2.1 tushare Demos.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 接口数据的获取、存储与应用\n",
    "\n",
    "### 以金融大数据tushare为例"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### https://tushare.pro/\n",
    "http://tushare.org/index.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1 目前在上映电影"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tushare as ts  \n",
    "import os,time,sys,re,datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "185 ms ± 24.6 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%timeit df = ts.realtime_boxoffice() #"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   BoxOffice Irank   MovieName boxPer  \\\n",
      "0    1787.63     1       冰雪奇缘2  46.96   \n",
      "1     655.14     2        利刃出鞘  17.21   \n",
      "2     596.47     3        两只老虎  15.67   \n",
      "3     273.15     4    一生有你2019   7.18   \n",
      "4      79.95     5         冰峰暴   2.10   \n",
      "5      59.08     6       海上钢琴师   1.55   \n",
      "6      59.05     7     平原上的夏洛克   1.55   \n",
      "7      40.28     8  若能与你共乘海浪之上   1.06   \n",
      "8      37.84     9       大约在冬季   0.99   \n",
      "9      37.49    10     衣柜里的冒险王   0.98   \n",
      "10    180.22    11          其它   1.00   \n",
      "\n",
      "                                          default_url  \\\n",
      "0   http://images.entgroup.cn/group2/M00/02/93/wKg...   \n",
      "1   http://images.entgroup.cn/group2/M00/02/95/wKg...   \n",
      "2   http://images.entgroup.cn/group2/M00/02/96/wKg...   \n",
      "3   http://images.entgroup.cn/group1/M00/05/26/wKg...   \n",
      "4   http://images.entgroup.cn/group1/M00/05/14/wKg...   \n",
      "5   http://images.entgroup.cn/group1/M00/05/28/wKg...   \n",
      "6   http://images.entgroup.cn/group2/M00/02/92/wKg...   \n",
      "7   http://images.entgroup.cn/group2/M00/02/95/wKg...   \n",
      "8   http://images.entgroup.cn/group1/M00/05/24/wKg...   \n",
      "9   http://images.entgroup.cn/group2/M00/02/94/wKg...   \n",
      "10                                                      \n",
      "\n",
      "                                           larger_url  \\\n",
      "0   http://images.entgroup.cn/group2/M00/02/95/wKg...   \n",
      "1                                                       \n",
      "2   http://images.entgroup.cn/group2/M00/02/96/wKg...   \n",
      "3                                                       \n",
      "4                                                       \n",
      "5   http://images.entgroup.cn/group2/M00/02/95/wKg...   \n",
      "6                                                       \n",
      "7                                                       \n",
      "8   http://images.entgroup.cn/group1/M00/05/28/wKg...   \n",
      "9                                                       \n",
      "10                                                      \n",
      "\n",
      "                                           moblie_url movieDay sumBoxOffice  \\\n",
      "0   http://images.entgroup.cn/group2/M00/02/93/wKg...        9     49438.62   \n",
      "1   http://images.entgroup.cn/group2/M00/02/95/wKg...        2      2578.33   \n",
      "2   http://images.entgroup.cn/group2/M00/02/96/wKg...        2      5140.56   \n",
      "3   http://images.entgroup.cn/group1/M00/05/26/wKg...        2      1710.34   \n",
      "4   http://images.entgroup.cn/group1/M00/05/14/wKg...        2       619.53   \n",
      "5   http://images.entgroup.cn/group1/M00/05/28/wKg...       16     13429.71   \n",
      "6   http://images.entgroup.cn/group2/M00/02/92/wKg...        2       539.25   \n",
      "7   http://images.entgroup.cn/group2/M00/02/95/wKg...       -6        42.41   \n",
      "8   http://images.entgroup.cn/group1/M00/05/24/wKg...       16     21218.74   \n",
      "9   http://images.entgroup.cn/group2/M00/02/94/wKg...        2       233.62   \n",
      "10                                                           0         0.00   \n",
      "\n",
      "                   time  \n",
      "0   2019-11-30 10:00:50  \n",
      "1   2019-11-30 10:00:50  \n",
      "2   2019-11-30 10:00:50  \n",
      "3   2019-11-30 10:00:50  \n",
      "4   2019-11-30 10:00:50  \n",
      "5   2019-11-30 10:00:50  \n",
      "6   2019-11-30 10:00:50  \n",
      "7   2019-11-30 10:00:50  \n",
      "8   2019-11-30 10:00:50  \n",
      "9   2019-11-30 10:00:50  \n",
      "10  2019-11-30 10:00:50  \n"
     ]
    }
   ],
   "source": [
    "df = ts.realtime_boxoffice()\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "??ts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2 获取指定股票数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      ts_code trade_date   open   high    low  close  pre_close  change  \\\n",
      "0   600728.SH   20190416  11.15  11.75  11.08  11.69      11.30    0.39   \n",
      "1   600728.SH   20190415  11.59  11.82  11.26  11.30      11.30    0.00   \n",
      "2   600728.SH   20190412  11.20  11.57  11.20  11.30      11.19    0.11   \n",
      "3   600728.SH   20190411  11.70  11.77  11.12  11.19      11.59   -0.40   \n",
      "4   600728.SH   20190410  11.77  11.77  11.24  11.59      11.76   -0.17   \n",
      "5   600728.SH   20190409  11.84  12.15  11.66  11.76      11.84   -0.08   \n",
      "6   600728.SH   20190408  12.26  12.37  11.58  11.84      12.38   -0.54   \n",
      "7   600728.SH   20190404  12.75  12.92  12.35  12.38      12.78   -0.40   \n",
      "8   600728.SH   20190403  12.68  13.06  12.61  12.78      12.85   -0.07   \n",
      "9   600728.SH   20190402  12.72  13.45  12.60  12.85      12.65    0.20   \n",
      "10  600728.SH   20190401  12.37  12.80  12.25  12.65      12.22    0.43   \n",
      "\n",
      "    pct_chg         vol       amount  \n",
      "0    3.4513   427840.42   492634.526  \n",
      "1    0.0000   445302.60   515447.283  \n",
      "2    0.9830   354701.80   403353.920  \n",
      "3   -3.4513   461470.89   524978.826  \n",
      "4   -1.4456   501787.09   578317.048  \n",
      "5   -0.6757   465878.30   551994.595  \n",
      "6   -4.3619   827695.12   987643.490  \n",
      "7   -3.1299   688628.17   863465.076  \n",
      "8   -0.5447   595345.76   760684.875  \n",
      "9    1.5810  1037668.78  1354271.928  \n",
      "10   3.5188   764361.93   958825.117  \n"
     ]
    }
   ],
   "source": [
    "pro = ts.pro_api('6cbb7738f458c6366abd8601dc143cf493649fb97a7e6b4f43966902')\n",
    "df = pro.daily(ts_code='600728.sh', start_date='20190401', end_date='20190416')\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df[['ts_code','open','close','pct_chg','vol','amount','trade_date']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ts_code</th>\n",
       "      <th>open</th>\n",
       "      <th>close</th>\n",
       "      <th>pct_chg</th>\n",
       "      <th>vol</th>\n",
       "      <th>amount</th>\n",
       "      <th>trade_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>600728.SH</td>\n",
       "      <td>12.37</td>\n",
       "      <td>12.65</td>\n",
       "      <td>3.5188</td>\n",
       "      <td>764361.93</td>\n",
       "      <td>958825.117</td>\n",
       "      <td>20190401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>600728.SH</td>\n",
       "      <td>12.72</td>\n",
       "      <td>12.85</td>\n",
       "      <td>1.5810</td>\n",
       "      <td>1037668.78</td>\n",
       "      <td>1354271.928</td>\n",
       "      <td>20190402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>600728.SH</td>\n",
       "      <td>12.68</td>\n",
       "      <td>12.78</td>\n",
       "      <td>-0.5447</td>\n",
       "      <td>595345.76</td>\n",
       "      <td>760684.875</td>\n",
       "      <td>20190403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>600728.SH</td>\n",
       "      <td>12.75</td>\n",
       "      <td>12.38</td>\n",
       "      <td>-3.1299</td>\n",
       "      <td>688628.17</td>\n",
       "      <td>863465.076</td>\n",
       "      <td>20190404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>600728.SH</td>\n",
       "      <td>12.26</td>\n",
       "      <td>11.84</td>\n",
       "      <td>-4.3619</td>\n",
       "      <td>827695.12</td>\n",
       "      <td>987643.490</td>\n",
       "      <td>20190408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>600728.SH</td>\n",
       "      <td>11.84</td>\n",
       "      <td>11.76</td>\n",
       "      <td>-0.6757</td>\n",
       "      <td>465878.30</td>\n",
       "      <td>551994.595</td>\n",
       "      <td>20190409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>600728.SH</td>\n",
       "      <td>11.77</td>\n",
       "      <td>11.59</td>\n",
       "      <td>-1.4456</td>\n",
       "      <td>501787.09</td>\n",
       "      <td>578317.048</td>\n",
       "      <td>20190410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>600728.SH</td>\n",
       "      <td>11.70</td>\n",
       "      <td>11.19</td>\n",
       "      <td>-3.4513</td>\n",
       "      <td>461470.89</td>\n",
       "      <td>524978.826</td>\n",
       "      <td>20190411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>600728.SH</td>\n",
       "      <td>11.20</td>\n",
       "      <td>11.30</td>\n",
       "      <td>0.9830</td>\n",
       "      <td>354701.80</td>\n",
       "      <td>403353.920</td>\n",
       "      <td>20190412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>600728.SH</td>\n",
       "      <td>11.59</td>\n",
       "      <td>11.30</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>445302.60</td>\n",
       "      <td>515447.283</td>\n",
       "      <td>20190415</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      ts_code   open  close  pct_chg         vol       amount trade_date\n",
       "10  600728.SH  12.37  12.65   3.5188   764361.93   958825.117   20190401\n",
       "9   600728.SH  12.72  12.85   1.5810  1037668.78  1354271.928   20190402\n",
       "8   600728.SH  12.68  12.78  -0.5447   595345.76   760684.875   20190403\n",
       "7   600728.SH  12.75  12.38  -3.1299   688628.17   863465.076   20190404\n",
       "6   600728.SH  12.26  11.84  -4.3619   827695.12   987643.490   20190408\n",
       "5   600728.SH  11.84  11.76  -0.6757   465878.30   551994.595   20190409\n",
       "4   600728.SH  11.77  11.59  -1.4456   501787.09   578317.048   20190410\n",
       "3   600728.SH  11.70  11.19  -3.4513   461470.89   524978.826   20190411\n",
       "2   600728.SH  11.20  11.30   0.9830   354701.80   403353.920   20190412\n",
       "1   600728.SH  11.59  11.30   0.0000   445302.60   515447.283   20190415"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.reindex(['trade_date'])\n",
    "df=df.sort_index()\n",
    "df=df.sort_values(by='trade_date')\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 理解日期类型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2019-11-30 2019-11-29\n",
      "2019-11-30\n",
      "20191129\n"
     ]
    }
   ],
   "source": [
    "TODAY = datetime.date.today()\n",
    "YTODAY=TODAY-datetime.timedelta(1)\n",
    "print(TODAY,YTODAY)\n",
    "CURRENTDAY=TODAY.strftime('%Y-%m-%d')\n",
    "print(CURRENTDAY)\n",
    "CURRENTDAY=YTODAY.strftime('%Y%m%d')\n",
    "print(CURRENTDAY)\n",
    "\n",
    "#时间"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ts_code</th>\n",
       "      <th>trade_date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>pre_close</th>\n",
       "      <th>change</th>\n",
       "      <th>pct_chg</th>\n",
       "      <th>vol</th>\n",
       "      <th>amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>600728.SH</td>\n",
       "      <td>20191129</td>\n",
       "      <td>8.66</td>\n",
       "      <td>8.91</td>\n",
       "      <td>8.64</td>\n",
       "      <td>8.75</td>\n",
       "      <td>8.63</td>\n",
       "      <td>0.12</td>\n",
       "      <td>1.3905</td>\n",
       "      <td>184276.00</td>\n",
       "      <td>162093.411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>600728.SH</td>\n",
       "      <td>20191128</td>\n",
       "      <td>8.60</td>\n",
       "      <td>8.71</td>\n",
       "      <td>8.55</td>\n",
       "      <td>8.63</td>\n",
       "      <td>8.59</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.4657</td>\n",
       "      <td>106042.77</td>\n",
       "      <td>91568.299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>600728.SH</td>\n",
       "      <td>20191127</td>\n",
       "      <td>8.51</td>\n",
       "      <td>8.64</td>\n",
       "      <td>8.51</td>\n",
       "      <td>8.59</td>\n",
       "      <td>8.53</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.7034</td>\n",
       "      <td>86742.01</td>\n",
       "      <td>74492.989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>600728.SH</td>\n",
       "      <td>20191126</td>\n",
       "      <td>8.63</td>\n",
       "      <td>8.66</td>\n",
       "      <td>8.51</td>\n",
       "      <td>8.53</td>\n",
       "      <td>8.59</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>-0.6985</td>\n",
       "      <td>86060.74</td>\n",
       "      <td>73798.775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>600728.SH</td>\n",
       "      <td>20191125</td>\n",
       "      <td>8.71</td>\n",
       "      <td>8.76</td>\n",
       "      <td>8.50</td>\n",
       "      <td>8.59</td>\n",
       "      <td>8.70</td>\n",
       "      <td>-0.11</td>\n",
       "      <td>-1.2644</td>\n",
       "      <td>119025.72</td>\n",
       "      <td>102305.740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>600728.SH</td>\n",
       "      <td>20191122</td>\n",
       "      <td>8.87</td>\n",
       "      <td>8.97</td>\n",
       "      <td>8.69</td>\n",
       "      <td>8.70</td>\n",
       "      <td>8.86</td>\n",
       "      <td>-0.16</td>\n",
       "      <td>-1.8059</td>\n",
       "      <td>167303.24</td>\n",
       "      <td>146969.877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>600728.SH</td>\n",
       "      <td>20191121</td>\n",
       "      <td>8.94</td>\n",
       "      <td>8.94</td>\n",
       "      <td>8.80</td>\n",
       "      <td>8.86</td>\n",
       "      <td>8.93</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>-0.7839</td>\n",
       "      <td>113459.72</td>\n",
       "      <td>100401.970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>600728.SH</td>\n",
       "      <td>20191120</td>\n",
       "      <td>9.06</td>\n",
       "      <td>9.12</td>\n",
       "      <td>8.91</td>\n",
       "      <td>8.93</td>\n",
       "      <td>9.05</td>\n",
       "      <td>-0.12</td>\n",
       "      <td>-1.3260</td>\n",
       "      <td>120809.72</td>\n",
       "      <td>108857.281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>600728.SH</td>\n",
       "      <td>20191119</td>\n",
       "      <td>8.88</td>\n",
       "      <td>9.10</td>\n",
       "      <td>8.88</td>\n",
       "      <td>9.05</td>\n",
       "      <td>8.87</td>\n",
       "      <td>0.18</td>\n",
       "      <td>2.0293</td>\n",
       "      <td>170884.54</td>\n",
       "      <td>154196.044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>600728.SH</td>\n",
       "      <td>20191118</td>\n",
       "      <td>8.78</td>\n",
       "      <td>8.90</td>\n",
       "      <td>8.76</td>\n",
       "      <td>8.87</td>\n",
       "      <td>8.82</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.5669</td>\n",
       "      <td>91700.23</td>\n",
       "      <td>81184.896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>600728.SH</td>\n",
       "      <td>20191115</td>\n",
       "      <td>8.91</td>\n",
       "      <td>9.02</td>\n",
       "      <td>8.82</td>\n",
       "      <td>8.82</td>\n",
       "      <td>8.98</td>\n",
       "      <td>-0.16</td>\n",
       "      <td>-1.7817</td>\n",
       "      <td>115782.48</td>\n",
       "      <td>103171.668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>600728.SH</td>\n",
       "      <td>20191114</td>\n",
       "      <td>8.81</td>\n",
       "      <td>9.03</td>\n",
       "      <td>8.81</td>\n",
       "      <td>8.98</td>\n",
       "      <td>8.84</td>\n",
       "      <td>0.14</td>\n",
       "      <td>1.5837</td>\n",
       "      <td>147286.36</td>\n",
       "      <td>131738.881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>600728.SH</td>\n",
       "      <td>20191113</td>\n",
       "      <td>8.91</td>\n",
       "      <td>8.95</td>\n",
       "      <td>8.80</td>\n",
       "      <td>8.84</td>\n",
       "      <td>8.96</td>\n",
       "      <td>-0.12</td>\n",
       "      <td>-1.3393</td>\n",
       "      <td>116428.85</td>\n",
       "      <td>103304.102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>600728.SH</td>\n",
       "      <td>20191112</td>\n",
       "      <td>8.89</td>\n",
       "      <td>9.00</td>\n",
       "      <td>8.76</td>\n",
       "      <td>8.96</td>\n",
       "      <td>8.89</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.7874</td>\n",
       "      <td>182276.73</td>\n",
       "      <td>161238.810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>600728.SH</td>\n",
       "      <td>20191111</td>\n",
       "      <td>9.13</td>\n",
       "      <td>9.13</td>\n",
       "      <td>8.83</td>\n",
       "      <td>8.89</td>\n",
       "      <td>9.24</td>\n",
       "      <td>-0.35</td>\n",
       "      <td>-3.7879</td>\n",
       "      <td>283354.63</td>\n",
       "      <td>254891.298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>600728.SH</td>\n",
       "      <td>20191108</td>\n",
       "      <td>9.32</td>\n",
       "      <td>9.38</td>\n",
       "      <td>9.21</td>\n",
       "      <td>9.24</td>\n",
       "      <td>9.23</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.1083</td>\n",
       "      <td>200727.41</td>\n",
       "      <td>186619.782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>600728.SH</td>\n",
       "      <td>20191107</td>\n",
       "      <td>9.32</td>\n",
       "      <td>9.37</td>\n",
       "      <td>9.17</td>\n",
       "      <td>9.23</td>\n",
       "      <td>9.32</td>\n",
       "      <td>-0.09</td>\n",
       "      <td>-0.9657</td>\n",
       "      <td>267452.37</td>\n",
       "      <td>247950.532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>600728.SH</td>\n",
       "      <td>20191106</td>\n",
       "      <td>9.50</td>\n",
       "      <td>9.64</td>\n",
       "      <td>9.24</td>\n",
       "      <td>9.32</td>\n",
       "      <td>9.52</td>\n",
       "      <td>-0.20</td>\n",
       "      <td>-2.1008</td>\n",
       "      <td>252818.20</td>\n",
       "      <td>238642.193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>600728.SH</td>\n",
       "      <td>20191105</td>\n",
       "      <td>9.65</td>\n",
       "      <td>9.66</td>\n",
       "      <td>9.45</td>\n",
       "      <td>9.52</td>\n",
       "      <td>9.62</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>-1.0395</td>\n",
       "      <td>217772.79</td>\n",
       "      <td>207688.999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>600728.SH</td>\n",
       "      <td>20191104</td>\n",
       "      <td>9.72</td>\n",
       "      <td>9.76</td>\n",
       "      <td>9.57</td>\n",
       "      <td>9.62</td>\n",
       "      <td>9.65</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.3109</td>\n",
       "      <td>248648.61</td>\n",
       "      <td>240074.115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>600728.SH</td>\n",
       "      <td>20191101</td>\n",
       "      <td>9.49</td>\n",
       "      <td>9.72</td>\n",
       "      <td>9.43</td>\n",
       "      <td>9.65</td>\n",
       "      <td>9.54</td>\n",
       "      <td>0.11</td>\n",
       "      <td>1.1530</td>\n",
       "      <td>236710.69</td>\n",
       "      <td>227447.827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>600728.SH</td>\n",
       "      <td>20191031</td>\n",
       "      <td>9.65</td>\n",
       "      <td>9.84</td>\n",
       "      <td>9.52</td>\n",
       "      <td>9.54</td>\n",
       "      <td>9.63</td>\n",
       "      <td>-0.09</td>\n",
       "      <td>-0.9346</td>\n",
       "      <td>307085.80</td>\n",
       "      <td>298056.267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>600728.SH</td>\n",
       "      <td>20191030</td>\n",
       "      <td>9.78</td>\n",
       "      <td>9.89</td>\n",
       "      <td>9.63</td>\n",
       "      <td>9.63</td>\n",
       "      <td>9.92</td>\n",
       "      <td>-0.29</td>\n",
       "      <td>-2.9234</td>\n",
       "      <td>405416.93</td>\n",
       "      <td>394373.676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>600728.SH</td>\n",
       "      <td>20191029</td>\n",
       "      <td>9.72</td>\n",
       "      <td>10.15</td>\n",
       "      <td>9.70</td>\n",
       "      <td>9.92</td>\n",
       "      <td>9.78</td>\n",
       "      <td>0.14</td>\n",
       "      <td>1.4315</td>\n",
       "      <td>662057.23</td>\n",
       "      <td>654604.818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>600728.SH</td>\n",
       "      <td>20191028</td>\n",
       "      <td>9.70</td>\n",
       "      <td>9.96</td>\n",
       "      <td>9.55</td>\n",
       "      <td>9.78</td>\n",
       "      <td>9.42</td>\n",
       "      <td>0.36</td>\n",
       "      <td>3.8217</td>\n",
       "      <td>585677.93</td>\n",
       "      <td>573473.642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>600728.SH</td>\n",
       "      <td>20191025</td>\n",
       "      <td>9.29</td>\n",
       "      <td>9.45</td>\n",
       "      <td>9.20</td>\n",
       "      <td>9.42</td>\n",
       "      <td>9.33</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.9646</td>\n",
       "      <td>262108.64</td>\n",
       "      <td>244829.276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>600728.SH</td>\n",
       "      <td>20191024</td>\n",
       "      <td>9.40</td>\n",
       "      <td>9.44</td>\n",
       "      <td>9.26</td>\n",
       "      <td>9.33</td>\n",
       "      <td>9.41</td>\n",
       "      <td>-0.08</td>\n",
       "      <td>-0.8502</td>\n",
       "      <td>235745.46</td>\n",
       "      <td>220184.182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>600728.SH</td>\n",
       "      <td>20191023</td>\n",
       "      <td>9.24</td>\n",
       "      <td>9.43</td>\n",
       "      <td>9.13</td>\n",
       "      <td>9.41</td>\n",
