{
 "cells": [
  {
   "metadata": {
    "SqlCellData": {
     "data_source_name": "leopard_dev@81.71.3.24",
     "variableName$1": "dailies_df"
    },
    "ExecuteTime": {
     "end_time": "2026-01-28T09:02:14.676305Z",
     "start_time": "2026-01-28T09:02:07.695819Z"
    }
   },
   "cell_type": "code",
   "source": [
    "%%sql\n",
    "select trade_date, open * factor as \"Open\", close * factor as \"Close\", high * factor as \"High\", low * factor as \"Low\", volume as \"Volume\", coalesce(factor, 1.0) as factor\n",
    "from leopard_daily daily\n",
    "         left join leopard_stock stock on stock.id = daily.stock_id\n",
    "where stock.code = '000001.SZ'\n",
    "  and daily.trade_date between '2024-01-01 00:00:00' and '2025-12-31 23:59:59'\n",
    "order by daily.trade_date"
   ],
   "id": "20775dafbd9f1ae6",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "     trade_date         Open        Close         High          Low  \\\n",
       "0    2024-01-02  1095.935070  1074.926730  1099.436460  1074.926730   \n",
       "1    2024-01-03  1072.592470  1073.759600  1076.093860  1067.923950   \n",
       "2    2024-01-04  1072.592470  1063.255430  1072.592470  1059.754040   \n",
       "3    2024-01-05  1062.088300  1081.929510  1101.770720  1058.586910   \n",
       "4    2024-01-08  1077.260990  1067.923950  1085.430900  1063.255430   \n",
       "..          ...          ...          ...          ...          ...   \n",
       "425  2025-10-09  1493.155774  1502.380920  1503.698798  1485.248506   \n",
       "426  2025-10-10  1498.427286  1506.334554  1514.241822  1497.109408   \n",
       "427  2025-10-13  1491.837896  1502.380920  1510.288188  1486.566384   \n",
       "428  2025-10-14  1501.063042  1524.784846  1528.738480  1497.109408   \n",
       "429  2025-10-15  1526.130396  1534.205160  1536.896748  1515.364044   \n",
       "\n",
       "         Volume    factor  \n",
       "0    1158366.45  116.7130  \n",
       "1     733610.31  116.7130  \n",
       "2     864193.99  116.7130  \n",
       "3    1991622.16  116.7130  \n",
       "4    1121156.19  116.7130  \n",
       "..          ...       ...  \n",
       "425  1047469.06  131.7878  \n",
       "426  1087947.75  131.7878  \n",
       "427  1168801.73  131.7878  \n",
       "428  1843428.36  131.7878  \n",
       "429  1271061.03  134.5794  \n",
       "\n",
       "[430 rows x 7 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>trade_date</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Volume</th>\n",
       "      <th>factor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2024-01-02</td>\n",
       "      <td>1095.935070</td>\n",
       "      <td>1074.926730</td>\n",
       "      <td>1099.436460</td>\n",
       "      <td>1074.926730</td>\n",
       "      <td>1158366.45</td>\n",
       "      <td>116.7130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2024-01-03</td>\n",
       "      <td>1072.592470</td>\n",
       "      <td>1073.759600</td>\n",
       "      <td>1076.093860</td>\n",
       "      <td>1067.923950</td>\n",
       "      <td>733610.31</td>\n",
       "      <td>116.7130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2024-01-04</td>\n",
       "      <td>1072.592470</td>\n",
       "      <td>1063.255430</td>\n",
       "      <td>1072.592470</td>\n",
       "      <td>1059.754040</td>\n",
       "      <td>864193.99</td>\n",
       "      <td>116.7130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2024-01-05</td>\n",
       "      <td>1062.088300</td>\n",
       "      <td>1081.929510</td>\n",
       "      <td>1101.770720</td>\n",
       "      <td>1058.586910</td>\n",
       "      <td>1991622.16</td>\n",
       "      <td>116.7130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2024-01-08</td>\n",
       "      <td>1077.260990</td>\n",
       "      <td>1067.923950</td>\n",
       "      <td>1085.430900</td>\n",
       "      <td>1063.255430</td>\n",
       "      <td>1121156.19</td>\n",
       "      <td>116.7130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>425</th>\n",
       "      <td>2025-10-09</td>\n",
       "      <td>1493.155774</td>\n",
       "      <td>1502.380920</td>\n",
