twp_304_xlp_strategy.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# XLP strategy\n",
    "\n",
    "Let's test a trading strategy described on the [Quantified Strategies](http://www.quantifiedstrategies.com/trade-the-boring-consumer-stocks-when-they-open-down-and-yesterday-was-a-down-day/) blog.  <br>\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "%run common.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import tradingWithPython as twp\n",
    "import tradingWithPython.lib.yahooFinance as yf # yahoo finance functions\n",
    "from tradingWithPython import sharpe # general trading toolbox functions\n",
    "import pandas as pd # pandas time series library\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Got 4588 days of data\n"
     ]
    }
   ],
   "source": [
    "ohlc = yf.getHistoricData('XLP')[['open','high','low','close']] # get data from yahoo finance\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</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>2018-03-21</th>\n",
       "      <td>52.29</td>\n",
       "      <td>52.50</td>\n",
       "      <td>51.82</td>\n",
       "      <td>51.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-22</th>\n",
       "      <td>51.78</td>\n",
       "      <td>52.19</td>\n",
       "      <td>51.46</td>\n",
       "      <td>51.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-23</th>\n",
       "      <td>51.68</td>\n",
       "      <td>51.89</td>\n",
       "      <td>50.81</td>\n",
       "      <td>50.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-26</th>\n",
       "      <td>51.24</td>\n",
       "      <td>51.64</td>\n",
       "      <td>51.08</td>\n",
       "      <td>51.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-27</th>\n",
       "      <td>51.68</td>\n",
       "      <td>52.10</td>\n",
       "      <td>51.33</td>\n",
       "      <td>51.56</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             open   high    low  close\n",
       "Date                                  \n",
       "2018-03-21  52.29  52.50  51.82  51.93\n",
       "2018-03-22  51.78  52.19  51.46  51.50\n",
       "2018-03-23  51.68  51.89  50.81  50.86\n",
       "2018-03-26  51.24  51.64  51.08  51.59\n",
       "2018-03-27  51.68  52.10  51.33  51.56"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ohlc.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Trading volume is not needed for this strategy, so I remove it for now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             open   high    low  close\n",
      "Date                                  \n",
      "1999-12-31  23.02  23.19  22.89  23.03\n",
      "2000-01-03  23.12  23.12  22.47  22.75\n",
      "2000-01-04  22.31  22.66  22.08  22.11\n",
      "2000-01-05  22.27  22.61  22.09  22.50\n",
      "2000-01-06  22.66  23.00  22.48  22.92\n",
      "...           ...    ...    ...    ...\n",
      "2018-03-21  52.29  52.50  51.82  51.93\n",
      "2018-03-22  51.78  52.19  51.46  51.50\n",
      "2018-03-23  51.68  51.89  50.81  50.86\n",
      "2018-03-26  51.24  51.64  51.08  51.59\n",
      "2018-03-27  51.68  52.10  51.33  51.56\n",
      "\n",
      "[4588 rows x 4 columns]\n"
     ]
    },
    {
     "data": {
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sstn6fH+LIYmMlMsx6CRlDBSpGQshRAzadqwktG3zVVO8cxcfpUTXXM2Gfkig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\nKYyFEEIIjf1/oyO+dj9csnYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc5ff68d7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ohlc.plot()\n",
    "print(ohlc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The strategy rules are:\n",
    "\n",
    "* Yesterday must have been a down day of at least 0.25%.\n",
    "* If XLP opens down more than 0.1% today, go long and exit on the close.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sharpe buy-and-hold: 0.372976063661\n"
     ]
    },
    {
     "data": {
