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feat(qmt): 优化定时重连机制避免与健康检查冲突

Browse Source 
- 添加 is_scheduled_reconnecting 标志位协调重连逻辑
- 增强定时重连任务的日志前缀便于追踪
- 改进异常处理和资源清理日志
- 优化代码格式和注释
This commit is contained in:
liaozhaorun
2026-02-09 22:12:14 +08:00
parent 7ae3d16a5e
commit e407225d29
11 changed files with 746 additions and 677 deletions
Download Patch File Download Diff File Expand all files Collapse all files

42
main/data/index_and_industry.ipynb
View File

@@ -83,32 +83,32 @@
"output_type": "stream",
"text": [
" ts_code trade_date close open high low \\\n",
"0 000905.SH 20260123 8590.1659 8422.3561 8590.1659 8417.7520 \n",
"1 000905.SH 20260122 8387.5950 8355.6781 8396.1328 8337.1950 \n",
"2 000905.SH 20260121 8340.1133 8196.5565 8351.4545 8196.5565 \n",
"3 000905.SH 20260120 8247.8049 8307.6416 8342.8738 8142.1424 \n",
"4 000905.SH 20260119 8287.9470 8199.4986 8318.3703 8195.0890 \n",
"0 000905.SH 20260206 8146.4128 8054.2180 8247.2968 8014.6599 \n",
"1 000905.SH 20260205 8146.1073 8203.0627 8224.7847 8081.4283 \n",
"2 000905.SH 20260204 8299.0574 8270.8420 8309.8579 8179.3522 \n",
"3 000905.SH 20260203 8286.7005 8135.8699 8286.7005 8075.3738 \n",
"4 000905.SH 20260202 8037.0456 8279.7152 8324.4341 8032.3500 \n",
"... ... ... ... ... ... ... \n",
"14029 399006.SZ 20100607 1069.4680 1005.0280 1075.2250 1001.7020 \n",
"14030 399006.SZ 20100604 1027.6810 989.6810 1027.6810 986.5040 \n",
"14031 399006.SZ 20100603 998.3940 1002.3550 1026.7020 997.7750 \n",
"14032 399006.SZ 20100602 997.1190 967.6090 997.1190 952.6110 \n",
"14033 399006.SZ 20100601 973.2330 986.0150 994.7930 948.1180 \n",
"14059 399006.SZ 20100607 1069.4680 1005.0280 1075.2250 1001.7020 \n",
"14060 399006.SZ 20100604 1027.6810 989.6810 1027.6810 986.5040 \n",
"14061 399006.SZ 20100603 998.3940 1002.3550 1026.7020 997.7750 \n",
"14062 399006.SZ 20100602 997.1190 967.6090 997.1190 952.6110 \n",
"14063 399006.SZ 20100601 973.2330 986.0150 994.7930 948.1180 \n",
"\n",
" pre_close change pct_chg vol amount \n",
"0 8387.5950 202.5709 2.4151 3.196901e+08 6.394214e+08 \n",
"1 8340.1133 47.4817 0.5693 2.688052e+08 5.461381e+08 \n",
"2 8247.8049 92.3084 1.1192 2.433044e+08 5.175922e+08 \n",
"3 8287.9470 -40.1421 -0.4843 2.898645e+08 5.881715e+08 \n",
"4 8232.6740 55.2730 0.6714 2.614974e+08 5.609261e+08 \n",
"0 8146.1073 0.3055 0.0038 2.186421e+08 4.183734e+08 \n",
"1 8299.0574 -152.9501 -1.8430 2.318405e+08 4.352777e+08 \n",
"2 8286.7005 12.3569 0.1491 2.688603e+08 5.018867e+08 \n",
"3 8037.0456 249.6549 3.1063 2.722271e+08 5.432645e+08 \n",
"4 8370.5203 -333.4747 -3.9839 2.996460e+08 5.460557e+08 \n",
"... ... ... ... ... ... \n",
"14029 1027.6810 41.7870 4.0661 2.655275e+06 9.106095e+06 \n",
"14030 998.3940 29.2870 2.9334 1.500295e+06 5.269441e+06 \n",
"14031 997.1190 1.2750 0.1279 1.616805e+06 6.240835e+06 \n",
"14032 973.2330 23.8860 2.4543 1.074628e+06 4.001206e+06 \n",
"14033 1000.0000 -26.7670 -2.6767 1.356285e+06 4.924177e+06 \n",
"14059 1027.6810 41.7870 4.0661 2.655275e+06 9.106095e+06 \n",
"14060 998.3940 29.2870 2.9334 1.500295e+06 5.269441e+06 \n",
"14061 997.1190 1.2750 0.1279 1.616805e+06 6.240835e+06 \n",
"14062 973.2330 23.8860 2.4543 1.074628e+06 4.001206e+06 \n",
"14063 1000.0000 -26.7670 -2.6767 1.356285e+06 4.924177e+06 \n",
"\n",
"[14034 rows x 11 columns]\n"
"[14064 rows x 11 columns]\n"
]
}
],

20
main/data/test.ipynb
View File

@@ -6,8 +6,8 @@
"metadata": {
"collapsed": true,
"ExecuteTime": {
"end_time": "2025-10-17T07:14:47.275Z",
"start_time": "2025-10-17T07:14:46.966401Z"
"end_time": "2026-01-30T19:47:50.093496Z",
"start_time": "2026-01-30T19:47:49.972697Z"
}
},
"source": [
@@ -20,13 +20,25 @@
"ts.set_token('3a0741c702ee7e5e5f2bf1f0846bafaafe4e320833240b2a7e4a685f')\n",
"pro = ts.pro_api()"
],
"outputs": [],
"outputs": [
{
"ename": "ModuleNotFoundError",
"evalue": "No module named 'tushare'",
"output_type": "error",
"traceback": [
"\u001B[31m---------------------------------------------------------------------------\u001B[39m",
"\u001B[31mModuleNotFoundError\u001B[39m Traceback (most recent call last)",
"\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[1]\u001B[39m\u001B[32m, line 3\u001B[39m\n\u001B[32m 1\u001B[39m \u001B[38;5;28;01mfrom\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34;01moperator\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[38;5;28;01mimport\u001B[39;00m index\n\u001B[32m----> \u001B[39m\u001B[32m3\u001B[39m \u001B[38;5;28;01mimport\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34;01mtushare\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[38;5;28;01mas\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34;01mts\u001B[39;00m\n\u001B[32m 4\u001B[39m \u001B[38;5;28;01mimport\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34;01mpandas\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[38;5;28;01mas\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34;01mpd\u001B[39;00m\n\u001B[32m 5\u001B[39m \u001B[38;5;28;01mimport\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34;01mtime\u001B[39;00m\n",
"\u001B[31mModuleNotFoundError\u001B[39m: No module named 'tushare'"
]
}
],
"execution_count": 1
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-10-17T07:15:47.631705Z",
"end_time": "2026-01-30T19:47:50.172803200Z",
"start_time": "2025-10-17T07:15:47.491485Z"
}
},

