From a4515bb27ada7035a2e8abff8e6d2d4ef6b484a6 Mon Sep 17 00:00:00 2001 From: liaozhaorun Date: Thu, 10 Apr 2025 23:17:22 +0800 Subject: [PATCH] =?UTF-8?q?RollingRank=E5=8B=89=E5=BC=BA=E8=B5=9A=E9=92=B1?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- code/data/index_and_industry.ipynb | 56 +- code/data/update/cyq-perf.ipynb | 41 +- code/data/update/sw_daily.ipynb | 39 +- code/data/update/update_daily_basic.ipynb | 225 +- code/data/update/update_daily_data.ipynb | 1116 ++++----- code/data/update/update_money_flow.ipynb | 35 +- code/data/update/update_stk_limit.ipynb | 81 +- code/train/RollingRank.ipynb | 2598 ++++++++++----------- code/train/predictions_test.tsv | 1111 +++++---- 9 files changed, 2596 insertions(+), 2706 deletions(-) diff --git a/code/data/index_and_industry.ipynb b/code/data/index_and_industry.ipynb index ba6435f..743ffda 100644 --- a/code/data/index_and_industry.ipynb +++ b/code/data/index_and_industry.ipynb @@ -5,8 +5,8 @@ "id": "initial_id", "metadata": { "ExecuteTime": { - "end_time": "2025-04-08T13:37:01.282302Z", - "start_time": "2025-04-08T13:37:00.318950Z" + "end_time": "2025-04-09T14:57:27.092313Z", + "start_time": "2025-04-09T14:57:26.124592Z" } }, "source": [ @@ -27,8 +27,8 @@ "id": "f448da220816bf98", "metadata": { "ExecuteTime": { - "end_time": "2025-04-08T13:37:02.801052Z", - "start_time": "2025-04-08T13:37:01.292316Z" + "end_time": "2025-04-09T14:57:37.680808Z", + "start_time": "2025-04-09T14:57:27.392846Z" } }, "source": [ @@ -63,7 +63,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\liaozhaorun\\AppData\\Local\\Temp\\ipykernel_25240\\3209233630.py:13: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + "C:\\Users\\liaozhaorun\\AppData\\Local\\Temp\\ipykernel_15500\\3209233630.py:13: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", " final_df = pd.concat(all_data, ignore_index=True)\n" ] } @@ -75,8 +75,8 @@ "id": "907f732d3c397bf", "metadata": { "ExecuteTime": { - "end_time": "2025-04-08T13:37:03.042004Z", - "start_time": "2025-04-08T13:37:03.007507Z" + "end_time": "2025-04-09T14:57:37.730922Z", + "start_time": "2025-04-09T14:57:37.695917Z" } }, "source": [ @@ -92,32 +92,32 @@ "output_type": "stream", "text": [ " ts_code trade_date close open high low \\\n", - "0 000905.SH 20250408 5326.9140 5279.7566 5371.1834 5249.2318 \n", - "1 000905.SH 20250407 5287.0333 5523.9636 5587.8502 5212.6773 \n", - "2 000905.SH 20250403 5845.5045 5842.6167 5906.7057 5817.9662 \n", - "3 000905.SH 20250402 5899.0865 5884.8925 5936.6467 5884.1126 \n", - "4 000905.SH 20250401 5892.8502 5870.9424 5931.5038 5867.8480 \n", + "0 000905.SH 20250409 5439.7716 5249.6841 5465.1449 5135.9655 \n", + "1 000905.SH 20250408 5326.9140 5279.7566 5371.1834 5249.2318 \n", + "2 000905.SH 20250407 5287.0333 5523.9636 5587.8502 5212.6773 \n", + "3 000905.SH 20250403 5845.5045 5842.6167 5906.7057 5817.9662 \n", + "4 000905.SH 20250402 5899.0865 5884.8925 5936.6467 5884.1126 \n", "... ... ... ... ... ... ... \n", - "13441 399006.SZ 20100607 1069.4680 1005.0280 1075.2250 1001.7020 \n", - "13442 399006.SZ 20100604 1027.6810 989.6810 1027.6810 986.5040 \n", - "13443 399006.SZ 20100603 998.3940 1002.3550 1026.7020 997.7750 \n", - "13444 399006.SZ 20100602 997.1190 967.6090 997.1190 952.6110 \n", - "13445 399006.SZ 20100601 973.2330 986.0150 994.7930 948.1180 \n", + "13444 399006.SZ 20100607 1069.4680 1005.0280 1075.2250 1001.7020 \n", + "13445 399006.SZ 20100604 1027.6810 989.6810 1027.6810 986.5040 \n", + "13446 399006.SZ 20100603 998.3940 1002.3550 1026.7020 997.7750 \n", + "13447 399006.SZ 20100602 997.1190 967.6090 997.1190 952.6110 \n", + "13448 399006.SZ 20100601 973.2330 986.0150 994.7930 948.1180 \n", "\n", " pre_close change pct_chg vol amount \n", - "0 5287.0333 39.8807 0.7543 2.238407e+08 2.618753e+08 \n", - "1 5845.5045 -558.4712 -9.5539 2.365227e+08 2.673974e+08 \n", - "2 5899.0865 -53.5820 -0.9083 1.349386e+08 1.736621e+08 \n", - "3 5892.8502 6.2363 0.1058 1.121600e+08 1.406421e+08 \n", - "4 5857.7721 35.0781 0.5988 1.364486e+08 1.793280e+08 \n", + "0 5326.9140 112.8576 2.1186 2.451180e+08 2.882574e+08 \n", + "1 5287.0333 39.8807 0.7543 2.238407e+08 2.618753e+08 \n", + "2 5845.5045 -558.4712 -9.5539 2.365227e+08 2.673974e+08 \n", + "3 5899.0865 -53.5820 -0.9083 1.349386e+08 1.736621e+08 \n", + "4 5892.8502 6.2363 0.1058 1.121600e+08 1.406421e+08 \n", "... ... ... ... ... ... \n", - "13441 1027.6810 41.7870 4.0661 2.655275e+06 9.106095e+06 \n", - "13442 998.3940 29.2870 2.9334 1.500295e+06 5.269441e+06 \n", - "13443 997.1190 1.2750 0.1279 1.616805e+06 6.240835e+06 \n", - "13444 973.2330 23.8860 2.4543 1.074628e+06 4.001206e+06 \n", - "13445 1000.0000 -26.7670 -2.6767 1.356285e+06 4.924177e+06 \n", + "13444 1027.6810 41.7870 4.0661 2.655275e+06 9.106095e+06 \n", + "13445 998.3940 29.2870 2.9334 1.500295e+06 5.269441e+06 \n", + "13446 997.1190 1.2750 0.1279 1.616805e+06 6.240835e+06 \n", + "13447 973.2330 23.8860 2.4543 1.074628e+06 4.001206e+06 \n", + "13448 1000.0000 -26.7670 -2.6767 1.356285e+06 4.924177e+06 \n", "\n", - "[13446 rows x 11 columns]\n" + "[13449 rows x 11 columns]\n" ] } ], diff --git a/code/data/update/cyq-perf.ipynb b/code/data/update/cyq-perf.ipynb index b0abf17..62a7ad6 100644 --- a/code/data/update/cyq-perf.ipynb +++ b/code/data/update/cyq-perf.ipynb @@ -5,8 +5,8 @@ "id": "f74ce078-f7e8-4733-a14c-14d8815a3626", "metadata": { "ExecuteTime": { - "end_time": "2025-04-08T13:37:05.760051Z", - "start_time": "2025-04-08T13:37:04.957429Z" + "end_time": "2025-04-09T14:57:34.662465Z", + "start_time": "2025-04-09T14:57:33.903794Z" } }, "source": [ @@ -22,8 +22,8 @@ "id": "44dd8d87-e60b-49e5-aed9-efaa7f92d4fe", "metadata": { "ExecuteTime": { - "end_time": "2025-04-08T13:37:11.825892Z", - "start_time": "2025-04-08T13:37:05.764874Z" + "end_time": "2025-04-09T14:57:41.818953Z", + "start_time": "2025-04-09T14:57:34.666469Z" } }, "source": [ @@ -57,15 +57,15 @@ "3 000006.SZ 20250312\n", "4 000007.SZ 20250312\n", "... ... ...\n", - "5386 920108.BJ 20250407\n", - "5387 920111.BJ 20250407\n", - "5388 920116.BJ 20250407\n", - "5389 920118.BJ 20250407\n", - "5390 920128.BJ 20250407\n", + "5387 920108.BJ 20250408\n", + "5388 920111.BJ 20250408\n", + "5389 920116.BJ 20250408\n", + "5390 920118.BJ 20250408\n", + "5391 920128.BJ 20250408\n", "\n", - "[7557329 rows x 2 columns]\n", - "20250407\n", - "start_date: 20250408\n" + "[7562721 rows x 2 columns]\n", + "20250408\n", + "start_date: 20250409\n" ] } ], @@ -76,8 +76,8 @@ "id": "747acc47-0884-4f76-90fb-276f6494e31d", "metadata": { "ExecuteTime": { - "end_time": "2025-04-08T13:37:13.619472Z", - "start_time": "2025-04-08T13:37:12.062668Z" + "end_time": "2025-04-09T14:57:45.660215Z", + "start_time": "2025-04-09T14:57:42.232250Z" } }, "source": [ @@ -115,15 +115,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "任务 20250417 完成\n", "任务 20250418 完成\n", - "任务 20250415 完成\n", + "任务 20250417 完成\n", "任务 20250416 完成\n", - "任务 20250411 完成\n", "任务 20250414 完成\n", - "任务 20250409 完成\n", + "任务 20250415 完成\n", + "任务 20250411 完成\n", "任务 20250410 完成\n", - "任务 20250408 完成\n" + "任务 20250409 完成\n" ] } ], @@ -134,8 +133,8 @@ "id": "c6765638-481f-40d8-a259-2e7b25362618", "metadata": { "ExecuteTime": { - "end_time": "2025-04-08T13:37:15.761420Z", - "start_time": "2025-04-08T13:37:13.644113Z" + "end_time": "2025-04-09T14:57:48.970445Z", + "start_time": "2025-04-09T14:57:45.698824Z" } }, "source": [ diff --git a/code/data/update/sw_daily.ipynb b/code/data/update/sw_daily.ipynb index f87c614..c65e73b 100644 --- a/code/data/update/sw_daily.ipynb +++ b/code/data/update/sw_daily.ipynb @@ -5,8 +5,8 @@ "id": "f74ce078-f7e8-4733-a14c-14d8815a3626", "metadata": { "ExecuteTime": { - "end_time": "2025-04-08T13:37:06.782320Z", - "start_time": "2025-04-08T13:37:06.021497Z" + "end_time": "2025-04-09T14:57:35.618124Z", + "start_time": "2025-04-09T14:57:34.837095Z" } }, "source": [ @@ -22,8 +22,8 @@ "id": "44dd8d87-e60b-49e5-aed9-efaa7f92d4fe", "metadata": { "ExecuteTime": { - "end_time": "2025-04-08T13:37:08.283747Z", - "start_time": "2025-04-08T13:37:06.788327Z" + "end_time": "2025-04-09T14:57:38.089531Z", + "start_time": "2025-04-09T14:57:35.854308Z" } }, "source": [ @@ -57,15 +57,15 @@ "3 801005.SI 20250221\n", "4 801010.SI 20250221\n", ".. ... ...