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+924 31987 2593 +925 32019 2558 +926 32051 2523 +927 32084 2489 +928 32116 2454 +929 32148 2419 +930 32181 2385 +931 32213 2350 +932 32244 2315 +933 32275 2280 +934 32307 2246 +935 32340 2211 +936 32372 2176 +937 32404 2141 +938 32438 2107 +939 32470 2072 +940 32502 2037 +941 32536 2003 +942 32568 1968 +943 32599 1933 +944 32633 1899 +945 32667 1864 +946 32703 1830 +947 32735 1795 +948 32769 1761 +949 32800 1726 +950 32832 1691 +951 32864 1657 +952 32896 1622 +953 32928 1587 +954 32961 1553 +955 32993 1518 +956 33028 1484 +957 33061 1449 +958 33094 1414 +959 33126 1380 +960 33157 1345 +961 33188 1310 +962 33219 1276 +963 33251 1241 +964 33282 1207 +965 33313 1172 diff --git a/dev.env b/dev.env new file mode 100644 index 0000000..ba5b7e3 --- /dev/null +++ b/dev.env @@ -0,0 +1,2 @@ +WORKSPACE_FOLDER=/mnt/d/PyProject/NewStock/main +PYTHONPATH=${workspaceFolder}/main:${PYTHONPATH} diff --git a/main/data/index_and_industry.ipynb b/main/data/index_and_industry.ipynb index 525ec00..87d32d2 100644 --- a/main/data/index_and_industry.ipynb +++ b/main/data/index_and_industry.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 5, "id": "initial_id", "metadata": { "ExecuteTime": { @@ -24,7 +24,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 6, "id": "f448da220816bf98", "metadata": { "ExecuteTime": { @@ -63,14 +63,14 @@ "final_df = pd.concat(all_data, ignore_index=True)\n", "\n", "# 存储到H5文件\n", - "final_df.to_hdf('../../data/index_data.h5', key='index_data', mode='w')\n", + "final_df.to_hdf('/mnt/d/PyProject/NewStock/data/index_data.h5', key='index_data', mode='w')\n", "\n", "print(\"数据已经成功存储到index_data.h5文件中\")" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 7, "id": "907f732d3c397bf", "metadata": { "ExecuteTime": { @@ -84,37 +84,37 @@ "output_type": "stream", "text": [ " ts_code trade_date close open high low \\\n", - "0 000905.SH 20250523 5653.0436 5697.1362 5738.0829 5653.0436 \n", - "1 000905.SH 20250522 5703.2797 5739.1909 5757.7946 5701.1614 \n", - "2 000905.SH 20250521 5757.9225 5741.6885 5763.0788 5733.8275 \n", - "3 000905.SH 20250520 5747.3670 5723.5055 5759.4582 5707.8101 \n", - "4 000905.SH 20250519 5720.7949 5719.4381 5729.0703 5669.7208 \n", + "0 000905.SH 20250530 5671.0723 5704.7710 5704.7710 5665.5177 \n", + "1 000905.SH 20250529 5719.9101 5637.0633 5724.5185 5637.0633 \n", + "2 000905.SH 20250528 5637.2378 5651.8755 5660.4696 5628.4165 \n", + "3 000905.SH 20250527 5652.1454 5666.3027 5667.8710 5629.1343 \n", + "4 000905.SH 20250526 5669.4609 5653.2063 5693.6250 5644.5794 \n", "... ... ... ... ... ... ... \n", - "13531 399006.SZ 20100607 1069.4680 1005.0280 1075.2250 1001.7020 \n", - "13532 399006.SZ 20100604 1027.6810 989.6810 1027.6810 986.5040 \n", - "13533 399006.SZ 20100603 998.3940 1002.3550 1026.7020 997.7750 \n", - "13534 399006.SZ 20100602 997.1190 967.6090 997.1190 952.6110 \n", - "13535 399006.SZ 20100601 973.2330 986.0150 994.7930 948.1180 \n", + "13546 399006.SZ 20100607 1069.4680 1005.0280 1075.2250 1001.7020 \n", + "13547 399006.SZ 20100604 1027.6810 989.6810 1027.6810 986.5040 \n", + "13548 399006.SZ 20100603 998.3940 1002.3550 1026.7020 997.7750 \n", + "13549 399006.SZ 20100602 997.1190 967.6090 997.1190 952.6110 \n", + "13550 399006.SZ 20100601 973.2330 986.0150 994.7930 948.1180 \n", "\n", " pre_close change pct_chg vol amount \n", - "0 5703.2797 -50.2361 -0.8808 1.143612e+08 1.481236e+08 \n", - "1 5757.9225 -54.6428 -0.9490 1.090577e+08 1.416209e+08 \n", - "2 5747.3670 10.5555 0.1837 1.158045e+08 1.551474e+08 \n", - "3 5720.7949 26.5721 0.4645 1.168966e+08 1.517512e+08 \n", - "4 5715.8491 4.9458 0.0865 1.153849e+08 1.410987e+08 \n", + "0 5719.9101 -48.8378 -0.8538 1.099007e+08 1.376706e+08 \n", + "1 5637.2378 82.6723 1.4665 1.146825e+08 1.480951e+08 \n", + "2 5652.1454 -14.9076 -0.2638 9.490888e+07 1.199598e+08 \n", + "3 5669.4609 -17.3155 -0.3054 9.514936e+07 1.252757e+08 \n", + "4 5653.0436 16.4173 0.2904 9.717099e+07 1.273436e+08 \n", "... ... ... ... ... ... \n", - "13531 1027.6810 41.7870 4.0661 2.655275e+06 9.106095e+06 \n", - "13532 998.3940 29.2870 2.9334 1.500295e+06 5.269441e+06 \n", - "13533 997.1190 1.2750 0.1279 1.616805e+06 6.240835e+06 \n", - "13534 973.2330 23.8860 2.4543 1.074628e+06 4.001206e+06 \n", - "13535 1000.0000 -26.7670 -2.6767 1.356285e+06 4.924177e+06 \n", + "13546 1027.6810 41.7870 4.0661 2.655275e+06 9.106095e+06 \n", + "13547 998.3940 29.2870 2.9334 1.500295e+06 5.269441e+06 \n", + "13548 997.1190 1.2750 0.1279 1.616805e+06 6.240835e+06 \n", + "13549 973.2330 23.8860 2.4543 1.074628e+06 4.001206e+06 \n", + "13550 1000.0000 -26.7670 -2.6767 1.356285e+06 4.924177e+06 \n", "\n", - "[13536 rows x 11 columns]\n" + "[13551 rows x 11 columns]\n" ] } ], "source": [ - "h5_filename = '../../data/index_data.h5'\n", + "h5_filename = '/mnt/d/PyProject/NewStock/data/index_data.h5'\n", "key = '/index_data'\n", "with pd.HDFStore(h5_filename, mode='r') as store:\n", " df = store[key]\n", @@ -124,7 +124,7 @@ ], "metadata": { "kernelspec": { - "display_name": "new_trader", + "display_name": "stock", "language": "python", "name": "python3" }, @@ -138,7 +138,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.11" + "version": "3.13.2" } }, "nbformat": 4, diff --git a/main/data/update/cyq-perf.ipynb b/main/data/update/cyq-perf.ipynb index 6a8ad5a..3a3eea3 100644 --- a/main/data/update/cyq-perf.ipynb +++ b/main/data/update/cyq-perf.ipynb @@ -39,15 +39,15 @@ "3 000006.SZ 20250312\n", "4 000007.SZ 20250312\n", "... ... ...\n", - "26947 920445.BJ 20250512\n", - "26948 920489.BJ 20250512\n", - "26949 920682.BJ 20250512\n", - "26950 920799.BJ 20250512\n", - "26951 920819.BJ 20250512\n", + "26917 920445.BJ 20250519\n", + "26918 920489.BJ 20250519\n", + "26919 920682.BJ 20250519\n", + "26920 920799.BJ 20250519\n", + "26921 920819.BJ 20250519\n", "\n", - "[7697412 rows x 2 columns]\n", - "20250516\n", - "start_date: 20250519\n" + "[7724334 rows x 2 columns]\n", + "20250523\n", + "start_date: 20250526\n" ] } ], @@ -55,7 +55,7 @@ "import pandas as pd\n", "import time\n", "\n", - "h5_filename = '../../../data/cyq_perf.h5'\n", + "h5_filename = '/mnt/d/PyProject/NewStock/data/cyq_perf.h5'\n", "key = '/cyq_perf'\n", "max_date = None\n", "with pd.HDFStore(h5_filename, mode='r') as store:\n", @@ -90,26 +90,21 @@ "任务 20250619 完成\n", "任务 20250618 完成\n", "任务 20250617 完成\n", - "任务 20250613 完成\n", "任务 20250616 完成\n", - "任务 20250611 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 20250529 完成\n", "任务 20250530 完成\n", - "任务 20250527 完成\n", + "任务 20250529 完成\n", "任务 20250528 完成\n", - "任务 20250526 完成\n", - "任务 20250523 完成\n", - "任务 20250522 完成\n", - "任务 20250521 完成\n", - "任务 20250520 完成\n", - "任务 20250519 完成\n" + "任务 20250527 完成\n", + "任务 20250526 完成\n" ] } ], @@ -177,7 +172,7 @@ ], "metadata": { "kernelspec": { - "display_name": "new_trader", + "display_name": "stock", "language": "python", "name": "python3" }, @@ -191,7 +186,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.11" + "version": "3.13.2" } }, "nbformat": 4, diff --git a/main/data/update/sw_daily.ipynb b/main/data/update/sw_daily.ipynb index 4cca328..88344a0 100644 --- a/main/data/update/sw_daily.ipynb +++ b/main/data/update/sw_daily.ipynb @@ -39,15 +39,15 @@ "3 801005.SI 20250221\n", "4 801010.SI 20250221\n", "... ... ...\n", - "2190 859811.SI 20250512\n", - "2191 859821.SI 20250512\n", - "2192 859822.SI 20250512\n", - "2193 859852.SI 20250512\n", - "2194 859951.SI 20250512\n", + "2190 859811.SI 20250519\n", + "2191 859821.SI 20250519\n", + "2192 859822.SI 20250519\n", + "2193 859852.SI 20250519\n", + "2194 859951.SI 20250519\n", "\n", - "[1068977 rows x 2 columns]\n", - "20250516\n", - "start_date: 20250519\n" + "[1071172 rows x 2 columns]\n", + "20250523\n", + "start_date: 20250526\n" ] } ], @@ -55,7 +55,7 @@ "import pandas as pd\n", "import time\n", "\n", - "h5_filename = '../../../data/sw_daily.h5'\n", + "h5_filename = '/mnt/d/PyProject/NewStock/data/sw_daily.h5'\n", "key = '/sw_daily'\n", "max_date = None\n", "with pd.HDFStore(h5_filename, mode='r') as store:\n", @@ -88,28 +88,23 @@ "text": [ "任务 20250619 完成\n", "任务 20250620 完成\n", - "任务 20250617 完成\n", "任务 20250618 完成\n", - "任务 20250613 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 20250610 完成\n", "任务 20250606 完成\n", - "任务 20250603 完成\n", + "任务 20250605 完成\n", "任务 20250604 完成\n", + "任务 20250603 完成\n", "任务 20250530 完成\n", "任务 20250529 完成\n", - "任务 20250528 完成\n", "任务 20250527 完成\n", - "任务 20250526 完成\n", - "任务 20250523 完成\n", - "任务 20250522 完成\n", - "任务 20250521 完成\n", - "任务 20250520 完成\n", - "任务 20250519 完成\n" + "任务 20250528 完成\n", + "任务 20250526 完成\n" ] } ], @@ -177,7 +172,7 @@ ], "metadata": { "kernelspec": { - "display_name": "new_trader", + "display_name": "stock", "language": "python", "name": "python3" }, @@ -191,7 +186,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.11" + "version": "3.13.2" } }, "nbformat": 4, diff --git a/main/data/update/update_daily_basic.ipynb b/main/data/update/update_daily_basic.ipynb index 6a11381..f86a7ac 100644 --- a/main/data/update/update_daily_basic.ipynb +++ b/main/data/update/update_daily_basic.ipynb @@ -62,7 +62,7 @@ " return True\n", " return False\n", "\n", - "name_change_df = pd.read_hdf('../../../data/name_change.h5', key='name_change')\n", + "name_change_df = pd.read_hdf('/mnt/d/PyProject/NewStock/data/name_change.h5', key='name_change')\n", "name_change_df = name_change_df.drop_duplicates(keep='first')\n", "\n", "# 确保 name_change_df 的日期格式正确\n", @@ -94,17 +94,17 @@ "output_type": "stream", "text": [ "\n", - "Index: 8647642 entries, 0 to 26951\n", + "Index: 8674588 entries, 0 to 26945\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: 197.9+ MB\n", + "memory usage: 198.5+ MB\n", "None\n", - "20250516\n", - "20250519\n" + "20250523\n", + "20250526\n" ] } ], @@ -112,7 +112,7 @@ "import time\n", "from concurrent.futures import ThreadPoolExecutor, as_completed\n", "\n", - "h5_filename = '../../../data/daily_basic.h5'\n", + "h5_filename = '/mnt/d/PyProject/NewStock/data/daily_basic.h5'\n", "key = '/daily_basic'\n", "max_date = None\n", "with pd.HDFStore(h5_filename, mode='r') as store:\n", @@ -144,10 +144,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "任务 20250717 完成\n", "任务 20250718 完成\n", - "任务 20250715 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 20250709 完成\n", @@ -160,12 +160,12 @@ "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 20250625 完成\n", "任务 20250624 完成\n", "任务 20250623 完成\n", - "任务 20250619 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", @@ -176,18 +176,13 @@ "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 20250529 完成\n", + "任务 20250604 完成\n", "任务 20250530 完成\n", - "任务 20250527 完成\n", + "任务 20250529 完成\n", "任务 20250528 完成\n", - "任务 20250526 完成\n", - "任务 20250523 完成\n", - "任务 20250522 完成\n", - "任务 20250521 完成\n", - "任务 20250520 完成\n", - "任务 20250519 完成\n" + "任务 20250527 完成\n", + "任务 20250526 完成\n" ] } ], @@ -258,58 +253,58 @@ "output_type": "stream", "text": [ " ts_code trade_date close turnover_rate turnover_rate_f \\\n", - "0 000839.SZ 20250523 2.67 0.8124 1.2782 \n", - "1 300274.SZ 20250523 60.60 3.2852 3.7071 \n", - "2 301356.SZ 20250523 17.59 5.0050 5.0698 \n", - "3 600152.SH 20250523 5.73 1.3359 2.0988 \n", - "4 300049.SZ 20250523 29.91 1.6066 1.7292 \n", + "0 603990.SH 20250530 14.96 3.7919 4.9168 \n", + "1 603666.SH 20250530 33.72 2.4954 4.7137 \n", + "2 001339.SZ 20250530 45.78 7.0710 7.0710 \n", + "3 002006.SZ 20250530 16.67 2.4368 3.4806 \n", + "4 603353.SH 20250530 15.21 1.3567 4.1316 \n", "... ... ... ... ... ... \n", - "26941 002458.SZ 20250519 8.36 2.1950 2.5416 \n", - "26942 600882.SH 20250519 27.18 2.2244 4.6853 \n", - "26943 001283.SZ 20250519 54.51 3.0453 3.0453 \n", - "26944 000718.SZ 20250519 2.20 1.4790 2.2404 \n", - "26945 002141.SZ 20250519 3.09 4.9267 7.1872 \n", + "26918 002670.SZ 20250526 11.86 0.7662 2.3092 \n", + "26919 839946.BJ 20250526 9.67 4.8520 6.8863 \n", + "26920 688076.SH 20250526 49.59 5.9483 9.5054 \n", + "26921 300519.SZ 20250526 14.44 2.4601 3.8976 \n", + "26922 300468.SZ 20250526 18.15 6.8275 8.8410 \n", "\n", - " volume_ratio pe pe_ttm pb ps ps_ttm dv_ratio \\\n", - "0 0.62 NaN NaN 7.4695 3.0824 3.1095 0.0000 \n", - "1 1.82 11.3840 9.8414 3.0807 1.6137 1.4907 1.1292 \n", - "2 1.43 NaN 18055.4366 1.2789 4.2618 3.3028 0.0000 \n", - "3 1.11 NaN NaN 1.7367 1.9844 2.0758 0.0000 \n", - "4 1.05 70.3242 80.3071 4.4707 5.9056 5.8725 0.0000 \n", - "... ... ... ... ... ... ... ... \n", - "26941 1.47 18.3588 24.2570 2.1403 2.9497 3.0116 2.3923 \n", - "26942 0.89 122.4919 89.9537 3.0986 2.8733 2.7144 0.0000 \n", - "26943 0.92 48.1520 36.6481 2.1043 0.8602 0.8229 0.8691 \n", - "26944 1.76 40.4178 55.0402 0.7058 3.1476 3.2425 3.6364 \n", - "26945 1.51 NaN NaN 3.8214 7.2461 4.4422 0.0000 \n", + " volume_ratio pe pe_ttm pb ps ps_ttm \\\n", + "0 0.65 NaN NaN 5.5665 9.8735 11.0137 \n", + "1 1.15 NaN NaN 3.2133 11.8990 10.3525 \n", + "2 1.22 91.7742 74.3709 5.3909 2.8419 2.7478 \n", + "3 0.81 58.9666 65.5384 3.6508 5.0124 5.4591 \n", + "4 1.10 90.1163 80.8019 1.5917 0.9380 0.9517 \n", + "... ... ... ... ... ... ... \n", + "26918 0.75 137.0866 106.8454 2.0610 15093.0115 14821.3328 \n", + "26919 0.55 NaN NaN 5.7695 2.5489 2.4978 \n", + "26920 3.15 27.5757 22.7263 3.7628 6.8632 6.0784 \n", + "26921 1.14 45.8504 44.3443 2.7022 8.6318 8.8737 \n", + "26922 1.08 142.9746 150.8960 5.8350 13.0086 13.6702 \n", "\n", - " dv_ttm total_share float_share free_share total_mv \\\n", - "0 NaN 391982.6352 391982.6352 249133.8007 1.046594e+06 \n", - "1 1.1292 207321.1424 158970.9449 140880.3307 1.256366e+07 \n", - "2 NaN 21600.0000 5481.0000 5410.9920 3.799440e+05 \n", - "3 NaN 52907.9375 52907.9375 33676.4965 3.031625e+05 \n", - "4 NaN 26635.6100 23351.5217 21696.0562 7.966711e+05 \n", - "... ... ... ... ... ... \n", - "26941 2.3577 110641.2915 74886.8285 64675.1303 9.249612e+05 \n", - "26942 NaN 51205.3647 51205.3647 24310.0793 1.391762e+06 \n", - "26943 0.8691 8061.0011 5785.5721 5785.5721 4.394052e+05 \n", - "26944 3.6364 303463.6384 228209.3122 150654.2061 6.676200e+05 \n", - "26945 NaN 103293.5798 103159.2875 70714.2228 3.191772e+05 \n", + " dv_ratio dv_ttm total_share float_share free_share total_mv \\\n", + "0 0.0000 NaN 30628.2731 30628.2731 23620.5583 4.581990e+05 \n", + "1 0.0000 NaN 20649.0816 20649.0816 10931.3716 6.962870e+05 \n", + "2 0.2622 0.3498 25042.9670 7313.0995 7313.0995 1.146467e+06 \n", + "3 0.7749 0.7749 51979.3440 45516.0000 31865.7600 8.664957e+05 \n", + "4 0.6462 1.3036 17339.4000 17041.8000 5596.0000 2.637323e+05 \n", + "... ... ... ... ... ... ... \n", + "26918 0.0000 NaN 193508.4653 162335.0634 53860.6790 2.295010e+06 \n", + "26919 NaN NaN 13499.0443 9702.8595 6836.5574 1.305358e+05 \n", + "26920 NaN NaN 22487.0915 22487.0915 14071.9565 1.115135e+06 \n", + "26921 2.7701 2.7701 16000.0000 11410.0000 7201.9100 2.310400e+05 \n", + "26922 0.3306 0.3306 53064.9275 52979.4065 40913.5262 9.631284e+05 \n", "\n", " circ_mv is_st \n", - "0 1.046594e+06 False \n", - "1 9.633639e+06 False \n", - "2 9.641079e+04 False \n", - "3 3.031625e+05 False \n", - "4 6.984440e+05 False \n", + "0 4.581990e+05 False \n", + "1 6.962870e+05 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4.6640 5.0668 0.0 \n", "\n", - " total_share float_share free_share total_mv circ_mv is_st \n", - "23 17600.0000 10126.2561 6359.4096 179520.0000 103287.8122 True \n", - "35 93629.1116 86984.9676 64375.7658 258416.3480 240078.5106 True \n", - "53 55792.2828 52663.7564 51638.5483 341448.7707 322302.1892 True \n", - "58 17420.0000 13370.7500 9364.1581 178903.4000 137317.6025 True \n", - "155 76297.9719 76250.0287 51632.2709 240338.6115 240187.5904 True \n", - "... ... ... ... ... ... ... \n", - "26880 66127.9045 65745.9042 50804.9121 581925.5596 578563.9570 True \n", - "26891 355000.0000 354999.9006 274999.9006 770350.0000 770349.7843 True \n", - "26910 19560.0000 19560.0000 9085.2748 124988.4000 124988.4000 True \n", - "26938 63105.2069 56592.2684 38956.2787 372320.7207 333894.3836 True \n", - "26945 103293.5798 103159.2875 70714.2228 319177.1616 318762.1984 True \n", + " dv_ttm total_share float_share free_share total_mv \\\n", + "16 NaN 29328.8133 29325.3240 23747.3240 254280.8113 \n", + "78 NaN 14684.1890 14684.1890 8677.2104 116592.4607 \n", + "112 NaN 34685.0017 29481.8767 26371.6067 113766.8056 \n", + "147 NaN 31297.7396 19735.2789 9511.5479 196549.8047 \n", + "158 NaN 32001.6000 32001.6000 11788.1468 183369.1680 \n", + "... ... ... ... ... ... \n", + "26733 NaN 59596.0158 59593.9625 29654.2988 296788.1587 \n", + "26751 NaN 16600.0000 16600.0000 6607.7948 123836.0000 \n", + "26785 NaN 64655.5179 29696.6877 28904.9696 195259.6641 \n", + "26885 NaN 175242.4858 175199.3158 109452.0915 310179.1999 \n", + "26905 NaN 50450.0508 50450.0508 19045.9689 310267.8124 \n", + "\n", + " circ_mv is_st \n", + "16 254250.5591 True \n", + "78 116592.4607 True \n", + "112 96700.5556 True \n", + "147 123937.5515 True \n", + "158 183369.1680 True \n", + "... ... ... \n", + "26733 296777.9333 True \n", + "26751 123836.0000 True \n", + "26785 89683.9969 True \n", + "26885 310102.7890 True \n", + "26905 310267.8124 True \n", "\n", "[944 rows x 19 columns]\n" ] @@ -422,7 +430,7 @@ "output_type": "stream", "text": [ "\n", - "Index: 8674588 entries, 0 to 26945\n", + "Index: 8701511 entries, 0 to 26922\n", "Data columns (total 3 columns):\n", " # Column Dtype \n", "--- ------ ----- \n", @@ -430,7 +438,7 @@ " 1 trade_date object\n", " 2 is_st bool \n", "dtypes: bool(1), object(2)\n", - "memory usage: 206.8+ MB\n", + "memory usage: 207.5+ MB\n", "None\n" ] } @@ -444,7 +452,7 @@ ], "metadata": { "kernelspec": { - "display_name": "new_trader", + "display_name": "stock", "language": "python", "name": "python3" }, @@ -458,7 +466,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.11" + "version": "3.13.2" } }, "nbformat": 4, diff --git a/main/data/update/update_daily_data.ipynb b/main/data/update/update_daily_data.ipynb index 53739cb..e5a0b94 100644 --- a/main/data/update/update_daily_data.ipynb +++ b/main/data/update/update_daily_data.ipynb @@ -38,22 +38,22 @@ "output_type": "stream", "text": [ "\n", - "Index: 8718823 entries, 0 to 26790\n", + "Index: 8745589 entries, 0 to 26765\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: 199.6+ MB\n", + "memory usage: 200.2+ MB\n", "None\n", - "20250516\n", - "20250519\n" + "20250523\n", + "20250526\n" ] } ], "source": [ - "h5_filename = '../../../data/daily_data.h5'\n", + "h5_filename = '/mnt/d/PyProject/NewStock/data/daily_data.h5'\n", "key = '/daily_data'\n", "max_date = None\n", "with pd.HDFStore(h5_filename, mode='r') as store:\n", @@ -87,92 +87,92 @@ "text": [ "任务 000001.SZ 完成\n", "任务 000002.SZ 完成\n", - "任务 000006.SZ 完成\n", "任务 000004.SZ 完成\n", + "任务 000006.SZ 完成\n", "任务 000007.SZ 完成\n", "任务 000008.SZ 完成\n", - "任务 000010.SZ 完成\n", "任务 000009.SZ 完成\n", - "任务 000011.SZ 完成\n", + "任务 000010.SZ 完成\n", "任务 000012.SZ 完成\n", + "任务 000011.SZ 完成\n", "任务 000014.SZ 完成\n", "任务 000016.SZ 完成\n", - "任务 000017.SZ 完成\n", "任务 000019.SZ 完成\n", + "任务 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完成\n", "任务 000046.SZ 完成\n", "任务 000047.SZ 完成\n", - "任务 000405.SZ 完成\n", "任务 000150.SZ 完成\n", + "任务 000405.SZ 完成\n", "任务 000406.SZ 完成\n", "任务 000412.SZ 完成\n", - "任务 000416.SZ 完成\n", "任务 000413.SZ 完成\n", + "任务 000416.SZ 完成\n", "任务 000418.SZ 完成\n", "任务 000502.SZ 完成\n", "任务 000508.SZ 完成\n", "任务 000511.SZ 完成\n", "任务 000515.SZ 完成\n", "任务 000522.SZ 完成\n", - "任务 000527.SZ 完成\n", "任务 000535.SZ 完成\n", - "任务 000540.SZ 完成\n", + "任务 000527.SZ 完成\n", "任务 000542.SZ 完成\n", - "任务 000549.SZ 完成\n", + "任务 000540.SZ 完成\n", "任务 000556.SZ 完成\n", - "任务 000562.SZ 完成\n", + "任务 000549.SZ 完成\n", "任务 000569.SZ 完成\n", + "任务 000562.SZ 完成\n", "任务 000578.SZ 完成\n", "任务 000583.SZ 完成\n", "任务 000585.SZ 完成\n", "任务 000587.SZ 完成\n", - "任务 000594.SZ 完成\n", "任务 000588.SZ 完成\n", + "任务 000594.SZ 完成\n", "任务 000602.SZ 完成\n", "任务 000606.SZ 完成\n", "任务 000611.SZ 完成\n", "任务 000613.SZ 完成\n", - "任务 000616.SZ 完成\n", "任务 000618.SZ 完成\n", - "任务 000621.SZ 完成\n", + "任务 000616.SZ 完成\n", "任务 000653.SZ 完成\n", - "任务 000658.SZ 完成\n", + "任务 000621.SZ 完成\n", "任务 000660.SZ 完成\n", - "任务 000662.SZ 完成\n", + "任务 000658.SZ 完成\n", "任务 000666.SZ 完成\n", - "任务 000667.SZ 完成\n", + "任务 000662.SZ 完成\n", "任务 000671.SZ 完成\n", + "任务 000667.SZ 完成\n", "任务 000673.SZ 完成\n", "任务 000675.SZ 完成\n", - "任务 000687.SZ 完成\n", "任务 000689.SZ 完成\n", + "任务 000687.SZ 完成\n", "任务 000693.SZ 完成\n", "任务 000699.SZ 完成\n", "任务 000730.SZ 完成\n", @@ -5572,12 +5572,12 @@ "任务 002070.SZ 完成\n", "任务 002071.SZ 完成\n", "任务 002087.SZ 完成\n", - "任务 002089.SZ 完成\n", "任务 002113.SZ 完成\n", - "任务 002118.SZ 完成\n", + "任务 002089.SZ 完成\n", "任务 002143.SZ 完成\n", - "任务 002147.SZ 完成\n", + "任务 002118.SZ 完成\n", "任务 002220.SZ 完成\n", + "任务 002147.SZ 完成\n", "任务 002260.SZ 完成\n", "任务 002280.SZ 完成\n", "任务 002288.SZ 完成\n", @@ -5742,10 +5742,10 @@ "任务 600813.SH 完成\n", "任务 600823.SH 完成\n", "任务 600832.SH 完成\n", - "任务 600840.SH 完成\n", "任务 600836.SH 完成\n", - "任务 600852.SH 完成\n", + "任务 600840.SH 完成\n", "任务 600842.SH 完成\n", + "任务 600852.SH 完成\n", "任务 600856.SH 完成\n", "任务 600870.SH 完成\n", "任务 600878.SH 完成\n", @@ -5777,7 +5777,7 @@ "from concurrent.futures import ThreadPoolExecutor, as_completed\n", "\n", "# 