49
main/data/update/cyq-perf.ipynb
View File

@@ -39,15 +39,15 @@
"3 000006.SZ 20250312\n",
"4 000007.SZ 20250312\n",
"... ... ...\n",
"27277 920978.BJ 20260112\n",
"27278 920981.BJ 20260112\n",
"27279 920982.BJ 20260112\n",
"27280 920985.BJ 20260112\n",
"27281 920992.BJ 20260112\n",
"27312 920978.BJ 20260126\n",
"27313 920981.BJ 20260126\n",
"27314 920982.BJ 20260126\n",
"27315 920985.BJ 20260126\n",
"27316 920992.BJ 20260126\n",
"\n",
"[8597722 rows x 2 columns]\n",
"20260116\n",
"start_date: 20260119\n"
"[8652365 rows x 2 columns]\n",
"20260130\n",
"start_date: 20260202\n"
]
}
],
@@ -64,7 +64,7 @@
" max_date = df['trade_date'].max()\n",
"\n",
"print(max_date)\n",
"trade_cal = pro.trade_cal(exchange='', start_date='20170101', end_date='20260201')\n",
"trade_cal = pro.trade_cal(exchange='', start_date='20170101', end_date='20260310')\n",
"trade_cal = trade_cal[trade_cal['is_open'] == 1] # 只保留交易日\n",
"trade_dates = trade_cal[trade_cal['cal_date'] > max_date]['cal_date'].tolist()\n",
"start_date = min(trade_dates)\n",
@@ -86,16 +86,27 @@
"name": "stdout",
"output_type": "stream",
"text": [
"任务 20260130 完成\n",
"任务 20260129 完成\n",
"任务 20260128 完成\n",
"任务 20260127 完成\n",
"任务 20260126 完成\n",
"任务 20260123 完成\n",
"任务 20260122 完成\n",
"任务 20260121 完成\n",
"任务 20260120 完成\n",
"任务 20260119 完成\n"
"任务 20260309 完成\n",
"任务 20260310 完成\n",
"任务 20260306 完成\n",
"任务 20260305 完成\n",
"任务 20260304 完成\n",
"任务 20260303 完成\n",
"任务 20260227 完成\n",
"任务 20260302 完成\n",
"任务 20260226 完成\n",
"任务 20260225 完成\n",
"任务 20260224 完成\n",
"任务 20260213 完成\n",
"任务 20260212 完成\n",
"任务 20260211 完成\n",
"任务 20260209 完成\n",
"任务 20260210 完成\n",
"任务 20260205 完成\n",
"任务 20260206 完成\n",
"任务 20260204 完成\n",
"任务 20260203 完成\n",
"任务 20260202 完成\n"
]
}
],

49
main/data/update/sw_daily.ipynb
View File

@@ -39,15 +39,15 @@
"3 801005.SI 20250221\n",
"4 801010.SI 20250221\n",
"... ... ...\n",
"2190 859811.SI 20260112\n",
"2191 859821.SI 20260112\n",
"2192 859822.SI 20260112\n",
"2193 859852.SI 20260112\n",
"2194 859951.SI 20260112\n",
"2190 859811.SI 20260126\n",
"2191 859821.SI 20260126\n",
"2192 859822.SI 20260126\n",
"2193 859852.SI 20260126\n",
"2194 859951.SI 20260126\n",
"\n",
"[1140973 rows x 2 columns]\n",
"20260116\n",
"start_date: 20260119\n"
"[1145363 rows x 2 columns]\n",
"20260130\n",
"start_date: 20260202\n"
]
}
],
@@ -64,7 +64,7 @@
" max_date = df['trade_date'].max()\n",
"\n",
"print(max_date)\n",
"trade_cal = pro.trade_cal(exchange='', start_date='20170101', end_date='20260201')\n",
"trade_cal = pro.trade_cal(exchange='', start_date='20170101', end_date='20260310')\n",
"trade_cal = trade_cal[trade_cal['is_open'] == 1] # 只保留交易日\n",
"trade_dates = trade_cal[trade_cal['cal_date'] > max_date]['cal_date'].tolist()\n",
"start_date = min(trade_dates)\n",
@@ -86,16 +86,27 @@
"name": "stdout",
"output_type": "stream",
"text": [
"任务 20260130 完成\n",
"任务 20260129 完成\n",
"任务 20260128 完成\n",
"任务 20260127 完成\n",
"任务 20260126 完成\n",
"任务 20260123 完成\n",
"任务 20260122 完成\n",
"任务 20260121 完成\n",
"任务 20260120 完成\n",
"任务 20260119 完成\n"
"任务 20260309 完成\n",
"任务 20260310 完成\n",
"任务 20260306 完成\n",
"任务 20260305 完成\n",
"任务 20260304 完成\n",
"任务 20260303 完成\n",
"任务 20260302 完成\n",
"任务 20260227 完成\n",
"任务 20260226 完成\n",
"任务 20260225 完成\n",
"任务 20260224 完成\n",
"任务 20260213 完成\n",
"任务 20260211 完成\n",
"任务 20260212 完成\n",
"任务 20260210 完成\n",
"任务 20260209 完成\n",
"任务 20260206 完成\n",
"任务 20260205 完成\n",
"任务 20260204 完成\n",
"任务 20260203 完成\n",
"任务 20260202 完成\n"
]
}
],