\n", - "434 859811.SI 20250407\n", - "435 859821.SI 20250407\n", - "436 859822.SI 20250407\n", - "437 859852.SI 20250407\n", - "438 859951.SI 20250407\n", + "434 859811.SI 20250408\n", + "435 859821.SI 20250408\n", + "436 859822.SI 20250408\n", + "437 859852.SI 20250408\n", + "438 859951.SI 20250408\n", "\n", - "[1057563 rows x 2 columns]\n", - "20250407\n", - "start_date: 20250408\n" + "[1058002 rows x 2 columns]\n", + "20250408\n", + "start_date: 20250409\n" ] } ], @@ -76,8 +76,8 @@ "id": "747acc47-0884-4f76-90fb-276f6494e31d", "metadata": { "ExecuteTime": { - "end_time": "2025-04-08T13:37:09.658356Z", - "start_time": "2025-04-08T13:37:08.473145Z" + "end_time": "2025-04-09T14:57:40.754159Z", + "start_time": "2025-04-09T14:57:38.104541Z" } }, "source": [ @@ -115,15 +115,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "任务 20250418 完成\n", "任务 20250417 完成\n", - "任务 20250416 完成\n", + "任务 20250418 完成\n", "任务 20250415 完成\n", + "任务 20250416 完成\n", "任务 20250414 完成\n", "任务 20250411 完成\n", "任务 20250410 完成\n", - "任务 20250409 完成\n", - "任务 20250408 完成\n" + "任务 20250409 完成\n" ] } ], @@ -134,8 +133,8 @@ "id": "c6765638-481f-40d8-a259-2e7b25362618", "metadata": { "ExecuteTime": { - "end_time": "2025-04-08T13:37:09.826834Z", - "start_time": "2025-04-08T13:37:09.671846Z" + "end_time": "2025-04-09T14:57:40.994975Z", + "start_time": "2025-04-09T14:57:40.773783Z" } }, "source": [ diff --git a/code/data/update/update_daily_basic.ipynb b/code/data/update/update_daily_basic.ipynb index a82c480..1fa5342 100644 --- a/code/data/update/update_daily_basic.ipynb +++ b/code/data/update/update_daily_basic.ipynb @@ -5,8 +5,8 @@ "id": "18d1d622-b083-4cc4-a6f8-7c1ed2d0edd2", "metadata": { "ExecuteTime": { - "end_time": "2025-04-08T13:37:08.050676Z", - "start_time": "2025-04-08T13:37:07.328483Z" + "end_time": "2025-04-09T14:57:36.913044Z", + "start_time": "2025-04-09T14:57:36.159612Z" } }, "source": [ @@ -22,8 +22,8 @@ "id": "14671a7f72de2564", "metadata": { "ExecuteTime": { - "end_time": "2025-04-08T13:37:10.251715Z", - "start_time": "2025-04-08T13:37:08.055681Z" + "end_time": "2025-04-09T14:57:39.128278Z", + "start_time": "2025-04-09T14:57:36.918051Z" } }, "source": [ @@ -82,8 +82,8 @@ "id": "e7f8cce2f80e2f20", "metadata": { "ExecuteTime": { - "end_time": "2025-04-08T13:37:37.727419Z", - "start_time": "2025-04-08T13:37:10.461897Z" + "end_time": "2025-04-09T14:58:09.296046Z", + "start_time": "2025-04-09T14:57:39.339423Z" } }, "source": [ @@ -111,17 +111,17 @@ "output_type": "stream", "text": [ "\n", - "Index: 8507519 entries, 0 to 5390\n", + "Index: 8512911 entries, 0 to 5391\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: 194.7+ MB\n", + "memory usage: 194.8+ MB\n", "None\n", - "20250407\n", - "20250408\n" + "20250408\n", + "20250409\n" ] } ], @@ -133,8 +133,8 @@ "metadata": { "scrolled": true, "ExecuteTime": { - "end_time": "2025-04-08T13:37:39.056144Z", - "start_time": "2025-04-08T13:37:37.770718Z" + "end_time": "2025-04-09T14:58:16.817010Z", + "start_time": "2025-04-09T14:58:09.326485Z" } }, "source": [ @@ -192,15 +192,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "任务 20250417 完成\n", "任务 20250418 完成\n", + "任务 20250417 完成\n", "任务 20250416 完成\n", "任务 20250415 完成\n", - "任务 20250411 完成\n", "任务 20250414 完成\n", + "任务 20250411 完成\n", "任务 20250410 完成\n", - "任务 20250409 完成\n", - "任务 20250408 完成\n" + "任务 20250409 完成\n" ] } ], @@ -211,8 +210,8 @@ "id": "919023c693d7a47a", "metadata": { "ExecuteTime": { - "end_time": "2025-04-08T13:37:39.072117Z", - "start_time": "2025-04-08T13:37:39.062189Z" + "end_time": "2025-04-09T14:58:16.864178Z", + "start_time": "2025-04-09T14:58:16.855084Z" } }, "source": [ @@ -225,58 +224,58 @@ "output_type": "stream", "text": [ " ts_code trade_date close turnover_rate turnover_rate_f \\\n", - "0 300504.SZ 20250408 12.65 2.5494 4.8465 \n", - "1 002223.SZ 20250408 34.24 0.9832 1.6194 \n", - "2 002036.SZ 20250408 9.13 7.4710 8.1827 \n", - "3 688207.SH 20250408 12.29 4.6144 4.6144 \n", - "4 002401.SZ 20250408 13.88 4.9037 9.6159 \n", + "0 300285.SZ 20250409 16.61 2.1086 2.2506 \n", + "1 300458.SZ 20250409 44.48 9.9286 11.7046 \n", + "2 605090.SH 20250409 23.81 0.6834 1.1888 \n", + "3 688686.SH 20250409 69.52 1.6005 5.7492 \n", + "4 002057.SZ 20250409 7.18 4.7461 7.1088 \n", "... ... ... ... ... ... \n", - "5387 600610.SH 20250408 7.56 18.8004 29.6937 \n", - "5388 002215.SZ 20250408 8.84 5.7658 6.7838 \n", - "5389 600694.SH 20250408 25.00 3.3101 5.4481 \n", - "5390 600121.SH 20250408 3.66 3.0305 6.3012 \n", - "5391 873167.BJ 20250408 21.56 7.8805 14.2434 \n", + "5390 301511.SZ 20250409 12.23 3.4040 4.6900 \n", + "5391 688355.SH 20250409 15.84 1.4154 4.4898 \n", + "5392 600019.SH 20250409 6.83 0.4729 1.2898 \n", + "5393 603507.SH 20250409 22.00 30.8936 42.4775 \n", + "5394 600886.SH 20250409 14.58 0.7795 2.4989 \n", "\n", - " volume_ratio pe pe_ttm pb ps ps_ttm dv_ratio \\\n", - "0 1.56 34.0479 220.6414 1.5349 1.3422 1.7126 1.5892 \n", - "1 1.07 14.3268 19.7636 2.8291 4.3058 4.6786 3.5030 \n", - "2 2.45 NaN NaN 3.6899 0.9822 0.9210 0.0000 \n", - "3 1.61 NaN NaN 1.5605 12.1348 26.4230 NaN \n", - "4 1.44 40.4258 40.4258 3.0931 2.8715 2.8715 1.2977 \n", - "... ... ... ... ... ... ... ... \n", - "5387 1.18 NaN NaN 122.1550 7.3648 7.3648 0.0000 \n", - "5388 2.49 37.7118 20.0533 2.2997 2.1570 1.7934 1.7092 \n", - "5389 3.51 15.4938 13.3524 0.9057 1.0676 1.1271 3.6364 \n", - "5390 1.13 15.7764 15.7764 2.3738 1.0605 1.0605 0.0000 \n", - "5391 0.79 33.5290 65.6770 3.2183 7.0572 9.9201 NaN \n", + " volume_ratio pe pe_ttm pb ps ps_ttm dv_ratio \\\n", + "0 1.11 29.0985 27.1266 2.5144 4.2913 4.1010 0.6020 \n", + "1 1.54 168.9309 168.9309 9.3966 12.3119 12.3119 0.3364 \n", + "2 1.00 11.8377 9.0427 1.7135 0.5819 0.6421 3.2226 \n", + "3 1.18 43.8690 61.1222 2.9105 9.0031 9.2377 NaN \n", + "4 1.35 19.8304 29.3370 1.7625 1.9656 2.0487 3.2191 \n", + "... ... ... ... ... ... ... ... \n", + "5390 1.36 58.1209 NaN 1.9116 1.1803 1.1129 0.3212 \n", + "5391 1.31 133.9017 29.7427 1.8103 3.6805 3.1067 NaN \n", + "5392 1.28 12.5281 15.7915 0.7518 0.4344 0.4503 4.4796 \n", + "5393 2.89 22.7537 22.7537 1.6401 1.0276 1.0276 1.3553 \n", + "5394 1.04 17.4059 16.1402 1.8424 2.0579 1.9930 3.1604 \n", "\n", - " dv_ttm total_share float_share free_share total_mv \\\n", - "0 1.5892 27102.4580 21826.2631 11481.0786 3.428461e+05 \n", - "1 3.5030 100247.6929 93867.3649 56990.4202 3.432481e+06 \n", - "2 NaN 105938.4915 105290.9483 96132.5171 9.672184e+05 \n", - "3 NaN 25897.3147 18867.6306 18867.6306 3.182780e+05 \n", - "4 1.2977 37166.8440 37136.3940 18937.9540 5.158758e+05 \n", - "... ... ... ... ... ... \n", - "5387 NaN 107127.4605 70872.6705 44872.6705 8.098836e+05 \n", - "5388 1.7092 100519.1310 79400.9515 67486.1454 8.885891e+05 \n", - "5389 3.6364 31305.2571 31305.2571 19020.4513 7.826314e+05 \n", - "5390 NaN 121841.2038 121841.2038 58597.2758 4.459388e+05 \n", - "5391 NaN 7086.1250 4178.1867 2311.6822 1.527769e+05 \n", + " dv_ttm total_share float_share free_share total_mv \\\n", + "0 0.6020 9.970483e+04 8.039498e+04 75323.2612 1.656097e+06 \n", + "1 0.3364 6.332851e+04 5.179696e+04 43937.3622 2.816852e+06 \n", + "2 3.2226 6.492580e+04 6.426965e+04 36946.4646 1.545883e+06 \n", + "3 NaN 1.222355e+04 1.222355e+04 3402.7889 8.497809e+05 \n", + "4 3.2191 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1.9331 2.4597 \n", + "283 002052.SZ 20250409 6.15 1.5326 2.5481 \n", "... ... ... ... ... ... \n", - "5261 603879.SH 20250408 4.13 4.3647 6.8212 \n", - "5273 002024.SZ 20250408 1.76 0.5005 1.3623 \n", - "5298 603828.SH 20250408 4.43 1.3711 2.7554 \n", - "5337 600234.SH 20250408 5.53 0.5518 1.0422 \n", - "5370 300536.SZ 20250408 7.99 2.2037 2.7214 \n", + "5286 002602.SZ 20250409 5.93 3.0376 3.5162 \n", + "5345 002501.SZ 20250409 1.89 4.3252 5.5834 \n", + "5364 600387.SH 20250409 2.34 0.0904 0.1163 \n", + "5366 002656.SZ 20250409 1.95 2.7047 3.0210 \n", + "5378 300013.SZ 20250409 3.57 2.8370 3.1107 \n", "\n", - " volume_ratio pe pe_ttm pb ps ps_ttm dv_ratio \\\n", - "20 0.69 NaN NaN 0.5590 0.2252 0.2252 0.0000 \n", - "21 0.35 NaN NaN 1.5767 1.3841 1.5604 0.0000 \n", - "88 2.09 NaN NaN NaN 0.4481 0.7781 0.0000 \n", - "124 1.71 30.0883 30.0883 1.7332 2.7432 2.7432 5.2053 \n", - "136 1.27 NaN NaN 1.7736 NaN NaN 0.0829 \n", - "... ... ... ... ... ... ... ... \n", - "5261 1.67 NaN NaN 5.6207 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--- a/code/data/update/update_daily_data.ipynb +++ b/code/data/update/update_daily_data.ipynb @@ -5,8 +5,8 @@ "id": "f294ba92-512a-48e6-bbaa-e19401c691ba", "metadata": { "ExecuteTime": { - "end_time": "2025-04-08T13:37:10.056146Z", - "start_time": "2025-04-08T13:37:09.245906Z" + "end_time": "2025-04-09T15:12:27.374193Z", + "start_time": "2025-04-09T15:12:26.421811Z" } }, "source": [ @@ -27,8 +27,8 @@ "id": "d31855fab4f8b1bc", "metadata": { "ExecuteTime": { - "end_time": "2025-04-08T13:37:34.909395Z", - "start_time": "2025-04-08T13:37:10.062153Z" + "end_time": "2025-04-09T15:12:34.394589Z", + "start_time": "2025-04-09T15:12:27.379199Z" } }, "source": [ @@ -53,17 +53,17 @@ "output_type": "stream", "text": [ "\n", - "Index: 8579473 entries, 0 to 5374\n", + "Index: 8584848 entries, 0 to 5374\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: 196.4+ MB\n", + "memory 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"任务 300487.SZ 完成\n", "任务 300488.SZ 完成\n", "任务 300489.SZ 完成\n", @@ -2085,16 +2085,16 @@ "任务 300500.SZ 完成\n", "任务 300501.SZ 完成\n", "任务 300502.SZ 完成\n", - "任务 300504.SZ 完成\n", "任务 300503.SZ 完成\n", + "任务 300504.SZ 完成\n", "任务 300505.SZ 完成\n", "任务 300506.SZ 完成\n", "任务 300507.SZ 完成\n", "任务 300508.SZ 完成\n", "任务 300509.SZ 完成\n", "任务 300510.SZ 完成\n", - "任务 300512.SZ 完成\n", "任务 300511.SZ 完成\n", + "任务 300512.SZ 完成\n", "任务 300513.SZ 完成\n", "任务 300514.SZ 完成\n", "任务 300515.SZ 完成\n", @@ -2103,12 +2103,12 @@ "任务 300518.SZ 