hft final report

8/3/2019 HFT Final Report

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NicholasBucheleres

&MatthewBinder

EECS399:StrategicReasoningGroupAutomatedTradingSystemThesis

ProfessorMichaelP.Wellman

December14,2011


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TableofContents

ProjectGoals...03

Foreword...03

ProjectAbstract.04

Introductory$GLD&$GVZCharts06

TestCase#1.11

TestCase#1Summary..17

TestCase#2..18

TestCase#2Summary...24

ProjectSummary25


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Intodaysbusinessworldoffinancialregulation,fraud,andinvestoruncertainty,international

assetmarketshavebecomeveryunsureofthemselves.Traditionalrisk-managementand

forecastingtechniquesarenolongerrelevant,andmanisbecomingincreasinglylessrelevant

comparedtothemachine.Thesearealltrendsthathavebecomeapparenttous,especiallytoNick

athistimeatBankofNewYork/Mellonoverthesummer.Wehadmanyconversationsaboutthe

inefficiencyofthemanualtradersatBNY,andthatsparkedourinterestinthefieldof

quantitative/automatedtrading.

Wehadageneralideaofwhatwewantedtoaccomplish,butbothofuswereadmittedlyquite

ignorantaboutthesecretivefieldofquantitativetrading.So,wegrabbedacouplebooks,anddove

rightin.Thisiswhatwefound.

Westartedoffthesemesterexperimentingwithquantbench.comsstatisticalanalysisplatform.

AfterspendingacoupleofweekswithQuantBench,wedecidedthatitwasnotcompatiblewith

ourgoalsforthesemesteranddidnotoffertheflexibilityandfreedomthatweneeded.Nextwe

workedwithGeniusTrader,anopensourceandhigh-powertechnicalanalysisandtrading

simulationplatformwritteninperl.WhileGeniusTraderhadthepotentialtoallowustodoour

research,itwasnotapracticaltoolforourpurposesbecauseitwaspoorlydocumented,

unsupported,andnearlyimpossibletomodifywithoutbreaking.Finallyafterseveralfalsestarts,

wefoundanopensourcetradingplatformcalledTradeLinkthatwaswritteninC#andappeared

tosuitallofourneeds.TradeLinkisanenterprisegradesoftwareprojectthatwasgraciously

distributedtothepublicdomainbyitsoriginalauthor.TradeLinkhasseveralcomponentsthat

allowittoserverasacompleteautomatedtradingsystemandconnecttoandplaceautomatic

tradeswithseveralofthemajoronlinebrokerages.WeweremainlyinterestedinTradeLink

becauseitwasopensourceandhadamodulardesignthatmadeiteasytoimplementcustom

algorithmsandstrategies.UnlikeGeniusTrader,TradeLinkisverythoroughlydocumentedandis

stillaliveprojectwithdozensofprogrammersreleasingupdatesonaregularbasis.Westarted

thesubstantivepartofourresearchafterdecidingthatTradeLinksuitedourneeds.Nickdesigned

threesimpletypesofalgorithmsforustotest:meanreversion,movingaverages,and


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Abstract:

Atthebeginningofthesemesterourgoalwassettoexecutethefollowingapproachinorderto

answerourfundamentalquestion:

Wewillparseourtimeseriesintotwopieces.Giventhefirstpiece,wewilldevelopeachstrategytooptimizehistoricalreturnsforthepastperiod,remainingconsciousofprevailingvolatilityeffectsthatwenoticeforeachtimeseriesontheperformanceofouralgorithms.

Oncewehaveoptimizedeachstrategyovertime[-1,0),wewillthentreatt=0asthepresent,andactasif(0,1]isoneyearintothefuture.Wewanttoavoidpurebacktestingbecauseourresultswillsimplyreflectourabilitytofitstrategiestohistorical

dataandwillnotnecessarilyhaveanypredictivecapability.Althoughsomefalse-startswereencountered,wehavemanagedtofullyrealizetheresearchvalue

ofourinitialgoal:toanalyzevariousautomatedtradingstrategiesofonepriceseriesgivenascope

ofdecreasingvolatility,netzerovolatility,andincreasingvolatility.