       "      <td>9.22</td>\n",
       "      <td>0.19</td>\n",
       "      <td>2.0607</td>\n",
       "      <td>300239.96</td>\n",
       "      <td>277872.194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>600728.SH</td>\n",
       "      <td>20191022</td>\n",
       "      <td>9.20</td>\n",
       "      <td>9.26</td>\n",
       "      <td>9.08</td>\n",
       "      <td>9.22</td>\n",
       "      <td>9.19</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.3264</td>\n",
       "      <td>255140.78</td>\n",
       "      <td>233907.795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>600728.SH</td>\n",
       "      <td>20191021</td>\n",
       "      <td>9.34</td>\n",
       "      <td>9.37</td>\n",
       "      <td>9.09</td>\n",
       "      <td>9.19</td>\n",
       "      <td>9.52</td>\n",
       "      <td>-0.33</td>\n",
       "      <td>-3.4664</td>\n",
       "      <td>386575.43</td>\n",
       "      <td>355105.369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>600728.SH</td>\n",
       "      <td>20190815</td>\n",
       "      <td>7.93</td>\n",
       "      <td>8.29</td>\n",
       "      <td>7.90</td>\n",
       "      <td>8.26</td>\n",
       "      <td>8.19</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.8547</td>\n",
       "      <td>206809.61</td>\n",
       "      <td>168051.562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>600728.SH</td>\n",
       "      <td>20190814</td>\n",
       "      <td>8.30</td>\n",
       "      <td>8.38</td>\n",
       "      <td>8.16</td>\n",
       "      <td>8.19</td>\n",
       "      <td>8.16</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.3676</td>\n",
       "      <td>170320.21</td>\n",
       "      <td>140988.730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>600728.SH</td>\n",
       "      <td>20190813</td>\n",
       "      <td>8.22</td>\n",
       "      <td>8.23</td>\n",
       "      <td>8.12</td>\n",
       "      <td>8.16</td>\n",
       "      <td>8.35</td>\n",
       "      <td>-0.19</td>\n",
       "      <td>-2.2754</td>\n",
       "      <td>131637.16</td>\n",
       "      <td>107405.409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>600728.SH</td>\n",
       "      <td>20190812</td>\n",
       "      <td>8.24</td>\n",
       "      <td>8.35</td>\n",
       "      <td>8.11</td>\n",
       "      <td>8.35</td>\n",
       "      <td>8.17</td>\n",
       "      <td>0.18</td>\n",
       "      <td>2.2032</td>\n",
       "      <td>164406.32</td>\n",
       "      <td>135062.445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>600728.SH</td>\n",
       "      <td>20190809</td>\n",
       "      <td>8.46</td>\n",
       "      <td>8.48</td>\n",
       "      <td>8.14</td>\n",
       "      <td>8.17</td>\n",
       "      <td>8.37</td>\n",
       "      <td>-0.20</td>\n",
       "      <td>-2.3895</td>\n",
       "      <td>178771.78</td>\n",
       "      <td>147988.101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>600728.SH</td>\n",
       "      <td>20190808</td>\n",
       "      <td>8.41</td>\n",
       "      <td>8.59</td>\n",
       "      <td>8.32</td>\n",
       "      <td>8.37</td>\n",
       "      <td>8.39</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.2384</td>\n",
       "      <td>160553.28</td>\n",
       "      <td>135400.840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>600728.SH</td>\n",
       "      <td>20190807</td>\n",
       "      <td>8.33</td>\n",
       "      <td>8.52</td>\n",
       "      <td>8.28</td>\n",
       "      <td>8.39</td>\n",
       "      <td>8.25</td>\n",
       "      <td>0.14</td>\n",
       "      <td>1.6970</td>\n",
       "      <td>301721.98</td>\n",
       "      <td>253990.280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>600728.SH</td>\n",
       "      <td>20190806</td>\n",
       "      <td>8.54</td>\n",
       "      <td>8.60</td>\n",
       "      <td>7.95</td>\n",
       "      <td>8.25</td>\n",
       "      <td>8.76</td>\n",
       "      <td>-0.51</td>\n",
       "      <td>-5.8219</td>\n",
       "      <td>451948.13</td>\n",
       "      <td>371222.820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>600728.SH</td>\n",
       "      <td>20190805</td>\n",
       "      <td>8.82</td>\n",
       "      <td>9.01</td>\n",
       "      <td>8.76</td>\n",
       "      <td>8.76</td>\n",
       "      <td>8.83</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>-0.7928</td>\n",
       "      <td>187707.36</td>\n",
       "      <td>166475.207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>600728.SH</td>\n",
       "      <td>20190802</td>\n",
       "      <td>8.94</td>\n",
       "      <td>9.02</td>\n",
       "      <td>8.75</td>\n",
       "      <td>8.83</td>\n",
       "      <td>9.13</td>\n",
       "      <td>-0.30</td>\n",
       "      <td>-3.2859</td>\n",
       "      <td>273041.90</td>\n",
       "      <td>242080.738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>600728.SH</td>\n",
       "      <td>20190801</td>\n",
       "      <td>9.07</td>\n",
       "      <td>9.19</td>\n",
       "      <td>9.03</td>\n",
       "      <td>9.13</td>\n",
       "      <td>9.13</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>132460.84</td>\n",
       "      <td>120723.645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>600728.SH</td>\n",
       "      <td>20190731</td>\n",
       "      <td>9.36</td>\n",
       "      <td>9.36</td>\n",
       "      <td>9.10</td>\n",
       "      <td>9.13</td>\n",
       "      <td>9.36</td>\n",
       "      <td>-0.23</td>\n",
       "      <td>-2.4573</td>\n",
       "      <td>216797.05</td>\n",
       "      <td>199230.316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>600728.SH</td>\n",
       "      <td>20190730</td>\n",
       "      <td>9.34</td>\n",
       "      <td>9.44</td>\n",
       "      <td>9.30</td>\n",
       "      <td>9.36</td>\n",
       "      <td>9.35</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.1070</td>\n",
       "      <td>144095.65</td>\n",
       "      <td>135113.789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>600728.SH</td>\n",
       "      <td>20190729</td>\n",
       "      <td>9.35</td>\n",
       "      <td>9.44</td>\n",
       "      <td>9.25</td>\n",
       "      <td>9.35</td>\n",
       "      <td>9.32</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.3219</td>\n",
       "      <td>134302.00</td>\n",
       "      <td>125325.425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>600728.SH</td>\n",
       "      <td>20190726</td>\n",
       "      <td>9.31</td>\n",
       "      <td>9.38</td>\n",
       "      <td>9.28</td>\n",
       "      <td>9.32</td>\n",
       "      <td>9.38</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>-0.6397</td>\n",
       "      <td>112085.87</td>\n",
       "      <td>104472.900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>600728.SH</td>\n",
       "      <td>20190725</td>\n",
       "      <td>9.36</td>\n",
       "      <td>9.42</td>\n",
       "      <td>9.28</td>\n",
       "      <td>9.38</td>\n",
       "      <td>9.35</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.3209</td>\n",
       "      <td>154812.27</td>\n",
       "      <td>144930.535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>600728.SH</td>\n",
       "      <td>20190724</td>\n",
       "      <td>9.22</td>\n",
       "      <td>9.46</td>\n",
       "      <td>9.22</td>\n",
       "      <td>9.35</td>\n",
       "      <td>9.15</td>\n",
       "      <td>0.20</td>\n",
       "      <td>2.1858</td>\n",
       "      <td>235305.09</td>\n",
       "      <td>220709.521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>600728.SH</td>\n",
       "      <td>20190723</td>\n",
       "      <td>9.04</td>\n",
       "      <td>9.22</td>\n",
       "      <td>9.04</td>\n",
       "      <td>9.15</td>\n",
       "      <td>9.04</td>\n",
       "      <td>0.11</td>\n",
       "      <td>1.2168</td>\n",
       "      <td>126654.07</td>\n",
       "      <td>115975.871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>600728.SH</td>\n",
       "      <td>20190722</td>\n",
       "      <td>9.22</td>\n",
       "      <td>9.28</td>\n",
       "      <td>8.96</td>\n",
       "      <td>9.04</td>\n",
       "      <td>9.23</td>\n",
       "      <td>-0.19</td>\n",
       "      <td>-2.0585</td>\n",
       "      <td>185134.86</td>\n",
       "      <td>168462.680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>600728.SH</td>\n",
       "      <td>20190719</td>\n",
       "      <td>9.13</td>\n",
       "      <td>9.43</td>\n",
       "      <td>9.13</td>\n",
       "      <td>9.23</td>\n",
       "      <td>9.11</td>\n",
       "      <td>0.12</td>\n",
       "      <td>1.3172</td>\n",
       "      <td>202094.68</td>\n",
       "      <td>187522.216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>600728.SH</td>\n",
       "      <td>20190718</td>\n",
       "      <td>9.26</td>\n",
       "      <td>9.28</td>\n",
       "      <td>9.05</td>\n",
       "      <td>9.11</td>\n",
       "      <td>9.29</td>\n",
       "      <td>-0.18</td>\n",
       "      <td>-1.9376</td>\n",
       "      <td>159253.03</td>\n",
       "      <td>145872.107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>600728.SH</td>\n",
       "      <td>20190717</td>\n",
       "      <td>9.33</td>\n",
       "      <td>9.44</td>\n",
       "      <td>9.25</td>\n",
       "      <td>9.29</td>\n",
       "      <td>9.30</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.1075</td>\n",
       "      <td>164234.77</td>\n",
       "      <td>153509.970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>600728.SH</td>\n",
       "      <td>20190716</td>\n",
       "      <td>9.34</td>\n",
       "      <td>9.48</td>\n",
       "      <td>9.24</td>\n",
       "      <td>9.30</td>\n",
       "      <td>9.30</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>199952.51</td>\n",
       "      <td>186588.271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>600728.SH</td>\n",
       "      <td>20190715</td>\n",
       "      <td>9.04</td>\n",
       "      <td>9.38</td>\n",
       "      <td>8.93</td>\n",
       "      <td>9.30</td>\n",
       "      <td>8.94</td>\n",
       "      <td>0.36</td>\n",
       "      <td>4.0268</td>\n",
       "      <td>336733.15</td>\n",
       "      <td>309359.313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>600728.SH</td>\n",
       "      <td>20190712</td>\n",
       "      <td>9.27</td>\n",
       "      <td>9.37</td>\n",
       "      <td>8.89</td>\n",
       "      <td>8.94</td>\n",
       "      <td>9.19</td>\n",
       "      <td>-0.25</td>\n",
       "      <td>-2.7203</td>\n",
       "      <td>429468.52</td>\n",
       "      <td>390292.644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>600728.SH</td>\n",
       "      <td>20190711</td>\n",
       "      <td>9.90</td>\n",
       "      <td>9.94</td>\n",
       "      <td>9.05</td>\n",
       "      <td>9.19</td>\n",
       "      <td>9.85</td>\n",
       "      <td>-0.66</td>\n",
       "      <td>-6.7005</td>\n",
       "      <td>343116.41</td>\n",
       "      <td>326476.368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>600728.SH</td>\n",
       "      <td>20190710</td>\n",
       "      <td>9.96</td>\n",
       "      <td>9.98</td>\n",
       "      <td>9.83</td>\n",
       "      <td>9.85</td>\n",
       "      <td>9.91</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>-0.6054</td>\n",
       "      <td>79307.76</td>\n",
       "      <td>78427.452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>600728.SH</td>\n",
       "      <td>20190709</td>\n",
       "      <td>9.90</td>\n",
       "      <td>9.95</td>\n",
       "      <td>9.80</td>\n",
       "      <td>9.91</td>\n",
       "      <td>9.93</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.2014</td>\n",
       "      <td>105024.89</td>\n",
       "      <td>103779.292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>600728.SH</td>\n",
       "      <td>20190708</td>\n",
       "      <td>10.07</td>\n",
       "      <td>10.07</td>\n",
       "      <td>9.82</td>\n",
       "      <td>9.93</td>\n",
       "      <td>10.07</td>\n",
       "      <td>-0.14</td>\n",
       "      <td>-1.3903</td>\n",
       "      <td>189208.49</td>\n",
       "      <td>187576.185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>600728.SH</td>\n",
       "      <td>20190705</td>\n",
       "      <td>10.04</td>\n",
       "      <td>10.10</td>\n",
       "      <td>10.03</td>\n",
       "      <td>10.07</td>\n",
       "      <td>10.02</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.4990</td>\n",
       "      <td>130502.30</td>\n",
       "      <td>131436.674</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      ts_code trade_date   open   high    low  close  pre_close  change  \\\n",
       "0   600728.SH   20191129   8.66   8.91   8.64   8.75       8.63    0.12   \n",
       "1   600728.SH   20191128   8.60   8.71   8.55   8.63       8.59    0.04   \n",
       "2   600728.SH   20191127   8.51   8.64   8.51   8.59       8.53    0.06   \n",
       "3   600728.SH   20191126   8.63   8.66   8.51   8.53       8.59   -0.06   \n",
       "4   600728.SH   20191125   8.71   8.76   8.50   8.59       8.70   -0.11   \n",
       "5   600728.SH   20191122   8.87   8.97   8.69   8.70       8.86   -0.16   \n",
       "6   600728.SH   20191121   8.94   8.94   8.80   8.86       8.93   -0.07   \n",
       "7   600728.SH   20191120   9.06   9.12   8.91   8.93       9.05   -0.12   \n",
       "8   600728.SH   20191119   8.88   9.10   8.88   9.05       8.87    0.18   \n",
       "9   600728.SH   20191118   8.78   8.90   8.76   8.87       8.82    0.05   \n",
       "10  600728.SH   20191115   8.91   9.02   8.82   8.82       8.98   -0.16   \n",
       "11  600728.SH   20191114   8.81   9.03   8.81   8.98       8.84    0.14   \n",
       "12  600728.SH   20191113   8.91   8.95   8.80   8.84       8.96   -0.12   \n",
       "13  600728.SH   20191112   8.89   9.00   8.76   8.96       8.89    0.07   \n",
       "14  600728.SH   20191111   9.13   9.13   8.83   8.89       9.24   -0.35   \n",
       "15  600728.SH   20191108   9.32   9.38   9.21   9.24       9.23    0.01   \n",
       "16  600728.SH   20191107   9.32   9.37   9.17   9.23       9.32   -0.09   \n",
       "17  600728.SH   20191106   9.50   9.64   9.24   9.32       9.52   -0.20   \n",
       "18  600728.SH   20191105   9.65   9.66   9.45   9.52       9.62   -0.10   \n",
       "19  600728.SH   20191104   9.72   9.76   9.57   9.62       9.65   -0.03   \n",
       "20  600728.SH   20191101   9.49   9.72   9.43   9.65       9.54    0.11   \n",
       "21  600728.SH   20191031   9.65   9.84   9.52   9.54       9.63   -0.09   \n",
       "22  600728.SH   20191030   9.78   9.89   9.63   9.63       9.92   -0.29   \n",
       "23  600728.SH   20191029   9.72  10.15   9.70   9.92       9.78    0.14   \n",
       "24  600728.SH   20191028   9.70   9.96   9.55   9.78       9.42    0.36   \n",
       "25  600728.SH   20191025   9.29   9.45   9.20   9.42       9.33    0.09   \n",
       "26  600728.SH   20191024   9.40   9.44   9.26   9.33       9.41   -0.08   \n",
       "27  600728.SH   20191023   9.24   9.43   9.13   9.41       9.22    0.19   \n",
       "28  600728.SH   20191022   9.20   9.26   9.08   9.22       9.19    0.03   \n",
       "29  600728.SH   20191021   9.34   9.37   9.09   9.19       9.52   -0.33   \n",
       "..        ...        ...    ...    ...    ...    ...        ...     ...   \n",
       "70  600728.SH   20190815   7.93   8.29   7.90   8.26       8.19    0.07   \n",
       "71  600728.SH   20190814   8.30   8.38   8.16   8.19       8.16    0.03   \n",
       "72  600728.SH   20190813   8.22   8.23   8.12   8.16       8.35   -0.19   \n",
       "73  600728.SH   20190812   8.24   8.35   8.11   8.35       8.17    0.18   \n",
       "74  600728.SH   20190809   8.46   8.48   8.14   8.17       8.37   -0.20   \n",
       "75  600728.SH   20190808   8.41   8.59   8.32   8.37       8.39   -0.02   \n",
       "76  600728.SH   20190807   8.33   8.52   8.28   8.39       8.25    0.14   \n",
       "77  600728.SH   20190806   8.54   8.60   7.95   8.25       8.76   -0.51   \n",
       "78  600728.SH   20190805   8.82   9.01   8.76   8.76       8.83   -0.07   \n",
       "79  600728.SH   20190802   8.94   9.02   8.75   8.83       9.13   -0.30   \n",
       "80  600728.SH   20190801   9.07   9.19   9.03   9.13       9.13    0.00   \n",
       "81  600728.SH   20190731   9.36   9.36   9.10   9.13       9.36   -0.23   \n",
       "82  600728.SH   20190730   9.34   9.44   9.30   9.36       9.35    0.01   \n",
       "83  600728.SH   20190729   9.35   9.44   9.25   9.35       9.32    0.03   \n",
       "84  600728.SH   20190726   9.31   9.38   9.28   9.32       9.38   -0.06   \n",
       "85  600728.SH   20190725   9.36   9.42   9.28   9.38       9.35    0.03   \n",
       "86  600728.SH   20190724   9.22   9.46   9.22   9.35       9.15    0.20   \n",
       "87  600728.SH   20190723   9.04   9.22   9.04   9.15       9.04    0.11   \n",
       "88  600728.SH   20190722   9.22   9.28   8.96   9.04       9.23   -0.19   \n",
       "89  600728.SH   20190719   9.13   9.43   9.13   9.23       9.11    0.12   \n",
       "90  600728.SH   20190718   9.26   9.28   9.05   9.11       9.29   -0.18   \n",
       "91  600728.SH   20190717   9.33   9.44   9.25   9.29       9.30   -0.01   \n",
       "92  600728.SH   20190716   9.34   9.48   9.24   9.30       9.30    0.00   \n",
       "93  600728.SH   20190715   9.04   9.38   8.93   9.30       8.94    0.36   \n",
       "94  600728.SH   20190712   9.27   9.37   8.89   8.94       9.19   -0.25   \n",
       "95  600728.SH   20190711   9.90   9.94   9.05   9.19       9.85   -0.66   \n",
       "96  600728.SH   20190710   9.96   9.98   9.83   9.85       9.91   -0.06   \n",
       "97  600728.SH   20190709   9.90   9.95   9.80   9.91       9.93   -0.02   \n",
       "98  600728.SH   20190708  10.07  10.07   9.82   9.93      10.07   -0.14   \n",
       "99  600728.SH   20190705  10.04  10.10  10.03  10.07      10.02    0.05   \n",
       "\n",
       "    pct_chg        vol      amount  \n",
       "0    1.3905  184276.00  162093.411  \n",
       "1    0.4657  106042.77   91568.299  \n",
       "2    0.7034   86742.01   74492.989  \n",
       "3   -0.6985   86060.74   73798.775  \n",
       "4   -1.2644  119025.72  102305.740  \n",
       "5   -1.8059  167303.24  146969.877  \n",
       "6   -0.7839  113459.72  100401.970  \n",
       "7   -1.3260  120809.72  108857.281  \n",
       "8    2.0293  170884.54  154196.044  \n",
       "9    0.5669   91700.23   81184.896  \n",
       "10  -1.7817  115782.48  103171.668  \n",
       "11   1.5837  147286.36  131738.881  \n",
       "12  -1.3393  116428.85  103304.102  \n",
       "13   0.7874  182276.73  161238.810  \n",
       "14  -3.7879  283354.63  254891.298  \n",
       "15   0.1083  200727.41  186619.782  \n",
       "16  -0.9657  267452.37  247950.532  \n",
       "17  -2.1008  252818.20  238642.193  \n",
       "18  -1.0395  217772.79  207688.999  \n",
       "19  -0.3109  248648.61  240074.115  \n",
       "20   1.1530  236710.69  227447.827  \n",
       "21  -0.9346  307085.80  298056.267  \n",
       "22  -2.9234  405416.93  394373.676  \n",
       "23   1.4315  662057.23  654604.818  \n",
       "24   3.8217  585677.93  573473.642  \n",
       "25   0.9646  262108.64  244829.276  \n",
       "26  -0.8502  235745.46  220184.182  \n",
       "27   2.0607  300239.96  277872.194  \n",
       "28   0.3264  255140.78  233907.795  \n",
       "29  -3.4664  386575.43  355105.369  \n",
       "..      ...        ...         ...  \n",
       "70   0.8547  206809.61  168051.562  \n",
       "71   0.3676  170320.21  140988.730  \n",
       "72  -2.2754  131637.16  107405.409  \n",
       "73   2.2032  164406.32  135062.445  \n",
       "74  -2.3895  178771.78  147988.101  \n",
       "75  -0.2384  160553.28  135400.840  \n",
       "76   1.6970  301721.98  253990.280  \n",
       "77  -5.8219  451948.13  371222.820  \n",
       "78  -0.7928  187707.36  166475.207  \n",
       "79  -3.2859  273041.90  242080.738  \n",
       "80   0.0000  132460.84  120723.645  \n",
       "81  -2.4573  216797.05  199230.316  \n",
       "82   0.1070  144095.65  135113.789  \n",
       "83   0.3219  134302.00  125325.425  \n",
       "84  -0.6397  112085.87  104472.900  \n",
       "85   0.3209  154812.27  144930.535  \n",
       "86   2.1858  235305.09  220709.521  \n",
       "87   1.2168  126654.07  115975.871  \n",
       "88  -2.0585  185134.86  168462.680  \n",
       "89   1.3172  202094.68  187522.216  \n",
       "90  -1.9376  159253.03  145872.107  \n",
       "91  -0.1075  164234.77  153509.970  \n",
       "92   0.0000  199952.51  186588.271  \n",
       "93   4.0268  336733.15  309359.313  \n",
       "94  -2.7203  429468.52  390292.644  \n",
       "95  -6.7005  343116.41  326476.368  \n",
       "96  -0.6054   79307.76   78427.452  \n",
       "97  -0.2014  105024.89  103779.292  \n",
       "98  -1.3903  189208.49  187576.185  \n",
       "99   0.4990  130502.30  131436.674  \n",
       "\n",
       "[100 rows x 11 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pro.daily(ts_code='600728.SH',end_date=CURRENTDAY)\n",
    "df.head(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4000, 11)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 获取所有交易标的某天的交易数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ts_code</th>\n",
       "      <th>trade_date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>pre_close</th>\n",
       "      <th>change</th>\n",
       "      <th>pct_chg</th>\n",
       "      <th>vol</th>\n",
       "      <th>amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>20181120</td>\n",
       "      <td>10.75</td>\n",
       "      <td>10.80</td>\n",
       "      <td>10.52</td>\n",
       "      <td>10.57</td>\n",
       "      <td>10.85</td>\n",
       "      <td>-0.28</td>\n",
       "      <td>-2.5806</td>\n",
       "      <td>1033877.53</td>\n",
       "      <td>1099654.683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>000002.SZ</td>\n",
       "      <td>20181120</td>\n",
       "      <td>25.78</td>\n",
       "      <td>26.43</td>\n",
       "      <td>25.23</td>\n",
       "      <td>25.65</td>\n",
       "      <td>25.97</td>\n",
       "      <td>-0.32</td>\n",
