Knowledge and Information Systems

, Volume 53, Issue 3, pp 767–804 | Cite as

An algorithmic framework for frequent intraday pattern recognition and exploitation in forex market

  • Nikitas Goumatianos
  • Ioannis T. Christou
  • Peter Lindgren
  • Ramjee Prasad
Regular Paper


We present a knowledge discovery-based framework that is capable of discovering, analyzing and exploiting new intraday price patterns in forex markets, beyond the well-known chart formations of technical analysis. We present a novel pattern recognition algorithm for Pattern Matching, that we successfully used to construct more than 16,000 new intraday price patterns. After processing and analysis, we extracted 3518 chart formations that are capable of predicting the short-term direction of prices. In our experiments, we used forex time series from 8 paired-currencies in various time frames. The system computes the probabilities of events such as “within next 5 periods, price will increase more than 20 pips”. Results show that the system is capable of finding patterns whose output signals (tested on unseen data) have predictive accuracy which varies between 60 and 85% depending on the type of pattern. We test the usefulness of the discovered patterns, via implementation of an expert system using a straightforward strategy based on the direction and the accuracy of the pattern predictions. We compare our method against three standard trading techniques plus a “random trader,” and we also test against the results presented in two recently published studies. Our framework performs very well against all systems we directly compare , and also, against all other published results.


Data mining Pattern recognition Hidden intraday patterns Template grid method Forex 


