Trade VWAP Calculation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing Libraries and Reading the File"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import backtesting\n",
    "backtesting.set_bokeh_output(notebook=True)\n",
    "from backtesting import Backtest, Strategy\n",
    "from backtesting.lib import crossover\n",
    "import ta.trend as tr\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.read_excel(\"D:DataSets/BankNiftyFutures_Data5Min.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Ticker</th>\n",
       "      <th>Date</th>\n",
       "      <th>Final Date format</th>\n",
       "      <th>Time</th>\n",
       "      <th>Open 5</th>\n",
       "      <th>High 5</th>\n",
       "      <th>Low 5</th>\n",
       "      <th>Close 5</th>\n",
       "      <th>Volume 5</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BANKNIFTY_F1</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>20150101</td>\n",
       "      <td>09:20:00</td>\n",
       "      <td>18861.3496</td>\n",
       "      <td>18867.9492</td>\n",
       "      <td>18793.0000</td>\n",
       "      <td>18808.3496</td>\n",
       "      <td>84825</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BANKNIFTY_F1</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>20150101</td>\n",
       "      <td>09:25:00</td>\n",
       "      <td>18808.0000</td>\n",
       "      <td>18835.0000</td>\n",
       "      <td>18802.1992</td>\n",
       "      <td>18829.0000</td>\n",
       "      <td>40925</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BANKNIFTY_F1</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>20150101</td>\n",
       "      <td>09:30:00</td>\n",
       "      <td>18826.9492</td>\n",
       "      <td>18833.4004</td>\n",
       "      <td>18820.0000</td>\n",
       "      <td>18826.0996</td>\n",
       "      <td>27000</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BANKNIFTY_F1</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>20150101</td>\n",
       "      <td>09:35:00</td>\n",
       "      <td>18825.0508</td>\n",
       "      <td>18829.0996</td>\n",
       "      <td>18810.0000</td>\n",
       "      <td>18810.0000</td>\n",
       "      <td>18025</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BANKNIFTY_F1</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>20150101</td>\n",
       "      <td>09:40:00</td>\n",
       "      <td>18805.0000</td>\n",
       "      <td>18808.9492</td>\n",
       "      <td>18775.0000</td>\n",
       "      <td>18803.9492</td>\n",
       "      <td>60100</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Ticker       Date  Final Date format      Time      Open 5  \\\n",
       "0  BANKNIFTY_F1 2015-01-01           20150101  09:20:00  18861.3496   \n",
       "1  BANKNIFTY_F1 2015-01-01           20150101  09:25:00  18808.0000   \n",
       "2  BANKNIFTY_F1 2015-01-01           20150101  09:30:00  18826.9492   \n",
       "3  BANKNIFTY_F1 2015-01-01           20150101  09:35:00  18825.0508   \n",
       "4  BANKNIFTY_F1 2015-01-01           20150101  09:40:00  18805.0000   \n",
       "\n",
       "       High 5       Low 5     Close 5  Volume 5  Year  \n",
       "0  18867.9492  18793.0000  18808.3496     84825  2015  \n",
       "1  18835.0000  18802.1992  18829.0000     40925  2015  \n",
       "2  18833.4004  18820.0000  18826.0996     27000  2015  \n",
       "3  18829.0996  18810.0000  18810.0000     18025  2015  \n",
       "4  18808.9492  18775.0000  18803.9492     60100  2015  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 91737 entries, 0 to 91736\n",
      "Data columns (total 10 columns):\n",
      " #   Column             Non-Null Count  Dtype         \n",
      "---  ------             --------------  -----         \n",
      " 0   Ticker             91737 non-null  object        \n",
      " 1   Date               91737 non-null  datetime64[ns]\n",
      " 2   Final Date format  91737 non-null  int64         \n",
      " 3   Time               91737 non-null  object        \n",
      " 4   Open 5             91737 non-null  float64       \n",
      " 5   High 5             91737 non-null  float64       \n",
      " 6   Low 5              91737 non-null  float64       \n",
      " 7   Close 5            91737 non-null  float64       \n",
      " 8   Volume 5           91737 non-null  int64         \n",
      " 9   Year               91737 non-null  int64         \n",
      "dtypes: datetime64[ns](1), float64(4), int64(3), object(2)\n",
      "memory usage: 7.0+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ticker                   1\n",
       "Date                  1226\n",
       "Final Date format     1226\n",
       "Time                   450\n",
       "Open 5               63890\n",
       "High 5               59286\n",
       "Low 5                61098\n",
       "Close 5              63852\n",
       "Volume 5             14248\n",
       "Year                     5\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 2 Backtest Using Python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Formation of reuired dataframe for backtest\n",
    "#Group By is by using the mean of 15 min time frame values \n",
    "\n",
    "df=data.groupby('Date')['Open 5','High 5','Low 5','Volume 5','Close 5'].mean()\n",
    "df.columns=['Open','High','Low','Volume','Close']\n",
    "df['Volume']=df['Volume'].astype(int)\n",
    "df.reset_index(inplace=True)\n",
    "df.set_index('Date',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start                     2015-01-01 00:00:00\n",
      "End                       2019-12-31 00:00:00\n",
      "Duration                   1825 days 00:00:00\n",
      "Exposure Time [%]                     49.6737\n",
      "Equity Final [$]                  1.32333e+06\n",
      "Equity Peak [$]                   1.55109e+06\n",
      "Return [%]                            32.3325\n",
      "Buy & Hold Return [%]                 71.7931\n",
      "Return (Ann.) [%]                     5.92736\n",
      "Volatility (Ann.) [%]                 10.2579\n",
      "Sharpe Ratio                         0.577832\n",
      "Sortino Ratio                        0.908453\n",
      "Calmar Ratio                          0.29168\n",
      "Max. Drawdown [%]                    -20.3215\n",
      "Avg. Drawdown [%]                    -2.75278\n",
      "Max. Drawdown Duration      701 days 00:00:00\n",
