code_main (6).ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4pvdtZoQaJaD"
      },
      "source": [
        "# 2024 NH 투자증권 빅데이터 경진대회"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q4ri19HlaJaG"
      },
      "source": [
        "#### 종합코드"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5RjOpk3NaJaG"
      },
      "source": [
        "## 1. 데이터 불러오기"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bLeGUdgqaJaH"
      },
      "source": [
        "### 필수 라이브러리"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vbxDVc1DaJaH"
      },
      "source": [
        "이윤서 코드 (기준 6개)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KWFFm5bUaJaH"
      },
      "source": [
        "1. 6번 파일(8월 27일 기준 ETF의 구성 종목)에서 시가총액 큰 것 순서대로 etf 추출하기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "_rJvfrxqNJhz"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import yfinance as yf"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import sys\n",
        "sys.version"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "6HiylhnvQY3O",
        "outputId": "dd44c2ee-696f-4849-df1a-ae754c6fbe6a"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'3.10.12 (main, Sep 11 2024, 15:47:36) [GCC 11.4.0]'"
            ],
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              "type": "string"
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          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "id": "BwEDfgvFaJaI"
      },
      "outputs": [],
      "source": [
        "etf_holdings = pd.read_csv('/content/NH_CONTEST_DATA_ETF_HOLDINGS.csv', encoding='cp949')\n",
        "nw_fc_stk_iem_ifo = pd.read_csv('/content/NH_CONTEST_NW_FC_STK_IEM_IFO.csv', encoding='cp949')\n",
        "stk_dt_qut = pd.read_csv('/content/NH_CONTEST_STK_DT_QUT.csv', encoding='cp949')\n",
        "historical_dividend = pd.read_csv('/content/NH_CONTEST_DATA_HISTORICAL_DIVIDEND.csv', encoding='cp949')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
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        "outputId": "aad329e7-a6ee-4a63-a8c9-0fd94936e9a0"
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        {
          "output_type": "display_data",
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            "text/plain": [
              "    etf_tck_cd        mkt_vlu\n",
              "107        VTI  1632609396735\n",
              "105        VOO  1168848786417\n",
              "83         SPY   537149441711\n",
              "40         IVV   486185746208\n",
              "67         QQQ   270418248248\n",
              "..         ...            ...\n",
              "121       YMAX       48564080\n",
              "34        HDRO       18093000\n",
              "95         TSL       10395509\n",
              "3         AMDL        9788048\n",
              "59         NVD      -67319541\n",
              "\n",
              "[122 rows x 2 columns]"
            ],
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              "      <th>107</th>\n",
              "      <td>VTI</td>\n",
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              "    <tr>\n",
              "      <th>105</th>\n",
              "      <td>VOO</td>\n",
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              "      <th>83</th>\n",
              "      <td>SPY</td>\n",
              "      <td>537149441711</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>IVV</td>\n",
              "      <td>486185746208</td>\n",
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              "      <th>67</th>\n",
              "      <td>QQQ</td>\n",
              "      <td>270418248248</td>\n",
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              "      <th>121</th>\n",
              "      <td>YMAX</td>\n",
              "      <td>48564080</td>\n",
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              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>HDRO</td>\n",
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              "    <tr>\n",
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              "      <td>TSL</td>\n",
              "      <td>10395509</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>AMDL</td>\n",
              "      <td>9788048</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>NVD</td>\n",
              "      <td>-67319541</td>\n",
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              "\n",
              "      .colab-df-generate:hover {\n",
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              "    </style>\n",
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              "            title=\"Generate code using this dataframe.\"\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "ETF_top",
              "summary": "{\n  \"name\": \"ETF_top\",\n  \"rows\": 122,\n  \"fields\": [\n    {\n      \"column\": \"etf_tck_cd\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 122,\n        \"samples\": [\n          \"XLV\",\n          \"QYLD\",\n          \"PAVE\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mkt_vlu\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 191968371173,\n        \"min\": -67319541,\n        \"max\": 1632609396735,\n        \"num_unique_values\": 122,\n        \"samples\": [\n          39691826082,\n          7898228977,\n          7539098455\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
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          "metadata": {}
        }
      ],
      "source": [
        "etf_holdings_result = etf_holdings.groupby('etf_tck_cd')['mkt_vlu'].sum().reset_index()\n",
        "ETF_top = etf_holdings_result.sort_values(by='mkt_vlu', ascending=False)\n",
        "display(ETF_top)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4qRGSUFqaJaJ"
      },
      "source": [
        "2. 1번 파일(8월 27일 기준 해외종목 정보)에서 업종분류명, 섹터분류명, 산업명 열 빼와서 6번 파일(8월 27일 기준 ETF의 구성 종목)과 병합하기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
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        },
        "id": "mg01nXAaaJaJ",
        "outputId": "ba5b7552-b1ae-4af6-f752-8d8aa52d556d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "      etf_tck_cd tck_iem_cd     mkt_vlu  \\\n",
              "0           AAPB       AAPL    36858666   \n",
              "1           AAPB        USD    18337384   \n",
              "2           AMDL        USD     3257693   \n",
              "3           AMDL        AMD     6530355   \n",
              "4           CLOU       TWLO    15400502   \n",
              "...          ...        ...         ...   \n",
              "18511        TAN       CSIQ    18712550   \n",
              "18512        TAN         AY    18652217   \n",
              "18513        TAN        RNW     7690201   \n",
              "18514        TAN       AMPS     5794798   \n",
              "18515        TMF        TLT  4204149128   \n",
              "\n",
              "                                  fc_sec_eng_nm  fc_sec_krl_nm   stk_qty  \\\n",
              "0                                          AAPL             애플    215737   \n",
              "1                                    US Dollars  프로셰어즈 울트라 반도체         0   \n",
              "2                                    US Dollars  프로셰어즈 울트라 반도체         0   \n",
              "3                                           AMD           에이엠디     36558   \n",
              "4                                TWILIO INC - A           트윌리오    254933   \n",
              "...                                         ...            ...       ...   \n",
              "18511                        Canadian Solar Inc        캐나디안 솔라   1382020   \n",
              "18512  Atlantica Sustainable Infrastructure PLC       아틀란티카 일드    843610   \n",
              "18513                   ReNew Energy Global PLC         리뉴 에너지   1296830   \n",
              "18514                           Altus Power Inc         알터스 파워   1833797   \n",
              "18515              ISHARES 20+ YEAR TREASURY BD   아이셰어즈 20년 국채  44105635   \n",
              "\n",
              "       wht_pct sec_tp                              btp_cfc_nm  ser_cfc_nm  \\\n",
              "0       66.778     ST                   Phones & Smart Phones  Technology   \n",
              "1       33.222     EF                                       -           -   \n",
              "2       33.282     EF                                       -           -   \n",
              "3       66.718     ST                    Semiconductors (NEC)  Technology   \n",
              "4        4.266     ST                          Software (NEC)  Technology   \n",
              "...        ...    ...                                     ...         ...   \n",
              "18511    1.897     ST  Photovoltaic Solar Systems & Equipment  Technology   \n",
              "18512    1.891     ST          Alternative Electric Utilities   Utilities   \n",
              "18513    0.780     ST                          Renewable IPPs   Utilities   \n",
              "18514    0.587     ST               Solar Electric Ultilities   Utilities   \n",
              "18515   20.922     EF                                       -           -   \n",
              "\n",
              "                          ids_nm  mkt_pr_tot_amt  \n",
              "0           Consumer Electronics      3454684.01  \n",
              "1                              -            0.00  \n",
              "2                              -            0.00  \n",
              "3                 Semiconductors       242772.28  \n",
              "4      Software - Infrastructure         9743.59  \n",
              "...                          ...             ...  \n",
              "18511                      Solar          895.02  \n",
              "18512      Utilities - Renewable         2588.02  \n",
              "18513      Utilities - Renewable         2063.37  \n",
              "18514      Utilities - Renewable          487.97  \n",
              "18515                          -            0.00  \n",
              "\n",
              "[18516 rows x 12 columns]"
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              "      <td>AAPB</td>\n",
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              "      <th>3</th>\n",
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              "      <th>4</th>\n",
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              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "      <td>Canadian Solar Inc</td>\n",
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              "      <td>1382020</td>\n",
              "      <td>1.897</td>\n",
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              "    <tr>\n",
              "      <th>18512</th>\n",
              "      <td>TAN</td>\n",
              "      <td>AY</td>\n",
              "      <td>18652217</td>\n",
              "      <td>Atlantica Sustainable Infrastructure PLC</td>\n",
              "      <td>아틀란티카 일드</td>\n",
              "      <td>843610</td>\n",
              "      <td>1.891</td>\n",
              "      <td>ST</td>\n",
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              "      <td>2588.02</td>\n",
              "    </tr>\n",
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              "      <th>18513</th>\n",
              "      <td>TAN</td>\n",
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              "      <td>7690201</td>\n",
              "      <td>ReNew Energy Global PLC</td>\n",
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              "      <td>0.780</td>\n",
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              "      <td>Renewable IPPs</td>\n",
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              "    <tr>\n",
              "      <th>18514</th>\n",
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              "      <td>-</td>\n",
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              "<p>18516 rows × 12 columns</p>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "etf_holdings_and_nw_fc_stk_iem_ifo",
              "summary": "{\n  \"name\": \"etf_holdings_and_nw_fc_stk_iem_ifo\",\n  \"rows\": 18516,\n  \"fields\": [\n    {\n      \"column\": \"etf_tck_cd\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 122,\n        \"samples\": [\n          \"AMZU\",\n          \"XLP\",\n          \"YMAX\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tck_iem_cd\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2984,\n        \"samples\": [\n          \"DKS\",\n          \"HRMY\",\n          \"MLP\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mkt_vlu\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2100690714,\n        \"min\": -135528902,\n        \"max\": 104945453823,\n        \"num_unique_values\": 18495,\n        \"samples\": [\n          73277,\n          297896,\n          10337819\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fc_sec_eng_nm\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7576,\n        \"samples\": [\n          \"ANDERSONS INC\",\n          \"DexCom Inc.\",\n          \"Dow Inc.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fc_sec_krl_nm\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2921,\n        \"samples\": [\n          \"\\uc5d0\\uc139\\uc2a4\",\n          \"\\uc634\\ub2c8\\uc140\",\n          \"\\ud53c\\ud53c\\uc9c0 \\uc778\\ub354\\uc2a4\\ud2b8\\ub9ac\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"stk_qty\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13753957,\n        \"min\": -1293832,\n        \"max\": 738344877,\n        \"num_unique_values\": 17555,\n        \"samples\": [\n          2455,\n          190794,\n          79979\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"wht_pct\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.2544404437737597,\n        \"min\": 0.0,\n        \"max\": 99.795,\n        \"num_unique_values\": 2279,\n        \"samples\": [\n          1.22,\n          0.66,\n          5.537\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sec_tp\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"ST\",\n          \"EF\",\n          \"-\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"btp_cfc_nm\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 445,\n        \"samples\": [\n          \"Electrical Transmission & Grid Equipment\",\n          \"Construction Supplies\",\n          \"Corporate Financial Services (NEC)\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ser_cfc_nm\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 12,\n        \"samples\": [\n          \"Utilities\",\n          \"Consumer Defensive\",\n          \"Technology\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ids_nm\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 145,\n        \"samples\": [\n          \"Electronic Components\",\n          \"Utilities - Independent Power Producers\",\n          \"Conglomerates\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mkt_pr_tot_amt\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 297163.4982556173,\n        \"min\": 0.0,\n        \"max\": 3454684.01,\n        \"num_unique_values\": 2855,\n        \"samples\": [\n          3483.8,\n          2440.22,\n          404.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "nw_fc_stk_iem_ifo_selected = nw_fc_stk_iem_ifo[['tck_iem_cd', 'btp_cfc_nm', 'ser_cfc_nm', 'ids_nm', 'mkt_pr_tot_amt']]\n",
        "etf_holdings_and_nw_fc_stk_iem_ifo = pd.merge(etf_holdings, nw_fc_stk_iem_ifo_selected, on='tck_iem_cd', how='inner')\n",
        "display(etf_holdings_and_nw_fc_stk_iem_ifo)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1RnIdGbMaJaK"
      },
      "source": [
        "(아래 df = etf명, etf 구성 종목, 보유종목가치, 보유종목비중, 업종분류명, 섹터분류명, 산업명)\n",
        "3. ETF 종목들을 섹터분류명(ser_cfc_nm), 총 12개 섹터로 분류"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "GCIyrR9YaJaK",
        "outputId": "eb42a97d-7173-4c86-94eb-db1cf5fd19f9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 ser_cfc_nm  wht_pct etf_tck_cd\n",
              "0                         -    0.098        VTI\n",
              "1           Basic Materials    2.153        VTI\n",
              "2    Communication Services    8.578        VTI\n",
              "3         Consumer Cyclical   10.047        VTI\n",
              "4        Consumer Defensive    5.465        VTI\n",
              "..                      ...      ...        ...\n",
              "657       Consumer Cyclical   55.556        TSL\n",
              "658                       -   33.282       AMDL\n",
              "659              Technology   66.718       AMDL\n",
              "660                       -   33.479        NVD\n",
              "661              Technology   66.521        NVD\n",
              "\n",
              "[662 rows x 3 columns]"
            ],
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "sector_result_df",
              "summary": "{\n  \"name\": \"sector_result_df\",\n  \"rows\": 662,\n  \"fields\": [\n    {\n      \"column\": \"ser_cfc_nm\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 12,\n        \"samples\": [\n          \"Technology\",\n          \"Real Estate\",\n          \"-\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"wht_pct\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19.93407677373292,\n        \"min\": 0.0,\n        \"max\": 100.001,\n        \"num_unique_values\": 628,\n        \"samples\": [\n          25.085,\n          0.234,\n          19.569\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"etf_tck_cd\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 122,\n        \"samples\": [\n          \"XLV\",\n          \"QYLD\",\n          \"PAVE\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 45
        }
      ],
      "source": [
        "df = etf_holdings_and_nw_fc_stk_iem_ifo[['etf_tck_cd','tck_iem_cd', 'mkt_vlu', 'wht_pct','btp_cfc_nm', 'ser_cfc_nm', 'ids_nm']]\n",
        "# ETF_top.etf_tck_cd.unique()\n",
        "# lst= ['VTI', 'VOO', 'SPY', 'IVV', 'QQQ', 'VEA', 'VIG', 'VWO', 'TQQQ',\n",
        "#        'VYM', 'XLK', 'IEMG', 'IWM', 'VNQ', 'SCHD', 'VT', 'SPLG', 'XLF',\n",
        "#        'XLV', 'XLE', 'SOXL', 'DIA', 'JEPI', 'QQQM', 'DGRO', 'SPYG',\n",
        "#        'SPYV', 'MGK', 'SMH', 'TMF', 'XLP', 'XLU', 'MOAT', 'JEPQ', 'GDX',\n",
        "#        'DGRW', 'SOXX', 'SPXL', 'QLD', 'NOBL', 'UPRO', 'SSO', 'PFF',\n",
        "#        'TECL', 'TNA', 'QYLD', 'IBB', 'PAVE', 'XBI', 'FAS', 'XLRE', 'CIBR',\n",
        "#        'ITA', 'NVDL', 'ARKK', 'SPYD', 'IHI', 'TSLL', 'SPHD', 'DIVO',\n",
        "#        'PGX', 'LABU', 'XYLD', 'YINN', 'SKYY', 'URA', 'BOTZ', 'DPST',\n",
        "#        'USD', 'ICLN', 'AOR', 'UDOW', 'AIQ', 'COPX', 'GRID', 'URTY',\n",
        "#        'ARKW', 'ARKG', 'RYLD', 'CONL', 'NVDU', 'LIT', 'TLTW', 'JETS',\n",
        "#        'NAIL', 'TAN', 'ARKF', 'ARKQ', 'RPAR', 'SVOL', 'CWEB', 'SDIV',\n",
        "#        'QCLN', 'DFEN', 'DIV', 'AMZU', 'SOXQ', 'METV', 'DRIV', 'WEBL',\n",
        "#        'CLOU', 'NUSI', 'AAPU', 'MSFU', 'KRBN', 'GGLL', 'FBL', 'ARKX',\n",
        "#        'KBWY', 'GPIQ', 'GPIX', 'KLIP', 'KORU', 'RETL', 'MSOX', 'AAPB',\n",
        "#        'AWAY', 'YMAX', 'HDRO', 'TSL', 'AMDL', 'NVD']\n",
        "\n",
        "df_array=[]\n",
        "sector_result_df = pd.DataFrame()\n",
        "\n",
        "for ticker in ETF_top.etf_tck_cd.unique():\n",
        "    filtered_df = df[df['etf_tck_cd'] == ticker]\n",
        "\n",
        "    # ser_cfc_nm으로 그룹화하고 wht_pct 합계 계산\n",
        "    grouped_df = filtered_df.groupby('ser_cfc_nm')['wht_pct'].sum().reset_index()\n",
        "\n",
        "    # ticker 열 추가\n",
        "    grouped_df['etf_tck_cd'] = ticker\n",
        "\n",
        "    # 결과를 합치기\n",
        "    sector_result_df = pd.concat([sector_result_df, grouped_df], ignore_index=True)\n",
        "\n",
        "sector_result_df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f86LSgZwaJaL"
      },
      "source": [
        "3. ETF 구성 종목 개수 (분산투자 정도 파악 목적, 기준3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "Y-_56ztiaJaL",
        "outputId": "e759e309-1640-4b10-a6d7-eb43e8aeee85"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    etf_tck_cd  tck_iem_cd_count\n",
              "0         AAPB                 2\n",
              "1         AAPU                10\n",
              "2          AIQ                85\n",
