{
 "metadata": {
  "name": "2. Pandas replication of Google Trends paper"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Reproduction [Quantifying Trading Behavior in Financial Markets Using Google Trends](http://www.nature.com/srep/2013/130425/srep01684/pdf/srep01684.pdf)\n",
      "=============================================="
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The first part of this tutorial covered the basics of `Pandas` and how to use it for analyzing financial data.\n",
      "\n",
      "In the second part we will reiterate these concepts by applying them to reproduce a paper published recently on using Google Trends search volume for specific terms (e.g. 'debt') to predict market movements. Several cells are left blank for you to fill in.\n",
      "\n",
      "In addition, we will see how we can test an actual trading strategy using `Pandas` and calculate its performance."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.core.display import HTML\n",
      "HTML(\"<iframe src=http://www.nature.com/srep/2013/130425/srep01684/full/srep01684.html width=900 height=400></iframe>\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<iframe src=http://www.nature.com/srep/2013/130425/srep01684/full/srep01684.html width=900 height=400></iframe>"
       ],
       "output_type": "pyout",
       "prompt_number": 1,
       "text": [
        "<IPython.core.display.HTML at 0xa7af14c>"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "\n",
      "import matplotlib.pyplot as plt\n",
      "import matplotlib as mpl\n",
      "mpl.rc('figure', figsize=(8, 7))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Tobias Preis was kind enough to provide me with the data used in this publication. There are many Google Trends search words but here we will only be using 'debt'."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = pd.read_csv('data/GoogleTrendsData.csv', index_col='Date', parse_dates=True)\n",
      "data.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>djia</th>\n",
        "      <th>debt</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>2004-01-14</th>\n",
        "      <td> 10485.18</td>\n",
        "      <td> 0.210000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2004-01-22</th>\n",
        "      <td> 10528.66</td>\n",
        "      <td> 0.210000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2004-01-28</th>\n",
        "      <td> 10702.51</td>\n",
        "      <td> 0.210000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2004-02-04</th>\n",
        "      <td> 10499.18</td>\n",
        "      <td> 0.213333</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2004-02-11</th>\n",
        "      <td> 10579.03</td>\n",
        "      <td> 0.200000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "                djia      debt\n",
        "Date                          \n",
        "2004-01-14  10485.18  0.210000\n",
        "2004-01-22  10528.66  0.210000\n",
        "2004-01-28  10702.51  0.210000\n",
        "2004-02-04  10499.18  0.213333\n",
        "2004-02-11  10579.03  0.200000"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data.plot(subplots=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "array([<matplotlib.axes.AxesSubplot object at 0xac0794c>,\n",
        "       <matplotlib.axes.AxesSubplot object at 0xac7f14c>], dtype=object)"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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GrKws8Pl8CAQCbnzcxMREaR03gyETNe2feuHCBbpx4wZ3OhgRUX5+Pvfzpk2baNasWURE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\nfv2UiDs5ORk3Nzesra319uvWrRupqalIkoROpwNoVznvqh3+unTp0uC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37qyM3TaEj48P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\n"
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The authors detect if search volume is relatively increasing or decreasing in any given week by forming a moving average and testing if the current value crosses the moving average of the past 3 weeks. \n",
      "\n",
      "Lets first compute the moving average."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data['debt_mavg'] = pd.rolling_mean(data.debt, 3)\n",
      "data.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>djia</th>\n",
        "      <th>debt</th>\n",
        "      <th>debt_mavg</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Date</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2004-01-14</th>\n",
        "      <td> 10485.18</td>\n",
        "      <td> 0.210000</td>\n",
