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# Default ignored files
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/workspace.xml
# Editor-based HTTP Client requests
/httpRequests/
# Datasource local storage ignored files
/dataSources/
/dataSources.local.xml
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../data/

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<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
<component name="Black">
<option name="sdkName" value="E:\Python\anaconda\envs\try_trader" />
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<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
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<module fileurl="file://$PROJECT_DIR$/.idea/NewStock.iml" filepath="$PROJECT_DIR$/.idea/NewStock.iml" />
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import tushare as ts
ts.set_token('3a0741c702ee7e5e5f2bf1f0846bafaafe4e320833240b2a7e4a685f')
pro = ts.pro_api()
import pandas as pd
import time
# 读取本地保存的股票列表 CSV 文件（假设文件名为 stocks_data.csv）
df = ts.pro_bar(ts_code='000001.SZ', adj='hfq', start_date='20180101')
print(df)

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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "17cc645336d4eb18",
"metadata": {
"ExecuteTime": {
"end_time": "2025-02-08T16:55:19.819017Z",
"start_time": "2025-02-08T16:55:18.958639Z"
}
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import tushare as ts"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "48ae71ed02d61819",
"metadata": {
"ExecuteTime": {
"end_time": "2025-02-08T16:55:27.578361Z",
"start_time": "2025-02-08T16:55:19.882313Z"
}
},
"outputs": [],
"source": [
"daily_basic = pd.read_hdf('../../data/daily_basic.h5', key='daily_basic')\n",
"name_change_df = pd.read_hdf('../../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",
"name_change_df['start_date'] = pd.to_datetime(name_change_df['start_date'], format='%Y%m%d')\n",
"name_change_df['end_date'] = pd.to_datetime(name_change_df['end_date'], format='%Y%m%d', errors='coerce')\n",
"name_change_df = name_change_df[name_change_df.name.str.contains('ST')]\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "e6606a96e5728b8",
"metadata": {
"ExecuteTime": {
"end_time": "2025-02-08T16:55:27.938078Z",
"start_time": "2025-02-08T16:55:27.584226Z"
}
},
"outputs": [],
"source": [
"name_change_dict = {}\n",
"for ts_code, group in name_change_df.groupby('ts_code'):\n",
" # 只保留 'ST' 和 '*ST' 的记录\n",
" st_data = group[(group['change_reason'] == 'ST') | (group['change_reason'] == '*ST')]\n",
" if not st_data.empty:\n",
" name_change_dict[ts_code] = st_data"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "initial_id",
"metadata": {
"ExecuteTime": {
"end_time": "2025-02-08T16:59:20.537632Z",
"start_time": "2025-02-08T16:55:27.971219Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"is st...\n",
" ts_code trade_date is_st\n",
"0 688283.SH 20250207 False\n",
"1 002635.SZ 20250207 False\n",
"2 002463.SZ 20250207 False\n",
"3 603959.SH 20250207 False\n",
"4 688361.SH 20250207 False\n"
]
}
],
"source": [
"from datetime import datetime\n",
"import pandas as pd\n",
"\n",
"\n",
"# 判断股票是否为 ST 的函数\n",
"#stock_code = 'xxxxxx.SH'\n",
"#target_date = '20200830'\n",
"#若为ST，返回True；否则返回False\n",
"def is_st(name_change_dict, stock_code, target_date):\n",
" target_date = datetime.strptime(target_date, '%Y%m%d')\n",
" if stock_code not in name_change_dict.keys():\n",
" return False\n",
" df = name_change_dict[stock_code]\n",
" for i in range(len(df)):\n",
" sds = df.iloc[i, 2]\n",
" eds = df.iloc[i, 3]\n",
" # sd = datetime.strptime(sds, '%Y%m%d')\n",
" if eds == None:\n",
" ed = datetime.now()\n",
" # else:\n",
" # ed = datetime.strptime(eds, '%Y%m%d')\n",
" if (target_date - sds).days >= 0 and (target_date - eds).days <= 0:\n",
" return True\n",
" return False\n",
"\n",
"\n",
"print('is st...')\n",
"# 创建一个新的列 is_st，判断每只股票是否是 ST\n",
"daily_basic['is_st'] = daily_basic.apply(\n",
" lambda row: is_st(name_change_dict, row['ts_code'], row['trade_date']), axis=1\n",
")\n",
"\n",
"# 保存结果到新的 HDF5 文件\n",
"daily_basic.to_hdf('../../data/daily_basic.h5', key='daily_basic', mode='w', format='table')\n",
"\n",
"# 输出部分结果\n",
"print(daily_basic[['ts_code', 'trade_date', 'is_st']].head())\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "30c882de-3a89-4056-900d-459a3a012af9",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.8.19"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

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{
"cells": [
{
"cell_type": "code",
"id": "18d1d622-b083-4cc4-a6f8-7c1ed2d0edd2",
"metadata": {
"ExecuteTime": {
"end_time": "2025-02-11T15:43:54.745322Z",
"start_time": "2025-02-11T15:43:53.837662Z"
}
},
"source": [
"import tushare as ts\n",
"ts.set_token('3a0741c702ee7e5e5f2bf1f0846bafaafe4e320833240b2a7e4a685f')\n",
"pro = ts.pro_api()"
],
"outputs": [],
"execution_count": 1
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-02-11T15:53:08.235573Z",
"start_time": "2025-02-11T15:53:07.753701Z"
}
},
"cell_type": "code",
"source": [
"from datetime import datetime\n",
"import pandas as pd\n",
"\n",
"def is_st(name_change_dict, stock_code, target_date):\n",
" target_date = datetime.strptime(target_date, '%Y%m%d')\n",
" if stock_code not in name_change_dict.keys():\n",
" return False\n",
" df = name_change_dict[stock_code]\n",
" for i in range(len(df)):\n",
" sds = df.iloc[i, 2]\n",
" eds = df.iloc[i, 3]\n",
" if eds is None or eds is pd.NaT:\n",
" eds = datetime.now()\n",
" if (target_date - sds).days >= 0 and (target_date - eds).days <= 0:\n",
" return True\n",
" return False\n",
"\n",
"name_change_df = pd.read_hdf('../../../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",
"name_change_df['start_date'] = pd.to_datetime(name_change_df['start_date'], format='%Y%m%d')\n",
"name_change_df['end_date'] = pd.to_datetime(name_change_df['end_date'], format='%Y%m%d', errors='coerce')\n",
"name_change_df = name_change_df[name_change_df.name.str.contains('ST')]\n",
"name_change_dict = {}\n",
"for ts_code, group in name_change_df.groupby('ts_code'):\n",
" # 只保留 'ST' 和 '*ST' 的记录\n",
" st_data = group[(group['change_reason'] == 'ST') | (group['change_reason'] == '*ST')]\n",
" if not st_data.empty:\n",
" name_change_dict[ts_code] = st_data"
],
"id": "14671a7f72de2564",
"outputs": [],
"execution_count": 31
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-02-11T15:53:19.812860Z",
"start_time": "2025-02-11T15:53:09.614377Z"
}
},
"cell_type": "code",
"source": [
"import time\n",
"from concurrent.futures import ThreadPoolExecutor, as_completed\n",
"\n",
"h5_filename = '../../../data/daily_basic.h5'\n",
"key = '/daily_basic'\n",
"max_date = None\n",
"with pd.HDFStore(h5_filename, mode='r') as store:\n",
" df = store[key][['ts_code', 'trade_date']]\n",
" print(df.info())\n",
" max_date = df['trade_date'].max()\n",
"\n",
"print(max_date)\n",
"trade_cal = pro.trade_cal(exchange='', start_date='20170101', end_date='20250220')\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",
"print(start_date)"
],
"id": "e7f8cce2f80e2f20",
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Index: 8295494 entries, 0 to 8295493\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: 189.9+ MB\n",
"None\n",
"20250210\n",
"20250211\n"
]
}
],
"execution_count": 32
},
{
"cell_type": "code",
"id": "553cfb36-f560-4cc4-b2bc-68323ccc5072",
"metadata": {
"scrolled": true,
"ExecuteTime": {
"end_time": "2025-02-11T15:53:24.100612Z",
"start_time": "2025-02-11T15:53:22.361257Z"
}
},
"source": [
"\n",
"\n",
"# 使用 HDFStore 存储数据\n",
"all_daily_data = []\n",
"\n",
"# API 调用计数和时间控制变量\n",
"api_call_count = 0\n",
"batch_start_time = time.time()\n",
"\n",
"\n",
"def get_data(trade_date):\n",
" daily_basic_data = pro.daily_basic(ts_code='', trade_date=trade_date)\n",
" if daily_basic_data is not None and not daily_basic_data.empty:\n",
" # 添加交易日期列标识\n",
" daily_basic_data['trade_date'] = trade_date\n",
" daily_basic_data['is_st'] = daily_basic_data.apply(\n",
" lambda row: is_st(name_change_dict, row['ts_code'], row['trade_date']), axis=1\n",
" )\n",
" time.sleep(0.2)\n",
" # print(f\"成功获取并保存 {trade_date} 的每日基础数据\")\n",
" return daily_basic_data\n",
"\n",
"\n",
"# 遍历每个交易日期并获取数据\n",
"with ThreadPoolExecutor(max_workers=2) as executor:\n",
" future_to_date = {executor.submit(get_data, td): td for td in trade_dates}\n",
"\n",
" for future in as_completed(future_to_date):\n",
" trade_date = future_to_date[future] # 获取对应的交易日期\n",
" try:\n",
" result = future.result() # 获取任务执行的结果\n",
" all_daily_data.append(result)\n",
" print(f\"任务 {trade_date} 完成\")\n",
" except Exception as e:\n",
" print(f\"获取 {trade_date} 数据时出错: {e}\")\n",
" # 计数一次 API 调用\n",
" api_call_count += 1\n",
"\n",
" # 每调用 300 次，检查时间是否少于 1 分钟，如果少于则等待剩余时间\n",
" if api_call_count % 150 == 0:\n",
" elapsed = time.time() - batch_start_time\n",
" if elapsed < 60:\n",
" sleep_time = 60 - elapsed\n",
" print(f\"已调用 150 次 API，等待 {sleep_time:.2f} 秒以满足速率限制...\")\n",
" time.sleep(sleep_time)\n",
" # 重置批次起始时间\n",
" batch_start_time = time.time()\n",
"\n"
],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"任务 20250220 完成\n",
"任务 20250219 完成\n",
"任务 20250218 完成\n",
"任务 20250217 完成\n",
"任务 20250214 完成\n",
"任务 20250213 完成\n",
"任务 20250212 完成\n",
"任务 20250211 完成\n"
]
}
],
"execution_count": 33
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-02-11T15:53:25.913933Z",
"start_time": "2025-02-11T15:53:25.902629Z"
}
},
"cell_type": "code",
"source": [
"all_daily_data_df = pd.concat(all_daily_data, ignore_index=True)\n",
"print(all_daily_data_df)"
],
"id": "919023c693d7a47a",
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" ts_code trade_date close turnover_rate turnover_rate_f \\\n",
"0 002512.SZ 20250211 5.03 5.9759 7.8713 \n",
"1 600966.SH 20250211 4.83 0.6904 1.3494 \n",
"2 600358.SH 20250211 3.68 8.5826 11.3780 \n",
"3 002893.SZ 20250211 9.73 1.9217 2.6415 \n",
"4 300648.SZ 20250211 22.90 1.7775 2.3188 \n",
"... ... ... ... ... ... \n",
"5380 300886.SZ 20250211 21.80 8.9341 13.4176 \n",
"5381 600050.SH 20250211 5.48 2.3899 5.6722 \n",
"5382 300149.SZ 20250211 6.73 3.5271 5.3077 \n",
"5383 002197.SZ 20250211 4.42 4.0058 4.6595 \n",
"5384 688270.SH 20250211 37.34 2.9212 2.9212 \n",
"\n",
" volume_ratio pe pe_ttm pb ps ps_ttm dv_ratio \\\n",
