Fuzzy-Evolutionary Modeling for Single-Position Day Trading

This chapter illustrates a data-mining approach to single-position day trading which uses an evolutionary algorithm to construct a fuzzy predictive model of a financial instrument. The model is expressed as a set of fuzzy IF-THEN rules. The model takes as inputs the open, high, low, and close prices, as well as the values of a number of popular technical indicators on day t and produces a go short, do nothing, go long trading signal for day t+1 based on a dataset of past observations of which actions would have been most profitable. The approach has been applied to trading several financial instruments (large-cap stocks and indices): the experimental results are presented and discussed. A method to enhance the performance of trading rules based on the approach by using ensembles of fuzzy models is finally illustrated. The results clearly indicate that, despite its simplicity, the approach may yield significant returns, outperforming a buy-and-hold strategy.

[1]  E. H. Mamdani,et al.  Advances in the linguistic synthesis of fuzzy controllers , 1976 .

[2]  Andrea G. B. Tettamanzi,et al.  Learning Fuzzy Classifiers with Evolutionary Algorithms , 2003 .

[3]  Zbigniew Michalewicz,et al.  Evolutionary Computation 2 : Advanced Algorithms and Operators , 2000 .

[4]  Anthony Brabazon,et al.  Evolving Market Index Trading Rules Using Grammatical Evolution , 2001, EvoWorkshops.

[5]  G. Vechkanov.  Investments , 2014, Canadian Medical Association journal.

[6]  Rick L. Riolo,et al.  Genetic Programming Theory and Practice XIX , 2008, Genetic and Evolutionary Computation.

[7]  Andrea G. B. Tettamanzi,et al.  AN EVOLUTIONARY ALGORITHM FOR FUZZY CONTROLLER SYNTHESIS AND OPTIMIZATION BASED ON SGS-THOMSON'S W.A.R.P. FUZZY PROCESSOR , 1997 .

[8]  Lotfi A. Zadeh,et al.  The concept of a linguistic variable and its application to approximate reasoning - II , 1975, Inf. Sci..

[9]  Bernard Manderick,et al.  Fine-Grained Parallel Genetic Algorithms , 1989, ICGA.

[10]  Terence Soule,et al.  Genetic Programming: Theory and Practice , 2003 .

[11]  Kenneth de Jong,et al.  Evolutionary computation: a unified approach , 2007, GECCO.

[12]  Takanori Shibata,et al.  Genetic Algorithms And Fuzzy Logic Systems Soft Computing Perspectives , 1997 .

[13]  Andrea G. B. Tettamanzi.  An evolutionary algorithm for fuzzy controller synthesis and optimization , 1995, 1995 IEEE International Conference on Systems, Man and Cybernetics. Intelligent Systems for the 21st Century.

[14]  Heinz Mühlenbein,et al.  Parallel Genetic Algorithms, Population Genetics, and Combinatorial Optimization , 1989, Parallelism, Learning, Evolution.

[15]  J. David Schaffer,et al.  Proceedings of the third international conference on Genetic algorithms , 1989 .

[16]  F. Sortino,et al.  DOWNSIDE RISK - CAPTURING WHATS AT STAKE IN INVESTMENT SITUATIONS , 1991 .

[17]  D. Fogel.  Evolutionary algorithms in theory and practice , 1997, Complex..

[18]  Francesco Masulli,et al.  Soft Computing Applications , 2003 .

[19]  Hans Hellendoorn,et al.  Defuzzification in Fuzzy Controllers , 1993, J. Intell. Fuzzy Syst..

[20]  Jean-Yves Potvin,et al.  Generating trading rules on the stock markets with genetic programming , 2004, Comput. Oper. Res..

[21]  W. Sharpe.  The Sharpe Ratio , 1994 .

[22]  Anthony Brabazon,et al.  Biologically inspired algorithms for financial modelling , 2006, Natural computing series.

[23]  Lucia Pannese,et al.  Business Intelligence for Strategic Marketing: Predictive Modelling of Customer Behaviour Using Fuzzy Logic and Evolutionary Algorithms , 2009, EvoWorkshops.

[24]  Elena Marchiori,et al.  Applications of Evolutionary Computing: Evoworkshops 2003 , 2003 .

[25]  Thomas Hellström,et al.  Parameter Tuning in Trading Algorithms Using ASTA , 1999 .

[26]  Erick Cantú-Paz,et al.  A Survey of Parallel Genetic Algorithms , 2000 .

[27]  Lotfi A. Zadeh,et al.  The Calculus of Fuzzy If/Then Rules , 1992, Fuzzy Days.

[28]  Jaume Bacardit,et al.  Empirical Evaluation of Ensemble Techniques for a Pittsburgh Learning Classifier System , 2007, IWLCS.

[29]  T. Ross.  Fuzzy Logic with Engineering Applications , 1994 .

[30]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[31]  Christopher J. Neely,et al.  Is Technical Analysis in the Foreign Exchange Market Profitable? A Genetic Programming Approach , 1996, Journal of Financial and Quantitative Analysis.

[32]  J. Murphy.  Technical Analysis of the Financial Markets , 1999 .

[33]  Franklin Allen,et al.  Using genetic algorithms to find technical trading rules , 1999 .

[34]  Tien Foo,et al.  Asset Allocation in a Downside Risk Framework , 2000 .

[35]  Shu-Heng Chen,et al.  Discovering Financial Technical Trading Rules Using Genetic Programming with Lambda Abstraction , 2005 .

[36]  Benjamin Kuipers,et al.  Designing safe, profitable automated stock trading agents using evolutionary algorithms , 2006, GECCO.

[37]  Lotfi A. Zadeh,et al.  The Concepts of a Linguistic Variable and its Application to Approximate Reasoning , 1975 .

[38]  Xin Yao,et al.  Recent Advances in Evolutionary Computation , 2006, Journal of Computer Science and Technology.

[39]  M. Dempster,et al.  A real-time adaptive trading system using genetic programming , 2001 .

[40]  Marney Colin Fyfe Heather Tarbert David Miller Jp,et al.  Risk Adjusted Returns to Technical Trading Rules: a Genetic Programming Approach , 2001 .

[41]  Michael A. H. Dempster,et al.  Computational learning techniques for intraday FX trading using popular technical indicators , 2001, IEEE Trans. Neural Networks.

[42]  Zbigniew Michalewicz,et al.  Evolutionary Computation 1 , 2018 .