BackTestingScript.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"InputData.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "\n",
    "def disaggregateInputData(df):\n",
    "    dfPrices = df.iloc[:, 0:6]\n",
    "    dfDurations = df.iloc[:, [0, 6, 7, 8, 9, 10]]\n",
    "    dfOptWeights = df.iloc[:, [0, 11, 12, 13, 14, 15]]\n",
    "    dfRollingStats = df.loc[:, [\"DateTime\", \"RollingAvg\", \"RollingStd\"]]\n",
    "\n",
    "    dfPrices.set_index(\"DateTime\", inplace=True)\n",
    "    dfDurations.set_index(\"DateTime\", inplace=True)\n",
    "    dfOptWeights.set_index(\"DateTime\", inplace=True)\n",
    "    dfRollingStats.set_index(\"DateTime\", inplace=True)\n",
    "\n",
    "    dfPrices.index = pd.to_datetime(dfPrices.index)\n",
    "    dfDurations.index = pd.to_datetime(dfDurations.index)\n",
    "    dfOptWeights.index = pd.to_datetime(dfOptWeights.index)\n",
    "    dfRollingStats.index = pd.to_datetime(dfRollingStats.index)\n",
    "    return (dfPrices, dfDurations, dfOptWeights, dfRollingStats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "class Positions:\n",
    "    def __init__(self, positions, startIdx, endIdx):\n",
    "        self.positions = np.array(positions)\n",
    "        self.startIdx = startIdx\n",
    "        self.endIdx = endIdx\n",
    "\n",
    "    def duration(self):\n",
    "        return self.endIdx - self.startIdx\n",
    "\n",
    "\n",
    "class PortPositions:\n",
    "    def __init__(self, timeSize, numEquities):\n",
    "        # print(timeSize)\n",
    "        self.startIdx = 0\n",
    "        self.endIdx = timeSize - 1\n",
    "        self.cumPositions = np.zeros((timeSize, numEquities))\n",
    "        self.numPositions = np.zeros(timeSize)\n",
    "\n",
    "    # todo: i in range number to number\n",
    "    # i in df.index[]\n",
    "    def addPositions(self, positionsChange):\n",
    "        for i in range(positionsChange.startIdx, positionsChange.endIdx):\n",
    "            #             print(\"##################################################\")\n",
    "            #             print(type(positionsChange.positions))\n",
    "            #             print(type(self.cumPositions[i,:]))\n",
    "            self.cumPositions[i, :] = self.cumPositions[i, :] + positionsChange.positions\n",
    "            self.numPositions[i] = self.numPositions[i] + 1\n",
    "\n",
    "    def maxPosJudge(self, positionsChange, maxPos):\n",
    "        startIdx = positionsChange.startIdx\n",
    "        # print(startIdx)\n",
    "        # print(self.numPositions[startIdx])\n",
    "        return self.numPositions[startIdx] < maxPos\n",
    "\n",
    "    def get_cumPositions():\n",
    "        return self.cumPositions\n",
    "\n",
    "    def get_numPositions():\n",
    "        return self.numPositions\n",
    "\n",
    "\n",
    "class BackTestingSystem:\n",
    "\n",
    "\n",
    "    def __init__(self, numEquities, pointPrices, tickSizePrices, margins, transactionCostCoeff):\n",
    "        self.numEquities = numEquities\n",
    "        if (len(pointPrices) == numEquities):\n",
    "            self.pointPrices = np.array(pointPrices)\n",
    "        else:\n",
    "            print(\"number of equities unmatch: point prices\")\n",
    "        if (len(tickSizePrices) == numEquities):\n",
    "            self.tickSizePrices = np.array(tickSizePrices)\n",
    "        else:\n",
    "            print(\"number of equities unmatch: tickSizes\")\n",
    "        if (len(margins) == numEquities):\n",
    "            self.margins = np.array(margins)\n",
    "        else:\n",
    "            print(\"number of equities unmatch: margins\")\n",
    "            \n",
    "        self.transactionCostCoeff = transactionCostCoeff\n",
    "        # \n",
    "\n",
    "    def set_rollDate(self, rollDate):\n",
    "        self.rollDate = rollDate\n",
    "\n",
    "    def get_rollDate(self):\n",
    "        return self.rollDate\n",
    "\n",
    "    def set_exitUpLevel(self, exitUpLevel):\n",
    "        self.exitUpLevel = exitUpLevel\n",
    "\n",
    "    def set_exitDownLevel(self, exitDownLevel):\n",
    "        self.exitDownLevel\n",
    "\n",
    "    def set_triggerS(self, triggerS):\n",
    "        self.triggerS = triggerS\n",
    "\n",
    "    def set_triggerT(self, triggerT):\n",
    "        self.triggerT = triggerT\n",
    "\n",
    "    def get_marginPrices(self):\n",
    "        return self.margins / self.pointPrices\n",
    "\n",
    "    def get_tickSizes(self):\n",
    "        return self.pointPrices * tickSizePrices\n",
    "\n",
    "    def set_AUM(self, AUM):\n",
    "        self.AUM = AUM\n",
    "\n",
    "    def set_rollingStats(self, dfRollingStats):\n",
    "        self.dfRollingStats = dfRollingStats\n",
    "        self.df = pd.concat([self.df, self.rollingStats], axis=1)\n",
    "\n",
    "    def set_maxPoistions(self, maxPositions):\n",
    "        self.maxPositions = 30\n",
    "\n",
    "    def set_percentageInvested(self, pctInvest):\n",
    "        self.percentageInvested = pctInvest\n",
    "\n",
    "    def set_maxPositions(self, maxPositions):\n",
    "        self.maxPositions = maxPositions\n",
    "\n",
    "    def set_exitUpLevel(self, exitUpLevel):\n",
    "        self.exitUpLevel = exitUpLevel\n",
    "\n",
    "    def set_exitDownLevel(self, exitDownLevel):\n",
    "        self.exitDownLevel = exitDownLevel\n",
    "\n",
    "    def input_data(self, dfPrices, dfDurations, dfOptWeights, dfRollingStats):\n",
    "        self.dfPrices = dfPrices\n",
    "        self.dfDurations = dfDurations\n",
    "        self.dfOptWeights = dfOptWeights\n",
    "        self.df = pd.concat([self.dfPrices, self.dfDurations, self.dfOptWeights, dfRollingStats], axis=1)\n",
    "\n",
    "    # todo: delete\n",
    "    #     def input_whole_data(self,df):\n",
    "    #         self.df = df\n",
    "\n",
    "    def get_df(self):\n",
    "        return self.df\n",
    "\n",
    "    def time_delta_365(self, timeDelta):\n",
    "        if (timeDelta.days > 0):\n",
    "            return timeDelta.days / 365\n",
    "        else:\n",
    "            return 0\n",
    "\n",
    "    def preprocessing(self):\n",
    "\n",