完成\n", "任务 300519.SZ 完成\n", "任务 300520.SZ 完成\n", - "任务 300522.SZ 完成\n", "任务 300521.SZ 完成\n", "任务 300523.SZ 完成\n", + "任务 300522.SZ 完成\n", + "任务 300527.SZ 完成\n", "任务 300525.SZ 完成\n", "任务 300528.SZ 完成\n", - "任务 300527.SZ 完成\n", "任务 300529.SZ 完成\n", "任务 300530.SZ 完成\n", "任务 300531.SZ 完成\n", @@ -2167,10 +2167,10 @@ "任务 300586.SZ 完成\n", "任务 300587.SZ 完成\n", "任务 300588.SZ 完成\n", - "任务 300590.SZ 完成\n", "任务 300589.SZ 完成\n", - "任务 300592.SZ 完成\n", + "任务 300590.SZ 完成\n", "任务 300591.SZ 完成\n", + "任务 300592.SZ 完成\n", "任务 300593.SZ 完成\n", "任务 300594.SZ 完成\n", "任务 300595.SZ 完成\n", @@ -2211,24 +2211,24 @@ "任务 300630.SZ 完成\n", "任务 300631.SZ 完成\n", "任务 300632.SZ 完成\n", - "任务 300633.SZ 完成\n", "任务 300634.SZ 完成\n", + "任务 300633.SZ 完成\n", "任务 300635.SZ 完成\n", "任务 300636.SZ 完成\n", - "任务 300637.SZ 完成\n", "任务 300638.SZ 完成\n", + "任务 300637.SZ 完成\n", "任务 300639.SZ 完成\n", "任务 300640.SZ 完成\n", - "任务 300641.SZ 完成\n", "任务 300642.SZ 完成\n", + "任务 300641.SZ 完成\n", "任务 300643.SZ 完成\n", "任务 300644.SZ 完成\n", "任务 300645.SZ 完成\n", "任务 300647.SZ 完成\n", "任务 300648.SZ 完成\n", "任务 300649.SZ 完成\n", - "任务 300651.SZ 完成\n", "任务 300650.SZ 完成\n", + "任务 300651.SZ 完成\n", "任务 300652.SZ 完成\n", "任务 300653.SZ 完成\n", "任务 300654.SZ 完成\n", @@ -2245,10 +2245,10 @@ "任务 300665.SZ 完成\n", "任务 300666.SZ 完成\n", "任务 300667.SZ 完成\n", - "任务 300668.SZ 完成\n", "任务 300669.SZ 完成\n", - "任务 300670.SZ 完成\n", + "任务 300668.SZ 完成\n", "任务 300671.SZ 完成\n", + "任务 300670.SZ 完成\n", "任务 300672.SZ 完成\n", "任务 300673.SZ 完成\n", "任务 300674.SZ 完成\n", @@ 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完成\n", "任务 301066.SZ 完成\n", "任务 301067.SZ 完成\n", - "任务 301068.SZ 完成\n", "任务 301069.SZ 完成\n", - "任务 301070.SZ 完成\n", + "任务 301068.SZ 完成\n", "任务 301071.SZ 完成\n", - "任务 301072.SZ 完成\n", + "任务 301070.SZ 完成\n", "任务 301073.SZ 完成\n", + "任务 301072.SZ 完成\n", "任务 301075.SZ 完成\n", "任务 301076.SZ 完成\n", "任务 301077.SZ 完成\n", @@ -2641,8 +2641,8 @@ "任务 301085.SZ 完成\n", "任务 301086.SZ 完成\n", "任务 301087.SZ 完成\n", - "任务 301089.SZ 完成\n", "任务 301088.SZ 完成\n", + "任务 301089.SZ 完成\n", "任务 301090.SZ 完成\n", "任务 301091.SZ 完成\n", "任务 301092.SZ 完成\n", @@ -2651,8 +2651,8 @@ "任务 301096.SZ 完成\n", "任务 301097.SZ 完成\n", "任务 301098.SZ 完成\n", - "任务 301099.SZ 完成\n", "任务 301100.SZ 完成\n", + "任务 301099.SZ 完成\n", "任务 301101.SZ 完成\n", "任务 301102.SZ 完成\n", "任务 301103.SZ 完成\n", @@ -2665,8 +2665,8 @@ "任务 301111.SZ 完成\n", "任务 301112.SZ 完成\n", "任务 301113.SZ 完成\n", - "任务 301115.SZ 完成\n", "任务 301116.SZ 完成\n", + "任务 301115.SZ 完成\n", "任务 301117.SZ 完成\n", "任务 301118.SZ 完成\n", "任务 301119.SZ 完成\n", @@ -2695,12 +2695,12 @@ "任务 301151.SZ 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完成\n", + "任务 301197.SZ 完成\n", "任务 301198.SZ 完成\n", "任务 301199.SZ 完成\n", "任务 301200.SZ 完成\n", - "任务 301202.SZ 完成\n", "任务 301201.SZ 完成\n", + "任务 301202.SZ 完成\n", "任务 301203.SZ 完成\n", "任务 301205.SZ 完成\n", "任务 301206.SZ 完成\n", "任务 301207.SZ 完成\n", - "任务 301209.SZ 完成\n", "任务 301208.SZ 完成\n", - "任务 301211.SZ 完成\n", + "任务 301209.SZ 完成\n", "任务 301210.SZ 完成\n", + "任务 301211.SZ 完成\n", "任务 301212.SZ 完成\n", "任务 301213.SZ 完成\n", "任务 301215.SZ 完成\n", @@ -2778,6 +2777,7 @@ "任务 301252.SZ 完成\n", "任务 301255.SZ 完成\n", "任务 301256.SZ 完成\n", + "任务 301185.SZ 完成\n", "任务 301257.SZ 完成\n", "任务 301258.SZ 完成\n", "任务 301259.SZ 完成\n", @@ -2823,8 +2823,8 @@ "任务 301306.SZ 完成\n", "任务 301307.SZ 完成\n", "任务 301308.SZ 完成\n", - "任务 301309.SZ 完成\n", "任务 301310.SZ 完成\n", + "任务 301309.SZ 完成\n", "任务 301311.SZ 完成\n", "任务 301312.SZ 完成\n", "任务 301313.SZ 完成\n", @@ -2841,20 +2841,20 @@ "任务 301325.SZ 完成\n", "任务 301326.SZ 完成\n", "任务 301327.SZ 完成\n", - "任务 301328.SZ 完成\n", "任务 301329.SZ 完成\n", - "任务 301330.SZ 完成\n", + "任务 301328.SZ 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"任务 301499.SZ 完成\n", "任务 301500.SZ 完成\n", - "任务 301503.SZ 完成\n", "任务 301502.SZ 完成\n", + "任务 301503.SZ 完成\n", "任务 301505.SZ 完成\n", "任务 301507.SZ 完成\n", "任务 301508.SZ 完成\n", "任务 301509.SZ 完成\n", - "任务 301511.SZ 完成\n", "任务 301510.SZ 完成\n", - "任务 301515.SZ 完成\n", + "任务 301511.SZ 完成\n", "任务 301512.SZ 完成\n", + "任务 301515.SZ 完成\n", "任务 301516.SZ 完成\n", "任务 301517.SZ 完成\n", "任务 301518.SZ 完成\n", @@ -2937,8 +2937,8 @@ "任务 301526.SZ 完成\n", "任务 301528.SZ 完成\n", "任务 301529.SZ 完成\n", - "任务 301536.SZ 完成\n", "任务 301533.SZ 完成\n", + "任务 301536.SZ 完成\n", "任务 301538.SZ 完成\n", "任务 301539.SZ 完成\n", "任务 301548.SZ 完成\n", @@ -3029,12 +3029,12 @@ "任务 600035.SH 完成\n", "任务 600036.SH 完成\n", "任务 600037.SH 完成\n", - "任务 600038.SH 完成\n", "任务 600039.SH 完成\n", + "任务 600038.SH 完成\n", "任务 600048.SH 完成\n", "任务 600050.SH 完成\n", - "任务 600051.SH 完成\n", "任务 600052.SH 完成\n", + "任务 600051.SH 完成\n", "任务 600053.SH 完成\n", "任务 600054.SH 完成\n", "任务 600055.SH 完成\n", @@ -3072,70 +3072,70 @@ "任务 600098.SH 完成\n", "任务 600099.SH 完成\n", "任务 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"任务 600666.SH 完成\n", + "任务 600665.SH 完成\n", "任务 600667.SH 完成\n", "任务 600668.SH 完成\n", "任务 600671.SH 完成\n", - "任务 600674.SH 完成\n", "任务 600673.SH 完成\n", "任务 600675.SH 完成\n", + "任务 600674.SH 完成\n", "任务 600676.SH 完成\n", - "任务 600679.SH 完成\n", "任务 600678.SH 完成\n", + "任务 600679.SH 完成\n", "任务 600681.SH 完成\n", "任务 600682.SH 完成\n", "任务 600683.SH 完成\n", @@ -3529,17 +3529,17 @@ "任务 600691.SH 完成\n", "任务 600692.SH 完成\n", "任务 600693.SH 完成\n", - "任务 600696.SH 完成\n", "任务 600694.SH 完成\n", + "任务 600696.SH 完成\n", "任务 600697.SH 完成\n", "任务 600698.SH 完成\n", - "任务 600702.SH 完成\n", "任务 600699.SH 完成\n", + "任务 600702.SH 完成\n", "任务 600703.SH 完成\n", "任务 600704.SH 完成\n", - "任务 600706.SH 完成\n", "任务 600705.SH 完成\n", "任务 600707.SH 完成\n", + "任务 600706.SH 完成\n", "任务 600708.SH 完成\n", "任务 600710.SH 完成\n", "任务 600711.SH 完成\n", @@ -3654,8 +3654,8 @@ "任务 600835.SH 完成\n", "任务 600837.SH 完成\n", "任务 600838.SH 完成\n", - "任务 600839.SH 完成\n", "任务 600841.SH 完成\n", + "任务 600839.SH 完成\n", "任务 600843.SH 完成\n", "任务 600844.SH 完成\n", "任务 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1362.18 1382.62 1367.29 \n", - "1 000002.SZ 20250408 1199.25 1242.86 1199.25 1235.59 1199.25 \n", - "2 000004.SZ 20250408 34.50 36.58 33.93 35.19 35.93 \n", - "3 000006.SZ 20250408 238.44 244.00 224.13 226.92 249.17 \n", - "4 000007.SZ 20250408 45.06 46.22 42.91 44.49 46.22 \n", + "0 000001.SZ 20250409 1371.12 1383.90 1362.18 1380.07 1382.62 \n", + "1 000002.SZ 20250409 1217.42 1302.82 1202.88 1282.83 1235.59 \n", + "2 000004.SZ 20250409 34.91 36.29 31.70 36.09 35.19 \n", + "3 000006.SZ 20250409 222.54 238.04 204.66 235.26 226.92 \n", + "4 000007.SZ 20250409 44.32 45.89 40.34 45.56 44.49 \n", "... ... ... ... ... ... ... ... \n", - "5370 920111.BJ 20250408 23.05 23.98 22.94 23.73 22.60 \n", - "5371 920116.BJ 20250408 76.77 79.28 74.18 77.15 74.16 \n", - "5372 920118.BJ 20250408 23.78 24.40 23.30 24.33 23.20 \n", - "5373 920128.BJ 20250408 26.21 27.76 26.21 27.13 26.27 \n", - "5374 689009.SH 20250408 54.16 56.87 54.16 54.70 53.66 \n", + "5356 873706.BJ 20250409 16.57 21.81 16.57 21.81 16.79 \n", + "5357 873726.BJ 20250409 20.96 24.75 20.33 24.19 21.68 \n", + "5358 873806.BJ 20250409 12.64 14.34 12.35 14.25 12.78 \n", + "5359 873833.BJ 20250409 18.37 21.47 17.66 20.86 19.24 \n", + "5360 689009.SH 20250409 52.65 56.90 52.11 54.86 54.70 \n", "\n", " change pct_chg vol amount \n", - "0 15.33 1.12 1463987.71 1576590.040 \n", - "1 36.34 3.03 1425550.14 962248.248 \n", - "2 -0.74 -2.06 69768.00 60315.630 \n", - "3 -22.25 -8.93 759990.08 438643.481 \n", - "4 -1.73 -3.74 65714.65 35143.987 \n", + "0 -2.55 -0.18 1064394.05 1145238.958 \n", + "1 47.24 3.82 1853079.77 1284483.339 \n", + "2 0.90 2.56 73354.00 61248.520 \n", + "3 8.34 3.68 685967.13 386042.322 \n", + "4 1.07 2.41 61500.00 32136.135 \n", "... ... ... ... ... \n", - "5370 1.13 5.00 34595.60 81278.233 \n", - "5371 2.99 4.03 36521.19 279699.317 \n", - "5372 1.13 4.87 9287.01 22345.957 \n", - "5373 0.86 3.27 18725.30 50284.791 \n", - "5374 1.04 1.94 118442.74 650396.418 \n", + "5356 5.02 29.90 101369.40 178514.657 \n", + "5357 2.51 11.58 34407.94 75592.009 \n", + "5358 1.47 11.50 92646.26 124407.734 \n", + "5359 1.62 8.42 82933.72 145386.999 \n", + "5360 0.16 0.29 102228.24 556103.751 \n", "\n", - "[5375 rows x 11 columns]\n" + "[5361 rows x 11 columns]\n" ] } ], @@ -5875,8 +5875,8 @@ "id": "446aeba1a846b34", "metadata": { "ExecuteTime": { - "end_time": "2025-04-08T13:50:38.034544Z", - "start_time": "2025-04-08T13:50:37.971871Z" + "end_time": "2025-04-09T15:25:31.728371Z", + "start_time": "2025-04-09T15:25:31.660508Z" } }, "source": [ diff --git a/code/data/update/update_money_flow.ipynb b/code/data/update/update_money_flow.ipynb index 133c4fd..0058ae4 100644 --- a/code/data/update/update_money_flow.ipynb +++ b/code/data/update/update_money_flow.ipynb @@ -5,8 +5,8 @@ "id": "b94bb1f2-5332-485e-ae1b-eea01f938106", "metadata": { "ExecuteTime": { - "end_time": "2025-04-08T13:37:11.623192Z", - "start_time": "2025-04-08T13:37:10.611486Z" + "end_time": "2025-04-09T14:57:40.184418Z", + "start_time": "2025-04-09T14:57:39.137312Z" } }, "source": [ @@ -23,8 +23,8 @@ "id": "742c29d453b9bb38", "metadata": { "ExecuteTime": { - "end_time": "2025-04-08T13:37:32.754262Z", - "start_time": "2025-04-08T13:37:11.629198Z" + "end_time": "2025-04-09T14:58:10.515830Z", + "start_time": "2025-04-09T14:57:40.190466Z" } }, "source": [ @@ -52,17 +52,17 @@ "output_type": "stream", "text": [ "\n", - "Index: 8348584 entries, 0 to 5125\n", + "Index: 8353711 entries, 0 to 5126\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: 191.1+ MB\n", + "memory usage: 191.2+ MB\n", "None\n", - "20250407\n", - "start_date: 20250408\n" + "20250408\n", + "start_date: 20250409\n" ] } ], @@ -74,8 +74,8 @@ "metadata": { "scrolled": true, "ExecuteTime": { - "end_time": "2025-04-08T13:37:34.659267Z", - "start_time": "2025-04-08T13:37:33.094502Z" + "end_time": "2025-04-09T14:58:17.197319Z", + "start_time": "2025-04-09T14:58:10.724923Z" } }, "source": [ @@ -115,13 +115,12 @@ "text": [ "任务 20250417 完成\n", "任务 20250418 完成\n", - "任务 20250415 完成\n", "任务 20250416 完成\n", - "任务 20250414 完成\n", + "任务 20250415 完成\n", "任务 20250411 完成\n", + "任务 20250414 完成\n", "任务 20250410 完成\n", - "任务 20250409 完成\n", - "任务 20250408 完成\n" + "任务 20250409 完成\n" ] } ], @@ -132,8 +131,8 @@ "id": "9af80516849d4e80", "metadata": { "ExecuteTime": { - "end_time": "2025-04-08T13:37:34.678164Z", - "start_time": "2025-04-08T13:37:34.674804Z" + "end_time": "2025-04-09T14:58:17.214168Z", + "start_time": "2025-04-09T14:58:17.210734Z" } }, "source": [ @@ -147,8 +146,8 @@ "id": "a2b05187-437f-4053-bc43-bd80d4cf8b0e", "metadata": { "ExecuteTime": { - "end_time": "2025-04-08T13:37:37.285649Z", - "start_time": "2025-04-08T13:37:34.694595Z" + "end_time": "2025-04-09T14:58:19.633456Z", + "start_time": "2025-04-09T14:58:17.229837Z" } }, "source": [ diff --git a/code/data/update/update_stk_limit.ipynb b/code/data/update/update_stk_limit.ipynb index 4a7adba..bbc1476 100644 --- a/code/data/update/update_stk_limit.ipynb +++ b/code/data/update/update_stk_limit.ipynb @@ -5,8 +5,8 @@ "id": "500802dc-7a20-48b7-a470-a4bae3ec534b", "metadata": { "ExecuteTime": { - "end_time": "2025-04-08T13:37:12.814092Z", - "start_time": "2025-04-08T13:37:11.953133Z" + "end_time": "2025-04-09T14:57:41.532210Z", + "start_time": "2025-04-09T14:57:40.584930Z" } }, "source": [ @@ -23,8 +23,8 @@ "id": "5a84bc9da6d54868", "metadata": { "ExecuteTime": { - "end_time": "2025-04-08T13:37:35.724923Z", - "start_time": "2025-04-08T13:37:12.820096Z" + "end_time": "2025-04-09T14:58:04.911924Z", + "start_time": "2025-04-09T14:57:41.540345Z" } }, "source": [ @@ -81,8 +81,8 @@ "metadata": { "scrolled": true, "ExecuteTime": { - "end_time": "2025-04-08T13:37:36.896959Z", - "start_time": "2025-04-08T13:37:35.931558Z" + "end_time": "2025-04-09T14:58:09.342522Z", + "start_time": "2025-04-09T14:58:05.259974Z" } }, "source": [ @@ -121,14 +121,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "任务 20250418 完成\n", "任务 20250417 完成\n", + "任务 20250418 完成\n", "任务 20250416 完成\n", "任务 20250415 完成\n", "任务 20250414 完成\n", - "任务 20250411 完成\n", + "任务 20250410 完成\n", "任务 20250409 完成\n", - "任务 20250410 完成\n" + "任务 20250411 完成\n" ] } ], @@ -139,8 +139,8 @@ "id": "96a81aa5890ea3c3", "metadata": { "ExecuteTime": { - "end_time": "2025-04-08T13:37:37.699901Z", - "start_time": "2025-04-08T13:37:36.909744Z" + "end_time": "2025-04-09T14:58:09.353560Z", + "start_time": "2025-04-09T14:58:09.346528Z" } }, "source": [ @@ -153,21 +153,20 @@ "name": "stdout", "output_type": "stream", "text": [ - "[]\n" - ] - }, - { - "ename": "ValueError", - "evalue": "No objects to concatenate", - "output_type": "error", - "traceback": [ - "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", - "\u001B[1;31mValueError\u001B[0m Traceback (most recent call last)", - "Cell \u001B[1;32mIn[4], line 3\u001B[0m\n\u001B[0;32m 1\u001B[0m \u001B[38;5;28mprint\u001B[39m(all_daily_data)\n\u001B[0;32m 2\u001B[0m \u001B[38;5;66;03m# 将所有数据合并为一个 DataFrame\u001B[39;00m\n\u001B[1;32m----> 3\u001B[0m all_daily_data_df \u001B[38;5;241m=\u001B[39m pd\u001B[38;5;241m.\u001B[39mconcat(all_daily_data, ignore_index\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m)\n", - "File \u001B[1;32mE:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\pandas\\core\\reshape\\concat.py:382\u001B[0m, in \u001B[0;36mconcat\u001B[1;34m(objs, axis, join, ignore_index, keys, levels, names, verify_integrity, sort, copy)\u001B[0m\n\u001B[0;32m 379\u001B[0m \u001B[38;5;28;01melif\u001B[39;00m copy \u001B[38;5;129;01mand\u001B[39;00m using_copy_on_write():\n\u001B[0;32m 380\u001B[0m copy \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mFalse\u001B[39;00m\n\u001B[1;32m--> 382\u001B[0m op \u001B[38;5;241m=\u001B[39m _Concatenator(\n\u001B[0;32m 383\u001B[0m objs,\n\u001B[0;32m 384\u001B[0m axis\u001B[38;5;241m=\u001B[39maxis,\n\u001B[0;32m 385\u001B[0m ignore_index\u001B[38;5;241m=\u001B[39mignore_index,\n\u001B[0;32m 386\u001B[0m join\u001B[38;5;241m=\u001B[39mjoin,\n\u001B[0;32m 387\u001B[0m keys\u001B[38;5;241m=\u001B[39mkeys,\n\u001B[0;32m 388\u001B[0m levels\u001B[38;5;241m=\u001B[39mlevels,\n\u001B[0;32m 389\u001B[0m names\u001B[38;5;241m=\u001B[39mnames,\n\u001B[0;32m 390\u001B[0m verify_integrity\u001B[38;5;241m=\u001B[39mverify_integrity,\n\u001B[0;32m 391\u001B[0m copy\u001B[38;5;241m=\u001B[39mcopy,\n\u001B[0;32m 392\u001B[0m sort\u001B[38;5;241m=\u001B[39msort,\n\u001B[0;32m 393\u001B[0m )\n\u001B[0;32m 395\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m op\u001B[38;5;241m.\u001B[39mget_result()\n", - "File \u001B[1;32mE:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\pandas\\core\\reshape\\concat.py:445\u001B[0m, in \u001B[0;36m_Concatenator.__init__\u001B[1;34m(self, objs, axis, join, keys, levels, names, ignore_index, verify_integrity, copy, sort)\u001B[0m\n\u001B[0;32m 442\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mverify_integrity \u001B[38;5;241m=\u001B[39m verify_integrity\n\u001B[0;32m 443\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mcopy \u001B[38;5;241m=\u001B[39m copy\n\u001B[1;32m--> 445\u001B[0m objs, keys \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_clean_keys_and_objs(objs, keys)\n\u001B[0;32m 447\u001B[0m \u001B[38;5;66;03m# figure out what our result ndim is going to be\u001B[39;00m\n\u001B[0;32m 448\u001B[0m ndims \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_get_ndims(objs)\n", - "File \u001B[1;32mE:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\pandas\\core\\reshape\\concat.py:507\u001B[0m, in \u001B[0;36m_Concatenator._clean_keys_and_objs\u001B[1;34m(self, objs, keys)\u001B[0m\n\u001B[0;32m 504\u001B[0m objs_list \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mlist\u001B[39m(objs)\n\u001B[0;32m 506\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mlen\u001B[39m(objs_list) \u001B[38;5;241m==\u001B[39m \u001B[38;5;241m0\u001B[39m:\n\u001B[1;32m--> 507\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mNo objects to concatenate\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[0;32m 509\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m keys \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[0;32m 510\u001B[0m objs_list \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mlist\u001B[39m(com\u001B[38;5;241m.\u001B[39mnot_none(\u001B[38;5;241m*\u001B[39mobjs_list))\n", - "\u001B[1;31mValueError\u001B[0m: No objects to concatenate" + "[ trade_date ts_code up_limit down_limit\n", + "0 20250409 000001.SZ 11.90 9.74\n", + "1 20250409 000002.SZ 7.48 6.12\n", + "2 20250409 000004.SZ 9.53 7.79\n", + "3 20250409 000006.SZ 6.28 5.14\n", + "4 20250409 000007.SZ 5.91 4.83\n", + "... ... ... ... ...\n", + "7077 20250409 920108.BJ 26.55 14.31\n", + "7078 20250409 920111.BJ 30.84 16.62\n", + "7079 20250409 920116.BJ 100.29 54.01\n", + "7080 20250409 920118.BJ 31.62 17.04\n", + "7081 20250409 920128.BJ 35.26 19.00\n", + "\n", + "[7082 rows x 4 columns]]\n" ] } ], @@ -175,14 +174,21 @@ }, { "cell_type": "code", - "execution_count": 5, "id": "ad9733a1-2f42-43ee-a98c-0bf699304c21", "metadata": { "ExecuteTime": { - "end_time": "2025-04-08T13:37:37.748574900Z", - "start_time": "2025-04-06T15:34:48.693158Z" + "end_time": "2025-04-09T14:58:09.674078Z", + "start_time": "2025-04-09T14:58:09.366441Z" } }, + "source": [ + "\n", + "\n", + "# 将数据保存为 HDF5 文件（table 格式）\n", + "all_daily_data_df.to_hdf(h5_filename, key='stk_limit', mode='a', format='table', append=True, data_columns=True)\n", + "\n", + "print(\"所有每日基础数据获取并保存完毕！\")" + ], "outputs": [ { "name": "stdout", @@ -192,27 +198,20 @@ ] } ], - "source": [ - "\n", - "\n", - "# 将数据保存为 HDF5 文件（table 格式）\n", - "all_daily_data_df.to_hdf(h5_filename, key='stk_limit', mode='a', format='table', append=True, data_columns=True)\n", - "\n", - "print(\"所有每日基础数据获取并保存完毕！