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liaozhaorun
2025-04-03 00:45:07 +08:00
parent 01092b8cae
commit ea3955f80f
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "initial_id",
"metadata": {
"ExecuteTime": {
"end_time": "2025-03-12T15:31:25.004019Z",
"start_time": "2025-03-12T15:31:24.322440Z"
}
},
"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": 7,
"id": "972a5ac9f79fe373",
"metadata": {
"ExecuteTime": {
"end_time": "2025-03-12T15:31:40.917015Z",
"start_time": "2025-03-12T15:31:35.958771Z"
}
},
"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 12.7 7.2 7.9 \n",
"1 000002.SZ 20180104 0.3 31.8 14.1 15.6 \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 5.9 5.9 \n",
"... ... ... ... ... ... ... \n",
"3091 603991.SH 20180104 12.0 67.8 26.4 27.0 \n",
"3092 603993.SH 20180104 1.5 8.1 5.6 5.8 \n",
"3093 603997.SH 20180104 5.4 31.5 9.9 10.2 \n",
"3094 603998.SH 20180104 3.9 18.9 9.8 10.1 \n",
"3095 603999.SH 20180104 5.4 30.9 6.9 7.2 \n",
"\n",
" cost_50pct cost_85pct cost_95pct weight_avg winner_rate \n",
"0 10.6 11.3 11.9 9.93 71.97 \n",
"1 20.1 23.1 24.3 19.62 99.34 \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 7.9 6.76 38.14 \n",
"... ... ... ... ... ... \n",
"3091 27.6 30.6 34.2 28.54 57.36 \n",
"3092 6.3 7.1 7.6 6.34 73.50 \n",
"3093 10.5 11.7 11.7 10.84 11.28 \n",
"3094 11.9 13.5 15.7 12.13 17.93 \n",
"3095 7.8 9.6 9.9 8.17 21.83 \n",
"\n",
"[3096 rows x 11 columns]\n"
]
}
],
"source": [
"\n",
"df = pro.cyq_perf(trade_date='20180104')\n",
"print(df)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "1b5a82fbf4e380de",
"metadata": {
"ExecuteTime": {
"end_time": "2025-03-12T15:30:20.421604Z",
"start_time": "2025-03-12T15:30:20.224851Z"
}
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import time\n",
"\n",
"h5_filename = '../../../data/sw_daily.h5'\n",
"\n",
"trade_cal = pro.trade_cal(exchange='', start_date='20170101', end_date='20250420')\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": "f448da220816bf98",
"metadata": {
"ExecuteTime": {
"start_time": "2025-03-12T15:30:20.436796Z"
},
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"任务 20250418 完成\n",
"任务 20250417 完成\n",
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]
}
],
"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",
" data = pro.cyq_perf(trade_date=trade_date)\n",
" if data is not None and not data.empty:\n",
" return 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"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "907f732d3c397bf",
"metadata": {
"ExecuteTime": {
"end_time": "2025-03-12T15:31:10.381348500Z",
"start_time": "2025-03-12T15:23:41.345460Z"
}
},
"outputs": [],
"source": [
"\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)\n",
"\n",
"# 将数据保存为 HDF5 文件（table 格式）\n",
"all_daily_data_df.to_hdf('../../data/cyq_perf.h5', key='cyq_perf', mode='w', format='table', data_columns=True)\n",
"\n",
"print(\"所有每日基础数据获取并保存完毕！\")"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "73e829ac-ff3d-408e-beb3-0b87f5b00b19",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" ts_code trade_date\n",
"0 000001.SZ 20250312\n",
"1 000002.SZ 20250312\n",
"2 000004.SZ 20250312\n",
"3 000006.SZ 20250312\n",
"4 000007.SZ 20250312\n",
"... ... ...\n",
"7465732 603991.SH 20180102\n",
"7465733 603993.SH 20180102\n",
"7465734 603997.SH 20180102\n",
"7465735 603998.SH 20180102\n",
"7465736 603999.SH 20180102\n",
"\n",
"[7465737 rows x 2 columns]\n"
]
}
],
"source": [
"h5_filename = '../../data/cyq_perf.h5'\n",
"key = '/cyq_perf'\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)\n",
" max_date = df['trade_date'].min()"
]
}
],
"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": "3.11.11"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

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{
"cells": [
{
"cell_type": "code",
"id": "initial_id",
"metadata": {
"ExecuteTime": {
"end_time": "2025-03-30T16:42:23.864275Z",
"start_time": "2025-03-30T16:42:22.963221Z"
}
},
"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
},
{
"cell_type": "code",
"id": "f448da220816bf98",
"metadata": {
"ExecuteTime": {
"end_time": "2025-03-30T16:42:25.559047Z",
"start_time": "2025-03-30T16:42:23.868783Z"
}
},
"source": [
"# 定义四个指数\n",
"index_list = ['399300.SH', '000905.SH', '000852.SH', '399006.SZ']\n",
"\n",
"# 获取并存储数据\n",
"all_data = []\n",
"\n",
"for ts_code in index_list:\n",
" df = pro.index_daily(ts_code=ts_code) # 可根据需要设置日期\n",
" df['ts_code'] = ts_code # 添加ts_code列来区分数据\n",
" all_data.append(df)\n",
"\n",
"# 合并所有数据\n",
"final_df = pd.concat(all_data, ignore_index=True)\n",
"\n",
"# 存储到H5文件\n",
"final_df.to_hdf('../../data/index_data.h5', key='index_data', mode='w')\n",
"\n",
"print(\"数据已经成功存储到index_data.h5文件中\")"
