Crunch the market : a Big Data approach to trading system optimization

dc.contributor.advisorAziz, Adnan
dc.creatorMauldin, Timothy Allanen
dc.date.accessioned2014-04-23T17:27:44Zen
dc.date.issued2013-12en
dc.date.submittedDecember 2013en
dc.date.updated2014-04-23T17:27:45Zen
dc.descriptiontexten
dc.description.abstractDue to the size of data needed, running software to analyze and tuning intraday trading strategies can take large amounts of time away from analysts, who would like to be able to evaluate strategies and optimize strategy parameters very quickly, ideally in the blink of an eye. Fortunately, Big Data technologies are evolving rapidly and can be leveraged for these purposes. These technologies include software systems for distributed computing, parallel hardware, and on demand computing resources in the cloud. This report presents a distributed software system for trading strategy analysis. It also demonstrates the effectiveness of Machine Learning techniques in decreasing parameter optimization workload. The results from tests run on two different commercial cloud service providers show linear scalability when analyzing intraday trading strategies.en
dc.description.departmentElectrical and Computer Engineeringen
dc.format.mimetypeapplication/pdfen
dc.identifier.urihttp://hdl.handle.net/2152/24094en
dc.subjectBig Dataen
dc.subjectTradingen
dc.subjectStocksen
dc.subjectMachine learningen
dc.subjectOptimizationen
dc.titleCrunch the market : a Big Data approach to trading system optimizationen
dc.typeThesisen
thesis.degree.departmentElectrical and Computer Engineeringen
thesis.degree.disciplineElectrical and Computer Engineeringen
thesis.degree.grantorThe University of Texas at Austinen
thesis.degree.levelMastersen
thesis.degree.nameMaster of Science in Engineeringen

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