      "Avg. Drawdown Duration       57 days 00:00:00\n",
      "# Trades                                    9\n",
      "Win Rate [%]                          33.3333\n",
      "Best Trade [%]                        28.1881\n",
      "Worst Trade [%]                      -4.36644\n",
      "Avg. Trade [%]                         3.1924\n",
      "Max. Trade Duration         433 days 00:00:00\n",
      "Avg. Trade Duration          99 days 00:00:00\n",
      "Profit Factor                         3.79112\n",
      "Expectancy [%]                        3.59905\n",
      "SQN                                  0.976186\n",
      "_strategy                            strategy\n",
      "_equity_curve                             ...\n",
      "_trades                      Size  EntryBa...\n",
      "dtype: object\n"
     ]
    },
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\",\"dtype\":\"float64\",\"order\":\"little\",\"shape\":[1226]},\"High\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"order\":\"little\",\"shape\":[1226]},\"Low\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"order\":\"little\",\"shape\":[1226]},\"Open\":{\"__ndarray__\":\"aCe48alr0kDxWhyo9rrSQJ+opbllu9JAZcQFoDFQ0kBr6jwqXgLSQB7WqtjMONJAsb0pvz9I0kAIYVssglnSQOi+nNm+c9JAEmWVv4hA0kDWuKibx9XSQFqeB3en1NJADS+bE4750kDhtThQ3SrTQE40SMETdNNAw/+9ISB000ABrHtRuKPTQNWBrKfmzNNAdJ8cBRgd1ECMOfVu2fPTQLwrTbGo1dNALXQ4zZVq00BgsYaLLEvTQJT8Ysnf7tJAodMGOj3n0kDEfjFlf43SQJKplRyqMtJAMe7zBYpZ0kA7GRwlz57SQP2T0kaIg9JAIgvHtHbt0kBBGGUjrfvSQNMGOm0w5dJASHAjZVu30kD9bOS6Ob7SQD81XrqhqtJAOzbvJcps0kCPPWhjsn/SQDICKhzxN9JAfftNOx+O0kDQz/yfdPnSQO3QnU0km9NAxDBN/oSZ00BRhxVu+cPTQPRoqidDQ9NALKbpVgH40kCj4hJaOcvSQGdrV/vuuNJAvezE0m3V0kCbFAzUT53SQEZzB+PMddJA20CnDTSU0kCatmyaZMPSQPlUO7A509JAmM/MpuZM0kBC6e3Z0i/SQMkDfno5CtJAHs9Udbz40UCco7QkLaTRQJk3h2sVrtFADfAfz2Lv0UCweOqRVg/SQEnhehQeAtJAI86dc/oy0kCSBre1hSLSQCOxynK/H9JAF2Pl0z9X0kDJgxqrmnLSQA+byMzFbtJAHNlvACCT0kBd57mYmTLSQKgdZQqBFdJAyX7wtC/50UB5XhdrhdDRQEUJIKI+u9FA97t31Ijc0UAEMwNjYKTRQGxlr2SUfdFAod0hxVCi0UBlxAWg0eDRQCHn/X883dFApZNLAccr0kDcjoMlwSHSQEJx2mY9ptFAFhBaD98q0UCSAgtgKlrRQNvhFjlfrtFAJf+misxW0UC8hAoOP3/RQOUSIYjWldFAH61XfkbC0UAsuyTi1NrRQEghyawO+NFAYqJBCq4a0kDjYbShNQbSQKfcasPuI9JALyVdmsDr0UDdDuf0f9HRQPx2/0Wt+tFA5bmrROYL0kCBCdy6qzbSQLZEui4PUdJApmH4iFjj0UAL9HZx6FLRQGlD684QRtFAt9lFvhhF0UBLfJQvDgPRQCnrNxODDtFAvICXGXZC0UBUSzrKwQ3RQD0K16OQ69BAbcYMhp8G0UDPcx4oyvTQQCsICxROKdFAJqsi3PRG0UAsflNY6VvRQDijyGRo3NFAafmBq9z00UBD3QcgJfrRQCybJllU9NFAALaW3Fvg0UDJkVN30YTRQM9CFRF4wtFAStxQHiP50UAw1kr7uSTSQJkUDNQvNtJAXiBEHmIr0kCh8Nk6CG/SQJ9EXulvHdJADz6nMyob0kATcyM/9T3SQOqcGixoZdJAMFvYwAN20kBY438F43XSQBjB/1ZSp9JATKWfcDa70kArHeFN6ZDSQG19WG8kkNJAWAHRpr120kAT0cJ4sIfSQPHVjuJcYtJAYqpdJ7zq0UCTVRFuouPRQGzWBhRX5tFACObo8fsC0kCLlhYIlU3SQLClZZqBkNJATmVR2AWF0kAPZZW/SJvSQHuymxndkdJArRbYYzKO0kA1r3ajGqbSQNXUeVSMZdJAZj942mf40UBVOIJUCtjRQJPQg6hYHdJAF45s6gBh0kCR6eO0SlfSQAyNOk25TtJAUbgehTsZ0kBqrxiISIjRQLYiwGSXttBA/Ym3u+SY0EAeyip/saHQQIrGLXWLvtBAX7GGi7wQ0UD5u3fU6N7QQPd3tkdPXNBA2kP7WPkG0EAor2rvTSHQQHu2Dg7mbM9AhetRuD50z0D7DcfgXpzPQBooAxp/QtBAfq6wCGP8z0CCE/dFj0PQQGCxhovsYtBA2vFMVZdn0EAr+cWS/4HQQERTJpI+F9FALKHO794H0UDgscVb9ADRQAe/+mUtq9BAKLou/MDC0EAQ2+RDh/jQQGDx1CN9ztBAnkRe6f/o0EB9bKXqAePQQGxlr2T0M9FAQx4KtuQ70UBcHmtG9kLRQBWQ9j+gINFAtN/tVKNF0UCtAqJNK07RQPCQbO3lMNFANwFzGi8s0UAiBU8hV1fRQJR9fZowUNFAE7hibG580UAQBVtapnfRQMM1d/QPWtFA0csVy4p/0UBHNf/CVXDRQGYEVDiSZtFAn4q+tOUm0UCmue8rmefQQEghyazO/tBAU5++ePnv0EDrKsqSNgjRQMNUu04wAdFAvVR/l1y10EDATtcA0rnQQJIuI+JTjNBAsW7PBjOR0ECtG0yRF4jQQGTmApenvNBA8ctzV7nH0EDneV2sJazQQMYX7fFCoNBAlINK4U3C0EBOllrvh6nQQAGiYMbUptBAz1ZLJx+q0EA/yogLcPPQQEfSmdU1FNFAHSPZI2QT0UAStO93RenQQEqIzG8r0tBAk1URboKZ0EAz5JJ/DKPQQFDq3p0fidBAaVch5fdo0ECik7ihfFbQQP5/nDABKdBAibdJCSkH0ECpVib8QtzPQIbrUbheL9BA8oEKNIVG0EBCnfLo5kvQQOsGU+QzZ9BAAuef8y1u0EDabL+PXXXQQFbBgg6tadBAaDpO5ByC0EA9V1iEuYzQQJtZSwHJjtBAEgf2hc2K0EBEtZvzJo/QQOmlPM3cVtBAMxZgMpg20EABo95m9CbQQH3dwzhCjs9Ad1Wz4VObz0D2shNLN0DPQCQEmB+l985AjAxfTFDMzkC4fCQlTYTOQGYTmbnwRM5Ayxqp5MmlzUCK/tDMk9HNQNIVf+7OCc1AXO1hLwR/zUCHNbvb8jrOQBPfUPjMXs5Aq7V8N5BMzkCMg16SbUHOQNkKrWFpBs5Ae61xI0IszkATnzvBzuzNQFc89UhzKM1A4etrXRoxzUCz1dLJD4vNQAeiJ2Xi6M1Ad8HgmsshzUBPffriZajMQLejErGrIsxALm6QNntIy0DJ/dXjXiXMQI7vi0vl8stAHVTiOoZoy0Agjwbd69jLQLCp86hY7stAv+ea3FIezEDaD572AY7LQDUpGKhfE8tAMP04h4jQykAJoanEUuTKQCRdRsRnNctANyTusST7y0DtUKpLEUrNQA6l41e5eM1AlnKrDbuPzUAWBj+Eq6fNQJh8xpgOss1A8NAuj8q9zUDDJjJzAcjNQGFnHWgYB85ADjSMqKYQzkAbnkteevXNQE7G65aLis5ASP/LtWhtzkDankZHD+rOQP1ZLEVyCc9Aw7oEuskBz0CrBhEGPODOQE3WqIcIvc5AXAAapXsnz0CkndMsIKHPQGwIjssImc9AYz4M9l60z0Azh7fhGjTPQKk0YmaftM5AH2yLRfaXzkC6mfcleJHOQP6Vx0dckc5A4BgCgGMXz0Bemd2A3N7PQI5WyG9Eqs9A+bt31BjUz0CPZYIJiVDQQPXyKIKVSdBAzwLHePdH0EAg+zOWw2fQQDHbO5ByitBAw34xZX930EAQTDWzJmHQQFkGwjs2SdBAfYDbAIlC0EBHC73rURDQQMKZww8MANBAdsYKkWbsz0DEi5jsWkXQQLy7u7sbYdBAOvxIJCRl0ED9gIy8GIvQQP8m7otOW9BACObo8Vsz0EBbo/gLsHjQQGHsqi1CS9BA5H5SVGNJ0EBBzvv/yDDQQJ/xV9t3I9BA8L3l1zwM0EAyc4HLE3fQQELUsk6ntNBAQlOJxdIC0UDNFjbwkB/RQCjDy+YEHNFAuTK7Aaka0UAnPsoXfwfRQM7MzMyMRNFAEOoihRJI0UBzbhPuFWfRQNWomjMldtFAY/S0jpFx0UAHBDqTtnDRQGN1q+cUIdFAbxErtiwt0UB7mTsNq0TRQNsWMxWBQtFAOfdXjxtX0UCgbMoVDkjRQDtZar0/QNFAmIbhI6I90UC4V9xAWUnRQKThlLkpvtBAIKK+UqMT0UAm+nyUcSLRQGbXvRVZQdFAq3z3/Dl00UBvx8GSuKzRQPSLJb9IudFAS/4UtNau0UCuuDgq17TRQMApNDiHmdFA+6wyU4r+0UDfXUGtPCjSQILmYCM2NtJAAmwtucdY0kBUO8oUYnrSQH+YhAsZpdJAOB/Eu/l90kBasj2NzpTSQEw+Y0wXctJAE9nLyv1C0kB0kNeDGWzSQDqy3wCAjtJAk8fmvbSO0kAO0nl6EqLSQA3QPn40mtJAfcUaLjKB0kCBp8mMR2rSQAirxE8WWNJA4nD5IpQ00kC/1DYLb4TSQE7q3p2/ltJAwXCblvWK0kD3MncaZlfSQD2eqeq4PtJAeyJ+hmdw0kA18+SaYojSQNQGOm0gnNJAilxwBo/X0kCMbyh8RvHSQAq9HGwS4tJAd6iAjjna0kBZY+PU4d/SQGDFvKHz79JArAKiTUva0kCw77wBR87SQOzCD84XFdNApdxqw05U00CKZQ3xonDTQMhI9gjlc9NAPZYJJhzV00D3Rq0wLRPUQLWVhDEvB9RA7wvC70nb00CiPzTz5G/TQM+wLE+wfNNAcbh8EQpv00A6Izewwq7TQI44d84ZhtNANo6N2VRx00ANA4ORBHvTQLzOczFDqtNAZjVdT9SY00BqSNxjiUTTQP/////vLdNAq9bCLAQt00Cxn9gdlQjTQPEf+AVB5dJAQv8EFxsp00AzUOSw2TbTQLTf7VRTONNAxUMFdPwh00DkfoeikAbTQFXQx49LCdNAtFn1uWqk0kBV5P2l4pjSQMrlLNkOsNJAdS7FVXXb0kBPOLu1vP7SQMqmjgLlMdNAzjj6hZQj00CDBLLEAF/TQI49aGNyVNNAEPjpZdEh00C94fVJG+PSQA7qIoWCItNAFm75SNoc00Atw5KF4tbSQKAYRmfm2tJAYtTTPfu30kC9+wlUzOjSQLht36Pu+NJAqsj7S0Wq0kDIyFnYU7LTQBh4IF+rg9NAEWBryT0d00BNeYfuzNXSQOlg/Z/zv9JAV0s6ylGj0kDK5lCkyR7SQI1MreQgCNJADn71y0oc0kBsEqlWrO7RQJKfepHmCdJAu4z4HEP40UAb6LSBHvzRQIN/JP+mDtJAQLZS9Sgj0kAkzp1zauHRQD1h7UnS59FApnpYlWoP0kAj2Lj+jRTSQEqS5/ruFtJAicOsKs410kBZa6P+jhfSQJWGGoWU+tFAZz942sf90UBInbmHZP7RQJ2i/SKL6tFAUsTSHbrj0UAgO4IuZLnRQDZbn1+RuNFA8ZpqQOeC0UBnyHiUGn7RQJBQR34NRdFArAy92I5M