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

Jin Zhang () and Dietmar Maringer
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Jin Zhang: University of Basel
Dietmar Maringer: University of Basel

Computational Economics, 2016, vol. 47, issue 4, No 3, 567 pages

Abstract: 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.

Keywords: Artificial intelligence; Algorithmic trading; Recurrent reinforcement learning; Genetic algorithm; Indicator selection; Sharpe ratio (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10614-015-9490-y

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