diff --git a/backtest.ipynb b/backtest.ipynb
index f8fea5e..fa5dc0a 100644
--- a/backtest.ipynb
+++ b/backtest.ipynb
@@ -2,404 +2,702 @@
  "cells": [
   {
    "metadata": {
+    "SqlCellData": {
+     "data_source_name": "leopard_dev@81.71.3.24",
+     "variableName$1": "dailies_df"
+    },
     "ExecuteTime": {
-     "end_time": "2026-01-19T10:00:35.820002Z",
-     "start_time": "2026-01-19T10:00:34.354216Z"
+     "end_time": "2026-01-20T02:26:30.134545Z",
+     "start_time": "2026-01-20T02:26:22.978651Z"
+    }
+   },
+   "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 '2025-01-01 00:00:00' and '2025-12-31 23:59:59'\n",
+    "order by daily.trade_date"
+   ],
+   "id": "93e66142c88e4d2f",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "     trade_date         open        close         high          low  \\\n",
+       "0    2025-01-02  1498.907493  1460.572263  1504.018857  1455.460899   \n",
+       "1    2025-01-03  1461.850104  1454.183058  1474.628514  1451.627376   \n",
+       "2    2025-01-06  1454.183058  1461.850104  1466.961468  1433.737602   \n",
+       "3    2025-01-07  1459.294422  1470.794991  1473.350673  1452.905217   \n",
+       "4    2025-01-08  1469.517150  1469.517150  1486.129083  1456.738740   \n",
+       "..          ...          ...          ...          ...          ...   \n",
+       "183  2025-10-09  1493.155774  1502.380920  1503.698798  1485.248506   \n",
+       "184  2025-10-10  1498.427286  1506.334554  1514.241822  1497.109408   \n",
+       "185  2025-10-13  1491.837896  1502.380920  1510.288188  1486.566384   \n",
+       "186  2025-10-14  1501.063042  1524.784846  1528.738480  1497.109408   \n",
+       "187  2025-10-15  1526.130396  1534.205160  1536.896748  1515.364044   \n",
+       "\n",
+       "         volume    factor  \n",
+       "0    1819596.99  127.7841  \n",
+       "1    1154680.44  127.7841  \n",
+       "2    1085536.30  127.7841  \n",
+       "3     747862.88  127.7841  \n",
+       "4    1062386.01  127.7841  \n",
+       "..          ...       ...  \n",
+       "183  1047469.06  131.7878  \n",
+       "184  1087947.75  131.7878  \n",
+       "185  1168801.73  131.7878  \n",
+       "186  1843428.36  131.7878  \n",
+       "187  1271061.03  134.5794  \n",
+       "\n",
+       "[188 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>2025-01-02</td>\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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2025-01-03</td>\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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2025-01-06</td>\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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2025-01-07</td>\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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2025-01-08</td>\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",
+       "    </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>183</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>184</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>185</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>186</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>187</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>188 rows × 7 columns</p>\n",
+       "</div>"
+      ]
+     },
+     "execution_count": 87,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 87
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-20T02:26:30.368092Z",
+     "start_time": "2026-01-20T02:26:30.241161Z"
     }
    },
    "cell_type": "code",
    "source": [
     "import pandas as pd\n",
     "\n",
-    "host = '81.71.3.24'\n",
-    "port = 6785\n",
-    "database = 'leopard_dev'\n",
-    "username = 'leopard'\n",
-    "password = '9NEzFzovnddf@PyEP?e*AYAWnCyd7UhYwQK$pJf>7?ccFiN^x4$eKEZ5~E<7<+~X'"
+    "dailies_df.rename(\n",
