Hubble: An LLM-Driven Agentic Framework for Safe and Automated Alpha Factor Discovery
Hubble is an LLM-driven framework designed to automate the discovery of predictive alpha factors in quantitative finance, combining AI-driven search with deterministic safety constraints for better interpretability and performance.
Computer Science > Artificial Intelligence
Title:Hubble: An LLM-Driven Agentic Framework for Safe and Automated Alpha Factor Discovery
View PDF HTML (experimental)Abstract:Discovering predictive alpha factors in quantitative finance remains a formidable challenge due to the vast combinatorial search space and inherently low signal-to-noise ratios in financial data. Existing automated methods, particularly genetic programming, often produce complex, uninterpretable formulas prone to overfitting. We introduce Hubble, a closed-loop factor mining framework that leverages Large Language Models (LLMs) as intelligent search heuristics, constrained by a domain-specific operator language and an Abstract Syntax Tree (AST)-based execution sandbox. The framework evaluates candidate factors through a rigorous statistical pipeline encompassing cross-sectional Rank Information Coefficient (RankIC), annualized Information Ratio, and portfolio turnover. An evolutionary feedback mechanism returns top-performing factors and structured error diagnostics to the LLM, enabling iterative refinement across multiple generation rounds. In experiments conducted on a panel of 30 U.S. equities over 752 trading days, the system evaluated 181 syntactically valid factors from 122 unique candidates across three rounds, achieving a peak composite score of 0.827 with 100% computational stability. Our results demonstrate that combining LLM-driven generation with deterministic safety constraints yields an effective, interpretable, and reproducible approach to automated factor discovery.
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Source: arXiv cs.AI Recent