              "3         AMDL                 2\n",
              "4         AMZU                 7\n",
              "..         ...               ...\n",
              "117        XLU                33\n",
              "118        XLV                63\n",
              "119       XYLD               498\n",
              "120       YINN                12\n",
              "121       YMAX                19\n",
              "\n",
              "[122 rows x 2 columns]"
            ],
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              "      <td>YMAX</td>\n",
              "      <td>19</td>\n",
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              "\n",
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              "\n",
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              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "etf_count_result",
              "summary": "{\n  \"name\": \"etf_count_result\",\n  \"rows\": 122,\n  \"fields\": [\n    {\n      \"column\": \"etf_tck_cd\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 122,\n        \"samples\": [\n          \"CWEB\",\n          \"KBWY\",\n          \"KORU\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tck_iem_cd_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1091,\n        \"min\": 2,\n        \"max\": 9163,\n        \"num_unique_values\": 85,\n        \"samples\": [\n          47,\n          2,\n          514\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 46
        }
      ],
      "source": [
        "etf_count = etf_holdings.groupby('etf_tck_cd')['tck_iem_cd'].count()\n",
        "\n",
        "etf_count_result = etf_count.reset_index(name='tck_iem_cd_count')\n",
        "etf_count_result"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TW7sOXa4aJaL"
      },
      "source": [
        "4. 시가총액 큰 것 순서대로 ETF들의 거래량 내림차순 정렬\n",
        "2번파일의 acl_trd_qty(누적거래수량) 활용"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "Mw13LNonaJaL",
        "outputId": "897f3616-7f7b-4bf6-8a4e-18d746111549"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    tck_iem_cd   acl_trd_qty\n",
              "77        SOXL  4.791392e+09\n",
              "94        TQQQ  3.532010e+09\n",
              "83         SPY  3.213682e+09\n",
              "96        TSLL  3.014736e+09\n",
              "113        XLF  2.363635e+09\n",
              "..         ...           ...\n",
              "32        GPIX  3.500320e+06\n",
              "11        ARKX  3.406643e+06\n",
              "34        HDRO  2.650521e+06\n",
              "71        RPAR  2.594178e+06\n",
              "12        AWAY  1.056584e+06\n",
              "\n",
              "[122 rows x 2 columns]"
            ],
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              "      <th></th>\n",
              "      <th>tck_iem_cd</th>\n",
              "      <th>acl_trd_qty</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>77</th>\n",
              "      <td>SOXL</td>\n",
              "      <td>4.791392e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>94</th>\n",
              "      <td>TQQQ</td>\n",
              "      <td>3.532010e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>83</th>\n",
              "      <td>SPY</td>\n",
              "      <td>3.213682e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>TSLL</td>\n",
              "      <td>3.014736e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>113</th>\n",
              "      <td>XLF</td>\n",
              "      <td>2.363635e+09</td>\n",
              "    </tr>\n",
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              "      <th>32</th>\n",
              "      <td>GPIX</td>\n",
              "      <td>3.500320e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>ARKX</td>\n",
              "      <td>3.406643e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>HDRO</td>\n",
              "      <td>2.650521e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>71</th>\n",
              "      <td>RPAR</td>\n",
              "      <td>2.594178e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>AWAY</td>\n",
              "      <td>1.056584e+06</td>\n",
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              "</table>\n",
              "<p>122 rows × 2 columns</p>\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "stk_dt_qut_result_sorted",
              "summary": "{\n  \"name\": \"stk_dt_qut_result_sorted\",\n  \"rows\": 122,\n  \"fields\": [\n    {\n      \"column\": \"tck_iem_cd\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 122,\n        \"samples\": [\n          \"XBI\",\n          \"SPYG\",\n          \"SSO\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"acl_trd_qty\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 762758712.674282,\n        \"min\": 1056584.0,\n        \"max\": 4791392454.0,\n        \"num_unique_values\": 122,\n        \"samples\": [\n          511450176.0,\n          181060584.0,\n          177947239.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 47
        }
      ],
      "source": [
        "etf_tck_cd_list = ETF_top['etf_tck_cd'].tolist()\n",
        "\n",
        "stk_dt_qut['tck_iem_cd'] = stk_dt_qut['tck_iem_cd'].str.strip()\n",
        "filtered_stk_dt_qut = stk_dt_qut[stk_dt_qut['tck_iem_cd'].isin(etf_tck_cd_list)]\n",
        "stk_dt_qut_result = filtered_stk_dt_qut.groupby('tck_iem_cd')['acl_trd_qty'].sum().reset_index()\n",
        "stk_dt_qut_result_sorted = stk_dt_qut_result.sort_values(by='acl_trd_qty', ascending=False)\n",
        "stk_dt_qut_result_sorted"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ef1l_ULYaJaM"
      },
      "source": [
        "5. 시가-종가 간 차이와 고가-저가 간 차이 사이의 괴리 파악 (주가 변동성 파악 목적)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 652
        },
        "id": "ORGIZ67WaJaM",
        "outputId": "ed8cae18-3285-46f6-9b9a-dd3c3c44c88d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-48-03f96f5aaf63>:4: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  filtered_stk_dt_qut['gap_ong_end'] = np.where(\n",
            "<ipython-input-48-03f96f5aaf63>:10: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  filtered_stk_dt_qut['gap_hi_low'] = filtered_stk_dt_qut['iem_hi_pr'] - filtered_stk_dt_qut['iem_low_pr']\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    tck_iem_cd  total_gap\n",
              "0         NVDU  14.416992\n",
              "1          USD  13.823870\n",
              "2          SMH  11.182910\n",
              "3         NAIL  11.779965\n",
              "4          QQQ  10.631935\n",
              "..         ...        ...\n",
              "117       QYLD   0.228814\n",
              "118        DIV   0.233584\n",
              "119       HDRO   0.199435\n",
              "120       RYLD   0.217886\n",
              "121        PGX   0.123984\n",
              "\n",
              "[122 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
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              "    <div>\n",
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              "        vertical-align: middle;\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tck_iem_cd</th>\n",
              "      <th>total_gap</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>NVDU</td>\n",
              "      <td>14.416992</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>USD</td>\n",
              "      <td>13.823870</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>SMH</td>\n",
              "      <td>11.182910</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>NAIL</td>\n",
              "      <td>11.779965</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>QQQ</td>\n",
              "      <td>10.631935</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>117</th>\n",
              "      <td>QYLD</td>\n",
              "      <td>0.228814</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>118</th>\n",
              "      <td>DIV</td>\n",
              "      <td>0.233584</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>119</th>\n",
              "      <td>HDRO</td>\n",
              "      <td>0.199435</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>120</th>\n",
              "      <td>RYLD</td>\n",
              "      <td>0.217886</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>121</th>\n",
              "      <td>PGX</td>\n",
              "      <td>0.123984</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>122 rows × 2 columns</p>\n",
              "</div>\n",
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              "      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",
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              "\n",
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              "        document.querySelector('#df-7a9c0070-ec39-4e38-90dd-bcae78e50fc2 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-7a9c0070-ec39-4e38-90dd-bcae78e50fc2');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "</svg>\n",
              "  </button>\n",
              "\n",
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              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-8ab70c01-fd1c-404b-8f82-c1a9e75f9af8 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_8de8ae70-6771-4007-894e-31e9ef3065d8\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('sum_result_avg')\"\n",
              "            title=\"Generate code using this dataframe.\"\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",
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              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_8de8ae70-6771-4007-894e-31e9ef3065d8 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('sum_result_avg');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "sum_result_avg",
              "summary": "{\n  \"name\": \"sum_result_avg\",\n  \"rows\": 122,\n  \"fields\": [\n    {\n      \"column\": \"tck_iem_cd\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 122,\n        \"samples\": [\n          \"FAS\",\n          \"TMF\",\n          \"SOXQ\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_gap\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.1553404423227285,\n        \"min\": 0.1239841269841272,\n        \"max\": 14.416992063492064,\n        \"num_unique_values\": 122,\n        \"samples\": [\n          5.559750793650792,\n          1.9956047619047617,\n          1.8204380952380963\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 48
        }
      ],
      "source": [
        "filtered_stk_dt_qut = stk_dt_qut[stk_dt_qut['tck_iem_cd'].isin(stk_dt_qut_result_sorted['tck_iem_cd'])]\n",
        "\n",
        "# gap_ong_end 열 생성 후, 시가와 종가의 차이를 열에 넣기\n",
        "filtered_stk_dt_qut['gap_ong_end'] = np.where(\n",
        "    filtered_stk_dt_qut['iem_ong_pr'] > filtered_stk_dt_qut['iem_end_pr'],\n",
        "    filtered_stk_dt_qut['iem_ong_pr'] - filtered_stk_dt_qut['iem_end_pr'],\n",
        "    filtered_stk_dt_qut['iem_end_pr'] - filtered_stk_dt_qut['iem_ong_pr']\n",
        ")\n",
        "# gap_hi_low 열 생성, 고가 - 저가를 열에 넣기\n",
        "filtered_stk_dt_qut['gap_hi_low'] = filtered_stk_dt_qut['iem_hi_pr'] - filtered_stk_dt_qut['iem_low_pr']\n",
        "filtered_stk_dt_qut\n",
        "\n",
        "# tck_iem_cd를 기준으로 평균 계산\n",
        "result_avg = filtered_stk_dt_qut.groupby('tck_iem_cd').agg(\n",
        "    avg_gap_ong_end=('gap_ong_end', 'mean'),\n",
        "    avg_gap_hi_low=('gap_hi_low', 'mean')\n",
        "    ).reset_index()\n",
        "result_avg\n",
        "\n",
        "# avg_gap_ong_end와 avg_gap_hi_low 기준으로 내림차순 정렬\n",
        "sorted_result_avg = result_avg.sort_values(by=['avg_gap_ong_end', 'avg_gap_hi_low'], ascending=False).reset_index(drop=True)\n",
        "sorted_result_avg\n",
        "\n",
        "##시가-종가 차이, 저가-고가 차이의 평균치를 합산하여 주가 변동성이 큰 순서대로 나열하기\n",
        "\n",
        "# avg_gap_ong_end와 avg_gap_hi_low의 합을 새로운 열로 추가\n",
        "sorted_result_avg['total_gap'] = sorted_result_avg['avg_gap_ong_end'] + sorted_result_avg['avg_gap_hi_low']\n",
        "\n",
        "# 결과를 새로운 데이터 프레임으로 생성\n",
        "sum_result_avg = sorted_result_avg[['tck_iem_cd', 'total_gap']]\n",
        "sum_result_avg"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XdEcptX7aJaM"
      },
      "source": [
        "6. 배당금의 평균치를 가장 최근의 종가로 나눠서 시가배당률을 계산한다.\n",
        "-> 이유 : 투자자들이 etf를 산 시점의 가격으로 배당률이 정해지기 때문에, 즉, 매일매일 시가배당률이 바뀌기 때문에 배당금 평균치와 최근 종가 데이터 모두 크롤링을 사용하여 구하는 게 베스트일 거라고 생각함.\n",
        "- 배당금 평균치는 가장 늦게 배당금을 지급하기 시작한 etf를 찾고, 그 일자를 기준으로 현재까지의 배당금 데이터를 추출. (기준 6)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "1V_JCCiBaJaM",
        "outputId": "281fc811-c3f9-4575-952b-b225389c6e1d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    etf_tck_cd   ediv_dt  ddn_amt  aed_stkp_ddn_amt ddn_bse_dt  ddn_pym_dt  \\\n",
              "0         AAPB  20231227  4.30641           4.30641   20231228    20231229   \n",
              "1         AAPU  20220920  0.01041           0.01041   20220921    20220927   \n",
              "2         AAPU  20230919  0.14642           0.14642   20230920    20230926   \n",
              "3         AAPU  20231221  0.27249           0.27249   20231222    20231229   \n",
              "4         AAPU  20230321  0.07802           0.07802   20230322    20230328   \n",
              "..         ...       ...      ...               ...        ...         ...   \n",
              "995       YMAX  20240717  0.65160           0.65160   20240717    20240718   \n",
              "996       YMAX  20240614  0.73000           0.73000   20240614    20240617   \n",
              "997       YMAX  20240215  0.53390           0.53390   20240216    20240220   \n",
              "998       YMAX  20240314  0.56800           0.56800   20240315    20240318   \n",
              "999       YMAX  20240515  0.73170           0.73170   20240516    20240517   \n",
              "\n",
              "       pba_dt ddn_pym_fcy_cd  \n",
              "0    20231226        Monthly  \n",
              "1    20220919      Quarterly  \n",
              "2    20230918      Quarterly  \n",
              "3    20231220      Quarterly  \n",
              "4    20230320      Quarterly  \n",
              "..        ...            ...  \n",
              "995  20240122        Monthly  \n",
              "996  20240122        Monthly  \n",
              "997  20240122        Monthly  \n",
              "998  20240122        Monthly  \n",
              "999  20240122        Monthly  \n",
              "\n",
              "[1000 rows x 8 columns]"
            ],
            "text/html": [
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>etf_tck_cd</th>\n",
              "      <th>ediv_dt</th>\n",
              "      <th>ddn_amt</th>\n",
              "      <th>aed_stkp_ddn_amt</th>\n",
              "      <th>ddn_bse_dt</th>\n",
              "      <th>ddn_pym_dt</th>\n",
              "      <th>pba_dt</th>\n",
              "      <th>ddn_pym_fcy_cd</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>AAPB</td>\n",
              "      <td>20231227</td>\n",
              "      <td>4.30641</td>\n",
              "      <td>4.30641</td>\n",
              "      <td>20231228</td>\n",
              "      <td>20231229</td>\n",
              "      <td>20231226</td>\n",
              "      <td>Monthly</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>AAPU</td>\n",
              "      <td>20220920</td>\n",
              "      <td>0.01041</td>\n",
              "      <td>0.01041</td>\n",
              "      <td>20220921</td>\n",
              "      <td>20220927</td>\n",
              "      <td>20220919</td>\n",
              "      <td>Quarterly</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>AAPU</td>\n",
              "      <td>20230919</td>\n",
              "      <td>0.14642</td>\n",
              "      <td>0.14642</td>\n",
              "      <td>20230920</td>\n",
              "      <td>20230926</td>\n",
              "      <td>20230918</td>\n",
              "      <td>Quarterly</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>AAPU</td>\n",
              "      <td>20231221</td>\n",
              "      <td>0.27249</td>\n",
              "      <td>0.27249</td>\n",
              "      <td>20231222</td>\n",
              "      <td>20231229</td>\n",
              "      <td>20231220</td>\n",
              "      <td>Quarterly</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>AAPU</td>\n",
              "      <td>20230321</td>\n",
              "      <td>0.07802</td>\n",
              "      <td>0.07802</td>\n",
              "      <td>20230322</td>\n",
              "      <td>20230328</td>\n",
              "      <td>20230320</td>\n",
              "      <td>Quarterly</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>995</th>\n",
              "      <td>YMAX</td>\n",
              "      <td>20240717</td>\n",
              "      <td>0.65160</td>\n",
              "      <td>0.65160</td>\n",
              "      <td>20240717</td>\n",
              "      <td>20240718</td>\n",
              "      <td>20240122</td>\n",
              "      <td>Monthly</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>996</th>\n",
              "      <td>YMAX</td>\n",
              "      <td>20240614</td>\n",
              "      <td>0.73000</td>\n",
              "      <td>0.73000</td>\n",
              "      <td>20240614</td>\n",
              "      <td>20240617</td>\n",
              "      <td>20240122</td>\n",
              "      <td>Monthly</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>997</th>\n",
              "      <td>YMAX</td>\n",
              "      <td>20240215</td>\n",
              "      <td>0.53390</td>\n",
              "      <td>0.53390</td>\n",
              "      <td>20240216</td>\n",
              "      <td>20240220</td>\n",
              "      <td>20240122</td>\n",
              "      <td>Monthly</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>998</th>\n",
              "      <td>YMAX</td>\n",
              "      <td>20240314</td>\n",
              "      <td>0.56800</td>\n",
              "      <td>0.56800</td>\n",
              "      <td>20240315</td>\n",
              "      <td>20240318</td>\n",
              "      <td>20240122</td>\n",
              "      <td>Monthly</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>999</th>\n",
              "      <td>YMAX</td>\n",
              "      <td>20240515</td>\n",
              "      <td>0.73170</td>\n",
              "      <td>0.73170</td>\n",
              "      <td>20240516</td>\n",
              "      <td>20240517</td>\n",
              "      <td>20240122</td>\n",
              "      <td>Monthly</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1000 rows × 8 columns</p>\n",
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              "type": "dataframe",
              "variable_name": "filtered_historical_dividend_with_ETF_top",
              "summary": "{\n  \"name\": \"filtered_historical_dividend_with_ETF_top\",\n  \"rows\": 1000,\n  \"fields\": [\n    {\n      \"column\": \"etf_tck_cd\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 113,\n        \"samples\": [\n          \"FAS\",\n          \"CONL\",\n          \"SKYY\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ediv_dt\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6793,\n        \"min\": 20220819,\n        \"max\": 20240821,\n        \"num_unique_values\": 234,\n        \"samples\": [\n          20231103,\n          20230302,\n          20240522\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ddn_amt\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.3863797953398092,\n        \"min\": 0.00011,\n        \"max\": 40.0223,\n        \"num_unique_values\": 925,\n        \"samples\": [\n          0.15087,\n          0.17773,\n          0.17\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"aed_stkp_ddn_amt\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5410979525499361,\n        \"min\": 0.00011,\n        \"max\": 8.35526,\n        \"num_unique_values\": 925,\n        \"samples\": [\n          0.15087,\n          0.17773,\n          0.17\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ddn_bse_dt\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 234,\n        \"samples\": [\n          \"20231106\",\n          \"20230303\",\n          \"20240523\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ddn_pym_dt\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6773,\n        \"min\": 20220830,\n        \"max\": 20240826,\n        \"num_unique_values\": 243,\n        \"samples\": [\n          20230830,\n          20221228,\n          20240731\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pba_dt\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 191,\n        \"samples\": [\n          \"20221123\",\n          \"20240322\",\n          \"20221003\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ddn_pym_fcy_cd\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Quarterly\",\n          \"Other\",\n          \"SemiAnnual\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 49
