频繁训练，选择性部署：加密货币市场中的前馈门控模型替换
Train Often, Deploy Selectively: Forward-Gated Model Replacement in Crypto Markets
AI总结：
针对加密货币市场预测模型部署问题，提出Shadow Before Swap策略，通过热适配评估挑战者模型后选择性升级，可降低负对数似然并减少模型变更量，效果稳定。
AI中文摘要：
生产预测系统会定期重新训练模型，但重新训练的候选模型未必优于持续学习的现有模型。本文提出Shadow Before Swap（SBS）部署策略，该策略在服务路径外对挑战者模型进行热适配，利用相同延迟一周的标签数据将其与持续维护的现有模型评估，仅当挑战者模型达到固定负对数似然（NLL）优势时才进行升级。在覆盖48个UTC周、3个随机种子、8个标的资产及2种永续合约类型的两个非重叠Binance历史回放实验中，SBS相较于按时间替换策略降低NLL达0.1472%，相较于匹配时间的自动升级策略降低0.0755%，相较于持续维护策略降低0.0428%；对应分时段的四周区间降幅分别为0.1139%-0.1754%、0.0521%-0.0980%、0.0301%-0.0554%。SBS从528个挑战者模型中仅升级114个，使已部署模型变更量减少78.4%，同时优化服务轨迹；该效果在不同随机种子、试验预算、升级阈值、早期20资产面板及拓扑匹配的监督目标下均保持方向一致，因此SBS是一种可提升概率预测能力且限制关键模型状态变更的实用部署策略。
英文摘要：
Production forecasting systems retrain models regularly, but a retrained candidate does not necessarily outperform a continuously maintained incumbent that has continued to learn. We introduce Shadow Before Swap (SBS), a deployment policy that warm-refits a challenger off the serving path, evaluates it against the maintained incumbent on the same next week of delayed labels, and promotes it only after a fixed paired negative-log-likelihood (NLL) advantage. In historical replay over two nonoverlapping Binance episodes spanning 48 UTC weeks, three seeds, eight underlyings, and two perpetual-futures contract types, SBS reduces NLL by 0.1472% relative to calendar replacement, 0.0755% relative to schedule-matched automatic promotion, and 0.0428% relative to continuous maintenance. The corresponding episode-stratified four-week block intervals are 0.1139%-0.1754%, 0.0521%-0.0980%, and 0.0301%-0.0554%, respectively. SBS promotes 114 of 528 challengers, reducing deployed model changes by 78.4% while improving the serving trajectory. The effect remains directionally consistent across seeds, trial budgets, promotion margins, an earlier 20-asset panel, and a topology-matched supervised objective. SBS thus provides a practical deployment policy that improves probabilistic forecasts while limiting consequential model-state transitions.
