Abstract
A developmental co-evolutionary genetic programming approach (PAM DGP) and a standard linear genetic programming (LGP) stock trading systemare applied to a number of stocks across market sectors. Both GP techniques were found to be robust to market fluctuations and reactive to opportunities associated with stock price rise and fall, with PAMDGP generating notably greater profit in some stock trend scenarios. Both algorithms were very accurate at buying to achieve profit and selling to protect assets, while exhibiting bothmoderate trading activity and the ability to maximize or minimize investment as appropriate. The content of the trading rules produced by both algorithms are also examined in relation to stock price trend scenarios.
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References
Brabazon, Anthony and O'Neill, Michael (2006). Biologically Inspired Algorithms for Financial Modelling. Natural Computing Series. Springer.
Brameier, Markus and Banzhaf, Wolfgang (2007). Linear Genetic Programming. Number XVI in Genetic and Evolutionary Computation. Springer.
Drezewski, Rafal and Sepielak, Jan (2008). Evolutionary system for generating investment strategies. In Giacobini, Mario, Brabazon, Anthony, Cagnoni, Stefano, Caro, Gianni Di, Drechsler, Rolf, Ekárt, Anikó, Esparcia-Alcázar, Anna, Farooq, Muddassar, Fink, Andreas, McCormack, Jon, O'Neill, Michael, Romero, Juan, Rothlauf, Franz, Squillero, Giovanni, Uyar, Sima, and Yang, Shengxiang, editors, EvoWorkshops, volume 4974 of Lecture Notes in Computer Science, pages 83–92. Springer.
Grosan, Crina and Abraham, Ajith (2006). Stock market modeling using genetic programming ensembles. In Nedjah, Nadia, de Macedo Mourelle, Luiza, and Abraham, Ajith, editors, Genetic Systems Programming, volume 13 of Studies in Computational Intelligence, pages 131–146. Springer.
Wilson, Garnett and Banzhaf, Wolfgang (2009). Prediction of interday stock prices using developmental and linear genetic programming. In Giacobini, Mario, De Falco, Ivanoe, and Ebner, Marc, editors, Applications of Evolutionary Computing, EvoWorkshops2009: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, EvoNUM, EvoPhD, EvoSTOC, EvoTRANSLOG, LNCS, Tubingen, Germany. Springer Verlag.
Wilson, Garnett and Heywood, Malcolm (2007). Introducing probabilistic adaptive mapping developmental genetic programming with redundant mappings. Genetic Programming and Evolvable Machines, 8(2):187–220. Special issue on developmental systems.
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Wilson, G., Banzhaf, W. (2010). Algorithmic Trading with Developmental and Linear Genetic Programming. In: Riolo, R., O'Reilly, UM., McConaghy, T. (eds) Genetic Programming Theory and Practice VII. Genetic and Evolutionary Computation. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-1626-6_8
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DOI: https://doi.org/10.1007/978-1-4419-1626-6_8
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