Code.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# High Frequency Pairs Trading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
      "  from pandas.core import datetools\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from statsmodels.tsa.stattools import adfuller\n",
    "from statsmodels.tsa.stattools import coint\n",
    "import statsmodels.api as sm\n",
    "import matplotlib.pyplot as plt\n",
    "pd.options.display.max_columns = 99"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#===============================\n",
    "#     Choose stock pairs\n",
    "#===============================\n",
    "\n",
    "def test_stationarity(stocks):\n",
    "    new_stocks_symbol=[]\n",
    "    for key,value in stocks.items():\n",
    "        test = adfuller(stocks[key]['Adj Close'].iloc[start_training:end_training+1],\n",
    "                        regression='ct') \n",
    "        if test[1]>0.05:\n",
    "            print(key,'is non-stationary')\n",
    "            new_stocks_symbol.append(key)\n",
    "        else:\n",
    "            print(key,'is stationary')  \n",
    "    return new_stocks_symbol\n",
    "\n",
    "def coint_test(y, x):\n",
    "    t_test, p_value, _ = coint(y, x)\n",
    "    l=[]\n",
    "    l.append(t_test)\n",
    "    l.append(p_value)\n",
    "    return l\n",
    "\n",
    "def hedge_ratio(y, x):\n",
    "    x = sm.add_constant(x)\n",
    "    model = sm.OLS(y, x).fit()\n",
    "    return model.params[0],model.params[1]\n",
    "\n",
    "def get_z_score_func(hedge, spreads):\n",
    "    if spreads.std()==0:\n",
    "        return lambda p1, p2: 0\n",
    "    return lambda p1, p2: (p1-hedge*p2-spreads.mean())/spreads.std()\n",
    "\n",
    "def target_pct(y_shares, x_shares, y_price, x_price):\n",
    "    y_dollars = y_shares * y_price\n",
    "    x_dollars = x_shares * x_price\n",
    "    y_target_pct = float(float(y_dollars) / float((abs(y_dollars) + abs(x_dollars))))\n",
    "    x_target_pct = float(float(x_dollars) / float((abs(y_dollars) + abs(x_dollars))))\n",
    "    return (y_target_pct, x_target_pct)\n",
    "\n",
    "def choose_pairs(pairs,start,end):\n",
    "    global portfolio\n",
    "    global position\n",
    "    \n",
    "    num_of_trades=0\n",
    "    position=0\n",
    "    pair_info={}\n",
    "    selected_pair_info={}\n",
    "    selected_pairs=[]\n",
    "    for pair in pairs:\n",
    "        s1,s2=pair\n",
    "        y = stocks[s1]['Adj Close'].iloc[start:end+1]\n",
    "        x = stocks[s2]['Adj Close'].iloc[start:end+1]\n",
    "        corr=x.corr(y)\n",
    "        if corr>=corr_threshold:\n",
    "            coint_result=coint_test(y, x)\n",
    "            t_test=coint_result[0]\n",
    "            p_value=coint_result[1]\n",
    "            if t_test<=t_test_threshold:\n",
    "                mean,hedge = hedge_ratio(y, x)\n",
    "                spreads = y - hedge*x\n",
    "                pair_info[pair] = {\n",
    "                    'correlation':corr,\n",
    "                    'p_value': p_value,\n",
    "                    't_test': t_test,\n",
    "                    'mean':mean,\n",
    "                    'hedge':hedge,\n",
    "                    'spreads':spreads\n",
    "                }        \n",
    "\n",
    "    pairs2 = []\n",
    "    for pair, value in pair_info.items():\n",
    "        pairs2.append((pair,value['t_test']))\n",
    "    pairs2.sort(key=lambda x: x[1])\n",
    "    \n",
    "    if len(pairs2)>=number_of_pairs:\n",
    "        for i in range(0,number_of_pairs):\n",
    "            selected_pairs.append(pairs2[i][0])\n",
    "            a,b= pairs2[i]\n",
    "            print('Selected pairs: ', a, ' t_test: ', b,' corr: ',\n",
    "                  pair_info[pairs2[i][0]]['correlation'])\n",
    "            selected_pair_info[pairs2[i][0]]= {\n",
    "                'correlation':pair_info[pairs2[i][0]]['correlation'],\n",
    "                'p_value': pair_info[pairs2[i][0]]['p_value'],\n",
    "                't_test': pair_info[pairs2[i][0]]['t_test'],\n",
    "                'mean': pair_info[pairs2[i][0]]['mean'],\n",
    "                'hedge': pair_info[pairs2[i][0]]['hedge'],\n",
    "                'spreads': pair_info[pairs2[i][0]]['spreads']\n",
    "            }\n",
    "    else:\n",
    "        for i in range(0,len(pairs2)):\n",
    "            selected_pairs.append(pairs2[i][0])\n",
    "            a,b= pairs2[i]\n",
    "            print('Selected pairs: ', a, ' t_test: ', b,' corr: ',\n",
    "                  pair_info[pairs2[i][0]]['correlation'])\n",
    "            selected_pair_info[pairs2[i][0]]= {\n",
    "                'correlation':pair_info[pairs2[i][0]]['correlation'],\n",
    "                'p_value': pair_info[pairs2[i][0]]['p_value'],\n",
    "                't_test': pair_info[pairs2[i][0]]['t_test'],\n",
    "                'mean': pair_info[pairs2[i][0]]['mean'],\n",
    "                'hedge': pair_info[pairs2[i][0]]['hedge'],\n",
    "                'spreads': pair_info[pairs2[i][0]]['spreads']\n",
    "            }\n",
    "        \n",
    "    for each in selected_pairs:\n",
    "        portfolio[(each,each[0])]={'# of shares':0,'transaction':0,'record':0}\n",
    "        portfolio[(each,each[1])]={'# of shares':0,'transaction':0,'record':0}\n",
    "        temp_record[(each[0],each[1])]=[0,0]\n",
    "        \n",
    "    return pair_info,selected_pair_info,selected_pairs\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#================================\n",
    "# Call check_pairs() every minute\n",
    "#================================\n",
    "\n",
    "def check_pairs(selected_pair_info, selected_pairs,k):\n",
    "    global portfolio\n",
    "    global position\n",
    "    if position<number_of_pairs:\n",
    "        check_pairs_for_entry(selected_pair_info, selected_pairs, k)\n",
    "    if position>0: \n",
    "        check_pairs_for_exit(selected_pair_info, selected_pairs, k)\n",
    "        \n",
    "        \n",
    "def check_pairs_for_entry(selected_pair_info, selected_pairs,k):\n",
    "    global portfolio\n",
    "    global position\n",
    "    global stocks\n",
    "    global pairs_symbol_traded\n",
    "    pairs = []\n",
    "    for pair, pair_info in selected_pair_info.items():\n",
    "        y=stocks[pair[0]].iloc[start_test:end_test+1]['Adj Close'].iloc[k]\n",
    "        x=stocks[pair[1]].iloc[start_test:end_test+1]['Adj Close'].iloc[k]\n",
    "\n",
    "        z_score_func=get_z_score_func(pair_info['hedge'],pair_info['spreads'])\n",
    "        z_score = z_score_func(y, x)\n",
    "        hedge = pair_info['hedge']\n",
    "        t_test=pair_info['t_test']\n",
    "        target1, target2 = target_pct(1, hedge, y, x)\n",
    "\n",
    "        s1, s2 = pair\n",
    "        if portfolio[(pair,s1)]['# of shares']==0 and portfolio[(pair,s2)]['# of shares']==0 and z_score > entry_threshold and z_score < stop_loss_threshold:\n",
    "            if pair in pairs_symbol_traded:\n",
    "                if capital[pair]>0:\n",
    "                    # short top, long bottom\n",
    "                    pairs.append((s1, s2, z_score, -target1, target2, y, x, hedge))\n",
    "                    pairs_symbol_traded.append(pair)\n",
    "            else:\n",
    "                # short top, long bottom\n",
    "                pairs.append((s1, s2, z_score, -target1, target2, y, x, hedge))\n",
    "                pairs_symbol_traded.append(pair)\n",
    "                \n",
    "        elif portfolio[(pair,s1)]['# of shares']==0 and portfolio[(pair,s2)]['# of shares']==0 and z_score < -entry_threshold and z_score > -stop_loss_threshold:\n",
    "            if pair in pairs_symbol_traded:\n",
    "                if capital[pair]>0:\n",
    "                    # long top, short bottom\n",
    "                    pairs.append((s1, s2, z_score, target1, -target2, y, x, hedge))\n",
    "                    pairs_symbol_traded.append(pair)\n",
    "            else:\n",
    "                # long top, short bottom\n",
    "                pairs.append((s1, s2, z_score, target1, -target2, y, x, hedge))\n",
    "                pairs_symbol_traded.append(pair)\n",
    "            \n",
    " \n",
    "    if len(pairs)>0:\n",
    "        for i in range(0,len(pairs)):\n",
    "            s1, s2, z_score, target1, target2, y, x, hedge = pairs[i]\n",
    "            pair_check=(s1,s2)\n",
    "            print('Enter: ', s1, s2, z_score, target1, target2, y, x, hedge)\n",
    "\n",
    "            n=0\n",
    "            for pair_symbol in pairs_symbol_traded:\n",
    "                if pair_check == pair_symbol:\n",
    "                    n+=1\n",
    "            if n==1:\n",
    "                order_target_percent(s1, s2, target1, target2, y, x, initial_capital/number_of_pairs)\n",
    "            else:\n",
    "                order_target_percent(s1, s2, target1, target2, y, x, capital[pair_check])\n",
    "            \n",
    "        position=position+len(pairs)\n",
    "             \n",
    "        \n",
    "def check_pairs_for_exit(selected_pair_info, selected_pairs, k):\n",
    "    global portfolio\n",
    "    global position\n",
    "    for pair in selected_pairs:\n",
    "        s1, s2 = pair\n",
