arXiv:2605.23007v1 Announce Type: cross 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. 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 evolvin
The convergence of advanced large language models (LLMs) and the increasing complexity of quantitative finance tasks is creating new opportunities for automated optimization at a rapid pace.
This development indicates a significant leap in the autonomy and sophistication of trading systems, potentially leading to more efficient markets and highly individualized alpha generation.
The ability to use LLMs for evolutionary optimization allows for more dynamic and adaptive trading strategies, moving beyond traditional, predefined algorithmic approaches.
- · Hedge funds
- · Quantitative trading firms
- · AI developers
- · Cryptocurrency markets
- · Traditional algorithmic trading platforms
- · Manual quantitative analysts
- · Brokerages reliant on human-driven analysis
AI-driven trading systems will become more prevalent and sophisticated in financial markets.
Increased efficiency and potential for new forms of market manipulation could emerge, necessitating novel regulatory frameworks.
The democratization of advanced trading strategies through LLM-enabled platforms might reduce information asymmetry, or conversely, consolidate power among those with superior AI infrastructure.
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Read at arXiv cs.LG