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1、策略更新

Browse Source 
2、新增qmt
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liaozhaorun
2025-11-29 00:23:12 +08:00
parent 0a942f92d1
commit c9b61db5b7
47 changed files with 97116 additions and 8867 deletions
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5903
main/data/daily_data_none.ipynb Normal file
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42
main/data/index_and_industry.ipynb
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@@ -83,32 +83,32 @@
"output_type": "stream",
"text": [
" ts_code trade_date close open high low \\\n",
"0 000905.SH 20251010 7398.2241 7499.3917 7509.1161 7373.9841 \n",
"1 000905.SH 20251009 7548.9226 7470.0474 7559.0920 7437.3242 \n",
"2 000905.SH 20250930 7412.3684 7372.5240 7428.0307 7372.0634 \n",
"3 000905.SH 20250929 7350.5599 7251.5221 7377.2217 7216.7357 \n",
"4 000905.SH 20250926 7240.9114 7311.8433 7351.7931 7237.0459 \n",
"0 000905.SH 20251121 6817.4103 6955.7485 6986.7784 6817.4103 \n",
"1 000905.SH 20251120 7061.9497 7174.1046 7180.7320 7056.9003 \n",
"2 000905.SH 20251119 7122.7465 7141.2641 7178.1495 7086.1232 \n",
"3 000905.SH 20251118 7151.0176 7215.0302 7230.5416 7118.4085 \n",
"4 000905.SH 20251117 7235.3512 7248.9216 7262.3306 7202.5932 \n",
"... ... ... ... ... ... ... \n",
"13810 399006.SZ 20100607 1069.4680 1005.0280 1075.2250 1001.7020 \n",
"13811 399006.SZ 20100604 1027.6810 989.6810 1027.6810 986.5040 \n",
"13812 399006.SZ 20100603 998.3940 1002.3550 1026.7020 997.7750 \n",
"13813 399006.SZ 20100602 997.1190 967.6090 997.1190 952.6110 \n",
"13814 399006.SZ 20100601 973.2330 986.0150 994.7930 948.1180 \n",
"13900 399006.SZ 20100607 1069.4680 1005.0280 1075.2250 1001.7020 \n",
"13901 399006.SZ 20100604 1027.6810 989.6810 1027.6810 986.5040 \n",
"13902 399006.SZ 20100603 998.3940 1002.3550 1026.7020 997.7750 \n",
"13903 399006.SZ 20100602 997.1190 967.6090 997.1190 952.6110 \n",
"13904 399006.SZ 20100601 973.2330 986.0150 994.7930 948.1180 \n",
"\n",
" pre_close change pct_chg vol amount \n",
"0 7548.9226 -150.6985 -1.9963 2.622566e+08 5.021274e+08 \n",
"1 7412.3684 136.5542 1.8422 2.831308e+08 5.357568e+08 \n",
"2 7350.5599 61.8085 0.8409 2.207075e+08 4.449564e+08 \n",
"3 7240.9114 109.6485 1.5143 2.335394e+08 4.338645e+08 \n",
"4 7341.3238 -100.4124 -1.3678 2.114441e+08 4.301976e+08 \n",
"0 7061.9497 -244.5394 -3.4628 2.089334e+08 3.109687e+08 \n",
"1 7122.7465 -60.7968 -0.8536 1.596187e+08 2.541582e+08 \n",
"2 7151.0176 -28.2711 -0.3953 1.627866e+08 2.567551e+08 \n",
"3 7235.3512 -84.3336 -1.1656 2.022141e+08 3.065400e+08 \n",
"4 7235.4617 -0.1105 -0.0015 2.030506e+08 3.108232e+08 \n",
"... ... ... ... ... ... \n",
"13810 1027.6810 41.7870 4.0661 2.655275e+06 9.106095e+06 \n",
"13811 998.3940 29.2870 2.9334 1.500295e+06 5.269441e+06 \n",
"13812 997.1190 1.2750 0.1279 1.616805e+06 6.240835e+06 \n",
"13813 973.2330 23.8860 2.4543 1.074628e+06 4.001206e+06 \n",
"13814 1000.0000 -26.7670 -2.6767 1.356285e+06 4.924177e+06 \n",
"13900 1027.6810 41.7870 4.0661 2.655275e+06 9.106095e+06 \n",
"13901 998.3940 29.2870 2.9334 1.500295e+06 5.269441e+06 \n",
"13902 997.1190 1.2750 0.1279 1.616805e+06 6.240835e+06 \n",
"13903 973.2330 23.8860 2.4543 1.074628e+06 4.001206e+06 \n",
"13904 1000.0000 -26.7670 -2.6767 1.356285e+06 4.924177e+06 \n",
"\n",
"[13815 rows x 11 columns]\n"
"[13905 rows x 11 columns]\n"
]
}
],

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main/data/qlib.ipynb Normal file
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@@ -0,0 +1,179 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"id": "2d9eb12f",
"metadata": {},
"outputs": [],
"source": [
"from operator import index\n",
"\n",
"import tushare as ts\n",
"import pandas as pd\n",
"import time\n",
"\n",
"ts.set_token('3a0741c702ee7e5e5f2bf1f0846bafaafe4e320833240b2a7e4a685f')\n",
"pro = ts.pro_api()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "0c5a87ba",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" ts_code trade_date his_low his_high cost_5pct cost_15pct \\\n",
"0 000001.SZ 20180104 0.2 12.3 7.2 8.7 \n",
"1 000002.SZ 20180104 0.2 25.6 15.0 17.6 \n",
"2 000004.SZ 20180104 0.8 53.2 21.6 22.0 \n",
"3 000008.SZ 20180104 0.1 13.9 7.2 7.8 \n",
"4 000009.SZ 20180104 0.3 15.0 5.8 5.9 \n",
"... ... ... ... ... ... ... \n",
"3095 603991.SH 20180104 12.0 67.8 26.4 27.0 \n",
"3096 603993.SH 20180104 1.4 8.6 5.4 5.6 \n",
"3097 603997.SH 20180104 5.4 31.5 9.9 10.2 \n",
"3098 603998.SH 20180104 3.8 18.3 9.5 9.8 \n",
"3099 603999.SH 20180104 3.6 30.6 6.9 6.9 \n",
"\n",
" cost_50pct cost_85pct cost_95pct weight_avg winner_rate \n",
"0 10.8 11.8 12.1 10.39 44.59 \n",
"1 22.2 24.4 24.8 21.31 97.14 \n",
"2 23.6 27.6 29.6 24.71 45.41 \n",
"3 8.6 9.2 10.5 8.64 47.04 \n",
"4 6.6 7.6 7.8 6.74 38.85 \n",
"... ... ... ... ... ... \n",
