{
 "cells": [
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-09T14:57:27.092313Z",
     "start_time": "2025-04-09T14:57:26.124592Z"
    }
   },
   "source": [
    "from operator import index\n",
    "\n",
    "import tushare as ts\n",
    "import pandas as pd\n",
    "import time\n",
    "\n",
    "ts.set_token('3a0741c702ee7e5e5f2bf1f0846bafaafe4e320833240b2a7e4a685f')\n",
    "pro = ts.pro_api()"
   ],
   "outputs": [],
   "execution_count": 1
  },
  {
   "cell_type": "code",
   "id": "f448da220816bf98",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-09T14:57:37.680808Z",
     "start_time": "2025-04-09T14:57:27.392846Z"
    }
   },
   "source": [
    "# 定义四个指数\n",
    "index_list = ['399300.SH', '000905.SH', '000852.SH', '399006.SZ']\n",
    "\n",
    "# 获取并存储数据\n",
    "all_data = []\n",
    "\n",
    "for ts_code in index_list:\n",
    "    df = pro.index_daily(ts_code=ts_code)  # 可根据需要设置日期\n",
    "    df['ts_code'] = ts_code  # 添加ts_code列来区分数据\n",
    "    all_data.append(df)\n",
    "\n",
    "# 合并所有数据\n",
    "final_df = pd.concat(all_data, ignore_index=True)\n",
    "\n",
    "# 存储到H5文件\n",
    "final_df.to_hdf('../../data/index_data.h5', key='index_data', mode='w')\n",
    "\n",
    "print(\"数据已经成功存储到index_data.h5文件中\")"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据已经成功存储到index_data.h5文件中\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\liaozhaorun\\AppData\\Local\\Temp\\ipykernel_15500\\3209233630.py:13: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
      "  final_df = pd.concat(all_data, ignore_index=True)\n"
     ]
    }
   ],
   "execution_count": 2
  },
  {
   "cell_type": "code",
   "id": "907f732d3c397bf",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-09T14:57:37.730922Z",
     "start_time": "2025-04-09T14:57:37.695917Z"
    }
   },
   "source": [
    "h5_filename = '../../data/index_data.h5'\n",
    "key = '/index_data'\n",
    "with pd.HDFStore(h5_filename, mode='r') as store:\n",
    "    df = store[key]\n",
    "    print(df)\n"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         ts_code trade_date      close       open       high        low  \\\n",
      "0      000905.SH   20250409  5439.7716  5249.6841  5465.1449  5135.9655   \n",
      "1      000905.SH   20250408  5326.9140  5279.7566  5371.1834  5249.2318   \n",
      "2      000905.SH   20250407  5287.0333  5523.9636  5587.8502  5212.6773   \n",
      "3      000905.SH   20250403  5845.5045  5842.6167  5906.7057  5817.9662   \n",
      "4      000905.SH   20250402  5899.0865  5884.8925  5936.6467  5884.1126   \n",
      "...          ...        ...        ...        ...        ...        ...   \n",
      "13444  399006.SZ   20100607  1069.4680  1005.0280  1075.2250  1001.7020   \n",
      "13445  399006.SZ   20100604  1027.6810   989.6810  1027.6810   986.5040   \n",
      "13446  399006.SZ   20100603   998.3940  1002.3550  1026.7020   997.7750   \n",
      "13447  399006.SZ   20100602   997.1190   967.6090   997.1190   952.6110   \n",
      "13448  399006.SZ   20100601   973.2330   986.0150   994.7930   948.1180   \n",
      "\n",
      "       pre_close    change  pct_chg           vol        amount  \n",
      "0      5326.9140  112.8576   2.1186  2.451180e+08  2.882574e+08  \n",
      "1      5287.0333   39.8807   0.7543  2.238407e+08  2.618753e+08  \n",
      "2      5845.5045 -558.4712  -9.5539  2.365227e+08  2.673974e+08  \n",
      "3      5899.0865  -53.5820  -0.9083  1.349386e+08  1.736621e+08  \n",
      "4      5892.8502    6.2363   0.1058  1.121600e+08  1.406421e+08  \n",
      "...          ...       ...      ...           ...           ...  \n",
      "13444  1027.6810   41.7870   4.0661  2.655275e+06  9.106095e+06  \n",
      "13445   998.3940   29.2870   2.9334  1.500295e+06  5.269441e+06  \n",
      "13446   997.1190    1.2750   0.1279  1.616805e+06  6.240835e+06  \n",
      "13447   973.2330   23.8860   2.4543  1.074628e+06  4.001206e+06  \n",
      "13448  1000.0000  -26.7670  -2.6767  1.356285e+06  4.924177e+06  \n",
      "\n",
      "[13449 rows x 11 columns]\n"
     ]
    }
   ],
   "execution_count": 3
  }
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