00_epat_august_2017.md

Executive Program in Algorithmic Trading (QuantInsti)

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

Online, 26. & 27. August 2017

Resources

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

Slides

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

Python

We are using Python 3.6. Download and install Miniconda 3.6 from https://conda.io/miniconda.html

If you have either Miniconda or Anaconda already installed, there is no need to install anything new. You should then execute the following lines on the shell/command prompt:

conda create -n epat python=3.6
(source) activate epat
conda install numpy pandas=0.19 scikit-learn matplotlib
conda install pandas-datareader pytables
conda install ipython jupyter
jupyter notebook

Management of environments: https://conda.io/docs/using/envs.html

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",
   "metadata": {},
   "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": {},
   "outputs": [],
   "source": [
    "from pandas_datareader import data as web"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = web.DataReader('AAPL', data_source='google')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 1925 entries, 2010-01-04 to 2017-08-25\n",
      "Data columns (total 5 columns):\n",
      "Open      1925 non-null float64\n",
      "High      1925 non-null float64\n",
      "Low       1925 non-null float64\n",
      "Close     1925 non-null float64\n",
      "Volume    1925 non-null int64\n",
      "dtypes: float64(4), int64(1)\n",
      "memory usage: 90.2 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</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>30.49</td>\n",
       "      <td>30.64</td>\n",
       "      <td>30.34</td>\n",
       "      <td>30.57</td>\n",
       "      <td>123432050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>30.66</td>\n",
       "      <td>30.80</td>\n",
       "      <td>30.46</td>\n",
       "      <td>30.63</td>\n",
       "      <td>150476004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>30.63</td>\n",
       "      <td>30.75</td>\n",
       "      <td>30.11</td>\n",
       "      <td>30.14</td>\n",
       "      <td>138039594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>30.25</td>\n",
       "      <td>30.29</td>\n",
       "      <td>29.86</td>\n",
       "      <td>30.08</td>\n",
       "      <td>119282324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-08</th>\n",
       "      <td>30.04</td>\n",
       "      <td>30.29</td>\n",
       "      <td>29.87</td>\n",
       "      <td>30.28</td>\n",
       "      <td>111969081</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Open   High    Low  Close     Volume\n",
       "Date                                             \n",
       "2010-01-04  30.49  30.64  30.34  30.57  123432050\n",
       "2010-01-05  30.66  30.80  30.46  30.63  150476004\n",
       "2010-01-06  30.63  30.75  30.11  30.14  138039594\n",
       "2010-01-07  30.25  30.29  29.86  30.08  119282324\n",
       "2010-01-08  30.04  30.29  29.87  30.28  111969081"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</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>2017-08-21</th>\n",
       "      <td>157.50</td>\n",
       "      <td>157.89</td>\n",
       "      <td>155.11</td>\n",
       "      <td>157.21</td>\n",
       "      <td>26368528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-22</th>\n",
       "      <td>158.23</td>\n",
       "      <td>160.00</td>\n",
       "      <td>158.02</td>\n",
       "      <td>159.78</td>\n",
       "      <td>21604585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-23</th>\n",
       "      <td>159.07</td>\n",
       "      <td>160.47</td>\n",
       "      <td>158.88</td>\n",
       "      <td>159.98</td>\n",
       "      <td>19399081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-24</th>\n",
       "      <td>160.43</td>\n",
       "      <td>160.74</td>\n",
       "      <td>158.55</td>\n",
       "      <td>159.27</td>\n",
       "      <td>19818918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-25</th>\n",
       "      <td>159.65</td>\n",
       "      <td>160.56</td>\n",
       "      <td>159.27</td>\n",
       "      <td>159.86</td>\n",
       "      <td>25480063</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Open    High     Low   Close    Volume\n",
       "Date                                                \n",
       "2017-08-21  157.50  157.89  155.11  157.21  26368528\n",
       "2017-08-22  158.23  160.00  158.02  159.78  21604585\n",
       "2017-08-23  159.07  160.47  158.88  159.98  19399081\n",
       "2017-08-24  160.43  160.74  158.55  159.27  19818918\n",
       "2017-08-25  159.65  160.56  159.27  159.86  25480063"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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GAy2llFJKdRnGGPy/XA/TPo12882aHj1h6/RSM8ixP0V+cLRd9xBsC9jgYZmubupAy3Xd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\ni2CrmpVy9gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ab8cc18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['Close'].plot(figsize=(10, 6), title='Apple, Inc. Stock Price');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Deriving Statistics (\"Studies\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data['SMA1'] = data['Close'].rolling(42).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data['SMA2'] = data['Close'].rolling(252).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-08-21</th>\n",
       "      <td>157.50</td>\n",
       "      <td>157.89</td>\n",
       "      <td>155.11</td>\n",
       "      <td>157.21</td>\n",
       "      <td>26368528</td>\n",
       "      <td>151.139286</td>\n",
       "      <td>131.532302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-22</th>\n",
       "      <td>158.23</td>\n",
       "      <td>160.00</td>\n",
       "      <td>158.02</td>\n",
       "      <td>159.78</td>\n",
       "      <td>21604585</td>\n",
       "      <td>151.476190</td>\n",
       "      <td>131.735754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-23</th>\n",
       "      <td>159.07</td>\n",
       "      <td>160.47</td>\n",
       "      <td>158.88</td>\n",
       "      <td>159.98</td>\n",
       "      <td>19399081</td>\n",
       "      <td>151.802381</td>\n",
       "      <td>131.938651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-24</th>\n",
       "      <td>160.43</td>\n",
       "      <td>160.74</td>\n",
       "      <td>158.55</td>\n",
       "      <td>159.27</td>\n",
       "      <td>19818918</td>\n",
       "      <td>152.122619</td>\n",
       "      <td>132.141984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-25</th>\n",
       "      <td>159.65</td>\n",
       "      <td>160.56</td>\n",
       "      <td>159.27</td>\n",
       "      <td>159.86</td>\n",
       "      <td>25480063</td>\n",
       "      <td>152.506667</td>\n",
       "      <td>132.349484</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Open    High     Low   Close    Volume        SMA1        SMA2\n",
       "Date                                                                        \n",
       "2017-08-21  157.50  157.89  155.11  157.21  26368528  151.139286  131.532302\n",
       "2017-08-22  158.23  160.00  158.02  159.78  21604585  151.476190  131.735754\n",
       "2017-08-23  159.07  160.47  158.88  159.98  19399081  151.802381  131.938651\n",
       "2017-08-24  160.43  160.74  158.55  159.27  19818918  152.122619  132.141984\n",
       "2017-08-25  159.65  160.56  159.27  159.86  25480063  152.506667  132.349484"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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Gq8ztp7wuwIQ+3Tc/q7w0QOFRP0NG2hkxNvLBnARaQgghIkZ561oGWqH3J6JT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\n/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wnmDDkKGgYp/yL2b/d25xKcpfIKMLqDf6E1V4069IdVdYpPNYpP30nxaT+KTzVqXpqF\nvGl84FZr7aPQcMmB/sD1wL3A+FjqfwaYZozpb61dRTDnyinW2jcS1O7WpBodHNUpPtUoPtUoPtUo\nPtWoGalHq2kWA9bsuTDtmwTXUHsYCBtjroyl/t5AXWxHxFr7RifaEVWjg6M6xacaxacaxacaxaca\nNSP1aDWB3XPdtN1mALsHaV8IfNMY8zdgKI0GDXYmqtHBUZ3iU43iU43iU43iU42al4JWM4ilfgd0\nB56NLd4JXAOMBFbZYEK7Tks1OjiqU3yqUXyqUXyqUXyqUfNQ0GoePpAEbAZGG2PuAMqAK6218xLa\nsrZDNTo4qlN8qlF8qlF8qlF8qlEz0PQOzcQYM5lgtvL5wB+stb9PcJPaHNXo4KhO8alG8alG8alG\n8alGTacereazjuC01ttscFkC+TzV6OCoTvGpRvGpRvGpRvGpRk2kHi0RERGRFqLpHURERERaiIKW\niIiISAtR0BIRERFpIQpaIiIiIi1EQUtERESkhShoiYiIiLQQzaMlIu2CMWY1waVA6oB6YCkwG3gg\ndoHbA23bD1gFRK21dS3bUhGRPdSjJSLtyRnW2kygL3AzcDWgmapFpM1Sj5aItDvW2u3As8aYUuBt\nY8ytBOHrN8BAYDvwe2vtL2KbvB77uc0YAzDDWvuWMeYi4MdAD+Ad4FJrbVHrvRMR6ejUoyUi7Za1\n9h2CS4QcB+wCvgFkAacBlxtjzo6tenzsZ5a1NiMWss4CrgH+C8gD3gAeb832i0jHpx4tEWnvSoAc\na+2rjZZ9YIx5HJgKPLOf7S4DbrLWLgMwxtwIXGOM6ateLRFpLgpaItLe9QK2GGMmEYzbGgkkAcnA\nnw+wXV/gzthhx9282PMpaIlIs1DQEpF2yxgzkSAYzSPoubobONVaW2WMuQPIja3q9rH5WuAGa+2f\nWqWxItIpaYyWiLQ7xpguxpjTgSeAP1prPwQygS2xkHUU8NVGm2wCfGBAo2X3AT81xoyIPWdXY8y5\nrfMORKSzUNASkfbkOWPMToLeqGuB24ALY499G/hV7PHrAbt7I2ttBXAD8KYxZpsxZrK19mngFuAJ\nY8wO4CPg1NZ7KyLSGXjO7atHXURERESaSj1aIiIiIi1EQUtERESkhShoiYiIiDTIyicAAAA2SURB\nVLQQBS0RERGRFqKgJSIiItJCFLREREREWoiCloiIiEgLUdASERERaSEKWiIiIiIt5P8AB+Nlj0hW\neW8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10beb77f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data[['Close', 'SMA1', 'SMA2']].plot(figsize=(10, 6));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data.dropna(inplace=True)"
   ]
  },
  {
   "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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-01-03</th>\n",
       "      <td>46.52</td>\n",
       "      <td>47.18</td>\n",
       "      <td>46.41</td>\n",
       "      <td>47.08</td>\n",
       "      <td>111280407</td>\n",
       "      <td>45.347381</td>\n",
       "      <td>37.173651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-04</th>\n",
       "      <td>47.49</td>\n",
       "      <td>47.50</td>\n",
       "      <td>46.88</td>\n",
       "      <td>47.33</td>\n",
       "      <td>77337001</td>\n",
       "      <td>45.410238</td>\n",
       "      <td>37.240159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-05</th>\n",
       "      <td>47.08</td>\n",
       "      <td>47.76</td>\n",
       "      <td>47.07</td>\n",
       "      <td>47.71</td>\n",
       "      <td>63879193</td>\n",
       "      <td>45.463571</td>\n",
       "      <td>37.307937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-06</th>\n",
       "      <td>47.82</td>\n",
       "      <td>47.89</td>\n",
       "      <td>47.56</td>\n",
       "      <td>47.68</td>\n",
       "      <td>75106626</td>\n",
       "      <td>45.520238</td>\n",
       "      <td>37.377540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-07</th>\n",
       "      <td>47.71</td>\n",
       "      <td>48.05</td>\n",
       "      <td>47.41</td>\n",
       "      <td>48.02</td>\n",
       "      <td>77982212</td>\n",
       "      <td>45.579762</td>\n",
       "      <td>37.448730</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Open   High    Low  Close     Volume       SMA1       SMA2\n",
       "Date                                                                   \n",
       "2011-01-03  46.52  47.18  46.41  47.08  111280407  45.347381  37.173651\n",
       "2011-01-04  47.49  47.50  46.88  47.33   77337001  45.410238  37.240159\n",
       "2011-01-05  47.08  47.76  47.07  47.71   63879193  45.463571  37.307937\n",
       "2011-01-06  47.82  47.89  47.56  47.68   75106626  45.520238  37.377540\n",
       "2011-01-07  47.71  48.05  47.41  48.02   77982212  45.579762  37.448730"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Formulating a Trading Strategy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Position'] = np.where(data['SMA1'] > data['SMA2'], 1, -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>SMA1</th>\n",
       "      <th>SMA2</th>\n",
       "      <th>Position</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-01-03</th>\n",
       "      <td>46.52</td>\n",
       "      <td>47.18</td>\n",
       "      <td>46.41</td>\n",
       "      <td>47.08</td>\n",
       "      <td>111280407</td>\n",
       "      <td>45.347381</td>\n",
       "      <td>37.173651</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-04</th>\n",
       "      <td>47.49</td>\n",
       "      <td>47.50</td>\n",
       "      <td>46.88</td>\n",
       "      <td>47.33</td>\n",
       "      <td>77337001</td>\n",
       "      <td>45.410238</td>\n",
       "      <td>37.240159</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-05</th>\n",
       "      <td>47.08</td>\n",
       "      <td>47.76</td>\n",
       "      <td>47.07</td>\n",
       "      <td>47.71</td>\n",
       "      <td>63879193</td>\n",
       "      <td>45.463571</td>\n",
       "      <td>37.307937</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-06</th>\n",
       "      <td>47.82</td>\n",
       "      <td>47.89</td>\n",
       "      <td>47.56</td>\n",
       "      <td>47.68</td>\n",
       "      <td>75106626</td>\n",
       "      <td>45.520238</td>\n",
       "      <td>37.377540</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-07</th>\n",
       "      <td>47.71</td>\n",
       "      <td>48.05</td>\n",
       "      <td>47.41</td>\n",
       "      <td>48.02</td>\n",
       "      <td>77982212</td>\n",
       "      <td>45.579762</td>\n",
       "      <td>37.448730</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Open   High    Low  Close     Volume       SMA1       SMA2  \\\n",
       "Date                                                                      \n",
       "2011-01-03  46.52  47.18  46.41  47.08  111280407  45.347381  37.173651   \n",
       "2011-01-04  47.49  47.50  46.88  47.33   77337001  45.410238  37.240159   \n",
       "2011-01-05  47.08  47.76  47.07  47.71   63879193  45.463571  37.307937   \n",
       "2011-01-06  47.82  47.89  47.56  47.68   75106626  45.520238  37.377540   \n",
       "2011-01-07  47.71  48.05  47.41  48.02   77982212  45.579762  37.448730   \n",
       "\n",
       "            Position  \n",
       "Date                  \n",
       "2011-01-03         1  \n",
       "2011-01-04         1  \n",
       "2011-01-05         1  \n",
       "2011-01-06         1  \n",
       "2011-01-07         1  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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pG3p2landDGVSGV67ZyHrIXWL+bZWhjJJUjf0/O3L0fGEqw6GMqkSfgpYAzXO4Uu1nKFM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\nOQp9NgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10bdfc780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['Position'].plot(figsize=(10, 6));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/n169ejF//vw6PddVV13FG2+8QVRUVJV9bf33lp2dzTvvvMPMmTPLp9PpqL75\n5hu2bdtWZXCPEM2lsLCQ119/nQsvvJDhw4e3dHXajRdeeIFzzjmH888/v6WrUoXaux3zz/ejzb4O\ntXk9trnzoe9ANF8geCyvhNs+OcDPRnXiioExLVbPxMREgDb1jaQ2o3Tn1rC/52nbjwGPNaxaoq4e\neugh0tLSagz43n33XZYsWUJpaSlDhw7l+uuvr9PznDx5kvnz51cb7AkhRGOSDF8H5FsfVxs0Atvl\nP6mye+0hawaMsd1qsXSa8FPnJl3ROp177rm1Om7+/Pl1zuhVFhsby8yZVaZlFEIIIRpEuV2ovFPW\nRjWTIB/JLWbx9izGdQujc5izyn5xdhLwCSGEaNVk0Ebja43ZU/M3N4Hbt4RpYNWAb3u6C1PBz0Z1\nbuaatQ+tdmiL/IG3TfJ7E0I0ltYYlIimoU6dLA/2tLGTIb5LlWNSTrqJDLQTHyq5qvpotXfNZrPh\n8XhwOFptFcVpPB4PNlur/Q5RJxK4yj0QQjQf9aU1vlO7VEe78rpqg/2UbDd9Y4Pki0A9tdpoKigo\nCLfbTXFxsfxy2wClFDabrc1PyCzvNX9yP0RrIF8+2jd1PBW1woABw7D96KfVHlPsMTmaW8w4GaxR\nb6024NM0jeDg4JauhhBCiBYiXzg6BrXWWvddu+DiMx7z4c6TmAqGdW670361tPbR/iaEEKLdkgxf\nO+cqgPBIbGOrX0ZVKcWne08xrlsYwxNCm7ly7YcEfEIIIVolyfB1DKqoECLOPLfr8fxS8ou9jO4q\nzbkNIQGfEEII0YG0uoypqxCCz5y523vSGr3bP7Zt9xFvaRLwCSFEB6Gys1DHjrR0Neqs1QUoonEV\n5EHImQO+PVlFBDlsJEVWnZtP1J4EfEII0UGY99+C+fCCOp+nvF7MZe+hCvP9y4uLUXu2N1b1qpAm\n3abTWu6tysuBtMNoPfpUu7+o1CT5aAED44Kw21pHnU2zbX4BkYBPCCE6Cq8XsAK4ulCb16E+eR+1\n9F3/8sWvYj59P2byV3XKwqm0I6jsrDrVQbRP5t03AqANPqfa/f9JOcXJIg/XDI9rzmqdVeqhkpau\nQr202mlZhGhJ0oQk96BdK8yDiOjaH3/qpPXTZvcrLmseVq89AwV5aNOuqNXlzEcWgM2G/eWPa3W8\nvBfbJ+XxVGz06l/tMTsyXHQJD2BQfOuYjsXrVezZ4ea8iTUfq+v6TOA5wA68ZhjGE6ftvxBYChz0\nFX1oGMbyNkjfAAAgAElEQVQfanNufUiGT4hKWkszhxBNqrS0bsfn51k/Q04bJVlpW+3aWqtLlX/I\nm2aNx8rfYzvn+8Kg3XIPmt1eZbdSir1ZRQyIbR1z8nq9ivVrCnC7av4Couu6HfgbcAkwGJir6/rg\nag792jCMkb5/f6jjuXUiAZ8QQnQAqmxReoDSOjZJncq2fno9/uWVl1IsKa7dtXLq3pQrGb52KjcH\nAC22U7W7Mws95Li9DIhvHQHfoX3FnMr2MnJsreozFkgxDOOAYRglwPvA7Fo+VUPOPSMJ+IQQoiPI\nyyl/qDZ8XadTVdYJ60GloFF5PLB1Q8VBu7ZifvdlzRfLSq/180qGr31T+aesB2eYg2/zsQIABsS1\nfMB37GgJe7a7iU9wkNSrVqOFuwJHK22n+spON0HX9W26rq/UdX1IHc+tk1bThy8urvV0yBQdV9kH\nTHh4eId/TwYHB2Oz2Tr8fWg3wsPg2bcrtuvye/3V78D0QmhExXkFef7Xq+11zxlTcV4Nx5b6mp5D\nQkLkfdjIWsU9nTQdRo6GpN5wWnCvFEwcFM7UoT3oGuFsoQpW1MXp8NK7j0ZomA3NlyrTdX1TpcNe\nMQzjlTpeegvQ3TCMAl3XLwU+Bvo1SqWr0WoCvqwsGbElWt6pU9Y3zvz8/A7/niwqKsI0zQ5/H9oL\ntekbzJefKt+23fsEWt+auwWpgjzMO30L2p9zHvZf3A+A+fYLqK//Y2Vn8k5BcAgUubC99FG1/bHK\nmB/9E7ViCQQFY39+8Vmf2+Pr71dYWCjvw0aklMLlcrX4PfU+egcEh2K/57Eq+3ZmuLjv8yM8MLkr\ngd3CW6B2Fk+pYvO3hWQc9zBpRhhRMVbYlJiYiGEYo89yahqQVGm7m6+snGEYeZUer9B1/UVd1+Nq\nc259tJqATwjR+kiTWvuh0o/5bZtP/hb7q8tqPjE/t+Lx/75DpR2B6Fgr2OvRF9vdf4T0NFTaYdRb\nf0Utew9z8iWkOyIIcdqICnJUf71a9MuT91/7pbIz4cgBtNnXVbt/30k3AP1beMBGym43Gcc9DDkn\nuDzYq6WNQD9d13thBWvXANdWPkDX9QQg3TAMpev6WKxudieBUzWdWx8S8AkhRAegPn6n+vLcHNSa\n5Wizr0WzVZOZy8/z2zQfWYDt9getjeg4tOAQ6NmPjMJSPuo7m+2ZSaStyMDUrOzRlYNimDUwmtiQ\nAOv5ygK+0weAiGbV0sG0+ewjVj16D6h2f8pJN3EhDqKCWy5MOZ5awv49xXTtHkDv/nVb5cMwDI+u\n6wuAz7CmVnnDMIwduq7f6tv/EjAHuE3XdQ9QBFxjGIYCqj23oa9HAj4hhOhAtCuuQX3yPgRZmRO1\n+DXUxq/R+g2GoaOqnlCQV7XMtCZutl16NQBbjhXw/J4ACrqMZXDuAcZk7STh/PPZk5rNx7vg413Z\nhDptDI4PYbonkoiI7oR53HSvqa6+oERG6bZD0bFw/Cj0q75bwb7sIvq14Nq5RS6TLd+6CIuwMWhE\n/bKMhmGsAFacVvZSpccvAC/U9tyGkoBPiGrIB4xoT9RRa15X7eqbsV10JaZSqE8N633umw9PFRZQ\nlvNR2ZkQHokW4MQ8bXUNAPOt563jAgPZeryQP61NIybYweNpH9L7wGbroIOrmA7MDunE5p8/ye7M\nIpJTC9gYNx3iphPkLebpU26SolruQ120HC0mHhUVixZQdUDG/44Xcjy/lCsGxLRAzcDjUfywxQXA\nmIlhBIe0jwlNJOATQoh2zlzyBoSFo50/3SoICgalMJ9/FC3UmjxZvfYMqlsviO+Mee88AE7e+ghB\nGRlUWda+MJ8TQTF8sNfDF2lHiQqy86eLehDd81LMFzb7HdrNlUH3QTFogzVOuko58fjvyDYdvNz/\nKu79zxGeuKgH3aPO3lwmX8DaIa8XzjC4Z/nubOJDHFzUN7KZK2XZ+X0R6WkeBg0PIiS0fQR7IAGf\nEEK0a8pdBLu2ol3+k/LgDrvvv/4fNqGGnltx7JpP0EZPpMARzB+GzyNldwja+Y/Qo/AEnYpPEVpS\nSGxJHqkh8WyIG4qZVsLsgdHMHR5PcIANRozBds/jmE/f71+H1/4C+s3EhEUQlb4LAoPoteUF7p14\nH29syeCRqUlUp6X7mYkmpEz/ibsr2Z9TzMiEEALszR9sncr2cORACT37Ouk7qH1lnyXgE0KI9izT\nmjRZ69azoqzyRLdFrvKHau1nuL9Zw0sDdQ6Ed+WG/Z9SYnOwoc8k9rrDcduclNgDiCgt5PLUr5k5\n7xq6xvtnYbQBQ7G9+G/Um89BaDjqvytQG75CbVyL7Q9/sw6KjKZLxnH0AeG8sT2X97dlcc3wM88J\nJxm+dsjrrbI2M8BJVyk5RR76tkD/vcJ8L+vWFBDg1Og3uH0FeyABnxDiDORDtp0oC+gqrXurjT4f\nTmagPny7Ytk0oMju5Dejfs2xkHiuOfgZVx79CoBrZo7CfPM5KCmm7F2hAbbYn1X7lFqAE23+b1BZ\n6aj/+vqdK4X50pPW46gYyDjOZT2D2JNnYmzP4rIB0YQH+gcAkuFrv5TprTbDVzYdS79mno5FmYqd\nW91oGlxwUThBwe2nKbdM+3tFQgghKhT7lkMLrMhYaDY7WlIva+NUNoRH4g4I4rFhN3M8OJb7fngT\n3bUDYuKtY6LjyldC0Hz/bPc/Xf00LpXFVMraxXaCtMPWNXpaiwnYXQVcNTgWr4LvjuY38IWKNsWs\nvkl3Z4YLuwa9ous2DUpD/S/ZxYm0UvoODGo3gzRO1z5flRD1JBkFf3I/2j7ltjImBJ6WMXH6AkCv\nB/oMYk2vyeyM6s2vd73P2GG9sD/+CpSWWMdEx6Jdc0v5qdqki9B69a/xuf0CwrJm5LjOaGMnW3Xb\n/QN9YgLpEh7Ax7uyOekqrddrFG2QaVYZtJFeUMLKfacYlxSOs5n675lexY7vi0g7UkrfQYH0G9y8\ngWZzkoBPCCHas7IMX9BpfZIqZ/y69+arsH70LDjGBRnfl++z/fIBKziLisU2cQb2V5dhe/TvaD+9\nrfbPX/Y8Tt8HaXwCdO9tNTGnHkLTNH42qjMZhaX8a1vVpb40TZPuBe1RNX34NqYVUOJV3DAyvtmq\nsX9PMQf2FNOjj5MBQ4Pa9ZdcCfiEEKI9K/Zl+IJOy/AFVmQyUroMZl9Edyaf2AKANnaS9bPPQGy3\n3I1WqelNS+hac1NuJbYnXsP21JvlAZ8Wn2B9qHbriUo7BMDormFMSArn26P5lHoluOsQqsnw7cl0\nExvsoEt41bn5mkJ+rpeU3W46JzoYPjoEm639BnsgAZ8QQrRv7qp9+KztigDw+eOhxLpPMfXERrRr\nf47WvU+jPb0WFoEWHVu+OgfxCVZ51x6wfzcqZRcAk3tFUFBi8sHOk1WuIRm+dsj0glYRgnhMxc5M\nFwPjm2ewRkmxybo1BdhsWr1X0mhrJOATQoj2rLgI7A40R4B/uS/DlxUYyZG8UmY5jhPuKUIbOKJp\n6lE2Wjgq1voZbk3nYj55LwAjEkIZ1y2Mf23L4mCOu/y09tzE1hJaTfBs+k+8/MrGdLJcHib1iGjy\np1ZKsWe7m9ISxfgLwwiPqH3Gui2TgE8IIdozt7tqdg/KB23sjugJwJDLZ2J/dRlal25NWh2trGl3\nxBi/crtNY8G4BOwafHXQf/3eugYp5rdf4v35laiyQSei9ak0SrfYY7J6/yku7hvF+O7hTf7Uu39w\ncyilhB59nEREdYxgDyTgE0KI9q3YXXXABqA5rGlYd/c9j0C7Rq/oJp5otixT57T6Z2nd+6BdejVo\nNpTXau6NCHIwIiGUDWkFDXoqtfx9K6A4drRB12nPWipzqjwezI/fgZRd5QFfSrYbr4LRXass4tfo\ncnO87N9TTNceAQwb1TGacsvUOPGyrutvAJcDGYZhDPWV/Rm4AigB9gM3GYZxyrfvPmAe4AV+ZRjG\nZ01UdyGaTKtp9mhBcg/aB1VcVHVKFp+CJ/7BxrUn6R/uxNHUHdbLAoyASh3yo+OsJbbyTkG01dQ7\nrHMIW77P5FCOm57R9Rw1GRoOHIesdOjReP0RRSNIPYj61LAe7/gfALszrX6mA+OaNgAryPfyzRf5\nOBwag0cEd7juArXJ8L0FzDyt7HNgqGEYw4G9wH0Auq4PBq4BhvjOeVHX9Y6TLxVCiNbGXVR1hK7P\n4oMl5Lg9XHuWZc0aj+/DtdKHrBbte96ciulYLuobRajTxpIdVQdv1Jqvb5j58Tv1v4ZoGpWW8mPE\nWAB2ZbroGuEkIqhpF//at8PqG9peV9KoSY2v2DCMtUD2aWX/MQzD49v8Dijr9DEbeN8wjGLDMA4C\nKcDYRqyvEE2qo33jq4ncj3aguPo+fF5Tsf5wHmO6hjO4U0jT16PsrVQ5c+zL6pFTEdyFBdoZ0zWM\n7ekulFJ1nodPHdxrNRcCnEhFHU5pYMVFo/IFfLb7n8b2i/vZk1XEprRCxnQNq+HEhknZ7Sb1cCk9\n+wYSEtrxgj1onLV0bwYW+x53xQoAy6T6yoQQQjQzlXEcjqdC30FV9u3KLCLH7eX8ZugkD1Rk9vwC\nPivDp3KyqPzVYmBcMP89mMfx/LqvvGE+fg8AuRf+lJz96RRvdVGS7sLrUXi9Cq8XvF5FaYnCU6pA\nA7tdIyTURlCwjaBgjagYB/EJDux2+cLTmMyvVqHeedHaCItAs9lYvf8UIQE2fjIstsme11VosucH\nN527Ohg4tIn7qrZiDQr4dF1/APAA79bj3PnAfADDMBpSDSGEENUwH/g5ANppffhMpXhnayZhThuj\nmzizUsZ29c2Yb78AvnV0AQgLB0cAZGfiffwebJdejTZyHKO7hqFtTGfVvhygdv1JlanIyvCQ2WcO\nxxLGU+SIhwGgFZkEnijF7tCw2zXsdrDZNcLCbQQ4rYDOU6pwFZrk55biditQxdjsENfJQVSMnc6J\nAURG2yXjXU9q5/eYix6qKLDZypfa25PpZkBcMCEBTdP7KyfLw7dfFYAGQ0cGY3d03N9hvQM+Xdf/\nD2swxzTDMMr+GtOApEqHdfOVVWEYxivAK75N6R0uhBCnUft3Q6cuaL4562p9nlJQUGlqk97+697u\nz3azK7OIW8d0JjigeZq3tJ79sD/0nH+ZpkF0LCrtCBzci/nGIux/fZ/40ACm9I5k6e4cZpzlmkop\nck56yTxRypGDJbhdCnrNIj5rK33PiabTK3cQOPNytOBg1DersT38V/B4YM8PMOScagM406vITPeQ\ncbyU7EwPe0942LujmLjODoaNCiYsXLql15X5/qvlj7V5d6KNnoTmcFBY4uVIbjETejRdlnnXtiIc\nDo3xF4YREtaxf3f1Cvh0XZ8JLAQmG4ZRqQcmy4D3dF3/C5AI9AM2NLiWQgjRwajd2zCf+R2EhGJ/\n7l91O3fNp6j3Xynf1s6b4rf/h3Trv+3zkpqpOfdsouPAt8QawRV9CW8b25l1h/MwTf8MX0mJyYnU\nUrLSPWRleCh2W/viOzsY9N0i4k7+gPPcMdhGTMZbWoDm8aAWvw6AOX822g0LUG+/gHb9L9AuOH08\nopX965wYQOdEa6Jqd5HJsSMl7Nnh5qtV+fTsF0j/wYEEONtmP7DmHn2vTG/5oBztMh1bpffivpNu\nFE03Ojdlt5uTmV6GnBNMeGTHDvagdtOy/Au4EIjTdT0VeBhrVG4g8Lmu6wDfGYZxq2EYO3RdN4Cd\nWE29vzQMw9tUlRdCiPbKfOZ31gNXYZ3PVRu+Kn9su+P3aKH+zbY70q1RkdHBTTsqsja0qBjU3u3W\nRnDFPGxOu41hnUPwZoJpKk6klXLkQDFZGR68HghwanTq4qBTlwDiOztw2j2Y7260Ti6b+iXAAR7/\nyZfV2y9YPxe/jpo4o8Z1gYOCbfQeEERidyfb/1fEgT3F5GR5GHpuMFExLX//mpLKzkR9ugRt7i1V\nV2qprYP7wF2EdvOdaOddWHFtpfh0bw5Ou0b/uMbvV5ef62XXVqvfXo8+zbM2b2tX47vVMIy51RS/\nfpbjHwMea0ilhBBCVFCmt8bAxI9vNQsAbcg5frsKSrzszCxqliWsaqXyCGJfwOf1KPJyvZzrCGM/\nGgdTislPL8QZqJHU00lSL2eVPnXKVWmAh29SaRxOKC215uUrzPd/3pJiOJUDMbWbkiYo2MboCaEc\nTy1hy7cuvv68gMEjgujVPxBbU89h2ATO1h9RHdkPXi/mkjdg30608RdC38H1eh61YwtoNrTho/2e\n82heCRtSC7h+RHyj999TSrFvlxubHUaMCZHBNz7t++uJEPUkkw7LPWhJVe59QX55J/daKftgTai6\nTNrLG9Mp8ZpM61O3foFNxpc5MjUbqZHnkvbJCXJcZUGghgbkKA99B9uYMDj8zB/elTN5dt9HW0AA\neEqt9YSrU1Jc5+p26eZkxiwH/0t2sXOrm5xsL6PGh7SbAR0qKx3z0Tv9C+11CxVUQR5q2ya0+ATI\nyoDoGLRQ/+4DO3zdCs5vgv57+/cUk3a4lD4DAgkMbJtN701BAj4hhGhtygZcdEmC40chP69OAZ8W\nHYfC6jNVmddUbEwtYFrvKAY08aoGtRbg5Fjn80jpNZuCsK6EZaTSb2gCYQkRxHd28NY/bJywlfJl\nVi7n284SHJRWyvCVBSiOAGviaY8H7cqfol0yB/PnV1Kq2flXr4vZlpxPbGQqvzovoU6T/joDbYyZ\nGMq+nW727ihmg6eQkWNDCAxq+8GF+uyjqoV1CIzNjd+gXnnKuhbAsNEQVvXLxc6MIqKDHSSE1bOp\n+Ay8HsX+3cXEJzgYNKLjTsFSHQn4hKikvXxLF23c4f0AaMNGo44fhYLcup3vm9DYVqnPFFid5Is8\nJiMSmmGi5VrIzfGwVU0hd1gUEfmHOWfb83TJ2IhtzINoPcaUH9crKpBPjhXywTvLufr6K6q/WOWA\nr+zPODQclZVuPQ5wotlsFNkDWTRoLpviBjNIedmYVsCvVhziuUt7ElmHoM9m0+g/xFr6bd8uN+u+\nKGDg8CASk9p4f7Ewq6lfu/om1JI3rbJaBnxKKdTi1/wLf9gEQ8/1K9qR7iI5NZ/x3cMb9f9cpRR7\nd7opKVb0HRjY4v+f+wa4PgfYgdcMw3jitP3XAfdivWPzgdsMw9jq23fIV+YFPIZhjG5ofdr+1xEh\nhGhn1N7toGlo544HwHzj2bo1sXs85U2llW09UYiGtV5tS1JKsWtbEevWFFBMEIN3/4MJGx6mS4Y1\n6ELlnfI7vm9sEENzUlhdHI1ZWlLdJf2bdH0DXbQefeDgXqsswInHVDw38CdsihvMtQdW8tjnD/P7\nqUnkuj28simdvOK6jTHUNCvoG3eB1fdw83oX3ye7KMhvu2MV1f5d0Lkr2owr0f7vV1Zhsbt2J2/f\nDLm+hblGnlderEVElz/2mIpF648RHexo9CX90o95SNlVTNfuAcR2atl8lm9Z2b8BlwCDgbm+5Wcr\nO4g128kw4FEqpqorM8UwjJGNEeyBBHxCCNHqqJX/tlakiO9sFWRnWv9qy+utGLjgU1DiZeXeHAbG\nBzf5mqVnU1qi2LqxiJRdxcR1cjAhOJmeqV9gU2bFQYUF5Q/LsjTnZ27jRHAcx4+cOMOFKzJ8quz8\npF4Vu+0O/vxNGhvih3LzvqXMOfIllJQwsksoPxoUwzeH83lm3bF6vaa4TgFceEk4vfsHkna0hLX/\nyeeHzS6Ki82aT24h6sBuvPf8n3+ZUnA4BW3gMDRNQ+s/1CqvZcBnrl4GkTHYXvwA2w0LIKErdEpE\nu/wn5cd8dTCXTJeHeaM60Tms8bKhHo9izw9FhIbZGDmuVfSpHAukGIZxwDCMEuB9rOVnyxmGsd4w\njBzfZuVlapuENOkKIUQrosxK2aHQipG0KmUXWmyn2l3EW1ol4Pv6UB45bi/3Ta7lNZqAx6NIXltA\nzkkvfQYEMmhEEOrl3VVn3i/M89tUpSUMzbGauZd+sJqfJ7iw37DA/xx3pYEZLmtEriswlNygGI6F\nxPNeehcOlBQwL/0rLktbB1A+TcgN53TC6bDxr21ZZBSU0qke/cpsNo0h5wTTe0AgO78v4vD+Eg7t\nLyGuk4Pe/QOJT3C0rtG826xsnMrPrZjYO++UlR3t4ls/oSwzl5tT/TUqUe4i2Pk9DBqBFhAAAQHY\nH/273zE7M1z8LfkEfWICG3WFF2UqvvtvAXm5JqMmhLSW+9wVOFppOxUYd5bj5wErK20rYLWu617g\nZd9iFQ3SagK+uLjGTe0KUR8O34dkWFhYh39PBgUFYbPZOvx9aHamF55923rcuXPF48gYiIqp3TWu\nmw9X/RQq/e4uCopk0qDu9IwOPMuJTcfrVRTmm0y5OIaQ0IplzbjtN1Dw84oDbXYIDSufLsVmsxEU\nFMQ5f3qGj4KiyQsYTVhRFqExMdYSXWVGjau4V4FB5IdHkz/xckInXk4/rPaymNAAIgImgLkQMo5b\n8/X57tHPJ8dyyYieBDpsxIY7aUiCKKm7lWQtLTEpKVYoZVXVGWjDGag16NoN5fVaXyhCr7iazsOH\nAgoCAyA8EsJCrXvYORGCfM3+f33X7/dxRp5S69yoWIiMrvaQ/oGl/OP6zvSICqQxYzJ3kfW+Cg61\n4XQ2383VdX1Tpc1X6huU6bo+BSvgm1ipeKJhGGm6rnfCmvN4t2EYaxtQ3dYT8GVlZbV0FYQgP9/K\nDBQUFHT496Tb7UYp1eHvQ3PzPrwAjh1Bu/hH2ObchEpLw/zzfQDYHlyE1r0PqrQUtXIJWq8BaMNG\nVbmG+ebzqN3bsD9pTZmqlOLWpfvpGxvMvZO6NuvrMU3FD5uLSDtcQkCgxsgxIcQnVGTQVKELtXUL\n6s1nrYLYTmj9h2C7+c7yuhft+J6Mt5/Go9m4e9y9xBbn8vilfXBUWpfX/Po/1goaoyfivuxabv7g\nCN0CShmz43PCPC4mXT6FkjHjKXs3e5+4D2LisS/4Xfk1thzI5dlvj/Pr8V2Y2rtxpq3xehXpx0pJ\n2VVMbo4XzQZ9BgTSf0hQi8wPVxbwFW7+jvQ3niovt/36EdSJVNTi17A99Saab+CP98+/g6hY7L96\niK8O5lJqKqb1jqzSZGp+/A7qUwPbbfeV9z2tzNiexbtbs7h6SCw/HRnfaK9n60YXRw6U0KVbAKMm\nNF9TbmJiIjX0ravVUrO6rg8HXgMuMQzjZFm5YRhpvp8Zuq5/hNVE3KCAT/rwCSFEa3LsCADaBRdb\nP/sPKd9lfvAP68G+HahP3sf86++rH8zh8YC9YjLbxT+cJKPQw6jE0KrHNqEil8l3XxVy5EAJiUlO\nzp8a5hfsAWihYdgmTK0ocAaiiotRphdlWn3gVIk1IMOhTH58eA17Invy9x8K/K6j1q22rnfjAtYW\nhVLkMZmX5GHOkTXMPPYdYSUuv+NxBlYZfXpBzwiCHBop2bUcpFALdrtGYpKTCy4KZ+K0MLp0CyBl\nVzFfLM+jIK/lBndoNv+Pf/O5R1DbNkJ8gn8mObYTqfuP8vTn+/jL+uM8/90JFq0/jlnpfae8XtSn\nhrURXHVAUGpeMe9uzeL87uHow2Ibpf75uV6++iyfIwdK6DMwsFmDvVraCPTTdb2XrutO4Bqs5WfL\n6breHfgQuN4wjL2VykN1XQ8vewxcBGxvaIVaTYZPiNZEJh0WLUF5PABos65F65RYsaNspQjfahvq\nf99W7CtyQUhFIKeOHrSWVnNaHeLzi70s3p7FBT0jmNZIWavaKC1VbFpXSF6ul6HnBtOrXw1NyQOH\nW82IudlQXIT5x7sgvgtaQBx4PdCjLxxO4aLjyRwJS+AzJnCtq5TYEF8A6QwEh4PtuYpXN6XTJyaI\nAZFmef9AbewF/s8X4PTv9wfYbRq9o4NIOXmGiZobKDrOwbmxdrr19PB9sov1XxYwfHQICV0bdy66\n2lDeaoLNXVvRxk72C5y+GafzQrgbW0YJF/eNJcxp44Od2ew7WcTQziEMig/hvBhF+W83xL9vntdU\nvLc1C6ddY/6YzjjtDc8zHT1UwvYtLux2jaHnBNOzr7O1BXsYhuHRdX0B8BnWtCxv+JafvdW3/yXg\nISAWeNG3TG3Z9CudgY98ZQ7gPcMwVjW0ThLwCSFEa+HyZa3CTptgOCrGCvhME++f74O9OzgRFMPf\nB/yYwtXHUA4HxR7F5QOimfn6o9Y5vqzYlmMFmAquGBDdbB+K7iKTrz/Px12kGDUhpFZz09nv/iMA\n3md+B5npkHEMjh6E0ZdZgXBkNLZXlkJhPlf86SFWJU7gkTVHuW1sAoM7hYC7iMODJvL65gxCnXYe\nntIN7fhBK+CLT7AGElSi9R6AWvkBKuOYX3DdLzaIFXtP4TUV9ibo/K9pGp27BDDuglC+3+Bi0/pC\nzh0XQpekgOYNWkzTCqJttoqpawB6VTSTF5R4efmAoqc7g3uD9hM3bj5KKRIjnKw/ks+6I/n8JyWX\nNwI0rug+has6e7F17+33NK9tTmfdkXyuHhJLVANHhyulSD1cytYNLqJi7IwYG0J4ROMuy9aYDMNY\nAaw4reylSo9/BvysmvMOACMauz7SpCuEEK1F2XQip2VJtKG+fnoFebB3BxtiB/OrsfewL7w7TryU\nuIsJsite2ZTOAz2uZl+4NbtDZmEpS3dnExVkp29s86w6kJvj4buvCigtUYyfElr3iYgDnP7r3hbk\nWaN2S0usqULCIkiICOKenK8oKjV55PNDfLbjBIUlXu6LmcHhU8X8cmyC3yTK2qCqn53alMtAmajN\n3/qV944JotRU7G/EZl2wghVz/ZryiaCjYhxMmBJGWJiNzd+6OLCn7su8NYjXCwEB2K64xq9YG281\nr5d6FS9tOEFhqcnP0/9LjNua/FvTNKb3ieKhKUm8M6cfj05LYkAYvNf7Em4Mv5Q/rU3D2J7F4h+y\nuH35AVbsPcXMflFcN6Lhg7/27y7m+2QXYRE2xl0Q2qqDvdZIAj4hhGgtfNORnL7uqHbV9dYDZyD7\nw1JzC2AAACAASURBVLrywkCdHgXH+evGp/lTxEGeX7mQxwq/Ye7gSA6GJXLvqF/x9ODruGfVIVJz\nS7jp3E7YmiF7VJjvJXltIcVuxbnjQ4nrVI+mSpsNSk8PfjQ4WWkewqBgxuft44E+JXQuSOfF709x\na8/rcWsBPDwliXFJvvvXsy+2W+9Fu2Z+lafRomKsqWuK/PsCnpsYRpjTxj+31mHew9o4ehD15rOo\nj94pL3IG2rjg4nASugawc6ub7VtczdedxPRaq48MG43tidfg3AnYnvsXWqj1ZWPprmy+PpzP3OFx\n9Mo9gtq8rvxUVVqC99E7UPNnM+zYVh7oUcT9P7zJhBg4mOPm3a1ZvLctiwC7xs9GdeJnozo3KHup\nlGLrRhe7trnp0i2AyReFE+CU8KWupElXCCFai8Lqm3Q1mx1GjsOzbRN/Gvdbguwad53flbgtuXAi\nFYDAzWv5ycxZXPryYzw7eC7rO40gMcDOH6d3Jymy6adiyc708O1/C9BsMGl6OOGR9cy+2O3lzdEA\nGspqlg2tlPUMCIDSErr/+28sOn6Ut/pczidJF9AvwO23bJymaTDq/DM/l8eDWvkBXHVjeVFEoJ1Z\nA2N4b1sWp9yehjdDml7YtrF8fV/lG5RTxmbTOGdcCNu3FHFwXwmOAGv1jiafS840wWkF5FpsJ+y3\n/bZ8165MF//ecZKx3cK4ZlgcXt88fOrwfkjqCQf2wJED1mX+/gTa5T9h9MldjB0ZhdY5EVepF6Ug\n1NnwDJzHo9i1tYgjB0ro0cfJ4JHBaK1jnr02RwI+IUS1ZOBK81NlTZmnZfgAtLAINsYMIjswkgf+\nn70zj4+rKv//+9zZJ5N9b5o2bdN93zdqSykIqODyZWRRUXZlEdGvIC7IF0VEQVBwAZefKIgjKiCL\nQAulhe4t3fc2bZM2+z6TyWz3/P64k2QmSdvsTdvzfr3yyr3nnHvvmcnN3Gee8zyfJ99LXlYyOrQt\nf3rrIRgkIdLMd3f8iSaTDddvPAMSF1ZTFWbzWh92h8b8JQk4XT1/0AvN1FGIuf0YixVZXwveBgTw\nlUOv8YXDb2J+4vk+eb0zhiTwwvYqtpc18bGCpNMfcArk6/9AvvpCW4O542PXbBFMneMgHJEc2B2g\nsV5n1gJn/xo2egTMHZf565rDPPhuCYk2jRtmGCLd4hNu5Ose9B99A7H4UsiOSvvk5kNpMXLPNmM/\nqr/ntPTNUquUki1rfZSfCDNspJXJMx2DLjnjbEL5RBUKhWKw4D25wVdtT+GFkZeS0VzLjFQN7A4A\nZKMRW0UwCHWtMl44I4EBeTh6GyOsX+VFSpg539krYw9ozURuQUiQCGLVimU4bMT2xWAxCczOntUI\nbv/lZmSqHZdVY2upr0fnizv3lvgYwc4MPjC8kTPnOxk/1U7Z8RDv/KeBqvJQp2P7hIiOaFdvORTR\n+ePmCgIRnQeW5pObGI2/zBveOka2ePeS09DuiSYIHd4PjgRE9J7sC8IhyZp3vZSfCDNxmp2pswed\n7MpZhzL4FIoY1AdKPOr9GGB8XiOGrRMtM48YQaUtlTv3ejDZbEZlBKsVjh9tHaM/8cMBnCyUHA3y\n3puNSAkLL3KRkt4Hi0Ynke0Qs2OKEGxZ07H/mls7tJ0OMWexsREKxrWbNMHMIS7eP9LA5uPeTo7s\nBiVF8fvayQ1iIQSF4+zMWujEYhWsW+Vj7w5/n3rbW8+lh40EmZj2xz48wftHGvjshHSGJrWFAcTF\nlIbDyHXvQd4wSIrK/Ej99JU4ukEoJNm6oYma6qikz5gzUx3mXEMZfAqFQnGGkaXF6M//BhpqISGx\ng6EdCOt8EE5jfuV2JtcdAqsVYTJB/kioq4kZ2LeZpaeiyaezc4uflFQTSy5NIqG3nr0WOhhEEnLy\nEBddcfJDHnsOLSpU3S1GjjV+BzpmyN4yK5vcRAt//qjnyRv6itc6NrZ4ZE9B7lAr85e4yMmzcGB3\ngM1rmgg0692//hv/IHLzFegbOhZoELoOUQ9fKKLzk1XHWVvs5frpmXyxfSUMV8yydqlRHlYsXGbE\nlrbg66Vh3DLnaF3c0pIQYyfZGTHapr549hHK4FMoFIozjP7Uj5Er30R+8E58ckKUP2+tpElqLC2L\nlu60GB4PMbTg5CftxEvYV/ibdFa91UgkIpk624kzoQ8fJaZ2S7oA2XnxD/0cQ3ZG+79fI758FyIp\npWfXiopTEwwgm3zIitLWLpfNxOKCJI7WB2hoDvfo9PLd/3RsrCw1EjlOg92hMXO+k8LxNspPhFj3\nvo/GblTmkFvXIf/9F2P72Z8jD+6JH6AbsiwAbx6oY32JF/ekdD4zvpN6ze11IQExvND4/ZWvGw0Z\nWV2e28kIhSSb1zRRVxNh2lwnYyYMjJTQ+YJK2lAoFIozTcWJ1k0xY0FcV0l9gNf31XLJyCQmrTxk\nNLYYKqkxy2iZOVBZBiYT2oNPd2o49gXBgM7WDU3ouuRjlyT2vRZaZ0ue7V6Lds9DUHwYkTsUkTu0\n59eyGoazft+NrU2mZ9uqX03MMozmnRVNLBjW/eQNMX0e8q1/w6hxcGgvZGRDVTnUVBnbpzteCMZP\ncZCWYWbTGh+r3m5k4VIXKWmnfnTLkiL0px+Oa9N/eq/x2lqqi0R0MJvZXdHEizuqmJrj5LqpJ6lx\nm5Ri/DTUtbVFl4O1BRch84ZDSu9KpgUDOjs2+yk7bnj28gu6qd+oOC3Kw6dQKBRnEBkKGl4tTUN7\n+Bm0z3wxrn/zCSNx4KrJmbT6uKKGStxDtkWsOTMHkT0E4epddmmnc9Ul61f5qK4IM3G6o3+Eb02n\nP6dITUdMmd3rSwlbRw9SbLzc6HQH6U4zz2+rIqz3II4uHAaHE+3qmxGfvwnty3cZ7TEGflfIHmJh\n6eVJ2GyCD1d4OXLg1CLN8qP1rdva9x5va9d1o5IJRLN0LTy7qZwEi4mvzsk56fmE2YLpsefiG20x\nMX7DCxHRDN2eEGjWef+tRk4UG8bemInKs9cfKINPoVAoziTFRRCJIG74BiKz40N3S6mPoUlWslwx\nGZVR74qILXLfUk+3RTKjj4mEjUD6upoI0+c6GT6qnwLptfjHkjCZ+k8iyNrJa4iJg7SYBNdPy6Sk\nIcjB6h7ERwaaweZAFIxGW3YFZBsl3GR56amP6wSHU2PBUhfpWWZ2bPGzbWMT/qbO4/paM4OnzYNh\noxBRnUH5r+fiknxqTE4O1wb4+OiUtozcrmLpGw+czxthw2ofwYBk4VKXMvb6EWXwKRQKxRlE/8n/\nAh2rawDsKm9iR5mP2XntlmdblnRjPXwOw+AT/WTw7d/dTMnREIXjbQwZ1oMKGl0lZklXu+N7vV4q\nPCWdePhoipdimZRtLOseqPZ3//zNfrDHXCPZMNDlC789yQGnxplgYvbCBIYWWCg5GmT1O40cOxzv\n7ZN6BEqKEJ9wY7r9fqMc3ZBhRt9b/4odyRaM+cwaktCl62vffaxtpw8MvnBYsvEDIzZx6mwnaZkq\nyqw/UQafQtEJSnRYvQcDTkFh3G5Elzy9oYxsl4X/mRQ1elqkL6JVG0iN8fC1CDDn9L3Bd/RQgEN7\nA+QXWBk/pZ/Fb2OXdAtGI7R+fEx15uHzxxt86U4LaQ4zB7rh4ZOlxUS++llk0X6wtWnTxb5vPf3/\nMpkF0+cmsHCpC7tDY9tGP7u3+QmHoudrMVhjl/RHjO6g/9esWXnJl0FWgpnhKV301ua0xUv29h7w\neSOsfLOBxnqdmQsSGKpi9vodZfApFArFmSRrCGL2og4xdwdrmjneEMQ9KQNXtESV9p2fod39YNvD\n1hHjmQkanh7RhWSA7uDzRtix2U96lpkJ0wdguS02aSNqpPTbl48W4WGrDXH9nca2v6PY8uh0O4dq\numbwydpq9Kd+ZMTvVVe0CmS3ID53/Umv0x1S0sxcsMxF3jALh/YGWL/KS5Mvglz7njHA2eYVFkkp\niM/fHHf84cQ8yiNmvrkwr8vGW18JKxcXBflwhZdwGOYuTiA7tx89xopWlP9UoYhB6T3Fo96PAaDJ\nG2+4RdkWrfIwPWa5TaSkxy1xCiHQbv02OF3I2mrDoxRTFaG3hIKS7Zv8CA2mz3ViHYiC9bHCyyZz\n/96DqWkgNMSXv47IyDZKuvk6GmKF6XY2lHg53hAkL+nUnij91w9DjLxL+2VjkZnL7qTh/Py1YiYN\nSeKrc3J6XHNW0wQz5ieQnRfko3VNvPdGI2P2lZJrSyehnVSNGDcZaTIhLv40HK+nxJnNcFuEcZl9\nVx3jdDQ2RNizzU/5iTBJKSamznL0jVi3okuod1qhUCjOEPrqt40SYQnxBl9dc5hX9tYwKdtJiv3U\nH9NillGBQgBywdI+NZB2bfVTXRFm0gwHdscALQjFejpPUoasrxB2J6ZnXgZAlhuZs/pTD6F9/wnE\nsJGt4y4elcLLu2v467ZK7l3UxSXz8VNhz7YOQsulBZN5YFoS4ZBg9dFGavxhfrxsWK/+bnnDrKSm\nm9j+YR17R1/N3tFXYz8sSKnzkZisRf92mZi+/TcsgWo4/k+qHGksSov3nEpdEgxKmv06zX7jdzAg\ncSVppGWYsdk1tFu/TXcdro0NEXZuMe4ls1lQOM7G2Ml2tP6sFazogDL4FAqF4gwhX3kBADFibFz7\nqiMNeIM6N8/snphtXxp7tVVhio8EGTHaRkHhwJW2EulZtNoTUQ/fgMSTZmbDpBmwcwtUlkKMwZfq\nMDMvP5GNx71IKU/9PptMMGEa2p0/MJIzxk2J6369qAk0jWfX/IjNt/yU3+5sZMXhepaN6qF4dBRn\ngok5GQdofP3/UZExhcZl11NXE6HsRAhi3j5dGn/LmVoijqo83nq5Hl2X6BHQT1PMIylZIyV9BolJ\nGknlIZwuEw6HQLQz3MJhSWN9hLrqCJXlIcpPhDFbYNQ4G8NH2fpWqFvRZZTBp1AoFGcAKSU01iEu\nvwoxfV5c37riRoan2ChIPTMSFb7GCGve82K3C0aPH+A6pultRu5AhhQIzYR2zS3o370NGQjQ/sqj\n0+2sOFxPhS9EtusUy7pNPkhJQ5jNiC/dEde1vczH8kN1LEzVSQ82cLG9lhXpLn61rowkm4k5Qztm\nancH+ZuHcQGuYycwzf8aAJGIJBQ0LL7imgDPrashE0ir3kHB9OlEIgJNM5JBNE1gtQnsDoHdrmFz\naFitgob6CFXlYWqqwpSWhDgWbLMgNQ2SUkwkJpvQNPB5dWqrwkSiRUGsNsHoCcaXhgHzEis6RRl8\nCoVCcSYIBgyXSrsSaCX1AXZX+Pn85H6UIzkFekSyZ3szQsAFyxKx2Qf4IZ3e0as5YBnjLVm7oWCH\nrtHpRqzbgerm0xp8wtl5lZPfbSwn1WHmulHGe2pq9vOTiydw1+tFvLSrptcGXwvaT55t3TaZBCaH\nYb7+bX8VRyMBMoGsmt1MnN41OZa0DDNpGW3mQrNfx9sQwefV8Tbq1NeEqSgNoeuQ4NLIH2ElI9tM\ncqpZefMGEcrgUygUij5C7tiMbPKizV18+sEtWZoxCRu6lDy0soREm4mlI5P7aZYnR49IVi/30lAX\nYcxEGw7nwD+sO6t+MWBYWgy+jpUshqfYMGuC/VV+Lhh+iiom/s6TcCp9IUoagnxlRiZZzgZ0gEgY\ni0lj2agUnttayfYyH1NyumaEdYrNgVh0caeZ2uXeIFtO+Pif8SnUlPX8EmDU+bU7tK5Uh1MMIpTp\nrVAoFH2AbPKi//JB5O8fi2/3eZGBTiQ9WrJBYzx828uaKPOGuHlW9qm9SP2Arkt2bfXTUBdh6mzH\nma94ENWvG7AYPmgTtA529PBZTIIp2U7ePVxPYyAS1ye3byRy8xXImkrjWGcnWddl0azrXFeb9Ixu\nnOfiUckMTbLy6AcnCEZOE0h3EmQ4BAE/dCLgHYpInlxbihBw0ajoF4mM7sWHKs5+lMGnUHSCEh1W\n70F3kW+/3Gm7fve16A/c0aFdnjgGtFXGOFYX4DcbyqIJAp0vCfYnh/cFOHIwyPBRVoaNtJ1RSR7t\nF39Fe/QPA39hswWE6HRJF+C6qZk0BnU+PNYQ165/uAIA+Y8/GQ3tlnT9IZ3X99WS5jAzLNnaKj0j\no4FuSXYzN8/KpjEQYUOJt2dzj+owdlY9ZEupl10Vfr46J4cMp6F5JxZd0rPrKM5alMGnUCgUvUQG\nA8h3XjF20jKQUiIb65H7dhpt1RUdD2qoNX5HPS2/31xOQyDCPQtysZoG9qO5tjrMgd3NZA8xM2WW\n8/QH9DPCldQaBzegiRtCGCXDgh2XdAFGpdlItJk6VN0Qmcbaptz0AWRkI2YuiOt/+2Adh2sD3DY7\n27hGq4evzZs3OdtJptPMS7uqqfOHuz/5Fs+g1lHTb11xIwlWLS5MQJhURNf5xmn/4m63+4/AJ4EK\nj8czKdqWBvwdKACOAG6Px1Mb7fsOcCMQAe7yeDxv9cvMFYp+QAkNK7qLlBL5l6cNI8FsgXAYuelD\n5DOPnvrAFqPCamN3RRPbypq4flpm72K4ekCgWWfdSi8Wq2Di9IET4R20WK1xS7r68lcRM+Yj0jIR\nQjA6zd6xzFpM/V0xfiqinejx2uJGClJszM2PLre2GFt629KwSRPcOCubxz88wS/XlfL9JUO793nU\nci5TvMHXEIiwscTLnDwXZk0QinRyrOK8oCtfI/8fcGm7tvuAFR6PZzSwIrqP2+2eAFwNTIwe82u3\n290zCXGFQqE4Gzi8D7lupbFtMkM4hFy/ssMw2eRFNvnQ//A4srIMAs0gBGXNkgffKyHDaebjo3un\nxdZdpC7Zu6OZcATmLnaR4Bp8H9cDGsMHRuJGNGlDehuQf/89+s+/29pdmG6nuD5Ac7jNOydrq9qO\nb6l3HKW6KcTeSj/zh8XE1rV4cCPxnrz5+Yl8YWomm0/4+OOWCkLdiedrOVc7g++JNSdoDksuG5Pa\n9XMpzklO6+HzeDyr3G53QbvmK4El0e0/AyuBe6PtL3o8ngBQ5Ha7DwJzgLV9NF+FIg7Z5APkSWUQ\nFIr+Rn/zpbadcZON6gqlJR3Hff1axOxF7N9bxB7xIanORIpHXcZ//3sUTcCPlg3rcYmtnrJnRzPH\nDgcpKLSSmDT4jL0zgtXW5uFr8ZpVtqW1jkl3oEv474FaPj0+3YjDO3oIcvIQYyYjFl8Wd7qn15dh\n0mBRbGZvy7JrJwbd5YVJHDl0jFf31pLhtHDl+LSuzbtF+C5mSbfWH2bLCR9XTUpnbIby3g40brf7\nUuBJwAT83uPxPNKuX0T7LweagC97PJ4tXTm2J/Q0UCTb4/G0FAssA1qSs/OA4phxJdE2haLPkVKi\nf+cm9K9fi2xuOtPTUZyvHNgFiclov3sZkTfc8LSEgoiFy9B+92+YMA0wih2sPtrAd2bcwf+zTeQX\nkbG8NHQJhWl2frxsGLmJA5uVW18b4fD+APkFVibNGNzGwMB6+KxIXyNy84cQbvPAyaa22saz8xL4\n05ZKDlT7oaEOGusRSz+J9sWvIRLb4uRKGgJsPuHjmsmZ8TV4TfFZurGYd23ijld+QGGggncP1xHR\nT//a5aG9yAO74s8NrDnWiAQWFZxCRkbRL0RXN58GLgMmANdEV0FjuQwYHf25BfhNN47tNr2ODPZ4\nPJK4wi1dw+123+J2uze53e5NvZ2D4jyl5Ehr7Iz84J0zO5dzFBXTeGpkOGwI7S65HGGUKzA8LQE/\nWK0IzQRSUmNN5GcTv8DjE65jhPcEv137MA8VeXhi/5948KJhjEwbWAkUX2OED1Y0YrEIxk2xq79z\nLFYr7N6K/tufItesaG2W//kbAGZNcPeCIZg1weojDRAOGQM6yY5de6wRgCUj2xlcrR6+jgafrKsB\n4NKi9zhSF+QPz72JLC466XRlZRn6I99G/ulJoyFq8B2rC/D89kpGptoYljzA1VIUYKxuHvR4PIc9\nHk8QeBFjFTSWK4HnPB6P9Hg864AUt9ud28Vju01P03TK3W53rsfjKY1OriUF7TiQHzNuaLStAx6P\n5xngmeiu0n9QdBu5c0vbztHDZ24iivMGuWcb5AxFpEarYPiMBzqJ0Qe6OfqR2uQDi43jDUH+X/Ii\nNiy4DoCri97iiuJV2PUQWUc3wZiJA/wKDPbtNJIOFl2cOOjLXQ18DF+bJ66l1jEA9jYvqMtqYsaQ\nBN46WM8Mm4XJYCTsxPD2wTr+s7eWsRmOVimUVrSTe/hoNCRflpZtZl/+NN5yjOIzf3mWzPsf7nS6\n8qN1cfsiavC9uKMKJNz3sfhFNiW3NGB0tuI5twtj8rp4bLfpqcH3KnA98Ej09ysx7S+43e7HgSEY\nbsoNXTlhRkbG6QcpFLFc8ilYdrnxQRsMQB/cQ16voYHlcrnO+3vSbrejadp5/z60InUYNtwwCFre\nkwQnPPEcZOcZBsGnr4HFFwOgJ6fRJBO4/ebP8p2QjyQRxu7PwfjIjOJKgvSBfX+b/TpzL0jFZheD\n3tgDMJvNWK3WgbsP7/lhWxWUWNKzjL9XlJ99Jp3jDUFMSLKfeA4yc1sFl5tCOnPGuFg0fhiZCWbs\n5nbvs5TGfZOSDsntkimuvNr4XJOS72Lii64ckiLTyMg4SULPwiXGTwtZuTRbHNy62EWK3Uy6M/4x\nHwgYCSkJCQnqf7uXtFuhfCbqyBq0dEWW5W8YCRoZbre7BHgAw9DzuN3uG4GjgBvA4/HscrvdHmA3\nEAZu93g8XUoCr6qqOv0ghSKGyHe/hhg9ATJykK+9aMRQab17gDU1GbGAjY2N5/092dzcjK7r5/37\n0ILcvRX9Fz8AwPTsqwDoLz6LXPUW2pMvICw+9LdfRv7jj9RZXDw+/3Z2m9J5uPw1EnevovFbD9No\nNiMrS5F/+AUA4qob0C759IC9hiZfhHdfb2TIMAvT5jjx+gb/Um4kEiEYDA7YfRh55jHYvKZDu7ju\nNrQll7fu6+tWssIxhj/sbOAXGx9nxI23IibP5GB1M/e+fYR0p4VffWIE3pBGeyllqevod38J8alr\n0K64Jq5Pf+ZnyKMHwdsATT5eLLyCN4ZewPeXDGVWXnxymmysR7/nSzBtLmxdD0DlbQ9w5wEXKXYT\nj368ANkU/5gPRhNSmpqa1P92LxgyZAgej2fWKYZ0ZcXzZGMsXTi223QlS/eak3RddJLxPwZ+3JtJ\nKRRdwtdolBGyReNTgoG4ZReFoi+RB3a3bYfDCLPZWOIdPRERXQYU0+ex/Z33eW7UJyjWUrl5VjZj\nV0ojZsXuQAwfhRg1jkiLwZczcDltoZBk2wY/QsD4KQ40bfAbe2cCYbF1HmMUE28nK04g//A4iybP\n5y+Zn+bRiV/iJ9JMU0OQJ9eeIMFi4ueXFmBr79lruYamgdA6j+HzNkBiMlQYeZFfSa5mTbCRd7aX\nMCtvXPxgv/EFVUyfjzxRTGNNHU+X2BHAwxcPJ9WhxJXPIBuB0W63ewSGsXY1cG27Ma8Cd7jd7hcx\nlmzro6FylV04ttsMfn++QtEOWVeDvuI1aPaDK7E1WFouf+U0RyoUPUNKiXztxbaGYDOyrhpKiw0v\nM9DQHObx/ToPTLuV0tR87lk4hMvHpCK+8DXEDd9ADB/V8cSZOQP0CmD/zmaqKsNMnunA4Tx7PvoH\nPIYv0nmVC/nis8hQNEGj2Q9AUs0JHhgZpNKewv0HrNzzZhHF9UFumJlFku00MjcWc1vCRyyN9eBK\nQlx7K+Ljn8FywcUsKv+ITVVhKhv88XN63QPAllAiXx13M9df8EN2ejVumJlFZoKl47kVA4bH4wkD\ndwBvAXuMJs8ut9t9m9vtvi067A3gMHAQeBb42qmO7e2clPmvOGuQoRCYzei/exQORr0tCUmGbhbR\nAOtPXn0GZ6g4Z6ltt/QVDMCRAyAlYuJ0AH61vowtJ7x8fnI6/zMxvbU8mnAmIOZf2Pl5Xcmdt/ch\nUkp2bPZz9HCQocMtDBupMjZPRUuN4077lr8KF1/Z6lnj+FHG//773JU5lf/MvY68dCc3zcwmpysS\nOzECz3F4GxDDC9Eu/IRxzVCQi599mreGzOPBlcd58pOjMGnCEIVes4J/5y/mbyUuMs1BZlXsxv2x\ncYwdrUSWBwMej+cNDKMutu23MdsSuL2rx/aWs+drnuK8Ruo6+re/gv7kD9uMPYAEF8I+sJIW5wsq\nmw9kTRWRe29EHtpnNIyKLqn5m5AN9cZ2chrH6gJsKPFy1cQMrp2S2fVauAn9X0at7HiIo4eCDBth\nZdL0M18ntycM6L0YjQPWfvzbDl3yX39GLn8FWV8b135B5TZ+dmEO31uS3zVjD6I1e4NxTVKPGFm6\nMckhwmIl/38+z117/05xY5g1R+oACK5azut5C/jLqE8wNcfJT2Y5uX/fC4zJT+/Oq1WcRygPn+Ls\noLrCCGLe9VFcs3AlQowwqf7HJxBfvtPQP1MoeoH+4rPI914HXUeuMJI0REo6EtAf+z4kGAH09RYX\nP3q3BIdZ4/KxXfOsiM9dj1z1Vr/fp5XlIbZv8uNK0pg8U8XtdQXtq99BHtyDyBqC9sCT6M/8HEpj\nFDKOHW6rg9xyzJN/Qzi7abxbbR3OQ3GRsaQ8dHhcs3A4mVu5k9ymKv602svatT72BfOoGj2B0el2\n7luSj9WkIZ/y9DpxTXHuou4MxaBHNnnR77+l886EJCPAOUrDxrW8t/Uoz2+r5LmPKmgMdK9SuBKg\njed8fT+kriNX/Af0aOmrFm22hGg91PoaOHEMMnN47VAjFd4QD1w49PRxW1G0Sz+H6eH+VXAIhyVb\n1zdhsQpmLUg4a429gY7hE5k5aNEleDF0BKb/ezp+gNncoXRet409AKvVqK/couUIyN3bjPONnxY/\n1ubAhOSOfR7Smqo56DczoqGEe5rW88glw9vCB5SxpzgFysOnGPTI1/4e3zBqHBzaa2y7ElsfypW2\nFL456+t49wSBagBWHWng3o/lMTpdZe8qukFZu1q4x48CIBZeBDl5SM8fANhzyZf5z74a5uW7RZaJ\nrQAAIABJREFUGJ81eJZLQyHJxtVemv2SBUsTSExWHu++Qq59z9iYMhu2b+z5iSxW2PUR+t3Xtcr8\nyEN7DGHv9tp8QwyFjvH1R/jpljYDVCy6pNuGvArVOH9RXwcUgx75Tkz2rdmC9tkvte0nuCAjm8op\ni/j5xOsIa2YeGlrHv68dyw+X5hOISJ5aV9alepSK8xNZX2uUSIttK9oPgFhwEaRlGJmTAA4nYtkV\n+Mx2VmVN48HSNBKsJq6dkjnQ0z4pUkr2bvdTXRlh6mwH6Zln9/f6weplFrlDe3eCGAkpGQ4Z9cC3\nbehU9FmkZ6H936/j2+YsRlz6ud7NQXFeoQw+xVmDuPY2tF+/hBgzCfGpa4zAZpsDIQS/HHsVRxOG\ncPvefzBJa0ATgum5Cdw8K5sjdQEeWllCdVMnEgiK8xoZCqJ/63rkc7+K7zi8HxwJiOvvBF+bbG4o\nJYMn1pZyw6IHeWLCtaQ5LTx+aQHDUgZH5qsekaxf5ePIwSAFhVaVkduf5A5D+9n/Q/vJsz06PM6L\nV1+L3LqhdbvT8blDYdIMY0fTEDd+A5GV26NrK85PlMGnOGsQ+QWt3/a1K67B9Iu/IoSgtDHIrspm\nrs4XLKzc3iaZAFwwPJHrpmawq6KJP39UeaamrhisRIvSty7TEfW2rPovFBTGx0Rl5vD3fV5WFjVw\nUWEqD12UzxOXjyBlkIjblh0P8c5/GqgsCzN+qp2J08+NMIYB1+E7BSJmdUGMGI1ISUNkZPfsZKlt\nZc3kupXIPzwOgHbH909+TFSCiqQUFa+n6DaD45NKoTgJsinqXRk7GVE4odMxL++pQQCLZhXCC4Df\nh9R1CDSjOZy4J2XQ0BzhtX21zM5zsaggqdPzKM4/ZNEBY8MRE3QfjQ8V+SPixv7rqgd5aVc1F41M\n5qtzBk4w+XRIKTmwJ8CBXc0kJpuYNsdO9hAlutunmM2ImQvRLvsf5AUXw6G9iCHDenVKcfGVUFOJ\nXP8+8uW/tnWMm3zyYxISjSogif2v36g491BfERSDm6pygFYR0vbsq/Lz3wN1fHJcKlmJdkjLhMoy\n5CsvoN91tREXA3xxWibDU2z8a3f1gE1dMbiRUiJfiT5ozW3ffVtKqImPfxYA7Z6HqF/yaTz7Gpg7\n1MXX5g4eYy8ckuza2sy+Hc1kZJuZtzjhnDT2zrSHz/Sbf6Hd9E0ARGI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jx4LouloeHmhk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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f287080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data[['Close', 'SMA1', 'SMA2', 'Position']].plot(figsize=(10, 6), secondary_y='Position');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simple vs. Log Returns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ST = 105"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "S0 = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "r = ST / S0 - 1  # simple return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.050000000000000044"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "105.0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "STc = S0 * (1 + r)\n",
    "STc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lr = math.log(ST / S0)  # log return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.04879016416943205"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "105.0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "STlc = S0 * math.exp(lr)\n",
    "STlc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.05"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "math.exp(lr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Vectorized Backtesting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data['Returns'] = np.log(data['Close'] / data['Close'].shift(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>SMA1</th>\n",
       "      <th>SMA2</th>\n",
       "      <th>Position</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-01-03</th>\n",
       "      <td>46.52</td>\n",
       "      <td>47.18</td>\n",
       "      <td>46.41</td>\n",
       "      <td>47.08</td>\n",
       "      <td>111280407</td>\n",
       "      <td>45.347381</td>\n",
       "      <td>37.173651</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-04</th>\n",
       "      <td>47.49</td>\n",
       "      <td>47.50</td>\n",
       "      <td>46.88</td>\n",
       "      <td>47.33</td>\n",
       "      <td>77337001</td>\n",
       "      <td>45.410238</td>\n",
       "      <td>37.240159</td>\n",
       "      <td>1</td>\n",
       "      <td>0.005296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-05</th>\n",
       "      <td>47.08</td>\n",
       "      <td>47.76</td>\n",
       "      <td>47.07</td>\n",
       "      <td>47.71</td>\n",
       "      <td>63879193</td>\n",