"3095 27.6 30.6 34.2 28.54 57.36 \n",
"3096 6.1 6.9 7.3 6.15 72.78 \n",
"3097 10.5 11.7 11.7 10.84 11.28 \n",
"3098 11.5 13.0 15.2 11.72 18.44 \n",
"3099 7.8 9.3 9.9 8.00 31.89 \n",
"\n",
"[3100 rows x 11 columns]\n"
]
}
],
"source": [
"\n",
"df = pro.cyq_perf(trade_date='20180104')\n",
"print(df)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "500292d5",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import time\n",
"\n",
"\n",
"trade_cal = pro.trade_cal(exchange='', start_date='20170101', end_date='20250820')\n",
"trade_cal = trade_cal[trade_cal['is_open'] == 1] # 只保留交易日\n",
"trade_dates = trade_cal['cal_date'].tolist()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4ae3cb65",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"✅ 日历文件已保存至: /mnt/d/PyProject/NewStock/data/qlib/calendar/day.txt\n",
"📅 共 2097 个交易日\n"
]
}
],
"source": [
"import os\n",
"\n",
"calendar_dir = \"/mnt/d/PyProject/NewStock/data/qlib/calendars\"\n",
"os.makedirs(calendar_dir, exist_ok=True) # 自动创建目录（包括父目录）\n",
"\n",
"# 排序为升序（Qlib 要求日历按时间升序）\n",
"trade_dates_sorted = sorted(trade_dates)\n",
"\n",
"# 写入 day.txt\n",
"day_txt_path = os.path.join(calendar_dir, \"day.txt\")\n",
"with open(day_txt_path, \"w\") as f:\n",
" for date_str in trade_dates_sorted:\n",
" f.write(date_str + \"\\n\")\n",
"\n",
"print(f\"✅ 日历文件已保存至: {day_txt_path}\")\n",
"print(f\"📅 共 {len(trade_dates_sorted)} 个交易日\")"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "7a6e529b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"✅ all.txt 已生成，共 5685 只股票\n"
]
}
],
"source": [
"stocks_df = pd.read_csv('/mnt/d/PyProject/NewStock/stocks_list.csv', encoding='utf-8-sig')\n",
"\n",
"import os\n",
"\n",
"# 假设你有一个包含所有股票代码的列表（来自 stocks_df['ts_code']）\n",
"# 例如：\n",
"# instrument_list = ['600000.SH', '000001.SZ', '300001.SZ', ...]\n",
"\n",
"instrument_list = stocks_df['ts_code'].unique().tolist()\n",
"\n",
"# 获取你的数据时间范围（从 trade_dates）\n",
"start_date = min(trade_dates) # e.g., '20201106'\n",
"end_date = max(trade_dates) # e.g., '20210125'\n",
"\n",
"# 创建 instruments 目录\n",
"instr_dir = \"/mnt/d/PyProject/NewStock/data/qlib/instruments\"\n",
"os.makedirs(instr_dir, exist_ok=True)\n",
"\n",
"# 写入 all.txt\n",
"with open(os.path.join(instr_dir, \"all.txt\"), \"w\") as f:\n",
" for inst in instrument_list:\n",
" f.write(f\"{inst}\\t{start_date}\\t{end_date}\\n\")\n",
"\n",
"print(f\"✅ all.txt 已生成，共 {len(instrument_list)} 只股票\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "stock",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.13.2"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

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@@ -0,0 +1,80 @@
{
"cells": [
{
"cell_type": "code",
"id": "initial_id",
"metadata": {
"collapsed": true,
"ExecuteTime": {
"end_time": "2025-10-17T07:14:47.275Z",
"start_time": "2025-10-17T07:14:46.966401Z"
}
},
"source": [
"from operator import index\n",
"\n",
"import tushare as ts\n",
"import pandas as pd\n",
"import time\n",
"\n",
"ts.set_token('3a0741c702ee7e5e5f2bf1f0846bafaafe4e320833240b2a7e4a685f')\n",
"pro = ts.pro_api()"
],
"outputs": [],
"execution_count": 1
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-10-17T07:15:47.631705Z",
"start_time": "2025-10-17T07:15:47.491485Z"
}
},
"cell_type": "code",
"source": [
"pro = ts.pro_api()\n",
"\n",
"#获取单个股票数据\n",
"df = pro.stk_limit(ts_code='603106.SH', start_date='20240924', end_date='20240928')\n",
"\n",
"print(df)"
],
"id": "72dcf1a049d09818",
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" trade_date ts_code up_limit down_limit\n",
"0 20240927 603106.SH 7.71 6.31\n",
"1 20240926 603106.SH 7.01 5.73\n",
"2 20240925 603106.SH 6.37 5.21\n",
"3 20240924 603106.SH 5.79 4.73\n"
]
}
],
"execution_count": 6
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

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main/data/update/cyq-perf.ipynb
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@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"execution_count": 3,
"id": "f74ce078-f7e8-4733-a14c-14d8815a3626",
"metadata": {
"ExecuteTime": {
@@ -19,7 +19,7 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 4,
"id": "44dd8d87-e60b-49e5-aed9-efaa7f92d4fe",
"metadata": {
"ExecuteTime": {
@@ -39,15 +39,15 @@
"3 000006.SZ 20250312\n",
"4 000007.SZ 20250312\n",
"... ... ...\n",
"27111 920445.BJ 20250922\n",
"27112 920489.BJ 20250922\n",
"27113 920682.BJ 20250922\n",
"27114 920799.BJ 20250922\n",
"27115 920819.BJ 20250922\n",
"21755 920978.BJ 20251117\n",
"21756 920981.BJ 20251117\n",
"21757 920982.BJ 20251117\n",
"21758 920985.BJ 20251117\n",
"21759 920992.BJ 20251117\n",
"\n",
"[8205543 rows x 2 columns]\n",
"20250926\n",
"start_date: 20250929\n"
"[8385278 rows x 2 columns]\n",
"20251120\n",
"start_date: 20251121\n"
]
}
],
@@ -64,7 +64,7 @@
" max_date = df['trade_date'].max()\n",