WefoundSPDRsgoldETF$GLDanenticingfitformultiplereasons:italmostexactly(inmost

cases,exactly)followsthespotpriceofgold;it(gold)isahighlyliquidassetthatissensitiveto

changesinvolatility,and,amongotherlessintuitivereasons,goldpricesarenotsubjecttothe

largelyunpredictable,news-drivenpricegapsthattraditionalequitiesface.

Ourfirststepwastodownloadthree,threemonthtimeseriesof$GLDintra-daytradedata.These

datawerechosenfrom,asaforementioned,oneperiodofconstantlydecreasingvolatility,one

periodofnetzerochangevolatility,andoneperiodofconstantlyincreasingvolatility.Asa

benchmarkforgoldvolatilityweusedtheCBOEs$GVZgoldvolatilityindex.

Themostmarkedperiodofrecent,constantlydecreasinggoldvolatilitywas11/25/2008through

2/25/2009;themostmarkedperiodofnetzerovolatilitywas9/15/2009through12/15/2009;

andthemostmarkedperiodofincreasingvolatilitywas6/30/2011through9/30/2011.

Ourfirstteststrategyisasimplemovingaverage(SMA)indicator,whichgeneratessignalswhen

$GLDinterfereswithourSMAdistinctions.Followingthat,weimplementedameanreversion


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strategy.Forthemeanreversionstrategy,weusedamovingaverageindicator,similartothe

SMA,butinsteadofplayingmomentumandbuyingup-trendsandsellingdownwardviolations,we

postulatedthatwhen$GLDmovesabovethemovingaverage(mean)thatitwouldreturnbackto

itsmean.

Thetestresultsareasfollows:

GoldpricesasreflectedthroughoneofthemostliquidETFs$GLD.


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GoldvolatilitymeasuredbytheCBOEgoldvolatilityindex$GVZ

Period#1:Decreasingperiodofgoldvolatilityfrom11/25/2008through2/25/2009.


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Period#1:$GLDpricemovementunderdecreasingvolatility.

Period#2:$GLDpricemovementundernetzerovolatility.


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Period#3:Goldvolatilityfrom6/30/2011through9/30/2011.


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Case#1SimpleMovingAverageOperationParameters2.5hourSimple(Unweighted)MovingAverageProfittarget=$20,000Positionsize=$100IntervalWindow=15minutesIntervalWindowMultiplier=10barsDecreasingVolatility

Left:ResultsfromtheSimpleMovingAverage(SMA)back-testedunderdecreasingvolatility.Right:Tickdatacorrespondingtoback-testeddecreasingvolatilitySMA.


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NetZeroVolatility

Left:IndicatingvalueofSMAatcrossoverpointsundernetzerovolatilityconditions.Right:DescriptionsoftimeofSMAcrossover,positionentry,direction,andexit.


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Left:Positionstakenduringback-testingperiod. Right:OrdersplacedgeneratedbySMAcrossovers.


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Left:Tickdatacorrespondingtonetzerovolatility$GLDpriceseries. Right:Strategyresultsfromnetzerovolatilityback-test.


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Above:Ordersfilledduringback-testsimulation.


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IncreasingVolatility

Left:MessagesdetailingsignalgenerationsfromSMAcrossovers.Right:ResultsfromSMAsback-testperformanceunderincreasingvolatility.


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TestCase#1SummaryUnderthethreemonthdecreasingvolatilityback-test,theSMAstrategyproducedagrosslossof

$328;underthethreemonthnetzerovolatilityback-test,theSMAstrategyproducedagrossloss

of$1043;andunderthethreemonthincreasingvolatilityback-test,theSMAstrategyproduceda

grosslossof$687.

Basedonourfindings,wecanconcludethattheSMAstrategywasnotrobustinthegeneralsense.

Wefind,though,thatthenetzerovolatilityconditionsyieldedthelargestlossofthethree

strategies.Wedeterminethatavolatilityconditionmarkedbymoredrasticandmorefrequent

reversals(zigzagpattern)leadtothefailureoftheSMAstrategyunderthenetzerovolatility

conditions.Duetothespecificexecutionparametersinterval,time,andsizethesefindings

mayormaynotbeageneral,stylized,objectivetrend.


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TestCase#2MeanReversionOperationParameters2.5hourSimple(Unweighted)MovingAverageProfittarget=$20,000Positionsize=$100IntervalWindow=15minutesIntervalWindowMultiplier=10barsDecreasingVolatility

Left:$GLDpriceseriesunderdecreasingvolatilitycondition.Right:Ordersfilledduringtestsimulation.


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Left:Resultsfrommeanreversionbacktestwithdecreasingvolatility.Right:Tickdataof$GLDunderdecreasingvolatilitycondition.


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NetZeroVolatility

Left:Signalsgeneratedwithmeanreversionstrategyundernetzerovolatility.Right:Resultsfrommeanreversionbacktestperformanceundernetzerovolatility.


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Above:Tickdataof$GLDundernetzerovolatilitycondition.


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IncreasingVolatility

Left:Positionsenteredwithmeanreversionstrategyunderincreasingvolatilitycondition.Right:Resultsofmeanreversionstrategyperformancewithincreasingvolatility.


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Above:Tickdataof$GLDunderincreasingvolatilitycondition.


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TestCase#2SummaryUnderthree,three-monthperiodsofdecreasing,netzero,andincreasingvolatility,we

experiencedthreedisparateresultsfromthemeanreversionstrategy.Underdecreasingvolatility

ourmeanreversionstrategyprofited$1,274;undernetzerovolatilityourmeanreversion

strategylost$364;andunderincreasingvolatilityourmeanreversionstrategyprofited$384.

TheseresultsnearlymirrortheresultsfromourSMAstrategy.Itseemsthatwiththemean

reversionstrategy,andwiththeSMAstrategy,thenetzerovolatilityperiodproducedthemost

significantloss.Thisislikelyduetothesameconjecturethatwasmadeabove:periodsinwhich

volatilityswingsbetweenupanddownmakeitmoredifficultforthestrategiestoenterintotight

androbusttrades,andthusdonotcapturetheintendedprofitportionofagivenpricemovement.

Themeanreversionstrategyprovedtobeprofitablefortheperiodsofdecreasingandincreasing

volatility,whereastheSMAstrategydidnotprovetobeprofitableforanyofthevolatilityperiods.


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ProjectSummary

GiventheresultssummarizedinTestCase#1SummaryandTestCase#2Summary,wedonot

findsufficientevidencetoanswerourthesisbeyondareasonabledoubt,butourresearchhas

shedsomelightontothetopic.Althoughwehavenotreachedaspecificresult,weareabletonote

specificoutcomesofourexperimentthatenlightenreadersfromourperspective.

Assummarizedabove,wehavenoteddisparateperformancesofbothstrategiesundereachofthe

threevolatilityconditions.Wehavebeenabletoextrapolatethatoneoftheeffectsthatvolatility

hasontheperformanceofautomatedstrategiescomesasafunctionofthepersistenceofsaid

volatility.Ourstrategiesperformedthebestunderperiodsofnetchange(increasingor

decreasing)volatilityinwhichvolatilitytendedtomovelinearlyasopposedtothevolatility

undulationthatwenotedinthenetzerovolatilityperiods.Weattributethefailureofthe

strategiesunderthenetzeroperiodstotheunpredictable,meanrevertingnatureofthevolatility

duringthoseperiods.

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