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

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.

Figures (5)

• Figure 1: Comparison of advantages and disadvantages of alpha factor mining methods. Manual Market Logic-driven factor mining (left) offers high interpretability but lacks scalability; Market data driven factor mining (middle) scales well but often lacks interpretability; Our method (right) offers high interpretability and scalability.
• Figure 2: The autonomous workflow of AlphaLogics. The framework operates in three stages: (1) Market Logic Mining extracts latent market logic from historical factor libraries; (2) Guided Factor Generation guides new factors generation and optimization with backtesting feedback; (3) Market Logic Generation generates 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.
• Figure 3: Comparison of market logic-guided factor generation on CSI 500 and S&P 500 (train 2015.01--2019.12, validation 2020.01--2020.12, test 2021.01--2024.12). The results demonstrate that across all models and market settings, factors generated with market logic guidance consistently outperform unconstrained generation in terms of IC and IR metrics.
• Figure 4: Evolution indicators of market logic. We test GPT-3.5-Turbo ouyang2022training, DeepSeek V3 liu2024deepseek_v3, and Gemini-2.5-Flash comanici2025gemini on CSI 500 and S&P 500, using early stopping of 3 for each logic and selecting the best-performing factor under the current logic in each round. The results show that as iteration rounds progress, the optimal factors corresponding to different rounds show an overall upward trend in key metrics such as IC, IR, cumulative returns, and stability.
• Figure 5: The influence of market logic quantity on factor quality. Increasing the number of market logic yields stable and nearly monotonic improvements across IC, ICIR, AR, and IR.