DataAndy.ipynb

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  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cd test"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "/home/maku-8qlabs/Downloads/Pygotham2014-master/test\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from copy import deepcopy\n",
      "from data_access import get_file\n",
      "from fund import FundsHolder\n",
      "from portfolio import PortfoliosHolder\n",
      "from quandl_streams import get_live\n",
      "from simple_files import FileHolder\n",
      "import pandas as pd"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "portfolios = PortfoliosHolder('portfolio')\n",
      "funds = FundsHolder('funds')\n",
      "model = FileHolder('model')\n",
      "market = get_live(get_file(\"sys\"), model)\n",
      "portfolio = portfolios.scenario_1\n",
      "cob_date = '8/1/2014'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "portfolio.components"
     ],
     "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>fund_name</th>\n",
        "      <th>quantity</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>position_id</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>         rip</td>\n",
        "      <td> 1000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> senior_care</td>\n",
        "      <td>  500</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "               fund_name  quantity\n",
        "position_id                       \n",
        "1                    rip      1000\n",
        "2            senior_care       500"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "funds.senior_care.components"
     ],
     "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>instrument_type</th>\n",
        "      <th>instrument</th>\n",
        "      <th>quantity</th>\n",
        "      <th>cost</th>\n",
        "      <th>start_date</th>\n",
        "      <th>end_date</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>position_id</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>1 </th>\n",
        "      <td> stock</td>\n",
        "      <td>  HCN</td>\n",
        "      <td> 16.70</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 2012-01-12</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2 </th>\n",
        "      <td> stock</td>\n",
        "      <td> AVIV</td>\n",
        "      <td>  1.97</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 2012-01-12</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3 </th>\n",
        "      <td> stock</td>\n",
        "      <td>  BKD</td>\n",
        "      <td>  1.59</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 2012-01-12</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4 </th>\n",
        "      <td> stock</td>\n",
        "      <td>  CSU</td>\n",
        "      <td>  1.45</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 2012-01-12</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6 </th>\n",
        "      <td> stock</td>\n",
        "      <td>  DVA</td>\n",
        "      <td> 12.06</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 2012-01-12</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7 </th>\n",
        "      <td> stock</td>\n",
        "      <td> NXTM</td>\n",
        "      <td>  1.69</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 2012-01-12</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8 </th>\n",
        "      <td> stock</td>\n",
        "      <td> RMTI</td>\n",
        "      <td>  0.90</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 2012-01-12</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9 </th>\n",
        "      <td> stock</td>\n",
        "      <td>  CHE</td>\n",
        "      <td>  5.24</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 2012-01-12</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
        "      <td> stock</td>\n",
        "      <td> ENSG</td>\n",
        "      <td>  2.57</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 2012-01-12</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
        "      <td> stock</td>\n",
        "      <td> AMED</td>\n",
        "      <td>  1.92</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 2012-01-12</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
        "      <td> stock</td>\n",
        "      <td> GTIV</td>\n",
        "      <td>  1.29</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 2012-01-12</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13</th>\n",
        "      <td> stock</td>\n",
        "      <td> AFAM</td>\n",
        "      <td>  1.11</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 2012-01-12</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14</th>\n",
        "      <td> stock</td>\n",
        "      <td> BIOS</td>\n",
        "      <td>  0.92</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 2012-01-12</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15</th>\n",
        "      <td> stock</td>\n",
        "      <td> VASC</td>\n",
        "      <td>  2.81</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 2012-01-12</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16</th>\n",
        "      <td> stock</td>\n",
