{
 "cells": [
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-22T02:00:48.759512Z",
     "start_time": "2025-01-22T01:48:26.785997Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import os\n",
    "\n",
    "# 加载\"C:\\Users\\lanyuanxiaoyao\\Documents\\Tushare\\日线行情\"路径下的所有CSV文件，放在一个dataframe表里，然后根据ts_code分组，把分组按ts_code命名，以CSV的格式存在“C:\\Users\\lanyuanxiaoyao\\Documents\\Tushare\\日线行情\\group”路径下\n",
    "import pandas as pd\n",
    "\n",
    "# 定义路径\n",
    "input_path = \"/Users/lanyuanxiaoyao/SynologyDrive/data/Tushare/日线行情\"\n",
    "output_path = \"/Users/lanyuanxiaoyao/Documents/日线行情\"\n",
    "\n",
    "# 确保输出路径存在\n",
    "os.makedirs(output_path, exist_ok=True)\n",
    "\n",
    "# 获取所有CSV文件\n",
    "csv_files = [f for f in os.listdir(input_path) if f.endswith('.csv')]\n",
    "\n",
    "# 初始化一个空的DataFrame\n",
    "df = pd.DataFrame()\n",
    "\n",
    "# 逐个加载CSV文件到DataFrame\n",
    "for file in csv_files:\n",
    "    file_path = os.path.join(input_path, file)\n",
    "    temp_df = pd.read_csv(file_path)\n",
    "    df = pd.concat([df, temp_df], ignore_index=True)\n",
    "\n",
    "# 根据ts_code分组\n",
    "grouped = df.groupby('ts_code')\n",
    "\n",
    "# 将每个分组保存为CSV文件\n",
    "for ts_code, group in grouped:\n",
    "    output_file_path = os.path.join(output_path, f\"{ts_code}.csv\")\n",
    "    group.to_csv(output_file_path, index=False)\n",
    "\n",
    "print(\"文件已成功分组并保存为CSV格式。\")\n"
   ],
   "id": "6a10e07f5f498bc6",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "文件已成功分组并保存为CSV格式。\n"
     ]
    }
   ],
   "execution_count": 1
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-22T07:54:35.124992Z",
     "start_time": "2025-01-22T07:54:35.014745Z"
    }
   },
   "cell_type": "code",
   "source": [
    "source_df = pd.read_csv(\"/Users/lanyuanxiaoyao/Documents/日线行情/000001.SZ.csv\") \\\n",
    "    [[\"trade_date\", \"open_qfq\", \"close_qfq\", \"high_qfq\", \"low_qfq\", \"macd\", \"macd_dif\", \"macd_dea\"]]\n",
    "df = pd.DataFrame()\n",
    "df[[\"date\", \"open\", \"close\", \"high\", \"low\", \"macd\", \"macd_dif\", \"macd_dea\"]] = \\\n",
    "    source_df[[\"trade_date\", \"open_qfq\", \"close_qfq\", \"high_qfq\", \"low_qfq\", \"macd\", \"macd_dif\", \"macd_dea\"]]\n",
    "df[\"datetime\"] = pd.to_datetime(df[\"date\"], format=\"%Y%m%d\")\n",
    "df[\"datetime_text\"] = df[\"datetime\"].apply(lambda x: x.strftime(\"%Y%m%d\"))\n",
    "df = df[df[\"datetime\"].dt.year >= 2024]\n",
    "df.sort_values(by='datetime', inplace=True)\n",
    "\n",
    "close_median = df[\"close\"].median()\n",
    "df[\"close_to_median\"] = df[\"close\"] - close_median\n",
    "macd_max = max(abs(df[\"macd\"].max()), abs(df[\"macd\"].min()))\n",
    "close_max = max(abs(df[\"close_to_median\"].max()), abs(df[\"close_to_median\"].min()))\n",
    "ratio = close_max / macd_max\n",
    "df[\"macd_close\"] = df[\"close_to_median\"] / ratio\n",
    "\n",
    "# df[\"macd_sign\"] = df[\"macd\"] > 0\n",
    "# df[\"group\"] = (df[\"macd_sign\"] != df[\"macd_sign\"].shift(1)).cumsum()\n",
    "\n",
    "df[\"macd_diff\"] = df[\"macd\"].diff()\n",
    "df[\"macd_diff\"] = df[\"macd_diff\"].fillna(0)\n",
    "df['macd_trend'] = df['macd_diff'].apply(lambda x: 1 if x > 0 else -1 if x < 0 else 0)\n",
    "df[\"macd_trend_group\"] = (df['macd_trend'] != df['macd_trend'].shift(1)).cumsum()\n",
    "\n",
    "df[\"pre_macd\"] = df[\"macd\"].shift(1)\n",
    "df[\"point\"] = (df[\"macd_dif\"] > 0) & (df[\"macd_dea\"] > 0) & (df[\"macd\"] > 0) & \\\n",
    "              ((df[\"macd_dif\"] > df[\"macd\"]) & (df[\"macd_dea\"] > df[\"macd\"])) & \\\n",
    "              (df[\"pre_macd\"] < df[\"macd\"])\n",
    "df.loc[df[\"point\"] & df[\"point\"].shift(1), \"point\"] = False\n",
    "\n",
    "df"
   ],