        }
      ],
      "source": [
        "# 내부 조인 수행\n",
        "historical_dividend_with_ETF_top = pd.merge(historical_dividend, ETF_top, on='etf_tck_cd', how='inner')\n",
        "\n",
        "required_columns = [\n",
        "    'etf_tck_cd',\n",
        "    'ediv_dt',\n",
        "    'ddn_amt',\n",
        "    'aed_stkp_ddn_amt',\n",
        "    'ddn_bse_dt',\n",
        "    'ddn_pym_dt',\n",
        "    'pba_dt',\n",
        "    'ddn_pym_fcy_cd'\n",
        "]\n",
        "filtered_historical_dividend_with_ETF_top = historical_dividend_with_ETF_top[required_columns]\n",
        "\n",
        "filtered_historical_dividend_with_ETF_top"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Izv8aVdLaJaM"
      },
      "source": [
        "배당금 제공하는 ETF들의 배당금 데이터 가져오기."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UphJAed9aJaM",
        "outputId": "9be3f03f-8317-4cc0-cf16-b0433c853485"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "       ETF                      Date  Dividends\n",
            "0     AAPB 2023-12-27 00:00:00-05:00      4.306\n",
            "1     AAPU 2022-09-20 00:00:00-04:00      0.010\n",
            "2     AAPU 2022-12-20 00:00:00-05:00      0.121\n",
            "3     AAPU 2023-03-21 00:00:00-04:00      0.078\n",
            "4     AAPU 2023-06-21 00:00:00-04:00      0.140\n",
            "...    ...                       ...        ...\n",
            "2041  YMAX 2024-07-17 00:00:00-04:00      0.652\n",
            "2042  YMAX 2024-08-15 00:00:00-04:00      0.591\n",
            "2043  YMAX 2024-09-19 00:00:00-04:00      0.227\n",
            "2044  YMAX 2024-09-26 00:00:00-04:00      0.222\n",
            "2045  YMAX 2024-10-03 00:00:00-04:00      0.208\n",
            "\n",
            "[2046 rows x 3 columns]\n"
          ]
        }
      ],
      "source": [
        "unique_etf_tickers_df = filtered_historical_dividend_with_ETF_top[['etf_tck_cd']].drop_duplicates()\n",
        "unique_etf_tickers_list = unique_etf_tickers_df['etf_tck_cd'].tolist()\n",
        "\n",
        "# 배당금 지급 이력을 저장할 데이터프레임 리스트\n",
        "dividend_records = []\n",
        "\n",
        "# 시작 날짜\n",
        "start_date = '2020-01-30'\n",
        "\n",
        "# 각 ETF에 대해 배당금 데이터 가져오기\n",
        "for etf in unique_etf_tickers_list:\n",
        "    ticker = yf.Ticker(etf)\n",
        "    dividends = ticker.dividends\n",
        "    dividends.index = pd.to_datetime(dividends.index)\n",
        "    # 2020년 1월 30일 이후의 데이터 필터링\n",
        "    filtered_dividends = dividends[dividends.index >= start_date]\n",
        "\n",
        "    if not filtered_dividends.empty:\n",
        "        filtered_dividends = filtered_dividends.reset_index()\n",
        "        filtered_dividends['ETF'] = etf  # ETF 이름 추가\n",
        "        dividend_records.append(filtered_dividends)\n",
        "\n",
        "# 결과 데이터프레임 생성\n",
        "result_df1 = pd.concat(dividend_records, ignore_index=True) if dividend_records else pd.DataFrame()\n",
        "\n",
        "# 결과 출력\n",
        "if not result_df1.empty:\n",
        "    print(result_df1[['ETF', 'Date', 'Dividends']])\n",
        "else:\n",
        "    print(\"No dividend data found for the specified date range.\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kh1DBIxBaJaN"
      },
      "source": [
        "월배당, 분기배당, 반기배당 모두 연배당으로 환산"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R64vOm6uaJaN",
        "outputId": "7e1b4263-381d-415e-863a-37f928584096"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "      ETF  Total Dividend  Average Annual Dividend\n",
            "0    AAPB           4.306                 4.306000\n",
            "1    AAPU           1.331                 0.443667\n",
            "2    AWAY           0.071                 0.035500\n",
            "3    CIBR           1.250                 0.250000\n",
            "4    CONL           0.109                 0.109000\n",
            "..    ...             ...                      ...\n",
            "108   VWO           5.649                 1.129800\n",
            "109   VYM          15.264                 3.052800\n",
            "110  WEBL           3.051                 3.051000\n",
            "111  YINN           5.894                 1.178800\n",
            "112  YMAX           5.096                 5.096000\n",
            "\n",
            "[113 rows x 3 columns]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
            "<ipython-input-51-a0e098075f3c>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n"
          ]
        }
      ],
      "source": [
        "# 배당금 지급 이력을 저장할 데이터프레임 리스트\n",
        "dividend_records = []\n",
        "\n",
        "# 각 ETF에 대해 배당금 데이터 가져오기\n",
        "for etf in unique_etf_tickers_list:\n",
        "    ticker = yf.Ticker(etf)\n",
        "    dividends = ticker.dividends\n",
        "    dividends.index = pd.to_datetime(dividends.index)\n",
        "    # 2020년 1월 30일 이후의 데이터 필터링\n",
        "    filtered_dividends = dividends[dividends.index >= '2020-01-30']\n",
        "\n",
        "    if not filtered_dividends.empty:\n",
        "        filtered_dividends = filtered_dividends.reset_index()\n",
        "        filtered_dividends['ETF'] = etf  # ETF 이름 추가\n",
        "        dividend_records.append(filtered_dividends)\n",
        "\n",
        "# 결과 데이터프레임 생성\n",
        "result_df2 = pd.concat(dividend_records, ignore_index=True) if dividend_records else pd.DataFrame()\n",
        "\n",
        "# 배당금 계산\n",
        "if not result_df2.empty:\n",
        "    # 각 ETF의 연도별 배당금 합산\n",
        "    annual_dividend_results = []\n",
        "    for etf in result_df2['ETF'].unique():\n",
        "        etf_data = result_df2[result_df2['ETF'] == etf]\n",
        "\n",
        "        # 연도별로 그룹화하여 배당금 합산\n",
        "        etf_data['Year'] = etf_data['Date'].dt.year  # 연도 열 추가\n",
        "        annual_totals = etf_data.groupby('Year')['Dividends'].sum().reset_index()\n",
        "\n",
        "        # 총 배당금 계산 (모든 연도의 합산)\n",
        "        total_dividend = annual_totals['Dividends'].sum()\n",
        "\n",
        "        # 연간 배당금 평균 계산\n",
        "        average_annual_dividend = total_dividend / len(annual_totals)\n",
        "\n",
        "        annual_dividend_results.append({\n",
        "            'ETF': etf,\n",
        "            'Total Dividend': total_dividend,\n",
        "            'Average Annual Dividend': average_annual_dividend\n",
        "        })\n",
        "\n",
        "    # 결과 데이터프레임 생성\n",
        "    result_df2 = pd.DataFrame(annual_dividend_results)\n",
        "\n",
        "    # 결과 출력\n",
        "    print(result_df2)\n",
        "else:\n",
        "    print(\"No dividend data found for the specified date range.\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VK1xjvlpaJaN"
      },
      "source": [
        "ETF 최근 종가 데이터. (평균 배당금과 나눠서 시가 배당률 계산하려고)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WusuiyexaJaO",
        "outputId": "2306dcc8-5061-4624-aaac-a02eb2221d5d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "yfinance.Ticker object <AAPB>\n",
            "yfinance.Ticker object <AAPU>\n",
            "yfinance.Ticker object <AWAY>\n",
            "yfinance.Ticker object <CIBR>\n",
            "yfinance.Ticker object <CONL>\n",
            "yfinance.Ticker object <CWEB>\n",
            "yfinance.Ticker object <DGRW>\n",
            "yfinance.Ticker object <DIA>\n",
            "yfinance.Ticker object <DIV>\n",
            "yfinance.Ticker object <DPST>\n",
            "yfinance.Ticker object <DRIV>\n",
            "yfinance.Ticker object <GDX>\n",
            "yfinance.Ticker object <GGLL>\n",
            "yfinance.Ticker object <GPIX>\n",
            "yfinance.Ticker object <GRID>\n",
            "yfinance.Ticker object <IBB>\n",
            "yfinance.Ticker object <ICLN>\n",
            "yfinance.Ticker object <IEMG>\n",
            "yfinance.Ticker object <IHI>\n",
            "yfinance.Ticker object <IVV>\n",
            "yfinance.Ticker object <IWM>\n",
            "yfinance.Ticker object <JEPI>\n",
            "yfinance.Ticker object <JEPQ>\n",
            "yfinance.Ticker object <KBWY>\n",
            "yfinance.Ticker object <KLIP>\n",
            "yfinance.Ticker object <KRBN>\n",
            "yfinance.Ticker object <LABU>\n",
            "yfinance.Ticker object <MOAT>\n",
            "yfinance.Ticker object <MSFU>\n",
            "yfinance.Ticker object <NOBL>\n",
            "yfinance.Ticker object <NUSI>\n",
            "yfinance.Ticker object <NVD>\n",
            "yfinance.Ticker object <NVDL>\n",
            "yfinance.Ticker object <NVDU>\n",
            "yfinance.Ticker object <PAVE>\n",
            "yfinance.Ticker object <PFF>\n",
            "yfinance.Ticker object <QCLN>\n",
            "yfinance.Ticker object <RPAR>\n",
            "yfinance.Ticker object <RYLD>\n",
            "yfinance.Ticker object <SDIV>\n",
            "yfinance.Ticker object <SKYY>\n",
            "yfinance.Ticker object <SOXL>\n",
            "yfinance.Ticker object <SOXQ>\n",
            "yfinance.Ticker object <SOXX>\n",
            "yfinance.Ticker object <SPY>\n",
            "yfinance.Ticker object <SPYD>\n",
            "yfinance.Ticker object <SVOL>\n",
            "yfinance.Ticker object <TAN>\n",
            "yfinance.Ticker object <TECL>\n",
            "yfinance.Ticker object <TLTW>\n",
            "yfinance.Ticker object <TNA>\n",
            "yfinance.Ticker object <TQQQ>\n",
            "yfinance.Ticker object <TSL>\n",
            "yfinance.Ticker object <TSLL>\n",
            "yfinance.Ticker object <UDOW>\n",
            "yfinance.Ticker object <UPRO>\n",
            "yfinance.Ticker object <URA>\n",
            "yfinance.Ticker object <USD>\n",
            "yfinance.Ticker object <VEA>\n",
            "yfinance.Ticker object <VNQ>\n",
            "yfinance.Ticker object <VT>\n",
            "yfinance.Ticker object <VTI>\n",
            "yfinance.Ticker object <XBI>\n",
            "yfinance.Ticker object <XLE>\n",
            "yfinance.Ticker object <XLF>\n",
            "yfinance.Ticker object <XLK>\n",
            "yfinance.Ticker object <XLP>\n",
            "yfinance.Ticker object <XLRE>\n",
            "yfinance.Ticker object <XLU>\n",
            "yfinance.Ticker object <XLV>\n",
            "yfinance.Ticker object <XYLD>\n",
            "yfinance.Ticker object <AIQ>\n",
            "yfinance.Ticker object <AMZU>\n",
            "yfinance.Ticker object <AOR>\n",
            "yfinance.Ticker object <ARKK>\n",
            "yfinance.Ticker object <BOTZ>\n",
            "yfinance.Ticker object <COPX>\n",
            "yfinance.Ticker object <DFEN>\n",
            "yfinance.Ticker object <DGRO>\n",
            "yfinance.Ticker object <DIVO>\n",
            "yfinance.Ticker object <FAS>\n",
            "yfinance.Ticker object <FBL>\n",
            "yfinance.Ticker object <GPIQ>\n",
            "yfinance.Ticker object <HDRO>\n",
            "yfinance.Ticker object <ITA>\n",
            "yfinance.Ticker object <KORU>\n",
            "yfinance.Ticker object <LIT>\n",
            "yfinance.Ticker object <METV>\n",
            "yfinance.Ticker object <MGK>\n",
            "yfinance.Ticker object <NAIL>\n",
            "yfinance.Ticker object <PGX>\n",
            "yfinance.Ticker object <QLD>\n",
            "yfinance.Ticker object <QQQ>\n",
            "yfinance.Ticker object <QQQM>\n",
            "yfinance.Ticker object <QYLD>\n",
            "yfinance.Ticker object <RETL>\n",
            "yfinance.Ticker object <SCHD>\n",
            "yfinance.Ticker object <SMH>\n",
            "yfinance.Ticker object <SPHD>\n",
            "yfinance.Ticker object <SPLG>\n",
            "yfinance.Ticker object <SPXL>\n",
            "yfinance.Ticker object <SPYG>\n",
            "yfinance.Ticker object <SPYV>\n",
            "yfinance.Ticker object <SSO>\n",
            "yfinance.Ticker object <TMF>\n",
            "yfinance.Ticker object <URTY>\n",
            "yfinance.Ticker object <VIG>\n",
            "yfinance.Ticker object <VOO>\n",
            "yfinance.Ticker object <VWO>\n",
            "yfinance.Ticker object <VYM>\n",
            "yfinance.Ticker object <WEBL>\n",
            "yfinance.Ticker object <YINN>\n",
            "yfinance.Ticker object <YMAX>\n",
            "    etf_tck_cd  recent_close\n",
            "0         AAPB     28.190001\n",
            "1         AAPU     35.700001\n",
            "2         AWAY     20.200001\n",
            "3         CIBR     60.580002\n",
            "4         CONL     21.830000\n",
            "..         ...           ...\n",
            "108        VWO     47.520000\n",
            "109        VYM    128.380005\n",
            "110       WEBL     19.374399\n",
            "111       YINN     38.959999\n",
            "112       YMAX     17.555000\n",
            "\n",
            "[113 rows x 2 columns]\n"
          ]
        }
      ],
      "source": [
        "# 종가 데이터를 저장할 데이터 프레임 생성\n",
        "closing_prices = {}\n",
        "\n",
        "# 각 ETF의 최근 종가를 가져오기\n",
        "for ticker in unique_etf_tickers_list:\n",
        "    stock_data = yf.Ticker(ticker)\n",
        "    print(stock_data)\n",
        "    # 최근 종가를 가져온 후 데이터 사전에 저장\n",
        "    closing_prices[ticker] = stock_data.history(period='5d')['Close'].iloc[-1]\n",
        "\n",
        "# 결과를 데이터 프레임으로 변환\n",
        "closing_prices_df = pd.DataFrame(list(closing_prices.items()), columns=['etf_tck_cd','recent_close'])\n",
        "\n",
        "\n",
        "# 결과 출력\n",
        "print(closing_prices_df)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pB4On9xTaJaO"
      },
      "source": [
        "마지막! 시가배당률 계산하기 = (평균 배당금 / 최근 종가) * 100"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k2cxc-OsaJaO",
        "outputId": "bda96f87-1498-41d1-ad94-c47b4ad81706"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "      ETF  Average Annual Dividend  recent_close  Dividend per Price\n",
            "0    AAPB                 4.306000     28.190001           15.274920\n",
            "1    AAPU                 0.443667     35.700001            1.242764\n",
            "2    AWAY                 0.035500     20.200001            0.175743\n",
            "3    CIBR                 0.250000     60.580002            0.412677\n",
            "4    CONL                 0.109000     21.830000            0.499313\n",
            "..    ...                      ...           ...                 ...\n",
            "108   VWO                 1.129800     47.520000            2.377525\n",
            "109   VYM                 3.052800    128.380005            2.377940\n",
            "110  WEBL                 3.051000     19.374399           15.747585\n",
            "111  YINN                 1.178800     38.959999            3.025667\n",
            "112  YMAX                 5.096000     17.555000           29.028766\n",
            "\n",
            "[113 rows x 4 columns]\n"
          ]
        }
      ],
      "source": [
        "# result_df2와 closing_prices_df를 ETF 코드로 병합\n",
        "merged_df = pd.merge(result_df2, closing_prices_df, left_on='ETF', right_on='etf_tck_cd', how='inner')\n",
        "\n",
        "# Average Annual Dividend를 recent_close로 나누기\n",
        "merged_df['Dividend per Price'] = merged_df['Average Annual Dividend'] / merged_df['recent_close'] * 100\n",
        "\n",
        "# 결과 출력\n",
        "print(merged_df[['ETF', 'Average Annual Dividend', 'recent_close', 'Dividend per Price']])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OkjF6y0naJaO"
      },
      "source": [
        "-----------------------------------------------------------------------"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jdWygoh_NJh5"
      },
      "source": [
        "### 최성현"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6ldHKNmuaJaZ"
      },
      "source": [
        "### 크롤링"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Tl_XQuFXNJh5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 384
        },
        "outputId": "c8fd3af5-9174-409c-9681-929a1017bf84"
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "ModuleNotFoundError",
          "evalue": "No module named 'selenium'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-82-8ac8512a24c1>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mcsv\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mselenium\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mwebdriver\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mselenium\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwebdriver\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcommon\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mby\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mBy\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mselenium\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwebdriver\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0medge\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mservice\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mService\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mEdgeService\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mdatetime\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdatetime\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'selenium'",
            "",
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0;32m\nNOTE: If your import is failing due to a missing package, you can\nmanually install dependencies using either !pip or !apt.\n\nTo view examples of installing some common dependencies, click the\n\"Open Examples\" button below.\n\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n"
          ],
          "errorDetails": {
            "actions": [
              {
                "action": "open_url",
                "actionText": "Open Examples",
                "url": "/notebooks/snippets/importing_libraries.ipynb"
              }
            ]
          }
        }
      ],
      "source": [
        "import csv\n",
        "from selenium import webdriver\n",
        "from selenium.webdriver.common.by import By\n",
        "from selenium.webdriver.edge.service import Service as EdgeService\n",
        "from datetime import datetime\n",
        "from selenium import webdriver\n",
        "from tqdm import tqdm\n",
        "from selenium.webdriver.support.ui import WebDriverWait\n",
        "from selenium.webdriver.support import expected_conditions as EC\n",
        "from selenium.webdriver.edge.options import Options\n",
        "from webdriver_manager.microsoft import EdgeChromiumDriverManager\n",
        "import os"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9N_A2BgQaJaa",
        "outputId": "bc367484-c198-4f6c-e630-c39d635957b8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['AAPB', 'AMDL', 'CLOU', 'DFEN', 'DPST', 'GDX', 'GPIQ', 'IWM', 'LIT', 'MOAT', 'NUSI', 'PAVE', 'QYLD', 'SPY', 'TSLL', 'URTY', 'XBI', 'AAPU', 'AMZU', 'IEMG', 'KLIP', 'KORU', 'MGK', 'NVDU', 'QCLN', 'SCHD', 'SOXQ', 'VNQ', 'XLE', 'ARKW', 'DGRO', 'DRIV', 'GGLL', 'ICLN', 'ITA', 'JETS', 'KBWY', 'NAIL', 'QLD', 'QQQM', 'RETL', 'SPYD', 'SSO', 'TNA', 'URA', 'XLP', 'XLRE', 'YMAX', 'AWAY', 'CWEB', 'DIA', 'FBL', 'JEPI', 'PFF', 'SKYY', 'TLTW', 'USD', 'VT', 'VWO', 'XLK', 'XLV', 'YINN', 'ARKG', 'ARKK', 'ARKX', 'CIBR', 'JEPQ', 'KRBN', 'MSFU', 'QQQ', 'SPHD', 'SPXL', 'TQQQ', 'VEA', 'VOO', 'BOTZ', 'DIV', 'IBB', 'METV', 'NOBL', 'SMH', 'SPYG', 'SPYV', 'UDOW', 'VTI', 'ARKF', 'CONL', 'DGRW', 'DIVO', 'HDRO', 'NVDL', 'SDIV', 'SOXL', 'SOXX', 'SPLG', 'SVOL', 'TECL', 'VYM', 'XLU', 'XYLD', 'AOR', 'ARKQ', 'GRID', 'RPAR', 'UPRO', 'VIG', 'WEBL', 'GPIX', 'IVV', 'MSOX', 'PGX', 'TSL', 'XLF', 'AIQ', 'COPX', 'FAS', 'IHI', 'LABU', 'NVD', 'RYLD', 'TAN', 'TMF']\n",
            "122\n"
          ]
        }
      ],
