论文标题
避风力 - 偏见和差异的低偏差和高强化学习实施实施了高频股票交易
Safe-FinRL: A Low Bias and Variance Deep Reinforcement Learning Implementation for High-Freq Stock Trading
论文作者
论文摘要
近年来，许多定量财务从业者试图使用深度强化学习（DRL）来建立更好的定量交易（QT）策略。然而，许多现有研究未能应对几个严重的挑战，例如非平稳的金融环境以及在实际金融市场应用DRL时的偏见和差异权衡。在这项工作中，我们提出了Safe-Finrl，这是一种基于DRL的新型高FREQ股票交易策略，该策略通过近部财务环境以及低偏差和差异估算而增强。我们的主要贡献是双重的：首先，我们将漫长的财务时间序列分为近乎固定的短期环境；其次，我们通过将一般逆转录运营商纳入软批评者中，在近部财务环境中实施Trace-SAC。对加密货币市场的广泛实验表明，Safe-Finrl提供了稳定的价值估计，并且在近部财务环境中显着改善了稳定的政策，并大大减少了偏见和差异。
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.