       "      <td>-1.2322</td>\n",
       "      <td>474151.80</td>\n",
       "      <td>1221340.780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>000004.SZ</td>\n",
       "      <td>20181120</td>\n",
       "      <td>17.33</td>\n",
       "      <td>17.48</td>\n",
       "      <td>16.80</td>\n",
       "      <td>16.92</td>\n",
       "      <td>17.33</td>\n",
       "      <td>-0.41</td>\n",
       "      <td>-2.3658</td>\n",
       "      <td>4911.00</td>\n",
       "      <td>8369.820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>000005.SZ</td>\n",
       "      <td>20181120</td>\n",
       "      <td>3.11</td>\n",
       "      <td>3.13</td>\n",
       "      <td>3.03</td>\n",
       "      <td>3.06</td>\n",
       "      <td>3.17</td>\n",
       "      <td>-0.11</td>\n",
       "      <td>-3.4700</td>\n",
       "      <td>138100.03</td>\n",
       "      <td>42348.709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>000006.SZ</td>\n",
       "      <td>20181120</td>\n",
       "      <td>5.66</td>\n",
       "      <td>5.70</td>\n",
       "      <td>5.47</td>\n",
       "      <td>5.49</td>\n",
       "      <td>5.81</td>\n",
       "      <td>-0.32</td>\n",
       "      <td>-5.5077</td>\n",
       "      <td>293993.88</td>\n",
       "      <td>164306.995</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     ts_code trade_date   open   high    low  close  pre_close  change  \\\n",
       "0  000001.SZ   20181120  10.75  10.80  10.52  10.57      10.85   -0.28   \n",
       "1  000002.SZ   20181120  25.78  26.43  25.23  25.65      25.97   -0.32   \n",
       "2  000004.SZ   20181120  17.33  17.48  16.80  16.92      17.33   -0.41   \n",
       "3  000005.SZ   20181120   3.11   3.13   3.03   3.06       3.17   -0.11   \n",
       "4  000006.SZ   20181120   5.66   5.70   5.47   5.49       5.81   -0.32   \n",
       "\n",
       "   pct_chg         vol       amount  \n",
       "0  -2.5806  1033877.53  1099654.683  \n",
       "1  -1.2322   474151.80  1221340.780  \n",
       "2  -2.3658     4911.00     8369.820  \n",
       "3  -3.4700   138100.03    42348.709  \n",
       "4  -5.5077   293993.88   164306.995  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pro.daily(trade_date='20181120')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3520, 11)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3 利用20日均线进行量化交易"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ts_code</th>\n",
       "      <th>trade_date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>pre_close</th>\n",
       "      <th>change</th>\n",
       "      <th>pct_chg</th>\n",
       "      <th>vol</th>\n",
       "      <th>amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>600728.SH</td>\n",
       "      <td>20181116</td>\n",
       "      <td>8.35</td>\n",
       "      <td>8.36</td>\n",
       "      <td>7.93</td>\n",
       "      <td>8.15</td>\n",
       "      <td>7.79</td>\n",
       "      <td>0.36</td>\n",
       "      <td>4.6213</td>\n",
       "      <td>1244701.10</td>\n",
       "      <td>1019665.838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>600728.SH</td>\n",
       "      <td>20181115</td>\n",
       "      <td>7.79</td>\n",
       "      <td>7.79</td>\n",
       "      <td>7.79</td>\n",
       "      <td>7.79</td>\n",
       "      <td>7.08</td>\n",
       "      <td>0.71</td>\n",
       "      <td>10.0282</td>\n",
       "      <td>175086.39</td>\n",
       "      <td>136392.298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>600728.SH</td>\n",
       "      <td>20181114</td>\n",
       "      <td>7.06</td>\n",
       "      <td>7.22</td>\n",
       "      <td>7.01</td>\n",
       "      <td>7.08</td>\n",
       "      <td>7.06</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.2833</td>\n",
       "      <td>235544.84</td>\n",
       "      <td>167637.213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>600728.SH</td>\n",
       "      <td>20181113</td>\n",
       "      <td>6.95</td>\n",
       "      <td>7.28</td>\n",
       "      <td>6.95</td>\n",
       "      <td>7.06</td>\n",
       "      <td>7.05</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.1418</td>\n",
       "      <td>310362.62</td>\n",
       "      <td>220376.360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>600728.SH</td>\n",
       "      <td>20181112</td>\n",
       "      <td>6.97</td>\n",
       "      <td>7.06</td>\n",
       "      <td>6.88</td>\n",
       "      <td>7.05</td>\n",
       "      <td>6.90</td>\n",
       "      <td>0.15</td>\n",
       "      <td>2.1739</td>\n",
       "      <td>180260.71</td>\n",
       "      <td>126159.363</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     ts_code trade_date  open  high   low  close  pre_close  change  pct_chg  \\\n",
       "0  600728.SH   20181116  8.35  8.36  7.93   8.15       7.79    0.36   4.6213   \n",
       "1  600728.SH   20181115  7.79  7.79  7.79   7.79       7.08    0.71  10.0282   \n",
       "2  600728.SH   20181114  7.06  7.22  7.01   7.08       7.06    0.02   0.2833   \n",
       "3  600728.SH   20181113  6.95  7.28  6.95   7.06       7.05    0.01   0.1418   \n",
       "4  600728.SH   20181112  6.97  7.06  6.88   7.05       6.90    0.15   2.1739   \n",
       "\n",
       "          vol       amount  \n",
       "0  1244701.10  1019665.838  \n",
       "1   175086.39   136392.298  \n",
       "2   235544.84   167637.213  \n",
       "3   310362.62   220376.360  \n",
       "4   180260.71   126159.363  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#某标的某段时间交易数据，不含均线数据\n",
    "df = pro.daily(ts_code='600728.sh', end_date='20181118')\n",
    "df.head() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>close</th>\n",
       "      <th>low</th>\n",
       "      <th>volume</th>\n",
       "      <th>price_change</th>\n",
       "      <th>p_change</th>\n",
       "      <th>ma5</th>\n",
       "      <th>ma10</th>\n",
       "      <th>ma20</th>\n",
       "      <th>v_ma5</th>\n",
       "      <th>v_ma10</th>\n",
       "      <th>v_ma20</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-11-18</th>\n",
       "      <td>8.78</td>\n",
       "      <td>8.90</td>\n",
       "      <td>8.87</td>\n",
       "      <td>8.76</td>\n",
       "      <td>86821.23</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.57</td>\n",
       "      <td>8.894</td>\n",
       "      <td>9.067</td>\n",
       "      <td>9.310</td>\n",
       "      <td>129719.13</td>\n",
       "      <td>187072.11</td>\n",
       "      <td>268477.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-15</th>\n",
       "      <td>8.91</td>\n",
       "      <td>9.02</td>\n",
       "      <td>8.82</td>\n",
       "      <td>8.82</td>\n",
       "      <td>115782.48</td>\n",
       "      <td>-0.16</td>\n",
       "      <td>-1.78</td>\n",
       "      <td>8.898</td>\n",
       "      <td>9.142</td>\n",
       "      <td>9.326</td>\n",
       "      <td>169025.81</td>\n",
       "      <td>203254.84</td>\n",
       "      <td>283465.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-14</th>\n",
       "      <td>8.81</td>\n",
       "      <td>9.03</td>\n",
       "      <td>8.98</td>\n",
       "      <td>8.81</td>\n",
       "      <td>147286.36</td>\n",
       "      <td>0.14</td>\n",
       "      <td>1.58</td>\n",
       "      <td>8.982</td>\n",
       "      <td>9.225</td>\n",
       "      <td>9.361</td>\n",
       "      <td>186014.79</td>\n",
       "      <td>215347.67</td>\n",
       "      <td>313837.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-13</th>\n",
       "      <td>8.91</td>\n",
       "      <td>8.95</td>\n",
       "      <td>8.84</td>\n",
       "      <td>8.80</td>\n",
       "      <td>116428.85</td>\n",
       "      <td>-0.12</td>\n",
       "      <td>-1.34</td>\n",
       "      <td>9.032</td>\n",
       "      <td>9.281</td>\n",
       "      <td>9.368</td>\n",
       "      <td>210048.00</td>\n",
       "      <td>231327.61</td>\n",
       "      <td>318427.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-12</th>\n",
       "      <td>8.89</td>\n",
       "      <td>9.00</td>\n",
       "      <td>8.96</td>\n",
       "      <td>8.76</td>\n",
       "      <td>182276.73</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.79</td>\n",
       "      <td>9.128</td>\n",
       "      <td>9.360</td>\n",
       "      <td>9.388</td>\n",
       "      <td>237325.87</td>\n",
       "      <td>260226.42</td>\n",
       "      <td>323683.86</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            open  high  close   low     volume  price_change  p_change    ma5  \\\n",
       "date                                                                            \n",
       "2019-11-18  8.78  8.90   8.87  8.76   86821.23          0.05      0.57  8.894   \n",
       "2019-11-15  8.91  9.02   8.82  8.82  115782.48         -0.16     -1.78  8.898   \n",
       "2019-11-14  8.81  9.03   8.98  8.81  147286.36          0.14      1.58  8.982   \n",
       "2019-11-13  8.91  8.95   8.84  8.80  116428.85         -0.12     -1.34  9.032   \n",
       "2019-11-12  8.89  9.00   8.96  8.76  182276.73          0.07      0.79  9.128   \n",
       "\n",
       "             ma10   ma20      v_ma5     v_ma10     v_ma20  \n",
       "date                                                       \n",
       "2019-11-18  9.067  9.310  129719.13  187072.11  268477.66  \n",
       "2019-11-15  9.142  9.326  169025.81  203254.84  283465.37  \n",
       "2019-11-14  9.225  9.361  186014.79  215347.67  313837.70  \n",
       "2019-11-13  9.281  9.368  210048.00  231327.61  318427.03  \n",
       "2019-11-12  9.360  9.388  237325.87  260226.42  323683.86  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#某标的某段时间交易数据，含均线数据\n",
    "df = ts.get_hist_data('600728')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(609, 13)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "def parse(scode):\n",
    "    '''process stock'''\n",
    "    is_buy    = 0\n",
    "    buy_val   = []\n",
    "    buy_date  = []\n",
    "    sell_val  = []\n",
    "    sell_date = []\n",
    "    df = ts.get_hist_data(scode)\n",
    "    ma10 = df[u'ma10']\n",
    "    close = df[u'close']\n",
    "    rate = 1.0\n",
    "    idx = len(ma10)\n",
    "    while idx > 0:\n",
    "        idx -= 1\n",
    "        close_val = close[idx]\n",
    "        ma10_val = ma10[idx]\n",
    "        if close_val > ma10_val:\n",
    "                if is_buy == 0:\n",
    "                        is_buy = 1\n",
    "                        buy_val.append(close_val)\n",
    "                        buy_date.append(close.keys()[idx])\n",
    "        elif close_val < ma10_val:\n",
    "                if is_buy == 1:\n",
    "                        is_buy = 0\n",
    "                        sell_val.append(close_val)\n",
    "                        sell_date.append(close.keys()[idx])\n",
    "\n",
    "    print(\"stock number: %s\" %STOCK)\n",
    "    print(\"buy count   : %d\" %len(buy_val))\n",
    "    print(\"sell count  : %d\" %len(sell_val))\n",
    "\n",
    "    for i in range(len(sell_val)):\n",
    "        rate = rate * (sell_val[i] * (1 - 0.002) / buy_val[i])\n",
    "        print(\"buy date : %s, buy price : %.2f\" %(buy_date[i], buy_val[i]))\n",
    "        print(\"sell date: %s, sell price: %.2f\" %(sell_date[i], sell_val[i]))\n",
    "\n",
    "    print(\"rate: %.2f\" % rate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stock number: 600728\n",
      "buy count   : 45\n",
      "sell count  : 45\n",
      "buy date : 2017-05-24, buy price : 7.20\n",
      "sell date: 2017-06-01, sell price: 7.24\n",
      "buy date : 2017-06-02, buy price : 7.37\n",
      "sell date: 2017-06-09, sell price: 7.35\n",
      "buy date : 2017-06-21, buy price : 7.37\n",
      "sell date: 2017-07-17, sell price: 7.87\n",
      "buy date : 2017-07-20, buy price : 8.89\n",
      "sell date: 2017-08-01, sell price: 8.62\n",
      "buy date : 2017-08-07, buy price : 8.58\n",
      "sell date: 2017-09-14, sell price: 11.34\n",
      "buy date : 2017-10-09, buy price : 11.00\n",
      "sell date: 2017-10-11, sell price: 10.70\n",
      "buy date : 2017-10-30, buy price : 10.13\n",
      "sell date: 2017-11-03, sell price: 9.53\n",
      "buy date : 2017-11-07, buy price : 9.90\n",
      "sell date: 2017-11-22, sell price: 10.18\n",
      "buy date : 2017-12-07, buy price : 8.64\n",
      "sell date: 2017-12-12, sell price: 8.52\n",
      "buy date : 2017-12-15, buy price : 8.58\n",
      "sell date: 2017-12-18, sell price: 8.40\n",
      "buy date : 2017-12-29, buy price : 8.25\n",
      "sell date: 2018-01-12, sell price: 8.40\n",
      "buy date : 2018-02-14, buy price : 6.89\n",
      "sell date: 2018-03-19, sell price: 10.65\n",
      "buy date : 2018-03-22, buy price : 10.76\n",
      "sell date: 2018-03-23, sell price: 9.67\n",
      "buy date : 2018-04-02, buy price : 10.37\n",
      "sell date: 2018-04-03, sell price: 9.97\n",
      "buy date : 2018-04-23, buy price : 9.63\n",
      "sell date: 2018-04-26, sell price: 9.07\n",
      "buy date : 2018-05-15, buy price : 8.53\n",
      "sell date: 2018-05-16, sell price: 8.39\n",
      "buy date : 2018-05-23, buy price : 8.33\n",
      "sell date: 2018-05-25, sell price: 8.06\n",
      "buy date : 2018-06-05, buy price : 7.93\n",
      "sell date: 2018-06-13, sell price: 7.93\n",
      "buy date : 2018-06-29, buy price : 7.29\n",
      "sell date: 2018-07-05, sell price: 6.81\n",
      "buy date : 2018-07-09, buy price : 7.09\n",
      "sell date: 2018-07-11, sell price: 7.01\n",
      "buy date : 2018-07-12, buy price : 7.24\n",
      "sell date: 2018-07-27, sell price: 7.43\n",
      "buy date : 2018-08-09, buy price : 7.22\n",
      "sell date: 2018-08-17, sell price: 7.01\n",
      "buy date : 2018-08-27, buy price : 7.22\n",
      "sell date: 2018-08-30, sell price: 7.05\n",
      "buy date : 2018-09-19, buy price : 6.81\n",
      "sell date: 2018-09-27, sell price: 6.63\n",
      "buy date : 2018-09-28, buy price : 7.01\n",
      "sell date: 2018-10-11, sell price: 6.30\n",
      "buy date : 2018-10-26, buy price : 6.05\n",
      "sell date: 2018-10-29, sell price: 5.95\n",
      "buy date : 2018-10-30, buy price : 6.11\n",
      "sell date: 2018-11-28, sell price: 7.90\n",
      "buy date : 2018-12-03, buy price : 8.08\n",
      "sell date: 2018-12-05, sell price: 7.88\n",
      "buy date : 2019-01-04, buy price : 6.95\n",
      "sell date: 2019-01-30, sell price: 7.92\n",
      "buy date : 2019-02-01, buy price : 8.13\n",
      "sell date: 2019-03-14, sell price: 11.03\n",
      "buy date : 2019-03-19, buy price : 11.89\n",
      "sell date: 2019-03-28, sell price: 12.04\n",
      "buy date : 2019-04-01, buy price : 12.65\n",
      "sell date: 2019-04-04, sell price: 12.38\n",
      "buy date : 2019-04-24, buy price : 11.37\n",
      "sell date: 2019-04-25, sell price: 10.67\n",
      "buy date : 2019-05-10, buy price : 10.51\n",
      "sell date: 2019-05-13, sell price: 10.20\n",
      "buy date : 2019-05-15, buy price : 10.35\n",
      "sell date: 2019-05-17, sell price: 10.08\n",
      "buy date : 2019-05-21, buy price : 10.28\n",
      "sell date: 2019-05-23, sell price: 10.08\n",
      "buy date : 2019-05-27, buy price : 10.30\n",
      "sell date: 2019-05-30, sell price: 10.19\n",
      "buy date : 2019-06-11, buy price : 10.20\n",
      "sell date: 2019-06-14, sell price: 9.88\n",
      "buy date : 2019-06-18, buy price : 10.05\n",
      "sell date: 2019-06-25, sell price: 10.06\n",
      "buy date : 2019-07-01, buy price : 10.33\n",
      "sell date: 2019-07-03, sell price: 10.03\n",
      "buy date : 2019-07-24, buy price : 9.35\n",
      "sell date: 2019-07-31, sell price: 9.13\n",
      "buy date : 2019-08-16, buy price : 8.76\n",
      "sell date: 2019-09-17, sell price: 9.89\n",
      "buy date : 2019-09-27, buy price : 10.32\n",
      "sell date: 2019-10-09, sell price: 9.05\n",
      "buy date : 2019-10-18, buy price : 9.52\n",
      "sell date: 2019-10-21, sell price: 9.19\n",
      "buy date : 2019-10-22, buy price : 9.22\n",
      "sell date: 2019-11-05, sell price: 9.52\n",
      "rate: 1.62\n"
     ]
    }
   ],
   "source": [
    "STOCK = '600728'       ##佳都\n",
    "parse(STOCK)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4 选取某三个行业中上市公司若干关键指标（市盈率，市净率等）的相关性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import OrderedDict \n",
    "import numpy as np \n",
    "import pandas as pd \n",
    "import datetime as dt \n",
    "import seaborn as sns \n",
    "import tushare as ts \n",
    "#from bokeh.io import output_file,show\n",
    "#from bokeh.plotting import figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>industry</th>\n",
       "      <th>area</th>\n",
       "      <th>pe</th>\n",
       "      <th>outstanding</th>\n",
       "      <th>totals</th>\n",
       "      <th>totalAssets</th>\n",
       "      <th>liquidAssets</th>\n",
       "      <th>fixedAssets</th>\n",
       "      <th>reserved</th>\n",
       "      <th>...</th>\n",
       "      <th>bvps</th>\n",
       "      <th>pb</th>\n",
       "      <th>timeToMarket</th>\n",
       "      <th>undp</th>\n",
       "      <th>perundp</th>\n",
       "      <th>rev</th>\n",
       "      <th>profit</th>\n",
       "      <th>gpr</th>\n",
       "      <th>npr</th>\n",
       "      <th>holders</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>code</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>002517</th>\n",
       "      <td>恺英网络</td>\n",
       "      <td>互联网</td>\n",
       "      <td>福建</td>\n",
       "      <td>63.59</td>\n",
       "      <td>16.00</td>\n",
       "      <td>21.53</td>\n",
       "      <td>61.68</td>\n",
       "      <td>24.85</td>\n",
       "      <td>0.24</td>\n",
       "      <td>7.40</td>\n",
       "      <td>...</td>\n",
       "      <td>2.29</td>\n",
       "      <td>1.23</td>\n",
       "      <td>20101207</td>\n",
       "      <td>26.48</td>\n",
       "      <td>1.23</td>\n",
       "      <td>-13.31</td>\n",
       "      <td>-85.51</td>\n",
       "      <td>68.63</td>\n",
       "      <td>4.74</td>\n",
       "      <td>75843.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>002247</th>\n",
       "      <td>聚力文化</td>\n",
       "      <td>互联网</td>\n",
       "      <td>浙江</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.45</td>\n",
       "      <td>8.51</td>\n",
       "      <td>31.50</td>\n",
       "      <td>22.43</td>\n",
       "      <td>3.44</td>\n",
       "      <td>30.24</td>\n",
       "      <td>...</td>\n",
       "      <td>2.37</td>\n",
       "      <td>1.24</td>\n",
       "      <td>20080612</td>\n",
       "      <td>-19.15</td>\n",
       "      <td>-2.25</td>\n",
       "      <td>-20.24</td>\n",
       "      <td>-102.08</td>\n",
       "      <td>10.87</td>\n",
       "      <td>-0.32</td>\n",
       "      <td>32982.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>002528</th>\n",
       "      <td>英飞拓</td>\n",
       "      <td>IT设备</td>\n",
       "      <td>深圳</td>\n",
       "      <td>234.38</td>\n",
       "      <td>10.46</td>\n",
       "      <td>11.99</td>\n",
       "      <td>60.70</td>\n",
       "      <td>36.85</td>\n",
       "      <td>1.22</td>\n",
       "      <td>23.01</td>\n",
       "      <td>...</td>\n",
       "      <td>3.11</td>\n",
       "      <td>1.75</td>\n",
       "      <td>20101224</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.04</td>\n",
       "      <td>8.88</td>\n",
       "      <td>-68.24</td>\n",
       "      <td>20.22</td>\n",
       "      <td>0.70</td>\n",
       "      <td>51316.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600770</th>\n",
       "      <td>综艺股份</td>\n",
       "      <td>综合类</td>\n",
       "      <td>江苏</td>\n",
       "      <td>329.71</td>\n",
       "      <td>13.00</td>\n",
       "      <td>13.00</td>\n",
       "      <td>58.76</td>\n",
       "      <td>23.69</td>\n",
       "      <td>11.64</td>\n",
       "      <td>22.26</td>\n",
       "      <td>...</td>\n",
       "      <td>2.67</td>\n",
       "      <td>2.04</td>\n",
       "      <td>19961120</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-2.61</td>\n",
       "      <td>-81.03</td>\n",
       "      <td>28.20</td>\n",
       "      <td>4.48</td>\n",
       "      <td>76440.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>601599</th>\n",
       "      <td>鹿港文化</td>\n",
       "      <td>影视音像</td>\n",
       "      <td>江苏</td>\n",
       "      <td>24.69</td>\n",
       "      <td>8.93</td>\n",
       "      <td>8.93</td>\n",
       "      <td>64.79</td>\n",
       "      <td>45.49</td>\n",
       "      <td>8.83</td>\n",
       "      <td>9.69</td>\n",
       "      <td>...</td>\n",
       "      <td>2.94</td>\n",
       "      <td>1.08</td>\n",
       "      <td>20110527</td>\n",
       "      <td>6.88</td>\n",
       "      <td>0.77</td>\n",
       "      <td>-18.14</td>\n",
       "      <td>32.66</td>\n",
       "      <td>18.72</td>\n",
       "      <td>3.41</td>\n",
       "      <td>34574.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000859</th>\n",
       "      <td>国风塑业</td>\n",
       "      <td>塑料</td>\n",
       "      <td>安徽</td>\n",
       "      <td>36.15</td>\n",
       "      <td>7.39</td>\n",
       "      <td>7.39</td>\n",
       "      <td>21.01</td>\n",
       "      <td>8.69</td>\n",
       "      <td>7.70</td>\n",
       "      <td>5.90</td>\n",
       "      <td>...</td>\n",
       "      <td>2.21</td>\n",
       "      <td>2.18</td>\n",
       "      <td>19981119</td>\n",
       "      <td>2.28</td>\n",
       "      <td>0.31</td>\n",
       "      <td>14.62</td>\n",
       "      <td>-1.11</td>\n",
       "      <td>12.34</td>\n",
       "      <td>7.36</td>\n",
       "      <td>72854.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>002502</th>\n",
       "      <td>鼎龙文化</td>\n",
       "      <td>影视音像</td>\n",
       "      <td>广东</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.97</td>\n",
       "      <td>8.60</td>\n",
       "      <td>17.65</td>\n",
       "      <td>12.36</td>\n",
       "      <td>0.69</td>\n",
       "      <td>16.37</td>\n",
       "      <td>...</td>\n",
       "      <td>1.94</td>\n",
       "      <td>1.97</td>\n",
       "      <td>20101117</td>\n",
       "      <td>-8.55</td>\n",
       "      <td>-0.99</td>\n",
       "      <td>512.63</td>\n",