"0 0.87 NaN NaN 12.8888 2.9340 3.0625 0.0000 \n",
"1 1.16 35.5101 15.2315 0.9534 0.3454 0.3402 0.5633 \n",
"2 1.38 NaN NaN 15.2661 3.4220 4.2041 0.0000 \n",
"3 0.85 48.9883 41.5405 2.2074 2.3641 2.3637 0.8222 \n",
"4 0.69 NaN NaN 4.1442 3.7325 3.3186 0.0000 \n",
"... ... ... ... ... ... ... ... \n",
"5380 3.00 NaN 111.0678 2.9043 6.0326 4.9204 0.0000 \n",
"5381 1.15 21.3231 19.5079 1.0668 0.4677 0.4574 2.6625 \n",
"5382 1.34 NaN NaN 2.5009 2.9440 3.3158 0.0000 \n",
"5383 1.41 NaN NaN 1.1195 2.0851 2.5837 0.0000 \n",
"5384 0.75 110.2738 170.0477 3.7594 28.4642 27.3030 NaN \n",
"\n",
" dv_ttm total_share float_share free_share total_mv \\\n",
"0 NaN 1.147095e+05 1.048455e+05 7.959795e+04 5.769885e+05 \n",
"1 0.5633 1.336844e+05 1.336844e+05 6.839785e+04 6.456958e+05 \n",
"2 NaN 5.049367e+04 5.049367e+04 3.808829e+04 1.858167e+05 \n",
"3 0.8222 2.636400e+04 2.027786e+04 1.475173e+04 2.565217e+05 \n",
"4 NaN 1.477839e+04 1.061894e+04 8.140048e+03 3.384251e+05 \n",
"... ... ... ... ... ... \n",
"5380 NaN 7.455500e+03 4.346405e+03 2.894040e+03 1.625299e+05 \n",
"5381 2.6625 3.180058e+06 3.128014e+06 1.317969e+06 1.742672e+07 \n",
"5382 NaN 4.979640e+04 4.970844e+04 3.303210e+04 3.351298e+05 \n",
"5383 NaN 6.143629e+04 5.340007e+04 4.590857e+04 2.715484e+05 \n",
"5384 NaN 2.140516e+04 1.442317e+04 1.442317e+04 7.992687e+05 \n",
"\n",
" circ_mv is_st \n",
"0 5.273728e+05 False \n",
"1 6.456958e+05 False \n",
"2 1.858167e+05 True \n",
"3 1.973036e+05 False \n",
"4 2.431738e+05 False \n",
"... ... ... \n",
"5380 9.475163e+04 False \n",
"5381 1.714152e+07 False \n",
"5382 3.345378e+05 False \n",
"5383 2.360283e+05 True \n",
"5384 5.385612e+05 False \n",
"\n",
"[5385 rows x 19 columns]\n"
]
}
],
"execution_count": 34
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-02-11T15:53:42.062142Z",
"start_time": "2025-02-11T15:53:42.044324Z"
}
},
"cell_type": "code",
"source": "print(all_daily_data_df[all_daily_data_df['is_st']])",
"id": "28cb78d032671b20",
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" ts_code trade_date close turnover_rate turnover_rate_f \\\n",
"2 600358.SH 20250211 3.68 8.5826 11.3780 \n",
"20 000889.SZ 20250211 2.48 2.0846 2.8167 \n",
"50 603879.SH 20250211 3.58 1.7126 2.7285 \n",
"62 002024.SZ 20250211 1.99 0.2997 0.8575 \n",
"65 600078.SH 20250211 5.77 1.0536 1.8102 \n",
"... ... ... ... ... ... \n",
"5327 688309.SH 20250211 13.80 0.5594 1.0928 \n",
"5328 002800.SZ 20250211 10.57 2.0449 3.9025 \n",
"5342 300368.SZ 20250211 4.50 1.5755 2.2505 \n",
"5375 600515.SH 20250211 3.64 0.4111 0.6804 \n",
"5383 002197.SZ 20250211 4.42 4.0058 4.6595 \n",
"\n",
" volume_ratio pe pe_ttm pb ps ps_ttm dv_ratio \\\n",
"2 1.38 NaN NaN 15.2661 3.4220 4.2041 0.0000 \n",
"20 1.08 NaN NaN 20.6126 1.6250 1.6047 0.0000 \n",
"50 1.08 NaN NaN 3.4116 3.8093 3.5391 0.0000 \n",
"62 1.01 NaN NaN 1.5246 0.2944 0.3546 0.0000 \n",
"65 0.97 NaN NaN 2.1866 1.2329 1.2311 0.5373 \n",
"... ... ... ... ... ... ... ... \n",
"5327 0.73 60.8452 186.0174 1.5353 6.7361 13.4432 NaN \n",
"5328 0.72 NaN NaN 3.0468 1.6938 1.3629 0.0000 \n",
"5342 0.99 NaN NaN 7.1301 6.7544 11.8519 0.0000 \n",
"5375 0.91 43.6494 110.6536 1.7765 6.1506 7.8214 0.0000 \n",
"5383 1.41 NaN NaN 1.1195 2.0851 2.5837 0.0000 \n",
"\n",
" dv_ttm total_share float_share free_share total_mv \\\n",
"2 NaN 5.049367e+04 50493.6660 38088.2934 1.858167e+05 \n",
"20 NaN 9.362911e+04 86984.9676 64375.7658 2.322002e+05 \n",
"50 NaN 3.593444e+04 35934.4440 22555.6496 1.286453e+05 \n",
"62 NaN 9.264768e+05 919834.5068 321453.1001 1.843689e+06 \n",
"65 0.5373 6.625729e+04 66257.2861 38563.8247 3.823045e+05 \n",
"... ... ... ... ... ... \n",
"5327 NaN 8.001073e+03 8001.0733 4095.6641 1.104148e+05 \n",
"5328 NaN 1.522531e+04 14165.4100 7422.5200 1.609315e+05 \n",
"5342 NaN 5.289435e+04 52894.3475 37030.2475 2.380246e+05 \n",
"5375 NaN 1.142531e+06 917601.2508 554411.0843 4.158813e+06 \n",
"5383 NaN 6.143629e+04 53400.0687 45908.5733 2.715484e+05 \n",
"\n",
" circ_mv is_st \n",
"2 1.858167e+05 True \n",
"20 2.157227e+05 True \n",
"50 1.286453e+05 True \n",
"62 1.830471e+06 True \n",
"65 3.823045e+05 True \n",
"... ... ... \n",
"5327 1.104148e+05 True \n",
"5328 1.497284e+05 True \n",
"5342 2.380246e+05 True \n",
"5375 3.340069e+06 True \n",
"5383 2.360283e+05 True \n",
"\n",
"[318 rows x 19 columns]\n"
]
}
],
"execution_count": 37
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-02-11T15:53:33.693894Z",
"start_time": "2025-02-11T15:53:33.609884Z"
}
},
"cell_type": "code",
"source": [
"# 将数据保存为 HDF5 文件（table 格式）\n",
"all_daily_data_df.to_hdf(h5_filename, key='daily_basic', mode='a', format='table', append=True, data_columns=True)\n",
"\n",
"print(\"所有每日基础数据获取并保存完毕！\")\n"
],
"id": "692b58674b7462c9",
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"所有每日基础数据获取并保存完毕！\n"
]
}
],
"execution_count": 36
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-02-11T15:54:27.868021Z",
"start_time": "2025-02-11T15:54:18.853803Z"
}
},
"cell_type": "code",
"source": [
"with pd.HDFStore(h5_filename, mode='r') as store:\n",
" df = store[key][['ts_code', 'trade_date', 'is_st']]\n",
" print(df.info())"
],
"id": "d7a773fc20293477",
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Index: 8300879 entries, 0 to 5384\n",
"Data columns (total 3 columns):\n",
" # Column Dtype \n",
"--- ------ ----- \n",
" 0 ts_code object\n",
" 1 trade_date object\n",
" 2 is_st bool \n",
"dtypes: bool(1), object(2)\n",
"memory usage: 197.9+ MB\n",
"None\n"
]
}
],
"execution_count": 39
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.8.19"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

5929
code/data/update/update_daily_data.ipynb Normal file
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149
code/data/update/update_is_st.ipynb Normal file
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{
"cells": [
{
"cell_type": "code",
"id": "17cc645336d4eb18",
"metadata": {
"ExecuteTime": {
"end_time": "2025-02-08T16:55:19.819017Z",
"start_time": "2025-02-08T16:55:18.958639Z"
}
},
"source": [
"import pandas as pd\n",
"import tushare as ts"
],
"outputs": [],
"execution_count": 1
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-02-08T16:55:27.578361Z",
"start_time": "2025-02-08T16:55:19.882313Z"
}
},
"cell_type": "code",
"source": [
"daily_basic = pd.read_hdf('../../data/daily_basic.h5', key='daily_basic', columns=['ts_code', 'trade_date '])\n",
"name_change_df = pd.read_hdf('../../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",
"name_change_df['start_date'] = pd.to_datetime(name_change_df['start_date'], format='%Y%m%d')\n",
"name_change_df['end_date'] = pd.to_datetime(name_change_df['end_date'], format='%Y%m%d', errors='coerce')\n",
"name_change_df = name_change_df[name_change_df.name.str.contains('ST')]\n"
],
"id": "48ae71ed02d61819",
"outputs": [],
"execution_count": 2
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-02-08T16:55:27.938078Z",
"start_time": "2025-02-08T16:55:27.584226Z"
}
},
"cell_type": "code",
"source": [
"name_change_dict = {}\n",
"for ts_code, group in name_change_df.groupby('ts_code'):\n",
" # 只保留 'ST' 和 '*ST' 的记录\n",
" st_data = group[(group['change_reason'] == 'ST') | (group['change_reason'] == '*ST')]\n",
" if not st_data.empty:\n",
" name_change_dict[ts_code] = st_data"
],
"id": "e6606a96e5728b8",
"outputs": [],
"execution_count": 3
},
{
"metadata": {
"collapsed": true,
"ExecuteTime": {
"end_time": "2025-02-08T16:59:20.537632Z",
"start_time": "2025-02-08T16:55:27.971219Z"
}
},
"cell_type": "code",
"source": [
"from datetime import datetime\n",
"import pandas as pd\n",
"\n",
"\n",
"# 判断股票是否为 ST 的函数\n",
"#stock_code = 'xxxxxx.SH'\n",
"#target_date = '20200830'\n",
"#若为ST，返回True；否则返回False\n",
"def is_st(name_change_dict, stock_code, target_date):\n",
" target_date = datetime.strptime(target_date, '%Y%m%d')\n",
" if stock_code not in name_change_dict.keys():\n",
" return False\n",
" df = name_change_dict[stock_code]\n",
" for i in range(len(df)):\n",
" sds = df.iloc[i, 2]\n",
" eds = df.iloc[i, 3]\n",
" # sd = datetime.strptime(sds, '%Y%m%d')\n",
" if eds == None:\n",
" ed = datetime.now()\n",
" # else:\n",
" # ed = datetime.strptime(eds, '%Y%m%d')\n",
" if (target_date - sds).days >= 0 and (target_date - eds).days <= 0:\n",
" return True\n",
" return False\n",
"\n",
"\n",
"print('is st...')\n",
"# 创建一个新的列 is_st，判断每只股票是否是 ST\n",
"daily_basic['is_st'] = daily_basic.apply(\n",
" lambda row: is_st(name_change_dict, row['ts_code'], row['trade_date']), axis=1\n",
")\n",
"\n",
"# 保存结果到新的 HDF5 文件\n",
"daily_basic.to_hdf('../../data/daily_basic_with_st.h5', key='daily_basic_with_st', mode='w', format='table')\n",
"\n",
"# 输出部分结果\n",
"print(daily_basic[['ts_code', 'trade_date', 'is_st']].head())\n"
],
"id": "initial_id",
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"is st...\n",
" ts_code trade_date is_st\n",
"0 603429.SH 20250127 False\n",
"1 300917.SZ 20250127 False\n",
"2 301266.SZ 20250127 False\n",
"3 688399.SH 20250127 False\n",
"4 603737.SH 20250127 False\n"
]
}
],
"execution_count": 4
}
],
"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
}

193
code/data/update/update_money_flow.ipynb Normal file
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@@ -0,0 +1,193 @@
{
"cells": [
{
"cell_type": "code",
"id": "b94bb1f2-5332-485e-ae1b-eea01f938106",
"metadata": {
"ExecuteTime": {
"end_time": "2025-02-11T15:21:54.821950Z",
"start_time": "2025-02-11T15:21:54.050569Z"
}
},
"source": [
"import tushare as ts\n",
"\n",
"ts.set_token('3a0741c702ee7e5e5f2bf1f0846bafaafe4e320833240b2a7e4a685f')\n",
"pro = ts.pro_api()"
],
"outputs": [],
"execution_count": 1
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-02-11T15:22:32.726905Z",
"start_time": "2025-02-11T15:22:25.018135Z"
}
},
"cell_type": "code",
"source": [
"import pandas as pd\n",
"import time\n",
"\n",
"h5_filename = '../../../data/money_flow.h5'\n",
"key = '/money_flow'\n",
"max_date = None\n",
"with pd.HDFStore(h5_filename, mode='r') as store:\n",
" df = store[key][['ts_code', 'trade_date']]\n",
" print(df.info())\n",
" max_date = df['trade_date'].max()\n",
"\n",
"print(max_date)\n",
"trade_cal = pro.trade_cal(exchange='', start_date='20170101', end_date='20250220')\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",
"print(f'start_date: {start_date}')"
