New Methodology for Measuring Counterfactual Outcomes in Algorithmic DEX Trading
A new methodology called Post-Rejection Follow-up Sampling (PRFS) has been introduced to measure what happens to cryptocurrency tokens after algorithmic trading systems on decentralised exchanges decline to trade them. The paper describes a tracking subsystem that monitors rejected tokens' price and liquidity for up to 24 hours, with a companion dataset covering 2,997 rejection events across 457 unique tokens over eight days in April 2026. The work addresses a blind spot in algorithmic trading evaluation, where filter performance is typically assessed against synthetic backtests rather than real market outcomes of rejected candidates.
Algorithmic trading systems operating on decentralised exchanges (DEXs) reject the vast majority of candidate tokens they evaluate, yet the subsequent market behaviour of those rejected tokens is almost never tracked. Researchers have introduced Post-Rejection Follow-up Sampling (PRFS), a methodology in which a separate subsystem samples each rejected token's price and liquidity at configurable intervals over a horizon of up to 24 hours post-rejection. A companion dataset contains 67,000 forward-outcome observation rows spanning 2,997 rejection events and 457 unique token mints, collected continuously from April 10–19, 2026 (UTC). Approximately 55 percent of rejection events received at least one forward observation, while coverage at the mint level was complete; the authors identify per-event horizon density—not event-level coverage—as the principal constraint on downstream classification tasks. The authors argue that PRFS enables evaluation of filter precision against actual market outcomes rather than synthetic backtest reconstructions. The methodology is described as dataset-independent and generalisable to any algorithmic decision system where rejections substantially outnumber executions. The paper is currently under review at the journal Ledger and is a companion to a previously posted dataset paper.
What's missing
The paper does not report baseline filter precision metrics or any preliminary classification results using the PRFS dataset, leaving open the question of whether the methodology demonstrably improves trading system evaluation in practice. It is also unclear how representative the eight-day collection window is of broader DEX market conditions, and the paper does not address potential survivorship or selection biases in which tokens are evaluated by the trading system in the first place.
What different sources said
- arXiv cs.LGCenter
Post-Rejection Follow-up Sampling: A Methodology for Counterfactual Outcome Measurement in Algorithmic DEX Trading
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