00_epat_october_2017.md

Executive Program in Algorithmic Trading (QuantInsti)

Python Sessions by Dr. Yves J. Hilpisch | The Python Quants GmbH

Online, 22. & 29. October 2017

Resources

• http://tpq.io
• http://hilpisch.com
• http://twitter.com/dyjh
• http://certificate.tpq.io

Slides

You find the introduction slides under http://hilpisch.com/epat.pdf

Python

If you have either Miniconda or Anaconda already installed, there is no need to install anything new.

The code that follows uses Python 3.6. For example, download and install Miniconda 3.6 from https://conda.io/miniconda.html if you do not have conda already installed.

In any case, for Linux/Mac you should execute the following lines on the shell to create a new environment with the needed packages:

conda create -n fxcm python=3.6
source activate fxcm
conda install numpy pandas matplotlib statsmodels
pip install plotly cufflinks
conda install ipython jupyter
jupyter notebook

On Windows, execute the following lines on the command prompt:

conda create -n fxcm python=3.6
activate fxcm
conda install numpy pandas matplotlib statsmodels
pip install plotly cufflinks
pip install win-unicode-console
set PYTHONIOENCODING=UTF-8
conda install ipython jupyter
jupyter notebook

Read more about the management of environments under https://conda.io/docs/using/envs.html

ZeroMQ

The major resource for the ZeroMQ distributed messaging package based on sockets is http://zeromq.org/

Cloud

Use this link to get a 10 USD bonus on DigitalOcean when signing up for a new account.

Books

Good book about everything import in Python data analysis: Python Data Science Handbook, O'Reilly

Good book covering object-oriented programming in Python: Fluent Python, O'Reilly

