MadEvolve: Evolutionary Optimization of Trading Systems with Large Language Models
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 wi...
| Authors | Yurii Kvasiuk, Tianyi Li, Owen Colegrove et al. |
|---|---|
| Primary category | q-fin.TR |
| Published | 21 May 2026 |
| Last updated at source | 21 May 2026 |
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