close_near_low_backtest.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://youtu.be/d3PqpaOb_Gs : Video explanation of strategy and code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import libraries\n",
    "import pandas_datareader as pdr\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import datetime\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "#define date range\n",
    "START = datetime.datetime(2000, 1, 1)\n",
    "END = datetime.datetime(2020, 12, 31)\n",
    "YEARS = (END - START).days/ 365.25"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "#define starting variables\n",
    "STARTING_BALANCE = 10000\n",
    "PCT_THRESH = 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-03</th>\n",
       "      <td>1478.000000</td>\n",
       "      <td>1438.359985</td>\n",
       "      <td>1469.250000</td>\n",
       "      <td>1455.219971</td>\n",
       "      <td>931800000</td>\n",
       "      <td>1455.219971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-04</th>\n",
       "      <td>1455.219971</td>\n",
       "      <td>1397.430054</td>\n",
       "      <td>1455.219971</td>\n",
       "      <td>1399.420044</td>\n",
       "      <td>1009000000</td>\n",
       "      <td>1399.420044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-05</th>\n",
       "      <td>1413.270020</td>\n",
       "      <td>1377.680054</td>\n",
       "      <td>1399.420044</td>\n",
       "      <td>1402.109985</td>\n",
       "      <td>1085500000</td>\n",
       "      <td>1402.109985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-06</th>\n",
       "      <td>1411.900024</td>\n",
       "      <td>1392.099976</td>\n",
       "      <td>1402.109985</td>\n",
       "      <td>1403.449951</td>\n",
       "      <td>1092300000</td>\n",
       "      <td>1403.449951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-07</th>\n",
       "      <td>1441.469971</td>\n",
       "      <td>1400.729980</td>\n",
       "      <td>1403.449951</td>\n",
       "      <td>1441.469971</td>\n",
       "      <td>1225200000</td>\n",
       "      <td>1441.469971</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   High          Low         Open        Close      Volume  \\\n",
       "Date                                                                         \n",
       "2000-01-03  1478.000000  1438.359985  1469.250000  1455.219971   931800000   \n",
       "2000-01-04  1455.219971  1397.430054  1455.219971  1399.420044  1009000000   \n",
       "2000-01-05  1413.270020  1377.680054  1399.420044  1402.109985  1085500000   \n",
       "2000-01-06  1411.900024  1392.099976  1402.109985  1403.449951  1092300000   \n",
       "2000-01-07  1441.469971  1400.729980  1403.449951  1441.469971  1225200000   \n",
       "\n",
       "              Adj Close  \n",
       "Date                     \n",
       "2000-01-03  1455.219971  \n",
       "2000-01-04  1399.420044  \n",
       "2000-01-05  1402.109985  \n",
       "2000-01-06  1403.449951  \n",
       "2000-01-07  1441.469971  "
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#download data\n",
    "price = pdr.get_data_yahoo('^GSPC', START, END)\n",
    "\n",
    "price.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        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>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-12-24</th>\n",
       "      <td>3703.820068</td>\n",
       "      <td>3689.320068</td>\n",
       "      <td>3694.030029</td>\n",
       "      <td>3703.060059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-28</th>\n",
       "      <td>3740.510010</td>\n",
       "      <td>3723.030029</td>\n",
       "      <td>3723.030029</td>\n",
       "      <td>3735.360107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-29</th>\n",
       "      <td>3756.120117</td>\n",
       "      <td>3723.310059</td>\n",
       "      <td>3750.010010</td>\n",
       "      <td>3727.040039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-30</th>\n",