],
"id": "742c29d453b9bb38",
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Index: 8153941 entries, 0 to 5120\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: 186.6+ MB\n",
"None\n",
"20250211\n",
"start_date: 20250212\n"
]
}
],
"execution_count": 6
},
{
"cell_type": "code",
"id": "679ce40e-8d62-4887-970c-e1d8cbdeee6b",
"metadata": {
"scrolled": true,
"ExecuteTime": {
"end_time": "2025-02-11T15:22:14.513527Z",
"start_time": "2025-02-11T15:22:12.973331Z"
}
},
"source": [
"from concurrent.futures import ThreadPoolExecutor, as_completed\n",
"\n",
"all_daily_data = []\n",
"\n",
"# API 调用计数和时间控制变量\n",
"api_call_count = 0\n",
"batch_start_time = time.time()\n",
"\n",
"\n",
"def get_data(trade_date):\n",
" time.sleep(0.1)\n",
" money_flow_data = pro.moneyflow(trade_date=trade_date)\n",
" if money_flow_data is not None and not money_flow_data.empty:\n",
" return money_flow_data\n",
"\n",
"\n",
"with ThreadPoolExecutor(max_workers=2) as executor:\n",
" future_to_date = {executor.submit(get_data, td): td for td in trade_dates}\n",
"\n",
" for future in as_completed(future_to_date):\n",
" trade_date = future_to_date[future] # 获取对应的交易日期\n",
" try:\n",
" result = future.result() # 获取任务执行的结果\n",
" all_daily_data.append(result)\n",
" print(f\"任务 {trade_date} 完成\")\n",
" except Exception as e:\n",
" print(f\"获取 {trade_date} 数据时出错: {e}\")\n",
"\n"
],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"任务 20250219 完成\n",
"任务 20250220 完成\n",
"任务 20250218 完成\n",
"任务 20250217 完成\n",
"任务 20250214 完成\n",
"任务 20250213 完成\n",
"任务 20250212 完成\n",
"任务 20250211 完成\n"
]
}
],
"execution_count": 3
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-02-11T15:22:16.656650Z",
"start_time": "2025-02-11T15:22:16.639271Z"
}
},
"cell_type": "code",
"source": "all_daily_data_df = pd.concat(all_daily_data, ignore_index=True)\n",
"id": "9af80516849d4e80",
"outputs": [],
"execution_count": 4
},
{
"cell_type": "code",
"id": "a2b05187-437f-4053-bc43-bd80d4cf8b0e",
"metadata": {
"ExecuteTime": {
"end_time": "2025-02-11T15:22:20.447350Z",
"start_time": "2025-02-11T15:22:19.145561Z"
}
},
"source": [
"\n",
"# 将所有数据合并为一个 DataFrame\n",
"\n",
"# 将数据保存为 HDF5 文件（table 格式）\n",
"all_daily_data_df.to_hdf(h5_filename, key='money_flow', mode='a', format='table', append=True, data_columns=True)\n",
"\n",
"print(\"所有每日基础数据获取并保存完毕！\")"
],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"所有每日基础数据获取并保存完毕！\n"
]
}
],
"execution_count": 5
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.8.19"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

5902
code/data/update/update_name_change.ipynb Normal file
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238
code/data/update/update_stk_limit.ipynb Normal file
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@@ -0,0 +1,238 @@
{
"cells": [
{
"cell_type": "code",
"id": "500802dc-7a20-48b7-a470-a4bae3ec534b",
"metadata": {
"ExecuteTime": {
"end_time": "2025-02-11T15:18:36.892437Z",
"start_time": "2025-02-11T15:18:36.020822Z"
}
},
"source": [
"import tushare as ts\n",
"\n",
"ts.set_token('3a0741c702ee7e5e5f2bf1f0846bafaafe4e320833240b2a7e4a685f')\n",
"pro = ts.pro_api()"
],
"outputs": [],
"execution_count": 1
},
{
"cell_type": "code",
"id": "5a84bc9da6d54868",
"metadata": {
"ExecuteTime": {
"end_time": "2025-02-11T15:20:12.573607Z",
"start_time": "2025-02-11T15:20:00.110127Z"
}
},
"source": [
"import pandas as pd\n",
"import time\n",
"\n",
"h5_filename = '../../../data/stk_limit.h5'\n",
"key = '/stk_limit'\n",
"max_date = None\n",
"with pd.HDFStore(h5_filename, mode='r') as store:\n",
" df = store[key][['ts_code', 'trade_date']]\n",
" print(df.sort_values(by='trade_date', ascending=True).tail())\n",
" print(df.info())\n",
" max_date = df['trade_date'].max()\n",
"\n",
"print(max_date)\n",
"trade_cal = pro.trade_cal(exchange='', start_date='20170101', end_date='20250220')\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",
"print(start_date)"
],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" ts_code trade_date\n",
"4682 600310.SH 20250211\n",
"4683 600312.SH 20250211\n",
"4684 600313.SH 20250211\n",
"4673 600299.SH 20250211\n",
"0 000001.SZ 20250211\n",
"<class 'pandas.core.frame.DataFrame'>\n",
"Index: 10040878 entries, 0 to 10040877\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: 229.8+ MB\n",
"None\n",
"20250211\n",
"20250212\n"
]
}
],
"execution_count": 5
},
{
"cell_type": "code",
"id": "bb3191de-27a2-4c89-a3b5-32a0d7b9496f",
"metadata": {
"scrolled": true,
"ExecuteTime": {
"end_time": "2025-02-11T15:21:27.831699Z",
"start_time": "2025-02-11T15:21:26.665039Z"
}
},
"source": [
"from concurrent.futures import ThreadPoolExecutor, as_completed\n",
"\n",
"all_daily_data = []\n",
"\n",
"# API 调用计数和时间控制变量\n",
"api_call_count = 0\n",
"batch_start_time = time.time()\n",
"\n",
"\n",
"def get_data(trade_date):\n",
" time.sleep(0.1)\n",
" stk_limit_data = pro.stk_limit(trade_date=trade_date)\n",
" if stk_limit_data is not None and not stk_limit_data.empty:\n",
" return stk_limit_data\n",
"\n",
"\n",
"with ThreadPoolExecutor(max_workers=2) as executor:\n",
" future_to_date = {executor.submit(get_data, td): td for td in trade_dates}\n",
"\n",
" for future in as_completed(future_to_date):\n",
" trade_date = future_to_date[future] # 获取对应的交易日期\n",
" try:\n",
" result = future.result() # 获取任务执行的结果\n",
" if result is not None:\n",
" all_daily_data.append(result)\n",
" print(f\"任务 {trade_date} 完成\")\n",
" except Exception as e:\n",
" print(f\"获取 {trade_date} 数据时出错: {e}\")\n",
"\n"
],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"任务 20250220 完成\n",
"任务 20250219 完成\n",
"任务 20250217 完成\n",
"任务 20250218 完成\n",
"任务 20250214 完成\n",
"任务 20250213 完成\n",
"任务 20250212 完成\n"
]
}
],
"execution_count": 10
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-02-11T15:21:29.294283Z",
"start_time": "2025-02-11T15:21:29.247112Z"
}
},
"cell_type": "code",
"source": [
"print(all_daily_data)\n",
"# 将所有数据合并为一个 DataFrame\n",
"all_daily_data_df = pd.concat(all_daily_data, ignore_index=True)"
],
"id": "96a81aa5890ea3c3",
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[]\n"
]
},
{
"ename": "ValueError",
"evalue": "No objects to concatenate",
"output_type": "error",
"traceback": [
"\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
"\u001B[1;31mValueError\u001B[0m Traceback (most recent call last)",
"Cell \u001B[1;32mIn[11], line 3\u001B[0m\n\u001B[0;32m 1\u001B[0m \u001B[38;5;28mprint\u001B[39m(all_daily_data)\n\u001B[0;32m 2\u001B[0m \u001B[38;5;66;03m# 将所有数据合并为一个 DataFrame\u001B[39;00m\n\u001B[1;32m----> 3\u001B[0m all_daily_data_df \u001B[38;5;241m=\u001B[39m \u001B[43mpd\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mconcat\u001B[49m\u001B[43m(\u001B[49m\u001B[43mall_daily_data\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mignore_index\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43;01mTrue\u001B[39;49;00m\u001B[43m)\u001B[49m\n",
"File \u001B[1;32mE:\\Python\\anaconda\\envs\\try_trader\\lib\\site-packages\\pandas\\core\\reshape\\concat.py:372\u001B[0m, in \u001B[0;36mconcat\u001B[1;34m(objs, axis, join, ignore_index, keys, levels, names, verify_integrity, sort, copy)\u001B[0m\n\u001B[0;32m 369\u001B[0m \u001B[38;5;28;01melif\u001B[39;00m copy \u001B[38;5;129;01mand\u001B[39;00m using_copy_on_write():\n\u001B[0;32m 370\u001B[0m copy \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mFalse\u001B[39;00m\n\u001B[1;32m--> 372\u001B[0m op \u001B[38;5;241m=\u001B[39m \u001B[43m_Concatenator\u001B[49m\u001B[43m(\u001B[49m\n\u001B[0;32m 373\u001B[0m \u001B[43m \u001B[49m\u001B[43mobjs\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 374\u001B[0m \u001B[43m \u001B[49m\u001B[43maxis\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43maxis\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 375\u001B[0m \u001B[43m \u001B[49m\u001B[43mignore_index\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mignore_index\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 376\u001B[0m \u001B[43m \u001B[49m\u001B[43mjoin\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mjoin\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 377\u001B[0m \u001B[43m \u001B[49m\u001B[43mkeys\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mkeys\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 378\u001B[0m \u001B[43m \u001B[49m\u001B[43mlevels\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mlevels\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 379\u001B[0m \u001B[43m \u001B[49m\u001B[43mnames\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mnames\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 380\u001B[0m \u001B[43m \u001B[49m\u001B[43mverify_integrity\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mverify_integrity\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 381\u001B[0m \u001B[43m \u001B[49m\u001B[43mcopy\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mcopy\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 382\u001B[0m \u001B[43m \u001B[49m\u001B[43msort\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43msort\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 383\u001B[0m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 385\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m op\u001B[38;5;241m.\u001B[39mget_result()\n",
"File \u001B[1;32mE:\\Python\\anaconda\\envs\\try_trader\\lib\\site-packages\\pandas\\core\\reshape\\concat.py:429\u001B[0m, in \u001B[0;36m_Concatenator.__init__\u001B[1;34m(self, objs, axis, join, keys, levels, names, ignore_index, verify_integrity, copy, sort)\u001B[0m\n\u001B[0;32m 426\u001B[0m objs \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mlist\u001B[39m(objs)\n\u001B[0;32m 428\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mlen\u001B[39m(objs) \u001B[38;5;241m==\u001B[39m \u001B[38;5;241m0\u001B[39m:\n\u001B[1;32m--> 429\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mNo objects to concatenate\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[0;32m 431\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m keys \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[0;32m 432\u001B[0m objs \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mlist\u001B[39m(com\u001B[38;5;241m.\u001B[39mnot_none(\u001B[38;5;241m*\u001B[39mobjs))\n",
