Trading Rules on the Stock Markets using Genetic Network Programming with Candlestick Chart

Izumi, Yoshinori; Yamaguchi, Tomohisa; Mabu, Shingo; Hirasawa, Kotaro; Hu, Jingle

doi:10.1109/cec.2006.1688600

Cited by 23 publications

(8 citation statements)

References 13 publications

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“…The values in Table III are the average profit and profitability of 30 independent simulations with different random seeds. For the comparison, Table III also shows the results of GNP-RL, GNP-Candlestick [16], GA and Buy&Hold method which is often considered as a benchmark in trading stocks simulations. Buy&Hold buys as much stocks as possible at the opening of the market on the first day in the simulations, and sells all the stocks at the opening on the last day.…”

Section: B Simulation Resultsmentioning

confidence: 99%

“…The features of the proposed method compared with other traditional methods are as follows: The TA-GNP method makes an investment strategy considering the recommendable information of Technical Indices and Candlestick Charts [16] by adapting to the change of stock prices. Reinforcement Learning is also used in this paper for taking appropriate actions.…”

Section: Introductionmentioning

confidence: 99%

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Constructing portfolio investment strategy based on Time Adapting Genetic Network Programming

Chen

Mabu

Ohkawa

et al. 2009

2009 IEEE Congress on Evolutionary Computation

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The classical portfolio problem is a problem of distributing capital to a set of stocks. By adapting to the change of stock prices, this study proposes an portfolio investment strategy based on an evolutionary method named "Genetic Network Programming" (GNP). This method makes use of the information from Technical Indices and Candlestick Chart. The proposed portfolio model, consisting of technical analysis rules, are trained to generate investment advice. Experimental results on the Japanese stock market show that the proposed investment strategy using Time Adapting GNP (TA-GNP) method outperforms other traditional models in terms of both accuracy and efficiency. We also compared the experimental results of the proposed model with the conventional GNP based methods, GA and Buy&Hold method to confirm its effectiveness, and it is clarified that the proposed investment strategy is effective on the portfolio optimization problem.

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Section: B Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Constructing portfolio investment strategy based on Time Adapting Genetic Network Programming

Chen

Mabu

Ohkawa

et al. 2009

2009 IEEE Congress on Evolutionary Computation

View full text Add to dashboard Cite

show abstract

“…The values in Table 4 are the average profit and profitability of 30 independent simulations with different random seeds. For the comparison, Table 4 also shows the results of GNP-RL, GNPCandlestick [33], GA and Buy&Hold method which is often considered as a benchmark in the stock market. Buy&Hold buys stocks as much as possible at the opening of the market on the first day, and sells all the stocks at the opening on the last day.…”

Section: Simulation Resultsmentioning

confidence: 99%

A model of portfolio optimization using time adapting genetic network programming

Chen

Mabu

Hirasawa

2010

Computers & Operations Research

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“…Particularly, there have been numerous studies in applying the soft computing techniques to the stock trading task, such as GA [37], GP [38], [39], GNP [40], neural network (NN) [41], etc., which soon becomes one of the most active research problems. However, this problem has been rarely addressed in the EDA literature, where a gap remains to be filled.…”

Section: Introductionmentioning

confidence: 99%

Creating stock trading rules using graph-based estimation of distribution algorithm

Wen

Hirasawa

2014

2014 IEEE Congress on Evolutionary Computation (CEC)

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Though there are numerous approaches developed currently, exploring the practical applications of estimation of distribution algorithm (EDA) has been reported to be one of the most important challenges in this field. This paper is dedicated to extend EDA to solve one of the most active research problems -stock trading, which has been rarely revealed in the EDA literature. A recent proposed graph-based EDA called reinforced probabilistic model building genetic network programming (RPMBGNP) is investigated to create stock trading rules. With its distinguished directed graph-based individual structure and the reinforcement learning-based probabilistic modeling, we demonstrate the effectiveness of RPMBGNP for the stock trading task through real-market stock data, where much higher profits are obtained than traditional non-EDA models.

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Trading Rules on the Stock Markets using Genetic Network Programming with Candlestick Chart

Cited by 23 publications

References 13 publications

Constructing portfolio investment strategy based on Time Adapting Genetic Network Programming

Constructing portfolio investment strategy based on Time Adapting Genetic Network Programming

A model of portfolio optimization using time adapting genetic network programming

Creating stock trading rules using graph-based estimation of distribution algorithm

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