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Merge branch 'dev' of \\wsl.localhost\ubuntu\mnt\d\PyProject\NewStock with conflicts.

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liaozhaorun
2025-10-13 15:30:34 +08:00
parent 0c12e6c2b1 79d5384de5
commit 1c903b34da
26 changed files with 4471 additions and 13176 deletions
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5600
main/data/daily_data.ipynb
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61
main/data/index_and_industry.ipynb
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@@ -2,12 +2,11 @@
"cells": [
{
"cell_type": "code",
"execution_count": 4,
"execution_count": 1,
"id": "initial_id",
"metadata": {
"ExecuteTime": {
"end_time": "2025-04-09T14:57:27.092313Z",
"start_time": "2025-04-09T14:57:26.124592Z"
"jupyter": {
"is_executing": true
}
},
"outputs": [],
@@ -24,11 +23,11 @@
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": 2,
"id": "f448da220816bf98",
"metadata": {
"ExecuteTime": {
"end_time": "2025-04-09T14:57:37.680808Z",
"end_time": "2025-07-26T10:23:18.517518100Z",
"start_time": "2025-04-09T14:57:27.392846Z"
}
},
@@ -70,11 +69,11 @@
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": 3,
"id": "907f732d3c397bf",
"metadata": {
"ExecuteTime": {
"end_time": "2025-04-09T14:57:37.730922Z",
"end_time": "2025-07-26T10:23:18.552166300Z",
"start_time": "2025-04-09T14:57:37.695917Z"
}
},
@@ -84,32 +83,32 @@
"output_type": "stream",
"text": [
" ts_code trade_date close open high low \\\n",
"0 000905.SH 20250606 5762.0778 5768.3989 5771.7558 5750.6592 \n",
"1 000905.SH 20250605 5769.9675 5741.0298 5778.0959 5719.5466 \n",
"2 000905.SH 20250604 5739.0058 5696.5692 5744.4543 5696.5692 \n",
"3 000905.SH 20250603 5694.8385 5653.6747 5710.4203 5653.2978 \n",
"4 000905.SH 20250530 5671.0723 5704.7710 5704.7710 5665.5177 \n",
"0 000905.SH 20251010 7398.2241 7499.3917 7509.1161 7373.9841 \n",
"1 000905.SH 20251009 7548.9226 7470.0474 7559.0920 7437.3242 \n",
"2 000905.SH 20250930 7412.3684 7372.5240 7428.0307 7372.0634 \n",
"3 000905.SH 20250929 7350.5599 7251.5221 7377.2217 7216.7357 \n",
"4 000905.SH 20250926 7240.9114 7311.8433 7351.7931 7237.0459 \n",
"... ... ... ... ... ... ... \n",
"13558 399006.SZ 20100607 1069.4680 1005.0280 1075.2250 1001.7020 \n",
"13559 399006.SZ 20100604 1027.6810 989.6810 1027.6810 986.5040 \n",
"13560 399006.SZ 20100603 998.3940 1002.3550 1026.7020 997.7750 \n",
"13561 399006.SZ 20100602 997.1190 967.6090 997.1190 952.6110 \n",
"13562 399006.SZ 20100601 973.2330 986.0150 994.7930 948.1180 \n",
"13810 399006.SZ 20100607 1069.4680 1005.0280 1075.2250 1001.7020 \n",
"13811 399006.SZ 20100604 1027.6810 989.6810 1027.6810 986.5040 \n",
"13812 399006.SZ 20100603 998.3940 1002.3550 1026.7020 997.7750 \n",
"13813 399006.SZ 20100602 997.1190 967.6090 997.1190 952.6110 \n",
"13814 399006.SZ 20100601 973.2330 986.0150 994.7930 948.1180 \n",
"\n",
" pre_close change pct_chg vol amount \n",
"0 5769.9675 -7.8897 -0.1367 1.082177e+08 1.480224e+08 \n",
"1 5739.0058 30.9617 0.5395 1.252236e+08 1.749701e+08 \n",
"2 5694.8385 44.1673 0.7756 1.161961e+08 1.503149e+08 \n",
"3 5671.0723 23.7662 0.4191 1.228539e+08 1.599968e+08 \n",
"4 5719.9101 -48.8378 -0.8538 1.099007e+08 1.376706e+08 \n",
"... ... ... ... ... ... \n",
"13558 1027.6810 41.7870 4.0661 2.655275e+06 9.106095e+06 \n",
"13559 998.3940 29.2870 2.9334 1.500295e+06 5.269441e+06 \n",
"13560 997.1190 1.2750 0.1279 1.616805e+06 6.240835e+06 \n",
"13561 973.2330 23.8860 2.4543 1.074628e+06 4.001206e+06 \n",