"\u001B[1;31mValueError\u001B[0m: No objects to concatenate"
]
}
],
"execution_count": 11
},
{
"cell_type": "code",
"id": "ad9733a1-2f42-43ee-a98c-0bf699304c21",
"metadata": {
"ExecuteTime": {
"end_time": "2025-02-11T15:20:37.999493Z",
"start_time": "2025-02-11T15:20:37.375220Z"
}
},
"source": [
"\n",
"\n",
"# 将数据保存为 HDF5 文件（table 格式）\n",
"all_daily_data_df.to_hdf(h5_filename, key='stk_limit', mode='a', format='table', append=True, data_columns=True)\n",
"\n",
"print(\"所有每日基础数据获取并保存完毕！\")"
],
"outputs": [
{
"ename": "ValueError",
"evalue": "All objects passed were None",
"output_type": "error",
"traceback": [
"\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
"\u001B[1;31mValueError\u001B[0m Traceback (most recent call last)",
"Cell \u001B[1;32mIn[7], line 2\u001B[0m\n\u001B[0;32m 1\u001B[0m \u001B[38;5;66;03m# 将所有数据合并为一个 DataFrame\u001B[39;00m\n\u001B[1;32m----> 2\u001B[0m all_daily_data_df \u001B[38;5;241m=\u001B[39m \u001B[43mpd\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mconcat\u001B[49m\u001B[43m(\u001B[49m\u001B[43mall_daily_data\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mignore_index\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43;01mTrue\u001B[39;49;00m\u001B[43m)\u001B[49m\n\u001B[0;32m 4\u001B[0m \u001B[38;5;66;03m# 将数据保存为 HDF5 文件（table 格式）\u001B[39;00m\n\u001B[0;32m 5\u001B[0m all_daily_data_df\u001B[38;5;241m.\u001B[39mto_hdf(h5_filename, key\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mstk_limit\u001B[39m\u001B[38;5;124m'\u001B[39m, mode\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124ma\u001B[39m\u001B[38;5;124m'\u001B[39m, \u001B[38;5;28mformat\u001B[39m\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mtable\u001B[39m\u001B[38;5;124m'\u001B[39m, append\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m, data_columns\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m)\n",
"File \u001B[1;32mE:\\Python\\anaconda\\envs\\try_trader\\lib\\site-packages\\pandas\\core\\reshape\\concat.py:372\u001B[0m, in \u001B[0;36mconcat\u001B[1;34m(objs, axis, join, ignore_index, keys, levels, names, verify_integrity, sort, copy)\u001B[0m\n\u001B[0;32m 369\u001B[0m \u001B[38;5;28;01melif\u001B[39;00m copy \u001B[38;5;129;01mand\u001B[39;00m using_copy_on_write():\n\u001B[0;32m 370\u001B[0m copy \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mFalse\u001B[39;00m\n\u001B[1;32m--> 372\u001B[0m op \u001B[38;5;241m=\u001B[39m \u001B[43m_Concatenator\u001B[49m\u001B[43m(\u001B[49m\n\u001B[0;32m 373\u001B[0m \u001B[43m \u001B[49m\u001B[43mobjs\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 374\u001B[0m \u001B[43m \u001B[49m\u001B[43maxis\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43maxis\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 375\u001B[0m \u001B[43m \u001B[49m\u001B[43mignore_index\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mignore_index\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 376\u001B[0m \u001B[43m \u001B[49m\u001B[43mjoin\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mjoin\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 377\u001B[0m \u001B[43m \u001B[49m\u001B[43mkeys\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mkeys\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 378\u001B[0m \u001B[43m \u001B[49m\u001B[43mlevels\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mlevels\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 379\u001B[0m \u001B[43m \u001B[49m\u001B[43mnames\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mnames\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 380\u001B[0m \u001B[43m \u001B[49m\u001B[43mverify_integrity\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mverify_integrity\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 381\u001B[0m \u001B[43m \u001B[49m\u001B[43mcopy\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mcopy\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 382\u001B[0m \u001B[43m \u001B[49m\u001B[43msort\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43msort\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 383\u001B[0m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 385\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m op\u001B[38;5;241m.\u001B[39mget_result()\n",
"File \u001B[1;32mE:\\Python\\anaconda\\envs\\try_trader\\lib\\site-packages\\pandas\\core\\reshape\\concat.py:452\u001B[0m, in \u001B[0;36m_Concatenator.__init__\u001B[1;34m(self, objs, axis, join, keys, levels, names, ignore_index, verify_integrity, copy, sort)\u001B[0m\n\u001B[0;32m 449\u001B[0m keys \u001B[38;5;241m=\u001B[39m Index(clean_keys, name\u001B[38;5;241m=\u001B[39mname, dtype\u001B[38;5;241m=\u001B[39m\u001B[38;5;28mgetattr\u001B[39m(keys, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mdtype\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;28;01mNone\u001B[39;00m))\n\u001B[0;32m 451\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mlen\u001B[39m(objs) \u001B[38;5;241m==\u001B[39m \u001B[38;5;241m0\u001B[39m:\n\u001B[1;32m--> 452\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mAll objects passed were None\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[0;32m 454\u001B[0m \u001B[38;5;66;03m# figure out what our result ndim is going to be\u001B[39;00m\n\u001B[0;32m 455\u001B[0m ndims \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mset\u001B[39m()\n",
"\u001B[1;31mValueError\u001B[0m: All objects passed were None"
]
}
],
"execution_count": 7
},
{
"cell_type": "code",
"execution_count": null,
"id": "7e777f1f-4d54-4a74-b916-691ede6af055",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.8.19"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

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{
"cells": [
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-02-09T14:52:54.170824Z",
"start_time": "2025-02-09T14:52:53.544850Z"
}
},
"cell_type": "code",
"source": [
"%load_ext autoreload\n",
"%autoreload 2\n",
"\n",
"from utils.utils import read_and_merge_h5_data"
],
"id": "79a7758178bafdd3",
"outputs": [],
"execution_count": 1
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-02-09T14:53:36.873700Z",
"start_time": "2025-02-09T14:52:54.170824Z"
}
},
"cell_type": "code",
"source": [
"print('daily data')\n",
"df = read_and_merge_h5_data('../../data/daily_data.h5', key='daily_data',\n",
" columns=['ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'vol'],\n",
" df=None)\n",
"\n",
"print('daily basic')\n",
"df = read_and_merge_h5_data('../../data/daily_basic.h5', key='daily_basic_with_st',\n",
" columns=['ts_code', 'trade_date', 'turnover_rate', 'pe_ttm', 'circ_mv', 'volume_ratio',\n",
" 'is_st'], df=df)\n",
"\n",
"print('stk limit')\n",
"df = read_and_merge_h5_data('../../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",
" 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)"
],
"id": "a79cafb06a7e0e43",
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"daily data\n",
"daily basic\n",
"stk limit\n",
"money flow\n"
]
}
],
"execution_count": 2
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-02-09T14:53:37.426404Z",
"start_time": "2025-02-09T14:53:36.955552Z"
}
},
"cell_type": "code",
"source": "origin_columns = df.columns.tolist()",
"id": "c4e9e1d31da6dba6",
"outputs": [],
"execution_count": 3
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-02-09T14:53:38.164112Z",
"start_time": "2025-02-09T14:53:38.070007Z"
}
},
"cell_type": "code",
"source": [
"import numpy as np\n",
"import talib\n",
"\n",
"\n",
"def get_technical_factor(df):\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['atr_14'] = talib.ATR(df['high'], df['low'], df['close'], timeperiod=14)\n",
" df['atr_6'] = talib.ATR(df['high'], df['low'], df['close'], timeperiod=6)\n",
"\n",
" df['obv'] = talib.OBV(df['close'], df['vol'])\n",
" df['maobv_6'] = talib.SMA(df['obv'], timeperiod=6)\n",
" df['obv-maobv_6'] = df['obv'] - df['maobv_6']\n",
"\n",
" df['rsi_3'] = talib.RSI(df['close'], timeperiod=3)\n",
" df['rsi_6'] = talib.RSI(df['close'], timeperiod=6)\n",
" df['rsi_9'] = talib.RSI(df['close'], timeperiod=9)\n",
"\n",
" df['return_10'] = df['close'] / df['close'].shift(10) - 1\n",
" df['return_20'] = df['close'] / df['close'].shift(20) - 1\n",
"\n",
" # # 计算 _rank_return_10 和 _rank_return_20\n",
" # df['_rank_return_10'] = df['return_10'].rank(pct=True)\n",
" # df['_rank_return_20'] = df['return_20'].rank(pct=True)\n",
"\n",
" # 计算 avg_close_5\n",
" df['avg_close_5'] = df['close'].rolling(window=5).mean() / df['close']\n",
"\n",
" # 计算 std_return_5, std_return_15, std_return_25, std_return_252, std_return_2522\n",
" df['std_return_5'] = df['close'].pct_change().shift(-1).rolling(window=5).std()\n",
" df['std_return_15'] = df['close'].pct_change().shift(-1).rolling(window=15).std()\n",
" df['std_return_25'] = df['close'].pct_change().shift(-1).rolling(window=25).std()\n",
" df['std_return_90'] = df['close'].pct_change().shift(-1).rolling(window=90).std()\n",
" df['std_return_90_2'] = df['close'].shift(10).pct_change().shift(-1).rolling(window=90).std()\n",
"\n",
" # 计算 std_return_5 / std_return_252 和 std_return_5 / std_return_25\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 std_return_252 - std_return_2522\n",
" df['std_return_90 - std_return_90_2'] = df['std_return_90'] - df['std_return_90_2']\n",
" return df\n",
"\n",
"\n",
"def get_act_factor(df):\n",
" # 计算 m_ta_ema(close, 5), m_ta_ema(close, 13), m_ta_ema(close, 20), m_ta_ema(close, 60)\n",
" df['ema_5'] = talib.EMA(df['close'], timeperiod=5)\n",
" df['ema_13'] = talib.EMA(df['close'], timeperiod=13)\n",
" df['ema_20'] = talib.EMA(df['close'], timeperiod=20)\n",
" df['ema_60'] = talib.EMA(df['close'], timeperiod=60)\n",
"\n",
" # 计算 act_factor1, act_factor2, act_factor3, act_factor4\n",
" df['act_factor1'] = np.arctan((df['ema_5'] / df['ema_5'].shift(1) - 1) * 100) * 57.3 / 50\n",
" df['act_factor2'] = np.arctan((df['ema_13'] / df['ema_13'].shift(1) - 1) * 100) * 57.3 / 40\n",
" df['act_factor3'] = np.arctan((df['ema_20'] / df['ema_20'].shift(1) - 1) * 100) * 57.3 / 21\n",
" df['act_factor4'] = np.arctan((df['ema_60'] / df['ema_60'].shift(1) - 1) * 100) * 57.3 / 10\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",
" # 根据 'trade_date' 进行分组，在每个组内分别计算 'act_factor1', 'act_factor2', 'act_factor3' 的排名\n",
" df['rank_act_factor1'] = df.groupby('trade_date')['act_factor1'].rank(ascending=False, pct=True)\n",
" df['rank_act_factor2'] = df.groupby('trade_date')['act_factor2'].rank(ascending=False, pct=True)\n",
" df['rank_act_factor3'] = df.groupby('trade_date')['act_factor3'].rank(ascending=False, pct=True)\n",
"\n",
" return df\n",
"\n",
"\n",
"def get_money_flow_factor(df):\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 - sell_lg_vol'] = (df['buy_lg_vol'] - df['sell_lg_vol']) / df['net_mf_vol']\n",
" df['buy_elg_vol - sell_elg_vol'] = (df['buy_elg_vol'] - df['sell_elg_vol']) / df['net_mf_vol']\n",
"\n",
" # # 你还提到了一些其他字段:\n",
" # df['net_active_buy_volume_main'] = df['net_mf_vol'] / df['buy_sm_vol']\n",
" # df['netflow_amount_main'] = df['net_mf_vol'] / df['buy_sm_vol'] # 这里假设 'net_mf_vol' 是主流资金流\n",
"\n",
" # df['active_sell_volume_large'] = df['sell_lg_vol'] / df['sell_sm_vol']\n",
" # df['active_sell_volume_big'] = df['sell_elg_vol'] / df['sell_sm_vol']\n",
" # df['active_sell_volume_small'] = df['sell_sm_vol'] / df['sell_sm_vol']\n",
"\n",
" return df\n",
"\n",
"\n",
"def get_alpha_factor(df):\n",
" df['alpha_022'] = df['close'] - df['close'].shift(5)\n",
"\n",
" # alpha_003: (close - open) / (high - low)\n",
" df['alpha_003'] = (df['close'] - df['open']) / (df['high'] - df['low'])\n",
"\n",
" # alpha_007: rank(correlation(close, volume, 5))\n",
" df['alpha_007'] = df['close'].rolling(5).corr(df['vol']).rank(axis=1)\n",
"\n",
" # alpha_013: rank(sum(close, 5) - sum(close, 20))\n",
" df['alpha_013'] = (df['close'].rolling(5).sum() - df['close'].rolling(20).sum()).rank(axis=1)\n",
" return df\n",
"\n",
"\n",
"def get_future_data(df):\n",
" df['future_return1'] = (df['close'].shift(-1) - df['close']) / df['close']\n",
" df['future_return2'] = (df['open'].shift(-2) - df['open'].shift(-1)) / df['open'].shift(-1)\n",
" df['future_return3'] = (df['close'].shift(-2) - df['close'].shift(-1)) / df['close'].shift(-1)\n",
" df['future_return4'] = (df['close'].shift(-2) - df['open'].shift(-1)) / df['open'].shift(-1)\n",
" df['future_return5'] = (df['close'].shift(-5) - df['open'].shift(-1)) / df['open'].shift(-1)\n",
" df['future_return6'] = (df['close'].shift(-10) - df['open'].shift(-1)) / df['open'].shift(-1)\n",
" df['future_return7'] = (df['close'].shift(-20) - df['open'].shift(-1)) / df['open'].shift(-1)\n",
" df['future_close1'] = (df['close'].shift(-1) - df['close']) / df['close']\n",
" df['future_close2'] = (df['close'].shift(-2) - df['close']) / df['close']\n",
" df['future_close3'] = (df['close'].shift(-3) - df['close']) / df['close']\n",
" df['future_close4'] = (df['close'].shift(-4) - df['close']) / df['close']\n",
" df['future_close5'] = (df['close'].shift(-5) - df['close']) / df['close']\n",
" df['future_af11'] = df['act_factor1'].shift(-1)\n",
" df['future_af12'] = df['act_factor1'].shift(-2)\n",
" df['future_af13'] = df['act_factor1'].shift(-3)\n",
" df['future_af14'] = df['act_factor1'].shift(-4)\n",
" df['future_af15'] = df['act_factor1'].shift(-5)\n",
" df['future_af21'] = df['act_factor2'].shift(-1)\n",
" df['future_af22'] = df['act_factor2'].shift(-2)\n",
" df['future_af23'] = df['act_factor2'].shift(-3)\n",
" df['future_af24'] = df['act_factor2'].shift(-4)\n",
" df['future_af25'] = df['act_factor2'].shift(-5)\n",
" df['future_af31'] = df['act_factor3'].shift(-1)\n",
" df['future_af32'] = df['act_factor3'].shift(-2)\n",
" df['future_af33'] = df['act_factor3'].shift(-3)\n",
" df['future_af34'] = df['act_factor3'].shift(-4)\n",
" df['future_af35'] = df['act_factor3'].shift(-5)\n",
"\n",
" return df\n"
],
"id": "a735bc02ceb4d872",
"outputs": [],
"execution_count": 4
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-02-09T14:53:49.153376Z",
"start_time": "2025-02-09T14:53:38.164112Z"
}
},
"cell_type": "code",
"source": [
"df = get_technical_factor(df)\n",
"df = get_act_factor(df)\n",
"df = get_money_flow_factor(df)\n",
"df = get_future_data(df)\n",
"# df = df.drop(columns=origin_columns)\n",
"\n",
"print(df.info())"
],
"id": "53f86ddc0677a6d7",
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 8364308 entries, 0 to 8364307\n",
"Data columns (total 83 columns):\n",
" # Column Dtype \n",
"--- ------ ----- \n",
" 0 ts_code object \n",
" 1 trade_date datetime64[ns]\n",
" 2 open float64 \n",
" 3 close float64 \n",
" 4 high float64 \n",
" 5 low float64 \n",
" 6 vol float64 \n",
" 7 is_st object \n",
" 8 up_limit float64 \n",
" 9 down_limit float64 \n",
" 10 buy_sm_vol float64 \n",
" 11 sell_sm_vol float64 \n",
" 12 buy_lg_vol float64 \n",
" 13 sell_lg_vol float64 \n",
" 14 buy_elg_vol float64 \n",
" 15 sell_elg_vol float64 \n",
" 16 net_mf_vol float64 \n",
" 17 up float64 \n",
" 18 down float64 \n",
" 19 atr_14 float64 \n",
" 20 atr_6 float64 \n",
" 21 obv float64 \n",
" 22 maobv_6 float64 \n",
" 23 obv-maobv_6 float64 \n",
" 24 rsi_3 float64 \n",
" 25 rsi_6 float64 \n",
" 26 rsi_9 float64 \n",
" 27 return_10 float64 \n",
" 28 return_20 float64 \n",
" 29 avg_close_5 float64 \n",
" 30 std_return_5 float64 \n",
" 31 std_return_15 float64 \n",
" 32 std_return_25 float64 \n",
" 33 std_return_90 float64 \n",
" 34 std_return_90_2 float64 \n",
" 35 std_return_5 / std_return_90 float64 \n",
" 36 std_return_5 / std_return_25 float64 \n",
" 37 std_return_90 - std_return_90_2 float64 \n",
" 38 ema_5 float64 \n",
" 39 ema_13 float64 \n",
" 40 ema_20 float64 \n",
" 41 ema_60 float64 \n",
" 42 act_factor1 float64 \n",
" 43 act_factor2 float64 \n",
" 44 act_factor3 float64 \n",
" 45 act_factor4 float64 \n",
" 46 act_factor5 float64 \n",
" 47 act_factor6 float64 \n",
" 48 rank_act_factor1 float64 \n",
" 49 rank_act_factor2 float64 \n",
" 50 rank_act_factor3 float64 \n",
" 51 active_buy_volume_large float64 \n",
" 52 active_buy_volume_big float64 \n",
" 53 active_buy_volume_small float64 \n",
" 54 buy_lg_vol - sell_lg_vol float64 \n",
" 55 buy_elg_vol - sell_elg_vol float64 \n",
" 56 future_return1 float64 \n",
" 57 future_return2 float64 \n",
" 58 future_return3 float64 \n",
" 59 future_return4 float64 \n",
" 60 future_return5 float64 \n",
" 61 future_return6 float64 \n",
" 62 future_return7 float64 \n",
" 63 future_close1 float64 \n",
" 64 future_close2 float64 \n",
" 65 future_close3 float64 \n",
" 66 future_close4 float64 \n",
" 67 future_close5 float64 \n",
" 68 future_af11 float64 \n",
" 69 future_af12 float64 \n",
" 70 future_af13 float64 \n",
" 71 future_af14 float64 \n",
" 72 future_af15 float64 \n",
" 73 future_af21 float64 \n",
" 74 future_af22 float64 \n",
" 75 future_af23 float64 \n",
" 76 future_af24 float64 \n",
" 77 future_af25 float64 \n",
" 78 future_af31 float64 \n",
" 79 future_af32 float64 \n",
" 80 future_af33 float64 \n",
" 81 future_af34 float64 \n",
" 82 future_af35 float64 \n",
"dtypes: datetime64[ns](1), float64(80), object(2)\n",
"memory usage: 5.2+ GB\n",
"None\n"
]
}
],
"execution_count": 5
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-02-09T14:55:28.712343Z",
"start_time": "2025-02-09T14:53:49.279168Z"
}
},
"cell_type": "code",
"source": [
"def filter_data(df):\n",
" df = df.groupby('trade_date').apply(lambda x: x.nlargest(1000, 'act_factor3'))\n",
" df = df[df['is_st'] == False]\n",
" df = df[df['is_st'] == False]\n",
" 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.reset_index(drop=True)\n",
" return df\n",
"\n",
"\n",
"df = filter_data(df)\n",
"print(df.info())"
],
"id": "dbe2fd8021b9417f",
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 1136157 entries, 0 to 1136156\n",
"Data columns (total 83 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 ts_code 1136157 non-null object \n",
" 1 trade_date 1136157 non-null datetime64[ns]\n",
" 2 open 1136157 non-null float64 \n",
" 3 close 1136157 non-null float64 \n",
" 4 high 1136157 non-null float64 \n",
" 5 low 1136157 non-null float64 \n",
" 6 vol 1136157 non-null float64 \n",
" 7 is_st 1136157 non-null object \n",
" 8 up_limit 1135878 non-null float64 \n",
" 9 down_limit 1135878 non-null float64 \n",
" 10 buy_sm_vol 1135663 non-null float64 \n",
" 11 sell_sm_vol 1135663 non-null float64 \n",
" 12 buy_lg_vol 1135663 non-null float64 \n",
" 13 sell_lg_vol 1135663 non-null float64 \n",
" 14 buy_elg_vol 1135663 non-null float64 \n",
" 15 sell_elg_vol 1135663 non-null float64 \n",
" 16 net_mf_vol 1135663 non-null float64 \n",
" 17 up 1136157 non-null float64 \n",
" 18 down 1136157 non-null float64 \n",
" 19 atr_14 1136157 non-null float64 \n",
" 20 atr_6 1136157 non-null float64 \n",
" 21 obv 1136157 non-null float64 \n",
" 22 maobv_6 1136157 non-null float64 \n",
" 23 obv-maobv_6 1136157 non-null float64 \n",
" 24 rsi_3 1136157 non-null float64 \n",
" 25 rsi_6 1136157 non-null float64 \n",
" 26 rsi_9 1136157 non-null float64 \n",
" 27 return_10 1136157 non-null float64 \n",
" 28 return_20 1136157 non-null float64 \n",
" 29 avg_close_5 1136157 non-null float64 \n",
" 30 std_return_5 1136157 non-null float64 \n",
" 31 std_return_15 1136157 non-null float64 \n",
" 32 std_return_25 1136157 non-null float64 \n",
" 33 std_return_90 1136131 non-null float64 \n",
" 34 std_return_90_2 1136129 non-null float64 \n",
" 35 std_return_5 / std_return_90 1136131 non-null float64 \n",
" 36 std_return_5 / std_return_25 1136157 non-null float64 \n",
" 37 std_return_90 - std_return_90_2 1136129 non-null float64 \n",
" 38 ema_5 1136157 non-null float64 \n",
" 39 ema_13 1136157 non-null float64 \n",
" 40 ema_20 1136157 non-null float64 \n",
" 41 ema_60 1136153 non-null float64 \n",
" 42 act_factor1 1136157 non-null float64 \n",
" 43 act_factor2 1136157 non-null float64 \n",
" 44 act_factor3 1136157 non-null float64 \n",
" 45 act_factor4 1136152 non-null float64 \n",
" 46 act_factor5 1136152 non-null float64 \n",
" 47 act_factor6 1136157 non-null float64 \n",
" 48 rank_act_factor1 1136157 non-null float64 \n",
" 49 rank_act_factor2 1136157 non-null float64 \n",
" 50 rank_act_factor3 1136157 non-null float64 \n",
" 51 active_buy_volume_large 1135659 non-null float64 \n",
" 52 active_buy_volume_big 1135636 non-null float64 \n",
" 53 active_buy_volume_small 1135663 non-null float64 \n",
" 54 buy_lg_vol - sell_lg_vol 1135660 non-null float64 \n",
" 55 buy_elg_vol - sell_elg_vol 1135640 non-null float64 \n",
" 56 future_return1 1136157 non-null float64 \n",
" 57 future_return2 1136157 non-null float64 \n",
" 58 future_return3 1136157 non-null float64 \n",
" 59 future_return4 1136157 non-null float64 \n",
" 60 future_return5 1136157 non-null float64 \n",
" 61 future_return6 1136157 non-null float64 \n",
" 62 future_return7 1136157 non-null float64 \n",
" 63 future_close1 1136157 non-null float64 \n",
" 64 future_close2 1136157 non-null float64 \n",
" 65 future_close3 1136157 non-null float64 \n",
" 66 future_close4 1136157 non-null float64 \n",
" 67 future_close5 1136157 non-null float64 \n",
" 68 future_af11 1136157 non-null float64 \n",
" 69 future_af12 1136157 non-null float64 \n",
" 70 future_af13 1136157 non-null float64 \n",
" 71 future_af14 1136157 non-null float64 \n",
" 72 future_af15 1136157 non-null float64 \n",
" 73 future_af21 1136157 non-null float64 \n",
" 74 future_af22 1136157 non-null float64 \n",
" 75 future_af23 1136157 non-null float64 \n",
" 76 future_af24 1136157 non-null float64 \n",
" 77 future_af25 1136157 non-null float64 \n",
" 78 future_af31 1136157 non-null float64 \n",
" 79 future_af32 1136157 non-null float64 \n",
" 80 future_af33 1136157 non-null float64 \n",
" 81 future_af34 1136157 non-null float64 \n",
" 82 future_af35 1136157 non-null float64 \n",
"dtypes: datetime64[ns](1), float64(80), object(2)\n",
"memory usage: 719.5+ MB\n",
"None\n"
]
}
],
"execution_count": 6
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-02-09T15:00:45.828404Z",
"start_time": "2025-02-09T15:00:45.294830Z"
}
},
"cell_type": "code",
"source": [
"def remove_outliers_iqr(series, lower_quantile=0.05, upper_quantile=0.95, threshold=1.5):\n",
" Q1 = series.quantile(lower_quantile)\n",
" Q3 = series.quantile(upper_quantile)\n",
" IQR = Q3 - Q1\n",
" lower_bound = Q1 - threshold * IQR\n",
" upper_bound = Q3 + threshold * IQR\n",
" # 过滤掉低于下边界或高于上边界的极值\n",
" return (series >= lower_bound) & (series <= upper_bound)\n",
"\n",
"\n",
"def neutralize_labels(labels, features, feature_columns, z_threshold=3, method='regression'):\n",
" labels_no_outliers = remove_outliers_iqr(labels)\n",
" return labels_no_outliers\n",
"\n",
"\n",
"train_data = df[df['trade_date'] <= '2023-01-01']\n",
"test_data = df[df['trade_date'] >= '2023-01-01']\n",
"\n",
"feature_columns = [col for col in df.columns if col not in ['trade_date',\n",
" 'ts_code',\n",
" 'label']]\n",
"feature_columns = [col for col in feature_columns if 'future' not in col]\n",
"feature_columns = [col for col in feature_columns if 'score' not in col]\n",
"feature_columns = [col for col in feature_columns if col not in origin_columns]\n",
"\n",
"# for column in [column for column in train_data.columns if 'future' in column]:\n",
"# label_index = neutralize_labels(train_data[column], train_data, feature_columns, z_threshold=3, method='regression')\n",
"# train_data = train_data[label_index]\n",
"# label_index = neutralize_labels(test_data[column], test_data, feature_columns, z_threshold=3, method='regression')\n",
"# test_data = test_data[label_index]\n",
"\n",
"print(len(train_data))\n",
"print(len(test_data))"
],
"id": "5f3d9aece75318cd",
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['up', 'down', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'obv-maobv_6', 'rsi_3', 'rsi_6', 'rsi_9', 'return_10', 'return_20', 'avg_close_5', 'std_return_5', 'std_return_15', 'std_return_25', 'std_return_90', 'std_return_90_2', 'std_return_5 / std_return_90', 'std_return_5 / std_return_25', 'std_return_90 - std_return_90_2', 'ema_5', 'ema_13', 'ema_20', 'ema_60', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'act_factor5', 'act_factor6', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol - sell_lg_vol', 'buy_elg_vol - sell_elg_vol']\n"
]
}
],
"execution_count": 19
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-02-09T14:56:05.319915Z",
"start_time": "2025-02-09T14:56:03.355725Z"
}
},
"cell_type": "code",
"source": [
"def get_qcuts(series, quantiles):\n",
" q = pd.qcut(series, q=quantiles, labels=False, duplicates='drop')\n",
" return q[-1] # 返回窗口最后一个元素的分位数标签\n",
"\n",
"\n",
"window = 5\n",
"quantiles = 20\n",
"\n",
"\n",
"def get_label(df):\n",
" labels = df['future_af13'] - df['act_factor1']\n",
" # labels = df['future_close3']\n",
" return labels\n",
"\n",
"\n",
"train_data['label'], test_data['label'] = get_label(train_data), get_label(test_data)\n",
"\n",
"train_data, test_data = train_data.dropna(subset=['label']), test_data.dropna(subset=['label'])\n",
"train_data, test_data = train_data.replace([np.inf, -np.inf], np.nan).dropna(), test_data.replace([np.inf, -np.inf],\n",
" np.nan).dropna()\n",
"train_data, test_data = train_data.reset_index(drop=True), test_data.reset_index(drop=True)\n",
"\n",
"print(len(train_data))\n",
"print(len(test_data))"
],
"id": "f4f16d63ad18d1bc",
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"875004\n",
"最小日期: 2017-01-03\n",
"最大日期: 2022-12-30\n",
"260581\n",
"最小日期: 2023-01-03\n",
"最大日期: 2025-01-27\n"
]
}
],
"execution_count": 13
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-02-09T14:56:05.480695Z",
"start_time": "2025-02-09T14:56:05.367238Z"
}
},
"cell_type": "code",
"source": [
"import lightgbm as lgb\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import optuna\n",
"from sklearn.model_selection import KFold\n",
"from sklearn.metrics import mean_absolute_error\n",
"import os\n",
"import json\n",
"import pickle\n",
"import hashlib\n",
"\n",
"\n",
"def objective(trial, X, y, num_boost_round, params):\n",
" # 参数网格\n",
" X, y = X.reset_index(drop=True), y.reset_index(drop=True)\n",
" param_grid = {\n",
" \"n_estimators\": trial.suggest_categorical(\"n_estimators\", [10000]),\n",
" \"learning_rate\": trial.suggest_float(\"learning_rate\", 0.01, 0.3),\n",
" \"num_leaves\": trial.suggest_int(\"num_leaves\", 20, 3000, step=25),\n",
" \"max_depth\": trial.suggest_int(\"max_depth\", 3, 16),\n",
" \"min_data_in_leaf\": trial.suggest_int(\"min_data_in_leaf\", 200, 10000, step=100),\n",
" \"lambda_l1\": trial.suggest_int(\"lambda_l1\", 0, 100, step=5),\n",
" \"lambda_l2\": trial.suggest_int(\"lambda_l2\", 0, 100, step=5),\n",
" \"min_gain_to_split\": trial.suggest_float(\"min_gain_to_split\", 0, 15),\n",
" \"bagging_fraction\": trial.suggest_float(\"bagging_fraction\", 0.2, 0.95, step=0.1),\n",
" \"bagging_freq\": trial.suggest_categorical(\"bagging_freq\", [1]),\n",
" \"feature_fraction\": trial.suggest_float(\"feature_fraction\", 0.2, 0.95, step=0.1),\n",
" \"random_state\": 1,\n",
" \"objective\": 'regression',\n",
" 'verbosity': -1\n",
" }\n",
" # 5折交叉验证\n",
" cv = KFold(n_splits=5, shuffle=False)\n",
"\n",
" cv_scores = np.empty(5)\n",
" for idx, (train_idx, test_idx) in enumerate(cv.split(X, y)):\n",
" X_train, X_test = X.iloc[train_idx], X.iloc[test_idx]\n",
" y_train, y_test = y[train_idx], y[test_idx]\n",
"\n",
" # LGBM建模\n",
" model = lgb.LGBMRegressor(**param_grid, num_boost_round=num_boost_round)\n",
" model.fit(\n",
" X_train,\n",
" y_train,\n",
" eval_set=[(X_test, y_test)],\n",
" eval_metric=\"l2\",\n",
" callbacks=[\n",
" # LightGBMPruningCallback(trial, \"l2\"),\n",
" lgb.early_stopping(50, first_metric_only=True),\n",
" lgb.log_evaluation(period=-1)\n",
" ],\n",
" )\n",
" # 模型预测\n",
" preds = model.predict(X_test)\n",
" # 优化指标logloss最小\n",
" cv_scores[idx] = mean_absolute_error(y_test, preds)\n",
"\n",
" return np.mean(cv_scores)\n",
"\n",
"def generate_key(params, feature_columns, num_boost_round):\n",
" key_data = {\n",
" \"params\": params,\n",
" \"feature_columns\": feature_columns,\n",
" \"num_boost_round\": num_boost_round\n",
" }\n",
" # 转换成排序后的 JSON 字符串，再生成 md5 hash\n",
" key_str = json.dumps(key_data, sort_keys=True)\n",
" return hashlib.md5(key_str.encode('utf-8')).hexdigest()\n",
"\n",
"def train_light_model(df, params, feature_columns, callbacks, evals,\n",
" print_feature_importance=True, num_boost_round=100,\n",
" use_optuna=False):\n",
" cache_file = 'light_model.pkl'\n",
" cache_key = generate_key(params, feature_columns, num_boost_round)\n",
"\n",
" # 检查缓存文件是否存在\n",
" if os.path.exists(cache_file):\n",
" try:\n",
" with open(cache_file, 'rb') as f:\n",
" cache_data = pickle.load(f)\n",
" if cache_data.get('key') == cache_key:\n",
" print(\"加载缓存模型...\")\n",
" return cache_data.get('model')\n",
" else:\n",
" print(\"缓存模型的参数与当前参数不匹配，重新训练模型。\")\n",
" except Exception as e:\n",
" print(f\"加载缓存失败: {e}，重新训练模型。\")\n",
" else:\n",
" print(\"未发现缓存模型，开始训练新模型。\")\n",
" # 确保数据按照 date 和 label 排序\n",
" df_sorted = df.sort_values(by=['trade_date', 'label'], ascending=[True, False]) # 按日期升序、标签降序排序\n",
" df_sorted = df_sorted.sort_values(by='trade_date')\n",
" unique_dates = df_sorted['trade_date'].unique()\n",
" val_date_count = int(len(unique_dates) * 0.1)\n",
" val_dates = unique_dates[-val_date_count:]\n",
" val_indices = df_sorted[df_sorted['trade_date'].isin(val_dates)].index\n",
" train_indices = df_sorted[~df_sorted['trade_date'].isin(val_dates)].index\n",
"\n",
" # 获取训练集和验证集的样本\n",
" train_df = df_sorted.iloc[train_indices]\n",
" val_df = df_sorted.iloc[val_indices]\n",
"\n",
" X_train = train_df[feature_columns]\n",
" y_train = train_df['label']\n",
"\n",
" X_val = val_df[feature_columns]\n",
" y_val = val_df['label']\n",
"\n",
" train_data = lgb.Dataset(X_train, label=y_train)\n",
" val_data = lgb.Dataset(X_val, label=y_val)\n",
" if use_optuna:\n",
" # study = optuna.create_study(direction='minimize' if classify else 'maximize')\n",
" study = optuna.create_study(direction='minimize')\n",
" study.optimize(lambda trial: objective(trial, X_train, y_train, num_boost_round, params), n_trials=20)\n",
"\n",
" print(f\"Best parameters: {study.best_trial.params}\")\n",
" print(f\"Best score: {study.best_trial.value}\")\n",
"\n",
" params.update(study.best_trial.params)\n",
" model = lgb.train(\n",
" params, train_data, num_boost_round=num_boost_round,\n",
" valid_sets=[train_data, val_data], valid_names=['train', 'valid'],\n",
" callbacks=callbacks\n",
" )\n",
"\n",
" # 打印特征重要性（如果需要）\n",
" if print_feature_importance:\n",
" lgb.plot_metric(evals)\n",
" lgb.plot_tree(model, figsize=(20, 8))\n",
" lgb.plot_importance(model, importance_type='split', max_num_features=20)\n",
" plt.show()\n",
" # with open(cache_file, 'wb') as f:\n",
" # pickle.dump({'key': cache_key,\n",
" # 'model': model,\n",
" # 'feature_columns': feature_columns}, f)\n",