   "id": "f6ca89b1047f1eb0",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "          date      open     close      high       low   macd  macd_dif  \\\n",
       "3576  20240102   8.57646   8.41205   8.60386   8.41205  0.107    -0.162   \n",
       "3577  20240103   8.39379   8.40292   8.42119   8.35725  0.098    -0.155   \n",
       "3578  20240104   8.39379   8.32072   8.39379   8.29332  0.080    -0.153   \n",
       "3579  20240105   8.31158   8.46686   8.62213   8.28418  0.087    -0.139   \n",
       "3580  20240108   8.43032   8.35725   8.49426   8.32072  0.076    -0.135   \n",
       "...        ...       ...       ...       ...       ...    ...       ...   \n",
       "3813  20241225  11.86000  11.92000  12.02000  11.84000  0.050     0.080   \n",
       "3814  20241226  11.92000  11.86000  11.93000  11.78000  0.051     0.087   \n",
       "3815  20241227  11.87000  11.83000  11.90000  11.66000  0.044     0.089   \n",
       "3816  20241230  11.78000  11.95000  11.97000  11.78000  0.052     0.100   \n",
       "3817  20241231  11.93000  11.70000  11.99000  11.70000  0.020     0.086   \n",
       "\n",
       "      macd_dea   datetime datetime_text  close_to_median  macd_close  \\\n",
       "3576    -0.216 2024-01-02      20240102         -1.50113   -0.339145   \n",
       "3577    -0.203 2024-01-03      20240103         -1.51026   -0.341208   \n",
       "3578    -0.193 2024-01-04      20240104         -1.59246   -0.359779   \n",
       "3579    -0.183 2024-01-05      20240105         -1.44632   -0.326762   \n",
       "3580    -0.173 2024-01-08      20240108         -1.55593   -0.351526   \n",
       "...        ...        ...           ...              ...         ...   \n",
       "3813     0.055 2024-12-25      20241225          2.00682    0.453394   \n",
       "3814     0.062 2024-12-26      20241226          1.94682    0.439838   \n",
       "3815     0.067 2024-12-27      20241227          1.91682    0.433061   \n",
       "3816     0.074 2024-12-30      20241230          2.03682    0.460172   \n",
       "3817     0.076 2024-12-31      20241231          1.78682    0.403690   \n",
       "\n",
       "      macd_diff  macd_trend  macd_trend_group  pre_macd  point  \n",
       "3576      0.000           0                 1       NaN  False  \n",
       "3577     -0.009          -1                 2     0.107  False  \n",
       "3578     -0.018          -1                 2     0.098  False  \n",
       "3579      0.007           1                 3     0.080  False  \n",
       "3580     -0.011          -1                 4     0.087  False  \n",
       "...         ...         ...               ...       ...    ...  \n",
       "3813      0.019           1                66     0.031  False  \n",
       "3814      0.001           1                66     0.050  False  \n",
       "3815     -0.007          -1                67     0.051  False  \n",
       "3816      0.008           1                68     0.044   True  \n",
       "3817     -0.032          -1                69     0.052  False  \n",
       "\n",
       "[242 rows x 17 columns]"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>open</th>\n",
       "      <th>close</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>macd</th>\n",
       "      <th>macd_dif</th>\n",
       "      <th>macd_dea</th>\n",
       "      <th>datetime</th>\n",
       "      <th>datetime_text</th>\n",
       "      <th>close_to_median</th>\n",
       "      <th>macd_close</th>\n",
       "      <th>macd_diff</th>\n",
       "      <th>macd_trend</th>\n",
       "      <th>macd_trend_group</th>\n",
       "      <th>pre_macd</th>\n",
       "      <th>point</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3576</th>\n",
       "      <td>20240102</td>\n",
       "      <td>8.57646</td>\n",