01_vecback_oop.ipynb

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   "cell_type": "markdown",
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   "source": [
    "<img src=\"http://hilpisch.com/tpq_logo.png\" width=350px align=right>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EPAT Session 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Executive Program in Algorithmic Trading**\n",
    "\n",
    "**_Vectorized Backtesting & Object Oriented Programming_**\n",
    "\n",
    "Dr. Yves J. Hilpisch | The Python Quants GmbH | http://tpq.io\n",
    "\n",
    "<img src=\"http://hilpisch.com/images/tpq_bootcamp.png\" width=350px align=left>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pylab import plt\n",
    "plt.style.use('ggplot')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading Financial Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = pd.read_csv('http://hilpisch.com/tr_eikon_eod_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1960 entries, 0 to 1959\n",
      "Data columns (total 11 columns):\n",
      "Date      1960 non-null object\n",
      "AAPL.O    1960 non-null float64\n",
      "MSFT.O    1960 non-null float64\n",
      "INTC.O    1960 non-null float64\n",
      "AMZN.O    1960 non-null float64\n",
      "GS.N      1960 non-null float64\n",
      "SPY       1960 non-null float64\n",
      ".SPX      1960 non-null float64\n",
      ".VIX      1960 non-null float64\n",
      "EUR=      1960 non-null float64\n",
      "XAU=      1960 non-null float64\n",
      "dtypes: float64(10), object(1)\n",
      "memory usage: 168.5+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
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   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>AAPL.O</th>\n",
       "      <th>MSFT.O</th>\n",
       "      <th>INTC.O</th>\n",
       "      <th>AMZN.O</th>\n",
       "      <th>GS.N</th>\n",
       "      <th>SPY</th>\n",
       "      <th>.SPX</th>\n",
       "      <th>.VIX</th>\n",
       "      <th>EUR=</th>\n",
       "      <th>XAU=</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010-01-04</td>\n",
       "      <td>30.572827</td>\n",
       "      <td>30.950</td>\n",
       "      <td>20.88</td>\n",
       "      <td>133.90</td>\n",
       "      <td>173.08</td>\n",
       "      <td>113.33</td>\n",
       "      <td>1132.99</td>\n",
       "      <td>20.04</td>\n",
       "      <td>1.4411</td>\n",
       "      <td>1120.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-01-05</td>\n",
       "      <td>30.625684</td>\n",
       "      <td>30.960</td>\n",
       "      <td>20.87</td>\n",
       "      <td>134.69</td>\n",
       "      <td>176.14</td>\n",
       "      <td>113.63</td>\n",
       "      <td>1136.52</td>\n",
       "      <td>19.35</td>\n",
       "      <td>1.4368</td>\n",
       "      <td>1118.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010-01-06</td>\n",
       "      <td>30.138541</td>\n",
       "      <td>30.770</td>\n",
       "      <td>20.80</td>\n",
       "      <td>132.25</td>\n",
       "      <td>174.26</td>\n",
       "      <td>113.71</td>\n",
       "      <td>1137.14</td>\n",
       "      <td>19.16</td>\n",
       "      <td>1.4412</td>\n",
       "      <td>1138.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010-01-07</td>\n",
       "      <td>30.082827</td>\n",
       "      <td>30.452</td>\n",
       "      <td>20.60</td>\n",
       "      <td>130.00</td>\n",
       "      <td>177.67</td>\n",
       "      <td>114.19</td>\n",
       "      <td>1141.69</td>\n",
       "      <td>19.06</td>\n",
       "      <td>1.4318</td>\n",
       "      <td>1131.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-01-08</td>\n",
       "      <td>30.282827</td>\n",
       "      <td>30.660</td>\n",
       "      <td>20.83</td>\n",
       "      <td>133.52</td>\n",
       "      <td>174.31</td>\n",
       "      <td>114.57</td>\n",
       "      <td>1144.98</td>\n",
       "      <td>18.13</td>\n",
       "      <td>1.4412</td>\n",
       "      <td>1136.10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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       "         Date     AAPL.O  MSFT.O  INTC.O  AMZN.O    GS.N     SPY     .SPX  \\\n",
       "0  2010-01-04  30.572827  30.950   20.88  133.90  173.08  113.33  1132.99   \n",
       "1  2010-01-05  30.625684  30.960   20.87  134.69  176.14  113.63  1136.52   \n",
       "2  2010-01-06  30.138541  30.770   20.80  132.25  174.26  113.71  1137.14   \n",
       "3  2010-01-07  30.082827  30.452   20.60  130.00  177.67  114.19  1141.69   \n",
       "4  2010-01-08  30.282827  30.660   20.83  133.52  174.31  114.57  1144.98   \n",
       "\n",
       "    .VIX    EUR=     XAU=  \n",
       "0  20.04  1.4411  1120.00  \n",
       "1  19.35  1.4368  1118.65  \n",
       "2  19.16  1.4412  1138.50  \n",
       "3  19.06  1.4318  1131.90  \n",
       "4  18.13  1.4412  1136.10  "
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     "output_type": "execute_result"
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    "data.head()"
   ]
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   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>AAPL.O</th>\n",
       "      <th>MSFT.O</th>\n",
       "      <th>INTC.O</th>\n",
       "      <th>AMZN.O</th>\n",
       "      <th>GS.N</th>\n",
       "      <th>SPY</th>\n",
       "      <th>.SPX</th>\n",
       "      <th>.VIX</th>\n",
       "      <th>EUR=</th>\n",
       "      <th>XAU=</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1955</th>\n",
       "      <td>2017-10-09</td>\n",
       "      <td>155.84</td>\n",
       "      <td>76.29</td>\n",
       "      <td>39.86</td>\n",
       "      <td>990.99</td>\n",
       "      <td>242.80</td>\n",
       "      <td>253.95</td>\n",
       "      <td>2544.73</td>\n",
       "      <td>10.33</td>\n",
       "      <td>1.1739</td>\n",
       "      <td>1283.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1956</th>\n",
       "      <td>2017-10-10</td>\n",
       "      <td>155.90</td>\n",
       "      <td>76.29</td>\n",
       "      <td>39.65</td>\n",
       "      <td>987.20</td>\n",
       "      <td>242.60</td>\n",
       "      <td>254.62</td>\n",
       "      <td>2550.64</td>\n",
       "      <td>10.08</td>\n",
       "      <td>1.1806</td>\n",
       "      <td>1287.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1957</th>\n",
       "      <td>2017-10-11</td>\n",
       "      <td>156.55</td>\n",
       "      <td>76.42</td>\n",
       "      <td>39.30</td>\n",
       "      <td>995.00</td>\n",
       "      <td>242.40</td>\n",
       "      <td>255.02</td>\n",
       "      <td>2555.24</td>\n",
       "      <td>9.85</td>\n",
       "      <td>1.1857</td>\n",
       "      <td>1291.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958</th>\n",
       "      <td>2017-10-12</td>\n",
       "      <td>156.00</td>\n",
       "      <td>77.12</td>\n",
       "      <td>39.19</td>\n",
       "      <td>1000.93</td>\n",
       "      <td>239.80</td>\n",
       "      <td>254.64</td>\n",
       "      <td>2550.93</td>\n",
       "      <td>9.91</td>\n",
       "      <td>1.1829</td>\n",
       "      <td>1293.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959</th>\n",
       "      <td>2017-10-13</td>\n",
