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Memetic Computing

, Volume 10, Issue 3, pp 301–306 | Cite as

Finding attractive technical patterns in cryptocurrency markets

  • Sungjoo Ha
  • Byung-Ro Moon
Regular Research Paper
  • 549 Downloads

Abstract

The cryptographic currency market is an emerging venue for traders looking to diversify their investments. We investigate the use of genetic programming (GP) for finding attractive technical patterns in a cryptocurrency market. We decompose the problem of automatic trading into two parts, mining useful signals and applying them to trading strategies, and focus our attention on the former. Extensive experiments are performed to analyze the factors that affect the quality of the solutions found by the proposed GP system. With the introduction of domain knowledge through extended function sets and the inclusion of diversity preserving mechanism, we show that the proposed GP system successfully finds attractive technical patterns. Out-of-sample performance of the patterns indicates that the GP consistently finds signals that are profitable and frequent. A trading simulation with the generated patterns suggests that the captured signals are indeed useful for portfolio optimization.

Keywords

Technical patterns Genetic programming Cryptocurrency Algorithmic trading 

Notes

Acknowledgements

This work was supported by Optus Investment Inc. The ICT at Seoul National University provided some research facilities for this study.

References

  1. 1.
    Allen F, Karjalainen R (1999) Using genetic algorithms to find technical trading rules. J Financ Econ 51(2):245–271CrossRefGoogle Scholar
  2. 2.
    Broder AZ (1997) On the resemblance and containment of documents. In: Compression and complexity of sequences 1997. Proceedings, pp 21–29Google Scholar
  3. 3.
    Chen SH, Kuo TW, Hoi KM (2008) Genetic programming and financial trading: how much about "what we know". In: Zopounidis C, Doumpos M, Pardalos PM (eds) Handbook of financial engineering. Springer, Boston, MA, pp 99–154CrossRefGoogle Scholar
  4. 4.
    Contreras I, Jiang Y, Hidalgo JI, Núñez-Letamendia L (2012) Using a gpu-cpu architecture to speed up a ga-based real-time system for trading the stock market. Soft Comput 16(2):203–215CrossRefGoogle Scholar
  5. 5.
    Ha MH, Moon BR (2017) The evolution of neural network-based chart patterns: a preliminary study. In: Genetic and evolutionary computation conference, pp 1113–1120Google Scholar
  6. 6.
    Ha MH, Lee S, Moon BR (2016) A genetic algorithm for rule-based chart pattern search in stock market prices. In: Genetic and evolutionary computation conference, pp 909–916Google Scholar
  7. 7.
    Ha S, Moon BR (2015) Fast knowledge discovery in time series with GPGPU on genetic programming. In: Genetic and evolutionary computation conference, pp 1159–1166Google Scholar
  8. 8.
    Hrbacek R, Sekanina L (2014) Towards highly optimized cartesian genetic programming: from sequential via SIMD and thread to massive parallel implementation. In: Genetic and evolutionary computation conference, pp 1015–1022Google Scholar
  9. 9.
    Jiang Z, Xu D, Liang J (2017) A deep reinforcement learning framework for the financial portfolio management problem. arXiv preprint arXiv:1706.10059
  10. 10.
    Kampouridis M, Tsang E (2010) Eddie for investment opportunities forecasting: extending the search space of the GP. In: 2010 IEEE congress on evolutionary computation (CEC), pp 1–8Google Scholar
  11. 11.
    Kaucic M (2010) Investment using evolutionary learning methods and technical rules. Eur J Oper Res 207(3):1717–1727CrossRefGoogle Scholar
  12. 12.
    Kim YB, Kim JG, Kim W, Im JH, Kim TH, Kang SJ, Kim CH (2016) Predicting fluctuations in cryptocurrency transactions based on user comments and replies. PloS One 11(8):e0161,197CrossRefGoogle Scholar
  13. 13.
    Lee SK, Moon BR (2010) A new modular genetic programming for finding attractive technical patterns in stock markets. In: Genetic and evolutionary computation conference, pp 1219–1226Google Scholar
  14. 14.
    Leskovec J, Rajaraman A, Ullman JD (2014) Mining of massive datasets. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  15. 15.
    Lipinski P (2007) ECGA vs. BOA in discovering stock market trading experts. In: Genetic and evolutionary computation conference, pp 531–538Google Scholar
  16. 16.
    Loginov A, Heywood MI (2014) On evolving multi-agent FX traders. In: European conference on the applications of evolutionary computation, pp 203–214Google Scholar
  17. 17.
    McKenney D, White T (2012) Stock trading strategy creation using GP on GPU. Soft Comput 16(2):247–259CrossRefGoogle Scholar
  18. 18.
    Murphy JJ (1999) Technical analysis of the financial markets: a comprehensive guide to trading methods and applications. Penguin, LondonGoogle Scholar
  19. 19.
    Nakamoto S (2008) Bitcoin: a peer-to-peer electronic cashsystem. https://bitcoin.org/bitcoin.pdf. Accessed 1 Aug 2018
  20. 20.
    Prechelt L (1998) Automatic early stopping using cross validation: quantifying the criteria. Neural Netw 11(4):761–767CrossRefGoogle Scholar
  21. 21.
    Rohrbach J, Suremann S, Osterrieder J (2017) Momentum and trend following trading strategies for currencies revisited-combining academia and industry. SSRN: https://ssrn.com/abstract=2949379. Accessed 6 June 2017
  22. 22.
    Shah D, Zhang K (2014) Bayesian regression and bitcoin. In: 2014 52nd Annual Allerton conference on communication, control, and computing (Allerton). IEEE, pp 409–414Google Scholar
  23. 23.
    Wilson G, Banzhaf W (2010) Interday foreign exchange trading using linear genetic programming. In: Proceedings of the 12th annual conference on genetic and evolutionary computation, pp 1139–1146Google Scholar
  24. 24.
    Zhou C, Yu L, Huang T, Wang S, Lai KK (2006) Selecting valuable stock using genetic algorithm. In: Asia-Pacific conference on simulated evolution and learning, pp 688–694Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Computer Science and EngineeringSeoul National UniversitySeoulKorea

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