    "        print(\"****************************************************************\")\n",
    "        print(\"Start preprocessing...\")\n",
    "        # basic setting\n",
    "        self.marginPrices = self.margins / self.pointPrices\n",
    "        self.maxInitMargin = self.AUM * self.percentageInvested\n",
    "        self.positionInitMargin = self.maxInitMargin / self.maxPositions\n",
    "        self.tickSizes = self.pointPrices * self.tickSizePrices\n",
    "        self.marginPrices = self.margins / self.pointPrices\n",
    "\n",
    "        # time to maturity\n",
    "        timeDeltas = self.rollDate - self.df.index\n",
    "        self.df['TimeToMaturity'] = timeDeltas\n",
    "        self.df.TimeToMaturity = self.df.TimeToMaturity.apply(self.time_delta_365)\n",
    "        self.timeToMaturity = self.df.TimeToMaturity\n",
    "        print(self.df.head())\n",
    "        # future duration\n",
    "        futureDurationsColumns = [\"dfFutureDuration\" + dur_str[8:] for dur_str in self.dfDurations.columns]\n",
    "        self.dfFutureDurations = pd.DataFrame(index=self.df.index, columns=futureDurationsColumns)\n",
    "        for index, row in self.dfDurations.iterrows():\n",
    "            self.dfFutureDurations.loc[index, :] = (row - self.df.TimeToMaturity[index]).values\n",
    "\n",
    "        # margin unit\n",
    "        #         self.marginUnit = pd.Series(index = self.df.index, name=\"MarginUnit\")\n",
    "        #         for index, row in self.dfOptWeights.iterrows():\n",
    "        #             self.marginUnit[index] = np.inner(np.abs(row.values), self.marginPrices)\n",
    "        self.marginUnit = self.dfOptWeights.apply(lambda x: np.inner(np.abs(x), self.marginPrices), axis=1)\n",
    "        self.marginUnit.rename(\"MarginUnit\")\n",
    "\n",
    "        # national\n",
    "        self.portNotional = self.positionInitMargin / self.marginUnit\n",
    "        self.portNotional.rename(\"PortNotional\", inplace=True)\n",
    "\n",
    "        # positions\n",
    "        positionsColumns = [\"dfPosition\" + dur_str[8:] for dur_str in self.dfDurations.columns]\n",
    "        self.dfPositions = pd.DataFrame(index=self.df.index, columns=positionsColumns)\n",
    "        for index, row in self.dfOptWeights.iterrows():\n",
    "            self.dfPositions.loc[index, :] = row.values * self.portNotional[index] / self.pointPrices\n",
    "        # tick size\n",
    "        self.portTickSize = self.dfPositions.apply(lambda x: np.inner(np.abs(x), self.tickSizes), axis=1)\n",
    "        self.portTickSize.rename(\"PortTickSize\", inplace=True)\n",
    "\n",
    "        # current price\n",
    "        self.portPrice = pd.Series(index=self.df.index, name=\"PortPrice\")\n",
    "        for idx in self.df.index:\n",
    "            self.portPrice[idx] = np.inner(self.dfPrices.loc[idx, :], self.dfOptWeights.loc[idx, :])\n",
    "\n",
    "        # tick size price\n",
    "        self.portTickSizePrice = pd.Series(index=self.df.index, name=\"PortTickSizePrice\")\n",
    "        for idx in self.df.index:\n",
    "            self.portTickSizePrice[idx] = self.portTickSize[idx] / self.portNotional[idx]\n",
    "\n",
    "        # z-score\n",
    "        self.ZScore = pd.Series(index=self.df.index, name=\"ZScore\")\n",
    "        for idx in self.df.index:\n",
    "            self.ZScore[idx] = (self.portPrice[idx] - self.df.RollingAvg[idx]) / self.df.RollingStd[idx]\n",
    "\n",
    "        # t-score\n",
    "        self.TScore = pd.Series(index=self.df.index, name=\"TScore\")\n",
    "        for idx in self.df.index:\n",
    "            self.TScore[idx] = (self.portPrice[idx] - self.df.RollingAvg[idx]) / self.portTickSizePrice[idx]\n",
    "\n",
    "        # concat all results\n",
    "        self.df = pd.concat([self.df, self.dfFutureDurations, self.marginUnit, self.portNotional,\n",
    "                             self.dfPositions, self.portTickSize, self.portPrice, self.portTickSizePrice,\n",
    "                             self.ZScore, self.TScore], axis=1)\n",
    "\n",
    "        print(\"Preprocessing finished!\")\n",
    "        print(\"****************************************************************\")\n",
    "        return self.df\n",
    "\n",
    "    def _enterSignal(self, time):\n",
    "        return self.ZScore[time] <= -self.triggerS and self.TScore[time] <= -self.triggerT and time < self.rollDate\n",
    "\n",
    "    def _exitTime(self, startTime,rollTime=None):\n",
    "        positions = self.dfPositions.loc[startTime, :]\n",
    "        p0 = np.sum(positions.values * self.dfPrices.loc[startTime, :].values * self.pointPrices)\n",
    "        exitUp = self.exitUpLevel * self.portTickSize[startTime]\n",
    "        exitDown = self.exitDownLevel * self.portTickSize[startTime]\n",
    "        startIdx = self.df.index.get_loc(startTime)\n",
    "        for time in self.df.index[startIdx:]:\n",
    "            price = np.sum(positions.values * self.dfPrices.loc[time, :].values * self.pointPrices)\n",
    "            #             print(price)\n",
    "            #             print(p0)\n",
    "            if (price - p0 >= exitUp):\n",
    "                break\n",
    "            if (price - p0 <= -exitDown):\n",
    "                break\n",
    "            #         print(time)\n",
    "        if(rollTime and time>rollTime):\n",
    "            time = rollTime\n",
    "            \n",
    "        return time\n",
    "\n",
    "    # todo: change misleading name upwards, \"port\" is the term for portfolio, if not execute, call \"df\"\n",
    "    def calculateCumPositions(self):\n",
    "        print(\"**************************************************\")\n",
    "        print(\"start calculate strategy positions\")\n",
    "        self.portPositions = PortPositions(len(self.df.index), self.numEquities)\n",
    "        for idx, time in enumerate(self.df.index):\n",
    "            positions = self.dfPositions.iloc[idx, :]\n",
    "            endTimeIdx = self.df.index.get_loc(self._exitTime(time,self.rollDate))\n",
    "            positionsChange = Positions(positions, idx, endTimeIdx)\n",
    "            #             print(\"positionsChange.positions\",positionsChange.positions)\n",
    "            if (self._enterSignal(time) and self.portPositions.maxPosJudge(positionsChange, self.maxPositions)):\n",
    "                self.portPositions.addPositions(positionsChange)\n",
    "                print(\"##########################################\")\n",
    "                print(\"add positions:\", positionsChange.positions)\n",
    "                print(\"time:\",time)\n",
    "                print(\"number of positions:\",self.portPositions.numPositions[idx])\n",
    "                print(\"startTime:\",self.df.index[positionsChange.startIdx])\n",
    "                print(\"endIdx:\",self.df.index[positionsChange.endIdx])\n",
    "                print(\"cumPositions:\",self.portPositions.cumPositions[idx])\n",
    "                print(\"##########################################\")\n",
    "        print(\"complete calculation\")\n",
    "        print(\"**************************************************\")\n",
    "        return self.portPositions\n",
    "    \n",
    "    def calculateInitMargin(self):\n",
    "        if(not self.portPositions):\n",
    "            calculateCumPositions()\n",
    "        \n",
    "        self.portInitMargin = np.inner(np.abs(self.portPositions.cumPositions),self.margins)\n",
    "        return self.portInitMargin\n",
    "    \n",
    "    def calculatePnL(self):\n",
    "        self.PnL = [0]\n",
    "        for idx,time in enumerate(self.df.index[1:]):\n",
    "            self.PnL.append(np.sum(self.pointPrices*self.portPositions.cumPositions[idx-1]\n",
    "                            *(self.dfPrices[idx] - self.dfPrices[idx-1])))\n",
    "        self.PnL = np.array(self.PnL)\n",
    "        return self.PnL\n",
    "    \n",
    "    def calculateTransactionCost(self):\n",
    "        self.transactionCost = [0]\n",
    "        for idx,time in enumerate(self.df.index[1:]):\n",
    "            self.transactionCost.append(np.inner(\n",
    "                np.abs(self.portPositions.cumPositions[idx] - self.portPositions.cumPositions[idx-1]),\n",
    "                self.tickSizes)*self.transactionCostCoeff)\n",
    "        self.transactionCost = np.array(self.transactionCost)\n",
    "        return self.transactionCost\n",
    "    \n",
    "    def calculateNetPnL(self):\n",
    "        if(not self.PnL):\n",
    "            calculatePnL()\n",
    "        if(not self.transactionCost):\n",
    "            calculateTransactionCost()\n",
    "        self.netPnL = self.PnL - self.transactionCost\n",
    "        return self.netPnL\n",
    "            \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "****************************************************************\n",
      "Start preprocessing...\n",
      "                        PriceTU     PriceFV     PriceTY    PriceUS    PriceUB  \\\n",
      "DateTime                                                                        \n",
      "2017-01-02 19:00:00  108.296875  117.523438  124.046875  150.21875  159.59375   \n",
      "2017-01-02 20:00:00  108.296875  117.539062  124.078125  150.31250  159.75000   \n",
      "2017-01-02 21:00:00  108.304688  117.554688  124.093750  150.34375  159.75000   \n",
      "2017-01-02 22:00:00  108.312500  117.578125  124.109375  150.34375  159.78125   \n",
      "2017-01-02 23:00:00  108.312500  117.585938  124.125000  150.43750  159.90625   \n",
      "\n",
      "                     DurationTU  DurationFV  DurationTY  DurationUS  \\\n",
      "DateTime                                                              \n",
      "2017-01-02 19:00:00    1.957515     4.24503    6.222844    13.85058   \n",
      "2017-01-02 20:00:00    1.957515     4.24503    6.222844    13.85058   \n",
      "2017-01-02 21:00:00    1.957515     4.24503    6.222844    13.85058   \n",
      "2017-01-02 22:00:00    1.957515     4.24503    6.222844    13.85058   \n",
      "2017-01-02 23:00:00    1.957515     4.24503    6.222844    13.85058   \n",
      "\n",
      "                     DurationUB  OptWeightTU  OptWeightFV  OptWeightTY  \\\n",
      "DateTime                                                                 \n",
      "2017-01-02 19:00:00   17.468457     0.239421     0.176399    -0.135898   \n",
      "2017-01-02 20:00:00   17.468457     0.205643     0.221380    -0.175470   \n",
      "2017-01-02 21:00:00   17.468457     0.306894     0.095349    -0.074645   \n",
      "2017-01-02 22:00:00   17.468457     0.352609    -0.105151     0.063064   \n",
      "2017-01-02 23:00:00   17.468457     0.315661    -0.160763     0.110985   \n",
      "\n",
      "                     OptWeightUS  OptWeightUB  RollingAvg  RollingStd  \\\n",
      "DateTime                                                                \n",
      "2017-01-02 19:00:00    -0.263656     0.184626   19.729368    0.027984   \n",
      "2017-01-02 20:00:00    -0.231472     0.166035   18.312467    0.026713   \n",
      "2017-01-02 21:00:00    -0.310858     0.212253   22.434865    0.031605   \n",
      "2017-01-02 22:00:00    -0.288999     0.190178   20.655034    0.028392   \n",
      "2017-01-02 23:00:00    -0.251162     0.161430   17.145529    0.023511   \n",
      "\n",
      "                     TimeToMaturity  \n",
      "DateTime                             \n",
      "2017-01-02 19:00:00        0.156164  \n",
      "2017-01-02 20:00:00        0.156164  \n",
      "2017-01-02 21:00:00        0.156164  \n",
      "2017-01-02 22:00:00        0.156164  \n",
      "2017-01-02 23:00:00        0.156164  \n",
      "Preprocessing finished!\n",
      "****************************************************************\n",
      "                        PriceTU     PriceFV     PriceTY    PriceUS    PriceUB  \\\n",
      "DateTime                                                                        \n",
      "2017-01-02 19:00:00  108.296875  117.523438  124.046875  150.21875  159.59375   \n",
      "2017-01-02 20:00:00  108.296875  117.539062  124.078125  150.31250  159.75000   \n",
      "2017-01-02 21:00:00  108.304688  117.554688  124.093750  150.34375  159.75000   \n",
      "2017-01-02 22:00:00  108.312500  117.578125  124.109375  150.34375  159.78125   \n",
      "2017-01-02 23:00:00  108.312500  117.585938  124.125000  150.43750  159.90625   \n",