"13562 1000.0000 -26.7670 -2.6767 1.356285e+06 4.924177e+06 \n",
" pre_close change pct_chg vol amount \n",
"0 7548.9226 -150.6985 -1.9963 2.622566e+08 5.021274e+08 \n",
"1 7412.3684 136.5542 1.8422 2.831308e+08 5.357568e+08 \n",
"2 7350.5599 61.8085 0.8409 2.207075e+08 4.449564e+08 \n",
"3 7240.9114 109.6485 1.5143 2.335394e+08 4.338645e+08 \n",
"4 7341.3238 -100.4124 -1.3678 2.114441e+08 4.301976e+08 \n",
"... ... ... ... ... ... \n",
"13810 1027.6810 41.7870 4.0661 2.655275e+06 9.106095e+06 \n",
"13811 998.3940 29.2870 2.9334 1.500295e+06 5.269441e+06 \n",
"13812 997.1190 1.2750 0.1279 1.616805e+06 6.240835e+06 \n",
"13813 973.2330 23.8860 2.4543 1.074628e+06 4.001206e+06 \n",
"13814 1000.0000 -26.7670 -2.6767 1.356285e+06 4.924177e+06 \n",
"\n",
"[13563 rows x 11 columns]\n"
"[13815 rows x 11 columns]\n"
]
}
],

42
main/data/update/cyq-perf.ipynb
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@@ -39,15 +39,15 @@
"3 000006.SZ 20250312\n",
"4 000007.SZ 20250312\n",
"... ... ...\n",
"26894 920445.BJ 20250526\n",
"26895 920489.BJ 20250526\n",
"26896 920682.BJ 20250526\n",
"26897 920799.BJ 20250526\n",
"26898 920819.BJ 20250526\n",
"27111 920445.BJ 20250922\n",
"27112 920489.BJ 20250922\n",
"27113 920682.BJ 20250922\n",
"27114 920799.BJ 20250922\n",
"27115 920819.BJ 20250922\n",
"\n",
"[7751233 rows x 2 columns]\n",
"20250530\n",
"start_date: 20250603\n"
"[8205543 rows x 2 columns]\n",
"20250926\n",
"start_date: 20250929\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='20250620')\n",
"trade_cal = pro.trade_cal(exchange='', start_date='20170101', end_date='20251020')\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",
@@ -86,20 +86,16 @@
"name": "stdout",
"output_type": "stream",
"text": [
"任务 20250620 完成\n",
"任务 20250619 完成\n",
"任务 20250618 完成\n",
"任务 20250617 完成\n",
"任务 20250616 完成\n",
"任务 20250613 完成\n",
"任务 20250612 完成\n",
"任务 20250611 完成\n",
"任务 20250610 完成\n",
"任务 20250609 完成\n",
"任务 20250605 完成\n",
"任务 20250606 完成\n",
"任务 20250604 完成\n",
"任务 20250603 完成\n"
"任务 20251020 完成\n",
"任务 20251017 完成\n",
"任务 20251016 完成\n",
"任务 20251015 完成\n",
"任务 20251014 完成\n",
"任务 20251013 完成\n",
"任务 20251010 完成\n",
"任务 20251009 完成\n",
"任务 20250930 完成\n",
"任务 20250929 完成\n"
]
}
],

42
main/data/update/sw_daily.ipynb
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@@ -39,15 +39,15 @@
"3 801005.SI 20250221\n",
"4 801010.SI 20250221\n",
"... ... ...\n",
"2190 859811.SI 20250526\n",
"2191 859821.SI 20250526\n",
"2192 859822.SI 20250526\n",
"2193 859852.SI 20250526\n",
"2194 859951.SI 20250526\n",
"2190 859811.SI 20250922\n",
"2191 859821.SI 20250922\n",
"2192 859822.SI 20250922\n",
"2193 859852.SI 20250922\n",
"2194 859951.SI 20250922\n",
"\n",
"[1073367 rows x 2 columns]\n",
"20250530\n",
"start_date: 20250603\n"
"[1110243 rows x 2 columns]\n",
"20250926\n",
"start_date: 20250929\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='20250620')\n",
"trade_cal = pro.trade_cal(exchange='', start_date='20170101', end_date='20251020')\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",
@@ -86,20 +86,16 @@
"name": "stdout",
"output_type": "stream",
"text": [
"任务 20250620 完成\n",
"任务 20250619 完成\n",
"任务 20250618 完成\n",
"任务 20250617 完成\n",
"任务 20250616 完成\n",
"任务 20250613 完成\n",
"任务 20250611 完成\n",
"任务 20250612 完成\n",
"任务 20250610 完成\n",
"任务 20250609 完成\n",
"任务 20250606 完成\n",
"任务 20250605 完成\n",
"任务 20250604 完成\n",
"任务 20250603 完成\n"
"任务 20251020 完成\n",
"任务 20251017 完成\n",
"任务 20251016 完成\n",
"任务 20251015 完成\n",
"任务 20251014 完成\n",
"任务 20251013 完成\n",
"任务 20251010 完成\n",