],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"数据已经成功存储到index_data.h5文件中\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\liaozhaorun\\AppData\\Local\\Temp\\ipykernel_6192\\3209233630.py:13: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
" final_df = pd.concat(all_data, ignore_index=True)\n"
]
}
],
"execution_count": 2
},
{
"cell_type": "code",
"id": "907f732d3c397bf",
"metadata": {
"ExecuteTime": {
"end_time": "2025-03-30T16:42:25.802535Z",
"start_time": "2025-03-30T16:42:25.766399Z"
}
},
"source": [
"h5_filename = '../../data/index_data.h5'\n",
"key = '/index_data'\n",
"with pd.HDFStore(h5_filename, mode='r') as store:\n",
" df = store[key]\n",
" print(df)\n"
],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" ts_code trade_date close open high low \\\n",
"0 000905.SH 20250328 5916.0314 5954.7297 5973.8015 5904.9159 \n",
"1 000905.SH 20250327 5957.6017 5932.5165 6000.6615 5891.7664 \n",
"2 000905.SH 20250326 5948.4986 5935.8537 5983.4739 5935.8537 \n",
"3 000905.SH 20250325 5946.9510 5969.4164 5993.9312 5929.6734 \n",
"4 000905.SH 20250324 5969.0789 5973.0466 5987.0606 5882.8780 \n",
"... ... ... ... ... ... ... \n",
"13423 399006.SZ 20100607 1069.4680 1005.0280 1075.2250 1001.7020 \n",
"13424 399006.SZ 20100604 1027.6810 989.6810 1027.6810 986.5040 \n",
"13425 399006.SZ 20100603 998.3940 1002.3550 1026.7020 997.7750 \n",
"13426 399006.SZ 20100602 997.1190 967.6090 997.1190 952.6110 \n",
"13427 399006.SZ 20100601 973.2330 986.0150 994.7930 948.1180 \n",
"\n",
" pre_close change pct_chg vol amount \n",
"0 5957.6017 -41.5703 -0.6978 1.342619e+08 1.688995e+08 \n",
"1 5948.4986 9.1031 0.1530 1.347089e+08 1.765905e+08 \n",
"2 5946.9510 1.5476 0.0260 1.367021e+08 1.716958e+08 \n",
"3 5969.0789 -22.1279 -0.3707 1.474839e+08 1.922270e+08 \n",
"4 5971.9302 -2.8513 -0.0477 1.691924e+08 2.200943e+08 \n",
"... ... ... ... ... ... \n",
"13423 1027.6810 41.7870 4.0661 2.655275e+06 9.106095e+06 \n",
"13424 998.3940 29.2870 2.9334 1.500295e+06 5.269441e+06 \n",
"13425 997.1190 1.2750 0.1279 1.616805e+06 6.240835e+06 \n",
"13426 973.2330 23.8860 2.4543 1.074628e+06 4.001206e+06 \n",
"13427 1000.0000 -26.7670 -2.6767 1.356285e+06 4.924177e+06 \n",
"\n",
"[13428 rows x 11 columns]\n"
]
}
],
"execution_count": 3
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
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"name": "ipython",
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"file_extension": ".py",
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"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
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"nbformat_minor": 5
}

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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "initial_id",
"metadata": {
"ExecuteTime": {
"end_time": "2025-03-12T15:28:49.275220Z",
"start_time": "2025-03-12T15:28:48.624632Z"
}
},
"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": 2,
"id": "972a5ac9f79fe373",
"metadata": {
"ExecuteTime": {
"end_time": "2025-03-12T15:28:49.280632Z",
"start_time": "2025-03-12T15:28:49.275220Z"
}
},
"outputs": [],
"source": [
"\n",
"# df = pro.cyq_perf(start_date='20220101', end_date='20220429')\n",
"# print(df)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "f448da220816bf98",
"metadata": {
"ExecuteTime": {
"end_time": "2025-03-12T15:39:50.128089Z",
"start_time": "2025-03-12T15:28:49.437760Z"
},
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"成功获取并保存 20250228 的每日基础数据\n",
"成功获取并保存 20250227 的每日基础数据\n",
"成功获取并保存 20250226 的每日基础数据\n",
"成功获取并保存 20250225 的每日基础数据\n",
"成功获取并保存 20250224 的每日基础数据\n",
"成功获取并保存 20250221 的每日基础数据\n",
"成功获取并保存 20250220 的每日基础数据\n",
"成功获取并保存 20250219 的每日基础数据\n",
"成功获取并保存 20250218 的每日基础数据\n",
"成功获取并保存 20250217 的每日基础数据\n",
"成功获取并保存 20250214 的每日基础数据\n",
"成功获取并保存 20250213 的每日基础数据\n",
"成功获取并保存 20250212 的每日基础数据\n",
"成功获取并保存 20250211 的每日基础数据\n",
"成功获取并保存 20250210 的每日基础数据\n",
"成功获取并保存 20250207 的每日基础数据\n",
"成功获取并保存 20250206 的每日基础数据\n",
"成功获取并保存 20250205 的每日基础数据\n",
"成功获取并保存 20250127 的每日基础数据\n",
"成功获取并保存 20250124 的每日基础数据\n",
"成功获取并保存 20250123 的每日基础数据\n",
"成功获取并保存 20250122 的每日基础数据\n",
"成功获取并保存 20250121 的每日基础数据\n",
"成功获取并保存 20250120 的每日基础数据\n",
"成功获取并保存 20250117 的每日基础数据\n",
"成功获取并保存 20250116 的每日基础数据\n",
"成功获取并保存 20250115 的每日基础数据\n",
"成功获取并保存 20250114 的每日基础数据\n",
"成功获取并保存 20250113 的每日基础数据\n",
"成功获取并保存 20250110 的每日基础数据\n",
"成功获取并保存 20250109 的每日基础数据\n",
"成功获取并保存 20250108 的每日基础数据\n",
"成功获取并保存 20250107 的每日基础数据\n",
"成功获取并保存 20250106 的每日基础数据\n",
"成功获取并保存 20250103 的每日基础数据\n",
"成功获取并保存 20250102 的每日基础数据\n",
"成功获取并保存 20241231 的每日基础数据\n",
"成功获取并保存 20241230 的每日基础数据\n",
"成功获取并保存 20241227 的每日基础数据\n",
"成功获取并保存 20241226 的每日基础数据\n",
"成功获取并保存 20241225 的每日基础数据\n",
"成功获取并保存 20241224 的每日基础数据\n",
"成功获取并保存 20241223 的每日基础数据\n",
"成功获取并保存 20241220 的每日基础数据\n",
"成功获取并保存 20241219 的每日基础数据\n",
"成功获取并保存 20241218 的每日基础数据\n",
