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

Battle Count: 96

Directly addresses core quantitative trading challenges: alpha factor generation, factor interpretability, cross-regime robustness, and scalable factor discovery. The system produces actionable trading signals (cross-sectional scores) evaluated with standard quant metrics (IC, IR, AR, MDD) on major equity indices. The market logic framework provides the economic rationale behind factors, addressing a key practitioner need for interpretable and auditable factor research. Results show 16.72% annualized excess return (IR=1.53) on CSI 500 and 13.75% (IR=1.27) on S&P 500 after trading costs.

High complexity due to: (1) multi-agent pipeline with 7+ specialized agents requiring careful prompt engineering; (2) three-stage architecture with nested inner/outer loops; (3) DSL-based factor generation with constraint compilation (Gamma); (4) backtesting integration with LightGBM; (5) iterative logic refinement with cross-factor evidence aggregation; (6) multiple LLM API calls per factor generation cycle. Requires expertise in both quantitative finance and LLM-based agent systems. The paper provides detailed agent schemas and DSL specifications that aid implementation.

The paper provides detailed methodology including agent prompt templates (Appendix A.7), factor DSL with operator families and parameter ranges (Appendix A.2-A.3), logic schema and compilation examples (Appendix A.4), evaluation metrics definitions (Appendix A.5), and a market logic optimization example (Appendix A.6). Data sources (Baostock, Yahoo Finance) and backtesting platform (Qlib) are specified. However, no GitHub repository is provided, and the multi-agent pipeline with multiple LLM calls per stage makes exact reproduction challenging. Budget alignment details are provided but full code is not released.