Strategy_3.ipynb

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    "colab": {
      "name": "Strategy-3",
      "provenance": [],
      "collapsed_sections": [
        "Z6x_eoBC95Ui",
        "XYmmboPka9zG",
        "ZEVuT23Na-NS",
        "aOyZ_BL-bFnM"
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        "id": "GBKI_Inbb9Zx",
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        },
        "outputId": "cb57070c-fad1-4348-d29a-83572face089"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "metadata": {
        "id": "DbIiecqldVN4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#셀트리온"
      ],
      "metadata": {
        "id": "mfixrz--a31N"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv('/content/gdrive/Shareddrives/CS360/madcamp-3-week/data/strategy-3/셀트리온(2008~현재).csv', encoding = 'cp949', thousands = ',')\n",
        "data"
      ],
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          "base_uri": "https://localhost:8080/",
          "height": 658
        },
        "outputId": "d608bf52-4ec8-4a88-93ab-0ccfcf726ee1",
        "id": "RWdfaFMDa31v"
      },
      "execution_count": null,
      "outputs": [
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              "              일자      시가      고가      저가      종가 전일비  Unnamed: 6   등락률  \\\n",
              "0     2022-07-15  189500  189500  183000  186500   ▼       -2500 -1.32   \n",
              "1     2022-07-14  190000  190500  186500  189000   ▼       -1000 -0.53   \n",
              "2     2022-07-13  188500  193000  187500  190000   ▲        2500  1.33   \n",
              "3     2022-07-12  187500  191500  185000  187500   ▲        1500  0.81   \n",
              "4     2022-07-11  181500  187500  181500  186000   ▲        3500  1.92   \n",
              "...          ...     ...     ...     ...     ...  ..         ...   ...   \n",
              "3590  2008-01-08    2850    2935    2830    2850   ▼         -60 -2.06   \n",
              "3591  2008-01-07    2885    2940    2855    2910   ▼         -55 -1.85   \n",
              "3592  2008-01-04    2945    2980    2870    2965   ▲          45  1.54   \n",
              "3593  2008-01-03    2945    2945    2910    2920   ▼         -25 -0.85   \n",
              "3594  2008-01-02    3055    3060    2900    2945   ▼         -85 -2.81   \n",
              "\n",
              "           거래량   금액(백만)   신용비      개인     기관  외인(수량)    외국계   프로그램    외인비  \n",
              "0     389861.0  72255.0  0.81   18164 -86053   45779  57166  39338  20.63  \n",
              "1     420165.0  79076.0  0.82   13421 -15809  -31578  38258 -78514  20.60  \n",
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              "3     423296.0  79507.0  0.85  -53603  30983   21555  15032  13698  20.60  \n",
              "4     344098.0  63851.0  0.83 -153713  61841   94447  62358  95288  20.58  \n",
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              "3593    7349.0     22.0  1.67       0      0       0      0      0   0.04  \n",
              "3594   33350.0     98.0  1.66       0      0       0      0      0   0.04  \n",
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              "    <tr>\n",
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              "      <td>2022-07-14</td>\n",
              "      <td>190000</td>\n",
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              "      <td>0.82</td>\n",
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              "      <td>38258</td>\n",
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              "      <td>20.60</td>\n",
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              "      <th>2</th>\n",
              "      <td>2022-07-13</td>\n",
              "      <td>188500</td>\n",
              "      <td>193000</td>\n",
              "      <td>187500</td>\n",
              "      <td>190000</td>\n",
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              "      <td>2500</td>\n",
              "      <td>1.33</td>\n",
              "      <td>513629.0</td>\n",
              "      <td>97775.0</td>\n",
              "      <td>0.83</td>\n",
              "      <td>-125090</td>\n",
              "      <td>92986</td>\n",
              "      <td>32324</td>\n",
              "      <td>47639</td>\n",
              "      <td>34758</td>\n",
              "      <td>20.62</td>\n",
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              "      <th>3</th>\n",
              "      <td>2022-07-12</td>\n",
              "      <td>187500</td>\n",
              "      <td>191500</td>\n",
              "      <td>185000</td>\n",
              "      <td>187500</td>\n",
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          "metadata": {},
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        "data = data.loc[:, ['일자', '시가', '종가', '등락률']]\n",
        "data"
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              "              일자      시가      종가   등락률\n",
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              "2     2022-07-13  188500  190000  1.33\n",
              "3     2022-07-12  187500  187500  0.81\n",
              "4     2022-07-11  181500  186000  1.92\n",
              "...          ...     ...     ...   ...\n",
              "3590  2008-01-08    2850    2850 -2.06\n",
              "3591  2008-01-07    2885    2910 -1.85\n",
              "3592  2008-01-04    2945    2965  1.54\n",
              "3593  2008-01-03    2945    2920 -0.85\n",
              "3594  2008-01-02    3055    2945 -2.81\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>일자</th>\n",
              "      <th>시가</th>\n",
              "      <th>종가</th>\n",
              "      <th>등락률</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2022-07-15</td>\n",
              "      <td>189500</td>\n",
              "      <td>186500</td>\n",
              "      <td>-1.32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2022-07-14</td>\n",
              "      <td>190000</td>\n",
              "      <td>189000</td>\n",
              "      <td>-0.53</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2022-07-13</td>\n",
              "      <td>188500</td>\n",
              "      <td>190000</td>\n",
              "      <td>1.33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2022-07-12</td>\n",
              "      <td>187500</td>\n",
              "      <td>187500</td>\n",
              "      <td>0.81</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2022-07-11</td>\n",
              "      <td>181500</td>\n",
              "      <td>186000</td>\n",
              "      <td>1.92</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3590</th>\n",
              "      <td>2008-01-08</td>\n",
              "      <td>2850</td>\n",
              "      <td>2850</td>\n",
              "      <td>-2.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3591</th>\n",
              "      <td>2008-01-07</td>\n",
              "      <td>2885</td>\n",
              "      <td>2910</td>\n",
              "      <td>-1.85</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3592</th>\n",
              "      <td>2008-01-04</td>\n",
              "      <td>2945</td>\n",
              "      <td>2965</td>\n",
              "      <td>1.54</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3593</th>\n",
              "      <td>2008-01-03</td>\n",
              "      <td>2945</td>\n",
              "      <td>2920</td>\n",
              "      <td>-0.85</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3594</th>\n",
              "      <td>2008-01-02</td>\n",
              "      <td>3055</td>\n",
              "      <td>2945</td>\n",
              "      <td>-2.81</td>\n",
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              "  </tbody>\n",
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              "<p>3595 rows × 4 columns</p>\n",
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              "\n",
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              "          const element = document.querySelector('#df-f545bac6-9db0-4f04-b2a2-f106747c8fd9');\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",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_copy = data.copy()\n",
        "data_copy['일자'] = data_copy['일자'].map(lambda x: x[:4])\n",
        "data_copy"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "9a0b80d8-8461-47d2-cd3d-4fc6e61fb8e6",
        "id": "U6eqK8U2a31y"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        일자      시가      종가   등락률\n",
              "0     2022  189500  186500 -1.32\n",
              "1     2022  190000  189000 -0.53\n",
              "2     2022  188500  190000  1.33\n",
              "3     2022  187500  187500  0.81\n",
              "4     2022  181500  186000  1.92\n",
              "...    ...     ...     ...   ...\n",
              "3590  2008    2850    2850 -2.06\n",
              "3591  2008    2885    2910 -1.85\n",
              "3592  2008    2945    2965  1.54\n",
              "3593  2008    2945    2920 -0.85\n",
              "3594  2008    3055    2945 -2.81\n",
              "\n",
              "[3595 rows x 4 columns]"
            ],
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              "      <td>187500</td>\n",
              "      <td>0.81</td>\n",
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              "<p>3595 rows × 4 columns</p>\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "year_info = data_copy.groupby('일자')['일자'].count()\n",
        "year_info = year_info[::-1]\n",
        "year_info"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "747b9f20-79fa-47d4-bdc5-263a82e8cf52",
        "id": "fRzldPqfa31z"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "일자\n",
              "2022    132\n",
              "2021    248\n",
              "2020    248\n",
              "2019    246\n",
              "2018    244\n",
              "2017    243\n",
              "2016    246\n",
              "2015    248\n",
              "2014    245\n",
              "2013    247\n",
              "2012    248\n",
              "2011    248\n",
              "2010    251\n",
              "2009    253\n",
              "2008    248\n",
              "Name: 일자, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataframe_by_year = []\n",
        "data_fragement = data.copy()\n",
        "\n",
        "for dates in year_info:\n",
        "  ret = data_fragement[:dates].iloc[1:3]\n",
        "  after_div_day = ret.iloc[:1]\n",
        "  before_div_day = ret.iloc[1:]\n",
        "  after_div_day.columns = ['대주주기준일-1', '시가(Day-1)', '종가(Day-1)', '등락률(Day-1)']\n",
        "  before_div_day.columns = ['대주주기준일-2', '시가(Day-2)', '종가(Day-2)', '등락률(Day-2)']\n",
        "  after_div_day.index = before_div_day.index\n",
        "  ret = pd.concat([before_div_day, after_div_day], axis = 1)\n",
        "  dataframe_by_year.append(ret)\n",
        "\n",
        "  data_fragement = data_fragement[dates:]\n",
        "\n",
        "dataframe_by_year = dataframe_by_year[1:]\n",
        "ret = pd.concat(dataframe_by_year)\n",
        "ret.index = range(len(ret.index))"
      ],
      "metadata": {
        "id": "fs0zmhEta310"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ret"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "outputId": "ac159ae0-c47b-41f1-81b9-5c4405e40489",
        "id": "EGetu8r1a310"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      대주주기준일-2  시가(Day-2)  종가(Day-2)  등락률(Day-2)    대주주기준일-1  시가(Day-1)  \\\n",
              "0   2021-12-28     204500     216000        5.88  2021-12-29     207500   \n",
              "1   2020-12-28     349500     333500       -4.03  2020-12-29     360500   \n",
              "2   2019-12-26     187000     189500        2.43  2019-12-27     183000   \n",
              "3   2018-12-26     204000     228000        8.31  2018-12-27     241500   \n",
              "4   2017-12-26     204200     195000       -3.42  2017-12-27     210000   \n",
              "5   2016-12-27     106700     106300        0.19  2016-12-28     107500   \n",
              "6   2015-12-28      77900      76700       -2.54  2015-12-29      79700   \n",
              "7   2014-12-26      40000      39450       -0.63  2014-12-29      38600   \n",
              "8   2013-12-26      38900      38050       -1.55  2013-12-27      37500   \n",
              "9   2012-12-26      29300      28600       -1.72  2012-12-27      25700   \n",
              "10  2011-12-27      35100      34000       -3.27  2011-12-28      33700   \n",
              "11  2010-12-28      31750      31000       -2.21  2010-12-29      31300   \n",
              "12  2009-12-28      15150      14600       -3.63  2009-12-29      14800   \n",
              "13  2008-12-26      10500       9980       -1.19  2008-12-29      10100   \n",
              "\n",
              "    종가(Day-1)  등락률(Day-1)  \n",
              "0      201000       -5.19  \n",
              "1      360500       10.08  \n",
              "2      184000        1.66  \n",
              "3      223000       -0.45  \n",
              "4      220000       15.06  \n",
              "5      106500        5.13  \n",
              "6       84000       12.75  \n",
              "7       38750        3.06  \n",
              "8       38200        3.24  \n",
              "9       26000        4.21  \n",
              "10      35650        4.85  \n",
              "11      31650        2.10  \n",
              "12      14900        2.05  \n",
              "13      10150        1.70  "
            ],
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              "      <th></th>\n",
              "      <th>대주주기준일-2</th>\n",
              "      <th>시가(Day-2)</th>\n",
              "      <th>종가(Day-2)</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2021-12-28</td>\n",
              "      <td>204500</td>\n",
              "      <td>216000</td>\n",
              "      <td>5.88</td>\n",
              "      <td>2021-12-29</td>\n",
              "      <td>207500</td>\n",
              "      <td>201000</td>\n",
              "      <td>-5.19</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-12-28</td>\n",
              "      <td>349500</td>\n",
              "      <td>333500</td>\n",
              "      <td>-4.03</td>\n",
              "      <td>2020-12-29</td>\n",
              "      <td>360500</td>\n",
              "      <td>360500</td>\n",
              "      <td>10.08</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2019-12-26</td>\n",
              "      <td>187000</td>\n",
              "      <td>189500</td>\n",
              "      <td>2.43</td>\n",
              "      <td>2019-12-27</td>\n",
              "      <td>183000</td>\n",
              "      <td>184000</td>\n",
              "      <td>1.66</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2018-12-26</td>\n",
              "      <td>204000</td>\n",
              "      <td>228000</td>\n",
              "      <td>8.31</td>\n",
              "      <td>2018-12-27</td>\n",
              "      <td>241500</td>\n",
              "      <td>223000</td>\n",
              "      <td>-0.45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2017-12-26</td>\n",
              "      <td>204200</td>\n",
              "      <td>195000</td>\n",
              "      <td>-3.42</td>\n",
              "      <td>2017-12-27</td>\n",
              "      <td>210000</td>\n",
              "      <td>220000</td>\n",
              "      <td>15.06</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2016-12-27</td>\n",
              "      <td>106700</td>\n",
              "      <td>106300</td>\n",
              "      <td>0.19</td>\n",
              "      <td>2016-12-28</td>\n",
              "      <td>107500</td>\n",
              "      <td>106500</td>\n",
              "      <td>5.13</td>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2015-12-28</td>\n",
              "      <td>77900</td>\n",
              "      <td>76700</td>\n",
              "      <td>-2.54</td>\n",
              "      <td>2015-12-29</td>\n",
              "      <td>79700</td>\n",
              "      <td>84000</td>\n",
              "      <td>12.75</td>\n",
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              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2014-12-26</td>\n",
              "      <td>40000</td>\n",
              "      <td>39450</td>\n",
              "      <td>-0.63</td>\n",
              "      <td>2014-12-29</td>\n",
              "      <td>38600</td>\n",
              "      <td>38750</td>\n",
              "      <td>3.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2013-12-26</td>\n",
              "      <td>38900</td>\n",
              "      <td>38050</td>\n",
              "      <td>-1.55</td>\n",
              "      <td>2013-12-27</td>\n",
              "      <td>37500</td>\n",
              "      <td>38200</td>\n",
              "      <td>3.24</td>\n",
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              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2012-12-26</td>\n",
