EVOQUANT: Self-Evolving Verifier-Guided Strategy Optimization for Robust Quantitative Trading
cs.AI, cs.CE
Submitted: 2026-07-14
Updated: 2026-09-10
Comments: 13 pages, 6 figures, 3 tables
License: http://creativecommons.org/licenses/by/4.0/
The gist: Quantitative strategy optimization remains largely manual, requiring domain experts to identify weak signals, tune risk-control rules, and repeatedly validate iterative revisions.
Terminology
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.
Sources
- AlphaSAGE: Structure-Aware Alpha Mining via GFlowNets for Robust Exploration
- In-Sample and Out-of-Sample Sharpe Ratios for Linear Predictive Models
- ReAct: Synergizing Reasoning and Acting in Language Models
- The Alpha Illusion: Reported Alpha from LLM Trading Agents Should Not Be Treated as Deployment Evidence
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