Event & News Monitoring System

Built a scalable event monitoring platform that ingests, processes, and scores news and corporate events in near real-time. The system identifies material events — earnings surprises, regulatory actions, management changes — and translates them into structured signals for downstream consumption by trading models.

Data Architecture

Designed a streaming data pipeline that ingests from multiple news and filing sources. Raw text is processed through a series of NLP stages: entity extraction, event classification, and sentiment scoring. The architecture is built for horizontal scalability and processes thousands of documents per hour with sub-minute latency.

Signal Construction

Events are mapped to a proprietary taxonomy that distinguishes between different materiality tiers. Signals are generated based on event type, historical base rates of similar events, and the magnitude of deviation from market expectations. Signal decay profiles are modeled to account for information diffusion speed across different event categories.

Integration & Monitoring

The platform exposes signals via a REST API and publishes to a message queue for real-time consumption. A monitoring dashboard tracks data freshness, processing latency, and signal quality metrics. Automated alerts flag anomalies in data volume or processing failures.

Validation Framework

Signal efficacy is evaluated using event-study methodology with proper controls for sector, market cap, and contemporaneous market moves. The validation framework runs continuously to detect signal degradation and generates periodic reports on hit rates and information coefficients across different event categories.