       "      <td>156.99</td>\n",
       "      <td>77.49</td>\n",
       "      <td>39.67</td>\n",
       "      <td>1002.94</td>\n",
       "      <td>238.53</td>\n",
       "      <td>254.95</td>\n",
       "      <td>2553.17</td>\n",
       "      <td>9.61</td>\n",
       "      <td>1.1822</td>\n",
       "      <td>1304.47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date  AAPL.O  MSFT.O  INTC.O   AMZN.O    GS.N     SPY     .SPX  \\\n",
       "1955  2017-10-09  155.84   76.29   39.86   990.99  242.80  253.95  2544.73   \n",
       "1956  2017-10-10  155.90   76.29   39.65   987.20  242.60  254.62  2550.64   \n",
       "1957  2017-10-11  156.55   76.42   39.30   995.00  242.40  255.02  2555.24   \n",
       "1958  2017-10-12  156.00   77.12   39.19  1000.93  239.80  254.64  2550.93   \n",
       "1959  2017-10-13  156.99   77.49   39.67  1002.94  238.53  254.95  2553.17   \n",
       "\n",
       "       .VIX    EUR=     XAU=  \n",
       "1955  10.33  1.1739  1283.90  \n",
       "1956  10.08  1.1806  1287.46  \n",
       "1957   9.85  1.1857  1291.70  \n",
       "1958   9.91  1.1829  1293.40  \n",
       "1959   9.61  1.1822  1304.47  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = pd.read_csv('http://hilpisch.com/tr_eikon_eod_data.csv',\n",
    "                  index_col=0, parse_dates=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAPL.O</th>\n",
       "      <th>MSFT.O</th>\n",
       "      <th>INTC.O</th>\n",
       "      <th>AMZN.O</th>\n",
       "      <th>GS.N</th>\n",
       "      <th>SPY</th>\n",
       "      <th>.SPX</th>\n",
       "      <th>.VIX</th>\n",
       "      <th>EUR=</th>\n",
       "      <th>XAU=</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-04</th>\n",
       "      <td>30.572827</td>\n",
       "      <td>30.950</td>\n",
       "      <td>20.88</td>\n",
       "      <td>133.90</td>\n",
       "      <td>173.08</td>\n",
       "      <td>113.33</td>\n",
       "      <td>1132.99</td>\n",
       "      <td>20.04</td>\n",
       "      <td>1.4411</td>\n",
       "      <td>1120.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>30.625684</td>\n",
       "      <td>30.960</td>\n",
       "      <td>20.87</td>\n",
       "      <td>134.69</td>\n",
       "      <td>176.14</td>\n",
       "      <td>113.63</td>\n",
       "      <td>1136.52</td>\n",
       "      <td>19.35</td>\n",
       "      <td>1.4368</td>\n",
       "      <td>1118.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>30.138541</td>\n",
       "      <td>30.770</td>\n",
       "      <td>20.80</td>\n",
       "      <td>132.25</td>\n",
       "      <td>174.26</td>\n",
       "      <td>113.71</td>\n",
       "      <td>1137.14</td>\n",
       "      <td>19.16</td>\n",
       "      <td>1.4412</td>\n",
       "      <td>1138.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>30.082827</td>\n",
       "      <td>30.452</td>\n",
       "      <td>20.60</td>\n",
       "      <td>130.00</td>\n",
       "      <td>177.67</td>\n",
       "      <td>114.19</td>\n",
       "      <td>1141.69</td>\n",
       "      <td>19.06</td>\n",
       "      <td>1.4318</td>\n",
       "      <td>1131.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-08</th>\n",
       "      <td>30.282827</td>\n",
       "      <td>30.660</td>\n",
       "      <td>20.83</td>\n",
       "      <td>133.52</td>\n",
       "      <td>174.31</td>\n",
       "      <td>114.57</td>\n",
       "      <td>1144.98</td>\n",
       "      <td>18.13</td>\n",
       "      <td>1.4412</td>\n",
       "      <td>1136.10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               AAPL.O  MSFT.O  INTC.O  AMZN.O    GS.N     SPY     .SPX   .VIX  \\\n",
       "Date                                                                            \n",
       "2010-01-04  30.572827  30.950   20.88  133.90  173.08  113.33  1132.99  20.04   \n",
       "2010-01-05  30.625684  30.960   20.87  134.69  176.14  113.63  1136.52  19.35   \n",
       "2010-01-06  30.138541  30.770   20.80  132.25  174.26  113.71  1137.14  19.16   \n",
       "2010-01-07  30.082827  30.452   20.60  130.00  177.67  114.19  1141.69  19.06   \n",
       "2010-01-08  30.282827  30.660   20.83  133.52  174.31  114.57  1144.98  18.13   \n",
       "\n",
       "              EUR=     XAU=  \n",
       "Date                         \n",
       "2010-01-04  1.4411  1120.00  \n",
       "2010-01-05  1.4368  1118.65  \n",
       "2010-01-06  1.4412  1138.50  \n",
       "2010-01-07  1.4318  1131.90  \n",
       "2010-01-08  1.4412  1136.10  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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D1oqHYIqKcS67PtjfbmaOWpqfNBleRCTP2c1ViZ1td4Ap26dXqpgCH7yXGEYa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\nYU46C/unm7F/+i32tRdg9FjMgUf0YUN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8AvtPD3PgEUGvUU/cs6Ee+/wTsKUKknJZmdLh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\niBTJ/wNL3GuKzmY2nwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112b2a3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['AAPL.O'].plot(figsize=(10, 6));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Vectorized Backtesting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = pd.DataFrame(data['AAPL.O'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data.columns = ['Prices']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 1960 entries, 2010-01-04 to 2017-10-13\n",
      "Data columns (total 1 columns):\n",
      "Prices    1960 non-null float64\n",
      "dtypes: float64(1)\n",
      "memory usage: 30.6 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SMAs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data['SMA1'] = data['Prices'].rolling(42).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data['SMA2'] = data['Prices'].rolling(252).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Prices</th>\n",
       "      <th>SMA1</th>\n",
       "      <th>SMA2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-04</th>\n",
       "      <td>30.572827</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>30.625684</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>30.138541</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>30.082827</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-08</th>\n",
       "      <td>30.282827</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Prices  SMA1  SMA2\n",
       "Date                             \n",
       "2010-01-04  30.572827   NaN   NaN\n",
       "2010-01-05  30.625684   NaN   NaN\n",
       "2010-01-06  30.138541   NaN   NaN\n",
       "2010-01-07  30.082827   NaN   NaN\n",
       "2010-01-08  30.282827   NaN   NaN"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Prices</th>\n",
       "      <th>SMA1</th>\n",
       "      <th>SMA2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-10-09</th>\n",
       "      <td>155.84</td>\n",
       "      <td>158.068095</td>\n",
       "      <td>137.951964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-10</th>\n",
       "      <td>155.90</td>\n",
       "      <td>158.081905</td>\n",
       "      <td>138.110099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-11</th>\n",
       "      <td>156.55</td>\n",
       "      <td>158.059762</td>\n",
       "      <td>138.269821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-12</th>\n",