0UClyDStl4jRQL2ECg7veNFAqHmhk9jK0UAyRT7rBpjRQCjESYckpdFA4R3zFHyX0UA9TxbRibDRQHL9uz6j4NFAJfRysJnp0UBCI/5OsPHRQLZBAIMnS9JA+UAFmhJz0kAkmGpm7XTSQFKFIXy/mtJAOkuj3OCq0kBtESu27MTSQOShAjrGsNJAEm+wSuyH0kALHcBexWbSQE5qQm3ujNJAQ9gWi+zD0kB846Qw707TQOET2IlIP9NAxHScn34e00DPAsd4V0zTQA8qcR1zntNANIKN61+600A/o5p/UfjTQPFE/Az/6tNALINE7XHe00DeBUoKbLvTQP6T0kZo1dNAxQQ1fBvH00AXTqpInMjTQDMqlkjPx9NAfYr2iwzE00DsveXXHBnUQB/URQpFGdRAGyIbucMx1ECW34/IhGPUQApcMTZXbtRAAeef871E1EB946Qw7zLUQH/27vbeW9RANi4JPZhZ1EDhCD9eRQ/UQP27PnO2O9RAnZRvo3431ECjYq+KWjjUQN8iHQtXRNRADU3stOhO1EAJ3UsHaKvUQGyMnfByu9RAuB6F63HL1EBwm6kQf77UQEkIaaCMrtRAyOG55GWd1EDkcMDB8W7UQAa60G8yYtRAdYxkj9Cn1EB3PFPVEZbUQFqoIgIruNRACMkV8UDa1EDaI9QMafrUQDLC24PgCNVAd7hDsOcC1UBVmpSCDiPVQOm0gU57GtVAPV5bUjgQ1UC6FGpgvvrUQDptoNMGKNVAcxHyVIox1UDb9j3q/zzVQBUQWg/fLNVA8tBk7FFe1UCldS6frxnVQN/OmFxPDNVAbqruke0E1UAFIsSVgznVQE6qkAUvadVAXV653ham1UASZZW/WL3VQHbaQKdtw9VAkf0Zy4Hd1UDU30vhAdPVQFD7B3P1FdZANwFzGq8p1kCXsvmlOz3WQNFCFRE4OdZAVuT9paJO1kAK0lf3hWTWQBS4YmzuMdZAaE0GWoRH1kA9J6qkO1HWQJijtCQtYdZA/SH6yec81kBdxfUCdj7WQIuSoxPcPNZAvmINF/kT1kC57XvU3wnWQKdcFqzLQ9ZAlGlHhPmb1kBFWHpai6nWQBH+WJu5p9ZAzkc/BxO21kB4In6GN7rWQNGHVD6xy9ZAXPd8utfW1kASB/aFHdzWQPNoqicD7dZAoxTYvY8K10B42gdG6/7WQPAV3XqN9tZAiNwMN7D31kDh8RP05OTWQOG1h6nW1dZAiJy+nm/i1kBgdmLpxgrXQAWUmeUlKNdAUJEw+Qwb10BVcObwe0DXQKpImHyGDddAtDNyA8vA1kCr+QRjd6vWQP1jR9AlyNZA5wZT5COU1kDmcMDBcbvWQLT4TWHFtdZAUT2sSoXM1kCor9SghfTWQJMkuO6k6tZA0V/JoGIT10DFa3Gg2iTXQP0WVATVENdAolNqCcxQ10BlvxQLyVfXQCRKjk6wbNdABaxC8PWJ10CWZHqQsZXXQJQp+eu4q9dAM25X1Tij10CdilQY68vXQLGfn7zC69dAcDBFPov710Dg9YboDVvYQFK4HoWLUthA0pXivU132EDD2FVb5JTYQCdz5vNQl9hAFMen7fxO2ECRTSuFADvYQA+l41fZb9hAWrI9jR4k2EDxg62uGfXXQL02LvZhwtdAAqPeZrSP10BNfrHkd6zXQMROnp8PnddAzWXE53jL10AHhNbDx3/XQH9ne/R2k9dAATEJFzJ010AI8AN9f6HXQL8283YDu9dAoqFGIQnR10AxW9jAA53XQOI11YCOwNdA2HdYuzCw10DhlaZYIOjXQO5l7jQswNdASxHNKQ7R10DkMHIpkbbXQIk+H2X00tdArzst4gXH10B0B9fJVgfYQIn/h87FLthAZKJBCh5I2ECnJtTmYlXYQGIr6uM8SNhAYSfPzxd62EBPvsYb1nPYQGgwM1lZddhAd3MEgw5B2ECHCAmQYfLXQDOR0mwOm9dAWjfLUriY10B3X85sB3TXQDgyfDFhPtdAABU7i8Oh10A1go3rP4fXQDcfxLvJlNdAWvKLJR+T10Aa9/kC/afXQDAbyNSPuNdAkl8s+UXJ10CaHidf88zXQAlrdreFmNdAJXam0GkY2EDshcYnWyHYQP7iLP8UINhAGjOcRRLP10CnnBwrznzXQLz2313hqNdAj1BHfq082EAesoF0IW7YQG8WHEslbNhArFYm/GJ92EA/9qwYF3XYQKsDIO5K4thAldBKR3bn2ECs6r/hyPLYQLxt36PeCtlAyWfdYBro2EDjehSuR83YQKCi/SK7w9hAqKFGIcnZ2EC2KKB2RfLYQLz2MNXGz9hA3ABZddPA2EAZ5EGNRdbYQFYepKc4TdlAC1wxNnc02UA3jo3ZxEjZQM896xr9MNlAz8ei1hEo2UCIV39xNjzZQENLqkXXLdlAVNriGs9S2UCafMaYDkDZQLH9d1dg9NhAg04b6BTP2ECqnNhNiKLYQHRLmFaAhthAoKfut7N+2ED/////n4DYQGLsqi3yvthAT/hsHVzT2EBUOIJUiqHYQAUJZIlRl9hABBN/FLV42EBXUCtf2unYQIGTWhVOC9lA96POyagS2UA3blfV6BzZQO+LJb+oAdlA1wqtYekM2UAIk+Lj8wbZQLHkF0sOENlAu7GgMMjh2EBtb8rv1/PYQCIiIiLi+dhAIMwT9b/G2EBrSsXZV9nYQGzn+6nx2dhAsjomeOD52EA3XrpJbCTZQLhlrY4pF9lAXtbqmBAG2UBuy1odYwrZQMODeaKOGdlA6CYxCIxc2UBbHRM8qGPZQAnzRP3LeNlASzeJQRAL2kDZ6pjgYQzaQEMZvS1LSdpAM8GDefKO2kAJrBxaFLvaQJ6ofyN0tNpAzfdT44Xs2kCO3pa1SsDaQO58PzVOstpAJngwT9TS2kAAjlBGHyLaQNXqmOARktlABQ/miarX2ED1KFyPcjDZQARWDi3SWNlAeHd3d+fq2ECxD59hNwzZQF0dEzy4+NhAt2Wtjpnf2EAxwYN5ksHYQCCF61EIZNhAgwfzRE122EDhlrU6pkvYQFodEzzYRdhAPu41F9Ku2ED5N0IZnQjZQJSKsw/v7NhAFIPAygGC2ECwpWWaqYjYQAzIXu9+NdhA+fabdo5M2EDrgsE1d7fXQFTa4hp/uNdAEE+ywTvf10BGYpXl3vLXQKbcasO+V9hADlFfqZEd2ECp10DNA1LYQHY3uRHLL9hAqVKzB3rX10D3HkEEL7nXQGbdZayj09dA6APcBui010A4Tss3qCbXQFJWDFeHV9dAgmxsiW/W10BGf2jmKbjXQDR4cmAMutdAPVdYhLno10CsyPtLZejXQAVNJRYL+tdAu942U5E32ED3WWWmNHvYQC1cku8DmdhAq4O8Hpx22EA9XltSqIHYQC6lQeRXrNhA/DHAGGSi2EAx8+SagrjYQH0PhFG2s9hAiQ0WTlKK2EBaC7PQblzYQKecHCuOYdhA4bU4UA1r2EDxaWEplUnYQPFuUr5dP9hAngCdXLbC2ECjNRlooQTZQMW+d64iDNlAsVbtmgAG2UBmepx8bRPZQLoKT9XqMtlALFdlzPpi2UDz0GTscXjZQLi3SMfSiNlAMZpvV8KU2UCf9oHRotTZQL/ArFBkEdpAxAlfchas2UBJhFl7Ao3ZQHZBfcs8StlABmt2t8U22UAgrNndxjvZQKU/NPNkJtlAkRDSQKke2UAt+cWSH4nZQJz7q8dd3tlA8XfvqIG82UB9gNsAKYHZQNEVf+5O4tlAjQxfTGAY2kCftjM5gsnZQBV4FNQ9mtlAaUPrznCt2UBl4digTA/aQL22kcXgvtlA4TCrikP32UDxPMsGbO7ZQLn3ll9TDdpAxKUXoiDu2UBMAz+q0dbZQO1HnZNBw9lA4+ZBZ4+p2UA6wSSCntfZQIWh6JQq8dlAHoXrUbjg2UBJEoQrABDaQPX4iywo+tlANOnJDx/W2UDygQo01bzZQBcGP4Qrw9lArI3XqU+U2UBLfJQvdqbZQE3q3p3/o9lA3GyGLkvj2UBn6/MrYOjZQAAAAABgENpAjNHTOhYs2kBGv7Z+ynHaQOqDeDdpV9pA1iMNbvst2kDhUyYiOULaQAV9Wsq1ddpA9bt31IhM2kDPr/nLEUXaQF6j+AuAUdpACj9eNexv2kCBn9hQzWvaQAmcf84XoNpAR2YI2ifw2kA22oSOIBnbQG+v3yY2INtA6qfA8QIT20Ah2Lj+vdXaQJYatGq6BNtAY2cdaKhQ20Dma88saUfbQDY5kKw8adtAVXIoVrCh20AbNw8625bbQBaBsb5BPttAYHtTfv9i20BSmlwHu03bQOXSC1EodttA6ttv2rmt20Dxs8pMSZfbQKCJnRYJddtAd+1hqpVP20Bc2SsZnZPbQMO12sPuj9tASeuVnyGm20B+IjzH7WTbQKnXQM1ThttAvnWkGB6C20A1wV3j0BnbQCDKKn9x0dpA1IvHMprj2kB07fgc5djaQOEUGpxPttpAyr4+TRCq2kAaaFGyLzjaQEmD29piiNpAydH2wjdG2kCWzaFIOyPaQKlX3f2k6tlAVIFt1+6T2UAMHOOddqLYQKWD9X+ecNhAkPkw2DvI2EAytE0E1JjYQI+0Z6kfrNhALzhdKV6E2ECjImHy6cbYQKeHncJsP9hANal7dx5G2EDABG7dffrXQE0qLTZwF9hAt8FjUnZ62EBA9qwYNzLYQDh3u16Ky9hAAJhFO4Gq2EAK4b77wAXZQAkEOpN299hAJR7pxqh72EBPudWGDabYQJ3ZrtDHWdhALKvpevKF2EBknJlzkz3YQFZ4Yj37J9hAPY9kaTor2EALKygfRXPYQCec3VrWUNhAtnIJmjm12EDkP6GyACjZQN6F5jodG9lADdJ5etI52UAWGnWaMjDZQJw20GljJtlAyXFyJjsC2UB9T9Lp1lzZQDqt7mtXd9lAa+VLlfWf2UAf7Cd2p6zZQFqB74kUk9lAVQvsMSGi2UBhmCZ/2pnZQDf3V49rjtlA/sTbXSq/2UB6In6G/9zZQE8uoCppFdpAsanzqJhL2kAaYye8JFLaQEEsmzmUMNpAo/T2bJkJ2kBfIt46b7PZQDHHBXqL2dlACSWAiOqv2UD189+Dh2vZQGPij6J+8tlAILbJh9ZO2kAtUb01AD3aQFFQZMCMbdpALMRJh9Rk2kDksA5a8rnaQM5waq1jndpAgIRO9YF72kDap+MxkznaQHsAeDGKB9pAoL/QI8Zr2kCM+0lRLaXaQGkS4reerNpAYknMxh2b2kDlyPDF5LraQIq5kZ8aitpAJCmPjJuf2kBBxV4VRd7aQCA/9SKdy9pA9TJ3GhYF20AOFjsHHALbQP21qJgS79pAEXMjP/W42kCKvruVRdbaQBqjdVRV89pAPZl/9L3g2kBeapuFT+raQAx9dyt78tpAEtbsbkva2kDs1kXkfuDaQBSuR+EaoNpAELsD83i62kCkNRlocTPaQFQVB730DNpAlP0Zy0Em2kAa2ahh0mjaQAadOxtUs9pA4PWG6M1n2kDMFjbwoLDaQDLu8wVqyNpAKus3E2Pd2kBQBZ04LrnaQAlrdreVodpAoHVnAMKq2kDlBJMIWnzaQLuZ9yX4YNpAH3TubMA42kDuQnOd9jXaQCb1i/84TtpArLm2ytZH2kBjDm/D9WvaQI37SVENTtpAH0WduRdV2kDl5kFnHzvaQEF79fFQgdpAX9QBI1K12kCcd5yiWwLbQK8Hk+LjENtAt/eWX7Mn20DyWhyoNm3bQMGiW69ptNtAR90/m2gL3EC6BSXfP0/cQDwjN7CyudxA7+TTY0vk3EAKyF7vHg7dQCQJrjtFH91A9yzPg0sk3UBQWzZNwqHcQFXQx48r3NxAQRM7LdJs3UAKXop5iYHdQFJ9+uIly91AaEPrztD43UAiWdPPzrfdQIM/1mYW5t1AKxzQTSeV3UBNw/ARQXrdQKx9FIhRfd1AD9bsbmtY3UD0S9cmWHndQDBFPusuMd1A0tUwVv5A3UCz1dLJL3ndQOh+h6IAyt1Axlc7inOy3UAHfVrKdT3dQH4dEtECFN1ATSotNvDn3EBazxCOWTTdQKylgLTfLt1AKmYntWQi3UDX477VmjbdQO0CJQUWWN1ADDo0PxEd3UACWPeiQBrdQKbIZ92AltxAdOmFKExb3EBUDGrSEHLcQM+MfjSMTtxAs2R7Go0y3EAjalmnP0PcQAk6baCTA9xA8locqGZ53EBXlaPthcXdQOMEkwiK7t1ANjmQrLzP3UDVcmcmaFfeQM8kxG8NKd5A0rNswLq53kDCafbZI8DeQPbPreofsd5A6Ax58Vu93kCYXnX3E8HeQNdP7I4ivt5AzYJjqfjt3kA9DkqYqY3eQIy0Z6m/Q95A9tkB14VU3kD+pwhdz3TeQG2MnfCCRt5AN5Aba2wY3kCRM03YbhPeQInI1iAJtt1AY50XFOO03UBh7KotssjdQLqP3JpU1d1AvN42U/Hs3UDIuU24Z/DdQA4Nnhxo9t1AEeoiheJH3kDgGAKA85veQBjG8Os6kd5AH96Z9nq63kB22kCnbZreQAeO8U4bwN5AyNsRTmvj3kA8z7IBS9HeQCbwYQl+Kd5A5s3hWi3p3UBZbf5f9d7dQLyGP7Aw+N1AwzV39F/03UD2RS+QfbndQGgDnTZA3N1AJwgLFL773UCOOD5tR97dQA5uMqqcXd1A6EMqn6iv3ECWXyz5ZZ7cQDjLPw0CWNxAMRXikfhm3EC1EPdrdazcQDMCKhzBrdxA8gIlBSai3EDDJjJzkSvcQDW9sY2l89tAgIk/inqg20Ar4RyIPxjbQLVtGAWxWttAzdG9YVVl20D67IDraivbQOqXrk3g0dtAh6HolCpn20Dg08JSeljbQDWfYOx6bdtAlOS5vj+z20CH0vGrLE7bQH865dHNP9tACpdV2OzJ2kAKpppZi1faQPinQb4Rx9pAWkHm3etv20BHsHH9CznbQNpU3SOr1NpAc6k3kJvC2kDwfZc/jGnaQAUYqQoAetpAZF0C3bRv2kDGfikWIqTaQH2nyYwH1tpALyrPqaEj20AdNpGZi1HbQP+nCF0vLttA4mbel6BN20BA+5EikufaQKwg8+6VptpA50Mqnyg52kA3fOVU5YvbQK5bF5G7j91AefZcpqZ+3UAOK++9sgndQCsrhqvTZN1APCM3sBJo3UBgSczG7bPcQA8H9oWNdtxAt0Vzd1D220AK16NwjarbQPmY/DwDattAMxC4mw2m20CgWJT/ZpXbQPbU1+BqiNtAA+sS6La620B5LQ5UM9DbQFnZKxnt9NtAS5wChrIh3EBvYIVuiWzcQIMY6Np3z9xA+TbqDr/Z3EA3VffI5q/cQHGRjoXLgdxAk/xiyT8e3UCrEefOmUzdQPGuoFZeg91ACqoNTrST3UAxlvxiqcDdQNmLxzLaod1AKfVSnray3UCkU2oJnPHdQBT4sARvFt5AOlRAxyQi3kCm7yI5JiveQAoX8ghe4N1AmMYvvCJR3kBcngd350jeQLPf7VQjcN5Afe2/uwKY3kA4xZd215veQCzlj3yocN5A/rEj6HKg3kBs6jwqruveQHtjCAC+C99AWiOVPGFI30BsBxAreTffQAe/+mU9IN9AuOiKP9f43kAx/TiH2BDfQDOupW1JNt9Arsj7S0Xp3kBv0dwd3LXeQK5+kigxmN5A8lABHfOH3kDh3Cbce8reQIUTvuTMSt9AajSmTVJI30BorxiICHXfQPkK0ow1ad9AzV/oEWN830CTEVDhSJLfQBaBsb7Rld9AguK0zTqU30As+cWSb2ffQPJuUr49vN9AGEaNHFzT30CTC6hKDqbfQA==\",\"dtype\":\"float64\",\"order\":\"little\",\"shape\":[1226]},\"Volume\":{\"__ndarray__\":\"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\",\"dtype\":\"int32\",\"order\":\"little\",\"shape\":[1226]},\"datetime\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"order\":\"little\",\"shape\":[1226]},\"ohlc_low\":{\"__ndarray__\":\"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       "  var render_items = [{\"docid\":\"ca9d09f0-6da1-4e4e-b8a9-497aa03116f9\",\"root_ids\":[\"1410\"],\"roots\":{\"1410\":\"68eca3be-781e-44c5-9e13-2c2dfb34bd5e\"}}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "1410"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Using Backtest Library and Pre-Provided Code,\n",
    "\n",
    "class strategy(Strategy):\n",
    "    def init(self):\n",
    "        cp = pd.Series(self.data.Close)\n",
    "        \n",
    "        self.ema1 = self.I(tr.ema_indicator,close=cp, window=20);\n",
    "        self.ema2 = self.I(tr.sma_indicator,close=cp, window=200)\n",
    "        \n",
    "\n",
    "    def next(self):\n",
    "        # Buy or sell on ema -> sma cross over\n",
    "        # Have a 200 pts stop loss for long trades and 130 points for short trades\n",
    "        if crossover(self.ema1, self.ema2):\n",
    "            self.buy(sl=self.data.High-200)\n",
    "        elif crossover(self.ema2, self.ema1):\n",
    "            self.sell(sl=self.data.Low+130)\n",
    "            \n",
    "bt = Backtest(df, strategy, commission=.002,cash=1000000,\n",
    "              exclusive_orders=True)\n",
    "# Run the backtest\n",
    "stats = bt.run()\n",
    " \n",
    "# Print the performance metrics for this strategy \n",
    "print(stats)\n",
    "\n",
    "# Plot the price movements, trades and other meta-data such as drawdown etc.\n",
    "bt.plot(resample=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>18013.938779</td>\n",
       "      <td>87300.856228</td>\n",
       "      <td>0.087869</td>\n",
       "      <td>2016-05-23</td>\n",
       "      <td>2017-01-04</td>\n",
       "      <td>226 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-60</td>\n",
       "      <td>495</td>\n",
       "      <td>497</td>\n",
       "      <td>17977.910901</td>\n",
       "      <td>18306.550704</td>\n",
       "      <td>-19718.388173</td>\n",
       "      <td>-0.018280</td>\n",
       "      <td>2017-01-04</td>\n",
       "      <td>2017-01-06</td>\n",
       "      <td>2 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>56</td>\n",
       "      <td>502</td>\n",
       "      <td>795</td>\n",
       "      <td>18937.508795</td>\n",
       "      <td>24275.625419</td>\n",
       "      <td>298934.530939</td>\n",
       "      <td>0.281881</td>\n",
       "      <td>2017-01-13</td>\n",
       "      <td>2018-03-22</td>\n",
       "      <td>433 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-56</td>\n",
       "      <td>795</td>\n",
       "      <td>803</td>\n",
       "      <td>24227.074168</td>\n",
       "      <td>24552.173227</td>\n",
       "      <td>-18205.547295</td>\n",
       "      <td>-0.013419</td>\n",
       "      <td>2018-03-22</td>\n",
       "      <td>2018-04-05</td>\n",
       "      <td>14 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>52</td>\n",
       "      <td>822</td>\n",
       "      <td>919</td>\n",
       "      <td>25675.257475</td>\n",
       "      <td>25225.853387</td>\n",
       "      <td>-23369.012589</td>\n",
       "      <td>-0.017503</td>\n",
       "      <td>2018-05-03</td>\n",
       "      <td>2018-09-24</td>\n",
       "      <td>144 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-53</td>\n",
       "      <td>927</td>\n",
       "      <td>930</td>\n",
       "      <td>24806.763091</td>\n",
       "      <td>25065.848779</td>\n",
       "      <td>-13731.541469</td>\n",
       "      <td>-0.010444</td>\n",
       "      <td>2018-10-05</td>\n",
       "      <td>2018-10-10</td>\n",
       "      <td>5 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>48</td>\n",
       "      <td>964</td>\n",
       "      <td>966</td>\n",
       "      <td>27006.479132</td>\n",
       "      <td>26662.397276</td>\n",
       "      <td>-16515.929102</td>\n",
       "      <td>-0.012741</td>\n",
       "      <td>2018-12-03</td>\n",
       "      <td>2018-12-05</td>\n",
       "      <td>2 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-48</td>\n",
       "      <td>1141</td>\n",
       "      <td>1143</td>\n",
       "      <td>26920.228989</td>\n",
       "      <td>28095.685419</td>\n",
       "      <td>-56421.908609</td>\n",
       "      <td>-0.043664</td>\n",
       "      <td>2019-08-23</td>\n",
       "      <td>2019-08-27</td>\n",
       "      <td>4 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>40</td>\n",
       "      <td>1184</td>\n",
       "      <td>1225</td>\n",
       "      <td>30281.916985</td>\n",
       "      <td>32408.223307</td>\n",
