@inproceedings{mao-etal-2026-evoquant,
title = "{EVOQUANT}: Self-Evolving Verifier-Guided Strategy Optimization for Robust Quantitative Trading",
author = "Mao, Jie and
Li, Changlun and
Li, Xiang and
Duan, Qiqi and
Yuan, Jinhui and
Liu, Xiang and
Luo, Yuyu and
Tang, Jing and
Tang, Nan",
editor = "Huang, Jimin and
Lee, Yongjae and
Ananiadou, Sophia and
Chen, Hsin-Hsi and
Takamura, Hiroya and
Chen, Chung-Chi",
booktitle = "Proceedings of the 11th Workshop on Financial Technology and Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.finnlp-1.60/",
pages = "717--729",
ISBN = "979-8-89176-466-8",
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 back-test 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, generate semantically controlled candidate edits, select the best strategy through a multi-stage verification pipeline, and distill 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 {\ensuremath{-}}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."
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<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 back-test 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, generate semantically controlled candidate edits, select the best strategy through a multi-stage verification pipeline, and distill 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 \ensuremath-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.</abstract>
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%0 Conference Proceedings
%T EVOQUANT: Self-Evolving Verifier-Guided Strategy Optimization for Robust Quantitative Trading
%A Mao, Jie
%A Li, Changlun
%A Li, Xiang
%A Duan, Qiqi
%A Yuan, Jinhui
%A Liu, Xiang
%A Luo, Yuyu
%A Tang, Jing
%A Tang, Nan
%Y Huang, Jimin
%Y Lee, Yongjae
%Y Ananiadou, Sophia
%Y Chen, Hsin-Hsi
%Y Takamura, Hiroya
%Y Chen, Chung-Chi
%S Proceedings of the 11th Workshop on Financial Technology and Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-466-8
%F mao-etal-2026-evoquant
%X 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 back-test 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, generate semantically controlled candidate edits, select the best strategy through a multi-stage verification pipeline, and distill 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 \ensuremath-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.
%U https://aclanthology.org/2026.finnlp-1.60/
%P 717-729
Markdown (Informal)
[EVOQUANT: Self-Evolving Verifier-Guided Strategy Optimization for Robust Quantitative Trading](https://aclanthology.org/2026.finnlp-1.60/) (Mao et al., FinNLP 2026)
ACL
- Jie Mao, Changlun Li, Xiang Li, Qiqi Duan, Jinhui Yuan, Xiang Liu, Yuyu Luo, Jing Tang, and Nan Tang. 2026. EVOQUANT: Self-Evolving Verifier-Guided Strategy Optimization for Robust Quantitative Trading. In Proceedings of the 11th Workshop on Financial Technology and Natural Language Processing, pages 717–729, Budapest, Hungary. Association for Computational Linguistics.