    "        pos1 = portfolio[(pair,s1)]['# of shares']\n",
    "        pos2 = portfolio[(pair,s2)]['# of shares']\n",
    "        \n",
    "        if pos1==0 and pos2==0:\n",
    "            continue\n",
    "        \n",
    "        pair_info = selected_pair_info[pair]\n",
    "            \n",
    "        y=stocks[pair[0]].iloc[start_test:end_test+1]['Adj Close'].iloc[k]\n",
    "        x=stocks[pair[1]].iloc[start_test:end_test+1]['Adj Close'].iloc[k]\n",
    "        \n",
    "        z_score_func=get_z_score_func(pair_info['hedge'],pair_info['spreads'])\n",
    "        z_score = z_score_func(y, x)\n",
    "        hedge = pair_info['hedge']\n",
    "\n",
    "        s1, s2 = pair\n",
    "        if pos1<0 and pos2>0:\n",
    "            if z_score < exit_threshold or z_score > stop_loss_threshold:\n",
    "                print('Close position by buying ', s1, s2, z_score, pos1, pos2, y, x, hedge)\n",
    "                order_target_percent(s1, s2, 0, 0, y, x, 0)\n",
    "                position=position-1\n",
    "        elif pos1>0 and pos2<0:\n",
    "            if z_score > -exit_threshold or z_score < -stop_loss_threshold: \n",
    "                print('Close position by selling ', s1, s2, z_score, pos1, pos2, y, x, hedge)\n",
    "                order_target_percent(s1, s2, 0, 0, y, x, 0)\n",
    "                position=position-1\n",
    "\n",
    "                         \n",
    "def order_target_percent(s1, s2, target1, target2, p1, p2, account_capital):\n",
    "    global initial_capital\n",
    "    global portfolio\n",
    "    global transaction_record\n",
    "    global num_of_trades\n",
    "    global temp_record\n",
    "    global winning\n",
    "    global lose\n",
    "    global capital\n",
    "    transaction_record=transaction_record+1\n",
    "    \n",
    "    temp_pair=(s1,s2)\n",
    "    \n",
    "    if target1>0 and target2<0 or target1<0 and target2>0:\n",
    "        num_of_trades+=1\n",
    "        temp_record[(s1,s2)]=[0,0]\n",
    "        \n",
    "        portfolio[((s1,s2),s1)]={'amount of money': (2*account_capital)*target1,\n",
    "                     '# of shares':float(float((2*account_capital)*target1)/float(p1)),\n",
    "                     'price_open':p1,\n",
    "                      'transaction': 1,\n",
    "                      'record':(2*account_capital)*target1/p1}    \n",
    "        portfolio[((s1,s2),s2)]={'amount of money': (2*account_capital)*target2,\n",
    "                     '# of shares':float(float((2*account_capital)*target2)/float(p2)),\n",
    "                     'price_open':p2,\n",
    "                      'transaction': 1,\n",
    "                      'record':(2*account_capital)*target2/p2}\n",
    "        \n",
    "        print('s1 # of shares'+str(portfolio[((s1,s2),s1)]['# of shares']))\n",
    "        print('s2 # of shares'+str(portfolio[((s1,s2),s2)]['# of shares']))\n",
    "        \n",
    "        \n",
    "        temp_record[(s1,s2)][0]=portfolio[((s1,s2),s1)]['# of shares']*p1+portfolio[((s1,s2),s2)]['# of shares']*p2\n",
    "      \n",
    "    \n",
    "    elif target1==0 and target2==0:\n",
    "        \n",
    "        capital[temp_pair]=initial_capital/number_of_pairs+p1*portfolio[((s1,s2),s1)]['# of shares']+p2*portfolio[((s1,s2),s2)]['# of shares']-portfolio[((s1,s2),s1)]['price_open']*portfolio[((s1,s2),s1)]['# of shares']-portfolio[((s1,s2),s2)]['price_open']*portfolio[((s1,s2),s2)]['# of shares']\n",
    "        \n",
    "        portfolio[((s1,s2),s1)]['amount of money']=0\n",
    "        portfolio[((s1,s2),s1)]['# of shares']=0\n",
    "        portfolio[((s1,s2),s1)]['transaction']=1\n",
    "        \n",
    "        portfolio[((s1,s2),s2)]['amount of money']=0\n",
    "        portfolio[((s1,s2),s2)]['# of shares']=0\n",
    "        portfolio[((s1,s2),s2)]['transaction']=1\n",
    "        \n",
    "        temp_record[(s1,s2)][1]=portfolio[((s1,s2),s1)]['# of shares']*p1+portfolio[((s1,s2),s2)]['# of shares']*p2\n",
    "        count_winning_and_loss(s1,s2)\n",
    "        print('account value'+str(capital[temp_pair]))\n",
    "           \n",
    "        \n",
    "def count_winning_and_loss(s1,s2):\n",
    "    global temp_record\n",
    "    global winning\n",
    "    global loss\n",
    "    if temp_record[(s1,s2)][0]==temp_record[(s1,s2)][1]:\n",
    "        print('error......')\n",
    "    if temp_record[(s1,s2)][0]<temp_record[(s1,s2)][1]:\n",
    "        print(s1,s2,' win')\n",
    "        winning+=1\n",
    "    if temp_record[(s1,s2)][0]>temp_record[(s1,s2)][1]:\n",
    "        print(s1,s2,' lose')\n",
    "        loss+=1\n",
    "    \n",
    "        \n",
    "def record(i,num,selected_pairs):\n",
    "    global dfpnl\n",
    "    global portfolio \n",
    "    global transaction_record \n",
    "    \n",
    "    # record pnl for each pair \n",
    "    for pair in selected_pairs:\n",
    "        s1, s2 = pair\n",
    "        pos1 = portfolio[(pair,s1)]['# of shares']\n",
    "        pos2 = portfolio[(pair,s2)]['# of shares']\n",
    "        if pos1==0 and pos2==0:\n",
    "            continue\n",
    "        temp=pos1*(stocks[s1].iloc[start_test:end_test+1]['Adj Close'].iloc[i+1]-stocks[s1].iloc[start_test:end_test+1]['Adj Close'].iloc[i])+pos2*(stocks[s2].iloc[start_test:end_test+1]['Adj Close'].iloc[i+1]-stocks[s2].iloc[start_test:end_test+1]['Adj Close'].iloc[i])\n",
    "        dfpnl[num][pair[0]+', '+pair[1]+' pnl'][i+1]=temp \n",
    "        \n",
    "    # record cumulative pnl for each pair \n",
    "    for pair in selected_pairs:\n",
    "        dfpnl[num][pair[0]+', '+pair[1]+' cumulative pnl']=dfpnl[num][pair[0]+', '+pair[1]+' pnl'].cumsum()\n",
    "            \n",
    "            \n",
    "    # pnl\n",
    "    change=0\n",
    "    for each, value in portfolio.items():\n",
    "        change=change+value['# of shares']*(stocks[each[1]].iloc[start_test:end_test+1]['Adj Close'].iloc[i+1]-stocks[each[1]].iloc[start_test:end_test+1]['Adj Close'].iloc[i])-value['transaction']*abs(value['record'])*transaction_cost_per_share\n",
    "        value['transaction']=0 \n",
    "    dfpnl[num]['PnL'][i+1]=change-transaction_record*2*transaction_cost \n",
    "    transaction_record=0 \n",
    "    \n",
    "    \n",
    "    # cumulative pnl\n",
    "    dfpnl[num]['Cumulative PnL']=dfpnl[num]['PnL'].cumsum()\n",
    "    \n",
    "    # value\n",
    "    \n",
    "    dfpnl[num]['value']=dfpnl[num]['Cumulative PnL']+initial_capital\n",
    "    \n",
    "    # return\n",
    "    dfpnl[num]['return']=dfpnl[num]['Cumulative PnL']/initial_capital\n",
    "\n",
    "    \n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#================\n",
    "# Read csv files\n",
    "#================\n",
    "\n",
    "df=pd.read_csv(\"test.csv\") #S&P500_Intraday\n",
    "stocks={}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#============================\n",
    "# Please choose frequency\n",
    "#============================\n",
    "\n",
    "freq=10\n",
    "\n",
    "lst = [i for i in range(0,len(df),freq)]\n",
    "df2=df.iloc[lst]\n",
    "for i in range(0,len(df2.columns),3):\n",
    "    stocks[df2.columns[i]]=df2.iloc[:,[i,i+1]].set_index(df2.columns[i])\n",
    "    stocks[df2.columns[i]].index.names=['Time']\n",
    "    stocks[df2.columns[i]].columns=['Adj Close']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A UN Equity is non-stationary\n",
      "AAL UW Equity is non-stationary\n",
      "AAP UN Equity is non-stationary\n",
      "AAPL UW Equity is non-stationary\n",
      "ABBV UN Equity is non-stationary\n",
      "ABC UN Equity is non-stationary\n",
      "ABT UN Equity is non-stationary\n",
      "ACN UN Equity is non-stationary\n",
      "ADBE UW Equity is non-stationary\n",
      "ADI UW Equity is stationary\n",
      "ADM UN Equity is non-stationary\n",
      "ADP UW Equity is non-stationary\n",
      "ADS UN Equity is non-stationary\n",
      "ADSK UW Equity is non-stationary\n",
      "AEE UN Equity is non-stationary\n",
      "AEP UN Equity is non-stationary\n",
      "AES UN Equity is non-stationary\n",
      "AET UN Equity is non-stationary\n",
      "AFL UN Equity is non-stationary\n",
      "AGN UN Equity is non-stationary\n",
      "AIG UN Equity is non-stationary\n",
      "AIV UN Equity is non-stationary\n",
      "AIZ UN Equity is non-stationary\n",
      "AJG UN Equity is non-stationary\n",
      "AKAM UW Equity is stationary\n",
      "ALB UN Equity is non-stationary\n",
      "ALGN UW Equity is non-stationary\n",
      "ALK UN Equity is non-stationary\n",
      "ALL UN Equity is non-stationary\n",
      "ALLE UN Equity is non-stationary\n",
      "ALXN UW Equity is non-stationary\n",
      "AMAT UW Equity is non-stationary\n",
      "AMD UR Equity is non-stationary\n",
      "AME UN Equity is non-stationary\n",
      "AMG UN Equity is non-stationary\n",
      "AMGN UW Equity is non-stationary\n",
      "AMP UN Equity is non-stationary\n",
      "AMT UN Equity is non-stationary\n",
      "AMZN UW Equity is non-stationary\n",
      "ANDV UN Equity is non-stationary\n",
      "ANSS UW Equity is non-stationary\n",
      "ANTM UN Equity is non-stationary\n",
      "AON UN Equity is non-stationary\n",
      "AOS UN Equity is non-stationary\n",
      "APA UN Equity is non-stationary\n",
      "APC UN Equity is non-stationary\n",
