Generate a one-page Market Posture summary with net exposure ceiling, growth-vs-value bias, participation breadth, and new-entry-allowed vs cash-priority recommendation by integrating signals from breadth, regime, and flow analysis skills.

Exposure Coach Overview Exposure Coach synthesizes outputs from market breadth analyzer, uptrend analyzer, macro regime detector, market top detector, ftd detector, theme detector, sector analyst, and institutional flow tracker into a unified control plane decision. The skill answers the solo trader's core question: "How much capital should I commit to equities right now?" before any individual stock analysis begins. When to Use Before initiating any new stock positions to determine appropriate capital commitment At the start of each trading week to calibrate portfolio exposure When multiple market signals conflict and a unified posture is needed After significant macro or market events to reassess exposure ceiling When transitioning between market regimes (broadening, concentration, contraction) Prerequisites Python 3.9+ FMP API key (set FMP API KEY environment variable) for institutional flow tracker data Input JSON files from upstream skills (see Workflow Step 1) Standard library + argparse , json , datetime Workflow Step 1: Gather Upstream Skill Outputs Collect the most recent JSON outputs from integrated skills. Each file provides a specific signal dimension: Skill Output File Pattern Signal Provided market breadth analyzer breadth .json Advance/decline ratios, new highs/lows uptrend analyzer uptrend .json Uptrend participation percentage macro regime detector regime .json Current regime (Concentration, Broadening, etc.) market top detector top risk .json Distribution day count, top probability score ftd detector ftd .json Follow Through Day quality (market bottom confirmation) theme detector theme detector .json or theme .json Active investment themes and rotation sector analyst sector .json Sector performance rankings institutional flow tracker institutional .json Net institutional buying/selling Step 2: Run Exposure Scoring Engine Execute the exposure scoring script with paths to upstream outputs: The script accepts partial inputs; missing files reduce confidence but do not block execution. Canonical macro regime reports must include nested regime.confidence and composite.data quality with valid integer component counts. Missing or malformed availability metadata, very low confidence, and zero usable components are treated as missing critical input. They do not contribute a regime score or bias, and the normal missing input haircut and confidence cap apply. Never override this degradation by manually copying the report's regime label into the exposure decision. Verification pitfall: After each run, inspect the generated JSON fields inputs provided and inputs missing . If a file you passed on the CLI still appears in inputs missing (for example a theme detector JSON that the exposure engine did not recognize), report the affected dimension as degraded and keep confidence capped; do not assume the supplied input was incorporated just because the CLI argument was present. Theme detector ingestion caveat: The theme detector commonly emits theme detector YYYY MM DD HHMMSS.json with a themes object. If that file is not recognized by calculate exposure.py and theme remains in inputs missing , do not fold theme strength into the exposure ceiling manually. Instead, keep the Exposure Coach confidence capped, state that the theme dimension was not incorporated, and summarize theme/sector findings separately in the broader trading brief. Step 3: Interpret the Market Posture Summary Review the generated posture report containing: 1. Exposure Ceiling Maximum recommended equity allocation (0 100%) 2. Bias Direction Growth vs Value tilt based on regime and flow 3. Participation Assessment Broad (healthy) vs Narrow (fragile) market 4. Action Recommendation NEW ENTRY ALLOWED, REDUCE ONLY, or CASH PRIORITY 5. Confidence Level HIGH, MEDIUM, or LOW based on input completeness Step 4: Apply Exposure Guidance Map the posture recommendation to portfolio actions: Recommendation Action NEW ENTRY ALLOWED Proceed with stock level analysis and new positions REDUCE ONLY No new entries; trim existing positions on strength CASH PRIORITY Raise cash aggressively; avoid all new commitments Output Format JSON Report Markdown Report The markdown report provides a one page summary suitable for quick review: Reports are saved to reports/ with filenames exposure posture YYYY MM DD HHMMSS.{json,md} . Resources scripts/calculate exposure.py Main orchestrator that scores and synthesizes inputs references/exposure framework.md Scoring rules and threshold definitions references/regime exposure map.md Regime to exposure ceiling mappings Key Principles 1. Safety First Default to lower exposure when inputs are incomplete or conflicting 2. Regime Alignment Let macro regime set the baseline; breadth adjusts within bounds 3. Actionable Output Always produce a clear recommendation, not just data aggregation

exposure-coach