      "\n",
      "                     DurationTU  DurationFV  DurationTY  DurationUS  \\\n",
      "DateTime                                                              \n",
      "2017-01-02 19:00:00    1.957515     4.24503    6.222844    13.85058   \n",
      "2017-01-02 20:00:00    1.957515     4.24503    6.222844    13.85058   \n",
      "2017-01-02 21:00:00    1.957515     4.24503    6.222844    13.85058   \n",
      "2017-01-02 22:00:00    1.957515     4.24503    6.222844    13.85058   \n",
      "2017-01-02 23:00:00    1.957515     4.24503    6.222844    13.85058   \n",
      "\n",
      "                     DurationUB    ...     dfPositionTU  dfPositionFV  \\\n",
      "DateTime                           ...                                  \n",
      "2017-01-02 19:00:00   17.468457    ...          6.93012       10.2118   \n",
      "2017-01-02 20:00:00   17.468457    ...          6.25113        13.459   \n",
      "2017-01-02 21:00:00   17.468457    ...          8.34136       5.18317   \n",
      "2017-01-02 22:00:00   17.468457    ...          10.3791      -6.19023   \n",
      "2017-01-02 23:00:00   17.468457    ...          9.98825      -10.1738   \n",
      "\n",
      "                     dfPositionTY  dfPositionUS  dfPositionUB  PortTickSize  \\\n",
      "DateTime                                                                      \n",
      "2017-01-02 19:00:00      -7.86725      -15.2632       10.6881   1121.968097   \n",
      "2017-01-02 20:00:00      -10.6679      -14.0725       10.0942   1124.718872   \n",
      "2017-01-02 21:00:00      -4.05767      -16.8982        11.538   1122.860139   \n",
      "2017-01-02 22:00:00       3.71258      -17.0134       11.1958   1150.081270   \n",
      "2017-01-02 23:00:00       7.02362      -15.8947       10.2161   1161.254815   \n",
      "\n",
      "                     PortPrice  PortTickSizePrice    ZScore    TScore  \n",
      "DateTime                                                               \n",
      "2017-01-02 19:00:00  19.660852           0.019381 -2.448433 -3.535253  \n",
      "2017-01-02 20:00:00  18.250280           0.018500 -2.327933 -3.361486  \n",
      "2017-01-02 21:00:00  22.355819           0.020656 -2.501032 -3.826729  \n",
      "2017-01-02 22:00:00  20.593131           0.019536 -2.180324 -3.168695  \n",
      "2017-01-02 23:00:00  17.092052           0.018350 -2.274573 -2.914337  \n",
      "\n",
      "[5 rows x 35 columns]\n",
      "**************************************************\n",
      "start calculate strategy positions\n",
      "##########################################\n",
      "add positions: [27.938301018440164 -10.907454268446479 -30.02891789099491\n",
      " 12.029339950961877 -2.986344748465737]\n",
      "time: 2017-01-04 21:00:00\n",
      "number of positions: 1.0\n",
      "startTime: 2017-01-04 21:00:00\n",
      "endIdx: 2017-01-06 09:00:00\n",
      "cumPositions: [ 27.93830102 -10.90745427 -30.02891789  12.02933995  -2.98634475]\n",
      "##########################################\n",
      "##########################################\n",
      "add positions: [16.972158881889467 -9.874538274566106 -19.74469933777951\n",
      " 14.457595528829536 -6.12848003684461]\n",
      "time: 2017-01-05 11:00:00\n",
      "number of positions: 2.0\n",
      "startTime: 2017-01-05 11:00:00\n",
      "endIdx: 2017-01-06 09:00:00\n",
      "cumPositions: [ 44.9104599  -20.78199254 -49.77361723  26.48693548  -9.11482479]\n",
      "##########################################\n",
      "##########################################\n",
      "add positions: [44.99141164519896 -66.32303741180979 0.40822706410940174\n",
      " 10.811983952797691 -3.169814415283048]\n",
      "time: 2017-01-17 16:00:00\n",
      "number of positions: 1.0\n",
      "startTime: 2017-01-17 16:00:00\n",
      "endIdx: 2017-01-18 05:00:00\n",
      "cumPositions: [ 44.99141165 -66.32303741   0.40822706  10.81198395  -3.16981442]\n",
      "##########################################\n",
      "##########################################\n",
      "add positions: [57.65150749845743 -84.15581768752 7.9989870681704 0.2079290918956022\n",
      " 3.9283646916612236]\n",
      "time: 2017-01-17 17:00:00\n",
      "number of positions: 2.0\n",
      "startTime: 2017-01-17 17:00:00\n",
      "endIdx: 2017-01-18 02:00:00\n",
      "cumPositions: [ 102.64291914 -150.4788551     8.40721413   11.01991304    0.75855028]\n",
      "##########################################\n",
      "##########################################\n",
      "add positions: [-28.468628014258012 25.380470296880503 3.035554083591675\n",
      " 11.998239635704623 -9.993962187556749]\n",
      "time: 2017-01-19 20:00:00\n",
      "number of positions: 1.0\n",
      "startTime: 2017-01-19 20:00:00\n",
      "endIdx: 2017-01-22 19:00:00\n",
      "cumPositions: [-28.46862801  25.3804703    3.03555408  11.99823964  -9.99396219]\n",
      "##########################################\n",
      "##########################################\n",
      "add positions: [-30.611952796273624 30.921612147895058 0.843085524979456\n",
      " 11.682367244039238 -9.838661514487125]\n",
      "time: 2017-01-20 05:00:00\n",
      "number of positions: 2.0\n",
      "startTime: 2017-01-20 05:00:00\n",
      "endIdx: 2017-01-20 16:00:00\n",
      "cumPositions: [-59.08058081  56.30208244   3.87863961  23.68060688 -19.8326237 ]\n",
      "##########################################\n",
      "##########################################\n",
      "add positions: [-26.303191006512826 22.750043302109557 2.418644790183066\n",
      " 12.799736025252372 -10.292563475212773]\n",
      "time: 2017-01-20 07:00:00\n",
      "number of positions: 3.0\n",
      "startTime: 2017-01-20 07:00:00\n",
      "endIdx: 2017-01-22 19:00:00\n",
      "cumPositions: [-85.38377182  79.05212575   6.2972844   36.4803429  -30.12518718]\n",
      "##########################################\n",
      "##########################################\n",
      "add positions: [-26.733442044222464 21.614806689557597 4.202254859867677\n",
      " 12.323109686825044 -10.16127307296388]\n",
      "time: 2017-01-20 08:00:00\n",
      "number of positions: 4.0\n",
      "startTime: 2017-01-20 08:00:00\n",
      "endIdx: 2017-01-22 19:00:00\n",
      "cumPositions: [-112.11721386  100.66693244   10.49953926   48.80345259  -40.28646025]\n",
      "##########################################\n",
      "##########################################\n",
      "add positions: [-23.91130343519642 11.203178876999722 10.241803061069485\n",
      " 12.117115401548057 -10.238151981873092]\n",
      "time: 2017-01-20 09:00:00\n",
      "number of positions: 5.0\n",
      "startTime: 2017-01-20 09:00:00\n",
      "endIdx: 2017-01-22 20:00:00\n",
      "cumPositions: [-136.0285173   111.87011131   20.74134232   60.92056799  -50.52461223]\n",
      "##########################################\n",
      "##########################################\n",
      "add positions: [-21.892402660045715 2.2025616810334254 16.216654602207708\n",
      " 11.620989768814628 -10.228638535099241]\n",
      "time: 2017-01-20 10:00:00\n",
      "number of positions: 6.0\n",
      "startTime: 2017-01-20 10:00:00\n",
      "endIdx: 2017-01-22 19:00:00\n",
      "cumPositions: [-157.92091996  114.07267299   36.95799692   72.54155776  -60.75325077]\n",
      "##########################################\n",
      "##########################################\n",
      "add positions: [-12.177915240844891 -9.435913604843668 14.744754632296894\n",
      " 12.518862200869197 -9.86644249759969]\n",
      "time: 2017-01-20 11:00:00\n",
      "number of positions: 7.0\n",
      "startTime: 2017-01-20 11:00:00\n",
      "endIdx: 2017-01-22 20:00:00\n",
      "cumPositions: [-170.0988352   104.63675939   51.70275155   85.06041996  -70.61969326]\n",
      "##########################################\n",
      "##########################################\n",
      "add positions: [-19.76686228221425 11.684886740982646 3.474549359024814 14.747309803071257\n",
      " -11.040780401248583]\n",
      "time: 2017-01-20 12:00:00\n",
      "number of positions: 8.0\n",
      "startTime: 2017-01-20 12:00:00\n",
      "endIdx: 2017-01-22 19:00:00\n",
      "cumPositions: [-189.86569748  116.32164613   55.17730091   99.80772977  -81.66047367]\n",
      "##########################################\n",
      "##########################################\n",
      "add positions: [-7.10558512182347 -16.758265294043923 15.707215087978188\n",
      " 11.991018534835641 -9.197522212568044]\n",
      "time: 2017-01-20 13:00:00\n",
      "number of positions: 9.0\n",
      "startTime: 2017-01-20 13:00:00\n",
      "endIdx: 2017-01-22 19:00:00\n",
      "cumPositions: [-196.9712826    99.56338084   70.884516    111.7987483   -90.85799588]\n",
      "##########################################\n",
      "##########################################\n",
      "add positions: [-3.765228079757782 -21.75617564470431 16.52610227474204 11.566029068785225\n",
      " -8.718754624018132]\n",
      "time: 2017-01-20 14:00:00\n",
      "number of positions: 10.0\n",
      "startTime: 2017-01-20 14:00:00\n",
      "endIdx: 2017-01-22 20:00:00\n",
      "cumPositions: [-200.73651068   77.80720519   87.41061828  123.36477737  -99.5767505 ]\n",
      "##########################################\n",
      "##########################################\n",
      "add positions: [2.5825029043802195 -34.85470950286981 22.67070020235506 8.468835534858718\n",
      " -6.728834932395074]\n",
      "time: 2017-01-20 15:00:00\n",
      "number of positions: 11.0\n",
      "startTime: 2017-01-20 15:00:00\n",
      "endIdx: 2017-01-23 20:00:00\n",
      "cumPositions: [-198.15400778   42.95249569  110.08131848  131.8336129  -106.30558544]\n",
      "##########################################\n",
      "##########################################\n",
      "add positions: [2.2398522137960915 -26.896877805396876 13.705488295567871\n",
      " 12.295452074500558 -8.465797877025592]\n",
      "time: 2017-01-20 17:00:00\n",
      "number of positions: 11.0\n",
      "startTime: 2017-01-20 17:00:00\n",
      "endIdx: 2017-01-23 21:00:00\n",
      "cumPositions: [-165.30220277  -14.86599426  122.94372125  132.44669774 -104.9327218 ]\n",
      "##########################################\n",
      "##########################################\n",
      "add positions: [0.7098099500469478 -42.17371840034787 34.079891703888244 3.967161493858546\n",
      " -4.961217018997823]\n",
      "time: 2017-01-25 10:00:00\n",
      "number of positions: 1.0\n",
      "startTime: 2017-01-25 10:00:00\n",
      "endIdx: 2017-01-31 11:00:00\n",
      "cumPositions: [  0.70980995 -42.1737184   34.0798917    3.96716149  -4.96121702]\n",
      "##########################################\n",
      "##########################################\n",
      "add positions: [2.9617190861163767 -43.999584448749616 33.07236934182762 4.022592343935649\n",
      " -4.735061349677059]\n",
      "time: 2017-01-25 11:00:00\n",
      "number of positions: 2.0\n",
      "startTime: 2017-01-25 11:00:00\n",
      "endIdx: 2017-01-31 11:00:00\n",
      "cumPositions: [  3.67152904 -86.17330285  67.15226105   7.98975384  -9.69627837]\n",
      "##########################################\n",
      "##########################################\n",
      "add positions: [-18.52458490915004 -4.841754543779564 19.062637408532293\n",
      " 10.824556331470923 -9.709954167188458]\n",
      "time: 2017-01-25 15:00:00\n",
      "number of positions: 3.0\n",
      "startTime: 2017-01-25 15:00:00\n",
      "endIdx: 2017-01-29 21:00:00\n",
      "cumPositions: [-14.85305587 -91.01505739  86.21489845  18.81431017 -19.40623254]\n",
      "##########################################\n",
      "##########################################\n",
      "add positions: [9.613933433649402 -56.74051828172508 38.84630894928742 0.5072901942852012\n",
      " -2.4362151920246964]\n",
      "time: 2017-01-25 16:00:00\n",
      "number of positions: 4.0\n",
      "startTime: 2017-01-25 16:00:00\n",
      "endIdx: 2017-01-31 11:00:00\n",
      "cumPositions: [  -5.23912244 -147.75557567  125.0612074    19.32160036  -21.84244773]\n",
      "##########################################\n",
      "##########################################\n",
      "add positions: [4.356415189936951 -43.332802460892296 30.34069665104139 4.9423943902416765\n",
      " -4.993024375610794]\n",
