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   20260213  8299.5850  8366.3327  8419.7618  8290.8388   \n",
      "1      000905.SH   20260212  8423.5695  8342.3650  8437.4202  8341.7705   \n",
      "2      000905.SH   20260211  8325.8107  8295.3326  8373.5033  8295.1223   \n",
      "3      000905.SH   20260210  8306.4448  8318.5325  8331.9858  8258.1519   \n",
      "4      000905.SH   20260209  8311.2829  8280.1088  8313.4692  8229.0026   \n",
      "...          ...        ...        ...        ...        ...        ...   \n",
      "14074  399006.SZ   20100607  1069.4680  1005.0280  1075.2250  1001.7020   \n",
      "14075  399006.SZ   20100604  1027.6810   989.6810  1027.6810   986.5040   \n",
      "14076  399006.SZ   20100603   998.3940  1002.3550  1026.7020   997.7750   \n",
      "14077  399006.SZ   20100602   997.1190   967.6090   997.1190   952.6110   \n",
      "14078  399006.SZ   20100601   973.2330   986.0150   994.7930   948.1180   \n",
      "\n",
      "       pre_close    change  pct_chg           vol        amount  \n",
      "0      8423.5695 -123.9845  -1.4719  2.027857e+08  4.063205e+08  \n",
      "1      8325.8107   97.7588   1.1742  2.121383e+08  4.383205e+08  \n",
      "2      8306.4448   19.3659   0.2331  2.040197e+08  3.886124e+08  \n",
      "3      8311.2829   -4.8381  -0.0582  1.958062e+08  3.942833e+08  \n",
      "4      8146.4128  164.8701   2.0238  2.116676e+08  4.279823e+08  \n",
      "...          ...       ...      ...           ...           ...  \n",
      "14074  1027.6810   41.7870   4.0661  2.655275e+06  9.106095e+06  \n",
      "14075   998.3940   29.2870   2.9334  1.500295e+06  5.269441e+06  \n",
      "14076   997.1190    1.2750   0.1279  1.616805e+06  6.240835e+06  \n",
      "14077   973.2330   23.8860   2.4543  1.074628e+06  4.001206e+06  \n",
      "14078  1000.0000  -26.7670  -2.6767  1.356285e+06  4.924177e+06  \n",
      "\n",
      "[14079 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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