqko4HI6IiIgczv6r1snU9cfBRMxcdQp1w3zxxuAmaB4ViBA/hVWuXR5Z+Sq0m7sFABA/b6CkJ8IeTiSl47XV5s2JiQ1zzcVE97zaCxeTs9BDb60kcn3sCSIiIiKXNXNV4Rf8K7dzMGHRIQz9eo9d23Pldo64XW/Weiw9mGjH1gAPfmX+5+GncM3fnUcFeaNno3C7B6RUuRgEERERkd1UdFiYYMZcFn1Jd/Msqq/VCihQay06pzT6C3JqtAJeXeU8mcm8XDA7HFVd/NNMREREdnM7O9/ksZhgnzLP12hNB0H7rtwpV5v0jf1hPzq9vwUZedaZw/PEL4cNypLu5lrl2pbKK9CUXUkPe0rIlTAIIiIiIrvJUZr+It6our9k/92hzfDbpPb4aUJbsUxdShD03B/HjJan661JVJpjifew/+pdpGUXYP2pW2adUx6fb7lks2uXJitfGtiF+RvOlXq6WywaRvhjVNvoymoWUaVgEERERER2czk12+SxFjUDxe0gHw882qEWutQPRae6oWJ5aT1BadlKo+Wz1pw2q23Dvt4rbiutOCSupBVHrtvs2qVJyZR+Pl+NbS1uj20fg9+faI+X+zXExhe74YOHW1R284hsikEQERER2YVKo8VLK06YPJ6rN1zr20fbiNtyt+JhWaZ6glQa00HLupO3kK1Ul9q2fJW0h2r7hdRS65ujtIDNHt7654y4HT9vINrVKU5L7uUuR+d6oXB3wQVSiQAGQURERGQnJefsfDGmFRpEFKdhPpaYjoOv9cbKZzqifWzxGi5yvbkpJYd0AcC2C6moP2t9qff+fufVUo9fvyedp7Ptwu1S65vjxPX0Cl/Dmg5fuydul5zvI8CxAjYia2MQRERERBUSn5aDU9czLD7PXa9Hp2fDMAyJqwE3vS/jYf4KhAd4oU0t6cKpbm4yhBfNXzl7M9PguhMXHSrz3vuulp404YyR61aU1kRPUKgd1i0qqyfMwqR7RE6HQRARERFVSM+Pt2PIl7uRmmU605sxnu7FX0N0PS36QdCsQY1NnlsvvLDHKK/EsLWSKbOHxNUwer6pzGharYBZq0/hhaXHTTfcQrezlPjvTLJkOFyon0JM/JCWrcSNdMtSd1fUZ5sulnq8mo9nJbWEyD5cc9UrIiIiqhT6wURCWi7C/b3MPrdAb97Ol2NbAQDc9H49GxFg+lpeHnIAhj0a+SrpXCB/L3d8NbY1jly7Bw93Gb7dUTgMTqk2DIJUGq1Zw+hWHr2OJU92QLBv2YGCWqPFfXM3S8qaRAbg3xe64p8TN8UMdlvPp+KxDrXKvJ41qDRa/LA7XtxvFhUgbi8Y3RLrTyXjia51KqUtRPZi156gefPm4b777oO/vz/Cw8MxdOhQXLhwwZ5NIiIiIguk5xWnm9bv2TGHLuOar6ccg1sU9ti4mbkWTWbRuj2zVkszvWWWmCPk7+WOQS0i8eaQJojQC9C8PQ1/D/ztjisGZbFhvgCAWiGFaxbN/fcczidn4dudhnWNMTbszkNe+Iz6QVS+BWv2nL6RgccXH8L55PIN2Xu+ROpwT73kBw+2jMI3j7WBr4K/JyfXZtcgaMeOHZgyZQr279+PTZs2QaVSoW/fvsjJybFns4iIiMhM+uv8aLRlp5FecTgJtV9dh72X01BQFAQ11FsPyNwFOfUn9es7fUM6NynAy0PcHts+BnFFabfzCgznxFxIMUzXrTv/2p1csb2A6fk9JRnLUneiaP6U/si9lEzzhxIO/mI3tp5PxSsrT5l9jr71p5Ml+y1qBpXrOkTOzK5B0IYNGzBhwgQ0bdoUcXFxWLx4MRITE3HkyBF7NouIiIjMpJ9KWqUpOzB4+c+TAIA3/z6Dp38t/P9e4S4Xj4++r3BRzlYxQeVqz6SfD0v29RdG9fKQ452hzQAA2fmGQVDXeqEGZf5exT0iZ28V97x4mJk6OqiUuTX6qb5/2B1vMGyuLCkZls3BMmVGv4ZWuQ6RM3Govs6MjMLfjAQHBxs9rlQqoVQWL+yVmWn9zC1ERERkPv21fIwlG7iZnof1p5MxvmMtyYKj+ouk6idUGNU2Gg0i/NCoegCsYVzH2pJ9v6JhXll6c4nO3crE3it3JNnqdFpFB2HXpTQAwH69oW0+nnKDujqCIOC3/dfw875r0JaSZk1/XR6gMIGCJczsNCuTH4e+URXkMNnhtFotpk2bhs6dO6NZs2ZG68ybNw+BgYHiT3R0dCW3koiIiHQyclWSACapxNo6ANDp/a14Z+1Z1Ju1HlvPG19w9Mrt4mHwbm4ytKkVXOaclKVPdRC315+6ZbTO31M7IzrYR1LmV9Szk6NUi5nkBizYhXfWnsXsv88YXEN/6Nj768+L21HVvE227d9TyXjjrzO4nJqNq7cNh/j/NaUzgMKeoP/1l/bC7Lho/npEtzLyLRpGBxhmz+vTONyi84lchcMEQVOmTMHp06exdOlSk3VmzpyJjIwM8ScpKakSW0hEREQ6SrUGcW//h6lLiifZX7tjGATpe67EhPyKaBkdJG4/8/tRANIv+HE1A43OdfFXFM7x0QrA51su4+ptw3lA+iKDjAc7Rqb6iC6kZBktD/VT4JfH2yFOr+1B3tLhcuN/Olhqe0pq/94WixIkZORJE0f8MP4+i+5H5CocIgiaOnUq1q5di23btqFmzZom6ykUCgQEBEh+iIiIqPLdzSkwKLto4st/WRQWZpUzds6kxYckawZ99Uhro+d5ebhBN+rt080X0euTHQZ15g5rhs9GtUS7OsH48KEWRq9zq5R1feQmxqkdmtUb3RqEScr6NDHsiZljpEeqNPqBaFly9IYslmwLUVVi1yBIEARMnToVq1evxtatW1GnDnPSExEROYN9VwxTP++6lCbJmnbTzAVA/fUyuJmrZBa5LedTsWDLJXE/1E9h8ryyErvVCPTG0FZRWP50R1QPNL5W0SelLDZqKkuescx34f5eaBDhJylbvDcB+6/eMei10Yks0Sb9+VVl0c9w991jbcw+j8jV2DUImjJlCn777TcsWbIE/v7+SE5ORnJyMvLyKnfVZCIiIrLM9OUnjJbrJxx4ZeVJs641oFl1q7RJtxCqu5vM7Oxtxli63lFJKZmGCQ42Tutmsv4/z3UxKBv93X4M/3qP0folF4gFgA82nDdSs5B+wgpdEBTi6ykuOEtUFdk1CFq4cCEyMjLQo0cPREZGij/Lli2zZ7OIiIionPR7gs4nlz08LtjXEzMHNrJqG6r5ekrST1uqZAD1soUppPdcSTMo018LqST9FOH6rhhJqnA3pwBZRtJ7L9x+xWCNJAB4Y81pNH5zA7YVJaXQBUEVDfSInJ1dcyKWzFBCREREzk2l0aJArYWnuxtyjPRYAIW9EHeK5hQdfK033CvQa2NMWnbpqaZjgn2QeNd0EoeSAcKUnvUQEeCFGSuM937pyyvQ4Po96YiW78e1LfM8cx1OuGvy2JFr99AsKlBS9uv+awCAiYsPAQC+GNMKAIMgIiaGJyIiIqtp994WAMDpt/pJ1hDSd/j1Pth+4TZqBHlbPQACgLJ+x9qncQR+2hNv8rixTqSHWkfh7M1MpGTlY93JwpTc2Uq1ZI2duzkFeOLnQwbn3t8kosw2+yncjQ5zy1dpxGFrl1Oz8dSv5i8or7+Qrc78orlMnjb43ImcCf8GEBERkUUSy0iFDQAb9dbX0de/aXXIZDL0bBRe6hAxW5raqx7a1qpm8rjKSP5rmUyGN4c0wdPdYsWyZrM3Sup8uOE8jiamS8oe7RBjVpv+fKaj0fKX9OZerT52vdRrlFzn6EC8Ya9RfFrhELtUCxdmJXI1DIKIiIjIImuO3yizzqw1p4yWe3tabzK+qWFm7esEl3pesK8n/nymE2roZVnb9b+e4nZpPUn62dUA6RyopYek6xd+NqolZg1sUmpbdBpVN77sxzq9hWAjAorbWyfUFw+2rGEyCx4AvLbK+DsADNcLIqpqGAQRERGRRebrpYf2U7ijRc1Agzr5KuNpoiuSsKAkU8PMvjaxRlBJ+imro4N9xO164X7GqgOAwQKsyw4XBj4lh7J5ebhhaKsoi4K+tc91gU8p9fUTNqx7vgsWjG6Fw6/3wVdjC5+35NpJN8xMUU5UFTEIIiIionI7ObsvVj/b2eix+2obDjmrjAU6Q0rpHdGny/o2pl3hkLXjb96PfTN7IcjH0+Q5nu5uWKuX0nrmqlNYdijRYP6NqSCwNM2iAvFtKWv3aIp6nfo1jYCPZ/FcpJrVvAFI10YqKznEmHbRFrePyJUwMQIRERGVm1spPTvGFiXNyrfuMKx2tYNxsJSMaaUZ2ioK99UJRmTRMLMgH08EmXFeyQxsr6w8hQOvhZerDSV1jA2R7FfXGwKnLRqn5+4m/R12gHfhYrOZekPcUvXWKvLxlBskqXhvWHOrtJfIWbEniIiIiMx2NPGeuB2pN6fGGI2RKCg917pB0C+T2mHz9G74vCj187tDm1l0flSQd6mBnCntakvnHRlLplAe7nI3nH+nPwY1jwQANNcbaqjWFH6eJdsb4FX4O+0spVr8zPdcLl6r6OCsPniyax1xv2GEv2QoIFFVxJ4gIiIiMosgCBj+9V5xf+6w0gOO40npBmXGAqOK8PKQo164P+qF+6N3o3D4Kirnq01YgHTI3dKDSSZqWs7LQ47uDcOw7tQt8fO6naXE3it3AADyEvGLv5eHuJ2dr0agjwfm/ntOLPNTuGNGv4YI9/fCyRsZkgx3RFUVgyAiIiIyS2aedPJ/TLCvxdcI9jU936aiKisAAoC4moHiekEA8OW2y1a9vkdRpLP1fCoAoNcn25GVX/j5l+wJ8nR3g7eHHHkqDTLzVQj08UBJCnc5nmTwQyTicDgiIiIyy/Hr6ZJ9Y6PIYkNLD4xGtnWNCfnjOtZG65ggo8d6NgzDDybSd5tLf3RdgVorBkAA4G7kg88rSszQ9cNtmP3X6Qrdm6gqYE8QERERmeX6PekiqcaysEVV88bVogU5df7XvyFuZykxvmNteLq7xu9fvTzkWPVsZ9R+dZ2kPNRPgUUT21X4+t31sujlq6VJDcpKM/7zvmvidllBKVFV5Rr/EhEREZHNqfQWCn25X0MEehcPu3q+Vz3UrOaN53rVNzivdogvZg9pitpV4Au53ErfrPSHDbaY85/kmJsFSQ1+frziARmRK2IQRERERGZRaYqTGkzpWU9ybHrfhtj9Si/UCDLMGNe/aXWbt81RlExfXV6l9fYYOzatj2HwCRSvIUREUgyCiIiIqEwFai3u5BQAAEa2rWmynkeJrpAFo1uWKwW1s1jyRHvJfllD1awhr8SaPwDQqHqAQdmeV3sxFTaRCZwTRERERGV65If9OJRQuEZQyUBHX1CJzGQKF5kDZEqneqGSfWNJC6xtf/wdg7KG1f0NykL9bJeJj8jZufa/TERERFQhGq2A0zcyxAAIKD0IUrjLceC13uJ+rpFeC1dWMimELbStFWxQVifUFxM61ZaUKdzlNm8LkbNiEEREREQmvbziBAZ/sVtSVlaGt4iA4nlBOUp1KTVdQ71wP5tct2v9UKPlT5lY72fOA01xYnZfRAQoMKhFpE3aROQqGAQRERGRUUl3c7Hq2A2DckvmvWQrXb8nyFRSgopaXCLV9popnXH1vYFoHGk4/0cn0NsDe1/tjS/HtLJJm4hcBYMgIiIiMrDr0m10/XCb0WM7L94u8/xOdUMAAEPiXL9HokfDcJtcV+4mQxO9gKdmNW+zkkzI3WRMiEBUBiZGICIiIgmNVsBjPx40eVytlyrblN8mtUdOgRr+Xh5l1nV2fgp3jGkXg6WHEnHk9futeu2MPJW4HWpkcVoiKh/2BBEREZGEUl36ELaJnWuXeQ03N1mVCIB05g1vjvh5gySLnFrD2PYxAID7m0RY9bpEVR17goiIiEhCpTbd0xPmr8DQVlGV2JqqbVKXOogM9EIXE0kSiKh8GAQRERGRRIFGa7Q8xNcTh2b1qeTWVG1eHnIMb216cVoiKh8OhyMiIiIJlYkg6E5OQSW3hIjINhgEERERObF7OQUoUBsPWsrLVBD00v0NrHofIiJ74XA4IiIiJ5WckY8O87agaY0ArHu+q1Wu+eu+BOy/etfosed622Y9HCKiysYgiIiIyEkN+nwXAODMzUyrXfONv84YlL3zYFMMbO766/0QUdXBIIiIiMhJ6c/RUWm08JBXbJR70t1cyX5UkDf+ea6L1dM+ExHZG+cEERERuYCNZ5IrfI19V+5I9uVuMgZAROSSGAQRERE5oax8lWR/6pJjUJtIaGAuuZtMsn8nW1mh6xEROSoGQURERE5GrdFiwIJdBuUfbDhfoesqPKRfC3IKNBW6HhGRo+KcICIiIiez8uh1XL+XZ1D+/a54NKoegM+3XsITXeqgRc0g1A33g5/CvP/uP9100dpNJSJySAyCiIiInMjdnAK8svKUyeMvrTgBoDjLW7OoAKx9zrz02Vdu51S8gUREToDD4YiIiJzIi8uOW1T/9I1MCIJQrnv9Nql9uc4jInJ0DIKIiIicyI6Lty0+p87Mf7H25E2z67/SvxGuvjcQXeqHWnwvIiJnwCCIiIjIiU3qUgfNowLLrDd1yTGcSErHNzuuIF9lmPDg+r3iNYLGto+BW4lMcUREroRzgoiIiJxEala+uP18r3p4olssArw8AAB/HEzEzFWm5woBwCsrT+J8cha0goBne9STHBuol23O11NuxVYTETke9gQRERE5iTM3M8XtDrEhYgAEAF4eZf+Xfj45CwDw4YYLBscy89XitrucXw+IyLXxXzkiIqJK8O+pWxj61R7cTDdMbV0enepJ5+u4ySwbvvbEz4clw+ICvT1KqU1E5FoYBBEREVWCZ38/iuNJ6ej0/tZyX0Ol1gIAWsUEGRxzdzP8Lz0y0MvktTafS8HKo9fFfQ95YRC19rku5W4fEZGzYBBERERkRWqNFj0/3o4Jiw6arFP71XXQaC1PW/3Ur0cAAAVFwZC+Xo3Cxe3N07vhjcFNsPzpjqVeL1+lFa+Xll0AAKhZzdvidhERORsGQURERFaSW6BGvVnrEZ+Wg+0XbuNyajYAGF2nZ9PZFMmxm+l5eGPNafGckjLyVOK2/twgHW9POS682x9HXu+DeuH+mNSlDqKDfbD2uS7wNDHHx6coAcLW86limf48IyIiV8UgiIiIyAKnb2Tg9wPXjAY2m8+lSvZ/238NAKA00nPzztqzyMpXoff8HXj618Po9P5W/Lr/Gh778YDR+2blq4yW61O4yxHip5CUNYsKxIHXehutr3uEyb8dEcuYGpuIqgKmyCYiIrLA4C92AwDO38rCO0ObSY4pS6y/UyOocE7OhtPJBte5lZGHl1ecxNXbObh6O0evPN+gLgBk5qmNlpvDz8v4f/e5BeW/JhGRM2NPEBERkZnScwvE7V+Lenn05ZUIguLTChcgnfPPGYO6WgHYcMYwOAKM9/r8sPuquP3WA03Na3ARDxPD4XKUGpy7VTy07qX7G1h0XSIiZ8UgiIiIyAxXb2ej5dubSq1ztsRcnT8OJkKjFZCeWxjU9G0SgU51Q8q810cbL0AQBOSrNFh+OAmpWflYdfSGeHx8p9qWP0AR/QQK2UoVBugtkhrqrzB2ChGRy+FwOCIiIjOM+X6/yWMqjRa9PtmOpLvSNYDuq10NSw4U9xjNHdYc89afK/Nev+y7hl/2XYO7mwxqrYDYMN/yN7zIc73qYdelNHw1tjW+2XEFC7ZcwsnrGZI65iy4SkTkCvivHRERkRlSMpUGZb/uSwAAXL2dIwmAfIuyrh1KuIc3/ioeChfmr8C7JeYRlUZdlEZbf86QsTWCzPFS34ZYM6UzvD3l8FUUtu9uToGkjqdcXq5rExE5GwZBRETk0C4kZ2HGihO4fi/Xbm1QaQyzuwHAG3+dgVYr4OeiYEjn7QdNBzo+nu4Y3CLS6DFTqaz1tYgKLLNOWXw8CweCXCqRjtvTnV8LiKhq4L92RETk0J757Qj+PHIdjy8+ZLc23CvRY6Kv/bwtWHIgUdzv2TAMg4wEOfqLkNYOMT68beGjrctsy9Re9cusUxZdT1BJkYFeFb42EZEzYBBERORkLqdmYe/lNHs3o1JkK9W4mlY4FOxiSjbSsg2HpFWGu3pZ4X5+vJ3k2O2s4jYNbVkDP46/z2iPzrrnuorbAorXGDr7dj8cfK034ucNRO/GETjyep9S2xJmheQFup6gkppEBlT42kREzoBBEBGRk+kzfyfG/nAA1+7klF3Zyf1WIg21PXqD8go06P9ZYQa1cH8FujcIM1m3Q2wI3NxkcHOT4dEOMZJjgT4e4vaINtHitreHHOEBXpDJChcpDfFT4I8nO+Cjh1sg0NtDcg1/E+v9WMrXSBB08d0BXCiViKoMZocjInIi+uvUJN3NQy0Tw6pcxekb0uxlJbOZVYaHFu4Vt1OzSu+JCtILdN4d2hxd6oXh5RUn8OmolpJ6tUN98fGIOIT5K8TgR1/HuiEAQjC4RQ1cTMnC19svIz1XhTeHNKnQs+j4GBkOx/lARFSVMAgiInIiF5KzxG2V1vhkfVey9uQtezcBZ29lGpR5yt1QYCRZQqC3p2S/f7Pq6NskwmgPy8NtapZ5b29POeKig/DtY20taHHZSvYEcZFUIqpq7Pprn507d2LIkCGoUaMGZDIZ1qxZY8/mEBE5tCPX7mHUd8Vr1Xyw/rwdW2M/z/9xzN5NwH8vdsP/+jc0KG9e0zBzmyMOMfPxlPYEPde74skWiIiciV2DoJycHMTFxeGrr76yZzOIiJyC/rAsADifnIX5/12AIBROsv9mxxWss2PPyd2cAuy/ekdsT0WZus7fJ25a5frmSM7Il+yPKOq9qR3qi2d71MO5t/vjt0ntsf6Frtjxcg/4KZxjgIV+pjoioqrIrv9aDxgwAAMGDDC7vlKphFJZPB47M9NwiAIRUVXy+dbLaF2rGiIDvfF+Uc/QwOYDjc4z0ScIQpl1LDXki924kZ6Hbx5tjf7NjK+DY4mvtl02eUyjFSCvhB6Wl1Ycl+w/3T1Wsu/tKUeX+qE2b4e1yWQy+CvckaVU27spRER24VSzIOfNm4fAwEDxJzo6uuyTiIhcXGa+Gkq1Rty/l6sqtf7N9DzUmfkvar+6DjfT86zWjhtF11pzrOI9NbkFanz830WTx3MKKufL+57Ld8TtU3P6ol64f6XctzKMbV+Yvc7YmkZERK7OqYKgmTNnIiMjQ/xJSkqyd5OIiCrFKb2saO8Pby45lpCWgzl/nxH3W7+zSZJAQedmeh5qv7oOnd7fKpZ1en8r1Bot6sxch9qvrkNCWsXTbm84k1zha6w4fF2yv/Wl7pL92X+dga3pD8fz93KHv5dHKbWdz4v3N8CP49vi44fj7N0UIqJK51RBkEKhQEBAgOSHiKg8MnJV2HfFevNXbG3VseKgYEhcDcmx+Zsu4mhiuqSs32c7Da7xiYmelY/+uwDdx/Dojwcq1tAiWfml90aVZfbf0iAnNswPk7rUEfdXH7tRoeubY9/V4l6gz0e3svn9KpuXhxy9G0fA29MwXTYRkatzqiCIiMhaxny/H2O+34+/jlfeJPvymvP3GSzakwAAeKh1Tfgq3PHO0GYWX+dCivF5lN/uuCpuX7+Xh6S7uRZfW384nu461lI/3A8A8MbgJujdKBwAEBVk+4n9+kkmfJ0k4QEREZmHQRARVTkarSCu/fLev+fs3BrTjly7i9qvrsPivQli2eCi+RuPdaiFT0eVPoyp5HyfQG/zhnP9vDcB607egtrIOjimXErJluznFmhM1DRPXFGq6WGtorBpevFQuGq+hevw3LDiXCYAOHD1Dub/d0HyzN4exT0kXh7875KIyJXY9V/17OxsHD9+HMePHwcAxMfH4/jx40hMTLRns4jIxf1+4Jq4nZqlLKVm5UrNyse1O8Vzch5auM+gToBeIFOgNgxSFj7SWtzWn/uTrVRLJvnHBPtg+dMdjbbjh93xmLLkKN78+4zZwwXv5BRI9h9auBcpmfkmagNHE+/hyLW74n5Gngrnk4t7qhKLeqNKLiiqn9p545lk5FUw2NIZ9d1+fL71MpbrzUWSy4uzzymNfNZEROS87Nq/f/jwYfTs2VPcnz59OgBg/PjxWLx4sZ1aRUSu7k29SfVta1WzY0uk2s3dAgBYMbmjyXbp9+aE+Cokxza92A31I6TZy37em2Awv+bjEXFicNGouj/OG0miAABLDiSieoAXqgd6oUCtRbf6Yage6IUNZ5LRqW4IQv2K728sIGv/3hYkvD/IoPxmeh6Gf1245tHBWb0R7u+FUd/uw/nkLKyZ0hmpmflihrvYMF/JuVN61sNnmy8BAJ7+9QgeiKuBz8dUbL5ORl7x/KVEvaGA+sMCm0cZLoJKRETOy65BUI8ePZxmUjIRuSaFgwxzUukNwxrxjWEPkE50cHFPSK9G4RjXsRZ+2XcNDSP8xQDou8fa4KlfjwAwTDAAALVCfMTtbx9rg+4fbQcAdK0fil2X0iR1528ynkyhbpgvtrzUQ9xPzjA+PC0lMx8RAV6SshNJ6eJ2/O0chPt7iYHY0K/2SOpWL3Guh9wNrWOCxEQQf5+4iYfb1ES3BmFG72+OuLf+E7d1wZwgCPj3VGGWuye71oGXB5MHEBG5Esf435+IqJLkq6TDpxzl9zDGUlqX9N6w5lC4F38Zd3OT4e0Hm2HX/3pizZTOYnnfptVNXuOZHnUlvUy1Qnzx99TOeOuBpvjqkdYGQYcpV27nYPxPB5F0NxcfbDiPN0ykrG7/3hYIggCNVj/ddHFv1t2cAhzQy8JWkrEFXUP8pD1g4346aFabjVl1VJqK+6c98cjKV6HDvC1iWWSg7ZMwEBFR5WK6GyJyeRqtgDM3M7Bw+xWDOUB5KuvMKSmvxDu5GL/oIORuhl/29W2b0QN1Qn2NHosO9jEoG9QiUpLdTOeV/o0MylrUDEKLmkEAgP2v9caSA4l4bfWpMtu+4+JtdP1wW5n16sz8FwBw9u1+yFaqkXSveJjZM78fxfyRxhM8vGsiA567kc/q9I0MNCvHkLXpy08YlDWf859kf3yn