Automate Strategy Finding with LLM in Quant investment

2024-09-10

arXiv.org

AI Summary

To address the poor stability and weak adaptability of deep learning models in quantitative investing, this paper proposes an LLM-driven multi-agent collaborative framework for automated discovery and dynamic ensemble optimization of multimodal (numerical, textual, and chart-based) alpha factors. The method innovatively integrates large language models (LLMs), multi-agent systems, and a dynamic weight gating mechanism to establish a market-state-aware, adaptive strategy generation paradigm. Empirically evaluated on the Chinese A-share market, the framework significantly outperforms state-of-the-art baselines: it achieves a 23.6% improvement in Sharpe ratio and a 31.2% reduction in maximum drawdown, effectively balancing return enhancement and risk control. By enabling interpretable, robust, and adaptive decision-making, the proposed framework establishes a novel paradigm for AI-powered quantitative investment.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Multiagent Systems: Adversarial Agents
• Search and Optimization: Learning to Search

Application Category

• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Despite significant progress in deep learning for financial trading, existing models often face instability and high uncertainty, hindering their practical application. Leveraging advancements in Large Language Models (LLMs) and multi-agent architectures, we propose a novel framework for quantitative stock investment in portfolio management and alpha mining. Our framework addresses these issues by integrating LLMs to generate diversified alphas and employing a multi-agent approach to dynamically evaluate market conditions. This paper proposes a framework where large language models (LLMs) mine alpha factors from multimodal financial data, ensuring a comprehensive understanding of market dynamics. The first module extracts predictive signals by integrating numerical data, research papers, and visual charts. The second module uses ensemble learning to construct a diverse pool of trading agents with varying risk preferences, enhancing strategy performance through a broader market analysis. In the third module, a dynamic weight-gating mechanism selects and assigns weights to the most relevant agents based on real-time market conditions, enabling the creation of an adaptive and context-aware composite alpha formula. Extensive experiments on the Chinese stock markets demonstrate that this framework significantly outperforms state-of-the-art baselines across multiple financial metrics. The results underscore the efficacy of combining LLM-generated alphas with a multi-agent architecture to achieve superior trading performance and stability. This work highlights the potential of AI-driven approaches in enhancing quantitative investment strategies and sets a new benchmark for integrating advanced machine learning techniques in financial trading can also be applied on diverse markets.

Problem

• Instability and high uncertainty in financial trading models
• Generating diversified alphas using LLMs for quant investment
• Dynamic market evaluation via multi-agent architecture

Innovation

• LLMs generate diversified alphas
• Multi-agent approach evaluates market dynamically
• Dynamic weight-gating selects relevant agents