Canslim Screener

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 3.1 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 = 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 ../../../

# Custom RS benchmark (Phase 3.1)
python3 screen_canslim.py --rs-benchmark SPY

# Disable L component (saves per-stock 365-day fetch; L fixed at neutral 50)
python3 screen_canslim.py --disable-rs

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:

# 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 (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:

# 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 3.1 (multi-period RS) Last Updated: 2026-05-03 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 (now includes Summary Table and schema_version: "3.1") Components Implemented: C, A, N, S, L, I, M (7 of 7, 100% coverage) Phase 3.1 additions: multi-period RS (3m/6m/12m), --rs-benchmark, --disable-rs, new RS fields (rs_rating, rs_rank_percentile, rs_3m_return, rs_6m_return, rs_12m_return, rs_benchmark, rs_benchmark_relative_return, rs_component_score, benchmark_52w_performance).

CANSLIM Methodology - William O'Neil's Growth Stock Selection System

Overview

CANSLIM is a proven growth stock selection methodology developed by William O'Neil, founder of Investor's Business Daily (IBD). Based on comprehensive analysis of the greatest winning stocks from 1953 to the present, CANSLIM identifies 7 common characteristics that multi-bagger stocks exhibit before their major price advances.

Historical Performance: IBD's studies show that stocks exhibiting all 7 CANSLIM traits delivered average gains of 100-300% over 1-3 year periods, with many producing returns exceeding 1,000%.

Investment Philosophy: CANSLIM focuses on identifying emerging growth leaders at the beginning of their major price advances - not after they've already run. The methodology emphasizes buying quality growth stocks during brief consolidations or pullbacks in confirmed market uptrends.

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The 7 CANSLIM Components

C - Current Quarterly Earnings (15% Weight)

O'Neil's Rule: "Look for companies whose current quarterly earnings per share are up at least 18-20% compared to the same quarter the prior year."

Why It Matters

Earnings are the primary fundamental driver of stock prices. Companies with accelerating quarterly earnings signal business momentum, competitive advantages, and management execution. O'Neil's research found that 3 out of 4 winning stocks showed EPS growth of at least 70% in the quarter before their major price advance began.

Quantitative Criteria

• Minimum: 18% year-over-year (YoY) quarterly EPS growth
• Preferred: 25%+ YoY growth
• Exceptional: 50%+ YoY growth with accelerating trend

Scoring Formula (0-100 Points)

100 points: EPS growth >= 50% AND revenue growth >= 25% (explosive acceleration)
 80 points: EPS growth 30-49% AND revenue growth >= 15% (strong growth)
 60 points: EPS growth 18-29% AND revenue growth >= 10% (meets minimum)
 40 points: EPS growth 10-17% (below threshold)
  0 points: EPS growth < 10% or negative

Revenue Growth Verification

Critical Check: EPS growth must be supported by revenue growth. If EPS grows significantly faster than revenue, investigate whether growth is driven by cost-cutting, share buybacks, or accounting adjustments rather than genuine business expansion.

Red Flag: EPS growth > 30% while revenue growth < 10% suggests unsustainable earnings quality.

Historical Examples

• AAPL (2009 Q3): EPS +45% YoY (iPhone 3GS launch), stock gained 200% over next 2 years
• NFLX (2013 Q1): EPS +278% YoY (streaming acceleration), stock up 400% in 18 months
• TSLA (2020 Q3): EPS turned positive (first sustained profitability), stock up 700% in 12 months
• NVDA (2023 Q2): EPS +429% YoY (AI chip demand), stock up 240% YTD

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A - Annual Earnings Growth (20% Weight)

O'Neil's Rule: "Annual earnings per share should be up 25% or more in each of the last three years."

Why It Matters

While current quarterly earnings (C) signal near-term momentum, annual earnings growth (A) validates sustained competitive advantages and business model strength. One-quarter wonders rarely sustain price gains. Multi-year consistency separates authentic growth companies from cyclical or temporary winners.

Quantitative Criteria

• Minimum: 25% annual EPS CAGR over 3 years
• Preferred: 30-40% annual CAGR
• Exceptional: 40%+ annual CAGR with no down years

3-Year CAGR Calculation

# Compound Annual Growth Rate formula
EPS_CAGR = ((EPS_current / EPS_3_years_ago) ^ (1/3)) - 1

# Example: MSFT 2017-2020
# EPS: $3.25 (2017) → $5.76 (2020)
# CAGR = (5.76 / 3.25) ^ (1/3) - 1 = 21.0%

Scoring Formula (0-100 Points)

90 points: EPS CAGR >= 40% AND stable (no down years) AND revenue CAGR >= 20%
70 points: EPS CAGR 30-39% AND stable
50 points: EPS CAGR 25-29% (meets CANSLIM minimum)
30 points: EPS CAGR 15-24% (below threshold)
 0 points: EPS CAGR < 15% or erratic (down years present)

Bonus: +10 points if all 3 years show year-over-year growth (stability bonus)
Penalty: -20% of score if revenue CAGR < 50% of EPS CAGR (buyback-driven growth)

Growth Stability Assessment

Stable Growth (preferred): EPS increases every year over the 3-year period

• Example: $1.00 → $1.30 → $1.69 → $2.20 (consistent 30% growth)

Erratic Growth (penalty): One or more years show declining EPS

• Example: $1.00 → $1.50 → $1.20 → $1.80 (volatile, uncertain)

O'Neil's Insight: "It's better to see three years of 25% growth than one year of 80% followed by two years of 10%."

Revenue Growth Validation

Healthy Growth: EPS CAGR and Revenue CAGR both strong (within 10 percentage points)

• Example: EPS 30% CAGR + Revenue 25% CAGR = Sustainable

Warning Sign: EPS CAGR >> Revenue CAGR (gap > 15 percentage points)

• Likely driven by margin expansion, buybacks, or cost cuts (less sustainable)
• Example: EPS 35% CAGR + Revenue 10% CAGR = Investigate quality

Historical Examples

• V (2015-2018): EPS CAGR 29%, Revenue CAGR 18% → 180% stock gain
• MA (2014-2017): EPS CAGR 33%, Revenue CAGR 14% → 200% stock gain
• MSFT (2017-2020): EPS CAGR 21%, Revenue CAGR 13% → 280% stock gain
• NVDA (2020-2023): EPS CAGR 76%, Revenue CAGR 52% → 450% stock gain

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N - New Products, Management, or Highs (15% Weight)

O'Neil's Rule: "Stocks making new price highs have no overhead supply (resistance), signaling strong demand. New products, services, or management can catalyze major moves."

Why It Matters

Price action near 52-week highs indicates institutional accumulation and investor demand. New products create earnings catalysts. New management brings strategic changes. The "N" component combines technical momentum (new highs) with fundamental catalysts (newness).

Key Insight: 95% of CANSLIM winning stocks made new all-time highs before their major advance. Stocks far from highs face resistance and lack sponsorship.

Quantitative Criteria

Price Position:

• Ideal: Within 5% of 52-week high
• Acceptable: Within 15% of 52-week high
• Caution: 15-25% below 52-week high
• Avoid: >25% below 52-week high (lacks momentum)

Breakout Pattern (bonus):

• New 52-week high achieved on volume 40-50%+ above average
• Signals institutional buying (large orders driving price)

Scoring Formula (0-100 Points)

# Distance from 52-week high
distance_pct = ((current_price / week_52_high) - 1) * 100

# Base score from price position
100 points: distance <= 5% from high AND breakout detected AND new product/catalyst
 80 points: distance <= 10% from high AND breakout detected
 60 points: distance <= 15% from high OR breakout detected
 40 points: distance <= 25% from high
 20 points: distance > 25% from high (insufficient momentum)

# Bonus for new product/catalyst signals (from news)
+10-20 points: Keywords detected - "FDA approval", "new product", "patent granted", "breakthrough"

New Product/Catalyst Detection (Supplementary)

High-Impact Catalysts:

• FDA drug approvals (pharmaceuticals)
• New platform/service launches (technology)
• Patent grants (defensive moat)
• Strategic acquisitions (expansion)
• New management from successful companies

Data Source: Keyword search in recent news headlines (FMP news API)

• "FDA approval" → +20 points
• "new product", "product launch" → +10 points
• "acquisition", "expansion" → +10 points

Note: Price action (new highs) is the primary signal (80% of N score). New product detection is supplementary (20% of N score).

Historical Examples

New Highs + New Products:

• AAPL (2007): iPhone launch + new highs → 600% gain in 5 years
• TSLA (2020): Model 3 production scale + new highs → 700% gain in 12 months
• NVDA (2023): AI chip (H100) demand + new highs → 240% YTD gain

New Highs After Consolidation:

• MSFT (2018): Azure cloud growth + breakout from 3-year consolidation → 350% gain in 3 years
• META (2023): AI efficiency gains + breakout from 2022 bear market → 200% gain in 12 months

Caution - Stocks Far from Highs:

• Stocks 30-50% below highs rarely lead; they often become "dead money" for years
• Exception: Deep pullbacks in bear markets can reset; wait for new highs in next bull market

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M - Market Direction (5% Weight)

O'Neil's Rule: "You can be right about a stock but wrong about the market, and still lose money. Three out of four stocks follow the market's trend."

Why It Matters

CANSLIM doesn't work in bear markets. Even the best growth stocks decline 20-50% in sustained downtrends. O'Neil's research shows:

• Bull markets: 75% of stocks move with market direction
• Bear markets: Profitable stock picking nearly impossible
• Market timing: Staying in cash during corrections preserves capital for next bull phase

Critical Insight: "The most important decision is not which stock to buy, but whether to be invested at all."