  1. 1.
    Alexander SS (1961) Price movements in speculative markets: trends or random walks. Ind Manag Rev 2:7–26Google Scholar
  2. 2.
    Bessembinder H, Chan K (1995) The profitability of technical trading rules in the Asian stock markets. Pac Basin Finance J 3(2–3):257–284CrossRefGoogle Scholar
  3. 3.
    Bo L, Linyan S, Mweene R (2005) Empirical study of trading rule discovery in China stock market. Expert Syst Appl 28:531–535CrossRefGoogle Scholar
  4. 4.
    Brock W, Lakonishok J, Lebaron B (1992) Simple technical trading rules and the stochastic properties of stock returns. J Finance 47:1731–1764CrossRefGoogle Scholar
  5. 5.
    Bulkowski TN (2008) Encyclopedia of candlestick charts, 2nd edn. Wiley, HobokenGoogle Scholar
  6. 6.
    Caginalp G, Laurent H (1998) The predictive power of price patterns. Appl Math Finance 5:181–205CrossRefMATHGoogle Scholar
  7. 7.
    Cheng C-H, Chen T-L, Wei L-Y (2010) A hybrid model based on rough sets theory and genetic algorithms for stock price forecasting. Inf Sci 180:1610–1629CrossRefGoogle Scholar
  8. 8.
    Duda R, Hart P (1973) Pattern classification and scene analysis. Wiley, New YorkMATHGoogle Scholar
  9. 9.
    Fama EF (1970) Efficient capital markets: a review of theory and empirical work. J Finance 25:383–417CrossRefGoogle Scholar
  10. 10.
    Goumatianos N, Christou IT, Lindgren P (2013a) Useful pattern mining on time series: applications in the stock market. In: Proceedings of 2nd international conference on pattern recognition applications and methods (ICPRAM 2013), Barcelona, Spain, pp 608–612Google Scholar
  11. 11.
    Goumatianos N, Christou IT, Lindgren P (2013) Stock selection system: building long/short portfolios using intraday patterns. Procedia Econ Finance 2:296–307 (Proc. Intl. Conf. Appl. Econ. 2013)Google Scholar
  12. 12.
    Haeri A, Hatefi S-M, Rezaie K (2015) Forecasting about EUR/JPY exchange rate using hidden Markova model and CART classification algorithm. J Adv Comput Sci Technol 4(1):84–89CrossRefGoogle Scholar
  13. 13.
    Hung K-K, Cheung Y-M, Xu L (2003) An extended ASLD trading system to enhance portfolio management. IEEE Trans Neural Netw 14(2):413–425CrossRefGoogle Scholar
  14. 14.
    Ilmanen A (2011) Expected returns: an investor’s guide to harvesting market rewards. Wiley, ChichesterCrossRefGoogle Scholar
  15. 15.
    Jensen MC, Bennington GA (1970) Random walks and technical theories: some additional evidence. J Finance 25(2):469–482CrossRefGoogle Scholar
  16. 16.
    Kao L, He T (2009) Developing actionable trading agents. Knowl Inf Syst 18(2):183–198CrossRefGoogle Scholar
  17. 17.
    Keogh E, Pazzani M (2000) A simple dimensionality reduction technique for fast similarity search in large time series databases. In: Proceedings of fourth Pacific-Asia conference on knowledge discovery and data mining, pp 122–133Google Scholar
  18. 18.
    Kong X, Qiang W, Guoqing C (2010) An approach to discovering multi-temporal patterns and its application to financial databases. Inf Sci 180:873–885CrossRefGoogle Scholar
  19. 19.
    Lee KH, Jo GS (1999) Expert system for predicting stock market timing using a candlestick chart. Expert Syst Appl 16:357–364CrossRefGoogle Scholar
  20. 20.
    Leigh W, Modani N, Purvis R, Roberts T (2002a) Stock market trading rule discovery using technical charting heuristics. Expert Syst Appl 23(2):155–159CrossRefGoogle Scholar
  21. 21.
    Leigh W, Purvis R, Ragusa JM (2002b) Forecasting the NYSE composite index with technical analysis, pattern recognizer, neural network, and genetic algorithm: a case study in romantic decision support. Decis Support Syst 32:161–174CrossRefGoogle Scholar
  22. 22.
    Leigh W, Modani N, Hightower R (2004) A computational implementation of stock charting: abrupt volume increase as signal for movement in New York stock exchange composite index. Decis Support Syst 37:515–530CrossRefGoogle Scholar
  23. 23.
    Leigh W, Flobish C, Hornik S, Purvis R, Roberts T (2008) Trading with a stock chart heuristic. IEEE Trans Syst Man Cybern Part A 38(1):93–104CrossRefGoogle Scholar
  24. 24.
    Maginn J, Tuttle D, McLeavey D, Pinto J (eds) (2007) Managing investment portfolios: a dynamic process, 3rd edn. Wiley, Hoboken , NJ, USAGoogle Scholar
  25. 25.
    Marshall BR, Young MR, Rose LC (2006) Candlestick technical trading strategies: can they create value for investors? J Bank Finance 30:2303–2323CrossRefGoogle Scholar
  26. 26.
    Nassirtoussi AK, Aghabozorgi S, Wah T-Y, Ngo DCL (2015) Text-mining of news-headlines for FOREX market prediction: a multi-layer dimension reduction algorithm with semantics and sentiment. Expert Syst Appl 42:306–324CrossRefGoogle Scholar
  27. 27.
    Ney H, Steinbiss V, Haeb-Umbach R, Tran B-H, Essen U (1994) An overview of the Phillips research system for large vocabulary continuous speech recognition. Int J Pattern Recognit Artif Intell 8(1):33. doi: 10.1142/S0218001494000036 CrossRefGoogle Scholar
  28. 28.
    Ozturk M (2015) Heuristic-based trading system on Forex data using technical indicator rules. M.Sc. thesis, Comp. Eng. Dept. Middle East Technical UniversityGoogle Scholar
  29. 29.
    Parracho P, Neves RF (2011) Trading with optimized uptrend and downtrend pattern templates using a genetic algorithm kernel. In: Conference: proceedings of IEEE congress on evolutionary computation, New Orleans, LA, USA, 5–8 June 2011Google Scholar
  30. 30.
    Petrov V-Yu, Tribelsky MI (2015) FOREX trades: can the Takens algorithm help to obtain steady profit at investment reallocations? Pis’ma v ZhETF 102(12):958–961Google Scholar
  31. 31.
    Poh KL (2000) An intelligent decision support system for investment analysis. Knowl Inf Syst 2(3):340–358CrossRefMATHGoogle Scholar
  32. 32.
    Raudys S (2013) Portfolio of automated trading systems: complexity and learning set size issues. IEEE Trans Neural Netw Learn Syst 24(3):448–459CrossRefGoogle Scholar
  33. 33.
    Theofilatos K, Likothanasis S, Karathanasopoulos A (2012) Modeling and trading the EUR/USD exchange rate using machine learning techniques. Eng Technol Appl Sci Res 2(5):269–272Google Scholar
  34. 34.
    Toshniwal D, Joshi RC (2005) Similarity search in time series data using time weighted slopes. Informatica 29(1):79–88Google Scholar
  35. 35.
    Wang J-L, Chan S-H (2007) Stock market trading rule discovery using pattern recognition and technical analysis. Expert Syst Appl 33:304–315CrossRefGoogle Scholar
  36. 36.
    Wang J-L, Chan S-H (2009) Trading rule discovery in the US stock market: an empirical study. Expert Syst Appl 36:5450–5455CrossRefGoogle Scholar
  37. 37.
    Walid B, Van Oppens H (2006) The performance analysis of chart patterns: Monte-Carlo simulation and evidence from the euro/dollar foreign exchange market. Empir Econ 30:947–971CrossRefGoogle Scholar
  38. 38.
    Wu J-L, Yu L-C, Chang P-C (2014) An intelligent stock trading system using comprehensive features. Appl Soft Comput 23:39–50CrossRefGoogle Scholar
  39. 39.
    Xu L, Cheung Y-M (1997) Adaptive supervised learning decision networks for trading and portfolio management. J Comput Finance 13(2):806–816Google Scholar
  40. 40.
    Zhang D, Zhou L (2004) Discovering golden nuggets: data mining in financial application. IEEE Trans Syst Man Cybern Part C 34(4):513–522CrossRefGoogle Scholar
  41. 41.
    Zhang Z, Jiang J, Liu X, Lau R, Wang H, Zhang R (2010) A real-time hybrid pattern matching scheme for stock time series. In: Proceedings of 21st Australasian conference on database technologies, vol 104, pp 161–170Google Scholar

Copyright information

© Springer-Verlag London 2017

Authors and Affiliations

  • Nikitas Goumatianos
    • 1
    • 2
  • Ioannis T. Christou
    • 1
  • Peter Lindgren
    • 3
  • Ramjee Prasad
    • 2
  1. 1.Athens Information TechnologyMarousiGreece
  2. 2.CTiFAalborg UniversityAalborgDenmark
  3. 3.Aarhus UniversityAarhus CDenmark

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