backtest.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Backtesting\n",
    "\n",
    "Using 2 strategies\n",
    "\n",
    "- self defined strategy\n",
    "- buy and hold\n",
    "\n",
    "## Self Strategy\n",
    "\n",
    "Initial cash = $100000\n",
    "\n",
    "Every buy/sell order = 100 stocks\n",
    "\n",
    "## Buy and Hold\n",
    "\n",
    "Buy on first day and hold till last.\n",
    "\n",
    "## Value-at-risk\n",
    "\n",
    "The maximum loss we can incur using self defined stratefy with `99%` confidence. VAR is calculted using the variance-covariance method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "sns.set(style='darkgrid', context='talk', palette='Dark2')\n",
    "my_year_month_fmt = mdates.DateFormatter('%m/%y')\n",
    "from scipy.stats import norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"formatted_aapl_stock_data_sentiment.csv\")\n",
    "data[['date']] = data[['date']].apply(pd.to_datetime)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def var_cov_var(P, c, mu, sigma):\n",
    "    \"\"\"\n",
    "    Variance-Covariance calculation of daily Value-at-Risk\n",
    "    using confidence level c, with mean of returns mu\n",
    "    and standard deviation of returns sigma, on a portfolio\n",
    "    of value P.\n",
    "    \"\"\"\n",
    "    alpha = norm.ppf(1-c, mu, sigma)\n",
    "    return P - P*(alpha + 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def backtest(data):\n",
    "    cash = 100000\n",
    "    position = 0\n",
    "    total = 0\n",
    "     \n",
    "    data['Total'] = 100000\n",
    "    data['BuyHold'] = 100000\n",
    "    # To compute the Buy and Hold value, invest all cash in the Adj Close on the first day of the backtest\n",
    "    positionBeginning = int(100000/float(data.iloc[0]['Adj Close']))\n",
    "    increment = 100\n",
    "    rets = []\n",
    "     \n",
    "    for row in data.iterrows():\n",
    "        price = float(row[1]['Adj Close'])\n",
    "        signal = float(row[1]['trading_positions'])\n",
    "        rets.append(cash)\n",
    "        if(signal > 0 and cash - increment * price > 0):\n",
    "            # Buy only if signal is buy and enough funds to buy\n",
    "            cash = cash - increment * price\n",
    "            position = position + increment\n",
    "            print(row[1]['date'].strftime('%d %b %Y')+\" Position = \"+str(position)+\" Cash = \"+str(cash)+\" // Total = {:,}\".format(int(position*price+cash)))\n",
    "             \n",
    "        elif(signal < 0 and abs(position*price) < cash and position > 0):\n",
    "            # Sell only if signal is sell and you are at a loss position\n",
    "            cash = cash + increment * price\n",
    "            position = position - increment\n",
    "            print(row[1]['date'].strftime('%d %b %Y')+\" Position = \"+str(position)+\" Cash = \"+str(cash)+\" // Total = {:,}\".format(int(position*price+cash)))\n",
    "             \n",
    "        data.loc[data.date == row[1]['date'], 'Total'] = float(position*price+cash)\n",
    "        data.loc[data.date == row[1]['date'], 'BuyHold'] = price*positionBeginning\n",
    "         \n",
    "    return position*price+cash, rets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "31 Dec 1999 Position = 100 Cash = 99754.1081 // Total = 100,000\n",
      "23 Oct 2007 Position = 0 Cash = 101535.0287 // Total = 101,535\n",
      "12 Nov 2007 Position = 100 Cash = 100064.0667 // Total = 101,535\n",
      "23 Jan 2008 Position = 200 Cash = 98733.6378 // Total = 101,394\n",
      "24 Jan 2008 Position = 300 Cash = 97436.4051 // Total = 101,328\n",
      "25 Jan 2008 Position = 400 Cash = 96192.65000000001 // Total = 101,167\n",
