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