"成功获取并保存 20241217 的每日基础数据\n",
"成功获取并保存 20241216 的每日基础数据\n",
"成功获取并保存 20241213 的每日基础数据\n",
"成功获取并保存 20241212 的每日基础数据\n",
"成功获取并保存 20241211 的每日基础数据\n",
"成功获取并保存 20241210 的每日基础数据\n",
"成功获取并保存 20241209 的每日基础数据\n",
"成功获取并保存 20241206 的每日基础数据\n",
"成功获取并保存 20241205 的每日基础数据\n",
"成功获取并保存 20241204 的每日基础数据\n",
"成功获取并保存 20241203 的每日基础数据\n",
"成功获取并保存 20241202 的每日基础数据\n",
"成功获取并保存 20241129 的每日基础数据\n",
"成功获取并保存 20241128 的每日基础数据\n",
"成功获取并保存 20241127 的每日基础数据\n",
"成功获取并保存 20241126 的每日基础数据\n",
"成功获取并保存 20241125 的每日基础数据\n",
"成功获取并保存 20241122 的每日基础数据\n",
"成功获取并保存 20241121 的每日基础数据\n",
"成功获取并保存 20241120 的每日基础数据\n",
"成功获取并保存 20241119 的每日基础数据\n",
"成功获取并保存 20241118 的每日基础数据\n",
"成功获取并保存 20241115 的每日基础数据\n",
"成功获取并保存 20241114 的每日基础数据\n",
"成功获取并保存 20241113 的每日基础数据\n",
"成功获取并保存 20241112 的每日基础数据\n",
"成功获取并保存 20241111 的每日基础数据\n",
"成功获取并保存 20241108 的每日基础数据\n",
"成功获取并保存 20241107 的每日基础数据\n",
"成功获取并保存 20241106 的每日基础数据\n",
"成功获取并保存 20241105 的每日基础数据\n",
"成功获取并保存 20241104 的每日基础数据\n",
"成功获取并保存 20241101 的每日基础数据\n",
"成功获取并保存 20241031 的每日基础数据\n",
"成功获取并保存 20241030 的每日基础数据\n",
"成功获取并保存 20241029 的每日基础数据\n",
"成功获取并保存 20241028 的每日基础数据\n",
"成功获取并保存 20241025 的每日基础数据\n",
"成功获取并保存 20241024 的每日基础数据\n",
"成功获取并保存 20241023 的每日基础数据\n",
"成功获取并保存 20241022 的每日基础数据\n",
"成功获取并保存 20241021 的每日基础数据\n",
"成功获取并保存 20241014 的每日基础数据\n",
"150 1741835004.3988936 1741834982.2357981\n",
"已调用 150 次 API，等待 37.84 秒以满足速率限制...\n",
"300 1741835064.0700593 1741835042.2372077\n",
"已调用 150 次 API，等待 38.17 秒以满足速率限制...\n",
"450 1741835124.4976892 1741835102.2381623\n",
"已调用 150 次 API，等待 37.74 秒以满足速率限制...\n"
]
},
{
"ename": "KeyboardInterrupt",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
"Cell \u001b[1;32mIn[4], line 22\u001b[0m\n\u001b[0;32m 19\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m trade_date \u001b[38;5;129;01min\u001b[39;00m trade_dates:\n\u001b[0;32m 20\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m 21\u001b[0m \u001b[38;5;66;03m# 获取每日基础数据\u001b[39;00m\n\u001b[1;32m---> 22\u001b[0m kpl_concept \u001b[38;5;241m=\u001b[39m pro\u001b[38;5;241m.\u001b[39mkpl_concept(trade_date\u001b[38;5;241m=\u001b[39mtrade_date)\n\u001b[0;32m 23\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m kpl_concept \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m kpl_concept\u001b[38;5;241m.\u001b[39mempty:\n\u001b[0;32m 24\u001b[0m all_daily_data\u001b[38;5;241m.\u001b[39mappend(kpl_concept)\n",
"File \u001b[1;32mE:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\tushare\\pro\\client.py:41\u001b[0m, in \u001b[0;36mDataApi.query\u001b[1;34m(self, api_name, fields, **kwargs)\u001b[0m\n\u001b[0;32m 33\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mquery\u001b[39m(\u001b[38;5;28mself\u001b[39m, api_name, fields\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m 34\u001b[0m req_params \u001b[38;5;241m=\u001b[39m {\n\u001b[0;32m 35\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mapi_name\u001b[39m\u001b[38;5;124m'\u001b[39m: api_name,\n\u001b[0;32m 36\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtoken\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m__token,\n\u001b[0;32m 37\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mparams\u001b[39m\u001b[38;5;124m'\u001b[39m: kwargs,\n\u001b[0;32m 38\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfields\u001b[39m\u001b[38;5;124m'\u001b[39m: fields\n\u001b[0;32m 39\u001b[0m }\n\u001b[1;32m---> 41\u001b[0m res \u001b[38;5;241m=\u001b[39m requests\u001b[38;5;241m.\u001b[39mpost(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m__http_url\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m/\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mapi_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, json\u001b[38;5;241m=\u001b[39mreq_params, timeout\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m__timeout)\n\u001b[0;32m 42\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m res:\n\u001b[0;32m 43\u001b[0m result \u001b[38;5;241m=\u001b[39m json\u001b[38;5;241m.\u001b[39mloads(res\u001b[38;5;241m.\u001b[39mtext)\n",
"File \u001b[1;32mE:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\requests\\api.py:115\u001b[0m, in \u001b[0;36mpost\u001b[1;34m(url, data, json, **kwargs)\u001b[0m\n\u001b[0;32m 103\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mpost\u001b[39m(url, data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, json\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m 104\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"Sends a POST request.\u001b[39;00m\n\u001b[0;32m 105\u001b[0m \n\u001b[0;32m 106\u001b[0m \u001b[38;5;124;03m :param url: URL for the new :class:`Request` object.\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 112\u001b[0m \u001b[38;5;124;03m :rtype: requests.Response\u001b[39;00m\n\u001b[0;32m 113\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m--> 115\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m request(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpost\u001b[39m\u001b[38;5;124m\"\u001b[39m, url, data\u001b[38;5;241m=\u001b[39mdata, json\u001b[38;5;241m=\u001b[39mjson, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
