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  "name": ""
 },
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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Valuation of the HDA Process for the Production of Benzene from Toluene"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Some basic calculations."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "P = dict()\n",
      "P['Benzene'] = 1431\n",
      "P['Toluene'] = 1290"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "P"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "{'Benzene': 1431, 'Toluene': 1290}"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas\n",
      "prices = pandas.read_csv('TolBenNGPrices.csv', index_col = 0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "prices"
     ],
     "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>Benzene</th>\n",
        "      <th>Toluene Blend</th>\n",
        "      <th>Natural Gas</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Prices</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>11-Feb</th>\n",
        "      <td> 4.2866</td>\n",
        "      <td> 2.9730</td>\n",
        "      <td> 5.77</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11-Mar</th>\n",
        "      <td> 4.0488</td>\n",
        "      <td> 3.3708</td>\n",
        "      <td> 5.21</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11-Apr</th>\n",
        "      <td> 4.1338</td>\n",
        "      <td> 4.2003</td>\n",
        "      <td> 5.34</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11-May</th>\n",
        "      <td> 4.1219</td>\n",
        "      <td> 3.6000</td>\n",
        "      <td> 5.21</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11-Jun</th>\n",
        "      <td> 3.7910</td>\n",
        "      <td> 3.5714</td>\n",
        "      <td> 5.21</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11-Jul</th>\n",
        "      <td> 3.9963</td>\n",
        "      <td> 4.0194</td>\n",
        "      <td> 5.05</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11-Aug</th>\n",
        "      <td> 4.0838</td>\n",
        "      <td> 3.6853</td>\n",
        "      <td> 5.21</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11-Sep</th>\n",
        "      <td> 3.7060</td>\n",
        "      <td> 3.3577</td>\n",
        "      <td> 4.84</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11-Oct</th>\n",
        "      <td> 3.1713</td>\n",
        "      <td> 3.3020</td>\n",
        "      <td> 4.71</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11-Nov</th>\n",
        "      <td> 3.1056</td>\n",
        "      <td> 3.0904</td>\n",
        "      <td> 4.64</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11-Dec</th>\n",
        "      <td> 3.3738</td>\n",
        "      <td> 3.2395</td>\n",
        "      <td> 4.59</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12-Jan</th>\n",
        "      <td> 4.0450</td>\n",
        "      <td> 3.6006</td>\n",
        "      <td> 4.59</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12-Feb</th>\n",
        "      <td> 4.1650</td>\n",
        "      <td> 3.7538</td>\n",
        "      <td> 4.19</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12-Mar</th>\n",
        "      <td> 4.1575</td>\n",
        "      <td> 3.9608</td>\n",
        "      <td> 3.71</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12-Apr</th>\n",
        "      <td> 4.1425</td>\n",
        "      <td> 4.3428</td>\n",
        "      <td> 3.21</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12-May</th>\n",
        "      <td> 4.0600</td>\n",
        "      <td> 4.2330</td>\n",
        "      <td> 3.02</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12-Jun</th>\n",
        "      <td> 4.1463</td>\n",
        "      <td> 3.9933</td>\n",
        "      <td> 3.34</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12-Jul</th>\n",
        "      <td> 4.6675</td>\n",
        "      <td> 4.2367</td>\n",
        "      <td> 3.60</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12-Aug</th>\n",
        "      <td> 4.3400</td>\n",
        "      <td> 4.2641</td>\n",
        "      <td> 3.83</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12-Sep</th>\n",
        "      <td> 4.3456</td>\n",
        "      <td> 4.3466</td>\n",
        "      <td> 3.56</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12-Oct</th>\n",
        "      <td> 4.7313</td>\n",
        "      <td> 3.8967</td>\n",
        "      <td> 3.95</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12-Nov</th>\n",
        "      <td> 4.8745</td>\n",
        "      <td> 3.1773</td>\n",
        "      <td> 4.46</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12-Dec</th>\n",
        "      <td> 5.1258</td>\n",
        "      <td> 3.2948</td>\n",
        "      <td> 4.72</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13-Jan</th>\n",
        "      <td> 4.9331</td>\n",
        "      <td> 3.8594</td>\n",
        "      <td> 4.60</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>24 rows \u00d7 3 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "        Benzene  Toluene Blend  Natural Gas\n",
