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Trading algorithms for high-frequency currency trading
Umeå University, Faculty of Science and Technology, Department of Physics.
2018 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

This thesis uses modern portfolio theory together with machine learning techniques to generate stable portfolio returns over eleven currency pairs with spreads included. The backtests show that support vector machine predicted future returns better than neural network and linear regression. Principal component analysis and data smoothing combined with the local outlier factor further improved the performance of the trading algorithm. However, the ensemble of the top performed predictor performed below the individual predictors. Also, the use of different error estimates showed the criticality of mean arctangent absolute percentage error over mean absolute error and over mean squared error for profitability. For obtaining sensible results in a transaction costless setting, adopting risk adjusted leverage proved necessary. Otherwise, the profit-maximizing leverage surpassed the risk adjusted in a spread setting.

Place, publisher, year, edition, pages
2018.
National Category
Mathematics
Identifiers
URN: urn:nbn:se:umu:diva-146315OAI: oai:DiVA.org:umu-146315DiVA, id: diva2:1195277
Subject / course
Examensarbete i teknisk fysik
Educational program
Master of Science Programme in Engineering Physics
Supervisors
Examiners
Available from: 2018-04-25 Created: 2018-04-04 Last updated: 2018-04-25Bibliographically approved

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fulltext(4029 kB)56 downloads
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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf