Towards robust deep reinforcement learning-based quantitative trading with neuro-symbolic trend analysis

Deep reinforcement learning (DRL) has revolutionized quantitative trading (Q-trading) by achieving decent performance without significant human expert knowledge. Despite its achievements, we observe that the current state-of-the-art DRL models are still ineffective in identifying the market trends, causing them to miss good trading opportunity or suffer from large drawdowns when encountering market crashes. To address this limitation, a natural approach is to incorporate human expert knowledge in identifying market trends. Whereas, such knowledge is abstract and hard to be quantified. In order to effectively leverage abstract human expert knowledge, in this paper, we propose a universal logic-guided deep reinforcement learning framework for Q-trading, called Logic-Q. In particular, Logic-Q adopts the program synthesis by sketching paradigm and introduces a neuro-symbolic trend analysis mechanism (NeSy-TA), which can dynamically determine the market trend and the corresponding tuning parameters for backbone models across different modalities. Extensive evaluations on two popular quantitative trading tasks demonstrate that Logic-Q outperforms the previous state-of-the-art baselines by a large margin, which includes the recently proposed powerful multimodal LLM-based trading strategy.