        "      <td> THOR</td>\n",
        "      <td>  2.74</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 2012-01-12</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17</th>\n",
        "      <td> stock</td>\n",
        "      <td> ELGX</td>\n",
        "      <td>  1.95</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 2012-01-12</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18</th>\n",
        "      <td> stock</td>\n",
        "      <td> ABMD</td>\n",
        "      <td>  1.22</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 2012-01-12</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19</th>\n",
        "      <td> stock</td>\n",
        "      <td> SPNC</td>\n",
        "      <td>  1.14</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 2012-01-12</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>20</th>\n",
        "      <td> stock</td>\n",
        "      <td>  HUM</td>\n",
        "      <td>  7.34</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 2012-01-12</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>21</th>\n",
        "      <td> stock</td>\n",
        "      <td>  UAM</td>\n",
        "      <td>  2.35</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 2012-01-12</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>22</th>\n",
        "      <td> stock</td>\n",
        "      <td> WMGI</td>\n",
        "      <td>  3.66</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 2012-01-12</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>23</th>\n",
        "      <td> stock</td>\n",
        "      <td> OFIX</td>\n",
        "      <td>  3.31</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 2012-01-12</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>24</th>\n",
        "      <td> stock</td>\n",
        "      <td> NUVA</td>\n",
        "      <td>  1.62</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 2012-01-12</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25</th>\n",
        "      <td> stock</td>\n",
        "      <td> GMED</td>\n",
        "      <td>  0.89</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 2012-01-12</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "            instrument_type instrument  quantity  cost  start_date  end_date\n",
        "position_id                                                                 \n",
        "1                     stock        HCN     16.70   NaN  2012-01-12       NaN\n",
        "2                     stock       AVIV      1.97   NaN  2012-01-12       NaN\n",
        "3                     stock        BKD      1.59   NaN  2012-01-12       NaN\n",
        "4                     stock        CSU      1.45   NaN  2012-01-12       NaN\n",
        "6                     stock        DVA     12.06   NaN  2012-01-12       NaN\n",
        "7                     stock       NXTM      1.69   NaN  2012-01-12       NaN\n",
        "8                     stock       RMTI      0.90   NaN  2012-01-12       NaN\n",
        "9                     stock        CHE      5.24   NaN  2012-01-12       NaN\n",
        "10                    stock       ENSG      2.57   NaN  2012-01-12       NaN\n",
        "11                    stock       AMED      1.92   NaN  2012-01-12       NaN\n",
        "12                    stock       GTIV      1.29   NaN  2012-01-12       NaN\n",
        "13                    stock       AFAM      1.11   NaN  2012-01-12       NaN\n",
        "14                    stock       BIOS      0.92   NaN  2012-01-12       NaN\n",
        "15                    stock       VASC      2.81   NaN  2012-01-12       NaN\n",
        "16                    stock       THOR      2.74   NaN  2012-01-12       NaN\n",
        "17                    stock       ELGX      1.95   NaN  2012-01-12       NaN\n",
        "18                    stock       ABMD      1.22   NaN  2012-01-12       NaN\n",
        "19                    stock       SPNC      1.14   NaN  2012-01-12       NaN\n",
        "20                    stock        HUM      7.34   NaN  2012-01-12       NaN\n",
        "21                    stock        UAM      2.35   NaN  2012-01-12       NaN\n",
        "22                    stock       WMGI      3.66   NaN  2012-01-12       NaN\n",
        "23                    stock       OFIX      3.31   NaN  2012-01-12       NaN\n",
        "24                    stock       NUVA      1.62   NaN  2012-01-12       NaN\n",
        "25                    stock       GMED      0.89   NaN  2012-01-12       NaN"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "funds.senior_care.components[funds.senior_care.components['instrument_type']=='stock'][funds.senior_care.components['instrument']=='HUM']"
     ],
     "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>instrument_type</th>\n",
        "      <th>instrument</th>\n",
        "      <th>quantity</th>\n",
        "      <th>cost</th>\n",
        "      <th>start_date</th>\n",
        "      <th>end_date</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>position_id</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>20</th>\n",
        "      <td> stock</td>\n",
        "      <td> HUM</td>\n",