        "Prices                                     \n",
        "11-Feb   4.2866         2.9730         5.77\n",
        "11-Mar   4.0488         3.3708         5.21\n",
        "11-Apr   4.1338         4.2003         5.34\n",
        "11-May   4.1219         3.6000         5.21\n",
        "11-Jun   3.7910         3.5714         5.21\n",
        "11-Jul   3.9963         4.0194         5.05\n",
        "11-Aug   4.0838         3.6853         5.21\n",
        "11-Sep   3.7060         3.3577         4.84\n",
        "11-Oct   3.1713         3.3020         4.71\n",
        "11-Nov   3.1056         3.0904         4.64\n",
        "11-Dec   3.3738         3.2395         4.59\n",
        "12-Jan   4.0450         3.6006         4.59\n",
        "12-Feb   4.1650         3.7538         4.19\n",
        "12-Mar   4.1575         3.9608         3.71\n",
        "12-Apr   4.1425         4.3428         3.21\n",
        "12-May   4.0600         4.2330         3.02\n",
        "12-Jun   4.1463         3.9933         3.34\n",
        "12-Jul   4.6675         4.2367         3.60\n",
        "12-Aug   4.3400         4.2641         3.83\n",
        "12-Sep   4.3456         4.3466         3.56\n",
        "12-Oct   4.7313         3.8967         3.95\n",
        "12-Nov   4.8745         3.1773         4.46\n",
        "12-Dec   5.1258         3.2948         4.72\n",
        "13-Jan   4.9331         3.8594         4.60\n",
        "\n",
        "[24 rows x 3 columns]"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "prices.plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x1096df850>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Q+OUXZqJ5+BBwdARCQjgXnYeHh0dT6J4NPJdcn/Bq1YAJE4BWrYA1a9g2FwQGAhMnMs+V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\nqIATQoiFogJOCCEW6v8ALX2edOwnVrEAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1096df250>"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "returns = dict()\n",
      "\n",
      "for c in list(prices.columns):\n",
      "    returns[c] = log(prices[:][c]) - log(prices[:][c].shift(1))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "returns = pandas.DataFrame(returns)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "returns"
     ],
     "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>Benzene</th>\n",
        "      <th>Natural Gas</th>\n",
        "      <th>Toluene Blend</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Prices</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>11-Feb</th>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11-Mar</th>\n",
        "      <td>-0.057073</td>\n",
        "      <td>-0.102092</td>\n",
        "      <td> 0.125579</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11-Apr</th>\n",
        "      <td> 0.020777</td>\n",
        "      <td> 0.024646</td>\n",
        "      <td> 0.220006</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11-May</th>\n",
        "      <td>-0.002883</td>\n",
        "      <td>-0.024646</td>\n",
        "      <td>-0.154222</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11-Jun</th>\n",
        "      <td>-0.083684</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td>-0.007976</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11-Jul</th>\n",
        "      <td> 0.052739</td>\n",
        "      <td>-0.031192</td>\n",
        "      <td> 0.118175</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11-Aug</th>\n",
        "      <td> 0.021659</td>\n",
        "      <td> 0.031192</td>\n",
        "      <td>-0.086781</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11-Sep</th>\n",
        "      <td>-0.097075</td>\n",
        "      <td>-0.073665</td>\n",
        "      <td>-0.093096</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11-Oct</th>\n",
        "      <td>-0.155812</td>\n",
        "      <td>-0.027227</td>\n",
        "      <td>-0.016728</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11-Nov</th>\n",
        "      <td>-0.020935</td>\n",
        "      <td>-0.014974</td>\n",
        "      <td>-0.066228</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11-Dec</th>\n",
        "      <td> 0.082833</td>\n",
        "      <td>-0.010834</td>\n",
        "      <td> 0.047118</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12-Jan</th>\n",
        "      <td> 0.181442</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.105682</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12-Feb</th>\n",
        "      <td> 0.029235</td>\n",
        "      <td>-0.091179</td>\n",
        "      <td> 0.041668</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12-Mar</th>\n",
        "      <td>-0.001802</td>\n",
        "      <td>-0.121669</td>\n",
        "      <td> 0.053677</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12-Apr</th>\n",
        "      <td>-0.003614</td>\n",
        "      <td>-0.144761</td>\n",
        "      <td> 0.092073</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12-May</th>\n",
        "      <td>-0.020116</td>\n",
        "      <td>-0.061014</td>\n",
        "      <td>-0.025608</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12-Jun</th>\n",
        "      <td> 0.021033</td>\n",
        "      <td> 0.100714</td>\n",
        "      <td>-0.058293</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12-Jul</th>\n",
        "      <td> 0.118407</td>\n",
        "      <td> 0.074963</td>\n",
        "      <td> 0.059167</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12-Aug</th>\n",
        "      <td>-0.072749</td>\n",
        "      <td> 0.061931</td>\n",
        "      <td> 0.006446</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12-Sep</th>\n",
        "      <td> 0.001289</td>\n",
        "      <td>-0.073104</td>\n",
        "      <td> 0.019163</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12-Oct</th>\n",
        "      <td> 0.085036</td>\n",
        "      <td> 0.103955</td>\n",
        "      <td>-0.109264</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12-Nov</th>\n",
        "      <td> 0.029818</td>\n",
        "      <td> 0.121433</td>\n",
        "      <td>-0.204098</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12-Dec</th>\n",
        "      <td> 0.050269</td>\n",
        "      <td> 0.056660</td>\n",
        "      <td> 0.036314</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13-Jan</th>\n",
        "      <td>-0.038319</td>\n",
        "      <td>-0.025752</td>\n",
        "      <td> 0.158166</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>24 rows \u00d7 3 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 24,
       "text": [
        "         Benzene  Natural Gas  Toluene Blend\n",
        "Prices                                      \n",
        "11-Feb       NaN          NaN            NaN\n",
        "11-Mar -0.057073    -0.102092       0.125579\n",
        "11-Apr  0.020777     0.024646       0.220006\n",
        "11-May -0.002883    -0.024646      -0.154222\n",
        "11-Jun -0.083684     0.000000      -0.007976\n",
        "11-Jul  0.052739    -0.031192       0.118175\n",
        "11-Aug  0.021659     0.031192      -0.086781\n",
        "11-Sep -0.097075    -0.073665      -0.093096\n",
        "11-Oct -0.155812    -0.027227      -0.016728\n",
        "11-Nov -0.020935    -0.014974      -0.066228\n",
        "11-Dec  0.082833    -0.010834       0.047118\n",
        "12-Jan  0.181442     0.000000       0.105682\n",
        "12-Feb  0.029235    -0.091179       0.041668\n",
        "12-Mar -0.001802    -0.121669       0.053677\n",
        "12-Apr -0.003614    -0.144761       0.092073\n",
        "12-May -0.020116    -0.061014      -0.025608\n",
        "12-Jun  0.021033     0.100714      -0.058293\n",
        "12-Jul  0.118407     0.074963       0.059167\n",
        "12-Aug -0.072749     0.061931       0.006446\n",
        "12-Sep  0.001289    -0.073104       0.019163\n",
        "12-Oct  0.085036     0.103955      -0.109264\n",
        "12-Nov  0.029818     0.121433      -0.204098\n",
        "12-Dec  0.050269     0.056660       0.036314\n",
        "13-Jan -0.038319    -0.025752       0.158166\n",
        "\n",
        "[24 rows x 3 columns]"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "returns?"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "returns.mean()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 26,
       "text": [
        "Benzene          0.006108\n",
        "Natural Gas     -0.009853\n",
        "Toluene Blend    0.011345\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "returns.std()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 29,
       "text": [
        "Benzene          0.073905\n",
        "Natural Gas      0.073862\n",
        "Toluene Blend    0.103191\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s = returns[1:23]['Benzene']\n",
      "\n",
      "pandas.tools.plotting.autocorrelation_plot(s)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 55,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x10a4ecb10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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qe5rPgGzoNWkC/Pqr9pkbFy/KPef37pULdrp21XY+orLk5Mj32MCBcqZbbq68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\nEZEFFgYiIrLw/yN7+Vm4SyDUAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x109ad0250>"
       ]
      }
     ],
     "prompt_number": 55
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "type(s)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 44,
       "text": [
        "pandas.core.series.Series"
       ]
      }
     ],
     "prompt_number": 44
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "size(returns)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 50,
       "text": [
        "72"
       ]
      }
     ],
     "prompt_number": 50
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(s)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 52,
       "text": [
        "24"
       ]
      }
     ],
     "prompt_number": 52
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}