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
• User asks "find every historical VCP in " or wants to study one ticker's past VCP setups with forward outcomes (--history --ticker SYM)

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/

Historical single-ticker mode

Walk one ticker's multi-year history, detect every VCP that ever formed, and attach forward-outcome stats (breakout / stop-hit / timeout, days-to-outcome, max gain, max loss) per detection. Useful for pattern study and backtesting context — not a real-time screener.

# Default: scan ~5 years (1260 trading days), 5-day stride, 60-day outcome window
python3 skills/vcp-screener/scripts/screen_vcp.py \
  --history --ticker FIX --output-dir reports/

# Custom scan length: 750 trading days (~3 years), 90-day outcome window
python3 skills/vcp-screener/scripts/screen_vcp.py \
  --history 750 --ticker TSLA \
  --stride-days 5 --outcome-days 90 \
  --output-dir reports/

# Long scan: 10 years (2520 trading days)
python3 skills/vcp-screener/scripts/screen_vcp.py \
  --history 2520 --ticker NVDA --output-dir reports/

Outputs (timestamped):

• vcp_history_<SYM>_<YYYY-MM-DD_HHMMSS>.json — timeline of detections with full analyzer payload + forward_outcome per detection + summary stats.
• vcp_history_<SYM>_<YYYY-MM-DD_HHMMSS>.md — human-readable timeline.

Mode-specific flags:

Notes:

• Two FMP API calls per scan (ticker + SPY history), not 100+ like the cross-sectional pipeline.
• marketCap and absolute RS percentile reflect the ticker in isolation, not against the live screening universe — use this report for pattern study, not portfolio sizing.
• Detections are deduplicated by (T1_high_date, last_low_date, pivot) so the same VCP isn't reported repeatedly as the cursor ages.

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/

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