My_Backtester.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Python based Equity Backtester\n",
    "\n",
    "## This script provides a basic Backtesting / Market Simulator infrastructure for a given equity trading strategy. \n",
    " \n",
    "Simply run the whole script to start the backtest \n",
    " \n",
    " - It runs over the previous 12 months from the current date. \n",
    "\n",
    " - Each day and it corresponding event (buy / sell etc) will print to console.\n",
    " \n",
    " - A final plot of PnL will be displayed.\n",
    "\n",
    "\n",
    "The idea is that you could 'plug-and-play' with various strategies -\n",
    "\n",
    "currently a Z-score Mean Reversion strategy for \"United Airlines\" (or any other ticker symbol) based on significant \n",
    "deviation (1.5 Sigma) from the previous 20 day period mean.\n",
    "\n",
    "It will buy or sell depending on the overbought / sold signal.  \n",
    "____________________________________________________________________________________________________________________________\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    " - The first two classes create our variables to be used, and creates \n",
    "    a way of storing our equity data in a Dictionary - This is similar to \n",
    "    the 'Initialize' Method in the Zipline Package.\n",
    "    \n",
    " - If there isn't an Equity ticker symbol in the Portfolio, then pull from Equity function. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class Portfolio:\n",
    "    def __init__(self):\n",
    "        self.__store__ = dict()\n",
    "\n",
    "    def add_end_p(self, time, symbol, price):\n",
    "        EQdata = Equity(symbol, time, price)\n",
    "        self.__store__[symbol] = EQdata\n",
    "\n",
    "    def add_start_p(self, time, symbol, price):\n",
    "        EQdata = self.PullEquity(symbol, time)\n",
    "        EQdata.start_p = price\n",
    "\n",
    "\n",
    "    def PullEquity(self, symbol, time):\n",
    "        if not symbol in self.__store__:\n",
    "            EQdata = Equity(symbol, time)\n",
    "            self.__store__[symbol] = EQdata\n",
    "\n",
    "        return self.__store__[symbol]\n",
    "\n",
    "    def get_end_p(self, symbol):\n",
    "        return self.__store__[symbol].end_p\n",
    "\n",
    "    def get_start_p(self, symbol):\n",
    "        return self.__store__[symbol].start_p\n",
    "\n",
    "    def pull_timestamp(self, symbol):\n",
    "        return self.__store__[symbol].timestamp\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class Equity:\n",
    "    def __init__(self, symbol, timestamp,\n",
    "                 end_p=0):\n",
    "        self.symbol = symbol\n",
    "        self.timestamp = timestamp\n",
    "        self.start_p = 0\n",
    "        self.end_p = end_p\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " - The StockPuller class, tells python what to do with the \n",
    "    DataFrame that we download from google finance. Here we are only\n",
    "    interested in the Open and Close price.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#from pandas_datareader import data as web\n",
    "import pandas.io.data as web\n",
    "\n",
    "class StockPuller:\n",
    "    def __init__(self):\n",
    "        self.EQ_event, self.Tsymbol, self.source = None, None, None\n",
    "        self.start, self.end = None, None\n",
    "        self.mp = Portfolio()\n",
    "\n",
    "    def PullData(self):\n",
    "        data = web.DataReader(self.Tsymbol, self.source,\n",
    "                              self.start, self.end)\n",
    "\n",
    "        for time, row in data.iterrows():\n",
    "            self.mp.add_end_p(time, self.Tsymbol,\n",
    "                                   row[\"Close\"])\n",
    "            self.mp.add_start_p(time, self.Tsymbol, row[\"Open\"])\n",
    "\n",
    "            if not self.EQ_event is None:\n",
    "                self.EQ_event(self.mp)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class Position:\n",
    "    def __init__(self):\n",
    "        self.symbol = None\n",
    "        self.buys, self.sells, self.net = 0, 0, 0\n",
    "        self.realized_pnl = 0\n",
    "        self.unrealized_pnl = 0\n",
