technical-analysis.ipynb

{
 "metadata": {
  "name": "",
  "signature": "sha256:b8431da5dca14175a455006c0114eee283e181a2ee78cf52412b843513afb349"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# \u555f\u52d5\u4e92\u52d5\u5f0f\u7e6a\u5716\u74b0\u5883\n",
      "%pylab inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# \u5f15\u5165\u76f8\u4f9d\u5957\u4ef6\n",
      "import numpy as np\n",
      "import pandas as pd\n",
      "from numpy import random\n",
      "import matplotlib.pyplot as plt\n",
      "# \u5f15\u5165\u6a94\u6848\n",
      "# \u8cc7\u6599\u4f86\u6e90 3008 \u5927\u7acb\u5149 2012-8/1 ~ 2014-12/09\n",
      "# http://www.twse.com.tw/ch/trading/exchange/STOCK_DAY/genpage/Report201412/201412_F3_1_8_3008.php?STK_NO=3008&myear=2014&mmon=12\n",
      "datapath = '/Users/wy/Desktop/3008.txt'\n",
      "data = pd.read_csv(datapath)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# \u770b\u6a94\u6848\u524dN\u7b46\n",
      "data.head(5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Date</th>\n",
        "      <th>Open</th>\n",
        "      <th>High</th>\n",
        "      <th>Low</th>\n",
        "      <th>Close</th>\n",
        "      <th>Volume</th>\n",
        "      <th>Adj Close</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 2014-12-09</td>\n",
        "      <td> 2265</td>\n",
        "      <td> 2300</td>\n",
        "      <td> 2235</td>\n",
        "      <td> 2290</td>\n",
        "      <td> 1137524</td>\n",
        "      <td> 2290.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 2014-12-08</td>\n",
        "      <td> 2375</td>\n",
        "      <td> 2395</td>\n",
        "      <td> 2275</td>\n",
        "      <td> 2285</td>\n",
        "      <td> 1389389</td>\n",
        "      <td> 2285.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 2014-12-05</td>\n",
        "      <td> 2400</td>\n",
        "      <td> 2430</td>\n",
        "      <td> 2340</td>\n",
        "      <td> 2370</td>\n",
        "      <td> 1059384</td>\n",
        "      <td> 2360.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 2014-12-04</td>\n",
        "      <td> 2405</td>\n",
        "      <td> 2440</td>\n",
        "      <td> 2395</td>\n",
        "      <td> 2400</td>\n",
        "      <td>  831362</td>\n",
        "      <td> 2400.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 2014-12-03</td>\n",
        "      <td> 2410</td>\n",
        "      <td> 2440</td>\n",
        "      <td> 2375</td>\n",
        "      <td> 2380</td>\n",
        "      <td> 1422947</td>\n",
        "      <td> 2380.00</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "         Date  Open  High   Low  Close   Volume Adj Close\n",
        "0  2014-12-09  2265  2300  2235   2290  1137524   2290.00\n",
        "1  2014-12-08  2375  2395  2275   2285  1389389   2285.00\n",
        "2  2014-12-05  2400  2430  2340   2370  1059384   2360.00\n",
        "3  2014-12-04  2405  2440  2395   2400   831362   2400.00\n",
        "4  2014-12-03  2410  2440  2375   2380  1422947   2380.00"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# data\u8a73\u7d30\u8cc7\u6599 \u7e3d\u6578,\u5e73\u5747\u6578,\u6a19\u6e96\u5dee...\n",
      "data.describe()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Open</th>\n",
        "      <th>High</th>\n",
        "      <th>Low</th>\n",
        "      <th>Close</th>\n",
        "      <th>Volume</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>count</th>\n",
        "      <td>  584.000000</td>\n",
        "      <td>  584.000000</td>\n",
        "      <td>  584.000000</td>\n",
        "      <td>  584.000000</td>\n",
        "      <td>     584.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
        "      <td> 1290.816781</td>\n",
        "      <td> 1309.916096</td>\n",
        "      <td> 1269.823630</td>\n",
        "      <td> 1288.195205</td>\n",
        "      <td> 1389292.299658</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
        "      <td>  625.521153</td>\n",
        "      <td>  636.663035</td>\n",
        "      <td>  612.475304</td>\n",
        "      <td>  623.215506</td>\n",
        "      <td>  867885.109465</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
        "      <td>  587.000000</td>\n",
        "      <td>  599.000000</td>\n",
        "      <td>  580.000000</td>\n",
        "      <td>  592.000000</td>\n",
        "      <td>  256330.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25%</th>\n",
        "      <td>  785.000000</td>\n",
        "      <td>  793.750000</td>\n",
        "      <td>  773.750000</td>\n",
        "      <td>  782.000000</td>\n",
        "      <td>  807529.250000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50%</th>\n",
        "      <td> 1015.000000</td>\n",
        "      <td> 1030.000000</td>\n",
        "      <td> 1000.000000</td>\n",
        "      <td> 1010.000000</td>\n",
        "      <td> 1176971.500000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75%</th>\n",
        "      <td> 1956.250000</td>\n",
        "      <td> 1980.000000</td>\n",
        "      <td> 1935.000000</td>\n",
        "      <td> 1950.000000</td>\n",
        "      <td> 1705747.750000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\n",
        "      <td> 2630.000000</td>\n",
        "      <td> 2640.000000</td>\n",
        "      <td> 2565.000000</td>\n",
        "      <td> 2635.000000</td>\n",
        "      <td> 6029834.000000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "              Open         High          Low        Close          Volume\n",
        "count   584.000000   584.000000   584.000000   584.000000      584.000000\n",
        "mean   1290.816781  1309.916096  1269.823630  1288.195205  1389292.299658\n",
        "std     625.521153   636.663035   612.475304   623.215506   867885.109465\n",
        "min     587.000000   599.000000   580.000000   592.000000   256330.000000\n",
        "25%     785.000000   793.750000   773.750000   782.000000   807529.250000\n",
        "50%    1015.000000  1030.000000  1000.000000  1010.000000  1176971.500000\n",
        "75%    1956.250000  1980.000000  1935.000000  1950.000000  1705747.750000\n",
        "max    2630.000000  2640.000000  2565.000000  2635.000000  6029834.000000"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# \u6280\u8853\u5206\u6790\u8cc7\u6599\u4f86\u6e90\n",
      "# http://hymar.myweb.hinet.net/study/stock/theory/"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Rise Ratio \u6f32\u5e45\u6bd4\n",
      "def RR(data):\n",
      "    # \u7531\u65bc data \u65b0\u5230\u820a 0~xxx\uff0c\u905e\u589e\uff0c\u56e0\u6b64\u9700\u53cd\u8f49\u9663\u5217\n",
      "    dataList = range(data['Date'].size)\n",
      "    dataList.reverse()\n",
      "    tmpList = []\n",
      "    \n",
      "    for item in dataList:\n",
      "        # \u9632\u6b62 \u7b2c\u4e00\u7b46data\u6c92\u6709\u66f4\u820a\u7684\n",
      "        if item-1 >=0:\n",
      "            # (\u4eca\u65e5\u6536\u76e4\u50f9 - \u6628\u65e5\u6536\u76e4\u50f9)/\u6628\u65e5\u6536\u76e4\u50f9\n",
      "            tmp = (data['Close'][item-1]-data['Close'][item])/data['Close'][item]*100\n",
      "            tmpList.append(tmp)\n",
      "        \n",
      "    # \u524dday \u6c92data\u6703\u51fa\u73feNA\n",
      "    tmpList.reverse()\n",
      "    tmpSeries = pd.Series(tmpList)\n",
      "    \n",
      "    # create  RR \u6b04\u4f4d\n",
      "    data['RR']=tmpSeries"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# \u5a01\u5ec9\u6307\u6a19(WMS%R\u6216%R)\n",
      "def WMS(data,day):\n",
      "    # \u7531\u65bc data \u65b0\u5230\u820a 0~xxx\uff0c\u905e\u589e\uff0c\u56e0\u6b64\u9700\u53cd\u8f49\u9663\u5217\n",
      "    dataList = range(data['Date'].size)\n",
      "    dataList.reverse()\n",
      "    tmpList = []\n",
      "    \n",
      "    for item in dataList:\n",
      "        # \u9632\u6b62\u524dday\u6c92\u6709data\n",
      "        if item-day+1 >= 0:\n",
      "            # 9\u65e5WMS%R =(9\u65e5\u5167\u6700\u9ad8\u50f9-\u7b2c9\u65e5\u6536\u76e4\u50f9) / (9\u65e5\u5167\u6700\u9ad8\u50f9-9\u65e5\u5167\u6700\u4f4e\u50f9)*100\n",
      "            # [item-day+1:item+1] \u4eca\u65e5\u5340\u9593 [item-day+1] \u7b2cN\u65e5 583-9=574+1=575\n",
      "            tmp = (data['High'][item-day+1:item+1].max()-data['Close'][item-day+1])/(data['High'][item-day+1:item+1].max()-data['Low'][item-day+1:item+1].min())*100\n",
      "            tmpList.append(tmp)\n",
      "        \n",
      "    # \u524dday \u6c92data\u6703\u51fa\u73feNA\n",
      "    tmpList.reverse()\n",
      "    tmpSeries = pd.Series(tmpList)\n",
      "    \n",
      "    # create  WMS \u6b04\u4f4d\n",
      "    data['WMS']=tmpSeries"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# \u8cb7\u8ce3\u610f\u9858\u6307\u6a19 day \u5efa\u8b7026\n",
      "def BR(data,day):\n",
      "    # \u7531\u65bc data \u65b0\u5230\u820a 0~xxx\uff0c\u905e\u589e\uff0c\u56e0\u6b64\u9700\u53cd\u8f49\u9663\u5217\n",
      "    dataList = range(data['Date'].size)\n",
      "    dataList.reverse()\n",
      "    tmpList = []\n",
      "    \n",
      "    for item in dataList:\n",
      "        # \u9632\u6b62\u524dday\u6c92\u6709data\n",
      "        if item-day >= 0:\n",
      "            # 26\u65e5BR = (\u4eca\u65e5\u6700\u9ad8\u50f9 - \u6628\u65e5\u6536\u76e4\u50f9)26\u5929\u7d2f\u8a08\u7e3d\u6578 / (\u6628\u65e5\u6536\u76e4\u50f9 - \u4eca\u65e5\u6700\u4f4e\u50f9)26\u5929\u7d2f\u8a08\u7e3d\u6578\n",
      "            # [(item-day+1)-1:(item+1)-1] \u6709-1 \u4eca\u65e5\u5340\u9593 [(item-day+1):(item+1)] \u6628\u65e5\u5340\u9593\n",
      "            tmp = (data['High'][(item-day+1)-1:(item+1)-1].sum()-data['Close'][item-day+1:item+1].sum())/(data['Close'][item-day+1:item+1].sum()-data['Low'][(item-day+1)-1:(item+1)-1].sum())\n",
      "            tmpList.append(tmp)\n",
      "        \n",
      "    # \u524dday \u6c92data\u6703\u51fa\u73feNA\n",
      "    tmpList.reverse()\n",
      "    tmpSeries = pd.Series(tmpList)\n",
      "    \n",
      "    # create  BR \u6b04\u4f4d\n",
      "    data['BR']=tmpSeries"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# \u8cb7\u8ce3\u6c23\u52e2\u6307\u6a19 day\u5efa\u8b7026\n",
      "def AR(data,day):\n",
      "    # \u7531\u65bc data \u65b0\u5230\u820a 0~xxx\uff0c\u905e\u589e\uff0c\u56e0\u6b64\u9700\u53cd\u8f49\u9663\u5217\n",
      "    dataList = range(data['Date'].size)\n",
      "    dataList.reverse()\n",
      "    tmpList = []\n",
      "    \n",
      "    for item in dataList:\n",
      "        # \u9632\u6b62\u524dday\u6c92\u6709data\n",
      "        if item-day+1 >= 0:\n",
      "            # 26\u65e5AR = (\u6700\u9ad8\u50f9 - \u958b\u76e4\u50f9)26\u5929\u7d2f\u8a08\u7e3d\u6578 / (\u958b\u76e4\u50f9 - \u6700\u4f4e\u50f9)26\u5929\u7d2f\u8a08\u7e3d\u6578\n",
      "            # [item-day+1:item+1] \u4eca\u65e5\u5340\u9593\n",
      "            tmp = (data['High'][item-day+1:item+1].sum()-data['Open'][item-day+1:item+1].sum())/(data['Open'][item-day+1:item+1].sum()-data['Low'][item-day+1:item+1].sum())\n",
      "            tmpList.append(tmp)\n",
      "        \n",
      "    # \u524dday \u6c92data\u6703\u51fa\u73feNA\n",
      "    tmpList.reverse()\n",
      "    tmpSeries = pd.Series(tmpList)\n",
      "    \n",
      "    # create  AR \u6b04\u4f4d\n",
      "    data['AR']=tmpSeries"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# \u5e73\u5747\u6210\u4ea4\u91cf mean volumn day\u5efa\u8b7012\n",
      "def MV(data,day):\n",
      "    # \u7531\u65bc data \u65b0\u5230\u820a 0~xxx\uff0c\u905e\u589e\uff0c\u56e0\u6b64\u9700\u53cd\u8f49\u9663\u5217\n",
      "    dataList = range(data['Date'].size)\n",
      "    dataList.reverse()\n",
      "    tmpList = []\n",
      "    \n",
      "    for item in dataList:\n",
      "        # \u9632\u6b62\u524dday\u6c92\u6709data\n",
      "        if item-day+1 >= 0:\n",
      "            # N\u65e5\u5e73\u5747\u91cf = N\u65e5\u5167\u7684\u6210\u4ea4\u91cf\u7e3d\u548c / N\n",
      "            # [item-day+1:item+1] \u4eca\u65e5\u5340\u9593\n",
      "            tmp = data['Volume'][item-day+1:item+1].mean()\n",
      "            tmpList.append(tmp)\n",
      "        \n",
      "    # \u524dday \u6c92data\u6703\u51fa\u73feNA\n",
      "    tmpList.reverse()\n",
      "    tmpSeries = pd.Series(tmpList)\n",
      "    \n",
      "    # create  MV \u6b04\u4f4d\n",
      "    data['MV']=tmpSeries"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# \u79fb\u52d5\u5e73\u5747\u7dda(MA\uff0cMoving Average) \u5efa\u8b7012\n",
      "def MA(data,day):\n",
      "    # \u7531\u65bc data \u65b0\u5230\u820a 0~xxx\uff0c\u905e\u589e\uff0c\u56e0\u6b64\u9700\u53cd\u8f49\u9663\u5217\n",
      "    dataList = range(data['Date'].size)\n",
      "    dataList.reverse()\n",
      "    tmpList = []\n",
      "    \n",
      "    for item in dataList:\n",
      "        # \u9632\u6b62\u524dday\u6c92\u6709data\n",
      "        if item-day+1 >= 0:\n",
      "            # \u79fb\u52d5\u5e73\u5747\u6578 = \u63a1\u6a23\u5929\u6578\u7684\u80a1\u50f9\u5408\u8a08 / \u63a1\u6a23\u5929\u6578\n",
      "            # [item-day+1:item+1] \u4eca\u65e5\u5340\u9593\n",
      "            tmp = data['Close'][item-day+1:item+1].mean()\n",
      "            tmpList.append(tmp)\n",
      "        \n",
      "    # \u524dday \u6c92data\u6703\u51fa\u73feNA\n",
      "    tmpList.reverse()\n",
      "    tmpSeries = pd.Series(tmpList)\n",
      "    \n",
      "    # create  MA \u6b04\u4f4d\n",
      "    data['MA'+str(day)]=tmpSeries"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# \u5fc3\u7406\u7dda(PSY) \u5efa\u8b7013\n",
      "def PSY(data,day):\n",
      "    # \u7531\u65bc data \u65b0\u5230\u820a 0~xxx\uff0c\u905e\u589e\uff0c\u56e0\u6b64\u9700\u53cd\u8f49\u9663\u5217\n",
      "    dataList = range(data['Date'].size)\n",
      "    dataList.reverse()\n",
      "    tmpList = []\n",
      "    \n",
      "    for item in dataList:\n",
      "        # \u9632\u6b62\u524dday\u6c92\u6709data\n",
      "        if item-day >= 0:\n",
      "            # 13\u65e5PSY\u503c = ( 13\u65e5\u5167\u4e4b\u4e0a\u6f32\u5929\u6578 / 13 ) * 100\n",
      "            # [item-day+1-1:item+1-1] \u8df3\u4e00\u5929 \u6700\u65e9\u7684\u5929\u6c92\u6709RR\u503c\n",
      "            count = 0\n",
      "            for a in data['RR'][item-day+1-1:item+1-1]:\n",
      "                if a > 0:\n",
      "                    count+=1\n",
      "            tmp = float(count)/float(13)*100\n",