      "28 Jan 2008 Position = 500 Cash = 94948.89490000001 // Total = 101,167\n",
      "29 Jan 2008 Position = 600 Cash = 93690.50290000002 // Total = 101,240\n",
      "30 Jan 2008 Position = 700 Cash = 92425.98830000003 // Total = 101,277\n",
      "06 Feb 2008 Position = 800 Cash = 91258.86190000003 // Total = 100,595\n",
      "07 Feb 2008 Position = 900 Cash = 90099.00570000004 // Total = 100,537\n",
      "17 Sep 2008 Position = 1000 Cash = 88876.10560000004 // Total = 101,105\n",
      "23 Sep 2008 Position = 1100 Cash = 87662.67630000004 // Total = 101,010\n",
      "29 Sep 2008 Position = 1200 Cash = 86655.69480000004 // Total = 98,739\n",
      "30 Sep 2008 Position = 1300 Cash = 85568.35370000004 // Total = 99,703\n",
      "01 Oct 2008 Position = 1400 Cash = 84524.44510000004 // Total = 99,139\n",
      "02 Oct 2008 Position = 1500 Cash = 83566.82710000004 // Total = 97,931\n",
      "03 Oct 2008 Position = 1600 Cash = 82638.19620000003 // Total = 97,496\n",
      "06 Oct 2008 Position = 1700 Cash = 81699.32890000004 // Total = 97,660\n",
      "07 Oct 2008 Position = 1800 Cash = 80846.36970000004 // Total = 96,199\n",
      "08 Oct 2008 Position = 1900 Cash = 79987.38360000004 // Total = 96,308\n",
      "09 Oct 2008 Position = 2000 Cash = 79138.44270000004 // Total = 96,117\n",
      "22 Apr 2010 Position = 1900 Cash = 81687.65740000004 // Total = 130,122\n",
      "23 Apr 2010 Position = 1800 Cash = 84278.58260000004 // Total = 130,915\n",
      "26 Apr 2010 Position = 1700 Cash = 86856.78480000004 // Total = 130,686\n",
      "15 Oct 2010 Position = 1600 Cash = 89867.77910000004 // Total = 138,043\n",
      "18 Oct 2010 Position = 1500 Cash = 92909.96150000005 // Total = 138,542\n",
      "20 Jul 2011 Position = 1400 Cash = 96611.28370000004 // Total = 148,429\n",
      "21 Jul 2011 Position = 1300 Cash = 100316.33740000005 // Total = 148,482\n",
      "26 Jul 2011 Position = 1200 Cash = 104175.60440000004 // Total = 150,486\n",
      "27 Jul 2011 Position = 1100 Cash = 107931.36120000004 // Total = 149,244\n",
      "19 Sep 2011 Position = 1000 Cash = 111869.26590000004 // Total = 151,248\n",
      "20 Sep 2011 Position = 900 Cash = 115824.58170000004 // Total = 151,422\n",
      "25 Jan 2012 Position = 800 Cash = 120097.60420000005 // Total = 154,281\n",
      "27 Jan 2012 Position = 700 Cash = 124376.55850000004 // Total = 154,329\n",
      "30 Jan 2012 Position = 600 Cash = 128710.32850000005 // Total = 154,712\n",
      "01 Feb 2012 Position = 500 Cash = 133074.52070000005 // Total = 154,895\n",
      "03 Feb 2012 Position = 400 Cash = 137472.09990000006 // Total = 155,062\n",
      "06 Feb 2012 Position = 300 Cash = 141910.72070000006 // Total = 155,226\n",
      "07 Feb 2012 Position = 200 Cash = 146395.83580000006 // Total = 155,366\n",
      "08 Feb 2012 Position = 100 Cash = 150956.04770000005 // Total = 155,516\n",
      "09 Feb 2012 Position = 0 Cash = 155674.01420000006 // Total = 155,674\n",
      "20 Apr 2012 Position = 100 Cash = 150192.53800000006 // Total = 155,674\n",
      "06 Jul 2012 Position = 0 Cash = 155988.75730000006 // Total = 155,988\n",
      "08 Oct 2012 Position = 100 Cash = 149695.29340000005 // Total = 155,988\n",
      "01 Nov 2012 Position = 200 Cash = 143812.37450000006 // Total = 155,578\n",
      "06 Nov 2012 Position = 300 Cash = 138064.46230000007 // Total = 155,308\n",
      "12 Nov 2012 Position = 400 Cash = 132535.24210000006 // Total = 154,652\n",
      "14 Nov 2012 Position = 500 Cash = 127066.62790000006 // Total = 154,409\n",
      "15 Nov 2012 Position = 600 Cash = 121712.70680000006 // Total = 153,836\n",
