A NEURAL NET FOR STOCK TREND PREDICTOR · Thus, neural network can be a suitable approach for it....

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A NEURAL NET FOR STOCK TREND PREDICTOR CS 297 Report Presented to Dr. Chris Pollett Department of Computer Science San Jose State University In Partial Fulfillment of the Requirements for the Class CS 297 By Sonal Kabra Dec 2016

Transcript of A NEURAL NET FOR STOCK TREND PREDICTOR · Thus, neural network can be a suitable approach for it....

Page 1: A NEURAL NET FOR STOCK TREND PREDICTOR · Thus, neural network can be a suitable approach for it. To test this, I am building a convolutional neural network for a small dataset. Convolutional

ANEURALNETFORSTOCKTRENDPREDICTOR

CS297Report

Presentedto

Dr.ChrisPollett

DepartmentofComputerScience

SanJoseStateUniversity

InPartialFulfillment

oftheRequirementsfortheClass

CS297

By

SonalKabra

Dec2016

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TABLE OF CONTENTS

INTRODUCTION................................................................................................................................... 3

DELIVERABLE 1................................................................................................................................... 5

DELIVERABLE 2................................................................................................................................... 9

DELIVERABLE 3...................................................................................................................................12

DELIVERABLE 4…...............................................................................................................................15

CONCLUSION .......................................................................................................................................16

REFERENCES .......................................................................................................................................17

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INTRODUCTION

The“NeuralNetStockTrendPredictor”isanapproachtodevelopingastocktrend

predictor.ThisreportdescribespreliminaryworktowardsmyCS297project.Stock

predictionusingcomputersisalsoknownasalgorithmictrading(AT)orautomatedtrading.

Sincethemid-2000s,nearlyallthefinancialtradesareexecutedviacomputers.Muchofthat

tradingoccursalgorithmically,withcomputersexecutingpurchasesandsalesinresponse

toamarket.About66percentofallequitytransactionsintheUnitedStatesnowaredone

viahighfrequencyoralgorithmictrading.[1]

Algorithmictradingistheprocessofusingprogrammedcomputerstofollowa

definedsetofinstructionsforplacingatradeinorder.Thishelpstogenerateprofitsata

speedandfrequencythatisimpossibleforahumantrader.Theseinstructionscanbebased

onvolume,timing,price,oranymathematicalmodel.Algorithmictradinghelpstotakeout

thehumanemotionalimpactontrading.Thus,increasingtheprofitopportunitiesforthe

trader.[1]

Forexample,ifacomputerisprovidedwithallthehistoricalstockdata,itcanlearn

andanalyzethepatternsinthedataandcanpredictthefuturevalueforastockbasedonits

findings.

IamusingQuandlforpoolingallthehistoricalstockdata,andYahoo-Financefor

realtimestockdata.Bothwebsitesprovidefinancialandeconomicaldata.Thestockdata

pooledfromtherecontains5featuresasshowninthefollowingfigure:

Therefore,theobjectiveofthisprojectistoanalyzethedataandgeneratefeatures

thathelppredictstockprices.Besidesthegeneratedfeaturestheaimistobuildacomputer

programmablemodelusinganeuralnetandvariousmachine-learningtechniques.Neural

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netisaninformation-processingparadigm.Theconceptisinspiredfromthewaybiological

nervoussystems,suchasthebrain,processinformation.

ThefollowingarethedeliverablesthatIhaveimplementedthissemesterto

understandtheessenceandtheworkingofalgorithmictradingsystem.

InDeliverable1,Icreatedapresentationcoveringthebasicsofanalgorithmic

tradingsystemandcalculatedsomebasictechnicalindicatorsfordataanalysis.In

deliverable2,Idevelopedaneuralnetworktopredictthestartingmonthforgivendata

series.Moredetailsonthiswillbediscussedunderthesectiontitled‘Deliverable2.’Inthe

nextdeliverable,i.e.Deliverable3,Idevelopedaprogramtopredicttheclosingpricefora

stockusingtheDecisiontreeregressionanalysisandKnearestneighborregression

analysis.InDeliverable4,Idevelopedaprogramthatpredictsthereturnsofaportfolioof

stock.