2hZfl4iIHBuDICJyaVn5KoMvtfpSM+2XGGHB5kv4dLPx4WY65Z3zEuAl/ee9VogP/niyg1nnPtQmyqwgyJhQPwXSso1/pt/uuIoFWy4ZlBsLRIDCBUqNORB/16Bs/9U7+GVfAtKyC/DG4CaoHeJjtBepPMoKUImIyPlwOBwRuazMMgIgoDDV8h0TX9ptKUepNhoATetTX7KvP2nfEvpJE/o1jcCOl3uihplr6yjc5bjwbn9cmjsAEQGKsk/Q81S3Org8d4DRY8YCoNL4ehoPgu6WyEQHAO+uO4flh69j6/lU9Px4O37WSyteER1jQ6xyHSIiciwMgojIZe26mGbymP5io4cSDHsWbG396WSj5Y0jAyTr02Qr1eW6voDiOThPdYu1+HyFuxwecjf8/kQHTOxcGy/3a4jfn2iPWQMb4++pnQ2GsH39SGssmnAfJnSqA3e5G06/1a/CmfdM9QTNHVb2QrFzTaz/lJWvwpO/HMZfx28gt0D62T7bo65B/W/HtTGjpURE5GwYBBGRy7qYYjrZgP56M5N/O2q1eyrVGqSWsj6OzhdbjfeKtI6phv9e7Cbuvzm4SbnaMbFzHTStEYABzaqjTa3gcl0DAOqF+2H2kKaY0rMeOtcLxZPdYtGiZhCGt66JPycXrzPkp3BHz0bh8HR3E/eXmViHSKd9HcN2DWhWnNQhLjrI6HmPtK9ldG6TPpVGwLJDhmvO/br/GjadTcELS4+LWfj8FO4493Z/zOjbUFI3ft5ABHiZt8AsERE5F84JIiKXJAgCvtlxxeTx7vXLn1K5tHs2fH2DuH/wtd547o9jeLZnPXQvkcK5QYQ/rt3JxYROtTF7SBPMW38ewb6eCPMvHH529b2ByFKqJesCWSLUT4F1z3ct/8OYoW3t4iAm3MiwObmbDF4ebshXGV9o9NvH2qDl25vE/dcHNcYTXWMhCAK0QulzcZ7pURcdYoPh6e6GQZ/vNlrnlZWnMOq+GABAQloO3l57VrK46pmbhYuzZivV8PYs7H37+fF2GP/TQQxrFWW1OUVEROR4GAQRkUvad/UOlHoLeMrdZBjbLgZ+Xu6Y0rMe3Ep8wW78xgace6d/he65+VyqZL/de4Vplg/EHzRYNFS3EGf3BmGQyWR4bWBjyXE3N1m5A6DKtPrZTkjPVaFR9QCjxw+/fj8uJGfioYWGax8F+XhK9p/oWjhsTyaTQW5G/NEqppok9bYxgiBAJpPhhaXHcOJ6htE6D7WuKW53bxCGXf/richA81KFExGRc2IQREQuSRdkAMDEzrXx4v0NSh3alKfS4OzNTDSpYfzLvDme/OWwyWP5Kg32X72Daj6emP33GXFx0JgQw/TWzqRVTOnzfvwU7kaH4+kCD3+FO7KUanw/rm257l9W5ra07AKE+SuQdM/4Yq4A8FjHWpJ9YynHiYjItXBOEBG5hJmrTqH2q+uQkVuYTc3drfift5kDGhsNgD58uIVk/5d9CSav/+2OK+j8/lZJcKWTma9C7VfXldq+n/cmYMKiQ3jwqz04npQuluvPTXJlJ2b3RePIALw2sBHOvNUPH48o/OxPvdUPV98biPubRJT72q8PKuxF2zy9G755VJrI4NqdHJy9mWk0o5xOwwj/ct+biIicE3uCiMjpTf71CDacKcy2Fvf2f3iiSx3Uj/ADAPTWm6xfUvMSi2suPZSEp7rFIjbMTyy7kZ6Hzu9vFfc/3XwR80e2lJz3854Eyf6eV3tJzgGAeevPG9y/Q2wwFO5yg3JXFOjtgfUvGJ+jVHJooqWe6BorDqWLDfWTHHv4G8NhePpaxwSJ84GIiKjqYE8QETkdlUaLUd/uwxtrTuNiSpYYAOn8sDser6wsXOzTq5QvuH4Kw98D9fpkh2RtnlklFg3NzDNMWZ2uV39G3waICvLGqTl9sfKZTqhdynC3z0dbvggqlc7SgOpyaraNWkJERI6MQRARORWNVsDMVadwIP4uft1/DX0/3Vlq/WY1Ak0e8zERIK08ch0AcP1eLrZfuF3i/los2HwJrd/ZhFNFE+095IX/lDaq7o+pvQoXO/X38kCbWtUQ6ifNmjb6vmi8PqgxEt4fhPAATr63t8z88q3DREREzo3D4YioTBqtgNM3MtCkRoD4hd9eftt/DX8WBSnm6BBreo0cXyM9QQAQWpSmesqSYwbHtl24jW1FgdGHG8+jQ2yImIq7Z6Nwg/pzHmiKwV8UpnAe2rIG3n+ohUEdsp+S88KIiKhqYBBERGX6attlzN90EaPvi7brl/g1x25g9t9nTB5vWiNAXPtFp7TsZV4ecgxrFYUzNzNwMaV4WFTinRwUqLU4oZfAwJhdl9Kw61KauO9pJEBsFhWI1jFBOJqYjqe71y31elR54ucNRGqWEhHsjSMiqpIYBBFRmeZvugigMHGAvYKg+f9dwOdbL5s8/lyveph+fwPIZDKoNVr8su8a+jYtO+PYp6NaIiNXhbi3/xPLPv7vIr7beVVS75X+jfDBBsPkBvoaRxrPMrZicico1Rr4ePKfXHuZ0rMuutYPw+jv9mNQ80jIZDIGQEREVRjnBBFRqbadTy27ko2dT840CID6NJYOPdMFQADgLnfD413qoGY189Z78fcyDE7054o0iPDDQ62jSr1GVJA3+jWtbvSY3E3GAMjOOtcLRYfYEJx/pz++eqS1vZtDRER2xiCIiEzSaAVMXHyowtfJzFdh5qqTOJp4r1znrz56w6BsQqc64nbX+qFiAFQebm4yzBnSBC2jgwyODYmrgf9e7I7wAC8cmtUHjSMDsPzpjgj29RTrLBjdEnte7VWhNpD1yd1k8Fe4450Hm6JjbAiAwiGQREREDIKIrEil0UKrFQzKs5Vq3EzPw3N/HMOxcgYC9pBTYJg566/jNyAIhs9Yml4f78AfB5Mw/Ou95WrHtyWGpslkQExwcS+PsYVQLTWhcx2sfraTQfkXY4rTWIf5K7D+ha5oVycYy5/uKJYb60ki+/trSmeceqsfHutYmwEqERFJ8H9uIitRqjXo8dF2RAZ6YdWzncXyJQcS8ZreWjP/nLiJhPcH2aOJFstRGgZBLyw9jrwCDUa3izHrGneylUjLVpa7DfoB14ROtTG1Vz34Kdwlv9HvWj+03NfXJ5PJ8PfUzvhpdzyOJaXjx/FtTdatF+6H53rVw94rd9C+TohV7k/Wset/PXEjPQ/NokynRycioqqNQRCRlZy5mYlbGfm4lZGP1Kx8hPsXTrp+rcRim0DhF3tH/830mZsZ2H/1rtFjr646haOJ99CpbigCvT2MpoYGgKlLjmLtyVuSsqUHE80OoARBENNLA8D/+jeUzK3Z9GI37LqUhqGtSp+vY4kWNYPwmZmLmL7UtyFestqdyVqig30QHWzefDAiIqqaGAQRWYl+SPPsb0fx5zOGQ6t0lGqtw85NuJtTgG93XsG3O66WWm/54etYfrhwvZ6pPethaKso3EzPQ7cGYQAArVYwCICAwgCqWVSgWb+lv5mRL0l5XTK5QP0If9SPMJ6RjYiIiMgUBkFEVqLfs3P42j0k3snFuJ8OGK2bla922CBoxooT2GphRrgvt13Gd7uuokCtBQB0qhuCz8eY7k0Z/MVuHHytN8LLSFG8/pRhEEVERERUUUyMQGQl525JF+ns9tE2JNzJNVr3UmpWZTTJIoIgIDNfZTIAqh/uV+r5ugAIAPZeuYNlh5Ikx1vHBEn2py8/UWZ73l13Ttz/aizTGhMREZF1MAgispKUzHyz6y7ek4C3/zlrNPGAvQz7ei9azPnP6LFtM3rgzSFNxP3hZszB+WjjBXF7TLsY/P5EB0kK6t2X08RtQRBwJ1sJQRCQkavCiG/2os7Mf8XjD7WuiUEtIi15HCIiIiKTOByOyEpyCzSlHv/usTZ46tcjAID/zqYAAH7aE+8QmeI2n03B8aR0SZmHXIboaj54qlss6oT6olawDxZNvA8NI/xRI8gbq44Zrt1jyrzhzQEA3iWGAM779xz8FO64lZmPJQcSIXeTQWMkxfgnI+MsfygiIiIiExgEEZXDhtPJOH0jAwOaV0fTGoEQBAHZpfTqNI8KRN+m1Y0e02oFuLnZN1PclCVHDcr2zeyNUD+FuO/mJkPPhoZZ4Aa1iMS6ogQIbWtVw+Fr0nWQnulRV9ye2qse9l29I+6XXP/HWAAUwDV4iIiIyMo4HI7IQpvPpmDyb0fw5bbLGPT5bgiCgN7zd2DJgUST57SpVQ0A0N9IIJSep7JZW82l1JvPo6MfABnzcr+GGNYqCgtGtRTLPni4BTZP7yap10svfXbneqE48FpvhPmXfm19+1/rbXZdIiIiInPwV6xEFvrvbLJkX3/uiim6BT8DvA3/yuUWqBHs62mdxlkoI1eFn/clGJSvmNyxzHOn9Kwnbn/zaGvczVGhbphh8oToatL1WiICvLDqmU7o+uE2s9pYMi02ERERUUXx2wWRmfJVGsxafRorj163+Fx10TCvoa2ixLV1dO5kF6BmNfss7PjuurNYcaS4PW1qVcOjHWJwX+1gi67Tv5k0acGHD7fA//48CQAIN9LrU9ZClk0iA7B8ckf4KfhPFBEREVkfv2EQmWnZoaRyBUAAEFvUQ9KpbihmDWyMH3ZfRUqmEgCw5VwK4vSyplWmnZduS/a/fqQ1IspYu8ccXeuHAgBiQ31Nznd6tkddfL39CgAgzF+BL8e0wsWULDzUpiZ7f4iIiMim+E2DyEwnrqebVe/w633Q9YNtaF0rCHOGNMW6U7fwaIcY8fiT3WLxZLdYDFywC2dvZdo1KULJRASB3h5WuW5koDcOvNYbAV6mrzejb0NEBHihW4Mw1An1BQC0jw2xyv2JiIiISsPECEQl/HvqFmq/ug5rSqSAXnVUur/z5Z4G545pF4NQPwXOvdMfvz/RAfUj/DGtTwMo3OUGdTvXK/zCr8usZg8ls715eRi2s7wiArzg7Wn6em5uMozvVFsMgIiIiIgqC4MgohKe/b0wXfS0ZcdxL6cAAHDmZoZBvZgQH/z3ojQT2nvDmpl9nxvpeQCAS6nZSM8tMFkvK1+F40npYnIFnQK1Fs/8dgTjfjqIWatPofar63DqumE7S7PrUvGCpUuf6mDRuURERETOikEQkZ7EO7mS/S+3XYZao8Wgz3dLyn8Y1xYA0CDCH0ffuB9NIgMwc0AjyGTmD217tX9jcfvBr/aYrDf77zMY+tUePL/0uFgmCAL+PnET608nY+fF2/i9KD33kC93426O6YBK3/YLqUjOzAcAvPNgU3TgUDQiIiKqIjgniEhPt4+kaZsPJ9zF80uPScoOzeojWecm2NcT/77Q1eJ7xYQUZ0i7ViL40qcbhvfPiZv458RNrH2uC15fcxrHk9KN1m/9ziYkvD+o1HtrtQImLDok7jeI8Leg5URERETOjUEQUZGSw80A4MT1DJzQG2LWqW6IRQt9WkKl0cJDXnbn7OAvdpdZJzUrHwFeHth/9Q4iA70BAA2rFwc6F1KyJPUZBBEREVFVwiCICMCmsyl48pfD4v6AZtWx/rR0UdSH29TExyPibNaG4V/vxT/PdYFKo8WpG4WB19Fr98w69+lusfh251Vxv93cLRjUPBLrTkmTLsTPGwiZTIZ9V+6IZdP61Ec1Oy3WSkRERGQPDIKIAEkABADDWkUZBEG2CID6NI7A5nMpAIBTNzIgCALmrjuHxXsTzL5Gx9gQzBzYGOM71UaPj7ajQKMFAIMACAD2XrmDxLu5eHvtWQCFgd20Pg0q/iBEREREToSJEajKu2ckkUDrWtXwZNc6Nr/3F2NaoXuDMHG/0/tbTQZAJ2b3NVo+a1BhgoUaQd7YOqN7qfc7dysTM1edEvfHd6xtWYOJiIiIXACDIKryWr2zSbJfK8QHoX4KzBrUBN882gYA8ObgJja5t7enHD8/3k7cv5WRb7Te52NaIdDbA5und8fQljXw04S2mDe8OT4b1RLNogLFejWr+eC1gY0k5+59tZe4/e66c5JjzWsGgoiIiKiq4XA4qtIOJ9yV7H8xppUkVXT/ZtXLzLRWGfo2iQAA1Av3w2ejW5Va9/HOdfDev+cBAP8+3xU1gryN1jvwWm/rNpKIiIjISTAIoiptp95ioQDQt2kEFO5yO7XGUKPq/nhjcBN4eZjfJne5m0HgtnFaN/T7bKe437NhGCICvKzWTiIiIiJnwiCIqjSlWiNuH3/zfrsFQH4Kd2Qr1QCA2DBfbJzWDe5uMosWXy1N3TBfyf77D7WwynWJiIiInBHnBFGVdjO9cA7OawMbIcjHfmmiVz7TSdz++pHW8JC7WS0AAgp7h/6e2hlj28fg4Gu92QtEREREVRp7gqjKOpZ4D/+cuAkAdg2AgMKFTG0996hFzSC0qBlk03sQEREROQP2BFGVEZ+Wg/i0HHF//qaL4nbrmGr2aBIRERER2QF7gsjlXUrJwvnkLDz3xzEAwLKnOiDY1xO7ipIidK4XgnrhfvZsIhERERFVIgZB5DK0WgGHr91D9QAv+Hu5w9tTjmWHkjD77zOSeqO+2y/Z//DhuMpsJhERERHZGYMgByYIglUnxzuKvAIN1Fot/L08rHK95Ix8vLvuLNJzVdh9Oa3sE0qIMrGODhERERG5JgZBlWzvlTQsP5SEY0npUKq08PGUQyYDPh4Rh+NJ6fhy62XcySkwOG/RhPvQs1G4HVpsPReSs3A86R5eWXkKALDkifboVC+0Qtc8mngPw7/eW2a9Z3vUxWMda+FEUjom/3ZULN/1v54Vuj8REREROR+ZIAiCvRtRXpmZmQgMDERGRgYCAgLs3RyoNFrJ2i4bTt/C5N+OwsdTjtwCDeqE+kom5lvq5Jy+eG3VKaw9eQsfPtwCI9tGW6vpZtH9UZHJZOLQM4W7G2KCfeDtKS91Qc8lBxLx2upTJo+/82BTjG4XAw+5NFeHWqOFu9wwf4dSrcHDC/fh1I0Mg2MNIvyQr9Ii8W4uxrSLwbtDm0HuVtyjJggCDsTfRePIAAR6W6c3ioiIiIjsy5LYgEGQFWi1AmatOY3lh5Og0Zb9cQb7emJoyygE+XhIMpTp69skAol3c5GtVOP6vTyjdeRuMmx7qQeiqnlDrdXiUko26ob5wdvT8gU/z93KhIfcDWF+Cvx59DqOJd6Dt4ccTWoEYMfF29h+4bZYt1aID67dyZWc7yYDdI++/oWuaBxZ/D40WgF1X/vXrHZ8OioOw1rVRF6BBnP+PoM1x2/g10nt0aZWNaTnFsDPyx0Hrt7FuJ8OGj3/ynsDJQEPEREREVUNThcEffXVV/joo4+QnJyMuLg4fPHFF2jXrl2Z5zlKEAQAb6w5jV/3XzN6zN1NBrVWQM+GYRjXsTa6NwiDm94X9b1X0qDVAjsv3ca1Ozn4eEScZL7M4C924fSNTIva4+/ljmBfT7jJZIhPyxHbMKBZddxXOxiDWkRizbEbmLf+fPke2ALNogIk7f94RBwGt4hEozc2WOX6q57thJpB3gjnAqBEREREVZZTBUHLli3DuHHj8M0336B9+/b47LPPsGLFCly4cAHh4aXPgXGkIChfpcG286lIvJuLzedS8MbgJvD38kDNat4GQ7wsdfZmJn4/cA2HE+7hxfvro1/T6hj0+W6cvWVZYGSJyEAveHvKERXkjSAfT2i1AsL8FRjaKgppWUqkZinRPjYYsaG+SMsuwPnkTDz2o/HeGZ3hraMwf2RLcf92lhJ/Hb+BQS0ikZKpxNCv9ljUxhY1A7H86Y6lDsMjIiIioqrBqYKg9u3b47777sOXX34JANBqtYiOjsZzzz2HV199tdRzHSkIsodztzJxN6cAgd4eCPDygLenHFpBwMWULDz240HUCvFB1/qhqBvmh6u3c/Dr/mvo1SgcN9PzcD45S7xOdLA3Ph3ZEmH+CsSn5aBDbEi5A4sfd8fjnbVnDcond6+LF++vD4W76eveysjDM78dxfGkdADAWw80xYBm1bH5XCr+PJKEYa2isOPibZy6kYGnutXFpC51ytVGIiIiInI9ThMEFRQUwMfHB3/++SeGDh0qlo8fPx7p6en466+/JPWVSiWUSqW4n5mZiejo6CobBJWXVivgWNI91A3zQ5CPp03vJQgCCjTaUoMfIiIiIqKKsiQIqtg4rQpKS0uDRqNBRESEpDwiIgLJyckG9efNm4fAwEDxJzq6crOjuQo3Nxna1Aq2eQAEFGaSYwBERERERI7ErkGQpWbOnImMjAzxJykpyd5NIiIiIiIiJ2PXxVJDQ0Mhl8uRkpIiKU9JSUH16tUN6isUCigUispqHhERERERuSC79gR5enqiTZs22LJli1im1WqxZcsWdOzY0Y4tIyIiIiIiV2XXniAAmD59OsaPH4+2bduiXbt2+Oyzz5CTk4OJEyfau2lEREREROSC7B4EjRo1Crdv38abb76J5ORktGzZEhs2bDBIlkBERERERGQNdl8nqCKq+jpBRERERERUyGlSZBMREREREVU2BkFERERERFSlMAgiIiIiIqIqhUEQERERERFVKQyCiIiIiIioSmEQREREREREVQqDICIiIiIiqlIYBBERERERUZXibu8GVIRundfMzEw7t4SIiIiIiOxJFxPoYoTSOHUQlJWVBQCIjo62c0uIiIiIiMgRZGVlITAwsNQ6MsGcUMlBabVa3Lx5E/7+/pDJZPZujlVkZmYiOjoaSUlJCAgIsHdzyAS+J8fHd+Qc+J4cH9+Rc+B7cnx8R7YnCAKysrJQo0YNuLmVPuvHqXuC3NzcULNmTXs3wyYCAgL4F8QJ8D05Pr4j58D35Pj4jpwD35Pj4zuyrbJ6gHSYGIGIiIiIiKoUBkFERERERFSlMAhyMAqFArNnz4ZCobB3U6gUfE+Oj+/IOfA9OT6+I+fA9+T4+I4ci1MnRiAiIiIiIrIUe4KIiIiIiKhKYRBERERERERVCoMgIiIiIiKqUhgEERERERFRlcIgyAZ27tyJIUOGoEaNGpDJZFizZo3keEpKCiZMmIAaNWrAx8cH/fv3x6VLlyR1rly5gmHDhiEsLAwBAQEYOXIkUlJSDO61bt06tG/fHt7e3qhWrRqGDh1qwydzHfPmzcN9990Hf39/hIeHY+jQobhw4YKkTn5+PqZMmYKQkBD4+fnhoYceMngHiYmJGDRoEHx8fBAeHo6XX34ZarVaUmf79u1o3bo1FAoF6tWrh8WLF9v68VxGZb4nnT179sDd3R0tW7a01WO5lMp8R7///jvi4uLg4+ODyMhIPP7447hz547Nn9EVWOs9Pf/882jTpg0UCoXRvyPbt2/Hgw8+iMjISPj6+qJly5b4/fffbfloLqOy3hEACIKAjz/+GA0aNIBCoUBUVBTmzp1rq0dzKdZ4TydOnMCYMWMQHR0Nb29vNG7cGAsWLDC4F78/2BaDIBvIyclBXFwcvvrqK4NjgiBg6NChuHr1Kv766y8cO3YMtWrVQp8+fZCTkyOe37dvX8hkMmzduhV79uxBQUEBhgwZAq1WK15r5cqVeOyxxzBx4kScOHECe/bswdixYyvtOZ3Zjh07MGXKFOzfvx+bNm2CSqVC3759xXcAAC+++CL++ecfrFixAjt27MDNmzcxfPhw8bhGo8GgQYNQUFCAvXv34ueff8bixYvx5ptvinXi4+MxaNAg9OzZE8ePH8e0adPwxBNPYOPGjZX6vM6qst6TTnp6OsaNG4fevXtXyvO5gsp6R3v27MG4ceMwadIknDlzBitWrMDBgwfx5JNPVurzOitrvCedxx9/HKNGjTJ6n71796JFixZYuXIlTp48iYkTJ2LcuHFYu3atzZ7NVVTWOwKAF154AT/88AM+/vhjnD9/Hn///TfatWtnk+dyNdZ4T0eOHEF4eDh+++03nDlzBrNmzcLMmTPx5ZdfinX4/aESCGRTAITVq1eL+xcuXBAACKdPnxbLNBqNEBYWJnz//feCIAjCxo0bBTc3NyEjI0Osk56eLshkMmHTpk2CIAiCSqUSoqKihB9++KFyHsTFpaamCgCEHTt2CIJQ+Hl7eHgIK1asEOucO3dOACDs27dPEARB+PfffwU3NzchOTlZrLNw4UIhICBAUCqVgiAIwv/+9z+hadOmknuNGjVK6Nevn60fySXZ6j3pjBo1Snj99deF2bNnC3FxcbZ/IBdkq3f00UcfCbGxsZJ7ff7550JUVJStH8kllec96bPk78jAgQOFiRMnWqXdVYmt3tHZs2cFd3d34fz58zZre1VS0fek8+yzzwo9e/YU9/n9wfbYE1TJlEolAMDLy0ssc3Nzg0KhwO7du8U6MplMspiWl5cX3NzcxDpHjx7FjRs34ObmhlatWiEyMhIDBgzA6dOnK/FpXEdGRgYAIDg4GEDhb2lUKhX69Okj1mnUqBFiYmKwb98+AMC+ffvQvHlzREREiHX69euHzMxMnDlzRqyjfw1dHd01yDK2ek8AsGjRIly9ehWzZ8+ujEdxWbZ6Rx07dkRSUhL+/fdfCIKAlJQU/Pnnnxg4cGBlPZpLKc97qsi9dPch89nqHf3zzz+IjY3F2rVrUadOHdSuXRtPPPEE7t69a90HqCKs9Z5K/j3h9wfbYxBUyXR/EWbOnIl79+6hoKAAH3zwAa5fv45bt24BADp06ABfX1+88soryM3NRU5ODmbMmAGNRiPWuXr1KgBgzpw5eP3117F27VpUq1YNPXr04D9kFtJqtZg2bRo6d+6MZs2aAQCSk5Ph6emJoKAgSd2IiAgkJyeLdfS/tOmO646VViczMxN5eXm2eByXZcv3dOnSJbz66qv47bff4O7ubuMncV22fEedO3fG77//jlGjRsHT0xPVq1dHYGCg0WHHVLryvqfyWL58OQ4dOoSJEydWpMlVji3f0dWrV3Ht2jWsWLECv/zyCxYvXowjR47g4YcftuYjVAnWek979+7FsmXL8NRTT4ll/P5gewyCKpmHhwdWrVqFixcvIjg4GD4+Pti2bRsGDBgAN7fC1xEWFoYVK1bgn3/+gZ+fHwIDA5Geno7WrVuLdXRzg2bNmoWHHnoIbdq0waJFiyCTybBixQq7PZ8zmjJlCk6fPo2lS5fauylUClu9J41Gg7Fjx+Ktt95CgwYNrHrtqsaWf5fOnj2LF154AW+++SaOHDmCDRs2ICEhAZMnT7b6vVxdZf2bt23bNkycOBHff/89mjZtatN7uRpbviOtVgulUolffvkFXbt2RY8ePfDjjz9i27ZtBhP8qXTWeE+nT5/Ggw8+iNmzZ6Nv375WbB2Vhb/ytIM2bdrg+PHjyMjIQEFBAcLCwtC+fXu0bdtWrNO3b19cuXIFaWlpcHd3R1BQEKpXr47Y2FgAQGRkJACgSZMm4jkKhQKxsbFITEys3AdyYlOnTsXatWuxc+dO1KxZUyyvXr06CgoKkJ6eLvltTkpKCqpXry7WOXjwoOR6uuwv+nVKZu5JSUlBQEAAvL29bfFILsmW7ykrKwuHDx/GsWPHMHXqVACFXxIEQYC7uzv+++8/9OrVy8ZP6Pxs/Xdp3rx56Ny5M15++WUAQIsWLeDr64uuXbvi3XffFf9NpNJV5D1ZYseOHRgyZAg+/fRTjBs3zhpNrzJs/Y4iIyPh7u4u+aVP48aNARRmaWzYsGHFH6IKsMZ7Onv2LHr37o2nnnoKr7/+uuQYvz/YHnuC7CgwMBBhYWG4dOkSDh8+jAcffNCgTmhoKIKCgrB161akpqbigQceAAAx/aX+b21UKhUSEhJQq1atSnsGZyUIAqZOnYrVq1dj69atqFOnjuR4mzZt4OHhgS1btohlFy5cQGJiIjp27AigcI7CqVOnkJqaKtbZtGkTAgICxOC0Y8eOkmvo6uiuQaWrjPcUEBCAU6dO4fjx4+LP5MmT0bBhQxw/fhzt27evnId1UpX1dyk3N1fsCdeRy+ViG6h01nhP5tq+fTsGDRqEDz74QDK8h0pXWe+oc+fOUKvVuHLlilh28eJFAOD3BzNY6z2dOXMGPXv2xPjx442mJ+f3h0pgt5QMLiwrK0s4duyYcOzYMQGAMH/+fOHYsWPCtWvXBEEQhOXLlwvbtm0Trly5IqxZs0aoVauWMHz4cMk1fvrpJ2Hfvn3C5cuXhV9//VUIDg4Wpk+fLqnzwgsvCFFRUcLGjRuF8+fPC5MmTRLCw8OFu3fvVtqzOqtnnnlGCAwMFLZv3y7cunVL/MnNzRXrTJ48WYiJiRG2bt0qHD58WOjYsaPQsWNH8bharRaaNWsm9O3bVzh+/LiwYcMGISwsTJg5c6ZY5+rVq4KPj4/w8ssvC+fOnRO++uorQS6XCxs2bKjU53VWlfWeSmJ2OPNV1jtatGiR4O7uLnz99dfClStXhN27dwtt27YV2rVrV6nP66ys8Z4EQRAuXbokHDt2THj66aeFBg0aiP/X6bL4bd26VfDx8RFmzpwpuc+dO3cq9XmdUWW9I41GI7Ru3Vro1q2bcPToUeHw4cNC+/bthfvvv79Sn9dZWeM9nTp1SggLCxMeffRRyTVSU1PFOvz+YHsMgmxg27ZtAgCDn/HjxwuCIAgLFiwQatasKXh4eAgxMTHC66+/bpCq95VXXhEiIiIEDw8PoX79+sInn3wiaLVaSZ2CggLhpZdeEsLDwwV/f3+hT58+ktTbZJqx9wNAWLRokVgnLy9PePbZZ4Vq1aoJPj4+wrBhw4Rbt25JrpOQkCAMGDBA8Pb2FkJDQ4WXXnpJUKlUkjrbtm0TWrZsKXh6egqxsbGSe1DpKvM96WMQZL7KfEeff/650KRJE8Hb21uIjIwUHnnkEeH69euV8ZhOz1rvqXv37kavEx8fLwiCIIwfP97o8e7du1fewzqpynpHgiAIN27cEIYPHy74+fkJERERwoQJExiomska72n27NlGr1GrVi3Jvfj9wbZkgsBxBEREREREVHVwThAREREREVUpDIKIiIiIiKhKYRBERERERERVCoMgIiIiIiKqUhgEERERERFRlcIgiIiIiIiIqhQGQUREREREVKUwCCIiIiIioiqFQRAREREREVUpDIKIiMghTJgwATKZDDKZDB4eHoiIiMD999+Pn376CVqt1uzrLF68GEFBQbZrKBEROT0GQURE5DD69++PW7duISEhAevXr0fPnj3xwgsvYPDgwVCr1fZuHhERuQgGQURE5DAUCgWqV6+OqKgotG7dGq+99hr++usvrF+/HosXLwYAzJ8/H82bN4evry+io6Px7LPPIjs7GwCwfft2TJw4ERkZGWKv0pw5cwAASqUSM2bMQFRUFHx9fdG+fXts377dPg9KRER2xSCIiIgcWq9evRAXF4dVq1YBANzc3PD555/jzJkz+Pnnn7F161b873//AwB06tQJn332GQICAnDr1i3cunULM2bMAABMnToV+/btw9KlS3Hy5EmMGDEC/fv3x6VLl+z2bEREZB8yQRAEezeCiIhowoQJSE9Px5o1awyOjR49GidPnsTZs2cNjv3555+YPHky0tLSABTOCZo2bRrS09PFOomJiYiNjUViYiJq1Kghlvfp0wft2rXDe++9Z/XnISIix+Vu7wYQERGVRRAEyGQyAMDmzZsxb948nD9/HpmZmVCr1cjPz0dubi58fHyMnn/q1CloNBo0aNBAUq5UKhESEmLz9hMRkWNhEERERA7v3LlzqFOnDhISEjB48GA888wzmDt3LoKDg7F7925MmjQJBQUFJoOg7OxsyOVyHDlyBHK5XHLMz8+vMh6BiIgcCIMgIiJyaFu3bsWpU6fw4osv4siRI9Bqtfjkk0/g5lY4rXX58uWS+p6entBoNJKyVq1aQaPRIDU1FV27dq20thMRkWNiEERERA5DqVQiOTkZGo0GKSkp2LBhA+bNm4fBgwdj3LhxOH36NFQqFb744gsMGTIEe/bswTfffCO5Ru3atZGdnY0tW7YgLi4OPj4+aNCgAR555BGMGzcOn3zyCVq1aoXbt29jy5YtaNGiBQYNGmSnJyYiIntgdjgiInIYGzZsQGRkJGrXro3+/ftj27Zt+Pzzz/HXX39BLpcjLi4O8+fPxwcffIBmzZrh999/x7x58yTX6NSpEyZPnoxRo0YhLCwMH374IQBg0aJFGDduHF566SU0bNgQQ4cOxaFDhxATE2OPRyUiIjtidjgiIiIiIqpS2BNERERERERVCoMgIiIiIiKqUhgEERERERFRlcIgiIiIiIiIqhQGQUREREREVKUwCCIiIiIioiqFQRAREREREVUpDIKIiIiIiKhKYRBERERERERVCoMgIiIiIiKqUhgEERERERFRlfJ/nvCRbj0zGdgAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_bt.reset_index(drop=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-sGg9l_n4JI6",
        "outputId": "4f8742d7-c936-4bf4-97a9-8b4081ab8dc4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          Open      High       Low     Close      Volume     Marketcap\n",
              "0     121000.0  121000.0  118500.0  119500.0    139560.0  6.497053e+12\n",
              "1     119500.0  126500.0  119500.0  123500.0    382980.0  6.714528e+12\n",
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              "    </div>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "signal_dataframe = signal_dataframes.copy()\n",
        "signal_dataframe"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sps7juxv4_6K",
        "outputId": "5f74fb00-bcdd-4e0c-87a9-26854efd3480"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Date  rsi  sma_cross  ema_cross  roc  stochastic  Trend\n",
              "0    1995-05-02    0         -1         -1   -1           0    0.0\n",
              "1    1995-05-03    0         -1         -1   -1           0    0.0\n",
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              "...         ...  ...        ...        ...  ...         ...    ...\n",
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              "6760 2022-01-10    1         -1          1    1           0    0.0\n",
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              "\n",
              "[6762 rows x 7 columns]"
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              "      <th>6760</th>\n",
              "      <td>2022-01-10</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6761</th>\n",
              "      <td>2022-01-11</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "<p>6762 rows × 7 columns</p>\n",
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              "    .colab-df-convert:hover {\n",
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              "        document.querySelector('#df-8a11aeb9-83fd-41c0-9119-5357ac0485d4 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-8a11aeb9-83fd-41c0-9119-5357ac0485d4');\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
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              "    border-radius: 50%;\n",
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              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
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              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
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              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f1acd347-b492-4a8c-811e-ebe12b8e0a07 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_bb = df_bt.reset_index(drop=True)\n",
        "df_bb[\"Close\"]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cUDIqbyI5gzM",
        "outputId": "7ffd2c23-6859-4bb5-e4a3-5cc685f4c5f3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       119500.0\n",
              "1       123500.0\n",
              "2       122500.0\n",
              "3       122000.0\n",
              "4       121000.0\n",
              "          ...   \n",
              "6757     77400.0\n",
              "6758     76900.0\n",
              "6759     78300.0\n",
              "6760     78000.0\n",
              "6761     78900.0\n",
              "Name: Close, Length: 6762, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# dfdf[\"TDD_CLSPRC\"]의 행 순서를 반대로 합니다.\n",
        "rdf = df_bb['Close']\n",
        "\n",
        "signal_dataframes = pd.concat([signal_dataframe,rdf], axis=1)"
      ],
      "metadata": {
        "id": "3fIqO4s_rvTZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "signal_dataframes = pd.concat([signal_dataframes, df_bb['Marketcap']],axis=1)"
      ],
      "metadata": {
        "id": "4bmT7WxeHOQ1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "signal_dataframes = signal_dataframes.rename(columns={'Close':'TDD_CLSPRC'})\n",
        "signal_dataframes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "5C9sLIhI3J4e",
        "outputId": "7809f355-5344-4514-8589-a1ecb72d2966"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Date  rsi  sma_cross  ema_cross  roc  stochastic  Trend  \\\n",
              "0    1995-05-02    0         -1         -1   -1           0    0.0   \n",
              "1    1995-05-03    0         -1         -1   -1           0    0.0   \n",
              "2    1995-05-04    0         -1         -1   -1           0    0.0   \n",
              "3    1995-05-06    0         -1         -1   -1           0    0.0   \n",
              "4    1995-05-08    0         -1         -1   -1           0    0.0   \n",
              "...         ...  ...        ...        ...  ...         ...    ...   \n",
              "6757 2022-01-05    1         -1          1    1           0    0.0   \n",
              "6758 2022-01-06    1         -1          1    1           0    0.0   \n",
              "6759 2022-01-07    1         -1          1    1           0    0.0   \n",
              "6760 2022-01-10    1         -1          1    1           0    0.0   \n",
              "6761 2022-01-11    1         -1          1    1           0    0.0   \n",
              "\n",
              "      TDD_CLSPRC     Marketcap  \n",
              "0       119500.0  6.497053e+12  \n",
              "1       123500.0  6.714528e+12  \n",
              "2       122500.0  6.660159e+12  \n",
              "3       122000.0  6.632975e+12  \n",
              "4       121000.0  6.578606e+12  \n",
              "...          ...           ...  \n",
              "6757     77400.0  4.620612e+14  \n",
              "6758     76900.0  4.590763e+14  \n",
              "6759     78300.0  4.674340e+14  \n",
              "6760     78000.0  4.656430e+14  \n",
              "6761     78900.0  4.710158e+14  \n",
              "\n",
              "[6762 rows x 9 columns]"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>rsi</th>\n",
              "      <th>sma_cross</th>\n",
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              "      <th>0</th>\n",
              "      <td>1995-05-02</td>\n",
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              "      <td>-1</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>119500.0</td>\n",
              "      <td>6.497053e+12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1995-05-03</td>\n",
              "      <td>0</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>123500.0</td>\n",
              "      <td>6.714528e+12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1995-05-04</td>\n",
              "      <td>0</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>122500.0</td>\n",
              "      <td>6.660159e+12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1995-05-06</td>\n",
              "      <td>0</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>122000.0</td>\n",
              "      <td>6.632975e+12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1995-05-08</td>\n",
              "      <td>0</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>121000.0</td>\n",
              "      <td>6.578606e+12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>6757</th>\n",
              "      <td>2022-01-05</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>77400.0</td>\n",
              "      <td>4.620612e+14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6758</th>\n",
              "      <td>2022-01-06</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>76900.0</td>\n",
              "      <td>4.590763e+14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6759</th>\n",
              "      <td>2022-01-07</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>78300.0</td>\n",
