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1、InternationalJournalofComputerApplications(0975–8887)Volume36–No.5,December2011GeneticAlgorithm:AnApplicationtoTechnicalTradingSystemDesignV.KapoorS.DeyA.P.KhuranaInstituteofEngineering&IndianInstituteofManagementSchoolofComputerScienceTechnology,IndoreDeviAhilyaUniversity,IndoreDeviAhilyaUniv
2、ersity,IndoreABSTRACTavailabilityofvirtuallyinfinitenumberoftechnicaltradingrulesthemultitudeofwaysinwhichtheycanbeapplied.Recentstudieshaveshownthatinthecontextoffinancialmarkets,technicalanalysisisaveryusefultoolforpredictingMorerecentlymovingaverageshavebeenusedbyBrocket.altrends.MovingAver
3、agerulesareusuallyusedtomake“buy”[2],theirworkshowsthatprofitableresultscouldbeobtainedor“sell”decisionsonadailybasis.Duetheirabilitytocoverusingthismethod.Asimplemovingaveragesystem,commonlylargesearchspaceswithrelativelylowcomputationaleffort,usedintradingsimulators,andhastwosetofparameters(
4、theGeneticAlgorithms(GA)couldbeeffectiveinoptimizationoflengthsoftwomovingaverages).Asmovingaveragelengthstechnicaltradingsystems.Thispaperstudiestheproblem:howcancovervarioustimeframes(from1dayto500daysetc.),thecanGAbeusedtoimprovetheperformanceofaparticularuseofthecorrectparameters(i.e.thele
5、ngths)inconstructingtradingrulebyoptimizingitsparameters,andhowchangesintradingrulesisimmenselyimportant.Also,thereexistalargethedesignoftheGAitselfcanaffectthesolutionqualitynumberofvariousparameterscombinationsformovingaverageobtainedincontextoftechnicaltradingsystem.Inourstudy,werulesmaking
6、itimpossibletotestallofthemmanuallyasaparthaveconcentratedonexploitingthepowerofgeneticalgorithmsofthedecisionmakingprocess.Therefore,itisimperativetotoadjusttechnicaltradingrulesparametersinbackgroundofdevelopautomatedmethodsforoptimizingtradingsystems.financialmarkets.Theresultsofexperiments
7、basedonrealtime-seriesdatademonstratethattheoptimizedruleobtainedusingOptimizationisasearchprocessbywhichthe?best?solutioncantheGAcanincreasetheprofitgeneratedsignificantlyasbediscovered.Itissimplyamethodoralgorithmthatallowsuscompareto