"File \u001b[1;32mE:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\requests\\api.py:59\u001b[0m, in \u001b[0;36mrequest\u001b[1;34m(method, url, **kwargs)\u001b[0m\n\u001b[0;32m 55\u001b[0m \u001b[38;5;66;03m# By using the 'with' statement we are sure the session is closed, thus we\u001b[39;00m\n\u001b[0;32m 56\u001b[0m \u001b[38;5;66;03m# avoid leaving sockets open which can trigger a ResourceWarning in some\u001b[39;00m\n\u001b[0;32m 57\u001b[0m \u001b[38;5;66;03m# cases, and look like a memory leak in others.\u001b[39;00m\n\u001b[0;32m 58\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m sessions\u001b[38;5;241m.\u001b[39mSession() \u001b[38;5;28;01mas\u001b[39;00m session:\n\u001b[1;32m---> 59\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m session\u001b[38;5;241m.\u001b[39mrequest(method\u001b[38;5;241m=\u001b[39mmethod, url\u001b[38;5;241m=\u001b[39murl, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
"File \u001b[1;32mE:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\requests\\sessions.py:589\u001b[0m, in \u001b[0;36mSession.request\u001b[1;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[0;32m 584\u001b[0m send_kwargs \u001b[38;5;241m=\u001b[39m {\n\u001b[0;32m 585\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtimeout\u001b[39m\u001b[38;5;124m\"\u001b[39m: timeout,\n\u001b[0;32m 586\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mallow_redirects\u001b[39m\u001b[38;5;124m\"\u001b[39m: allow_redirects,\n\u001b[0;32m 587\u001b[0m }\n\u001b[0;32m 588\u001b[0m send_kwargs\u001b[38;5;241m.\u001b[39mupdate(settings)\n\u001b[1;32m--> 589\u001b[0m resp \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msend(prep, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39msend_kwargs)\n\u001b[0;32m 591\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m resp\n",
"File \u001b[1;32mE:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\requests\\sessions.py:724\u001b[0m, in \u001b[0;36mSession.send\u001b[1;34m(self, request, **kwargs)\u001b[0m\n\u001b[0;32m 721\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m allow_redirects:\n\u001b[0;32m 722\u001b[0m \u001b[38;5;66;03m# Redirect resolving generator.\u001b[39;00m\n\u001b[0;32m 723\u001b[0m gen \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mresolve_redirects(r, request, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m--> 724\u001b[0m history \u001b[38;5;241m=\u001b[39m [resp \u001b[38;5;28;01mfor\u001b[39;00m resp \u001b[38;5;129;01min\u001b[39;00m gen]\n\u001b[0;32m 725\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 726\u001b[0m history \u001b[38;5;241m=\u001b[39m []\n",
"File \u001b[1;32mE:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\requests\\sessions.py:724\u001b[0m, in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m 721\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m allow_redirects:\n\u001b[0;32m 722\u001b[0m \u001b[38;5;66;03m# Redirect resolving generator.\u001b[39;00m\n\u001b[0;32m 723\u001b[0m gen \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mresolve_redirects(r, request, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m--> 724\u001b[0m history \u001b[38;5;241m=\u001b[39m [resp \u001b[38;5;28;01mfor\u001b[39;00m resp \u001b[38;5;129;01min\u001b[39;00m gen]\n\u001b[0;32m 725\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 726\u001b[0m history \u001b[38;5;241m=\u001b[39m []\n",
"File \u001b[1;32mE:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\requests\\sessions.py:265\u001b[0m, in \u001b[0;36mSessionRedirectMixin.resolve_redirects\u001b[1;34m(self, resp, req, stream, timeout, verify, cert, proxies, yield_requests, **adapter_kwargs)\u001b[0m\n\u001b[0;32m 263\u001b[0m \u001b[38;5;28;01myield\u001b[39;00m req\n\u001b[0;32m 264\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 265\u001b[0m resp \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msend(\n\u001b[0;32m 266\u001b[0m req,\n\u001b[0;32m 267\u001b[0m stream\u001b[38;5;241m=\u001b[39mstream,\n\u001b[0;32m 268\u001b[0m timeout\u001b[38;5;241m=\u001b[39mtimeout,\n\u001b[0;32m 269\u001b[0m verify\u001b[38;5;241m=\u001b[39mverify,\n\u001b[0;32m 270\u001b[0m cert\u001b[38;5;241m=\u001b[39mcert,\n\u001b[0;32m 271\u001b[0m proxies\u001b[38;5;241m=\u001b[39mproxies,\n\u001b[0;32m 272\u001b[0m allow_redirects\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[0;32m 273\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39madapter_kwargs,\n\u001b[0;32m 274\u001b[0m )\n\u001b[0;32m 276\u001b[0m extract_cookies_to_jar(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcookies, prepared_request, resp\u001b[38;5;241m.\u001b[39mraw)\n\u001b[0;32m 278\u001b[0m \u001b[38;5;66;03m# extract redirect url, if any, for the next loop\u001b[39;00m\n",