       "      <td>45.463571</td>\n",
       "      <td>37.307937</td>\n",
       "      <td>1</td>\n",
       "      <td>0.007997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-06</th>\n",
       "      <td>47.82</td>\n",
       "      <td>47.89</td>\n",
       "      <td>47.56</td>\n",
       "      <td>47.68</td>\n",
       "      <td>75106626</td>\n",
       "      <td>45.520238</td>\n",
       "      <td>37.377540</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.000629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-07</th>\n",
       "      <td>47.71</td>\n",
       "      <td>48.05</td>\n",
       "      <td>47.41</td>\n",
       "      <td>48.02</td>\n",
       "      <td>77982212</td>\n",
       "      <td>45.579762</td>\n",
       "      <td>37.448730</td>\n",
       "      <td>1</td>\n",
       "      <td>0.007106</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Open   High    Low  Close     Volume       SMA1       SMA2  \\\n",
       "Date                                                                      \n",
       "2011-01-03  46.52  47.18  46.41  47.08  111280407  45.347381  37.173651   \n",
       "2011-01-04  47.49  47.50  46.88  47.33   77337001  45.410238  37.240159   \n",
       "2011-01-05  47.08  47.76  47.07  47.71   63879193  45.463571  37.307937   \n",
       "2011-01-06  47.82  47.89  47.56  47.68   75106626  45.520238  37.377540   \n",
       "2011-01-07  47.71  48.05  47.41  48.02   77982212  45.579762  37.448730   \n",
       "\n",
       "            Position   Returns  \n",
       "Date                            \n",
       "2011-01-03         1       NaN  \n",
       "2011-01-04         1  0.005296  \n",
       "2011-01-05         1  0.007997  \n",
       "2011-01-06         1 -0.000629  \n",
       "2011-01-07         1  0.007106  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.005296061526080561"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "math.log(47.33 / 47.08)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.007996675609859118"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "math.log(47.71 / 47.33)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9D0nS4AZZo98M/DIifgMcAn6emQ8BtwFXRMRvgcvLtiRpRPpeo8/M3wEfX2b/fwP/MEhR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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f51f390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['Returns'].hist(bins=35);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data['Strategy'] = data['Position'].shift(1) * data['Returns']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 1    1220\n",
       "-1     453\n",
       "Name: Position, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Position'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>SMA1</th>\n",
       "      <th>SMA2</th>\n",
       "      <th>Position</th>\n",
       "      <th>Returns</th>\n",
       "      <th>Strategy</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>2011-01-04</th>\n",
       "      <td>47.49</td>\n",
       "      <td>47.50</td>\n",
       "      <td>46.88</td>\n",
       "      <td>47.33</td>\n",
       "      <td>77337001</td>\n",
       "      <td>45.410238</td>\n",
       "      <td>37.240159</td>\n",
       "      <td>1</td>\n",
       "      <td>0.005296</td>\n",
       "      <td>0.005296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-05</th>\n",
       "      <td>47.08</td>\n",
       "      <td>47.76</td>\n",
       "      <td>47.07</td>\n",
       "      <td>47.71</td>\n",
       "      <td>63879193</td>\n",
       "      <td>45.463571</td>\n",
       "      <td>37.307937</td>\n",
       "      <td>1</td>\n",
       "      <td>0.007997</td>\n",
       "      <td>0.007997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-06</th>\n",
       "      <td>47.82</td>\n",
       "      <td>47.89</td>\n",
       "      <td>47.56</td>\n",
       "      <td>47.68</td>\n",
       "      <td>75106626</td>\n",
       "      <td>45.520238</td>\n",
       "      <td>37.377540</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.000629</td>\n",
       "      <td>-0.000629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-07</th>\n",
       "      <td>47.71</td>\n",
       "      <td>48.05</td>\n",
       "      <td>47.41</td>\n",
       "      <td>48.02</td>\n",
       "      <td>77982212</td>\n",
       "      <td>45.579762</td>\n",
       "      <td>37.448730</td>\n",
       "      <td>1</td>\n",
       "      <td>0.007106</td>\n",
       "      <td>0.007106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-10</th>\n",
       "      <td>48.40</td>\n",
       "      <td>49.03</td>\n",
       "      <td>48.17</td>\n",
       "      <td>48.92</td>\n",
       "      <td>112139482</td>\n",
       "      <td>45.669524</td>\n",
       "      <td>37.522698</td>\n",
       "      <td>1</td>\n",
       "      <td>0.018569</td>\n",
       "      <td>0.018569</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Open   High    Low  Close     Volume       SMA1       SMA2  \\\n",
       "Date                                                                      \n",
       "2011-01-04  47.49  47.50  46.88  47.33   77337001  45.410238  37.240159   \n",
       "2011-01-05  47.08  47.76  47.07  47.71   63879193  45.463571  37.307937   \n",
       "2011-01-06  47.82  47.89  47.56  47.68   75106626  45.520238  37.377540   \n",
       "2011-01-07  47.71  48.05  47.41  48.02   77982212  45.579762  37.448730   \n",
       "2011-01-10  48.40  49.03  48.17  48.92  112139482  45.669524  37.522698   \n",
       "\n",
       "            Position   Returns  Strategy  \n",
       "Date                                      \n",
       "2011-01-04         1  0.005296  0.005296  \n",
       "2011-01-05         1  0.007997  0.007997  \n",
       "2011-01-06         1 -0.000629 -0.000629  \n",
       "2011-01-07         1  0.007106  0.007106  \n",
       "2011-01-10         1  0.018569  0.018569  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data[['MPerformance', 'SPerformance']] = data[['Returns', 'Strategy']].cumsum().apply(np.exp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>SMA1</th>\n",
       "      <th>SMA2</th>\n",
       "      <th>Position</th>\n",
       "      <th>Returns</th>\n",
       "      <th>Strategy</th>\n",
       "      <th>MPerformance</th>\n",
       "      <th>SPerformance</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-01-04</th>\n",
       "      <td>47.49</td>\n",
       "      <td>47.50</td>\n",
       "      <td>46.88</td>\n",
       "      <td>47.33</td>\n",
       "      <td>77337001</td>\n",
       "      <td>45.410238</td>\n",
       "      <td>37.240159</td>\n",
       "      <td>1</td>\n",
       "      <td>0.005296</td>\n",
       "      <td>0.005296</td>\n",
       "      <td>1.005310</td>\n",
       "      <td>1.005310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-05</th>\n",
       "      <td>47.08</td>\n",
       "      <td>47.76</td>\n",
       "      <td>47.07</td>\n",
       "      <td>47.71</td>\n",
       "      <td>63879193</td>\n",
       "      <td>45.463571</td>\n",
       "      <td>37.307937</td>\n",
       "      <td>1</td>\n",
       "      <td>0.007997</td>\n",
       "      <td>0.007997</td>\n",
       "      <td>1.013381</td>\n",
       "      <td>1.013381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-06</th>\n",
       "      <td>47.82</td>\n",
       "      <td>47.89</td>\n",
       "      <td>47.56</td>\n",
       "      <td>47.68</td>\n",
       "      <td>75106626</td>\n",
       "      <td>45.520238</td>\n",
       "      <td>37.377540</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.000629</td>\n",
       "      <td>-0.000629</td>\n",
       "      <td>1.012744</td>\n",
       "      <td>1.012744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-07</th>\n",
       "      <td>47.71</td>\n",
       "      <td>48.05</td>\n",
       "      <td>47.41</td>\n",
       "      <td>48.02</td>\n",
       "      <td>77982212</td>\n",
       "      <td>45.579762</td>\n",
       "      <td>37.448730</td>\n",
       "      <td>1</td>\n",
       "      <td>0.007106</td>\n",
       "      <td>0.007106</td>\n",
       "      <td>1.019966</td>\n",
       "      <td>1.019966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-10</th>\n",
       "      <td>48.40</td>\n",
       "      <td>49.03</td>\n",
       "      <td>48.17</td>\n",
       "      <td>48.92</td>\n",
       "      <td>112139482</td>\n",
       "      <td>45.669524</td>\n",
       "      <td>37.522698</td>\n",
       "      <td>1</td>\n",
       "      <td>0.018569</td>\n",
       "      <td>0.018569</td>\n",
       "      <td>1.039082</td>\n",
       "      <td>1.039082</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Open   High    Low  Close     Volume       SMA1       SMA2  \\\n",
       "Date                                                                      \n",
       "2011-01-04  47.49  47.50  46.88  47.33   77337001  45.410238  37.240159   \n",
       "2011-01-05  47.08  47.76  47.07  47.71   63879193  45.463571  37.307937   \n",
       "2011-01-06  47.82  47.89  47.56  47.68   75106626  45.520238  37.377540   \n",
       "2011-01-07  47.71  48.05  47.41  48.02   77982212  45.579762  37.448730   \n",
       "2011-01-10  48.40  49.03  48.17  48.92  112139482  45.669524  37.522698   \n",
       "\n",
       "            Position   Returns  Strategy  MPerformance  SPerformance  \n",
       "Date                                                                  \n",
       "2011-01-04         1  0.005296  0.005296      1.005310      1.005310  \n",
       "2011-01-05         1  0.007997  0.007997      1.013381      1.013381  \n",
       "2011-01-06         1 -0.000629 -0.000629      1.012744      1.012744  \n",
       "2011-01-07         1  0.007106  0.007106      1.019966      1.019966  \n",
       "2011-01-10         1  0.018569  0.018569      1.039082      1.039082  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>SMA1</th>\n",
       "      <th>SMA2</th>\n",
       "      <th>Position</th>\n",
       "      <th>Returns</th>\n",
       "      <th>Strategy</th>\n",
       "      <th>MPerformance</th>\n",
       "      <th>SPerformance</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-08-21</th>\n",
       "      <td>157.50</td>\n",
       "      <td>157.89</td>\n",
       "      <td>155.11</td>\n",
       "      <td>157.21</td>\n",
       "      <td>26368528</td>\n",
       "      <td>151.139286</td>\n",
       "      <td>131.532302</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.001843</td>\n",
       "      <td>-0.001843</td>\n",
       "      <td>3.339210</td>\n",
       "      <td>4.830197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-22</th>\n",
       "      <td>158.23</td>\n",
       "      <td>160.00</td>\n",
       "      <td>158.02</td>\n",
       "      <td>159.78</td>\n",
       "      <td>21604585</td>\n",
       "      <td>151.476190</td>\n",
       "      <td>131.735754</td>\n",
       "      <td>1</td>\n",
       "      <td>0.016215</td>\n",
       "      <td>0.016215</td>\n",
       "      <td>3.393798</td>\n",
       "      <td>4.909159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-23</th>\n",
       "      <td>159.07</td>\n",
       "      <td>160.47</td>\n",
       "      <td>158.88</td>\n",
       "      <td>159.98</td>\n",
       "      <td>19399081</td>\n",
       "      <td>151.802381</td>\n",
       "      <td>131.938651</td>\n",
       "      <td>1</td>\n",
       "      <td>0.001251</td>\n",
       "      <td>0.001251</td>\n",
       "      <td>3.398046</td>\n",
       "      <td>4.915304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-24</th>\n",
       "      <td>160.43</td>\n",
       "      <td>160.74</td>\n",
       "      <td>158.55</td>\n",
       "      <td>159.27</td>\n",
       "      <td>19818918</td>\n",
       "      <td>152.122619</td>\n",
       "      <td>132.141984</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.004448</td>\n",
       "      <td>-0.004448</td>\n",
       "      <td>3.382965</td>\n",
       "      <td>4.893490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-25</th>\n",
       "      <td>159.65</td>\n",
       "      <td>160.56</td>\n",
       "      <td>159.27</td>\n",
       "      <td>159.86</td>\n",
       "      <td>25480063</td>\n",
       "      <td>152.506667</td>\n",
       "      <td>132.349484</td>\n",
       "      <td>1</td>\n",
       "      <td>0.003698</td>\n",
       "      <td>0.003698</td>\n",
       "      <td>3.395497</td>\n",
       "      <td>4.911617</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Open    High     Low   Close    Volume        SMA1        SMA2  \\\n",
       "Date                                                                           \n",
       "2017-08-21  157.50  157.89  155.11  157.21  26368528  151.139286  131.532302   \n",
       "2017-08-22  158.23  160.00  158.02  159.78  21604585  151.476190  131.735754   \n",
       "2017-08-23  159.07  160.47  158.88  159.98  19399081  151.802381  131.938651   \n",
       "2017-08-24  160.43  160.74  158.55  159.27  19818918  152.122619  132.141984   \n",
       "2017-08-25  159.65  160.56  159.27  159.86  25480063  152.506667  132.349484   \n",
       "\n",
       "            Position   Returns  Strategy  MPerformance  SPerformance  \n",
       "Date                                                                  \n",
       "2017-08-21         1 -0.001843 -0.001843      3.339210      4.830197  \n",
       "2017-08-22         1  0.016215  0.016215      3.393798      4.909159  \n",
       "2017-08-23         1  0.001251  0.001251      3.398046      4.915304  \n",
       "2017-08-24         1 -0.004448 -0.004448      3.382965      4.893490  \n",
       "2017-08-25         1  0.003698  0.003698      3.395497      4.911617  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MPerformance    3.395497\n",
       "SPerformance    4.911617\n",
       "Name: 2017-08-25 00:00:00, dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[['MPerformance', 'SPerformance']].iloc[-1]  # absolute performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MPerformance    2.395497\n",
       "SPerformance    3.911617\n",
       "Name: 2017-08-25 00:00:00, dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[['MPerformance', 'SPerformance']].iloc[-1] - 1  # relative performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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eymmnndblNp933nlceumlnHrqqYwfP57Ro0d3eM1DDz3EH//4R+677z5sNhuPP/44U6ZM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922fvQcbJ4RsRC4ALgF2EKZI/Bs969gtMwxz12ZeUN97ITM3NbzPG/5+3DVR/+cT7mU/XbgL9g/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\nSZIa+S8DbsN4BRkHcgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f73b0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data[['MPerformance', 'SPerformance']].plot(figsize=(10, 6));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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G8rct5eyvbsJiim7u2Df5dE2rbY+2uv408HSrbXuByVFtDHG2dm1XJoGI+NHf\nXzfpHhJCiPjT5PJ+t1w6Poc/+Mbd7a108PxW75jhuxYUUZDetck5ZqOBGcPTuX2+d9Li+PzkTo7o\n3+Iqk2c0GnG5XJjNcdUs0QGXy9WvZ9YKIeJTP//tKKKgyZfJs5qNpFlNGA2wp8rBhkPeLtwTcyOv\n3ZpqMdLgbFnPIcNq4r7iERRlRm08XlyKq2jKZrPhcDhoamoK6UMX8UnXdYxGY+IUSZYvFSFiruVP\nv3wgo6f/PZdv76nmkxJvMOcvneLRCQR4g9MsXVql4tGLR9PsDl20a2xuYmfxIM6CPIPBQHJy4j/p\nQgghhAjP6fbw+w0ty2hafbNjs20mqnzr1Vo6WMYsnAyrCe9ciIFF+tmEEEKIAaC/dJBds2p3yPW8\nFG8+6pfzijh5kDcRZB3gNU0jFVeZPCGEEAObf6iOjMkbWJpcHsxGAw3NbuqbW7pVb5szjMFp3skV\nI7Ks3LVgBO/urWHCIOn1i4QEeUIIIYSImS1HG/jF294lX78/c0jIbacWprXZf84JHS8AIFpIkCeE\nEEIksjjPin55vDFw+cGPvevB/3DWECYVpMaqSQlDxuQJIUL0l3E7IsHFeWDSP8Xnhzs7zNJkEwal\nhN0uukaeQSHi8++eEAOS/0eGxHgDz53zi9h6rIErTsnDKL82o0KCPCGEEELEjMNX9HhklpWJg1Ni\n3JrEIt21QvhI5kCIOCIfyKiJ56fyX7ur+W9pAyBlUXqDZPKEEAFStkLEnHTXDhgNzW4e2egtepyT\nbMbcxQLHonOSyRNCCBE35Gt+4Fi3pzpw+Y75RbKcaS+QTJ4QQggh+lRtk5unNh9n4uAUbp83XCZa\n9BLJ5AkhhIgf/u966a9NaA9v8NbDO39MlgR4vUiCPCH85EtFiJiTGG9gqHG4AZhVlB7jliQ2CfKE\nEEKIgSCOEmbNbg+nD02VyRa9TII8MeDJnxgh4pCk8hJafbOHVIsp1s1IeBLkCSGEiB9SQiXh6bpO\nfbObVKsEeb1NgjwhAuRrRYhYa8msy+cxauLsqSypbcbu9DA8IynWTUl4EuQJIYLE2beBECLh3PDa\nPgBOK0yLcUsSX8R18jRNMwGbgBKl1KJWtxmAB4ELADtwjVJqczQbKoToIzJIUcSSTK/tNfHw0S63\nOwOXB6VZYtiSgaErmbzvA1+2c9tCYKzv33Lgjz1slxB9R2o0CRFH5POYyHaWNwJwX/GIGLdkYIgo\nyNM0bRhR5PvhAAAgAElEQVRwIfBEO7tcDDyrlNKVUhuALE3ThkSpjUIIIQYISeQltkq7C4DBaTIe\nry9E2l37O+AnQHtVC4cCh4KuH/ZtK+1+04ToW/KlIkQckQ9k1MTTU1ntcGMyQFpSYk4J0DStGO/w\nNRPwhFLq7la3nwusBvb5Nq1SSv0qkmO7o9MgT9O0RUCZUuozX+O6TdO05Xi7c1FK9eRUQojeoINB\nustELMnbL6HtrnQwJD0pIZcy881deARYgDfZ9ammaa8opb5oteu/w8xtiPTYLokkk3cmcJGmaRcA\nNiBD07S/KqW+EbRPCTA86Pow37YQSqnHgcd9V+Ppx4UQQog4Il8QiafJ5eGLMjvnj8mKdVN6y3Rg\nt1JqL4Cmac/jHc4WSaDWk2Pb1WmQp5T6KfBT352eC/y4VYAH8AqwwteoGUCNUkq6aoUQQnRJIL8j\nUV7CeWVHJc1unalDUmPdlN4SbujajDD7zdI0bQveZNiPlVLbu3Bsl0RcQqU1TdOuB1BKPQqswVs+\nZTfeEirfiuQceXl53b17IaKmrKQaaCQ3JxerbWBXYDeZ7SQlWeSzKWLG7WwE6snIyCAvL2GDgT5l\nMTcDdaSnp5OX197Q+t63YFIaCyaN5IRsG/11yVpN0zYFXX3c10PZFZuBIqVUva+H9GW8lUl6RZeC\nPKXUe8B7vsuPBm3XgRu6eufl5eVdPUSIqKtvaAKgoqKCJGtiDgaOlMvlorlZl8+miJmaGu/sy9ra\nWsp95TZEz9TXuQGoq6ujvLwpZu249fV9HKhuYvVV42LWhp4oLCxEKTWtg106HbqmlKoNurxG07Q/\naJqWF8mx3dHtTJ4QQggRbVJCpRfFOHuWajFyyuCU2Daid30KjNU0bRTeAG0JsDR4B03TCoBjSild\n07TpeEvZVQDVnR3bHQM7bSEEMf+7J4QIJlFewnLrYErgP7hKKRewAngT7+IRSim1XdO06/1D3IDF\nwDZN0z4Hfg8s8dUYDntsT9skmTwhhBBC9DqPrmMyJnZuSSm1Bu88heBtwcPbHgYejvTYnkrsZ1sI\nIYQY6OIkK+r26AlZHy+eSZAnhAglf4NFDAV6a+MkMEkksf5ou3UwSdTRp+TpFkIIET9iHYmIXuPR\ndUySyetTEuQJIX9zhBCi17k9SJDXxyTIE8JHeoeEEIkoXv62eXSdBJ93EXfk6RZCCBE3/IkeXQbl\nJRyZeNH3JMgTQrTQpfdaiIQV4w93otfJi0cS5AkhhIgjEgUkKreuY+qvi9b2U1IMWQghRNyR3trE\n0ej08OrOSmocbsnk9THJ5AnhJ18qQsScDNlKPJtK6nnu83IAdlU4YtyagUWCPCFEgMS5Qohoq292\nBy5PKkiNYUsGHumuFUIIEX/kF0fCaHB6APjr4rGkJUluqS9JkCcGPOkdEiJ+BEqoxLYZood0XafJ\nrfPbD4+w8XA9AGlJRgzSH9+nJMgTQgghElkMIuYnPivjtZ1VgeuTClIkwIsBCfKEEELED4kDEkJw\ngPfoRScwJD0phq0ZuKRzXAgRSr5kRTyQ/tqo66uP9pHa5sDlu+YXSYAXQxLkCSFBjRBxw/9xlBgv\nNtTWci55bkePlpXbeLgOgD9dPJqTB6dEq2miG6S7VgghRPxJ8Cjvb1uOs+N4I7+aVxTrpuD26Pzu\no1IGpVl4aXsFAE6PTlI3KxdvPWanKDOJQWmWaDZTdIMEeUIIIeLHAMmsv7DVG0zZnW5SLKaYtuV3\nH5Wy/kBtyDaHSycpwmZV2J28trOKDw/WseSUPKoaXQxKlQAvHkiQJ4RokeDZE9F/DJS3Yl1T7IO8\nvVVtV6FocnnAGlm7fvLmAcrtLgAe/LiU3GQzo7JtUW2j6B4ZkyeECDFAEikiTgXefwMkyqtxuDvf\nKVra+XBn2UyMyrZyz3kjAtuaXJ6ITrm/yhEI8PwqGl1kJ0sOKR5IkCeEECJ+DLBfGbVNHQd5uq7z\nzt6akKXBuqu0tpktRxtCtjW7PXxxvJEpBamMy09m5eyhgLe7NhJ//bycVIuRa08bxI1nFAS2nzpE\nli+LB52G2pqm2YD1gNW3/0tKqdta7XMusBrY59u0Sin1q+g2VYje1YPJZEII0S2dBXnbyxp58ONS\nisdm8d3pBR3u2x7/37a/fH6cfXoTq68aF7itpLYZjw6jc7zdq2m+gXh1EQSVdU1uPi2pZ8HoTL42\nLge3RyfZYmRaYRpWs+SQ4kEk+dQmYK5Sql7TNAvwgaZpbyilNrTa799KqUXRb6IQQoiBoiWRl5i/\nutwend99XBq4XuNwdbA37PONl3N7ovd8PL+1nOIxWWQlmzlY3QRAUZYVgPwU74SJ4w3OTs/jH8s3\nwnesyWjgzKKMqLVT9FynQZ5SSgfqfVctvn+J+ekTYoCTD7aIOf/atQn6Zjxa72T9/paZrJ1l8g77\nCgtn2no+xs3/lP59SzlbjjawcvYwDtY0YzJAoa9gcW6KGaMByuo7D/L8AeJEqYUXoGlaMfAgYAKe\nUErd3er2q4Bb8L7T64DvKqU+992237fNDbiUUtN62p6I8qmappk0TfsvUAasU0ptDLPbLE3Ttmia\n9oamaSf3tGFCCCFELDQ6PVz83A7e2VsDQK3DRWldcydHda6+yc2L28pDtq36orLDY/ZVeQMpu7Pn\nY/JygyZDbC9r5KoXd/Hp4XpGZduw+GrimYwGcpPNfHK4Hqe7/ckX5XYnT3xWBkCKRbpmwRsrAY8A\nC4EJwJWapk1otds+YLZS6hTgduDxVrfPUUpNiUaABxEGeUopt1JqCjAMmK5p2sRWu2wGipRSk4CH\ngJfDnUfTtOWapm3SNG1TTxothBAiUcV+5kWdL7v25OYy3t9Xw/Wv7OX6V/b2uMv0j58e5d193ize\n0kl5ge0dnbfBNzaut2bhHqhpYkqrSRKzR2VyoKaJDw/WtXtcZdCM2tQYl4CJI9OB3UqpvUqpZuB5\n4OLgHZRSHyml/Av7bsAbV/WaLoXfSqlq4F2guNX2WqVUve/yGsCiaVpemOMfV0pNi1aEKoQQIjHF\nsrvW4Isz65rc/PajUhqc3oxWaX3PsnnB49wunZDDqGzvWLbVX7afzWv2ZdPKIhgj1xm3DrNHth0z\nNzrHGnL90gk5QMeBpf85mTk8jbQI6+kNAEOBQ0HXD/u2tec7wBtB13XgLU3TPtM0bXk0GhTJ7Np8\nwKmUqtY0LRlYANzTap8C4JhSStc0bTre4LGis3Pn5bWJA4Xoc+VHa4BGcnJySEkd2LWdTKYGrFar\nfDYTiN3pwWw0dHuJqr5Wb3UCtaSlpZGXlxmTNmTm6DyzLLvN9sL0JGqb3NjMRrJsXQ9sbrswHacv\na1eYY+MPV+RS1uAk2WwkLyMp7DG/vTwdt65jMhjIybJi7MbLaDQ0AXV884yRjBydxo+NBiobWzJx\nY3JCCxfnAc8syyQn2UxOO/XuZmZk88zQwRRlWvvNeysaWvVEPq6Uat3dGul55uAN8s4K2nyWUqpE\n07RBwDpN03Yopdb3oLkRza4dAjzj62s2Akop9ZqmadfjvfIosBj4rqZpLqARWOKbsNGh8vLyznYR\notfV13vHvFRWVmJvHNhjS9x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1le5215qNBlKTTIHxi50Fef65EdF86jN9Ba4f3ni0w0xi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\nu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cDzQB9gPWCstTubet1W7dVvjLnGGPOxMeZjgEnn+IMgqkal3jl1NP0G9z/kvNoJHsCJ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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f913438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data[['MPerformance', 'SPerformance', 'Position']].plot(figsize=(10, 6), secondary_y='Position');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Financial Base Class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class FinData(object):\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fd = FinData()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<__main__.FinData at 0x10f87ba58>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fd.number = 10  # data attribute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fd.name = 'Peter'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fd.number"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Peter'"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fd.name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class FinData(object):\n",