      "source": [
        "# 모든 etf 티커명 추출\n",
        "tickers = etf_holdings['etf_tck_cd'].unique()\n",
        "\n",
        "# 각 요소에서 공백을 제거\n",
        "tickers = [ticker.strip() for ticker in tickers]\n",
        "\n",
        "# 공백을 제거한 후 결과 출력\n",
        "print(tickers)\n",
        "\n",
        "# 리스트의 요소 개수 출력\n",
        "print(len(tickers))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 211
        },
        "id": "Qu25mDATaJab",
        "outputId": "5efb5cbc-c6df-4e94-e7fc-e0221344bfdb"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Tickers Processed: 0it [00:00, ?it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Done!\n"
          ]
        }
      ],
      "source": [
        "# 엣지 옵션 설정\n",
        "edge_options = Options()\n",
        "edge_options.add_experimental_option('excludeSwitches', ['enable-logging', 'enable-automation'])\n",
        "edge_options.add_experimental_option(\"useAutomationExtension\", False)\n",
        "edge_options.add_argument('--disable-blink-features=AutomationControlled')\n",
        "\n",
        "user_agent = \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/129.0.0.0 Safari/537.36 Edg/129.0.0.0\"\n",
        "edge_options.add_argument('user-agent=' + user_agent)\n",
        "\n",
        "# 엣지 웹드라이버 설정\n",
        "driver = webdriver.Edge(service=EdgeService(EdgeChromiumDriverManager().install()), options=edge_options)\n",
        "\n",
        "# 결과를 저장할 CSV 파일 경로 설정\n",
        "output_file = \"news_results.csv\"\n",
        "\n",
        "# 기존에 저장된 결과 파일을 불러오기\n",
        "def get_existing_tickers():\n",
        "    if os.path.exists(output_file):\n",
        "        df = pd.read_csv(output_file, encoding='utf-8')\n",
        "        return df['Ticker'].unique()  # 이미 저장된 티커 목록 반환\n",
        "    return []\n",
        "\n",
        "# CSV 파일에서 ticker에 해당하는 데이터 삭제\n",
        "def delete_ticker_data(ticker):\n",
        "    if os.path.exists(output_file):\n",
        "        df = pd.read_csv(output_file, encoding='utf-8')\n",
        "        df = df[df['Ticker'] != ticker]  # 해당 티커를 제외한 데이터\n",
        "        df.to_csv(output_file, index=False)  # CSV 파일 덮어쓰기\n",
        "        print(f\"{ticker}에 해당하는 데이터가 CSV에서 삭제되었습니다.\")\n",
        "\n",
        "# 뉴스 크롤링 함수\n",
        "def crawl_news_for_ticker(ticker):\n",
        "    url = f\"https://finance.yahoo.com/quote/{ticker}/news/\"\n",
        "    print(f\"Loading news for {ticker} from {url}\")\n",
        "    try:\n",
        "        driver.get(url)\n",
        "    except Exception as e:\n",
        "        print(f\"Error loading URL for {ticker}: {e}\")\n",
        "        return False\n",
        "\n",
        "    # 페이지 로딩이 완료될 때까지 대기\n",
        "    wait = WebDriverWait(driver, 30)\n",
        "\n",
        "    try:\n",
        "        # 뉴스 기사 목록이 로드될 때까지 대기\n",
        "        wait.until(EC.presence_of_element_located((By.XPATH, '//li[@class=\"stream-item story-item yf-1usaaz9\"]')))\n",
        "        print(f\"News loaded for {ticker}\")\n",
        "    except Exception as e:\n",
        "        print(f\"Error loading news for {ticker}: {e}\")\n",
        "        return False\n",
        "\n",
        "    # 뉴스 항목 추출\n",
        "    try:\n",
        "        news_items = driver.find_elements(By.XPATH, '//li[@class=\"stream-item story-item yf-1usaaz9\"]')\n",
        "        print(f\"Ticker: {ticker} - 뉴스 제목 및 내용:\")\n",
        "\n",
        "        for item in news_items:\n",
        "            # 뉴스 제목 추출\n",
        "            title_element = item.find_element(By.XPATH, './/h3[@class=\"clamp  yf-1sxfjua\"]')\n",
        "            title = title_element.text\n",
        "            print(f\"\\n- 제목: {title}\")\n",
        "\n",
        "            # 뉴스 기사 링크 추출\n",
        "            link_element = item.find_element(By.XPATH, './/div[@class=\"content yf-1sxfjua\"]//a[@class=\"subtle-link fin-size-small titles noUnderline yf-1e4diqp\"]')\n",
        "            news_link = link_element.get_attribute('href')\n",
        "            print(f\"링크: {news_link}\")\n",
        "\n",
        "            # 뉴스 기사 본문 및 작성일 크롤링\n",
        "            driver.execute_script(f\"window.open('{news_link}', '_blank');\")\n",
        "            driver.switch_to.window(driver.window_handles[1])\n",
        "\n",
        "            # 새 페이지가 로드될 때까지 대기\n",
        "            wait.until(EC.presence_of_element_located((By.XPATH, '//div[@class=\"body yf-5ef8bf\"]')))\n",
        "            content = driver.find_element(By.XPATH, '//div[@class=\"body yf-5ef8bf\"]').text\n",
        "            print(f\"내용: {content[:500]}...\")\n",
        "\n",
        "            # 작성일 크롤링 및 datetime 형식 변환\n",
        "            try:\n",
        "                date_element = wait.until(EC.presence_of_element_located((By.XPATH, '//time[@class=\"byline-attr-meta-time\"]')))\n",
        "                date_raw = date_element.get_attribute('datetime')  # 원본 날짜 값 가져오기\n",
        "                date = datetime.strptime(date_raw, '%Y-%m-%dT%H:%M:%S.%fZ').strftime('%Y-%m-%d')  # datetime 형식 변환\n",
        "                print(f\"작성일: {date}\")\n",
        "            except Exception as e:\n",
        "                date = \"작성일을 가져오지 못했습니다.\"\n",
        "                print(f\"작성일 오류: {e}\")\n",
        "\n",
        "            # 결과를 CSV 파일에 저장\n",
        "            with open(output_file, mode='a', newline='', encoding='utf-8') as file:\n",
        "                writer = csv.writer(file)\n",
        "                writer.writerow([ticker, date, title, news_link, content])\n",
        "\n",
        "            driver.close()\n",
        "            driver.switch_to.window(driver.window_handles[0])  # 원래 탭으로 돌아옴\n",
        "\n",
        "    except Exception as e:\n",
        "        print(f\"Error retrieving news for {ticker}: {e}\")\n",
        "        return False\n",
        "\n",
        "    return True\n",
        "\n",
        "# 이미 크롤링된 티커 불러오기\n",
        "existing_tickers = get_existing_tickers()\n",
        "\n",
        "# 크롤링할 티커 목록에서 이미 저장된 티커는 제외\n",
        "remaining_tickers = [ticker for ticker in tickers if ticker not in existing_tickers]\n",
        "\n",
        "# 각 티커에 대해 뉴스 제목 및 내용 크롤링\n",
        "for ticker in tqdm(remaining_tickers, desc=\"Tickers Processed\"):\n",
        "    success = crawl_news_for_ticker(ticker)\n",
        "    if not success:\n",
        "        print(f\"Error occurred for ticker {ticker}, stopping and deleting its data.\")\n",
        "        delete_ticker_data(ticker)\n",
        "        break\n",
        "print(\"Done!\")\n",
        "\n",
        "\n",
        "# 브라우저 종료\n",
        "driver.quit()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DhaXfu3_NJh6"
      },
      "source": [
        "### 박찬우"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "2jLtU0wINJh6"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import MinMaxScaler"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "etf_holdings_result"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "dUnKnkxgAegk",
        "outputId": "b9a20c72-721b-409c-ced7-8323243c7a79"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    etf_tck_cd      mkt_vlu\n",
              "0         AAPB     55196050\n",
              "1         AAPU    333423423\n",
              "2          AIQ   1969949877\n",
              "3         AMDL      9788048\n",
              "4         AMZU    465277883\n",
              "..         ...          ...\n",
              "117        XLU  15432230720\n",
              "118        XLV  39691826082\n",
              "119       XYLD   2813629261\n",
              "120       YINN   2795877317\n",
              "121       YMAX     48564080\n",
              "\n",
              "[122 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-94c7c880-ef08-4a1c-8e20-bcc5e9f45d94\" 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",
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>etf_tck_cd</th>\n",
              "      <th>mkt_vlu</th>\n",
              "    </tr>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>AAPB</td>\n",
              "      <td>55196050</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>AAPU</td>\n",
              "      <td>333423423</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>AIQ</td>\n",
              "      <td>1969949877</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>AMDL</td>\n",
              "      <td>9788048</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>AMZU</td>\n",
              "      <td>465277883</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>117</th>\n",
              "      <td>XLU</td>\n",
              "      <td>15432230720</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>118</th>\n",
              "      <td>XLV</td>\n",
              "      <td>39691826082</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>119</th>\n",
              "      <td>XYLD</td>\n",
              "      <td>2813629261</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>120</th>\n",
              "      <td>YINN</td>\n",
              "      <td>2795877317</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>121</th>\n",
              "      <td>YMAX</td>\n",
              "      <td>48564080</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>122 rows × 2 columns</p>\n",
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              "        const element = document.querySelector('#df-94c7c880-ef08-4a1c-8e20-bcc5e9f45d94');\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",
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              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "  }\n",
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              "    border: 2px solid var(--fill-color);\n",
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              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
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              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "</div>\n",
              "\n",
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              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
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              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
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              "\n",
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              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('etf_holdings_result')\"\n",
              "            title=\"Generate code using this dataframe.\"\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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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('etf_holdings_result');\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "etf_holdings_result",
              "summary": "{\n  \"name\": \"etf_holdings_result\",\n  \"rows\": 122,\n  \"fields\": [\n    {\n      \"column\": \"etf_tck_cd\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 122,\n        \"samples\": [\n          \"CWEB\",\n          \"KBWY\",\n          \"KORU\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mkt_vlu\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 191968371173,\n        \"min\": -67319541,\n        \"max\": 1632609396735,\n        \"num_unique_values\": 122,\n        \"samples\": [\n          695410930,\n          217435734,\n          136872435\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "result_df = merged_df\n",
        "# etf_count_result를 'tck_iem_cd'와 'etf_tck_cd'를 기준으로 병합\n",
        "result_df = result_df.rename(columns={'ETF':'tck_iem_cd'})\n",
        "result_df = result_df.merge(etf_count_result, left_on='tck_iem_cd', right_on='etf_tck_cd', how='inner')\n",
        "\n",
        "result_df = result_df.merge(sum_result_avg, on='tck_iem_cd', how='inner')\n",
        "\n",
        "result_df = result_df.merge(stk_dt_qut, on='tck_iem_cd', how='inner')\n",
        "\n",
        "#etf_holdings_result rename\n",
        "ETF_top = ETF_top.rename(columns={'etf_tck_cd':'tck_iem_cd'})\n",
        "\n",
        "result_df = result_df.merge(ETF_top, on='tck_iem_cd', how='inner')\n",
        "unique_df = result_df.drop_duplicates(subset='tck_iem_cd')\n",
        "#unique_df[['tck_iem_cd','mkt_pr_tot_amt','Dividend per Price','total_gap','acl_trd_qty','tck_iem_cd_count']]\n",
        "unique_df=unique_df[['tck_iem_cd','Dividend per Price','total_gap','acl_trd_qty','tck_iem_cd_count','mkt_vlu']]"
      ],
      "metadata": {
        "id": "qN33VpSkaiH2"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 스케일러 객체 생성\n",
        "scaler = MinMaxScaler()\n",
        "\n",
        "# 스케일을 적용할 컬럼 선택\n",
        "columns_to_scale = ['Dividend per Price','total_gap','acl_trd_qty','tck_iem_cd_count','mkt_vlu']\n",
        "\n",
        "# 선택한 컬럼에 MinMaxScaler 적용\n",
        "unique_df[columns_to_scale] = scaler.fit_transform(unique_df[columns_to_scale])"
      ],
      "metadata": {
        "id": "78v4LEhShnM0"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "unique_df['score']=unique_df['Dividend per Price']-unique_df['total_gap']+unique_df['acl_trd_qty']+unique_df['tck_iem_cd_count']+unique_df['mkt_vlu']"
      ],
      "metadata": {
        "id": "asRbIiMZbWhA"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "unique_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "4uNkCgSJiDNL",
        "outputId": "0555ac28-dcbf-4670-ec93-9fa421db02ee"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     tck_iem_cd  Dividend per Price  total_gap  acl_trd_qty  tck_iem_cd_count  \\\n",
              "0          AAPB            0.072590   0.108606     0.003153          0.000000   \n",
              "63         AAPU            0.005782   0.145770     0.004543          0.000873   \n",
              "126        AWAY            0.000702   0.021661     0.000000          0.002838   \n",
              "188        CIBR            0.001830   0.081479     0.007699          0.003166   \n",
              "250        CONL            0.002242   0.576283     0.048329          0.000000   \n",
              "...         ...                 ...        ...          ...               ...   \n",
              "6791        VWO            0.011185   0.027549     0.100721          0.589564   \n",
              "6854        VYM            0.011187   0.109400     0.011534          0.058509   \n",
              "6917       WEBL            0.074840   0.065606     0.004056          0.004912   \n",
              "6980       YINN            0.014271   0.070535     0.045100          0.001092   \n",
              "7043       YMAX            0.138072   0.022656     0.006189          0.001856   \n",
              "\n",
              "       mkt_vlu     score  \n",
              "0     0.000075 -0.032789  \n",
              "63    0.000245 -0.134327  \n",
              "126   0.000074 -0.018047  \n",
              "188   0.004009 -0.064775  \n",
              "250   0.000856 -0.524855  \n",
              "...        ...       ...  \n",
              "6791  0.052047  0.725968  \n",
              "6854  0.040562  0.012391  \n",
              "6917  0.000282  0.018484  \n",
              "6980  0.001754 -0.008319  \n",
              "7043  0.000071  0.123532  \n",
              "\n",
              "[113 rows x 7 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-56582f47-d147-44fb-979d-8fdb0182c0f0\" class=\"colab-df-container\">\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tck_iem_cd</th>\n",
              "      <th>Dividend per Price</th>\n",
              "      <th>total_gap</th>\n",
              "      <th>acl_trd_qty</th>\n",
              "      <th>tck_iem_cd_count</th>\n",
              "      <th>mkt_vlu</th>\n",
              "      <th>score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>AAPB</td>\n",
              "      <td>0.072590</td>\n",
              "      <td>0.108606</td>\n",
              "      <td>0.003153</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000075</td>\n",
              "      <td>-0.032789</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>63</th>\n",
              "      <td>AAPU</td>\n",
              "      <td>0.005782</td>\n",
              "      <td>0.145770</td>\n",
              "      <td>0.004543</td>\n",
              "      <td>0.000873</td>\n",
              "      <td>0.000245</td>\n",
              "      <td>-0.134327</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>126</th>\n",
              "      <td>AWAY</td>\n",
              "      <td>0.000702</td>\n",
              "      <td>0.021661</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.002838</td>\n",
              "      <td>0.000074</td>\n",
              "      <td>-0.018047</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>188</th>\n",
              "      <td>CIBR</td>\n",
              "      <td>0.001830</td>\n",
              "      <td>0.081479</td>\n",
              "      <td>0.007699</td>\n",
              "      <td>0.003166</td>\n",
              "      <td>0.004009</td>\n",
              "      <td>-0.064775</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>250</th>\n",
              "      <td>CONL</td>\n",
              "      <td>0.002242</td>\n",
              "      <td>0.576283</td>\n",
              "      <td>0.048329</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000856</td>\n",
              "      <td>-0.524855</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6791</th>\n",
              "      <td>VWO</td>\n",
              "      <td>0.011185</td>\n",
              "      <td>0.027549</td>\n",
              "      <td>0.100721</td>\n",
              "      <td>0.589564</td>\n",
              "      <td>0.052047</td>\n",
              "      <td>0.725968</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6854</th>\n",
              "      <td>VYM</td>\n",
              "      <td>0.011187</td>\n",
              "      <td>0.109400</td>\n",
              "      <td>0.011534</td>\n",
              "      <td>0.058509</td>\n",
              "      <td>0.040562</td>\n",
              "      <td>0.012391</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6917</th>\n",
              "      <td>WEBL</td>\n",
              "      <td>0.074840</td>\n",
              "      <td>0.065606</td>\n",
              "      <td>0.004056</td>\n",
              "      <td>0.004912</td>\n",
              "      <td>0.000282</td>\n",
              "      <td>0.018484</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6980</th>\n",
              "      <td>YINN</td>\n",
              "      <td>0.014271</td>\n",
              "      <td>0.070535</td>\n",
              "      <td>0.045100</td>\n",
              "      <td>0.001092</td>\n",
              "      <td>0.001754</td>\n",
              "      <td>-0.008319</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7043</th>\n",
              "      <td>YMAX</td>\n",
              "      <td>0.138072</td>\n",
              "      <td>0.022656</td>\n",
              "      <td>0.006189</td>\n",
              "      <td>0.001856</td>\n",
              "      <td>0.000071</td>\n",
              "      <td>0.123532</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>113 rows × 7 columns</p>\n",
              "</div>\n",
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              "\n",
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              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('unique_df')\"\n",
              "            title=\"Generate code using this dataframe.\"\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=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_183c49ad-0e53-4104-82a3-6d954e902e3e button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('unique_df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "unique_df",
