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

Published: 07 July 2007 Publication History

Abstract

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 clarified that GNP works well especially in dynamic environments since GNP can create quite compact programs and has an implicit memory function. In this paper, GNP is applied to creating a stock trading model. There are three important points: The first important point is to combine GNP with Sarsa Learning which is one of the reinforcement learning algorithms. Evolution-based methods evolve their programs after task execution because they must calculate fitness values, while reinforcement learning can change programs during task execution, therefore the programs can be created efficiently. The second important point is that GNP uses candlestick chart and selects appropriate technical indices to judge the buying and selling timing of stocks. The third important point is that sub-nodes are used in each node to determine appropriate actions (buying/selling) and to select appropriate stock price information depending on the situation. In the simulations, the trading model is trained using the stock prices of 16 brands in 2001, 2002 and 2003. Then the generalization ability is tested using the stock prices in 2004. From the simulation results, it is clarified that the trading rules of the proposed method obtain much higher profits than Buy&Hold method and its effectiveness has been confirmed.

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  • (2022)Agent-based artificial financial market with evolutionary algorithmEconomic Research-Ekonomska Istraživanja10.1080/1331677X.2021.202109835:1(5037-5057)Online publication date: 31-Jan-2022
  • (2020)On the Computation of Optimized Trading Policies Using Deep Reinforcement LearningTelematics and Computing10.1007/978-3-030-62554-2_7(83-96)Online publication date: 28-Oct-2020
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  1. Trading rules on stock markets using genetic network programming with sarsa learning

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    cover image ACM Conferences
    GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
    July 2007
    2313 pages
    ISBN:9781595936974
    DOI:10.1145/1276958

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 07 July 2007

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    Author Tags

    1. candlestick chart
    2. genetic network programming
    3. reinforcement learning
    4. sarsa
    5. stock trading model
    6. technical index

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    GECCO '07 Paper Acceptance Rate 266 of 577 submissions, 46%;
    Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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    View all
    • (2024)Optimizing Multi-Vessel Collision Avoidance Decision Making for Autonomous Surface Vessels: A COLREGs-Compliant Deep Reinforcement Learning ApproachJournal of Marine Science and Engineering10.3390/jmse1203037212:3(372)Online publication date: 22-Feb-2024
    • (2022)Agent-based artificial financial market with evolutionary algorithmEconomic Research-Ekonomska Istraživanja10.1080/1331677X.2021.202109835:1(5037-5057)Online publication date: 31-Jan-2022
    • (2020)On the Computation of Optimized Trading Policies Using Deep Reinforcement LearningTelematics and Computing10.1007/978-3-030-62554-2_7(83-96)Online publication date: 28-Oct-2020
    • (2016)Generating ternary stock trading signals using fuzzy genetic network programming2016 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS)10.1109/NAFIPS.2016.7851630(1-6)Online publication date: Oct-2016
    • (2015)Modelling stock-market investors as Reinforcement Learning agents2015 IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS)10.1109/EAIS.2015.7368789(1-6)Online publication date: Dec-2015
    • (2014)Creating stock trading rules using graph-based estimation of distribution algorithm2014 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2014.6900421(731-738)Online publication date: Jul-2014
    • (2012)Stock trading system based on portfolio beta and evolutionary algorithms2012 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr)10.1109/CIFEr.2012.6327816(1-8)Online publication date: Mar-2012
    • (2009)Genetic Network Programming with Rule Accumulation Considering Judgment Order2009 IEEE Congress on Evolutionary Computation10.1109/CEC.2009.4983346(3176-3182)Online publication date: May-2009
    • (2009)Genetic Network Programming with Reconstructed Individuals2009 IEEE Congress on Evolutionary Computation10.1109/CEC.2009.4983034(854-859)Online publication date: May-2009

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