       "      <td>156.00</td>\n",
       "      <td>157.968095</td>\n",
       "      <td>138.423234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-13</th>\n",
       "      <td>156.99</td>\n",
       "      <td>157.858333</td>\n",
       "      <td>138.582004</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Prices        SMA1        SMA2\n",
       "Date                                      \n",
       "2017-10-09  155.84  158.068095  137.951964\n",
       "2017-10-10  155.90  158.081905  138.110099\n",
       "2017-10-11  156.55  158.059762  138.269821\n",
       "2017-10-12  156.00  157.968095  138.423234\n",
       "2017-10-13  156.99  157.858333  138.582004"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rVLq33ENqjJlxmb0/olVwwIfbZTB9ThzxiX0zTSmBlhBCiK4rOYE2dFRjde/0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\nuXE1wd/8COn3ITetgckzjN9jiJd31SOBmVmDJ7SkbqwubJ8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Wx5CSbohAkX5Uno+1+wo2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4EyY0yhtbbUGFMIrN+L59gjzjnc049AnyK8SYfhhdo+Q+q21OL++mCwkJmNd/p39+xJNwXJjXf8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\nERERkThRoiUiIiISJ/8PZAwbbS2jLs4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112b2a908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot(figsize=(10, 6));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Positions "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data['Positions'] = np.where(data['SMA1'] > data['SMA2'], 1, -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+IblqwDv6N4XHEa1ZkwdPk/um8XDMLMmyTIShTJIk55SpBgxlTeOEZq1GW8ZL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\nasBQJkmSVAP/HyEeF35E0D4xAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113dd6710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['Positions'].plot(figsize=(10, 6));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Prices</th>\n",
       "      <th>SMA1</th>\n",
       "      <th>SMA2</th>\n",
       "      <th>Positions</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>2010-12-31</th>\n",
       "      <td>46.079954</td>\n",
       "      <td>45.280967</td>\n",
       "      <td>37.120735</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-03</th>\n",
       "      <td>47.081381</td>\n",
       "      <td>45.349708</td>\n",
       "      <td>37.186246</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-04</th>\n",
       "      <td>47.327096</td>\n",
       "      <td>45.412599</td>\n",
       "      <td>37.252521</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-05</th>\n",
       "      <td>47.714238</td>\n",
       "      <td>45.466102</td>\n",
       "      <td>37.322266</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-06</th>\n",
       "      <td>47.675667</td>\n",
       "      <td>45.522565</td>\n",
       "      <td>37.392079</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Prices       SMA1       SMA2  Positions\n",
       "Date                                                  \n",
       "2010-12-31  46.079954  45.280967  37.120735          1\n",
       "2011-01-03  47.081381  45.349708  37.186246          1\n",
       "2011-01-04  47.327096  45.412599  37.252521          1\n",
       "2011-01-05  47.714238  45.466102  37.322266          1\n",
       "2011-01-06  47.675667  45.522565  37.392079          1"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dropna().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Vp+KPiopi06ZNjBw5krKyMvLz8+vcn5WVxeDBg1m6dCnXXnstADExMbz66qskJSWxZ88e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\nCVHQEhEREUmIgpaIiIhIQv4/lCd7J/44S34AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113eaaf28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.dropna().plot(figsize=(10, 6));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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CzFqDQY4Hfc4of/++LkyCPiGQQEcIITq1smL/Y1S0/zEiEgB9wnkQG+cvayToU6GjfyEs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\nLHKR4LSRGt2+oce+3dXsza2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iK1KJS7Chd6L33jCMtSG7zwVmJWnJfabgD/omhRRPMk0z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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113c2f3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.dropna().plot(figsize=(10, 6), secondary_y='Positions');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Log Returns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data['Returns'] = np.log(data['Prices'] / data['Prices'].shift(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Prices</th>\n",
       "      <th>SMA1</th>\n",
       "      <th>SMA2</th>\n",
       "      <th>Positions</th>\n",
       "      <th>Returns</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>2010-01-04</th>\n",
       "      <td>30.572827</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>30.625684</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.001727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>30.138541</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>-0.016034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>30.082827</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>-0.001850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-08</th>\n",
       "      <td>30.282827</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.006626</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Prices  SMA1  SMA2  Positions   Returns\n",
       "Date                                                  \n",
       "2010-01-04  30.572827   NaN   NaN         -1       NaN\n",
       "2010-01-05  30.625684   NaN   NaN         -1  0.001727\n",
       "2010-01-06  30.138541   NaN   NaN         -1 -0.016034\n",
       "2010-01-07  30.082827   NaN   NaN         -1 -0.001850\n",
       "2010-01-08  30.282827   NaN   NaN         -1  0.006626"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.001727395398339415"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = math.log(30.625684 / 30.572827)\n",
    "lr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0017288882052027"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "math.exp(lr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Backtesting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data['Strategy'] = data['Positions'].shift(1) * data['Returns']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Returns</th>\n",
       "      <th>Strategy</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-10-09</th>\n",
       "      <td>0.003471</td>\n",
       "      <td>0.003471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-10</th>\n",
       "      <td>0.000385</td>\n",
       "      <td>0.000385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-11</th>\n",
       "      <td>0.004161</td>\n",
       "      <td>0.004161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-12</th>\n",
       "      <td>-0.003519</td>\n",
       "      <td>-0.003519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-13</th>\n",
       "      <td>0.006326</td>\n",
       "      <td>0.006326</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Returns  Strategy\n",
       "Date                          \n",
       "2017-10-09  0.003471  0.003471\n",
       "2017-10-10  0.000385  0.000385\n",
       "2017-10-11  0.004161  0.004161\n",
       "2017-10-12 -0.003519 -0.003519\n",
