End-to-End Python implementation of the AIMM-X market integrity monitoring system for detecting suspicious trading windows (Neela, 2026). Fuses public attention data (Reddit, StockTwits, Wikipedia, Search Trends) with price/volatility signals, applies robust anomaly detection, and generates auditable evidence trails. 

Research and diagnostic data documenting how historical disinformation, when encoded into AI and financial algorithms, ceases to be a narrative and becomes a Systemic Risk Contagion. Using the cannabis industry as a high-fidelity case study for model recalibration in emerging and stigmatized markets.