Claude-trading-skills 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.

install

source · Clone the upstream repo

git clone https://github.com/tradermonty/claude-trading-skills

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/tradermonty/claude-trading-skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/exposure-coach" ~/.claude/skills/tradermonty-claude-trading-skills-exposure-coach && rm -rf "$T"

manifest: skills/exposure-coach/SKILL.md

source content

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:

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:

Exposure Ceiling -- Maximum recommended equity allocation (0-100%)
Bias Direction -- Growth vs Value tilt based on regime and flow
Participation Assessment -- Broad (healthy) vs Narrow (fragile) market
Action Recommendation -- NEW_ENTRY_ALLOWED, REDUCE_ONLY, or CASH_PRIORITY
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

{
  "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:

# 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

Safety First -- Default to lower exposure when inputs are incomplete or conflicting
Regime Alignment -- Let macro regime set the baseline; breadth adjusts within bounds
Actionable Output -- Always produce a clear recommendation, not just data aggregation