vcp-screener
Screen S&P 500 stocks for Mark Minervini's Volatility Contraction Pattern (VCP) and detect historical VCPs in a single ticker's price path. Identifies Stage 2 uptrend stocks forming tight bases with contracting volatility near breakout pivot points; in historical single-ticker mode walks a multi-year history and emits every VCP that formed with forward-outcome stats (breakout / stop-hit / timeout). Use when user requests VCP screening, Minervini-style setups, tight base patterns, volatility contraction breakout candidates, Stage 2 momentum stock scanning, or historical VCP pattern study on a s
npx skills add BaggaT236/AI-Trading-Skills --skill vcp-screener --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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 <TICKER>" 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: ```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
- When to Use
- Prerequisites
- Workflow
- Step 1: Prepare and Execute Screening
- Strict Mode (Minervini pure setup)
- Historical single-ticker mode
- Advanced Tuning (for backtesting)
- Step 2: Review Results
- Step 3: Present Analysis
- Step 4: Provide Actionable Guidance
- 3-Phase Pipeline
- Output
- Resources
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 python3 skills/vcp-screener/scripts/screen_vcp.py --strict --output-dir reports/ python3 skills/vcp-screener/scripts/screen_vcp.py \ Custom scan length: 750 trading days (~3 years), 90-day outcome window Long scan: 10 years (2520 trading days)
What does the vcp-screener skill do?
Screen S&P 500 stocks for Mark Minervini's Volatility Contraction Pattern (VCP) and detect historical VCPs in a single ticker's price path. Identifies Stage 2 uptrend stocks forming tight bases with contracting volatility near breakout pivot points; in historical single-ticker mode walks a multi-year history and emits every VCP that formed with forward-outcome stats (breakout / stop-hit / timeout). Use when user requests VCP screening, Minervini-style setups, tight base patterns, volatility contraction breakout candidates, Stage 2 momentum stock scanning, or historical VCP pattern study on a s
How do I install it?
Run `npx skills add BaggaT236/AI-Trading-Skills --skill vcp-screener --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BaggaT236/AI-Trading-Skills, a repository with 118 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.