    "    def __init__(self, symbol):\n",
    "        self.symbol = symbol  # class attribute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "fd = FinData('AAPL')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'AAPL'"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fd.symbol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class FinData(object):\n",
    "    def __init__(self, symbol):\n",
    "        self.symbol = symbol  # class attribute\n",
    "        self.get_data()  # executes method during instantiation\n",
    "        \n",
    "    def get_data(self):\n",
    "        self.data = pd.DataFrame(web.DataReader(self.symbol,\n",
    "                                data_source='google')['Close'])\n",
    "        self.data['Returns'] = np.log(self.data['Close'] / self.data['Close'].shift(1))\n",
    "        self.data.dropna(inplace=True)\n",
    "        \n",
    "    def plot_data(self, column='Close'):\n",
    "        self.data[column].plot(figsize=(10, 6), title=self.symbol)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fd = FinData('AAPL')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'AAPL'"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fd.symbol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# fd.get_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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>Close</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>2010-01-05</th>\n",
       "      <td>30.63</td>\n",
       "      <td>0.001961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>30.14</td>\n",
       "      <td>-0.016127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>30.08</td>\n",
       "      <td>-0.001993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-08</th>\n",
       "      <td>30.28</td>\n",
       "      <td>0.006627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-11</th>\n",
       "      <td>30.02</td>\n",
       "      <td>-0.008624</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Close   Returns\n",
       "Date                       \n",
       "2010-01-05  30.63  0.001961\n",
       "2010-01-06  30.14 -0.016127\n",
       "2010-01-07  30.08 -0.001993\n",
       "2010-01-08  30.28  0.006627\n",
       "2010-01-11  30.02 -0.008624"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fd.data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RySnr+4mgB/Bv+FX2m5IDs0fuw25YCx+/B7vtg3PZH3Bm/im49vA92A/nddxG7I1H6XPs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\nB6ulJfPejumSpp3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zYMaX+DPrn0hSyro1wVo9wL7/dmrb/foT+e1tEMsev90uODP/3NMu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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f6ab438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fd.plot_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Financial Backtesting Class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class SMABacktester(FinData):\n",
    "    def __init__(self, symbol, SMA1, SMA2):\n",
    "        FinData.__init__(self, symbol)\n",
    "        self.SMA1 = SMA1\n",
    "        self.SMA2 = SMA2\n",
    "        self.prepare_data()\n",
    "        \n",
    "    def prepare_data(self):\n",
    "        self.data['SMA1'] = self.data['Close'].rolling(self.SMA1).mean()\n",
    "        self.data['SMA2'] = self.data['Close'].rolling(self.SMA2).mean()\n",
    "        self.data.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "sma = SMABacktester('AAPL', 42, 252)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "42"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sma.SMA1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'AAPL'"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sma.symbol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 1673 entries, 2011-01-04 to 2017-08-25\n",
      "Data columns (total 4 columns):\n",
      "Close      1673 non-null float64\n",
      "Returns    1673 non-null float64\n",
      "SMA1       1673 non-null float64\n",
      "SMA2       1673 non-null float64\n",
      "dtypes: float64(4)\n",
      "memory usage: 65.4 KB\n"
     ]
    }
   ],
   "source": [
    "sma.data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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CwsJm569YsYIpU6awatWqhrbk5GRee+011q5dy/PPP8+9997bPV9GCCGE6MtqqgBQsXHN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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f6ab780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sma.plot_data(['Close', 'SMA1', 'SMA2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class SMABacktester(FinData):\n",
    "    def __init__(self, symbol, SMA1, SMA2):\n",
    "        FinData.__init__(self, symbol)\n",
    "        self.SMA1 = SMA1\n",
    "        self.SMA2 = SMA2\n",
    "        self.prepare_data()\n",
    "        \n",
    "    def prepare_data(self):\n",
    "        self.data['SMA1'] = self.data['Close'].rolling(self.SMA1).mean()\n",
    "        self.data['SMA2'] = self.data['Close'].rolling(self.SMA2).mean()\n",
    "        self.data.dropna(inplace=True)\n",
    "        \n",
    "    def run_strategy(self):\n",
    "        self.data['Position'] = np.where(self.data['SMA1'] > self.data['SMA2'], 1, -1)\n",
    "        self.data['Strategy'] = self.data['Position'].shift(1) * self.data['Returns']\n",
    "        self.data.dropna(inplace=True)\n",
    "        self.data[['MPerformance', 'SPerformance']] = self.data[['Returns',\n",
    "                                                'Strategy']].cumsum().apply(np.exp)\n",
    "        return self.data[['MPerformance', 'SPerformance']].iloc[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 211 ms, sys: 3.58 ms, total: 215 ms\n",
      "Wall time: 717 ms\n"
     ]
    }
   ],
   "source": [
    "%time sma = SMABacktester('AAPL', 42, 252)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 7.88 ms, sys: 1.24 ms, total: 9.12 ms\n",
      "Wall time: 7.92 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "MPerformance    3.377562\n",
       "SPerformance    4.885674\n",
       "Name: 2017-08-25 00:00:00, dtype: float64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%time sma.run_strategy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 216 ms, sys: 3.39 ms, total: 220 ms\n",
      "Wall time: 690 ms\n"
     ]
    }
   ],
   "source": [
    "%time sma = SMABacktester('AAPL', 42, 150)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 7.91 ms, sys: 1.4 ms, total: 9.32 ms\n",
      "Wall time: 8 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "MPerformance    4.313546\n",
       "SPerformance    4.539850\n",
       "Name: 2017-08-25 00:00:00, dtype: float64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%time sma.run_strategy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Some Improvements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class SMABacktester(FinData):\n",
    "        \n",
    "    def prepare_data(self):\n",
    "        self.copy = self.data.copy()\n",
    "        self.copy['SMA1'] = self.copy['Close'].rolling(self.SMA1).mean()\n",
    "        self.copy['SMA2'] = self.copy['Close'].rolling(self.SMA2).mean()\n",
    "        self.copy.dropna(inplace=True)\n",
    "        \n",
    "    def run_strategy(self, SMA1, SMA2):\n",
    "        self.SMA1 = SMA1\n",
    "        self.SMA2 = SMA2\n",
    "        self.prepare_data()\n",
    "        self.copy['Position'] = np.where(self.copy['SMA1'] > self.copy['SMA2'], 1, -1)\n",
    "        self.copy['Strategy'] = self.copy['Position'].shift(1) * self.copy['Returns']\n",
    "        self.copy.dropna(inplace=True)\n",
    "        self.copy[['MPerformance', 'SPerformance']] = self.copy[['Returns',\n",
    "                                                'Strategy']].cumsum().apply(np.exp)\n",
    "        return self.copy[['MPerformance', 'SPerformance']].iloc[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 211 ms, sys: 3.58 ms, total: 215 ms\n",
      "Wall time: 684 ms\n"
     ]
    }
   ],
   "source": [
    "%time sma = SMABacktester('AAPL')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 11 ms, sys: 1.42 ms, total: 12.4 ms\n",
      "Wall time: 11.3 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "MPerformance    3.377562\n",
       "SPerformance    4.885674\n",
       "Name: 2017-08-25 00:00:00, dtype: float64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%time sma.run_strategy(42, 252)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 11.1 ms, sys: 978 µs, total: 12 ms\n",
      "Wall time: 11.3 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "MPerformance    4.313546\n",
       "SPerformance    4.539850\n",
       "Name: 2017-08-25 00:00:00, dtype: float64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%time sma.run_strategy(42, 150)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from itertools import product"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SMA1 | SMA2 | market |  strat\n",
      "  20 |  100 | 4.3559 | 2.0089\n",
      "  20 |  150 | 4.3135 | 4.0306\n",
      "  20 |  250 | 3.4692 | 4.1092\n",
      "  30 |  100 | 4.3559 | 1.5594\n",
      "  30 |  150 | 4.3135 | 3.0314\n",
      "  30 |  250 | 3.4692 | 4.5157\n",
      "  40 |  100 | 4.3559 | 1.2321\n",
      "  40 |  150 | 4.3135 | 4.4103\n",
      "  40 |  250 | 3.4692 | 3.8505\n",
      "  50 |  100 | 4.3559 | 1.6677\n",
      "  50 |  150 | 4.3135 | 4.0487\n",
      "  50 |  250 | 3.4692 | 3.8408\n",
      "  60 |  100 | 4.3559 | 2.8643\n",
      "  60 |  150 | 4.3135 | 4.6822\n",
      "  60 |  250 | 3.4692 | 3.9911\n",
      "  70 |  100 | 4.3559 | 2.7562\n",
      "  70 |  150 | 4.3135 | 3.7820\n",
      "  70 |  250 | 3.4692 | 3.3759\n",
      "CPU times: user 176 ms, sys: 6.77 ms, total: 183 ms\n",
      "Wall time: 181 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "print('%4s | %4s | %6s | %6s' % ('SMA1', 'SMA2', 'market', 'strat'))\n",
    "for SMA in product([20, 30, 40, 50, 60, 70], [100, 150, 250]):\n",
    "    perf = sma.run_strategy(SMA[0], SMA[1])\n",
    "    print('%4d | %4d | %6.4f | %6.4f' % (SMA[0], SMA[1],\n",
    "                    perf['MPerformance'], perf['SPerformance']))"
   ]
  },
  {
   "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": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}

