Stock trading strategies by genetic network programming with flag nodes

Authors:
Shingo Mabu;Yan Chen;Etsushi Ohkawa;Kotaro Hirasawa
Affiliations:
Waseda University, Kitakyushu, Japan;Waseda University, Kitakyushu, Japan;Waseda University, Kitakyushu, Japan;Waseda University, Kitakyushu, Japan
Venue:
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Year:
2008

Citing 2
Cited 0

Generating trading rules on the stock markets with genetic programming

Computers and Operations Research
A Graph-Based Evolutionary Algorithm: Genetic Network Programming (GNP) and Its Extension Using Reinforcement Learning

Evolutionary Computation

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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.