216
main/data/update/update_daily_basic.ipynb
View File

@@ -94,17 +94,17 @@
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Index: 9547667 entries, 0 to 27281\n",
"Index: 9602310 entries, 0 to 27316\n",
"Data columns (total 2 columns):\n",
" # Column Dtype \n",
"--- ------ ----- \n",
" 0 ts_code object\n",
" 1 trade_date object\n",
"dtypes: object(2)\n",
"memory usage: 218.5+ MB\n",
"memory usage: 219.8+ MB\n",
"None\n",
"20260116\n",
"20260119\n"
"20260130\n",
"20260202\n"
]
}
],
@@ -121,7 +121,7 @@
" max_date = df['trade_date'].max()\n",
"\n",
"print(max_date)\n",
"trade_cal = pro.trade_cal(exchange='', start_date='20170101', end_date='20260201')\n",
"trade_cal = pro.trade_cal(exchange='', start_date='20170101', end_date='20260310')\n",
"trade_cal = trade_cal[trade_cal['is_open'] == 1] # 只保留交易日\n",
"trade_dates = trade_cal[trade_cal['cal_date'] > max_date]['cal_date'].tolist()\n",
"start_date = min(trade_dates)\n",
@@ -144,22 +144,27 @@
"name": "stdout",
"output_type": "stream",
"text": [
"任务 20260130 完成\n",
"任务 20260129 完成\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"任务 20260127 完成\n",
"任务 20260128 完成\n",
"任务 20260126 完成\n",
"任务 20260123 完成\n",
"任务 20260122 完成\n",
"任务 20260121 完成\n",
"任务 20260120 完成\n",
"任务 20260119 完成\n"
"任务 20260309 完成\n",
"任务 20260310 完成\n",
"任务 20260305 完成\n",
"任务 20260306 完成\n",
"任务 20260303 完成\n",
"任务 20260304 完成\n",
"任务 20260302 完成\n",
"任务 20260227 完成\n",
"任务 20260226 完成\n",
"任务 20260225 完成\n",
"任务 20260224 完成\n",
"任务 20260213 完成\n",
"任务 20260212 完成\n",
"任务 20260211 完成\n",
"任务 20260210 完成\n",
"任务 20260209 完成\n",
"任务 20260206 完成\n",
"任务 20260205 完成\n",
"任务 20260204 完成\n",
"任务 20260203 完成\n",
"任务 20260202 完成\n"
]
}
],
@@ -230,58 +235,58 @@
"output_type": "stream",
"text": [
" ts_code trade_date close turnover_rate turnover_rate_f \\\n",
"0 301586.SZ 20260123 52.80 4.4195 6.0484 \n",
"1 600871.SH 20260123 2.63 3.5599 17.1067 \n",
"2 002067.SZ 20260123 5.91 9.5542 9.8833 \n",
"3 601225.SH 20260123 21.41 0.4692 1.3502 \n",
"4 688800.SH 20260123 90.22 4.3421 7.2546 \n",
"0 001389.SZ 20260206 99.50 6.1259 6.1259 \n",
"1 600841.SH 20260206 9.55 4.4177 14.0519 \n",
"2 300968.SZ 20260206 13.71 1.0258 2.1909 \n",
"3 300634.SZ 20260206 28.46 4.0862 6.8510 \n",
"4 300295.SZ 20260206 11.99 3.5950 3.8451 \n",
"... ... ... ... ... ... \n",
"27321 688659.SH 20260119 10.42 1.7256 3.1386 \n",
"27322 301021.SZ 20260119 55.92 5.8451 10.3979 \n",
"27323 300102.SZ 20260119 34.23 14.1090 22.0304 \n",
"27324 300088.SZ 20260119 6.36 1.9148 2.3308 \n",
"27325 002261.SZ 20260119 33.77 4.6567 5.1897 \n",
"27325 603766.SH 20260202 14.89 1.1630 1.9407 \n",
"27326 603408.SH 20260202 12.92 0.7963 3.0049 \n",
"27327 000004.SZ 20260202 11.99 0.2338 0.3161 \n",
"27328 601628.SH 20260202 48.34 0.1384 1.9210 \n",
"27329 301042.SZ 20260202 86.10 3.6207 5.4797 \n",
"\n",
" volume_ratio pe pe_ttm pb ps ps_ttm dv_ratio \\\n",
"0 0.98 43.6343 68.1615 3.4308 6.9893 5.8003 0.9091 \n",
"1 1.91 78.9369 79.8327 5.2226 0.6148 0.6127 NaN \n",
"2 0.80 122.3105 140.3743 1.3170 1.5908 1.5491 NaN \n",
"3 1.02 9.2832 10.8507 2.1981 1.1272 1.1740 5.9699 \n",
"4 0.79 105.8709 61.3816 8.0916 7.6847 5.8974 0.2971 \n",
"0 0.83 62.6474 46.6770 11.6304 11.3424 8.6639 0.4819 \n",
"1 1.02 NaN NaN 4.1908 2.0493 2.4531 NaN \n",
"2 0.80 106.8945 225.1215 2.9871 4.6434 4.4246 0.3647 \n",
"3 0.68 55.7987 55.5119 4.4068 7.7725 7.2020 0.5590 \n",
"4 0.40 NaN NaN 2.0169 18.5210 23.1598 NaN \n",
"... ... ... ... ... ... ... ... \n",
"27321 1.01 NaN NaN 3.1071 2.6478 2.4387 NaN \n",
"27322 0.72 391.5181 198.4990 8.4757 19.1357 17.1491 0.1772 \n",
"27323 0.69 327.8681 232.9793 7.4375 12.9485 9.5346 0.1313 \n",
"27324 0.52 44.5231 45.4726 1.8179 1.4366 1.3961 0.9198 \n",
"27325 0.38 NaN NaN 16.3185 10.3586 13.1287 NaN \n",
"27325 1.04 27.2701 16.9932 3.2106 1.8177 1.5958 2.6864 \n",
"27326 0.99 11.9989 13.3289 1.7275 1.1550 1.1674 3.8700 \n",
"27327 0.05 NaN NaN 42.9590 16.0879 21.5107 NaN \n",
"27328 0.95 12.7771 8.0270 2.2069 2.5849 2.1373 1.4233 \n",
"27329 1.92 84.1572 534.3244 5.7108 8.6920 12.7424 NaN \n",
"\n",
" dv_ttm total_share float_share free_share total_mv \\\n",
"0 0.3788 8.297550e+03 5.291107e+03 3866.1069 4.381107e+05 \n",
"1 NaN 1.895705e+06 1.354701e+06 281911.5987 4.985703e+06 \n",
"2 NaN 1.474854e+05 1.337604e+05 129305.3853 8.716386e+05 \n",
"3 5.4881 9.695000e+05 9.695000e+05 336903.9335 2.075700e+07 \n",
"4 0.2971 2.056743e+04 2.056743e+04 12310.0935 1.855594e+06 \n",
"0 0.4819 4.256875e+04 1.511580e+04 15115.7957 4.235591e+06 \n",
"1 NaN 1.387822e+05 1.043024e+05 32790.9410 1.325370e+06 \n",
"2 0.3647 4.133800e+04 4.133800e+04 19355.6537 5.667440e+05 \n",
"3 0.5590 4.512109e+04 4.345735e+04 25919.5274 1.284146e+06 \n",
"4 NaN 1.896137e+04 1.675259e+04 15662.9042 2.273468e+05 \n",
"... ... ... ... ... ... \n",
"27321 NaN 1.600000e+04 1.600000e+04 8796.6880 1.667200e+05 \n",
"27322 0.1772 1.528528e+04 1.527393e+04 8586.0802 8.547528e+05 \n",
"27323 0.1313 9.203339e+04 9.163399e+04 58685.2206 3.150303e+06 \n",
"27324 0.9198 2.497734e+05 2.485504e+05 204186.7350 1.588559e+06 \n",
"27325 NaN 1.259831e+05 1.145652e+05 102798.2760 4.254451e+06 \n",
"27325 4.0296 2.053542e+05 2.053542e+05 123065.9980 3.057724e+06 \n",
"27326 5.3406 4.475730e+04 4.475730e+04 11860.1633 5.782643e+05 \n",
"27327 NaN 1.323803e+04 1.262878e+04 9339.3580 1.587240e+05 \n",
"27328 1.4233 2.826470e+06 2.082353e+06 150000.0000 1.366316e+08 \n",