       "      <td>85052.252850</td>\n",
       "      <td>0.070217</td>\n",
       "      <td>2019-10-31</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>61 days</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Size  EntryBar  ExitBar    EntryPrice     ExitPrice            PnL  \\\n",
       "0    60       341      495  16558.924508  18013.938779   87300.856228   \n",
       "1   -60       495      497  17977.910901  18306.550704  -19718.388173   \n",
       "2    56       502      795  18937.508795  24275.625419  298934.530939   \n",
       "3   -56       795      803  24227.074168  24552.173227  -18205.547295   \n",
       "4    52       822      919  25675.257475  25225.853387  -23369.012589   \n",
       "5   -53       927      930  24806.763091  25065.848779  -13731.541469   \n",
       "6    48       964      966  27006.479132  26662.397276  -16515.929102   \n",
       "7   -48      1141     1143  26920.228989  28095.685419  -56421.908609   \n",
       "8    40      1184     1225  30281.916985  32408.223307   85052.252850   \n",
       "\n",
       "   ReturnPct  EntryTime   ExitTime Duration  \n",
       "0   0.087869 2016-05-23 2017-01-04 226 days  \n",
       "1  -0.018280 2017-01-04 2017-01-06   2 days  \n",
       "2   0.281881 2017-01-13 2018-03-22 433 days  \n",
       "3  -0.013419 2018-03-22 2018-04-05  14 days  \n",
       "4  -0.017503 2018-05-03 2018-09-24 144 days  \n",
       "5  -0.010444 2018-10-05 2018-10-10   5 days  \n",
       "6  -0.012741 2018-12-03 2018-12-05   2 days  \n",
       "7  -0.043664 2019-08-23 2019-08-27   4 days  \n",
       "8   0.070217 2019-10-31 2019-12-31  61 days  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#DataFrame of Trades\n",
    "\n",
    "stats._trades"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 3 Converting into 15 min Time Frames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>Ticker</th>\n",
       "      <th>Date</th>\n",
       "      <th>Final Date format</th>\n",
       "      <th>Time</th>\n",
       "      <th>Open 5</th>\n",
       "      <th>High 5</th>\n",
       "      <th>Low 5</th>\n",
       "      <th>Close 5</th>\n",
       "      <th>Volume 5</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BANKNIFTY_F1</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>20150101</td>\n",
       "      <td>09:20:00</td>\n",
       "      <td>18861.3496</td>\n",
       "      <td>18867.9492</td>\n",
       "      <td>18793.0000</td>\n",
       "      <td>18808.3496</td>\n",
       "      <td>84825</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BANKNIFTY_F1</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>20150101</td>\n",
       "      <td>09:25:00</td>\n",
       "      <td>18808.0000</td>\n",
       "      <td>18835.0000</td>\n",
       "      <td>18802.1992</td>\n",
       "      <td>18829.0000</td>\n",
       "      <td>40925</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BANKNIFTY_F1</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>20150101</td>\n",
       "      <td>09:30:00</td>\n",
       "      <td>18826.9492</td>\n",
       "      <td>18833.4004</td>\n",
       "      <td>18820.0000</td>\n",
       "      <td>18826.0996</td>\n",
       "      <td>27000</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BANKNIFTY_F1</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>20150101</td>\n",
       "      <td>09:35:00</td>\n",
       "      <td>18825.0508</td>\n",
       "      <td>18829.0996</td>\n",
       "      <td>18810.0000</td>\n",
       "      <td>18810.0000</td>\n",
       "      <td>18025</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BANKNIFTY_F1</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>20150101</td>\n",
       "      <td>09:40:00</td>\n",
       "      <td>18805.0000</td>\n",
       "      <td>18808.9492</td>\n",
       "      <td>18775.0000</td>\n",
       "      <td>18803.9492</td>\n",
       "      <td>60100</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Ticker       Date  Final Date format      Time      Open 5  \\\n",
       "0  BANKNIFTY_F1 2015-01-01           20150101  09:20:00  18861.3496   \n",
       "1  BANKNIFTY_F1 2015-01-01           20150101  09:25:00  18808.0000   \n",
       "2  BANKNIFTY_F1 2015-01-01           20150101  09:30:00  18826.9492   \n",
       "3  BANKNIFTY_F1 2015-01-01           20150101  09:35:00  18825.0508   \n",
       "4  BANKNIFTY_F1 2015-01-01           20150101  09:40:00  18805.0000   \n",
       "\n",
       "       High 5       Low 5     Close 5  Volume 5  Year  \n",
       "0  18867.9492  18793.0000  18808.3496     84825  2015  \n",
       "1  18835.0000  18802.1992  18829.0000     40925  2015  \n",
       "2  18833.4004  18820.0000  18826.0996     27000  2015  \n",
       "3  18829.0996  18810.0000  18810.0000     18025  2015  \n",
       "4  18808.9492  18775.0000  18803.9492     60100  2015  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Since the tickare has 0 variance and final date format is same as date, we will remove them\n",
    "\n",
    "data.drop(['Ticker','Final Date format'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Code for 15 min time frame\n",
    "\n",
    "j=0\n",
    "now=pd.DataFrame()\n",
    "for k in data['Date'].unique():\n",
    "    new=data[data['Date']==k]\n",
    "    new.reset_index(inplace=True,drop=True)\n",
    "    new['Open']=\" \"\n",
    "    new['Close']=\" \"\n",
    "    new['Low']=\" \"\n",
    "    new['Volume']=\" \"\n",
    "    new['High']=\" \"\n",
    "    \n",
    "    for i in range(new.index[0],len(new),3):\n",
    "        #Since 5 min time frame is given so, step size of 3 will make it 15 min time frame\n",
    "        j=i+2\n",
    "        new['High'][j+1]=new['High 5'][i:j+1].mean()\n",
    "        new['Open'][j+1]=new['Open 5'][i:j+1].mean()\n",
    "        new['Low'][j+1]=new['Low 5'][i:j+1].mean()\n",
    "        new['Close'][j+1]=new['Close 5'][i:j+1].mean()\n",
    "        new['Volume'][j+1]=new['Volume 5'][i:j+1].mean()\n",
    "        \n",
    "        #Storing the 15 min time frames value to the every fourth index of data\n",
    "        \n",
    "    now=pd.concat([new[new['Open']!=\" \"],now])\n",
    "    \n",
    "final_data=now.drop(['High 5','Open 5','Low 5','Close 5','Volume 5'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>Time</th>\n",
       "      <th>Year</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>Low</th>\n",