              "      <td>29300</td>\n",
              "      <td>28600</td>\n",
              "      <td>-1.72</td>\n",
              "      <td>2012-12-27</td>\n",
              "      <td>25700</td>\n",
              "      <td>26000</td>\n",
              "      <td>4.21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>2011-12-27</td>\n",
              "      <td>35100</td>\n",
              "      <td>34000</td>\n",
              "      <td>-3.27</td>\n",
              "      <td>2011-12-28</td>\n",
              "      <td>33700</td>\n",
              "      <td>35650</td>\n",
              "      <td>4.85</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>2010-12-28</td>\n",
              "      <td>31750</td>\n",
              "      <td>31000</td>\n",
              "      <td>-2.21</td>\n",
              "      <td>2010-12-29</td>\n",
              "      <td>31300</td>\n",
              "      <td>31650</td>\n",
              "      <td>2.10</td>\n",
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              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>2009-12-28</td>\n",
              "      <td>15150</td>\n",
              "      <td>14600</td>\n",
              "      <td>-3.63</td>\n",
              "      <td>2009-12-29</td>\n",
              "      <td>14800</td>\n",
              "      <td>14900</td>\n",
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              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>2008-12-26</td>\n",
              "      <td>10500</td>\n",
              "      <td>9980</td>\n",
              "      <td>-1.19</td>\n",
              "      <td>2008-12-29</td>\n",
              "      <td>10100</td>\n",
              "      <td>10150</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np"
      ],
      "metadata": {
        "id": "1CQE2jgMa311"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ret_day_minus_2=ret.loc[::-1, ['대주주기준일-2',\t'등락률(Day-2)']]\n",
        "ret_day_minus_1=ret.loc[::-1, ['대주주기준일-1',\t'등락률(Day-1)']]"
      ],
      "metadata": {
        "id": "lwfWDQDua312"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "label = ret_day_minus_2['대주주기준일-2']\n",
        "index = np.arange(len(label))\n",
        "\n",
        "plt.figure(figsize=(20, 5))\n",
        "plt.subplot(1,2,1)\n",
        "plt.bar(index, ret_day_minus_2['등락률(Day-2)'], width = 0.5, color=[  'red' if x_return > 0 else 'blue' for x_return in list(ret_day_minus_2['등락률(Day-2)']) ] )\n",
        "plt.ylabel('return(%)', fontsize = 16)\n",
        "plt.xlabel('Day-2', fontsize = 16)\n",
        "plt.xticks(index, label, fontsize = 10, rotation = 45)\n",
        "\n",
        "label = ret_day_minus_1['대주주기준일-1']\n",
        "index = np.arange(len(label))\n",
        "\n",
        "plt.subplot(1,2,2)\n",
        "plt.bar(index, ret_day_minus_1['등락률(Day-1)'], width = 0.5, color=[ 'red' if x_return > 0 else 'blue' for x_return in list(ret_day_minus_1['등락률(Day-1)']) ])\n",
        "plt.ylabel('return(%)', fontsize = 16)\n",
        "plt.xlabel('Day-1', fontsize = 16)\n",
        "plt.xticks(index, label, fontsize = 10, rotation = 45)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "outputId": "0c383ce8-24bf-4ae2-d518-e86e9bec9194",
        "id": "I2izCHA4a313"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x360 with 2 Axes>"
            ],
            "image/png": 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vAmcCLwQ+V1U1hKySJEkTz76VJEmadAMNwA1QVT8AXt0+SLITsA3wo6r61XDiSZIkTSf7VpIkaVINXEyar70LiSRJkpaBfStJkjQpbnUxKckdaL49m6OqrlhSIkmSpBXIvpUkSZoUiyomJbkd8Cbg/wNus8Bsq5YaSpKkoUqW/h4OYaNlYN9KkiRNosWemfRW4LHAu4ALae44IkmSpFtnJH2rJJcDPwNuAjZU1cy810NT1DoKuAF4alWdO4wskiRp8i22mHQE8BdV9dZhhJEkSVphRtm3ekhV/XCB144E9m8f9wHe1v6UJEnayK0ZM+mby55CkiRp5epC3+pY4J+rqoCzkuyU5I5Vde24g0nSonk5uzR0Wyxy/pOBRwwjiCRJ0go0qr5VAacl+WqStX1e3xO4suf5Ve00SZKkjSz2zKTTgL9PsgNwKnD9/Bmq6vO3NkySvYF/Bnaj6fScWFVvurXvJ0mS1HFD7Vv1eEBVXd3eMe70JN+oqjMX+yZtIWotwJo1a5YhliRJmkSLLSZ9ov25L/DUnukFpP25lDuObACeV1Xntp2qryY5vaouWcJ7SpIkddWw+1bNm1Vd3f78QZKPAQcDvcWkq4G9e57v1U6b/z4nAicCzMzMeA2IJEkr1GKLSQ8ZSopWe13+te3vP0vydZpTrC0mSZKkaTTUvhVAku2ALdq+1XbA4cAr5s12CnB8kpNpBt7+ieMlaSOOQyNJag1cTEqyNXAQ8G9VddHwIv16efsA9wLOHvayJEmSRm2EfavdgI+lKQRsCby/qj6b5FkAVfV2mkvsjgIuBW4AnjbEPJIkacINXEyqqhuTvAp4+BDzAJBke+AjwHOr6qd9Xvd6fUmSNNFG1beqqsuAA/tMf3vP7wX88TBzSJKk6bHYu7l9HbjzMILMSrIVTSHpfVX10X7zVNWJVTVTVTOrV68eZhxJkqRhGnrfSpIkabkttpj0UuAlSe4xjDBpzr9+F/D1qnrDMJYhSZLUIUPtW0mSJA3DYgfg/ktge+BrSS6nGSy7dxS9qqoHLSHPIcCTgAuTnNdOe1FVnbqE95QkSeqqYfetJEmSlt1ii0k3McQ7q1XVl2hugytJkrQSDLVvJUmSNAyLKiZV1YOHlEOSJGnFsW8lSZIm0WLPTJIkSZKk8ckyXMhQtfl5JEkLWlQxKckDNzdPVZ156+NIkiStHPatJEnSJFrsmUlfYO6gkP2sunVRJEmSVpwvYN9KkiRNmMUWkx7SZ9ouwDHAg4Djl5xIkiRp5bBvJUmSJs5iB+D+4gIvfTTJG4FHAJ9ZcipJkqQVwL6VJEmaRFss43t9Gvi9ZXw/SZKklcy+lSRJ6qTlLCYdANy8jO8nSZK0ktm3kiRJnbTYu7k9uc/krYG7A88AProcoSRJklYC+1aSJGkSLXYA7vcsMP2XwAeB5ywpjSRJ0sryngWm27eSJEmdtdhi0r59pv2iqq5bjjCSJEkrjH0rSZI0cRZbTCrg2qr61fwXkmwJ7FFVVyxLMkmSpOln30qSJE2cxQ7A/V3gXgu8dmD7uiRJkgZj30qaZMnSH5I0gRZbTNrU0W4rvOOIJEnSYti3kiRJE2ezl7kl2QnYuWfSnknuPG+22wJPAb6/jNkkSZKmjn0rSZI06QYZM+k5wMtorukv4MMLzJd2PkmSJC3MvpWk5bUcl8tVLf09JK0YgxSTPg5cTtOhOQn4G+A78+b5JXBJVV2wrOkkSZKmj30rSZI00TZbTKqq84HzAZIU8Omq+uGwg0mSJE0j+1aSJGnSLWoA7qr6p6r6YZJdkxyT5ClJdgZIsk2SxQ7oLUmStGINu2+VZO8k/57kkiQXJ3lOn3kenOQnSc5rHy9dyjIlSdL0G+QytzmSvBb4E2Brmuv8fwe4HvgE8CXglcsZUJIkaZoNuW+1AXheVZ2bZAfgq0lOr6pL5s33H1V1zBKWI0mSVpBFfduV5EXA8cArgPsw93a2nwTshEiSJA1o2H2rqrq2qs5tf/8Z8HVgz6W8pyRJ0mLPTHom8Iqq+rskq+a9dimw3/LEkiRJWhFG1rdKsg9wL+DsPi/fL8n5wDXA86vq4gXeYy2wFmDNmjXLFU2SJE2YxV6Hvydw1gKv3Qhst7Q4kiRJK8pI+lZJtgc+Ajy3qn467+VzgTtV1YHAW2juNtdXVZ1YVTNVNbN69erliCZJkibQYotJVwN3X+C1A4HvLi2OJEnSijL0vlWSrWgKSe+rqo/Of72qflpVP29/PxXYKsmuS12uJEmaXostJv0r8NIkh/RMqyS/ATwPOHnZkkmSJE2/ofatkgR4F/D1qnrDAvPs3s5HkoNp+oc/WspyJUnSdFvsmEkvB+4PnAl8r532r8DewH8Br1q2ZJIkSdPv5Qy3b3UI8CTgwiTntdNeBKwBqKq3A48D/v8kG4D/BZ5QVbXE5UqSpCk2cDEpydbA+4GX0Vzf/3CagSF/RHPL2vdV1YZhhJQkSZo2o+hbVdWXmHuHuH7znACcsJTlaMiyyU04GOuDkqRlNHAxqapuTHIY8Kaq+hfgX4YXS5IkabrZt5oAFnEkSeprsWMm/Sdw32EEkSRJWoHsW0mSpImz2DGTngd8PMnPaW4bey0w5+uWqrp5mbJJkiRNO/tWkiRp4iz2zKQLgf2AN9EMEnkj8Kuex43Lmk6SJGm62beSJEkTZ7FnJr2Ced+WLbckR9B0qFYB76wq7xAnaTI51oakzRt632oiefyUJKnTFlVMqqqXDykHAElWAW8FHgZcBZyT5JSqumSYy5UkSRqHYfetJEnqjOX4ogD8sqAjFnuZ27AdDFxaVZdV1Y3AycCxY84kSZIkSZKkVteKSXsCV/Y8v6qdJkmSJEmSpA5Y7JhJnZBkLbAWYM2aNUNcztLfYznOwDPH8ueA7mQxx/Tm6MopuF3aZ7qyTrqSw7YqSZKkSdS1M5OuBvbueb5XO22Oqjqxqmaqamb16tUjCydJkiRJkrTSda2YdA6wf5J9k2wNPAE4ZcyZJEmSJEmS1OrUZW5VtSHJ8cDngFXASVV18ZhjSZIkSZIkqdWpYhJAVZ0KnDruHJIkSZIkSdpY1y5zkyRJkiRJUodZTJIkSZIkSdLALCZJkiRJkiRpYBaTJEmSJEmSNDCLSZIkSZIkSRqYxSRJkiRJkiQNzGKSJEmSJEmSBrbluANImh5V404gSZIkSRo2z0ySJEmackmOSPLNJJcmeWGf12+T5IPt62cn2Wf0KSVJ0qSwmCRJkjTFkqwC3gocCdwVOC7JXefN9gzgx1V1F+CNwKtHm1KSJE0Si0mSJEnT7WDg0qq6rKpuBE4Gjp03z7HAP7W/fxg4NElGmFGSJE0Qi0mSJEnTbU/gyp7nV7XT+s5TVRuAnwC7jCSdJEmaOA7ALUmSpIEkWQusBVizZs3wFtSVOzqYYy5zbKwrWcwxlznmWI7zTJflv9KR9QF0Jktnts2t4JlJkiRJ0+1qYO+e53u10/rOk2RLYEfgR/PfqKpOrKqZqppZvXr1kOJKkqSus5gkSZI03c4B9k+yb5KtgScAp8yb5xTgKe3vjwM+X9WRr20lSVLneJmbJEnSFKuqDUmOBz4HrAJOqqqLk7wCWFdVpwDvAv4lyaXA9TQFJ0mSpL4sJkmSJE25qjoVOHXetJf2/P4L4PGjziVJkiaTxSRJkiRJkqaUFy1rGCwmbYI7nSRJkiRJ0lwOwC1JkiRJkqSBWUySJEmSJEnSwCwmSZIkSZIkaWAWkyRJkiRJkjQwi0mSJEmSJEkamMUkSZIkSZIkDcxikiRJkiRJkga25bgDSFq6qnEnkCRJkiStFJ6ZJEmSJEmSpIFZTJIkSZIkSdLAvMxNWgIvL5MkSZIkrTSemSRJkiRJkqSBdaaYlOS1Sb6R5IIkH0uy07gzSZIkSZIkaa7OFJOA04G7V9U9gW8BfzXmPJIkSZIkSZqnM8Wkqjqtqja0T88C9hpnHkmSJEmSJG2sM8WkeZ4OfGbcISRJkiRJkjTXSO/mluQMYPc+L724qj7RzvNiYAPwvk28z1pgLcCaNWuGkFSSJEmSJEn9jLSYVFWHber1JE8FjgEOrVr4putVdSJwIsDMzIw3Z5ckSZIkSRqRkRaTNiXJEcALgAdV1Q3jziNJkiRJkqSNdWnMpBOAHYDTk5yX5O3jDiRJkiRJkqS5OnNmUlXdZdwZtGkLX3goSZK6JslrgUcANwLfAZ5WVf/dZ77LgZ8BNwEbqmpmlDklSdLk6dKZSZIkSVo+pwN3r6p7At8C/moT8z6kqg6ykCRJkgZhMUmSJGkKVdVpVbWhfXoWsNc480iSpOlhMUmSJGn6PR34zAKvFXBakq8mWbupN0myNsm6JOvWr1+/7CElSdJk6MyYSZIkSVqcJGcAu/d56cVV9Yl2nhcDG4D3LfA2D6iqq5PcgeZGKN+oqjP7zVhVJwInAszMzDiaoiRJK5TFJEmSpAlVVYdt6vUkTwWOAQ6t6n8rjaq6uv35gyQfAw4G+haTJEmSwMvcJE2hqqU/JGnSJTkCeAHwyKq6YYF5tkuyw+zvwOHARaNLKUmSJpHFJEmSpOl0ArADzaVr5yV5O0CSPZKc2s6zG/ClJOcDXwE+XVWfHU9cSZI0KbzMTZIkaQpV1V0WmH4NcFT7+2XAgaPMJUmSJp9nJkmSJEmSJGlgFpMkSZIkSZI0MItJkiRJkiRJGpjFJEmSJEmSJA3MYpIkSZIkSZIGZjFJkiRJkiRJA7OYJEmSJEmSpIFZTJIkSZIkSdLALCZJkiRJkiRpYBaTJEmSJEmSNDCLSZIkSZIkSRqYxSRJkiRJkiQNbMtxB5AWq2rcCSRJkiRJWrk8M0mSJEmSJEkDs5gkSZIkSZKkgXmZmyRNOS8NlSRJkrScPDNJkiRJkiRJA7OYJEmSJEmSpIFZTJIkSZIkSdLAHDNpAjjeiSRJkiRJ6grPTJIkSZIkSdLALCZJkiRNqSQvT3J1kvPax1ELzHdEkm8muTTJC0edU5IkTRYvc5MkSZpub6yq1y30YpJVwFuBhwFXAeckOaWqLhlVQEmSNFk6d2ZSkuclqSS7jjuLJEnSCnAwcGlVXVZVNwInA8eOOZMkSeqwThWTkuwNHA5cMe4skiRJU+L4JBckOSnJ7fu8vidwZc/zq9ppkiRJfXWqmAS8EXgB4P3LJEmSBpDkjCQX9XkcC7wN2A84CLgWeP0Sl7U2ybok69avX78M6SVJ0iTqzJhJbYfn6qo6P8m440iSJE2EqjpskPmSvAP4VJ+Xrgb27nm+Vzut37JOBE4EmJmZ8cs/SZJWqJEWk5KcAeze56UXAy+iucRtkPdZC6wFWLNmzbLlkyRplMo/xTVkSe5YVde2Tx8NXNRntnOA/ZPsS1NEegLw+yOKKEnSijXJfcGRFpMW+uYsyT2AfYHZs5L2As5NcnBVfb/P+/itmCRJ0ua9JslBNEMIXA78EUCSPYB3VtVRVbUhyfHA54BVwElVdfG4AkuSpO7rxGVuVXUhcIfZ50kuB2aq6odjCyVJkjThqupJC0y/Bjiq5/mpwKmjyiVJkiZb1wbgliRJkiRJUod14syk+apqn3FnkCRJkiRJ0sY8M0mSJEmSJEkDs5gkSZIkSZKkgVlMkiRJkiRJ0sAsJkmSJEmSJGlgFpMkSZIkSZI0MItJkiRJkiRJGpjFJEmSJEmSJA3MYpIkSZIkSZIGlqoad4YlSbIe+N4YI+wK/HCMy59ljrnMMVdXckB3sphjLnPMZY6NjTPLnapq9ZiWrQWMuQ/WlX2jKzmgO1nMMZc55upKDuhOFnPMZY65xp1jwT7YxBeTxi3JuqqaMYc5zDGYrmQxhznMsThdyiJ1pT12JQd0J4s5zDEJOaA7WcxhjknI0Y+XuUmSJEmSJGlgFpMkSZIkSZI0MItJS3fiuAO0zDGXOebqSg7oThZzzGWOucyxsS5lkbrSHruSA7qTxRxzmWOuruSA7mQxx1zmmKsrOTbimEmSJEmSJEkamGcmSZIkSZIkaWAWkyRJkiRJkjQwi0n6tSQZdwYYf44k23chh7Q5tlUNyjYi3aJ3f+jKvmGObunK52uSLbqQo1dXsnQlx7jZVjfK0Yn10asrWYaRw2LSmCR5aJI/6kCO/ZLMAA+M1cQAACAASURBVFRVjauxdyjHMcCnkmScOdosj0zyunEtvyfHkUlebI5f57hbksOT3K59vqLbalfaKXSqjXRinXTouNqJfUbqsdNsO2z3jbH0h5Osnv3Dp8Y4iGmHcnTiWNGhz9djgY/DeLdLm6UrbWTPJHt2IIdtdW6OTrTVrqyPNsuK2GcsJo1BkkcCbwGunjd9pA0+ydHAJ4HXJjkLxvMHR4dyPAx4ObAP8LLZHKPM0JPlCOD/Av81juX35DgaeC1wiTl+/SH1YeC5wOlJVo2jjXSlrXalnbZZutJGOrFOOnRc7cQ+I81KciTNvvHqJO8AqKqbx9T3+SzwhiTvTbLjKJffwRydOFZ06PP1YcBfAwckecaolz8vS1fayCOAzwGvT/L+JPdKsuUYcthWN84x9rbalfXRZlk5+0xV+RjhA7gN8C/Ag9rn2wM7jyHH3YCvAfdun58C3HkF5zgU+CZwH+DuwNuA242xnfwN8Nj2912BewN3ALYZcY63AA9vf98J2Be4zRjWx9hzAPsBZwEHts8/2raXVSPO0Zm22pV22pU20pV10qHjaif2GR8+Zh/AQcBFwIOArYAvAWcCt21f32JEOX4L+CpwcPv8Y+0+e7f2eVZYjk4cK7ry+QocBlzcttNHAa8ddYYOtpF9ab6kuWf7/E3AhcDRo/yst61ulKMTbbUr66PNsqL2Gc9MGr2bgB2BVUl2Az4FnJTks0nuCiM7Q+lnNA17fZJdgEOA/9vmuNtKyZHGbYEDgKdV1dnAz4H7A48Z1nI3laf99Y7AHZNsB3yCpsL+DuCZs6dMDjtHm2V34PZJbk/TVt8CfDzJ0RnBZQFdydH6MXAFsF2SnWg+OF9AczrtMUlWDXPhXWqrXWmns1m60Ea6tE7ozvF9rPuM1EcBn6+qL1bVr4A/o/nj8N3QnKE0ohz/S9Op/2b7/Nk0xa3nz57lMKJ9tCs5/Hy9JcftaP7/a6vqi8C3gCclefSocszTlTbyU+D7wI3t8+e3046m+dxdEZ9rttW+Oca+PuZZUfuMxaQRq6oNNNeU3gP4P8B7q+pRNJdmvKGdZxSn5N1IU9j6W+A/gVdV1XFtjjeulBzV+N+q+oeq+q8kW1XV5cBLgKcl2W9Yy14oT/vrPwK/TVNZf0dVHQu8F3gAzdkOQ8/RZnk3cE/g1cC7quoY4HTgj4DtVkqO1s9oPhxeCHwReHVVPbbN8axh5+hSW+1KO53N0oU20qV1QneO72PdZ6Q+Atw/zbiVOwFHAq8CdkzyghHmuInmDMpHpPki8Q9pLpvZEXgdjGwf7UoOP19vyfFT4K+r6j/bHJfQrJdHJBnVZ0ivrrSRG4Br2hyHAS+mOfujgFeOMIdtle601a6sj3lW1D4z8utMV6I0A6DelebU6m8CF9A08m2ADwBU1Z8n+UySO1fVZUPKcQjN6X/fojm1+0U0jf0lNN+gz+Y4Lcn+VfXtKc/xYOBhNN8wfLmqLqA5AAB8pc23BvhOki1qiN9YtlkOA66kOSXxXOAbwONoTqelqv41yXHAbwKXDylH77Y5n+YD86h2mZ9tc7whyaE03wKsm/IcD+aW7XJGVb0yyR7AK4Av9+Q4nOa01rOHmGPsbbUr7bTN0sU20pV9d9zH1bHvM9Ks3j5YVZ2b5PU0f/CsB3apqiOSfJvmS75h5ujdR78AvBl4Ek1Ba5uqemyS/YGnrZAcD6YDx4oOfb4eAtyX5u+Ei2g+Kza0L18AHAvsAvxwBH3SLraRzwD/ADwBeArN38FPTrINzeU7o8phW+1IW+3K+mizrNh9xjOThizNAFz/DDyEplP/2KpaB7wdWAUckmSmPSVwD+AnQ8pxDM035nsDjwb+FPh5+wfFt4B7Jdm1zbEbzWmc057jzcAvaa4pfW6S1bMHmqr6PvBd4IS2yj3MA9BslhvbLH9GM7bWR2gGTXtAkqcneSzN6fgXDzHH7LZ5FM3polfSjPH1XZq2+ogkj2rnuXIF5JjdLncGXpxkt6q6hqZI8NtJ7pFmQP09ge8NOcdY22pX2mlPlq61ka7su104ro51n5FmzeuDvTTJk6vqAzQd+uNpCtDQFJLunGRVsvyXH/TZR/+8qj5PM4DvM4DHt7MeCeyXZKsVkGPsx4qOfb7+I7AXzR/is3/w0eY4t81xUpItR9An7Vob2Y9mYOXLaQZ6fjLNH8e0P++UZBvb6sppq11ZHz1ZVu4+U2MYmGqlPGgGALsI+J32+ZNoLjm4Tfv8QTR/fPwzTfXwnkPKsT/Nt/KzA4EdCpxBO/A3zcHgVJpT8M4B7jHlOdbQnI56//b5b7bL3Kd9nvbnVjRFvzsOsY30y/KRnix70Fz3+27gRNoB/0a0bf6tZ9vcHXgszeU6H1wBOTbXRn633R6fbPfpqW6rXWmnE9BGurLvdvG4OpJ9xoeP2Qeb6YO100JTWLoK+K0h5djkMaudtkVPjrtOeY5OHCsGyDGqz9d+2+X0nmP4Fu3PvYC3MsSb9nS8jfz683V2+7Q5rqQd2Ni2ujLaalfWxybWyYraZ4ayYn38eqPtSNN52aJn2qeY12GhudxtpyHm2BJ4Ij13E6IZt+mhPc/3pKns7rECcqyiGZTttj3TPgA8ad58WzDkOzBtIsuT+6y7rca5bdppWwPbroAcm90uNNfF7wrcYQw5RtpWu9JOJ62NjGKdTMBxdaT7jA8fsw8W7oMd0PN8e+DpDKmQ1C5joX30IT3PV9F8a/2bKyBHJ44VHfp83ex2aadtDew4rBwT0kbmb5t7A78xhhy21QXaRzttqG21K+tj0HUy7fuMYyYNSZJU1U+SnFxVN6cZvf0mmo7LTu08dwOuq6ofAr8YUo4tqhn0+73t89kcN9MOEpfkYODqqvruMDJ0LEfa5X50Xo7raccQS/JQ4HtV9R2GtF0GyLKqJ8sVVXXpEHMMum2uqaqruOWuANOaY9DtcmUNadyZAXKMrK12pZ22y5m0NtKVfXecx9WR7TPSrM30wXZu57kbzSUp7662lz2EHJvaR7dvpx1Mc6z4zDAydCxHJ44VHfp8HXS7DPUzbZFZxtlG5m+bc8eUw7Y6prbalfXRLsd9BsdMGprZjkk1t56F9sAD/AC4Ns3YFa8ZQY7514jObvMr2xyPoLmTyVBvh9uhHPM7jLPXil7R5jga+BvgVwzZIrIMrfPQ5hhk2/wdtwxqN+05Bt0uv+xIjqG21a600zbLpLWRLuy7XTquDnWfkWYN2Ad7Lc23yUMpJLXLH3QfHeptojuUoxPHig59vnbiM20RWbrURrrS97GtdrP/NYq/49xn8G5uQ9dWC6uqZv+ouILmOtJdgWdWc1bS0Jffk2O2If0IeAfNHzvPrKprV2iO2bsP/BJ4J80tFJ9eVVcMM0eXsnR423QlR1e2y4rOsUAW24jrQ1qQfbDO5+jKsbMrOcayXbqUpcPbxhy2j81lWZHrxGLSMklyIHAn4Os0lxTckPY0syT3BParqo/RDEr2IOCgGsLlD0nuQ3Nbyu8CF1bV9T05ZoB7VdU7gNvR3Cr3rtWcBjitOX4X+B3gOuDfq+qanhz3Bx5YVa+iqdZuBfz+MLZLl7J0aNt0JUdXtos5Ns5iG5mbw/Uh9WEfrLM5OnGs6FCOTmyXLmXp0LYxx9wcto+Ns7hO+qkhDkq1Uh7AMTR3DPkIcDLwmJ7XHgRc2G5YaG7VN5TB4tocF9JUIU8Cngds2b52P+A84GHt89uskBzn01xOeALNt5Hbt6/dG/gqcEz7fGtg3yG3kbFn6di26UqOrmwXc9hGXB8+fCzygX2wLucY+7GiYznGvl26lKVj28Yctg/Xya3JNOwFTPsDuBdwCU01EuBPgU/0vP5s2o4NPXcUGUKOu7UNeTbHo2lGk79N+/wPgKPa34d5Z6Gu5NgP+A9uuSXwvWk6mbu0z4+hHWl/mDm6lKVD26YrObqyXcxhG3F9+PBxKx7YB+tqjk4cKzqUoxPbpUtZOrRtzGH7cJ0sJdeoFjStD2Bv4A/nTfscsGbetFVDznF7YC2QnmmnAzMrNMcO7c7de0vgU4FHjDJHl7J0aNt0JUdXtos5bCOuDx8+bsXDPlhnc3TiWNGhHJ3YLl3K0qFtYw7bh+tkCQ/v5rZEVXUl8C8ASbZMsg3NtZKzt2XeP8m21dyib5g5fgycVFWVZHYsrA3cckvAg5LsuIJy/Az4YDW3BJ7Nsb7NQpIHJNl12Dm6lKVD26YrObqyXcyxcRbbyNwcrg+pD/tgnc3RiWNFh3J0Yrt0KUuHto055uawfWycxXWyCRaTboUk90/ypJ5Js3cJSVX9gmZArGuSPB74W5prJ4eR46FJ/rJnUs2b5VrgB0keQ3O7xmnPcWSSN/fJMfvz+8D1SR41zBxdytKhbdOVHF3ZLubYOIttZG4O14fUh32wzuboxLGiQzk6sV26lKVD28Ycc3PYPjbO4joZ1ChPg5qGB/Aw4Abgw8Cze6b3nvr2NuD9wDnAPYaU41Dgv2lOs/u7num9p769Hvj3NsfdpzzH/WlufbgOeO8COf4auHiYObqUpUPbpis5urJdzGEbcX348HErHtgH62qOThwrOpSjE9ulS1k6tG3MYftwnSzjwzOTFm9v4JXAO4B7Jnk2QFVVkq3aee4CPBw4rqouHFKOnYBXAccDeyZ5VZvj5p4cOwMzbY6LpjzHdsDLgUOA2yR5f58cNwK7DDlHl7J0Zdt0JUdXtos5NmYbmcv1IfVnH6ybObpyrOhKjq5sly5l6cq2Mcdcto+NuU4WIW1VSwNKc41iaK6TPAZ4KHBxVZ3QM899gJ9V1SVDzrJtVd2QZAb4E+D7VfWXPa/vAey0gnLcrqp+mmRn4O3ATVV1XPvaNjQ75e2r6tJh5uhSlg5tm67k6Mp2McfGWWwjc3O4PqR57IN1OkcnjhUdytGJ7dKlLB3aNuaYm8P2sXEW18mgGS0m3XpJtgeOAh5Cc6rbtsANVfWhEefYkub2uMfT3CL3OmDnqnrDSszRZtmV5lT3HwFnAb8FvKjGMDBsF7J0Zdt0JUebZezbxRx9c9hG5mZwfUh92AfrZo42SyeOFV3I0bHt0qUsY9825tgog+1j4xyuk83lspi0eUlSC6yoJDsA9wZeA/wm8LtVdcEo87U5tgLuQHOLwDXAg4aVYzPrY2Q5BpHkm22eoebY1DoZZZaubJskW1TVzV3O0TPPULfLIBlGlGP7qvr5uHP0LKcrbdV9d5FG1UYksA/WZ1keK+Yuw2P4xsuzD7aIDCPK0Zk+WFfaalf23c1lWYnH1cVwzKRNSLImye1pb/3XTtui/blde4D6Gc01k2uA+w3pAHTbJFvPm7aq/blzkm2q6lc0YwTsDBwypBy7JNmud2drK7YjzdGz7FU9v89ulzu2nUvSjGy/FfCAIR4Id2pPhexdJyPPkmT32WX1yTHKNnK3JLtUcz3v7PLHkeOBSXbv7UCMabscBjwyzamo48zxEOAlaW6dPX+7jHqfOSDJnsCuPdPG0UZ27rPvzh7PRrltPK5KfdgH2yiHx4q5y7f/tXEW+2Bzc9gHm5vD/tfGWTyuLpHFpAUkeSRwMvBB4A/aaavaA/RDaEZxn/3w+AVwdA3huskkxwLvAj6Q5PAkdwKoqpvaHG+iGShsNseRQ8rxGOADwKeT/GGaMQmoqg1JHjrCHIcn+at22TfN7mjtdnkQ8FZuOUje2Oa4eLlztFkeAbwX+EyS30+yQ5KMOkuSo2nuXHMy8LQkq9pO9s0jbiN3pbnU4IQku7XLn91nHjzCHIcD/wTcqWdaetbHqLbLw4H3AP9Tze2q6dkuDx1xjvcDzwbuMtvJHOM+8z7gBOCPkmw777g6qjZyLE0beW+SJ6W5Jn72eDbKNuJxVerDPthGOTxWzM1h/2vjLPbB5uawDzY3h/2vjbN4XF0ONYZbyHX9ARwEXATcg2aAx9OAHdrX7gh8BXjcCHIcCFwI3BN4NM3B6HU0p3LvCJwNPHYEOfYAvklzKvnhwItoBgE7lGaMglHleCDwA+AbwOt6pq+iqRafDTxmRG3kYW0bmQEeT3Pq433a13YZVRbgaOBrwO/QjB3xeZqB2KA58Jw9irbaLm9Lmg7Vm4APAXu103cbYRt5OHA+cN/2+W1ob6FJ84fHWcPOAQTYBvjI7LLa/XVHYHXbXs8a0THkEcC5wH40Awh+GNh+1O20Xd4BPcfVewMfA3ZpX7vdqNoq8BvtcfWu7THltTSdid+l+bZn6G2kzeFx1YePPg/sg83P4bFibg77X/3z2Acr+2AL5LD/tXEWj6vL9Pj1qcOaYw1wSVVdmOR7wP8B3pzkK8A1wO9V1eVJAs0taYeU405tjguAC5KsBp5E0+g+SnMbwMtGkGMVcEVVnQuQ5FKaD4zHAkWzs101ghx7AC+m+b9/KMnrq+p51Qw8dn2SR1XVtW1Ft4aVI80piIfQ7PTrgHVJ9qPp1JxdVT8aVRbgt4GXVNU5SXan+bB8dZIvAFcCx1bV94e9bdr/5+xpxF+g6Xy/IsnJ7fTHVdWVI2gjhwG3raqz2v3lb4HbJTkT+CJwRFX99zBztO/5i/bYcVaaQWI/TrPfbgBOobnO+ZfDzJHmtO7DgBdU1XeSfImmvewG/LxtpyNpH61dgava4+r2NJ2rtya5DPgqcFSbadhZZnNcApDmMppnA78HrKfZZ64bQY4t6cZxdU86cFyVetgHm8s+WMv+V3/2wW5hH6wv+18bsw+2TLzMrb+vALsm+RDwdZoDz0eBvYHfajsxW1RriDkuBH6V5Mnt893bPAfSjCR/GbQta4g5qupKmgb9uvb5ZTTfFF4H7N3ubENfH1V1MvDhqvoR8AzgwCR/3zPL/7bz3TzkHDcB/wB8LC2aDu4demb78YiyvKKqPpVkW5pvGj5N86F5d+ABwHUj2jY3VzOw4GeAX1bVX9N0YP4V2KrtxIwix18AX0xyDvAJmm8NP0rzx8lRwE+GnWP2g6f1TuCVwLuB59Gsj8cBuyfNYH/DylHNad0vqKoz2knn0dxC9BU983y//TmKD6hzgNsmOQP4Ds0lCm+g2XceTHOsG8Vx9SLgp0n+T/v8XsC3aE5h3qeqroORHFevaHOM+7j6AeDj4z6uSj3sg/WwDzYng/2v/lnsg7Xsg/Vl/2se+2DLx2JSK8mDk/xekuPanftpNDvbl6vqb6vqk8DngAelGTRss3cGWGKOJ1TVd2m+YXhkks/QnMa7FvhP2jEEhiXJXkl27Jn0d8C2SZ4PUFXfAdYBx6UZnGxY62NOjqqa7SRcDqwF7p7kpUkeR3MN8FbDyNEnyw+r6ic9B5lvAr9q5zsO+L30DKI2rBw9FfMbgMdX1Uur6lTgdJpv77YexbZpK+YAWwP3SHIIcD/gszRt5I4jbCN/SHOq7Ker6h+q6oM0HxAPZATro+dg/xfAZTSnwp9RVVfR7M8bgKF9KLQ5dgKoql+207Zol/cs4PZpxjQYut5tU1U30nzr83LgtKp6TVV9BTiD5gyALUfURm6g6WDOJDkFuFdV/QnNqejHzeuILneOQ5I8JskT20kvAXYYw3F1Nsfvt8td33asL2fEx1UJ7IP1yWEfbOEcK77/1SeLfTD7YP1y2P+am8U+2BBYTOLXo+x/gKZy/hdJ3gr8b1WdAnx/dmPTXFe6iuZgPewcL2irpWcATwf+FHhUO+uWwE+GkaHN8ah2uc9Ic5oqNNdyfhLYL8mb2mnb03yAD+tDuzfHru20Xx9o2iryUcAfAe8ATq1mxP2hZ+lzgLkJuDnJU4GXAl+p5hu0YeeoJFu06