"27329 NaN 6.972358e+03 6.602083e+03 4362.2827 6.003200e+05 \n",
"\n",
" circ_mv is_st \n",
"0 2.793704e+05 False \n",
"1 3.562864e+06 False \n",
"2 7.905239e+05 False \n",
"3 2.075700e+07 False \n",
"4 1.855594e+06 False \n",
"0 1.504022e+06 False \n",
"1 9.960884e+05 False \n",
"2 5.667440e+05 False \n",
"3 1.236796e+06 False \n",
"4 2.008635e+05 False \n",
"... ... ... \n",
"27321 1.667200e+05 False \n",
"27322 8.541180e+05 False \n",
"27323 3.136631e+06 False \n",
"27324 1.580780e+06 False \n",
"27325 3.868868e+06 False \n",
"27325 3.057724e+06 False \n",
"27326 5.782643e+05 False \n",
"27327 1.514190e+05 True \n",
"27328 1.006609e+08 False \n",
"27329 5.684393e+05 False \n",
"\n",
"[27326 rows x 19 columns]\n"
"[27330 rows x 19 columns]\n"
]
}
],
@@ -306,58 +311,45 @@
"output_type": "stream",
"text": [
" ts_code trade_date close turnover_rate turnover_rate_f \\\n",
"54 000615.SZ 20260123 3.32 0.7420 0.9114 \n",
"60 600228.SH 20260123 5.63 3.4275 4.8943 \n",
"88 000430.SZ 20260123 7.30 1.4748 2.3665 \n",
"96 603389.SH 20260123 48.20 0.8319 2.1042 \n",
"110 000752.SZ 20260123 10.96 1.4753 1.8177 \n",
"52 002713.SZ 20260206 9.52 4.6530 6.2693 \n",
"107 000609.SZ 20260206 8.23 0.2427 0.3213 \n",
"113 300052.SZ 20260206 13.86 3.5983 4.3260 \n",
"116 600624.SH 20260206 5.31 0.6260 0.8827 \n",
"119 300555.SZ 20260206 14.31 1.4520 1.7474 \n",
"... ... ... ... ... ... \n",
"27130 002586.SZ 20260119 4.68 1.7164 2.9874 \n",
"27154 600265.SH 20260119 19.29 0.2879 0.8066 \n",
"27193 688287.SH 20260119 6.44 0.9802 1.9881 \n",
"27195 300338.SZ 20260119 3.61 1.4127 1.5675 \n",
"27223 000669.SZ 20260119 2.79 1.1437 1.4424 \n",
"27182 000668.SZ 20260202 13.85 3.5014 5.9253 \n",
"27240 003032.SZ 20260202 6.37 2.2066 3.1223 \n",
"27305 000691.SZ 20260202 8.30 1.9108 2.2317 \n",
"27312 300167.SZ 20260202 4.65 2.1705 2.3552 \n",
"27327 000004.SZ 20260202 11.99 0.2338 0.3161 \n",
"\n",
" volume_ratio pe pe_ttm pb ps ps_ttm dv_ratio \\\n",
"54 0.72 NaN NaN NaN 5.4217 5.8063 NaN \n",
"60 1.60 NaN NaN 5.6813 9.6204 6.3997 NaN \n",
"88 1.11 NaN NaN 22.0731 13.6938 12.9047 NaN \n",
"96 0.95 NaN NaN 54.1434 62.5718 60.6578 NaN \n",
"110 0.96 110.3513 19.6504 4.7026 6.8589 6.4652 NaN \n",
"... ... ... ... ... ... ... ... \n",
"27130 1.55 NaN NaN 1.7970 2.1568 2.2158 NaN \n",
"27154 0.79 NaN NaN 288.1848 5.6010 10.1170 NaN \n",
"27193 0.59 NaN NaN 3.1120 26.5172 26.3673 NaN \n",
"27195 0.58 NaN NaN NaN 8.5110 10.1280 NaN \n",
"27223 0.84 NaN NaN NaN 1.4522 1.5001 NaN \n",
" volume_ratio pe pe_ttm pb ps ps_ttm dv_ratio dv_ttm \\\n",
"52 1.02 NaN NaN NaN 6.9890 12.8336 NaN NaN \n",
"107 0.05 NaN NaN NaN 8.0655 15.8232 NaN NaN \n",
"113 1.51 NaN NaN 9.6004 15.9784 18.8191 NaN NaN \n",
"116 0.60 NaN NaN 5.7984 5.5900 5.4551 NaN NaN \n",
"119 1.24 NaN NaN 6.0224 16.1604 18.5602 NaN NaN \n",
"... ... .. ... ... ... ... ... ... \n",
"27182 1.32 NaN NaN 2.8792 15.2387 7.2708 NaN NaN \n",
"27240 1.15 NaN NaN 2.0451 10.4354 7.6772 NaN NaN \n",
"27305 0.69 NaN NaN NaN 9.0850 9.0002 NaN NaN \n",
"27312 0.71 NaN 134.128 63.0032 4.3517 3.8269 NaN NaN \n",
"27327 0.05 NaN NaN 42.9590 16.0879 21.5107 NaN NaN \n",
"\n",
" dv_ttm total_share float_share free_share total_mv \\\n",
"54 NaN 178749.2693 92531.6398 75331.7788 5.934476e+05 \n",
"60 NaN 41667.2427 41603.7177 29135.5053 2.345866e+05 \n",
"88 NaN 80963.5372 37055.6486 23092.8156 5.910338e+05 \n",
"96 NaN 26275.2000 26275.2000 10387.7487 1.266465e+06 \n",
"110 NaN 26375.8491 26375.8491 21407.3042 2.890793e+05 \n",
"... ... ... ... ... ... \n",
"27130 NaN 114422.3714 108751.8003 62484.0799 5.354967e+05 \n",
"27154 NaN 12980.0000 12980.0000 4633.1947 2.503842e+05 \n",
"27193 NaN 37051.5600 37051.5600 18267.2898 2.386120e+05 \n",
"27195 NaN 40262.4692 34936.1242 31485.3582 1.453475e+05 \n",
"27223 NaN 68040.8797 68040.8797 53950.9653 1.898341e+05 \n",
" total_share float_share free_share total_mv circ_mv is_st \n",
"52 95140.5184 52650.0831 39075.6229 905737.7352 501228.7911 True \n",
"107 29926.5522 29105.8272 21991.3472 246295.5246 239540.9579 True \n",
"113 26185.8710 26185.1210 21780.6332 362936.1721 362925.7771 True \n",
"116 67934.6942 67934.6942 48180.1806 360733.2262 360733.2262 True \n",
"119 20000.0000 19798.4863 16450.7792 286200.0000 283316.3390 True \n",
"... ... ... ... ... ... ... \n",
"27182 14684.1890 14684.1890 8677.2104 203376.0177 203376.0177 True \n",
"27240 40244.7500 27621.9885 19520.7308 256359.0575 175952.0667 True \n",
"27305 48490.5000 32327.0000 27679.8405 402471.1500 268314.1000 True \n",
"27312 38937.4000 35889.3250 33073.6637 181058.9100 166885.3613 True \n",
"27327 13238.0282 12628.7768 9339.3580 158723.9581 151419.0338 True \n",
"\n",
" circ_mv is_st \n",
"54 3.072050e+05 True \n",
"60 2.342289e+05 True \n",
"88 2.705062e+05 True \n",
"96 1.266465e+06 True \n",
"110 2.890793e+05 True \n",
"... ... ... \n",
"27130 5.089584e+05 True \n",
"27154 2.503842e+05 True \n",
"27193 2.386120e+05 True \n",
"27195 1.261194e+05 True \n",
"27223 1.898341e+05 True \n",
"\n",
"[886 rows x 19 columns]\n"
"[872 rows x 19 columns]\n"
]
}
],
@@ -407,7 +399,7 @@
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Index: 9574993 entries, 0 to 27325\n",
"Index: 9629640 entries, 0 to 27329\n",
"Data columns (total 3 columns):\n",
" # Column Dtype \n",
"--- ------ ----- \n",
@@ -415,7 +407,7 @@
" 1 trade_date object\n",
" 2 is_st bool \n",
"dtypes: bool(1), object(2)\n",
"memory usage: 228.3+ MB\n",
"memory usage: 229.6+ MB\n",
"None\n"
]
}

