NewStock/main/data/update/update_stk_limit.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "500802dc-7a20-48b7-a470-a4bae3ec534b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-09T14:57:41.532210Z",
     "start_time": "2025-04-09T14:57:40.584930Z"
    }
   },
   "outputs": [],
   "source": [
    "import tushare as ts\n",
    "\n",
    "ts.set_token('3a0741c702ee7e5e5f2bf1f0846bafaafe4e320833240b2a7e4a685f')\n",
    "pro = ts.pro_api()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5a84bc9da6d54868",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-09T14:58:04.911924Z",
     "start_time": "2025-04-09T14:57:41.540345Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        ts_code trade_date\n",
      "2364  300067.SZ   20250508\n",
      "2363  300066.SZ   20250508\n",
      "2362  300065.SZ   20250508\n",
      "2373  300076.SZ   20250508\n",
      "7111  920819.BJ   20250508\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 10450519 entries, 0 to 7111\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: 239.2+ MB\n",
      "None\n",
      "20250508\n",
      "20250509\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import time\n",
    "\n",
    "h5_filename = '../../../data/stk_limit.h5'\n",
    "key = '/stk_limit'\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.sort_values(by='trade_date', ascending=True).tail())\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='20250720')\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(start_date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bb3191de-27a2-4c89-a3b5-32a0d7b9496f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-09T14:58:09.342522Z",
     "start_time": "2025-04-09T14:58:05.259974Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "任务 20250718 完成\n",
      "任务 20250717 完成\n",
      "任务 20250715 完成\n",
      "任务 20250716 完成\n",
      "任务 20250714 完成\n",
      "任务 20250711 完成\n",
      "任务 20250710 完成\n",
      "任务 20250709 完成\n",
      "任务 20250708 完成\n",
      "任务 20250707 完成\n",
      "任务 20250704 完成\n",
      "任务 20250703 完成\n",
      "任务 20250702 完成\n",
      "任务 20250701 完成\n",
      "任务 20250630 完成\n",
      "任务 20250627 完成\n",
      "任务 20250626 完成\n",
      "任务 20250625 完成\n",
      "任务 20250624 完成\n",
      "任务 20250623 完成\n",
      "任务 20250620 完成\n",
      "任务 20250619 完成\n",
      "任务 20250618 完成\n",
      "任务 20250617 完成\n",
      "任务 20250616 完成\n",
      "任务 20250613 完成\n",
      "任务 20250612 完成\n",
      "任务 20250611 完成\n",
      "任务 20250610 完成\n",
      "任务 20250609 完成\n",
      "任务 20250606 完成\n",
      "任务 20250605 完成\n",
      "任务 20250604 完成\n",
      "任务 20250603 完成\n",
      "任务 20250530 完成\n",
      "任务 20250528 完成\n",
      "任务 20250529 完成\n",
      "任务 20250527 完成\n",
      "任务 20250526 完成\n",
      "任务 20250523 完成\n",
      "任务 20250522 完成\n",
      "任务 20250521 完成\n",
      "任务 20250520 完成\n",
      "任务 20250519 完成\n",
      "任务 20250516 完成\n",
      "任务 20250515 完成\n",
      "任务 20250514 完成\n",
      "任务 20250513 完成\n",
      "任务 20250512 完成\n",
      "任务 20250509 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",
    "    stk_limit_data = pro.stk_limit(trade_date=trade_date)\n",
    "    if stk_limit_data is not None and not stk_limit_data.empty:\n",
    "        return stk_limit_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",
    "            if result is not None:\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": "96a81aa5890ea3c3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-09T14:58:09.353560Z",
     "start_time": "2025-04-09T14:58:09.346528Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[     trade_date    ts_code  up_limit  down_limit\n",
      "0      20250509  000001.SZ     12.19        9.97\n",
      "1      20250509  000002.SZ      7.57        6.19\n",
      "2      20250509  000004.SZ      7.86        7.12\n",
      "3      20250509  000006.SZ      7.33        5.99\n",
      "4      20250509  000007.SZ      7.66        6.26\n",
      "...         ...        ...       ...         ...\n",
      "7109   20250509  920445.BJ     13.14        7.08\n",
      "7110   20250509  920489.BJ     31.70       17.08\n",
      "7111   20250509  920682.BJ     16.17        8.71\n",
      "7112   20250509  920799.BJ     78.39       42.21\n",
      "7113   20250509  920819.BJ      5.74        3.10\n",
      "\n",
      "[7114 rows x 4 columns]]\n"
     ]
    }
   ],
   "source": [
    "print(all_daily_data)\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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ad9733a1-2f42-43ee-a98c-0bf699304c21",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-09T14:58:09.674078Z",
     "start_time": "2025-04-09T14:58:09.366441Z"
    }
   },
   "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",
    "\n",
    "# 将数据保存为 HDF5 文件（table 格式）\n",
    "all_daily_data_df.to_hdf(h5_filename, key='stk_limit', 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": null,
   "id": "7e777f1f-4d54-4a74-b916-691ede6af055",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-09T14:58:09.689422Z",
     "start_time": "2025-04-09T14:58:09.686524Z"
    }
   },
   "outputs": [],
   "source": []
  }
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