"任务 20251009 完成\n",
"任务 20250930 完成\n",
"任务 20250929 完成\n"
]
}
],

223
main/data/update/update_daily_basic.ipynb
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@@ -94,17 +94,17 @@
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Index: 8701511 entries, 0 to 26922\n",
"Index: 9155905 entries, 0 to 27115\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: 199.2+ MB\n",
"memory usage: 209.6+ MB\n",
"None\n",
"20250530\n",
"20250603\n"
"20250926\n",
"20250929\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='20250720')\n",
"trade_cal = pro.trade_cal(exchange='', start_date='20170101', end_date='20251020')\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",
@@ -144,40 +144,16 @@
"name": "stdout",
"output_type": "stream",
"text": [
"任务 20250718 完成\n",
"任务 20250717 完成\n",
"任务 20250716 完成\n",
"任务 20250715 完成\n",
"任务 20250714 完成\n",
"任务 20250711 完成\n",
"任务 20250710 完成\n",
"任务 20250709 完成\n",
"任务 20250708 完成\n",
"任务 20250707 完成\n",
"任务 20250703 完成\n",
"任务 20250704 完成\n",
"任务 20250702 完成\n",
"任务 20250701 完成\n",
"任务 20250630 完成\n",
"任务 20250627 完成\n",
"任务 20250626 完成\n",
"任务 20250625 完成\n",
"任务 20250624 完成\n",
"任务 20250623 完成\n",
"任务 20250620 完成\n",
"任务 20250619 完成\n",
"任务 20250618 完成\n",
"任务 20250617 完成\n",
"任务 20250616 完成\n",
"任务 20250613 完成\n",
"任务 20250612 完成\n",
"任务 20250611 完成\n",
"任务 20250610 完成\n",
"任务 20250609 完成\n",
"任务 20250605 完成\n",
"任务 20250606 完成\n",
"任务 20250604 完成\n",
"任务 20250603 完成\n"
"任务 20251017 完成\n",
"任务 20251020 完成\n",
"任务 20251015 完成\n",
"任务 20251016 完成\n",
"任务 20251014 完成\n",
"任务 20251013 完成\n",
"任务 20251010 完成\n",
"任务 20251009 完成\n",
"任务 20250930 完成\n",
"任务 20250929 完成\n"
]
}
],
@@ -248,58 +224,58 @@
"output_type": "stream",
"text": [
" ts_code trade_date close turnover_rate turnover_rate_f \\\n",
"0 600845.SH 20250605 25.68 0.8243 2.5243 \n",
"1 000153.SZ 20250605 6.12 1.9588 2.7729 \n",
"2 002086.SZ 20250605 2.76 5.2365 6.0861 \n",
"3 300020.SZ 20250605 3.87 2.2399 2.5078 \n",
"4 605567.SH 20250605 9.90 2.5088 4.5825 \n",
"0 600642.SH 20251010 8.03 0.4806 1.3835 \n",
"1 600295.SH 20251010 10.76 0.8549 3.7056 \n",
"2 600444.SH 20251010 19.00 9.6611 17.4605 \n",
"3 605100.SH 20251010 28.72 3.4770 7.6902 \n",
"4 301399.SZ 20251010 19.53 3.9562 4.6772 \n",
"... ... ... ... ... ... \n",
"21540 000068.SZ 20250603 3.38 1.1289 2.0176 \n",
"21541 301135.SZ 20250603 25.84 4.8553 4.8553 \n",
"21542 603026.SH 20250603 33.42 0.4772 0.7542 \n",
"21543 002079.SZ 20250603 9.45 1.0524 1.3694 \n",
"21544 688335.SH 20250603 12.69 1.1169 2.2103 \n",
"21679 600653.SH 20250929 2.13 2.1746 2.9589 \n",
"21680 002344.SZ 20250929 4.49 1.7080 3.6338 \n",
"21681 301162.SZ 20250929 60.30 2.8491 3.5744 \n",
"21682 920077.BJ 20250929 14.43 1.1113 1.6410 \n",
"21683 300283.SZ 20250929 7.04 4.8583 5.7018 \n",
"\n",
" volume_ratio pe pe_ttm pb ps ps_ttm dv_ratio \\\n",
"0 1.47 32.6889 34.9249 6.1529 5.4276 5.7895 3.2460 \n",
"1 0.95 17.6853 18.7324 1.4099 0.6639 0.6930 1.7509 \n",
"2 1.00 NaN NaN 3.8361 15.8946 15.5013 0.0000 \n",
"3 0.66 NaN NaN 0.9763 5.6130 21.2702 0.0000 \n",
"4 0.99 242.4925 78.2360 1.8181 0.7875 0.7674 0.0000 \n",
"0 1.49 9.9635 10.2617 1.1073 1.3268 1.3600 4.9816 \n",
"1 1.56 16.3053 16.4683 1.4839 1.0603 1.1230 7.4349 \n",
"2 2.84 69.2746 55.7147 3.8398 3.6313 3.5392 0.5263 \n",
"3 0.55 66.7896 123.2961 2.7276 5.3634 6.7180 2.0794 \n",
"4 0.94 60.7990 75.8958 2.7675 6.8812 7.1828 1.2177 \n",
"... ... ... ... ... ... ... ... \n",
"21540 1.02 259.4835 175.8911 4.9250 3.3696 3.4641 0.0000 \n",
"21541 0.98 68.9144 62.8352 2.0805 2.0868 1.9475 1.1264 \n",