       "      <td>-1494.35</td>\n",
       "      <td>6.84</td>\n",
       "      <td>-58.86</td>\n",
       "      <td>38986.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600599</th>\n",
       "      <td>熊猫金控</td>\n",
       "      <td>多元金融</td>\n",
       "      <td>湖南</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.66</td>\n",
       "      <td>1.66</td>\n",
       "      <td>8.11</td>\n",
       "      <td>7.76</td>\n",
       "      <td>0.01</td>\n",
       "      <td>5.16</td>\n",
       "      <td>...</td>\n",
       "      <td>3.94</td>\n",
       "      <td>2.61</td>\n",
       "      <td>20010828</td>\n",
       "      <td>-0.46</td>\n",
       "      <td>-0.28</td>\n",
       "      <td>-52.67</td>\n",
       "      <td>29.42</td>\n",
       "      <td>35.99</td>\n",
       "      <td>-17.91</td>\n",
       "      <td>28186.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300341</th>\n",
       "      <td>麦克奥迪</td>\n",
       "      <td>电气设备</td>\n",
       "      <td>福建</td>\n",
       "      <td>34.74</td>\n",
       "      <td>5.10</td>\n",
       "      <td>5.10</td>\n",
       "      <td>15.95</td>\n",
       "      <td>10.87</td>\n",
       "      <td>2.86</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>1.86</td>\n",
       "      <td>5.12</td>\n",
       "      <td>20120726</td>\n",
       "      <td>4.05</td>\n",
       "      <td>0.79</td>\n",
       "      <td>14.70</td>\n",
       "      <td>10.20</td>\n",
       "      <td>44.23</td>\n",
       "      <td>13.21</td>\n",
       "      <td>13949.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300459</th>\n",
       "      <td>金科文化</td>\n",
       "      <td>互联网</td>\n",
       "      <td>浙江</td>\n",
       "      <td>12.73</td>\n",
       "      <td>22.69</td>\n",
       "      <td>35.44</td>\n",
       "      <td>113.23</td>\n",
       "      <td>33.05</td>\n",
       "      <td>3.35</td>\n",
       "      <td>21.28</td>\n",
       "      <td>...</td>\n",
       "      <td>2.01</td>\n",
       "      <td>1.53</td>\n",
       "      <td>20150515</td>\n",
       "      <td>19.95</td>\n",
       "      <td>0.56</td>\n",
       "      <td>-9.30</td>\n",
       "      <td>-15.17</td>\n",
       "      <td>77.90</td>\n",
       "      <td>35.20</td>\n",
       "      <td>55934.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000835</th>\n",
       "      <td>长城动漫</td>\n",
       "      <td>影视音像</td>\n",
       "      <td>四川</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.03</td>\n",
       "      <td>3.27</td>\n",
       "      <td>8.22</td>\n",
       "      <td>1.21</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.95</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>-63.07</td>\n",
       "      <td>19990625</td>\n",
       "      <td>-4.41</td>\n",
       "      <td>-1.35</td>\n",
       "      <td>-30.40</td>\n",
       "      <td>-99.28</td>\n",
       "      <td>18.64</td>\n",
       "      <td>-72.93</td>\n",
       "      <td>20000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>002174</th>\n",
       "      <td>游族网络</td>\n",
       "      <td>互联网</td>\n",
       "      <td>福建</td>\n",
       "      <td>15.97</td>\n",
       "      <td>6.45</td>\n",
       "      <td>8.88</td>\n",
       "      <td>94.25</td>\n",
       "      <td>60.20</td>\n",
       "      <td>7.19</td>\n",
       "      <td>8.56</td>\n",
       "      <td>...</td>\n",
       "      <td>5.97</td>\n",
       "      <td>2.83</td>\n",
       "      <td>20070925</td>\n",
       "      <td>39.30</td>\n",
       "      <td>4.42</td>\n",
       "      <td>0.73</td>\n",
       "      <td>3.29</td>\n",
       "      <td>53.90</td>\n",
       "      <td>26.69</td>\n",
       "      <td>39569.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300494</th>\n",
       "      <td>盛天网络</td>\n",
       "      <td>互联网</td>\n",
       "      <td>湖北</td>\n",
       "      <td>82.39</td>\n",
       "      <td>1.48</td>\n",
       "      <td>2.40</td>\n",
       "      <td>14.99</td>\n",
       "      <td>9.99</td>\n",
       "      <td>0.52</td>\n",
       "      <td>3.37</td>\n",
       "      <td>...</td>\n",
       "      <td>4.19</td>\n",
       "      <td>3.95</td>\n",
       "      <td>20151231</td>\n",
       "      <td>3.63</td>\n",
       "      <td>1.51</td>\n",
       "      <td>15.52</td>\n",
       "      <td>-30.71</td>\n",
       "      <td>27.31</td>\n",
       "      <td>8.52</td>\n",
       "      <td>22906.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300200</th>\n",
       "      <td>高盟新材</td>\n",
       "      <td>化工原料</td>\n",
       "      <td>北京</td>\n",
       "      <td>16.49</td>\n",
       "      <td>2.02</td>\n",
       "      <td>2.67</td>\n",
       "      <td>18.22</td>\n",
       "      <td>8.61</td>\n",
       "      <td>2.26</td>\n",
       "      <td>10.73</td>\n",
       "      <td>...</td>\n",
       "      <td>6.03</td>\n",
       "      <td>1.91</td>\n",
       "      <td>20110407</td>\n",
       "      <td>2.28</td>\n",
       "      <td>0.85</td>\n",
       "      <td>-4.64</td>\n",
       "      <td>109.91</td>\n",
       "      <td>42.68</td>\n",
       "      <td>19.10</td>\n",
       "      <td>13525.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>002892</th>\n",
       "      <td>科力尔</td>\n",
       "      <td>电气设备</td>\n",
       "      <td>湖南</td>\n",
       "      <td>45.31</td>\n",
       "      <td>0.45</td>\n",
       "      <td>1.42</td>\n",
       "      <td>8.18</td>\n",
       "      <td>6.08</td>\n",
       "      <td>1.94</td>\n",
       "      <td>2.98</td>\n",
       "      <td>...</td>\n",
       "      <td>4.68</td>\n",
       "      <td>6.22</td>\n",
       "      <td>20170817</td>\n",
       "      <td>2.03</td>\n",
       "      <td>1.43</td>\n",
       "      <td>21.03</td>\n",
       "      <td>40.97</td>\n",
       "      <td>23.32</td>\n",
       "      <td>10.80</td>\n",
       "      <td>7190.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>002942</th>\n",
       "      <td>新农股份</td>\n",
       "      <td>农药化肥</td>\n",
       "      <td>浙江</td>\n",
       "      <td>19.12</td>\n",
       "      <td>0.30</td>\n",
       "      <td>1.20</td>\n",
       "      <td>12.59</td>\n",
       "      <td>7.63</td>\n",
       "      <td>2.43</td>\n",
       "      <td>3.57</td>\n",
       "      <td>...</td>\n",
       "      <td>7.55</td>\n",
       "      <td>3.94</td>\n",
       "      <td>20181205</td>\n",
       "      <td>3.22</td>\n",
       "      <td>2.68</td>\n",
       "      <td>16.74</td>\n",
       "      <td>28.36</td>\n",
       "      <td>33.81</td>\n",
       "      <td>16.97</td>\n",
       "      <td>19373.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>002733</th>\n",
       "      <td>雄韬股份</td>\n",
       "      <td>电气设备</td>\n",
       "      <td>深圳</td>\n",
       "      <td>37.84</td>\n",
       "      <td>3.27</td>\n",
       "      <td>3.50</td>\n",
       "      <td>47.75</td>\n",
       "      <td>32.63</td>\n",
       "      <td>5.00</td>\n",
       "      <td>13.65</td>\n",
       "      <td>...</td>\n",
       "      <td>7.07</td>\n",
       "      <td>3.14</td>\n",
       "      <td>20141203</td>\n",
       "      <td>7.24</td>\n",
       "      <td>2.07</td>\n",
       "      <td>-4.04</td>\n",
       "      <td>114.30</td>\n",
       "      <td>17.00</td>\n",
       "      <td>7.12</td>\n",
       "      <td>42550.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>002965</th>\n",
       "      <td>祥鑫科技</td>\n",
       "      <td>汽车配件</td>\n",
       "      <td>广东</td>\n",
       "      <td>36.27</td>\n",
       "      <td>0.38</td>\n",
       "      <td>1.51</td>\n",
       "      <td>15.47</td>\n",
       "      <td>9.70</td>\n",
       "      <td>3.27</td>\n",
       "      <td>1.38</td>\n",
       "      <td>...</td>\n",
       "      <td>10.54</td>\n",
       "      <td>3.56</td>\n",
       "      <td>20191025</td>\n",
       "      <td>5.86</td>\n",
       "      <td>3.89</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>25.69</td>\n",
       "      <td>10.45</td>\n",
       "      <td>42000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300796</th>\n",
       "      <td>贝斯美</td>\n",
       "      <td>化工原料</td>\n",
       "      <td>浙江</td>\n",
       "      <td>43.77</td>\n",
       "      <td>0.30</td>\n",
       "      <td>1.21</td>\n",
       "      <td>7.67</td>\n",
       "      <td>3.84</td>\n",
       "      <td>2.36</td>\n",
       "      <td>3.17</td>\n",
       "      <td>...</td>\n",
       "      <td>8.14</td>\n",
       "      <td>3.05</td>\n",
       "      <td>20191115</td>\n",
       "      <td>1.71</td>\n",
       "      <td>1.41</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>30.28</td>\n",
       "      <td>13.77</td>\n",
       "      <td>60576.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>603022</th>\n",
       "      <td>新通联</td>\n",
       "      <td>广告包装</td>\n",
       "      <td>上海</td>\n",
       "      <td>97.84</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>8.35</td>\n",
       "      <td>4.18</td>\n",
       "      <td>1.98</td>\n",
       "      <td>1.35</td>\n",
       "      <td>...</td>\n",
       "      <td>3.17</td>\n",
       "      <td>5.27</td>\n",
       "      <td>20150518</td>\n",
       "      <td>2.77</td>\n",
       "      <td>1.38</td>\n",
       "      <td>1.61</td>\n",
       "      <td>-19.60</td>\n",
       "      <td>20.01</td>\n",
       "      <td>5.14</td>\n",
       "      <td>16168.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>603021</th>\n",
       "      <td>山东华鹏</td>\n",
       "      <td>玻璃</td>\n",
       "      <td>山东</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.20</td>\n",
       "      <td>3.20</td>\n",
       "      <td>29.44</td>\n",
       "      <td>7.17</td>\n",
       "      <td>13.18</td>\n",
       "      <td>6.03</td>\n",
       "      <td>...</td>\n",
       "      <td>4.10</td>\n",
       "      <td>2.28</td>\n",
       "      <td>20150423</td>\n",
       "      <td>3.57</td>\n",
       "      <td>1.12</td>\n",
       "      <td>-3.28</td>\n",
       "      <td>-717.88</td>\n",
       "      <td>19.74</td>\n",
       "      <td>-4.31</td>\n",
       "      <td>11944.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300797</th>\n",
       "      <td>钢研纳克</td>\n",
       "      <td>电器仪表</td>\n",
       "      <td>北京</td>\n",
       "      <td>73.08</td>\n",
       "      <td>0.62</td>\n",
       "      <td>2.48</td>\n",
       "      <td>7.18</td>\n",
       "      <td>5.42</td>\n",
       "      <td>1.11</td>\n",
       "      <td>0.97</td>\n",
       "      <td>...</td>\n",
       "      <td>2.82</td>\n",
       "      <td>7.22</td>\n",
       "      <td>20191101</td>\n",
       "      <td>1.59</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>44.16</td>\n",
       "      <td>13.74</td>\n",
       "      <td>118577.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>002967</th>\n",
       "      <td>广电计量</td>\n",
       "      <td>综合类</td>\n",
       "      <td>广东</td>\n",
       "      <td>73.93</td>\n",
       "      <td>0.83</td>\n",
       "      <td>3.31</td>\n",
       "      <td>23.78</td>\n",
       "      <td>10.58</td>\n",
       "      <td>8.75</td>\n",
       "      <td>2.55</td>\n",
       "      <td>...</td>\n",
       "      <td>4.32</td>\n",
       "      <td>4.84</td>\n",
       "      <td>20191108</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>45.80</td>\n",
       "      <td>7.03</td>\n",
       "      <td>152800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>002191</th>\n",
       "      <td>劲嘉股份</td>\n",
       "      <td>广告包装</td>\n",
       "      <td>深圳</td>\n",
       "      <td>17.93</td>\n",
       "      <td>12.72</td>\n",
       "      <td>14.65</td>\n",
       "      <td>82.60</td>\n",
       "      <td>30.67</td>\n",
       "      <td>16.93</td>\n",
       "      <td>14.36</td>\n",
       "      <td>...</td>\n",
       "      <td>4.58</td>\n",
       "      <td>2.40</td>\n",
       "      <td>20071205</td>\n",
       "      <td>33.37</td>\n",
       "      <td>2.28</td>\n",
       "      <td>21.35</td>\n",
       "      <td>23.44</td>\n",
       "      <td>42.89</td>\n",
       "      <td>23.35</td>\n",
       "      <td>66176.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>603530</th>\n",
       "      <td>神马电力</td>\n",
       "      <td>电气设备</td>\n",
       "      <td>江苏</td>\n",
       "      <td>94.44</td>\n",
       "      <td>0.40</td>\n",
       "      <td>4.00</td>\n",
       "      <td>12.34</td>\n",
       "      <td>7.39</td>\n",
       "      <td>1.99</td>\n",
       "      <td>1.66</td>\n",
       "      <td>...</td>\n",
       "      <td>2.57</td>\n",
       "      <td>9.75</td>\n",
       "      <td>20190805</td>\n",
       "      <td>4.04</td>\n",
       "      <td>1.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>41.97</td>\n",
       "      <td>18.56</td>\n",
       "      <td>22902.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300602</th>\n",
       "      <td>飞荣达</td>\n",
       "      <td>元器件</td>\n",
       "      <td>深圳</td>\n",
       "      <td>47.69</td>\n",
       "      <td>0.95</td>\n",
       "      <td>3.06</td>\n",
       "      <td>32.21</td>\n",
       "      <td>19.18</td>\n",
       "      <td>4.84</td>\n",
       "      <td>2.39</td>\n",
       "      <td>...</td>\n",
       "      <td>4.65</td>\n",
       "      <td>11.57</td>\n",
       "      <td>20170126</td>\n",
       "      <td>8.39</td>\n",
       "      <td>2.74</td>\n",
       "      <td>84.72</td>\n",
       "      <td>115.08</td>\n",
       "      <td>30.31</td>\n",
       "      <td>15.49</td>\n",
       "      <td>18124.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000038</th>\n",
       "      <td>深大通</td>\n",
       "      <td>广告包装</td>\n",
       "      <td>深圳</td>\n",
       "      <td>3214.26</td>\n",
       "      <td>2.32</td>\n",
       "      <td>5.23</td>\n",
       "      <td>31.65</td>\n",
       "      <td>24.12</td>\n",
       "      <td>0.42</td>\n",
       "      <td>43.13</td>\n",
       "      <td>...</td>\n",
       "      <td>5.42</td>\n",
       "      <td>2.49</td>\n",
       "      <td>19940808</td>\n",
       "      <td>-20.39</td>\n",
       "      <td>-3.90</td>\n",
       "      <td>-29.78</td>\n",
       "      <td>-99.25</td>\n",
       "      <td>6.57</td>\n",
       "      <td>0.11</td>\n",
       "      <td>32536.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300675</th>\n",
       "      <td>建科院</td>\n",
       "      <td>建筑工程</td>\n",
       "      <td>深圳</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.81</td>\n",
       "      <td>1.47</td>\n",
       "      <td>8.30</td>\n",
       "      <td>4.42</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.28</td>\n",
       "      <td>...</td>\n",
       "      <td>2.78</td>\n",
       "      <td>5.61</td>\n",
       "      <td>20170719</td>\n",
       "      <td>1.19</td>\n",
       "      <td>0.81</td>\n",
       "      <td>-3.07</td>\n",
       "      <td>-759.80</td>\n",
       "      <td>19.74</td>\n",
       "      <td>-10.23</td>\n",
       "      <td>16904.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>002624</th>\n",
       "      <td>完美世界</td>\n",
       "      <td>影视音像</td>\n",
       "      <td>浙江</td>\n",
       "      <td>22.10</td>\n",
       "      <td>11.60</td>\n",
       "      <td>12.93</td>\n",
       "      <td>173.60</td>\n",
       "      <td>118.52</td>\n",
       "      <td>3.63</td>\n",
       "      <td>23.41</td>\n",
       "      <td>...</td>\n",
       "      <td>7.38</td>\n",
       "      <td>4.56</td>\n",
       "      <td>20111028</td>\n",
       "      <td>56.80</td>\n",
       "      <td>4.39</td>\n",
       "      <td>5.43</td>\n",
       "      <td>12.00</td>\n",
       "      <td>65.68</td>\n",
       "      <td>25.39</td>\n",
       "      <td>22181.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>002961</th>\n",
       "      <td>瑞达期货</td>\n",
       "      <td>多元金融</td>\n",
       "      <td>福建</td>\n",
       "      <td>138.72</td>\n",
       "      <td>0.45</td>\n",
       "      <td>4.45</td>\n",
       "      <td>59.92</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.37</td>\n",
       "      <td>2.67</td>\n",
       "      <td>...</td>\n",
       "      <td>3.78</td>\n",
       "      <td>10.16</td>\n",
       "      <td>20190905</td>\n",
       "      <td>7.50</td>\n",
       "      <td>1.69</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>13.05</td>\n",
       "      <td>34613.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>603196</th>\n",
       "      <td>日播时尚</td>\n",
       "      <td>服饰</td>\n",
       "      <td>上海</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.65</td>\n",
       "      <td>2.40</td>\n",
       "      <td>11.75</td>\n",
       "      <td>7.67</td>\n",
       "      <td>1.63</td>\n",
       "      <td>3.83</td>\n",
       "      <td>...</td>\n",
       "      <td>3.83</td>\n",
       "      <td>2.36</td>\n",
       "      <td>20170531</td>\n",
       "      <td>2.46</td>\n",
       "      <td>1.02</td>\n",
       "      <td>-0.22</td>\n",
       "      <td>-103.71</td>\n",
       "      <td>59.18</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>13628.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>603079</th>\n",
       "      <td>圣达生物</td>\n",
       "      <td>食品</td>\n",
       "      <td>浙江</td>\n",
       "      <td>75.11</td>\n",
       "      <td>0.37</td>\n",
       "      <td>1.12</td>\n",
       "      <td>15.12</td>\n",
       "      <td>7.44</td>\n",
       "      <td>3.23</td>\n",
       "      <td>4.31</td>\n",
       "      <td>...</td>\n",
       "      <td>7.34</td>\n",
       "      <td>5.44</td>\n",
       "      <td>20170823</td>\n",
       "      <td>2.48</td>\n",
       "      <td>2.22</td>\n",
       "      <td>5.44</td>\n",
       "      <td>-13.65</td>\n",
       "      <td>34.47</td>\n",
       "      <td>11.94</td>\n",
       "      <td>5823.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300468</th>\n",
       "      <td>四方精创</td>\n",
       "      <td>软件服务</td>\n",
       "      <td>深圳</td>\n",
       "      <td>90.35</td>\n",
       "      <td>1.86</td>\n",
       "      <td>1.89</td>\n",
       "      <td>11.56</td>\n",
       "      <td>6.68</td>\n",
       "      <td>2.82</td>\n",
       "      <td>5.30</td>\n",
       "      <td>...</td>\n",
       "      <td>5.75</td>\n",
       "      <td>6.29</td>\n",
       "      <td>20150527</td>\n",
       "      <td>3.37</td>\n",
       "      <td>1.78</td>\n",
       "      <td>1.93</td>\n",
       "      <td>3.19</td>\n",
       "      <td>47.13</td>\n",
       "      <td>17.31</td>\n",
       "      <td>18370.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300216</th>\n",
       "      <td>千山药机</td>\n",
       "      <td>医疗保健</td>\n",
       "      <td>湖南</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.92</td>\n",
       "      <td>3.61</td>\n",
       "      <td>23.66</td>\n",
       "      <td>9.58</td>\n",
       "      <td>4.17</td>\n",
       "      <td>2.24</td>\n",
       "      <td>...</td>\n",
       "      <td>-6.28</td>\n",
       "      <td>0.00</td>\n",
       "      <td>20110511</td>\n",
       "      <td>-29.24</td>\n",
       "      <td>-8.09</td>\n",
       "      <td>-15.83</td>\n",
       "      <td>-17.28</td>\n",
       "      <td>19.37</td>\n",
       "      <td>-321.83</td>\n",
       "      <td>48186.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300104</th>\n",
       "      <td>乐视网</td>\n",
       "      <td>互联网</td>\n",
       "      <td>北京</td>\n",
       "      <td>0.00</td>\n",
       "      <td>39.66</td>\n",
       "      <td>39.89</td>\n",
       "      <td>79.24</td>\n",
       "      <td>27.19</td>\n",
       "      <td>2.30</td>\n",
       "      <td>90.48</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.32</td>\n",
       "      <td>0.00</td>\n",
       "      <td>20100812</td>\n",
       "      <td>-265.38</td>\n",
       "      <td>-6.65</td>\n",
       "      <td>-71.69</td>\n",
       "      <td>-584.36</td>\n",
       "      <td>28.36</td>\n",
       "      <td>-2671.50</td>\n",
       "      <td>280736.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300028</th>\n",
       "      <td>金亚科技</td>\n",
       "      <td>通信设备</td>\n",
       "      <td>四川</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.44</td>\n",
       "      <td>3.44</td>\n",
       "      <td>2.03</td>\n",
       "      <td>0.32</td>\n",
       "      <td>1.04</td>\n",
       "      <td>3.63</td>\n",
       "      <td>...</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.00</td>\n",
       "      <td>20091030</td>\n",
       "      <td>-5.78</td>\n",
       "      <td>-1.68</td>\n",
       "      <td>-80.46</td>\n",
       "      <td>-157.72</td>\n",
       "      <td>-158.78</td>\n",
       "      <td>-849.87</td>\n",
       "      <td>44900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>002968</th>\n",
       "      <td>新大正</td>\n",
       "      <td>房产服务</td>\n",
       "      <td>重庆</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.68</td>\n",
       "      <td>3.18</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.81</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1.07</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>21.33</td>\n",
       "      <td>9.70</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>002604</th>\n",
       "      <td>*ST龙力</td>\n",
       "      <td>食品</td>\n",
       "      <td>山东</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.13</td>\n",
       "      <td>6.00</td>\n",
       "      <td>13.02</td>\n",
       "      <td>3.72</td>\n",
       "      <td>5.83</td>\n",
       "      <td>18.42</td>\n",
       "      <td>...</td>\n",
       "      <td>-5.92</td>\n",
       "      <td>0.00</td>\n",
       "      <td>20110728</td>\n",
       "      <td>-60.83</td>\n",
       "      <td>-10.15</td>\n",
       "      <td>-53.99</td>\n",
       "      <td>-26.71</td>\n",
       "      <td>18.00</td>\n",
       "      <td>-103.34</td>\n",
       "      <td>48426.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>002260</th>\n",
       "      <td>*ST德奥</td>\n",
       "      <td>家用电器</td>\n",
       "      <td>广东</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.65</td>\n",
       "      <td>2.65</td>\n",
