AlgoEvolve: LLM-driven Meta-evolution of Algorithmic Trading Programs
Researchers have introduced AlgoEvolve, an LLM-driven evolutionary framework that automatically generates, evaluates, and optimizes Python-based algorithmic trading strategies. The system demonstrates emergent regime-adaptive logic and utilizes a meta-evolutionary loop to optimize prompts, outperforming human-designed instructions.
AlgoEvolve: LLM-driven Meta-evolution of Algorithmic Trading Programs
Recent work shows that Large Language Models (LLMs) can act as semantic mutation operators for the evolutionary discovery of programs and proofs. Most current applications focus on static coding benchmarks. We extend this paradigm to algorithmic trading. This domain is uniquely challenging because it is noisy, non-stationary, and highly discontinuous.
We present AlgoEvolve, an LLM-driven evolutionary framework that generates, evaluates, and iteratively improves executable trading strategies. These strategies are expressed as Python code and evaluated through a rigorous testing protocol. Across multiple experiments, the system exhibits emergent regime-adaptive strategy logic, including autonomous shifts in trading rules.
We further introduce a meta-evolutionary outer loop that evolves the prompts guiding program synthesis in the inner loop. This outer loop discovers improved search heuristics. These heuristics balance exploration and exploitation while reducing zero-trade failures. They consistently outperform initial human-designed instructions. The results demonstrate that LLM-based semantic evolution provides a viable approach for continual program synthesis in complex environments.
Source: arXiv cs.AI Recent
