Qcon2014VaR.ipynb

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   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Python \"why are the big dealers making big bets?\""
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Hot Technologies behind Modern Finance"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "QCON 2014\n",
      "Wednesday June 11th 2014\n",
      "1:45pm - 2:35pm"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Andy Fundinger Senior Consultant at Risk Focus, Python Evangelist"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Mario Morales Machine Learning Research Scientist and Quant Developer"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Abstract"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In the last few years a number of large dealers and many other financial institutions have made big investments in Python.\n",
      "We will examine the reasons behind this trend and discuss techniques that facilitate successful projects.\n",
      "Python's strength is in its flexibility. Starting with dynamic typing and extending to more advanced meta-programming capabilities,\n",
      "Python allows code to be written many different ways and is especially good at integrating with C and C++ for optimized code.\n",
      "This makes Python a great fit for finance (especially Risk projects) as it is pretty common to have different groups that want\n",
      "to use different languages for the same project: e.g. Quants usually write in C/C++, while Application Development prefers\n",
      "to use a language with better support for plumbing and UI functionality.\n",
      "We will demonstrate the design and use of an environment for quantative researchers building a market risk simulation first\n",
      "as a basic system and then adding a hypothetical systemic shock. We will also discuss how we can leverage the dynamic typing\n",
      "of the language without sacrificing some of the benefits of a strongly typed languages."
     ]
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Goal of this Presentation:"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Show a simple example of a Risk Analysis in Python using calls to quantdl with utilities programmed by Andy Fundinger. Then some case study with plots and equations from a correlation analysis based in historical exchange rate information."
     ]
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Data Handling"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "* Importing Data with Quandl handlers\n",
      "* Data Exploration and Visualization"
     ]
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Data Analysis"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "* VaR Definition\n",
      "* Exchange Rates Model (Stepwise Implementation)\n",
      "* Schock Model"
     ]
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Case Study"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "* Alien Invasion October 31st 2013\n",
      "* Effects in the world economy reflected in the exchange rate modeling (Stress testing over impulse-response functions)\n",
      "* Plotting results"
     ]
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Required Libraries"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "* Numpy\n",
      "* SciPy\n",
      "* Pandas\n",
      "* SciKit-Learn\n",
      "* StatsModels\n",
      "* Patsy\n",
      "* Matplotlib\n",
      "* Ipython\n",
      "* Quandl\n",
      "* Dingus"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Dingus Example\n",
      "from data_access import get_dummy\n",
      "print(repr(get_dummy(\"test\")))\n",
      "dummyCollection = get_dummy(\"market1\")\n",
      "print(repr(dummyCollection))\n",
      "print(repr(dummyCollection.news))\n",
      "print(repr(dummyCollection.news['IBM']))\n",
      "print(repr(dummyCollection.news['IBM']['5-14-2010']))\n",
      "print(repr(dummyCollection.fx.usd_eur['High (est)']['2010-05-14']))\n",
      "print(repr(dummyCollection.fx.usd_eur['Low (est)']['2010-05-14']))\n",
      "print(repr(dummyCollection.fx.usd_eur.Rate['2010-05-14']))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "<Dingus test_env>\n",
        "<Dingus market1_env>\n",
        "<Dingus market1_env.news>\n",
        "<Dingus market1_env.news[IBM]>\n",
        "<Dingus market1_env.news[IBM][5-14-2010]>\n",
        "<Dingus market1_env.fx.usd_eur[High (est)][2010-05-14]>\n",
        "<Dingus market1_env.fx.usd_eur[Low (est)][2010-05-14]>\n",
        "<Dingus market1_env.fx.usd_eur.Rate[2010-05-14]>\n"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from data_access import get_file\n",
      "import pandas as pd\n",
      "import numpy as np\n",
      "\n",
      "market = get_file('market1')\n",
      "model = get_file('model')\n",
      "\n",
      "def make_ccy_matrix(market, model):\n",
      "    return pd.DataFrame({ccy:curve.Rate for ccy,curve in [(ccy,market.fx[ccy+'_usd']) for ccy in model.ccyConfig]})\n",
      "\n",
      "ccy_matrix=make_ccy_matrix(market, model)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ccy_matrix.tail()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
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        "      <th></th>\n",
        "      <th>aed</th>\n",
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        "      <th>all</th>\n",
        "      <th>ang</th>\n",
        "      <th>aoa</th>\n",
        "      <th>ars</th>\n",
        "      <th>aud</th>\n",
        "      <th>awg</th>\n",
        "      <th>bbd</th>\n",
        "      <th>bdt</th>\n",
        "      <th>bgn</th>\n",
        "      <th>bhd</th>\n",
        "      <th>bif</th>\n",
        "      <th>bmd</th>\n",
        "      <th>bnd</th>\n",
        "      <th>bob</th>\n",
        "      <th>brl</th>\n",
        "      <th>bsd</th>\n",
        "      <th>btn</th>\n",
        "      <th>bwp</th>\n",
        "      <th></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",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
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        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2014-05-20</th>\n",
        "      <td> 0.272375</td>\n",
        "      <td> 0.017439</td>\n",
        "      <td> 0.009838</td>\n",
        "      <td> 0.556593</td>\n",
        "      <td> 0.010238</td>\n",
        "      <td> 0.124287</td>\n",
        "      <td> 0.935728</td>\n",
        "      <td> 0.560754</td>\n",
        "      <td> 0.500429</td>\n",
        "      <td> 0.012864</td>\n",
        "      <td> 0.700729</td>\n",
        "      <td> 2.65412</td>\n",
        "      <td> 0.000636</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0.799320</td>\n",
        "      <td> 0.144863</td>\n",
        "      <td> 0.451703</td>\n",
        "      <td> 0.999543</td>\n",
        "      <td> 0.016991</td>\n",
        "      <td> 0.115147</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-05-21</th>\n",
        "      <td> 0.272375</td>\n",
        "      <td> 0.017439</td>\n",
        "      <td> 0.009838</td>\n",
        "      <td> 0.556593</td>\n",
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        "      <td> 0.124038</td>\n",
        "      <td> 0.930881</td>\n",
        "      <td> 0.560754</td>\n",
        "      <td> 0.500246</td>\n",
        "      <td> 0.012864</td>\n",
        "      <td> 0.700741</td>\n",
        "      <td> 2.65292</td>\n",
        "      <td> 0.000636</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0.799618</td>\n",
        "      <td> 0.144839</td>\n",
        "      <td> 0.452419</td>\n",
        "      <td> 0.999970</td>\n",
        "      <td> 0.016991</td>\n",
        "      <td> 0.114875</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-05-22</th>\n",
        "      <td> 0.272375</td>\n",
        "      <td> 0.017439</td>\n",
        "      <td> 0.009838</td>\n",
        "      <td> 0.556593</td>\n",
        "      <td> 0.010238</td>\n",
        "      <td> 0.124063</td>\n",
        "      <td> 0.925006</td>\n",
        "      <td> 0.560754</td>\n",
        "      <td> 0.499910</td>\n",
        "      <td> 0.012864</td>\n",
        "      <td> 0.700100</td>\n",
        "      <td> 2.65292</td>\n",
        "      <td> 0.000636</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0.798514</td>\n",
        "      <td> 0.144735</td>\n",
        "      <td> 0.452761</td>\n",
        "      <td> 1.000600</td>\n",
        "      <td> 0.016991</td>\n",
        "      <td> 0.114875</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-05-23</th>\n",
        "      <td> 0.272375</td>\n",
        "      <td> 0.017439</td>\n",
        "      <td> 0.009838</td>\n",
        "      <td> 0.556593</td>\n",
        "      <td> 0.010238</td>\n",
        "      <td> 0.124063</td>\n",
        "      <td> 0.923839</td>\n",
        "      <td> 0.559834</td>\n",
        "      <td> 0.500046</td>\n",
        "      <td> 0.012864</td>\n",
        "      <td> 0.699248</td>\n",
        "      <td> 2.65292</td>\n",
        "      <td> 0.000636</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0.798218</td>\n",
        "      <td> 0.144761</td>\n",
        "      <td> 0.452618</td>\n",
        "      <td> 1.000360</td>\n",
        "      <td> 0.016991</td>\n",
        "      <td> 0.114927</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-05-26</th>\n",
        "      <td> 0.272375</td>\n",
        "      <td> 0.017439</td>\n",
        "      <td> 0.009736</td>\n",
        "      <td> 0.556593</td>\n",
        "      <td> 0.010238</td>\n",
        "      <td> 0.124063</td>\n",
        "      <td> 0.923627</td>\n",
        "      <td> 0.560754</td>\n",
        "      <td> 0.500327</td>\n",
        "      <td> 0.012864</td>\n",
        "      <td> 0.697697</td>\n",
        "      <td> 2.65208</td>\n",
        "      <td> 0.000636</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0.798335</td>\n",
        "      <td> 0.144761</td>\n",
        "      <td> 0.450726</td>\n",
        "      <td> 0.999717</td>\n",
        "      <td> 0.016991</td>\n",
        "      <td> 0.115064</td>\n",
        "      <td>...</td>\n",
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        "</table>\n",
        "<p>5 rows \u00d7 148 columns</p>\n",
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       "text": [
        "                 aed       afn       all       ang       aoa       ars  \\\n",
        "Date                                                                     \n",
        "2014-05-20  0.272375  0.017439  0.009838  0.556593  0.010238  0.124287   \n",
        "2014-05-21  0.272375  0.017439  0.009838  0.556593  0.010238  0.124038   \n",
        "2014-05-22  0.272375  0.017439  0.009838  0.556593  0.010238  0.124063   \n",
        "2014-05-23  0.272375  0.017439  0.009838  0.556593  0.010238  0.124063   \n",
        "2014-05-26  0.272375  0.017439  0.009736  0.556593  0.010238  0.124063   \n",
        "\n",
        "                 aud       awg       bbd       bdt       bgn      bhd  \\\n",
        "Date                                                                    \n",
        "2014-05-20  0.935728  0.560754  0.500429  0.012864  0.700729  2.65412   \n",
        "2014-05-21  0.930881  0.560754  0.500246  0.012864  0.700741  2.65292   \n",
        "2014-05-22  0.925006  0.560754  0.499910  0.012864  0.700100  2.65292   \n",
        "2014-05-23  0.923839  0.559834  0.500046  0.012864  0.699248  2.65292   \n",
        "2014-05-26  0.923627  0.560754  0.500327  0.012864  0.697697  2.65208   \n",
        "\n",
        "                 bif  bmd       bnd       bob       brl       bsd       btn  \\\n",
        "Date                                                                          \n",
        "2014-05-20  0.000636    1  0.799320  0.144863  0.451703  0.999543  0.016991   \n",
        "2014-05-21  0.000636    1  0.799618  0.144839  0.452419  0.999970  0.016991   \n",
        "2014-05-22  0.000636    1  0.798514  0.144735  0.452761  1.000600  0.016991   \n",
        "2014-05-23  0.000636    1  0.798218  0.144761  0.452618  1.000360  0.016991   \n",
        "2014-05-26  0.000636    1  0.798335  0.144761  0.450726  0.999717  0.016991   \n",
        "\n",
        "                 bwp      \n",
        "Date                      \n",
        "2014-05-20  0.115147 ...  \n",
        "2014-05-21  0.114875 ...  \n",
        "2014-05-22  0.114875 ...  \n",
        "2014-05-23  0.114927 ...  \n",
        "2014-05-26  0.115064 ...  \n",
        "\n",
        "[5 rows x 148 columns]"
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     ],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ccy_matrix.columns"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "Index([u'aed', u'afn', u'all', u'ang', u'aoa', u'ars', u'aud', u'awg', u'bbd', u'bdt', u'bgn', u'bhd', u'bif', u'bmd', u'bnd', u'bob', u'brl', u'bsd', u'btn', u'bwp', u'bzd', u'cad', u'chf', u'clp', u'cny', u'cop', u'crc', u'cup', u'cve', u'czk', u'djf', u'dkk', u'dop', u'dzd', u'ecs', u'eek', u'egp', u'etb', u'eur', u'fjd', u'fkp', u'frf', u'gbp', u'ghc', u'gip', u'gmd', u'gnf', u'gtq', u'gyd', u'hkd', u'hnl', u'hrk', u'htg', u'huf', u'idr', u'ils', u'inr', u'iqd', u'irr', u'isk', u'jmd', u'jod', u'jpy', u'kes', u'khr', u'kmf', u'kpw', u'krw', u'kwd', u'kyd', u'kzt', u'lak', u'lbp', u'lkr', u'lrd', u'lsl', u'ltl', u'lvl', u'lyd', u'mad', u'mgf', u'mmk', u'mnt', u'mop', u'mro', u'mur', u'mvr', u'mwk', u'mxn', u'myr', u'mzm', u'mzn', u'nad', u'ngn', u'nio', u'nok', u'npr', u'nzd', u'omr', u'pab', ...], dtype='object')"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "corr=ccy_matrix.corr()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "corr"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