Quantitative Criteria

Primary Signal: S&P 500 vs. 50-day Exponential Moving Average (EMA)

• Uptrend: S&P 500 closes above 50-day EMA for 3+ consecutive days
• Choppy/Neutral: S&P 500 oscillating around 50-day EMA (±2%)
• Downtrend: S&P 500 closes below 50-day EMA for 3+ consecutive days

Secondary Signal: VIX (Fear Gauge)

• Low Fear: VIX < 15 (complacent, supportive environment)
• Normal: VIX 15-20 (healthy market)
• Elevated: VIX 20-30 (caution, potential volatility)
• Panic: VIX > 30 (sell signal, go to cash)

Scoring Formula (0-100 Points)

# Calculate distance from 50-day EMA
distance_from_ema = (sp500_price / sp500_ema_50) - 1

# Market trend scoring
100 points: sp500 > EMA by 2%+ AND VIX < 15 AND follow-through day detected
 80 points: sp500 > EMA by 1-2% AND VIX < 20
 60 points: sp500 > EMA by 0-1% (early uptrend)
 40 points: sp500 within ±2% of EMA (choppy, neutral)
 20 points: sp500 < EMA by 1-3% (early downtrend)
  0 points: sp500 < EMA by 3%+ OR VIX > 30 (bear market - DO NOT BUY)

Follow-Through Day (FTD) - O'Neil's Bull Market Confirmation

Definition: A day when a major index (S&P 500, Nasdaq) rallies 1.25%+ on volume higher than the previous day, occurring on Day 4-10 of an attempted rally after a correction.

Significance:

• Signals institutional buying has resumed
• Confirms market bottoming process complete
• Green light to begin buying leading growth stocks

Without FTD: Rally attempts often fail; premature buying leads to losses

Market Direction Interpretation

100 Points (Strong Uptrend):

• S&P 500 well above 50-day EMA
• VIX low (complacent)
• Follow-through day confirmed
• Action: Aggressively buy leading growth stocks (CANSLIM candidates)

60 Points (Early Uptrend):

• S&P 500 just crossed above 50-day EMA
• Market still establishing trend
• Action: Start small positions (25-50% allocation)

40 Points (Choppy/Neutral):

• S&P 500 oscillating around 50-day EMA
• Direction unclear
• Action: Reduce exposure to 25-50%, wait for confirmation

0 Points (Downtrend/Bear Market):

• S&P 500 below 50-day EMA and declining
• VIX elevated or spiking
• Action: Sell all stocks, raise 80-100% cash, wait for FTD

Historical Examples

Bull Market Phases (M Score 80-100):

• 2009-2011: Post-financial crisis recovery (AAPL, PCLN major winners)
• 2013-2015: QE-driven rally (NFLX, FB explosive gains)
• 2016-2018: Tax reform bull run (NVDA, AMD, FANG stocks)
• 2020-2021: Pandemic recovery (TSLA, NVDA, growth stocks)
• 2023-2024: AI boom (NVDA, META, MAG7)

Bear Market Phases (M Score 0-20):

• 2008 (Financial Crisis): Even best stocks fell 40-60%
• 2011 (Debt Ceiling Crisis): 20% correction
• 2015-2016 (China Devaluation): Growth stocks down 20-30%
• 2018 (Fed Tightening): Nasdaq down 23%
• 2022 (Inflation/Fed): Nasdaq down 33%, growth stocks down 50-80%

Key Takeaway: In bear markets (M score < 20), CANSLIM candidates still decline despite strong fundamentals. Market direction trumps stock selection.

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S - Supply and Demand (15% Weight)

O'Neil's Rule: "Volume is the gas in the tank of a stock. Without gas, the car doesn't go anywhere. Look for stocks where volume expands on up days and contracts on down days."

Why It Matters

Volume patterns reveal institutional accumulation (buying) or distribution (selling). Individual investors trade in small lots (100-1,000 shares), but institutions move millions of shares. When institutions accumulate, volume surges on up days. When they distribute, volume spikes on down days. Volume precedes price - smart money moves before the crowd notices.

Key Principle: UP-DAY VOLUME > DOWN-DAY VOLUME = Accumulation (bullish)

Quantitative Criteria

Accumulation/Distribution Analysis (60-day period):

• Classify each day as up-day (close > previous close) or down-day (close < previous close)
• Calculate average volume for up-days vs. down-days
• Accumulation Ratio = Avg Up-Day Volume / Avg Down-Day Volume

Thresholds:

• Strong Accumulation: Ratio ≥ 2.0 (institutions aggressively buying)
• Accumulation: Ratio 1.5-2.0 (bullish volume pattern)
• Neutral: Ratio 1.0-1.5 (slightly positive)
• Distribution: Ratio 0.5-0.7 (institutions selling)
• Strong Distribution: Ratio < 0.5 (heavy selling pressure)

Scoring Formula (0-100 Points)

# Calculate accumulation/distribution ratio
up_days_volume = [volume for day in last_60_days if close > prev_close]
down_days_volume = [volume for day in last_60_days if close < prev_close]

avg_up_volume = sum(up_days_volume) / len(up_days_volume)
avg_down_volume = sum(down_days_volume) / len(down_days_volume)

ratio = avg_up_volume / avg_down_volume

# Scoring
100 points: ratio >= 2.0 (Strong Accumulation)
 80 points: ratio 1.5-2.0 (Accumulation)
 60 points: ratio 1.0-1.5 (Neutral/Weak Accumulation)
 40 points: ratio 0.7-1.0 (Neutral/Weak Distribution)
 20 points: ratio 0.5-0.7 (Distribution)
  0 points: ratio < 0.5 (Strong Distribution)

Historical Examples

• NVDA (2023): Up/down ratio 2.3 before 500% run (massive institutional accumulation)
• META (2023): Up/down ratio 1.8 during recovery from 2022 lows
• TSLA (2019-2020): Ratio 2.1 during Model 3 production ramp
• AAPL (2019): Ratio 1.7 before iPhone 11 cycle launched

Red Flag: Ratio < 0.7 signals distribution - institutions are selling into strength. Avoid or exit positions.

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I - Institutional Sponsorship (10% Weight)

O'Neil's Rule: "You need some of the big boys on your side. Look for stocks with increasing institutional sponsorship, but not too much. The sweet spot is 50-100 institutional holders with 30-60% ownership."

Why It Matters

Institutional investors (mutual funds, pension funds, hedge funds) have research teams, long time horizons, and large capital pools. Their sponsorship provides: 1. Liquidity: Enables smooth trading without excessive slippage 2. Price Support: Large holders defend positions during pullbacks 3. Discovery: Attracts additional institutional and retail capital 4. Validation: Smart money confirms the investment thesis

Key Insight: Too little institutional ownership (< 20%) means the stock is neglected. Too much (> 80%) means institutions have already bought - no buying power left to drive future gains.

Quantitative Criteria

Holder Count:

• Sweet Spot: 50-100 institutional holders
• Good: 30-50 holders (building interest)
• Acceptable: 100-150 holders (getting crowded)
• Avoid: < 30 holders (underowned) or > 150 holders (overcrowded)

Ownership Percentage:

• Ideal Range: 30-60% institutional ownership
• Acceptable: 20-30% or 60-80%
• Caution: < 20% (neglected) or > 80% (saturated)

Quality Signal: Superinvestor Presence (bonus)

• Holdings by legendary investors: Berkshire Hathaway, Baupost Group, Pershing Square, Greenlight Capital, Third Point, Appaloosa Management

Scoring Formula (0-100 Points)

# Calculate institutional ownership %
total_shares_held = sum(holder.shares for holder in institutional_holders)
ownership_pct = (total_shares_held / shares_outstanding) * 100

# Scoring logic
if 50 <= num_holders <= 100 and 30 <= ownership_pct <= 60:
    score = 100  # O'Neil's sweet spot

elif superinvestor_present and 30 <= num_holders <= 150:
    score = 90  # Superinvestor quality signal

elif (30 <= num_holders < 50 and 20 <= ownership_pct <= 40) or \
     (100 < num_holders <= 150 and 40 <= ownership_pct <= 70):
    score = 80  # Good ranges

elif (20 <= num_holders < 30 and 20 <= ownership_pct <= 50) or \
     (50 <= num_holders <= 150 and 20 <= ownership_pct <= 70):
    score = 60  # Acceptable

elif ownership_pct < 20 or ownership_pct > 80:
    score = 40  # Suboptimal ownership

elif ownership_pct < 10 or ownership_pct > 90:
    score = 20  # Extreme ownership (avoid)

else:
    score = 50  # Default

# Superinvestor bonus
if superinvestor_present and score < 100:
    score = min(score + 10, 100)

Interpretation

100 Points (Ideal):

• 50-100 holders, 30-60% ownership
• Action: Perfect institutional backing - proceed with confidence

90 Points (Quality Signal):

• Superinvestor holding + good holder count
• Action: Quality investors provide margin of safety

60 Points (Acceptable):

• Decent institutional interest, but not optimal
• Action: Proceed cautiously, monitor for changes

40 Points (Suboptimal):

• Either underowned (< 20%) or overcrowded (> 80%)
• Action: Investigate why institutions are avoiding or have saturated

20 Points (Avoid):

• Extreme ownership levels (< 10% or > 90%)
• Action: Pass - either neglected or no buying power left

Historical Examples

• NVDA (2023 Q2): 74 holders, 44% ownership → 240% gain YTD
• META (2023 Q1): 68 holders, 51% ownership → 194% recovery
• TSLA (2020): Increased from 35 to 89 holders during 700% run
• AAPL (2019): Berkshire Hathaway holding (superinvestor) → confidence signal

Warning: Stocks with > 150 holders and > 80% ownership often underperform - institutions have already bought, creating selling pressure during any weakness.

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Phase 2 Implementation Notes

This Phase 2 implementation includes C, A, N, S, I, M components, covering 80% of the full CANSLIM methodology weight. Phase 2 adds two critical components to Phase 1:

1. S (Supply/Demand): Volume-based accumulation/distribution analysis - reveals institutional buying/selling 2. I (Institutional Sponsorship): Holder count and ownership percentage - validates "smart money" backing

Key Improvements Over Phase 1:

• More accurate filtering: Volume patterns (S) eliminate stocks with distribution
• Quality validation: Institutional backing (I) confirms investment thesis
• Better ranking: Stocks with both strong fundamentals AND institutional support rise to top
• API efficient: Phase 2 requires ~203 calls for 40 stocks (81% of free tier)

Component Weights (Phase 2 - Renormalized)

Future Phases:

• Phase 3: Add L (Leadership/RS Rank) → 100% complete CANSLIM
• Phase 4: FINVIZ Elite integration for 10x speed improvement

Interpretation for Phase 2 Scores

Phase 2 implements 6 of 7 components (80% weight), providing substantially more accurate screening than Phase 1:

• 90-100 points (Phase 2): Exceptional+ (Rare multi-bagger setup with institutional backing)
• 80-89 points (Phase 2): Exceptional (Outstanding fundamentals + accumulation)
• 70-79 points (Phase 2): Strong (High-quality CANSLIM stock)
• 60-69 points (Phase 2): Above Average (Watchlist - monitor for improvement)
• <60 points (Phase 2): Below CANSLIM standards

Minimum Thresholds (Phase 2): All 6 components must meet baseline criteria:

• C ≥ 60 (18%+ quarterly EPS growth)
• A ≥ 50 (25%+ annual CAGR)
• N ≥ 40 (within 15% of 52-week high)
• S ≥ 40 (accumulation pattern, ratio ≥ 1.0)
• I ≥ 40 (30+ holders OR 20%+ ownership)
• M ≥ 40 (market in uptrend)

Key Difference from Phase 1: Phase 2 scores reflect institutional validation (S, I). A stock with great fundamentals (C, A) but distribution pattern (S < 40) will score poorly, protecting against false positives.