"File \u001b[1;32mE:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\requests\\sessions.py:703\u001b[0m, in \u001b[0;36mSession.send\u001b[1;34m(self, request, **kwargs)\u001b[0m\n\u001b[0;32m 700\u001b[0m start \u001b[38;5;241m=\u001b[39m preferred_clock()\n\u001b[0;32m 702\u001b[0m \u001b[38;5;66;03m# Send the request\u001b[39;00m\n\u001b[1;32m--> 703\u001b[0m r \u001b[38;5;241m=\u001b[39m adapter\u001b[38;5;241m.\u001b[39msend(request, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 705\u001b[0m \u001b[38;5;66;03m# Total elapsed time of the request (approximately)\u001b[39;00m\n\u001b[0;32m 706\u001b[0m elapsed \u001b[38;5;241m=\u001b[39m preferred_clock() \u001b[38;5;241m-\u001b[39m start\n",
"File \u001b[1;32mE:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\requests\\adapters.py:667\u001b[0m, in \u001b[0;36mHTTPAdapter.send\u001b[1;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[0;32m 664\u001b[0m timeout \u001b[38;5;241m=\u001b[39m TimeoutSauce(connect\u001b[38;5;241m=\u001b[39mtimeout, read\u001b[38;5;241m=\u001b[39mtimeout)\n\u001b[0;32m 666\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 667\u001b[0m resp \u001b[38;5;241m=\u001b[39m conn\u001b[38;5;241m.\u001b[39murlopen(\n\u001b[0;32m 668\u001b[0m method\u001b[38;5;241m=\u001b[39mrequest\u001b[38;5;241m.\u001b[39mmethod,\n\u001b[0;32m 669\u001b[0m url\u001b[38;5;241m=\u001b[39murl,\n\u001b[0;32m 670\u001b[0m body\u001b[38;5;241m=\u001b[39mrequest\u001b[38;5;241m.\u001b[39mbody,\n\u001b[0;32m 671\u001b[0m headers\u001b[38;5;241m=\u001b[39mrequest\u001b[38;5;241m.\u001b[39mheaders,\n\u001b[0;32m 672\u001b[0m redirect\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[0;32m 673\u001b[0m assert_same_host\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[0;32m 674\u001b[0m preload_content\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[0;32m 675\u001b[0m decode_content\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[0;32m 676\u001b[0m retries\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmax_retries,\n\u001b[0;32m 677\u001b[0m timeout\u001b[38;5;241m=\u001b[39mtimeout,\n\u001b[0;32m 678\u001b[0m chunked\u001b[38;5;241m=\u001b[39mchunked,\n\u001b[0;32m 679\u001b[0m )\n\u001b[0;32m 681\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (ProtocolError, \u001b[38;5;167;01mOSError\u001b[39;00m) \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[0;32m 682\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mConnectionError\u001b[39;00m(err, request\u001b[38;5;241m=\u001b[39mrequest)\n",
"File \u001b[1;32mE:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\urllib3\\connectionpool.py:787\u001b[0m, in \u001b[0;36mHTTPConnectionPool.urlopen\u001b[1;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, preload_content, decode_content, **response_kw)\u001b[0m\n\u001b[0;32m 784\u001b[0m response_conn \u001b[38;5;241m=\u001b[39m conn \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m release_conn \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m 786\u001b[0m \u001b[38;5;66;03m# Make the request on the HTTPConnection object\u001b[39;00m\n\u001b[1;32m--> 787\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_make_request(\n\u001b[0;32m 788\u001b[0m conn,\n\u001b[0;32m 789\u001b[0m method,\n\u001b[0;32m 790\u001b[0m url,\n\u001b[0;32m 791\u001b[0m timeout\u001b[38;5;241m=\u001b[39mtimeout_obj,\n\u001b[0;32m 792\u001b[0m body\u001b[38;5;241m=\u001b[39mbody,\n\u001b[0;32m 793\u001b[0m headers\u001b[38;5;241m=\u001b[39mheaders,\n\u001b[0;32m 794\u001b[0m chunked\u001b[38;5;241m=\u001b[39mchunked,\n\u001b[0;32m 795\u001b[0m retries\u001b[38;5;241m=\u001b[39mretries,\n\u001b[0;32m 796\u001b[0m response_conn\u001b[38;5;241m=\u001b[39mresponse_conn,\n\u001b[0;32m 797\u001b[0m preload_content\u001b[38;5;241m=\u001b[39mpreload_content,\n\u001b[0;32m 798\u001b[0m decode_content\u001b[38;5;241m=\u001b[39mdecode_content,\n\u001b[0;32m 799\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mresponse_kw,\n\u001b[0;32m 800\u001b[0m )\n\u001b[0;32m 802\u001b[0m \u001b[38;5;66;03m# Everything went great!\u001b[39;00m\n\u001b[0;32m 803\u001b[0m clean_exit \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n",
