20160515Demo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import requests\n",
    "payload={\n",
    "'qdate':'105/05/13',\n",
    "'select2':'24',\n",
    "'sorting':'by_issue'    \n",
    "}\n",
    "url = 'http://www.twse.com.tw/ch/trading/fund/T86/T86.php'\n",
    "res = requests.post(url, data=payload)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#print res.text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from bs4 import BeautifulSoup as bs\n",
    "soup = bs(res.text)\n",
    "tb = soup.select('#tbl-sortable')[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "dfs = pd.read_html(tb.prettify('utf-8'), encoding='utf-8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>證券  代號</th>\n",
       "      <th>證券  名稱</th>\n",
       "      <th>外資  買進股數</th>\n",
       "      <th>外資  賣出股數</th>\n",
       "      <th>外資  買賣超股數</th>\n",
       "      <th>投信  買進股數</th>\n",
       "      <th>投信  賣出股數</th>\n",
       "      <th>投信  買賣超股數</th>\n",
       "      <th>自營商  買賣超股數</th>\n",
       "      <th>自營商  買進股數  (自行買賣)</th>\n",
       "      <th>自營商  賣出股數  (自行買賣)</th>\n",
       "      <th>自營商  買賣超股數  (自行買賣)</th>\n",
       "      <th>自營商  買進股數  (避險)</th>\n",
       "      <th>自營商  賣出股數  (避險)</th>\n",
       "      <th>自營商  買賣超股數  (避險)</th>\n",
       "      <th>三大法人  買賣超股數</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2408</td>\n",
       "      <td>南亞科</td>\n",
       "      <td>18079000</td>\n",
       "      <td>4097000</td>\n",
       "      <td>13982000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>329000</td>\n",
       "      <td>317000</td>\n",
       "      <td>17000</td>\n",
       "      <td>300000</td>\n",
       "      <td>154000</td>\n",
       "      <td>125000</td>\n",
       "      <td>29000</td>\n",
       "      <td>14311000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3474</td>\n",
       "      <td>華亞科</td>\n",
       "      <td>9560000</td>\n",
       "      <td>4665000</td>\n",
       "      <td>4895000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113000</td>\n",
       "      <td>201000</td>\n",
       "      <td>30000</td>\n",
       "      <td>171000</td>\n",
       "      <td>0</td>\n",
       "      <td>58000</td>\n",
       "      <td>-58000</td>\n",
       "      <td>5008000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2449</td>\n",
       "      <td>京元電</td>\n",
       "      <td>8513011</td>\n",
       "      <td>4469755</td>\n",
       "      <td>4043256</td>\n",
       "      <td>58000</td>\n",
       "      <td>490000</td>\n",
       "      <td>-432000</td>\n",
       "      <td>472000</td>\n",
       "      <td>0</td>\n",
       "      <td>107000</td>\n",
       "      <td>-107000</td>\n",
       "      <td>1266000</td>\n",
       "      <td>687000</td>\n",
       "      <td>579000</td>\n",
       "      <td>4083256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3413</td>\n",
       "      <td>京鼎</td>\n",
       "      <td>529000</td>\n",
       "      <td>24000</td>\n",
       "      <td>505000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1000</td>\n",
       "      <td>0</td>\n",
       "      <td>1000</td>\n",
       "      <td>-1000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>504000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3016</td>\n",
       "      <td>嘉晶</td>\n",
       "      <td>440000</td>\n",
       "      <td>91000</td>\n",
       "      <td>349000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-12000</td>\n",
       "      <td>2000</td>\n",
       "      <td>14000</td>\n",
       "      <td>-12000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>337000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   證券  代號 證券  名稱  外資  買進股數  外資  賣出股數  外資  買賣超股數  投信  買進股數  投信  賣出股數  \\\n",
       "0    2408    南亞科  18079000   4097000   13982000         0         0   \n",
       "1    3474    華亞科   9560000   4665000    4895000         0         0   \n",
       "2    2449    京元電   8513011   4469755    4043256     58000    490000   \n",
       "3    3413     京鼎    529000     24000     505000         0         0   \n",
       "4    3016     嘉晶    440000     91000     349000         0         0   \n",
       "\n",
       "   投信  買賣超股數  自營商  買賣超股數  自營商  買進股數  (自行買賣)  自營商  賣出股數  (自行買賣)  \\\n",
       "0          0      329000             317000              17000   \n",
       "1          0      113000             201000              30000   \n",
       "2    -432000      472000                  0             107000   \n",
       "3          0       -1000                  0               1000   \n",
       "4          0      -12000               2000              14000   \n",
       "\n",
       "   自營商  買賣超股數  (自行買賣)  自營商  買進股數  (避險)  自營商  賣出股數  (避險)  自營商  買賣超股數  (避險)  \\\n",
       "0              300000           154000           125000             29000   \n",
       "1              171000                0            58000            -58000   \n",
       "2             -107000          1266000           687000            579000   \n",
       "3               -1000                0                0                 0   \n",
       "4              -12000                0                0                 0   \n",
       "\n",
       "   三大法人  買賣超股數  \n",
       "0     14311000  \n",
       "1      5008000  \n",
       "2      4083256  \n",
       "3       504000  \n",
       "4       337000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfs[0].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2016-05-15 09:37:17.229000\n",
      "105/05/14\n",
      "105/05/13\n",
      "105/05/12\n",
      "105/05/11\n",
      "105/05/10\n",
      "105/05/09\n",
      "105/05/08\n",
      "105/05/07\n",
      "105/05/06\n"
     ]
    }
   ],
   "source": [
    "from datetime import datetime, timedelta\n",
    "print datetime.now()\n",
    "#print datetime.now() - datetime.timedelta(days = 1)\n",
    "currentdate = datetime.now()\n",
    "\n",
    "\n",
    "for i in range(1,10):\n",
    "    ymd = currentdate - timedelta(days = i)\n",
    "    year = str(int(ymd.strftime('%Y')) - 1911)\n",
    "    monthdate = ymd.strftime('/%m/%d')\n",
    "    print year + monthdate\n",
    "           "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def getTWDate(dt):\n",
    "    year = int(dt.strftime('%Y')) - 1911\n",
    "    monthdate = dt.strftime('%m/%d')\n",
    "    ymd = '{}/{}'.format(year, monthdate)\n",
    "    return ymd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "url = 'http://www.twse.com.tw/ch/trading/fund/T86/T86.php'\n",
    "payload={\n",
    "'qdate':'105/05/13',\n",
    "'select2':'24',\n",
    "'sorting':'by_issue'    \n",
    "}\n",
    "from bs4 import BeautifulSoup as bs\n",
    "import pandas as pd\n",
    "\n",
    "def getTradingVolume(date):\n",
    "    payload['qdate'] = getTWDate(date)\n",
    "    res = requests.post(url, data=payload)    \n",
    "    soup = bs(res.text)\n",
    "    tb = soup.select('#tbl-sortable')[0]\n",
    "    dfs = pd.read_html(tb.prettify('utf-8'), encoding='utf-8')\n",
    "    stockdf = dfs[0]\n",
    "    stockdf['date'] = date.date()\n",
    "    return stockdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dt = datetime.strptime('2016/05/12', '%Y/%m/%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2016-05-12\n"
     ]
    }
   ],
   "source": [
    "dt\n",
    "print dt.date()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = getTradingVolume(dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import requests\n",
    "\n",
    "currentdate = datetime.now()\n",
    "dfall = []\n",
    "for i in range(1,30):\n",
    "    ymd = currentdate - timedelta(days = i)\n",
    "    df = getTradingVolume(ymd)\n",
    "    dfall.append(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(dfall)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "stockdf = pd.concat(dfall)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#dftotal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([            u'證券  代號',             u'證券  名稱',           u'外資  買進股數',\n",
       "                 u'外資  賣出股數',          u'外資  買賣超股數',           u'投信  買進股數',\n",
       "                 u'投信  賣出股數',          u'投信  買賣超股數',         u'自營商  買賣超股數',\n",
       "        u'自營商  買進股數  (自行買賣)',  u'自營商  賣出股數  (自行買賣)', u'自營商  買賣超股數  (自行買賣)',\n",
       "          u'自營商  買進股數  (避險)',    u'自營商  賣出股數  (避險)',   u'自營商  買賣超股數  (避險)',\n",
       "              u'三大法人  買賣超股數',               u'date'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stockdf.columns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df2330 = stockdf[stockdf[u'證券  代號'] == 2330]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>證券  代號</th>\n",
       "      <th>證券  名稱</th>\n",
       "      <th>外資  買進股數</th>\n",
       "      <th>外資  賣出股數</th>\n",
       "      <th>外資  買賣超股數</th>\n",
       "      <th>投信  買進股數</th>\n",
       "      <th>投信  賣出股數</th>\n",
       "      <th>投信  買賣超股數</th>\n",
       "      <th>自營商  買賣超股數</th>\n",
       "      <th>自營商  買進股數  (自行買賣)</th>\n",
       "      <th>自營商  賣出股數  (自行買賣)</th>\n",
       "      <th>自營商  買賣超股數  (自行買賣)</th>\n",
       "      <th>自營商  買進股數  (避險)</th>\n",
       "      <th>自營商  賣出股數  (避險)</th>\n",
       "      <th>自營商  買賣超股數  (避險)</th>\n",
       "      <th>三大法人  買賣超股數</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>2330</td>\n",
       "      <td>台積電</td>\n",
       "      <td>21174000</td>\n",
       "      <td>54670407</td>\n",
       "      <td>-33496407</td>\n",
       "      <td>329000</td>\n",
       "      <td>9000</td>\n",
       "      <td>320000</td>\n",
       "      <td>934000</td>\n",
       "      <td>1306000</td>\n",
       "      <td>328000</td>\n",
       "      <td>978000</td>\n",
       "      <td>384000</td>\n",
       "      <td>428000</td>\n",
       "      <td>-44000</td>\n",
       "      <td>-32242407</td>\n",
       "      <td>2016-05-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>2330</td>\n",
       "      <td>台積電</td>\n",
       "      <td>12719000</td>\n",
       "      <td>19946830</td>\n",
       "      <td>-7227830</td>\n",
       "      <td>54000</td>\n",
       "      <td>200000</td>\n",
       "      <td>-146000</td>\n",
       "      <td>-712000</td>\n",
       "      <td>81000</td>\n",
       "      <td>573000</td>\n",
       "      <td>-492000</td>\n",
       "      <td>180000</td>\n",
       "      <td>400000</td>\n",
       "      <td>-220000</td>\n",
       "      <td>-8085830</td>\n",
       "      <td>2016-05-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>2330</td>\n",
       "      <td>台積電</td>\n",
       "      <td>23622636</td>\n",
       "      <td>40207253</td>\n",
       "      <td>-16584617</td>\n",
       "      <td>461938</td>\n",
       "      <td>40000</td>\n",
