299 lines
9.8 KiB
Plaintext
299 lines
9.8 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "500802dc-7a20-48b7-a470-a4bae3ec534b",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-04-09T14:57:41.532210Z",
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"start_time": "2025-04-09T14:57:40.584930Z"
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}
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},
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"outputs": [],
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"source": [
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"import tushare as ts\n",
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"\n",
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"ts.set_token('3a0741c702ee7e5e5f2bf1f0846bafaafe4e320833240b2a7e4a685f')\n",
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"pro = ts.pro_api()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "5a84bc9da6d54868",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-04-09T14:58:04.911924Z",
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"start_time": "2025-04-09T14:57:41.540345Z"
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}
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" ts_code trade_date\n",
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"4959 600211.SH 20260116\n",
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"4960 600212.SH 20260116\n",
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"4961 600215.SH 20260116\n",
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"4949 600197.SH 20260116\n",
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"3732 301501.SZ 20260116\n",
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"<class 'pandas.core.frame.DataFrame'>\n",
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"Index: 11701107 entries, 0 to 37139\n",
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"Data columns (total 2 columns):\n",
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" # Column Dtype \n",
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"--- ------ ----- \n",
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" 0 ts_code object\n",
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" 1 trade_date object\n",
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"dtypes: object(2)\n",
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"memory usage: 267.8+ MB\n",
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"None\n",
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"20260116\n",
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"20260119\n"
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]
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}
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],
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"source": [
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"import pandas as pd\n",
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"import time\n",
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"\n",
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"h5_filename = '/mnt/d/PyProject/NewStock/data/stk_limit.h5'\n",
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"key = '/stk_limit'\n",
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"max_date = None\n",
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"with pd.HDFStore(h5_filename, mode='r') as store:\n",
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" df = store[key][['ts_code', 'trade_date']]\n",
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" print(df.sort_values(by='trade_date', ascending=True).tail())\n",
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" print(df.info())\n",
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" max_date = df['trade_date'].max()\n",
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"\n",
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"print(max_date)\n",
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"trade_cal = pro.trade_cal(exchange='', start_date='20170101', end_date='20260201')\n",
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"trade_cal = trade_cal[trade_cal['is_open'] == 1] # 只保留交易日\n",
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"trade_dates = trade_cal[trade_cal['cal_date'] > max_date]['cal_date'].tolist()\n",
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"start_date = min(trade_dates)\n",
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"print(start_date)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "bb3191de-27a2-4c89-a3b5-32a0d7b9496f",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-04-09T14:58:09.342522Z",
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"start_time": "2025-04-09T14:58:05.259974Z"
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},
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"scrolled": true
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"任务 20260130 完成\n",
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"任务 20260129 完成\n",
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"任务 20260128 完成\n",
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"任务 20260127 完成\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"任务 20260126 完成\n",
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"任务 20260123 完成\n",
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"任务 20260122 完成\n",
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"任务 20260121 完成\n",
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"任务 20260120 完成\n",
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"任务 20260119 完成\n"
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]
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}
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],
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"source": [
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"from concurrent.futures import ThreadPoolExecutor, as_completed\n",
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"\n",
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"all_daily_data = []\n",
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"\n",
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"# API 调用计数和时间控制变量\n",
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"api_call_count = 0\n",
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"batch_start_time = time.time()\n",
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"\n",
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"\n",
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"def get_data(trade_date):\n",
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" time.sleep(0.1)\n",
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" stk_limit_data = pro.stk_limit(trade_date=trade_date)\n",
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" if stk_limit_data is not None and not stk_limit_data.empty:\n",
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" return stk_limit_data\n",
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"\n",
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"\n",
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"with ThreadPoolExecutor(max_workers=2) as executor:\n",
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" future_to_date = {executor.submit(get_data, td): td for td in trade_dates}\n",
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"\n",
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" for future in as_completed(future_to_date):\n",
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" trade_date = future_to_date[future] # 获取对应的交易日期\n",
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" try:\n",
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" result = future.result() # 获取任务执行的结果\n",
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" if result is not None:\n",
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" all_daily_data.append(result)\n",
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" print(f\"任务 {trade_date} 完成\")\n",
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" except Exception as e:\n",
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" print(f\"获取 {trade_date} 数据时出错: {e}\")\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "96a81aa5890ea3c3",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-04-09T14:58:09.353560Z",
