Backtester_Step_By_Step.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "73f654d6",
   "metadata": {},
   "source": [
    "# Backtesting "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d9592fd",
   "metadata": {},
   "source": [
    "## 0. Backfilling  Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e7a87ff1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ReqId: -1 Error: 2104 Message: Market data farm connection is OK:hfarm\n",
      "ReqId: -1 Error: 2104 Message: Market data farm connection is OK:usfuture\n",
      "ReqId: -1 Error: 2106 Message: HMDS data farm connection is OK:euhmds\n",
      "ReqId: -1 Error: 2106 Message: HMDS data farm connection is OK:hkhmds\n",
      "ReqId: -1 Error: 2106 Message: HMDS data farm connection is OK:fundfarm\n",
      "ReqId: -1 Error: 2106 Message: HMDS data farm connection is OK:ushmds\n",
      "ReqId: -1 Error: 2158 Message: Sec-def data farm connection is OK:secdefhk\n",
      "ReqId: 0 Historical Data Ended from 20210412  02:43:39 to 20210502  02:43:39\n",
      "Data saved to: ./Data/TATAMOTOR_20210502_20D_5mins.csv\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<script>Jupyter.notebook.kernel.restart()</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from broker_to_csv import TradingApp, multi_historical_retriver\n",
    "import pandas as pd\n",
    "\n",
    "import threading\n",
    "import time\n",
    "##########  Starting App as Thread #############\n",
    "def websocket_con():\n",
    "    app.run()\n",
    "    \n",
    "\n",
    "event = threading.Event() \n",
    "\n",
    "app = TradingApp()\n",
    "app.event=event\n",
    "app.connect(host='127.0.0.1', port=7496, clientId=23) #port 4002 for ib gateway paper trading/7497 for TWS paper trading\n",
    "\n",
    "\n",
    "con_thread = threading.Thread(target=websocket_con, daemon=True)\n",
    "con_thread.start()\n",
    "time.sleep(1) # some latency added to ensure that the connection is established\n",
    " \n",
    "##################################################\n",
    "\n",
    "\n",
    "##########  From Datetime #############\n",
    "import datetime\n",
    "today_date=(datetime.datetime.today().strftime(\"%Y%m%d %H:%M:%S\"))\n",
    "########################################\n",
    "\n",
    "\n",
    "########## Historical Data ############\n",
    "tickers=['TATAMOTOR']\n",
    "multi_historical_retriver(ticker_list=tickers,\n",
    "                         app_obj=app, \n",
    "                         from_date=today_date,\n",
    "                         duration=\"20 D\",\n",
    "                         bar_size=\"5 mins\",\n",
    "                         event=event\n",
    "                         )\n",
    "\n",
    "\n",
    "########################################\n",
    "\n",
    "\n",
    "def kernel_restarter():\n",
    "    from IPython.display import display_html\n",
    "    display_html(\"<script>Jupyter.notebook.kernel.restart()</script>\",raw=True)\n",
    "kernel_restarter()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0a4bba1f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tatamotor_1min :\n",
      "Table exist, updating record.\n",
      "Done\n",
      "tatamotor_5mins :\n",
      "Table exist, updating record.\n",
      "Done\n",
      "tatamotor_5mins :\n",
      "Table exist, updating record.\n",
      "Done\n",
      "tatamotor_5mins :\n",
      "Table exist, updating record.\n",
      "Done\n"
     ]
    }
   ],
   "source": [
    "# Old Csv to sql\n",
    "\n",
    "from csv_to_sql import sql_ingester\n",
    "data_dir=\"./Data\"\n",
    "\n",
    "import sqlalchemy\n",
    "sql_obj = sqlalchemy.create_engine('postgresql://krh:krh@123@localhost:5432/krh')\n",
    "    \n",
    "df=sql_ingester(data_dir, sql_obj, False)\n",
    "\n",
    "####################################################################################################################"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9eac7c94",
   "metadata": {},
   "source": [
    "##  1. Getting Data from SQL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1b194812",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13870</th>\n",
       "      <td>2021-04-26 15:05:00</td>\n",
       "      <td>295.15</td>\n",
       "      <td>295.70</td>\n",
       "      <td>295.00</td>\n",
       "      <td>295.60</td>\n",
       "      <td>41603.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13871</th>\n",
       "      <td>2021-04-26 15:10:00</td>\n",
       "      <td>295.65</td>\n",
       "      <td>295.65</td>\n",
       "      <td>295.20</td>\n",
       "      <td>295.40</td>\n",
       "      <td>27936.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13872</th>\n",
