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.

Dual-market support: This skill uses bin/stock-cli for data fetching, supporting both US and KR markets. Original FMP scripts preserved in scripts/ for reference.

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: Current Earnings, Annual Growth, Newness/New Highs, Supply/Demand, Leadership/RS Rank, Institutional Sponsorship, and Market 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 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%
• I (Institutional): 10%
• M (Market Direction): 5%

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 3.7+
• 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 3.7+ 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):

uv run python .claude/skills/canslim-screener/scripts/screen_canslim.py

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

uv run python .claude/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 = 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: Fetch Data and Execute CANSLIM Screening

Fetch fundamentals and price data for the candidate universe using bin/stock-cli:

# Fetch fundamentals (for C and A components: earnings, growth rates)
bin/stock-cli fundamentals-batch AAPL,MSFT,NVDA,AMZN,META --market US

# Fetch 365 days of price data (for N, S, L, M components)
bin/stock-cli price-batch AAPL,MSFT,NVDA,AMZN,META --market US --days 365

# KR market
bin/stock-cli fundamentals-batch 005930,000660,035420,051910 --market KR
bin/stock-cli price-batch 005930,000660,035420,051910 --market KR --days 365

Note: KR earnings growth data (C and A components) may be limited — yfinance/PyKRX quarterly earnings coverage is less complete than FMP for US stocks.

The original script scripts/screen_canslim.py is preserved for reference (FMP API required for full automated run).

Analysis workflow using fetched data (Phase 3 - Full CANSLIM):

1. Market Direction (M): Assess S&P 500 (or KOSPI for KR) trend vs 50-day EMA from price-batch output
   • If bear market detected (M=0), warn user to raise cash
2. Stock Analysis: For each ticker, calculate:
   • C Component: Quarterly EPS/revenue growth (YoY) from fundamentals-batch
   • A Component: 3-year EPS CAGR and stability from fundamentals-batch
   • N Component: Distance from 52-week high, breakout detection from price-batch
   • S Component: Volume-based accumulation/distribution (up-day vs down-day volume) from price-batch
   • L Component: 52-week Relative Strength vs benchmark from price-batch
   • I Component: Institutional holder count + ownership % (use WebSearch for KR if needed)
3. Composite Scoring: Weighted average with all 7 component breakdown
4. Ranking: Sort by composite score (highest first)
5. 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:

# Find the latest report
ls -lt canslim_screener_*.md | head -1

# Read the report
cat canslim_screener_YYYY-MM-DD_HHMMSS.md

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: "52wk: +45.2% (+22.1% vs S&P) RS: 88"
• I Component: "6199 holders, 68.3% ownership ⭐ Superinvestor"

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:

# CANSLIM Stock Screening Results (Phase 3 - Full CANSLIM)
**Date:** YYYY-MM-DD
**Market Condition:** [Trend] - M Score: [X]/100
**Stocks Analyzed:** [N]
**Components:** C, A, N, S, L, I, M (7 of 7, 100% coverage)

## Market Summary
[2-3 sentences on current market environment based on M component]
[If bear market: WARNING - Consider raising cash allocation]

## Top 5 CANSLIM Candidates

### 1. [SYMBOL] - [Company Name] ⭐⭐⭐
**Score:** [X.X]/100 ([Rating])
**Price:** $[XXX.XX] | **Sector:** [Sector]

**Component Breakdown:**
- C (Earnings): [X]/100 - [EPS growth]% QoQ, [Revenue growth]% revenue
- A (Growth): [X]/100 - [CAGR]% 3yr EPS CAGR
- N (Newness): [X]/100 - [Distance]% from 52wk high
- S (Supply/Demand): [X]/100 - Up/Down Volume Ratio: [X.XX]
- L (Leadership): [X]/100 - 52wk: [+X.X]% ([+X.X]% vs S&P) RS: [XX]
- I (Institutional): [X]/100 - [N] holders, [X.X]% ownership [⭐ Superinvestor if present]
- M (Market): [X]/100 - [Trend]

**Interpretation:** [Rating description and guidance]
**Weakest Component:** [X] ([score])
**Data Source Note:** [If Finviz used: "Institutional data from Finviz"]

[Repeat for top 5 stocks]

## Investment Recommendations

**Immediate Buy List (90+ score):**
- [List stocks with exceptional+ ratings]
- Position sizing: 15-20% each

**Strong Buy List (80-89 score):**
- [List stocks with exceptional ratings]
- Position sizing: 10-15% each

**Watchlist (70-79 score):**
- [List stocks with strong ratings]
- Buy on pullback

## Risk Factors
- [Identify any quality warnings from components]
- [Market condition warnings]
- [Sector concentration risks if applicable]
- [Data source reliability notes if Finviz heavily used]

## Next Steps
1. Conduct detailed fundamental analysis on top 3 candidates
2. Check earnings calendars for upcoming reports
3. Review technical charts for entry timing
4. [If bear market: Wait for market recovery before deploying capital]

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**Note:** This is Phase 3 (Full CANSLIM: C, A, N, S, L, I, M - 100% coverage).

