MadEvolve: Evolutionary Optimization of Trading Systems with Large Language Models
The paper titled 'MadEvolve' presents a framework for optimizing trading algorithms using large language models. The authors demonstrate significant improvements in algorithmic trading strategies, particularly in Bitcoin trading, through evolutionary optimization techniques. Their findings suggest that AI-driven methods can enhance performance in quantitative finance tasks.
- ▪MadEvolve is inspired by DeepMind's Alpha-Evolve and is designed for algorithm optimization in quantitative finance.
- ▪The framework was tested on Bitcoin trading, showing substantial improvements in various trading tasks.
- ▪The study compares MadEvolve with other optimization approaches and evaluates the risk of p-hacking.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.23007 |
| Publication time | Mon, 25 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-25T04:07:35.648Z |
| Last seen | 2026-05-25T04:07:35.648Z |
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| Summary source text | contentText |
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| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
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Quantitative Finance > Trading and Market Microstructure arXiv:2605.23007 (q-fin) [Submitted on 21 May 2026] Title:MadEvolve: Evolutionary Optimization of Trading Systems with Large Language Models Authors:Yurii Kvasiuk, Tianyi Li, Owen Colegrove, Moritz Münchmeyer View a PDF of the paper titled MadEvolve: Evolutionary Optimization of Trading Systems with Large Language Models, by Yurii Kvasiuk and 3 other authors View PDF HTML (experimental) Abstract:We explore the application of LLM-driven algorithm optimization to several common tasks in quantitative finance. MadEvolve, a general-purpose algorithm optimization framework inspired by DeepMind's Alpha-Evolve, was recently developed to optimize algorithms in computational cosmology.
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