# Hard to Fool: A bounded LLM research loop for quantitative finance > Source: > Published: 2026-11-05 15:10:41+00:00 # Quantitative Finance > Computational Finance [Submitted on 4 Nov 2026] # Title:Hard to Fool: A Bounded LLM Research Loop for Quantitative Finance, and What Two Dozen Campaigns Found [View PDF](https://arxiv.org/pdf/2611.06607) [HTML (experimental)](https://arxiv.org/html/2611.06607v1) Abstract:Agentic research loops that let a language model propose a change, run a fixed experiment and keep whatever improves the metric work well in machine learning. In trading research the same loop is a machine for producing false discoveries: a backtest that "improves the metric" is, by default, overfit, look-ahead, a multiple-comparisons artifact, cost-free, or market drift wearing a strategy. We describe Autonomous Quant Researcher, an open-source loop built on the opposite premise: the model proposes, and only trusted code decides. Proposals are strict declarative JSON over a fixed vocabulary, so there is no model-written code to sandbox. Evaluation runs on immutable chronological stores split into discovery, validation and a final holdout that can be opened exactly once, after a durable receipt is written. Acceptance is a conjunction of absolute gates (positive edge and net P&L after costs, a positive weekly-cluster bootstrap lower bound, survival at elevated costs, per-year stability and sample floors), never a single score. Duplicates are refused before they spend a trial, and every formulation, refusal, crash and result is kept in a hash-chained, append-only ledger. We report every campaign run to date, across 0DTE options priced from traded option bars, overnight SPX verticals, daily ETF rules, technical-analysis premises, Kalshi crypto binaries, earnings options, off-exchange volume and a pre-registered alternative-data campaign. Most hypotheses fail, and we report them. Two results survive their pre-registered bars. The clearest example of the loop working is a failure: a language-model survivor that passed discovery and validation with Sharpe ratios near 3 and fell to 0.26 on its single holdout use. The value of such a loop lies less in what it finds than in how hard it is to make it report something that is not there. ### Current browse context: q-fin.CP ### References & Citations Loading... # Bibliographic and Citation Tools Bibliographic Explorer *(*[What is the Explorer?](https://info.arxiv.org/labs/showcase.html#arxiv-bibliographic-explorer)) Connected Papers *(*[What is Connected Papers?](https://www.connectedpapers.com/about)) Litmaps *(*[What is Litmaps?](https://www.litmaps.co/)) scite Smart Citations *(*[What are Smart Citations?](https://www.scite.ai/)) # Code, Data and Media Associated with this Article alphaXiv *(*[What is alphaXiv?](https://alphaxiv.org/)) CatalyzeX Code Finder for Papers *(*[What is CatalyzeX?](https://www.catalyzex.com)) DagsHub *(*[What is DagsHub?](https://dagshub.com/)) Gotit.pub *(*[What is GotitPub?](http://gotit.pub/faq)) Hugging Face *(*[What is Huggingface?](https://huggingface.co/huggingface)) ScienceCast *(*[What is ScienceCast?](https://sciencecast.org/welcome)) # Demos # Recommenders and Search Tools Influence Flower *(*[What are Influence Flowers?](https://influencemap.cmlab.dev/)) CORE Recommender *(*[What is CORE?](https://core.ac.uk/services/recommender)) # arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? [**Learn more about arXivLabs**](https://info.arxiv.org/labs/index.html).