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About this paper
Zhangyuhua Weng, Shengli Zhang, Taotao Wang, Yihan Xia
AlphaLogics, developed by Shenzhen University, implements a multi-agent system that explicitly models, extracts, and iteratively optimizes market logic to guide the generation of alpha factors. This approach achieved annualized returns of 16.72% and an Information Ratio of 1.5266 on CSI 500, and 13.75% AR with an IR of 1.2658 on S&P 500, outperforming various baselines in both markets.
Key insights
- Explicitly modeling and optimizing market logic as an intermediate, verifiable representation can effectively bridge the gap between scalable factor discovery and interpretability.
- A nested multi-agent architecture allows for the simultaneous and iterative refinement of both individual alpha factors and their guiding market logics.
- Enforcing structured, executable market logic constraints during factor generation guides LLMs to prioritize economically meaningful patterns, enhancing stability and reducing search space.
Summary by alphaXiv.
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