       "      <td>3.30</td>\n",
       "      <td>1.68</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.09</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.29</td>\n",
       "      <td>0.00</td>\n",
       "      <td>20080716</td>\n",
       "      <td>-6.37</td>\n",
       "      <td>-2.40</td>\n",
       "      <td>-45.01</td>\n",
       "      <td>114.14</td>\n",
       "      <td>23.08</td>\n",
       "      <td>3.03</td>\n",
       "      <td>24704.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000995</th>\n",
       "      <td>*ST皇台</td>\n",
       "      <td>白酒</td>\n",
       "      <td>甘肃</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.77</td>\n",
       "      <td>1.77</td>\n",
       "      <td>2.51</td>\n",
       "      <td>1.08</td>\n",
       "      <td>0.54</td>\n",
       "      <td>3.06</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.42</td>\n",
       "      <td>0.00</td>\n",
       "      <td>20000807</td>\n",
       "      <td>-7.41</td>\n",
       "      <td>-4.18</td>\n",
       "      <td>316.44</td>\n",
       "      <td>66.72</td>\n",
       "      <td>68.05</td>\n",
       "      <td>-35.24</td>\n",
       "      <td>10275.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000939</th>\n",
       "      <td>*ST凯迪</td>\n",
       "      <td>新型电力</td>\n",
       "      <td>湖北</td>\n",
       "      <td>0.00</td>\n",
       "      <td>30.42</td>\n",
       "      <td>39.30</td>\n",
       "      <td>323.65</td>\n",
       "      <td>59.31</td>\n",
       "      <td>154.36</td>\n",
       "      <td>63.81</td>\n",
       "      <td>...</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.00</td>\n",
       "      <td>19990923</td>\n",
       "      <td>-98.17</td>\n",
       "      <td>-2.50</td>\n",
       "      <td>5.62</td>\n",
       "      <td>17.89</td>\n",
       "      <td>10.79</td>\n",
       "      <td>-74.92</td>\n",
       "      <td>79373.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>688399</th>\n",
       "      <td>硕世生物</td>\n",
       "      <td>医疗保健</td>\n",
       "      <td>江苏</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.18</td>\n",
       "      <td>2.55</td>\n",
       "      <td>0.55</td>\n",
       "      <td>2.06</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>82.01</td>\n",
       "      <td>24.61</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>688358</th>\n",
       "      <td>祥生医疗</td>\n",
       "      <td>医疗保健</td>\n",
       "      <td>江苏</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.93</td>\n",
       "      <td>2.39</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.31</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>61.16</td>\n",
       "      <td>25.75</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>688357</th>\n",
       "      <td>建龙微纳</td>\n",
       "      <td>化工原料</td>\n",
       "      <td>河南</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.30</td>\n",
       "      <td>2.67</td>\n",
       "      <td>3.07</td>\n",
       "      <td>1.25</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1.10</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>39.21</td>\n",
       "      <td>22.30</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>688310</th>\n",
       "      <td>迈得医疗</td>\n",
       "      <td>医疗保健</td>\n",
       "      <td>浙江</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>2.05</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.51</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>46.66</td>\n",
       "      <td>19.69</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>688258</th>\n",
       "      <td>卓易信息</td>\n",
       "      <td>软件服务</td>\n",
       "      <td>江苏</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.86</td>\n",
       "      <td>2.80</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.69</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1.61</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>51.43</td>\n",
       "      <td>11.67</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>688218</th>\n",
       "      <td>江苏北人</td>\n",
       "      <td>专用机械</td>\n",
       "      <td>江苏</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>7.74</td>\n",
       "      <td>6.64</td>\n",
       "      <td>0.61</td>\n",
       "      <td>1.76</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>25.20</td>\n",
       "      <td>12.94</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>688196</th>\n",
       "      <td>卓越新能</td>\n",
       "      <td>环境保护</td>\n",
       "      <td>福建</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>8.59</td>\n",
       "      <td>4.87</td>\n",
       "      <td>2.60</td>\n",
       "      <td>0.88</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>4.80</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>16.29</td>\n",
       "      <td>13.56</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>688138</th>\n",
       "      <td>清溢光电</td>\n",
       "      <td>元器件</td>\n",
       "      <td>深圳</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>7.84</td>\n",
       "      <td>2.60</td>\n",
       "      <td>3.78</td>\n",
       "      <td>0.27</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>3.10</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>34.55</td>\n",
       "      <td>16.63</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>688118</th>\n",
       "      <td>普元信息</td>\n",
       "      <td>软件服务</td>\n",
       "      <td>上海</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.06</td>\n",
       "      <td>3.92</td>\n",
       "      <td>0.09</td>\n",
       "      <td>1.16</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1.19</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>58.14</td>\n",
       "      <td>-1.07</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>603995</th>\n",
       "      <td>甬金股份</td>\n",
       "      <td>钢加工</td>\n",
       "      <td>浙江</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>36.68</td>\n",
       "      <td>14.85</td>\n",
       "      <td>16.65</td>\n",
       "      <td>2.10</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>11.73</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.60</td>\n",
       "      <td>2.11</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>603390</th>\n",
       "      <td>通达电气</td>\n",
       "      <td>汽车配件</td>\n",
       "      <td>广东</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>11.37</td>\n",
       "      <td>7.70</td>\n",
       "      <td>1.43</td>\n",
       "      <td>1.82</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>3.68</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>35.82</td>\n",
       "      <td>20.06</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>601916</th>\n",
       "      <td>浙商银行</td>\n",
       "      <td>银行</td>\n",
       "      <td>浙江</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>17372.69</td>\n",
       "      <td>0.00</td>\n",
       "      <td>112.23</td>\n",
       "      <td>221.30</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>248.58</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>33.39</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>601658</th>\n",
       "      <td>邮储银行</td>\n",
       "      <td>银行</td>\n",
       "      <td>北京</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100671.76</td>\n",
       "      <td>0.00</td>\n",
       "      <td>447.44</td>\n",
       "      <td>746.48</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1521.17</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>26.40</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600610</th>\n",
       "      <td>*ST毅达</td>\n",
       "      <td>建筑工程</td>\n",
       "      <td>上海</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.76</td>\n",
       "      <td>10.71</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.83</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.45</td>\n",
       "      <td>0.00</td>\n",
       "      <td>19920805</td>\n",
       "      <td>-19.37</td>\n",
       "      <td>-1.81</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.45</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>69536.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600074</th>\n",
       "      <td>*ST保千</td>\n",
       "      <td>IT设备</td>\n",
       "      <td>江苏</td>\n",
       "      <td>0.00</td>\n",
       "      <td>10.26</td>\n",
       "      <td>24.38</td>\n",
       "      <td>6.90</td>\n",
       "      <td>4.43</td>\n",
       "      <td>1.69</td>\n",
       "      <td>7.12</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.20</td>\n",
       "      <td>0.00</td>\n",
       "      <td>19970623</td>\n",
       "      <td>-82.54</td>\n",
       "      <td>-3.39</td>\n",
       "      <td>-8.32</td>\n",
       "      <td>14.92</td>\n",
       "      <td>37.35</td>\n",
       "      <td>-294.40</td>\n",
       "      <td>92835.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300809</th>\n",
       "      <td>华辰装备</td>\n",
       "      <td>机床制造</td>\n",
       "      <td>江苏</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>9.62</td>\n",
       "      <td>6.65</td>\n",
       "      <td>1.25</td>\n",
       "      <td>2.65</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>2.66</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>47.39</td>\n",
       "      <td>35.05</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300806</th>\n",
       "      <td>斯迪克</td>\n",
       "      <td>塑料</td>\n",
       "      <td>江苏</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>18.67</td>\n",
       "      <td>9.15</td>\n",
       "      <td>6.85</td>\n",
       "      <td>2.72</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>3.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>24.86</td>\n",
       "      <td>6.55</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300805</th>\n",
       "      <td>电声股份</td>\n",
       "      <td>广告包装</td>\n",
       "      <td>广东</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>17.23</td>\n",
       "      <td>16.14</td>\n",
       "      <td>0.22</td>\n",
       "      <td>1.71</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>5.28</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>21.00</td>\n",
       "      <td>7.24</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300798</th>\n",
       "      <td>锦鸡股份</td>\n",
       "      <td>染料涂料</td>\n",
       "      <td>江苏</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>12.85</td>\n",
       "      <td>7.85</td>\n",
       "      <td>2.84</td>\n",
       "      <td>1.02</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>5.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>21.24</td>\n",
       "      <td>8.75</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3745 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         name industry area       pe  outstanding  totals  totalAssets  \\\n",
       "code                                                                     \n",
       "002517   恺英网络      互联网   福建    63.59        16.00   21.53        61.68   \n",
       "002247   聚力文化      互联网   浙江     0.00         5.45    8.51        31.50   \n",
       "002528    英飞拓     IT设备   深圳   234.38        10.46   11.99        60.70   \n",
       "600770   综艺股份      综合类   江苏   329.71        13.00   13.00        58.76   \n",
       "601599   鹿港文化     影视音像   江苏    24.69         8.93    8.93        64.79   \n",
       "000859   国风塑业       塑料   安徽    36.15         7.39    7.39        21.01   \n",
       "002502   鼎龙文化     影视音像   广东     0.00         6.97    8.60        17.65   \n",
       "600599   熊猫金控     多元金融   湖南     0.00         1.66    1.66         8.11   \n",
       "300341   麦克奥迪     电气设备   福建    34.74         5.10    5.10        15.95   \n",
       "300459   金科文化      互联网   浙江    12.73        22.69   35.44       113.23   \n",
       "000835   长城动漫     影视音像   四川     0.00         3.03    3.27         8.22   \n",
       "002174   游族网络      互联网   福建    15.97         6.45    8.88        94.25   \n",
       "300494   盛天网络      互联网   湖北    82.39         1.48    2.40        14.99   \n",
       "300200   高盟新材     化工原料   北京    16.49         2.02    2.67        18.22   \n",
       "002892    科力尔     电气设备   湖南    45.31         0.45    1.42         8.18   \n",
       "002942   新农股份     农药化肥   浙江    19.12         0.30    1.20        12.59   \n",
       "002733   雄韬股份     电气设备   深圳    37.84         3.27    3.50        47.75   \n",
       "002965   祥鑫科技     汽车配件   广东    36.27         0.38    1.51        15.47   \n",
       "300796    贝斯美     化工原料   浙江    43.77         0.30    1.21         7.67   \n",
       "603022    新通联     广告包装   上海    97.84         2.00    2.00         8.35   \n",
       "603021   山东华鹏       玻璃   山东     0.00         3.20    3.20        29.44   \n",
       "300797   钢研纳克     电器仪表   北京    73.08         0.62    2.48         7.18   \n",
       "002967   广电计量      综合类   广东    73.93         0.83    3.31        23.78   \n",
       "002191   劲嘉股份     广告包装   深圳    17.93        12.72   14.65        82.60   \n",
       "603530   神马电力     电气设备   江苏    94.44         0.40    4.00        12.34   \n",
       "300602    飞荣达      元器件   深圳    47.69         0.95    3.06        32.21   \n",
       "000038    深大通     广告包装   深圳  3214.26         2.32    5.23        31.65   \n",
       "300675    建科院     建筑工程   深圳     0.00         0.81    1.47         8.30   \n",
       "002624   完美世界     影视音像   浙江    22.10        11.60   12.93       173.60   \n",
       "002961   瑞达期货     多元金融   福建   138.72         0.45    4.45        59.92   \n",
       "...       ...      ...  ...      ...          ...     ...          ...   \n",
       "603196   日播时尚       服饰   上海     0.00         0.65    2.40        11.75   \n",
       "603079   圣达生物       食品   浙江    75.11         0.37    1.12        15.12   \n",
       "300468   四方精创     软件服务   深圳    90.35         1.86    1.89        11.56   \n",
       "300216   千山药机     医疗保健   湖南     0.00         2.92    3.61        23.66   \n",
       "300104    乐视网      互联网   北京     0.00        39.66   39.89        79.24   \n",
       "300028   金亚科技     通信设备   四川     0.00         3.44    3.44         2.03   \n",
       "002968    新大正     房产服务   重庆     0.00         0.00    0.00         4.68   \n",
       "002604  *ST龙力       食品   山东     0.00         5.13    6.00        13.02   \n",
       "002260  *ST德奥     家用电器   广东     0.00         2.65    2.65         3.30   \n",
       "000995  *ST皇台       白酒   甘肃     0.00         1.77    1.77         2.51   \n",
       "000939  *ST凯迪     新型电力   湖北     0.00        30.42   39.30       323.65   \n",
       "688399   硕世生物     医疗保健   江苏     0.00         0.00    0.00         4.18   \n",
       "688358   祥生医疗     医疗保健   江苏     0.00         0.00    0.00         2.93   \n",
       "688357   建龙微纳     化工原料   河南     0.00         0.00    0.00         6.30   \n",
       "688310   迈得医疗     医疗保健   浙江     0.00         0.00    0.00         3.00   \n",
       "688258   卓易信息     软件服务   江苏     0.00         0.00    0.00         3.86   \n",
       "688218   江苏北人     专用机械   江苏     0.00         0.00    0.00         7.74   \n",
       "688196   卓越新能     环境保护   福建     0.00         0.00    0.00         8.59   \n",
       "688138   清溢光电      元器件   深圳     0.00         0.00    0.00         7.84   \n",
       "688118   普元信息     软件服务   上海     0.00         0.00    0.00         4.06   \n",
       "603995   甬金股份      钢加工   浙江     0.00         0.00    0.00        36.68   \n",
       "603390   通达电气     汽车配件   广东     0.00         0.00    0.00        11.37   \n",
       "601916   浙商银行       银行   浙江     0.00         0.00    0.00     17372.69   \n",
       "601658   邮储银行       银行   北京     0.00         0.00    0.00    100671.76   \n",
       "600610  *ST毅达     建筑工程   上海     0.00         3.76   10.71         0.27   \n",
       "600074  *ST保千     IT设备   江苏     0.00        10.26   24.38         6.90   \n",
       "300809   华辰装备     机床制造   江苏     0.00         0.00    0.00         9.62   \n",
       "300806    斯迪克       塑料   江苏     0.00         0.00    0.00        18.67   \n",
       "300805   电声股份     广告包装   广东     0.00         0.00    0.00        17.23   \n",
       "300798   锦鸡股份     染料涂料   江苏     0.00         0.00    0.00        12.85   \n",
       "\n",
       "        liquidAssets  fixedAssets  reserved  ...   bvps     pb  timeToMarket  \\\n",
       "code                                         ...                               \n",
       "002517         24.85         0.24      7.40  ...   2.29   1.23      20101207   \n",
       "002247         22.43         3.44     30.24  ...   2.37   1.24      20080612   \n",
       "002528         36.85         1.22     23.01  ...   3.11   1.75      20101224   \n",
       "600770         23.69        11.64     22.26  ...   2.67   2.04      19961120   \n",
       "601599         45.49         8.83      9.69  ...   2.94   1.08      20110527   \n",
       "000859          8.69         7.70      5.90  ...   2.21   2.18      19981119   \n",
       "002502         12.36         0.69     16.37  ...   1.94   1.97      20101117   \n",
       "600599          7.76         0.01      5.16  ...   3.94   2.61      20010828   \n",
       "300341         10.87         2.86      0.00  ...   1.86   5.12      20120726   \n",
       "300459         33.05         3.35     21.28  ...   2.01   1.53      20150515   \n",
       "000835          1.21         1.50      0.95  ...  -0.06 -63.07      19990625   \n",
       "002174         60.20         7.19      8.56  ...   5.97   2.83      20070925   \n",
       "300494          9.99         0.52      3.37  ...   4.19   3.95      20151231   \n",
       "300200          8.61         2.26     10.73  ...   6.03   1.91      20110407   \n",
       "002892          6.08         1.94      2.98  ...   4.68   6.22      20170817   \n",
       "002942          7.63         2.43      3.57  ...   7.55   3.94      20181205   \n",
       "002733         32.63         5.00     13.65  ...   7.07   3.14      20141203   \n",
       "002965          9.70         3.27      1.38  ...  10.54   3.56      20191025   \n",
       "300796          3.84         2.36      3.17  ...   8.14   3.05      20191115   \n",
       "603022          4.18         1.98      1.35  ...   3.17   5.27      20150518   \n",
       "603021          7.17        13.18      6.03  ...   4.10   2.28      20150423   \n",
       "300797          5.42         1.11      0.97  ...   2.82   7.22      20191101   \n",
       "002967         10.58         8.75      2.55  ...   4.32   4.84      20191108   \n",
       "002191         30.67        16.93     14.36  ...   4.58   2.40      20071205   \n",
       "603530          7.39         1.99      1.66  ...   2.57   9.75      20190805   \n",
       "300602         19.18         4.84      2.39  ...   4.65  11.57      20170126   \n",
       "000038         24.12         0.42     43.13  ...   5.42   2.49      19940808   \n",
       "300675          4.42         1.00      1.28  ...   2.78   5.61      20170719   \n",
       "002624        118.52         3.63     23.41  ...   7.38   4.56      20111028   \n",
       "002961          0.00         0.37      2.67  ...   3.78  10.16      20190905   \n",
       "...              ...          ...       ...  ...    ...    ...           ...   \n",
       "603196          7.67         1.63      3.83  ...   3.83   2.36      20170531   \n",
       "603079          7.44         3.23      4.31  ...   7.34   5.44      20170823   \n",
       "300468          6.68         2.82      5.30  ...   5.75   6.29      20150527   \n",
       "300216          9.58         4.17      2.24  ...  -6.28   0.00      20110511   \n",
       "300104         27.19         2.30     90.48  ...  -3.32   0.00      20100812   \n",
       "300028          0.32         1.04      3.63  ...   0.38   0.00      20091030   \n",
       "002968          3.18         0.95      0.81  ...   0.00   0.00             0   \n",
       "002604          3.72         5.83     18.42  ...  -5.92   0.00      20110728   \n",
       "002260          1.68         0.67      0.09  ...  -1.29   0.00      20080716   \n",
       "000995          1.08         0.54      3.06  ...  -1.42   0.00      20000807   \n",
       "000939         59.31       154.36     63.81  ...   0.19   0.00      19990923   \n",
       "688399          2.55         0.55      2.06  ...   0.00   0.00             0   \n",
       "688358          2.39         0.20      0.31  ...   0.00   0.00             0   \n",
       "688357          2.67         3.07      1.25  ...   0.00   0.00             0   \n",
       "688310          2.05         0.48      0.51  ...   0.00   0.00             0   \n",
       "688258          2.80         0.56      0.69  ...   0.00   0.00             0   \n",
       "688218          6.64         0.61      1.76  ...   0.00   0.00             0   \n",
       "688196          4.87         2.60      0.88  ...   0.00   0.00             0   \n",
       "688138          2.60         3.78      0.27  ...   0.00   0.00             0   \n",
       "688118          3.92         0.09      1.16  ...   0.00   0.00             0   \n",
       "603995         14.85        16.65      2.10  ...   0.00   0.00             0   \n",
       "603390          7.70         1.43      1.82  ...   0.00   0.00             0   \n",
       "601916          0.00       112.23    221.30  ...   0.00   0.00             0   \n",
       "601658          0.00       447.44    746.48  ...   0.00   0.00             0   \n",
       "600610          0.27         0.00      3.83  ...  -0.45   0.00      19920805   \n",
       "600074          4.43         1.69      7.12  ...  -2.20   0.00      19970623   \n",
       "300809          6.65         1.25      2.65  ...   0.00   0.00             0   \n",
       "300806          9.15         6.85      2.72  ...   0.00   0.00             0   \n",
       "300805         16.14         0.22      1.71  ...   0.00   0.00             0   \n",
       "300798          7.85         2.84      1.02  ...   0.00   0.00             0   \n",
       "\n",
       "           undp  perundp     rev   profit     gpr      npr   holders  \n",
       "code                                                                  \n",
       "002517    26.48     1.23  -13.31   -85.51   68.63     4.74   75843.0  \n",
       "002247   -19.15    -2.25  -20.24  -102.08   10.87    -0.32   32982.0  \n",
       "002528     0.46     0.04    8.88   -68.24   20.22     0.70   51316.0  \n",
