A new evolutionary method named "Genetic Network Programming with Control Nodes, GNPcn" has been proposed and applied to determine the timing of buying and selling stocks. GNPcn represents its solution as a directed graph structure which has some useful features inherently. For example, GNPcn has the implicit memory function which memorizes the past action sequences of agents and GNPcn can re-use nodes repeatedly in the network flow, so highly compact graph structures can be made. GNPcn can improve the strategy of buying and selling stocks of multi issues. Its effectiveness is confirmed by some simulations.

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[1]

S. Mabu, K. Hirasawa and J. Hu, A Graph-Based Evolutionary Algorithm: Genetic Network Programming and Its Extension Using Reinforcement Learning, Evolutionary Computation, MIT Press, Vol. 15, No. 3, pp. 369--398, 2007.

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Varying portfolio construction of stocks using genetic network programming with control nodes
1. Applied computing
  1. Enterprise computing

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GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation

July 2008

1814 pages

ISBN:9781605581309

DOI:10.1145/1389095

Conference Chair:
Conor Ryan
University of Limerick, Ireland
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Maarten Keijzer
Chordiant Software International B.V., The Netherlands

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Published: 12 July 2008

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Trading rules on stock markets using genetic network programming with sarsa learning

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