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新环境3

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liaozhaorun
2025-06-02 22:23:44 +08:00
parent 76ebd72fb3
commit ff2a5f8b18
28 changed files with 9951 additions and 7448 deletions
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56
main/data/index_and_industry.ipynb
View File

@@ -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,

37
main/data/update/cyq-perf.ipynb
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@@ -39,15 +39,15 @@
"3 000006.SZ 20250312\n",
"4 000007.SZ 20250312\n",
"... ... ...\n",
"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,

41
main/data/update/sw_daily.ipynb
View File

@@ -39,15 +39,15 @@
"3 801005.SI 20250221\n",
"4 801010.SI 20250221\n",
"... ... ...\n",
"2190 859811.SI 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,

212
main/data/update/update_daily_basic.ipynb
View File

@@ -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": [
"<class 'pandas.core.frame.DataFrame'>\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 False \n",
"2 3.347937e+05 False \n",
"3 7.587517e+05 False \n",
"4 2.592058e+05 False \n",
"... ... ... \n",
"26941 6.260539e+05 False \n",
"26942 1.391762e+06 False \n",
"26943 3.153715e+05 False \n",
"26944 5.020605e+05 False \n",
"26945 3.187622e+05 True \n",
"26918 1.925294e+06 False \n",
"26919 9.382665e+04 False \n",
"26920 1.115135e+06 False \n",
"26921 1.647604e+05 False \n",
"26922 9.615762e+05 False \n",
"\n",
"[26946 rows x 19 columns]\n"
"[26923 rows x 19 columns]\n"
]
}
],
@@ -334,43 +329,56 @@
"output_type": "stream",
"text": [
" ts_code trade_date close turnover_rate turnover_rate_f \\\n",
"23 002898.SZ 20250523 10.20 22.8874 36.4442 \n",
"35 000889.SZ 20250523 2.76 1.6609 2.2443 \n",
"53 300379.SZ 20250523 6.12 9.3935 9.5800 \n",
"58 300268.SZ 20250523 10.27 1.8178 2.5956 \n",
"155 000615.SZ 20250523 3.15 1.1640 1.7189 \n",
"16 300536.SZ 20250530 8.67 2.8854 3.5632 \n",
"78 000668.SZ 20250530 7.94 4.1498 7.0226 \n",
"112 002231.SZ 20250530 3.28 8.9944 10.0552 \n",
"147 300313.SZ 20250530 6.28 6.0110 12.4720 \n",
"158 603838.SH 20250530 5.73 0.9777 2.6542 \n",
"... ... ... ... ... ... \n",
"26880 300147.SZ 20250519 8.80 6.8409 8.8527 \n",
"26891 002501.SZ 20250519 2.17 4.4260 5.7136 \n",
"26910 600421.SH 20250519 6.39 3.4329 7.3909 \n",
"26938 600289.SH 20250519 5.90 1.1380 1.6532 \n",
"26945 002141.SZ 20250519 3.09 4.9267 7.1872 \n",
"26733 603828.SH 20250526 4.98 0.9734 1.9562 \n",
"26751 600599.SH 20250526 7.46 2.5125 6.3118 \n",
"26785 000820.SZ 20250526 3.02 13.6997 14.0750 \n",
"26885 002005.SZ 20250526 1.77 0.3214 0.5145 \n",
"26905 603869.SH 20250526 6.15 0.3000 0.7946 \n",
"\n",
" volume_ratio pe pe_ttm pb ps ps_ttm dv_ratio dv_ttm \\\n",
"23 10.43 NaN NaN 3.6011 6.8112 7.2338 0.1961 0.1961 \n",
"35 0.52 NaN NaN 27.2957 1.7661 1.7554 0.0000 NaN \n",
"53 0.89 NaN NaN 1.0993 4.5062 4.1828 0.0000 NaN \n",
"58 0.99 NaN NaN NaN 0.5235 0.5833 0.0000 NaN \n",
"155 0.99 NaN NaN NaN 2.1957 2.2727 0.0000 NaN \n",
"... ... .. ... ... ... ... ... ... \n",
"26880 1.55 NaN NaN 6.0171 3.1309 3.4015 0.0000 NaN \n",
"26891 1.83 NaN NaN 23.5587 23.0948 27.1516 0.0000 NaN \n",
"26910 0.92 NaN NaN 173.6254 10.6672 10.8459 0.0000 NaN \n",
"26938 0.46 NaN NaN 3.0370 11.6255 11.9049 0.0000 NaN \n",
"26945 1.51 NaN NaN 3.8214 7.2461 4.4422 0.0000 NaN \n",
" volume_ratio pe pe_ttm pb ps ps_ttm dv_ratio \\\n",
"16 0.55 NaN NaN 4.9112 10.9775 12.1174 0.0 \n",
"78 1.07 NaN NaN 1.6212 8.7361 5.6924 0.0 \n",
"112 0.74 NaN NaN 4.3227 3.9056 5.3690 0.0 \n",
"147 0.92 NaN NaN NaN 14.2840 13.5826 0.0 \n",
"158 1.06 NaN NaN 1.9039 6.4291 5.8279 0.0 \n",
"... ... ... ... ... ... ... ... \n",
"26733 0.56 345.783 1670.8958 3.9261 1.2065 1.3013 0.0 \n",
"26751 0.68 NaN NaN 11.2319 3.8238 3.9211 0.0 \n",
"26785 2.40 NaN NaN 12.4588 15.8309 20.1399 0.0 \n",
"26885 0.48 NaN NaN 15.9120 4.2066 4.2221 0.0 \n",
"26905 1.00 149.594 167.2545 0.8344 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": [
"<class 'pandas.core.frame.DataFrame'>\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,