       "      <th>Volume</th>\n",
       "      <th>High</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>09:31:00</td>\n",
       "      <td>2019</td>\n",
       "      <td>32461.7</td>\n",
       "      <td>32410</td>\n",
       "      <td>32398.6</td>\n",
       "      <td>78020</td>\n",
       "      <td>32473.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>09:46:00</td>\n",
       "      <td>2019</td>\n",
       "      <td>32397.4</td>\n",
       "      <td>32391.8</td>\n",
       "      <td>32379.1</td>\n",
       "      <td>46920</td>\n",
       "      <td>32410.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>10:01:00</td>\n",
       "      <td>2019</td>\n",
       "      <td>32372.5</td>\n",
       "      <td>32368.1</td>\n",
       "      <td>32355.3</td>\n",
       "      <td>38413.3</td>\n",
       "      <td>32383.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>10:16:00</td>\n",
       "      <td>2019</td>\n",
       "      <td>32375.1</td>\n",
       "      <td>32379.4</td>\n",
       "      <td>32369.1</td>\n",
       "      <td>25973.3</td>\n",
       "      <td>32391.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>10:31:00</td>\n",
       "      <td>2019</td>\n",
       "      <td>32378.6</td>\n",
       "      <td>32373.9</td>\n",
       "      <td>32362.2</td>\n",
       "      <td>14673.3</td>\n",
       "      <td>32384.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date      Time  Year     Open    Close      Low   Volume     High\n",
       "3  2019-12-31  09:31:00  2019  32461.7    32410  32398.6    78020  32473.7\n",
       "6  2019-12-31  09:46:00  2019  32397.4  32391.8  32379.1    46920  32410.2\n",
       "9  2019-12-31  10:01:00  2019  32372.5  32368.1  32355.3  38413.3  32383.3\n",
       "12 2019-12-31  10:16:00  2019  32375.1  32379.4  32369.1  25973.3  32391.6\n",
       "15 2019-12-31  10:31:00  2019  32378.6  32373.9  32362.2  14673.3  32384.3"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 4-8 and After, Intraday Calculations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Converting all values to float for easy \n",
    "\n",
    "for i in ['Open','Close','Low','Volume','High']:\n",
    "    final_data[i]=final_data[i].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date      datetime64[ns]\n",
       "Time              object\n",
       "Year               int64\n",
       "Open             float64\n",
       "Close            float64\n",
       "Low              float64\n",
       "Volume           float64\n",
       "High             float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_data.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Calculation of VWAP "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculateVwap(data):\n",
    "\n",
    "        data['TP'] = (data['High']+data['Low']+data['Close'])/3.0\n",
    "        data['TradedValue']  = data['TP']*data['Volume']\n",
    "        data['CumVolume'] = data['Volume'].cumsum()\n",
    "        data['CumTradedValue'] = data['TradedValue'].cumsum()\n",
    "        data['VWAP'] = data['CumTradedValue'] / (data['CumVolume'])\n",
    "        return data\n",
    "        \n",
    "data_vwap= final_data.groupby('Date').apply(calculateVwap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>Time</th>\n",
       "      <th>Year</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>Low</th>\n",
       "      <th>Volume</th>\n",
       "      <th>High</th>\n",
       "      <th>TP</th>\n",
       "      <th>TradedValue</th>\n",
       "      <th>CumVolume</th>\n",
       "      <th>CumTradedValue</th>\n",
       "      <th>VWAP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>09:35:00</td>\n",
       "      <td>2015</td>\n",
       "      <td>18832.099600</td>\n",
       "      <td>18821.149733</td>\n",
       "      <td>18805.066400</td>\n",
       "      <td>50916.666667</td>\n",
       "      <td>18845.449867</td>\n",
       "      <td>18823.888667</td>\n",
       "      <td>9.584497e+08</td>\n",
       "      <td>50916.666667</td>\n",
       "      <td>9.584497e+08</td>\n",
       "      <td>18823.888667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-01-02</td>\n",
       "      <td>09:31:00</td>\n",
       "      <td>2015</td>\n",
       "      <td>18931.966800</td>\n",
       "      <td>18965.199867</td>\n",
       "      <td>18926.566400</td>\n",
       "      <td>73608.333333</td>\n",
       "      <td>18980.016933</td>\n",
       "      <td>18957.261067</td>\n",
       "      <td>1.395412e+09</td>\n",
       "      <td>73608.333333</td>\n",
       "      <td>1.395412e+09</td>\n",
       "      <td>18957.261067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-01-05</td>\n",
       "      <td>09:32:00</td>\n",
       "      <td>2015</td>\n",
       "      <td>19205.333333</td>\n",
       "      <td>19186.333333</td>\n",
       "      <td>19161.850267</td>\n",
       "      <td>126600.000000</td>\n",
       "      <td>19239.799467</td>\n",
       "      <td>19195.994356</td>\n",
       "      <td>2.430213e+09</td>\n",
       "      <td>126600.000000</td>\n",
       "      <td>2.430213e+09</td>\n",
       "      <td>19195.994356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-01-06</td>\n",
       "      <td>09:33:00</td>\n",
       "      <td>2015</td>\n",
       "      <td>18997.266933</td>\n",
       "      <td>18922.866533</td>\n",
       "      <td>18903.450533</td>\n",
       "      <td>180825.000000</td>\n",
       "      <td>19020.216800</td>\n",
       "      <td>18948.844622</td>\n",
       "      <td>3.426425e+09</td>\n",
       "      <td>180825.000000</td>\n",
       "      <td>3.426425e+09</td>\n",
       "      <td>18948.844622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-01-07</td>\n",
       "      <td>09:34:00</td>\n",
       "      <td>2015</td>\n",
       "      <td>18500.649733</td>\n",
       "      <td>18493.500000</td>\n",
       "      <td>18446.333333</td>\n",
       "      <td>143591.666667</td>\n",
       "      <td>18578.633467</td>\n",
       "      <td>18506.155600</td>\n",
       "      <td>2.657330e+09</td>\n",
       "      <td>143591.666667</td>\n",
       "      <td>2.657330e+09</td>\n",
       "      <td>18506.155600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date      Time  Year          Open         Close           Low  \\\n",