    "        self.position_value = 0\n",
    "\n",
    "    def bought(self, timestamp, is_buy, qty, price):\n",
    "        if is_buy:\n",
    "            self.buys += qty\n",
    "        else:\n",
    "            self.sells += qty\n",
    "\n",
    "        self.net = self.buys - self.sells\n",
    "        changed_value = qty * price * (-1 if is_buy else 1)\n",
    "        self.position_value += changed_value\n",
    "\n",
    "        if self.net == 0:\n",
    "            self.realized_pnl = self.position_value\n",
    "\n",
    "    def update_unrealized_pnl(self, price):\n",
    "        if self.net == 0:\n",
    "            self.unrealized_pnl = 0\n",
    "        else:\n",
    "            self.unrealized_pnl = price * self.net + \\\n",
    "                                  self.position_value\n",
    "\n",
    "        return self.unrealized_pnl\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class Signal:\n",
    "    def __init__(self, timestamp, symbol, qty, is_buy,\n",
    "                 is_market_order, price=0):\n",
    "        self.timestamp = timestamp\n",
    "        self.symbol = symbol\n",
    "        self.qty = qty\n",
    "        self.price = price\n",
    "        self.is_buy = is_buy\n",
    "        self.is_market_order = is_market_order\n",
    "        self.is_filled = False\n",
    "        self.filled_price = 0\n",
    "        self.filled_time = None\n",
    "        self.filled_qty = 0\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " - This is a framework for all future strategies to run through, should we add new strategies. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class Framework:\n",
    "    def __init__(self):\n",
    "        self.event_create = None\n",
    "\n",
    "    def EQ_event(self, market_data):\n",
    "        pass\n",
    "\n",
    "    def event_order(self, order):\n",
    "        pass\n",
    "\n",
    "    def current_position(self, positions):\n",
    "        pass\n",
    "\n",
    "    def send_market_order(self, symbol, qty, is_buy, timestamp):\n",
    "        if not self.event_create is None:\n",
    "            order = Signal(timestamp, symbol, qty, is_buy, True)\n",
    "            self.event_create(order)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " - This is the part that could be change out from it's current 'Mean Reversion' strategy to any other algo you choose (RSI, Moving Average, etc etc...)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class MeanReversion(Framework):\n",
    "    def __init__(self, symbol,\n",
    "                 periods=20,\n",
    "                 buy_signal=-1.5,\n",
    "                 sell_signal=1.5):\n",
    "        Framework.__init__(self)\n",
    "        self.symbol = symbol\n",
    "        self.periods = periods\n",
    "        self.buy_signal = buy_signal\n",
    "        self.sell_signal = sell_signal\n",
    "        self.prices = pd.DataFrame()\n",
    "        self.is_long, self.is_short = False, False\n",
    "\n",
    "    def current_position(self, positions):\n",
    "        if self.symbol in positions:\n",
    "            position = positions[self.symbol]\n",
    "            self.is_long = True if position.net > 0 else False\n",
    "            self.is_short = True if position.net < 0 else False\n",
    "\n",
    "    def EQ_event(self, market_data):\n",
    "        self.store_prices(market_data)\n",
    "\n",
    "        if len(self.prices) < self.periods:\n",
    "            return\n",
    "\n",
    "        sigma_signal = self.sigma()\n",
    "        timestamp = market_data.pull_timestamp(self.symbol)\n",
    "\n",
    "        if sigma_signal < self.buy_signal:\n",
    "            self.when_buy(timestamp)\n",
    "        elif sigma_signal > self.sell_signal:\n",
    "            self.when_sell(timestamp)\n",
    "\n",
    "    def sigma(self):\n",
    "        self.prices = self.prices[-self.periods:]\n",
    "        returns = self.prices[\"close\"].pct_change().dropna()\n",
    "        z_score = ((returns-returns.mean())/returns.std())[-1]\n",
    "        return z_score\n",
    "\n",
    "    def when_buy(self, timestamp):\n",
    "        if not self.is_long:\n",