       "      <td>3744.629883</td>\n",
       "      <td>3730.209961</td>\n",
       "      <td>3736.189941</td>\n",
       "      <td>3732.040039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-31</th>\n",
       "      <td>3760.199951</td>\n",
       "      <td>3726.879883</td>\n",
       "      <td>3733.270020</td>\n",
       "      <td>3756.070068</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   High          Low         Open        Close\n",
       "Date                                                          \n",
       "2020-12-24  3703.820068  3689.320068  3694.030029  3703.060059\n",
       "2020-12-28  3740.510010  3723.030029  3723.030029  3735.360107\n",
       "2020-12-29  3756.120117  3723.310059  3750.010010  3727.040039\n",
       "2020-12-30  3744.629883  3730.209961  3736.189941  3732.040039\n",
       "2020-12-31  3760.199951  3726.879883  3733.270020  3756.070068"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#drop redundant columns\n",
    "price = price.drop(['Volume', 'Adj Close'], 1)\n",
    "\n",
    "price.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot chart\n",
    "plt.style.use('seaborn-muted')\n",
    "plt.plot(price.Close)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "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>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>Return</th>\n",
       "      <th>Bench_Bal</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-12-24</th>\n",
       "      <td>3703.820068</td>\n",
       "      <td>3689.320068</td>\n",
       "      <td>3694.030029</td>\n",
       "      <td>3703.060059</td>\n",
       "      <td>1.003537</td>\n",
       "      <td>25446.737491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-28</th>\n",
       "      <td>3740.510010</td>\n",
       "      <td>3723.030029</td>\n",
       "      <td>3723.030029</td>\n",
       "      <td>3735.360107</td>\n",
       "      <td>1.008723</td>\n",
       "      <td>25668.697397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-29</th>\n",
       "      <td>3756.120117</td>\n",
       "      <td>3723.310059</td>\n",
       "      <td>3750.010010</td>\n",
       "      <td>3727.040039</td>\n",
       "      <td>0.997773</td>\n",
       "      <td>25611.523440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-30</th>\n",
       "      <td>3744.629883</td>\n",
       "      <td>3730.209961</td>\n",
       "      <td>3736.189941</td>\n",
       "      <td>3732.040039</td>\n",
       "      <td>1.001342</td>\n",
       "      <td>25645.882507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-31</th>\n",
       "      <td>3760.199951</td>\n",
       "      <td>3726.879883</td>\n",
       "      <td>3733.270020</td>\n",
       "      <td>3756.070068</td>\n",
       "      <td>1.006439</td>\n",
       "      <td>25811.012383</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   High          Low         Open        Close    Return  \\\n",
       "Date                                                                       \n",
       "2020-12-24  3703.820068  3689.320068  3694.030029  3703.060059  1.003537   \n",
       "2020-12-28  3740.510010  3723.030029  3723.030029  3735.360107  1.008723   \n",
       "2020-12-29  3756.120117  3723.310059  3750.010010  3727.040039  0.997773   \n",
       "2020-12-30  3744.629883  3730.209961  3736.189941  3732.040039  1.001342   \n",
       "2020-12-31  3760.199951  3726.879883  3733.270020  3756.070068  1.006439   \n",
       "\n",
       "               Bench_Bal  \n",
       "Date                      \n",
       "2020-12-24  25446.737491  \n",
       "2020-12-28  25668.697397  \n",
       "2020-12-29  25611.523440  \n",
       "2020-12-30  25645.882507  \n",
       "2020-12-31  25811.012383  "
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#calculate benchmark return and balance\n",
    "price['Return'] = price.Close / price.Close.shift(1)\n",