+    "    columns={'open': 'Open', 'close': 'Close', 'high': 'High', 'low': 'Low', 'volume': 'Volume'}, inplace=True\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": "d815a3591c4f9463",
+   "id": "c0ed078ffd5b57fd",
+   "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  \n",
+       "trade_date            \n",
+       "2025-01-02  127.7841  \n",
+       "2025-01-03  127.7841  \n",
+       "2025-01-06  127.7841  \n",
+       "2025-01-07  127.7841  \n",
+       "2025-01-08  127.7841  \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",
+       "[188 rows x 6 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>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>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",
+       "    </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",
+       "    </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",
+       "    </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",
+       "    </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",
+       "    </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>188 rows × 6 columns</p>\n",
+       "</div>"
+      ]
+     },
+     "execution_count": 88,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 88
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-20T02:28:38.147008Z",
+     "start_time": "2026-01-20T02:28:38.094513Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "dailies_df['sma30'] = dailies_df['Close'].rolling(10).mean()\n",
+    "dailies_df['sma60'] = dailies_df['Close'].rolling(30).mean()\n",
+    "dailies_df.tail()"
+   ],
+   "id": "c558d68773d228c1",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "                   Open        Close         High          Low      Volume  \\\n",
+       "trade_date                                                                   \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  \n",
+       "trade_date                                      \n",
+       "2025-10-09  131.7878  1504.094161  1546.925196  \n",
+       "2025-10-10  131.7878  1504.357737  1543.762289  \n",
+       "2025-10-13  131.7878  1503.698798  1540.862958  \n",
+       "2025-10-14  131.7878  1506.202766  1536.997182  \n",
+       "2025-10-15  134.5794  1507.803737  1533.840781  "
+      ],
+      "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>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",
+       "    </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",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\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>1504.094161</td>\n",
+       "      <td>1546.925196</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>1504.357737</td>\n",
+       "      <td>1543.762289</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>1503.698798</td>\n",
+       "      <td>1540.862958</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>1506.202766</td>\n",
+       "      <td>1536.997182</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>1507.803737</td>\n",
+       "      <td>1533.840781</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ]
+     },
+     "execution_count": 97,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 97
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-20T02:28:09.232747Z",
+     "start_time": "2026-01-20T02:28:09.215190Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "from backtesting import Strategy\n",
+    "from backtesting.lib import crossover\n",
+    "\n",
+    "\n",
+    "class SmaCross(Strategy):\n",
+    "    def init(self):\n",
+    "        pass\n",
+    "\n",
+    "    def next(self):\n",
+    "        if crossover(self.data.sma30, self.data.sma60):\n",
+    "            self.position.close()\n",
+    "            self.buy()\n",
+    "        elif crossover(self.data.sma60, self.data.sma30):\n",
+    "            self.position.close()\n",
+    "            self.sell()"
+   ],
+   "id": "b56aaaf4cad7bc7d",
    "outputs": [],
-   "execution_count": 2
-  },
-  {
-   "cell_type": "code",
-   "id": "initial_id",
-   "metadata": {
