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Published in: Knowledge and Information Systems 3/2017

22-04-2017 | Regular Paper

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

Authors: Nikitas Goumatianos, Ioannis T. Christou, Peter Lindgren, Ramjee Prasad

Published in: Knowledge and Information Systems | Issue 3/2017

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Abstract

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.

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Literature
1.
go back to reference Alexander SS (1961) Price movements in speculative markets: trends or random walks. Ind Manag Rev 2:7–26 Alexander SS (1961) Price movements in speculative markets: trends or random walks. Ind Manag Rev 2:7–26
2.
go back to reference Bessembinder H, Chan K (1995) The profitability of technical trading rules in the Asian stock markets. Pac Basin Finance J 3(2–3):257–284CrossRef Bessembinder H, Chan K (1995) The profitability of technical trading rules in the Asian stock markets. Pac Basin Finance J 3(2–3):257–284CrossRef
3.
go back to reference Bo L, Linyan S, Mweene R (2005) Empirical study of trading rule discovery in China stock market. Expert Syst Appl 28:531–535CrossRef Bo L, Linyan S, Mweene R (2005) Empirical study of trading rule discovery in China stock market. Expert Syst Appl 28:531–535CrossRef
4.
go back to reference Brock W, Lakonishok J, Lebaron B (1992) Simple technical trading rules and the stochastic properties of stock returns. J Finance 47:1731–1764CrossRef Brock W, Lakonishok J, Lebaron B (1992) Simple technical trading rules and the stochastic properties of stock returns. J Finance 47:1731–1764CrossRef
5.
go back to reference Bulkowski TN (2008) Encyclopedia of candlestick charts, 2nd edn. Wiley, Hoboken Bulkowski TN (2008) Encyclopedia of candlestick charts, 2nd edn. Wiley, Hoboken
6.
go back to reference Caginalp G, Laurent H (1998) The predictive power of price patterns. Appl Math Finance 5:181–205CrossRefMATH Caginalp G, Laurent H (1998) The predictive power of price patterns. Appl Math Finance 5:181–205CrossRefMATH
7.
go back to reference 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 180:1610–1629CrossRef 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 180:1610–1629CrossRef
8.
go back to reference Duda R, Hart P (1973) Pattern classification and scene analysis. Wiley, New YorkMATH Duda R, Hart P (1973) Pattern classification and scene analysis. Wiley, New YorkMATH
9.
go back to reference Fama EF (1970) Efficient capital markets: a review of theory and empirical work. J Finance 25:383–417CrossRef Fama EF (1970) Efficient capital markets: a review of theory and empirical work. J Finance 25:383–417CrossRef
10.
go back to reference 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 608–612 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 608–612
11.
go back to reference 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) 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)
12.
go back to reference 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):84–89CrossRef 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):84–89CrossRef
13.
go back to reference Hung K-K, Cheung Y-M, Xu L (2003) An extended ASLD trading system to enhance portfolio management. IEEE Trans Neural Netw 14(2):413–425CrossRef Hung K-K, Cheung Y-M, Xu L (2003) An extended ASLD trading system to enhance portfolio management. IEEE Trans Neural Netw 14(2):413–425CrossRef
14.
go back to reference Ilmanen A (2011) Expected returns: an investor’s guide to harvesting market rewards. Wiley, ChichesterCrossRef Ilmanen A (2011) Expected returns: an investor’s guide to harvesting market rewards. Wiley, ChichesterCrossRef
15.
go back to reference Jensen MC, Bennington GA (1970) Random walks and technical theories: some additional evidence. J Finance 25(2):469–482CrossRef Jensen MC, Bennington GA (1970) Random walks and technical theories: some additional evidence. J Finance 25(2):469–482CrossRef
16.
go back to reference Kao L, He T (2009) Developing actionable trading agents. Knowl Inf Syst 18(2):183–198CrossRef Kao L, He T (2009) Developing actionable trading agents. Knowl Inf Syst 18(2):183–198CrossRef
17.
go back to reference 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 122–133 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 122–133
18.
go back to reference Kong X, Qiang W, Guoqing C (2010) An approach to discovering multi-temporal patterns and its application to financial databases. Inf Sci 180:873–885CrossRef Kong X, Qiang W, Guoqing C (2010) An approach to discovering multi-temporal patterns and its application to financial databases. Inf Sci 180:873–885CrossRef
19.
go back to reference Lee KH, Jo GS (1999) Expert system for predicting stock market timing using a candlestick chart. Expert Syst Appl 16:357–364CrossRef Lee KH, Jo GS (1999) Expert system for predicting stock market timing using a candlestick chart. Expert Syst Appl 16:357–364CrossRef
20.
go back to reference Leigh W, Modani N, Purvis R, Roberts T (2002a) Stock market trading rule discovery using technical charting heuristics. Expert Syst Appl 23(2):155–159CrossRef Leigh W, Modani N, Purvis R, Roberts T (2002a) Stock market trading rule discovery using technical charting heuristics. Expert Syst Appl 23(2):155–159CrossRef
21.