    "            self.send_market_order(self.symbol, 100,\n",
    "                                   True, timestamp)\n",
    "\n",
    "    def when_sell(self, timestamp):\n",
    "        if not self.is_short:\n",
    "            self.send_market_order(self.symbol, 100,\n",
    "                                   False, timestamp)\n",
    "    def store_prices(self, market_data):\n",
    "        timestamp = market_data.pull_timestamp(self.symbol)\n",
    "        self.prices.loc[timestamp, \"close\"] = \\\n",
    "            market_data.get_end_p(self.symbol)\n",
    "        self.prices.loc[timestamp, \"open\"] = \\\n",
    "            market_data.get_start_p(self.symbol)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " - This is where the main functionality of the algo is found, (Similar to the 'Handle-Date'Method in Zipline). It handles the output to the console and manages all the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import datetime as dt\n",
    "import pandas as pd\n",
    "\n",
    "class MyBacktest:\n",
    "    def __init__(self, symbol, start_date, end_date,\n",
    "                 data_source=\"google\"):\n",
    "        self.target_symbol = symbol\n",
    "        self.data_source = data_source\n",
    "        self.start_dt = start_date\n",
    "        self.end_dt = end_date\n",
    "        self.strategy = None\n",
    "        self.open_orders = []\n",
    "        self.positions = dict()\n",
    "        self.current_prices = None\n",
    "        self.rpnl, self.upnl = pd.DataFrame(), pd.DataFrame()\n",
    "        \n",
    "    def portfolio_positions(self, symbol):\n",
    "        if symbol not in self.positions:\n",
    "            position = Position()\n",
    "            position.symbol = symbol\n",
    "            self.positions[symbol] = position\n",
    "\n",
    "        return self.positions[symbol]\n",
    "\n",
    "    def order_mgt(self, order):\n",
    "        self.open_orders.append(order)\n",
    "\n",
    "        print self.pull_date(), \\\n",
    "            \"Order received to\", \\\n",
    "            \"BUY\" if order.is_buy else \"SELL\", order.qty, \\\n",
    "             \"units of\", order.symbol        \n",
    "\n",
    "    def pull_timestamp(self):\n",
    "        return self.current_prices.pull_timestamp(\n",
    "            self.target_symbol)\n",
    "\n",
    "    def pull_date(self):\n",
    "        timestamp = self.pull_timestamp()\n",
    "        return timestamp.strftime(\"%Y-%m-%d\")\n",
    "\n",
    "    def changed_positions(self, symbol, qty, is_buy,\n",
    "                               price, timestamp):\n",
    "        position = self.portfolio_positions(symbol)\n",
    "        position.bought(timestamp, is_buy, qty, price)\n",
    "        self.strategy.current_position(self.positions)\n",
    "        self.rpnl.loc[timestamp, \"PnL\"] = position.realized_pnl\n",
    "\n",
    "        print self.pull_date(), \\\n",
    "            \"You just\", \"BOUGHT\" if is_buy else \"SOLD\", \\\n",
    "            qty, symbol, \"at\", price\n",
    "\n",
    "    def sync_portfolio(self, prices):\n",
    "        if len(self.open_orders) > 0:\n",
    "            self.open_orders = \\\n",
    "                [order for order in self.open_orders\n",
    "                 if self.unsynced(order, prices)]\n",
    "\n",
    "    def unsynced(self, order, prices):\n",
    "        symbol = order.symbol\n",
    "        timestamp = prices.pull_timestamp(symbol)\n",
    "\n",
    "        if order.is_market_order and timestamp > order.timestamp:\n",
    "\n",
    "            order.is_filled = True\n",
    "            start_p = prices.get_start_p(symbol)\n",
    "            order.filled_timestamp = timestamp\n",
    "            order.filled_price = start_p\n",
    "            self.changed_positions(symbol,order.qty,\n",
    "                                        order.is_buy, start_p, timestamp)\n",
    "            self.strategy.event_order(order)\n",
    "            return False\n",
    "\n",
    "        return True\n",
    "\n",
    "    def Output(self, symbol, prices):\n",
    "        if symbol in self.positions:\n",
    "            position = self.positions[symbol]\n",