-    "collapsed": true,
-    "ExecuteTime": {
-     "end_time": "2026-01-19T10:00:42.739695Z",
-     "start_time": "2026-01-19T10:00:35.820726Z"
-    }
-   },
-   "source": [
-    "import psycopg2\n",
-    "\n",
-    "dailies_df = pd.DataFrame()\n",
-    "\n",
-    "with psycopg2.connect(host=host, port=port, database=database, user=username, password=password) as connection:\n",
-    "    with connection.cursor() as cursor:\n",
-    "        # language=PostgreSQL\n",
-    "        cursor.execute(\n",
-    "            \"\"\"select trade_date, open, close, high, low, 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 '2025-01-01 00:00:00' and '2025-12-31 23:59:59'\n",
-    "order by daily.trade_date\"\"\"\n",
-    "            )\n",
-    "        rows = cursor.fetchall()\n",
-    "\n",
-    "        dailies_df = pd.DataFrame.from_records(rows, columns=['trade_date', 'open', 'close', 'high', 'low', 'factor'])\n",
-    "\n",
-    "dailies_df"
-   ],
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "     trade_date   open  close   high    low    factor\n",
-       "0    2025-01-02  11.73  11.43  11.77  11.39  127.7841\n",
-       "1    2025-01-03  11.44  11.38  11.54  11.36  127.7841\n",
-       "2    2025-01-06  11.38  11.44  11.48  11.22  127.7841\n",
-       "3    2025-01-07  11.42  11.51  11.53  11.37  127.7841\n",
-       "4    2025-01-08  11.50  11.50  11.63  11.40  127.7841\n",
-       "..          ...    ...    ...    ...    ...       ...\n",
-       "183  2025-10-09  11.33  11.40  11.41  11.27  131.7878\n",
-       "184  2025-10-10  11.37  11.43  11.49  11.36  131.7878\n",
-       "185  2025-10-13  11.32  11.40  11.46  11.28  131.7878\n",
-       "186  2025-10-14  11.39  11.57  11.60  11.36  131.7878\n",
-       "187  2025-10-15  11.34  11.40  11.42  11.26  134.5794\n",
-       "\n",
-       "[188 rows x 6 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>factor</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2025-01-02</td>\n",
-       "      <td>11.73</td>\n",
-       "      <td>11.43</td>\n",
-       "      <td>11.77</td>\n",
-       "      <td>11.39</td>\n",
-       "      <td>127.7841</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2025-01-03</td>\n",
-       "      <td>11.44</td>\n",
-       "      <td>11.38</td>\n",
-       "      <td>11.54</td>\n",
-       "      <td>11.36</td>\n",
-       "      <td>127.7841</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2025-01-06</td>\n",
-       "      <td>11.38</td>\n",
-       "      <td>11.44</td>\n",
-       "      <td>11.48</td>\n",
-       "      <td>11.22</td>\n",
-       "      <td>127.7841</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2025-01-07</td>\n",
-       "      <td>11.42</td>\n",
-       "      <td>11.51</td>\n",
-       "      <td>11.53</td>\n",
-       "      <td>11.37</td>\n",
-       "      <td>127.7841</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2025-01-08</td>\n",
-       "      <td>11.50</td>\n",
-       "      <td>11.50</td>\n",
-       "      <td>11.63</td>\n",
-       "      <td>11.40</td>\n",
-       "      <td>127.7841</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>183</th>\n",
-       "      <td>2025-10-09</td>\n",
-       "      <td>11.33</td>\n",
-       "      <td>11.40</td>\n",
-       "      <td>11.41</td>\n",
-       "      <td>11.27</td>\n",
-       "      <td>131.7878</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>184</th>\n",
-       "      <td>2025-10-10</td>\n",
-       "      <td>11.37</td>\n",
-       "      <td>11.43</td>\n",
-       "      <td>11.49</td>\n",
-       "      <td>11.36</td>\n",
-       "      <td>131.7878</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>185</th>\n",
-       "      <td>2025-10-13</td>\n",
-       "      <td>11.32</td>\n",
-       "      <td>11.40</td>\n",
-       "      <td>11.46</td>\n",
-       "      <td>11.28</td>\n",
-       "      <td>131.7878</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>186</th>\n",