      "            tmpList.append(tmp)\n",
      "        \n",
      "    # \u524dday \u6c92data\u6703\u51fa\u73feNA\n",
      "    tmpList.reverse()\n",
      "    tmpSeries = pd.Series(tmpList)\n",
      "    \n",
      "    # create  PSY \u6b04\u4f4d\n",
      "    data['PSY']=tmpSeries"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# \u80fd\u91cf\u6f6e(OBV) \u5efa\u8b7012\n",
      "def OBV(data,day):\n",
      "    # \u7531\u65bc data \u65b0\u5230\u820a 0~xxx\uff0c\u905e\u589e\uff0c\u56e0\u6b64\u9700\u53cd\u8f49\u9663\u5217\n",
      "    dataList = range(data['Date'].size)\n",
      "    dataList.reverse()\n",
      "    tmpList = []\n",
      "    \n",
      "    for item in dataList:\n",
      "        # \u9632\u6b62\u524dday\u6c92\u6709data\n",
      "        if item-day >= 0:\n",
      "            # \u4eca\u65e5OBV\u503c = \u6700\u8fd112\u5929\u80a1\u50f9\u4e0a\u6f32\u65e5\u6210\u4ea4\u91cf\u7e3d\u548c - \u6700\u8fd112\u5929\u80a1\u50f9\u4e0b\u8dcc\u65e5\u6210\u4ea4\u91cf\u7e3d\u548c\n",
      "            # \u5148\u7531 ['RR'] \u6c42\u51faboolean\u503c > 0 True \u5957\u5165['Volume']\u7b26\u5408True\u5168\u52a0\u8d77\u4f86\n",
      "            bolRise = data['RR'][item-day+1-1:item+1-1]>0\n",
      "            sumVolRise = data['Volume'][item-day+1-1:item+1-1][bolRise].sum() \n",
      "            bolDesc = data['RR'][item-day+1-1:item+1-1]<0\n",
      "            sumVolDesc = data['Volume'][item-day+1-1:item+1-1][bolDesc].sum()   \n",
      "            \n",
      "            tmp = sumVolRise-sumVolDesc\n",
      "#             \u53ef\u5207\u63db OBV\u7d2f\u7a4d12\u65e5\u79fb\u52d5\u5e73\u5747\u503c = (\u6700\u8fd112\u5929\u80a1\u50f9\u4e0a\u6f32\u65e5\u6210\u4ea4\u91cf\u7e3d\u548c - \u6700\u8fd112\u5929\u80a1\u50f9\u4e0b\u8dcc\u65e5\u6210\u4ea4\u91cf\u7e3d\u548c) / 12\n",
      "#             tmp = (sumVolRise-sumVolDesc)/12\n",
      "            tmpList.append(tmp)\n",
      "            \n",
      "    # \u524dday \u6c92data\u6703\u51fa\u73feNA\n",
      "    tmpList.reverse()\n",
      "    tmpSeries = pd.Series(tmpList)\n",
      "    \n",
      "    # create  OBV \u6b04\u4f4d\n",
      "    data['OBV']=tmpSeries"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# \u6578\u91cf\u6307\u6a19(VR) \u5efa\u8b7012\n",
      "def VR(data,day):\n",
      "    # \u7531\u65bc data \u65b0\u5230\u820a 0~xxx\uff0c\u905e\u589e\uff0c\u56e0\u6b64\u9700\u53cd\u8f49\u9663\u5217\n",
      "    dataList = range(data['Date'].size)\n",
      "    dataList.reverse()\n",
      "    tmpList = []\n",
      "    \n",
      "    for item in dataList:\n",
      "        # \u9632\u6b62\u524dday\u6c92\u6709data\n",
      "        if item-day >= 0:\n",
      "            # VR = ( N\u65e5\u5167\u4e0a\u6f32\u65e5\u6210\u4ea4\u503c\u7e3d\u548c + 1/2*N\u65e5\u5167\u5e73\u76e4\u65e5\u6210\u4ea4\u503c\u7e3d\u548c) / ( N\u65e5\u5167\u4e0b\u8dcc\u65e5\u6210\u4ea4\u503c\u7e3d\u548c + 1/2*N\u65e5\u5167\u5e73\u76e4\u65e5\u6210\u4ea4\u503c\u7e3d\u548c)* 100\uff05\n",
      "            # \u5148\u7531 ['RR'] \u6c42\u51faboolean\u503c > 0 True \u5957\u5165['Volume']\u7b26\u5408True\u5168\u52a0\u8d77\u4f86\n",
      "            bolRise = data['RR'][item-day+1-1:item+1-1]>0\n",
      "            sumVolRise = data['Volume'][item-day+1-1:item+1-1][bolRise].sum()\n",
      "            \n",
      "            bolNorm = data['RR'][item-day+1-1:item+1-1] == 0\n",
      "            sumVolNorm = data['Volume'][item-day+1-1:item+1-1][bolNorm].sum()\n",
      "            \n",
      "            bolDesc = data['RR'][item-day+1-1:item+1-1]<0\n",
      "            sumVolDesc = data['Volume'][item-day+1-1:item+1-1][bolDesc].sum()   \n",
      "            \n",
      "            tmp = (sumVolRise+0.5*sumVolNorm)/(sumVolDesc+0.5*sumVolNorm)*100\n",
      "            tmpList.append(tmp)\n",
      "            \n",
      "    # \u524dday \u6c92data\u6703\u51fa\u73feNA\n",
      "    tmpList.reverse()\n",
      "    tmpSeries = pd.Series(tmpList)\n",
      "    \n",
      "    # create  VR \u6b04\u4f4d\n",
      "    data['VR']=tmpSeries"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# \u76f8\u5c0d\u5f37\u5f31\u6307\u6a19(RSI) \u5efa\u8b706\n",
      "def RSI(data,day):\n",
      "    # \u7531\u65bc data \u65b0\u5230\u820a 0~xxx\uff0c\u905e\u589e\uff0c\u56e0\u6b64\u9700\u53cd\u8f49\u9663\u5217\n",
      "    dataList = range(data['Date'].size)\n",
      "    dataList.reverse()\n",
      "    tmpList = []\n",
      "    \n",
      "    for item in dataList:\n",
      "        # \u9632\u6b62\u524dday\u6c92\u6709data\n",
      "        if item-day >= 0:\n",
      "            # 6\u65e5RSI=100*6\u65e5\u5167\u6536\u76e4\u4e0a\u6f32\u7e3d\u5e45\u5ea6\u5e73\u5747\u503c / (6\u65e5\u5167\u6536\u76e4\u4e0a\u6f32\u7e3d\u5e45\u5ea6\u5e73\u5747\u503c - 6\u65e5\u5167\u6536\u76e4\u4e0b\u8dcc\u7e3d\u5e45\u5ea6\u5e73\u5747\u503c)\n",
      "            # \u5148\u7531 ['RR'] \u6c42\u51faboolean\u503c > 0 True \u5957\u5165['Volume']\u7b26\u5408True\u5168\u52a0\u8d77\u4f86\n",
      "            bolRise = data['RR'][item-day+1-1:item+1-1]>0\n",
      "            meanRise = data['RR'][item-day+1-1:item+1-1][bolRise].mean() \n",
      "            bolDesc = data['RR'][item-day+1-1:item+1-1]<0\n",
      "            meanDesc = data['RR'][item-day+1-1:item+1-1][bolDesc].mean()   \n",
      "            \n",
      "            tmp = 100*meanRise/(meanRise-meanDesc)\n",
      "            tmpList.append(tmp)\n",
      "            \n",
      "    # \u524dday \u6c92data\u6703\u51fa\u73feNA\n",
      "    tmpList.reverse()\n",
      "    tmpSeries = pd.Series(tmpList)\n",
      "    \n",
      "    # create  RSI \u6b04\u4f4d\n",
      "    data['RSI']=tmpSeries"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# \u4e56\u96e2\u7387(BIAS)\n",
      "def BIAS(data,day):\n",
      "    # \u7531\u65bc data \u65b0\u5230\u820a 0~xxx\uff0c\u905e\u589e\uff0c\u56e0\u6b64\u9700\u53cd\u8f49\u9663\u5217\n",
      "    dataList = range(data['Date'].size)\n",
      "    dataList.reverse()\n",
      "    tmpList = []\n",
      "    \n",
      "    for item in dataList:\n",
      "        # \u9632\u6b62\u524dday\u6c92\u6709data\n",
      "        if item-day+1 >= 0:\n",
      "            # N\u65e5\u4e56\u96e2\u7387 = (\u7576\u65e5\u80a1\u50f9 - N\u65e5\u80a1\u50f9\u79fb\u52d5\u5e73\u5747\u6578) / N\u65e5\u5e73\u5747\u80a1\u50f9\n",
      "            tmp = (data['Close'][item-day+1]-data['MA'+str(day)][item-day+1])/data['MA'+str(day)][item-day+1]*100\n",
      "            tmpList.append(tmp)\n",
      "        \n",
      "    # \u524dday \u6c92data\u6703\u51fa\u73feNA\n",
      "    tmpList.reverse()\n",
      "    tmpSeries = pd.Series(tmpList)\n",
      "    \n",
      "    # create  BIAS \u6b04\u4f4d\n",
      "    data['BIAS']=tmpSeries"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# RR\u6f32\u5e45\u6bd4\u9808\u5148\u7b97\u51fa\u4f86\uff0c\u5f8c\u7e8c\u6307\u6a19\u9700\u7528\u5230\u6b64\u9805\n",
      "RR(data)\n",
      "WMS(data,9)\n",
      "BR(data,26)\n",
      "AR(data,26)\n",
      "MV(data,12)\n",
      "MA(data,12)\n",
      "# \u7b97BIAS\u9808\u5148\u6c42\u51faMA\u503c\n",
      "BIAS(data,12)\n",
      "PSY(data,13)\n",
      "OBV(data,12)\n",
      "VR(data,12)\n",
      "RSI(data,6)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "MA(data,20)\n",
      "MA(data,60)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Date</th>\n",
        "      <th>Open</th>\n",
        "      <th>High</th>\n",
        "      <th>Low</th>\n",
        "      <th>Close</th>\n",
        "      <th>Volume</th>\n",
        "      <th>Adj Close</th>\n",
        "      <th>RR</th>\n",
        "      <th>WMS</th>\n",
        "      <th>BR</th>\n",
        "      <th>AR</th>\n",
        "      <th>MV</th>\n",
        "      <th>MA12</th>\n",
        "      <th>BIAS</th>\n",
        "      <th>PSY</th>\n",
        "      <th>OBV</th>\n",
        "      <th>VR</th>\n",
        "      <th>RSI</th>\n",
        "      <th>MA20</th>\n",
        "      <th>MA60</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0  </th>\n",
        "      <td> 2014-12-09</td>\n",
        "      <td> 2265</td>\n",
        "      <td> 2300</td>\n",
        "      <td> 2235</td>\n",
        "      <td> 2290</td>\n",
        "      <td> 1137524</td>\n",
        "      <td> 2290.00</td>\n",
        "      <td> 0.218818</td>\n",
        "      <td> 77.083333</td>\n",
        "      <td> 1.549133</td>\n",
        "      <td> 0.925764</td>\n",
        "      <td> 1210435.166667</td>\n",
        "      <td> 2370.000000</td>\n",
        "      <td>-3.375527</td>\n",
        "      <td> 53.846154</td>\n",
        "      <td> 2186310</td>\n",
        "      <td> 135.437646</td>\n",
        "      <td> 13.012632</td>\n",
        "      <td> 2293.75</td>\n",
        "      <td> 2228.500000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1  </th>\n",
        "      <td> 2014-12-08</td>\n",
        "      <td> 2375</td>\n",
        "      <td> 2395</td>\n",
        "      <td> 2275</td>\n",
        "      <td> 2285</td>\n",
        "      <td> 1389389</td>\n",
        "      <td> 2285.00</td>\n",
        "      <td>-3.586498</td>\n",
        "      <td> 95.000000</td>\n",
        "      <td> 1.753086</td>\n",
        "      <td> 0.973451</td>\n",
        "      <td> 1227501.916667</td>\n",
        "      <td> 2373.750000</td>\n",
        "      <td>-3.738810</td>\n",
        "      <td> 53.846154</td>\n",
        "      <td> -293539</td>\n",
        "      <td>  96.092285</td>\n",
        "      <td> 34.167668</td>\n",
        "      <td> 2285.25</td>\n",
        "      <td> 2228.333333</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2  </th>\n",
        "      <td> 2014-12-05</td>\n",
        "      <td> 2400</td>\n",
        "      <td> 2430</td>\n",
        "      <td> 2340</td>\n",
        "      <td> 2370</td>\n",
        "      <td> 1059384</td>\n",
        "      <td> 2360.00</td>\n",
        "      <td>-1.250000</td>\n",
        "      <td> 58.333333</td>\n",
        "      <td> 2.079137</td>\n",
        "      <td> 1.077670</td>\n",
        "      <td> 1318681.416667</td>\n",
        "      <td> 2378.333333</td>\n",
        "      <td>-0.350385</td>\n",
        "      <td> 61.538462</td>\n",
        "      <td> 3579393</td>\n",
        "      <td> 158.463963</td>\n",
        "      <td> 32.991976</td>\n",
        "      <td> 2276.75</td>\n",
        "      <td> 2227.750000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3  </th>\n",
        "      <td> 2014-12-04</td>\n",
        "      <td> 2405</td>\n",
        "      <td> 2440</td>\n",
        "      <td> 2395</td>\n",
        "      <td> 2400</td>\n",
        "      <td>  831362</td>\n",
        "      <td> 2400.00</td>\n",
        "      <td> 0.840336</td>\n",
        "      <td> 41.666667</td>\n",
        "      <td> 2.119403</td>\n",
        "      <td> 1.079602</td>\n",
        "      <td> 1374651.166667</td>\n",
        "      <td> 2363.333333</td>\n",
        "      <td> 1.551481</td>\n",
        "      <td> 69.230769</td>\n",
        "      <td> 6369798</td>\n",
        "      <td> 225.810546</td>\n",
        "      <td> 29.695832</td>\n",
        "      <td> 2265.50</td>\n",
        "      <td> 2228.250000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4  </th>\n",
        "      <td> 2014-12-03</td>\n",
        "      <td> 2410</td>\n",
        "      <td> 2440</td>\n",
        "      <td> 2375</td>\n",
        "      <td> 2380</td>\n",
        "      <td> 1422947</td>\n",
        "      <td> 2380.00</td>\n",
        "      <td> 0.210526</td>\n",
        "      <td> 52.777778</td>\n",
        "      <td> 2.117647</td>\n",
        "      <td> 1.099010</td>\n",
        "      <td> 1352415.083333</td>\n",
        "      <td> 2340.833333</td>\n",
        "      <td> 1.673193</td>\n",
        "      <td> 61.538462</td>\n",
        "      <td> 6102965</td>\n",
        "      <td> 220.540299</td>\n",
        "      <td> 33.138487</td>\n",
        "      <td> 2246.00</td>\n",
        "      <td> 2229.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5  </th>\n",
        "      <td> 2014-12-02</td>\n",
        "      <td> 2445</td>\n",
        "      <td> 2445</td>\n",
        "      <td> 2365</td>\n",
        "      <td> 2375</td>\n",
        "      <td> 1278528</td>\n",
        "      <td> 2375.00</td>\n",
        "      <td>-3.651116</td>\n",
        "      <td> 38.461538</td>\n",
        "      <td> 2.172662</td>\n",
        "      <td> 1.130435</td>\n",
        "      <td> 1284325.750000</td>\n",
        "      <td> 2318.750000</td>\n",
        "      <td> 2.425876</td>\n",
        "      <td> 61.538462</td>\n",
        "      <td> 4074143</td>\n",
        "      <td> 171.868532</td>\n",
        "      <td> 43.850538</td>\n",
        "      <td> 2231.25</td>\n",
        "      <td> 2230.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6  </th>\n",
        "      <td> 2014-12-01</td>\n",
        "      <td> 2320</td>\n",
        "      <td> 2465</td>\n",
        "      <td> 2320</td>\n",
        "      <td> 2465</td>\n",
        "      <td> 1362046</td>\n",
        "      <td> 2460.00</td>\n",
        "      <td> 3.354298</td>\n",
        "      <td>  2.857143</td>\n",
        "      <td> 2.311111</td>\n",
        "      <td> 1.118483</td>\n",
        "      <td> 1259344.000000</td>\n",
        "      <td> 2297.916667</td>\n",
        "      <td> 7.271079</td>\n",
        "      <td> 69.230769</td>\n",
        "      <td> 6331418</td>\n",
        "      <td> 244.211983</td>\n",
        "      <td> 57.139589</td>\n",
        "      <td> 2220.50</td>\n",
        "      <td> 2230.666667</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7  </th>\n",
        "      <td> 2014-11-28</td>\n",
        "      <td> 2395</td>\n",
        "      <td> 2410</td>\n",
        "      <td> 2375</td>\n",
        "      <td> 2385</td>\n",
        "      <td>  743512</td>\n",
        "      <td> 2385.00</td>\n",
        "      <td> 0.421053</td>\n",
        "      <td> 24.657534</td>\n",
        "      <td> 2.412698</td>\n",
        "      <td> 0.954545</td>\n",
        "      <td> 1274118.166667</td>\n",
        "      <td> 2269.166667</td>\n",
        "      <td> 5.104664</td>\n",
        "      <td> 61.538462</td>\n",
        "      <td> 6508708</td>\n",
        "      <td> 248.250153</td>\n",
        "      <td> 61.000854</td>\n",
        "      <td> 2207.75</td>\n",
        "      <td> 2230.416667</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8  </th>\n",