    "price.Return.iat[0] = 1\n",
    "price['Bench_Bal'] = STARTING_BALANCE * price.Return.cumprod()\n",
    "\n",
    "price.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-56.78"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#calculate benchmark drawdown\n",
    "price['Bench_Peak'] = price.Bench_Bal.cummax()\n",
    "price['Bench_DD'] = price.Bench_Bal - price.Bench_Peak\n",
    "\n",
    "bench_dd = round(((price.Bench_DD / price.Bench_Peak).min() * 100), 2)\n",
    "\n",
    "bench_dd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "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>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>Return</th>\n",
       "      <th>Bench_Bal</th>\n",
       "      <th>Bench_Peak</th>\n",
       "      <th>Bench_DD</th>\n",
       "      <th>Range</th>\n",
       "      <th>Dist</th>\n",
       "      <th>Pct</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-12-24</th>\n",
       "      <td>3703.820068</td>\n",
       "      <td>3689.320068</td>\n",
       "      <td>3694.030029</td>\n",
       "      <td>3703.060059</td>\n",
       "      <td>1.003537</td>\n",
       "      <td>25446.737491</td>\n",
       "      <td>25580.187569</td>\n",
       "      <td>-133.450078</td>\n",
       "      <td>14.500000</td>\n",
       "      <td>13.739990</td>\n",
       "      <td>94.758553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-28</th>\n",
       "      <td>3740.510010</td>\n",
       "      <td>3723.030029</td>\n",
       "      <td>3723.030029</td>\n",
       "      <td>3735.360107</td>\n",
       "      <td>1.008723</td>\n",
       "      <td>25668.697397</td>\n",
       "      <td>25668.697397</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>17.479980</td>\n",
       "      <td>12.330078</td>\n",
       "      <td>70.538283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-29</th>\n",
       "      <td>3756.120117</td>\n",
       "      <td>3723.310059</td>\n",
       "      <td>3750.010010</td>\n",
       "      <td>3727.040039</td>\n",
       "      <td>0.997773</td>\n",
       "      <td>25611.523440</td>\n",
       "      <td>25668.697397</td>\n",
       "      <td>-57.173957</td>\n",
       "      <td>32.810059</td>\n",
       "      <td>3.729980</td>\n",
       "      <td>11.368405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-30</th>\n",
       "      <td>3744.629883</td>\n",
       "      <td>3730.209961</td>\n",
       "      <td>3736.189941</td>\n",
       "      <td>3732.040039</td>\n",
       "      <td>1.001342</td>\n",
       "      <td>25645.882507</td>\n",
       "      <td>25668.697397</td>\n",
       "      <td>-22.814890</td>\n",
       "      <td>14.419922</td>\n",
       "      <td>1.830078</td>\n",
       "      <td>12.691318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-31</th>\n",
       "      <td>3760.199951</td>\n",
       "      <td>3726.879883</td>\n",
       "      <td>3733.270020</td>\n",
       "      <td>3756.070068</td>\n",
       "      <td>1.006439</td>\n",
       "      <td>25811.012383</td>\n",
       "      <td>25811.012383</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>33.320068</td>\n",
       "      <td>29.190186</td>\n",
       "      <td>87.605419</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   High          Low         Open        Close    Return  \\\n",
       "Date                                                                       \n",
       "2020-12-24  3703.820068  3689.320068  3694.030029  3703.060059  1.003537   \n",
       "2020-12-28  3740.510010  3723.030029  3723.030029  3735.360107  1.008723   \n",
       "2020-12-29  3756.120117  3723.310059  3750.010010  3727.040039  0.997773   \n",
       "2020-12-30  3744.629883  3730.209961  3736.189941  3732.040039  1.001342   \n",
       "2020-12-31  3760.199951  3726.879883  3733.270020  3756.070068  1.006439   \n",
       "\n",
       "               Bench_Bal    Bench_Peak    Bench_DD      Range       Dist  \\\n",