-       "      <td>2025-10-14</td>\n",
-       "      <td>11.39</td>\n",
-       "      <td>11.57</td>\n",
-       "      <td>11.60</td>\n",
-       "      <td>11.36</td>\n",
-       "      <td>131.7878</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>187</th>\n",
-       "      <td>2025-10-15</td>\n",
-       "      <td>11.34</td>\n",
-       "      <td>11.40</td>\n",
-       "      <td>11.42</td>\n",
-       "      <td>11.26</td>\n",
-       "      <td>134.5794</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>188 rows × 6 columns</p>\n",
-       "</div>"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "execution_count": 3
+   "execution_count": 94
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-01-19T10:01:46.133661Z",
-     "start_time": "2026-01-19T10:01:46.044719Z"
+     "end_time": "2026-01-20T02:28:12.350117Z",
+     "start_time": "2026-01-20T02:28:12.330024Z"
     }
    },
    "cell_type": "code",
    "source": [
-    "dailies_df['factor'] = dailies_df['factor'].fillna(1.0)\n",
-    "dailies_df['open'] = dailies_df['open'] * dailies_df['factor']\n",
-    "dailies_df['close'] = dailies_df['close'] * dailies_df['factor']\n",
-    "dailies_df['high'] = dailies_df['high'] * dailies_df['factor']\n",
-    "dailies_df['low'] = dailies_df['low'] * dailies_df['factor']\n",
-    "dailies_df"
+    "def stats_print(stats):\n",
+    "    indicator_name_mapping = {\n",
+    "        'Start': '回测开始时间',\n",
+    "        'End': '回测结束时间',\n",
+    "        'Duration': '回测持续时长',\n",
+    "        'Exposure Time [%]': '持仓时间占比（%）',\n",
+    "        'Equity Final [$]': '最终权益',\n",
+    "        'Equity Peak [$]': '权益峰值',\n",
+    "        'Return [%]': '总收益率（%）',\n",
+    "        'Buy & Hold Return [%]': '买入并持有收益率（%）',\n",
+    "        'Return (Ann.) [%]': '年化收益率（%）',\n",
+    "        'Volatility (Ann.) [%]': '年化波动率（%）',\n",
+    "        'CAGR [%]': '复合年均增长率（%）',\n",
+    "        'Sharpe Ratio': '夏普比率',\n",
+    "        'Sortino Ratio': '索提诺比率',\n",
+    "        'Calmar Ratio': '卡尔玛比率',\n",
+    "        'Alpha [%]': '阿尔法系数（%）',\n",
+    "        'Beta': '贝塔系数',\n",
+    "        'Max. Drawdown [%]': '最大回撤（%）',\n",
+    "        'Avg. Drawdown [%]': '平均回撤（%）',\n",
+    "        'Max. Drawdown Duration': '最大回撤持续时长',\n",
+    "        'Avg. Drawdown Duration': '平均回撤持续时长',\n",
+    "        '# Trades': '交易总次数',\n",
+    "        'Win Rate [%]': '胜率（%）',\n",
+    "        'Best Trade [%]': '最佳单笔交易收益率（%）',\n",
+    "        'Worst Trade [%]': '最差单笔交易收益率（%）',\n",
+    "        'Avg. Trade [%]': '平均单笔交易收益率（%）',\n",
+    "        'Max. Trade Duration': '单笔交易最长持有时长',\n",
+    "        'Avg. Trade Duration': '单笔交易平均持有时长',\n",
+    "        'Profit Factor': '盈利因子',\n",
+    "        'Expectancy [%]': '期望收益（%）',\n",
+    "        'SQN': '系统质量数',\n",
+    "        'Kelly Criterion': '凯利准则',\n",
+    "    }\n",
+    "    for k, v in stats.items():\n",
+    "        if k in indicator_name_mapping:\n",
+    "            cn_name = indicator_name_mapping.get(k, k)\n",
+    "            if isinstance(v, (int, float)):\n",
+    "                if \"%\" in cn_name or k in ['Sharpe Ratio', 'Sortino Ratio', 'Calmar Ratio', 'Profit Factor']:\n",
+    "                    formatted_value = f\"{v:.2f}\"\n",
+    "                elif \"$\" in cn_name:\n",
+    "                    formatted_value = f\"{v:.2f}\"\n",
+    "                elif \"次数\" in cn_name:\n",
+    "                    formatted_value = f\"{v:.0f}\"\n",
+    "                else:\n",
+    "                    formatted_value = f\"{v:.4f}\"\n",
+    "            else:\n",
+    "                formatted_value = str(v)\n",
+    "            print(f'{cn_name}: {formatted_value}')"
    ],
-   "id": "29226cf1c6fe9f94",
+   "id": "a23e811212958477",
+   "outputs": [],
+   "execution_count": 95
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-20T02:28:43.128279Z",
