DTW_strategy_0223.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#coding: utf-8\n",
    "import cython\n",
    "from DTW import min_dis as min_dis_jit\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy\n",
    "import pymysql\n",
    "from numba import jit\n",
    "from sqlalchemy import create_engine\n",
    "import matplotlib.pyplot as plt\n",
    "import wrapt\n",
    "import time\n",
    "import os\n",
    "from multiprocessing import Pool\n",
    "%matplotlib inline\n",
    "%load_ext Cython"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "%%cython\n",
    "cimport numpy as np\n",
    "import numpy as np\n",
    "cimport cython\n",
    "\n",
    "\n",
    "@cython.boundscheck(False)\n",
    "@cython.wraparound(False)\n",
    "@cython.nonecheck(False)\n",
    "cdef double distance(np.ndarray[double,ndim=1] p1,np.ndarray[double,ndim=1] p2):\n",
    "    cdef np.ndarray[double,ndim=1] sqr=(p1-p2)**2\n",
    "    cdef Py_ssize_t l=len(sqr)\n",
    "    cdef Py_ssize_t i\n",
    "    cdef double ss=0\n",
    "    for i in range(l):\n",
    "        ss+=sqr[i]\n",
    "    cdef double x=np.sqrt(ss)\n",
    "    return x\n",
    "\n",
    "@cython.boundscheck(False)\n",
    "@cython.wraparound(False)\n",
    "@cython.nonecheck(False)\n",
    "cdef np.ndarray[double,ndim=2] grid(np.ndarray[double,ndim=2] X,np.ndarray[double,ndim=2] Y):\n",
    "#     assert (X.dtype==np.float and Y.dtype==np.float)\n",
    "    cdef Py_ssize_t size\n",
    "    size=X.size\n",
    "    cdef Py_ssize_t m\n",
    "    m=X.shape[0]\n",
    "    cdef Py_ssize_t n\n",
    "    cdef Py_ssize_t lx,wx\n",
    "    cdef Py_ssize_t ly,xy\n",
    "    cdef double dis\n",
    "    n=X.shape[1]\n",
    "    if m==size:\n",
    "        \n",
    "        lx=X.shape[0]\n",
    "        \n",
    "        wx=1\n",
    "    else:\n",
    "        lx=X.shape[0]\n",
    "        wx=X.shape[1]\n",
    "    if Y.shape[0]==Y.size:\n",
    "        ly=Y.shape[0]\n",
    "        wy=1\n",
    "    else:\n",
    "        ly=Y.shape[0]\n",
    "        wy=Y.shape[1]\n",
    "\n",
    "\n",
    "#     cdef np.ndarray[double,ndim=2] X_,Y_\n",
    "#     X_=np.reshape(X,(lx,wx))\n",
    "#     Y_=np.reshape(Y,(ly,wy))\n",
    "\n",
    "    if wx!=wy:\n",
    "        raise IndexError('Wrong X and Y dimension')\n",
    "\n",
    "\n",
    "    \n",
    "\n",
    "    cdef np.ndarray[double,ndim=2] g=np.empty((lx,ly))\n",
    "    cdef np.ndarray[double,ndim=1] p1, p2\n",
    "    cdef Py_ssize_t i,j\n",
    "    for i in range(lx):\n",
    "\n",
    "        for j in range(ly):\n",
    "            p1=X[i,0:wx]\n",
    "            p2=Y[j,0:wy]\n",
    "            dis=distance(p1,p2)\n",
    "            g[i,j]=dis\n",
    "    return g\n",
    "\n",
    "@cython.boundscheck(False)\n",
    "@cython.wraparound(False)\n",
    "@cython.nonecheck(False)\n",
    "cdef np.ndarray[double,ndim=2] dtw(np.ndarray[double,ndim=2] X,np.ndarray[double,ndim=2] Y):\n",
    "#     assert (X.dtype==np.float and Y.dtype==np.float)\n",
    "    cdef np.ndarray[double,ndim=2] g,g_1\n",
    "    cdef Py_ssize_t dx,dy\n",
    "    g=grid(X,Y)\n",
    "    dx=g.shape[0]\n",
    "    dy=g.shape[1]\n",
    "    g_1=np.empty((dx,dy))\n",
    "    g_1[0,0]=g[0,0]\n",
    "    cdef Py_ssize_t i,j\n",
    "    cdef double d_min\n",
    "    for i in range(dx):\n",
    "        for j in range(dy):\n",
    "            if i==0 and j==0:\n",
    "                pass\n",
    "            elif i==0 and j>0:\n",
    "                g_1[i,j]=g_1[i,j-1]+g[i,j]\n",
    "            elif i>0 and j==0:\n",
    "                g_1[i,j]=g_1[i-1,j]+g[i,j]\n",
    "            else:\n",
    "                d1=g_1[i,j-1]\n",
    "                d2=g_1[i-1,j]\n",
    "                d3=g_1[i-1,j-1]\n",
    "                d_min=min((d1,d2,d3))\n",
    "                g_1[i,j]=g[i,j]+d_min\n",
    "    return g_1\n",
    "\n",
    "\n",
    "@cython.boundscheck(False)\n",
    "@cython.wraparound(False)\n",
    "@cython.nonecheck(False)\n",
    "cpdef double min_dis(np.ndarray[double,ndim=2] X,np.ndarray[double,ndim=2] Y):\n",
    "#     assert (X.dtype==np.float and Y.dtype==np.float)\n",
    "    cdef np.ndarray[double,ndim=2] g_1\n",
    "    g_1=dtw(X,Y)\n",
    "    cdef Py_ssize_t m,n\n",
    "    m=g_1.shape[0]\n",
    "    n=g_1.shape[1]\n",
    "    cdef np.ndarray[double,ndim=1] d1,d2\n",
    "    d1=g_1[m-1,0:n]\n",
    "    d2=g_1[0:m,n-1]\n",
    "    cdef double min_d1, min_d2\n",
    "    min_d1=min(d1)\n",
    "    min_d2=min(d2)\n",
    "    cdef double d_min\n",
    "    d_min=min((min_d1,min_d2))\n",
    "    return d_min"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "@wrapt.decorator\n",
    "def timeit(func, args, kwargs):\n",
    "    t=time.time()\n",
    "    ans=func(*args, **kwargs)\n",
    "    t=time.time()-t\n",
    "    print func.__name__, t\n",
    "    return ans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#导入持仓量数据，先用锌来实验"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def cal_ratio(series):\n",
    "    \n",
    "    col_t=series.diff()\n",
    "    series=series.shift(1)\n",
    "    col_t=col_t/series\n",
    "    return col_t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def comp_ohl_with_c(df):\n",
    "    col_list=list(df.columns)\n",
    "    open_col=col_list.index('open')\n",
    "    high_col=col_list.index('high')\n",
    "    low_col=col_list.index('low')\n",
    "    close_col=col_list.index('close_raw')\n",
    "    df_mat=df.as_matrix()\n",
    "    close_array=df_mat[:,close_col]\n",
    "    open_array=df_mat[:,open_col]\n",
    "    high_array=df_mat[:,high_col]\n",
    "    low_array=df_mat[:,low_col]\n",
    "    N=len(close_array)\n",
    "    o_array=np.zeros(N)\n",
    "    h_array=np.zeros(N)\n",
    "    l_array=np.zeros(N)\n",
    "    for i in xrange(1,N):\n",
    "        o_array[i]=(open_array[i]-close_array[i-1])/close_array[i-1]\n",
    "        h_array[i]=(high_array[i]-close_array[i-1])/close_array[i-1]\n",
    "        l_array[i]=(low_array[i]-close_array[i-1])/close_array[i-1]\n",
    "    df['open']=o_array\n",
    "    df['high']=h_array\n",
    "    df['low']=l_array\n",
    "#     df=df.drop(['close_raw'],axis=1)\n",
    "    return df\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "@jit\n",
    "def cut_sample(update_date,data,L,exist_result):\n",
    "    #传入参数包括：日期序列，数据序列本身，移动窗口长度，现有的片段池\n",
    "    #N为日期数（也即数据序列本身的长度）\n",
    "    N=len(update_date)\n",
    "    #如果数据总元素数量等于数据的长度，则表明该数据仅有一列，否则的话有多列（reshape之后能够避免bug）\n",
    "    if data.shape[0]==data.size:\n",
    "        data=data.reshape((data.size,1))\n",
    "    else:\n",
    "        data=data\n",
    "#     #进度条长度\n",
    "#     width=(N-L)/100\n",
    "    #从L-1开始循环，因为python下标从0开始，所以L-1正好是第L个，是第一个能够取到完整的L长度子序列的下标\n",
    "    for i in range(L-1,N):\n",
    "#         if i%100==0:\n",
    "#             p=i/100\n",
    "#             print '['+p*'#'+(width-p)*'-'+']'+str(100.*i/(N-L))+'\\r',\n",
    "#         else:\n",
    "#             pass\n",
    "        start=i-L+1\n",
    "        stop=i\n",
    "        if pd.to_datetime(update_date[i]) in exist_result.keys():\n",
    "            pass\n",
    "        else:\n",
    "            exist_result[pd.to_datetime(update_date[i])]=data[start:stop,:]\n",
    "    return exist_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def loop_day(update_date,df,return_dict,k,L):\n",
    "    pool={}\n",
    "    result={}\n",
    "    result_={}\n",
    "    cnt=0\n",
    "    for day in update_date:\n",
    "        t0=time.time()\n",
    "        if cnt%100==0:\n",
    "            l=len(update_date)\n",
    "            print (cnt/100)*'#',((l-cnt)/100)*'-',cnt*100./l,'\\r',\n",
    "            cnt+=1\n",
    "        dt=df[df['update_date']<=pd.to_datetime(day)]\n",
    "        dt=dt.sort('update_date')\n",
    "        if len(dt)>100:\n",
    "            ud=np.array(dt['update_date'])\n",
    "#             dt['net_chg']=dt['net_chg']/dt['close']\n",
    "#             dt['net_chg']=dt['net_chg']/pd.rolling_mean(dt['net_chg'],50)\n",
    "            dd=dt[['close','open','high','low','volume']].as_matrix()\n",
    "#             dd=dt[['close']].as_matrix()\n",
    "            pool=cut_sample(ud,dd,L,pool)\n",
    "            m=dd.shape[0]\n",
    "            n=dd.shape[1]\n",
    "            Y=dd[m-11:m,:]\n",
    "            d_list=[]\n",
    "            for item in pool.items():\n",
    "                X=item[1]\n",
    "                #将模板和备选序列都倒序排列\n",
    "                X=X[::-1,:]\n",
    "                Y=Y[::-1,:]\n",
    "                key=item[0]\n",
    "                tmp=min_dis_jit(Y,X)\n",
    "                d_list.append((key,tmp))\n",
    "#             d_list=filter(lambda x: x[1]>0,d_list)\n",
    "            d_list=filter(lambda x: pd.to_datetime(x[0])!=pd.to_datetime(day),d_list)\n",
    "            dis_list=[a[1] for a in sorted(d_list,key=lambda x:x[1])][:k]\n",
    "            date_list=[a[0] for a in sorted(d_list,key=lambda x:x[1])][:k]\n",
    "         \n",
    "        \n",
    "#             print day,date_list[0]\n",
    "            \n",
    "            try:\n",
    "                tom_return=[return_dict[x] for x in date_list]\n",