1216
main/data/update/update_daily_data.ipynb
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File diff suppressed because it is too large Load Diff

27
main/data/update/update_money_flow.ipynb
View File

@@ -34,17 +34,17 @@
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\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,

249
main/data/update/update_stk_limit.ipynb
View File

@@ -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",
"<class 'pandas.core.frame.DataFrame'>\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 9.44\n",
"3 20250529 000006.SZ 7.78 6.36\n",
"4 20250529 000007.SZ 8.48 6.94\n",
"... ... ... ... ...\n",
"7132 20250529 920445.BJ 13.28 7.16\n",
"7133 20250529 920489.BJ 31.73 17.09\n",
"7134 20250529 920682.BJ 13.55 7.31\n",
"7135 20250529 920799.BJ 73.17 39.41\n",
"7136 20250529 920819.BJ 5.86 3.16\n",
"\n",
"[7137 rows x 4 columns], trade_date ts_code up_limit down_limit\n",
"0 20250528 000001.SZ 12.64 10.34\n",
"1 20250528 000002.SZ 7.34 6.00\n",
"2 20250528 000004.SZ 10.52 9.52\n",
"3 20250528 000006.SZ 7.96 6.52\n",
"4 20250528 000007.SZ 8.51 6.97\n",
"... ... ... ... ...\n",
"7130 20250528 920445.BJ 13.50 7.28\n",
"7131 20250528 920489.BJ 32.70 17.62\n",
"7132 20250528 920682.BJ 13.71 7.39\n",
"7133 20250528 920799.BJ 73.60 39.64\n",
"7134 20250528 920819.BJ 5.87 3.17\n",
"\n",
"[7135 rows x 4 columns], trade_date ts_code up_limit down_limit\n",
"0 20250527 000001.SZ 12.56 10.28\n",
"1 20250527 000002.SZ 7.29 5.97\n",
"2 20250527 000004.SZ 10.02 9.06\n",
"3 20250527 000006.SZ 7.58 6.20\n",
"4 20250527 000007.SZ 8.37 6.85\n",
"... ... ... ... ...\n",
"7128 20250527 920445.BJ 13.28 7.16\n",
"7129 20250527 920489.BJ 33.96 18.30\n",
"7130 20250527 920682.BJ 13.59 7.33\n",
"7131 20250527 920799.BJ 73.38 39.52\n",
"7132 20250527 920819.BJ 5.55 2.99\n",
"\n",
"[7133 rows x 4 columns], trade_date ts_code up_limit down_limit\n",
"0 20250526 000001.SZ 12.61 10.31\n",
"1 20250526 000002.SZ 7.29 5.97\n",
"2 20250526 000004.SZ 9.54 8.64\n",
"3 20250526 000006.SZ 7.44 6.08\n",
"4 20250526 000007.SZ 8.66 7.08\n",
"... ... ... ... ...\n",
"7130 20250526 920445.BJ 12.88 6.94\n",
"7131 20250526 920489.BJ 31.96 17.22\n",
"7132 20250526 920682.BJ 12.77 6.89\n",
"7133 20250526 920799.BJ 72.35 38.97\n",
"7134 20250526 920819.BJ 5.48 2.96\n",
"\n",
"[7135 rows x 4 columns], trade_date ts_code up_limit down_limit\n",
"0 20250523 000001.SZ 12.71 10.40\n",