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DELIVERABLE1Inordertomovetowardsimplementation,itwillbeusefultounderstandthebasic

ofalgorithmictrading.Theprocessofalgorithmictradingisasshowninfigure1.[2]

Figure1:AlgorithmicTradingProcess[2]

Thetradingprocessisdividedintofivestagesasfollows:

1.Dataaccessingandcleaning:

Algorithmictradingobtainsdatafromvarioussourcesandcleansthedataaccording

tothealgorithmicrequirements.Thedatacanberawdata,cleaneddataoranalyzeddata.

2.Pre-tradeanalysis:

Inthisstage,basedoninputteddata,theATanalyzesthevariouspropertiesofthe

financialinstrumentsinordertoidentifytradingopportunities.Thepropertiescanbevalue

ofacompanyorsentimentanalysisforacompany.Theanalysiscanbe:

FundamentalAnalysis:Inthisthefactors,whichmightaffecttheinstrument’svaluesuch

astheeconomicstateofacountry,orpricetoearningsratioofacompanyareanalyzed.

TechnicalAnalysis:Ittriestopredictfuturepricemovementsofaninstrument.Thisis

basedonitspricehistory,varioustradinghistorieslikevolumetradedpreviously.Thus,

tryingtoidentifythetradingpatterns.

QuantitativeAnalysis:Thisisamathematicalandstatisticalanalysisofaninstrument.It

describestherandomnessofthepriceofaninstrument.Thisismainlyusefulinperforming

riskmanagement.

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3.Tradingsignalgeneration:

Inthisstage,itgeneratestheportfolioofinstrumentsbasedonthepreviousstage

analysisresultinginwhattotrade,howmuchtotradeandwhentotrade.

4.Tradeexecution:

Inthisstage,thetrademayormaynotbeexecutedviaautomationbecauseofthe

constrainingfactorssuchasliquidityofmarketandsizeoforder.Thus,ittriestoexecute

thetradeinsuchawaythatimpactonmarketandriskoftimingisminimized.

5.Post-tradeanalysis:

Inthisstage,itanalyzestheresultsofthetrade.Itcalculatesthedifferencebetween

thepricewhenabuy/selldecisionwasmadeandthefinalexecutionprice.Thisisimportant

becausebasedonthisanalysisandresultstheabovemodelsarereadjustedforgenerating

moreprofit.

AfterlearningATprocess,Iunderstoodthattechnicalanalysisiscrucialinanalyzing

howthestockmarketoraparticularstockismoving.Forthe297partoftheproject,Ihave

implementedthefollowingtechnicalindicators:

DailyReturns:ThisisthepercentagedifferenceofAdjustedClosePriceofi-thday

comparedto(i-1)-thday:

IhavecalculatedthedailyreturnsontheadjustedclosingpriceofApple’s(Stocksymbol:

AAPL)stockdata.Ihavepooledhistoricalstockdatafrom01-01-2014to09-12-2016from

YahooFinance.Tocalculatethis,Ihaveusedthepct_change()functionofPandas,aPython

librarymodule.Pandasisadataanalysislibrary.Itoffersdatastructuresandoperationsfor

manipulatingnumericaltablesandtimeseries.pct_change()isapercentchangeovera

periodoftimet.

Thisgivesusthepercentchangeoveragivennumberofperiods.Theoutputcanbeseenin

thefollowingscreenshot.Thes_returnsindicatethestockreturnsbasedonadjacentclose

(s_adjclose)valueoftheApplestock.

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Volatility:Volatilityisthestandarddeviationofthereturnsofthestock.Itgivesusasense

ofhowmuchthestockreturnsfluctuateandhowriskyitisincomparisontotheoverall

marketreturn.

Iamcalculatingvolatilitybyusingthecovariancematrix.Thefirstelementofamatrixwill

giveusthevolatility.Thus,itgivesasenseofhowriskythestockiscomparedtomarket

return.