"\n",
"print(max_date)\n",
"trade_cal = pro.trade_cal(exchange='', start_date='20170101', end_date='20251020')\n",
"trade_cal = pro.trade_cal(exchange='', start_date='20170101', end_date='20251220')\n",
"trade_cal = trade_cal[trade_cal['is_open'] == 1] # 只保留交易日\n",
"trade_dates = trade_cal[trade_cal['cal_date'] > max_date]['cal_date'].tolist()\n",
"start_date = min(trade_dates)\n",
@@ -73,7 +73,7 @@
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": 5,
"id": "747acc47-0884-4f76-90fb-276f6494e31d",
"metadata": {
"ExecuteTime": {
@@ -86,16 +86,27 @@
"name": "stdout",
"output_type": "stream",
"text": [
"任务 20251020 完成\n",
"任务 20251017 完成\n",
"任务 20251016 完成\n",
"任务 20251015 完成\n",
"任务 20251014 完成\n",
"任务 20251013 完成\n",
"任务 20251010 完成\n",
"任务 20251009 完成\n",
"任务 20250930 完成\n",
"任务 20250929 完成\n"
"任务 20251219 完成\n",
"任务 20251218 完成\n",
"任务 20251216 完成\n",
"任务 20251217 完成\n",
"任务 20251215 完成\n",
"任务 20251212 完成\n",
"任务 20251211 完成\n",
"任务 20251210 完成\n",
"任务 20251209 完成\n",
"任务 20251208 完成\n",
"任务 20251205 完成\n",
"任务 20251204 完成\n",
"任务 20251203 完成\n",
"任务 20251202 完成\n",
"任务 20251201 完成\n",
"任务 20251128 完成\n",
"任务 20251127 完成\n",
"任务 20251126 完成\n",
"任务 20251125 完成\n",
"任务 20251124 完成\n",
"任务 20251121 完成\n"
]
}
],
@@ -132,7 +143,7 @@
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": 6,
"id": "c6765638-481f-40d8-a259-2e7b25362618",
"metadata": {
"ExecuteTime": {
@@ -177,7 +188,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.13.2"
"version": "3.12.11"
}
},
"nbformat": 4,

63
main/data/update/sw_daily.ipynb
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@@ -32,22 +32,22 @@
"name": "stdout",
"output_type": "stream",
"text": [
" ts_code trade_date\n",
"0 801001.SI 20250221\n",
"1 801002.SI 20250221\n",
"2 801003.SI 20250221\n",
"3 801005.SI 20250221\n",
"4 801010.SI 20250221\n",
"... ... ...\n",
"2190 859811.SI 20250922\n",
"2191 859821.SI 20250922\n",
"2192 859822.SI 20250922\n",
"2193 859852.SI 20250922\n",
"2194 859951.SI 20250922\n",
" ts_code trade_date\n",
"0 801001.SI 20250221\n",
"1 801002.SI 20250221\n",
"2 801003.SI 20250221\n",
"3 801005.SI 20250221\n",
"4 801010.SI 20250221\n",
".. ... ...\n",
"873 859811.SI 20251120\n",
"874 859821.SI 20251120\n",
"875 859822.SI 20251120\n",
"876 859852.SI 20251120\n",
"877 859951.SI 20251120\n",
"\n",
"[1110243 rows x 2 columns]\n",
"20250926\n",
"start_date: 20250929\n"
"[1123852 rows x 2 columns]\n",
"20251120\n",
"start_date: 20251121\n"
]
}
],
@@ -64,7 +64,7 @@
" max_date = df['trade_date'].max()\n",
"\n",
"print(max_date)\n",
"trade_cal = pro.trade_cal(exchange='', start_date='20170101', end_date='20251020')\n",
"trade_cal = pro.trade_cal(exchange='', start_date='20170101', end_date='20251220')\n",
"trade_cal = trade_cal[trade_cal['is_open'] == 1] # 只保留交易日\n",
"trade_dates = trade_cal[trade_cal['cal_date'] > max_date]['cal_date'].tolist()\n",
"start_date = min(trade_dates)\n",
@@ -86,16 +86,27 @@
"name": "stdout",
"output_type": "stream",
"text": [
"任务 20251020 完成\n",
"任务 20251017 完成\n",
"任务 20251016 完成\n",
"任务 20251015 完成\n",
"任务 20251014 完成\n",
"任务 20251013 完成\n",
"任务 20251010 完成\n",
"任务 20251009 完成\n",
"任务 20250930 完成\n",
"任务 20250929 完成\n"
"任务 20251218 完成\n",
"任务 20251219 完成\n",
"任务 20251217 完成\n",
"任务 20251216 完成\n",
"任务 20251215 完成\n",
"任务 20251212 完成\n",
"任务 20251211 完成\n",
"任务 20251210 完成\n",
"任务 20251209 完成\n",
"任务 20251208 完成\n",
"任务 20251204 完成\n",
"任务 20251205 完成\n",
"任务 20251202 完成\n",
"任务 20251203 完成\n",
"任务 20251201 完成\n",
"任务 20251128 完成\n",
"任务 20251127 完成\n",
"任务 20251126 完成\n",
"任务 20251125 完成\n",
"任务 20251124 完成\n",
"任务 20251121 完成\n"
]
}
],

232
main/data/update/update_daily_basic.ipynb
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@@ -94,17 +94,17 @@
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Index: 9155905 entries, 0 to 27115\n",
"Index: 9335158 entries, 0 to 21759\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: 209.6+ MB\n",
"memory usage: 213.7+ MB\n",
"None\n",
"20250926\n",
"20250929\n"
"20251120\n",
"20251121\n"
]
}
],
@@ -121,7 +121,7 @@
" max_date = df['trade_date'].max()\n",
"\n",
"print(max_date)\n",
"trade_cal = pro.trade_cal(exchange='', start_date='20170101', end_date='20251020')\n",
"trade_cal = pro.trade_cal(exchange='', start_date='20170101', end_date='20251220')\n",
"trade_cal = trade_cal[trade_cal['is_open'] == 1] # 只保留交易日\n",
"trade_dates = trade_cal[trade_cal['cal_date'] > max_date]['cal_date'].tolist()\n",
"start_date = min(trade_dates)\n",
@@ -144,16 +144,27 @@
"name": "stdout",
"output_type": "stream",
"text": [
"任务 20251017 完成\n",
"任务 20251020 完成\n",
"任务 20251015 完成\n",
"任务 20251016 完成\n",
"任务 20251014 完成\n",
"任务 20251013 完成\n",
"任务 20251010 完成\n",
"任务 20251009 完成\n",
"任务 20250930 完成\n",
"任务 20250929 完成\n"
"任务 20251219 完成\n",
"任务 20251218 完成\n",
"任务 20251217 完成\n",
"任务 20251216 完成\n",
