金融大數據分析_week3作業_技術分析與K線圖類型.ipynb

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        "<a href=\"https://colab.research.google.com/github/Kimeteor309/Financial-big-data-analysis/blob/main/%E9%87%91%E8%9E%8D%E5%A4%A7%E6%95%B8%E6%93%9A%E5%88%86%E6%9E%90_week3%E4%BD%9C%E6%A5%AD_%E6%8A%80%E8%A1%93%E5%88%86%E6%9E%90%E8%88%87K%E7%B7%9A%E5%9C%96%E9%A1%9E%E5%9E%8B.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "# **取得股價資料**"
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              "          Date    Open    High     Low   Close    Volume\n",
              "0   2024-04-01   783.0   783.0   769.0   770.0  22348250\n",
              "1   2024-04-02   784.0   790.0   783.0   790.0  42219075\n",
              "2   2024-04-03   783.0   785.0   778.0   780.0  32909892\n",
              "3   2024-04-08   789.0   792.0   783.0   783.0  40567580\n",
              "4   2024-04-09   795.0   820.0   792.0   819.0  61642573\n",
              "..         ...     ...     ...     ...     ...       ...\n",
              "118 2024-09-23   971.0   977.0   971.0   977.0  15399658\n",
              "119 2024-09-24   976.0   987.0   971.0   987.0  29324170\n",
              "120 2024-09-25  1000.0  1005.0   998.0  1005.0  43039991\n",
              "121 2024-09-26  1010.0  1015.0  1005.0  1015.0  43341810\n",
              "122 2024-09-27  1020.0  1025.0  1000.0  1000.0  37665070\n",
              "\n",
              "[123 rows x 6 columns]"
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              "      <th>1</th>\n",
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              "      <td>2024-09-26</td>\n",
              "      <td>1010.0</td>\n",
              "      <td>1015.0</td>\n",
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 123,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2024-04-01 00:00:00\",\n        \"max\": \"2024-09-27 00:00:00\",\n        \"num_unique_values\": 123,\n        \"samples\": [\n          \"2024-04-29 00:00:00\",\n          \"2024-06-06 00:00:00\",\n          \"2024-06-11 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 78.32439285301663,\n        \"min\": 740.0,\n        \"max\": 1065.0,\n        \"num_unique_values\": 88,\n        \"samples\": [\n          950.0,\n          783.0,\n          830.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"High\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 80.47194939528644,\n        \"min\": 757.0,\n        \"max\": 1080.0,\n        \"num_unique_values\": 89,\n        \"samples\": [\n          925.0,\n          977.0,\n          872.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 78.30396879291524,\n        \"min\": 740.0,\n        \"max\": 1055.0,\n        \"num_unique_values\": 91,\n        \"samples\": [\n          911.0,\n          818.0,\n          1000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 81.00175730270023,\n        \"min\": 742.0,\n        \"max\": 1080.0,\n        \"num_unique_values\": 88,\n        \"samples\": [\n          964.0,\n          770.0,\n          864.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 24207747,\n        \"min\": 14456888,\n        \"max\": 159662795,\n        \"num_unique_values\": 123,\n        \"samples\": [\n          30704200,\n          67300344,\n          57435637\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 64
        }
      ],
      "source": [
        "import requests\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# 將民國年轉換為西元年\n",
        "def convert_to_gregorian(date_str):\n",
        "    year, month, day = date_str.split('/')\n",
        "    year = str(int(year) + 1911)  # 民國年轉換為西元年\n",
        "    return f'{year}/{month}/{day}'\n",
        "\n",
        "# 從台灣證券交易所 API 獲取每日收盤價\n",
        "def get_twse_data(stock_id, date):\n",
        "    url = f\"https://www.twse.com.tw/exchangeReport/STOCK_DAY?response=json&date={date}&stockNo={stock_id}\"\n",
        "    response = requests.get(url)\n",
        "    data = response.json()\n",
        "\n",
        "    # 解析 JSON 並轉換為 DataFrame\n",
        "    columns = data['fields']\n",
        "    rows = data['data']\n",
        "    df = pd.DataFrame(rows, columns=columns)\n",
        "\n",
        "    # 將日期轉換為西元年\n",
        "    df['日期'] = df['日期'].apply(convert_to_gregorian)\n",
        "    df['日期'] = pd.to_datetime(df['日期'], format='%Y/%m/%d')  # 指定西元年格式\n",
        "\n",
        "    # 只保留你需要的欄位，並重新命名為英文字段\n",
        "    df = df[['日期', '開盤價', '最高價', '最低價', '收盤價', '成交股數']].copy()\n",
        "    df.columns = ['Date', 'Open', 'High', 'Low', 'Close', 'Volume']\n",
        "\n",
        "    # 將數值欄位轉為數字類型，並去除逗號\n",
        "    df['Open'] = pd.to_numeric(df['Open'].str.replace(',', ''))\n",
        "    df['High'] = pd.to_numeric(df['High'].str.replace(',', ''))\n",
        "    df['Low'] = pd.to_numeric(df['Low'].str.replace(',', ''))\n",
        "    df['Close'] = pd.to_numeric(df['Close'].str.replace(',', ''))\n",
        "    df['Volume'] = pd.to_numeric(df['Volume'].str.replace(',', ''))\n",
        "\n",
        "    return df\n",
        "\n",
        "# 獲取一段日期範圍內的股價資料\n",
        "def get_data_range(stock_id, start_year, start_month, end_month):\n",
        "    all_data = pd.DataFrame()  # 用於存放所有月份的數據\n",
        "    for month in range(start_month, end_month + 1):\n",
        "        # 日期格式：YYYYMMDD，只需要指定月份的第一天\n",
        "        date = f\"{start_year}{month:02d}01\"\n",
        "        df_month = get_twse_data(stock_id, date)\n",
        "        all_data = pd.concat([all_data, df_month], ignore_index=True)  # 合併時確保索引正確\n",
        "\n",
        "    return all_data\n",
        "\n",
        "# 取得台積電從4月至9月的股價資料\n",
        "stock_id = '2330'  # 台積電股票代號\n",
        "df = get_data_range(stock_id, 2024, 4, 9)\n",
        "\n",
        "# 顯示資料\n",
        "df"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **1. 計算技術指標（SMA、RSI、MACD）**\n"
      ],
      "metadata": {
        "id": "TXxsxTi0d22c"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1.1 計算簡單移動平均線（SMA）"
      ],
      "metadata": {
        "id": "q5Pu9qLCgXSr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 計算8日 SMA\n",
        "df['8_SMA'] = df['Close'].rolling(window=8).mean()\n",
        "\n",
        "# 計算13日 SMA\n",
        "df['13_SMA'] = df['Close'].rolling(window=13).mean()\n",
        "\n",
        "# 計算20日 SMA\n",
        "df['20_SMA'] = df['Close'].rolling(window=20).mean()\n",
        "\n",
        "# 列印最後筆數\n",
        "df.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "xkzPB15Md8Z6",
        "outputId": "a5270fd5-a0f9-4c70-89fa-3001fae03cec"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          Date    Open    High     Low   Close    Volume    8_SMA      13_SMA  \\\n",
              "118 2024-09-23   971.0   977.0   971.0   977.0  15399658  948.250  930.615385   \n",
              "119 2024-09-24   976.0   987.0   971.0   987.0  29324170  959.000  938.153846   \n",
              "120 2024-09-25  1000.0  1005.0   998.0  1005.0  43039991  967.125  946.076923   \n",
              "121 2024-09-26  1010.0  1015.0  1005.0  1015.0  43341810  975.625  953.538462   \n",
              "122 2024-09-27  1020.0  1025.0  1000.0  1000.0  37665070  982.250  961.307692   \n",
              "\n",
              "     20_SMA  \n",
              "118  936.45  \n",
              "119  938.30  \n",
              "120  941.45  \n",
              "121  944.00  \n",
              "122  946.85  "
            ],
            "text/html": [
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              "    <div>\n",
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              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Open</th>\n",
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              "      <td>2024-09-26</td>\n",
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              "      <td>1005.0</td>\n",
              "      <td>1015.0</td>\n",
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              "    <tr>\n",
              "      <th>122</th>\n",
              "      <td>2024-09-27</td>\n",
              "      <td>1020.0</td>\n",
              "      <td>1025.0</td>\n",
              "      <td>1000.0</td>\n",
              "      <td>1000.0</td>\n",
              "      <td>37665070</td>\n",
