Trading Rules on Stock Markets Using Genetic Network Programming with Reinforcement Learning and Importance Index

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  • Mabu Shingo
    Graduate School of Information, Production, and Systems, Waseda University
  • Hirasawa Kotaro
    Graduate School of Information, Production, and Systems, Waseda University
  • Furuzuki Takayuki
    Graduate School of Information, Production, and Systems, Waseda University

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  • 強化学習と重要度指標を用いた遺伝的ネットワークプログラミングによる株式売買モデル
  • キョウカ ガクシュウ ト ジュウヨウド シヒョウ オ モチイタ イデンテキ ネットワーク プログラミング ニ ヨル カブシキ バイバイ モデル

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Abstract

Genetic Network Programming (GNP) is an evolutionary computation which represents its solutions using graph structures. Since GNP can create quite compact programs and has an implicit memory function, it has been clarified that GNP works well especially in dynamic environments. In addition, a study on creating trading rules on stock markets using GNP with Importance Index (GNP-IMX) has been done. IMX is a new element which is a criterion for decision making. In this paper, we combined GNP-IMX with Actor-Critic (GNP-IMX&AC) and create trading rules on stock markets. Evolution-based methods evolve their programs after enough period of time because they must calculate fitness values, however reinforcement learning can change programs during the period, therefore the trading rules can be created efficiently. In the simulation, the proposed method is trained using the stock prices of 10 brands in 2002 and 2003. Then the generalization ability is tested using the stock prices in 2004. The simulation results show that the proposed method can obtain larger profits than GNP-IMX without AC and Buy&Hold.

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