Screen US stocks using William O'Neil's CANSLIM growth stock methodology. Use when user requests CANSLIM stock screening, growth stock analysis, momentum stock identification, or wants to find stocks with strong earnings and price momentum following O'Neil's investment system.

CANSLIM Stock Screener Phase 3 (Full CANSLIM) Overview This skill screens US stocks using William O'Neil's proven CANSLIM methodology, a systematic approach for identifying growth stocks with strong fundamentals and price momentum. CANSLIM analyzes 7 key components: C urrent Earnings, A nnual Growth, N ewness/New Highs, S upply/Demand, L eadership/RS Rank, I nstitutional Sponsorship, and M arket Direction. Phase 3 implements all 7 of 7 components (C, A, N, S, L, I, M), representing 100% of the full methodology . Two Stage Approach: 1. Stage 1 (FMP API + Finviz) : Analyze stock universe with all 7 CANSLIM components 2. Stage 2 (Reporting) : Rank by composite score and generate actionable reports Key Features: Composite scoring (0 100 scale) with weighted components Finviz fallback for institutional ownership data (automatic when FMP data incomplete) Progressive filtering to optimize API usage JSON + Markdown output formats Interpretation bands: Exceptional+ (90+), Exceptional (80 89), Strong (70 79), Above Average (60 69) Bear market protection (M component gating) Phase 3.1 Component Weights (Original O'Neil weights): C (Current Earnings): 15% A (Annual Growth): 20% N (Newness): 15% S (Supply/Demand): 15% L (Leadership/RS Rank): 20% — multi period weighted RS (3m/6m/12m vs configurable benchmark) I (Institutional): 10% M (Market Direction): 5% Weighted RS Formula: Available periods are re normalized when some are missing. Default benchmark is ^GSPC ; override with rs benchmark SPY/QQQ/IWM/... . Fallback hierarchy when multi period data is incomplete: 1. No benchmark → weighted absolute stock performance + 20% penalty. 2. All multi period windows missing but =50 bars of price history → fall back to the legacy 365 day full window absolute return as the scoring input (20% penalty if no benchmark). 3. <50 bars of price history → score=0 with error set. Future Phases: Phase 4: FINVIZ Elite integration → 10x faster execution When to Use This Skill Explicit Triggers: "Find CANSLIM stocks" "Screen for growth stocks using O'Neil's method" "Which stocks have strong earnings and momentum?" "Identify stocks near 52 week highs with accelerating earnings" "Run a CANSLIM screener on [sector/universe]" Implicit Triggers: User wants to identify multi bagger candidates User is looking for growth stocks with proven fundamentals User wants systematic stock selection based on historical winners User needs a ranked list of stocks meeting O'Neil's criteria When NOT to Use: Value investing focus (use value dividend screener instead) Income/dividend focus (use dividend growth pullback screener instead) Bear market conditions (M component will flag consider raising cash) Prerequisites API Requirements: FMP API key (free tier: 250 calls/day, sufficient for 35 stocks; Starter tier $29.99/mo for 40+ stocks) Sign up: https://site.financialmodelingprep.com/developer/docs Set via environment variable: export FMP API KEY=your key here Python Dependencies: Python 3.9+ requests (FMP API calls) beautifulsoup4 (Finviz web scraping) lxml (HTML parsing) Installation: Output Output Directory: reports/ (default) or custom via output dir Generated Files: canslim screener YYYY MM DD HHMMSS.json Structured data for programmatic use canslim screener YYYY MM DD HHMMSS.md Human readable report Report Contents: Market Condition Summary (trend, M score, warnings) Top N CANSLIM Candidates (ranked by composite score) Component Breakdown for each stock (C, A, N, S, L, I, M scores with details) Rating interpretation (Exceptional+/Exceptional/Strong/Above Average) Quality warnings and data source notes Summary statistics (rating distribution) Rating Bands: Exceptional+ (90 100): All components near perfect, aggressive buy Exceptional (80 89): Outstanding fundamentals + momentum, strong buy Strong (70 79): Solid across components, standard buy Above Average (60 69): Meets thresholds with minor weaknesses, buy on pullback Workflow Step 1: Verify API Access and Requirements Check if user has FMP API key configured: Requirements: FMP API key (free tier: 250 calls/day, sufficient for 40 stocks) Python 3.9+ with required libraries: requests (FMP API calls) beautifulsoup4 (Finviz web scraping) lxml (HTML parsing) Installation: If API key is missing, guide user to: 1. Sign up at https://site.financialmodelingprep.com/developer/docs 2. Get free API key (250 calls/day) 3. Set environment variable: export FMP API KEY=your key here Step 2: Determine Stock Universe Option A: Default Universe (Recommended) Use top 40 S&P 500 stocks by market cap (predefined in script): Option B: Custom Universe User provides specific symbols or sector: Option C: Sector Specific User can provide sector focused list (Technology, Healthcare, etc.) API Budget Considerations (Phase 3): 40 stocks × 7 FMP calls/stock = 280 API calls FMP: 7 calls/stock (profile, quote, income×2, historical 90d, historical 365d, institutional) Finviz: ~1.8 calls/stock (institutional ownership fallback, 2s rate limit, not counted in FMP budget) Market data (^GSPC quote, ^VIX quote, ^GSPC 52 week history): 3 FMP calls Total: ~283 FMP calls per screening run (exceeds 250 free tier) Recommendation : Use max candidates 35 for free tier (35 × 7 + 3 = 248 calls), or upgrade to FMP Starter tier ($29.99/mo, 750 calls/day) for full 40 stock screening Step 3: Execute CANSLIM Screening Script Run the main screening script with appropriate parameters: Script Workflow (Phase 3 Full CANSLIM): 1. Market Direction (M) : Analyze S&P 