"任务 20251215 完成\n",
"任务 20251212 完成\n",
"任务 20251211 完成\n",
"任务 20251210 完成\n",
"任务 20251209 完成\n",
"任务 20251208 完成\n",
"任务 20251205 完成\n",
"任务 20251204 完成\n",
"任务 20251203 完成\n",
"任务 20251202 完成\n",
"任务 20251201 完成\n",
"任务 20251128 完成\n",
"任务 20251127 完成\n",
"任务 20251126 完成\n",
"任务 20251125 完成\n",
"任务 20251124 完成\n",
"任务 20251121 完成\n"
]
}
],
@@ -223,59 +234,59 @@
"name": "stdout",
"output_type": "stream",
"text": [
" ts_code trade_date close turnover_rate turnover_rate_f \\\n",
"0 600642.SH 20251010 8.03 0.4806 1.3835 \n",
"1 600295.SH 20251010 10.76 0.8549 3.7056 \n",
"2 600444.SH 20251010 19.00 9.6611 17.4605 \n",
"3 605100.SH 20251010 28.72 3.4770 7.6902 \n",
"4 301399.SZ 20251010 19.53 3.9562 4.6772 \n",
"... ... ... ... ... ... \n",
"21679 600653.SH 20250929 2.13 2.1746 2.9589 \n",
"21680 002344.SZ 20250929 4.49 1.7080 3.6338 \n",
"21681 301162.SZ 20250929 60.30 2.8491 3.5744 \n",
"21682 920077.BJ 20250929 14.43 1.1113 1.6410 \n",
"21683 300283.SZ 20250929 7.04 4.8583 5.7018 \n",
" ts_code trade_date close turnover_rate turnover_rate_f \\\n",
"0 000559.SZ 20251121 11.64 4.8762 13.4563 \n",
"1 002981.SZ 20251121 27.84 1.5833 4.5574 \n",
"2 301053.SZ 20251121 32.50 1.0110 2.9907 \n",
"3 603093.SH 20251121 18.29 0.7403 3.2151 \n",
"4 600269.SH 20251121 5.25 0.8423 1.8459 \n",
"... ... ... ... ... ... \n",
"5439 600243.SH 20251121 4.78 1.7524 2.1078 \n",
"5440 300759.SZ 20251121 28.39 1.0514 1.6405 \n",
"5441 600054.SH 20251121 11.10 1.3130 3.1101 \n",
"5442 603579.SH 20251121 23.85 2.2265 4.3412 \n",
"5443 002528.SZ 20251121 3.03 1.9087 4.0726 \n",
"\n",
" volume_ratio pe pe_ttm pb ps ps_ttm dv_ratio \\\n",
"0 1.49 9.9635 10.2617 1.1073 1.3268 1.3600 4.9816 \n",
"1 1.56 16.3053 16.4683 1.4839 1.0603 1.1230 7.4349 \n",
"2 2.84 69.2746 55.7147 3.8398 3.6313 3.5392 0.5263 \n",
"3 0.55 66.7896 123.2961 2.7276 5.3634 6.7180 2.0794 \n",
"4 0.94 60.7990 75.8958 2.7675 6.8812 7.1828 1.2177 \n",
"... ... ... ... ... ... ... ... \n",
"21679 0.72 107.4073 227.6354 5.4498 0.9887 0.9724 0.0000 \n",
"21680 0.70 64.8238 75.9239 0.6834 5.5516 5.5560 0.9577 \n",
"21681 0.96 85.4251 76.2427 5.3380 14.5424 12.3677 0.5586 \n",
"21682 0.51 90.3399 82.4861 3.3572 5.2895 4.1636 NaN \n",
"21683 0.94 NaN NaN 3.2821 1.1161 0.9970 0.2499 \n",
" volume_ratio pe pe_ttm pb ps ps_ttm dv_ratio \\\n",
"0 1.09 40.5790 38.2942 4.1055 2.9989 2.7785 1.2842 \n",
"1 1.44 33.9003 28.1141 3.4000 2.2070 1.9328 0.9280 \n",
"2 1.24 56.6010 98.7688 4.0251 4.4406 4.0870 0.2389 \n",
"3 1.21 24.3641 24.7359 2.5390 1.9536 5.0927 0.3609 \n",
"4 1.32 9.5849 6.9841 0.6165 2.0486 2.1055 3.0476 \n",
"... ... ... ... ... ... ... ... \n",
"5439 1.37 NaN NaN 3.3110 8.8659 8.4702 0.0000 \n",
"5440 0.86 28.1501 33.3780 3.4547 4.1124 3.7273 0.7056 \n",
"5441 1.53 25.7012 28.5474 1.6912 4.1924 3.9403 1.8829 \n",
"5442 1.23 25.2677 30.2644 1.7649 3.0372 3.0683 3.8598 \n",
"5443 0.61 NaN NaN 35.8962 3.8438 6.1411 0.0000 \n",
"\n",
" dv_ttm total_share float_share free_share total_mv \\\n",
"0 5.6040 489407.9376 489381.3156 170006.8520 3.929946e+06 \n",
"1 5.5762 279877.6254 197557.6254 45577.9458 3.011483e+06 \n",
"2 0.5789 14642.1932 14642.1932 8101.7360 2.782017e+05 \n",
"3 1.0446 17113.2000 16993.2000 7683.2000 4.914911e+05 \n",
"4 1.0594 18502.0000 5468.3586 4625.5000 3.613441e+05 \n",
"... ... ... ... ... ... \n",
"21679 NaN 194638.0317 194638.0317 143048.5612 4.145790e+05 \n",
"21680 0.8463 128261.6960 128145.0092 60233.0025 5.758950e+05 \n",
"21681 0.9704 13258.3724 8522.5548 6793.1764 7.994799e+05 \n",
"21682 NaN 58768.1817 31695.6817 21464.7599 8.480249e+05 \n",
"21683 NaN 49697.8222 36721.8502 31289.2680 3.498727e+05 \n",
" dv_ttm total_share float_share free_share total_mv \\\n",
"0 1.5410 331535.8444 331454.4214 120110.9588 3.859077e+06 \n",
"1 0.9187 13748.6115 11941.3915 4148.6777 3.827613e+05 \n",
"2 0.8961 8421.7803 7749.4689 2619.7738 2.737079e+05 \n",
"3 0.4117 61006.5893 61006.5893 14046.4993 1.115811e+06 \n",
"4 3.2381 233540.7014 233540.7014 106564.7107 1.226089e+06 \n",
"... ... ... ... ... ... \n",
"5439 NaN 43885.0000 43885.0000 36485.0000 2.097703e+05 \n",
"5440 0.7045 177819.5525 141938.4613 90967.4278 5.048297e+06 \n",
"5441 1.5495 72937.9440 51330.0000 21670.4250 8.096112e+05 \n",
"5442 1.2636 20335.5564 20335.5564 10429.5044 4.850030e+05 \n",
"5443 NaN 119867.5082 105021.9577 49219.1551 3.631985e+05 \n",
"\n",
" circ_mv is_st \n",
"0 3.929732e+06 False \n",
"1 2.125720e+06 False \n",
"2 2.782017e+05 False \n",
"3 4.880447e+05 False \n",
"4 1.067970e+05 False \n",
"... ... ... \n",
"21679 4.145790e+05 False \n",
"21680 5.753711e+05 False \n",
"21681 5.139101e+05 False \n",
"21682 4.573687e+05 False \n",
"21683 2.585218e+05 False \n",
" circ_mv is_st \n",
"0 3.858129e+06 False \n",
"1 3.324483e+05 False \n",
"2 2.518577e+05 False \n",
"3 1.115811e+06 False \n",