+Xqnll3AW6muQZ52W1ifXyc5puGjwJ/DjyV5vr5kbURgPbD6VU9s+5Cs41Gsj7ayTcDb6Y5tfpd7TY6HNiHpjMzzBxPn7c+bm6XfwPNt2MHDWP5C2T59XGkqn5RVV8Cfp7khe2sd6EZ4HAUbWR1VW2oqtOBx9B8aD+mnXUH4L+H2ME8CjiR5lvrFyX522pO9f4Yoz2u9uZ4cZLXwC2ncI/yuCqBfbA+OeyDbSLHSu9/LZDFPph9sH457H/dksU+2JBkiNttIrQ796uBa6vqjWmucX0XzWmyfwccSbNRb0tzXeMTazi3SeyX490015C+oqp+2M73fJpr9n+/hjPa/2qaAfyuAK6iOQif3FZNt6G5zvalNDv+3sCTq+prI8hxXZvjh/PmexzNgJhHD2N9bC5LT8fqnsCXaE6Lf3pVfWOUOebN92yaUyWfWlUXjjJHmmux/xg4q6q+2K6frdpvRUaWY958f0zzLffTRrg+PlhV69vXt6Hp0BTNh8ezRr1d5s13BM1lAvcAbhjGh/fmsqT5Vu54muPILgzvuLrg8WzefM+laSNPHNK22Z/mj9TnVtWXkuwDvIXm7IZfAXdmNMfVfjneBDyT5tv+aucb+nFVAvtgfXLYBxswx0rsf20ui30w+2D2v/pmsQ82RCu+mASQ5Ak014y+rJqBv7al6URcUVV/kWRfmgPQ+dVcYznKHCcB11fVs9t5XsH/a+9OYySrygAMvx8MyyCKoCwDKEIQMARkNUhAjBojjoFExBBCZKIgvxADhkVEJTHiEuNGUFHCyCLLQMQECULY1ZCoILsohsXoGJDFCTvC549zC2ranpnqtm/dc6vfJznpqeqqrreq79ScuXUXuDwz72ipYV3KUf//TDmLynuAB4Bl2ezb29xuc+D5zGzl07nVdFySmY/Ga6fVPJLyKdR9bXSM0tLcZgPKvtgnt/hmuKbXZAHluAEnAT/poGNZlgMJrpuZL0bEgsxs5dOfNXS8uoxQPmH4ErC0g9fj0mz2/25utz6wdmY+M+aOlf7ONLfdqK2/u2toubR5b1uXcjyHXYG/ZubyMXcMXpNoPl0+AfhVZt7dUsfbKZtzXxpl94s3Uo6vcdTwY0Y5iOtzLb6vTtdxZdNxz9DrsYRyMNvW3lelAedgKzU4B5tBR3ObeTP/WkOLczDnYM6/pm9xDtamrOCUcl0MyprH9Sifdm0DXEg53ejC5vsbUA4ct7jjjoWUI+0f3HLHWymbjm8w5fpDKGv0j20u71VJx+5jWEZm1AKs13HHrs3XBTW8HhX8Xt7ZfF2r4449K3k9dmuzo6aWGXTsMYaOdSifDA+uG3yIcwGw7RiXkVE6Wl9GHI5M52DTdDgH+z86mPD5V09/N87B6pz31NLR6vxrqMU5WMtjXh4zKSIWU8548H3Kp04vUjY7Ow7YP8qB6p6lHESv647ngOtoaf/eoY6rgDOBcyNip8H3MvNyyuk8N42IK4AbImLLCjpujoit2uiYRcstze/qhY47fhsRW2YLn0TN4ndTwzLy6+b1mPMD6M2w48ZKXo9b2uqoqWWGHTe19T4y1HEWcMFQx4Lm6ybA66IcCPLieO3YKF12LIuIzSLaPQim5jfnYNN2OAebXcfEz79m0eIcbJ7NwXra0dr8a0qLc7C2db02a5wDCMqnUHdRNmXeHDgR+BtlX/zFwHnNOIOyj+cO86zjc8ByYOcpt70AeAjYZVI7amqxw44+dNTUUnnHCVM7KP95vYxyrI+dJ7XD4RiMVSyTtcx9aumo5T3LDv99tcOOXnWspsU5WIuj84CxP+FydPazKROGwSZmxwMPA4uALYGDKQfi2nGedhxHOTPFDs3lRcC9tLj5XS0dNbXYYUcfOmpq6UnHjs3lb1GOH7DTpHc4HIOximWylrlPLR21vWfZ0VFHTS122NGHjhFanIPN9fPsOmBsT7Sc/nBvypHrLwFOnPL9UyhrB1vZ77qHHScCS3ntuAEbTnJHTS122NGHjppaetbxU8oE40Bgu0nucDgGY4Rlspa5Ty0dNb1n2THmjppa7LCjDx0zaHEONtfPt+uAsTzJchT5Oyn7ap4JHETZxO6Uodu8jbL2MOx4teNH86GjphY77OhDR00tPez4cSW/l1Y7HI7BmMEyWcvcp5aOWt6z7BhjR00tdtjRh44ZtjgHm+MxOPjTxIqIfYFvAodn5u0RcTbwLmBf4Nbm1HwXA/sBe1BO0/ekHewH7DnpHTW12GFHHzpqaulpx+4RsUlmPjGpHdJAT+c+tXTU8p5lh/++2mFHlR2zaHEONte6XpvV9qD8ApcMXd4U+GXz5+0omzOfRTn1aysHA7Oj3o6aWuywow8dNbXYUWeHwzEYtSyTdtjRh46aWuywow8dNbXU0jHu0XlA60+w7I/4hqE/bw3cDixqrtuGcnq+jeyYfx01tdhhRx86amqxo84Oh2Mwalkm7bCjDx01tdhhRx86amqppWPcYy0mXGa+nJkrmosBPAU8kZnLI+II4PPAOpn5bzvmX0dNLXbY0YeOmlrsqLNDGqhlmbTDjj501NRihx196KippZaOcRucpm5eiYilwHLgg5TN0e6yw44aW+ywow8dNbXYUWeHNFDLMmmHHX3oqKnFDjv60FFTSy0dbZpXK5MiIoB1gPuar+/PzL/YYUdtLXbY0YeOmlrsqLNDGqhlmbTDjj501NRihx196KippZaOcZhXK5MGImIJ8LvMvMcOO2puscOOPnTU1GJHnR3SQC3LpB129KGjphY77OhDR00ttXS0ab6uTIqs4InbUWcH1NNihx196IB6Wuyos0MaqGWZtMOOPnRAPS122NGHDqinpZaONs3LlUmSJEmSJEmanYk/m5skSZIkSZLmjiuTJEmSJEmSNDJXJkmSJEmSJGlkrkyS1JqIWBIROTSeiYiHIuLnEfHx5tSZ4+zZKyLOjog/RcSzEfFIRFwYEduOs0OSJKkttc2/mqavRsQ1EfF407Rk3A2S5pYrkySNw6HAu4EPA6cBLwAXAddGxMIxdhwG7Ax8DzgQOBnYA/h9RLxljB2SJEltq2X+BXAssBC4csyPK6klC7oOkDQv/DEzHxi6fH5ELAOWAd+gTDDG4euZ+djwFRHxG+BB4Gjgi2PqkCRJalst8y+AE+y9eAAAA2xJREFUjTLzlYjYHvjEGB9XUkvcMklSJzLzcuAXwNERsQFARJweEbdFxIqI+FdEXB8R+wzuExFbRMSLEXHc1J8XEV9udl3beDWP+dg01z0MPAZsNRfPS5IkqVZdzL+ax31lrp+LpG65MklSl64C1gP2ai5vBXwbOBhYAjwK3BwRuwBk5j+BK4BPD/+QiFgb+BRwaWY+OZOAiHgHsBlw36yfhSRJUn90Pv+S1H/u5iapS480XxcBZOZRg280E5SrgXuAo4DBp2FnATdExP6ZeUtz3WJga+CHM3nwiFjQ3Ocx4JxZPgdJkqQ+6XT+JWkyuGWSpC4NziaSABHxgYi4ISIeB/4DvATsAOw4uENm3gjcCxwz9HOOAe7MzFubn7N2RCwYGqt6rzsT2Bc4wk/UJEnSPNH1/EvSBPAvuKQuDc6gtjwi9qBsdv00ZZPpfYC9gTuA9afc7wfAxyLiTRGxDfAhVv5U7DrKRGgw/ufA2hHxNcrm2p/MzGvm7BlJkiTVrbP5l6TJ4W5ukrq0GHge+ANwKuXTsI9m5kuDGzQHdHxqyv3OA86g7Ne/MfAscOHQ948BXj90+R/Dd46IU4GTgGMz8/y5eCKSJEk90cn8S9JkcWWSpE5ExCHAQcB3M/PZ5owiL9Nsct3c5n3AW4EHh++bmSsi4kLKpGVD4KLMXDH0/ftX87ifAb4CnJqZZ87hU5IkSapaV/MvSZPHlUmSxmG3iHgzsC5lcvIR4FDgWuCU5jZXA58FlkbEuZR99U8D/r6Kn3kWr+23P9KBHyPiMOA7zWOtdNpbYEVm3jvyM5IkSapbFfMvgIg4ANgU2KK5aq+IeBogMy8b9edIqkdk5ppvJUmzEBFLgHOHrnqecrrZ24CfAZfl0JtQRBwLHE+ZaNxNmeh8ASAz3zvNz7+fshJo7xF7lgJHruLbN033GJIkSX1S2/yruc+NwAHTfS8zY7rrJdXNlUmSeikidgTuA47OzHO67pEkSZp0zr8kDbgySVKvRMTWwPbA6c3X7TPzuW6rJEmSJpfzL0lTrdV1gCTN0FHA9cDmwOFOZCRJklrn/EvSStwySZIkSZIkSSNzyyRJkiRJkiSNzJVJkiRJkiRJGpkrkyRJkiRJkjQyVyZJkiRJkiRpZK5MkiRJkiRJ0shcmSRJkiRJkqSR/Rf7NgkqK8ALWwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# KODEX 코스닥 150"
      ],
      "metadata": {
        "id": "Z6x_eoBC95Ui"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv('/content/gdrive/Shareddrives/CS360/madcamp-3-week/data/strategy-3/KODEX_코스닥_150(2016~현재).csv', encoding = 'cp949', thousands = ',')\n",
        "data"
      ],
      "metadata": {
        "id": "BULhqLm0dbCe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 572
        },
        "outputId": "e4a4b6ce-95ab-44d4-b4e0-64d404698b1a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              일자       시가       고가       저가       종가  전일비  Unnamed: 6   등락률  \\\n",
              "0     2022-07-18  11015.0  11075.0  10850.0  11010.0    ▲       150.0  1.38   \n",
              "1     2022-07-15  10955.0  10965.0  10735.0  10860.0    ▼        -5.0 -0.05   \n",
              "2     2022-07-14  10840.0  10975.0  10795.0  10865.0    ▼       -40.0 -0.37   \n",
              "3     2022-07-13  10615.0  10905.0  10590.0  10905.0    ▲       315.0  2.97   \n",
              "4     2022-07-12  10710.0  10745.0  10555.0  10590.0    ▼      -235.0 -2.17   \n",
              "...          ...      ...      ...      ...      ...  ...         ...   ...   \n",
              "1666         NaN      NaN      NaN      NaN      NaN  NaN         NaN   NaN   \n",
              "1667         NaN      NaN      NaN      NaN      NaN  NaN         NaN   NaN   \n",
              "1668         NaN      NaN      NaN      NaN      NaN  NaN         NaN   NaN   \n",
              "1669         NaN      NaN      NaN      NaN      NaN  NaN         NaN   NaN   \n",
              "1670         NaN      NaN      NaN      NaN      NaN  NaN         NaN   NaN   \n",
              "\n",
              "            거래량   금액(백만)   신용비        개인        기관   외인(수량)  외국계  프로그램   외인비  \n",
              "0     7961398.0  87649.0  0.00  306846.0 -268938.0 -38881.0  0.0   0.0  0.73  \n",
              "1     6584636.0  71633.0  1.82  121553.0 -113722.0 -10905.0  0.0   0.0  0.81  \n",
              "2     6613272.0  72081.0  1.94   77122.0  -56415.0  52721.0  1.0   0.0  0.84  \n",
              "3     6165852.0  66931.0  1.76 -386256.0  414111.0 -69154.0  0.0   0.0  0.75  \n",
              "4     5700546.0  60749.0  2.07  289603.0 -337076.0 -12281.0  0.0   0.0  0.90  \n",
              "...         ...      ...   ...       ...       ...      ...  ...   ...   ...  \n",
              "1666        NaN      NaN   NaN       NaN       NaN      NaN  NaN   NaN   NaN  \n",
              "1667        NaN      NaN   NaN       NaN       NaN      NaN  NaN   NaN   NaN  \n",
              "1668        NaN      NaN   NaN       NaN       NaN      NaN  NaN   NaN   NaN  \n",
              "1669        NaN      NaN   NaN       NaN       NaN      NaN  NaN   NaN   NaN  \n",
              "1670        NaN      NaN   NaN       NaN       NaN      NaN  NaN   NaN   NaN  \n",
              "\n",
              "[1671 rows x 17 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a8650587-215c-499d-945d-5a3bcfdf8824\">\n",
              "    <div 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",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>일자</th>\n",
              "      <th>시가</th>\n",
              "      <th>고가</th>\n",
              "      <th>저가</th>\n",
              "      <th>종가</th>\n",
              "      <th>전일비</th>\n",
              "      <th>Unnamed: 6</th>\n",
              "      <th>등락률</th>\n",
              "      <th>거래량</th>\n",
              "      <th>금액(백만)</th>\n",
              "      <th>신용비</th>\n",
              "      <th>개인</th>\n",
              "      <th>기관</th>\n",
              "      <th>외인(수량)</th>\n",
              "      <th>외국계</th>\n",
              "      <th>프로그램</th>\n",
              "      <th>외인비</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2022-07-18</td>\n",
              "      <td>11015.0</td>\n",
              "      <td>11075.0</td>\n",
              "      <td>10850.0</td>\n",
              "      <td>11010.0</td>\n",
              "      <td>▲</td>\n",
              "      <td>150.0</td>\n",
              "      <td>1.38</td>\n",
              "      <td>7961398.0</td>\n",
              "      <td>87649.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>306846.0</td>\n",
              "      <td>-268938.0</td>\n",
              "      <td>-38881.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.73</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2022-07-15</td>\n",
              "      <td>10955.0</td>\n",
              "      <td>10965.0</td>\n",
              "      <td>10735.0</td>\n",
              "      <td>10860.0</td>\n",
              "      <td>▼</td>\n",
              "      <td>-5.0</td>\n",
              "      <td>-0.05</td>\n",
              "      <td>6584636.0</td>\n",
              "      <td>71633.0</td>\n",
              "      <td>1.82</td>\n",
              "      <td>121553.0</td>\n",
              "      <td>-113722.0</td>\n",
              "      <td>-10905.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.81</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2022-07-14</td>\n",
              "      <td>10840.0</td>\n",
              "      <td>10975.0</td>\n",
              "      <td>10795.0</td>\n",
              "      <td>10865.0</td>\n",
              "      <td>▼</td>\n",
              "      <td>-40.0</td>\n",
              "      <td>-0.37</td>\n",
              "      <td>6613272.0</td>\n",
              "      <td>72081.0</td>\n",
              "      <td>1.94</td>\n",
              "      <td>77122.0</td>\n",
              "      <td>-56415.0</td>\n",
              "      <td>52721.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.84</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2022-07-13</td>\n",
              "      <td>10615.0</td>\n",
              "      <td>10905.0</td>\n",
              "      <td>10590.0</td>\n",
              "      <td>10905.0</td>\n",
              "      <td>▲</td>\n",
              "      <td>315.0</td>\n",
              "      <td>2.97</td>\n",
              "      <td>6165852.0</td>\n",
              "      <td>66931.0</td>\n",
              "      <td>1.76</td>\n",
              "      <td>-386256.0</td>\n",
              "      <td>414111.0</td>\n",
              "      <td>-69154.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.75</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2022-07-12</td>\n",
              "      <td>10710.0</td>\n",
              "      <td>10745.0</td>\n",
              "      <td>10555.0</td>\n",
              "      <td>10590.0</td>\n",
              "      <td>▼</td>\n",
              "      <td>-235.0</td>\n",
              "      <td>-2.17</td>\n",
              "      <td>5700546.0</td>\n",
              "      <td>60749.0</td>\n",
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              "      <td>-337076.0</td>\n",
              "      <td>-12281.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <th>...</th>\n",
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              "      <td>...</td>\n",
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              "      <th>1666</th>\n",
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              "      <td>NaN</td>\n",
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              "      <th>1667</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <th>1668</th>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>1670</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "  </tbody>\n",
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              "<p>1671 rows × 17 columns</p>\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = data.dropna()\n",
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 676
        },
        "id": "ZigJCiAwBfmC",
        "outputId": "a80ce598-cf3a-4d54-fb94-2ea294d45eeb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              일자       시가       고가       저가       종가 전일비  Unnamed: 6   등락률  \\\n",
              "0     2022-07-18  11015.0  11075.0  10850.0  11010.0   ▲       150.0  1.38   \n",
              "1     2022-07-15  10955.0  10965.0  10735.0  10860.0   ▼        -5.0 -0.05   \n",
              "2     2022-07-14  10840.0  10975.0  10795.0  10865.0   ▼       -40.0 -0.37   \n",
              "3     2022-07-13  10615.0  10905.0  10590.0  10905.0   ▲       315.0  2.97   \n",
              "4     2022-07-12  10710.0  10745.0  10555.0  10590.0   ▼      -235.0 -2.17   \n",
              "...          ...      ...      ...      ...      ...  ..         ...   ...   \n",
              "1603  2016-01-08  10800.0  11120.0  10750.0  11050.0   ▲        25.0  0.23   \n",
              "1604  2016-01-07  11080.0  11200.0  10915.0  11025.0   ▼      -125.0 -1.12   \n",
              "1605  2016-01-06  11065.0  11150.0  10910.0  11150.0   ▲       100.0  0.90   \n",
              "1606  2016-01-05  10775.0  11050.0  10725.0  11050.0   ▲       110.0  1.01   \n",
              "1607  2016-01-04  11040.0  11130.0  10810.0  10940.0   ▼       -75.0 -0.68   \n",
              "\n",
              "            거래량   금액(백만)   신용비        개인        기관   외인(수량)  외국계  프로그램   외인비  \n",
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              "...         ...      ...   ...       ...       ...      ...  ...   ...   ...  \n",
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              "1606   882234.0   9665.0  0.03   -3062.0    3062.0      0.0  0.0   0.0  0.00  \n",
              "1607   619538.0   6812.0  0.03     150.0    -200.0      0.0  0.0   0.0  0.00  \n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2022-07-12</td>\n",
              "      <td>10710.0</td>\n",
              "      <td>10745.0</td>\n",
              "      <td>10555.0</td>\n",
              "      <td>10590.0</td>\n",
              "      <td>▼</td>\n",
              "      <td>-235.0</td>\n",
              "      <td>-2.17</td>\n",
              "      <td>5700546.0</td>\n",
              "      <td>60749.0</td>\n",
              "      <td>2.07</td>\n",
              "      <td>289603.0</td>\n",
              "      <td>-337076.0</td>\n",
              "      <td>-12281.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.90</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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1603</th>\n",
              "      <td>2016-01-08</td>\n",
              "      <td>10800.0</td>\n",
              "      <td>11120.0</td>\n",
              "      <td>10750.0</td>\n",
              "      <td>11050.0</td>\n",
              "      <td>▲</td>\n",
              "      <td>25.0</td>\n",
              "      <td>0.23</td>\n",
              "      <td>3667733.0</td>\n",
              "      <td>40473.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>188.0</td>\n",
              "      <td>-188.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1604</th>\n",
              "      <td>2016-01-07</td>\n",
              "      <td>11080.0</td>\n",
              "      <td>11200.0</td>\n",
              "      <td>10915.0</td>\n",
              "      <td>11025.0</td>\n",
              "      <td>▼</td>\n",
              "      <td>-125.0</td>\n",
              "      <td>-1.12</td>\n",
              "      <td>853205.0</td>\n",
              "      <td>9452.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>-48806.0</td>\n",
              "      <td>48806.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1605</th>\n",
              "      <td>2016-01-06</td>\n",
              "      <td>11065.0</td>\n",
              "      <td>11150.0</td>\n",
              "      <td>10910.0</td>\n",
              "      <td>11150.0</td>\n",
              "      <td>▲</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.90</td>\n",
              "      <td>545702.0</td>\n",
              "      <td>6048.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>-1404.0</td>\n",
              "      <td>1404.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1606</th>\n",
              "      <td>2016-01-05</td>\n",
              "      <td>10775.0</td>\n",
              "      <td>11050.0</td>\n",
              "      <td>10725.0</td>\n",
              "      <td>11050.0</td>\n",
              "      <td>▲</td>\n",
              "      <td>110.0</td>\n",
              "      <td>1.01</td>\n",
              "      <td>882234.0</td>\n",
              "      <td>9665.0</td>\n",
              "      <td>0.03</td>\n",
              "      <td>-3062.0</td>\n",
              "      <td>3062.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1607</th>\n",
              "      <td>2016-01-04</td>\n",
              "      <td>11040.0</td>\n",
              "      <td>11130.0</td>\n",
              "      <td>10810.0</td>\n",
              "      <td>10940.0</td>\n",
              "      <td>▼</td>\n",
