281 lines
10 KiB
Plaintext
281 lines
10 KiB
Plaintext
{
|
||
"cells": [
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 1,
|
||
"id": "b94bb1f2-5332-485e-ae1b-eea01f938106",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-04-09T14:57:40.184418Z",
|
||
"start_time": "2025-04-09T14:57:39.137312Z"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"import tushare as ts\n",
|
||
"\n",
|
||
"ts.set_token('3a0741c702ee7e5e5f2bf1f0846bafaafe4e320833240b2a7e4a685f')\n",
|
||
"pro = ts.pro_api()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 2,
|
||
"id": "742c29d453b9bb38",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-04-09T14:58:10.515830Z",
|
||
"start_time": "2025-04-09T14:57:40.190466Z"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"<class 'pandas.core.frame.DataFrame'>\n",
|
||
"Index: 9336127 entries, 0 to 25845\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: 213.7+ MB\n",
|
||
"None\n",
|
||
"20260116\n",
|
||
"start_date: 20260119\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"import pandas as pd\n",
|
||
"import time\n",
|
||
"\n",
|
||
"h5_filename = '/mnt/d/PyProject/NewStock/data/money_flow.h5'\n",
|
||
"key = '/money_flow'\n",
|
||
"max_date = None\n",
|
||
"with pd.HDFStore(h5_filename, mode='r') as store:\n",
|
||
" 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='20260201')\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": "679ce40e-8d62-4887-970c-e1d8cbdeee6b",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-04-09T14:58:17.197319Z",
|
||
"start_time": "2025-04-09T14:58:10.724923Z"
|
||
},
|
||
"scrolled": true
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"任务 20260129 完成\n",
|
||
"任务 20260130 完成\n",
|
||
"任务 20260128 完成\n",
|
||
"任务 20260127 完成\n",
|
||
"任务 20260126 完成\n",
|
||
"任务 20260123 完成\n",
|
||
"任务 20260122 完成\n",
|
||
"任务 20260121 完成\n",
|
||
"任务 20260120 完成\n",
|
||
"任务 20260119 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",
|
||
"\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"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 4,
|
||
"id": "9af80516849d4e80",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-04-09T14:58:17.214168Z",
|
||
"start_time": "2025-04-09T14:58:17.210734Z"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"all_daily_data_df = pd.concat(all_daily_data, ignore_index=True)\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 5,
|
||
"id": "a2b05187-437f-4053-bc43-bd80d4cf8b0e",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-04-09T14:58:19.633456Z",
|
||
"start_time": "2025-04-09T14:58:17.229837Z"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"所有每日基础数据获取并保存完毕！\n"
|
||
]
|
||
}
|
||
],
|
||
"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！\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 6,
|
||
"id": "e6f2a2fe",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
" ts_code trade_date buy_sm_vol buy_sm_amount sell_sm_vol \\\n",
|
||
"0 300284.SZ 20260123 57213 4682.01 45561 \n",
|
||
"1 002835.SZ 20260123 10930 1886.39 9809 \n",
|
||
"2 603175.SH 20260123 28945 21106.65 29993 \n",
|
||
"3 600284.SH 20260123 62561 5324.31 55101 \n",
|
||
"4 300855.SZ 20260123 41944 15903.55 33566 \n",
|
||
"... ... ... ... ... ... \n",
|
||
"25876 600211.SH 20260119 10915 4796.09 12174 \n",
|
||
"25877 601229.SH 20260119 225161 21704.91 267726 \n",
|
||
"25878 003042.SZ 20260119 17500 2893.60 11703 \n",
|
||
"25879 601155.SH 20260119 75731 11076.12 70817 \n",
|
||
"25880 600169.SH 20260119 81734 2005.16 84188 \n",
|
||
"\n",
|
||
" sell_sm_amount buy_md_vol buy_md_amount sell_md_vol sell_md_amount \\\n",
|
||
"0 3728.28 48486 3966.28 59366 4856.95 \n",
|
||
"1 1693.39 6499 1121.44 6017 1038.84 \n",
|
||
"2 21840.97 22701 16511.38 23142 16835.79 \n",
|
||
"3 4687.48 68181 5800.70 62114 5285.81 \n",
|
||
"4 12712.79 35383 13409.44 40111 15213.47 \n",
|
||
"... ... ... ... ... ... \n",
|
||
"25876 5348.74 9594 4215.26 8253 3626.54 \n",
|
||
"25877 25799.99 204041 19664.19 207361 19990.67 \n",
|
||
"25878 1933.74 11780 1946.25 14398 2379.72 \n",
|
||
"25879 10351.64 45622 6659.41 46251 6765.59 \n",
|
||
"25880 2063.68 129391 3172.21 137053 3360.95 \n",
|
||
"\n",
|
||
" buy_lg_vol buy_lg_amount sell_lg_vol sell_lg_amount buy_elg_vol \\\n",
|
||
"0 39133 3201.55 32920 2693.69 6576 \n",
|
||
"1 4067 702.11 4633 799.04 241 \n",
|
||
"2 11291 8249.67 11377 8302.90 3400 \n",
|
||
"3 46517 3960.29 57644 4906.75 23366 \n",
|
||
"4 24315 9212.24 24640 9342.34 5111 \n",
|
||
"... ... ... ... ... ... \n",
|
||
"25876 5507 2419.57 5204 2286.10 2 \n",
|
||
"25877 134694 12983.30 86737 8363.03 24453 \n",
|
||
"25878 4038 668.49 7218 1194.87 0 \n",
|
||
"25879 23725 3458.86 23634 3453.46 21891 \n",
|
||
"25880 63062 1545.61 49278 1208.05 5904 \n",
|
||
"\n",
|
||
" buy_elg_amount sell_elg_vol sell_elg_amount net_mf_vol \\\n",
|
||
"0 539.18 13561 1110.11 -4914 \n",
|
||
"1 41.60 1278 220.26 1923 \n",
|
||
"2 2437.55 1825 1325.59 1620 \n",
|
||
"3 1991.65 25767 2196.91 46602 \n",
|
||
"4 1934.73 8437 3191.36 -19491 \n",
|
||
"... ... ... ... ... \n",
|
||
"25876 0.88 388 170.41 -44 \n",
|
||
"25877 2359.23 26525 2557.95 -37774 \n",
|
||
"25878 0.00 0 0.00 6587 \n",
|
||
"25879 3125.39 26267 3749.10 28653 \n",
|
||
"25880 144.65 9571 234.95 -10865 \n",
|
||
"\n",
|
||
" net_mf_amount \n",
|
||
"0 -391.89 \n",
|
||
"1 333.04 \n",
|
||
"2 1372.40 \n",
|
||
"3 3982.07 \n",
|
||
"4 -7354.89 \n",
|
||
"... ... \n",
|
||
"25876 -16.04 \n",
|
||
"25877 -3603.98 \n",
|
||
"25878 1092.73 \n",
|
||
"25879 4152.29 \n",
|
||
"25880 -256.23 \n",
|
||
"\n",
|
||
"[25881 rows x 20 columns]\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"print(all_daily_data_df)"
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "stock",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.12.11"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 5
|
||
}
|