    "                dis_list=np.array(dis_list)\n",
    "                tom_return=np.array(tom_return)\n",
    "                w=1./np.array(dis_list,dtype=np.float)\n",
    "                pre_tom_return=np.dot(w,tom_return.T)/np.sum(w)\n",
    "                time_delta=pd.to_datetime(day)-pd.to_datetime(date_list[0])\n",
    "                t1=time.time()\n",
    "                result[pd.to_datetime(day)]=pre_tom_return\n",
    "                result_[pd.to_datetime(day)]=time_delta.days\n",
    "            except:\n",
    "                pass\n",
    "\n",
    "        else:\n",
    "            pass\n",
    "\n",
    "#     print day, t1-t0\n",
    "    return result,result_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def dtw_match(df,k,L):\n",
    "    df=df.sort('update_date')\n",
    "    update_date=np.array(df['update_date'])\n",
    "    data=df[['close','open','high','low','volume']].as_matrix()\n",
    "    df['return_rate']=(df['close_raw']-df['open_raw'])/df['open_raw']\n",
    "#     df['return_rate']=df['return_rate'].diff()\n",
    "    #用于存储对应时间段之后一天的收益率\n",
    "    return_dict={}\n",
    "    return_array=np.array(df['return_rate'])\n",
    "    for i in range(len(update_date)-1):\n",
    "        return_dict[pd.to_datetime(update_date[i])]=return_array[i+1]\n",
    "    \n",
    "    result,result_=loop_day(update_date,df,return_dict,k,L)\n",
    "\n",
    "    return result,result_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "engine=create_engine(\"mysql+pymysql://liyuefan:1994050306@localhost/gtja_intern?charset=utf8\")\n",
    "\n",
    "buy_vol=pd.read_sql_query('select distinct update_date, sum(hold_vol_buy) as buy_chg \\\n",
    "                          from gtja_intern.al_volume_data where rank_2<=10 group by update_date'\n",
    "                      ,engine)\n",
    "\n",
    "sell_vol=pd.read_sql_query('select distinct update_date, sum(hold_vol_sell) as sell_chg \\\n",
    "                          from gtja_intern.al_volume_data where rank_3<=10 group by update_date'\n",
    "                      ,engine)\n",
    "\n",
    "buy_vol['update_date']=pd.to_datetime(buy_vol['update_date'])\n",
    "sell_vol['update_date']=pd.to_datetime(sell_vol['update_date'])\n",
    "\n",
    "znfi=pd.read_csv('ALFI_zhulian.csv',encoding='gbk')\n",
    "znfi['update_date']=pd.to_datetime(znfi['update_date'])\n",
    "##cauz Wind always fill null OHL data with close price, these data need to be filtered\n",
    "def filter_data(o,h,l,c):\n",
    "    if o==h and h==l and l==c:\n",
    "        return False\n",
    "    elif o>0 and h>0 and l>0 and c>0:\n",
    "        return True\n",
    "    else:\n",
    "        return False\n",
    "\n",
    "znfi=znfi[map(filter_data,znfi['open'],znfi['high'],znfi['low'],znfi['close'])]\n",
    "\n",
    "df=pd.merge(znfi[['update_date','open','high','low','close','volume']],buy_vol,on=['update_date'],how='inner')\n",
    "\n",
    "df=pd.merge(df,sell_vol,on=['update_date'],how='inner')\n",
    "\n",
    "df['net_chg']=df['buy_chg']-df['sell_chg']\n",
    "df=df.drop(['buy_chg','sell_chg'],axis=1)\n",
    "\n",
    "df['close_raw']=df['close']\n",
    "df['close']=cal_ratio(df['close'])\n",
    "df['volume']=cal_ratio(df['volume'])\n",
    "df['net_chg']=cal_ratio(df['net_chg'])\n",
    "df['open_raw']=df['open']\n",
    "\n",
    "df=comp_ohl_with_c(df)\n",
    "df['return_rate']=(df['close_raw']-df['open_raw'])/df['open_raw']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "df_1=df[df['update_date']>=pd.to_datetime('2014-12-05')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def execute(p):\n",
    "    prediction,prediction_=dtw_match(df,10,p)\n",
    "    result=pd.DataFrame.from_dict(prediction,orient='index')\n",
    "    result=result.rename(columns={0:'prediction'})\n",
    "    result_=pd.DataFrame.from_dict(prediction_,orient='index')\n",
    "    result_=result_.rename(columns={0:'time_delta'})\n",
    "    result=pd.concat([result,result_],axis=1,join='inner')\n",
    "    # df['return_rate']=np.log(df['close'])\n",
    "    # df['return_rate']=df['return_rate'].diff()\n",
    "    \n",
    "    df.index=[df['update_date']]\n",
    "    ddf=pd.concat([result,df],axis=1,join='inner')\n",
    "    ddf=ddf.drop(['update_date'],axis=1)\n",
    "    ddf['prediction']=ddf['prediction'].shift(1)\n",
    "    ddf['direction']=ddf['prediction'].apply(lambda x:1 if x>0 else -1)\n",
    "    ddf['daily_return']=ddf['direction']*ddf['return_rate']\n",
    "    ddf['cum_return']=ddf['daily_return'].cumsum()\n",
    "    ddf['real_direction']=ddf['return_rate'].apply(lambda x:1 if x>0 else -1)\n",
    "    acc=len(ddf[ddf['real_direction']==ddf['direction']])/float(len(ddf))\n",
    "    print p,acc,ddf['cum_return'][-1]\n",
    "    return ddf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parent process 48931.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/liyuefan/anaconda2/lib/python2.7/site-packages/ipykernel/__main__.py:2: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n",
      "  from ipykernel import kernelapp as app\n",
      "/Users/liyuefan/anaconda2/lib/python2.7/site-packages/ipykernel/__main__.py:2: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n",
      "  from ipykernel import kernelapp as app\n",
      "/Users/liyuefan/anaconda2/lib/python2.7/site-packages/ipykernel/__main__.py:2: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n",
      "  from ipykernel import kernelapp as app\n",
      "/Users/liyuefan/anaconda2/lib/python2.7/site-packages/ipykernel/__main__.py:2: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Waiting for all subprocesses done...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/liyuefan/anaconda2/lib/python2.7/site-packages/ipykernel/__main__.py:13: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n",
      "/Users/liyuefan/anaconda2/lib/python2.7/site-packages/ipykernel/__main__.py:13: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n",
      "/Users/liyuefan/anaconda2/lib/python2.7/site-packages/ipykernel/__main__.py:13: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n",
      "/Users/liyuefan/anaconda2/lib/python2.7/site-packages/ipykernel/__main__.py:13: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6 0.514500537057 -0.611910499047\n",
      "8 0.518796992481 -0.462523589995\n",
      "10 0.513426423201 -0.661435595388\n",
      "12 0.515574650913 -0.583608880942\n",
      "14 0.520408163265 -0.313578863794\n",
      "16 0.523093447905 -0.122094478998\n",
      "18 0.52470461869 -0.0519812040996\n",
      "20 0.523093447905 -0.0465633877383\n",
      "22 0.523630504834 -0.177104811283\n",
      "24 0.524167561762 -0.103430486602\n",
      "26 0.522556390977 -0.21504414436\n",
      "28 0.519871106337 -0.518554865483\n",
      "All subprocesses done.\n"
     ]
    }
   ],
   "source": [
    "if __name__=='__main__':\n",
    "    print('Parent process %s.' % os.getpid())\n",
    "    p = Pool(4)\n",
    "    for i in range(6,30,2):\n",
    "        p.apply_async(execute, args=(i,))\n",
    "    print('Waiting for all subprocesses done...')\n",
    "    p.close()\n",
    "    p.join()\n",
    "    print('All subprocesses done.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/liyuefan/anaconda2/lib/python2.7/site-packages/ipykernel/__main__.py:2: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n",
      "  from ipykernel import kernelapp as app\n",
      "/Users/liyuefan/anaconda2/lib/python2.7/site-packages/ipykernel/__main__.py:13: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18 0.506444683136 0.305506333239\n"
     ]
    }
   ],
   "source": [
    "ddf=execute(18)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "ddf['benchmark']=ddf['return_rate'].cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1260adc90>"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3YAAAJCCAYAAACMMVWCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAg/aU7nlAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x125ce8690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "plt.plot(ddf['cum_return'],label='cum_return')\n",
    "# ax2=plt.twinx()\n",
    "plt.plot(ddf['benchmark'],color='orange',label='benchmark')\n",
    "# ax3=ax2.twinx()\n",
    "# ax3.bar(ddf.index,ddf['direction'],color='red',alpha=0.2)\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.LineCollection at 0x116d4db10>"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3wAAAJCCAYAAACbE8VLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x124a39690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "plt.plot(ddf['prediction'],ddf['return_rate'],'o')\n",
    "plt.vlines(0,np.max(ddf['return_rate']),np.min(ddf['return_rate']),linestyles='dashed',color='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "from scipy.stats import pearsonr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.0019194064780725501, 0.93405325939485506)"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dx=ddf.dropna(axis=0)\n",
    "X=np.array(dx['prediction'])\n",
    "Y=np.array(dx['return_rate'])\n",
    "pearsonr(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "ddf['corr_or_not']=map(lambda x,y: 1 if x==y else 0, ddf['direction'],ddf['real_direction'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "tt=ddf[['time_delta','corr_or_not']]\n",
