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  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Real trade-weighted index values of USD, gold, and SPX"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We examine the value of USD against a basket of 26 foreign currencies using real trade numbers. Trade statistics are released annually, however, the Fed uses international inflation data to adjust the weights monthly."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "*Dependencies:*\n",
      "     - Linux, bash\n",
      "     - Python: matplotlib, pandas\n",
      "     - Modules: yi_1tools, yi_fred, yi_plot, yi_timeseries\n",
      "     \n",
      "*CHANGE LOG*\n",
      "\n",
      "    2015-01-20  Code review.\n",
      "    2014-08-24  First version.\n",
      "    \n",
      "Reference: Mico Loretan, Federal Reserve Bulletin, Winter 2005, \n",
      "*\"Indexes of the Foreign Exchange Value of the Dollar\",* \n",
      "[http://www.federalreserve.gov/pubs/bulletin/2005/winter05_index.pdf](http://www.federalreserve.gov/pubs/bulletin/2005/winter05_index.pdf)    "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#  NOTEBOOK settings and system details:   [00-tpl v14.09.28]\n",
      "\n",
      "#  Assume that the backend is LINUX (our particular distro is Ubuntu, running bash shell):\n",
      "print '\\n ::  TIMESTAMP of last notebook execution:'\n",
      "!date\n",
      "print '\\n ::  IPython version:'\n",
      "!ipython --version\n",
      "\n",
      "#  Automatically reload modified modules:\n",
      "%load_ext autoreload\n",
      "%autoreload 2   \n",
      "#           0 will disable autoreload.\n",
      "#  Generate plots inside notebook:\n",
      "%matplotlib inline\n",
      "\n",
      "#  DISPLAY options\n",
      "from IPython.display import Image \n",
      "#  e.g. Image(filename='holt-winters-equations.png', embed=True)\n",
      "from IPython.display import YouTubeVideo\n",
      "#  e.g. YouTubeVideo('1j_HxD4iLn8')\n",
      "from IPython.display import HTML # useful for snippets\n",
      "#  e.g. HTML('<iframe src=http://en.mobile.wikipedia.org/?useformat=mobile width=700 height=350></iframe>')\n",
      "import pandas as pd\n",
      "print '\\n ::  pandas version:'\n",
      "print pd.__version__\n",
      "#      pandas DataFrames are represented as text by default; enable HTML representation:\n",
      "#      [Deprecated: pd.core.format.set_printoptions( notebook_repr_html=True ) ]\n",
      "pd.set_option( 'display.notebook_repr_html', False )\n",
      "\n",
      "#  MATH display, use %%latex, rather than the following:\n",
      "#                from IPython.display import Math\n",
      "#                from IPython.display import Latex\n",
      "\n",
      "print '\\n ::  Working directory (set as $workd):'\n",
      "workd, = !pwd\n",
      "print workd + '\\n'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        " ::  TIMESTAMP of last notebook execution:\n",
        "Fri Jan 23 22:29:33 PST 2015"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        " ::  IPython version:\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2.3.0\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        " ::  pandas version:\n",
        "0.15.0\n",
        "\n",
        " ::  Working directory (set as $workd):\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "/home/yaya/Dropbox/ipy/fecon235/nb\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from yi_1tools import *\n",
      "from yi_fred import *\n",
      "from yi_plot import *\n",
      "from yi_timeseries import *"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "RTB stands for \"real trade-weighted: broad\" where broad consists of 26 currencies. There is a sub-index for \"major\" currencies where the weights are adjusted annually."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#  USD in RTB terms:\n",
      "usdrtb = getfred( m4usdrtb )\n",
      "plotfred( usdrtb, 'm4 USDRTB' )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
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3v7HskpUrGxvyfOCFVt59F268Mf/Hd2RG2XnkYYxf+UlXXxDxcWis7+KL7fG8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\n/H9SrlBtBayZ0wAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0xad3b9e4c>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " ::  Finished: plotdf-m4_USDRTB.png\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "stats( usdrtb )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "                Y\n",
        "count  503.000000\n",
        "mean    95.749771\n",
        "std      9.473351\n",
        "min     80.536000\n",
        "25%     88.508500\n",
        "50%     94.007000\n",
        "75%    100.410000\n",
        "max    128.437000\n",
        "\n",
        " ::  Index on min:\n",
        "Y   2011-07-01\n",
        "dtype: datetime64[ns]\n",
        "\n",
        " ::  Index on max:\n",
        "Y   1985-03-01\n",
        "dtype: datetime64[ns]\n",
        "\n",
        " ::  Head:\n",
        "                  Y\n",
        "T                  \n",
        "1973-01-01  107.616\n",
        "1973-02-01  103.046\n",
        "1973-03-01  100.000\n",
        "1973-04-01  100.376\n",
        "1973-05-01   99.263\n",
        "1973-06-01   97.483\n",
        "1973-07-01   94.995"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\n",
        " ::  Tail:\n",
        "                 Y\n",
        "T                 \n",
        "2014-05-01  84.993\n",
        "2014-06-01  85.118\n",
        "2014-07-01  84.848\n",
        "2014-08-01  85.362\n",
        "2014-09-01  86.582\n",
        "2014-10-01  87.561\n",
        "2014-11-01  89.007\n",
        "\n",
        " ::  Correlation matrix:\n",
        "   Y\n",
        "Y  1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "We can also value other assets in terms of RTB, i.e. their international valuations ex-USD."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#  Gold in RTB terms:\n",
      "xaurtb = getfred( m4xaurtb )\n",
      "plotfred( xaurtb, 'm4 XAURTB' )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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l8tZaAd4Rkb+ISEOgVEROAhCRI4AoTIawHNhDVZsmPoBv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\nyB3HcfIeD+SO4zh5jgdyx3GcPOf/A192k62pXmKVAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0xad2e102c>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " ::  Finished: plotdf-m4_XAURTB.png\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#  SP500 -- US equities in international RTB terms:\n",
      "spxrtb = getfred( m4spxrtb )\n",
      "plotfred( spxrtb )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " ::  S&P 500 prepend successfully goes back to 1957.\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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lt/+UKTplXPzcn/E9cqgI5BMnajkFv6hub7nIObc88no5EJnbg9bA4pj9FgM5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\nH3vrv9ZUva8TAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0xad1df30c>"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "American equities see record high even in international terms."
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "To evaluate the above as financial assets, we compute the geometric mean returns. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "georet(usdrtb, 12)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "[-0.45, -0.36, 4.4, 12]"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "georet(xaurtb, 12)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "[6.4, 7.93, 17.51, 12]"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "georet(spxrtb, 12)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "[6.38, 7.21, 12.95, 12]"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Going back to 1973, we expect USD to decline around 50 bp each year against a basket of 26 currencies. \n",
      "\n",
      "For non-Americans, gold appears to equal US equities returns despite much higher volatility. "
     ]
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Caution: SPX above does not include dividends, thus US equities would be superior to gold as investment."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "[We do not forecast here, because of the lag in data release.]"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Thus RTB allows us to view assets from a global non-dollar perspective."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    }
   ],
   "metadata": {}
  }
 ]
}