AlphaLogics: A Market Logic-Driven Multi-Agent System for Scalable and Interpretable Alpha Factor Generation

TL;DR

AlphaLogics is a multi-agent system that extracts, refines, and utilizes market logic to generate and optimize alpha factors, leading to improved predictive performance and interpretability in factor investing.

Contribution

It introduces a novel market logic-driven framework for scalable and interpretable alpha factor generation, addressing the limitations of prior complex and opaque factor mining methods.

Findings

Consistently improves predictive metrics on CSI 500 and S&P 500

Enhances risk-adjusted returns compared to baseline methods

Produces a useful market logic library for ongoing factor discovery

Abstract

Factor investing is ultimately grounded in market logic - the latent mechanism behind observed alpha factors that explains why they should persist across assets and regimes. However, recent factor mining prioritizes factor discovery over logic discovery, producing complex alpha factors with unclear rationale, while market logic remains largely handcrafted and difficult to scale. To address this challenge, we propose AlphaLogics, a market logic-driven multi-agent system for factor mining. AlphaLogics consists of three key components: (i) Market Logic Mining: reverse-extracting market logic from historical factor libraries to construct an initial market logic library; (ii) Factor Generation and Optimization: using new market logics generated in (i) to guide factor generation, and optimizing factors with backtesting feedback; and (iii) Market Logic Generation and Optimization: generating new market logics conditioned on the initial market logic library, and refining each market logic by aggregating the backtest outcomes of its guided factors, continuously refreshing the library. Experiments on CSI 500 and S&P 500 show that AlphaLogics consistently improves predictive metrics and risk-adjusted returns over representative baselines, while producing a market logic library that remains empirically useful for guiding further factor discovery.