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  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Euro currency qua Foreign Exchange"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "INTRO:_ We examine euro FX data from the Fed Reserve FRED database. Our synthetic time-series, which takes us far back as 1971, give additional perspective to observe the cross-rates against U.S. dollar and Japanese yen."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "*Dependencies:*\n",
      "\n",
      "    - Linux, bash [not crucial, cross-platform prefered]\n",
      "    - Python: matplotlib, pandas [recommend Anaconda distribution]\n",
      "    - Modules: yi_1tools, yi_plot, yi_timeseries, yi_fred\n",
      "     \n",
      "*CHANGE LOG*\n",
      "\n",
      "    2015-05-16  Code review and revision.\n",
      "    2014-10-09  First version using template dated 2014-09-28."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#  NOTEBOOK settings and system details:   [00-tpl v14.12.21]\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 disables 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) # url= also works\n",
      "from IPython.display import YouTubeVideo\n",
      "#  e.g. YouTubeVideo('1j_HxD4iLn8', start='43', width=600, height=400)\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",
      "\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"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Tue May 19 17:03:53 PDT 2015\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_plot import *\n",
      "from yi_timeseries import *\n",
      "from yi_fred import *"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      " ::  EURUSD"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#  Daily frequency:\n",
      "eurusd = getfred( d4eurusd )\n",
      "#  Note that the synthetic preprend back to 1971 is only for monthly data."
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plotfred( eurusd )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
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//tep4777cjvvihXp59q0yblsPiNHMrdpk93xN2yQfXv0YJ42jfmuu5gvvDC9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\npbUw4QAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0xad285cec>"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The 2006 support for EURUSD as of Oct 2014 was around 1.24. Its breach requires us to know the resistance from the synthetic euro series. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#  Monthly frequency (daily frequency is not available):\n",
      "eursyn = getfred( m4eurusd )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " ::  EURUSD synthetically goes back monthly to 1971.\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plotfred(eursyn, 'EURUSD synthetic')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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UKRJHd4ZNVB9HMtlMAwdKWNUveW+8MxX/W7JEvpjrrpPloiKZ+7BnT1l2/gB0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\n",
       "text": [
        "<matplotlib.figure.Figure at 0xad26d7ec>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " ::  Finished: plotdf-EURUSD_synthetic.png\n"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The support from 1985 implies the euro could easily descend to the 1.06 level based on the *synthetic series composed of the mean between DEM and FRF (converted at their official euro rates).*"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\"**On 22 January 2015, Mario Draghi committed the ECB to a QE quantitative easing program worth at least 1.1 trillion euros** to counter the threat of a deflationary spiral. The ECB president shrugged off determined opposition led by German officials with a pledge to buy 60 billion euros every month through September 2016 in a once-and-for-all push to put more cash into circulation and revive inflation. To assuage critics, the region\u2019s 19 national central banks will make 80 percent of the purchases and take on any risk they carry.\" "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#  Define the day before QE-EU:\n",
      "qeeu = '2015-01-21'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#  What is the EURUSD low since QE-EU announcement?\n",
      "#  On the day before, on 2015-01-21, the rate was 1.1584.\n",
      "stats( eurusd[qeeu:] )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "               Y\n",
        "count  83.000000\n",
        "mean    1.106006\n",
        "std     0.029410\n",
        "min     1.052400\n",
        "25%     1.079750\n",
        "50%     1.114500\n",
        "75%     1.133350\n",
        "max     1.158400\n",
        "\n",
        " ::  Index on min:\n",
        "Y   2015-03-13\n",
        "dtype: datetime64[ns]\n",
        "\n",
        " ::  Index on max:\n",
        "Y   2015-01-21\n",
        "dtype: datetime64[ns]\n",
        "\n",
        " ::  Head:\n",
        "                 Y\n",
        "T                 \n",
        "2015-01-21  1.1584\n",
        "2015-01-22  1.1414\n",
        "2015-01-23  1.1279\n",
        "2015-01-26  1.1290\n",
        "2015-01-27  1.1370\n",
        "2015-01-28  1.1342\n",
        "2015-01-29  1.1308\n",
        "\n",
        " ::  Tail:\n",
        "                 Y\n",
        "T                 \n",
        "2015-05-07  1.1283\n",
        "2015-05-08  1.1241\n",
        "2015-05-11  1.1142\n",
        "2015-05-12  1.1240\n",
        "2015-05-13  1.1372\n",
        "2015-05-14  1.1368\n",
        "2015-05-15  1.1428\n",
        "\n",
        " ::  Correlation matrix:\n",
        "   Y\n",
        "Y  1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "As of 2015-05-16: the EURUSD low post-QE-EU is **1.0524** on 2015-03-13 -- which a 9% decline in less than two months."