              "      <td>-75.0</td>\n",
              "      <td>-0.68</td>\n",
              "      <td>619538.0</td>\n",
              "      <td>6812.0</td>\n",
              "      <td>0.03</td>\n",
              "      <td>150.0</td>\n",
              "      <td>-200.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1608 rows × 17 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4a1b2aaf-ce8f-4e62-b166-ba88a21199a1')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
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              "\n",
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              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-4a1b2aaf-ce8f-4e62-b166-ba88a21199a1 button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-4a1b2aaf-ce8f-4e62-b166-ba88a21199a1');\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",
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              "  "
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          },
          "metadata": {},
          "execution_count": 16
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    {
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        "data = data.loc[:, ['일자', '시가', '종가', '등락률']]\n",
        "data"
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              "              일자       시가       종가   등락률\n",
              "0     2022-07-18  11015.0  11010.0  1.38\n",
              "1     2022-07-15  10955.0  10860.0 -0.05\n",
              "2     2022-07-14  10840.0  10865.0 -0.37\n",
              "3     2022-07-13  10615.0  10905.0  2.97\n",
              "4     2022-07-12  10710.0  10590.0 -2.17\n",
              "...          ...      ...      ...   ...\n",
              "1603  2016-01-08  10800.0  11050.0  0.23\n",
              "1604  2016-01-07  11080.0  11025.0 -1.12\n",
              "1605  2016-01-06  11065.0  11150.0  0.90\n",
              "1606  2016-01-05  10775.0  11050.0  1.01\n",
              "1607  2016-01-04  11040.0  10940.0 -0.68\n",
              "\n",
              "[1608 rows x 4 columns]"
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              "      <td>10905.0</td>\n",
              "      <td>2.97</td>\n",
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              "      <th>1603</th>\n",
              "      <td>2016-01-08</td>\n",
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              "      <td>-1.12</td>\n",
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              "      <td>2016-01-04</td>\n",
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              "      <td>-0.68</td>\n",
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              "<p>1608 rows × 4 columns</p>\n",
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              "                                                     [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",
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              "  "
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          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_copy = data.copy()\n",
        "data_copy['일자'] = data_copy['일자'].map(lambda x: x[:4])\n",
        "data_copy"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "GE_10Qjhk0wu",
        "outputId": "0593d09a-243b-4b48-8614-4725532bb0f4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        일자       시가       종가   등락률\n",
              "0     2022  11015.0  11010.0  1.38\n",
              "1     2022  10955.0  10860.0 -0.05\n",
              "2     2022  10840.0  10865.0 -0.37\n",
              "3     2022  10615.0  10905.0  2.97\n",
              "4     2022  10710.0  10590.0 -2.17\n",
              "...    ...      ...      ...   ...\n",
              "1603  2016  10800.0  11050.0  0.23\n",
              "1604  2016  11080.0  11025.0 -1.12\n",
              "1605  2016  11065.0  11150.0  0.90\n",
              "1606  2016  10775.0  11050.0  1.01\n",
              "1607  2016  11040.0  10940.0 -0.68\n",
              "\n",
              "[1608 rows x 4 columns]"
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              "\n",
              "  <div id=\"df-ae36ef87-8bc9-4e9c-858d-0138e1f9182d\">\n",
              "    <div 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",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>일자</th>\n",
              "      <th>시가</th>\n",
              "      <th>종가</th>\n",
              "      <th>등락률</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2022</td>\n",
              "      <td>11015.0</td>\n",
              "      <td>11010.0</td>\n",
              "      <td>1.38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2022</td>\n",
              "      <td>10955.0</td>\n",
              "      <td>10860.0</td>\n",
              "      <td>-0.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2022</td>\n",
              "      <td>10840.0</td>\n",
              "      <td>10865.0</td>\n",
              "      <td>-0.37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2022</td>\n",
              "      <td>10615.0</td>\n",
              "      <td>10905.0</td>\n",
              "      <td>2.97</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2022</td>\n",
              "      <td>10710.0</td>\n",
              "      <td>10590.0</td>\n",
              "      <td>-2.17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1603</th>\n",
              "      <td>2016</td>\n",
              "      <td>10800.0</td>\n",
              "      <td>11050.0</td>\n",
              "      <td>0.23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1604</th>\n",
              "      <td>2016</td>\n",
              "      <td>11080.0</td>\n",
              "      <td>11025.0</td>\n",
              "      <td>-1.12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1605</th>\n",
              "      <td>2016</td>\n",
              "      <td>11065.0</td>\n",
              "      <td>11150.0</td>\n",
              "      <td>0.90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1606</th>\n",
              "      <td>2016</td>\n",
              "      <td>10775.0</td>\n",
              "      <td>11050.0</td>\n",
              "      <td>1.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1607</th>\n",
              "      <td>2016</td>\n",
              "      <td>11040.0</td>\n",
              "      <td>10940.0</td>\n",
              "      <td>-0.68</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1608 rows × 4 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ae36ef87-8bc9-4e9c-858d-0138e1f9182d')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ae36ef87-8bc9-4e9c-858d-0138e1f9182d 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-ae36ef87-8bc9-4e9c-858d-0138e1f9182d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "year_info = data_copy.groupby('일자')['일자'].count()\n",
        "year_info = year_info[::-1]\n",
        "year_info"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZBWHRWiIlfIb",
        "outputId": "26df07ab-dd6b-496c-cbfb-88d4f129c368"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "일자\n",
              "2022    133\n",
              "2021    248\n",
              "2020    248\n",
              "2019    246\n",
              "2018    244\n",
              "2017    243\n",
              "2016    246\n",
              "Name: 일자, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataframe_by_year = []\n",
        "data_fragement = data.copy()\n",
        "\n",
        "for dates in year_info:\n",
        "  ret = data_fragement[:dates].iloc[1:3]\n",
        "  after_div_day = ret.iloc[:1]\n",
        "  before_div_day = ret.iloc[1:]\n",
        "  after_div_day.columns = ['대주주기준일-1', '시가(Day-1)', '종가(Day-1)', '등락률(Day-1)']\n",
        "  before_div_day.columns = ['대주주기준일-2', '시가(Day-2)', '종가(Day-2)', '등락률(Day-2)']\n",
        "  after_div_day.index = before_div_day.index\n",
        "  ret = pd.concat([before_div_day, after_div_day], axis = 1)\n",
        "  dataframe_by_year.append(ret)\n",
        "\n",
        "  data_fragement = data_fragement[dates:]\n",
        "\n",
        "dataframe_by_year = dataframe_by_year[1:]\n",
        "ret = pd.concat(dataframe_by_year)\n",
        "ret.index = range(len(ret.index))"
      ],
      "metadata": {
        "id": "DeMWLNmKhbAW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ret"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "S_HjlmMItyCl",
        "outputId": "d9c99a94-843e-4fdf-df26-bcffbe18d7a9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     대주주기준일-2  시가(Day-2)  종가(Day-2)  등락률(Day-2)    대주주기준일-1  시가(Day-1)  \\\n",
              "0  2021-12-28    15195.0    15415.0        3.28  2021-12-29    15405.0   \n",
              "1  2020-12-28    14955.0    15115.0        1.92  2020-12-29    15195.0   \n",
              "2  2019-12-26     9830.0    10070.0        2.76  2019-12-27    10075.0   \n",
              "3  2018-12-26    11475.0    11820.0        1.24  2018-12-27    12180.0   \n",
              "4  2017-12-26    13310.0    13380.0        0.60  2017-12-27    13800.0   \n",
              "5  2016-12-27     9045.0     9130.0        0.61  2016-12-28     9235.0   \n",
              "\n",
              "   종가(Day-1)  등락률(Day-1)  \n",
              "0    15095.0       -2.08  \n",
              "1    15490.0        2.48  \n",
              "2    10250.0        1.79  \n",
              "3    11750.0       -0.59  \n",
              "4    14105.0        5.42  \n",
              "5     9315.0        2.03  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d55d527a-d405-4213-9560-1369f234cacf\">\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>대주주기준일-2</th>\n",
              "      <th>시가(Day-2)</th>\n",
              "      <th>종가(Day-2)</th>\n",
              "      <th>등락률(Day-2)</th>\n",
              "      <th>대주주기준일-1</th>\n",
              "      <th>시가(Day-1)</th>\n",
              "      <th>종가(Day-1)</th>\n",
              "      <th>등락률(Day-1)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2021-12-28</td>\n",
              "      <td>15195.0</td>\n",
              "      <td>15415.0</td>\n",
              "      <td>3.28</td>\n",
              "      <td>2021-12-29</td>\n",
              "      <td>15405.0</td>\n",
              "      <td>15095.0</td>\n",
              "      <td>-2.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-12-28</td>\n",
              "      <td>14955.0</td>\n",
              "      <td>15115.0</td>\n",
              "      <td>1.92</td>\n",
              "      <td>2020-12-29</td>\n",
              "      <td>15195.0</td>\n",
              "      <td>15490.0</td>\n",
              "      <td>2.48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2019-12-26</td>\n",
              "      <td>9830.0</td>\n",
              "      <td>10070.0</td>\n",
              "      <td>2.76</td>\n",
              "      <td>2019-12-27</td>\n",
              "      <td>10075.0</td>\n",
              "      <td>10250.0</td>\n",
              "      <td>1.79</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2018-12-26</td>\n",
              "      <td>11475.0</td>\n",
              "      <td>11820.0</td>\n",
              "      <td>1.24</td>\n",
              "      <td>2018-12-27</td>\n",
              "      <td>12180.0</td>\n",
              "      <td>11750.0</td>\n",
              "      <td>-0.59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2017-12-26</td>\n",
              "      <td>13310.0</td>\n",
              "      <td>13380.0</td>\n",
              "      <td>0.60</td>\n",
              "      <td>2017-12-27</td>\n",
              "      <td>13800.0</td>\n",
              "      <td>14105.0</td>\n",
              "      <td>5.42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2016-12-27</td>\n",
              "      <td>9045.0</td>\n",
              "      <td>9130.0</td>\n",
              "      <td>0.61</td>\n",
              "      <td>2016-12-28</td>\n",
              "      <td>9235.0</td>\n",
              "      <td>9315.0</td>\n",
              "      <td>2.03</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d55d527a-d405-4213-9560-1369f234cacf')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d55d527a-d405-4213-9560-1369f234cacf 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-d55d527a-d405-4213-9560-1369f234cacf');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np"
      ],
      "metadata": {
        "id": "BE0m7Jp8aboH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ret_day_minus_2=ret.loc[::-1, ['대주주기준일-2',\t'등락률(Day-2)']]\n",
        "ret_day_minus_1=ret.loc[::-1, ['대주주기준일-1',\t'등락률(Day-1)']]"
      ],
      "metadata": {
        "id": "SE_UE6xbJ3Vx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "label = ret_day_minus_2['대주주기준일-2']\n",
        "index = np.arange(len(label))\n",
        "\n",
        "plt.figure(figsize=(20, 5))\n",
        "plt.subplot(1,2,1)\n",
        "plt.bar(index, ret_day_minus_2['등락률(Day-2)'], width = 0.5, color=[  'red' if x_return > 0 else 'blue' for x_return in list(ret_day_minus_2['등락률(Day-2)']) ] )\n",
        "plt.ylabel('return(%)', fontsize = 16)\n",
        "plt.xlabel('Day-2', fontsize = 16)\n",
        "plt.xticks(index, label, fontsize = 10, rotation = 45)\n",
        "\n",
        "label = ret_day_minus_1['대주주기준일-1']\n",
        "index = np.arange(len(label))\n",
        "\n",
        "plt.subplot(1,2,2)\n",
        "plt.bar(index, ret_day_minus_1['등락률(Day-1)'], width = 0.5, color=[ 'red' if x_return > 0 else 'blue' for x_return in list(ret_day_minus_1['등락률(Day-1)']) ])\n",
        "plt.ylabel('return(%)', fontsize = 16)\n",
        "plt.xlabel('Day-1', fontsize = 16)\n",
        "plt.xticks(index, label, fontsize = 10, rotation = 45)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "id": "Vye9ETPrzJZY",
        "outputId": "da7985e1-6d1f-4e3d-b4f6-b3c74ac09bc6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x360 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#카카오"
      ],
      "metadata": {
        "id": "XYmmboPka9zG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv('/content/gdrive/Shareddrives/CS360/madcamp-3-week/data/strategy-3/카카오(2013~현재).csv', encoding = 'cp949', thousands = ',')\n",
        "data = data.dropna()\n",
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 696
        },
        "outputId": "c1096849-9d88-4780-c701-d389a82c0fe0",
        "id": "mylr-GY8a9zH"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              일자        시가        고가        저가        종가 전일비  Unnamed: 6  \\\n",
              "0     2022-07-18   71100.0   73200.0   70600.0   72800.0   ▲      2700.0   \n",
              "1     2022-07-15   72100.0   72200.0   69400.0   70100.0   ▼     -2000.0   \n",
              "2     2022-07-14   71000.0   73400.0   70400.0   72100.0   ▲       200.0   \n",
              "3     2022-07-13   70100.0   72200.0   70100.0   71900.0   ▲      1600.0   \n",
              "4     2022-07-12   70700.0   71000.0   69400.0   70300.0   ▼     -1000.0   \n",
              "...          ...       ...       ...       ...       ...  ..         ...   \n",
              "2343  2013-01-08  102100.0  103700.0  101500.0  103000.0   ▼      -700.0   \n",
              "2344  2013-01-07  101900.0  104200.0   99900.0  103700.0   ▲      3800.0   \n",
              "2345  2013-01-04   94600.0  100500.0   94000.0   99900.0   ▲      6200.0   \n",
              "2346  2013-01-03   94300.0   94500.0   92500.0   93700.0   ▲       400.0   \n",
              "2347  2013-01-02   91100.0   93500.0   90700.0   93300.0   ▲      2200.0   \n",
              "\n",
              "       등락률        거래량    금액(백만)   신용비        개인        기관    외인(수량)       외국계  \\\n",
              "0     3.85  1669517.0  120612.0  0.00 -394158.0  293242.0       0.0   -1132.0   \n",
              "1    -2.77  1605289.0  112792.0  0.56  522053.0 -168410.0 -336970.0  -79611.0   \n",
              "2     0.28  1597400.0  114770.0  0.55 -117969.0  216344.0 -125837.0  -17818.0   \n",
              "3     2.28  1156844.0   82679.0  0.55 -248264.0  260471.0  -39484.0  -18877.0   \n",
              "4    -1.40  1082539.0   75753.0  0.55   34316.0 -175518.0  140111.0  107057.0   \n",
              "...    ...        ...       ...   ...       ...       ...       ...       ...   \n",
              "2343 -0.68   155050.0   15888.0  0.49  -12787.0  -84458.0   98800.0   65574.0   \n",
              "2344  3.80   277976.0   28538.0  0.67  -55927.0 -109140.0  167053.0  127838.0   \n",
              "2345  6.62   263981.0   25978.0  0.73 -110555.0   21664.0   88886.0   87037.0   \n",
              "2346  0.43    81956.0    7693.0  0.80  -26128.0   20523.0    2097.0    7566.0   \n",
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          "metadata": {},
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    {
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        "data = data.loc[:, ['일자', '시가', '종가', '등락률']]\n",
        "data"
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      "outputs": [
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              "              일자        시가        종가   등락률\n",
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              "      <td>2013-01-02</td>\n",
