arXiv:2604.20421cs.LG2026-04被引 4

首个完整记录去中心化预测市场全生命周期的数据集,支持研究与分析。

Unlocking the Forecasting Economy: A Suite of Datasets for the Full Lifecycle of Prediction Market: [Experiments \& Analysis]

论文配图:Unlocking the Forecasting Economy: A Suite of Datasets for the Full Lifecycle of Prediction Market: [Experiments \& Analysis]
图 1 · 摘自论文原文
  • 构建统一关系数据系统,整合市场元数据、交易记录与预言机事件
  • 覆盖2020年10月至2026年3月,含超77万市场、9.43亿笔交易和近200万预言机事件
  • 适合研究市场机制、集体信念建模及金融预测的学者与开发者

预测市场是交易未来事件结果的市场,其价格持续反映群体信念。在Polymarket等去中心化平台中,市场生命周期涵盖创建、代币注册、交易、预言机交互、争议及最终结算,但相关数据分散于异构的链上与链下来源。本文首次构建了一个持续维护的去中心化预测市场全生命周期数据集,基于Polymarket实现。为应对大规模跨源整合、链接不全与持续同步的挑战,我们建立统一的关系数据系统,通过标识符解析、链上恢复与增量更新,集成市场元数据、逐笔交易记录与预言机结算事件。数据集覆盖2020年10月至2026年3月,包含超过77万条市场记录、9.43亿条成交记录和近200万条预言机事件。我们详细描述了数据模型、采集管道与一致性机制,确保数据可复现与可扩展,并通过市场活动描述性分析及两个下游案例研究(NBA结果校准、CPI预期重建)展示其应用价值。

原文摘要 · Abstract (English)

Prediction markets are markets for trading claims on future events, such as presidential elections, and their prices provide continuously updated signals of collective beliefs. In decentralized platforms such as Polymarket, the market lifecycle spans market creation, token registration, trading, oracle interaction, dispute, and final settlement, yet the corresponding data are fragmented across heterogeneous off-chain and on-chain sources. We present the first continuously maintained dataset suite for the full lifecycle of decentralized prediction markets, built on Polymarket. To address the challenges of large-scale cross-source integration, incomplete linkage, and continuous synchronization, we build a unified relational data system that integrates three canonical layers: market metadata, fill-level trading records, and oracle-resolution events, through identifier resolution, on-chain recovery, and incremental updates. The resulting dataset spans October 2020 to March 2026 and comprises more than 770 thousand market records, over 943 million fill records, and nearly 2 million oracle events. We describe the data model, collection pipeline, and consistency mechanisms that make the dataset reproducible and extensible, and we demonstrate its utility through descriptive analyses of market activity and two downstream case studies: NBA outcome calibration and CPI expectation reconstruction.

预测市场数据集链上数据集体信念

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