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Look-Ahead-Freedom as Temporal Non-Interference: A Verifiable Correctness Property for Backtesting and Agentic Trading Pipelines

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  • Xavier Fonseca

Abstract

Look-ahead bias (using information from after a decision epoch to make the decision at that epoch) is the dominant way a backtest or a machine-learning evaluation flatters a system that will disappoint in deployment. The field manages it with construct-specific recipes and empirical detectors, which are sound only channel by channel and certify nothing by their silence. We show that look-ahead-freedom is a formal property in disguise: fixing an epoch, the demand that the future not influence the present is temporal non-interference over a time-indexed information lattice. From this identification we develop a pipeline calculus separating a datum's availability from its reference time, and settle the problem's boundary. Where availability may depend on data values, look-ahead-freedom is undecidable (indeed Pi-0-1-hard): leakage is recursively enumerable but freedom is not. On the value-independent fragment (covering windowing, resampling, joins, point-in-time and vintage reads, and agentic retrieval) we give a type-and-effect system that is sound and decidable in linear time. An artifact confirms the theory: the check scales linearly, an independent oracle witnesses no leak in any accepted pipeline, and the checker catches every planted leak that differential and tiling detectors miss.

Suggested Citation

  • Xavier Fonseca, 2026. "Look-Ahead-Freedom as Temporal Non-Interference: A Verifiable Correctness Property for Backtesting and Agentic Trading Pipelines," Papers 2607.04958, arXiv.org.
    • Handle: RePEc:arx:papers:2607.04958
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    References listed on IDEAS

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    1. Mostapha Benhenda, 2026. "Look-Ahead-Bench: a Standardized Benchmark of Look-ahead Bias in Point-in-Time LLMs for Finance," Papers 2601.13770, arXiv.org.
    2. Junyi Yao & Zihao Zheng, 2026. "Beyond Agent Architecture: Execution Assumptions and Reproducibility in LLM-Based Trading Systems," Papers 2606.08285, arXiv.org.
    3. Fan Zhang & Zhen Li & Sijia Peng & Yu Chen, 2026. "When Alpha Disappears: A One-Switch Benchmark for Decision-Time Leakage in Financial Backtests," Papers 2605.23959, arXiv.org.
    4. Xavier Fonseca, 2026. "The Decision Geometry of Covariance Estimation for the Global Minimum-Variance Portfolio under Heavy Tails," Papers 2606.27462, arXiv.org.
    5. Brown, Stephen J, et al, 1992. "Survivorship Bias in Performance Studies," The Review of Financial Studies, Society for Financial Studies, vol. 5(4), pages 553-580.
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