       "      <td>2021-04-26 15:15:00</td>\n",
       "      <td>295.45</td>\n",
       "      <td>295.75</td>\n",
       "      <td>295.20</td>\n",
       "      <td>295.40</td>\n",
       "      <td>40760.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13873</th>\n",
       "      <td>2021-04-26 15:20:00</td>\n",
       "      <td>295.40</td>\n",
       "      <td>295.85</td>\n",
       "      <td>295.25</td>\n",
       "      <td>295.50</td>\n",
       "      <td>42255.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13874</th>\n",
       "      <td>2021-04-26 15:25:00</td>\n",
       "      <td>295.50</td>\n",
       "      <td>295.90</td>\n",
       "      <td>295.30</td>\n",
       "      <td>295.85</td>\n",
       "      <td>28243.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     date    open    high     low   close   volume\n",
       "13870 2021-04-26 15:05:00  295.15  295.70  295.00  295.60  41603.0\n",
       "13871 2021-04-26 15:10:00  295.65  295.65  295.20  295.40  27936.0\n",
       "13872 2021-04-26 15:15:00  295.45  295.75  295.20  295.40  40760.0\n",
       "13873 2021-04-26 15:20:00  295.40  295.85  295.25  295.50  42255.0\n",
       "13874 2021-04-26 15:25:00  295.50  295.90  295.30  295.85  28243.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "import sqlalchemy\n",
    "sql_obj = sqlalchemy.create_engine('postgresql://krh:krh@123@localhost:5432/krh')\n",
    "\n",
    "df_1min = pd.read_sql_table('tatamotor_1min', sql_obj, parse_dates={'date': {'format': '%Y-%m-%d %H:%M:%S'}})\n",
    "df_5mins = pd.read_sql_table('tatamotor_5mins', sql_obj, parse_dates={'date': {'format': '%Y-%m-%d %H:%M:%S'}})\n",
    "\n",
    "df_time_frames=[df_5mins]\n",
    "df_time_frames[0].tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd194ecd",
   "metadata": {},
   "source": [
    "## 2. Creating Strategy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7c48a026",
   "metadata": {},
   "outputs": [],
   "source": [
    "import backtrader as bt\n",
    "import backtrader.indicators as btind\n",
    "import numpy as np\n",
    "import datetime\n",
    "\n",
    "class ReversalAction(bt.Strategy):\n",
    "    \n",
    "    \n",
    "    \n",
    "    params = (\n",
    "        ('short_period',10),\n",
    "        ('long_period',180),\n",
    "        ('reversal_tol_factor',0.5),\n",
    "        ('breakout_tol_factor',0.3),\n",
    "        ('strike_at',\"sr_price\"),\n",
    "        ('target_percentage',2),\n",
    "        ('stop_percentage',0.5),\n",
    "        ('closing_time',\"15:10\"),\n",
    "        ('show_trades', False),\n",
    "        ('execute_breakout',True),\n",
    "        ('sr_levels',[]),\n",
    "        ('order_time',\"2:00\"),\n",
    "        ('order_at',\"close_price\"),\n",
    "        ('show_trade_object',False),\n",
    "        ('allow_shorting',False),\n",
    "        ('cerebro', \"\")\n",
    "        )\n",
    "    \n",
    "    def log(self,txt,dt=None):\n",
    "        if dt is None:\n",
    "            dt=self.datas[0].datetime.datetime()\n",
    "        print(dt,txt)\n",
    "        pass\n",
    "\n",
    "    def tolerance(self,base_x,y,tolerance, dt=None):\n",
    "        z=(base_x-y)/base_x\n",
    "        z=np.abs(z)*100\n",
    "        z=z<tolerance\n",
    "        return z\n",
    "        \n",
    "        \n",
    "    ############## Copied from Documenation #####################\n",
    "    def notify_order(self, order):\n",
    "        if self.params.show_trades:\n",
    "            if order.status in [order.Submitted, order.Accepted]:\n",
    "                # Buy/Sell order submitted/accepted to/by broker - Nothing to do\n",
    "                return\n",
    "\n",
    "            # Check if an order has been completed\n",
    "            # Attention: broker could reject order if not enough cash\n",
    "        \n",
    "            if order.status in [order.Completed]:\n",
    "                if order.isbuy():\n",
    "                    self.log('BUY EXECUTED, %.2f' % order.executed.price)\n",
    "                elif order.issell():\n",
    "                    self.log('SELL EXECUTED, %.2f' % order.executed.price)\n",
    "\n",
    "                self.bar_executed = len(self)\n",
    "\n",
    "            elif order.status == order.Canceled:\n",
    "                self.log('Order Canceled')\n",
    "            elif order.status ==order.Margin: \n",
    "                    self.log('Order Margin')\n",
    "            elif order.status ==order.Rejected: \n",
    "                    self.log('Order Rejected')\n",
    "            \n",
    "            # Write down: no pending order\n",
    "            self.order = None\n",
    "        #################################################################\n",