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2024-09-23 00:00:00\",\n        \"max\": \"2024-09-27 00:00:00\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"2024-09-24 00:00:00\",\n          \"2024-09-27 00:00:00\",\n          \"2024-09-25 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 21.279097725232617,\n        \"min\": 971.0,\n        \"max\": 1020.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          976.0,\n          1020.0,\n          1000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"High\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19.728152473052308,\n        \"min\": 977.0,\n        \"max\": 1025.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          987.0,\n          1025.0,\n          1005.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16.62828914831589,\n        \"min\": 971.0,\n        \"max\": 1005.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          998.0,\n          1000.0,\n          971.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.973309587395834,\n        \"min\": 977.0,\n        \"max\": 1015.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          987.0,\n          1000.0,\n          1005.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11725585,\n        \"min\": 15399658,\n        \"max\": 43341810,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          29324170,\n          37665070,\n          43039991\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"8_SMA\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13.425896152585123,\n        \"min\": 948.25,\n        \"max\": 982.25,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          959.0,\n          982.25,\n          967.125\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"13_SMA\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12.138671149598949,\n        \"min\": 930.6153846153846,\n        \"max\": 961.3076923076923,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          938.1538461538462,\n          961.3076923076923,\n          946.0769230769231\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"20_SMA\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.200505921909886,\n        \"min\": 936.45,\n        \"max\": 946.85,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          938.3,\n          946.85,\n          941.45\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1.2 計算相對強弱指數（RSI）\n",
        "\n",
        "<img src=\"https://images.stockfeel.com.tw/stockfeelimage/2015/10/%E6%95%99%E4%BD%A0%E7%9C%8B%E6%87%82-RSI-%E7%9B%B8%E5%B0%8D%E5%BC%B7%E5%BC%B1%E6%8C%87%E6%A8%99-01.jpg\" alt=\"rsi\" width=\"500\"/>\n",
        "<img src=\"https://images.stockfeel.com.tw/stockfeelimage/2015/10/%E6%95%99%E4%BD%A0%E7%9C%8B%E6%87%82-RSI-%E7%9B%B8%E5%B0%8D%E5%BC%B7%E5%BC%B1%E6%8C%87%E6%A8%99-02.jpg\" alt=\"rs\" width=\"500\"/>\n",
        "\n",
        "**RSI 的計算步驟如下：**\n",
        "\n",
        "**1. 變動計算 (Change)：**計算每個交易日的價格變動，即當天收盤價與前一天收盤價的差異。\n",
        "\n",
        "**2. 增益與損失：**將變動中的正數部分計為增益（Gain），負數部分計為損失（Loss），並將它們取為正值或 0。\n",
        "\n",
        "**3. 平均增益與損失：**使用滾動窗口（14天）來計算過去14天的平均增益和平均損失。\n",
        "\n",
        "**4. 計算 RSI：**計算相對強度（RS）作為平均增益和平均損失的比值，最後根據 RSI 公式計算 RSI。\n"
      ],
      "metadata": {
        "id": "oaf8ojuMgelG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 計算每個交易日的價格變動，即當天收盤價與前一天收盤價的差異\n",
        "df['Change'] = df['Close'] - df['Close'].shift(1)\n",
        "\n",
        "# 將變動值中的正數部分視為“Gain”（增益），如果變動為負則記為0\n",
        "df['Gain'] = df['Change'].apply(lambda x: x if x > 0 else 0)\n",
        "\n",
        "# 將變動值中的負數部分視為“Loss”（損失），取其絕對值，如果變動為正則記為0\n",
        "df['Loss'] = df['Change'].apply(lambda x: abs(x) if x < 0 else 0)\n",
        "\n",
        "df.tail()\n",
        "\n",
        "# 計算過去14個交易日的平均增益\n",
        "df['Avg Gain'] = df['Gain'].rolling(window=14).mean()\n",
        "\n",
        "# 計算過去14個交易日的平均損失\n",
        "df['Avg Loss'] = df['Loss'].rolling(window=14).mean()\n",
        "\n",
        "# 計算相對強弱指數的相對強度（RS），即平均增益與平均損失的比值\n",
        "df['RS'] = df['Avg Gain'] / df['Avg Loss']\n",
        "\n",
        "# 計算相對強弱指數（RSI），使用RS公式計算出來\n",
        "df['RSI'] = 100 - (100 / (1 + df['RS']))\n",
        "\n",
        "# 顯示最後幾行的數據\n",
        "df.tail()"
      ],
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          "height": 330
        },
        "id": "UdmKcStNghpf",
        "outputId": "feb94ee1-5ff8-43d3-caec-1649b25ea6e1"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          Date    Open    High     Low   Close    Volume    8_SMA      13_SMA  \\\n",
              "118 2024-09-23   971.0   977.0   971.0   977.0  15399658  948.250  930.615385   \n",
              "119 2024-09-24   976.0   987.0   971.0   987.0  29324170  959.000  938.153846   \n",
              "120 2024-09-25  1000.0  1005.0   998.0  1005.0  43039991  967.125  946.076923   \n",
              "121 2024-09-26  1010.0  1015.0  1005.0  1015.0  43341810  975.625  953.538462   \n",
              "122 2024-09-27  1020.0  1025.0  1000.0  1000.0  37665070  982.250  961.307692   \n",
              "\n",
              "     20_SMA  Change  Gain  Loss   Avg Gain  Avg Loss        RS        RSI  \n",
              "118  936.45     4.0   4.0   0.0   8.285714  6.214286  1.333333  57.142857  \n",
              "119  938.30    10.0  10.0   0.0   9.000000  5.642857  1.594937  61.463415  \n",
              "120  941.45    18.0  18.0   0.0  10.285714  2.000000  5.142857  83.720930  \n",
              "121  944.00    10.0  10.0   0.0  10.071429  2.000000  5.035714  83.431953  \n",
              "122  946.85   -15.0   0.0  15.0   8.928571  3.071429  2.906977  74.404762  "
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              "      <td>2024-09-26</td>\n",
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              "      <td>43341810</td>\n",
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            }
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1.3 計算平滑異同移動平均線（MACD）\n",
        "\n",
        "要計算移動平均收斂發散指數（MACD），首先需要計算以下幾個關鍵指標：\n",
        "\n",
        "1. 8天的指數平滑移動平均線（EMA_8）。\n",
        "2. 13天的指數平滑移動平均線（EMA_13）。\n",
        "3. MACD 線：8天 EMA 減去 13天 EMA。\n",
        "\n",
        "---\n",
        "補充：常見的標準 MACD 使用 12日和 26日 EMA，適合較中長期的趨勢分析。而 8日和 13日 EMA 的組合則更加適合短期的波動。\n",
        "\n"
      ],
      "metadata": {
        "id": "e30duzPPmAAu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 計算8天的指數移動平均線（EMA），並將其存儲在 'EMA_8' 列中\n",
        "# adjust=False 表示在計算 EMA 時，只考慮當前值和前一個 EMA 的遞歸計算方式，而不考慮所有歷史數據\n",
        "df['EMA_8'] = df['Close'].ewm(span=8, adjust=False).mean()\n",
        "\n",
        "# 計算13天的指數移動平均線（EMA），並將其存儲在 'EMA_13' 列中\n",
        "df['EMA_13'] = df['Close'].ewm(span=13, adjust=False).mean()\n",
        "\n",
        "# 計算MACD線：8天EMA減去13天EMA，並將其存儲在 'MACD' 列中\n",
        "df['MACD'] = df['EMA_8'] - df['EMA_13']\n",
        "\n",
        "# 顯示DataFrame的最後幾行數據\n",
        "df.head(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 233
        },
        "id": "B1vX61J2mFiZ",
        "outputId": "9d9e2231-8c96-4f30-b5ee-a3d14123f74d"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Date   Open   High    Low  Close    Volume  8_SMA  13_SMA  20_SMA  \\\n",
              "0 2024-04-01  783.0  783.0  769.0  770.0  22348250    NaN     NaN     NaN   \n",
              "1 2024-04-02  784.0  790.0  783.0  790.0  42219075    NaN     NaN     NaN   \n",
              "2 2024-04-03  783.0  785.0  778.0  780.0  32909892    NaN     NaN     NaN   \n",
              "\n",
              "   Change  Gain  Loss  Avg Gain  Avg Loss  RS  RSI       EMA_8      EMA_13  \\\n",
              "0     NaN   0.0   0.0       NaN       NaN NaN  NaN  770.000000  770.000000   \n",
              "1    20.0  20.0   0.0       NaN       NaN NaN  NaN  774.444444  772.857143   \n",
              "2   -10.0   0.0  10.0       NaN       NaN NaN  NaN  775.679012  773.877551   \n",
              "\n",
              "       MACD  \n",
              "0  0.000000  \n",
              "1  1.587302  \n",
              "2  1.801461  "
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              "    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-be2d4de7-3e3e-4873-bb77-80b04200f362 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 123,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2024-04-01 00:00:00\",\n        \"max\": \"2024-09-27 00:00:00\",\n        \"num_unique_values\": 123,\n        \"samples\": [\n          \"2024-04-29 00:00:00\",\n          \"2024-06-06 00:00:00\",\n          \"2024-06-11 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 78.32439285301663,\n        \"min\": 740.0,\n        \"max\": 1065.0,\n        \"num_unique_values\": 88,\n        \"samples\": [\n          950.0,\n          783.0,\n          830.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"High\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 80.47194939528644,\n        \"min\": 757.0,\n        \"max\": 1080.0,\n        \"num_unique_values\": 89,\n        \"samples\": [\n          925.0,\n          977.0,\n          872.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 78.30396879291524,\n        \"min\": 740.0,\n        \"max\": 1055.0,\n        \"num_unique_values\": 91,\n        \"samples\": [\n          911.0,\n          818.0,\n          1000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 81.00175730270023,\n        \"min\": 742.0,\n        \"max\": 1080.0,\n        \"num_unique_values\": 88,\n        \"samples\": [\n          964.0,\n          770.0,\n          864.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 24207747,\n        \"min\": 14456888,\n        \"max\": 159662795,\n        \"num_unique_values\": 123,\n        \"samples\": [\n          30704200,\n          67300344,\n          57435637\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"8_SMA\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 75.68923448653082,\n        \"min\": 770.25,\n        \"max\": 1045.625,\n        \"num_unique_values\": 114,\n        \"samples\": [\n          906.0,\n          809.25,\n          856.75\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"13_SMA\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 73.14503303371698,\n        \"min\": 777.0769230769231,\n        \"max\": 1025.3076923076924,\n        \"num_unique_values\": 108,\n        \"samples\": [\n          920.6923076923077,\n          777.5384615384615,\n          789.9230769230769\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"20_SMA\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 69.07615962141581,\n        \"min\": 786.0,\n        \"max\": 1003.05,\n        \"num_unique_values\": 97,\n        \"samples\": [\n          984.15,\n          916.65,\n          938.