      "17 Dec 2012 Position = 700 Cash = 116427.94840000005 // Total = 153,421\n",
      "26 Dec 2012 Position = 800 Cash = 111202.57300000005 // Total = 153,005\n",
      "14 Jan 2013 Position = 900 Cash = 106091.79080000005 // Total = 152,088\n",
      "28 Jan 2013 Position = 1000 Cash = 101509.85990000004 // Total = 147,329\n",
      "30 Jan 2013 Position = 1100 Cash = 96856.62730000004 // Total = 148,042\n",
      "31 Jan 2013 Position = 1200 Cash = 92217.04450000003 // Total = 147,892\n",
      "01 Feb 2013 Position = 1300 Cash = 87596.50960000003 // Total = 147,663\n",
      "04 Mar 2013 Position = 1400 Cash = 83137.04000000004 // Total = 145,569\n",
      "06 Mar 2013 Position = 1500 Cash = 78618.01190000004 // Total = 146,403\n",
      "02 May 2013 Position = 1400 Cash = 83347.88360000004 // Total = 149,566\n",
      "09 May 2013 Position = 1300 Cash = 88431.16870000004 // Total = 154,513\n",
      "13 May 2013 Position = 1200 Cash = 93491.86320000004 // Total = 154,220\n",
      "28 Jun 2013 Position = 1300 Cash = 89078.97520000003 // Total = 146,446\n",
      "29 Jul 2013 Position = 1200 Cash = 94062.32480000003 // Total = 153,862\n",
      "30 Jul 2013 Position = 1100 Cash = 99107.21540000003 // Total = 154,601\n",
      "01 Aug 2013 Position = 1000 Cash = 104189.49840000003 // Total = 155,012\n",
      "02 Aug 2013 Position = 900 Cash = 109336.99630000003 // Total = 155,664\n",
      "06 Aug 2013 Position = 800 Cash = 114514.65320000003 // Total = 155,935\n",
      "07 Aug 2013 Position = 700 Cash = 119689.30490000003 // Total = 155,911\n",
      "08 Aug 2013 Position = 600 Cash = 125066.68370000004 // Total = 157,330\n",
      "09 Aug 2013 Position = 500 Cash = 130367.54350000004 // Total = 156,871\n",
      "12 Aug 2013 Position = 400 Cash = 135818.99120000005 // Total = 157,624\n",
      "14 Aug 2013 Position = 300 Cash = 141633.66600000006 // Total = 159,077\n",
      "16 Aug 2013 Position = 200 Cash = 147493.01510000005 // Total = 159,211\n",
      "19 Aug 2013 Position = 100 Cash = 153415.46880000006 // Total = 159,337\n",
      "20 Aug 2013 Position = 0 Cash = 159260.12100000007 // Total = 159,260\n",
      "13 Sep 2013 Position = 100 Cash = 153837.36860000007 // Total = 159,260\n",
      "18 Oct 2013 Position = 0 Cash = 159773.23630000008 // Total = 159,773\n",
      "31 Jan 2014 Position = 100 Cash = 153686.76210000008 // Total = 159,773\n",
      "03 Feb 2014 Position = 200 Cash = 147588.98150000008 // Total = 159,784\n",
      "13 Feb 2014 Position = 100 Cash = 154496.04330000008 // Total = 161,403\n",
      "14 Feb 2014 Position = 0 Cash = 161397.5257000001 // Total = 161,397\n",
      "16 Oct 2014 Position = 100 Cash = 152458.86070000008 // Total = 161,397\n",
      "21 Oct 2014 Position = 0 Cash = 161974.18280000007 // Total = 161,974\n",
      "02 Jan 2015 Position = 100 Cash = 151777.81970000008 // Total = 161,974\n",
      "22 Jan 2015 Position = 0 Cash = 162260.49870000008 // Total = 162,260\n",
      "25 Mar 2015 Position = 100 Cash = 150708.3892000001 // Total = 162,260\n",
      "23 Apr 2015 Position = 0 Cash = 162849.4353000001 // Total = 162,849\n",
      "29 Jun 2015 Position = 100 Cash = 151140.9461000001 // Total = 162,849\n",
      "09 Jul 2015 Position = 200 Cash = 139851.7936000001 // Total = 162,430\n",
      "16 Jul 2015 Position = 100 Cash = 151934.4878000001 // Total = 164,017\n",
      "17 Jul 2015 Position = 0 Cash = 164121.54450000008 // Total = 164,121\n",
      "29 Jul 2015 Position = 100 Cash = 152557.85130000007 // Total = 164,121\n",
      "31 Jul 2015 Position = 200 Cash = 141153.05160000006 // Total = 163,962\n",
      "07 Aug 2015 Position = 300 Cash = 130242.53300000007 // Total = 162,974\n",