    "    def notify_trade(self, trade):\n",
    "        \n",
    "        trade.historyon=True\n",
    "        \n",
    "        dt = self.data.datetime.datetime()\n",
    "        if trade.isclosed:\n",
    "            dt=self.datas[0].datetime.datetime()\n",
    "            h1=[\"Date\", 'Avg Price',  'Gross Profit',     'Net Profit',            'Len']\n",
    "            r1=[dt,    trade.price,  round(trade.pnl,2),  round(trade.pnlcomm,2),  trade.barlen]\n",
    "            table_values=[h1,r1]\n",
    "            \n",
    "            from tabulate import tabulate\n",
    "            if self.params.show_trade_object:\n",
    "                print(tabulate(table_values,))\n",
    "            \n",
    "\n",
    "    \n",
    "    def __init__(self):\n",
    "        cerebro=self.params.cerebro\n",
    "        \n",
    "\n",
    "        self.start_datetime=self.datas[0].p.fromdate\n",
    "        \n",
    "        self.start_portfolio_value = cerebro.broker.getvalue()\n",
    "        \n",
    "        \n",
    "        self.brought_today=False\n",
    "        self.order =None\n",
    "        \n",
    "        self.sma_short = btind.EMA(self.datas[0], period=self.params.short_period)\n",
    "        \n",
    "        self.sma_long= btind.EMA(self.datas[0], period=self.params.long_period)\n",
    "        \n",
    "    #################  Printing Profit At end ################################\n",
    "    def stop(self):   \n",
    "        from tabulate import tabulate\n",
    "        from babel.numbers import format_currency  as inr\n",
    "\n",
    "\n",
    "\n",
    "        start_portfolio_value=self.start_portfolio_value\n",
    "        end_portfolio_value=int(cerebro.broker.getvalue())\n",
    "        pnl=end_portfolio_value-self.start_portfolio_value\n",
    "        \n",
    "        start_portfolio_value  =   inr(start_portfolio_value, \"INR\", locale='en_IN')\n",
    "        end_portfolio_value    =   inr(end_portfolio_value,   \"INR\", locale='en_IN')\n",
    "        pnl                    =   inr(pnl,                   \"INR\", locale='en_IN')\n",
    "        \n",
    "        start_datetime=self.start_datetime\n",
    "        end_datetime=self.datas[0].datetime.datetime()\n",
    "        start_date=start_datetime.date()\n",
    "        end_date=end_datetime.date()\n",
    "        time_delta=end_datetime-self.start_datetime\n",
    "        \n",
    "        table_values= [\n",
    "                        [\"Date Time\",start_date,            end_date,            time_delta.days],\n",
    "                        [\"Amount\",   start_portfolio_value, end_portfolio_value, pnl],\n",
    "                      ]        \n",
    "        \n",
    "        print (tabulate(table_values, \n",
    "                        headers=[\"Values\",\"Started\",\"Ended\",\"Delta\"],\n",
    "                        tablefmt=\"grid\"))\n",
    "\n",
    "    ###############################################    \n",
    "    \n",
    "    \n",
    "    def next(self):\n",
    "        mid_bar_value= (self.datas[0].high[0] + self.datas[0].low[0] )/2\n",
    "        open_p=self.datas[0].open[0]\n",
    "        low_p=self.datas[0].low[0]\n",
    "        high_p=self.datas[0].high[0]\n",
    "        close_p=self.datas[0].close[0]\n",
    "        \n",
    "        open_p1=self.datas[0].open[-1]\n",
    "        low_p1=self.datas[0].low[-1]\n",
    "        high_p1=self.datas[0].high[-1]\n",
    "        close_p1=self.datas[0].close[-1]\n",
    "        \n",
    "        \n",
    "        #################  Long Trend ################################\n",
    "        if mid_bar_value>self.sma_long:\n",
    "            long_trend=\"Up\"\n",
    "        else:\n",
    "            long_trend=\"Down\"\n",
    "        ##############################################################\n",
    "        \n",
    "        \n",
    "        #################  Short Trend ################################\n",
    "        if mid_bar_value>self.sma_short:\n",
    "            short_trend=\"Up\"\n",
    "        else:\n",
    "            short_trend=\"Down\"\n",
    "        ##############################################################\n",
    "\n",
    "        \n",
    "        #################  SR Area ################################\n",
    "        sr=self.params.sr_levels\n",
    "        \n",
    "        \n",
    "        tol_factor=self.params.reversal_tol_factor\n",
    "        if short_trend==\"Up\":\n",
    "            z=self.tolerance(high_p,sr,tol_factor)\n",
    "        else:\n",
    "            z=self.tolerance(low_p,sr,tol_factor)\n",
    "    \n",
    "        z=np.matmul(z,np.transpose(sr))\n",
