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
- (artificial
- algorithms,
- arithmetic
- comparison
- criteria,
- data
- decision
- discovery,
- domain-related
- exchange
- financial
- fitness
- foreign
- genetic
- high-frequency
- indicator
- intelligence),
- knowledge
- knowledge-intensive
- learning
- learning,
- logical
- machine
- markets,
- mathematical
- measure,
- mining,
- model
- operators,
- optimisation
- performance,
- processing,
- programming,
- properties,
- representation,
- restrictions,
- return
- risk-adjusted
- robust
- semantic
- specification
- structuring,
- symmetry
- symmetry,
- trading
- trading,
- types,
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