  Deep reinforcement learning (DRL) has revolutionized quantitative finance 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 trend, causing them to
miss good trading opportunities or suffer from large drawdowns when
encountering market crashes. To tackle this limitation, a natural idea is to
embed human expert knowledge regarding the market trend. Whereas, such
knowledge is abstract and hard to be quantified. In this paper, we propose a
universal neuro-symbolic tuning framework, called program sketch-based tuning
(PST). Particularly, PST first proposes using a novel symbolic program sketch
to embed the abstract human expert knowledge of market trends. Then we utilize
the program sketch to tune a trained DRL policy according to the different
market trend of the moment. Finally, in order to optimize this neural-symbolic
framework, we propose a novel hybrid optimization method. Extensive evaluations
on two popular quantitative trading tasks demonstrate that PST can
significantly enhance the performance of previous state-of-the-art DRL
strategies while being extremely lightweight.