    "tt=tt[tt['time_delta']>5][tt['time_delta']<750]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x125261290>]"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA20AAAJCCAYAAABTfy+dAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n2P8D+ixVy09aBNcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x124a39650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "plt.plot(tt['time_delta'],tt['corr_or_not'],'bo')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x124a427d0>"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlMAAAFpCAYAAAC4SK2+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nU5IkSQUMU5IkSQX+PySCZLM/Ul0wAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c6e5d90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tt_cor=tt[tt['corr_or_not']==1]\n",
    "tt_not_cor=tt[tt['corr_or_not']==0]\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.hist(tt_cor['time_delta'],bins=40,alpha=0.3,label='correct');\n",
    "plt.hist(tt_not_cor['time_delta'],bins=40,alpha=0.3,label='uncorrect');\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>prediction</th>\n",
       "      <th>time_delta</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>net_chg</th>\n",
       "      <th>close_raw</th>\n",
       "      <th>open_raw</th>\n",
       "      <th>return_rate</th>\n",
       "      <th>direction</th>\n",
       "      <th>daily_return</th>\n",
       "      <th>cum_return</th>\n",
       "      <th>real_direction</th>\n",
       "      <th>benchmark</th>\n",
       "      <th>corr_or_not</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2009-06-05</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>0.039790</td>\n",
       "      <td>0.039790</td>\n",
       "      <td>0.019520</td>\n",
       "      <td>0.023273</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.011675</td>\n",
       "      <td>13630</td>\n",
       "      <td>13850</td>\n",
       "      <td>-0.015884</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.015884</td>\n",
       "      <td>0.015884</td>\n",
       "      <td>-1</td>\n",
       "      <td>-0.015884</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-06-08</th>\n",
       "      <td>0.002985</td>\n",
       "      <td>5</td>\n",
       "      <td>-0.007337</td>\n",
       "      <td>-0.002201</td>\n",
       "      <td>-0.015407</td>\n",
       "      <td>-0.009538</td>\n",
       "      <td>-0.359736</td>\n",
       "      <td>-0.040026</td>\n",
       "      <td>13500</td>\n",
       "      <td>13530</td>\n",
       "      <td>-0.002217</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.002217</td>\n",
       "      <td>0.013667</td>\n",
       "      <td>-1</td>\n",
       "      <td>-0.018102</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-06-09</th>\n",
       "      <td>0.005106</td>\n",
       "      <td>4</td>\n",
       "      <td>0.009630</td>\n",
       "      <td>0.009630</td>\n",
       "      <td>-0.002222</td>\n",
       "      <td>0.001481</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100256</td>\n",
       "      <td>13520</td>\n",
       "      <td>13630</td>\n",
       "      <td>-0.008070</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.008070</td>\n",
       "      <td>0.005597</td>\n",
       "      <td>-1</td>\n",
       "      <td>-0.026172</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-06-10</th>\n",
       "      <td>0.003994</td>\n",
       "      <td>124</td>\n",
       "      <td>0.007396</td>\n",
       "      <td>0.011834</td>\n",
       "      <td>0.002589</td>\n",
       "      <td>0.011834</td>\n",
       "      <td>-0.221649</td>\n",
       "      <td>-0.028472</td>\n",
       "      <td>13680</td>\n",
       "      <td>13620</td>\n",
       "      <td>0.004405</td>\n",
       "      <td>1</td>\n",
       "      <td>0.004405</td>\n",
       "      <td>0.010002</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.021767</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-06-11</th>\n",
       "      <td>0.006126</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.002924</td>\n",
       "      <td>-0.002924</td>\n",
       "      <td>-0.008772</td>\n",
       "      <td>-0.006944</td>\n",
       "      <td>-0.314570</td>\n",
       "      <td>-0.010460</td>\n",
       "      <td>13585</td>\n",
       "      <td>13640</td>\n",
       "      <td>-0.004032</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.004032</td>\n",
       "      <td>0.005970</td>\n",
       "      <td>-1</td>\n",
       "      <td>-0.025799</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-06-12</th>\n",
       "      <td>0.004530</td>\n",
       "      <td>2</td>\n",
       "      <td>0.004785</td>\n",
       "      <td>0.005889</td>\n",
       "      <td>0.002576</td>\n",
       "      <td>0.002576</td>\n",
       "      <td>0.231884</td>\n",
       "      <td>-0.071161</td>\n",
       "      <td>13620</td>\n",
       "      <td>13650</td>\n",
       "      <td>-0.002198</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.002198</td>\n",
       "      <td>0.003772</td>\n",
       "      <td>-1</td>\n",
       "      <td>-0.027997</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-06-15</th>\n",
       "      <td>0.005249</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.007709</td>\n",
       "      <td>-0.005140</td>\n",
       "      <td>-0.015419</td>\n",
       "      <td>-0.008811</td>\n",
       "      <td>0.019608</td>\n",
       "      <td>-0.017707</td>\n",
       "      <td>13500</td>\n",
       "      <td>13515</td>\n",
       "      <td>-0.001110</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.001110</td>\n",
       "      <td>0.002662</td>\n",
       "      <td>-1</td>\n",
       "      <td>-0.029107</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-06-16</th>\n",
       "      <td>0.001556</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.016296</td>\n",
       "      <td>-0.004815</td>\n",
       "      <td>-0.016296</td>\n",
       "      <td>-0.006667</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>0.014337</td>\n",
       "      <td>13410</td>\n",
       "      <td>13280</td>\n",
       "      <td>0.009789</td>\n",
       "      <td>1</td>\n",
       "      <td>0.009789</td>\n",
       "      <td>0.012451</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.019318</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-06-17</th>\n",
       "      <td>0.005319</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.000746</td>\n",
       "      <td>0.010440</td>\n",
       "      <td>-0.001491</td>\n",
       "      <td>0.010440</td>\n",
       "      <td>-0.387574</td>\n",
       "      <td>-0.027949</td>\n",
       "      <td>13550</td>\n",
       "      <td>13400</td>\n",
       "      <td>0.011194</td>\n",
       "      <td>1</td>\n",
       "      <td>0.011194</td>\n",
       "      <td>0.023645</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.008124</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-06-18</th>\n",
       "      <td>0.005027</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.001107</td>\n",
       "      <td>0.000738</td>\n",
       "      <td>-0.002214</td>\n",
       "      <td>0.000738</td>\n",
       "      <td>0.225845</td>\n",
       "      <td>-0.025798</td>\n",
       "      <td>13560</td>\n",
       "      <td>13535</td>\n",
       "      <td>0.001847</td>\n",
       "      <td>1</td>\n",
       "      <td>0.001847</td>\n",
       "      <td>0.025492</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.006277</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-06-19</th>\n",
       "      <td>0.007573</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001844</td>\n",
       "      <td>-0.002212</td>\n",
       "      <td>-0.000369</td>\n",
       "      <td>0.370443</td>\n",
       "      <td>-0.015835</td>\n",
       "      <td>13555</td>\n",
       "      <td>13560</td>\n",
       "      <td>-0.000369</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.000369</td>\n",
       "      <td>0.025124</td>\n",
       "      <td>-1</td>\n",
       "      <td>-0.006645</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-06-22</th>\n",
       "      <td>0.006811</td>\n",
       "      <td>137</td>\n",
       "      <td>-0.002582</td>\n",
       "      <td>0.001844</td>\n",
       "      <td>-0.003320</td>\n",
       "      <td>-0.002582</td>\n",
       "      <td>-0.238677</td>\n",
       "      <td>-0.033994</td>\n",
       "      <td>13520</td>\n",
       "      <td>13520</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.025124</td>\n",
       "      <td>-1</td>\n",
       "      <td>-0.006645</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-06-23</th>\n",
       "      <td>0.005253</td>\n",
       "      <td>39</td>\n",
       "      <td>-0.028476</td>\n",
       "      <td>-0.005178</td>\n",
       "      <td>-0.028476</td>\n",
       "      <td>-0.006287</td>\n",
       "      <td>0.349386</td>\n",
       "      <td>0.031753</td>\n",
       "      <td>13435</td>\n",
       "      <td>13135</td>\n",
       "      <td>0.022840</td>\n",
       "      <td>1</td>\n",
       "      <td>0.022840</td>\n",