       "3 2015-01-01  09:35:00  2015  18832.099600  18821.149733  18805.066400   \n",
       "3 2015-01-02  09:31:00  2015  18931.966800  18965.199867  18926.566400   \n",
       "3 2015-01-05  09:32:00  2015  19205.333333  19186.333333  19161.850267   \n",
       "3 2015-01-06  09:33:00  2015  18997.266933  18922.866533  18903.450533   \n",
       "3 2015-01-07  09:34:00  2015  18500.649733  18493.500000  18446.333333   \n",
       "\n",
       "          Volume          High            TP   TradedValue      CumVolume  \\\n",
       "3   50916.666667  18845.449867  18823.888667  9.584497e+08   50916.666667   \n",
       "3   73608.333333  18980.016933  18957.261067  1.395412e+09   73608.333333   \n",
       "3  126600.000000  19239.799467  19195.994356  2.430213e+09  126600.000000   \n",
       "3  180825.000000  19020.216800  18948.844622  3.426425e+09  180825.000000   \n",
       "3  143591.666667  18578.633467  18506.155600  2.657330e+09  143591.666667   \n",
       "\n",
       "   CumTradedValue          VWAP  \n",
       "3    9.584497e+08  18823.888667  \n",
       "3    1.395412e+09  18957.261067  \n",
       "3    2.430213e+09  19195.994356  \n",
       "3    3.426425e+09  18948.844622  \n",
       "3    2.657330e+09  18506.155600  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_vwap.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### If candle closes above VWAP, we will buy and if candle closes below VWAP, we will sell\n",
    "\n",
    "#### Assumptions:-\n",
    "1. Buy column is used to mention where close is above VWAP, buy will take place.\n",
    "2. Buy Prie is the High Price at which buy occurs\n",
    "3. Stop loss of buy is at the low price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_vwap['Buy']=\" \"\n",
    "data_vwap['Buy Price']=\" \"\n",
    "data_vwap['Stop Loss']=\" \"\n",
    "data_vwap.reset_index(inplace=True,drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(0,len(data_vwap)):\n",
    "    if data_vwap['Close'][i]>data_vwap['VWAP'][i]:\n",
    "        data_vwap['Buy'][i]='Buy'\n",
    "        data_vwap['Buy Price'][i]=data_vwap['High'][i]\n",
    "        data_vwap['Stop Loss'][i]=data_vwap['Low'][i]   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### If stop loss is not hit, then all trades are exited at 3:15 PM and trade starts only after 2nd 15-minute candle (9:30 to 9:45)\n",
    "\n",
    "#### Assumptions:-\n",
    "1. Sell column is used define where sell occurs of all the buys.\n",
    "2. The sell will occur of total buys before that sell options on that day.\n",
    "3. Example, if 4 buys occurs in a day consequetively, then next sell will occur of 4 units that were purchased before on that day\n",
    "4. Sell is at the low price of that moment.\n",
    "5. Amount is the cost at which sell or buy occurs.\n",
    "6. Units are total units by or sell occurs.\n",
    "7. 1 unit of buy takes place only everytime.\n",
    "8. Amount of sell = Number of units * Low Price\n",
    "9. Since, ending of day is not exact st 3:15 PM but at 3:16 to 3:20 PM. Hence these timings are used for convenience."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "count=0\n",
    "df_Amount=pd.DataFrame()\n",
    "\n",
    "for k in data_vwap.Date.unique():\n",
    "    #providing the dataframe filtered with dates\n",
    "    \n",
    "    d=data_vwap[data_vwap['Date']==k]\n",
    "    d.reset_index(inplace=True,drop=True)\n",
    "    d['Sell']=\" \"\n",
    "    d['Amount (Rs)']=\" \"\n",
    "    d['Units']=\" \"\n",
    "\n",
    "    for i in d.index:\n",
    "        \n",
    "        if d['Buy'][i]=='Buy':\n",
    "            d['Amount (Rs)'][i]=-d['High'][i]\n",
    "            d['Units'][i]=1\n",
    "            count=count+1\n",
    "            try:\n",
    "                if d['Buy'][i+1]==' ':\n",
    "                    d['Sell'][i+1]='Sell'\n",
    "                    d['Amount (Rs)'][i+1]=float(d['Low'][i+1])*count\n",
    "                    d['Units'][i+1]=count          \n",
    "                    count=0\n",
    "            except:\n",
    "                d['Sell'][i]=\"Sell\"\n",
    "                d['Amount (Rs)'][i]=float(d['Low'][i])*count\n",
    "                #As per above Instructions\n",
    "                d['Units'][i]=count \n",
    "                \n",
    "    df_Amount=pd.concat([d,df_Amount])\n",
    "df_Amount.reset_index(inplace=True,drop=True)   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 9 Calculation of Overall Profit/Loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The overall profit or loss is Rs 210985339.5157\n"
     ]
    }
   ],
   "source": [
    "print(\"The overall profit or loss is Rs\", \"{:.4f}\".format(df_Amount['Amount (Rs)'].replace(\" \",0).sum()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 10 Calculation of Profit/Loss Year Wise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " The profit for year 2019 is Rs 54378707.9860\n",
      " The profit for year 2018 is Rs 47691359.6821\n",
      " The profit for year 2017 is Rs 47529940.5416\n",
      " The profit for year 2016 is Rs 34634987.0409\n",
      " The profit for year 2015 is Rs 26750344.2650\n"
     ]
    }
   ],
   "source": [
    "year_list=[]\n",
    "amount_list=[]\n",
    "for i in df_Amount['Year'].unique():\n",
    "        print(\" The profit for year {0} is Rs {1}\".format(i, \"{:.4f}\".format(df_Amount[df_Amount['Year']==i]['Amount (Rs)'].replace(\" \",0).sum())))\n",
    "        amount_list.append(df_Amount[df_Amount['Year']==i]['Amount (Rs)'].replace(\" \",0).sum())\n",
    "        year_list.append(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Profit Earned')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(year_list,amount_list)\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel('Profit Earned')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 10 Calculation of Profit/Loss Day Wise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_Amount['Profit/Loss (Rs)']=\" \"\n",
    "for i in df_Amount['Date'].unique():\n",
    "    df_Amount['Profit/Loss (Rs)'][df_Amount[df_Amount['Date']==i].index[-1]]=df_Amount[df_Amount['Date']==i]['Amount (Rs)'].replace(\" \",0).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "Pro_Loss_DateWise=df_Amount[df_Amount['Profit/Loss (Rs)']!=\" \"].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Date wise profit/loss dataframe head is \n"
