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

Arati Uday Kamat

MPRA Paper from University Library of Munich, Germany

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.

Keywords: algorithmic trading; counterfactual evaluation; decentralized exchange; memecoin markets; shadow-mode A/B testing; rejection sampling; exit-logic validation; research instrumentation; market microstructure; cryptocurrency (search for similar items in EconPapers)
JEL-codes: C18 C58 G12 G14 G17 (search for similar items in EconPapers)
Date: 2026-04-20
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