AI 中文总结
研究 Kalshi 按需组合事件市场的实例化结构，发现市场对象数量庞大但层级高度集中，提出经济广度需多层级衡量而非仅凭代码数。
AI 中文摘要
Kalshi 的多变量事件架构按需从精确选定的腿（legs）生成市场对象。在注册的七天时间间隔内，190 个独立验证的时间分片在排除 5,777 个边界重叠观测后，产生 7,611,594 个唯一的 REST MVE 市场代码；该总体以平均每天 108.7 万个对象的速度创建，并具有强烈的每小时突发性。层级相对于市场对象数量被急剧压缩：5,262,526 个精确事件键、三个集合键、83,701 个选定腿原语，以及 66,344,938 个选定腿出现次数。最大的集合占 7,217,085 个对象（94.82%），有效集合数为 1.11；有效原语数约为 720。REST 和 WebSocket 是不同的观测表面：1,487,330 个创建通知代码与 REST 总体仅在 276,177 个精确代码上相交。所有三个观测到的集合键都有当前端点确认，但没有建立历史集合或规则版本。精确结构签名产生 7,611,594 个唯一签名，零冲突组。在检索时，2,692,787 个对象（35.38%）具有正的累计成交量或未平仓量；所有观测到的 24 小时成交量值均为零，交易计数字段不可用。这是当前快照的活动资格，而非历史交易。核心结果是，一个非常大的按需市场对象总体由集中的集合层和复用的原语词汇表生成；经济广度必须在多个层级上衡量，而不能仅凭代码数量。精确的公共端点证据可用于 7,357,576 个 MVE 对象，而只有 126,806 个具有精确的确定到端点对：终端状态覆盖率和生命周期路径覆盖率是不同的统计对象。
英文摘要
Kalshi's multivariate-event architecture produces market objects on demand from exact selected legs. Across a registered seven-day interval, 190 independently validated temporal shards yield 7,611,594 unique REST MVE market tickers after excluding 5,777 boundary-overlap observations; the population was created at an average rate of 1.087 million objects per day, with strong hourly burstiness. The hierarchy is sharply compressed relative to the market-object count: 5,262,526 exact event keys, three collection keys, 83,701 selected-leg primitives, and 66,344,938 selected-leg occurrences. The largest collection accounts for 7,217,085 objects (94.82 percent), with an effective collection count of 1.11; the effective primitive count is approximately 720. REST and WebSocket are distinct observation surfaces: 1,487,330 created-notification tickers intersect the REST population in only 276,177 exact tickers. All three observed collection keys have current endpoint confirmation without establishing historical collection or rule versions. The exact structural signature yields 7,611,594 unique signatures with zero collision groups. At retrieval, 2,692,787 objects (35.38 percent) have positive cumulative volume or open interest; all observed 24-hour-volume values are zero and trade-count fields are unavailable. This is current-snapshot activity eligibility, not historical trading. The central result is that a very large on-demand market-object population is generated by a concentrated collection layer and reused primitive vocabulary; economic breadth must be measured at several hierarchical levels rather than by ticker count alone. Exact public endpoint evidence is available for 7,357,576 MVE objects, whereas only 126,806 have an exact determination-to-endpoint pair: terminal-state coverage and lifecycle-path coverage are distinct statistical objects.
Comments14 pages, 4 figures. SSRN: https://ssrn.com/abstract=7421260