    "            close_price = prices.get_end_p(symbol)\n",
    "            \n",
    "            \n",
    "\n",
    "            print self.pull_date(), \\\n",
    "                \"Net Change:\", position.net, \\\n",
    "                \"Order Value:\", position.position_value, \\\n",
    "                \"PnL:\", position.realized_pnl\n",
    "                \n",
    "    def Handle_data(self, prices):\n",
    "        self.current_prices = prices\n",
    "        self.strategy.EQ_event(prices)\n",
    "        self.sync_portfolio(prices)\n",
    "        self.Output(self.target_symbol, prices)\n",
    "\n",
    "    \n",
    "    def run_Backtest(self):\n",
    "        self.strategy = MeanReversion(self.target_symbol)\n",
    "        self.strategy.event_create = self.order_mgt\n",
    "\n",
    "        mds = StockPuller()\n",
    "        mds.EQ_event = self.Handle_data\n",
    "        mds.Tsymbol = self.target_symbol\n",
    "        mds.source = self.data_source\n",
    "        mds.start, mds.end = self.start_dt, self.end_dt\n",
    "\n",
    "        print \"Starting Backtest...\"\n",
    "        mds.PullData()\n",
    "        print \"...Backtest Completed.\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "---- This bit of  code runs the above Backtesting Framework-----\n",
    "\n",
    " - It will print the daily values on the console as well as the time taken. It will then display a Plot showing the relative Profit & Loss of the strategy.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    This allows the backtest to be run at anytime and always be up to date\n",
    "    on a trailing 12 month basis.  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting Backtest...\n",
      "2015-03-27 Order received to SELL 100 units of UAL\n",
      "2015-03-30 You just SOLD 100 UAL at 68.25\n",
      "2015-03-30 Net Change: -100 Order Value: 6825.0 PnL: 0\n",
      "2015-03-31 Net Change: -100 Order Value: 6825.0 PnL: 0\n",
      "2015-04-01 Order received to BUY 100 units of UAL\n",
      "2015-04-01 Net Change: -100 Order Value: 6825.0 PnL: 0\n",
      "2015-04-02 You just BOUGHT 100 UAL at 64.24\n",
      "2015-04-02 Net Change: 0 Order Value: 401.0 PnL: 401.0\n",
      "2015-04-06 Net Change: 0 Order Value: 401.0 PnL: 401.0\n",
      "2015-04-07 Net Change: 0 Order Value: 401.0 PnL: 401.0\n",
      "2015-04-08 Net Change: 0 Order Value: 401.0 PnL: 401.0\n",
      "2015-04-09 Net Change: 0 Order Value: 401.0 PnL: 401.0\n",
      "2015-04-10 Net Change: 0 Order Value: 401.0 PnL: 401.0\n",
      "2015-04-13 Net Change: 0 Order Value: 401.0 PnL: 401.0\n",
      "2015-04-14 Net Change: 0 Order Value: 401.0 PnL: 401.0\n",
      "2015-04-15 Net Change: 0 Order Value: 401.0 PnL: 401.0\n",
      "2015-04-16 Net Change: 0 Order Value: 401.0 PnL: 401.0\n",
      "2015-04-17 Net Change: 0 Order Value: 401.0 PnL: 401.0\n",
      "2015-04-20 Net Change: 0 Order Value: 401.0 PnL: 401.0\n",
      "2015-04-21 Net Change: 0 Order Value: 401.0 PnL: 401.0\n",
      "2015-04-22 Net Change: 0 Order Value: 401.0 PnL: 401.0\n",
      "2015-04-23 Net Change: 0 Order Value: 401.0 PnL: 401.0\n",
      "2015-04-24 Net Change: 0 Order Value: 401.0 PnL: 401.0\n",
      "2015-04-27 Net Change: 0 Order Value: 401.0 PnL: 401.0\n",
      "2015-04-28 Net Change: 0 Order Value: 401.0 PnL: 401.0\n",
      "2015-04-29 Net Change: 0 Order Value: 401.0 PnL: 401.0\n",
      "2015-04-30 Net Change: 0 Order Value: 401.0 PnL: 401.0\n",
      "2015-05-01 Order received to SELL 100 units of UAL\n",
      "2015-05-01 Net Change: 0 Order Value: 401.0 PnL: 401.0\n",
      "2015-05-04 You just SOLD 100 UAL at 61.94\n",
      "2015-05-04 Net Change: -100 Order Value: 6595.0 PnL: 401.0\n",
      "2015-05-05 Order received to BUY 100 units of UAL\n",
      "2015-05-05 Net Change: -100 Order Value: 6595.0 PnL: 401.0\n",
      "2015-05-06 You just BOUGHT 100 UAL at 59.11\n",
      "2015-05-06 Net Change: 0 Order Value: 684.0 PnL: 684.0\n",
      "2015-05-07 Order received to SELL 100 units of UAL\n",
      "2015-05-07 Net Change: 0 Order Value: 684.0 PnL: 684.0\n",
      "2015-05-08 You just SOLD 100 UAL at 62.24\n",
      "2015-05-08 Net Change: -100 Order Value: 6908.0 PnL: 684.0\n",
      "2015-05-11 Net Change: -100 Order Value: 6908.0 PnL: 684.0\n",