       "      <td>1503.698798</td>\n",
       "      <td>1485.248506</td>\n",
       "      <td>1047469.06</td>\n",
       "      <td>131.7878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426</th>\n",
       "      <td>2025-10-10</td>\n",
       "      <td>1498.427286</td>\n",
       "      <td>1506.334554</td>\n",
       "      <td>1514.241822</td>\n",
       "      <td>1497.109408</td>\n",
       "      <td>1087947.75</td>\n",
       "      <td>131.7878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>427</th>\n",
       "      <td>2025-10-13</td>\n",
       "      <td>1491.837896</td>\n",
       "      <td>1502.380920</td>\n",
       "      <td>1510.288188</td>\n",
       "      <td>1486.566384</td>\n",
       "      <td>1168801.73</td>\n",
       "      <td>131.7878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>428</th>\n",
       "      <td>2025-10-14</td>\n",
       "      <td>1501.063042</td>\n",
       "      <td>1524.784846</td>\n",
       "      <td>1528.738480</td>\n",
       "      <td>1497.109408</td>\n",
       "      <td>1843428.36</td>\n",
       "      <td>131.7878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429</th>\n",
       "      <td>2025-10-15</td>\n",
       "      <td>1526.130396</td>\n",
       "      <td>1534.205160</td>\n",
       "      <td>1536.896748</td>\n",
       "      <td>1515.364044</td>\n",
       "      <td>1271061.03</td>\n",
       "      <td>134.5794</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>430 rows × 7 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 115
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-01-28T09:02:14.810005Z",
     "start_time": "2026-01-28T09:02:14.717550Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import pandas as pd\n",
    "\n",
    "dailies_df[\"trade_date\"] = pd.to_datetime(dailies_df[\"trade_date\"], format='%Y-%m-%d')\n",
    "dailies_df.set_index(\"trade_date\", inplace=True)\n",
    "dailies_df"
   ],
   "id": "4a56612617ffcebe",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                   Open        Close         High          Low      Volume  \\\n",
       "trade_date                                                                   \n",
       "2024-01-02  1095.935070  1074.926730  1099.436460  1074.926730  1158366.45   \n",
       "2024-01-03  1072.592470  1073.759600  1076.093860  1067.923950   733610.31   \n",
       "2024-01-04  1072.592470  1063.255430  1072.592470  1059.754040   864193.99   \n",
       "2024-01-05  1062.088300  1081.929510  1101.770720  1058.586910  1991622.16   \n",
       "2024-01-08  1077.260990  1067.923950  1085.430900  1063.255430  1121156.19   \n",
       "...                 ...          ...          ...          ...         ...   \n",
       "2025-10-09  1493.155774  1502.380920  1503.698798  1485.248506  1047469.06   \n",
       "2025-10-10  1498.427286  1506.334554  1514.241822  1497.109408  1087947.75   \n",
       "2025-10-13  1491.837896  1502.380920  1510.288188  1486.566384  1168801.73   \n",
       "2025-10-14  1501.063042  1524.784846  1528.738480  1497.109408  1843428.36   \n",
       "2025-10-15  1526.130396  1534.205160  1536.896748  1515.364044  1271061.03   \n",
       "\n",
       "              factor  \n",
       "trade_date            \n",
       "2024-01-02  116.7130  \n",
       "2024-01-03  116.7130  \n",
       "2024-01-04  116.7130  \n",
       "2024-01-05  116.7130  \n",
       "2024-01-08  116.7130  \n",
       "...              ...  \n",
       "2025-10-09  131.7878  \n",
       "2025-10-10  131.7878  \n",
       "2025-10-13  131.7878  \n",
       "2025-10-14  131.7878  \n",
       "2025-10-15  134.5794  \n",
       "\n",
       "[430 rows x 6 columns]"
      ],
      "text/html": [
       "<div>\n",
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       "    }\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>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Volume</th>\n",
       "      <th>factor</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trade_date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2024-01-02</th>\n",
       "      <td>1095.935070</td>\n",
       "      <td>1074.926730</td>\n",