    "    \n",
    "        if z>0:\n",
    "            area_of_value=\"In\"\n",
    "            area=z\n",
    "        else:\n",
    "            area_of_value=\"Out\"\n",
    "            area=\"\"\n",
    "        ###############################################################\n",
    "        \n",
    "        \n",
    "        ################# Volume Support ################################\n",
    "        if self.datas[0].volume[0]>self.datas[0].volume[-1]:\n",
    "            volume_support=\"yes\"\n",
    "        else:\n",
    "            volume_support=\"no\"\n",
    "        ###############################################################\n",
    "        \n",
    "        \n",
    "        ################# Bar Lenght Support ################################\n",
    "        bar_lenght_support=\"\"\n",
    "        if np.abs(open_p-close_p) > np.abs(open_p1-close_p1):\n",
    "            bar_lenght_support=\"yes\"\n",
    "        else:\n",
    "            bar_lenght_support=\"no\"\n",
    "        ###############################################################\n",
    "        \n",
    "        \n",
    "        #################  Red Green Conversion ################################\n",
    "        # Current Bar Color\n",
    "        if close_p>open_p:\n",
    "            bar_color=\"green\"\n",
    "        else:\n",
    "            bar_color=\"red\"\n",
    "            \n",
    "        # Previous Bar Color\n",
    "        if close_p1>open_p1:\n",
    "            previous_bar_color=\"green\"\n",
    "        else:\n",
    "            previous_bar_color=\"red\"\n",
    "        \n",
    "        trend_change=\"\"\n",
    "        if volume_support==\"yes\" and bar_lenght_support==\"yes\":\n",
    "            if previous_bar_color==\"green\" and bar_color==\"red\":\n",
    "                trend_change=\"green_to_red\"\n",
    "            elif previous_bar_color==\"red\" and bar_color==\"green\":\n",
    "                trend_change=\"red_to_green\"\n",
    "            \n",
    "            elif previous_bar_color==\"green\" and bar_color==\"green\":\n",
    "                trend_change=\"green_to_green\"\n",
    "            elif previous_bar_color==\"red\" and bar_color==\"red\":\n",
    "                trend_change=\"red_to_red\"\n",
    "                \n",
    "        ########################################################################\n",
    "        \n",
    "        \n",
    "        #################  To Buy/Sell/Wait ################################\n",
    "        \n",
    "        ############### Reversal\n",
    "        order_signal=\"\"\n",
    "        if long_trend==\"Up\":\n",
    "            if short_trend==\"Down\":\n",
    "                if area_of_value==\"In\":\n",
    "                    if trend_change==\"red_to_green\":\n",
    "                        order_signal=\"Buy\"\n",
    "        \n",
    "        if long_trend==\"Down\":\n",
    "            if short_trend==\"Up\":\n",
    "                if area_of_value==\"In\":\n",
    "                    if trend_change==\"green_to_red\":\n",
    "                        order_signal=\"Sell\"\n",
    "        \n",
    "        ############### Breakout\n",
    "        if self.params.execute_breakout:\n",
    "\n",
    "            breakout_tol=self.params.breakout_tol_factor\n",
    "            if long_trend==\"Up\":\n",
    "                if short_trend==\"Up\":\n",
    "                    if area_of_value==\"In\":\n",
    "                        if ((close_p-area)/close_p)*100 >breakout_tol:\n",
    "                            if trend_change==\"green_to_green\":\n",
    "                                order_signal=\"Buy\"                            \n",
    "\n",
    "            if long_trend==\"Down\":\n",
    "                if short_trend==\"Down\":\n",
    "                    if area_of_value==\"In\":\n",
    "                        if ((close_p-area)/close_p)*100 <breakout_tol:\n",
    "                            if trend_change==\"red_to_red\":\n",
    "                                order_signal=\"Sell\"                            \n",
    "        else:\n",
    "            pass\n",
    "        #################  Placing Bracket Order ################################\n",
    "        strike_at=self.params.strike_at\n",
    "        \n",
    "        \n",
    "        if strike_at ==\"mid_bar_price\":\n",
    "            strike_price=mid_bar_value\n",
    "        elif strike_at==\"sr_price\":\n",
    "            if area==\"\":\n",
    "                strike_price=0\n",
    "            else:\n",
    "                strike_price=area\n",
    "            \n",
    "        ###### Target\n",
    "        target_percentage=self.params.target_percentage\n",