" # print(\"模型训练完成并已保存缓存。\")\n",
" return model\n",
"\n",
"\n",
"from catboost import CatBoostRegressor\n",
"import pandas as pd\n",
"\n",
"\n",
"def train_catboost(df, num_boost_round, params=None):\n",
" \"\"\"\n",
" 训练 CatBoost 排序模型\n",
" - df: 包含因子、date、instrument 和 label 的 DataFrame\n",
" - num_boost_round: 训练的轮数\n",
" - print_feature_importance: 是否打印特征重要性\n",
" - plot: 是否绘制特征重要性图\n",
" - split_date: 用于划分训练集和验证集的日期（比如 '2020-01-01'）\n",
"\n",
" 返回训练好的模型\n",
" \"\"\"\n",
" df_sorted = df.sort_values(by=['date', 'label'], ascending=[True, False])\n",
"\n",
" # 提取特征和标签\n",
" feature_columns = [col for col in df.columns if col not in ['date',\n",
" 'instrument',\n",
" 'label']]\n",
" feature_columns = [col for col in feature_columns if 'future' not in col]\n",
" feature_columns = [col for col in feature_columns if 'score' not in col]\n",
"\n",
" df_sorted = df_sorted.sort_values(by='date')\n",
" unique_dates = df_sorted['date'].unique()\n",
" val_date_count = int(len(unique_dates) * 0.1)\n",
" val_dates = unique_dates[-val_date_count:]\n",
" val_indices = df_sorted[df_sorted['date'].isin(val_dates)].index\n",
" train_indices = df_sorted[~df_sorted['date'].isin(val_dates)].index\n",
"\n",
" # 获取训练集和验证集的样本\n",
" train_df = df_sorted.iloc[train_indices].sort_values(by=['date', 'label'], ascending=[True, False])\n",
" val_df = df_sorted.iloc[val_indices].sort_values(by=['date', 'label'], ascending=[True, False])\n",
"\n",
" X_train = train_df[feature_columns]\n",
" y_train = train_df['label']\n",
"\n",
" X_val = val_df[feature_columns]\n",
" y_val = val_df['label']\n",
"\n",
" model = CatBoostRegressor(**params, iterations=num_boost_round)\n",
" model.fit(X_train,\n",
" y_train,\n",
" eval_set=(X_val, y_val))\n",
"\n",
" return model"
],
"id": "8f134d435f71e9e2",
"outputs": [],
"execution_count": 14
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-02-09T14:56:05.576927Z",
"start_time": "2025-02-09T14:56:05.480695Z"
}
},
"cell_type": "code",
"source": [
"light_params = {\n",
" 'objective': 'regression',\n",
" 'metric': 'l2',\n",
" 'learning_rate': 0.05,\n",
" 'is_unbalance': True,\n",
" 'num_leaves': 2048,\n",
" 'min_data_in_leaf': 16,\n",
" 'max_depth': 32,\n",
" 'max_bin': 1024,\n",
" 'nthread': 2,\n",
" 'feature_fraction': 0.7,\n",
" 'bagging_fraction': 0.7,\n",
" 'bagging_freq': 5,\n",
" 'lambda_l1': 80,\n",
" 'lambda_l2': 65,\n",
" 'verbosity': -1\n",
"}"
],
"id": "4a4542e1ed6afe7d",
"outputs": [],
"execution_count": 15
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-02-09T14:57:25.341222Z",
"start_time": "2025-02-09T14:56:05.640256Z"
}
},
"cell_type": "code",
"source": [
"print('train data size: ', len(train_data))\n",
"df = train_data\n",
"\n",
"evals = {}\n",
"light_model = train_light_model(train_data, light_params, feature_columns,\n",
" [lgb.log_evaluation(period=500),\n",
" lgb.callback.record_evaluation(evals),\n",
" lgb.early_stopping(50, first_metric_only=True)\n",
" ], evals,\n",
" num_boost_round=1000, use_optuna=False,\n",
" print_feature_importance=False)"
],
"id": "beeb098799ecfa6a",
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"train data size: 875004\n",
"未发现缓存模型，开始训练新模型。\n",
"Training until validation scores don't improve for 50 rounds\n",
"Early stopping, best iteration is:\n",
"[378]\ttrain's l2: 0.435049\tvalid's l2: 0.589178\n",
"Evaluated only: l2\n"
]
}
],
"execution_count": 16
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-02-09T14:57:27.394697Z",
"start_time": "2025-02-09T14:57:25.373274Z"
}
},
"cell_type": "code",
"source": [
"test_data['score'] = light_model.predict(test_data[feature_columns])\n",
"predictions = test_data.loc[test_data.groupby('trade_date')['score'].idxmax()]"
],
"id": "5bb96ca8492e74d",
"outputs": [],
"execution_count": 17
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-02-09T14:57:27.489570Z",
"start_time": "2025-02-09T14:57:27.397368Z"
}
},
"cell_type": "code",
"source": "predictions[['trade_date', 'score', 'ts_code']].to_csv('predictions.csv', index=False)",
"id": "5d1522a7538db91b",
"outputs": [],
"execution_count": 18
}
],
"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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2023-09-13,0.9974851537103986,601127.SH
2023-09-14,1.2529240943908486,603005.SH
2023-09-15,0.6551278623697204,603306.SH
2023-09-18,0.62491397271537,000851.SZ
2023-09-19,0.9590430560641906,600293.SH
2023-09-20,0.9192992329949209,600895.SH
2023-09-21,1.303323539412371,603667.SH
2023-09-22,0.6392556165728372,002406.SZ
2023-09-25,0.550363061204975,603933.SH
2023-09-26,0.8885810012289143,000766.SZ
2023-09-27,1.3119504188158804,605588.SH
2023-09-28,0.6383963467007131,002885.SZ
2023-10-09,0.5559833174181884,603277.SH
2023-10-10,0.6746335129218759,002855.SZ
2023-10-11,0.48076279797688554,001319.SZ
2023-10-12,0.4648032423789084,002953.SZ
2023-10-13,0.7973569102606312,605588.SH
2023-10-16,0.6753500013003589,603929.SH
2023-10-17,0.939032197442827,002786.SZ
2023-10-18,1.3316765134484991,603005.SH
2023-10-19,1.5107094655109568,002728.SZ
2023-10-20,1.8033472200834302,603890.SH
2023-10-23,2.0156758075487904,000526.SZ
2023-10-24,1.733095955568728,600186.SH
2023-10-25,1.0763644734039597,002456.SZ
2023-10-26,0.6254989123134431,600155.SH
2023-10-27,0.9189894086467906,000712.SZ
2023-10-30,0.82608918468284,600839.SH
2023-10-31,0.7212314254570089,001319.SZ
2023-11-01,0.7217175319235539,002657.SZ
2023-11-02,0.7778599932153227,002456.SZ
2023-11-03,1.3422820103526423,603985.SH
2023-11-06,0.5239681122691597,600595.SH
2023-11-07,0.5067988914432168,000520.SZ
2023-11-08,0.8068628626883659,603009.SH
2023-11-09,0.795444145270753,601127.SH
2023-11-10,0.7853290033363176,600630.SH
2023-11-13,0.6675304106579922,601595.SH
2023-11-14,0.2361369659661822,000828.SZ
2023-11-15,1.3945937102768664,603598.SH
2023-11-16,0.9576360953800751,000628.SZ
2023-11-17,0.6890730621251102,603108.SH
2023-11-20,0.7909511281469108,000628.SZ
2023-11-21,0.8142228770378246,000925.SZ
2023-11-22,0.797507102177366,000550.SZ
2023-11-23,0.7063630358338048,000676.SZ
2023-11-24,1.036008941153352,002181.SZ
2023-11-27,1.0291531391754778,000503.SZ
2023-11-28,0.9076486100273481,600775.SH
2023-11-29,0.7882491062137358,000669.SZ
2023-11-30,1.0967518981113826,000669.SZ
2023-12-01,0.7311870736612255,000625.SZ
2023-12-04,1.0691371238894785,603286.SH
2023-12-05,1.3131270390024472,605188.SH
2023-12-06,0.7752257820655966,000628.SZ
2023-12-07,0.8197027217611897,600107.SH
2023-12-08,1.3967047074635028,603488.SH
2023-12-11,0.687045285463665,002277.SZ
2023-12-12,0.665884682887716,600593.SH
2023-12-13,0.654047737248992,600476.SH
2023-12-14,1.0532572750390772,603106.SH
2023-12-15,1.0658083351666034,603358.SH
2023-12-18,0.962400311858336,000766.SZ
2023-12-19,0.7287547482546808,603103.SH
2023-12-20,1.2148728495986132,002647.SZ
2023-12-21,0.7541763251442525,002858.SZ
2023-12-22,0.8726322448001688,600178.SH
2023-12-25,0.8241484608574933,605011.SH
2023-12-26,0.9109440061301425,603992.SH
2023-12-27,0.8418823218408088,002660.SZ
2023-12-28,0.6918104133614209,002238.SZ
2023-12-29,0.587505902687491,002495.SZ
2024-01-02,0.9211111544777676,002587.SZ
2024-01-03,0.7226235020970133,000691.SZ
2024-01-04,0.9569550769001772,002962.SZ
2024-01-05,1.1926365483735857,605117.SH
2024-01-08,1.2688927804343633,603032.SH
2024-01-09,0.658120719671456,002862.SZ
2024-01-10,1.0753659753822198,000698.SZ
2024-01-11,0.8894494773121738,603097.SH
2024-01-12,0.9087175987157813,600593.SH
2024-01-15,0.5892323752732013,603212.SH
2024-01-16,0.7839193778147607,000698.SZ
2024-01-17,1.0625258983405035,603172.SH
2024-01-18,1.2631347573071583,002033.SZ
2024-01-19,1.178331671274573,603579.SH
2024-01-22,1.6352965224693876,605268.SH
2024-01-23,1.4266068895392103,605117.SH
2024-01-24,0.8002314972730927,600138.SH
2024-01-25,0.6269522471780932,603099.SH
2024-01-26,1.1309900405971738,605111.SH
2024-01-29,1.125066361142788,002253.SZ
2024-01-30,1.351423375727843,003007.SZ
2024-01-31,1.5739771441257069,601138.SH
2024-02-01,1.499054147286471,002033.SZ
2024-02-02,1.5167645269468597,601038.SH
2024-02-05,1.903436398968963,000998.SZ
2024-02-06,0.8212170920332696,600705.SH
2024-02-07,0.8351364611756162,603199.SH
2024-02-08,0.6822013314565247,601998.SH
2024-02-19,0.5444839345994206,000999.SZ
2024-02-20,0.5734873097155654,603369.SH
2024-02-21,0.43260992475178217,600023.SH
2024-02-22,0.4837658423417254,000526.SZ
2024-02-23,0.25932845682914313,000157.SZ
2024-02-26,0.37391625217883473,000983.SZ
2024-02-27,0.06249364085384111,002039.SZ
2024-02-28,1.038623993098607,000680.SZ
2024-02-29,0.11792404338777529,603871.SH
2024-03-01,0.30810066963043975,603605.SH
2024-03-04,0.17622933681947947,600375.SH
2024-03-05,1.0199617166369774,002009.SZ
2024-03-06,0.7025950679251926,002209.SZ
2024-03-07,0.8130361341508562,002317.SZ
2024-03-08,0.6714948117029352,603960.SH
2024-03-11,0.7527103580484402,603960.SH
2024-03-12,0.773794092704635,603960.SH
2024-03-13,0.6395980350337678,603499.SH
2024-03-14,0.6983134888804106,603660.SH
2024-03-15,0.990357376475744,601138.SH
2024-03-18,0.4182273803259301,002075.SZ
2024-03-19,0.8116021296004745,603050.SH
2024-03-20,0.6916582321872963,605580.SH
2024-03-21,0.7126108686308776,002698.SZ
2024-03-22,0.795964501966808,603190.SH
2024-03-25,1.0652068531193497,603286.SH
2024-03-26,1.2060727717193194,002331.SZ
2024-03-27,1.8308956146424886,001696.SZ
2024-03-28,1.5762953683649072,002645.SZ
2024-03-29,0.8133804882766478,002055.SZ
2024-04-01,0.6881987381521469,002735.SZ
2024-04-02,0.9728619403924941,002085.SZ
2024-04-03,0.9735850429701983,002625.SZ
2024-04-08,0.9464502611855133,002805.SZ
2024-04-09,1.10422053943107,002085.SZ
2024-04-10,0.699553803327553,603822.SH
2024-04-11,1.2813227488155954,002544.SZ
2024-04-12,0.9677010248477915,605198.SH
2024-04-15,1.2722648770139777,002290.SZ
2024-04-16,2.1135975645782725,002521.SZ
2024-04-17,1.0448007042761251,000737.SZ
2024-04-18,1.3682438721410928,603619.SH
2024-04-19,0.6510347025537216,000933.SZ
2024-04-22,1.1007294592332375,600529.SH
2024-04-23,1.0096847778899667,002078.SZ
2024-04-24,0.5132039397179469,000949.SZ
2024-04-25,1.7289140437217827,600066.SH
2024-04-26,1.048178737250126,603556.SH
2024-04-29,0.28151131629200876,000949.SZ
2024-04-30,0.5902959535550075,605098.SH
2024-05-06,0.8659440618533316,001696.SZ
2024-05-07,0.4125483888931327,002225.SZ