        "      <td>      NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2004-01-22</th>\n",
        "      <td> 10528.66</td>\n",
        "      <td> 0.210000</td>\n",
        "      <td>      NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2004-01-28</th>\n",
        "      <td> 10702.51</td>\n",
        "      <td> 0.210000</td>\n",
        "      <td> 0.210000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2004-02-04</th>\n",
        "      <td> 10499.18</td>\n",
        "      <td> 0.213333</td>\n",
        "      <td> 0.211111</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2004-02-11</th>\n",
        "      <td> 10579.03</td>\n",
        "      <td> 0.200000</td>\n",
        "      <td> 0.207778</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "                djia      debt  debt_mavg\n",
        "Date                                     \n",
        "2004-01-14  10485.18  0.210000        NaN\n",
        "2004-01-22  10528.66  0.210000        NaN\n",
        "2004-01-28  10702.51  0.210000   0.210000\n",
        "2004-02-04  10499.18  0.213333   0.211111\n",
        "2004-02-11  10579.03  0.200000   0.207778"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Since we want to see if the current value is above the moving average of the *preceeding* weeks, we have to shift the moving average timeseries forward by one."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data['debt_mavg'] = data.debt_mavg.shift(1)\n",
      "data.head(10)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>djia</th>\n",
        "      <th>debt</th>\n",
        "      <th>debt_mavg</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Date</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2004-01-14</th>\n",
        "      <td> 10485.18</td>\n",
        "      <td> 0.210000</td>\n",
        "      <td>      NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2004-01-22</th>\n",
        "      <td> 10528.66</td>\n",
        "      <td> 0.210000</td>\n",
        "      <td>      NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2004-01-28</th>\n",
        "      <td> 10702.51</td>\n",
        "      <td> 0.210000</td>\n",
        "      <td>      NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2004-02-04</th>\n",
        "      <td> 10499.18</td>\n",
        "      <td> 0.213333</td>\n",
        "      <td> 0.210000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2004-02-11</th>\n",
        "      <td> 10579.03</td>\n",
        "      <td> 0.200000</td>\n",
        "      <td> 0.211111</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2004-02-19</th>\n",
        "      <td> 10714.88</td>\n",
        "      <td> 0.203333</td>\n",
        "      <td> 0.207778</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2004-02-25</th>\n",
        "      <td> 10609.62</td>\n",
        "      <td> 0.200000</td>\n",
        "      <td> 0.205555</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2004-03-03</th>\n",
        "      <td> 10678.14</td>\n",
        "      <td> 0.200000</td>\n",
        "      <td> 0.201111</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2004-03-10</th>\n",
        "      <td> 10529.48</td>\n",
        "      <td> 0.196667</td>\n",
        "      <td> 0.201111</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2004-03-17</th>\n",
        "      <td> 10102.89</td>\n",
        "      <td> 0.196667</td>\n",
        "      <td> 0.198889</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "                djia      debt  debt_mavg\n",
        "Date                                     \n",
        "2004-01-14  10485.18  0.210000        NaN\n",
        "2004-01-22  10528.66  0.210000        NaN\n",
        "2004-01-28  10702.51  0.210000        NaN\n",
        "2004-02-04  10499.18  0.213333   0.210000\n",
        "2004-02-11  10579.03  0.200000   0.211111\n",
        "2004-02-19  10714.88  0.203333   0.207778\n",
        "2004-02-25  10609.62  0.200000   0.205555\n",
        "2004-03-03  10678.14  0.200000   0.201111\n",
        "2004-03-10  10529.48  0.196667   0.201111\n",
        "2004-03-17  10102.89  0.196667   0.198889"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Generate the order signals.\n",
      "===========================\n",
      "\n",
      "From the paper:\n",
      "\n",
      "\"We use Google Trends to determine how many searches n(t \u2013 1) have been carried out for a specific search term such as debt in week t \u2013 1, where Google defines weeks as ending on a Sunday, relative to the total number of searches carried out on Google during that time.\"\n",
      "\n",