      "2015-05-12 Net Change: -100 Order Value: 6908.0 PnL: 684.0\n",
      "2015-05-13 Net Change: -100 Order Value: 6908.0 PnL: 684.0\n",
      "2015-05-14 Net Change: -100 Order Value: 6908.0 PnL: 684.0\n",
      "2015-05-15 Net Change: -100 Order Value: 6908.0 PnL: 684.0\n",
      "2015-05-18 Net Change: -100 Order Value: 6908.0 PnL: 684.0\n",
      "2015-05-19 Net Change: -100 Order Value: 6908.0 PnL: 684.0\n",
      "2015-05-20 Order received to BUY 100 units of UAL\n",
      "2015-05-20 Net Change: -100 Order Value: 6908.0 PnL: 684.0\n",
      "2015-05-21 You just BOUGHT 100 UAL at 54.6\n",
      "2015-05-21 Net Change: 0 Order Value: 1448.0 PnL: 1448.0\n",
      "2015-05-22 Net Change: 0 Order Value: 1448.0 PnL: 1448.0\n",
      "2015-05-26 Net Change: 0 Order Value: 1448.0 PnL: 1448.0\n",
      "2015-05-27 Net Change: 0 Order Value: 1448.0 PnL: 1448.0\n",
      "2015-05-28 Net Change: 0 Order Value: 1448.0 PnL: 1448.0\n",
      "2015-05-29 Net Change: 0 Order Value: 1448.0 PnL: 1448.0\n",
      "2015-06-01 Net Change: 0 Order Value: 1448.0 PnL: 1448.0\n",
      "2015-06-02 Net Change: 0 Order Value: 1448.0 PnL: 1448.0\n",
      "2015-06-03 Net Change: 0 Order Value: 1448.0 PnL: 1448.0\n",
      "2015-06-04 Net Change: 0 Order Value: 1448.0 PnL: 1448.0\n",
      "2015-06-05 Net Change: 0 Order Value: 1448.0 PnL: 1448.0\n",
      "2015-06-08 Net Change: 0 Order Value: 1448.0 PnL: 1448.0\n",
      "2015-06-09 Net Change: 0 Order Value: 1448.0 PnL: 1448.0\n",
      "2015-06-10 Net Change: 0 Order Value: 1448.0 PnL: 1448.0\n",
      "2015-06-11 Net Change: 0 Order Value: 1448.0 PnL: 1448.0\n",
      "2015-06-12 Net Change: 0 Order Value: 1448.0 PnL: 1448.0\n",
      "2015-06-15 Net Change: 0 Order Value: 1448.0 PnL: 1448.0\n",
      "2015-06-16 Net Change: 0 Order Value: 1448.0 PnL: 1448.0\n",
      "2015-06-17 Net Change: 0 Order Value: 1448.0 PnL: 1448.0\n",
      "2015-06-18 Net Change: 0 Order Value: 1448.0 PnL: 1448.0\n",
      "2015-06-19 Net Change: 0 Order Value: 1448.0 PnL: 1448.0\n",
      "2015-06-22 Net Change: 0 Order Value: 1448.0 PnL: 1448.0\n",
      "2015-06-23 Net Change: 0 Order Value: 1448.0 PnL: 1448.0\n",
      "2015-06-24 Net Change: 0 Order Value: 1448.0 PnL: 1448.0\n",
      "2015-06-25 Net Change: 0 Order Value: 1448.0 PnL: 1448.0\n",
      "2015-06-26 Net Change: 0 Order Value: 1448.0 PnL: 1448.0\n",
      "2015-06-29 Order received to BUY 100 units of UAL\n",
      "2015-06-29 Net Change: 0 Order Value: 1448.0 PnL: 1448.0\n",
      "2015-06-30 You just BOUGHT 100 UAL at 53.4\n",
      "2015-06-30 Net Change: 100 Order Value: -3892.0 PnL: 1448.0\n",
      "2015-07-01 Net Change: 100 Order Value: -3892.0 PnL: 1448.0\n",
      "2015-07-02 Net Change: 100 Order Value: -3892.0 PnL: 1448.0\n",
      "2015-07-06 Order received to SELL 100 units of UAL\n",
      "2015-07-06 Net Change: 100 Order Value: -3892.0 PnL: 1448.0\n",
      "2015-07-07 You just SOLD 100 UAL at 53.99\n",
      "2015-07-07 Net Change: 0 Order Value: 1507.0 PnL: 1507.0\n",
      "2015-07-08 Net Change: 0 Order Value: 1507.0 PnL: 1507.0\n",
      "2015-07-09 Net Change: 0 Order Value: 1507.0 PnL: 1507.0\n",
      "2015-07-10 Order received to SELL 100 units of UAL\n",
      "2015-07-10 Net Change: 0 Order Value: 1507.0 PnL: 1507.0\n",
      "2015-07-13 You just SOLD 100 UAL at 56.91\n",
      "2015-07-13 Net Change: -100 Order Value: 7198.0 PnL: 1507.0\n",
      "2015-07-14 Net Change: -100 Order Value: 7198.0 PnL: 1507.0\n",
      "2015-07-15 Net Change: -100 Order Value: 7198.0 PnL: 1507.0\n",
      "2015-07-16 Net Change: -100 Order Value: 7198.0 PnL: 1507.0\n",
      "2015-07-17 Net Change: -100 Order Value: 7198.0 PnL: 1507.0\n",
      "2015-07-20 Net Change: -100 Order Value: 7198.0 PnL: 1507.0\n",
      "2015-07-21 Net Change: -100 Order Value: 7198.0 PnL: 1507.0\n",
      "2015-07-22 Net Change: -100 Order Value: 7198.0 PnL: 1507.0\n",
      "2015-07-23 Net Change: -100 Order Value: 7198.0 PnL: 1507.0\n",
      "2015-07-24 Net Change: -100 Order Value: 7198.0 PnL: 1507.0\n",
      "2015-07-27 Net Change: -100 Order Value: 7198.0 PnL: 1507.0\n",
      "2015-07-28 Net Change: -100 Order Value: 7198.0 PnL: 1507.0\n",
      "2015-07-29 Net Change: -100 Order Value: 7198.0 PnL: 1507.0\n",
