通过多源注意力信号与透明评分，识别市场异常时段并解释原因。
An Explainable Market Integrity Monitoring System with Multi-Source Attention Signals and Transparent Scoring
- 融合价格/成交量与新闻讨论等多源信号检测异常时段
- 采用可解释的加性得分分解，定位关键驱动因素
- 适合合规团队、交易所和研究人员做审计筛查
市场完整性监控困难，因异常价格/量行为可能由多种良性机制引起，而现有检测系统多依赖难以审计的黑箱模型。我们提出AIMM-X，一个可解释的监控流程：结合基于OHLCV时间序列的市场微观结构信号与多源公开注意力信号（如新闻和在线讨论代理），识别需分析师审查的时间窗口。系统通过透明阈值与聚合检测候选异常时段，并生成可分解的可解释完整性评分，使从业者能追溯被标记原因及主导因素。提供端到端可复现实现，涵盖数据下载、注意力特征构建、统一面板建立、窗口检测、组件信号计算及摘要图表生成。目标并非直接标注操纵行为，而是为合规团队、交易所或研究者提供实用、可审计的筛查工具，支持后续调查。
原文摘要 · Abstract (English)
Market integrity monitoring is difficult because suspicious price/volume behavior can arise from many benign mechanisms, while modern detection systems often rely on opaque models that are hard to audit and communicate. We present AIMM-X, an explainable monitoring pipeline that combines market microstructure-style signals derived from OHLCV time series with multi-source public attention signals (e.g., news and online discussion proxies) to surface time windows that merit analyst review. The system detects candidate anomalous windows using transparent thresholding and aggregation, then assigns an interpretable integrity score decomposed into a small set of additive components, allowing practitioners to trace why a window was flagged and which factors drove the score. We provide an end-to-end, reproducible implementation that downloads data, constructs attention features, builds unified panels, detects windows, computes component signals, and generates summary figures/tables. Our goal is not to label manipulation, but to provide a practical, auditable screening tool that supports downstream investigation by compliance teams, exchanges, or researchers.
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