NewStock/main/data/index_and_industry.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "initial_id",
   "metadata": {
    "jupyter": {
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    }
   },
   "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   20260123  8590.1659  8422.3561  8590.1659  8417.7520   \n",
      "1      000905.SH   20260122  8387.5950  8355.6781  8396.1328  8337.1950   \n",
      "2      000905.SH   20260121  8340.1133  8196.5565  8351.4545  8196.5565   \n",
      "3      000905.SH   20260120  8247.8049  8307.6416  8342.8738  8142.1424   \n",
      "4      000905.SH   20260119  8287.9470  8199.4986  8318.3703  8195.0890   \n",
      "...          ...        ...        ...        ...        ...        ...   \n",
      "14029  399006.SZ   20100607  1069.4680  1005.0280  1075.2250  1001.7020   \n",
      "14030  399006.SZ   20100604  1027.6810   989.6810  1027.6810   986.5040   \n",
      "14031  399006.SZ   20100603   998.3940  1002.3550  1026.7020   997.7750   \n",
      "14032  399006.SZ   20100602   997.1190   967.6090   997.1190   952.6110   \n",
      "14033  399006.SZ   20100601   973.2330   986.0150   994.7930   948.1180   \n",
      "\n",
      "       pre_close    change  pct_chg           vol        amount  \n",
      "0      8387.5950  202.5709   2.4151  3.196901e+08  6.394214e+08  \n",
      "1      8340.1133   47.4817   0.5693  2.688052e+08  5.461381e+08  \n",
      "2      8247.8049   92.3084   1.1192  2.433044e+08  5.175922e+08  \n",
      "3      8287.9470  -40.1421  -0.4843  2.898645e+08  5.881715e+08  \n",
      "4      8232.6740   55.2730   0.6714  2.614974e+08  5.609261e+08  \n",
      "...          ...       ...      ...           ...           ...  \n",
      "14029  1027.6810   41.7870   4.0661  2.655275e+06  9.106095e+06  \n",
      "14030   998.3940   29.2870   2.9334  1.500295e+06  5.269441e+06  \n",
      "14031   997.1190    1.2750   0.1279  1.616805e+06  6.240835e+06  \n",
      "14032   973.2330   23.8860   2.4543  1.074628e+06  4.001206e+06  \n",
      "14033  1000.0000  -26.7670  -2.6767  1.356285e+06  4.924177e+06  \n",
      "\n",
      "[14034 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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