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Change\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 21.884821231900798,\n        \"min\": -88.0,\n        \"max\": 65.0,\n        \"num_unique_values\": 59,\n        \"samples\": [\n          20.0,\n          5.0,\n          10.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gain\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12.444840311544702,\n        \"min\": 0.0,\n        \"max\": 65.0,\n        \"num_unique_values\": 32,\n        \"samples\": [\n          7.0,\n          11.0,\n          19.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Loss\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.17360141045868,\n        \"min\": 0.0,\n        \"max\": 88.0,\n        \"num_unique_values\": 28,\n        \"samples\": [\n          5.0,\n          21.0,\n          17.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Avg Gain\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.0484926353790054,\n        \"min\": 3.2142857142857144,\n        \"max\": 16.857142857142858,\n        \"num_unique_values\": 66,\n        \"samples\": [\n          13.357142857142858,\n          6.071428571428571,\n          5.714285714285714\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Avg Loss\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.394555212117103,\n        \"min\": 0.8571428571428571,\n        \"max\": 23.285714285714285,\n        \"num_unique_values\": 64,\n        \"samples\": [\n          3.7857142857142856,\n          9.357142857142858,\n          7.714285714285714\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RS\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.5111027902694176,\n        \"min\": 0.30981595092024544,\n        \"max\": 8.916666666666668,\n        \"num_unique_values\": 105,\n        \"samples\": [\n          1.0,\n          0.6153846153846153,\n          0.5203619909502262\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RSI\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15.761623110088705,\n        \"min\": 23.65339578454332,\n        \"max\": 89.91596638655463,\n        \"num_unique_values\": 105,\n        \"samples\": [\n          50.0,\n          38.095238095238095,\n          34.22619047619047\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EMA_8\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 77.85943415718721,\n        \"min\": 770.0,\n        \"max\": 1037.2117664403186,\n        \"num_unique_values\": 123,\n        \"samples\": [\n          779.8616887975522,\n          856.9324476279534,\n          866.5393818983915\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EMA_13\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 76.86572317155293,\n        \"min\": 770.0,\n        \"max\": 1022.0512714450828,\n        \"num_unique_values\": 123,\n        \"samples\": [\n          780.9521955541065,\n          851.1416972138446,\n          859.1041040754777\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MACD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.987898279673773,\n        \"min\": -21.78861294239482,\n        \"max\": 20.997847840523946,\n        \"num_unique_values\": 123,\n        \"samples\": [\n          -1.090506756554305,\n          5.790750414108743,\n          7.435277822913804\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **2. 可視化 K 線圖**"
      ],
      "metadata": {
        "id": "_uOQvE-Zd80q"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2.1 可視化收盤價折線圖"
      ],
      "metadata": {
        "id": "PtYLQBwdrrfC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# 畫布尺寸大小設定\n",
        "plt.figure(figsize=(12, 7))\n",
        "\n",
        "# 設定要繪製的資料欄位\n",
        "plt.plot(df['Close'], label='Close')\n",
        "\n",
        "# 設定 x 軸的時間\n",
        "num = 10\n",
        "date = df[\"Date\"].dt.strftime('%Y-%m-%d')\n",
        "plt.xticks(date[::len(date)//num].index,  # 設定在 x 軸上顯示標籤的位置（索引），這些位置間隔為 len(date)//num\n",
        "           date[::len(date)//num],  # 設定顯示在 x 軸上的日期標籤，數量為 num，間隔為 len(date)//num\n",
        "           rotation = 45) # 讓日期標籤旋轉 45 度，以避免標籤之間重疊，提高可讀性\n",
        "\n",
        "# 設定圖表的標題，x 軸和 y 軸的標籤\n",
        "plt.title(f'{stock_id}')   # 將股票代號設為圖標\n",
        "plt.xlabel('Date')         # x 軸標籤\n",
        "plt.ylabel('Price')  # y 軸標籤\n",
        "plt.legend(loc='upper left') # 在左上角顯示圖例\n",
        "plt.grid(True) # 在圖上顯示網格\n",
        "plt.tight_layout()  # 自動調整圖表中的間距，避免元素之間的重疊\n",
        "\n",
        "# 顯示圖表\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 597
        },
        "id": "zFHposuueAXR",
        "outputId": "88c41837-1c7b-467d-b6d5-c6a4eb44589a"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x700 with 1 Axes>"
            ],
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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2.2 加入技術指標"
      ],
      "metadata": {
        "id": "mqhqC-5fxMhQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 畫布尺寸大小設定\n",
        "plt.figure(figsize=(12, 7))\n",
        "\n",
        "# 設定要繪製的資料欄位\n",
        "plt.plot(df['Date'], df['Close'], label='Closing Price')\n",
        "\n",
        "# 8 天的 SMA\n",
        "plt.plot(df['Date'], df['8_SMA'], label='7-Day SMA', color='red', alpha=.5)\n",
        "\n",
        "# 13 天的 SMA\n",
        "plt.plot(df['Date'], df['13_SMA'], label='13-Day SMA', color='orange', alpha=.5)\n",
        "\n",
        "# 20 天的 SMA\n",
        "plt.plot(df['Date'], df['20_SMA'], label='20-Day SMA', color='green', alpha=.5)\n",
        "\n",
        "# 將股票代號設為圖標\n",
        "plt.title(f'{stock_id}')\n",
        "plt.xlabel('Date')\n",
        "plt.ylabel('Price')\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 633
        },
        "id": "o376p7NDxPWS",
        "outputId": "fbbe15d5-34c0-4891-ca7e-85312273509b"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2.3 K 線"
      ],
      "metadata": {
        "id": "VFWq6MxQxZqC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install mplfinance"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MhrtRPeDxre8",
        "outputId": "21fc73d0-e879-4a2a-82fe-adebdcdc6553"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting mplfinance\n",
            "  Downloading mplfinance-0.12.10b0-py3-none-any.whl.metadata (19 kB)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from mplfinance) (3.7.1)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from mplfinance) (2.1.4)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->mplfinance) (1.3.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib->mplfinance) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->mplfinance) (4.53.1)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->mplfinance) (1.4.7)\n",
            "Requirement already satisfied: numpy>=1.20 in /usr/local/lib/python3.10/dist-packages (from matplotlib->mplfinance) (1.26.4)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->mplfinance) (24.1)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->mplfinance) (10.4.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->mplfinance) (3.1.4)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib->mplfinance) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->mplfinance) (2024.2)\n",
            "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas->mplfinance) (2024.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib->mplfinance) (1.16.0)\n",
            "Downloading mplfinance-0.12.10b0-py3-none-any.whl (75 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m75.0/75.0 kB\u001b[0m \u001b[31m2.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: mplfinance\n",
            "Successfully installed mplfinance-0.12.10b0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 將原始的 dataframe 再建立一個出來\n",
        "kplot_df = df.set_index('Date')\n",
        "kplot_df['2024-8-01':'2024-09-18'].tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 361
        },
        "id": "OuugeTKOxwCh",
        "outputId": "26738324-d048-4f33-f92c-3d1f3970fb96"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             Open   High    Low  Close    Volume    8_SMA      13_SMA  20_SMA  \\\n",
              "Date                                                                            \n",
              "2024-09-11  906.0  906.0  900.0  901.0  19513256  912.625  926.461538  938.00   \n",
              "2024-09-12  936.0  944.0  928.0  940.0  43749260  911.625  925.692308  937.85   \n",
              "2024-09-13  955.0  955.0  939.0  947.0  28307441  912.500  926.076923  936.75   \n",
              "2024-09-16  952.0  952.0  943.0  947.0  14456888  919.750  924.769231  935.45   \n",
              "2024-09-18  945.0  948.0  933.0  941.0  30094662  924.625  924.615385  933.85   \n",
              "\n",
              "            Change  Gain  Loss  Avg Gain  Avg Loss        RS        RSI  \\\n",
              "Date                                                                      \n",
              "2024-09-11    -3.0   0.0   3.0  4.428571  8.000000  0.553571  35.632184   \n",