"21542 0.95 412.5304 NaN 1.7468 1.2212 1.1453 0.1197 \n",
"21543 0.93 103.8909 74.2709 2.4969 1.3579 1.4180 0.4011 \n",
"21544 0.87 NaN NaN 1.6474 10.0514 8.7963 NaN \n",
"21679 0.72 107.4073 227.6354 5.4498 0.9887 0.9724 0.0000 \n",
"21680 0.70 64.8238 75.9239 0.6834 5.5516 5.5560 0.9577 \n",
"21681 0.96 85.4251 76.2427 5.3380 14.5424 12.3677 0.5586 \n",
"21682 0.51 90.3399 82.4861 3.3572 5.2895 4.1636 NaN \n",
"21683 0.94 NaN NaN 3.2821 1.1161 0.9970 0.2499 \n",
"\n",
" dv_ttm total_share float_share free_share total_mv \\\n",
"0 3.2460 288380.3858 213374.0521 69678.6847 7.405608e+06 \n",
"1 1.6340 46477.3722 45294.3722 31996.8047 2.844415e+05 \n",
"2 NaN 195894.6500 151702.1291 130526.0564 5.406692e+05 \n",
"3 NaN 79467.7974 76663.9584 68475.6577 3.075404e+05 \n",
"4 NaN 20000.0000 20000.0000 10949.3050 1.980000e+05 \n",
"0 5.6040 489407.9376 489381.3156 170006.8520 3.929946e+06 \n",
"1 5.5762 279877.6254 197557.6254 45577.9458 3.011483e+06 \n",
"2 0.5789 14642.1932 14642.1932 8101.7360 2.782017e+05 \n",
"3 1.0446 17113.2000 16993.2000 7683.2000 4.914911e+05 \n",
"4 1.0594 18502.0000 5468.3586 4625.5000 3.613441e+05 \n",
"... ... ... ... ... ... \n",
"21540 NaN 100667.1464 100667.1464 56326.7969 3.402550e+05 \n",
"21541 1.1264 10195.2000 5558.9000 5558.9000 2.634440e+05 \n",
"21542 0.1197 20268.0000 20268.0000 12822.4285 6.773566e+05 \n",
"21543 0.4011 81013.9316 80937.8478 62203.4223 7.655817e+05 \n",
"21544 NaN 14803.4592 14803.4592 7480.3745 1.878559e+05 \n",
"21679 NaN 194638.0317 194638.0317 143048.5612 4.145790e+05 \n",
"21680 0.8463 128261.6960 128145.0092 60233.0025 5.758950e+05 \n",
"21681 0.9704 13258.3724 8522.5548 6793.1764 7.994799e+05 \n",
"21682 NaN 58768.1817 31695.6817 21464.7599 8.480249e+05 \n",
"21683 NaN 49697.8222 36721.8502 31289.2680 3.498727e+05 \n",
"\n",
" circ_mv is_st \n",
"0 5.479446e+06 False \n",
"1 2.772016e+05 False \n",
"2 4.186979e+05 False \n",
"3 2.966895e+05 True \n",
"4 1.980000e+05 False \n",
"0 3.929732e+06 False \n",
"1 2.125720e+06 False \n",
"2 2.782017e+05 False \n",
"3 4.880447e+05 False \n",
"4 1.067970e+05 False \n",
"... ... ... \n",
"21540 3.402550e+05 False \n",
"21541 1.436420e+05 False \n",
"21542 6.773566e+05 False \n",
"21543 7.648627e+05 False \n",
"21544 1.878559e+05 False \n",
"21679 4.145790e+05 False \n",
"21680 5.753711e+05 False \n",
"21681 5.139101e+05 False \n",
"21682 4.573687e+05 False \n",
"21683 2.585218e+05 False \n",
"\n",
"[21545 rows x 19 columns]\n"
"[21684 rows x 19 columns]\n"
]
}
],
@@ -324,58 +300,45 @@
"output_type": "stream",
"text": [
" ts_code trade_date close turnover_rate turnover_rate_f \\\n",
"3 300020.SZ 20250605 3.87 2.2399 2.5078 \n",
"5 000506.SZ 20250605 8.11 11.2852 16.8442 \n",
"43 600243.SH 20250605 3.09 2.3385 2.8128 \n",
"48 002528.SZ 20250605 2.63 1.7748 3.7890 \n",
"78 300044.SZ 20250605 3.43 3.7959 3.9616 \n",
"9 300313.SZ 20251010 8.84 3.1146 6.4625 \n",
"20 603838.SH 20251010 7.80 0.5503 1.5146 \n",
"29 603813.SH 20251010 24.06 1.5835 4.5173 \n",
"48 002742.SZ 20251010 4.65 1.0473 1.2924 \n",
"69 603559.SH 20251010 8.50 0.2072 0.2945 \n",
"... ... ... ... ... ... \n",
"21429 600243.SH 20250603 3.06 3.3544 4.0348 \n",
"21434 002528.SZ 20250603 2.52 1.4622 3.1216 \n",
"21464 300044.SZ 20250603 3.45 4.3894 4.5810 \n",
"21494 300097.SZ 20250603 4.89 2.6755 3.1205 \n",
"21515 600200.SH 20250603 2.59 6.4745 7.8264 \n",
"21466 603021.SH 20250929 4.62 1.3860 2.3418 \n",
"21552 300020.SZ 20250929 3.58 1.5031 1.6828 \n",
"21554 000506.SZ 20250929 10.88 10.5560 15.7565 \n",