2024-05-08,0.49991019731911135,002597.SZ
2024-05-09,0.8523675809088074,002590.SZ
2024-05-10,0.6072304373378594,001696.SZ
2024-05-13,0.9449133844307348,600326.SH
2024-05-14,0.756524501371889,002025.SZ
2024-05-15,0.7974348512463216,600682.SH
2024-05-16,0.9752446361705888,600644.SH
2024-05-17,1.1846986232709233,000702.SZ
2024-05-20,0.7167728676668204,002851.SZ
2024-05-21,1.071890852713521,603683.SH
2024-05-22,0.7124540930368298,600463.SH
2024-05-23,1.5016154741945653,002225.SZ
2024-05-24,1.2198831133459211,002968.SZ
2024-05-27,0.9536299254414154,000656.SZ
2024-05-28,0.8425457543469408,002761.SZ
2024-05-29,0.652762060180136,600979.SH
2024-05-30,0.9000889953940735,002455.SZ
2024-05-31,0.9330771253458983,601825.SH
2024-06-03,1.2914669574335471,001267.SZ
2024-06-04,0.829699499403838,600530.SH
2024-06-05,1.0758350766018299,002922.SZ
2024-06-06,0.8595311066455215,603360.SH
2024-06-07,1.0888503051113,002889.SZ
2024-06-11,1.0628038633716435,002475.SZ
2024-06-12,1.1159013979424315,603009.SH
2024-06-13,1.1812561799680747,600996.SH
2024-06-14,0.8256170648299659,600039.SH
2024-06-17,0.8645410455127762,000528.SZ
2024-06-18,0.6063406599939511,600996.SH
2024-06-19,0.7166446473651414,600192.SH
2024-06-20,0.932110291603618,605598.SH
2024-06-21,0.9558209107020993,603936.SH
2024-06-24,1.119014534304907,600992.SH
2024-06-25,1.3527294398026957,600584.SH
2024-06-26,0.7188166389093542,000504.SZ
2024-06-27,1.9515093336713365,002452.SZ
2024-06-28,0.8843544096493936,600889.SH
2024-07-01,0.6545882280229176,003004.SZ
2024-07-02,0.6762487770835988,600584.SH
2024-07-03,0.9128444390874889,601179.SH
2024-07-04,1.1423863246899275,603225.SH
2024-07-05,0.9324658736527716,603936.SH
2024-07-08,1.780537827812583,603328.SH
2024-07-09,0.7579237257512456,000679.SZ
2024-07-10,0.6704021737995826,605117.SH
2024-07-11,0.6117132499929072,000622.SZ
2024-07-12,1.0566477568456074,002452.SZ
2024-07-15,0.6602660923500289,605081.SH
2024-07-16,0.5502090318853736,002420.SZ
2024-07-17,0.6837622842594867,002355.SZ
2024-07-18,0.8242173773899442,002384.SZ
2024-07-19,0.9979238076969816,002384.SZ
2024-07-22,0.4982073979858695,000421.SZ
2024-07-23,1.1543952284060066,002428.SZ
2024-07-24,1.2334918071766405,605111.SH
2024-07-25,0.8987252044027945,002496.SZ
2024-07-26,0.8562640506752258,600171.SH
2024-07-29,0.5774106541432314,002750.SZ
2024-07-30,0.5406184547635255,600563.SH
2024-07-31,0.5575394015591096,002700.SZ
2024-08-01,0.3836358457058139,600099.SH
2024-08-02,0.670543769867587,605081.SH
2024-08-05,1.2561423052432321,605117.SH
2024-08-06,0.9373802633817199,000584.SZ
2024-08-07,0.8074459777954732,603032.SH
2024-08-08,0.8017404350976902,600611.SH
2024-08-09,0.9444750578832374,000659.SZ
2024-08-12,0.8308531709022774,000880.SZ
2024-08-13,0.6345130236563948,600266.SH
2024-08-14,0.7618996741024034,000159.SZ
2024-08-15,0.7957196976564392,000880.SZ
2024-08-16,0.8007104277247147,603050.SH
2024-08-19,0.8488617680848443,603444.SH
2024-08-20,1.049247341754445,002488.SZ
2024-08-21,1.2543709456137933,002488.SZ
2024-08-22,1.0702026523317902,002208.SZ
2024-08-23,1.0790725273037058,002369.SZ
2024-08-26,0.8933596937509765,000532.SZ
2024-08-27,0.9555026539273229,605318.SH
2024-08-28,0.8517730333686951,002547.SZ
2024-08-29,0.8787028549560587,003001.SZ
2024-08-30,0.5949301031458588,601898.SH
2024-09-02,1.0927036539101782,603826.SH
2024-09-03,0.5011518255679451,000810.SZ
2024-09-04,0.913670019713004,001298.SZ
2024-09-05,0.5722903163709645,600375.SH
2024-09-06,1.054280211341685,002622.SZ
2024-09-09,1.1145869455283224,600375.SH
2024-09-10,0.6071645644461853,600804.SH
2024-09-11,0.8120257237744295,002808.SZ
2024-09-12,0.7226510652354332,600148.SH
2024-09-13,1.4358476935516946,603615.SH
2024-09-18,0.8351081061832145,002946.SZ
2024-09-19,0.605865384829211,600898.SH
2024-09-20,0.9533885243905273,603398.SH
2024-09-23,1.046054385469168,601162.SH
2024-09-24,0.3758184358111333,603559.SH
2024-09-25,0.8156363720747627,600756.SH
2024-09-26,0.5051488053045387,600550.SH
2024-09-27,0.16901120016969293,002016.SZ
2024-09-30,0.03361915273177237,603106.SH
2024-10-08,0.02829952163781644,603106.SH
2024-10-09,0.15132702693149014,601336.SH
2024-10-10,0.5264407322666304,000402.SZ
2024-10-11,1.0343311919140215,600657.SH
2024-10-14,0.47180187869811324,603383.SH
2024-10-15,0.8703255693900985,603106.SH
2024-10-16,0.8602575981884926,002987.SZ
2024-10-17,0.8415090474760394,000567.SZ
2024-10-18,0.15292428350076237,002542.SZ
2024-10-21,0.4922089945140812,002457.SZ
2024-10-22,0.4176771447393169,002423.SZ
2024-10-23,0.8519606263347589,000158.SZ
2024-10-24,0.5430046541942165,000935.SZ
2024-10-25,0.18041199007445233,603016.SH
2024-10-28,0.5571845419116188,000503.SZ
2024-10-29,0.4049481417593221,002851.SZ
2024-10-30,0.7134818524722009,600463.SH
2024-10-31,0.5829203522209173,603117.SH
2024-11-01,1.2246655429458404,002851.SZ
2024-11-04,0.8176452500049061,600843.SH
2024-11-05,0.896531400955133,002514.SZ
2024-11-06,0.8080466337179094,600292.SH
2024-11-07,0.8536978345761432,600481.SH
2024-11-08,0.735404763610977,002047.SZ
2024-11-11,0.7749352489908305,002428.SZ
2024-11-12,0.8967548353595826,002428.SZ
2024-11-13,1.0199850479686239,603859.SH
2024-11-14,0.9361081815051415,603559.SH
2024-11-15,1.5997213068383358,002086.SZ
2024-11-18,1.5789594007558525,002709.SZ
2024-11-19,1.5249787983366927,603825.SH
2024-11-20,0.736292965730012,002808.SZ
2024-11-21,0.7544341372210354,603859.SH
2024-11-22,1.2744170240551567,600593.SH
2024-11-25,0.8324187454861728,600787.SH
2024-11-26,1.0752006153991913,600736.SH
2024-11-27,0.6500606694243171,002245.SZ
2024-11-28,0.8456761058883665,000833.SZ
2024-11-29,0.8489157795122361,002354.SZ
2024-12-02,0.6832024025795977,002611.SZ
2024-12-03,0.8939114413301544,603366.SH
2024-12-04,1.0277665925094743,002822.SZ
2024-12-05,0.9103466279163851,002175.SZ
2024-12-06,0.7686238693084577,002467.SZ
2024-12-09,1.0846053237578963,601933.SH
2024-12-10,1.0700371757624498,600193.SH
2024-12-11,0.7673727634361369,002520.SZ
2024-12-12,0.8497906144901559,002362.SZ
2024-12-13,1.254346634364545,603429.SH
2024-12-16,1.0386293779387827,000727.SZ
2024-12-17,1.2458109349303497,600157.SH
2024-12-18,1.3409657396440136,003002.SZ
2024-12-19,1.1408332938187322,600593.SH
2024-12-20,0.8248161307709121,002512.SZ
2024-12-23,1.1576027817247634,002336.SZ
2024-12-24,0.8652560164174093,603610.SH
2024-12-25,0.8928903216570524,002045.SZ
2024-12-26,0.7436802683703838,603214.SH
2024-12-27,0.8278524197563604,002945.SZ
2024-12-30,0.6846259683364331,600223.SH
2024-12-31,1.0514482797503029,001309.SZ
2025-01-02,1.4104671874229635,603668.SH
1 trade_date score ts_code
2 2023-01-03 0.6847274162535452 600965.SH
3 2023-01-04 0.7414192412896188 000615.SZ
4 2023-01-05 0.5542654290126795 000430.SZ
5 2023-01-06 0.6959513776962396 002640.SZ
6 2023-01-09 0.7412055912703203 600785.SH
7 2023-01-10 0.6855907318251123 002762.SZ
8 2023-01-11 0.7292345443148791 002719.SZ
9 2023-01-12 0.7309633018913224 600996.SH
10 2023-01-13 0.8190324397886146 002279.SZ
11 2023-01-16 0.784606092607123 000850.SZ
12 2023-01-17 0.7241262895910806 002441.SZ
13 2023-01-18 0.7040191348893544 002467.SZ
14 2023-01-19 1.4428906481799044 002195.SZ
15 2023-01-20 0.8043769403690574 000561.SZ
16 2023-01-30 0.8713260866140435 000913.SZ
17 2023-01-31 0.7091214216170246 002560.SZ
18 2023-02-01 0.8674507984004312 002576.SZ
19 2023-02-02 0.8435316672358789 002576.SZ
20 2023-02-03 0.3637794960484866 603698.SH
21 2023-02-06 0.8498784343039595 002401.SZ
22 2023-02-07 0.5210597972482388 002767.SZ
23 2023-02-08 0.9567872514253289 002368.SZ
24 2023-02-09 0.5267235816880836 002155.SZ
25 2023-02-10 0.8402723790587989 002348.SZ
26 2023-02-13 0.4846072502710197 002348.SZ
27 2023-02-14 0.7626025684565886 002877.SZ
28 2023-02-15 0.697045569808066 600225.SH
29 2023-02-16 0.8282986278708487 002748.SZ
30 2023-02-17 1.8315250204409614 601020.SH
31 2023-02-20 0.9746443150927466 003021.SZ
32 2023-02-21 1.33028636760986 002339.SZ
33 2023-02-22 0.7778102046341324 002167.SZ
34 2023-02-23 1.1313865958453686 600100.SH
35 2023-02-24 0.5636940136703907 000716.SZ
36 2023-02-27 1.1864163478203489 600557.SH
37 2023-02-28 1.0001756905854564 603129.SH
38 2023-03-01 0.859829916936714 603025.SH
39 2023-03-02 0.7059826582301507 603186.SH
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405 2024-08-30 0.5949301031458588 601898.SH
406 2024-09-02 1.0927036539101782 603826.SH
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485 2024-12-31 1.0514482797503029 001309.SZ
486 2025-01-02 1.4104671874229635 603668.SH

265
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trade_date,score,ts_code
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2024-08-01,0.5070284832062075,600834.SH
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2025-01-07,1.327995034218316,002881.SZ
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2025-02-07,0.8152925075270021,000856.SZ
2025-02-10,0.4527649047879118,603166.SH
1 trade_date,score,ts_code
2 2024-01-02,0.8906433047229376,002587.SZ
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4 2024-01-04,0.918203870395468,002962.SZ
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50
code/utils/utils.py Normal file
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import pandas as pd
import pandas as pd
def read_and_merge_h5_data(h5_filename, key, columns, df=None):
"""
读取 HDF5 文件中的数据，根据指定的 columns 筛选数据，
如果传入 df 参数，则将其与读取的数据根据 ts_code 和 trade_date 合并。
参数:
- h5_filename: HDF5 文件名
- key: 数据存储在 HDF5 文件中的 key
- columns: 要读取的列名列表
- df: 需要合并的 DataFrame（如果为空，则不进行合并）
返回:
- 合并后的 DataFrame
"""
# 处理 _ 开头的列名
processed_columns = []
for col in columns:
if col.startswith('_'):
processed_columns.append(col[1:]) # 去掉下划线
else:
processed_columns.append(col)
# 从 HDF5 文件读取数据，选择需要的列
data = pd.read_hdf(h5_filename, key=key, columns=processed_columns)
# 修改列名，如果列名以前有 _，加上 _
for col in data.columns:
if col not in columns: # 只有不在 columns 中的列才需要加下划线
new_col = f'_{col}'
data.rename(columns={col: new_col}, inplace=True)
# 如果传入的 df 不为空，则进行合并
if df is not None and not df.empty:
# 确保两个 DataFrame 都有 ts_code 和 trade_date 列
df['trade_date'] = pd.to_datetime(df['trade_date'], format='%Y%m%d')
data['trade_date'] = pd.to_datetime(data['trade_date'], format='%Y%m%d')
# 根据 ts_code 和 trade_date 合并
merged_df = pd.merge(df, data, on=['ts_code', 'trade_date'], how='left')
else:
# 如果 df 为空，则直接返回读取的数据
merged_df = data
return merged_df