       "Date                                                                       \n",
       "2020-12-24  25446.737491  25580.187569 -133.450078  14.500000  13.739990   \n",
       "2020-12-28  25668.697397  25668.697397    0.000000  17.479980  12.330078   \n",
       "2020-12-29  25611.523440  25668.697397  -57.173957  32.810059   3.729980   \n",
       "2020-12-30  25645.882507  25668.697397  -22.814890  14.419922   1.830078   \n",
       "2020-12-31  25811.012383  25811.012383    0.000000  33.320068  29.190186   \n",
       "\n",
       "                  Pct  \n",
       "Date                   \n",
       "2020-12-24  94.758553  \n",
       "2020-12-28  70.538283  \n",
       "2020-12-29  11.368405  \n",
       "2020-12-30  12.691318  \n",
       "2020-12-31  87.605419  "
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#calculate additional columns for strategy\n",
    "\n",
    "#daily range\n",
    "price['Range'] = price.High - price.Low\n",
    "#distance between close and daily low\n",
    "price['Dist'] = abs(price.Close - price.Low)\n",
    "#distance as % of range\n",
    "price['Pct'] = (price.Dist / price.Range) * 100\n",
    "\n",
    "price.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "#identify entries and allocate trading fees\n",
    "price['Long'] = price.Pct < PCT_THRESH"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>Return</th>\n",
       "      <th>Bench_Bal</th>\n",
       "      <th>Bench_Peak</th>\n",
       "      <th>Bench_DD</th>\n",
       "      <th>Range</th>\n",
       "      <th>Dist</th>\n",
       "      <th>Pct</th>\n",
       "      <th>Long</th>\n",
       "      <th>Sys_Ret</th>\n",
       "      <th>Sys_Bal</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-12-24</th>\n",
       "      <td>3703.820068</td>\n",
       "      <td>3689.320068</td>\n",
       "      <td>3694.030029</td>\n",
       "      <td>3703.060059</td>\n",
       "      <td>1.003537</td>\n",
       "      <td>25446.737491</td>\n",
       "      <td>25580.187569</td>\n",
       "      <td>-133.450078</td>\n",
       "      <td>14.500000</td>\n",
       "      <td>13.739990</td>\n",
       "      <td>94.758553</td>\n",
       "      <td>False</td>\n",
       "      <td>1.003537</td>\n",
       "      <td>61611.928467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-28</th>\n",
       "      <td>3740.510010</td>\n",
       "      <td>3723.030029</td>\n",
       "      <td>3723.030029</td>\n",
       "      <td>3735.360107</td>\n",
       "      <td>1.008723</td>\n",
       "      <td>25668.697397</td>\n",
       "      <td>25668.697397</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>17.479980</td>\n",
       "      <td>12.330078</td>\n",
       "      <td>70.538283</td>\n",
       "      <td>False</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>61611.928467</td>\n",
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       "    <tr>\n",
       "      <th>2020-12-29</th>\n",
       "      <td>3756.120117</td>\n",
       "      <td>3723.310059</td>\n",
       "      <td>3750.010010</td>\n",
       "      <td>3727.040039</td>\n",
       "      <td>0.997773</td>\n",
       "      <td>25611.523440</td>\n",
       "      <td>25668.697397</td>\n",
       "      <td>-57.173957</td>\n",
       "      <td>32.810059</td>\n",
       "      <td>3.729980</td>\n",
       "      <td>11.368405</td>\n",
       "      <td>True</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>61611.928467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-30</th>\n",
       "      <td>3744.629883</td>\n",
       "      <td>3730.209961</td>\n",
       "      <td>3736.189941</td>\n",
       "      <td>3732.040039</td>\n",
       "      <td>1.001342</td>\n",
       "      <td>25645.882507</td>\n",
       "      <td>25668.697397</td>\n",
       "      <td>-22.814890</td>\n",
       "      <td>14.419922</td>\n",
       "      <td>1.830078</td>\n",
       "      <td>12.691318</td>\n",
