We study the microstructure of Polymarket, the largest on-chain prediction market, using a continuous tick-level archive of the public WebSocket 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-concentration profile closer to a uniform geometric grid than to the top-of-book pattern often assumed for prediction markets; a null block-clock alignment effect; broad maker-wallet diversity with a concentrated tail; category-conditional differences in effective spread; 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 the network-classifier benchmarks of Cong et al. (2023) for unregulated cryptocurrency token exchanges (a sanity bound, not an apples-to-apples reference, since the venues face different wash incentives); and a depth decay near resolution with a within-category slope of 0.55 on log seconds-to-close (t=3.85). The paper also contributes a measurement result: trade direction inferred from Polymarket's public order-book feed agrees with on-chain ground truth only ~59% of the time (panel mean 0.615, 95% CI [0.58, 0.65]), barely above the 50% chance baseline. On the comparable subset of the top-100 panel, the effective half-spread changes sign between feed- and on-chain directions on 67% of markets in a first 7-day window and 50% in a second non-overlapping window, with Kyle's lambda flipping on 60% and 43% respectively; neither window recovers the on-chain sign at anything close to the ~80% rate that Lee-Ready achieves on equity venues. 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：本文研究Polymarket（最大的链上预测市场）的微观结构，基于连续tick级别的公开WebSocket订单簿数据流（52天内300亿个事件）与权威链上交易记录的联合数据。针对预注册的600个市场分层面板，我们报告八项程式化事实：冷门赔率溢价；深度集中度曲线更接近均匀几何网格而非预测市场常假设的top-of-book模式；区块时钟对齐效应为零；做市商钱包广泛分布但尾部集中；不同类别市场的有效价差存在差异；中位数存档摄取延迟低于50毫秒但尾部达数秒；自对手方洗售交易的中位数占比为1%，上尾为22%，远低于Cong等人（2023）针对未受监管加密货币代币交易所的网络分类器基准（此为合理性边界而非直接比较参考，因不同交易场所面临不同的洗售动机）；以及在结算临近时深度衰减，对日志结算秒数的类别内斜率为0.55（t=3.85）。本文还贡献了一项测量结果：从Polymarket公开订单簿数据流推断的交易方向与链上真实数据的一致性仅为~59%（面板均值0.615，95%置信区间[0.58, 0.65]），略高于50%的随机基线。在前100市场面板的可比子集中，有效半价差在67%的市场（第一个7天窗口）和50%的市场（第二个不重叠窗口）中因数据流与链上方向不同而符号反转，Kyle's lambda的符号翻转比例分别为60%和43%；两个窗口均未能以接近~80%的比率（Lee-Ready方法在股票交易场所的典型表现）恢复链上符号。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；我们发布包含数据联合操作的复制工具包。