       "      <td>421938</td>\n",
       "      <td>-156000</td>\n",
       "      <td>688000</td>\n",
       "      <td>493000</td>\n",
       "      <td>195000</td>\n",
       "      <td>355000</td>\n",
       "      <td>706000</td>\n",
       "      <td>-351000</td>\n",
       "      <td>-16318679</td>\n",
       "      <td>2016-05-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>2330</td>\n",
       "      <td>台積電</td>\n",
       "      <td>27595804</td>\n",
       "      <td>36472619</td>\n",
       "      <td>-8876815</td>\n",
       "      <td>124107</td>\n",
       "      <td>0</td>\n",
       "      <td>124107</td>\n",
       "      <td>444000</td>\n",
       "      <td>692000</td>\n",
       "      <td>708000</td>\n",
       "      <td>-16000</td>\n",
       "      <td>570000</td>\n",
       "      <td>110000</td>\n",
       "      <td>460000</td>\n",
       "      <td>-8308708</td>\n",
       "      <td>2016-05-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>2330</td>\n",
       "      <td>台積電</td>\n",
       "      <td>24509300</td>\n",
       "      <td>34812293</td>\n",
       "      <td>-10302993</td>\n",
       "      <td>145000</td>\n",
       "      <td>0</td>\n",
       "      <td>145000</td>\n",
       "      <td>-905000</td>\n",
       "      <td>61000</td>\n",
       "      <td>1018000</td>\n",
       "      <td>-957000</td>\n",
       "      <td>258000</td>\n",
       "      <td>206000</td>\n",
       "      <td>52000</td>\n",
       "      <td>-11062993</td>\n",
       "      <td>2016-05-09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    證券  代號 證券  名稱  外資  買進股數  外資  賣出股數  外資  買賣超股數  投信  買進股數  投信  賣出股數  \\\n",
       "66    2330    台積電  21174000  54670407  -33496407    329000      9000   \n",
       "67    2330    台積電  12719000  19946830   -7227830     54000    200000   \n",
       "70    2330    台積電  23622636  40207253  -16584617    461938     40000   \n",
       "68    2330    台積電  27595804  36472619   -8876815    124107         0   \n",
       "71    2330    台積電  24509300  34812293  -10302993    145000         0   \n",
       "\n",
       "    投信  買賣超股數  自營商  買賣超股數  自營商  買進股數  (自行買賣)  自營商  賣出股數  (自行買賣)  \\\n",
       "66     320000      934000            1306000             328000   \n",
       "67    -146000     -712000              81000             573000   \n",
       "70     421938     -156000             688000             493000   \n",
       "68     124107      444000             692000             708000   \n",
       "71     145000     -905000              61000            1018000   \n",
       "\n",
       "    自營商  買賣超股數  (自行買賣)  自營商  買進股數  (避險)  自營商  賣出股數  (避險)  自營商  買賣超股數  (避險)  \\\n",
       "66              978000           384000           428000            -44000   \n",
       "67             -492000           180000           400000           -220000   \n",
       "70              195000           355000           706000           -351000   \n",
       "68              -16000           570000           110000            460000   \n",
       "71             -957000           258000           206000             52000   \n",
       "\n",
       "    三大法人  買賣超股數        date  \n",
       "66    -32242407  2016-05-13  \n",
       "67     -8085830  2016-05-12  \n",
       "70    -16318679  2016-05-11  \n",
       "68     -8308708  2016-05-10  \n",
       "71    -11062993  2016-05-09  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2330.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>證券  代號</th>\n",
       "      <th>外資  買進股數</th>\n",
       "      <th>外資  賣出股數</th>\n",
       "      <th>外資  買賣超股數</th>\n",
       "      <th>投信  買進股數</th>\n",
       "      <th>投信  賣出股數</th>\n",
       "      <th>投信  買賣超股數</th>\n",
       "      <th>自營商  買賣超股數</th>\n",
       "      <th>自營商  買進股數  (自行買賣)</th>\n",
       "      <th>自營商  賣出股數  (自行買賣)</th>\n",
       "      <th>自營商  買賣超股數  (自行買賣)</th>\n",
       "      <th>自營商  買進股數  (避險)</th>\n",
       "      <th>自營商  賣出股數  (避險)</th>\n",
       "      <th>自營商  買賣超股數  (避險)</th>\n",
       "      <th>三大法人  買賣超股數</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>19</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>19.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2330</td>\n",
       "      <td>22138587.842105</td>\n",
       "      <td>33736859.526316</td>\n",
       "      <td>-11598271.684211</td>\n",
       "      <td>109791.842105</td>\n",
       "      <td>237828.421053</td>\n",
       "      <td>-128036.578947</td>\n",
       "      <td>460171.052632</td>\n",
       "      <td>646263.157895</td>\n",
       "      <td>371157.894737</td>\n",
       "      <td>275105.263158</td>\n",
       "      <td>706421.052632</td>\n",
       "      <td>521355.263158</td>\n",
       "      <td>185065.789474</td>\n",
       "      <td>-11266137.210526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0</td>\n",
       "      <td>6805220.410908</td>\n",
       "      <td>16872066.956777</td>\n",
       "      <td>14408106.192703</td>\n",
       "      <td>124063.276872</td>\n",
       "      <td>290786.082910</td>\n",
       "      <td>360325.253183</td>\n",
       "      <td>1268529.128144</td>\n",
       "      <td>628386.367532</td>\n",
       "      <td>271460.302635</td>\n",
       "      <td>769795.852789</td>\n",
       "      <td>548304.894479</td>\n",
       "      <td>548826.897471</td>\n",
       "      <td>802783.317547</td>\n",
       "      <td>13663364.764087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2330</td>\n",
       "      <td>11925989.000000</td>\n",
       "      <td>9734929.000000</td>\n",
       "      <td>-47307028.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1187000.000000</td>\n",
       "      <td>-2240749.000000</td>\n",
       "      <td>43000.000000</td>\n",
       "      <td>37000.000000</td>\n",
       "      <td>-957000.000000</td>\n",
       "      <td>180000.000000</td>\n",
       "      <td>59000.000000</td>\n",
       "      <td>-2036749.000000</td>\n",
       "      <td>-45206028.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2330</td>\n",
       "      <td>16411205.000000</td>\n",
       "      <td>18224430.000000</td>\n",
       "      <td>-16441120.500000</td>\n",
       "      <td>13500.000000</td>\n",
       "      <td>40500.000000</td>\n",
       "      <td>-265000.000000</td>\n",
       "      <td>-64500.000000</td>\n",
       "      <td>60500.000000</td>\n",
       "      <td>185000.000000</td>\n",
       "      <td>-179000.000000</td>\n",
       "      <td>322000.000000</td>\n",
       "      <td>198000.000000</td>\n",
       "      <td>-21000.000000</td>\n",
       "      <td>-15718651.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2330</td>\n",
       "      <td>23622636.000000</td>\n",
       "      <td>34812293.000000</td>\n",
       "      <td>-8876815.000000</td>\n",
       "      <td>84000.000000</td>\n",
       "      <td>165000.000000</td>\n",
       "      <td>-146000.000000</td>\n",
       "      <td>363000.000000</td>\n",
       "      <td>393000.000000</td>\n",
       "      <td>259000.000000</td>\n",
       "      <td>-16000.000000</td>\n",
       "      <td>423000.000000</td>\n",
       "      <td>355000.000000</td>\n",
       "      <td>112000.000000</td>\n",
       "      <td>-8308708.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2330</td>\n",
       "      <td>25227748.000000</td>\n",
       "      <td>40945676.500000</td>\n",
       "      <td>-4878821.000000</td>\n",
       "      <td>136000.000000</td>\n",
       "      <td>294500.000000</td>\n",
       "      <td>99053.500000</td>\n",
       "      <td>1230500.000000</td>\n",
       "      <td>1175500.000000</td>\n",
       "      <td>533000.000000</td>\n",
       "      <td>993000.000000</td>\n",
       "      <td>1054500.000000</td>\n",
       "      <td>690500.500000</td>\n",
       "      <td>547000.000000</td>\n",
       "      <td>-4137110.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2330</td>\n",
       "      <td>37765618.000000</td>\n",
       "      <td>66262467.000000</td>\n",
       "      <td>11166864.000000</td>\n",
       "      <td>461938.000000</td>\n",
       "      <td>1187000.000000</td>\n",
       "      <td>421938.000000</td>\n",
       "      <td>2868000.000000</td>\n",
       "      <td>2046000.000000</td>\n",
       "      <td>1018000.000000</td>\n",
       "      <td>1840000.000000</td>\n",
       "      <td>2145000.000000</td>\n",
       "      <td>2336749.000000</td>\n",
       "      <td>1587000.000000</td>\n",
       "      <td>9809864.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       證券  代號         外資  買進股數         外資  賣出股數        外資  買賣超股數  \\\n",
       "count      19        19.000000        19.000000        19.000000   \n",
       "mean     2330  22138587.842105  33736859.526316 -11598271.684211   \n",
       "std         0   6805220.410908  16872066.956777  14408106.192703   \n",
       "min      2330  11925989.000000   9734929.000000 -47307028.000000   \n",
       "25%      2330  16411205.000000  18224430.000000 -16441120.500000   \n",
       "50%      2330  23622636.000000  34812293.000000  -8876815.000000   \n",
       "75%      2330  25227748.000000  40945676.500000  -4878821.000000   \n",
       "max      2330  37765618.000000  66262467.000000  11166864.000000   \n",
       "\n",
       "            投信  買進股數        投信  賣出股數       投信  買賣超股數      自營商  買賣超股數  \\\n",
       "count      19.000000       19.000000       19.000000       19.000000   \n",
       "mean   109791.842105   237828.421053  -128036.578947   460171.052632   \n",
       "std    124063.276872   290786.082910   360325.253183  1268529.128144   \n",
       "min         0.000000        0.000000 -1187000.000000 -2240749.000000   \n",
       "25%     13500.000000    40500.000000  -265000.000000   -64500.000000   \n",
       "50%     84000.000000   165000.000000  -146000.000000   363000.000000   \n",
       "75%    136000.000000   294500.000000    99053.500000  1230500.000000   \n",
       "max    461938.000000  1187000.000000   421938.000000  2868000.000000   \n",
       "\n",
       "       自營商  買進股數  (自行買賣)  自營商  賣出股數  (自行買賣)  自營商  買賣超股數  (自行買賣)  \\\n",
       "count          19.000000          19.000000           19.000000   \n",
       "mean       646263.157895      371157.894737       275105.263158   \n",
       "std        628386.367532      271460.302635       769795.852789   \n",
       "min         43000.000000       37000.000000      -957000.000000   \n",
       "25%         60500.000000      185000.000000      -179000.000000   \n",
       "50%        393000.000000      259000.000000       -16000.000000   \n",
       "75%       1175500.000000      533000.000000       993000.000000   \n",
       "max       2046000.000000     1018000.000000      1840000.000000   \n",