       "      <td>8.41205</td>\n",
       "      <td>8.60386</td>\n",
       "      <td>8.41205</td>\n",
       "      <td>0.107</td>\n",
       "      <td>-0.162</td>\n",
       "      <td>-0.216</td>\n",
       "      <td>2024-01-02</td>\n",
       "      <td>20240102</td>\n",
       "      <td>-1.50113</td>\n",
       "      <td>-0.339145</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3577</th>\n",
       "      <td>20240103</td>\n",
       "      <td>8.39379</td>\n",
       "      <td>8.40292</td>\n",
       "      <td>8.42119</td>\n",
       "      <td>8.35725</td>\n",
       "      <td>0.098</td>\n",
       "      <td>-0.155</td>\n",
       "      <td>-0.203</td>\n",
       "      <td>2024-01-03</td>\n",
       "      <td>20240103</td>\n",
       "      <td>-1.51026</td>\n",
       "      <td>-0.341208</td>\n",
       "      <td>-0.009</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.107</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3578</th>\n",
       "      <td>20240104</td>\n",
       "      <td>8.39379</td>\n",
       "      <td>8.32072</td>\n",
       "      <td>8.39379</td>\n",
       "      <td>8.29332</td>\n",
       "      <td>0.080</td>\n",
       "      <td>-0.153</td>\n",
       "      <td>-0.193</td>\n",
       "      <td>2024-01-04</td>\n",
       "      <td>20240104</td>\n",
       "      <td>-1.59246</td>\n",
       "      <td>-0.359779</td>\n",
       "      <td>-0.018</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.098</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3579</th>\n",
       "      <td>20240105</td>\n",
       "      <td>8.31158</td>\n",
       "      <td>8.46686</td>\n",
       "      <td>8.62213</td>\n",
       "      <td>8.28418</td>\n",
       "      <td>0.087</td>\n",
       "      <td>-0.139</td>\n",
       "      <td>-0.183</td>\n",
       "      <td>2024-01-05</td>\n",
       "      <td>20240105</td>\n",
       "      <td>-1.44632</td>\n",
       "      <td>-0.326762</td>\n",
       "      <td>0.007</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.080</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3580</th>\n",
       "      <td>20240108</td>\n",
       "      <td>8.43032</td>\n",
       "      <td>8.35725</td>\n",
       "      <td>8.49426</td>\n",
       "      <td>8.32072</td>\n",
       "      <td>0.076</td>\n",
       "      <td>-0.135</td>\n",
       "      <td>-0.173</td>\n",
       "      <td>2024-01-08</td>\n",
       "      <td>20240108</td>\n",
       "      <td>-1.55593</td>\n",
       "      <td>-0.351526</td>\n",
       "      <td>-0.011</td>\n",
       "      <td>-1</td>\n",
       "      <td>4</td>\n",
       "      <td>0.087</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3813</th>\n",
       "      <td>20241225</td>\n",
       "      <td>11.86000</td>\n",
       "      <td>11.92000</td>\n",
       "      <td>12.02000</td>\n",
       "      <td>11.84000</td>\n",
       "      <td>0.050</td>\n",
       "      <td>0.080</td>\n",
       "      <td>0.055</td>\n",
       "      <td>2024-12-25</td>\n",
       "      <td>20241225</td>\n",
       "      <td>2.00682</td>\n",
       "      <td>0.453394</td>\n",
       "      <td>0.019</td>\n",
       "      <td>1</td>\n",
       "      <td>66</td>\n",
       "      <td>0.031</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3814</th>\n",
       "      <td>20241226</td>\n",
       "      <td>11.92000</td>\n",
       "      <td>11.86000</td>\n",
       "      <td>11.93000</td>\n",
       "      <td>11.78000</td>\n",
       "      <td>0.051</td>\n",
       "      <td>0.087</td>\n",
       "      <td>0.062</td>\n",
       "      <td>2024-12-26</td>\n",
       "      <td>20241226</td>\n",
       "      <td>1.94682</td>\n",
       "      <td>0.439838</td>\n",
       "      <td>0.001</td>\n",
