Post-Rejection Follow-up Sampling: A Methodology for Counterfactual Outcome Measurement in Algorithmic DEX Trading

Abstract:Algorithmic trading systems on decentralised exchanges (DEXs) reject most candidate tokens they evaluate. The counterfactual outcome of rejected candidates (what would have happened had the system entered) is rarely measured. This paper introduces Post-Rejection Follow-up Sampling (PRFS). A separate tracking subsystem samples each rejected token's price and liquidity at a configurable cadence, over a horizon of up to twenty-four hours. PRFS produces the data needed to evaluate filter precision against actual market outcomes of rejected candidates, not against synthetic backtest reconstructions. The methodology, data architecture, and deposit format are described in Section III. The companion dataset contains 67,000 forward-outcome observation rows across 2,997 rejection events spanning 457 unique mints, collected over a continuous eight-day window (2026-04-10 to 2026-04-19, UTC). Approximately 55 percent of rejection events receive at least one forward observation; coverage at the mint level is complete. The principal binding constraint on downstream classification is per-event horizon density, not event-level coverage. PRFS is dataset-independent. It generalises to any algorithmic decision system in which rejections substantially outnumber executions.

12 pages. Companion methodology paper to RED-2400 (arXiv:2605.12151). Currently under review at Ledger. SSRN abstract ID 6607301. Zenodo concept DOI https://doi.org/10.5281/zenodo.20043516

Trading and Market Microstructure (q-fin.TR); Machine Learning (cs.LG); Computational Finance (q-fin.CP); Statistical Finance (q-fin.ST)

arXiv:2606.08228 [q-fin.TR]

(or arXiv:2606.08228v1 [q-fin.TR] for this version)

https://doi.org/10.48550/arXiv.2606.08228 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

https://doi.org/10.5281/zenodo.20043516 Focus to learn more DOI(s) linking to related resources

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