读取本地保存的股票列表 CSV 文件（假设文件名为 stocks_data.csv）\n", - "stocks_df = pd.read_csv('../../../stocks_list.csv', encoding='utf-8-sig')\n", + "stocks_df = pd.read_csv('/mnt/d/PyProject/NewStock/stocks_list.csv', encoding='utf-8-sig')\n", "\n", "# 用于存放所有股票的日线数据（每次获取的 DataFrame）\n", "daily_data_list = []\n", @@ -5836,32 +5836,32 @@ "output_type": "stream", "text": [ " ts_code trade_date open high low close pre_close \\\n", - "0 000001.SZ 20250523 1475.91 1482.30 1460.57 1464.41 1475.91 \n", - "1 000001.SZ 20250522 1463.13 1477.18 1461.85 1475.91 1466.96 \n", - "2 000001.SZ 20250521 1456.74 1481.02 1455.46 1466.96 1455.46 \n", - "3 000001.SZ 20250520 1456.74 1465.68 1452.91 1455.46 1452.91 \n", - "4 000001.SZ 20250519 1456.74 1465.68 1451.63 1452.91 1454.18 \n", + "0 000001.SZ 20250530 1465.68 1479.74 1461.85 1477.18 1464.41 \n", + "1 000001.SZ 20250529 1472.07 1475.91 1463.13 1464.41 1473.35 \n", + "2 000001.SZ 20250528 1469.52 1475.91 1461.85 1473.35 1468.24 \n", + "3 000001.SZ 20250527 1463.13 1474.63 1459.29 1468.24 1459.29 \n", + "4 000001.SZ 20250526 1461.85 1469.52 1456.74 1459.29 1464.41 \n", "... ... ... ... ... ... ... ... \n", - "26761 689009.SH 20250523 68.37 70.32 66.62 66.63 68.32 \n", - "26762 689009.SH 20250522 67.16 68.66 66.74 68.32 67.32 \n", - "26763 689009.SH 20250521 65.45 67.75 64.73 67.32 65.45 \n", - "26764 689009.SH 20250520 65.56 66.73 64.75 65.45 65.44 \n", - "26765 689009.SH 20250519 64.05 65.95 63.99 65.44 63.94 \n", + "26736 920128.BJ 20250530 42.50 42.95 38.13 38.13 44.88 \n", + "26737 920128.BJ 20250529 41.00 46.98 40.78 44.88 39.52 \n", + "26738 920128.BJ 20250528 38.90 42.98 38.50 39.52 39.30 \n", + "26739 920128.BJ 20250527 37.97 43.29 36.62 39.30 39.25 \n", + "26740 920128.BJ 20250526 35.55 40.00 34.80 39.25 34.99 \n", "\n", " change pct_chg vol amount \n", - "0 -11.50 -0.78 962643.11 1108636.101 \n", - "1 8.95 0.61 1002968.06 1155265.064 \n", - "2 11.50 0.79 1328278.10 1528350.368 \n", - "3 2.55 0.18 643634.80 734455.266 \n", - "4 -1.27 -0.09 831390.60 949883.545 \n", + "0 12.77 0.87 1130849.31 1303433.716 \n", + "1 -8.94 -0.61 919806.76 1056726.384 \n", + "2 5.11 0.35 658179.47 757571.319 \n", + "3 8.95 0.61 801915.14 921912.281 \n", + "4 -5.12 -0.35 699158.58 800495.685 \n", "... ... ... ... ... \n", - "26761 -1.69 -2.47 88999.73 599644.428 \n", - "26762 1.00 1.49 61685.15 417079.978 \n", - "26763 1.87 2.86 63715.11 420469.740 \n", - "26764 0.01 0.02 64488.23 421060.706 \n", - "26765 1.50 2.35 95078.93 615893.951 \n", + "26736 -6.75 -15.04 80798.97 324916.233 \n", + "26737 5.36 13.56 101158.14 448509.533 \n", + "26738 0.22 0.56 67022.73 273635.585 \n", + "26739 0.05 0.13 94793.54 378706.573 \n", + "26740 4.26 12.17 77292.59 289655.061 \n", "\n", - "[26766 rows x 11 columns]\n" + "[26741 rows x 11 columns]\n" ] } ], @@ -5903,7 +5903,7 @@ ], "metadata": { "kernelspec": { - "display_name": "new_trader", + "display_name": "stock", "language": "python", "name": "python3" }, @@ -5917,7 +5917,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.11" + "version": "3.13.2" } }, "nbformat": 4, diff --git a/main/data/update/update_money_flow.ipynb b/main/data/update/update_money_flow.ipynb index ea6f435..2307f06 100644 --- a/main/data/update/update_money_flow.ipynb +++ b/main/data/update/update_money_flow.ipynb @@ -34,17 +34,17 @@ "output_type": "stream", "text": [ "\n", - "Index: 8481815 entries, 0 to 25622\n", + "Index: 8507431 entries, 0 to 25615\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: 194.1+ MB\n", + "memory usage: 194.7+ MB\n", "None\n", - "20250516\n", - "start_date: 20250519\n" + "20250523\n", + "start_date: 20250526\n" ] } ], @@ -52,7 +52,7 @@ "import pandas as pd\n", "import time\n", "\n", - "h5_filename = '../../../data/money_flow.h5'\n", + "h5_filename = '/mnt/d/PyProject/NewStock/data/money_flow.h5'\n", "key = '/money_flow'\n", "max_date = None\n", "with pd.HDFStore(h5_filename, mode='r') as store:\n", @@ -84,8 +84,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "任务 20250718 完成\n", "任务 20250717 完成\n", + "任务 20250718 完成\n", "任务 20250715 完成\n", "任务 20250716 完成\n", "任务 20250714 完成\n", @@ -106,12 +106,12 @@ "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 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 20250611 完成\n", "任务 20250610 完成\n", "任务 20250609 完成\n", "任务 20250606 完成\n", @@ -122,12 +122,7 @@ "任务 20250529 完成\n", "任务 20250528 完成\n", "任务 20250527 完成\n", - "任务 20250526 完成\n", - "任务 20250523 完成\n", - "任务 20250522 完成\n", - "任务 20250521 完成\n", - "任务 20250520 完成\n", - "任务 20250519 完成\n" + "任务 20250526 完成\n" ] } ], @@ -209,7 +204,7 @@ ], "metadata": { "kernelspec": { - "display_name": "new_trader", + "display_name": "stock", "language": "python", "name": "python3" }, @@ -223,7 +218,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.11" + "version": "3.13.2" } }, "nbformat": 4, diff --git a/main/data/update/update_stk_limit.ipynb b/main/data/update/update_stk_limit.ipynb index 5f55036..a0d279c 100644 --- a/main/data/update/update_stk_limit.ipynb +++ b/main/data/update/update_stk_limit.ipynb @@ -34,23 +34,23 @@ "output_type": "stream", "text": [ " ts_code trade_date\n", - "2364 300067.SZ 20250508\n", - "2363 300066.SZ 20250508\n", - "2362 300065.SZ 20250508\n", - "2373 300076.SZ 20250508\n", - "7111 920819.BJ 20250508\n", + "2365 300067.SZ 20250509\n", + "2364 300066.SZ 20250509\n", + "2363 300065.SZ 20250509\n", + "2374 300076.SZ 20250509\n", + "7113 920819.BJ 20250509\n", "\n", - "Index: 10450519 entries, 0 to 7111\n", + "Index: 10457633 entries, 0 to 7113\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: 239.2+ MB\n", + "memory usage: 239.4+ MB\n", "None\n", - "20250508\n", - "20250509\n" + "20250509\n", + "20250512\n" ] } ], @@ -58,7 +58,7 @@ "import pandas as pd\n", "import time\n", "\n", - "h5_filename = '../../../data/stk_limit.h5'\n", + "h5_filename = '/mnt/d/PyProject/NewStock/data/stk_limit.h5'\n", "key = '/stk_limit'\n", "max_date = None\n", "with pd.HDFStore(h5_filename, mode='r') as store:\n", @@ -91,32 +91,32 @@ "name": "stdout", "output_type": "stream", "text": [ - "任务 20250718 完成\n", "任务 20250717 完成\n", - "任务 20250715 完成\n", + "任务 20250718 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 20250708 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 20250630 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 20250618 完成\n", "任务 20250613 完成\n", + "任务 20250616 完成\n", "任务 20250612 完成\n", "任务 20250611 完成\n", "任务 20250610 完成\n", @@ -126,21 +126,20 @@ "任务 20250604 完成\n", "任务 20250603 完成\n", "任务 20250530 完成\n", - "任务 20250528 完成\n", "任务 20250529 完成\n", + "任务 20250528 完成\n", "任务 20250527 完成\n", "任务 20250526 完成\n", "任务 20250523 完成\n", - "任务 20250522 完成\n", "任务 20250521 完成\n", - "任务 20250520 完成\n", + "任务 20250522 完成\n", "任务 20250519 完成\n", + "任务 20250520 完成\n", "任务 20250516 完成\n", "任务 20250515 完成\n", "任务 20250514 完成\n", "任务 20250513 完成\n", - "任务 20250512 完成\n", - "任务 20250509 完成\n" + "任务 20250512 完成\n" ] } ], @@ -192,19 +191,201 @@ "output_type": "stream", "text": [ "[ trade_date ts_code up_limit down_limit\n", - "0 20250509 000001.SZ 12.19 9.97\n", - "1 20250509 000002.SZ 7.57 6.19\n", - "2 20250509 000004.SZ 7.86 7.12\n", - "3 20250509 000006.SZ 7.33 5.99\n", - "4 20250509 000007.SZ 7.66 6.26\n", + "0 20250530 000001.SZ 12.61 10.31\n", + "1 20250530 000002.SZ 7.37 6.03\n", + "2 20250530 000004.SZ 10.38 9.40\n", + "3 20250530 000006.SZ 7.69 6.29\n", + "4 20250530 000007.SZ 8.61 7.05\n", "... ... ... ... ...\n", - "7109 20250509 920445.BJ 13.14 7.08\n", - "7110 20250509 920489.BJ 31.70 17.08\n", - "7111 20250509 920682.BJ 16.17 8.71\n", - "7112 20250509 920799.BJ 78.39 42.21\n", - "7113 20250509 920819.BJ 5.74 3.10\n", + "7136 20250530 920445.BJ 13.61 7.33\n", + "7137 20250530 920489.BJ 32.64 17.58\n", + "7138 20250530 920682.BJ 13.81 7.45\n", + "7139 20250530 920799.BJ 78.92 42.50\n", + "7140 20250530 920819.BJ 5.90 3.18\n", "\n", - "[7114 rows x 4 columns]]\n" + "[7141 rows x 4 columns], trade_date ts_code up_limit down_limit\n", + "0 20250529 000001.SZ 12.68 10.38\n", + "1 20250529 000002.SZ 7.35 6.01\n", + "2 20250529 000004.SZ 10.44 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@@ -258,7 +439,7 @@ ], "metadata": { "kernelspec": { - "display_name": "new_trader", + "display_name": "stock", "language": "python", "name": "python3" }, @@ -272,7 +453,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.11" + "version": "3.13.2" } }, "nbformat": 4, diff --git a/main/test.py b/main/test.py index 20586f0..4025490 100644 --- a/main/test.py +++ b/main/test.py @@ -2,3 +2,4 @@ import sys print(sys.path) from main.utils.utils import read_and_merge_h5_data, merge_with_industry_data + diff --git a/main/train/AnalyzeData.ipynb b/main/train/AnalyzeData.ipynb index 13aab3f..822df2a 100644 --- a/main/train/AnalyzeData.ipynb +++ b/main/train/AnalyzeData.ipynb @@ -29,396 +29,6 @@ { "cell_type": "code", "execution_count": 2, - "id": "2c66084a979c42dd", - "metadata": { - "ExecuteTime": { - "end_time": "2025-04-09T16:39:30.914968Z", - "start_time": "2025-04-09T16:39:30.858395Z" - }, - "jupyter": { - "source_hidden": true - } - }, - "outputs": [], - "source": [ - "\n", - "import talib\n", - "\n", - "\n", - "def get_rolling_factor(df):\n", - " old_columns = df.columns.tolist()[:]\n", - "\n", - " # 按股票和日期排序（如果尚未排序）\n", - " df = df.sort_values(by=['ts_code', 'trade_date'])\n", - "\n", - " grouped = df.groupby('ts_code', group_keys=False)\n", - "\n", - " window = 20\n", - " df['_is_positive'] = (df['pct_chg'] > 0).astype(int)\n", - " df['_is_negative'] = (df['pct_chg'] < 0).astype(int)\n", - " df['cat_is_positive'] = (df['pct_chg'] > 0).astype(int)\n", - "\n", - " # 分离正负收益率 (用于计算各自的均值和平方均值)\n", - " # 注意：这里我们保留原始收益率用于计算，而不是 clip 到 0\n", - " df['_pos_returns'] = df['pct_chg'].where(df['pct_chg'] > 0, 0) # 非正设为0，便于求和\n", - " df['_neg_returns'] = df['pct_chg'].where(df['pct_chg'] < 0, 0) # 非负设为0，便于求和\n", - "\n", - " # 计算收益率的平方 (用于计算 E[X^2])\n", - " df['_pos_returns_sq'] = np.square(df['_pos_returns'])\n", - " df['_neg_returns_sq'] = np.square(df['_neg_returns']) # 平方后负数变正\n", - "\n", - " # 4. 计算滚动统计量 (使用内置函数，速度较快)\n", - " # 计算正收益日的统计量\n", - " rolling_pos_count = grouped['_is_positive'].rolling(window, min_periods=max(1, window // 2)).sum()\n", - " rolling_pos_sum = grouped['_pos_returns'].rolling(window, min_periods=max(1, window // 2)).sum()\n", - " rolling_pos_sum_sq = grouped['_pos_returns_sq'].rolling(window, min_periods=max(1, window // 2)).sum()\n", - "\n", - " # 计算负收益日的统计量\n", - " rolling_neg_count = grouped['_is_negative'].rolling(window, min_periods=max(1, window // 2)).sum()\n", - " rolling_neg_sum = grouped['_neg_returns'].rolling(window, min_periods=max(1, window // 2)).sum()\n", - " rolling_neg_sum_sq = grouped['_neg_returns_sq'].rolling(window, min_periods=max(1, window // 2)).sum()\n", - "\n", - " # 5. 计算方差和标准差\n", - " pos_mean_sq = rolling_pos_sum_sq / rolling_pos_count\n", - " pos_mean = rolling_pos_sum / rolling_pos_count\n", - " pos_var = pos_mean_sq - np.square(pos_mean)\n", - " pos_var = pos_var.where(rolling_pos_count >= 2, np.nan).clip(lower=0)\n", - " upside_vol = np.sqrt(pos_var)\n", - "\n", - " neg_mean_sq = rolling_neg_sum_sq / rolling_neg_count\n", - " neg_mean = rolling_neg_sum / rolling_neg_count # 注意 neg_mean 是负数\n", - " neg_var = neg_mean_sq - np.square(neg_mean)\n", - " neg_var = neg_var.where(rolling_neg_count >= 2, np.nan).clip(lower=0)\n", - " downside_vol = np.sqrt(neg_var)\n", - "\n", - " # rolling 操作后结果带有 MultiIndex，需要去除股票代码层级以便合并\n", - " df['upside_vol'] = upside_vol.reset_index(level=0, drop=True)\n", - " df['downside_vol'] = downside_vol.reset_index(level=0, drop=True)\n", - "\n", - " df['vol_ratio'] = df['upside_vol'] / df['downside_vol']\n", - " df['vol_ratio'] = df['vol_ratio'].replace([np.inf, -np.inf], np.nan).fillna(0) # 或 fillna(np.nan)\n", - "\n", - " df['return_skew'] = grouped['pct_chg'].rolling(window=5).skew().reset_index(0, drop=True)\n", - " df['return_kurtosis'] = grouped['pct_chg'].rolling(window=5).kurt().reset_index(0, drop=True)\n", - "\n", - " # 因子 1：短期成交量变化率\n", - " df['volume_change_rate'] = (\n", - " grouped['vol'].rolling(window=2).mean() /\n", - " grouped['vol'].rolling(window=10).mean() - 1\n", - " ).reset_index(level=0, drop=True) # 确保索引对齐\n", - "\n", - " # 因子 2：成交量突破信号\n", - " max_volume = grouped['vol'].rolling(window=5).max().reset_index(level=0, drop=True) # 确保索引对齐\n", - " df['cat_volume_breakout'] = (df['vol'] > max_volume)\n", - "\n", - " # 因子 3：换手率均线偏离度\n", - " mean_turnover = grouped['turnover_rate'].rolling(window=3).mean().reset_index(level=0, drop=True)\n", - " std_turnover = grouped['turnover_rate'].rolling(window=3).std().reset_index(level=0, drop=True)\n", - " df['turnover_deviation'] = (df['turnover_rate'] - mean_turnover) / std_turnover\n", - "\n", - " # 因子 4：换手率激增信号\n", - " df['cat_turnover_spike'] = (df['turnover_rate'] > mean_turnover + 2 * std_turnover)\n", - "\n", - " # 因子 5：量比均值\n", - " df['avg_volume_ratio'] = grouped['volume_ratio'].rolling(window=3).mean().reset_index(level=0, drop=True)\n", - "\n", - " # 因子 6：量比突破信号\n", - " max_volume_ratio = grouped['volume_ratio'].rolling(window=5).max().reset_index(level=0, drop=True)\n", - " df['cat_volume_ratio_breakout'] = (df['volume_ratio'] > max_volume_ratio)\n", - "\n", - " df['vol_spike'] = grouped.apply(\n", - " lambda x: pd.Series(x['vol'].rolling(20).mean(), index=x.index)\n", - " )\n", - " df['vol_std_5'] = grouped['vol'].pct_change().rolling(window=5).std()\n", - "\n", - " # 计算 ATR\n", - " df['atr_14'] = grouped.apply(\n", - " lambda x: pd.Series(talib.ATR(x['high'].values, x['low'].values, x['close'].values, timeperiod=14),\n", - " index=x.index)\n", - " )\n", - " df['atr_6'] = grouped.apply(\n", - " lambda x: pd.Series(talib.ATR(x['high'].values, x['low'].values, x['close'].values, timeperiod=6),\n", - " index=x.index)\n", - " )\n", - "\n", - " # 计算 OBV 及其均线\n", - " df['obv'] = grouped.apply(\n", - " lambda x: pd.Series(talib.OBV(x['close'].values, x['vol'].values), index=x.index)\n", - " )\n", - " print(df.columns)\n", - " df['maobv_6'] = grouped.apply(\n", - " lambda x: pd.Series(talib.SMA(x['obv'].values, timeperiod=6), index=x.index)\n", - " )\n", - "\n", - " df['rsi_3'] = grouped.apply(\n", - " lambda x: pd.Series(talib.RSI(x['close'].values, timeperiod=3), index=x.index)\n", - " )\n", - " # df['rsi_6'] = grouped.apply(\n", - " # lambda x: pd.Series(talib.RSI(x['close'].values, timeperiod=6), index=x.index)\n", - " # )\n", - " # df['rsi_9'] = grouped.apply(\n", - " # lambda x: pd.Series(talib.RSI(x['close'].values, timeperiod=9), index=x.index)\n", - " # )\n", - "\n", - " # 计算 return_10 和 return_20\n", - " df['return_5'] = grouped['close'].apply(lambda x: x / x.shift(5) - 1)\n", - " # df['return_10'] = grouped['close'].apply(lambda x: x / x.shift(10) - 1)\n", - " df['return_20'] = grouped['close'].apply(lambda x: x / x.shift(20) - 1)\n", - "\n", - " # df['avg_close_5'] = grouped['close'].apply(lambda x: x.rolling(window=5).mean() / x)\n", - "\n", - " # 计算标准差指标\n", - " df['std_return_5'] = grouped['close'].apply(lambda x: x.pct_change().rolling(window=5).std())\n", - " # df['std_return_15'] = grouped['close'].apply(lambda x: x.pct_change().rolling(window=15).std())\n", - " # df['std_return_25'] = grouped['close'].apply(lambda x: x.pct_change().rolling(window=25).std())\n", - " df['std_return_90'] = grouped['close'].apply(lambda x: x.pct_change().rolling(window=90).std())\n", - " df['std_return_90_2'] = grouped['close'].apply(lambda x: x.shift(10).pct_change().rolling(window=90).std())\n", - "\n", - " # 计算 EMA 指标\n", - " df['_ema_5'] = grouped['close'].apply(\n", - " lambda x: pd.Series(talib.EMA(x.values, timeperiod=5), index=x.index)\n", - " )\n", - " df['_ema_13'] = grouped['close'].apply(\n", - " lambda x: pd.Series(talib.EMA(x.values, timeperiod=13), index=x.index)\n", - " )\n", - " df['_ema_20'] = grouped['close'].apply(\n", - " lambda x: pd.Series(talib.EMA(x.values, timeperiod=20), index=x.index)\n", - " )\n", - " df['_ema_60'] = grouped['close'].apply(\n", - " lambda x: pd.Series(talib.EMA(x.values, timeperiod=60), index=x.index)\n", - " )\n", - "\n", - " # 计算 act_factor1, act_factor2, act_factor3, act_factor4\n", - " df['act_factor1'] = grouped['_ema_5'].apply(\n", - " lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 50\n", - " )\n", - " df['act_factor2'] = grouped['_ema_13'].apply(\n", - " lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 40\n", - " )\n", - " df['act_factor3'] = grouped['_ema_20'].apply(\n", - " lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 21\n", - " )\n", - " df['act_factor4'] = grouped['_ema_60'].apply(\n", - " lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 10\n", - " )\n", - "\n", - " # 根据 trade_date 截面计算排名\n", - " df['rank_act_factor1'] = df.groupby('trade_date', group_keys=False)['act_factor1'].rank(ascending=False, pct=True)\n", - " df['rank_act_factor2'] = df.groupby('trade_date', group_keys=False)['act_factor2'].rank(ascending=False, pct=True)\n", - " df['rank_act_factor3'] = df.groupby('trade_date', group_keys=False)['act_factor3'].rank(ascending=False, pct=True)\n", - "\n", - " df['log(circ_mv)'] = np.log(df['circ_mv'])\n", - "\n", - " window_high_volume = 5\n", - " window_close_stddev = 20\n", - " period_delta = 5\n", - "\n", - " # 计算每只股票的滚动协方差\n", - " def calculate_rolling_cov(group):\n", - " return group['high'].rolling(window_high_volume).cov(group['vol'])\n", - "\n", - " df['cov'] = grouped.apply(calculate_rolling_cov)\n", - "\n", - " # 计算每只股票的协方差差分\n", - " def calculate_delta_cov(group):\n", - " return group['cov'].diff(period_delta)\n", - "\n", - " df['delta_cov'] = grouped.apply(calculate_delta_cov)\n", - "\n", - " # 计算每只股票的滚动标准差\n", - " def calculate_stddev_close(group):\n", - " return group['close'].rolling(window_close_stddev).std()\n", - "\n", - " df['_stddev_close'] = grouped.apply(calculate_stddev_close)\n", - " 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", - "\n", - " df['alpha_007'] = grouped.apply(lambda x: x['close'].rolling(5).corr(x['vol']))\n", - " df['alpha_007'] = df.groupby('trade_date', group_keys=False)['alpha_007'].rank(ascending=True, pct=True)\n", - "\n", - " df['alpha_013'] = grouped['close'].transform(lambda x: x.rolling(5).sum() - x.rolling(20).sum())\n", - " df['alpha_013'] = df.groupby('trade_date', group_keys=False)['alpha_013'].rank(ascending=True, pct=True)\n", - "\n", - " df['cat_up_limit'] = (df['close'] == df['up_limit']) # 是否涨停（1表示涨停，0表示未涨停）\n", - " df['cat_down_limit'] = (df['close'] == df['down_limit']) # 是否跌停（1表示跌停，0表示未跌停）\n", - " df['up_limit_count_10d'] = grouped['cat_up_limit'].rolling(window=10, min_periods=1).sum().reset_index(level=0,\n", - " drop=True)\n", - " df['down_limit_count_10d'] = grouped['cat_down_limit'].rolling(window=10, min_periods=1).sum().reset_index(level=0,\n", - " drop=True)\n", - "\n", - " # 3. 最近连续涨跌停天数\n", - " def calculate_consecutive_limits(series):\n", - " \"\"\"\n", - " 计算连续涨停/跌停天数。\n", - " \"\"\"\n", - " consecutive_up = series * (series.groupby((series != series.shift()).cumsum()).cumcount() + 1)\n", - " consecutive_down = series * (series.groupby((series != series.shift()).cumsum()).cumcount() + 1)\n", - " return consecutive_up, consecutive_down\n", - "\n", - " # 连续涨停天数\n", - " df['consecutive_up_limit'] = grouped['cat_up_limit'].apply(\n", - " lambda x: calculate_consecutive_limits(x)[0]\n", - " )\n", - "\n", - " df['vol_break'] = np.where((df['close'] > df['cost_85pct']) & (df['volume_ratio'] > 2), 1, 0)\n", - "\n", - " df['weight_roc5'] = grouped['weight_avg'].apply(lambda x: x.pct_change(5))\n", - "\n", - " def rolling_corr(group):\n", - " roc_close = group['close'].pct_change()\n", - " roc_weight = group['weight_avg'].pct_change()\n", - " return roc_close.rolling(10).corr(roc_weight)\n", - "\n", - " df['price_cost_divergence'] = grouped.apply(rolling_corr)\n", - "\n", - " df['smallcap_concentration'] = (1 / df['log(circ_mv)']) * (df['cost_85pct'] - df['cost_15pct'])\n", - "\n", - " # 16. 筹码稳定性指数 (20日波动率)\n", - " df['weight_std20'] = grouped['weight_avg'].apply(lambda x: x.rolling(20).std())\n", - " df['cost_stability'] = df['weight_std20'] / grouped['weight_avg'].transform(lambda x: x.rolling(20).mean())\n", - "\n", - " # 17. 成本区间突破标记\n", - " df['high_cost_break_days'] = grouped.apply(lambda g: g['close'].gt(g['cost_95pct']).rolling(5).sum())\n", - "\n", - " # 20. 筹码-流动性风险\n", - " df['liquidity_risk'] = (df['cost_95pct'] - df['cost_5pct']) * (\n", - " 1 / grouped['vol'].transform(lambda x: x.rolling(10).mean()))\n", - "\n", - " # # 7. 市值波动率因子\n", - " # df['turnover_std'] = df.groupby('ts_code')['turnover_rate'].transform(lambda x: x.rolling(window=20).std())\n", - " # df['mv_volatility'] = grouped.apply(lambda x: x['turnover_std'] / x['log(circ_mv)'])\n", - " #\n", - " # # 8. 