       "      <td>1099.436460</td>\n",
       "      <td>1074.926730</td>\n",
       "      <td>1158366.45</td>\n",
       "      <td>116.7130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-03</th>\n",
       "      <td>1072.592470</td>\n",
       "      <td>1073.759600</td>\n",
       "      <td>1076.093860</td>\n",
       "      <td>1067.923950</td>\n",
       "      <td>733610.31</td>\n",
       "      <td>116.7130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-04</th>\n",
       "      <td>1072.592470</td>\n",
       "      <td>1063.255430</td>\n",
       "      <td>1072.592470</td>\n",
       "      <td>1059.754040</td>\n",
       "      <td>864193.99</td>\n",
       "      <td>116.7130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-05</th>\n",
       "      <td>1062.088300</td>\n",
       "      <td>1081.929510</td>\n",
       "      <td>1101.770720</td>\n",
       "      <td>1058.586910</td>\n",
       "      <td>1991622.16</td>\n",
       "      <td>116.7130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-08</th>\n",
       "      <td>1077.260990</td>\n",
       "      <td>1067.923950</td>\n",
       "      <td>1085.430900</td>\n",
       "      <td>1063.255430</td>\n",
       "      <td>1121156.19</td>\n",
       "      <td>116.7130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025-10-09</th>\n",
       "      <td>1493.155774</td>\n",
       "      <td>1502.380920</td>\n",
       "      <td>1503.698798</td>\n",
       "      <td>1485.248506</td>\n",
       "      <td>1047469.06</td>\n",
       "      <td>131.7878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025-10-10</th>\n",
       "      <td>1498.427286</td>\n",
       "      <td>1506.334554</td>\n",
       "      <td>1514.241822</td>\n",
       "      <td>1497.109408</td>\n",
       "      <td>1087947.75</td>\n",
       "      <td>131.7878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025-10-13</th>\n",
       "      <td>1491.837896</td>\n",
       "      <td>1502.380920</td>\n",
       "      <td>1510.288188</td>\n",
       "      <td>1486.566384</td>\n",
       "      <td>1168801.73</td>\n",
       "      <td>131.7878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025-10-14</th>\n",
       "      <td>1501.063042</td>\n",
       "      <td>1524.784846</td>\n",
       "      <td>1528.738480</td>\n",
       "      <td>1497.109408</td>\n",
       "      <td>1843428.36</td>\n",
       "      <td>131.7878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025-10-15</th>\n",
       "      <td>1526.130396</td>\n",
       "      <td>1534.205160</td>\n",
       "      <td>1536.896748</td>\n",
       "      <td>1515.364044</td>\n",
       "      <td>1271061.03</td>\n",
       "      <td>134.5794</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>430 rows × 6 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 116
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-01-28T09:02:15.548521Z",
     "start_time": "2026-01-28T09:02:15.510447Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import talib\n",
    "\n",
    "dailies_df['sma30'] = talib.SMA(dailies_df['Close'], timeperiod=30)\n",
    "dailies_df['sma60'] = talib.SMA(dailies_df['Close'], timeperiod=60)\n",
    "dailies_df['sma120'] = talib.SMA(dailies_df['Close'], timeperiod=120)\n",
    "\n",
    "macd, signal, hist = talib.MACD(dailies_df[\"Close\"], fastperiod=10, slowperiod=20, signalperiod=9)\n",
    "dailies_df['macd'] = macd\n",
    "dailies_df['signal'] = signal\n",
    "dailies_df['hist'] = hist\n",
    "\n",
    "dailies_df = dailies_df.loc['2025-01-01':'2025-12-31']\n",
    "dailies_df"
   ],
   "id": "8a48b99475109677",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                   Open        Close         High          Low      Volume  \\\n",
       "trade_date                                                                   \n",
       "2025-01-02  1498.907493  1460.572263  1504.018857  1455.460899  1819596.99   \n",
       "2025-01-03  1461.850104  1454.183058  1474.628514  1451.627376  1154680.44   \n",
       "2025-01-06  1454.183058  1461.850104  1466.961468  1433.737602  1085536.30   \n",
       "2025-01-07  1459.294422  1470.794991  1473.350673  1452.905217   747862.88   \n",
