发表机构
National University of Singapore; Singapore Management University; University of Science and Technology of China; E Fund Management Co., Ltd.（新加坡国立大学; 新加坡管理大学; 中国科学技术大学; 易方达基金管理有限公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
AlphaPADI通过池感知分层离散扩散，在生成公式化Alpha时考虑池上下文与公式互补性，结合语法约束初始化、分层重建和奖励引导优化，在中美股市实证中优于基线方法。
AI 中文摘要
公式化Alpha发现旨在寻找能够预测横截面资产收益的符号表达式。在实际部署中，多个公式被组合成一个Alpha池，其中每个公式的价值通过其为联合预测性能贡献的互补信息来评估。尽管强化学习和生成流网络已成为生成公式化Alpha的有前景的范式，但现有框架面临三个相关挑战。首先，单独生成公式会将池上下文和公式间互补性排除在生成状态之外。其次，逐公式生成缺乏在不同层级上保留和修改结构的统一机制。第三，池级奖励共同反映预测性能和冗余性，但无法通过符号评估直接微分来训练生成器。为克服这些挑战，我们引入了AlphaPADI（基于池感知分层离散扩散的公式化Alpha发现），这是一个新颖的框架，包含三个组成部分：（1）语法约束的缓冲区初始化，构建语法有效的池候选；（2）池感知的分层扩散，在当前池上下文下在多个结构尺度上重建完整池；（3）奖励引导的池优化，评估联合预测性能和内部多样性，更新精英缓冲区，并通过重建和偏好学习训练反向模型。在中国和美国股票市场的实证结果表明，AlphaPADI在预测和组合性能上均优于所评估的基线方法，从而验证了池感知生成作为自动Alpha发现的有效框架。
英文摘要
Formulaic alpha discovery seeks symbolic expressions that predict cross-sectional asset returns. In deployment, multiple formulas are combined into an alpha pool, where each formula is valued through the complementary information it contributes to joint predictive performance. While Reinforcement Learning and Generative Flow Networks have emerged as promising paradigms for generating formulaic alphas, existing frameworks face three related challenges. First, generating formulas individually leaves pool context and inter-formula complementarity outside the generative state. Second, formula-wise generation lacks a unified mechanism for preserving and revising structures at different levels. Third, pool-level rewards jointly reflect predictive performance and redundancy but cannot be differentiated directly through symbolic evaluation to train the generator. To overcome these challenges, we introduce AlphaPADI (Formulaic Alpha Discovery via Pool-Aware Hierarchical Discrete Diffusion), a novel framework built around three components: (1) grammar-constrained buffer initialization that constructs syntactically valid pool candidates, (2) pool-aware hierarchical diffusion that reconstructs complete pools at multiple structural scales under the current pool context, and (3) reward-guided pool refinement that evaluates joint predictive performance and inner diversity, updates the elite buffer, and trains the reverse model through reconstruction and preference learning. Empirical results on the Chinese and U.S. stock markets demonstrate that AlphaPADI outperforms the evaluated baselines in both predictive and portfolio performance, thereby validating pool-aware generation as an effective framework for automated alpha discovery.
