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

الملخص EN

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.