       "\n",
       "       自營商  買進股數  (避險)  自營商  賣出股數  (避險)  自營商  買賣超股數  (避險)      三大法人  買賣超股數  \n",
       "count        19.000000        19.000000         19.000000        19.000000  \n",
       "mean     706421.052632    521355.263158     185065.789474 -11266137.210526  \n",
       "std      548304.894479    548826.897471     802783.317547  13663364.764087  \n",
       "min      180000.000000     59000.000000   -2036749.000000 -45206028.000000  \n",
       "25%      322000.000000    198000.000000     -21000.000000 -15718651.500000  \n",
       "50%      423000.000000    355000.000000     112000.000000  -8308708.000000  \n",
       "75%     1054500.000000    690500.500000     547000.000000  -4137110.000000  \n",
       "max     2145000.000000   2336749.000000    1587000.000000   9809864.000000  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2330.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "?df2330.plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 磁碟區 C 中的磁碟是 OS 7\n",
      " 磁碟區序號:  C485-A13C\n",
      "\n",
      " C:\\Users\\User\\pyfinance 的目錄\n",
      "\n",
      "2016/05/15  上午 09:55    <DIR>          .\n",
      "2016/05/15  上午 09:55    <DIR>          ..\n",
      "2016/03/28  上午 09:41               826 .gitignore\n",
      "2016/05/15  上午 09:17    <DIR>          .ipynb_checkpoints\n",
      "2016/05/08  上午 01:37         3,745,521 20160508 Python網路爬蟲入門-財經為例.pdf\n",
      "2016/05/08  上午 01:37         3,693,897 20160508 Python網路爬蟲入門-財經為例.pptx\n",
      "2016/05/09  下午 06:00           127,887 20160508Demo.ipynb\n",
      "2016/05/15  上午 09:55            30,675 20160515Demo.ipynb\n",
      "2016/05/15  上午 07:42             1,730 downlad.csv\n",
      "2016/05/08  上午 01:36         1,385,472 finance.sqlite\n",
      "2016/03/28  下午 03:48            34,380 foo.png\n",
      "2016/05/10  下午 11:21           463,539 location.png\n",
      "2016/05/08  上午 10:27           417,212 lunch.jpg\n",
      "2016/05/08  上午 11:04           321,575 lunch2.jpg\n",
      "2016/03/28  下午 03:20    <DIR>          names\n",
      "2016/05/08  上午 01:39    <DIR>          old\n",
      "2015/12/31  上午 01:17            30,969 pandas_readhtml.ipynb\n",
      "2016/05/08  上午 01:39            73,661 Python_Finance.ipynb\n",
      "2016/05/08  上午 01:45               560 README.md\n",
      "2016/05/09  下午 06:17             6,733 sitca.ipynb\n",
      "2016/05/15  上午 08:56            32,144 supplement.ipynb\n",
      "2016/05/08  下午 01:58               339 tb.html\n",
      "2016/05/08  下午 11:09            30,494 Untitled.ipynb\n",
      "              18 個檔案      10,397,614 位元組\n",
      "               5 個目錄  227,765,788,672 位元組可用\n"
     ]
    }
   ],
   "source": [
    "%ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: pylab import has clobbered these variables: ['datetime']\n",
      "`%matplotlib` prevents importing * from pylab and numpy\n"
     ]
    }
   ],
   "source": [
    "%pylab inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xe6b3dd8>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OXXWVc19+qdsKC51r1Mi5r7/2N0Y/TJvmXNeufkfhj9WrnatVy7nNm/2OJDHX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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd711390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2330.plot(kind=\"line\", x = 'date', y = u'外資  買進股數')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xe43ed30>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HLx4lPjm+wOeLT44nMSWRKmUKvu7AGGtDvgiPDWfWrlmM7z7ebn3u2we+vhAW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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd0c6748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2330.plot(kind=\"line\", x = 'date', y = [u'外資  買進股數', u'外資  賣出股數'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xd435198>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW8AAAEJCAYAAABbkaZTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nMrwlqUGGtyQ1yPCWpAYZ3pLUoP8Ppe6eNHbX2/cAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd41f278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2330.plot(kind=\"box\", y = u'外資  買進股數')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 三大法人買賣超日報 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from bs4 import BeautifulSoup as bs\n",
    "from datetime import datetime, timedelta\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "url = 'http://www.twse.com.tw/ch/trading/fund/T86/T86.php'\n",
    "payload={\n",
    "'qdate':'105/05/13',\n",
    "'select2':'24',\n",
    "'sorting':'by_issue'    \n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "def getTWDate(dt):\n",
    "    year = int(dt.strftime('%Y')) - 1911\n",
    "    monthdate = dt.strftime('%m/%d')\n",
    "    ymd = '{}/{}'.format(year, monthdate)\n",
    "    return ymd\n",
    "\n",
    "def getTradingVolume(date):\n",
    "    payload['qdate'] = getTWDate(date)\n",
    "    res = requests.post(url, data=payload)    \n",
    "    soup = bs(res.text)\n",
    "    tb = soup.select('#tbl-sortable')[0]\n",
    "    dfs = pd.read_html(tb.prettify('utf-8'), encoding='utf-8')\n",
    "    stockdf = dfs[0]\n",
    "    stockdf['date'] = date.date()\n",
    "    return stockdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import requests\n",
    "\n",
    "currentdate = datetime.now()\n",
    "dfall = []\n",
    "for i in range(1,3):\n",
    "    ymd = currentdate - timedelta(days = i)\n",
    "    df = getTradingVolume(ymd)\n",
    "    dfall.append(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "stockdf = pd.concat(dfall)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SQLite 操作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import sqlite3 as lite\n",
    "con = lite.connect('finance2.sqlite')\n",
    "con.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(u'3.6.21',)\n"
     ]
    }
   ],
   "source": [
    "import sqlite3 as lite\n",
    "con = lite.connect('finance2.sqlite')\n",
    "cur = con.cursor()\n",
    "cur.execute('SELECT SQLITE_VERSION()')\n",
    "data = cur.fetchone()\n",
    "print data\n",
    "con.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(u'0912173381', u'United State'), (u'0928375018', u'Tokyo Japan'), (u'0957209108', u'Taipei')]\n"
     ]
    }
   ],
   "source": [
    "import sqlite3 as lite\n",
    "con = lite.connect('finance2.sqlite')\n",
    "cur = con.cursor()\n",
    "\n",
    "cur.execute(\"DROP TABLE IF EXISTS PhoneAddress\") \n",
    "cur.execute(\"CREATE TABLE PhoneAddress(phone CHAR(10) PRIMARY KEY, address TEXT, name TEXT unique, age INT NOT NULL)\") \n",
    "cur.execute(\"INSERT INTO PhoneAddress VALUES('0912173381','United State','Jhon Doe',53)\") \n",
    "cur.execute(\"INSERT INTO PhoneAddress VALUES('0928375018','Tokyo Japan','MuMu Cat',6)\") \n",
    "cur.execute(\"INSERT INTO PhoneAddress VALUES('0957209108','Taipei','Richard',29)\") \n",
    "con.commit()\n",
    "cur.execute(\"SELECT phone,address FROM PhoneAddress\") \n",
    "\n",
    "data = cur.fetchall()\n",
    "print data\n",
    "con.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0912173381 United State\n",
      "[(u'0928375018', u'Tokyo Japan'), (u'0957209108', u'Taipei')]\n"
     ]
    }
   ],
   "source": [
    "import sqlite3 as lite\n",
    "con = lite.connect('finance2.sqlite')\n",
    "cur = con.cursor()\n",
    "\n",
    "cur.execute(\"SELECT phone,address FROM PhoneAddress\") \n",
    "\n",
    "data = cur.fetchone()\n",
    "print data[0], data[1]\n",
    "\n",
    "data = cur.fetchall()\n",
    "print data\n",
    "con.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['__add__',\n",
       " '__class__',\n",
       " '__contains__',\n",
       " '__delattr__',\n",
       " '__delitem__',\n",
       " '__delslice__',\n",
       " '__doc__',\n",
       " '__eq__',\n",
       " '__format__',\n",
       " '__ge__',\n",
       " '__getattribute__',\n",
       " '__getitem__',\n",
       " '__getslice__',\n",
       " '__gt__',\n",
       " '__hash__',\n",
       " '__iadd__',\n",
       " '__imul__',\n",
       " '__init__',\n",
       " '__iter__',\n",
       " '__le__',\n",
       " '__len__',\n",
       " '__lt__',\n",
       " '__mul__',\n",
       " '__ne__',\n",
       " '__new__',\n",
       " '__reduce__',\n",
       " '__reduce_ex__',\n",
       " '__repr__',\n",
       " '__reversed__',\n",
       " '__rmul__',\n",
       " '__setattr__',\n",
       " '__setitem__',\n",
       " '__setslice__',\n",
       " '__sizeof__',\n",
       " '__str__',\n",
       " '__subclasshook__',\n",
       " 'append',\n",
       " 'count',\n",
       " 'extend',\n",
       " 'index',\n",
       " 'insert',\n",
       " 'pop',\n",
       " 'remove',\n",
       " 'reverse',\n",
       " 'sort']"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = (1,2)\n",
    "b = [1,2]\n",
    "dir(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>證券  代號</th>\n",
       "      <th>證券  名稱</th>\n",
       "      <th>外資  買進股數</th>\n",
       "      <th>外資  賣出股數</th>\n",
       "      <th>外資  買賣超股數</th>\n",
       "      <th>投信  買進股數</th>\n",
       "      <th>投信  賣出股數</th>\n",
       "      <th>投信  買賣超股數</th>\n",
       "      <th>自營商  買賣超股數</th>\n",
       "      <th>自營商  買進股數  (自行買賣)</th>\n",
       "      <th>自營商  賣出股數  (自行買賣)</th>\n",
       "      <th>自營商  買賣超股數  (自行買賣)</th>\n",
       "      <th>自營商  買進股數  (避險)</th>\n",
       "      <th>自營商  賣出股數  (避險)</th>\n",
       "      <th>自營商  買賣超股數  (避險)</th>\n",
       "      <th>三大法人  買賣超股數</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2408</td>\n",
       "      <td>南亞科</td>\n",
       "      <td>18079000</td>\n",
       "      <td>4097000</td>\n",
       "      <td>13982000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>329000</td>\n",
       "      <td>317000</td>\n",
       "      <td>17000</td>\n",
       "      <td>300000</td>\n",
       "      <td>154000</td>\n",
       "      <td>125000</td>\n",
       "      <td>29000</td>\n",
       "      <td>14311000</td>\n",
       "      <td>2016-05-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3474</td>\n",
       "      <td>華亞科</td>\n",
       "      <td>9560000</td>\n",
       "      <td>4665000</td>\n",
       "      <td>4895000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113000</td>\n",
       "      <td>201000</td>\n",
       "      <td>30000</td>\n",
       "      <td>171000</td>\n",
       "      <td>0</td>\n",
       "      <td>58000</td>\n",
       "      <td>-58000</td>\n",
       "      <td>5008000</td>\n",
       "      <td>2016-05-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2449</td>\n",
       "      <td>京元電</td>\n",
       "      <td>8513011</td>\n",
       "      <td>4469755</td>\n",
       "      <td>4043256</td>\n",
       "      <td>58000</td>\n",
       "      <td>490000</td>\n",