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              "      <td>93300.0</td>\n",
              "      <td>2.41</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>2344 rows × 4 columns</p>\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",
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              "  "
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          "metadata": {},
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    },
    {
      "cell_type": "code",
      "source": [
        "data_copy = data.copy()\n",
        "data_copy['일자'] = data_copy['일자'].map(lambda x: x[:4])\n",
        "data_copy"
      ],
      "metadata": {
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        "id": "v_Ce8reYa9zI"
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      "execution_count": null,
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          "data": {
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              "        일자        시가        종가   등락률\n",
              "0     2022   71100.0   72800.0  3.85\n",
              "1     2022   72100.0   70100.0 -2.77\n",
              "2     2022   71000.0   72100.0  0.28\n",
              "3     2022   70100.0   71900.0  2.28\n",
              "4     2022   70700.0   70300.0 -1.40\n",
              "...    ...       ...       ...   ...\n",
              "2343  2013  102100.0  103000.0 -0.68\n",
              "2344  2013  101900.0  103700.0  3.80\n",
              "2345  2013   94600.0   99900.0  6.62\n",
              "2346  2013   94300.0   93700.0  0.43\n",
              "2347  2013   91100.0   93300.0  2.41\n",
              "\n",
              "[2344 rows x 4 columns]"
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              "<p>2344 rows × 4 columns</p>\n",
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "year_info = data_copy.groupby('일자')['일자'].count()\n",
        "year_info = year_info[::-1]\n",
        "year_info"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6e5a2c35-c6b6-46f9-d2c4-d88db9151336",
        "id": "kj6U_reLa9zJ"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "일자\n",
              "2022    133\n",
              "2021    245\n",
              "2020    248\n",
              "2019    246\n",
              "2018    244\n",
              "2017    243\n",
              "2016    246\n",
              "2015    248\n",
              "2014    244\n",
              "2013    247\n",
              "Name: 일자, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
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    {
      "cell_type": "code",
      "source": [
        "dataframe_by_year = []\n",
        "data_fragement = data.copy()\n",
        "\n",
        "for dates in year_info:\n",
        "  ret = data_fragement[:dates].iloc[1:3]\n",
        "  after_div_day = ret.iloc[:1]\n",
        "  before_div_day = ret.iloc[1:]\n",
        "  after_div_day.columns = ['대주주기준일-1', '시가(Day-1)', '종가(Day-1)', '등락률(Day-1)']\n",
        "  before_div_day.columns = ['대주주기준일-2', '시가(Day-2)', '종가(Day-2)', '등락률(Day-2)']\n",
        "  after_div_day.index = before_div_day.index\n",
        "  ret = pd.concat([before_div_day, after_div_day], axis = 1)\n",
        "  dataframe_by_year.append(ret)\n",
        "\n",
        "  data_fragement = data_fragement[dates:]\n",
        "\n",
        "dataframe_by_year = dataframe_by_year[1:]\n",
        "ret = pd.concat(dataframe_by_year)\n",
        "ret.index = range(len(ret.index))"
      ],
      "metadata": {
        "id": "xM4oBYbza9zJ"
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      "execution_count": null,
      "outputs": []
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              "     대주주기준일-2  시가(Day-2)  종가(Day-2)  등락률(Day-2)    대주주기준일-1  시가(Day-1)  \\\n",
              "0  2021-12-28   113000.0   113500.0        0.44  2021-12-29   115500.0   \n",
              "1  2020-12-28   374500.0   373000.0       -0.27  2020-12-29   381000.0   \n",
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              "7  2014-12-26   123200.0   122000.0       -1.05  2014-12-29   123200.0   \n",
              "8  2013-12-26    87500.0    85600.0       -2.17  2013-12-27    85600.0   \n",
              "\n",
              "   종가(Day-1)  등락률(Day-1)  \n",
              "0   114000.0        0.44  \n",
              "1   384500.0        3.08  \n",
              "2   153500.0        3.72  \n",
              "3   101500.0        3.05  \n",
              "4   139500.0       10.71  \n",
              "5    75500.0        0.94  \n",
              "6   113400.0        2.53  \n",
              "7   123100.0        0.90  \n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2018-12-26</td>\n",
              "      <td>99000.0</td>\n",
              "      <td>98500.0</td>\n",
              "      <td>-2.48</td>\n",
              "      <td>2018-12-27</td>\n",
              "      <td>100500.0</td>\n",
              "      <td>101500.0</td>\n",
              "      <td>3.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2017-12-26</td>\n",
              "      <td>130500.0</td>\n",
              "      <td>126000.0</td>\n",
              "      <td>-2.70</td>\n",
              "      <td>2017-12-27</td>\n",
              "      <td>130000.0</td>\n",
              "      <td>139500.0</td>\n",
              "      <td>10.71</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2016-12-27</td>\n",
              "      <td>75500.0</td>\n",
              "      <td>74800.0</td>\n",
              "      <td>-0.40</td>\n",
              "      <td>2016-12-28</td>\n",
              "      <td>75000.0</td>\n",
              "      <td>75500.0</td>\n",
              "      <td>0.94</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2015-12-28</td>\n",
              "      <td>112100.0</td>\n",
              "      <td>110600.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>2015-12-29</td>\n",
              "      <td>111500.0</td>\n",
              "      <td>113400.0</td>\n",
              "      <td>2.53</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2014-12-26</td>\n",
              "      <td>123200.0</td>\n",
              "      <td>122000.0</td>\n",
              "      <td>-1.05</td>\n",
              "      <td>2014-12-29</td>\n",
              "      <td>123200.0</td>\n",
              "      <td>123100.0</td>\n",
              "      <td>0.90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2013-12-26</td>\n",
              "      <td>87500.0</td>\n",
              "      <td>85600.0</td>\n",
              "      <td>-2.17</td>\n",
              "      <td>2013-12-27</td>\n",
              "      <td>85600.0</td>\n",
              "      <td>85200.0</td>\n",
              "      <td>-0.47</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-05dbe5f5-6cb7-4d97-981f-73767e9e903e')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      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",
              "    [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-05dbe5f5-6cb7-4d97-981f-73767e9e903e 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-05dbe5f5-6cb7-4d97-981f-73767e9e903e');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np"
      ],
      "metadata": {
        "id": "nda5gkYxa9zK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ret_day_minus_2=ret.loc[::-1, ['대주주기준일-2',\t'등락률(Day-2)']]\n",
        "ret_day_minus_1=ret.loc[::-1, ['대주주기준일-1',\t'등락률(Day-1)']]"
      ],
      "metadata": {
        "id": "o_v0adP5a9zK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "label = ret_day_minus_2['대주주기준일-2']\n",
        "index = np.arange(len(label))\n",
        "\n",
        "plt.figure(figsize=(20, 5))\n",
        "plt.subplot(1,2,1)\n",
        "plt.bar(index, ret_day_minus_2['등락률(Day-2)'], width = 0.5, color=[  'red' if x_return > 0 else 'blue' for x_return in list(ret_day_minus_2['등락률(Day-2)']) ] )\n",
        "plt.ylabel('return(%)', fontsize = 16)\n",
        "plt.xlabel('Day-2', fontsize = 16)\n",
        "plt.xticks(index, label, fontsize = 10, rotation = 45)\n",
        "\n",
        "label = ret_day_minus_1['대주주기준일-1']\n",
        "index = np.arange(len(label))\n",
        "\n",
        "plt.subplot(1,2,2)\n",
        "plt.bar(index, ret_day_minus_1['등락률(Day-1)'], width = 0.5, color=[ 'red' if x_return > 0 else 'blue' for x_return in list(ret_day_minus_1['등락률(Day-1)']) ])\n",
        "plt.ylabel('return(%)', fontsize = 16)\n",
        "plt.xlabel('Day-1', fontsize = 16)\n",
        "plt.xticks(index, label, fontsize = 10, rotation = 45)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "outputId": "6dc2af65-422e-4d8f-a31b-94790bae2d18",
        "id": "6tewZ3vea9zK"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x360 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#NAVER"
      ],
      "metadata": {
        "id": "ZEVuT23Na-NS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv('/content/gdrive/Shareddrives/CS360/madcamp-3-week/data/strategy-3/네이버(2011~현재).csv', encoding = 'cp949', thousands = ',')\n",
        "data = data.dropna()\n",
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 696
        },
        "outputId": "c3c2fc5a-0510-40b2-f4b5-de8c74a8365a",
        "id": "P4En0jSPa-NS"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              일자        시가        고가        저가        종가 전일비  Unnamed: 6  \\\n",
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              "3     2022-07-13  232000.0  239000.0  232000.0  237500.0   ▲      4000.0   \n",
              "4     2022-07-12  236500.0  237500.0  230500.0  233500.0   ▼     -7000.0   \n",
              "...          ...       ...       ...       ...       ...  ..         ...   \n",
              "2839  2011-01-07  221500.0  227500.0  221500.0  225000.0   ▲      3500.0   \n",
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              "2842  2011-01-04  223500.0  225500.0  217000.0  221500.0             0.0   \n",
              "2843  2011-01-03  229000.0  229500.0  220000.0  221500.0   ▼     -5500.0   \n",
              "\n",
              "       등락률       거래량    금액(백만)   신용비        개인        기관    외인(수량)       외국계  \\\n",
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              "...    ...       ...       ...   ...       ...       ...       ...       ...   \n",
              "2839  1.58  202713.0   45700.0  0.16  -18960.0   16780.0   -8244.0   15246.0   \n",
              "2840 -0.45  191166.0   42411.0  0.16   -7800.0     630.0    4553.0     347.0   \n",
              "2841  0.45  187526.0   42102.0  0.12  -14708.0   10743.0   -7780.0   -8861.0   \n",
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              "      <td>142584.0</td>\n",
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    {
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              "              일자        시가        종가   등락률\n",
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              "      <td>234000.0</td>\n",
              "      <td>246500.0</td>\n",
              "      <td>6.71</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2022-07-15</td>\n",
              "      <td>234500.0</td>\n",
              "      <td>231000.0</td>\n",
              "      <td>-1.07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2022-07-14</td>\n",
              "      <td>232000.0</td>\n",
              "      <td>233500.0</td>\n",
              "      <td>-1.68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2022-07-13</td>\n",
              "      <td>232000.0</td>\n",
              "      <td>237500.0</td>\n",
              "      <td>1.71</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2022-07-12</td>\n",
              "      <td>236500.0</td>\n",
              "      <td>233500.0</td>\n",
              "      <td>-2.91</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2839</th>\n",
              "      <td>2011-01-07</td>\n",
              "      <td>221500.0</td>\n",
              "      <td>225000.0</td>\n",
              "      <td>1.58</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2840</th>\n",
              "      <td>2011-01-06</td>\n",
              "      <td>224500.0</td>\n",
              "      <td>221500.0</td>\n",
              "      <td>-0.45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2841</th>\n",
              "      <td>2011-01-05</td>\n",
              "      <td>219000.0</td>\n",
              "      <td>222500.0</td>\n",
              "      <td>0.45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2842</th>\n",
              "      <td>2011-01-04</td>\n",
              "      <td>223500.0</td>\n",
              "      <td>221500.0</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2843</th>\n",
              "      <td>2011-01-03</td>\n",
              "      <td>229000.0</td>\n",
              "      <td>221500.0</td>\n",
              "      <td>-2.42</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2820 rows × 4 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0843944a-fd8b-47a3-b4eb-8c1aef3be679')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
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              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0843944a-fd8b-47a3-b4eb-8c1aef3be679 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-0843944a-fd8b-47a3-b4eb-8c1aef3be679');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_copy = data.copy()\n",
        "data_copy['일자'] = data_copy['일자'].map(lambda x: x[:4])\n",
        "data_copy"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "d993e24a-2876-42d2-c64f-62fa835481b1",
        "id": "CCI5X2-ga-NT"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        일자        시가        종가   등락률\n",
              "0     2022  234000.0  246500.0  6.71\n",
              "1     2022  234500.0  231000.0 -1.07\n",
              "2     2022  232000.0  233500.0 -1.68\n",
              "3     2022  232000.0  237500.0  1.71\n",
              "4     2022  236500.0  233500.0 -2.91\n",
              "...    ...       ...       ...   ...\n",
              "2839  2011  221500.0  225000.0  1.58\n",
              "2840  2011  224500.0  221500.0 -0.45\n",
              "2841  2011  219000.0  222500.0  0.45\n",
              "2842  2011  223500.0  221500.0  0.00\n",
              "2843  2011  229000.0  221500.0 -2.42\n",
              "\n",
              "[2820 rows x 4 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8c067630-2e4a-44ab-bf3a-dba6cd1ff432\">\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>일자</th>\n",
              "      <th>시가</th>\n",
              "      <th>종가</th>\n",
              "      <th>등락률</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2022</td>\n",
              "      <td>234000.0</td>\n",
              "      <td>246500.0</td>\n",
              "      <td>6.71</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2022</td>\n",
              "      <td>234500.0</td>\n",
              "      <td>231000.0</td>\n",
              "      <td>-1.07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2022</td>\n",
              "      <td>232000.0</td>\n",
              "      <td>233500.0</td>\n",
              "      <td>-1.68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2022</td>\n",
              "      <td>232000.0</td>\n",
              "      <td>237500.0</td>\n",
              "      <td>1.71</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2022</td>\n",
              "      <td>236500.0</td>\n",
              "      <td>233500.0</td>\n",
              "      <td>-2.91</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>2839</th>\n",
              "      <td>2011</td>\n",
              "      <td>221500.0</td>\n",
              "      <td>225000.0</td>\n",
              "      <td>1.58</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2840</th>\n",
              "      <td>2011</td>\n",
              "      <td>224500.0</td>\n",
              "      <td>221500.0</td>\n",
              "      <td>-0.45</td>\n",
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              "    <tr>\n",
              "      <th>2841</th>\n",
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              "      <td>0.45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2842</th>\n",
              "      <td>2011</td>\n",
              "      <td>223500.0</td>\n",
              "      <td>221500.0</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2843</th>\n",
              "      <td>2011</td>\n",
              "      <td>229000.0</td>\n",
              "      <td>221500.0</td>\n",
              "      <td>-2.42</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2820 rows × 4 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8c067630-2e4a-44ab-bf3a-dba6cd1ff432')\"\n",
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              "      </button>\n",
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              "\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-8c067630-2e4a-44ab-bf3a-dba6cd1ff432');\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",
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              "        }\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "year_info = data_copy.groupby('일자')['일자'].count()\n",