      "APD UN Equity is non-stationary\n",
      "APH UN Equity is non-stationary\n",
      "APTV UN Equity is non-stationary\n",
      "ARE UN Equity is non-stationary\n",
      "Selected pairs:  ('ABT UN Equity', 'APH UN Equity')  t_test:  -3.603271935  corr:  0.939364414285\n",
      "Date:  10/31/17 09:30\n",
      "Date:  10/31/17 09:40\n",
      "Date:  10/31/17 09:50\n",
      "Date:  10/31/17 10:00\n",
      "Date:  10/31/17 10:10\n",
      "Date:  10/31/17 10:20\n",
      "Date:  10/31/17 10:30\n",
      "Date:  10/31/17 10:40\n",
      "Date:  10/31/17 10:50\n",
      "Date:  10/31/17 11:00\n",
      "Date:  10/31/17 11:10\n",
      "Date:  10/31/17 11:20\n",
      "Date:  10/31/17 11:30\n",
      "Date:  10/31/17 11:40\n",
      "Date:  10/31/17 11:50\n",
      "Date:  10/31/17 12:00\n",
      "Date:  10/31/17 12:10\n",
      "Date:  10/31/17 12:20\n",
      "Date:  10/31/17 12:30\n",
      "Date:  10/31/17 12:40\n",
      "Date:  10/31/17 12:50\n",
      "Date:  10/31/17 13:00\n",
      "Date:  10/31/17 13:10\n",
      "Date:  10/31/17 13:20\n",
      "Date:  10/31/17 13:30\n",
      "Date:  10/31/17 13:40\n",
      "Date:  10/31/17 13:50\n",
      "Date:  10/31/17 14:00\n",
      "Date:  10/31/17 14:10\n",
      "Date:  10/31/17 14:20\n",
      "Date:  10/31/17 14:30\n",
      "Date:  10/31/17 14:40\n",
      "Date:  10/31/17 14:50\n",
      "Date:  10/31/17 15:00\n",
      "Date:  10/31/17 15:10\n",
      "Date:  10/31/17 15:20\n",
      "Date:  10/31/17 15:30\n",
      "Date:  10/31/17 15:40\n",
      "Date:  10/31/17 15:50\n",
      "Date:  11/1/17 09:30\n",
      "Date:  11/1/17 09:40\n",
      "Date:  11/1/17 09:50\n",
      "Date:  11/1/17 10:00\n",
      "Date:  11/1/17 10:10\n",
      "Date:  11/1/17 10:20\n",
      "Date:  11/1/17 10:30\n",
      "Date:  11/1/17 10:40\n",
      "Date:  11/1/17 10:50\n",
      "Date:  11/1/17 11:00\n",
      "Date:  11/1/17 11:10\n",
      "Date:  11/1/17 11:20\n",
      "Date:  11/1/17 11:30\n",
      "Date:  11/1/17 11:40\n",
      "Date:  11/1/17 11:50\n",
      "Date:  11/1/17 12:00\n",
      "Date:  11/1/17 12:10\n",
      "Date:  11/1/17 12:20\n",
      "Date:  11/1/17 12:30\n",
      "Date:  11/1/17 12:40\n",
      "Date:  11/1/17 12:50\n",
      "Date:  11/1/17 13:00\n",
      "Date:  11/1/17 13:10\n",
      "Date:  11/1/17 13:20\n",
      "Date:  11/1/17 13:30\n",
      "Date:  11/1/17 13:40\n",
      "Date:  11/1/17 13:50\n",
      "Date:  11/1/17 14:00\n",
      "Date:  11/1/17 14:10\n",
      "Date:  11/1/17 14:20\n",
      "Date:  11/1/17 14:30\n",
      "Date:  11/1/17 14:40\n",
      "Date:  11/1/17 14:50\n",
      "Date:  11/1/17 15:00\n",
      "Date:  11/1/17 15:10\n",
      "Date:  11/1/17 15:20\n",
      "Date:  11/1/17 15:30\n",
      "Date:  11/1/17 15:40\n",
      "Date:  11/1/17 15:50\n",
      "Date:  11/2/17 09:30\n",
      "Date:  11/2/17 09:40\n",
      "Date:  11/2/17 09:50\n",
      "Date:  11/2/17 10:00\n",
      "Date:  11/2/17 10:10\n",
      "Date:  11/2/17 10:20\n",
      "Date:  11/2/17 10:30\n",
      "Date:  11/2/17 10:40\n",
      "Date:  11/2/17 10:50\n",
      "Date:  11/2/17 11:00\n",
      "Date:  11/2/17 11:10\n",
      "Date:  11/2/17 11:20\n",
      "Date:  11/2/17 11:30\n",
      "Date:  11/2/17 11:40\n",
      "Date:  11/2/17 11:50\n",
      "Date:  11/2/17 12:00\n",
      "Date:  11/2/17 12:10\n",
      "Date:  11/2/17 12:20\n",
      "Date:  11/2/17 12:30\n",
      "Date:  11/2/17 12:40\n",
      "Date:  11/2/17 12:50\n",
      "Date:  11/2/17 13:00\n",
      "Date:  11/2/17 13:10\n",
      "Date:  11/2/17 13:20\n",
      "Date:  11/2/17 13:30\n",
      "Date:  11/2/17 13:40\n",
      "Date:  11/2/17 13:50\n",
      "Date:  11/2/17 14:00\n",
      "Date:  11/2/17 14:10\n",
      "Date:  11/2/17 14:20\n",
      "Enter:  ABT UN Equity APH UN Equity -2.50314246746 0.4660180785335484 -0.5339819214664516 54.16 87.78 0.706979576744\n",
      "s1 # of shares17208.93938454758\n",
      "s2 # of shares-12166.36868230694\n",
      "Date:  11/2/17 14:30\n",
      "Date:  11/2/17 14:40\n",
      "Date:  11/2/17 14:50\n",
      "Date:  11/2/17 15:00\n",
      "Date:  11/2/17 15:10\n",
      "Date:  11/2/17 15:20\n",
      "Date:  11/2/17 15:30\n",
      "Date:  11/2/17 15:40\n",
      "Date:  11/2/17 15:50\n",
      "Date:  11/3/17 09:30\n",
      "Date:  11/3/17 09:40\n",
      "Close position by selling  ABT UN Equity APH UN Equity -1.43878849341 17208.93938454758 -12166.36868230694 54.54 87.23 0.706979576744\n",
      "ABT UN Equity APH UN Equity  win\n",
      "account value1013230.89974\n",
      "Date:  11/3/17 09:50\n",
      "Date:  11/3/17 10:00\n",
      "Date:  11/3/17 10:10\n",
      "Date:  11/3/17 10:20\n",
      "Date:  11/3/17 10:30\n",
      "Date:  11/3/17 10:40\n",
      "Date:  11/3/17 10:50\n",
      "Date:  11/3/17 11:00\n",
      "Date:  11/3/17 11:10\n",
      "Date:  11/3/17 11:20\n",
      "Date:  11/3/17 11:30\n",
      "Date:  11/3/17 11:40\n",
      "Date:  11/3/17 11:50\n",
      "Date:  11/3/17 12:00\n",
      "Date:  11/3/17 12:10\n",
      "Date:  11/3/17 12:20\n",
      "Date:  11/3/17 12:30\n",
      "Date:  11/3/17 12:40\n",
      "Date:  11/3/17 12:50\n",
      "Date:  11/3/17 13:00\n",
      "Date:  11/3/17 13:10\n",
      "Date:  11/3/17 13:20\n",
      "Date:  11/3/17 13:30\n",
      "Date:  11/3/17 13:40\n",
      "Date:  11/3/17 13:50\n",
      "Date:  11/3/17 14:00\n",
      "Date:  11/3/17 14:10\n",
      "Date:  11/3/17 14:20\n",
      "Date:  11/3/17 14:30\n",
      "Date:  11/3/17 14:40\n",
      "Date:  11/3/17 14:50\n",
      "Date:  11/3/17 15:00\n",
      "Date:  11/3/17 15:10\n",
      "Date:  11/3/17 15:20\n",
      "Date:  11/3/17 15:30\n",
      "Date:  11/3/17 15:40\n",
      "Date:  11/3/17 15:50\n",
      "Date:  11/6/17 09:30\n",
      "Date:  11/6/17 09:40\n",
      "Date:  11/6/17 09:50\n",
      "Date:  11/6/17 10:00\n",
      "Date:  11/6/17 10:10\n",
      "Date:  11/6/17 10:20\n",
      "Date:  11/6/17 10:30\n",
      "Date:  11/6/17 10:40\n",
      "Date:  11/6/17 10:50\n",
      "Date:  11/6/17 11:00\n",
      "Date:  11/6/17 11:10\n",
      "Date:  11/6/17 11:20\n",
      "Date:  11/6/17 11:30\n",
      "Date:  11/6/17 11:40\n",
      "Date:  11/6/17 11:50\n",
      "Date:  11/6/17 12:00\n",
      "Date:  11/6/17 12:10\n",
      "Date:  11/6/17 12:20\n",
      "Date:  11/6/17 12:30\n",
      "Date:  11/6/17 12:40\n",
      "Date:  11/6/17 12:50\n",
      "Date:  11/6/17 13:00\n",
      "Date:  11/6/17 13:10\n",
      "Date:  11/6/17 13:20\n",
      "Date:  11/6/17 13:30\n",
      "Date:  11/6/17 13:40\n",
      "Date:  11/6/17 13:50\n",
      "Date:  11/6/17 14:00\n",
      "Date:  11/6/17 14:10\n",
      "Date:  11/6/17 14:20\n",
      "Date:  11/6/17 14:30\n",
      "Date:  11/6/17 14:40\n",
      "Date:  11/6/17 14:50\n",
      "Date:  11/6/17 15:00\n",
      "Date:  11/6/17 15:10\n",
      "Date:  11/6/17 15:20\n",
      "Date:  11/6/17 15:30\n",
      "Date:  11/6/17 15:40\n",
      "Date:  11/6/17 15:50\n",
      "Date:  11/7/17 09:30\n",
      "Date:  11/7/17 09:40\n",
      "Date:  11/7/17 09:50\n",
      "Date:  11/7/17 10:00\n",
      "Date:  11/7/17 10:10\n",
      "Date:  11/7/17 10:20\n",
      "Date:  11/7/17 10:30\n",
      "Date:  11/7/17 10:40\n",
      "Date:  11/7/17 10:50\n",
      "Date:  11/7/17 11:00\n",
      "Date:  11/7/17 11:10\n",
      "Date:  11/7/17 11:20\n",
      "Date:  11/7/17 11:30\n",
      "Date:  11/7/17 11:40\n",
      "Date:  11/7/17 11:50\n",
      "Date:  11/7/17 12:00\n",
      "Date:  11/7/17 12:10\n",
      "Date:  11/7/17 12:20\n",
      "Date:  11/7/17 12:30\n",
      "Date:  11/7/17 12:40\n",
      "Date:  11/7/17 12:50\n",
      "Date:  11/7/17 13:00\n",
      "Date:  11/7/17 13:10\n",
      "Date:  11/7/17 13:20\n",
      "Date:  11/7/17 13:30\n",
      "Date:  11/7/17 13:40\n",
      "Date:  11/7/17 13:50\n",
      "Date:  11/7/17 14:00\n",
      "Date:  11/7/17 14:10\n",
      "Date:  11/7/17 14:20\n",
      "Date:  11/7/17 14:30\n",
      "Date:  11/7/17 14:40\n",
      "Date:  11/7/17 14:50\n",
      "Date:  11/7/17 15:00\n",
      "Date:  11/7/17 15:10\n",
      "Date:  11/7/17 15:20\n",
      "Date:  11/7/17 15:30\n",
      "Date:  11/7/17 15:40\n",
      "Date:  11/7/17 15:50\n",
      "Date:  11/8/17 09:30\n",
      "Date:  11/8/17 09:40\n",
      "Date:  11/8/17 09:50\n",
      "Date:  11/8/17 10:00\n",
      "Date:  11/8/17 10:10\n",
      "Date:  11/8/17 10:20\n",
      "Date:  11/8/17 10:30\n",
      "Date:  11/8/17 10:40\n",
      "Date:  11/8/17 10:50\n",
      "Date:  11/8/17 11:00\n",
      "Date:  11/8/17 11:10\n",
      "Date:  11/8/17 11:20\n",
      "Date:  11/8/17 11:30\n",
      "Date:  11/8/17 11:40\n",
      "Date:  11/8/17 11:50\n",
      "Date:  11/8/17 12:00\n",
      "Date:  11/8/17 12:10\n",
      "Date:  11/8/17 12:20\n",
      "Date:  11/8/17 12:30\n",
      "Date:  11/8/17 12:40\n",
      "Date:  11/8/17 12:50\n",
      "Date:  11/8/17 13:00\n",
      "Date:  11/8/17 13:10\n",
      "Date:  11/8/17 13:20\n",
      "Date:  11/8/17 13:30\n",
      "Date:  11/8/17 13:40\n",
      "Date:  11/8/17 13:50\n",
      "Date:  11/8/17 14:00\n",
      "Date:  11/8/17 14:10\n",
      "Date:  11/8/17 14:20\n",
      "Date:  11/8/17 14:30\n",
      "Date:  11/8/17 14:40\n",
      "Date:  11/8/17 14:50\n",
      "Date:  11/8/17 15:00\n",
      "Date:  11/8/17 15:10\n",
      "Date:  11/8/17 15:20\n",
      "Date:  11/8/17 15:30\n",
