283 lines
8.1 KiB
Plaintext
283 lines
8.1 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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"4745 600276.SH 20250506\n",
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"4746 600278.SH 20250506\n",
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"4747 600279.SH 20250506\n",
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"4736 600262.SH 20250506\n",
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"281 000791.SZ 20250506\n",
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"<class 'pandas.core.frame.DataFrame'>\n",
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"Index: 10436295 entries, 0 to 113592\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: 238.9+ MB\n",
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"None\n",
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"20250506\n",
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"20250507\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 = '../../../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 20250715 完成\n",
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"任务 20250716 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 20250626 完成\n",
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"任务 20250625 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 20250618 完成\n",
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"任务 20250617 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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"任务 20250529 完成\n",
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"任务 20250530 完成\n",
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"任务 20250528 完成\n",
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"任务 20250527 完成\n",
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"任务 20250526 完成\n",
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"任务 20250523 完成\n",
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"任务 20250522 完成\n",
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"任务 20250521 完成\n",
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"任务 20250520 完成\n",
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"任务 20250519 完成\n",
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"任务 20250516 完成\n",
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"任务 20250515 完成\n",
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"任务 20250514 完成\n",
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"任务 20250513 完成\n",
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"任务 20250512 完成\n",
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"任务 20250509 完成\n",
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"任务 20250508 完成\n",
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"任务 20250507 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 20250507 000001.SZ 12.06 9.86\n",
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"1 20250507 000002.SZ 7.51 6.15\n",
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"2 20250507 000004.SZ 7.95 7.19\n",
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"3 20250507 000006.SZ 7.11 5.81\n",
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"4 20250507 000007.SZ 7.50 6.14\n",
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"... ... ... ... ...\n",
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"7107 20250507 920445.BJ 13.42 7.24\n",
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"7108 20250507 920489.BJ 31.69 17.07\n",
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"7109 20250507 920682.BJ 16.41 8.85\n",
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"7110 20250507 920799.BJ 78.58 42.32\n",
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"7111 20250507 920819.BJ 5.82 3.14\n",
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"\n",
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"[7112 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": "new_trader",
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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": "3.11.11"
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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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