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        "      <th></th>\n",
        "      <th>aed</th>\n",
        "      <th>afn</th>\n",
        "      <th>all</th>\n",
        "      <th>ang</th>\n",
        "      <th>aoa</th>\n",
        "      <th>ars</th>\n",
        "      <th>aud</th>\n",
        "      <th>awg</th>\n",
        "      <th>bbd</th>\n",
        "      <th>bdt</th>\n",
        "      <th>bgn</th>\n",
        "      <th>bhd</th>\n",
        "      <th>bif</th>\n",
        "      <th>bmd</th>\n",
        "      <th>bnd</th>\n",
        "      <th>bob</th>\n",
        "      <th>brl</th>\n",
        "      <th>bsd</th>\n",
        "      <th>btn</th>\n",
        "      <th>bwp</th>\n",
        "      <th></th>\n",
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        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>aed</th>\n",
        "      <td> 1.000000</td>\n",
        "      <td>-0.387602</td>\n",
        "      <td>-0.215562</td>\n",
        "      <td> 0.318342</td>\n",
        "      <td> 0.150919</td>\n",
        "      <td> 0.345806</td>\n",
        "      <td>-0.429843</td>\n",
        "      <td> 0.204882</td>\n",
        "      <td>-0.036890</td>\n",
        "      <td> 0.421365</td>\n",
        "      <td>-0.294120</td>\n",
        "      <td> 0.061025</td>\n",
        "      <td> 0.296765</td>\n",
        "      <td> 0.061892</td>\n",
        "      <td>-0.472499</td>\n",
        "      <td> 0.127788</td>\n",
        "      <td> 0.031011</td>\n",
        "      <td> 0.117436</td>\n",
        "      <td> 0.308459</td>\n",
        "      <td> 0.369275</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>afn</th>\n",
        "      <td>-0.387602</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.850153</td>\n",
        "      <td>-0.658482</td>\n",
        "      <td>-0.386002</td>\n",
        "      <td>-0.880705</td>\n",
        "      <td> 0.730158</td>\n",
        "      <td>-0.116045</td>\n",
        "      <td> 0.191257</td>\n",
        "      <td>-0.870072</td>\n",
        "      <td> 0.844934</td>\n",
        "      <td> 0.124149</td>\n",
        "      <td>-0.748667</td>\n",
        "      <td>-0.104156</td>\n",
        "      <td> 0.568621</td>\n",
        "      <td>-0.731615</td>\n",
        "      <td>-0.342465</td>\n",
        "      <td>-0.432514</td>\n",
        "      <td>-0.457017</td>\n",
        "      <td>-0.656391</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>all</th>\n",
        "      <td>-0.215562</td>\n",
        "      <td> 0.850153</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td>-0.493636</td>\n",
        "      <td>-0.391092</td>\n",
        "      <td>-0.726466</td>\n",
        "      <td> 0.660349</td>\n",
        "      <td>-0.001542</td>\n",
        "      <td> 0.059841</td>\n",
        "      <td>-0.695829</td>\n",
        "      <td> 0.753363</td>\n",
        "      <td> 0.164095</td>\n",
        "      <td>-0.607221</td>\n",
        "      <td>-0.113611</td>\n",
        "      <td> 0.509434</td>\n",
        "      <td>-0.630260</td>\n",
        "      <td>-0.222071</td>\n",
        "      <td>-0.556689</td>\n",
        "      <td>-0.189262</td>\n",
        "      <td>-0.484713</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ang</th>\n",
        "      <td> 0.318342</td>\n",
        "      <td>-0.658482</td>\n",
        "      <td>-0.493636</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td>-0.064658</td>\n",
        "      <td> 0.713985</td>\n",
        "      <td>-0.389470</td>\n",
        "      <td>-0.009043</td>\n",
        "      <td>-0.568945</td>\n",
        "      <td> 0.827816</td>\n",
        "      <td>-0.782344</td>\n",
        "      <td>-0.048932</td>\n",
        "      <td> 0.880685</td>\n",
        "      <td>-0.001264</td>\n",
        "      <td>-0.292906</td>\n",
        "      <td> 0.751659</td>\n",
        "      <td> 0.693543</td>\n",
        "      <td> 0.104828</td>\n",
        "      <td> 0.739517</td>\n",
        "      <td> 0.827118</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>aoa</th>\n",
        "      <td> 0.150919</td>\n",
        "      <td>-0.386002</td>\n",
        "      <td>-0.391092</td>\n",
        "      <td>-0.064658</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.391747</td>\n",
        "      <td>-0.487989</td>\n",
        "      <td> 0.197351</td>\n",
        "      <td> 0.212811</td>\n",
        "      <td> 0.183662</td>\n",
        "      <td>-0.108598</td>\n",
        "      <td>-0.041409</td>\n",
        "      <td>-0.093057</td>\n",
        "      <td> 0.085109</td>\n",
        "      <td>-0.409163</td>\n",
        "      <td> 0.029092</td>\n",
        "      <td>-0.204268</td>\n",
        "      <td> 0.106388</td>\n",
        "      <td>-0.108879</td>\n",
        "      <td>-0.021772</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ars</th>\n",
        "      <td> 0.345806</td>\n",
        "      <td>-0.880705</td>\n",
        "      <td>-0.726466</td>\n",
        "      <td> 0.713985</td>\n",
        "      <td> 0.391747</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td>-0.605686</td>\n",
        "      <td> 0.082945</td>\n",
        "      <td>-0.289172</td>\n",
        "      <td> 0.885343</td>\n",
        "      <td>-0.882117</td>\n",
        "      <td>-0.092293</td>\n",
        "      <td> 0.829553</td>\n",
        "      <td> 0.028990</td>\n",
        "      <td>-0.470184</td>\n",
        "      <td> 0.813758</td>\n",
        "      <td> 0.548284</td>\n",
        "      <td> 0.138454</td>\n",
        "      <td> 0.629170</td>\n",
        "      <td> 0.730753</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>aud</th>\n",
        "      <td>-0.429843</td>\n",
        "      <td> 0.730158</td>\n",
        "      <td> 0.660349</td>\n",
        "      <td>-0.389470</td>\n",
        "      <td>-0.487989</td>\n",
        "      <td>-0.605686</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td>-0.289410</td>\n",
        "      <td>-0.033362</td>\n",
        "      <td>-0.660589</td>\n",
        "      <td> 0.519028</td>\n",
        "      <td> 0.094448</td>\n",
        "      <td>-0.321689</td>\n",
        "      <td>-0.160546</td>\n",
        "      <td> 0.922478</td>\n",
        "      <td>-0.219091</td>\n",
        "      <td> 0.121693</td>\n",
        "      <td>-0.292971</td>\n",
        "      <td>-0.234422</td>\n",
        "      <td>-0.322210</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>awg</th>\n",
        "      <td> 0.204882</td>\n",
        "      <td>-0.116045</td>\n",
        "      <td>-0.001542</td>\n",
        "      <td>-0.009043</td>\n",
        "      <td> 0.197351</td>\n",
        "      <td> 0.082945</td>\n",
        "      <td>-0.289410</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.124027</td>\n",
        "      <td> 0.098901</td>\n",
        "      <td>-0.022411</td>\n",
        "      <td> 0.009140</td>\n",
        "      <td>-0.041812</td>\n",
        "      <td> 0.032224</td>\n",
        "      <td>-0.359452</td>\n",
        "      <td>-0.147456</td>\n",
        "      <td>-0.236472</td>\n",
        "      <td>-0.039029</td>\n",
        "      <td>-0.009498</td>\n",
        "      <td> 0.090142</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>bbd</th>\n",
        "      <td>-0.036890</td>\n",
        "      <td> 0.191257</td>\n",
        "      <td> 0.059841</td>\n",
        "      <td>-0.568945</td>\n",
        "      <td> 0.212811</td>\n",
        "      <td>-0.289172</td>\n",
        "      <td>-0.033362</td>\n",
        "      <td> 0.124027</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td>-0.340147</td>\n",
        "      <td> 0.308121</td>\n",
        "      <td> 0.074803</td>\n",
        "      <td>-0.499189</td>\n",
        "      <td> 0.067200</td>\n",
        "      <td>-0.091067</td>\n",
        "      <td>-0.463276</td>\n",
        "      <td>-0.577734</td>\n",
        "      <td> 0.071432</td>\n",
        "      <td>-0.471832</td>\n",
        "      <td>-0.432564</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>bdt</th>\n",
        "      <td> 0.421365</td>\n",
        "      <td>-0.870072</td>\n",
        "      <td>-0.695829</td>\n",
        "      <td> 0.827816</td>\n",
        "      <td> 0.183662</td>\n",
        "      <td> 0.885343</td>\n",
        "      <td>-0.660589</td>\n",
        "      <td> 0.098901</td>\n",
        "      <td>-0.340147</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td>-0.908591</td>\n",
        "      <td>-0.109724</td>\n",
        "      <td> 0.911894</td>\n",
        "      <td> 0.037454</td>\n",
        "      <td>-0.584562</td>\n",
        "      <td> 0.771226</td>\n",
        "      <td> 0.555676</td>\n",
        "      <td> 0.271985</td>\n",
        "      <td> 0.738603</td>\n",
        "      <td> 0.897028</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>bgn</th>\n",
        "      <td>-0.294120</td>\n",
        "      <td> 0.844934</td>\n",
        "      <td> 0.753363</td>\n",
        "      <td>-0.782344</td>\n",
        "      <td>-0.108598</td>\n",
        "      <td>-0.882117</td>\n",
        "      <td> 0.519028</td>\n",
        "      <td>-0.022411</td>\n",
        "      <td> 0.308121</td>\n",
        "      <td>-0.908591</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.108716</td>\n",
        "      <td>-0.921780</td>\n",
        "      <td> 0.000407</td>\n",
        "      <td> 0.403354</td>\n",
        "      <td>-0.874450</td>\n",
        "      <td>-0.624626</td>\n",
        "      <td>-0.273341</td>\n",
        "      <td>-0.603399</td>\n",
        "      <td>-0.763768</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>bhd</th>\n",
        "      <td> 0.061025</td>\n",
        "      <td> 0.124149</td>\n",
        "      <td> 0.164095</td>\n",
        "      <td>-0.048932</td>\n",
        "      <td>-0.041409</td>\n",
        "      <td>-0.092293</td>\n",
        "      <td> 0.094448</td>\n",
        "      <td> 0.009140</td>\n",
        "      <td> 0.074803</td>\n",
        "      <td>-0.109724</td>\n",
        "      <td> 0.108716</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td>-0.085641</td>\n",
        "      <td>-0.040208</td>\n",
        "      <td> 0.072872</td>\n",
        "      <td>-0.098268</td>\n",
        "      <td>-0.011610</td>\n",
        "      <td>-0.221818</td>\n",
        "      <td> 0.006997</td>\n",
        "      <td>-0.086353</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>bif</th>\n",
        "      <td> 0.296765</td>\n",
        "      <td>-0.748667</td>\n",
        "      <td>-0.607221</td>\n",
        "      <td> 0.880685</td>\n",
        "      <td>-0.093057</td>\n",
        "      <td> 0.829553</td>\n",
        "      <td>-0.321689</td>\n",
        "      <td>-0.041812</td>\n",
        "      <td>-0.499189</td>\n",
        "      <td> 0.911894</td>\n",
        "      <td>-0.921780</td>\n",
        "      <td>-0.085641</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td>-0.039498</td>\n",
        "      <td>-0.240366</td>\n",
        "      <td> 0.897580</td>\n",
        "      <td> 0.785959</td>\n",
        "      <td> 0.154794</td>\n",
        "      <td> 0.791384</td>\n",
        "      <td> 0.902843</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>bmd</th>\n",
        "      <td> 0.061892</td>\n",
        "      <td>-0.104156</td>\n",
        "      <td>-0.113611</td>\n",
        "      <td>-0.001264</td>\n",
        "      <td> 0.085109</td>\n",
        "      <td> 0.028990</td>\n",
        "      <td>-0.160546</td>\n",
        "      <td> 0.032224</td>\n",
        "      <td> 0.067200</td>\n",
        "      <td> 0.037454</td>\n",
        "      <td> 0.000407</td>\n",
        "      <td>-0.040208</td>\n",
        "      <td>-0.039498</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td>-0.131807</td>\n",
        "      <td>-0.042271</td>\n",
        "      <td>-0.143303</td>\n",
        "      <td> 0.183243</td>\n",
        "      <td>-0.049115</td>\n",
        "      <td>-0.001988</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>bnd</th>\n",
        "      <td>-0.472499</td>\n",
        "      <td> 0.568621</td>\n",
        "      <td> 0.509434</td>\n",
        "      <td>-0.292906</td>\n",
        "      <td>-0.409163</td>\n",
        "      <td>-0.470184</td>\n",
        "      <td> 0.922478</td>\n",
        "      <td>-0.359452</td>\n",
        "      <td>-0.091067</td>\n",
        "      <td>-0.584562</td>\n",
        "      <td> 0.403354</td>\n",
        "      <td> 0.072872</td>\n",
        "      <td>-0.240366</td>\n",
        "      <td>-0.131807</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td>-0.000410</td>\n",
        "      <td> 0.226425</td>\n",
        "      <td>-0.246194</td>\n",
        "      <td>-0.253331</td>\n",
        "      <td>-0.435712</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>bob</th>\n",
        "      <td> 0.127788</td>\n",
        "      <td>-0.731615</td>\n",
        "      <td>-0.630260</td>\n",
        "      <td> 0.751659</td>\n",
        "      <td> 0.029092</td>\n",
        "      <td> 0.813758</td>\n",
        "      <td>-0.219091</td>\n",
        "      <td>-0.147456</td>\n",
        "      <td>-0.463276</td>\n",
        "      <td> 0.771226</td>\n",
        "      <td>-0.874450</td>\n",
        "      <td>-0.098268</td>\n",
        "      <td> 0.897580</td>\n",
        "      <td>-0.042271</td>\n",
        "      <td>-0.000410</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.816374</td>\n",
