lanyuanxiaoyao/leopard-analysis
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leopard-analysis/backtest.ipynb

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{
"cells": [
{
"metadata": {
"SqlCellData": {
"data_source_name": "leopard_dev@81.71.3.24",
"variableName$1": "dailies_df"
},
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"end_time": "2026-01-20T06:18:47.059850Z",
"start_time": "2026-01-20T06:18:40.058469Z"
}
},
"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": "93e66142c88e4d2f",
"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]"
],
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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>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": 110,
"metadata": {},
"output_type": "execute_result"
}
],
"execution_count": 110
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2026-01-20T06:18:47.288743Z",
"start_time": "2026-01-20T06:18:47.107554Z"
}
},
"cell_type": "code",
"source": [
"import pandas as pd\n",
"\n",
"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": "c0ed078ffd5b57fd",
"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]"
],
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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",
" </tr>\n",
" <tr>\n",
" <th>trade_date</th>\n",
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" <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": 111,
"metadata": {},
"output_type": "execute_result"
}
],
"execution_count": 111
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2026-01-20T06:18:47.649279Z",
"start_time": "2026-01-20T06:18:47.592859Z"
}
},
"cell_type": "code",
"source": [
"dailies_df['sma10'] = dailies_df['Close'].rolling(10).mean()\n",
"dailies_df['sma30'] = dailies_df['Close'].rolling(30).mean()\n",
"dailies_df['sma60'] = dailies_df['Close'].rolling(60).mean()\n",
"dailies_df['sma120'] = dailies_df['Close'].rolling(120).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 sma10 sma30 sma60 sma120 \n",
"trade_date \n",
"2025-10-09 131.7878 1504.094161 1546.925196 1593.490219 1544.474925 \n",
"2025-10-10 131.7878 1504.357737 1543.762289 1589.646408 1545.431305 \n",
"2025-10-13 131.7878 1503.698798 1540.862958 1586.329749 1546.312145 \n",
"2025-10-14 131.7878 1506.202766 1536.997182 1583.232735 1547.358386 \n",
"2025-10-15 134.5794 1507.803737 1533.840781 1580.841843 1548.429886 "
],
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Open</th>\n",
" <th>Close</th>\n",
" <th>High</th>\n",
" <th>Low</th>\n",
" <th>Volume</th>\n",
" <th>factor</th>\n",
" <th>sma10</th>\n",
" <th>sma30</th>\n",
" <th>sma60</th>\n",
" <th>sma120</th>\n",
" </tr>\n",
" <tr>\n",
" <th>trade_date</th>\n",
" <th></th>\n",
" <th></th>\n",
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" <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",
" <td>1593.490219</td>\n",
" <td>1544.474925</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",
" <td>1589.646408</td>\n",
" <td>1545.431305</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",
" <td>1586.329749</td>\n",
" <td>1546.312145</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",
" <td>1583.232735</td>\n",
" <td>1547.358386</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",
" <td>1580.841843</td>\n",
" <td>1548.429886</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
]
},
"execution_count": 112,
"metadata": {},
"output_type": "execute_result"
}
],
"execution_count": 112
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2026-01-20T06:18:48.525845Z",
"start_time": "2026-01-20T06:18:48.288567Z"
}
},
"cell_type": "code",
"source": [
"# 指标计算完成后截取后面指标的完整的部份使用\n",
"dailies_df = dailies_df.loc['2025-01-01':'2025-12-31']\n",
"dailies_df.tail()"
],
"id": "b6f5da567c935aa",
"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 sma10 sma30 sma60 sma120 \n",
"trade_date \n",
"2025-10-09 131.7878 1504.094161 1546.925196 1593.490219 1544.474925 \n",
"2025-10-10 131.7878 1504.357737 1543.762289 1589.646408 1545.431305 \n",
"2025-10-13 131.7878 1503.698798 1540.862958 1586.329749 1546.312145 \n",
"2025-10-14 131.7878 1506.202766 1536.997182 1583.232735 1547.358386 \n",
"2025-10-15 134.5794 1507.803737 1533.840781 1580.841843 1548.429886 "
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" <th>Open</th>\n",
" <th>Close</th>\n",
" <th>High</th>\n",
" <th>Low</th>\n",
" <th>Volume</th>\n",
" <th>factor</th>\n",
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" <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",
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" <td>1497.109408</td>\n",
" <td>1087947.75</td>\n",
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" <th>2025-10-13</th>\n",
" <td>1491.837896</td>\n",
" <td>1502.380920</td>\n",
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" <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",
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" <th>2025-10-15</th>\n",
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},