              "summary": "{\n  \"name\": \"unique_df\",\n  \"rows\": 113,\n  \"fields\": [\n    {\n      \"column\": \"tck_iem_cd\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 113,\n        \"samples\": [\n          \"FAS\",\n          \"CONL\",\n          \"SKYY\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Dividend per Price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.10249936492801487,\n        \"min\": 0.0,\n        \"max\": 0.9999999999999999,\n        \"num_unique_values\": 113,\n        \"samples\": [\n          0.0028625211004421115,\n          0.0022424520817986107,\n          0.0015694716290145028\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_gap\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.22690863185834168,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 113,\n        \"samples\": [\n          0.38030949753986704,\n          0.5762826645729209,\n          0.1519377606655268\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"acl_trd_qty\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.16889451175565962,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 113,\n        \"samples\": [\n          0.01824686668527687,\n          0.048329185632858984,\n          0.0011323747705895978\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tck_iem_cd_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.12342527102459969,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 82,\n        \"samples\": [\n          0.010151730160462832,\n          0.0,\n          0.0013099006658661718\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mkt_vlu\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.12192728448154863,\n        \"min\": 0.0,\n        \"max\": 0.9999999999999999,\n        \"num_unique_values\": 113,\n        \"samples\": [\n          0.0043798376363880015,\n          0.0008562279519662612,\n          0.0017337751826684825\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3425611971316865,\n        \"min\": -0.9817665973450769,\n        \"max\": 1.2543677682476244,\n        \"num_unique_values\": 113,\n        \"samples\": [\n          -0.3460876010119856,\n          -0.5248547989062969,\n          -0.14095263575392336\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from sklearn.cluster import KMeans\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "X = unique_df[['score']].values\n",
        "kmeans = KMeans(n_clusters=2, random_state=42)\n",
        "\n",
        "# 모델 학습\n",
        "kmeans.fit(X)\n",
        "\n",
        "# 클러스터 라벨 저장\n",
        "unique_df['cluster'] = kmeans.labels_"
      ],
      "metadata": {
        "id": "JeJlkBqqCdoV"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sse = []\n",
        "k_range = range(1, 11)  # k=1부터 k=10까지 시도\n",
        "\n",
        "for k in k_range:\n",
        "    kmeans = KMeans(n_clusters=k, random_state=42)\n",
        "    kmeans.fit(unique_df[['score']])\n",
        "    sse.append(kmeans.inertia_)\n",
        "\n",
        "# 엘보우 그래프 그리기\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.plot(k_range, sse, marker='o')\n",
        "plt.title('엘보우 방법을 통한 최적의 클러스터 수 결정')\n",
        "plt.xlabel('클러스터 수 (k)')\n",
        "plt.ylabel('SSE (Sum of Squared Errors)')\n",
        "plt.xticks(k_range)\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "piykdu8RtRxC",
        "outputId": "c5a1656c-bcfa-423b-f715-35c3cd39fb31"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 50648 (\\N{HANGUL SYLLABLE EL}) missing from current font.\n",
            "  fig.canvas.print_figure(bytes_io, **kw)\n",
            "/usr/local/lib/python3.10/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 48372 (\\N{HANGUL SYLLABLE BO}) missing from current font.\n",
            "  fig.canvas.print_figure(bytes_io, **kw)\n",
            "/usr/local/lib/python3.10/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 50864 (\\N{HANGUL SYLLABLE U}) missing from current font.\n",
            "  fig.canvas.print_figure(bytes_io, **kw)\n",
            "/usr/local/lib/python3.10/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 48169 (\\N{HANGUL SYLLABLE BANG}) missing from current font.\n",
            "  fig.canvas.print_figure(bytes_io, **kw)\n",
            "/usr/local/lib/python3.10/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 48277 (\\N{HANGUL SYLLABLE BEOB}) missing from current font.\n",
            "  fig.canvas.print_figure(bytes_io, **kw)\n",
            "/usr/local/lib/python3.10/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 51012 (\\N{HANGUL SYLLABLE EUL}) missing from current font.\n",
            "  fig.canvas.print_figure(bytes_io, **kw)\n",
            "/usr/local/lib/python3.10/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 53685 (\\N{HANGUL SYLLABLE TONG}) missing from current font.\n",
            "  fig.canvas.print_figure(bytes_io, **kw)\n",
            "/usr/local/lib/python3.10/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 54620 (\\N{HANGUL SYLLABLE HAN}) missing from current font.\n",
            "  fig.canvas.print_figure(bytes_io, **kw)\n",
            "/usr/local/lib/python3.10/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 52572 (\\N{HANGUL SYLLABLE COE}) missing from current font.\n",
            "  fig.canvas.print_figure(bytes_io, **kw)\n",
            "/usr/local/lib/python3.10/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 51201 (\\N{HANGUL SYLLABLE JEOG}) missing from current font.\n",
            "  fig.canvas.print_figure(bytes_io, **kw)\n",
            "/usr/local/lib/python3.10/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 51032 (\\N{HANGUL SYLLABLE YI}) missing from current font.\n",
            "  fig.canvas.print_figure(bytes_io, **kw)\n",
            "/usr/local/lib/python3.10/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 53364 (\\N{HANGUL SYLLABLE KEUL}) missing from current font.\n",
            "  fig.canvas.print_figure(bytes_io, **kw)\n",
            "/usr/local/lib/python3.10/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 47084 (\\N{HANGUL SYLLABLE REO}) missing from current font.\n",
            "  fig.canvas.print_figure(bytes_io, **kw)\n",
            "/usr/local/lib/python3.10/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 49828 (\\N{HANGUL SYLLABLE SEU}) missing from current font.\n",
            "  fig.canvas.print_figure(bytes_io, **kw)\n",
            "/usr/local/lib/python3.10/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 53552 (\\N{HANGUL SYLLABLE TEO}) missing from current font.\n",
            "  fig.canvas.print_figure(bytes_io, **kw)\n",
            "/usr/local/lib/python3.10/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 49688 (\\N{HANGUL SYLLABLE SU}) missing from current font.\n",
            "  fig.canvas.print_figure(bytes_io, **kw)\n",
            "/usr/local/lib/python3.10/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 44208 (\\N{HANGUL SYLLABLE GYEOL}) missing from current font.\n",
            "  fig.canvas.print_figure(bytes_io, **kw)\n",
            "/usr/local/lib/python3.10/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 51221 (\\N{HANGUL SYLLABLE JEONG}) missing from current font.\n",
            "  fig.canvas.print_figure(bytes_io, **kw)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8, 6))\n",
        "sns.violinplot(x='cluster', y='score', data=unique_df, palette='viridis')\n",
        "plt.title('클러스터별 Score 바이올린 플롯')\n",
        "plt.xlabel('클러스터')\n",
        "plt.ylabel('Score')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "TcX4uYPWrqSF",
        "outputId": "004bdf17-f82b-42a1-f58e-48ddcd216abc"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-60-79a425f35221>:2: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.violinplot(x='cluster', y='score', data=unique_df, palette='viridis')\n",
            "/usr/local/lib/python3.10/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 53364 (\\N{HANGUL SYLLABLE KEUL}) missing from current font.\n",
            "  fig.canvas.print_figure(bytes_io, **kw)\n",
            "/usr/local/lib/python3.10/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 47084 (\\N{HANGUL SYLLABLE REO}) missing from current font.\n",
            "  fig.canvas.print_figure(bytes_io, **kw)\n",
            "/usr/local/lib/python3.10/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 49828 (\\N{HANGUL SYLLABLE SEU}) missing from current font.\n",
            "  fig.canvas.print_figure(bytes_io, **kw)\n",
            "/usr/local/lib/python3.10/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 53552 (\\N{HANGUL SYLLABLE TEO}) missing from current font.\n",
            "  fig.canvas.print_figure(bytes_io, **kw)\n",
            "/usr/local/lib/python3.10/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 48324 (\\N{HANGUL SYLLABLE BYEOL}) missing from current font.\n",
            "  fig.canvas.print_figure(bytes_io, **kw)\n",
            "/usr/local/lib/python3.10/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 48148 (\\N{HANGUL SYLLABLE BA}) missing from current font.\n",
            "  fig.canvas.print_figure(bytes_io, **kw)\n",
            "/usr/local/lib/python3.10/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 51060 (\\N{HANGUL SYLLABLE I}) missing from current font.\n",
            "  fig.canvas.print_figure(bytes_io, **kw)\n",
            "/usr/local/lib/python3.10/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 50732 (\\N{HANGUL SYLLABLE OL}) missing from current font.\n",
            "  fig.canvas.print_figure(bytes_io, **kw)\n",
            "/usr/local/lib/python3.10/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 47536 (\\N{HANGUL SYLLABLE RIN}) missing from current font.\n",
            "  fig.canvas.print_figure(bytes_io, **kw)\n",
            "/usr/local/lib/python3.10/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 54540 (\\N{HANGUL SYLLABLE PEUL}) missing from current font.\n",
            "  fig.canvas.print_figure(bytes_io, **kw)\n",
            "/usr/local/lib/python3.10/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 47215 (\\N{HANGUL SYLLABLE ROS}) missing from current font.\n",
            "  fig.canvas.print_figure(bytes_io, **kw)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "u5VmJ4v1rdr9"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "주가예측"
      ],
      "metadata": {
        "id": "IaGNurFvy4Av"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "stk_dt_qut_medel=stk_dt_qut[['bse_dt', 'tck_iem_cd', 'iem_end_pr', 'acl_trd_qty']]\n",
        "stk_dt_qut_medel"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "thEG5AX3y5EW",
        "outputId": "7f141527-b4b4-4662-bc63-92855a2d102a"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          bse_dt tck_iem_cd  iem_end_pr  acl_trd_qty\n",
              "0       20240722        AAL       10.69   38895463.0\n",
              "1       20240603        AAL       11.53   52899042.0\n",
              "2       20240531        AAL       11.50   60868405.0\n",
              "3       20240625        AAL       11.13   28547274.0\n",
              "4       20240605        AAL       11.62   34340914.0\n",
              "...          ...        ...         ...          ...\n",
              "194826  20240702         ZS      196.66    1725207.0\n",
              "194827  20240807         ZS      166.20    1362130.0\n",
              "194828  20240617         ZS      184.38    1790442.0\n",
              "194829  20240815         ZS      190.34    1583690.0\n",
              "194830  20240715         ZS      204.47    2555283.0\n",
              "\n",
              "[194831 rows x 4 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-251676f8-25d5-491d-b92d-3d26cdd5d21e\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "    }\n",
              "\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>bse_dt</th>\n",
              "      <th>tck_iem_cd</th>\n",
              "      <th>iem_end_pr</th>\n",
              "      <th>acl_trd_qty</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>20240722</td>\n",
              "      <td>AAL</td>\n",
              "      <td>10.69</td>\n",
              "      <td>38895463.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>20240603</td>\n",
              "      <td>AAL</td>\n",
              "      <td>11.53</td>\n",
              "      <td>52899042.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>20240531</td>\n",
              "      <td>AAL</td>\n",
              "      <td>11.50</td>\n",
              "      <td>60868405.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>20240625</td>\n",
              "      <td>AAL</td>\n",
              "      <td>11.13</td>\n",
              "      <td>28547274.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>20240605</td>\n",
              "      <td>AAL</td>\n",
              "      <td>11.62</td>\n",
              "      <td>34340914.0</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>194826</th>\n",
              "      <td>20240702</td>\n",
              "      <td>ZS</td>\n",
              "      <td>196.66</td>\n",
              "      <td>1725207.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>194827</th>\n",
              "      <td>20240807</td>\n",
              "      <td>ZS</td>\n",
              "      <td>166.20</td>\n",
              "      <td>1362130.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>194828</th>\n",
              "      <td>20240617</td>\n",
              "      <td>ZS</td>\n",
              "      <td>184.38</td>\n",
              "      <td>1790442.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>194829</th>\n",
              "      <td>20240815</td>\n",
              "      <td>ZS</td>\n",
              "      <td>190.34</td>\n",
              "      <td>1583690.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>194830</th>\n",
              "      <td>20240715</td>\n",
              "      <td>ZS</td>\n",
              "      <td>204.47</td>\n",
              "      <td>2555283.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('stk_dt_qut_medel');\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "stk_dt_qut_medel"
            }
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#stk_dt_qut_medel  to datetime\n",
        "stk_dt_qut_medel['bse_dt'] = pd.to_datetime(stk_dt_qut_medel['bse_dt'], format='%Y%m%d')\n",
        "stk_dt_qut_medel\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 548
        },
        "id": "V0M_IJEN8HHq",
        "outputId": "fe8e135f-9960-487e-bb8a-a1bfac3d6b73"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-66-9bb4868074b6>:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  stk_dt_qut_medel['bse_dt'] = pd.to_datetime(stk_dt_qut_medel['bse_dt'], format='%Y%m%d')\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           bse_dt tck_iem_cd  iem_end_pr  acl_trd_qty\n",
              "0      2024-07-22        AAL       10.69   38895463.0\n",
              "1      2024-06-03        AAL       11.53   52899042.0\n",
              "2      2024-05-31        AAL       11.50   60868405.0\n",
              "3      2024-06-25        AAL       11.13   28547274.0\n",
              "4      2024-06-05        AAL       11.62   34340914.0\n",
              "...           ...        ...         ...          ...\n",
              "194826 2024-07-02         ZS      196.66    1725207.0\n",
              "194827 2024-08-07         ZS      166.20    1362130.0\n",
              "194828 2024-06-17         ZS      184.38    1790442.0\n",
              "194829 2024-08-15         ZS      190.34    1583690.0\n",
              "194830 2024-07-15         ZS      204.47    2555283.0\n",
              "\n",
              "[194831 rows x 4 columns]"
            ],
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              "      <th>bse_dt</th>\n",
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              "      <th>3</th>\n",
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              "      <td>11.13</td>\n",
              "      <td>28547274.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2024-06-05</td>\n",
              "      <td>AAL</td>\n",
              "      <td>11.62</td>\n",
              "      <td>34340914.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <th>194826</th>\n",
              "      <td>2024-07-02</td>\n",
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              "      <td>1725207.0</td>\n",
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              "      <th>194827</th>\n",
              "      <td>2024-08-07</td>\n",
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              "      <td>166.20</td>\n",
              "      <td>1362130.0</td>\n",
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              "      <th>194828</th>\n",
              "      <td>2024-06-17</td>\n",
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              "      <td>1790442.0</td>\n",
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              "      <th>194829</th>\n",
              "      <td>2024-08-15</td>\n",
              "      <td>ZS</td>\n",
              "      <td>190.34</td>\n",
              "      <td>1583690.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>194830</th>\n",
              "      <td>2024-07-15</td>\n",
              "      <td>ZS</td>\n",
              "      <td>204.47</td>\n",
              "      <td>2555283.0</td>\n",
              "    </tr>\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "\n",
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              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('stk_dt_qut_medel')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
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              "\n",
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              "    </button>\n",
              "    <script>\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('stk_dt_qut_medel');\n",
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              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "stk_dt_qut_medel"
            }
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# bse_dt 컬럼 rename\n",
        "stk_dt_qut_medel = stk_dt_qut_medel.rename(columns={'bse_dt':'Date'})"
      ],
      "metadata": {
        "id": "VKsrOJ1iAngu"
      },
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "news=pd.read_csv(\"/content/news_results.csv\")\n",
        "news['Date'] = pd.to_datetime(news['Date'], errors='coerce')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0tul5cu4y6A6",
        "outputId": "522cdb15-9c17-4fda-d6ea-b11d7b799e2d"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-68-6eb8114027d6>:2: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
            "  news['Date'] = pd.to_datetime(news['Date'], errors='coerce')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#news rename\n",
        "news = news.rename(columns={'Ticker':'tck_iem_cd','Date\t':'bse_dt\t'})\n",
        "news"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 771
        },
        "id": "AyDV7lFG1DxO",
        "outputId": "10d23262-04d6-4044-88bf-4942b10c9eb7"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     tck_iem_cd       Date                                              Title  \\\n",
              "0          AAPB 2004-10-24  Is Invesco S&P 500 Equal Weight Financials ETF...   \n",
              "1          AAPB 2003-10-24  American Century Debuting European ETFs With A...   \n",
              "2          AAPB 2003-10-24  Unsure Which AI Stock to Buy? Check Out This E...   \n",
              "3          AAPB 2002-10-24  Sector Update: Financial Stocks Decline Wednes...   \n",
              "4          AAPB 2002-10-24  How investors can get in on the China stock ra...   \n",
              "...         ...        ...                                                ...   \n",
              "1322        TAN 2024-02-21  SolarEdge stock tanks on 'abrupt slowdown in d...   \n",
              "1323        TAN 2023-12-19  Why solar energy stocks may be down, but not o...   \n",
              "1324        TAN 2023-12-15       Solar stocks pop after Fed signals rate cuts   \n",
              "1325        TAN 2023-12-15  Jefferies initiates coverage on solar energy s...   \n",
              "1326        TMF 2024-05-10  Direxion's TMF Sees $264 Million in Inflows; E...   \n",
              "\n",
              "                                                   Link  \\\n",
              "0     https://finance.yahoo.com/news/invesco-p-500-e...   \n",
              "1     https://finance.yahoo.com/news/american-centur...   \n",
              "2     https://finance.yahoo.com/news/unsure-ai-stock...   \n",
              "3     https://finance.yahoo.com/news/sector-financia...   \n",
              "4     https://finance.yahoo.com/video/investors-chin...   \n",
              "...                                                 ...   \n",
              "1322  https://finance.yahoo.com/news/solaredge-stock...   \n",
              "1323  https://finance.yahoo.com/video/why-solar-ener...   \n",