        "      <td> 0.153895</td>\n",
        "      <td> 0.588403</td>\n",
        "      <td> 0.640485</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>brl</th>\n",
        "      <td> 0.031011</td>\n",
        "      <td>-0.342465</td>\n",
        "      <td>-0.222071</td>\n",
        "      <td> 0.693543</td>\n",
        "      <td>-0.204268</td>\n",
        "      <td> 0.548284</td>\n",
        "      <td> 0.121693</td>\n",
        "      <td>-0.236472</td>\n",
        "      <td>-0.577734</td>\n",
        "      <td> 0.555676</td>\n",
        "      <td>-0.624626</td>\n",
        "      <td>-0.011610</td>\n",
        "      <td> 0.785959</td>\n",
        "      <td>-0.143303</td>\n",
        "      <td> 0.226425</td>\n",
        "      <td> 0.816374</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td>-0.138637</td>\n",
        "      <td> 0.683922</td>\n",
        "      <td> 0.523793</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>bsd</th>\n",
        "      <td> 0.117436</td>\n",
        "      <td>-0.432514</td>\n",
        "      <td>-0.556689</td>\n",
        "      <td> 0.104828</td>\n",
        "      <td> 0.106388</td>\n",
        "      <td> 0.138454</td>\n",
        "      <td>-0.292971</td>\n",
        "      <td>-0.039029</td>\n",
        "      <td> 0.071432</td>\n",
        "      <td> 0.271985</td>\n",
        "      <td>-0.273341</td>\n",
        "      <td>-0.221818</td>\n",
        "      <td> 0.154794</td>\n",
        "      <td> 0.183243</td>\n",
        "      <td>-0.246194</td>\n",
        "      <td> 0.153895</td>\n",
        "      <td>-0.138637</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td>-0.074173</td>\n",
        "      <td> 0.191240</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>btn</th>\n",
        "      <td> 0.308459</td>\n",
        "      <td>-0.457017</td>\n",
        "      <td>-0.189262</td>\n",
        "      <td> 0.739517</td>\n",
        "      <td>-0.108879</td>\n",
        "      <td> 0.629170</td>\n",
        "      <td>-0.234422</td>\n",
        "      <td>-0.009498</td>\n",
        "      <td>-0.471832</td>\n",
        "      <td> 0.738603</td>\n",
        "      <td>-0.603399</td>\n",
        "      <td> 0.006997</td>\n",
        "      <td> 0.791384</td>\n",
        "      <td>-0.049115</td>\n",
        "      <td>-0.253331</td>\n",
        "      <td> 0.588403</td>\n",
        "      <td> 0.683922</td>\n",
        "      <td>-0.074173</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.831562</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>bwp</th>\n",
        "      <td> 0.369275</td>\n",
        "      <td>-0.656391</td>\n",
        "      <td>-0.484713</td>\n",
        "      <td> 0.827118</td>\n",
        "      <td>-0.021772</td>\n",
        "      <td> 0.730753</td>\n",
        "      <td>-0.322210</td>\n",
        "      <td> 0.090142</td>\n",
        "      <td>-0.432564</td>\n",
        "      <td> 0.897028</td>\n",
        "      <td>-0.763768</td>\n",
        "      <td>-0.086353</td>\n",
        "      <td> 0.902843</td>\n",
        "      <td>-0.001988</td>\n",
        "      <td>-0.435712</td>\n",
        "      <td> 0.640485</td>\n",
        "      <td> 0.523793</td>\n",
        "      <td> 0.191240</td>\n",
        "      <td> 0.831562</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>bzd</th>\n",
        "      <td>-0.199241</td>\n",
        "      <td> 0.540240</td>\n",
        "      <td> 0.466551</td>\n",
        "      <td>-0.580590</td>\n",
        "      <td>-0.025197</td>\n",
        "      <td>-0.572888</td>\n",
        "      <td> 0.206827</td>\n",
        "      <td> 0.077406</td>\n",
        "      <td> 0.306965</td>\n",
        "      <td>-0.571049</td>\n",
        "      <td> 0.619528</td>\n",
        "      <td> 0.147967</td>\n",
        "      <td>-0.632425</td>\n",
        "      <td> 0.037079</td>\n",
        "      <td> 0.061620</td>\n",
        "      <td>-0.661113</td>\n",
        "      <td>-0.529474</td>\n",
        "      <td>-0.189046</td>\n",
        "      <td>-0.385241</td>\n",
        "      <td>-0.489066</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>cad</th>\n",
        "      <td>-0.453087</td>\n",
        "      <td> 0.873579</td>\n",
        "      <td> 0.791226</td>\n",
        "      <td>-0.550645</td>\n",
        "      <td>-0.398290</td>\n",
        "      <td>-0.731101</td>\n",
        "      <td> 0.919697</td>\n",
        "      <td>-0.238847</td>\n",
        "      <td> 0.067953</td>\n",
        "      <td>-0.802544</td>\n",
        "      <td> 0.692853</td>\n",
        "      <td> 0.154649</td>\n",
        "      <td>-0.539977</td>\n",
        "      <td>-0.154205</td>\n",
        "      <td> 0.833482</td>\n",
        "      <td>-0.442866</td>\n",
        "      <td>-0.032108</td>\n",
        "      <td>-0.433541</td>\n",
        "      <td>-0.345770</td>\n",
        "      <td>-0.545858</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>chf</th>\n",
        "      <td>-0.472706</td>\n",
        "      <td> 0.781060</td>\n",
        "      <td> 0.672975</td>\n",
        "      <td>-0.499283</td>\n",
        "      <td>-0.475194</td>\n",
        "      <td>-0.727928</td>\n",
        "      <td> 0.920995</td>\n",
        "      <td>-0.269338</td>\n",
        "      <td> 0.031152</td>\n",
        "      <td>-0.774685</td>\n",
        "      <td> 0.638527</td>\n",
        "      <td> 0.069894</td>\n",
        "      <td>-0.418038</td>\n",
        "      <td>-0.109487</td>\n",
        "      <td> 0.900661</td>\n",
        "      <td>-0.330737</td>\n",
        "      <td>-0.062455</td>\n",
        "      <td>-0.244996</td>\n",
        "      <td>-0.454563</td>\n",
        "      <td>-0.400865</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>clp</th>\n",
        "      <td>-0.011513</td>\n",
        "      <td>-0.100243</td>\n",
        "      <td> 0.046357</td>\n",
        "      <td> 0.471420</td>\n",
        "      <td>-0.313592</td>\n",
        "      <td> 0.335740</td>\n",
        "      <td> 0.361547</td>\n",
        "      <td>-0.256289</td>\n",
        "      <td>-0.466367</td>\n",
        "      <td> 0.233044</td>\n",
        "      <td>-0.385934</td>\n",
        "      <td> 0.007332</td>\n",
        "      <td> 0.672774</td>\n",
        "      <td>-0.233081</td>\n",
        "      <td> 0.510523</td>\n",
        "      <td> 0.647831</td>\n",
        "      <td> 0.864075</td>\n",
        "      <td>-0.213372</td>\n",
        "      <td> 0.534517</td>\n",
        "      <td> 0.586455</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>cny</th>\n",
        "      <td>-0.549585</td>\n",
        "      <td> 0.680301</td>\n",
        "      <td> 0.512385</td>\n",
        "      <td>-0.569450</td>\n",
        "      <td>-0.276438</td>\n",
        "      <td>-0.647739</td>\n",
        "      <td> 0.802625</td>\n",
        "      <td>-0.291698</td>\n",
        "      <td> 0.114466</td>\n",
        "      <td>-0.772915</td>\n",
        "      <td> 0.574869</td>\n",
        "      <td> 0.065915</td>\n",
        "      <td>-0.549888</td>\n",
        "      <td>-0.086805</td>\n",
        "      <td> 0.885626</td>\n",
        "      <td>-0.252936</td>\n",
        "      <td>-0.059130</td>\n",
        "      <td>-0.239085</td>\n",
        "      <td>-0.608535</td>\n",
        "      <td>-0.683191</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>cop</th>\n",
        "      <td> 0.009631</td>\n",
        "      <td>-0.433227</td>\n",
        "      <td>-0.322577</td>\n",
        "      <td> 0.650822</td>\n",
        "      <td>-0.205107</td>\n",
        "      <td> 0.541071</td>\n",
        "      <td> 0.127890</td>\n",
        "      <td>-0.247805</td>\n",
        "      <td>-0.494096</td>\n",
        "      <td> 0.563507</td>\n",
        "      <td>-0.667506</td>\n",
        "      <td>-0.041960</td>\n",
        "      <td> 0.784924</td>\n",
        "      <td>-0.102921</td>\n",
        "      <td> 0.300819</td>\n",
        "      <td> 0.882521</td>\n",
        "      <td> 0.912125</td>\n",
        "      <td> 0.034106</td>\n",
        "      <td> 0.545439</td>\n",
        "      <td> 0.485731</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>crc</th>\n",
        "      <td> 0.348785</td>\n",
        "      <td>-0.885344</td>\n",
        "      <td>-0.762344</td>\n",
        "      <td> 0.846919</td>\n",
        "      <td> 0.125663</td>\n",
        "      <td> 0.895699</td>\n",
        "      <td>-0.506377</td>\n",
        "      <td> 0.003518</td>\n",
        "      <td>-0.389607</td>\n",
        "      <td> 0.961488</td>\n",
        "      <td>-0.942022</td>\n",
        "      <td>-0.104512</td>\n",
        "      <td> 0.947301</td>\n",
        "      <td> 0.034966</td>\n",
        "      <td>-0.360269</td>\n",
        "      <td> 0.885446</td>\n",
        "      <td> 0.634442</td>\n",
        "      <td> 0.308179</td>\n",
        "      <td> 0.708035</td>\n",
        "      <td> 0.858453</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>cup</th>\n",
        "      <td> 0.285120</td>\n",
        "      <td>-0.637896</td>\n",
        "      <td>-0.554477</td>\n",
        "      <td> 0.146498</td>\n",
        "      <td> 0.663040</td>\n",
        "      <td> 0.532399</td>\n",
        "      <td>-0.637070</td>\n",
        "      <td> 0.204727</td>\n",
        "      <td> 0.201305</td>\n",
        "      <td> 0.398836</td>\n",
        "      <td>-0.313963</td>\n",
        "      <td>-0.100620</td>\n",
        "      <td> 0.223209</td>\n",
        "      <td> 0.162909</td>\n",
        "      <td>-0.537177</td>\n",
        "      <td> 0.245808</td>\n",
        "      <td>-0.245160</td>\n",
        "      <td> 0.339180</td>\n",
        "      <td> 0.122099</td>\n",
        "      <td> 0.293874</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>cve</th>\n",
        "      <td>-0.281813</td>\n",
        "      <td> 0.646713</td>\n",
        "      <td> 0.774032</td>\n",
        "      <td>-0.167737</td>\n",
        "      <td>-0.562190</td>\n",
        "      <td>-0.464940</td>\n",
        "      <td> 0.800037</td>\n",
        "      <td>-0.211071</td>\n",
        "      <td>-0.260553</td>\n",
        "      <td>-0.440949</td>\n",
        "      <td> 0.389292</td>\n",
        "      <td> 0.122245</td>\n",
        "      <td>-0.177614</td>\n",
        "      <td>-0.173909</td>\n",
        "      <td> 0.775205</td>\n",
        "      <td>-0.108349</td>\n",
        "      <td> 0.321730</td>\n",
        "      <td>-0.498408</td>\n",
        "      <td> 0.045690</td>\n",
        "      <td>-0.286904</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>czk</th>\n",
        "      <td>-0.423761</td>\n",
        "      <td> 0.885967</td>\n",
        "      <td> 0.878067</td>\n",
        "      <td>-0.534048</td>\n",
        "      <td>-0.482354</td>\n",
        "      <td>-0.784530</td>\n",
        "      <td> 0.867029</td>\n",
        "      <td>-0.212554</td>\n",
        "      <td> 0.006670</td>\n",
        "      <td>-0.791373</td>\n",
        "      <td> 0.741821</td>\n",
        "      <td> 0.124666</td>\n",
        "      <td>-0.600789</td>\n",
        "      <td>-0.112132</td>\n",
        "      <td> 0.793116</td>\n",
        "      <td>-0.478516</td>\n",
        "      <td>-0.076543</td>\n",
        "      <td>-0.451483</td>\n",
        "      <td>-0.318893</td>\n",
        "      <td>-0.637217</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>djf</th>\n",
        "      <td> 0.365357</td>\n",
        "      <td>-0.700002</td>\n",
        "      <td>-0.563285</td>\n",
        "      <td> 0.733248</td>\n",
        "      <td> 0.093883</td>\n",
        "      <td> 0.648250</td>\n",
        "      <td>-0.472857</td>\n",
        "      <td> 0.050697</td>\n",
        "      <td>-0.393457</td>\n",
        "      <td> 0.803719</td>\n",
        "      <td>-0.619773</td>\n",
        "      <td>-0.086952</td>\n",
        "      <td> 0.712739</td>\n",
        "      <td> 0.052515</td>\n",
        "      <td>-0.416053</td>\n",
        "      <td> 0.606786</td>\n",
        "      <td> 0.455050</td>\n",
        "      <td> 0.266779</td>\n",
        "      <td> 0.706562</td>\n",
        "      <td> 0.638934</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>dkk</th>\n",
        "      <td>-0.267451</td>\n",
        "      <td> 0.792155</td>\n",
        "      <td> 0.891255</td>\n",
        "      <td>-0.293724</td>\n",
        "      <td>-0.606721</td>\n",
        "      <td>-0.643265</td>\n",
        "      <td> 0.807941</td>\n",
        "      <td>-0.153512</td>\n",
        "      <td>-0.125295</td>\n",
        "      <td>-0.612471</td>\n",
        "      <td> 0.593182</td>\n",
        "      <td> 0.117878</td>\n",
        "      <td>-0.223099</td>\n",
        "      <td>-0.145687</td>\n",
        "      <td> 0.720488</td>\n",
        "      <td>-0.326513</td>\n",
        "      <td> 0.056700</td>\n",
        "      <td>-0.500497</td>\n",
        "      <td>-0.100039</td>\n",
        "      <td>-0.126269</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>dop</th>\n",
        "      <td> 0.372574</td>\n",
        "      <td>-0.954266</td>\n",
        "      <td>-0.803434</td>\n",
        "      <td> 0.736741</td>\n",
        "      <td> 0.317621</td>\n",
        "      <td> 0.922816</td>\n",
        "      <td>-0.666496</td>\n",
        "      <td> 0.059636</td>\n",
        "      <td>-0.298306</td>\n",
        "      <td> 0.910653</td>\n",
        "      <td>-0.885294</td>\n",
        "      <td>-0.102793</td>\n",
        "      <td> 0.846984</td>\n",
        "      <td> 0.069948</td>\n",