"4 1.226089e+06 False \n",
"... ... ... \n",
"5439 2.097703e+05 True \n",
"5440 4.029633e+06 False \n",
"5441 5.697630e+05 False \n",
"5442 4.850030e+05 False \n",
"5443 3.182165e+05 True \n",
"\n",
"[21684 rows x 19 columns]\n"
"[5444 rows x 19 columns]\n"
]
}
],
@@ -299,46 +310,59 @@
"name": "stdout",
"output_type": "stream",
"text": [
" ts_code trade_date close turnover_rate turnover_rate_f \\\n",
"9 300313.SZ 20251010 8.84 3.1146 6.4625 \n",
"20 603838.SH 20251010 7.80 0.5503 1.5146 \n",
"29 603813.SH 20251010 24.06 1.5835 4.5173 \n",
"48 002742.SZ 20251010 4.65 1.0473 1.2924 \n",
"69 603559.SH 20251010 8.50 0.2072 0.2945 \n",
"... ... ... ... ... ... \n",
"21466 603021.SH 20250929 4.62 1.3860 2.3418 \n",
"21552 300020.SZ 20250929 3.58 1.5031 1.6828 \n",
"21554 000506.SZ 20250929 10.88 10.5560 15.7565 \n",
"21603 600636.SH 20250929 8.29 0.4693 0.7963 \n",
"21661 603843.SH 20250929 5.17 0.3798 0.5364 \n",
" ts_code trade_date close turnover_rate turnover_rate_f \\\n",
"55 000909.SZ 20251121 5.63 0.5785 0.9877 \n",
"62 002485.SZ 20251121 4.61 0.9593 3.9009 \n",
"134 300096.SZ 20251121 7.31 1.6490 1.9675 \n",
"154 300343.SZ 20251121 5.48 4.1298 4.7019 \n",
"166 600525.SH 20251121 3.53 1.8869 2.7053 \n",
"... ... ... ... ... ... \n",
"5340 300368.SZ 20251121 14.86 7.3423 10.4878 \n",
"5381 300020.SZ 20251121 3.63 1.9995 2.2386 \n",
"5383 000506.SZ 20251121 11.55 2.5685 3.8339 \n",
"5439 600243.SH 20251121 4.78 1.7524 2.1078 \n",
"5443 002528.SZ 20251121 3.03 1.9087 4.0726 \n",
"\n",
" volume_ratio pe pe_ttm pb ps ps_ttm dv_ratio dv_ttm \\\n",
"9 1.30 NaN NaN NaN 20.1067 20.9731 0.0000 NaN \n",
"20 0.57 NaN NaN 2.6121 8.7517 6.9304 0.0000 NaN \n",
"29 1.88 NaN NaN 4.5222 8.4776 7.5124 1.0313 NaN \n",
"48 1.28 NaN NaN NaN 1.6800 2.1226 0.0000 NaN \n",
"69 0.60 NaN NaN 3.5043 9.5964 8.2315 0.0000 NaN \n",
"... ... .. ... ... ... ... ... ... \n",
"21466 0.80 NaN NaN NaN 3.5891 3.7851 0.0000 NaN \n",
"21552 1.00 NaN NaN 0.9812 5.1924 18.4036 0.0000 NaN \n",
"21554 3.17 NaN NaN 16.4257 30.3341 23.4860 0.0000 NaN \n",
"21603 0.81 NaN NaN 1.7909 12.8512 11.0116 0.4825 0.6031 \n",
"21661 0.05 NaN NaN 12.5612 2.6558 3.1369 0.0000 NaN \n",
" volume_ratio pe pe_ttm pb ps ps_ttm dv_ratio \\\n",
"55 0.99 NaN NaN 2.4818 7.6504 7.4923 0.0 \n",
"62 0.51 NaN NaN 2.1295 3.0458 3.2777 0.0 \n",
"134 0.81 NaN 50.1694 8.9654 5.6290 6.2215 0.0 \n",
"154 0.72 267.9489 106.2988 3.0411 6.7430 6.5207 0.0 \n",
"166 0.72 NaN NaN 1.2373 0.5912 0.5968 0.0 \n",
"... ... ... ... ... ... ... ... \n",
"5340 0.94 NaN NaN 42.1875 42.9123 57.8502 0.0 \n",
"5381 1.00 NaN NaN 1.0776 5.2649 21.5375 0.0 \n",
"5383 0.78 NaN 239.4225 16.7572 32.2021 20.7023 0.0 \n",
"5439 1.37 NaN NaN 3.3110 8.8659 8.4702 0.0 \n",
"5443 0.61 NaN NaN 35.8962 3.8438 6.1411 0.0 \n",
"\n",
" total_share float_share free_share total_mv circ_mv is_st \n",
"9 31297.7396 19735.2789 9511.5479 2.766720e+05 1.744599e+05 True \n",
"20 32001.6000 32001.6000 11627.0468 2.496125e+05 2.496125e+05 True \n",
"29 10501.5000 10501.5000 3681.2000 2.526661e+05 2.526661e+05 True \n",
"48 43200.0000 43185.8082 34994.8239 2.008800e+05 2.008140e+05 True \n",
"69 40127.6979 40127.6979 28231.9697 3.410854e+05 3.410854e+05 True \n",
"... ... ... ... ... ... ... \n",
"21466 31994.8070 31994.8070 18936.7934 1.478160e+05 1.478160e+05 True \n",
"21552 79467.7974 76663.9584 68475.6577 2.844947e+05 2.744570e+05 True \n",
"21554 92901.7761 92858.4361 62210.1427 1.010771e+06 1.010300e+06 True \n",
"21603 43863.6802 43863.6802 25849.6552 3.636299e+05 3.636299e+05 True \n",
"21661 69962.3237 69962.3237 49541.4702 3.617052e+05 3.617052e+05 True \n",
" dv_ttm total_share float_share free_share total_mv \\\n",
"55 NaN 43771.4245 43771.0570 25634.2299 2.464331e+05 \n",
"62 NaN 54400.0000 54400.0000 13377.7333 2.507840e+05 \n",
"134 NaN 43000.0000 43000.0000 36039.3251 3.143300e+05 \n",
"154 NaN 106896.9119 106621.9389 93649.7579 5.857951e+05 \n",
"166 NaN 131878.0152 131878.0152 91981.1744 4.655294e+05 \n",
"... ... ... ... ... ... \n",
"5340 NaN 52894.3475 52894.3475 37030.2475 7.860100e+05 \n",
"5381 NaN 79467.7974 76663.9584 68475.6577 2.884681e+05 \n",
"5383 NaN 92901.7761 92858.4361 62210.1427 1.073016e+06 \n",
"5439 NaN 43885.0000 43885.0000 36485.0000 2.097703e+05 \n",
"5443 NaN 119867.5082 105021.9577 49219.1551 3.631985e+05 \n",
"\n",
"[749 rows x 19 columns]\n"
" circ_mv is_st \n",
"55 2.464311e+05 True \n",
"62 2.507840e+05 True \n",
"134 3.143300e+05 True \n",
"154 5.842882e+05 True \n",
"166 4.655294e+05 True \n",
"... ... ... \n",
"5340 7.860100e+05 True \n",
"5381 2.782902e+05 True \n",
"5383 1.072515e+06 True \n",
"5439 2.097703e+05 True \n",
"5443 3.182165e+05 True \n",
"\n",
"[186 rows x 19 columns]\n"
]
}
],
@@ -388,7 +412,7 @@
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Index: 9177589 entries, 0 to 21683\n",