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "EURUSD deviation from long-term trend"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "syntrend = trend( eursyn )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " ::  regresstime slope = 0.000783027874247\n"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plotfred( syntrend )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0xad273fcc>"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#  Detrend:\n",
      "plotfred( eursyn - syntrend )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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zjhX3hx5KXdzffltSGZXH7mbEaVFR1/Pc582TtvPK5s0i7qedJqOU7cjPjx+c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\nShU5v2lvl+Hj3bvLkHKvDBgg2QLq1ra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dRFRJRA1EVB95fUdEYLNFINvLjmyE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\nAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0xac9efccc>"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tailvalue( syntrend )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "1.3243276659569534"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "October 2014: Looking at the detrended series, a deviation of -0.3 (i.e. -3000 pips) is plausible from the trend. Thus the statistically 1.3280 - 0.3 = 1.0280 is realistic. Given ECB implicitly wants the euro to weaken (LSAP not yet in effect, as of October 2014), we see the long-term support between 1.03 and 1.06."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "May 2015: Given QE announcement in January, 1.0524 definitively is the technical support dating back to 1985. The uptrend is unbroken, however, ..."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Though monetary policy is the primary reason for weakening euro, Greece is expected to face default conditions beginning the start of 2016, intensifying into the summer of 2016. *The euro crisis will be revisited as Grexit is a possibility on the horizon.*"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#  Forecast monthly for next 24 months:\n",
      "holtfred( eursyn )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "    Forecast\n",
        "0   1.124100\n",
        "1   1.065482\n",
        "2   1.038455\n",
        "3   1.011428\n",
        "4   0.984401\n",
        "5   0.957374\n",
        "6   0.930347\n",
        "7   0.903320\n",
        "8   0.876293\n",
        "9   0.849266\n",
        "10  0.822239\n",
        "11  0.795212\n",
        "12  0.768185\n",
        "13  0.741158\n",
        "14  0.714131\n",
        "15  0.687104\n",
        "16  0.660077\n",
        "17  0.633050\n",
        "18  0.606023\n",
        "19  0.578996\n",
        "20  0.551969\n",
        "21  0.524942\n",
        "22  0.497915\n",
        "23  0.470888\n",
        "24  0.443861"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#  Look at geometric return since 2006:\n",
      "georet( eurusd[t06:] )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "[-0.37, 0.12, 9.95, 256]"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "October 2014: Interestingly the annual geometric return post-US-Great-Recession is positive at 0.62% (which does not seem so in the charts visually). The volatility for EURUSD going back to 2006 is 10%."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "May 2015: The annual geometric return on EURUSD as FX has become negative at -0.55% due chiefly to QE-EU. That does not include negative rates on euro deposits! The one-year ahead forecast is 0.7662 which means the long-term trend will be broken. "
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      " ::  EURJPY"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let's derive the long-term synthetic rates for EURJPY."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#  We retrieve the monthly frequency:\n",
      "usdjpy = getfred( m4usdjpy )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#  Since monthly eursysn is expressed in terms of EURUSD:\n",
      "eurjpy = todf( eursyn * usdjpy )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plotfred( eurjpy, 'EURJPY synthetic' )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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GxRZQV6xcab7oPfe07dati+snz9SQO47j1ES98JFv3QpNm1pkSCxa5fDD4f/9PzjiiDqT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\niGl8OwMAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0xac89474c>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " ::  Finished: plotdf-EURJPY_synthetic.png\n"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "*The EURJPY trend since 1995 looks sideways, bouncing between 90 and 170.*"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#  Verify trend claim:\n",
      "ej95trend = trend( eurjpy['1995-01-01':] )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " ::  regresstime slope = -0.00246059121507\n"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#  The plot is uninteresting, just a straight line along 129.50 !\n",
      "#  Look at the slope and the std for confirmation:\n",
      "ej95trend.describe()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "                Y\n",
        "count  245.000000\n",
        "mean   129.483756\n",
        "std      0.174381\n",
        "min    129.183564\n",
        "25%    129.333660\n",
        "50%    129.483756\n",
        "75%    129.633852\n",
        "max    129.783949"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "So basically the **EURJPY range is 130 \u00b1 40 since 1995.** *This is only apparent by observing the synthetic time-series.* \n",
      "\n",
      "It has large width, \u00b1 31% -- but the 130 level provides \"fair\" benchmark (cf. IMF assessment) for quick comparison."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#  Euro strength against the yen on a scale of 100:\n",
      "int((tailvalue( eurjpy ) - 90) / 0.80) \n",
      "\n",
      "#  2014-12-22: 71  One month prior to QE-EU, EURJPY = 146.83\n",
      "#  2015-01-21: 58  One day prior to QE-EU, EURJPY = 136.52\n",
      "#  2015-04-01: 51  Two months after QE-EU, EURJPY = 130.80\n",
      "#  2015-05-18: 55  Four months after QE-EU"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 37,
       "text": [
        "55"
       ]
      }
     ],
     "prompt_number": 37
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Following the Great Recession, which devaluing faster, EUR or JPY, against the USD?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "georet( eurjpy[t06:], 12 )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "[-0.39, 0.24, 11.23, 12]"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Surprisingly, the answer seems to be: neither, i.e. equally fast, from solely the FX viewpoint. Japan though embarked on its QE program many years ago, and still has not reversed that policy. Japan seems on its way out of economic misery whereas Europe seems to be under economic suppression."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Concluding remarks:_ \n",
      "\n",
      "- From the viewpoint of carry trades, the short side has to be the euro, simply because of the negative deposit rates. One is paid to go short!\n",
      "\n",
      "- The US is only country among the three major FX countries considering a upward hike in rates. Thus fundamentally the long side would be the dollar.\n",
      "\n",
      "- May 2015: EU has just begun their QE, and there is the possibility of Grexit looming -- all which indicate further weakening of the euro. The euro against the dollar may test the synthetic lows registered during the 1980's [All-time EURUSD low: 0.62 circa Feb 1985].\n",
      "\n",
      "- May 2015: Given 130 as fair EURJPY benchmark, Euro becomes a buy against the yen as it approaches 90 yen. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 21
    }
   ],
   "metadata": {}
  }
 ]
}