       "600770     0.14     0.01   -2.61   -81.03   28.20     4.48   76440.0  \n",
       "601599     6.88     0.77  -18.14    32.66   18.72     3.41   34574.0  \n",
       "000859     2.28     0.31   14.62    -1.11   12.34     7.36   72854.0  \n",
       "002502    -8.55    -0.99  512.63 -1494.35    6.84   -58.86   38986.0  \n",
       "600599    -0.46    -0.28  -52.67    29.42   35.99   -17.91   28186.0  \n",
       "300341     4.05     0.79   14.70    10.20   44.23    13.21   13949.0  \n",
       "300459    19.95     0.56   -9.30   -15.17   77.90    35.20   55934.0  \n",
       "000835    -4.41    -1.35  -30.40   -99.28   18.64   -72.93   20000.0  \n",
       "002174    39.30     4.42    0.73     3.29   53.90    26.69   39569.0  \n",
       "300494     3.63     1.51   15.52   -30.71   27.31     8.52   22906.0  \n",
       "300200     2.28     0.85   -4.64   109.91   42.68    19.10   13525.0  \n",
       "002892     2.03     1.43   21.03    40.97   23.32    10.80    7190.0  \n",
       "002942     3.22     2.68   16.74    28.36   33.81    16.97   19373.0  \n",
       "002733     7.24     2.07   -4.04   114.30   17.00     7.12   42550.0  \n",
       "002965     5.86     3.89    0.00     0.00   25.69    10.45   42000.0  \n",
       "300796     1.71     1.41    0.00     0.00   30.28    13.77   60576.0  \n",
       "603022     2.77     1.38    1.61   -19.60   20.01     5.14   16168.0  \n",
       "603021     3.57     1.12   -3.28  -717.88   19.74    -4.31   11944.0  \n",
       "300797     1.59     0.64    0.00     0.00   44.16    13.74  118577.0  \n",
       "002967     3.26     0.98    0.00     0.00   45.80     7.03  152800.0  \n",
       "002191    33.37     2.28   21.35    23.44   42.89    23.35   66176.0  \n",
       "603530     4.04     1.01    0.00     0.00   41.97    18.56   22902.0  \n",
       "300602     8.39     2.74   84.72   115.08   30.31    15.49   18124.0  \n",
       "000038   -20.39    -3.90  -29.78   -99.25    6.57     0.11   32536.0  \n",
       "300675     1.19     0.81   -3.07  -759.80   19.74   -10.23   16904.0  \n",
       "002624    56.80     4.39    5.43    12.00   65.68    25.39   22181.0  \n",
       "002961     7.50     1.69    0.00     0.00    0.00    13.05   34613.0  \n",
       "...         ...      ...     ...      ...     ...      ...       ...  \n",
       "603196     2.46     1.02   -0.22  -103.71   59.18    -0.10   13628.0  \n",
       "603079     2.48     2.22    5.44   -13.65   34.47    11.94    5823.0  \n",
       "300468     3.37     1.78    1.93     3.19   47.13    17.31   18370.0  \n",
       "300216   -29.24    -8.09  -15.83   -17.28   19.37  -321.83   48186.0  \n",
       "300104  -265.38    -6.65  -71.69  -584.36   28.36 -2671.50  280736.0  \n",
       "300028    -5.78    -1.68  -80.46  -157.72 -158.78  -849.87   44900.0  \n",
       "002968     1.07     0.00    0.00     0.00   21.33     9.70       0.0  \n",
       "002604   -60.83   -10.15  -53.99   -26.71   18.00  -103.34   48426.0  \n",
       "002260    -6.37    -2.40  -45.01   114.14   23.08     3.03   24704.0  \n",
       "000995    -7.41    -4.18  316.44    66.72   68.05   -35.24   10275.0  \n",
       "000939   -98.17    -2.50    5.62    17.89   10.79   -74.92   79373.0  \n",
       "688399     0.93     0.00    0.00     0.00   82.01    24.61       0.0  \n",
       "688358     1.01     0.00    0.00     0.00   61.16    25.75       0.0  \n",
       "688357     1.10     0.00    0.00     0.00   39.21    22.30       0.0  \n",
       "688310     0.82     0.00    0.00     0.00   46.66    19.69       0.0  \n",
       "688258     1.61     0.00    0.00     0.00   51.43    11.67       0.0  \n",
       "688218     1.01     0.00    0.00     0.00   25.20    12.94       0.0  \n",
       "688196     4.80     0.00    0.00     0.00   16.29    13.56       0.0  \n",
       "688138     3.10     0.00    0.00     0.00   34.55    16.63       0.0  \n",
       "688118     1.19     0.00    0.00     0.00   58.14    -1.07       0.0  \n",
       "603995    11.73     0.00    0.00     0.00    5.60     2.11       0.0  \n",
       "603390     3.68     0.00    0.00     0.00   35.82    20.06       0.0  \n",
       "601916   248.58     0.00    0.00     0.00    0.00    33.39       0.0  \n",
       "601658  1521.17     0.00    0.00     0.00    0.00    26.40       0.0  \n",
       "600610   -19.37    -1.81    0.00     4.45    0.00     0.00   69536.0  \n",
       "600074   -82.54    -3.39   -8.32    14.92   37.35  -294.40   92835.0  \n",
       "300809     2.66     0.00    0.00     0.00   47.39    35.05       0.0  \n",
       "300806     3.70     0.00    0.00     0.00   24.86     6.55       0.0  \n",
       "300805     5.28     0.00    0.00     0.00   21.00     7.24       0.0  \n",
       "300798     5.02     0.00    0.00     0.00   21.24     8.75       0.0  \n",
       "\n",
       "[3745 rows x 22 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cls = ts.get_industry_classified() \n",
    "stk = ts.get_stock_basics() \n",
    "\n",
    "cls = cls.set_index('code') \n",
    "tcls = cls[['c_name']] \n",
    "tstk = stk[['pe', 'pb', 'esp', 'bvps']] \n",
    "df = tcls.join(tstk, how='inner') \n",
    "clist = [df.iloc[i]['c_name'] for i in range(3)]\n",
    "\n",
    "def neq(a, b, eps=1e-6): return abs(a - b) > eps \n",
    "tdf = df.loc[df['c_name'].isin(clist) & neq(df['pe'], 0.0) & neq(df['pb'], 0.0) & neq(df['esp'], 0.0) & neq(df['bvps'], 0.0)] \n",
    "\n",
    "col_mapping = {'pe' : u'P/E', 'pb' : u'P/BV', 'esp' : u'EPS', 'bvps' : u'BVPS'} \n",
    "tdf.rename(columns=col_mapping, inplace=True) \n",
    "sns.pairplot(tdf, hue='c_name', size=2.5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>c_name</th>\n",
       "      <th>P/E</th>\n",
       "      <th>P/BV</th>\n",
       "      <th>EPS</th>\n",
       "      <th>BVPS</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>code</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>000001</th>\n",
       "      <td>金融行业</td>\n",
       "      <td>10.11</td>\n",
       "      <td>1.19</td>\n",
       "      <td>1.320</td>\n",
       "      <td>13.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000002</th>\n",
       "      <td>房地产</td>\n",
       "      <td>12.42</td>\n",
       "      <td>1.81</td>\n",
       "      <td>1.630</td>\n",
       "      <td>14.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000005</th>\n",
       "      <td>房地产</td>\n",
       "      <td>52.93</td>\n",
       "      <td>2.05</td>\n",
       "      <td>0.042</td>\n",
       "      <td>1.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000006</th>\n",
       "      <td>房地产</td>\n",
       "      <td>11.56</td>\n",
       "      <td>1.09</td>\n",
       "      <td>0.335</td>\n",
       "      <td>4.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000009</th>\n",
       "      <td>房地产</td>\n",
       "      <td>43.76</td>\n",
       "      <td>2.17</td>\n",
       "      <td>0.078</td>\n",
       "      <td>2.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000011</th>\n",
       "      <td>房地产</td>\n",
       "      <td>39.68</td>\n",
       "      <td>1.67</td>\n",
       "      <td>0.173</td>\n",
       "      <td>5.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000029</th>\n",
       "      <td>房地产</td>\n",
       "      <td>18.94</td>\n",
       "      <td>3.11</td>\n",
       "      <td>0.434</td>\n",
       "      <td>3.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000031</th>\n",
       "      <td>房地产</td>\n",
       "      <td>8.70</td>\n",
       "      <td>1.64</td>\n",
       "      <td>0.620</td>\n",
       "      <td>4.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000036</th>\n",
       "      <td>房地产</td>\n",
       "      <td>7.04</td>\n",
       "      <td>1.21</td>\n",
       "      <td>0.410</td>\n",
       "      <td>3.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000038</th>\n",
       "      <td>房地产</td>\n",
       "      <td>3214.26</td>\n",
       "      <td>2.49</td>\n",
       "      <td>0.003</td>\n",
       "      <td>5.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000040</th>\n",
       "      <td>房地产</td>\n",
       "      <td>60.25</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.050</td>\n",
       "      <td>9.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000042</th>\n",
       "      <td>房地产</td>\n",
       "      <td>5.36</td>\n",
       "      <td>0.87</td>\n",
       "      <td>1.393</td>\n",
       "      <td>11.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000043</th>\n",
       "      <td>房地产</td>\n",
       "      <td>81.36</td>\n",
       "      <td>3.14</td>\n",
       "      <td>0.207</td>\n",
       "      <td>7.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000046</th>\n",
       "      <td>房地产</td>\n",
       "      <td>6.64</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.480</td>\n",
       "      <td>4.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000056</th>\n",
       "      <td>房地产</td>\n",
       "      <td>31.87</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.110</td>\n",
       "      <td>4.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000078</th>\n",
       "      <td>生物制药</td>\n",
       "      <td>18.82</td>\n",
       "      <td>1.44</td>\n",
       "      <td>0.132</td>\n",
       "      <td>2.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000153</th>\n",
       "      <td>生物制药</td>\n",
       "      <td>19.88</td>\n",
       "      <td>1.51</td>\n",
       "      <td>0.241</td>\n",
       "      <td>4.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000166</th>\n",
       "      <td>金融行业</td>\n",
       "      <td>18.79</td>\n",
       "      <td>1.45</td>\n",
       "      <td>0.200</td>\n",
       "      <td>3.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000402</th>\n",
       "      <td>房地产</td>\n",
       "      <td>12.62</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.460</td>\n",
       "      <td>10.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000403</th>\n",
       "      <td>生物制药</td>\n",
       "      <td>53.71</td>\n",
       "      <td>11.92</td>\n",
       "      <td>0.467</td>\n",
       "      <td>2.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000415</th>\n",
       "      <td>金融行业</td>\n",
       "      <td>7.76</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.336</td>\n",
       "      <td>6.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000423</th>\n",
       "      <td>生物制药</td>\n",
       "      <td>82.91</td>\n",
       "      <td>2.17</td>\n",
       "      <td>0.319</td>\n",
       "      <td>16.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000505</th>\n",
       "      <td>房地产</td>\n",
       "      <td>31.58</td>\n",
       "      <td>1.52</td>\n",
       "      <td>0.124</td>\n",
       "      <td>3.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000513</th>\n",
       "      <td>生物制药</td>\n",
       "      <td>22.27</td>\n",
       "      <td>2.84</td>\n",
       "      <td>1.112</td>\n",
       "      <td>11.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000514</th>\n",
       "      <td>房地产</td>\n",
       "      <td>217.88</td>\n",
       "      <td>1.08</td>\n",
       "      <td>0.013</td>\n",
       "      <td>3.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000517</th>\n",
       "      <td>房地产</td>\n",
       "      <td>9.51</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.204</td>\n",
       "      <td>1.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000518</th>\n",
       "      <td>生物制药</td>\n",
       "      <td>285.14</td>\n",
       "      <td>5.92</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000526</th>\n",
       "      <td>房地产</td>\n",
       "      <td>38.57</td>\n",
       "      <td>22.76</td>\n",
       "      <td>0.665</td>\n",
       "      <td>1.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000534</th>\n",
       "      <td>房地产</td>\n",
       "      <td>41.21</td>\n",
       "      <td>4.31</td>\n",
       "      <td>0.165</td>\n",
       "      <td>2.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000537</th>\n",
       "      <td>房地产</td>\n",
       "      <td>3.99</td>\n",
       "      <td>1.01</td>\n",
       "      <td>1.321</td>\n",
       "      <td>6.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600976</th>\n",
       "      <td>生物制药</td>\n",
       "      <td>26.17</td>\n",
       "      <td>2.17</td>\n",
       "      <td>0.479</td>\n",
       "      <td>7.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600993</th>\n",
       "      <td>生物制药</td>\n",
       "      <td>20.48</td>\n",
       "      <td>3.21</td>\n",
       "      <td>0.680</td>\n",
       "      <td>5.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600999</th>\n",
       "      <td>金融行业</td>\n",
       "      <td>17.18</td>\n",
       "      <td>1.61</td>\n",
       "      <td>0.630</td>\n",
       "      <td>10.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>601009</th>\n",
       "      <td>金融行业</td>\n",
       "      <td>5.54</td>\n",
       "      <td>0.98</td>\n",
       "      <td>1.166</td>\n",
       "      <td>8.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>601099</th>\n",
       "      <td>金融行业</td>\n",
       "      <td>41.72</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.059</td>\n",
       "      <td>1.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>601166</th>\n",
       "      <td>金融行业</td>\n",
       "      <td>5.63</td>\n",
       "      <td>0.87</td>\n",
       "      <td>2.570</td>\n",
       "      <td>22.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>601169</th>\n",
       "      <td>金融行业</td>\n",
       "      <td>4.92</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.830</td>\n",
       "      <td>8.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>601198</th>\n",
       "      <td>金融行业</td>\n",
       "      <td>20.12</td>\n",
       "      <td>1.46</td>\n",
       "      <td>0.402</td>\n",
       "      <td>7.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>601288</th>\n",
       "      <td>金融行业</td>\n",
       "      <td>5.23</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.510</td>\n",
       "      <td>4.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>601318</th>\n",
       "      <td>金融行业</td>\n",
       "      <td>9.41</td>\n",
       "      <td>2.53</td>\n",
       "      <td>7.290</td>\n",
       "      <td>35.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>601328</th>\n",
       "      <td>金融行业</td>\n",
       "      <td>5.14</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.770</td>\n",
       "      <td>9.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>601336</th>\n",
       "      <td>金融行业</td>\n",
       "      <td>8.36</td>\n",
       "      <td>1.82</td>\n",
       "      <td>4.168</td>\n",
       "      <td>25.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>601377</th>\n",
       "      <td>金融行业</td>\n",
       "      <td>17.96</td>\n",
       "      <td>1.22</td>\n",
       "      <td>0.262</td>\n",
       "      <td>5.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>601398</th>\n",
       "      <td>金融行业</td>\n",
       "      <td>6.21</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.706</td>\n",
       "      <td>6.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>601555</th>\n",
       "      <td>金融行业</td>\n",
       "      <td>21.19</td>\n",
       "      <td>1.26</td>\n",
       "      <td>0.309</td>\n",
       "      <td>6.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>601588</th>\n",
       "      <td>房地产</td>\n",
       "      <td>5.91</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.387</td>\n",
       "      <td>4.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>601601</th>\n",
       "      <td>金融行业</td>\n",
       "      <td>10.56</td>\n",
       "      <td>1.89</td>\n",
       "      <td>2.529</td>\n",
       "      <td>18.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>601628</th>\n",
       "      <td>金融行业</td>\n",
       "      <td>12.89</td>\n",
       "      <td>2.60</td>\n",
       "      <td>2.030</td>\n",
       "      <td>13.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>601688</th>\n",
       "      <td>金融行业</td>\n",
       "      <td>18.20</td>\n",
       "      <td>1.30</td>\n",
       "      <td>0.750</td>\n",
       "      <td>13.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>601788</th>\n",
       "      <td>金融行业</td>\n",
       "      <td>17.11</td>\n",
       "      <td>1.05</td>\n",
       "      <td>0.492</td>\n",
       "      <td>10.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>601818</th>\n",
       "      <td>金融行业</td>\n",
       "      <td>5.43</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.570</td>\n",
       "      <td>5.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>601901</th>\n",
       "      <td>金融行业</td>\n",
       "      <td>37.91</td>\n",
       "      <td>1.41</td>\n",
       "      <td>0.131</td>\n",
       "      <td>4.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>601939</th>\n",
       "      <td>金融行业</td>\n",
       "      <td>5.98</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.901</td>\n",
       "      <td>8.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>601988</th>\n",
       "      <td>金融行业</td>\n",
       "      <td>5.02</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.520</td>\n",
       "      <td>5.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>601998</th>\n",
       "      <td>金融行业</td>\n",
       "      <td>5.50</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.810</td>\n",
       "      <td>8.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>603168</th>\n",
       "      <td>生物制药</td>\n",
       "      <td>46.16</td>\n",
       "      <td>1.59</td>\n",
       "      <td>0.121</td>\n",
       "      <td>4.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>603222</th>\n",
       "      <td>生物制药</td>\n",
       "      <td>291.35</td>\n",
       "      <td>19.00</td>\n",
       "      <td>0.129</td>\n",
       "      <td>2.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>603456</th>\n",
       "      <td>生物制药</td>\n",
       "      <td>60.79</td>\n",
       "      <td>4.00</td>\n",
       "      <td>0.168</td>\n",
       "      <td>3.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>603567</th>\n",
       "      <td>生物制药</td>\n",
       "      <td>21.63</td>\n",
       "      <td>1.88</td>\n",
       "      <td>0.398</td>\n",
       "      <td>6.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>603998</th>\n",
       "      <td>生物制药</td>\n",
       "      <td>39.73</td>\n",
       "      <td>3.35</td>\n",
       "      <td>0.162</td>\n",
       "      <td>2.55</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>289 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       c_name      P/E   P/BV    EPS   BVPS\n",
       "code                                       \n",
       "000001   金融行业    10.11   1.19  1.320  13.82\n",
       "000002    房地产    12.42   1.81  1.630  14.80\n",
       "000005    房地产    52.93   2.05  0.042   1.45\n",
       "000006    房地产    11.56   1.09  0.335   4.73\n",
       "000009    房地产    43.76   2.17  0.078   2.09\n",
       "000011    房地产    39.68   1.67  0.173   5.47\n",
       "000029    房地产    18.94   3.11  0.434   3.53\n",
       "000031    房地产     8.70   1.64  0.620   4.39\n",
       "000036    房地产     7.04   1.21  0.410   3.19\n",
       "000038    房地产  3214.26   2.49  0.003   5.42\n",
       "000040    房地产    60.25   0.41  0.050   9.72\n",
       "000042    房地产     5.36   0.87  1.393  11.44\n",
       "000043    房地产    81.36   3.14  0.207   7.16\n",
       "000046    房地产     6.64   0.96  0.480   4.41\n",
       "000056    房地产    31.87   0.93  0.110   4.64\n",
       "000078   生物制药    18.82   1.44  0.132   2.31\n",
       "000153   生物制药    19.88   1.51  0.241   4.22\n",
       "000166   金融行业    18.79   1.45  0.200   3.28\n",
       "000402    房地产    12.62   0.71  0.460  10.85\n",
       "000403   生物制药    53.71  11.92  0.467   2.81\n",
       "000415   金融行业     7.76   0.56  0.336   6.21\n",
       "000423   生物制药    82.91   2.17  0.319  16.25\n",
       "000505    房地产    31.58   1.52  0.124   3.44\n",
       "000513   生物制药    22.27   2.84  1.112  11.62\n",
       "000514    房地产   217.88   1.08  0.013   3.56\n",
       "000517    房地产     9.51   1.50  0.204   1.73\n",
       "000518   生物制药   285.14   5.92  0.009   0.59\n",
       "000526    房地产    38.57  22.76  0.665   1.50\n",
       "000534    房地产    41.21   4.31  0.165   2.11\n",
       "000537    房地产     3.99   1.01  1.321   6.93\n",
       "...       ...      ...    ...    ...    ...\n",
       "600976   生物制药    26.17   2.17  0.479   7.72\n",
       "600993   生物制药    20.48   3.21  0.680   5.79\n",
       "600999   金融行业    17.18   1.61  0.630  10.21\n",
       "601009   金融行业     5.54   0.98  1.166   8.79\n",
       "601099   金融行业    41.72   2.18  0.059   1.50\n",
       "601166   金融行业     5.63   0.87  2.570  22.85\n",
       "601169   金融行业     4.92   0.64  0.830   8.78\n",
       "601198   金融行业    20.12   1.46  0.402   7.39\n",
       "601288   金融行业     5.23   0.73  0.510   4.91\n",
       "601318   金融行业     9.41   2.53  7.290  35.08\n",
       "601328   金融行业     5.14   0.61  0.770   9.10\n",
       "601336   金融行业     8.36   1.82  4.168  25.58\n",
       "601377   金融行业    17.96   1.22  0.262   5.14\n",
       "601398   金融行业     6.21   0.86  0.706   6.79\n",
       "601555   金融行业    21.19   1.26  0.309   6.91\n",
       "601588    房地产     5.91   0.71  0.387   4.28\n",
       "601601   金融行业    10.56   1.89  2.529  18.79\n",
       "601628   金融行业    12.89   2.60  2.030  13.48\n",
       "601688   金融行业    18.20   1.30  0.750  13.21\n",
       "601788   金融行业    17.11   1.05  0.492  10.68\n",
       "601818   金融行业     5.43   0.72  0.570   5.99\n",
       "601901   金融行业    37.91   1.41  0.131   4.70\n",
       "601939   金融行业     5.98   0.87  0.901   8.29\n",
       "601988   金融行业     5.02   0.66  0.520   5.53\n",
       "601998   金融行业     5.50   0.69  0.810   8.91\n",
       "603168   生物制药    46.16   1.59  0.121   4.71\n",
       "603222   生物制药   291.35  19.00  0.129   2.64\n",
       "603456   生物制药    60.79   4.00  0.168   3.42\n",
       "603567   生物制药    21.63   1.88  0.398   6.09\n",
       "603998   生物制药    39.73   3.35  0.162   2.55\n",
       "\n",