"21603 600636.SH 20250929 8.29 0.4693 0.7963 \n",
"21661 603843.SH 20250929 5.17 0.3798 0.5364 \n",
"\n",
" volume_ratio pe pe_ttm pb ps ps_ttm dv_ratio \\\n",
"3 0.66 NaN NaN 0.9763 5.6130 21.2702 0.0 \n",
"5 5.96 NaN NaN 14.2472 22.6112 19.7704 0.0 \n",
"43 0.52 NaN NaN 2.1216 5.7313 5.8761 0.0 \n",
"48 1.08 NaN NaN 17.3769 3.3364 4.0382 0.0 \n",
"78 1.05 NaN NaN 25.1987 18.2860 27.0836 0.0 \n",
"... ... ... ... ... ... ... ... \n",
"21429 0.68 NaN NaN 2.1010 5.6757 5.8190 0.0 \n",
"21434 0.77 NaN NaN 16.6502 3.1969 3.8693 0.0 \n",
"21464 1.26 NaN NaN 25.3456 18.3927 27.2415 0.0 \n",
"21494 1.55 NaN NaN 3.0435 3.6740 4.2734 0.0 \n",
"21515 0.79 26.1689 NaN 1.0523 1.1539 1.5214 0.0 \n",
" volume_ratio pe pe_ttm pb ps ps_ttm dv_ratio dv_ttm \\\n",
"9 1.30 NaN NaN NaN 20.1067 20.9731 0.0000 NaN \n",
"20 0.57 NaN NaN 2.6121 8.7517 6.9304 0.0000 NaN \n",
"29 1.88 NaN NaN 4.5222 8.4776 7.5124 1.0313 NaN \n",
"48 1.28 NaN NaN NaN 1.6800 2.1226 0.0000 NaN \n",
"69 0.60 NaN NaN 3.5043 9.5964 8.2315 0.0000 NaN \n",
"... ... .. ... ... ... ... ... ... \n",
"21466 0.80 NaN NaN NaN 3.5891 3.7851 0.0000 NaN \n",
"21552 1.00 NaN NaN 0.9812 5.1924 18.4036 0.0000 NaN \n",
"21554 3.17 NaN NaN 16.4257 30.3341 23.4860 0.0000 NaN \n",
"21603 0.81 NaN NaN 1.7909 12.8512 11.0116 0.4825 0.6031 \n",
"21661 0.05 NaN NaN 12.5612 2.6558 3.1369 0.0000 NaN \n",
"\n",
" dv_ttm total_share float_share free_share total_mv circ_mv \\\n",
"3 NaN 79467.7974 76663.9584 68475.6577 307540.3759 296689.5190 \n",
"5 NaN 92901.7761 92867.0961 62218.8027 753433.4042 753152.1494 \n",
"43 NaN 43885.0000 43885.0000 36485.0000 135604.6500 135604.6500 \n",
"48 NaN 119867.5082 104974.0608 49171.2582 315251.5466 276081.7799 \n",
"78 NaN 76386.9228 76375.7508 73182.1277 262007.1452 261968.8252 \n",
"... ... ... ... ... ... ... \n",
"21429 NaN 43885.0000 43885.0000 36485.0000 134288.1000 134288.1000 \n",
"21434 NaN 119867.5082 104974.0608 49171.2582 302066.1207 264534.6332 \n",
"21464 NaN 76386.9228 76375.7508 73182.1277 263534.8837 263496.3403 \n",
"21494 NaN 28854.9669 27000.9948 23150.5534 141100.7881 132034.8646 \n",
"21515 NaN 71215.1832 71087.9480 58808.3718 184447.3245 184117.7853 \n",
" total_share float_share free_share total_mv circ_mv is_st \n",
"9 31297.7396 19735.2789 9511.5479 2.766720e+05 1.744599e+05 True \n",
"20 32001.6000 32001.6000 11627.0468 2.496125e+05 2.496125e+05 True \n",
"29 10501.5000 10501.5000 3681.2000 2.526661e+05 2.526661e+05 True \n",
"48 43200.0000 43185.8082 34994.8239 2.008800e+05 2.008140e+05 True \n",
"69 40127.6979 40127.6979 28231.9697 3.410854e+05 3.410854e+05 True \n",
"... ... ... ... ... ... ... \n",
"21466 31994.8070 31994.8070 18936.7934 1.478160e+05 1.478160e+05 True \n",
"21552 79467.7974 76663.9584 68475.6577 2.844947e+05 2.744570e+05 True \n",
"21554 92901.7761 92858.4361 62210.1427 1.010771e+06 1.010300e+06 True \n",
"21603 43863.6802 43863.6802 25849.6552 3.636299e+05 3.636299e+05 True \n",
"21661 69962.3237 69962.3237 49541.4702 3.617052e+05 3.617052e+05 True \n",
"\n",
" is_st \n",
"3 True \n",
"5 True \n",
"43 True \n",
"48 True \n",
"78 True \n",
"... ... \n",
"21429 True \n",
"21434 True \n",
"21464 True \n",
"21494 True \n",
"21515 True \n",
"\n",
"[753 rows x 19 columns]\n"
"[749 rows x 19 columns]\n"
]
}
],
@@ -425,7 +388,7 @@
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Index: 8723056 entries, 0 to 21544\n",
"Index: 9177589 entries, 0 to 21683\n",
"Data columns (total 3 columns):\n",
" # Column Dtype \n",
"--- ------ ----- \n",