       "      <td>0.047963</td>\n",
       "      <td>1</td>\n",
       "      <td>0.016194</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-06-24</th>\n",
       "      <td>0.005506</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.003349</td>\n",
       "      <td>0.008560</td>\n",
       "      <td>-0.003349</td>\n",
       "      <td>0.008560</td>\n",
       "      <td>-0.398880</td>\n",
       "      <td>-0.055884</td>\n",
       "      <td>13550</td>\n",
       "      <td>13390</td>\n",
       "      <td>0.011949</td>\n",
       "      <td>1</td>\n",
       "      <td>0.011949</td>\n",
       "      <td>0.059913</td>\n",
       "      <td>1</td>\n",
       "      <td>0.028144</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-06-25</th>\n",
       "      <td>0.005968</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.000738</td>\n",
       "      <td>0.002952</td>\n",
       "      <td>-0.000738</td>\n",
       "      <td>0.000738</td>\n",
       "      <td>0.790454</td>\n",
       "      <td>-0.017426</td>\n",
       "      <td>13560</td>\n",
       "      <td>13540</td>\n",
       "      <td>0.001477</td>\n",
       "      <td>1</td>\n",
       "      <td>0.001477</td>\n",
       "      <td>0.061390</td>\n",
       "      <td>1</td>\n",
       "      <td>0.029621</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-06-26</th>\n",
       "      <td>0.005638</td>\n",
       "      <td>4</td>\n",
       "      <td>0.001106</td>\n",
       "      <td>0.005531</td>\n",
       "      <td>0.000369</td>\n",
       "      <td>0.003687</td>\n",
       "      <td>-0.347204</td>\n",
       "      <td>-0.015255</td>\n",
       "      <td>13610</td>\n",
       "      <td>13575</td>\n",
       "      <td>0.002578</td>\n",
       "      <td>1</td>\n",
       "      <td>0.002578</td>\n",
       "      <td>0.063968</td>\n",
       "      <td>1</td>\n",
       "      <td>0.032199</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-06-29</th>\n",
       "      <td>0.007479</td>\n",
       "      <td>6</td>\n",
       "      <td>-0.006245</td>\n",
       "      <td>0.000735</td>\n",
       "      <td>-0.006245</td>\n",
       "      <td>-0.004776</td>\n",
       "      <td>0.749004</td>\n",
       "      <td>-0.013197</td>\n",
       "      <td>13545</td>\n",
       "      <td>13525</td>\n",
       "      <td>0.001479</td>\n",
       "      <td>1</td>\n",
       "      <td>0.001479</td>\n",
       "      <td>0.065447</td>\n",
       "      <td>1</td>\n",
       "      <td>0.033678</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-06-30</th>\n",
       "      <td>0.006982</td>\n",
       "      <td>43</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.004061</td>\n",
       "      <td>-0.004799</td>\n",
       "      <td>-0.004799</td>\n",
       "      <td>-0.577449</td>\n",
       "      <td>0.024690</td>\n",
       "      <td>13480</td>\n",
       "      <td>13545</td>\n",
       "      <td>-0.004799</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.004799</td>\n",
       "      <td>0.060648</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.028879</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-07-01</th>\n",
       "      <td>0.003861</td>\n",
       "      <td>1</td>\n",
       "      <td>0.005193</td>\n",
       "      <td>0.005935</td>\n",
       "      <td>0.003709</td>\n",
       "      <td>0.005935</td>\n",
       "      <td>-0.575472</td>\n",
       "      <td>-0.032053</td>\n",
       "      <td>13560</td>\n",
       "      <td>13550</td>\n",
       "      <td>0.000738</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000738</td>\n",
       "      <td>0.061386</td>\n",
       "      <td>1</td>\n",
       "      <td>0.029617</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-07-02</th>\n",
       "      <td>0.004160</td>\n",
       "      <td>1</td>\n",
       "      <td>0.002212</td>\n",
       "      <td>0.003687</td>\n",
       "      <td>0.000737</td>\n",
       "      <td>0.002950</td>\n",
       "      <td>2.253968</td>\n",
       "      <td>-0.021321</td>\n",
       "      <td>13600</td>\n",
       "      <td>13590</td>\n",
       "      <td>0.000736</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000736</td>\n",
       "      <td>0.062122</td>\n",
       "      <td>1</td>\n",
       "      <td>0.030353</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-07-03</th>\n",
       "      <td>0.003629</td>\n",
       "      <td>45</td>\n",
       "      <td>-0.005147</td>\n",
       "      <td>0.011029</td>\n",
       "      <td>-0.005147</td>\n",
       "      <td>0.000368</td>\n",
       "      <td>0.882927</td>\n",
       "      <td>0.075718</td>\n",
       "      <td>13605</td>\n",
       "      <td>13530</td>\n",
       "      <td>0.005543</td>\n",
       "      <td>1</td>\n",
       "      <td>0.005543</td>\n",
       "      <td>0.067665</td>\n",
       "      <td>1</td>\n",
       "      <td>0.035896</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-07-06</th>\n",
       "      <td>0.007064</td>\n",
       "      <td>4</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.006248</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000368</td>\n",
       "      <td>-0.406736</td>\n",
       "      <td>-0.012771</td>\n",
       "      <td>13610</td>\n",
       "      <td>13605</td>\n",
       "      <td>0.000368</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000368</td>\n",
       "      <td>0.068032</td>\n",
       "      <td>1</td>\n",
       "      <td>0.036264</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-07-07</th>\n",
       "      <td>0.007400</td>\n",
       "      <td>1</td>\n",
       "      <td>0.008082</td>\n",
       "      <td>0.008082</td>\n",
       "      <td>0.002204</td>\n",
       "      <td>0.002939</td>\n",
       "      <td>-0.244541</td>\n",
       "      <td>-0.002329</td>\n",
       "      <td>13650</td>\n",
       "      <td>13720</td>\n",
       "      <td>-0.005102</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.005102</td>\n",
       "      <td>0.062930</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.031161</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-07-08</th>\n",
       "      <td>-0.001282</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.002564</td>\n",
       "      <td>-0.002198</td>\n",
       "      <td>-0.004396</td>\n",
       "      <td>-0.002930</td>\n",
       "      <td>0.075145</td>\n",
       "      <td>0.259744</td>\n",
       "      <td>13610</td>\n",
       "      <td>13615</td>\n",
       "      <td>-0.000367</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.000367</td>\n",
       "      <td>0.063298</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.030794</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-07-09</th>\n",
       "      <td>0.002554</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.002204</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.002939</td>\n",
       "      <td>-0.001102</td>\n",
       "      <td>0.026882</td>\n",
       "      <td>0.072699</td>\n",
       "      <td>13595</td>\n",
       "      <td>13580</td>\n",
       "      <td>0.001105</td>\n",
       "      <td>1</td>\n",
       "      <td>0.001105</td>\n",
       "      <td>0.064402</td>\n",
       "      <td>1</td>\n",
       "      <td>0.031899</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-07-10</th>\n",
       "      <td>0.005939</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.000736</td>\n",
       "      <td>0.000368</td>\n",
       "      <td>-0.002207</td>\n",
       "      <td>-0.001471</td>\n",
       "      <td>0.062827</td>\n",
       "      <td>0.012611</td>\n",
       "      <td>13575</td>\n",
       "      <td>13585</td>\n",
       "      <td>-0.000736</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.000736</td>\n",
       "      <td>0.063666</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.031163</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-07-13</th>\n",
       "      <td>0.001595</td>\n",
       "      <td>3</td>\n",
       "      <td>0.002578</td>\n",
       "      <td>0.002578</td>\n",
       "      <td>-0.002578</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.216749</td>\n",
       "      <td>-0.019764</td>\n",
       "      <td>13575</td>\n",
       "      <td>13610</td>\n",
       "      <td>-0.002572</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.002572</td>\n",
       "      <td>0.061094</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.028591</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-07-14</th>\n",
       "      <td>0.002883</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000368</td>\n",
       "      <td>0.000368</td>\n",
       "      <td>-0.004788</td>\n",
       "      <td>-0.004788</td>\n",
       "      <td>-0.214575</td>\n",
       "      <td>-0.001464</td>\n",
       "      <td>13510</td>\n",
       "      <td>13580</td>\n",
       "      <td>-0.005155</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.005155</td>\n",
       "      <td>0.055940</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.023436</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-07-15</th>\n",
