为去中心化交易中被拒代币设计追踪方法，量化其真实市场表现。
Post-Rejection Follow-up Sampling: A Methodology for Counterfactual Outcome Measurement in Algorithmic DEX Trading
- 对被拒绝代币进行定时价格与流动性采样，获取真实后续数据
- 8天内收集2997次拒单事件，55%事件有后续观测，覆盖全部发行方
- 适用于任何拒单远多于执行的自动化决策系统
去中心化交易所（DEX）的算法交易系统会拒绝大部分评估代币。被拒代币的真实市场结果（若进入会如何）很少被测量。本文提出后拒跟踪采样（PRFS）：通过独立追踪子系统，在最长24小时的时间窗口内，以可配置频率采样每个被拒代币的价格与流动性。该方法生成真实市场结果数据，用于评估过滤器精度，而非依赖合成回测重建。第Ⅲ节详述方法、数据架构与存款格式。配套数据集包含67,000条未来结果观测，覆盖2,997个拒单事件、457个唯一发行代币，时间跨度为2026年4月10日至19日（UTC）。约55%拒单事件至少有一次后续观测；在发行方层面覆盖率完整。下游分类的主要约束是事件级时间密度，而非事件覆盖率。PRFS与数据集无关，可推广至任何拒单远多于执行的算法决策系统。
原文摘要 · Abstract (English)
Algorithmic trading systems on decentralised exchanges (DEXs) reject most candidate tokens they evaluate. The counterfactual outcome of rejected candidates (what would have happened had the system entered) is rarely measured. This paper introduces Post-Rejection Follow-up Sampling (PRFS). A separate tracking subsystem samples each rejected token's price and liquidity at a configurable cadence, over a horizon of up to twenty-four hours. PRFS produces the data needed to evaluate filter precision against actual market outcomes of rejected candidates, not against synthetic backtest reconstructions. The methodology, data architecture, and deposit format are described in Section III. The companion dataset contains 67,000 forward-outcome observation rows across 2,997 rejection events spanning 457 unique mints, collected over a continuous eight-day window (2026-04-10 to 2026-04-19, UTC). Approximately 55 percent of rejection events receive at least one forward observation; coverage at the mint level is complete. The principal binding constraint on downstream classification is per-event horizon density, not event-level coverage. PRFS is dataset-independent. It generalises to any algorithmic decision system in which rejections substantially outnumber executions.
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