AlphaPROBE: Alpha Mining via Principled Retrieval and On-graph biased evolution
Taian Guo, Haiyang Shen, Junyu Luo, Binqi Chen, Hongjun Ding, Jinsheng Huang, Luchen Liu, Yun Ma, Ming Zhang

TL;DR
AlphaPROBE introduces a novel graph-based framework for alpha factor mining in quantitative finance, leveraging a DAG structure to improve diversity, efficiency, and predictive accuracy over traditional methods.
Contribution
It models alpha factor discovery as navigation on a DAG, integrating a Bayesian retriever and context-aware generator for more effective and diverse factor extraction.
Findings
Significantly improves predictive accuracy and return stability.
Enhances training efficiency in alpha mining.
Outperforms 8 competitive baselines on Chinese stock datasets.
Abstract
Extracting signals through alpha factor mining is a fundamental challenge in quantitative finance. Existing automated methods primarily follow two paradigms: Decoupled Factor Generation, which treats factor discovery as isolated events, and Iterative Factor Evolution, which focuses on local parent-child refinements. However, both paradigms lack a global structural view, often treating factor pools as unstructured collections or fragmented chains, which leads to redundant search and limited diversity. To address these limitations, we introduce AlphaPROBE (Alpha Mining via Principled Retrieval and On-graph Biased Evolution), a framework that reframes alpha mining as the strategic navigation of a Directed Acyclic Graph (DAG). By modeling factors as nodes and evolutionary links as edges, AlphaPROBE treats the factor pool as a dynamic, interconnected ecosystem. The framework consists of two…
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Taxonomy
TopicsStock Market Forecasting Methods · Time Series Analysis and Forecasting · Evolutionary Algorithms and Applications