       "      <td>1</td>\n",
       "      <td>66</td>\n",
       "      <td>0.050</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3815</th>\n",
       "      <td>20241227</td>\n",
       "      <td>11.87000</td>\n",
       "      <td>11.83000</td>\n",
       "      <td>11.90000</td>\n",
       "      <td>11.66000</td>\n",
       "      <td>0.044</td>\n",
       "      <td>0.089</td>\n",
       "      <td>0.067</td>\n",
       "      <td>2024-12-27</td>\n",
       "      <td>20241227</td>\n",
       "      <td>1.91682</td>\n",
       "      <td>0.433061</td>\n",
       "      <td>-0.007</td>\n",
       "      <td>-1</td>\n",
       "      <td>67</td>\n",
       "      <td>0.051</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3816</th>\n",
       "      <td>20241230</td>\n",
       "      <td>11.78000</td>\n",
       "      <td>11.95000</td>\n",
       "      <td>11.97000</td>\n",
       "      <td>11.78000</td>\n",
       "      <td>0.052</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0.074</td>\n",
       "      <td>2024-12-30</td>\n",
       "      <td>20241230</td>\n",
       "      <td>2.03682</td>\n",
       "      <td>0.460172</td>\n",
       "      <td>0.008</td>\n",
       "      <td>1</td>\n",
       "      <td>68</td>\n",
       "      <td>0.044</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3817</th>\n",
       "      <td>20241231</td>\n",
       "      <td>11.93000</td>\n",
       "      <td>11.70000</td>\n",
       "      <td>11.99000</td>\n",
       "      <td>11.70000</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.086</td>\n",
       "      <td>0.076</td>\n",
       "      <td>2024-12-31</td>\n",
       "      <td>20241231</td>\n",
       "      <td>1.78682</td>\n",
       "      <td>0.403690</td>\n",
       "      <td>-0.032</td>\n",
       "      <td>-1</td>\n",
       "      <td>69</td>\n",
       "      <td>0.052</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>242 rows × 17 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 64
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-22T08:02:09.486300Z",
     "start_time": "2025-01-22T08:02:08.742146Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 根据macd、macd_dif、macd_dea的值绘制macd图像\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(20, 6))\n",
    "plt.plot(df['datetime_text'], df['macd_dif'], label='DIF', color='red')\n",
    "plt.plot(df['datetime_text'], df['macd_dea'], label='DEA', color='green')\n",
    "# plt.plot(df['datetime_text'], df['macd_close'], label='Close', color='gray')\n",
    "plt.bar(df['datetime_text'], df['macd'], label='MACD')\n",
    "point_df = df[df[\"point\"]]\n",
    "# plt.scatter(point_df['datetime_text'], point_df[\"point\"], label='Point', color='blue')\n",
    "for i, row in point_df.iterrows():\n",
    "    plt.axvline(x=row['datetime_text'], color='blue', linestyle='--')\n",
    "\n",
    "for group_name, group_data in df.groupby(\"macd_trend_group\"):\n",
    "    first_row = group_data.iloc[0]\n",
    "    last_row = group_data.iloc[-1]\n",
    "    x_start, y_start = first_row['datetime_text'], first_row['macd']\n",
    "    x_end, y_end = last_row['datetime_text'], last_row['macd']\n",
    "    plt.plot([x_start, x_end], [y_start, y_end], color='cyan')\n",
    "\n",
    "# plt.xlabel('datetime_text')\n",
    "# plt.ylabel('MACD')\n",
    "plt.axis('off')\n",
    "plt.title('MACD')\n",
    "# plt.gca().xaxis.set_major_locator(mdates.DayLocator(interval=15))\n",
    "plt.legend()\n",
    "\n",
    "plt.show()\n",
    "point_df"
   ],
   "id": "25cd7f58d7b8c044",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 2000x600 with 1 Axes>"
      ],
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "          date      open     close      high       low   macd  macd_dif  \\\n",