      "image/png": 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XN2DbsJvh4pHXmHAmaDY++kRMsIUgHYnblD1s2DB88IG652iiqbJprIFKL+Su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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc5ff4b2f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "stratData = pd.DataFrame(index=ohlc.index)\n",
    "stratData['cc'] = 100*ohlc['close'].pct_change() # close-to-close change in %\n",
    "stratData['co'] = 100*(ohlc['open']/ohlc['close'].shift(1)-1) # previous close to open change in %\n",
    "stratData['oc'] = 100*(ohlc['close']/ohlc['open']-1) # open to close change in %\n",
    "stratData.plot()\n",
    "print('Sharpe buy-and-hold:', sharpe(stratData['cc']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a look how these CC, CO and OC returns accumulate over time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc5fc916160>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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sbIIg6s2R7ApMXnLSpbKeNzoJb45pg3/1jcW3N3WwjGeVWSOA3x3XFgIDyvUm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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc5ff5dd940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "stratData.cumsum().plot(grid=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Clearly most of the returns are occuring overnight, from close to open. \n",
    "\n",
    "Now let's simulate the strategy.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>cc</th>\n",
       "      <th>co</th>\n",
       "      <th>oc</th>\n",
       "      <th>goLong</th>\n",
       "      <th>pnl</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-02-28</th>\n",
       "      <td>-1.04</td>\n",
       "      <td>0.59</td>\n",
       "      <td>-1.62</td>\n",
       "      <td>False</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-01</th>\n",
       "      <td>-0.22</td>\n",
       "      <td>-0.24</td>\n",
       "      <td>0.02</td>\n",
       "      <td>True</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-02</th>\n",
       "      <td>0.84</td>\n",
       "      <td>-0.38</td>\n",
       "      <td>1.22</td>\n",
       "      <td>False</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-05</th>\n",
       "      <td>0.87</td>\n",
       "      <td>-0.54</td>\n",
       "      <td>1.42</td>\n",
       "      <td>False</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-06</th>\n",
       "      <td>0.07</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.04</td>\n",
       "      <td>False</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-21</th>\n",
       "      <td>-1.24</td>\n",
       "      <td>-0.55</td>\n",
       "      <td>-0.69</td>\n",
       "      <td>True</td>\n",
       "      <td>-0.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-22</th>\n",
       "      <td>-0.83</td>\n",
       "      <td>-0.29</td>\n",
       "      <td>-0.54</td>\n",
       "      <td>True</td>\n",
       "      <td>-0.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-23</th>\n",
       "      <td>-1.24</td>\n",
       "      <td>0.35</td>\n",
       "      <td>-1.59</td>\n",
       "      <td>False</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-26</th>\n",
       "      <td>1.44</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.68</td>\n",
       "      <td>False</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-27</th>\n",
       "      <td>-0.06</td>\n",
       "      <td>0.17</td>\n",
       "      <td>-0.23</td>\n",
       "      <td>False</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              cc    co    oc  goLong   pnl\n",
       "Date                                      \n",
       "2018-02-28 -1.04  0.59 -1.62   False  0.00\n",
       "2018-03-01 -0.22 -0.24  0.02    True  0.02\n",
       "2018-03-02  0.84 -0.38  1.22   False  0.00\n",
       "2018-03-05  0.87 -0.54  1.42   False  0.00\n",
       "2018-03-06  0.07  0.04  0.04   False  0.00\n",
       "...          ...   ...   ...     ...   ...\n",
       "2018-03-21 -1.24 -0.55 -0.69    True -0.69\n",
       "2018-03-22 -0.83 -0.29 -0.54    True -0.54\n",
       "2018-03-23 -1.24  0.35 -1.59   False  0.00\n",