        "      <td> 7.34</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 2012-01-12</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "            instrument_type instrument  quantity  cost  start_date  end_date\n",
        "position_id                                                                 \n",
        "20                    stock        HUM      7.34   NaN  2012-01-12       NaN"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from __future__ import division\n",
      "import numpy as np\n",
      "import pandas as pd\n",
      "from datetime import datetime"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "from scipy.optimize import fmin\n",
      "\n",
      "def minusLogLikelihood(pdf, X, parameters):\n",
      "    return -np.sum(np.log(pdf(X, parameters)))\n",
      "\n",
      "def fit2pdf(X, pdf, parameterseed):\n",
      "    def mLL(parameterlist):\n",
      "        return minusLogLikelihood(pdf, X, parameterlist)\n",
      "    \n",
      "    varargout = fmin(mLL, parameterseed, xtol=1e-8, disp=0)\n",
      "    logLikelihood = -mLL(varargout)\n",
      "    return logLikelihood, varargout"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def gaussPDF(x, args):\n",
      "    mu, sigma = args     \n",
      "    return np.exp(-(x-mu)**2/(2 * sigma**2)) / (np.sqrt(2 * np.pi) * sigma)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def fitReturns():\n",
      "    LL, (mu, sigma) =  fit2pdf(logReturn, gaussPDF, [0, 1])\n",
      "    print \"Estimated parameters:\", mu, sigma\n",
      "    print \"Sample estimates:\", logReturn.mean(), logReturn.std()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def modelPDF(x):\n",
      "    return gaussPDF(x, (mu, sigma))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def simulateLossMC(logReturns, S0):\n",
      "    nu0 = 5  # seed for estimating d.o.f.\n",
      "    modelDF = pd.DataFrame(index=logReturns.columns, columns=['mean', 'sigma', 'nu', \n",
      "                                                              'PDF', 'CDF', 'INV'])\n",
      "    # Fit student t distribution\n",
      "    for n in logReturns.columns:\n",
      "        params, modelDF.loc[n, 'PDF'], modelDF.loc[n, 'CDF'], modelDF.loc[n, 'INV'] = fit2StudentT(logReturns[n], nu0)\n",
      "        modelDF.loc[n, ['mean', 'sigma', 'nu']] = params\n",
      "            \n",
      "    # Transform to U[0,1] marginals\n",
      "    u_margin = pd.DataFrame(index=logReturns.index, columns=[], dtype=np.float)\n",
      "    for n in logReturns.columns:\n",
      "        u_margin[n] = modelDF.loc[n, 'CDF'](logReturns[n])\n",
      "        \n",
      "    # Estimate correlation for Gaussin copula\n",
      "    n1, n2 = u_margin.columns[0], u_margin.columns[1]\n",
      "    rho = np.corrcoef(modelDF.loc[n1, 'INV'](u_margin[n1]), modelDF.loc[n2, 'INV'](u_margin[n2]))\n",
      "    \n",
      "    # Simulation time!\n",
      "    # Generate samples from estimated copula\n",
      "    M = 5e4\n",
      "    U = gaussianCopulaRand(M, rho)\n",
      "    \n",
      "    simulated_price = pd.DataFrame(index=np.arange(M), \n",
      "                             columns=logReturns.columns,\n",
      "                             dtype=np.float)\n",
      "    for i, n in enumerate([n1, n2]):\n",
      "        simulated_price[n] = S0[n] * np.exp( modelDF.loc[n, 'INV'](U[:,i]) )\n",
      "    \n",
      "    return simulated_price"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "bootstrapindices = np.random.randint(0, 10, size=10)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def simulateLossHistoric(logReturns, S0):\n",
      "    M = 1e3\n",
      "    bootstrapindices = np.random.randint(0, logReturns.shape[0], size=M)\n",
      "    simulated_price = pd.DataFrame(index=np.arange(M), \n",
      "                             columns=logReturns.columns,\n",
      "                             dtype=np.float)\n",
      "    for n in logReturns.columns:\n",
      "        simulated_price[n] = S0[n] * np.exp( logReturns.iloc[bootstrapindices].values )\n",
      "    return simulated_price"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from scipy.stats import t as tdist\n",
      "\n",
      "def locationScaleTcdf(x, mu, sigma, nu):\n",
      "    return tdist.cdf((x - mu)/sigma, nu)\n",
      "\n",
      "def locationScaleTinv(p, mu, sigma, nu):\n",
      "    return mu + sigma * tdist.ppf(p, nu)\n",
      "\n",
      "def locationScaleTpdf(x, mu, sigma, nu):\n",
      "    return tdist.pdf((x - mu)/sigma, nu) / sigma\n",
      "\n",
      "def fit2StudentT(X, nu0):\n",
      "\n",
      "    ## Seed for mu,sigma determined from moment matching\n",
      "    mu0    = np.mean(X);                        # match first moment\n",
      "    sigma0 = np.sqrt(np.var(X) * (nu0 - 2)/nu0) # match second moment\n",
      "\n",
      "    ## ML fit \n",
      "    def PDFhandle(X, args):\n",
      "        _mu, _sigma, _nu = args\n",
      "        return locationScaleTpdf(X, _mu, _sigma, _nu)\n",
      "        \n",
      "    LL, argsout = fit2pdf(X, PDFhandle, [mu0, sigma0, nu0])\n",
      "    mu, sigma, nu = argsout\n",
      "    \n",
      "    ## Model pdf, cdf and inv\n",