市值成长性因子\n", - " # df['volume_growth'] = df.groupby('ts_code')['vol'].pct_change(periods=20)\n", - " # df['mv_growth'] = df['volume_growth'] / df['log(circ_mv)']\n", - " #\n", - " # df[\"ar\"] = df.groupby('ts_code').apply(lambda x: (x[\"high\"].div(x[\"open\"]).rolling(3).sum()) / (x[\"open\"].div(x[\"low\"]).rolling(3).sum()) * 100).reset_index(level='ts_code', drop=True)\n", - " # df[\"pre_close\"] = df.groupby('ts_code')[\"close\"].shift(1)\n", - " # df[\"br_up\"] = (df[\"high\"] - df[\"pre_close\"]).clip(lower=0)\n", - " # df[\"br_down\"] = (df[\"pre_close\"] - df[\"low\"]).clip(lower=0)\n", - " # df[\"br\"] = df.groupby('ts_code').apply(lambda x: (x[\"br_up\"].rolling(3).sum()) / (x[\"br_down\"].rolling(3).sum()) * 100).reset_index(level='ts_code', drop=True)\n", - " # df['arbr'] = df['ar'] - df['br']\n", - " # df.drop(columns=[\"pre_close\", \"br_up\", \"br_down\", 'ar', 'br'], inplace=True)\n", - "\n", - " # 7. 市值波动率因子 (使用 grouped)\n", - " df['turnover_std'] = grouped['turnover_rate'].transform(lambda x: x.rolling(window=20).std())\n", - " df['mv_volatility'] = grouped.apply(lambda x: x['turnover_std'] / x['log(circ_mv)'])\n", - "\n", - " # 8. 市值成长性因子\n", - " df['volume_growth'] = grouped['vol'].pct_change(periods=20)\n", - " 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", - "\n", - " # BR 指标\n", - " df[\"pre_close\"] = grouped[\"close\"].shift(1)\n", - " df[\"br_up\"] = (df[\"high\"] - df[\"pre_close\"]).clip(lower=0)\n", - " df[\"br_down\"] = (df[\"pre_close\"] - df[\"low\"]).clip(lower=0)\n", - " df[\"br\"] = grouped.apply(lambda x: (x[\"br_up\"].rolling(3).sum()) / (x[\"br_down\"].rolling(3).sum()) * 100)\n", - "\n", - " # ARBR\n", - " df['arbr'] = df['ar'] - df['br']\n", - " df.drop(columns=[\"pre_close\", \"br_up\", \"br_down\", 'ar', 'br'], inplace=True)\n", - "\n", - " df.drop(columns=['weight_std20'], inplace=True, errors='ignore')\n", - " df.drop(\n", - " columns=['_is_positive', '_is_negative', '_pos_returns', '_neg_returns', '_pos_returns_sq', '_neg_returns_sq'],\n", - " inplace=True, errors='ignore')\n", - " new_columns = [col for col in df.columns.tolist()[:] if col not in old_columns]\n", - "\n", - " return df, new_columns\n", - "\n", - "\n", - "def get_simple_factor(df):\n", - " old_columns = df.columns.tolist()[:]\n", - " df = df.sort_values(by=['ts_code', 'trade_date'])\n", - "\n", - " alpha = 0.5\n", - " df['momentum_factor'] = df['volume_change_rate'] + alpha * df['turnover_deviation']\n", - " df['resonance_factor'] = df['volume_ratio'] * df['pct_chg']\n", - " df['log_close'] = np.log(df['close'])\n", - "\n", - " df['cat_vol_spike'] = df['vol'] > 2 * df['vol_spike']\n", - "\n", - " df['up'] = (df['high'] - df[['close', 'open']].max(axis=1)) / df['close']\n", - " df['down'] = (df[['close', 'open']].min(axis=1) - df['low']) / df['close']\n", - "\n", - " df['obv-maobv_6'] = df['obv'] - df['maobv_6']\n", - "\n", - " # 计算比值指标\n", - " df['std_return_5 / std_return_90'] = df['std_return_5'] / df['std_return_90']\n", - " # df['std_return_5 / std_return_25'] = df['std_return_5'] / df['std_return_25']\n", - "\n", - " # 计算标准差差值\n", - " df['std_return_90 - std_return_90_2'] = df['std_return_90'] - df['std_return_90_2']\n", - "\n", - " # df['cat_af1'] = df['act_factor1'] > 0\n", - " df['cat_af2'] = df['act_factor2'] > df['act_factor1']\n", - " df['cat_af3'] = df['act_factor3'] > df['act_factor2']\n", - " df['cat_af4'] = df['act_factor4'] > df['act_factor3']\n", - "\n", - " # 计算 act_factor5 和 act_factor6\n", - " df['act_factor5'] = df['act_factor1'] + df['act_factor2'] + df['act_factor3'] + df['act_factor4']\n", - " df['act_factor6'] = (df['act_factor1'] - df['act_factor2']) / np.sqrt(\n", - " df['act_factor1'] ** 2 + df['act_factor2'] ** 2)\n", - "\n", - " df['active_buy_volume_large'] = df['buy_lg_vol'] / df['net_mf_vol']\n", - " df['active_buy_volume_big'] = df['buy_elg_vol'] / df['net_mf_vol']\n", - " df['active_buy_volume_small'] = df['buy_sm_vol'] / df['net_mf_vol']\n", - "\n", - " df['buy_lg_vol_minus_sell_lg_vol'] = (df['buy_lg_vol'] - df['sell_lg_vol']) / df['net_mf_vol']\n", - " df['buy_elg_vol_minus_sell_elg_vol'] = (df['buy_elg_vol'] - df['sell_elg_vol']) / df['net_mf_vol']\n", - "\n", - " df['log(circ_mv)'] = np.log(df['circ_mv'])\n", - "\n", - " df['ctrl_strength'] = (df['cost_85pct'] - df['cost_15pct']) / (df['his_high'] - df['his_low'])\n", - "\n", - " df['low_cost_dev'] = (df['close'] - df['cost_5pct']) / (df['cost_50pct'] - df['cost_5pct'])\n", - "\n", - " df['asymmetry'] = (df['cost_95pct'] - df['cost_50pct']) / (df['cost_50pct'] - df['cost_5pct'])\n", - "\n", - " df['lock_factor'] = df['turnover_rate'] * (\n", - " 1 - (df['cost_95pct'] - df['cost_5pct']) / (df['his_high'] - df['his_low']))\n", - "\n", - " df['cat_vol_break'] = (df['close'] > df['cost_85pct']) & (df['volume_ratio'] > 2)\n", - "\n", - " df['cost_atr_adj'] = (df['cost_95pct'] - df['cost_5pct']) / df['atr_14']\n", - "\n", - " # 12. 小盘股筹码集中度\n", - " df['smallcap_concentration'] = (1 / df['log(circ_mv)']) * (df['cost_85pct'] - df['cost_15pct'])\n", - "\n", - " df['cat_golden_resonance'] = ((df['close'] > df['weight_avg']) &\n", - " (df['volume_ratio'] > 1.5) &\n", - " (df['winner_rate'] > 0.7))\n", - "\n", - " df['mv_turnover_ratio'] = df['turnover_rate'] / df['log(circ_mv)']\n", - "\n", - " df['mv_adjusted_volume'] = df['vol'] / df['log(circ_mv)']\n", - "\n", - " df['mv_weighted_turnover'] = df['turnover_rate'] * (1 / df['log(circ_mv)'])\n", - "\n", - " df['nonlinear_mv_volume'] = df['vol'] / df['log(circ_mv)']\n", - "\n", - " df['mv_volume_ratio'] = df['volume_ratio'] / df['log(circ_mv)']\n", - "\n", - " df['mv_momentum'] = df['turnover_rate'] * df['volume_ratio'] / df['log(circ_mv)']\n", - "\n", - " drop_columns = [col for col in df.columns if col.startswith('_')]\n", - " df.drop(columns=drop_columns, inplace=True, errors='ignore')\n", - "\n", - " new_columns = [col for col in df.columns.tolist()[:] if col not in old_columns]\n", - " return df, new_columns\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, "id": "a79cafb06a7e0e43", "metadata": { "ExecuteTime": { @@ -445,8 +55,8 @@ "cyq perf\n", "left merge on ['ts_code', 'trade_date']\n", "\n", - "RangeIndex: 4051406 entries, 0 to 4051405\n", - "Data columns (total 31 columns):\n", + "RangeIndex: 8692146 entries, 0 to 8692145\n", + "Data columns (total 33 columns):\n", " # Column Dtype \n", "--- ------ ----- \n", " 0 ts_code object \n", @@ -456,204 +66,95 @@ " 4 high float64 \n", " 5 low float64 \n", " 6 vol float64 \n", - " 7 pct_chg float64 \n", - " 8 turnover_rate float64 \n", - " 9 pe_ttm float64 \n", - " 10 circ_mv float64 \n", - " 11 volume_ratio float64 \n", - " 12 is_st bool \n", - " 13 up_limit float64 \n", - " 14 down_limit float64 \n", - " 15 buy_sm_vol float64 \n", - " 16 sell_sm_vol float64 \n", - " 17 buy_lg_vol float64 \n", - " 18 sell_lg_vol float64 \n", - " 19 buy_elg_vol float64 \n", - " 20 sell_elg_vol float64 \n", - " 21 net_mf_vol float64 \n", - " 22 his_low float64 \n", - " 23 his_high float64 \n", - " 24 cost_5pct float64 \n", - " 25 cost_15pct float64 \n", - " 26 cost_50pct float64 \n", - " 27 cost_85pct float64 \n", - " 28 cost_95pct float64 \n", - " 29 weight_avg float64 \n", - " 30 winner_rate float64 \n", - "dtypes: bool(1), datetime64[ns](1), float64(28), object(1)\n", - "memory usage: 931.2+ MB\n", + " 7 amount float64 \n", + " 8 pct_chg float64 \n", + " 9 turnover_rate float64 \n", + " 10 pe_ttm float64 \n", + " 11 circ_mv float64 \n", + " 12 total_mv float64 \n", + " 13 volume_ratio float64 \n", + " 14 is_st bool \n", + " 15 up_limit float64 \n", + " 16 down_limit float64 \n", + " 17 buy_sm_vol float64 \n", + " 18 sell_sm_vol float64 \n", + " 19 buy_lg_vol float64 \n", + " 20 sell_lg_vol float64 \n", + " 21 buy_elg_vol float64 \n", + " 22 sell_elg_vol float64 \n", + " 23 net_mf_vol float64 \n", + " 24 his_low float64 \n", + " 25 his_high float64 \n", + " 26 cost_5pct float64 \n", + " 27 cost_15pct float64 \n", + " 28 cost_50pct float64 \n", + " 29 cost_85pct float64 \n", + " 30 cost_95pct float64 \n", + " 31 weight_avg float64 \n", + " 32 winner_rate float64 \n", + "dtypes: bool(1), datetime64[ns](1), float64(30), object(1)\n", + "memory usage: 2.1+ GB\n", "None\n" ] } ], "source": [ - "from code.utils.utils import read_and_merge_h5_data\n", + "from main.utils.utils import read_and_merge_h5_data\n", "\n", "print('daily data')\n", - "df1 = read_and_merge_h5_data('../../data-copy/daily_data.h5', key='daily_data',\n", - " columns=['ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'vol', 'pct_chg'],\n", + "df = read_and_merge_h5_data('/mnt/d/PyProject/NewStock/data/daily_data.h5', key='daily_data',\n", + " columns=['ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'vol', 'amount', 'pct_chg'],\n", " df=None)\n", - "df1 = df1[df1['trade_date'] >= '2022-01-01']\n", "\n", "print('daily basic')\n", - "df1 = read_and_merge_h5_data('../../data-copy/daily_basic.h5', key='daily_basic',\n", - " columns=['ts_code', 'trade_date', 'turnover_rate', 'pe_ttm', 'circ_mv', 'volume_ratio',\n", - " 'is_st'], df=df1, join='inner')\n", + "df = read_and_merge_h5_data('/mnt/d/PyProject/NewStock/data/daily_basic.h5', key='daily_basic',\n", + " columns=['ts_code', 'trade_date', 'turnover_rate', 'pe_ttm', 'circ_mv', 'total_mv', 'volume_ratio',\n", + " 'is_st'], df=df, join='inner')\n", "\n", "print('stk limit')\n", - "df1 = read_and_merge_h5_data('../../data-copy/stk_limit.h5', key='stk_limit',\n", + "df = read_and_merge_h5_data('/mnt/d/PyProject/NewStock/data/stk_limit.h5', key='stk_limit',\n", " columns=['ts_code', 'trade_date', 'pre_close', 'up_limit', 'down_limit'],\n", - " df=df1)\n", + " df=df)\n", "print('money flow')\n", - "df1 = read_and_merge_h5_data('../../data-copy/money_flow.h5', key='money_flow',\n", + "df = read_and_merge_h5_data('/mnt/d/PyProject/NewStock/data/money_flow.h5', key='money_flow',\n", " columns=['ts_code', 'trade_date', 'buy_sm_vol', 'sell_sm_vol', 'buy_lg_vol', 'sell_lg_vol',\n", " 'buy_elg_vol', 'sell_elg_vol', 'net_mf_vol'],\n", - " df=df1)\n", + " df=df)\n", "print('cyq perf')\n", - "df1 = read_and_merge_h5_data('../../data-copy/cyq_perf.h5', key='cyq_perf',\n", + "df = read_and_merge_h5_data('/mnt/d/PyProject/NewStock/data/cyq_perf.h5', key='cyq_perf',\n", " columns=['ts_code', 'trade_date', 'his_low', 'his_high', 'cost_5pct', 'cost_15pct',\n", " 'cost_50pct',\n", " 'cost_85pct', 'cost_95pct', 'weight_avg', 'winner_rate'],\n", - " df=df1)\n", - "print(df1.info())" + " df=df)\n", + "print(df.info())" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "8d2acede", + "metadata": {}, + "outputs": [], + "source": [ + "fina_indicator_df = read_and_merge_h5_data('/mnt/d/PyProject/NewStock//data/fina_indicator.h5', key='fina_indicator',\n", + " columns=['ts_code', 'ann_date', 'undist_profit_ps', 'ocfps', 'bps', 'roa', 'roe'],\n", + " df=None)\n", + "cashflow_df = read_and_merge_h5_data('/mnt/d/PyProject/NewStock//data/cashflow.h5', key='cashflow',\n", + " columns=['ts_code', 'ann_date', 'n_cashflow_act'],\n", + " df=None)\n", + "balancesheet_df = read_and_merge_h5_data('/mnt/d/PyProject/NewStock//data/balancesheet.h5', key='balancesheet',\n", + " columns=['ts_code', 'ann_date', 'money_cap', 'total_liab'],\n", + " df=None)\n", + "top_list_df = read_and_merge_h5_data('/mnt/d/PyProject/NewStock//data/top_list.h5', key='top_list',\n", + " columns=['ts_code', 'trade_date', 'reason'],\n", + " df=None)\n", + "\n", + "top_list_df = top_list_df.sort_values(by='trade_date', ascending=False).drop_duplicates(subset=['ts_code', 'trade_date'], keep='first').sort_values(by='trade_date')\n" ] }, { "cell_type": "code", "execution_count": 4, - "id": "cac01788dac10678", - "metadata": { - "ExecuteTime": { - "end_time": "2025-04-09T16:40:23.694912Z", - "start_time": "2025-04-09T16:40:19.488481Z" - }, - "jupyter": { - "source_hidden": true - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "industry\n" - ] - } - ], - "source": [ - "print('industry')\n", - "industry_df1 = read_and_merge_h5_data('../../data-copy/industry_data.h5', key='industry_data',\n", - " columns=['ts_code', 'l2_code', 'in_date'],\n", - " df=None, on=['ts_code'], join='left')\n", - "\n", - "\n", - "def merge_with_industry_data(df, industry_df):\n", - " # 确保日期字段是 datetime 类型\n", - " df['trade_date'] = pd.to_datetime(df['trade_date'])\n", - " industry_df['in_date'] = pd.to_datetime(industry_df['in_date'])\n", - "\n", - " # 对 industry_df 按 ts_code 和 in_date 排序\n", - " industry_df_sorted = industry_df.sort_values(['in_date', 'ts_code'])\n", - "\n", - " # 对原始 df 按 ts_code 和 trade_date 排序\n", - " df_sorted = df.sort_values(['trade_date', 'ts_code'])\n", - "\n", - " # 使用 merge_asof 进行向后合并\n", - " merged = pd.merge_asof(\n", - " df_sorted,\n", - " industry_df_sorted,\n", - " by='ts_code', # 按 ts_code 分组\n", - " left_on='trade_date',\n", - " right_on='in_date',\n", - " direction='backward'\n", - " )\n", - "\n", - " # 获取每个 ts_code 的最早 in_date 记录\n", - " min_in_date_per_ts = (industry_df_sorted\n", - " .groupby('ts_code')\n", - " .first()\n", - " .reset_index()[['ts_code', 'l2_code']])\n", - "\n", - " # 填充未匹配到的记录（trade_date 早于所有 in_date 的情况）\n", - " merged['l2_code'] = merged['l2_code'].fillna(\n", - " merged['ts_code'].map(min_in_date_per_ts.set_index('ts_code')['l2_code'])\n", - " )\n", - "\n", - " # 保留需要的列并重置索引\n", - " result = merged.reset_index(drop=True)\n", - " return result\n", - "\n", - "\n", - "# 使用示例\n", - "df1 = merge_with_industry_data(df1, industry_df1)\n", - "# print(mdf[mdf['ts_code'] == '600751.SH'][['ts_code', 'trade_date', 'l2_code']])" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "5f7a8b42681606f6", - "metadata": { - "ExecuteTime": { - "end_time": "2025-04-09T16:40:30.145830Z", - "start_time": "2025-04-09T16:40:23.712071Z" - }, - "jupyter": { - "source_hidden": true - } - }, - "outputs": [], - "source": [ - "from code.utils.factor import get_act_factor\n", - "\n", - "\n", - "def read_industry_data(h5_filename):\n", - " # 读取 H5 文件中所有的行业数据\n", - " industry_data = pd.read_hdf(h5_filename, key='sw_daily', columns=[\n", - " 'ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'pe', 'pb', 'vol'\n", - " ]) # 假设 H5 文件的键是 'industry_data'\n", - " industry_data = industry_data.sort_values(by=['ts_code', 'trade_date'])\n", - " industry_data = industry_data.reindex()\n", - " industry_data['trade_date'] = pd.to_datetime(industry_data['trade_date'], format='%Y%m%d')\n", - "\n", - " grouped = industry_data.groupby('ts_code', group_keys=False)\n", - " industry_data['obv'] = grouped.apply(\n", - " lambda x: pd.Series(talib.OBV(x['close'].values, x['vol'].values), index=x.index)\n", - " )\n", - " industry_data['return_5'] = grouped['close'].apply(lambda x: x / x.shift(5) - 1)\n", - " industry_data['return_20'] = grouped['close'].apply(lambda x: x / x.shift(20) - 1)\n", - "\n", - " industry_data = get_act_factor(industry_data, cat=False)\n", - " industry_data = industry_data.sort_values(by=['trade_date', 'ts_code'])\n", - "\n", - " # # 计算每天每个 ts_code 的因子和当天所有 ts_code 的中位数的偏差\n", - " # factor_columns = ['obv', 'return_5', 'return_20', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4'] # 因子列\n", - " #\n", - " # for factor in factor_columns:\n", - " # if factor in industry_data.columns:\n", - " # # 计算每天每个 ts_code 的因子值与当天所有 ts_code 的中位数的偏差\n", - " # industry_data[f'{factor}_deviation'] = industry_data.groupby('trade_date')[factor].transform(\n", - " # lambda x: x - x.mean())\n", - "\n", - " industry_data['return_5_percentile'] = industry_data.groupby('trade_date')['return_5'].transform(\n", - " lambda x: x.rank(pct=True))\n", - " industry_data['return_20_percentile'] = industry_data.groupby('trade_date')['return_20'].transform(\n", - " lambda x: x.rank(pct=True))\n", - " industry_data = industry_data.drop(columns=['open', 'close', 'high', 'low', 'pe', 'pb', 'vol'])\n", - "\n", - " industry_data = industry_data.rename(\n", - " columns={col: f'industry_{col}' for col in industry_data.columns if col not in ['ts_code', 'trade_date']})\n", - "\n", - " industry_data = industry_data.rename(columns={'ts_code': 'cat_l2_code'})\n", - " return industry_data\n", - "\n", - "\n", - "industry_df1 = read_industry_data('../../data-copy/sw_daily.h5')\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, "id": "85c3e3d0235ffffa", "metadata": { "ExecuteTime": { @@ -669,30 +170,142 @@ "name": "stdout", "output_type": "stream", "text": [ + "使用 'ann_date' 作为财务数据生效日期。\n", + "警告: 从 financial_data_subset 中移除了 366 行，因为其 'ts_code' 或 'ann_date' 列存在空值。\n", + "使用 'ann_date' 作为财务数据生效日期。\n", + "警告: 从 financial_data_subset 中移除了 366 行，因为其 'ts_code' 或 'ann_date' 列存在空值。\n", + "使用 'ann_date' 作为财务数据生效日期。\n", + "警告: 从 financial_data_subset 中移除了 366 行，因为其 'ts_code' 或 'ann_date' 列存在空值。\n", + "使用 'ann_date' 作为财务数据生效日期。\n", + "警告: 从 financial_data_subset 中移除了 366 行，因为其 'ts_code' 或 'ann_date' 列存在空值。\n", + "开始计算因子: AR, BR (原地修改)...\n", + "因子 AR, BR 计算成功。\n", + "因子 AR, BR 计算流程结束。\n", + "使用 'ann_date' 作为财务数据生效日期。\n", + "使用 'ann_date' 作为财务数据生效日期。\n", + "使用 'ann_date' 作为财务数据生效日期。\n", + "使用 'ann_date' 作为财务数据生效日期。\n", + "警告: 从 financial_data_subset 中移除了 366 行，因为其 'ts_code' 或 'ann_date' 列存在空值。\n", + "计算 BBI...\n", + "--- 计算日级别偏离度 (使用 pct_chg) ---\n", + "--- 计算日级别动量基准 (使用 pct_chg) ---\n", + "日级别动量基准计算完成 (使用 pct_chg)。\n", + "日级别偏离度计算完成 (使用 pct_chg)。\n", + "--- 计算日级别行业偏离度 (使用 pct_chg 和行业基准) ---\n", + "--- 计算日级别行业动量基准 (使用 pct_chg 和 cat_l2_code) ---\n", + "错误: 计算日级别行业动量基准需要以下列: ['pct_chg', 'cat_l2_code', 'trade_date', 'ts_code']。\n", + "错误: 计算日级别行业偏离度需要以下列: ['pct_chg', 'daily_industry_positive_benchmark', 'daily_industry_negative_benchmark']。请先运行 daily_industry_momentum_benchmark(df)。\n", "Index(['ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'vol',\n", - " 'pct_chg', 'turnover_rate', 'pe_ttm', 'circ_mv', 'volume_ratio',\n", - " 'is_st', 'up_limit', 'down_limit', 'buy_sm_vol', 'sell_sm_vol',\n", - " 'buy_lg_vol', 'sell_lg_vol', 'buy_elg_vol', 'sell_elg_vol',\n", - " 'net_mf_vol', 'his_low', 'his_high', 'cost_5pct', 'cost_15pct',\n", - " 'cost_50pct', 'cost_85pct', 'cost_95pct', 'weight_avg', 'winner_rate',\n", - " 'l2_code', '_is_positive', '_is_negative', 'cat_is_positive',\n", - " '_pos_returns', '_neg_returns', '_pos_returns_sq', '_neg_returns_sq',\n", - " 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew',\n", - " 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout',\n", - " 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio',\n", - " 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14',\n", - " 'atr_6', 'obv'],\n", + " 'amount', 'pct_chg', 'turnover_rate', 'pe_ttm', 'circ_mv', 'total_mv',\n", + " 'volume_ratio', 'is_st', 'up_limit', 'down_limit', 'buy_sm_vol',\n", + " 'sell_sm_vol', 'buy_lg_vol', 'sell_lg_vol', 'buy_elg_vol',\n", + " 'sell_elg_vol', 'net_mf_vol', 'his_low', 'his_high', 'cost_5pct',\n", + " 'cost_15pct', 'cost_50pct', 'cost_85pct', 'cost_95pct', 'weight_avg',\n", + " 'winner_rate', 'undist_profit_ps', 'ocfps', 'roa', 'roe', 'AR', 'BR',\n", + " 'AR_BR', 'log_circ_mv', 'cashflow_to_ev_factor', 'book_to_price_ratio',\n", + " 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor',\n", + " 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity',\n", + " 'sm_net_buy_vol', 'flow_divergence_diff', 'flow_divergence_ratio',\n", + " 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change',\n", + " 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel',\n", + " 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy',\n", + " 'cost_support_15pct_change', 'cat_winner_price_zone',\n", + " 'flow_chip_consistency', 'profit_taking_vs_absorb', '_is_positive',\n", + " '_is_negative', 'cat_is_positive', '_pos_returns', '_neg_returns',\n", + " '_pos_returns_sq', '_neg_returns_sq', 'upside_vol', 'downside_vol',\n", + " 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate',\n", + " 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike',\n", + " 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike',\n", + " 'vol_std_5', 'atr_14', 'atr_6', 'obv'],\n", " dtype='object')\n", + "Calculating lg_flow_mom_corr_20_60...\n", + "Finished lg_flow_mom_corr_20_60.\n", + "Calculating lg_flow_accel...\n", + "Finished lg_flow_accel.\n", + "Calculating profit_pressure...\n", + "Finished profit_pressure.\n", + "Calculating underwater_resistance...\n", + "Finished underwater_resistance.\n", + "Calculating cost_conc_std_20...\n", + "Finished cost_conc_std_20.\n", + "Calculating profit_decay_20...\n", + "Finished profit_decay_20.\n", + "Calculating vol_amp_loss_20...\n", + "Finished vol_amp_loss_20.\n", + "Calculating vol_drop_profit_cnt_5...\n", + "Finished vol_drop_profit_cnt_5.\n", + "Calculating lg_flow_vol_interact_20...\n", + "Finished lg_flow_vol_interact_20.\n", + "Calculating cost_break_confirm_cnt_5...\n", + "Finished cost_break_confirm_cnt_5.\n", + "Calculating atr_norm_channel_pos_14...\n", + "Finished atr_norm_channel_pos_14.\n", + "Calculating turnover_diff_skew_20...\n", + "Finished turnover_diff_skew_20.\n", + "Calculating lg_sm_flow_diverge_20...\n", + "Finished lg_sm_flow_diverge_20.\n", + "Calculating pullback_strong_20_20...\n", + "Finished pullback_strong_20_20.\n", + "Calculating vol_wgt_hist_pos_20...\n", + "Finished vol_wgt_hist_pos_20.\n", + "Calculating vol_adj_roc_20...\n", + "Finished vol_adj_roc_20.\n", + "Calculating cs_rank_net_lg_flow_val...\n", + "Finished cs_rank_net_lg_flow_val.\n", + "Calculating cs_rank_flow_divergence...\n", + "Finished cs_rank_flow_divergence.\n", + "Calculating cs_rank_ind_adj_lg_flow...\n", + "Error calculating cs_rank_ind_adj_lg_flow: Missing 'cat_l2_code' column. Assigning NaN.