    "        buy_target=strike_price*(1+(target_percentage/100))\n",
    "        sell_target=strike_price*(1-(target_percentage/100))\n",
    "        \n",
    "        ###### Stop Loss\n",
    "        stop_percentage=self.params.stop_percentage\n",
    "        buy_loss=strike_price*(1-(stop_percentage/100))\n",
    "        sell_loss=strike_price*(1+(stop_percentage/100))\n",
    "        \n",
    "        \n",
    "        ################### Placing Order ######################################\n",
    "        order_hour=self.params.order_time.split(\":\")[0]\n",
    "        order_hour=int(order_hour)\n",
    "        order_minute=self.params.order_time.split(\":\")[1]\n",
    "        order_minute=int(order_minute)\n",
    "        \n",
    "        \n",
    "        \n",
    "        if self.data.datetime.time() < datetime.time(order_hour,order_minute) \\\n",
    "            and not self.position.size\\\n",
    "            and (order_signal==\"Buy\" or order_signal==\"Sell\"):\n",
    "\n",
    "            order_at=self.params.order_at\n",
    "            if order_at==\"close_price\":\n",
    "                order_price=close_p\n",
    "            elif order_at==\"mid_bar_price\":\n",
    "                order_price=mid_bar_value\n",
    "            elif order_at==\"sr_price\":\n",
    "                order_price=area\n",
    "                \n",
    "            cash=cerebro.broker.getvalue()\n",
    "            cash=cash*(1-0.05)\n",
    "            no_of_shares=int(cash/order_price)\n",
    "            \n",
    "            lots=int(no_of_shares/100)\n",
    "            size=lots*100\n",
    "            \n",
    "            \n",
    "            if order_signal==\"Buy\":\n",
    "                if self.params.show_trades:\n",
    "                    print(\"-------------------Buyed---------------:\",size)\n",
    "                self.order = self.buy_bracket(limitprice=buy_target, \n",
    "                                              price=order_price, \n",
    "                                              stopprice=buy_loss,\n",
    "                                              size=size)\n",
    "            if order_signal==\"Sell\" and self.params.allow_shorting:\n",
    "                if self.params.show_trades:\n",
    "                    print(\"-------------------Sold---------------\",size)\n",
    "                self.order = self.sell_bracket(limitprice=sell_target, \n",
    "                                               price=order_price, \n",
    "                                               stopprice=sell_loss,\n",
    "                                               size=size)\n",
    "        ########################################################################\n",
    "\n",
    "        \n",
    "        \n",
    "        #################  Closing all Postion before 3:10 ################################\n",
    "        close_hour=self.params.closing_time.split(\":\")[0]\n",
    "        close_hour=int(close_hour)\n",
    "        close_minute=self.params.closing_time.split(\":\")[1]\n",
    "        close_minute=int(close_minute)\n",
    "        \n",
    "        if self.position.size != 0:\n",
    "            if self.data.datetime.time() > datetime.time(close_hour,close_minute):\n",
    "                    self.close(exectype=bt.Order.Market, size=self.position.size)\n",
    "        ########################################################################\n",
    "        \n",
    "        \n",
    "        self.log(\"Close: \"+str(close_p))\n",
    "#         +\n",
    "#                 \" Long Trend:\"+long_trend+\n",
    "#                 \" Short Trend:\"+short_trend+\n",
    "#                 \" Area :\"+area_of_value+str(area)+\n",
    "#                 \" Trend Change: \"+trend_change+\n",
    "#                 \" Order Signal: \"+ order_signal)\n",
    "    \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9faaa158",
   "metadata": {},
   "source": [
    "# 3. Creating Analyzer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bcb95c9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def printTradeAnalysis(analyzer):\n",
    "    '''\n",
    "    Function to print the Technical Analysis results in a nice format.\n",
    "    '''\n",
    "    #Get the results we are interested in\n",
    "    total_open = analyzer.total.open\n",
    "    total_closed = analyzer.total.closed\n",
    "    total_won = analyzer.won.total\n",
    "    total_lost = analyzer.lost.total\n",
    "    win_streak = analyzer.streak.won.longest\n",
    "    lose_streak = analyzer.streak.lost.longest\n",
    "    pnl_net = round(analyzer.pnl.net.total,2)\n",
