Enhanced Strongly Typed Genetic Programming for Algorithmic Trading

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{christodoulaki:2023:GECCO,

• author = "Evangelia Christodoulaki and Michael Kampouridis and Maria Kyropoulou",
• title = "Enhanced Strongly Typed Genetic Programming for Algorithmic Trading",
• booktitle = "Proceedings of the 2023 Genetic and Evolutionary Computation Conference",
• year = "2023",
• editor = "Sara Silva and Luis Paquete and Leonardo Vanneschi and Nuno Lourenco and Ales Zamuda and Ahmed Kheiri and Arnaud Liefooghe and Bing Xue and Ying Bi and Nelishia Pillay and Irene Moser and Arthur Guijt and Jessica Catarino and Pablo Garcia-Sanchez and Leonardo Trujillo and Carla Silva and Nadarajen Veerapen",
• pages = "1055--1063",
• address = "Lisbon, Portugal",
• series = "GECCO '23",
• month = "15-19 " # jul,
• organisation = "SIGEVO",
• publisher = "Association for Computing Machinery",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming, algorithmic trading, technical analysis, sentiment analysis",
• isbn13 = "9798400701191",
• DOI = "doi:10.1145/3583131.3590359",
• size = "9 pages",
• abstract = "This paper proposes a novel strongly typed Genetic Programming (STGP) algorithm that combines Technical (TA) and Sentiment analysis (SA) indicators to produce trading strategies. While TA and SA have been successful when used individually, their combination has not been considered extensively. Our proposed STGP algorithm has a novel fitness function, which rewards not only a tree's trading performance, but also the trading performance of its TA and SA subtrees. To achieve this, the fitness function is equal to the sum of three components: the fitness function for the complete tree, the fitness function of the TA subtree, and the fitness function of the SA subtree. In doing so, we ensure that the evolved trees contain profitable trading strategies that take full advantage of both technical and sentiment analysis. We run experiments on 35 international stocks and compare the STGP's performance to four other GP algorithms, as well as multilayer perceptron, support vector machines, and buy and hold. Results show that the proposed GP algorithm statistically and significantly outperforms all benchmarks and it improves the financial performance of the trading strategies produced by other GP algorithms by up to a factor of two for the median rate of return.",
• notes = "GECCO-2023 A Recombination of the 32nd International Conference on Genetic Algorithms (ICGA) and the 28th Annual Genetic Programming Conference (GP)",

}

Genetic Programming entries for Eva Christodoulaki Michael Kampouridis Maria Kyropoulou

Citations