+     "start_time": "2026-01-20T02:28:43.044156Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "from backtesting import Backtest\n",
+    "\n",
+    "bt = Backtest(dailies_df, SmaCross, cash=100000, commission=.002, finalize_trades=True)\n",
+    "stats = bt.run()\n",
+    "stats_print(stats)"
+   ],
+   "id": "afc750727129ff2e",
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "     trade_date           open          close           high            low  \\\n",
-       "0    2025-01-02  191536.544976  186637.912112  192189.696025  185984.761064   \n",
-       "1    2025-01-03  186801.199875  185821.473302  188434.077496  185494.897778   \n",
-       "2    2025-01-06  185821.473302  186801.199875  187454.350923  183208.869108   \n",
-       "3    2025-01-07  186474.624350  187944.214209  188270.789734  185658.185540   \n",
-       "4    2025-01-08  187780.926447  187780.926447  189903.667355  186148.048826   \n",
-       "..          ...            ...            ...            ...            ...   \n",
-       "183  2025-10-09  196779.714513  197995.476209  198169.156451  195737.633059   \n",
-       "184  2025-10-10  197474.435482  198516.516936  199558.598389  197300.755240   \n",
-       "185  2025-10-13  196606.034270  197995.476209  199037.557663  195911.313301   \n",
-       "186  2025-10-14  197821.795966  200948.040328  201469.081055  197300.755240   \n",
-       "187  2025-10-15  205385.713015  206472.409910  206834.642208  203936.783823   \n",
-       "\n",
-       "       factor  \n",
-       "0    127.7841  \n",
-       "1    127.7841  \n",
-       "2    127.7841  \n",
-       "3    127.7841  \n",
-       "4    127.7841  \n",
-       "..        ...  \n",
-       "183  131.7878  \n",
-       "184  131.7878  \n",
-       "185  131.7878  \n",
-       "186  131.7878  \n",
-       "187  134.5794  \n",
-       "\n",
-       "[188 rows x 6 columns]"
-      ],
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
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-       "\n",
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-       "        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>trade_date</th>\n",
-       "      <th>open</th>\n",
-       "      <th>close</th>\n",
-       "      <th>high</th>\n",
-       "      <th>low</th>\n",
-       "      <th>factor</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2025-01-02</td>\n",
-       "      <td>191536.544976</td>\n",
-       "      <td>186637.912112</td>\n",
-       "      <td>192189.696025</td>\n",
-       "      <td>185984.761064</td>\n",
-       "      <td>127.7841</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2025-01-03</td>\n",
-       "      <td>186801.199875</td>\n",
-       "      <td>185821.473302</td>\n",
-       "      <td>188434.077496</td>\n",
-       "      <td>185494.897778</td>\n",
-       "      <td>127.7841</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2025-01-06</td>\n",
-       "      <td>185821.473302</td>\n",
-       "      <td>186801.199875</td>\n",
-       "      <td>187454.350923</td>\n",
-       "      <td>183208.869108</td>\n",
-       "      <td>127.7841</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2025-01-07</td>\n",
-       "      <td>186474.624350</td>\n",
-       "      <td>187944.214209</td>\n",
-       "      <td>188270.789734</td>\n",
-       "      <td>185658.185540</td>\n",
-       "      <td>127.7841</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2025-01-08</td>\n",
-       "      <td>187780.926447</td>\n",
-       "      <td>187780.926447</td>\n",
-       "      <td>189903.667355</td>\n",
-       "      <td>186148.048826</td>\n",
-       "      <td>127.7841</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>183</th>\n",
-       "      <td>2025-10-09</td>\n",
-       "      <td>196779.714513</td>\n",
-       "      <td>197995.476209</td>\n",
-       "      <td>198169.156451</td>\n",
-       "      <td>195737.633059</td>\n",
-       "      <td>131.7878</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>184</th>\n",
