**Market Posture Summary** **Net Exposure Ceiling:** [Insert Value] **Growth-vs-Value Bias:** [Insert Value] **Participation Breadth:** [Insert Value] **New-Entry-Allowed vs Cash-Priority Recommendation:** [Insert Value] **Integration of

--- name: exposure-coach description: 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` | Failure-to-deliver anomalies | | theme-detector | `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: ```bash python3 skills/exposure-coach/scripts/calculate_exposure.py --breadth reports/breadth_latest.json --uptrend reports/uptrend_latest.json --regime reports/regime_latest.json --top-risk reports/top_risk_latest.json --ftd reports/ftd_latest.json --theme reports/theme_latest.json --sector reports/sector_latest.json --institutional reports/institutional_latest.json --output-dir reports/ ``` The script accepts partial inputs; missing files reduce confidence but do not block execution. ### 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 ```json { "schema_version": "1.0", "generated_at": "2026-03-16T07:00:00Z", "exposure_ceiling_pct": 70, "bias": "GROWTH", "participation": "BROAD", "recommendation": "NEW_ENTRY_ALLOWED", "confidence": "HIGH", "component_scores": { "breadth_score": 65, "uptrend_score": 72, "regime_score": 80, "top_risk_score": 25, "ftd_score": 10, "theme_score": 68, "sector_score": 70, "institutional_score": 75 }, "inputs_provided": ["breadth", "uptrend", "regime", "top_risk"], "inputs_missing": ["ftd", "theme", "sector", "institutional"], "rationale": "Broad participation with low top risk supports elevated exposure." } ``` ### Markdown Report The markdown report provides a one-page summary suitable for quick review: ```markdown # Market Posture Summary **Date:** 2026-03-16 | **Confidence:** HIGH ## Exposure Ceiling: 70% | Dimension | Score | Status | |-----------|-------|--------| | Breadth | 65 | Healthy | | Uptrend Participation | 72% | Broad | | Regime | Broadening | Favorable | | Top Risk | 25 | Low | ## Recommendation: NEW_ENTRY_ALLOWED **Bias:** Growth > Value **Participation:** Broad (healthy internals) ### Rationale Broad participation with low distribution day count supports elevated equity exposure. New positions allowed within the 70% ceiling. ``` 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