Exposure Coach

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:

Step 2: Run Exposure Scoring Engine

Execute the exposure scoring script with paths to upstream outputs:

bashCopy code
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.

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:

Output Format

JSON Report

jsonCopy code
{
  "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:

markdownCopy code
# 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