        "      <td> 2014-11-27</td>\n",
        "      <td> 2440</td>\n",
        "      <td> 2475</td>\n",
        "      <td> 2370</td>\n",
        "      <td> 2375</td>\n",
        "      <td> 1411415</td>\n",
        "      <td> 2375.00</td>\n",
        "      <td>-2.061856</td>\n",
        "      <td> 27.027027</td>\n",
        "      <td> 2.298507</td>\n",
        "      <td> 1.018265</td>\n",
        "      <td> 1334315.166667</td>\n",
        "      <td> 2243.750000</td>\n",
        "      <td> 5.849582</td>\n",
        "      <td> 61.538462</td>\n",
        "      <td> 4299320</td>\n",
        "      <td> 173.414454</td>\n",
        "      <td> 77.115052</td>\n",
        "      <td> 2195.25</td>\n",
        "      <td> 2230.750000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9  </th>\n",
        "      <td> 2014-11-26</td>\n",
        "      <td> 2385</td>\n",
        "      <td> 2440</td>\n",
        "      <td> 2365</td>\n",
        "      <td> 2425</td>\n",
        "      <td> 1772042</td>\n",
        "      <td> 2420.00</td>\n",
        "      <td> 1.677149</td>\n",
        "      <td>  4.477612</td>\n",
        "      <td> 2.174825</td>\n",
        "      <td> 0.931915</td>\n",
        "      <td> 1315727.916667</td>\n",
        "      <td> 2222.500000</td>\n",
        "      <td> 9.111361</td>\n",
        "      <td> 61.538462</td>\n",
        "      <td> 6899103</td>\n",
        "      <td> 255.216841</td>\n",
        "      <td> 83.173660</td>\n",
        "      <td> 2181.25</td>\n",
        "      <td> 2231.250000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10 </th>\n",
        "      <td> 2014-11-25</td>\n",
        "      <td> 2320</td>\n",
        "      <td> 2385</td>\n",
        "      <td> 2295</td>\n",
        "      <td> 2385</td>\n",
        "      <td> 1086333</td>\n",
        "      <td> 2380.00</td>\n",
        "      <td> 3.470716</td>\n",
        "      <td>  0.000000</td>\n",
        "      <td> 2.087838</td>\n",
        "      <td> 0.896266</td>\n",
        "      <td> 1293641.083333</td>\n",
        "      <td> 2196.666667</td>\n",
        "      <td> 8.573596</td>\n",
        "      <td> 61.538462</td>\n",
        "      <td> 3620061</td>\n",
        "      <td> 160.822798</td>\n",
        "      <td> 82.206694</td>\n",
        "      <td> 2166.00</td>\n",
        "      <td> 2232.666667</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11 </th>\n",
        "      <td> 2014-11-24</td>\n",
        "      <td> 2355</td>\n",
        "      <td> 2385</td>\n",
        "      <td> 2305</td>\n",
        "      <td> 2305</td>\n",
        "      <td> 1030740</td>\n",
        "      <td> 2305.00</td>\n",
        "      <td>-1.284797</td>\n",
        "      <td> 22.857143</td>\n",
        "      <td> 1.813253</td>\n",
        "      <td> 0.768939</td>\n",
        "      <td> 1340803.416667</td>\n",
        "      <td> 2176.666667</td>\n",
        "      <td> 5.895865</td>\n",
        "      <td> 53.846154</td>\n",
        "      <td> 4186009</td>\n",
        "      <td> 170.331627</td>\n",
        "      <td> 84.049086</td>\n",
        "      <td> 2150.75</td>\n",
        "      <td> 2234.166667</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12 </th>\n",
        "      <td> 2014-11-21</td>\n",
        "      <td> 2360</td>\n",
        "      <td> 2365</td>\n",
        "      <td> 2295</td>\n",
        "      <td> 2335</td>\n",
        "      <td> 1342325</td>\n",
        "      <td> 2335.00</td>\n",
        "      <td>-0.213675</td>\n",
        "      <td>  9.090909</td>\n",
        "      <td> 1.489583</td>\n",
        "      <td> 0.701068</td>\n",
        "      <td> 1405655.166667</td>\n",
        "      <td> 2152.083333</td>\n",
        "      <td> 8.499516</td>\n",
        "      <td> 53.846154</td>\n",
        "      <td> 3407788</td>\n",
        "      <td> 150.635502</td>\n",
        "      <td> 88.576391</td>\n",
        "      <td> 2133.00</td>\n",
        "      <td> 2237.666667</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13 </th>\n",
        "      <td> 2014-11-20</td>\n",
        "      <td> 2215</td>\n",
        "      <td> 2340</td>\n",
        "      <td> 2215</td>\n",
        "      <td> 2340</td>\n",
        "      <td> 2483543</td>\n",
        "      <td> 2335.00</td>\n",
        "      <td> 6.849315</td>\n",
        "      <td>  0.000000</td>\n",
        "      <td> 1.561856</td>\n",
        "      <td> 0.781362</td>\n",
        "      <td> 1390235.083333</td>\n",
        "      <td> 2131.250000</td>\n",
        "      <td> 9.794721</td>\n",
        "      <td> 53.846154</td>\n",
        "      <td> 3592829</td>\n",
        "      <td> 154.894289</td>\n",
        "      <td> 84.194981</td>\n",
        "      <td> 2117.75</td>\n",
        "      <td> 2241.083333</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14 </th>\n",
        "      <td> 2014-11-19</td>\n",
        "      <td> 2150</td>\n",
        "      <td> 2220</td>\n",
        "      <td> 2125</td>\n",
        "      <td> 2190</td>\n",
        "      <td> 1731021</td>\n",
        "      <td> 2190.00</td>\n",
        "      <td> 2.816901</td>\n",
        "      <td> 14.634146</td>\n",
        "      <td> 1.229075</td>\n",
        "      <td> 0.648208</td>\n",
        "      <td> 1311212.000000</td>\n",
        "      <td> 2116.250000</td>\n",
        "      <td> 3.484938</td>\n",
        "      <td> 53.846154</td>\n",
        "      <td> -425980</td>\n",
        "      <td>  94.728141</td>\n",
        "      <td> 54.664922</td>\n",
        "      <td> 2102.00</td>\n",
        "      <td> 2244.583333</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15 </th>\n",
        "      <td> 2014-11-18</td>\n",
        "      <td> 2130</td>\n",
        "      <td> 2140</td>\n",
        "      <td> 2110</td>\n",
        "      <td> 2130</td>\n",
        "      <td>  564529</td>\n",
        "      <td> 2130.00</td>\n",
        "      <td> 0.709220</td>\n",
        "      <td> 41.860465</td>\n",
        "      <td> 1.198238</td>\n",
        "      <td> 0.641447</td>\n",
        "      <td> 1274055.250000</td>\n",
        "      <td> 2117.916667</td>\n",
        "      <td> 0.570529</td>\n",
        "      <td> 53.846154</td>\n",
        "      <td> -871861</td>\n",
        "      <td>  89.209991</td>\n",
        "      <td> 39.703643</td>\n",
        "      <td> 2095.00</td>\n",
        "      <td> 2249.083333</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16 </th>\n",
        "      <td> 2014-11-17</td>\n",
        "      <td> 2160</td>\n",
        "      <td> 2160</td>\n",
        "      <td> 2105</td>\n",
        "      <td> 2115</td>\n",
        "      <td>  605875</td>\n",
        "      <td> 2115.00</td>\n",
        "      <td>-0.470588</td>\n",
        "      <td> 48.837209</td>\n",
        "      <td> 1.133891</td>\n",
        "      <td> 0.688742</td>\n",
        "      <td> 1297663.083333</td>\n",
        "      <td> 2118.333333</td>\n",
        "      <td>-0.157356</td>\n",
        "      <td> 46.153846</td>\n",
        "      <td> -588567</td>\n",
        "      <td>  92.715991</td>\n",
        "      <td> 38.940173</td>\n",
        "      <td> 2096.00</td>\n",
        "      <td> 2254.416667</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17 </th>\n",
        "      <td> 2014-11-14</td>\n",
        "      <td> 2145</td>\n",
        "      <td> 2160</td>\n",
        "      <td> 2110</td>\n",
        "      <td> 2125</td>\n",
        "      <td>  978747</td>\n",
        "      <td> 2125.00</td>\n",
        "      <td> 0.235849</td>\n",
        "      <td> 46.666667</td>\n",
        "      <td> 1.195745</td>\n",
        "      <td> 0.686275</td>\n",
        "      <td> 1325490.666667</td>\n",
        "      <td> 2116.666667</td>\n",
        "      <td> 0.393701</td>\n",
        "      <td> 53.846154</td>\n",
        "      <td> -922498</td>\n",
        "      <td>  89.036406</td>\n",
        "      <td> 58.101249</td>\n",
        "      <td> 2098.00</td>\n",
        "      <td> 2258.916667</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18 </th>\n",
        "      <td> 2014-11-13</td>\n",
        "      <td> 2085</td>\n",
        "      <td> 2145</td>\n",
        "      <td> 2085</td>\n",
        "      <td> 2120</td>\n",
        "      <td> 1539336</td>\n",
        "      <td> 2120.00</td>\n",
        "      <td> 1.923077</td>\n",
        "      <td> 48.888889</td>\n",
        "      <td> 1.192469</td>\n",
        "      <td> 0.723684</td>\n",
        "      <td> 1384254.500000</td>\n",
        "      <td> 2116.250000</td>\n",
        "      <td> 0.177200</td>\n",
        "      <td> 53.846154</td>\n",
        "      <td> -217332</td>\n",
        "      <td>  97.417079</td>\n",
        "      <td> 56.324827</td>\n",
        "      <td> 2099.75</td>\n",
        "      <td> 2264.166667</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19 </th>\n",
        "      <td> 2014-11-12</td>\n",
        "      <td> 2115</td>\n",
        "      <td> 2130</td>\n",
        "      <td> 2035</td>\n",
        "      <td> 2080</td>\n",
        "      <td> 1465876</td>\n",
        "      <td> 2075.00</td>\n",
        "      <td>-1.886792</td>\n",
        "      <td> 66.666667</td>\n",
        "      <td> 1.066406</td>\n",
        "      <td> 0.653125</td>\n",
        "      <td> 1438014.166667</td>\n",
        "      <td> 2112.916667</td>\n",
        "      <td>-1.557878</td>\n",
        "      <td> 46.153846</td>\n",
        "      <td>  427784</td>\n",
        "      <td> 105.084076</td>\n",
        "      <td> 57.318331</td>\n",
        "      <td> 2109.75</td>\n",
        "      <td> 2269.833333</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>20 </th>\n",
        "      <td> 2014-11-11</td>\n",
        "      <td> 2140</td>\n",
        "      <td> 2170</td>\n",
        "      <td> 2100</td>\n",
        "      <td> 2120</td>\n",
        "      <td> 1188368</td>\n",
        "      <td> 2120.00</td>\n",
        "      <td> 0.236407</td>\n",
        "      <td> 48.888889</td>\n",
        "      <td> 1.164609</td>\n",
        "      <td> 0.675159</td>\n",
        "      <td> 1480802.000000</td>\n",
        "      <td> 2102.083333</td>\n",
        "      <td> 0.852329</td>\n",
        "      <td> 53.846154</td>\n",
        "      <td>  -85670</td>\n",
        "      <td>  99.040397</td>\n",
        "      <td> 56.444339</td>\n",
        "      <td> 2116.50</td>\n",
        "      <td> 2275.083333</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>21 </th>\n",
        "      <td> 2014-11-10</td>\n",
        "      <td> 2155</td>\n",
        "      <td> 2220</td>\n",
        "      <td> 2110</td>\n",
        "      <td> 2115</td>\n",
        "      <td> 1507000</td>\n",
        "      <td> 2115.00</td>\n",
        "      <td>-1.398601</td>\n",
        "      <td> 51.111111</td>\n",
        "      <td> 1.307359</td>\n",
        "      <td> 0.741830</td>\n",
        "      <td> 1492986.333333</td>\n",
        "      <td> 2094.583333</td>\n",
        "      <td> 0.974736</td>\n",
        "      <td> 46.153846</td>\n",
        "      <td>   60542</td>\n",
        "      <td> 100.678141</td>\n",
        "      <td> 65.595582</td>\n",
        "      <td> 2124.25</td>\n",
        "      <td> 2280.500000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>22 </th>\n",
        "      <td> 2014-11-07</td>\n",
        "      <td> 2015</td>\n",
        "      <td> 2150</td>\n",
        "      <td> 2015</td>\n",
        "      <td> 2145</td>\n",
        "      <td> 1652281</td>\n",
        "      <td> 2140.00</td>\n",
        "      <td> 6.716418</td>\n",
        "      <td> 28.813559</td>\n",
        "      <td> 1.310044</td>\n",
        "      <td> 0.769231</td>\n",
        "      <td> 1533952.833333</td>\n",
        "      <td> 2087.083333</td>\n",
        "      <td> 2.775005</td>\n",
        "      <td> 46.153846</td>\n",
        "      <td> -431056</td>\n",
        "      <td>  95.423667</td>\n",
        "      <td> 56.536485</td>\n",
        "      <td> 2132.25</td>\n",
        "      <td> 2285.166667</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>23 </th>\n",
        "      <td> 2014-11-06</td>\n",
        "      <td> 2125</td>\n",
        "      <td> 2140</td>\n",
        "      <td> 2005</td>\n",
        "      <td> 2010</td>\n",
        "      <td> 1808961</td>\n",
        "      <td> 2010.00</td>\n",
        "      <td>-3.597122</td>\n",
        "      <td> 74.576271</td>\n",
        "      <td> 1.243590</td>\n",
        "      <td> 0.732673</td>\n",
        "      <td> 1570399.750000</td>\n",
        "      <td> 2079.166667</td>\n",
        "      <td>-3.326653</td>\n",
        "      <td> 38.461538</td>\n",
        "      <td>-4172981</td>\n",
        "      <td>  63.741235</td>\n",
        "      <td> 50.780466</td>\n",
        "      <td> 2138.75</td>\n",
        "      <td> 2289.833333</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>24 </th>\n",
        "      <td> 2014-11-05</td>\n",
        "      <td> 2190</td>\n",
        "      <td> 2190</td>\n",
        "      <td> 2080</td>\n",
        "      <td> 2085</td>\n",
        "      <td> 1157284</td>\n",
        "      <td> 2080.00</td>\n",
        "      <td>-3.472222</td>\n",
        "      <td> 49.152542</td>\n",
        "      <td> 1.179916</td>\n",
        "      <td> 0.730897</td>\n",
        "      <td> 1524903.833333</td>\n",
        "      <td> 2090.833333</td>\n",
        "      <td>-0.278996</td>\n",
        "      <td> 38.461538</td>\n",
        "      <td>-3627030</td>\n",
        "      <td>  66.915529</td>\n",
        "      <td> 51.512550</td>\n",
        "      <td> 2152.00</td>\n",
        "      <td> 2296.833333</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25 </th>\n",
        "      <td> 2014-11-04</td>\n",
        "      <td> 2210</td>\n",
        "      <td> 2230</td>\n",
        "      <td> 2160</td>\n",
        "      <td> 2160</td>\n",
        "      <td> 1535266</td>\n",
        "      <td> 2160.00</td>\n",
        "      <td>-2.262443</td>\n",
        "      <td> 23.728814</td>\n",
        "      <td> 1.343612</td>\n",
        "      <td> 0.853659</td>\n",
        "      <td> 1639096.500000</td>\n",
        "      <td> 2096.666667</td>\n",
        "      <td> 3.020668</td>\n",
        "      <td> 38.461538</td>\n",
        "      <td>-4997342</td>\n",
        "      <td>  59.480737</td>\n",
        "      <td> 67.057787</td>\n",