      "time: 2017-01-25 17:00:00\n",
      "number of positions: 5.0\n",
      "startTime: 2017-01-25 17:00:00\n",
      "endIdx: 2017-01-31 11:00:00\n",
      "cumPositions: [  -0.88270725 -191.08837814  155.40190405   24.26399475  -26.8354721 ]\n",
      "##########################################\n",
      "##########################################\n",
      "add positions: [4.3633602527772135 -43.69353601414969 30.7553675516781 4.765633947798756\n",
      " -4.914668545638629]\n",
      "time: 2017-01-25 19:00:00\n",
      "number of positions: 6.0\n",
      "startTime: 2017-01-25 19:00:00\n",
      "endIdx: 2017-01-31 11:00:00\n",
      "cumPositions: [   3.480653   -234.78191415  186.15727161   29.0296287   -31.75014065]\n",
      "##########################################\n",
      "##########################################\n",
      "add positions: [11.218090543743148 -56.97325540833741 36.86381109012686 1.078429379541047\n",
      " -2.5119262760110774]\n",
      "time: 2017-01-25 20:00:00\n",
      "number of positions: 7.0\n",
      "startTime: 2017-01-25 20:00:00\n",
      "endIdx: 2017-01-31 11:00:00\n",
      "cumPositions: [  14.69874355 -291.75516956  223.0210827    30.10805808  -34.26206693]\n",
      "##########################################\n",
      "##########################################\n",
      "add positions: [2.939964430908914 -45.282353928317335 34.67020451784761 3.3501361185802936\n",
      " -4.449921329104751]\n",
      "time: 2017-01-25 21:00:00\n",
      "number of positions: 8.0\n",
      "startTime: 2017-01-25 21:00:00\n",
      "endIdx: 2017-01-31 10:00:00\n",
      "cumPositions: [  17.63870798 -337.03752349  257.69128721   33.4581942   -38.71198825]\n",
      "##########################################\n",
      "##########################################\n",
      "add positions: [7.730949824939369 -49.434752928966205 32.83629272948354 3.340010805108943\n",
      " -3.908191557782813]\n",
      "time: 2017-01-25 22:00:00\n",
      "number of positions: 9.0\n",
      "startTime: 2017-01-25 22:00:00\n",
      "endIdx: 2017-01-31 11:00:00\n",
      "cumPositions: [  25.3696578  -386.47227642  290.52757994   36.798205    -42.62017981]\n",
      "##########################################\n",
      "##########################################\n",
      "add positions: [-6.446674886033295 -22.051512535824358 21.073841299545318\n",
      " 9.699741821800636 -8.15750583787743]\n",
      "time: 2017-01-25 23:00:00\n",
      "number of positions: 10.0\n",
      "startTime: 2017-01-25 23:00:00\n",
      "endIdx: 2017-01-30 09:00:00\n",
      "cumPositions: [  18.92298292 -408.52378895  311.60142124   46.49794683  -50.77768565]\n",
      "##########################################\n",
      "##########################################\n",
      "add positions: [1.0844826097650202 -43.14570189332032 34.74427837334311 3.625021170433917\n",
      " -4.774787534985529]\n",
      "time: 2017-01-26 00:00:00\n",
      "number of positions: 11.0\n",
      "startTime: 2017-01-26 00:00:00\n",
      "endIdx: 2017-01-30 14:00:00\n",
      "cumPositions: [  20.00746553 -451.66949084  346.34569962   50.122968    -55.55247318]\n",
      "##########################################\n",
      "##########################################\n",
      "add positions: [2.368818555081568 -44.90143292587849 35.012824922835755 3.3031439739803643\n",
      " -4.486835740800401]\n",
      "time: 2017-01-26 01:00:00\n",
      "number of positions: 12.0\n",
      "startTime: 2017-01-26 01:00:00\n",
      "endIdx: 2017-01-31 00:00:00\n",
      "cumPositions: [  22.37628408 -496.57092377  381.35852454   53.42611197  -60.03930893]\n",
      "##########################################\n",
      "##########################################\n",
      "add positions: [-9.931881437140317 -20.077900992715914 24.033488289878928\n",
      " 8.536931495636228 -7.941607329291567]\n",
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      "endIdx: 2017-01-29 21:00:00\n",
      "cumPositions: [  12.44440264 -516.64882476  405.39201283   61.96304347  -67.98091625]\n",
      "##########################################\n",
      "##########################################\n",
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      "time: 2017-01-26 03:00:00\n",
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      "startTime: 2017-01-26 03:00:00\n",
      "endIdx: 2017-01-26 15:00:00\n",
      "cumPositions: [  -9.52587456 -517.84634623  425.36201711   72.49091602  -77.85026282]\n",
      "##########################################\n",
      "##########################################\n",
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      "endIdx: 2017-01-31 08:00:00\n",
      "cumPositions: [  -4.67226571 -568.94922769  467.32159645   68.67797421  -78.22159571]\n",
      "##########################################\n",
      "##########################################\n",
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      " -4.377299110642131 -0.6517277018341789]\n",
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      "endIdx: 2017-01-29 20:00:00\n",
      "cumPositions: [  -5.63031919 -614.28472499  511.22248397   64.3006751   -78.87332342]\n",
      "##########################################\n",
      "##########################################\n",
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      "time: 2017-01-26 06:00:00\n",
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      "endIdx: 2017-01-26 13:00:00\n",
      "cumPositions: [ -32.98216061 -627.05180607  553.18310233   65.70604582  -85.16618011]\n",
      "##########################################\n",
      "##########################################\n",
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      " -2.7110329723114868 -2.263595131743969]\n",
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      "endIdx: 2017-01-27 05:00:00\n",
      "cumPositions: [ -40.61983659 -665.24656319  597.43761688   62.99501285  -87.42977524]\n",
      "##########################################\n",
      "##########################################\n",
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      "endIdx: 2017-01-29 21:00:00\n",