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"start_time": "2025-04-09T14:58:09.346528Z"
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}
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[ trade_date ts_code up_limit down_limit\n",
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"0 20260123 000001.SZ 12.18 9.96\n",
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"1 20260123 000002.SZ 5.45 4.46\n",
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"2 20260123 000004.SZ 12.59 11.39\n",
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"3 20260123 000006.SZ 10.62 8.69\n",
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"4 20260123 000007.SZ 12.47 10.21\n",
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"... ... ... ... ...\n",
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"7435 20260123 920978.BJ 44.56 24.00\n",
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"7436 20260123 920981.BJ 45.61 24.57\n",
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"7437 20260123 920982.BJ 295.08 158.90\n",
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"7438 20260123 920985.BJ 10.20 5.50\n",
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"7439 20260123 920992.BJ 24.32 13.10\n",
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"\n",
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"[7440 rows x 4 columns], trade_date ts_code up_limit down_limit\n",
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"0 20260122 000001.SZ 12.18 9.96\n",
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"1 20260122 000002.SZ 5.51 4.51\n",
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"2 20260122 000004.SZ 11.99 10.85\n",
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"3 20260122 000006.SZ 10.62 8.69\n",
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"4 20260122 000007.SZ 12.41 10.15\n",
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"... ... ... ... ...\n",
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"7433 20260122 920978.BJ 45.48 24.50\n",
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"7434 20260122 920981.BJ 45.61 24.57\n",
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"7435 20260122 920982.BJ 301.76 162.50\n",
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"7436 20260122 920985.BJ 9.84 5.30\n",
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"7437 20260122 920992.BJ 23.95 12.91\n",
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"\n",
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"[7438 rows x 4 columns], trade_date ts_code up_limit down_limit\n",
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"0 20260121 000001.SZ 12.28 10.04\n",
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"1 20260121 000002.SZ 5.27 4.31\n",
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"2 20260121 000004.SZ 12.02 10.88\n",
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"3 20260121 000006.SZ 10.27 8.41\n",
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"4 20260121 000007.SZ 12.08 9.88\n",
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"... ... ... ... ...\n",
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"7433 20260121 920978.BJ 45.60 24.56\n",
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"7434 20260121 920981.BJ 43.81 23.59\n",
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"7435 20260121 920982.BJ 304.34 163.88\n",
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"7436 20260121 920985.BJ 9.90 5.34\n",
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"7437 20260121 920992.BJ 24.11 12.99\n",
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"\n",
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"[7438 rows x 4 columns], trade_date ts_code up_limit down_limit\n",
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"0 20260120 000001.SZ 12.23 10.01\n",
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"1 20260120 000002.SZ 5.20 4.26\n",
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"2 20260120 000004.SZ 11.46 10.36\n",
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"3 20260120 000006.SZ 10.07 8.24\n",
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"4 20260120 000007.SZ 12.49 10.22\n",
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"... ... ... ... ...\n",
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"7431 20260120 920978.BJ 46.41 24.99\n",
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"7432 20260120 920981.BJ 44.26 23.84\n",
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"7433 20260120 920982.BJ 310.42 167.16\n",
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"7434 20260120 920985.BJ 9.97 5.37\n",
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"7435 20260120 920992.BJ 24.49 13.19\n",
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"\n",
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"[7436 rows x 4 columns], trade_date ts_code up_limit down_limit\n",
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"0 20260119 000001.SZ 12.31 10.07\n",
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"1 20260119 000002.SZ 5.20 4.26\n",
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"2 20260119 000004.SZ 11.70 10.58\n",
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"3 20260119 000006.SZ 10.13 8.29\n",
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"4 20260119 000007.SZ 12.43 10.17\n",
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"... ... ... ... ...\n",
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"7429 20260119 920978.BJ 46.26 24.92\n",
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"7430 20260119 920981.BJ 45.51 24.51\n",
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"7431 20260119 920982.BJ 305.50 164.50\n",
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"7432 20260119 920985.BJ 9.88 5.32\n",
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"7433 20260119 920992.BJ 24.28 13.08\n",
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"\n",
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"[7434 rows x 4 columns]]\n"
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]
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}
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],
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"source": [
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"print(all_daily_data)\n",
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"# 将所有数据合并为一个 DataFrame\n",
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"all_daily_data_df = pd.concat(all_daily_data, ignore_index=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "ad9733a1-2f42-43ee-a98c-0bf699304c21",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-04-09T14:58:09.674078Z",
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"start_time": "2025-04-09T14:58:09.366441Z"
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}
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"所有每日基础数据获取并保存完毕！\n"
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]
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}
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],
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"source": [
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"\n",
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"\n",
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"# 将数据保存为 HDF5 文件（table 格式）\n",
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"all_daily_data_df.to_hdf(h5_filename, key='stk_limit', mode='a', format='table', append=True, data_columns=True)\n",
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"\n",
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"print(\"所有每日基础数据获取并保存完毕！\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "7e777f1f-4d54-4a74-b916-691ede6af055",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-04-09T14:58:09.689422Z",
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"start_time": "2025-04-09T14:58:09.686524Z"
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}
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},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "stock",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.11"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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