      "    modelPdf = lambda x: locationScaleTpdf(x, mu, sigma, nu)\n",
      "    modelCdf = lambda x: locationScaleTcdf(x, mu, sigma, nu)\n",
      "    modelInv = lambda p: locationScaleTinv(p, mu, sigma, nu)\n",
      "    return [mu, sigma, nu], modelPdf, modelCdf, modelInv"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from numpy.linalg import cholesky\n",
      "from scipy.stats import norm\n",
      "\n",
      "def gaussianCopulaRand(M, rho):\n",
      "    rho = np.asarray(rho)\n",
      "    D = rho.shape[0]\n",
      "    L = cholesky(rho).T\n",
      "    X = np.random.randn(D, M)     \n",
      "    Z = np.dot(L, X)                          \n",
      "    U = norm.cdf(Z)   # Matrix [D, M]\n",
      "    U = U.T           # Matrix [M, D]\n",
      "    return U"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def constructPortfolio(N=5, horizon=5, simulateLoss='historic'):\n",
      "\n",
      "    if simulateLoss == 'historic':\n",
      "        simulateLoss = simulateLossHistoric\n",
      "    else:\n",
      "        simulateLoss = simulateLossMC\n",
      "    \n",
      "    ## Portfolio of stocks from the rip fund. \n",
      "    ## portfolioComposition holds the assets held in the portfolio\n",
      "    wghts=funds.senior_care.components[['instrument','quantity']]\n",
      "    portfolioComposition = wghts.set_index('instrument')\n",
      "    portfolioComposition=portfolioComposition['quantity']\n",
      "    \n",
      "    S=pd.DataFrame({ccy:curve['Adj. Close'] for ccy,curve in [(ccy,market.stock[ccy]) for ccy in portfolioComposition.index]})\n",
      "    N = 500\n",
      "    S = S.iloc[-N:]\n",
      "    S0 = S.iloc[-1]\n",
      "    print \"Behavior of stock over last %s days\" % N\n",
      "    ## Compute returns for specified time horizons\n",
      "    # Horizon for the risk measures\n",
      "    horizon = 5\n",
      "    \n",
      "    # logreturns   \n",
      "    logReturns = np.log(S / S.shift(horizon))\n",
      "    logReturns.dropna(inplace=True)\n",
      "    \n",
      "    ## portfolio\n",
      "    portfolio=pd.DataFrame({ccy:curve['Adj. Close'] for ccy,curve in [(ccy,market.stock[ccy]) for ccy in portfolioComposition.index]})\n",
      "    \n",
      "    print \"Current value per asset:\"\n",
      "    print portfolio.head()\n",
      "    print\n",
      "    print \"Weight in portfolio:\"\n",
      "    print portfolioComposition.head()\n",
      "    print\n",
      "    \n",
      "    # Next we simulate the potential returns of the portfolio using either the\n",
      "    # historic method (bootstrapping of historic data)\n",
      "    # Monte Carlo (Fitting a copula to historic data, and simulating new data)\n",
      "    simulated_assets = simulateLoss(logReturns, S0)    \n",
      "    simulated_portfolio = (simulated_assets * portfolioComposition).sum(axis=1)\n",
      "    #simulated_portfolio.hist(bins=25)\n",
      "    current_portfolio = (portfolio * portfolioComposition).sum()\n",
      "    \n",
      "    print \"Simulated loss of portfolio\"\n",
      "    loss_simulated = (-(simulated_portfolio - current_portfolio))\n",
      "    \n",
      "    ## Finally, we compute the VaR at 5% and the estimated loss\n",
      "    p = .95\n",
      "    VaR = loss_simulated.quantile(.95)\n",
      "    indexTail = loss_simulated > VaR\n",
      "    ES = np.mean(loss_simulated.ix[indexTail])\n",
      "   \n",
      "    print \"Value-at-Risk: %.2f\" % VaR\n",
      "    print \"Expected Shortfall: %.2f\" % ES"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "wghts=funds.senior_care.components[['instrument','quantity']]\n",
      "portfolioComposition = wghts.set_index('instrument')\n",
      "portfolioComposition=portfolioComposition['quantity']\n",
      "    \n",
      "S=pd.DataFrame({ccy:curve['Adj. Close'] for ccy,curve in [(ccy,market.stock[ccy]) for ccy in portfolioComposition.index]})\n",
      "S=S[:'8/1/2014']\n",
      "N = 262\n",
      "S = S.iloc[-N:]\n",
      "S0 = S.iloc[-1]\n",
      "\n",
      "horizon = 5\n",
      "    \n",
      "logReturns = np.log(S / S.shift(horizon))\n",
      "logReturns.dropna(inplace=True)\n",
      "portfolio=S0\n",
      "\n",
      "simulated_assets = simulateLossHistoric(logReturns, S0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "simulated_portfolio = (simulated_assets * portfolioComposition).sum(axis=1)\n",
      "current_portfolio = (portfolio * portfolioComposition).sum()\n",
      "loss_simulated = (-(simulated_portfolio - current_portfolio))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "p = .99\n",
      "VaR = loss_simulated.quantile(.99)\n",
      "indexTail = loss_simulated > VaR\n",
      "ES = np.mean(loss_simulated.ix[indexTail])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "print VaR\n",
      "print ES"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "780.131387282\n",
        "823.242651987\n"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "simulated_portfolio.std()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 22,
       "text": [
        "227.58819867030454"
       ]
      }
     ],
     "prompt_number": 22
    }
   ],
   "metadata": {}
  }
 ]
}