gistfile1.txt

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "# Import some packages\n",
    "import csv                                              # Python standard library\n",
    "import math                                          # Python standard library\n",
    "import numpy                                       # A 3rd party package\n",
    "from datetime import datetime          # Python standard library\n",
    "\n",
    "# Import data from csv files\n",
    "EURUSDreader = csv.reader(open('EURUSD.csv'))\n",
    "\n",
    "EURUSD = []\n",
    "for line in EURUSDreader:\n",
    "\tEURUSD.append(line)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Time data processing\n",
    "EURUSD_Time = []\n",
    "for row in EURUSD:\n",
    "\tEURUSD_Time.append(datetime.strptime((row[0] + \" \" + row[1]), \"%Y.%m.%d %H:%M\"))\n",
    "\n",
    "\n",
    "# Price and volume data processing\n",
    "EURUSD_Open = []\n",
    "for row in EURUSD:\n",
    "\tEURUSD_Open.append(float(row[2]))\n",
    "\n",
    "EURUSD_High = []\n",
    "for row in EURUSD:\n",
    "\tEURUSD_High.append(float(row[3]))\n",
    "\n",
    "EURUSD_Low = []\n",
    "for row in EURUSD:\n",
    "\tEURUSD_Low.append(float(row[4]))\n",
    "\n",
    "EURUSD_Close = []\t\n",
    "for row in EURUSD:\n",
    "\tEURUSD_Close.append(float(row[5]))\n",
    "\n",
    "EURUSD_Volume = []\n",
    "for row in EURUSD:\n",
    "\tEURUSD_Volume.append(float(row[6]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Rate of return calculation\n",
    "EURUSD_Return = []\n",
    "for index in (range(len(EURUSD_Close) - 1)):\n",
    "\ttemp = math.log(EURUSD_Close[index + 1]) - math.log(EURUSD_Close[index])\n",
    "\tEURUSD_Return.append(temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.00128620478494\n"
     ]
    }
   ],
   "source": [
    "# Volatility using standard deviation, the function is from the 3rd party package \"Numpy\"\n",
    "sd_EURUSD = numpy.std(EURUSD_Return)\t\n",
    "\n",
    "print sd_EURUSD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0252409711408\n"
     ]
    }
   ],
   "source": [
    "# Volatility using Exponetial Weighted Moving Average model, with the \"Pandas\" package\n",
    "\n",
    "import pandas                        # Import a 3rd party package \"Pandas\"\n",
    "\n",
    "EURUSD_ReturnSeries = pandas.Series(EURUSD_Return)\n",
    "EWMA = pandas.stats.moments.ewma(EURUSD_ReturnSeries, (2/0.94-1))\n",
    "\n",
    "# Print the volatility to users\n",
    "print EURUSD_ReturnSeries[len(EURUSD_ReturnSeries)-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes.AxesSubplot at 0x7f34e3fdb410>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VEOJZNeL6YIeWKm0HWsLenNn/o8x+VQH5XswU8jbS+Z+yiiGuSC/LJ0HvivFceSXDi1i1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\nYf5d4PNjyuc7Y8p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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3500252c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the EWMA volatility series\n",
    "EWMA.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "celltoolbar": "Raw Cell Format",
  "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
}