NewStock/main/data/index_and_industry.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "initial_id",
   "metadata": {
    "jupyter": {
     "is_executing": true
    }
   },
   "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": "2025-07-26T10:23:18.517518100Z",
     "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('/mnt/d/PyProject/NewStock/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": "2025-07-26T10:23:18.552166300Z",
     "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   20251121  6817.4103  6955.7485  6986.7784  6817.4103   \n",
      "1      000905.SH   20251120  7061.9497  7174.1046  7180.7320  7056.9003   \n",
      "2      000905.SH   20251119  7122.7465  7141.2641  7178.1495  7086.1232   \n",
      "3      000905.SH   20251118  7151.0176  7215.0302  7230.5416  7118.4085   \n",
      "4      000905.SH   20251117  7235.3512  7248.9216  7262.3306  7202.5932   \n",
      "...          ...        ...        ...        ...        ...        ...   \n",
      "13900  399006.SZ   20100607  1069.4680  1005.0280  1075.2250  1001.7020   \n",
      "13901  399006.SZ   20100604  1027.6810   989.6810  1027.6810   986.5040   \n",
      "13902  399006.SZ   20100603   998.3940  1002.3550  1026.7020   997.7750   \n",
      "13903  399006.SZ   20100602   997.1190   967.6090   997.1190   952.6110   \n",
      "13904  399006.SZ   20100601   973.2330   986.0150   994.7930   948.1180   \n",
      "\n",
      "       pre_close    change  pct_chg           vol        amount  \n",
      "0      7061.9497 -244.5394  -3.4628  2.089334e+08  3.109687e+08  \n",
      "1      7122.7465  -60.7968  -0.8536  1.596187e+08  2.541582e+08  \n",
      "2      7151.0176  -28.2711  -0.3953  1.627866e+08  2.567551e+08  \n",
      "3      7235.3512  -84.3336  -1.1656  2.022141e+08  3.065400e+08  \n",
      "4      7235.4617   -0.1105  -0.0015  2.030506e+08  3.108232e+08  \n",
      "...          ...       ...      ...           ...           ...  \n",
      "13900  1027.6810   41.7870   4.0661  2.655275e+06  9.106095e+06  \n",
      "13901   998.3940   29.2870   2.9334  1.500295e+06  5.269441e+06  \n",
      "13902   997.1190    1.2750   0.1279  1.616805e+06  6.240835e+06  \n",
      "13903   973.2330   23.8860   2.4543  1.074628e+06  4.001206e+06  \n",
      "13904  1000.0000  -26.7670  -2.6767  1.356285e+06  4.924177e+06  \n",
      "\n",
      "[13905 rows x 11 columns]\n"
     ]
    }
   ],
   "source": [
    "h5_filename = '/mnt/d/PyProject/NewStock/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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