      "and\n",
      "\n",
      "\"We implement this strategy by selling the DJIA at the closing price p(t) on the first trading day of week t, if \u0394n(t \u2212 1, \u0394t) > 0, and buying the DJIA at price p(t + 1) at the end of the first trading day of the following week. [...]. If instead \u0394n(t \u2212 1, \u0394t) < 0, then we buy the DJIA at the closing price p(t) on the first trading day of week t and sell the DJIA at price p(t + 1) at the end of the first trading day of the coming week.\""
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data['order'] = 0\n",
      "data['order'][data.debt > data.debt_mavg] = -1 # Short if search volume goes up relative to mavg.\n",
      "data['order'][data.debt < data.debt_mavg] = 1 # Long if search volume goes down relative to mavg.\n",
      "data.head(10)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>djia</th>\n",
        "      <th>debt</th>\n",
        "      <th>debt_mavg</th>\n",
        "      <th>order</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Date</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2004-01-14</th>\n",
        "      <td> 10485.18</td>\n",
        "      <td> 0.210000</td>\n",
        "      <td>      NaN</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2004-01-22</th>\n",
        "      <td> 10528.66</td>\n",
        "      <td> 0.210000</td>\n",
        "      <td>      NaN</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2004-01-28</th>\n",
        "      <td> 10702.51</td>\n",
        "      <td> 0.210000</td>\n",
        "      <td>      NaN</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2004-02-04</th>\n",
        "      <td> 10499.18</td>\n",
        "      <td> 0.213333</td>\n",
        "      <td> 0.210000</td>\n",
        "      <td>-1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2004-02-11</th>\n",
        "      <td> 10579.03</td>\n",
        "      <td> 0.200000</td>\n",
        "      <td> 0.211111</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2004-02-19</th>\n",
        "      <td> 10714.88</td>\n",
        "      <td> 0.203333</td>\n",
        "      <td> 0.207778</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2004-02-25</th>\n",
        "      <td> 10609.62</td>\n",
        "      <td> 0.200000</td>\n",
        "      <td> 0.205555</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2004-03-03</th>\n",
        "      <td> 10678.14</td>\n",
        "      <td> 0.200000</td>\n",
        "      <td> 0.201111</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2004-03-10</th>\n",
        "      <td> 10529.48</td>\n",
        "      <td> 0.196667</td>\n",
        "      <td> 0.201111</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2004-03-17</th>\n",
        "      <td> 10102.89</td>\n",
        "      <td> 0.196667</td>\n",
        "      <td> 0.198889</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "                djia      debt  debt_mavg  order\n",
        "Date                                            \n",
        "2004-01-14  10485.18  0.210000        NaN      0\n",
        "2004-01-22  10528.66  0.210000        NaN      0\n",
        "2004-01-28  10702.51  0.210000        NaN      0\n",
        "2004-02-04  10499.18  0.213333   0.210000     -1\n",
        "2004-02-11  10579.03  0.200000   0.211111      1\n",
        "2004-02-19  10714.88  0.203333   0.207778      1\n",
        "2004-02-25  10609.62  0.200000   0.205555      1\n",
        "2004-03-03  10678.14  0.200000   0.201111      1\n",
        "2004-03-10  10529.48  0.196667   0.201111      1\n",
        "2004-03-17  10102.89  0.196667   0.198889      1"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Compute returns\n",
      "==============="
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data['ret_djia'] = data.djia.pct_change()\n",
      "data.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>djia</th>\n",
        "      <th>debt</th>\n",
        "      <th>debt_mavg</th>\n",
        "      <th>order</th>\n",
        "      <th>ret_djia</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>2004-01-14</th>\n",
        "      <td> 10485.18</td>\n",
        "      <td> 0.210000</td>\n",
        "      <td>      NaN</td>\n",
        "      <td> 0</td>\n",
        "      <td>      NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2004-01-22</th>\n",
        "      <td> 10528.66</td>\n",
        "      <td> 0.210000</td>\n",
        "      <td>      NaN</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.004147</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2004-01-28</th>\n",