       "3645  20240418   9.66336   9.86430  10.07437   9.64509  0.017     0.043   \n",
       "3650  20240425   9.59029   9.69076   9.69989   9.57202  0.017     0.057   \n",
       "3652  20240429   9.64509   9.87343   9.95563   9.60856  0.039     0.075   \n",
       "3658  20240510   9.83690   9.99217  10.01044   9.82776  0.040     0.111   \n",
       "3662  20240516   9.90997  10.20224  10.23878   9.89170  0.046     0.133   \n",
       "3808  20241218  11.58000  11.65000  11.74000  11.57000  0.005     0.048   \n",
       "3811  20241223  11.64000  11.73000  11.84000  11.64000  0.008     0.049   \n",
       "3816  20241230  11.78000  11.95000  11.97000  11.78000  0.052     0.100   \n",
       "\n",
       "      macd_dea   datetime datetime_text  close_to_median  macd_close  \\\n",
       "3645     0.034 2024-04-18      20240418         -0.04888   -0.011043   \n",
       "3650     0.048 2024-04-25      20240425         -0.22242   -0.050251   \n",
       "3652     0.055 2024-04-29      20240429         -0.03975   -0.008981   \n",
       "3658     0.091 2024-05-10      20240510          0.07899    0.017846   \n",
       "3662     0.110 2024-05-16      20240516          0.28906    0.065306   \n",
       "3808     0.045 2024-12-18      20241218          1.73682    0.392394   \n",
       "3811     0.045 2024-12-23      20241223          1.81682    0.410468   \n",
       "3816     0.074 2024-12-30      20241230          2.03682    0.460172   \n",
       "\n",
       "      macd_diff  macd_trend  macd_trend_group  pre_macd  point  \n",
       "3645      0.055           1                23    -0.038   True  \n",
       "3650      0.004           1                25     0.013   True  \n",
       "3652      0.022           1                27     0.017   True  \n",
       "3658      0.010           1                29     0.030   True  \n",
       "3662      0.026           1                31     0.020   True  \n",
       "3808      0.001           1                64     0.004   True  \n",
       "3811      0.013           1                66    -0.005   True  \n",
       "3816      0.008           1                68     0.044   True  "
      ],
      "text/html": [
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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>open</th>\n",
       "      <th>close</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>macd</th>\n",
       "      <th>macd_dif</th>\n",
       "      <th>macd_dea</th>\n",
       "      <th>datetime</th>\n",
       "      <th>datetime_text</th>\n",
       "      <th>close_to_median</th>\n",
       "      <th>macd_close</th>\n",
       "      <th>macd_diff</th>\n",
       "      <th>macd_trend</th>\n",
       "      <th>macd_trend_group</th>\n",
       "      <th>pre_macd</th>\n",
       "      <th>point</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3645</th>\n",
       "      <td>20240418</td>\n",
       "      <td>9.66336</td>\n",
       "      <td>9.86430</td>\n",
       "      <td>10.07437</td>\n",
       "      <td>9.64509</td>\n",
       "      <td>0.017</td>\n",
       "      <td>0.043</td>\n",
       "      <td>0.034</td>\n",
       "      <td>2024-04-18</td>\n",
       "      <td>20240418</td>\n",
       "      <td>-0.04888</td>\n",
       "      <td>-0.011043</td>\n",
       "      <td>0.055</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>-0.038</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3650</th>\n",
       "      <td>20240425</td>\n",
       "      <td>9.59029</td>\n",
       "      <td>9.69076</td>\n",
       "      <td>9.69989</td>\n",
       "      <td>9.57202</td>\n",
       "      <td>0.017</td>\n",
       "      <td>0.057</td>\n",
       "      <td>0.048</td>\n",
       "      <td>2024-04-25</td>\n",
       "      <td>20240425</td>\n",
