Deep reinforcement learning (DRL) has revolutionized quantitative finance by
achieving excellent performance without significant manual effort. Whereas we
observe that the DRL models behave unstably in a dynamic stock market due to
the low signal-to-noise ratio nature of the financial data. In this paper, we
propose a novel logic-guided trading framework, termed as SYENS (Program
Synthesis-based Ensemble Strategy). Different from the previous
state-of-the-art ensemble reinforcement learning strategy which arbitrarily
selects the best-performing agent for testing based on a single measurement,
our framework proposes regularizing the model's behavior in a hierarchical
manner using the program synthesis by sketching paradigm. First, we propose a
high-level, domain-specific language (DSL) that is used for the depiction of
the market environment and action. Then based on the DSL, a novel program
sketch is introduced, which embeds human expert knowledge in a logical manner.
Finally, based on the program sketch, we adopt the program synthesis by
sketching a paradigm and synthesizing a logical, hierarchical trading strategy.
We evaluate SYENS on the 30 Dow Jones stocks under the cash trading and the
margin trading settings. Experimental results demonstrate that our proposed
framework can significantly outperform the baselines with much higher
cumulative return and lower maximum drawdown under both settings.

本文提出了一种名为 SYENS 的新型逻辑引导交易框架，它通过程序合成和层次化交易策略在动态股市中稳定地行为。实验证明，SYENS 在现金交易和保证金交易设置下，在累积收益和最大回撤方面都明显优于基准。