     ]
    },
    {
     "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>Time</th>\n",
       "      <th>Year</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>Low</th>\n",
       "      <th>Volume</th>\n",
       "      <th>High</th>\n",
       "      <th>TP</th>\n",
       "      <th>TradedValue</th>\n",
       "      <th>CumVolume</th>\n",
       "      <th>CumTradedValue</th>\n",
       "      <th>VWAP</th>\n",
       "      <th>Buy</th>\n",
       "      <th>Buy Price</th>\n",
       "      <th>Stop Loss</th>\n",
       "      <th>Sell</th>\n",
       "      <th>Amount (Rs)</th>\n",
       "      <th>Units</th>\n",
       "      <th>Profit/Loss (Rs)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>15:16:00</td>\n",
       "      <td>2019</td>\n",
       "      <td>32389.733067</td>\n",
       "      <td>32389.666667</td>\n",
       "      <td>32380.483733</td>\n",
       "      <td>31740.000000</td>\n",
       "      <td>32399.366533</td>\n",
       "      <td>32389.838978</td>\n",
       "      <td>1.028053e+09</td>\n",
       "      <td>687373.333333</td>\n",
       "      <td>2.227799e+10</td>\n",
       "      <td>32410.327496</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Sell</td>\n",
       "      <td>453327</td>\n",
       "      <td>14</td>\n",
       "      <td>-903.144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-12-30</td>\n",
       "      <td>15:16:00</td>\n",
       "      <td>2019</td>\n",
       "      <td>32554.216800</td>\n",
       "      <td>32550.716800</td>\n",
       "      <td>32533.816400</td>\n",
       "      <td>33900.000000</td>\n",
       "      <td>32574.833333</td>\n",
       "      <td>32553.122178</td>\n",
       "      <td>1.103551e+09</td>\n",
       "      <td>815946.666667</td>\n",
       "      <td>2.659189e+10</td>\n",
       "      <td>32590.236045</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>784291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-12-27</td>\n",
       "      <td>15:16:00</td>\n",
       "      <td>2019</td>\n",
       "      <td>32569.566400</td>\n",
       "      <td>32589.583333</td>\n",
       "      <td>32559.666667</td>\n",
       "      <td>46760.000000</td>\n",
       "      <td>32601.099600</td>\n",
       "      <td>32583.449867</td>\n",
       "      <td>1.523602e+09</td>\n",
       "      <td>760513.333333</td>\n",
       "      <td>2.468450e+10</td>\n",
       "      <td>32457.684219</td>\n",
       "      <td>Buy</td>\n",
       "      <td>32601.1</td>\n",
       "      <td>32559.7</td>\n",
       "      <td>Sell</td>\n",
       "      <td>781432</td>\n",
       "      <td>24</td>\n",
       "      <td>33746.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-12-26</td>\n",
       "      <td>15:16:00</td>\n",
       "      <td>2019</td>\n",
       "      <td>32007.800133</td>\n",
       "      <td>31998.199867</td>\n",
       "      <td>31985.617200</td>\n",
       "      <td>51453.333333</td>\n",
       "      <td>32019.500000</td>\n",
       "      <td>32001.105689</td>\n",
       "      <td>1.646564e+09</td>\n",
       "      <td>719200.000000</td>\n",
       "      <td>2.311712e+10</td>\n",
       "      <td>32142.827919</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>-25.8997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-12-24</td>\n",
       "      <td>15:16:00</td>\n",
       "      <td>2019</td>\n",
       "      <td>32309.850267</td>\n",
       "      <td>32310.500000</td>\n",
       "      <td>32297.283867</td>\n",
       "      <td>33900.000000</td>\n",
       "      <td>32329.466800</td>\n",
       "      <td>32312.416889</td>\n",
       "      <td>1.095391e+09</td>\n",
       "      <td>477033.333333</td>\n",
       "      <td>1.542554e+10</td>\n",
       "      <td>32336.408773</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>-295.686</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date      Time  Year          Open         Close           Low  \\\n",
       "0 2019-12-31  15:16:00  2019  32389.733067  32389.666667  32380.483733   \n",
       "1 2019-12-30  15:16:00  2019  32554.216800  32550.716800  32533.816400   \n",
       "2 2019-12-27  15:16:00  2019  32569.566400  32589.583333  32559.666667   \n",
       "3 2019-12-26  15:16:00  2019  32007.800133  31998.199867  31985.617200   \n",
       "4 2019-12-24  15:16:00  2019  32309.850267  32310.500000  32297.283867   \n",
       "\n",
       "         Volume          High            TP   TradedValue      CumVolume  \\\n",
       "0  31740.000000  32399.366533  32389.838978  1.028053e+09  687373.333333   \n",
       "1  33900.000000  32574.833333  32553.122178  1.103551e+09  815946.666667   \n",
       "2  46760.000000  32601.099600  32583.449867  1.523602e+09  760513.333333   \n",
       "3  51453.333333  32019.500000  32001.105689  1.646564e+09  719200.000000   \n",
       "4  33900.000000  32329.466800  32312.416889  1.095391e+09  477033.333333   \n",
       "\n",
       "   CumTradedValue          VWAP  Buy Buy Price Stop Loss  Sell Amount (Rs)  \\\n",
       "0    2.227799e+10  32410.327496                           Sell      453327   \n",
       "1    2.659189e+10  32590.236045                                              \n",
       "2    2.468450e+10  32457.684219  Buy   32601.1   32559.7  Sell      781432   \n",
       "3    2.311712e+10  32142.827919                                              \n",
       "4    1.542554e+10  32336.408773                                              \n",
       "\n",
       "  Units Profit/Loss (Rs)  \n",
       "0    14         -903.144  \n",
       "1                 784291  \n",
       "2    24          33746.8  \n",
       "3               -25.8997  \n",
       "4               -295.686  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"The Date wise profit/loss dataframe head is \")\n",
    "pd.set_option(\"display.max_columns\",35)\n",
    "Pro_Loss_DateWise.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 11 Best Performing Year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The best performing year is with maximum profit or minimum loss 2019 with amount 54378707.986\n"
     ]
    }
   ],
   "source": [
    "print(\"The best performing year is with maximum profit or minimum loss\", year_list[np.argmax(amount_list)] , \"with amount\",max(amount_list) )"
   ]
  }
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