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Investment Philosophy and Best Practices

When CANSLIM Works Best

1. Confirmed Bull Markets: S&P 500 above 50-day and 200-day moving averages 2. Growth-Favoring Environment: Low interest rates, economic expansion, innovation cycles 3. Sector Rotation into Growth: Technology, healthcare, consumer discretionary leading 4. Positive Market Breadth: Majority of stocks in uptrends (>50% above 200-day MA)

When to Avoid CANSLIM

1. Bear Markets: Major indices below 200-day MA, VIX > 30 2. Value-Favoring Markets: High inflation, rising rates favor defensive stocks 3. Sector Rotation into Defensives: Utilities, consumer staples, REITs leading 4. Negative Breadth: Majority of stocks in downtrends, breadth deteriorating

Position Management Rules

Entry:

• Buy stocks scoring 80+ (Phase 1) or 140+ (Full CANSLIM)
• Enter on pullback to 10-week moving average (best risk/reward)
• Position size: 10-20% of portfolio per stock, 5-10 total positions

Stops:

• Initial stop loss: 7-8% below entry (O'Neil's strict rule)
• Move to breakeven once stock up 15%
• Trail stop 10-15% below peak as stock advances

Profit Taking:

• Sell 20-25% of position when up 20-25% (lock in partial gains)
• Sell another 20-25% when up 50%
• Let winners run if still in Stage 2 uptrend (hold final 50%)

Sell Signals: 1. Stop loss hit (7-8% loss) - no exceptions 2. Market enters correction (M score drops to 0-20) 3. Fundamental deterioration (C or A component score drops below 40) 4. Climax top (parabolic move on extreme volume, then reversal) 5. Distribution (heavy volume on down days, institutional selling)

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Common Mistakes and How to Avoid Them

Mistake 1: Ignoring Market Direction (M Component)

Error: Buying CANSLIM stocks in bear markets because "they have great fundamentals"

Reality: 75% of stocks follow market direction. Even perfect CANSLIM candidates decline 20-50% in sustained downtrends.

Solution:

• Check M component FIRST before analyzing individual stocks
• If M score < 40, raise cash to 80-100% and wait
• "Being right about a stock but wrong about the market still loses money"

Mistake 2: Chasing Stocks Far from Highs

Error: Buying stocks 30-50% below 52-week highs because "they're on sale"

Reality: Stocks trading far from highs lack institutional sponsorship and face overhead resistance. They rarely lead the next advance.

Solution:

• Focus on N component: stocks within 15% of 52-week high
• Wait for proper base breakout (7-8 weeks minimum consolidation)
• "Leaders of the last bull market rarely lead the next one"

Mistake 3: Overweighting Quarterly Earnings, Ignoring Annual Growth

Error: Buying stocks with explosive Q1 EPS growth (C) but erratic 3-year history (A)

Reality: One-quarter wonders often revert. Sustainable winners show consistent multi-year growth.

Solution:

• Require BOTH C >= 60 AND A >= 50 (Phase 1 scoring)
• View A component as validation of C component
• "Consistency beats volatility in long-term wealth building"

Mistake 4: Not Cutting Losses Quickly

Error: Holding losing positions hoping they "come back"

Reality: O'Neil's studies show that cutting losses at 7-8% prevents small losses from becoming large disasters.

Solution:

• Set stop loss at entry (7-8% below buy price)
• No exceptions: stop hit = sell immediately
• "Your first loss is your smallest loss - take it"

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Conclusion

CANSLIM is a time-tested, quantitative methodology for identifying emerging growth leaders before major price advances. The system combines fundamental analysis (C, A) with technical confirmation (N, M) to filter for stocks with institutional sponsorship and momentum.

Phase 1 MVP (C, A, N, M) provides immediate value by screening for stocks with:

• Accelerating quarterly earnings (C)
• Consistent multi-year growth (A)
• Price momentum near new highs (N)
• Confirmation of market uptrend (M)

Future Phases will add supply/demand analysis (S), institutional sponsorship tracking (I), and relative strength leadership ranking (L) to create a complete implementation of O'Neil's system.

Remember: CANSLIM is a growth stock methodology best applied during confirmed bull markets. In bear markets, the best action is to raise cash and wait for the next follow-through day signaling institutional buying has resumed.

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References and Further Reading

• "How to Make Money in Stocks" by William J. O'Neil (4th Edition, 2009)
• "The Successful Investor" by William J. O'Neil (2004)
• IBD (Investor's Business Daily) - Daily CANSLIM stock screens and market analysis
• MarketSmith - IBD's institutional-grade stock analysis platform with official RS Ranks
• Historical Studies: IBD's analysis of 600+ winning stocks (1953-2008) validating CANSLIM

FMP API Endpoints - CANSLIM Screener Phase 3 (Full CANSLIM)

Overview

This document specifies the Financial Modeling Prep (FMP) API endpoints required for the CANSLIM screener Phase 3 implementation (all 7 components: C, A, N, S, L, I, M).

Base URL: https://financialmodelingprep.com/api/v3

Authentication: All requests require apikey parameter

Rate Limiting:

• Free tier: 250 requests/day
• Recommended delay: 0.3 seconds between requests (200 requests/minute max)

---

C Component - Current Quarterly Earnings

Endpoint: Income Statement (Quarterly)

URL: /income-statement/{symbol}?period=quarter&limit=8

Method: GET

Parameters:

• symbol: Stock ticker (e.g., "AAPL")
• period: "quarter" (quarterly data)
• limit: 8 (fetch last 8 quarters = 2 years)
• apikey: Your FMP API key

Example Request:

curl "https://financialmodelingprep.com/api/v3/income-statement/AAPL?period=quarter&limit=8&apikey=YOUR_KEY"

Response Fields Used:

[
  {
    "date": "2023-09-30",  # Quarter end date
    "symbol": "AAPL",
    "reportedCurrency": "USD",
    "fillingDate": "2023-11-02",
    "eps": 1.46,           # Diluted EPS ← KEY
    "epsdiلuted": 1.46,     # Alternative field
    "revenue": 89498000000, # Total revenue ← KEY
    "grossProfit": 41104000000,
    "operatingIncome": 26982000000,
    "netIncome": 22956000000
  },
  # ... 7 more quarters
]

Usage:

• Compare eps from most recent quarter to quarter 4 positions back (YoY comparison)
• Compare revenue same way
• Calculate YoY growth percentage

API Calls: 1 per stock

---

A Component - Annual EPS Growth

Endpoint: Income Statement (Annual)

URL: /income-statement/{symbol}?period=annual&limit=5

Method: GET

Parameters:

• symbol: Stock ticker
• period: "annual" (annual data)
• limit: 5 (fetch last 5 years for 4-year CAGR calculation)
• apikey: Your FMP API key

Example Request:

curl "https://financialmodelingprep.com/api/v3/income-statement/AAPL?period=annual&limit=5&apikey=YOUR_KEY"

Response Fields Used:

[
  {
    "date": "2023-09-30",   # Fiscal year end
    "symbol": "AAPL",
    "eps": 6.13,            # Annual diluted EPS ← KEY
    "revenue": 383285000000, # Annual revenue ← KEY
    "netIncome": 96995000000
  },
  # ... 4 more years
]

Usage:

• Use 4 most recent years to calculate 3-year CAGR
• CAGR = ((EPS_current / EPS_3years_ago) ^ (1/3)) - 1
• Check for stability (no down years)
• Validate with revenue CAGR

API Calls: 1 per stock

---

N Component - Newness / New Highs

Endpoint 1: Historical Prices (Daily)

URL: /historical-price-full/{symbol}?timeseries=365

Method: GET

Parameters:

• symbol: Stock ticker
• timeseries: 365 (fetch last 365 days)
• apikey: Your FMP API key

Example Request:

curl "https://financialmodelingprep.com/api/v3/historical-price-full/AAPL?timeseries=365&apikey=YOUR_KEY"

Response Fields Used:

{
  "symbol": "AAPL",
  "historical": [
    {
      "date": "2024-01-10",
      "open": 185.16,
      "high": 186.40,     # Daily high ← KEY
      "low": 184.00,      # Daily low ← KEY
      "close": 185.92,    # Close price ← KEY
      "volume": 50123456  # Daily volume ← KEY
    },
    # ... 364 more days
  ]
}

Usage:

• Calculate 52-week high: max(historical[0:252].high)
• Calculate 52-week low: min(historical[0:252].low)
• Current price: historical[0].close
• Distance from high: (current / 52wk_high - 1) * 100
• Detect breakout: Check if recent high >= 52wk_high with elevated volume

API Calls: 1 per stock

Endpoint 2: Quote (Real-Time Price)

URL: /quote/{symbol}

Method: GET

Parameters:

• symbol: Stock ticker (can be comma-separated for batch)
• apikey: Your FMP API key

Example Request:

curl "https://financialmodelingprep.com/api/v3/quote/AAPL?apikey=YOUR_KEY"

Response Fields Used:

[
  {
    "symbol": "AAPL",
    "name": "Apple Inc.",
    "price": 185.92,               # Current price ← KEY
    "changesPercentage": 1.23,
    "change": 2.25,
    "dayLow": 184.00,
    "dayHigh": 186.40,
    "yearHigh": 198.23,            # 52-week high ← KEY
    "yearLow": 164.08,             # 52-week low ← KEY
    "marketCap": 2913000000000,
    "volume": 50123456,
    "avgVolume": 48000000,         # Average volume ← KEY
    "exchange": "NASDAQ",
    "sector": "Technology"
  }
]

Usage:

• Alternative to historical prices for 52-week high/low
• Faster (1 call vs historical prices) but less granular
• Use for quick screening; historical prices for detailed analysis

API Calls: 1 per stock (or batch multiple)

Endpoint 3: Stock News (Optional - New Product Detection)

URL: /stock_news?tickers={symbol}&limit=50

Method: GET

Parameters:

• tickers: Stock ticker (comma-separated for multiple)
• limit: 50 (recent news articles)
• apikey: Your FMP API key

Example Request:

curl "https://financialmodelingprep.com/api/v3/stock_news?tickers=AAPL&limit=50&apikey=YOUR_KEY"

Response Fields Used:

[
  {
    "symbol": "AAPL",
    "publishedDate": "2024-01-10T14:30:00.000Z",
    "title": "Apple Launches Revolutionary AI Chip",  # ← KEY (keyword search)
    "image": "https://...",
    "site": "Reuters",
    "text": "Apple Inc announced today...",
    "url": "https://..."
  },
  # ... 49 more articles
]

Usage:

• Search title field for keywords:
• High impact: "FDA approval", "patent granted", "breakthrough"
• Moderate: "new product", "product launch", "acquisition"
• Bonus points for N component if catalyst detected
• Optional: Can skip to reduce API calls (N component primarily uses price action)

API Calls: 1 per stock (optional, can be skipped to save quota)

---

M Component - Market Direction

Endpoint 1: Quote (Major Indices)

URL: /quote/^GSPC,^IXIC,^DJI

Method: GET

Parameters:

• Symbol: ^GSPC (S&P 500), ^IXIC (Nasdaq), ^DJI (Dow Jones)
• Can batch multiple indices in single call
• apikey: Your FMP API key

Example Request:

curl "https://financialmodelingprep.com/api/v3/quote/%5EGSPC,%5EIXIC,%5EDJI?apikey=YOUR_KEY"

Response Fields Used:

[
  {
    "symbol": "^GSPC",
    "name": "S&P 500",
    "price": 4783.45,         # Current level ← KEY
    "changesPercentage": 0.85,
    "change": 40.25,
    "dayLow": 4750.20,
    "dayHigh": 4790.10,
    "yearHigh": 4818.62,
    "yearLow": 4103.78,
    "marketCap": null,
    "volume": null,
    "avgVolume": null
  },
  # IXIC, DJI...
]

Usage:

• Get current S&P 500 price for trend analysis
• Compare to 50-day EMA (from separate call or calculated locally)

API Calls: 1 (batch call for all indices)

Endpoint 2: Historical Prices (S&P 500 for EMA Calculation)

URL: /historical-price-full/^GSPC?timeseries=60

Method: GET

Parameters:

• Symbol: ^GSPC (S&P 500)
• timeseries: 60 (fetch 60 days for 50-day EMA calculation)
• apikey: Your FMP API key

Example Request:

curl "https://financialmodelingprep.com/api/v3/historical-price-full/%5EGSPC?timeseries=60&apikey=YOUR_KEY"

Response Fields Used:

{
  "symbol": "^GSPC",
  "historical": [
    {
      "date": "2024-01-10",
      "close": 4783.45  # ← KEY (for EMA calculation)
    },
    # ... 59 more days
  ]
}

Usage:

• Calculate 50-day EMA from closing prices
• EMA formula: EMA_today = (Price_today * k) + (EMA_yesterday * (1 - k)) where k = 2/(50+1)
• Alternative: Use simple moving average (SMA) for simplicity

API Calls: 1 (reused for all stocks)

Endpoint 3: VIX (Fear Gauge)

URL: /quote/^VIX

Method: GET

Parameters:

• Symbol: ^VIX (CBOE Volatility Index)
• apikey: Your FMP API key

Example Request:

curl "https://financialmodelingprep.com/api/v3/quote/%5EVIX?apikey=YOUR_KEY"

Response Fields Used:

[
  {
    "symbol": "^VIX",
    "name": "CBOE Volatility Index",
    "price": 13.24,  # Current VIX level ← KEY
    "changesPercentage": -2.15,
    "change": -0.29
  }
]

Usage:

• VIX < 15: Low fear (bullish environment)
• VIX 15-20: Normal (healthy market)
• VIX 20-30: Elevated (caution)
• VIX > 30: Panic (bear market signal)

API Calls: 1 (reused for all stocks)

---

L Component - Leadership / Relative Strength (Phase 3)

Endpoint: Historical Prices (52-Week)

URL: /v3/historical-price-full/{symbol}?timeseries=365

Purpose: Calculate 52-week stock performance vs S&P 500 benchmark for RS Rank estimation

Request:

curl "https://financialmodelingprep.com/api/v3/historical-price-full/NVDA?timeseries=365&apikey=YOUR_KEY"

Response Structure:

{
  "symbol": "NVDA",
  "historical": [
    {
      "date": "2025-01-10",
      "close": 148.50,
      "open": 146.20,
      "high": 149.80,
      "low": 145.90,
      "volume": 250000000
    }
    // ... 364 more days
  ]
}

Usage:

# Stock 52-week performance
current_price = historical[0]['close']
price_52w_ago = historical[-1]['close']  # ~252 trading days
stock_perf = ((current_price / price_52w_ago) - 1) * 100

# Compare vs S&P 500 (^GSPC) for RS calculation
relative_perf = stock_perf - sp500_perf

S&P 500 Benchmark Data: S&P 500 52-week historical prices are fetched once using ^GSPC (same endpoint, shared for both M component EMA and L component RS calculation). Both quote and historical use ^GSPC to ensure price scale consistency.

API Calls: 1 per stock (52-week data) + 1 shared S&P 500 call

---

S Component - Supply and Demand

Endpoint: Historical Prices (Already Fetched for N Component)

URL: /v3/historical-price-full/{symbol}?timeseries=90

Purpose: Volume-based accumulation/distribution analysis

Data Reuse: S component uses the same historical_prices data already fetched for N component (52-week high calculation). No additional API calls required.

Algorithm:

# Classify last 60 days into up-days and down-days
for day in last_60_days:
    if close > previous_close:
        up_days.append(volume)
    elif close < previous_close:
        down_days.append(volume)

# Calculate accumulation/distribution ratio
avg_up_volume = sum(up_days) / len(up_days)
avg_down_volume = sum(down_days) / len(down_days)
ratio = avg_up_volume / avg_down_volume

Scoring:

• Ratio ≥ 2.0: 100 points (Strong Accumulation)
• Ratio 1.5-2.0: 80 points (Accumulation)
• Ratio 1.0-1.5: 60 points (Neutral/Weak Accumulation)
• Ratio 0.7-1.0: 40 points (Neutral/Weak Distribution)
• Ratio 0.5-0.7: 20 points (Distribution)
• Ratio < 0.5: 0 points (Strong Distribution)

API Calls: 0 (data already fetched)

---

I Component - Institutional Sponsorship (Phase 2)

Endpoint: Institutional Holders

URL: /v3/institutional-holder/{symbol}

Purpose: Analyze institutional holder count and ownership percentage

Authentication: Requires FMP API key (available on free tier)

Request:

curl "https://financialmodelingprep.com/api/v3/institutional-holder/AAPL?apikey=YOUR_KEY"

Response Structure:

[
  {
    "holder": "Vanguard Group Inc",
    "shares": 1295611697,
    "dateReported": "2024-09-30",
    "change": 12500000,
    "changePercent": 0.0097
  },
  {
    "holder": "Blackrock Inc.",
    "shares": 1042156037,
    "dateReported": "2024-09-30",
    "change": -5234567,
    "changePercent": -0.0050
  },
  {
    "holder": "Berkshire Hathaway Inc",
    "shares": 915560382,
    "dateReported": "2024-09-30",
    "change": 0,
    "changePercent": 0.0000
  }
  // ... hundreds more holders ...
]

Key Fields:

• holder: Institution name (string)
• shares: Number of shares held (int)
• dateReported: 13F filing date (string, YYYY-MM-DD)
• change: Change in shares from previous quarter (int)
• changePercent: Percentage change (float)

Typical Response Size: 100-7,000 holders per stock (AAPL has ~7,111 holders)

Free Tier Availability: ✅ Available (tested with AAPL on 2026-01-12)

Usage:

# Calculate total institutional ownership
total_shares_held = sum(holder['shares'] for holder in institutional_holders)
ownership_pct = (total_shares_held / shares_outstanding) * 100

# Count unique holders
num_holders = len(institutional_holders)

# Detect superinvestors
SUPERINVESTORS = [
    "BERKSHIRE HATHAWAY",
    "BAUPOST GROUP",
    "PERSHING SQUARE",
    # ...
]
superinvestor_present = any(
    superinvestor in holder['holder'].upper()
    for holder in institutional_holders
    for superinvestor in SUPERINVESTORS
)

Scoring (O'Neil's Criteria):

• 50-100 holders + 30-60% ownership: 100 points (Sweet spot)
• Superinvestor present + good holder count: 90 points
• 30-50 holders + 20-40% ownership: 80 points
• Acceptable ranges: 60 points
• Suboptimal (< 20% or > 80% ownership): 40 points
• Extreme (< 10% or > 90% ownership): 20 points

API Calls: 1 per stock

Data Freshness: Updated quarterly (13F filings due 45 days after quarter-end)

Quality Notes:

• Large-cap stocks (AAPL, MSFT): 5,000-10,000 holders
• Mid-cap stocks: 500-2,000 holders
• Small-cap stocks: 50-500 holders
• Micro-cap stocks: < 50 holders (may lack institutional interest)

---

API Call Summary (Per Stock)

Phase 3 (Full CANSLIM: C, A, N, S, L, I, M)

Per-Stock Calls: 1. Profile (for company info): 1 call 2. Quote (for current price): 1 call 3. Income Statement (Quarterly): 1 call 4. Income Statement (Annual): 1 call 5. Historical Prices (90 days): 1 call (reused for N and S) 6. Historical Prices (365 days): 1 call (for L component RS calculation) 7. Institutional Holders: 1 call

Per-Stock Total: 7 calls (profile, quote, income×2, historical_90d, historical_365d, institutional)

Market Data Calls (One-Time per Session): 1. S&P 500 Quote (^GSPC): 1 call (shared) 2. S&P 500 Historical (^GSPC, 365 days): 1 call (shared, used for both M component EMA and L component RS benchmark) 3. VIX Quote (^VIX): 1 call (shared)

Market Data Total: 3 calls

Important: Both the quote and historical data use ^GSPC (S&P 500 index) to ensure price scale consistency for M component EMA calculation. Using SPY (ETF, ~500) for historical while ^GSPC (~5000) for quote would cause a ~10× scale mismatch.