       "      <td>True</td>\n",
       "      <td>1.001342</td>\n",
       "      <td>61694.583775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-31</th>\n",
       "      <td>3760.199951</td>\n",
       "      <td>3726.879883</td>\n",
       "      <td>3733.270020</td>\n",
       "      <td>3756.070068</td>\n",
       "      <td>1.006439</td>\n",
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       "      <td>33.320068</td>\n",
       "      <td>29.190186</td>\n",
       "      <td>87.605419</td>\n",
       "      <td>False</td>\n",
       "      <td>1.006439</td>\n",
       "      <td>62091.825669</td>\n",
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      ],
      "text/plain": [
       "                   High          Low         Open        Close    Return  \\\n",
       "Date                                                                       \n",
       "2020-12-24  3703.820068  3689.320068  3694.030029  3703.060059  1.003537   \n",
       "2020-12-28  3740.510010  3723.030029  3723.030029  3735.360107  1.008723   \n",
       "2020-12-29  3756.120117  3723.310059  3750.010010  3727.040039  0.997773   \n",
       "2020-12-30  3744.629883  3730.209961  3736.189941  3732.040039  1.001342   \n",
       "2020-12-31  3760.199951  3726.879883  3733.270020  3756.070068  1.006439   \n",
       "\n",
       "               Bench_Bal    Bench_Peak    Bench_DD      Range       Dist  \\\n",
       "Date                                                                       \n",
       "2020-12-24  25446.737491  25580.187569 -133.450078  14.500000  13.739990   \n",
       "2020-12-28  25668.697397  25668.697397    0.000000  17.479980  12.330078   \n",
       "2020-12-29  25611.523440  25668.697397  -57.173957  32.810059   3.729980   \n",
       "2020-12-30  25645.882507  25668.697397  -22.814890  14.419922   1.830078   \n",
       "2020-12-31  25811.012383  25811.012383    0.000000  33.320068  29.190186   \n",
       "\n",
       "                  Pct   Long   Sys_Ret       Sys_Bal  \n",
       "Date                                                  \n",
       "2020-12-24  94.758553  False  1.003537  61611.928467  \n",
       "2020-12-28  70.538283  False  1.000000  61611.928467  \n",
       "2020-12-29  11.368405   True  1.000000  61611.928467  \n",
       "2020-12-30  12.691318   True  1.001342  61694.583775  \n",
       "2020-12-31  87.605419  False  1.006439  62091.825669  "
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#calculate system return and balance\n",
    "price['Sys_Ret'] = np.where(price.Long.shift(1) == True, price.Return, 1)\n",
    "price['Sys_Bal'] = (STARTING_BALANCE * price.Sys_Ret.cumprod())\n",
    "\n",
    "price.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "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": [
    "#plot balance\n",
    "plt.plot(price.Bench_Bal)\n",
    "plt.plot(price.Sys_Bal)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-29.45"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#calculate system drawdown\n",
    "price['Sys_Peak'] = price.Sys_Bal.cummax()\n",
    "price['Sys_DD'] = price.Sys_Bal - price.Sys_Peak\n",
    "\n",
    "sys_dd = round(((price.Sys_DD / price.Sys_Peak).min()) * 100, 2)\n",
    "\n",
    "sys_dd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Benchmark Total return: 158.11%\n",
      "Benchmark CAGR: 4.62\n",
      "Benchmark DD: -56.78%\n",
      "\n",
      "System Total return: 520.92%\n",
      "System CAGR: 9.08\n",
      "System DD: -29.45%\n",
      "Time in Market: 21.0%\n",
      "Trades Won: 661\n",
      "Trades Loss: 452\n",
      "Winrate: 59.39%\n"
     ]
    }
   ],
   "source": [
    "#calculate metrics\n",
    "bench_return = round(((price.Bench_Bal[-1]/price.Bench_Bal[0]) - 1) * 100, 2)\n",
    "bench_cagr = round(((((price.Bench_Bal[-1]/price.Bench_Bal[0])**(1/YEARS))-1)*100), 2)\n",
    "sys_return = round(((price.Sys_Bal[-1]/price.Sys_Bal[0]) - 1) * 100, 2)\n",