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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, 1.5-2.0 = 80pts, 1.0-1.5 = 60pts

• leadership_calculator.py - L component (Leadership/Relative Strength)

  • 52-week stock performance vs S&P 500 benchmark
  • RS Rank estimation (1-99 scale, O'Neil style)
  • Scoring: RS 90+ outperforming market = 100pts, RS 80-89 = 80pts

• institutional_calculator.py - I component (Institutional)

  • Institutional holder count (from FMP)
  • Ownership % (from FMP or Finviz fallback)
  • Superinvestor detection (Berkshire Hathaway, Baupost, etc.)
  • Scoring: 50-100 holders + 30-60% ownership = 100pts

• market_calculator.py - M component (Market Direction)

  • S&P 500 vs 50-day EMA
  • VIX-adjusted scoring
  • Scoring: Strong uptrend = 100pts, Uptrend = 80pts, Bear market = 0pts

Supporting Modules:

• scorer.py - Composite score calculation

  • Phase 3 weighted average: C×15% + A×20% + N×15% + S×15% + L×20% + I×10% + M×5%
  • Rating interpretation (Exceptional+/Exceptional/Strong/etc.)
  • Minimum threshold validation (all 7 components must meet baseline)

• report_generator.py - Output generation

  • JSON export (programmatic use)
  • Markdown export (human-readable)
  • Phase 3 component breakdown tables (all 7 components)
  • Summary statistics calculation

References Directory (references/)

Knowledge Bases:

• references/canslim_methodology.md (27KB) - Complete CANSLIM explanation

  • All 7 components with O'Neil's original thresholds
  • S component (Volume accumulation/distribution) detailed explanation
  • L component (Leadership/Relative Strength) detailed explanation
  • I component (Institutional sponsorship) detailed explanation
  • Historical examples (AAPL 2009, NFLX 2013, TSLA 2019, NVDA 2023)

• references/scoring_system.md (21KB) - Technical scoring specification (Phase 3)

  • Phase 3 component weights and formulas (all 7 components)
  • Interpretation bands (90-100, 80-89, etc.)
  • Minimum thresholds for all 7 components
  • Composite score calculation examples

• references/fmp_api_endpoints.md (18KB) - API integration guide (Phase 3)

  • Required endpoints for all 7 components
  • L component: 52-week historical prices endpoint
  • Institutional holder endpoint documentation
  • Finviz fallback strategy explanation
  • Rate limiting strategy
  • Cost analysis (Phase 3: ~283 FMP calls for 40 stocks, exceeds 250 free tier)

• references/interpretation_guide.md (18KB) - User guidance

  • Portfolio construction rules
  • Position sizing by rating
  • Entry/exit strategies
  • Bear market protection rules

How to Use References:

• Read references/canslim_methodology.md first to understand O'Neil's system (now includes S and I)
• Consult references/interpretation_guide.md when analyzing results
• Reference references/scoring_system.md if scores seem unexpected
• Check references/fmp_api_endpoints.md for API troubleshooting or Finviz fallback issues

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Troubleshooting

Issue 1: FMP API Rate Limit Exceeded

Symptoms:

ERROR: 429 Too Many Requests - Rate limit exceeded
Retrying in 60 seconds...

Causes:

• Running multiple screenings within short time window
• Exceeding 250 calls/day (free tier limit)
• Other applications using same API key

Solutions:

1. Wait and Retry: Script auto-retries after 60s
2. Reduce Universe: Use --max-candidates 30 to lower API usage
3. Check Daily Usage: Free tier resets at midnight UTC
4. Upgrade Plan: FMP Starter ($29.99/month) provides 750 calls/day

Issue 2: Missing Required Libraries

Symptoms:

ERROR: required libraries not found. Install with: pip install beautifulsoup4 requests lxml

Solutions:

# Install all required libraries
pip install requests beautifulsoup4 lxml

# Or install individually
pip install beautifulsoup4
pip install requests
pip install lxml

Issue 3: Finviz Fallback Slow Execution

Symptoms:

Execution time: 2 minutes 30 seconds for 40 stocks (slower than expected)

Causes:

• Finviz rate limiting (2.0s per request)
• All stocks triggering fallback due to FMP data gaps

Solutions:

1. Accept Delay: 1-2 minutes for 40 stocks is normal with Finviz fallback
2. Monitor Fallback Usage: Check logs for "Using Finviz institutional ownership" messages
3. Reduce Rate Limit (advanced): Edit finviz_stock_client.py, change rate_limit_seconds=2.0 to 1.5 (risk: IP ban)

Note: Finviz fallback adds ~2 seconds per stock but significantly improves I component accuracy (35 → 60-100 points).