      "2015-07-30 Net Change: -100 Order Value: 7198.0 PnL: 1507.0\n",
      "2015-07-31 Net Change: -100 Order Value: 7198.0 PnL: 1507.0\n",
      "2015-08-03 Net Change: -100 Order Value: 7198.0 PnL: 1507.0\n",
      "2015-08-04 Net Change: -100 Order Value: 7198.0 PnL: 1507.0\n",
      "2015-08-05 Net Change: -100 Order Value: 7198.0 PnL: 1507.0\n",
      "2015-08-06 Net Change: -100 Order Value: 7198.0 PnL: 1507.0\n",
      "2015-08-07 Net Change: -100 Order Value: 7198.0 PnL: 1507.0\n",
      "2015-08-10 Net Change: -100 Order Value: 7198.0 PnL: 1507.0\n",
      "2015-08-11 Net Change: -100 Order Value: 7198.0 PnL: 1507.0\n",
      "2015-08-12 Net Change: -100 Order Value: 7198.0 PnL: 1507.0\n",
      "2015-08-13 Net Change: -100 Order Value: 7198.0 PnL: 1507.0\n",
      "2015-08-14 Net Change: -100 Order Value: 7198.0 PnL: 1507.0\n",
      "2015-08-17 Net Change: -100 Order Value: 7198.0 PnL: 1507.0\n",
      "2015-08-18 Net Change: -100 Order Value: 7198.0 PnL: 1507.0\n",
      "2015-08-19 Net Change: -100 Order Value: 7198.0 PnL: 1507.0\n",
      "2015-08-20 Order received to BUY 100 units of UAL\n",
      "2015-08-20 Net Change: -100 Order Value: 7198.0 PnL: 1507.0\n",
      "2015-08-21 You just BOUGHT 100 UAL at 55.92\n",
      "2015-08-21 Net Change: 0 Order Value: 1606.0 PnL: 1606.0\n",
      "2015-08-24 Order received to BUY 100 units of UAL\n",
      "2015-08-24 Net Change: 0 Order Value: 1606.0 PnL: 1606.0\n",
      "2015-08-25 You just BOUGHT 100 UAL at 54.37\n",
      "2015-08-25 Net Change: 100 Order Value: -3831.0 PnL: 1606.0\n",
      "2015-08-26 Order received to SELL 100 units of UAL\n",
      "2015-08-26 Net Change: 100 Order Value: -3831.0 PnL: 1606.0\n",
      "2015-08-27 You just SOLD 100 UAL at 54.38\n",
      "2015-08-27 Net Change: 0 Order Value: 1607.0 PnL: 1607.0\n",
      "2015-08-28 Order received to SELL 100 units of UAL\n",
      "2015-08-28 Net Change: 0 Order Value: 1607.0 PnL: 1607.0\n",
      "2015-08-31 You just SOLD 100 UAL at 57.18\n",
      "2015-08-31 Net Change: -100 Order Value: 7325.0 PnL: 1607.0\n",
      "2015-09-01 Net Change: -100 Order Value: 7325.0 PnL: 1607.0\n",
      "2015-09-02 Net Change: -100 Order Value: 7325.0 PnL: 1607.0\n",
      "2015-09-03 Net Change: -100 Order Value: 7325.0 PnL: 1607.0\n",
      "2015-09-04 Net Change: -100 Order Value: 7325.0 PnL: 1607.0\n",
      "2015-09-08 Net Change: -100 Order Value: 7325.0 PnL: 1607.0\n",
      "2015-09-09 Net Change: -100 Order Value: 7325.0 PnL: 1607.0\n",
      "2015-09-10 Net Change: -100 Order Value: 7325.0 PnL: 1607.0\n",
      "2015-09-11 Net Change: -100 Order Value: 7325.0 PnL: 1607.0\n",
      "2015-09-14 Net Change: -100 Order Value: 7325.0 PnL: 1607.0\n",
      "2015-09-15 Net Change: -100 Order Value: 7325.0 PnL: 1607.0\n",
      "2015-09-16 Net Change: -100 Order Value: 7325.0 PnL: 1607.0\n",
      "2015-09-17 Net Change: -100 Order Value: 7325.0 PnL: 1607.0\n",
      "2015-09-18 Net Change: -100 Order Value: 7325.0 PnL: 1607.0\n",
      "2015-09-21 Net Change: -100 Order Value: 7325.0 PnL: 1607.0\n",
      "2015-09-22 Order received to BUY 100 units of UAL\n",
      "2015-09-22 Net Change: -100 Order Value: 7325.0 PnL: 1607.0\n",
      "2015-09-23 You just BOUGHT 100 UAL at 58.52\n",
      "2015-09-23 Net Change: 0 Order Value: 1473.0 PnL: 1473.0\n",
      "2015-09-24 Net Change: 0 Order Value: 1473.0 PnL: 1473.0\n",
      "2015-09-25 Net Change: 0 Order Value: 1473.0 PnL: 1473.0\n",
      "2015-09-28 Net Change: 0 Order Value: 1473.0 PnL: 1473.0\n",
      "2015-09-29 Net Change: 0 Order Value: 1473.0 PnL: 1473.0\n",
      "2015-09-30 Net Change: 0 Order Value: 1473.0 PnL: 1473.0\n",
      "2015-10-01 Net Change: 0 Order Value: 1473.0 PnL: 1473.0\n",
      "2015-10-02 Net Change: 0 Order Value: 1473.0 PnL: 1473.0\n",
      "2015-10-05 Net Change: 0 Order Value: 1473.0 PnL: 1473.0\n",
      "2015-10-06 Order received to BUY 100 units of UAL\n",
      "2015-10-06 Net Change: 0 Order Value: 1473.0 PnL: 1473.0\n",
      "2015-10-07 You just BOUGHT 100 UAL at 51.04\n",
      "2015-10-07 Net Change: 100 Order Value: -3631.0 PnL: 1473.0\n",
      "2015-10-08 Net Change: 100 Order Value: -3631.0 PnL: 1473.0\n",
      "2015-10-09 Order received to SELL 100 units of UAL\n",