    "sys_cagr = round(((((price.Sys_Bal[-1]/price.Sys_Bal[0])**(1/YEARS))-1)*100), 2)\n",
    "sys_in_market = round((price.Long.value_counts().loc[True] / len(price)) * 100)\n",
    "sys_win = price.Sys_Ret[price.Sys_Ret > 1.0].count()\n",
    "sys_loss = price.Sys_Ret[price.Sys_Ret < 1.0].count()\n",
    "sys_winrate = round(sys_win / (sys_win + sys_loss) * 100, 2)\n",
    "\n",
    "print(f'Benchmark Total return: {bench_return}%')\n",
    "print(f'Benchmark CAGR: {bench_cagr}')\n",
    "print(f'Benchmark DD: {bench_dd}%')\n",
    "print('')\n",
    "print(f'System Total return: {sys_return}%')\n",
    "print(f'System CAGR: {sys_cagr}')\n",
    "print(f'System DD: {sys_dd}%')\n",
    "print(f'Time in Market: {sys_in_market}%')\n",
    "print(f'Trades Won: {sys_win}')\n",
    "print(f'Trades Loss: {sys_loss}')\n",
    "print(f'Winrate: {sys_winrate}%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "#define list of ETFs to backtest\n",
    "symbols = ['^GSPC', '^DJI', '^IXIC', '^RUT', '^N225']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "#create backtesting function\n",
    "def backtest(symbol):\n",
    "    #download data\n",
    "    price = pdr.get_data_yahoo(symbol, START, END)\n",
    "    \n",
    "    #drop redundant columns\n",
    "    price = price.drop(['Volume', 'Adj Close'], 1)\n",
    "    \n",
    "    #calculate benchmark return and balance\n",
    "    price['Return'] = price.Close / price.Close.shift(1)\n",
    "    price.Return.iat[0] = 1\n",
    "    price['Bench_Bal'] = STARTING_BALANCE * price.Return.cumprod()\n",
    "    \n",
    "    #calculate benchmark drawdown\n",
    "    price['Bench_Peak'] = price.Bench_Bal.cummax()\n",
    "    price['Bench_DD'] = price.Bench_Bal - price.Bench_Peak\n",
    "    \n",
    "    #calculate additional columns for strategy\n",
    "    #daily range\n",
    "    price['Range'] = price.High - price.Low\n",
    "    #distance between close and daily low\n",
    "    price['Dist'] = abs(price.Close - price.Low)\n",
    "    #distance as % of range\n",
    "    price['Pct'] = (price.Dist / price.Range) * 100\n",
    "    \n",
    "    #identify entries and allocate trading fees\n",
    "    price['Long'] = price.Pct < PCT_THRESH\n",
    "    \n",
    "    #calculate system return and balance\n",
    "    price['Sys_Ret'] = np.where(price.Long.shift(1) == True, price.Return, 1)\n",
    "    price['Sys_Bal'] = (STARTING_BALANCE * price.Sys_Ret.cumprod())\n",
    "    \n",
    "    #calculate system drawdown\n",
    "    price['Sys_Peak'] = price.Sys_Bal.cummax()\n",
    "    price['Sys_DD'] = price.Sys_Bal - price.Sys_Peak\n",
    "    \n",
    "    #calculate metrics\n",
    "    bench_cagr = round(((((price.Bench_Bal[-1]/price.Bench_Bal[0])**(1/YEARS))-1)*100), 2)\n",
    "    bench_dd = round((price.Bench_DD / price.Bench_Peak).min() * 100, 2)\n",
    "    sys_cagr = round(((((price.Sys_Bal[-1]/price.Sys_Bal[0])**(1/YEARS))-1)*100), 2)\n",
    "    sys_dd = round(((price.Sys_DD / price.Sys_Peak).min()) * 100, 2)\n",
    "    \n",
    "    return bench_cagr, sys_cagr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "#backtest multiple symbols\n",
    "bc = []\n",
    "sc = []\n",
    "\n",
    "\n",
    "for symbol in symbols:\n",
    "    bench_cagr, sys_cagr = backtest(symbol)\n",
    "    bc.append(bench_cagr)\n",
    "    sc.append(sys_cagr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "#plot data\n",
    "x_indices = np.arange(len(symbols))\n",
    "width = 0.2\n",
    "\n",
    "plt.bar(x_indices - width / 2, bc, width = width, label = 'Benchmark')\n",
    "plt.bar(x_indices + width / 2, sc, width = width, label = 'System')\n",
    "   \n",
    "plt.xticks(ticks = x_indices, labels = symbols)\n",
    "\n",
    "plt.legend()\n",
    "\n",
    "plt.title('Backtest CAGR')\n",
    "plt.xlabel('Symbols')\n",
    "plt.ylabel('% CAGR')\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
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