@@ -433,7 +396,7 @@
" 1 trade_date object\n",
" 2 is_st bool \n",
"dtypes: bool(1), object(2)\n",
"memory usage: 208.0+ MB\n",
"memory usage: 218.8+ MB\n",
"None\n"
]
}

1136
main/data/update/update_daily_data.ipynb
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File diff suppressed because it is too large Load Diff

137
main/data/update/update_money_flow.ipynb
View File

@@ -34,17 +34,17 @@
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Index: 8533029 entries, 0 to 25597\n",
"Index: 8964780 entries, 0 to 25739\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: 195.3+ MB\n",
"memory usage: 205.2+ MB\n",
"None\n",
"20250530\n",
"start_date: 20250603\n"
"20250926\n",
"start_date: 20250929\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='20250720')\n",
"trade_cal = pro.trade_cal(exchange='', start_date='20170101', end_date='20251020')\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",
@@ -84,40 +84,16 @@
"name": "stdout",
"output_type": "stream",
"text": [
"任务 20250718 完成\n",
"任务 20250717 完成\n",
"任务 20250716 完成\n",
"任务 20250715 完成\n",
"任务 20250714 完成\n",
"任务 20250711 完成\n",
"任务 20250710 完成\n",
"任务 20250709 完成\n",
"任务 20250708 完成\n",
"任务 20250707 完成\n",
"任务 20250704 完成\n",
"任务 20250703 完成\n",
"任务 20250702 完成\n",
"任务 20250701 完成\n",
"任务 20250630 完成\n",
"任务 20250627 完成\n",
"任务 20250626 完成\n",
"任务 20250625 完成\n",
"任务 20250624 完成\n",
"任务 20250623 完成\n",
"任务 20250620 完成\n",
"任务 20250619 完成\n",
"任务 20250618 完成\n",
"任务 20250617 完成\n",
"任务 20250616 完成\n",
"任务 20250613 完成\n",
"任务 20250612 完成\n",
"任务 20250611 完成\n",
"任务 20250610 完成\n",
"任务 20250609 完成\n",
"任务 20250606 完成\n",
"任务 20250605 完成\n",
"任务 20250604 完成\n",
"任务 20250603 完成\n"
"任务 20251020 完成\n",
"任务 20251017 完成\n",
"任务 20251016 完成\n",
"任务 20251015 完成\n",
"任务 20251014 完成\n",
"任务 20251013 完成\n",
"任务 20251009 完成\n",
"任务 20251010 完成\n",
"任务 20250929 完成\n",
"任务 20250930 完成\n"
]
}
],
@@ -195,6 +171,89 @@
"\n",
"print(\"所有每日基础数据获取并保存完毕！\")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "e6f2a2fe",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" ts_code trade_date buy_sm_vol buy_sm_amount sell_sm_vol \\\n",
"0 603290.SH 20251009 45532 52028.67 42778 \n",
"1 600936.SH 20251009 42537 1545.21 42382 \n",
"2 300429.SZ 20251009 81914 11768.07 64063 \n",
"3 300879.SZ 20251009 15330 5366.90 11651 \n",
"4 300031.SZ 20251009 51381 12650.70 43869 \n",
"... ... ... ... ... ... \n",
"20574 688083.SH 20250930 13247 10094.95 11236 \n",
"20575 002939.SZ 20250930 372609 43083.12 232240 \n",
"20576 688303.SH 20250930 62478 18094.19 55086 \n",
"20577 300146.SZ 20250930 50078 5792.85 35214 \n",
"20578 688351.SH 20250930 15096 3333.84 14017 \n",
"\n",
" sell_sm_amount buy_md_vol buy_md_amount sell_md_vol sell_md_amount \\\n",
"0 48942.98 53824 61495.85 54076 61851.39 \n",
"1 1538.97 24175 878.06 31948 1160.07 \n",
"2 9211.49 88583 12730.36 88244 12682.05 \n",
"3 4089.33 15591 5464.12 17057 5976.94 \n",
"4 10822.65 56173 13836.60 49423 12190.63 \n",
"... ... ... ... ... ... \n",
"20574 8561.02 10482 7994.12 9858 7514.37 \n",
"20575 26867.01 279904 32371.96 324997 37595.57 \n",
"20576 15952.67 55867 16177.83 53776 15573.61 \n",
"20577 4076.10 46159 5337.00 39420 4560.91 \n",
"20578 3095.89 6482 1430.69 6675 1474.59 \n",
"\n",
" buy_lg_vol buy_lg_amount sell_lg_vol sell_lg_amount buy_elg_vol \\\n",
"0 36150 41253.53 36789 41932.43 10514 \n",
"1 11158 405.04 9212 334.60 5672 \n",
"2 64282 9239.06 72904 10475.38 8221 \n",
"3 10167 3562.24 12327 4313.59 3221 \n",
"4 40306 9938.01 41035 10103.23 6112 \n",