      "2015-10-09 Net Change: 100 Order Value: -3631.0 PnL: 1473.0\n",
      "2015-10-12 You just SOLD 100 UAL at 56.1\n",
      "2015-10-12 Net Change: 0 Order Value: 1979.0 PnL: 1979.0\n",
      "2015-10-13 Net Change: 0 Order Value: 1979.0 PnL: 1979.0\n",
      "2015-10-14 Net Change: 0 Order Value: 1979.0 PnL: 1979.0\n",
      "2015-10-15 Net Change: 0 Order Value: 1979.0 PnL: 1979.0\n",
      "2015-10-16 Net Change: 0 Order Value: 1979.0 PnL: 1979.0\n",
      "2015-10-19 Net Change: 0 Order Value: 1979.0 PnL: 1979.0\n",
      "2015-10-20 Net Change: 0 Order Value: 1979.0 PnL: 1979.0\n",
      "2015-10-21 Net Change: 0 Order Value: 1979.0 PnL: 1979.0\n",
      "2015-10-22 Net Change: 0 Order Value: 1979.0 PnL: 1979.0\n",
      "2015-10-23 Order received to SELL 100 units of UAL\n",
      "2015-10-23 Net Change: 0 Order Value: 1979.0 PnL: 1979.0\n",
      "2015-10-26 You just SOLD 100 UAL at 59.42\n",
      "2015-10-26 Net Change: -100 Order Value: 7921.0 PnL: 1979.0\n",
      "2015-10-27 Net Change: -100 Order Value: 7921.0 PnL: 1979.0\n",
      "2015-10-28 Net Change: -100 Order Value: 7921.0 PnL: 1979.0\n",
      "2015-10-29 Net Change: -100 Order Value: 7921.0 PnL: 1979.0\n",
      "2015-10-30 Net Change: -100 Order Value: 7921.0 PnL: 1979.0\n",
      "2015-11-02 Net Change: -100 Order Value: 7921.0 PnL: 1979.0\n",
      "2015-11-03 Net Change: -100 Order Value: 7921.0 PnL: 1979.0\n",
      "2015-11-04 Net Change: -100 Order Value: 7921.0 PnL: 1979.0\n",
      "2015-11-05 Net Change: -100 Order Value: 7921.0 PnL: 1979.0\n",
      "2015-11-06 Net Change: -100 Order Value: 7921.0 PnL: 1979.0\n",
      "2015-11-09 Net Change: -100 Order Value: 7921.0 PnL: 1979.0\n",
      "2015-11-10 Net Change: -100 Order Value: 7921.0 PnL: 1979.0\n",
      "2015-11-11 Net Change: -100 Order Value: 7921.0 PnL: 1979.0\n",
      "2015-11-12 Net Change: -100 Order Value: 7921.0 PnL: 1979.0\n",
      "2015-11-13 Net Change: -100 Order Value: 7921.0 PnL: 1979.0\n",
      "2015-11-16 Net Change: -100 Order Value: 7921.0 PnL: 1979.0\n",
      "2015-11-17 Net Change: -100 Order Value: 7921.0 PnL: 1979.0\n",
      "2015-11-18 Net Change: -100 Order Value: 7921.0 PnL: 1979.0\n",
      "2015-11-19 Net Change: -100 Order Value: 7921.0 PnL: 1979.0\n",
      "2015-11-20 Net Change: -100 Order Value: 7921.0 PnL: 1979.0\n",
      "2015-11-23 Net Change: -100 Order Value: 7921.0 PnL: 1979.0\n",
      "2015-11-24 Order received to BUY 100 units of UAL\n",
      "2015-11-24 Net Change: -100 Order Value: 7921.0 PnL: 1979.0\n",
      "2015-11-25 You just BOUGHT 100 UAL at 57.08\n",
      "2015-11-25 Net Change: 0 Order Value: 2213.0 PnL: 2213.0\n",
      "2015-11-27 Net Change: 0 Order Value: 2213.0 PnL: 2213.0\n",
      "2015-11-30 Net Change: 0 Order Value: 2213.0 PnL: 2213.0\n",
      "2015-12-01 Order received to SELL 100 units of UAL\n",
      "2015-12-01 Net Change: 0 Order Value: 2213.0 PnL: 2213.0\n",
      "2015-12-02 You just SOLD 100 UAL at 58.0\n",
      "2015-12-02 Net Change: -100 Order Value: 8013.0 PnL: 2213.0\n",
      "2015-12-03 Net Change: -100 Order Value: 8013.0 PnL: 2213.0\n",
      "2015-12-04 Net Change: -100 Order Value: 8013.0 PnL: 2213.0\n",
      "2015-12-07 Net Change: -100 Order Value: 8013.0 PnL: 2213.0\n",
      "2015-12-08 Order received to BUY 100 units of UAL\n",
      "2015-12-08 Net Change: -100 Order Value: 8013.0 PnL: 2213.0\n",
      "2015-12-09 You just BOUGHT 100 UAL at 58.7\n",
      "2015-12-09 Net Change: 0 Order Value: 2143.0 PnL: 2143.0\n",
      "2015-12-10 Net Change: 0 Order Value: 2143.0 PnL: 2143.0\n",
      "2015-12-11 Order received to BUY 100 units of UAL\n",
      "2015-12-11 Net Change: 0 Order Value: 2143.0 PnL: 2143.0\n",
      "2015-12-14 You just BOUGHT 100 UAL at 57.37\n",
      "2015-12-14 Net Change: 100 Order Value: -3594.0 PnL: 2143.0\n",
      "2015-12-15 Net Change: 100 Order Value: -3594.0 PnL: 2143.0\n",
      "2015-12-16 Net Change: 100 Order Value: -3594.0 PnL: 2143.0\n",
      "2015-12-17 Net Change: 100 Order Value: -3594.0 PnL: 2143.0\n",
      "2015-12-18 Net Change: 100 Order Value: -3594.0 PnL: 2143.0\n",
      "2015-12-21 Net Change: 100 Order Value: -3594.0 PnL: 2143.0\n",
