Skip to main content
SpringerLink
Log in

Genetic Programming for Combining Directional Changes Indicators in International Stock Markets

  • Conference paper
  • First Online:
Parallel Problem Solving from Nature – PPSN XVII (PPSN 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13399))

Included in the following conference series:

Abstract

The majority of algorithmic trading studies use data under fixed physical time intervals, such as daily closing prices, which makes the flow of time discontinuous. An alternative approach, namely directional changes (DC), is able to convert physical time interval series into event-based series and allows traders to analyse price movement in a novel way. Previous work on DC has focused on proposing new DC-based indicators, similar to indicators derived from technical analysis. However, very little work has been done in combining these indicators under a trading strategy. Meanwhile, genetic programming (GP) has also demonstrated competitiveness in algorithmic trading, but the performance of GP under the DC framework remains largely unexplored.

In this paper, we present a novel GP that uses DC-based indicators to form trading strategies, namely GP-DC. We evaluate the cumulative return, rate of return, risk, and Sharpe ratio of the GP-DC trading strategies under 33 datasets from 3 international stock markets, and we compare the GP’s performance to strategies derived under physical time, namely GP-PT, and also to a buy and hold trading strategy. Our results show that the GP-DC is able to outperform both GP-PT and the buy and hold strategy, making DC-based trading strategies a powerful complementary approach for algorithmic trading.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 139.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Adegboye, A., Kampouridis, M.: Machine learning classification and regression models for predicting directional changes trend reversal in FX markets. Exp. Syst. Appl. 173, 114645 (2021)

    Article  Google Scholar 

  2. Adegboye, A., Kampouridis, M., Johnson, C.G.: Regression genetic programming for estimating trend end in foreign exchange market. In: 2017 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1–8. IEEE (2017)

    Google Scholar 

  3. Aloud, M.E.: Time series analysis indicators under directional changes: the case of Saudi stock market. Int. J. Econ. Financ. Issues 6(1), 55–64 (2016)

    Google Scholar 

  4. Aloud, M.E., Alkhamees, N.: Intelligent algorithmic trading strategy using reinforcement learning and directional change. IEEE Access 9, 114659–114671 (2021)

    Article  Google Scholar 

  5. Aloud, M., Fasli, M.: Exploring trading strategies and their effects in the foreign exchange market: exploring trading strategies. Comput. Intell. 33(2), 280–307 (2016)

    Article  Google Scholar 

  6. Ao, H., Tsang, E.: Trading algorithms built with directional changes. In: 2019 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr), pp. 1–7. IEEE (2019)

    Google Scholar 

  7. Bakhach, A., Tsang, E., Ng, W.L., Chinthalapati, V.R.: Backlash agent: a trading strategy based on directional change. In: 2016 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1–9. IEEE (2016)

    Google Scholar 

  8. Brabazon, A., Kampouridis, M., O’Neill, M.: Applications of genetic programming to finance and economics: past, present, future. Genet. Program Evolvable Mach. 21(1), 33–53 (2020)

    Article  Google Scholar 

  9. Christodoulaki, E., Kampouridis, M., Kanellopoulos, P.: Technical and sentiment analysis in financial forecasting with genetic programming. In: IEEE Symposium on Computational Intelligence for Financial Engineering and Economics (CIFEr) (2022)

    Google Scholar 

  10. Claveria, O., Monte, E., Torra, S.: Evolutionary computation for macroeconomic forecasting. Comput. Econ. 53(2), 833–849 (2019)

    Article  Google Scholar 

  11. Glattfelder, J.B., Dupuis, A., Olsen, R.B.: Patterns in high-frequency FX data: discovery of 12 empirical scaling laws. Quant. Financ. 11(4), 599–614 (2011)

    Article  MathSciNet  Google Scholar 

  12. Guillaume, D.M., Dacorogna, M.M., Davé, R.R., Müller, U.A., Olsen, R.B., Pictet, O.V.: From the bird’s eye to the microscope: a survey of new stylized facts of the intra-daily foreign exchange markets. Financ. Stochast. 1(2), 95–129 (1997)

    Article  Google Scholar 

  13. Hamida, S.B., Abdelmalek, W., Abid, F.: Applying dynamic training-subset selection methods using genetic programming for forecasting implied volatility. arXiv preprint arXiv:2007.07207 (2020)

  14. Hussein, S.M.: Event-based microscopic analysis of the FX market. Ph.D. thesis, University of Essex (2013)

    Google Scholar 

  15. Kampouridis, M., Adegboye, A., Johnson, C.: Evolving directional changes trading strategies with a new event-based indicator. In: Shi, Y., et al. (eds.) SEAL 2017. LNCS, vol. 10593, pp. 727–738. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-68759-9_59

    Chapter  Google Scholar 

  16. Kampouridis, M., Tsang, E.: Investment opportunities forecasting: extending the grammar of a GP-based tool. Int. J. Comput. Intell. Syst. 5(3), 530–541 (2012)

    Article  Google Scholar 

  17. Kelotra, A., Pandey, P.: Stock market prediction using optimized Deep-ConvLSTM model. Big Data 8(1), 5–24 (2020)

    Article  Google Scholar 

  18. Poli, R., Langdon, W.B., McPhee, N.F.: A field guide to genetic programming (2008). Published via http://lulu.com and freely available at http://www.gp-field-guide.org.uk. (With contributions by JR Koza)

  19. Tsang, E.: Directional changes, definitions. Working Paper WP050-10 Centre for Computational Finance and Economic Agents (CCFEA), University of Essex Revised 1, Technical report (2010)

    Google Scholar 

  20. Tsang, E. P. K., Tao, R., Ma, S.: Profiling financial market dynamics under directional changes. Quantit. Finan. (2016). https://doi.org/10.1080/14697688.2016.1164887

  21. Tsang, E.P., Tao, R., Serguieva, A., Ma, S.: Profiling high-frequency equity price movements in directional changes. Quant. Financ. 17(2), 217–225 (2017)

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computer Science and Electronic Engineering, University of Essex, Wivenhoe Park, UK

    Xinpeng Long, Michael Kampouridis & Panagiotis Kanellopoulos

Authors
  1. Xinpeng Long
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Michael Kampouridis
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Panagiotis Kanellopoulos
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xinpeng Long .

Editor information

Editors and Affiliations

  1. TU Dortmund, Dortmund, Germany

    Günter Rudolph

  2. Leiden University, Leiden, The Netherlands

    Anna V. Kononova

  3. Shinshu University, Nagano, Japan

    Hernán Aguirre

  4. Technische Universität Dresden, Dresden, Germany

    Pascal Kerschke

  5. University of Stirling, Stirling, UK

    Gabriela Ochoa

  6. Jožef Stefan Institute, Ljubljana, Slovenia

    Tea Tušar

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Long, X., Kampouridis, M., Kanellopoulos, P. (2022). Genetic Programming for Combining Directional Changes Indicators in International Stock Markets. In: Rudolph, G., Kononova, A.V., Aguirre, H., Kerschke, P., Ochoa, G., Tušar, T. (eds) Parallel Problem Solving from Nature – PPSN XVII. PPSN 2022. Lecture Notes in Computer Science, vol 13399. Springer, Cham. https://doi.org/10.1007/978-3-031-14721-0_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-14721-0_3

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-14720-3

  • Online ISBN: 978-3-031-14721-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation