Knowledge-intensive genetic discovery in foreign exchange markets
Bhattacharyya, S.
Pictet, O.V.
Zumbach, G.
Dept. of Inf. & Decision Sci., Illinois Univ., Chicago, IL;
This paper appears in: Evolutionary Computation, IEEE Transactions on
Publication Date: Apr 2002
Volume: 6,
Issue: 2
On page(s): 169-181
ISSN: 1089-778X
References Cited: 35
CODEN: ITEVF5
INSPEC Accession Number: 7256658
Digital Object Identifier: 10.1109/4235.996016
Current Version Published: 2002-08-07
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
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