       "      <td>-0.000335</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.000740</td>\n",
       "      <td>0.009252</td>\n",
       "      <td>-0.000740</td>\n",
       "      <td>0.001480</td>\n",
       "      <td>0.072165</td>\n",
       "      <td>-0.035517</td>\n",
       "      <td>13530</td>\n",
       "      <td>13500</td>\n",
       "      <td>0.002222</td>\n",
       "      <td>-1</td>\n",
       "      <td>-0.002222</td>\n",
       "      <td>0.053718</td>\n",
       "      <td>1</td>\n",
       "      <td>0.025659</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-07-16</th>\n",
       "      <td>0.000322</td>\n",
       "      <td>3</td>\n",
       "      <td>0.006652</td>\n",
       "      <td>0.011826</td>\n",
       "      <td>0.003326</td>\n",
       "      <td>0.003695</td>\n",
       "      <td>0.498077</td>\n",
       "      <td>-0.029855</td>\n",
       "      <td>13580</td>\n",
       "      <td>13620</td>\n",
       "      <td>-0.002937</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.002937</td>\n",
       "      <td>0.050781</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.022722</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-06</th>\n",
       "      <td>0.004601</td>\n",
       "      <td>316</td>\n",
       "      <td>-0.044859</td>\n",
       "      <td>0.007543</td>\n",
       "      <td>-0.044859</td>\n",
       "      <td>-0.004764</td>\n",
       "      <td>-0.075949</td>\n",
       "      <td>0.011259</td>\n",
       "      <td>12535</td>\n",
       "      <td>12030</td>\n",
       "      <td>0.041978</td>\n",
       "      <td>1</td>\n",
       "      <td>0.041978</td>\n",
       "      <td>0.467395</td>\n",
       "      <td>1</td>\n",
       "      <td>0.389943</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-09</th>\n",
       "      <td>0.003566</td>\n",
       "      <td>3</td>\n",
       "      <td>0.011169</td>\n",
       "      <td>0.011169</td>\n",
       "      <td>-0.001596</td>\n",
       "      <td>0.001197</td>\n",
       "      <td>0.399543</td>\n",
       "      <td>0.011846</td>\n",
       "      <td>12550</td>\n",
       "      <td>12675</td>\n",
       "      <td>-0.009862</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.009862</td>\n",
       "      <td>0.457533</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.380081</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-10</th>\n",
       "      <td>-0.003974</td>\n",
       "      <td>316</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.018327</td>\n",
       "      <td>-0.003187</td>\n",
       "      <td>0.016733</td>\n",
       "      <td>1.481240</td>\n",
       "      <td>0.078826</td>\n",
       "      <td>12760</td>\n",
       "      <td>12550</td>\n",
       "      <td>0.016733</td>\n",
       "      <td>-1</td>\n",
       "      <td>-0.016733</td>\n",
       "      <td>0.440800</td>\n",
       "      <td>1</td>\n",
       "      <td>0.396814</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-11</th>\n",
       "      <td>-0.001192</td>\n",
       "      <td>2360</td>\n",
       "      <td>0.003527</td>\n",
       "      <td>0.012147</td>\n",
       "      <td>0.001176</td>\n",
       "      <td>0.009013</td>\n",
       "      <td>-0.485865</td>\n",
       "      <td>0.169101</td>\n",
       "      <td>12875</td>\n",
       "      <td>12805</td>\n",
       "      <td>0.005467</td>\n",
       "      <td>-1</td>\n",
       "      <td>-0.005467</td>\n",
       "      <td>0.435333</td>\n",
       "      <td>1</td>\n",
       "      <td>0.402281</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-12</th>\n",
       "      <td>0.004470</td>\n",
       "      <td>1</td>\n",
       "      <td>0.013592</td>\n",
       "      <td>0.017476</td>\n",
       "      <td>-0.001165</td>\n",
       "      <td>0.017476</td>\n",
       "      <td>0.480818</td>\n",
       "      <td>-0.049412</td>\n",
       "      <td>13100</td>\n",
       "      <td>13050</td>\n",
       "      <td>0.003831</td>\n",
       "      <td>1</td>\n",
       "      <td>0.003831</td>\n",
       "      <td>0.439165</td>\n",
       "      <td>1</td>\n",
       "      <td>0.406112</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-13</th>\n",
       "      <td>-0.000051</td>\n",
       "      <td>1</td>\n",
       "      <td>0.014885</td>\n",
       "      <td>0.029008</td>\n",
       "      <td>0.000763</td>\n",
       "      <td>0.012214</td>\n",
       "      <td>-0.208981</td>\n",
       "      <td>0.372343</td>\n",
       "      <td>13260</td>\n",
       "      <td>13295</td>\n",
       "      <td>-0.002633</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.002633</td>\n",
       "      <td>0.441797</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.403480</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-16</th>\n",
       "      <td>-0.001282</td>\n",
       "      <td>4</td>\n",
       "      <td>0.013952</td>\n",
       "      <td>0.017345</td>\n",
       "      <td>-0.005279</td>\n",
       "      <td>-0.003394</td>\n",
       "      <td>-0.014192</td>\n",
       "      <td>-0.058982</td>\n",
       "      <td>13215</td>\n",
       "      <td>13445</td>\n",
       "      <td>-0.017107</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.017107</td>\n",
       "      <td>0.458904</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.386373</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-17</th>\n",
       "      <td>0.002179</td>\n",
       "      <td>1</td>\n",
       "      <td>0.011351</td>\n",
       "      <td>0.017404</td>\n",
       "      <td>-0.005297</td>\n",
       "      <td>0.002649</td>\n",
       "      <td>3.596013</td>\n",
       "      <td>-0.092811</td>\n",
       "      <td>13250</td>\n",
       "      <td>13365</td>\n",
       "      <td>-0.008605</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.008605</td>\n",
       "      <td>0.450299</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.377769</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-18</th>\n",
       "      <td>-0.003606</td>\n",
       "      <td>1</td>\n",
       "      <td>0.009434</td>\n",
       "      <td>0.009434</td>\n",
       "      <td>-0.006038</td>\n",
       "      <td>0.000755</td>\n",
       "      <td>-0.395692</td>\n",
       "      <td>0.035909</td>\n",
       "      <td>13260</td>\n",
       "      <td>13375</td>\n",
       "      <td>-0.008598</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.008598</td>\n",
       "      <td>0.458897</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.369170</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-19</th>\n",
       "      <td>0.002083</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.009804</td>\n",
       "      <td>-0.003771</td>\n",
       "      <td>0.001885</td>\n",
       "      <td>-0.036045</td>\n",
       "      <td>0.062795</td>\n",
       "      <td>13285</td>\n",
       "      <td>13260</td>\n",
       "      <td>0.001885</td>\n",
       "      <td>1</td>\n",
       "      <td>0.001885</td>\n",
       "      <td>0.460783</td>\n",
       "      <td>1</td>\n",
       "      <td>0.371056</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-20</th>\n",
       "      <td>0.000800</td>\n",
       "      <td>3</td>\n",
       "      <td>0.004893</td>\n",
       "      <td>0.007151</td>\n",
       "      <td>-0.001882</td>\n",
       "      <td>-0.001129</td>\n",
       "      <td>0.062293</td>\n",
       "      <td>0.016109</td>\n",
       "      <td>13270</td>\n",
       "      <td>13350</td>\n",
       "      <td>-0.005993</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.005993</td>\n",
       "      <td>0.454790</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.365063</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-23</th>\n",
       "      <td>-0.001598</td>\n",
       "      <td>1025</td>\n",
       "      <td>0.004898</td>\n",
       "      <td>0.039563</td>\n",
       "      <td>0.003014</td>\n",
       "      <td>0.026375</td>\n",
       "      <td>0.781170</td>\n",
       "      <td>0.112011</td>\n",
       "      <td>13620</td>\n",
       "      <td>13335</td>\n",
       "      <td>0.021372</td>\n",
       "      <td>-1</td>\n",
       "      <td>-0.021372</td>\n",
       "      <td>0.433418</td>\n",
       "      <td>1</td>\n",
       "      <td>0.386436</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-24</th>\n",
       "      <td>0.001573</td>\n",
       "      <td>1</td>\n",
       "      <td>0.002570</td>\n",
       "      <td>0.008811</td>\n",
       "      <td>-0.001836</td>\n",
       "      <td>0.003304</td>\n",
       "      <td>-0.542279</td>\n",
       "      <td>-0.102642</td>\n",
       "      <td>13665</td>\n",
       "      <td>13655</td>\n",
       "      <td>0.000732</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000732</td>\n",
       "      <td>0.434150</td>\n",
       "      <td>1</td>\n",
       "      <td>0.387168</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-25</th>\n",
       "      <td>-0.000565</td>\n",
       "      <td>1</td>\n",
       "      <td>0.005488</td>\n",
       "      <td>0.028174</td>\n",
       "      <td>0.005488</td>\n",
       "      <td>0.023783</td>\n",
       "      <td>0.314643</td>\n",
       "      <td>0.001460</td>\n",