        "      <td>-0.501796</td>\n",
        "      <td> 0.807447</td>\n",
        "      <td> 0.505837</td>\n",
        "      <td> 0.337840</td>\n",
        "      <td> 0.619557</td>\n",
        "      <td> 0.725782</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>dzd</th>\n",
        "      <td> 0.152865</td>\n",
        "      <td>-0.412030</td>\n",
        "      <td>-0.116705</td>\n",
        "      <td> 0.813462</td>\n",
        "      <td>-0.310091</td>\n",
        "      <td> 0.593780</td>\n",
        "      <td>-0.055478</td>\n",
        "      <td>-0.062925</td>\n",
        "      <td>-0.586596</td>\n",
        "      <td> 0.673636</td>\n",
        "      <td>-0.639105</td>\n",
        "      <td>-0.038256</td>\n",
        "      <td> 0.688973</td>\n",
        "      <td>-0.100741</td>\n",
        "      <td>-0.028709</td>\n",
        "      <td> 0.727086</td>\n",
        "      <td> 0.838841</td>\n",
        "      <td>-0.153389</td>\n",
        "      <td> 0.843791</td>\n",
        "      <td> 0.766005</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ecs</th>\n",
        "      <td> 0.226241</td>\n",
        "      <td>-0.590237</td>\n",
        "      <td>-0.429281</td>\n",
        "      <td> 0.848856</td>\n",
        "      <td>-0.273312</td>\n",
        "      <td> 0.673744</td>\n",
        "      <td>-0.130047</td>\n",
        "      <td>-0.121210</td>\n",
        "      <td>-0.558198</td>\n",
        "      <td> 0.789424</td>\n",
        "      <td>-0.801406</td>\n",
        "      <td>-0.077157</td>\n",
        "      <td> 0.958210</td>\n",
        "      <td>-0.048470</td>\n",
        "      <td>-0.015757</td>\n",
        "      <td> 0.866850</td>\n",
        "      <td> 0.843678</td>\n",
        "      <td> 0.099525</td>\n",
        "      <td> 0.757371</td>\n",
        "      <td> 0.900966</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>eek</th>\n",
        "      <td>-0.306820</td>\n",
        "      <td> 0.824974</td>\n",
        "      <td> 0.889314</td>\n",
        "      <td>-0.330187</td>\n",
        "      <td>-0.609808</td>\n",
        "      <td>-0.678623</td>\n",
        "      <td> 0.834215</td>\n",
        "      <td>-0.152937</td>\n",
        "      <td>-0.116934</td>\n",
        "      <td>-0.620285</td>\n",
        "      <td> 0.590253</td>\n",
        "      <td> 0.136461</td>\n",
        "      <td>-0.413347</td>\n",
        "      <td>-0.147079</td>\n",
        "      <td> 0.720467</td>\n",
        "      <td>-0.376009</td>\n",
        "      <td> 0.052290</td>\n",
        "      <td>-0.511686</td>\n",
        "      <td>-0.142538</td>\n",
        "      <td>-0.402063</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>egp</th>\n",
        "      <td> 0.341283</td>\n",
        "      <td>-0.891633</td>\n",
        "      <td>-0.725893</td>\n",
        "      <td> 0.752436</td>\n",
        "      <td> 0.278387</td>\n",
        "      <td> 0.954123</td>\n",
        "      <td>-0.538448</td>\n",
        "      <td> 0.037340</td>\n",
        "      <td>-0.345088</td>\n",
        "      <td> 0.907456</td>\n",
        "      <td>-0.927112</td>\n",
        "      <td>-0.104884</td>\n",
        "      <td> 0.875006</td>\n",
        "      <td>-0.005835</td>\n",
        "      <td>-0.421141</td>\n",
        "      <td> 0.871999</td>\n",
        "      <td> 0.627184</td>\n",
        "      <td> 0.198952</td>\n",
        "      <td> 0.679301</td>\n",
        "      <td> 0.749145</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>etb</th>\n",
        "      <td> 0.497018</td>\n",
        "      <td>-0.729454</td>\n",
        "      <td>-0.469469</td>\n",
        "      <td> 0.786349</td>\n",
        "      <td> 0.125252</td>\n",
        "      <td> 0.754809</td>\n",
        "      <td>-0.618847</td>\n",
        "      <td> 0.217501</td>\n",
        "      <td>-0.320727</td>\n",
        "      <td> 0.884619</td>\n",
        "      <td>-0.708321</td>\n",
        "      <td>-0.056498</td>\n",
        "      <td> 0.816481</td>\n",
        "      <td> 0.043717</td>\n",
        "      <td>-0.683150</td>\n",
        "      <td> 0.525198</td>\n",
        "      <td> 0.359795</td>\n",
        "      <td> 0.119904</td>\n",
        "      <td> 0.797625</td>\n",
        "      <td> 0.889520</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>eur</th>\n",
        "      <td>-0.269434</td>\n",
        "      <td> 0.892211</td>\n",
        "      <td> 0.937373</td>\n",
        "      <td>-0.446876</td>\n",
        "      <td>-0.712588</td>\n",
        "      <td>-0.745535</td>\n",
        "      <td> 0.830175</td>\n",
        "      <td>-0.169452</td>\n",
        "      <td>-0.255841</td>\n",
        "      <td>-0.771663</td>\n",
        "      <td> 0.761817</td>\n",
        "      <td> 0.115989</td>\n",
        "      <td>-0.670708</td>\n",
        "      <td>-0.160867</td>\n",
        "      <td> 0.723654</td>\n",
        "      <td>-0.403427</td>\n",
        "      <td> 0.423902</td>\n",
        "      <td>-0.509193</td>\n",
        "      <td>-0.018739</td>\n",
        "      <td>-0.418475</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>fjd</th>\n",
        "      <td> 0.035610</td>\n",
        "      <td> 0.283634</td>\n",
        "      <td> 0.483270</td>\n",
        "      <td> 0.266024</td>\n",
        "      <td>-0.510935</td>\n",
        "      <td>-0.127003</td>\n",
        "      <td> 0.473197</td>\n",
        "      <td>-0.035760</td>\n",
        "      <td>-0.289542</td>\n",
        "      <td> 0.049558</td>\n",
        "      <td> 0.016993</td>\n",
        "      <td> 0.118295</td>\n",
        "      <td> 0.228282</td>\n",
        "      <td>-0.139000</td>\n",
        "      <td> 0.374787</td>\n",
        "      <td> 0.059945</td>\n",
        "      <td> 0.440704</td>\n",
        "      <td>-0.311888</td>\n",
        "      <td> 0.498509</td>\n",
        "      <td> 0.316547</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>fkp</th>\n",
        "      <td> 0.290632</td>\n",
        "      <td> 0.291960</td>\n",
        "      <td> 0.390765</td>\n",
        "      <td>-0.081465</td>\n",
        "      <td>-0.305917</td>\n",
        "      <td>-0.400538</td>\n",
        "      <td>-0.015974</td>\n",
        "      <td> 0.236614</td>\n",
        "      <td> 0.179989</td>\n",
        "      <td>-0.107565</td>\n",
        "      <td> 0.275523</td>\n",
        "      <td> 0.128592</td>\n",
        "      <td>-0.225664</td>\n",
        "      <td> 0.032721</td>\n",
        "      <td>-0.268398</td>\n",
        "      <td>-0.549563</td>\n",
        "      <td>-0.315953</td>\n",
        "      <td> 0.003070</td>\n",
        "      <td> 0.082461</td>\n",
        "      <td> 0.064514</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>frf</th>\n",
        "      <td> 0.078423</td>\n",
        "      <td> 0.034048</td>\n",
        "      <td> 0.362338</td>\n",
        "      <td>-0.056027</td>\n",
        "      <td> 0.016188</td>\n",
        "      <td>-0.036185</td>\n",
        "      <td> 0.038247</td>\n",
        "      <td> 0.009866</td>\n",
        "      <td> 0.042123</td>\n",
        "      <td>-0.219296</td>\n",
        "      <td> 0.267130</td>\n",
        "      <td>-0.028498</td>\n",
        "      <td> 0.357084</td>\n",
        "      <td> 0.003374</td>\n",
        "      <td> 0.150844</td>\n",
        "      <td> 0.019529</td>\n",
        "      <td>-0.275947</td>\n",
        "      <td>-0.004960</td>\n",
        "      <td> 0.070580</td>\n",
        "      <td> 0.552877</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>gbp</th>\n",
        "      <td> 0.198093</td>\n",
        "      <td> 0.418628</td>\n",
        "      <td> 0.629831</td>\n",
        "      <td>-0.040627</td>\n",
        "      <td>-0.354506</td>\n",
        "      <td>-0.300839</td>\n",
        "      <td> 0.263544</td>\n",
        "      <td> 0.177207</td>\n",
        "      <td>-0.011851</td>\n",
        "      <td>-0.163598</td>\n",
        "      <td> 0.262560</td>\n",
        "      <td> 0.187614</td>\n",
        "      <td>-0.190560</td>\n",
        "      <td>-0.105645</td>\n",
        "      <td> 0.008118</td>\n",
        "      <td>-0.415807</td>\n",
        "      <td>-0.025130</td>\n",
        "      <td>-0.404481</td>\n",
        "      <td> 0.196157</td>\n",
        "      <td>-0.057143</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ghc</th>\n",
        "      <td> 0.238646</td>\n",
        "      <td>-0.662917</td>\n",
        "      <td>-0.488464</td>\n",
        "      <td> 0.838368</td>\n",
        "      <td>-0.177579</td>\n",
        "      <td> 0.768597</td>\n",
        "      <td>-0.198624</td>\n",
        "      <td>-0.066709</td>\n",
        "      <td>-0.513935</td>\n",
        "      <td> 0.859103</td>\n",
        "      <td>-0.897746</td>\n",
        "      <td>-0.078509</td>\n",
        "      <td> 0.968479</td>\n",
        "      <td>-0.074814</td>\n",
        "      <td>-0.130454</td>\n",
        "      <td> 0.887924</td>\n",
        "      <td> 0.804984</td>\n",
        "      <td> 0.111157</td>\n",
        "      <td> 0.805154</td>\n",
        "      <td> 0.907353</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>gip</th>\n",
        "      <td> 0.242070</td>\n",
        "      <td> 0.406983</td>\n",
        "      <td> 0.620892</td>\n",
        "      <td>-0.042861</td>\n",
        "      <td>-0.345612</td>\n",
        "      <td>-0.292430</td>\n",
        "      <td> 0.261222</td>\n",
        "      <td> 0.178861</td>\n",
        "      <td>-0.016360</td>\n",
        "      <td>-0.159610</td>\n",
        "      <td> 0.257145</td>\n",
        "      <td> 0.178483</td>\n",
        "      <td>-0.149335</td>\n",
        "      <td>-0.105253</td>\n",
        "      <td> 0.010071</td>\n",
        "      <td>-0.406151</td>\n",
        "      <td>-0.021948</td>\n",
        "      <td>-0.386169</td>\n",
        "      <td> 0.190944</td>\n",
        "      <td> 0.060706</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>gmd</th>\n",
        "      <td> 0.337680</td>\n",
        "      <td>-0.855159</td>\n",
        "      <td>-0.669550</td>\n",
        "      <td> 0.824441</td>\n",
        "      <td> 0.147401</td>\n",
        "      <td> 0.932108</td>\n",
        "      <td>-0.449617</td>\n",
        "      <td> 0.008638</td>\n",
        "      <td>-0.410544</td>\n",
        "      <td> 0.925727</td>\n",
        "      <td>-0.943172</td>\n",
        "      <td>-0.086023</td>\n",
        "      <td> 0.940376</td>\n",
        "      <td>-0.014428</td>\n",
        "      <td>-0.334220</td>\n",
        "      <td> 0.915588</td>\n",
        "      <td> 0.720269</td>\n",
        "      <td> 0.161537</td>\n",
        "      <td> 0.740525</td>\n",
        "      <td> 0.817723</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>gnf</th>\n",
        "      <td> 0.420406</td>\n",
        "      <td>-0.880266</td>\n",
        "      <td>-0.725866</td>\n",
        "      <td> 0.839325</td>\n",
        "      <td> 0.111805</td>\n",
        "      <td> 0.868082</td>\n",
        "      <td>-0.621521</td>\n",
        "      <td> 0.086621</td>\n",
        "      <td>-0.364075</td>\n",
        "      <td> 0.986562</td>\n",
        "      <td>-0.905172</td>\n",
        "      <td>-0.113438</td>\n",
        "      <td> 0.906936</td>\n",
        "      <td> 0.057952</td>\n",
        "      <td>-0.530253</td>\n",
        "      <td> 0.784254</td>\n",
        "      <td> 0.511606</td>\n",
        "      <td> 0.313527</td>\n",
        "      <td> 0.719367</td>\n",
        "      <td> 0.900801</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>gtq</th>\n",
        "      <td> 0.221984</td>\n",
        "      <td>-0.561640</td>\n",
        "      <td>-0.389876</td>\n",
        "      <td> 0.871405</td>\n",
        "      <td>-0.252027</td>\n",
        "      <td> 0.651091</td>\n",
        "      <td>-0.152613</td>\n",
        "      <td>-0.101397</td>\n",
        "      <td>-0.572989</td>\n",
        "      <td> 0.745386</td>\n",
        "      <td>-0.753727</td>\n",
        "      <td>-0.029111</td>\n",
        "      <td> 0.920064</td>\n",
        "      <td>-0.064549</td>\n",
        "      <td>-0.034800</td>\n",
        "      <td> 0.815004</td>\n",
        "      <td> 0.849479</td>\n",
        "      <td> 0.079950</td>\n",
        "      <td> 0.767544</td>\n",
        "      <td> 0.777738</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>gyd</th>\n",
        "      <td> 0.333371</td>\n",
        "      <td>-0.723412</td>\n",
        "      <td>-0.561287</td>\n",
        "      <td> 0.916805</td>\n",
        "      <td>-0.128698</td>\n",
        "      <td> 0.763326</td>\n",
        "      <td>-0.456701</td>\n",
        "      <td> 0.021901</td>\n",
        "      <td>-0.557773</td>\n",
        "      <td> 0.903610</td>\n",
        "      <td>-0.848233</td>\n",
        "      <td>-0.082451</td>\n",
        "      <td> 0.942143</td>\n",
        "      <td>-0.006609</td>\n",
        "      <td>-0.356368</td>\n",
        "      <td> 0.792263</td>\n",
        "      <td> 0.670593</td>\n",
        "      <td> 0.155681</td>\n",
        "      <td> 0.761953</td>\n",
        "      <td> 0.878185</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>hkd</th>\n",
        "      <td>-0.060102</td>\n",
        "      <td>-0.109962</td>\n",
        "      <td>-0.114604</td>\n",
        "      <td> 0.429178</td>\n",
        "      <td>-0.435259</td>\n",
        "      <td> 0.210980</td>\n",
        "      <td> 0.168034</td>\n",
        "      <td>-0.215658</td>\n",
        "      <td>-0.406000</td>\n",
        "      <td> 0.209758</td>\n",