"1 20250523 000002.SZ 7.34 6.00\n",
"2 20250523 000004.SZ 9.87 8.93\n",
"3 20250523 000006.SZ 7.54 6.17\n",
"4 20250523 000007.SZ 8.80 7.20\n",
"... ... ... ... ...\n",
"7130 20250523 920445.BJ 13.01 7.01\n",
"7131 20250523 920489.BJ 30.58 16.48\n",
"7132 20250523 920682.BJ 12.83 6.91\n",
"7133 20250523 920799.BJ 74.10 39.90\n",
"7134 20250523 920819.BJ 5.56 3.00\n",
"\n",
"[7135 rows x 4 columns], trade_date ts_code up_limit down_limit\n",
"0 20250521 000001.SZ 12.53 10.25\n",
"1 20250521 000002.SZ 7.46 6.10\n",
"2 20250521 000004.SZ 9.47 8.57\n",
"3 20250521 000006.SZ 7.61 6.23\n",
"4 20250521 000007.SZ 8.28 6.78\n",
"... ... ... ... ...\n",
"7129 20250521 920445.BJ 14.02 7.56\n",
"7130 20250521 920489.BJ 32.89 17.71\n",
"7131 20250521 920682.BJ 13.83 7.45\n",
"7132 20250521 920799.BJ 77.87 41.93\n",
"7133 20250521 920819.BJ 5.95 3.21\n",
"\n",
"[7134 rows x 4 columns], trade_date ts_code up_limit down_limit\n",
"0 20250522 000001.SZ 12.63 10.33\n",
"1 20250522 000002.SZ 7.44 6.08\n",
"2 20250522 000004.SZ 9.94 9.00\n",
"3 20250522 000006.SZ 7.43 6.08\n",
"4 20250522 000007.SZ 8.43 6.89\n",
"... ... ... ... ...\n",
"7130 20250522 920445.BJ 13.68 7.38\n",
"7131 20250522 920489.BJ 32.95 17.75\n",
"7132 20250522 920682.BJ 13.41 7.23\n",
"7133 20250522 920799.BJ 77.42 41.70\n",
"7134 20250522 920819.BJ 5.81 3.13\n",
"\n",
"[7135 rows x 4 columns], trade_date ts_code up_limit down_limit\n",
"0 20250519 000001.SZ 12.52 10.24\n",
"1 20250519 000002.SZ 7.45 6.09\n",
"2 20250519 000004.SZ 8.68 7.86\n",
"3 20250519 000006.SZ 7.17 5.87\n",
"4 20250519 000007.SZ 8.05 6.59\n",
"... ... ... ... ...\n",
"7128 20250519 920445.BJ 13.96 7.52\n",
"7129 20250519 920489.BJ 30.29 16.31\n",
"7130 20250519 920682.BJ 13.35 7.19\n",
"7131 20250519 920799.BJ 77.87 41.93\n",
"7132 20250519 920819.BJ 5.91 3.19\n",
"\n",
"[7133 rows x 4 columns], trade_date ts_code up_limit down_limit\n",
"0 20250520 000001.SZ 12.51 10.23\n",
"1 20250520 000002.SZ 7.48 6.12\n",
"2 20250520 000004.SZ 9.02 8.16\n",
"3 20250520 000006.SZ 7.66 6.26\n",
"4 20250520 000007.SZ 8.18 6.70\n",
"... ... ... ... ...\n",
"7128 20250520 920445.BJ 13.97 7.53\n",
"7129 20250520 920489.BJ 31.75 17.11\n",
"7130 20250520 920682.BJ 13.23 7.13\n",
"7131 20250520 920799.BJ 77.83 41.91\n",
"7132 20250520 920819.BJ 5.86 3.16\n",
"\n",
"[7133 rows x 4 columns], trade_date ts_code up_limit down_limit\n",
"0 20250516 000001.SZ 12.53 10.25\n",
