# QuantEvolve: Automating Quantitative Strategy Discovery through Multi-Agent Evolutionary Framework

arXiv: https://arxiv.org/abs/2510.18569 · HTML: https://mlsanity.com/paper/2510.18569/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 35 references that are in the map.
- First posted on arXiv 21 Oct 2025.

## Builds on (most cited first, 9 of 9)

- [AlphaEvolve: A coding agent for scientific and algorithmic discovery](https://mlsanity.com/paper/2506.13131/) (2025)
- [Darwin Godel Machine: Open-Ended Evolution of Self-Improving Agents](https://mlsanity.com/paper/2505.22954/) (2025)
- [FinMem: A Performance-Enhanced LLM Trading Agent with Layered Memory and Character Design](https://mlsanity.com/paper/2311.13743/) (2023)
- [A Multimodal Foundation Agent for Financial Trading: Tool-Augmented, Diversified, and Generalist](https://mlsanity.com/paper/2402.18485/) (2024)
- [Alpha-GPT: Human-AI Interactive Alpha Mining for Quantitative Investment](https://mlsanity.com/paper/2308.00016/) (2023)
- [R&D-Agent-Quant: A Multi-Agent Framework for Data-Centric Factors and Model Joint Optimization](https://mlsanity.com/paper/2505.15155/) (2025)
- [AlphaAgents: Large Language Model based Multi-Agents for Equity Portfolio Constructions](https://mlsanity.com/paper/2508.11152/) (2025)
- [Alpha-GPT 2.0: Human-in-the-Loop AI for Quantitative Investment](https://mlsanity.com/paper/2402.09746/) (2024)
- [MASS: Muli-agent simulation scaling for portfolio construction](https://mlsanity.com/paper/2505.10278/) (2025)

## Built on by (most cited first, 6 of 6)

- [Alpha-R1: Alpha Screening with LLM Reasoning via Reinforcement Learning](https://mlsanity.com/paper/2512.23515/) (2025)
- [Agentic Trading: When LLM Agents Meet Financial Markets](https://mlsanity.com/paper/2605.19337/) (2026)
- [TrustTrade: Human-Inspired Selective Consensus Reduces Decision Uncertainty in LLM Trading Agents](https://mlsanity.com/paper/2603.22567/) (2026)
- [MadEvolve: Evolutionary Optimization of Trading Systems with Large Language Models](https://mlsanity.com/paper/2605.23007/) (2026)
- [Enhancing Decision-Making with Large Language Models through Multi-Agent Fictitious Play](https://mlsanity.com/paper/2606.19308/) (2026)
- [Trading Strategy Optimization via Textual Gradient](https://mlsanity.com/paper/2610.03128/) (2026)

## Abstract (arXiv)

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.