\")" - ] + "execution_count": 5 }, { "cell_type": "code", - "execution_count": null, "id": "7e777f1f-4d54-4a74-b916-691ede6af055", "metadata": { "ExecuteTime": { - "end_time": "2025-04-08T13:37:37.762102Z", - "start_time": "2025-04-06T15:34:48.977771Z" + "end_time": "2025-04-09T14:58:09.689422Z", + "start_time": "2025-04-09T14:58:09.686524Z" } }, + "source": [], "outputs": [], - "source": [] + "execution_count": null } ], "metadata": { diff --git a/code/train/RollingRank.ipynb b/code/train/RollingRank.ipynb index a466b18..a197c70 100644 --- a/code/train/RollingRank.ipynb +++ b/code/train/RollingRank.ipynb @@ -8,8 +8,8 @@ "source_hidden": true }, "ExecuteTime": { - "end_time": "2025-04-09T14:48:04.783264Z", - "start_time": "2025-04-09T14:48:04.779754Z" + "end_time": "2025-04-10T13:32:04.258788Z", + "start_time": "2025-04-10T13:32:03.855779Z" } }, "source": [ @@ -24,7 +24,7 @@ "pd.set_option('display.max_columns', None)\n" ], "outputs": [], - "execution_count": 25 + "execution_count": 1 }, { "cell_type": "code", @@ -32,8 +32,8 @@ "metadata": { "scrolled": true, "ExecuteTime": { - "end_time": "2025-04-09T14:48:50.995967Z", - "start_time": "2025-04-09T14:48:04.834658Z" + "end_time": "2025-04-10T13:32:50.139059Z", + "start_time": "2025-04-10T13:32:04.267804Z" } }, "source": [ @@ -82,7 +82,7 @@ "cyq perf\n", "left merge on ['ts_code', 'trade_date']\n", "\n", - "RangeIndex: 5113004 entries, 0 to 5113003\n", + "RangeIndex: 5123740 entries, 0 to 5123739\n", "Data columns (total 31 columns):\n", " # Column Dtype \n", "--- ------ ----- \n", @@ -118,20 +118,20 @@ " 29 weight_avg float64 \n", " 30 winner_rate float64 \n", "dtypes: bool(1), datetime64[ns](1), float64(28), object(1)\n", - "memory usage: 1.1+ GB\n", + "memory usage: 1.2+ GB\n", "None\n" ] } ], - "execution_count": 26 + "execution_count": 2 }, { "cell_type": "code", "id": "cac01788dac10678", "metadata": { "ExecuteTime": { - "end_time": "2025-04-09T14:48:56.021834Z", - "start_time": "2025-04-09T14:48:51.037076Z" + "end_time": "2025-04-10T13:32:55.189416Z", + "start_time": "2025-04-10T13:32:50.335162Z" } }, "source": [ @@ -191,15 +191,15 @@ ] } ], - "execution_count": 27 + "execution_count": 3 }, { "cell_type": "code", "id": "c4e9e1d31da6dba6", "metadata": { "ExecuteTime": { - "end_time": "2025-04-09T14:48:56.082954Z", - "start_time": "2025-04-09T14:48:56.026849Z" + "end_time": "2025-04-10T13:32:55.289023Z", + "start_time": "2025-04-10T13:32:55.203565Z" } }, "source": [ @@ -280,7 +280,7 @@ "index_data = index_data.dropna()\n" ], "outputs": [], - "execution_count": 28 + "execution_count": 4 }, { "cell_type": "code", @@ -290,8 +290,8 @@ "source_hidden": true }, "ExecuteTime": { - "end_time": "2025-04-09T14:48:56.140563Z", - "start_time": "2025-04-09T14:48:56.110420Z" + "end_time": "2025-04-10T13:32:55.359628Z", + "start_time": "2025-04-10T13:32:55.317246Z" } }, "source": [ @@ -487,7 +487,6 @@ " df['_rank_stddev'] = df.groupby('trade_date')['_stddev_close'].rank(pct=True)\n", " df['alpha_22_improved'] = -1 * df['delta_cov'] * df['_rank_stddev']\n", "\n", - "\n", " df['alpha_003'] = np.where(df['high'] != df['low'],\n", " (df['close'] - df['open']) / (df['high'] - df['low']),\n", " 0)\n", @@ -552,7 +551,8 @@ " df['mv_growth'] = df['volume_growth'] / df['log(circ_mv)']\n", "\n", " # AR 指标\n", - " df[\"ar\"] = grouped.apply(lambda x: (x[\"high\"].div(x[\"open\"]).rolling(3).sum()) / (x[\"open\"].div(x[\"low\"]).rolling(3).sum()) * 100)\n", + " df[\"ar\"] = grouped.apply(\n", + " lambda x: (x[\"high\"].div(x[\"open\"]).rolling(3).sum()) / (x[\"open\"].div(x[\"low\"]).rolling(3).sum()) * 100)\n", "\n", " # BR 指标\n", " df[\"pre_close\"] = grouped[\"close\"].shift(1)\n", @@ -654,7 +654,7 @@ " return df, new_columns\n" ], "outputs": [], - "execution_count": 29 + "execution_count": 5 }, { "cell_type": "code", @@ -662,8 +662,8 @@ "metadata": { "scrolled": true, "ExecuteTime": { - "end_time": "2025-04-09T14:49:01.535858Z", - "start_time": "2025-04-09T14:48:56.166990Z" + "end_time": "2025-04-10T13:33:00.552850Z", + "start_time": "2025-04-10T13:32:55.363645Z" } }, "source": [ @@ -714,15 +714,15 @@ "industry_df = read_industry_data('../../data/sw_daily.h5')\n" ], "outputs": [], - "execution_count": 30 + "execution_count": 6 }, { "cell_type": "code", "id": "dbe2fd8021b9417f", "metadata": { "ExecuteTime": { - "end_time": "2025-04-09T14:49:01.569403Z", - "start_time": "2025-04-09T14:49:01.565741Z" + "end_time": "2025-04-10T13:33:00.573680Z", + "start_time": "2025-04-10T13:33:00.569106Z" } }, "source": [ @@ -742,15 +742,15 @@ ] } ], - "execution_count": 31 + "execution_count": 7 }, { "cell_type": "code", "id": "85c3e3d0235ffffa", "metadata": { "ExecuteTime": { - "end_time": "2025-04-09T14:50:18.282461Z", - "start_time": "2025-04-09T14:49:01.600962Z" + "end_time": "2025-04-10T13:34:15.568065Z", + "start_time": "2025-04-10T13:33:00.592221Z" } }, "source": [ @@ -804,7 +804,7 @@ " 'atr_6', 'obv'],\n", " dtype='object')\n", "\n", - "Index: 3142516 entries, 0 to 3142515\n", + "Index: 3148690 entries, 0 to 3148689\n", "Columns: 119 entries, ts_code to mv_momentum\n", "dtypes: bool(12), datetime64[ns](1), float64(101), int32(2), int64(1), object(2)\n", "memory usage: 2.5+ GB\n", @@ -812,7 +812,7 @@ ] } ], - "execution_count": 32 + "execution_count": 8 }, { "cell_type": "code", @@ -822,8 +822,8 @@ "source_hidden": true }, "ExecuteTime": { - "end_time": "2025-04-09T14:50:18.967476Z", - "start_time": "2025-04-09T14:50:18.940918Z" + "end_time": "2025-04-10T13:34:15.976910Z", + "start_time": "2025-04-10T13:34:15.967986Z" } }, "source": [ @@ -864,15 +864,15 @@ " return df, ret_feature_columns\n" ], "outputs": [], - "execution_count": 33 + "execution_count": 9 }, { "cell_type": "code", "id": "40e6b68a91b30c79", "metadata": { "ExecuteTime": { - "end_time": "2025-04-09T14:50:19.189566Z", - "start_time": "2025-04-09T14:50:19.074786Z" + "end_time": "2025-04-10T14:45:38.780120Z", + "start_time": "2025-04-10T14:45:38.545898Z" } }, "source": [ @@ -885,22 +885,27 @@ "\n", "\n", "def remove_shifted_features(train_data, feature_columns, ks_threshold=0.05, wasserstein_threshold=0.1, size=0.8,\n", - " log=True):\n", + " log=True, val_data=None):\n", " dropped_features = []\n", "\n", - " all_dates = sorted(train_data['trade_date'].unique().tolist()) # 获取所有唯一的 trade_date\n", - " split_date = all_dates[int(len(all_dates) * size)] # 划分点为倒数第 validation_days 天\n", - " train_data_split = train_data[train_data['trade_date'] < split_date] # 训练集\n", - " val_data_split = train_data[train_data['trade_date'] >= split_date] # 验证集\n", + " if val_data is None:\n", + " all_dates = sorted(train_data['trade_date'].unique().tolist()) # 获取所有唯一的 trade_date\n", + " split_date = all_dates[int(len(all_dates) * size)] # 划分点为倒数第 validation_days 天\n", + " train_data_split = train_data[train_data['trade_date'] < split_date] # 训练集\n", + " val_data_split = train_data[train_data['trade_date'] >= split_date] # 验证集\n", + " else:\n", + " train_data_split = train_data\n", + " val_data_split = val_data\n", "\n", " # **统计数据漂移**\n", " numeric_columns = train_data_split.select_dtypes(include=['float64', 'int64']).columns\n", " numeric_columns = [col for col in numeric_columns if col in feature_columns]\n", " for feature in numeric_columns:\n", " ks_stat, p_value = ks_2samp(train_data_split[feature], val_data_split[feature])\n", - " wasserstein_dist = wasserstein_distance(train_data_split[feature], val_data_split[feature])\n", + " # wasserstein_dist = wasserstein_distance(train_data_split[feature], val_data_split[feature])\n", "\n", - " if p_value < ks_threshold or wasserstein_dist > wasserstein_threshold:\n", + " # if p_value < ks_threshold or wasserstein_dist > wasserstein_threshold:\n", + " if p_value < ks_threshold:\n", " dropped_features.append(feature)\n", " if log:\n", " print(f\"检测到 {len(dropped_features)} 个可能漂移的特征: {dropped_features}\")\n", @@ -1093,6 +1098,7 @@ "\n", " return df\n", "\n", + "\n", "def time_series_quantile_filter(df, features, lower_quantile=0.01, upper_quantile=0.99, window=60):\n", " df = df.copy()\n", " # 确保按股票和时间排序\n", @@ -1109,6 +1115,7 @@ " df[col] = np.clip(df[col], rolling_lower, rolling_upper)\n", " return df\n", "\n", + "\n", "def cross_sectional_quantile_filter(df, features, lower_quantile=0.01, upper_quantile=0.99):\n", " df = df.copy()\n", " grouped = df.groupby('trade_date')\n", @@ -1121,22 +1128,22 @@ " return df" ], "outputs": [], - "execution_count": 34 + "execution_count": 120 }, { "cell_type": "code", "id": "1c46817a-b5dd-4bec-8bb4-e6e80bfd9d66", "metadata": { "ExecuteTime": { - "end_time": "2025-04-09T14:50:19.205714Z", - "start_time": "2025-04-09T14:50:19.197581Z" + "end_time": "2025-04-10T14:38:09.383969Z", + "start_time": "2025-04-10T14:38:09.381447Z" } }, "source": [ "# print(test_data.head()[['act_factor1', 'act_factor2', 'ts_code', 'trade_date']])" ], "outputs": [], - "execution_count": 35 + "execution_count": 105 }, { "cell_type": "code", @@ -1146,8 +1153,8 @@ "source_hidden": true }, "ExecuteTime": { - "end_time": "2025-04-09T14:50:19.291833Z", - "start_time": "2025-04-09T14:50:19.280710Z" + "end_time": "2025-04-10T14:38:09.436214Z", + "start_time": "2025-04-10T14:38:09.426468Z" } }, "source": [ @@ -1246,15 +1253,15 @@ " return model, scaler, None" ], "outputs": [], - "execution_count": 36 + "execution_count": 106 }, { "cell_type": "code", "id": "ff19e3f1e051a489", "metadata": { "ExecuteTime": { - "end_time": "2025-04-09T14:51:32.646948Z", - "start_time": "2025-04-09T14:50:19.359656Z" + "end_time": "2025-04-10T14:50:14.928322Z", + "start_time": "2025-04-10T14:48:29.721868Z" } }, "source": [ @@ -1264,11 +1271,12 @@ "# df['future_return'] = df.groupby('ts_code', group_keys=False)['close'].apply(lambda x: x.shift(-days) / x - 1)\n", "df['future_return'] = (df.groupby('ts_code')['close'].shift(-days) - df.groupby('ts_code')['open'].shift(-1)) / \\\n", " df.groupby('ts_code')['open'].shift(-1)\n", + "# df['future_return'] = df.groupby('ts_code')['pct_chg'].shift(-1)\n", "df['future_volatility'] = (\n", " df.groupby('ts_code')['pct_chg']\n", " .transform(lambda x: x.rolling(days).std().shift(-days))\n", ")\n", - "df['future_score'] = calculate_score(df, days=2, lambda_param=0.3)\n", + "df['future_score'] = calculate_score(df, days=days, lambda_param=0.3)\n", "df['label'] = df.groupby('trade_date', group_keys=False)['future_score'].transform(\n", " lambda x: pd.qcut(x, q=20, labels=False, duplicates='drop')\n", ")\n", @@ -1281,29 +1289,29 @@ "# )" ], "outputs": [], - "execution_count": 37 + "execution_count": 128 }, { "cell_type": "code", "id": "27dba27b2e108316", "metadata": { "ExecuteTime": { - "end_time": "2025-04-09T14:51:45.505400Z", - "start_time": "2025-04-09T14:51:32.722125Z" + "end_time": "2025-04-10T14:50:24.097167Z", + "start_time": "2025-04-10T14:50:15.128276Z" } }, "source": [ "def select_pre_zt_stocks_dynamic(stock_df):\n", " def select_stocks(group):\n", - " max_stocks = 150\n", - " initial_data = group.nlargest(100, 'return_20')\n", + " max_stocks = 300\n", + " initial_data = group.nlargest(300, 'return_5')\n", " unique_labels = initial_data['label'].nunique()\n", "\n", " if unique_labels >= 20 or unique_labels == 0: # 包含标签种类为0的情况\n", " return initial_data\n", "\n", " for i in range(110, max_stocks + 1, 10):\n", - " data = group.nlargest(i, 'return_20')\n", + " data = group.nlargest(i, 'return_5')\n", " unique_labels = data['label'].nunique()\n", " if unique_labels >= 20:\n", " return data\n", @@ -1326,19 +1334,19 @@ "name": "stdout", "output_type": "stream", "text": [ - "2025-04-07 00:00:00\n" + "2025-04-09 00:00:00\n" ] } ], - "execution_count": 38 + "execution_count": 129 }, { "cell_type": "code", "id": "ca96fb81e17c4a90", "metadata": { "ExecuteTime": { - "end_time": "2025-04-09T14:51:46.074673Z", - "start_time": "2025-04-09T14:51:45.512822Z" + "end_time": "2025-04-10T14:50:25.416842Z", + "start_time": "2025-04-10T14:50:24.111269Z" } }, "source": [ @@ -1377,15 +1385,15 @@ ] } ], - "execution_count": 39 + "execution_count": 130 }, { "cell_type": "code", "id": "81d4570663ae21d7", "metadata": { "ExecuteTime": { - "end_time": "2025-04-09T14:52:38.849524Z", - "start_time": "2025-04-09T14:51:46.113849Z" + "end_time": "2025-04-10T14:52:09.850326Z", + "start_time": "2025-04-10T14:50:25.514971Z" } }, "source": [ @@ -1395,33 +1403,19 @@ "pdf = cross_sectional_standardization(pdf, numeric_columns)\n", "\n", "pdf = pdf.sort_values(by=['ts_code', 'trade_date'])\n", - "\n", - "filter_index = pdf['future_return'].between(pdf['future_return'].quantile(0.01), pdf['future_return'].quantile(0.99))\n", - "\n", - "feature_columns = remove_highly_correlated_features(pdf, feature_columns)\n", - "\n", - "# print(pdf[pdf['trade_date'] == '2025-03-26'][['ts_code', 'trade_date', 'pct_chg']])\n", - "#\n", - "# pdf = select_pre_zt_stocks_dynamic(df[(df['trade_date'] >= '2022-03-26') & (df['trade_date'] <= '2029-03-26')])\n", - "# pdf = pdf.sort_values(['ts_code', 'trade_date'])\n", - "# pdf = pdf.replace([np.inf, -np.inf], np.nan)\n", - "#\n", - "# # pdf = time_series_quantile_filter(pdf, numeric_columns)\n", - "#\n", - "# pdf = cross_sectional_standardization(pdf, numeric_columns)\n", - "#\n", - "# print(pdf[pdf['trade_date'] == '2025-03-26'][['ts_code', 'trade_date', 'pct_chg']])" + "filter_index = pdf['future_return'].between(pdf['future_return'].quantile(0.01),\n", + " pdf['future_return'].quantile(0.99))" ], "outputs": [], - "execution_count": 40 + "execution_count": 131 }, { "cell_type": "code", "id": "92428d543f4727ad", "metadata": { "ExecuteTime": { - "end_time": "2025-04-09T14:52:38.972693Z", - "start_time": "2025-04-09T14:52:38.887044Z" + "end_time": "2025-04-10T14:52:10.203284Z", + "start_time": "2025-04-10T14:52:09.941361Z" } }, "source": [ @@ -1464,20 +1458,20 @@ "0" ] }, - "execution_count": 41, + "execution_count": 132, "metadata": {}, "output_type": "execute_result" } ], - "execution_count": 41 + "execution_count": 132 }, { "cell_type": "code", "id": "8f134d435f71e9e2", "metadata": { "ExecuteTime": { - "end_time": "2025-04-09T14:52:39.087454Z", - "start_time": "2025-04-09T14:52:39.016514Z" + "end_time": "2025-04-10T15:08:28.458002Z", + "start_time": "2025-04-10T15:08:28.217183Z" } }, "source": [ @@ -1499,7 +1493,8 @@ "\n", " for start in range(start_index, n - train_days - test_days + 1, test_days):\n", "\n", - " train_dates = unique_dates[start: start + train_days]\n", + " # train_dates = unique_dates[start: start + train_days]\n", + " train_dates = unique_dates[max(0, start - days + 1): start + train_days - days + 1]\n", " test_dates = unique_dates[start + train_days: start + train_days + test_days]\n", "\n", " # 根据日期筛选数据\n", @@ -1510,7 +1505,9 @@ " train_data = train_data.sort_values('trade_date')\n", " test_data = test_data.sort_values('trade_date')\n", "\n", - " feature_columns, _ = remove_shifted_features(train_data, feature_columns_origin, size=0.8, log=False)\n", + " feature_columns, _ = remove_shifted_features(train_data, feature_columns_origin, size=0.8, log=False,\n", + " val_data=df[filter_index & (df['trade_date'] ==\n", + " unique_dates[start + train_days - days + 1])])\n", "\n", " train_data = train_data.dropna(subset=feature_columns)\n", " train_data = train_data.dropna(subset=['label'])\n", @@ -1523,7 +1520,8 @@ "\n", " # print(len(train_data))\n", " # print(f\"最小日期: {train_data['trade_date'].min().strftime('%Y-%m-%d')}\")\n", - " print(f\"train_data最大日期: {train_data['trade_date'].max().strftime('%Y-%m-%d')}\")\n", + " print(\n", + " f\"train_data最大日期: {train_data['trade_date'].max().strftime('%Y-%m-%d')}, 训练天数{train_data['trade_date'].nunique()}\")\n", " # # print(len(test_data))\n", " # print(f\"最小日期: {test_data['trade_date'].min().strftime('%Y-%m-%d')}\")\n", " print(f\"test_data最大日期: {test_data['trade_date'].max().strftime('%Y-%m-%d')}\")\n", @@ -1544,14 +1542,17 @@ " # print(f'feature_columns: {feature_columns}')\n", " # feature_contri = [2 if feat.startswith('act_factor') else 1 for feat in feature_columns]\n", " # params['feature_contri'] = feature_contri\n", - " model, _, _ = train_light_model(train_data.dropna(subset=['label']),\n", - " params, feature_columns,\n", - " [lgb.log_evaluation(period=100),\n", - " lgb.callback.record_evaluation(evals),\n", - " # lgb.early_stopping(100, first_metric_only=True)\n", - " ], evals,\n", - " num_boost_round=100, validation_days=validation_days,\n", - " print_feature_importance=False, use_pca=False)\n", + " try:\n", + " model, _, _ = train_light_model(train_data.dropna(subset=['label']),\n", + " params, feature_columns,\n", + " [lgb.log_evaluation(period=100),\n", + " lgb.callback.record_evaluation(evals),\n", + " # lgb.early_stopping(100, first_metric_only=True)\n", + " ], evals,\n", + " num_boost_round=24, validation_days=validation_days,\n", + " print_feature_importance=False, use_pca=False)\n", + " except Exception as e:\n", + " print(train_data['label'].unique().tolist())\n", "\n", " score_df = test_data.copy()\n", " score_df['score'] = model.predict(score_df[feature_columns])\n", @@ -1561,85 +1562,10 @@ "\n", " final_predictions = pd.concat(predictions_list, ignore_index=True)\n", " return final_predictions\n", - "\n", - "\n", - "from concurrent.futures import ThreadPoolExecutor\n", - "\n", - "\n", - "def worker(train_data, test_data, train_days, test_days, feature_columns_origin, unique_dates, start, filter_index,\n", - " validation_days, params=None):\n", - " train_data = train_data.sort_values('trade_date')\n", - " test_data = test_data.sort_values('trade_date')\n", - "\n", - " # feature_columns, _ = remove_shifted_features(train_data, feature_columns_origin, size=0.8, log=False)\n", - "\n", - " train_data = train_data.dropna(subset=feature_columns + ['label'])\n", - " train_data = train_data.reset_index(drop=True)\n", - "\n", - " test_data = test_data.dropna(subset=feature_columns)\n", - " test_data = test_data.reset_index(drop=True)\n", - "\n", - " cat_columns = [col for col in df.columns if col.startswith('cat')]\n", - " for col in cat_columns:\n", - " if col in train_data.columns:\n", - " train_data[col] = train_data[col].astype('category')\n", - " if col in test_data.columns:\n", - " test_data[col] = test_data[col].astype('category')\n", - "\n", - " label_gain = list(range(len(train_data['label'].unique())))\n", - " label_gain = [(gain + 1) * (gain + 1) for gain in label_gain]\n", - " current_light_params = params.copy()\n", - " current_light_params['label_gain'] = label_gain\n", - "\n", - " model, _, _ = train_light_model(train_data.dropna(subset=['label']),\n", - " current_light_params, feature_columns,\n", - " [lgb.log_evaluation(period=100),\n", - " lgb.callback.record_evaluation(evals),\n", - " # lgb.early_stopping(100, first_metric_only=True)\n", - " ], evals,\n", - " num_boost_round=100, validation_days=validation_days,\n", - " print_feature_importance=False, use_pca=False)\n", - "\n", - " score_df = test_data.copy()\n", - " score_df['score'] = model.predict(score_df[feature_columns])\n", - " if not score_df.empty:\n", - " score_df = score_df.loc[score_df.groupby('trade_date')['score'].idxmax()]\n", - " score_df = score_df[['trade_date', 'score', 'ts_code']]\n", - " return score_df\n", - " return None\n", - "\n", - "\n", - "def rolling_train_predict_mt(df, train_days, test_days, feature_columns_origin, days=5, use_pca=False,\n", - " validation_days=60, filter_index=None, num_threads=4):\n", - " unique_dates = df['trade_date'].unique().tolist()\n", - " unique_dates = sorted(unique_dates)\n", - " n = len(unique_dates)\n", - " extra_days = (n - train_days) % test_days\n", - " start_index = extra_days\n", - "\n", - " predictions_list = []\n", - " with ThreadPoolExecutor(max_workers=num_threads) as executor:\n", - " futures = []\n", - " for start in range(start_index, n - train_days - test_days + 1, test_days):\n", - " train_dates = unique_dates[start: start + train_days]\n", - " test_dates = unique_dates[start + train_days: start + train_days + test_days]\n", - "\n", - " train_data = df[filter_index & df['trade_date'].isin(train_dates)].copy()\n", - " test_data = df[df['trade_date'].isin(test_dates)].copy()\n", - " future = executor.submit(worker, train_data, test_data, train_days, test_days, feature_columns_origin,\n", - " unique_dates, start, filter_index, validation_days)\n", - " futures.append(future)\n", - "\n", - " for future in futures:\n", - " result = future.result()\n", - " if result is not None:\n", - " predictions_list.append(result)\n", - "\n", - " final_predictions = pd.concat(predictions_list, ignore_index=True)\n", - " return final_predictions\n" + "\n" ], "outputs": [], - "execution_count": 42 + "execution_count": 147 }, { "cell_type": "code", @@ -1652,8 +1578,8 @@ }, "tags": [], "ExecuteTime": { - "end_time": "2025-04-09T14:53:52.944545Z", - "start_time": "2025-04-09T14:52:39.125461Z" + "end_time": "2025-04-10T15:15:47.062878Z", + "start_time": "2025-04-10T15:11:48.632695Z" } }, "source": [ @@ -1664,7 +1590,7 @@ "# qdf = qdf[qdf['trade_date'] >= '2022-01-01']\n", "\n", "final_predictions = rolling_train_predict(\n", - " pdf[(pdf['trade_date'] >= '2022-12-01') & (pdf['trade_date'] <= '2029-03-26')], 5, 1, feature_columns,\n", + " pdf[(pdf['trade_date'] >= '2022-12-01') & (pdf['trade_date'] <= '2029-03-26')], 3, 1, feature_columns,\n", " days=days, validation_days=0, filter_index=filter_index, params=light_params)\n", "final_predictions.to_csv('predictions_test.tsv', index=False)\n" ], @@ -1674,1704 +1600,1665 @@ "output_type": "stream", "text": [ "finish\n", - "train_data最大日期: 2022-12-07\n", - "test_data最大日期: 2022-12-08\n", - "划分后的训练集大小: 487, 验证集大小: 90\n", - "train_data最大日期: 2022-12-08\n", - "test_data最大日期: 2022-12-09\n", - "划分后的训练集大小: 497, 验证集大小: 104\n", - "train_data最大日期: 2022-12-09\n", - "test_data最大日期: 2022-12-12\n", - "划分后的训练集大小: 481, 验证集大小: 94\n", - "train_data最大日期: 2022-12-12\n", - "test_data最大日期: 2022-12-13\n", - "划分后的训练集大小: 492, 验证集大小: 102\n", - "train_data最大日期: 2022-12-13\n", - "test_data最大日期: 2022-12-14\n", - "划分后的训练集大小: 531, 验证集大小: 141\n", - "train_data最大日期: 2022-12-14\n", - "test_data最大日期: 2022-12-15\n", - "划分后的训练集大小: 536, 验证集大小: 95\n", - "train_data最大日期: 2022-12-15\n", - "test_data最大日期: 2022-12-16\n", - "划分后的训练集大小: 568, 验证集大小: 136\n", - "train_data最大日期: 2022-12-16\n", - "test_data最大日期: 2022-12-19\n", - "划分后的训练集大小: 566, 验证集大小: 92\n", - "train_data最大日期: 2022-12-19\n", - "test_data最大日期: 2022-12-20\n", - "划分后的训练集大小: 600, 验证集大小: 136\n", - "train_data最大日期: 2022-12-20\n", - "test_data最大日期: 2022-12-21\n", - "划分后的训练集大小: 595, 验证集大小: 136\n", - "train_data最大日期: 2022-12-21\n", - "test_data最大日期: 2022-12-22\n", - "划分后的训练集大小: 593, 验证集大小: 93\n", - "train_data最大日期: 2022-12-22\n", - "test_data最大日期: 2022-12-23\n", - "划分后的训练集大小: 577, 验证集大小: 120\n", - "train_data最大日期: 2022-12-23\n", - "test_data最大日期: 2022-12-26\n", - "划分后的训练集大小: 578, 验证集大小: 93\n", - "train_data最大日期: 2022-12-26\n", - "test_data最大日期: 2022-12-27\n", - "划分后的训练集大小: 537, 验证集大小: 95\n", - "train_data最大日期: 2022-12-27\n", - "test_data最大日期: 2022-12-28\n", - "划分后的训练集大小: 492, 验证集大小: 91\n", - "train_data最大日期: 2022-12-28\n", + "train_data最大日期: 2022-12-27, 训练天数19\n", "test_data最大日期: 2022-12-29\n", - "划分后的训练集大小: 491, 验证集大小: 92\n", - "train_data最大日期: 2022-12-29\n", + "划分后的训练集大小: 5351, 验证集大小: 282\n", + "train_data最大日期: 2022-12-28, 训练天数20\n", "test_data最大日期: 2022-12-30\n", - "划分后的训练集大小: 509, 验证集大小: 138\n", - "train_data最大日期: 2022-12-30\n", + "划分后的训练集大小: 5631, 验证集大小: 280\n", + "train_data最大日期: 2022-12-29, 训练天数20\n", "test_data最大日期: 2023-01-03\n", - "划分后的训练集大小: 548, 验证集大小: 132\n", - "train_data最大日期: 2023-01-03\n", + "划分后的训练集大小: 5641, 验证集大小: 282\n", + "train_data最大日期: 2022-12-30, 训练天数20\n", "test_data最大日期: 2023-01-04\n", - "划分后的训练集大小: 596, 验证集大小: 143\n", - "train_data最大日期: 2023-01-04\n", + "划分后的训练集大小: 5650, 验证集大小: 287\n", + "train_data最大日期: 2023-01-03, 训练天数20\n", "test_data最大日期: 2023-01-05\n", - "划分后的训练集大小: 641, 验证集大小: 136\n", - "train_data最大日期: 2023-01-05\n", + "划分后的训练集大小: 5663, 验证集大小: 284\n", + "train_data最大日期: 2023-01-04, 训练天数20\n", "test_data最大日期: 2023-01-06\n", - "划分后的训练集大小: 692, 验证集大小: 143\n", - "train_data最大日期: 2023-01-06\n", + "划分后的训练集大小: 5663, 验证集大小: 280\n", + "train_data最大日期: 2023-01-05, 训练天数20\n", "test_data最大日期: 2023-01-09\n", - "划分后的训练集大小: 650, 验证集大小: 96\n", - "train_data最大日期: 2023-01-09\n", + "划分后的训练集大小: 5669, 验证集大小: 282\n", + "train_data最大日期: 2023-01-06, 训练天数20\n", "test_data最大日期: 2023-01-10\n", - "划分后的训练集大小: 613, 验证集大小: 95\n", - "train_data最大日期: 2023-01-10\n", + "划分后的训练集大小: 5671, 验证集大小: 284\n", + "train_data最大日期: 2023-01-09, 训练天数20\n", "test_data最大日期: 2023-01-11\n", - "划分后的训练集大小: 574, 验证集大小: 104\n", - "train_data最大日期: 2023-01-11\n", 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验证集大小: 290\n", + "train_data最大日期: 2025-02-24, 训练天数20\n", "test_data最大日期: 2025-02-26\n", - "划分后的训练集大小: 663, 验证集大小: 143\n", - "train_data最大日期: 2025-02-26\n", + "划分后的训练集大小: 5746, 验证集大小: 284\n", + "train_data最大日期: 2025-02-25, 训练天数20\n", "test_data最大日期: 2025-02-27\n", - "划分后的训练集大小: 701, 验证集大小: 132\n", - "train_data最大日期: 2025-02-27\n", + "划分后的训练集大小: 5752, 验证集大小: 293\n", + "train_data最大日期: 2025-02-26, 训练天数20\n", "test_data最大日期: 2025-02-28\n", - "划分后的训练集大小: 694, 验证集大小: 136\n", - "train_data最大日期: 2025-02-28\n", + "划分后的训练集大小: 5746, 验证集大小: 282\n", + "train_data最大日期: 2025-02-27, 训练天数20\n", "test_data最大日期: 2025-03-03\n", - "划分后的训练集大小: 670, 验证集大小: 120\n", - "train_data最大日期: 2025-03-03\n", + "划分后的训练集大小: 5731, 验证集大小: 271\n", + "train_data最大日期: 2025-02-28, 训练天数20\n", "test_data最大日期: 2025-03-04\n", - "划分后的训练集大小: 671, 验证集大小: 140\n", - "train_data最大日期: 2025-03-04\n", + "划分后的训练集大小: 5721, 验证集大小: 283\n", + "train_data最大日期: 2025-03-03, 训练天数20\n", "test_data最大日期: 2025-03-05\n", - "划分后的训练集大小: 673, 验证集大小: 145\n", - "train_data最大日期: 2025-03-05\n", + "划分后的训练集大小: 5712, 验证集大小: 286\n", + "train_data最大日期: 2025-03-04, 训练天数20\n", "test_data最大日期: 2025-03-06\n", - "划分后的训练集大小: 650, 验证集大小: 109\n", - "train_data最大日期: 2025-03-06\n", + "划分后的训练集大小: 5712, 验证集大小: 291\n", + "train_data最大日期: 2025-03-05, 训练天数20\n", "test_data最大日期: 2025-03-07\n", - "划分后的训练集大小: 659, 验证集大小: 145\n", - "train_data最大日期: 2025-03-07\n", + "划分后的训练集大小: 5713, 验证集大小: 286\n", + "train_data最大日期: 2025-03-06, 训练天数20\n", "test_data最大日期: 2025-03-10\n", - "划分后的训练集大小: 682, 验证集大小: 143\n", - "train_data最大日期: 2025-03-10\n", + "划分后的训练集大小: 5722, 验证集大小: 292\n", + "train_data最大日期: 2025-03-07, 训练天数20\n", "test_data最大日期: 2025-03-11\n", - "划分后的训练集大小: 638, 验证集大小: 96\n", - "train_data最大日期: 2025-03-11\n", + "划分后的训练集大小: 5729, 验证集大小: 293\n", + "train_data最大日期: 2025-03-10, 训练天数20\n", "test_data最大日期: 2025-03-12\n", - "划分后的训练集大小: 640, 验证集大小: 147\n", - "train_data最大日期: 2025-03-12\n", + "划分后的训练集大小: 5731, 验证集大小: 287\n", + "train_data最大日期: 2025-03-11, 训练天数20\n", "test_data最大日期: 2025-03-13\n", - "划分后的训练集大小: 674, 验证集大小: 143\n", - "train_data最大日期: 2025-03-13\n", + "划分后的训练集大小: 5742, 验证集大小: 293\n", + "train_data最大日期: 2025-03-12, 训练天数20\n", "test_data最大日期: 2025-03-14\n", - "划分后的训练集大小: 678, 验证集大小: 149\n", - "train_data最大日期: 2025-03-14\n", + "划分后的训练集大小: 5746, 验证集大小: 289\n", + "train_data最大日期: 2025-03-13, 训练天数20\n", "test_data最大日期: 2025-03-17\n", - "划分后的训练集大小: 642, 验证集大小: 107\n", - "train_data最大日期: 2025-03-17\n", + "划分后的训练集大小: 5754, 验证集大小: 295\n", + "train_data最大日期: 2025-03-14, 训练天数20\n", "test_data最大日期: 2025-03-18\n", - "划分后的训练集大小: 691, 验证集大小: 145\n", - "train_data最大日期: 2025-03-18\n", + "划分后的训练集大小: 5757, 验证集大小: 290\n", + "train_data最大日期: 2025-03-17, 训练天数20\n", "test_data最大日期: 2025-03-19\n", - "划分后的训练集大小: 633, 验证集大小: 89\n", - "train_data最大日期: 2025-03-19\n", + "划分后的训练集大小: 5749, 验证集大小: 284\n", + "train_data最大日期: 2025-03-18, 训练天数20\n", "test_data最大日期: 2025-03-20\n", - "划分后的训练集大小: 610, 验证集大小: 120\n", - "train_data最大日期: 2025-03-20\n", + "划分后的训练集大小: 