alpha-dominance two-objective Optimization Genetic Programming for Algorithmic Trading under a Directional Changes Environment

Created by W.Langdon from gp-bibliography.bib Revision:1.8349

@InProceedings{Long:2024:CIFEr,

• author = "Xinpeng Long and Michael Kampouridis",
• title = "alpha-dominance two-objective Optimization Genetic Programming for Algorithmic Trading under a Directional Changes Environment",
• booktitle = "2024 IEEE Symposium on Computational Intelligence for Financial Engineering and Economics (CIFEr)",
• year = "2024",
• month = oct,
• keywords = "genetic algorithms, genetic programming, Event detection, Transforms, Benchmark testing, Stock markets, Optimisation, Computational intelligence, Investment, Convergence, Directional Changes, Algorithmic Trading, Ge-netic Programming, Multi-Objective Optimisation",
• ISSN = "2640-7701",
• DOI = "doi:10.1109/CIFEr62890.2024.10772764",
• abstract = "We present a novel genetic programming (GP) algorithm that combines physical time and event-based time indicators to trade on the stock market. Rather than only using data in fixed intervals (e.g. daily closing prices), we use directional changes to transform physical time into events and allow the GP to make trading decisions based on when significant price movements have occurred. We use a two-objective fitness function, which simultaneously optimises return and risk. To overcome challenges with the convergence ability of the multi-objective GP, we apply an a-dominance strategy, which is able to relax the strict Pareto dominance criteria. We run experiments on 110 stocks from 10 international markets and compare results against a single-objective GP, as well as strategies based on technical analysis indicators and buy-and-hold. Results show that the proposed GP algorithm offers statistically significant improvements when compared to the above benchmarks.",
• notes = "Also known as \cite{10772764}",

}

Genetic Programming entries for Xinpeng Long Michael Kampouridis

Citations