        "      <td> 2163.00</td>\n",
        "      <td> 2302.666667</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>26 </th>\n",
        "      <td> 2014-11-03</td>\n",
        "      <td> 2155</td>\n",
        "      <td> 2230</td>\n",
        "      <td> 2140</td>\n",
        "      <td> 2210</td>\n",
        "      <td> 1285140</td>\n",
        "      <td> 2195.00</td>\n",
        "      <td> 3.512881</td>\n",
        "      <td>  6.779661</td>\n",
        "      <td> 1.231405</td>\n",
        "      <td> 0.849315</td>\n",
        "      <td> 1727913.916667</td>\n",
        "      <td> 2096.666667</td>\n",
        "      <td> 5.405405</td>\n",
        "      <td> 46.153846</td>\n",
        "      <td>-6063151</td>\n",
        "      <td>  54.749427</td>\n",
        "      <td> 57.775931</td>\n",
        "      <td> 2173.50</td>\n",
        "      <td> 2306.500000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>27 </th>\n",
        "      <td> 2014-10-31</td>\n",
        "      <td> 2115</td>\n",
        "      <td> 2145</td>\n",
        "      <td> 2115</td>\n",
        "      <td> 2135</td>\n",
        "      <td>  847823</td>\n",
        "      <td> 2135.00</td>\n",
        "      <td> 1.909308</td>\n",
        "      <td> 28.571429</td>\n",
        "      <td> 1.058394</td>\n",
        "      <td> 0.735385</td>\n",
        "      <td> 1773595.000000</td>\n",
        "      <td> 2105.833333</td>\n",
        "      <td> 1.385042</td>\n",
        "      <td> 38.461538</td>\n",
        "      <td>-5514978</td>\n",
        "      <td>  58.840557</td>\n",
        "      <td> 51.204840</td>\n",
        "      <td> 2182.50</td>\n",
        "      <td> 2307.666667</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>28 </th>\n",
        "      <td> 2014-10-30</td>\n",
        "      <td> 2120</td>\n",
        "      <td> 2125</td>\n",
        "      <td> 2085</td>\n",
        "      <td> 2095</td>\n",
        "      <td>  939806</td>\n",
        "      <td> 2095.00</td>\n",
        "      <td>-1.179245</td>\n",
        "      <td> 42.857143</td>\n",
        "      <td> 1.031915</td>\n",
        "      <td> 0.741641</td>\n",
        "      <td> 1873131.833333</td>\n",
        "      <td> 2112.500000</td>\n",
        "      <td>-0.828402</td>\n",
        "      <td> 30.769231</td>\n",
        "      <td>-8405066</td>\n",
        "      <td>  45.567712</td>\n",
        "      <td> 58.226685</td>\n",
        "      <td> 2189.25</td>\n",
        "      <td> 2310.833333</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>29 </th>\n",
        "      <td> 2014-10-29</td>\n",
        "      <td> 2100</td>\n",
        "      <td> 2160</td>\n",
        "      <td> 2085</td>\n",
        "      <td> 2120</td>\n",
        "      <td> 1683913</td>\n",
        "      <td> 2115.00</td>\n",
        "      <td> 1.923077</td>\n",
        "      <td> 48.611111</td>\n",
        "      <td> 1.017422</td>\n",
        "      <td> 0.754545</td>\n",
        "      <td> 1875432.500000</td>\n",
        "      <td> 2127.500000</td>\n",
        "      <td>-0.352526</td>\n",
        "      <td> 30.769231</td>\n",
        "      <td>-7465260</td>\n",
        "      <td>  50.182530</td>\n",
        "      <td> 47.386065</td>\n",
        "      <td> 2195.00</td>\n",
        "      <td> 2314.416667</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>554</th>\n",
        "      <td> 2012-09-12</td>\n",
        "      <td>  647</td>\n",
        "      <td>  659</td>\n",
        "      <td>  645</td>\n",
        "      <td>  651</td>\n",
        "      <td> 1816815</td>\n",
        "      <td>  651.00</td>\n",
        "      <td> 1.401869</td>\n",
        "      <td> 16.666667</td>\n",
        "      <td> 1.818182</td>\n",
        "      <td> 1.368056</td>\n",
        "      <td> 1431117.166667</td>\n",
        "      <td>  636.166667</td>\n",
        "      <td> 2.331674</td>\n",
        "      <td> 69.230769</td>\n",
        "      <td> 6457200</td>\n",
        "      <td> 220.512801</td>\n",
        "      <td> 32.253303</td>\n",
        "      <td>  628.85</td>\n",
        "      <td>         NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>555</th>\n",
        "      <td> 2012-09-11</td>\n",
        "      <td>  655</td>\n",
        "      <td>  655</td>\n",
        "      <td>  641</td>\n",
        "      <td>  642</td>\n",
        "      <td>  960640</td>\n",
        "      <td>  642.00</td>\n",
        "      <td>-1.834862</td>\n",
        "      <td> 35.416667</td>\n",
        "      <td> 1.731707</td>\n",
        "      <td> 1.366197</td>\n",
        "      <td> 1565427.166667</td>\n",
        "      <td>  635.000000</td>\n",
        "      <td> 1.102362</td>\n",
        "      <td> 69.230769</td>\n",
        "      <td> 8068920</td>\n",
        "      <td> 250.592850</td>\n",
        "      <td> 32.506709</td>\n",
        "      <td>  626.70</td>\n",
        "      <td>         NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>556</th>\n",
        "      <td> 2012-09-10</td>\n",
        "      <td>  651</td>\n",
        "      <td>  654</td>\n",
        "      <td>  647</td>\n",
        "      <td>  654</td>\n",
        "      <td>  957356</td>\n",
        "      <td>  652.00</td>\n",
        "      <td> 1.710731</td>\n",
        "      <td> 10.416667</td>\n",
        "      <td> 1.571429</td>\n",
        "      <td> 1.168675</td>\n",
        "      <td> 1558444.000000</td>\n",
        "      <td>  632.833333</td>\n",
        "      <td> 3.344746</td>\n",
        "      <td> 69.230769</td>\n",
        "      <td> 9906402</td>\n",
        "      <td> 325.275392</td>\n",
        "      <td> 49.819844</td>\n",
        "      <td>  625.30</td>\n",
        "      <td>         NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>557</th>\n",
        "      <td> 2012-09-07</td>\n",
        "      <td>  657</td>\n",
        "      <td>  659</td>\n",
        "      <td>  643</td>\n",
        "      <td>  643</td>\n",
        "      <td> 1540887</td>\n",
        "      <td>  643.00</td>\n",
        "      <td> 0.155763</td>\n",
        "      <td> 33.333333</td>\n",
        "      <td> 1.339623</td>\n",
        "      <td> 1.055249</td>\n",
        "      <td> 1523994.916667</td>\n",
        "      <td>  629.500000</td>\n",
        "      <td> 2.144559</td>\n",
        "      <td> 61.538462</td>\n",
        "      <td> 8949046</td>\n",
        "      <td> 291.651109</td>\n",
        "      <td> 44.308368</td>\n",
        "      <td>  623.10</td>\n",
        "      <td>         NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>558</th>\n",
        "      <td> 2012-09-06</td>\n",
        "      <td>  650</td>\n",
        "      <td>  659</td>\n",
        "      <td>  642</td>\n",
        "      <td>  642</td>\n",
        "      <td> 2916863</td>\n",
        "      <td>  642.00</td>\n",
        "      <td> 0.469484</td>\n",
        "      <td> 35.416667</td>\n",
        "      <td>      NaN</td>\n",
        "      <td> 1.205882</td>\n",
        "      <td> 1483828.916667</td>\n",
        "      <td>  627.083333</td>\n",
        "      <td> 2.378738</td>\n",
        "      <td> 53.846154</td>\n",
        "      <td> 6349264</td>\n",
        "      <td> 210.839481</td>\n",
        "      <td> 58.139092</td>\n",
        "      <td>  621.00</td>\n",
        "      <td>         NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>559</th>\n",
        "      <td> 2012-09-05</td>\n",
        "      <td>  636</td>\n",
        "      <td>  639</td>\n",
        "      <td>  630</td>\n",
        "      <td>  639</td>\n",
        "      <td>  960852</td>\n",
        "      <td>  638.00</td>\n",
        "      <td> 0.629921</td>\n",
        "      <td> 17.948718</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td> 1311019.416667</td>\n",
        "      <td>  624.833333</td>\n",
        "      <td> 2.267271</td>\n",
        "      <td> 46.153846</td>\n",
        "      <td> 3432401</td>\n",
        "      <td> 155.812161</td>\n",
        "      <td> 59.017498</td>\n",
        "      <td>  619.35</td>\n",
        "      <td>         NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>560</th>\n",
        "      <td> 2012-09-04</td>\n",
        "      <td>  643</td>\n",
        "      <td>  645</td>\n",
        "      <td>  629</td>\n",
        "      <td>  635</td>\n",
        "      <td> 1688727</td>\n",
        "      <td>  634.00</td>\n",
        "      <td>-1.244168</td>\n",
        "      <td> 28.205128</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td> 1293946.250000</td>\n",
        "      <td>  622.833333</td>\n",
        "      <td> 1.953439</td>\n",
        "      <td> 38.461538</td>\n",
        "      <td> 2471549</td>\n",
        "      <td> 137.861301</td>\n",
        "      <td> 48.812911</td>\n",
        "      <td>  617.30</td>\n",
        "      <td>         NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>561</th>\n",
        "      <td> 2012-09-03</td>\n",
        "      <td>  624</td>\n",
        "      <td>  644</td>\n",
        "      <td>  618</td>\n",
        "      <td>  643</td>\n",
        "      <td> 1818053</td>\n",
        "      <td>  643.00</td>\n",
        "      <td> 3.210273</td>\n",
        "      <td>  7.142857</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td> 1277915.250000</td>\n",
        "      <td>  621.166667</td>\n",
        "      <td> 3.514891</td>\n",
        "      <td> 46.153846</td>\n",
        "      <td> 2663921</td>\n",
        "      <td> 142.047320</td>\n",
        "      <td> 53.950377</td>\n",
        "      <td>  615.40</td>\n",
        "      <td>         NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>562</th>\n",
        "      <td> 2012-08-31</td>\n",
        "      <td>  612</td>\n",
        "      <td>  623</td>\n",
        "      <td>  611</td>\n",
        "      <td>  623</td>\n",
        "      <td>  919990</td>\n",
        "      <td>  622.00</td>\n",
        "      <td> 0.483871</td>\n",
        "      <td> 54.761905</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td> 1206406.083333</td>\n",
        "      <td>  619.000000</td>\n",
        "      <td> 0.646204</td>\n",
        "      <td> 38.461538</td>\n",
        "      <td> 1805811</td>\n",
        "      <td> 128.502915</td>\n",
        "      <td> 42.379114</td>\n",
        "      <td>  612.85</td>\n",
        "      <td>         NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>563</th>\n",
        "      <td> 2012-08-30</td>\n",
        "      <td>  619</td>\n",
        "      <td>  622</td>\n",
        "      <td>  616</td>\n",
        "      <td>  620</td>\n",
        "      <td>  792807</td>\n",
        "      <td>  620.00</td>\n",
        "      <td>-0.481541</td>\n",
        "      <td> 61.904762</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td> 1213885.083333</td>\n",
        "      <td>  617.750000</td>\n",
        "      <td> 0.364225</td>\n",
        "      <td> 38.461538</td>\n",
        "      <td> -123917</td>\n",
        "      <td>  98.312969</td>\n",
        "      <td> 46.896247</td>\n",
        "      <td>  612.70</td>\n",
        "      <td>         NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>564</th>\n",
        "      <td> 2012-08-29</td>\n",
        "      <td>  619</td>\n",
        "      <td>  624</td>\n",
        "      <td>  613</td>\n",
        "      <td>  623</td>\n",
        "      <td>  884487</td>\n",
        "      <td>  622.00</td>\n",
        "      <td> 0.646204</td>\n",
        "      <td> 54.761905</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td> 1300819.000000</td>\n",
        "      <td>  617.250000</td>\n",
        "      <td> 0.931551</td>\n",
        "      <td> 46.153846</td>\n",
        "      <td> 2504904</td>\n",
        "      <td> 138.228440</td>\n",
        "      <td> 49.427466</td>\n",
        "      <td>  612.85</td>\n",
        "      <td>         NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>565</th>\n",
        "      <td> 2012-08-28</td>\n",
        "      <td>  633</td>\n",
        "      <td>  639</td>\n",
        "      <td>  618</td>\n",
        "      <td>  619</td>\n",
        "      <td> 1915929</td>\n",
        "      <td>  619.00</td>\n",
        "      <td>-2.825746</td>\n",
        "      <td> 64.285714</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td> 1323282.000000</td>\n",
        "      <td>  616.166667</td>\n",
        "      <td> 0.459832</td>\n",
        "      <td> 38.461538</td>\n",
        "      <td> 2774460</td>\n",
        "      <td> 142.342252</td>\n",
        "      <td> 55.551521</td>\n",
        "      <td>     NaN</td>\n",
        "      <td>         NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>566</th>\n",
        "      <td> 2012-08-27</td>\n",
        "      <td>  624</td>\n",
        "      <td>  646</td>\n",
        "      <td>  622</td>\n",
        "      <td>  637</td>\n",
        "      <td> 3428535</td>\n",
        "      <td>  637.00</td>\n",
        "      <td> 3.409091</td>\n",
        "      <td> 21.428571</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td> 1351032.333333</td>\n",
        "      <td>  614.666667</td>\n",
        "      <td> 3.633406</td>\n",
        "      <td> 46.153846</td>\n",
        "      <td> 2441456</td>\n",
        "      <td> 135.458108</td>\n",
        "      <td> 91.990121</td>\n",
        "      <td>     NaN</td>\n",
        "      <td>         NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>567</th>\n",
        "      <td> 2012-08-24</td>\n",
        "      <td>  609</td>\n",
        "      <td>  620</td>\n",
        "      <td>  607</td>\n",
        "      <td>  616</td>\n",
        "      <td>  876842</td>\n",
        "      <td>  615.00</td>\n",
        "      <td> 0.325733</td>\n",
        "      <td> 47.826087</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td> 1392435.250000</td>\n",
        "      <td>  612.333333</td>\n",
        "      <td> 0.598802</td>\n",
        "      <td> 46.153846</td>\n",
        "      <td> 2938291</td>\n",
        "      <td> 142.673815</td>\n",
        "      <td> 57.235665</td>\n",
        "      <td>     NaN</td>\n",
        "      <td>         NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>568</th>\n",
        "      <td> 2012-08-23</td>\n",
        "      <td>  612</td>\n",
        "      <td>  614</td>\n",
        "      <td>  608</td>\n",
        "      <td>  614</td>\n",
        "      <td>  543967</td>\n",
