R & Python

Python and R for Quantitative Finance

An Introduction

Luca [email protected]

@lsbardel

LondonR - Nov 09

Overview

1)Puttingthingsintocontext2)PythonandR3)Examples

How can quantitative finance pratictioners best leverage their expertise without reinventing the wheel and spending lots of their precious time writing low level code?

opensource technologies

1Context

CurrentApproach

ExtensiveuseoflargeproprietyC,C++orJavalibraries. Serversideapplicationswrittenin

.Net/C#Windowsservers JavaWindows&Linux/Unix

VBA/Excelontheclientside:( Someweb/basedclients:) Limitedifinexistentuseofpowerfulopensource

technologies/libraries.

Problems

Fulldevelopmentcycleinlowlevellanguagesisexpensive

C++:Python=7:1 Writingcodealreadydevelopedmanytimesbefore Difficulttoadjusttonewtechnologiesastheybecome

available Oftenlibrariesaredeployedontheclientmachine Sometechnologies(.Net)forceadecisionontheplatform

tobeused(Windows)

Adifferentapproach

Limitlowleveldevelopmenttocriticalnumbercrunchingcomponents

Useavailablehighstandardopensourcetechnologies Flexible,multiplatformserversideconfiguration Multilanguagesupport Abstractionwhenpossible Remoteprocedurecalls(RPC)

Aproposedsolution

UseofPythonasmainserversidedriver LegacyC,C++asPythonmodules(BoostPython) Interactionwithothertechnologies

Rforstatistics Erlang/Haskellfunctionalprogrammingand

concurrency qkdb+timeseriesdatabase(commercial) Javascript/flexwebGUIfrontend

2PythonandR

Combine two of the most used and powerful opensourcelanguagestocreateatrulydynamicapplicationframework.

Benefitfromanactivecommunityofdevelopersandavastarrayofreadyavailablelibraries.

SeeminglessintegrationwithC/C++libraries

Python?

General-purpose high-level programming language

Named after Monty Python by Guido Van Rossum in the late 1980s. Large community

PythonFeatures

Dynamicallytyped Multiparadigmdesign

ObjectOriented Metaprogramming(reflection) Functionalprogramming

Emphasizessemplicity ExtendibilityviaCorC++(boost.python,Cpython)

Where?

FastPrototyping Scientificprogramming(numpy,scipy,) Databaseabstraction(pyodbc,django) RPCservers(JSON,XML,Binary,...) DomainSpecificLanguages(DSP) Webservers(mod_python,twisted,...) WebFrameworks(django,zope,...) Glueforanythingyoucanthinkof

Rlanguage

OpensourceimplementationofS/SPLUS Scriptinglanguagethatexcelsin

dataanalysis statisticsandeconometric graphics

Hugecollectionofstatisticalpackagesavailable Canbeusedasstandaloneserversidelanguagebutnot

ideal

UsingRontheserverside

UseRonlyforwhathasbeendesignfor Statistics&Math

EmbedRwithinPythondomain

Installing InstallPython InstallRandanyRlibraryofyourchoice Installnumpyandrpy2 LaunchPythonandrunrpy2test

$ python

>>> from rpy2 import tests>>> import unittest>>> tr = unittest.TextTestRunner(verbosity = 1)>>> tr.run(tests.suite())

3Examples

1)Sharingmemoryandarrays

2)Smallserverforrollingstatisticscalculation

3)AtimeseriesDSL(DomainSpecificLanguage).Notpresented.

3.1MemoryandVectors

Whenusingrpy2PythonandRdomainsarecoexisting Pythonmanagesobjectspointingtodatastoredand

administeredintheRspace RvariablesareexistingwithinanembeddedRworkspace,

andcanbeaccessedfromPythonthroughtheirpythonobjectrepresentations(Sexpandsubclasses).

3.1MemoryandVectors

import rpy2.rinterface as riimport numpy as ny

# Create an integer array in R domainrx = ri.SexpVector([1,2,3,4], ri.INTSXP)

# Create a local copy in Python domainnx = ny.array(rx)

# Proxy to R vector without copingnx_nc = ny.asarray(rx)

3.2Asmallserver

AminimalistClientserverapplication ThePythonserverusesRpackagestoperformtimeseries

analysisonhistoricalstockprices TheserverexposesaRPCJSONinterfacetotheclient Implementonefunctionwhichcalculateasimplerolling

movingavarage

londonr1109/__init__.pyroll.pyplot.pyserver1.pyjsonrpc/

__init__.pyjsonlib.pyproxy.pyserver.py

Codestructure

Code can be found at http://github.com

http://github.com/lsbardel/statplay/tree/master/examples/londonr1109/

SystemRequirements

Python2.6orabove R2.8orabove rpy2andnumpy(corePythonRpackages) matplotlib(forplottingclient) proformanceAnalytics(Rpackage) quantmod(Rpackage)

To start the server type

$ python server1.py 8080

The server is now ready to listen to requests on port 8080

To test the server the plot.py client can be used

To plot Google moving averages with rolloing window of 60 days type

$ python plot.py GOOG 60

References

Python: www.python.org R:www.rproject.org rpy2:http://rpy.sourceforge.net/ numpy:www.numpy.org matplotlib:http://matplotlib.sourceforge.net twisted:http://twistedmatrix.com boost:http://www.boost.org/

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