        "      <td>-0.378072</td>\n",
        "      <td>-0.065085</td>\n",
        "      <td> 0.559695</td>\n",
        "      <td>-0.168752</td>\n",
        "      <td> 0.319890</td>\n",
        "      <td> 0.555415</td>\n",
        "      <td> 0.535638</td>\n",
        "      <td>-0.064183</td>\n",
        "      <td> 0.223727</td>\n",
        "      <td> 0.478450</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>hnl</th>\n",
        "      <td> 0.322095</td>\n",
        "      <td>-0.831364</td>\n",
        "      <td>-0.693674</td>\n",
        "      <td> 0.837562</td>\n",
        "      <td> 0.091320</td>\n",
        "      <td> 0.869564</td>\n",
        "      <td>-0.452145</td>\n",
        "      <td>-0.015723</td>\n",
        "      <td>-0.433512</td>\n",
        "      <td> 0.949312</td>\n",
        "      <td>-0.919763</td>\n",
        "      <td>-0.094759</td>\n",
        "      <td> 0.943566</td>\n",
        "      <td> 0.013319</td>\n",
        "      <td>-0.303709</td>\n",
        "      <td> 0.889753</td>\n",
        "      <td> 0.679364</td>\n",
        "      <td> 0.236043</td>\n",
        "      <td> 0.742417</td>\n",
        "      <td> 0.844837</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>hrk</th>\n",
        "      <td>-0.272419</td>\n",
        "      <td> 0.761040</td>\n",
        "      <td> 0.859400</td>\n",
        "      <td>-0.209352</td>\n",
        "      <td>-0.637587</td>\n",
        "      <td>-0.599853</td>\n",
        "      <td> 0.792714</td>\n",
        "      <td>-0.179936</td>\n",
        "      <td>-0.209158</td>\n",
        "      <td>-0.512127</td>\n",
        "      <td> 0.512571</td>\n",
        "      <td> 0.128002</td>\n",
        "      <td>-0.291571</td>\n",
        "      <td>-0.149459</td>\n",
        "      <td> 0.695352</td>\n",
        "      <td>-0.281486</td>\n",
        "      <td> 0.183944</td>\n",
        "      <td>-0.521732</td>\n",
        "      <td> 0.004179</td>\n",
        "      <td>-0.300486</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>htg</th>\n",
        "      <td> 0.327099</td>\n",
        "      <td>-0.877362</td>\n",
        "      <td>-0.700920</td>\n",
        "      <td> 0.797521</td>\n",
        "      <td> 0.151423</td>\n",
        "      <td> 0.934521</td>\n",
        "      <td>-0.494790</td>\n",
        "      <td> 0.014867</td>\n",
        "      <td>-0.383936</td>\n",
        "      <td> 0.927642</td>\n",
        "      <td>-0.964697</td>\n",
        "      <td>-0.091978</td>\n",
        "      <td> 0.927157</td>\n",
        "      <td>-0.033257</td>\n",
        "      <td>-0.351518</td>\n",
        "      <td> 0.903723</td>\n",
        "      <td> 0.677004</td>\n",
        "      <td> 0.200875</td>\n",
        "      <td> 0.688782</td>\n",
        "      <td> 0.779286</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>huf</th>\n",
        "      <td>-0.022811</td>\n",
        "      <td> 0.421512</td>\n",
        "      <td> 0.672464</td>\n",
        "      <td> 0.157765</td>\n",
        "      <td>-0.608683</td>\n",
        "      <td>-0.267795</td>\n",
        "      <td> 0.526422</td>\n",
        "      <td>-0.070513</td>\n",
        "      <td>-0.348501</td>\n",
        "      <td>-0.069417</td>\n",
        "      <td> 0.175576</td>\n",
        "      <td> 0.103084</td>\n",
        "      <td> 0.092769</td>\n",
        "      <td>-0.114273</td>\n",
        "      <td> 0.409771</td>\n",
        "      <td>-0.043406</td>\n",
        "      <td> 0.397528</td>\n",
        "      <td>-0.394168</td>\n",
        "      <td> 0.433527</td>\n",
        "      <td> 0.168849</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>idr</th>\n",
        "      <td> 0.184041</td>\n",
        "      <td>-0.407302</td>\n",
        "      <td>-0.204383</td>\n",
        "      <td> 0.669713</td>\n",
        "      <td>-0.191544</td>\n",
        "      <td> 0.465468</td>\n",
        "      <td>-0.025229</td>\n",
        "      <td>-0.117823</td>\n",
        "      <td>-0.476360</td>\n",
        "      <td> 0.579276</td>\n",
        "      <td>-0.507197</td>\n",
        "      <td>-0.059701</td>\n",
        "      <td> 0.808894</td>\n",
        "      <td>-0.040356</td>\n",
        "      <td> 0.137901</td>\n",
        "      <td> 0.678737</td>\n",
        "      <td> 0.671590</td>\n",
        "      <td> 0.059336</td>\n",
        "      <td> 0.698549</td>\n",
        "      <td> 0.838638</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ils</th>\n",
        "      <td>-0.081974</td>\n",
        "      <td> 0.015911</td>\n",
        "      <td> 0.183158</td>\n",
        "      <td> 0.279310</td>\n",
        "      <td>-0.250584</td>\n",
        "      <td> 0.159134</td>\n",
        "      <td> 0.402300</td>\n",
        "      <td>-0.317575</td>\n",
        "      <td>-0.392211</td>\n",
        "      <td> 0.023592</td>\n",
        "      <td>-0.117854</td>\n",
        "      <td>-0.034320</td>\n",
        "      <td> 0.535477</td>\n",
        "      <td>-0.097994</td>\n",
        "      <td> 0.700928</td>\n",
        "      <td> 0.576100</td>\n",
        "      <td> 0.701482</td>\n",
        "      <td>-0.228711</td>\n",
        "      <td> 0.295341</td>\n",
        "      <td> 0.502137</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>inr</th>\n",
        "      <td> 0.350360</td>\n",
        "      <td>-0.425962</td>\n",
        "      <td>-0.131864</td>\n",
        "      <td> 0.722619</td>\n",
        "      <td>-0.112216</td>\n",
        "      <td> 0.597434</td>\n",
        "      <td>-0.185822</td>\n",
        "      <td> 0.013017</td>\n",
        "      <td>-0.462807</td>\n",
        "      <td> 0.707862</td>\n",
        "      <td>-0.556329</td>\n",
        "      <td>-0.009622</td>\n",
        "      <td> 0.819149</td>\n",
        "      <td>-0.050317</td>\n",
        "      <td>-0.258316</td>\n",
        "      <td> 0.537012</td>\n",
        "      <td> 0.647518</td>\n",
        "      <td>-0.089928</td>\n",
        "      <td> 0.977068</td>\n",
        "      <td> 0.900253</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>iqd</th>\n",
        "      <td> 0.094215</td>\n",
        "      <td>-0.519443</td>\n",
        "      <td>-0.301567</td>\n",
        "      <td> 0.726516</td>\n",
        "      <td>-0.203779</td>\n",
        "      <td> 0.615711</td>\n",
        "      <td>-0.033666</td>\n",
        "      <td>-0.105346</td>\n",
        "      <td>-0.467952</td>\n",
        "      <td> 0.703757</td>\n",
        "      <td>-0.770251</td>\n",
        "      <td>-0.036130</td>\n",
        "      <td> 0.416672</td>\n",
        "      <td>-0.058239</td>\n",
        "      <td> 0.060555</td>\n",
        "      <td> 0.818672</td>\n",
        "      <td> 0.771284</td>\n",
        "      <td> 0.061587</td>\n",
        "      <td> 0.675666</td>\n",
        "      <td> 0.601116</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>irr</th>\n",
        "      <td> 0.284378</td>\n",
        "      <td>-0.777097</td>\n",
        "      <td>-0.608830</td>\n",
        "      <td> 0.675498</td>\n",
        "      <td> 0.309622</td>\n",
        "      <td> 0.902861</td>\n",
        "      <td>-0.455765</td>\n",
        "      <td> 0.044134</td>\n",
        "      <td>-0.346917</td>\n",
        "      <td> 0.818273</td>\n",
        "      <td>-0.789208</td>\n",
        "      <td>-0.091319</td>\n",
        "      <td> 0.817895</td>\n",
        "      <td> 0.022585</td>\n",
        "      <td>-0.408583</td>\n",
        "      <td> 0.747172</td>\n",
        "      <td> 0.543537</td>\n",
        "      <td> 0.110337</td>\n",
        "      <td> 0.707960</td>\n",
        "      <td> 0.787432</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>isk</th>\n",
        "      <td> 0.534270</td>\n",
        "      <td>-0.303555</td>\n",
        "      <td>-0.060160</td>\n",
        "      <td> 0.496844</td>\n",
        "      <td>-0.026104</td>\n",
        "      <td> 0.375547</td>\n",
        "      <td>-0.396357</td>\n",
        "      <td> 0.321293</td>\n",
        "      <td>-0.103961</td>\n",
        "      <td> 0.531127</td>\n",
        "      <td>-0.384884</td>\n",
        "      <td> 0.085736</td>\n",
        "      <td> 0.445136</td>\n",
        "      <td>-0.048437</td>\n",
        "      <td>-0.577675</td>\n",
        "      <td> 0.059158</td>\n",
        "      <td> 0.155944</td>\n",
        "      <td>-0.011955</td>\n",
        "      <td> 0.621578</td>\n",
        "      <td> 0.698772</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>148 rows \u00d7 148 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "          aed       afn       all       ang       aoa       ars       aud  \\\n",
        "aed  1.000000 -0.387602 -0.215562  0.318342  0.150919  0.345806 -0.429843   \n",
        "afn -0.387602  1.000000  0.850153 -0.658482 -0.386002 -0.880705  0.730158   \n",
        "all -0.215562  0.850153  1.000000 -0.493636 -0.391092 -0.726466  0.660349   \n",
        "ang  0.318342 -0.658482 -0.493636  1.000000 -0.064658  0.713985 -0.389470   \n",
        "aoa  0.150919 -0.386002 -0.391092 -0.064658  1.000000  0.391747 -0.487989   \n",
        "ars  0.345806 -0.880705 -0.726466  0.713985  0.391747  1.000000 -0.605686   \n",
        "aud -0.429843  0.730158  0.660349 -0.389470 -0.487989 -0.605686  1.000000   \n",
        "awg  0.204882 -0.116045 -0.001542 -0.009043  0.197351  0.082945 -0.289410   \n",
        "bbd -0.036890  0.191257  0.059841 -0.568945  0.212811 -0.289172 -0.033362   \n",
        "bdt  0.421365 -0.870072 -0.695829  0.827816  0.183662  0.885343 -0.660589   \n",
        "bgn -0.294120  0.844934  0.753363 -0.782344 -0.108598 -0.882117  0.519028   \n",
        "bhd  0.061025  0.124149  0.164095 -0.048932 -0.041409 -0.092293  0.094448   \n",
        "bif  0.296765 -0.748667 -0.607221  0.880685 -0.093057  0.829553 -0.321689   \n",
        "bmd  0.061892 -0.104156 -0.113611 -0.001264  0.085109  0.028990 -0.160546   \n",
        "bnd -0.472499  0.568621  0.509434 -0.292906 -0.409163 -0.470184  0.922478   \n",
        "bob  0.127788 -0.731615 -0.630260  0.751659  0.029092  0.813758 -0.219091   \n",
        "brl  0.031011 -0.342465 -0.222071  0.693543 -0.204268  0.548284  0.121693   \n",
        "bsd  0.117436 -0.432514 -0.556689  0.104828  0.106388  0.138454 -0.292971   \n",
        "btn  0.308459 -0.457017 -0.189262  0.739517 -0.108879  0.629170 -0.234422   \n",
        "bwp  0.369275 -0.656391 -0.484713  0.827118 -0.021772  0.730753 -0.322210   \n",
        "bzd -0.199241  0.540240  0.466551 -0.580590 -0.025197 -0.572888  0.206827   \n",
        "cad -0.453087  0.873579  0.791226 -0.550645 -0.398290 -0.731101  0.919697   \n",
        "chf -0.472706  0.781060  0.672975 -0.499283 -0.475194 -0.727928  0.920995   \n",
        "clp -0.011513 -0.100243  0.046357  0.471420 -0.313592  0.335740  0.361547   \n",
        "cny -0.549585  0.680301  0.512385 -0.569450 -0.276438 -0.647739  0.802625   \n",
        "cop  0.009631 -0.433227 -0.322577  0.650822 -0.205107  0.541071  0.127890   \n",
        "crc  0.348785 -0.885344 -0.762344  0.846919  0.125663  0.895699 -0.506377   \n",
        "cup  0.285120 -0.637896 -0.554477  0.146498  0.663040  0.532399 -0.637070   \n",
        "cve -0.281813  0.646713  0.774032 -0.167737 -0.562190 -0.464940  0.800037   \n",
        "czk -0.423761  0.885967  0.878067 -0.534048 -0.482354 -0.784530  0.867029   \n",
        "djf  0.365357 -0.700002 -0.563285  0.733248  0.093883  0.648250 -0.472857   \n",
        "dkk -0.267451  0.792155  0.891255 -0.293724 -0.606721 -0.643265  0.807941   \n",
        "dop  0.372574 -0.954266 -0.803434  0.736741  0.317621  0.922816 -0.666496   \n",
        "dzd  0.152865 -0.412030 -0.116705  0.813462 -0.310091  0.593780 -0.055478   \n",
        "ecs  0.226241 -0.590237 -0.429281  0.848856 -0.273312  0.673744 -0.130047   \n",
        "eek -0.306820  0.824974  0.889314 -0.330187 -0.609808 -0.678623  0.834215   \n",
        "egp  0.341283 -0.891633 -0.725893  0.752436  0.278387  0.954123 -0.538448   \n",
        "etb  0.497018 -0.729454 -0.469469  0.786349  0.125252  0.754809 -0.618847   \n",
        "eur -0.269434  0.892211  0.937373 -0.446876 -0.712588 -0.745535  0.830175   \n",
        "fjd  0.035610  0.283634  0.483270  0.266024 -0.510935 -0.127003  0.473197   \n",
        "fkp  0.290632  0.291960  0.390765 -0.081465 -0.305917 -0.400538 -0.015974   \n",
        "frf  0.078423  0.034048  0.362338 -0.056027  0.016188 -0.036185  0.038247   \n",
        "gbp  0.198093  0.418628  0.629831 -0.040627 -0.354506 -0.300839  0.263544   \n",
        "ghc  0.238646 -0.662917 -0.488464  0.838368 -0.177579  0.768597 -0.198624   \n",
        "gip  0.242070  0.406983  0.620892 -0.042861 -0.345612 -0.292430  0.261222   \n",
        "gmd  0.337680 -0.855159 -0.669550  0.824441  0.147401  0.932108 -0.449617   \n",
        "gnf  0.420406 -0.880266 -0.725866  0.839325  0.111805  0.868082 -0.621521   \n",
        "gtq  0.221984 -0.561640 -0.389876  0.871405 -0.252027  0.651091 -0.152613   \n",
        "gyd  0.333371 -0.723412 -0.561287  0.916805 -0.128698  0.763326 -0.456701   \n",
        "hkd -0.060102 -0.109962 -0.114604  0.429178 -0.435259  0.210980  0.168034   \n",
        "hnl  0.322095 -0.831364 -0.693674  0.837562  0.091320  0.869564 -0.452145   \n",
        "hrk -0.272419  0.761040  0.859400 -0.209352 -0.637587 -0.599853  0.792714   \n",
        "htg  0.327099 -0.877362 -0.700920  0.797521  0.151423  0.934521 -0.494790   \n",
        "huf -0.022811  0.421512  0.672464  0.157765 -0.608683 -0.267795  0.526422   \n",
        "idr  0.184041 -0.407302 -0.204383  0.669713 -0.191544  0.465468 -0.025229   \n",