618
main/data/update/update_daily_data.ipynb
View File

File diff suppressed because it is too large Load Diff

143
main/data/update/update_money_flow.ipynb
View File

@@ -34,17 +34,17 @@
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Index: 9336127 entries, 0 to 25845\n",
"Index: 9387872 entries, 0 to 25863\n",
"Data columns (total 2 columns):\n",
" # Column Dtype \n",
"--- ------ ----- \n",
" 0 ts_code object\n",
" 1 trade_date object\n",
"dtypes: object(2)\n",
"memory usage: 213.7+ MB\n",
"memory usage: 214.9+ MB\n",
"None\n",
"20260116\n",
"start_date: 20260119\n"
"20260130\n",
"start_date: 20260202\n"
]
}
],
@@ -61,7 +61,7 @@
" max_date = df['trade_date'].max()\n",
"\n",
"print(max_date)\n",
"trade_cal = pro.trade_cal(exchange='', start_date='20170101', end_date='20260201')\n",
"trade_cal = pro.trade_cal(exchange='', start_date='20170101', end_date='20260310')\n",
"trade_cal = trade_cal[trade_cal['is_open'] == 1] # 只保留交易日\n",
"trade_dates = trade_cal[trade_cal['cal_date'] > max_date]['cal_date'].tolist()\n",
"start_date = min(trade_dates)\n",
@@ -84,16 +84,27 @@
"name": "stdout",
"output_type": "stream",
"text": [
"任务 20260129 完成\n",
"任务 20260130 完成\n",
"任务 20260128 完成\n",
"任务 20260127 完成\n",
"任务 20260126 完成\n",
"任务 20260123 完成\n",
"任务 20260122 完成\n",
"任务 20260121 完成\n",
"任务 20260120 完成\n",
"任务 20260119 完成\n"
"任务 20260310 完成\n",
"任务 20260309 完成\n",
"任务 20260306 完成\n",
"任务 20260305 完成\n",
"任务 20260304 完成\n",
"任务 20260303 完成\n",
"任务 20260302 完成\n",
"任务 20260227 完成\n",
"任务 20260226 完成\n",
"任务 20260225 完成\n",
"任务 20260224 完成\n",
"任务 20260213 完成\n",
"任务 20260212 完成\n",
"任务 20260211 完成\n",
"任务 20260210 完成\n",
"任务 20260209 完成\n",
"任务 20260206 完成\n",
"任务 20260205 完成\n",
"任务 20260204 完成\n",
"任务 20260203 完成\n",
"任务 20260202 完成\n"
]
}
],
@@ -183,71 +194,71 @@
"output_type": "stream",
"text": [
" ts_code trade_date buy_sm_vol buy_sm_amount sell_sm_vol \\\n",
"0 300284.SZ 20260123 57213 4682.01 45561 \n",
"1 002835.SZ 20260123 10930 1886.39 9809 \n",
"2 603175.SH 20260123 28945 21106.65 29993 \n",
"3 600284.SH 20260123 62561 5324.31 55101 \n",
"4 300855.SZ 20260123 41944 15903.55 33566 \n",
"0 002397.SZ 20260206 143016 6566.04 142640 \n",
"1 603882.SH 20260206 32234 10009.25 31819 \n",
"2 002824.SZ 20260206 58407 13856.33 47164 \n",
"3 603379.SH 20260206 31545 21744.99 30955 \n",
"4 600223.SH 20260206 73778 5582.50 67552 \n",
"... ... ... ... ... ... \n",
"25876 600211.SH 20260119 10915 4796.09 12174 \n",
"25877 601229.SH 20260119 225161 21704.91 267726 \n",
"25878 003042.SZ 20260119 17500 2893.60 11703 \n",
"25879 601155.SH 20260119 75731 11076.12 70817 \n",
"25880 600169.SH 20260119 81734 2005.16 84188 \n",
"25871 002774.SZ 20260202 32854 3675.61 20566 \n",
"25872 300188.SZ 20260202 54005 7416.58 48176 \n",
"25873 688173.SH 20260202 64149 10373.20 68049 \n",
"25874 300145.SZ 20260202 132990 6043.37 133529 \n",
"25875 300205.SZ 20260202 8361 364.75 6236 \n",
"\n",
" sell_sm_amount buy_md_vol buy_md_amount sell_md_vol sell_md_amount \\\n",
"0 3728.28 48486 3966.28 59366 4856.95 \n",
"1 1693.39 6499 1121.44 6017 1038.84 \n",
"2 21840.97 22701 16511.38 23142 16835.79 \n",
"3 4687.48 68181 5800.70 62114 5285.81 \n",
"4 12712.79 35383 13409.44 40111 15213.47 \n",
"0 6550.08 114346 5247.29 121777 5580.76 \n",
"1 9880.13 15560 4828.56 14519 4506.56 \n",
"2 11195.49 27988 6622.95 30015 7118.27 \n",
"3 21323.49 21708 14968.66 21044 14503.09 \n",
"4 5110.61 53914 4075.02 53354 4037.48 \n",
"... ... ... ... ... ... \n",
"25876 5348.74 9594 4215.26 8253 3626.54 \n",
"25877 25799.99 204041 19664.19 207361 19990.67 \n",
"25878 1933.74 11780 1946.25 14398 2379.72 \n",
"25879 10351.64 45622 6659.41 46251 6765.59 \n",
"25880 2063.68 129391 3172.21 137053 3360.95 \n",
"25871 2300.99 20761 2320.61 21785 2437.00 \n",
"25872 6623.07 52821 7250.13 53449 7347.52 \n",
"25873 11000.03 35320 5706.28 30270 4895.56 \n",
"25874 6066.65 114646 5208.71 97260 4422.03 \n",
"25875 272.25 9374 408.60 9827 427.93 \n",
"\n",
" buy_lg_vol buy_lg_amount sell_lg_vol sell_lg_amount buy_elg_vol \\\n",
"0 39133 3201.55 32920 2693.69 6576 \n",
"1 4067 702.11 4633 799.04 241 \n",
"2 11291 8249.67 11377 8302.90 3400 \n",
"3 46517 3960.29 57644 4906.75 23366 \n",
"4 24315 9212.24 24640 9342.34 5111 \n",
"0 85477 3926.44 87204 3989.37 28400 \n",
"1 5474 1697.68 6390 1982.08 721 \n",
"2 11723 2778.17 15330 3630.71 3939 \n",
"3 13069 9000.74 15789 10882.14 7583 \n",
"4 27279 2062.59 22583 1709.48 10118 \n",
"... ... ... ... ... ... \n",
"25876 5507 2419.57 5204 2286.10 2 \n",
"25877 134694 12983.30 86737 8363.03 24453 \n",
"25878 4038 668.49 7218 1194.87 0 \n",
"25879 23725 3458.86 23634 3453.46 21891 \n",
"25880 63062 1545.61 49278 1208.05 5904 \n",
"25871 8975 1002.99 18793 2100.36 0 \n",
"25872 31050 4271.36 35903 4923.97 7655 \n",
"25873 11829 1915.52 12687 2050.68 2000 \n",
"25874 81164 3682.66 113414 5149.25 41421 \n",
"25875 6887 302.35 8559 375.51 0 \n",
"\n",
" buy_elg_amount sell_elg_vol sell_elg_amount net_mf_vol \\\n",
"0 539.18 13561 1110.11 -4914 \n",
"1 41.60 1278 220.26 1923 \n",
"2 2437.55 1825 1325.59 1620 \n",
"3 1991.65 25767 2196.91 46602 \n",
"4 1934.73 8437 3191.36 -19491 \n",
"0 1293.83 19618 913.39 42201 \n",
"1 223.34 1261 390.07 -823 \n",
"2 935.53 9548 2248.51 2997 \n",
"3 5221.72 6117 4227.39 6215 \n",
"4 766.07 21600 1628.62 -9839 \n",
"... ... ... ... ... \n",
"25876 0.88 388 170.41 -44 \n",
"25877 2359.23 26525 2557.95 -37774 \n",
"25878 0.00 0 0.00 6587 \n",
"25879 3125.39 26267 3749.10 28653 \n",
"25880 144.65 9571 234.95 -10865 \n",
"25871 0.00 1446 160.86 -20182 \n",
"25872 1053.35 8003 1096.85 -12903 \n",
"25873 319.00 2291 367.73 -5670 \n",
"25874 1881.32 26018 1178.15 -70082 \n",
"25875 0.00 0 0.00 -1678 \n",
"\n",
" net_mf_amount \n",
"0 -391.89 \n",
"1 333.04 \n",
"2 1372.40 \n",
"3 3982.07 \n",
"4 -7354.89 \n",
"0 1943.09 \n",
"1 -246.33 \n",
"2 705.13 \n",
"3 4367.94 \n",
"4 -741.40 \n",
"... ... \n",
"25876 -16.04 \n",
"25877 -3603.98 \n",
"25878 1092.73 \n",
"25879 4152.29 \n",
"25880 -256.23 \n",
"25871 -2251.19 \n",
"25872 -1769.73 \n",
"25873 -918.58 \n",
"25874 -3146.31 \n",
"25875 -68.42 \n",
"\n",
"[25881 rows x 20 columns]\n"
"[25876 rows x 20 columns]\n"
]
}
],