\n", + "Calculating cs_rank_elg_buy_ratio...\n", + "Finished cs_rank_elg_buy_ratio.\n", + "Calculating cs_rank_rel_profit_margin...\n", + "Finished cs_rank_rel_profit_margin.\n", + "Calculating cs_rank_cost_breadth...\n", + "Finished cs_rank_cost_breadth.\n", + "Calculating cs_rank_dist_to_upper_cost...\n", + "Finished cs_rank_dist_to_upper_cost.\n", + "Calculating cs_rank_winner_rate...\n", + "Finished cs_rank_winner_rate.\n", + "Calculating cs_rank_intraday_range...\n", + "Finished cs_rank_intraday_range.\n", + "Calculating cs_rank_close_pos_in_range...\n", + "Finished cs_rank_close_pos_in_range.\n", + "Calculating cs_rank_opening_gap...\n", + "Error calculating cs_rank_opening_gap: Missing 'pre_close' column. Assigning NaN.\n", + "Calculating cs_rank_pos_in_hist_range...\n", + "Finished cs_rank_pos_in_hist_range.\n", + "Calculating cs_rank_vol_x_profit_margin...\n", + "Finished cs_rank_vol_x_profit_margin.\n", + "Calculating cs_rank_lg_flow_price_concordance...\n", + "Finished cs_rank_lg_flow_price_concordance.\n", + "Calculating cs_rank_turnover_per_winner...\n", + "Finished cs_rank_turnover_per_winner.\n", + "Calculating cs_rank_ind_cap_neutral_pe (Placeholder - requires statsmodels)...\n", + "Finished cs_rank_ind_cap_neutral_pe (Placeholder).\n", + "Calculating cs_rank_volume_ratio...\n", + "Finished cs_rank_volume_ratio.\n", + "Calculating cs_rank_elg_buy_sell_sm_ratio...\n", + "Finished cs_rank_elg_buy_sell_sm_ratio.\n", + "Calculating cs_rank_cost_dist_vol_ratio...\n", + "Finished cs_rank_cost_dist_vol_ratio.\n", + "Calculating cs_rank_size...\n", + "Finished cs_rank_size.\n", "\n", - "RangeIndex: 2425287 entries, 0 to 2425286\n", - "Columns: 137 entries, ts_code to industry_return_20_percentile\n", - "dtypes: bool(12), datetime64[ns](1), float64(119), int32(2), int64(1), object(2)\n", - "memory usage: 2.3+ GB\n", - "None\n" + "RangeIndex: 2511964 entries, 0 to 2511963\n", + "Columns: 180 entries, ts_code to cs_rank_size\n", + "dtypes: bool(10), datetime64[ns](1), float64(165), int64(3), object(1)\n", + "memory usage: 3.2+ GB\n", + "None\n", + "['ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'vol', 'amount', 'pct_chg', 'turnover_rate', 'pe_ttm', 'circ_mv', 'total_mv', 'volume_ratio', 'is_st', 'up_limit', 'down_limit', 'buy_sm_vol', 'sell_sm_vol', 'buy_lg_vol', 'sell_lg_vol', 'buy_elg_vol', 'sell_elg_vol', 'net_mf_vol', 'his_low', 'his_high', 'cost_5pct', 'cost_15pct', 'cost_50pct', 'cost_85pct', 'cost_95pct', 'weight_avg', 'winner_rate', 'undist_profit_ps', 'ocfps', 'roa', 'roe', 'AR', 'BR', 'AR_BR', 'log_circ_mv', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'flow_divergence_diff', 'flow_divergence_ratio', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'price_cost_divergence', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_flow_divergence', 'cs_rank_ind_adj_lg_flow', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_opening_gap', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_ind_cap_neutral_pe', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size']\n" ] } ], "source": [ + "# df1\n", + "\n", + "import numpy as np\n", + "from main.factor.factor import *\n", + "\n", "def filter_data(df):\n", " # df = df.groupby('trade_date').apply(lambda x: x.nlargest(1000, 'act_factor1'))\n", " df = df[~df['is_st']]\n", @@ -700,25 +313,125 @@ " df = df[~df['ts_code'].str.startswith('30')]\n", " df = df[~df['ts_code'].str.startswith('68')]\n", " df = df[~df['ts_code'].str.startswith('8')]\n", + " df = df[df['trade_date'] >= '2022-01-01']\n", " if 'in_date' in df.columns:\n", " df = df.drop(columns=['in_date'])\n", " df = df.reset_index(drop=True)\n", " return df\n", "\n", + "import gc\n", + "gc.collect()\n", "\n", - "df1 = filter_data(df1)\n", - "df1, _ = get_rolling_factor(df1)\n", - "df1, _ = get_simple_factor(df1)\n", - "df1 = df1.rename(columns={'l2_code': 'cat_l2_code'})\n", - "df1 = df1.merge(industry_df1, on=['cat_l2_code', 'trade_date'], how='left')\n", + "df = filter_data(df)\n", + "df = df.sort_values(by=['ts_code', 'trade_date'])\n", "\n", + "# df = price_minus_deduction_price(df, n=120)\n", + "# df = price_deduction_price_diff_ratio_to_sma(df, n=120)\n", + "# df = cat_price_vs_sma_vs_deduction_price(df, n=120)\n", + "# df = cat_reason(df, top_list_df)\n", + "# df = cat_is_on_top_list(df, top_list_df)\n", "\n", - "print(df1.info())" + "# df = cat_senti_mom_vol_spike(\n", + "# df,\n", + "# return_period=3,\n", + "# return_threshold=0.03, # 近3日涨幅超3%\n", + "# volume_ratio_threshold=1.3,\n", + "# current_pct_chg_min=0.0, # 当日必须收红\n", + "# current_pct_chg_max=0.05,\n", + "# ) # 当日涨幅不宜过大\n", + "\n", + "# df = cat_senti_pre_breakout(\n", + "# df,\n", + "# atr_short_N=10,\n", + "# atr_long_M=40,\n", + "# vol_atrophy_N=10,\n", + "# vol_atrophy_M=40,\n", + "# price_stab_N=5,\n", + "# price_stab_threshold=0.06,\n", + "# current_pct_chg_min_signal=0.002,\n", + "# current_pct_chg_max_signal=0.05,\n", + "# volume_ratio_signal_threshold=1.1,\n", + "# )\n", + "\n", + "# df = ts_turnover_rate_acceleration_5_20(df)\n", + "# df = ts_vol_sustain_10_30(df)\n", + "# # df = cs_turnover_rate_relative_strength_20(df)\n", + "# df = cs_amount_outlier_10(df)\n", + "# df = ts_ff_to_total_turnover_ratio(df)\n", + "# df = ts_price_volume_trend_coherence_5_20(df)\n", + "# # df = ts_turnover_rate_trend_strength_5(df)\n", + "# df = ts_ff_turnover_rate_surge_10(df)\n", + "\n", + "df = add_financial_factor(df, fina_indicator_df, factor_value_col='undist_profit_ps')\n", + "df = add_financial_factor(df, fina_indicator_df, factor_value_col='ocfps')\n", + "df = add_financial_factor(df, fina_indicator_df, factor_value_col='roa')\n", + "df = add_financial_factor(df, fina_indicator_df, factor_value_col='roe')\n", + "\n", + "calculate_arbr(df, N=26)\n", + "df['log_circ_mv'] = np.log(df['circ_mv'])\n", + "df = calculate_cashflow_to_ev_factor(df, cashflow_df, balancesheet_df)\n", + "df = caculate_book_to_price_ratio(df, fina_indicator_df)\n", + "\n", + "df = turnover_rate_n(df, n=5)\n", + "df = variance_n(df, n=20)\n", + "df = bbi_ratio_factor(df)\n", + "df = daily_deviation(df)\n", + "df = daily_industry_deviation(df)\n", + "df, _ = get_rolling_factor(df)\n", + "df, _ = get_simple_factor(df)\n", + "\n", + "df = df.rename(columns={'l1_code': 'cat_l1_code'})\n", + "df = df.rename(columns={'l2_code': 'cat_l2_code'})\n", + "\n", + "lg_flow_mom_corr(df, N=20, M=60)\n", + "lg_flow_accel(df)\n", + "profit_pressure(df)\n", + "underwater_resistance(df)\n", + "cost_conc_std(df, N=20)\n", + "profit_decay(df, N=20)\n", + "vol_amp_loss(df, N=20)\n", + "vol_drop_profit_cnt(df, N=20, M=5)\n", + "lg_flow_vol_interact(df, N=20)\n", + "cost_break_confirm_cnt(df, M=5)\n", + "atr_norm_channel_pos(df, N=14)\n", + "turnover_diff_skew(df, N=20)\n", + "lg_sm_flow_diverge(df, N=20)\n", + "pullback_strong(df, N=20, M=20)\n", + "vol_wgt_hist_pos(df, N=20)\n", + "vol_adj_roc(df, N=20)\n", + "\n", + "cs_rank_net_lg_flow_val(df)\n", + "cs_rank_flow_divergence(df)\n", + "cs_rank_industry_adj_lg_flow(df) # Needs cat_l2_code\n", + "cs_rank_elg_buy_ratio(df)\n", + "cs_rank_rel_profit_margin(df)\n", + "cs_rank_cost_breadth(df)\n", + "cs_rank_dist_to_upper_cost(df)\n", + "cs_rank_winner_rate(df)\n", + "cs_rank_intraday_range(df)\n", + "cs_rank_close_pos_in_range(df)\n", + "cs_rank_opening_gap(df) # Needs pre_close\n", + "cs_rank_pos_in_hist_range(df) # Needs his_low, his_high\n", + "cs_rank_vol_x_profit_margin(df)\n", + "cs_rank_lg_flow_price_concordance(df)\n", + "cs_rank_turnover_per_winner(df)\n", + "cs_rank_ind_cap_neutral_pe(df) # Placeholder - needs external libraries\n", + "cs_rank_volume_ratio(df) # Needs volume_ratio\n", + "cs_rank_elg_buy_sell_sm_ratio(df)\n", + "cs_rank_cost_dist_vol_ratio(df) # Needs volume_ratio\n", + "cs_rank_size(df) # Needs circ_mv\n", + "\n", + "df1 = df.copy()\n", + "\n", + "# df = df.merge(index_data, on='trade_date', how='left')\n", + "\n", + "print(df.info())\n", + "print(df.columns.tolist())" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 5, "id": "5dabff1e7bdd48c0", "metadata": { "ExecuteTime": { @@ -744,8 +457,8 @@ "cyq perf\n", "left merge on ['ts_code', 'trade_date']\n", "\n", - "RangeIndex: 4062142 entries, 0 to 4062141\n", - "Data columns (total 31 columns):\n", + "RangeIndex: 8692146 entries, 0 to 8692145\n", + "Data columns (total 33 columns):\n", " # Column Dtype \n", "--- ------ ----- \n", " 0 ts_code object \n", @@ -755,157 +468,72 @@ " 4 high float64 \n", " 5 low float64 \n", " 6 vol float64 \n", - " 7 pct_chg float64 \n", - " 8 turnover_rate float64 \n", - " 9 pe_ttm float64 \n", - " 10 circ_mv float64 \n", - " 11 volume_ratio float64 \n", - " 12 is_st bool \n", - " 13 up_limit float64 \n", - " 14 down_limit float64 \n", - " 15 buy_sm_vol float64 \n", - " 16 sell_sm_vol float64 \n", - " 17 buy_lg_vol float64 \n", - " 18 sell_lg_vol float64 \n", - " 19 buy_elg_vol float64 \n", - " 20 sell_elg_vol float64 \n", - " 21 net_mf_vol float64 \n", - " 22 his_low float64 \n", - " 23 his_high float64 \n", - " 24 cost_5pct float64 \n", - " 25 cost_15pct float64 \n", - " 26 cost_50pct float64 \n", - " 27 cost_85pct float64 \n", - " 28 cost_95pct float64 \n", - " 29 weight_avg float64 \n", - " 30 winner_rate float64 \n", - "dtypes: bool(1), datetime64[ns](1), float64(28), object(1)\n", - "memory usage: 933.6+ MB\n", + " 7 amount float64 \n", + " 8 pct_chg float64 \n", + " 9 turnover_rate float64 \n", + " 10 pe_ttm float64 \n", + " 11 circ_mv float64 \n", + " 12 total_mv float64 \n", + " 13 volume_ratio float64 \n", + " 14 is_st bool \n", + " 15 up_limit float64 \n", + " 16 down_limit float64 \n", + " 17 buy_sm_vol float64 \n", + " 18 sell_sm_vol float64 \n", + " 19 buy_lg_vol float64 \n", + " 20 sell_lg_vol float64 \n", + " 21 buy_elg_vol float64 \n", + " 22 sell_elg_vol float64 \n", + " 23 net_mf_vol float64 \n", + " 24 his_low float64 \n", + " 25 his_high float64 \n", + " 26 cost_5pct float64 \n", + " 27 cost_15pct float64 \n", + " 28 cost_50pct float64 \n", + " 29 cost_85pct float64 \n", + " 30 cost_95pct float64 \n", + " 31 weight_avg float64 \n", + " 32 winner_rate float64 \n", + "dtypes: bool(1), datetime64[ns](1), float64(30), object(1)\n", + "memory usage: 2.1+ GB\n", "None\n" ] } ], "source": [ - "from code.utils.utils import read_and_merge_h5_data\n", + "from main.utils.utils import read_and_merge_h5_data\n", "\n", "print('daily data')\n", - "df2 = read_and_merge_h5_data('../../data/daily_data.h5', key='daily_data',\n", - " columns=['ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'vol', 'pct_chg'],\n", + "df = read_and_merge_h5_data('/mnt/d/PyProject/NewStock/data/daily_data.h5', key='daily_data',\n", + " columns=['ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'vol', 'amount', 'pct_chg'],\n", " df=None)\n", - "df2 = df2[df2['trade_date'] >= '2022-01-01']\n", "\n", "print('daily basic')\n", - "df2 = read_and_merge_h5_data('../../data/daily_basic.h5', key='daily_basic',\n", - " columns=['ts_code', 'trade_date', 'turnover_rate', 'pe_ttm', 'circ_mv', 'volume_ratio',\n", - " 'is_st'], df=df2, join='inner')\n", + "df = read_and_merge_h5_data('/mnt/d/PyProject/NewStock/data/daily_basic.h5', key='daily_basic',\n", + " columns=['ts_code', 'trade_date', 'turnover_rate', 'pe_ttm', 'circ_mv', 'total_mv', 'volume_ratio',\n", + " 'is_st'], df=df, join='inner')\n", "\n", "print('stk limit')\n", - "df2 = read_and_merge_h5_data('../../data/stk_limit.h5', key='stk_limit',\n", + "df = read_and_merge_h5_data('/mnt/d/PyProject/NewStock/data/stk_limit.h5', key='stk_limit',\n", " columns=['ts_code', 'trade_date', 'pre_close', 'up_limit', 'down_limit'],\n", - " df=df2)\n", + " df=df)\n", "print('money flow')\n", - "df2 = read_and_merge_h5_data('../../data/money_flow.h5', key='money_flow',\n", + "df = read_and_merge_h5_data('/mnt/d/PyProject/NewStock/data/money_flow.h5', key='money_flow',\n", " columns=['ts_code', 'trade_date', 'buy_sm_vol', 'sell_sm_vol', 'buy_lg_vol', 'sell_lg_vol',\n", " 'buy_elg_vol', 'sell_elg_vol', 'net_mf_vol'],\n", - " df=df2)\n", + " df=df)\n", "print('cyq perf')\n", - "df2 = read_and_merge_h5_data('../../data/cyq_perf.h5', key='cyq_perf',\n", + "df = read_and_merge_h5_data('/mnt/d/PyProject/NewStock/data/cyq_perf.h5', key='cyq_perf',\n", " columns=['ts_code', 'trade_date', 'his_low', 'his_high', 'cost_5pct', 'cost_15pct',\n", " 'cost_50pct',\n", " 'cost_85pct', 'cost_95pct', 'weight_avg', 'winner_rate'],\n", - " df=df2)\n", - "print(df2.info())" + " df=df)\n", + "print(df.info())" ] }, { "cell_type": "code", - "execution_count": 8, - "id": "7da9e79ee7f2eeb2", - "metadata": { - "ExecuteTime": { - "end_time": "2025-04-09T16:42:33.590224Z", - "start_time": "2025-04-09T16:42:29.605171Z" - }, - "jupyter": { - "source_hidden": true - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "industry\n" - ] - } - ], - "source": [ - "print('industry')\n", - "industry_df2 = read_and_merge_h5_data('../../data/industry_data.h5', key='industry_data',\n", - " columns=['ts_code', 'l2_code', 'in_date'],\n", - " df=None, on=['ts_code'], join='left')\n", - "\n", - "\n", - "def merge_with_industry_data(df, industry_df):\n", - " # 确保日期字段是 datetime 类型\n", - " df['trade_date'] = pd.to_datetime(df['trade_date'])\n", - " industry_df['in_date'] = pd.to_datetime(industry_df['in_date'])\n", - "\n", - " # 对 industry_df 按 ts_code 和 in_date 排序\n", - " industry_df_sorted = industry_df.sort_values(['in_date', 'ts_code'])\n", - "\n", - " # 对原始 df 按 ts_code 和 trade_date 排序\n", - " df_sorted = df.sort_values(['trade_date', 'ts_code'])\n", - "\n", - " # 使用 merge_asof 进行向后合并\n", - " merged = pd.merge_asof(\n", - " df_sorted,\n", - " industry_df_sorted,\n", - " by='ts_code', # 按 ts_code 分组\n", - " left_on='trade_date',\n", - " right_on='in_date',\n", - " direction='backward'\n", - " )\n", - "\n", - " # 获取每个 ts_code 的最早 in_date 记录\n", - " min_in_date_per_ts = (industry_df_sorted\n", - " .groupby('ts_code')\n", - " .first()\n", - " .reset_index()[['ts_code', 'l2_code']])\n", - "\n", - " # 填充未匹配到的记录（trade_date 早于所有 in_date 的情况）\n", - " merged['l2_code'] = merged['l2_code'].fillna(\n", - " merged['ts_code'].map(min_in_date_per_ts.set_index('ts_code')['l2_code'])\n", - " )\n", - "\n", - " # 保留需要的列并重置索引\n", - " result = merged.reset_index(drop=True)\n", - " return result\n", - "\n", - "\n", - "# 使用示例\n", - "df2 = merge_with_industry_data(df2, industry_df2)\n", - "# print(mdf[mdf['ts_code'] == '600751.SH'][['ts_code', 'trade_date', 'l2_code']])" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "7f0830ced3ce1050", - "metadata": { - "ExecuteTime": { - "end_time": "2025-04-09T16:42:39.280494Z", - "start_time": "2025-04-09T16:42:33.613600Z" - } - }, - "outputs": [], - "source": [ - "industry_df2 = read_industry_data('../../data/sw_daily.h5')\n" - ] - }, - { - "cell_type": "code", - "execution_count": 10, + "execution_count": 6, "id": "ee9d7511597a312b", "metadata": { "ExecuteTime": { @@ -921,30 +549,148 @@ "name": "stdout", "output_type": "stream", "text": [ + "使用 'ann_date' 作为财务数据生效日期。\n", + "警告: 从 financial_data_subset 中移除了 366 行，因为其 'ts_code' 或 'ann_date' 列存在空值。\n", + "使用 'ann_date' 作为财务数据生效日期。\n", + "警告: 从 financial_data_subset 中移除了 366 行，因为其 'ts_code' 或 'ann_date' 列存在空值。\n", + "使用 'ann_date' 作为财务数据生效日期。\n", + "警告: 从 financial_data_subset 中移除了 366 行，因为其 'ts_code' 或 'ann_date' 列存在空值。\n", + "使用 'ann_date' 作为财务数据生效日期。\n", + "警告: 从 financial_data_subset 中移除了 366 行，因为其 'ts_code' 或 'ann_date' 列存在空值。\n", + "开始计算因子: AR, BR (原地修改)...\n", + "因子 AR, BR 计算成功。\n", + "因子 AR, BR 计算流程结束。\n", + "使用 'ann_date' 作为财务数据生效日期。\n", + "使用 'ann_date' 作为财务数据生效日期。\n", + "使用 'ann_date' 作为财务数据生效日期。\n", + "使用 'ann_date' 作为财务数据生效日期。\n", + "警告: 从 financial_data_subset 中移除了 366 行，因为其 'ts_code' 或 'ann_date' 列存在空值。\n", + "计算 BBI...\n", + "--- 计算日级别偏离度 (使用 pct_chg) ---\n", + "--- 计算日级别动量基准 (使用 pct_chg) ---\n", + "日级别动量基准计算完成 (使用 pct_chg)。\n", + "日级别偏离度计算完成 (使用 pct_chg)。\n", + "--- 计算日级别行业偏离度 (使用 pct_chg 和行业基准) ---\n", + "--- 计算日级别行业动量基准 (使用 pct_chg 和 cat_l2_code) ---\n", + "错误: 计算日级别行业动量基准需要以下列: ['pct_chg', 'cat_l2_code', 'trade_date', 'ts_code']。\n", + "错误: 计算日级别行业偏离度需要以下列: ['pct_chg', 'daily_industry_positive_benchmark', 'daily_industry_negative_benchmark']。请先运行 daily_industry_momentum_benchmark(df)。\n", "Index(['ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'vol',\n", - " 'pct_chg', 'turnover_rate', 'pe_ttm', 'circ_mv', 'volume_ratio',\n", - " 'is_st', 'up_limit', 'down_limit', 'buy_sm_vol', 'sell_sm_vol',\n", - " 'buy_lg_vol', 'sell_lg_vol', 'buy_elg_vol', 'sell_elg_vol',\n", - " 'net_mf_vol', 'his_low', 'his_high', 'cost_5pct', 'cost_15pct',\n", - " 'cost_50pct', 'cost_85pct', 'cost_95pct', 'weight_avg', 'winner_rate',\n", - " 'l2_code', '_is_positive', '_is_negative', 'cat_is_positive',\n", - " '_pos_returns', '_neg_returns', '_pos_returns_sq', '_neg_returns_sq',\n", - " 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew',\n", - " 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout',\n", - " 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio',\n", - " 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14',\n", - " 'atr_6', 'obv'],\n", + " 'amount', 'pct_chg', 'turnover_rate', 'pe_ttm', 'circ_mv', 'total_mv',\n", + " 'volume_ratio', 'is_st', 'up_limit', 'down_limit', 'buy_sm_vol',\n", + " 'sell_sm_vol', 'buy_lg_vol', 'sell_lg_vol', 'buy_elg_vol',\n", + " 'sell_elg_vol', 'net_mf_vol', 'his_low', 'his_high', 'cost_5pct',\n", + " 'cost_15pct', 'cost_50pct', 'cost_85pct', 'cost_95pct', 'weight_avg',\n", + " 'winner_rate', 'undist_profit_ps', 'ocfps', 'roa', 'roe', 'AR', 'BR',\n", + " 'AR_BR', 'log_circ_mv', 'cashflow_to_ev_factor', 'book_to_price_ratio',\n", + " 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor',\n", + " 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity',\n", + " 'sm_net_buy_vol', 'flow_divergence_diff', 'flow_divergence_ratio',\n", + " 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change',\n", + " 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel',\n", + " 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy',\n", + " 'cost_support_15pct_change', 'cat_winner_price_zone',\n", + " 'flow_chip_consistency', 'profit_taking_vs_absorb', '_is_positive',\n", + " '_is_negative', 'cat_is_positive', '_pos_returns', '_neg_returns',\n", + " '_pos_returns_sq', '_neg_returns_sq', 'upside_vol', 'downside_vol',\n", + " 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate',\n", + " 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike',\n", + " 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike',\n", + " 'vol_std_5', 'atr_14', 'atr_6', 'obv'],\n", " dtype='object')\n", + "Calculating lg_flow_mom_corr_20_60...\n", + "Finished lg_flow_mom_corr_20_60.\n", + "Calculating lg_flow_accel...\n", + "Finished lg_flow_accel.\n", + "Calculating profit_pressure...\n", + "Finished profit_pressure.\n", + "Calculating underwater_resistance...\n", + "Finished underwater_resistance.\n", + "Calculating cost_conc_std_20...\n", + "Finished cost_conc_std_20.\n", + "Calculating profit_decay_20...\n", + "Finished profit_decay_20.\n", + "Calculating vol_amp_loss_20...\n", + "Finished vol_amp_loss_20.\n", + "Calculating vol_drop_profit_cnt_5...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Finished vol_drop_profit_cnt_5.\n", + "Calculating lg_flow_vol_interact_20...\n", + "Finished lg_flow_vol_interact_20.\n", + "Calculating cost_break_confirm_cnt_5...\n", + "Finished cost_break_confirm_cnt_5.\n", + "Calculating atr_norm_channel_pos_14...\n", + "Finished atr_norm_channel_pos_14.\n", + "Calculating turnover_diff_skew_20...\n", + "Finished turnover_diff_skew_20.\n", + "Calculating lg_sm_flow_diverge_20...\n", + "Finished lg_sm_flow_diverge_20.\n", + "Calculating pullback_strong_20_20...\n", + "Finished pullback_strong_20_20.\n", + "Calculating vol_wgt_hist_pos_20...\n", + "Finished vol_wgt_hist_pos_20.\n", + "Calculating vol_adj_roc_20...\n", + "Finished vol_adj_roc_20.\n", + "Calculating cs_rank_net_lg_flow_val...\n", + "Finished cs_rank_net_lg_flow_val.\n", + "Calculating cs_rank_flow_divergence...\n", + "Finished cs_rank_flow_divergence.\n", + "Calculating cs_rank_ind_adj_lg_flow...\n", + "Error calculating cs_rank_ind_adj_lg_flow: Missing 'cat_l2_code' column. Assigning NaN.\n", + "Calculating cs_rank_elg_buy_ratio...\n", + "Finished cs_rank_elg_buy_ratio.\n", + "Calculating cs_rank_rel_profit_margin...\n", + "Finished cs_rank_rel_profit_margin.\n", + "Calculating cs_rank_cost_breadth...\n", + "Finished cs_rank_cost_breadth.\n", + "Calculating cs_rank_dist_to_upper_cost...\n", + "Finished cs_rank_dist_to_upper_cost.\n", + "Calculating cs_rank_winner_rate...\n", + "Finished cs_rank_winner_rate.\n", + "Calculating cs_rank_intraday_range...\n", + "Finished cs_rank_intraday_range.\n", + "Calculating cs_rank_close_pos_in_range...\n", + "Finished cs_rank_close_pos_in_range.\n", + "Calculating cs_rank_opening_gap...\n", + "Error calculating cs_rank_opening_gap: Missing 'pre_close' column. Assigning NaN.\n", + "Calculating cs_rank_pos_in_hist_range...\n", + "Finished cs_rank_pos_in_hist_range.\n", + "Calculating cs_rank_vol_x_profit_margin...\n", + "Finished cs_rank_vol_x_profit_margin.\n", + "Calculating cs_rank_lg_flow_price_concordance...