/canslim-screener

name: canslim-screener
description: 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:**

Weighted RS = 0.40 × rel_3m + 0.30 × rel_6m + 0.30 × rel_12m

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

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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)

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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:**

pip install requests beautifulsoup4 lxml

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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

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Workflow

Step 1: Verify API Access and Requirements

Check if user has FMP API key configured:

# Check environment variable
echo $FMP_API_KEY

# If not set, prompt user to provide it

**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:**

pip install requests beautifulsoup4 lxml

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):

python3 skills/canslim-screener/scripts/screen_canslim.py

**Option B: Custom Universe** User provides specific symbols or sector:

python3 skills/canslim-screener/scripts/screen_canslim.py \
  --universe AAPL MSFT GOOGL AMZN NVDA META TSLA

**Option C: Sector-Specific** User can provide sector-focused list (Technology, Healthcare, etc.)

**API Budget Considerations (Phase 3):**

• 40 stocks × 7 FMP calls/stock