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Deep learning models as advisors to execute trades on financial markets
KTH, School of Electrical Engineering and Computer Science (EECS).
2018 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Modeller med djupa artificiella neuronnät som rådgivare vid affärer på finansmarknader (Swedish)
Abstract [en]

Recent work has shown that convolutional networks can successfully handle time series as input in various different problems. This thesis embraces this observation and introduces a new method combining machine learning techniques in order to create profitable trading strategies. The method addresses a binary classification problem: given a specific time, access to prices before this moment and an exit policy, the goal is to forecast the next price movement. The classification method is based on convolutional networks combining two major improvements: a special form of bagging and a weight propagation, to enhance the accuracy and reduce the overall variance of the model. The rolling learning and the convolutional layers are able to exploit the time dependency to strongly improve the trading strategy. The presented architecture is able to surpass the expert traders.

Abstract [sv]

Nyligen utförda arbeten har visat att faltningsnätverk framgångsrikt kan hantera tidsserier som indata i olika problem. Observationen utnyttjas i detta examensarbete som introducerar en ny metod som kombinerar tekniker för maskininlärning för att skapa lönsamma handelsstrategier. Metoden löser ett binärt klassificeringsproblem: beroende på en viss tidpunkt, tillgång till priser före denna tidpunkt och ett säljkriterium så är målet att förutsäga nästa prisvariation. Klassificeringsmetoden baseras på faltningsnätverk som kombinerar två stora förbättringar: en speciell form av bagging och en viktpropagering för att förbättra noggrannheten och reducera modellens varians. Det rullande lärandet och faltningsnätverken kan utnyttja tidsberoendet för att förbättra handelsstrategin. Den presenterade arkitekturen klarar av att prestera bättre än experthandlare.

Place, publisher, year, edition, pages
2018. , p. 49
Series
TRITA-EECS-EX ; 2018:605
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-235782OAI: oai:DiVA.org:kth-235782DiVA, id: diva2:1253330
Supervisors
Examiners
Available from: 2018-10-04 Created: 2018-10-04 Last updated: 2018-10-04Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
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  • vancouver
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  • de-DE
  • en-GB
  • en-US
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  • Other locale
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