Machine Trading: Deploying Computer Algorithms to Conquer the Markets

254 Pages·2016·3.51 MB·English

by  Ernest P. Chan

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ACHINE RADING
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M
ACHINE
T
RADING
Deploying Computer Algorithms to Conquer
the Markets
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Ernest P. Chan
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Copyright©2017byErnestP.Chan.Allrightsreserved.
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To my mom, Ching, my spouse, Ben, and to the memory
of my beloved father, Hung Yip.
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CONTENTS
Preface ix
CHAPTER1 TheBasicsofAlgorithmicTrading 1
vii
(cid:2) CHAPTER2 FactorModels 27 (cid:2)
CHAPTER3 Time-SeriesAnalysis 59
CHAPTER4 ArtificialIntelligenceTechniques 83
CHAPTER5 OptionsStrategies 119
CHAPTER6 IntradayTradingandMarket
Microstructure 159
CHAPTER7 Bitcoins 201
CHAPTER8 AlgorithmicTradingIsGoodforBody
andSoul 215
Bibliography 227
AbouttheAuthor 235
Index 237
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PREFACE
Thebestwaytolearnsomethingreallywellistoteachittosomeoneelse
(BarghandSchul,1980).SoIconfessthatonemajormotivationformy
writingthisbook,thethirdandthemostadvancedtodateinaseries,isto
forcemyselftostudyinmoredepththefollowingtopics:
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■ The latest backtesting and trading platforms and the best and most
cost-effectivevendorsforallmannersofdata(Chapter1);
■ Howtopickthebestbrokerforalgorithmicexecutionsandwhatprecau-
tionsweshouldtake(Chapter1);
■ Thesimplestwaytooptimizeallocationstodifferentassetsandstrategies
(Chapter1);
■ Factormodelsinalltheirglory,includingthosederivedfromtheoptions
market,andwhytheycanbeusefultoshort-termtraders(Chapter2);
■ Timeseriestechniques:ARIMA,VAR,andstatespacemodels(withhid-
denvariables)asappliedtopracticaltrading(Chapter3);
■ Artificialintelligence/machinelearningtechniques:particularlymethods
thatwillreduceoverfitting(Chapter4);
■ Options and volatility trading strategies, including those that involve
portfoliosofoptions(Chapter5);
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■ Intraday and higher frequency trading: market microstructure, order
types and routing optimization, dark pools, adverse selection, order
flow,andhowtobacktestintradaystrategieswithtickdata(Chapter6);
■ Bitcoins: bringing some of the techniques we covered to this new asset
class(Chapter7);
■ Howtokeepupwiththelatestknowledge(Chapter8);
■ Transitioning from a proprietary trader to an investment advisor
(Chapter8).
I don’t know if these topics will excite you or bring you profits, but my
study of them has certainly improved my own money management skills.
Besides,sharingknowledgeandideasisfunandultimatelyconducivetocre-
ativityandprofits.
You will find most of the materials quite accessible to anyone who has
some experience in a quantitative field, be it computer science, engineer-
ing,orphysics.Notmuchpriorknowledgeoftradingandfinanceisassumed
(except for the chapter on options, where we do assume basic familiar-
ity).However,ifyouarecompletelynewtotrading,youmayfindmymore
basictreatmentsinQuantitativeTrading(Chan,2009)andAlgorithmicTrading
x
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(Chan,2013)easiertounderstand.Thisbookcanbetreatedasacontinua-
tionofmyfirsttwobooks,withcoverageontopicsthatIhavenotdiscussed
E before,butitcanalsobereadindependently.
C
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F Although many prototype trading strategies have been included as
E
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P examples,oneshoulddefinitelynottreatthemasshrink-wrappedproducts
ready to deploy in live trading. As I have emphasized in my previous
books, nobody should trade someone else’s strategies without a thorough,
independentbacktest,removingalllikelysourcesofbiasesanddataerrors,
and adding various variations for improvement. Most, if not all, the
strategies I describe contain hidden biases in one way or another, waiting
foryoutounearthandeliminate.
I use MATLAB for all of my research in trading. I find it extremely
user-friendly, with constantly improving and new features, and with an