      "Date:  11/8/17 15:40\n",
      "Date:  11/8/17 15:50\n",
      "Date:  11/9/17 09:30\n",
      "Date:  11/9/17 09:40\n",
      "Date:  11/9/17 09:50\n",
      "Date:  11/9/17 10:00\n",
      "Date:  11/9/17 10:10\n",
      "Date:  11/9/17 10:20\n",
      "Date:  11/9/17 10:30\n",
      "Date:  11/9/17 10:40\n",
      "Date:  11/9/17 10:50\n",
      "Date:  11/9/17 11:00\n",
      "Date:  11/9/17 11:10\n",
      "Date:  11/9/17 11:20\n",
      "Date:  11/9/17 11:30\n",
      "Date:  11/9/17 11:40\n",
      "Date:  11/9/17 11:50\n",
      "Date:  11/9/17 12:00\n",
      "Date:  11/9/17 12:10\n",
      "Date:  11/9/17 12:20\n",
      "Date:  11/9/17 12:30\n",
      "Date:  11/9/17 12:40\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Date:  11/9/17 12:50\n",
      "Date:  11/9/17 13:00\n",
      "Date:  11/9/17 13:10\n",
      "Date:  11/9/17 13:20\n",
      "Date:  11/9/17 13:30\n",
      "Date:  11/9/17 13:40\n",
      "Date:  11/9/17 13:50\n",
      "Date:  11/9/17 14:00\n",
      "Date:  11/9/17 14:10\n",
      "Date:  11/9/17 14:20\n",
      "Date:  11/9/17 14:30\n",
      "Date:  11/9/17 14:40\n",
      "Date:  11/9/17 14:50\n",
      "Date:  11/9/17 15:00\n",
      "Date:  11/9/17 15:10\n",
      "Date:  11/9/17 15:20\n",
      "Date:  11/9/17 15:30\n",
      "Date:  11/9/17 15:40\n",
      "Date:  11/9/17 15:50\n",
      "Date:  11/10/17 09:30\n",
      "Date:  11/10/17 09:40\n",
      "Date:  11/10/17 09:50\n",
      "Date:  11/10/17 10:00\n",
      "Date:  11/10/17 10:10\n",
      "Date:  11/10/17 10:20\n",
      "Date:  11/10/17 10:30\n",
      "Date:  11/10/17 10:40\n",
      "Date:  11/10/17 10:50\n",
      "Date:  11/10/17 11:00\n",
      "Date:  11/10/17 11:10\n",
      "Date:  11/10/17 11:20\n",
      "Date:  11/10/17 11:30\n",
      "Date:  11/10/17 11:40\n",
      "Date:  11/10/17 11:50\n",
      "Date:  11/10/17 12:00\n",
      "Date:  11/10/17 12:10\n",
      "Date:  11/10/17 12:20\n",
      "Date:  11/10/17 12:30\n",
      "Date:  11/10/17 12:40\n",
      "Date:  11/10/17 12:50\n",
      "Date:  11/10/17 13:00\n",
      "Date:  11/10/17 13:10\n",
      "Date:  11/10/17 13:20\n",
      "Date:  11/10/17 13:30\n",
      "Date:  11/10/17 13:40\n",
      "Date:  11/10/17 13:50\n",
      "Date:  11/10/17 14:00\n",
      "Date:  11/10/17 14:10\n",
      "Date:  11/10/17 14:20\n",
      "Date:  11/10/17 14:30\n",
      "Date:  11/10/17 14:40\n",
      "Date:  11/10/17 14:50\n",
      "Date:  11/10/17 15:00\n",
      "Date:  11/10/17 15:10\n",
      "Date:  11/10/17 15:20\n",
      "Date:  11/10/17 15:30\n",
      "Date:  11/10/17 15:40\n",
      "Date:  11/10/17 15:50\n",
      "Date:  11/13/17 09:30\n",
      "Date:  11/13/17 09:40\n",
      "Date:  11/13/17 09:50\n",
      "Date:  11/13/17 10:00\n",
      "Date:  11/13/17 10:10\n",
      "Date:  11/13/17 10:20\n",
      "Date:  11/13/17 10:30\n",
      "Date:  11/13/17 10:40\n",
      "Date:  11/13/17 10:50\n",
      "Date:  11/13/17 11:00\n",
      "Date:  11/13/17 11:10\n",
      "Date:  11/13/17 11:20\n",
      "Date:  11/13/17 11:30\n",
      "Date:  11/13/17 11:40\n",
      "Date:  11/13/17 11:50\n",
      "Date:  11/13/17 12:00\n",
      "Date:  11/13/17 12:10\n",
      "Date:  11/13/17 12:20\n",
      "Date:  11/13/17 12:30\n",
      "Date:  11/13/17 12:40\n",
      "Date:  11/13/17 12:50\n",
      "Date:  11/13/17 13:00\n",
      "Date:  11/13/17 13:10\n",
      "Date:  11/13/17 13:20\n",
      "Date:  11/13/17 13:30\n",
      "Date:  11/13/17 13:40\n",
      "Date:  11/13/17 13:50\n",
      "Date:  11/13/17 14:00\n",
      "Date:  11/13/17 14:10\n",
      "Date:  11/13/17 14:20\n",
      "Date:  11/13/17 14:30\n",
      "Date:  11/13/17 14:40\n",
      "Date:  11/13/17 14:50\n",
      "Date:  11/13/17 15:00\n",
      "Date:  11/13/17 15:10\n",
      "Date:  11/13/17 15:20\n",
      "Date:  11/13/17 15:30\n",
      "Date:  11/13/17 15:40\n",
      "Date:  11/13/17 15:50\n",
      "Date:  11/14/17 09:30\n",
      "Date:  11/14/17 09:40\n",
      "Date:  11/14/17 09:50\n",
      "Date:  11/14/17 10:00\n",
      "Date:  11/14/17 10:10\n",
      "Date:  11/14/17 10:20\n",
      "Date:  11/14/17 10:30\n",
      "Date:  11/14/17 10:40\n",
      "Date:  11/14/17 10:50\n",
      "Date:  11/14/17 11:00\n",
      "Date:  11/14/17 11:10\n",
      "Date:  11/14/17 11:20\n",
      "Date:  11/14/17 11:30\n",
      "Date:  11/14/17 11:40\n",
      "Date:  11/14/17 11:50\n",
      "Enter:  ABT UN Equity APH UN Equity -2.61402504577 0.46612817406647933 -0.5338718259335207 54.9 88.94 0.706979576744\n",
      "s1 # of shares17205.663723285736\n",
      "s2 # of shares-12164.052856694483\n",
      "Date:  11/14/17 12:00\n",
      "Date:  11/14/17 12:10\n",
      "Date:  11/14/17 12:20\n",
      "Date:  11/14/17 12:30\n",
      "Date:  11/14/17 12:40\n",
      "Date:  11/14/17 12:50\n",
      "Date:  11/14/17 13:00\n",
      "Date:  11/14/17 13:10\n",
      "Date:  11/14/17 13:20\n",
      "Date:  11/14/17 13:30\n",
      "Date:  11/14/17 13:40\n",
      "Date:  11/14/17 13:50\n",
      "Date:  11/14/17 14:00\n",
      "Date:  11/14/17 14:10\n",
      "Date:  11/14/17 14:20\n",
      "Date:  11/14/17 14:30\n",
      "Date:  11/14/17 14:40\n",
      "Date:  11/14/17 14:50\n",
      "Date:  11/14/17 15:00\n",
      "Date:  11/14/17 15:10\n",
      "Date:  11/14/17 15:20\n",
      "Date:  11/14/17 15:30\n",
      "Date:  11/14/17 15:40\n",
      "Date:  11/14/17 15:50\n",
      "Date:  11/15/17 09:30\n",
      "Date:  11/15/17 09:40\n",
      "Date:  11/15/17 09:50\n",
      "Date:  11/15/17 10:00\n",
      "Date:  11/15/17 10:10\n",
      "Date:  11/15/17 10:20\n",
      "Date:  11/15/17 10:30\n",
      "Date:  11/15/17 10:40\n",
      "Date:  11/15/17 10:50\n",
      "Date:  11/15/17 11:00\n",
      "Date:  11/15/17 11:10\n",
      "Date:  11/15/17 11:20\n",
      "Date:  11/15/17 11:30\n",
      "Date:  11/15/17 11:40\n",
      "Date:  11/15/17 11:50\n",
      "Close position by selling  ABT UN Equity APH UN Equity -1.98146488379 17205.663723285736 -12164.052856694483 55.06 88.52 0.706979576744\n",
      "ABT UN Equity APH UN Equity  win\n",
      "account value1007861.8084\n",
      "Date:  11/15/17 12:00\n",
      "Date:  11/15/17 12:10\n",
      "Date:  11/15/17 12:20\n",
      "Date:  11/15/17 12:30\n",
      "Date:  11/15/17 12:40\n",
      "Date:  11/15/17 12:50\n",
      "Date:  11/15/17 13:00\n",
      "Date:  11/15/17 13:10\n",
      "Date:  11/15/17 13:20\n",
      "Date:  11/15/17 13:30\n",
      "Date:  11/15/17 13:40\n",
      "Date:  11/15/17 13:50\n",
      "Date:  11/15/17 14:00\n",
      "Date:  11/15/17 14:10\n",
      "Date:  11/15/17 14:20\n",
      "Date:  11/15/17 14:30\n",
      "Date:  11/15/17 14:40\n",
      "Date:  11/15/17 14:50\n",
      "Date:  11/15/17 15:00\n",
      "Date:  11/15/17 15:10\n",
      "Date:  11/15/17 15:20\n",
      "Date:  11/15/17 15:30\n",
      "Date:  11/15/17 15:40\n",
      "Date:  11/15/17 15:50\n",
      "Date:  11/16/17 09:30\n",
      "Date:  11/16/17 09:40\n",
      "Date:  11/16/17 09:50\n",
      "Date:  11/16/17 10:00\n",
      "Date:  11/16/17 10:10\n",
      "Date:  11/16/17 10:20\n",
      "Date:  11/16/17 10:30\n",
      "Date:  11/16/17 10:40\n",
      "Date:  11/16/17 10:50\n",
      "Date:  11/16/17 11:00\n",
      "Date:  11/16/17 11:10\n",
      "Date:  11/16/17 11:20\n",
      "Date:  11/16/17 11:30\n",
      "Date:  11/16/17 11:40\n",
      "Date:  11/16/17 11:50\n",
      "Date:  11/16/17 12:00\n",
      "Date:  11/16/17 12:10\n",
      "Date:  11/16/17 12:20\n",
      "Date:  11/16/17 12:30\n",
      "Date:  11/16/17 12:40\n",
      "Date:  11/16/17 12:50\n",
      "Date:  11/16/17 13:00\n",
      "Date:  11/16/17 13:10\n",
      "Date:  11/16/17 13:20\n",
      "Date:  11/16/17 13:30\n",
      "Date:  11/16/17 13:40\n",
      "Date:  11/16/17 13:50\n",
      "Date:  11/16/17 14:00\n",
      "Date:  11/16/17 14:10\n",
      "Date:  11/16/17 14:20\n",
      "Date:  11/16/17 14:30\n",
      "Date:  11/16/17 14:40\n",
      "Date:  11/16/17 14:50\n",
      "Date:  11/16/17 15:00\n",
      "Date:  11/16/17 15:10\n",
      "Date:  11/16/17 15:20\n",
      "Date:  11/16/17 15:30\n",
      "Date:  11/16/17 15:40\n",
      "Date:  11/16/17 15:50\n",
      "Date:  11/17/17 09:30\n",
      "Date:  11/17/17 09:40\n",
      "Date:  11/17/17 09:50\n",
      "Date:  11/17/17 10:00\n",
      "Date:  11/17/17 10:10\n",
      "Date:  11/17/17 10:20\n",
      "Enter:  ABT UN Equity APH UN Equity -2.51074740034 0.4667176868187433 -0.5332823131812567 55.42 89.57 0.706979576744\n",
      "s1 # of shares16975.349399037197\n",
      "s2 # of shares-12001.225333219087\n",
      "Date:  11/17/17 10:30\n",
      "Date:  11/17/17 10:40\n",
      "Date:  11/17/17 10:50\n",
      "Date:  11/17/17 11:00\n",
      "Date:  11/17/17 11:10\n",
      "Date:  11/17/17 11:20\n",
      "Date:  11/17/17 11:30\n",
      "Date:  11/17/17 11:40\n",
      "Date:  11/17/17 11:50\n",
      "Date:  11/17/17 12:00\n",
      "Date:  11/17/17 12:10\n",
      "Date:  11/17/17 12:20\n",
      "Date:  11/17/17 12:30\n",
      "Date:  11/17/17 12:40\n",
      "Date:  11/17/17 12:50\n",
      "Date:  11/17/17 13:00\n",
      "Date:  11/17/17 13:10\n",
      "Date:  11/17/17 13:20\n",
      "Date:  11/17/17 13:30\n",
      "Date:  11/17/17 13:40\n",
      "Date:  11/17/17 13:50\n",
      "Date:  11/17/17 14:00\n",
      "Date:  11/17/17 14:10\n",