"... ... ... ... ... ... \n",
"20574 6674 5082.80 8224 6273.43 3329 \n",
"20575 204229 23631.31 285167 32986.98 132696 \n",
"20576 33304 9638.04 34809 10074.64 5032 \n",
"20577 47161 5454.07 36321 4202.88 8662 \n",
"20578 2513 555.48 3398 749.54 0 \n",
"\n",
" buy_elg_amount sell_elg_vol sell_elg_amount net_mf_vol \\\n",
"0 12073.88 12377 14125.13 20027 \n",
"1 205.33 0 0.00 -21182 \n",
"2 1183.11 17790 2551.67 -840 \n",
"3 1133.90 3275 1147.29 -4996 \n",
"4 1507.28 19645 4816.08 1531 \n",
"... ... ... ... ... \n",
"20574 2538.01 4413 3361.05 7612 \n",
"20575 15366.29 147033 17003.12 84949 \n",
"20576 1459.24 13010 3768.39 15188 \n",
"20577 1000.95 41105 4744.98 -16754 \n",
"20578 0.00 0 0.00 3406 \n",
"\n",
" net_mf_amount \n",
"0 22734.35 \n",
"1 -766.75 \n",
"2 -90.83 \n",
"3 -1741.72 \n",
"4 385.00 \n",
"... ... \n",
"20574 5816.07 \n",
"20575 9927.60 \n",
"20576 4417.72 \n",
"20577 -1928.39 \n",
"20578 752.20 \n",
"\n",
"[20579 rows x 20 columns]\n"
]
}
],
"source": [
"print(all_daily_data_df)"
]
}
],
"metadata": {

152
main/data/update/update_stk_limit.ipynb
View File

@@ -34,23 +34,23 @@
"output_type": "stream",
"text": [
" ts_code trade_date\n",
"4763 600259.SH 20250530\n",
"4764 600261.SH 20250530\n",
"4765 600262.SH 20250530\n",
"4754 600248.SH 20250530\n",
"7116 900957.BJ 20250530\n",
"4872 600206.SH 20250926\n",
"4873 600207.SH 20250926\n",
"4874 600208.SH 20250926\n",
"4876 600211.SH 20250926\n",
"7280 920037.BJ 20250926\n",
"<class 'pandas.core.frame.DataFrame'>\n",
"Index: 10564598 entries, 0 to 106964\n",
"Index: 11170571 entries, 0 to 36462\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: 241.8+ MB\n",
"memory usage: 255.7+ MB\n",
"None\n",
"20250530\n",
"20250603\n"
"20250926\n",
"20250929\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='20250720')\n",
"trade_cal = pro.trade_cal(exchange='', start_date='20170101', end_date='20251020')\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",
@@ -91,40 +91,16 @@
"name": "stdout",
"output_type": "stream",
"text": [
"任务 20250718 完成\n",
"任务 20250717 完成\n",
"任务 20250716 完成\n",
"任务 20250715 完成\n",
"任务 20250714 完成\n",
"任务 20250711 完成\n",
"任务 20250710 完成\n",
"任务 20250709 完成\n",
"任务 20250708 完成\n",
"任务 20250707 完成\n",
"任务 20250704 完成\n",
"任务 20250703 完成\n",
"任务 20250702 完成\n",
"任务 20250701 完成\n",
"任务 20250630 完成\n",
"任务 20250627 完成\n",
"任务 20250625 完成\n",
"任务 20250626 完成\n",
"任务 20250624 完成\n",
"任务 20250623 完成\n",
"任务 20250620 完成\n",
"任务 20250619 完成\n",
"任务 20250617 完成\n",
"任务 20250618 完成\n",
"任务 20250616 完成\n",
"任务 20250613 完成\n",
"任务 20250612 完成\n",
"任务 20250611 完成\n",
"任务 20250610 完成\n",
"任务 20250609 完成\n",
"任务 20250606 完成\n",
"任务 20250605 完成\n",
"任务 20250603 完成\n",
"任务 20250604 完成\n"
"任务 20251020 完成\n",
"任务 20251017 完成\n",
"任务 20251015 完成\n",
"任务 20251016 完成\n",
"任务 20251013 完成\n",
"任务 20251014 完成\n",
"任务 20251010 完成\n",
"任务 20251009 完成\n",
"任务 20250929 完成\n",
"任务 20250930 完成\n"
]
}
],
@@ -176,58 +152,58 @@
"output_type": "stream",
"text": [
"[ trade_date ts_code up_limit down_limit\n",
"0 20250606 000001.SZ 12.84 10.50\n",
"1 20250606 000002.SZ 7.30 5.98\n",
"2 20250606 000004.SZ 10.35 9.37\n",
"3 20250606 000006.SZ 7.48 6.12\n",
"4 20250606 000007.SZ 9.06 7.42\n",
"0 20251010 000001.SZ 12.54 10.26\n",
"1 20251010 000002.SZ 7.47 6.11\n",
"2 20251010 000004.SZ 12.26 11.10\n",
"3 20251010 000006.SZ 11.94 9.77\n",
"4 20251010 000007.SZ 8.12 6.64\n",
"... ... ... ... ...\n",
"7144 20250606 920445.BJ 13.36 7.20\n",
"7145 20250606 920489.BJ 31.46 16.94\n",
"7146 20250606 920682.BJ 13.14 7.08\n",