       "[289 rows x 5 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x182888ea748>"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 780x720 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "sns.pairplot(tdf, hue='c_name', size=2.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5 均线和candlestick图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "import datetime as dt \n",
    "#import pandas.io.data as web \n",
    "from pandas_datareader import data, wb\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.dates as mdates \n",
    "import matplotlib.mlab as mlab \n",
    "import matplotlib.pyplot as plt \n",
    "import matplotlib.font_manager as font_manager \n",
    "\n",
    "import mpl_finance as finance\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "import tushare as ts  \n",
    "pro = ts.pro_api('6cbb7738f458c6366abd8601dc143cf493649fb97a7e6b4f43966902')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     ts_code trade_date  open  high   low  close  pre_close  change  pct_chg  \\\n",
      "0  600728.SH   20181121  7.95  8.14  7.66   7.97       7.90    0.07   0.8861   \n",
      "1  600728.SH   20181120  8.05  8.30  7.88   7.90       8.29   -0.39  -4.7045   \n",
      "2  600728.SH   20181119  8.15  8.47  8.12   8.29       8.15    0.14   1.7178   \n",
      "3  600728.SH   20181116  8.35  8.36  7.93   8.15       7.79    0.36   4.6213   \n",
      "4  600728.SH   20181115  7.79  7.79  7.79   7.79       7.08    0.71  10.0282   \n",
      "\n",
      "          vol       amount  \n",
      "0   578565.40   458542.728  \n",
      "1   614633.81   494248.015  \n",
      "2   891646.23   738173.447  \n",
      "3  1244701.10  1019665.838  \n",
      "4   175086.39   136392.298  \n"
     ]
    }
   ],
   "source": [
    "begin_time = '20181101' \n",
    "end_time = '20181121' \n",
    "code = \"600728.sh\" \n",
    "#df = ts.get_hist_data(code, start=begin_time, end=end_time) \n",
    "df = pro.daily(ts_code='600728.sh',start_date=begin_time, end_date=end_time)\n",
    "df = df.sort_index(0) \n",
    "df_idx = df.index.values \n",
    "# df.plot() \n",
    "print(df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABZ4AAAJVCAYAAABeYuBnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nOzdf5Tnd10f+uer2Qguq3VCdNOAEtpSWuE2w80cpLWtO41AsJY0Vcvmaom9eAMVzGGbY1PPrayi3ttUYzQVG9MjEmrNKkFOc7yocNudqz1evGzgC01ECiElJssimDF0WORHfN8/9hu/389kNjuz8558Z2Yfj3O+J/N9f96f97y/zzPZSZ7nu+9vtdYCAAAAAAC9/LlZbwAAAAAAgN1F8QwAAAAAQFeKZwAAAAAAulI8AwAAAADQleIZAAAAAICuFM8AAAAAAHSleAYAgF2gqi6pqlZVe2a9FwAAUDwDAHDOqaqDVfWhqvpsVd1XVX97PH55Vf1+VZ2sqqNV9aype55SVW+uqs9U1Ymq+mdT176zqlamHifHJfBl4+vfX1X3VNX/qKr7q+r7V+1nvqp+u6oeqaoHq+oNT1YWAACwFRTPAACcU6rqxUluTPJPknxFkr+T5GNVdWGSX03yg0kuSHIsyS9P3fpDSZ6T5FlJFpP886q6Iklaa/+htbbvsUeS703ysSTve+zbJnllkrkkVyR5XVUdnFr7l5L81vj7flOSf1pVL+/80gEA4EmjeAYA4Fzzw0ne2Fp7T2vtT1trD7XWHkryD5Pc21p7W2vtT3KqaL60qv7q+L5XJvmR1tpya+1DSf5dku8+zfe4JslbW2stSVpr/7q19r7W2pdaax9O8h+TfOPU/EuS/IfW2qOttfuS/Jckz1tr4ap6YVUdG7/z+pNV9ZOrpnxnVT1QVZ+uqv99Q8kAAEAnimcAAM4ZVXVekoUkX11VHx0fa/EzVfXlOVX0fuCxua21zya5L8nzqmouycXT18dfP64cHh/P8XeSvPU0e6gkfzvJvVPDP5XklVV1flU9N8nfSPJ/n+Zl/HSSn26tfWWSv5TkV1Zd/1tJnpvk8iRvqKq/dpp1AABgyyieAQA4l+xPcn6Sb8+p8nc+yQuS/Msk+5I8smr+Izl1HMe+qeerr632yiS/3Vq7/zR7+KGc+u/wX5ga+7Xxnj6X5PeT/Hxr7b2nuf+LSf5yVV3YWltprb1n1fUfbq19rrX2gZwqxy89zToAALBlFM8AAJxLPjf+579prX2itfbpJD+Z5FuSrCT5ylXzvzLJ/xhfy6rrj11b7ZVJbl/rm1fV68bX/15r7fPjsQuS/EaSNyZ5apKvTfLSqvre07yGVyX5K0l+v6reW1Xfuur6iamvT2ZSmgMAwJNG8QwAwDmjtbac5MEkbY3L92bq3cFV9bScOsri3vF9n8jw3cOXZnhcRqrqG3PqSI47Vy9eVf9rkn+R5PLW2oNTl/5ikkdba28dnwH9YJIjOVWGr/UaPtJauzrJ1+TUhyTeOd4rAABsG4pnAADONb+Q5Puq6mvGZze/PqeOunhHkudX1bdV1VOTvCHJB1trvz++761J/mVVzY0/cPB/S/KWVWtfk+TtrbXBO6Gr6juT/B9JXtxa+9iqe/7bqSn1v1TVn6uqi5K8IsPzpKfX+q6q+urW2p8m+ePx8KMbDQEAALaS4hkAgHPNjyR5b04Vvh9K8v4kP9Za+1SSb0vyY0mWk3xDkoNT9x3OqQ8b/HiS/yfJj7fWfuOxi+Oy+h9l7WM2fjTJ05O8t6pWxo9bk6S19pkk/zDJofH3HSW5Z7yPVNXXjed/3XitK5LcW1UrOfVBgwdba3+yuUgAAKCvam2tv2UIAAAAAABnxzueAQAAAADoSvEMAAAAAEBXimcAAAAAALpSPAMAAAAA0JXiGQAAAACArvbMegNrufDCC9sll1wy623MxGc/+9k87WlPm/U2tg15TMhiSB5D8piQxZA8JmQxJI8heUzIYkgeQ/KYkMWQPIbkMSGLIXlMyGJoJ+dx9913f7q19tWrx7dl8XzJJZfk2LFjs97GTCwtLeXAgQOz3sa2IY8JWQzJY0geE7IYkseELIbkMSSPCVkMyWNIHhOyGJLHkDwmZDEkjwlZDO3kPKrq42uNO2oDAAAAAICuFM8AAAAAAHSleAYAAAAAoCvFMwAAAAAAXSmeAQAAAADoSvEMAAAAAEBXimcAAAAAALpSPAMAAAAA0JXiGQAAAACArhTPAAAAAAB0ta7iuaoOVdW9VXVPVd1RVU9ddf27q+pTVTUaP75n6to1VfWR8eOa3i8AAAAAAIDtZc+ZJlTVM5Jcl+TrW2ufq6pfSXIwyVtWTf3l1trrVt17QZLDSRaStCR3V9VdrbXlHpsHAAAAAGD7We9RG3uSfHlV7UmyN8nxdd730iTvbq09PC6b353kio1vEwAAAACAneKMxXNr7aEkP5HkgSSfSPJIa+1da0z9tqr6YFXdWVVfOx57RpI/mJrz4HgMAAAAAIBdqlprTzyhai7J25O8IskfJ3lbkjtba784NefpSVZaa5+vqtck+Uettb9bVd+f5CmttR8dz/vBJCdbazet8X2uTXJtkuzfv/+yI0eOdHmBO83Kykr27ds3621sG/KYkMWQPIbkMSGLIXlMyGJIHkPymJDFkDyG5DEhiyF5DMljQhZD8piQxdBOzmNxcfHu1trC6vEznvGc5JuT3N9a+1SSVNWvJvmbSf6seG6t/dHU/H+X5Mbx1w8mOTB17ZlJltb6Jq2125LcliQLCwvtwIEDa03b9ZaWlnKuvva1yGNCFkPyGJLHhCyG5DEhiyF5DMljQhZD8hiSx4QshuQxJI8JWQzJY0IWQ7sxj/Wc8fxAkhdV1d6qqiSXJ/nQ9ISq+gtTT18+df03k7ykqubG75x+yXgMAAAAAIBd6ozveG6t/W5V3ZnkfUm+lOT9SW6rqjcmOdZauyvJdVX18vH1h5N89/jeh6vqR5K8d7zcG1trD/d/GQAAAAAAbBfrOWojrbXDSQ6vGn7D1PUfSPIDp7n3zUnefLYbBAAAAABgZ1nPURsAAAAAALBuimcAAAAAALpSPAMAAAAA0JXiGQAAAACArhTPwMYsLs56BwAAAABsc4pnAAAAAAC6UjwDAAAAANCV4hkAAAAAgK4UzwAAAAAAdKV4BgAAAACgK8UzAAAAAABdKZ4BAAAAAOhK8QwAAAAAQFeKZwAAAAAAulI8AwAAAADQleIZAAAAAICuFM8AAAAAAHSleAYAAAAAoCvFMwAAAAAAXSmeAQAAAADoSvEMAAAAAEBXimcAAAAAALpSPAMAAAAA0JXiGQAAAACArhTPAAAAAAB0pXgGAAAAAKArxTMAAAAAAF0pngEAAAAA6ErxDAAAAABAV4pnAAAAAAC6UjwDAAAAANCV4hkAAAAAgK4UzwAAAAAAdKV4BgAAAACgK8UzAAAAAABdKZ4BZmzx9sVZbwEAAACgK8UzAAAAAABdKZ4BAAAAAOhqz6w3ALBTzN04tyXrrnxhZdNrH37W4Vx141WDseUblje1JgAAAMDZUjwDbMD8RfPd1xydGG163b3n7x2sMTox2uy2AAAAAM6aozYAAAAAAOhK8QwAAAAAQFeKZwAAAAAAulI8AwAAAADQleIZAAAAAICuFM8AAAAAAHSleAYAAAAAoCvFMwAAAAAAXSmeAQAAAADoSvEMAAAAAEBXimcAAAAAALpSPAMAAAAA0JXimV1v8fbFWW8BAAAAAM4pimcAAAAAALpSPAMAAAAA0NWeWW8A2EJzc/3XXFnZmnWXl/uvCQAAAMBMKJ5ht5uf77veaLQ1awIAAACwazhqAwAAAACArhTPAAAAAAB0pXgGAAAAAKArxTMAAAAAAF0pngEAAAAA6GpdxXNVHaqqe6vqnqq6o6qeuur6P6uq36uqD1bVf6qqZ01de7SqRuPHXb1fAAAAAAAA28sZi+eqekaS65IstNaen+S8JAdXTXv/+PpfT3Jnkn89de1zrbX58ePlnfYNAAAAAMA2td6jNvYk+fKq2pNkb5Lj0xdba0dbayfHT9+T5Jn9tggAAAAAwE5yxuK5tfZQkp9I8kCSTyR5pLX2rie45VVJfn3q+VOr6lhVvaeq/sGmdgsAAAAAwLZXrbUnnlA1l+TtSV6R5I+TvC3Jna21X1xj7ncleV2Sb2qtfX48dnFr7XhV/cUk/znJ5a21+9a499ok1ybJ/v37Lzty5MimXthOtbKykn379s16G9tGjzwOjQ7l5vmbO+1ods4qi9Eo2bu36z4uvemmfOD667uumZMnk/n5Dd0yi39XRidG2Xt+3zyT5Kb7b8r1z95cphecd0EefvThP3t+8osnM3/RxjLdLfw5OiSPCVkMyWNIHhOyGJLHkDwmZDEkjyF5TMhiSB4TshjayXksLi7e3VpbWD2+nuL5O5Jc0Vp71fj5K5O8qLX2vavmfXOSf5NTpfMfnmattyT5tdbanU/0PRcWFtqxY8eecF+71dLSUg4cODDrbWwbPfJYvH0xR6852mdDM3RWWczNbbjQPaPRaGvWXF7e0C2z+Hdl7sa5LSlzRydGm1736n1X546VOwZrLt+wsUx3C3+ODsljQhZD8hiSx4QshuQxJI8JWQzJY0geE7IYkseELIZ2ch5VtWbxvJ4znh9I8qKq2ltVleTyJB9atfgLkvxckpdPl85VNVdVTxl/fWGSb0zye2f/MgAAAAAA2O72nGlCa+13q+rOJO9L8qUk709yW1W9Mcmx1tpdSX48yb4kbzvVTeeB1trLk/y1JD9XVX+aUyX3v2qtKZ4BAAAAAHaxMxbPSdJaO5zk8KrhN0xd/+bT3Pc7Sf6ns94dAAAAAAA7znqO2gAAAAAAgHVTPAMAAAAA0JXiGQAAAACArhTPAAAAAAB0pXgGAAAAAKArxTMAAAAAAF0pngEAAAAA6ErxDAAAAABAV4pnAAAAAAC6UjwDAAAAANCV4hkAAAAAgK4UzwAAAAAAdKV4BgAAAACgK8UzAAAAAABdKZ4BAAAAAOhK8QwAAAAAQFeKZwAAAAAAulI8AwAAAADQleIZAAAAAICuFM8AAAAAAHSleAYAAAAAoCvFMwAAAAAAXSmeAQAAAADoSvEMAAAAAEBXe2a9AWCLjUZ911tZ6b/mDjI60f+1r3xhZdPrXvmsK7dkbwAAAABnQ/EMu9nycv81FxeTo0f7r7sDLN+wBXkmWbx9MUev2VymS0tLWT64NfsDAAAA2ChHbQAAAAAA0JXieZu59NChWW8BAAAAAGBTFM8AAAAAAHSleAYAAAAAoCvFMwAAAAAAXSmeAQAAAADoSvEMAAAAAEBXimdgY44enfUOAAAAANjmFM8AAAAAAHSleAYAAAAAoCvFMwAAAAAAXSmeAQAAAADoSvEMAAAAAEBXimcAAAAAALpSPAMAAAAA0JXiGQAAAACArhTPAAAAAAB0pXgGAAAAAKArxTMAAAAAAF0pngEAAAAA6ErxDAAAAABAV4pnAAAAAAC6UjwDAAAAANCV4hkAAAAAgK4UzwAAAAAAdKV4BgAAAACgqz2z3gA8Zu7GuceNHX7W4Vx141WbWnflCytrrr1Zyzcsd18TAAAAAHYDxTPbyvxF84Pne8/f+7ixjRqdGG16jbXWBAAAAADW5qgNAAAAAAC6UjwDAAAAANCV4hkAAAAAgK4UzwAAAAAAdKV4BgAAAACgK8UzAAAAAABdKZ4BAAAAAOhK8QwAAAAAQFfrKp6r6lBV3VtV91TVHVX11FXXn1JVv1xVH62q362qS6au/cB4/MNV9dK+2wcAAAAAYLs5Y/FcVc9Icl2Shdba85Ocl+TgqmmvSrLcWvvLSW5OcuP43q8fz31ekiuS/GxVnddv+wAAAAAAbDfrPWpjT5Ivr6o9SfYmOb7q+pVJbh9/fWeSy6uqxuNHWmufb63dn+SjSV64+W0DAAAAALBdnbF4bq09lOQnkjyQ5BNJHmmtvWvVtGck+YPx/C8leSTJ06fHxx4cjwEAAAAAsEtVa+2JJ1TNJXl7klck+eMkb0tyZ2vtF6fm3Jvkpa21B8fP78updza/Mcn/+9jcqvr5JO9srb19je9zbZJrk2T//v2XHTlyZPOvbgd6/nXX5Z5bbpn1NmZidGKUvefvHYxdcN4FefjRhze17k3335Trn339ptZY7eQXT2b+ovmua57JyspK9u3b96R+z+1sN+VxaHQoN8/fvKk1dlMemyWLIXlMyGJIHkPymJDFkDyG5DEhiyF5DMljQhZD8piQxdBOzmNxcfHu1trC6vE967j3m5Pc31r7VJJU1a8m+ZtJfnFqzoNJvjbJg+PjOP58koenxh/zzDz+mI4kSWvttiS3JcnCwkI7cODAOra2+yyfd17O1dd+1Y1XPa7MvXrf1blj5Y5NrfuHj/7hptdYbXRilOWDy13XPJOlpaVz9mdjLbspj6/6+Fdt+rXspjw2SxZD8piQxZA8huQxIYsheQzJY0IWQ/IYkseELIbkMSGLod2Yx3rOeH4gyYuqau/43ObLk3xo1Zy7klwz/vrbk/znduqt1HclOVhVT6mqZyd5TpL/r8/WAQAAAADYjs74jufW2u9W1Z1J3pfkS0nen+S2qnpjkmOttbuS/HySf19VH82pdzofHN97b1X9SpLfG9/72tbao1vzUgAAAAAA2A7Wc9RGWmuHkxxeNfyGqet/kuQ7TnPvjyX5sbPdIAAAAAAAO8t6jtoAAAAAAIB1UzwDAAAAANCV4hkAAAAAgK4UzwAAAAAAdLWuDxfkNObm+q954YVbs+7ycv81AQAAAADWoHjerPn5vusdP95/zdGo73oAAAAAAE/AURsAAAAAAHSleAYAAAAAoCvFMwAAAAAAXSmeAQAAAADoyocLblbvD+678EIfBggAAAAA7GiK581YXu6/5gtekHzkI/3XBQAAAAB4kjhqAwAAAACArhTPAAAAAAB0pXgGAAAAAKArxTMAAAAAAF0pngFm7Og1R2e9BQAAAICuFM8AAAAAAHSleAYAAAAAoCvFMwAAAAAAXSmeAQAAAADoSvEMAAAAAEBXimcAAAAAALpSPAMAAAAA0NWeWW8Apo1OjAbPr3zWlY8b26iVL6xseg0AAAAAYP0Uz2wbyzcsP25saWkpywcfP74Ri7cv5ug1Rze1BgAAAACwfo7aAAAAAACgK8UzAAAAAABdKZ4BAAAAAOhK8QwAAAAAQFeKZ3Y9HywIAAAAAE8uxTMAAAAAAF0pngEAAAAA6ErxDAAAAABAV4pnAAAAAAC6UjwDAAAAANCV4hkAAAAAgK4UzwAAAAAAdKV4BgAAAACgK8UzAAAAAABdKZ4BAAAAAOhK8QwAAAAAQFeKZwAAAAAAulI8AwAAAADQleIZAAAAAICuFM8AAAAAAHSleAYAAAAAoCvFMwAAAAAAXSmeAQAAAADoSvEMAAAAAEBXimcAAAAAALpSPAMAAAAA0JXiGQAAAACArhTPAAAAAAB0pXgGAAAAAKCrPbPeAADnrsXbF7uvefW+q/PDt/9w93WPXnO0+5oAAACwWymeAZiZrShzl5aWcvTblMQAAAAwS47aAAAAAACgK8UzAAAAAABdKZ4BAAAAAOhK8QwAAAAAQFeKZwAAAAAAujpj8VxVz62q0dTjM1X1+lVzvn/q+j1V9WhVXTC+9t+r6r+Orx3bqhcCAAAAAMD2sOdME1prH04ynyRVdV6Sh5K8Y9WcH0/y4+M5fz/Jodbaw1NTFltrn+61aQAAAAAAtq+NHrVxeZL7Wmsff4I5Vye54+y3BAAAAADATrbR4vlgnqBUrqq9Sa5I8vap4ZbkXVV1d1Vdu/EtAgAAAACwk1RrbX0Tq74syfEkz2utffI0c16R5Ltaa39/auzi1trxqvqaJO9O8n2ttd9a495rk1ybJPv377/syJEjG34xu8Hzr7su99xyy6y3sW2srKxk3759s97GtiCLIXkMyWNCFkPymJDFkDyG5DEhiyF5DMljQhZD8hiSx4QshuQxIYuhnZzH4uLi3a21hdXjZzzjecrLkrzvdKXz2OPeEd1aOz7+5x9W1TuSvDDJ44rn1tptSW5LkoWFhXbgwIENbG33WD7vvJyrr30tS0tL8hiTxZA8huQxIYsheUzIYkgeQ/KYkMWQPIbkMSGLIXkMyWNCFkPymJDF0G7MYyNHbTzh2c1V9eeTfFOS/zg19rSq+orHvk7ykiT3nN1WAQAAAADYCdb1jufx2c0vTvLqqbHXJElr7dbx0FVJ3tVa++zUrfuTvKOqHvtev9Ra+40O+wYAAAAAYJtaV/HcWjuZ5Omrxm5d9fwtSd6yauxjSS7d1A4BAAAAANhRNnLUBgAAAAAAnJHiGQAAAACArhTPAAAAAAB0pXgGAAAAAKArxTMAAAAAAF0pngEAAAAA6ErxDAAAAABAV4pnAAAAAAC6UjwDAAAAANCV4hkAAAAAgK4UzwAAAAAAdKV4BgAAAACgK8UzAAAAAABdKZ4BAAAAAOhK8QwAAAAAQFeKZwAAAAAAulI8AwAAAADQleIZAAAAAICuFM8AAAAAAHSleAYAAAAAoCvFMwAAAAAAXSmeAQAAAADoSvG8zXzg5ptnvQUAAAAAgE1RPAMAAAAA0JXiGQAAAACArhTPAAAAAAB0pXgGAAAAAKArxTMAAAAAAF0pngEAAAAA6ErxDAAAAABAV4pnAAAAAAC6UjwDAAAAANCV4hkAAAAAgK4UzwAAAAAAdKV4BgAAAACgK8UzAAAAAABdKZ4BAAAAAOhK8QwAAAAAQFeKZwAAAAAAulI8AwAAAADQleIZAAAAAICuFM8AAAAAAHSleAYAAAAAoCvFMwAAAAAAXSmeAQAAAADoSvEMAAAAAEBXimcAAAAAALpSPAMAAAAA0JXiGQAAAACArhTPAAAAAAB0pXgGAAAAAKArxTMAAAAAAF0pngEAAAAA6ErxDAAAAABAV4pnAAAAAAC6UjwDAAAAANCV4hkAAAAAgK4UzwAAAAAAdKV4BgAAAACgK8UzAAAAAABdKZ4BAAAAAOhK8QwAAAAAQFdnLJ6r6rlVNZp6fKaqXr9qzoGqemRqzhumrl1RVR+uqo9W1b/YihcBAAAAAMD2sedME1prH04ynyRVdV6Sh5K8Y42pv91a+9bpgfH8NyV5cZIHk7y3qu5qrf3eZjcOAAAAAMD2tNGjNi5Pcl9r7ePrnP/CJB9trX2stfaFJEeSXLnB7wkAAAAAwA6y0eL5YJI7TnPtb1TVB6rq16vqeeOxZyT5g6k5D47HAAAAAADYpaq1tr6JVV+W5HiS57XWPrnq2lcm+dPW2kpVfUuSn26tPaeqviPJS1tr3zOe94+TvLC19n1rrH9tkmuTZP/+/ZcdOXJkM69rx1pZWcm+fftmvY1tQx4TshiSx5A8JmQxJI8JWQzJY0geE7IYkseQPCZkMSSPIXlMyGJIHhOyGNrJeSwuLt7dWltYPX7GM56nvCzJ+1aXzknSWvvM1NfvrKqfraoLc+odzl87NfWZOVVeP05r7bYktyXJwsJCO3DgwAa2tnssLS3lXH3ta5HHhCyG5DEkjwlZDMljQhZD8hiSx4QshuQxJI8JWQzJY0geE7IYkseELIZ2Yx4bOWrj6pzmmI2quqiqavz1C8fr/lGS9yZ5TlU9e/yO6YNJ7trclgEAAAAA2M7W9Y7nqtqb5MVJXj019pokaa3dmuTbk/zTqvpSks8lOdhOneHxpap6XZLfTHJekje31u7t+xIAAAAAANhO1lU8t9ZOJnn6qrFbp77+mSQ/c5p735nknZvYIwAAAAAAO8hGjtoAAAAAAIAzUjwDAAAAANCV4hkAAAAAgK4UzwAAAAAAdKV4BgAAYMdYvH1x1lsAANZB8QwAAAAAQFeKZwAAAAAAulI8AwAAAADQleIZAAAAAICuFM8AAAAAAHSleAYAAAAAoCvFMwAAAAAAXSmeAQAAAADoSvEMAAAAAEBXimcAAAAAALpSPAMAAMC5bHFx1jsAYBdSPAMAAAAA0JXiGQAAAACArhTPAAAAAAB0pXgGAAAAAKArxTMAAAAAAF3tmfUGAAAA2J3mbpzrvubKF1a2ZN0kWb5heUvWBYBzkeIZAACALTN/0XzX9UYnRt3XfGxdAKAfR20AAAAAANCV4hkAAAAAgK4UzwAAAAAAdKV4BgAAAACgK8UzAAAAAABdKZ4BAAAAAOhK8QwAAAAAQFeKZwAAAAAAulI8AwAAAADQleIZAAAAAICuFM8AAAAAAHSleAYAAAAAoCvFMwAAAAAAXSmeAQAAAADoSvEMAAAAAEBXimcAAAAAALpSPAMAAAAA0JXiGQAAAACArhTPAAAAAAB0pXgGAAAAAKArxTMAAAAAAF0pngEAAAAA6ErxDAAAAABAV4pnAAAAAAC6UjwDAAAAANCV4hkAAAAAgK4UzwAAAAAAdKV4BgAAAACgK8UzAAAAAABdKZ4BAAAAAOhK8QwAAAAAQFeKZwAAAAAAulI8AwAAAADQleIZAAAAAICuFM8AAAAAAHSleAYAeDItLs56BwAAAFtO8QwAAAAAQFeKZwAAAAAAulI8AwAAAADQ1RmL56p6blWNph6fqarXr5rznVX1wfHjd6rq0qlr/72q/uv43mNb8SIAAAAAANg+9pxpQmvtw0nmk6SqzkvyUJJ3rJp2f5Jvaq0tV9XLktyW5Bumri+21j7dZ8sAAAAAAGxnZyyeV7k8yX2ttY9PD7bWfmfq6XuSPHOzGwMAAAAAYGfa6BnPB5PccYY5r0ry61PPW5J3VdXdVXXtBr8fAAAAAAA7TLXW1jex6suSHE/yvNbaJ08zZzHJzyb5W621PxqPXdxaO15VX5Pk3Um+r7X2W2vce22Sa5Nk//79lx05cuRsXs+Ot7Kykn379s16G9uGPCZkMSSPIXlMyGJIHhPbJYtLDx3KB26+edbb2DZ5bBfymJDFkDyGNprH6MQoe8/f23UPN91/U65/9vVd10ySk188mfmL5tc9fzf9bPT43bSb8uhBHhOyGJLHhCyGdnIei4uLd7fWFlaPb6R4vjLJa1trLznN9b+eU2c/v6y19t9OM+eHkqy01h1v5EUAABrwSURBVH7iib7XwsJCO3bs3PwcwqWlpRw4cGDW29g25DEhiyF5DMljQhZD8pjYNlksLiZHj856F9snj21CHhOyGJLH0EbzmLtxbkNl7nqMToy6r/nYuss3LK97/q762ejwu2lX5dGBPCZkMSSPCVkM7eQ8qmrN4nkjR21cndMcs1FVX5fkV5P84+nSuaqeVlVf8djXSV6S5J6NbBwAAAAAgJ1lXR8uWFV7k7w4yaunxl6TJK21W5O8IcnTk/xsVSXJl8Yt9/4k7xiP7UnyS6213+j5AgAAAAAA2F7WVTy31k7mVLE8PXbr1Nffk+R71rjvY0ku3eQeAQAAAADYQTZy1AYAAAAAAJzRut7xDAAAAGwDc3P911xZ2fy6hw8nV101HFte/4c1ArD7KJ4BAABgJ5mf77veaLT5NffuHa4xGm1uPQB2PEdtAAAAAADQleIZAHhii4uz3gHAjrZ4uz9HAYBzj+IZAAAAAICuFM8AAAAAAHSleAYAAAAAoCvFMwAAAAAAXSmeAQAAAADoSvEMAAAAAEBXimcAAAAAALpSPAMAAAAA0JXiGQAAAACArhTPAAAAAAB0pXgGAAAA6OjSQ4dmvQWAmVM8AwAAAADQleIZAAAAAICuFM8AAAAAAHSleAYAAAAAoCvFMwAAAAAAXe2Z9QYAAADYvUYnRl3XW/nCSvc1AYD+FM8AAABsieUblruvuXj7Yo5ec7T7ugBAX47aAAAAAACgK8UzAADwZw6NDs16CwAA7AKKZwAAAAAAulI8AwAAAADQlQ8XBAAAGJu7cW7w/PCzDueqG6/a1JorX1h53Lo9bMUH9wEA9KJ4BgAAmDJ/0fyffb33/L2D52djdGK06TXWWhMAYDtz1AYAAAAAAF0pngEAAAAA6ErxDAAAAABAV4pnAAAAAAC6UjwDAACwYxy95uistwAArIPiGQAAAACArhTPAAAAAAB0pXgGAAAAAKArxTMAAAAAAF0pngEAAAAA6ErxDAAAAABAV4pnAAAAAAC62jPrDQAAAADMxNzc1qx74YVbs/bycv81AbaI4hkAAAA4d83P91/z+PH+645GfdcD2GKO2gAAAAAAoCvveAaA3WT1X+k8fDi56qrNrbmy4q+KAgAAsCGKZwDYbab/WufevZv/a56jkb8qCgAAwIY4agMAAAAAgK4UzwAAAAAAdKV4BgAAAACgK2c8A8BuM31+8pVXbv485ZWVc/dM5q34UEUf1tjf4mJy9OisdwEAAExRPAPAbrK6fFxa2nwhea6XelvxwYo+rBEAANjlHLUBAAAAAEBX3vEMAMCTZ7seX3L4cHLVVcOxc/n4EgAA2CTFMwAAT67teHzJ3r3DNRxfAgAAm+KoDQAAAAAAulI8AwAAAADQleIZAAAAAICuFM8AAAAAAHSleAYAAAAAoCvFMwAAAAAAXSmeAQAAAADoSvEMAAAAAEBXZyyeq+q5VTWaenymql6/ak5V1S1V9dGq+mBV/c9T166pqo+MH9dsxYsAAAAAAGD72HOmCa21DyeZT5KqOi/JQ0nesWray5I8Z/z4hiT/Nsk3VNUFSQ4nWUjSktxdVXe11pa7vQIAAAAAALaVjR61cXmS+1prH181fmWSt7ZT3pPkq6rqLyR5aZJ3t9YeHpfN705yxaZ3DQA8eY4enfUOAAAA2GE2WjwfTHLHGuPPSPIHU88fHI+dbhwAAAAAgF2qWmvrm1j1ZUmOJ3lea+2Tq679X0n+z9bafxk//09J/nmSv5vkKa21Hx2P/2CSk621m9ZY/9ok1ybJ/v37Lzty5MhZv6idbGVlJfv27Zv1NrYNeUzIYkgeQ/KYkMWQPCbOKovRKNm7t+s+Lr3ppnzg+uu7rpmTJ5P5+Q3dMrOfjW2a6coFF2Tfww9PBs4i093iuruvyy2X3TLrbczM6MQoe8+f/IxecN4FefjRh5/gjjO76f6bcv2z+/57f/KLJzN/0ZP/M+r3ysRM/xzt7NI3vSkfeO1rN7XGysUXZ9/x48PBnfDn6Bb8XkqS599yS+657rq+i+6Q302jE8Of0YufcnGOf/74aWavz5seeFNe+3Wb+xldiz9HZ0sWQzs5j8XFxbtbawurxzdSPF+Z5LWttZesce3nkiy11u4YP/9wkgOPPVprr15r3uksLCy0Y8eOrWtfu83S0lIOHDgw621sG/KYkMWQPIbkMSGLIXlMnFUWc3P9/wdvNNqaNZc39hEaM/vZ2KaZLl19dQ7cMfWfqGeR6W7xgp96Qd7/+vfPehszM3fj3KCIuHrf1blj5Qn/9+WMRidG3cuN0YlRlm948n9G/V6Z2FVZLC5u+mitHZvHVvxeSrJ8/HjmLr6476I75HeTP0ef2I79d2ULyGJoJ+dRVWsWzxs5auPqrH3MRpLcleSVdcqLkjzSWvtEkt9M8pKqmququSQvGY8BAAAAALBL7VnPpKram+TFSV49NfaaJGmt3ZrknUm+JclHk5xM8k/G1x6uqh9J8t7xbW9srW3u76kBAAAAALCtrat4bq2dTPL0VWO3Tn3dkqx52E5r7c1J3ryJPQIAAAAAsINs5KgNAAAAAAA4I8UzAAAAAABdKZ4BAAAAAOhK8QwAAAAAQFfr+nBBAADoZjTqu97KyubXvPLK/vt6EszdONd9zQvrwi1Zd/mG5e5rAgCwfSmeAQB48ixvQfm4uJgcPbq5NZaWtmZvT4L5i+a7rnf8U8cz/9V91xyd2HmlPgAAm+OoDQAAAAAAulI8AwAA/397dx98WV3fB/z9KQ8qKlDDg3VqaDo+pZoJKKP1IcLGp9ZKjKJREEVNRTvRVNpE4qTW1piqsQ1KpykSjDJRNFarNs3U6tTdSdokahzW1GIMLY1PlPi0SjcVEfz0j3OX+zvLrhA5+zu//Z3Xa4bZ3z337NnP78295577Od/zPQAAMCmNZwAAAAAAJqXxDAAAAADApNxcEADge9k98U3R9u6dfpsAAABbjMYzAMDB7NkzdwUAAACHJVNtAAAAAAAwKY1nAAAAAAAmpfEMAAAAAMCkNJ4BAAAAAJiUxjMAAAAAAJPSeAYAAAAAYFJHzl0AAADAVrL7+t23/vzUU546evz92HvT3ju9DQCAw43GMwAAwMqei/aMHu/atSt7nr3nIGvfMTuu2JGd5++8U9sAADjcmGoDAAAAAIBJaTwDAAAAADApjWcAAAAAACal8QwAAAAAwKTcXBAAAA5ju6/fPen2TqgTJt8mAADLo/EMAACHqT0X7Zl8m6e96bRc8/JrJt8uAADLYqoNAAAAAAAmpfEMAAAAAMCkNJ4BAAAOoZ3n75y7BACATafxDAAAAADApDSeAQA4vO00mhQAALYajWcAAAAAACal8QwAAAAAwKQ0ngEAAAAAmJTGMwAAAAAAk9J4BgAAAABgUhrPAADArS4+9eK5SwAAYBvQeAYAAAAAYFIazwAAAAAATErjGQAAAACASWk8AwAAAAAwKY1nAAAAAAAmpfEMAAAAS7Zz59wVALANaTwDAAAAADCpI+cuAAAAAAC4rR1X7Dgk2919/e6ceu9TJ9/uzvNdQcGaxjMAAAAAbEGHqpG744odmsQcchrPAAAAAExm9/W7b/35qac8dfT4+7H3pr13ehvA5tN4BgAAAGASey7aM3q8a9eu7Hn2noOsfccYnTs9ebIZ3FwQAAAAAIBJaTwDAAAAADApjWcAAAAAACal8QwAAAAAwKTcXBAAAABYrt27p9/mCSccmu0CHEY0ngEAAIBl2rPn0Gz3tNOSa645NNsGOEyYagMAAAAAgElpPAMAAAAAMCmNZwAAAAAAJqXxDAAAAADApDSeAQAAAACYlMYzAAAAAACT0ngGAAAAAGBSGs8AAAAAAEzqyDuyUlUdn+TyJA9J0kle2N1/sOH5n0/ynA3b/OEkJ3b316vqz5L83yS3JLm5u0+frnwAAAAAALaaO9R4TvLmJB/q7mdU1dFJjtn4ZHe/Mckbk6SqzkpyYXd/fcMqO7r7q1MUDAAAAADA1na7jeeqOjbJY5M8P0m6+6YkN32Pv3JOkndNURwAAAAAAIefOzLH899M8pUkb6uqq6rq8qq6+4FWrKpjkvydJO/bsLiTfLiqPllVF9zpigEAAAAA2NKqu7/3ClWnJ/nDJI/u7o9V1ZuT3NDdrzrAus9Kcl53n7Vh2X26+7qqOinJR5K8rLt/9wB/94IkFyTJySef/LB3v/vdd+b3Omzt3bs397jHPeYuY8uQx5osxuQxJo81WYzJY00WY/IYk8eaLMbkMSaPNVmMyWPsIT/7s/n0JZfMXcaWMMVr48LdF+biUy+eqKJ5ea+syWLscM5jx44dnzzQff3uyBzPX0zyxe7+2Orxe5P8wkHWfXb2m2aju69b/fnlqnp/kocnuU3jubsvS3JZkpx++ul95pln3oHStp9du3Zlqb/7gchjTRZj8hiTx5osxuSxJosxeYzJY00WY/IYk8eaLMbkMbbrkkvksTLFa+P4zx2/bfL0XlmTxdh2zON2p9ro7uuTfKGqHrha9LgkV++/XlUdl+SMJB/csOzuVXXPfT8neWKST09QNwAAAAAAW9QdGfGcJC9L8s6qOjrJtUleUFUvSZLuvnS1ztOSfLi7/2LD3zs5yfurat+/dWV3f2iSygEAAAAA2JLuUOO5u3cn2X+ejkv3W+ftSd6+37Jrk/zo918eAAAAAACHm9udagMAAAAAAP4yNJ4BAAAAAJiUxjMAAAAAW9bO83fOXQLwfdB4BgAAAABgUhrPAAAAAABMSuMZAAAAAIBJaTwDAAAAADApjWcAAAAAACal8QwAAAAAwKQ0ngEAAAAAmJTGMwAAAAAAk9J4BgAAAABgUhrPAAAAAABMSuMZAAAAAIBJaTwDAAAAADApjWcAAAAAACal8QwAAAAAwKQ0ngEAAAAAmJTGMwAAAAAAk9J4BgAAAABgUhrPAAAAAABMSuMZAAAAAIBJaTwDAAAAADApjWcAAAAAACal8QwAAAAAwKQ0ngEAAAAAmFR199w13EZVfSXJ5+auYyYnJPnq3EVsIfJYk8WYPMbksSaLMXmsyWJMHmPyWJPFmDzG5LEmizF5jMljTRZj8liTxdjhnMcp3X3i/gu3ZON5yarqj7r79Lnr2CrksSaLMXmMyWNNFmPyWJPFmDzG5LEmizF5jMljTRZj8hiTx5osxuSxJoux7ZiHqTYAAAAAAJiUxjMAAAAAAJPSeN56Lpu7gC1GHmuyGJPHmDzWZDEmjzVZjMljTB5rshiTx5g81mQxJo8xeazJYkwea7IY23Z5mOMZAAAAAIBJGfEMAAAAAMCkNJ4BOCxVVc1dw1YhCw6kqu4+dw1bSVXd23sFAAA2j8bzJqqqB1bVI6vqqKo6Yu565iSL25LDWlXdr6pOr6q7zF3L3KrqwVV1RlX9wNy1bAVV9Ziqem6SdHcvuYkki7GqOquq/uHcdWwVVfXUJG+oqpPmrmUrqKonJXl/kvvOXcvcqupvV9VzV38ePXc9c5PHWlXdf3X8dYTj0rGlf8ZycF4bY/IYkweY43nTVNXTk/yLJF9a/fdHSd7e3TfMWtgMZDFWVQ/o7j9d/XxEd98yd01zqqqnZHh9fC3J9UlevS+fpamqv5vkDUmuTXJUkp/u7uvnrWoeVfVXkhyT5GNJKskl3X3pvue6+7tz1reZZHFbVfXEJL+S5Oe7+yNz1zO3qjojyVuSvEweo9fH8Uk+2N2LPUFRVT+R5LVJrkpy9ySv7O5r5q1qPvJYq6qfTPLPk/zPJF9M8tkkV3T3X8xa2Eyq6hFJ7prk/3X3J1bLqhf65bmqjl3qd7X9VdVDMxyH3dTdH5+7nrnJY6yqHpnkuCS3LP0YbPVd9oTu/s25a2FeRjxvgqo6KsmzMjSNHpfkgxlG3Lyiqo6dtbhNJouxVZN1d1VdmSTdfcuSR5hU1aOS/Msk53f3jiR7kvzCvFXNo6rOTPLmJH+/u38yyU1JHjJrUTPq7u92994kVyR5a5JHVdWF+56btbhNJoux1X7jN5Nc0N0fqarjquqUqjpm7tpm9LAkl6/yuE9VPaGqHlFVx81d2Garqscn+bUkz0ly/yQ/XFWPnbeqeayunPmZJOd29/lJbkhyalWdVFV3nbe6zSePtVUWL05yTnefneRTSV6Q5MKquuesxc1g1Sx5R4b9xi9W1VuT5V5dtBo09Hurz5FF9w9W393emuSCJD9XVS+euaRZyWOsqp6c5NIkP57k5auTm/ueW9S+Y/U5+g+SXLa6Co8FW/QHxyY7NsMXnmS41PM/Jjk6yblL2wlFFklunXvzpUlenuSmqnpHovmc5PXdfdXq51cnuddCp9z48yQv7u6PV9W9kzwiyUur6i1V9YwlvVf2c3OGk1VXJHl4Vf1qVb2uBkv7TJPF4GtJvpPkr62aJx9I8m+TvH3B75WbN/z83iQvzPB582+q6q/OU9JsjkjyvO7+HxlGtH42yYOT5X0JzPC6uFuSB61O9p+Z5HlJ3pTkn9Ty5gSXx9rNSe6R5N5J0t2/keRzSU5M8pQZ69p0q2Pw85O8prsvyPCaeGBVvTdZXvO5qv5Gkn+U5MtJLkzy0CX9/htV1WkZrsp8fnc/L8m/S/KgeauajzzGViO/X5PkJd39igxX0qRWU54tbd/R3Tcm+e0MAw3fVFXnJ7deucnC+J++Cbr7O0l+NcnTq+rHViPS/muS3UkeM2txm0wWa6tLF1+Y5MokP5fkrhubz3PWNqOPJfn3ya0H/ndJckqGkxX7RuQsQnd/prt3rh7+dJJfW418/sMkz0xywmzFzeuDSa7v7v+SYZqelyQ5tgdLG+0riyTd/dkkfy/JxRlG6V2ZoVHyoSRnJ1laozVJPprkRVX17iS/3t3nZDiRtzfJw2etbJN193/u7t9fTUPzjSS/k+TVVfUjS7tkvru/meSSJK9M8uEkb+vus5JcnuSvJ7nfjOVtOnmsrbJ4Z5IX1DDf9S8nuTHJ1UmeMGtxm2x1DH7Vhsc3dPdjkpxcVW9ZLVvSvuO7SX6xu5+Q4fXwT5M8rKqO3LjSQhpqd8twPP6p1eOrkjy6qu67kN9/f/IYOzLJS7v7D6rqXhm+578oyb+qqn+dLGffsbrKPRlOWL0vyTMynNB9Q5KLFz7IbpE0njfP72U4qH1uVT22u2/p7iuT3CfJj85b2qaTxUp3X9fde7v7qxkucbzbvuZzVT20qhZ11nj1Wtg3f1wl+UaSr3f3V6rqOUleW1V3m6/CeXT3L3f3a1c/vy3JPbPcG2R9K8PIoxdlaLS+PskPLvTSPlmsrL70PCXJ67r711fTkfxGhqbzD85b3ebr7k9nOKH5iCQ/tFp2bYbRvyfOWNps9p2M6e4PJbksyVMWeHVAuvu9SR6f4VjsqtWyj2b4XDllxtJmIY+Rd2U4YffjSY7p7vO6+y1JTqoFTIdXVQ/Y8PBLSS6qqo2fH09L8gNV9bc2t7J57Mujuz+fYYBQuvs1ST6R4UTmaav1fmT13LZtqG3I4vczNNH2DZC5LsMVit9cjWa9/8G3sn3IY2xDHh9P8onVccU5Ge4ZcFaGk5sPqWEaxW1tQxbfWS364yRP7+5PZrj3yIVJjlzwILvFOvL2V2EK3X1jVb0zSSd55aqh+O0kJyf5P7MWt8lkcWDd/bVVw+iNVfUnGRoEO2YuazbdfXOSvVX1hap6XZInZriU61szl7apqsY3sqmqszO8V66br6r5dPd1VfWFJK9K8jPd/dtVtSPDzZAWRRZj3X11htFYSW59r5yY5X6u/KcMzYF/VlWfWy07LcMJiqX7VIYvP7+yxC8/3b2nqj6a5Keq6qYMN1D7oQxfEBdHHoN9o56r6l37TtRU1fOS3CvJtn6f1DBP7Xuq6j9097O7+x1V9cAk/62qHt3dn+/ur1bVzRlOSmxrG/L4YHef093frKqju/um7v6lqnpVhvm/P5/kyVX1+O7+8sxlHxIHeG18ZXUFzS1VdWOG72upqucmeWZVnd/de2Yt+hCSx9gB3iu3rJZf3t3fTpLu/mJVXZvhfj3b1oYsPtDd564WfyPJV6rqpzKMAH9Nhn3H73b3b81VK5uvtvHJyS2pqo5O8ugMo1tvTPLmXs9nuyiyOLAabhJ2UZIndPd/n7ueuawu0ToqyWdWfz6uF3qn+SSpYZ7r8zLMs/es1YjGRaqq+yY5aXX2PKsD3sVMLbGRLG5rte94QYYRv8/sYW7fxaphzsFnZJi66O1L/lzZqKrek+QV3f1nc9cyh6o6PsPctWdnOAZ7xYbLpRdHHrdVVS/MsB991nbeb9Qwl/f7Mkz19qgkd1lNT5Sq+qUkP5HhBqUnZDgOe3J3/++Zyj3kDpDHkd193uq5u+xrplXVriQPSPKk7fr6uJ0sjshwdea7knwzyakZ7idw9UE2d9iTx9jt5HHkahDVvptzvjLJM7r7cwfb3uHsdrJ4fYaT/ed29/uq6owkX+ruRQ6UWSqN55msds699AZBIouNarjp03uS/OPuXtRIm4Opqucn+YTmUR2VYZ7F/9XDnLaLt/9o8CWTxdqq8XxGhvmv/2TuethavFfGquqeGb4P3HC7Ky+APNaq6pQkRy2hOVBV90lyQ4bR7pcm+c6G5vPTMtx08WFJ3rSEE/8HyOPGfU2k1fMPSPJbGa5E3NYnaO5AFh/I0IB/2hKOz+Ux9r3yWH13uyDDSN/zt/u+4wBZ3NTd566mHrlfd/+pY7Dl0niGLaaq7trDXWCJJgEAAJujhhtZX5ahaXJOVT04yd7tOlLx9mzI41vdfV5VnZrhpt9X93CPmsU4QBb3z3CF1Tu288jeg5HH2AHyeFCSJyX5nSWcwNvoIPuNb3f3Z2YujZloPAMAAECSqjohyRszXDJ+RJIzu/uL81Y1nw15PDJDHmd09yLvNbIhi0evFv1Yd//5jCXNSh5j++07Kslju/v6eauaxwH2GzuWvB9dukXdyRsAAAAOZjWS94+THJdhyoBFN0s25HF8kqcvtemcjLI4NsnZS26yJvLY3377jrOX2nRODrjfWPR+dOk0ngEAACC33nPlyUmeuF1vnPeXIY81WYzJY0wea7JgI1NtAAAAwIp7rozJY00WY/IYk8eaLNhH4xkAAAAAgEmZagMAAAAAgElpPAMAAAAAMCmNZwAAAAAAJqXxDAAAAADApDSeAQAAAACYlMYzAAAAAACT0ngGAAAAAGBS/x/Oph6TI/p4HgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1800x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(25, 10)) # 设置图片大小。 \n",
    "# https://matplotlib.org/api/finance_api.html#module-matplotlib.finance \n",