        "ils -0.081974  0.015911  0.183158  0.279310 -0.250584  0.159134  0.402300   \n",
        "inr  0.350360 -0.425962 -0.131864  0.722619 -0.112216  0.597434 -0.185822   \n",
        "iqd  0.094215 -0.519443 -0.301567  0.726516 -0.203779  0.615711 -0.033666   \n",
        "irr  0.284378 -0.777097 -0.608830  0.675498  0.309622  0.902861 -0.455765   \n",
        "isk  0.534270 -0.303555 -0.060160  0.496844 -0.026104  0.375547 -0.396357   \n",
        "          ...       ...       ...       ...       ...       ...       ...   \n",
        "\n",
        "          awg       bbd       bdt       bgn       bhd       bif       bmd  \\\n",
        "aed  0.204882 -0.036890  0.421365 -0.294120  0.061025  0.296765  0.061892   \n",
        "afn -0.116045  0.191257 -0.870072  0.844934  0.124149 -0.748667 -0.104156   \n",
        "all -0.001542  0.059841 -0.695829  0.753363  0.164095 -0.607221 -0.113611   \n",
        "ang -0.009043 -0.568945  0.827816 -0.782344 -0.048932  0.880685 -0.001264   \n",
        "aoa  0.197351  0.212811  0.183662 -0.108598 -0.041409 -0.093057  0.085109   \n",
        "ars  0.082945 -0.289172  0.885343 -0.882117 -0.092293  0.829553  0.028990   \n",
        "aud -0.289410 -0.033362 -0.660589  0.519028  0.094448 -0.321689 -0.160546   \n",
        "awg  1.000000  0.124027  0.098901 -0.022411  0.009140 -0.041812  0.032224   \n",
        "bbd  0.124027  1.000000 -0.340147  0.308121  0.074803 -0.499189  0.067200   \n",
        "bdt  0.098901 -0.340147  1.000000 -0.908591 -0.109724  0.911894  0.037454   \n",
        "bgn -0.022411  0.308121 -0.908591  1.000000  0.108716 -0.921780  0.000407   \n",
        "bhd  0.009140  0.074803 -0.109724  0.108716  1.000000 -0.085641 -0.040208   \n",
        "bif -0.041812 -0.499189  0.911894 -0.921780 -0.085641  1.000000 -0.039498   \n",
        "bmd  0.032224  0.067200  0.037454  0.000407 -0.040208 -0.039498  1.000000   \n",
        "bnd -0.359452 -0.091067 -0.584562  0.403354  0.072872 -0.240366 -0.131807   \n",
        "bob -0.147456 -0.463276  0.771226 -0.874450 -0.098268  0.897580 -0.042271   \n",
        "brl -0.236472 -0.577734  0.555676 -0.624626 -0.011610  0.785959 -0.143303   \n",
        "bsd -0.039029  0.071432  0.271985 -0.273341 -0.221818  0.154794  0.183243   \n",
        "btn -0.009498 -0.471832  0.738603 -0.603399  0.006997  0.791384 -0.049115   \n",
        "bwp  0.090142 -0.432564  0.897028 -0.763768 -0.086353  0.902843 -0.001988   \n",
        "bzd  0.077406  0.306965 -0.571049  0.619528  0.147967 -0.632425  0.037079   \n",
        "cad -0.238847  0.067953 -0.802544  0.692853  0.154649 -0.539977 -0.154205   \n",
        "chf -0.269338  0.031152 -0.774685  0.638527  0.069894 -0.418038 -0.109487   \n",
        "clp -0.256289 -0.466367  0.233044 -0.385934  0.007332  0.672774 -0.233081   \n",
        "cny -0.291698  0.114466 -0.772915  0.574869  0.065915 -0.549888 -0.086805   \n",
        "cop -0.247805 -0.494096  0.563507 -0.667506 -0.041960  0.784924 -0.102921   \n",
        "crc  0.003518 -0.389607  0.961488 -0.942022 -0.104512  0.947301  0.034966   \n",
        "cup  0.204727  0.201305  0.398836 -0.313963 -0.100620  0.223209  0.162909   \n",
        "cve -0.211071 -0.260553 -0.440949  0.389292  0.122245 -0.177614 -0.173909   \n",
        "czk -0.212554  0.006670 -0.791373  0.741821  0.124666 -0.600789 -0.112132   \n",
        "djf  0.050697 -0.393457  0.803719 -0.619773 -0.086952  0.712739  0.052515   \n",
        "dkk -0.153512 -0.125295 -0.612471  0.593182  0.117878 -0.223099 -0.145687   \n",
        "dop  0.059636 -0.298306  0.910653 -0.885294 -0.102793  0.846984  0.069948   \n",
        "dzd -0.062925 -0.586596  0.673636 -0.639105 -0.038256  0.688973 -0.100741   \n",
        "ecs -0.121210 -0.558198  0.789424 -0.801406 -0.077157  0.958210 -0.048470   \n",
        "eek -0.152937 -0.116934 -0.620285  0.590253  0.136461 -0.413347 -0.147079   \n",
        "egp  0.037340 -0.345088  0.907456 -0.927112 -0.104884  0.875006 -0.005835   \n",
        "etb  0.217501 -0.320727  0.884619 -0.708321 -0.056498  0.816481  0.043717   \n",
        "eur -0.169452 -0.255841 -0.771663  0.761817  0.115989 -0.670708 -0.160867   \n",
        "fjd -0.035760 -0.289542  0.049558  0.016993  0.118295  0.228282 -0.139000   \n",
        "fkp  0.236614  0.179989 -0.107565  0.275523  0.128592 -0.225664  0.032721   \n",
        "frf  0.009866  0.042123 -0.219296  0.267130 -0.028498  0.357084  0.003374   \n",
        "gbp  0.177207 -0.011851 -0.163598  0.262560  0.187614 -0.190560 -0.105645   \n",
        "ghc -0.066709 -0.513935  0.859103 -0.897746 -0.078509  0.968479 -0.074814   \n",
        "gip  0.178861 -0.016360 -0.159610  0.257145  0.178483 -0.149335 -0.105253   \n",
        "gmd  0.008638 -0.410544  0.925727 -0.943172 -0.086023  0.940376 -0.014428   \n",
        "gnf  0.086621 -0.364075  0.986562 -0.905172 -0.113438  0.906936  0.057952   \n",
        "gtq -0.101397 -0.572989  0.745386 -0.753727 -0.029111  0.920064 -0.064549   \n",
        "gyd  0.021901 -0.557773  0.903610 -0.848233 -0.082451  0.942143 -0.006609   \n",
        "hkd -0.215658 -0.406000  0.209758 -0.378072 -0.065085  0.559695 -0.168752   \n",
        "hnl -0.015723 -0.433512  0.949312 -0.919763 -0.094759  0.943566  0.013319   \n",
        "hrk -0.179936 -0.209158 -0.512127  0.512571  0.128002 -0.291571 -0.149459   \n",
        "htg  0.014867 -0.383936  0.927642 -0.964697 -0.091978  0.927157 -0.033257   \n",
        "huf -0.070513 -0.348501 -0.069417  0.175576  0.103084  0.092769 -0.114273   \n",
        "idr -0.117823 -0.476360  0.579276 -0.507197 -0.059701  0.808894 -0.040356   \n",
        "ils -0.317575 -0.392211  0.023592 -0.117854 -0.034320  0.535477 -0.097994   \n",
        "inr  0.013017 -0.462807  0.707862 -0.556329 -0.009622  0.819149 -0.050317   \n",
        "iqd -0.105346 -0.467952  0.703757 -0.770251 -0.036130  0.416672 -0.058239   \n",
        "irr  0.044134 -0.346917  0.818273 -0.789208 -0.091319  0.817895  0.022585   \n",
        "isk  0.321293 -0.103961  0.531127 -0.384884  0.085736  0.445136 -0.048437   \n",
        "          ...       ...       ...       ...       ...       ...       ...   \n",
        "\n",
        "          bnd       bob       brl       bsd       btn       bwp      \n",
        "aed -0.472499  0.127788  0.031011  0.117436  0.308459  0.369275 ...  \n",
        "afn  0.568621 -0.731615 -0.342465 -0.432514 -0.457017 -0.656391 ...  \n",
        "all  0.509434 -0.630260 -0.222071 -0.556689 -0.189262 -0.484713 ...  \n",
        "ang -0.292906  0.751659  0.693543  0.104828  0.739517  0.827118 ...  \n",
        "aoa -0.409163  0.029092 -0.204268  0.106388 -0.108879 -0.021772 ...  \n",
        "ars -0.470184  0.813758  0.548284  0.138454  0.629170  0.730753 ...  \n",
        "aud  0.922478 -0.219091  0.121693 -0.292971 -0.234422 -0.322210 ...  \n",
        "awg -0.359452 -0.147456 -0.236472 -0.039029 -0.009498  0.090142 ...  \n",
        "bbd -0.091067 -0.463276 -0.577734  0.071432 -0.471832 -0.432564 ...  \n",
        "bdt -0.584562  0.771226  0.555676  0.271985  0.738603  0.897028 ...  \n",
        "bgn  0.403354 -0.874450 -0.624626 -0.273341 -0.603399 -0.763768 ...  \n",
        "bhd  0.072872 -0.098268 -0.011610 -0.221818  0.006997 -0.086353 ...  \n",
        "bif -0.240366  0.897580  0.785959  0.154794  0.791384  0.902843 ...  \n",
        "bmd -0.131807 -0.042271 -0.143303  0.183243 -0.049115 -0.001988 ...  \n",
        "bnd  1.000000 -0.000410  0.226425 -0.246194 -0.253331 -0.435712 ...  \n",
        "bob -0.000410  1.000000  0.816374  0.153895  0.588403  0.640485 ...  \n",
        "brl  0.226425  0.816374  1.000000 -0.138637  0.683922  0.523793 ...  \n",
        "bsd -0.246194  0.153895 -0.138637  1.000000 -0.074173  0.191240 ...  \n",
        "btn -0.253331  0.588403  0.683922 -0.074173  1.000000  0.831562 ...  \n",
        "bwp -0.435712  0.640485  0.523793  0.191240  0.831562  1.000000 ...  \n",
        "bzd  0.061620 -0.661113 -0.529474 -0.189046 -0.385241 -0.489066 ...  \n",
        "cad  0.833482 -0.442866 -0.032108 -0.433541 -0.345770 -0.545858 ...  \n",
        "chf  0.900661 -0.330737 -0.062455 -0.244996 -0.454563 -0.400865 ...  \n",
        "clp  0.510523  0.647831  0.864075 -0.213372  0.534517  0.586455 ...  \n",
        "cny  0.885626 -0.252936 -0.059130 -0.239085 -0.608535 -0.683191 ...  \n",
        "cop  0.300819  0.882521  0.912125  0.034106  0.545439  0.485731 ...  \n",
        "crc -0.360269  0.885446  0.634442  0.308179  0.708035  0.858453 ...  \n",
        "cup -0.537177  0.245808 -0.245160  0.339180  0.122099  0.293874 ...  \n",
        "cve  0.775205 -0.108349  0.321730 -0.498408  0.045690 -0.286904 ...  \n",
        "czk  0.793116 -0.478516 -0.076543 -0.451483 -0.318893 -0.637217 ...  \n",
        "djf -0.416053  0.606786  0.455050  0.266779  0.706562  0.638934 ...  \n",
        "dkk  0.720488 -0.326513  0.056700 -0.500497 -0.100039 -0.126269 ...  \n",
        "dop -0.501796  0.807447  0.505837  0.337840  0.619557  0.725782 ...  \n",
        "dzd -0.028709  0.727086  0.838841 -0.153389  0.843791  0.766005 ...  \n",
        "ecs -0.015757  0.866850  0.843678  0.099525  0.757371  0.900966 ...  \n",
        "eek  0.720467 -0.376009  0.052290 -0.511686 -0.142538 -0.402063 ...  \n",
        "egp -0.421141  0.871999  0.627184  0.198952  0.679301  0.749145 ...  \n",
        "etb -0.683150  0.525198  0.359795  0.119904  0.797625  0.889520 ...  \n",
        "eur  0.723654 -0.403427  0.423902 -0.509193 -0.018739 -0.418475 ...  \n",
        "fjd  0.374787  0.059945  0.440704 -0.311888  0.498509  0.316547 ...  \n",
        "fkp -0.268398 -0.549563 -0.315953  0.003070  0.082461  0.064514 ...  \n",
        "frf  0.150844  0.019529 -0.275947 -0.004960  0.070580  0.552877 ...  \n",
        "gbp  0.008118 -0.415807 -0.025130 -0.404481  0.196157 -0.057143 ...  \n",
        "ghc -0.130454  0.887924  0.804984  0.111157  0.805154  0.907353 ...  \n",
        "gip  0.010071 -0.406151 -0.021948 -0.386169  0.190944  0.060706 ...  \n",
        "gmd -0.334220  0.915588  0.720269  0.161537  0.740525  0.817723 ...  \n",
        "gnf -0.530253  0.784254  0.511606  0.313527  0.719367  0.900801 ...  \n",
        "gtq -0.034800  0.815004  0.849479  0.079950  0.767544  0.777738 ...  \n",
        "gyd -0.356368  0.792263  0.670593  0.155681  0.761953  0.878185 ...  \n",
        "hkd  0.319890  0.555415  0.535638 -0.064183  0.223727  0.478450 ...  \n",
        "hnl -0.303709  0.889753  0.679364  0.236043  0.742417  0.844837 ...  \n",
        "hrk  0.695352 -0.281486  0.183944 -0.521732  0.004179 -0.300486 ...  \n",
        "htg -0.351518  0.903723  0.677004  0.200875  0.688782  0.779286 ...  \n",
        "huf  0.409771 -0.043406  0.397528 -0.394168  0.433527  0.168849 ...  \n",
        "idr  0.137901  0.678737  0.671590  0.059336  0.698549  0.838638 ...  \n",
        "ils  0.700928  0.576100  0.701482 -0.228711  0.295341  0.502137 ...  \n",
        "inr -0.258316  0.537012  0.647518 -0.089928  0.977068  0.900253 ...  \n",
        "iqd  0.060555  0.818672  0.771284  0.061587  0.675666  0.601116 ...  \n",
        "irr -0.408583  0.747172  0.543537  0.110337  0.707960  0.787432 ...  \n",
        "isk -0.577675  0.059158  0.155944 -0.011955  0.621578  0.698772 ...  \n",
        "          ...       ...       ...       ...       ...       ...      \n",
        "\n",
        "[148 rows x 148 columns]"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print 'Expected Value'\n",
      "from IPython.display import display, Math, Latex\n",
      "display(Math(r'E(x) = \\sum_{1}^{n} x_{i} p(x_{i})'))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Expected Value\n"
       ]
      },
      {
       "latex": [
        "$$E(x) = \\sum_{1}^{n} x_{i} p(x_{i})$$"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Math at 0xa026eec>"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import display, Math, Latex\n",
      "display(Math(r'E[P]=\\begin{pmatrix} \\alpha_{1} & \\alpha_{2} & \\cdots & \\alpha_{N} \\end{pmatrix} \\begin{pmatrix} E[r_{1}] \\\\ E[r_{2}] \\\\ \\vdots \\\\ E[r_{N}] \\end{pmatrix}'))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "latex": [
        "$$E[P]=\\begin{pmatrix} \\alpha_{1} & \\alpha_{2} & \\cdots & \\alpha_{N} \\end{pmatrix} \\begin{pmatrix} E[r_{1}] \\\\ E[r_{2}] \\\\ \\vdots \\\\ E[r_{N}] \\end{pmatrix}$$"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Math at 0xa07c7ec>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "from IPython.display import display, Math, Latex\n",
      "display(Math(r'\\sigma_{P}^2 = \\begin{pmatrix} \\alpha_{1} & \\alpha_{2} & \\cdots & \\alpha_{N} \\end{pmatrix} \\begin{pmatrix} \\sigma_{1}^2 & \\sigma_{1,2} & \\cdots & \\sigma_{1,n} \\\\ \\sigma_{2,1} & \\sigma_{2}^2 & \\cdots & \\sigma_{2,n} \\\\ \\vdots  & \\vdots  & \\ddots & \\vdots  \\\\ \\sigma_{m,1} & \\sigma_{m,2} & \\cdots & \\sigma_{N}^2 \\end{pmatrix} \\begin{pmatrix} \\alpha_{1} \\\\ \\alpha_{2} \\\\ \\vdots \\\\ \\alpha_{N} \\end{pmatrix}'))\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "latex": [
        "$$\\sigma_{P}^2 = \\begin{pmatrix} \\alpha_{1} & \\alpha_{2} & \\cdots & \\alpha_{N} \\end{pmatrix} \\begin{pmatrix} \\sigma_{1}^2 & \\sigma_{1,2} & \\cdots & \\sigma_{1,n} \\\\ \\sigma_{2,1} & \\sigma_{2}^2 & \\cdots & \\sigma_{2,n} \\\\ \\vdots  & \\vdots  & \\ddots & \\vdots  \\\\ \\sigma_{m,1} & \\sigma_{m,2} & \\cdots & \\sigma_{N}^2 \\end{pmatrix} \\begin{pmatrix} \\alpha_{1} \\\\ \\alpha_{2} \\\\ \\vdots \\\\ \\alpha_{N} \\end{pmatrix}$$"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Math at 0xa07c6cc>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import display, Math, Latex\n",
      "display(Math(r'\\begin{pmatrix} \\alpha_{1} \\\\ \\alpha_{2} \\\\ \\cdots \\\\ \\alpha_{n} \\end{pmatrix}'))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "latex": [