-       "      <td>2025-10-10</td>\n",
-       "      <td>197474.435482</td>\n",
-       "      <td>198516.516936</td>\n",
-       "      <td>199558.598389</td>\n",
-       "      <td>197300.755240</td>\n",
-       "      <td>131.7878</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>185</th>\n",
-       "      <td>2025-10-13</td>\n",
-       "      <td>196606.034270</td>\n",
-       "      <td>197995.476209</td>\n",
-       "      <td>199037.557663</td>\n",
-       "      <td>195911.313301</td>\n",
-       "      <td>131.7878</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>186</th>\n",
-       "      <td>2025-10-14</td>\n",
-       "      <td>197821.795966</td>\n",
-       "      <td>200948.040328</td>\n",
-       "      <td>201469.081055</td>\n",
-       "      <td>197300.755240</td>\n",
-       "      <td>131.7878</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>187</th>\n",
-       "      <td>2025-10-15</td>\n",
-       "      <td>205385.713015</td>\n",
-       "      <td>206472.409910</td>\n",
-       "      <td>206834.642208</td>\n",
-       "      <td>203936.783823</td>\n",
-       "      <td>134.5794</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>188 rows × 6 columns</p>\n",
-       "</div>"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "回测开始时间: 2025-01-02 00:00:00\n",
+      "回测结束时间: 2025-10-15 00:00:00\n",
+      "回测持续时长: 286 days 00:00:00\n",
+      "持仓时间占比（%）: 71.81\n",
+      "最终权益: 114727.4805\n",
+      "权益峰值: 117838.4813\n",
+      "总收益率（%）: 14.73\n",
+      "买入并持有收益率（%）: 5.04\n",
+      "年化收益率（%）: 20.22\n",
+      "年化波动率（%）: 17.17\n",
+      "复合年均增长率（%）: 12.87\n",
+      "夏普比率: 1.18\n",
+      "索提诺比率: 2.45\n",
+      "卡尔玛比率: 2.21\n",
+      "阿尔法系数（%）: 14.96\n",
+      "贝塔系数: -0.0456\n",
+      "最大回撤（%）: -9.13\n",
+      "平均回撤（%）: -3.40\n",
+      "最大回撤持续时长: 97 days 00:00:00\n",
+      "平均回撤持续时长: 33 days 00:00:00\n",
+      "交易总次数: 3\n",
+      "胜率（%）: 66.67\n",
+      "最佳单笔交易收益率（%）: 10.02\n",
+      "最差单笔交易收益率（%）: -0.49\n",
+      "平均单笔交易收益率（%）: 4.76\n",
+      "单笔交易最长持有时长: 78 days 00:00:00\n",
+      "单笔交易平均持有时长: 68 days 00:00:00\n",
+      "盈利因子: 30.80\n",
+      "期望收益（%）: 4.84\n",
+      "系统质量数: 1.6469\n",
+      "凯利准则: 0.6455\n"
+     ]
     }
    ],
-   "execution_count": 5
+   "execution_count": 98
   }
  ],
  "metadata": {
diff --git a/note.md b/note.md
new file mode 100644
index 0000000..16494b6
--- /dev/null
+++ b/note.md
@@ -0,0 +1,126 @@
+# 基础时间与权益指标
+
+## Exposure Time [%] - 持仓时间占比（%）
+
+衡量策略在统计周期内实际持有头寸的时间占总时间的百分比。反映策略的“活跃度”（如占比100%=全程持仓，占比低=多数时间空仓），辅助判断交易频率和资金使用效率。
+
+## Equity Final [$] - 最终权益
+
+策略运行结束时的账户总资金（本金+盈亏），是最基础的收益结果指标，直接体现最终资金规模。
+
+## Equity Peak [$] - 权益峰值
+
+统计周期内账户权益的最大值。对比最终权益可判断策略是否从峰值回落，结合回撤指标能评估收益稳定性。
+
+# 收益率类指标（核心收益衡量）
+
+## Return [%] - 总收益率（%）
+
+周期内总收益占初始本金的百分比（`(最终权益-初始本金)/初始本金×100%`），直观反映整体盈利/亏损程度，但未考虑时间因素。
+
+## Buy & Hold Return [%] - 买入并持有收益率（%）
+
+作为“基准收益率”，模拟“买入标的后长期持有不操作”的收益。用于对比策略收益，判断策略是否跑赢简单持有，是评估策略有效性的核心参照。
+
+## Return (Ann.) [%] - 年化收益率（%）
+
+将周期总收益折算为按年计算的收益（`(1+总收益率)^(365/持仓天数) - 1`），消除时间周期差异，是跨周期对比不同策略收益的关键指标。
+
+## CAGR [%] - 复合年均增长率（%）
+
+即“年均复合收益率”（`(最终权益/初始本金)^(1/年数) - 1`），衡量资金长期复利增长速度，更贴合多年期策略的收益评估，避免短期高收益误导。
+
+# 风险类指标（衡量亏损与波动风险）
+
+## Volatility (Ann.) [%] - 年化波动率（%）
+
+收益波动程度的年化标准差，反映收益稳定性。波动率越高，收益起伏越大，策略“震荡风险”越高；波动率低则收益更平稳。
+
+## Max. Drawdown [%] - 最大回撤（%）
+
+权益从峰值回落至谷值的最大幅度（`(峰值-谷值)/峰值×100%`），核心风险指标。比如最大回撤20%，说明策略曾从最高点亏掉20%，直接体现抗风险能力。
+
+## Avg. Drawdown [%] - 平均回撤（%）
+
+所有回撤的平均幅度。若最大回撤远高于平均回撤，说明策略曾出现极端亏损；两者接近则回撤风险更均匀。
+
+## Max. Drawdown Duration - 最大回撤持续时长
+
+最大回撤从峰值回落至恢复峰值的时间（天/月），反映策略从最大亏损中回血的速度，时长越长则恢复能力越弱。
+
+## Avg. Drawdown Duration - 平均回撤持续时长
+
+所有回撤周期的平均时间，辅助判断策略亏损后的恢复效率。
+
+# 风险收益比指标（性价比核心）
+
+## Sharpe Ratio - 夏普比率
+
+衡量“每承担1单位风险的超额收益”（`(年化收益率-无风险利率)/年化波动率`
+），无风险利率通常取国债利率（3%左右）。比率越高性价比越高（>1较好，>2优秀）。
+
+## Sortino Ratio - 索提诺比率
+
+改进版夏普比率，仅用“下行波动率”（亏损方向波动）计算风险，更贴合投资者对“亏损风险”的关注，适合评估稳健型策略。
+
+## Calmar Ratio - 卡尔玛比率
+
