Optimizing the Pairs-Trading Strategy Using Deep Reinforcement Learning with Trading and Stop-Loss Boundaries

Taewook Kim and Ha Young Kim

Complexity, 2019, vol. 2019, 1-20

Abstract:

Many researchers have tried to optimize pairs trading as the numbers of opportunities for arbitrage profit have gradually decreased. Pairs trading is a market-neutral strategy; it profits if the given condition is satisfied within a given trading window, and if not, there is a risk of loss. In this study, we propose an optimized pairs-trading strategy using deep reinforcement learning—particularly with the deep Q-network—utilizing various trading and stop-loss boundaries. More specifically, if spreads hit trading thresholds and reverse to the mean, the agent receives a positive reward. However, if spreads hit stop-loss thresholds or fail to reverse to the mean after hitting the trading thresholds, the agent receives a negative reward. The agent is trained to select the optimum level of discretized trading and stop-loss boundaries given a spread to maximize the expected sum of discounted future profits. Pairs are selected from stocks on the S&P 500 Index using a cointegration test. We compared our proposed method with traditional pairs-trading strategies which use constant trading and stop-loss boundaries. We find that our proposed model is trained well and outperforms traditional pairs-trading strategies.

Date: 2019
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:hin:complx:3582516

DOI: 10.1155/2019/3582516

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