02_event_based_backtesting.ipynb

tick_client.py

#
# Simple Tick Client with ZeroMQ
#
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, 'AAPL')

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

tick_collector.py

#
# Simple Tick Collector with ZeroMQ
#
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, 'AAPL')

data = pd.DataFrame()

ticks = 0

while ticks < 100:
    ticks += 1
    msg = socket.recv_string()
    t = datetime.datetime.now()
    print('%s | ' % str(t), msg)
    sym, value = msg.split()
    data = data.append(pd.DataFrame({sym: float(value)}, index=[t]))
    if ticks % 10 == 0:
        h5 = pd.HDFStore('data.h5', 'w')
        h5['data'] = data
        h5.close()

tick_server.py

#
# Simple Tick Data Server with ZeroMQ
#
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 %.4f' % AAPL
    socket.send_string(msg)
    print(msg)
    time.sleep(random.random() * 2)

tick_trader.py

#
# Simple Tick Trader with ZeroMQ
#
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, 'AAPL')

data = pd.DataFrame()

ticks = 0
position = 0

while ticks < 200:
    ticks += 1
    msg = socket.recv_string()
    t = datetime.datetime.now()
    sym, value = msg.split()
    data = data.append(pd.DataFrame({sym: float(value)}, index=[t]))

    resam = data.resample('2s', label='right').last()
    resam['SMA1'] = resam['AAPL'].rolling(5).mean()
    resam['SMA2'] = resam['AAPL'].rolling(10).mean()
    resam.dropna(inplace=True)

    if len(resam) > 2:
        print('%3d | %s | ' % (ticks, str(t)), msg, '| %.3f | %.3f' %
                (resam['SMA1'].iloc[-2], resam['SMA2'].iloc[-2]))
        if position == 0 and resam['SMA1'].iloc[-2] > resam['SMA2'].iloc[-2]:
            print('going long the market')
            position = 1
            #
            # place trading code here (for buy order)
            #
        elif position == 1 and resam['SMA1'].iloc[-2] < resam['SMA2'].iloc[-2]:
            print('going market neutral')
            position = 0
            #
            # place trading code here (for sell order)
            #

    #if ticks % 10 == 0:
    #    h5 = pd.HDFStore('data.h5', 'w')
    #    h5['data'] = data
    #    h5.close()

tick_trader_ls.py

#
# Simple Tick Trader LS with ZeroMQ
#
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, 'AAPL')

data = pd.DataFrame()

ticks = 0
position = 0

while ticks < 200:
    ticks += 1
    msg = socket.recv_string()
    t = datetime.datetime.now()
    sym, value = msg.split()
    data = data.append(pd.DataFrame({sym: float(value)}, index=[t]))

    resam = data.resample('2s', label='right').last()
    resam['SMA1'] = resam['AAPL'].rolling(5).mean()
    resam['SMA2'] = resam['AAPL'].rolling(10).mean()
    resam.dropna(inplace=True)

    if len(resam) > 2:
        print('%3d | %s | ' % (ticks, str(t)), msg, '| %.3f | %.3f' %
                (resam['SMA1'].iloc[-2], resam['SMA2'].iloc[-2]))
        if position in [0, -1] and resam['SMA1'].iloc[-2] > resam['SMA2'].iloc[-2]:
            print('going long the market')
            position = 1
            #
            # place trading code here (for buy order)
            #
        elif position in [0, 1] and resam['SMA1'].iloc[-2] < resam['SMA2'].iloc[-2]:
            print('going short the market')
            position = -1
            #
            # place trading code here (for sell order)
            #