              "1324  https://finance.yahoo.com/news/solar-stocks-po...   \n",
              "1325  https://finance.yahoo.com/video/jefferies-init...   \n",
              "1326  https://finance.yahoo.com/news/direxions-tmf-s...   \n",
              "\n",
              "                                                Content  \n",
              "0     The Invesco S&P 500 Equal Weight Financials ET...  \n",
              "1     American Century has made its European debut w...  \n",
              "2     Investing in artificial intelligence (AI) can ...  \n",
              "3     Sector Update: Financial Stocks Decline Wednes...  \n",
              "4     China stocks (^HSI) have extended their rally ...  \n",
              "...                                                 ...  \n",
              "1322  SolarEdge (SEDG) stock sank around 14% on Wedn...  \n",
              "1323  Solar energy stocks have taken a hit in 2023 d...  \n",
              "1324  Market optimism for rate cuts has sent renewab...  \n",
              "1325  Higher interest rates have been a headwind for...  \n",
              "1326  Top 10 Creations (All ETFs)\\nTicker\\nName\\nNet...  \n",
              "\n",
              "[1327 rows x 5 columns]"
            ],
            "text/html": [
              "\n",
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              "      <th></th>\n",
              "      <th>tck_iem_cd</th>\n",
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              "      <td>AAPB</td>\n",
              "      <td>2004-10-24</td>\n",
              "      <td>Is Invesco S&amp;P 500 Equal Weight Financials ETF...</td>\n",
              "      <td>https://finance.yahoo.com/news/invesco-p-500-e...</td>\n",
              "      <td>The Invesco S&amp;P 500 Equal Weight Financials ET...</td>\n",
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              "      <td>2003-10-24</td>\n",
              "      <td>American Century Debuting European ETFs With A...</td>\n",
              "      <td>https://finance.yahoo.com/news/american-centur...</td>\n",
              "      <td>American Century has made its European debut w...</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>AAPB</td>\n",
              "      <td>2003-10-24</td>\n",
              "      <td>Unsure Which AI Stock to Buy? Check Out This E...</td>\n",
              "      <td>https://finance.yahoo.com/news/unsure-ai-stock...</td>\n",
              "      <td>Investing in artificial intelligence (AI) can ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>AAPB</td>\n",
              "      <td>2002-10-24</td>\n",
              "      <td>Sector Update: Financial Stocks Decline Wednes...</td>\n",
              "      <td>https://finance.yahoo.com/news/sector-financia...</td>\n",
              "      <td>Sector Update: Financial Stocks Decline Wednes...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>AAPB</td>\n",
              "      <td>2002-10-24</td>\n",
              "      <td>How investors can get in on the China stock ra...</td>\n",
              "      <td>https://finance.yahoo.com/video/investors-chin...</td>\n",
              "      <td>China stocks (^HSI) have extended their rally ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1322</th>\n",
              "      <td>TAN</td>\n",
              "      <td>2024-02-21</td>\n",
              "      <td>SolarEdge stock tanks on 'abrupt slowdown in d...</td>\n",
              "      <td>https://finance.yahoo.com/news/solaredge-stock...</td>\n",
              "      <td>SolarEdge (SEDG) stock sank around 14% on Wedn...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1323</th>\n",
              "      <td>TAN</td>\n",
              "      <td>2023-12-19</td>\n",
              "      <td>Why solar energy stocks may be down, but not o...</td>\n",
              "      <td>https://finance.yahoo.com/video/why-solar-ener...</td>\n",
              "      <td>Solar energy stocks have taken a hit in 2023 d...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1324</th>\n",
              "      <td>TAN</td>\n",
              "      <td>2023-12-15</td>\n",
              "      <td>Solar stocks pop after Fed signals rate cuts</td>\n",
              "      <td>https://finance.yahoo.com/news/solar-stocks-po...</td>\n",
              "      <td>Market optimism for rate cuts has sent renewab...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1325</th>\n",
              "      <td>TAN</td>\n",
              "      <td>2023-12-15</td>\n",
              "      <td>Jefferies initiates coverage on solar energy s...</td>\n",
              "      <td>https://finance.yahoo.com/video/jefferies-init...</td>\n",
              "      <td>Higher interest rates have been a headwind for...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1326</th>\n",
              "      <td>TMF</td>\n",
              "      <td>2024-05-10</td>\n",
              "      <td>Direxion's TMF Sees $264 Million in Inflows; E...</td>\n",
              "      <td>https://finance.yahoo.com/news/direxions-tmf-s...</td>\n",
              "      <td>Top 10 Creations (All ETFs)\\nTicker\\nName\\nNet...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1327 rows × 5 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c0d4d5af-142d-4b33-a56e-2784092bb9f4')\"\n",
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              "      padding: 0 0 0 0;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
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              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "  .colab-df-quickchart:hover {\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "\n",
              "  .colab-df-spinner {\n",
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              "    animation:\n",
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              "      border-right-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "  }\n",
              "</style>\n",
              "\n",
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              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "\n",
              "  <div id=\"id_73cfb2cb-23f9-489c-9e79-8293b46887bf\">\n",
              "    <style>\n",
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              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('news')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
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              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "news",
              "summary": "{\n  \"name\": \"news\",\n  \"rows\": 1327,\n  \"fields\": [\n    {\n      \"column\": \"tck_iem_cd\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 122,\n        \"samples\": [\n          \"AMZU\",\n          \"XLP\",\n          \"YMAX\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2001-01-24 00:00:00\",\n        \"max\": \"2031-10-23 00:00:00\",\n        \"num_unique_values\": 208,\n        \"samples\": [\n          \"2019-03-24 00:00:00\",\n          \"2007-03-24 00:00:00\",\n          \"2031-07-24 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Title\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 497,\n        \"samples\": [\n          \"Marjorie Taylor Greene Continues Adding to Her Portfolio. Here Are 6 Stocks She Just Bought.\",\n          \"The S&P 500 is too tech-heavy. This alternative ETF keeps beating it.\",\n          \"Trading Platforms Suffer Service Breaks as Markets Plunge\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Link\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 493,\n        \"samples\": [\n          \"https://finance.yahoo.com/news/defiance-announces-shift-weekly-distributions-134100958.html\",\n          \"https://finance.yahoo.com/news/p-500-too-tech-heavy-160100786.html\",\n          \"https://finance.yahoo.com/news/56-ownership-shares-coupang-inc-110015739.html\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Content\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 592,\n        \"samples\": [\n          \"The Simplify US Equity PLUS QIS ETF is designed to provide 100% exposure to U.S. equities with 50% exposure to a diversified basket of quantitative investment strategies in the form of the QIS ETF\\nNEW YORK, November 14, 2023--(BUSINESS WIRE)--Simplify Asset Management (\\\"Simplify\\\"), an innovative provider of Exchange Traded Funds (\\\"ETFs\\\"), is today celebrating the launch of its newest ETF, the Simplify US Equity PLUS QIS ETF (SPQ).\\nSPQ is designed to seek long-term capital appreciation by overlaying a diversified basket of Quantitative Investment Strategies on top of core U.S. equity exposure. Specifically, 100% of SPQ?\\uc172 assets will be invested in U.S. equities via low-cost equity ETFs and equity futures, while approximately 50% of SPQ?\\uc172 assets will be invested in shares of the Simplify Multi-QIS Alternative ETF (QIS), a fund that provides exposure to an optimized collection of 10 to 20 best-in-class quant-driven strategies.\\nQIS launched in July of 2023 and has already gathered well over $100 million in assets.\\n\\\"The goal of this 100% equity plus 50% QIS portfolio is to enhance both the absolute and risk-adjusted returns of a core equity investment,\\\" said David Berns, PhD, Simplify?\\uc172 Chief Investment Officer and Cofounder. \\\"We?\\uc176e spoken with a number of investors and advisors who have been seeking a simple way to gain exposure to an alternative return source without reducing equity exposure, and that is exactly what we have designed with SPQ.\\\"\\nAs with all of Simplify?\\uc172 ETFs, SPQ includes no lockups or incentive fees, nor will it deliver a K-1 tax form. Additionally, the fund?\\uc172 expense ratio of 100bps is inclusive of QIS??fees, meaning there are no hidden fees passed along to investors.\\nIn addition to QIS, SPQ joins a lineup of alternative ETFs from Simplify that also includes the Simplify Volatility Premium ETF (SVOL), Simplify Interest Rate Hedge ETF (PFIX), and Simplify Market Neutral Equity Long/Short ETF (EQLS).\\n\\\"Complex markets require access to sophisticated solutions, and we?\\uc171e very pleased to be adding SPQ to our fast-growing family of alternative ETFs,\\\" added Berns.\\nABOUT SIMPLIFY ASSET MANAGEMENT INC\\nSimplify Asset Management Inc. is a Registered Investment Adviser founded in 2020 to help advisors tackle the most pressing portfolio challenges with an innovative set of options-based strategies. By accounting for real-world investor needs and market behavior, along with the non-linear power of options, our strategies allow for the tailored portfolio outcomes for which clients are looking. For more information, visit www.simplify.us.\\nStory Continues\\n\\n\\n\\n\\nView Comments\",\n          \"SolarEdge (SEDG) stock sank around 14% on Wednesday after the company posted weaker-than-expected revenue guidance for the current quarter due to a slowdown in residential demand and increasing inventories.\\nThe maker of inverters, which convert solar power into usable energy, expects first quarter revenue in the range of $175 million to $215 million versus Wall Street expectations for $373.2 million.\\n\\\"We continue to face challenges from general market dynamics as well as the inventory levels of our products in the channels due to the abrupt slowdown in demand in the second half of 2023,\\\" CEO Zvi Lando said during the company's fourth quarter earnings call.\\nExecutives noted weakness across Europe and the US residential market, where product sell-through \\u2014 the quantity sold of the available inventory \\u2014 was down 8% year over year.\\n\\\"We do not expect significant changes in the residential market dynamics in the US until such time as interest rates decline,\\\" said Lando.\\nHigher rates have impacted the renewable sector because projects are capital-intensive. The Department of Energy estimates about 85% of residential solar systems in the US are financed.\\nA slowdown in California, the largest US solar market, has also contributed to the current installation weakness. A policy change in the Golden State last year reduced the amount of money credited to panel owners for sending excess power they generate into the grid, removing a key incentive.\\nA visitor inspects products at the SolarEdge booth during the event at The Queen Sirikit National Convention Center. (Peerapon Boonyakiat/SOPA Images/LightRocket via Getty Images) \\u00b7 (SOPA Images via Getty Images)\\nSolarEdge expects the US commercial segment, which hit record levels in the fourth quarter, to continue growing as companies pursue net-zero pledges and take advantage of government incentives provided by the Inflation Reduction Act.\\nThe company expects a gradual improvement in installation rates in Europe in the first quarter for both residential and commercial segments along with a clearing of inventory.\\nSolarEdge announced cuts to roughly 16% of its workforce last month. The stock fell 66% last year as renewables took a beating.\\nThe Invesco Solar ETF (TAN) is 14% lower since the start of the year as headwinds continue across the industry.\\nInes Ferre is a senior business reporter for Yahoo Finance. Follow her on Twitter at @ines_ferre.\\nView Comments (11)\",\n          \"US market averages (^DJI, ^IXIC, ^GSPC) closed Monday's session higher after last week's sell-off. NorthEnd Private Wealth CIO Alex McGrath joins Market Domination Overtime to discuss the state of the market ahead of the Federal Reserve's first interest rate cut.\\n\\\"We're coming off the back end of a pretty long inflation battle here with rates being at historic peaks. And, you know, you look at the underlying macro data that we're staring at every day, and it's not exceptional, but it's not bad either. It's just kind of in this middling phase. And certainly a much better start to this week than we had last week,\\\" McGrath explains.\\nHe argues that the Fed's cuts will not immediately come to the rescue as its cuts will likely \\\"take time to work through.\\\" He adds, \\\"I don't think you have to look much further than a lot of the numbers we've seen from the consumer discretionary companies, where they're slashing forecast because the demand just isn't there like it has been. And that's not entirely surprising.\\\"\\nRelated Videos\\n02:43\\nDenver tops list of 5 hottest labor markets\\nYahoo Finance Video\\n?\\n7 hours ago\\n27:58\\nMarket takeaways, Cathie Wood on OpenAI: Asking for a Trend\\nYahoo Finance Video\\n?\\n23 hours ago\\n06:03\\nHow options can help traders manage risk\\nYahoo Finance Video\\n?\\n23 hours ago\\n05:23\\nJobs report, gold prices, and China: Market takeaways\\nYahoo Finance Video\\n?\\nyesterday\\nWhile inflation is down, he argues, \\\"the problem is there's 20 to 30% built up in there that's still increasing on a month-over-month basis. And it's just the consumer's got to get tapped out at some point. And I'm not saying that's like a death and destruction phase coming, but you are starting to see inklings of that.\\\" However, easing rates should help counter that issue.\\nMoving forward, McGrath believes the equities market \\\"could be a bit rocky,\\\" so investors should take more of a defensive approach and take a position in sectors like industrials (XLI), utilities (XLU), healthcare (XLV), real estate (XLRE), and consumer staples (XLP). He also recommends semiconductors, calling them \\\"the new industrial.\\\" On the fixed-income side, he argues that \\\"it's probably time to start taking some duration there\\\" as the Fed kicks off its rate easing cycle.\\nFor more expert insight and the latest market action, click here to watch this full episode of Market Domination Overtime.\\nThis post was written by Melanie Riehl\\nView Comments (15)\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#tck_iem_cd\tDate 기준으로 merge\n",
        "df2 = pd.merge(stk_dt_qut_medel, news, on=['tck_iem_cd', 'Date'], how='left')\n",
        "df2=df2.sort_values(by=['tck_iem_cd', 'Date'])\n"
      ],
      "metadata": {
        "id": "XWKzeLY89Xbk"
      },
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df2=df2[['tck_iem_cd', 'Date', 'iem_end_pr', 'acl_trd_qty', 'Title']]\n",
        "df2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "OG-NXePb2NDU",
        "outputId": "fdca6b38-0f21-42eb-9d5a-a10611f06838"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       tck_iem_cd       Date  iem_end_pr  acl_trd_qty Title\n",
              "100878         AA 2024-05-28       43.20    7888482.0   NaN\n",
              "100891         AA 2024-05-29       43.93    5275412.0   NaN\n",
              "100847         AA 2024-05-30       44.54    5824970.0   NaN\n",
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              "100837       ZYXI 2024-08-26        8.35     131525.0   NaN\n",
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              "      <th>tck_iem_cd</th>\n",
              "      <th>Date</th>\n",
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              "      <td>ZYXI</td>\n",
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          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from gensim.models import Word2Vec\n",
        "import numpy as np\n",
        "\n",
        "\n",
        "df2['Title'] = df2['Title'].fillna('')\n",
        "\n",
        "# Title을 공백으로 분리하여 단어 리스트로 변환\n",
        "sentences = [title.split() for title in df2['Title']]\n",
        "\n",
        "# Word2Vec 모델 학습\n",
        "model = Word2Vec(sentences, vector_size=100, window=5, min_count=1, workers=4)\n",
        "\n",
        "# 각 Title에 대한 벡터 생성\n",
        "def get_vector(title):\n",
        "    if title == \"\":\n",
        "        return np.zeros(100)  # null값에 대해 0 벡터\n",
        "    vector = np.zeros(100)  # 100 차원 벡터\n",
        "    words = title.split()\n",
        "    for word in words:\n",
        "        if word in model.wv:\n",
        "            vector += model.wv[word]\n",
        "    return vector / len(words) if words else vector\n",
        "\n",
        "# 벡터 생성하여 새로운 DataFrame에 추가\n",
        "df_vectors = pd.DataFrame(df2['Title'].apply(get_vector).tolist(), columns=[f'vector_{i+1}' for i in range(100)])\n",
        "\n",
        "result_df = pd.concat([df2, df_vectors], axis=1)"
      ],
      "metadata": {
        "id": "WCEB-H1TBLQX"
      },
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "result_df['iem_end_pr_shift'] = result_df.groupby('tck_iem_cd')['iem_end_pr'].shift(-1)\n",
        "\n",
        "\n",
        "result_df['iem_end'] = (result_df['iem_end_pr'] < result_df['iem_end_pr_shift']).astype(int)"
      ],
      "metadata": {
        "id": "8e95HZTYCEjG"
      },
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "result_df['tck_iem_cd'] = pd.factorize(result_df['tck_iem_cd'])[0]\n",
        "#Title컬럼 제거\n",
        "result_df_train=result_df.drop('Title',axis=1)\n",
        "\n",
        "result_df_train=result_df_train.drop('iem_end_pr_shift',axis=1)"
      ],
      "metadata": {
        "id": "wmdZyOncDKh0"
      },
      "execution_count": 203,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "result_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "id": "-yQND8FJsmW2",
        "outputId": "41d5f35d-8e72-45fb-931f-37823b04038f"
      },
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        tck_iem_cd       Date  iem_end_pr  acl_trd_qty Title  vector_1  \\\n",
              "100878           0 2024-05-28       43.20    7888482.0             0.0   \n",
              "100891           0 2024-05-29       43.93    5275412.0             0.0   \n",
              "100847           0 2024-05-30       44.54    5824970.0             0.0   \n",
              "100887           0 2024-05-31       44.27    4527342.0             0.0   \n",
              "100874           0 2024-06-03       44.01    3887113.0             0.0   \n",
              "...            ...        ...         ...          ...   ...       ...   \n",
              "100819        3093 2024-08-20        7.65      80521.0             0.0   \n",
              "100795        3093 2024-08-21        7.73      53251.0             0.0   \n",
              "100815        3093 2024-08-22        7.75      78763.0             0.0   \n",
              "100804        3093 2024-08-23        8.19     109444.0             0.0   \n",
              "100837        3093 2024-08-26        8.35     131525.0             0.0   \n",