Issue 4: Finviz Web Scraping Failure

Symptoms:

WARNING: Finviz request failed with status 403 for NVDA
⚠️ Using Finviz institutional ownership data - FMP shares outstanding unavailable. Finviz fallback also unavailable. Score reduced by 50%.

Causes:

• Finviz blocking scraping requests (User-Agent detection)
• Rate limit exceeded (too many requests)
• Network issues or Finviz downtime

Solutions:

1. Wait and Retry: Rate limit resets after a few minutes
2. Check Internet Connection: Verify network access to finviz.com
3. Fallback Accepted: Script continues with FMP holder count only (I score capped at 70/100)
4. Manual Verification: Check Finviz website manually for blocked IP

Graceful Degradation:

• Script never fails due to Finviz issues
• Falls back to FMP holder count only
• User sees quality warning in report

Issue 5: No Stocks Meet Minimum Thresholds

Symptoms:

✓ Successfully analyzed 40 stocks
Top 5 Stocks:
  1. AAPL  -  58.3 (Average)
  2. MSFT  -  55.1 (Average)
  ...

Causes:

• Bear market conditions (M component low)
• Selected universe lacks growth stocks
• Market rotation away from growth

Solutions:

1. Check M Component: If M=0 (bear market), raise cash per CANSLIM rules
2. Expand Universe: Try different sectors or market cap ranges
3. Lower Expectations: Average scores (55-65) may still be actionable in weak markets
4. Wait for Better Setup: CANSLIM works best in bull markets

Issue 6: Data Quality Warnings

Symptoms:

⚠️ Revenue declining despite EPS growth (possible buyback distortion)
⚠️ Using Finviz institutional ownership data (68.3%) - FMP shares outstanding unavailable.

Interpretation:

• These are not errors - they are quality flags from calculators
• Revenue warning: EPS growth may be from share buybacks, not organic growth
• Finviz warning: Data source switched from FMP to Finviz (still accurate)

Actions:

1. Review component details in full report
2. Cross-check with fundamental analysis
3. Adjust position sizing based on risk level
4. Finviz data is reliable - no action needed for data source warnings

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Important Notes

Phase 3 Implementation Status

This is Phase 3 implementing all 7 of 7 CANSLIM components:

• ✅ C (Current Earnings) - Implemented
• ✅ A (Annual Growth) - Implemented
• ✅ N (Newness) - Implemented
• ✅ S (Supply/Demand) - Implemented
• ✅ L (Leadership/RS Rank) - Implemented
• ✅ I (Institutional) - Implemented
• ✅ M (Market Direction) - Implemented

Implications:

• Composite scores represent 100% of full CANSLIM methodology
• Uses original O'Neil component weights (C 15%, A 20%, N 15%, S 15%, L 20%, I 10%, M 5%)
• L component (20% weight) is the largest individual factor alongside A, emphasizing relative strength leadership
• M component uses real 50-day EMA from historical data (not fallback estimate)

Finviz Integration Benefits

Automatic Fallback System:

• When FMP API doesn't provide sharesOutstanding, Finviz automatically activates
• Scrapes institutional ownership % from Finviz.com (free, no API key)
• Improves I component accuracy from 35/100 (partial) to 60-100/100 (full)

Data Source Priority:

1. FMP API (primary): Institutional holder count + shares outstanding calculation
2. Finviz (fallback): Direct institutional ownership % from web page
3. Partial Data (last resort): Holder count only, 50% penalty applied

Tested Reliability:

• 39/39 stocks successfully retrieved ownership % via Finviz (100% success rate)
• Average execution time: 2.54 seconds per stock
• No errors or IP blocks during testing

Future Enhancements

Phase 4 (Planned):

• FINVIZ Elite integration for pre-screening
• Execution time: 2 minutes → 10-15 seconds
• FMP API usage reduction: 90%
• Larger universe possible (100+ stocks)

Data Source Attribution

• FMP API: Income statements, quotes, historical prices, key metrics, institutional holders
• Finviz: Institutional ownership % (fallback), market data
• Methodology: William O'Neil's "How to Make Money in Stocks" (4th edition)
• Scoring System: Adapted from IBD MarketSmith proprietary system

Disclaimer

This screener is for educational and informational purposes only.

• Not investment advice
• Past performance does not guarantee future results
• CANSLIM methodology works best in bull markets (M component confirms)
• Conduct your own research and consult a financial advisor before making investment decisions
• O'Neil's historical winners include AAPL (2009: +1,200%), NFLX (2013: +800%), but many stocks fail to perform

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Version: Phase 3 Last Updated: 2026-02-20 API Requirements: FMP API (free tier: up to 35 stocks; Starter tier recommended for 40 stocks) + BeautifulSoup/requests/lxml for Finviz Execution Time: ~2 minutes for 40 stocks Output Formats: JSON + Markdown Components Implemented: C, A, N, S, L, I, M (7 of 7, 100% coverage)