用语法约束搜索高效发现可解释的量化交易因子
Alpha Discovery via Grammar-Guided Learning and Search
- 基于语法生成树结构空间，限制因子形式合法性
- 在中美股市数据上提升策略收益与搜索效率
- 适合量化研究者用于自动发现金融因子
自动发现公式化阿尔法因子是量化金融的核心问题。现有方法常忽略语法和语义约束，依赖对无结构、无界空间的穷举搜索。本文提出AlphaCFG，一种基于语法的框架，用于定义和发现语法合法、金融可解释且计算高效的阿尔法因子。AlphaCFG采用面向阿尔法的上下文无关语法，构建树状、大小可控的搜索空间，并将因子发现建模为树状语言马尔可夫决策过程，通过语法感知的蒙特卡洛树搜索求解，由语法敏感的价值网络和策略网络引导。在中、美股票市场数据集上的实验表明，AlphaCFG在搜索效率和交易盈利性方面均优于当前最优基线。除交易策略外，AlphaCFG还可作为量化金融中符号因子发现与优化的通用框架，适用于资产定价与组合构建。
原文摘要 · Abstract (English)
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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