       "2018-03-26  1.44  0.75  0.68   False  0.00\n",
       "2018-03-27 -0.06  0.17 -0.23   False  0.00\n",
       "\n",
       "[20 rows x 5 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idx = (stratData['cc']<-0.25).shift(1) & (stratData['co'] < -0.1) # find days that satisfy the strategy rules\n",
    "idx[0] = False # fill first entry with False (needed because .shift(1) adds a NaN in the first element)\n",
    "\n",
    "stratData['goLong'] = idx\n",
    "stratData['pnl'] = 0. # init pnl column with zeros (Watch out: if initialized with integer value (0), an error will pop later on)\n",
    "stratData.loc[idx,'pnl'] = stratData.loc[idx,'oc'] # set pnl column values to daily return wehere 'goLong' is true\n",
    "\n",
    "stratData.tail(20) # show last 10 rows of stratData "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sharpe: 1.00708745001\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc5fc842278>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kBqlQi0SItRZK92JGnIlJawNpbTATJnkPTrwyuuFEJGapUIs0EltVBRvXgRuC\nQAD7nw+83t0tE6MdTUR8RIVapJG4990Om9cfMt+ccV4U0oiIX6nXt0gjsOXl4SLt3HJPzQN9BmDa\nBKOUSkT8SC1qkQZkd27Dvj8Lu3gBAOayH2AGnIo5/Qzs/A+hVXKUE4qI36hQizQg++lc7EfvQLA9\nfONkzLhvAWCuvQ0z4kzo2CXKCUXEb1SoRRpS9VCgzu+fxhgTnm2Mgb4Do5VKRHxMx6hFGlJlBcQn\n1CrSIiL1Ue8Wteu6TJ48mTZt2jB58mR27NjBww8/TElJCd27d+fmm28mLk4Nd2m6Kr/Kx25cD9l9\nsIvmQ3x8tCOJSBNS7wr6zjvv0KlTJ/bt2wfA9OnTOe+88xg5ciRPP/00s2fP5qyzNOKSNB22aCf2\n3ZmYoaMhsxNFP/l/tRfo0Dk6wUSkSarXru/CwkIWLVrEmWeeCXgjLy1fvpzhw4cDMG7cOPLy8uqf\nUiSG2Neew370Du67r2KXfgKAGT4Oc/oZ0DoV59LvRTmhiDQl9WpRP/fcc1x55ZXh1vTevXtJSkoi\nEAgA0KZNG4qKiuqfUiSG2M8+9e4U7IAvlgJgrr0V4wSimEpEmqo6F+qFCxeSmppKdnY2y5cvP+H1\nc3Nzyc3NBWDKlCkEg/4YBCIuLs43WcF/eSG2M7v7StlZ7v0wZdsm7LZNJPQfRHq79tENdoJi+T0+\nHL/lBf9l9lte8F/muuatc6H+8ssv+fTTT1m8eDEVFRXs27eP5557jrKyMkKhEIFAgKKiItq0aXPY\n9XNycsjJyQlPFxQU1DVKRAWDQd9kBf/lhdjO7L78vwCY7/80PMJYar+BMZv3SGL5PT4cv+UF/2X2\nW17wX+aD83bs2PG416tzob7iiiu44oorAFi+fDlvvvkmt9xyCw899BALFixg5MiRfPTRRwwZMqSu\nTyESc+yuQgDM0NHhU7CclDTw0T8LEfGXBj+P+rvf/S5vvfUWN998MyUlJYwfP76hn0KkUdnPF+K+\n8yp27+7a85fmwaJ5kN1H50mLSMQ0yAnO/fv3p3///gC0b9+e+++/vyE2KxIV7vOPQXER9vW/eUOA\nhkJQVYWdPxsAZ8JlUU4oIs2JRiIROYgt2QPFNWcq2E//A4E4CAQgsxPm5GGYAadGMaGINDcq1CIH\ncZ9/PHzfjDwT55pbo5hGRESFWqS23UWQnILzh79CnIYCFZHoU6GWZseWleL+/qfeLu6+A3CuvQ33\ntivCj5sN9WuPAAAgAElEQVQhozAJLaKYUESkhgq1NCv2szzcx35XM+PzhbiP3xeeNBMmYQaPiEIy\nEZHDU6GWZsVuWAOAmXQ9pm0m7hv/B/tKw4873/lutKKJiByWCrU0SdbaQ851toU7sW+8CICTcz4A\ngZOHARD6/vmRDSgicpxUqKXJsYU7cH99I+aM8zAXXAWhKlj7Je7brxxxHfP/bsIkJkUwpYjI8VGh\nlqZn5zaorMC+/zr2/de93ttVleGHnbsfOWQVZ7SumS4isanBhxAViSa7aR12wYe1Z/YdGL5rvnc7\npnP3CKcSEak7tailSXEf+S0UV18449pbMcPHYZwAtqIcKsoxySlRTigicmJUqKXJcD98O1ykiYvH\nDBiKcQIA3nnROjdaRHxIhVqaDHtQZzHnsZcxGllMRJoAFWqJaXbRPNz3/n7Yx0yXbJwrb/SW27we\ndu/y5p9xnoq0iDQZKtQSs+xXq3GfnOJN9DsFDj4tevli7LpV2Eu/h0logf10LgDOTb+GgUMiH1ZE\npJGoUEtMsFs24L7wOFRWUhgXR6hkD+zYCoAZcSbOtbWvYuV+8Cb25f+F/BXYLj2wb83wHuje65CB\nTkRE/EyFWqLOlpd7RXrNShgwBKdFC0hOgXYdvPObBww9ZB3Tqz8WcKfeU/sB9eoWkSZGhVqizv73\nQ69IZ7QjcMvdpAeDFBQUHH2lrG5gDFgbnmWGn4FxNDSAiDQtKtQSVXbNSuzyxQA49z563OsZx8H5\n/dOQkIBdtgh2bsXoghoi0gSpUEvU2MoK3Ad+CaEQZHXHtDyxsbZNsL13O/LMxognIhITVKglevbv\nh1AIc+4lmLMvjHYaEZGYpAN6Ej0V+73btpmYpFbRzSIiEqPUopaIsOvzvY5fXbKx05/ErlsFm77y\nHmyRGNVsIiKxTIVa6sQumof7zms1M+LicK78ESar9pWprBvCvjUD++bL3owuPWDDmlrLmEHDGjuu\niIhvqVDLCbM7tuI+8xDEx0OPb0CoClYswa7+4tBC/fH7NUUawkXaDB+H3bMbM2I8Jj4hkvFFRHxF\nhVqOm/vv92H7Fuw/vbG3zfBxOP/vJm/AkpsugYLtANjy/RAf711ecvZbh2zHnHMRzkVXRzS7iIhf\nqVDLcbH7y7AvPA6BAMTFYU4bh6m+IAYJXovYvv86bsfO2OcehUAczo/vgq0bMWech108H4qLcP78\nmlrQIiInQIVaDsuWlsC+UmidBssX4b74FwCc7/8Mc+qIWssaYzDjzsV+9A72lb96M0NVuI/+xrvf\ntQfOd66AUJWKtIjICVKhlkPYvXtwf34tVFVCIA6694ayEsyZ34b+gw67jpkwCcr3Q1UldsUSKN2L\n87P7IS4OuvbEBAIRfhUiIk2DCrXUYstKvNHCqiqha09Ynw/5K6DPAJzLvn/E9UxqOua622q2Y62u\nYiUi0gBUqKW21V/A1o0AODf/GhKTwHXDx6GPl4q0iEjDUKFupuzuXVBZER4vG8CGQrh//h8AnN8+\ngUlNj1Y8ERGppkLdTLkP/gp2F+FM+SusW+UdS87M8lrPaRnQvlO0I4qICCrUzVf17m37+t+wH77t\nzcvMAsB8+zJd11lEJEaoUDdz9sO3oV1H7/zokj2QHsRkdYt2LBERqaZCLVBRTuCBadFOISIih6FC\n3QzZ1StqJjp0xpxxXvTCiIjIUdW5UBcUFPDEE09QXFyMMYacnBzOPfdcSkpKmDp1Kjt37qRt27bc\nfvvtJCcnN2RmqSf36QcAMGddgHPJtVFOIyIiR1PnQh0IBLjqqqvIzs5m3759TJ48mYEDB/LRRx8x\nYMAAJk6cyKxZs5g1axZXXnllQ2aWerBuCHbvwpw1EXPxNdGOIyIix1Dnrr3p6elkZ2cDkJiYSKdO\nnSgqKiIvL4+xY8cCMHbsWPLy8homqTSMkr1gXchop0FJRER8oEHOwdmxYwfr1q2jZ8+e7N69m/R0\nb6CM9PR09uzZ0xBPIQ1lT7F32zotujlEROS41Lsz2f79+3nwwQe55pprSEpKOu71cnNzyc3NBWDK\nlCkEg8H6RomIuLg432SFQ/OWb15HMZDWuSsJMfo6/P4e+4HfMvstL/gvs9/ygv8y1zVvvQp1VVUV\nDz74IKNHj+a0004DIDU1lV27dpGens6uXbtISUk57Lo5OTnk5OSEpwsKCuoTJWKCwaAvstrSEtj8\nFampqezeXw5demCMwd3sDXSy27WYGH0dfnmPD/BbXvBfZr/lBf9l9lte8F/mg/N27NjxuNerc6G2\n1vLUU0/RqVMnJkyYEJ4/ZMgQ5syZw8SJE5kzZw5Dhw6t61NIHdjycvh8Ie5TUwDYVT3f+dn92PQM\n7PuzvBlJ6okvIuIHdS7UX375JR9//DFdunThZz/7GQCXX345EydOZOrUqcyePZtgMMgdd9zRYGHl\n2NyfXwNlpeHp1j/8GXv/8gDsK8Xm/du7bGXbTEhJjV5IERE5bnUu1H379uWVV1457GN33313nQNJ\n3VnXrSnSHTrj/Hoq8furp0MhbHERtEjE+d2TGCcQvaAiInLcNDJZU1Kxv+Z+y0RMfAKm0ptnd++C\nJQsAMAEVaRERv9AlkpqSr/Jr7q9b5d0GvN9i9sWnohBIRETqS4W6KamsPGRWoEMWdO0ZnnZuvTeC\ngUREpL5UqJuSUFX4rvnuj7xbx8G54ofVMw1ktI1GMhERqSMdo25C7LKF4fvmoIJssvvgPPx/YAxG\np2WJiPiKCnUTYnd5J9KbU0dCdt9aj5lWraMRSURE6km7vn3G7ivDrliCtbb2/PJyKN8HPfri3PAL\nTCu1nEVEmgIVap+xr03DnXo3bFhTM2/TOtybLoFVy6FFyyimExGRhqZd3z5jN6z17hw0+pj7m1sB\nMENHY759eTRiiYhII1GL2kfcf7wEX632Jqq8U7Hs+pqWtTn3EkyHrGhEExGRRqJC7SP2zZdqJior\nvNu9uwFwbpiMyeoW+VAiItKotOvbp2xlJVRV4j79gDcj2C66gUREpFGoRe0T9uujjuWvgN27YF8p\ntEiEDp2jE0xERBqVWtR+sT6/1qT95GNIbAWAOf8yTEKLaKQSEZFGpha1Xxw4Jn1AWSn23de8+ylp\nkc8jIiIRoRa1D7hz/4V9/rEjPm5OGxe5MCIiElEq1DHM/fAd7Ky/1Tpn2lz+A+xLT3sTCS0wF1yF\nMSZKCUVEpLFp13eMsju2eNeQPrhIX3srzvgJ4enAE6/i5JwfjXgiIhIhalFHgS0uhNISTKeuh3+8\nohz3nptqzQv87z9qJk4eBqs+b8yIIiISI1SoI8x+uQz3T3cB4Nx6L/Toi0lM8h77LA/3uUe960pX\nVWFGnIm59LpDOpIFbvpVpGOLiEiUqFBHmPv2KzX3H7kXc9ZE6NEX99lHvKtfAeaMcyG+Beaci3R5\nShGRZk6FOkKstV5Les0X0GcAzkXX4P7597CrEHfmC+EiDeBccUMUk4qISCxRZ7JICVV5x5W79MA5\n9xJM916QkobN+zfs2AInD8N85wqIT4h2UhERiSFqUUfK9q0AmCEjMf0GAeBc8UNs/hfe/IFDvStf\nTbgsahFFRCT2qFBHiPuPFwEwbTuE55kefTE9+kYrkoiI+IB2fUdKZQWkZWBOGR7tJCIi4iMq1JFS\nVQkZbaOdQkREfEaFOlIqKyEuPtopRETEZ1SoG5ktLsL96F3v+tGle6MdR0REfEadyRqZfW8m9oM3\nvYlNX0U1i4iI+I9a1I3IrlvtFemMdtGOIiIiPqUWdT3YinLsv97AnH0hJi4O67oQCkHAwTgB7Gd5\nAJgxZ0ObIKaNOpOJiMiJUaGuB/vWDOy7r0FKGmb0WbgP3wNfLIUWLXFu/jX2rZe9++deEu2oIiLi\nU9r1XR/FRd7t5vW4uf+Ar1ZD61Qo3x++QhZqRYuISD2oRV1Hdttm7PzZ3v0DncUATh0BC+eFJ53v\n3R7paCIi0oSoRX0Mdv8+7M5t2D27as13X3/hsMs7Q0djLrkWAtW/gRJaNnZEERFpwppsi9ru2Ip9\nbyZm7DmYrj3rvB333puhcAcAzn1PYdPTvQf2FNdazrnnEUhOxaS1wQB24FDs+jWQ2anOzy0iItIo\nhXrJkiVMmzYN13U588wzmThxYmM8zVHZJQuw/34fAPP/bqr7hqqLNID7qxsoufAq+NYl3khjBzMB\nTFqbmsnMLExmVt2fV0REhEbY9e26Ln/961+58847mTp1Kv/5z3/YtGlTQz/NsX29kDYQt2C7d6fq\na9tv3bpRnk9ERJq3Bm9R5+fnk5mZSfv27QEYMWIEeXl5ZGVFuHVZWeHdGlPnTdiDWtMH7P/4fSjY\nAZvXA+BMnQ4VFZiU9Do/j4iIyJE0eKEuKioiIyMjPJ2RkcHq1asb+mmOyP1PLnbm83DguHQoVOdt\n2eWLDv/AiiXebY++mOSUOm9fRETkWBq8UFtrD5lnDtOqzc3NJTc3F4ApU6YQDAYb5Pn3tW7Nnr27\nMRvWYIEW8XGk1nHbpQGHkoOmTcsk7P4yEs+5ELC0HDGehAbK3Vji4uIa7L2NFL9l9lte8F9mv+UF\n/2X2W17wX+a65m3wQp2RkUFhYWF4urCwkPT0Q3cL5+TkkJOTE54uKChokOe3XbyWtK3ulV1eVlbn\nbbvVr8P5y+sYJ0Dot7fCxnXsTwvinDmBCoAGyt1YgsFgg723keK3zH7LC/7L7Le84L/MfssL/st8\ncN6OHTse93oN3pmsR48ebN26lR07dlBVVcW8efMYMmRIQz/NkaW2wUyYFJ60n3yMXfV53bZVvg8S\nWmCcAADOxdcSaN8RM/qbDZFURETkmBq8RR0IBLjuuuv4n//5H1zX5YwzzqBz584N/TRHZIzBfOe7\nhN6aEZ5n87/A9D7phLZjN6/Hvj8LDjoGbfoNIvjUa776BSciIv7WKOdRDx48mMGDBzfGpo+bufY2\nCFVhX3gc3BPvUGYXL/C2M2RkQ0cTERE5bk12CFFnxHjMKG8XtX3jxRNa11ZVQskeAMwVNzR4NhER\nkePVZIcQhdq9za21h+19fjjuI7+BlZ8dsg0REZFIa7It6kNUlB/XYnbrxpoiPfHKxkwkIiJyTE26\nRQ1Ai5ZQvh/2lXn3j8F+7g1y4tx6D+akUxs7nYiIyFE1+Ra1+e6PvDvl+4+5rHVD2Ff+CvEJ0D+6\nneFERESgGRRq4qp3GoSqjr3s/upi3q2njk2LiEhMaPKF2gS8wUqOa8zv6uPYZtiYRkwkIiJy/Jr+\nMepAvHdbXIjNzMLEx+P+dw7278/DgWHJAwHMWRdgX33Wm05oEZWoIiIiX9fkW9RUt6jdR3+Le8+P\nAbDPPQpFBZh+gyAlDQq2Y198KnxpTJPVLVppRUREamn6hfrgq3nt3IYtLgIs9BmAc80tOJdd7xXr\namb4GZguPSKfU0RE5DCafqHO7o05dSTm25cDYHPfgKoqTJ8BAJie/XD+8GzN8l2zo5FSRETksJr8\nMWrTqjXmhl9gK8qxb76E/efr3gNtM2uWiYvDTLoeE2wHA4dFKamIiMihmnyhDnMO2nmQkHDIxTac\nnPMjHEhEROTYmv6u7wMOKtTO93+KiYuPYhgREZHj03wKtTm4RX3soURFRERiQbPZ9W2MgVOGQ+FO\n6Ngl2nFERESOS7Mp1ACBG++MdgQREZET0nx2fYuIiPiQCrWIiEgMU6EWERGJYSrUIiIiMUyFWkRE\nJIapUIuIiMQwFWoREZEYpkItIiISw1SoRUREYpgKtYiISAxToRYREYlhxlprox1CREREDk8t6hM0\nefLkaEc4IX7LC/7L7Le84L/MfssL/svst7zgv8x1zatCLSIiEsNUqEVERGJY4N5777032iH8Jjs7\nO9oRTojf8oL/MvstL/gvs9/ygv8y+y0v+C9zXfKqM5mIiEgM065vERGRGKZCLSIiEsNUqI/Add1o\nRzhhfjqKsWfPHsA/mdeuXcvevXujHaNO/PIeg/++d37LW1xcDPjrM7Fy5Uq2bdsW7RjH7cBnoiHf\n47gG21ITsHbtWrZs2cKoUaNwnNj/DZOfn88HH3xA586dGT9+PC1btox2pGNat24dL7/8Mt26dePy\nyy/HGBPtSEe1bt06pk+fzooVK3jyySejHee4rFq1irlz55Kdnc24ceNi/j3Oz89n1apVnHvuub74\n3gGsWbOGd955h/bt2zNmzBgyMzOjHemo1q1bx9/+9jfatWvHDTfcEPOfCfD+H7/44ousWLGC++67\nL9pxjmnVqlXMnj2bjh07kpOTQ1JSUoNtW72+8X75zJgxg2effZbNmzfTuXNngsEgruvG5Ac6FArx\n7LPPkpuby7Bhw1i4cCFffvklQ4YMiXa0I3JdlyeeeILc3FxycnKYMGFCtCMdVWVlJX/961/Jzc1l\nwoQJ7N69m/j4eLp37461NiY/FwALFizgxRdfZPjw4SxdupR169aRkpJCampqtKMd1ttvv820adNY\nsWIFnTt3pkOHDjH7vQPvc/zss8/y/vvvM3LkSDZt2sSyZcsYNGgQcXGx1+6x1vL888/z1ltvcfbZ\nZ3PxxRdHO9IxVVVV8cwzz5Cbm8t3vvMdysrKMMbQu3fvmP1srFixgmnTpjF69Gi2bNnC6tWrCYVC\nDfYDLvY+WVFgjCErK4vbbruNwsJCcnNz6du3b8z+uj/wob3yyitp2bIl3bp141//+heu68ZsZsdx\nKC0tJSsrizFjxgDe7u/WrVvH5Bdv165dZGdnc80115CQkMDWrVspKSmJ6SINsHHjRk477TTGjBnD\nwIEDeeyxx3Ach4yMDFq1ahXteIfIzMxk8uTJbN++nVmzZnHKKafgOE7Mvs+O43DSSSdx2WWX0apV\nK/r27ctrr71GIBCIdrTDMsawf/9+unfvztixYwHYtm0b7dq1i9n/FVVVVfTr1y/83du7dy8rV64k\nFArF7Pu8du1a+vTpw6hRoxg0aBAvvPAC8+bNo1u3bqSlpdV7+822RT1v3jyWLl2KtZZgMEhWVhaZ\nmZkkJCTw+eefU1VVRZcuXWLmF9y8efNYsmQJxhiCwSBdu3YlLi6OJUuWcP/995Oens7GjRvJzs4m\nPj4+2nGBmvfYdV2CwSCDBg3i+eefxxjDyy+/zBdffMHSpUvp3bs3LVq0iHZc5s2bx+LFiwkEAnTu\n3Jns7OzwP4YlS5awe/duBg8eHDOfCah5jwEyMjLYsGED+/btIzs7m9atW7N06VL2799Pq1atYmL3\n7KpVqygvL6d169YAdOjQgdTUVNq3b09eXh579+6lV69eMfWj8+uZs7KySEhI4LPPPuN3v/sdiYmJ\nbNy4kYyMDFJSUqKc9tC8/fr145VXXqG0tJSXXnqJVatWsXjxYoLBIOnp6VFO6zk4cyAQoGvXruHv\n3tq1a9m1axdDhw6Nme/e19/jkpIS1q5dS69evUhNTWXZsmXs27ePyspKevToUe/na3aF2nVdZs6c\nyezZs+natSszZ84kLS2Nzp07Y4yhZcuWuK7L/PnzOfnkk6NeQL6e97XXXiM9PZ1OnToBsHXrVoYP\nH87555/Pv//973BLMJr/5A73Hrdu3ZoePXpQWVnJe++9x3XXXce4cePIy8ujpKSEHj16RC3zwXm7\ndevGq6++SlpaGllZWeF/DElJScycOZOcnJyY+CF0uM9F+/btyczMZOXKlcybN4/58+cD0KpVKxIT\nE+nWrVvUWqqlpaU8+OCD/P3vf6dVq1ZkZ2cTFxeHtRbHcQgEAqSlpfHGG28wfPhwEhMTI57xRDIb\nYygpKWHQoEFcdtllrFy5knXr1tGlS5eo9RU5Ut74+HistcydO5errrqKc889lzVr1rB582a6du0a\n1f9xh8t8IO+B9zkxMZHp06czfvz4qP8/PtJ7DLB+/Xpmz57NJ598QllZGT179qSqqoo+ffrU+3vX\n7Aq1MYbc3FwuuOACRo0aRVpaGu+99x49e/YkJSWFQCBAYmIiGzZsoKysjO7du7Nt2zaSk5NjMm+H\nDh3o0KEDgUAAYwwfffQR48ePj+qvziNl7tOnD6eddhpnnnkmHTt2pEWLFgQCgXDmWMvbs2fP8LHd\nUCjEzp07ad++fUy0Qr6eOTU1lffee4+hQ4cydOhQ4uPjCQaDXH755ezdu5dFixZx+umnR+1zUVJS\nQlVVFcOGDQv3PO7QoUOtPG3btmX9+vVs2rSJ/v37k5+fT5s2baKSF46duU2bNnTo0AGAhIQE5s2b\nx+jRo6P2Q+5IeQF69erFiBEj6NSpE4FAgJYtWzJ37lxGjx4d1WPrR3uPjTG4rktSUhIbN24kISEh\n3ECJlbzWWjp06EDr1q0ZMGAAwWAw/L0rKytjwYIFjBo1qt7fu9jYt9TI5syZw4oVKygtLQUgNTWV\n0tJSQqEQp512GllZWcyfPz/crb5du3aMHDmSGTNmcPXVV5Ofnx+Teb9u586d9OzZMyqnjBwrc5cu\nXZg7dy7W2lq9Ibdv3x6VzCf6mWjZsiXbt28Pf+GicXrL0TIPHz6czMxM5s2bR3JyMiNGjAj/+Nmy\nZQtDhw6NWt6ysjLatGlDTk4Op59+OvHx8axevZqioiKg5r10HIcLL7yQN954g6uvvpq1a9dG/H0+\n3sxft3btWtLS0iJ+DPVE8h7c2Fi7di1t2rSJyl6sE/lcOI5DZWUl4P0YOjA/VvLm5+eH88bFxXHS\nSScxatQowHuPBw0a1CAZmmyL2lpLcXExf/zjH1m/fj2FhYXk5eVx8skns2HDBoqLi+nevTsJCQlk\nZmYyc+ZMhg0bRmJiIps2beKxxx6jQ4cO3HbbbQwcODAm8w4dOpS4uDiWLVvGY489RnFxMRdddFHE\njpPVNXNiYiLLli3jkUceYffu3Vx44YXhYz2xlvfAZyIhIYFPP/0U13Xp06dPxFqm9X2P//CHP1BZ\nWclZZ53VoKeLnGjeb3zjGyQlJREXF0cgEGDt2rVUVVXRtWvXcMtp+/btPP7447Rr14477riDwYMH\nR+R9rktmgLKyMr744gsefvhhiouLueyyyxqk41Bj5a2srGTlypU89NBDFBcXM2nSpIjkrWvmA5+L\n+Ph4/vvf/1JeXk7//v1j+jMB3nnfDzzwAHv37mXChAkN0omzSRbqAx1Rdu3axbp16/j5z3/OKaec\nwvLly1m4cCEXXXQRb7/9Nh06dCAlJYX09HSWL1/Ovn376NWrF6FQiC5dukTsi1fXvOXl5fTp04e1\na9fSr18/Jk2aFLFd9PXJ3LNnTzZu3EifPn0ilrm+nwmAU045hZNOOqnRszZUZmstnTt3ZtKkSREp\n0kfK+8UXXzB37lxGjBgBQDAYZPPmzRQWFpKdnR3+ZxwKhejcuTOXXHJJxE4nq2tma214L0vv3r2Z\nNGlSRH5s1idvQkJCuA9LpPLWJ/OBzwXAqaeeyoABA2I6bygUIi4uDsdxyMrKYtKkSQ12pkWTKtSh\nUIiXX36Zzz77jLi4OIqKiti8eTPDhg3DcRwGDRrE008/zWmnnUZycjJLly6lqqqKzp07k5eXx2mn\nnUZGRgaJiYl07Ngx5vMOGzaMjIwMOnfuHLFjNw2VuWPHjhHJ3FCfCSBixx4bKnNycnLMvMfPP/88\nPXr0oG3btgB06dKFJUuW8Pe//53XXnuNkSNHkp6eTrt27Ro9b0NlHjVqFN26dfPNezxq1KiY+19x\nPJkTExMjsou+vnlnzpzJyJEjycjICPcNaChN5hj1ihUrmDx5MqWlpWRmZjJjxgzi4uJYvnx5+Biz\n4zhcfPHFTJ8+nXHjxjFw4EDmzJnDz3/+83ArWnmbTma/5fVj5uPJa4zh4osv5tVXXw2vt2jRIv75\nz3/StWtX/vSnP4V/DPkpc6Q6uvktrx8zx/rnuMm0qAsKCsjKyuLCCy8kOzubNWvWEBcXx8knn8yM\nGTP45je/ieu6tG3bls8++4zevXvTq1cvBgwYwLBhw8jJyYlo70e/5fVjZr/l9WPm480bDAZZsWIF\nPXr0oFWrVmzbto2cnBy+9a1vRfx0Jr9l9lteP2aO9bxNpkWdnZ3N6aefHu6l26dPHwoKChg3bhyu\n6/Luu+/iOA6FhYUEAgGCwSAAaWlptG/fXnmbYGa/5fVj5hPJ6zhOeNf20KFD6devX8Tz+jGz3/L6\nMXOs520yQ4h+/UT4zz77LNwT78Ybb+SDDz5gypQpbNmyhZycnGhErMVvecF/mf2WF/yXuS55oz08\nqN8y+y0v+C9zrOdtMoX6gAO/iHbv3h2+SEViYiKXX345GzdupF27dlEdROHr/JYX/JfZb3nBf5lP\nJG8sDAEJ/svst7zgv8yxmrfJHKM+WCgUYtmyZSQnJzN9+nTy8/MZPHgwHTp0iImhCb/Ob3nBf5n9\nlhf8l9lvecF/mf2WF/yXORbzNrkWtTGGdevWMXfuXHbs2MEZZ5wR1eEpj8VvecF/mf2WF/yX2W95\nwX+Z/ZYX/Jc5VvMaG42xEBtZYWEhH3/8MRMmTIiJCygci9/ygv8y+y0v+C+z3/KC/zL7LS/4L3Ms\n5m2ShVpERKSpaDKnZ4mIiDRFKtQiIiIxTIVaREQkhqlQi4iIxDAVahERkRimQi0iIhLDmtyAJyLN\n1Y9//GOKi4sJBALhi9ePGTOGnJycY17Pd8eOHdx000289NJLBAKBCCUWkeOhQi3ShPziF79g4MCB\nlJWVsWLFCqZNm0Z+fj433nhjtKOJSB2pUIs0QUlJSQwZMoS0tDTuuusuJkyYQEFBAS+//DLbt28n\nKSmJM844g0svvRSAe+65B4BrrrkGgF//+tf07t2b2bNn8+abb1JcXEzPnj35wQ9+QNu2baP1skSa\nJR2jFmnCevbsSZs2bVi5ciUtWrTgpptuYtq0aUyePJl//etffPLJJwD85je/AeC5557jb3/7G717\n9+aTTz7h9ddf5yc/+QnPPPMMffv25ZFHHonmyxFpllSoRZq4Nm3aUFJSQv/+/enSpQuO49C1a1dG\njjwXeVUAAAGGSURBVBzJihUrjrhebm4uF1xwAVlZWQQCAS644AK++uordu7cGcH0IqJd3yJNXFFR\nEcnJyaxevZoXX3yRDRs2UFVVRVVVFcOHDz/iejt37mTatGm88MIL4XnWWoqKirT7WySCVKhFmrD8\n/HyKioro27cvDzzwAGeffTa//OUvSUhI4LnnnmPPnj2Ad3m/rwsGg1x44YWMHj060rFF5CDa9S3S\nBJWVlbFw4UIeeeQRRo8eTZcuXdi3bx/JyckkJCSQn5/P3Llzw8unpKRgjGH79u3hed/85jeZNWsW\nGzduDG9z/vz5EX8tIs2dLnMp0kQcfB61MYasrCxGjx7NWWedheM4LFiwgBdeeIGSkhL69etH27Zt\nKS0t5ZZbbgFgxowZvP/++4RCIe6880569+7Nxx9/zBtvvEFBQQFJSUkMGDBAp3qJRJgKtYiISAzT\nrm8REZEYpkItIiL/v706FgAAAAAY5G89jR0lEWOiBoAxUQPAmKgBYEzUADAmagAYEzUAjIkaAMYC\nA2bQfkwKPSMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc63a1e6c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Sharpe:' , sharpe(stratData['pnl']))\n",
    "stratData['pnl'].cumsum().plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok, this is nice. The result matches with what Oddmund have found. <br> \n",
    "But this is actually where the fun really starts, now I'll backtest this strategy with different parameters and also test it on other symbols. <br>\n",
    "First, let's rewrite the strategy as a function so it is easy to use."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Rewrite strategy to a single function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here I'll just copy & paste all the code written above and put it into a function, which returns the pnl timeseries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def backtest(ohlc, ccThresh=-0.25, coThresh=-0.1):\n",
    "    ''' Function to backtest the Boring Consumer Stocks strategy '''\n",
    "    stratData = pd.DataFrame(index=ohlc.index)\n",
    "    stratData['cc'] = 100*ohlc['close'].pct_change() # close-to-close change in %\n",
    "    stratData['co'] = 100*(ohlc['open']/ohlc['close'].shift(1)-1) # previous close to open change in %\n",
    "    stratData['oc'] = 100*(ohlc['close']/ohlc['open']-1) # open to close change in %\n",
    "    \n",
    "    idx = (stratData['cc']<ccThresh).shift(1) & (stratData['co'] < coThresh) # find days that satisfy the strategy rules\n",
    "    idx[0] = False # fill first entry with False (needed because .shift(1) adds a NaN in the first element)\n",
    "\n",
    "    stratData['goLong'] = idx\n",
    "    stratData['pnl'] = 0. # init pnl column with zeros (Watch out: if initialized with integer value (0), an error will pop later on)\n",
    "    stratData.loc[idx,'pnl'] = stratData.loc[idx, 'oc'] # set pnl column values to daily return wehere 'goLong' is true\n",
    "\n",
    "    return stratData['pnl']\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, test the function with OHLC data, result should be exactly as before"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sharpe: 1.00708745001\n"
     ]
    },