      "2015-12-22 Net Change: 100 Order Value: -3594.0 PnL: 2143.0\n",
      "2015-12-23 Net Change: 100 Order Value: -3594.0 PnL: 2143.0\n",
      "2015-12-24 Net Change: 100 Order Value: -3594.0 PnL: 2143.0\n",
      "2015-12-28 Net Change: 100 Order Value: -3594.0 PnL: 2143.0\n",
      "2015-12-29 Net Change: 100 Order Value: -3594.0 PnL: 2143.0\n",
      "2015-12-30 Net Change: 100 Order Value: -3594.0 PnL: 2143.0\n",
      "2015-12-31 Net Change: 100 Order Value: -3594.0 PnL: 2143.0\n",
      "2016-01-04 Net Change: 100 Order Value: -3594.0 PnL: 2143.0\n",
      "2016-01-05 Net Change: 100 Order Value: -3594.0 PnL: 2143.0\n",
      "2016-01-06 Net Change: 100 Order Value: -3594.0 PnL: 2143.0\n",
      "2016-01-07 Net Change: 100 Order Value: -3594.0 PnL: 2143.0\n",
      "2016-01-08 Net Change: 100 Order Value: -3594.0 PnL: 2143.0\n",
      "2016-01-11 Net Change: 100 Order Value: -3594.0 PnL: 2143.0\n",
      "2016-01-12 Net Change: 100 Order Value: -3594.0 PnL: 2143.0\n",
      "2016-01-13 Net Change: 100 Order Value: -3594.0 PnL: 2143.0\n",
      "2016-01-14 Net Change: 100 Order Value: -3594.0 PnL: 2143.0\n",
      "2016-01-15 Net Change: 100 Order Value: -3594.0 PnL: 2143.0\n",
      "2016-01-19 Net Change: 100 Order Value: -3594.0 PnL: 2143.0\n",
      "2016-01-20 Net Change: 100 Order Value: -3594.0 PnL: 2143.0\n",
      "2016-01-21 Net Change: 100 Order Value: -3594.0 PnL: 2143.0\n",
      "2016-01-22 Order received to SELL 100 units of UAL\n",
      "2016-01-22 Net Change: 100 Order Value: -3594.0 PnL: 2143.0\n",
      "2016-01-25 You just SOLD 100 UAL at 46.91\n",
      "2016-01-25 Net Change: 0 Order Value: 1097.0 PnL: 1097.0\n",
      "2016-01-26 Net Change: 0 Order Value: 1097.0 PnL: 1097.0\n",
      "2016-01-27 Net Change: 0 Order Value: 1097.0 PnL: 1097.0\n",
      "2016-01-28 Net Change: 0 Order Value: 1097.0 PnL: 1097.0\n",
      "2016-01-29 Order received to SELL 100 units of UAL\n",
      "2016-01-29 Net Change: 0 Order Value: 1097.0 PnL: 1097.0\n",
      "2016-02-01 You just SOLD 100 UAL at 47.71\n",
      "2016-02-01 Net Change: -100 Order Value: 5868.0 PnL: 1097.0\n",
      "2016-02-02 Net Change: -100 Order Value: 5868.0 PnL: 1097.0\n",
      "2016-02-03 Net Change: -100 Order Value: 5868.0 PnL: 1097.0\n",
      "2016-02-04 Net Change: -100 Order Value: 5868.0 PnL: 1097.0\n",
      "2016-02-05 Net Change: -100 Order Value: 5868.0 PnL: 1097.0\n",
      "2016-02-08 Net Change: -100 Order Value: 5868.0 PnL: 1097.0\n",
      "2016-02-09 Net Change: -100 Order Value: 5868.0 PnL: 1097.0\n",
      "2016-02-10 Net Change: -100 Order Value: 5868.0 PnL: 1097.0\n",
      "2016-02-11 Net Change: -100 Order Value: 5868.0 PnL: 1097.0\n",
      "2016-02-12 Net Change: -100 Order Value: 5868.0 PnL: 1097.0\n",
      "2016-02-16 Net Change: -100 Order Value: 5868.0 PnL: 1097.0\n",
      "2016-02-17 Net Change: -100 Order Value: 5868.0 PnL: 1097.0\n",
      "2016-02-18 Net Change: -100 Order Value: 5868.0 PnL: 1097.0\n",
      "2016-02-19 Net Change: -100 Order Value: 5868.0 PnL: 1097.0\n",
      "...Backtest Completed.\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0YAAAIwCAYAAACiKHtwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nMcYYY4wxPc//AxHE6bl8l6BzAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6b0ff10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Duration: 0:00:22.417000\n"
     ]
    }
   ],
   "source": [
    "if __name__ == \"__main__\":\n",
    "    from datetime import datetime\n",
    "    start_time = datetime.now()\n",
    "\n",
    "    runtest = MyBacktest(\"UAL\",\n",
    "                            dt.datetime.now() - dt.timedelta(weeks =52),\n",
    "                            dt.datetime.now())\n",
    "    runtest.run_Backtest()\n",
    "\n",
    "    import matplotlib.pyplot as plt\n",
    "    %matplotlib inline\n",
    "\n",
    "    plt.rcParams['figure.figsize'] = (14.0,10.0)\n",
    "    runtest.rpnl.plot()\n",
    "    plt.title('Net PnL - Trading 100 units of stock based on 1.5 stdev mean reversion'  )\n",
    "    plt.ylabel('Profit - Loss $')\n",
    "    plt.grid()\n",
    "    plt.show()\n",
    "    end_time = datetime.now()\n",
    "    print('Duration: {}'.format(end_time - start_time))    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}