AlgoEvolve: LLM-driven Meta-evolution of Algorithmic Trading Programs
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.
- ▪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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Record
| Original publisher | arXiv.org |
| Canonical URL | https://arxiv.org/abs/2606.26173 |
| Publication time | Fri, 26 Jun 2026 00:00:00 -0400 |
| Retrieval time | 2026-06-26T05:20:41.012Z |
| Last seen | 2026-06-26T05:20:41.012Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | phA4vpFrF6I1 |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
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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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv.org.
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