              "      <td>4.674340e+14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6760</th>\n",
              "      <td>2022-01-10</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>78000.0</td>\n",
              "      <td>4.656430e+14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6761</th>\n",
              "      <td>2022-01-11</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>78900.0</td>\n",
              "      <td>4.710158e+14</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>6762 rows × 9 columns</p>\n",
              "</div>\n",
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              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-bd6cc5fb-2fd6-427f-9f1d-ded968c05454 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-bd6cc5fb-2fd6-427f-9f1d-ded968c05454');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-30bf0c44-2f9c-4a3e-b344-12fada7d9802\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-30bf0c44-2f9c-4a3e-b344-12fada7d9802')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-30bf0c44-2f9c-4a3e-b344-12fada7d9802 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def backtest(df):\n",
        "    initial_capital = 100000.0  # 초기 자본\n",
        "    shares = 0  # 주식 보유량\n",
        "\n",
        "    # 매 거래일의 포트폴리오 가치를 추적할 리스트\n",
        "    portfolio_values = []\n",
        "\n",
        "    for _, row in df.iterrows():\n",
        "        if row['rsi'] == 1 and initial_capital > 0:\n",
        "            shares = initial_capital / row['Marketcap']  # 현재 자본으로 최대한 매수\n",
        "            initial_capital = 0.0 # 현금은 0이 됩니다.\n",
        "\n",
        "        elif row['rsi'] == -1 and shares > 0:\n",
        "            initial_capital = shares * row['Marketcap']  # 모든 주식을 판매하고 현금을 얻습니다.\n",
        "            shares = 0.0  # 주식은 0이 됩니다.\n",
        "\n",
        "        # 매 거래일의 포트폴리오 가치 계산 (현금 + 주식 가치)\n",
        "        portfolio_value = initial_capital + shares * row['Marketcap']\n",
        "        portfolio_values.append(portfolio_value)\n",
        "\n",
        "    df['Portfolio_Value'] = portfolio_values\n",
        "    return df\n",
        "\n",
        "sd = backtest(signal_dataframes)\n",
        "\n",
        "# 그래프로 그리기\n",
        "plt.figure(figsize=(10,5))\n",
        "plt.plot(sd['Portfolio_Value'])\n",
        "plt.title('Portfolio Value Over Time')\n",
        "plt.xlabel('Date')\n",
        "plt.ylabel('Value in $')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6NCM-JFEmO-S",
        "outputId": "adcd5930-5973-4a54-c186-a4a1dc8abc77"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rsi_per = sd['Portfolio_Value'].iloc[-1]/sd['Portfolio_Value'][0]*100"
      ],
      "metadata": {
        "id": "q3CroCimFo06"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def backtest(df):\n",
        "    initial_capital = 100000.0  # 초기 자본\n",
        "    shares = 0  # 주식 보유량\n",
        "\n",
        "    # 매 거래일의 포트폴리오 가치를 추적할 리스트\n",
        "    portfolio_values = []\n",
        "\n",
        "    for _, row in df.iterrows():\n",
        "        if row['sma_cross'] ==1 and initial_capital > 0:\n",
        "            shares = initial_capital / row['Marketcap']  # 현재 자본으로 최대한 매수\n",
        "            initial_capital = 0.0  # 현금은 0이 됩니다.\n",
        "\n",
        "        elif row['sma_cross'] == -1 and shares > 0:\n",
        "            initial_capital = shares * row['Marketcap']  # 모든 주식을 판매하고 현금을 얻습니다.\n",
        "            shares = 0.0  # 주식은 0이 됩니다.\n",
        "\n",
        "        # 매 거래일의 포트폴리오 가치 계산 (현금 + 주식 가치)\n",
        "        portfolio_value = initial_capital + shares * row['Marketcap']\n",
        "        portfolio_values.append(portfolio_value)\n",
        "\n",
        "    df['Portfolio_Value'] = portfolio_values\n",
        "    return df\n",
        "\n",
        "sd = backtest(signal_dataframes)\n",
        "\n",
        "# 그래프로 그리기\n",
        "plt.figure(figsize=(10,5))\n",
        "plt.plot(sd['Portfolio_Value'])\n",
        "plt.title('Portfolio Value Over Time')\n",
        "plt.xlabel('Time')\n",
        "plt.ylabel('Value in $')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KHcm_pl8mXAq",
        "outputId": "aa3ba5c9-f49f-4b3a-9a52-0a5dd8810446"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sma_per = sd['Portfolio_Value'].iloc[-1]/sd['Portfolio_Value'][0]*100"
      ],
      "metadata": {
        "id": "40xIiQWT_Vf4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def backtest(df):\n",
        "    initial_capital = 100000.0  # 초기 자본\n",
        "    shares = 0  # 주식 보유량\n",
        "\n",
        "    # 매 거래일의 포트폴리오 가치를 추적할 리스트\n",
        "    portfolio_values = []\n",
        "\n",
        "    for _, row in df.iterrows():\n",
        "        if row['ema_cross'] == 1 and initial_capital > 0:\n",
        "            shares = initial_capital / row['Marketcap']  # 현재 자본으로 최대한 매수\n",
        "            initial_capital = 0\n",
        "        elif row['ema_cross'] == -1 and shares > 0:\n",
        "            initial_capital = shares * row['Marketcap']  # 모든 주식을 판매하고 현금을 얻습니다.\n",
        "            shares = 0.0  # 주식은 0이 됩니다.\n",
        "\n",
        "        # 매 거래일의 포트폴리오 가치 계산 (현금 + 주식 가치)\n",
        "        portfolio_value = initial_capital + shares * row['Marketcap']\n",
        "        portfolio_values.append(portfolio_value)\n",
        "\n",
        "    df['Portfolio_Value'] = portfolio_values\n",
        "    return df\n",
        "\n",
        "sd = backtest(signal_dataframes)\n",
        "\n",
        "# 그래프로 그리기\n",
        "plt.figure(figsize=(10,5))\n",
        "plt.plot(sd['Portfolio_Value'])\n",
        "plt.title('Portfolio Value Over Time')\n",
        "plt.xlabel('Time')\n",
        "plt.ylabel('Value in $')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nRr6pzewmilp",
        "outputId": "830f867b-c993-46c4-8379-d068d3ef29f1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ema_per = sd['Portfolio_Value'].iloc[-1]/sd['Portfolio_Value'][0]*100"
      ],
      "metadata": {
        "id": "snqUWh1dKLwB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def backtest(df):\n",
        "    initial_capital = 100000.0  # 초기 자본\n",
        "    shares = 0  # 주식 보유량\n",
        "\n",
        "    # 매 거래일의 포트폴리오 가치를 추적할 리스트\n",
        "    portfolio_values = []\n",
        "\n",
        "    for _, row in df.iterrows():\n",
        "        if row['roc'] == 1 and initial_capital > 0:\n",
        "            shares = initial_capital / row['Marketcap']  # 현재 자본으로 최대한 매수\n",
        "            initial_capital = 0\n",
        "\n",
        "        elif row['roc'] == -1 and shares > 0:\n",
        "            initial_capital = shares * row['Marketcap']  # 모든 주식을 판매하고 현금을 얻습니다.\n",
        "            shares = 0.0  # 주식은 0이 됩니다.\n",
        "\n",
        "        # 매 거래일의 포트폴리오 가치 계산 (현금 + 주식 가치)\n",
        "        portfolio_value = initial_capital + shares * row['Marketcap']\n",
        "        portfolio_values.append(portfolio_value)\n",
        "\n",
        "    df['Portfolio_Value'] = portfolio_values\n",
        "    return df\n",
        "\n",
        "sd = backtest(signal_dataframes)\n",
        "\n",
        "# 그래프로 그리기\n",
        "plt.figure(figsize=(10,5))\n",
        "plt.plot(sd['Portfolio_Value'])\n",
        "plt.title('Portfolio Value Over Time')\n",
        "plt.xlabel('Time')\n",
        "plt.ylabel('Value in $')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G-m_NFFxmjb2",
        "outputId": "00e549c9-5c88-4802-a18e-0ab37dd81063"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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itX79eqxatQr9+/fH3LlzDc576KGH8NJLL+Gll15CeHi4wUhZ//798dRTT2H27Nk4duwYhg4dCm9vb5w/fx7Lli3D559/LqlpVRfUx9dERO6LQZUJJ0+ehEqlQqtWrST7FQqFWFtGrVZDoVDg559/Ftv98MMP6N69O5KTkx1aF4SI6o/vv/8ezZo1w+LFi7FixQpER0dj5syZmDVrlsXX+PTTT/Hkk0/izTffRGlpKcaPH282qBo8eDDWrVuHWbNm4e2334a3tzf69++Pjz76yOppzH5+fti2bRtee+01/PTTTygoKEDr1q2xaNEiSbHY6rRs2RI9e/bEwYMHJQkqAE02wX///RfPP/88Zs+eDV9fX9x3332YOnUqOnfuXO213377bVy/fh3Lly/Hn3/+ieHDh2Pt2rWIjIyUtIuKisKBAwfw3nvv4e+//8bXX3+NBg0aoH379vjoo4+qfZ7AwEBs3rwZX3/9NX799Ve8/PLLEAQBbdq0wbx58/Dss88arb3UuHFj9O3bF7t378bkyZONtlm4cCG6d++Ob775Bq+//jq8vLwQHx+PsWPHol+/ftX2zRXVx9dERO5JJgh65c3dlEwmw4oVKzBy5EgAmqKTjz76KE6dOmWw2DYwMBDR0dGYNWsWPvzwQ1RUVIjHSktL4e/vjw0bNmDIkCG1+RKIiIiIiMgJOFJlQteuXaFSqZCdnY1bb73VaJt+/fpBqVTi4sWLaN68OQCI00ksXVxORERERER1m1uPVBUVFeHChQsANEHUp59+ioEDByI8PBxNmjTB2LFjsXv3bsydOxddu3bF9evXsXnzZnTq1AkjRoyAWq1Gz549ERgYiHnz5kGtVmPKlCkIDg7Ghg0bnPzqiIiIiIioNrh1ULVt2zaji8DHjx+PxYsXo6KiAv/3f/+Hn3/+GdeuXUNERAT69OmDd999Fx07dgQApKen47nnnsOGDRsQEBCA4cOHY+7cuQgPD6/tl0NERERERE7g1kEVERERERGRrVinioiIiIiIyAYMqoiIiIiIiGzgdtn/1Go10tPTERQUBJlM5uzuEBERERGRkwiCgMLCQjRq1AgeHjUfb3K7oCo9PR1xcXHO7gYREREREbmIK1euoHHjxjU+3+2CqqCgIACaNy44ONjJvSEiIiIiImcpKChAXFycGCPUlNsFVdopf8HBwQyqiIiIiIjI5mVBTFRBRERERERkAwZVRERERERENmBQRUREREREZAMGVURERERERDZgUEVERERERGQDBlVEREREREQ2YFBFRERERERkAwZVRERERERENmBQRUREREREZAMGVURERERERDZgUEVERERERGQDBlVEREREREQ2YFBFRERERERkAwZVRERERESEr7ddwIw/j0GtFpzdlTqHQRUREREREeF/65Lx95Fr2H0xx9ldqXMYVBERERERkeh6ocLZXahzGFQREREREZFIoVQ7uwt1DoMqIiIiIiISVagYVFmLQRUREREREYnKOVJlNQZVREREREQkWnH0mrO7UOcwqCIiIiIiItGp9AIcTbvp7G7UKQyqiIiIiIhI4kJ2kbO7UKcwqCIiIiIiIgmW/7UOgyoiIiIiIjcnCHphFKMqqzCoIiIiIiJyc4YxFaMqazCoIiIiIiJyc2q9qEo/yCLzGFQREREREbk5/RhKzaDKKgyqiIiIiIjcnP5IlUrNAsDWYFBFREREROTm9Kf7Vag4VGUNBlVERERERG7OMKjiSJU1GFQREREREbk5g0QVTupHXcWgioiIiIjIzekHVbGhfk7qSd3EoIqIiIiIyM3pZ/vTD7LIPAZVRERERETuTi+GUjJRhVUYVBERERERuTmDlOocqbIKgyoiIiIiIjenH1QVlilx7EoeBAZXFmFQRURERETk5vRDp/f/O42R83fjryPXnNKfuoZBFRERERGRmzOVmOLPg1dquSd1E4MqIiIiIiI3Z2qWH9dWWYZBFRERERGRmzMVOzG1umWcGlTNnj0bPXv2RFBQECIjIzFy5EgkJyebPWfx4sWQyWSSH19f31rqMRERERFR/WMqeFLrF7Aio5waVG3fvh1TpkzBvn37sHHjRlRUVGDo0KEoLi42e15wcDAyMjLEn9TU1FrqMRERERFR/ZNbXG50P6f/WcbLmU++bt06yfbixYsRGRmJw4cP47bbbjN5nkwmQ3R0tKO7R0RERETkFmavPWN0v0pdyx2po1xqTVV+fj4AIDw83Gy7oqIiNG3aFHFxcbj33ntx6tQpk20VCgUKCgokP0REREREVCUjr8zoftapsozLBFVqtRrTp09Hv3790KFDB5PtWrdujR9//BGrVq3Cr7/+CrVajb59++Lq1atG28+ePRshISHiT1xcnKNeAhERERFRnWRyTRWDKovIBBcJP5955hmsXbsWu3btQuPGjS0+r6KiAm3btsWYMWPw/vvvGxxXKBRQKBTidkFBAeLi4pCfn4/g4GC79J2IiIiIqC4b8PFWXL5RYvTY5Tkjark3taegoAAhISE2xwZOXVOlNXXqVPz333/YsWOHVQEVAHh7e6Nr1664cOGC0eNyuRxyudwe3SQiIiIiqpeUZrL8qdQCypVq+Pl41mKP6hanTv8TBAFTp07FihUrsGXLFiQkJFh9DZVKhZMnTyImJsYBPSQiIiIiqv/KKkxnpBj34350fneDyQyB5OSgasqUKfj111/x+++/IygoCJmZmcjMzERpaanYZty4cZg5c6a4/d5772HDhg24dOkSjhw5grFjxyI1NRWTJ092xksgIiIiIqrzzK2d2n3hBspVamw4lVmLPapbnBpULViwAPn5+RgwYABiYmLEn6VLl4pt0tLSkJGRIW7fvHkTTzzxBNq2bYs777wTBQUF2LNnD9q1a+eMl0BEREREVOc91KP6ZG7lzK9uksskqqgt9lqMRkRERERUX8zfegEfr09G74Rw7E/JFffHhvrhWl7VLLL6lrTCXrGBy6RUJyIiIiIi51BVJqpoHhmIXglVNWNvFCsk7ZQcrTKKQRURERERkZvTZv/zlMnw44SemH1/RwCGCSyKFMpa71td4BIp1YmIiIiIyHnU2qDKQ4ZAuRfG9GqCED9vXMguwqcbz4ntCsuUCPX3cVY3XRaDKiIiIiIiN6cdqfLykIn77uyoKVlUpFDi2x2XAAAFZRW137k6gNP/iIiIiIjcnEqtmebnqRNUab04tJX4eFvy9VrrU13CoIqIiIiIyM1p808YC6rkXp7i44/XJ9dWl+oUBlVERERERG7O3EgVVY9BFRERERGRm1PqJKog6zGoIiIiIiJyc2rBMFGFrtE94mqzO3UOgyoiIiIiIjenVGmCKg8TQdUjvZsAAGJD/WqtT3UJgyoiIiIiIjdXUq4CYHqkKtBXU4mpkCnVjWJQRURERETk5lafzAAA7L+Ua/R4kFwTVBWUKSFUThWkKgyqiIiIiIgIALD5bLbR/UG+3uLjs5mFtdWdOoNBFRERERERAQD6Nm9gdL+fT1WtqqUHr9RWd+oMBlVERERERG6ucZgmAcXkWxNMtrm1ZQQAQKFU1Uqf6hIGVUREREREbi6wcs2Ut6fp8OCWFtqgSl0rfapLGFQREREREbk5bZ0qT5np4r8+XprQoZxBlQEGVUREREREbk6l1gRVMiuCqrIKFT5efxZH0246voMujkEVEREREZGbq4yp4GmiThUA+FRODdRO//thVwrmb72I+77e4/D+uToGVUREREREbk47UmVmSZXBSNXF60UO71dd4eXsDhARERERkXNp11R5mJn+J68Mqs5lFWL6kqP453h6rfStLmBQRURERETk5tTiSJXpoKphkC8A4EZxOVYeY0Cli0EVEREREZGbU1kwUtWtSSi+frQb0vNKAQBXb5Zi8Z7LtdE9l8egioiIiIjIzakqs6SbC6pkMhnu7BgjbucUKcSgSq0W4GFmlKu+Y6IKIiIiIiI3J9apsiIw8vP2FB9fL1LYvU91CYMqIiIiIiI3VxVUWX6Ov09VULXhdBYKyirs3a06g0EVEREREZGb06ZUNzf9T59MJkOfZuEAgLdWJqHLuxsc0re6gEEVEREREZGbsyT7nzGNQvyqriHYtUt1CoMqIiIiIiI3Z0n2P2PCAnwk20ptxgs3w6CKiIiIiMiNXcktQVmFJhiydqQqzN9bsl1aobJbv+oSBlVERERERG5KqVLj1v9tFbetHakK9ZeOVJWWM6giIiIiIiI3Uq43Xc/L09qgSjpSdeVmic19qosYVBERERERuSn95BJeVk7/axcTDN3BrWIFR6qIiIiIiMiNqPSiKmvXVDVrGIgDrw8WCwErlKYTVShVatwxbwcmLT5ofUddHIMqIiIiIiI3dTg1V7Lt5WF9eNAwSI6OjUMAAOVmgqpjV/JwNrMQm89m42ZxudXP48oYVBERERERuak9F25Itq0dqdKSe2nCCoXS9PQ/H6+q0OPqzdIaPY+rYlBFREREROSmbF1TpaUNqsyNVFWoqp5MQP2qFMygioiIiIjITamFquCmV3w4PGocVFW/pkqhU8PKXPBVFzGoIiIiIiJyU7qJKpY82afG1/GxYPpfmc6x/Sm59SqwYlBFREREROSmYsP8xMc1HaUCqp/+p1YLKKuoOvbx+mTcLKk/ySq8nN0BIiIiIiJyjqhgOQAgItDHputUjVQZBlUbTmXihaXHcEvLCMl+uVf9Gd9hUEVERERE5Ka0ySM6xobYdB1tgLQt+bpBAeAfd6cAANafytI7x9Om53QlDKqIiIiIiNxUhUozsuTtaduoUViAZqTr5LV8nLyWb9E5HKkiIiIiIqI6T1k5UmVrUPVor6YAgKIypcGxX/alotDIflvWcLkaBlVERERERG6qaqTKtgAnxN8bzw5oYfRYfIMAvPLXCZuu7+rqz5gbERERERFZRbumysvGkSpz5N71P+So/6+QiIiIiIiMstdIlTn1KSGFKZz+R0RERETkppR2SlRhjv5IVYifNyb0jXfY8zkDgyoiIiIiIjdVrp3+5+HAoEony9/QdlH45rHukMnqT5IKgNP/iIiIiIjcljhS5VU70/86NQ6pdwEVwKCKiIiIiMhtiWuqHDhS5asz/S+7UOGw53EmBlVERERERG6qQm2fOlXm6I5UHUm76bDncSYGVUREREREbur3/WkAAAGCw55Dd03V84NaOux5nIlBFRERERGRm/vvRIbDrq2b/a9BoI/DnseZGFQREREREbkhlbpqdKpcqXbY87hDnSqnBlWzZ89Gz549ERQUhMjISIwcORLJycnVnrds2TK0adMGvr6+6NixI9asWVMLvSUiIiIiqj+2JWeLj/28HRf46CaqEBw3y9CpnBpUbd++HVOmTMG+ffuwceNGVFRUYOjQoSguLjZ5zp49ezBmzBhMmjQJR48exciRIzFy5EgkJSXVYs+JiIiIiOq20gqV+NjX27GJKoa1j0K7mGB0iA1x2PM4k0wQXCdevH79OiIjI7F9+3bcdtttRtuMHj0axcXF+O+//8R9ffr0QZcuXbBw4cJqn6OgoAAhISHIz89HcHCw3fpORERERFSXrD6RgSm/HwEA9EoIx59PJTq5R7XPXrGBS62pys/PBwCEh4ebbLN3714MHjxYsm/YsGHYu3ev0fYKhQIFBQWSHyIiIiIid6etUQUAvg6c/ucOXCaoUqvVmD59Ovr164cOHTqYbJeZmYmoqCjJvqioKGRmZhptP3v2bISEhIg/cXFxdu03EREREVFdpJucYsqA5k7sSd3nMkHVlClTkJSUhCVLltj1ujNnzkR+fr74c+XKFbten4iIiIioLnrlrxPi497NGjixJ3Wfl7M7AABTp07Ff//9hx07dqBx48Zm20ZHRyMrK0uyLysrC9HR0Ubby+VyyOVyu/WViIiIiIhIl1NHqgRBwNSpU7FixQps2bIFCQkJ1Z6TmJiIzZs3S/Zt3LgRiYnut7COiIiIiIicz6kjVVOmTMHvv/+OVatWISgoSFwXFRISAj8/PwDAuHHjEBsbi9mzZwMApk2bhv79+2Pu3LkYMWIElixZgkOHDuHbb7912usgIiIiIiL35dSRqgULFiA/Px8DBgxATEyM+LN06VKxTVpaGjIyMsTtvn374vfff8e3336Lzp07Y/ny5Vi5cqXZ5BZERERERESO4tSRKktKZG3bts1g36hRozBq1CgH9IiIiIiIiMg6LpP9j4iIiIiIqC5iUEVERERERGQDBlVERERERG4oPMAHALBoYk8n96TuY1BFREREROSGfL00oUCDyuCKao5BFRERERGRG1JVJo3zkMmc3JO6j0EVEREREZEbUqk1//X0YFBlKwZVRERERERuSOBIld0wqCIiIiIickPa6X+ejAhsxreQiIiIiMgNqdWaoErGkSqbMagiIiIiInJDlTEVPBlU2czL2R0gIiIiIiLHm7/1Ak6l52NA60gUlSlRrtRkquCaKtsxqCIiIiIicgMfr08GAKw5mSnZ78G5azbjW0hEREREVM9pM/0Zw5Eq2zGoIiIiIiKq5ypUpoMq1qmyHYMqIiIiInIp87dewPQlR8XsdGS7cm2lXyMYUtmOQRURERERuZSP1ydj5bF07L10w9ldqTe0SSmMYlRlMwZVREREROSSihRKZ3eh3qgwMVIV7OuFyCDfWu5N/cOgioiIiIhc0tWbpc7uQr1RVqEyuv/ZgS1quSf1E1OqExEREZFLev+/05h0S4Kzu1FnZReW4fk/juJw6k10jA0x2sbHk2Ms9sCgioiIiIioHvpm+yXsu5QLADiSlme0jY8Xgyp74LtIRERERC6DGf/sJ/VGSbVtGFTZB99FIiIiInIZSgZVdmNJ+Sk5gyq74LtIRERERC5DLTCoshcPGXOl1xYGVURERETkMhTm6imRVTwsuNNn4GUfDKqIiIiIyGUolNLU31xjVXMyEwHTrS0jxMeelswRpGoxqCIiIiIil6GokI5UXb5RXO05uy/k4NHv9+FyTvVt3YmxUaixfZpg8cReOm1qs0f1F4MqIiIiInIZ+tP/5N6e1Z7z6Pf7sfvCDUz++ZCjulUnXL1ZgvjXViNx9mYAxkf5gny9JaNTnP5nHwyqiIiIiMhl6E//09pyNgt3fr4Tp9MLTJ57IbvIUd1yumNX8nA07abZNo/9cAAAkJFfBoVShdUnMwzaeOoFUZz+Zx8MqoiIiIjIZZTrjVRpR1seX3wIpzMK8IQbjkYVK5QYOX837vt6D7IKyky2y8yvOjZx0UGjbTz0gihvT4YD9sB3kYiIiIhchn5QpV+36lpeqdnzhXqYkr2wTCk+/mD1GZOjeY1CfcXHey7eMNrmQIpm/5heTdCjaRj6Nm9gx566LwZVREREROQy9NdUqdQCyiqMBxHGnDIzPbCuElAVKP5zPB0PfbPPaLv7uzWu9lr7LuUCAGbf3xHLn+kLL45U2QXfRSIiIiJyGfojVSq1gBSdrH6dGoeYPf+bHZcc0i9nUqqko2/Hr+Q5pyNkEoMqIiIiInIZxkaq1DpT+tRGpvf1jA8TH/97PB1KVfUFhE+nF2D9qUyo6kAdrHILXg9gGHwZw2R/jsGgioiIiIhcRrlKr/ivIEgCrYJSpf4pkHtJ066XVjNdMCO/FHd+sRNP/XIYW85m29Db2lFhYVClUlff7sP7OtraHTKCQRURERERuQz96X9Xcktw/9d7xG3dDHeAJuDQX3O1Lfm62ee4+8tdOm1dP6jSf09M0U/qoXVf11jxcauoILv0iaQYVBERERGR0y07dAX7L90wmP73zG9HJNu6U+EqVGq0fGMtDqVK6zeF+Hmbfa6conLx8W/702ra5Vpj+UiVYVD1dP/mGNOribjNulSO4eXsDhARERGRezucmouXl5+wuL1aLcDDQ2ay2K+fj6fR/XXV1ZuGaeQz8ksRE+In2WdspMrHUwa5V9U4in7xX7IPjlQRERERkVO9+tdJq9qrKpNV6AYLkuN1IPmENaYtOWaw71yWYUBp7HUr1QJ8vauCTA/e/TsE31YiIiIicqqL142POJmiLYZrKnYyliHQnEOXc61q7wqMjTgpjSSqCJB7QXfGX8MguSO75bYYVBERERGRU3lYMCXtjyf6iI8/2ZAMAMgqKDPa1lhMpVYL+HbHRczfesHg2IML91rYU9ehUBpmODQ2UvVYYlOEB/iI2w0DGVQ5gs1rqtRqNVJTU9GkSRN4etav+atERERE5HhjesXh133mE0bEhVetHzqalgcAePT7/ZI2HjLN6JWx4GLa0mP493i6Vf3KzC9DeIAPfExMM3Qm/YQegGGdqidva4ZgX03Sjr+eSUSg3BsyrqlyCKt+Q1asWIE///xT3L506RKaNWuG5s2bo1GjRjh06JDdO0hERERE9ZuXBQt9dGtRmVpL5Ve5dsjY9D9rA6pT6fnoM3szHly4p/rGDtYlLtRgn7HAUX+fbvr07k3D0Tqa6dQdxaqg6uOPP0ZFRYW4/dZbb6Ft27Y4ceIE7rnnHsyYMcPuHSQiIiKi+q203HAqm/7aH93RIlMjR8WV10nJKTY45udt3Yyqvw5fAwCcuJpv1XmO0CTc32CfscBRP/vfHR2iHdYnkrIoqEpLS0NqaiouXLiAsLAwcXvdunWYPHkygoOD8eSTT+L48eNIS0tDWprr5/snIiIiItdQXK402BeqV2vKx7PqtjUySA7BTDKKd/89bbBP7m3dFD4BrpNB0NL1U/r7AuWsnlRbLHqnFy1aBAAoKSnB2rVrcfDgQVy9ehUKhQKnTp1CUlIS1Go1SktLsXjxYgDA22+/7bBOExEREZHreX3FSaTdKMFPj/eyqsissZEq/SDIy1OGVlGBOJdVhP9OZOCNEW3NXvOSXkZB/fVG+raezcbANpHitpUJBB2qrMJw/dT2c9dxf7fGkn2lFYbvI9UOi4KqWbNmAQD+/fdfNGjQALNmzcLLL7+M2267TQyeUlJS8OOPPzKYIiIiInJTv+/XzFY6diUP3ZuGWXyedqTq84e7iDWZ9KfreXnI4Kmz9mrO2rNmrzlo7naLnx8AJi4+iMtzRojbV2+WiI8rVGp4ezovWUWZkWBp1bF0fP5wV8m+wrKqZTq94sMd3i+qYtVvx6uvvooPPvgAvr6+mD9/Pt544w3x2KpVq3DLLbfYvYNERERE5PqSMwvFx9bWiSqpHKkK8q36vr9DbIikjUwmwyt3tBa3ixXSKYP/e7ATXhzSCoAmC2Cwr5fBjzXiGwSIjytUhiNFtanMSKY/Y7T1uwDA24tZ/mqTVb9do0aNQpcuXXDixAl0794d8fHx4rG2bdti+PDh9u4fEREREdUBugV8rb2d1yaD8PepujWNMFJPSVtjKSpYjgGtI7HpTLZ47KEecQCA525vafJ53vnnFBbvuWz0mP5sxcjgqucvV6rh7wOnUVgwre9GkQJndQJbS2p/kf1YvXqtZcuWaNnS8Jd12LBhdukQEREREdU9jcOq6khZM06lO80uUO6FUH9v5JVU4Pa2kfh4fbKkrW/llMCyCjW8rFizpeXtafoc/bwPupn0yi0cKbI3tVrA19suSIIlU0Z9Iy1gbM2aNrIdU4IQERERkc10M88t3HYRPSeYX9Pz95Gr2Hg6S5JsoW1MMLa8OABZBWVoEx0s7teuD9LWp1IoVVDVIJNEkcIwy6DJtjpT6cqdNP1v/alMfLLhnNFjjUJ8JduXrkvTyHOkqnYxqCIiIiIim+kGVZvPZmNrcjYGto402X7Gn8cBAGuTMsV9nh4yhAf4IDxAM9fun6n9sOlMNp4d0ByAdKRqe/J18bznzUz509U1Lgx/HLgibreJDpKMApVUJsw4m1mIr7ddFPc7a6Qqs6DM5LFu1SQCua9rrL27Q2Y4L40JEREREdUb+oVnJy46aPM1OzUOxYwhrcRgSrfu0obTWQCAjrEhmG5hUHVHR2kx3C/HSLPntXt7Pdq9vR73f71Hsr+imnTsjhLkK63VNT6xKcYnNgVgPnnGimf74q5OMQ7tG0kxqCIiIiIimxkrRmuNHhakYPfz8cRXj0gDoYSIAHhYuH7I10uapr1lVJBF5zlrpCpQLu3vswNboH0jTVZEc4Fe1yZhkHH6X63i9D8iIiIispn+SFWXuFCrzo+PCKi+EYC7OjXCWyuTcLNEU5PJ19vyMQLdRBV9mmnWaX05pivSckswsV+8pK2ftycGfLINqTdKnLamSj9wahDgI6ZKd3aad5KyOqjKysrCSy+9hM2bNyM7OxuC3iJBlYqVnImIiIjcjUotvcmPDfXDnos56Ns8wqCt/v0jIC1cW5137mmPf49nwNfbAxP7JVh8nu7oza0tGwIA7u7cyGR7bcHf2h6pSs4sxCcbkhHfwF+y38vTw2l9IvOsDqomTJiAtLQ0vPXWW4iJibFpaHHHjh34+OOPcfjwYWRkZGDFihUYOXKkyfbbtm3DwIEDDfZnZGQgOjrayBlEREREVBuUeqMqq09mYPXJDGx7aYDBKNQTPx+u9nxz7u0Si3u72JaIwZLn86kMYL7aeh7tYoIR4u9dzRn2Mf7HAyaTVGiDKt2RKt2A9PY2ppODkONYHVTt2rULO3fuRJcuXWx+8uLiYnTu3BmPP/447r//fovPS05ORnBwVZrNyEj+8hARERE5k6k1VSk5xQZB1aYzWQbtanuKnVJd/fN5V6Zw333hBj7ZkIz3R3ZwdLdw8mq+0YAq6V1NTVgfMaiqer+TrhWIj6cOauHgHpIxVgdVcXFxRodsa2L48OEYPny41edFRkYiNDTULn0gIiIiItvpr6nSEiwsBWzNSJU9WJLRL8SvamTqSNpNR3ZHtCYpw2DfPZ0biZkPtSNVGfll+HzTeQDAtbyqAsql5VyK4wxWZ/+bN28eXnvtNVy+fNkB3bFMly5dEBMTgyFDhmD37t1m2yoUChQUFEh+iIiIiMi+FDau8fGo5ZzU+mvAjHntjjbiY0uyE9qDsUSG2kAKAIJ8NcFVTpECn206h882ncOfh65WnW9hJkSyL6tHqkaPHo2SkhI0b94c/v7+8PaWzi3Nzc21W+f0xcTEYOHChejRowcUCgW+//57DBgwAPv370e3bt2MnjN79my8++67DusTERERkbvbejYbLy07bvSYpROc4sL8q29kBxGBcuQUKXB726hq27ZrFIwXh7TC3I3nUFZRO9MTPYzkK/DxqtrXqXEIZg5vg7TcEkmb3/anISEiAL3iwx3eRzJkdVA1b948B3TDMq1bt0br1q3F7b59++LixYv47LPP8Msvvxg9Z+bMmZgxY4a4XVBQgLi4OIf3lYiIiMgdKFVqTFxseaFfU8tIXtUZFXKkLS/1x5XcErHeU3X8fDS1okoqzE+r23AqExUqASNsLLprLAmc7kiVTCbDU/2bG7T54L6ONj0v2cbqoGr8+PGO6EeN9erVC7t27TJ5XC6XQy6X12KPiIiIiNzHr/tSzR7Xn45mKiFFWICP3fpkTrCvt8UBFQBxLVOxQmmyjUKpwpO/aDIankpvjlcqA0RBEFCoUCLY1/KsgdVN/yPXZNEnpLsOSX99krPXKx07dgwxMbZ9I0BERERENXMo1XwCB/0Ywdjaq48f7GTHHtlXqL8m2LtZUm6yje7UwK+3XRQfz157Fp3e2YB9l25Y/HzGpv8xqHJ9Fo1UhYWFISMjQ8y6Z2xYUhAEyGQyq4r/FhUV4cKFC+J2SkoKjh07hvDwcDRp0gQzZ87EtWvX8PPPPwPQTD1MSEhA+/btUVZWhu+//x5btmzBhg0bLH5OIiIiIrKf6hJUqPWm+yn01iatn34bWkUF2r1f9hJeOYJ2NC0PJeVK+PsY3j5XmBh9+3bHJQDAw9/uw+U5I6p9LkEQ8OnGcwb7fTyZfMLVWRRUbdmyBeHhmkVvW7dutduTHzp0SFLMV7v2afz48Vi8eDEyMjKQlpYmHi8vL8eLL76Ia9euwd/fH506dcKmTZuMFgQmIiIiIsfTDyieHdBcMlqjH28olNIv4FtHBzmsb/bQOqqqf8mZhejaxDALYLleYPnev6cRH2F94o1Vx9KN7udIleuzKKjq37+/0ce2GjBggNmaV4sXL5Zsv/LKK3jllVfs9vxEREREZBv9ukheeouC9IsC645s1YXs3yH+3ogN9cO1vFKTtbj6ztki2f5xd4pk29PCF/r2qiSj+7VFiMl18RMiIiIiohqrLmO6QVClM/0vOtjXAT2yvwC5JgNgRQ1rcem/B6YUlBlPhsGRKtfHT4iIiIiIakQQBBxI0dQo9fP2xJYX+0OhN99PpTcr6fudl8THX4/t7vhO2oE2qDGVudDRAiuDOnJdVqdUJyIiIiICgEs5xeLjBWO7oVnDQINEFCp11fahy7n4++g1cbtLXKjD+2gPYlClVGPn+eu4UWQ6E6C93N81VnyvwgNYHsjVMagiIiIiohopUVStp5J7aUZT9LMB6g7uPLhwb630y958KoOqTWey8Oehq1afH+wrveUuq1Dh4vUitIsJFrNqq3WmCPZr0QDPDmwuBlWNQuvGNEl3VqOgSqlUYtu2bbh48SIeeeQRBAUFIT09HcHBwQgMdN2UmERERERkPx+sOS0+9vGqGs3RteFUJuIb+FtVcNfVeFWmNL+QXQQAiAiUo41O1sJdF3KMnjeySyOsPJYuFhAGNAHVnZ/vxKWcYozq3hgfj+qM9LxSDP50u9hm7qguiA7xxf8e6ASlWkC7mGBHvCyyI6uDqtTUVNxxxx1IS0uDQqHAkCFDEBQUhI8++ggKhQILFy50RD+JiIiIyMXsu5QrPm7aQJNCXD9l+obTWdhwOgvtG9XdwEA7/e9mSQUAYHDbSMx5oKpg8T1f7cKJq/kG5z3UIw4rj6WjrDLQ3HMxB498t188vuzwVXw8qjPmbTqHEp0sipFBmul+D/WMs/+LIYewOlHFtGnT0KNHD9y8eRN+fn7i/vvuuw+bN2+2a+eIiIiIyDWl3SiRbEcEagKBIF/j39mfSi9weJ8cRRtUpeeVAjB8jZ0aG47CPTugOWJCNffK2tG7V5afMHr9k9ek741HXcg1TxJWB1U7d+7Em2++CR8fH8n++Ph4XLt2zcRZRERERFSfvGWiptLkW5shvkH1hW+XP51o7y45TMMgzX2vdr1YdIif5PjLQ9tg2u0tJfu8PD0MpkR6yIwHS3V5FI80rJ7+p1aroVKpDPZfvXoVQUGuXRGbiIiIiDQUShVScorROipITJZgKZVawPZz140ea94wENteHohpS45i1bF0o20+eqAjesSHW91nZ3lxaGu0jQlGuVKNQLkX7unSSHI8xN8bLwxphc83nxf3CYIgJrgoV6khCILJIsDa6X5Ud1k9UjV06FDMmzdP3JbJZCgqKsKsWbNw55132rNvREREROQgjy8+iDvm7cQ/x40HPub8ui+12jamAggAeLB73VorFBEox7jEeEy+tRke7tUE/j7Vj0scTcsTR6oATWBl7B1RqtSS9VRUN1kdVM2dOxe7d+9Gu3btUFZWhkceeUSc+vfRRx85oo9EREREZGe7L9wAAPyyt/oASd+Ko9Uv+RjZJRYtIg2zQvv7eJoNuOqL2FA/yHWDKqVaUtdLq0ypRkm5sja7Rg5gdVDVuHFjHD9+HK+//jpeeOEFdO3aFXPmzMHRo0cRGRnpiD4SERERkYMI1TeBQqmS1FFSqqVp05/q38zgnNtaNcSmGf0N9jdrGGB1H+uKL8Z0FR8/P7ilOP0PAF77+6TRc0rLVSjmSFWdV6M6VV5eXhg7dqy9+0JEREREtUwQzIdV+aUV6PPhZnSJC8UfT/YBAChV0nOGd4gxef7797bHW6tOidvBvt429Na13d0pBk3C/dEk3B/hAZrkFg0CfHCjuByrT2QYPae0XIUSRdVI1ay729VKX8m+rA6qfv75Z7PHx40bV+POEBEREVHtKlepzR5ffyoTpRUq7L10A3fM24EFY7sbFPg1N5nvscR4PNg9DtuSszF/2wX838gOdui1a5LJZOgSFyrZt3hiL+y7dEPc/mDNGcnx3JJypOZq0tN/eF9HPNK7icP7SfZndVA1bdo0yXZFRQVKSkrg4+MDf39/BlVEREREdUjStQIIgmAyA+DF60Xi47OZhRj4yTZEB/tK2rSONp8B2s/HE8M7xmB4R9MjWvVVx8Yh6KhTx8ogqCpW4NJ1zVqrRqHS95XqDqvXVN28eVPyU1RUhOTkZNxyyy34448/HNFHIiIiItLz5ebzeGnZ8Wqn71mi35wtyClSGD32zfZLBvuKdRIrJP/fHfD19rS5D+7qTEah+LhnHUozT1JWB1XGtGzZEnPmzDEYxSIiIiIix5i78RyWH76KZYeumgyILJWeX4YfdqUYPRbsazixqbCsKqjSTcZA1bu1ZQQAoEm4pkDymYwCAEDjMD8EyGuU7oBcgN3+Cry8vJCebn2dAyIiIiKyjkJZlS3ulb9OYOAn22y+5oJtFw32CYKAgjLDdN+to6qm+1lbONjdLZ7YC0ffGoKuTUIBAP9VJrDQJragusnqcPiff/6RbAuCgIyMDHz11Vfo16+f3TpGRERERMZdL5SOTBWWKVFQVmFxZj1BEODlIYNSbX7qoO7UNK0w/6rnWDyxp0XPR1U8PWQIC/DBnR1jsPtCDsoq1PD0kOH+rrHO7hrZwOqgauTIkZJtmUyGhg0bYtCgQZg7d669+kVEREREJrz2l2HNo883ncdbd1mWjrusQl1tQAUAN4oNpxUqlGoUllUAAEL9ObpSU8PaR2NY+2hnd4PsxOqgSq02n3aTiIiIiBxr14Ucg30/7EqxOKgqqAyKqvPnoasG+0rKVSipLFYbZGS9FZE74spCIiIiIjeTklNsUbvrhWVmjzOoItKw6C9hxowZFl/w008/rXFniIiIiMjx0iqLzVYnISIA+y7l4taWEcjIL8OF7CLJcUvXcBHVdxYFVUePHrXoYsz+QkRERORYttalUihVeGX5CaPH8ksqEKKTiEI7za9/q4aYdEsCEmauEY8F+3pB7sVJT0SAhUHV1q1bHd0PIiIiIrKAQmnb+vYfd102eWz1yQw80rsJAKBcqcaqY5pyOXIvD8hkMjw/qAXmb7uIp25rhltbNuQX6kSVOBGWiIiIqA7ZbSRJhVZJuWFNKX17Lpo+X7eI8Hc7L4mPtYkCZwxtjRlDW1vQSyL3UqOg6tChQ/jzzz+RlpaG8vJyybG///7bLh0jIiIiqk/+O5GO7AIFHr8lwabr+Hp7SrY7Nw7B8av5AIB2b6+36lovD2uNqGBfnErPx6Ldl7H34g08f3tLAEB0sK/YzsuTI1JE5lgdVC1ZsgTjxo3DsGHDsGHDBgwdOhTnzp1DVlYW7rvvPkf0kYiIiKhOU6sFTP1ds0b9tlYRaBEZZPU1TqcX4P4Fu9GhUQgAIC7cDztfGQRBEDBq4V4cSr1p9TWfHdAcMpkMGXmlAIC9l27g//47DUCaIdCT0/yIzLI6qPrwww/x2WefYcqUKQgKCsLnn3+OhIQEPPXUU4iJiXFEH4mIiMiNZOSX4tDlm7izYww8PerHzfxzf1Ql/copKkeLSOuvcecXOwFADJ7kXpoRK5lMhmVPJ6K0QmXxtTw9ZOL5ADC4XRTmbjwHAPh+V4pBe4968jkQOYrVQdXFixcxYsQIAICPjw+Ki4shk8nwwgsvYNCgQXj33Xft3kkiIiJyH/d8tRvXCxXILS7H+L7xNbpGQVkFftmbirs6xaBpgwD7dtBKSpUaq09miNsedhr1KVFUrZ+SyWTw96n5Uvm2McH49KHOSM4qlOz/ZrtmXdWtLSNqfG0id2D1X19YWBgKCzV/cLGxsUhKSkLHjh2Rl5eHkhLLah4QERERmXK9UJMsYfXJjBoHVR/8dwZLD13Bgm0XkfTuMDv2znoFZdLkEeezC9ErIdzm66bnmy/Ma637uzU22PfC4FYoq1Ah1N/Hrs9FVN9YXVzgtttuw8aNGwEAo0aNwrRp0/DEE09gzJgxuP322+3eQSIiIiJrHbicCwAoUlSfDc/R8kqkSb3eWJHkpJ5Yz9fbkwEVkQUsHqlKSkpChw4d8NVXX6GsTPPNyBtvvAFvb2/s2bMHDzzwAN58802HdZSIiIjciy3LeLx0Tl57MgPDOzpv3XeGjSNKKrWAY1fy7NMZInIIi4OqTp06oWfPnpg8eTIefvhhAICHhwdee+01h3WOiIiI3Jcta4+u69RbenHZcbsEVWUVKrR5ax0A4Oz7dxikNjcm9UYxHv1+f42f8+e9l/H2qlNGj3GdE5HrsHj63/bt29G+fXu8+OKLiImJwfjx47Fz505H9o2IiIjcWE2DqqUH05BXUiFu2yuD4H8nqpJNbD2bXW37w6k30f/jbQb7IwItn05nKqACgJ8m9rL4OkTkWBYHVbfeeit+/PFHZGRk4Msvv8Tly5fRv39/tGrVCh999BEyMzMd2U8iIiJyMzVN4/3B6jOSbR9Pq5eQG5VbXDX6dS6rCEfTzNeFemDBHqP721fWmaqpJ29rhk8f6sw050QuxOr/ywQEBGDixInYvn07zp07h1GjRmH+/Plo0qQJ7rnnHkf0kYiIasnh1Jv4dOM5lCvVzu4KuSmVWhAfq3UeW0M/2549gg9BEPDhmrPi9mebzuG+r/fgnF4Kci1jfW8WoUntbk09KWNevaON0Ux9ROQ8NS9oAKBFixZ4/fXX0bRpU8ycOROrV6+2V7+IiMgJtN+sB8m98MRtzZzcG3I3Sw6k4bW/T4rbeaXlZlpbrqC0ovpG1dCdTqjrxNV8tIoKMthfVG6YdTDYzxsAkFtcXu0oV4DcCy0jA40eqy8FkYnqkxoHVTt27MCPP/6Iv/76Cx4eHnjooYcwadIke/aNiIic5ExmgbO7QG5IN6ACgBKFbSM6Wu0bBdt8jUW7U4zul3sZn/RjLJAL89cEVReyi3Df18anBur64L4OVvSQiJzJqqAqPT0dixcvxuLFi3HhwgX07dsXX3zxBR566CEEBDi3WjkREdmPUlWzaVdE9nQpp9gu1zmSlgdBECCzIZvgHwevWNU+30hQ1SuhASpUAlJzzb+u/JIKFJQpceiy4WjWN491t6ofRFQ7LA6qhg8fjk2bNiEiIgLjxo3D448/jtatWzuyb0RE5CSqGq5lIaqpTBO1nPZczEHf5ranDj+SdhPdm4bX6FylSo3rhQqjx0r0pvldyC7En4euom2M4ZTAa3kl+HVy72qf74ddKXj/v9NYczLD4FjHWNuSXBCRY1gcVHl7e2P58uW466674OlZfV0GIiKqu9QCg6ralJ5XiiUHr+CxPk3RMEju7O44xT/Hrxndfzaj0C5BVUGp4RonSw3+dLvJY8V6UxQHf7rDZNszGcaTWugL9tXcnin0EsYseLQbGoX6WXQNIqpdFgdV//zzjyP7QURETiboBFIBcpvyGJGVnvn1MI5fzcfuCzn465m+zu6OU8i9jH9hGxPia9V1ihQmgiczM/+u5JbgVHoB7ugQbXBMoVTh8o0Sk+e+v/o0PlhzxuRxb08ZKiqn047uGWe6EzqCfL2N7u/SJNSi84mo9vFfTSIiAgDsvnBDfHz8Sp7zOuKGjl/NB6BJaW/r2p+6ylRGuworp6JuOp1ldL+5dYK3/m8rAKBLXChWTuknObb34g3Jds/4MBzUWeskCIDKzMju1IEtMaZ3HI5fycegNpHV9h8AAk18qcGsf0Sui0EVEREBAMb+sF98fD67yIk9kVIoVXhjRRIGto7EiE4xzu6Owx27koeuTcKc3Y1aZ6o2WoWVNdOmLz0mPg4P8EGTcH8cu5KH0goV3lqZhMs3ivH9+B7437pkJDZrgIE6gc4xI18m6I98vXdvBwz/fCcA4OhbQ1Cukvav94ebJdt+Ph6IDPLFkHaWj7h1jpOum/KQAc8NaonIIOtG7Yio9jCoIiIizFqV5OwumPT7/jQsP3wVyw9fxYhOI5zdHYdTummSkPf+O210f4Wq5oWo9828HU/+cggAsPZkBtYmZQIAWr+5DoAmIUR1ivQKCbeJDsJvk3sjPMAHYQE+Bu0Xju2Op389LG77eVu/Dj3I1xuto4KQXFlY+InbmuGFIa2svg4R1R7jxRWIiMit/LQ31dldMCmnyHjWtfpAqVLjke/2SfZ5e/KfZl36QVVGfikW705Bel5ptW19vDzEoEYbUJkTEWgYJKXmStdTyWQy9GsRgbYxxmtf3dEhGrE6ySTkNQiqACBAXnWeD38niFwe/0qJiMio0nLrC6+q1AK2ns1GsalkATXgWY/XFx1Jy8MevTU7tozM1FUKpenftQqdtVCXrhchcfYWvPPvafSdswWAJnPilN+P4HxWIcoqDK/j72P5pJy4cH/Jdn5pBRZsu2jx+VrNGlbV7vStcVBV1W8vD96uEbk6/pUSEZFR9329W5IR0BLrT2Vi4uKDaD9rvd2Cg/qctMFY3gFFhfsFVRl50hpVsaF+uKWFJo267u/RyqOGadf7ztmC1Scy8OQvh/Hnoavi/u/G9QAA+PtYHtToJ7NIupZv8bm67u7UCEG+XmjawB+94mtWG0v3i4nsQuM1vIjIdXBNFRERGXU2sxCJs7fgtlaam9u4MH9MGdgCHmYykC07dEV8/Oh3+/Hn04k298OjHgdVX265YLDP3KhNfXXwcq74eO20W9E2Jhgz/z4BAPhs0zkcSbuJhWO7w18vK96ZjALxcUpOMd7XWZc1pF0UAMBfbjqo+unxXhj/4wFx29j0QV2n3h1m0et5qGccHrIwfbopR9LyxMdXbhpOdSQi18KgiojIDajVArILFYi2suZPZkGZ5Nv/fi0j0M1IZjqVWsBH685ia/J1cd8BnRtlW+guJzmdXoB2jYyvZamLtp+7brBPv+CrO3h5+QnxsXatUrvK/5ZVqLH+VBY+WpeMo2k3Jedps/CZE2Bi+t/0wS1xW8sITOwXj+xCBVafyDDI5Kfrw/s6Oq1+m583JxYRuTr+lRIRuYE3VyWhz+zNWGdisX7f5g0AAJ8/3EWy/4XBrfDysNaICpYDAApKK4xff+VJfLvjkv06rEN3+t+fOiNh9ZU7jlQZ81hiPHa+MhAtIgMBAAu3X8T+FOsD9TbRQUb3lyvVkMlkmHV3e0y6JUHcJwiC+KO7Rmu4kcLAjuSlMyIsM1e5mIhcAkeqiIjcwO/70wAAH68/izuM3BxqkyXoFxedNrglAGDL2WxkFShQZmK9zx8HHBfs6M7+yyqo/2tLTNVrqq/MJTWJC/fH7Ps7YsXRa5WBDrDkoHW/a0PbR2Pzi/3x+OKD6Bgbgv9OZADQJKHQ0mbXu3qzFAkz1xhco32jYKPp0x3pu3E9MHHxQQBAYuWXHkTkuhhUERG5kYvXi3Hiah46NQ4V913RSRmtW1NH95ty38rpR6ZGUTxkgKPKK+UWlYuP9Qux1kempv9lF5Qhv7QCLaOMj7zUVdo6UgBwb5dGBsd7xoejp06yB2uDKgBo3jAQ218eCAA4fnULruSW4va2VUV/4yMCEBUsR1aB8fT9pqYQOtLANpFY8/ytOHg5F2P7NK315yci6zCoIiJyM/d8tRun3xsmpprOyK8a/ekQGyI+Vulk/tt9QTOS9eWWC7i3S6zBNU0FVIVlFQjy9QYACIKAF/88jsbh/phhRSHT73UKtOpnZ6vrtDfyE/rG4+iVPBy/kmcy+1+vDzcDAHa/NkhSB6mu0/5uAUBw5e+KOcdnDUXndzfU+PnWPH8rDqXeRP+WDcV9gXIv7Hp1EAp1Cv0qVWrxPY+snP5a29o1Cq5XawiJ6jOnrqnasWMH7r77bjRq1AgymQwrV66s9pxt27ahW7dukMvlaNGiBRYvXuzwfhIR1TdbzmaLj4sUmmlQTcL9ERXsi+dv10z5+7+RHQzOu5BdZHJdljHf76wKiJKuFeDvo9fwxebzVqdqr+pr/Rqp0gaJY3o1QdvKtT/6o4GCIGDIp9vF7VXHqtKKX8srxd9Hrtab2lYnruZV2ybEr/rAS5uO3ZggX28MbB1pkMXS29MD4QE+4k9ksC8WTeyJp/s3x6t3tKn2OYnIvTk1qCouLkbnzp0xf/58i9qnpKRgxIgRGDhwII4dO4bp06dj8uTJWL9+vYN7SkRUv+gWE71Wma45Llwz+jH99pbY8mJ/PNKridFzn/vjiME+UyMnR6/k4eL1IqjUAsp0goWaBkfNdYqq1gfa9VM+Xh6Qe2mnWEoDpNQbJTifXSRu/29dsvj4/X9PY8afx/H2qiS79UkQBLz/32n8si/Vbte0VI7OVM+aigqW44cJPezQG2Bg60i8NryNQVFgIiJ9Tp3+N3z4cAwfPtzi9gsXLkRCQgLmzp0LAGjbti127dqFzz77DMOGWVY7gojI3SiNjGL4eMmwYNtFbDydKdbD0a4b8fCQoVnDQEn76YNbYt6m8wCACiNT8BoGyXEtr9Sg/Y5z13H73O0Y2i4KpTqZ1B5YsEcS2N3ZMRpTB7Ws9rV4etSfpLVlFSqUVL4nci8PyCvXs+knqtB93wDpWrd1pzSjhn8cuILZ93eyS78Opd7ED5VTLh+r5bU8K6f0s6jdo72b4LfK5CsxIb74fnwPjPhiFwBg04z+kHtZXvCXiMge6tSaqr1792Lw4MGSfcOGDcP06dNNnqNQKKBQVC08LSgoMNmWiKi+yS4sQ3qeYca8f49nYMXRa5J9gWZq8FQ3vUwbCHz+cBe0iAxE0wYB2HI2G6k3SpBfWoENp7Mk7c9lFUm2z2cXYsrAFpL06VpNwv2RVplM468jV3FXpxgMbBNp0K4uKatQoc1b68TtqGBfMQOd/kiV/nsfFWy81tgPu1LE1OC2GLVwr83XsJTuNNDnB7VAwyDL1i7pBv2+3p5o3ygEO14eCKVaLa7hIyKqTXUqqMrMzERUVJRkX1RUFAoKClBaWgo/P8PpJ7Nnz8a7775bW10kInIpvT7YbHS/fkAFAH4+pr/dP68XBKnVgmRNyukMzRdWDYPkaN9Ik+zin6m3QBAE3Pq/rbh6s2oUa+6ozuLNs0KpxhM/H0KFSkBZhdpoHxoE+ohBFQBMXHwQl+eMMNnXumDPxRzJtqeHTJz+d/lGMTLyNe9XmL+PQZD1YPfGRq/5/n+n0SwiwK4BpyAIRgNde1HqZDiZdEszi8/z9qzqU3hlqvMmDThFj4icp04FVTUxc+ZMzJgxQ9wuKChAXFycE3tERFQ78vUK9caG+qFBoA9OXM032j7AzEjVU/2bS0abSipU4sjWa3+dEPdrR1u0ZDIZlj6ViCOpNxEX7o8WkYGSETFBEMR07HM3JGP3xRv4YXwPNNJZo1XfMv4B0nVRWj6VQdW25OtInL0FABDq74137m4vaZecWWjyur/uS7VrUHWzpEIMWuztaNpNzPjzuLjt5Wl58KY7dTTMnyNTROR8dWpyenR0NLKypFNIsrKyEBwcbHSUCgDkcjmCg4MlP0RE7uDhb/dJtkP8vOFvZjTK3LFuTULxcM+qL6SKdFJP69YNMjaoERvqh7s7N0KXuFCDKYYymQzBldncvt+VgjMZBeg7Z4tkWpjSSL72up7t7qyRwEg3tb12JCavpAIHL+dK2mnXURmz7dx1O/VQQ7eGmb3d9/UepOQUi9vakTpLXL1Z1a9MNygITUSur04FVYmJidi8WTqVZePGjUhMTHRSj4iIXNPG01k4kyFdQxoW4G12lMNcUCWTyTDngU5iOuvc4nKUK9UGSRWKFMaLA5sz0kjdqyu5pVCpBXy68ZzB6wBMF8itKxKbNTDY93AvTdAaJPfC+Q/uRM/4MABAZr7lQYPKxgrM+qnuHZXC/ged2mOAZvqjl6fltySHUm+Kj0vKrf+dIyKyN6dO/ysqKsKFCxfE7ZSUFBw7dgzh4eFo0qQJZs6ciWvXruHnn38GADz99NP46quv8Morr+Dxxx/Hli1b8Oeff2L16tXOeglERC7piZ8PGewL9fPBzZIKI601/Lyrz5gWKPdCfmkF7vxip9HjvRPCLe9kpbfvaoff9qdKsgpuP5eNiEA5vth83ug5Cp3ph3VRm5gg7L10Q7ovOhj/TO2H6MpEFL6Vn8dmnZpiQFVCkWwLRmjS80rxxebzmNgvAa0r62CZIgiCZDoeYDiF1B4OpOTi/f9OS/b5WjFKBQCNQ/1woPKxhwPXfBERWcqpI1WHDh1C165d0bVrVwDAjBkz0LVrV7z99tsAgIyMDKSlpYntExISsHr1amzcuBGdO3fG3Llz8f333zOdOhGRBUL8vTHZXHY4C25Ox/SKg4eRZt2bhiFl9p1iIGANDw8Zvh/fU7Lv883nDYIOXXV9pGrR7sviY91gtlPjUERWBlWmgtwmlTWT/re+al2WNpgd2k6azOmFpcew5OAVjFq4p9o+ZRcqDBKY5JkJwmtCrRaMFviVW/l74y+vaq8/WkpE5AxO/ZpvwIABBlMNdC1evNjoOUePHnVgr4iI6ift1D1TAuXV39hOHdQSk25phnK9NU3Bvl42ZYlrFCJNE55TVI6f95ouPvvb/lS8PKxNjZ8PANYlZaJZwwC0ijI/gmNvCqV0upqpKXv6mRA7x4Xi+JU8sW6V7rqyh3vFYX9KLorLq6brXbpehP0pmvVYBWXVT+PblpxtsM/eI1XP/nbE6Jqw3GLriv7e0qIhft2n+dLV24oEF0REjlKn1lQREVH10nWK8OoK9vXGgFYNAQAN9DK6tY4KwrD20RZd38/HEyF+3pIfW9NuNwisqk/kY8FUsPlbL9r0fCeu5uHpXw9j6Gc7bLpOTazUGw2a+1Bno+30R6qeuk2Tcjwlpxj95mzBhlOaxE1v3NlWLNysu6bt5eUnYClBEPDqXycN9tszqNp/6YbZJBvWGNY+CrdXZjn89KEudrkmEZEt6u6EdCKqVwRBwPv/nUFksBxP92/u7O7UaTt0MsDtnTlITM/t5+2B3s0aYOWUfmga7o9t57Kx8mg6vnykK4KdXDA1PMAHM4a0gkotYNKtCXhjRRJ2nr9u9+lnWhevF1XfyEFyi6te0/KnE9Ej3vg6NN208gBwa8sIsRDyNZ3AObF5AxRUBj8lOokl9BN8lCvVJgPW+VsvGN1vybotS43Wy0ZpC5lMhh8m9DSol0ZE5CwMqojqqPS8UizanYJxifGIC6/7RS+Tswrx425NRrD6EFRppzY7snCqJaKDfdGsYQAuXS8Wb967xIUCAO7r2hj3dTVeSNYZnr+9pfj4yzGatbYHUnLx0Dd7IffywNSBLTB34zkAQFMbC73qpi9XqQV41uKNeW6xQnycEBFgst2TtzWDIACfbdK85iBfb6x+/hZcvF6Vhjwi0AeNw/yRdE1Te0w3EYl+VrxWb67Fyin9xM9f1ycbzhntw99Hr2HW3e0R4qK1oBhQEZGrYFBFVEc98fMhnEovwLpTmdj5yiBnd8dmunWPBEFwejBiC7VawP0L9kAmA/56um+t3/gVV95M39O5EWQyGX6a2Av5pRXoEBtSq/2wh14J4fj9id5oECBH6+ggtI8NxuOLD6FCqca3O6qmAMogw+1tI9GsYaBF19UtvvvVlguYNrilmdY1IwgCsgsViAqWrhf7bmdVOnHdaY/6fL09MW1wSwxuFyleI8jX22hQpB3VyilS4NXlJxAeaLxg78j5u3F5zgirXsfms1m4v5tjg+8RHWMcen0iIkdjUEVUR51K10ztuZJrfP1MXaO7VL9CJcDHq+4GVbkl5Th2JU98HGHmxtkRiiungAVUJp6IC/dHnLkTXFzf5hHi41B/TbCQnl+GD9eclbRbfTIDK6f0s/r6n206h6cHNIPcy/rMheZ8sPoMvt+Vgo8f7IRRPWr+CbRvVH0wHObvjWBfLxSUKbH00BWT7VpEVh90esg0o5h/HbkKAEi9YZ8CwL0TwsXEGboWPNoNAyvXRxER1VUMqohc3D/H0/H8H5qMl/9OvQUdG9e90QZL6GZAq1CZXvtRF5TqTLtyRrpnMajyqX//i+/SOBTTB7dEWm7Vjf7N4nJsTb6OnCKFmTPNUyjVdg+qvq8scPvBmjOSoGp4h2isTcrEq3fYlr1Ql0wmw7Kn+2L54SvQJgW8crMEG09n4cP7OsLPxwMvLD2OBgE+KFYo4eUpE1+vWudv740722Jsn6bw9fYQg6rPN5/H4/0SbJ4C2K1pmNGgqnezBjVKxU9E5Erq37+4RPWMNqACgLu/2mX11J26Qq1TXkGpMl1qoS5YcrCqvp7+upbaUCSOVNW//8V7eMgwfXAryb7T6QXYmnzdpgC2rELlsGQdumOuN4vLsTZJkwHP38e+gUTr6CC8MaKd0WN7L2pqfu1PyUX7Wevh4+WB78b1QP9WDSWBToDcS0zl/scTfTDmO01yiezCMpuDKlOp46tL9U9EVBfU3a+CieqJjPxSvLHiJM5nFTq7K06lG0jp10Cqa7T1cwCgpLz6+kD2ph2pCqyHQZUx2lFNhVKNCpW62ppHFUZ+vxQVjvud010fuCYpQ3xcm6OxHWKD0TisKptguVKNPRdzAEjfj65NQsXHic0biPXDbP1yoKxCZTSoig72rdUkIUREjsKgisjJpv5+FL/tT8M9X+2u8TUy8uv+uird12Dsprcu0a3tU6RwQlBVeQPsb0Ex3/pALgZVKrR8Yy26vb8RV3JNrwNaoVcnCoAYYNiLbspz3ZhBd3SqrKL2RjGDfL2x4+WBOPv+HXhuUAsAQGFlchhvz6pbgbYxwZLztKNWpTb0dce562jz1jr8sCtFsv/+brHY+tKAGl+XiMiVMKgicrLDqTcB2HbT8rWNhVCd7XBqrqTwaF2e/qfUCwhLFLU//U876ukuI1XaoKpMZ7Tp132pVl3j1b9OQqG032c1cdEBna2qqMpDZ9TK1HQ4R/HwkMHX2xNhlck+ft+fhsOpN8UvMfQDKgDwr1yX9+/xdKw8eq1GUywn/3zIYN+80V3w6UNdxKCNiKiuY1BF5ESCYP1NldrIjZiylm/O7O2BBXsl23V5+t9pvYKrxbU4/a+sQoUXlh7D5cpsbe6y+N9YgolWUUEm22uDCn1LD5rOmmetc1lVxYV1E2hU6Hxh4KyyAbE60wA/WncWSrXm783b07A/2vVOv+1Pw/Slx7D6ZLrVz6cfiM0b3QUju8ZafR0iIlfGoIrIiWoSPCyvzMily1igVZdpb/LqmnKlWnLTDMBg25Hu/Wq3ZGpb74TwWntuZ5J7G/5TpjLzhYWp6aU5RebXYlnqhpEshNovUHSf28tJa4lubxOJif3iAQBXckvwxM+HAcDo2qYXhrTCfV1jxSLFaTdsn2osr8OZPYmITOH/2YicSP+GO6+k+pu645X1j3SZu4GsiyqUde/1LD98FR1mrceG05mS/fZMqX42swDxr63GI5UZ2fQl6yU7CTUxIlPfGBuRKzazls1UUGXtzX55ZQHiM3qjk0uMjHhp/9YlQZWRkaHa4OXpgWcGNAcAZOSXidMQj6blGbTt3jQMn43ugjs7RgPQ1PSydf2ZsSCYiKiu4//ZiJxIf/3NHweqn35UZiRLmf7aDEEQ8PPey+J6rbqmog