"7147 20250606 920799.BJ 77.80 41.90\n",
"7148 20250606 920819.BJ 5.70 3.08\n",
"7309 20251010 920978.BJ 50.08 26.98\n",
"7310 20251010 920981.BJ 48.04 25.88\n",
"7311 20251010 920982.BJ 354.64 190.96\n",
"7312 20251010 920985.BJ 11.86 6.40\n",
"7313 20251010 920992.BJ 27.87 15.01\n",
"\n",
"[7149 rows x 4 columns], trade_date ts_code up_limit down_limit\n",
"0 20250605 000001.SZ 13.02 10.66\n",
"1 20250605 000002.SZ 7.28 5.96\n",
"2 20250605 000004.SZ 10.63 9.61\n",
"3 20250605 000006.SZ 7.41 6.07\n",
"4 20250605 000007.SZ 9.19 7.52\n",
"[7314 rows x 4 columns], trade_date ts_code up_limit down_limit\n",
"0 20251009 000001.SZ 12.47 10.21\n",
"1 20251009 000002.SZ 7.58 6.20\n",
"2 20251009 000004.SZ 11.68 10.56\n",
"3 20251009 000006.SZ 11.32 9.26\n",
"4 20251009 000007.SZ 8.02 6.56\n",
"... ... ... ... ...\n",
"7143 20250605 920445.BJ 13.49 7.27\n",
"7144 20250605 920489.BJ 31.00 16.70\n",
"7145 20250605 920682.BJ 13.22 7.12\n",
"7146 20250605 920799.BJ 76.24 41.06\n",
"7147 20250605 920819.BJ 5.70 3.08\n",
"7306 20251009 920978.BJ 50.44 27.16\n",
"7307 20251009 920981.BJ 48.11 25.91\n",
"7308 20251009 920982.BJ 366.06 197.12\n",
"7309 20251009 920985.BJ 12.01 6.47\n",
"7310 20251009 920992.BJ 27.39 14.75\n",
"\n",
"[7148 rows x 4 columns], trade_date ts_code up_limit down_limit\n",
"0 20250603 000001.SZ 12.72 10.40\n",
"1 20250603 000002.SZ 7.30 5.98\n",
"2 20250603 000004.SZ 10.90 9.86\n",
"3 20250603 000006.SZ 7.62 6.24\n",
"4 20250603 000007.SZ 8.65 7.07\n",
"[7311 rows x 4 columns], trade_date ts_code up_limit down_limit\n",
"0 20250929 000001.SZ 12.54 10.26\n",
"1 20250929 000002.SZ 7.48 6.12\n",
"2 20250929 000004.SZ 11.00 9.96\n",
"3 20250929 000006.SZ 10.46 8.56\n",
"4 20250929 000007.SZ 7.63 6.25\n",
"... ... ... ... ...\n",
"7137 20250603 920445.BJ 13.18 7.10\n",
"7138 20250603 920489.BJ 31.25 16.83\n",
"7139 20250603 920682.BJ 13.20 7.12\n",
"7140 20250603 920799.BJ 76.31 41.09\n",
"7141 20250603 920819.BJ 5.72 3.08\n",
"7302 20250929 920445.BJ 14.37 7.75\n",
"7303 20250929 920489.BJ 29.34 15.80\n",
"7304 20250929 920682.BJ 13.10 7.06\n",
"7305 20250929 920799.BJ 70.78 38.12\n",
"7306 20250929 920819.BJ 5.52 2.98\n",
"\n",
"[7142 rows x 4 columns], trade_date ts_code up_limit down_limit\n",
"0 20250604 000001.SZ 12.99 10.63\n",
"1 20250604 000002.SZ 7.24 5.92\n",
"2 20250604 000004.SZ 10.77 9.75\n",
"3 20250604 000006.SZ 7.41 6.07\n",
"4 20250604 000007.SZ 8.88 7.26\n",
"[7307 rows x 4 columns], trade_date ts_code up_limit down_limit\n",
"0 20250930 000001.SZ 12.51 10.23\n",
"1 20250930 000002.SZ 7.49 6.13\n",
"2 20250930 000004.SZ 11.12 10.06\n",
"3 20250930 000006.SZ 10.29 8.42\n",
"4 20250930 000007.SZ 7.92 6.48\n",
"... ... ... ... ...\n",
"7140 20250604 920445.BJ 13.29 7.17\n",
"7141 20250604 920489.BJ 31.18 16.80\n",
"7142 20250604 920682.BJ 13.26 7.14\n",
"7143 20250604 920799.BJ 76.93 41.43\n",
"7144 20250604 920819.BJ 5.73 3.09\n",
"7305 20250930 920445.BJ 14.67 7.91\n",
"7306 20250930 920489.BJ 29.26 15.76\n",
"7307 20250930 920682.BJ 12.92 6.96\n",
"7308 20250930 920799.BJ 73.19 39.41\n",
"7309 20250930 920819.BJ 5.55 2.99\n",
"\n",
"[7145 rows x 4 columns]]\n"
"[7310 rows x 4 columns]]\n"
]
}
],

7
main/factor/operators.py
View File

@@ -1,7 +0,0 @@
from main.utils.utils import read_and_merge_h5_data, merge_with_industry_data
import sys
print(sys.path)

396
main/train/Classify2.ipynb
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1903
main/train/Classify2_load_model.ipynb Normal file
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