Multi-Agent LLM Framework for Formulaic Alpha Generation and Selection in Quantitative Trading

Abstract

This study proposes a multi-agent Large Language Model (LLM) framework for formulaic alpha generation and selection in quantitative finance. In this context, an alpha refers to a predictive trading signal or mathematical expression designed to capture excess returns beyond market benchmarks. While a single LLM can generate candidate alphas, relying on one model often results in redundancy, limited diversity, and suboptimal signal quality. To address this, we adopt a multi-agent approach where DeepSeek is responsible for generating a broad pool of formulaic alphas, and Gemini serves as an evaluator that selects the top ten most important alphas from this pool based on criteria such as predictive strength and diversity of signals. To assess the effectiveness of the framework, we compare two experimental setups: (1) an equal-weighted portfolio constructed from the top ten alphas selected by Gemini, and (2) an equal-weighted portfolio of all 50 alphas generated by DeepSeek. Empirical results demonstrate that the multi-agent framework can achieve better portfolio performance, delivering higher cumulative returns, improved risk-adjusted measures, and lower maximum drawdown relative to the full alpha pool. These findings highlight the potential of multi-agent LLM collaboration in enhancing alpha discovery and systematic trading strategies.