"File \u001b[1;32mE:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\urllib3\\connectionpool.py:534\u001b[0m, in \u001b[0;36mHTTPConnectionPool._make_request\u001b[1;34m(self, conn, method, url, body, headers, retries, timeout, chunked, response_conn, preload_content, decode_content, enforce_content_length)\u001b[0m\n\u001b[0;32m 532\u001b[0m \u001b[38;5;66;03m# Receive the response from the server\u001b[39;00m\n\u001b[0;32m 533\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 534\u001b[0m response \u001b[38;5;241m=\u001b[39m conn\u001b[38;5;241m.\u001b[39mgetresponse()\n\u001b[0;32m 535\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (BaseSSLError, \u001b[38;5;167;01mOSError\u001b[39;00m) \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m 536\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_raise_timeout(err\u001b[38;5;241m=\u001b[39me, url\u001b[38;5;241m=\u001b[39murl, timeout_value\u001b[38;5;241m=\u001b[39mread_timeout)\n",
"File \u001b[1;32mE:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\urllib3\\connection.py:516\u001b[0m, in \u001b[0;36mHTTPConnection.getresponse\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 513\u001b[0m _shutdown \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msock, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mshutdown\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m 515\u001b[0m \u001b[38;5;66;03m# Get the response from http.client.HTTPConnection\u001b[39;00m\n\u001b[1;32m--> 516\u001b[0m httplib_response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39mgetresponse()\n\u001b[0;32m 518\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m 519\u001b[0m assert_header_parsing(httplib_response\u001b[38;5;241m.\u001b[39mmsg)\n",
"File \u001b[1;32mE:\\Python\\anaconda\\envs\\new_trader\\Lib\\http\\client.py:1395\u001b[0m, in \u001b[0;36mHTTPConnection.getresponse\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 1393\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m 1394\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 1395\u001b[0m response\u001b[38;5;241m.\u001b[39mbegin()\n\u001b[0;32m 1396\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mConnectionError\u001b[39;00m:\n\u001b[0;32m 1397\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mclose()\n",
"File \u001b[1;32mE:\\Python\\anaconda\\envs\\new_trader\\Lib\\http\\client.py:325\u001b[0m, in \u001b[0;36mHTTPResponse.begin\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 323\u001b[0m \u001b[38;5;66;03m# read until we get a non-100 response\u001b[39;00m\n\u001b[0;32m 324\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[1;32m--> 325\u001b[0m version, status, reason \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_read_status()\n\u001b[0;32m 326\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m status \u001b[38;5;241m!=\u001b[39m CONTINUE:\n\u001b[0;32m 327\u001b[0m \u001b[38;5;28;01mbreak\u001b[39;00m\n",
"File \u001b[1;32mE:\\Python\\anaconda\\envs\\new_trader\\Lib\\http\\client.py:286\u001b[0m, in \u001b[0;36mHTTPResponse._read_status\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 285\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_read_status\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m--> 286\u001b[0m line \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mstr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfp\u001b[38;5;241m.\u001b[39mreadline(_MAXLINE \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m), \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124miso-8859-1\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 287\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(line) \u001b[38;5;241m>\u001b[39m _MAXLINE:\n\u001b[0;32m 288\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m LineTooLong(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstatus line\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
"File \u001b[1;32mE:\\Python\\anaconda\\envs\\new_trader\\Lib\\socket.py:718\u001b[0m, in \u001b[0;36mSocketIO.readinto\u001b[1;34m(self, b)\u001b[0m\n\u001b[0;32m 716\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[0;32m 717\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 718\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sock\u001b[38;5;241m.\u001b[39mrecv_into(b)\n\u001b[0;32m 719\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m timeout:\n\u001b[0;32m 720\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_timeout_occurred \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n",
"\u001b[1;31mKeyboardInterrupt\u001b[0m: "
]
}
],
"source": [
"import tushare as ts\n",
"import pandas as pd\n",
"import time\n",
"\n",
"\n",
"# 获取交易日历\n",
"trade_cal = pro.trade_cal(exchange='', start_date='20170101', end_date='20250301')\n",
"trade_cal = trade_cal[trade_cal['is_open'] == 1] # 只保留交易日\n",
"trade_dates = trade_cal['cal_date'].tolist() # 获取所有交易日期列表\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",
"# 遍历每个交易日期并获取数据\n",
"for trade_date in trade_dates:\n",
" try:\n",
" # 获取每日基础数据\n",
