Automate Strategy Finding with LLM in Quant Investment (2025)

Abstract: We present a novel three-stage framework leveraging Large Language Models (LLMs) within a risk-aware multi-agent system for automate strategy finding in quantitative finance. Our approach addresses the brittleness of traditional deep learning models in financial applications by: employing prompt-engineered LLMs to generate executable alpha factor candidates across diverse financial data, implementing multimodal agent-based evaluation that filters factors based on market status, predictive quality while maintaining category balance, and deploying dynamic weight optimization that adapts to market conditions. Experimental results demonstrate the robust performance of the strategy in Chinese & US market regimes compared to established benchmarks. Our work extends LLMs capabilities to quantitative trading, providing a scalable architecture for financial signal extraction and portfolio construction. The overall framework significantly outperforms all benchmarks with 53.17% cumulative return on SSE50 (Jan 2023 to Jan 2024), demonstrating superior risk-adjusted performance and downside protection on the market.

## introduction recent advances in llms and multi-agent systems are converging to transform quantitative finance. this synergistic relationship leverages llms' text comprehension and generation capabilities alongside multi-agent frameworks that simulate market dynamics, creating sophisticated approaches to portfolio management @xcite . llms have evolved from supporting analytical tools to active participants in financial decision-making @xcite . for exam-ple, bloomberggpt demonstrates superior performance in parsing market sentiment and answering domain-specific questions @xcite . research shows that llms effectively generate trading actions by contextualizing price trends with news and earnings reports @xcite . concurrently, multi-agent systems offer powerful approaches to portfolio optimization through decentralized interaction. the multi-agent portfolio system demonstrates the portfolio managed by agents have achieved well-diversified returns with improved risk-adjusted performance @xcite . these frameworks capture complex market dynamics such as information sharing and strategic arbitrage that single-agent models cannot address @xcite . the integration of these technologies creates sophisticated financial environments where llm-enhanced agents demonstrate adaptive behavior. stockagent exemplifies this approach with llm-powered agents mimic diverse investor personas responding to market events @xcite . hierarchical structures such as fincon organize agents in manager-analyst relationships, facilitating collaboration through natural language communication @xcite . this convergence heralds a future of intelligent, distributed financial decision making that combines data-driven learning with human-like reasoning capabilities for more robust investment strategies @xcite . alpha mining is the process of discovering trading signals that generate excess returns in financial markets, we identify three critical challenges in alpha mining: rigidity of traditional methods that lack adaptability to dynamic markets @xcite ; data diversity and integration challenges despite machine learning advances @xcite ; and adaptation to market variability despite progress in prediction @xcite and strategy formulation @xcite . our llm-driven framework addresses these lim-itations through three components: flexible alpha mining employs llms to extract, categorize, and filter alpha factors from financial literature, organizing them as momentum, fundamental, or liquidity factors with established independence @xcite . multi-agent multimodal market evaluation conducts rigorous backtesting across diverse market conditions, with specialized agents evaluating factor effectiveness from multiple perspectives. dynamic strategy optimization implements a weight gating layer that assigns optimal alpha factor weights based on current market conditions, ensuring adaptive strategy development. our methodology synthesizes cutting-edge machine learning techniques with established financial domain knowledge to create a robust interdisciplinary framework for alpha identification and optimization across diverse asset classes. this research is grounded in empirical quantitative investment practices, bridging theoretical advancements with practical applications in portfolio management. our main contributions are three-fold: ❶ a novel framework for identifying formulaic alpha factors using llms, leveraging their exploratory capabilities to establish an alpha factory from multimodal information with incremental update functionality; ❷ introduction of a multi-agent approach to portfolio management for evaluating relationships between market conditions and alpha factors, enabling specialized evaluation under different scenarios; ❸ integration of advanced techniques from machine learning and finance, representing a significant advancement in developing robust, adaptive investment strategies without human intervention. the proposed framework demonstrates versatility across various asset classes, enhancing its utility and practical effectiveness. to support future research and ensure reproducibility, we make source code publicly available at https://github.com /kouzhizhuo/automate-strategy-finding-w ith-llm-in-quant-investment . ## problem formulation this section establishes the theoretical foundation for our research on alpha factor strategies in quantitative finance. we formulate a framework addressing three interconnected challenges: mathematically formalizing these alphas, developing dynamic methodologies to generate seed alphas, and defining and optimizing alpha factor strategies. our approach integrates llms and multi-agent systems to overcome limitations in traditional quantitative trading methods. we employ consistent notation throughout: n stocks observed over trading periods @xmath0, 2, . . . , t , each characterized by m financial features. alpha factors are denoted as α (t) ij for stock i in category j at time t, with corresponding weights w i . market conditions at time t are represented by m (t) , and alpha factor predictive power is measured using the information coefficient (ic) 1. a higher ic indicates stronger predictive relationships between alpha values and future returns. where σ(u, v) is correlation coefficient between predicted alphas u and actual future returns v. ## experiment our research aims to develop a comprehensive llm-driven alpha mining framework that operates without human intervention. this framework is uniquely capable of processing multimodal information and adapting to varying market conditions. to validate the effectiveness of our framework, we have conducted a series of experiments. ## conclusion in this paper, we proposed a novel quantitative investment framework integrating llms and multi-agent architectures to address instability in traditional approaches. our system generates diversified alpha factors from multimodal financial data, constructs risk-calibrated trading agents, and employs a deep learning mechanism for dynamic agent weighting based on market conditions. experimental results confirm the framework's effectiveness across chinese and us markets, our framework demonstrates significant outperformance versus sota alpha generation methods and benchmark indices across key financial metrics. this work successfully extends llm capabilities to quantitative trading, creating a scalable, adaptive architecture for financial signal extraction that functions effectively without human intervention across diverse market regimes.