+`年化收益率/最大回撤（绝对值）`，直接反映“收益能否覆盖最大亏损”，比率越高，策略用小回撤换高收益的能力越强。
+
+# 资产定价系数（市场相关性）
+
+## Alpha [%] - 阿尔法系数（%）
+
+衡量策略“超额收益”（超出市场基准的收益），Alpha为正说明策略能跑赢市场，反映主动管理能力；为负则跑输市场。
+
+## Beta - 贝塔系数
+
+衡量策略与市场基准的波动同步性。Beta=1=和市场波动一致；Beta>1=策略更激进（波动更大）；Beta<1=策略更稳健；Beta<0=与市场反向波动。
+
+# 交易行为与质量指标
+
+## Trades - 交易总次数
+
+统计周期内总交易笔数，反映策略交易频率（高频/低频），结合胜率可判断策略有效性。
+
+## Win Rate [%] - 胜率（%）
+
+盈利交易次数/总次数×100%，直观反映“赚钱交易的比例”，但需结合盈亏比（平均盈利/平均亏损）使用（如胜率50%+盈亏比3:1仍盈利）。
+
+## Best Trade [%] - 最佳单笔交易收益率（%）
+
+单次盈利幅度最大值，反映策略盈利上限，但需警惕是否由偶然高收益拉高整体表现。
+
+## Worst Trade [%] - 最差单笔交易收益率（%）
+
+单次亏损幅度最大值（通常为负），反映单笔交易的最大亏损风险。
+
+## Avg. Trade [%] - 平均单笔交易收益率（%）
+
+所有交易收益率的平均值，反映单笔交易的平均盈利能力，为正则单笔交易平均赚钱。
+
+## Max. Trade Duration - 单笔交易最长持有时长
+
+单次持仓的最长时间，定义策略风格（日内/短线/中长线）。
+
+## Avg. Trade Duration - 单笔交易平均持有时长
+
+所有交易持仓时长的平均值，辅助判断资金周转效率。
+
+# 综合绩效指标（策略可持续性）
+
+## Profit Factor - 盈利因子
+
+`总盈利金额/总亏损金额（绝对值）`，核心指标。>1说明总盈利>总亏损，数值越高盈利能力越强（如盈利因子2=每亏1元赚2元）。
+
+## Expectancy [%] - 期望收益（%）
+
+每笔交易的预期收益（`胜率×平均盈利 - (1-胜率)×平均亏损`），为正则策略长期可盈利，数值越高预期收益越好。
+
+## SQN - 系统质量数
+
+衡量策略收益的“统计显著性”（`√交易次数 × 期望收益 / 交易收益标准差`）。SQN>1.6合格，>2.5优秀，>3.0卓越，判断收益是否为偶然结果。
+
+## Kelly Criterion - 凯利准则
+
+计算“最优仓位比例”（`(胜率×(盈亏比+1)-1)/盈亏比`），确定每笔交易的最佳资金投入比例，最大化长期复利收益，同时控制破产风险（实际应用中通常减半使用）。
diff --git a/pyproject.toml b/pyproject.toml
index f4ccb4b..37f6a56 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -5,8 +5,10 @@ description = "Stock analysis"
 requires-python = ">=3.14"
 dependencies = [
     "backtesting~=0.6.5",
+    "duckdb>=1.4.3",
     "jupyter~=1.1.1",
     "matplotlib~=3.10.8",
+    "mplfinance>=0.12.10b0",
     "pandas~=2.3.3",
     "pandas-stubs~=2.3.3",
     "peewee~=3.19.0",
diff --git a/sql2dataframe.ipynb b/sql2dataframe.ipynb
new file mode 100644
index 0000000..87ad9a0
--- /dev/null
+++ b/sql2dataframe.ipynb
@@ -0,0 +1,72 @@
+{
+ "cells": [
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "host = '81.71.3.24'\n",
+    "port = 6785\n",
+    "database = 'leopard_dev'\n",
+    "username = 'leopard'\n",
+    "password = '9NEzFzovnddf@PyEP?e*AYAWnCyd7UhYwQK$pJf>7?ccFiN^x4$eKEZ5~E<7<+~X'"
+   ],
+   "id": "1e8d815ee9b8c936"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "initial_id",
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import psycopg2\n",
+    "\n",
+    "dailies_df = pd.DataFrame()\n",
+    "\n",
+    "with psycopg2.connect(host=host, port=port, database=database, user=username, password=password) as connection:\n",
+    "    with connection.cursor() as cursor:\n",
+    "        # language=PostgreSQL\n",
+    "        cursor.execute(\n",
+    "            \"\"\"select trade_date, open, close, high, low, 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 '2025-01-01 00:00:00' and '2025-12-31 23:59:59'\n",
+    "order by daily.trade_date\"\"\"\n",
+    "            )\n",
+    "        rows = cursor.fetchall()\n",
+    "\n",
+    "        dailies_df = pd.DataFrame.from_records(rows, columns=['trade_date', 'open', 'close', 'high', 'low', 'factor'])\n",
+    "\n",
+    "dailies_df"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/uv.lock b/uv.lock
index 350e2e3..49a0887 100644
--- a/uv.lock
+++ b/uv.lock
@@ -338,6 +338,21 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/07/6c/aa3f2f849e01cb6a001cd8554a88d4c77c5c1a31c95bdf1cf9301e6d9ef4/defusedxml-0.7.1-py2.py3-none-any.whl", hash = "sha256:a352e7e428770286cc899e2542b6cdaedb2b4953ff269a210103ec58f6198a61", size = 25604, upload-time = "2021-03-08T10:59:24.45Z" },
 ]
 
+[[package]]
+name = "duckdb"
+version = "1.4.3"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/7f/da/17c3eb5458af69d54dedc8d18e4a32ceaa8ce4d4c699d45d6d8287e790c3/duckdb-1.4.3.tar.gz", hash = "sha256:fea43e03604c713e25a25211ada87d30cd2a044d8f27afab5deba26ac49e5268", size = 18478418, upload-time = "2025-12-09T10:59:22.945Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/b6/f4/a38651e478fa41eeb8e43a0a9c0d4cd8633adea856e3ac5ac95124b0fdbf/duckdb-1.4.3-cp314-cp314-macosx_10_15_universal2.whl", hash = "sha256:316711a9e852bcfe1ed6241a5f654983f67e909e290495f3562cccdf43be8180", size = 29042272, upload-time = "2025-12-09T10:58:51.826Z" },