       "2017-10-13  0.006326  0.006326"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[['Returns', 'Strategy']].tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data.dropna(inplace=True)\n",
    "data = data.iloc[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Returns     1.225804\n",
       "Strategy    1.595036\n",
       "dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[['Returns', 'Strategy']].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Returns     3.406904\n",
       "Strategy    4.928507\n",
       "dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.exp(data[['Returns', 'Strategy']].sum())  # absolute return per USD invested"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Returns     2.406904\n",
       "Strategy    3.928507\n",
       "dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.exp(data[['Returns', 'Strategy']].sum()) - 1  # relative/net return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Returns</th>\n",
       "      <th>Strategy</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-01-03</th>\n",
       "      <td>0.021500</td>\n",
       "      <td>0.021500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-04</th>\n",
       "      <td>0.026705</td>\n",
       "      <td>0.026705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-05</th>\n",
       "      <td>0.034852</td>\n",
       "      <td>0.034852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-06</th>\n",
       "      <td>0.034043</td>\n",
       "      <td>0.034043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-07</th>\n",
       "      <td>0.041179</td>\n",
       "      <td>0.041179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-10</th>\n",
       "      <td>0.059851</td>\n",
       "      <td>0.059851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-11</th>\n",
       "      <td>0.057468</td>\n",
       "      <td>0.057468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-12</th>\n",
       "      <td>0.065573</td>\n",
       "      <td>0.065573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-13</th>\n",
       "      <td>0.069224</td>\n",
       "      <td>0.069224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-14</th>\n",
       "      <td>0.077292</td>\n",
       "      <td>0.077292</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Returns  Strategy\n",
       "Date                          \n",
       "2011-01-03  0.021500  0.021500\n",
       "2011-01-04  0.026705  0.026705\n",
       "2011-01-05  0.034852  0.034852\n",
       "2011-01-06  0.034043  0.034043\n",
       "2011-01-07  0.041179  0.041179\n",
       "2011-01-10  0.059851  0.059851\n",
       "2011-01-11  0.057468  0.057468\n",
       "2011-01-12  0.065573  0.065573\n",
       "2011-01-13  0.069224  0.069224\n",
       "2011-01-14  0.077292  0.077292"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[['Returns', 'Strategy']].cumsum().iloc[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Returns</th>\n",
       "      <th>Strategy</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-01-03</th>\n",
       "      <td>1.021732</td>\n",
       "      <td>1.021732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-04</th>\n",
       "      <td>1.027065</td>\n",
       "      <td>1.027065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-05</th>\n",
       "      <td>1.035466</td>\n",
       "      <td>1.035466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-06</th>\n",
       "      <td>1.034629</td>\n",
       "      <td>1.034629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-07</th>\n",
       "      <td>1.042039</td>\n",
       "      <td>1.042039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-10</th>\n",
       "      <td>1.061678</td>\n",
       "      <td>1.061678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-11</th>\n",
       "      <td>1.059152</td>\n",
       "      <td>1.059152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-12</th>\n",
       "      <td>1.067770</td>\n",
       "      <td>1.067770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-13</th>\n",
       "      <td>1.071677</td>\n",
       "      <td>1.071677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-14</th>\n",
       "      <td>1.080357</td>\n",
       "      <td>1.080357</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Returns  Strategy\n",
       "Date                          \n",
       "2011-01-03  1.021732  1.021732\n",
       "2011-01-04  1.027065  1.027065\n",
       "2011-01-05  1.035466  1.035466\n",
       "2011-01-06  1.034629  1.034629\n",
       "2011-01-07  1.042039  1.042039\n",
       "2011-01-10  1.061678  1.061678\n",
       "2011-01-11  1.059152  1.059152\n",
       "2011-01-12  1.067770  1.067770\n",
       "2011-01-13  1.071677  1.071677\n",
       "2011-01-14  1.080357  1.080357"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.exp(data[['Returns', 'Strategy']].cumsum()).iloc[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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C9CT+sGhMzxqPlBwQQgghRB9ptHl6mlItjMu1MjonuPyA2QR6F0HTqh2NNNnd/M+xwxif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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11437e6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data[['Returns', 'Strategy']].cumsum().apply(np.exp).plot(figsize=(10, 6));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9rQ2NPTg8OiePKyHfJmFBJpDMm8hKMmBfiOTL9nzN9kNOlm1pZXuLg9vPGJ/S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\nzMzMKsTFm5mZmVmFuHgzMzMzq5D/A0qO0dJSyM//AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11435eba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = data[['Returns', 'Strategy']].cumsum().apply(np.exp).plot(figsize=(10, 6))\n",
    "data['Positions'].plot(ax=ax, secondary_y='Positions');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Financial Base Class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class FinancialData(object):\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fd = FinancialData()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<__main__.FinancialData at 0x112b88978>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def f(x):\n",
    "    return x ** 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class FinancialData(object):\n",
    "    def __init__(self):\n",
    "        pass\n",
    "    \n",
    "    def f(self, x):\n",
    "        return x ** 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fd = FinancialData()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "144"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fd.f(12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class FinancialData(object):\n",
    "    def __init__(self, x):  # special method\n",
    "        self.x = x\n",
    "    \n",
    "    def f(self):\n",
    "        return self.x ** 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fd = FinancialData(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fd.x  # attribute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fd.f()  # method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class FinancialData(object):\n",
    "    def __init__(self, symbol):  # special method\n",
    "        self.symbol = symbol\n",
    "        self.prepare_data()\n",
    "    \n",
    "    def prepare_data(self):\n",
    "        self.raw = pd.read_csv('http://hilpisch.com/tr_eikon_eod_data.csv',\n",
    "                               index_col=0, parse_dates=True)\n",
    "        self.data = pd.DataFrame(self.raw[self.symbol])\n",
    "        self.data['Returns'] = np.log(self.data / self.data.shift(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 13.1 ms, sys: 2.88 ms, total: 15.9 ms\n",
      "Wall time: 166 ms\n"
     ]
    }
   ],
   "source": [
    "%time fd = FinancialData('AAPL.O')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'AAPL.O'"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fd.symbol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 1960 entries, 2010-01-04 to 2017-10-13\n",
      "Data columns (total 10 columns):\n",
      "AAPL.O    1960 non-null float64\n",
      "MSFT.O    1960 non-null float64\n",
      "INTC.O    1960 non-null float64\n",
      "AMZN.O    1960 non-null float64\n",
      "GS.N      1960 non-null float64\n",
      "SPY       1960 non-null float64\n",
      ".SPX      1960 non-null float64\n",
      ".VIX      1960 non-null float64\n",
      "EUR=      1960 non-null float64\n",
      "XAU=      1960 non-null float64\n",
      "dtypes: float64(10)\n",
      "memory usage: 168.4 KB\n"
     ]
    }
   ],
   "source": [
    "fd.raw.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAPL.O</th>\n",
       "      <th>Returns</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-04</th>\n",
       "      <td>30.572827</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>30.625684</td>\n",
       "      <td>0.001727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>30.138541</td>\n",
       "      <td>-0.016034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>30.082827</td>\n",
       "      <td>-0.001850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-08</th>\n",
       "      <td>30.282827</td>\n",
       "      <td>0.006626</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               AAPL.O   Returns\n",
       "Date                           \n",
       "2010-01-04  30.572827       NaN\n",
       "2010-01-05  30.625684  0.001727\n",
       "2010-01-06  30.138541 -0.016034\n",
       "2010-01-07  30.082827 -0.001850\n",
       "2010-01-08  30.282827  0.006626"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fd.data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Class for Vectorized Backtesting "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class SMABacktester(FinancialData):\n",
    "    def __init__(self, symbol, SMA1, SMA2):\n",
    "        FinancialData.__init__(self, symbol)\n",
    "        self.SMA1 = SMA1\n",
    "        self.SMA2 = SMA2\n",
    "        self.prepare_studies()\n",
    "        \n",
    "    def prepare_studies(self):\n",
    "        self.data['SMA1'] = self.data[self.symbol].rolling(self.SMA1).mean()\n",
    "        self.data['SMA2'] = self.data[self.symbol].rolling(self.SMA2).mean()\n",
    "        \n",
    "    def plot_data(self, cols=None):\n",
    "        if cols is None:\n",
    "            cols = [self.symbol]\n",
    "        self.data[cols].plot(figsize=(10, 6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sma = SMABacktester('AAPL.O', 42, 252)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 1960 entries, 2010-01-04 to 2017-10-13\n",
      "Data columns (total 4 columns):\n",
      "AAPL.O     1960 non-null float64\n",
      "Returns    1959 non-null float64\n",
      "SMA1       1919 non-null float64\n",
      "SMA2       1709 non-null float64\n",
      "dtypes: float64(4)\n",
      "memory usage: 76.6 KB\n"
     ]
    }
   ],
   "source": [
    "sma.data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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g14xK6vMuBIOKj9+vQwFjJ9h7rfp7R2RESwghRLepjathUCba1NmhhpPHQu35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d3HWuuMMa6NbXcCd8YWXVVV1a42Zbf5zz8GZSvwTpmJl5m1a48R681qzi9gHSFC518OGZmUV1Ry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\nRCRB/h/AiGEO0DTX2gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114518f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sma.plot_data(['AAPL.O', 'SMA1', 'SMA2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class SMABacktester(SMABacktester):\n",
    "    def run_strategy(self, net=False):\n",
    "        self.results = self.data.copy().dropna()\n",
    "        self.results['Positions'] = np.where(\n",
    "                self.results['SMA1'] > self.results['SMA2'], 1, -1)\n",
    "        self.results['Strategy'] = self.results['Positions'].shift(1) * self.results['Returns']\n",
    "        self.results.dropna(inplace=True)\n",
    "        perf = self.results[['Returns', 'Strategy']].sum().apply(np.exp)\n",
    "        if net is True:\n",
    "            return perf - 1\n",
    "        return perf\n",
    "        \n",
    "    def plot_results(self):\n",
    "        try:\n",
    "            self.results\n",
    "        except:\n",
    "            self.run_strategy()\n",
    "        self.results[['Returns', 'Strategy']].cumsum().apply(np.exp).plot(figsize=(10, 6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sma = SMABacktester('AAPL.O', 42, 252)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Returns     3.406904\n",
       "Strategy    4.928507\n",
       "dtype: float64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sma.run_strategy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Returns     2.406904\n",
       "Strategy    3.928507\n",
       "dtype: float64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sma.run_strategy(net=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RwYmjs3DpcMmz22i0uXhvTxPffXEn22qDA66w9/YEWtE+++SA47zVy6cUpAb1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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1142bdd68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sma.plot_results()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Some Backtests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sma = SMABacktester('AAPL.O', 42, 252)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Returns     3.406904\n",
       "Strategy    4.928507\n",
       "dtype: float64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sma.run_strategy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sma = SMABacktester('AAPL.O', 30, 252)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Returns     3.406904\n",
       "Strategy    4.539994\n",
       "dtype: float64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sma.run_strategy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sma = SMABacktester('AAPL.O', 30, 180)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Returns     3.879995\n",
       "Strategy    4.139461\n",
       "dtype: float64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sma.run_strategy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Some Improvements "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class SMABacktester(FinancialData):\n",
    "    def __init__(self, symbol, SMA1, SMA2):\n",
    "        FinancialData.__init__(self, symbol)\n",
    "        self.SMA1 = SMA1\n",
    "        self.SMA2 = SMA2\n",
    "        self.prepare_studies()\n",
    "        \n",
    "    def prepare_studies(self):\n",
    "        self.data['SMA1'] = self.data[self.symbol].rolling(self.SMA1).mean()\n",
