# Vcp Screener

> Screen S&P 500 stocks for Mark Minervini's Volatility Contraction Pattern (VCP). Identifies Stage 2 uptrend stocks forming tight bases with contracting volatility near breakout pivot points. Use when user requests VCP screening, Minervini-style setups, tight base patterns, volatility contraction breakout candidates, or Stage 2 momentum stock scanning.

- Skill: `tools-only/vcp-screener-2` (Agent Skill, multi-file: 3 files)
- Install (CLI): `npx skillmds@latest add tools-only/vcp-screener-2`
- Raw SKILL.md: https://api.skillmd.com/api/skills/tools-only/vcp-screener-2/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Productivity
- Author: tools-only (https://skillmd.com/u/tools-only)
- Updated: 2026-09-22
- Page: https://skillmd.com/skills/tools-only/vcp-screener-2

---


# VCP Screener - Minervini Volatility Contraction Pattern

Screen S&P 500 stocks for Mark Minervini's Volatility Contraction Pattern (VCP), identifying Stage 2 uptrend stocks with contracting volatility near breakout pivot points.

## When to Use

- User asks for VCP screening or Minervini-style setups
- User wants to find tight base / volatility contraction patterns
- User requests Stage 2 momentum stock scanning
- User asks for breakout candidates with defined risk

## Prerequisites

- FMP API key (set `FMP_API_KEY` environment variable or pass `--api-key`)
- Free tier (250 calls/day) is sufficient for default screening (top 100 candidates)
- Paid tier recommended for full S&P 500 screening (`--full-sp500`)

## Workflow

### Step 1: Prepare and Execute Screening

Run the VCP screener script:

```bash
# Default: S&P 500, top 100 candidates
python3 skills/vcp-screener/scripts/screen_vcp.py --output-dir skills/vcp-screener/scripts

# Custom universe
python3 skills/vcp-screener/scripts/screen_vcp.py --universe AAPL NVDA MSFT AMZN META --output-dir skills/vcp-screener/scripts

# Full S&P 500 (paid API tier)
python3 skills/vcp-screener/scripts/screen_vcp.py --full-sp500 --output-dir skills/vcp-screener/scripts
```

### Advanced Tuning (for backtesting)

Adjust VCP detection parameters for research and backtesting:

```bash
python3 skills/vcp-screener/scripts/screen_vcp.py \
  --min-contractions 3 \
  --t1-depth-min 12.0 \
  --breakout-volume-ratio 2.0 \
  --trend-min-score 90 \
  --atr-multiplier 1.5 \
  --output-dir reports/
```

| Parameter | Default | Range | Effect |
|-----------|---------|-------|--------|
| `--min-contractions` | 2 | 2-4 | Higher = fewer but higher-quality patterns |
| `--t1-depth-min` | 8.0% | 1-50 | Higher = excludes shallow first corrections |
| `--breakout-volume-ratio` | 1.5x | 0.5-10 | Higher = stricter volume confirmation |
| `--trend-min-score` | 85 | 0-100 | Higher = stricter Stage 2 filter |
| `--atr-multiplier` | 1.5 | 0.5-5 | Lower = more sensitive swing detection |
| `--contraction-ratio` | 0.75 | 0.1-1 | Lower = requires tighter contractions |
| `--min-contraction-days` | 5 | 1-30 | Higher = longer minimum contraction |
| `--lookback-days` | 120 | 30-365 | Longer = finds older patterns |

### Step 2: Review Results

1. Read the generated JSON and Markdown reports
2. Load `references/vcp_methodology.md` for pattern interpretation context
3. Load `references/scoring_system.md` for score threshold guidance

### Step 3: Present Analysis

For each top candidate, present:
- VCP composite score and rating
- Contraction details (T1/T2/T3 depths and ratios)
- Trade setup: pivot price, stop-loss, risk percentage
- Volume dry-up ratio
- Relative strength rank

### Step 4: Provide Actionable Guidance

Based on ratings:
- **Textbook VCP (90+):** Buy at pivot with aggressive sizing
- **Strong VCP (80-89):** Buy at pivot with standard sizing
- **Good VCP (70-79):** Buy on volume confirmation above pivot
- **Developing (60-69):** Add to watchlist, wait for tighter contraction
- **Weak/No VCP (<60):** Monitor only or skip

## 3-Phase Pipeline

1. **Pre-Filter** - Quote-based screening (price, volume, 52w position) ~101 API calls
2. **Trend Template** - 7-point Stage 2 filter with 260-day histories ~100 API calls
3. **VCP Detection** - Pattern analysis, scoring, report generation (no additional API calls)

## Output

- `vcp_screener_YYYY-MM-DD_HHMMSS.json` - Structured results
- `vcp_screener_YYYY-MM-DD_HHMMSS.md` - Human-readable report

## Resources

- `references/vcp_methodology.md` - VCP theory and Trend Template explanation
- `references/scoring_system.md` - Scoring thresholds and component weights
- `references/fmp_api_endpoints.md` - API endpoints and rate limits