ANEURALNETFORSTOCKTRENDPREDICTOR

CS297Report

Presentedto

Dr.ChrisPollett

DepartmentofComputerScience

SanJoseStateUniversity

InPartialFulfillment

oftheRequirementsfortheClass

CS297

By

SonalKabra

Dec2016

NeuralNetStockTrendPredictor 2

TABLE OF CONTENTS

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

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

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

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

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

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

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

NeuralNetStockTrendPredictor 3

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

NeuralNetStockTrendPredictor 4

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.

NeuralNetStockTrendPredictor 5

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.

NeuralNetStockTrendPredictor 6

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.

NeuralNetStockTrendPredictor 7

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.

NeuralNetStockTrendPredictor 8

Momentum:Momentumisameasureofthepastreturnsoveracertainperiod.For

example,the1-yearmomentumwillbethe1-yearreturnofthestock.Itmeasurestherate

oftheriseorfallinstockprices.Itisaveryusefulindicatorofthestrengthsandweaknesses

ofastock.Theformulaforcalculatingmomentumisasfollows.

Alpha:Alphaofastockgivesyouameasureoftheexcessreturnwithrespecttothemarket.

Positivealphasforastockorportfoliogivesyouasenseofhowwellyourstock

outperformedamarket.Alphaisahistoricalmeasureofthestockreturnsoninvestment

comparedtotheriskadjustedexpectedreturn.

RESULT:

TheoutputofDeliverable1foralltheabove-mentionedtechnicalindicatorsisasfollows.

NeuralNetStockTrendPredictor 9

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

NeuralNetStockTrendPredictor 10

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.

NeuralNetStockTrendPredictor 11

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.

NeuralNetStockTrendPredictor 12

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.

NeuralNetStockTrendPredictor 13

Therefore,tofindthebestparameters,Iusedagridsearchmethod.Usingthose

parameterstrainthedataandcalculatetherootmeansquarederrorwiththetestdataand

predictthevalueforthetestdata.

RESULTS:

1.UsingDecisionTree:

FollowingistheoutputfortheFacebookstockpriceusingDecisionTree.Here

predictiondateswerenotprovided.DataistrainedfordatafromJan2012tillDec2016.

TheactualclosingpricesareFB:120.57,CSCO:30.63,AAPL:115.82

NeuralNetStockTrendPredictor 14

2.UsingKNN:

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

Facebook,CiscoandApple.Herepredictiondateisprovided.ThedatasetisfromJan.2016

tillDec.2016.

NeuralNetStockTrendPredictor 15

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.

NeuralNetStockTrendPredictor 16

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.

NeuralNetStockTrendPredictor 17

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)