gistfile1.txt

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "raw_mimetype": "text/html",
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# This project is to implement volatility for Trading Symbol Selection.\n",
    "\n",
    "Data source here is the MT4 exported csv file with trading time, Open, High, Low, Close and volume. For production, the data source could be from database."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Volatility with EWMA model, Python\n",
    "\"\"\"\n",
    "\n",
    "# Import some Python standard libraries\n",
    "import csv\n",
    "import math\n",
    "from datetime import datetime\n",
    "# Import 3rd party package Numpy\n",
    "import numpy\n",
    "\n",
    "# Import data from csv files, read data\n",
    "EURUSDreader = csv.reader(open('EURUSD.csv'))\n",
    "\n",
    "EURUSD = []\n",
    "for line in EURUSDreader:\n",
    "\tEURUSD.append(line)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following Input is for data preparation, mainly to get time data series (EURUSD_Time) and Close price data series (EURUSD_Close)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "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",
    "# 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": "markdown",
   "metadata": {},
   "source": [
    "Use Close price data series to calculate and get Return series of the Forex. The volatility calculation is based on Return data series.\n",
    "\n",
    "To calculate Return series data, I create an empty list \"EURUSD_Return\", \n",
    "\n",
    "then loop through EURUSD_Close, the Return of each item can be got from two agjacent Close price: Return_i = log_e(Close_i+1) - log_e(Close_i)\n",
    "\n",
    "Then I append each Return item into the list EURUSD_Return."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Rate of return calculation\n",
    "EURUSD_Return = []\n",
    "\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": "markdown",
   "metadata": {},
   "source": [
    "Use standard deviation to show volatility, as a comparison for later method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": "markdown",
   "metadata": {},
   "source": [
    "Use EWMA model to calculate volatility, to show users the recent volatility condition of the Forex product, more accurate than standard deviation.\n",
    "\n",
    "For Java reference:\n",
    "\n",
    "https://github.com/dropwizard/metrics/blob/master/metrics-core/src/main/java/com/codahale/metrics/EWMA.java\n",
    "http://grepcode.com/file/repo1.maven.org/maven2/com.codahale.metrics/metrics-core/3.0.1/com/codahale/metrics/EWMA.java"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Volatility using Exponetial Weighted Moving Average model, with the \"Pandas\" package\n",
    "import pandas   # Import a 3rd party package \"Pandas\"\n",
    "\n",
    "\n",
    "# Function of EWMA Volatility\n",
    "def FX_Volatility(FX_Close_raw, FX_Time, starting_time, ending_time):\n",
    "\t\"\"\"\n",
    "\tInput data as FX_Close price series 1-dimension list, starting time and ending time.\n",
    "\tUse EWMA model to calculate volatility and return the volatility value.\n",
    "\t\"\"\"\n",
    "\tindex_1 = FX_Time.index(starting_time)\n",
    "\tindex_2 = FX_Time.index(ending_time) + 1\n",
    "\tFX_Close = FX_Close_raw[index_1 : index_2]\n",
    "\n",
    "\n",
    "\tFX_Return = []\n",
    "\n",
    "\tfor index in (range(len(FX_Close) - 1)):\n",
    "\t\ttemp = math.log(FX_Close[index + 1]) - math.log(FX_Close[index])\n",
    "\t\tFX_Return.append(temp)\n",
    "\n",
    "\tFX_ReturnSeries = pandas.Series(FX_Return)\n",
    "\tEWMA = pandas.stats.moments.ewma(FX_ReturnSeries, (2/0.94-1))\n",
    "\n",
    "\treturn EWMA[len(EWMA)-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.000439327583636\n"
     ]
    }
   ],
   "source": [
    "Volatility_example = FX_Volatility(EURUSD_Close, EURUSD_Time, datetime(2014, 01, 01, 23, 0), datetime(2015, 04, 01, 07, 0))\n",
    "print Volatility_example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0119323335189\n"
     ]
    }
   ],
   "source": [
    "# Another EWMA test\n",
    "EURUSD_ReturnSeries = pandas.Series(EURUSD_Return)\n",
    "EWMA_1 = pandas.stats.moments.ewma(EURUSD_ReturnSeries, (2/0.94-1))\n",
    "\n",
    "print EWMA_1[len(EWMA_1)-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes.AxesSubplot at 0x7f65869cc110>"
      ]
     },
     "execution_count": 9,
     "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 0x7f65869bed90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "EWMA_1.plot()"
   ]
  },
  {
   "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
}