    "        self.data['SMA2'] = self.data[self.symbol].rolling(self.SMA2).mean()\n",
    "        \n",
    "    def run_strategy(self, SMA=None, net=False):\n",
    "        if SMA is not None:\n",
    "            self.SMA1 = SMA[0]\n",
    "            self.SMA2 = SMA[1]\n",
    "            self.prepare_studies()\n",
    "        self.results = self.data.copy().dropna()\n",
    "        self.results['Positions'] = np.where(\n",
    "                self.results['SMA1'] > self.results['SMA2'], 1, -1)\n",
    "        self.results['Strategy'] = self.results['Positions'].shift(1) * self.results['Returns']\n",
    "        self.results.dropna(inplace=True)\n",
    "        perf = self.results[['Returns', 'Strategy']].sum().apply(np.exp)\n",
    "        if net is True:\n",
    "            return perf - 1\n",
    "        return perf\n",
    "    \n",
    "    def plot_data(self, cols=None):\n",
    "        if cols is None:\n",
    "            cols = [self.symbol]\n",
    "        self.data[cols].plot(figsize=(10, 6))\n",
    "        \n",
    "    def plot_results(self):\n",
    "        try:\n",
    "            self.results\n",
    "        except:\n",
    "            self.run_strategy()\n",
    "        self.results[['Returns', 'Strategy']].cumsum().apply(np.exp).plot(figsize=(10, 6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sma = SMABacktester('AAPL.O', 42, 252)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Returns     3.406904\n",
       "Strategy    4.928507\n",
       "dtype: float64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sma.run_strategy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Returns     3.406904\n",
       "Strategy    4.539994\n",
       "dtype: float64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sma.run_strategy(SMA=(30, 252))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Returns     3.879995\n",
       "Strategy    4.139461\n",
       "dtype: float64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sma.run_strategy(SMA=(30, 180))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from itertools import product"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('a', 0), ('a', 1), ('a', 2), ('b', 0), ('b', 1), ('b', 2)]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(product('ab', range(3)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(10, 150),\n",
       " (10, 175),\n",
       " (10, 200),\n",
       " (10, 225),\n",
       " (10, 250),\n",
       " (15, 150),\n",
       " (15, 175),\n",
       " (15, 200),\n",
       " (15, 225),\n",
       " (15, 250),\n",
       " (20, 150),\n",
       " (20, 175),\n",
       " (20, 200),\n",
       " (20, 225),\n",
       " (20, 250),\n",
       " (25, 150),\n",
       " (25, 175),\n",
       " (25, 200),\n",
       " (25, 225),\n",
       " (25, 250),\n",
       " (30, 150),\n",
       " (30, 175),\n",
       " (30, 200),\n",
       " (30, 225),\n",
       " (30, 250)]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(product([10, 15, 20, 25, 30], [150, 175, 200, 225, 250]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10, 150) [ 4.22517926  4.72056343]\n",
      "(10, 175) [ 4.1152303   6.39081245]\n",
      "(10, 200) [ 3.45575817  4.15380312]\n",
      "(10, 225) [ 3.50692845  5.18822687]\n",
      "(10, 250) [ 3.37831196  3.33515728]\n",
      "(15, 150) [ 4.22517926  4.63868746]\n",
      "(15, 175) [ 4.1152303   3.72830617]\n",
      "(15, 200) [ 3.45575817  4.3930667 ]\n",
      "(15, 225) [ 3.50692845  4.39343367]\n",
      "(15, 250) [ 3.37831196  4.55017383]\n",
      "(20, 150) [ 4.22517926  3.94727034]\n",
      "(20, 175) [ 4.1152303   3.65333787]\n",
      "(20, 200) [ 3.45575817  4.4950541 ]\n",
      "(20, 225) [ 3.50692845  5.28375109]\n",
      "(20, 250) [ 3.37831196  4.0020055 ]\n",
      "(25, 150) [ 4.22517926  3.3911836 ]\n",
      "(25, 175) [ 4.1152303   4.22332554]\n",
      "(25, 200) [ 3.45575817  4.36375008]\n",
      "(25, 225) [ 3.50692845  4.53595687]\n",
      "(25, 250) [ 3.37831196  3.76791343]\n",
      "(30, 150) [ 4.22517926  2.96801167]\n",
      "(30, 175) [ 4.1152303   4.22315719]\n",
      "(30, 200) [ 3.45575817  5.19526157]\n",
      "(30, 225) [ 3.50692845  4.51065691]\n",
      "(30, 250) [ 3.37831196  4.39707035]\n"
     ]
    }
   ],
   "source": [
    "for SMA in product([10, 15, 20, 25, 30], [150, 175, 200, 225, 250]):\n",
    "    print(SMA, sma.run_strategy(SMA=SMA).values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class SMABacktester(SMABacktester):\n",
    "    def optimize_parameters(self, SMA1_list, SMA2_list):\n",
    "        self.opt_results = pd.DataFrame()\n",
    "        for i, SMA in enumerate(product(SMA1_list, SMA2_list)):\n",
    "            perf = sma.run_strategy(SMA=SMA).values\n",
    "            self.opt_results = self.opt_results.append(pd.DataFrame({'SMA1': SMA[0], 'SMA2': SMA[1],\n",
    "                                                  'BENCH': perf[0], 'STRAT': perf[1]}, index=[i]))\n",
    "        self.opt_results = self.opt_results[['SMA1', 'SMA2', 'BENCH', 'STRAT']]\n",
    "        print('Optimal results:')\n",
    "        print(self.opt_results.iloc[sma.opt_results['STRAT'].argmax()])\n",
    "        \n",
    "    def print_opt_results(self):\n",
    "        try:\n",
    "            self.opt_results\n",
    "        except:\n",
    "            print('no optimization results available yet')\n",
    "        print(self.opt_results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sma = SMABacktester('AAPL.O', 42, 252)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimal results:\n",
      "SMA1      10.000000\n",
      "SMA2     175.000000\n",
      "BENCH      4.115230\n",
      "STRAT      6.390812\n",
      "Name: 1, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "sma.optimize_parameters([10, 15, 20, 25], [150, 175, 200, 225, 250])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.3908124460789706"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sma.opt_results['STRAT'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sma.opt_results['STRAT'].argmax()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    SMA1  SMA2     BENCH     STRAT\n",
      "0     10   150  4.225179  4.720563\n",
      "1     10   175  4.115230  6.390812\n",
      "2     10   200  3.455758  4.153803\n",
      "3     10   225  3.506928  5.188227\n",
      "4     10   250  3.378312  3.335157\n",
      "5     15   150  4.225179  4.638687\n",
      "6     15   175  4.115230  3.728306\n",
      "7     15   200  3.455758  4.393067\n",
      "8     15   225  3.506928  4.393434\n",
      "9     15   250  3.378312  4.550174\n",
      "10    20   150  4.225179  3.947270\n",
      "11    20   175  4.115230  3.653338\n",
      "12    20   200  3.455758  4.495054\n",
      "13    20   225  3.506928  5.283751\n",
      "14    20   250  3.378312  4.002006\n",
      "15    25   150  4.225179  3.391184\n",
      "16    25   175  4.115230  4.223326\n",
      "17    25   200  3.455758  4.363750\n",
      "18    25   225  3.506928  4.535957\n",
      "19    25   250  3.378312  3.767913\n"
     ]
    }
   ],
   "source": [
    "sma.print_opt_results()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"http://hilpisch.com/tpq_logo.png\" width=350px align=right>"
   ]
  }
 ],
 "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": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}