increasingnumberofspecializedtoolboxesthatIcandrawon.Forexample,
without the Statistics and Machine Learning Toolbox, it would take much
longer to explore using AI/ML techniques for trading. (See why Google
scientistandmachinelearningexpertKevinMurphyprefersMATLABtoR
forAI/MLresearchinMurphy,2015.)Inthepast,readershavecomplained
aboutthehighpriceofaMATLABlicense.Butnow,itcostsonly$150fora
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‘‘Home’’license,witheachadditionaltoolboxcostingonly$45.Noserious
traders should compromise their productivity because of this small cost.
IamalsofamiliarwithR,whichisacloserelativetoMATLAB.Butfrankly,
itisnomatchforMATLABintermsofperformanceanduser-friendliness.
AdetailedcomparisonoftheselanguagescanbefoundinChapters1and6.
Ifyoudon’talreadyknowMATLAB,itisveryeasytorequestaone-month
trial license from mathworks.com and use its many free online tutorials
to learn the language. One great advantage of MATLAB over R or other
open-source languages is that there is excellent customer support: If you
haveaquestion,justemailorcallthestaffatMathworks.(Often,someone
withaPhDwillansweryourquestions.)
Ihavetaughtmanyofthesetopicstobothretailandinstitutionaltraders
atmybiannualworkshopsinLondon,aswellasonline(www.epchan.com).
Inordertofacilitatelecturerswhowouldliketousethisasatextbookfora
specialtopicscourseonAlgorithmicTrading,Ihaveincludedmanyexercises
at the end of most chapters. Some of these exercises should be treated as
suggestionsforopen-endedprojects;therearenoready-madeanswers.
Readers will also find all of the software and some data used in the
examples on epchan.com/book3. The userid and password are embedded
in Box 1.1. But unlike my previous books, some of the data involved in
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the example strategies are under strict licensing restrictions and therefore
are unavailable for free download from my website. Readers are invited
P
to purchase or rent them from their original sources, all of which are R
E
describedinChapter1. FA
C
I have benefited from tips, ideas, and help from many people in putting E
thecontenttogether.Anincompletelistwouldinclude:
■ StephenAikin,arenownedauthor(Aikin,2012)andlecturer,whohelped
me understand implied quotes due to calendar spreads in the futures
markets(Chapter6).
■ DavidDonandJosephSignorelliofLimeBrokerage,whocorrectedsome
ofmymisunderstandingofthemarketmicrostructure(Chapter6).
■ Jonathan Shore, infinitely knowledgeable about bitcoins, who helped
compile some order book data in that market and shared that with me
(Chapter7).
■ Dr. Roger Hunter, CTO at our firm, QTS Capital Management, who
reviewed my manuscript and who never failed to find software bugs in
mycodes.
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■ The team at Interactive Brokers (especially Joanne, Ragini, Mike, Greg,
Ian,andRalph)whoseinfinitepatiencewithmyquestionsaboutallissues
relatedtotradingaremuchappreciated.
I would like to thank Professor Thomas Miller of Northwestern Uni-
versity for hiring me to teach the Risk Analytics course at the Master of
Science in Predictive Analytics program. In the same vein, I would also
like to thank Matthew Clements and Jim Biss at Global Markets Training
for organizing the London workshops for me over the years. Quite a few
nuggets of knowledge in this book come out of materials or discussions
fromthesecoursesandworkshops.
Tradingandresearchhavebeenmadealotmoreinterestingandenjoyable
becauseIwasabletoworkcloselywithourteamatQTS,whocontributed
toresearch,ideas,andgeneralknowledge,someofwhichfindtheirwayinto
thisbook.Amongthem,Roger,ofcourse,withoutwhomtherewouldn’tbe
QTS,butalsoYang,Marcin,Sam,andlastbutnotleast,Ray.
Of course, none of my books would come into existence without the
supportofWiley,especiallymylong-timeeditorBillFalloon,development
editorJulieKerr,productioneditorCarolineMaria,andcopyeditorCheryl
Ferguson(fromwhomnomissing‘‘end’’toa‘‘for’’-loopcanescape).Itwas
xii trulyagreatpleasuretoworkwiththem,andtheirenthusiasmandprofes-
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sionalismaregreatlyappreciated.
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