        "      <td>  613.00</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 50.000000</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td> 1454304.916667</td>\n",
        "      <td>  610.833333</td>\n",
        "      <td> 0.518417</td>\n",
        "      <td> 46.153846</td>\n",
        "      <td> 3680727</td>\n",
        "      <td> 153.456469</td>\n",
        "      <td> 85.880139</td>\n",
        "      <td>     NaN</td>\n",
        "      <td>         NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>569</th>\n",
        "      <td> 2012-08-22</td>\n",
        "      <td>  613</td>\n",
        "      <td>  615</td>\n",
        "      <td>  604</td>\n",
        "      <td>  614</td>\n",
        "      <td> 1058895</td>\n",
        "      <td>  612.00</td>\n",
        "      <td>-0.162602</td>\n",
        "      <td> 46.428571</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td> 1477309.333333</td>\n",
        "      <td>  609.416667</td>\n",
        "      <td> 0.752085</td>\n",
        "      <td> 46.153846</td>\n",
        "      <td> 4500747</td>\n",
        "      <td> 168.054115</td>\n",
        "      <td> 75.207211</td>\n",
        "      <td>     NaN</td>\n",
        "      <td>         NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>570</th>\n",
        "      <td> 2012-08-21</td>\n",
        "      <td>  615</td>\n",
        "      <td>  620</td>\n",
        "      <td>  615</td>\n",
        "      <td>  615</td>\n",
        "      <td>  843149</td>\n",
        "      <td>  615.00</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 30.769231</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td> 1521128.166667</td>\n",
        "      <td>  607.583333</td>\n",
        "      <td> 1.220683</td>\n",
        "      <td> 46.153846</td>\n",
        "      <td> 3974921</td>\n",
        "      <td> 155.676555</td>\n",
        "      <td> 62.140943</td>\n",
        "      <td>     NaN</td>\n",
        "      <td>         NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>571</th>\n",
        "      <td> 2012-08-20</td>\n",
        "      <td>  615</td>\n",
        "      <td>  622</td>\n",
        "      <td>  612</td>\n",
        "      <td>  615</td>\n",
        "      <td>  755974</td>\n",
        "      <td>  614.00</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 30.769231</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td> 1639213.750000</td>\n",
        "      <td>  608.000000</td>\n",
        "      <td> 1.151316</td>\n",
        "      <td>       NaN</td>\n",
        "      <td> 1714745</td>\n",
        "      <td> 119.099601</td>\n",
        "      <td> 65.052146</td>\n",
        "      <td>     NaN</td>\n",
        "      <td>         NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>572</th>\n",
        "      <td> 2012-08-17</td>\n",
        "      <td>  616</td>\n",
        "      <td>  622</td>\n",
        "      <td>  612</td>\n",
        "      <td>  615</td>\n",
        "      <td> 1496355</td>\n",
        "      <td>  615.00</td>\n",
        "      <td>-0.324149</td>\n",
        "      <td> 30.769231</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td> 1778671.666667</td>\n",
        "      <td>  608.666667</td>\n",
        "      <td> 1.040526</td>\n",
        "      <td>       NaN</td>\n",
        "      <td>     NaN</td>\n",
        "      <td>        NaN</td>\n",
        "      <td> 58.150206</td>\n",
        "      <td>     NaN</td>\n",
        "      <td>         NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>573</th>\n",
        "      <td> 2012-08-16</td>\n",
        "      <td>  614</td>\n",
        "      <td>  617</td>\n",
        "      <td>  609</td>\n",
        "      <td>  617</td>\n",
        "      <td>  959943</td>\n",
        "      <td>  616.00</td>\n",
        "      <td> 1.480263</td>\n",
        "      <td> 27.500000</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>            NaN</td>\n",
        "      <td>         NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>       NaN</td>\n",
        "      <td>     NaN</td>\n",
        "      <td>        NaN</td>\n",
        "      <td> 54.432362</td>\n",
        "      <td>     NaN</td>\n",
        "      <td>         NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>574</th>\n",
        "      <td> 2012-08-15</td>\n",
        "      <td>  620</td>\n",
        "      <td>  620</td>\n",
        "      <td>  607</td>\n",
        "      <td>  608</td>\n",
        "      <td> 1009738</td>\n",
        "      <td>  608.00</td>\n",
        "      <td>-0.977199</td>\n",
        "      <td> 50.000000</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>            NaN</td>\n",
        "      <td>         NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>       NaN</td>\n",
        "      <td>     NaN</td>\n",
        "      <td>        NaN</td>\n",
        "      <td> 47.589506</td>\n",
        "      <td>     NaN</td>\n",
        "      <td>         NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>575</th>\n",
        "      <td> 2012-08-14</td>\n",
        "      <td>  615</td>\n",
        "      <td>  627</td>\n",
        "      <td>  613</td>\n",
        "      <td>  614</td>\n",
        "      <td> 1836014</td>\n",
        "      <td>  614.00</td>\n",
        "      <td> 0.655738</td>\n",
        "      <td> 38.095238</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>            NaN</td>\n",
        "      <td>         NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>       NaN</td>\n",
        "      <td>     NaN</td>\n",
        "      <td>        NaN</td>\n",
        "      <td> 43.245720</td>\n",
        "      <td>     NaN</td>\n",
        "      <td>         NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>576</th>\n",
        "      <td> 2012-08-13</td>\n",
        "      <td>  607</td>\n",
        "      <td>  611</td>\n",
        "      <td>  601</td>\n",
        "      <td>  610</td>\n",
        "      <td> 1154043</td>\n",
        "      <td>  607.00</td>\n",
        "      <td> 1.497504</td>\n",
        "      <td>       NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>            NaN</td>\n",
        "      <td>         NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>       NaN</td>\n",
        "      <td>     NaN</td>\n",
        "      <td>        NaN</td>\n",
        "      <td> 27.167099</td>\n",
        "      <td>     NaN</td>\n",
        "      <td>         NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>577</th>\n",
        "      <td> 2012-08-10</td>\n",
        "      <td>  599</td>\n",
        "      <td>  610</td>\n",
        "      <td>  599</td>\n",
        "      <td>  601</td>\n",
        "      <td> 2248933</td>\n",
        "      <td>  601.00</td>\n",
        "      <td>-1.313629</td>\n",
        "      <td>       NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>            NaN</td>\n",
        "      <td>         NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>       NaN</td>\n",
        "      <td>     NaN</td>\n",
        "      <td>        NaN</td>\n",
        "      <td> 31.120882</td>\n",
        "      <td>     NaN</td>\n",
        "      <td>         NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>578</th>\n",
        "      <td> 2012-08-09</td>\n",
        "      <td>  589</td>\n",
        "      <td>  615</td>\n",
        "      <td>  588</td>\n",
        "      <td>  609</td>\n",
        "      <td> 3925370</td>\n",
        "      <td>  609.00</td>\n",
        "      <td> 1.839465</td>\n",
        "      <td>       NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>            NaN</td>\n",
        "      <td>         NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>       NaN</td>\n",
        "      <td>     NaN</td>\n",
        "      <td>        NaN</td>\n",
        "      <td>       NaN</td>\n",
        "      <td>     NaN</td>\n",
        "      <td>         NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>579</th>\n",
        "      <td> 2012-08-08</td>\n",
        "      <td>  592</td>\n",
        "      <td>  602</td>\n",
        "      <td>  592</td>\n",
        "      <td>  598</td>\n",
        "      <td> 1619278</td>\n",
        "      <td>  597.00</td>\n",
        "      <td> 0.167504</td>\n",
        "      <td>       NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>            NaN</td>\n",
        "      <td>         NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>       NaN</td>\n",
        "      <td>     NaN</td>\n",
        "      <td>        NaN</td>\n",
        "      <td>       NaN</td>\n",
        "      <td>     NaN</td>\n",
        "      <td>         NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>580</th>\n",
        "      <td> 2012-08-07</td>\n",
        "      <td>  593</td>\n",
        "      <td>  602</td>\n",
        "      <td>  593</td>\n",
        "      <td>  597</td>\n",
        "      <td>  820020</td>\n",
        "      <td>  596.00</td>\n",
        "      <td> 0.844595</td>\n",
        "      <td>       NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>            NaN</td>\n",
        "      <td>         NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>       NaN</td>\n",
        "      <td>     NaN</td>\n",
        "      <td>        NaN</td>\n",
        "      <td>       NaN</td>\n",
        "      <td>     NaN</td>\n",
        "      <td>         NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>581</th>\n",
        "      <td> 2012-08-06</td>\n",
        "      <td>  628</td>\n",
        "      <td>  628</td>\n",
        "      <td>  590</td>\n",
        "      <td>  592</td>\n",
        "      <td> 1584721</td>\n",
        "      <td>  592.00</td>\n",
        "      <td>-4.516129</td>\n",
        "      <td>       NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>            NaN</td>\n",
        "      <td>         NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>       NaN</td>\n",
        "      <td>     NaN</td>\n",
        "      <td>        NaN</td>\n",
        "      <td>       NaN</td>\n",
        "      <td>     NaN</td>\n",
        "      <td>         NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>582</th>\n",
        "      <td> 2012-08-03</td>\n",
        "      <td>  627</td>\n",
        "      <td>  627</td>\n",
        "      <td>  608</td>\n",
        "      <td>  620</td>\n",
        "      <td> 2260176</td>\n",
        "      <td>  619.00</td>\n",
        "      <td>-0.481541</td>\n",
        "      <td>       NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>            NaN</td>\n",
        "      <td>         NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>       NaN</td>\n",
        "      <td>     NaN</td>\n",
        "      <td>        NaN</td>\n",
        "      <td>       NaN</td>\n",
        "      <td>     NaN</td>\n",
        "      <td>         NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>583</th>\n",
        "      <td> 2012-08-01</td>\n",
        "      <td>  614</td>\n",
        "      <td>  630</td>\n",
        "      <td>  611</td>\n",
        "      <td>  623</td>\n",
        "      <td> 2429469</td>\n",
        "      <td>  622.00</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>       NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>            NaN</td>\n",
        "      <td>         NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>       NaN</td>\n",
        "      <td>     NaN</td>\n",
        "      <td>        NaN</td>\n",
        "      <td>       NaN</td>\n",
        "      <td>     NaN</td>\n",
        "      <td>         NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>584 rows \u00d7 20 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "           Date  Open  High   Low  Close   Volume Adj Close        RR  \\\n",
        "0    2014-12-09  2265  2300  2235   2290  1137524   2290.00  0.218818   \n",
        "1    2014-12-08  2375  2395  2275   2285  1389389   2285.00 -3.586498   \n",
        "2    2014-12-05  2400  2430  2340   2370  1059384   2360.00 -1.250000   \n",
        "3    2014-12-04  2405  2440  2395   2400   831362   2400.00  0.840336   \n",
        "4    2014-12-03  2410  2440  2375   2380  1422947   2380.00  0.210526   \n",
        "5    2014-12-02  2445  2445  2365   2375  1278528   2375.00 -3.651116   \n",
        "6    2014-12-01  2320  2465  2320   2465  1362046   2460.00  3.354298   \n",
        "7    2014-11-28  2395  2410  2375   2385   743512   2385.00  0.421053   \n",
        "8    2014-11-27  2440  2475  2370   2375  1411415   2375.00 -2.061856   \n",
        "9    2014-11-26  2385  2440  2365   2425  1772042   2420.00  1.677149   \n",
        "10   2014-11-25  2320  2385  2295   2385  1086333   2380.00  3.470716   \n",
        "11   2014-11-24  2355  2385  2305   2305  1030740   2305.00 -1.284797   \n",
        "12   2014-11-21  2360  2365  2295   2335  1342325   2335.00 -0.213675   \n",
        "13   2014-11-20  2215  2340  2215   2340  2483543   2335.00  6.849315   \n",
        "14   2014-11-19  2150  2220  2125   2190  1731021   2190.00  2.816901   \n",
        "15   2014-11-18  2130  2140  2110   2130   564529   2130.00  0.709220   \n",
        "16   2014-11-17  2160  2160  2105   2115   605875   2115.00 -0.470588   \n",
        "17   2014-11-14  2145  2160  2110   2125   978747   2125.00  0.235849   \n",
        "18   2014-11-13  2085  2145  2085   2120  1539336   2120.00  1.923077   \n",
        "19   2014-11-12  2115  2130  2035   2080  1465876   2075.00 -1.886792   \n",
        "20   2014-11-11  2140  2170  2100   2120  1188368   2120.00  0.236407   \n",
        "21   2014-11-10  2155  2220  2110   2115  1507000   2115.00 -1.398601   \n",
        "22   2014-11-07  2015  2150  2015   2145  1652281   2140.00  6.716418   \n",
        "23   2014-11-06  2125  2140  2005   2010  1808961   2010.00 -3.597122   \n",
        "24   2014-11-05  2190  2190  2080   2085  1157284   2080.00 -3.472222   \n",
        "25   2014-11-04  2210  2230  2160   2160  1535266   2160.00 -2.262443   \n",
        "26   2014-11-03  2155  2230  2140   2210  1285140   2195.00  3.512881   \n",
        "27   2014-10-31  2115  2145  2115   2135   847823   2135.00  1.909308   \n",
        "28   2014-10-30  2120  2125  2085   2095   939806   2095.00 -1.179245   \n",
        "29   2014-10-29  2100  2160  2085   2120  1683913   2115.00  1.923077   \n",
        "..          ...   ...   ...   ...    ...      ...       ...       ...   \n",
