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 specific ticker (e.g. FIX, TSLA).
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# 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 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)`: ```bash 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. ```bash # 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: | Parameter | Default | Range | Effect | |-----------|---------|-------|--------| | `--history [DAYS]` | (off) / 1260 if bare | 100-5040 | Enable historical mode; optionally specify trading-day scan window (requires `--ticker`) | | `--ticker SYM` | — | — | Ticker to scan | | `--stride-days` | 5 | 1-60 | Trading-day step between as-of cursor positions | | `--outcome-days` | 60 | 5-252 | Forward window evaluated per detection | 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: ```bash 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` | 10.0% | 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` | 0.70 | 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 | | `--max-sma200-extension` | 50.0% | — | SMA200 distance threshold for Overextended state and penalty | | `--wide-and-loose-threshold` | 15.0% | — | Final contraction depth above which wide-and-loose flag triggers | | `--strict` | off | — | 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
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