    {
     "data": {
      "image/png": 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kBqlQi0SItRZK92JGnIlJawNpbTATJnkPTrwyuuFEJGapUIs0EltVBRvXgRuC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\nA2bQfkwKPSMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc5fc905550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pnl = backtest(ohlc)\n",
    "pnl.cumsum().plot()\n",
    "print('Sharpe:', sharpe(pnl))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Indeed, same result. Now we can proceed to scanning parameter values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Make a scan of ALL parameters \n",
    "We'll also use a nice progress bar to display calculation progress\n",
    "\n",
    "[ref]: # (plotting - heatmap)\n",
    "[ref]: # (backtesting - parameter scan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " [*********************100%***********************]  29 of 29 complete"
     ]
    },
    {
     "data": {
      "image/png": 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MJpPLYxEREXjrrbe8khQREVGncXTw1g3cOpWqoaEBISEhLo+FhISgrq7OK0kR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\nsxBCCFFipJyFEEKIEvMbZxvfNRU21KUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc5ff5f8d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from tradingWithPython.lib.extra import ProgressBar\n",
    "\n",
    "ccThresh = np.linspace(-1,1,30)\n",
    "coThresh = np.linspace(-0.5,0.5, 30)\n",
    "\n",
    "SH = np.zeros((len(ccThresh),len(coThresh)))\n",
    "\n",
    "pbar = ProgressBar(len(ccThresh)-1)\n",
    "\n",
    "for i, cc in enumerate(ccThresh):\n",
    "    pbar.animate(i)\n",
    "    for j, co in enumerate(coThresh):\n",
    "        pnl = backtest(ohlc, ccThresh=cc, coThresh=co)\n",
    "        SH[i,j] = sharpe(pnl)\n",
    "        \n",
    "pcolor(coThresh,ccThresh, SH)\n",
    "xlabel('opening gap [%]')\n",
    "ylabel('previous day change [%]');\n",
    "colorbar();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No find the ompimal parameters corresponding to maximum Sharpe ratio."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimum CC 0.17\n",
      "Optimum CO 0.12\n"
     ]
    }
   ],
   "source": [
    "i,j = np.unravel_index(SH.argmax(), SH.shape)\n",
    "SH[i,j]\n",
    "print('Optimum CC %.2f' % ccThresh[i])\n",
    "print('Optimum CO %.2f' % coThresh[j])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sharpe: 1.40688387138\n"
     ]
    },
    {
     "data": {
      "image/png": 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x8+s9rn5ey+y1vOC9zMfmTU5OLtd6FT7rOyYmJrBLOzs7m+joaMDfgz46yJ49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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc5fb009b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pnl = backtest(ohlc, ccThresh = 0.03, coThresh= 0.09)\n",
    "print('Sharpe:', sharpe(pnl))\n",
    "_ = pnl.cumsum().plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The rules of -0.25% / -0.1% can be further improved to achieve a higher Sharpe. However, the strategy performance does not vary that much, it is very stable and performs well for a wide range of settings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Improvements"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Walk-Forward Optimization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[ref]: # (backtesting - walk-forward)\n",
    "\n",
    "In order to avoid [curve-fitting](http://en.wikipedia.org/wiki/Curve_fitting), we will perform a [walk-forward optimization](http://en.wikipedia.org/wiki/Walk_forward_optimization)\n",
    "\n",
    "* http://www.easyexpertforex.com/walk-forward-analysis.html\n",
    "\n",
    "* http://www.tradestation.com/trading-technology/tradestation-platform/analyze/walk-forward-optimizer\n",
    "\n",
    "A neat way to do this is to create a function that generates two datasets: in- and out-of-sample. The first one is used to optimize the strategy, second one to test its performance. \n",