        "554  2012-09-12   647   659   645    651  1816815    651.00  1.401869   \n",
        "555  2012-09-11   655   655   641    642   960640    642.00 -1.834862   \n",
        "556  2012-09-10   651   654   647    654   957356    652.00  1.710731   \n",
        "557  2012-09-07   657   659   643    643  1540887    643.00  0.155763   \n",
        "558  2012-09-06   650   659   642    642  2916863    642.00  0.469484   \n",
        "559  2012-09-05   636   639   630    639   960852    638.00  0.629921   \n",
        "560  2012-09-04   643   645   629    635  1688727    634.00 -1.244168   \n",
        "561  2012-09-03   624   644   618    643  1818053    643.00  3.210273   \n",
        "562  2012-08-31   612   623   611    623   919990    622.00  0.483871   \n",
        "563  2012-08-30   619   622   616    620   792807    620.00 -0.481541   \n",
        "564  2012-08-29   619   624   613    623   884487    622.00  0.646204   \n",
        "565  2012-08-28   633   639   618    619  1915929    619.00 -2.825746   \n",
        "566  2012-08-27   624   646   622    637  3428535    637.00  3.409091   \n",
        "567  2012-08-24   609   620   607    616   876842    615.00  0.325733   \n",
        "568  2012-08-23   612   614   608    614   543967    613.00  0.000000   \n",
        "569  2012-08-22   613   615   604    614  1058895    612.00 -0.162602   \n",
        "570  2012-08-21   615   620   615    615   843149    615.00  0.000000   \n",
        "571  2012-08-20   615   622   612    615   755974    614.00  0.000000   \n",
        "572  2012-08-17   616   622   612    615  1496355    615.00 -0.324149   \n",
        "573  2012-08-16   614   617   609    617   959943    616.00  1.480263   \n",
        "574  2012-08-15   620   620   607    608  1009738    608.00 -0.977199   \n",
        "575  2012-08-14   615   627   613    614  1836014    614.00  0.655738   \n",
        "576  2012-08-13   607   611   601    610  1154043    607.00  1.497504   \n",
        "577  2012-08-10   599   610   599    601  2248933    601.00 -1.313629   \n",
        "578  2012-08-09   589   615   588    609  3925370    609.00  1.839465   \n",
        "579  2012-08-08   592   602   592    598  1619278    597.00  0.167504   \n",
        "580  2012-08-07   593   602   593    597   820020    596.00  0.844595   \n",
        "581  2012-08-06   628   628   590    592  1584721    592.00 -4.516129   \n",
        "582  2012-08-03   627   627   608    620  2260176    619.00 -0.481541   \n",
        "583  2012-08-01   614   630   611    623  2429469    622.00       NaN   \n",
        "\n",
        "           WMS        BR        AR              MV         MA12      BIAS  \\\n",
        "0    77.083333  1.549133  0.925764  1210435.166667  2370.000000 -3.375527   \n",
        "1    95.000000  1.753086  0.973451  1227501.916667  2373.750000 -3.738810   \n",
        "2    58.333333  2.079137  1.077670  1318681.416667  2378.333333 -0.350385   \n",
        "3    41.666667  2.119403  1.079602  1374651.166667  2363.333333  1.551481   \n",
        "4    52.777778  2.117647  1.099010  1352415.083333  2340.833333  1.673193   \n",
        "5    38.461538  2.172662  1.130435  1284325.750000  2318.750000  2.425876   \n",
        "6     2.857143  2.311111  1.118483  1259344.000000  2297.916667  7.271079   \n",
        "7    24.657534  2.412698  0.954545  1274118.166667  2269.166667  5.104664   \n",
        "8    27.027027  2.298507  1.018265  1334315.166667  2243.750000  5.849582   \n",
        "9     4.477612  2.174825  0.931915  1315727.916667  2222.500000  9.111361   \n",
        "10    0.000000  2.087838  0.896266  1293641.083333  2196.666667  8.573596   \n",
        "11   22.857143  1.813253  0.768939  1340803.416667  2176.666667  5.895865   \n",
        "12    9.090909  1.489583  0.701068  1405655.166667  2152.083333  8.499516   \n",
        "13    0.000000  1.561856  0.781362  1390235.083333  2131.250000  9.794721   \n",
        "14   14.634146  1.229075  0.648208  1311212.000000  2116.250000  3.484938   \n",
        "15   41.860465  1.198238  0.641447  1274055.250000  2117.916667  0.570529   \n",
        "16   48.837209  1.133891  0.688742  1297663.083333  2118.333333 -0.157356   \n",
        "17   46.666667  1.195745  0.686275  1325490.666667  2116.666667  0.393701   \n",
        "18   48.888889  1.192469  0.723684  1384254.500000  2116.250000  0.177200   \n",
        "19   66.666667  1.066406  0.653125  1438014.166667  2112.916667 -1.557878   \n",
        "20   48.888889  1.164609  0.675159  1480802.000000  2102.083333  0.852329   \n",
        "21   51.111111  1.307359  0.741830  1492986.333333  2094.583333  0.974736   \n",
        "22   28.813559  1.310044  0.769231  1533952.833333  2087.083333  2.775005   \n",
        "23   74.576271  1.243590  0.732673  1570399.750000  2079.166667 -3.326653   \n",
        "24   49.152542  1.179916  0.730897  1524903.833333  2090.833333 -0.278996   \n",
        "25   23.728814  1.343612  0.853659  1639096.500000  2096.666667  3.020668   \n",
        "26    6.779661  1.231405  0.849315  1727913.916667  2096.666667  5.405405   \n",
        "27   28.571429  1.058394  0.735385  1773595.000000  2105.833333  1.385042   \n",
        "28   42.857143  1.031915  0.741641  1873131.833333  2112.500000 -0.828402   \n",
        "29   48.611111  1.017422  0.754545  1875432.500000  2127.500000 -0.352526   \n",
        "..         ...       ...       ...             ...          ...       ...   \n",
        "554  16.666667  1.818182  1.368056  1431117.166667   636.166667  2.331674   \n",
        "555  35.416667  1.731707  1.366197  1565427.166667   635.000000  1.102362   \n",
        "556  10.416667  1.571429  1.168675  1558444.000000   632.833333  3.344746   \n",
        "557  33.333333  1.339623  1.055249  1523994.916667   629.500000  2.144559   \n",
        "558  35.416667       NaN  1.205882  1483828.916667   627.083333  2.378738   \n",
        "559  17.948718       NaN       NaN  1311019.416667   624.833333  2.267271   \n",
        "560  28.205128       NaN       NaN  1293946.250000   622.833333  1.953439   \n",
        "561   7.142857       NaN       NaN  1277915.250000   621.166667  3.514891   \n",
        "562  54.761905       NaN       NaN  1206406.083333   619.000000  0.646204   \n",
        "563  61.904762       NaN       NaN  1213885.083333   617.750000  0.364225   \n",
        "564  54.761905       NaN       NaN  1300819.000000   617.250000  0.931551   \n",
        "565  64.285714       NaN       NaN  1323282.000000   616.166667  0.459832   \n",
        "566  21.428571       NaN       NaN  1351032.333333   614.666667  3.633406   \n",
        "567  47.826087       NaN       NaN  1392435.250000   612.333333  0.598802   \n",
        "568  50.000000       NaN       NaN  1454304.916667   610.833333  0.518417   \n",
        "569  46.428571       NaN       NaN  1477309.333333   609.416667  0.752085   \n",
        "570  30.769231       NaN       NaN  1521128.166667   607.583333  1.220683   \n",
        "571  30.769231       NaN       NaN  1639213.750000   608.000000  1.151316   \n",
        "572  30.769231       NaN       NaN  1778671.666667   608.666667  1.040526   \n",
        "573  27.500000       NaN       NaN             NaN          NaN       NaN   \n",
        "574  50.000000       NaN       NaN             NaN          NaN       NaN   \n",
        "575  38.095238       NaN       NaN             NaN          NaN       NaN   \n",
        "576        NaN       NaN       NaN             NaN          NaN       NaN   \n",
        "577        NaN       NaN       NaN             NaN          NaN       NaN   \n",
        "578        NaN       NaN       NaN             NaN          NaN       NaN   \n",
        "579        NaN       NaN       NaN             NaN          NaN       NaN   \n",
        "580        NaN       NaN       NaN             NaN          NaN       NaN   \n",
        "581        NaN       NaN       NaN             NaN          NaN       NaN   \n",
        "582        NaN       NaN       NaN             NaN          NaN       NaN   \n",
        "583        NaN       NaN       NaN             NaN          NaN       NaN   \n",
        "\n",
        "           PSY      OBV          VR        RSI     MA20         MA60  \n",
        "0    53.846154  2186310  135.437646  13.012632  2293.75  2228.500000  \n",
        "1    53.846154  -293539   96.092285  34.167668  2285.25  2228.333333  \n",
        "2    61.538462  3579393  158.463963  32.991976  2276.75  2227.750000  \n",
        "3    69.230769  6369798  225.810546  29.695832  2265.50  2228.250000  \n",
        "4    61.538462  6102965  220.540299  33.138487  2246.00  2229.000000  \n",
        "5    61.538462  4074143  171.868532  43.850538  2231.25  2230.000000  \n",
        "6    69.230769  6331418  244.211983  57.139589  2220.50  2230.666667  \n",
        "7    61.538462  6508708  248.250153  61.000854  2207.75  2230.416667  \n",
        "8    61.538462  4299320  173.414454  77.115052  2195.25  2230.750000  \n",
        "9    61.538462  6899103  255.216841  83.173660  2181.25  2231.250000  \n",
        "10   61.538462  3620061  160.822798  82.206694  2166.00  2232.666667  \n",
        "11   53.846154  4186009  170.331627  84.049086  2150.75  2234.166667  \n",
        "12   53.846154  3407788  150.635502  88.576391  2133.00  2237.666667  \n",
        "13   53.846154  3592829  154.894289  84.194981  2117.75  2241.083333  \n",
        "14   53.846154  -425980   94.728141  54.664922  2102.00  2244.583333  \n",
        "15   53.846154  -871861   89.209991  39.703643  2095.00  2249.083333  \n",
        "16   46.153846  -588567   92.715991  38.940173  2096.00  2254.416667  \n",
        "17   53.846154  -922498   89.036406  58.101249  2098.00  2258.916667  \n",
        "18   53.846154  -217332   97.417079  56.324827  2099.75  2264.166667  \n",
        "19   46.153846   427784  105.084076  57.318331  2109.75  2269.833333  \n",
        "20   53.846154   -85670   99.040397  56.444339  2116.50  2275.083333  \n",
        "21   46.153846    60542  100.678141  65.595582  2124.25  2280.500000  \n",
        "22   46.153846  -431056   95.423667  56.536485  2132.25  2285.166667  \n",
        "23   38.461538 -4172981   63.741235  50.780466  2138.75  2289.833333  \n",
        "24   38.461538 -3627030   66.915529  51.512550  2152.00  2296.833333  \n",
        "25   38.461538 -4997342   59.480737  67.057787  2163.00  2302.666667  \n",
        "26   46.153846 -6063151   54.749427  57.775931  2173.50  2306.500000  \n",
        "27   38.461538 -5514978   58.840557  51.204840  2182.50  2307.666667  \n",
        "28   30.769231 -8405066   45.567712  58.226685  2189.25  2310.833333  \n",
        "29   30.769231 -7465260   50.182530  47.386065  2195.00  2314.416667  \n",
        "..         ...      ...         ...        ...      ...          ...  \n",
        "554  69.230769  6457200  220.512801  32.253303   628.85          NaN  \n",
        "555  69.230769  8068920  250.592850  32.506709   626.70          NaN  \n",
        "556  69.230769  9906402  325.275392  49.819844   625.30          NaN  \n",
        "557  61.538462  8949046  291.651109  44.308368   623.10          NaN  \n",
        "558  53.846154  6349264  210.839481  58.139092   621.00          NaN  \n",
        "559  46.153846  3432401  155.812161  59.017498   619.35          NaN  \n",
        "560  38.461538  2471549  137.861301  48.812911   617.30          NaN  \n",
        "561  46.153846  2663921  142.047320  53.950377   615.40          NaN  \n",
        "562  38.461538  1805811  128.502915  42.379114   612.85          NaN  \n",
        "563  38.461538  -123917   98.312969  46.896247   612.70          NaN  \n",
        "564  46.153846  2504904  138.228440  49.427466   612.85          NaN  \n",
        "565  38.461538  2774460  142.342252  55.551521      NaN          NaN  \n",
        "566  46.153846  2441456  135.458108  91.990121      NaN          NaN  \n",
        "567  46.153846  2938291  142.673815  57.235665      NaN          NaN  \n",
        "568  46.153846  3680727  153.456469  85.880139      NaN          NaN  \n",
        "569  46.153846  4500747  168.054115  75.207211      NaN          NaN  \n",
        "570  46.153846  3974921  155.676555  62.140943      NaN          NaN  \n",
        "571        NaN  1714745  119.099601  65.052146      NaN          NaN  \n",
        "572        NaN      NaN         NaN  58.150206      NaN          NaN  \n",
        "573        NaN      NaN         NaN  54.432362      NaN          NaN  \n",
        "574        NaN      NaN         NaN  47.589506      NaN          NaN  \n",
        "575        NaN      NaN         NaN  43.245720      NaN          NaN  \n",
        "576        NaN      NaN         NaN  27.167099      NaN          NaN  \n",
        "577        NaN      NaN         NaN  31.120882      NaN          NaN  \n",
        "578        NaN      NaN         NaN        NaN      NaN          NaN  \n",
        "579        NaN      NaN         NaN        NaN      NaN          NaN  \n",
        "580        NaN      NaN         NaN        NaN      NaN          NaN  \n",
        "581        NaN      NaN         NaN        NaN      NaN          NaN  \n",
        "582        NaN      NaN         NaN        NaN      NaN          NaN  \n",
        "583        NaN      NaN         NaN        NaN      NaN          NaN  \n",
        "\n",
        "[584 rows x 20 columns]"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Rise Ratio \u6f32\u5e45\u6bd4\n",
      "# 0\u70ba\u6700\u65b0\u8cc7\u6599 \u5411\u5de6\u8d8a\u4f86\u8d8a\u65b0\n",
      "fig = plt.figure()\n",
      "ax = fig.add_subplot(1, 1, 1)\n",
      "ax.plot(data['RR'])\n",
      "ax.set_title('RR')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "<matplotlib.text.Text at 0x10782add0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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hMuic9vbqChuIK/feXnM+Lr0UeP3r4+Ruq7t2X/CHSRblPjpq3vt5xhn+yt1m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\ngICAgICAgICAgICAbsb/B7US9mC2wVYvAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10770f610>"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# \u5a01\u5ec9\u6307\u6a19(WMS%R\u6216%R)\n",
      "# 0\u70ba\u6700\u65b0\u8cc7\u6599 \u5411\u5de6\u8d8a\u4f86\u8d8a\u65b0\n",
      "fig = plt.figure()\n",
      "ax = fig.add_subplot(1, 1, 1)\n",
      "ax.plot(data['WMS'])\n",
