exposure-coach

To consolidate market signals into an equity exposure recommendation, you normalize breadth, regime detection, and institutional flow data into a weighted composite score. This produces a confidence-scored market posture summary detailing allocation ceilings and new-entry permissions.

Market posture analysis evaluates breadth, regime, and institutional flow signals to determine your equity capital commitment. You need this posture analysis during weekly exposure reviews or after major macro events to decide equity allocation and bias between growth and value.

You generate a one-page posture report by feeding partial breadth and uptrend JSON outputs into a unified scoring engine. The engine applies missing-input haircuts and confidence reduction rules to produce a Markdown summary with an exposure ceiling and explicit recommendation.

Yes, regime detection and FTD anomaly signals are designed for solo traders and small portfolio managers performing weekly exposure reviews. The system handles partial inputs to help you decide equity allocation and new-entry permissions without requiring complete datasets.

Yes, the exposure posture scoring engine supports optional FMP API integration to supplement institutional flow and sector leadership data. It handles partial inputs and applies confidence reduction rules if the optional FMP API integration is unavailable.

A unified market posture score is limited by its dependency on upstream JSON inputs and optional FMP API data. Missing inputs trigger confidence reduction haircuts, meaning incomplete breadth or institutional flow data will lower the reliability of the exposure ceiling recommendation.