       "      <td>-432000</td>\n",
       "      <td>472000</td>\n",
       "      <td>0</td>\n",
       "      <td>107000</td>\n",
       "      <td>-107000</td>\n",
       "      <td>1266000</td>\n",
       "      <td>687000</td>\n",
       "      <td>579000</td>\n",
       "      <td>4083256</td>\n",
       "      <td>2016-05-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3413</td>\n",
       "      <td>京鼎</td>\n",
       "      <td>529000</td>\n",
       "      <td>24000</td>\n",
       "      <td>505000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1000</td>\n",
       "      <td>0</td>\n",
       "      <td>1000</td>\n",
       "      <td>-1000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>504000</td>\n",
       "      <td>2016-05-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3016</td>\n",
       "      <td>嘉晶</td>\n",
       "      <td>440000</td>\n",
       "      <td>91000</td>\n",
       "      <td>349000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-12000</td>\n",
       "      <td>2000</td>\n",
       "      <td>14000</td>\n",
       "      <td>-12000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>337000</td>\n",
       "      <td>2016-05-13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   證券  代號 證券  名稱  外資  買進股數  外資  賣出股數  外資  買賣超股數  投信  買進股數  投信  賣出股數  \\\n",
       "0    2408    南亞科  18079000   4097000   13982000         0         0   \n",
       "1    3474    華亞科   9560000   4665000    4895000         0         0   \n",
       "2    2449    京元電   8513011   4469755    4043256     58000    490000   \n",
       "3    3413     京鼎    529000     24000     505000         0         0   \n",
       "4    3016     嘉晶    440000     91000     349000         0         0   \n",
       "\n",
       "   投信  買賣超股數  自營商  買賣超股數  自營商  買進股數  (自行買賣)  自營商  賣出股數  (自行買賣)  \\\n",
       "0          0      329000             317000              17000   \n",
       "1          0      113000             201000              30000   \n",
       "2    -432000      472000                  0             107000   \n",
       "3          0       -1000                  0               1000   \n",
       "4          0      -12000               2000              14000   \n",
       "\n",
       "   自營商  買賣超股數  (自行買賣)  自營商  買進股數  (避險)  自營商  賣出股數  (避險)  自營商  買賣超股數  (避險)  \\\n",
       "0              300000           154000           125000             29000   \n",
       "1              171000                0            58000            -58000   \n",
       "2             -107000          1266000           687000            579000   \n",
       "3               -1000                0                0                 0   \n",
       "4              -12000                0                0                 0   \n",
       "\n",
       "   三大法人  買賣超股數        date  \n",
       "0     14311000  2016-05-13  \n",
       "1      5008000  2016-05-13  \n",
       "2      4083256  2016-05-13  \n",
       "3       504000  2016-05-13  \n",
       "4       337000  2016-05-13  "
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stockdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda2\\lib\\site-packages\\ipykernel\\__main__.py:3: UserWarning: The spaces in these column names will not be changed. In pandas versions < 0.14, spaces were converted to underscores.\n",
      "  app.launch_new_instance()\n"
     ]
    }
   ],
   "source": [
    "import sqlite3 as lite\n",
    "con = lite.connect('finance2.sqlite')\n",
    "stockdf.to_sql(name = 'trading_volume', index=False, con = con, if_exists=\"replace\")\n",
    "#con.commit()\n",
    "con.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n",
    "#stockdf.head()\n",
    "#len(stockdf.columns)\n",
    "#stockdf.columns = ['id', 'name', 'buy', 'sale', '4','5', '6', '7' '8','9','10','11','12', '13', '14', '15', '16', 'date']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda2\\lib\\site-packages\\ipykernel\\__main__.py:2: UserWarning: The spaces in these column names will not be changed. In pandas versions < 0.14, spaces were converted to underscores.\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    }
   ],
   "source": [
    "with lite.connect('finance2.sqlite') as con:\n",
    "    stockdf.to_sql(name = 'trading_volume', index=False, con = con, if_exists=\"replace\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>證券  代號</th>\n",
       "      <th>證券  名稱</th>\n",
       "      <th>外資  買進股數</th>\n",
       "      <th>外資  賣出股數</th>\n",
       "      <th>外資  買賣超股數</th>\n",
       "      <th>投信  買進股數</th>\n",
       "      <th>投信  賣出股數</th>\n",
       "      <th>投信  買賣超股數</th>\n",
       "      <th>自營商  買賣超股數</th>\n",
       "      <th>自營商  買進股數  (自行買賣)</th>\n",
       "      <th>自營商  賣出股數  (自行買賣)</th>\n",
       "      <th>自營商  買賣超股數  (自行買賣)</th>\n",
       "      <th>自營商  買進股數  (避險)</th>\n",
       "      <th>自營商  賣出股數  (避險)</th>\n",
       "      <th>自營商  買賣超股數  (避險)</th>\n",
       "      <th>三大法人  買賣超股數</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2408</td>\n",
       "      <td>南亞科</td>\n",
       "      <td>18079000</td>\n",
       "      <td>4097000</td>\n",
       "      <td>13982000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>329000</td>\n",
       "      <td>317000</td>\n",
       "      <td>17000</td>\n",
       "      <td>300000</td>\n",
       "      <td>154000</td>\n",
       "      <td>125000</td>\n",
       "      <td>29000</td>\n",
       "      <td>14311000</td>\n",
       "      <td>2016-05-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3474</td>\n",
       "      <td>華亞科</td>\n",
       "      <td>9560000</td>\n",
       "      <td>4665000</td>\n",
       "      <td>4895000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113000</td>\n",
       "      <td>201000</td>\n",
       "      <td>30000</td>\n",
       "      <td>171000</td>\n",
       "      <td>0</td>\n",
       "      <td>58000</td>\n",
       "      <td>-58000</td>\n",
       "      <td>5008000</td>\n",
       "      <td>2016-05-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2449</td>\n",
       "      <td>京元電</td>\n",
       "      <td>8513011</td>\n",
       "      <td>4469755</td>\n",
       "      <td>4043256</td>\n",
       "      <td>58000</td>\n",
       "      <td>490000</td>\n",
       "      <td>-432000</td>\n",
       "      <td>472000</td>\n",
       "      <td>0</td>\n",
       "      <td>107000</td>\n",
       "      <td>-107000</td>\n",
       "      <td>1266000</td>\n",
       "      <td>687000</td>\n",
       "      <td>579000</td>\n",
       "      <td>4083256</td>\n",
       "      <td>2016-05-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3413</td>\n",
       "      <td>京鼎</td>\n",
       "      <td>529000</td>\n",
       "      <td>24000</td>\n",
       "      <td>505000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1000</td>\n",
       "      <td>0</td>\n",
       "      <td>1000</td>\n",
       "      <td>-1000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>504000</td>\n",
       "      <td>2016-05-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3016</td>\n",
       "      <td>嘉晶</td>\n",
       "      <td>440000</td>\n",
       "      <td>91000</td>\n",
       "      <td>349000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-12000</td>\n",
       "      <td>2000</td>\n",
       "      <td>14000</td>\n",
       "      <td>-12000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>337000</td>\n",
       "      <td>2016-05-13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   證券  代號 證券  名稱  外資  買進股數  外資  賣出股數  外資  買賣超股數  投信  買進股數  投信  賣出股數  \\\n",
       "0    2408    南亞科  18079000   4097000   13982000         0         0   \n",
       "1    3474    華亞科   9560000   4665000    4895000         0         0   \n",
       "2    2449    京元電   8513011   4469755    4043256     58000    490000   \n",
       "3    3413     京鼎    529000     24000     505000         0         0   \n",
       "4    3016     嘉晶    440000     91000     349000         0         0   \n",
       "\n",
       "   投信  買賣超股數  自營商  買賣超股數  自營商  買進股數  (自行買賣)  自營商  賣出股數  (自行買賣)  \\\n",
       "0          0      329000             317000              17000   \n",
       "1          0      113000             201000              30000   \n",
       "2    -432000      472000                  0             107000   \n",
       "3          0       -1000                  0               1000   \n",
       "4          0      -12000               2000              14000   \n",
       "\n",
       "   自營商  買賣超股數  (自行買賣)  自營商  買進股數  (避險)  自營商  賣出股數  (避險)  自營商  買賣超股數  (避險)  \\\n",
       "0              300000           154000           125000             29000   \n",
       "1              171000                0            58000            -58000   \n",
       "2             -107000          1266000           687000            579000   \n",
       "3               -1000                0                0                 0   \n",
       "4              -12000                0                0                 0   \n",
       "\n",
       "   三大法人  買賣超股數        date  \n",
       "0     14311000  2016-05-13  \n",
       "1      5008000  2016-05-13  \n",
       "2      4083256  2016-05-13  \n",
       "3       504000  2016-05-13  \n",
       "4       337000  2016-05-13  "
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "with lite.connect('finance2.sqlite') as con:\n",
    "    df2 = pd.read_sql_query('select * from trading_volume', con)\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'證券  代號', u'證券  名稱', u'外資  買進股數', u'外資  賣出股數', u'外資  買賣超股數',\n",
       "       u'投信  買進股數', u'投信  賣出股數', u'投信  買賣超股數', u'自營商  買賣超股數',\n",
       "       u'自營商  買進股數  (自行買賣)', u'自營商  賣出股數  (自行買賣)', u'自營商  買賣超股數  (自行買賣)',\n",
       "       u'自營商  買進股數  (避險)', u'自營商  賣出股數  (避險)', u'自營商  買賣超股數  (避險)',\n",
       "       u'三大法人  買賣超股數', u'date'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>證券  代號</th>\n",
       "      <th>證券  名稱</th>\n",
       "      <th>外資  買進股數</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2408</td>\n",
       "      <td>南亞科</td>\n",
       "      <td>18079000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3474</td>\n",
       "      <td>華亞科</td>\n",
       "      <td>9560000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2449</td>\n",
       "      <td>京元電</td>\n",
       "      <td>8513011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3413</td>\n",
       "      <td>京鼎</td>\n",
       "      <td>529000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3016</td>\n",
       "      <td>嘉晶</td>\n",
       "      <td>440000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   證券  代號 證券  名稱  外資  買進股數\n",
       "0    2408    南亞科  18079000\n",
       "1    3474    華亞科   9560000\n",