\n", + "Finished cs_rank_lg_flow_price_concordance.\n", + "Calculating cs_rank_turnover_per_winner...\n", + "Finished cs_rank_turnover_per_winner.\n", + "Calculating cs_rank_ind_cap_neutral_pe (Placeholder - requires statsmodels)...\n", + "Finished cs_rank_ind_cap_neutral_pe (Placeholder).\n", + "Calculating cs_rank_volume_ratio...\n", + "Finished cs_rank_volume_ratio.\n", + "Calculating cs_rank_elg_buy_sell_sm_ratio...\n", + "Finished cs_rank_elg_buy_sell_sm_ratio.\n", + "Calculating cs_rank_cost_dist_vol_ratio...\n", + "Finished cs_rank_cost_dist_vol_ratio.\n", + "Calculating cs_rank_size...\n", + "Finished cs_rank_size.\n", "\n", - "RangeIndex: 2431461 entries, 0 to 2431460\n", - "Columns: 137 entries, ts_code to industry_return_20_percentile\n", - "dtypes: bool(12), datetime64[ns](1), float64(119), int32(2), int64(1), object(2)\n", + "RangeIndex: 1784215 entries, 0 to 1784214\n", + "Columns: 180 entries, ts_code to cs_rank_size\n", + "dtypes: bool(10), datetime64[ns](1), float64(165), int64(3), object(1)\n", "memory usage: 2.3+ GB\n", - "None\n" + "None\n", + "['ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'vol', 'amount', 'pct_chg', 'turnover_rate', 'pe_ttm', 'circ_mv', 'total_mv', 'volume_ratio', 'is_st', 'up_limit', 'down_limit', 'buy_sm_vol', 'sell_sm_vol', 'buy_lg_vol', 'sell_lg_vol', 'buy_elg_vol', 'sell_elg_vol', 'net_mf_vol', 'his_low', 'his_high', 'cost_5pct', 'cost_15pct', 'cost_50pct', 'cost_85pct', 'cost_95pct', 'weight_avg', 'winner_rate', 'undist_profit_ps', 'ocfps', 'roa', 'roe', 'AR', 'BR', 'AR_BR', 'log_circ_mv', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'flow_divergence_diff', 'flow_divergence_ratio', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'price_cost_divergence', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_flow_divergence', 'cs_rank_ind_adj_lg_flow', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_opening_gap', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_ind_cap_neutral_pe', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size']\n" ] } ], "source": [ + "# df2\n", + "\n", + "import numpy as np\n", + "from main.factor.factor import *\n", + "\n", "def filter_data(df):\n", " # df = df.groupby('trade_date').apply(lambda x: x.nlargest(1000, 'act_factor1'))\n", " df = df[~df['is_st']]\n", @@ -952,24 +698,166 @@ " df = df[~df['ts_code'].str.startswith('30')]\n", " df = df[~df['ts_code'].str.startswith('68')]\n", " df = df[~df['ts_code'].str.startswith('8')]\n", + " df = df[df['trade_date'] >= '2023-01-01']\n", " if 'in_date' in df.columns:\n", " df = df.drop(columns=['in_date'])\n", " df = df.reset_index(drop=True)\n", " return df\n", "\n", + "import gc\n", + "gc.collect()\n", "\n", - "df2 = filter_data(df2)\n", - "df2, _ = get_rolling_factor(df2)\n", - "df2, _ = get_simple_factor(df2)\n", - "df2 = df2.rename(columns={'l2_code': 'cat_l2_code'})\n", - "df2 = df2.merge(industry_df2, on=['cat_l2_code', 'trade_date'], how='left')\n", + "df = filter_data(df)\n", + "df = df.sort_values(by=['ts_code', 'trade_date'])\n", "\n", - "print(df2.info())" + "# df = price_minus_deduction_price(df, n=120)\n", + "# df = price_deduction_price_diff_ratio_to_sma(df, n=120)\n", + "# df = cat_price_vs_sma_vs_deduction_price(df, n=120)\n", + "# df = cat_reason(df, top_list_df)\n", + "# df = cat_is_on_top_list(df, top_list_df)\n", + "\n", + "# df = cat_senti_mom_vol_spike(\n", + "# df,\n", + "# return_period=3,\n", + "# return_threshold=0.03, # 近3日涨幅超3%\n", + "# volume_ratio_threshold=1.3,\n", + "# current_pct_chg_min=0.0, # 当日必须收红\n", + "# current_pct_chg_max=0.05,\n", + "# ) # 当日涨幅不宜过大\n", + "\n", + "# df = cat_senti_pre_breakout(\n", + "# df,\n", + "# atr_short_N=10,\n", + "# atr_long_M=40,\n", + "# vol_atrophy_N=10,\n", + "# vol_atrophy_M=40,\n", + "# price_stab_N=5,\n", + "# price_stab_threshold=0.06,\n", + "# current_pct_chg_min_signal=0.002,\n", + "# current_pct_chg_max_signal=0.05,\n", + "# volume_ratio_signal_threshold=1.1,\n", + "# )\n", + "\n", + "# df = ts_turnover_rate_acceleration_5_20(df)\n", + "# df = ts_vol_sustain_10_30(df)\n", + "# # df = cs_turnover_rate_relative_strength_20(df)\n", + "# df = cs_amount_outlier_10(df)\n", + "# df = ts_ff_to_total_turnover_ratio(df)\n", + "# df = ts_price_volume_trend_coherence_5_20(df)\n", + "# # df = ts_turnover_rate_trend_strength_5(df)\n", + "# df = ts_ff_turnover_rate_surge_10(df)\n", + "\n", + "df = add_financial_factor(df, fina_indicator_df, factor_value_col='undist_profit_ps')\n", + "df = add_financial_factor(df, fina_indicator_df, factor_value_col='ocfps')\n", + "df = add_financial_factor(df, fina_indicator_df, factor_value_col='roa')\n", + "df = add_financial_factor(df, fina_indicator_df, factor_value_col='roe')\n", + "\n", + "calculate_arbr(df, N=26)\n", + "df['log_circ_mv'] = np.log(df['circ_mv'])\n", + "df = calculate_cashflow_to_ev_factor(df, cashflow_df, balancesheet_df)\n", + "df = caculate_book_to_price_ratio(df, fina_indicator_df)\n", + "\n", + "df = turnover_rate_n(df, n=5)\n", + "df = variance_n(df, n=20)\n", + "df = bbi_ratio_factor(df)\n", + "df = daily_deviation(df)\n", + "df = daily_industry_deviation(df)\n", + "df, _ = get_rolling_factor(df)\n", + "df, _ = get_simple_factor(df)\n", + "\n", + "df = df.rename(columns={'l1_code': 'cat_l1_code'})\n", + "df = df.rename(columns={'l2_code': 'cat_l2_code'})\n", + "\n", + "lg_flow_mom_corr(df, N=20, M=60)\n", + "lg_flow_accel(df)\n", + "profit_pressure(df)\n", + "underwater_resistance(df)\n", + "cost_conc_std(df, N=20)\n", + "profit_decay(df, N=20)\n", + "vol_amp_loss(df, N=20)\n", + "vol_drop_profit_cnt(df, N=20, M=5)\n", + "lg_flow_vol_interact(df, N=20)\n", + "cost_break_confirm_cnt(df, M=5)\n", + "atr_norm_channel_pos(df, N=14)\n", + "turnover_diff_skew(df, N=20)\n", + "lg_sm_flow_diverge(df, N=20)\n", + "pullback_strong(df, N=20, M=20)\n", + "vol_wgt_hist_pos(df, N=20)\n", + "vol_adj_roc(df, N=20)\n", + "\n", + "cs_rank_net_lg_flow_val(df)\n", + "cs_rank_flow_divergence(df)\n", + "cs_rank_industry_adj_lg_flow(df) # Needs cat_l2_code\n", + "cs_rank_elg_buy_ratio(df)\n", + "cs_rank_rel_profit_margin(df)\n", + "cs_rank_cost_breadth(df)\n", + "cs_rank_dist_to_upper_cost(df)\n", + "cs_rank_winner_rate(df)\n", + "cs_rank_intraday_range(df)\n", + "cs_rank_close_pos_in_range(df)\n", + "cs_rank_opening_gap(df) # Needs pre_close\n", + "cs_rank_pos_in_hist_range(df) # Needs his_low, his_high\n", + "cs_rank_vol_x_profit_margin(df)\n", + "cs_rank_lg_flow_price_concordance(df)\n", + "cs_rank_turnover_per_winner(df)\n", + "cs_rank_ind_cap_neutral_pe(df) # Placeholder - needs external libraries\n", + "cs_rank_volume_ratio(df) # Needs volume_ratio\n", + "cs_rank_elg_buy_sell_sm_ratio(df)\n", + "cs_rank_cost_dist_vol_ratio(df) # Needs volume_ratio\n", + "cs_rank_size(df) # Needs circ_mv\n", + "\n", + "df2 = df\n", + "\n", + "# df = df.merge(index_data, on='trade_date', how='left')\n", + "\n", + "print(df.info())\n", + "print(df.columns.tolist())" ] }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 7, + "id": "770520c3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2022-01-04 00:00:00\n", + "2023-01-03 00:00:00\n" + ] + } + ], + "source": [ + "print(df1['trade_date'].min())\n", + "print(df2['trade_date'].min())\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "3cff0731", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Empty DataFrame\n", + "Columns: [ts_code, trade_date, open, close, high, low, vol, amount, pct_chg, turnover_rate, pe_ttm, circ_mv, total_mv, volume_ratio, is_st, up_limit, down_limit, buy_sm_vol, sell_sm_vol, buy_lg_vol, sell_lg_vol, buy_elg_vol, sell_elg_vol, net_mf_vol, his_low, his_high, cost_5pct, cost_15pct, cost_50pct, cost_85pct, cost_95pct, weight_avg, winner_rate, undist_profit_ps, ocfps, roa, roe, AR, BR, AR_BR, log_circ_mv, cashflow_to_ev_factor, book_to_price_ratio, turnover_rate_mean_5, variance_20, bbi_ratio_factor, daily_deviation, lg_elg_net_buy_vol, flow_lg_elg_intensity, sm_net_buy_vol, flow_divergence_diff, flow_divergence_ratio, total_buy_vol, lg_elg_buy_prop, flow_struct_buy_change, lg_elg_net_buy_vol_change, flow_lg_elg_accel, chip_concentration_range, chip_skewness, floating_chip_proxy, cost_support_15pct_change, cat_winner_price_zone, flow_chip_consistency, profit_taking_vs_absorb, cat_is_positive, upside_vol, downside_vol, vol_ratio, return_skew, return_kurtosis, volume_change_rate, cat_volume_breakout, turnover_deviation, cat_turnover_spike, avg_volume_ratio, cat_volume_ratio_breakout, vol_spike, vol_std_5, atr_14, atr_6, obv, maobv_6, rsi_3, return_5, return_20, std_return_5, std_return_90, std_return_90_2, act_factor1, act_factor2, act_factor3, act_factor4, rank_act_factor1, rank_act_factor2, rank_act_factor3, cov, delta_cov, alpha_22_improved, alpha_003, alpha_007, ...]\n", + "Index: []\n" + ] + } + ], + "source": [ + "\n", + "print(df1[df1['ts_code'] == '002259.SZ'])" + ] + }, + { + "cell_type": "code", + "execution_count": 9, "id": "4ae711775caefbe5", "metadata": { "ExecuteTime": { @@ -979,32 +867,35 @@ }, "outputs": [], "source": [ - "# print(df1[df1['trade_date'] == '2025-04-07'][['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']].tail())\n", - "# print(df2[df2['trade_date'] == '2025-04-07'][['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']].tail())\n", - "# print(df1[df1['trade_date'] == '2025-04-07'].equals(df2[df2['trade_date'] == '2025-04-07']))\n", - "days = 2\n", - "df1 = df1.sort_values(by=['ts_code', 'trade_date'])\n", - "# df['future_return'] = df.groupby('ts_code', group_keys=False)['close'].apply(lambda x: x.shift(-days) / x - 1)\n", - "df1['future_return'] = (df1.groupby('ts_code')['close'].shift(-days) - df1.groupby('ts_code')['open'].shift(-1)) / \\\n", - " df1.groupby('ts_code')['open'].shift(-1)\n", - "df1['future_score'] = calculate_score(df1, days=2, lambda_param=0.3)\n", - "df1['label'] = df1.groupby('trade_date', group_keys=False)['future_score'].transform(\n", - " lambda x: pd.qcut(x, q=20, labels=False, duplicates='drop')\n", - ")\n", + "# # print(df1[df1['trade_date'] == '2025-04-07'][['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']].tail())\n", + "# # print(df2[df2['trade_date'] == '2025-04-07'][['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']].tail())\n", + "# # print(df1[df1['trade_date'] == '2025-04-07'].equals(df2[df2['trade_date'] == '2025-04-07']))\n", "\n", - "df2 = df2.sort_values(by=['ts_code', 'trade_date'])\n", - "# df['future_return'] = df.groupby('ts_code', group_keys=False)['close'].apply(lambda x: x.shift(-days) / x - 1)\n", - "df2['future_return'] = (df2.groupby('ts_code')['close'].shift(-days) - df2.groupby('ts_code')['open'].shift(-1)) / \\\n", - " df2.groupby('ts_code')['open'].shift(-1)\n", - "df2['future_score'] = calculate_score(df2, days=2, lambda_param=0.3)\n", - "df2['label'] = df2.groupby('trade_date', group_keys=False)['future_score'].transform(\n", - " lambda x: pd.qcut(x, q=20, labels=False, duplicates='drop')\n", - ")" + "# from main.utils.factor_processor import calculate_score\n", + "\n", + "# days = 2\n", + "# df1 = df1.sort_values(by=['ts_code', 'trade_date'])\n", + "# # df['future_return'] = df.groupby('ts_code', group_keys=False)['close'].apply(lambda x: x.shift(-days) / x - 1)\n", + "# df1['future_return'] = (df1.groupby('ts_code')['close'].shift(-days) - df1.groupby('ts_code')['open'].shift(-1)) / \\\n", + "# df1.groupby('ts_code')['open'].shift(-1)\n", + "# df1['future_score'] = calculate_score(df1, days=2, lambda_param=0.3)\n", + "# df1['label'] = df1.groupby('trade_date', group_keys=False)['future_score'].transform(\n", + "# lambda x: pd.qcut(x, q=20, labels=False, duplicates='drop')\n", + "# )\n", + "\n", + "# df2 = df2.sort_values(by=['ts_code', 'trade_date'])\n", + "# # df['future_return'] = df.groupby('ts_code', group_keys=False)['close'].apply(lambda x: x.shift(-days) / x - 1)\n", + "# df2['future_return'] = (df2.groupby('ts_code')['close'].shift(-days) - df2.groupby('ts_code')['open'].shift(-1)) / \\\n", + "# df2.groupby('ts_code')['open'].shift(-1)\n", + "# df2['future_score'] = calculate_score(df2, days=2, lambda_param=0.3)\n", + "# df2['label'] = df2.groupby('trade_date', group_keys=False)['future_score'].transform(\n", + "# lambda x: pd.qcut(x, q=20, labels=False, duplicates='drop')\n", + "# )" ] }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 10, "id": "350bf91df8c3dfc2", "metadata": { "ExecuteTime": { @@ -1019,33 +910,410 @@ "text": [ "日期: 2025-03-26\n", "------------------------------\n", - "Slice 1 形状: (3086, 141)\n", - "Slice 2 形状: (3086, 141)\n", + "Slice 1 形状: (3064, 184)\n", + "Slice 2 形状: (3064, 184)\n", "!!! 索引不同，尝试按 ts_code 对齐 !!!\n", "------------------------------\n", "使用 compare() 方法查找差异:\n", "!!! 发现差异 (compare结果):\n", - "MultiIndex([('vol_std_5', 'self'),\n", - " ('vol_std_5', 'other')],\n", + "MultiIndex([( 'turnover_rate_mean_5', 'self'),\n", + " ( 'turnover_rate_mean_5', 'other'),\n", + " ( 'variance_20', 'self'),\n", + " ( 'variance_20', 'other'),\n", + " ( 'bbi_ratio_factor', 'self'),\n", + " ( 'bbi_ratio_factor', 'other'),\n", + " ( 'upside_vol', 'self'),\n", + " ( 'upside_vol', 'other'),\n", + " ( 'downside_vol', 'self'),\n", + " ( 'downside_vol', 'other'),\n", + " ( 'vol_ratio', 'self'),\n", + " ( 'vol_ratio', 'other'),\n", + " ( 'return_skew', 'self'),\n", + " ( 'return_skew', 'other'),\n", + " ( 'return_kurtosis', 'self'),\n", + " ( 'return_kurtosis', 'other'),\n", + " ( 'volume_change_rate', 'self'),\n", + " ( 'volume_change_rate', 'other'),\n", + " ( 'turnover_deviation', 'self'),\n", + " ( 'turnover_deviation', 'other'),\n", + " ( 'avg_volume_ratio', 'self'),\n", + " ( 'avg_volume_ratio', 'other'),\n", + " ( 'vol_spike', 'self'),\n", + " ( 'vol_spike', 'other'),\n", + " ( 'vol_std_5', 'self'),\n", + " ( 'vol_std_5', 'other'),\n", + " ( 'atr_14', 'self'),\n", + " ( 'atr_14', 'other'),\n", + " ( 'atr_6', 'self'),\n", + " ( 'atr_6', 'other'),\n", + " ( 'obv', 'self'),\n", + " ( 'obv', 'other'),\n", + " ( 'maobv_6', 'self'),\n", + " ( 'maobv_6', 'other'),\n", + " ( 'std_return_5', 'self'),\n", + " ( 'std_return_5', 'other'),\n", + " ( 'std_return_90', 'self'),\n", + " ( 'std_return_90', 'other'),\n", + " ( 'std_return_90_2', 'self'),\n", + " ( 'std_return_90_2', 'other'),\n", + " ( 'act_factor2', 'self'),\n", + " ( 'act_factor2', 'other'),\n", + " ( 'act_factor3', 'self'),\n", + " ( 'act_factor3', 'other'),\n", + " ( 'act_factor4', 'self'),\n", + " ( 'act_factor4', 'other'),\n", + " ( 'cov', 'self'),\n", + " ( 'cov', 'other'),\n", + " ( 'delta_cov', 'self'),\n", + " ( 'delta_cov', 'other'),\n", + " ( 'alpha_22_improved', 'self'),\n", + " ( 'alpha_22_improved', 'other'),\n", + " ( 'alpha_013', 'self'),\n", + " ( 'alpha_013', 'other'),\n", + " ( 'price_cost_divergence', 'self'),\n", + " ( 'price_cost_divergence', 'other'),\n", + " ( 'cost_stability', 'self'),\n", + " ( 'cost_stability', 'other'),\n", + " ( 'liquidity_risk', 'self'),\n", + " ( 'liquidity_risk', 'other'),\n", + " ( 'turnover_std', 'self'),\n", + " ( 'turnover_std', 'other'),\n", + " ( 'mv_volatility', 'self'),\n", + " ( 'mv_volatility', 'other'),\n", + " ( 'momentum_factor', 'self'),\n", + " ( 'momentum_factor', 'other'),\n", + " ( 'obv_maobv_6', 'self'),\n", + " ( 'obv_maobv_6', 'other'),\n", + " ( 'std_return_5_over_std_return_90', 'self'),\n", + " ( 'std_return_5_over_std_return_90', 'other'),\n", + " ('std_return_90_minus_std_return_90_2', 'self'),\n", + " ('std_return_90_minus_std_return_90_2', 'other'),\n", + " ( 'act_factor5', 'self'),\n", + " ( 'act_factor5', 'other'),\n", + " ( 'act_factor6', 'self'),\n", + " ( 'act_factor6', 'other'),\n", + " ( 'cost_atr_adj', 'self'),\n", + " ( 'cost_atr_adj', 'other'),\n", + " ( 'lg_flow_mom_corr_20_60', 'self'),\n", + " ( 'lg_flow_mom_corr_20_60', 'other'),\n", + " ( 'cost_conc_std_20', 'self'),\n", + " ( 'cost_conc_std_20', 'other'),\n", + " ( 'vol_amp_loss_20', 'self'),\n", + " ( 'vol_amp_loss_20', 'other'),\n", + " ( 'lg_flow_vol_interact_20', 'self'),\n", + " ( 'lg_flow_vol_interact_20', 'other'),\n", + " ( 'turnover_diff_skew_20', 'self'),\n", + " ( 'turnover_diff_skew_20', 'other'),\n", + " ( 'lg_sm_flow_diverge_20', 'self'),\n", + " ( 'lg_sm_flow_diverge_20', 'other'),\n", + " ( 'vol_wgt_hist_pos_20', 'self'),\n", + " ( 'vol_wgt_hist_pos_20', 'other'),\n", + " ( 'vol_adj_roc_20', 'self'),\n", + " ( 'vol_adj_roc_20', 'other')],\n", " )\n", - " vol_std_5 \n", - " self other\n", - "ts_code \n", - "000004.SZ 1.076957 1.076957\n", - "000006.SZ 1.228637 1.228637\n", - "000007.SZ 0.533913 0.533913\n", - "000008.SZ 0.368086 0.368086\n", - "000009.SZ 0.393264 0.393264\n", - "... ... ...\n", - "605580.SH 1.164645 1.164645\n", - "605588.SH 0.314876 0.314876\n", - "605589.SH 0.562543 0.562543\n", - "605598.SH 1.057029 1.057029\n", - "605599.SH 0.193314 0.193314\n", + " turnover_rate_mean_5 variance_20 bbi_ratio_factor \\\n", + " self other self other self \n", + "ts_code \n", + "000001.SZ NaN NaN 1.350932 1.350932 1.010161 \n", + "000002.SZ NaN NaN 2.117668 2.117668 NaN \n", + "000004.SZ NaN NaN 8.061740 8.061740 NaN \n", + "000006.SZ NaN NaN 7.298740 7.298740 NaN \n", + "000007.SZ NaN NaN NaN NaN NaN \n", + "... ... ... ... ... ... \n", + "605580.SH NaN NaN NaN NaN NaN \n", + "605588.SH NaN NaN 6.613036 6.613036 NaN \n", + "605589.SH 1.2223 1.2223 7.727591 7.727591 NaN \n", + "605598.SH NaN NaN 3.079582 3.079582 NaN \n", + "605599.SH NaN NaN 3.339119 3.339119 NaN \n", "\n", - "[3001 rows x 2 columns]\n", + " upside_vol downside_vol vol_ratio \\\n", + " other self other self other self \n", + "ts_code \n", + "000001.SZ 1.010161 NaN NaN NaN NaN NaN \n", + "000002.SZ NaN NaN NaN NaN NaN NaN \n", + "000004.SZ NaN 1.074360 1.074360 2.400379 2.400379 0.447579 \n", + "000006.SZ NaN NaN NaN NaN NaN NaN \n", + "000007.SZ NaN NaN NaN 1.277065 1.277065 1.098127 \n", + "... ... ... ... ... ... ... \n", + "605580.SH NaN NaN NaN 0.699229 0.699229 2.174044 \n", + "605588.SH NaN NaN NaN NaN NaN NaN \n", + "605589.SH NaN NaN NaN 1.540787 1.540787 1.064451 \n", + "605598.SH NaN 1.374078 1.374078 NaN NaN 1.528319 \n", + "605599.SH NaN NaN NaN NaN NaN NaN \n", "\n", - "存在差异的列: ['vol_std_5']\n" + " return_skew return_kurtosis \\\n", + " other self other self other \n", + "ts_code \n", + "000001.SZ NaN NaN NaN NaN NaN \n", + "000002.SZ NaN NaN NaN NaN NaN \n", + "000004.SZ 0.447579 -1.534768 -1.534768 2.792027 2.792027 \n", + "000006.SZ NaN -0.577787 -0.577787 -0.476524 -0.476524 \n", + "000007.SZ 1.098127 NaN NaN NaN NaN \n", + "... ... ... ... ... ... \n", + "605580.SH 2.174044 2.126640 2.126640 4.642559 4.642559 \n", + "605588.SH NaN 0.609743 0.609743 -3.243014 -3.243014 \n", + "605589.SH 1.064451 NaN NaN NaN NaN \n", + "605598.SH 1.528319 0.278034 0.278034 0.049195 0.049195 \n", + "605599.SH NaN 0.030262 0.030262 -0.694891 -0.694891 \n", + "\n", + " volume_change_rate turnover_deviation \\\n", + " self other self other \n", + "ts_code \n", + "000001.SZ -0.530526 -0.530526 -0.566673 -0.566673 \n", + "000002.SZ NaN NaN -0.446047 -0.446047 \n", + "000004.SZ NaN NaN -0.948364 -0.948364 \n", + "000006.SZ NaN NaN 1.052435 1.052435 \n", + "000007.SZ NaN NaN 1.111991 1.111991 \n", + "... ... ... ... ... \n", + "605580.SH NaN NaN NaN NaN \n", + "605588.SH NaN NaN -0.678620 -0.678620 \n", + "605589.SH NaN NaN -0.938677 -0.938677 \n", + "605598.SH 0.166773 0.166773 0.933048 0.933048 \n", + "605599.SH NaN NaN 1.075588 1.075588 \n", + "\n", + " avg_volume_ratio vol_spike vol_std_5 \\\n", + " self other self other self other \n", + "ts_code \n", + "000001.SZ NaN NaN NaN NaN 0.232868 0.232868 \n", + "000002.SZ 0.876667 0.876667 NaN NaN NaN NaN \n", + "000004.SZ NaN NaN NaN NaN NaN NaN \n", + "000006.SZ NaN NaN NaN NaN NaN NaN \n", + "000007.SZ NaN NaN NaN NaN NaN NaN \n", + "... ... ... ... ... ... ... \n", + "605580.SH NaN NaN NaN NaN 1.164645 1.164645 \n", + "605588.SH 0.596667 0.596667 NaN NaN 0.314876 0.314876 \n", + "605589.SH NaN NaN NaN NaN 0.562543 0.562543 \n", + "605598.SH NaN NaN NaN NaN 1.057029 1.057029 \n", + "605599.SH NaN NaN NaN NaN 0.193314 0.193314 \n", + "\n", + " atr_14 atr_6 obv \\\n", + " self other self other self other \n", + "ts_code \n", + "000001.SZ NaN NaN NaN NaN 11801312.72 2738247.22 \n", + "000002.SZ NaN NaN NaN NaN 41291828.48 25339065.82 \n", + "000004.SZ 2.030307 2.030307 NaN NaN 6090154.93 5915712.53 \n", + "000006.SZ NaN NaN NaN NaN 27874233.68 14515751.96 \n", + "000007.SZ 1.713940 1.713940 NaN NaN 1716807.04 270569.37 \n", + "... ... ... ... ... ... ... \n", + "605580.SH NaN NaN NaN NaN 4089325.91 3674511.22 \n", + "605588.SH NaN NaN NaN NaN 1537384.91 1376415.97 \n", + "605589.SH NaN NaN NaN NaN 6078107.94 5044023.07 \n", + "605598.SH NaN NaN NaN NaN 3839018.04 1797711.65 \n", + "605599.SH NaN NaN NaN NaN 2485523.73 2575349.58 \n", + "\n", + " maobv_6 std_return_5 std_return_90 \\\n", + " self other self other self \n", + "ts_code \n", + "000001.SZ 1.315290e+07 4.089838e+06 0.004030 0.004030 0.010625 \n", + "000002.SZ 4.132689e+07 2.537412e+07 0.010652 0.010652 0.023221 \n", + "000004.SZ 6.210621e+06 6.036178e+06 NaN NaN NaN \n", + "000006.SZ 2.735946e+07 1.400098e+07 NaN NaN 0.035182 \n", + "000007.SZ 1.642407e+06 1.961695e+05 0.032904 0.032904 0.022552 \n", + "... ... ... ... ... ... \n", + "605580.SH 