              "2024-09-12    39.0  39.0   0.0  7.214286  7.857143  0.918182  47.867299   \n",
              "2024-09-13     7.0   7.0   0.0  7.642857  7.857143  0.972727  49.308756   \n",
              "2024-09-16     0.0   0.0   0.0  7.642857  7.285714  1.049020  51.196172   \n",
              "2024-09-18    -6.0   0.0   6.0  6.071429  7.714286  0.787037  44.041451   \n",
              "\n",
              "                 EMA_8      EMA_13      MACD  \n",
              "Date                                          \n",
              "2024-09-11  912.528220  920.382034 -7.853815  \n",
              "2024-09-12  918.633060  923.184601 -4.551541  \n",
              "2024-09-13  924.936824  926.586801 -1.649976  \n",
              "2024-09-16  929.839752  929.502972  0.336780  \n",
              "2024-09-18  932.319807  931.145405  1.174403  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d104c7cd-836c-4b2a-91c4-3fe484d58bd8\" 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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              "      <th>Volume</th>\n",
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              "      <th>Avg Loss</th>\n",
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              "      <th>Date</th>\n",
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              "      <th></th>\n",
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              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
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              "      <th>2024-09-11</th>\n",
              "      <td>906.0</td>\n",
              "      <td>906.0</td>\n",
              "      <td>900.0</td>\n",
              "      <td>901.0</td>\n",
              "      <td>19513256</td>\n",
              "      <td>912.625</td>\n",
              "      <td>926.461538</td>\n",
              "      <td>938.00</td>\n",
              "      <td>-3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
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              "      <td>8.000000</td>\n",
              "      <td>0.553571</td>\n",
              "      <td>35.632184</td>\n",
              "      <td>912.528220</td>\n",
              "      <td>920.382034</td>\n",
              "      <td>-7.853815</td>\n",
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              "    <tr>\n",
              "      <th>2024-09-12</th>\n",
              "      <td>936.0</td>\n",
              "      <td>944.0</td>\n",
              "      <td>928.0</td>\n",
              "      <td>940.0</td>\n",
              "      <td>43749260</td>\n",
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              "      <td>925.692308</td>\n",
              "      <td>937.85</td>\n",
              "      <td>39.0</td>\n",
              "      <td>39.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.214286</td>\n",
              "      <td>7.857143</td>\n",
              "      <td>0.918182</td>\n",
              "      <td>47.867299</td>\n",
              "      <td>918.633060</td>\n",
              "      <td>923.184601</td>\n",
              "      <td>-4.551541</td>\n",
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              "    <tr>\n",
              "      <th>2024-09-13</th>\n",
              "      <td>955.0</td>\n",
              "      <td>955.0</td>\n",
              "      <td>939.0</td>\n",
              "      <td>947.0</td>\n",
              "      <td>28307441</td>\n",
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              "      <td>926.076923</td>\n",
              "      <td>936.75</td>\n",
              "      <td>7.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.642857</td>\n",
              "      <td>7.857143</td>\n",
              "      <td>0.972727</td>\n",
              "      <td>49.308756</td>\n",
              "      <td>924.936824</td>\n",
              "      <td>926.586801</td>\n",
              "      <td>-1.649976</td>\n",
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              "    <tr>\n",
              "      <th>2024-09-16</th>\n",
              "      <td>952.0</td>\n",
              "      <td>952.0</td>\n",
              "      <td>943.0</td>\n",
              "      <td>947.0</td>\n",
              "      <td>14456888</td>\n",
              "      <td>919.750</td>\n",
              "      <td>924.769231</td>\n",
              "      <td>935.45</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.642857</td>\n",
              "      <td>7.285714</td>\n",
              "      <td>1.049020</td>\n",
              "      <td>51.196172</td>\n",
              "      <td>929.839752</td>\n",
              "      <td>929.502972</td>\n",
              "      <td>0.336780</td>\n",
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              "    <tr>\n",
              "      <th>2024-09-18</th>\n",
              "      <td>945.0</td>\n",
              "      <td>948.0</td>\n",
              "      <td>933.0</td>\n",
              "      <td>941.0</td>\n",
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              "      <td>933.85</td>\n",
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              "      <td>6.0</td>\n",
              "      <td>6.071429</td>\n",
              "      <td>7.714286</td>\n",
              "      <td>0.787037</td>\n",
              "      <td>44.041451</td>\n",
              "      <td>932.319807</td>\n",
              "      <td>931.145405</td>\n",
              "      <td>1.174403</td>\n",
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       \"samples\": [\n          0.9181818181818182,\n          0.787037037037037,\n          0.9727272727272728\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RSI\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.164387006075496,\n        \"min\": 35.632183908045974,\n        \"max\": 51.196172248803826,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          47.867298578199055,\n          44.04145077720207,\n          49.30875576036866\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EMA_8\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.124010454210708,\n        \"min\": 912.5282195959167,\n        \"max\": 932.3198072320982,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          918.633059685713,\n          932.3198072320982,\n          924.936824199999\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EMA_13\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.428593686580646,\n        \"min\": 920.3820341428043,\n        \"max\": 931.1454045185652,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          923.1846006938324,\n          931.1454045185652,\n          926.5868005947136\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MACD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.712911873277776,\n        \"min\": -7.853814546887634,\n        \"max\": 1.174402713533027,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          -4.551541008119443,\n          1.174402713533027,\n          -1.6499763947145993\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import mplfinance as mpf\n",
        "# 設置繪圖風格\n",
        "my_color = mpf.make_marketcolors(up='r', down='g', inherit=True)\n",
        "my_style = mpf.make_mpf_style(base_mpf_style='default',\n",
        "                marketcolors=my_color)\n",
        "\n",
        "# 使用 mplfinance 繪製 K 線圖\n",
        "mpf.plot(kplot_df, type='candle', style=my_style, title=f'{stock_id}', figsize=(15, 6))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 501
        },
        "id": "jzh5tpSzxz3Q",
        "outputId": "dc0ebf01-133f-4483-f575-4ef1dd892c1e"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2.4 捕捉 Hammer 型態\n",
        "錘形線（Hammer）\n",
        "\n",
        "<img src=\"https://drive.google.com/uc?id=1M5t0g5kKCm2xfzRaFHfKt_CT41XSRjgb\" alt=\"drawing\" width=\"600\"/>"
      ],
      "metadata": {
        "id": "fFR7EB40x9mO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 計算 K 線長度 = abs(收盤-開盤)\n",
        "df['body_length'] = abs(df['Close'] - df['Open'])\n",
        "\n",
        "# 計算下影線長度 = abs(最高-最小)\n",
        "df['lower_shadow'] = abs(df['Low'] - df[['Open', 'Close']].min(axis=1))\n",
        "\n",
        "# 計算上影線長度 = abs(最高-最小)\n",
        "df['upper_shadow'] = abs(df['High'] - df[['Open', 'Close']].max(axis=1))\n",
        "\n",
        "# 定義 Hammer 型態的條件\n",
        "hammer_condition = (\n",
        "    (df['lower_shadow'] > 2 * df['body_length']) &  # 下影線長於實體的两倍\n",
        "    (df['upper_shadow'] < df['body_length'] * 0.1) &  # 上影線很短\n",
        "    (df['body_length'] < (df['High'] - df['Low']) * 0.4)  # 實體相對較小\n",
        ")\n",
        "\n",
        "# 提取 Hammer 型態\n",
        "hammer_df = df[hammer_condition]\n",
        "hammer_df.set_index('Date', inplace=True)\n",
        "\n",
        "hammer_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 196
        },
        "id": "HzKtSuuyx9PO",
        "outputId": "90b8c167-8321-47b1-b4bb-3f3f363c597a"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             Open   High    Low  Close    Volume   8_SMA      13_SMA  20_SMA  \\\n",
              "Date                                                                           \n",
              "2024-07-03  976.0  979.0  967.0  979.0  25022531  959.75  959.538462   936.7   \n",