       "2    2449    京元電   8513011\n",
       "3    3413     京鼎    529000\n",
       "4    3016     嘉晶    440000"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "with lite.connect('finance2.sqlite') as con:\n",
    "    df2 = pd.read_sql_query(\\\n",
    "        \"select \\\"證券  代號\\\",\\\"證券  名稱\\\",\\\"外資  買進股數\\\" from trading_volume\".decode('utf-8'), con)\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df2.to_excel('test.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df3 = pd.read_excel('test.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2408</td>\n",
       "      <td>南亞科</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3474</td>\n",
       "      <td>華亞科</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2449</td>\n",
       "      <td>京元電</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3413</td>\n",
       "      <td>京鼎</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3016</td>\n",
       "      <td>嘉晶</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8016</td>\n",
       "      <td>矽創</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2401</td>\n",
       "      <td>凌陽</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2388</td>\n",
       "      <td>威盛</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2337</td>\n",
       "      <td>旺宏</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2379</td>\n",
       "      <td>瑞昱</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3035</td>\n",
       "      <td>智原</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2344</td>\n",
       "      <td>華邦電</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>3519</td>\n",
       "      <td>綠能</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>6239</td>\n",
       "      <td>力成</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>3189</td>\n",
       "      <td>景碩</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>3034</td>\n",
       "      <td>聯詠</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2325</td>\n",
       "      <td>矽品</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2311</td>\n",
       "      <td>日月光</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2303</td>\n",
       "      <td>聯電</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2330</td>\n",
       "      <td>台積電</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id name\n",
       "0   2408  南亞科\n",
       "1   3474  華亞科\n",
       "2   2449  京元電\n",
       "3   3413   京鼎\n",
       "4   3016   嘉晶\n",
       "5   8016   矽創\n",
       "6   2401   凌陽\n",
       "7   2388   威盛\n",
       "8   2337   旺宏\n",
       "9   2379   瑞昱\n",
       "10  3035   智原\n",
       "11  2344  華邦電\n",
       "12  3519   綠能\n",
       "13  6239   力成\n",
       "14  3189   景碩\n",
       "15  3034   聯詠\n",
       "16  2325   矽品\n",
       "17  2311  日月光\n",
       "18  2303   聯電\n",
       "19  2330  台積電"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2330　102年第1季 IFRS 合併財務報表預覽"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ISO-8859-1\n",
      "big5\n"
     ]
    }
   ],
   "source": [
    "import requests\n",
    "url = 'http://mops.twse.com.tw/server-java/t164sb01'\n",
    "payload = {\n",
    "'step':'1',\n",
    "'CO_ID':'2330',\n",
    "'SYEAR':'2013',\n",
    "'SSEASON':'1',\n",
    "'REPORT_ID':'C'\n",
    "}\n",
    "res = requests.post(url, data= payload)\n",
    "print res.encoding\n",
    "res.encoding = 'big5'\n",
    "print res.encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from bs4 import BeautifulSoup as bs\n",
    "soup = bs(res.text)\n",
    "tb = soup.select('.result_table')[0]\n",
    "#print tb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "dfs = pd.read_html(tb.prettify('utf-8'), encoding = 'utf-8', header=0, skiprows=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "stockdf = dfs[0]\n",
    "stockdf['stockno'] = '2330'\n",
    "import sqlite3 as lite\n",
    "with lite.connect('finance2.sqlite') as con:\n",
    "    stockdf.to_sql('ifrs', con = con, if_exists='replace')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 三大法人買賣明細資訊\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Document(Doc):\n",
    "requests    ------------> document: <html> data </html>\n",
    "100 * 3\n",
    "    \n",
    "AJAX(XHR):\n",
    "requests    ------------> document: <html></html>\n",
    "requests    ------------> javascript: $('html').html(data)\n",
    "requests    ------------> data: data\n",
    "2 + 1 + 100 * 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "XML:\n",
    "<employee>\n",
    "    <name> qoo </name>\n",
    "    <age> 10 <age>\n",
    "</employee>\n",
    "\n",
    "JSON:\n",
    "{employee:{name:qoo, age:10}}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<Response [200]>\n"
     ]
    }
   ],
   "source": [
    "import requests\n",
    "res = requests.get('http://www.tpex.org.tw/web/stock/3insti/daily_trade/3itrade_hedge_result.php?l=zh-tw&se=EW&t=D&d=105/05/10&_=1463283795803')\n",
    "print res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#print res.text\n",
    "import json\n",
    "jd = json.loads(res.text)\n",
    "#print jd['aaData']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>006201</td>\n",
       "      <td>寶富櫃</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-45,000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45,000</td>\n",
       "      <td>-45,000</td>\n",
       "      <td>-45,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1258</td>\n",
       "      <td>F-其祥</td>\n",
       "      <td>3,000</td>\n",
       "      <td>0</td>\n",
       "      <td>3,000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1,000</td>\n",
       "      <td>0</td>\n",
       "      <td>1,000</td>\n",
       "      <td>-1,000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1259</td>\n",
       "      <td>安心</td>\n",
       "      <td>14,000</td>\n",
       "      <td>0</td>\n",
       "      <td>14,000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1264</td>\n",
       "      <td>德麥</td>\n",
       "      <td>4,000</td>\n",
       "      <td>1,000</td>\n",
       "      <td>3,000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1565</td>\n",
       "      <td>精華</td>\n",
       "      <td>148,000</td>\n",
       "      <td>136,000</td>\n",
       "      <td>12,000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1,000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1,000</td>\n",
       "      <td>2,000</td>\n",
       "      <td>-1,000</td>\n",
       "      <td>11,000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        0       1                  2                  3                  4   \\\n",
       "0  006201    寶富櫃                    0                  0                  0   \n",
       "1  1258     F-其祥                3,000                  0              3,000   \n",
       "2  1259     安心                 14,000                  0             14,000   \n",
       "3  1264     德麥                  4,000              1,000              3,000   \n",
       "4  1565     精華                148,000            136,000             12,000   \n",
       "\n",
       "                  5                  6                  7                  8   \\\n",
       "0                  0                  0                  0            -45,000   \n",
       "1                  0                  0                  0             -1,000   \n",
       "2                  0                  0                  0                  0   \n",
       "3                  0                  0                  0                  0   \n",
       "4                  0                  0                  0             -1,000   \n",
       "\n",
       "                  9                  10                 11                 12  \\\n",
       "0                  0                  0                  0                  0   \n",
       "1                  0              1,000             -1,000                  0   \n",
       "2                  0                  0                  0                  0   \n",
       "3                  0                  0                  0                  0   \n",
       "4                  0                  0                  0              1,000   \n",
       "\n",
       "                  13                 14                 15  \n",
       "0             45,000            -45,000            -45,000  \n",
       "1                  0                  0              2,000  \n",
       "2                  0                  0             14,000  \n",
       "3                  0                  0              3,000  \n",
       "4              2,000             -1,000             11,000  "
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.DataFrame(jd['aaData'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 三大法人交易資訊"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<Response [200]>\n"
     ]
    }
   ],
   "source": [
    "import requests\n",
    "url = 'http://www.taifex.com.tw/chinese/3/7_12_1.asp'\n",
    "payload = {\n",
    "'DATA_DATE_Y':'2016',\n",
    "'DATA_DATE_M':'5',\n",
    "'DATA_DATE_D':'12',\n",
    "'syear':'2016',\n",
    "'smonth':'5',\n",
    "'sday':'12',\n",
    "'datestart':'2016/5/12'    \n",
    "}\n",
    "res = requests.post(url, data = payload)\n",
    "res.encoding='utf-8'\n",
    "print res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#print res.text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from bs4 import BeautifulSoup as bs\n",
    "soup = bs(res.text)\n",
    "tb = soup.select('.table_f')[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "?pd.read_html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "dfs = pd.read_html(tb.prettify('utf-8'), \\\n",
    "            encoding = 'utf-8', skiprows=[0,1], header=0)\n",
    "stockdf = dfs[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>身份別</th>\n",
       "      <th>多方口數</th>\n",
       "      <th>多方契約金額</th>\n",
       "      <th>空方口數</th>\n",
       "      <th>空方契約金額</th>\n",
       "      <th>多空淨額口數</th>\n",
       "      <th>多空淨額契約金額</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>自營商</td>\n",
       "      <td>288779</td>\n",
       "      <td>39991</td>\n",
       "      <td>266375</td>\n",
       "      <td>46217</td>\n",
       "      <td>22404</td>\n",
       "      <td>-6226</td>\n",
       "      <td>2016/5/12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>投信</td>\n",
       "      <td>4753</td>\n",
       "      <td>7459</td>\n",
       "      <td>4011</td>\n",
       "      <td>6302</td>\n",
       "      <td>742</td>\n",
       "      <td>1157</td>\n",
       "      <td>2016/5/12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>外資</td>\n",
       "      <td>174311</td>\n",
       "      <td>89034</td>\n",