      "ax.set_title('WMS')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "<matplotlib.text.Text at 0x107a13450>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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k48ScZx5XILKfnLQfM396HBsDPvzhVmVP1kKZZM9tnE4oe1eAltbdd1/z6fLs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\nAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1078a17d0>"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# \u8cb7\u8ce3\u610f\u9858\u6307\u6a19\n",
      "# 0\u70ba\u6700\u65b0\u8cc7\u6599 \u5411\u5de6\u8d8a\u4f86\u8d8a\u65b0\n",
      "fig = plt.figure()\n",
      "ax = fig.add_subplot(1, 1, 1)\n",
      "ax.plot(data['BR'])\n",
      "ax.set_title('BR')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 22,
       "text": [
        "<matplotlib.text.Text at 0x107b8d690>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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TOfdcmXbrltqxSkvFcdy4Ua4xr0TdGivBT8MGpiPqtcBPgWOBocBNQKNEuhde\ngG9/WyoDm4ov+4GXXrK94lg3m+Vd798vHXT9/Odyo1uv6tHs2CGi4AXnn5/+A6JLF2889U2b7ArR\nCRNs0QHJ6pgyBS64QH5DZ6dk+Y4l6sOGSZbSRx+JZwne5ow7j5FI1O+8U2K+48bZmUV+CG3t2ycP\nwxkz5CHZooU4FCNHyvZUB9Y+7DBxEK66St6UvRL1FStkumWLN8fLBumI+lbA6lJqH7AcKHXuMGyY\nDM92553wP/8jsfVsNdzIBJ98Ysex491sU6fCpZfKfFER3HKLCFssvvoquY63ssXhh8OqVekfZ9Mm\nuckAJk60B/4A8aLef1+89169Uu/VMpdYot6zpzyUfvMbW9TBu0pLpyAlymgqLZXGapdcYq/zQz/j\nc+ZIg6E77xQvvaYm8l5I9Y3RCn1OmiQPV69E/eOP5aETFE/dSV/gBCAiqau6WirvjjlGKsvOOafp\nisN8pq5Omu8PHSrL8UR9zJjIlp1nnx2/F7l8E/VTT4V//cv2ltziFPVorJZ/oZDsM3WqPzzLv/1N\n3sAsER8yBP7978yI+t694hgsWpT8d4YOlRj/0UfDtGkiRvnM6tVyvT38cONtw4fL0I6pYN1zXbp4\nI+rTp4tD+swzcNFF/mpf4UV7rHbAK8CtiMf+NbW1ZTzwgMyHQiEGDgx9HaPyG1betVWxmeyAv927\nxw8xWCly+YIVA3/vPfe27dsnD/N4D6tvfxvmzpUb+sABeOwxuO22/M9fnzVLbnKro7SBA2V64on2\nPu3buxeTV16RDKGLL5ZjnHOOPRxhshQVyYPyppvk9507150t2WDNmvhN+8vLUz/eiBF2FpgXoj5h\ngoQiTztNKvydIUSvKS8vp9xNoeOQrqi3AP4OTAFej944enQZ48fby9u2SYMcP7JypYQOunWTpsnJ\nCl737lLZcv758vS/4AK5UO64Q1rOJdNFbjapqxNb/+//4MILU//+Bx9IY5B42TutW9s5+KNGSaXp\n5s35J+qLF4uQ33yzxHtXrYJrrrG3W6I+fLi9Lh1PffRomRojx2iqh86muO46cThefjl+e4p8YM0a\nOOMM747XrJld3+WFqO/aJWHC/v3lP9m/X96cBg9O39ZoQqEQoVDo6+WJEyemdbx0RL0IeAZYBvwh\n1g7RT+KjjsK3nvrKlXIjd+smf3ayObRWvy4zZsgr57/+Jcfq2lVEwGoskS8UF8uDyG0WzIwZ8uBK\nhltvlYf2bpkKAAAPVElEQVSHV10TeMmkSRJyqauTSshVq+QGt+jXT17NnWGmdETd+u6WLXY3ym4Y\nPVo+M2dKq9d8FfUvv5QhIDNBu3bp1StUV4uj4awE37cPjj9e2iok09leLkknpn46cDlwFvBJ+HOe\ncwfryWkxYIDcHE01nc83jIFHHhEBGjhQ/uBUPAyn+N9zj8SS77pLxgzNp9CLk44dEzdk2rgR3nyz\n8fqlSyNTGBPRq1djUbeaeG/aZDfiaoqGBm/j8g0NkiL63nvwu99JQ7Cqqsh6lGbNpOGWE7eiboz9\nZnPxxfJGm25WVIcO+V1hunlz/HqXdEnXU1+7VirEnS20v/xS6suy1Zd+OqQj6v8Of/94pJL0BOAt\n5w7RPQW2bStPuVxWOsyZk/zg0EuXyqDQP/uZLDs9tVQZPRpefFFu/EGD5FXOq3RGr0lG1C++WDzy\n/fsjM5rWrEktlGI1Gvnd76Ryb+ZM+V1WrBDB/0PMd8BI+vYVb9orNm2SvkTOOktu5EsugauvTtwg\n7NBD3Y1E9NVXcuyaGrk3Fi5MvyvZkpLI/mnSpapKUlFvvDH9Y9XUyAMnU9d/uqmlmzY1Tg/u00eu\nhbfeiv2dfCKjLUpjhRb27pULNhcZDxUV0pz73/+WWGgiIRg+XJq7P/qo3OTRbx7JUlkpgj5ypHix\nffrI28qAAe6Ol2mSEfWtWyWcNm2aTKdPF+9r69bYrWTjMXCgxOHvvlv6y7nsMql8tFL0kglzbdgg\n/5NXfP65lAnsGOpttyX+Xteu7voI2bpV3gJatLDPl+4rfocO3or6Cy/AFVdIa+EFC+Ravv56d8ey\n3kRS6QYgFbp1s4eXdMP27bFbtJ51lmTv5XuDuaz3Rvzzn8sNvGWLnVuaSZ56Si7Eq66SOPGBA+IN\nffBB0xd9ba149F98IR76LTHbyyaHVRlqdQlqjFQYWsKRbyQS9YYGuTHvvttulm5Vqh5+eGp9XI8Y\nId+98EJ54J17rvz299wjFa7btsX/7sKF0iAMvG3G/fnndkXoiBHygEnGc+7WzV2f8du22SJiea/J\ndBPRFOmGX+rq5B455RTxfNetk/+7slLErbRURoD6y19SP/aWLYkbVaVDjx4S0lu3LjLlNBlqayP/\nDyft2snxli3LTIWpV2S9l8bx4yX7IdbwU1VV6Td+sbpHHTlS4mrXXSeNhh5/XMR9zBiJmUHk4BTR\nfPqpvNanE3KJR1GRHNuvol5RIQ+qM8+0u0sFya0uK0vtXG3bylvMM8/IsHennirHnT1bBL6plnxX\nX22HZ7zsqdDpqffuLb0wJoNbD9EpIj/7mdS5pEuq4ZfokORll8k99OSTsrxmjfSgeNppcp9av0m8\nbjCMid8mJdOi3r27pDdG9z0Uj7lz5T8wRvpBmj49ft8zQ4bkfxuAnHS9e8QRkRfD/ffLj/i970lI\nwm1oxuoGtUcPEe4OHWT94MES9x09WoTi/vtlvfNCnjnTbs7/wQfw29/C6ae7syMZ7rsv9QYW2aJj\nx6a9vM2bxVMbOlRCWJZ3+fLLIrSp8t3vxg439OjROMd/716pwGxokP/05ptlfcuW8lbmBStWuHvg\ndu8e+WZRVSUPuUTXs1PUBw+GBx9M/dzRxPLUd+yQMJXTu161SnoFPf10CaWBZHctXAj//Kfsu3at\nXVcybJhk/hx7rNxLb7/d+NzGyG84bFhkRXdlpdxzn3+e2RRWq98Yq4l/U9TXS9mvvFIeBJ9/LtdX\nvL5nzj8/ufF/CxUTj4ceMmbYMGOefNKYN980prTUmNGjrQ5ZjZk/P+5XjTHGNDTEXt+5s3z/1VeN\nufFG+3j19cY89ZQx1dXGfPihrFuwwJhevezvnnyyMRMmGPPSS8a0bSv7PPlk03YUKitXSvk/+6zx\ntlWrjLnzTmPOO89e9+67xvz1r97bsXSp2DF5sixPmWL/p59+akzXrsY8+qgsz5hhzJlnxj7Ojh2p\nnffww4354ovU7V2xwpgBA+zl8nKx7Y03mv7eXXcZc//9qZ+vKX71K2PGj49c162b/fv9+c/GrF1r\nzPnny3JxsTEjRhizYYMxJ55ozLRpct/ceqvs06GDMRUVkcd74QVjLrzQmNpamX/1VWMuvlh+g2ee\nkePOm2fvf8wx9vmt/zRTWOfZubPp/az/yPqccYYxhxxizD/+kVn7mgLI2zbWcY2uqTHm0ktFPIuK\njDn2WLkwJkww5tprjXnggdjfq6oy5uqr5cdfvlwuOouGBvuPOXDAmIMHjdm6Vb4TTUOD2NC6tTF7\n9xrzox/Z3z3iCBGId9+VbUHlhhvkhjbGmNdeM+bLL2X+ppvkd3r77czbUF1tzHXXGXPoocbMnCmi\n8Je/GHPOOXIdnHSSPGTOPtuYffvketq3L/IYe/aIvbNnJ3fOqipjWrWS6zFVvvrKmPbtbafjT38y\npkULY/r1M2bhQnu/hgZjJk0ypq5Oli+/XJwOL3nsMfkPLRYvNqZjR2OaN7ev9WbNjOnUSR7Iy5eL\nowXG9Oljl2HvXlnXt2/jc2zfLv/Nvfca841vGDNypH3sH/1IxPGJJ+z9Tz3VPv+nn3pb3mgsO/7z\nn/j73H+//D/jx8tvAMZMnSqf7dsza19T4EdRN0aEd/t2Y84915jnn7fXv/mmPOnff99eN3++Mbt2\nyb6WNw7GHHaYMevWyVP15pvFc1u1Kvkf78wzjXnwQTlWSYl4L+vXp/YHFCqzZxszeLAxL74ov09p\nqQjdyJHy0Msm06aJaHTuLEI4d67YNHRo5H7HHy9vYE4ef1z2ffrp5M61YIExxx3nzs6GBvnNXntN\nhAHEUSkpMaZLF/GMjTFm2TLZ9tFH8hDq2FGuYy956SVjLrnEXp4wwZg77jDmvffEG7/3XnGmrrkm\n8nvr19sPcItly+KL8EknSVk+/liWa2tFzPv1M+ayy4z5yU/sfQcNMuaTT+S3sR5omeLgQWPGjjXm\n97+PvX3mTGN69xbb586VdXPnxo8CZBP8KuoW0X9uQ4O8mpWUiOhPmCA//LBh8qmuNuatt+QV+dpr\njRkzxpizzpJ9nCGBZLjvPvnepEn58WfmE1VVIlD9+hkzapT89i++KMK6eXN2bbG87SuusNdNnSqv\n+04uv1we0nv22OtGjhThuf325M719NOR50mV55835rTTxAN+8EFbyO+5R65VY+yQ0aBBMj3/fPfn\ni8fs2cacfrq9PGqUPGyc7NvX+M0mVSZNskOcFr16ybrXXzfmW9+y13frZsyWLemdLxX+8Q9jTjgh\nMvy2caMxV14p18SLL2bPllTA76Iej7FjRVB69pQwADSOc82bFxkP+9OfUjvH/v3y0Ni9Oy1TA4EV\nIz3++Nyc/403EgvQL38pNnbtKuGZ0lIJGzz3nKz/7W8lnLBtm+w/b54dJ549W94eb7rJmEcecW/n\nzp1yrltuiVy/b58xRx1lzEUXyRvi7bfb1+1bb7k/XzxWrDCmf3+ZX7vWmJYtG3vgXlBX1/jt+PXX\njXn4YamX6NdP1tXWSugl0x66k+pqY374Q3m4rVwp6+68U37zE0+MfBDlExSqqO/cKTfmkiXiRW/Y\n0HifhgZ57XvnHWM++ED+RCUz7NplzA9+IBVL+UpFhVTu/uc/xlx1lYRSduyQt46f/jTSAViyRKb/\n/d/GrFkj86efLmESSwDcMnu21NlEc/CgCHvbtnKtPv545FuFl1RWGtOundwfhxwi5cv226gVitm1\nSzzkHj2ye37LhksuMeb735cHeufO8ja1eHH2bUkW0hT1NJs4NEnYPkXJPcZI1wwffihD0e3eHTm4\n9+mnS252aan0n50pduyQlqNWum2mMEbSPDt1gv/93+Rztr3myislbbC6WrrImDw5+zasXi1tH+66\nS9I1k213kCuKpOWZa21WUVcCx/z50iXBbbdJnzPXXy8tJ9MdHCTf6NBBWsLmsrFMRQX86EeS815V\nlZteI42RRoS7dknDotNOy74NqaCirihKTKqrpX8VZ2+DucAYEXS3fcR7wdSp0hnX88/nzoZkUVFX\nFEUpINIV9Zx0E6AoiqJkhnRE/TxgBfAFcKc35iiKoijp4FbUi4E/IsJ+DDAGOLrJbxQYXg4Um49o\n+fxLIZcNCr986eJW1E8BVgFfArXA/wMymAiWfxT6haXl8y+FXDYo/PKli1tRPwzY4FjeGF6nKIqi\n5BC3oq5pLYqiKHmI27SZoUAZElMHuAtoAH7t2GcVkObwuYqiKIFjNZCBMdeapnn4xH2BlsCnBKyi\nVFEUpdAYBXyOeOQejKqoKIqiKIqiKErG8XvDpGeBbcBix7pOwExgJfAOUOLYdhdS1hXAuVmyMR16\nA+8DS4ElwC3h9YVSxkOAeUhYcBnwUHh9oZQPpK3IJ8D08HIhle1L4DOkfPPD6wqpfCXAK8By5Pr8\nJnlevmIkJNMXaIE/4+3DgBOIFPXfAD8Pz98JPByePwYpYwukzKvI/+4XegDHh+fbIWG0oymsMlr9\nATYH/gOcQWGV73bgBeCf4eVCKttaROScFFL5ngPGhuebAx3I8/KdCrzlWP5F+OM3+hIp6iuA7uH5\nHuFlkKeo823kLSQ7yE+8DpxNYZaxDbAAOJbCKV8vYBZwFranXihlAxH1zlHrCqV8HYA1MdZ7Vr5M\nKH6hNkzqjoRkCE+tP6AUKaOF38rbF3krmUdhlbEZ4uFsww41FUr5fg+MQ9KILQqlbCDtYGYBC4Fr\nw+sKpXz9gApgMvAx8BTQFg/LlwlRD0LDpERDTvnlN2gH/B24Fdgbtc3vZWxAQky9gDMRr9aJX8t3\nAbAdiTfHa2fi17JZnI44GqOAm5BwqBM/l685MAT4U3haReNIRlrly4Sob0Iq4ix6E/mk8SvbkNci\ngJ7IjQWNy9srvC7faYEI+t+Q8AsUXhkBdgNvAidSGOU7DbgQCVFMA0Yg/2EhlM1iS3haAbyG9DVV\nKOXbGP4sCC+/goj7VvK4fIXSMKkvjStKrdjWL2hckdESebVaTWYHH/GCIuB55DXeSaGUsQt29kBr\n4ANgJIVTPovh2DH1QilbG6B9eL4t8H9IxkehlA/kehwYni9Dypb35fN7w6RpwGagBqkfuBqpjZ9F\n7JSj8UhZVwDfyqql7jgDCU98irzGf4KkoRZKGY9D4pWfIqlx48LrC6V8FsOxs18KpWz9kP/tUyTd\n1tKPQikfwGDEU18EvIpUnhZS+RRFURRFURRFURRFURRFURRFURRFURRFURRFURRFURRFURRFURRF\nyUf+PzZXxoGjiO2jAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x107a8d290>"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# \u8cb7\u8ce3\u6c23\u52e2\u6307\u6a19\n",
      "# 0\u70ba\u6700\u65b0\u8cc7\u6599 \u5411\u5de6\u8d8a\u4f86\u8d8a\u65b0\n",
      "fig = plt.figure()\n",
      "ax = fig.add_subplot(1, 1, 1)\n",
      "ax.plot(data['AR'])\n",
      "ax.set_title('AR')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 23,
       "text": [
        "<matplotlib.text.Text at 0x107db2050>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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sDt69/fYLfNTvzN/JGkYxHHw+IZpKOzaKjV9glGIJfFeXbMc/rqMQB9/Z6aUd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\n/x+Wxiam656qjwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10782a150>"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# \u5e73\u5747\u6210\u4ea4\u91cf mean volumn\n",
      "# 0\u70ba\u6700\u65b0\u8cc7\u6599 \u5411\u5de6\u8d8a\u4f86\u8d8a\u65b0\n",
      "fig = plt.figure()\n",
      "ax = fig.add_subplot(1, 1, 1)\n",
      "ax.plot(data['MV'])\n",
      "ax.set_title('MV')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 24,
       "text": [
        "<matplotlib.text.Text at 0x107ecf750>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Fdt8ZvLK/6urk+rzIZJuW+nr/KabThYqKi2XLpGgrEhGftForSliEGVMJEqh/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\n36ehQQTFK+24T5/ELBWQSbx+9zvvIsz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       "text": [
        "<matplotlib.figure.Figure at 0x107e1bbd0>"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# \u79fb\u52d5\u5e73\u5747\u7dda(MA\uff0cMoving Average)\n",
      "# 0\u70ba\u6700\u65b0\u8cc7\u6599 \u5411\u5de6\u8d8a\u4f86\u8d8a\u65b0\n",
      "fig = plt.figure()\n",
      "ax = fig.add_subplot(1, 1, 1)\n",
      "ax.plot(data['MA12'])\n",
      "ax.set_title('MA12')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 25,
       "text": [
        "<matplotlib.text.Text at 0x1081ee950>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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7zuDBjtOt25m/nPM7dMjOP358we8vXWrXef750O7tiisKP7cfoUQgIqGaNs2S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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10784ea90>"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# \u4e56\u96e2\u7387(BIAS)\n",
      "# 0\u70ba\u6700\u65b0\u8cc7\u6599 \u5411\u5de6\u8d8a\u4f86\u8d8a\u65b0\n",
      "fig = plt.figure()\n",
      "ax = fig.add_subplot(1, 1, 1)\n",
      "ax.plot(data['BIAS'])\n",
      "ax.set_title('BIAS')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 26,
       "text": [
        "<matplotlib.text.Text at 0x10829eb90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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D73539O9HcfpJZPp795KwmuWARS1OvPPSS/QYdm4EGW/Nm0eG5mc/S070vXrb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\nRVEURVGU2uT/AzGKnrp5pcHrAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10823fa50>"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# \u5fc3\u7406\u7dda(PSY)\n",
      "# 0\u70ba\u6700\u65b0\u8cc7\u6599 \u5411\u5de6\u8d8a\u4f86\u8d8a\u65b0\n",
      "fig = plt.figure()\n",
      "ax = fig.add_subplot(1, 1, 1)\n",
      "ax.plot(data['PSY'])\n",
      "ax.set_title('PSY')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 27,
       "text": [
        "<matplotlib.text.Text at 0x1083bbd90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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d0QEnnOD+b9Qo2aZVx+oR11OfOhX23FP299oLttkmnfjlkViLrh5/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\niqIoeeP/AYPjuvWul3r6AAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1083789d0>"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# \u80fd\u91cf\u6f6e(OBV)\n",
      "# 0\u70ba\u6700\u65b0\u8cc7\u6599 \u5411\u5de6\u8d8a\u4f86\u8d8a\u65b0\n",
      "fig = plt.figure()\n",
      "ax = fig.add_subplot(1, 1, 1)\n",
      "ax.plot(data['OBV'])\n",
      "ax.set_title('OBV')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 28,
       "text": [
        "<matplotlib.text.Text at 0x1084dcf50>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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pC+C114DDDqsMiAPUEUcpANzJ9/YaQWidWUDFInD++UYW0Oiosc9qnhMgAiAIEXLzzfRo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\nYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x108111290>"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# \u6578\u91cf\u6307\u6a19(VR)\n",
      "# 0\u70ba\u6700\u65b0\u8cc7\u6599 \u5411\u5de6\u8d8a\u4f86\u8d8a\u65b0\n",
      "fig = plt.figure()\n",
      "ax = fig.add_subplot(1, 1, 1)\n",
      "ax.plot(data['VR'])\n",
      "ax.set_title('VR')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 29,
       "text": [
        "<matplotlib.text.Text at 0x1085fe950>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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DNNK4H3gBmAecB6zITF8BXJB5fz5wM3KT2ARsAE7yz1R3SLnHaU0a/+Al+j7o\nUToJsT/tNPi//1fCObt3S4aOpt81NnqNtoODcN11MpaAu+7lzsbJt8wknX1Tk3djjKv8Q6GYfVNT\n9jmwc6cM6F6KE3TFfmCgtJh9EmKvYZxHHoHf/Cb+bBzIFnt/352+Pi96AGPPqeuug+98J9qy/Q20\nLnocqpGoMftDgLcCjwGzgO2Z6dsznwHmAm6i4Wbk5pCFnnzlqB9TTnKFcYJOguHh+MV++nSJV06c\n6Dl7kIusocHraDUwAGvWSL1xpVAYRxt877gj+3elUImYvS5T3W9chd0KxewnTcoWo/vukxCSv+E+\nCnrMNFSj+8t9cqu0s29qgpNOEiMSd6cqyC32w8OyTdopEMaK/RVXwJVXEgl/A63L5MnxZ1zFRZQS\nx63Az4AvAPt8/0tn/nIx5n89PcsyrwBdDA93kU7HX3el3PgbaN0T0i/2STh7xS/2kyaNFfvBwex1\nChuzf+45uaGsWlXaukPlYvau2MdlNAo5e7/o3nOPVzdm3hg7FMzatfDVr8JNN2Uv0+/s3fWotNi7\n5+fBB4shyXdcV6+WAW2WL8/9HXdf6/ybm8c6+/7+/M4eCmdQ+cl1zUH8zr67u5vu7u5Y5hV2Myci\nQn8TcHtm2nZgNhLmmQNkZIQtSKOuMj8zLYuGhmUAzJ7t9X7zl4itRfwx+3zOPg6x7+/PdjaKVgN0\nnX1jo9fRqhSxh/huypVw9gMDss801BFXHL2Qs3fDOKOjcO+9UhHz+efhPe8Jt4zXX8+uQJorZl8t\nzt4f8jjySMmSyZf189JL8MQT+ecb5Oz9wwJqGOegg7xpQedU1DBergZaXZc4xb6rq4uurq4/fl6e\n7w5YgDAPsCnge8Aa4BvO9FXA0sz7pXg3gVXAxUATsAg4AnjcP1M9+dxH6HqI27sx+6OO8kZ8CorZ\nxxHG2bcv27koeiHkC+NEFXu9KUN8oY9KOHsVe3X2cYl9GGevYvTii7JPTzzRC4+FwZ+um8vZu/u0\n0s7eL4yFjmuY0hJhxH7/fkknda+PoFz4OJ19c3P1xuzDbOZpwEeB3wNPZ6ZdBVwLrAQ+hTTEatLW\nmsz0NcAIcCkBYRw92O5ddXg42KXWEq676+jwBlIPSn+Lw9nv3x8s9nqxu2GcxkavTk5//1ixz5cZ\nM22aDC2nlEPsk3b2cYt9mJi9LmvPHmlEb2oaW88lH/5zRkWvr08ae7UCYzFin4RIBQljoeM6NJR/\nfbXCpWpkAG6mAAAgAElEQVRFLrFX3BBZHGMq12oDbRix/w25nwDOyjH9msxfTvSR1hWNemikzXXB\nJxXGyeXs/WLf1CR/KvZRnf2MGRJ6aGuTZcYp9rmWmZSz10JWSYRxwmbj9PXJzSZqr8tczn7tWvms\nNw53PaIIUK4bb7H09o7t01Gqs+/pyb5Z5xP7G2/MTnnOJ/ajo9LJ7qijci9b169cYZw4qVgPWr0z\njo56O87EPjqFxH7GDHltasrOFBgYkOX7xT7Xxa43De2hG1fMPt8yy+Xs48qL9o9B68d12K7YR1l+\nLrF3a9dDcTF7iL9D0GuvyTgLLoXEvpCz37pVRmVTVEtcsdeb3cKF2cckn9g/91y4TmWFGmirNRun\n4mJ/4EB8XairgVzuOKnUy/37s1PLFB0c4thj5XXSpOzH3mKcPXhiX66Yfa010IaN2avYR+1inyuM\n43LTTXDmmdnLrYTYp9NSlG/hwuzppTp7v9gHOXvdJwcfXFjs9bt9feGEupwNtHFSVWJf684+nZa/\nICFMwtmn07mdvQ7ioANHqMABtLdHF3u/sy9XzL7WGmjDZuP098fr7N2SDx/9aLbzDCP2KvJxjmi1\nY4fcUP1mpNBx9T9x+gkj9npOzZ+ffU7nc/Zha1WVM/UyTiqW6OgOT6bD3tW62B84ICIYFOKIu4G2\nt1cekbWLth9Nk/THbkEaW4PEvq+vsLPX2jv14Oyjiv3AgHQo0wqjfsI4+1Jj9nrO/PKXXq2jxkbp\ntHTHHcH7ctKksSM4+dH5jI6Gq94ZhrVrpWyHn0LHtVhn39bmiXUqJb12J0+WUiWKvxezrg9410Qh\n6rmBNhEmTYInnxSXeeihcnAWL67eIkJhyJeNEXe5hDvvlHzlIFcP2Tnxvb3isH72M/nc3j42G+eB\nB0QsPvjB4Pk1N8s2xB2zr6SzjxrGufdeuPji3GIfJmbvhnGam4t39u9/v1R8dJ3reeflXm6hbVQB\nGx2V78Yh9ldcAZ/4xNjppcbse3uzz/sgZz84CG97m7z/0Ie8tNJ8YZywo4nla6DNlXq5fj0cfnhl\nO41WNIzztreJ0NcL+Zxd3DH7/fulhndQvB6yxX7KFDnJ8jn7l16S11NOyb3MhQu9NLZ6cPZHHCHh\nqTAX+FNPwSc/mf87xWbjFBvGGRoK58LDiL0KWJy173fuDO4sVmrM3h8z9zfQptPZgpxKeTf2SZOk\nv4hmL+n6gJe0UIhiwjinnz626m25qXjMvp7I5+w0Y0Ir8PX3SxpksWLf3y+jH+Vy9kEddfLF7F9+\nGb7yFXm6ysWzz0qDF8Qn9vnEKmln/8EPwlVXhRO3DRtkZLF8RMnG0Zh9MQ20GlcPK/bNzYUbHlVA\n4wxDuO1ELmFi9lHE3nX2ejNsago+R5uapKOVOySqG7MPG8aJ0kA7OCh9VNavLzzvJKk6sY/SwaTa\nyBfG0cc3rS1/6qnSTb5Yse/rE0HPJfbvehf8+Z9nT9N9Pn26/N4V+02bguOr/t/HJfJKIWd/xRXx\nj9/rilBYJ+s+KeUKNZYjZu+uq4YBC4n9lCmFxV7dapzOPlcpjzAxe207CMIvtnr+aG2cfM5bf/fk\nk2P7JAwMyDEsZDCipl6+9pq8btiQf75JUzVin05LmGDLmCo6tUOYkYqUP/xBXksRe8gt9v/xH2ML\nlemFN3u2iNfwsCf2r7xSWOzBuxDiakwvFLMH+OlP41mWMjDgnX/FiL1ueyqVnQUTJhvHn3oZNYzj\nr+oYxtlPmVK4FEJSzt4ttayEidlD7uPij5n7b9zu8fXjpnnrsXOdvbv8XEQN4+iY0Cb2Dq+8IoWS\napXbby/8qK9oOKfYkW3UPeSK2adSYxuD9KKYM0dCQOCdmHv3Siy/EP7OO6Wyd29usdf1f/LJeJal\nlOrsXeF0x5SNUi6hlJi9S9xiH6ezzxXGCROzd1/9+J391KlS80kbR7WHdBDu7/7zP7310fXNt9xc\ny3eZOnVsCPWVVySbLe4n1KhUldjXMk895dXBCYM7ek4xcelCzj4IV+xff13euyd4mGOiYh+Hs//h\nD+F738st9lraQdfVpb1dsoeKwRWhoFS8IHKJvXvs8okAeEKaTsvNuthsHP/nQsctjNjrfFpbpfG/\nVPQ8CboRlersg/bzzJmy7r29XqZTEPq7E0+EX/xC3qupiMPZz5njDX6urFsnySiVDlFXldivWVO7\nJY79BziI11/3yq26Yl+McJYi9gcdJILT3Cwn4OhoYaFS4nL2P/4x3H+/vM8l9iqwQeO27tkzdgD3\nsLhiHzZs4U9lve02ee/GlQsJb0ODCIuW3tUG2qh59i75XKwSxdl3dGRXNy2WXPF6KNxAG9XZKy0t\ncqPasyf3U6r+7gMf8L6j66Lnwb/8C3z/+7nXL1+evWuklBdekH4Q+/yjgJSZqhJ7t/NDEqTThetk\ng7j0qINQ79sHS5bAsmW5v9PS4t3dSxX7QmGcIPTiU9czOiqNtVu3hnf2ccTs+/th6VLp9OLO048+\nDucapFt79Ubhl7+UlEDdF9OnFx4EPJXKHplr/374i7+Q9+665wpbuCxaJKamp0eeTkp19mGWGUXs\nZ8wIH4rMR771KtRAG8bZB52r+lQSRuzb2+FjH5P3ui66vF//On98PV+bgF/sDxyQ433SSfE8MZVC\nVYn9xInhWsOL5Te/kZ1eiCuv9PLNV60K95i/f7/koF99de7v6EWXTnsiPWFC+Z29vg4Oyg32pZei\nh3FKcfbPPy/H+NVX5XOup6Jczl5TD9vboy972TJ49FFvH8yc6Y3Lm49167xMJO2TAN65mk57Y9vm\n4/3vlw5xW7ZIL9BSGmghv/AoYcM4cTr7XI2zUHibS3H2vb3hxH7KFK8Tmp7Tbp+TfNlL+lQWxOzZ\ncmxPOEFSlRsbxUydcIKJfRapVLwNRH504I5C6EmaTsPf/E12B4xc5KpR49LQINvX3y8iceSRIlzf\n/na49XIpRezdfX/ooeJa8/UKdNGnr2Kd/b593lOTCmVQTB48Qdi/P3sfqeMvpjdif784V71YOzu9\n0buC0Mbz3l6vQ5l7PuhxGBoSs1JonY49Vvb39u3iAkt19lA4HTass580SZx9XGKfy9kXGuCjmJg9\nRHP2U6ZIOPOxx8Y6+y1b8q9fPrHX+T/7rLRHAbz97fIEOW7FPl+nhKTEXh9PCxV70oPf2yvitH8/\nXHst/OpXuX+Tq/qkH3Ufg4OegP3P/1n4d37UeUQR+8ZGEYbJk+Xv7/9ewgovv5w/DulyxhlygRTr\n7N/yFvjc57zPN96Ye/tvu02eAgA+8xlvut60iw1/DQ97N/QZM+TJIdc54T5lfu5zcPTR2WKvF/Du\n3eEG3pkyRfo0aAgnDrEPs8woYRwV+5tugq9/PfryIL/YF2onGRz0OkjlW1c/YZy93oy1ncgtD63r\nlE4XL/YgbVFTp8qT68KFUoG0pUV+F2ehuahUlbPX6UkVElIHGeaRFjyh379f7tT5GgTDOHvIFvtS\nMpJ0G6LE7LVkwuTJInBf+Yo4jl275AQP2zheqCPQJZfIo2sQGjLRfXXxxVLfJYj58+Xm4EfFvphS\nCrrf9GJtbJR1yRW3dwtr/ff/Ls58zRrv/2vXirjMnh3uyailRXpSaiGvUhtow6A1eQo1ivpj9i++\nKOGrYsjXQKvXeK6U46EhOSZJOHtFz3W3sdjVnXwapL2fc3HmmbJtW7bADTdIL+0JE8RgJDH0Y1iq\nUuyTcvaa51rocUoPdE+PvFfBz/e4H4fYf/rTcM45hecBXoXKKM4eoLtbXOXkySJgbW3i5MIIlVKo\ni/93vwsrVwb/70/+RF5PPtl7yoiKVnAspWHbvVjzxe11O6dNk/WdMkUa8JU77/SeVML0l2hpERGY\nM0c+R3X2/rTRMKRShXvR6pOdG7PfvdtLf41KPmevT5i5btaDg+KMw3aqUpqaxDnv3FlY7IOcvbu8\nQjH7XO0RIPt70SKphdPe7t1Y4kprLZaqE/skwzjaEBhW7PVJQMU+X0NelDDO+94nnXEmTYK77pLf\nXXaZOKl77/W6V+ejv18eEVU0wvL2t2d/bmuT7YrylOG60f7+YPeXqw7IrFlw+eXibFtbw8XdL79c\nXlVM9UIsRezdizVf3F6FWMd21U5/biaQZm6EedLQm4zOL6rY79/vrXuUsVMLhXJcZ69i39NTfLpg\nvgZayD+iU7HOPpWSc2rr1mjO3g3jaInyUsI4IAZicFCud8XEPmB6UmEcdSmFdvjgoFyM73+/9/19\n++Jx9hq/A9nWt75V5n/jjd6j/f/6X4Xn09cn2UXHH1/4u/lobZWLO4rYuwL1V38V3Ov5hhvG3gQ2\nbhQBOfNMWW7YENTXv579xKfnRz6x//nPvcfzAwe8/Hb9jXuxamecIPxi/5nPwLvfnb3N2q4QJotM\nj72m3jY0iBsNG8vVctUQrU5RIbEPitmXKvb5ntryxe0LOft8fUJaWqTHaqFMLb1Zu2Lf3y83iYMP\nLl3s9Wbjir2O31wpqk7sk3T2e/fK/MM4e7dSYJgwTlhnf+21Xs14dwCNgQF4+mn4y78MNzRaX1+0\neH0uNIwT1dmrCGrnoiCefTb78+GHSw7z5MlyEbgXQiFcV1TI2afTsh/VcauQa4kIyL5Y81WF9It9\nezs8/LDnqr/+de8CDuPsdZtVDFKpaMXQ9u/3RD6K2Le3Z2+/HzcbR2P2pYRx8sXsIb/YF+vsQfbv\n0097teyD2LZNetBCttjv2yeO/PDDc6/b8LAcs0IDsweJvTn7gOlJOfs9e8Q9hxF79ztxxuzBE45J\nk7JFZ+1aaegLM4ScdrcvFQ3jRInZ+1PndEATXbdUSgatyNXo2dwsF0GUm1Vrqyequuxc4rpnj3zn\n5Zflsx5LtzaJu+/yuV6/2Csq9m5l0ShhHDfM0NJSeCQppbcXfvITeZqIEsY5+2wJGeZCwzjNzXIM\n+/pKc/Z3353//MyXfqnOPt/NIJ9ZhLHj3rrMmuW994v97Nny1JZr2YXi9UqQ2H/1q5Wt/VXRkaqC\nSNrZH310uDCOe5Kr2OdzX2GdPXiPmJMmjXUIc+ZkZ3vkWr+JE6Nd7Lloa5MTOIqz18dRt/qjoiV3\nOztz98RUZx9F7Nvawjt7bZvZtEle1dm7Yu+6zjDO3j+Cl+77I4+ULK3jjy8ujANw3HHShrNkSf7f\n6rq8613yGsXZn3lm/jTK3l5Zt1TKC+X09BSX8ZROw4oVXmXXIHI5+3S6NGd///3SMBq2D4Yr9nv3\nSoP74CA8+GDw98OEcMA7vq7Yv/vd4dYpKcKcLjcC2wF3nJUO4H5gHXAf4EbIrgLWA2uBnKdvpWL2\n+Zx9KiUnqD8tTFMwh4dzO8Binb2fOXMK3+zCnnBhUMGNGsZpbJSGZHXcfX3SMPbyy3Ih5ytDUIzY\nu4/AAwMitrnEXhvW/c5ey2c3N2cLZb6UuKEheeT/8Iezpx99tLymUiLWEC4bR4+bK/Z/+qewenXh\n3/oNRRSxP+ig/GUQVOzBE/vdu4tz9r29sp062H0QucR+eFiObXNzcWI/axacdVb4dfU7+4ULvZHc\nggh77en1FIchi4swp8v3AX9C4JWI2C8GHsx8BjgGuCjzeg5wQ65l5LowXGf/xhvxjUmrB6+zMzsO\n+dvfZnfXf+kl+e7tt3vTdu6Uk2LWrNwZOVHEXi9094RV8Zg9u/DNLq4QDnjrHCWMo7976SW5OPr7\nJUZ6/fXwf/6PrFshsZ86NVraaGsrfPGLMv+BAfl9Pmc/caLn7P1i779Y86Ul5hKWf/7n7NBL2Ita\ns0Dcm+vRR4cbxcgVZIgm9h0d+cV+//5ssd+2TRqNdYi/KOzaVbhuUa5sHA3R5ErBVudfKGYeFhV7\nbbxvbs4fYiqUY+/Ot9oIc7r8J+C/bM8DVmTerwAuyLw/H7gZGAY2ARuAwGo0uS5U9yDPmlVcKQGQ\n3mvuybJ3rwjEcceJwCvveAf87//tfdYeluedJxkl06aJU2xtldh0rrh9lDCOnizuo+YJJ0jDUL7H\nVyVOZ+92LoqCir1W8XzxRdlP27YVdvbNzXDBBdF6Z7a1Sc/db35TjlFbW+4Qw9atkuXkd/Y6iIT/\nRlnI2eeq4+S686j7zxWr1tb8mTIbNojx8Z9jUVxjoQJn7o2ks1NulC0txWWQ7Noly8tHLmdfqK6+\n1u+Pa8Q0Fft9+7xU4HyNx2Fj9tXk6JVid9ksJLRD5lWbPOYCbon+zcC8oBnkEnvd0XqCXXqpN8hA\nFBYulLo2yt13ywV2wQXSUJVOe5UM58zxhKOnx+tw9JnPwEMPSQywtTV3PraelGFDIUEnS2enuLsw\nncriFHt/Le+wtLXJ/nMbZ3/3O8n4COPsW1q88WzDoDcVTYtrbc19DnV3S9hFxV5j9rfcIq9xOHs/\nUcXeFQO3Gmo6DT/7WfZ3jzgC/ut/Lc3Zt7bmH1C7t9e7kcyfL2mzU6Z4hb2i8Oab4Zx9kKDmGh5x\nwwYxZWGPR1hU7Pfu9Z408/UBCHvtVaPYx/Gwkc785fv/GG67bdkfxbarq4uuri7AO8irV8sweVu2\niJvTRqkodHd773WA4Vmz5PG0t1fcKMjjuN5ctmzJFu1jjpEDP316bmcfxdWDXLz+xjj38b6Q8MYZ\nxlGijqKjzn72bG/aunUixtOny8WeT+yjcs01ckHefLOURj7llGCxHx6WG/RNN0ntH21cd/FfrIWc\nfRhxiXJxd3Vld25zs4G2bYP/8l/kHE2lvPz7PXvkidQ9z2bODC/E2ov2Bz+Av/7rsf93byTz58uw\nli0tUh7i+ee9ns/5WLNGvn/LLcWLfS5nv3Il/I//AR/9aHHnTy5cZ69PalpeYnR07A3VvSnm46ST\n4hH87u5uul0hK4FixX47MBvYBswBtJ7kFmCB8735mWlj+M53lmUJhaLdyV9+WTIg/uZvpAGrGFTM\nFXX6U6eKcGhD3u7dniBs3Zp9Mqnwn3SSiEJQzD5KvB5kG++91/vc2gqnneYtL5fYDw/L6EwdHfE5\ne5DtDTP4iouK/XHHSSZKQ4PkN2s1x3zOvph4a0eHhH1uvtlbfpDY79ol+7e9XczCpk1ybC+7TCp8\nXnvt2IHYC2XjxO3sH3oo+/OUKZ6z14qefX1eByGQ/gm//jWce673u/vuC9cnQ9m7V0pyBIm9G7Nf\nsECunbY2Ob7PPTf2+0GsWydPJmHCOLni4ursc2XlPfBA9BIh+VAx37PHm69W3x0YGHudhTV273hH\ncZlMflwjDLB8+fKi51VsGGcVsDTzfilwuzP9YqAJWAQcATweNIMgoQdxPddcI49sp5wij+89PdEb\nahsasn/T2SkFiUDEfs8eEft587LziVeuHHuQnnhC1icuZ+9n3z740IfkfT6xf+QRGTQjzjAOBBcb\nK0RrqxfGefZZGYwEZN2DwjiuMBdTmhiyzxl/zF4b83ft8rKdDjlESlPce6+s79/+rXzvH/8xe775\nwjhhSz9fdZXMvxi0IiJ4ZkIbf/3jmbpCN3NmtFCYEnQt+Z39xo2yX446KnwxNDVGpYRxcjl7fYJZ\nty5esQe5UW/cmD3f9vax+x5Kv9YrSRixvxl4FDgSeA34BHAtcDaSenlG5jPAGmBl5vVu4FLyh3jG\ncNZZIj4dHeJ2J00ScSgU2nAf1dNp74R2K1jqwZw2TVzO1q0SpnFTzPbtG1vP+0//VH6bS+yjOvt8\n5BN7fSyMO4xzxx3ZjdZhaGsTQdJY+jynZWbyZNnHu3d7wh7FgeaiocHrset39rNmSVtMT48n9osW\nSUP9L3+Zv7duHGGcyy+XTKFicJ29X+z9jaOnn17cMsCrrx4kYv6YPcg+yxeO86PX3ObNhcW+tTW4\nI1mumP2WLTLPV17JbhiPg8mT4Utf8kK9IOeTv8fxPfdI5dN6FvsPIw2vTUiI5vvALuAsJPVyCeCe\nPtcAhwNHAfcSkUmTJEb45pterRgNu+Ti+efl4j/7bPm8b5/XuLRzp4jCyIgXnnHDOMccI9/ZulVE\n3a2b7qfSYq8X09698Tr7efO8kbnCoturDbSu2Dc3Z8crBwfj6ztx5pnyGpR6+cor2c5+gRNQzHeB\nxtFAWwqus1ej4Yq97uu3vx0+8pHil/PJT8LixcEhOzeM09kpr1Om5Ha4Qeg2bNhQOIxz2GHBQ//l\ncvabN0tI6dVX43f2U6eKsL/nPd60ILFfvlyOSz2LfcUpJParV8NFF4k73bPHc3cqznrBqMvUMM4b\nb0gPyN/9TtoHpkyRdMtcqWa5snHifLTLV+tbL6bt2+MV+2LQEatU7F0n589K6umJx9mD1w29sdET\ne3XFv/mNFK/zlzaA4GlKHM6+FPI5+/37Paf9ve+V7mpnzw6ukeOGcfTcSqfDi/0LL8A//IO8X7++\nsLM/6qixbWoQ7Oz37ZPwzamnyg09brHX+bnzDRJ7PUYm9gkybVr+2iHPPCO9HBculJPBFfudO7PT\nqsC7eezfL3Fd5emn5TXXwcxV93zNmvy1OKKQr9a3nmzbtsWfjRMVrbuvYr94sTzmwlhh37pVpi1e\nXHonOb1hHzjgib02tD/6qLzqE9xHPuId93xOs1BtnHKIvY5NrOeXmpt9+7y4vJvmWiyzZo119loL\nxx/q6u8PJ/Zf+Yo8IesIXps2FRb7I4+U769ZI6UVlCBnf++9EtJduDAZZ9/W5i1TMbGvEIWc/Qsv\nSJxfxV4biDQ1zU2rcufX2ysOdft2ydCYF9gjwCMojPPKK5LOpg2scZArlKMn24YNXqy8UhxxBHzn\nO56IplLyGLx5s/SkdTnxRHFmcaXMPf64LEtviCr2OoC5jmR18MHwta/J+3ziM3Vq7qe5coh9Y6P8\nDQ3JudjQkB3G0XBUodBIGILO4euvl059/lTBvj7vWslXgvmmm8ZOK7Suc+fKTeTuu+FHP/KmBzn7\nDRsk46ujQ27ySYRx/E9MQWJfzOhw1URdiP2uXRJiUbHfulWE+7TTJE65dWv2CTJtmrgbfXQ96CCp\nf+6OQBTE9OnZBcBAxk8991x45ztL20aXQmL/wguFb0xJk0pJCp8/s2bevOAMkUcfje9p5O1vl32k\nx+H3v5ces4pbkEzDPvnEp709/7CESYs9eKGcV14RYXPFftYsucHFUSIgqMH1iSekFIUfHQ2tpSV/\nL9qgsQAKOftUSq7Zp5/OftIIcvY7dsh3NRQXdwNtW9vYec6dmz205oED3jGJUpq7mqgZsc8XxtGw\nzeLFUshs82YRnc99Thz7D36QLfbTp4vj27bNO3ATJxYWowkT5CRubpbHz6EhWLVKYpXFphPm2p7r\nrhs7XS+qTZu8xutq5oILvPcPPxxvO4OOltXfL2mPur8++MHssEAYsdfMoaAQ09at5XmK0kZav9jv\n3y/nrn+EsWIJ6v+Qq5aNhuMKhXLcEJim8RYSe5CnjKeeyhZ7dfbTp3uN1Tt2yHd1nuVw9moc9Ynm\ngQe8wm5xlWooNzWx2vv2SR53rhOup0dOyNNPl9Kkmzd7jVpnnCFC454gl13mNTBGvUvPnCl3+d//\nXsIGHR3JiIEr9tu2iRC5DqrSzj4Mv/iFF2devTqcAIRFxf63v5Vem1rp8NBDs2PbKvb5GmgnT5YQ\nRlAj8po1+as3xsWWLZLW9/rr8pSyY4c01D/ySLzipgPMuwSJ/fnnS09eiCb2mi0VpnTIzJnylLpz\np1fGQZ39ggVyHY+OemKvNxJ/anSpBDl7FfuGBrj1VunApqHaYgZ9rwZqQuy1ETUoF3x0VFxQe7s4\not275QJRse/slAvIdXY6/BpEfzTWtLTGRunle+ih0X4fhbvukiEK58yRnp+1JvYgT1baASqOmLMy\ncaLM86GHsnPP/eGvadPkr1AP11yCVi6xv+UW6dDX2Snn7vbtkn3z6KPxlgcICuO4fROU22/3Op/N\nnJl/lCt3n8+aFb7Okt6UGxq8dhZ19s3Nct7s2CE3g85O2Q8PP5w/PboYgpy9G8bp6ZHzQEtZuym9\ntUQVFuIcyze/KRfsz38uTtwdBGDfPjkxVLT/7M+k443GjVVg/O672BKkeoL29YnDWLSouPnk47Of\nhW99Szp5HHmkVMN8/HER/fPOEycbd9wyKW68Eb7wBWkAjFPsGxvhySdFJG68UaadffbYUggLFsg+\nK4SKvRse6+kRd52rt3ecXHihpA8feqicq08+6ZUpiDNsECWMoxx6qFc00I9/wJYobRz6RHDccfL0\nOn9+do/lgw8Wd63OHpIZACTI2buN1Xv2eDd9rVlUi9SEswe5YL/7XRFzl927s13J+edL0SYdg1Kd\nuF/si23s0pOup0ecfRJir9ksxx0nuePf/raccL29Egf/6lfjX2aS6D7TYxEH8+fLjfC116QOCcij\ntr/AXHs7/PCHhecX5Ox37BCnWq6Le+VKGXJw1iwJEZ56qoSWtBRFHPjr2qfTwc7e5bDDPLHfsEGu\nQ41l790r+04zVKKEOLTX+9y5XtxeS5GDhFJWr5Ybehxpp7mYNSs4FPv66/BP/yQ1oHbulPWpVaGH\nGhL7E0+U+Lsf/4n60Y9K3r0elFzOvlSx3707ObHXdfeXd/CXuK0VVOTjdPadnZKn3dMTT8Nve7sI\nixYeg2xHWQ7+4i/EzeoYqWef7ZXbjgu/s9fe5vmuh8MP98R+5Uq45BJvZK3du2Xf6TUYxXn/5Cey\nv2fPhs9/XsK0rnm74gppX+voiL9R1mXp0uCEiNmz5Vj8+tdiLKqxbHEUakbsUykZXxIkLKBo42wu\nkhT7l15KNmavbkmzkV59tXZi9S5NTXLDilPslXzHPgozZsh5df753rSdO8sr9oo6Wx29LE78DbRv\nvpnf1YMInY4nq7F7HSdZxf7LXxYzop3twnDQQXJzmz1bjNM73iH1hXR9Tj5ZbkRJx8gbGnLrwcyZ\n5Wu3SZqaEXvwYpfXX+/FCl991WuMDaKtTQ5kXDH7975Xnh6SDOMoxx4rr9OmeY1EOq3WmDkzGbGP\nC/oN71YAAAaCSURBVM08cZ8StGGw3KRSksmkocg40fRizTzauLGwYTnmGAlp9PSI2B97rCf+KvYf\n/3jxT1j+NhH35vPaa9LxqlKobpjYV4BLL5VXHbNzwwbpzZkL7dnpdwfFOvtDD5Vc7tdekwsmqRzs\nnh6pxAci9torWFMJa43vfU9CcdXKeedJfwzX9ZY7jONywQXJ5HOnUtmhnLVrpU5NPhoa5Ng9/rjE\n1s88U0Kln/gE/Ou/lv505U/TdOfX0RHf01sxvOUtMmjKJz9ZuXWIi5oT+//3/0RstY7N+vX5xR7g\nzjvHimQxNdyVo4+W8VDf857kGmza270Y4aRJcnMqZZ0rzbvfXZ6eqMUyYYJkw2zaJOGIt75VSiNX\nwtknjRvKWbtWwjSFOPlk+MAHJPXxjDOkk9Ejj0gOeqnlA7SNwl2/aqGlRWr/lCMjK2lqTuwhu+jS\nhg3SgBSVq68eO1xdWI46Slyf1lZPmlRKbla1GsKpFSZPFoe/dKk410cekXTXeqOjQ55MN2+WoTvD\nhItOPtkL/Zx6quyrD3xAPm/aVNr6nHeeXItayK6axL6eqEmxP/JIrzzqq68WV3GyoaG0zJbm5vJ2\nm5461cS+HHz60zLgtz6FxFWmoJqYPh2+8Q2p5zQyEm58Zx3v4Prr5Wnn6KNlqM7Zs0vv9JVKybWo\n4SQT+2SoiU5Vfo48Ej72MUkB27278hUgy8Fb3iIXl5Esp5wijfcXXijVGAuFCGuR6dOlptOePfCp\nT4ULRc6dm1076NZbxWQtWRKf6Wlvl6f2WukwWGvUpNhrStqXviSP2bVamCgKq1ZVeg3GBy0t0mnt\n4x+Hv/zL+jy3Ojq8Qmth4vVBaG2pOPPfUykvy8eIn5o8lVtbpaHoqadqM+/cqG6+9CV5WoySM15L\nuNdMoUwco36oSWcPXt6rib1hROOzn5USBM3N0cceNmqXSlV6SKdLHZ8Oybm/8ELo6ip9hQzDMKqd\nlDSwFKXbNS32hmEY44lSxD6pmP05wFpgPXBFQsswDMMwQpKE2DcA30IE/xjgw0ACJZ2ql+7u7kqv\nQqLY9tUu9bxtUP/bVwpJiP1JwAZgEzAM/BQ4P98P6o16P+Fs+2qXet42qP/tK4UkxH4e8JrzeXNm\nmmEYhlEhkhB7a3k1DMOoMpLIxjkFWIbE7AGuAkaBrznf2QAclsCyDcMw6pmNQBGlH5OhEVmhQ4Am\n4BnGWQOtYRjGeOFc4EXEwV9V4XUxDMMwDMMwDCMJar3D1Y3AduA5Z1oHcD+wDrgPcAdSuwrZ1rXA\nkjKtYyksAB4C/gA8D3w+M71etnEy8BgSXlwD/FNmer1sH0hfl6eBOzOf62nbNgG/R7bv8cy0etq+\nduA24AXk/DyZGt2+BiS0cwgwkdqM578LeCvZYn8dkBkxliuAazPvj0G2cSKyzRuo/kqjs4ETMu9b\nkXDc0dTXNurQ2I3A74B3Ul/b97fAjwEtjF1P2/YyIn4u9bR9KwAd8bYRmEaNbt+pwD3O5yszf7XG\nIWSL/VpAR9KcnfkMctd1n17uQbKVaonbgbOoz22cAjwBvIX62b75wAPA6XjOvl62DUTsZ/im1cv2\nTQNeCpgey/aV+y5Qrx2uZiGhHTKvemDmItuo1Nr2HoI8xTxGfW3jBMQRbccLWdXL9v0z8HdIurNS\nL9sG0o/nAWA1cElmWr1s3yJgB/B94Cng34EWYtq+cov9eOhwlSb/dtbKPmgFfgZ8Adjn+1+tb+Mo\nEqqaD7wbccEutbp9fw68gcSzc/WhqdVtU05DDMi5wGVIWNWllrevEXgbcEPmtZexkY+it6/cYr8F\naQBUFpB9Z6pVtiOPVwBzkAsOxm7v/My0amciIvQ3IWEcqL9tBNgD3AWcSH1s3zuA85BQx83AGcgx\nrIdtU17PvO4AfoHU4qqX7duc+Xsi8/k2RPS3UYPbVy8drg5hbAOtxs6uZGwDShPyiLaRyo0hEJYU\n8EMkHOBSL9vYiZfN0Az8GjiT+tk+5c/wYvb1sm1TAB31tgV4BMlAqZftAzkfF2feL0O2rWa3r9Y7\nXN0MbAWGkPaHTyDZAQ8QnBr198i2rgXeU9Y1LY53ImGOZ5BwwNNIumy9bONxSDz0GSSF7+8y0+tl\n+5Q/w8vGqZdtW4Qct2eQtGDVj3rZPoA/QZz9s8DPkUbbeto+wzAMwzAMwzAMwzAMwzAMwzAMwzAM\nwzAMwzAMwzAMwzAMwzAMwzCM8vD/ARgJtH2lYHTcAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1085bc0d0>"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# \u76f8\u5c0d\u5f37\u5f31\u6307\u6a19(RSI)\n",
      "# 0\u70ba\u6700\u65b0\u8cc7\u6599 \u5411\u5de6\u8d8a\u4f86\u8d8a\u65b0\n",
      "fig = plt.figure()\n",
      "ax = fig.add_subplot(1, 1, 1)\n",
      "ax.plot(data['RSI'])\n",
      "ax.set_title('RSI')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 30,
       "text": [
        "<matplotlib.text.Text at 0x10882b350>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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HKfMl51RyRZiy/+53gfvvp/+jPHugXNnLNNdRBCd7ijqYfi8VtgpXzz4szr6z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\n35wOZU4re3X2G1/4xYtpmb4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Inif6RZH9W95CqnpgIFjzgM+r0p69tOqYsOSYRpwBWpto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\nHh4eHh4eHh4eHh4eHh4eHtXF/weHVnlBBAZg4AAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1081fab10>"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# MA20 \u548c MA60 \u6bd4\u8f03 \u770b\u51fa\u9ec3\u91d1\u4ea4\u53c9\u548c\u6b7b\u4ea1\u4ea4\u53c9\n",
      "# 0\u70ba\u6700\u65b0\u8cc7\u6599 \u5411\u5de6\u8d8a\u4f86\u8d8a\u65b0\n",
      "fig = plt.figure()\n",
      "ax = fig.add_subplot(1, 1, 1)\n",
      "ax.plot(data['MA20'],color='#9D442F',label='MA20')\n",
      "ax.plot(data['MA60'],color='#5D947E',label='MA60')\n",
      "ax.legend(loc='best')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 32,
       "text": [
        "<matplotlib.legend.Legend at 0x108c1e690>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ldwOXkg68/3FO/vg1NVVVOoxMCKGPmpIIlgMHgD7AZWAR8B5wEs0YwVjgeW3d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\nZeKAEUwaOAKbLlYtPl7Kwd0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       "text": [
        "<matplotlib.figure.Figure at 0x108990ed0>"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}