              "\n",
              "            Change  Gain  ...  Avg Gain  Avg Loss   RS    RSI       EMA_8  \\\n",
              "Date                      ...                                               \n",
              "2024-07-03    19.0  19.0  ...  7.857143  3.571429  2.2  68.75  962.391305   \n",
              "\n",
              "                EMA_13       MACD  body_length  lower_shadow  upper_shadow  \n",
              "Date                                                                        \n",
              "2024-07-03  952.063502  10.327803          3.0           9.0           0.0  \n",
              "\n",
              "[1 rows x 21 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-db98ce26-2d03-46b8-93f9-0f792667c981\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Volume</th>\n",
              "      <th>8_SMA</th>\n",
              "      <th>13_SMA</th>\n",
              "      <th>20_SMA</th>\n",
              "      <th>Change</th>\n",
              "      <th>Gain</th>\n",
              "      <th>...</th>\n",
              "      <th>Avg Gain</th>\n",
              "      <th>Avg Loss</th>\n",
              "      <th>RS</th>\n",
              "      <th>RSI</th>\n",
              "      <th>EMA_8</th>\n",
              "      <th>EMA_13</th>\n",
              "      <th>MACD</th>\n",
              "      <th>body_length</th>\n",
              "      <th>lower_shadow</th>\n",
              "      <th>upper_shadow</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2024-07-03</th>\n",
              "      <td>976.0</td>\n",
              "      <td>979.0</td>\n",
              "      <td>967.0</td>\n",
              "      <td>979.0</td>\n",
              "      <td>25022531</td>\n",
              "      <td>959.75</td>\n",
              "      <td>959.538462</td>\n",
              "      <td>936.7</td>\n",
              "      <td>19.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>...</td>\n",
              "      <td>7.857143</td>\n",
              "      <td>3.571429</td>\n",
              "      <td>2.2</td>\n",
              "      <td>68.75</td>\n",
              "      <td>962.391305</td>\n",
              "      <td>952.063502</td>\n",
              "      <td>10.327803</td>\n",
              "      <td>3.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1 rows × 21 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-db98ce26-2d03-46b8-93f9-0f792667c981')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-db98ce26-2d03-46b8-93f9-0f792667c981 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-db98ce26-2d03-46b8-93f9-0f792667c981');\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=\"id_0bd3b97c-5383-4f90-8de5-618a7f001a4b\">\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('hammer_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_0bd3b97c-5383-4f90-8de5-618a7f001a4b button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('hammer_df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "hammer_df"
            }
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mpf.plot(hammer_df, type='candle', style=my_style, title=f'{stock_id}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 553
        },
        "id": "pz4BmChVyVzW",
        "outputId": "cb11862a-b7bb-4dac-cdc7-5a7a81a0ebed"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x575 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2.5 捕捉 Doji 型態\n",
        "十字線（Doji）\n",
        "\n",
        "<img src=\"https://drive.google.com/uc?id=1BDfcn8ZmMVgl0Xlku7mSD338WHeffrgc\" alt=\"drawing\" width=\"600\"/>"
      ],
      "metadata": {
        "id": "ZUzi5Dm_ybGU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 計算 K 線長度\n",
        "df['body_length'] = abs(df['Close'] - df['Open'])\n",
        "\n",
        "# 定義Doji型態的條件\n",
        "# 一般來說，body 長度 如果 小於等於 (最高價-最低價) * 0.1\n",
        "doji_condition = (\n",
        "    df['body_length'] <= (df['High'] - df['Low']) * 0.1  # 中間區域非常小\n",
        ")\n",
        "\n",
        "# 提取Doji形态\n",
        "doji_df = df[doji_condition]\n",
        "doji_df.set_index('Date', inplace=True)\n",
        "\n",
        "doji_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 634
        },
        "id": "3LMV4THWym4C",
        "outputId": "d9c8df9e-f380-4612-f85c-beb68fb883ae"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              Open    High     Low   Close    Volume     8_SMA       13_SMA  \\\n",
              "Date                                                                          \n",
              "2024-04-10   815.0   819.0   810.0   815.0  31109466       NaN          NaN   \n",
              "2024-05-20   834.0   838.0   822.0   835.0  30148330   824.000   810.153846   \n",
              "2024-05-23   875.0   877.0   867.0   875.0  40771207   844.375   828.769231   \n",
              "2024-06-06   893.0   899.0   885.0   894.0  67300344   851.750   856.153846   \n",
              "2024-06-18   944.0   950.0   940.0   943.0  40870419   908.750   882.153846   \n",
              "2024-07-01   968.0   977.0   965.0   968.0  20936005   961.250   952.000000   \n",
              "2024-07-05  1005.0  1010.0  1000.0  1005.0  21735614   975.375   970.769231   \n",
              "2024-07-15  1040.0  1045.0  1025.0  1040.0  44123104  1036.250  1009.461538   \n",
              "2024-08-29   942.0   947.0   937.0   943.0  46275772   953.750   954.153846   \n",
              "2024-08-30   944.0   952.0   941.0   944.0  75533393   950.125   954.384615   \n",
              "\n",
              "            20_SMA  Change  Gain  ...   Avg Gain  Avg Loss        RS  \\\n",
              "Date                              ...                                  \n",
              "2024-04-10     NaN    -4.0   0.0  ...        NaN       NaN       NaN   \n",
              "2024-05-20  797.20     0.0   0.0  ...   5.357143  2.500000  2.142857   \n",
              "2024-05-23  812.25    11.0  11.0  ...   7.642857  0.857143  8.916667   \n",
              "2024-06-06  846.30    40.0  40.0  ...   8.714286  4.500000  1.936508   \n",
              "2024-06-18  875.30    22.0  22.0  ...  10.357143  4.785714  2.164179   \n",
              "2024-07-01  924.00     2.0   2.0  ...   9.071429  3.000000  3.023810   \n",
              "2024-07-05  949.80     0.0   0.0  ...   9.500000  3.500000  2.714286   \n",
              "2024-07-15  992.15     0.0   0.0  ...  10.214286  3.428571  2.979167   \n",
              "2024-08-29  934.60   -21.0   0.0  ...   4.785714  4.142857  1.155172   \n",
              "2024-08-30  936.65     1.0   1.0  ...   4.428571  4.142857  1.068966   \n",
              "\n",
              "                  RSI        EMA_8       EMA_13       MACD  body_length  \\\n",
              "Date                                                                      \n",
              "2024-04-10        NaN   792.888753   786.234843   6.653911          0.0   \n",
              "2024-05-20  68.181818   825.137466   816.308513   8.828952          1.0   \n",
              "2024-05-23  89.915966   845.067422   833.124312  11.943110          0.0   \n",
              "2024-06-06  65.945946   856.932448   851.141697   5.790750          1.0   \n",
              "2024-06-18  68.396226   909.337320   894.505534  14.831786          1.0   \n",
              "2024-07-01  75.147929   956.973382   945.503100  11.470282          0.0   \n",
              "2024-07-05  73.076923   979.224370   966.107879  13.116491          0.0   \n",
              "2024-07-15  74.869110  1032.129414  1015.014231  17.115183          0.0   \n",
              "2024-08-29  53.600000   950.751921   949.570518   1.181404          1.0   \n",
              "2024-08-30  51.666667   949.251494   948.774729   0.476765          0.0   \n",
              "\n",
              "            lower_shadow  upper_shadow  \n",
              "Date                                    \n",
              "2024-04-10           5.0           4.0  \n",
              "2024-05-20          12.0           3.0  \n",
              "2024-05-23           8.0           2.0  \n",
              "2024-06-06           8.0           5.0  \n",
              "2024-06-18           3.0           6.0  \n",
              "2024-07-01           3.0           9.0  \n",
              "2024-07-05           5.0           5.0  \n",
              "2024-07-15          15.0           5.0  \n",
              "2024-08-29           5.0           4.0  \n",
              "2024-08-30           3.0           8.0  \n",
              "\n",
              "[10 rows x 21 columns]"
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              "      <th></th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Volume</th>\n",
              "      <th>8_SMA</th>\n",
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              "      <th>20_SMA</th>\n",
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              "      <th>RS</th>\n",
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              "      <th>MACD</th>\n",
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              "    <tr>\n",
              "      <th>Date</th>\n",
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              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
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              "    <tr>\n",