       "      <td>168547</td>\n",
       "      <td>86050</td>\n",
       "      <td>5764</td>\n",
       "      <td>2984</td>\n",
       "      <td>2016/5/12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>合計</td>\n",
       "      <td>467843</td>\n",
       "      <td>136484</td>\n",
       "      <td>438933</td>\n",
       "      <td>138569</td>\n",
       "      <td>28910</td>\n",
       "      <td>-2085</td>\n",
       "      <td>2016/5/12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   身份別    多方口數  多方契約金額    空方口數  空方契約金額  多空淨額口數  多空淨額契約金額       date\n",
       "0  自營商  288779   39991  266375   46217   22404     -6226  2016/5/12\n",
       "1   投信    4753    7459    4011    6302     742      1157  2016/5/12\n",
       "2   外資  174311   89034  168547   86050    5764      2984  2016/5/12\n",
       "3   合計  467843  136484  438933  138569   28910     -2085  2016/5/12"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stockdf.columns = ['身份別','多方口數','多方契約金額',\\\n",
    "                   '空方口數','空方契約金額',\\\n",
    "                   '多空淨額口數','多空淨額契約金額']\n",
    "stockdf['date'] = '2016/5/12' \n",
    "stockdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 證交所三大法人csv檔抓取"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "url = 'http://www.twse.com.tw/ch/trading/fund/T86/T86.php'\n",
    "payload = {\n",
    "'download':'csv',\n",
    "'qdate':'105/05/13',\n",
    "'select2':'01',\n",
    "'sorting':'by_issue'    \n",
    "}\n",
    "res = requests.post(url , payload)\n",
    "res.encoding= 'big5'\n",
    "\n",
    "with open('20160513.csv', 'w') as f:\n",
    "    f.write(res.text.encode('utf-8'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>證券代號</th>\n",
       "      <th>證券名稱</th>\n",
       "      <th>外資買進股數</th>\n",
       "      <th>外資賣出股數</th>\n",
       "      <th>外資買賣超股數</th>\n",
       "      <th>投信買進股數</th>\n",
       "      <th>投信賣出股數</th>\n",
       "      <th>投信買賣超股數</th>\n",
       "      <th>自營商買賣超股數</th>\n",
       "      <th>自營商買進股數(自行買賣)</th>\n",
       "      <th>自營商賣出股數(自行買賣)</th>\n",
       "      <th>自營商買賣超股數(自行買賣)</th>\n",
       "      <th>自營商買進股數(避險)</th>\n",
       "      <th>自營商賣出股數(避險)</th>\n",
       "      <th>自營商買賣超股數(避險)</th>\n",
       "      <th>三大法人買賣超股數</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>=\"1108\"</td>\n",
       "      <td>幸福</td>\n",
       "      <td>2,000</td>\n",
       "      <td>2,000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>=\"1110\"</td>\n",
       "      <td>東泥</td>\n",
       "      <td>13,000</td>\n",
       "      <td>16,000</td>\n",
       "      <td>-3,000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-3,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>=\"1104\"</td>\n",
       "      <td>環泥</td>\n",
       "      <td>61,000</td>\n",
       "      <td>118,000</td>\n",
       "      <td>-57,000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-9,000</td>\n",
       "      <td>0</td>\n",
       "      <td>9,000</td>\n",
       "      <td>-9,000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-66,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>=\"1103\"</td>\n",
       "      <td>嘉泥</td>\n",
       "      <td>0</td>\n",
       "      <td>79,000</td>\n",
       "      <td>-79,000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-79,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>=\"1102\"</td>\n",
       "      <td>亞泥</td>\n",
       "      <td>2,308,000</td>\n",
       "      <td>3,899,000</td>\n",
       "      <td>-1,591,000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>221,000</td>\n",
       "      <td>0</td>\n",
       "      <td>84,000</td>\n",
       "      <td>-84,000</td>\n",
       "      <td>305,000</td>\n",
       "      <td>0</td>\n",
       "      <td>305,000</td>\n",
       "      <td>-1,370,000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      證券代號            證券名稱     外資買進股數     外資賣出股數     外資買賣超股數 投信買進股數 投信賣出股數  \\\n",
       "2  =\"1108\"  幸福                  2,000      2,000           0      0      0   \n",
       "3  =\"1110\"  東泥                 13,000     16,000      -3,000      0      0   \n",
       "4  =\"1104\"  環泥                 61,000    118,000     -57,000      0      0   \n",
       "5  =\"1103\"  嘉泥                      0     79,000     -79,000      0      0   \n",
       "6  =\"1102\"  亞泥              2,308,000  3,899,000  -1,591,000      0      0   \n",
       "\n",
       "  投信買賣超股數 自營商買賣超股數 自營商買進股數(自行買賣) 自營商賣出股數(自行買賣) 自營商買賣超股數(自行買賣) 自營商買進股數(避險)  \\\n",
       "2       0        0             0             0              0           0   \n",
       "3       0        0             0             0              0           0   \n",
       "4       0   -9,000             0         9,000         -9,000           0   \n",
       "5       0        0             0             0              0           0   \n",
       "6       0  221,000             0        84,000        -84,000     305,000   \n",
       "\n",
       "  自營商賣出股數(避險) 自營商買賣超股數(避險)   三大法人買賣超股數  \n",
       "2           0            0           0  \n",
       "3           0            0      -3,000  \n",
       "4           0            0     -66,000  \n",
       "5           0            0     -79,000  \n",
       "6           0      305,000  -1,370,000  "
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv('20160513.csv', names=[\"證券代號\",\"證券名稱\",\"外資買進股數\",\"外資賣出股數\",\\\n",
    "                \"外資買賣超股數\",\"投信買進股數\",\"投信賣出股數\",\"投信買賣超股數\",\"自營商買賣超股數\",\\\n",
    "                \"自營商買進股數(自行買賣)\",\"自營商賣出股數(自行買賣)\",\"自營商買賣超股數(自行買賣)\",\\\n",
    "                \"自營商買進股數(避險)\",\"自營商賣出股數(避險)\",\"自營商買賣超股數(避險)\",\"三大法人買賣超股數\"])\n",
    "df = df.dropna()\n",
    "df = df.drop(1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'證券代號', u'證券名稱', u'外資買進股數', u'外資賣出股數', u'外資買賣超股數', u'投信買進股數',\n",
       "       u'投信賣出股數', u'投信買賣超股數', u'自營商買賣超股數', u'自營商買進股數(自行買賣)', u'自營商賣出股數(自行買賣)',\n",
       "       u'自營商買賣超股數(自行買賣)', u'自營商買進股數(避險)', u'自營商賣出股數(避險)', u'自營商買賣超股數(避險)',\n",
       "       u'三大法人買賣超股數'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>證券代號</th>\n",
       "      <th>證券名稱</th>\n",
       "      <th>外資買進股數</th>\n",
       "      <th>外資賣出股數</th>\n",
       "      <th>外資買賣超股數</th>\n",
       "      <th>投信買進股數</th>\n",
       "      <th>投信賣出股數</th>\n",
       "      <th>投信買賣超股數</th>\n",
       "      <th>自營商買賣超股數</th>\n",
       "      <th>自營商買進股數(自行買賣)</th>\n",
       "      <th>自營商賣出股數(自行買賣)</th>\n",
       "      <th>自營商買賣超股數(自行買賣)</th>\n",
       "      <th>自營商買進股數(避險)</th>\n",
       "      <th>自營商賣出股數(避險)</th>\n",
       "      <th>自營商買賣超股數(避險)</th>\n",
       "      <th>三大法人買賣超股數</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1108</td>\n",
       "      <td>幸福</td>\n",
       "      <td>2,000</td>\n",
       "      <td>2,000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1110</td>\n",
       "      <td>東泥</td>\n",
       "      <td>13,000</td>\n",
       "      <td>16,000</td>\n",
       "      <td>-3,000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-3,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1104</td>\n",
       "      <td>環泥</td>\n",
       "      <td>61,000</td>\n",
       "      <td>118,000</td>\n",
       "      <td>-57,000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-9,000</td>\n",
       "      <td>0</td>\n",
       "      <td>9,000</td>\n",
       "      <td>-9,000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-66,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1103</td>\n",
       "      <td>嘉泥</td>\n",
       "      <td>0</td>\n",
       "      <td>79,000</td>\n",
       "      <td>-79,000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-79,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1102</td>\n",
       "      <td>亞泥</td>\n",
       "      <td>2,308,000</td>\n",
       "      <td>3,899,000</td>\n",
       "      <td>-1,591,000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>221,000</td>\n",
       "      <td>0</td>\n",
       "      <td>84,000</td>\n",
       "      <td>-84,000</td>\n",
       "      <td>305,000</td>\n",
       "      <td>0</td>\n",
       "      <td>305,000</td>\n",
       "      <td>-1,370,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1101</td>\n",
       "      <td>台泥</td>\n",
       "      <td>1,008,000</td>\n",
       "      <td>5,384,000</td>\n",
       "      <td>-4,376,000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>350,000</td>\n",
       "      <td>194,000</td>\n",
       "      <td>2,000</td>\n",
       "      <td>192,000</td>\n",
       "      <td>258,000</td>\n",
       "      <td>100,000</td>\n",
       "      <td>158,000</td>\n",
       "      <td>-4,026,000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   證券代號            證券名稱     外資買進股數     外資賣出股數     外資買賣超股數 投信買進股數 投信賣出股數  \\\n",
       "2  1108  幸福                  2,000      2,000           0      0      0   \n",
       "3  1110  東泥                 13,000     16,000      -3,000      0      0   \n",
       "4  1104  環泥                 61,000    118,000     -57,000      0      0   \n",
       "5  1103  嘉泥                      0     79,000     -79,000      0      0   \n",
       "6  1102  亞泥              2,308,000  3,899,000  -1,591,000      0      0   \n",
       "7  1101  台泥              1,008,000  5,384,000  -4,376,000      0      0   \n",
       "\n",
       "  投信買賣超股數 自營商買賣超股數 自營商買進股數(自行買賣) 自營商賣出股數(自行買賣) 自營商買賣超股數(自行買賣) 自營商買進股數(避險)  \\\n",
       "2       0        0             0             0              0           0   \n",
       "3       0        0             0             0              0           0   \n",
       "4       0   -9,000             0         9,000         -9,000           0   \n",
       "5       0        0             0             0              0           0   \n",
       "6       0  221,000             0        84,000        -84,000     305,000   \n",
       "7       0  350,000       194,000         2,000        192,000     258,000   \n",
       "\n",
       "  自營商賣出股數(避險) 自營商買賣超股數(避險)   三大法人買賣超股數  \n",
       "2           0            0           0  \n",
       "3           0            0      -3,000  \n",
       "4           0            0     -66,000  \n",
       "5           0            0     -79,000  \n",
       "6           0      305,000  -1,370,000  \n",
       "7     100,000      158,000  -4,026,000  "
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['證券代號']= df['證券代號'].str.strip('=').str.strip('\\\"')\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DataFrame 操作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>david</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>mary</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       0  1\n",
       "0  david  1\n",
       "1   mary  2"