02_event_based_backtesting.ipynb

tick_client.py

#
# Simple Tick Data Client
#
import zmq
import datetime

context = zmq.Context()
socket = context.socket(zmq.SUB)
socket.connect('tcp://127.0.0.1:5555')

socket.setsockopt_string(zmq.SUBSCRIBE, '')

while True:
    msg = socket.recv_string()
    t = datetime.datetime.now()
    print(str(t) + ' | ' + msg)

tick_collector.py

#
# Simple Tick Data Collector
#
import zmq
import datetime
import pandas as pd

context = zmq.Context()
socket = context.socket(zmq.SUB)
socket.connect('tcp://127.0.0.1:5555')

socket.setsockopt_string(zmq.SUBSCRIBE, '')

raw = pd.DataFrame()

while True:
    msg = socket.recv_string()
    t = datetime.datetime.now()
    print(str(t) + ' | ' + msg)
    symbol, price = msg.split()
    raw = raw.append(pd.DataFrame({'SYM': symbol, 'PRICE': price}, index=[t]))
    data = raw.resample('5s', label='right').last()
    if len(data) % 4 == 0:
        print(50 * '=')
        print(data.tail())
        print(50 * '=')
    # simple way of storing data, needs to be adjusted for your purposes
    if len(data) % 20 == 0:
        # h5 = pd.HDFStore('database.h5', 'a')
        # h5['data'] = data
        # h5.close()
        pass

tick_server.py

#
# Simple Tick Data Server
#
import zmq
import time
import random

context = zmq.Context()
socket = context.socket(zmq.PUB)
socket.bind('tcp://127.0.0.1:5555')

AAPL = 100.

while True:
    AAPL += random.gauss(0, 1) * 0.5
    msg = 'AAPL %.3f' % AAPL
    socket.send_string(msg)
    print(msg)
    time.sleep(random.random() * 2)