167
main/data/update/update_stk_limit.ipynb
View File

@@ -34,23 +34,23 @@
"output_type": "stream",
"text": [
" ts_code trade_date\n",
"4959 600211.SH 20260116\n",
"4960 600212.SH 20260116\n",
"4961 600215.SH 20260116\n",
"4949 600197.SH 20260116\n",
"3732 301501.SZ 20260116\n",
"4971 600208.SH 20260130\n",
"4972 600210.SH 20260130\n",
"4973 600211.SH 20260130\n",
"4961 600195.SH 20260130\n",
"7441 920964.BJ 20260130\n",
"<class 'pandas.core.frame.DataFrame'>\n",
"Index: 11701107 entries, 0 to 37139\n",
"Index: 11775527 entries, 0 to 37233\n",
"Data columns (total 2 columns):\n",
" # Column Dtype \n",
"--- ------ ----- \n",
" 0 ts_code object\n",
" 1 trade_date object\n",
"dtypes: object(2)\n",
"memory usage: 267.8+ MB\n",
"memory usage: 269.5+ MB\n",
"None\n",
"20260116\n",
"20260119\n"
"20260130\n",
"20260202\n"
]
}
],
@@ -68,7 +68,7 @@
" max_date = df['trade_date'].max()\n",
"\n",
"print(max_date)\n",
"trade_cal = pro.trade_cal(exchange='', start_date='20170101', end_date='20260201')\n",
"trade_cal = pro.trade_cal(exchange='', start_date='20170101', end_date='20260310')\n",
"trade_cal = trade_cal[trade_cal['is_open'] == 1] # 只保留交易日\n",
"trade_dates = trade_cal[trade_cal['cal_date'] > max_date]['cal_date'].tolist()\n",
"start_date = min(trade_dates)\n",
@@ -91,22 +91,27 @@
"name": "stdout",
"output_type": "stream",
"text": [
"任务 20260130 完成\n",
"任务 20260129 完成\n",
"任务 20260128 完成\n",
"任务 20260127 完成\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"任务 20260126 完成\n",
"任务 20260123 完成\n",
"任务 20260122 完成\n",
"任务 20260121 完成\n",
"任务 20260120 完成\n",
"任务 20260119 完成\n"
"任务 20260310 完成\n",
"任务 20260309 完成\n",
"任务 20260306 完成\n",
"任务 20260305 完成\n",
"任务 20260303 完成\n",
"任务 20260304 完成\n",
"任务 20260302 完成\n",
"任务 20260227 完成\n",
"任务 20260226 完成\n",
"任务 20260225 完成\n",
"任务 20260224 完成\n",
"任务 20260213 完成\n",
"任务 20260212 完成\n",
"任务 20260211 完成\n",
"任务 20260210 完成\n",
"任务 20260209 完成\n",
"任务 20260206 完成\n",
"任务 20260205 完成\n",
"任务 20260204 完成\n",
"任务 20260203 完成\n",
"任务 20260202 完成\n"
]
}
],
@@ -158,71 +163,71 @@
"output_type": "stream",
"text": [
"[ trade_date ts_code up_limit down_limit\n",
"0 20260123 000001.SZ 12.18 9.96\n",
"1 20260123 000002.SZ 5.45 4.46\n",
"2 20260123 000004.SZ 12.59 11.39\n",
"3 20260123 000006.SZ 10.62 8.69\n",
"4 20260123 000007.SZ 12.47 10.21\n",
"0 20260206 000001.SZ 12.20 9.98\n",
"1 20260206 000002.SZ 5.37 4.39\n",
"2 20260206 000004.SZ 10.79 9.77\n",
"3 20260206 000006.SZ 9.90 8.10\n",
"4 20260206 000007.SZ 12.31 10.07\n",
"... ... ... ... ...\n",
"7435 20260123 920978.BJ 44.56 24.00\n",
"7436 20260123 920981.BJ 45.61 24.57\n",
"7437 20260123 920982.BJ 295.08 158.90\n",
"7438 20260123 920985.BJ 10.20 5.50\n",
"7439 20260123 920992.BJ 24.32 13.10\n",
"7459 20260206 920978.BJ 41.60 22.40\n",
"7460 20260206 920981.BJ 41.63 22.43\n",
"7461 20260206 920982.BJ 298.63 160.81\n",
"7462 20260206 920985.BJ 11.37 6.13\n",
"7463 20260206 920992.BJ 22.32 12.02\n",
"\n",
"[7440 rows x 4 columns], trade_date ts_code up_limit down_limit\n",
"0 20260122 000001.SZ 12.18 9.96\n",
"1 20260122 000002.SZ 5.51 4.51\n",
"2 20260122 000004.SZ 11.99 10.85\n",
"3 20260122 000006.SZ 10.62 8.69\n",
"4 20260122 000007.SZ 12.41 10.15\n",
"[7464 rows x 4 columns], trade_date ts_code up_limit down_limit\n",
"0 20260205 000001.SZ 12.07 9.87\n",
"1 20260205 000002.SZ 5.39 4.41\n",
"2 20260205 000004.SZ 11.36 10.28\n",
"3 20260205 000006.SZ 9.97 8.15\n",
"4 20260205 000007.SZ 12.32 10.08\n",
"... ... ... ... ...\n",
"7433 20260122 920978.BJ 45.48 24.50\n",
"7434 20260122 920981.BJ 45.61 24.57\n",
"7435 20260122 920982.BJ 301.76 162.50\n",
"7436 20260122 920985.BJ 9.84 5.30\n",
"7437 20260122 920992.BJ 23.95 12.91\n",
"7454 20260205 920978.BJ 41.48 22.34\n",
"7455 20260205 920981.BJ 42.00 22.62\n",
"7456 20260205 920982.BJ 289.43 155.85\n",
"7457 20260205 920985.BJ 12.16 6.56\n",
"7458 20260205 920992.BJ 22.32 12.02\n",
"\n",
"[7438 rows x 4 columns], trade_date ts_code up_limit down_limit\n",
"0 20260121 000001.SZ 12.28 10.04\n",
"1 20260121 000002.SZ 5.27 4.31\n",
"2 20260121 000004.SZ 12.02 10.88\n",
"3 20260121 000006.SZ 10.27 8.41\n",
"4 20260121 000007.SZ 12.08 9.88\n",
"[7459 rows x 4 columns], trade_date ts_code up_limit down_limit\n",
"0 20260204 000001.SZ 11.92 9.76\n",
"1 20260204 000002.SZ 5.17 4.23\n",
"2 20260204 000004.SZ 11.96 10.82\n",
"3 20260204 000006.SZ 9.77 7.99\n",
"4 20260204 000007.SZ 12.32 10.08\n",
"... ... ... ... ...\n",
"7433 20260121 920978.BJ 45.60 24.56\n",
"7434 20260121 920981.BJ 43.81 23.59\n",
"7435 20260121 920982.BJ 304.34 163.88\n",
"7436 20260121 920985.BJ 9.90 5.34\n",
"7437 20260121 920992.BJ 24.11 12.99\n",
"7453 20260204 920978.BJ 38.15 20.55\n",
"7454 20260204 920981.BJ 43.01 23.17\n",
"7455 20260204 920982.BJ 279.50 150.50\n",
"7456 20260204 920985.BJ 10.66 5.74\n",
"7457 20260204 920992.BJ 22.28 12.00\n",
"\n",
"[7438 rows x 4 columns], trade_date ts_code up_limit down_limit\n",
"0 20260120 000001.SZ 12.23 10.01\n",
"1 20260120 000002.SZ 5.20 4.26\n",
"2 20260120 000004.SZ 11.46 10.36\n",
"3 20260120 000006.SZ 10.07 8.24\n",
"4 20260120 000007.SZ 12.49 10.22\n",
"[7458 rows x 4 columns], trade_date ts_code up_limit down_limit\n",
"0 20260203 000001.SZ 11.95 9.77\n",
"1 20260203 000002.SZ 5.15 4.21\n",
"2 20260203 000004.SZ 12.59 11.39\n",
"3 20260203 000006.SZ 9.58 7.84\n",
"4 20260203 000007.SZ 12.09 9.89\n",
"... ... ... ... ...\n",
"7431 20260120 920978.BJ 46.41 24.99\n",
"7432 20260120 920981.BJ 44.26 23.84\n",
"7433 20260120 920982.BJ 310.42 167.16\n",
"7434 20260120 920985.BJ 9.97 5.37\n",
"7435 20260120 920992.BJ 24.49 13.19\n",
"7453 20260203 920978.BJ 37.93 20.43\n",
"7454 20260203 920981.BJ 42.52 22.90\n",
"7455 20260203 920982.BJ 279.35 150.43\n",
"7456 20260203 920985.BJ 10.15 5.47\n",
"7457 20260203 920992.BJ 21.95 11.83\n",
"\n",
"[7436 rows x 4 columns], trade_date ts_code up_limit down_limit\n",
"0 20260119 000001.SZ 12.31 10.07\n",
"1 20260119 000002.SZ 5.20 4.26\n",
"2 20260119 000004.SZ 11.70 10.58\n",
"3 20260119 000006.SZ 10.13 8.29\n",
"4 20260119 000007.SZ 12.43 10.17\n",
"[7458 rows x 4 columns], trade_date ts_code up_limit down_limit\n",
"0 20260202 000001.SZ 11.91 9.75\n",
"1 20260202 000002.SZ 5.37 4.39\n",
"2 20260202 000004.SZ 13.25 11.99\n",
"3 20260202 000006.SZ 9.86 8.06\n",
"4 20260202 000007.SZ 11.97 9.79\n",
"... ... ... ... ...\n",
"7429 20260119 920978.BJ 46.26 24.92\n",
"7430 20260119 920981.BJ 45.51 24.51\n",
"7431 20260119 920982.BJ 305.50 164.50\n",
"7432 20260119 920985.BJ 9.88 5.32\n",
"7433 20260119 920992.BJ 24.28 13.08\n",
"7450 20260202 920978.BJ 40.36 21.74\n",
"7451 20260202 920981.BJ 45.50 24.50\n",
"7452 20260202 920982.BJ 287.80 154.98\n",
"7453 20260202 920985.BJ 10.33 5.57\n",
"7454 20260202 920992.BJ 22.63 12.19\n",
"\n",
"[7434 rows x 4 columns]]\n"
"[7455 rows x 4 columns]]\n"
]
}
],

