用大模型进化优化比特币交易策略，效果显著提升。
MadEvolve: Evolutionary Optimization of Trading Systems with Large Language Models
- 基于大模型的演化算法自动优化交易特征与策略组件。
- 在模拟环境中显著提升信号生成与执行策略性能。
- 适合量化交易研究者和算法优化工程师参考。
我们探索了大语言模型驱动的算法优化在量化金融常见任务中的应用。MadEvolve 是一个受 DeepMind Alpha-Evolve 启发的通用算法优化框架，最初用于计算宇宙学算法优化。本文展示了其在比特币交易中优化算法交易策略和因子生成的实用性。在模拟与回测设置下，我们在所有任务中均取得显著改进，包括演化信号生成的特征集、优化交易策略的独立组件，以及联合演化特征管道与执行策略。此外，我们将该方法与 Claude Code 等代理搜索方法进行比较，并在模拟环境中仔细评估了 p 值伪造概率。结果有力支持了人工智能驱动的代理式与演化算法在算法交易与量化金融中的有效性。
原文摘要 · Abstract (English)
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. Here we demonstrate the utility of MadEvolve to optimize algorithmic trading strategies and alpha generation at the example of Bitcoin trading. On our simulation and backtesting setup, we achieve significant improvements on all tasks we considered, such as evolving feature sets for signal generation, optimizing separate components of the trading strategy, and jointly evolving the feature pipeline together with the execution strategy. Additionally, we compare our method to other agentic search approaches, specifically Claude Code, and carefully evaluate p-hacking probabilities on our simulation setup. Our findings strongly support the utility of AI-driven agentic and evolutionary algorithms for algorithmic trading and quantitative finance.
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