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

Zhang, Jin and Maringer, Dietmar. (2015) Using a Genetic Algorithm to Improve Recurrent Reinforcement Learning for Equity Trading. Computational Economics, 47 (4). pp. 551-567.

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Official URL: http://edoc.unibas.ch/39490/

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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.
Faculties and Departments:06 Faculty of Business and Economics > Departement Wirtschaftswissenschaften > Professuren Wirtschaftswissenschaften > Computational Economics and Finance (Maringer)
UniBasel Contributors:Maringer, Dietmar and Zhang, Jin
Item Type:Article, refereed
Article Subtype:Research Article
Publisher:Springer US
Note:Publication type according to Uni Basel Research Database: Journal article
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Last Modified:30 Jun 2016 10:59
Deposited On:27 Apr 2016 08:33

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