500 trend vs 50 day EMA (using real historical data for accurate EMA) If bear market detected (M=0), warn user to raise cash 2. S&P 500 Historical Data : Fetch 52 week data for M component EMA and L component RS calculation 3. Stock Analysis : For each stock, calculate: C Component : Quarterly EPS/revenue growth (YoY) A Component : 3 year EPS CAGR and stability N Component : Distance from 52 week high, breakout detection S Component : Volume based accumulation/distribution (up day vs down day volume) L Component : 52 week Relative Strength vs S&P 500 I Component : Institutional holder count + ownership % (with Finviz fallback) 4. Composite Scoring : Weighted average with all 7 component breakdown 5. Ranking : Sort by composite score (highest first) 6. Reporting : Generate JSON + Markdown outputs Expected Execution Time (Phase 3): 40 stocks: ~2 minutes (additional 52 week history fetch per stock for L component) Finviz fallback adds ~2 seconds per stock (rate limiting) L component requires 365 day historical data for each stock Finviz Fallback Behavior: Triggers automatically when FMP sharesOutstanding unavailable Scrapes institutional ownership % from Finviz.com (free, no API key) Increases I component accuracy from 35/100 (partial data) to 60 100/100 (full data) User sees: ✅ Using Finviz institutional ownership for NVDA: 68.3% Step 4: Read and Parse Screening Results The script generates two output files: canslim screener YYYY MM DD HHMMSS.json Structured data canslim screener YYYY MM DD HHMMSS.md Human readable report Read the Markdown report to identify top candidates: Report Structure (Phase 3 Full CANSLIM): Market Condition Summary (trend, M score, warnings) Top N CANSLIM Candidates (ranked, N = top parameter) For each stock: Composite Score and Rating (Exceptional+/Exceptional/Strong/etc.) Component Breakdown (C, A, N, S, L, I, M scores with details) Interpretation (rating description, guidance, weakest component) Warnings (quality issues, market conditions, data source notes) Summary Statistics (rating distribution) Methodology note (Phase 3: 7 components, 100% coverage) Component Details in Report: S Component : "Up/Down Volume Ratio: 1.06 ✓ Accumulation" L Component (Phase 3.1) : "3m/6m/12m: +12.4%/+18.7%/+44.1% (rel +5.2%/+8.3%/+22.0%) RS: 88 (Strong)" I Component : "6199 holders, 68.3% ownership ⭐ Superinvestor" A new Summary Table appears above the candidate list in Phase 3.1 reports, showing rank, symbol, composite score, rating, RS rating, and RS percentile for quick scanning. Step 5: Analyze Top Candidates and Provide Recommendations Review the top ranked stocks and cross reference with knowledge bases: Reference Documents to Consult: 1. references/interpretation guide.md Understand rating bands and portfolio sizing 2. references/canslim methodology.md Deep dive into component meanings (now includes S and I) 3. references/scoring system.md Understand scoring formulas (Phase 3 weights) Analysis Framework: For Exceptional+ stocks (90 100 points) : All components near perfect (C≥85, A≥85, N≥85, S≥80, L≥85, I≥80, M≥80) Guidance: Immediate buy, aggressive position sizing (15 20% of portfolio) Example: "NVDA scores 97.2 explosive quarterly earnings (100), strong 3 year growth (95), at new highs (98), volume accumulation (85), RS leader (92), strong institutional support (90), uptrend market (100)" For Exceptional stocks (80 89 points) : Outstanding fundamentals + strong momentum Guidance: Strong buy, standard sizing (10 15% of portfolio) For Strong stocks (70 79 points) : Solid across all components, minor weaknesses Guidance: Buy, standard sizing (8 12% of portfolio) Phase 3 Example: "Stock scores 77.5 strong earnings (85), solid growth (80), near high (70), accumulation (60), RS leader (75), good institutions (60), uptrend (90)" For Above Average stocks (60 69 points) : Meets thresholds, one component weak Guidance: Buy on pullback, conservative sizing (5 8% of portfolio) Bear Market Override: If M component = 0 (bear market detected), do NOT buy regardless of other scores Guidance: Raise 80 100% cash, wait for market recovery CANSLIM does not work in bear markets (3 out of 4 stocks follow market trend) Step 6: Generate User Facing Report Create a concise, actionable summary for the user: Report Format: Resources Scripts Directory ( scripts/ ) Main Scripts: screen canslim.py Main orchestrator script Entry point for screening workflow Handles argument parsing, API coordination, ranking, reporting Usage: python3 screen canslim.py api key KEY [options] fmp client.py FMP API client wrapper Rate limiting (0.3s between calls) 429 error handling with 60s retry Session based caching Methods: get income statement() , get quote() , get historical prices() , get institutional holders() finviz stock client.py Finviz web scraping client ← NEW BeautifulSoup based HTML parsing Fetches institutional ownership % from Finviz.com Rate limiting (2.0s between calls) No API key required (free web scraping) Methods: get institutional ownership() , get stock data() Calculators ( scripts/calculators/ ): earnings calculator.py C component (Current Earnings) Quarterly EPS/revenue growth (YoY) Scoring: 50%+ = 100pts, 30 49% = 80pts, 18 29% = 60pts growth calculator.py A component (Annual Growth) 3 year EPS CAGR calculation Stability check (no negative growth years) Scoring: 40%+ = 90pts, 30 39% = 70pts, 25 29% = 50pts new highs calculator.py N component (Newness) Distance from 52 week high Volume confirmed breakout detection Scoring: 5% of high + breakout = 100pts, 10% + breakout = 80pts supply demand calculator.py S component (Supply/Demand) ← NEW Volume based accumulation/distribution analysis Up day volume vs down day volume ratio (60 day lookback) Scoring: ratio ≥2.0 = 100pts

canslim-screener