"1 20250516 000002.SZ 7.47 6.11\n",
"2 20250516 000004.SZ 9.14 8.27\n",
"3 20250516 000006.SZ 7.17 5.87\n",
"4 20250516 000007.SZ 8.03 6.57\n",
"... ... ... ... ...\n",
"7125 20250516 920445.BJ 14.80 7.98\n",
"7126 20250516 920489.BJ 30.31 16.33\n",
"7127 20250516 920682.BJ 13.71 7.39\n",
"7128 20250516 920799.BJ 78.03 42.03\n",
"7129 20250516 920819.BJ 5.74 3.10\n",
"\n",
"[7130 rows x 4 columns], trade_date ts_code up_limit down_limit\n",
"0 20250515 000001.SZ 12.57 10.29\n",
"1 20250515 000002.SZ 7.58 6.20\n",
"2 20250515 000004.SZ 8.90 8.06\n",
"3 20250515 000006.SZ 7.26 5.94\n",
"4 20250515 000007.SZ 8.01 6.55\n",
"... ... ... ... ...\n",
"7119 20250515 920445.BJ 14.80 7.98\n",
"7120 20250515 920489.BJ 31.12 16.76\n",
"7121 20250515 920682.BJ 16.96 9.14\n",
"7122 20250515 920799.BJ 82.13 44.23\n",
"7123 20250515 920819.BJ 5.59 3.01\n",
"\n",
"[7124 rows x 4 columns], trade_date ts_code up_limit down_limit\n",
"0 20250514 000001.SZ 12.42 10.16\n",
"1 20250514 000002.SZ 7.55 6.17\n",
"2 20250514 000004.SZ 8.96 8.10\n",
"3 20250514 000006.SZ 7.14 5.84\n",
"4 20250514 000007.SZ 8.02 6.56\n",
"... ... ... ... ...\n",
"7117 20250514 920445.BJ 14.04 7.56\n",
"7118 20250514 920489.BJ 31.42 16.92\n",
"7119 20250514 920682.BJ 17.23 9.29\n",
"7120 20250514 920799.BJ 78.22 42.12\n",
"7121 20250514 920819.BJ 5.59 3.01\n",
"\n",
"[7122 rows x 4 columns], trade_date ts_code up_limit down_limit\n",
"0 20250513 000001.SZ 12.28 10.04\n",
"1 20250513 000002.SZ 7.54 6.17\n",
"2 20250513 000004.SZ 8.53 7.71\n",
"3 20250513 000006.SZ 7.12 5.82\n",
"4 20250513 000007.SZ 7.82 6.40\n",
"... ... ... ... ...\n",
"7116 20250513 920445.BJ 13.36 7.20\n",
"7117 20250513 920489.BJ 31.07 16.73\n",
"7118 20250513 920682.BJ 16.73 9.01\n",
"7119 20250513 920799.BJ 80.47 43.33\n",
"7120 20250513 920819.BJ 5.60 3.02\n",
"\n",
"[7121 rows x 4 columns], trade_date ts_code up_limit down_limit\n",
"0 20250512 000001.SZ 12.27 10.04\n",
"1 20250512 000002.SZ 7.46 6.10\n",
"2 20250512 000004.SZ 8.12 7.34\n",
"3 20250512 000006.SZ 7.08 5.80\n",
"4 20250512 000007.SZ 7.81 6.39\n",
"... ... ... ... ...\n",
"7112 20250512 920445.BJ 13.19 7.11\n",
"7113 20250512 920489.BJ 30.55 16.45\n",
"7114 20250512 920682.BJ 16.34 8.80\n",
"7115 20250512 920799.BJ 78.13 42.07\n",
"7116 20250512 920819.BJ 5.57 3.01\n",
"\n",
"[7117 rows x 4 columns]]\n"
]
}
],
@@ -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,