AlgoEvolve: LLM-driven Meta-evolution of Algorithmic Trading Programs
arXiv:2606.26173v1 Announce Type: new 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 iterati
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Computer Science > Artificial Intelligence arXiv:2606.26173 (cs) [Submitted on 24 Jun 2026] Title:AlgoEvolve: LLM-driven Meta-evolution of Algorithmic Trading Programs Authors:Dhruv Sharma, Gautam Shroff View a PDF of the paper titled AlgoEvolve: LLM-driven Meta-evolution of Algorithmic Trading Programs, by Dhruv Sharma and Gautam Shroff View PDF HTML (experimental) 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.
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