       "2025-01-08  1469.517150  1469.517150  1486.129083  1456.738740  1062386.01   \n",
       "...                 ...          ...          ...          ...         ...   \n",
       "2025-10-09  1493.155774  1502.380920  1503.698798  1485.248506  1047469.06   \n",
       "2025-10-10  1498.427286  1506.334554  1514.241822  1497.109408  1087947.75   \n",
       "2025-10-13  1491.837896  1502.380920  1510.288188  1486.566384  1168801.73   \n",
       "2025-10-14  1501.063042  1524.784846  1528.738480  1497.109408  1843428.36   \n",
       "2025-10-15  1526.130396  1534.205160  1536.896748  1515.364044  1271061.03   \n",
       "\n",
       "              factor        sma30        sma60       sma120       macd  \\\n",
       "trade_date                                                               \n",
       "2025-01-02  127.7841  1481.060314  1488.045844  1386.170379   4.048710   \n",
       "2025-01-03  127.7841  1480.165825  1486.768003  1387.794876  -0.026455   \n",
       "2025-01-06  127.7841  1480.847340  1486.171678  1389.410319  -2.378865   \n",
       "2025-01-07  127.7841  1482.252965  1485.085513  1391.110725  -3.304617   \n",
       "2025-01-08  127.7841  1483.232643  1484.233619  1392.612908  -4.043327   \n",
       "...              ...          ...          ...          ...        ...   \n",
       "2025-10-09  131.7878  1546.925196  1593.490219  1544.474925 -16.948867   \n",
       "2025-10-10  131.7878  1543.762289  1589.646408  1545.431305 -15.506860   \n",
       "2025-10-13  131.7878  1540.862958  1586.329749  1546.312145 -14.513190   \n",
       "2025-10-14  131.7878  1536.997182  1583.232735  1547.358386 -11.586571   \n",
       "2025-10-15  134.5794  1533.840781  1580.841843  1548.429886  -8.403867   \n",
       "\n",
       "               signal      hist  \n",
       "trade_date                       \n",
       "2025-01-02   7.092149 -3.043439  \n",
       "2025-01-03   5.668428 -5.694884  \n",
       "2025-01-06   4.058970 -6.437834  \n",
       "2025-01-07   2.586252 -5.890869  \n",
       "2025-01-08   1.260337 -5.303663  \n",
       "...               ...       ...  \n",
       "2025-10-09 -17.863171  0.914304  \n",
       "2025-10-10 -17.391909  1.885048  \n",
       "2025-10-13 -16.816165  2.302975  \n",
       "2025-10-14 -15.770246  4.183676  \n",
       "2025-10-15 -14.296970  5.893103  \n",
       "\n",
       "[188 rows x 12 columns]"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Volume</th>\n",
       "      <th>factor</th>\n",
       "      <th>sma30</th>\n",
       "      <th>sma60</th>\n",
       "      <th>sma120</th>\n",
       "      <th>macd</th>\n",
       "      <th>signal</th>\n",
       "      <th>hist</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trade_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",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2025-01-02</th>\n",
       "      <td>1498.907493</td>\n",
       "      <td>1460.572263</td>\n",
       "      <td>1504.018857</td>\n",
       "      <td>1455.460899</td>\n",
       "      <td>1819596.99</td>\n",
       "      <td>127.7841</td>\n",
       "      <td>1481.060314</td>\n",
       "      <td>1488.045844</td>\n",
       "      <td>1386.170379</td>\n",
       "      <td>4.048710</td>\n",
       "      <td>7.092149</td>\n",
       "      <td>-3.043439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025-01-03</th>\n",
       "      <td>1461.850104</td>\n",
       "      <td>1454.183058</td>\n",
       "      <td>1474.628514</td>\n",
       "      <td>1451.627376</td>\n",
       "      <td>1154680.44</td>\n",
       "      <td>127.7841</td>\n",
       "      <td>1480.165825</td>\n",
       "      <td>1486.768003</td>\n",
       "      <td>1387.794876</td>\n",
       "      <td>-0.026455</td>\n",
       "      <td>5.668428</td>\n",
       "      <td>-5.694884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025-01-06</th>\n",
       "      <td>1454.183058</td>\n",
       "      <td>1461.850104</td>\n",
       "      <td>1466.961468</td>\n",
       "      <td>1433.737602</td>\n",
       "      <td>1085536.30</td>\n",
       "      <td>127.7841</td>\n",
