{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "initial_id",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-09T14:57:27.092313Z",
     "start_time": "2025-04-09T14:57:26.124592Z"
    }
   },
   "outputs": [],
   "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()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f448da220816bf98",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-09T14:57:37.680808Z",
     "start_time": "2025-04-09T14:57:27.392846Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据已经成功存储到index_data.h5文件中\n"
     ]
    }
   ],
   "source": [
    "# 定义四个指数\n",
    "index_list = [\n",
    "    # '399300.SZ', \n",
    "    '000905.SH', \n",
    "    '000852.SH', \n",
    "    '399006.SZ'\n",
    "              ]\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",
    "    # print(df)\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\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "907f732d3c397bf",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-09T14:57:37.730922Z",
     "start_time": "2025-04-09T14:57:37.695917Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         ts_code trade_date      close       open       high        low  \\\n",
      "0      000905.SH   20250523  5653.0436  5697.1362  5738.0829  5653.0436   \n",
      "1      000905.SH   20250522  5703.2797  5739.1909  5757.7946  5701.1614   \n",
      "2      000905.SH   20250521  5757.9225  5741.6885  5763.0788  5733.8275   \n",
      "3      000905.SH   20250520  5747.3670  5723.5055  5759.4582  5707.8101   \n",
      "4      000905.SH   20250519  5720.7949  5719.4381  5729.0703  5669.7208   \n",
      "...          ...        ...        ...        ...        ...        ...   \n",
      "13531  399006.SZ   20100607  1069.4680  1005.0280  1075.2250  1001.7020   \n",
      "13532  399006.SZ   20100604  1027.6810   989.6810  1027.6810   986.5040   \n",
      "13533  399006.SZ   20100603   998.3940  1002.3550  1026.7020   997.7750   \n",
      "13534  399006.SZ   20100602   997.1190   967.6090   997.1190   952.6110   \n",
      "13535  399006.SZ   20100601   973.2330   986.0150   994.7930   948.1180   \n",
      "\n",
      "       pre_close   change  pct_chg           vol        amount  \n",
      "0      5703.2797 -50.2361  -0.8808  1.143612e+08  1.481236e+08  \n",
      "1      5757.9225 -54.6428  -0.9490  1.090577e+08  1.416209e+08  \n",
      "2      5747.3670  10.5555   0.1837  1.158045e+08  1.551474e+08  \n",
      "3      5720.7949  26.5721   0.4645  1.168966e+08  1.517512e+08  \n",
      "4      5715.8491   4.9458   0.0865  1.153849e+08  1.410987e+08  \n",
      "...          ...      ...      ...           ...           ...  \n",
      "13531  1027.6810  41.7870   4.0661  2.655275e+06  9.106095e+06  \n",
      "13532   998.3940  29.2870   2.9334  1.500295e+06  5.269441e+06  \n",
      "13533   997.1190   1.2750   0.1279  1.616805e+06  6.240835e+06  \n",
      "13534   973.2330  23.8860   2.4543  1.074628e+06  4.001206e+06  \n",
      "13535  1000.0000 -26.7670  -2.6767  1.356285e+06  4.924177e+06  \n",
      "\n",
      "[13536 rows x 11 columns]\n"
     ]
    }
   ],
   "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"
   ]
  }
 ],
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