" kpl_concept = pro.kpl_concept(trade_date=trade_date)\n",
" if kpl_concept is not None and not kpl_concept.empty:\n",
" all_daily_data.append(kpl_concept)\n",
" print(f\"成功获取并保存 {trade_date} 的每日基础数据\")\n",
"\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",
" print(api_call_count,time.time(), batch_start_time)\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",
" except Exception as e:\n",
" print(f\"获取 {trade_date} 数据时出错: {e}\")\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "907f732d3c397bf",
"metadata": {
"ExecuteTime": {
"end_time": "2025-03-12T15:39:50.141920800Z",
"start_time": "2025-03-12T15:23:41.345460Z"
}
},
"outputs": [],
"source": [
"\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)\n",
"\n",
"# 将数据保存为 HDF5 文件（table 格式）\n",
"all_daily_data_df.to_hdf('../../data/kpl_concept.h5', key='kpl_concept', mode='w', format='table', data_columns=True)\n",
"\n",
"print(\"所有每日基础数据获取并保存完毕！\")"
]
}
],
"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": "3.11.11"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

194
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@@ -0,0 +1,194 @@
{
"cells": [
{
"cell_type": "code",
"id": "f74ce078-f7e8-4733-a14c-14d8815a3626",
"metadata": {
"ExecuteTime": {
"end_time": "2025-03-30T16:42:31.596637Z",
"start_time": "2025-03-30T16:42:30.883319Z"
}
},
"source": [
"import tushare as ts\n",
"ts.set_token('3a0741c702ee7e5e5f2bf1f0846bafaafe4e320833240b2a7e4a685f')\n",
"pro = ts.pro_api()"
],
"outputs": [],
"execution_count": 1
},
{
"cell_type": "code",
"id": "44dd8d87-e60b-49e5-aed9-efaa7f92d4fe",
"metadata": {
"ExecuteTime": {
"end_time": "2025-03-30T16:42:37.590148Z",
"start_time": "2025-03-30T16:42:31.596637Z"
}
},
"source": [
"import pandas as pd\n",
"import time\n",
"\n",
"h5_filename = '../../../data/cyq_perf.h5'\n",
"key = '/cyq_perf'\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)\n",
" max_date = df['trade_date'].max()\n",
"\n",
"print(max_date)\n",
"trade_cal = pro.trade_cal(exchange='', start_date='20170101', end_date='20250420')\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}')"
],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" ts_code trade_date\n",
"0 000001.SZ 20250312\n",
"1 000002.SZ 20250312\n",
"2 000004.SZ 20250312\n",
"3 000006.SZ 20250312\n",
"4 000007.SZ 20250312\n",
"... ... ...\n",
"32304 920108.BJ 20250314\n",
"32305 920111.BJ 20250314\n",
"32306 920116.BJ 20250314\n",
"32307 920118.BJ 20250314\n",
"32308 920128.BJ 20250314\n",
"\n",
"[7503415 rows x 2 columns]\n",
"20250321\n",
"start_date: 20250324\n"
]
}
],
"execution_count": 2
},
{
"cell_type": "code",
"id": "747acc47-0884-4f76-90fb-276f6494e31d",
"metadata": {
"ExecuteTime": {
"end_time": "2025-03-30T16:43:29.275885Z",
"start_time": "2025-03-30T16:42:37.858763Z"
}
},
"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",
" data = pro.cyq_perf(trade_date=trade_date)\n",
" if data is not None and not data.empty:\n",
" return 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 20250418 完成\n",
"任务 20250417 完成\n",
"任务 20250415 完成\n",
"任务 20250416 完成\n",
"任务 20250411 完成\n",
"任务 20250414 完成\n",
"任务 20250409 完成\n",
"任务 20250410 完成\n",
"任务 20250408 完成\n",
"任务 20250407 完成\n",
"任务 20250403 完成\n",
"任务 20250402 完成\n",
"任务 20250401 完成\n",
"任务 20250331 完成\n",
"任务 20250328 完成\n",
"任务 20250327 完成\n",
"任务 20250326 完成\n",
"任务 20250325 完成\n",
"任务 20250324 完成\n"
]
}
],
"execution_count": 3
},
{
"cell_type": "code",
"id": "c6765638-481f-40d8-a259-2e7b25362618",
"metadata": {
"ExecuteTime": {
"end_time": "2025-03-30T16:43:30.100678Z",
"start_time": "2025-03-30T16:43:29.311710Z"
}
},
"source": [
"all_daily_data_df = pd.concat(all_daily_data, ignore_index=True)\n",
"\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=key, 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": 4
}
],
"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": "3.11.11"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

194
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@@ -0,0 +1,194 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "f74ce078-f7e8-4733-a14c-14d8815a3626",
"metadata": {},
"outputs": [],
"source": [
"import tushare as ts\n",
"ts.set_token('3a0741c702ee7e5e5f2bf1f0846bafaafe4e320833240b2a7e4a685f')\n",
"pro = ts.pro_api()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "44dd8d87-e60b-49e5-aed9-efaa7f92d4fe",
"metadata": {},
"outputs": [
{
"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",
"1044388 857344.SI 20170103\n",
"1044389 857411.SI 20170103\n",
"1044390 857421.SI 20170103\n",
"1044391 857431.SI 20170103\n",
"1044392 858811.SI 20170103\n",
"\n",
"[1044393 rows x 2 columns]\n",
"20250221\n",
"start_date: 20250224\n"
]
}
],
"source": [
"import pandas as pd\n",
"import time\n",
"\n",
"h5_filename = '../../../data/sw_daily.h5'\n",