        "      <td> 10702.51</td>\n",
        "      <td> 0.210000</td>\n",
        "      <td>      NaN</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.016512</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2004-02-04</th>\n",
        "      <td> 10499.18</td>\n",
        "      <td> 0.213333</td>\n",
        "      <td> 0.210000</td>\n",
        "      <td>-1</td>\n",
        "      <td>-0.018998</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2004-02-11</th>\n",
        "      <td> 10579.03</td>\n",
        "      <td> 0.200000</td>\n",
        "      <td> 0.211111</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0.007605</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "                djia      debt  debt_mavg  order  ret_djia\n",
        "Date                                                      \n",
        "2004-01-14  10485.18  0.210000        NaN      0       NaN\n",
        "2004-01-22  10528.66  0.210000        NaN      0  0.004147\n",
        "2004-01-28  10702.51  0.210000        NaN      0  0.016512\n",
        "2004-02-04  10499.18  0.213333   0.210000     -1 -0.018998\n",
        "2004-02-11  10579.03  0.200000   0.211111      1  0.007605"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Returns at week `t` are relative to week `t-1`. However, we are buying at week `t` and selling at week `t+1`, so we have to adjust by shifting the returns upward."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data['ret_djia'] = data['ret_djia'].shift(-1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The algorithm that is used by the authors makes a decision every Monday of whether to long or short the Dow Jones. After this week passed, we exit all positions (sell if we longed, buy if we shorted) and make a new trading decision.\n",
      "\n",
      "The `ret` column contains the weekly returns. Thus, if we buy at week `t` sell at week `t+1` we make the returns of week `t+1`. Conversely, if we short at week `t` and buy back at week `t+1` we make the negative returns of week `t+1`."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Compute returns of our strategy\n",
      "data['ret_google'] = data.order * data.ret_djia\n",
      "data.head(10)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>djia</th>\n",
        "      <th>debt</th>\n",
        "      <th>debt_mavg</th>\n",
        "      <th>order</th>\n",
        "      <th>ret_djia</th>\n",
        "      <th>ret_google</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Date</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2004-01-14</th>\n",
        "      <td> 10485.18</td>\n",
        "      <td> 0.210000</td>\n",
        "      <td>      NaN</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.004147</td>\n",
        "      <td> 0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2004-01-22</th>\n",
        "      <td> 10528.66</td>\n",
        "      <td> 0.210000</td>\n",
        "      <td>      NaN</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.016512</td>\n",
        "      <td> 0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2004-01-28</th>\n",
        "      <td> 10702.51</td>\n",
        "      <td> 0.210000</td>\n",
        "      <td>      NaN</td>\n",
        "      <td> 0</td>\n",
        "      <td>-0.018998</td>\n",
        "      <td>-0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2004-02-04</th>\n",
        "      <td> 10499.18</td>\n",
        "      <td> 0.213333</td>\n",
        "      <td> 0.210000</td>\n",
        "      <td>-1</td>\n",
        "      <td> 0.007605</td>\n",
        "      <td>-0.007605</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2004-02-11</th>\n",
        "      <td> 10579.03</td>\n",
        "      <td> 0.200000</td>\n",
        "      <td> 0.211111</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0.012841</td>\n",
        "      <td> 0.012841</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2004-02-19</th>\n",
        "      <td> 10714.88</td>\n",
        "      <td> 0.203333</td>\n",
        "      <td> 0.207778</td>\n",
        "      <td> 1</td>\n",
        "      <td>-0.009824</td>\n",
        "      <td>-0.009824</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2004-02-25</th>\n",
        "      <td> 10609.62</td>\n",
        "      <td> 0.200000</td>\n",
        "      <td> 0.205555</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0.006458</td>\n",
        "      <td> 0.006458</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2004-03-03</th>\n",
        "      <td> 10678.14</td>\n",
