We study the microstructure of Polymarket, the largest on-chain prediction market, using a continuous tick-level archive of the public order-book feed (30 billion events over 52 days) joined to the authoritative on-chain trade record. On a pre-registered stratified panel of 600 markets we report eight stylized facts: a longshot spread premium; a depth profile closer to uniform than to top-of-book; a null block-clock alignment effect; broad maker-wallet diversity with a concentrated tail; category-conditional effective-spread differences; a sub-50 ms median archive-ingestion delay with a multi-second tail; a self-counterparty wash share with median 1% and a 22% upper tail (well below Cong et al. 2023's 25-70% for unregulated crypto venues -- a sanity bound, not an apples-to-apples reference); and a cross-sectional depth profile explained by market duration, price level, and volume, with no residual time-to-close effect. The paper also contributes a measurement result: trade direction inferred from Polymarket's public order-book feed agrees with on-chain ground truth on only ~59% of buckets (panel mean 0.615, 95% CI [0.58, 0.65]), well below the ~80% Lee-Ready accuracy on Nasdaq. The effective half-spread changes sign between feed- and on-chain trade directions on 67%/50% of markets across two 7-day windows; Kyle's lambda on 60%/43%. Microstructure work on Polymarket therefore needs to source trade direction from on-chain OrderFilled events; we release a replication package that performs the join.

翻译：本文利用连续档位级别的公开订单簿数据（52天内的300亿个事件），结合权威链上交易记录，对最大链上预测市场Polymarket的微观结构进行研究。基于600个市场预注册的分层面板数据，我们报告了八大典型特征：冷门股报价价差溢价；深度分布更接近均匀分布而非顶部集中；区块时钟对齐效应为零；做市商钱包多样性广泛但尾部集中；条件类别有效价差差异显著；中位数低于50毫秒的归档延迟，但尾部可达数秒；自对手方洗盘比例为中位数1%、上尾部22%（远低于Cong等人2023年报告的无监管加密货币平台25-70%的水平——此数据为合理边界而非同类对比参考）；以及由市场存续期、价格水平和成交量解释的横截面深度分布，且不存在残差到期时间效应。本文还贡献了一项测量结果：从Polymarket公开订单簿推断的交易方向与链上真实数据仅在约59%的时间段内一致（面板均值0.615，95%置信区间[0.58,0.65]），远低于纳斯达克约80%的Lee-Ready准确率。在两个7天窗口中，有效半价差符号在67%/50%的市场中会因订单簿与链上交易方向差异而反转；Kyle's lambda在60%/43%的市场中亦出现此现象。因此，针对Polymarket的微观结构研究需从链上OrderFilled事件获取交易方向。我们发布了执行此数据匹配的复现工具包。