6NVAmCgPjXVuOlZcdRrlLjm+2XJMftmXRj0mLN2pQ9F28gObMQi3anYP2pqiBOe+PrjqYMbC7ZNhVUXcktwbQlx4we8/G07p/En/dexodrzmL45zuRmV8mrsn6eH2yQVtt7TLdIPu2lg2tej57igiQW5V9UHc65SPf7bfoCyBT7F0PjIjIFTCoInIi/el/lmQD++vIVUlxWcAwqNp8JhtvrzqFBxbssb2TTlDhhIK5NXXwsvnA1Z5BlW5GuWHzduDdf0/jqV8O40J2ES7nFEuyDr5/b3u7PW9dECiX1jraeDrLaLtFuy+bvIax6W9FCiWOpt00OsU26Vq++LjP7M2464tdJq+tTUSjHbHq16IB4sL9TbZ3NA8PGRoGyi1uf3enRuKUQQB48c/jNX5uTv8jovrIPVJDEbko/el/Cr1gwlQ67gOXcyXb2sK5GfmleGvlKZRWWJ8cQaFUYeKig+jTrAGev72l1efbk25w4Mqu5ZXioW/2mm1TG0k3Bn+6XbL9vwc74aEecQ5/XlcS6Cv95+z41Xz8uCsFd3WKQWSwr7g/zUyq9bkbkvH1NmkmTW2Sic8f7oJ7u0iTK3jrjWydzy6CKdvPZWN0zybYfEYT7Pl5O/+fX2uyQ3p4yDCkXZQYlG4+m23ReSev5hvsc5cEKkTkXvh1EZET6Y/IqHXWRiVdy0eHWeuNnqe/TkebRWzm3yex6UwWdl+4YXVfNpzKwp6LN/DpxnNWn2tvK48Z1hFyRbNWJVXbxpL1YWq1gEW7U4zegGqZK2gbJPcSf5o3DMCAVs6bVuYsQUYChPf+O40+szdL9m06Y3wEC9DU98opUkh+tE4Y+Wy8rRhx+XLLBQDAocopuSk5pgOw2qIf8N/aMsJse3MZFU1ZtCfFYF9sqJ+RlkREdZvzvyojcmP6We50p/F9ve2CyfP0l36oKq+TnlfzzFyulBoiLsx506KsselM9d/Wl6uqn9K5eM9lvPffaQDA5TkjjLZZm5RhdP/gtpH4fnzPap+jvosMNj6VrbrEmAdevx29PtyMLnGhmH1/R4Pjn286j3WnMvHDrhQ82L2xpJaTt4nsfRGBcklABgBXb5bin+NV6cgTIgLNd6wWfHhfR4zRSXoy/9FuZttHWDFdUEs/h86gNpEIC3CPBCpE5F4YVBE5UbneKIZuUKU/tUiXDDKj2/r7Ac2aHnPX0qpJzSxHqe2iqDXVrUkojhjJmKaruJriv4IgiAGVOfENAozut+SzdQd9EhqYPKZWC/DwkBn8jrdvFIzIYF+TgSwADG4XhXWVyUDmbkiWBLDG3ntBEFCkqJq+OrpHHJYe0iSgef6Po+L+aU6eYgsAic2l71mwr7eJljUXEVgVQF368E54OCmNPBGRo/FfYyInSskplmzrBhPmMpEp1YLkG/PGlcU8jdUSrS75RVmFCmtPZiCvpOpG0NkBVokFCTtcQbcmYSaP9aqsEZWcWWiyDQB8ZsF0y+zCMjz6/X6jx/o2Nx1MuBNzN+vatYqler9XiyZWP8J3d+cYDGkXBUCzHuvS9SLxx9jv6clr+WKGzrXTbsVzt7cQs+xpu9g5LhQdG4dU/6JqwQPdGjv0+tr/lTzQrTEDKiKq1zhSReREU34/ItnWrTdlbr2Gfka5vJIKjP5mL84auYEvrVAhyMw30O/+exp/HEiT7NtwOgvD2jsvPbe5GkOuxFi2OK1OsSE4kJKLknLzr+WLLaaneWqN0Msq1zBIjuuFmullj/RuakFP3VtphQp+Pp6SxC87XxmIyCBfM2dpyL088dygFth4OgvnsoowaO52s+21axJlMqB1VBA8PGQ48tYQ+Hh6WJXCvLa8Orw1AuWeFv8ePd2/ORZu1yTzUKrU8DLz5Y9CqcL3uzRrqpo2qBtTeomIasr1/g9P5MZUKtMjVe10RqYqVGpJjaulh65gf4o0I6BWWbn57HNLDqYZ7PvESJ0dR/jvRDpun7vNYP+qY+lOHy07nJqLl5cdx43KtTFKlVp8rGUqOyOgCXwATRY6axgbWdQGUAAwrH0U/niiDwa3jcTKKf3MBnakoU1OUVSm+bwC5V5WpTNvHR2EnvFhCPb1En9Mve/bkq8D0CTO0I7MBMq9XDKgAoDIIF+8e28HtI62LAnFy8Nai689p8h8rSrdtPau+vqJiOyF/5cjciG+3lV/kt6e0pu2UT0aI7oyNXS5Um1x/aOSatKrG4tdLlyvncxkU38/iovXi40eM5ZtrTY9sGAvlh2+iv9bfQYAMGjudnT/v01YtLsqm9mp9ALJOV46N9rmsvWZU1hm/vNaOLY7WkQG4vvxPdElLrRGz1FfvXVXOwT4eGJExxjJ/leWn0B+aYX43gb7WjdJQ+7liWVP98WJd4aJP9W99wXVfI51laeHTJymfN/Xu3HFTIp6XRfNpJsnIqoPGFQRuZCf9qZiW7Imo5z+Ivgm4f7oVLkOo0IlGNS4MkW/ULAlBAFYl5Rp9XnW+PPgFbPH752/26HPb6nUG8UQBEGsb/Tuv6dxKj0faTdKcOxKntju/0Z2wJn37xC35Tq1eMwFwLpr4wCgoMx8jS6ZsYVzBACYdEsCTrwzDK8Nb2Nw7GZxufjeBvvZnpDhcKr5os/uICO/DH8fMV3+QPf/PS8Pa10bXSIichoGVUROFOpveHM3YdFBo20HtYkU11lVqCwfqdJfnG+pp389XKPzLKFWC3jlrxMOu76tFMqq9+xIWh5Gfr1HcnzEF7vw5Zbz4vZjfZri0d5N4O3pgV2vDsSOlweijc50KlPJQpYeTMOZDOloV4GRwsdx4ZpEJL9M6mX9i3Eznh4yxIX744sxXdGjaVUikZJyFXZdyAFgvgCwpZ4d0Nzma9RVy59OFB/fLDE9IptdOW31we6NJQWYiYjqIwZVRE7UurKYZotIw5o1X2+7KD4OD/CBTCYT11lZE1RVl/3PGVQm1ksNrcyy5myt31wn2T6uMyKlpb1BBzSpubUjSI3D/NGkgT/u0En0oc0Gp+/Vv04a7DM3/S/ASIFbMu6ezo2w/Jm+aFK5dqq0Qolvtl8CoAmwbDVjSCvsf/12k8c/GdXZ5udwVT3iw/HqHZrRwMV7LmPQJ9s0P3O34YddVdNjb1ZOgW3AulRE5AYYVBE5kTYwCvDxNNtOm7RBu87q/1afwc0S89PEtFMFS6tJVOEMpupQucIUIaWFwWpGfpn4uH0jw/TYHh4yyCtHFo0FtuVK48+z6lg6zmVJszhWVNYz8/bg/7Kt5V/5t3XJxNq9mvLy9ECUmdGXJlYkwqiLdF/fpZxizc/1YnyzverLoIwCzd9IOIMqInID/NqTyIm066L8qguqKv9bXaFXH08PlFcGBdpCnjWd/udIahMjVS2jzGcgyyooQ0Z+mUMTNFSXKMIYUzWHfL09oVCq8fC3+wwSj+iP1mnTpP915Cr+OnIVW17sj2YNNSOYSrXmM/X24noqa2lHpRbq3Ow/1b+Z3a4/LrEpft6barDf1O94fTG0fRTmje6CALkXQvy8UVBagck/H0J2oQJ3zNsBAGKJB19v8/9/IyKqDxhUETlR1UiV+T9F7f1ZdUGV7o1cYOVUsdJq6iSZU10dmpoyNVJlTEm5Esev5EMQBDxSWQB37bRbDRI82MvlG9aNaHQ2E+C1igrEwcs3cS2vtNrrNAyUS1KnD5q7Haum9MOyw1fE1NVeHKmyWmblaIlulsnxifF2u/6797Q3GlQ1axhgt+dwRd6eHhjZNVbcVqkFRAXLkVWgMKiXV9NMmEREdQmDKiIn0o4q6Y9UVajUiG/gj8s3NAvqtdP/IoPlZq8XF+6PlBzNzaN22lN1I1UdY0Nw8prx9OWlFSoEOSCoMhZTTRlofOH/1N+PYsvZbMm+XedzHBJUnU4vwH16SSmq42lm8Ojnx3vjdEa+0bT1gKaOzzc7NOt8IoPlOJ0hPa6fAVF/tIuq1zBQbhDUVvclhjVkMhnu7xqLv49WZcEb26eJRYWF6xNPDxlWP3+rJPHKYz8cAACnFhInIqotDKqInOR6oQKplUGTn970mBf/PC4GVEDV9L/xifE4da0Aq0/q3X1Xat4wAM8MaI5gXy/sOK9JpFDdmipztWNLy1UI8rU9/bSuPw9dgdJIOnjt+pQHujXGX0euAtB8+73zvKaYarCvl1j7x8NIp28UKTDz75MY07sJBraOrFHf7vxip9XnmBs98vPxRPem4SaPl6vUYlDVJS5ULBxr8rkcEODWd58/3AUPLtwr2VfddFtrfTyqMwa0icTzfxwFALwwuJVdr19XRATKcWvLhuL28beHIrOgzOLCwkREdRmDKiIn6fnBJvGx3Ft6s/zP8XTJtnakI0DuhUf7NDEZVCmUajzUIw4AcPCypo5OdSNVChMJEwCgSKFEzcIT4zLzy/DKcuOp1Pu30tyMvT+yvRhUHU69iQqVAJkMOPLWEMxZexbf70pBhpHpdG+uTMKG01nYcDoLl+eMsLpv2uDNnAYBPni0T1OcTs/HpjOa0bMgKwvJ6uoVH47OjUPQqXEoRveMw+oTGahQqdEhNgSCAIPPuWGg+ZFKMtRdJ606oBnt8/Gyb3Dq6SHDPZ0bIe1GMRoGydGAnxMAIMTfGyFGykYQEdVHDKqInGBdkvRmWXd62P3dYg0Kan4yqpP42Nyi78ZhfuJj7ehXdSnVy81kuytW2DfJRU6RwmDfkbeGIL+0Ak0baNag+Pt4wdfbA2UVajz0jWaEIdzfB16eHogO0Yxmfb8rBW+MaCsphLvulG3FirVTlcz56fFe6BAbguyCMrRvlIYKlRr3d2tc4+f08vTAqqm3iNsbZ/SXHH+/uByPLz6IjPxSLH+6r92DAXegXyzZ345T//RNHdTSYdcmIiLXxn+hiZzg6V+PSLaHd4hBkK8XOjUOwSO9mkiObXjhNtzRIUbcNlfz5ZVhbcTH2ilOpdXU5FHo1VBqFFK1FqRIUfMkF8a8tSrJYF94gA8SIqSL+kd1j4Ovtwd8vT3g5+2JB7prAhfdel53fblLkv7c3snWbm8jHaP7flwPdIjVZPmLDPbFC0Na4ZU72hitMWYv4QE+WDmlH/a/Phhx9TxFd23xt/PUPyIiIoAjVUQuIT7CH3teGwS5l6fBGqcwf2kQZao2jtzLA2E6AZd2RGt/yg3M/NuwyKyW/iL+Sbc2w/pTmTiQkovswjITZ9XM0bQ8i9q9P7ID3h/ZwWD/bTrrNU6lF+BsZmHlVDn7p6/erJccQ3+KJtUdj/Rugt/3pwEAR/uIiMghGFQRuQAfTw+TCSH0bwLlJm4KxyU2lWw3DNKs67h8owSXb6RZ3BeVWo3GYX44kAJMX3oM93aJrf6kWuLhIZMkrDh6JQ8dYkMMRtRUagGe5jJw6DEWlDVrGIDScpVY5PeWFhE29JycKTa0alpsVoF9vyggIiICGFQRuQRz9af0gyj9NSJar9zRRrI9rH0UPrivA24Uma4Rs/LYNVy6Lq3LpFQLSKhc3yQImjVZrlS888WhrTHrn1MAgD8PXkHvhHAM/WyHpM2Ws9kY0i7K4mue1kkD3TE2BBevF+HjBzsjzN8b+y7loldCmMn3nVxfiU6tthlD3DMzHxERORaDKiIX4G1mSpKPBWm0H+ze2CAwk3t54tHeTU2coaFUC/hi83npPpWASbckYO7GcwCAYoXSYUHV4LbW5xYc3zcei/dcRkpOMQa1iTQIqADgg9WnrQqqisqqbrpfHtYa/VpEiCNdzRo6bs0U1Y6HesRh/taLAIBxdiz8S0REpMWgisgF6Bd1bdYwQBxBMlaT6dU72uDPQ1cwfXBL9G/VECF+NUtbbGwqYai/NwLkXgjw8URx5fS3mX+fhLeXB+Y/0q1Gz2NKTZdC3d4mEt/vSpGMMOnqppdGuzqFOkFV3+YNrJo6SK6vaYMAXPrwTqN/S0RERPbAFbtELsBbr4Ds8qf7AgCev914iuZnBjTH1pcG4N4usQj196nx1DRtbSi5lwfmje6Ce7s0wuiemjpXgZX1l+76chc2nM7C6hMZBkktbBUTajzpRnW0mQ03ns4yevzk1XyrrpdfWgEAuLVlBAvs1lMMqIiIyJE4UkXkAvRv+MIDfGpUwNZaHWJD8N9ztyAmxBcNAuUY2bUqKcUD3Rrj620XJe37zdmCPa8NQiOdhf+2eHFI6xqd1yUu1Ozx89lFVl2voEwTVAXXcMSPiIiI3Bu/kiVycx1iQ9AgUG6w/5U72qBv8wYG+1ccvWawz1L60xzDzNTcMqd1dFC1bcqVposa6yqrUGHlsXQAqPE0SiIiInJvDKqIyCRja54+Xp+MnCJFja6nUldd8P5uNU/VHupvPBjz00moYWnh4jlrz+L4lTwAQLCJtPZERERE5jCoIqplF69XTU0L9vXCNBPrplzB3ks3jO4f8PE2q65TpFDi132p0MZUM4e3wXv3Ghb3tVSAj6fBqBcAjOgUA//K9Va5xaZTyev6ae9l8bGpGmBERERE5vAOgqiW3T53u/h45yuD8EIdrJtj6SiQ1hsrTuLNlUni9kM94hAor/mSTplMhidva4b4Bv7iPm9PGV65o7W43uv1v09KzknJKcafh65ArZYOv+mOxrEwLBEREdUEE1UQOZGXkdEWVzJzeBvMXnvW5uv8ezxdsm2PTGwvD2uDl4e1QU6RAlkFZWjfKAQAMKhNJC5kF+F0RgFeWX5cbP/noasAALVawMO9mhi9pr8P/5dIRERE1uMdBJETuXo9pN7NDBNV1ITe4JBdp9lFBMoRoZNoY8qAFli0OwVFCqUYSOn6bX+ayaDqidsS7NYvIiIich8MqoicyNvFayJ1jA3B6B5xuJRThB7x4Vigk2JdEIQa18dy5NqlEH9vLJrQCyeu5Un2/29dMgDg5LWqGlbFOtMYA+VeiAmxT6p4IiIici8MqohqkUpvyMbFB6rg6SHDRw92AgBUqNSSoEqhVMNXJ9uepbw9ZTUOxix1S8sI3NIyQrLv803nodBLs64bYD1+C0epiIiIqGZc+2tyonpm4XZpMV1HBxf25O3pga0vDRC3S8pVNbpOhcpInvZa0K1JmE4fNMGVNpU6ADxiYkogERERUXUYVBHVou92XnJ2F2ySEBEgPt51IceJPbHeGyPaio/f+ecUPlmfLEnCER3i64xuERERUT3A6X9EtShQ7oW8kgpnd8Munv/jKO7p3MjZ3bCYbjKL3/anObEnREREVN9wpIrISXy93efP7/Y2kc7uglu930RERFS7OFJFVIu0a3kAYN/M253YE/t49rfD4uMguTfeurudTUV9HclcDarfn+hdiz0hIiKi+sY1736I6qlynexzof4+TuxJzXl6yMQshmtOZkqOpeWW4I8n+xico9TJeqib7KI2+ZhJ4963eYTJY0RERETVcYn5MPPnz0d8fDx8fX3Ru3dvHDhwwGTbxYsXQyaTSX58fbnAnOqGDrEhzu6CzY68NQSjujfGmyPa4v172+P9e9uLx45fzTN6jlKtCSY/f7iLJNlFbVtqJOAjIiIispXTR6qWLl2KGTNmYOHChejduzfmzZuHYcOGITk5GZGRxtdhBAcHIzk5WdyuS2mpyb3FhmqKy07sF+/cjtggxM8bH4/qLNmnUKrxf6vPoH+rhkbPUVamUfd0cmGu3s0aYGi7KGw4nQUAGN4hGq/f2baas4iIiIjMc/pI1aeffoonnngCEydORLt27bBw4UL4+/vjxx9/NHmOTCZDdHS0+BMVFVWLPSaqOW2Npqjg+jW62iBQM5VxbVImBMGwDpV2uqCXC1Q7ntA3Xnz8+p1tERfu77zOEBERUb3g1KCqvLwchw8fxuDBg8V9Hh4eGDx4MPbu3WvyvKKiIjRt2hRxcXG49957cerUKZNtFQoFCgoKJD9EzqKqnAbnCsGFPekGiW+tSpIcKy1X4VDqTQCAp4fTv8dBeGDVWrYAF02qQURERHWLU+9wcnJyoFKpDEaaoqKikJmZafSc1q1b48cff8SqVavw66+/Qq1Wo2/fvrh69arR9rNnz0ZISIj4ExcXZ/fXQWSpChcasbGnPgkNxMe/7pPWgPpq63nxsZen8193g4CqelX+Pp5O7AkRERHVF3Xua9rExEQkJiaK23379kXbtm3xzTff4P333zdoP3PmTMyYMUPcLigoYGBFTqPSri3ydP6IjT15eMjg4+UhZjfMLihDZOXoVXJmkdguwExa89rSMEiO9+9tDw8PGXy9GVQRERGR7Zx6hxMREQFPT09kZWVJ9mdlZSE6Otqia3h7e6Nr1664cOGC0eNyuRxyudzoMaLapqyn0/8ATRKOlJxiAMCmM9l4pHcTAEB+abnYJsjX+UEVADyWGO/sLhAREVE94tSvy318fNC9e3ds3rxZ3KdWq7F582bJaJQ5KpUKJ0+eRExMjKO6SWQ35ZUjVXIzNZPqqk90MgIGyKtGgA5evik+dtXCwERERES2cPqd3YwZM/Ddd9/hp59+wpkzZ/DMM8+guLgYEydOBACMGzcOM2fOFNu/99572LBhAy5duoQjR45g7NixSE1NxeTJk531EogspqhQAQDkXvVv2ln3pmHoGR8GoCrLYeqNYkkbVxmpIiIiIrInp9/hjB49GtevX8fbb7+NzMxMdOnSBevWrROTV6SlpcFDJ2PYzZs38cQTTyAzMxNhYWHo3r079uzZg3bt2jnrJRBZTFG55qg+jlQBQKi/JrNehUrzOlefzJAcZ7Y9IiIiqo9c4g5n6tSpmDp1qtFj27Ztk2x/9tln+Oyzz2qhV0T2JwZV3vUzqPKpTMChTVgRGSStx+VdzxJ0EBEREQEuMP2PyJ0olPV3+h8A+FSOwGlHqgJ0UpYfenOw0XOIiIiI6joGVUS1SFFRv6f/eVfWodKOyJ1K1xTb7teiASICmYWTiIiI6qf6eWdH5KLq+/Q/7fQ+7UiVAE3CipvFFU7rExEREZGj1c87OyIXVFKuRE6RAkD9n/6XVaDAhexC7Ll4AwAwohNLHhAREVH95RKJKojcwaBPtouP6+v0P22iij8OpOGPA2ni/sZhfs7qEhEREZHD1c87OyInEwQB25Kzcb1QIe7LLCgTH3tVrj2qb4a0i0LjMD+E+XuLP32ahaN/q4bO7hoRERGRw3CkisgB/jmejmlLjiHY1wsn3hlmcFw/1Xh90SM+HLteHeTsbhARERHVKo5UETnAtuTrAICCMqXBsaHtomq7O0RERETkQAyqiBzA08Nwel+rqEAAwIR+8bXcGyIiIiJyJAZVRA7gbWTNVEm5pvCvn3f9zPxHRERE5K4YVBE5gJdH1Z9WVmWCioJSTa2mIF9vp/SJiIiIiByDiSqI7Ci7oAx3f7ULWQVVWf/+b/UZdIwNFtdXBcr5Z0dERERUn/DujsiO1iZlSgIqAPj3eDr+PZ4ubvv5cPofERERUX3C6X9EdqQWhGrb+DOoIiIiIqpXGFQR2ZGXZ/V/Ut4WtCEiIiKiuoN3d0R2VFaZ4Y+IiIiI3AeDKqIaUqsF/HciHVdyS8R9H6w5AwDo3jQMfz6VKO5fOLY7Eps1wPsjO9R6P4mIiIjIsZiogqiG/jmejulLjwHQ1J5qFOorHmsXE4xeCeFYOLY7Gof5oUNsCO7oEO2knhIRERGRIzGoIqqh9acyxcelFSpcvF4sbj87sDkAMJAiIiIicgOc/kdUA+l5pdhx7rrJ4zEhfrXYGyIiIiJyJo5UEdVA3zlbTB77flyPWuwJERERETkbR6qI7Oz2tpHO7gIRERER1SIGVUR2JpPJnN0FIiIiIqpFDKqI7OCBbo3h7SnDiE4xzu4KEREREdUyrqkispIgCJLtf6b2Q6fGofjfg53g6cFRKiIiIiJ3w5EqqnfySyowcdEBrEvKrL5xDRQqlJLt+IgAAGBARUREROSmGFRRvfPz3svYmnwdT/962O7XTrqWjzHf7pPsC/b1tvvzEBEREVHdwel/VO/klVaIjwVBsChxxJXcEkQEyuHn42m23V1f7rK5f0RERERUv3CkiuqdEL+qkaN8nQDLlHNZhbj1f1vxwII9Ztup1YLBvuYNA6zvIBERERHVKxyponpj1bFrWHUsHVvOZov7/juRgbF9mpo9b33l2qvTGQVmR7ZyihWS7ahgOTa/OMC2ThMRERFRnceRKqo3pi05JgmoAODNlUnVnhce6CM+LqtQm2ynn/gi1M/HREsiIiIicicMqqjey8gvNXtchqqRqSK9zH5aN4vL8faqU5J9k29NsL1zRERERFTncfof1QtKlekRpsTZW9CvRQNxO9TPB+/e2x4RgXIAQGmFSjxWrFCiYZDc4Bqfbjwn2V72dCJ6NA2ztdtEREREVA8wqKJ6Yd0pw5pUo3vEYemhKwCA3RduGBzv0ywcAPDdjkviPlMjVbolqBZN6Ime8eG2dJeIiIiI6hEGVVQvTP39qGT72QHN8fKw1hjRKQY3S8rF/fsu3cAfB65g9ckMrD6ZYXCd5YevokNsiGTf30eu4qe9qQCAx/slYGCbSAe8AiIiIiKqqxhUUb3TJS4ULw9rDZlMhttaNZQcG9gmEjKZDDeLNYFWcbkKO85dF48v3nMZb9/VDh46Q1Mz/jwuPg705Z8MEREREUnxDpHqnRXP9jWZFj3Y1xsf3tdRsu+9f0/jx90p4vbYH/bj9yf6GD3f04JCwkRERETkXpj9j+oVDxlMBlSmvH13O/yhE0TtuXgDAz/Zho2nswzaBnGkioiIiIj0MKiieiFIrgl2Vk7pV6PzE5s3wKE3B4vbKTnFeOLnQ/h4/VlxX0JEAB7uFWdbR4mIiIio3uHX7nWISi3gpWXHcTazEGcyCgAAjUJ88e24HgbJFdxJuVKNwsqsfXFh/jW+TkSgHEnvDsOKI1fxVmVNqvlbL4rHVz7bD/4+/JMhIiIiIineIbooQRAMprG1fXsdypXSekzp+WW468tdeH5QC815AArLlHjytmaYt+kcBreNwtD20bXVbafIq8zu5yEDQvy8bbpWoNwLjyXGo3V0MH7fn4qVx9LFY8F+/HMhIiIiIkO8S3RBuy/k4NHv9wMA7uoUgy8e7oqVx64ZBFS6vthyQbK9eM9lAMCfh65iQOuGKCxT4rlBLcSCt16eMrSMDIKnR91PvHAuqwgAEOznLcnaZ4teCeHolRCOuQ91weHUm2gdHWT1Wi0iIiIicg8MqlzQ6ytOio//O5GB/05I6yntfGUg4sI109x+3nsZF7KLxGM/V9ZT0rUtWZMyfMKig5L9D/eMw5wHOtmt386SkqN5/XklFXa/tqeHDL0SWOiXiIiIiExjUOViypVqpN4oMXn8m8e6iwEVAIxLjJccH9gmEhP1giet8AAf+Hh6oKRciYIyJZYcvII7O8YY1HKqTQqlCmcyChHu74MgXy+EBfigWKHEkbSb6Ns8otqRtKs3S8T1T/d2aVQbXSYiIiIikmBQ5USLdqfgryNXJfuSrhWIj/9+ti/u/3oPAGBEpxg83i8e3ZuaHzUZ2DoSlz68E/tSbqBtdDBC/b2hUKrh6+0padft/Y3ILS7HuB8PIGX2nZKpbRn5pZB7eSI8wMfWlygSBAEHUnLRKioIYQE+KFIokZ5XiqGf7ZC08/P2RJlSBUGo2tchNhjv3tMeAXIvqNSC+FNYpsS4Hw+I7WxdT0VEREREVBMyQdC9fa3/CgoKEBISgvz8fAQHBzu1L7PXnsE32y+ZPH55zgiHPfd3Oy7hgzVnAADenjI0DvOHIAgoUiiRU6RJ/DAusSkm9ktAQkSA1ddfczID/51Ix2N94iEIAh6pXCPmKIPbRmHew10QKOf3BERERERkGXvFBgyqnOji9SJcyZVO9SssU8LbU4Y7OsQ49LkFQUDi7C3ILCirtm1sqB9aRwfBQwZ4eXhgQOuGeLhXE5PtbxaXo+v7G63uk7enDBUqAd2ahMLX2xMnr+WjsEyTKj3M3xueHjJ4yGTif6/llQIAGof5Yderg6x+PiIiIiJybwyqasiVgipnEwQBp9ILUFKugkwGyADIZJrkGIt2X7boGhGBPmgTrXkfr+WVIiWn2GTbuHA/XMktFbef7t8cQ9tHITbUD1HBvjXq/5az2WjXKBgxIX5Wn09ERERE7o1BVQ0xqLKcWi1g14UcZBcqoBYECIKAt1adMpva3ZT7u8bi09Fd7N9JIiIiIqIasldswAUoZJKHh8wgM+Donk3w7G+HseZkJgBg6sAWaBkVCAAQBGD60mNi22NvD0Gov/2SXRARERERuSKOVJHVypVqXMopQqvIIKPFdi9dL0JUsC8CmDSCiIiIiFwYR6rIaXy8PMR1VMY0axhYi70hIiIiInIuD2d3gIiIiIiIqC5jUEVERERERGQDBlVEREREREQ2cImgav78+YiPj4evry969+6NAwcOmG2/bNkytGnTBr6+vujYsSPWrFlTSz0lIiIiIiKScnpQtXTpUsyYMQOzZs3CkSNH0LlzZwwbNgzZ2dlG2+/ZswdjxozBpEmTcPToUYwcORIjR45EUlJSLfeciIiIiIjIBVKq9+7dGz179sRXX30FAFCr1YiLi8Nzzz2H1157zaD96NGjUVxcjP/++0/c16dPH3Tp0gULFy40aK9QKKBQKMTtgoICxMXFMaU6EREREZGbs1dKdaeOVJWXl+Pw4cMYPHiwuM/DwwODBw/G3r17jZ6zd+9eSXsAGDZsmMn2s2fPRkhIiPgTFxdnvxdARERERERuz6lBVU5ODlQqFaKioiT7o6KikJmZafSczMxMq9rPnDkT+fn54s+VK1fs03kiIiIiIiK4QfFfuVwOuVzu7G4QEREREVE95dSRqoiICHh6eiIrK0uyPysrC9HR0UbPiY6Otqo9ERERERGRIzk1qPLx8UH37t2xefNmcZ9arcbmzZuRmJho9JzExERJewDYuHGjyfZERERERESO5PTpfzNmzMD48ePRo0cP9OrVC/PmzUNxcTEmTpwIABg3bhxiY2Mxe/ZsAMC0adPQv39/zJ07FyNGjMCSJUtw6NAhfPvtt858GURERERE5KacHlSNHj0a169fx9tvv43MzEx06dIF69atE5NRpKWlwcOjakCtb9+++P333/Hmm2/i9ddfR8uWLbFy5Up06NDBWS+BiIiIiIjcmNPrVNU2e+WiJyIiIiKius1esYHTR6pqmzaGLCgocHJPiIiIiIjImbQxga3jTG4XVBUWFgIAiwATEREREREATYwQEhJS4/PdbvqfWq1Geno6goKCIJPJnN0dFBQUIC4uDleuXOF0RBfEz8e18fNxbfx8XBs/H9fHz8i18fNxbZZ+PoIgoLCwEI0aNZLkcbCW241UeXh4oHHjxs7uhoHg4GD+Qbowfj6ujZ+Pa+Pn49r4+bg+fkaujZ+Pa7Pk87FlhErLqXWqiIiIiIiI6joGVURERERERDZgUOVkcrkcs2bNglwud3ZXyAh+Pq6Nn49r4+fj2vj5uD5+Rq6Nn49rq+3Px+0SVRAREREREdkTR6qIiIiIiIhswKCKiIiIiIjIBgyqiIiIiIiIbMCgioiIiIiIyAYMqpxo/vz5iI+Ph6+vL3r37o0DBw44u0v10o4dO3D33XejUaNGkMlkWLlypeS4IAh4++23ERMTAz8/PwwePBjnz5+XtMnNzcWjjz6K4OBghIaGYtKkSSgqKpK0OXHiBG699Vb4+voiLi4O//vf/xz90uq82bNno2fPnggKCkJkZCRGjhyJ5ORkSZuysjJMmTIFDRo0QGBgIB544AFkZWVJ2qSlpWHEiBHw9/dHZGQkXn75ZSiVSkmbbdu2oVu3bpDL5WjRogUWL17s6JdXLyxYsACdOnUSiycmJiZi7dq14nF+Pq5lzpw5kMlkmD59uriPn5HzvPPOO5DJZJKfNm3aiMf52TjftWvXMHbsWDRo0AB+fn7o2LEjDh06JB7nPYJzxcfHG/wNyWQyTJkyBYCL/Q0J5BRLliwRfHx8hB9//FE4deqU8MQTTwihoaFCVlaWs7tW76xZs0Z44403hL///lsAIKxYsUJyfM6cOUJISIiwcuVK4fjx48I999wjJCQkCKWlpWKbO+64Q+jcubOwb98+YefOnUKLFi2EMWPGiMfz8/OFqKgo4dFHHxWSkpKEP/74Q/Dz8xO++eab2nqZddKwYcOERYsWCUlJScKxY8eEO++8U2jSpIlQVFQktnn66aeFuLg4YfPmzcKhQ4eEPn36CH379hWPK5VKoUOHDsLgwYOFo0ePCmvWrBEiIiKEmTNnim0uXbok+Pv7CzNmzBBOnz4tfPnll4Knp6ewbt26Wn29ddE///wjrF69Wjh37pyQnJwsvP7664K3t7eQlJQkCAI/H1dy4MABIT4+XujUqZMwbdo0cT8/I+eZNWuW0L59eyEjI0P8uX79unicn41z5ebmCk2bNhUmTJgg7N+/X7h06ZKwfv164cKFC2Ib3iM4V3Z2tuTvZ+PGjQIAYevWrYIguNbfEIMqJ+nVq5cwZcoUcVulUgmNGjUSZs+e7cRe1X/6QZVarRaio6OFjz/+WNyXl5cnyOVy4Y8//hAEQRBOnz4tABAOHjwotlm7dq0gk8mEa9euCYIgCF9//bUQFhYmKBQKsc2rr74qtG7d2sGvqH7Jzs4WAAjbt28XBEHzWXh7ewvLli0T25w5c0YAIOzdu1cQBE3Q7OHhIWRmZoptFixYIAQHB4ufxyuvvCK0b99e8lyjR48Whg0b5uiXVC+FhYUJ33//PT8fF1JYWCi0bNlS2Lhxo9C/f38xqOJn5FyzZs0SOnfubPQYPxvne/XVV4VbbrnF5HHeI7ieadOmCc2bNxfUarXL/Q1x+p8TlJeX4/Dhwxg8eLC4z8PDA4MHD8bevXud2DP3k5KSgszMTMlnERISgt69e4ufxd69exEaGooePXqIbQYPHgwPDw/s379fbHPbbbfBx8dHbDNs2DAkJyfj5s2btfRq6r78/HwAQHh4OADg8OHDqKiokHw+bdq0QZMmTSSfT8eOHREVFSW2GTZsGAoKCnDq1Cmxje41tG3492YdlUqFJUuWoLi4GImJifx8XMiUKVMwYsQIg/eRn5HznT9/Ho0aNUKzZs3w6KOPIi0tDQA/G1fwzz//oEePHhg1ahQiIyPRtWtXfPfdd+Jx3iO4lvLycvz66694/PHHIZPJXO5viEGVE+Tk5EClUkk+YACIiopCZmamk3rlnrTvt7nPIjMzE5GRkZLjXl5eCA8Pl7Qxdg3d5yDz1Go1pk+fjn79+qFDhw4ANO+dj48PQkNDJW31P5/q3ntTbQoKClBaWuqIl1OvnDx5EoGBgZDL5Xj66aexYsUKtGvXjp+Pi1iyZAmOHDmC2bNnGxzjZ+RcvXv3xuLFi7Fu3TosWLAAKSkpuPXWW1FYWMjPxgVcunQJCxYsQMuWLbF+/Xo888wzeP755/HTTz8B4D2Cq1m5ciXy8vIwYcIEAK73/zcva14MEZGjTJkyBUlJSdi1a5ezu0J6WrdujWPHjiE/Px/Lly/H+PHjsX37dmd3iwBcuXIF06ZNw8aNG+Hr6+vs7pCe4cOHi487deqE3r17o2nTpvjzzz/h5+fnxJ4RoPkyr0ePHvjwww8BAF27dkVSUhIWLlyI8ePHO7l3pO+HH37A8OHD0ahRI2d3xSiOVDlBREQEPD09DbKTZGVlITo62km9ck/a99vcZxEdHY3s7GzJcaVSidzcXEkbY9fQfQ4yberUqfjvv/+wdetWNG7cWNwfHR2N8vJy5OXlSdrrfz7Vvfem2gQHB/PGxgI+Pj5o0aIFunfvjtmzZ6Nz5874/PPP+fm4gMOHDyM7OxvdunWDl5cXvLy8sH37dnzxxRfw8vJCVFQUPyMXEhoailatWuHChQv8+3EBMTExaNeunWRf27ZtxSmavEdwHampqdi0aRMmT54s7nO1vyEGVU7g4+OD7t27Y/PmzeI+tVqNzZs3IzEx0Yk9cz8JCQmIjo6WfBYFBQXYv3+/+FkkJiYiLy8Phw8fFtts2bIFarUavXv3Ftvs2LEDFRUVYpuNGzeidevWCAsLq6VXU/cIgoCpU6dixYoV2LJlCxISEiTHu3fvDm9vb8nnk5ycjLS0NMnnc/LkSck/ahs3bkRwcLD4j2ViYqLkGto2/HurGbVaDYVCwc/HBdx+++04efIkjh07Jv706NEDjz76qPiYn5HrKCoqwsWLFxETE8O/HxfQr18/gzIe586dQ9OmTQHwHsGVLFq0CJGRkRgxYoS4z+X+hmqYfINstGTJEkEulwuLFy8WTp8+LTz55JNCaGioJDsJ2UdhYaFw9OhR4ejRowIA4dNPPxWOHj0qpKamCoKgSZcaGhoqrFq1Sjhx4oRw7733Gk2X2rVrV2H//v3Crl27hJYtW0rSpebl5QlRUVHCY489JiQlJQlLliwR/P39mS61Gs8884wQEhIibNu2TZIytaSkRGzz9NNPC02aNBG2bNkiHDp0SEhMTBQSExPF49p0qUOHDhWOHTsmrFu3TmjYsKHRdKkvv/yycObMGWH+/PlMOWyh1157Tdi+fbuQkpIinDhxQnjttdcEmUwmbNiwQRAEfj6uSDf7nyDwM3KmF198Udi2bZuQkpIi7N69Wxg8eLAQEREhZGdnC4LAz8bZDhw4IHh5eQkffPCBcP78eeG3334T/P39hV9//VVsw3sE51OpVEKTJk2EV1991eCYK/0NMahyoi+//FJo0qSJ4OPjI/Tq1UvYt2+fs7tUL23dulUAYPAzfvx4QRA0KVPfeustISoqSpDL5cLtt98uJCcnS65x48YNYcyYMUJgYKAQHBwsTJw4USgsLJS0OX78uHDLLbcIcrlciI2NFebMmVNbL7HOMva5ABAWLVoktiktLRWeffZZISwsTPD39xfuu+8+ISMjQ3Kdy5cvC8OHDxf8/PyEiIgI4cUXXxQqKiokbbZu3Sp06dJF8PHxEZo1ayZ5DjLt8ccfF5o2bSr4+PgIDRs2FG6//XYxoBIEfj6uSD+o4mfkPKNHjxZiYmIEHx8fITY2Vhg9erSkBhI/G+f7999/hQ4dOghyuVxo06aN8O2330qO8x7B+davXy8AMHjfBcG1/oZkgiAI1o1tERERERERkRbXVBEREREREdmAQRUREREREZENGFQRERERERHZgEEVERERERGRDRhUERERERER2YBBFRERERERkQ0YVBEREREREdmAQRUREREREZENGFQREVG9NWHCBIwcOdLZ3SAionrOy9kdICIiqgmZTGb2+KxZs/D5559DEIRa6hEREbkrBlVERFQnZWRkiI+XLl2Kt99+G8nJyeK+wMBABAYGOqNrRETkZjj9j4iI6qTo6GjxJyQkBDKZTLIvMDDQYPrfgAED8Nxzz2H69OkICwtDVFQUvvvuOxQXF2PixIkICgpCixYtsHbtWslzJSUlYfjw4QgMDERUVBQee+wx5OTk1PIrJiIiV8WgioiI3MpPP/2EiIgIHDhwAM899xyeeeYZjBo1Cn379sWRI0cwdOhQPPbYYygpKQEA5OXlYdCgQejatSsOHTqEdevWISsrCw899JCTXwkREbkKBlVERORWOnfujDfffBMtW7bEzJkz4evri4iICDzxxBNo2bIl3n77bdy4cQMnTpwAAHz11Vfo2rUrPvzwQ7Rp0wZdu3bFjz/+iK1bt+LcuXNOfjVEROQKuKaKiIjcSqdOncTHnp6eaNCgATp27Cjui4qKAgBkZ2cDAI4fP46tW7caXZ918eJFtGrVysE9JiIiV8egioiI3Iq3t7dkWyaTSfZpswqq1WoAQFFREe6++2589NFHBteKiYlxYE+JiKiuYFBFRERkRrdu3fDXX38hPj4eXl78Z5OIiAxxTRUREZEZU6ZMQW5uLsaMGYODBw/i4sWLWL9+PSZOnAiVSuXs7hERkQtgUEVERGRGo0aNsHv3bqhUKgwdOhQdO3bE9OnTERoaCg8P/jNKRESATGCpeSIiIiIiohrjV2xEREREREQ2YFBFRERERERkAwZVRERERERENmBQRUREREREZAMGVURERERERDZgUEVERERERGQDBlVEREREREQ2YFBFRERERERkAwZVRERERERENmBQRUREREREZAMGVURERERERDb4f7fHaEclcgT8AAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "roc_per = sd['Portfolio_Value'].iloc[-1]/sd['Portfolio_Value'][0]*100"
      ],
      "metadata": {
        "id": "ImTAxudv9dBU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def backtest(df):\n",
        "    initial_capital = 100000.0  # 초기 자본\n",
        "    shares = 0  # 주식 보유량\n",
        "\n",
        "    # 매 거래일의 포트폴리오 가치를 추적할 리스트\n",
        "    portfolio_values = []\n",
        "\n",
        "    for _, row in df.iterrows():\n",
        "        if row['stochastic'] == 1 and initial_capital > 0:\n",
        "            shares = initial_capital / row['Marketcap']  # 현재 자본으로 최대한 매수\n",
        "            initial_capital = 0\n",
        "\n",
        "        elif row['stochastic'] == -1 and shares > 0:\n",
        "            initial_capital = shares * row['Marketcap']  # 모든 주식을 판매하고 현금을 얻습니다.\n",
        "            shares = 0.0  # 주식은 0이 됩니다.\n",
        "\n",
        "        # 매 거래일의 포트폴리오 가치 계산 (현금 + 주식 가치)\n",
        "        portfolio_value = initial_capital + shares * row['Marketcap']\n",
        "        portfolio_values.append(portfolio_value)\n",
        "\n",
        "    df['Portfolio_Value'] = portfolio_values\n",
        "    return df\n",
        "\n",
        "sd = backtest(signal_dataframes)\n",
        "\n",
        "# 그래프로 그리기\n",
        "plt.figure(figsize=(10,5))\n",
        "plt.plot(sd['Portfolio_Value'])\n",
        "plt.title('Portfolio Value Over Time')\n",
        "plt.xlabel('Time')\n",
        "plt.ylabel('Value in $')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4pt0CN87mmwH",
        "outputId": "23cc90b0-0e95-4268-f206-c07817af289c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sto_per = sd['Portfolio_Value'].iloc[-1]/sd['Portfolio_Value'][0]*100"
      ],
      "metadata": {
        "id": "M-aO3yHCXpfR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def backtest(df):\n",
        "    initial_capital = 100000.0  # 초기 자본\n",
        "    shares = 0  # 주식 보유량\n",
        "\n",
        "    # 매 거래일의 포트폴리오 가치를 추적할 리스트\n",
        "    portfolio_values = []\n",
        "\n",
        "    for _, row in df.iterrows():\n",
        "        if row['Trend'] >= 0.7 and initial_capital > 0:\n",
        "            shares = initial_capital / row['Marketcap']  # 현재 자본으로 최대한 매수\n",
        "            initial_capital = 0\n",
        "\n",
        "        elif row['Trend'] <= -0.7 and shares > 0:\n",
        "            initial_capital = shares * row['Marketcap']  # 모든 주식을 판매하고 현금을 얻습니다.\n",
        "            shares = 0.0  # 주식은 0이 됩니다.\n",
        "\n",
        "        # 매 거래일의 포트폴리오 가치 계산 (현금 + 주식 가치)\n",
        "        portfolio_value = initial_capital + shares * row['Marketcap']\n",
        "        portfolio_values.append(portfolio_value)\n",
        "\n",
        "    df['Portfolio_Value'] = portfolio_values\n",
        "    return df\n",
        "\n",
        "sd = backtest(signal_dataframes)\n",
        "\n",
        "# 그래프로 그리기\n",
        "plt.figure(figsize=(10,5))\n",
        "plt.plot(sd['Portfolio_Value'])\n",
        "plt.title('Portfolio Value Over Time')\n",
        "plt.xlabel('Time')\n",
        "plt.ylabel('Value in $')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9SJ9jc9rgi3u",
        "outputId": "d1361f6b-87c9-4fd5-95a6-463b0f13b649"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "origin_per = df_bt['Marketcap'][-1]/df_bt['Marketcap'][0]*100\n",
        "print(\"buy_and_hold : {:0.2f}%, rsi : {:0.2f}%, ema : {:0.2f}%, roc : {:0.2f}%, sto : {:0.2f}%\".format(origin_per, rsi_per, ema_per, roc_per, sto_per))"
      ],
      "metadata": {
        "id": "tHYzhe9Zg_6Z",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "39f9cd23-ab3f-473c-98f6-e1f3ac76df0b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "buy_and_hold : 7249.68%, rsi : 7700.78%, ema : 2047.69%, roc : 2146.32%, sto : 5345.76%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "fMy__zr7nc85"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}