用多智能体进化框架自动发现适配市场的个性化交易策略
QuantEvolve: Automating Quantitative Strategy Discovery through Multi-Agent Evolutionary Framework
- 结合质量-多样性优化与假设驱动的多智能体系统探索策略空间
- 生成多样化且适应市场变化的复杂策略，性能超越传统基线
- 适合量化交易研究者和需要个性化投资方案的从业者
在动态市场中自动化开发量化交易策略极具挑战性，尤其面对日益增长的个性化投资需求。现有方法往往难以在保持策略多样性的同时有效探索广阔的策略空间。我们提出QuantEvolve，一种融合质量-多样性优化与假设驱动策略生成的进化框架。该框架通过与投资者偏好（如策略类型、风险水平、换手率、收益特征）对齐的特征映射，维持一组高效且多样化的策略。同时，引入假设驱动的多智能体系统，通过迭代生成与评估系统性地探索策略空间。该方法生成的策略既多样化又具备复杂性，能适应市场周期变化与个体投资需求。实证结果表明，QuantEvolve显著优于传统基线方法。我们还发布了演化出的策略数据集以支持后续研究。
原文摘要 · Abstract (English)
Automating quantitative trading strategy development in dynamic markets is challenging, especially with increasing demand for personalized investment solutions. Existing methods often fail to explore the vast strategy space while preserving the diversity essential for robust performance across changing market conditions. We present QuantEvolve, an evolutionary framework that combines quality-diversity optimization with hypothesis-driven strategy generation. QuantEvolve employs a feature map aligned with investor preferences, such as strategy type, risk profile, turnover, and return characteristics, to maintain a diverse set of effective strategies. It also integrates a hypothesis-driven multi-agent system to systematically explore the strategy space through iterative generation and evaluation. This approach produces diverse, sophisticated strategies that adapt to both market regime shifts and individual investment needs. Empirical results show that QuantEvolve outperforms conventional baselines, validating its effectiveness. We release a dataset of evolved strategies to support future research.
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