4.143541e+06 3.728727e+06 0.031017 0.031017 0.026001 \n", + "605588.SH 1.534916e+06 1.373947e+06 NaN NaN 0.028813 \n", + "605589.SH 6.139128e+06 5.105043e+06 0.017784 0.017784 0.023788 \n", + "605598.SH 3.879454e+06 1.838147e+06 0.030152 0.030152 0.034004 \n", + "605599.SH 2.480334e+06 2.570160e+06 NaN NaN 0.020242 \n", + "\n", + " std_return_90_2 act_factor2 act_factor3 \\\n", + " other self other self other self \n", + "ts_code \n", + "000001.SZ 0.010625 0.010835 0.010835 NaN NaN NaN \n", + "000002.SZ 0.023221 0.024306 0.024306 NaN NaN NaN \n", + "000004.SZ NaN 0.045928 0.045928 NaN NaN NaN \n", + "000006.SZ 0.035182 0.041835 0.041835 NaN NaN NaN \n", + "000007.SZ 0.022552 0.026021 0.026021 NaN NaN 0.100465 \n", + "... ... ... ... ... ... ... \n", + "605580.SH 0.026001 0.025335 0.025335 NaN NaN NaN \n", + "605588.SH 0.028813 0.029796 0.029796 NaN NaN NaN \n", + "605589.SH 0.023788 0.024998 0.024998 NaN NaN NaN \n", + "605598.SH 0.034004 0.033996 0.033996 NaN NaN NaN \n", + "605599.SH 0.020242 0.019792 0.019792 NaN NaN NaN \n", + "\n", + " act_factor4 cov \\\n", + " other self other self other \n", + "ts_code \n", + "000001.SZ NaN -0.232135 -0.232135 NaN NaN \n", + "000002.SZ NaN -0.920105 -0.920105 NaN NaN \n", + "000004.SZ NaN -3.500298 -3.500299 NaN NaN \n", + "000006.SZ NaN -0.349179 -0.349179 1.083770e+06 1.083770e+06 \n", + "000007.SZ 0.100465 -0.608592 -0.608472 NaN NaN \n", + "... ... ... ... ... ... \n", + "605580.SH NaN 1.490573 1.490573 NaN NaN \n", + "605588.SH NaN 0.353965 0.353965 NaN NaN \n", + "605589.SH NaN 0.537608 0.537608 NaN NaN \n", + "605598.SH NaN -0.092286 -0.092286 NaN NaN \n", + "605599.SH NaN 0.777405 0.777405 NaN NaN \n", + "\n", + " delta_cov alpha_22_improved \\\n", + " self other self other \n", + "ts_code \n", + "000001.SZ 6.374603e+06 6.374603e+06 -6.262257e+06 -6.262257e+06 \n", + "000002.SZ NaN NaN NaN NaN \n", + "000004.SZ NaN NaN NaN NaN \n", + "000006.SZ 1.064318e+06 1.064318e+06 -9.302364e+05 -9.302364e+05 \n", + "000007.SZ NaN NaN NaN NaN \n", + "... ... ... ... ... \n", + "605580.SH NaN NaN NaN NaN \n", + "605588.SH NaN NaN NaN NaN \n", + "605589.SH NaN NaN NaN NaN \n", + "605598.SH 4.773888e+03 4.773888e+03 -1.441203e+03 -1.441203e+03 \n", + "605599.SH NaN NaN NaN NaN \n", + "\n", + " alpha_013 price_cost_divergence cost_stability \\\n", + " self other self other self \n", + "ts_code \n", + "000001.SZ NaN NaN 0.903682 0.903682 0.001266 \n", + "000002.SZ NaN NaN -0.063527 -0.063527 0.003122 \n", + "000004.SZ NaN NaN 0.448699 0.448699 0.039623 \n", + "000006.SZ NaN NaN -0.578683 -0.578683 0.014733 \n", + "000007.SZ NaN NaN -0.437721 -0.437721 0.012050 \n", + "... ... ... ... ... ... \n", + "605580.SH NaN NaN NaN NaN 0.007148 \n", + "605588.SH NaN NaN 0.135146 0.135146 0.011301 \n", + "605589.SH NaN NaN 0.653798 0.653798 0.012510 \n", + "605598.SH NaN NaN 0.386083 0.386083 0.002406 \n", + "605599.SH NaN NaN 0.170158 0.170158 0.002202 \n", + "\n", + " liquidity_risk turnover_std mv_volatility \\\n", + " other self other self other self \n", + "ts_code \n", + "000001.SZ 0.001266 NaN NaN 0.447536 0.447536 0.026465 \n", + "000002.SZ 0.003122 NaN NaN 0.583259 0.583259 0.037007 \n", + "000004.SZ 0.039623 NaN NaN NaN NaN NaN \n", + "000006.SZ 0.014733 NaN NaN 0.879670 0.879670 0.064097 \n", + "000007.SZ 0.012050 NaN NaN 0.574015 0.574015 0.046657 \n", + "... ... ... ... ... ... ... \n", + "605580.SH 0.007148 NaN NaN 0.795605 0.795605 0.062561 \n", + "605588.SH 0.011301 NaN NaN NaN NaN NaN \n", + "605589.SH 0.012510 NaN NaN 0.824547 0.824547 0.056549 \n", + "605598.SH 0.002406 NaN NaN 0.617382 0.617382 0.046860 \n", + "605599.SH 0.002202 NaN NaN 0.430581 0.430581 0.031348 \n", + "\n", + " momentum_factor obv_maobv_6 \\\n", + " other self other self other \n", + "ts_code \n", + "000001.SZ 0.026465 -0.813862 -0.813862 -1.351591e+06 -1.351591e+06 \n", + "000002.SZ 0.037007 -0.500416 -0.500416 -3.505751e+04 -3.505751e+04 \n", + "000004.SZ NaN -0.480611 -0.480611 -1.204658e+05 -1.204658e+05 \n", + "000006.SZ 0.064097 0.690214 0.690214 5.147734e+05 5.147734e+05 \n", + "000007.SZ 0.046657 1.021245 1.021245 7.439987e+04 7.439987e+04 \n", + "... ... ... ... ... ... \n", + "605580.SH 0.062561 NaN NaN -5.421535e+04 -5.421535e+04 \n", + "605588.SH NaN -0.582460 -0.582460 NaN NaN \n", + "605589.SH 0.056549 -0.673217 -0.673217 -6.101995e+04 -6.101995e+04 \n", + "605598.SH 0.046860 0.633297 0.633297 -4.043576e+04 -4.043575e+04 \n", + "605599.SH 0.031348 0.072720 0.072720 5.189752e+03 5.189752e+03 \n", + "\n", + " std_return_5_over_std_return_90 \\\n", + " self other \n", + "ts_code \n", + "000001.SZ 0.379306 0.379306 \n", + "000002.SZ 0.458738 0.458738 \n", + "000004.SZ NaN NaN \n", + "000006.SZ 0.971216 0.971216 \n", + "000007.SZ 1.459001 1.459001 \n", + "... ... ... \n", + "605580.SH 1.192910 1.192910 \n", + "605588.SH 0.976527 0.976527 \n", + "605589.SH 0.747608 0.747608 \n", + "605598.SH 0.886730 0.886730 \n", + "605599.SH 0.981816 0.981816 \n", + "\n", + " std_return_90_minus_std_return_90_2 act_factor5 \\\n", + " self other self other \n", + "ts_code \n", + "000001.SZ -0.000210 -0.000210 -1.146214 -1.146214 \n", + "000002.SZ NaN NaN -2.818276 -2.818276 \n", + "000004.SZ -0.001451 -0.001451 -8.723004 -8.723004 \n", + "000006.SZ NaN NaN 1.744042 1.744042 \n", + "000007.SZ -0.003469 -0.003469 0.744885 0.745004 \n", + "... ... ... ... ... \n", + "605580.SH NaN NaN 2.746294 2.746294 \n", + "605588.SH -0.000984 -0.000984 0.419094 0.419094 \n", + "605589.SH NaN NaN -1.461317 -1.461317 \n", + "605598.SH 0.000008 0.000008 -1.883670 -1.883670 \n", + "605599.SH NaN NaN 1.765644 1.765644 \n", + "\n", + " act_factor6 cost_atr_adj lg_flow_mom_corr_20_60 \\\n", + " self other self other self \n", + "ts_code \n", + "000001.SZ NaN NaN NaN NaN 0.611662 \n", + "000002.SZ NaN NaN NaN NaN 0.928029 \n", + "000004.SZ NaN NaN 1.379102 1.379102 -0.146624 \n", + "000006.SZ NaN NaN NaN NaN 0.834084 \n", + "000007.SZ NaN NaN 0.816831 0.816831 0.539901 \n", + "... ... ... ... ... ... \n", + "605580.SH NaN NaN NaN NaN 0.202900 \n", + "605588.SH NaN NaN NaN NaN 0.679962 \n", + "605589.SH NaN NaN NaN NaN -0.008683 \n", + "605598.SH NaN NaN NaN NaN 0.824691 \n", + "605599.SH NaN NaN NaN NaN 0.243661 \n", + "\n", + " cost_conc_std_20 vol_amp_loss_20 \\\n", + " other self other self other \n", + "ts_code \n", + "000001.SZ 0.611662 0.012551 0.012551 NaN NaN \n", + "000002.SZ 0.928029 0.013871 0.013871 NaN NaN \n", + "000004.SZ -0.146624 NaN NaN NaN NaN \n", + "000006.SZ 0.834084 0.024574 0.024574 NaN NaN \n", + "000007.SZ 0.539901 NaN NaN NaN NaN \n", + "... ... ... ... ... ... \n", + "605580.SH 0.202899 0.005251 0.005251 NaN NaN \n", + "605588.SH 0.679961 0.010481 0.010481 NaN NaN \n", + "605589.SH -0.008683 0.024145 0.024145 NaN NaN \n", + "605598.SH 0.824690 0.013963 0.013963 NaN NaN \n", + "605599.SH 0.243661 0.007508 0.007508 NaN NaN \n", + "\n", + " lg_flow_vol_interact_20 turnover_diff_skew_20 \\\n", + " self other self other \n", + "ts_code \n", + "000001.SZ 0.083196 0.083196 NaN NaN \n", + "000002.SZ 0.134502 0.134502 NaN NaN \n", + "000004.SZ 0.135444 0.135444 NaN NaN \n", + "000006.SZ 0.190142 0.190142 NaN NaN \n", + "000007.SZ NaN NaN NaN NaN \n", + "... ... ... ... ... \n", + "605580.SH NaN NaN NaN NaN \n", + "605588.SH 0.094075 0.094075 NaN NaN \n", + "605589.SH 0.123490 0.123490 -0.580349 -0.580349 \n", + "605598.SH 0.068323 0.068323 0.586522 0.586522 \n", + "605599.SH 0.079483 0.079483 1.398138 1.398138 \n", + "\n", + " lg_sm_flow_diverge_20 vol_wgt_hist_pos_20 \\\n", + " self other self other \n", + "ts_code \n", + "000001.SZ NaN NaN NaN NaN \n", + "000002.SZ NaN NaN NaN NaN \n", + "000004.SZ NaN NaN NaN NaN \n", + "000006.SZ NaN NaN NaN NaN \n", + "000007.SZ NaN NaN NaN NaN \n", + "... ... ... ... ... \n", + "605580.SH -0.036004 -0.036004 NaN NaN \n", + "605588.SH 0.035758 0.035758 NaN NaN \n", + "605589.SH NaN NaN NaN NaN \n", + "605598.SH 0.013836 0.013836 NaN NaN \n", + "605599.SH -0.049469 -0.049469 NaN NaN \n", + "\n", + " vol_adj_roc_20 \n", + " self other \n", + "ts_code \n", + "000001.SZ -0.010456 -0.010456 \n", + "000002.SZ -0.056096 -0.056096 \n", + "000004.SZ -0.067600 -0.067600 \n", + "000006.SZ 0.008398 0.008398 \n", + "000007.SZ NaN NaN \n", + "... ... ... \n", + "605580.SH NaN NaN \n", + "605588.SH -0.008329 -0.008329 \n", + "605589.SH -0.018240 -0.018240 \n", + "605598.SH -0.015839 -0.015839 \n", + "605599.SH 0.022997 0.022997 \n", + "\n", + "[3064 rows x 94 columns]\n", + "\n", + "存在差异的列: ['turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'turnover_deviation', 'avg_volume_ratio', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor2', 'act_factor3', 'act_factor4', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_013', 'price_cost_divergence', 'cost_stability', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'momentum_factor', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'act_factor5', 'act_factor6', 'cost_atr_adj', 'lg_flow_mom_corr_20_60', 'cost_conc_std_20', 'vol_amp_loss_20', 'lg_flow_vol_interact_20', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20']\n" ] } ], @@ -1120,12 +1388,45 @@ " print(\"索引或列在对齐后仍然不匹配，无法使用 compare()。请检查对齐逻辑。\")\n", "\n", "get_diff(slice1, slice2)\n", - "# print(df1['trade_date'].unique().tolist()[-5:])" + "# print(df1['trade_date'].unique().tolist()[-5:])\n" ] }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 11, + "id": "d56f61c4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "set()\n", + "set()\n", + "3064\n", + "3064\n", + "3064\n", + "3064\n" + ] + } + ], + "source": [ + "s1 = set(df1[df1['trade_date'] == date_to_compare].columns.to_list())\n", + "s2 = set(df2[df2['trade_date'] == date_to_compare].columns.to_list())\n", + "\n", + "print(s2 - s1)\n", + "print(s1 - s2)\n", + "\n", + "print(len(df1[df1['trade_date'] == date_to_compare]))\n", + "print(len(df2[df2['trade_date'] == date_to_compare]))\n", + "\n", + "print(df1[df1['trade_date'] == date_to_compare]['ts_code'].nunique())\n", + "print(df2[df2['trade_date'] == date_to_compare]['ts_code'].nunique())" + ] + }, + { + "cell_type": "code", + "execution_count": 12, "id": "9df2781fc6c7ae44", "metadata": { "ExecuteTime": { @@ -1375,7 +1676,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": null, "id": "99f677aca6a286d0", "metadata": { "ExecuteTime": { @@ -1385,35 +1686,14 @@ }, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Timestamp('2025-04-01 00:00:00')] 19 [19. 0. 5. 2. 1. 6. 10. 18. 4. 12. 17. 16. 11. 8. 15. 7. 14. 9.\n", - " 13.]\n", - "[Timestamp('2025-04-02 00:00:00')] 19 [18. 19. 1. 0. 3. 7. 17. 10. 16. 5. 9. 15. 8. 6. 4. 13. 2. 11.\n", - " 14.]\n", - "[Timestamp('2025-04-03 00:00:00')] 0 [nan]\n", - "[Timestamp('2025-04-07 00:00:00')] 0 [nan]\n", - "2025-04-07 00:00:00\n", - "[Timestamp('2025-04-01 00:00:00')] 19 [19. 0. 5. 2. 1. 6. 10. 18. 4. 12. 17. 16. 11. 8. 15. 7. 14. 9.\n", - " 13.]\n", - "[Timestamp('2025-04-02 00:00:00')] 19 [18. 19. 1. 0. 3. 7. 17. 10. 16. 5. 9. 15. 8. 6. 4. 13. 2. 11.\n", - " 14.]\n", - "[Timestamp('2025-04-03 00:00:00')] 19 [ 2. 15. 19. 0. 1. 5. 18. 17. 4. 6. 16. 8. 13. 14. 9. 7. 12. 11.\n", - " 3.]\n", - "[Timestamp('2025-04-07 00:00:00')] 19 [ 0. 18. 4. 17. 1. 19. 9. 13. 7. 5. 2. 16. 15. 6. 12. 11. 3. 14.\n", - " 8.]\n", - "[Timestamp('2025-04-08 00:00:00')] 0 [nan]\n", - "[Timestamp('2025-04-09 00:00:00')] 0 [nan]\n", - "2025-04-09 00:00:00\n", - "日期: 2025-04-07\n", - "------------------------------\n", - "Slice 1 形状: (100, 159)\n", - "Slice 2 形状: (110, 159)\n", - "!!! 形状不同 !!!\n", - "!!! 索引不同，尝试按 ts_code 对齐 !!!\n", - "------------------------------\n", - "索引或列在对齐后仍然不匹配，无法使用 compare()。请检查对齐逻辑。\n" + "ename": "NameError", + "evalue": "name 'industry_df1' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mNameError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[13]\u001b[39m\u001b[32m, line 81\u001b[39m\n\u001b[32m 77\u001b[39m feature_columns = remove_highly_correlated_features(pdf, feature_columns)\n\u001b[32m 79\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m pdf, feature_columns, filter_index\n\u001b[32m---> \u001b[39m\u001b[32m81\u001b[39m pdf1, feature_columns1, filter_index1 = get_pdf(df1[df1[\u001b[33m'\u001b[39m\u001b[33mtrade_date\u001b[39m\u001b[33m'\u001b[39m] >= \u001b[33m'\u001b[39m\u001b[33m2025-04-01\u001b[39m\u001b[33m'\u001b[39m], \u001b[43mindustry_df1\u001b[49m)\n\u001b[32m 82\u001b[39m pdf2, feature_columns2, filter_index2 = get_pdf(df2[df2[\u001b[33m'\u001b[39m\u001b[33mtrade_date\u001b[39m\u001b[33m'\u001b[39m] >= \u001b[33m'\u001b[39m\u001b[33m2025-04-01\u001b[39m\u001b[33m'\u001b[39m], industry_df2)\n\u001b[32m 84\u001b[39m \u001b[38;5;66;03m# date_to_compare = '2025-04-07'\u001b[39;00m\n", + "\u001b[31mNameError\u001b[39m: name 'industry_df1' is not defined" ] } ], @@ -1509,7 +1789,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": null, "id": "1b863e4115252d2d", "metadata": { "jupyter": { @@ -1692,7 +1972,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": null, "id": "ddb5b67a9852e2", "metadata": { "scrolled": true @@ -2084,16 +2364,16 @@ "evalue": "Forced splits file includes feature index 0, but maximum feature index in dataset is -1", "output_type": "error", "traceback": [ - "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", - "\u001B[1;31mLightGBMError\u001B[0m Traceback (most recent call last)", - "Cell \u001B[1;32mIn[36], line 38\u001B[0m\n\u001B[0;32m 34\u001B[0m final_predictions\u001B[38;5;241m.\u001B[39mto_csv(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mpredictions_test.tsv\u001B[39m\u001B[38;5;124m'\u001B[39m, index\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mFalse\u001B[39;00m)\n\u001B[0;32m 36\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m final_predictions\n\u001B[1;32m---> 38\u001B[0m final_predictions1 \u001B[38;5;241m=\u001B[39m train(pdf1, feature_columns1, filter_index1)\n\u001B[0;32m 39\u001B[0m final_predictions2 \u001B[38;5;241m=\u001B[39m train(pdf2, feature_columns2, filter_index2)\n", - "Cell \u001B[1;32mIn[36], line 31\u001B[0m, in \u001B[0;36mtrain\u001B[1;34m(pdf, feature_columns, filter_index)\u001B[0m\n\u001B[0;32m 4\u001B[0m light_params \u001B[38;5;241m=\u001B[39m {\n\u001B[0;32m 5\u001B[0m \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mlabel_gain\u001B[39m\u001B[38;5;124m'\u001B[39m: label_gain,\n\u001B[0;32m 6\u001B[0m \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mobjective\u001B[39m\u001B[38;5;124m'\u001B[39m: \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mlambdarank\u001B[39m\u001B[38;5;124m'\u001B[39m,\n\u001B[1;32m (...)\u001B[0m\n\u001B[0;32m 26\u001B[0m \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mseed\u001B[39m\u001B[38;5;124m'\u001B[39m: \u001B[38;5;241m7\u001B[39m\n\u001B[0;32m 27\u001B[0m }\n\u001B[0;32m 29\u001B[0m gc\u001B[38;5;241m.\u001B[39mcollect()\n\u001B[1;32m---> 31\u001B[0m final_predictions \u001B[38;5;241m=\u001B[39m rolling_train_predict(\n\u001B[0;32m 32\u001B[0m pdf[(pdf[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mtrade_date\u001B[39m\u001B[38;5;124m'\u001B[39m] \u001B[38;5;241m>\u001B[39m\u001B[38;5;241m=\u001B[39m \u001B[38;5;124m'\u001B[39m\u001B[38;5;124m2022-12-01\u001B[39m\u001B[38;5;124m'\u001B[39m) \u001B[38;5;241m&\u001B[39m (pdf[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mtrade_date\u001B[39m\u001B[38;5;124m'\u001B[39m] \u001B[38;5;241m<\u001B[39m\u001B[38;5;241m=\u001B[39m \u001B[38;5;124m'\u001B[39m\u001B[38;5;124m2029-03-26\u001B[39m\u001B[38;5;124m'\u001B[39m)], \u001B[38;5;241m5\u001B[39m, \u001B[38;5;241m1\u001B[39m, feature_columns,\n\u001B[0;32m 33\u001B[0m days\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m0\u001B[39m, validation_days\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m0\u001B[39m, filter_index\u001B[38;5;241m=\u001B[39mfilter_index, params\u001B[38;5;241m=\u001B[39mlight_params)\n\u001B[0;32m 34\u001B[0m final_predictions\u001B[38;5;241m.\u001B[39mto_csv(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mpredictions_test.tsv\u001B[39m\u001B[38;5;124m'\u001B[39m, index\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mFalse\u001B[39;00m)\n\u001B[0;32m 36\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m final_predictions\n", - "Cell \u001B[1;32mIn[33], line 154\u001B[0m, in \u001B[0;36mrolling_train_predict\u001B[1;34m(df, train_days, test_days, feature_columns_origin, days, use_pca, validation_days, filter_index, params)\u001B[0m\n\u001B[0;32m 146\u001B[0m \u001B[38;5;66;03m# ud = train_data[\"trade_date\"].unique()\u001B[39;00m\n\u001B[0;32m 147\u001B[0m \u001B[38;5;66;03m# date_weights = {date: weight for date, weight in zip(ud, np.linspace(1, 2, len(unique_dates)))}\u001B[39;00m\n\u001B[0;32m 148\u001B[0m \u001B[38;5;66;03m# params['weight'] = train_data[\"trade_date\"].map(date_weights).tolist()\u001B[39;00m\n\u001B[1;32m (...)\u001B[0m\n\u001B[0;32m 151\u001B[0m \u001B[38;5;66;03m# feature_contri = [2 if feat.startswith('act_factor') else 1 for feat in feature_columns]\u001B[39;00m\n\u001B[0;32m 152\u001B[0m \u001B[38;5;66;03m# params['feature_contri'] = feature_contri\u001B[39;00m\n\u001B[0;32m 153\u001B[0m evals \u001B[38;5;241m=\u001B[39m {}\n\u001B[1;32m--> 154\u001B[0m model, _, _ \u001B[38;5;241m=\u001B[39m train_light_model(train_data\u001B[38;5;241m.\u001B[39mdropna(subset\u001B[38;5;241m=\u001B[39m[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mlabel\u001B[39m\u001B[38;5;124m'\u001B[39m]),\n\u001B[0;32m 155\u001B[0m params, feature_columns,\n\u001B[0;32m 156\u001B[0m [lgb\u001B[38;5;241m.\u001B[39mlog_evaluation(period\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m100\u001B[39m),\n\u001B[0;32m 157\u001B[0m lgb\u001B[38;5;241m.\u001B[39mcallback\u001B[38;5;241m.\u001B[39mrecord_evaluation(evals),\n\u001B[0;32m 158\u001B[0m \u001B[38;5;66;03m# lgb.early_stopping(100, first_metric_only=True)\u001B[39;00m\n\u001B[0;32m 159\u001B[0m ], evals,\n\u001B[0;32m 160\u001B[0m num_boost_round\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m100\u001B[39m, validation_days\u001B[38;5;241m=\u001B[39mvalidation_days,\n\u001B[0;32m 161\u001B[0m print_feature_importance\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mFalse\u001B[39;00m, use_pca\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mFalse\u001B[39;00m)\n\u001B[0;32m 163\u001B[0m score_df \u001B[38;5;241m=\u001B[39m test_data\u001B[38;5;241m.\u001B[39mcopy()\n\u001B[0;32m 164\u001B[0m score_df[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mscore\u001B[39m\u001B[38;5;124m'\u001B[39m] \u001B[38;5;241m=\u001B[39m model\u001B[38;5;241m.\u001B[39mpredict(score_df[feature_columns])\n", - "Cell \u001B[1;32mIn[33], line 81\u001B[0m, in \u001B[0;36mtrain_light_model\u001B[1;34m(train_data_df, params, feature_columns, callbacks, evals, print_feature_importance, num_boost_round, validation_days, use_pca, split_date)\u001B[0m\n\u001B[0;32m 75\u001B[0m model \u001B[38;5;241m=\u001B[39m lgb\u001B[38;5;241m.\u001B[39mtrain(\n\u001B[0;32m 76\u001B[0m params, train_dataset, num_boost_round\u001B[38;5;241m=\u001B[39mnum_boost_round,\n\u001B[0;32m 77\u001B[0m valid_sets\u001B[38;5;241m=\u001B[39m[train_dataset, val_dataset], valid_names\u001B[38;5;241m=\u001B[39m[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mtrain\u001B[39m\u001B[38;5;124m'\u001B[39m, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mvalid\u001B[39m\u001B[38;5;124m'\u001B[39m],\n\u001B[0;32m 78\u001B[0m callbacks\u001B[38;5;241m=\u001B[39mcallbacks\n\u001B[0;32m 79\u001B[0m )\n\u001B[0;32m 80\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m---> 81\u001B[0m model \u001B[38;5;241m=\u001B[39m lgb\u001B[38;5;241m.\u001B[39mtrain(\n\u001B[0;32m 82\u001B[0m params, train_dataset, num_boost_round\u001B[38;5;241m=\u001B[39mnum_boost_round, callbacks\u001B[38;5;241m=\u001B[39mcallbacks\n\u001B[0;32m 83\u001B[0m )\n\u001B[0;32m 85\u001B[0m \u001B[38;5;66;03m# 打印特征重要性（如果需要）\u001B[39;00m\n\u001B[0;32m 86\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m print_feature_importance:\n", - "File \u001B[1;32mE:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\lightgbm\\engine.py:297\u001B[0m, in \u001B[0;36mtrain\u001B[1;34m(params, train_set, num_boost_round, valid_sets, valid_names, feval, init_model, keep_training_booster, callbacks)\u001B[0m\n\u001B[0;32m 295\u001B[0m \u001B[38;5;66;03m# construct booster\u001B[39;00m\n\u001B[0;32m 296\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m--> 297\u001B[0m booster \u001B[38;5;241m=\u001B[39m Booster(params\u001B[38;5;241m=\u001B[39mparams, train_set\u001B[38;5;241m=\u001B[39mtrain_set)\n\u001B[0;32m 298\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m is_valid_contain_train:\n\u001B[0;32m 299\u001B[0m booster\u001B[38;5;241m.\u001B[39mset_train_data_name(train_data_name)\n", - "File \u001B[1;32mE:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\lightgbm\\basic.py:3660\u001B[0m, in \u001B[0;36mBooster.__init__\u001B[1;34m(self, params, train_set, model_file, model_str)\u001B[0m\n\u001B[0;32m 3658\u001B[0m params\u001B[38;5;241m.\u001B[39mupdate(train_set\u001B[38;5;241m.\u001B[39mget_params())\n\u001B[0;32m 3659\u001B[0m params_str \u001B[38;5;241m=\u001B[39m _param_dict_to_str(params)\n\u001B[1;32m-> 3660\u001B[0m _safe_call(\n\u001B[0;32m 3661\u001B[0m _LIB\u001B[38;5;241m.