              "      <th>2024-04-10</th>\n",
              "      <td>815.0</td>\n",
              "      <td>819.0</td>\n",
              "      <td>810.0</td>\n",
              "      <td>815.0</td>\n",
              "      <td>31109466</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>-4.0</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>792.888753</td>\n",
              "      <td>786.234843</td>\n",
              "      <td>6.653911</td>\n",
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              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
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              "    <tr>\n",
              "      <th>2024-05-20</th>\n",
              "      <td>834.0</td>\n",
              "      <td>838.0</td>\n",
              "      <td>822.0</td>\n",
              "      <td>835.0</td>\n",
              "      <td>30148330</td>\n",
              "      <td>824.000</td>\n",
              "      <td>810.153846</td>\n",
              "      <td>797.20</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>5.357143</td>\n",
              "      <td>2.500000</td>\n",
              "      <td>2.142857</td>\n",
              "      <td>68.181818</td>\n",
              "      <td>825.137466</td>\n",
              "      <td>816.308513</td>\n",
              "      <td>8.828952</td>\n",
              "      <td>1.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>3.0</td>\n",
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              "    <tr>\n",
              "      <th>2024-05-23</th>\n",
              "      <td>875.0</td>\n",
              "      <td>877.0</td>\n",
              "      <td>867.0</td>\n",
              "      <td>875.0</td>\n",
              "      <td>40771207</td>\n",
              "      <td>844.375</td>\n",
              "      <td>828.769231</td>\n",
              "      <td>812.25</td>\n",
              "      <td>11.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>...</td>\n",
              "      <td>7.642857</td>\n",
              "      <td>0.857143</td>\n",
              "      <td>8.916667</td>\n",
              "      <td>89.915966</td>\n",
              "      <td>845.067422</td>\n",
              "      <td>833.124312</td>\n",
              "      <td>11.943110</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.0</td>\n",
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              "      <th>2024-06-06</th>\n",
              "      <td>893.0</td>\n",
              "      <td>899.0</td>\n",
              "      <td>885.0</td>\n",
              "      <td>894.0</td>\n",
              "      <td>67300344</td>\n",
              "      <td>851.750</td>\n",
              "      <td>856.153846</td>\n",
              "      <td>846.30</td>\n",
              "      <td>40.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>...</td>\n",
              "      <td>8.714286</td>\n",
              "      <td>4.500000</td>\n",
              "      <td>1.936508</td>\n",
              "      <td>65.945946</td>\n",
              "      <td>856.932448</td>\n",
              "      <td>851.141697</td>\n",
              "      <td>5.790750</td>\n",
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              "      <th>2024-06-18</th>\n",
              "      <td>944.0</td>\n",
              "      <td>950.0</td>\n",
              "      <td>940.0</td>\n",
              "      <td>943.0</td>\n",
              "      <td>40870419</td>\n",
              "      <td>908.750</td>\n",
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              "      <td>875.30</td>\n",
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              "      <td>22.0</td>\n",
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              "      <td>4.785714</td>\n",
              "      <td>2.164179</td>\n",
              "      <td>68.396226</td>\n",
              "      <td>909.337320</td>\n",
              "      <td>894.505534</td>\n",
              "      <td>14.831786</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>6.0</td>\n",
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              "      <th>2024-07-01</th>\n",
              "      <td>968.0</td>\n",
              "      <td>977.0</td>\n",
              "      <td>965.0</td>\n",
              "      <td>968.0</td>\n",
              "      <td>20936005</td>\n",
              "      <td>961.250</td>\n",
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              "      <td>924.00</td>\n",
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              "      <td>2.0</td>\n",
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              "      <td>9.071429</td>\n",
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              "      <td>3.023810</td>\n",
              "      <td>75.147929</td>\n",
              "      <td>956.973382</td>\n",
              "      <td>945.503100</td>\n",
              "      <td>11.470282</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>9.0</td>\n",
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              "    <tr>\n",
              "      <th>2024-07-05</th>\n",
              "      <td>1005.0</td>\n",
              "      <td>1010.0</td>\n",
              "      <td>1000.0</td>\n",
              "      <td>1005.0</td>\n",
              "      <td>21735614</td>\n",
              "      <td>975.375</td>\n",
              "      <td>970.769231</td>\n",
              "      <td>949.80</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>9.500000</td>\n",
              "      <td>3.500000</td>\n",
              "      <td>2.714286</td>\n",
              "      <td>73.076923</td>\n",
              "      <td>979.224370</td>\n",
              "      <td>966.107879</td>\n",
              "      <td>13.116491</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
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              "    <tr>\n",
              "      <th>2024-07-15</th>\n",
              "      <td>1040.0</td>\n",
              "      <td>1045.0</td>\n",
              "      <td>1025.0</td>\n",
              "      <td>1040.0</td>\n",
              "      <td>44123104</td>\n",
              "      <td>1036.250</td>\n",
              "      <td>1009.461538</td>\n",
              "      <td>992.15</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>10.214286</td>\n",
              "      <td>3.428571</td>\n",
              "      <td>2.979167</td>\n",
              "      <td>74.869110</td>\n",
              "      <td>1032.129414</td>\n",
              "      <td>1015.014231</td>\n",
              "      <td>17.115183</td>\n",
              "      <td>0.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>5.0</td>\n",
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              "    <tr>\n",
              "      <th>2024-08-29</th>\n",
              "      <td>942.0</td>\n",
              "      <td>947.0</td>\n",
              "      <td>937.0</td>\n",
              "      <td>943.0</td>\n",
              "      <td>46275772</td>\n",
              "      <td>953.750</td>\n",
              "      <td>954.153846</td>\n",
              "      <td>934.60</td>\n",
              "      <td>-21.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>4.785714</td>\n",
              "      <td>4.142857</td>\n",
              "      <td>1.155172</td>\n",
              "      <td>53.600000</td>\n",
              "      <td>950.751921</td>\n",
              "      <td>949.570518</td>\n",
              "      <td>1.181404</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
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              "      <th>2024-08-30</th>\n",
              "      <td>944.0</td>\n",
              "      <td>952.0</td>\n",
              "      <td>941.0</td>\n",
              "      <td>944.0</td>\n",
              "      <td>75533393</td>\n",
              "      <td>950.125</td>\n",
              "      <td>954.384615</td>\n",
              "      <td>936.65</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>4.428571</td>\n",
              "      <td>4.142857</td>\n",
              "      <td>1.068966</td>\n",
              "      <td>51.666667</td>\n",
              "      <td>949.251494</td>\n",
              "      <td>948.774729</td>\n",
              "      <td>0.476765</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>8.0</td>\n",
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              "        <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-96421ecd-659e-4527-b6af-5ca76bbf5b20 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_db0d0bf3-c07d-4c21-8c10-45ffded59dd1\">\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('doji_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_db0d0bf3-c07d-4c21-8c10-45ffded59dd1 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('doji_df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "doji_df"
            }
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mpf.plot(doji_df, type='candle', style=my_style, title=f'{stock_id}', figsize=(10, 6))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 582
        },
        "id": "33SPAL-zysPO",
        "outputId": "07a87339-30fe-4438-abc8-21cdc1cb1ac5"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **3. 技術指標結合機器學習（線性回歸、決策樹、隨機森林）預測股價漲跌。使用技術指標作為特徵來預測第 n 天的股價**\n",