34
main/train/Classify2_load_model.ipynb
View File

@@ -78,7 +78,7 @@
"cyq perf\n",
"left merge on ['ts_code', 'trade_date']\n",
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 9359748 entries, 0 to 9359747\n",
"RangeIndex: 9410807 entries, 0 to 9410806\n",
"Data columns (total 33 columns):\n",
" # Column Dtype \n",
"--- ------ ----- \n",
@@ -116,7 +116,7 @@
" 31 weight_avg float64 \n",
" 32 winner_rate float64 \n",
"dtypes: bool(1), datetime64[ns](1), float64(30), object(1)\n",
"memory usage: 2.2+ GB\n",
"memory usage: 2.3+ GB\n",
"None\n"
]
}
@@ -600,7 +600,7 @@
"Calculating cs_rank_size...\n",
"Finished cs_rank_size.\n",
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 5042085 entries, 0 to 5042084\n",
"RangeIndex: 5072276 entries, 0 to 5072275\n",
"Columns: 181 entries, ts_code to cs_rank_size\n",
"dtypes: bool(10), datetime64[ns](1), float64(165), int64(3), object(2)\n",
"memory usage: 6.5+ GB\n",
@@ -1409,7 +1409,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
"MAD Filtering: 100%|██████████| 131/131 [00:13<00:00, 9.39it/s]\n"
"MAD Filtering: 100%|██████████| 131/131 [00:13<00:00, 10.05it/s]\n"
]
},
{
@@ -1424,7 +1424,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
"MAD Filtering: 100%|██████████| 131/131 [00:15<00:00, 8.45it/s]\n"
"MAD Filtering: 100%|██████████| 131/131 [00:16<00:00, 8.05it/s]\n"
]
},
{
@@ -1464,13 +1464,13 @@
"截面 MAD 去极值处理完成。\n",
"feature_columns: ['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'winner_rate', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size', 'cat_up_limit', 'industry_obv', 'industry_return_5', 'industry_return_20', 'industry__ema_5', 'industry__ema_13', 'industry__ema_20', 'industry__ema_60', 'industry_act_factor1', 'industry_act_factor2', 'industry_act_factor3', 'industry_act_factor4', 'industry_act_factor5', 'industry_act_factor6', 'industry_rank_act_factor1', 'industry_rank_act_factor2', 'industry_rank_act_factor3', 'industry_return_5_percentile', 'industry_return_20_percentile', '000852.SH_MACD', '000905.SH_MACD', '399006.SZ_MACD', '000852.SH_MACD_hist', '000905.SH_MACD_hist', '399006.SZ_MACD_hist', '000852.SH_RSI', '000905.SH_RSI', '399006.SZ_RSI', '000852.SH_Signal_line', '000905.SH_Signal_line', '399006.SZ_Signal_line', '000852.SH_amount_change_rate', '000905.SH_amount_change_rate', '399006.SZ_amount_change_rate', '000852.SH_amount_mean', '000905.SH_amount_mean', '399006.SZ_amount_mean', '000852.SH_daily_return', '000905.SH_daily_return', '399006.SZ_daily_return', '000852.SH_up_ratio_20d', '000905.SH_up_ratio_20d', '399006.SZ_up_ratio_20d', '000852.SH_volatility', '000905.SH_volatility', '399006.SZ_volatility', '000852.SH_volume_change_rate', '000905.SH_volume_change_rate', '399006.SZ_volume_change_rate']\n",
"df最小日期: 2019-01-02\n",
"df最大日期: 2026-01-23\n",
"2055951\n",
"df最大日期: 2026-02-06\n",
"2055944\n",
"train_data最小日期: 2020-01-02\n",
"train_data最大日期: 2022-12-30\n",
"2267560\n",
"2297633\n",
"test_data最小日期: 2023-01-03\n",
"test_data最大日期: 2026-01-23\n",
"test_data最大日期: 2026-02-06\n",
" ts_code trade_date log_circ_mv\n",
"0 000001.SZ 2019-01-02 16.574219\n",
"1 000001.SZ 2019-01-03 16.583965\n",
@@ -1754,7 +1754,7 @@
{
"data": {
"text/plain": [
"<catboost.core.CatBoostClassifier at 0x7cca5687f800>"
"<catboost.core.CatBoostClassifier at 0x7b3eec333d70>"
]
},
"execution_count": 19,
@@ -1841,7 +1841,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
"5584 2055951\n",
"5584 2055944\n",
" ts_code trade_date turnover_rate\n",
"0 000001.SZ 2023-01-03 1.1307\n",
"1 000001.SZ 2023-01-04 1.1284\n",
@@ -1849,13 +1849,13 @@
"3 000001.SZ 2023-01-06 0.6162\n",
"4 000001.SZ 2023-01-09 0.5450\n",
"... ... ... ...\n",
"2267555 605599.SH 2026-01-19 0.6158\n",
"2267556 605599.SH 2026-01-20 0.9493\n",
"2267557 605599.SH 2026-01-21 1.1732\n",
"2267558 605599.SH 2026-01-22 0.8848\n",
"2267559 605599.SH 2026-01-23 0.9550\n",
"2297628 605599.SH 2026-02-02 2.6968\n",
"2297629 605599.SH 2026-02-03 1.6084\n",
"2297630 605599.SH 2026-02-04 3.9328\n",
"2297631 605599.SH 2026-02-05 3.0072\n",
"2297632 605599.SH 2026-02-06 3.1129\n",
"\n",
"[2267560 rows x 3 columns]\n"
"[2297633 rows x 3 columns]\n"
]
}
],