go back to reference 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 32:161–174CrossRef 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 32:161–174CrossRef
22.
go back to reference 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 37:515–530CrossRef 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 37:515–530CrossRef
23.
go back to reference 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):93–104CrossRef 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):93–104CrossRef
24.
go back to reference Maginn J, Tuttle D, McLeavey D, Pinto J (eds) (2007) Managing investment portfolios: a dynamic process, 3rd edn. Wiley, Hoboken , NJ, USA Maginn J, Tuttle D, McLeavey D, Pinto J (eds) (2007) Managing investment portfolios: a dynamic process, 3rd edn. Wiley, Hoboken , NJ, USA
25.
go back to reference Marshall BR, Young MR, Rose LC (2006) Candlestick technical trading strategies: can they create value for investors? J Bank Finance 30:2303–2323CrossRef Marshall BR, Young MR, Rose LC (2006) Candlestick technical trading strategies: can they create value for investors? J Bank Finance 30:2303–2323CrossRef
26.
go back to reference 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 42:306–324CrossRef 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 42:306–324CrossRef
27.
go back to reference 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 CrossRef 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/​S021800149400003​6 CrossRef
28.
go back to reference Ozturk M (2015) Heuristic-based trading system on Forex data using technical indicator rules. M.Sc. thesis, Comp. Eng. Dept. Middle East Technical University Ozturk M (2015) Heuristic-based trading system on Forex data using technical indicator rules. M.Sc. thesis, Comp. Eng. Dept. Middle East Technical University
29.
go back to reference 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 2011 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 2011
30.
go back to reference 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):958–961 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):958–961
31.
go back to reference Poh KL (2000) An intelligent decision support system for investment analysis. Knowl Inf Syst 2(3):340–358CrossRefMATH Poh KL (2000) An intelligent decision support system for investment analysis. Knowl Inf Syst 2(3):340–358CrossRefMATH
32.
go back to reference Raudys S (2013) Portfolio of automated trading systems: complexity and learning set size issues. IEEE Trans Neural Netw Learn Syst 24(3):448–459CrossRef Raudys S (2013) Portfolio of automated trading systems: complexity and learning set size issues. IEEE Trans Neural Netw Learn Syst 24(3):448–459CrossRef
33.
go back to reference 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):269–272 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):269–272
34.
go back to reference Toshniwal D, Joshi RC (2005) Similarity search in time series data using time weighted slopes. Informatica 29(1):79–88 Toshniwal D, Joshi RC (2005) Similarity search in time series data using time weighted slopes. Informatica 29(1):79–88
35.
go back to reference Wang J-L, Chan S-H (2007) Stock market trading rule discovery using pattern recognition and technical analysis. Expert Syst Appl 33:304–315CrossRef Wang J-L, Chan S-H (2007) Stock market trading rule discovery using pattern recognition and technical analysis. Expert Syst Appl 33:304–315CrossRef
36.
go back to reference Wang J-L, Chan S-H (2009) Trading rule discovery in the US stock market: an empirical study. Expert Syst Appl 36:5450–5455CrossRef Wang J-L, Chan S-H (2009) Trading rule discovery in the US stock market: an empirical study. Expert Syst Appl 36:5450–5455CrossRef
37.
go back to reference 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 30:947–971CrossRef 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 30:947–971CrossRef
38.
go back to reference Wu J-L, Yu L-C, Chang P-C (2014) An intelligent stock trading system using comprehensive features. Appl Soft Comput 23:39–50CrossRef Wu J-L, Yu L-C, Chang P-C (2014) An intelligent stock trading system using comprehensive features. Appl Soft Comput 23:39–50CrossRef
39.
go back to reference Xu L, Cheung Y-M (1997) Adaptive supervised learning decision networks for trading and portfolio management. J Comput Finance 13(2):806–816 Xu L, Cheung Y-M (1997) Adaptive supervised learning decision networks for trading and portfolio management. J Comput Finance 13(2):806–816
40.
go back to reference Zhang D, Zhou L (2004) Discovering golden nuggets: data mining in financial application. IEEE Trans Syst Man Cybern Part C 34(4):513–522CrossRef Zhang D, Zhou L (2004) Discovering golden nuggets: data mining in financial application. IEEE Trans Syst Man Cybern Part C 34(4):513–522CrossRef
41.
go back to reference 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 161–170 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 161–170
Metadata
Title
An algorithmic framework for frequent intraday pattern recognition and exploitation in forex market
Authors
Nikitas Goumatianos
Ioannis T. Christou
Peter Lindgren
Ramjee Prasad
Publication date
22-04-2017
Publisher
Springer London
Published in
Knowledge and Information Systems / Issue 3/2017
Print ISSN: 0219-1377
Electronic ISSN: 0219-3116
DOI
https://doi.org/10.1007/s10115-017-1052-2

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