Evolving efficient limit order strategy using Grammatical Evolution

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

doi:10.1109/cec.2010.5586040

Cited by 3 publications

(5 citation statements)

References 26 publications

(26 reference statements)

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“…Li et al (2010) extend this evolutionary algorithm approach with the introduction of subroutines in the programming that act as small scale models within the overall model (an approach somewhat similar in intention to bootstrapping in regular finance econometrics). Cui et al (2010) shows how grammatical evolution, a subtechnique of GE, can be used to improve trader ordering efficiency by lowering cost.…”

Section: Nomentioning

confidence: 99%

Machine learning in finance: A topic modeling approach

Aziz

Dowling

Hammami

et al. 2021

Euro Fin Management

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We identify the core topics of research applying machine learning to finance. We use a probabilistic topic modeling approach to make sense of this diverse body of research spanning across multiple disciplines. Through a latent Dirichlet allocation topic modeling technique, we extract 15 coherent research topics that are the focus of 5942 academic studies from 1990 to 2020. We find that these topics can be grouped into four categories: Priceforecasting techniques, financial markets analysis, risk forecasting and financial perspectives. We first describe and structure these topics and then further show how the topic focus has evolved over the last three decades. A notable trend we find is the emergence of text-based machine learning, for example, for sentiment analysis, in recent years. Our study thus provides a structured topography for finance researchers seeking to integrate machine learning research approaches in their exploration of finance phenomena. We also showcase the benefits to finance researchers of the method of probabilistic EUROPEAN FINANCIAL MANAGEMENTWe thank John A. Doukas, the editor, and an anonymous referee of European Financial Management as the study has enormously benefited from their comments. We also thank Muhammad Farooq Ahmad and participants of IFABS 2019, Angers France for their valuable comments. Saqib Aziz and Michael Dowling acknowledge financial assistance from the B<>COM project: Prospect 2030. The views expressed in this article are those of the authors and all errors are our own. modeling of topics for deep comprehension of a body of literature.

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Section: Nomentioning

confidence: 99%

Machine learning in finance: A topic modeling approach

Aziz

Dowling

Hammami

et al. 2021

Euro Fin Management

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“…The algorithm follows a generational approach with a population of 500 individuals (feature-extractors) for 40 generations. In the GP literature it is most common to use 50 generations [27], however 40 is also often used [9]. In their review of the GP literature, Poli et al [45] state that the number of generations typically falls in the range [10,50], where the most productive search is usually performed.…”

Section: Ge Configurationmentioning

confidence: 99%

“…The solid line (solid line) represents the performance achieved when, during the feature-extraction phase, we use a validation set that includes only subjects 1-6. The dashed line (dashed line) represents the performance achieved when, during the feature-extraction phase, we use a validation set that includes all subjects (1)(2)(3)(4)(5)(6)(7)(8)(9). In both cases, extracted features are evaluated on a test set including all subjects (1)(2)(3)(4)(5)(6)(7)(8)(9).…”

Section: Subject Authenticationmentioning

confidence: 99%

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Feature extraction by grammatical evolution for one-class time series classification

Mauceri

Sweeney

Nicolau

et al. 2021

Genet Program Evolvable Mach

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When dealing with a new time series classification problem, modellers do not know in advance which features could enable the best classification performance. We propose an evolutionary algorithm based on grammatical evolution to attain a data-driven feature-based representation of time series with minimal human intervention. The proposed algorithm can select both the features to extract and the sub-sequences from which to extract them. These choices not only impact classification performance but also allow understanding of the problem at hand. The algorithm is tested on 30 problems outperforming several benchmarks. Finally, in a case study related to subject authentication, we show how features learned for a given subject are able to generalise to subjects unseen during the extraction phase.

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“…A novel approach was taken by [32,33] where grammatical evolution (GE) was used to evolve a dynamic trade execution strategy, with the resulting rule adapting to changing market conditions. Based on the finance literature analysing the relationship between order placement and the information content of limit order books, six order book metrics were selected as potential inputs for an execution strategy.…”

Section: Trading System Designmentioning

confidence: 99%