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

Kamat, Arati Uday (2026): Post-Rejection Follow-up Sampling: A Methodology for Counterfactual Outcome Measurement in Algorithmic DEX Trading.

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.

• Item Type: MPRA Paper
• Language: English
• Keywords: algorithmic trading; counterfactual evaluation; decentralized exchange; memecoin markets; shadow-mode A/B testing; rejection sampling; exit-logic validation; research instrumentation; market microstructure; cryptocurrency
• Subjects: C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C18 - Methodological Issues: General; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C58 - Financial Econometrics; G - Financial Economics > G1 - General Financial Markets > G12 - Asset Pricing ; Trading Volume ; Bond Interest Rates; G - Financial Economics > G1 - General Financial Markets > G14 - Information and Market Efficiency ; Event Studies ; Insider Trading; G - Financial Economics > G1 - General Financial Markets > G17 - Financial Forecasting and Simulation

References

[1] M. Dudík, J. Langford, and L. Li, "Doubly robust policy evaluation and learning," in Proc. 28th Int. Conf. Machine Learning, 2011, pp. 1097–1104.

[2] A. Swaminathan and T. Joachims, "Counterfactual risk minimization: Learning from logged bandit feedback," in Proc. 32nd Int. Conf. Machine Learning, 2015, pp. 814–823.

[3] K. M. Kaminski and A. W. Lo, "When do stop-loss rules stop losses?" Journal of Financial Markets, vol. 18, pp. 234–262, 2014.

[4] R. Kohavi, D. Tang, and Y. Xu, Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing. Cambridge University Press, 2020.

[5] A. Kamat, "Hour-Aware Adaptive Risk Management for Autonomous Memecoin Trading: A Multi-Layer Intelligence Framework," SSRN Working Paper ID 6564803, 2026.

[6] F. Black, "Noise," Journal of Finance, vol. 41, no. 3, pp. 529–543, 1986.

[7] P. Jorion, Value at Risk: The New Benchmark for Managing Financial Risk, 3rd ed. McGraw-Hill, 2007.