VCP Screener - Minervini Volatility Contraction Pattern

SkillDev tools

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.

Available today. Use it from your connected AI after setup.

Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.

Then ask your AI: use the VCP Screener - Minervini Volatility Contraction Pattern skill

What this skill tells your AI

The instructions your AI receives, as published by mphinance/alpha-skills in skills/vcp-screener/SKILL.md and read by ahel’s review.

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

Strict Mode (Minervini pure setup)

Only return stocks with valid_vcp=True AND execution_state in (Pre-breakout, Breakout):

python3 skills/vcp-screener/scripts/screen_vcp.py --strict --output-dir reports/

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/
ParameterDefaultRangeEffect
--min-contractions22-4Higher = fewer but higher-quality patterns
--t1-depth-min10.0%1-50Higher = excludes shallow first corrections
--breakout-volume-ratio1.5x0.5-10Higher = stricter volume confirmation
--trend-min-score850-100Higher = stricter Stage 2 filter
--atr-multiplier1.50.5-5Lower = more sensitive swing detection
--contraction-ratio0.700.1-1Lower = requires tighter contractions
--min-contraction-days51-30Higher = longer minimum contraction
--lookback-days12030-365Longer = finds older patterns
--max-sma200-extension50.0%—SMA200 distance threshold for Overextended state and penalty
--wide-and-loose-threshold15.0%—Final contraction depth above which wide-and-loose flag triggers
--strictoff—Minervini strict mode: only Pre-breakout or Breakout with valid VCP

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:

  • Quality (composite_score / rating) — how well-formed is the VCP pattern?
  • Execution State (execution_state) — is it buyable now? (Pre-breakout / Breakout = actionable)
  • Pattern Type (pattern_type) — Textbook VCP / VCP-adjacent / Post-breakout / Extended Leader / Damaged
  • ★ marker if a State Cap was applied (raw score was downgraded)
  • Contraction details (T1/T2/T3 depths and ratios)
  • Trade setup: pivot price, stop-loss, risk percentage
  • Volume dry-up ratio and breakout_volume_score
  • Relative strength rank

Step 4: Provide Actionable Guidance

By Execution State (primary filter):

  • Pre-breakout / Breakout: Pattern is in the active entry window — apply rating-based sizing
  • Early-post-breakout: Breakout underway but above ideal entry — reduced size or wait for pullback
  • Extended / Overextended: Trade missed — add to watchlist for next base
  • Damaged / Invalid: Setup invalidated — do not enter

By Rating (secondary, after state confirms actionability):

  • Textbook VCP (90+): Buy at pivot with aggressive sizing (1.5-2x)
  • Strong VCP (80-89): Buy at pivot with standard sizing (1x)
  • Good VCP (70-79): Buy on volume confirmation above pivot (0.75x)
  • 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

Signals

GitHub stars
27
Forks
5
Last commit
Sep 2026

ahel review

  • K1binfo
    installs-packages (in scripts/fmp_client.py)

Automated review, not a security audit. Ruleset v1+k2.

Advanced
Item type
skill
Key
vcp-screener-mphinance
Source
github.com/mphinance/alpha-skills