Alpha Discovery via Grammar-Guided Learning and Search
This addresses the challenge of discovering interpretable and efficient trading factors for quantitative finance practitioners, representing a novel method rather than an incremental improvement.
The paper tackles the problem of automatically discovering formulaic alpha factors in quantitative finance by introducing AlphaCFG, a grammar-based framework that ensures syntactic validity and financial interpretability, resulting in improved search efficiency and trading profitability on Chinese and U.S. stock market datasets.
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.