      "Date:  11/17/17 14:20\n",
      "Date:  11/17/17 14:30\n",
      "Date:  11/17/17 14:40\n",
      "Date:  11/17/17 14:50\n",
      "Date:  11/17/17 15:00\n",
      "Date:  11/17/17 15:10\n",
      "Date:  11/17/17 15:20\n",
      "Date:  11/17/17 15:30\n",
      "Date:  11/17/17 15:40\n",
      "Date:  11/17/17 15:50\n",
      "Date:  11/20/17 09:30\n",
      "Date:  11/20/17 09:40\n",
      "Date:  11/20/17 09:50\n",
      "Date:  11/20/17 10:00\n",
      "Date:  11/20/17 10:10\n",
      "Date:  11/20/17 10:20\n",
      "Date:  11/20/17 10:30\n",
      "Date:  11/20/17 10:40\n",
      "Date:  11/20/17 10:50\n",
      "Date:  11/20/17 11:00\n",
      "Date:  11/20/17 11:10\n",
      "Date:  11/20/17 11:20\n",
      "Date:  11/20/17 11:30\n",
      "Date:  11/20/17 11:40\n",
      "Date:  11/20/17 11:50\n",
      "Date:  11/20/17 12:00\n",
      "Date:  11/20/17 12:10\n",
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      "Date:  11/20/17 12:30\n",
      "Date:  11/20/17 12:40\n",
      "Date:  11/20/17 12:50\n",
      "Date:  11/20/17 13:00\n",
      "Date:  11/20/17 13:10\n",
      "Date:  11/20/17 13:20\n",
      "Date:  11/20/17 13:30\n",
      "Date:  11/20/17 13:40\n",
      "Date:  11/20/17 13:50\n",
      "Date:  11/20/17 14:00\n",
      "Date:  11/20/17 14:10\n",
      "Date:  11/20/17 14:20\n",
      "Date:  11/20/17 14:30\n",
      "Date:  11/20/17 14:40\n",
      "Date:  11/20/17 14:50\n",
      "Date:  11/20/17 15:00\n",
      "Date:  11/20/17 15:10\n",
      "Date:  11/20/17 15:20\n",
      "Date:  11/20/17 15:30\n",
      "Date:  11/20/17 15:40\n",
      "Date:  11/20/17 15:50\n",
      "Date:  11/21/17 09:30\n",
      "Date:  11/21/17 09:40\n",
      "Date:  11/21/17 09:50\n",
      "Date:  11/21/17 10:00\n",
      "Date:  11/21/17 10:10\n",
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      "Date:  11/21/17 11:00\n",
      "Date:  11/21/17 11:10\n",
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      "Date:  11/21/17 11:40\n",
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      "Date:  11/21/17 12:00\n",
      "Date:  11/21/17 12:10\n",
      "Date:  11/21/17 12:20\n",
      "Date:  11/21/17 12:30\n",
      "Date:  11/21/17 12:40\n",
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      "Date:  11/21/17 13:00\n",
      "Date:  11/21/17 13:10\n",
      "Date:  11/21/17 13:20\n",
      "Date:  11/21/17 13:30\n",
      "Date:  11/21/17 13:40\n",
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      "Date:  11/21/17 14:00\n",
      "Date:  11/21/17 14:10\n",
      "Date:  11/21/17 14:20\n",
      "Date:  11/21/17 14:30\n",
      "Date:  11/21/17 14:40\n",
      "Date:  11/21/17 14:50\n",
      "Date:  11/21/17 15:00\n",
      "Date:  11/21/17 15:10\n",
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      "Date:  11/21/17 15:40\n",
      "Date:  11/21/17 15:50\n",
      "Date:  11/22/17 09:30\n",
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      "Date:  11/22/17 11:40\n",
      "Date:  11/22/17 11:50\n",
      "Date:  11/22/17 12:00\n",
      "Date:  11/22/17 12:10\n",
      "Date:  11/22/17 12:20\n",
      "Date:  11/22/17 12:30\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Date:  11/22/17 12:40\n",
      "Date:  11/22/17 12:50\n",
      "Date:  11/22/17 13:00\n",
      "Date:  11/22/17 13:10\n",
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      "Date:  11/22/17 13:30\n",
      "Date:  11/22/17 13:40\n",
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      "Date:  11/22/17 14:10\n",
      "Date:  11/22/17 14:20\n",
      "Date:  11/22/17 14:30\n",
      "Date:  11/22/17 14:40\n",
      "Date:  11/22/17 14:50\n",
      "Date:  11/22/17 15:00\n",
      "Date:  11/22/17 15:10\n",
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      "Date:  11/22/17 15:30\n",
      "Date:  11/22/17 15:40\n",
      "Date:  11/22/17 15:50\n",
      "Date:  11/23/17 09:30\n",
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      "Date:  11/24/17 09:30\n",
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      "Date:  11/27/17 09:39\n",
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      "Date:  11/30/17 14:49\n",
      "Date:  11/30/17 14:59\n",
      "Date:  11/30/17 15:09\n",
      "Date:  11/30/17 15:19\n",
      "Date:  11/30/17 15:29\n",
      "Date:  11/30/17 15:39\n",
      "Date:  11/30/17 15:49\n",
      "Date:  11/30/17 15:59\n",
      "Date:  12/1/17 09:39\n",
      "Date:  12/1/17 09:49\n",
      "Close position by selling  ABT UN Equity APH UN Equity -1.64890067388 16975.349399037197 -12001.225333219087 56.29 89.92 0.706979576744\n",
      "ABT UN Equity APH UN Equity  win\n",
      "account value1010568.12511\n",
      "Date:  12/1/17 09:59\n",
      "Date:  12/1/17 10:09\n",
      "Date:  12/1/17 10:19\n",
      "Date:  12/1/17 10:29\n",
      "Date:  12/1/17 10:39\n",
      "Date:  12/1/17 10:49\n",
      "Date:  12/1/17 10:59\n",
      "Date:  12/1/17 11:09\n",
      "Date:  12/1/17 11:19\n",
      "Date:  12/1/17 11:29\n",
      "Date:  12/1/17 11:39\n",
      "Date:  12/1/17 11:49\n",
      "Date:  12/1/17 11:59\n",
      "Date:  12/1/17 12:09\n",
      "Date:  12/1/17 12:19\n",
      "Date:  12/1/17 12:29\n",
      "Date:  12/1/17 12:39\n",
      "Date:  12/1/17 12:49\n",
      "Date:  12/1/17 12:59\n",
      "Date:  12/1/17 13:09\n",
      "Date:  12/1/17 13:19\n",
      "Date:  12/1/17 13:29\n",
      "Date:  12/1/17 13:39\n",
      "Date:  12/1/17 13:49\n",
      "Date:  12/1/17 13:59\n",
      "Date:  12/1/17 14:09\n",
      "Date:  12/1/17 14:19\n",
      "Date:  12/1/17 14:29\n",
      "Date:  12/1/17 14:39\n",
      "Date:  12/1/17 14:49\n",
      "Date:  12/1/17 14:59\n",
      "Date:  12/1/17 15:09\n",
      "Date:  12/1/17 15:19\n",
      "Date:  12/1/17 15:29\n",
      "Date:  12/1/17 15:39\n",
      "Date:  12/1/17 15:49\n",
      "Date:  12/1/17 15:59\n",
      "Date:  12/4/17 09:39\n",
      "Date:  12/4/17 09:49\n",
      "Date:  12/4/17 09:59\n",
      "Date:  12/4/17 10:09\n",
      "Date:  12/4/17 10:19\n",
      "Date:  12/4/17 10:29\n",
      "Date:  12/4/17 10:39\n",
      "Date:  12/4/17 10:49\n",
      "Date:  12/4/17 10:59\n",
      "Date:  12/4/17 11:09\n",
      "Date:  12/4/17 11:19\n",
      "Date:  12/4/17 11:29\n",
      "Date:  12/4/17 11:39\n",
      "Date:  12/4/17 11:49\n",
      "Date:  12/4/17 11:59\n",
      "Date:  12/4/17 12:09\n",
      "Date:  12/4/17 12:19\n",
      "Date:  12/4/17 12:29\n",
      "Date:  12/4/17 12:39\n",
      "Date:  12/4/17 12:49\n",
      "Date:  12/4/17 12:59\n",
      "Date:  12/4/17 13:09\n",
      "Date:  12/4/17 13:19\n",
      "Date:  12/4/17 13:29\n",
      "Date:  12/4/17 13:39\n",
      "Date:  12/4/17 13:49\n",
      "Date:  12/4/17 13:59\n",
      "Date:  12/4/17 14:09\n",
      "Date:  12/4/17 14:19\n",
      "Date:  12/4/17 14:29\n",
      "Date:  12/4/17 14:39\n",
      "Date:  12/4/17 14:49\n",
      "Date:  12/4/17 14:59\n",
      "Date:  12/4/17 15:09\n",
      "Date:  12/4/17 15:19\n",
      "Date:  12/4/17 15:29\n",
      "Date:  12/4/17 15:39\n",
      "Date:  12/4/17 15:49\n",
      "Date:  12/4/17 15:59\n",
      "Date:  12/5/17 09:39\n",
      "Date:  12/5/17 09:49\n",
      "Date:  12/5/17 09:59\n",
      "Date:  12/5/17 10:09\n",
      "Date:  12/5/17 10:19\n",
      "Date:  12/5/17 10:29\n",
      "Date:  12/5/17 10:39\n",
      "Date:  12/5/17 10:49\n",
      "Date:  12/5/17 10:59\n",
      "Date:  12/5/17 11:09\n",
      "Date:  12/5/17 11:19\n",
      "Date:  12/5/17 11:29\n",
      "Date:  12/5/17 11:39\n",
      "Date:  12/5/17 11:49\n",
      "Date:  12/5/17 11:59\n",
      "Date:  12/5/17 12:09\n"
     ]
    }
   ],
   "source": [
    "#====================================\n",
    "# Feel free to change the parameters\n",
    "#====================================\n",
    "corr_threshold=0.9\n",
    "t_test_threshold=-3.34\n",
    "number_of_pairs=1\n",
    "\n",
    "entry_threshold=2.5\n",
    "gap=0.5\n",
    "exit_threshold =entry_threshold-gap\n",
    "stop_loss_threshold=5\n",
    "transaction_cost=0 #fixed\n",
    "transaction_cost_per_share=0 # per share\n",
    "\n",
    "number=1 # modify\n",
    "\n",
    "\n",
    "#============================\n",
    "#       do not change\n",
    "#============================\n",
    "initial_capital=1000000\n",
    "transaction_record=0 \n",
    "portfolio={}\n",
    "dfpnl={}\n",
    "trainning_interval=int(19500/freq)\n",
    "interval=int(9750/freq)\n",
    "\n",
    "pairs_symbol_traded=[]\n",
    "capital={}\n",
    "\n",
    "length=len(stocks['A UN Equity'])\n",
    "\n",
    "num_of_trades=0\n",
    "trade_record={}\n",
    "temp_record={} # for compute winnging and loss trades\n",
    "winning=0\n",
    "lose=0\n",
    "\n",
    "start_training=0\n",
    "end_training=trainning_interval-1\n",
    "\n",
    "start_test=0\n",
    "end_test=0\n",
    "\n",
    "store_selected_pairs={}\n",
    "store_selected_pair_info={}\n",
    "\n",
    "#============================================\n",
    "#                   START!\n",
    "#============================================\n",
    "for i in range(number):\n",
    "    pairs_symbol_traded.clear()\n",
    "    num_of_trades=0\n",
    "    winning=0\n",
    "    loss=0\n",
    "    if trainning_interval+interval*i<length:\n",
    "        start_training+=i*interval\n",
    "        end_training+=i*interval\n",
    "        \n",