Covariancematrix:Covarianceindicatestheleveltowhichtwovariablesvarytogether.

Theformulatocalculatecovariancematrixis:

Ihaveusedcov()functionofNumpylibrary.Numpyisafundamentalpackageforscientific

computing.

Beta:Betaofastockisameasureoftherelativeriskofthestockwithrespecttothemarket.

Itindicateswhetherthestockismoreorlessvolatilethanthemarket.

Ifthebetavalueisgreaterthan1meansthatthestockreturnsamplifythemarketreturns

onboththeupsideanddownside.Whenthebetavalueisless,then1meansthatthestock

returnsaresubduedincomparisontothemarketreturns.Ihaveusedthefollowing

formulatocalculatethevalueofBeta.

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Momentum:Momentumisameasureofthepastreturnsoveracertainperiod.For

example,the1-yearmomentumwillbethe1-yearreturnofthestock.Itmeasurestherate

oftheriseorfallinstockprices.Itisaveryusefulindicatorofthestrengthsandweaknesses

ofastock.Theformulaforcalculatingmomentumisasfollows.

Alpha:Alphaofastockgivesyouameasureoftheexcessreturnwithrespecttothemarket.

Positivealphasforastockorportfoliogivesyouasenseofhowwellyourstock

outperformedamarket.Alphaisahistoricalmeasureofthestockreturnsoninvestment

comparedtotheriskadjustedexpectedreturn.

RESULT:

TheoutputofDeliverable1foralltheabove-mentionedtechnicalindicatorsisasfollows.

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DELIVERABLE2 Forthisdeliverable,Ihavebuiltaneuralnetwork,whichcantellthestartingmonth

ofagivenlabeleddataseries.Forexample,givenaseriesofsalesnumberofiPhonelabeled

eachwithmonthforanalyzing,oneshouldbeabletotellwhatcouldbethemonthofthe

firstsalespointinagivensimilardataset(size=10)withoutlabels.

Myneuralnetworkinthisdeliverablewillbedoingthesamething.Forthis

problem,Iusedanemploymentdatasetofgoodsproducingindustry.Idownloadedthis

datasetfromtheQuandlwebsite.Thedatasetcontainstwocolumnsofdatesand

employmentvalues.Theneuralnetwilltellusthemonthofthefirstemploymentdatavalue

ofgiventestset.

Aneuralnetworkisaprogrammingparadigmthatenablesacomputertolearnfrom

observationaldata.Itcanrecognizecomplexpatternsveryeasily.Usually,aneuralnetwork

getsalargenumberofinputandexpectedoutputforthatinput.Itthensearchesforthe

relationsbetweeninputandoutput.Oncethesepatternsareidentifiedthentheneural

networkcanproduceoutputonanyadditionalgivendataset.

Instockmarket,alotofinformationisproducedinashortperiodoftime.The

analysisandunderstandingofsuchavastdatainlimitedtimeisnotfeasibleforhumans.

Thus,neuralnetworkcanbeasuitableapproachforit.Totestthis,Iambuildinga

convolutionalneuralnetworkforasmalldataset.

Convolutionalneuralnetworkisafeedforwardneuralnetwork.Itisinspiredby

howthebrainprocessesvisualinputbydividingthesignalsintosmallerareascalled

receptivefields.Similarly,Convolutionallayersexploitthespatialrelationshipbetween

groupsofdatapointsandgeneratethefeaturemapbasedonthem[6].Theselayerstakethe

smallsizeofinput,multiplyitwithweightsandaddbiases.Thisiscalledasanactivation

function.TheConvolutionalneuralnetworkusesitfromrawinputdatatooutputclass

values.

Fordevelopingsuchaconvolutionalneuralnet,IamusingKerasPythonlibrary,

whichisbuiltontopoftheTensorflowlibrary.Tensorflowisanopensourcesoftware

libraryfornumericalcomputationusingdataflowgraphs.Kerasisahigh-levelneural

networklibrarythatfacilitatesfastandeasyprototypingforbuildinganeuralnetwork.

Theemploymentdatasetisaone-dimensionalinputvector.Therearetotalof924

datapointsinadataset.Ipreprocessedthedatatoreducethevaluestorangefrom0to1

anddivideditintoatestsetandatrainset.AsIampredictingthemonth,mytargetvariable

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isavaluefrom1to12.Totransformitintoabinarymatrix,Iusedonehotencodingonthe

targetvariable.Thebasicarchitectureformyconvolutionalneuralnetisasshowninthe

followingfigure:

Figure2:CNNarchitecture

Myfirstlayerofneuralnetisaconvolutionallayer.Thislayertakestheinputinthe

shapeof(12,1).Fromthisitgenerates18featuremapsofstride1.Thefeaturelengthisof

size3.Iusedrectifieractivationfunction.Afterthis,Iusedamaxpoollayerwiththepool

sizeof2andfollowingthis,Iagainusedaconvolutionallayer.Thisgenerates6-featuremap

ofsize2.Finally,Iusedadropoutlayerfollowedbyadenselayertogeneratetheoutput.

Theoutputlayercontains12classes.Thedenselayerusedasoftmaxactivationfunctionto

outputprobability-likepredictionsforeachclass.

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RESULT:

Theoutputforthisconvolutionalneuralnetworkisasfollows.

Forthisproblem,Itrainedmyneuralnetworkfor300epochs.Inmachine-learning

parlance,anepochisacompletepassthroughagivendataset.Thatis,bytheendofone

epoch,myneuralnetwork,inthiscase-aconvolutionalnet,willhavebeenexposedto

everyrecordtoexamplewithinthedatasetonce.AfterthatIachieved75%accuracy.The

numbersinthelastarrayindicatetheoutputclassificationforastartingmonth.The

expectedoutputwas[8,7,6,5,4,3,2,1,12,11,10,9].

Toevaluatewhethermyneuralnetisperformingwellinthisproblem,Idecidedtotestthe

sameproblemwith3people.Theresultiscompiledinthefollowingtable.

PredictedResult ActualResult

Person1 [10,9] [9,8]

Person2 [7,8] [5,4]

Person3 [10,9] [12,11]

Table1:HumanExperiment

AsshownintheTable1,theclosestanswergivenhasadifferenceofonemonthinactual

resultsandpredictedresults.Thus,theresultsgivenbytheneuralnetarecertainlymore

accuratethanhumans.

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DELIVERABLE3 Thethirddeliverablewastodevelopaprogramtopredicttheclosingpriceofa

stockusingthehistoricaldataofthesamestock.Forexample,stockdataforCISCOSystems

isgivenfromtheyear2010tilltoday.Basedonthisdata,aprogramhastopredictwhatwill

betheclosingpriceofCISCOtoday.

Instockmarketprediction,wepredictthepriceofaparticularstock.Aspriceisa

continuousvariable,theregressionmethodworksbetter.Becauseifwecomparetostock

trends,theexactincrementinstockindexmayprovidemoreinformationforbuildinga

predictionmodel.

Andsimilarly,ingeneralhumanstendtodosimilartrading.Theytrytobuyorsell

thestockbasedonthepastperformanceofthestock.Peoplechangetheirstrategyonly

whenthereissomemajorcurrentnewsthatinfluencesthestockmarket.

Forthisproblem,IusedtheDecisionTreeandKNearestNeighbors(KNN)machine

learningtechniques,becausetheycancapturethepastdataandmakeareliableprediction

basedonit.Thus,decisiontreesandKNNregressiontechniquesaregoodchoices.

Decisiontreessplitthedataintosmallgroupsbasedonmaximizinginformation

gain.Thus,thesegroupscapturethepastdataandpreciselypredictdatathataresimilarto

thepastdata.K-nearestneighborspredicttheresultusingk-nearestdatapointsfromthe

pastdata.Hence,italsocancapturethepastdataandassumesimilardatapointswillhave

similarresults.

Forimplementingtheaboveapproach,IusedScikit-learnPythonlibrary.Scikit-

learnisaspeciallibraryformachinelearning.Itprovidessimpleandefficienttoolsfordata

mining.

