Market-Dependent Communication in Multi-Agent Alpha Generation

Battle Count: 69

Highly relevant to quantitative trading. Directly addresses organizational design for multi-strategy hedge funds, alpha generation via LLM agents, and how communication structures affect trading performance. Key findings on market-dependent communication effectiveness (competitive for volatile tech, collaborative for stable general stocks) provide actionable insights for fund managers. The demonstration that strategy convergence occurs regardless of transparency challenges common assumptions about information sharing. The WorldQuant-style alpha expression framework is directly applicable to systematic trading. The finding that conversation quality metrics don't predict returns has practical implications for monitoring trading team effectiveness.

Moderate complexity. Requires LLM API access (GPT-4o-mini), implementation of WorldQuant-style alpha expression evaluation with 50+ mathematical operations, multi-agent conversation orchestration with persistent memory, returns-based capital reallocation logic, and statistical analysis infrastructure. The core trading logic is well-defined with clear function libraries and prompt templates. Main challenges include: (1) robust alpha expression parsing and evaluation, (2) managing multi-agent conversation state across months, (3) implementing the 5 organizational structures with proper information isolation, (4) statistical rigor with 30 iterations per configuration. The GitHub repository provides code, reducing implementation burden. No specialized hardware beyond a single GPU (L40S) is required.

Code and data available at GitHub (https://github.com/Jerick-1380/multi-agent-alpha-generation). Detailed prompt templates provided in Appendix B. Complete experimental parameters specified (temperature, max_tokens, function library, market universes, data sources). 30 independent iterations with distinct random seeds. Statistical tests (paired t-tests, ANOVA, bootstrap CIs) fully reported. However, results depend on GPT-4o-mini API availability and Yahoo Finance data, which may change over time. The 21-month period (Jan 2024 - Sep 2025) is a fixed historical window.