Title :
Knowledge-intensive genetic discovery in foreign exchange markets
Author :
Bhattacharyya, Siddhartha ; Pictet, Olivier V. ; Zumbach, Gilles
Author_Institution :
Dept. of Inf. & Decision Sci., Illinois Univ., Chicago, IL, USA
fDate :
4/1/2002 12:00:00 AM
Abstract :
This paper considers the discovery of trading decision models from high-frequency foreign exchange (FX) markets data using genetic programming (GP). It presents a domain-related structuring of the representation and incorporation of semantic restrictions for GP-based searching of trading decision models. A defined symmetry property provides a basis for the semantics of FX trading models. The symmetry properties of basic indicator types useful in formulating trading models are defined, together with semantic restrictions governing their use in trading model specification. The semantics for trading model specification have been defined with respect to regular arithmetic, comparison and logical operators. This study also explores the use of two fitness criteria for optimization, showing more robust performance with a risk-adjusted measure of returns
Keywords :
data mining; financial data processing; foreign exchange trading; genetic algorithms; knowledge representation; learning (artificial intelligence); mathematical operators; symmetry; arithmetic operators; comparison operators; data mining; domain-related structuring; financial markets; genetic programming; high-frequency foreign exchange markets; indicator types; knowledge-intensive genetic discovery; logical operators; machine learning; optimization fitness criteria; risk-adjusted return measure; robust performance; semantic restrictions; symmetry properties; trading decision model discovery; trading model specification; Arithmetic; Asset management; Availability; Data analysis; Data mining; Fractals; Genetic programming; Investments; Machine learning; Robustness;
Journal_Title :
Evolutionary Computation, IEEE Transactions on
DOI :
10.1109/4235.996016