+    { url = "https://files.pythonhosted.org/packages/16/de/2cf171a66098ce5aeeb7371511bd2b3d7b73a2090603b0b9df39f8aaf814/duckdb-1.4.3-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:9e625b2b4d52bafa1fd0ebdb0990c3961dac8bb00e30d327185de95b68202131", size = 15419343, upload-time = "2025-12-09T10:58:54.439Z" },
+    { url = "https://files.pythonhosted.org/packages/35/28/6b0a7830828d4e9a37420d87e80fe6171d2869a9d3d960bf5d7c3b8c7ee4/duckdb-1.4.3-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:130c6760f6c573f9c9fe9aba56adba0fab48811a4871b7b8fd667318b4a3e8da", size = 13748905, upload-time = "2025-12-09T10:58:56.656Z" },
+    { url = "https://files.pythonhosted.org/packages/15/4d/778628e194d63967870873b9581c8a6b4626974aa4fbe09f32708a2d3d3a/duckdb-1.4.3-cp314-cp314-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:20c88effaa557a11267706b01419c542fe42f893dee66e5a6daa5974ea2d4a46", size = 18487261, upload-time = "2025-12-09T10:58:58.866Z" },
+    { url = "https://files.pythonhosted.org/packages/c6/5f/87e43af2e4a0135f9675449563e7c2f9b6f1fe6a2d1691c96b091f3904dd/duckdb-1.4.3-cp314-cp314-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:1b35491db98ccd11d151165497c084a9d29d3dc42fc80abea2715a6c861ca43d", size = 20497138, upload-time = "2025-12-09T10:59:01.241Z" },
+    { url = "https://files.pythonhosted.org/packages/94/41/abec537cc7c519121a2a83b9a6f180af8915fabb433777dc147744513e74/duckdb-1.4.3-cp314-cp314-win_amd64.whl", hash = "sha256:23b12854032c1a58d0452e2b212afa908d4ce64171862f3792ba9a596ba7c765", size = 12836056, upload-time = "2025-12-09T10:59:03.388Z" },
+    { url = "https://files.pythonhosted.org/packages/b1/5a/8af5b96ce5622b6168854f479ce846cf7fb589813dcc7d8724233c37ded3/duckdb-1.4.3-cp314-cp314-win_arm64.whl", hash = "sha256:90f241f25cffe7241bf9f376754a5845c74775e00e1c5731119dc88cd71e0cb2", size = 13527759, upload-time = "2025-12-09T10:59:05.496Z" },
+]
+
 [[package]]
 name = "executing"
 version = "2.2.1"
@@ -850,8 +865,10 @@ version = "0.1.0"
 source = { virtual = "." }
 dependencies = [
     { name = "backtesting" },
+    { name = "duckdb" },
     { name = "jupyter" },
     { name = "matplotlib" },
+    { name = "mplfinance" },
     { name = "pandas" },
     { name = "pandas-stubs" },
     { name = "peewee" },
@@ -861,8 +878,10 @@ dependencies = [
 [package.metadata]
 requires-dist = [
     { name = "backtesting", specifier = "~=0.6.5" },
+    { name = "duckdb", specifier = ">=1.4.3" },
     { name = "jupyter", specifier = "~=1.1.1" },
     { name = "matplotlib", specifier = "~=3.10.8" },
+    { name = "mplfinance", specifier = ">=0.12.10b0" },
     { name = "pandas", specifier = "~=2.3.3" },
     { name = "pandas-stubs", specifier = "~=2.3.3" },
     { name = "peewee", specifier = "~=3.19.0" },
@@ -953,6 +972,19 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/9b/f7/4a5e785ec9fbd65146a27b6b70b6cdc161a66f2024e4b04ac06a67f5578b/mistune-3.2.0-py3-none-any.whl", hash = "sha256:febdc629a3c78616b94393c6580551e0e34cc289987ec6c35ed3f4be42d0eee1", size = 53598, upload-time = "2025-12-23T11:36:33.211Z" },
 ]
 
+[[package]]
+name = "mplfinance"
+version = "0.12.10b0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "matplotlib" },
+    { name = "pandas" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/6c/a9/34e7998d02fb58fae04f750444ce4e95e75f3a08dad17fb2d32098a97834/mplfinance-0.12.10b0.tar.gz", hash = "sha256:7da150b5851aa5119ad6e06b55e48338b619bb6773f1b85df5de67a5ffd917bf", size = 70117, upload-time = "2023-08-02T15:13:53.829Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/d7/d9/31c436ea7673c21a5bf3fc747bc7f63377582dfe845c3004d3e46f9deee0/mplfinance-0.12.10b0-py3-none-any.whl", hash = "sha256:76d3b095f05ff35de730751649de063bea4064d0c49b21b6182c82997a7f52bb", size = 75016, upload-time = "2023-08-02T15:13:52.022Z" },
+]
+
 [[package]]
 name = "narwhals"
 version = "2.15.0"