    "        #-------------------\n",
    "        # Stationarity test \n",
    "        #-------------------\n",
    "        symbol=test_stationarity(stocks)\n",
    "        \n",
    "        #-------------------\n",
    "        # Pair permutation\n",
    "        #-------------------\n",
    "        pairs = []\n",
    "        for c in range(len(symbol)-1):\n",
    "            for j in range(c+1,len(symbol)):\n",
    "                pairs.append((symbol[c],symbol[j]))\n",
    "              \n",
    "        #---------------------------------------     \n",
    "        # Create dfpnl to store testing results\n",
    "        #---------------------------------------\n",
    "        start_test=end_training+1 # start date of test\n",
    "        end_test=start_test+interval-1 # end date of test\n",
    "        dfpnl[i]=pd.DataFrame(columns=['value','PnL','Cumulative PnL'],\n",
    "                              index=stocks['A UN Equity'].index).iloc[start_test:end_test+1]\n",
    "   \n",
    "        \n",
    "             \n",
    "        #--------------\n",
    "        #     MAIN\n",
    "        #--------------\n",
    "        # Choose pairs\n",
    "        #--------------\n",
    "        d_pair_info,d_selected_pair_info,l_selected_pairs=choose_pairs(pairs,start_training,end_training)\n",
    "        store_selected_pairs[i]=l_selected_pairs\n",
    "        store_selected_pair_info[i]=d_selected_pair_info\n",
    "        \n",
    "        for each in l_selected_pairs: # add columns in dfpnl[i] to record pairs' pnl\n",
    "            dfpnl[i][each[0]+', '+each[1]+' pnl']=None \n",
    "            dfpnl[i][each[0]+', '+each[1]+' cumulative pnl']=None \n",
    "        \n",
    "        a=len(stocks['A UN Equity'].iloc[start_test:end_test+1])\n",
    "        \n",
    "        #----------------\n",
    "        # Start trading   \n",
    "        #----------------\n",
    "        for b in range(0,a-1):\n",
    "            print('Date: ',dfpnl[i].index[b])\n",
    "            check_pairs(d_selected_pair_info, l_selected_pairs,b)\n",
    "            record(b,i,l_selected_pairs)\n",
    "            \n",
    "        #-----------------------------------------------\n",
    "        # Close all positions at the end of test period\n",
    "        #-----------------------------------------------\n",
    "        for pair in l_selected_pairs:\n",
    "            s1, s2 = pair\n",
    "            pos1 = portfolio[(pair,s1)]['# of shares']\n",
    "            pos2 = portfolio[(pair,s2)]['# of shares']\n",
    "            if pos1==0 and pos2==0:\n",
    "                continue\n",
    "            print('Close position by selling ',s1, s2, 0, pos1, pos2, 0, 0, 0)\n",
    "            order_target_percent(s1, s2, 0, 0, stocks[s1].iloc[start_test:end_test+1]['Adj Close'][-1],stocks[s2].iloc[start_test:end_test+1]['Adj Close'][-1],0)\n",
    "            position=position-1\n",
    "       \n",
    "        #----------------------------------------------\n",
    "        # Record the number of winning and loss trades\n",
    "        #----------------------------------------------\n",
    "        trade_record[i]={'number of trades:':num_of_trades,\n",
    "                        'numer of winning trades:':winning,\n",
    "                        'numer of loss trades:':loss}\n",
    "        \n",
    "    #-----------------------------\n",
    "    # For the last testing period\n",
    "    #-----------------------------       \n",
    "    elif trainning_interval+interval*i>length and training_interval+i*interval-length<interval:\n",
    "        start_training+=i*interval\n",
    "        end_training+=i*interval\n",
    "        \n",
    "        #-------------------\n",
    "        # Stationarity test \n",
    "        #-------------------\n",
    "        symbol=test_stationarity(stocks)\n",
    "        \n",
    "        #------------------\n",
    "        # Pair permutation\n",
    "        #------------------\n",
    "        pairs = []\n",
    "        for c in range(len(symbol)-1):\n",
    "            for j in range(c+1,len(symbol)):\n",
    "                pairs.append((symbol[c],symbol[j]))\n",
    "                                \n",
    "        #---------------------------------------   \n",
    "        # Create dfpnl to store testing results\n",
    "        #---------------------------------------\n",
    "        start_test=end_training+1 # start date of test\n",
    "        end_test=length-1 # end date of test\n",
    "        dfpnl[i]=pd.DataFrame(columns=['value','PnL','Cumulative PnL'],index=stocks['A UN Equity'].index).iloc[start_test:end_test+1]\n",
    "        \n",
    "        #--------------\n",
    "        #     MAIN\n",
    "        #--------------\n",
    "        # Choose pairs\n",
    "        #--------------\n",
    "        d_pair_info,d_selected_pair_info,l_selected_pairs=choose_pairs(pairs,start_training,end_training)\n",
    "        store_selected_pairs[i]=l_selected_pairs\n",
    "        store_selected_pair_info[i]=d_selected_pair_info\n",
    "        \n",
    "        for each in l_selected_pairs: # add columns in dfpnl[i] to record pairs' pnl\n",
    "            dfpnl[i][each[0]+', '+each[1]+' pnl']=None \n",
    "            dfpnl[i][each[0]+', '+each[1]+' cumulative pnl']=None \n",
    "            \n",
    "        a=len(stocks['A UN Equity'].iloc[start_test:end_test+1])\n",
    "        \n",
    "        #---------------\n",
    "        # Start trading   \n",
    "        #---------------\n",
    "        for b in range(0,a-1):\n",
    "            print('Date: ',dfpnl[i].index[b])\n",
    "            check_pairs(d_selected_pair_info, l_selected_pairs,b)\n",
    "            record(b,i,l_selected_pairs)\n",
    "                   \n",
    "        #-----------------------------------------------\n",
    "        # Close all positions at the end of test period\n",
    "        #-----------------------------------------------\n",
    "        for pair in l_selected_pairs:\n",
    "            s1, s2 = pair\n",
    "            pos1 = portfolio[(pair,s1)]['# of shares']\n",
    "            pos2 = portfolio[(pair,s2)]['# of shares']\n",
    "            if pos1==0 and pos2==0:\n",
    "                continue\n",
    "            print('Close position by selling ',s1, s2, 0, pos1, pos2, 0, 0, 0)\n",
    "            order_target_percent(s1, s2, 0, 0, stocks[s1].iloc[start_test:end_test+1]['Adj Close'][-1],stocks[s2].iloc[start_test:end_test+1]['Adj Close'][-1],0)\n",
    "            position=position-1\n",
    "  \n",
    "        #----------------------------------------------\n",
    "        # Record the number of winning and loss trades\n",
    "        #----------------------------------------------\n",
    "        trade_record[i]={'number of trades:':num_of_trades,\n",
    "                        'numer of winning trades:':winning,\n",
    "                        'numer of loss trades:':loss}\n",
    "        \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('ABT UN Equity', 'APH UN Equity')]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the selected pairs\n",
    "store_selected_pairs[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'correlation': 0.93936441428531325,\n",
       " 'hedge': 0.70697957674436851,\n",
       " 'mean': -6.0905159964290654,\n",
       " 'p_value': 0.024274126716016343,\n",
       " 'spreads': Time\n",
       " 8/21/17 09:30    -6.535291\n",
       " 8/21/17 09:40    -6.590640\n",
       " 8/21/17 09:50    -6.664780\n",
       " 8/21/17 10:00    -6.717710\n",
       " 8/21/17 10:10    -6.737199\n",
       " 8/21/17 10:20    -6.641640\n",
       " 8/21/17 10:30    -6.484082\n",
       " 8/21/17 10:40    -6.487012\n",
       " 8/21/17 10:50    -6.526501\n",
       " 8/21/17 11:00    -6.520129\n",
       " 8/21/17 11:10    -6.597199\n",
       " 8/21/17 11:20    -6.618757\n",
       " 8/21/17 11:30    -6.565478\n",
       " 8/21/17 11:40    -6.624269\n",
       " 8/21/17 11:50    -6.708757\n",
       " 8/21/17 12:00    -6.651338\n",
       " 8/21/17 12:10    -6.515989\n",
       " 8/21/17 12:20    -6.575478\n",
       " 8/21/17 12:30    -6.558757\n",
       " 8/21/17 12:40    -6.560827\n",
       " 8/21/17 12:50    -6.559618\n",
       " 8/21/17 13:00    -6.574967\n",
       " 8/21/17 13:10    -6.560827\n",
       " 8/21/17 13:20    -6.639106\n",
       " 8/21/17 13:30    -6.676176\n",
       " 8/21/17 13:40    -6.662897\n",
       " 8/21/17 13:50    -6.674106\n",
       " 8/21/17 14:00    -6.740827\n",
       " 8/21/17 14:10    -6.832734\n",
       " 8/21/17 14:20    -6.855665\n",
       "                     ...   \n",
       " 10/30/17 11:00   -6.149546\n",
       " 10/30/17 11:10   -6.289546\n",
       " 10/30/17 11:20   -6.428336\n",
       " 10/30/17 11:30   -6.448499\n",
       " 10/30/17 11:40   -6.517638\n",
       " 10/30/17 11:50   -6.490057\n",
       " 10/30/17 12:00   -6.515406\n",
       " 10/30/17 12:10   -6.487127\n",
       " 10/30/17 12:20   -6.444197\n",
       " 10/30/17 12:30   -6.370755\n",
       " 10/30/17 12:40   -6.380755\n",
       " 10/30/17 12:50   -6.370755\n",
       " 10/30/17 13:00   -6.366104\n",
       " 10/30/17 13:10   -6.311964\n",
       " 10/30/17 13:20   -6.202476\n",
       " 10/30/17 13:30   -6.171266\n",
       " 10/30/17 13:40   -6.100569\n",
       " 10/30/17 13:50   -6.148848\n",
       " 10/30/17 14:00   -6.211778\n",
       " 10/30/17 14:10   -6.380057\n",
       " 10/30/17 14:20   -6.394197\n",
       " 10/30/17 14:30   -6.377127\n",
       " 10/30/17 14:40   -6.368848\n",
       " 10/30/17 14:50   -6.324708\n",
       " 10/30/17 15:00   -6.375917\n",
       " 10/30/17 15:10   -6.386429\n",
       " 10/30/17 15:20   -6.381080\n",
       " 10/30/17 15:30   -6.398150\n",
       " 10/30/17 15:40   -6.362987\n",
       " 10/30/17 15:50   -6.264708\n",
       " Name: Adj Close, Length: 1950, dtype: float64,\n",
       " 't_test': -3.6032719350013971}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# specific information of 10 selected pairs\n",
    "# for example:\n",
    "store_selected_pair_info[0][('ABT UN Equity', 'APH UN Equity')]# 'IDXX UW Equity', 'SO UN Equity'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: {'number of trades:': 3,\n",
       "  'numer of loss trades:': 0,\n",
       "  'numer of winning trades:': 3}}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# total number trades, winning trades and loss trades\n",
    "trade_record"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>value</th>\n",
       "      <th>PnL</th>\n",
       "      <th>Cumulative PnL</th>\n",
       "      <th>ABT UN Equity, APH UN Equity pnl</th>\n",
       "      <th>ABT UN Equity, APH UN Equity cumulative pnl</th>\n",
       "      <th>return</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12/5/17 11:39</th>\n",
       "      <td>1.031661e+06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31660.833247</td>\n",
       "      <td>4973.410006</td>\n",
       "      <td>31660.833247</td>\n",
       "      <td>0.031661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12/5/17 11:49</th>\n",
       "      <td>1.031661e+06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31660.833247</td>\n",
       "      <td>4973.410006</td>\n",
       "      <td>31660.833247</td>\n",
       "      <td>0.031661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12/5/17 11:59</th>\n",
       "      <td>1.031661e+06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31660.833247</td>\n",
       "      <td>4973.410006</td>\n",
       "      <td>31660.833247</td>\n",
       "      <td>0.031661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12/5/17 12:09</th>\n",
       "      <td>1.031661e+06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31660.833247</td>\n",
       "      <td>4973.410006</td>\n",
       "      <td>31660.833247</td>\n",
       "      <td>0.031661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12/5/17 12:19</th>\n",
       "      <td>1.031661e+06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31660.833247</td>\n",
       "      <td>4973.410006</td>\n",
       "      <td>31660.833247</td>\n",
       "      <td>0.031661</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      value  PnL  Cumulative PnL  \\\n",
       "Time                                               \n",
       "12/5/17 11:39  1.031661e+06  0.0    31660.833247   \n",
       "12/5/17 11:49  1.031661e+06  0.0    31660.833247   \n",
       "12/5/17 11:59  1.031661e+06  0.0    31660.833247   \n",
       "12/5/17 12:09  1.031661e+06  0.0    31660.833247   \n",
       "12/5/17 12:19  1.031661e+06  0.0    31660.833247   \n",
       "\n",
       "               ABT UN Equity, APH UN Equity pnl  \\\n",
       "Time                                              \n",
       "12/5/17 11:39                       4973.410006   \n",
       "12/5/17 11:49                       4973.410006   \n",
       "12/5/17 11:59                       4973.410006   \n",
       "12/5/17 12:09                       4973.410006   \n",
       "12/5/17 12:19                       4973.410006   \n",
       "\n",
       "               ABT UN Equity, APH UN Equity cumulative pnl    return  \n",
       "Time                                                                  \n",
       "12/5/17 11:39                                 31660.833247  0.031661  \n",
       "12/5/17 11:49                                 31660.833247  0.031661  \n",
       "12/5/17 11:59                                 31660.833247  0.031661  \n",
       "12/5/17 12:09                                 31660.833247  0.031661  \n",
       "12/5/17 12:19                                 31660.833247  0.031661  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cumulative PnL of portfolio and each traded pair\n",
    "dfpnl[0].fillna(method='ffill').tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# you can run this cell to save the pnl results as a csv file\n",
    "dfpnl[0].to_csv('result.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Some graphs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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jYNgt6vh5QUhtlRnVGsL68nBxYu9Tk/n3XwbX+x4tIdRXBXRLt8VTWm4kJbeY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\nP9/opYytN5LhAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12c13b0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# =============================================\n",
    "# show that a selected pair looks cointegrated\n",
    "# =============================================\n",
    "stock1='ABT UN Equity'\n",
    "stock2='APH UN Equity'\n",
    "# normalize the stock prices\n",
    "series1=stocks[stock1].iloc[0:end_training]['Adj Close']\n",
    "normalized_series1=(series1-series1.mean())/(series1.std())\n",
    "series2=stocks[stock2].iloc[0:end_training]['Adj Close']\n",
    "normalized_series2=(series2-series2.mean())/(series2.std())\n",
    "\n",
    "# plot\n",
    "plt.figure(figsize=(15,6))\n",
    "plt.ylabel('Normalized Stock Prices')\n",
    "normalized_series1.plot(label=stock1)\n",
    "normalized_series2.plot(label=stock2)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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m57eCIiIidYBpmhgeo/Qcgd7Gw2kAfLr64PmuloiI1GFeS8ZiOGZ+/X/AdtM0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\ngGGEHgAAwDBCDwAAYBihBwAAMMwniYUgZ99i15YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ec5c860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# =============================================\n",
    "# plot the time series of the spread of a pair\n",
    "# =============================================\n",
    "t=0\n",
    "\n",
    "std=store_selected_pair_info[t][(stock1,stock2)]['spreads'].std()\n",
    "plt.figure(figsize=(15,6))\n",
    "plt.ylabel('Residual')\n",
    "temp_series=store_selected_pair_info[t][(stock1,stock2)]['spreads']-store_selected_pair_info[t][(stock1,stock2)]['mean']\n",
    "temp_series.plot(label='residual')\n",
    "\n",
    "plt.axhline(0, linewidth=1,color='black',label='mean') # Add the mean of residual\n",
    "plt.axhline(std*entry_threshold, color='green', linestyle='--',label='entry threshold') \n",
    "plt.axhline(-std*entry_threshold, color='green', linestyle='--') \n",
    "plt.axhline(std*exit_threshold, color='blue', linestyle='--',label='exit threshold') \n",
    "plt.axhline(-std*exit_threshold, color='blue', linestyle='--') \n",
    "plt.axhline(std*stop_loss_threshold, color='red', linestyle='--',label='stop loss threshold') \n",
    "plt.axhline(-std*stop_loss_threshold, color='red', linestyle='--') \n",
    "\n",
    "plt.ylim(-std*stop_loss_threshold-1,std*stop_loss_threshold+1)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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BXYNFVTVU9UQAa2FmCY/Lu19hZhvrSlW/q6qnqOop/f399X45IiIiIqKWZmS4dMZSsCCF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\nWOsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ebbdda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ============================================\n",
    "#   plot the Cumulative PnL of the portfolio\n",
    "# ============================================\n",
    "t=0\n",
    "\n",
    "plt.figure(figsize=(15,6))\n",
    "plt.ylabel('Cumulative PnL')\n",
    "plt.title('Cumulative PnL for Pairs Trading Strategy')\n",
    "dfpnl[t]['Cumulative PnL'].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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CYEHZkEZZKCs4J3GCUlso20FQhgYb/I/rdo/g5d+8PZg8/Ul5QrufdsoJymxe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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1111a0668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# =============================================\n",
    "#         plot the PnL of the portfolio\n",
    "# =============================================\n",
    "t=0\n",
    "\n",
    "plt.figure(figsize=(15,6))\n",
    "plt.ylabel('PnL')\n",
    "plt.title('PnL for Pairs Trading Strategy')\n",
    "dfpnl[t]['PnL'].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1.0</th>\n",
       "      <th>1.5</th>\n",
       "      <th>2.0</th>\n",
       "      <th>2.5</th>\n",
       "      <th>3.0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.005</th>\n",
       "      <td>22748.61</td>\n",
       "      <td>21497.14</td>\n",
       "      <td>25069.66</td>\n",
       "      <td>26382.61</td>\n",
       "      <td>24090.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.020</th>\n",
       "      <td>23159.21</td>\n",
       "      <td>21872.84</td>\n",
       "      <td>24743.22</td>\n",
       "      <td>26217.71</td>\n",
       "      <td>24953.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.050</th>\n",