        "\n",
        "技術指標當特徵，用線性回歸、決策樹、隨機森林預測股價漲跌"
      ],
      "metadata": {
        "id": "UulaFTSTeAqt"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3.1 資料前處理"
      ],
      "metadata": {
        "id": "B4SbwzXp2GYg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np  # 引入 numpy，用於數據處理和數值運算\n",
        "from sklearn.model_selection import train_test_split  # 引入 train_test_split，用於將數據集拆分為訓練集和測試集\n",
        "from sklearn.metrics import mean_squared_error  # 引入 mean_squared_error 用於評估模型性能"
      ],
      "metadata": {
        "id": "DsAW7LWHy1Gh"
      },
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 標籤生成: 預測未來1天的價格，這裡以2024-04-01的技術指標 預測隔天的收盤價\n",
        "n = 1\n",
        "df['Future_Price'] = df['Close'].shift(-n)\n",
        "\n",
        "# 去除缺失值\n",
        "df.dropna(inplace=True)\n",
        "\n",
        "# 使用特徵\n",
        "X = df[['20_SMA', 'RSI', 'MACD']]\n",
        "\n",
        "# 標籤（未來價格）\n",
        "y = df['Future_Price']\n",
        "\n",
        "X.shape, y.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SI3MDp-fy5oI",
        "outputId": "22280dba-b06b-48d1-9eb8-4f33bba51881"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((103, 3), (103,))"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 將數據集 X 和標籤 y 拆分為訓練集和測試集\n",
        "# test_size=0.2 表示測試集佔全部數據的 20% --> 80% 的資料作為訓練，20% 的資料作為測試\n",
        "# shuffle=False 表示在拆分數據時不打亂原始數據的順序\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, shuffle=False)\n",
        "\n",
        "# 查看拆分後訓練集和測試集的形狀\n",
        "X_train.shape, X_test.shape, y_train.shape, y_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pfezQM1-19K6",
        "outputId": "542a7008-273a-43d2-d3e9-b7435e1f2920"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((82, 3), (21, 3), (82,), (21,))"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3.2 線性回歸（Linear Regression）"
      ],
      "metadata": {
        "id": "cZRI--Mw2CqT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 引入線性回歸模型\n",
        "from sklearn.linear_model import LinearRegression\n",
        "\n",
        "# 建立並訓練線性回歸模型\n",
        "model = LinearRegression()\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "# 預測\n",
        "y_pred = model.predict(X_test)\n",
        "\n",
        "# 評估\n",
        "mse = mean_squared_error(y_test, y_pred)\n",
        "rmse = np.sqrt(mse)\n",
        "print(f\"線性回歸模型RMSE: {rmse:.2f}\")\n",
        "\n",
        "# 使用模型進行未來價格走勢預測\n",
        "future_pred = model.predict(X[-1:])  # 使用最新的數據進行預測\n",
        "print(f\"線性回歸模型預測未來{n}天的價格: {future_pred[0]:.2f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9TErtzKo19p5",
        "outputId": "8d380cad-89cd-4483-93b9-e2186f8c027a"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "線性回歸模型RMSE: 23.67\n",
            "線性回歸模型預測未來1天的價格: 998.77\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 畫出測試集的實際價格與預測價格走勢圖\n",
        "plt.figure(figsize=(14, 7))\n",
        "plt.plot(df['Date'], df['Close'], label='Actual Prices', color='black', linewidth=2)\n",
        "plt.plot(df['Date'][-len(y_test):], y_pred, label='Linear Regression Predictions', linestyle='--', color='blue')\n",
        "plt.xlabel('Date')\n",
        "plt.ylabel('Price')\n",
        "plt.title('2230.TW Actual vs Predicted Prices on Test Set')\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "id": "e607P2L12Oq_",
        "outputId": "81f0def0-99ca-4922-ae4e-9bba2dbf8665"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x700 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3.2 決策樹（Decision Tree）"
      ],
      "metadata": {
        "id": "rn0wWfvL2CWl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeRegressor\n",
        "\n",
        "# 建立並訓練決策數模型\n",
        "model = DecisionTreeRegressor()\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "# 預測\n",
        "y_pred = model.predict(X_test)\n",
        "\n",
        "# 評估\n",
        "mse = mean_squared_error(y_test, y_pred)\n",
        "rmse = np.sqrt(mse)\n",
        "print(f\"決策樹模型RMSE: {rmse:.2f}\")\n",
        "\n",
        "future_pred = model.predict(X[-1:])\n",
        "print(f\"決策樹模型預測未來{n}天的價格: {future_pred[0]:.2f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fMBsVp76219p",
        "outputId": "ea211884-cfa0-4171-d595-ada575b01588"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "決策樹模型RMSE: 40.26\n",
            "決策樹模型預測未來1天的價格: 1035.00\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 畫出測試集的實際價格與預測價格走勢圖\n",
        "plt.figure(figsize=(14, 7))\n",
        "plt.plot(df['Date'], df['Close'], label='Actual Prices', color='black', linewidth=2)\n",
        "plt.plot(df['Date'][-len(y_test):], y_pred, label='Decision Tree Predictions', linestyle='--', color='blue')\n",
        "plt.xlabel('Date')\n",
        "plt.ylabel('Price')\n",
        "plt.title('2230.TW  Actual vs Predicted Prices on Test Set')\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "id": "g0YRgUAu29SO",
        "outputId": "097a2dd8-ae9a-4f39-adc9-6b55b3059a4e"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3.3 隨機森林（Random Forest）"
      ],
      "metadata": {
        "id": "Jqiwxg3N3Duk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 引入隨機森林模型\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "\n",
        "# 建立並訓練決策數模型\n",
        "model = RandomForestRegressor()\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "# 預測\n",
        "y_pred = model.predict(X_test)\n",
        "\n",
        "# 評估\n",
        "mse = mean_squared_error(y_test, y_pred)\n",
        "rmse = np.sqrt(mse)\n",
        "print(f\"隨機森林模型RMSE: {rmse:.2f}\")\n",
        "\n",
        "future_pred = model.predict(X[-1:])\n",
        "print(f\"隨機森林模型預測未來{n}天的價格: {future_pred[0]:.2f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jNELgk983gI4",
        "outputId": "07520c74-9225-45d6-fdcf-5494fed71e01"
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "隨機森林模型RMSE: 28.44\n",
            "隨機森林模型預測未來1天的價格: 1016.54\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 畫出測試集的實際價格與預測價格走勢圖\n",
        "plt.figure(figsize=(14, 7))\n",
        "plt.plot(df['Date'], df['Close'], label='Actual Prices', color='black', linewidth=2)\n",
        "plt.plot(df['Date'][-len(y_test):], y_pred, label='Random Forest Predictions', linestyle='--', color='blue')\n",
        "plt.xlabel('Date')\n",
        "plt.ylabel('Price')\n",
        "plt.title('2230.TW  Actual vs Predicted Prices on Test Set')\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "id": "E569y9Tr3j0Q",
        "outputId": "6f0d6f9a-8cc6-4226-9ffc-1844b148c83b"
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x700 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **補充：在 Colab 上使用中文字體**"
      ],
      "metadata": {
        "id": "CTRuoMCm3c8C"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Colab 進行matplotlib繪圖時顯示繁體中文\n",
        "# 下載台北思源黑體並命名taipei_sans_tc_beta.ttf，移至指定路徑\n",
        "!wget -O TaipeiSansTCBeta-Regular.ttf https://drive.google.com/uc?id=1eGAsTN1HBpJAkeVM57_C7ccp7hbgSz3_&export=download\n",
        "\n",
        "import matplotlib\n",
        "\n",
        "# 改style要在改font之前\n",
        "# plt.style.use('seaborn')\n",
        "\n",
        "matplotlib.font_manager.fontManager.addfont('TaipeiSansTCBeta-Regular.ttf')\n",
        "matplotlib.rc('font', family='Taipei Sans TC Beta')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gH3No0zb3fbS",
        "outputId": "5c22d38e-5bbc-4ca4-9560-bd543c8b26d4"
      },
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2024-09-28 09:03:15--  https://drive.google.com/uc?id=1eGAsTN1HBpJAkeVM57_C7ccp7hbgSz3_\n",
            "Resolving drive.google.com (drive.google.com)... 74.125.196.101, 74.125.196.100, 74.125.196.138, ...\n",
            "Connecting to drive.google.com (drive.google.com)|74.125.196.101|:443... connected.\n",
            "HTTP request sent, awaiting response... 303 See Other\n",
            "Location: https://drive.usercontent.google.com/download?id=1eGAsTN1HBpJAkeVM57_C7ccp7hbgSz3_ [following]\n",
            "--2024-09-28 09:03:15--  https://drive.usercontent.google.com/download?id=1eGAsTN1HBpJAkeVM57_C7ccp7hbgSz3_\n",
            "Resolving drive.usercontent.google.com (drive.usercontent.google.com)... 74.125.141.132, 2607:f8b0:400c:c06::84\n",
            "Connecting to drive.usercontent.google.com (drive.usercontent.google.com)|74.125.141.132|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 20659344 (20M) [application/octet-stream]\n",
            "Saving to: ‘TaipeiSansTCBeta-Regular.ttf’\n",
            "\n",
            "TaipeiSansTCBeta-Re 100%[===================>]  19.70M  --.-KB/s    in 0.1s    \n",
            "\n",
            "2024-09-28 09:03:18 (167 MB/s) - ‘TaipeiSansTCBeta-Regular.ttf’ saved [20659344/20659344]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 繪製範例圖表\n",
        "plt.title(\"這是一個範例標題\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "A6Z8j1cW3pgp",
        "outputId": "1b31d455-5248-4100-8314-31491ba293f0"
      },
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Pandas Shift**"
      ],
      "metadata": {
        "id": "IpD3vhQo3vlV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sample_df = pd.DataFrame({\n",
        "    'Date': ['2024-02-01', '2024-02-02', '2024-02-03', '2024-02-04', '2024-02-05', '2024-02-06'],\n",
