Exposure Coach

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.

FMP Optional

Download Skill Package (.skill) View Source on GitHub

Table of Contents

1. 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.

2. 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)

3. 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

4. Quick Start 

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/

5. 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:

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.

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

6. Resources

References:

  • skills/exposure-coach/references/exposure_framework.md
  • skills/exposure-coach/references/regime_exposure_map.md

Scripts:

  • skills/exposure-coach/scripts/calculate_exposure.py

Strict provenance for market-regime-daily

Add --require-provenance --run-id daily-20261017-unique --as-of 2026-10-17T13:00:00Z --max-age-days 3 to validate breadth and uptrend before scoring. Both producers must receive the same unique run ID; pass their exact printed JSON paths. --as-of is the timezone-aware evaluation instant, not an override of the source business date.

The gate checks schema 1.0, expected run ID, UTC retrieval no later than evaluation, nonfuture source date and configured age. Default three calendar days is a conservative policy, not an exchange holiday calendar. Failure stops before report output. Successful output records accepted lineage and verification limits. Optional top-risk/other inputs and auxiliary summary dates remain outside this gate; unknown quality flags stay false. Legacy mode retains its existing schema. Run IDs are consistency markers, not authenticity or point-in-time proof.