Evolutionary Money Management

Abstract. Trading-rule representation is an important factor to consider when designing a quantitative trading system. This study implements a trading strategy as a rule-based policy. The result is an intuitive human-readable format which allows for seamless integration of domain knowledge. The components of a policy are specified and represented as a set of rewrite rules in a context-free grammar. These rewrite rules define how the components can be legally assembled. Thus, strategies derived from the grammar are well-formed, domain-specific, solutions. A grammar-based Evolutionary Algorithm, Grammatical Evolution (GE), is then employed to automatically evolve intra-day trading strategies for the U.S. Stock Market. The GE methodology managed to discover profitable rules with realistic transaction costs included. The paper concludes with a number of suggestions for future work.

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Evolving Trading Rule-Based Policies

Bradley

Brabazon

O’Neill

2010

Applications of Evolutionary Computation

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A Stock Market Decision Support System with a Hybrid Evolutionary Algorithm for Many-Core Graphics Processors

Lipiński

2011

Euro-Par 2010 Parallel Processing Workshops

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Parallel Evolutionary Algorithms for Stock Market Trading Rule Selection on Many-Core Graphics Processors

Lipiński

2011

Natural Computing in Computational Finance

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Evolutionary Money Management

Cited by 8 publications

References 13 publications

Evolving Trading Rule-Based Policies

Evolving Trading Rule-Based Policies

A Stock Market Decision Support System with a Hybrid Evolutionary Algorithm for Many-Core Graphics Processors

Parallel Evolutionary Algorithms for Stock Market Trading Rule Selection on Many-Core Graphics Processors

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