        "$$\\begin{pmatrix} \\alpha_{1} \\\\ \\alpha_{2} \\\\ \\cdots \\\\ \\alpha_{n} \\end{pmatrix}$$"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Math at 0xa07c38c>"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ret=ccy_matrix.pct_change()\n",
      "#choose the quantile\n",
      "quantile=0.05\n",
      "#the vol window\n",
      "volwindow=50\n",
      "#and the Var window for rolling \n",
      "varwindow=250"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import YouTubeVideo\n",
      "from datetime import timedelta\n",
      "start=int(timedelta(hours=0, minutes=0, seconds=0).total_seconds())\n",
      "end=int(timedelta(hours=0, minutes=0, seconds=100).total_seconds())\n",
      "YouTubeVideo(\"9-3FY_WszOg\", start=start, end=end, autoplay=0, theme=\"light\", color=\"blue\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "\n",
        "        <iframe\n",
        "            width=\"400\"\n",
        "            height=300\"\n",
        "            src=\"https://www.youtube.com/embed/9-3FY_WszOg?color=blue&start=0&theme=light&end=100&autoplay=0\"\n",
        "            frameborder=\"0\"\n",
        "            allowfullscreen\n",
        "        ></iframe>\n",
        "        "
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 37,
       "text": [
        "<IPython.lib.display.YouTubeVideo at 0xb38f96c>"
       ]
      }
     ],
     "prompt_number": 37
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ccy_matrix['aed']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 38,
       "text": [
        "Date\n",
        "1991-03-05   NaN\n",
        "1991-03-06   NaN\n",
        "1991-03-07   NaN\n",
        "1991-03-08   NaN\n",
        "1991-03-11   NaN\n",
        "1991-03-12   NaN\n",
        "1991-03-13   NaN\n",
        "1991-03-14   NaN\n",
        "1991-03-15   NaN\n",
        "1991-03-18   NaN\n",
        "1991-03-19   NaN\n",
        "1991-03-20   NaN\n",
        "1991-03-21   NaN\n",
        "1991-03-22   NaN\n",
        "1991-03-25   NaN\n",
        "...\n",
        "2014-05-06    0.272375\n",
        "2014-05-07    0.272375\n",
        "2014-05-08    0.272375\n",
        "2014-05-09    0.272375\n",
        "2014-05-12    0.272375\n",
        "2014-05-13    0.272375\n",
        "2014-05-14    0.272375\n",
        "2014-05-15    0.272375\n",
        "2014-05-16    0.272375\n",
        "2014-05-19    0.272375\n",
        "2014-05-20    0.272375\n",
        "2014-05-21    0.272375\n",
        "2014-05-22    0.272375\n",
        "2014-05-23    0.272375\n",
        "2014-05-26    0.272375\n",
        "Freq: B, Name: aed, Length: 6060"
       ]
      }
     ],
     "prompt_number": 38
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a=ccy_matrix.columns\n",
      "print a"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Index([u'aed', u'afn', u'all', u'ang', u'aoa', u'ars', u'aud', u'awg', u'bbd', u'bdt', u'bgn', u'bhd', u'bif', u'bmd', u'bnd', u'bob', u'brl', u'bsd', u'btn', u'bwp', u'bzd', u'cad', u'chf', u'clp', u'cny', u'cop', u'crc', u'cup', u'cve', u'czk', u'djf', u'dkk', u'dop', u'dzd', u'ecs', u'eek', u'egp', u'etb', u'eur', u'fjd', u'fkp', u'frf', u'gbp', u'ghc', u'gip', u'gmd', u'gnf', u'gtq', u'gyd', u'hkd', u'hnl', u'hrk', u'htg', u'huf', u'idr', u'ils', u'inr', u'iqd', u'irr', u'isk', u'jmd', u'jod', u'jpy', u'kes', u'khr', u'kmf', u'kpw', u'krw', u'kwd', u'kyd', u'kzt', u'lak', u'lbp', u'lkr', u'lrd', u'lsl', u'ltl', u'lvl', u'lyd', u'mad', u'mgf', u'mmk', u'mnt', u'mop', u'mro', u'mur', u'mvr', u'mwk', u'mxn', u'myr', u'mzm', u'mzn', u'nad', u'ngn', u'nio', u'nok', u'npr', u'nzd', u'omr', u'pab', ...], dtype='object')\n"
       ]
      }
     ],
     "prompt_number": 39
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ccy_matrix['jpy']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 40,
       "text": [
        "Date\n",
        "1991-03-05    0.007437\n",
        "1991-03-06    0.007335\n",
        "1991-03-07    0.007363\n",
        "1991-03-08    0.007335\n",
        "1991-03-11    0.007335\n",
        "1991-03-12    0.007335\n",
        "1991-03-13    0.007335\n",
        "1991-03-14    0.007363\n",
        "1991-03-15    0.007335\n",
        "1991-03-18    0.007335\n",
        "1991-03-19    0.007236\n",
        "1991-03-20    0.007236\n",
        "1991-03-21    0.007265\n",
        "1991-03-22    0.007265\n",
        "1991-03-25    0.007236\n",
        "...\n",
        "2014-05-06    0.009762\n",
        "2014-05-07    0.009762\n",
        "2014-05-08    0.009762\n",
        "2014-05-09    0.009762\n",
        "2014-05-12    0.009762\n",
        "2014-05-13    0.009762\n",
        "2014-05-14    0.009762\n",
        "2014-05-15    0.009762\n",
        "2014-05-16    0.009762\n",
        "2014-05-19    0.009861\n",
        "2014-05-20    0.009861\n",
        "2014-05-21    0.009861\n",
        "2014-05-22    0.009861\n",
        "2014-05-23    0.009838\n",
        "2014-05-26    0.009838\n",
        "Freq: B, Name: jpy, Length: 6060"
       ]
      }
     ],
     "prompt_number": 40
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ccy_matrix['eur']['2010-10-31':'2014-02-15'].plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 84,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x123c1cec>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ly4CHH3Y9Um9sNO+w4uKoFnJqqmvH72lERysSimGkbk9HqcycYbTPyZMUVN14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\nQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1157262c>"
       ]
      }
     ],
     "prompt_number": 84
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ret['jpy'].plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 42,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0xb1b320c>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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56y2EP/zQGV+J/ICtp59BSXyr1+Pr11t7hiGLj7e2ypff1iYOu+vTZrOhq8vz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\nfGQm0coQk20AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0xb3a162c>"
       ]
      }
     ],
     "prompt_number": 42
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from statsmodels.formula.api import ols\n",
      "lm1 = ols('jpy~aed', ccy_matrix).fit()\n",
      "lm1.summary()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<table class=\"simpletable\">\n",
        "<caption>OLS Regression Results</caption>\n",
        "<tr>\n",
        "  <th>Dep. Variable:</th>           <td>jpy</td>       <th>  R-squared:         </th>  <td>   0.263</td> \n",
        "</tr>\n",
        "<tr>\n",
        "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   0.263</td> \n",
        "</tr>\n",
        "<tr>\n",
        "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>   1825.</td> \n",
        "</tr>\n",
        "<tr>\n",
        "  <th>Date:</th>             <td>Sat, 07 Jun 2014</td> <th>  Prob (F-statistic):</th>   <td>  0.00</td>  \n",
        "</tr>\n",
        "<tr>\n",
        "  <th>Time:</th>                 <td>20:17:56</td>     <th>  Log-Likelihood:    </th>  <td>  26976.</td> \n",
        "</tr>\n",
        "<tr>\n",
        "  <th>No. Observations:</th>      <td>  5115</td>      <th>  AIC:               </th> <td>-5.395e+04</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>Df Residuals:</th>          <td>  5113</td>      <th>  BIC:               </th> <td>-5.393e+04</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>      <td> </td>    \n",
        "</tr>\n",
        "</table>\n",
        "<table class=\"simpletable\">\n",
        "<tr>\n",
        "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th> <th>[95.0% Conf. Int.]</th> \n",
        "</tr>\n",
        "<tr>\n",
        "  <th>Intercept</th> <td>    4.3032</td> <td>    0.101</td> <td>   42.816</td> <td> 0.000</td> <td>    4.106     4.500</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>aed</th>       <td>  -15.7668</td> <td>    0.369</td> <td>  -42.721</td> <td> 0.000</td> <td>  -16.490   -15.043</td>\n",
        "</tr>\n",
        "</table>\n",
        "<table class=\"simpletable\">\n",
        "<tr>\n",
        "  <th>Omnibus:</th>       <td>2582.831</td> <th>  Durbin-Watson:     </th> <td>   0.082</td> \n",
        "</tr>\n",
        "<tr>\n",
        "  <th>Prob(Omnibus):</th>  <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>69611.799</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>Skew:</th>           <td> 1.855</td>  <th>  Prob(JB):          </th> <td>    0.00</td> \n",
        "</tr>\n",
        "<tr>\n",
        "  <th>Kurtosis:</th>       <td>20.688</td>  <th>  Cond. No.          </th> <td>2.29e+04</td> \n",
        "</tr>\n",
        "</table>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 40,
       "text": [
        "<class 'statsmodels.iolib.summary.Summary'>\n",
        "\"\"\"\n",
        "                            OLS Regression Results                            \n",
        "==============================================================================\n",
        "Dep. Variable:                    jpy   R-squared:                       0.263\n",
        "Model:                            OLS   Adj. R-squared:                  0.263\n",
        "Method:                 Least Squares   F-statistic:                     1825.\n",
        "Date:                Sat, 07 Jun 2014   Prob (F-statistic):               0.00\n",
        "Time:                        20:17:56   Log-Likelihood:                 26976.\n",
        "No. Observations:                5115   AIC:                        -5.395e+04\n",
        "Df Residuals:                    5113   BIC:                        -5.393e+04\n",
        "Df Model:                           1                                         \n",
        "==============================================================================\n",
        "                 coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
        "------------------------------------------------------------------------------\n",
        "Intercept      4.3032      0.101     42.816      0.000         4.106     4.500\n",
        "aed          -15.7668      0.369    -42.721      0.000       -16.490   -15.043\n",
        "==============================================================================\n",
        "Omnibus:                     2582.831   Durbin-Watson:                   0.082\n",
        "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            69611.799\n",
        "Skew:                           1.855   Prob(JB):                         0.00\n",
        "Kurtosis:                      20.688   Cond. No.                     2.29e+04\n",
        "==============================================================================\n",
        "\n",
        "Warnings:\n",
        "[1] The condition number is large, 2.29e+04. This might indicate that there are\n",
        "strong multicollinearity or other numerical problems.\n",
        "\"\"\""
       ]
      }
     ],
     "prompt_number": 40
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ccy_matrix.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>aed</th>\n",
        "      <th>afn</th>\n",
        "      <th>all</th>\n",
        "      <th>ang</th>\n",
        "      <th>aoa</th>\n",
        "      <th>ars</th>\n",
        "      <th>aud</th>\n",
        "      <th>awg</th>\n",
        "      <th>bbd</th>\n",
        "      <th>bdt</th>\n",
        "      <th>bgn</th>\n",
        "      <th>bhd</th>\n",
        "      <th>bif</th>\n",
        "      <th>bmd</th>\n",
        "      <th>bnd</th>\n",
        "      <th>bob</th>\n",
        "      <th>brl</th>\n",
        "      <th>bsd</th>\n",
        "      <th>btn</th>\n",
        "      <th>bwp</th>\n",
        "      <th></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",
        "      <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>1991-03-05</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1991-03-06</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1991-03-07</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1991-03-08</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1991-03-11</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 148 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 44,
       "text": [
        "            aed  afn  all  ang  aoa  ars  aud  awg  bbd  bdt  bgn  bhd  bif  \\\n",
        "Date                                                                          \n",
        "1991-03-05  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
        "1991-03-06  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
        "1991-03-07  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
        "1991-03-08  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
        "1991-03-11  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
        "\n",
        "            bmd  bnd  bob  brl  bsd  btn  bwp      \n",
        "Date                                               \n",
        "1991-03-05  NaN  NaN  NaN  NaN  NaN  NaN  NaN ...  \n",
        "1991-03-06  NaN  NaN  NaN  NaN  NaN  NaN  NaN ...  \n",
        "1991-03-07  NaN  NaN  NaN  NaN  NaN  NaN  NaN ...  \n",
        "1991-03-08  NaN  NaN  NaN  NaN  NaN  NaN  NaN ...  \n",
        "1991-03-11  NaN  NaN  NaN  NaN  NaN  NaN  NaN ...  \n",
        "\n",
        "[5 rows x 148 columns]"
       ]
      }
     ],
     "prompt_number": 44
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#simple VaR using all the data\n",
      "unnormedquantile=pd.expanding_quantile(ccy_matrix['jpy'],quantile)\n",
      "\n",
      "#similar one using a rolling window \n",