       "      <td>1480.847340</td>\n",
       "      <td>1486.171678</td>\n",
       "      <td>1389.410319</td>\n",
       "      <td>-2.378865</td>\n",
       "      <td>4.058970</td>\n",
       "      <td>-6.437834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025-01-07</th>\n",
       "      <td>1459.294422</td>\n",
       "      <td>1470.794991</td>\n",
       "      <td>1473.350673</td>\n",
       "      <td>1452.905217</td>\n",
       "      <td>747862.88</td>\n",
       "      <td>127.7841</td>\n",
       "      <td>1482.252965</td>\n",
       "      <td>1485.085513</td>\n",
       "      <td>1391.110725</td>\n",
       "      <td>-3.304617</td>\n",
       "      <td>2.586252</td>\n",
       "      <td>-5.890869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025-01-08</th>\n",
       "      <td>1469.517150</td>\n",
       "      <td>1469.517150</td>\n",
       "      <td>1486.129083</td>\n",
       "      <td>1456.738740</td>\n",
       "      <td>1062386.01</td>\n",
       "      <td>127.7841</td>\n",
       "      <td>1483.232643</td>\n",
       "      <td>1484.233619</td>\n",
       "      <td>1392.612908</td>\n",
       "      <td>-4.043327</td>\n",
       "      <td>1.260337</td>\n",
       "      <td>-5.303663</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025-10-09</th>\n",
       "      <td>1493.155774</td>\n",
       "      <td>1502.380920</td>\n",
       "      <td>1503.698798</td>\n",
       "      <td>1485.248506</td>\n",
       "      <td>1047469.06</td>\n",
       "      <td>131.7878</td>\n",
       "      <td>1546.925196</td>\n",
       "      <td>1593.490219</td>\n",
       "      <td>1544.474925</td>\n",
       "      <td>-16.948867</td>\n",
       "      <td>-17.863171</td>\n",
       "      <td>0.914304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025-10-10</th>\n",
       "      <td>1498.427286</td>\n",
       "      <td>1506.334554</td>\n",
       "      <td>1514.241822</td>\n",
       "      <td>1497.109408</td>\n",
       "      <td>1087947.75</td>\n",
       "      <td>131.7878</td>\n",
       "      <td>1543.762289</td>\n",
       "      <td>1589.646408</td>\n",
       "      <td>1545.431305</td>\n",
       "      <td>-15.506860</td>\n",
       "      <td>-17.391909</td>\n",
       "      <td>1.885048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025-10-13</th>\n",
       "      <td>1491.837896</td>\n",
       "      <td>1502.380920</td>\n",
       "      <td>1510.288188</td>\n",
       "      <td>1486.566384</td>\n",
       "      <td>1168801.73</td>\n",
       "      <td>131.7878</td>\n",
       "      <td>1540.862958</td>\n",
       "      <td>1586.329749</td>\n",
       "      <td>1546.312145</td>\n",
       "      <td>-14.513190</td>\n",
       "      <td>-16.816165</td>\n",
       "      <td>2.302975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025-10-14</th>\n",
       "      <td>1501.063042</td>\n",
       "      <td>1524.784846</td>\n",
       "      <td>1528.738480</td>\n",
       "      <td>1497.109408</td>\n",
       "      <td>1843428.36</td>\n",
       "      <td>131.7878</td>\n",
       "      <td>1536.997182</td>\n",
       "      <td>1583.232735</td>\n",
       "      <td>1547.358386</td>\n",
       "      <td>-11.586571</td>\n",
       "      <td>-15.770246</td>\n",
       "      <td>4.183676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025-10-15</th>\n",
       "      <td>1526.130396</td>\n",
       "      <td>1534.205160</td>\n",
       "      <td>1536.896748</td>\n",
       "      <td>1515.364044</td>\n",
       "      <td>1271061.03</td>\n",
       "      <td>134.5794</td>\n",
       "      <td>1533.840781</td>\n",
       "      <td>1580.841843</td>\n",
       "      <td>1548.429886</td>\n",
       "      <td>-8.403867</td>\n",
       "      <td>-14.296970</td>\n",
       "      <td>5.893103</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>188 rows × 12 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 117
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-01-28T09:02:16.150130Z",
     "start_time": "2026-01-28T09:02:15.595633Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import mplfinance as mpf\n",
    "\n",