"key = '/sw_daily'\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)\n",
" max_date = df['trade_date'].max()\n",
"\n",
"print(max_date)\n",
"trade_cal = pro.trade_cal(exchange='', start_date='20170101', end_date='20250420')\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}')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "747acc47-0884-4f76-90fb-276f6494e31d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"任务 20250417 完成\n",
"任务 20250418 完成\n",
"任务 20250416 完成\n",
"任务 20250415 完成\n",
"任务 20250411 完成\n",
"任务 20250414 完成\n",
"任务 20250410 完成\n",
"任务 20250409 完成\n",
"任务 20250408 完成\n",
"任务 20250403 完成\n",
"任务 20250407 完成\n",
"任务 20250402 完成\n",
"任务 20250401 完成\n",
"任务 20250331 完成\n",
"任务 20250328 完成\n",
"任务 20250327 完成\n",
"任务 20250326 完成\n",
"任务 20250325 完成\n",
"任务 20250324 完成\n",
"任务 20250321 完成\n",
"任务 20250320 完成\n",
"任务 20250319 完成\n",
"任务 20250317 完成\n",
"任务 20250314 完成\n",
"任务 20250318 完成\n",
"任务 20250313 完成\n",
"任务 20250312 完成\n",
"任务 20250311 完成\n",
"任务 20250310 完成\n",
"任务 20250307 完成\n",
"任务 20250306 完成\n",
"任务 20250305 完成\n",
"任务 20250304 完成\n",
"任务 20250303 完成\n",
"任务 20250228 完成\n",
"任务 20250227 完成\n",
"任务 20250226 完成\n",
"任务 20250225 完成\n",
"任务 20250224 完成\n"
]
}
],
"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",
"index_list = ['399300.SH', '000905.SH', '000852.SH', '399006.SZ']\n",
"def get_data(trade_date):\n",
" time.sleep(0.1)\n",
" data = pro.sw_daily(trade_date=trade_date)\n",
" if data is not None and not data.empty:\n",
" return 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"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "c6765638-481f-40d8-a259-2e7b25362618",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"所有每日基础数据获取并保存完毕！\n"
]
}
],
"source": [
"all_daily_data_df = pd.concat(all_daily_data, ignore_index=True)\n",
"\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=key, 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！\")"
]
}
],
"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": "3.11.11"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

194
code/data/update/sw_daily.ipynb Normal file
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@@ -0,0 +1,194 @@
{
"cells": [
{
"cell_type": "code",
"id": "f74ce078-f7e8-4733-a14c-14d8815a3626",
"metadata": {
"ExecuteTime": {
"end_time": "2025-03-30T16:42:32.996500Z",
"start_time": "2025-03-30T16:42:32.209631Z"
}
},
"source": [
"import tushare as ts\n",
"ts.set_token('3a0741c702ee7e5e5f2bf1f0846bafaafe4e320833240b2a7e4a685f')\n",
"pro = ts.pro_api()"
],
"outputs": [],
"execution_count": 1
},
{
"cell_type": "code",
"id": "44dd8d87-e60b-49e5-aed9-efaa7f92d4fe",
"metadata": {
"ExecuteTime": {
"end_time": "2025-03-30T16:42:34.591433Z",
"start_time": "2025-03-30T16:42:32.996500Z"
}
},
"source": [
"import pandas as pd\n",
"import time\n",
"\n",
"h5_filename = '../../../data/sw_daily.h5'\n",
"key = '/sw_daily'\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)\n",
" max_date = df['trade_date'].max()\n",
"\n",
"print(max_date)\n",
"trade_cal = pro.trade_cal(exchange='', start_date='20170101', end_date='20250420')\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}')"
],
"outputs": [
{
"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",
"2629 859811.SI 20250314\n",
"2630 859821.SI 20250314\n",
"2631 859822.SI 20250314\n",
"2632 859852.SI 20250314\n",
"2633 859951.SI 20250314\n",
"\n",
"[1053173 rows x 2 columns]\n",
"20250321\n",
"start_date: 20250324\n"
]
}
],
"execution_count": 2
},
{
"cell_type": "code",
"id": "747acc47-0884-4f76-90fb-276f6494e31d",
"metadata": {
"ExecuteTime": {
"end_time": "2025-03-30T16:42:37.718270Z",
"start_time": "2025-03-30T16:42:34.817305Z"
}
},
"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",
" data = pro.sw_daily(trade_date=trade_date)\n",
" if data is not None and not data.empty:\n",
" return 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 20250417 完成\n",
"任务 20250418 完成\n",
"任务 20250416 完成\n",
"任务 20250415 完成\n",
"任务 20250414 完成\n",
"任务 20250411 完成\n",
"任务 20250410 完成\n",
"任务 20250409 完成\n",
"任务 20250408 完成\n",
"任务 20250407 完成\n",
"任务 20250403 完成\n",
"任务 20250402 完成\n",
"任务 20250401 完成\n",
"任务 20250331 完成\n",
"任务 20250328 完成\n",
"任务 20250327 完成\n",
"任务 20250326 完成\n",
"任务 20250325 完成\n",
"任务 20250324 完成\n"
]
}
],
"execution_count": 3
},
{
"cell_type": "code",
"id": "c6765638-481f-40d8-a259-2e7b25362618",
"metadata": {
"ExecuteTime": {
"end_time": "2025-03-30T16:42:37.922827Z",
"start_time": "2025-03-30T16:42:37.739040Z"
}
},
"source": [
"all_daily_data_df = pd.concat(all_daily_data, ignore_index=True)\n",
"\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=key, 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": 4
}
],
"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": "3.11.11"
}
},
"nbformat": 4,
"nbformat_minor": 5
}