"Index: 9340602 entries, 0 to 5443\n",
"Data columns (total 3 columns):\n",
" # Column Dtype \n",
"--- ------ ----- \n",
@@ -396,7 +420,7 @@
" 1 trade_date object\n",
" 2 is_st bool \n",
"dtypes: bool(1), object(2)\n",
"memory usage: 218.8+ MB\n",
"memory usage: 222.7+ MB\n",
"None\n"
]
}
@@ -424,7 +448,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.13.2"
"version": "3.12.11"
}
},
"nbformat": 4,

963
main/data/update/update_daily_data.ipynb
View File

File diff suppressed because it is too large Load Diff

29
main/data/update/update_is_st.ipynb
View File

@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
"execution_count": 3,
"execution_count": 1,
"id": "17cc645336d4eb18",
"metadata": {
"ExecuteTime": {
@@ -18,7 +18,7 @@
},
{
"cell_type": "code",
"execution_count": 8,
"execution_count": 2,
"id": "48ae71ed02d61819",
"metadata": {
"ExecuteTime": {
@@ -26,14 +26,27 @@
"start_time": "2025-02-08T16:55:19.882313Z"
}
},
"outputs": [],
"outputs": [
{
"ename": "FileNotFoundError",
"evalue": "File ../../../data/daily_basic.h5 does not exist",
"output_type": "error",
"traceback": [
"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
"\u001b[31mFileNotFoundError\u001b[39m Traceback (most recent call last)",
"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[2]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m daily_basic = \u001b[43mpd\u001b[49m\u001b[43m.\u001b[49m\u001b[43mread_hdf\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43m../../../data/daily_basic.h5\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkey\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mdaily_basic\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n",
"\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/stock/lib/python3.12/site-packages/pandas/io/pytables.py:437\u001b[39m, in \u001b[36mread_hdf\u001b[39m\u001b[34m(path_or_buf, key, mode, errors, where, start, stop, columns, iterator, chunksize, **kwargs)\u001b[39m\n\u001b[32m 434\u001b[39m exists = \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[32m 436\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m exists:\n\u001b[32m--> \u001b[39m\u001b[32m437\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mFileNotFoundError\u001b[39;00m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mFile \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpath_or_buf\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m does not exist\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 439\u001b[39m store = HDFStore(path_or_buf, mode=mode, errors=errors, **kwargs)\n\u001b[32m 440\u001b[39m \u001b[38;5;66;03m# can't auto open/close if we are using an iterator\u001b[39;00m\n\u001b[32m 441\u001b[39m \u001b[38;5;66;03m# so delegate to the iterator\u001b[39;00m\n",
"\u001b[31mFileNotFoundError\u001b[39m: File ../../../data/daily_basic.h5 does not exist"
]
}
],
"source": [
"daily_basic = pd.read_hdf('../../../data/daily_basic.h5', key='daily_basic')\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": null,
"id": "e6606a96e5728b8",
"metadata": {
"ExecuteTime": {
@@ -93,7 +106,7 @@
},
{
"cell_type": "code",
"execution_count": 9,
"execution_count": null,
"id": "41bc125d",
"metadata": {},
"outputs": [
@@ -163,7 +176,7 @@
},
{
"cell_type": "code",
"execution_count": 10,
"execution_count": null,
"id": "initial_id",
"metadata": {
"ExecuteTime": {
@@ -209,7 +222,7 @@
],
"metadata": {
"kernelspec": {
"display_name": "new_trader",
"display_name": "stock",
"language": "python",
"name": "python3"
},
@@ -223,7 +236,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.11"
"version": "3.13.2"
}
},
"nbformat": 4,

154
main/data/update/update_money_flow.ipynb
View File

@@ -34,17 +34,17 @@
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Index: 8964780 entries, 0 to 25739\n",
"Index: 9134824 entries, 0 to 20632\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: 205.2+ MB\n",
"memory usage: 209.1+ MB\n",
"None\n",
"20250926\n",
"start_date: 20250929\n"
"20251120\n",
"start_date: 20251121\n"
]
}
],
@@ -61,7 +61,7 @@
" max_date = df['trade_date'].max()\n",
"\n",
"print(max_date)\n",
"trade_cal = pro.trade_cal(exchange='', start_date='20170101', end_date='20251020')\n",
"trade_cal = pro.trade_cal(exchange='', start_date='20170101', end_date='20251220')\n",
"trade_cal = trade_cal[trade_cal['is_open'] == 1] # 只保留交易日\n",
"trade_dates = trade_cal[trade_cal['cal_date'] > max_date]['cal_date'].tolist()\n",
"start_date = min(trade_dates)\n",
@@ -84,16 +84,27 @@
"name": "stdout",
"output_type": "stream",
"text": [
"任务 20251020 完成\n",
"任务 20251017 完成\n",
"任务 20251016 完成\n",
"任务 20251015 完成\n",
"任务 20251014 完成\n",
"任务 20251013 完成\n",
"任务 20251009 完成\n",
"任务 20251010 完成\n",
"任务 20250929 完成\n",
"任务 20250930 完成\n"
"任务 20251218 完成\n",
"任务 20251219 完成\n",
"任务 20251217 完成\n",
"任务 20251216 完成\n",
