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Stock trading strategies by genetic network programming with flag nodes

Authors:

Shingo Mabu,

Yan Chen,

Etsushi Ohkawa,

Kotaro HirasawaAuthors Info & Claims

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

Pages 1709 - 1710

https://doi.org/10.1145/1389095.1389421

Published: 12 July 2008 Publication History

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Abstract

Genetic Network Programming (GNP) has been proposed as a graph-based evolutionary algorithm. GNP works well especially in dynamic environments due to its graph structures. In addition, a stock trading model using GNP with Importance Index (GNP-IMX) has been proposed. IMX is one of the criterions for decision making. However, the values of IMXs must be determined by our experience/knowledge. Therefore in this paper, IMXs are adjusted appropriately during the stock trading in order to determine buying or selling stocks. Moreover, newly defined flag nodes are introduced to GNP, which can appropriately judge the current situation, and also contributes to the use of many kinds of nodes in GNP programs. In the stock trading simulations, the effectiveness of the proposed method is confirmed.

References

[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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[2]

N. Baba, N. Inoue and Y. Yanjun. Utilization of soft computing techniques for constructing reliable decision support systems for dealing stocks. In Proc. of International Joint Conference on Neural Networks, 2002.

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[3]

Jean-Yves Potvin, Patrick Soriano and Maxime Vallee. Generating trading rules on the stock markets with genetic programming. Computers & Operations Research, Vol. 31, pp. 1033--1047, 2004.

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[4]

S. Mabu, Y. Chen, K. Hirasawa and J. Hu. Stock Trading Rules Using Genetic Network Programming with Actor-Crotic. 2007 IEEE Congress on Evolutionary Computation (CEC2007), pp. 508--515, 2007.

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[5]

Y. Chen, S. Mabu, K. Hirasawa and J. Hu. Genetic Network Programming with Sarsa Learning and Its Application to Creating Stock Trading Rules. 2007 IEEE Congress on Evolutionary Computation (CEC2007), pp. 220--227 2007.

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Index Terms

Stock trading strategies by genetic network programming with flag nodes
1. Computing methodologies
  1. Artificial intelligence

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GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation

In this paper, the Genetic Network Programming (GNP) for creating trading rules on stocks is described. GNP is an evolutionary computation, which represents its solutions using graph structures and has some useful features inherently. It has been ...

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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
,
Editor:
Maarten Keijzer
Chordiant Software International B.V., The Netherlands

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 July 2008

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GECCO08: Genetic and Evolutionary Computation Conference

July 12 - 16, 2008

GA, Atlanta, USA

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Co‐evolved genetic programs for stock market trading

Generating trading rules on the stock markets with genetic programming

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

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