将金融KOL的模糊投资言论转化为可执行策略，保留其意图并补全操作细节。
When Missing Becomes Structure: Intent-Preserving Policy Completion from Financial KOL Discourse
- 将KOL言论视为不完整交易策略，用离线强化学习补全买卖时机、数量等执行细节。
- 在YouTube和X平台测试中，收益率提升18.9%，夏普比率最优，无错误持仓或方向反转。
- 适合想从社交媒体内容中自动提取可落地投资策略的研究者与量化交易开发者。
社交媒体上的关键意见领袖（KOL）言论被广泛用作投资参考，但如何在不引入对未明确执行决策的假设前提下，将其转化为可执行交易策略，仍是开放性问题。我们观察到，KOL表述中的信息缺口并非随机缺失，而是结构化分离：他们明确表达方向性意图（买/卖什么及原因），而系统性省略执行细节（何时、买多少、持多久）。基于此，我们提出一种意图保持的策略补全框架（KICL），将KOL言论视为部分交易策略，利用离线强化学习在保留其意图的前提下，完成缺失的执行决策。在2022–2025年来自YouTube和X平台的多模态KOL言论数据上进行实验，结果表明KICL在两个平台均实现最优收益与夏普比率，同时保持零未支持持仓和零方向反转；消融实验确认，完整框架相较仅对齐KOL意图的基线，带来18.9%的收益提升。
原文摘要 · Abstract (English)
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.
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