    "    strike_rate = int((total_won / total_closed) * 100)\n",
    "    \n",
    "    #Designate the rows\n",
    "    h1 = ['Total Open',   'Total Won',  'Win Streak',   'Strike Rate']\n",
    "    h2 = ['Total Closed', 'Total Lost', 'Losing Streak','PnL Net']\n",
    "    r1 = [total_open,     total_won,     win_streak,     strike_rate]\n",
    "    r2 = [total_closed,   total_lost,    lose_streak,    pnl_net]\n",
    "    \n",
    "    from tabulate import tabulate\n",
    "\n",
    "        \n",
    "    table_values= [h1,\n",
    "                   r1,\n",
    "                   h2,\n",
    "                   r2]        \n",
    "\n",
    "    print(\"\\n\\nTrade Analysis Results:\")\n",
    "    print (tabulate(table_values,))\n",
    "\n",
    "def printSQN(analyzer):\n",
    "    sqn = round(analyzer.sqn,2)\n",
    "    print('SQN: {}'.format(sqn))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "262097e3",
   "metadata": {},
   "source": [
    "## 3. Starting App"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8b05e8b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-----------+--------------+--------------+------------+\n",
      "| Values    | Started      | Ended        | Delta      |\n",
      "+===========+==============+==============+============+\n",
      "| Date Time | 2021-02-01   | 2021-04-26   | 84         |\n",
      "+-----------+--------------+--------------+------------+\n",
      "| Amount    | ₹1,60,000.00 | ₹2,12,745.00 | ₹52,745.00 |\n",
      "+-----------+--------------+--------------+------------+\n",
      "\n",
      "\n",
      "Trade Analysis Results:\n",
      "------------  ----------  -------------  -----------\n",
      "Total Open    Total Won   Win Streak     Strike Rate\n",
      "0             34          8              66\n",
      "Total Closed  Total Lost  Losing Streak  PnL Net\n",
      "51            17          4              52745.78\n",
      "------------  ----------  -------------  -----------\n",
      "SQN: 3.78\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Font family ['Arial'] not found. Falling back to DejaVu Sans.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "if __name__=='__main__':\n",
    "    ticker_name=\"TATAMOTOR-STK-NSE\"\n",
    "    \n",
    "    cerebro=bt.Cerebro()\n",
    "\n",
    "    \n",
    "    #Add data    \n",
    "    for df in df_time_frames:\n",
    "        data=bt.feeds.PandasData(dataname=df,\n",
    "                        datetime=0,\n",
    "                        fromdate=datetime.datetime(2021, 2, 1),\n",
    "\n",
    "                        )\n",
    "        cerebro.adddata(data)\n",
    "\n",
    "        \n",
    "    #Set Cash\n",
    "    cerebro.broker.setcash(160000)\n",
    "    \n",
    "    \n",
    "    \n",
    "\n",
    "    #Add stratedgy to Cerebro \n",
    "    sr_levels=np.array([339.01,324.11,319.38,312.96,304.37,299.17,295.17,293.48,291.24,283.31]) \n",
    "    cerebro.addstrategy(ReversalAction, \n",
    "                        short_period=50,\n",
    "                        long_period=200,\n",
    "                        sr_levels=sr_levels,\n",
    "        \n",
    "                        reversal_tol_factor=1.5,\n",
    "                        breakout_tol_factor=.3,\n",
    "                        \n",
    "                        order_time=\"15:10\",\n",
    "                        closing_time=\"15:10\",\n",
    "                        \n",
    "                        show_trades= False,\n",
    "                        show_trade_object=False,\n",
    "                        \n",
    "                        strike_at=\"sr_price\",\n",
    "                        order_at=\"mid_bar_price\",\n",
    "                        \n",
    "                        target_percentage=2,\n",
    "                        stop_percentage=1.2,\n",
    "\n",
    "                        execute_breakout=True,\n",
    "                        allow_shorting=True,\n",
    "                       \n",
    "                       cerebro=cerebro)\n",
    "    \n",
    "    \n",
    "    #Adding Anylizer\n",
    "    cerebro.addanalyzer(bt.analyzers.PyFolio, _name='PyFolio')\n",
    "    cerebro.addanalyzer(bt.analyzers.TradeAnalyzer, _name=\"ta\")\n",
    "    cerebro.addanalyzer(bt.analyzers.SQN, _name=\"sqn\")\n",
    "    \n",
    "    # Adding Observer\n",
    "    cerebro.addobserver(bt.observers.DrawDown, plot=True, subplot=False)\n",
    "    \n",
    "\n",
    "    \n",
    "    #RUN Cerebro Engine\n",
    "    strategies=cerebro.run()\n",
    "    \n",
    "    # Capture results\n",
    "    firstStrat = strategies[0]\n",
    "    \n",
    "    # print the analyzers\n",
    "    printTradeAnalysis(firstStrat.analyzers.ta.get_analysis())\n",