Initially,IqueriedthestockdatafromQuandlbasedonthereadtickersymboland

userinputteddates.Iusedonly‘Open,’‘High,’and‘Volume’featuresoftheretrieveddataset.

Ialsoaskedtheusertoinputthepredictiondateforwhichtheywanttheprediction.

Iftherearenogivenpredictiondates,thenquerythemostrecentdatafromYahoo

Finance.Idividedtheaboveretrieveddatasetusingcross-validationtocreateatrainand

testdatasetbysplittingthedatain75:25ratio.

IusedensemblebaggingregressormethodforbothdecisiontreeandKNN

regressor.Abaggingregressorisamethodofcombiningmultiplepredictorsandthenusing

anaveragingorvotingmethodtogetthevalue.

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Therefore,tofindthebestparameters,Iusedagridsearchmethod.Usingthose

parameterstrainthedataandcalculatetherootmeansquarederrorwiththetestdataand

predictthevalueforthetestdata.

RESULTS:

1.UsingDecisionTree:

FollowingistheoutputfortheFacebookstockpriceusingDecisionTree.Here

predictiondateswerenotprovided.DataistrainedfordatafromJan2012tillDec2016.

TheactualclosingpricesareFB:120.57,CSCO:30.63,AAPL:115.82

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2.UsingKNN:

The following screen shot shows the predicted and actual values for stocks of

Facebook,CiscoandApple.Herepredictiondateisprovided.ThedatasetisfromJan.2016

tillDec.2016.

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DELIVERABLE4 Forthisdeliverable,Ipredictedreturnportfolioofastock.Whenapersoninvestsin

more than one stock then it creates the portfolio of a stock. People buy, sell, or hold the

portfoliobasedonprofitorlossgeneratedfromit.Inthisdeliverable,Icalculatedprofitor

lossofaportfolio.ThisdeliverableisanextensionofDeliverable3.

Icalculatedreturnofstockbasedonpredictedclosepriceforindividualstock,and

later,summedupallthereturnsofastock.Iftheresultingvalueispositive,thenportfoliois

generatingprofit.

RESULT:

The portfolio of a stock contains Facebook, Cisco and Apple shares. Here, suppose I am

investingonDec.01,2016,thenmypredictedreturnofportfolioforDec.12isaround7%

asfollows.

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CONCLUSIONIhavecompletedthebasicimplementationpartofmyproject.Iamnowawareof

whatpartsIneedtoemphasizemoreforCS298.

DuringthespanofCS297,Istartedbygaininganunderstandingofhowthestock

predictionsystemworks,andwhataretheimportantfeaturesthataffectthetrendingofa

stock.Stockpredictionisnotastraightproblem.Therearevariouspatternsinthedataand

allthefeaturesofapredictionmodeldependoneachother.

InCS297,Iexploredallthesedependenciesofdatabyusingdifferenttypesof

approacheslikeneuralnet,decisiontreeandk-nearestneighbors.Ialsoperformed

technicalanalysesondatasetinordertomeasurethereturn,riskofinvesting,and

performanceofstockfactors.

InCS298,Iwillmergethesefeatureswithotherapproachestoimprovetheir

predictioncapabilities.Iwillalsoaddthefundamentalanalysismodule,whichwillperform

theanalysisofacompanyforwhichwewanttopredictthestockprice.Forthat,Ineedto

studyindepththevariouseconomicindicators.Iwillalsoexploretheothermachine

learningtechniquesforstockprediction.

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REFERENCES1. Readmore:BasicsofAlgorithmicTrading:ConceptsandExamples|Investopedia

http://www.investopedia.com/articles/active-trading/101014/basics-algorithmic-

trading-concepts-and-examples.asp#ixzz4PA8nb8Kq

2. Philip,T.,etall,algorithmicTradingReview

3. BarryJohnson-AlgorithmicTrading&DMA

4. PerryKaufman-TradingSystemsandMethods(5thed)(2013)


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