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In recent years, many practitioners in quantitative finance have attempted touse Deep Reinforcement Learning (DRL) to build better quantitative trading (QT)strategies. Nevertheless, many existing studies fail to address several seriouschallenges, such as the non-stationary financial environment and the bias andvariance trade-off when applying DRL in the real financial market. In thiswork, we proposed Safe-FinRL, a novel DRL-based high-freq stock tradingstrategy enhanced by the near-stationary financial environment and low bias andvariance estimation. Our main contributions are twofold: firstly, we separatethe long financial time series into the near-stationary short environment;secondly, we implement Trace-SAC in the near-stationary financial environmentby incorporating the general retrace operator into the Soft Actor-Critic.Extensive experiments on the cryptocurrency market have demonstrated thatSafe-FinRL has provided a stable value estimation and a steady policyimprovement and reduced bias and variance significantly in the near-stationaryfinancial environment.

Safe-FinRL: A Low Bias and Variance Deep Reinforcement Learning Implementation for High-Freq Stock Trading