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:

# 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:

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