    "# matplotlib.finance.candlestick2_ochl(ax, opens, closes, highs, lows, width=4, colorup='r', colordown='g', alpha=0.75) \n",
    "finance.candlestick2_ochl(ax = ax, \n",
    "                  opens=df[\"open\"].values, \n",
    "                  closes=df[\"close\"].values, \n",
    "                  highs=df[\"high\"].values, \n",
    "                  lows=df[\"low\"].values, \n",
    "                  width=0.5, colorup='r', colordown='g', alpha=0.75) \n",
    "ax.xaxis.set_major_locator(matplotlib.ticker.MaxNLocator(25)) \n",
    "# 设置自动格式化时间。 \n",
    "def mydate_formatter(x,pos): \n",
    "    try: \n",
    "        return df_idx[int(x)] \n",
    "    except IndexError: \n",
    "        return '' \n",
    "\n",
    "ax.xaxis.set_major_formatter(matplotlib.ticker.FuncFormatter(mydate_formatter)) \n",
    "plt.setp(plt.gca().get_xticklabels(), rotation=45, horizontalalignment='right') \n",
    "ax.grid(True) \n",
    "plt.title(code) \n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7  交易明细数据与分时数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "import datetime as dt \n",
    "import seaborn as sns \n",
    "import tushare as ts "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>price</th>\n",
       "      <th>change</th>\n",
       "      <th>volume</th>\n",
       "      <th>amount</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>09:25:02</td>\n",
       "      <td>5.99</td>\n",
       "      <td>0.04</td>\n",
       "      <td>754</td>\n",
       "      <td>451646</td>\n",
       "      <td>卖盘</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>09:30:02</td>\n",
       "      <td>5.98</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>470</td>\n",
       "      <td>280683</td>\n",
       "      <td>中性盘</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>09:30:05</td>\n",
       "      <td>5.98</td>\n",
       "      <td>0.00</td>\n",
       "      <td>265</td>\n",
       "      <td>158598</td>\n",
       "      <td>买盘</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>09:30:08</td>\n",
       "      <td>5.99</td>\n",
       "      <td>0.01</td>\n",
       "      <td>130</td>\n",
       "      <td>77768</td>\n",
       "      <td>买盘</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>09:30:10</td>\n",
       "      <td>5.97</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>12</td>\n",
       "      <td>7164</td>\n",
       "      <td>卖盘</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>09:30:14</td>\n",
       "      <td>5.99</td>\n",
       "      <td>0.02</td>\n",
       "      <td>64</td>\n",
       "      <td>38257</td>\n",
       "      <td>买盘</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>09:30:23</td>\n",
       "      <td>5.99</td>\n",
       "      <td>0.00</td>\n",
       "      <td>7</td>\n",
       "      <td>4193</td>\n",
       "      <td>买盘</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>09:30:25</td>\n",
       "      <td>5.99</td>\n",
       "      <td>0.00</td>\n",
       "      <td>27</td>\n",
       "      <td>16173</td>\n",
       "      <td>买盘</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>09:30:32</td>\n",
       "      <td>5.98</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>9</td>\n",
       "      <td>5382</td>\n",
       "      <td>卖盘</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>09:30:41</td>\n",
       "      <td>5.99</td>\n",
       "      <td>0.01</td>\n",
       "      <td>2</td>\n",
       "      <td>1198</td>\n",
       "      <td>买盘</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       time  price  change  volume  amount type\n",
       "0  09:25:02   5.99    0.04     754  451646   卖盘\n",
       "1  09:30:02   5.98   -0.01     470  280683  中性盘\n",
       "2  09:30:05   5.98    0.00     265  158598   买盘\n",
       "3  09:30:08   5.99    0.01     130   77768   买盘\n",
       "4  09:30:10   5.97   -0.02      12    7164   卖盘\n",
       "5  09:30:14   5.99    0.02      64   38257   买盘\n",
       "6  09:30:23   5.99    0.00       7    4193   买盘\n",
       "7  09:30:25   5.99    0.00      27   16173   买盘\n",
       "8  09:30:32   5.98   -0.01       9    5382   卖盘\n",
       "9  09:30:41   5.99    0.01       2    1198   买盘"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = ts.get_tick_data('600728',date='2018-10-30',retry_count=5, pause=0.5,src='tt')\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>close</th>\n",
       "      <th>low</th>\n",
       "      <th>volume</th>\n",
       "      <th>price_change</th>\n",
       "      <th>p_change</th>\n",
       "      <th>ma5</th>\n",
       "      <th>ma10</th>\n",
       "      <th>ma20</th>\n",
       "      <th>v_ma5</th>\n",
       "      <th>v_ma10</th>\n",
       "      <th>v_ma20</th>\n",
       "      <th>turnover</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [open, high, close, low, volume, price_change, p_change, ma5, ma10, ma20, v_ma5, v_ma10, v_ma20, turnover]\n",
       "Index: []"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = ts.get_hist_data('600728',start='2018-11-01',end='2018-11-21',ktype='60',retry_count=3, pause=0.5)\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## 8  talib 技术指标\n",
    "\n",
    "http://www.lfd.uci.edu/~gohlke/pythonlibs/ 下载安装 TA-Lib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "#coding=utf-8\n",
    "import tushare as ts\n",
    "import talib as ta\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os,time,sys,re,datetime\n",
    "import csv\n",
    "import scipy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "#两个函数\n",
    "#1、get_stock_basics()，这里得到是对应的dataframe数据结构，code,代码 name,名称 industry,所属行业 area,地区 pe,市盈率 \n",
    "# outstanding,流通股本 totals,总股本(万) totalAssets,总资产(万)liquidAssets,流动资产 fixedAssets,固定资产 reserved,公积金\n",
    "# reservedPerShare,每股公积金 eps,每股收益 bvps,每股净资 pb,市净率 timeToMarket,上市日期\n",
    "\n",
    "#2、get_hist_data(),获取每只股票的历史价格和成交量 对应的列有index列,0 - 6列是 date：日期 open：开盘价 high：最高价 close：收盘价 \n",
    "# low：最低价 volume：成交量 price_change：价格变动 p_change：涨跌幅\n",
    "# 7-12列是 ma5：5日均价 ma10：10日均价 ma20:20日均价 v_ma5:5日均量v_ma10:10日均量 v_ma20:20日均量\n",
    "\n",
    "#1 、首先是获取沪深两市的股票列表\n",
    "def Get_Stock_List():\n",
    "    df = ts.get_stock_basics()\n",
    "    return df\n",
    "\n",
    "#然后定义通过MACD判断买入卖出\n",
    "def Get_MACD(df_Code):\n",
    "    operate_array=[]\n",
    "    stcok_code=df_Code.index\n",
    "    for code in df_Code.index:\n",
    "        df = ts.get_hist_data(code,start='2016-01-03')\n",
    "        if df is None:           #如code代表的是一个未上市的新股\n",
    "            stcok_code=stcok_code.drop(code)\n",
    "            continue   \n",
    "        dflen = df.shape[0]\n",
    "        operate = 0\n",
    "        if dflen>35:\n",
    "            macd, macdsignal, macdhist = ta.MACD(np.array(df['close']), fastperiod=12, slowperiod=26, signalperiod=9)\n",
    "                   \n",
    "            SignalMA5 = ta.MA(macdsignal, timeperiod=5, matype=0)\n",
    "            SignalMA10 = ta.MA(macdsignal, timeperiod=10, matype=0)\n",
    "            SignalMA20 = ta.MA(macdsignal, timeperiod=20, matype=0)\n",
    "            \n",
    "            #在后面增加3列，分别是13-15列，对应的是 DIFF  DEA  DIFF-DEA       \n",
    "            df['macd']=pd.Series(macd,index=df.index)            #DIFF\n",
    "            df['macdsignal']=pd.Series(macdsignal,index=df.index)#DEA\n",
    "            df['macdhist']=pd.Series(macdhist,index=df.index)    #DIFF-DEA\n",
    "\n",
    "            MAlen = len(SignalMA5)\n",
    "\n",
    "            #2个数组 1.DIFF、DEA均为正，DIFF向上突破DEA，买入信号。 2.DIFF、DEA均为负，DIFF向下跌破DEA，卖出信号。\n",
    "\n",
    "            if df.iat[(dflen-1),13]>0:\n",
    "                if df.iat[(dflen-1),14]>0:\n",
    "                    if df.iat[(dflen-1),13]>df.iat[(dflen-1),14]:\n",
    "                        operate = operate + 1#买入\n",
    "            else:\n",
    "                if df.iat[(dflen-1),14]<0:\n",
    "                    if df.iat[(dflen-1),13]<0:\n",
    "                        operate = operate - 1#卖出\n",
    "       \n",
    "            #3.DEA线与K线发生背离，行情反转信号。\n",
    "            if df.iat[(dflen-1),7]>=df.iat[(dflen-1),8] and df.iat[(dflen-1),8]>=df.iat[(dflen-1),9]:     #K线上涨\n",
    "                if SignalMA5[MAlen-1]<=SignalMA10[MAlen-1] and SignalMA10[MAlen-1]<=SignalMA20[MAlen-1]:  #DEA下降\n",
    "                    operate = operate - 1\n",
    "            elif df.iat[(dflen-1),7]<=df.iat[(dflen-1),8] and df.iat[(dflen-1),8]<=df.iat[(dflen-1),9]:   #K线下降\n",
    "                if SignalMA5[MAlen-1]>=SignalMA10[MAlen-1] and SignalMA10[MAlen-1]>=SignalMA20[MAlen-1]:  #DEA上涨\n",
    "                    operate = operate + 1\n",
    "                    \n",
    "            #4.分析MACD柱状线，由负变正，买入信号。\n",
    "            if df.iat[(dflen-1),15]>0 and dflen >30 :\n",
    "                for i in range(1,26):\n",
    "                    if df.iat[(dflen-1-i),15]<=0:#\n",
    "                        operate = operate + 1\n",
    "                        break\n",
    "            #由正变负，卖出信号   \n",
    "            if df.iat[(dflen-1),15]<0 and dflen >30 :\n",
    "                for i in range(1,26):\n",
    "                    if df.iat[(dflen-1-i),15]>=0:#\n",
    "                        operate = operate - 1\n",
    "                        break\n",
    "                        \n",
    "        operate_array.append(operate)        \n",
    "    df_Code['MACD']=pd.Series(operate_array,index=stcok_code)   \n",
    "    return df_Code\n",
    "\n",
    " \n",
    "\n",
    "#输出CSV文件\n",
    "def Output_Csv(df,Dist,industry_name):\n",
    "    TODAY = datetime.date.today()\n",
    "    CURRENTDAY=TODAY.strftime('%Y-%m-%d')\n",
    "    df.to_csv(Dist+industry_name+CURRENTDAY+'stock.csv',encoding='gbk')#选择保存   \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df = Get_Stock_List()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>industry</th>\n",
       "      <th>area</th>\n",
       "      <th>pe</th>\n",
       "      <th>outstanding</th>\n",
       "      <th>totals</th>\n",
       "      <th>totalAssets</th>\n",
       "      <th>liquidAssets</th>\n",
       "      <th>fixedAssets</th>\n",
       "      <th>reserved</th>\n",
       "      <th>...</th>\n",
       "      <th>bvps</th>\n",
       "      <th>pb</th>\n",
       "      <th>timeToMarket</th>\n",
       "      <th>undp</th>\n",
       "      <th>perundp</th>\n",
       "      <th>rev</th>\n",
       "      <th>profit</th>\n",
       "      <th>gpr</th>\n",
       "      <th>npr</th>\n",
       "      <th>holders</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>code</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>688138</th>\n",
       "      <td>N清溢</td>\n",
       "      <td>元器件</td>\n",
       "      <td>深圳</td>\n",
       "      <td>70.70</td>\n",
       "      <td>0.61</td>\n",
       "      <td>2.67</td>\n",
       "      <td>8.22</td>\n",
       "      <td>2.73</td>\n",
       "      <td>3.70</td>\n",
       "      <td>0.27</td>\n",
       "      <td>...</td>\n",
       "      <td>4.15</td>\n",
       "      <td>4.71</td>\n",
       "      <td>20191120</td>\n",
       "      <td>3.29</td>\n",
       "      <td>1.23</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>33.90</td>\n",
       "      <td>15.84</td>\n",
       "      <td>54542.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600397</th>\n",
       "      <td>安源煤业</td>\n",
       "      <td>煤炭开采</td>\n",
       "      <td>江西</td>\n",
       "      <td>0.00</td>\n",
       "      <td>9.90</td>\n",
       "      <td>9.90</td>\n",
       "      <td>64.38</td>\n",
       "      <td>21.81</td>\n",
       "      <td>32.45</td>\n",
       "      <td>18.63</td>\n",
       "      <td>...</td>\n",
       "      <td>0.77</td>\n",
       "      <td>2.95</td>\n",
       "      <td>20020702</td>\n",
       "      <td>-24.36</td>\n",
       "      <td>-2.46</td>\n",
       "      <td>13.71</td>\n",
       "      <td>-205.26</td>\n",
       "      <td>7.35</td>\n",
       "      <td>-2.15</td>\n",
       "      <td>53192.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600532</th>\n",
       "      <td>宏达矿业</td>\n",
       "      <td>普钢</td>\n",
       "      <td>上海</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.16</td>\n",
       "      <td>5.16</td>\n",
       "      <td>33.22</td>\n",
       "      <td>27.16</td>\n",
       "      <td>0.03</td>\n",
       "      <td>10.48</td>\n",
       "      <td>...</td>\n",
       "      <td>3.25</td>\n",
       "      <td>1.24</td>\n",
       "      <td>20021031</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.19</td>\n",
       "      <td>205.86</td>\n",
       "      <td>-232.79</td>\n",
       "      <td>3.34</td>\n",
       "      <td>-3.83</td>\n",
       "      <td>30835.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>002354</th>\n",
       "      <td>天神娱乐</td>\n",
       "      <td>互联网</td>\n",
       "      <td>辽宁</td>\n",
       "      <td>0.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>9.32</td>\n",
       "      <td>72.53</td>\n",
       "      <td>19.63</td>\n",
       "      <td>1.01</td>\n",
       "      <td>58.87</td>\n",
       "      <td>...</td>\n",
       "      <td>1.71</td>\n",
       "      <td>1.79</td>\n",
       "      <td>20100209</td>\n",
       "      <td>-53.02</td>\n",
       "      <td>-5.69</td>\n",
       "      <td>-44.46</td>\n",
       "      <td>-261.61</td>\n",
       "      <td>55.28</td>\n",
       "      <td>-39.14</td>\n",
       "      <td>51788.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>002528</th>\n",
       "      <td>英飞拓</td>\n",
       "      <td>IT设备</td>\n",
       "      <td>深圳</td>\n",
       "      <td>258.03</td>\n",
       "      <td>10.46</td>\n",
       "      <td>11.99</td>\n",
       "      <td>60.70</td>\n",
       "      <td>36.85</td>\n",
       "      <td>1.22</td>\n",
       "      <td>23.01</td>\n",
       "      <td>...</td>\n",
       "      <td>3.11</td>\n",
       "      <td>1.93</td>\n",
       "      <td>20101224</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.04</td>\n",
       "      <td>8.88</td>\n",
       "      <td>-68.24</td>\n",
       "      <td>20.22</td>\n",
       "      <td>0.70</td>\n",
       "      <td>51316.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        name industry area      pe  outstanding  totals  totalAssets  \\\n",
       "code                                                                   \n",
       "688138   N清溢      元器件   深圳   70.70         0.61    2.67         8.22   \n",
       "600397  安源煤业     煤炭开采   江西    0.00         9.90    9.90        64.38   \n",
       "600532  宏达矿业       普钢   上海    0.00         5.16    5.16        33.22   \n",
       "002354  天神娱乐      互联网   辽宁    0.00         7.00    9.32        72.53   \n",
       "002528   英飞拓     IT设备   深圳  258.03        10.46   11.99        60.70   \n",
       "\n",
       "        liquidAssets  fixedAssets  reserved  ...  bvps    pb  timeToMarket  \\\n",
       "code                                         ...                             \n",
       "688138          2.73         3.70      0.27  ...  4.15  4.71      20191120   \n",
       "600397         21.81        32.45     18.63  ...  0.77  2.95      20020702   \n",
       "600532         27.16         0.03     10.48  ...  3.25  1.24      20021031   \n",
       "002354         19.63         1.01     58.87  ...  1.71  1.79      20100209   \n",
       "002528         36.85         1.22     23.01  ...  3.11  1.93      20101224   \n",
       "\n",
       "         undp  perundp     rev  profit    gpr    npr  holders  \n",
       "code                                                           \n",
       "688138   3.29     1.23    0.00    0.00  33.90  15.84  54542.0  \n",
       "600397 -24.36    -2.46   13.71 -205.26   7.35  -2.15  53192.0  \n",
       "600532   0.98     0.19  205.86 -232.79   3.34  -3.83  30835.0  \n",
       "002354 -53.02    -5.69  -44.46 -261.61  55.28 -39.14  51788.0  \n",
       "002528   0.46     0.04    8.88  -68.24  20.22   0.70  51316.0  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "code\n",
       "688138     元器件\n",
       "600397    煤炭开采\n",
       "600532      普钢\n",
       "002354     互联网\n",
       "002528    IT设备\n",
       "000509    其他建材\n",
       "000892    影视音像\n",
       "000677      化纤\n",
       "000523    日用化工\n",
       "603336    农业综合\n",
       "300655    化工原料\n",
       "000713     种植业\n",
       "300748     元器件\n",
       "603496    IT设备\n",
       "002967     综合类\n",
       "002405    软件服务\n",
       "600651    电器仪表\n",
       "300803    软件服务\n",
       "300799    软件服务\n",
       "300127     元器件\n",
       "002256    染料涂料\n",
       "000587     批发业\n",
       "000506      黄金\n",
       "300802    IT设备\n",
       "603976    医疗保健\n",
       "002417    多元金融\n",
       "300796    化工原料\n",
       "300797    电器仪表\n",
       "300168    软件服务\n",
       "300341    电气设备\n",
       "          ... \n",
       "002359    通信设备\n",
       "600980     元器件\n",
       "300777      化纤\n",
       "002684    电气设备\n",
       "600127    农业综合\n",
       "300659    软件服务\n",
       "603655    汽车配件\n",
       "600634     互联网\n",
       "002901    医疗保健\n",
       "600856    供气供热\n",
       "002089    通信设备\n",
       "000613    旅游服务\n",
       "002862    文教休闲\n",
       "002005    家用电器\n",
       "002021    纺织机械\n",
       "603882    医疗保健\n",
       "002850    机械基件\n",
       "600792    焦炭加工\n",
       "600654    软件服务\n",
       "000700    汽车配件\n",
       "000034     综合类\n",
       "603858     中成药\n",
       "600313     种植业\n",
       "601258    汽车服务\n",
       "600725    焦炭加工\n",
       "600614     综合类\n",
       "603533     互联网\n",
       "300014    电气设备\n",
       "600311      黄金\n",
       "603517      食品\n",
       "Name: industry, Length: 100, dtype: object"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['industry'].head(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1=df[df['industry']=='酒店餐饮']\n",
    "df2=df[df['industry']=='电信运营']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['002306', '600754', '000428', '002186', '000721', '601007', '600258',\n",
       "       '000524', '000007'],\n",
       "      dtype='object', name='code')"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:73: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:73: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    }
   ],
   "source": [
    "df11 = Get_MACD(df1)\n",
    "df22 = Get_MACD(df2)\n",
    "Dist = 'data/'\n",
    "name1 = '酒店餐饮'\n",
    "name2 = '电信运营'\n",
    "Output_Csv(df11,Dist,name1)\n",
    "Output_Csv(df22,Dist,name2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "from lxml import etree\n",
    "\n",
    "#设置抓取的网址信息\n",
    "get_url = 'https://sh.xiaozhu.com/putuo-duanzufang-8/'\n",
    "\n",
    "#设置User-Agent信息\n",
    "#最好设置自己的User-Agentpost_url\n",
    "headers = {\n",
    "    'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/78.0.3904.108 Safari/537.36'\n",
    "}\n",
    "\n",
    "#使用requests访问网页，获取网页内容\n",
    "ss = requests.Session()\n",
    "\n",
    "#获取网页内容\n",
    "content = ss.get(url=get_url,headers=headers).text\n",
    "\n",
    "#使用lxml构造文档对象模型DOM\n",
    "html = etree.HTML(content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24\n",
      "[[1, 1], [2, 2], [1, 1], [2, 2]]\n"
     ]
    }
   ],
   "source": [
    "def get_url(url,headers):\n",
    "    ss = requests.Session()\n",
    "    resp = ss.get(url, headers=headers)\n",
    "    html = resp.text\n",
    "  \n",
    "    return html\n",
    "\n",
    "#解析网页内容，利用1完成的内容，把里面缺失的代码补齐。\n",
    "house=[]  #存放抓取的房源信息\n",
    "def parse_html(content):\n",
    "    html = etree.HTML(content)\n",
    "    house_list = html.xpath('//div[@id=\"page_list\"]//ul/li')\n",
    "    print(len(house_list))\n",
    "\n",
    "    house=[]  #存放抓取的房源信息\n",
    "    if len(house_list):\n",
    "        for tr in house_list:\n",
    "            item = {}   #临时存放当前的一条房源信息\n",
    "\n",
    "            #房源标题与价格\n",
    "            #Problem 1a  ： 获取房源标题、价格存放在字典item的键title与price中，价格只保留数字 \n",
    "            #your code\n",
    "            item['title'] = tr.xpath('./div[2]/div[2]/a/span/text()')[0]#换成您的code\n",
    "            item['price'] = tr.xpath('./div[2]/div[1]/span/i/text()')[0]#换成您的code\n",
    "\n",
    "\n",
    "            #房源基本信息：出租类型、房型、床位数、宜住人数\n",
    "            #Problem 1b  ： 先获取房源基本信息存放在houseinfo中\n",
    "            #your code\n",
    "            houseinfo= tr.xpath('./div[2]/div[2]/em/text()')[0]#换成您的code\n",
    "\n",
    "\n",
    "            #再拆分成：出租类型、房型、床位数houseinfo、宜住人数 ,注意并不是每个房源都有这4个信息\n",
    "            #your code\n",
    "\n",
    "            list1=[]\n",
    "            list1=houseinfo.split('/')\n",
    "            if len(list1)==4:\n",
    "                renttype=list1[0] \n",
    "                housetype=list1[1] \n",
    "                beds=list1[2] \n",
    "                person=list1[3]\n",
    "            else:\n",
    "                renttype=list1[0]  \n",
    "                housetype=''\n",
    "                beds=list1[1] \n",
    "                person=list1[2]\n",
    "\n",
    "\n",
    "            #上面代码处理好后再存入字典item\n",
    "            item['renttype'] =str(renttype).strip()\n",
    "            item['housetype'] =str(housetype).strip()\n",
    "            item['beds'] =str(beds).strip()\n",
    "            item['person'] =str(person).strip()\n",
    "\n",
    "            #房源评分、评论数\n",
    "            #先获取房源评论信息reviews，\n",
    "            reviews=\"\".join(tr.xpath('.//em[@class=\"hiddenTxt\"]/span/text()'))\n",
    "            #再拆分成：房源评分、评论数\n",
    "            reviews=reviews.split('/')\n",
    "            if len(reviews)==2:\n",
    "                score,reviewnum=reviews\n",
    "            else:\n",
    "                score=0\n",
    "                reviewnum=reviews[0]\n",
    "            item['score'] =str(score).strip()\n",
    "            item['reviewnum'] =str(reviewnum).strip()        \n",
    "\n",
    "            house.append([item['title'],item['price'],item['renttype'],item['housetype'],item['beds'],item['person'],item['score'],item['reviewnum']])\n",
    "    return house\n",
    "\n",
    "html=get_url(\"https://sh.xiaozhu.com/putuo-duanzufang-8/\", headers)\n",
    "house = parse_html(html)\n",
    "#print(house)\n",
    "\n",
    "print([[1,1],[2,2]] + [[1,1],[2,2]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}