    "# https://www.cnblogs.com/shclbear/p/17231948.html\n",
    "\n",
    "figure = mpf.figure(figsize=(12, 8))\n",
    "\n",
    "ax1 = figure.add_axes([0.1, 0.4, 1, 0.6])\n",
    "ax2 = figure.add_axes([0.1, 0.2, 1, 0.2], sharex=ax1)\n",
    "ax3 = figure.add_axes([0.1, 0.0, 1, 0.2], sharex=ax1)\n",
    "\n",
    "ax3.set_ylabel('MACD')\n",
    "\n",
    "ap = [\n",
    "    mpf.make_addplot(dailies_df[['sma30', 'sma60', 'sma120']], ax=ax1),\n",
    "    mpf.make_addplot(dailies_df[['macd', 'signal']], ax=ax3),\n",
    "]\n",
    "\n",
    "mpf.plot(dailies_df, ax=ax1, volume=ax2, addplot=ap)"
   ],
   "id": "a6be892a675bdb6d",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Mpf_Figure size 1200x800 with 3 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 118
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-01-28T09:02:16.319469Z",
     "start_time": "2026-01-28T09:02:16.309796Z"
    }
   },
   "cell_type": "code",
   "source": [
    "dailies_df.reset_index(inplace=True)\n",
    "dailies_df_inc = dailies_df[dailies_df[\"Close\"] > dailies_df[\"Open\"]]\n",
    "dailies_df_dec = dailies_df[dailies_df[\"Close\"] < dailies_df[\"Open\"]]"
   ],
   "id": "29759a1d33476c0f",
   "outputs": [],
   "execution_count": 119
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-01-28T09:07:40.981251Z",
     "start_time": "2026-01-28T09:07:40.392017Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from bokeh.io import show, output_notebook\n",
    "from bokeh.plotting import figure\n",
    "\n",
    "output_notebook()\n",
    "\n",
    "p = figure(\n",
    "    x_axis_type=\"datetime\",\n",
    "    sizing_mode=\"stretch_width\",\n",
    "    tools=\"pan,wheel_zoom,box_zoom,reset,save,hover\",\n",
    ")\n",
    "p.min_border = 0\n",
    "\n",
    "p.xaxis.major_label_overrides = {\n",
    "    i: date.strftime(\"%Y-%m-%d\") for i, date in zip(dailies_df.index, dailies_df[\"trade_date\"])\n",
    "}\n",
    "\n",
    "p.segment(dailies_df.index, dailies_df[\"High\"], dailies_df.index, dailies_df[\"Low\"], color=\"black\")\n",
    "\n",
    "p.vbar(dailies_df_inc.index, 0.6, dailies_df_inc[\"Open\"], dailies_df_inc[\"Close\"], color=\"grey\")\n",
    "p.vbar(dailies_df_dec.index, 0.6, dailies_df_dec[\"Open\"], dailies_df_dec[\"Close\"], color=\"black\")\n",
    "\n",
    "p.line(dailies_df.index, dailies_df[\"sma30\"], color=\"orange\")\n",
    "p.line(dailies_df.index, dailies_df[\"sma60\"], color=\"red\")\n",
    "\n",
    "show(p)"
   ],
   "id": "96bdbedea2ddc1c",
   "outputs": [
    {
     "data": {
      "text/html": [
       "    <style>\n",
       "        .bk-notebook-logo {\n",
       "            display: block;\n",
       "            width: 20px;\n",
       "            height: 20px;\n",
       "            background-image: url(data:image/png;base64,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);\n",
       "        }\n",
       "    </style>\n",
       "    <div>\n",
       "        <a href=\"https://bokeh.org\" target=\"_blank\" class=\"bk-notebook-logo\"></a>\n",
       "        <span id=\"b86774a5-59b5-4032-84a1-593268fb9b24\">Loading BokehJS ...</span>\n",
       "    </div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    },
    {
     "data": {
      "application/javascript": "'use strict';\n(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  const force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\nconst JS_MIME_TYPE = 'application/javascript';\n  const HTML_MIME_TYPE = 'text/html';\n  const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n  const CLASS_NAME = 'output_bokeh rendered_html';\n\n  /**\n   * Render data to the DOM node\n   */\n  function render(props, node) {\n    const script = document.createElement(\"script\");\n    node.appendChild(script);\n  }\n\n  /**\n   * Handle when an output is cleared or removed\n   */\n  function handleClearOutput(event, handle) {\n    function drop(id) {\n      const view = Bokeh.index.get_by_id(id)\n      if (view != null) {\n        view.model.document.clear()\n        Bokeh.index.delete(view)\n      }\n    }\n\n    const cell = handle.cell;\n\n    const id = cell.output_area._bokeh_element_id;\n    const server_id = cell.output_area._bokeh_server_id;\n\n    // Clean up Bokeh references\n    if (id != null) {\n      drop(id)\n    }\n\n    if (server_id !