    "    printSQN(firstStrat.analyzers.sqn.get_analysis())\n",
    "    \n",
    "    portfolio_stats = firstStrat.analyzers.getbyname('PyFolio')\n",
    "    returns, positions, transactions, gross_lev = portfolio_stats.get_pf_items()\n",
    "    returns.index = returns.index.tz_convert(None)\n",
    "\n",
    "    import quantstats\n",
    "    report_name=ticker_name.split(\"-\")[0]\n",
    "    quantstats.reports.html(returns, output=str(report_name+'.html'), title=report_name)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cda44cc7",
   "metadata": {},
   "source": [
    "# 6. Going Live on Paper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "911b3177",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Server Version: 76\n",
      "TWS Time at connection:20210502 02:51:15 India Standard Time\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-9-4159f62242c7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     60\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     61\u001b[0m     \u001b[0;31m############# RUN Cerebro Engine####################\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 62\u001b[0;31m     \u001b[0mcerebro\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\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[0m\u001b[1;32m     63\u001b[0m     \u001b[0;31m##################################################\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     64\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Software/miniconda3/envs/quant/lib/python3.8/site-packages/backtrader/cerebro.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m   1125\u001b[0m             \u001b[0;31m# let's skip process \"spawning\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1126\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0miterstrat\u001b[0m \u001b[0;32min\u001b[0m \u001b[0miterstrats\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1127\u001b[0;31m                 \u001b[0mrunstrat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrunstrategies\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterstrat\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   1128\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrunstrats\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrunstrat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1129\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dooptimize\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Software/miniconda3/envs/quant/lib/python3.8/site-packages/backtrader/cerebro.py\u001b[0m in \u001b[0;36mrunstrategies\u001b[0;34m(self, iterstrat, predata)\u001b[0m\n\u001b[1;32m   1185\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_broker\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_order_history\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0morders\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0monotify\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1186\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1187\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_broker\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart\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[0m\u001b[1;32m   1188\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1189\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mfeed\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfeeds\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Software/miniconda3/envs/quant/lib/python3.8/site-packages/backtrader/brokers/ibbroker.py\u001b[0m in \u001b[0;36mstart\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    283\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    284\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnected\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[0;32m--> 285\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreqAccountUpdates\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[0m\u001b[1;32m    286\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstartingcash\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcash\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_acc_cash\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    287\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstartingvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_acc_value\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[0;32m~/Software/miniconda3/envs/quant/lib/python3.8/site-packages/backtrader/stores/ibstore.py\u001b[0m in \u001b[0;36mreqAccountUpdates\u001b[0;34m(self, subscribe, account)\u001b[0m\n\u001b[1;32m   1336\u001b[0m         '''\n\u001b[1;32m   1337\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0maccount\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1338\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_event_managed_accounts\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\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[0m\u001b[1;32m   1339\u001b[0m             \u001b[0maccount\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmanaged_accounts\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1340\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Software/miniconda3/envs/quant/lib/python3.8/threading.