Dynamic trade execution: a grammatical evolution approach

Cui, Wei; Brabazon, Anthony; O’Neill, Michael

doi:10.1504/ijfmd.2011.038526

Cited by 8 publications

(3 citation statements)

References 82 publications

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“…In the implementation, GE was found to be able to evolve quality trade execution strategies and its results proved highly competitive against two basic benchmark execution strategies. A detailed discussion of the application and the relevant background finance literature is provided in [34].…”

Section: Trading System Designmentioning

confidence: 99%

Applications of genetic programming to finance and economics: past, present, future

Brabazon

Kampouridis

O’Neill

2019

Genet Program Evolvable Mach

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Section: Trading System Designmentioning

confidence: 99%

Applications of genetic programming to finance and economics: past, present, future

Brabazon

Kampouridis

O’Neill

2019

Genet Program Evolvable Mach

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“…trading agents, can be represented in several ways. Rule-based strategies, sequence modeling with neural networks and LSTMs (Long Short-Term Memory), and symbolic regression using Genetic Programming or Grammatical Evolution are common approaches [1,12]. Frequency of trade, fundamental versus technical indicators, choice of trading instruments, transaction costs, and vocabulary of order types are crucial design decisions in building such agents.…”

Section: Stock Tradingmentioning

confidence: 99%

Enhanced optimization with composite objectives and novelty pulsation

Shahrzad

Hodjat

Dollé

et al. 2019

Proceedings of the Genetic and Evolutionary Computation Conference Companion

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An important benefit of multi-objective search is that it maintains a diverse population of candidates, which helps in deceptive problems in particular. Not all diversity is useful, however: candidates that optimize only one objective while ignoring others are rarely helpful. A recent solution is to replace the original objectives by their linear combinations, thus focusing the search on the most useful tradeoffs between objectives. To compensate for the loss of diversity, this transformation is accompanied by a selection mechanism that favors novelty. This paper improves this approach further by introducing novelty pulsation, i.e. a systematic method to alternate between novelty selection and local optimization. In the highly deceptive problem of discovering minimal sorting networks, it finds state-of-the-art solutions significantly faster than before. In fact, our method so far has established a new world record for the 20-line sorting network with 91 comparators. In the real-world problem of stock trading, it discovers solutions that generalize significantly better on unseen data. Composite Novelty Pulsation is therefore a promising approach to solving deceptive real-world problems through multi-objective optimization.

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“…Reflecting this is the wide range of application domains to which GE has been applied: highlights include architecture and engineering design [87,399,481,485], fi-nance [75,122,447], music and art [396,397,444,467,573], computer games [506,507,570,571], animation [428,429], ecosystem modelling [448], and autonomous networks [264,265,266]. The grammar contains the rules governing how the development of the phenotype is conducted, and as such can contain domain knowledge biasing the form a phenotypic solution can take.…”

Section: Grammatical Evolutionmentioning

confidence: 99%