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      "##########################################\n",
      "##########################################\n",
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      "##########################################\n",
      "##########################################\n",
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      "##########################################\n",
      "##########################################\n",
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      "##########################################\n",
      "##########################################\n",
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      "##########################################\n",
      "##########################################\n",
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      "##########################################\n",
      "##########################################\n",
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      "number of positions: 7.0\n",
      "startTime: 2017-02-28 22:00:00\n",
      "endIdx: 2017-03-01 00:00:00\n",
      "cumPositions: [-104.67013341  259.88146227  -56.92081167  -87.70230571   50.542204  ]\n",
      "##########################################\n",
      "##########################################\n",
      "add positions: [-18.530657500287376 43.85523678683674 -11.177715644231041\n",
      " -10.66653747678765 6.004904225213734]\n",
      "time: 2017-02-28 23:00:00\n",
      "number of positions: 8.0\n",
      "startTime: 2017-02-28 23:00:00\n",
      "endIdx: 2017-03-01 00:00:00\n",
      "cumPositions: [-123.20079091  303.73669906  -68.09852731  -98.36884318   56.54710823]\n",
      "##########################################\n",
      "complete calculation\n",
      "**************************************************\n"
     ]
    },
    {
     "ename": "AttributeError",
     "evalue": "'BackTestingSystem' object has no attribute 'PnL'",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-102-d7d7f1085927>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     50\u001b[0m \u001b[0mportCumPositions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbackTesting\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcalculateCumPositions\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     51\u001b[0m \u001b[0mportInitMargin\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbackTesting\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcalculateInitMargin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 52\u001b[0;31m \u001b[0mportNetPnL\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbackTesting\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcalculateNetPnL\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-100-508f6b78cab6>\u001b[0m in \u001b[0;36mcalculateNetPnL\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    269\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    270\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mcalculateNetPnL\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 271\u001b[0;31m         \u001b[0;32mif\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPnL\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    272\u001b[0m             \u001b[0mcalculatePnL\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    273\u001b[0m         \u001b[0;32mif\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransactionCost\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'BackTestingSystem' object has no attribute 'PnL'"
     ],
     "output_type": "error"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "# from datetime import datetime\n",
    "# from BackTesting import BackTestingSystem\n",
    "# from utils import disaggregateInputData\n",
    "\n",
    "df = pd.read_csv(\"InputData.csv\")\n",
    "\n",
    "# input data\n",
    "(dfPrices,dfDurations,dfOptWeights,dfRollingStats) = disaggregateInputData(df)\n",
    "\n",
    "# print(rollingStats.head())\n",
    "pointPrices = [2000,1000,1000,1000,1000]\n",
    "tickSizePrices = [1/128,1/128,1/64,1/32,1/32]\n",
    "margins = [380,625,1300,2700,3700]\n",
    "transactionCostCoeff = 0.5\n",
    "# hyper parameteres\n",
    "numEquities = 5\n",
    "AUM = 10000000\n",
    "\n",
    "# model parameters\n",
    "rollDate = datetime(2017,3,1)\n",
    "triggerS = 2\n",
    "triggerT = 5\n",
    "exitUpLevel = 2\n",
    "exitDownLevel = 20\n",
    "pctInvested = 0.3\n",
    "maxPositions = 30\n",
    "\n",
    "# plug in\n",
    "# HyperParameteres\n",
    "backTesting = BackTestingSystem(numEquities,pointPrices,tickSizePrices,margins,transactionCostCoeff)\n",
    "# Model Parameters\n",
    "backTesting.set_AUM(AUM)\n",
    "backTesting.set_percentageInvested(pctInvested)\n",
    "backTesting.set_maxPoistions(maxPositions)\n",
    "backTesting.set_rollDate(rollDate)\n",
    "backTesting.set_triggerS(triggerS)\n",
    "backTesting.set_triggerT(triggerT)\n",
    "backTesting.set_exitUpLevel(exitUpLevel)\n",
    "backTesting.set_exitDownLevel(exitDownLevel)\n",
    "# History Data\n",
    "backTesting.input_data(dfPrices,dfDurations,dfOptWeights,dfRollingStats)\n",
    "\n",
    "# pre-processing\n",
    "df2 = backTesting.preprocessing()\n",
    "print(df2.head())\n",
    "\n",
    "# strategy's cumulative positions\n",
    "portCumPositions = backTesting.calculateCumPositions()\n",
    "portInitMargin = backTesting.calculateInitMargin()\n",