== undefined) {\n      // Clean up Bokeh references\n      const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n      cell.notebook.kernel.execute(cmd_clean, {\n        iopub: {\n          output: function(msg) {\n            const id = msg.content.text.trim()\n            drop(id)\n          }\n        }\n      });\n      // Destroy server and session\n      const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n      cell.notebook.kernel.execute(cmd_destroy);\n    }\n  }\n\n  /**\n   * Handle when a new output is added\n   */\n  function handleAddOutput(event, handle) {\n    const output_area = handle.output_area;\n    const output = handle.output;\n\n    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n    if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n      return\n    }\n\n    const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n\n    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n      // store reference to embed id on output_area\n      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n    }\n    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n      const bk_div = document.createElement(\"div\");\n      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n      const script_attrs = bk_div.children[0].attributes;\n      for (let i = 0; i < script_attrs.length; i++) {\n        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n        toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n      }\n      // store reference to server id on output_area\n      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n    }\n  }\n\n  function register_renderer(events, OutputArea) {\n\n    function append_mime(data, metadata, element) {\n      // create a DOM node to render to\n      const toinsert = this.create_output_subarea(\n        metadata,\n        CLASS_NAME,\n        EXEC_MIME_TYPE\n      );\n      this.keyboard_manager.register_events(toinsert);\n      // Render to node\n      const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n      render(props, toinsert[toinsert.length - 1]);\n      element.append(toinsert);\n      return toinsert\n    }\n\n    /* Handle when an output is cleared or removed */\n    events.on('clear_output.CodeCell', handleClearOutput);\n    events.on('delete.Cell', handleClearOutput);\n\n    /* Handle when a new output is added */\n    events.on('output_added.OutputArea', handleAddOutput);\n\n    /**\n     * Register the mime type and append_mime function with output_area\n     */\n    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n      /* Is output safe? */\n      safe: true,\n      /* Index of renderer in `output_area.display_order` */\n      index: 0\n    });\n  }\n\n  // register the mime type if in Jupyter Notebook environment and previously unregistered\n  if (root.Jupyter !== undefined) {\n    const events = require('base/js/events');\n    const OutputArea = require('notebook/js/outputarea').OutputArea;\n\n    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n      register_renderer(events, OutputArea);\n    }\n  }\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  const NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded(error = null) {\n    const el = document.getElementById(\"b86774a5-59b5-4032-84a1-593268fb9b24\");\n    if (el != null) {\n      const html = (() => {\n        if (typeof root.Bokeh === \"undefined\") {\n          if (error == null) {\n            return \"BokehJS is loading ...\";\n          } else {\n            return \"BokehJS failed to load.\";\n          }\n        } else {\n          const prefix = `BokehJS ${root.Bokeh.version}`;\n          if (error == null) {\n            return `${prefix} successfully loaded.`;\n          } else {\n            return `${prefix} <b>encountered errors</b> while loading and may not function as expected.