"execution_count": 113,
"metadata": {},
"output_type": "execute_result"
}
],
"execution_count": 113
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2026-01-20T06:23:39.497068Z",
"start_time": "2026-01-20T06:23:39.478960Z"
}
},
"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",
" self.sma10 = self.I(lambda x: x, self.data.sma10)\n",
" self.sma30 = self.I(lambda x: x, self.data.sma30)\n",
" # self.sma60 = self.I(lambda x: x, self.data.sma60)\n",
" # self.sma120 = self.I(lambda x: x, self.data.sma120)\n",
"\n",
" def next(self):\n",
" if crossover(self.data.sma10, self.data.sma30):\n",
" self.position.close()\n",
" self.buy()\n",
" elif crossover(self.data.sma30, self.data.sma10):\n",
" self.position.close()\n",
" self.sell()"
],
"id": "b56aaaf4cad7bc7d",
"outputs": [],
"execution_count": 127
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2026-01-20T06:18:49.016553Z",
"start_time": "2026-01-20T06:18:49.003517Z"
}
},
"cell_type": "code",
"source": [
"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 [$]': '最终收益',\n",
" 'Equity Peak [$]': '峰值收益',\n",
" 'Return [%]': '总收益率（%）',\n",
" 'Buy & Hold Return [%]': '买入并持有收益率（%）',\n",
" 'Return (Ann.) [%]': '年化收益率（%）',\n",
" 'Volatility (Ann.) [%]': '年化波动率（%）',\n",
" # 'CAGR [%]': '复合年均增长率（%）',\n",
" # 'Sharpe Ratio': '夏普比率',\n",
" 'Sortino Ratio': '索提诺比率',\n",
" 'Calmar Ratio': '卡尔玛比率',\n",
" # 'Alpha [%]': '阿尔法系数（%）',\n",
" # 'Beta': '贝塔系数',\n",
" 'Max. Drawdown [%]': '最大回撤（%）',\n",
" 'Avg. Drawdown [%]': '平均回撤（%）',\n",
" 'Max. Drawdown Duration': '最大回撤持续时长',\n",
" 'Avg. Drawdown Duration': '平均回撤持续时长',\n",
" '# Trades': '总交易次数',\n",
" 'Win Rate [%]': '胜率（%）',\n",
" # 'Best Trade [%]': '最佳单笔交易收益率（%）',\n",
" # 'Worst Trade [%]': '最差单笔交易收益率（%）',\n",
" # 'Avg. Trade [%]': '平均单笔交易收益率（%）',\n",
" # 'Max. Trade Duration': '单笔交易最长持有时长',\n",
" # 'Avg. Trade Duration': '单笔交易平均持有时长',\n",
" # 'Profit Factor': '盈利因子',\n",
" # 'Expectancy [%]': '期望收益（%）',\n",
" 'SQN': '系统质量数',\n",
" # 'Kelly Criterion': '凯利准则',\n",
" }\n",
" for k, v in stats.items():\n",
" if k in indicator_name_mapping:\n",
" cn_name = indicator_name_mapping.get(k, k)\n",
" if isinstance(v, (int, float)):\n",
" if \"%\" in cn_name or k in ['Sharpe Ratio', 'Sortino Ratio', 'Calmar Ratio', 'Profit Factor']:\n",
" formatted_value = f\"{v:.2f}\"\n",
" elif \"$\" 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": "a23e811212958477",
"outputs": [],
"execution_count": 115
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2026-01-20T06:23:41.360832Z",
"start_time": "2026-01-20T06:23:41.286075Z"
}
},
"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": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"最终收益: 107164.5540\n",
"峰值收益: 110089.8234\n",
"总收益率（%）: 7.16\n",
"买入并持有收益率（%）: 5.04\n",
"年化收益率（%）: 9.72\n",
"年化波动率（%）: 18.21\n",
"索提诺比率: 0.91\n",
"卡尔玛比率: 0.97\n",
"最大回撤（%）: -10.05\n",
"平均回撤（%）: -5.17\n",
"最大回撤持续时长: 155 days 00:00:00\n",
"平均回撤持续时长: 55 days 00:00:00\n",
"总交易次数: 5\n",
"胜率（%）: 40.00\n",
"系统质量数: 0.5772\n"
]
}
],
"execution_count": 128
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2026-01-20T06:23:44.380577Z",
"start_time": "2026-01-20T06:23:43.872193Z"
}
},
"cell_type": "code",
"source": "bt.plot()",
"id": "d3e3b4d4836ab51b",
"outputs": [
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style=\"display: table-cell;\">flow_mode&nbsp;=&nbsp;'block',</div></div><div class=\"p3666\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">height&nbsp;=&nbsp;None,</div></div><div class=\"p3666\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">height_policy&nbsp;=&nbsp;'auto',</div></div><div class=\"p3666\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">html_attributes&nbsp;=&nbsp;{},</div></div><div class=\"p3666\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">html_id&nbsp;=&nbsp;None,</div></div><div class=\"p3666\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">js_event_callbacks&nbsp;=&nbsp;{},</div></div><div class=\"p3666\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">js_property_callbacks&nbsp;=&nbsp;{},</div></div><div class=\"p3666\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">margin&nbsp;=&nbsp;None,</div></div><div class=\"p3666\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">max_height&nbsp;=&nbsp;None,</div></div><div class=\"p3666\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">max_width&nbsp;=&nbsp;None,</div></div><div class=\"p3666\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">min_height&nbsp;=&nbsp;None,</div></div><div class=\"p3666\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">min_width&nbsp;=&nbsp;None,</div></div><div class=\"p3666\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div 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]
},
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"metadata": {},
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}
],
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}
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