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Using a Genetic Algorithm to Improve Recurrent Reinforcement Learning for Equity Trading

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Listed:
  • Jin Zhang

    (University of Basel)

  • Dietmar Maringer

    (University of Basel)

Abstract

Recurrent reinforcement learning (RRL) has been found to be a successful machine learning technique for building financial trading systems. In this paper, we use a genetic algorithm (GA) to improve the trading results of a RRL-type equity trading system. The proposed trading system takes the advantage of GA’s capability to select an optimal combination of technical indicators, fundamental indicators and volatility indicators for improving out-of-sample trading performance. In our experiment, we use the daily data of 180 S&P stocks (from the period January 2009 to April 2014) to examine the profitability and the stability of the proposed GA-RRL trading system. We find that, after feeding the indicators selected by the GA into the RRL trading system, the out-of-sample trading performance improves as the number of companies with a significantly positive Sharpe ratio increases.

Suggested Citation

  • Jin Zhang & Dietmar Maringer, 2016. "Using a Genetic Algorithm to Improve Recurrent Reinforcement Learning for Equity Trading," Computational Economics, Springer;Society for Computational Economics, vol. 47(4), pages 551-567, April.
    • Handle: RePEc:kap:compec:v:47:y:2016:i:4:d:10.1007_s10614-015-9490-y
    DOI: 10.1007/s10614-015-9490-y
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    References listed on IDEAS

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    Cited by:

    1. Geng, Yini & Liu, Yifan & Lu, Yikang & Shen, Chen & Shi, Lei, 2022. "Reinforcement learning explains various conditional cooperation," Applied Mathematics and Computation, Elsevier, vol. 427(C).

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