AI 中文总结
本文将跨截面学习排序应用于SPXW零到期日期权，结合保证金头寸规模等控制，经多窗口测试表明该方法的夏普比率远超被动基准与内部基线，特征交互项对其性能至关重要。
AI 中文摘要
本文开发了跨截面学习排序在标普500周度期权（SPXW）零到期日期限结构上的首个端到端应用，整合了考虑保证金的头寸规模设定、由模型不确定性驱动的弃权（不执行）规则以及严格的时间外完整性检验。LightGBM LambdaRank排序器对每日9种策略的跨截面组合进行评分，该组合由8个针对德尔塔（delta）的看跌期权空头头寸和1个“SKIP”候选项构成，训练采用基于路径的、以1分钟分辨率计算的Sortino-on-bars标签。该框架在指数期权保证金要求、分层费用表以及买卖价差中值（bid-to-mid）执行假设下，通过2021-2024年的四窗口滚动向前测试和2025年严格预留的时间外切片进行评估。7种规模设定方法的时间外年化夏普比率介于4.31至5.76之间，核心方法在单一预留年份的概率夏普比率为0.964，样本期最大回撤为-2.28%，而滚动向前范围内的最大回撤为1.90至3.11。时间外测试中，所有方法的夏普比率均至少超过3个被动基准（CBOE PUT、CBOE WPUT、SPX买入并持有）3.84，且至少超过5个内部选择基准3.69。针对置信门与尾部风险特征的2×2消融实验显示，在与CBOE PUT的时间外夏普比率5.59的差距中，5.05来自排序器和选择层，两个风险控制共同贡献0.54。在置信门起作用的滚动向前测试中，两个控制的表现均远不及核心方法单独作用，且它们的交互作用提供了大部分结果。15组特征消融实验表明，移除乘法制度交互项会使滚动向前统计置信度崩溃。
英文摘要
This paper develops the first end-to-end application of cross-sectional learning-to-rank to the S&P 500 weekly options (SPXW) zero-day-to-expiration surface, integrated with margin-aware position sizing, an abstention rule driven by model uncertainty, and a strict out-of-time integrity check. A LightGBM LambdaRank ranker scores a daily nine-strategy cross-section composed of eight delta-targeted short-put positions and a \textit{SKIP} candidate, trained against a path-aware Sortino-on-bars label computed at one-minute resolution. The framework is evaluated under index-option margin requirements, a tiered fee schedule, and bid-to-mid execution assumptions across a four-window walk-forward over 2021-2024 and a strictly held-out 2025 out-of-time slice. Seven sizing methods produce out-of-time annualized Sharpe ratios between 4.31 and 5.76, with the headline method reaching a Probabilistic Sharpe Ratio of 0.964 and a sample-period maximum drawdown of -2.28%, on a single hold-out year against a walk-forward range of 1.90 to 3.11. Out of time, every method exceeds three passive benchmarks (CBOE PUT, CBOE WPUT, SPX buy-and-hold) by at least 3.84 in Sharpe ratio and five internal selection baselines by at least 3.69. A two-by-two ablation of the confidence gate against the tail-risk features places 5.05 of the 5.59 out-of-time Sharpe gap over the CBOE PUT with the ranker and the selection layer, the two risk controls adding 0.54 between them. On walk-forward, where the gate binds, neither control comes close to the headline alone and their interaction supplies most of the result. A fifteen-group feature ablation shows that removing the multiplicative regime interactions collapses walk-forward statistical confidence.