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = [['david', 1], ['mary', 2]]\n",
    "df1 = pd.DataFrame(a)\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>david</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>mary</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id   name\n",
       "0   1  david\n",
       "1   2   mary"
      ]
     },
     "execution_count": 245,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = [{'name':'david', 'id':1}, {'name':'mary', 'id':2}]\n",
    "df2 = pd.DataFrame(b)\n",
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 財報狗抓取"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import requests\n",
    "headers = {\n",
    "'X-Requested-With':'XMLHttpRequest',\n",
    "'User-Agent':'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/50.0.2661.102 Safari/537.36'    \n",
    "}\n",
    "url = 'https://statementdog.com/pick/pick_ajax/?type=1&idx1=15&tm1=2&op1=1&val1=15&_=1463292903371'\n",
    "res = requests.get(url, headers = headers)\n",
    "\n",
    "#print res.text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data</th>\n",
       "      <th>id</th>\n",
       "      <th>nm</th>\n",
       "      <th>price</th>\n",
       "      <th>roi</th>\n",
       "      <th>ticker</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[{u'tm': 2, u'val': 18.45, u'idx': 15}]</td>\n",
       "      <td>18</td>\n",
       "      <td>福懋油</td>\n",
       "      <td>30.45</td>\n",
       "      <td>[44.9, 132.1, 101.1]</td>\n",
       "      <td>1225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[{u'tm': 2, u'val': 21.5, u'idx': 15}]</td>\n",
       "      <td>19</td>\n",
       "      <td>佳格</td>\n",
       "      <td>77.10</td>\n",
       "      <td>[1.6, -19.9, -8.0]</td>\n",
       "      <td>1227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[{u'tm': 2, u'val': 25.3, u'idx': 15}]</td>\n",
       "      <td>22</td>\n",
       "      <td>大統益</td>\n",
       "      <td>77.90</td>\n",
       "      <td>[-0.8, 47.9, 47.7]</td>\n",
       "      <td>1232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[{u'tm': 2, u'val': 24.54, u'idx': 15}]</td>\n",
       "      <td>27</td>\n",
       "      <td>F-鮮活</td>\n",
       "      <td>118.50</td>\n",
       "      <td>[-45.2, 72.8, None]</td>\n",
       "      <td>1256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[{u'tm': 2, u'val': 17.6, u'idx': 15}]</td>\n",
       "      <td>28</td>\n",
       "      <td>F-其祥</td>\n",
       "      <td>38.25</td>\n",
       "      <td>[33.8, 56.1, None]</td>\n",
       "      <td>1258</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      data  id    nm   price  \\\n",
       "0  [{u'tm': 2, u'val': 18.45, u'idx': 15}]  18   福懋油   30.45   \n",
       "1   [{u'tm': 2, u'val': 21.5, u'idx': 15}]  19    佳格   77.10   \n",
       "2   [{u'tm': 2, u'val': 25.3, u'idx': 15}]  22   大統益   77.90   \n",
       "3  [{u'tm': 2, u'val': 24.54, u'idx': 15}]  27  F-鮮活  118.50   \n",
       "4   [{u'tm': 2, u'val': 17.6, u'idx': 15}]  28  F-其祥   38.25   \n",
       "\n",
       "                    roi  ticker  \n",
       "0  [44.9, 132.1, 101.1]    1225  \n",
       "1    [1.6, -19.9, -8.0]    1227  \n",
       "2    [-0.8, 47.9, 47.7]    1232  \n",
       "3   [-45.2, 72.8, None]    1256  \n",
       "4    [33.8, 56.1, None]    1258  "
      ]
     },
     "execution_count": 260,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import json\n",
    "jd = json.loads(res.text)\n",
    "#print jd[1]\n",
    "df = pd.DataFrame(jd[1])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CK101 抓取範例"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import requests\n",
    "headers = {\n",
    "#'User-Agent':'qoo'\n",
    "'User-Agent':'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/50.0.2661.102 Safari/537.36'    \n",
    "}\n",
    "res = requests.get('http://ck101.com/', headers = headers)\n",
    "#print res.text"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## gogofund"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import requests\n",
    "headers = {\n",
    "'Referer':'http://www.gogofund.com/convch/fund/sectorperform.asp',\n",
    "'User-Agent':'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/50.0.2661.102 Safari/537.36'    \n",
    "}\n",
    "url = 'http://www.gogofund.com/convch/fund/forframe/sectorperform_sql.asp?FundTypeID=EG&PerformMoney=TWD&Prd=0&O=5&P=1&r=0.22726785669738292'\n",
    "res = requests.get(url, headers = headers)\n",
    "#print res.text"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Selenium"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "from selenium import webdriver\n",
    "from selenium.webdriver.common.by import By\n",
    "from selenium.webdriver.common.keys import Keys\n",
    "from selenium.webdriver.support.ui import Select\n",
    "from selenium.common.exceptions import NoSuchElementException\n",
    "from selenium.common.exceptions import NoAlertPresentException\n",
    "import unittest, time, re\n",
    "from bs4 import BeautifulSoup as bs\n",
    "import pandas as pd\n",
    "\n",
    "driver = webdriver.Firefox()\n",
    "driver.implicitly_wait(30)\n",
    "\n",
    "base_url = \"http://www.gogofund.com/convch/index.asp\"\n",
    "    \n",
    "driver.get(base_url)\n",
    "driver.find_element_by_css_selector(u\"a[title=\\\"各基金類型績效排行\\\"]\").click()\n",
    "soup = bs(driver.page_source)\n",
    "tb = soup.select('#sectorperform_output table')[0]\n",
    "dfs = pd.read_html(tb.prettify('utf-8'), encoding = 'utf-8')\n",
    "dfs[0]\n",
    "\n",
    "driver.find_element_by_xpath(u\"//input[@value='下一頁']\").click()\n",
    "soup = bs(driver.page_source)\n",
    "tb = soup.select('#sectorperform_output table')[0]\n",
    "dfs = pd.read_html(tb.prettify('utf-8'), encoding = 'utf-8')\n",
    "dfs[0]\n",
    "driver.close()\n",
    "#sectorperform_output \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## myindex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>地域</th>\n",
       "      <th>PER</th>\n",
       "      <th>PBR</th>\n",
       "      <th>配当利回り</th>\n",
       "      <th>時価総額</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>全世界</td>\n",
       "      <td>17.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.64%</td>\n",
       "      <td>36.4兆ドル</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>先進国</td>\n",
       "      <td>18.1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.59%</td>\n",
       "      <td>33.2兆ドル</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>エマージング国</td>\n",
       "      <td>13.2</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3.17%</td>\n",
       "      <td>3.2兆ドル</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ヨーロッパ</td>\n",
       "      <td>16.8</td>\n",
       "      <td>1.7</td>\n",
       "      <td>3.51%</td>\n",
       "      <td>8兆ドル</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>アジア・パシフィック</td>\n",
       "      <td>13.4</td>\n",
       "      <td>1.2</td>\n",
       "      <td>2.75%</td>\n",
       "      <td>5.2兆ドル</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           地域   PER  PBR  配当利回り     時価総額\n",
       "0         全世界  17.5  2.0  2.64%  36.4兆ドル\n",
       "1         先進国  18.1  2.0  2.59%  33.2兆ドル\n",
       "2     エマージング国  13.2  1.5  3.17%   3.2兆ドル\n",
       "3       ヨーロッパ  16.8  1.7  3.51%     8兆ドル\n",
       "4  アジア・パシフィック  13.4  1.2  2.75%   5.2兆ドル"
      ]
     },
     "execution_count": 308,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import requests\n",
    "from bs4 import BeautifulSoup as bs\n",
    "import pandas as pd\n",
    "res = requests.get('http://myindex.jp/global_per.php')\n",
    "soup = bs(res.text)\n",
    "tb = soup.select('.mgn_l_2em table')[0]\n",
    "dfs = pd.read_html(tb.prettify('utf-8'), encoding = 'utf-8')\n",
    "stockdf = dfs[0]\n",
    "stockdf.columns = ['地域','PER','PBR','配当利回り','時価総額']\n",
    "stockdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import requests\n",
    "from bs4 import BeautifulSoup as bs\n",
    "import pandas as pd\n",
    "def getIndex(url, ym):\n",
    "    res = requests.get('http://myindex.jp/global_per.php')\n",
    "    soup = bs(res.text)\n",
    "    tb = soup.select('.mgn_l_2em table')[0]\n",
    "    dfs = pd.read_html(tb.prettify('utf-8'), encoding = 'utf-8')\n",
    "    stockdf = dfs[0]\n",
    "    stockdf.columns = ['地域','PER','PBR','配当利回り','時価総額']\n",
    "    stockdf['date'] = ym\n",
    "    return stockdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n",
    "from datetime import datetime, timedelta\n",
    "from dateutil.relativedelta import relativedelta\n",
    "url = 'http://myindex.jp/global_per_old.php?m={}'\n",
    "currentdate = datetime.today()\n",
    "dfall = []\n",
    "for i in range(1,10):\n",
    "    ym =  (currentdate+ relativedelta(months=-i)).strftime('%Y%m')\n",
    "    dfall.append(getIndex(url.format(ym), ym))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>地域</th>\n",
       "      <th>PER</th>\n",
       "      <th>PBR</th>\n",
       "      <th>配当利回り</th>\n",
       "      <th>時価総額</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>全世界</td>\n",
       "      <td>17.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.64%</td>\n",
       "      <td>36.4兆ドル</td>\n",
       "      <td>201604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>先進国</td>\n",
       "      <td>18.1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.59%</td>\n",
       "      <td>33.2兆ドル</td>\n",
       "      <td>201604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>エマージング国</td>\n",
       "      <td>13.2</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3.17%</td>\n",
       "      <td>3.2兆ドル</td>\n",
       "      <td>201604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ヨーロッパ</td>\n",
       "      <td>16.8</td>\n",
       "      <td>1.7</td>\n",
       "      <td>3.51%</td>\n",
       "      <td>8兆ドル</td>\n",
       "      <td>201604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>アジア・パシフィック</td>\n",
       "      <td>13.4</td>\n",
       "      <td>1.2</td>\n",
       "      <td>2.75%</td>\n",
       "      <td>5.2兆ドル</td>\n",
       "      <td>201604</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           地域   PER  PBR  配当利回り     時価総額    date\n",
       "0         全世界  17.5  2.0  2.64%  36.4兆ドル  201604\n",
       "1         先進国  18.1  2.0  2.59%  33.2兆ドル  201604\n",
       "2     エマージング国  13.2  1.5  3.17%   3.2兆ドル  201604\n",
       "3       ヨーロッパ  16.8  1.7  3.51%     8兆ドル  201604\n",
       "4  アジア・パシフィック  13.4  1.2  2.75%   5.2兆ドル  201604"
      ]
     },
     "execution_count": 326,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indexdf = pd.concat(dfall)\n",
    "indexdf.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 神秘金字塔"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import requests\n",
