EVOQUANT: Self-Evolving Verifier-Guided Strategy Optimization for Robust Quantitative Trading

2026-07-14

AI Summary

This work addresses the longstanding reliance on manual intervention in quantitative trading strategy optimization, which suffers from challenges such as difficulty in signal identification, cumbersome rule tuning, and high validation costs. Direct application of large language models (LLMs) often leads to strategy hallucination, drift, and overfitting. To overcome these issues, the paper proposes a self-evolving, verifier-guided optimization framework that leverages LLMs to diagnose strategy bottlenecks and generate semantically controllable refinement suggestions, integrated with multi-stage validation and knowledge distillation for continuous self-improvement. This approach uniquely combines self-evolution with rigorous verification, substantially enhancing strategy robustness and automation. Experiments on seven strategies across A-share and cryptocurrency markets demonstrate an average test Sharpe ratio improvement from −0.298 to 0.538, with the best-performing strategy achieving a 199% relative gain, while maintaining robustness under stress testing.

Abstract

Quantitative strategy optimization remains largely manual, requiring domain experts to identify weak signals, tune risk-control rules, and repeatedly validate iterative revisions. Large language models can accelerate this process, but directly relying on them to rewrite trading strategies often introduces hallucinated edits, strategy drift, and backtest overfitting. We propose EVOQUANT, a self-Evolving Verifier-guided framework for strategy Optimization in Quantitative trading. Our method utilizes LLMs to deeply diagnose performance bottlenecks, generates semantically controlled candidate edits, selects the best strategy through a multi-stage verification pipeline, and distills optimization experience into reusable knowledge for continual self-improvement. We evaluate our method using seven representative strategies: four from the A-share market and three from the Crypto market. Experimental results show that our method significantly improves the Sharpe ratio across all tested strategies: the average test Sharpe increases from -0.298 to 0.538, and the best-performing strategy achieves a 199% relative improvement. Ablation studies and stress tests under stricter conditions further validate the effectiveness and robustness of the framework. Overall, this work transforms quantitative strategy optimization from costly manual trial and error into an automated and verifiable iterative paradigm, offering a new path for applying large language models to financial strategy research.

Problem

• quantitative trading
• strategy optimization
• large language models
• backtest overfitting
• strategy drift

Innovation

• Self-Evolving
• Verifier-Guided
• Quantitative Trading
• LLM-based Optimization
• Strategy Robustness