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arXiv · 2601.22119

Alpha Discovery via Grammar-Guided Learning and Search

Han Yang·Dong Hao·Zhuohan Wang·Qi Shi·Xingtong Li
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Abstract

Automatically discovering formulaic alpha factors is a central problem in quantitative finance. Existing methods often ignore syntactic and semantic constraints, relying on exhaustive search over unstructured and unbounded spaces. We present AlphaCFG, a grammar-based framework for defining and discovering alpha factors that are syntactically valid, financially interpretable, and computationally efficient. AlphaCFG uses an alpha-oriented context-free grammar to define a tree-structured, size-controlled search space, and formulates alpha discovery as a tree-structured linguistic Markov decision process, which is then solved using a grammar-aware Monte Carlo Tree Search guided by syntax-sensitive value and policy networks. Experiments on Chinese and U.S. stock market datasets show that AlphaCFG outperforms state-of-the-art baselines in both search efficiency and trading profitability. Beyond trading strategies, AlphaCFG serves as a general framework for symbolic factor discovery and refinement across quantitative finance, including asset pricing and portfolio construction.

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Han Yang, Dong Hao, Zhuohan Wang, Qi Shi, Xingtong Li. 2026-01-29. Alpha Discovery via Grammar-Guided Learning and Search. https://arxiv.org/abs/2601.22119

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