exposure-coach

To synthesize market regime, breadth, and flow signals into a portfolio exposure decision, load upstream signal JSONs into a signal-fusion script that outputs a unified market posture with an exposure ceiling, bias, and entry recommendation.

Signal fusion for market posture optimization is the process of integrating breadth, regime, and flow data to determine capital allocation limits and growth, value, or defensive bias, helping traders align risk with macro context.

You generate JSON and Markdown reports for weekly portfolio reviews by running an exposure-coach script that processes upstream signals and outputs structured summaries with a clear market posture, rationale, and confidence level.

Yes, the market posture script handles missing upstream signal data robustly, ensuring that incomplete breadth, regime, or flow inputs still produce a valid exposure ceiling and bias recommendation with adjusted confidence.

To calculate an exposure ceiling and entry recommendation, you need to provide upstream JSON files containing breadth, regime, top risk, uptrend, sector, and institutional flow signals as inputs for the fusion process.

The best way to avoid overexposure using data signals is to fuse breadth, regime, and flow inputs to calculate a strict exposure ceiling and receive clear recommendations like NEW_ENTRY_ALLOWED, REDUCE_ONLY, or CASH_PRIORITY.