    "portNetPnL = backTesting.calculateNetPnL()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "0",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m/Users/xiayuxuan/anaconda/lib/python3.6/site-packages/pandas/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2133\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2134\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2135\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas/index.c:4433)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas/index.c:4279)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/src/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:13742)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/src/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:13696)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 0",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-103-44e1ba15c86c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mbackTesting\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcalculatePnL\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-100-508f6b78cab6>\u001b[0m in \u001b[0;36mcalculatePnL\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    255\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtime\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    256\u001b[0m             self.PnL.append(np.sum(self.pointPrices*self.portPositions.cumPositions[idx-1]\n\u001b[0;32m--> 257\u001b[0;31m                             *(self.dfPrices[idx] - self.dfPrices[idx-1])))\n\u001b[0m\u001b[1;32m    258\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPnL\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPnL\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    259\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPnL\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/xiayuxuan/anaconda/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2057\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2058\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2059\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2060\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2061\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/xiayuxuan/anaconda/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_getitem_column\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2064\u001b[0m         \u001b[0;31m# get column\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2065\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2066\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_item_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2067\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2068\u001b[0m         \u001b[0;31m# duplicate columns & possible reduce dimensionality\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/xiayuxuan/anaconda/lib/python3.6/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_get_item_cache\u001b[0;34m(self, item)\u001b[0m\n\u001b[1;32m   1384\u001b[0m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1385\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1386\u001b[0;31m             \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1387\u001b[0m             \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_box_item_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1388\u001b[0m             \u001b[0mcache\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/xiayuxuan/anaconda/lib/python3.6/site-packages/pandas/core/internals.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, item, fastpath)\u001b[0m\n\u001b[1;32m   3541\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3542\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misnull\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3543\u001b[0;31m                 \u001b[0mloc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3544\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3545\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0misnull\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/xiayuxuan/anaconda/lib/python3.6/site-packages/pandas/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2134\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2135\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2136\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2137\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2138\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas/index.c:4433)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas/index.c:4279)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/src/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:13742)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/src/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:13696)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 0"
     ],
     "output_type": "error"
    }
   ],
   "source": [
    "backTesting.calculatePnL()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PriceTU    108.296875\n",
       "PriceFV    117.539062\n",
       "PriceTY    124.078125\n",
       "PriceUS    150.312500\n",
       "PriceUB    159.750000\n",
       "Name: 2017-01-02 20:00:00, dtype: float64"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "backTesting.dfPrices.iloc[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(backTesting.pointPrices*backTesting.portPositions.cumPositions[0]*(backTesting.dfPrices.iloc[1] - backTesting.dfPrices.iloc[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([14, 32])"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.inner([1,2,3],[[1,2,3],[4,5,6]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([      0.,       0.,       0.,       0.,       0.,       0.,\n",
       "             0.,       0.,       0.,       0.,       0.,       0.,\n",
       "             0.,       0.,       0.,       0.,       0.,       0.,\n",
       "             0.,       0.,       0.,       0.,       0.,       0.,\n",
       "             0.,       0.,       0.,       0.,       0.,       0.,\n",
       "             0.,       0.,       0.,       0.,       0.,       0.,\n",
       "             0.,       0.,       0.,       0.,       0.,       0.,\n",
       "             0.,       0.,       0.,       0.,       0.,       0.,\n",
       "        100000.,  100000.,  100000.,  100000.,  100000.,  100000.,\n",
       "        100000.,  100000.,  100000.,  100000.,  100000.,  100000.,\n",
       "        100000.,  100000.,  200000.,  200000.,  200000.,  200000.,\n",
       "        200000.,  200000.,  200000.,  200000.,  200000.,  200000.,\n",
       "        200000.,  200000.,  200000.,  200000.,  200000.,  200000.,\n",
       "        200000.,  200000.,  200000.,  200000.,  200000.,       0.,\n",
       "             0.,       0.,       0.,       0.,       0.,       0.,\n",
       "             0.,       0.,       0.,       0.,       0.,       0.,\n",
       "             0.,       0.,       0.,       0.])"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.inner(margins,np.abs(portCumPositions.cumPositions))[0:100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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