Total for 40 Stocks:

• Per-stock: 40 stocks × 7 calls = 280 calls
• Market data: 3 calls
• Grand Total: ~283 calls (exceeds 250 free tier limit) ⚠️

Key Efficiency:

• S component: 0 extra calls (reuses historical_prices from N component's 90-day fetch)
• L component: +1 call per stock (365-day historical prices, separate cache key from 90-day)
• S&P 500 historical data: shared between M (EMA) and L (RS benchmark)

Free Tier Workaround: Use --max-candidates 35 (35 × 7 + 3 = 248 calls, within 250 limit). For full 40-stock screening, upgrade to FMP Starter tier ($29.99/mo, 750 calls/day).

Note on `mktCap` field: FMP profile API returns mktCap (not marketCap). The screener handles both field names for compatibility.

---

Rate Limiting Strategy

Implementation

import time

def rate_limited_get(url, params):
    """Enforce 0.3s delay between requests (200 requests/minute max)"""
    response = requests.get(url, params=params)
    time.sleep(0.3)  # 300ms delay
    return response

Handling 429 Errors (Rate Limit Exceeded)

def handle_rate_limit(response):
    """Retry once with 60-second wait if rate limit hit"""
    if response.status_code == 429:
        print("WARNING: Rate limit exceeded. Waiting 60 seconds...")
        time.sleep(60)
        return True  # Signal to retry
    return False  # No retry needed

Free Tier Management

Daily Quota: 250 requests/day

Strategies to Stay Within Limits: 1. Batch calls where possible: Use comma-separated symbols in quote endpoint 2. Cache market data: Fetch S&P 500/VIX once, reuse for all stocks 3. Skip optional calls: News endpoint can be omitted to save 40 calls 4. Limit universe: Analyze top 35 stocks for free tier, or 40 with Starter tier 5. Progressive filtering: Apply cheap filters first (market cap, sector) before expensive API calls

---

Error Handling

Common Errors

401 Unauthorized:

• Cause: Invalid or missing API key
• Solution: Verify apikey parameter, check environment variable

404 Not Found:

• Cause: Invalid symbol or endpoint
• Solution: Verify ticker symbol exists, check endpoint URL

429 Too Many Requests:

• Cause: Exceeded daily/minute rate limit
• Solution: Wait 60 seconds (minute limit) or 24 hours (daily limit)

500 Internal Server Error:

• Cause: FMP server issue
• Solution: Retry after 5 seconds, skip stock if persistent

Empty Response `[]`:

• Cause: Symbol exists but no data available (e.g., recent IPO, delisted stock)
• Solution: Skip stock, log warning

Retry Logic

MAX_RETRIES = 1
retry_count = 0

while retry_count <= MAX_RETRIES:
    response = make_request()
    if response.status_code == 200:
        return response.json()
    elif response.status_code == 429:
        time.sleep(60)
        retry_count += 1
    else:
        print(f"ERROR: {response.status_code}")
        return None

print("ERROR: Max retries exceeded")
return None

---

Data Quality Considerations

Freshness

• Quarterly/Annual Data: Updated within 1-2 days of earnings release
• Price Data: Real-time (15-minute delay on free tier)
• News Data: Updated continuously

Completeness

• Large-cap stocks: Complete historical data (10+ years)
• Mid-cap stocks: Mostly complete (5+ years typical)
• Small-cap/Recent IPOs: May have gaps (<2 years data)

Validation

Always check for:

• null or 0 values in critical fields (EPS, revenue)
• Negative EPS when calculating growth rates (use absolute value in denominator)
• Missing quarters (delisted stocks, special situations)

---

Example API Call Sequence

For analyzing NVDA:

# 1. Quarterly income statement (C component)
curl "https://financialmodelingprep.com/api/v3/income-statement/NVDA?period=quarter&limit=8&apikey=YOUR_KEY"

# 2. Annual income statement (A component)
curl "https://financialmodelingprep.com/api/v3/income-statement/NVDA?period=annual&limit=5&apikey=YOUR_KEY"

# 3. Historical prices (N component)
curl "https://financialmodelingprep.com/api/v3/quote/NVDA?apikey=YOUR_KEY"

# 4. S&P 500 for market direction (M component - once per session)
curl "https://financialmodelingprep.com/api/v3/quote/%5EGSPC&apikey=YOUR_KEY"
curl "https://financialmodelingprep.com/api/v3/historical-price-full/%5EGSPC?timeseries=60&apikey=YOUR_KEY"
curl "https://financialmodelingprep.com/api/v3/quote/%5EVIX&apikey=YOUR_KEY"

Total: 7 calls per stock (profile, quote, income×2, historical_90d, historical_365d, institutional) + 3 market calls (reused for all stocks)

---

Cost Analysis

Free Tier (250 requests/day)

• 40 stocks × 7 calls = 280 calls
• Market data: 3 calls
• Total: ~283 calls (exceeds 250 quota) ⚠️
• Workaround: Use --max-candidates 35 (35 × 7 + 3 = 248 calls)

Paid Tiers

• Starter ($29.99/month): 750 requests/day → ~106 stocks/run ((750 - 3) / 7)
• Professional ($79.99/month): 2000 requests/day → ~285 stocks/run ((2000 - 3) / 7)

Recommendation for Phase 3: Free tier supports up to 35 stocks (--max-candidates 35). For the default 40-stock universe, upgrade to Starter tier ($29.99/mo).

---

This API reference provides complete documentation for implementing Phase 3 (Full CANSLIM) screening. Free tier users should use --max-candidates 35 to stay within the 250 calls/day limit.

CANSLIM Score Interpretation Guide - Phase 3 (Full CANSLIM)

Overview

This guide helps users interpret CANSLIM screening results and translate composite scores into actionable investment decisions. Phase 3 implements all 7 components (C, A, N, S, L, I, M) with original O'Neil weights, presented on a 0-100 scale.

---

Composite Score Interpretation

Score Bands and Meanings

---

Component-Level Analysis

How to Read Individual Component Scores

Each component contributes to the composite score with different weights:

• C (Current Earnings): 15% weight - Quarterly EPS acceleration
• A (Annual Growth): 20% weight - Multi-year consistency
• N (Newness): 15% weight - Momentum and price action
• S (Supply/Demand): 15% weight - Volume accumulation/distribution
• L (Leadership/RS Rank): 20% weight - Relative strength vs market (highest weight, tied with A!)
• I (Institutional): 10% weight - Smart money confirmation
• M (Market Direction): 5% weight - Macro environment filter

Minimum Thresholds (all must be met for "buy" rating):

• C >= 60 (18%+ quarterly EPS growth)
• A >= 50 (25%+ annual EPS CAGR)
• N >= 40 (within 25% of 52-week high)
• S >= 40 (accumulation pattern, ratio >= 1.0)
• L >= 50 (RS Rank 60+, outperforming market)
• I >= 40 (30+ holders or 20%+ ownership)
• M >= 40 (market not in downtrend)

Component Score Interpretation

---

Action Recommendations by Score

90-100 Points (Exceptional+)

Characteristics:

• All 7 components aligned (C, A, N, S, L, I, M all 80+)
• Explosive earnings growth (C: 90-100)
• Multi-year growth validation (A: 90-100)
• At or near new highs with catalyst (N: 90-100)
• Strong volume accumulation (S: 80+)
• Relative strength leader (L: 90+)
• Strong institutional backing (I: 80+)
• Strong bull market environment (M: 90-100)

Historical Examples:

• NVDA (2023 Q2): Score ~97 (AI chip explosion)
• AAPL (2009 Q3): Score ~95 (iPhone 3GS launch)
• TSLA (2020 Q3): Score ~92 (Model 3 profitability)

Investment Action:

• Immediate buy - Do not wait for pullback (may never come)
• Position size: 15-20% of portfolio (high conviction)
• Entry timing: Buy now or on minor 2-3% dip
• Stop loss: 7-8% below entry (O'Neil's rule)
• Profit target: Let winners run; these can become 200-500% gainers

Risk Management:

• Even exceptional setups can fail - honor stop loss
• If market deteriorates (M drops to 0-20), sell regardless of stock quality
• Take partial profits (20-25% of position) at 20-25% gain

---

80-89 Points (Exceptional)

Characteristics:

• Most components strong, one may be slightly lower
• Strong earnings and growth (C + A both 70+)
• Good momentum (N 70+)
• Favorable market (M 60+)

Typical Weakest Component:

• Often A component (recovering from prior downturn, 3-year CAGR improving but not yet 40%+)
• Sometimes N component (8-12% from high, not quite breakout)

Investment Action:

• Strong buy
• Position size: 10-15% of portfolio (standard sizing)
• Entry timing: Ideal entry on pullback to 10-week moving average; can buy now if urgent
• Stop loss: 7-8% below entry
• Profit target: Sell 20-25% at 20-25% gain, hold remainder for 50-100%+ move

Example:

• Stock scoring 85: C=90, A=78, N=88, M=80
• Weakest = A (78): Growth is strong but not yet exceptional
• Action: Buy, expect 50-150% upside over 12-18 months if fundamentals hold

---

70-79 Points (Strong)

Characteristics:

• All components meet CANSLIM minimums
• One component may be outstanding, others solid
• No major red flags

Typical Profile:

• C: 70-80 (strong earnings, not explosive)
• A: 60-75 (meets 25% CAGR, could be more consistent)
• N: 65-80 (10-15% from high, good but not perfect momentum)
• M: 60-80 (bull market confirmed, not extended)

Investment Action:

• Buy (standard recommendation)
• Position size: 8-12% of portfolio
• Entry timing: Wait for pullback to 10-week MA or minor consolidation
• Stop loss: 7-8% below entry
• Profit target: 30-80% upside typical over 6-12 months

Risk Consideration:

• Monitor weakest component for deterioration
• If weakest drops below 50, consider reducing position
• These are quality candidates but not "home run" setups

---

60-69 Points (Above Average)

Characteristics:

• Meets CANSLIM minimums but lacks conviction
• Typically one component weak (40-50 range)
• Remaining components solid (60-70 range)

Common Weak Points:

• N weak (40-50): Stock 20-25% from 52-week high → Lacks momentum
• A weak (50-60): 3-year CAGR only 25-28% → Marginal growth rate
• C weak (50-60): Quarterly EPS growth 18-22% → Just meets minimum

Investment Action:

• Buy on pullback or Watchlist
• Position size: 5-8% of portfolio (conservative)
• Entry timing: Wait for confirmation (improvement in weak component, or pullback to support)
• Stop loss: 7-8% below entry
• Profit target: 20-40% upside typical

Monitoring Strategy:

• Set alerts for earnings reports (watch if C/A component improves)
• Check weekly if price approaches 52-week high (N component improving)
• If composite score rises to 70+, increase position

---

50-59 Points (Average)

Characteristics:

• Barely meets CANSLIM criteria
• Multiple components in 50-60 range (marginal)
• May have one strong component (70-80) but others drag down composite

Investment Action:

• Watchlist only - Do not buy yet
• Position size: 0% (not ready)
• Monitoring: Track for improvement in weak components
• Potential catalyst: Upcoming earnings report could boost C score

When to Reconsider:

• If next quarter shows earnings acceleration (C rises to 70+)
• If stock breaks to new 52-week high (N rises to 70+)
• If market strengthens significantly (M rises to 80+)
• Goal: Wait for composite to reach 65-70+ before buying

Common Scenario:

• Stock recovering from pullback
• Fundamentals improving but not yet exceptional
• Price consolidating, building base for next move
• Patient approach: Wait 1-2 quarters for confirmation

---

40-49 Points (Below Average)

Characteristics:

• Fails one or more key CANSLIM thresholds
• Typically C or A component below 50 (earnings/growth insufficient)
• Or N component very weak (stock far from highs, no momentum)

Investment Action:

• Do not buy
• Position size: 0%
• Action: Remove from watchlist unless fundamental catalyst expected

Why Avoid:

• Stocks scoring <50 rarely lead market advances
• Even if they rally, gains are typically modest (10-20%)
• Better opportunities exist in 70+ scorers
• "You don't get paid to be right about mediocre stocks"

Exception:

• If M (Market) component is weak (0-20) but C, A, N are all 70+
• This indicates bear market is suppressing good stocks
• Add to watchlist, wait for M to recover (next bull market)

---

<40 Points (Weak)

Characteristics:

• Multiple components fail CANSLIM thresholds
• Earnings decelerating (C < 40)
• Or inconsistent growth history (A < 40)
• Or price action weak (N < 40)

Investment Action:

• Avoid entirely
• These stocks are not CANSLIM candidates
• Focus efforts on higher-scoring opportunities

---

Weakest Component Analysis

Every stock report identifies the weakest component (lowest individual score). This guides risk assessment:

If Weakest = C (Current Earnings)

Risk: Earnings deceleration or miss Monitor: Upcoming quarterly earnings reports Warning signs:

• Revenue growth slowing
• Margin compression
• Guidance lowered

Action:

• If C drops to <40 post-earnings → Sell immediately
• Set tighter stop loss (5-6% vs. standard 7-8%)

---

If Weakest = A (Annual Growth)

Risk: Inconsistent multi-year history, or recovering from prior downturn Monitor: Multi-quarter trends (is growth accelerating consistently?) Warning signs:

• Next quarter shows deceleration (C component weakens)
• Revenue growth lags EPS growth significantly

Action:

• Acceptable if A >= 50 and trending up
• If A drops below 50 or C also weakens → Sell
• This is often a "prove it" situation - stock must deliver for 2-3 more quarters

---

If Weakest = N (Newness)

Risk: Lacks price momentum, overhead resistance Monitor: Weekly chart for breakout to new highs Warning signs:

• Stock fails to participate in sector rallies
• Continues drifting further from 52-week high
• Volume declining (waning interest)

Action:

• Wait for N to improve (breakout confirmation) before buying
• If N drops to <30 → Remove from watchlist
• Stocks far from highs rarely lead; don't fight the tape

---

If Weakest = M (Market Direction)

Risk: Macro headwinds, bear market Monitor: S&P 500 vs. 50-day EMA, VIX level Warning signs:

• S&P breaks below 50-day and 200-day MAs
• VIX spikes above 25-30
• Breadth deteriorates (more declining than advancing stocks)

Action:

• If M = 0-20 → Raise cash immediately to 80-100%
• Do not buy any stocks, even with perfect C, A, N
• Wait for follow-through day (FTD) signaling market bottom before re-entering

O'Neil's Wisdom: "The best offense in a bear market is cash. You can't lose money sitting on the sidelines."

---

Market Direction Override

Critical Rule: If M component score = 0-20 (bear market), do not buy any stocks regardless of composite score.

Why This Matters

Historical data shows:

• Bull markets: 75% of stocks move with market direction
• Bear markets: Even best growth stocks decline 20-50%
• Poor timing: Buying great stocks in bear markets = losses

Example Scenario

Stock XYZ:

• C Score: 100 (exceptional earnings)
• A Score: 95 (exceptional growth)
• N Score: 98 (new highs)
• M Score: 0 (bear market)
• Composite: 85.3 (Exceptional by fundamentals)

What happens: 1. Initial purchase at $100 2. Bear market persists (M remains 0-20) 3. Stock declines to $70-80 despite strong fundamentals (-20-30%) 4. Stop loss triggered at $92-93 (-7-8% loss)

Lesson: Even 85-point stocks fail in bear markets. Wait for M > 40 (market recovery) before buying.

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Portfolio Construction Guidelines

Position Sizing by Score

Diversification Rules

Total Portfolio:

• 5-10 positions maximum (concentrated portfolio, O'Neil style)
• 10-20% cash reserve (for new opportunities)
• Rebalance when any position exceeds 25% due to gains

Sector Limits:

• Maximum 2 stocks per sector (avoid concentration risk)
• Exception: If sector is clearly leading (e.g., Technology in AI boom), can have 3

Score Distribution:

• 60-70% of portfolio: Stocks scoring 80+ (high conviction)
• 30-40% of portfolio: Stocks scoring 70-79 (solid candidates)
• 0% of portfolio: Stocks scoring <70 (only if very high conviction and specific catalyst)

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Entry and Exit Rules

Entry Timing

Best Entry Points (O'Neil's research): 1. Breakout from base (7-8 week consolidation minimum)

• Stock makes new high on volume 40-50%+ above average
• Buy within 5% of breakout price ("buy point")

2. Pullback to 10-week moving average

• Stock in uptrend pulls back 8-12%
• Finds support at 10-week MA
• Buy when bounces off MA with volume

3. Early-stage base (Stages 1-2 of Weinstein cycle)

• Stock just emerging from consolidation
• Beginning Stage 2 uptrend
• Low-risk entry before major advance

Stop Loss Discipline

Initial Stop: 7-8% below entry price (non-negotiable)

Why 7-8%:

• O'Neil's 50-year research: Cutting losses at 7-8% prevents small losses from becoming disasters
• Average successful trade: +20-25% gain
• Risk/reward: Lose 7%, make 20% = 3:1 favorable

Stop Loss Adjustments:

• Once stock up 15%: Move stop to breakeven (protect capital)
• Once stock up 25%: Trail stop 10-15% below peak
• In strong uptrend: Use 10-week MA as trailing stop

Profit Taking

Partial Profit Strategy: 1. First 20-25% gain: Sell 20-25% of position (lock in profit) 2. 50% gain: Sell another 20-25% 3. Remainder: Hold for potential multi-bagger (100-500%)

Full Sell Signals: 1. Stop loss hit: 7-8% loss → Sell immediately 2. Climax top: Parabolic move on extreme volume, then reversal → Sell 50-100% 3. Distribution: Heavy selling volume (stock down on high volume 4-5 days in week) → Sell 4. Fundamental deterioration: C or A component drops below 40 → Sell 5. Market enters correction: M score drops to 0-20 → Sell all stocks

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Common Interpretation Mistakes

Mistake 1: Ignoring M Component

Error: "This stock has a 95 composite score, I must buy it!"

Reality: If M = 0, composite score is misleading. Stock will likely decline in bear market.

Correct Approach:

• Always check M component FIRST
• If M < 40, question whether to buy
• If M = 0, do NOT buy regardless of C, A, N scores

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Mistake 2: Chasing Low-Scoring Stocks

Error: "This 55-point stock is cheap, it could bounce!"

Reality: Stocks scoring <60 rarely lead market advances. Even if they rally, gains are modest.

Correct Approach:

• Focus on 70+ scorers
• "It's not about being right, it's about making money"
• Better to buy fewer high-quality stocks than many mediocre ones

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Mistake 3: Overweighting N Component

Error: "This stock is 40% below its high, so it's a bargain!"

Reality: Stocks far from highs lack institutional sponsorship and face overhead resistance.

Correct Approach:

• N >= 60 (within 15% of high) is minimum
• N >= 80 (within 10% of high) is ideal
• "Leaders make new highs; laggards don't"

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Mistake 4: Ignoring Weakest Component

Error: "Composite score is 75, that's strong enough to buy"

Reality: If weakest component is C = 40 (earnings decelerating), stock is at risk of sharp decline on next earnings miss.

Correct Approach:

• Identify weakest component
• Understand the specific risk it represents
• Monitor that component closely
• Set appropriate stop loss based on risk

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Mistake 5: Not Cutting Losses

Error: "I'll hold this losing position; it will come back eventually"

Reality: Small losses turn into large disasters. O'Neil's data shows 90% of losses come from not selling losers early.

