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

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  • Kamat, Arati Uday

Abstract

Algorithmic trading systems operating on decentralized exchanges continuously evaluate candidate tokens against filter stacks, rejecting the majority. Unlike executed trades, these rejections leave no performance trace — the system retains no record of whether each rejection was correct. We introduce a post-rejection follow-up sampling methodology that captures this counterfactual by logging initial candidate state at rejection time and revisiting the same assets across multiple subsequent horizons. Applied to a production memecoin trading system over a two-week observation window, the method produced a dataset of approximately 67,000 rejection snapshots with multi-horizon price and liquidity samples on 457 unique tokens. Analysis of a subset of exit-category rejections revealed a statistically notable proportion of stopped-out positions experienced subsequent price recovery exceeding meaningful thresholds, indicating systematic over-sensitivity in the exit logic. The instrument enables paper-mode A/B/C testing of alternative exit hypotheses, filter calibrations, and entry-signal modifications while preserving the production system as control. We argue this post-rejection follow-up framework is a generalizable contribution to algorithmic trading research infrastructure, independent of any specific strategy, market, or parameter choice.

Suggested Citation

  • Kamat, Arati Uday, 2026. "Post-Rejection Follow-up Sampling: A Methodology for Counterfactual Outcome Measurement in Algorithmic DEX Trading," MPRA Paper 128870, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:128870
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    References listed on IDEAS

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    1. Arati U. Kamat, 2026. "RED-2400: A Public Benchmark of Algorithmically-Rejected Trading Events with Outcome Labels," Papers 2605.12151, arXiv.org, revised Jun 2026.
    2. Fan Fang & Carmine Ventre & Michail Basios & Leslie Kanthan & David Martinez-Rego & Fan Wu & Lingbo Li, 2022. "Cryptocurrency trading: a comprehensive survey," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-59, December.
    3. Kaminski, Kathryn M. & Lo, Andrew W., 2014. "When do stop-loss rules stop losses?," Journal of Financial Markets, Elsevier, vol. 18(C), pages 234-254.
    4. Crook, Jonathan & Banasik, John, 2004. "Does reject inference really improve the performance of application scoring models?," Journal of Banking & Finance, Elsevier, vol. 28(4), pages 857-874, April.
    5. Fan Fang & Carmine Ventre & Michail Basios & Leslie Kanthan & Lingbo Li & David Martinez-Regoband & Fan Wu, 2020. "Cryptocurrency Trading: A Comprehensive Survey," Papers 2003.11352, arXiv.org, revised Jan 2022.
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    JEL classification:

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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