\u001B[39mLGBM_BoosterCreate(\n\u001B[0;32m 3662\u001B[0m train_set\u001B[38;5;241m.\u001B[39m_handle,\n\u001B[0;32m 3663\u001B[0m _c_str(params_str),\n\u001B[0;32m 3664\u001B[0m ctypes\u001B[38;5;241m.\u001B[39mbyref(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_handle),\n\u001B[0;32m 3665\u001B[0m )\n\u001B[0;32m 3666\u001B[0m )\n\u001B[0;32m 3667\u001B[0m \u001B[38;5;66;03m# save reference to data\u001B[39;00m\n\u001B[0;32m 3668\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mtrain_set \u001B[38;5;241m=\u001B[39m train_set\n", - "File \u001B[1;32mE:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\lightgbm\\basic.py:313\u001B[0m, in \u001B[0;36m_safe_call\u001B[1;34m(ret)\u001B[0m\n\u001B[0;32m 305\u001B[0m \u001B[38;5;250m\u001B[39m\u001B[38;5;124;03m\"\"\"Check the return value from C API call.\u001B[39;00m\n\u001B[0;32m 306\u001B[0m \n\u001B[0;32m 307\u001B[0m \u001B[38;5;124;03mParameters\u001B[39;00m\n\u001B[1;32m (...)\u001B[0m\n\u001B[0;32m 310\u001B[0m \u001B[38;5;124;03m The return value from C API calls.\u001B[39;00m\n\u001B[0;32m 311\u001B[0m \u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[0;32m 312\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m ret \u001B[38;5;241m!=\u001B[39m \u001B[38;5;241m0\u001B[39m:\n\u001B[1;32m--> 313\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m LightGBMError(_LIB\u001B[38;5;241m.\u001B[39mLGBM_GetLastError()\u001B[38;5;241m.\u001B[39mdecode(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mutf-8\u001B[39m\u001B[38;5;124m\"\u001B[39m))\n", - "\u001B[1;31mLightGBMError\u001B[0m: Forced splits file includes feature index 0, but maximum feature index in dataset is -1" + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mLightGBMError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[36], line 38\u001b[0m\n\u001b[0;32m 34\u001b[0m final_predictions\u001b[38;5;241m.\u001b[39mto_csv(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpredictions_test.tsv\u001b[39m\u001b[38;5;124m'\u001b[39m, index\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[0;32m 36\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m final_predictions\n\u001b[1;32m---> 38\u001b[0m final_predictions1 \u001b[38;5;241m=\u001b[39m train(pdf1, feature_columns1, filter_index1)\n\u001b[0;32m 39\u001b[0m final_predictions2 \u001b[38;5;241m=\u001b[39m train(pdf2, feature_columns2, filter_index2)\n", + "Cell \u001b[1;32mIn[36], line 31\u001b[0m, in \u001b[0;36mtrain\u001b[1;34m(pdf, feature_columns, filter_index)\u001b[0m\n\u001b[0;32m 4\u001b[0m light_params \u001b[38;5;241m=\u001b[39m {\n\u001b[0;32m 5\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlabel_gain\u001b[39m\u001b[38;5;124m'\u001b[39m: label_gain,\n\u001b[0;32m 6\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mobjective\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlambdarank\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 26\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mseed\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m7\u001b[39m\n\u001b[0;32m 27\u001b[0m }\n\u001b[0;32m 29\u001b[0m gc\u001b[38;5;241m.\u001b[39mcollect()\n\u001b[1;32m---> 31\u001b[0m final_predictions \u001b[38;5;241m=\u001b[39m rolling_train_predict(\n\u001b[0;32m 32\u001b[0m pdf[(pdf[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtrade_date\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m2022-12-01\u001b[39m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;241m&\u001b[39m (pdf[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtrade_date\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m2029-03-26\u001b[39m\u001b[38;5;124m'\u001b[39m)], \u001b[38;5;241m5\u001b[39m, \u001b[38;5;241m1\u001b[39m, feature_columns,\n\u001b[0;32m 33\u001b[0m days\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m, validation_days\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m, filter_index\u001b[38;5;241m=\u001b[39mfilter_index, params\u001b[38;5;241m=\u001b[39mlight_params)\n\u001b[0;32m 34\u001b[0m final_predictions\u001b[38;5;241m.\u001b[39mto_csv(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpredictions_test.tsv\u001b[39m\u001b[38;5;124m'\u001b[39m, index\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[0;32m 36\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m final_predictions\n", + "Cell \u001b[1;32mIn[33], line 154\u001b[0m, in \u001b[0;36mrolling_train_predict\u001b[1;34m(df, train_days, test_days, feature_columns_origin, days, use_pca, validation_days, filter_index, params)\u001b[0m\n\u001b[0;32m 146\u001b[0m \u001b[38;5;66;03m# ud = train_data[\"trade_date\"].unique()\u001b[39;00m\n\u001b[0;32m 147\u001b[0m \u001b[38;5;66;03m# date_weights = {date: weight for date, weight in zip(ud, np.linspace(1, 2, len(unique_dates)))}\u001b[39;00m\n\u001b[0;32m 148\u001b[0m \u001b[38;5;66;03m# params['weight'] = train_data[\"trade_date\"].map(date_weights).tolist()\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 151\u001b[0m \u001b[38;5;66;03m# feature_contri = [2 if feat.startswith('act_factor') else 1 for feat in feature_columns]\u001b[39;00m\n\u001b[0;32m 152\u001b[0m \u001b[38;5;66;03m# params['feature_contri'] = feature_contri\u001b[39;00m\n\u001b[0;32m 153\u001b[0m evals \u001b[38;5;241m=\u001b[39m {}\n\u001b[1;32m--> 154\u001b[0m model, _, _ \u001b[38;5;241m=\u001b[39m train_light_model(train_data\u001b[38;5;241m.\u001b[39mdropna(subset\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlabel\u001b[39m\u001b[38;5;124m'\u001b[39m]),\n\u001b[0;32m 155\u001b[0m params, feature_columns,\n\u001b[0;32m 156\u001b[0m [lgb\u001b[38;5;241m.\u001b[39mlog_evaluation(period\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m100\u001b[39m),\n\u001b[0;32m 157\u001b[0m lgb\u001b[38;5;241m.\u001b[39mcallback\u001b[38;5;241m.\u001b[39mrecord_evaluation(evals),\n\u001b[0;32m 158\u001b[0m \u001b[38;5;66;03m# lgb.early_stopping(100, first_metric_only=True)\u001b[39;00m\n\u001b[0;32m 159\u001b[0m ], evals,\n\u001b[0;32m 160\u001b[0m num_boost_round\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m100\u001b[39m, validation_days\u001b[38;5;241m=\u001b[39mvalidation_days,\n\u001b[0;32m 161\u001b[0m print_feature_importance\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, use_pca\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[0;32m 163\u001b[0m score_df \u001b[38;5;241m=\u001b[39m test_data\u001b[38;5;241m.\u001b[39mcopy()\n\u001b[0;32m 164\u001b[0m score_df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mscore\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m model\u001b[38;5;241m.\u001b[39mpredict(score_df[feature_columns])\n", + "Cell \u001b[1;32mIn[33], line 81\u001b[0m, in \u001b[0;36mtrain_light_model\u001b[1;34m(train_data_df, params, feature_columns, callbacks, evals, print_feature_importance, num_boost_round, validation_days, use_pca, split_date)\u001b[0m\n\u001b[0;32m 75\u001b[0m model \u001b[38;5;241m=\u001b[39m lgb\u001b[38;5;241m.\u001b[39mtrain(\n\u001b[0;32m 76\u001b[0m params, train_dataset, num_boost_round\u001b[38;5;241m=\u001b[39mnum_boost_round,\n\u001b[0;32m 77\u001b[0m valid_sets\u001b[38;5;241m=\u001b[39m[train_dataset, val_dataset], valid_names\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtrain\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mvalid\u001b[39m\u001b[38;5;124m'\u001b[39m],\n\u001b[0;32m 78\u001b[0m callbacks\u001b[38;5;241m=\u001b[39mcallbacks\n\u001b[0;32m 79\u001b[0m )\n\u001b[0;32m 80\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m---> 81\u001b[0m model \u001b[38;5;241m=\u001b[39m lgb\u001b[38;5;241m.\u001b[39mtrain(\n\u001b[0;32m 82\u001b[0m params, train_dataset, num_boost_round\u001b[38;5;241m=\u001b[39mnum_boost_round, callbacks\u001b[38;5;241m=\u001b[39mcallbacks\n\u001b[0;32m 83\u001b[0m )\n\u001b[0;32m 85\u001b[0m \u001b[38;5;66;03m# 打印特征重要性（如果需要）\u001b[39;00m\n\u001b[0;32m 86\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m print_feature_importance:\n", + "File \u001b[1;32mE:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\lightgbm\\engine.py:297\u001b[0m, in \u001b[0;36mtrain\u001b[1;34m(params, train_set, num_boost_round, valid_sets, valid_names, feval, init_model, keep_training_booster, callbacks)\u001b[0m\n\u001b[0;32m 295\u001b[0m \u001b[38;5;66;03m# construct booster\u001b[39;00m\n\u001b[0;32m 296\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 297\u001b[0m booster \u001b[38;5;241m=\u001b[39m Booster(params\u001b[38;5;241m=\u001b[39mparams, train_set\u001b[38;5;241m=\u001b[39mtrain_set)\n\u001b[0;32m 298\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_valid_contain_train:\n\u001b[0;32m 299\u001b[0m booster\u001b[38;5;241m.\u001b[39mset_train_data_name(train_data_name)\n", + "File \u001b[1;32mE:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\lightgbm\\basic.py:3660\u001b[0m, in \u001b[0;36mBooster.__init__\u001b[1;34m(self, params, train_set, model_file, model_str)\u001b[0m\n\u001b[0;32m 3658\u001b[0m params\u001b[38;5;241m.\u001b[39mupdate(train_set\u001b[38;5;241m.\u001b[39mget_params())\n\u001b[0;32m 3659\u001b[0m params_str \u001b[38;5;241m=\u001b[39m _param_dict_to_str(params)\n\u001b[1;32m-> 3660\u001b[0m _safe_call(\n\u001b[0;32m 3661\u001b[0m _LIB\u001b[38;5;241m.\u001b[39mLGBM_BoosterCreate(\n\u001b[0;32m 3662\u001b[0m train_set\u001b[38;5;241m.\u001b[39m_handle,\n\u001b[0;32m 3663\u001b[0m _c_str(params_str),\n\u001b[0;32m 3664\u001b[0m ctypes\u001b[38;5;241m.\u001b[39mbyref(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_handle),\n\u001b[0;32m 3665\u001b[0m )\n\u001b[0;32m 3666\u001b[0m )\n\u001b[0;32m 3667\u001b[0m \u001b[38;5;66;03m# save reference to data\u001b[39;00m\n\u001b[0;32m 3668\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrain_set \u001b[38;5;241m=\u001b[39m train_set\n", + "File \u001b[1;32mE:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\lightgbm\\basic.py:313\u001b[0m, in \u001b[0;36m_safe_call\u001b[1;34m(ret)\u001b[0m\n\u001b[0;32m 305\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Check the return value from C API call.\u001b[39;00m\n\u001b[0;32m 306\u001b[0m \n\u001b[0;32m 307\u001b[0m \u001b[38;5;124;03mParameters\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 310\u001b[0m \u001b[38;5;124;03m The return value from C API calls.\u001b[39;00m\n\u001b[0;32m 311\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 312\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ret \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m--> 313\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m LightGBMError(_LIB\u001b[38;5;241m.\u001b[39mLGBM_GetLastError()\u001b[38;5;241m.\u001b[39mdecode(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mutf-8\u001b[39m\u001b[38;5;124m\"\u001b[39m))\n", + "\u001b[1;31mLightGBMError\u001b[0m: Forced splits file includes feature index 0, but maximum feature index in dataset is -1" ] } ], @@ -2157,7 +2437,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "stock", "language": "python", "name": "python3" }, @@ -2171,7 +2451,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.11" + "version": "3.13.2" } }, "nbformat": 4, diff --git a/main/train/Classify2.ipynb b/main/train/Classify2.ipynb index de4d694..7d415c7 100644 --- a/main/train/Classify2.ipynb +++ b/main/train/Classify2.ipynb @@ -18,7 +18,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "/mnt/d/PyProject/NewStock/main/train\n" + "/mnt/d/PyProject/NewStock\n" ] } ], @@ -29,7 +29,7 @@ "import gc\n", "import os\n", "import sys\n", - "sys.path.append('../../')\n", + "sys.path.append('/mnt/d/PyProject/NewStock/')\n", "print(os.getcwd())\n", "import pandas as pd\n", "from main.factor.factor import get_rolling_factor, get_simple_factor\n", @@ -80,7 +80,7 @@ "cyq perf\n", "left merge on ['ts_code', 'trade_date']\n", "\n", - "RangeIndex: 8665405 entries, 0 to 8665404\n", + "RangeIndex: 8692146 entries, 0 to 8692145\n", "Data columns (total 33 columns):\n", " # Column Dtype \n", "--- ------ ----- \n", @@ -127,26 +127,26 @@ "from main.utils.utils import read_and_merge_h5_data\n", "\n", "print('daily data')\n", - "df = read_and_merge_h5_data('../../data/daily_data.h5', key='daily_data',\n", + "df = read_and_merge_h5_data('/mnt/d/PyProject/NewStock/data/daily_data.h5', key='daily_data',\n", " columns=['ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'vol', 'amount', 'pct_chg'],\n", " df=None)\n", "\n", "print('daily basic')\n", - "df = read_and_merge_h5_data('../../data/daily_basic.h5', key='daily_basic',\n", + "df = read_and_merge_h5_data('/mnt/d/PyProject/NewStock/data/daily_basic.h5', key='daily_basic',\n", " columns=['ts_code', 'trade_date', 'turnover_rate', 'pe_ttm', 'circ_mv', 'total_mv', 'volume_ratio',\n", " 'is_st'], df=df, join='inner')\n", "\n", "print('stk limit')\n", - "df = read_and_merge_h5_data('../../data/stk_limit.h5', key='stk_limit',\n", + "df = read_and_merge_h5_data('/mnt/d/PyProject/NewStock/data/stk_limit.h5', key='stk_limit',\n", " columns=['ts_code', 'trade_date', 'pre_close', 'up_limit', 'down_limit'],\n", " df=df)\n", "print('money flow')\n", - "df = read_and_merge_h5_data('../../data/money_flow.h5', key='money_flow',\n", + "df = read_and_merge_h5_data('/mnt/d/PyProject/NewStock/data/money_flow.h5', key='money_flow',\n", " columns=['ts_code', 'trade_date', 'buy_sm_vol', 'sell_sm_vol', 'buy_lg_vol', 'sell_lg_vol',\n", " 'buy_elg_vol', 'sell_elg_vol', 'net_mf_vol'],\n", " df=df)\n", "print('cyq perf')\n", - "df = read_and_merge_h5_data('../../data/cyq_perf.h5', key='cyq_perf',\n", + "df = read_and_merge_h5_data('/mnt/d/PyProject/NewStock/data/cyq_perf.h5', key='cyq_perf',\n", " columns=['ts_code', 'trade_date', 'his_low', 'his_high', 'cost_5pct', 'cost_15pct',\n", " 'cost_50pct',\n", " 'cost_85pct', 'cost_95pct', 'weight_avg', 'winner_rate'],\n", @@ -175,7 +175,7 @@ ], "source": [ "print('industry')\n", - "industry_df = read_and_merge_h5_data('../../data/industry_data.h5', key='industry_data',\n", + "industry_df = read_and_merge_h5_data('/mnt/d/PyProject/NewStock/data/industry_data.h5', key='industry_data',\n", " columns=['ts_code', 'l2_code', 'in_date'],\n", " df=None, on=['ts_code'], join='left')\n", "\n", @@ -317,7 +317,7 @@ "\n", "\n", "# 使用函数\n", - "h5_filename = '../../data/index_data.h5'\n", + "h5_filename = '/mnt/d/PyProject/NewStock/data/index_data.h5'\n", "index_data = generate_index_indicators(h5_filename)\n", "index_data = index_data.dropna()\n" ] @@ -402,7 +402,7 @@ " return industry_data\n", "\n", "\n", - "industry_df = read_industry_data('../../data/sw_daily.h5')\n" + "industry_df = read_industry_data('/mnt/d/PyProject/NewStock/data/sw_daily.h5')\n" ] }, { @@ -445,16 +445,16 @@ }, "outputs": [], "source": [ - "fina_indicator_df = read_and_merge_h5_data('../../data/fina_indicator.h5', key='fina_indicator',\n", + "fina_indicator_df = read_and_merge_h5_data('/mnt/d/PyProject/NewStock/data/fina_indicator.h5', key='fina_indicator',\n", " columns=['ts_code', 'ann_date', 'undist_profit_ps', 'ocfps', 'bps', 'roa', 'roe'],\n", " df=None)\n", - "cashflow_df = read_and_merge_h5_data('../../data/cashflow.h5', key='cashflow',\n", + "cashflow_df = read_and_merge_h5_data('/mnt/d/PyProject/NewStock/data/cashflow.h5', key='cashflow',\n", " columns=['ts_code', 'ann_date', 'n_cashflow_act'],\n", " df=None)\n", - "balancesheet_df = read_and_merge_h5_data('../../data/balancesheet.h5', key='balancesheet',\n", + "balancesheet_df = read_and_merge_h5_data('/mnt/d/PyProject/NewStock/data/balancesheet.h5', key='balancesheet',\n", " columns=['ts_code', 'ann_date', 'money_cap', 'total_liab'],\n", " df=None)\n", - "top_list_df = read_and_merge_h5_data('../../data/top_list.h5', key='top_list',\n", + "top_list_df = read_and_merge_h5_data('/mnt/d/PyProject/NewStock/data/top_list.h5', key='top_list',\n", " columns=['ts_code', 'trade_date', 'reason'],\n", " df=None)\n", "\n", @@ -476,12 +476,6 @@ "name": "stdout", "output_type": "stream", "text": [ - "计算因子 ts_turnover_rate_acceleration_5_20\n", - "计算因子 ts_vol_sustain_10_30\n", - "计算因子 cs_amount_outlier_10\n", - "计算因子 ts_ff_to_total_turnover_ratio\n", - "计算因子 ts_price_volume_trend_coherence_5_20\n", - "计算因子 ts_ff_turnover_rate_surge_10\n", "使用 'ann_date' 作为财务数据生效日期。\n", "警告: 从 financial_data_subset 中移除了 366 行，因为其 'ts_code' 或 'ann_date' 列存在空值。\n", "使用 'ann_date' 作为财务数据生效日期。\n", @@ -513,11 +507,8 @@ " 'sell_sm_vol', 'buy_lg_vol', 'sell_lg_vol', 'buy_elg_vol',\n", " 'sell_elg_vol', 'net_mf_vol', 'his_low', 'his_high', 'cost_5pct',\n", " 'cost_15pct', 'cost_50pct', 'cost_85pct', 'cost_95pct', 'weight_avg',\n", - " 'winner_rate', 'l2_code', 'ts_turnover_rate_acceleration_5_20',\n", - " 'ts_vol_sustain_10_30', 'cs_amount_outlier_10',\n", - " 'ts_ff_to_total_turnover_ratio', 'ts_price_volume_trend_coherence_5_20',\n", - " 'ts_ff_turnover_rate_surge_10', 'undist_profit_ps', 'ocfps', 'roa',\n", - " 'roe', 'AR', 'BR', 'AR_BR', 'log_circ_mv', 'cashflow_to_ev_factor',\n", + " 'winner_rate', 'l2_code', 'undist_profit_ps', 'ocfps', 'roa', 'roe',\n", + " 'AR', 'BR', 'AR_BR', 'log_circ_mv', 'cashflow_to_ev_factor',\n", " 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20',\n", " 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol',\n", " 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'flow_divergence_diff',\n", @@ -607,12 +598,12 @@ "Calculating cs_rank_size...\n", "Finished cs_rank_size.\n", "\n", - "RangeIndex: 4539678 entries, 0 to 4539677\n", - "Columns: 187 entries, ts_code to cs_rank_size\n", - "dtypes: bool(10), datetime64[ns](1), float64(171), int64(3), object(2)\n", - "memory usage: 6.0+ GB\n", + "RangeIndex: 4554725 entries, 0 to 4554724\n", + "Columns: 181 entries, ts_code to cs_rank_size\n", + "dtypes: bool(10), datetime64[ns](1), float64(165), int64(3), object(2)\n", + "memory usage: 5.8+ GB\n", "None\n", - "['ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'vol', 'amount', 'pct_chg', 'turnover_rate', 'pe_ttm', 'circ_mv', 'total_mv', 'volume_ratio', 'is_st', 'up_limit', 'down_limit', 'buy_sm_vol', 'sell_sm_vol', 'buy_lg_vol', 'sell_lg_vol', 'buy_elg_vol', 'sell_elg_vol', 'net_mf_vol', 'his_low', 'his_high', 'cost_5pct', 'cost_15pct', 'cost_50pct', 'cost_85pct', 'cost_95pct', 'weight_avg', 'winner_rate', 'cat_l2_code', 'ts_turnover_rate_acceleration_5_20', 'ts_vol_sustain_10_30', 'cs_amount_outlier_10', 'ts_ff_to_total_turnover_ratio', 'ts_price_volume_trend_coherence_5_20', 'ts_ff_turnover_rate_surge_10', 'undist_profit_ps', 'ocfps', 'roa', 'roe', 'AR', 'BR', 'AR_BR', 'log_circ_mv', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'flow_divergence_diff', 'flow_divergence_ratio', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'price_cost_divergence', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_flow_divergence', 'cs_rank_ind_adj_lg_flow', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_opening_gap', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_ind_cap_neutral_pe', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size']\n" + "['ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'vol', 'amount', 'pct_chg', 'turnover_rate', 'pe_ttm', 'circ_mv', 'total_mv', 'volume_ratio', 'is_st', 'up_limit', 'down_limit', 'buy_sm_vol', 'sell_sm_vol', 'buy_lg_vol', 'sell_lg_vol', 'buy_elg_vol', 'sell_elg_vol', 'net_mf_vol', 'his_low', 'his_high', 'cost_5pct', 'cost_15pct', 'cost_50pct', 'cost_85pct', 'cost_95pct', 'weight_avg', 'winner_rate', 'cat_l2_code', 'undist_profit_ps', 'ocfps', 'roa', 'roe', 'AR', 'BR', 'AR_BR', 'log_circ_mv', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'flow_divergence_diff', 'flow_divergence_ratio', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'price_cost_divergence', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_flow_divergence', 'cs_rank_ind_adj_lg_flow', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_opening_gap', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_ind_cap_neutral_pe', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size']\n" ] } ], @@ -645,36 +636,10 @@ "# df = cat_reason(df, top_list_df)\n", "# df = cat_is_on_top_list(df, top_list_df)\n", "\n", - "# df = cat_senti_mom_vol_spike(\n", - "# df,\n", - "# return_period=3,\n", - "# return_threshold=0.03, # 近3日涨幅超3%\n", - "# volume_ratio_threshold=1.3,\n", - "# current_pct_chg_min=0.0, # 当日必须收红\n", - "# current_pct_chg_max=0.05,\n", - "# ) # 当日涨幅不宜过大\n", - "\n", - "# df = cat_senti_pre_breakout(\n", - "# df,\n", - "# atr_short_N=10,\n", - "# atr_long_M=40,\n", - "# vol_atrophy_N=10,\n", - "# vol_atrophy_M=40,\n", - "# price_stab_N=5,\n", - "# price_stab_threshold=0.06,\n", - "# current_pct_chg_min_signal=0.002,\n", - "# current_pct_chg_max_signal=0.05,\n", - "# volume_ratio_signal_threshold=1.1,\n", - "# )\n", - "\n", - "df = ts_turnover_rate_acceleration_5_20(df)\n", - "df = ts_vol_sustain_10_30(df)\n", + "# df = ts_turnover_rate_acceleration_5_20(df)\n", + "# df = ts_vol_sustain_10_30(df)\n", "# df = cs_turnover_rate_relative_strength_20(df)\n", - "df = cs_amount_outlier_10(df)\n", - "df = ts_ff_to_total_turnover_ratio(df)\n", - "df = ts_price_volume_trend_coherence_5_20(df)\n", - "# df = ts_turnover_rate_trend_strength_5(df)\n", - "df = ts_ff_turnover_rate_surge_10(df)\n", + "# df = cs_amount_outlier_10(df)\n", "\n", "df = add_financial_factor(df, fina_indicator_df, factor_value_col='undist_profit_ps')\n", "df = add_financial_factor(df, fina_indicator_df, factor_value_col='ocfps')\n", @@ -1370,7 +1335,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "197\n" + "191\n" ] } ], @@ -1430,7 +1395,7 @@ "0 000001.SZ 2019-01-02 16.574219\n", "1 000001.SZ 2019-01-03 16.583965\n", "2 000001.SZ 2019-01-04 16.633371\n", - "['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'winner_rate', 'ts_turnover_rate_acceleration_5_20', 'ts_vol_sustain_10_30', 'cs_amount_outlier_10', 'ts_ff_to_total_turnover_ratio', 'ts_price_volume_trend_coherence_5_20', 'ts_ff_turnover_rate_surge_10', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size', 'cat_up_limit', 'industry_obv', 'industry_return_5', 'industry_return_20', 'industry__ema_5', 'industry__ema_13', 'industry__ema_20', 'industry__ema_60', 'industry_act_factor1', 'industry_act_factor2', 'industry_act_factor3', 'industry_act_factor4', 'industry_act_factor5', 'industry_act_factor6', 'industry_rank_act_factor1', 'industry_rank_act_factor2', 'industry_rank_act_factor3', 'industry_return_5_percentile', 'industry_return_20_percentile', '000852.SH_MACD', '000905.SH_MACD', '399006.SZ_MACD', '000852.SH_MACD_hist', '000905.SH_MACD_hist', '399006.SZ_MACD_hist', '000852.SH_RSI', '000905.SH_RSI', '399006.SZ_RSI', '000852.SH_Signal_line', '000905.SH_Signal_line', '399006.SZ_Signal_line', '000852.SH_amount_change_rate', '000905.SH_amount_change_rate', '399006.SZ_amount_change_rate', '000852.SH_amount_mean', '000905.SH_amount_mean', '399006.SZ_amount_mean', '000852.SH_daily_return', '000905.SH_daily_return', '399006.SZ_daily_return', '000852.SH_up_ratio_20d', '000905.SH_up_ratio_20d', '399006.SZ_up_ratio_20d', '000852.SH_volatility', '000905.SH_volatility', '399006.SZ_volatility', '000852.SH_volume_change_rate', '000905.SH_volume_change_rate', '399006.SZ_volume_change_rate']\n", + "['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'winner_rate', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size', 'cat_up_limit', 'industry_obv', 'industry_return_5', 'industry_return_20', 'industry__ema_5', 'industry__ema_13', 'industry__ema_20', 'industry__ema_60', 'industry_act_factor1', 'industry_act_factor2', 'industry_act_factor3', 'industry_act_factor4', 'industry_act_factor5', 'industry_act_factor6', 'industry_rank_act_factor1', 'industry_rank_act_factor2', 'industry_rank_act_factor3', 'industry_return_5_percentile', 'industry_return_20_percentile', '000852.SH_MACD', '000905.SH_MACD', '399006.SZ_MACD', '000852.SH_MACD_hist', '000905.SH_MACD_hist', '399006.SZ_MACD_hist', '000852.SH_RSI', '000905.SH_RSI', '399006.SZ_RSI', '000852.SH_Signal_line', '000905.SH_Signal_line', '399006.SZ_Signal_line', '000852.SH_amount_change_rate', '000905.SH_amount_change_rate', '399006.SZ_amount_change_rate', '000852.SH_amount_mean', '000905.SH_amount_mean', '399006.SZ_amount_mean', '000852.SH_daily_return', '000905.SH_daily_return', '399006.SZ_daily_return', '000852.SH_up_ratio_20d', '000905.SH_up_ratio_20d', '399006.SZ_up_ratio_20d', '000852.SH_volatility', '000905.SH_volatility', '399006.SZ_volatility', '000852.SH_volume_change_rate', '000905.SH_volume_change_rate', '399006.SZ_volume_change_rate']\n", "去除极值\n", "开始截面 MAD 去极值处理 (k=3.0)...\n" ] @@ -1439,7 +1404,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "MAD Filtering: 100%|██████████| 137/137 [00:14<00:00, 9.48it/s]\n" + "MAD Filtering: 100%|██████████| 131/131 [00:12<00:00, 10.28it/s]\n" ] }, { @@ -1454,7 +1419,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "MAD Filtering: 100%|██████████| 137/137 [00:15<00:00, 8.83it/s]\n" + "MAD Filtering: 100%|██████████| 131/131 [00:11<00:00, 11.00it/s]\n" ] }, { @@ -1492,15 +1457,15 @@ "output_type": "stream", "text": [ "截面 MAD 去极值处理完成。\n", - "feature_columns: ['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'winner_rate', 'ts_turnover_rate_acceleration_5_20', 'ts_vol_sustain_10_30', 'cs_amount_outlier_10', 'ts_ff_to_total_turnover_ratio', 'ts_price_volume_trend_coherence_5_20', 'ts_ff_turnover_rate_surge_10', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size', 'cat_up_limit', 'industry_obv', 'industry_return_5', 'industry_return_20', 'industry__ema_5', 'industry__ema_13', 'industry__ema_20', 'industry__ema_60', 'industry_act_factor1', 'industry_act_factor2', 'industry_act_factor3', 'industry_act_factor4', 'industry_act_factor5', 'industry_act_factor6', 'industry_rank_act_factor1', 'industry_rank_act_factor2', 'industry_rank_act_factor3', 'industry_return_5_percentile', 'industry_return_20_percentile', '000852.SH_MACD', '000905.SH_MACD', '399006.SZ_MACD', '000852.SH_MACD_hist', '000905.SH_MACD_hist', '399006.SZ_MACD_hist', '000852.SH_RSI', '000905.SH_RSI', '399006.SZ_RSI', '000852.SH_Signal_line', '000905.SH_Signal_line', '399006.SZ_Signal_line', '000852.SH_amount_change_rate', '000905.SH_amount_change_rate', '399006.SZ_amount_change_rate', '000852.SH_amount_mean', '000905.SH_amount_mean', '399006.SZ_amount_mean', '000852.SH_daily_return', '000905.SH_daily_return', '399006.SZ_daily_return', '000852.SH_up_ratio_20d', '000905.SH_up_ratio_20d', '399006.SZ_up_ratio_20d', '000852.SH_volatility', '000905.SH_volatility', '399006.SZ_volatility', '000852.SH_volume_change_rate', '000905.SH_volume_change_rate', '399006.SZ_volume_change_rate']\n", + "feature_columns: ['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'winner_rate', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size', 'cat_up_limit', 'industry_obv', 'industry_return_5', 'industry_return_20', 'industry__ema_5', 'industry__ema_13', 'industry__ema_20', 'industry__ema_60', 'industry_act_factor1', 'industry_act_factor2', 'industry_act_factor3', 'industry_act_factor4', 'industry_act_factor5', 'industry_act_factor6', 'industry_rank_act_factor1', 'industry_rank_act_factor2', 'industry_rank_act_factor3', 'industry_return_5_percentile', 'industry_return_20_percentile', '000852.SH_MACD', '000905.SH_MACD', '399006.SZ_MACD', '000852.SH_MACD_hist', '000905.SH_MACD_hist', '399006.SZ_MACD_hist', '000852.SH_RSI', '000905.SH_RSI', '399006.SZ_RSI', '000852.SH_Signal_line', '000905.SH_Signal_line', '399006.SZ_Signal_line', '000852.SH_amount_change_rate', '000905.SH_amount_change_rate', '399006.SZ_amount_change_rate', '000852.SH_amount_mean', '000905.SH_amount_mean', '399006.SZ_amount_mean', '000852.SH_daily_return', '000905.SH_daily_return', '399006.SZ_daily_return', '000852.SH_up_ratio_20d', '000905.SH_up_ratio_20d', '399006.SZ_up_ratio_20d', '000852.SH_volatility', '000905.SH_volatility', '399006.SZ_volatility', '000852.SH_volume_change_rate', '000905.SH_volume_change_rate', '399006.SZ_volume_change_rate']\n", "df最小日期: 2019-01-02\n", - "df最大日期: 2025-05-23\n", - "2057539\n", + "df最大日期: 2025-05-30\n", + "2057465\n", "train_data最小日期: 2020-01-02\n", "train_data最大日期: 2022-12-30\n", - "1766694\n", + "1781706\n", "test_data最小日期: 2023-01-03\n", - "test_data最大日期: 2025-05-23\n", + "test_data最大日期: 2025-05-30\n", " ts_code trade_date log_circ_mv\n", "0 000001.SZ 2019-01-02 16.574219\n", "1 000001.SZ 2019-01-03 16.583965\n", @@ -1636,7 +1601,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "id": "3ff2d1c5", "metadata": {}, "outputs": [], @@ -1704,7 +1669,7 @@ " 'learning_rate': 0.01,\n", " 'depth': 10, # 控制模型复杂度\n", " 'l2_leaf_reg': 50, # L2 正则化\n", - " 'verbose': 100,\n", + " 'verbose': 5000,\n", " 'early_stopping_rounds': 300,\n", " # 'od_type': 'Iter', # Overfitting detector type\n", " # 'od_wait': 300, # Number of iterations to wait after the bes\n", @@ -1777,7 +1742,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 106, "id": "c6eb5cd4-e714-420a-ac48-39af3e11ee81", "metadata": { "ExecuteTime": { @@ -1811,7 +1776,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "306af53af6f24cce84719f44ae19a9b0", + "model_id": "0a4d14d383d0499e81773abe038f7d1d", "version_major": 2, "version_minor": 0 }, @@ -1826,21 +1791,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "0:\tlearn: 0.6889525\ttest: 0.6893155\tbest: 0.6893155 (0)\ttotal: 1.83s\tremaining: 45m 41s\n", - "100:\tlearn: 0.5065717\ttest: 0.5493420\tbest: 0.5493420 (100)\ttotal: 14.7s\tremaining: 3m 23s\n", - "200:\tlearn: 0.4728006\ttest: 0.5277064\tbest: 0.5277064 (200)\ttotal: 27.7s\tremaining: 2m 59s\n", - "300:\tlearn: 0.4572238\ttest: 0.5226125\tbest: 0.5225487 (298)\ttotal: 43s\tremaining: 2m 51s\n", - "400:\tlearn: 0.4478623\ttest: 0.5208601\tbest: 0.5208601 (400)\ttotal: 57.6s\tremaining: 2m 37s\n", - "500:\tlearn: 0.4408993\ttest: 0.5198696\tbest: 0.5198644 (463)\ttotal: 1m 12s\tremaining: 2m 23s\n", - "600:\tlearn: 0.4349650\ttest: 0.5201926\tbest: 0.5198644 (463)\ttotal: 1m 26s\tremaining: 2m 9s\n", - "700:\tlearn: 0.4287194\ttest: 0.5200216\tbest: 0.5198644 (463)\ttotal: 1m 40s\tremaining: 1m 54s\n", - "800:\tlearn: 0.4220811\ttest: 0.5194681\tbest: 0.5194258 (799)\ttotal: 1m 54s\tremaining: 1m 40s\n", - "900:\tlearn: 0.4156361\ttest: 0.5195695\tbest: 0.5193854 (845)\ttotal: 2m 9s\tremaining: 1m 26s\n", - "1000:\tlearn: 0.4083819\ttest: 0.5195108\tbest: 0.5193854 (845)\ttotal: 2m 25s\tremaining: 1m 12s\n", - "1100:\tlearn: 0.4002510\ttest: 0.5196046\tbest: 0.5193854 (845)\ttotal: 2m 41s\tremaining: 58.5s\n", - "bestTest = 0.5193854246\n", - "bestIteration = 845\n", - "Shrink model to first 846 iterations.\n" + "0:\tlearn: 0.6888297\ttest: 0.6894367\tbest: 0.6894367 (0)\ttotal: 30.9ms\tremaining: 30.9s\n", + "Stopped by overfitting detector (300 iterations wait)\n", + "\n", + "bestTest = 0.5057357077\n", + "bestIteration = 665\n", + "\n", + "Shrink model to first 666 iterations.\n" ] } ], @@ -1862,7 +1819,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 107, "id": "5d1522a7538db91b", "metadata": { "ExecuteTime": { @@ -1900,7 +1857,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 108, "id": "09b1799e", "metadata": {}, "outputs": [ @@ -1908,8 +1865,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "197\n", - "['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'winner_rate', 'ts_turnover_rate_acceleration_5_20', 'ts_vol_sustain_10_30', 'cs_amount_outlier_10', 'ts_ff_to_total_turnover_ratio', 'ts_price_volume_trend_coherence_5_20', 'ts_ff_turnover_rate_surge_10', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size', 'cat_up_limit', 'industry_obv', 'industry_return_5', 'industry_return_20', 'industry__ema_5', 'industry__ema_13', 'industry__ema_20', 'industry__ema_60', 'industry_act_factor1', 'industry_act_factor2', 'industry_act_factor3', 'industry_act_factor4', 'industry_act_factor5', 'industry_act_factor6', 'industry_rank_act_factor1', 'industry_rank_act_factor2', 'industry_rank_act_factor3', 'industry_return_5_percentile', 'industry_return_20_percentile', '000852.SH_MACD', '000905.SH_MACD', '399006.SZ_MACD', '000852.SH_MACD_hist', '000905.SH_MACD_hist', '399006.SZ_MACD_hist', '000852.SH_RSI', '000905.SH_RSI', '399006.SZ_RSI', '000852.SH_Signal_line', '000905.SH_Signal_line', '399006.SZ_Signal_line', '000852.SH_amount_change_rate', '000905.SH_amount_change_rate', '399006.SZ_amount_change_rate', '000852.SH_amount_mean', '000905.SH_amount_mean', '399006.SZ_amount_mean', '000852.SH_daily_return', '000905.SH_daily_return', '399006.SZ_daily_return', '000852.SH_up_ratio_20d', '000905.SH_up_ratio_20d', '399006.SZ_up_ratio_20d', '000852.SH_volatility', '000905.SH_volatility', '399006.SZ_volatility', '000852.SH_volume_change_rate', '000905.SH_volume_change_rate', '399006.SZ_volume_change_rate']\n", + "191\n", + "['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'winner_rate', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size', 'cat_up_limit', 'industry_obv', 'industry_return_5', 'industry_return_20', 'industry__ema_5', 'industry__ema_13', 'industry__ema_20', 'industry__ema_60', 'industry_act_factor1', 'industry_act_factor2', 'industry_act_factor3', 'industry_act_factor4', 'industry_act_factor5', 'industry_act_factor6', 'industry_rank_act_factor1', 'industry_rank_act_factor2', 'industry_rank_act_factor3', 'industry_return_5_percentile', 'industry_return_20_percentile', '000852.SH_MACD', '000905.SH_MACD', '399006.SZ_MACD', '000852.SH_MACD_hist', '000905.SH_MACD_hist', '399006.SZ_MACD_hist', '000852.SH_RSI', '000905.SH_RSI', '399006.SZ_RSI', '000852.SH_Signal_line', '000905.SH_Signal_line', '399006.SZ_Signal_line', '000852.SH_amount_change_rate', '000905.SH_amount_change_rate', '399006.SZ_amount_change_rate', '000852.SH_amount_mean', '000905.SH_amount_mean', '399006.SZ_amount_mean', '000852.SH_daily_return', '000905.SH_daily_return', '399006.SZ_daily_return', '000852.SH_up_ratio_20d', '000905.SH_up_ratio_20d', '399006.SZ_up_ratio_20d', '000852.SH_volatility', '000905.SH_volatility', '399006.SZ_volatility', '000852.SH_volume_change_rate', '000905.SH_volume_change_rate', '399006.SZ_volume_change_rate']\n", "[]\n" ] } @@ -1922,7 +1879,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 109, "id": "e53b209a", "metadata": {}, "outputs": [ @@ -1930,7 +1887,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "5595 2057539\n", + "5595 2057465\n", " ts_code trade_date turnover_rate\n", "0 000001.SZ 2023-01-03 1.1307\n", "1 000001.SZ 2023-01-04 1.1284\n", @@ -1938,13 +1895,13 @@ "3 000001.SZ 2023-01-06 0.6162\n", "4 000001.SZ 2023-01-09 0.5450\n", "... ... ... ...\n", - "1766689 605599.SH 2025-05-19 0.4952\n", - "1766690 605599.SH 2025-05-20 1.6447\n", - "1766691 605599.SH 2025-05-21 1.2658\n", - "1766692 605599.SH 2025-05-22 0.7522\n", - "1766693 605599.SH 2025-05-23 0.6051\n", + "1781701 605599.SH 2025-05-26 0.6188\n", + "1781702 605599.SH 2025-05-27 1.2576\n", + "1781703 605599.SH 2025-05-28 2.0432\n", + "1781704 605599.SH 2025-05-29 2.0954\n", + "1781705 605599.SH 2025-05-30 1.4392\n", "\n", - "[1766694 rows x 3 columns]\n" + "[1781706 rows x 3 columns]\n" ] } ], @@ -1955,7 +1912,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 110, "id": "364e821a", "metadata": {}, "outputs": [], @@ -2039,7 +1996,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 111, "id": "1f6e6336", "metadata": {}, "outputs": [ @@ -2047,16 +2004,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "6e+04-9e+04" + "6e+04-9e+04\n", + "9e+04-1e+05\n", + "1e+05-1e+05\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "\n", - "9e+04-1e+05\n", - "1e+05-1e+05\n", "1e+05-1e+05\n", "1e+05-2e+05\n", "2e+05-2e+05\n", @@ -2065,21 +2021,21 @@ "2e+05-3e+05\n", "3e+05-3e+05\n", "二分类评估指标:\n", - "accuracy: 0.6525\n", - "precision: 0.4625\n", - "recall: 0.2437\n", - "f1: 0.3192\n", - "roc_auc: 0.6190\n", - "fpr: (array of length 7459)\n", - "tpr: (array of length 7459)\n", - "thresholds: (array of length 7459)\n", - "score_return_correlation: -0.0381\n", - "mv_roc_auc: {'6e+04-9e+04': np.float64(0.5297001153402537), '9e+04-1e+05': np.float64(0.5480807161280534), '1e+05-1e+05': np.float64(0.5803400535039577), '1e+05-2e+05': np.float64(0.5801592577513709), '2e+05-2e+05': np.float64(0.6041226723862076), '2e+05-3e+05': np.float64(0.6108816749042437), '3e+05-3e+05': np.float64(0.6029078699377564)}\n" + "accuracy: 0.6687\n", + "precision: 0.4667\n", + "recall: 0.0134\n", + "f1: 0.0260\n", + "roc_auc: 0.6166\n", + "fpr: (array of length 7520)\n", + "tpr: (array of length 7520)\n", + "thresholds: (array of length 7520)\n", + "score_return_correlation: -0.0419\n", + "mv_roc_auc: {'6e+04-9e+04': np.float64(0.6129972565157751), '9e+04-1e+05': np.float64(0.5481528934443733), '1e+05-1e+05': np.float64(0.5819692706968757), '1e+05-2e+05': np.float64(0.5802354633555421), '2e+05-2e+05': np.float64(0.610526564518331), '2e+05-3e+05': np.float64(0.6141327685032996), '3e+05-3e+05': np.float64(0.6069017365995996)}\n" ] }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -2115,7 +2071,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 112, "id": "7e9023cc", "metadata": {}, "outputs": [], @@ -2315,53 +2271,17 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 113, "id": "a0000d75", "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n", - "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n", - "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n", - "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n", - "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n", - "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n", - "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n", - "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n", - "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n", - "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n", - "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n", - "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n", - "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n", - "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n", - "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n", - "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n", - "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n", - "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n", - "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n", - "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n", - "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n", - "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n", - "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n", - "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n", - "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n", - "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n", - "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n", - "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n", - "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n", - "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n" - ] - }, { "name": "stdout", "output_type": "stream", "text": [ "开始分析 'score' 在 'circ_mv' 和 'future_return' 下的表现...\n", "准备数据，处理 NaN 值...\n", - "原始数据 17280 行，移除 NaN 后剩余 16971 行用于分析。\n", + "原始数据 17430 行，移除 NaN 后剩余 17140 行用于分析。\n", "对 'circ_mv' 和 'future_return' 进行 100 分位数分箱...\n", "按二维分箱分组计算 Spearman Rank IC...\n", "整理结果用于绘图...\n", @@ -2394,233 +2314,13 @@ "99 NaN NaN NaN NaN NaN NaN NaN \n", "\n", "[100 rows x 100 columns]\n", - "生成热力图...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n", - "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n", - "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n", - "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n", - "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n", - "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n", - "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n", - "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n", - "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n", - "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n", - "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n", - "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n", - "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n", - "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n", - "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n", - 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"findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n", - "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n", - "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n", - "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n", - "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n", - "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n", - "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n", - "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n", - "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n", - "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n", - "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n", - "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n", - "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n", - "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n", - "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n", - "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n", - "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "生成热力图...\n", "分析完成。\n" ] }, { "data": { - "image/png": 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", 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"text/plain": [ "
" ] @@ -2814,6 +2514,26 @@ "except Exception as e:\n", " print(f\"发生未知错误: {e}\")" ] + }, + { + "cell_type": "code", + "execution_count": 114, + "id": "a436dba4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Empty DataFrame\n", + "Columns: [ts_code, trade_date, is_st]\n", + "Index: []\n" + ] + } + ], + "source": [ + "print(df[(df['ts_code'] == '600242.SH') & (df['trade_date'] >= '2023-06-01')][['ts_code', 'trade_date', 'is_st']])" + ] } ], "metadata": { diff --git a/main/train/catboost_info/catboost_training.json b/main/train/catboost_info/catboost_training.json index 8d3d694..08d6909 100644 --- a/main/train/catboost_info/catboost_training.json +++ b/main/train/catboost_info/catboost_training.json @@ -1,1150 +1,760 @@ { 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