20
predictions_test.tsv
View File

@@ -1483,3 +1483,23 @@ trade_date,score,ts_code
2026-01-22,0.5392295112943043,002193.SZ
2026-01-23,0.4433520823626358,001211.SZ
2026-01-23,0.5104712391758388,002193.SZ
2026-01-26,0.44845793700606645,600768.SH
2026-01-26,0.5184014597753998,002193.SZ
2026-01-27,0.38801810040527523,002719.SZ
2026-01-27,0.4660343777911184,002193.SZ
2026-01-28,0.3752951609014855,002963.SZ
2026-01-28,0.3844631540327193,002193.SZ
2026-01-29,0.3957310272373612,002188.SZ
2026-01-29,0.4154973074156361,002193.SZ
2026-01-30,0.4406255680708698,002193.SZ
2026-01-30,0.6387329035097701,600768.SH
2026-02-02,0.46452469502542476,600543.SH
2026-02-02,0.48096075649083675,002719.SZ
2026-02-03,0.4682296506349894,002719.SZ
2026-02-03,0.6074115272186936,002193.SZ
2026-02-04,0.441433034204454,600847.SH
2026-02-04,0.6491590676980965,002193.SZ
2026-02-05,0.43576482005305384,002719.SZ
2026-02-05,0.6383257473564533,002193.SZ
2026-02-06,0.45969830258066396,600493.SH
2026-02-06,0.6311025962732058,002193.SZ
1 trade_date,score,ts_code
1483 2026-01-22,0.5392295112943043,002193.SZ
1484 2026-01-23,0.4433520823626358,001211.SZ
1485 2026-01-23,0.5104712391758388,002193.SZ
1486 2026-01-26,0.44845793700606645,600768.SH
1487 2026-01-26,0.5184014597753998,002193.SZ
1488 2026-01-27,0.38801810040527523,002719.SZ
1489 2026-01-27,0.4660343777911184,002193.SZ
1490 2026-01-28,0.3752951609014855,002963.SZ
1491 2026-01-28,0.3844631540327193,002193.SZ
1492 2026-01-29,0.3957310272373612,002188.SZ
1493 2026-01-29,0.4154973074156361,002193.SZ
1494 2026-01-30,0.4406255680708698,002193.SZ
1495 2026-01-30,0.6387329035097701,600768.SH
1496 2026-02-02,0.46452469502542476,600543.SH
1497 2026-02-02,0.48096075649083675,002719.SZ
1498 2026-02-03,0.4682296506349894,002719.SZ
1499 2026-02-03,0.6074115272186936,002193.SZ
1500 2026-02-04,0.441433034204454,600847.SH
1501 2026-02-04,0.6491590676980965,002193.SZ
1502 2026-02-05,0.43576482005305384,002719.SZ
1503 2026-02-05,0.6383257473564533,002193.SZ
1504 2026-02-06,0.45969830258066396,600493.SH
1505 2026-02-06,0.6311025962732058,002193.SZ

65
qmt/qmt_engine.py
View File

@@ -209,39 +209,41 @@ class AutoReconnectScheduler:
time.sleep(60)
def _scheduled_reconnect(self):
"""执行定时重连任务"""
self.logger.info(f"执行定时重连任务，时间: {self.reconnect_time}")
"""执行定时重连任务（强制重连模式）"""
self.logger.info(f"[AutoReconnectScheduler] 执行定时重连任务，时间: {self.reconnect_time}")
# 1. 检测当前连接状态
statuses = self.manager.get_all_status()
connected_count = sum(1 for s in statuses if s.is_connected)
self.logger.info(f"当前连接状态: {connected_count}/{len(statuses)} 个终端在线")
# 设置重连中标志位，通知主循环暂停健康检查重连
self.manager.is_scheduled_reconnecting = True
# 2. 如果有连接，先断开
if connected_count > 0:
self.logger.info("正在断开所有终端连接...")
try:
# 强制断开所有终端连接（无论当前是否在线）
self.logger.info("[AutoReconnectScheduler] 正在断开所有终端连接...")
for unit in self.manager.units.values():
try:
if unit.xt_trader:
unit.cleanup()
self.logger.info(f"[AutoReconnectScheduler] 已断开终端 {unit.alias} 的连接")
except Exception as e:
self.logger.warning(f"断开终端 {unit.alias} 失败: {e}")
self.logger.warning(f"[AutoReconnectScheduler] 断开终端 {unit.alias} 失败: {e}")
# 3. 等待几秒后重新连接
self.logger.info("等待 3 秒后重新连接...")
time.sleep(3)
# 等待几秒后重新连接（固定等待时间）
self.logger.info("[AutoReconnectScheduler] 等待 3 秒后重新连接...")
time.sleep(3)
# 4. 重新连接所有终端
self.logger.info("正在重新连接所有终端...")
success_count = 0
for unit in self.manager.units.values():
if unit.connect():
success_count += 1
self.logger.info(f"终端 {unit.alias} 重连成功")
else:
self.logger.warning(f"终端 {unit.alias} 重连失败")
# 重新连接所有终端
self.logger.info("[AutoReconnectScheduler] 正在重新连接所有终端...")
success_count = 0
for unit in self.manager.units.values():
if unit.connect():
success_count += 1
self.logger.info(f"[AutoReconnectScheduler] 终端 {unit.alias} 重连成功")
else:
self.logger.warning(f"[AutoReconnectScheduler] 终端 {unit.alias} 重连失败")
self.logger.info(f"定时重连完成: {success_count}/{len(self.manager.units)} 个终端重连成功")
self.logger.info(f"[AutoReconnectScheduler] 定时重连完成: {success_count}/{len(self.manager.units)} 个终端重连成功")
finally:
# 确保无论成功与否都重置标志位
self.manager.is_scheduled_reconnecting = False
def start(self):
"""启动定时任务"""
@@ -351,11 +353,11 @@ class TradingUnit:
try:
logging.getLogger("QMT_Engine").info(f"正在销毁终端 {self.alias} 的旧资源...")
self.xt_trader.stop()
self.xt_trader = None # 显式置空
self.xt_trader = None # 显式置空
self.callback = None
time.sleep(1.5) # 给 C++ 引擎留出释放 down_queue 锁的时间
except:
pass
time.sleep(1.5) # 给 C++ 引擎留出释放 down_queue 锁的时间
except Exception as e:
logging.getLogger("QMT_Engine").warning(f"销毁终端 {self.alias} 资源时出现异常: {e}")
def connect(self):
"""连接 QMT 终端"""
@@ -401,6 +403,7 @@ class MultiEngineManager:
self.config = {}
self.is_running = True
self.start_time = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')
self.is_scheduled_reconnecting = False # 定时重连调度器正在执行标志
self._initialized = True
def initialize(self, config_file='config.json'):
@@ -467,8 +470,12 @@ class MultiEngineManager:
if not is_unit_alive:
# 避让 QMT 夜间重启高峰 (21:32 - 21:50)
if not ('213200' <= curr_hms <= '215000'):
self.logger.warning(f"🚫 终端 {unit.alias} 物理连接丢失，执行重连...")
unit.connect()
# 检查是否正在执行定时重连调度
if self.is_scheduled_reconnecting:
self.logger.info(f"⏳ 定时重连调度器正在执行，跳过健康检查重连...")
else:
self.logger.warning(f"🚫 终端 {unit.alias} 物理连接丢失，执行重连...")
unit.connect()
else:
self.logger.info(f"⏳ 处于 QMT 重启时段 ({curr_hms})，跳过重连操作...")