      "unnormedquantileR=pd.rolling_quantile(ccy_matrix['jpy'],varwindow,quantile)\n",
      "\n",
      "#we can also normalize the returns by the vol\n",
      "vol=pd.rolling_std(ccy_matrix['jpy'],volwindow)*np.sqrt(256)\n",
      "unitvol=ccy_matrix['jpy']/vol\n",
      "\n",
      "#and get the expanding or rolling quantiles\n",
      "Var=pd.expanding_quantile(unitvol,quantile)\n",
      "VarR=pd.rolling_quantile(unitvol,varwindow,quantile)\n",
      "\n",
      "normedquantile=Var*vol\n",
      "normedquantileR=VarR*vol\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 46
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "VarR.plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 47,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0xe975ecc>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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dnfyLotWYGkOHKh8YSKl9AvBs/e67lf3wHD4MJCb2Lls1+wbCwwM3SsDdbv5Q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\nWBQScIIgCItCAk4QBGFR/j9Vg/HuM//GGwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0xe99560c>"
       ]
      }
     ],
     "prompt_number": 47
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "type(ccy_matrix)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 48,
       "text": [
        "pandas.core.frame.DataFrame"
       ]
      }
     ],
     "prompt_number": 48
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from stress import point_tweak\n",
      "from operator import add, mul\n",
      "tweakedCollection = point_tweak(ccy_matrix, 'eur', '2013-10-31', add, .001)\n",
      "plot(tweakedCollection.jpy)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 23,
       "text": [
        "[<matplotlib.lines.Line2D at 0xb42764c>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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z5wNfQdTX86QqoSAeeIB/nmvX+lYuT2N70Wlq8r878i5dgBEjpOfFxcCYMfxx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\nAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x107d19cc>"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sorted(globals().keys())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 81,
       "text": [
        "['ALLOW_THREADS',\n",
        " 'Annotation',\n",
        " 'Arrow',\n",
        " 'Artist',\n",
        " 'AutoLocator',\n",
        " 'Axes',\n",
        " 'BUFSIZE',\n",
        " 'Button',\n",
        " 'CLIP',\n",
        " 'Circle',\n",
        " 'ComplexWarning',\n",
        " 'DAILY',\n",
        " 'DataSource',\n",
        " 'DateFormatter',\n",
        " 'DateLocator',\n",
        " 'DayLocator',\n",
        " 'ERR_CALL',\n",
        " 'ERR_DEFAULT',\n",
        " 'ERR_DEFAULT2',\n",
        " 'ERR_IGNORE',\n",
        " 'ERR_LOG',\n",
        " 'ERR_PRINT',\n",
        " 'ERR_RAISE',\n",
        " 'ERR_WARN',\n",
        " 'FLOATING_POINT_SUPPORT',\n",
        " 'FPE_DIVIDEBYZERO',\n",
        " 'FPE_INVALID',\n",
        " 'FPE_OVERFLOW',\n",
        " 'FPE_UNDERFLOW',\n",
        " 'FR',\n",
        " 'False_',\n",
        " 'Figure',\n",
        " 'FigureCanvasBase',\n",
        " 'FixedFormatter',\n",
        " 'FixedLocator',\n",
        " 'FormatStrFormatter',\n",
        " 'Formatter',\n",
        " 'FuncFormatter',\n",
        " 'GridSpec',\n",
        " 'HOURLY',\n",
        " 'HourLocator',\n",
        " 'In',\n",
        " 'IndexDateFormatter',\n",
        " 'IndexLocator',\n",
        " 'Inf',\n",
        " 'Infinity',\n",
        " 'Latex',\n",
        " 'LinAlgError',\n",
        " 'Line2D',\n",
        " 'LinearLocator',\n",
        " 'Locator',\n",
        " 'LogFormatter',\n",
        " 'LogFormatterExponent',\n",
        " 'LogFormatterMathtext',\n",
        " 'LogLocator',\n",
        " 'MAXDIMS',\n",
        " 'MINUTELY',\n",
        " 'MO',\n",
        " 'MONTHLY',\n",
        " 'MachAr',\n",
        " 'Math',\n",
        " 'MaxNLocator',\n",
        " 'MinuteLocator',\n",
        " 'ModuleDeprecationWarning',\n",
        " 'MonthLocator',\n",
        " 'MultipleLocator',\n",
        " 'NAN',\n",
        " 'NINF',\n",
        " 'NZERO',\n",
        " 'NaN',\n",
        " 'Normalize',\n",
        " 'NullFormatter',\n",
        " 'NullLocator',\n",
        " 'Out',\n",
        " 'PINF',\n",
        " 'PZERO',\n",
        " 'PackageLoader',\n",
        " 'PolarAxes',\n",
        " 'Polygon',\n",
        " 'RAISE',\n",
        " 'RRuleLocator',\n",
        " 'RankWarning',\n",
        " 'Rectangle',\n",
        " 'SA',\n",
        " 'SECONDLY',\n",
        " 'SHIFT_DIVIDEBYZERO',\n",
        " 'SHIFT_INVALID',\n",
        " 'SHIFT_OVERFLOW',\n",
        " 'SHIFT_UNDERFLOW',\n",
        " 'SU',\n",
        " 'ScalarFormatter',\n",
        " 'ScalarType',\n",
        " 'SecondLocator',\n",
        " 'Slider',\n",
        " 'Subplot',\n",
        " 'SubplotTool',\n",
        " 'TH',\n",
        " 'TU',\n",
        " 'Tester',\n",
        " 'Text',\n",
        " 'TickHelper',\n",
        " 'True_',\n",
        " 'UFUNC_BUFSIZE_DEFAULT',\n",
        " 'UFUNC_PYVALS_NAME',\n",
        " 'Var',\n",
        " 'VarR',\n",
        " 'WE',\n",
        " 'WEEKLY',\n",
        " 'WRAP',\n",
        " 'WeekdayLocator',\n",
        " 'Widget',\n",
        " 'YEARLY',\n",
        " 'YearLocator',\n",
        " 'YouTubeVideo',\n",
        " '_',\n",
        " '_13',\n",
        " '_14',\n",
        " '_16',\n",
        " '_17',\n",
        " '_18',\n",
        " '_19',\n",
        " '_21',\n",
        " '_22',\n",
        " '_24',\n",
        " '_27',\n",
        " '_28',\n",
        " '_3',\n",
        " '_30',\n",
        " '_37',\n",
        " '_38',\n",
        " '_4',\n",
        " '_40',\n",
        " '_41',\n",
        " '_42',\n",
        " '_43',\n",
        " '_44',\n",
        " '_47',\n",
        " '_48',\n",
        " '_58',\n",
        " '_59',\n",
        " '_6',\n",
        " '_60',\n",
        " '_61',\n",
        " '_67',\n",
        " '_72',\n",
        " '_73',\n",
        " '_74',\n",
        " '_75',\n",
        " '_76',\n",
        " '_79',\n",
        " '_80',\n",
        " '__',\n",
        " '___',\n",
        " '__builtin__',\n",
        " '__builtins__',\n",
        " '__doc__',\n",
        " '__name__',\n",
        " '__package__',\n",
        " '__version__',\n",
        " '_dh',\n",
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        " 'semilogx',\n",
        " 'semilogy',\n",
        " 'set_cmap',\n",
        " 'set_numeric_ops',\n",
        " 'set_printoptions',\n",
        " 'set_state',\n",
        " 'set_string_function',\n",
        " 'setbufsize',\n",
        " 'setdiff1d',\n",
        " 'seterr',\n",
        " 'seterrcall',\n",
        " 'seterrobj',\n",
        " 'setp',\n",
        " 'setxor1d',\n",
        " 'shape',\n",
        " 'short',\n",
        " 'show',\n",
        " 'show_config',\n",
        " 'shuffle',\n",
        " 'sign',\n",
        " 'signbit',\n",
        " 'signedinteger',\n",
        " 'silent_list',\n",
        " 'sin',\n",
        " 'sinc',\n",
        " 'single',\n",
        " 'singlecomplex',\n",
        " 'sinh',\n",
        " 'size',\n",
        " 'slogdet',\n",
        " 'slopes',\n",
        " 'solve',\n",
        " 'sometrue',\n",
        " 'sort',\n",
        " 'sort_complex',\n",
        " 'source',\n",
        " 'spacing',\n",
        " 'specgram',\n",
        " 'spectral',\n",
        " 'split',\n",
        " 'spring',\n",
        " 'spy',\n",
        " 'sqrt',\n",
        " 'square',\n",
        " 'squeeze',\n",
        " 'stackplot',\n",
        " 'standard_cauchy',\n",
        " 'standard_exponential',\n",
        " 'standard_gamma',\n",
        " 'standard_normal',\n",
        " 'standard_t',\n",
        " 'start',\n",
        " 'std',\n",
        " 'stem',\n",
        " 'step',\n",
        " 'step_tweak',\n",
        " 'stineman_interp',\n",
        " ...]"
       ]
      }
     ],
     "prompt_number": 81
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from stress import step_tweak\n",
      "from operator import add, mul\n",
      "steptweakedCollection = step_tweak(market, 'fx.usd_eur.Rate', '2013-10-31', '2014-01-15', add, .001)\n",
      "plot(steptweakedCollection.fx.usd_jpy.Rate)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 24,
       "text": [
        "[<matplotlib.lines.Line2D at 0xaf3d30c>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Dh52/X3PC39jouxsDf98DB4Cbb1Y/R23Gr8axY2zMzgZ699bEPIIIOgwv/MrywomJbOTN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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x11a2a74c>"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from stress import step_tweak\n",
      "from operator import add, mul\n",
      "steptweakedCollection = step_tweak(market, 'fx.usd_eur.Rate', '2012-10-31', '2014-02-15', mul, 1.5)\n",
      "plot(steptweakedCollection.fx.usd_eur.Rate)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 85,
       "text": [
        "[<matplotlib.lines.Line2D at 0x10982aac>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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zdKl6fflyio4BgMGDyeXy/PPqYywjdjp0qPv5xbjAjBk0MezFF4HkZErz3K8f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\nRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0xf706f2c>"
       ]
      }
     ],
     "prompt_number": 85
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(ccy_matrix.jpy)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 61,
       "text": [
        "[<matplotlib.lines.Line2D at 0xc567e2c>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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z5wNfQdTX86QqoSAeeIB/nmvX+lYuT2N70Wlq8r878i5dgBEjpOfFxcCYMfxx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\nAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0xc5e1b6c>"
       ]
      }
     ],
     "prompt_number": 61
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "lm1.params"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "Intercept     4.303166\n",
        "aed         -15.766818\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "type(lm1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 24,
       "text": [
        "statsmodels.regression.linear_model.RegressionResultsWrapper"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "import numpy as np\n",
      "import matplotlib.pylab as plt\n",
      " \n",
      "from sklearn.linear_model import LinearRegression\n",
      "from sklearn.metrics import r2_score\n",
      "\n",
      "X=np.asarray(steptweakedCollection.fx.usd_eur.Rate['2007-10-31':'2012-10-30'])[:, np.newaxis]\n",
      "Y=np.asarray(steptweakedCollection.fx.usd_jpy.Rate['2007-10-31':'2012-10-30'])\n",
      "\n",
      "lr=LinearRegression()\n",
      "lr.fit(X,Y)\n",
      " \n",
      "# Testing Data\n",
      "test_X=np.asarray(steptweakedCollection.fx.usd_eur.Rate['2012-10-31':'2014-02-15'])[:, np.newaxis]\n",
      "test_Y=np.asarray(steptweakedCollection.fx.usd_jpy.Rate['2012-10-31':'2014-02-15'])\n",
      "\n",
      "# Plot Prediction\n",
      "pred = lr.predict(test_X)\n",
      " \n",
      "plt.scatter(X, Y, color='blue')\n",
      "plt.scatter(test_X, pred, color='red')\n",
      "plt.plot(test_X, lr.predict(test_X), color='black',linewidth=1)\n",
      "plt.xlabel('eur')\n",
      "plt.ylabel('jpy')\n",
      "plt.title('Scatterplot jpy~eur and prediction under stress (red)')\n",
      "plt.grid(True)\n",
      "plt.xticks(())\n",
      "plt.yticks(())\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x12de9dec>"
       ]
      }
     ],
     "prompt_number": 110
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.figure()\n",
      "ccy_matrix['eur'].plot(label='eur')\n",
      "ccy_matrix['jpy'].plot(secondary_y=True, style='g',label='jpy')\n",
      "plt.legend(loc='best')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 102,
       "text": [
        "<matplotlib.legend.Legend at 0x12c2506c>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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+efgbs+19pD56moeOUKlToajg9s2/g/p7b9bddE4n3QhReDgBd7BZtyTi+FjG1censhIo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\nDBUeFAqFQjEZKjwoFAqFYjL/D8Cu9hKwfvF+AAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x12c251ac>"
       ]
      }
     ],
     "prompt_number": 102
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.figure()\n",
      "steptweakedCollection.fx.usd_eur.Rate.plot(label='eur')\n",
      "steptweakedCollection.fx.usd_jpy.Rate.plot(secondary_y=True, style='g',label='jpy')\n",
      "plt.legend(loc='best')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 101,
       "text": [
        "<matplotlib.legend.Legend at 0x12b68c6c>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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2PFOoVdfq2V/MzaT4GJoPZR2C9EOzZik92OojSf0EQHem4Oamk/m0Xig8u+1Z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\ng8FgMHj+H12NniaQ8nA9AAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x12b68cec>"
       ]
      }
     ],
     "prompt_number": 101
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(X)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 59,
       "text": [
        "[<matplotlib.lines.Line2D at 0x1204998c>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x1216690c>"
       ]
      }
     ],
     "prompt_number": 59
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##References\n",
      "\n",
      "* Benninga, Simon. *Financial Modeling*. The MIT Press.\n",
      "* Bodie, Zvi, A. Kane, and A.Marcus.  *Essentials of Investments*. McGraw-Hill.\n",
      "* Ross, Sheldon. *A First Course in Probability.*  Pearson Prentice Hall.\n",
      "* (http://gosmej1977.blogspot.com/2013/06/value-at-risk.html)"
     ]
    }
   ],
   "metadata": {}
  }
 ]
}