Explainable Market Integrity Monitoring via Multi-Source Attention Signals and Transparent Scoring (AIMM-X)

Battle Count: 52

Directly relevant to quantitative trading through market integrity monitoring, anomaly detection in price/volume/attention dynamics, and risk management. The multi-source attention fusion and window-based detection approach could inform trading signals around unusual market episodes. However, the paper focuses on surveillance/triage rather than alpha generation or execution. Useful for compliance-aware trading desks, identifying periods of potential manipulation that affect strategy performance, and understanding attention-driven volatility regimes. The framework's emphasis on public data and interpretability makes it accessible for independent quant researchers.

Moderate complexity. Core pipeline uses standard statistical methods (z-scores, rolling windows, hysteresis segmentation) implementable in Python with pandas/numpy/scipy. No ML model training required. Main complexity lies in: (1) multi-source attention data acquisition and fusion across 5 platforms with different cadences, (2) proper baseline estimation with warm-up periods, (3) factor normalization for balanced scoring, (4) configuration management for reproducibility. Runs in <15 minutes on commodity hardware (4-core CPU, 16GB RAM). Modular architecture with JSON configuration. No GPU needed. The conceptual framework is straightforward but production-quality implementation requires careful handling of data quality, missingness, and scaling.

High reproducibility: uses publicly accessible data (Polygon.io OHLCV, public attention proxies), all parameters documented in config.json, modular Python pipeline with deterministic outputs, complete algorithmic pseudocode provided, and code planned for open-source release (MIT license) upon publication. However, attention signals in preprint phase are stylized proxies rather than authenticated API feeds, and exact numerical replication may vary due to API changes or rate limits. No GPU required; runs in <15 minutes on commodity hardware.