       "      <td>-0.22242</td>\n",
       "      <td>-0.050251</td>\n",
       "      <td>0.004</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>0.013</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3652</th>\n",
       "      <td>20240429</td>\n",
       "      <td>9.64509</td>\n",
       "      <td>9.87343</td>\n",
       "      <td>9.95563</td>\n",
       "      <td>9.60856</td>\n",
       "      <td>0.039</td>\n",
       "      <td>0.075</td>\n",
       "      <td>0.055</td>\n",
       "      <td>2024-04-29</td>\n",
       "      <td>20240429</td>\n",
       "      <td>-0.03975</td>\n",
       "      <td>-0.008981</td>\n",
       "      <td>0.022</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>0.017</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3658</th>\n",
       "      <td>20240510</td>\n",
       "      <td>9.83690</td>\n",
       "      <td>9.99217</td>\n",
       "      <td>10.01044</td>\n",
       "      <td>9.82776</td>\n",
       "      <td>0.040</td>\n",
       "      <td>0.111</td>\n",
       "      <td>0.091</td>\n",
       "      <td>2024-05-10</td>\n",
       "      <td>20240510</td>\n",
       "      <td>0.07899</td>\n",
       "      <td>0.017846</td>\n",
       "      <td>0.010</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>0.030</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3662</th>\n",
       "      <td>20240516</td>\n",
       "      <td>9.90997</td>\n",
       "      <td>10.20224</td>\n",
       "      <td>10.23878</td>\n",
       "      <td>9.89170</td>\n",
       "      <td>0.046</td>\n",
       "      <td>0.133</td>\n",
       "      <td>0.110</td>\n",
       "      <td>2024-05-16</td>\n",
       "      <td>20240516</td>\n",
       "      <td>0.28906</td>\n",
       "      <td>0.065306</td>\n",
       "      <td>0.026</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>0.020</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3808</th>\n",
       "      <td>20241218</td>\n",
       "      <td>11.58000</td>\n",
       "      <td>11.65000</td>\n",
       "      <td>11.74000</td>\n",
       "      <td>11.57000</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.048</td>\n",
       "      <td>0.045</td>\n",
       "      <td>2024-12-18</td>\n",
       "      <td>20241218</td>\n",
       "      <td>1.73682</td>\n",
       "      <td>0.392394</td>\n",
       "      <td>0.001</td>\n",
       "      <td>1</td>\n",
       "      <td>64</td>\n",
       "      <td>0.004</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3811</th>\n",
       "      <td>20241223</td>\n",
       "      <td>11.64000</td>\n",
       "      <td>11.73000</td>\n",
       "      <td>11.84000</td>\n",
       "      <td>11.64000</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.049</td>\n",
       "      <td>0.045</td>\n",
       "      <td>2024-12-23</td>\n",
       "      <td>20241223</td>\n",
       "      <td>1.81682</td>\n",
       "      <td>0.410468</td>\n",
       "      <td>0.013</td>\n",
       "      <td>1</td>\n",
       "      <td>66</td>\n",
       "      <td>-0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3816</th>\n",
       "      <td>20241230</td>\n",
       "      <td>11.78000</td>\n",
       "      <td>11.95000</td>\n",
       "      <td>11.97000</td>\n",
       "      <td>11.78000</td>\n",
       "      <td>0.052</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0.074</td>\n",
       "      <td>2024-12-30</td>\n",
       "      <td>20241230</td>\n",
       "      <td>2.03682</td>\n",
       "      <td>0.460172</td>\n",
       "      <td>0.008</td>\n",
       "      <td>1</td>\n",
       "      <td>68</td>\n",
       "      <td>0.044</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 72
  }
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