      "04 Sep 2015 Position = 400 Cash = 119922.30980000008 // Total = 161,203\n",
      "22 Oct 2015 Position = 300 Cash = 130830.94090000007 // Total = 163,556\n",
      "29 Oct 2015 Position = 200 Cash = 142214.63990000007 // Total = 164,982\n",
      "30 Oct 2015 Position = 100 Cash = 153501.05810000008 // Total = 164,787\n",
      "02 Nov 2015 Position = 0 Cash = 164946.1478000001 // Total = 164,946\n",
      "13 Nov 2015 Position = 100 Cash = 154290.5547000001 // Total = 164,946\n",
      "19 Nov 2015 Position = 0 Cash = 165556.99070000008 // Total = 165,556\n",
      "11 Dec 2015 Position = 100 Cash = 154821.7200000001 // Total = 165,556\n",
      "14 Dec 2015 Position = 200 Cash = 144152.84720000008 // Total = 165,490\n",
      "21 Dec 2015 Position = 300 Cash = 133972.45860000007 // Total = 164,513\n",
      "22 Dec 2015 Position = 400 Cash = 123801.55480000007 // Total = 164,485\n",
      "29 Dec 2015 Position = 500 Cash = 113487.42590000007 // Total = 165,058\n",
      "12 Jan 2016 Position = 600 Cash = 104006.09120000008 // Total = 160,894\n",
      "13 Jan 2016 Position = 700 Cash = 94768.52250000008 // Total = 159,431\n",
      "14 Jan 2016 Position = 800 Cash = 85328.92210000008 // Total = 160,845\n",
      "19 Jan 2016 Position = 900 Cash = 76160.59630000009 // Total = 158,675\n",
      "21 Jan 2016 Position = 1000 Cash = 67026.41660000008 // Total = 158,368\n",
      "01 Feb 2016 Position = 1100 Cash = 57879.90700000009 // Total = 158,491\n",
      "02 Feb 2016 Position = 1200 Cash = 48918.356100000085 // Total = 156,456\n",
      "10 Feb 2016 Position = 1300 Cash = 39928.20410000009 // Total = 156,800\n",
      "11 Feb 2016 Position = 1400 Cash = 30992.41230000009 // Total = 156,093\n",
      "02 May 2016 Position = 1500 Cash = 22062.34260000009 // Total = 156,013\n",
      "11 May 2016 Position = 1600 Cash = 13186.321800000092 // Total = 155,202\n",
      "24 Jun 2016 Position = 1700 Cash = 4224.908200000093 // Total = 156,568\n",
      "Backtest => 386,648.56630000006 USD\n",
      "6846\n",
      "Annual return => 15.073544240140233%\n",
      "\n",
      "Value-at-Risk => 3,652.8960335396405 USD\n",
      "\n",
      "Buy and Hold => 9,180,457 USD\n",
      "Annual return => 477.49993745692973%\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Backtest\n",
    "backtestResult, rets = backtest(data)\n",
    "print((\"Backtest => {:,} USD\").format(backtestResult))\n",
    "perf = (float(backtestResult)/100000-1)*100\n",
    "daysDiff = (data.tail(1).date-data.head(1).date[0]).iloc[0].days\n",
    "print(daysDiff)\n",
    "perf = (perf/(daysDiff))*360\n",
    "print(\"Annual return => \"+str(perf)+\"%\")\n",
    "print()\n",
    "\n",
    "data['rets'] = rets\n",
    "data['rets_percent_change'] = data['rets'].pct_change()\n",
    "P = 100000   # 100,000 USD\n",
    "c = 0.99  # 99% confidence interval\n",
    "mu = np.mean(data[\"rets_percent_change\"])\n",
    "sigma = np.std(data[\"rets_percent_change\"])\n",
    "\n",
    "var = var_cov_var(P, c, mu, sigma)\n",
    "print((\"Value-at-Risk => {:,} USD\").format(var))\n",
    "print()\n",
    " \n",
    "# Buy and Hold\n",
    "perfBuyAndHold = float(data.tail(1)['Close'])/float(data.head(1)['Adj Close'])-1\n",
    "print((\"Buy and Hold => {:,} USD\").format(int((1+perfBuyAndHold)*100000)))\n",
    "perfBuyAndHold = (perfBuyAndHold/(daysDiff))*360\n",
    "print(\"Annual return => \"+str(perfBuyAndHold*100)+\"%\")\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(16,9))\n",
    "ax.plot(data.date, data['Total'], label='Total', color='g')\n",
    "ax.plot(data.date, data['BuyHold'], label='BuyHold', color='r')\n",
    "ax.legend(loc=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}