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    556\u001b[0m             \u001b[0msignaled\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_flag\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    557\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0msignaled\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 558\u001b[0;31m                 \u001b[0msignaled\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cond\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\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    559\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0msignaled\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    560\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Software/miniconda3/envs/quant/lib/python3.8/threading.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    300\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m    \u001b[0;31m# restore state no matter what (e.g., KeyboardInterrupt)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    301\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 302\u001b[0;31m                 \u001b[0mwaiter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\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[0m\u001b[1;32m    303\u001b[0m                 \u001b[0mgotit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    304\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import backtrader as bt\n",
    "# from strategy import ReversalAction\n",
    "import datetime\n",
    "\n",
    "\n",
    "if __name__=='__main__':\n",
    "\n",
    "    #Creating Cerebro Obect########\n",
    "    cerebro=bt.Cerebro()\n",
    "    ###############################\n",
    "\n",
    "    ticker_name=\"TATAMOTOR-STK-NSE\"\n",
    "    \n",
    "    ######### Add data to cerebro############   \n",
    "    ibstore = bt.stores.IBStore(host='127.0.0.1', port=7496, clientId=35)\n",
    "    cerebro.broker = ibstore.getbroker()\n",
    "    #################################################################\n",
    "    \n",
    "    # Data preparation\n",
    "    back_data=bt.feeds.PandasData(dataname=df_time_frames[0],\n",
    "                                  datetime=0,\n",
    "                                  fromdate=datetime.datetime(2021, 2, 1))\n",
    "    data = bt.feeds.IBData(dataname=ticker_name, backtfill_from=back_data)\n",
    "    \n",
    "    cerebro.adddata(data)\n",
    "    ###################################################\n",
    "    \n",
    "    cerebro.resampledata(data, timeframe=bt.TimeFrame.Minutes, compression=5)\n",
    "    \n",
    "    \n",
    "    ######### Add stratedgy to Cerebro ###############\n",
    "    sr_levels=np.array([339.01,324.11,319.38,312.96,304.37,299.17,295.17,293.48,291.24,283.31]) \n",
    "    cerebro.addstrategy(ReversalAction, \n",
    "                        short_period=50,\n",
    "                        long_period=200,\n",
    "                        sr_levels=sr_levels,\n",
    "        \n",
    "                        reversal_tol_factor=.8,\n",
    "                        breakout_tol_factor=.3,\n",
    "                        \n",
    "                        order_time=\"15:10\",\n",
    "                        closing_time=\"15:10\",\n",
    "                        \n",
    "                        show_trades= False,\n",
    "                        show_trade_object=False,\n",
    "                        \n",
    "                        strike_at=\"sr_price\",\n",
    "                        order_at=\"mid_bar_price\",\n",
    "                        \n",
    "                        target_percentage=2,\n",
    "                        stop_percentage=1.2,\n",
    "\n",
    "                        execute_breakout=True,\n",
    "                        allow_shorting=True,\n",
    "                       \n",
    "                        cerebro=cerebro)\n",
    "    \n",
    "    ##########################################\n",
    "\n",
    "    ############# RUN Cerebro Engine####################\n",
    "    cerebro.run()\n",
    "    ##################################################\n",
    "       \n",
    "        "
   ]
  }
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