AlgoEvolve: LLM-driven Meta-evolution of Algorithmic Trading Programs

2026-06-24

AI Summary

This work addresses the challenge of consistently generating effective and adaptive executable trading strategies in noisy, non-stationary, and highly discontinuous algorithmic trading environments. The authors propose a two-level evolutionary framework: at the inner level, a large language model (LLM) serves as a semantic mutation operator to iteratively generate and refine Python-based trading strategies; at the outer level, a meta-evolutionary mechanism automatically optimizes prompting instructions, autonomously discovering program synthesis heuristics that outperform human-designed ones. This approach represents the first integration of LLMs into the strategy evolution process for algorithmic trading, combining evolutionary algorithms with meta-learning. Rigorous backtesting demonstrates that the system adaptively responds to market regimes, dynamically switches trading logic, significantly reduces zero-trade failures, and consistently outperforms baseline strategies guided by manually crafted prompts.

Abstract

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.

Problem

• algorithmic trading
• program synthesis
• non-stationary environment
• semantic evolution
• continual learning

Innovation

• LLM-driven evolution
• algorithmic trading
• meta-evolution
• semantic mutation
• prompt optimization