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CANSLIM Stock Screener - Phase 2

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 2 implements 6 of 7 components (C, A, N, S, I, M), representing 80% of the full methodology. This phase adds critical volume analysis (S) and institutional ownership tracking (I) to the Phase 1 foundation.

Two-Stage Approach:

1. Stage 1 (FMP API + Finviz) : Analyze stock universe with 6 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 2 Component Weights (Renormalized for 6 components):

• C (Current Earnings): 19%
• A (Annual Growth): 25%
• N (Newness): 19%
• S (Supply/Demand): 19% ← NEW
• I (Institutional): 13% ← NEW
• M (Market Direction): 6%

Future Phases:

• Phase 3: Add L (Leadership/RS Rank) → 100% coverage (full CANSLIM)
• 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)

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):

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 2):

• 40 stocks × ~5.1 calls/stock = ~203 API calls (within free tier)
  • FMP: ~3.3 calls/stock (income statements, quotes, prices, institutional holders)
  • Finviz: ~1.8 calls/stock (institutional ownership fallback, 2s rate limit)
• Market data (S&P 500, VIX): 3 calls
• Total: ~206 calls per screening run

Step 3: Execute CANSLIM Screening Script

Run the main screening script with appropriate parameters:

cd /Users/takueisaotome/PycharmProjects/claude-trading-skills/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 2):

1. Market Direction (M) : Analyze S&P 500 trend vs 50-day EMA
   • If bear market detected (M=0), warn user to raise cash
2. 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) ← NEW
   • I Component : Institutional holder count + ownership % (with Finviz fallback) ← NEW
3. Composite Scoring : Weighted average with component breakdown
4. Ranking : Sort by composite score (highest first)
5. Reporting : Generate JSON + Markdown outputs

Expected Execution Time (Phase 2):

• 40 stocks: ~1 minute 40 seconds (tested: 101.42 seconds)
• Finviz fallback adds ~2 seconds per stock (rate limiting)
• Much faster than Phase 1 due to optimized API calls

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 2):

• Market Condition Summary (trend, M score, warnings)
• Top 20 CANSLIM Candidates (ranked)
• For each stock:
  • Composite Score and Rating (Exceptional+/Exceptional/Strong/etc.)
  • Component Breakdown (C, A, N, S , I , M scores with details) ← S and I added
  • Interpretation (rating description, guidance, weakest component)
  • Warnings (quality issues, market conditions, data source notes)
• Summary Statistics (rating distribution)
• Methodology note (Phase 2: 6 components)

New Component Details in Report:

• S Component : "Up/Down Volume Ratio: 1.06 ✓ Accumulation"
• 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 2 weights)

Analysis Framework:

For Exceptional+ stocks (90-100 points) :

• All components near-perfect (C≥85, A≥85, N≥85, S≥80, 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), 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 2 Example: "NVDA scores 77.5 - explosive earnings (100), strong growth (90), near high (60), accumulation (60), 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 2)
**Date:** YYYY-MM-DD
**Market Condition:** [Trend] - M Score: [X]/100
**Stocks Analyzed:** [N]
**Components:** C, A, N, S, I, M (6 of 7)

## 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]
- 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 2 (C, A, N, S, I, M components - 80% coverage). Phase 3 will add L component for full 7-component CANSLIM.

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

• institutional_calculator.py - I component (Institutional) ←

Supporting Modules:

• scorer.py - Composite score calculation

  • Phase 2 weighted average: C×19% + A×25% + N×19% + S×19% + I×13% + M×6%
  • Rating interpretation (Exceptional+/Exceptional/Strong/etc.)
  • Minimum threshold validation (all 6 components must meet baseline)

• report_generator.py - Output generation

  • JSON export (programmatic use)
  • Markdown export (human-readable)
  • Phase 2 component breakdown tables
  • Summary statistics calculation

References Directory (references/)

Knowledge Bases:

• canslim_methodology.md (27KB) - Complete CANSLIM explanation ← Updated for Phase 2

  • All 7 components with O'Neil's original thresholds
  • NEW : S component (Volume accumulation/distribution) detailed explanation
  • NEW : I component (Institutional sponsorship) detailed explanation
  • Historical examples (AAPL 2009, NFLX 2013, TSLA 2019, NVDA 2023)
  • Phase 2 implementation notes

• scoring_system.md (21KB) - Technical scoring specification ← Updated for Phase 2

  • Phase 2 component weights and formulas
  • Interpretation bands (90-100, 80-89, etc.)
  • Minimum thresholds for 6 components
  • Composite score calculation examples

• fmp_api_endpoints.md (18KB) - API integration guide ← Updated for Phase 2

  • Required endpoints for each component
  • NEW : Institutional holder endpoint documentation
  • NEW : Finviz fallback strategy explanation
  • Rate limiting strategy
  • Cost analysis (Phase 2: ~203 calls for 40 stocks, within free tier)

How to Use References:

• Read canslim_methodology.md first to understand O'Neil's system (now includes S and I)
• Consult interpretation_guide.md when analyzing results
• Reference scoring_system.md if scores seem unexpected
• Check 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:

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

Important Notes

Phase 2 Implementation Status

This is Phase 2 implementing 6 of 7 CANSLIM components:

• ✅ C (Current Earnings) - Implemented
• ✅ A (Annual Growth) - Implemented
• ✅ N (Newness) - Implemented
• ✅ S (Supply/Demand) - Implemented (Phase 2) ← NEW
• ❌ L (Leadership/RS Rank) - Not implemented (Phase 3)
• ✅ I (Institutional) - Implemented (Phase 2) ← NEW
• ✅ M (Market Direction) - Implemented

Implications:

• Composite scores represent 80% of full CANSLIM methodology
• Top scores typically max out at ~95 (full CANSLIM can reach 200+)
• Missing component: Relative strength rank (L component)
• Phase 2 provides highly accurate screening with volume and institutional analysis

Score Conversion:

• Phase 2 score 85+ ≈ Full CANSLIM 145-165 (Strong to Exceptional)
• Phase 2 score 70-84 ≈ Full CANSLIM 125-144 (Above Average to Strong)
• Phase 2 score 60-69 ≈ Full CANSLIM 110-124 (Average to Above Average)

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 3 (Planned):

• Add L component: RS Rank estimation (52-week high proxy, 80% accuracy)
• Full 7-component CANSLIM: C 15%, A 20%, N 15%, S 15%, L 20%, I 10%, M 5%
• Coverage: 100% of full CANSLIM

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 2 Last Updated: 2026-01-12 API Requirements: FMP API (free tier sufficient) + BeautifulSoup/requests/lxml for Finviz Execution Time: ~1 minute 40 seconds for 40 stocks Output Formats: JSON + Markdown Components Implemented: C, A, N, S, I, M (6 of 7, 80% coverage)

Weekly Installs

51

Repository

nicepkg/ai-workflow

GitHub Stars

142

First Seen

Jan 24, 2026

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