    "The function `generateFWD` below does just that. Take a look at the [generator concept](http://docs.python.org/2/tutorial/classes.html#tut-generators) used in this function. In this way the `yield` of the function can be used in a loop: \n",
    "\n",
    "`for trainSet,testSet, tp in generateFWD(ohlc)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " [****************      33%                       ]  5 of 15 complete"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jev/Development/tradingWithPython/lib/functions.py:294: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  return  np.sqrt(250)*pnl.mean()/pnl.std()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " [*********************100%***********************]  15 of 15 complete"
     ]
    }
   ],
   "source": [
    "def generateFWD(data,nTrain = 250, nTest=50):\n",
    "    ''' \n",
    "    Walk forward generator. Returns two subsections (in-sample, out-of-sample) of provided dataframe to \n",
    "    be used in a walk-forward testing.\n",
    "    \n",
    "    Parameters\n",
    "    -------------\n",
    "    data : DataFrame containing full dataset\n",
    "    nTrain : number of periods in a train (in-sample) set\n",
    "    nTest : number of periods in a test (out-of-sample) set\n",
    "    \n",
    "    Returns\n",
    "    ----------\n",
    "    (trainSet,testSet,testPoint)\n",
    "    \n",
    "    '''\n",
    "    nTotal = nTrain+nTest\n",
    "    \n",
    "    for tp in range(nTrain,len(data) - nTotal, nTest) :  # xrange() is a little more efficient than range()\n",
    "        # tp is a 'testpoint' which is at in-sample/out-of-sample boundary\n",
    "        yield (data.iloc[tp-nTrain:tp,:], data.iloc[tp:tp+nTest],tp) # return test and train datasets\n",
    "\n",
    "def optimize(ohlc):\n",
    "    ''' find optimal strategy parameters '''\n",
    "    ccThresh = np.linspace(-1,1,30)\n",
    "    coThresh = np.linspace(-0.5,0.5, 30)\n",
    "    \n",
    "    SH = np.zeros((len(ccThresh),len(coThresh)))\n",
    "    \n",
    "    for i, cc in enumerate(ccThresh):\n",
    "        for j, co in enumerate(coThresh):\n",
    "            pnl = backtest(ohlc, ccThresh=cc, coThresh=co)\n",
    "            SH[i,j] = sharpe(pnl)\n",
    "            \n",
    "    i,j = np.unravel_index(SH.argmax(), SH.shape)\n",
    "    SH[i,j]\n",
    "    \n",
    "    return ccThresh[i], coThresh[j]  \n",
    "   \n",
    "nTest = 250    # number of days in testing period\n",
    "pnl = []       # results of individual tests, will be combined to a single series later\n",
    "\n",
    "#we'll need some work to prepare the progress bar by first generating the testpoints. ProgressBar is optional of course.\n",
    "testPoints = [ tp for _,_,tp in generateFWD(ohlc, nTest=nTest)]\n",
    "    \n",
    "pbar =ProgressBar(len(testPoints)-1)    \n",
    "    \n",
    "for trainSet,testSet, tp in generateFWD(ohlc,nTest=nTest):\n",
    "    (cc,co) = optimize(trainSet)\n",
    "    pnl.append(backtest(testSet, ccThresh = cc, coThresh= co))\n",
    "    pbar.animate(testPoints.index(tp))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sharpe: 1.40\n"
     ]
    },
    {
     "data": {
      "image/png": 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RcY+CWuQw9u2Z2NdfAsDceDfm9LMwUdEuVyUizZmCWuQwdrX/mermutvxnHm2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\nVVcxevRoJk+ezNy5c0lLS2P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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc5ff5adc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "PNL = pd.concat(pnl) # combine to a single series\n",
    "PNL.cumsum().plot()\n",
    "\n",
    "print('Sharpe: %.2f' % sharpe(PNL))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, the strategy seems to perform quite well out-of-sample"
   ]
  }
 ],
 "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": "3.6.2"
  },
  "toc": {
   "nav_menu": {},
   "number_sections": false,
   "sideBar": true,
   "skip_h1_title": false,
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": "block",
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}