5741, 验证集大小: 280\n", + "train_data最大日期: 2025-03-19, 训练天数20\n", "test_data最大日期: 2025-03-21\n", - "划分后的训练集大小: 601, 验证集大小: 140\n", - "train_data最大日期: 2025-03-21\n", + "划分后的训练集大小: 5736, 验证集大小: 280\n", + "train_data最大日期: 2025-03-20, 训练天数20\n", "test_data最大日期: 2025-03-24\n", - "划分后的训练集大小: 629, 验证集大小: 135\n", - "train_data最大日期: 2025-03-24\n", + "划分后的训练集大小: 5733, 验证集大小: 284\n", + "train_data最大日期: 2025-03-21, 训练天数20\n", "test_data最大日期: 2025-03-25\n", - "划分后的训练集大小: 596, 验证集大小: 112\n", - "train_data最大日期: 2025-03-25\n", + "划分后的训练集大小: 5714, 验证集大小: 271\n", + "train_data最大日期: 2025-03-24, 训练天数20\n", "test_data最大日期: 2025-03-26\n", - "划分后的训练集大小: 651, 验证集大小: 144\n", - "train_data最大日期: 2025-03-26\n", + "划分后的训练集大小: 5720, 验证集大小: 290\n", + "train_data最大日期: 2025-03-25, 训练天数20\n", "test_data最大日期: 2025-03-27\n", - "划分后的训练集大小: 671, 验证集大小: 140\n", - "train_data最大日期: 2025-03-27\n", + "划分后的训练集大小: 5720, 验证集大小: 293\n", + "train_data最大日期: 2025-03-26, 训练天数20\n", "test_data最大日期: 2025-03-28\n", - "划分后的训练集大小: 623, 验证集大小: 92\n", - "train_data最大日期: 2025-03-28\n", + "划分后的训练集大小: 5721, 验证集大小: 283\n", + "train_data最大日期: 2025-03-27, 训练天数20\n", "test_data最大日期: 2025-03-31\n", - "划分后的训练集大小: 633, 验证集大小: 145\n", - "train_data最大日期: 2025-03-31\n", + "划分后的训练集大小: 5734, 验证集大小: 284\n", + "train_data最大日期: 2025-03-28, 训练天数20\n", "test_data最大日期: 2025-04-01\n", - "划分后的训练集大小: 666, 验证集大小: 145\n", - "train_data最大日期: 2025-04-01\n", + "划分后的训练集大小: 5742, 验证集大小: 291\n", + "train_data最大日期: 2025-03-31, 训练天数20\n", "test_data最大日期: 2025-04-02\n", - "划分后的训练集大小: 637, 验证集大小: 115\n", - "train_data最大日期: 2025-04-02\n", + "划分后的训练集大小: 5743, 验证集大小: 287\n", + "train_data最大日期: 2025-04-01, 训练天数20\n", "test_data最大日期: 2025-04-03\n", - "划分后的训练集大小: 620, 验证集大小: 123\n", - "train_data最大日期: 2025-04-02\n", + "划分后的训练集大小: 5739, 验证集大小: 287\n", + "train_data最大日期: 2025-04-02, 训练天数20\n", "test_data最大日期: 2025-04-07\n", - "划分后的训练集大小: 528, 验证集大小: 123\n" + "划分后的训练集大小: 5738, 验证集大小: 285\n", + "train_data最大日期: 2025-04-03, 训练天数20\n", + "test_data最大日期: 2025-04-08\n", + "划分后的训练集大小: 5733, 验证集大小: 287\n", + "train_data最大日期: 2025-04-07, 训练天数20\n", + "test_data最大日期: 2025-04-09\n", + "划分后的训练集大小: 5725, 验证集大小: 285\n" ] } ], - "execution_count": 43 + "execution_count": 149 }, { "cell_type": "code", "id": "e01fe33b-e30d-4bc6-bf40-de91e61862b4", "metadata": { "ExecuteTime": { - "end_time": "2025-04-09T14:53:53.045759Z", - "start_time": "2025-04-09T14:53:53.040829Z" + "end_time": "2025-04-10T14:59:10.659121Z", + "start_time": "2025-04-10T14:59:10.651976Z" } }, "source": [ @@ -3383,60 +3270,81 @@ "output_type": "stream", "text": [ " ts_code trade_date score\n", - "557 002851.SZ 2025-03-31 1.547811\n", - "558 002847.SZ 2025-04-01 0.596230\n", - "559 601595.SH 2025-04-02 -0.014094\n", - "560 603353.SH 2025-04-03 1.512002\n", - "561 600600.SH 2025-04-07 0.640985\n" + "561 000953.SZ 2025-04-02 1.055776\n", + "562 002667.SZ 2025-04-03 0.869295\n", + "563 002005.SZ 2025-04-07 -0.000617\n", + "564 603609.SH 2025-04-08 0.882270\n", + "565 600258.SH 2025-04-09 0.900722\n" ] } ], - "execution_count": 44 + "execution_count": 142 }, { "cell_type": "code", "id": "0dc75517-c857-4f1d-8815-e807400a6d33", "metadata": { "ExecuteTime": { - "end_time": "2025-04-09T14:53:53.055527Z", - "start_time": "2025-04-09T14:53:53.049774Z" + "end_time": "2025-04-10T14:59:10.691231Z", + "start_time": "2025-04-10T14:59:10.687575Z" } }, "source": [ "# pdf1 = select_pre_zt_stocks_dynamic(df[(df['trade_date'] >= '2022-03-26') & (df['trade_date'] <= '2029-03-26')])\n", + "# pdf1 = pdf1.merge(industry_df, on=['cat_l2_code', 'trade_date'], how='left')\n", "# pdf1 = pdf1.replace([np.inf, -np.inf], np.nan)\n", "#\n", + "# feature_columns = [col for col in pdf1.columns if col in pdf.columns]\n", + "# feature_columns = [col for col in feature_columns if col not in ['trade_date',\n", + "# 'ts_code',\n", + "# 'label']]\n", + "# feature_columns = [col for col in feature_columns if 'future' not in col]\n", + "# feature_columns = [col for col in feature_columns if 'label' not in col]\n", + "# feature_columns = [col for col in feature_columns if 'score' not in col]\n", + "# feature_columns = [col for col in feature_columns if 'gen' not in col]\n", + "# feature_columns = [col for col in feature_columns if 'pe_ttm' not in col]\n", + "# feature_columns = [col for col in feature_columns if 'volatility' not in col]\n", + "# feature_columns = [col for col in feature_columns if 'cat_l2_code' not in col]\n", + "# feature_columns = [col for col in feature_columns if col not in origin_columns]\n", + "# feature_columns = [col for col in feature_columns if not col.startswith('_')]\n", + "# # feature_columns = [col for col in feature_columns if col not in ['ts_code', 'trade_date', 'vol_std_5', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_007', 'consecutive_up_limit', 'mv_volatility', 'volume_growth', 'mv_growth', 'arbr']]\n", "#\n", - "# pdf1 = time_series_quantile_filter(pdf1, numeric_columns)\n", + "# print(feature_columns)\n", + "# numeric_columns = pdf.select_dtypes(include=['float64', 'int64']).columns\n", + "# numeric_columns = [col for col in numeric_columns if col in feature_columns]\n", + "#\n", + "# pdf1 = cross_sectional_quantile_filter(pdf1, numeric_columns)\n", + "# pdf1 = cross_sectional_standardization(pdf1, numeric_columns)\n", "#\n", "# # pdf1 = cross_sectional_standardization(pdf1, numeric_columns)\n", "# # pdf1 = pdf1[pdf1['trade_date'] <= '2025-03-26']\n", - "# # pdf1 = pdf1.sort_values(by=['ts_code', 'trade_date'])\n", + "# pdf1 = pdf1.sort_values(by=['ts_code', 'trade_date'])\n", "# filter_index1 = pdf1['future_return'].between(pdf1['future_return'].quantile(0.01), pdf1['future_return'].quantile(0.99))\n", "#\n", "# print('-----------------------------------------')\n", "# pdf2 = select_pre_zt_stocks_dynamic(df[(df['trade_date'] >= '2022-03-26') & (df['trade_date'] <= '2025-03-26')])\n", + "# pdf2 = pdf2.merge(industry_df, on=['cat_l2_code', 'trade_date'], how='left')\n", "# pdf2 = pdf2.replace([np.inf, -np.inf], np.nan)\n", "#\n", - "#\n", - "# pdf2 = time_series_quantile_filter(pdf2, numeric_columns)\n", + "# pdf2 = cross_sectional_quantile_filter(pdf2, numeric_columns)\n", + "# pdf2 = cross_sectional_standardization(pdf2, numeric_columns)\n", "#\n", "# # pdf2 = cross_sectional_standardization(pdf2, numeric_columns)\n", "#\n", "# # pdf2 = pdf2[pdf2['trade_date'] <= '2025-03-26']\n", - "# # pdf2 = pdf2.sort_values(by=['ts_code', 'trade_date'])\n", + "# pdf2 = pdf2.sort_values(by=['ts_code', 'trade_date'])\n", "# filter_index2 = pdf2['future_return'].between(pdf2['future_return'].quantile(0.01), pdf2['future_return'].quantile(0.99))\n" ], "outputs": [], - "execution_count": 45 + "execution_count": 143 }, { "cell_type": "code", "id": "8299a6f461097f14", "metadata": { "ExecuteTime": { - "end_time": "2025-04-09T14:53:53.135966Z", - "start_time": "2025-04-09T14:53:53.131344Z" + "end_time": "2025-04-10T14:59:10.827746Z", + "start_time": "2025-04-10T14:59:10.824106Z" } }, "source": [ @@ -3450,38 +3358,38 @@ "# print(are_equal) # 输出 True 或 False" ], "outputs": [], - "execution_count": 46 + "execution_count": 144 }, { "cell_type": "code", "id": "3f5079aa2c937c22", "metadata": { "ExecuteTime": { - "end_time": "2025-04-09T14:53:53.148195Z", - "start_time": "2025-04-09T14:53:53.138983Z" + "end_time": "2025-04-10T14:59:10.845209Z", + "start_time": "2025-04-10T14:59:10.837769Z" } }, "source": [ "# final_predictions1 = rolling_train_predict(\n", - "# pdf1[(pdf1['trade_date'] >= '2024-12-01') & (pdf1['trade_date'] <= '2029-03-26')], 5, 1, feature_columns,\n", + "# pdf1[(pdf1['trade_date'] >= '2024-12-01')], 5, 1, feature_columns,\n", "# days=days, validation_days=0, filter_index=filter_index1, params=light_params)\n", "# final_predictions.to_csv('test1.tsv', index=False)\n", "#\n", "# final_predictions2 = rolling_train_predict(\n", - "# pdf2[(pdf2['trade_date'] >= '2024-12-01') & (pdf2['trade_date'] <= '2029-03-26')], 5, 1, feature_columns,\n", + "# pdf2[(pdf2['trade_date'] >= '2024-12-01')], 5, 1, feature_columns,\n", "# days=days, validation_days=0, filter_index=filter_index2, params=light_params)\n", "# final_predictions2.to_csv('test2.tsv', index=False)" ], "outputs": [], - "execution_count": 47 + "execution_count": 145 }, { "cell_type": "code", "id": "199b12e7e20e4e6a", "metadata": { "ExecuteTime": { - "end_time": "2025-04-09T14:53:53.186822Z", - "start_time": "2025-04-09T14:53:53.184469Z" + "end_time": "2025-04-10T14:59:10.901682Z", + "start_time": "2025-04-10T14:59:10.887182Z" } }, "source": [ @@ -3492,7 +3400,7 @@ "# print(are_equal) # 输出 True 或 False" ], "outputs": [], - "execution_count": 48 + "execution_count": 146 } ], "metadata": { diff --git a/code/train/predictions_test.tsv b/code/train/predictions_test.tsv index 59e543e..cdac322 100644 --- a/code/train/predictions_test.tsv +++ b/code/train/predictions_test.tsv @@ -1,563 +1,550 @@ trade_date,score,ts_code -2022-12-08,1.2708337806641494,603816.SH -2022-12-09,1.4207120834806832,603567.SH 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