       "      <td>13990</td>\n",
       "      <td>13740</td>\n",
       "      <td>0.018195</td>\n",
       "      <td>-1</td>\n",
       "      <td>-0.018195</td>\n",
       "      <td>0.415955</td>\n",
       "      <td>1</td>\n",
       "      <td>0.405363</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-26</th>\n",
       "      <td>0.003683</td>\n",
       "      <td>2131</td>\n",
       "      <td>-0.006791</td>\n",
       "      <td>-0.002144</td>\n",
       "      <td>-0.028234</td>\n",
       "      <td>-0.016083</td>\n",
       "      <td>-0.586282</td>\n",
       "      <td>0.225013</td>\n",
       "      <td>13765</td>\n",
       "      <td>13895</td>\n",
       "      <td>-0.009356</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.009356</td>\n",
       "      <td>0.406599</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.396007</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-03</th>\n",
       "      <td>0.001545</td>\n",
       "      <td>634</td>\n",
       "      <td>-0.001090</td>\n",
       "      <td>0.003996</td>\n",
       "      <td>-0.014530</td>\n",
       "      <td>-0.005449</td>\n",
       "      <td>-0.503512</td>\n",
       "      <td>-0.060930</td>\n",
       "      <td>13690</td>\n",
       "      <td>13750</td>\n",
       "      <td>-0.004364</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.004364</td>\n",
       "      <td>0.402236</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.391644</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-06</th>\n",
       "      <td>-0.002152</td>\n",
       "      <td>11</td>\n",
       "      <td>-0.002922</td>\n",
       "      <td>0.010592</td>\n",
       "      <td>-0.014609</td>\n",
       "      <td>-0.009496</td>\n",
       "      <td>1.465511</td>\n",
       "      <td>0.006944</td>\n",
       "      <td>13560</td>\n",
       "      <td>13650</td>\n",
       "      <td>-0.006593</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.006593</td>\n",
       "      <td>0.408829</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.385050</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-07</th>\n",
       "      <td>0.001462</td>\n",
       "      <td>298</td>\n",
       "      <td>0.003687</td>\n",
       "      <td>0.007375</td>\n",
       "      <td>-0.007006</td>\n",
       "      <td>-0.002212</td>\n",
       "      <td>-0.136298</td>\n",
       "      <td>-0.015631</td>\n",
       "      <td>13530</td>\n",
       "      <td>13610</td>\n",
       "      <td>-0.005878</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.005878</td>\n",
       "      <td>0.402951</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.379172</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-08</th>\n",
       "      <td>-0.000875</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.003695</td>\n",
       "      <td>0.007391</td>\n",
       "      <td>-0.004435</td>\n",
       "      <td>0.004435</td>\n",
       "      <td>-0.333887</td>\n",
       "      <td>-0.039354</td>\n",
       "      <td>13590</td>\n",
       "      <td>13480</td>\n",
       "      <td>0.008160</td>\n",
       "      <td>-1</td>\n",
       "      <td>-0.008160</td>\n",
       "      <td>0.394791</td>\n",
       "      <td>1</td>\n",
       "      <td>0.387332</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-09</th>\n",
       "      <td>-0.001100</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.002575</td>\n",
       "      <td>0.011773</td>\n",
       "      <td>-0.002575</td>\n",
       "      <td>0.005887</td>\n",
       "      <td>0.481297</td>\n",
       "      <td>-0.053914</td>\n",
       "      <td>13670</td>\n",
       "      <td>13555</td>\n",
       "      <td>0.008484</td>\n",
       "      <td>-1</td>\n",
       "      <td>-0.008484</td>\n",
       "      <td>0.386307</td>\n",
       "      <td>1</td>\n",
       "      <td>0.395816</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-10</th>\n",
       "      <td>-0.001687</td>\n",
       "      <td>1</td>\n",
       "      <td>0.005121</td>\n",
       "      <td>0.005852</td>\n",
       "      <td>-0.009510</td>\n",
       "      <td>-0.000732</td>\n",
       "      <td>-0.184343</td>\n",
       "      <td>-0.061205</td>\n",
       "      <td>13660</td>\n",
       "      <td>13740</td>\n",
       "      <td>-0.005822</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.005822</td>\n",
       "      <td>0.392129</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.389994</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-13</th>\n",
       "      <td>-0.001451</td>\n",
       "      <td>2498</td>\n",
       "      <td>0.002928</td>\n",
       "      <td>0.020864</td>\n",
       "      <td>0.002928</td>\n",
       "      <td>0.013543</td>\n",
       "      <td>0.424149</td>\n",
       "      <td>-0.063553</td>\n",
       "      <td>13845</td>\n",
       "      <td>13700</td>\n",
       "      <td>0.010584</td>\n",
       "      <td>-1</td>\n",
       "      <td>-0.010584</td>\n",
       "      <td>0.381545</td>\n",
       "      <td>1</td>\n",
       "      <td>0.400578</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-14</th>\n",
       "      <td>-0.000934</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.009751</td>\n",
       "      <td>0.028891</td>\n",
       "      <td>-0.009751</td>\n",
       "      <td>0.028530</td>\n",
       "      <td>-0.097101</td>\n",
       "      <td>-0.109552</td>\n",
       "      <td>14240</td>\n",
       "      <td>13710</td>\n",
       "      <td>0.038658</td>\n",
       "      <td>-1</td>\n",
       "      <td>-0.038658</td>\n",
       "      <td>0.342888</td>\n",
       "      <td>1</td>\n",
       "      <td>0.439236</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-15</th>\n",
       "      <td>0.009432</td>\n",
       "      <td>5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.005969</td>\n",
       "      <td>-0.014747</td>\n",
       "      <td>0.000702</td>\n",
       "      <td>-0.311396</td>\n",
       "      <td>0.140962</td>\n",
       "      <td>14250</td>\n",
       "      <td>14240</td>\n",
       "      <td>0.000702</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000702</td>\n",
       "      <td>0.343590</td>\n",
       "      <td>1</td>\n",
       "      <td>0.439938</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-16</th>\n",
       "      <td>0.002802</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.001754</td>\n",
       "      <td>0.016842</td>\n",
       "      <td>-0.008421</td>\n",
       "      <td>0.001754</td>\n",
       "      <td>9.715851</td>\n",
       "      <td>0.027495</td>\n",
       "      <td>14275</td>\n",
       "      <td>14225</td>\n",
       "      <td>0.003515</td>\n",
       "      <td>1</td>\n",
       "      <td>0.003515</td>\n",
       "      <td>0.347105</td>\n",
       "      <td>1</td>\n",
       "      <td>0.443453</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-17</th>\n",
       "      <td>0.003651</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.004904</td>\n",
       "      <td>0.002802</td>\n",
       "      <td>-0.022767</td>\n",
       "      <td>-0.020665</td>\n",
       "      <td>-0.301386</td>\n",
       "      <td>-0.004391</td>\n",
       "      <td>13980</td>\n",
       "      <td>14205</td>\n",
       "      <td>-0.015839</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.015839</td>\n",
       "      <td>0.331265</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.427613</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-20</th>\n",
       "      <td>-0.000603</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.002861</td>\n",
       "      <td>0.008584</td>\n",
       "      <td>-0.011087</td>\n",
       "      <td>0.004649</td>\n",
       "      <td>-0.294433</td>\n",
       "      <td>-0.095758</td>\n",
       "      <td>14045</td>\n",
       "      <td>13940</td>\n",
       "      <td>0.007532</td>\n",
       "      <td>-1</td>\n",
       "      <td>-0.007532</td>\n",
       "      <td>0.323733</td>\n",
       "      <td>1</td>\n",
       "      <td>0.435146</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-21</th>\n",
       "      <td>0.000492</td>\n",
       "      <td>1</td>\n",
       "      <td>0.001424</td>\n",
       "      <td>0.016020</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.006408</td>\n",
       "      <td>-0.139673</td>\n",
       "      <td>0.147211</td>\n",
       "      <td>14135</td>\n",
       "      <td>14065</td>\n",
       "      <td>0.004977</td>\n",
       "      <td>1</td>\n",
       "      <td>0.004977</td>\n",
       "      <td>0.328710</td>\n",
       "      <td>1</td>\n",
       "      <td>0.440123</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-22</th>\n",
       "      <td>0.000706</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.003891</td>\n",
       "      <td>0.004599</td>\n",
       "      <td>-0.021578</td>\n",
       "      <td>-0.019455</td>\n",
       "      <td>-0.174660</td>\n",
       "      <td>0.109088</td>\n",
       "      <td>13860</td>\n",
       "      <td>14080</td>\n",
       "      <td>-0.015625</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.015625</td>\n",