        "    'SMA': ['SMA_1', 'SMA_2', 'SMA_3', 'SMA_4', 'SMA_5', 'SMA_6'],\n",
        "    'RSI': ['RSI_1', 'RSI_2', 'RSI_3', 'RSI_4', 'RSI_5', 'RSI_6'],\n",
        "    'MACD': ['MACD_1', 'MACD_2', 'MACD_3', 'MACD_4', 'MACD_5', 'MACD_6'],\n",
        "    'Close': [111, 222, 333, 444, 555, 666]\n",
        "})\n",
        "sample_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "s8IDNnQL31K4",
        "outputId": "adc2ab38-ea4e-49e1-dfec-3022d348c7bb"
      },
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Date    SMA    RSI    MACD  Close\n",
              "0  2024-02-01  SMA_1  RSI_1  MACD_1    111\n",
              "1  2024-02-02  SMA_2  RSI_2  MACD_2    222\n",
              "2  2024-02-03  SMA_3  RSI_3  MACD_3    333\n",
              "3  2024-02-04  SMA_4  RSI_4  MACD_4    444\n",
              "4  2024-02-05  SMA_5  RSI_5  MACD_5    555\n",
              "5  2024-02-06  SMA_6  RSI_6  MACD_6    666"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-692e916a-8d45-4610-b026-30efcb7113dd\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>SMA</th>\n",
              "      <th>RSI</th>\n",
              "      <th>MACD</th>\n",
              "      <th>Close</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2024-02-01</td>\n",
              "      <td>SMA_1</td>\n",
              "      <td>RSI_1</td>\n",
              "      <td>MACD_1</td>\n",
              "      <td>111</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2024-02-02</td>\n",
              "      <td>SMA_2</td>\n",
              "      <td>RSI_2</td>\n",
              "      <td>MACD_2</td>\n",
              "      <td>222</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2024-02-03</td>\n",
              "      <td>SMA_3</td>\n",
              "      <td>RSI_3</td>\n",
              "      <td>MACD_3</td>\n",
              "      <td>333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2024-02-04</td>\n",
              "      <td>SMA_4</td>\n",
              "      <td>RSI_4</td>\n",
              "      <td>MACD_4</td>\n",
              "      <td>444</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2024-02-05</td>\n",
              "      <td>SMA_5</td>\n",
              "      <td>RSI_5</td>\n",
              "      <td>MACD_5</td>\n",
              "      <td>555</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2024-02-06</td>\n",
              "      <td>SMA_6</td>\n",
              "      <td>RSI_6</td>\n",
              "      <td>MACD_6</td>\n",
              "      <td>666</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
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              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
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              "      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-692e916a-8d45-4610-b026-30efcb7113dd 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-692e916a-8d45-4610-b026-30efcb7113dd');\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-de651b53-fb7b-4640-902a-0968ee96bf43\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-de651b53-fb7b-4640-902a-0968ee96bf43')\"\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-de651b53-fb7b-4640-902a-0968ee96bf43 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_85ce42e7-b357-4f12-817e-fb2d7a1aeace\">\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('sample_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_85ce42e7-b357-4f12-817e-fb2d7a1aeace button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('sample_df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "sample_df",
              "summary": "{\n  \"name\": \"sample_df\",\n  \"rows\": 6,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"2024-02-01\",\n          \"2024-02-02\",\n          \"2024-02-06\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SMA\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"SMA_1\",\n          \"SMA_2\",\n          \"SMA_6\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RSI\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"RSI_1\",\n          \"RSI_2\",\n          \"RSI_6\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MACD\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"MACD_1\",\n          \"MACD_2\",\n          \"MACD_6\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 207,\n        \"min\": 111,\n        \"max\": 666,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          111,\n          222,\n          666\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sample_df['Future_Close'] = sample_df['Close'].shift(-1)\n",
        "sample_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "CXOeLDBL36qL",
        "outputId": "9a639043-00fe-4657-c394-0cb17f906d29"
      },
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Date    SMA    RSI    MACD  Close  Future_Close\n",
              "0  2024-02-01  SMA_1  RSI_1  MACD_1    111         222.0\n",
              "1  2024-02-02  SMA_2  RSI_2  MACD_2    222         333.0\n",
              "2  2024-02-03  SMA_3  RSI_3  MACD_3    333         444.0\n",
              "3  2024-02-04  SMA_4  RSI_4  MACD_4    444         555.0\n",
              "4  2024-02-05  SMA_5  RSI_5  MACD_5    555         666.0\n",
              "5  2024-02-06  SMA_6  RSI_6  MACD_6    666           NaN"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7464ea16-734e-4098-8070-c33ab73fccc3\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>SMA</th>\n",
              "      <th>RSI</th>\n",
              "      <th>MACD</th>\n",
              "      <th>Close</th>\n",
              "      <th>Future_Close</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2024-02-01</td>\n",
              "      <td>SMA_1</td>\n",
              "      <td>RSI_1</td>\n",
              "      <td>MACD_1</td>\n",
              "      <td>111</td>\n",
              "      <td>222.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2024-02-02</td>\n",
              "      <td>SMA_2</td>\n",
              "      <td>RSI_2</td>\n",
              "      <td>MACD_2</td>\n",
              "      <td>222</td>\n",
              "      <td>333.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2024-02-03</td>\n",
              "      <td>SMA_3</td>\n",
              "      <td>RSI_3</td>\n",
              "      <td>MACD_3</td>\n",
              "      <td>333</td>\n",
              "      <td>444.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2024-02-04</td>\n",
              "      <td>SMA_4</td>\n",
              "      <td>RSI_4</td>\n",
              "      <td>MACD_4</td>\n",
              "      <td>444</td>\n",
              "      <td>555.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2024-02-05</td>\n",
              "      <td>SMA_5</td>\n",
              "      <td>RSI_5</td>\n",
              "      <td>MACD_5</td>\n",
              "      <td>555</td>\n",
              "      <td>666.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2024-02-06</td>\n",
              "      <td>SMA_6</td>\n",
              "      <td>RSI_6</td>\n",
              "      <td>MACD_6</td>\n",
              "      <td>666</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7464ea16-734e-4098-8070-c33ab73fccc3')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .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-7464ea16-734e-4098-8070-c33ab73fccc3 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-7464ea16-734e-4098-8070-c33ab73fccc3');\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-5a1f76d5-2c75-4dcf-a2a4-325970459703\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-5a1f76d5-2c75-4dcf-a2a4-325970459703')\"\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-5a1f76d5-2c75-4dcf-a2a4-325970459703 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_0b27de68-852a-43b5-942c-bdb457182df8\">\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('sample_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_0b27de68-852a-43b5-942c-bdb457182df8 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('sample_df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "sample_df",
              "summary": "{\n  \"name\": \"sample_df\",\n  \"rows\": 6,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"2024-02-01\",\n          \"2024-02-02\",\n          \"2024-02-06\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SMA\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"SMA_1\",\n          \"SMA_2\",\n          \"SMA_6\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RSI\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"RSI_1\",\n          \"RSI_2\",\n          \"RSI_6\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MACD\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"MACD_1\",\n          \"MACD_2\",\n          \"MACD_6\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 207,\n        \"min\": 111,\n        \"max\": 666,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          111,\n          222,\n          666\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Future_Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 175.50641013934506,\n        \"min\": 222.0,\n        \"max\": 666.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          333.0,\n          666.0,\n          444.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 95
        }
      ]
    }
  ]
}