Correct Approach:

• Set stop loss at entry (7-8% below)
• Execute stop loss without hesitation
• "Your first loss is your smallest loss"
• Free up capital for better opportunities

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Summary Checklist

Before buying any stock, verify:

• [ ] Composite score >= 70 (Strong or higher)
• [ ] M component >= 40 (Market not in downtrend)
• [ ] C component >= 60 (Meets earnings minimum)
• [ ] A component >= 50 (Meets growth minimum)
• [ ] N component >= 40 (Not too far from highs)
• [ ] S component >= 40 (Accumulation pattern present)
• [ ] L component >= 50 (Outperforming market, RS leader)
• [ ] I component >= 40 (Institutional backing present)
• [ ] Weakest component identified and understood
• [ ] Stop loss set at 7-8% below entry
• [ ] Position size appropriate (based on score tier)
• [ ] Sector exposure acceptable (max 2-3 stocks per sector)
• [ ] Cash reserve maintained (10-20% of portfolio)

If all boxes checked → Execute purchase

If any box unchecked → Do not buy (or wait for condition to be met)

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This interpretation guide provides a systematic framework for translating CANSLIM scores into disciplined investment decisions. By following these guidelines, you implement O'Neil's research-backed methodology for identifying growth stock leaders while managing risk effectively.

CANSLIM Scoring System - Phase 3 (Full CANSLIM)

Overview

This document specifies the composite scoring system for the CANSLIM screener. Phase 3 implements all 7 of 7 components (C, A, N, S, L, I, M), representing 100% of the full CANSLIM methodology weight using O'Neil's original component weights.

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Component Weights

Phase 3 Weights (Full CANSLIM - 7 Components)

Legacy Phase Weights (Reference Only)

Phase 1 MVP (4 components - C, A, N, M): Renormalized to C 27%, A 36%, N 27%, M 10% Phase 2 (6 components - C, A, N, S, I, M): Renormalized to C 19%, A 25%, N 19%, S 19%, I 13%, M 6%

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Component Scoring Formulas (0-100 Scale)

Each component is scored on a 0-100 point scale based on O'Neil's quantitative thresholds.

C - Current Quarterly Earnings (0-100 Points)

Input Data Required:

• Latest quarterly EPS (most recent quarter)
• Year-ago quarterly EPS (same quarter, prior year)
• Latest quarterly revenue
• Year-ago quarterly revenue

Calculation:

eps_growth_pct = ((latest_qtr_eps - year_ago_qtr_eps) / abs(year_ago_qtr_eps)) * 100
revenue_growth_pct = ((latest_qtr_revenue - year_ago_qtr_revenue) / year_ago_qtr_revenue) * 100

Scoring Logic:

if eps_growth_pct >= 50 and revenue_growth_pct >= 25:
    c_score = 100  # Explosive growth
elif eps_growth_pct >= 30 and revenue_growth_pct >= 15:
    c_score = 80   # Strong growth
elif eps_growth_pct >= 18 and revenue_growth_pct >= 10:
    c_score = 60   # Meets CANSLIM minimum
elif eps_growth_pct >= 10:
    c_score = 40   # Below threshold
else:
    c_score = 0    # Weak or negative growth

Interpretation:

• 100 points: Exceptional - Top-tier earnings acceleration
• 80 points: Strong - Well above CANSLIM threshold
• 60 points: Acceptable - Meets minimum 18% threshold
• 40 points: Weak - Below CANSLIM standards
• 0 points: Fails - Insufficient growth

Quality Checks:

• If revenue growth < 50% of EPS growth → Investigate earnings quality (potential buyback-driven)
• If revenue is negative while EPS is positive → Red flag (cost-cutting, not growth)

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A - Annual EPS Growth (0-100 Points)

Input Data Required:

• Annual EPS for current year and previous 3 years (4 years total)
• Annual revenue for same 4 years (validation)

Calculation:

# 3-year CAGR (Compound Annual Growth Rate)
eps_cagr_3yr = (((current_year_eps / eps_3_years_ago) ** (1/3)) - 1) * 100
revenue_cagr_3yr = (((current_year_revenue / revenue_3_years_ago) ** (1/3)) - 1) * 100

# Growth stability check
eps_values = [year1_eps, year2_eps, year3_eps, year4_eps]  # chronological order
stable = all(eps_values[i] >= eps_values[i-1] for i in range(1, 4))  # No down years

Scoring Logic:

# Base score from EPS CAGR
if eps_cagr_3yr >= 40:
    base_score = 90
elif eps_cagr_3yr >= 30:
    base_score = 70
elif eps_cagr_3yr >= 25:
    base_score = 50  # Meets CANSLIM minimum
elif eps_cagr_3yr >= 15:
    base_score = 30
else:
    base_score = 0

# Revenue growth validation penalty
if revenue_cagr_3yr < (eps_cagr_3yr * 0.5):
    base_score = int(base_score * 0.8)  # 20% penalty for weak revenue growth

# Stability bonus
if stable:  # No down years
    base_score += 10

a_score = min(base_score, 100)  # Cap at 100

Interpretation:

• 90-100 points: Exceptional - Sustainable high growth with stability
• 70-89 points: Strong - Well above 25% threshold
• 50-69 points: Acceptable - Meets CANSLIM minimum
• 30-49 points: Weak - Below threshold
• 0-29 points: Fails - Insufficient or erratic growth

Quality Checks:

• Stability bonus (+10) rewards consistency (no down years)
• Revenue validation prevents buyback-driven EPS growth from scoring high

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N - Newness / New Highs (0-100 Points)

Input Data Required:

• Current stock price
• 52-week high price
• 52-week low price
• Recent daily volume data (30 days)
• Average volume (30-day average)
• Recent news headlines (optional, for new product detection)

Calculation:

# Distance from 52-week high
distance_from_high_pct = ((current_price / week_52_high) - 1) * 100

# Breakout detection (new high on volume)
breakout_detected = (
    current_price >= week_52_high * 0.995 and  # Within 0.5% of high
    recent_volume > avg_volume * 1.4           # Volume 40%+ above average
)

# New product signal detection (keyword search in news)
new_product_signals = search_news_keywords([
    "FDA approval", "patent granted", "breakthrough", "game-changer",
    "new product", "product launch", "expansion", "acquisition"
])

Scoring Logic:

# Base score from price position
if distance_from_high_pct >= -5 and breakout_detected and new_product_signals:
    base_score = 100  # Perfect setup
elif distance_from_high_pct >= -10 and breakout_detected:
    base_score = 80   # Strong momentum
elif distance_from_high_pct >= -15 or breakout_detected:
    base_score = 60   # Acceptable
elif distance_from_high_pct >= -25:
    base_score = 40   # Weak momentum
else:
    base_score = 20   # Too far from highs

# Bonus for new product/catalyst signals (optional data)
if new_product_signals:
    if "FDA approval" in signals or "breakthrough" in signals:
        base_score += 20  # High-impact catalyst
    elif "new product" in signals or "acquisition" in signals:
        base_score += 10  # Moderate catalyst

n_score = min(base_score, 100)  # Cap at 100

Interpretation:

• 90-100 points: Exceptional - At new highs with catalysts
• 70-89 points: Strong - Near highs with volume confirmation
• 50-69 points: Acceptable - Within 15% of highs
• 30-49 points: Weak - Lacks momentum
• 0-29 points: Fails - Too far from highs, no sponsorship

Note: Price position is primary signal (80% of score). New product detection is supplementary (20% bonus).

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M - Market Direction (0-100 Points)

Input Data Required:

• S&P 500 current price
• S&P 500 50-day Exponential Moving Average (EMA)
• VIX current level
• Follow-through day detection (optional advanced feature)

Calculation:

# Distance from 50-day EMA
distance_from_ema_pct = ((sp500_price / sp500_ema_50) - 1) * 100

# Trend determination
if distance_from_ema_pct >= 2.0:
    trend = "strong_uptrend"
elif distance_from_ema_pct >= 0:
    trend = "uptrend"
elif distance_from_ema_pct >= -2.0:
    trend = "choppy"
elif distance_from_ema_pct >= -5.0:
    trend = "downtrend"
else:
    trend = "bear_market"

Scoring Logic:

# Base score from trend
if trend == "strong_uptrend" and vix < 15:
    base_score = 100  # Ideal conditions
elif trend == "strong_uptrend" or (trend == "uptrend" and vix < 20):
    base_score = 80   # Favorable
elif trend == "uptrend":
    base_score = 60   # Acceptable
elif trend == "choppy":
    base_score = 40   # Neutral/caution
elif trend == "downtrend":
    base_score = 20   # Weak market
else:  # bear_market or vix > 30
    base_score = 0    # Avoid stocks entirely

# VIX adjustment (fear gauge)
if vix < 15:
    base_score += 10  # Low fear, bullish
elif vix > 30:
    base_score = 0    # Panic, override trend

# Follow-through day bonus (optional advanced feature)
if follow_through_day_detected:
    base_score += 10  # Confirmed institutional buying

m_score = min(max(base_score, 0), 100)  # Cap between 0-100

Interpretation:

• 90-100 points: Strong bull market - Aggressive buying recommended
• 70-89 points: Bull market - Standard position sizing
• 50-69 points: Early uptrend - Small initial positions
• 30-49 points: Choppy/neutral - Reduce exposure, be selective
• 10-29 points: Downtrend - Defensive posture, minimal positions
• 0 points: Bear market - Raise 80-100% cash, do not buy

Critical Rule: If M score = 0, do not buy any stocks regardless of other component scores. Market direction trumps stock selection.

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L - Leadership / Relative Strength (0-100 Points)

Input Data Required:

• Stock 52-week historical prices
• S&P 500 52-week historical prices (benchmark)

Calculation:

# 52-week stock performance
stock_perf = ((current_price / price_52w_ago) - 1) * 100

# 52-week S&P 500 performance
sp500_perf = ((sp500_current / sp500_52w_ago) - 1) * 100

# Relative performance
relative_perf = stock_perf - sp500_perf

# RS Rank estimate (1-99 scale)
rs_rank = calculate_rs_rank(relative_perf)

Scoring Logic:

if rs_rank >= 90:
    base_score = 100  # Top decile leader
elif rs_rank >= 80:
    base_score = 80   # Strong leader
elif rs_rank >= 70:
    base_score = 60   # Above average
elif rs_rank >= 60:
    base_score = 40   # Average
else:
    base_score = 20   # Laggard

Interpretation:

• 90-100 points: Top RS leader - stock significantly outperforming market
• 70-89 points: Strong relative strength - outperforming market
• 50-69 points: Average relative strength
• 30-49 points: Below average - underperforming market
• 0-29 points: Laggard - significantly underperforming, avoid per CANSLIM

O'Neil's Rule: "Buy stocks with an RS rating of 80 or higher. Avoid laggards below 70."

