🇺🇸English
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:
- Stage 1 (FMP API + Finviz) : Analyze stock universe with all 7 CANSLIM components
- 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
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 3.7+
requests (FMP API calls)beautifulsoup4 (Finviz web scraping)lxml (HTML parsing)
Installation:
pip install requests beautifulsoup4 lxml
Output
Output Directory: reports/ (default) or custom via --output-dir
Generated Files:
canslim_screener_YYYY-MM-DD_HHMMSS.json - Structured data for programmatic usecanslim_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:
# 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:
- Sign up at https://site.financialmodelingprep.com/developer/docs
- Get free API key (250 calls/day)
- 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 = 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:
cd skills/canslim-screener/scripts
# Basic run (40 stocks, top 20 in report)
python3 screen_canslim.py --api-key $FMP_API_KEY
# Custom parameters
python3 screen_canslim.py \
--api-key $FMP_API_KEY \
--max-candidates 40 \
--top 20 \
--output-dir ../../../
Script Workflow (Phase 3 - Full CANSLIM):
- 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
- S &P 500 Historical Data: Fetch 52-week data for M component EMA and L component RS calculation
- 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)
- Composite Scoring : Weighted average with all 7 component breakdown
- Ranking : Sort by composite score (highest first)
- 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 datacanslim_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:
references/interpretation_guide.md - Understand rating bands and portfolio sizingreferences/canslim_methodology.md - Deep dive into component meanings (now includes S and I)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]
---
**Note:** This is Phase 3 (Full CANSLIM: C, A, N, S, L, I, M - 100% coverage).
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 ←
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)
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
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
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:
- Wait and Retry : Script auto-retries after 60s
- Reduce Universe : Use
--max-candidates 30 to lower API usage
- Check Daily Usage : Free tier resets at midnight UTC
- 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:
- Accept Delay : 1-2 minutes for 40 stocks is normal with Finviz fallback
- Monitor Fallback Usage : Check logs for "Using Finviz institutional ownership" messages
- 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:
- Wait and Retry : Rate limit resets after a few minutes
- Check Internet Connection : Verify network access to finviz.com
- Fallback Accepted : Script continues with FMP holder count only (I score capped at 70/100)
- 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:
- Check M Component : If M=0 (bear market), raise cash per CANSLIM rules
- Expand Universe : Try different sectors or market cap ranges
- Lower Expectations : Average scores (55-65) may still be actionable in weak markets
- 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:
- Review component details in full report
- Cross-check with fundamental analysis
- Adjust position sizing based on risk level
- Finviz data is reliable - no action needed for data source warnings
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:
- FMP API (primary): Institutional holder count + shares outstanding calculation
- Finviz (fallback): Direct institutional ownership % from web page
- 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
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)
Weekly Installs
217
Repository
tradermonty/cla…g-skills
GitHub Stars
394
First Seen
Jan 26, 2026
Security Audits
Gen Agent Trust HubPassSocketPassSnykWarn
Installed on
gemini-cli205
cursor205
opencode205
codex202
github-copilot198
kimi-cli197
