gistfile1.txt

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Calculate Benchmark of the DOllar Index\n",
    "\"\"\"\n",
    "\n",
    "# Import some Python standard libraries\n",
    "import csv\n",
    "from datetime import datetime\n",
    "\n",
    "# Read data from csv files\n",
    "EURUSDreader = csv.reader(open('EURUSD.csv'))\n",
    "GBPUSDreader = csv.reader(open('GBPUSD.csv'))\n",
    "USDJPYreader = csv.reader(open('USDJPY.csv'))\n",
    "USDCADreader = csv.reader(open('USDCAD.csv'))\n",
    "USDCHFreader = csv.reader(open('USDCHF.csv'))\n",
    "USDSEKreader = csv.reader(open('USDSEK.csv'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 3 functions to process data\n",
    "def FX(FXreader):\n",
    "\tFX = []\n",
    "\tfor line in FXreader:\n",
    "\t\tFX.append(line)\n",
    "\treturn FX\n",
    "\n",
    "def FX_Time(FX):\n",
    "\tFX_Time = []\n",
    "\tfor row in FX:\n",
    "\t\tFX_Time.append(datetime.strptime((row[0] + \" \" + row[1]), \"%Y.%m.%d %H:%M\"))\n",
    "\treturn FX_Time\n",
    "\n",
    "def FX_Close(FX):\n",
    "\tFX_Close = []\n",
    "\tfor row in FX:\n",
    "\t\tFX_Close.append(float(row[5]))\n",
    "\treturn FX_Close"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Call the functions and get Time data series and Close_price series of each FX pair\n",
    "EURUSD = FX(EURUSDreader)\n",
    "GBPUSD = FX(GBPUSDreader)\n",
    "USDJPY = FX(USDJPYreader)\n",
    "USDCAD = FX(USDCADreader)\n",
    "USDCHF = FX(USDCHFreader)\n",
    "USDSEK = FX(USDSEKreader)\n",
    "\n",
    "EURUSD_Time = FX_Time(EURUSD)\n",
    "GBPUSD_Time = FX_Time(GBPUSD)\n",
    "USDJPY_Time = FX_Time(USDJPY)\n",
    "USDCAD_Time = FX_Time(USDCAD)\n",
    "USDCHF_Time = FX_Time(USDCHF)\n",
    "USDSEK_Time = FX_Time(USDSEK)\n",
    "\n",
    "EURUSD_Close = FX_Close(EURUSD)\n",
    "GBPUSD_Close = FX_Close(GBPUSD)\n",
    "USDJPY_Close = FX_Close(USDJPY)\n",
    "USDCAD_Close = FX_Close(USDCAD)\n",
    "USDCHF_Close = FX_Close(USDCHF)\n",
    "USDSEK_Close = FX_Close(USDSEK)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Get the Dollar_Index\n",
    "Dollar_Index = []\n",
    "Index_Time = []\n",
    "for item in USDJPY_Time:\n",
    "\tif (item in EURUSD_Time) and (item in GBPUSD_Time) and (item in USDCAD_Time) and (item in USDCHF_Time) and (item in USDSEK_Time):\n",
    "\t\tEUR = EURUSD_Close[EURUSD_Time.index(item)]\n",
    "\t\tGBP = GBPUSD_Close[GBPUSD_Time.index(item)]\n",
    "\t\tJPY = USDJPY_Close[USDJPY_Time.index(item)]\n",
    "\t\tCAD = USDCAD_Close[USDCAD_Time.index(item)]\n",
    "\t\tCHF = USDCHF_Close[USDCHF_Time.index(item)]\n",
    "\t\tSEK = USDSEK_Close[USDSEK_Time.index(item)]\n",
    "\t\ttemp_element = 50.14348112 * EUR**(-0.576) * JPY**(0.136) * GBP**(-0.119) * CAD**(0.091) * SEK**(0.042) * CHF**(0.036)\n",
    "\t\tDollar_Index.append(temp_element)\n",
    "\t\tIndex_Time.append(item)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f1654091f50>]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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1dOX43Km5M4umlSHA8cC3gIp/lE1X3gGk6Mo7gBRdeQdQR1feAaToyjuAFF15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\nRERERERERERERERERERERAaK/wdVQbiudN/XjQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1655860f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the Dollar Index series\n",
    "import matplotlib.pyplot as plt\n",
    "plt.plot(Index_Time, Dollar_Index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}