"任务 20251215 完成\n",
"任务 20251212 完成\n",
"任务 20251211 完成\n",
"任务 20251210 完成\n",
"任务 20251209 完成\n",
"任务 20251208 完成\n",
"任务 20251205 完成\n",
"任务 20251204 完成\n",
"任务 20251203 完成\n",
"任务 20251202 完成\n",
"任务 20251201 完成\n",
"任务 20251128 完成\n",
"任务 20251127 完成\n",
"任务 20251126 完成\n",
"任务 20251125 完成\n",
"任务 20251124 完成\n",
"任务 20251121 完成\n"
]
}
],
@@ -182,72 +193,59 @@
"name": "stdout",
"output_type": "stream",
"text": [
" ts_code trade_date buy_sm_vol buy_sm_amount sell_sm_vol \\\n",
"0 603290.SH 20251009 45532 52028.67 42778 \n",
"1 600936.SH 20251009 42537 1545.21 42382 \n",
"2 300429.SZ 20251009 81914 11768.07 64063 \n",
"3 300879.SZ 20251009 15330 5366.90 11651 \n",
"4 300031.SZ 20251009 51381 12650.70 43869 \n",
"... ... ... ... ... ... \n",
"20574 688083.SH 20250930 13247 10094.95 11236 \n",
"20575 002939.SZ 20250930 372609 43083.12 232240 \n",
"20576 688303.SH 20250930 62478 18094.19 55086 \n",
"20577 300146.SZ 20250930 50078 5792.85 35214 \n",
"20578 688351.SH 20250930 15096 3333.84 14017 \n",
" ts_code trade_date buy_sm_vol buy_sm_amount sell_sm_vol \\\n",
"0 002593.SZ 20251121 369428 21109.32 239444 \n",
"1 300405.SZ 20251121 173424 11775.01 115988 \n",
"2 001336.SZ 20251121 11378 2729.92 10423 \n",
"3 002403.SZ 20251121 24219 3104.96 19841 \n",
"4 688268.SH 20251121 12369 7423.62 12330 \n",
"... ... ... ... ... ... \n",
"5156 000881.SZ 20251121 146959 11936.56 155068 \n",
"5157 300676.SZ 20251121 21428 9913.61 15092 \n",
"5158 603138.SH 20251121 31243 4558.85 30559 \n",
"5159 301526.SZ 20251121 172815 9552.38 105860 \n",
"5160 300903.SZ 20251121 124772 20586.88 96098 \n",
"\n",
" sell_sm_amount buy_md_vol buy_md_amount sell_md_vol sell_md_amount \\\n",
"0 48942.98 53824 61495.85 54076 61851.39 \n",
"1 1538.97 24175 878.06 31948 1160.07 \n",
"2 9211.49 88583 12730.36 88244 12682.05 \n",
"3 4089.33 15591 5464.12 17057 5976.94 \n",
"4 10822.65 56173 13836.60 49423 12190.63 \n",
"... ... ... ... ... ... \n",
"20574 8561.02 10482 7994.12 9858 7514.37 \n",
"20575 26867.01 279904 32371.96 324997 37595.57 \n",
"20576 15952.67 55867 16177.83 53776 15573.61 \n",
"20577 4076.10 46159 5337.00 39420 4560.91 \n",
"20578 3095.89 6482 1430.69 6675 1474.59 \n",
" sell_sm_amount buy_md_vol buy_md_amount sell_md_vol sell_md_amount \\\n",
"0 13673.67 256325 14655.03 298786 17088.39 \n",
"1 7859.14 154296 10473.88 176589 11973.97 \n",
"2 2498.94 5274 1266.93 5893 1415.57 \n",
"3 2546.44 17292 2218.64 18180 2333.03 \n",
"4 7430.97 16104 9682.18 16670 10042.76 \n",
"... ... ... ... ... ... \n",
"5156 12623.78 107103 8717.66 97089 7896.18 \n",
"5157 6975.73 17857 8249.34 16607 7679.15 \n",
"5158 4458.47 15126 2208.57 11879 1733.73 \n",
"5159 5855.69 155749 8607.76 160962 8892.48 \n",
"5160 15867.99 92082 15223.39 105748 17449.56 \n",
"\n",
" buy_lg_vol buy_lg_amount sell_lg_vol sell_lg_amount buy_elg_vol \\\n",
"0 36150 41253.53 36789 41932.43 10514 \n",
"1 11158 405.04 9212 334.60 5672 \n",
"2 64282 9239.06 72904 10475.38 8221 \n",
"3 10167 3562.24 12327 4313.59 3221 \n",
"4 40306 9938.01 41035 10103.23 6112 \n",
"... ... ... ... ... ... \n",
"20574 6674 5082.80 8224 6273.43 3329 \n",
"20575 204229 23631.31 285167 32986.98 132696 \n",
"20576 33304 9638.04 34809 10074.64 5032 \n",
"20577 47161 5454.07 36321 4202.88 8662 \n",
"20578 2513 555.48 3398 749.54 0 \n",
" buy_lg_vol buy_lg_amount sell_lg_vol sell_lg_amount buy_elg_vol \\\n",
"0 125303 7153.65 190306 10868.03 13733 \n",
"1 68396 4621.42 100633 6820.12 12166 \n",
"2 326 77.32 662 159.66 0 \n",
"3 7131 916.27 8891 1137.58 0 \n",
"4 9155 5523.81 9780 5877.77 2793 \n",
"... ... ... ... ... ... \n",
"5156 63727 5186.84 54928 4460.74 8415 \n",
"5157 12528 5781.44 16425 7596.83 3906 \n",
"5158 5884 857.88 8048 1175.32 0 \n",
"5159 63089 3481.66 115498 6376.52 13568 \n",
"5160 58186 9624.92 77536 12811.46 25445 \n",
"\n",
" buy_elg_amount sell_elg_vol sell_elg_amount net_mf_vol \\\n",
"0 12073.88 12377 14125.13 20027 \n",
"1 205.33 0 0.00 -21182 \n",
"2 1183.11 17790 2551.67 -840 \n",
"3 1133.90 3275 1147.29 -4996 \n",
"4 1507.28 19645 4816.08 1531 \n",
"... ... ... ... ... \n",
"20574 2538.01 4413 3361.05 7612 \n",
"20575 15366.29 147033 17003.12 84949 \n",
"20576 1459.24 13010 3768.39 15188 \n",
"20577 1000.95 41105 4744.98 -16754 \n",
"20578 0.00 0 0.00 3406 \n",
" buy_elg_amount sell_elg_vol sell_elg_amount net_mf_vol net_mf_amount \n",
"0 781.20 36253 2069.12 -103672 -5866.51 \n",
"1 813.01 15071 1030.08 -34131 -2297.62 \n",
"2 0.00 0 0.00 -1180 -271.00 \n",
"3 0.00 1730 222.81 194 30.22 \n",
"4 1708.30 1640 986.41 476 282.30 \n",
"... ... ... ... ... ... \n",
"5156 686.43 19119 1546.77 -50922 -4113.23 \n",
"5157 1805.21 7595 3497.90 -4085 -1873.36 \n",
"5158 0.00 1768 257.78 713 110.42 \n",
"5159 744.87 22900 1261.99 -64224 -3539.76 \n",
"5160 4179.40 21103 3485.60 -29335 -4855.38 \n",
"\n",