    "import re\n",
    "res = requests.get('http://norway.twsthr.info/StockHolders.aspx?stock=2330')\n",
    "m = re.findall('series:.*', res.text)\n",
    "#print m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'__VIEWSTATE': '/wEPDwUJOTAxMjkxMjg3D2QWAgIDD2QWBAIBD2QWAmYPZBYEZg9kFgJmDxAPFgYeB0NoZWNrZWRnHglGb3JlQ29sb3IKAB4EXyFTQgIEZGRkZAIBD2QWAgIBDw8WBB8BCgAfAgIEZGQCAw9kFgQCAw8PFgIeBFRleHRlZGQCCQ8PFgIeB1Zpc2libGVoZGQYAgUeX19Db250cm9sc1JlcXVpcmVQb3N0QmFja0tleV9fFgMFElJhZGlvQnV0dG9uX05vcm1hbAUQUmFkaW9CdXR0b25fRXhjZAUQUmFkaW9CdXR0b25fRXhjZAUPQ2FwdGNoYUNvbnRyb2wxDwUkZjg5OThkMzQtNDQyNC00YmU4LTgyNTEtMzRiODU4NmI3ZDAzZBGxoe0r1STBr4Ya7XiuAl8=', '__EVENTVALIDATION': '/wEdAAad3qEU9CvHTPX9XlRvVSA0fMmuxAJNAJcNkRRsVeJwwqKURZav/+YrVMqaWE2hvMxJwK4Ohf3nRgXHkFhQen1PRSciNdPYWmINCrip1wqw01PJCA0uL9aE2sjICZqv6Gp5WylOwUKR0ZkYuzJKq4N2', 'btnOK': '\\xe6\\x9f\\xa5\\xe8\\xa9\\xa2', 'TextBox_Stkno': '1101', 'RadioButton_Normal': 'RadioButton_Normal'}\n"
     ]
    }
   ],
   "source": [
    "import requests\n",
    "from bs4 import BeautifulSoup as bs\n",
    "payload = {\n",
    "'RadioButton_Normal':'RadioButton_Normal',\n",
    "'TextBox_Stkno':'1101',\n",
    "#'CaptchaControl1':'VQAFD',\n",
    "'btnOK':'查詢'       \n",
    "}\n",
    "rs = requests.session()\n",
    "res = rs.get('http://bsr.twse.com.tw/bshtm/bsMenu.aspx')\n",
    "soup = bs(res.text)\n",
    "for ele in soup.select('input[type==hidden]'):\n",
    "    payload[ele['name']] = ele['value']\n",
    "print payload"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>序</th>\n",
       "      <th>證券商</th>\n",
       "      <th>成交單價</th>\n",
       "      <th>買進股數</th>\n",
       "      <th>賣出股數</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1020 合　　庫</td>\n",
       "      <td>28.9</td>\n",
       "      <td>7000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>1020</td>\n",
       "      <td>29.0</td>\n",
       "      <td>2000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>1020</td>\n",
       "      <td>29.3</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>1023 合庫高雄</td>\n",
       "      <td>29.1</td>\n",
       "      <td>0</td>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9</td>\n",
       "      <td>1023</td>\n",
       "      <td>29.5</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   序        證券商  成交單價  買進股數  賣出股數\n",
       "0  1  1020 合　　庫  28.9  7000     0\n",
       "1  3       1020  29.0  2000     0\n",
       "2  5       1020  29.3  1000     0\n",
       "3  7  1023 合庫高雄  29.1     0  5000\n",
       "4  9       1023  29.5     0   100"
      ]
     },
     "execution_count": 352,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import requests\n",
    "res2 = rs.post('http://bsr.twse.com.tw/bshtm/bsMenu.aspx', data=payload)\n",
    "res3 = rs.get('http://bsr.twse.com.tw/bshtm/bsContent.aspx?v=t')\n",
    "soup = bs(res3.text)\n",
    "dfsall = []\n",
    "for tb in soup.select('#table2 table'):\n",
    "    #print tb\n",
    "    dfs = pd.read_html(tb.prettify('utf-8'), encoding = 'utf-8', header=0)\n",
    "    dfsall.append(dfs[0])\n",
    "stockdf = pd.concat(dfsall)\n",
    "stockdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 354,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import sqlite3 as lite\n",
    "with  lite.connect('finance2.sqlite') as con:\n",
    "    stockdf.to_sql('bshtm', con=con, if_exists=\"replace\") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 網站登入"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import requests\n",
    "from bs4 import BeautifulSoup as bs \n",
    "\n",
    "payload = {\n",
    "'user[email]':'',\n",
    "'user[password]':'',\n",
    "'user[remember_me]':'1'    \n",
    "}\n",
    "rs = requests.session()\n",
    "res = rs.get('https://statementdog.com/users/sign_in')\n",
    "soup = bs(res.text)\n",
    "for ele in soup.select('input[type==hidden]'):\n",
    "    payload[ele['name']] = ele['value']\n",
    "#print payload\n",
    "res2 = rs.post('https://statementdog.com/users/sign_in', data = payload)\n",
    "res3 = rs.get('https://statementdog.com/users/login_success')\n",
    "#print res3.text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 388,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import requests\n",
    "from bs4 import BeautifulSoup as bs \n",
    "payload = {\n",
    "'stk_code':'1333',\n",
    "'auth_num':'UCD8J'    \n",
    "}\n",
    "res = requests.post('http://www.tpex.org.tw/web/stock/aftertrading/broker_trading/brokerBS.php?l=zh-tw', data= payload)\n",
    "res.encoding = 'utf-8'\n",
    "soup = bs(res.text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 389,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>序號</th>\n",
       "      <th>券商</th>\n",
       "      <th>價格</th>\n",
       "      <th>買進股數</th>\n",
       "      <th>賣出股數</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>116A 日盛木柵</td>\n",
       "      <td>2.81</td>\n",
       "      <td>10000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>116A</td>\n",
       "      <td>2.83</td>\n",
       "      <td>10000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>116C 日盛八德</td>\n",
       "      <td>2.80</td>\n",
       "      <td>0</td>\n",
       "      <td>4000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>116Z 日盛中和</td>\n",
       "      <td>2.75</td>\n",
       "      <td>0</td>\n",
       "      <td>6000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5269 大慶台中</td>\n",
       "      <td>2.75</td>\n",
       "      <td>0</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>538G 第一金中和</td>\n",
       "      <td>2.83</td>\n",
       "      <td>0</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>7005 兆豐台中港</td>\n",
       "      <td>2.80</td>\n",
       "      <td>0</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>913I 群益萬華</td>\n",
       "      <td>2.83</td>\n",
       "      <td>0</td>\n",
       "      <td>6000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>9661 富邦新店</td>\n",
       "      <td>2.75</td>\n",
       "      <td>7000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>981Q 元大太平</td>\n",
       "      <td>2.81</td>\n",
       "      <td>0</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>981Q</td>\n",
       "      <td>2.83</td>\n",
       "      <td>0</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>989C 元大板橋</td>\n",
       "      <td>2.80</td>\n",
       "      <td>6000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    序號          券商    價格   買進股數   賣出股數\n",
       "0    1   116A 日盛木柵  2.81  10000      0\n",
       "1    2        116A  2.83  10000      0\n",
       "2    3   116C 日盛八德  2.80      0   4000\n",
       "3    4   116Z 日盛中和  2.75      0   6000\n",
       "4    5   5269 大慶台中  2.75      0   1000\n",
       "5    6  538G 第一金中和  2.83      0   1000\n",
       "6    7  7005 兆豐台中港  2.80      0   2000\n",
       "7    8   913I 群益萬華  2.83      0   6000\n",
       "8    9   9661 富邦新店  2.75   7000      0\n",
       "9   10   981Q 元大太平  2.81      0  10000\n",
       "10  11        981Q  2.83      0   3000\n",
       "11  12   989C 元大板橋  2.80   6000      0"
      ]
     },
     "execution_count": 389,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tb = soup.select('#data_01 table')[0]\n",
    "dfs = pd.read_html(tb.prettify('utf-8'), encoding = 'utf-8', header=0)\n",
    "dfs[0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 355,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "      <th>f</th>\n",
       "      <th>g</th>\n",
       "      <th>h</th>\n",
       "      <th>i</th>\n",
       "      <th>j</th>\n",
       "      <th>k</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>008201</td>\n",
       "      <td>標智上證50中國指數基金®</td>\n",
       "      <td>3549100</td>\n",
       "      <td>0</td>\n",
       "      <td>75.50</td>\n",
       "      <td>76.51</td>\n",
       "      <td>-1.33</td>\n",
       "      <td>76.51</td>\n",
       "      <td>20160513</td>\n",
       "      <td>15:00:00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0052</td>\n",
       "      <td>FB科技</td>\n",
       "      <td>8,000,000</td>\n",
       "      <td>0</td>\n",
       "      <td>34.41</td>\n",
       "      <td>34.23</td>\n",
       "      <td>0.53</td>\n",
       "      <td>34.68</td>\n",
       "      <td>20160513</td>\n",
       "      <td>13:35:00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0057</td>\n",
       "      <td>FB摩台</td>\n",
       "      <td>8,527,000</td>\n",
       "      <td>0</td>\n",
       "      <td>35.94</td>\n",
       "      <td>36.16</td>\n",
       "      <td>-0.61</td>\n",
       "      <td>36.39</td>\n",
       "      <td>20160513</td>\n",
       "      <td>13:35:00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0058</td>\n",
       "      <td>FB發達</td>\n",
       "      <td>5,196,000</td>\n",
       "      <td>0</td>\n",
       "      <td>34.60</td>\n",
       "      <td>34.60</td>\n",
       "      <td>-</td>\n",
       "      <td>34.67</td>\n",
       "      <td>20160513</td>\n",
       "      <td>13:35:00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0059</td>\n",
       "      <td>FB金融</td>\n",
       "      <td>6,973,000</td>\n",
       "      <td>0</td>\n",
       "      <td>28.29</td>\n",
       "      <td>28.19</td>\n",
       "      <td>0.35</td>\n",
       "      <td>28.32</td>\n",
       "      <td>20160513</td>\n",
       "      <td>13:35:00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        a              b          c  d      e      f      g      h         i  \\\n",
       "0  008201  標智上證50中國指數基金®    3549100  0  75.50  76.51  -1.33  76.51  20160513   \n",
       "1    0052           FB科技  8,000,000  0  34.41  34.23   0.53  34.68  20160513   \n",
       "2    0057           FB摩台  8,527,000  0  35.94  36.16  -0.61  36.39  20160513   \n",
       "3    0058           FB發達  5,196,000  0  34.60  34.60      -  34.67  20160513   \n",
       "4    0059           FB金融  6,973,000  0  28.29  28.19   0.35  28.32  20160513   \n",
       "\n",
       "          j  k  \n",
       "0  15:00:00  2  \n",
       "1  13:35:00  1  \n",
       "2  13:35:00  1  \n",
       "3  13:35:00  1  \n",
       "4  13:35:00  1  "
      ]
     },
     "execution_count": 355,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import requests\n",
    "import json\n",
    "res = requests.get('http://mis.twse.com.tw/stock/data/all_etf.txt?_=1463295247230')\n",
    "jd = json.loads(res.text)\n",
    "ary = []\n",
    "for ele  in jd['a1']:\n",
    "    #print ele['msgArray']\n",
    "    if 'msgArray' in ele:\n",
    "        ary.extend(ele['msgArray'])\n",
    "#print ary\n",
    "df = pd.DataFrame(ary)\n",
    "df.head()"
   ]
  }
 ],
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