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              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "    }\n",
              "    30% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
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              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "</div>\n",
              "\n",
              "  <div id=\"id_09b46dbc-433b-46a4-afc4-a2395d5772ab\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
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              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
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              "\n",
              "      .colab-df-generate: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",
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              "\n",
              "      [theme=dark] .colab-df-generate {\n",
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              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
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              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('result_df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
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              "        document.querySelector('#id_09b46dbc-433b-46a4-afc4-a2395d5772ab button.colab-df-generate');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('result_df');\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "result_df"
            }
          },
          "metadata": {},
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = result_df[['iem_end_pr', 'acl_trd_qty', 'tck_iem_cd','Date'] + [f'vector_{i+1}' for i in range(100)]].values"
      ],
      "metadata": {
        "id": "CE-R7PU1rfoL"
      },
      "execution_count": 96,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#result_df_train=result_df_train[['tck_iem_cd','Date','iem_end_pr','acl_trd_qty','iem_end']]"
      ],
      "metadata": {
        "id": "OTlCCiS3lkfb"
      },
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras import layers\n",
        "\n",
        "# Ensure reproducibility\n",
        "np.random.seed(42)\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# 1. 데이터 로드 및 준비\n",
        "# 예시 DataFrame 로드 (실제 데이터에 맞게 수정하세요)\n",
        "# result_df = pd.read_csv('your_data.csv')\n",
        "\n",
        "# 날짜를 0부터 시작하는 정수로 매핑\n",
        "result_df['Date_mapped'], date_unique = pd.factorize(result_df['Date'])\n",
        "\n",
        "# 매핑된 날짜를 포함한 feature_columns 정의\n",
        "feature_columns = ['iem_end_pr', 'acl_trd_qty', 'tck_iem_cd', 'Date_mapped'] + [f'vector_{i+1}' for i in range(100)]\n",
        "data = result_df[feature_columns].values\n",
        "# 데이터 정규화 (수치형 컬럼 기준)\n",
        "data_min = data.min(axis=0)\n",
        "data_max = data.max(axis=0)\n",
        "normalized_data = (data - data_min) / (data_max - data_min)\n",
        "\n",
        "features = normalized_data  # 모든 컬럼이 숫자형\n"
      ],
      "metadata": {
        "id": "aOrp3KU8MGmw"
      },
      "execution_count": 191,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 2. GAN 아키텍처 정의\n",
        "\n",
        "# Generator 모델 정의\n",
        "def build_generator(input_dim, output_dim):\n",
        "    model = tf.keras.Sequential([\n",
        "        layers.Dense(256, activation='relu', input_dim=input_dim),\n",
        "        layers.Dense(512, activation='relu'),\n",
        "        layers.Dense(output_dim, activation='sigmoid')  # 정규화된 출력을 위해 sigmoid 사용\n",
        "    ])\n",
        "    return model\n",
        "\n",
        "# Discriminator 모델 정의\n",
        "def build_discriminator(input_dim):\n",
        "    model = tf.keras.Sequential([\n",
        "        layers.Dense(512, activation='relu', input_dim=input_dim),\n",
        "        layers.Dense(256, activation='relu'),\n",
        "        layers.Dense(1, activation='sigmoid')  # 진짜 또는 가짜를 구분\n",
        "    ])\n",
        "    return model\n",
        "\n",
        "# 입력 차원 정의\n",
        "noise_dim = 100  # Generator 입력 노이즈 차원\n",
        "data_dim = features.shape[1]  # 데이터의 피처 수\n",
        "\n",
        "# 모델 빌드\n",
        "generator = build_generator(noise_dim, data_dim)\n",
        "discriminator = build_discriminator(data_dim)\n",
        "\n",
        "# Discriminator 컴파일\n",
        "discriminator.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "# 3. GAN 모델 생성\n",
        "discriminator.trainable = False  # GAN 훈련 시 Generator만 훈련\n",
        "\n",
        "gan_input = layers.Input(shape=(noise_dim,))\n",
        "generated_data = generator(gan_input)\n",
        "gan_output = discriminator(generated_data)\n",
        "\n",
        "gan = tf.keras.Model(gan_input, gan_output)\n",
        "gan.compile(optimizer='adam', loss='binary_crossentropy')\n",
        "\n",
        "# 4. GAN 훈련 함수 정의\n",
        "def train_gan(epochs, batch_size):\n",
        "    for epoch in range(1, epochs + 1):\n",
        "        # 진짜 데이터 샘플링\n",
        "        idx = np.random.randint(0, features.shape[0], batch_size)\n",
        "        real_data = features[idx]\n",
        "\n",
        "        # 가짜 데이터 생성\n",
        "        noise = np.random.normal(0, 1, (batch_size, noise_dim))\n",
        "        fake_data = generator.predict(noise)\n",
        "\n",
        "        # 레이블 생성 (진짜: 1, 가짜: 0)\n",
        "        real_labels = np.ones((batch_size, 1))\n",
        "        fake_labels = np.zeros((batch_size, 1))\n",
        "\n",
        "        # Discriminator 훈련\n",
        "        d_loss_real = discriminator.train_on_batch(real_data, real_labels)\n",
        "        d_loss_fake = discriminator.train_on_batch(fake_data, fake_labels)\n",
        "\n",
        "        # Generator 훈련 (Discriminator의 가중치는 고정)\n",
        "        noise = np.random.normal(0, 1, (batch_size, noise_dim))\n",
        "        valid_labels = np.ones((batch_size, 1))  # Generator가 Discriminator를 속이기를 원함\n",
        "        g_loss = gan.train_on_batch(noise, valid_labels)\n",
        "\n",
        "        # 진행 상황 출력\n",
        "        if epoch % 100 == 0 or epoch == 1:\n",
        "            d_loss = 0.5 * (d_loss_real[0] + d_loss_fake[0])\n",
        "            d_acc = 100 * d_loss_fake[1]\n",
        "\n",
        "\n",
        "# GAN 훈련 실행\n",
        "train_gan(epochs=100, batch_size=64)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GyrUsKVLNsRv",
        "outputId": "e9af5a14-3842-40a0-a750-dddeacd5402f"
      },
      "execution_count": 194,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step \n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step \n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step \n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step \n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step \n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step \n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step \n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step \n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step \n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step \n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step \n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step \n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step \n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step \n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step \n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step \n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step \n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step \n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step \n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step \n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step \n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step \n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step \n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step \n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step \n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step \n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step \n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step \n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step \n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step \n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step \n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step \n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step \n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step \n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step \n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step \n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step \n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step \n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 5. 합성 데이터 생성\n",
        "num_samples_to_generate = 10000\n",
        "noise = np.random.normal(0, 1, (num_samples_to_generate, noise_dim))\n",
        "synthetic_data = generator.predict(noise)\n",
        "\n",
        "# 원래 범위로 스케일 복원\n",
        "synthetic_data_rescaled = synthetic_data * (data_max - data_min) + data_min\n",
        "\n",
        "# 합성 데이터를 DataFrame으로 변환\n",
        "synthetic_df = pd.DataFrame(synthetic_data_rescaled, columns=feature_columns)\n",
        "\n",
        "# 매핑된 Date를 원래 날짜로 되돌리기 위해 'Date_mapped'를 날짜로 변환\n",
        "# 가장 많이 사용하는 방법은 무작위로 원래 날짜 중에서 선택하는 것입니다\n",
        "synthetic_df['Date'] = np.random.choice(date_unique, size=num_samples_to_generate)\n",
        "\n",
        "# 필요에 따라 'Date_mapped' 컬럼 제거\n",
        "synthetic_df = synthetic_df.drop('Date_mapped', axis=1)\n",
        "\n",
        "# 합성 데이터 확인\n",
        "print(synthetic_df)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DTOvGXt1PooE",
        "outputId": "01054a89-7b32-4934-f8a7-b567169639e9"
      },
      "execution_count": 201,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step\n",
            "         iem_end_pr   acl_trd_qty   tck_iem_cd  vector_1  vector_2  vector_3  \\\n",
            "0     680249.699362  1.882758e+08  3092.991520 -0.001984  0.003629 -0.003782   \n",
            "1     680134.291707  2.857348e+08  3092.971978 -0.001984  0.003628 -0.003781   \n",
            "2     680170.098088  1.674603e+08  3092.967737 -0.001984  0.003629 -0.003781   \n",
            "3     680301.766624  2.147387e+08  3092.996866 -0.001984  0.003629 -0.003782   \n",
            "4     679559.727034  2.747773e+08  3092.873347 -0.001983  0.003628 -0.003780   \n",
            "...             ...           ...          ...       ...       ...       ...   \n",
            "9995  680294.994636  2.438425e+08  3092.996128 -0.001984  0.003629 -0.003782   \n",
            "9996  680163.244998  2.417765e+08  3092.900447 -0.001983  0.003628 -0.003781   \n",
            "9997  680091.834991  1.664677e+08  3092.966631 -0.001984  0.003628 -0.003781   \n",
            "9998  680237.453012  1.814013e+08  3092.989307 -0.001984  0.003629 -0.003781   \n",
            "9999  680214.947303  1.781403e+08  3092.961285 -0.001984  0.003629 -0.003781   \n",
            "\n",
            "      vector_4  vector_5  vector_6  vector_7  ...  vector_92  vector_93  \\\n",
            "0    -0.004652  0.001264 -0.003373 -0.002740  ...   0.001563  -0.001466   \n",
            "1    -0.004651  0.001030 -0.003373 -0.002737  ...   0.001546  -0.001464   \n",
            "2    -0.004651  0.001166 -0.003372 -0.002739  ...   0.001554  -0.001464   \n",
            "3    -0.004652  0.001163 -0.003373 -0.002741  ...   0.001563  -0.001466   \n",
            "4    -0.004651  0.001139 -0.003372 -0.002732  ...   0.001532  -0.001464   \n",
            "...        ...       ...       ...       ...  ...        ...        ...   \n",
            "9995 -0.004652  0.001131 -0.003373 -0.002741  ...   0.001562  -0.001466   \n",
            "9996 -0.004651  0.001018 -0.003372 -0.002732  ...   0.001543  -0.001464   \n",
            "9997 -0.004651  0.001152 -0.003373 -0.002739  ...   0.001548  -0.001466   \n",
            "9998 -0.004651  0.001232 -0.003373 -0.002740  ...   0.001559  -0.001465   \n",
            "9999 -0.004652  0.001169 -0.003373 -0.002740  ...   0.001557  -0.001465   \n",
            "\n",
            "      vector_94  vector_95  vector_96  vector_97  vector_98  vector_99  \\\n",
            "0     -0.002365   0.003687   0.002632   0.002244   0.003431  -0.002982   \n",
            "1     -0.002364   0.003687   0.002655   0.002244   0.003431  -0.002982   \n",
            "2     -0.002365   0.003687   0.002185   0.002244   0.003431  -0.002982   \n",
            "3     -0.002365   0.003687   0.002467   0.002244   0.003431  -0.002983   \n",
            "4     -0.002364   0.003687   0.002203   0.002244   0.003431  -0.002979   \n",
            "...         ...        ...        ...        ...        ...        ...   \n",
            "9995  -0.002365   0.003687   0.002904   0.002244   0.003431  -0.002983   \n",
            "9996  -0.002364   0.003687   0.002278   0.002244   0.003431  -0.002982   \n",
            "9997  -0.002365   0.003687   0.002450   0.002244   0.003431  -0.002982   \n",
            "9998  -0.002365   0.003687   0.002723   0.002244   0.003431  -0.002983   \n",
            "9999  -0.002365   0.003687   0.002100   0.002244   0.003431  -0.002983   \n",
            "\n",
            "      vector_100       Date  \n",
            "0       -0.00551 2024-07-03  \n",
            "1       -0.00551 2024-08-08  \n",
            "2       -0.00551 2024-07-01  \n",
            "3       -0.00551 2024-06-21  \n",
            "4       -0.00551 2024-08-02  \n",
            "...          ...        ...  \n",
            "9995    -0.00551 2024-08-06  \n",
            "9996    -0.00551 2024-07-02  \n",
            "9997    -0.00551 2024-07-10  \n",
            "9998    -0.00551 2024-08-06  \n",
            "9999    -0.00551 2024-06-27  \n",
            "\n",
            "[10000 rows x 104 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#synthetic_df 와 result_df_train concat\n",
        "result_df_train = pd.concat([result_df_train, synthetic_df], ignore_index=True)\n",
        "\n",
        "result_df_train['iem_end_pr_shift'] = result_df_train.groupby('tck_iem_cd')['iem_end_pr'].shift(-1)\n",
        "\n",
        "\n",
        "result_df_train['iem_end'] = (result_df_train['iem_end_pr'] < result_df_train['iem_end_pr_shift']).astype(int)\n",
        "result_df_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 652
        },
        "id": "_DEmxeGYR7pX",
        "outputId": "b33c7383-7cdb-4730-a11e-3be9b55a02ac"
      },
      "execution_count": 207,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-207-89e472715ada>:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  result_df_train['iem_end_pr_shift'] = result_df_train.groupby('tck_iem_cd')['iem_end_pr'].shift(-1)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         tck_iem_cd       Date     iem_end_pr   acl_trd_qty  vector_1  \\\n",
              "0          0.000000 2024-05-28      43.200000  7.888482e+06  0.000000   \n",
              "1          0.000000 2024-05-29      43.930000  5.275412e+06  0.000000   \n",
              "2          0.000000 2024-05-30      44.540000  5.824970e+06  0.000000   \n",
              "3          0.000000 2024-05-31      44.270000  4.527342e+06  0.000000   \n",
              "4          0.000000 2024-06-03      44.010000  3.887113e+06  0.000000   \n",
              "...             ...        ...            ...           ...       ...   \n",
              "214827  3092.996128 2024-08-06  680294.994636  2.438425e+08 -0.001984   \n",
              "214828  3092.900447 2024-07-02  680163.244998  2.417765e+08 -0.001983   \n",
              "214829  3092.966631 2024-07-10  680091.834991  1.664677e+08 -0.001984   \n",
              "214830  3092.989307 2024-08-06  680237.453012  1.814013e+08 -0.001984   \n",
              "214831  3092.961285 2024-06-27  680214.947303  1.781403e+08 -0.001984   \n",
              "\n",
              "        vector_2  vector_3  vector_4  vector_5  vector_6  ...  vector_94  \\\n",
              "0       0.000000  0.000000  0.000000  0.000000  0.000000  ...   0.000000   \n",
              "1       0.000000  0.000000  0.000000  0.000000  0.000000  ...   0.000000   \n",
              "2       0.000000  0.000000  0.000000  0.000000  0.000000  ...   0.000000   \n",
              "3       0.000000  0.000000  0.000000  0.000000  0.000000  ...   0.000000   \n",
              "4       0.000000  0.000000  0.000000  0.000000  0.000000  ...   0.000000   \n",
              "...          ...       ...       ...       ...       ...  ...        ...   \n",
              "214827  0.003629 -0.003782 -0.004652  0.001131 -0.003373  ...  -0.002365   \n",
              "214828  0.003628 -0.003781 -0.004651  0.001018 -0.003372  ...  -0.002364   \n",
              "214829  0.003628 -0.003781 -0.004651  0.001152 -0.003373  ...  -0.002365   \n",
              "214830  0.003629 -0.003781 -0.004651  0.001232 -0.003373  ...  -0.002365   \n",
              "214831  0.003629 -0.003781 -0.004652  0.001169 -0.003373  ...  -0.002365   \n",
              "\n",
              "        vector_95  vector_96  vector_97  vector_98  vector_99  vector_100  \\\n",
              "0        0.000000   0.000000   0.000000   0.000000   0.000000     0.00000   \n",
              "1        0.000000   0.000000   0.000000   0.000000   0.000000     0.00000   \n",
              "2        0.000000   0.000000   0.000000   0.000000   0.000000     0.00000   \n",
              "3        0.000000   0.000000   0.000000   0.000000   0.000000     0.00000   \n",
              "4        0.000000   0.000000   0.000000   0.000000   0.000000     0.00000   \n",
              "...           ...        ...        ...        ...        ...         ...   \n",
              "214827   0.003687   0.002904   0.002244   0.003431  -0.002983    -0.00551   \n",
              "214828   0.003687   0.002278   0.002244   0.003431  -0.002982    -0.00551   \n",
              "214829   0.003687   0.002450   0.002244   0.003431  -0.002982    -0.00551   \n",
              "214830   0.003687   0.002723   0.002244   0.003431  -0.002983    -0.00551   \n",
              "214831   0.003687   0.002100   0.002244   0.003431  -0.002983    -0.00551   \n",
              "\n",
              "        iem_end  Date_mapped  iem_end_pr_shift  \n",
              "0             1          0.0             43.93  \n",
              "1             1          1.0             44.54  \n",
              "2             0          2.0             44.27  \n",
              "3             0          3.0             44.01  \n",
              "4             0          4.0             42.01  \n",
              "...         ...          ...               ...  \n",
              "214827        0          NaN               NaN  \n",
              "214828        0          NaN               NaN  \n",
              "214829        0          NaN               NaN  \n",
              "214830        0          NaN               NaN  \n",
              "214831        0          NaN               NaN  \n",
              "\n",
              "[214832 rows x 107 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-44d274bf-e04c-48c6-86f3-0d799fa9033b\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "        vertical-align: top;\n",
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              "\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>tck_iem_cd</th>\n",
              "      <th>Date</th>\n",
              "      <th>iem_end_pr</th>\n",
              "      <th>acl_trd_qty</th>\n",
              "      <th>vector_1</th>\n",
              "      <th>vector_2</th>\n",
              "      <th>vector_3</th>\n",
              "      <th>vector_4</th>\n",
              "      <th>vector_5</th>\n",
              "      <th>vector_6</th>\n",
              "      <th>...</th>\n",
              "      <th>vector_94</th>\n",
              "      <th>vector_95</th>\n",
              "      <th>vector_96</th>\n",
              "      <th>vector_97</th>\n",
              "      <th>vector_98</th>\n",
              "      <th>vector_99</th>\n",
              "      <th>vector_100</th>\n",
              "      <th>iem_end</th>\n",
              "      <th>Date_mapped</th>\n",
              "      <th>iem_end_pr_shift</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>2024-05-28</td>\n",
              "      <td>43.200000</td>\n",