" net_mf_amount \n",
"0 22734.35 \n",
"1 -766.75 \n",
"2 -90.83 \n",
"3 -1741.72 \n",
"4 385.00 \n",
"... ... \n",
"20574 5816.07 \n",
"20575 9927.60 \n",
"20576 4417.72 \n",
"20577 -1928.39 \n",
"20578 752.20 \n",
"\n",
"[20579 rows x 20 columns]\n"
"[5161 rows x 20 columns]\n"
]
}
],
@@ -272,7 +270,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.13.2"
"version": "3.12.11"
}
},
"nbformat": 4,

114
main/data/update/update_stk_limit.ipynb
View File

@@ -34,23 +34,23 @@
"output_type": "stream",
"text": [
" ts_code trade_date\n",
"4872 600206.SH 20250926\n",
"4873 600207.SH 20250926\n",
"4874 600208.SH 20250926\n",
"4876 600211.SH 20250926\n",
"7280 920037.BJ 20250926\n",
"4915 600221.SH 20251120\n",
"4916 600222.SH 20251120\n",
"4917 600223.SH 20251120\n",
"4919 600227.SH 20251120\n",
"3693 301448.SZ 20251120\n",
"<class 'pandas.core.frame.DataFrame'>\n",
"Index: 11170571 entries, 0 to 36462\n",
"Index: 11412627 entries, 0 to 29456\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: 255.7+ MB\n",
"memory usage: 261.2+ MB\n",
"None\n",
"20250926\n",
"20250929\n"
"20251120\n",
"20251121\n"
]
}
],
@@ -68,7 +68,7 @@
" max_date = df['trade_date'].max()\n",
"\n",
"print(max_date)\n",
"trade_cal = pro.trade_cal(exchange='', start_date='20170101', end_date='20251020')\n",
"trade_cal = pro.trade_cal(exchange='', start_date='20170101', end_date='20251220')\n",
"trade_cal = trade_cal[trade_cal['is_open'] == 1] # 只保留交易日\n",
"trade_dates = trade_cal[trade_cal['cal_date'] > max_date]['cal_date'].tolist()\n",
"start_date = min(trade_dates)\n",
@@ -91,16 +91,27 @@
"name": "stdout",
"output_type": "stream",
"text": [
"任务 20251020 完成\n",
"任务 20251017 完成\n",
"任务 20251015 完成\n",
"任务 20251016 完成\n",
"任务 20251013 完成\n",
"任务 20251014 完成\n",
"任务 20251010 完成\n",
"任务 20251009 完成\n",
"任务 20250929 完成\n",
"任务 20250930 完成\n"
"任务 20251219 完成\n",
"任务 20251218 完成\n",
"任务 20251217 完成\n",
"任务 20251216 完成\n",
"任务 20251215 完成\n",
"任务 20251212 完成\n",
"任务 20251211 完成\n",
"任务 20251210 完成\n",
"任务 20251209 完成\n",
"任务 20251208 完成\n",
"任务 20251205 完成\n",
"任务 20251204 完成\n",
"任务 20251203 完成\n",
"任务 20251202 完成\n",
"任务 20251201 完成\n",
"任务 20251128 完成\n",
"任务 20251127 完成\n",
"任务 20251126 完成\n",
"任务 20251125 完成\n",
"任务 20251124 完成\n",
"任务 20251121 完成\n"
]
}
],
@@ -152,58 +163,19 @@
"output_type": "stream",
"text": [
"[ trade_date ts_code up_limit down_limit\n",
"0 20251010 000001.SZ 12.54 10.26\n",
"1 20251010 000002.SZ 7.47 6.11\n",
"2 20251010 000004.SZ 12.26 11.10\n",
"3 20251010 000006.SZ 11.94 9.77\n",
"4 20251010 000007.SZ 8.12 6.64\n",
"0 20251121 000001.SZ 13.04 10.67\n",
"1 20251121 000002.SZ 6.82 5.58\n",
"2 20251121 000004.SZ 11.64 10.54\n",
"3 20251121 000006.SZ 12.07 9.87\n",
"4 20251121 000007.SZ 11.00 9.00\n",
"... ... ... ... ...\n",
"7309 20251010 920978.BJ 50.08 26.98\n",
"7310 20251010 920981.BJ 48.04 25.88\n",
"7311 20251010 920982.BJ 354.64 190.96\n",
"7312 20251010 920985.BJ 11.86 6.40\n",
"7313 20251010 920992.BJ 27.87 15.01\n",
"7363 20251121 920978.BJ 49.06 26.42\n",
"7364 20251121 920981.BJ 46.99 25.31\n",
"7365 20251121 920982.BJ 300.67 161.91\n",
"7366 20251121 920985.BJ 11.75 6.33\n",
"7367 20251121 920992.BJ 24.06 12.96\n",
"\n",
"[7314 rows x 4 columns], trade_date ts_code up_limit down_limit\n",
"0 20251009 000001.SZ 12.47 10.21\n",
"1 20251009 000002.SZ 7.58 6.20\n",
"2 20251009 000004.SZ 11.68 10.56\n",
"3 20251009 000006.SZ 11.32 9.26\n",
"4 20251009 000007.SZ 8.02 6.56\n",
"... ... ... ... ...\n",
"7306 20251009 920978.BJ 50.44 27.16\n",
"7307 20251009 920981.BJ 48.11 25.91\n",
"7308 20251009 920982.BJ 366.06 197.12\n",
"7309 20251009 920985.BJ 12.01 6.47\n",
"7310 20251009 920992.BJ 27.39 14.75\n",
"\n",
"[7311 rows x 4 columns], trade_date ts_code up_limit down_limit\n",
"0 20250929 000001.SZ 12.54 10.26\n",
"1 20250929 000002.SZ 7.48 6.12\n",
"2 20250929 000004.SZ 11.00 9.96\n",
"3 20250929 000006.SZ 10.46 8.56\n",
"4 20250929 000007.SZ 7.63 6.25\n",
"... ... ... ... ...\n",
"7302 20250929 920445.BJ 14.37 7.75\n",
"7303 20250929 920489.BJ 29.34 15.80\n",
"7304 20250929 920682.BJ 13.10 7.06\n",
"7305 20250929 920799.BJ 70.78 38.12\n",
"7306 20250929 920819.BJ 5.52 2.98\n",
"\n",
"[7307 rows x 4 columns], trade_date ts_code up_limit down_limit\n",
"0 20250930 000001.SZ 12.51 10.23\n",
"1 20250930 000002.SZ 7.49 6.13\n",
"2 20250930 000004.SZ 11.12 10.06\n",
"3 20250930 000006.SZ 10.29 8.42\n",
"4 20250930 000007.SZ 7.92 6.48\n",
"... ... ... ... ...\n",
"7305 20250930 920445.BJ 14.67 7.91\n",
"7306 20250930 920489.BJ 29.26 15.76\n",
"7307 20250930 920682.BJ 12.92 6.96\n",
"7308 20250930 920799.BJ 73.19 39.41\n",
"7309 20250930 920819.BJ 5.55 2.99\n",
"\n",
"[7310 rows x 4 columns]]\n"
"[7368 rows x 4 columns]]\n"
]
}
],
@@ -271,7 +243,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.13.2"
"version": "3.12.11"
}
},
"nbformat": 4,