VCP Screener - Minervini Volatility Contraction Pattern Skill

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

Read the generated JSON and Markdown reports
Load references/vcp_methodology.md for pattern interpretation context
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

Pre-Filter - Quote-based screening (price, volume, 52w position) ~101 API calls
Trend Template - 7-point Stage 2 filter with 260-day histories ~100 API calls
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

Agent Skills: VCP Screener - Minervini Volatility Contraction Pattern

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