304 lines
9.8 KiB
Plaintext
304 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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"4763 600259.SH 20250530\n",
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"4764 600261.SH 20250530\n",
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"4765 600262.SH 20250530\n",
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"4754 600248.SH 20250530\n",
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"7116 900957.BJ 20250530\n",
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"<class 'pandas.core.frame.DataFrame'>\n",
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"Index: 10564598 entries, 0 to 106964\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: 241.8+ MB\n",
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"None\n",
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"20250530\n",
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"20250603\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='20250720')\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 20250718 完成\n",
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"任务 20250717 完成\n",
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"任务 20250716 完成\n",
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"任务 20250715 完成\n",
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"任务 20250714 完成\n",
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"任务 20250711 完成\n",
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"任务 20250710 完成\n",
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"任务 20250709 完成\n",
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"任务 20250708 完成\n",
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"任务 20250707 完成\n",
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"任务 20250704 完成\n",
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"任务 20250703 完成\n",
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"任务 20250702 完成\n",
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"任务 20250701 完成\n",
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"任务 20250630 完成\n",
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"任务 20250627 完成\n",
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"任务 20250625 完成\n",
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"任务 20250626 完成\n",
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"任务 20250624 完成\n",
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"任务 20250623 完成\n",
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"任务 20250620 完成\n",
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"任务 20250619 完成\n",
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"任务 20250617 完成\n",
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"任务 20250618 完成\n",
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"任务 20250616 完成\n",
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"任务 20250613 完成\n",
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"任务 20250612 完成\n",
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"任务 20250611 完成\n",
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"任务 20250610 完成\n",
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"任务 20250609 完成\n",
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"任务 20250606 完成\n",
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"任务 20250605 完成\n",
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"任务 20250603 完成\n",
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"任务 20250604 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 20250606 000001.SZ 12.84 10.50\n",
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"1 20250606 000002.SZ 7.30 5.98\n",
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"2 20250606 000004.SZ 10.35 9.37\n",
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"3 20250606 000006.SZ 7.48 6.12\n",
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"4 20250606 000007.SZ 9.06 7.42\n",
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"... ... ... ... ...\n",
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"7144 20250606 920445.BJ 13.36 7.20\n",
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"7145 20250606 920489.BJ 31.46 16.94\n",
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"7146 20250606 920682.BJ 13.14 7.08\n",
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"7147 20250606 920799.BJ 77.80 41.90\n",
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"7148 20250606 920819.BJ 5.70 3.08\n",
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"\n",
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"[7149 rows x 4 columns], trade_date ts_code up_limit down_limit\n",
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"0 20250605 000001.SZ 13.02 10.66\n",
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"1 20250605 000002.SZ 7.28 5.96\n",
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"2 20250605 000004.SZ 10.63 9.61\n",
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"3 20250605 000006.SZ 7.41 6.07\n",
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"4 20250605 000007.SZ 9.19 7.52\n",
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"... ... ... ... ...\n",
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"7143 20250605 920445.BJ 13.49 7.27\n",
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"7144 20250605 920489.BJ 31.00 16.70\n",
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"7145 20250605 920682.BJ 13.22 7.12\n",
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"7146 20250605 920799.BJ 76.24 41.06\n",
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"7147 20250605 920819.BJ 5.70 3.08\n",
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"\n",
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"[7148 rows x 4 columns], trade_date ts_code up_limit down_limit\n",
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"0 20250603 000001.SZ 12.72 10.40\n",
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"1 20250603 000002.SZ 7.30 5.98\n",
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"2 20250603 000004.SZ 10.90 9.86\n",
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"3 20250603 000006.SZ 7.62 6.24\n",
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"4 20250603 000007.SZ 8.65 7.07\n",
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"... ... ... ... ...\n",
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"7137 20250603 920445.BJ 13.18 7.10\n",
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"7138 20250603 920489.BJ 31.25 16.83\n",
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"7139 20250603 920682.BJ 13.20 7.12\n",
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"7140 20250603 920799.BJ 76.31 41.09\n",
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"7141 20250603 920819.BJ 5.72 3.08\n",
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"\n",
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"[7142 rows x 4 columns], trade_date ts_code up_limit down_limit\n",
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"0 20250604 000001.SZ 12.99 10.63\n",
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"1 20250604 000002.SZ 7.24 5.92\n",
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"2 20250604 000004.SZ 10.77 9.75\n",
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"3 20250604 000006.SZ 7.41 6.07\n",
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"4 20250604 000007.SZ 8.88 7.26\n",
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"... ... ... ... ...\n",
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"7140 20250604 920445.BJ 13.29 7.17\n",
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"7141 20250604 920489.BJ 31.18 16.80\n",
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"7142 20250604 920682.BJ 13.26 7.14\n",
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"7143 20250604 920799.BJ 76.93 41.43\n",
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"7144 20250604 920819.BJ 5.73 3.09\n",
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"\n",
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"[7145 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": "3.13.2"
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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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