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High-frequency equity index futures trading using recurrent reinforcement learning with candlesticks
Department of Information Technology, University of Borås, Sweden.
Department of Information Technology, University of Borås, Sweden.ORCID iD: 0000-0003-0412-6199
2015 (English)In: Proceedings - 2015 IEEE Symposium Series on Computational Intelligence, SSCI 2015, IEEE, 2015, 734-741 p.Conference paper, Published paper (Refereed)
Abstract [en]

In 1997, Moody and Wu presented recurrent reinforcement learning (RRL) as a viable machine learning method within algorithmic trading. Subsequent research has shown a degree of controversy with regards to the benefits of incorporating technical indicators in the recurrent reinforcement learning framework. In 1991, Nison introduced Japanese candlesticks to the global research community as an alternative to employing traditional indicators within the technical analysis of financial time series. The literature accumulated over the past two and a half decades of research contains conflicting results with regards to the utility of using Japanese candlestick patterns to exploit inefficiencies in financial time series. In this paper, we combine features based on Japanese candlesticks with recurrent reinforcement learning to produce a high-frequency algorithmic trading system for the E-mini S&P 500 index futures market. Our empirical study shows a statistically significant increase in both return and Sharpe ratio compared to relevant benchmarks, suggesting the existence of exploitable spatio-Temporal structure in Japanese candlestick patterns and the ability of recurrent reinforcement learning to detect and take advantage of this structure in a high-frequency equity index futures trading environment.

Place, publisher, year, edition, pages
IEEE, 2015. 734-741 p.
Keyword [en]
Artificial intelligence, Commerce, Financial data processing, Financial markets, Learning systems, Reinforcement learning, Time series, Time series analysis, Algorithmic trading, Algorithmic trading system, Financial time series, Japanese candlesticks, Machine learning methods, Recurrent reinforcement learning, Research communities, Spatio-temporal structures, Electronic trading
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:hj:diva-38117DOI: 10.1109/SSCI.2015.111Scopus ID: 2-s2.0-84964931411ISBN: 9781479975600 (print)OAI: oai:DiVA.org:hj-38117DiVA: diva2:1163922
Conference
IEEE Symposium Series on Computational Intelligence, SSCI 2015, 8 December 2015 through 10 December 2015
Available from: 2017-12-08 Created: 2017-12-08 Last updated: 2018-01-13Bibliographically approved

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Johansson, Ulf

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CiteExportLink to record
Permanent link

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Citation style
  • apa
  • harvard1
  • 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