        "year_info = year_info[::-1]\n",
        "year_info"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0f496d79-0ce3-43d9-f69b-fa7ff410bc95",
        "id": "q9ndBslda-NT"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "일자\n",
              "2022    133\n",
              "2021    248\n",
              "2020    248\n",
              "2019    246\n",
              "2018    241\n",
              "2017    243\n",
              "2016    246\n",
              "2015    248\n",
              "2014    245\n",
              "2013    226\n",
              "2012    248\n",
              "2011    248\n",
              "Name: 일자, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataframe_by_year = []\n",
        "data_fragement = data.copy()\n",
        "\n",
        "for dates in year_info:\n",
        "  ret = data_fragement[:dates].iloc[1:3]\n",
        "  after_div_day = ret.iloc[:1]\n",
        "  before_div_day = ret.iloc[1:]\n",
        "  after_div_day.columns = ['대주주기준일-1', '시가(Day-1)', '종가(Day-1)', '등락률(Day-1)']\n",
        "  before_div_day.columns = ['대주주기준일-2', '시가(Day-2)', '종가(Day-2)', '등락률(Day-2)']\n",
        "  after_div_day.index = before_div_day.index\n",
        "  ret = pd.concat([before_div_day, after_div_day], axis = 1)\n",
        "  dataframe_by_year.append(ret)\n",
        "\n",
        "  data_fragement = data_fragement[dates:]\n",
        "\n",
        "dataframe_by_year = dataframe_by_year[1:]\n",
        "ret = pd.concat(dataframe_by_year)\n",
        "ret.index = range(len(ret.index))"
      ],
      "metadata": {
        "id": "F_2_F-R-a-NT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ret"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "outputId": "cd562699-1fc6-40ed-ff7f-adf84e4b8551",
        "id": "ybPBovtna-NT"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      대주주기준일-2  시가(Day-2)  종가(Day-2)  등락률(Day-2)    대주주기준일-1  시가(Day-1)  \\\n",
              "0   2021-12-28   380000.0   384500.0        1.05  2021-12-29   389000.0   \n",
              "1   2020-12-28   282000.0   281000.0       -0.35  2020-12-29   281500.0   \n",
              "2   2019-12-26   181500.0   180500.0       -0.82  2019-12-27   182000.0   \n",
              "3   2018-12-26   121000.0   119000.0       -3.25  2018-12-27   120500.0   \n",
              "4   2017-12-26   865000.0   874000.0        1.39  2017-12-27   872000.0   \n",
              "5   2016-12-27   766000.0   772000.0        0.92  2016-12-28   785000.0   \n",
              "6   2015-12-28   647000.0   641000.0       -0.93  2015-12-29   649000.0   \n",
              "7   2014-12-26   733000.0   736000.0       -0.67  2014-12-29   728000.0   \n",
              "8   2013-12-26   734000.0   714000.0       -2.72  2013-12-27   723000.0   \n",
              "9   2012-12-26   233000.0   226000.0       -1.74  2012-12-27   226000.0   \n",
              "10  2011-12-27   215500.0   217000.0        0.70  2011-12-28   220000.0   \n",
              "\n",
              "    종가(Day-1)  등락률(Day-1)  \n",
              "0    382000.0       -0.65  \n",
              "1    283000.0        0.71  \n",
              "2    183500.0        1.66  \n",
              "3    122500.0        2.94  \n",
              "4    877000.0        0.34  \n",
              "5    763000.0       -1.17  \n",
              "6    642000.0        0.16  \n",
              "7    723000.0       -1.77  \n",
              "8    734000.0        2.80  \n",
              "9    225000.0       -0.44  \n",
              "10   210000.0       -3.23  "
            ],
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              "      <th></th>\n",
              "      <th>대주주기준일-2</th>\n",
              "      <th>시가(Day-2)</th>\n",
              "      <th>종가(Day-2)</th>\n",
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              "      <th>대주주기준일-1</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2021-12-28</td>\n",
              "      <td>380000.0</td>\n",
              "      <td>384500.0</td>\n",
              "      <td>1.05</td>\n",
              "      <td>2021-12-29</td>\n",
              "      <td>389000.0</td>\n",
              "      <td>382000.0</td>\n",
              "      <td>-0.65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-12-28</td>\n",
              "      <td>282000.0</td>\n",
              "      <td>281000.0</td>\n",
              "      <td>-0.35</td>\n",
              "      <td>2020-12-29</td>\n",
              "      <td>281500.0</td>\n",
              "      <td>283000.0</td>\n",
              "      <td>0.71</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2019-12-26</td>\n",
              "      <td>181500.0</td>\n",
              "      <td>180500.0</td>\n",
              "      <td>-0.82</td>\n",
              "      <td>2019-12-27</td>\n",
              "      <td>182000.0</td>\n",
              "      <td>183500.0</td>\n",
              "      <td>1.66</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2018-12-26</td>\n",
              "      <td>121000.0</td>\n",
              "      <td>119000.0</td>\n",
              "      <td>-3.25</td>\n",
              "      <td>2018-12-27</td>\n",
              "      <td>120500.0</td>\n",
              "      <td>122500.0</td>\n",
              "      <td>2.94</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2017-12-26</td>\n",
              "      <td>865000.0</td>\n",
              "      <td>874000.0</td>\n",
              "      <td>1.39</td>\n",
              "      <td>2017-12-27</td>\n",
              "      <td>872000.0</td>\n",
              "      <td>877000.0</td>\n",
              "      <td>0.34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2016-12-27</td>\n",
              "      <td>766000.0</td>\n",
              "      <td>772000.0</td>\n",
              "      <td>0.92</td>\n",
              "      <td>2016-12-28</td>\n",
              "      <td>785000.0</td>\n",
              "      <td>763000.0</td>\n",
              "      <td>-1.17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2015-12-28</td>\n",
              "      <td>647000.0</td>\n",
              "      <td>641000.0</td>\n",
              "      <td>-0.93</td>\n",
              "      <td>2015-12-29</td>\n",
              "      <td>649000.0</td>\n",
              "      <td>642000.0</td>\n",
              "      <td>0.16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2014-12-26</td>\n",
              "      <td>733000.0</td>\n",
              "      <td>736000.0</td>\n",
              "      <td>-0.67</td>\n",
              "      <td>2014-12-29</td>\n",
              "      <td>728000.0</td>\n",
              "      <td>723000.0</td>\n",
              "      <td>-1.77</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2013-12-26</td>\n",
              "      <td>734000.0</td>\n",
              "      <td>714000.0</td>\n",
              "      <td>-2.72</td>\n",
              "      <td>2013-12-27</td>\n",
              "      <td>723000.0</td>\n",
              "      <td>734000.0</td>\n",
              "      <td>2.80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2012-12-26</td>\n",
              "      <td>233000.0</td>\n",
              "      <td>226000.0</td>\n",
              "      <td>-1.74</td>\n",
              "      <td>2012-12-27</td>\n",
              "      <td>226000.0</td>\n",
              "      <td>225000.0</td>\n",
              "      <td>-0.44</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>2011-12-27</td>\n",
              "      <td>215500.0</td>\n",
              "      <td>217000.0</td>\n",
              "      <td>0.70</td>\n",
              "      <td>2011-12-28</td>\n",
              "      <td>220000.0</td>\n",
              "      <td>210000.0</td>\n",
              "      <td>-3.23</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np"
      ],
      "metadata": {
        "id": "RnjEdYxEa-NU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ret_day_minus_2=ret.loc[::-1, ['대주주기준일-2',\t'등락률(Day-2)']]\n",
        "ret_day_minus_1=ret.loc[::-1, ['대주주기준일-1',\t'등락률(Day-1)']]"
      ],
      "metadata": {
        "id": "f7QR7Dq8a-NU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "label = ret_day_minus_2['대주주기준일-2']\n",
        "index = np.arange(len(label))\n",
        "\n",
        "plt.figure(figsize=(20, 5))\n",
        "plt.subplot(1,2,1)\n",
        "plt.bar(index, ret_day_minus_2['등락률(Day-2)'], width = 0.5, color=[  'red' if x_return > 0 else 'blue' for x_return in list(ret_day_minus_2['등락률(Day-2)']) ] )\n",
        "plt.ylabel('return(%)', fontsize = 16)\n",
        "plt.xlabel('Day-2', fontsize = 16)\n",
        "plt.xticks(index, label, fontsize = 10, rotation = 45)\n",
        "\n",
        "label = ret_day_minus_1['대주주기준일-1']\n",
        "index = np.arange(len(label))\n",
        "\n",
        "plt.subplot(1,2,2)\n",
        "plt.bar(index, ret_day_minus_1['등락률(Day-1)'], width = 0.5, color=[ 'red' if x_return > 0 else 'blue' for x_return in list(ret_day_minus_1['등락률(Day-1)']) ])\n",
        "plt.ylabel('return(%)', fontsize = 16)\n",
        "plt.xlabel('Day-1', fontsize = 16)\n",
        "plt.xticks(index, label, fontsize = 10, rotation = 45)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "outputId": "d06c16a0-cd71-47db-b9be-5e6bd007e2da",
        "id": "I31q1A3Pa-NU"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x360 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#삼성전자"
      ],
      "metadata": {
        "id": "aOyZ_BL-bFnM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv('/content/gdrive/Shareddrives/CS360/madcamp-3-week/data/strategy-3/삼성전자(2000~현재).csv', encoding = 'cp949', thousands = ',')\n",
        "data = data.dropna()\n",
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 696
        },
        "outputId": "9a533c76-c46c-40d8-94e4-31d07208185e",
        "id": "6o6nZ7KGbFnO"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              일자        시가        고가        저가        종가 전일비  Unnamed: 6  \\\n",
              "0     2022-07-18   60600.0   62000.0   60500.0   61900.0   ▲      1900.0   \n",
              "1     2022-07-15   58400.0   60000.0   58100.0   60000.0   ▲      2500.0   \n",
              "2     2022-07-14   57500.0   58200.0   57400.0   57500.0   ▼      -500.0   \n",
              "3     2022-07-13   58300.0   58600.0   58000.0   58000.0   ▼      -100.0   \n",
              "4     2022-07-12   58600.0   58700.0   58100.0   58100.0   ▼      -700.0   \n",
              "...          ...       ...       ...       ...       ...  ..         ...   \n",
              "5560  2000-01-10  280000.0  288500.0  279000.0  288500.0   ▲     11500.0   \n",
              "5561  2000-01-07  278000.0  283500.0  268000.0  277000.0   ▼     -4000.0   \n",
              "5562  2000-01-06  287500.0  289000.0  279000.0  281000.0   ▲      2000.0   \n",
              "5563  2000-01-05  290000.0  303000.0  276000.0  279000.0   ▼    -26500.0   \n",
              "5564  2000-01-04  300000.0  305500.0  283000.0  305500.0   ↑     39500.0   \n",
              "\n",
              "        등락률         거래량     금액(백만)   신용비         개인         기관     외인(수량)  \\\n",
              "0      3.17  20832517.0  1277727.0  0.00 -2118292.0 -1243492.0  3587045.0   \n",
              "1      4.35  18685583.0  1104045.0  0.17 -4467156.0  -908681.0  5277292.0   \n",
              "2     -0.86  15067012.0   869131.0  0.17  1221298.0 -4061258.0  2642802.0   \n",
              "3     -0.17  10841315.0   631712.0  0.17   457539.0  1628232.0 -1883354.0   \n",
              "4     -1.19   9336061.0   544403.0  0.17   139534.0  -194812.0    66179.0   \n",
              "...     ...         ...        ...   ...        ...        ...        ...   \n",
              "5560   4.15    937615.0   265754.0  0.55        0.0        0.0        0.0   \n",
              "5561  -1.42    806195.0   222718.0  0.55        0.0        0.0        0.0   \n",
              "5562   0.72   1087810.0   308005.0  0.58        0.0        0.0        0.0   \n",
              "5563  -8.67   1493604.0   433783.0  0.57        0.0        0.0        0.0   \n",
              "5564  14.85   1483967.0   444233.0  0.57        0.0        0.0        0.0   \n",
              "\n",
              "            외국계       프로그램    외인비  \n",
              "0     5281378.0   570459.0  49.80  \n",
              "1     4909988.0  1679992.0  49.74  \n",
              "2     2402687.0  2103427.0  49.65  \n",
              "3    -2153510.0  -675926.0  49.61  \n",
              "4       57400.0   478026.0  49.64  \n",
              "...         ...        ...    ...  \n",
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          "metadata": {},
          "execution_count": 49
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    {
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        "data = data.loc[:, ['일자', '시가', '종가', '등락률']]\n",
        "data"
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        "id": "Nn6evA8KbFnP"
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      "execution_count": null,
      "outputs": [
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              "              일자        시가        종가    등락률\n",
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          "metadata": {},
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    },
    {
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        "data_copy = data.copy()\n",
        "data_copy['일자'] = data_copy['일자'].map(lambda x: x[:4])\n",
        "data_copy"
      ],
      "metadata": {
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        "id": "RxrPl4FtbFnR"
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      "execution_count": null,
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              "        일자        시가        종가    등락률\n",
              "0     2022   60600.0   61900.0   3.17\n",
              "1     2022   58400.0   60000.0   4.35\n",
              "2     2022   57500.0   57500.0  -0.86\n",
              "3     2022   58300.0   58000.0  -0.17\n",
              "4     2022   58600.0   58100.0  -1.19\n",
              "...    ...       ...       ...    ...\n",
              "5560  2000  280000.0  288500.0   4.15\n",
              "5561  2000  278000.0  277000.0  -1.42\n",
              "5562  2000  287500.0  281000.0   0.72\n",
              "5563  2000  290000.0  279000.0  -8.67\n",
              "5564  2000  300000.0  305500.0  14.85\n",
              "\n",
              "[5562 rows x 4 columns]"
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              "                                                     [key], {});\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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          "metadata": {},
          "execution_count": 51
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    },
    {
      "cell_type": "code",
      "source": [
        "year_info = data_copy.groupby('일자')['일자'].count()\n",
        "year_info = year_info[::-1]\n",
        "year_info"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d7318423-4d83-422c-c435-cc3577eadd58",
        "id": "2mrqwEKlbFnS"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "일자\n",
              "2022    133\n",
              "2021    248\n",
              "2020    248\n",
              "2019    246\n",
              "2018    241\n",
              "2017    243\n",
              "2016    246\n",
              "2015    248\n",
              "2014    245\n",
              "2013    247\n",
              "2012    248\n",
              "2011    248\n",
              "2010    251\n",
              "2009    253\n",
              "2008    248\n",
              "2007    246\n",
              "2006    247\n",
              "2005    249\n",
              "2004    249\n",
              "2003    247\n",
              "2002    244\n",
              "2001    246\n",
              "2000    241\n",
              "Name: 일자, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataframe_by_year = []\n",
        "data_fragement = data.copy()\n",
        "\n",
        "for dates in year_info:\n",
        "  ret = data_fragement[:dates].iloc[1:3]\n",
        "  after_div_day = ret.iloc[:1]\n",
        "  before_div_day = ret.iloc[1:]\n",
        "  after_div_day.columns = ['대주주기준일-1', '시가(Day-1)', '종가(Day-1)', '등락률(Day-1)']\n",
        "  before_div_day.columns = ['대주주기준일-2', '시가(Day-2)', '종가(Day-2)', '등락률(Day-2)']\n",
        "  after_div_day.index = before_div_day.index\n",
        "  ret = pd.concat([before_div_day, after_div_day], axis = 1)\n",
        "  dataframe_by_year.append(ret)\n",
        "\n",
        "  data_fragement = data_fragement[dates:]\n",
        "\n",
        "dataframe_by_year = dataframe_by_year[1:]\n",
        "ret = pd.concat(dataframe_by_year)\n",
        "ret.index = range(len(ret.index))"
      ],
      "metadata": {
        "id": "kR5TV3eHbFnU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ret"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 739
        },
        "outputId": "b16387ba-8ec6-4951-e3ab-c03c6ff732ca",
        "id": "C3lFMRdWbFnV"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      대주주기준일-2  시가(Day-2)  종가(Day-2)  등락률(Day-2)    대주주기준일-1  시가(Day-1)  \\\n",
              "0   2021-12-28    80200.0    80300.0        0.12  2021-12-29    80200.0   \n",
              "1   2020-12-28    79000.0    78700.0        1.16  2020-12-29    78800.0   \n",
              "2   2019-12-26    54700.0    55400.0        0.73  2019-12-27    55700.0   \n",
              "3   2018-12-26    38400.0    38350.0       -1.16  2018-12-27    38700.0   \n",
              "4   2017-12-26  2488000.0  2410000.0       -3.02  2017-12-27  2448000.0   \n",
              "5   2016-12-27  1799000.0  1799000.0        0.06  2016-12-28  1792000.0   \n",
              "6   2015-12-28  1285000.0  1266000.0       -1.48  2015-12-29  1265000.0   \n",
              "7   2014-12-26  1343000.0  1352000.0        0.67  2014-12-29  1345000.0   \n",
              "8   2013-12-26  1408000.0  1408000.0       -0.49  2013-12-27  1410000.0   \n",
              "9   2012-12-26  1480000.0  1470000.0       -0.27  2012-12-27  1470000.0   \n",
              "10  2011-12-27  1068000.0  1073000.0        0.66  2011-12-28  1073000.0   \n",
              "11  2010-12-28   928000.0   938000.0        1.74  2010-12-29   935000.0   \n",
              "12  2009-12-28   790000.0   787000.0       -0.38  2009-12-29   786000.0   \n",
              "13  2008-12-26   460500.0   456000.0       -0.87  2008-12-29   456000.0   \n",
              "14  2007-12-26   579000.0   576000.0       -0.35  2007-12-27   574000.0   \n",
              "15  2006-12-26   609000.0   606000.0       -0.66  2006-12-27   611000.0   \n",
              "16  2005-12-27   659000.0   662000.0        1.22  2005-12-28   658000.0   \n",
              "17  2004-12-28   436000.0   436000.0       -0.34  2004-12-29   434000.0   \n",
              "18  2003-12-26   439000.0   441500.0        0.57  2003-12-29   440000.0   \n",
              "19  2002-12-26   342500.0   339500.0       -0.88  2002-12-27   335000.0   \n",
              "20  2001-12-26   256500.0   258000.0        2.18  2001-12-27   260000.0   \n",
              "21  2000-12-21   156000.0   162000.0       -2.99  2000-12-22   162000.0   \n",
              "\n",
              "    종가(Day-1)  등락률(Day-1)  \n",
              "0     78800.0       -1.87  \n",
              "1     78300.0       -0.51  \n",
              "2     56500.0        1.99  \n",
              "3     38250.0       -0.26  \n",
              "4   2468000.0        2.41  \n",
              "5   1788000.0       -0.61  \n",
              "6   1254000.0       -0.95  \n",
              "7   1329000.0       -1.70  \n",
              "8   1396000.0       -0.85  \n",
              "9   1496000.0        1.77  \n",
              "10  1061000.0       -1.12  \n",
              "11   945000.0        0.75  \n",
              "12   786000.0       -0.13  \n",
              "13   454000.0       -0.44  \n",
              "14   570000.0       -1.04  \n",
              "15   612000.0        0.99  \n",
              "16   661000.0       -0.15  \n",
              "17   444500.0        1.95  \n",
              "18   440000.0       -0.34  \n",
              "19   336000.0       -1.03  \n",
              "20   270000.0        4.65  \n",
              "21   157000.0       -3.09  "
            ],
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              "      <th>시가(Day-2)</th>\n",
              "      <th>종가(Day-2)</th>\n",
              "      <th>등락률(Day-2)</th>\n",
              "      <th>대주주기준일-1</th>\n",
              "      <th>시가(Day-1)</th>\n",
              "      <th>종가(Day-1)</th>\n",
              "      <th>등락률(Day-1)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2021-12-28</td>\n",
              "      <td>80200.0</td>\n",
              "      <td>80300.0</td>\n",
              "      <td>0.12</td>\n",
              "      <td>2021-12-29</td>\n",
              "      <td>80200.0</td>\n",
              "      <td>78800.0</td>\n",
              "      <td>-1.87</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-12-28</td>\n",
              "      <td>79000.0</td>\n",
              "      <td>78700.0</td>\n",
              "      <td>1.16</td>\n",
              "      <td>2020-12-29</td>\n",
              "      <td>78800.0</td>\n",
              "      <td>78300.0</td>\n",
              "      <td>-0.51</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2019-12-26</td>\n",
              "      <td>54700.0</td>\n",
              "      <td>55400.0</td>\n",
              "      <td>0.73</td>\n",
              "      <td>2019-12-27</td>\n",
              "      <td>55700.0</td>\n",
              "      <td>56500.0</td>\n",
              "      <td>1.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2018-12-26</td>\n",
              "      <td>38400.0</td>\n",
              "      <td>38350.0</td>\n",
              "      <td>-1.16</td>\n",
              "      <td>2018-12-27</td>\n",
              "      <td>38700.0</td>\n",
              "      <td>38250.0</td>\n",
              "      <td>-0.26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2017-12-26</td>\n",
              "      <td>2488000.0</td>\n",
              "      <td>2410000.0</td>\n",
              "      <td>-3.02</td>\n",
              "      <td>2017-12-27</td>\n",
              "      <td>2448000.0</td>\n",
              "      <td>2468000.0</td>\n",
              "      <td>2.41</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2016-12-27</td>\n",
              "      <td>1799000.0</td>\n",
              "      <td>1799000.0</td>\n",
              "      <td>0.06</td>\n",
              "      <td>2016-12-28</td>\n",
              "      <td>1792000.0</td>\n",
              "      <td>1788000.0</td>\n",
              "      <td>-0.61</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2015-12-28</td>\n",
              "      <td>1285000.0</td>\n",
              "      <td>1266000.0</td>\n",
              "      <td>-1.48</td>\n",
              "      <td>2015-12-29</td>\n",
              "      <td>1265000.0</td>\n",
              "      <td>1254000.0</td>\n",
              "      <td>-0.95</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2014-12-26</td>\n",
              "      <td>1343000.0</td>\n",
              "      <td>1352000.0</td>\n",
              "      <td>0.67</td>\n",
              "      <td>2014-12-29</td>\n",
              "      <td>1345000.0</td>\n",
              "      <td>1329000.0</td>\n",
              "      <td>-1.70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2013-12-26</td>\n",
              "      <td>1408000.0</td>\n",
              "      <td>1408000.0</td>\n",
              "      <td>-0.49</td>\n",
              "      <td>2013-12-27</td>\n",
              "      <td>1410000.0</td>\n",
              "      <td>1396000.0</td>\n",
              "      <td>-0.85</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2012-12-26</td>\n",
              "      <td>1480000.0</td>\n",
              "      <td>1470000.0</td>\n",
              "      <td>-0.27</td>\n",
              "      <td>2012-12-27</td>\n",
              "      <td>1470000.0</td>\n",
              "      <td>1496000.0</td>\n",
              "      <td>1.77</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>2011-12-27</td>\n",
              "      <td>1068000.0</td>\n",
              "      <td>1073000.0</td>\n",
              "      <td>0.66</td>\n",
              "      <td>2011-12-28</td>\n",
              "      <td>1073000.0</td>\n",
              "      <td>1061000.0</td>\n",
              "      <td>-1.12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>2010-12-28</td>\n",
              "      <td>928000.0</td>\n",
              "      <td>938000.0</td>\n",
              "      <td>1.74</td>\n",
              "      <td>2010-12-29</td>\n",
              "      <td>935000.0</td>\n",
              "      <td>945000.0</td>\n",
              "      <td>0.75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>2009-12-28</td>\n",
              "      <td>790000.0</td>\n",
              "      <td>787000.0</td>\n",
              "      <td>-0.38</td>\n",
              "      <td>2009-12-29</td>\n",
              "      <td>786000.0</td>\n",
              "      <td>786000.0</td>\n",
              "      <td>-0.13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>2008-12-26</td>\n",
              "      <td>460500.0</td>\n",
              "      <td>456000.0</td>\n",
              "      <td>-0.87</td>\n",
              "      <td>2008-12-29</td>\n",
              "      <td>456000.0</td>\n",
              "      <td>454000.0</td>\n",
              "      <td>-0.44</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>2007-12-26</td>\n",
              "      <td>579000.0</td>\n",
              "      <td>576000.0</td>\n",
              "      <td>-0.35</td>\n",
              "      <td>2007-12-27</td>\n",
              "      <td>574000.0</td>\n",
              "      <td>570000.0</td>\n",
              "      <td>-1.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>2006-12-26</td>\n",
              "      <td>609000.0</td>\n",
              "      <td>606000.0</td>\n",
              "      <td>-0.66</td>\n",
              "      <td>2006-12-27</td>\n",
              "      <td>611000.0</td>\n",
              "      <td>612000.0</td>\n",
              "      <td>0.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>2005-12-27</td>\n",
              "      <td>659000.0</td>\n",
              "      <td>662000.0</td>\n",
              "      <td>1.22</td>\n",
              "      <td>2005-12-28</td>\n",
              "      <td>658000.0</td>\n",
              "      <td>661000.0</td>\n",
              "      <td>-0.15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>2004-12-28</td>\n",
              "      <td>436000.0</td>\n",
              "      <td>436000.0</td>\n",
              "      <td>-0.34</td>\n",
              "      <td>2004-12-29</td>\n",
              "      <td>434000.0</td>\n",
              "      <td>444500.0</td>\n",
              "      <td>1.95</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>2003-12-26</td>\n",
              "      <td>439000.0</td>\n",
              "      <td>441500.0</td>\n",
              "      <td>0.57</td>\n",
              "      <td>2003-12-29</td>\n",
              "      <td>440000.0</td>\n",
              "      <td>440000.0</td>\n",
              "      <td>-0.34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>2002-12-26</td>\n",
              "      <td>342500.0</td>\n",
              "      <td>339500.0</td>\n",
              "      <td>-0.88</td>\n",
              "      <td>2002-12-27</td>\n",
              "      <td>335000.0</td>\n",
              "      <td>336000.0</td>\n",
              "      <td>-1.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>2001-12-26</td>\n",
              "      <td>256500.0</td>\n",
              "      <td>258000.0</td>\n",
              "      <td>2.18</td>\n",
              "      <td>2001-12-27</td>\n",
              "      <td>260000.0</td>\n",
              "      <td>270000.0</td>\n",
              "      <td>4.65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>2000-12-21</td>\n",
              "      <td>156000.0</td>\n",
              "      <td>162000.0</td>\n",
              "      <td>-2.99</td>\n",
              "      <td>2000-12-22</td>\n",
              "      <td>162000.0</td>\n",
              "      <td>157000.0</td>\n",
              "      <td>-3.09</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ca2a1f58-f410-4d06-827d-4e624048cc66')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ca2a1f58-f410-4d06-827d-4e624048cc66 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-ca2a1f58-f410-4d06-827d-4e624048cc66');\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",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np"
      ],
      "metadata": {
        "id": "REpz2VtwbFnW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ret_day_minus_2=ret.loc[::-1, ['대주주기준일-2',\t'등락률(Day-2)']]\n",
        "ret_day_minus_1=ret.loc[::-1, ['대주주기준일-1',\t'등락률(Day-1)']]"
      ],
      "metadata": {
        "id": "h1G1EB-ebFnX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "label = ret_day_minus_2['대주주기준일-2']\n",
        "index = np.arange(len(label))\n",
        "\n",
        "plt.figure(figsize=(20, 5))\n",
        "plt.subplot(1,2,1)\n",
        "plt.bar(index, ret_day_minus_2['등락률(Day-2)'], width = 0.5, color=[  'red' if x_return > 0 else 'blue' for x_return in list(ret_day_minus_2['등락률(Day-2)']) ] )\n",
        "plt.ylabel('return(%)', fontsize = 16)\n",
        "plt.xlabel('Day-2', fontsize = 16)\n",
        "plt.xticks(index, label, fontsize = 10, rotation = 45)\n",
        "\n",
        "label = ret_day_minus_1['대주주기준일-1']\n",
        "index = np.arange(len(label))\n",
        "\n",
        "plt.subplot(1,2,2)\n",
        "plt.bar(index, ret_day_minus_1['등락률(Day-1)'], width = 0.5, color=[ 'red' if x_return > 0 else 'blue' for x_return in list(ret_day_minus_1['등락률(Day-1)']) ])\n",
        "plt.ylabel('return(%)', fontsize = 16)\n",
        "plt.xlabel('Day-1', fontsize = 16)\n",
        "plt.xticks(index, label, fontsize = 10, rotation = 45)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 362
        },
        "outputId": "f240e52e-cc8b-4c45-c272-59d9d850cd2e",
        "id": "SAjPKmTnbFnX"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x360 with 2 Axes>"
            ],
            "image/png": 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SpG5Zg0mSpIkx6GDSUVWykCRJ0lKswSSNJqL9vN7DQNIyWg8mRcQ2wG2BT2bmJfVSkiRJ0jxrMEmSNGla34A7M68GXgrsVi8dSdryeWNzSYOwBpMkSZNm0Ke5XQbcrEYikiRJWpQ1mCRJmhiDDia9APiriLh1jWQkSZK0IGswSZI0MQa9AfdzgB2BL0fEd4AfUJ4eMi8z88iOcpMkSVJhDSZJkibGoINJ1wGX1khEkiRJi7IGkyRJE2OgwaTMvHulPCRJkrQIazBJkjRJBr1nkiRJkiRJkmbYQGcmRcTdlpsnM88fPh1JkiT1swaTJEmTZNB7Jv0bN7zZ40JWDZeKNHlyud4uSdLK+DeswSRJ0oQYdDDpqAXe2x04HjgSePLIGUmSJKmfNZgkSZoYg96A+zOLfHRGRLwKuB/wkZGzkiRJ0u9Zg0mSpEnS5Q24zwEe0mE8SZIkLc8aTJIkraguB5MOBq7vMJ4kSZKWZw0mSZJW1KBPc3vUAm9vAxwKPB44o4ukJEmStJk1mCRJmiSD3oD7bYu8/zvgPcDTRspGkiRJC3nbIu9bg0mSpBU36GDSAQu899vM/GEXyUiSJGlB1mCSJGliDDqYlMAPMvOa/g8iYjWwd2Z+r5PMJEmSNM8aTJIkTYxBb8D9beB2i3x2WPO5JEmSumUNJkmSJsagg0mxxGdb45NEpLHJbDdJkqZS9RosIo6JiK9HxDcj4rmjxpMkSVuuZS9zi4hdgN163tonIm7WN9t2wKOB/+kwNw3AQQJJkrYsK1mDRcQq4HXAvYArgS9GxAcz89JR4kqSpC1Tm3smPQ14IeVa/QTev8h80cw3lIh4MPAi4JbAHTJz47CxJEmStgArUoM17gB8MzO/BRARpwMnAA4mSZKkP9BmMOks4DuUQuUtwN8C/903z++ASzPzqyPkcgnwQOCNI8SQJEnaUqxUDQawD3BFz+srgTuOGFOSJG2hlh1MysyLgIsAIiKBczLzx10nkpmXNb+j69CSJElTZ6VqsEFExHpgPcC6desq/p528w10mX+lewLUyHWQcnjc66BKrmNuq0FSqNVWW+J6HXdfhenZXu1XW2auY2+rjg10A+7M/NfM/HFE7BERx0fEoyNiN4CIWBMRg97QW5IkSctYgRrsKmC/ntf7Nu/157EhM+cyc27t2rUj/kpJkjSt2lzmdgMRcSrwFGAbyvX7twd+CpwNfBZ48RL/9xPAXgt8dEpmnj1ADityVEySJGlSjFKDtfBF4KCIOIAyiPQw4OEjJSxJkrZYAx3FiojnA08G/oZyHX3vyVcfAo5f6v9n5j0z89AFptYDSU0cj4pJkqSZMWoNtpzMvLaJ/zHgMuC9mfm1UWJKkqQt16BnJv0Z8DeZ+ZLmEbK9vgkc2E1akiRJ6lG9BsvMc4FzR42j2TKue3Vs6VyvkibdoNfX7wNcsMhnVwM7DJtIRJwYEVcCfwycExEfGzaWJEnSFqZaDSZJkjSoQQeTrgIOXeSzw4BvD5tIZp6Zmftm5raZuWdm3mfYWJIkSVuYajWYJEnSoAYdTHof8IKIOKLnvYyIPwJOBk7vLDNJkiTNswaTJEkTY9DBpBcBlwPnA//VvPc+4OLm9Us7y0ySJEnzXoQ1mCRpRmS2nzQerW/AHRHbAO8CXki5bv8+lBs+/oTyKNrTmieBSJIkqSPWYJIkadK0HkzKzKsj4p7AqzPzHcA76qUlSdL4ebRLk8AabPb43SNJmnSDXub2OeBONRKRJEnSoqzBJEnSxGh9ZlLjZOCsiPg1cBbwA+AGx04y8/qOcpMkSVJhDSZJkibGoGcmXQwcCLwa+C5wNXBNz3R1p9lJkiQJrMEkSdIEGfTMpL+h7yiYJEmSqrMGkyaQ97eSNKsGGkzKzBdVykOSJEmLsAaTJEmTZNAzkyRJkiRJksTsnqE46D2TJEmSJEmSNMMcTJIkSZIkSVJrXuYmSZIkSdIWblYvx1IdnpkkSZIkSZKk1hxMkiRJkiRJUmsOJkmSJEmSJKk1B5MkSZIkSZLUmoNJkiRJkiRJas2nuUmSFuVTPyRJkiT188wkSZIkSZIkteaZSZIkSZKkLZ5nXEvd8cwkSZKkGRYRD46Ir0XE9RExN+58JEnS5HMwSZIkabZdAjwQOH/ciUiSpOngZW6SJEkzLDMvA4iIcaciScLL8TQdPDNJkiRJkiRJrXlmkiRJ0hYuIj4B7LXAR6dk5tkDxFkPrAdYt25dR9lJkqRp42CSJEnSFi4z79lRnA3ABoC5uTkvxMDLUaaJbSVJ3fEyN0mSJEmSJLXmYJIkSdIMi4gTI+JK4I+BcyLiY+POSdL0yGw3SdqyeJmbJEnSDMvMM4Ezx52HJEmaHp6ZJEmSJEmSpNYcTJIkSZIkSVJrDiZJkiRJkiSpNQeTJEmSJEmS1JqDSZIkSZIkSWrNwSRJkiRJkiS15mCSJEmSJEmSWnMwSZIkSZIkSa05mCRJkiRJkqTWJmYwKSJOjYjLI+KrEXFmROwy7pwkSZIkSZJ0QxMzmAScBxyambcBvgE8b8z5SJIkSZIkqc/EDCZl5scz89rm5QXAvuPMR5IkSZIkSX9oYgaT+jwO+Mi4k5AkSZIkSdINrV7JXxYRnwD2WuCjUzLz7GaeU4BrgdOWiLMeWA+wbt26CplKkiRJkiRpISs6mJSZ91zq84h4DHA8cI/MzCXibAA2AMzNzS06nyRJkiRJkrq1ooNJS4mIY4BnA0dm5m/GnY8kSZIkSZL+0CTdM+m1wE7AeRHxlYh4w7gTkiRJkiRJ0g1NzJlJmXnzcecgSZIkSVJbi9+cRdqyTdKZSZIkSZIkSZpwDiZJkiRJkiSpNQeTJEmSJEmS1JqDSZIkSZIkSWptYm7ALUmSJEmSNOum4cbunpkkSZI0wyLi1Ii4PCK+GhFnRsQu485JkiRNNgeTJEmSZtt5wKGZeRvgG8DzxpyPJEmacA4mSZIkzbDM/HhmXtu8vADYd5z5SJKkyedgkiRJkuY9DvjIYh9GxPqI2BgRGzdt2rSCaUmSpEniDbglSZK2cBHxCWCvBT46JTPPbuY5BbgWOG2xOJm5AdgAMDc3NwW3B5UkSTU4mCRJkrSFy8x7LvV5RDwGOB64R+Y0PENGkiSNk4NJkiRJMywijgGeDRyZmb8Zdz6SJGnyec8kSZKk2fZaYCfgvIj4SkS8YdwJSZKkyeaZSZIkSTMsM28+7hwkSdJ08cwkSZIkSZIkteZgkiRJkiRJklpzMEmSJEmSJEmtOZgkSZIkSZKk1hxMkiRJkiRJUmsOJkmSJEmSJKk1B5MkSZIkSZLUmoNJkiRJkiRJas3BJEmSJEmSJLXmYJIkSZIkSZJaczBJkiRJkiRJrTmYJEmSJEmSpNYcTJIkSZIkSVJrDiZJkiRJkiSpNQeTJEmSJEmS1JqDSZIkSZIkSWrNwSRJkiRJkiS15mCSJEmSJEmSWnMwSZIkSZIkSa05mCRJkiRJkqTWHEySJEmSJElSaw4mSZIkSZIkqTUHkyRJkiRJktSag0mSJEkzLCJeHBFfjYivRMTHI2LvceckSZImm4NJkiRJs+3UzLxNZt4W+DDwgnEnJEmSJtvEDCZ5VEySJGnlZeYve17uAOS4cpEkSdNhYgaT8KiYJEnSWETE30XEFcCfskQNFhHrI2JjRGzctGnTyiUoSZImysQMJnlUTJIkqY6I+EREXLLAdAJAZp6SmfsBpwFPXixOZm7IzLnMnFu7du1KpS