        "      <td> 0.200000</td>\n",
        "      <td> 0.201111</td>\n",
        "      <td> 1</td>\n",
        "      <td>-0.013922</td>\n",
        "      <td>-0.013922</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2004-03-10</th>\n",
        "      <td> 10529.48</td>\n",
        "      <td> 0.196667</td>\n",
        "      <td> 0.201111</td>\n",
        "      <td> 1</td>\n",
        "      <td>-0.040514</td>\n",
        "      <td>-0.040514</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2004-03-17</th>\n",
        "      <td> 10102.89</td>\n",
        "      <td> 0.196667</td>\n",
        "      <td> 0.198889</td>\n",
        "      <td> 1</td>\n",
        "      <td>-0.003775</td>\n",
        "      <td>-0.003775</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "                djia      debt  debt_mavg  order  ret_djia  ret_google\n",
        "Date                                                                  \n",
        "2004-01-14  10485.18  0.210000        NaN      0  0.004147    0.000000\n",
        "2004-01-22  10528.66  0.210000        NaN      0  0.016512    0.000000\n",
        "2004-01-28  10702.51  0.210000        NaN      0 -0.018998   -0.000000\n",
        "2004-02-04  10499.18  0.213333   0.210000     -1  0.007605   -0.007605\n",
        "2004-02-11  10579.03  0.200000   0.211111      1  0.012841    0.012841\n",
        "2004-02-19  10714.88  0.203333   0.207778      1 -0.009824   -0.009824\n",
        "2004-02-25  10609.62  0.200000   0.205555      1  0.006458    0.006458\n",
        "2004-03-03  10678.14  0.200000   0.201111      1 -0.013922   -0.013922\n",
        "2004-03-10  10529.48  0.196667   0.201111      1 -0.040514   -0.040514\n",
        "2004-03-17  10102.89  0.196667   0.198889      1 -0.003775   -0.003775"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now we just have to compound the returns. As we are reinvesting our earns, returns actually do not compound by summing them up but by taking their cumulative product:\n",
      "\n",
      "$$i_t = (i_{t-1} + i_{t-1} \\cdot r_t) = (1 + r_t) \\cdot i_{t-1}, \\quad i_0 = 1$$"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.figsize(10, 3)\n",
      "(1 + data.ret_google).cumprod().plot();\n",
      "plt.ylabel('Portfolio value')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "<matplotlib.text.Text at 0xae19f6c>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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iY1mzBhg82NRREGmXZ8EVFBSECRMmID4+HhMnTsSECRMwYcIEDBs2DBUqVCjJ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\n"
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.core.display import Image \n",
      "Image(\"http://www.nature.com/srep/2013/130425/srep01684/carousel/srep01684-f2.jpg\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "jpeg": 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kyudJ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MmbDM4+5QjyZcfyJcOtlkLSaIhLq2ysa3ELxi4ivXLvHV0WU9njxqsEg8ROLQey7z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\n",
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "<IPython.core.display.Image at 0xaeb4f4c>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Limitations\n",
      "-----------\n",
      "\n",
      "We have seen that `Pandas` can do very flexible yet powerful processing of time-series financial data.\n",
      "\n",
      "Using some basic financial math we can even implement a simple backtest.\n",
      "\n",
      "However, there are many complexities involved if we actually wanted to run this strategy in the real world that we are neglecting here:\n",
      "\n",
      "* Error prone\n",
      "* Not taking transaction costs into account\n",
      "* Slippage: Influence of our own orders on the price.\n",
      "* Look-ahead bias: The whole history is available to us which makes it very easy to use information we would not have access to.\n",
      "\n",
      "Thus, to really backtest a strategy properly we need to simulate all these things."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.core.display import Image \n",
      "Image(\"http://cdn.meme.li/instances/300x300/39833146.jpg\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "jpeg": 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ne8YT/wAe54695dJ1WwtLLyricxyYvht8i4f/AF2BHykTL831474rLmreyvyv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       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "<IPython.core.display.Image at 0xae3202c>"
       ]
      }
     ],
     "prompt_number": 13
    }
   ],
   "metadata": {}
  }
 ]
}