       "      <td>0.313085</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.424498</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-23</th>\n",
       "      <td>0.000989</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.000361</td>\n",
       "      <td>0.010101</td>\n",
       "      <td>-0.010462</td>\n",
       "      <td>-0.007937</td>\n",
       "      <td>0.105345</td>\n",
       "      <td>-0.082176</td>\n",
       "      <td>13750</td>\n",
       "      <td>13855</td>\n",
       "      <td>-0.007578</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.007578</td>\n",
       "      <td>0.305506</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.416919</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1862 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            prediction  time_delta      open      high       low     close  \\\n",
       "2009-06-05         NaN           2  0.039790  0.039790  0.019520  0.023273   \n",
       "2009-06-08    0.002985           5 -0.007337 -0.002201 -0.015407 -0.009538   \n",
       "2009-06-09    0.005106           4  0.009630  0.009630 -0.002222  0.001481   \n",
       "2009-06-10    0.003994         124  0.007396  0.011834  0.002589  0.011834   \n",
       "2009-06-11    0.006126           1 -0.002924 -0.002924 -0.008772 -0.006944   \n",
       "2009-06-12    0.004530           2  0.004785  0.005889  0.002576  0.002576   \n",
       "2009-06-15    0.005249           3 -0.007709 -0.005140 -0.015419 -0.008811   \n",
       "2009-06-16    0.001556           1 -0.016296 -0.004815 -0.016296 -0.006667   \n",
       "2009-06-17    0.005319           1 -0.000746  0.010440 -0.001491  0.010440   \n",
       "2009-06-18    0.005027           1 -0.001107  0.000738 -0.002214  0.000738   \n",
       "2009-06-19    0.007573           1  0.000000  0.001844 -0.002212 -0.000369   \n",
       "2009-06-22    0.006811         137 -0.002582  0.001844 -0.003320 -0.002582   \n",
       "2009-06-23    0.005253          39 -0.028476 -0.005178 -0.028476 -0.006287   \n",
       "2009-06-24    0.005506           2 -0.003349  0.008560 -0.003349  0.008560   \n",
       "2009-06-25    0.005968           2 -0.000738  0.002952 -0.000738  0.000738   \n",
       "2009-06-26    0.005638           4  0.001106  0.005531  0.000369  0.003687   \n",
       "2009-06-29    0.007479           6 -0.006245  0.000735 -0.006245 -0.004776   \n",
       "2009-06-30    0.006982          43  0.000000  0.004061 -0.004799 -0.004799   \n",
       "2009-07-01    0.003861           1  0.005193  0.005935  0.003709  0.005935   \n",
       "2009-07-02    0.004160           1  0.002212  0.003687  0.000737  0.002950   \n",
       "2009-07-03    0.003629          45 -0.005147  0.011029 -0.005147  0.000368   \n",
       "2009-07-06    0.007064           4  0.000000  0.006248  0.000000  0.000368   \n",
       "2009-07-07    0.007400           1  0.008082  0.008082  0.002204  0.002939   \n",
       "2009-07-08   -0.001282           1 -0.002564 -0.002198 -0.004396 -0.002930   \n",
       "2009-07-09    0.002554           1 -0.002204  0.000000 -0.002939 -0.001102   \n",
       "2009-07-10    0.005939           1 -0.000736  0.000368 -0.002207 -0.001471   \n",
       "2009-07-13    0.001595           3  0.002578  0.002578 -0.002578  0.000000   \n",
       "2009-07-14    0.002883           1  0.000368  0.000368 -0.004788 -0.004788   \n",
       "2009-07-15   -0.000335           2 -0.000740  0.009252 -0.000740  0.001480   \n",
       "2009-07-16    0.000322           3  0.006652  0.011826  0.003326  0.003695   \n",
       "...                ...         ...       ...       ...       ...       ...   \n",
       "2017-01-06    0.004601         316 -0.044859  0.007543 -0.044859 -0.004764   \n",
       "2017-01-09    0.003566           3  0.011169  0.011169 -0.001596  0.001197   \n",
       "2017-01-10   -0.003974         316  0.000000  0.018327 -0.003187  0.016733   \n",
       "2017-01-11   -0.001192        2360  0.003527  0.012147  0.001176  0.009013   \n",
       "2017-01-12    0.004470           1  0.013592  0.017476 -0.001165  0.017476   \n",
       "2017-01-13   -0.000051           1  0.014885  0.029008  0.000763  0.012214   \n",
       "2017-01-16   -0.001282           4  0.013952  0.017345 -0.005279 -0.003394   \n",
       "2017-01-17    0.002179           1  0.011351  0.017404 -0.005297  0.002649   \n",
       "2017-01-18   -0.003606           1  0.009434  0.009434 -0.006038  0.000755   \n",
       "2017-01-19    0.002083           2  0.000000  0.009804 -0.003771  0.001885   \n",
       "2017-01-20    0.000800           3  0.004893  0.007151 -0.001882 -0.001129   \n",
       "2017-01-23   -0.001598        1025  0.004898  0.039563  0.003014  0.026375   \n",
       "2017-01-24    0.001573           1  0.002570  0.008811 -0.001836  0.003304   \n",
       "2017-01-25   -0.000565           1  0.005488  0.028174  0.005488  0.023783   \n",
       "2017-01-26    0.003683        2131 -0.006791 -0.002144 -0.028234 -0.016083   \n",
       "2017-02-03    0.001545         634 -0.001090  0.003996 -0.014530 -0.005449   \n",
       "2017-02-06   -0.002152          11 -0.002922  0.010592 -0.014609 -0.009496   \n",
       "2017-02-07    0.001462         298  0.003687  0.007375 -0.007006 -0.002212   \n",
       "2017-02-08   -0.000875           1 -0.003695  0.007391 -0.004435  0.004435   \n",
       "2017-02-09   -0.001100           3 -0.002575  0.011773 -0.002575  0.005887   \n",
       "2017-02-10   -0.001687           1  0.005121  0.005852 -0.009510 -0.000732   \n",
       "2017-02-13   -0.001451        2498  0.002928  0.020864  0.002928  0.013543   \n",
       "2017-02-14   -0.000934           1 -0.009751  0.028891 -0.009751  0.028530   \n",
       "2017-02-15    0.009432           5  0.000000  0.005969 -0.014747  0.000702   \n",
       "2017-02-16    0.002802           2 -0.001754  0.016842 -0.008421  0.001754   \n",
       "2017-02-17    0.003651           1 -0.004904  0.002802 -0.022767 -0.020665   \n",
       "2017-02-20   -0.000603           3 -0.002861  0.008584 -0.011087  0.004649   \n",
       "2017-02-21    0.000492           1  0.001424  0.016020  0.000000  0.006408   \n",
       "2017-02-22    0.000706           2 -0.003891  0.004599 -0.021578 -0.019455   \n",
       "2017-02-23    0.000989           1 -0.000361  0.010101 -0.010462 -0.007937   \n",
       "\n",
       "              volume   net_chg  close_raw  open_raw  return_rate  direction  \\\n",
       "2009-06-05  0.000000 -0.011675      13630     13850    -0.015884         -1   \n",
       "2009-06-08 -0.359736 -0.040026      13500     13530    -0.002217          1   \n",
       "2009-06-09  1.000000  0.100256      13520     13630    -0.008070          1   \n",
       "2009-06-10 -0.221649 -0.028472      13680     13620     0.004405          1   \n",
       "2009-06-11 -0.314570 -0.010460      13585     13640    -0.004032          1   \n",
       "2009-06-12  0.231884 -0.071161      13620     13650    -0.002198          1   \n",
       "2009-06-15  0.019608 -0.017707      13500     13515    -0.001110          1   \n",
       "2009-06-16  0.040000  0.014337      13410     13280     0.009789          1   \n",
       "2009-06-17 -0.387574 -0.027949      13550     13400     0.011194          1   \n",
       "2009-06-18  0.225845 -0.025798      13560     13535     0.001847          1   \n",
       "2009-06-19  0.370443 -0.015835      13555     13560    -0.000369          1   \n",
       "2009-06-22 -0.238677 -0.033994      13520     13520     0.000000          1   \n",
       "2009-06-23  0.349386  0.031753      13435     13135     0.022840          1   \n",
       "2009-06-24 -0.398880 -0.055884      13550     13390     0.011949          1   \n",
       "2009-06-25  0.790454 -0.017426      13560     13540     0.001477          1   \n",
       "2009-06-26 -0.347204 -0.015255      13610     13575     0.002578          1   \n",
       "2009-06-29  0.749004 -0.013197      13545     13525     0.001479          1   \n",
       "2009-06-30 -0.577449  0.024690      13480     13545    -0.004799          1   \n",
       "2009-07-01 -0.575472 -0.032053      13560     13550     0.000738          1   \n",
       "2009-07-02  2.253968 -0.021321      13600     13590     0.000736          1   \n",
       "2009-07-03  0.882927  0.075718      13605     13530     0.005543          1   \n",
       "2009-07-06 -0.406736 -0.012771      13610     13605     0.000368          1   \n",
       "2009-07-07 -0.244541 -0.002329      13650     13720    -0.005102          1   \n",
       "2009-07-08  0.075145  0.259744      13610     13615    -0.000367         -1   \n",
       "2009-07-09  0.026882  0.072699      13595     13580     0.001105          1   \n",
       "2009-07-10  0.062827  0.012611      13575     13585    -0.000736          1   \n",
       "2009-07-13  0.216749 -0.019764      13575     13610    -0.002572          1   \n",
       "2009-07-14 -0.214575 -0.001464      13510     13580    -0.005155          1   \n",
       "2009-07-15  0.072165 -0.035517      13530     13500     0.002222         -1   \n",
       "2009-07-16  0.498077 -0.029855      13580     13620    -0.002937          1   \n",
       "...              ...       ...        ...       ...          ...        ...   \n",
