Real Time Updating Genetic Network Programming for Adapting to the Change of Stock Prices

• Chen Yan

  Graduate school of Information, Production and Systems, Waseda University

• Mabu Shingo

  Graduate school of Information, Production and Systems, Waseda University

• Shimada Kaoru

  Graduate school of Information, Production and Systems, Waseda University

• Hirasawa Kotaro

  Graduate school of Information, Production and Systems, Waseda University

抄録

The key in stock trading model is to take the right actions for trading at the right time, primarily based on the accurate forecast of future stock trends. Since an effective trading with given information of stock prices needs an intelligent strategy for the decision making, we applied Genetic Network Programming (GNP) to creating a stock trading model. In this paper, we propose a new method called Real Time Updating Genetic Network Programming (RTU-GNP) for adapting to the change of stock prices. There are three important points in this paper: First, the RTU-GNP method makes a stock trading decision considering both the recommendable information of technical indices and the candlestick charts according to the real time stock prices. Second, we combine RTU-GNP with a Sarsa learning algorithm to create the programs efficiently. Also, sub-nodes are introduced in each judgment and processing node to determine appropriate actions (buying/selling) and to select appropriate stock price information depending on the situation. Third, a Real Time Updating system has been firstly introduced in our paper considering the change of the trend of stock prices. The experimental results on the Japanese stock market show that the trading model with the proposed RTU-GNP method outperforms other models without real time updating. We also compared the experimental results using the proposed method with Buy&Hold method to confirm its effectiveness, and it is clarified that the proposed trading model can obtain much higher profits than Buy&Hold method.

収録刊行物

• 電気学会論文誌Ｃ（電子・情報・システム部門誌）

  電気学会論文誌Ｃ（電子・情報・システム部門誌） 129 (2), 344-354, 2009

  一般社団法人 電気学会

キーワード

• Genetic Network Programming
• reinforcement learning
• RTU-GNP
• stock trading model
• technical index
• candlestick chart

詳細情報

• CRID: 1390001204606005760
• NII論文ID: 10023999962
• NII書誌ID: AN10065950
• DOI: 10.1541/ieejeiss.129.344
• ISSN: 13488155
• ISSN: 03854221
• NDL書誌ID: 10163271
• Web Site: https://ndlsearch.ndl.go.jp/books/R000000004-I10163271
• Web Site: http://www.jstage.jst.go.jp/article/ieejeiss/129/2/129_2_344/_pdf
• 本文言語コード: en
• データソース種別:
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  • NDL
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  • CiNii Articles
• 抄録ライセンスフラグ: 使用不可