9JkibM6nEn0Csi/g54FPAL4Kgl5lsPrAdYt25dtXzS4SxJkrQFyMx7tpz1NOBc4IUV05EkSVNuRc9M8qiYJKmWzHaTpBuKiIN6Xp4AXD6uXOa5PUuSNNlW9Mwkj4pJkiRNnJdGxMHA9cB3gT8fcz6SJGnCTcxlbhFxUGb+V/NyIo6KSZIkbeky80HjzkGSJE2XiRlMwqNikiRJkiRJE29iBpM8KiZJkiRJkjT5VvQG3JIkSZIkSZpuDiZJkiRJkiSpNQeTJEmSJEmS1JqDSZIkSZIkSWrNwSRJkiRJkiS15mCSJEmSJEmSWnMwSZIkSZIkSa05mCRJkiRJkqTWIjPHncNIImIT8N0V/JV7AD+ekrjmaq7TErNWXHM112mJWSvuNOW6lJtm5toV/H1qwRpsi4hZK665muu0xKwV11zNdZpyXcqiNdjUDyattIjYmJlz0xDXXM11WmLWimuu5jotMWvFnaZcpeVMU3+elpi14pqruU5LzFpxzdVcpynXYXmZmyRJkiRJklpzMEmSJEmSJEmtOZg0uA1TFNdczXVaYtaKa67mOi0xa8Wdplyl5UxTf56WmLXimqu5TkvMWnHN1VynKdeheM8kSZIkSZIkteaZSZIkSZIkSWrNwSRtUSIiOoy1VdcxtVmN9WpbSXW4bWlLMum1QqWYsdDPkxZzqd/RQawqdV3XcadtvdaMOQ1q/70wq+u1lmn7+27S83QwaQXNd96OY943Ik6ZhrgRcXREPLHLmE3c/SLiQIDMzC42uog4AThrPuao8ZqYtlXEgRExB522Vecxe2Lv2Pw70V/klfpAjZi3ioh7R8SNmte21QSrtL1W6wPSUiJil4hYDRNfK3Qes7HL/DI3y99FTVojZq3vnirrtVLcWut17fy+ssP+WiPm1NQKFftV5+u1L36X67RKe9Wo6yq2V9f9qmr7N7+jk1wdTFohEXE08PCI2LXDmMcBpwKXdhWzVtyIuD/wT8BVfe+P1JEj4ljgI8DrIuIjMHrhERH3Av4aODgiHj9Kfj0xbauS64eAUyPiAuikrTqP2RP7eODDEREdxrx/RLxi1Dh9MWv0gRoxjwfeDzwdOC8iVnVYeM5sWzVxa+Xa9fZarQ9IS4mI+wAfBP45Il4FoxfolWqFzmM2ce9L2Z5fFhFvAsjM60fcnjuP2cSt8d1Ta73W6AM11+tHgVdGxDsjYucOcq0Rc2pqhYr9qvP12sTdJyL2gU4H/qq0V6W6rlZ7dd2vqrR/E7vzPkBmOlWegCOA64HzgIcCu3YU95+A+zQ/7wIcAGw7aXGBbYF3AEc2r3cEdusgz9sBXwX+uHn9bmCXEWPeE/gacCTwAOBU26qTtroV8GXg8Ob1B4GbTVrMntj3AjYC3wFe1FHMY4CLgQd2Ea9m36rQrw4ELgAOa16fAdwRWGVbjdxWnedaaXut1gecnJaamv365cD9gP8DvA94eAcxO60VKtYftwUuaeJuDXwWOB/Yrvl8q0mI2fy/Gt89tdZrjT5Qa73eEvgScIfm9ZnNer5V8zomJObU1AoV+1Xn67X5f/dr+tbpwLsof0OtHjHXKu3VdVtVbq+u+1WV9q/VBzLTM5Nqi4hVwK6UQaQ3AscDx/SeoTToCGY0gL2AXZtYH6b8oXJWRBwXQ5wSWysucB2wM7AqIvZsYr4lIj4aEYfM/+4h4l4PfCozPx8R+wJHU45knRUR2w8St1n0G1G+ZNZn5meAbwCPjIgTh8itN65tBb+ifBluiojdKQOsf9fEvdWQcWvEJCLuAbwW+AvK9rpn0zdGdRfKjuaMiNgjIg6PiBtHxJphgtXoAxX71c+A7wE7RMQulO3s2ZQjOcc335MDm+W2qpVro8a2VaUPSItpNrvtgbsCz8nMDwEXNtPeI8TstFaoVX/0SEqt9JnMvAb4S8ofgW+FctbLhMSEDr97Ktd1tdqr1nr9X8pBh683r59EGax6ZjRnkgzxnV4j5sTXCiuwvXa+XiPiAOB5lEH0hwGbgLcD94mIbUfItfP26rquq9lelWrQGttVzT7gYFJtmXkd8GngnMx8P+W0tfsCx0bEbs08A51mlg3KzuU2wMuAN2fm8ZSzn54I7DBErrXiXku5PvXWwP8D3pmZD6Bc6vHK+d89aFzgWuCWEfEaypGbV1JOs7yGMpLbOm6z6L8E/jozPxcRW2fmpcBzgftFxB5D5GdbbXY1ZaDq74HPAS/NzJOauMNectBpzGaHsx1wMPDYzPwC8GvgzsADB8ztBnGbH28C3CQidgDOBl4IvAn4s2iuix5EjT5Qq19R/kC4mLI9fQZ4WWY+qIn554PGtK3q5dqosb122gek5TSb3W+AtwFfioitmn57GXCHEWJ2WivUqj96BHDnKGfZDBQAACAASURBVPdC3IVSg74U2Dkinj1BMaHD757KdV2t9qq1Xq+jnA17vygHBp9AuTRpZ+AVMNR3eo2YE18rrMD2WmO9/hL4H8r2BfDM5r3jKDXEKAeKO2mvWnVdjfaqlWujRvtDvT7gZW61JmAOeBRwOH2XXgGPpIwG3osygvuSljGPAJ5BGf3cr5leSxlIeWDPfOcAcwPk2nncvuXfoXl9NvAx4M49832EAU5h7o3bvD6Ectr6O4GdeuY7F1g7wPKf3Cz//s170fx7OGVg6uDmdatTjG2rP8h1F2AtcFDT9/+oZ76PAweNK+YSv2vr5t/7U3aSB44Y7w6UwYS3A49p3nsw5XTT/cfcB2rEvDvwt5SBkgOb9/YG/gW4a898HwXuaFu1/x5YgVy72l5XrA84OfVOlP3Yoyn7tZ36Prsv5ewPKPXY01rGrFUrdBqzZ/l7a6WTmm33NOCjzXvHAM8aZ8yedVCjVqixXmv0gZVYrztSzt5/axP3A808BwF/P+aYd2dKaoXK/arT9doXfztK7fEsyuVeLwJeA7wOeMeAsVakvUZtq5rtValfVWv/rvtA/+SZSRVEuXHW24GjKGd33L95f/4pIu+g3JfmFcBTgPe2iHk85TK5/SjXej42M69o4nwbOCIi7hcRD2jmuaJlrp3H7Vv+vwIelJkbgTcAq5qYc83phXsDv2iZa2/cF0TEo7KMLl9OGRE+vJnvIcCN2Tz62mb59wVOAB4VPZeHZOaFzXp4S0SszhanGNtWf5DricBTgV9n5n9RTi+9XZRLck4E9qScKrviMXti3z0i/i4inhgRt2nevq759z+b+OuaeVt9bzYx/7aJeWvKpRWXU66H3h4gM98HbAPcomXMGn2gVszXULbBmwGnRMSemfl9yvX1/ycibh3lZu/7AN9ts/xN7JltqxXKtcvttUofkJbSs1+7O6UGO7F5f/6Si28BFzfzPZVyFH25mDVrhc5iNnEXqpXeDTwWeDJwbDPrrYGbRcSq5Y5I14jZtw5q1Apdr9cafWCl1uszMvNTlLP3H0854ABlYPXAiNi6RR+oFXMqaoUV6Fedrdcm7u9rBcoA7euBnSiD7DfLzKdSBlp+0ybPnlw7b69KdV2t9qrVrzpt/55cO+0DCxplJMppwZG/Qyk3t7p98/qRwL/T3GSVzSOiJ1JODzykRcyDKDf3mr8Z1z2AT9LcGLn5nQ+inJ3zHpoboY0j7iLL/7me5T+Scg342ylnutymo/X6OMrNuN9Oud7+0CGX/7ye5d+q+Xdfysjtsjeitq0WzfUTPbmeQDlz7P3AF4FbjyNmT+zjm77zAsrp5G+h76w24PmUG/dtPULMHYCbU47ovKvpsw8CLgL2G1MfqBFzHWU7unPz+hZNu+zfvL4rsIHytJrP2VYDfQ+sVK6jbq/V+oCT01ITy9QKzXv7U+65+BWam5ouE3OlaoWRYi6x/L/frzfvBWWw4krgluOIucQ6qFErdLFea/SBlVyvn+zNiXKbk/m4I/8dMmTMqakVVrhfjbReF1j+lzfLvz3lIHGw+W/RJ1LOIFoz/95Kt1fXbVW5vVaiX43c/rX6wKK/a5j/5LRk4+1M2SFs1fPeh2lOo+t5794DfCmsBh4BrOl57yzg6L75tgG2HyDXzuMusfy37JtvDQM8ea1NXMpTQG4LrBtx+Y9aYPl3tq26y5VyxOIAYO9xxWz+z3I7x/kv260pZ2vdZMiYH+iJuTfluuq3Una8bQcSavSBGjFXUa4Z367nvXcDj+p5vQOwB3Bj26r1eh1broNuW7X6gJPTchMtajDKHxCfpecSqmVirmStMHTMAZZ/R8qgc9vBic5jLrMOatQKo67XGn1gpdfrUT2vV1HOdLjFGGNOU60wtn41xHpdslaYXweUAYoraDGgXqu9arRVrfYaV78atP1r9oHFptWoMxERmfmLiDg9M6+Pctf162ger97Mcyvgqsz8eMuYW2W5KfI7m9fzMa+nubFZRNwB+H5mXkmLS7tqxV1m+XfpWf4fZuaPgd+2zLVt3O9nZqtLm5ZZ/h2HXH7bqn2uV2Xmt8cVs8dVwD9TzmYjMy+PiGsoR1i+k803LuUU1qdnZpv1sFDMq4G7NTG/D3w/Iv6TsvO5ZrmAlfpArX51HeXxsL0xf0rZIRIRRwNXZLmMYRAz21YTkOsg22vNPiAtqm0Nlplfi4hjMvPXLWKudK0wVMxBlp9y6clbe74zVzRmi3VQo1YYZb3W6APjWK+9uX4vMz8yxphTUytMQL9qvV4bi9UKd6U8vp7MzIi4CLhHZn6jRa612qvzuq5We3Wda8X2XyzXkfrAUrxnUofmO1JPET9/ff6PgB9Eueb7VGg/iJd/eA3nfJtd0cS8H/ASNl+vOba4LZf/5YPkOWDc1o+grLT8tlW7XF9K+bIcW0zYvHPMzDMy839j8/00fkqzjUZ5osqBmXl9y53YUjF/v8ONiJtn5rVt/uCH6emvCxS889d1f6+JeRzlcqzftY0JttWYcx10e63SB6TltNyvvSIi9sgWA0lNrKn47m3itq1B17QdnKgRs4k3rlphmmqwmuu19VObKsWcmlphzP1qoPU64PJf2HYQoUZ71WirJteVrsFq9quB2n+IXFv3gaV4ZlIFTUNmZs6Pdn6Pck3mHsCfZTnTY6BYPTHndzw/oTz++eom5g+GybHruL2xu1j+WnFrLL9tNV3rteXO8RTgYeOMCdOzXheIeW3z0e8oT/r4PvC4zPxe25hgW01TrrX6gNTWrNYK/fG72q/XiDlN69UabHr2EzX2ldPUr1awVhi5vVYw14lcr9P0t81yHEwaQUQcBtwUuIxyKu5vojlNLcod3g/MzDMp1+cfCdw2M7+5TMw7Up7K823g4sz8aU/MOeB2mfkm4EbAIZT7Lv13i1w7j1tj+WvFrbT8ttUUrdcFfk/nO8euYk7Leo2IuwK3B34IfDozv98T887A3TLzpcA1lOvIH96mXy3we2aqraYp15XqA1I/a4Uqyz/ztYI12PTuJ7rYV05Tv6qx/E2c6u01yXVNjVyn+W+bZeUIN1ya5Ylyl/RLKDe0Oh14YM9nRwIXUzY2gANpcaPHJubFTWO/hfK4vtXNZ39MefLIvZrX27aJWStujeWfpvVqW03Xem3mvyvwDOBPaW7mCaxq/r0z8Nzm57+g7EBvPqaYU7Fem5gXUS6HfC3laOqOzWeHA18Cjm9ebwMcYFu13ramIteafcDJaakJa4Vayz/TtULFXGd2vTJFtcI09asay1+zvSrmOvHrtVaetdbroFOnwWZlAm4HXEoZRQR4KnB2z+dPotlZ0POUhmVi3qrpTPMxT6Tc0X3+Me1/Chzb/Nzq0Yi14tZY/mlar7bVdK3XZv7Od46VYk7FeqUUvP/O5kcaH04pknfvWTdH2VZDfQ9MRa41+4CT01IT1go1ln/ma4VKuc70emWKaoVp6lc1lr9me1XKdSrWa608a63XYabOA87CBOwHPKHvvY/R90h6mpHBljF3BdZDebRg8955wNywMWvFrbH807RebaupW6+d7xxrxJym9QrsRNkB9j7S+FzgfrbVSOt1mnKt0gecnJabsFaosfzWCtZgU7OfoE6tME39qlat0Hl7Vcx1KtZrxX41MQf0fJrbEDLzCuAdABGxOiLWUK5xnH+c6UERsX2WR/y1jfkz4C2ZmRExfy+ra9l85/XbRsTOg8SsFbfG8teKW2n5baspWq+Up6O8gTJCT2ZeSFkHd25efzgzP91cu3zN4mGqx5ya9ZqZvwLek+WRxvMxNzVxiYi7RHlikm012DqYmlwr9gFpSdYKVZZ/5msFa7Cp2k90vq+cpn5FvVqhRntNTV1TI9cp+9tmKA4mtRQRd46IR/a8Nf/khcjyCMAfAt+PiAcDf0+55nG5mEdHxHN63sq+WX4A/CgiHkh5jOGyMWvFrbH8teJWWn7baorWa68aO8cuY07Leo2I+0bEaxaIOf/v/wA/jYgHtI3ZbxbbappyXYk+IC3EWqHK8s98rWANNp37ia72ldPUr3p1XCtUba9Jr2tq5Dqtf9sMLSue9rSlTMC9gN8A7wee1PN+7ylr/wy8C/gicOsWMe8B/JxyqttLet7vPbXwH4BPNzEPbZlr53FrLP80rVfbarrWa/P/7gu8puf1qr5/XwbcEXgA5TTRfcYUcyrWK+VIx/eBjcA7F4n518DXbKuBvwemIteafcDJaakJa4Uayz/ztUKlXGd6vTJFtcI09asay1+zvSrlOhXrtVaetdZrF5NnJrWzH/Bi4E3AbSLiSQCZmRGxdTPPzYH7ACdl5sUtYu4CvBR4MrBPRLy0iXl9T8zdgLkm5iUtc60Rt8by14pbY/ltqylar1EeW/pm4M4R8c4m5nURsVVuHqH/LeWJCqcA/zczr1rpmI1pWa87AC8CjgC2jYh3LRDzamD3AWLOfFtNU65U6gNSC7NeK9RY/pmvFSrFnfX1OjW1AlPUryrWCp231zTVNdPUryqu19HVGqXakibKdY1bA9sBD6YcVXhy3zx3BA4ZMO72zb9zwL8CL+v7fO9BY9aIW3H5p2a9znpbTdN6pRwZXE85bfR9wLt6Ptu6+fcUyqm7rR6RWSPmFK7XGzX/7ga8F3h3z2drKMXGoMs+0201TbnW6gNOTstNzHitUGP5a63TaVqvNeK6XqenVpiyflWzVui0vSrnOhXrtdJ2VW29jjpF88vVUkTsCBwLHEU5RW174DeZ+d4RYq6mPD70yZRHiP4Q2C0zXzlirp3HrbH8teJWWn7bagrWa0TcKDN/GRG7UW5Qd11mntR8toZyNGbXzPzmOGP2xZ/49doTdw9KkfwT4ALglsDzc4hrs2e9raYp1764nfUBqS1rhSrLP7O1QuVcXa8TXiv0xJ74flW7VmjidNJe01TXTFO/Wok+MJSVHLnaUibKoxOPBL4A/AK4TQcxtwb2AS4CftZFzFHi0nN9d5fLXyMuPdehrsR6HWGdbjtNbTWGXGttA3tQRvHfADyGck3xSI8rHyVmjXVQYxtYKmbPPF+3rTr9bpmaXGv1ASen5aYa+7VK2/PQMWvs12vEbP6vNdgK9tWKuU7NfmLYfWWt5R/DNjDs8i9b13XdXsPmWrO9pqVf1VqvXU8r/gsnfaIcOdi27735G1vtML8hAidT7sY+yOl0W/X/TDmtcE3z8+OAK9rGpJx+uM1Cv2PYuJTHCm63SMyhlx+4GZtPpYwu4gJ3APboePmPBu7U+8XQUVsdRXOqc09/GjXXdcCuNKc3dthW2y3Qr1aNmOvuwA59760edb0OMjHgzpEldg7DxAT2ohSWnfUt4FbA7n2xRo15N2CvvvfmY94E2Kn5+QHAt4BbDdAGnRcywI4VYh5M2fmv7aqtKvaratsWPYVJV33AyWmpiSmpwahQfzXzd16DUaH+aua3BrMG+31ufcs/8n6CDveVVKi/mvk7r8H68+ti+Zt579m0xZou24sZrsFqblcDLM/YD+iN5ZdO6gQ8EDgD+BRwPHBgz2dHU0b/dm5e/wVweIuYdwMe1/O6t5i5O/AOmj/cgIfT/u7zx1OeBvEp4O7Ne6tGidvE/CDlDvT36s2XsiMeePmbeQ+n7EhfQjm9rzfXoeIC9wYuBg7reW++SBp2+Y8BrgTu0/Neb57DttW9gZ8CvwP27Vuvw+Z6f+A/gI8Dj+linTbzntD0qw80ed+057Oh1gFlu/o48G/AE4A79m1XQ63Xvt/RWTFDuXne9n3vjRrzOMq2eg5lp7KKG25bw6zXQ4AfAe8G9uzrA8P2q3sD3+5ro+jJ8wzggOb1scDBA7RR54VMk9PLKPep6C/kho15P8qTTc4E/h/lj9suvgd2W6BfrR4x1863raYPPK+/jZqfjxylDzg5LTUxJTUYFeqvnrid1mBUqL+aea3BZrgGo+J+go5rMCrUX828nddgzbydH9Cj3PD9Sprvlb6YRw/bXsxwDcaU/W1TcxrbL560CTgA+AZwe+ARwOuAFwK3BtYC/wk8aMCY96aMFn4GOLnvs5s0MR84RK7HUHbid6XspL40v+EPG5dSxFxIOSL0BODzNKOtlKMv/wn8yZDrdhfgY5Ri5qU0I83DxqXsxC+k2Tn3bWx7Uk4nbt1WQAA7Ugq4+zbv7UC5ydk2lCOFXxhm+XvW6yHA85rl32bEtrotcEnTN49vvsx26os5TK6HNf3qNsCJlILmFcAtgJ0HXa9NzL0po+aHN9vD8ylF1j0oO4iBY/ZtX50WM5Sd2Iebbfbh9BzJGiHmccCXKd8tx1KKml2bz/YYoW+tBk4HXk25aeJ8kTzwNtD8v/tQTsO9U/N6WzbvvHaiXD8/bFt1Xsg0MX8A/Aq4RV/MYdvq4J5t63BKMTN/1HGU74ETgA81OT0SmOv5bKhBuhrbFqWI/RFwOfCKnvdXUQqxLzDEPsvJabmJKanBqFB/Nf+3Sg1Gx/VX83+twWa4Bqu5n6DjGoxK9Vfz/zutwZr/2+kBvWbbWkMZnHxQ897OzbS2abMLhuyvM1uD1diu+vrAVB3QG+svn6SJ8iX+qZ7Xc8DfAn9F+eKc31CCFqfINfM+ibLzuh3wfnqKGcqpsfOdYasBYq4BXgU8pOe9vwP+svl5Z2D/QXKlXM/5CuCY5vW+lC/yUylnE9wZOGjQ5e9Ztj2ajXg9ZQT7ecAfU3aWfzTEen078MPm5xsB/9C8dyJlp7vfEDFXNzG2ohRfH2ra7GXAg2hO5R4w5q6URzTeo3l9IuUIxnbN610Gbatm3vsD7+1Z/guAtwL/l/KFOXDMnrjv6Xm9Hvh34NmUR9nebIhc9wPO63l9sybP11MGEfYdMtfOixnKkxIuoWz7DwbOpdmhU05lHapAAl4AHN/8vBflj48NlELprmw+cjHIet2KUnyf3vSrFzZ97d5NOw6zDZwKfKP5eS3l0cbvofzBdCiwyxAxqxQylILzQuBA4CmUbXXHDtrqCOCjzc87Al9t1vHfU74Hdu9Zrrbr4I8ofyAc0vTbUynfA3elfPcONUhH+WOw020LOInyh+zuwCeBf+j7/CY9/a/19urktNzEFNRgVKi/mnmr1GBUqL+a+a3BZrgGAx5Ghf0EFWowKtRfPcvWdQ1W84DeKymXje1IuRn8e4DTgIfSXFY8YK4zXYNRof5q5p/KA3pbIQAy8yLg5xHxlOb1RuBsyhfxmsy8PCIiGy1jvp7SsS4BXgvcKSKe3Xz2M+D7zc/XDxDzt02sj0bEqubtX1J2NGTmLzLzO83PrXLNzGuA52bmRyNiJ8offhdQNuJHUHZg/zXo8vcs24+Bj1CO1ryOzaPNv8rMb0TEVgOu10cBn42IyykFx/eAL1JOOTw8M68Yoq2uBa6j7GT+kXJTs5c2se8GXD1onsDPKdfof7L5HWdSdmanNq9/PmhbNf4T2CMi3gtcRjkt/gxKX71lZn5niFyhfNleExGPal7v1cQ/jHJ6/LcGzTUzrwB+GhGvaF5/i3IU74eUne2VQ+a6N+URmEcAt4uIf2jiX5eZPwUekJlnRMRWERHLBWu2pSMoX94bM/N9wPmUgobM/MmgMXvWwd9k5ocjYntKvz8HOIsyQHMX4IdDbAPXZ+avKdvV7zLzryl/6LyPcv+GK4aI+SzgMxHxRcp335cp/Wod5ejHL4aImc131neBC5qn25zF5u3sT4AjM/P90VguZvPEinsCz87M/wY+C/yacjRwvq1OaNqqVcweXwS2i4hPAP8NvJNShH2fctr6NUP01z2AKzPz0sw8v8l3N+AhlELshMz8wKC5Zub3gF92uW1l5ruBs5p1+HjgsIj4x55Z/reZr/U+S2ojp6AGywr1VzNvlRosK9RfTVxrsBmuwTLzdOD9Xe4natVgWaH+6lm2TmswSl2zXWZeEBFrKd81746IvwBuShlsHqhW6JvvX4AXUwY+T25y/RNgr0G2V2uwOvVXYx86/NtmxeQEjGiNa6J0zIcAj2xeP4BydOhhPfM8jlLQbD1gzEf0vb+Gcird+4DHUr4gH0n7G9LenTKC/JAFPjsOeHnz80OBew8Y8+F979+i5+eTKMVCq+XvWwcP73nveZQjGXej3ITsTMpRx90HjPnonvfeDbyk5/XDKTv11UO2/00pRx2/yOaR+oMpO55dB1z+hwIn9bw3f3ThEMqpy7ccsq+e1JPr/Sk79Pl5jqTs2LYfIu7Dmtd/RjnC8BHgnOa9JwGvHiDmvjT3Cmhe35ZS0D+zr89+lJ775ww6sflU5f2BTwD/2PPZLkPEuzHlyHI006OAt/d83jrX3nXADW/4uG/Pz0dRTude8gkzi8Sc709PAJ5D2fF8m3K06f00RwYHbavmvX8CTul5fY9B8uyLNX969iubPvWqZr3uS/m+fV/Tlwc6y6U3l6at3gecNmQ/6u+vayhF5jt63rsF5btltyHi36hpl//XvH5Rs45PpTkTYYBYR1D+YHtE8/oQ4I2jbFs9MXu/r+fb7WbNtvUCStH5HAbYFzg5LTUxJTUYFeqvvrid1WBUqL/64j665z1rsBmpwfpj9n3WyX6CjmowKtRfC8TtpAZb4He8qen//9G0+0MpZ+c9u1knbf9m7O8DqyiDyd8A9m7e24WyDe83RJ4zV4NRof5a5PfMX4q8Px38bbMS08yemRQRR1E2onXA0yPilZTrH78N3D4iTm5m/V/KiGubI+b9MV8fEfvA749o/Qfly/Y5lBHiL2fm9QPE3Q94TkT8c0Ts3TPL1cB1EfFw4G8oZwEMEvMZ8zGb0enLe0Y8k/I401Z9pW8dPCMi3hARu1KKgbtSTqt8KuWU0KR8wQ0S8ykR8aaI2CUzT8rM5/XkGpSjhMvmukBbvbb5fx8Brqd80QDckvKFu3rA5d8POLnpA3v3tPNPmlh3aRNvgVyfFRGvA/43Mz8I/E/T7lC+MFdR7jEwaNxnNyPsn6AU70+lFPY0+f6iZcwHNDEe3xxZgXK65oeAAyPi1c17OwLX0KL9e2LvGxE7z7/OcmSZLEcW1wOHRsQLIuJPgCdGxNYDxvxxliPLmeVb++tNjkTEScBDeo5GLxWzdx3skZnZcxThqp5Zd6f0tTZ59sec709nUS7hOAN4BuXxoBfRbru6Qcz59zPzKZQjwr15Xtcmz57Y+0bEzs16BHgW5SaBxwGfyMwrKTctvBZodfSyiblLk+Pvmve2av7vnwO7RsS92+bY/P8/6K+Z+dvM/Czw64h4bjPrzSnfA636a1+/+g3l+34uIj4I3K5ZxxcCJw1whPFYylH7Q4HnR8TfZ+allD8Mh9q2+mKeEhEvh3Lku/n3W5Sz0p5IKXLPzXIWhTSSaanBatRfC8TtpAarUX8tENcabMZqsIVqhd791ij7ia5rsBr11yJxR67Bmrj9de0TKGclnpOZr8/M91DOeLkb5V5fbf5mXKi2ux54DeUSqjc36+PelAGLawfIdSZrsBr1V0/sIyLigfPfI5m5qdkPfIch/7ZZceMezRrHRNnhvZzN17mvoTnCQjkKcl/KF8QngUspHW+YmKdRNt69euZ7IuW03baPnVw2LuU62/+PUigte7RlmZh79sz3Z5Q76re9U/5icV9FeXzmq9l8c8Wg73GKA8Z8bV+uT2hyvfWQMedvdLgv5frad1BGsjcywKPkW/aBhwJfozz+dckzMpZZ/n2aNjqDUoB9dcRc393kukfPfM+k7BjbPIFhbbPNvJVyKu1T2DzCvqbpA++hXAd/MS22q57YD6AURM+g594JffNsQykWftayH/xBzL7P5yiF0WMop5rfokXM/nXw5EViP4ly7X6bPBeNSdlxPYdyqdh8u27TYZ5/Qcvtarm2ony3frbpq0E5Gvt5WhzBW6qtmljbUa6pf/YAeS65DiiF1gcp9xcYZNvqzfUGj7WlXLYw/1SS9bQ82gwc1LTDXZrX+1P+OLhRs+wDb1uLxDy7WS+9R3P/BPgOY35aiNOWMzElNVibmAxYf7WIO1QNtkTMoeuvZeJag81ADbZAzMVqhYH3E3Rcgw2Qa+v6a7m4DFmDLbf83PCm9g+h7Jt3HDLX3jpkDWVQ5I3A5wZYBzNbg1Gh/uqJfSzlu+gFzb8vX2Cegf62Gcc09gTGtuDlBnJvYPPjHLen3I3/VX0daJDTgBeKeTrw+p55nj5oZ1guLuU61U8x2Jf4cjEPpozkdpHre7nhqdCrOojZm+vezeeD/MG7UMz3AP/UM8/uwI26zLVnvkFO2V4s11Ob1wdQboa3rutcKUdaD2sZbxvKExi2pdwo79WUYqb/Ead7ssjp0ovE7byYWSomm0+xPozylIr/oMVA0jLr4MbN56ub/vrqtv11ufXK5ifTtLq0oGWeW7H5xqqDbFedFzIDtP8xlLOfdqDFZXNLrIM9ez6/EeUIdtvLBhct5vvmezrlD4S2feAgmktsKEe8dqccvTy0b7692m5bi8T8/Pz2M78OKYX8QJeEODktNzElNdhyMRmi/moZd+AabIl1OnT91TJXa7AttAZbIubva4Xm30czwH6CCjVYi1wHrr/arFeGq8GqHNBbItc9++ZbQ/sB5ZmuwahQf/XE3SIO6I09gRVd2HLa67aUkcSbUo4u3IvNT3bYnjJafWKHMbdrYp7Qca7zce9HOU1z2Q48QMxjKX9Mbtfher1wkHUwQK7HUXY6y173XKP9B8z1/h331S8Dx1XKdZC2Wkf50t++7/0HUY6yPaV5PTdIrj1xOi9mlovZs47Pod3Opu06uE3z77JFxwAxBzkK0jbmYb3rtoO2GqWQadX+zc9tvgfbroPb/v/t3XnMbPccx/H3t1rUmpaibtWSpiV22gRJQxBBhQSlkRue0OpfVbE3RUhEkYg1RUV6LbW1ohJENLRqicSuKEFULRW13tTtpv3643fGnT7uc58zj/Obc37PvF/JL+3MM/Odz1lm5nvPnKXC6/g/GgAACyJJREFUujrbQPNy+u1tcCTl8/2guftmNT7G3qtRPXLBdaBPzYWn3+HY36CRHmyBmr37rwXr9u7Bes7ThfqvBbPag9XJOmoPtkDN3v3HujqD9WALZO3df9WeB5tNPwv+oLdA1t69wiJZ5x67bXowKvRf6+pvmx/0VuacSRFxAmU31PdQLt94A2WX0tOB4yPi8MzcQ9miecOANa/tavY6JnULdcnMGzNzv8dTL1jzgCxXKrh2oKx7KMfE9j0ud5GssysQXD9Qzt7LfwtZbxqw5h7gor45t5B1kWX1Rcou3+dGxP1nf8vMz1Aub3xYRFwIXLzuXBOb1T4yIm5N+eK/LDOv72peSvkQfm730Id2r/fhzLx8iJoR8fBuHj8zMy8bcB58qzt/w37n74I1L+0zXxes+Y1155nYrPZm8/U53eMe2b3edZn5r/+z5mz5P2RuOvp8DvadB1/vu74ukPXh3Wu9PTN/0jPr2cDH5rLOzh1yKHD7iNgJfHLu/BhD1Tw/Iu7W53wC0mZa6cFq9F9bqNurB6vRf20hqz1Ynayj9WBb6D929Mna1R60B6vRf21xHgzdKzw4M3cDrxy4B72kQtZt14PV6L/WZT0IuCIzP93dfXOWq+D9mnKOJ9j7b5tdm/3bZnRjbMFa5qD8YnIvynGMj6Ps3vkqyhUtdlB+VflIN84Cfg8cveyaZm2nplk3rPkK4CrW7YpJ2YJ/BYvtVnwC5XLO51B2J7//ur8/i7L794WU3aB3DFzzGjbZrbbGPGil5v+5rO657Jo158HQ6+oGWV++PivlHyMXUM5Dtd/dn2vUdDj6jA3Wvcl9V9aoaVazLjnrduoV9tuD1cpacx4sOP2t9TUtZd2wB9sg52C90lzWD85npdv7ibIB60HATuCXlCscLnS14zHG6AGWMpFl97FzKB/as93GXka56sbhlGNon0E5AdYxY9U0azs1zbphzdMpJ4o7urt9OOUEqr12V6WxBqnSPGil5kovqxGzHtPdfjvwK/qfz2vwmg5Hn7HBuje578oaNc1q1iVnXYleoVbWGnVrTb9ZR1mvttp/besf9EYPUHXiyqUEj6Mch/gp1p1lHjijW3CbHutds6ZZ26lp1l41XwXsYu85ADa9AsW650++QaoxD1qp6bIaPeuHu9d9CnC/MWo6HH1Gj3VvEt+VNWqa1awjZt3WvUKtrDXnwZDTb9ZR16st90qbZG36B73RA1SbMHga5VKCX6Mcm/l0ylbJM+Yec59uwfbahaxGTbO2U9OsC9X8wCJZu+c10SDVmAet1HRZTSLrBxfIOXhNh6PPWGDds1cwq1ntFXp9V9bKWrGufU0DWRfIuXCv1DNr0z/ojR6gykTBY4DL6c6wT/mgfhNlN9Irgdd2C3eNclm+TS8RWqOmWdupadZ6WbtaTTRIrcxXl1XV99YUsh46Rk2Ho89o5TOt4meEWc3aRNZa09/VGvS7sqVlVWP6zTqZ9ap3r7RA1qZ/0Bs9QJWJKivG2tztw4AvdP9/P8oupWdTLr/Z91jPwWuatZ2aZq2etYkGqZX56rKqOl9byjr4OuBwbDZaWZ8rf06a1ayTz1p5+mv0Ci0tq5Z6hZXNOqH3QLM/6I0eoMpElV3F7jT3/0cAP6C7MgBwb8rl/e48Zk2ztlPTrFWzttQgNTFfXVbV5mtLWausAw7HZqOV9bni56RZzdpE1orTX6NXaGlZtdQrrHTWlt4DUx2jB6g+gWUFuAPwle72TsqubwdPqaZZ26lp1mFr1vggr/Xl0NJ8dVkNPw9aylq7psPRZ7SyPtd6j5jVrK1kHbJm7e/KqS+r2tNv1umvV8uYr1MZB7LNZea/gWsi4ncRcRbwJMqWwmunVNOs7dQ067A1M/MmYHd3M4B/AH/LzKsiYidwPPDSzPznmDX38RqTnq81aq76smopa+2aUh+trM+r+v1rVrPWqFn7u3Lqy6qlXsGsw+dcRtYpmV2abtuKiAAOohy3eBDwhMz85dRqmrWdmmatl3Wu/i7gKvZ+kF82tZqtzFeXVd150ELW2uuAtJFW1me/f8266llb6hVaWlZztXcx8V5hrvYuVjBrS++BKdn2G5NmImIN+E5m/nTKNWvVXfWsqz79teoOXbPRBmmNic/XGjVXfVm1lLVmTamPVtbnVf7+rVnXrO1kbaFXmKu9xsSXVUu9gln/W3uNRt4DU7BKG5MiB57YGjVr1V31rKs+/bXqVsy6xsQbpLm6TcxXl1W1+bpGO1mrrAPSZlpZn/3+NeuqZ22sV2hpWa3RTq+wxgpnbek9MAUrszFJUjtaapBW3aovq5aySpI0hlX/rmxp+s1aR0tZF+HGJEmSJEmSJPV2wNgBJEmSJEmS1A43JkmSJEmSJKk3NyZJkiRJkiSpNzcmSaomItYiIufGvyLiioj4bEQ8p7tc5jLzHBsR50TEzyNiT0RcGRHnRcR9l5lDkiSplqn1X12mN0fElyPir12mtWVnkDQsNyZJWoYTgUcDTwVeB1wPfAK4KCIOXmKOk4AHAu8GngK8BngE8N2IuNcSc0iSJNU2lf4L4DTgYODzS35dSZUcOHYASSvhh5n5q7nbH42I84HzgbdRGoxleGtmXj1/R0R8E/gNcArw+iXlkCRJqm0q/RfAnTPz5og4Cnj+El9XUiXumSRpFJn5GeBzwCkRcTuAiHhjRHw/InZHxF8i4qsR8ajZcyLiHhFxQ0Scvr5eRLyhO3TtkP285tX7uO+3wNXAjiGmS5IkaarG6L+617156GmRNC43Jkka0xeB2wDHdrd3AO8AngGsAX8GLo2IBwNk5p+AC4EXzxeJiFsBLwI+nZl/XyRARDwAuBtw+ZanQpIkqR2j91+S2udhbpLGdGX338MBMvPk2R+6BuVLwE+Bk4HZr2FnAxdHxPGZ+fXuvhOAI4D3L/LiEXFg95yrgQ9tcRokSZJaMmr/JWl7cM8kSWOaXU0kASLiiRFxcUT8Ffg3cCNwNHDM7AmZeQnwM+DUuTqnAj/OzG93dW4VEQfOjY0+694LPAbY6S9qkiRpRYzdf0naBnyDSxrT7ApqV0XEIyi7XV9D2WX6UcBxwI+A26573vuAZ0fEXSLi3sCTueWvYl+hNEKz8T8n1o6It1B2135hZn55sCmSJEmattH6L0nbh4e5SRrTCcB1wPeAMym/hj0zM2+cPaA7oeM/1j3vI8BZlOP6DwH2AOfN/f1U4I5zt/84/+SIOBN4NXBaZn50iAmRJElqxCj9l6TtxY1JkkYREc8Cng68KzP3dFcUuYlul+vuMY8HjgR+M//czNwdEedRmpY7AJ/IzN1zf//Ffl73JcCbgDMz870DTpIkSdKkjdV/Sdp+3JgkaRkeFhF3BW5NaU6eBpwIXASc0T3mS8BLgV0RcS7lWP3XAX/YoObZ7D1uv9eJHyPiJOCd3Wvd4rK3wO7M/FnvKZIkSZq2SfRfABHxWOAw4B7dXcdGxDUAmXlB3zqSpiMyc/NHSdIWRMQacO7cXddRLjf7feDjwAU59yEUEacBL6M0Gj+hNDqvBcjMx+2j/i8oG4GO65lnF/CCDf78tX29hiRJUkum1n91z7kEeOy+/paZsa/7JU2bG5MkNSkijgEuB07JzA+NnUeSJGm7s/+SNOPGJElNiYgjgKOAN3b/PSozrx03lSRJ0vZl/yVpvQPGDiBJCzoZ+Cpwd+B5NjKSJEnV2X9JugX3TJIkSZIkSVJv7pkkSZIkSZKk3tyYJEmSJEmSpN7cmCRJkiRJkqTe3JgkSZIkSZKk3tyYJEmSJEmSpN7cmCRJkiRJkqTe/gMOvG2UYWD7IwAAAABJRU5ErkJggg==\n"
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            "needs_background": "light"
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        }
      ]
    }
  ]
}