              "      <td>7.888482e+06</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>43.93</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>2024-05-29</td>\n",
              "      <td>43.930000</td>\n",
              "      <td>5.275412e+06</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>0.00000</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>44.54</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>2024-05-30</td>\n",
              "      <td>44.540000</td>\n",
              "      <td>5.824970e+06</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>44.27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>2024-05-31</td>\n",
              "      <td>44.270000</td>\n",
              "      <td>4.527342e+06</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>44.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>2024-06-03</td>\n",
              "      <td>44.010000</td>\n",
              "      <td>3.887113e+06</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "      <td>3092.996128</td>\n",
              "      <td>2024-08-06</td>\n",
              "      <td>680294.994636</td>\n",
              "      <td>2.438425e+08</td>\n",
              "      <td>-0.001984</td>\n",
              "      <td>0.003629</td>\n",
              "      <td>-0.003782</td>\n",
              "      <td>-0.004652</td>\n",
              "      <td>0.001131</td>\n",
              "      <td>-0.003373</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.002365</td>\n",
              "      <td>0.003687</td>\n",
              "      <td>0.002904</td>\n",
              "      <td>0.002244</td>\n",
              "      <td>0.003431</td>\n",
              "      <td>-0.002983</td>\n",
              "      <td>-0.00551</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>214828</th>\n",
              "      <td>3092.900447</td>\n",
              "      <td>2024-07-02</td>\n",
              "      <td>680163.244998</td>\n",
              "      <td>2.417765e+08</td>\n",
              "      <td>-0.001983</td>\n",
              "      <td>0.003628</td>\n",
              "      <td>-0.003781</td>\n",
              "      <td>-0.004651</td>\n",
              "      <td>0.001018</td>\n",
              "      <td>-0.003372</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.002364</td>\n",
              "      <td>0.003687</td>\n",
              "      <td>0.002278</td>\n",
              "      <td>0.002244</td>\n",
              "      <td>0.003431</td>\n",
              "      <td>-0.002982</td>\n",
              "      <td>-0.00551</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>214829</th>\n",
              "      <td>3092.966631</td>\n",
              "      <td>2024-07-10</td>\n",
              "      <td>680091.834991</td>\n",
              "      <td>1.664677e+08</td>\n",
              "      <td>-0.001984</td>\n",
              "      <td>0.003628</td>\n",
              "      <td>-0.003781</td>\n",
              "      <td>-0.004651</td>\n",
              "      <td>0.001152</td>\n",
              "      <td>-0.003373</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.002365</td>\n",
              "      <td>0.003687</td>\n",
              "      <td>0.002450</td>\n",
              "      <td>0.002244</td>\n",
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              "      <td>-0.002982</td>\n",
              "      <td>-0.00551</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>214830</th>\n",
              "      <td>3092.989307</td>\n",
              "      <td>2024-08-06</td>\n",
              "      <td>680237.453012</td>\n",
              "      <td>1.814013e+08</td>\n",
              "      <td>-0.001984</td>\n",
              "      <td>0.003629</td>\n",
              "      <td>-0.003781</td>\n",
              "      <td>-0.004651</td>\n",
              "      <td>0.001232</td>\n",
              "      <td>-0.003373</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.002365</td>\n",
              "      <td>0.003687</td>\n",
              "      <td>0.002723</td>\n",
              "      <td>0.002244</td>\n",
              "      <td>0.003431</td>\n",
              "      <td>-0.002983</td>\n",
              "      <td>-0.00551</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>214831</th>\n",
              "      <td>3092.961285</td>\n",
              "      <td>2024-06-27</td>\n",
              "      <td>680214.947303</td>\n",
              "      <td>1.781403e+08</td>\n",
              "      <td>-0.001984</td>\n",
              "      <td>0.003629</td>\n",
              "      <td>-0.003781</td>\n",
              "      <td>-0.004652</td>\n",
              "      <td>0.001169</td>\n",
              "      <td>-0.003373</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.002365</td>\n",
              "      <td>0.003687</td>\n",
              "      <td>0.002100</td>\n",
              "      <td>0.002244</td>\n",
              "      <td>0.003431</td>\n",
              "      <td>-0.002983</td>\n",
              "      <td>-0.00551</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>214832 rows × 107 columns</p>\n",
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              "\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "result_df_train"
            }
          },
          "metadata": {},
          "execution_count": 207
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "result_df_train"
      ],
      "metadata": {
        "id": "klSss1ZwSYfM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_X = result_df_train[result_df_train['Date'] == pd.to_datetime('2024-08-26')].drop(['iem_end', 'Date','Date_mapped','iem_end_pr_shift'], axis=1)\n",
        "# test_Y: '2024-08-26'인 행의 'iem_end' 추출\n",
        "test_Y = result_df_train[result_df_train['Date'] == pd.to_datetime('2024-08-26')]['iem_end']\n",
        "\n",
        "# train_X: '2024-08-26'이 아닌 행에서 'iem_end'와 'Date' 컬럼 제거\n",
        "train_X = result_df_train[result_df_train['Date'] != pd.to_datetime('2024-08-26')].drop(['iem_end', 'Date','Date_mapped','iem_end_pr_shift'], axis=1)\n",
        "\n",
        "# train_Y: '2024-08-26'이 아닌 행의 'iem_end' 추출\n",
        "train_Y = result_df_train[result_df_train['Date'] != pd.to_datetime('2024-08-26')]['iem_end']"
      ],
      "metadata": {
        "id": "Zp9nVt4RIVxC"
      },
      "execution_count": 208,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "24ZHUnholg7D"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pip install catboost"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uXB7kjztLm_q",
        "outputId": "cb80b265-f439-42bd-84d2-5e249ac81230"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: catboost in /usr/local/lib/python3.10/dist-packages (1.2.7)\n",
            "Requirement already satisfied: graphviz in /usr/local/lib/python3.10/dist-packages (from catboost) (0.20.3)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from catboost) (3.7.1)\n",
            "Requirement already satisfied: numpy<2.0,>=1.16.0 in /usr/local/lib/python3.10/dist-packages (from catboost) (1.26.4)\n",
            "Requirement already satisfied: pandas>=0.24 in /usr/local/lib/python3.10/dist-packages (from catboost) (2.2.2)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from catboost) (1.13.1)\n",
            "Requirement already satisfied: plotly in /usr/local/lib/python3.10/dist-packages (from catboost) (5.24.1)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from catboost) (1.16.0)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.24->catboost) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.24->catboost) (2024.2)\n",
            "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.24->catboost) (2024.2)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (1.3.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (4.54.1)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (1.4.7)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (24.1)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (10.4.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (3.1.4)\n",
            "Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from plotly->catboost) (9.0.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "H2ypWthjlZit"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "from sklearn.ensemble import ExtraTreesClassifier\n",
        "from sklearn import svm\n",
        "from catboost import CatBoostClassifier\n",
        "from lightgbm import LGBMClassifier\n",
        "from sklearn.ensemble import HistGradientBoostingClassifier\n",
        "from sklearn.metrics import f1_score\n",
        "from sklearn.metrics import recall_score\n",
        "from sklearn.model_selection import cross_val_score, KFold\n",
        "from sklearn.svm import SVR\n",
        "from sklearn.metrics import accuracy_score\n",
        "import datetime\n",
        "import nltk\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "##import gensim\n",
        "from gensim.models import Word2Vec\n",
        "from nltk.tokenize import word_tokenize\n",
        "#주가데이터 가져오기\n",
        "\n",
        "#모델 라이브러리\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import BaggingClassifier\n",
        "import xgboost as xgb\n",
        "from sklearn.svm import SVC\n",
        "\n",
        "#모델저장 라이브러리\n",
        "import os\n",
        "models = {\n",
        "\n",
        "    \"Random Forest Classifier\": RandomForestClassifier(random_state=41),\n",
        "\n",
        "\n",
        "\n",
        "    \"Bagging\": BaggingClassifier(random_state=41),\n",
        "\n",
        "   # \"svr\":SVR(),\n",
        "    \"XGBClassifier Classifier\": xgb.XGBClassifier(seed=41),\n",
        "    'LGBM':LGBMClassifier(n_estimators=100,random_state=41,verbose=0),\n",
        "    'catbost':CatBoostClassifier(random_state=41,verbose=0),\n",
        "\n",
        "\n",
        "}\n",
        "lst=[]\n",
        "# 각 모델의 정확도를 출력합니다.\n",
        "for name, model in models.items():\n",
        "\n",
        "\n",
        "        # X_val_fold, y_val_fold를 검증 데이터셋으로 사용하여 모델을 평가\n",
        "      model.fit(train_X, train_Y )\n",
        "      predicted_probabilities = model.predict_proba(test_X)\n",
        "      y_pred=model.predict(test_X)\n",
        "\n",
        "      # 결과 출력\n",
        "      print(predicted_probabilities)\n",
        "\n",
        "      accuracy = accuracy_score(y_pred, test_Y)\n",
        "      # 평균 정확도, F1 점수, Recall 점수 계산\n",
        "      lst.append(y_pred)\n",
        "      print(\"{} Mean Cross-Validation Accuracy Score: {} : \".format(name, accuracy))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pTMTOlVvKQoO",
        "outputId": "853c1a72-643d-4cce-d13a-00ade6a5cf20"
      },
      "execution_count": 209,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0.86       0.14      ]\n",
            " [0.57       0.43      ]\n",
            " [0.52       0.48      ]\n",
            " ...\n",
            " [0.37145062 0.62854938]\n",
            " [0.615      0.385     ]\n",
            " [0.54583333 0.45416667]]\n",
            "Random Forest Classifier Mean Cross-Validation Accuracy Score: 0.5431034482758621 : \n",
            "[[1.   0.  ]\n",
            " [0.5  0.5 ]\n",
            " [0.3  0.7 ]\n",
            " ...\n",
            " [0.2  0.8 ]\n",
            " [0.55 0.45]\n",
            " [0.7  0.3 ]]\n",
            "Bagging Mean Cross-Validation Accuracy Score: 0.5933908045977011 : \n",
            "[[0.5105687  0.48943132]\n",
            " [0.5475247  0.45247534]\n",
            " [0.5111648  0.48883522]\n",
            " ...\n",
            " [0.11699009 0.8830099 ]\n",
            " [0.58698934 0.41301066]\n",
            " [0.5592879  0.44071212]]\n",
            "XGBClassifier Classifier Mean Cross-Validation Accuracy Score: 0.3201149425287356 : \n",
            "[[0.50618909 0.49381091]\n",
            " [0.53805237 0.46194763]\n",
            " [0.48142758 0.51857242]\n",
            " ...\n",
            " [0.20376385 0.79623615]\n",
            " [0.51373761 0.48626239]\n",
            " [0.50202278 0.49797722]]\n",
            "LGBM Mean Cross-Validation Accuracy Score: 0.28735632183908044 : \n",
            "[[0.58735305 0.41264695]\n",
            " [0.587213   0.412787  ]\n",
            " [0.58941955 0.41058045]\n",
            " ...\n",
            " [0.18172955 0.81827045]\n",
            " [0.41693531 0.58306469]\n",
            " [0.63721025 0.36278975]]\n",
            "catbost Mean Cross-Validation Accuracy Score: 0.3454022988505747 : \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "result_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "id": "gPbHjL0tLheN",
        "outputId": "dcac1c0e-214f-4738-84b7-5f806fe36b54"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        tck_iem_cd       Date  iem_end_pr  acl_trd_qty Title  vector_1  \\\n",
              "100878           0 2024-05-28       43.20    7888482.0             0.0   \n",
              "100891           0 2024-05-29       43.93    5275412.0             0.0   \n",
              "100847           0 2024-05-30       44.54    5824970.0             0.0   \n",
              "100887           0 2024-05-31       44.27    4527342.0             0.0   \n",
              "100874           0 2024-06-03       44.01    3887113.0             0.0   \n",
              "...            ...        ...         ...          ...   ...       ...   \n",
              "100819        3093 2024-08-20        7.65      80521.0             0.0   \n",
              "100795        3093 2024-08-21        7.73      53251.0             0.0   \n",
              "100815        3093 2024-08-22        7.75      78763.0             0.0   \n",
              "100804        3093 2024-08-23        8.19     109444.0             0.0   \n",
              "100837        3093 2024-08-26        8.35     131525.0             0.0   \n",
              "\n",
              "        vector_2  vector_3  vector_4  vector_5  ...  vector_93  vector_94  \\\n",
              "100878       0.0       0.0       0.0       0.0  ...        0.0        0.0   \n",
              "100891       0.0       0.0       0.0       0.0  ...        0.0        0.0   \n",
              "100847       0.0       0.0       0.0       0.0  ...        0.0        0.0   \n",
              "100887       0.0       0.0       0.0       0.0  ...        0.0        0.0   \n",
              "100874       0.0       0.0       0.0       0.0  ...        0.0        0.0   \n",
              "...          ...       ...       ...       ...  ...        ...        ...   \n",
              "100819       0.0       0.0       0.0       0.0  ...        0.0        0.0   \n",
              "100795       0.0       0.0       0.0       0.0  ...        0.0        0.0   \n",
              "100815       0.0       0.0       0.0       0.0  ...        0.0        0.0   \n",
              "100804       0.0       0.0       0.0       0.0  ...        0.0        0.0   \n",
              "100837       0.0       0.0       0.0       0.0  ...        0.0        0.0   \n",
              "\n",
              "        vector_95  vector_96  vector_97  vector_98  vector_99  vector_100  \\\n",
              "100878        0.0        0.0        0.0        0.0        0.0         0.0   \n",
              "100891        0.0        0.0        0.0        0.0        0.0         0.0   \n",
              "100847        0.0        0.0        0.0        0.0        0.0         0.0   \n",
              "100887        0.0        0.0        0.0        0.0        0.0         0.0   \n",
              "100874        0.0        0.0        0.0        0.0        0.0         0.0   \n",
              "...           ...        ...        ...        ...        ...         ...   \n",
              "100819        0.0        0.0        0.0        0.0        0.0         0.0   \n",
              "100795        0.0        0.0        0.0        0.0        0.0         0.0   \n",
              "100815        0.0        0.0        0.0        0.0        0.0         0.0   \n",
              "100804        0.0        0.0        0.0        0.0        0.0         0.0   \n",
              "100837        0.0        0.0        0.0        0.0        0.0         0.0   \n",
              "\n",
              "        iem_end_pr_shift  iem_end  \n",
              "100878             43.93        1  \n",
              "100891             44.54        1  \n",
              "100847             44.27        0  \n",
              "100887             44.01        0  \n",
              "100874             42.01        0  \n",
              "...                  ...      ...  \n",
              "100819              7.73        1  \n",
              "100795              7.75        1  \n",
              "100815              8.19        1  \n",
              "100804              8.35        1  \n",
              "100837               NaN        0  \n",
              "\n",
              "[194832 rows x 107 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3edbdef2-27b8-494a-93cc-6e48894c1b98\" class=\"colab-df-container\">\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tck_iem_cd</th>\n",
              "      <th>Date</th>\n",
              "      <th>iem_end_pr</th>\n",
              "      <th>acl_trd_qty</th>\n",
              "      <th>Title</th>\n",
              "      <th>vector_1</th>\n",
              "      <th>vector_2</th>\n",
              "      <th>vector_3</th>\n",
              "      <th>vector_4</th>\n",
              "      <th>vector_5</th>\n",
              "      <th>...</th>\n",
              "      <th>vector_93</th>\n",
              "      <th>vector_94</th>\n",
              "      <th>vector_95</th>\n",
              "      <th>vector_96</th>\n",
              "      <th>vector_97</th>\n",
              "      <th>vector_98</th>\n",
              "      <th>vector_99</th>\n",
              "      <th>vector_100</th>\n",
              "      <th>iem_end_pr_shift</th>\n",
              "      <th>iem_end</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>100878</th>\n",
              "      <td>0</td>\n",
              "      <td>2024-05-28</td>\n",
              "      <td>43.20</td>\n",
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              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>100891</th>\n",
              "      <td>0</td>\n",
              "      <td>2024-05-29</td>\n",
              "      <td>43.93</td>\n",
              "      <td>5275412.0</td>\n",
              "      <td></td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>100847</th>\n",
              "      <td>0</td>\n",
              "      <td>2024-05-30</td>\n",
              "      <td>44.54</td>\n",
              "      <td>5824970.0</td>\n",
              "      <td></td>\n",
              "      <td>0.0</td>\n",
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              "      <td>44.27</td>\n",
              "      <td>0</td>\n",
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              "      <th>100887</th>\n",
              "      <td>0</td>\n",
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              "      <td>44.27</td>\n",
              "      <td>4527342.0</td>\n",
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              "      <td>3887113.0</td>\n",
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              "      <th>100819</th>\n",
              "      <td>3093</td>\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-3edbdef2-27b8-494a-93cc-6e48894c1b98 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-3edbdef2-27b8-494a-93cc-6e48894c1b98');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-c138d1bb-facd-49e3-bebd-170de1bf4d15\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c138d1bb-facd-49e3-bebd-170de1bf4d15')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-c138d1bb-facd-49e3-bebd-170de1bf4d15 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_8f0f0576-1907-4125-941a-a9d8680cd9a5\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('result_df')\"\n",
              "            title=\"Generate code using this dataframe.\"\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=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_8f0f0576-1907-4125-941a-a9d8680cd9a5 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('result_df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "result_df"
            }
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "mRjy2niejbWR"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.12.7"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}