       "2017-01-06 -0.075949  0.011259      12535     12030     0.041978          1   \n",
       "2017-01-09  0.399543  0.011846      12550     12675    -0.009862          1   \n",
       "2017-01-10  1.481240  0.078826      12760     12550     0.016733         -1   \n",
       "2017-01-11 -0.485865  0.169101      12875     12805     0.005467         -1   \n",
       "2017-01-12  0.480818 -0.049412      13100     13050     0.003831          1   \n",
       "2017-01-13 -0.208981  0.372343      13260     13295    -0.002633         -1   \n",
       "2017-01-16 -0.014192 -0.058982      13215     13445    -0.017107         -1   \n",
       "2017-01-17  3.596013 -0.092811      13250     13365    -0.008605          1   \n",
       "2017-01-18 -0.395692  0.035909      13260     13375    -0.008598         -1   \n",
       "2017-01-19 -0.036045  0.062795      13285     13260     0.001885          1   \n",
       "2017-01-20  0.062293  0.016109      13270     13350    -0.005993          1   \n",
       "2017-01-23  0.781170  0.112011      13620     13335     0.021372         -1   \n",
       "2017-01-24 -0.542279 -0.102642      13665     13655     0.000732          1   \n",
       "2017-01-25  0.314643  0.001460      13990     13740     0.018195         -1   \n",
       "2017-01-26 -0.586282  0.225013      13765     13895    -0.009356          1   \n",
       "2017-02-03 -0.503512 -0.060930      13690     13750    -0.004364          1   \n",
       "2017-02-06  1.465511  0.006944      13560     13650    -0.006593         -1   \n",
       "2017-02-07 -0.136298 -0.015631      13530     13610    -0.005878          1   \n",
       "2017-02-08 -0.333887 -0.039354      13590     13480     0.008160         -1   \n",
       "2017-02-09  0.481297 -0.053914      13670     13555     0.008484         -1   \n",
       "2017-02-10 -0.184343 -0.061205      13660     13740    -0.005822         -1   \n",
       "2017-02-13  0.424149 -0.063553      13845     13700     0.010584         -1   \n",
       "2017-02-14 -0.097101 -0.109552      14240     13710     0.038658         -1   \n",
       "2017-02-15 -0.311396  0.140962      14250     14240     0.000702          1   \n",
       "2017-02-16  9.715851  0.027495      14275     14225     0.003515          1   \n",
       "2017-02-17 -0.301386 -0.004391      13980     14205    -0.015839          1   \n",
       "2017-02-20 -0.294433 -0.095758      14045     13940     0.007532         -1   \n",
       "2017-02-21 -0.139673  0.147211      14135     14065     0.004977          1   \n",
       "2017-02-22 -0.174660  0.109088      13860     14080    -0.015625          1   \n",
       "2017-02-23  0.105345 -0.082176      13750     13855    -0.007578          1   \n",
       "\n",
       "            daily_return  cum_return  real_direction  benchmark  corr_or_not  \n",
       "2009-06-05      0.015884    0.015884              -1  -0.015884            1  \n",
       "2009-06-08     -0.002217    0.013667              -1  -0.018102            0  \n",
       "2009-06-09     -0.008070    0.005597              -1  -0.026172            0  \n",
       "2009-06-10      0.004405    0.010002               1  -0.021767            1  \n",
       "2009-06-11     -0.004032    0.005970              -1  -0.025799            0  \n",
       "2009-06-12     -0.002198    0.003772              -1  -0.027997            0  \n",
       "2009-06-15     -0.001110    0.002662              -1  -0.029107            0  \n",
       "2009-06-16      0.009789    0.012451               1  -0.019318            1  \n",
       "2009-06-17      0.011194    0.023645               1  -0.008124            1  \n",
       "2009-06-18      0.001847    0.025492               1  -0.006277            1  \n",
       "2009-06-19     -0.000369    0.025124              -1  -0.006645            0  \n",
       "2009-06-22      0.000000    0.025124              -1  -0.006645            0  \n",
       "2009-06-23      0.022840    0.047963               1   0.016194            1  \n",
       "2009-06-24      0.011949    0.059913               1   0.028144            1  \n",
       "2009-06-25      0.001477    0.061390               1   0.029621            1  \n",
       "2009-06-26      0.002578    0.063968               1   0.032199            1  \n",
       "2009-06-29      0.001479    0.065447               1   0.033678            1  \n",
       "2009-06-30     -0.004799    0.060648              -1   0.028879            0  \n",
       "2009-07-01      0.000738    0.061386               1   0.029617            1  \n",
       "2009-07-02      0.000736    0.062122               1   0.030353            1  \n",
       "2009-07-03      0.005543    0.067665               1   0.035896            1  \n",
       "2009-07-06      0.000368    0.068032               1   0.036264            1  \n",
       "2009-07-07     -0.005102    0.062930              -1   0.031161            0  \n",
       "2009-07-08      0.000367    0.063298              -1   0.030794            1  \n",
       "2009-07-09      0.001105    0.064402               1   0.031899            1  \n",
       "2009-07-10     -0.000736    0.063666              -1   0.031163            0  \n",
       "2009-07-13     -0.002572    0.061094              -1   0.028591            0  \n",
       "2009-07-14     -0.005155    0.055940              -1   0.023436            0  \n",
       "2009-07-15     -0.002222    0.053718               1   0.025659            0  \n",
       "2009-07-16     -0.002937    0.050781              -1   0.022722            0  \n",
       "...                  ...         ...             ...        ...          ...  \n",
       "2017-01-06      0.041978    0.467395               1   0.389943            1  \n",
       "2017-01-09     -0.009862    0.457533              -1   0.380081            0  \n",
       "2017-01-10     -0.016733    0.440800               1   0.396814            0  \n",
       "2017-01-11     -0.005467    0.435333               1   0.402281            0  \n",
       "2017-01-12      0.003831    0.439165               1   0.406112            1  \n",
       "2017-01-13      0.002633    0.441797              -1   0.403480            1  \n",
       "2017-01-16      0.017107    0.458904              -1   0.386373            1  \n",
       "2017-01-17     -0.008605    0.450299              -1   0.377769            0  \n",
       "2017-01-18      0.008598    0.458897              -1   0.369170            1  \n",
       "2017-01-19      0.001885    0.460783               1   0.371056            1  \n",
       "2017-01-20     -0.005993    0.454790              -1   0.365063            0  \n",
       "2017-01-23     -0.021372    0.433418               1   0.386436            0  \n",
       "2017-01-24      0.000732    0.434150               1   0.387168            1  \n",
       "2017-01-25     -0.018195    0.415955               1   0.405363            0  \n",
       "2017-01-26     -0.009356    0.406599              -1   0.396007            0  \n",
       "2017-02-03     -0.004364    0.402236              -1   0.391644            0  \n",
       "2017-02-06      0.006593    0.408829              -1   0.385050            1  \n",
       "2017-02-07     -0.005878    0.402951              -1   0.379172            0  \n",
       "2017-02-08     -0.008160    0.394791               1   0.387332            0  \n",
       "2017-02-09     -0.008484    0.386307               1   0.395816            0  \n",
       "2017-02-10      0.005822    0.392129              -1   0.389994            1  \n",
       "2017-02-13     -0.010584    0.381545               1   0.400578            0  \n",
       "2017-02-14     -0.038658    0.342888               1   0.439236            0  \n",
       "2017-02-15      0.000702    0.343590               1   0.439938            1  \n",
       "2017-02-16      0.003515    0.347105               1   0.443453            1  \n",
       "2017-02-17     -0.015839    0.331265              -1   0.427613            0  \n",
       "2017-02-20     -0.007532    0.323733               1   0.435146            0  \n",
       "2017-02-21      0.004977    0.328710               1   0.440123            1  \n",
       "2017-02-22     -0.015625    0.313085              -1   0.424498            0  \n",
       "2017-02-23     -0.007578    0.305506              -1   0.416919            0  \n",
       "\n",
       "[1862 rows x 17 columns]"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ddf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "dt=ddf[ddf['corr_or_not']==1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x112bf7450>]"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAD8CAYAAABzTgP2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nX5PcTlyEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ce061d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(dt['time_delta'],dt['return_rate'],'ro')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x125072f10>]"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAD8CAYAAABzTgP2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ntYU1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112c0d890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(ddf['time_delta'],ddf['return_rate'],'ro')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}