Key Opinion Leader (KOL) discourse on social media is widely consumed as investment guidance, yet turning it into executable trading strategies without injecting assumptions about unspecified execution decisions remains an open problem. We observe that the gaps in KOL statements are not random deficiencies but a structured separation: KOLs express directional intent (what to buy or sell and why) while leaving execution decisions (when, how much, how long) systematically unspecified. Building on this observation, we propose an intent-preserving policy completion framework that treats KOL discourse as a partial trading policy and uses offline reinforcement learning to complete the missing execution decisions around the KOL-expressed intent. Experiments on multimodal KOL discourse from YouTube and X (2022-2025) show that KICL achieves the best return and Sharpe ratio on both platforms while maintaining zero unsupported entries and zero directional reversals, and ablations confirm that the full framework yields an 18.9% return improvement over the KOL-aligned baseline.

翻译：社交媒体上的关键意见领袖（KOL）话语被广泛用作投资指导，但在不引入关于未指定执行决策的假设的情况下，将其转化为可执行的交易策略仍是一个未解难题。我们观察到，KOL陈述中的缺失并非随机缺陷，而是一种结构性的分离：KOL表达方向性意图（买卖什么及其原因），同时系统地省略执行决策（何时、多少、多久）。基于这一观察，我们提出了一种意图保持型策略补全框架，将KOL话语视为部分交易策略，并利用离线强化学习围绕KOL表达的意图补全缺失的执行决策。在来自YouTube和X平台（2022-2025年）的多模态KOL话语上的实验表明，KICL在两个平台上均实现了最佳收益率和夏普比率，同时保持零未支持的条目和零方向反转。消融实验证实，完整框架相比于KOL对齐的基线提升了18.9%的收益率。