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Composite Score Calculation

Formula (Phase 3 - Full CANSLIM)

composite_score = (
    c_score * 0.15 +  # Current Earnings: 15% weight
    a_score * 0.20 +  # Annual Growth: 20% weight
    n_score * 0.15 +  # Newness: 15% weight
    s_score * 0.15 +  # Supply/Demand: 15% weight
    l_score * 0.20 +  # Leadership/RS Rank: 20% weight
    i_score * 0.10 +  # Institutional: 10% weight
    m_score * 0.05    # Market Direction: 5% weight
)

# Result: 0-100 composite score

Interpretation Bands (Phase 3)

Weakest Component Identification

For each stock, identify the component with the lowest individual score to guide further analysis:

components = {
    'C': c_score,
    'A': a_score,
    'N': n_score,
    'S': s_score,
    'L': l_score,
    'I': i_score,
    'M': m_score
}

weakest_component = min(components, key=components.get)
weakest_score = components[weakest_component]

Use Case: Helps user understand risks

• Weakest = C → Earnings deceleration risk
• Weakest = A → Lack of sustained growth history
• Weakest = N → Lacks momentum, far from highs
• Weakest = S → Distribution pattern, institutions selling
• Weakest = L → Lagging market, not a sector leader
• Weakest = I → Underowned or overcrowded, investigate further
• Weakest = M → Poor market timing, consider waiting

Formula (Phase 2 - 6 Components)

composite_score = (
    c_score * 0.19 +  # Current Earnings: 19% weight
    a_score * 0.25 +  # Annual Growth: 25% weight
    n_score * 0.19 +  # Newness: 19% weight
    s_score * 0.19 +  # Supply/Demand: 19% weight (NEW)
    i_score * 0.13 +  # Institutional: 13% weight (NEW)
    m_score * 0.06    # Market Direction: 6% weight
)

# Result: 0-100 composite score (Phase 2)

Interpretation Bands (Phase 2)

Key Improvement: Phase 2 scores include institutional validation (S, I components), making them more predictive than Phase 1.

Minimum Thresholds (Phase 2)

All 6 components must meet baseline criteria to qualify as a CANSLIM candidate:

thresholds = {
    "C": 60,  # 18%+ quarterly EPS growth
    "A": 50,  # 25%+ annual CAGR
    "N": 40,  # Within 15% of 52-week high
    "S": 40,  # Accumulation pattern (ratio ≥ 1.0)
    "I": 40,  # 30+ holders OR 20%+ ownership
    "M": 40   # Market in uptrend
}

# Stock passes if ALL components >= thresholds
passes_threshold = all(score >= thresholds[comp] for comp, score in scores.items())

Failure Interpretation:

• Fails C threshold → Earnings deceleration, avoid
• Fails A threshold → Lacks sustained growth, not a growth stock
• Fails N threshold → Too far from highs, wait for strength
• Fails S threshold → Distribution pattern, institutions selling
• Fails I threshold → Neglected by institutions, lacks backing
• Fails M threshold → Bear market, wait for market recovery

Weakest Component Identification (Phase 2)

components = {
    'C': c_score,
    'A': a_score,
    'N': n_score,
    'S': s_score,  # NEW
    'I': i_score,  # NEW
    'M': m_score
}

weakest_component = min(components, key=components.get)
weakest_score = components[weakest_component]

Additional Interpretations:

• Weakest = S → Distribution pattern, institutions selling - caution
• Weakest = I → Underowned or overcrowded, investigate further

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Example Calculations

Example 1: NVDA (2023 Q2) - Exceptional Setup

Component Scores:

• C Score: 100 points (EPS +429% YoY, Revenue +101% YoY)
• A Score: 95 points (3yr CAGR 89%, stable, revenue strong)
• N Score: 98 points (New all-time high, AI catalyst, breakout volume)
• M Score: 100 points (S&P 500 in strong uptrend, VIX <15)

Composite Calculation:

composite = (100 * 0.27) + (95 * 0.36) + (98 * 0.27) + (100 * 0.10)
          = 27.0 + 34.2 + 26.46 + 10.0
          = 97.66 points

Rating: Exceptional (97.66/100) Interpretation: Textbook CANSLIM setup - all components aligned, rare multi-bagger candidate Weakest Component: A (95) - even this is exceptional Action: Strong buy, aggressive position sizing (15-20% of portfolio)

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Example 2: META (2023 Q3) - Strong Setup

Component Scores:

• C Score: 85 points (EPS +164% YoY, Revenue +23% YoY)
• A Score: 78 points (3yr CAGR 28%, recovery from 2022 trough, stable recent)
• N Score: 88 points (5% from 52-week high, breakout pattern)
• M Score: 80 points (S&P 500 above EMA, VIX 18)

Composite Calculation:

composite = (85 * 0.27) + (78 * 0.36) + (88 * 0.27) + (80 * 0.10)
          = 22.95 + 28.08 + 23.76 + 8.0
          = 82.79 points

Rating: Exceptional (82.79/100) Interpretation: Strong CANSLIM candidate, slight weakness in historical growth Weakest Component: A (78) - recovering from prior downturn Action: Buy, standard position sizing (10-15% of portfolio)

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Example 3: Hypothetical "Average" Stock

Component Scores:

• C Score: 60 points (EPS +20% YoY - meets minimum)
• A Score: 55 points (3yr CAGR 26%, one down year)
• N Score: 65 points (12% from high, no catalyst)
• M Score: 60 points (S&P 500 just above EMA, early uptrend)

Composite Calculation:

composite = (60 * 0.27) + (55 * 0.36) + (65 * 0.27) + (60 * 0.10)
          = 16.2 + 19.8 + 17.55 + 6.0
          = 59.55 points

Rating: Average (59.55/100) Interpretation: Meets minimum thresholds but lacks conviction Weakest Component: A (55) - inconsistent growth history Action: Watchlist only, wait for A or N component to strengthen

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Example 4: Bear Market Scenario (M Score = 0)

Component Scores:

• C Score: 100 points (Excellent earnings)
• A Score: 90 points (Excellent growth)
• N Score: 95 points (New highs)
• M Score: 0 points (S&P 500 in bear market, VIX > 30)

Composite Calculation:

composite = (100 * 0.27) + (90 * 0.36) + (95 * 0.27) + (0 * 0.10)
          = 27.0 + 32.4 + 25.65 + 0
          = 85.05 points

Rating: Exceptional fundamentals (85.05) BUT bear market Interpretation: DO NOT BUY despite high score - market direction overrides stock quality Weakest Component: M (0) - bear market environment Action: Raise cash, wait for M score > 40 (market recovery signal)

Critical Lesson: This example illustrates O'Neil's principle: "You can be right about a stock but wrong about the market, and still lose money."

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Scoring Evolution History

Phase 3 (current) uses the original O'Neil weights across all 7 components. Previous phases used renormalized weights to compensate for missing components:

• Phase 1 MVP (4 components): C 27%, A 36%, N 27%, M 10%
• Phase 2 (6 components): C 19%, A 25%, N 19%, S 19%, I 13%, M 6%
• Phase 3 (7 components): C 15%, A 20%, N 15%, S 15%, L 20%, I 10%, M 5% (original O'Neil weights)

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Usage Notes

For Screener Implementation

1. Calculate all 7 component scores (C, A, N, S, L, I, M) for each stock 2. Apply composite formula with Phase 3 weights 3. Identify weakest component for each stock 4. Rank stocks by composite score (highest to lowest) 5. Apply market filter FIRST: If M score < 40, warn user to reduce exposure

For User Reports

Include in output:

• Composite score (0-100)
• Rating (Exceptional / Strong / Above Average / Average / Below Average / Weak)
• Individual component scores (C, A, N, S, L, I, M)
• Weakest component identification
• Interpretation guidance
• Recommended action (buy / watchlist / avoid)

Format Example:

NVDA - NVIDIA Corporation
Composite Score: 95.2 / 100 (Exceptional+)

Component Breakdown:
C (Current Earnings): 100 / 100 - Explosive growth (EPS +429% YoY)
A (Annual Growth): 95 / 100 - Exceptional 3yr CAGR (89%)
N (Newness): 98 / 100 - At new highs with AI catalyst
S (Supply/Demand): 85 / 100 - Strong accumulation pattern
L (Leadership): 92 / 100 - RS Rank 95, sector leader
I (Institutional): 90 / 100 - 6199 holders, 68% ownership
M (Market Direction): 100 / 100 - Strong bull market

Weakest Component: S (85) - Strong accumulation
Recommendation: Strong buy - Rare multi-bagger setup

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Validation and Testing

Test Cases

Validate scoring system with known CANSLIM winners:

Expected Results (Phase 1 MVP):

• NVDA (2023 Q2): 95-100 points (Exceptional)
• META (2023 Q3): 80-90 points (Exceptional/Strong)
• AAPL (2009 Q3): 85-95 points (Exceptional)
• TSLA (2020 Q3): 80-90 points (Exceptional)

Expected Results for Non-CANSLIM Stocks:

• Declining earnings stocks: C < 40 → Composite < 50
• Stocks far from highs: N < 40 → Composite < 60
• In bear markets: M = 0 → Warning generated regardless of other scores

Scoring System Integrity Checks

1. Range Validation: All component scores must be 0-100 2. Weight Validation: Sum of weights = 100% (0.27 + 0.36 + 0.27 + 0.10 = 1.00) 3. Monotonicity: Higher inputs → Higher scores (linear or step-function increases) 4. Boundary Conditions: Test edge cases (zero EPS, negative growth, etc.) 5. Historical Validation: Backtest on known winners 2019-2024

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This scoring system provides a quantitative, objective framework for implementing O'Neil's complete CANSLIM methodology. Phase 3 implements all 7 components with original O'Neil weights, providing comprehensive growth stock screening.