       "      <td>22872.42</td>\n",
       "      <td>21077.28</td>\n",
       "      <td>25408.26</td>\n",
       "      <td>23844.72</td>\n",
       "      <td>24934.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.100</th>\n",
       "      <td>23066.21</td>\n",
       "      <td>21623.94</td>\n",
       "      <td>24612.95</td>\n",
       "      <td>23223.83</td>\n",
       "      <td>25036.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.300</th>\n",
       "      <td>23147.58</td>\n",
       "      <td>13229.65</td>\n",
       "      <td>21785.46</td>\n",
       "      <td>20460.00</td>\n",
       "      <td>25419.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.500</th>\n",
       "      <td>18145.80</td>\n",
       "      <td>14758.37</td>\n",
       "      <td>22767.74</td>\n",
       "      <td>17909.68</td>\n",
       "      <td>20135.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.000</th>\n",
       "      <td>5210.76</td>\n",
       "      <td>14195.56</td>\n",
       "      <td>19079.12</td>\n",
       "      <td>18172.47</td>\n",
       "      <td>15349.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            1.0       1.5       2.0       2.5       3.0\n",
       "0.005  22748.61  21497.14  25069.66  26382.61  24090.92\n",
       "0.020  23159.21  21872.84  24743.22  26217.71  24953.16\n",
       "0.050  22872.42  21077.28  25408.26  23844.72  24934.09\n",
       "0.100  23066.21  21623.94  24612.95  23223.83  25036.76\n",
       "0.300  23147.58  13229.65  21785.46  20460.00  25419.94\n",
       "0.500  18145.80  14758.37  22767.74  17909.68  20135.36\n",
       "1.000   5210.76  14195.56  19079.12  18172.47  15349.33"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# =======================================================\n",
    "# plot the heat map to optimize entry and exit thresholds\n",
    "# =======================================================\n",
    "\n",
    "import seaborn as sns\n",
    "store_cum_pnl=[[22748.61,21497.14,25069.66,26382.61,24090.92],\n",
    "               [23159.21,21872.84,24743.22,26217.71,24953.16],\n",
    "               [22872.42,21077.28,25408.26,23844.72,24934.09],\n",
    "               [23066.21,21623.94,24612.95,23223.83,25036.76],\n",
    "               [23147.58,13229.65,21785.46,20460.00,25419.94],\n",
    "               [18145.80,14758.37,22767.74,17909.68,20135.36],\n",
    "               [5210.76,14195.56,19079.12,18172.47,15349.33]]\n",
    "\n",
    "rows = [0.005,0.02,0.05,0.1,0.3,0.5,1]\n",
    "columns =[1,1.5,2,2.5,3]\n",
    "df=pd.DataFrame(store_cum_pnl,columns=columns,index=rows)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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srCwrYgMAoMUJm3glKSMjQxkZGWbHAgBwGipeAAAs5MBzvM47YgAAbETFCwCw\nD61mAAAs5MDES6sZAAALUfECAOzjwIqXxAsAsA+rmgEAgJmoeAEA9qHVDACAhRyYeGk1AwBgISpe\nAIB9XM6r/0i8AAD7xNBqjpgTJ04oEAjIMAz98Y9/1Nq1a2UYhlnTAQAQFUypeJ9//nm98MILMgxD\nffv2VTAYVGJionbv3q2ZM2eaMSUAIBrRao6M9evX67XXXtNXX32l66+/Xps3b5YkjRo1yozpAADR\nyoGrmk1JvA0NDTp9+rTatWunWbNmSZKCwaBqa2vNmA4AEK24clVk3HHHHcrJyVFDQ4Oys7MlSbff\nfrtyc3PNmA4AgKhhSsU7aNAgZWdnK+Y7f8k89dRT8ng8ZkwHAIhWDmw1m1bjx/xT+8Dj8ai0tNSs\n6QAA0cgVE7ktSpgeaUNDQ+jfiYmJZk8HAECzZkqr+dChQ1qwYIH27Nkjt9uthoYGpaamyufzmTEd\nACBaObDVbEriLSws1JQpU9SzZ8/QWEVFhXw+n0pKSsyYEgAQjVjVHBnBYLBR0pWkXr16mTEVAABR\nxZSKNy0tTT6fTxkZGfJ6vQoEAiorK1NaWpoZ0wEAohWt5siYPXu2Nm7cqPLycvn9fnk8HmVlZYW+\n0wsAgCTLVyPv2rVLjzzyiFasWKEPP/xQc+fOVWxsrOLj47Vw4UL9/Oc/1+rVq1VSUiK3263x48cr\nKytL1dXVmjp1qo4fP66kpCQtXLhQbdu2VUVFhebPn6/Y2Filp6drwoQJYWMwJfG6XC5lZ2eTaAEA\nzcayZcv06quvhr5hM3/+fD344IPq1q2bSkpKtGzZMo0bN04rVqzQmjVrVFNTo/z8fF1xxRUqLi5W\namqqJk6cqA0bNqioqEgzZszQrFmztHjxYnXs2FEFBQXau3evunfvftY4nHdWGwDQfMS4IreFkZKS\nosWLF4d+fvTRR9WtWzdJUn19vRISErR792717t1b8fHx8nq9SklJ0b59+1ReXq6MjAxJUmZmprZu\n3Sq/369gMKiUlBS5XC6lp6dry5Yt4Q/5J75VAAD86yy8gMagQYPkdv+j0XvOOedIknbu3KmVK1dq\n7Nix8vv98nq9oX2SkpLk9/sbjSclJamqqip0KvW7+1ZVVYWNw5RWMwAA0eC1117TU089paVLl6pt\n27byeDwKBAKhxwOBgLxeb6PxQCCg5OTk7903OTk57JxUvAAA+7hckdt+pFdeeUUrV67UihUr1LFj\nR0lSjx5nXbiAAAAOIElEQVQ9VF5erpqaGlVVVamyslKpqam65JJLVFZWJknatGmT+vTpI4/Ho7i4\nOB08eFCGYWjz5s269NJLw85LxQsAsI9N11iur6/X/Pnzdd5552nixImSpMsuu0z33nuvRo8erfz8\nfBmGocmTJyshIUF5eXmaNm2a8vLyFBcXp0WLFkmS5syZo/vvv1/19fVKT08/4xoW38dlGIZh6tH9\nBC+3/YXdITRb188ZZXcIzdq78160O4Rmq+3P4uwOodl68fAJu0No1uYGvzLttetLHonYa8XedH/E\nXstMVLwAAPv8gNXILQ2JFwBgHwdeuYrFVQAAWIiKFwBgnyi6gX2kkHgBAPZx4Dle5/2pAQCAjah4\nAQD2odUMAICFWNUMAADM1Cwr3qt6cuWqphh//7vdITRrX9fV2x1Cs9XrV+faHUKzdbObGsQ2tJoB\nALAQq5oBAICZqHgBAPah1QwAgIVY1QwAAMxExQsAsE+M8+o/Ei8AwD60mgEAgJmoeAEA9mFVMwAA\nFqLVDAAAzETFCwCwD6uaAQCwEK1mAABgJipeAIB9WNUMAICFaDUDAAAzUfECAOxDqxkAAAvF0GoG\nAAAmouIFANiHVjMAABZiVTMAADATFS8AwD60mgEAsI7Lga1mUxLv/v371blzZ0lSWVmZ9u7dq9/8\n5jfKzMw0YzoAQLRyYMVryhHPnDlTkrR06VK98MILat26tV566SUtWbLEjOkAAIgapv6p8c477+j3\nv/+98vPz9cQTT2jr1q1mTgcAiDaumMhtUcKUVvOXX36pvXv3qn379vL7/WrTpo2qq6tVU1NjxnQA\ngGjFlasiY/jw4Xruuef0t7/9TatWrZLf79c111yjMWPGmDEdAABRw5SKd+zYsWeMrV+/Xh6Px4zp\nAADRKopaxJFi2RF7PB6VlpZaNR0AIBq4XJHbooTpibehoSH078TERLOnAwCgWTOl1Xzo0CEtWLBA\ne/bskdvtVkNDg1JTU+Xz+cyYDgAQrRzYajYl8RYWFmrKlCnq2bNnaKyiokI+n08lJSVmTAkAiEZR\n1CKOFFP+1AgGg42SriT16tXLjKkAAIgqplS8aWlp8vl8ysjIkNfrVSAQUFlZmdLS0syYDgAQrWg1\nR8bs2bO1ceNGlZeXy+/3y+PxKCsrS9nZ2WZMBwCIVg68gIYpidflcik7O5tECwDAP+G2gAAA+9Bq\nBgDAQqxqBgAAZqLiBQDYh1YzAAAWotUMAADMRMULALAPrWYAACwU47zE67wjBgDARlS8AADbuBy4\nuIrECwCwjwPP8TrviAEAsBEVLwDAPrSaAQCwkANbzc0y8bbeUGZ3CM1W/aLf2h1Cs3bpBW3sDqHZ\nMurq7Q6h2epyXU+7Q4CDNMvECwBwCItazbW1tXrggQd0+PBhxcTEaO7cuXK73XrggQfkcrnUtWtX\nzZo1SzExMVq9erVKSkrkdrs1fvx4ZWVlqbq6WlOnTtXx48eVlJSkhQsXqm3btj8pFhIvAMA+Fl1A\no6ysTHV1dSopKdG7776rxx9/XLW1tZo0aZL+/d//XTNnztSbb76pXr16acWKFVqzZo1qamqUn5+v\nK664QsXFxUpNTdXEiRO1YcMGFRUVacaMGT8pFuc11wEAjnPBBReovr5eDQ0N8vv9crvd+uCDD9S3\nb19JUmZmprZs2aLdu3erd+/eio+Pl9frVUpKivbt26fy8nJlZGSE9t26detPjoWKFwBgH4tazT/7\n2c90+PBhXXPNNfrqq6/09NNPa8eOHaELeCQlJamqqkp+v19erzf0vKSkJPn9/kbj3+77U5F4AQD2\nsWhV8/Lly5Wenq4pU6bo6NGjuuWWW1RbWxt6PBAIKDk5WR6PR4FAoNG41+ttNP7tvj8VrWYAQIuX\nnJwcqlhbt26turo6de/eXdu3b5ckbdq0SZdeeql69Oih8vJy1dTUqKqqSpWVlUpNTdUll1yisrKy\n0L59+vT5ybG4DMMw/vVDirBTX9sdQbPF14nO7vNXttgdQrP1M2+C3SE0W0m9L7Q7hGbN/ejLpr22\ncXBPxF7LlXJRk48FAgFNnz5dx44dU21trcaMGaOLLrpIDz74oGpra3XhhRdq3rx5io2N1erVq/Xi\niy/KMAzdeeedGjRokE6fPq1p06bp2LFjiouL06JFi9S+ffufFieJN7qQeM+OxNs0Em/TSLxnZ27i\n/SBir+VK+U3EXstMtJoBALAQi6sAAPbhWs0AAFjIgYmXVjMAABai4gUA2Mh5FS+JFwBgH1rNAADA\nTFS8AAD7OK/gJfECAOzkvMxLqxkAAAtR8QIA7OPAxVUkXgCAfRyYeGk1AwBgISpeAICNnFfxkngB\nAPah1QwAAMxExQsAsJHzKl4SLwDAPg5sNZuWeL/88kvt2LFDVVVVSk5OVq9evXTOOeeYNR0AIBo5\nMPGaco63tLRUBQUF2rlzp44cOaLy8nLdddddKi4uNmM6AACihikV75o1a1RcXKy4uLjQWDAYVF5e\nnvLy8syYEgAQlZxX8ZqSeOvq6lRTU9Mo8VZXV8vlwJYCAKBpTswLpiTeu+++Wzk5OerUqZO8Xq/8\nfr8OHDggn89nxnQAAEQNUxLvgAEDlJmZqcrKSvn9fnk8HnXp0kVuN4uoAQDfQcUbwRd2u5WWltZo\nrLS0VLm5uWZNCQCIOs5LvJZeuSoxMdHK6QAAaHYs7f0OHTrUyukAAM0drebIGD16tGpraxuNGYYh\nl8ulkpISM6YEAEQjEm9k3H///ZoxY4Z+//vfKzY21owpAACISqYk3p49e+r666/XRx99pOzsbDOm\nAAC0CFS8ETNu3DizXhoA0FI4sNXM/XgBALAQV7QAANjHeQUviRcAYCfnZV5azQAAWIiKFwBgHwcu\nriLxAgDs48DES6sZAAALUfECAGzkvIqXxAsAsA+tZgAAYCYqXgCAfRxY8ZJ4AQA2cl7ipdUMAICF\nqHgBAPZxYKvZZRiGYXcQAAA4Ba1mAAAsROIFAMBCJF4AACxE4gUAwEIkXgAALETiBQDAQiReAAAs\nROL9J7t27dLo0aPPGH/rrbc0bNgwjRw5UqtXr7Yhsuahqfdn+fLlGjJkiEaPHq3Ro0fr448/tiE6\ne9TW1mrq1KnKz8/X8OHD9eabbzZ63OmfnXDvj5M/O/X19fL5fLrpppuUl5env/71r40ed/pnp8Uy\nELJ06VJj6NChRm5ubqPxYDBoDBw40Dhx4oRRU1Nj5OTkGMeOHbMpSvs09f4YhmFMmTLFeP/9922I\nyn4vvfSSMW/ePMMwDOOrr74y+vfvH3qMz87Z3x/DcPZn589//rPxwAMPGIZhGNu2bTPuuuuu0GN8\ndlouKt7vSElJ0eLFi88Yr6ysVEpKilq3bq34+Hj16dNHO3bssCFCezX1/kjSBx98oKVLlyovL09/\n+MMfLI7MXoMHD9Z9990nSTIMQ7GxsaHH+Oyc/f2RnP3ZGThwoObOnStJOnLkiJKTk0OP8dlpuUi8\n3zFo0CC53Wdevtrv98vr9YZ+TkpKkt/vtzK0ZqGp90eShgwZotmzZ+t///d/VV5errffftvi6OyT\nlJQkj8cjv9+ve++9V5MmTQo9xmfn7O+P5OzPjiS53W5NmzZNc+fO1XXXXRca57PTcpF4fwCPx6NA\nIBD6ORAINPofwukMw9Att9yitm3bKj4+Xv3799fevXvtDstSR48e1ZgxY3T99dc3+uXJZ+cbTb0/\nfHa+sXDhQv3pT3/Sgw8+qFOnTknis9OSkXh/gC5duujAgQM6ceKEgsGg3nvvPfXu3dvusJoNv9+v\noUOHKhAIyDAMbd++XRdddJHdYVnmiy++0G233aapU6dq+PDhjR7js3P298fpn521a9eG2uuJiYly\nuVyKifnm1zKfnZaL2wKexbp163Tq1CmNHDlSDzzwgG6//XYZhqFhw4bp3HPPtTs82333/Zk8ebLG\njBmj+Ph4XX755erfv7/d4Vnm6aef1smTJ1VUVKSioiJJUm5urk6fPs1nR+HfHyd/dq6++mr5fD6N\nGjVKdXV1mj59uv785z/ze6eF47aAAABYiFYzAAAWIvECAGAhEi8AABYi8QIAYCESLwAAFiLxAiby\n+Xw6fPiw3WEAaEZIvICJtm/fLr6xB+C7uIAG8CMtXbpUr7/+uurr65Wenq68vDxNnDhRXbt21Ycf\nfqh27drpiSee0OrVq/X555+roKBAq1at0rBhw9SjRw99+OGH6tevn1q3bq3f/va3kr6pjDMyMnTt\ntdfafHQAzEbFC/wImzZt0p49e/TSSy9p7dq1+uyzz7Ru3Trt27dPt956q9avX6/k5GStW7dOBQUF\nOuecc7R06VL927/9myQpMzNTf/rTnzRu3DitX79ehmHo1KlT2rp1qwYOHGjz0QGwAhUv8CNs3bpV\nu3fvVk5OjiSpurpahmGoXbt26t69uySpa9eu+vrrr7/3+T179pQkdezYUR06dNCOHTt05MgR9e/f\nX/Hx8dYcBABbkXiBH6G+vl633HKLbr31VknSyZMn9fe//10VFRWhfVwuV5PndRMSEkL/HjZsmNav\nX68jR45o4sSJ5gYOoNmg1Qz8CP369dMrr7yiQCCguro63XPPPdqzZ0+T+8fGxqq+vv57Hxs8eLC2\nbt2qL774IlQJA2j5qHiBH2HAgAHat2+fRowYofr6emVkZOiyyy5rcv8rr7xSBQUFeuaZZ854rFWr\nVurZs6fS0tLMDBlAM8PdiQAbGIahQCCgkSNHavny5Wrfvr3dIQGwCK1mwAbvv/++BgwYoBEjRpB0\nAYeh4gUAwEJUvAAAWIjECwCAhUi8AABYiMQLAICFSLwAAFjo/wFiyGcnSqwbEgAAAABJRU5ErkJg\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ebc1518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,7))\n",
    "sns.heatmap(df,cmap=plt.cm.Reds)\n",
    "plt.xlabel('entry')\n",
    "plt.ylabel('entry-exit (gap)')\n",
    "\n",
    "plt.show()\n",
    "plt.close()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}