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

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.