`;\n          }\n        }\n      })();\n      el.innerHTML = html;\n\n      if (error != null) {\n        const wrapper = document.createElement(\"div\");\n        wrapper.style.overflow = \"auto\";\n        wrapper.style.height = \"5em\";\n        wrapper.style.resize = \"vertical\";\n        const content = document.createElement(\"div\");\n        content.style.fontFamily = \"monospace\";\n        content.style.whiteSpace = \"pre-wrap\";\n        content.style.backgroundColor = \"rgb(255, 221, 221)\";\n        content.textContent = error.stack ?? error.toString();\n        wrapper.append(content);\n        el.append(wrapper);\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(() => display_loaded(error), 100);\n    }\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error(url) {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.8.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.8.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.8.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.8.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.8.2.min.js\"];\n  const css_urls = [];\n\n  const inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {\n    }\n  ];\n\n  function run_inline_js() {\n    if (root.Bokeh !== undefined || force === true) {\n      try {\n            for (let i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\n\n      } catch (error) {display_loaded(error);throw error;\n      }if (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      const cell = $(document.getElementById(\"b86774a5-59b5-4032-84a1-593268fb9b24\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));",
      "application/vnd.bokehjs_load.v0+json": "'use strict';\n(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  const force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  const NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded(error = null) {\n    const el = document.getElementById(\"b86774a5-59b5-4032-84a1-593268fb9b24\");\n    if (el != null) {\n      const html = (() => {\n        if (typeof root.Bokeh === \"undefined\") {\n          if (error == null) {\n            return \"BokehJS is loading ...\";\n          } else {\n            return \"BokehJS failed to load.\";\n          }\n        } else {\n          const prefix = `BokehJS ${root.Bokeh.version}`;\n          if (error == null) {\n            return `${prefix} successfully loaded.`;\n          } else {\n            return `${prefix} <b>encountered errors</b> while loading and may not function as expected.`;\n          }\n        }\n      })();\n      el.innerHTML = html;\n\n      if (error != null) {\n        const wrapper = document.createElement(\"div\");\n        wrapper.style.overflow = \"auto\";\n        wrapper.style.height = \"5em\";\n        wrapper.style.resize = \"vertical\";\n        const content = document.createElement(\"div\");\n        content.style.fontFamily = \"monospace\";\n        content.style.whiteSpace = \"pre-wrap\";\n        content.style.backgroundColor = \"rgb(255, 221, 221)\";\n        content.textContent = error.stack ?? error.toString();\n        wrapper.append(content);\n        el.append(wrapper);\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(() => display_loaded(error), 100);\n    }\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error(url) {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.8.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.8.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.8.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.8.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.8.2.min.js\"];\n  const css_urls = [];\n\n  const inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {\n    }\n  ];\n\n  function run_inline_js() {\n    if (root.Bokeh !== undefined || force === true) {\n      try {\n            for (let i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\n\n      } catch (error) {display_loaded(error);throw error;\n      }if (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      const cell = $(document.getElementById(\"b86774a5-59b5-4032-84a1-593268fb9b24\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    },
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"caf1a2ad-09d2-4ce5-bf68-6763f01a2967\" data-root-id=\"p5747\" style=\"display: contents;\"></div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    },
    {
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