CANSLIM Stock Screener - Phase 3 (Full CANSLIM)

Overview

This skill screens US stocks using William O'Neil's proven CANSLIM methodology, a systematic approach for identifying growth stocks with strong fundamentals and price momentum. CANSLIM analyzes 7 key components:

C

urrent Earnings,

A

nnual Growth,

N

ewness/New Highs,

S

upply/Demand,

L

eadership/RS Rank,

I

nstitutional Sponsorship, and

M

arket Direction.

Phase 3

implements all 7 of 7 components (C, A, N, S, L, I, M), representing

100% of the full methodology

.

Two-Stage Approach:

Stage 1 (FMP API + Finviz)

Analyze stock universe with all 7 CANSLIM components

Stage 2 (Reporting)

Rank by composite score and generate actionable reports Key Features: Composite scoring (0-100 scale) with weighted components Finviz fallback for institutional ownership data (automatic when FMP data incomplete) Progressive filtering to optimize API usage JSON + Markdown output formats Interpretation bands: Exceptional+ (90+), Exceptional (80-89), Strong (70-79), Above Average (60-69) Bear market protection (M component gating) Phase 3 Component Weights (Original O'Neil weights): C (Current Earnings): 15% A (Annual Growth): 20% N (Newness): 15% S (Supply/Demand): 15% L (Leadership/RS Rank): 20% I (Institutional): 10% M (Market Direction): 5% Future Phases: Phase 4: FINVIZ Elite integration → 10x faster execution When to Use This Skill Explicit Triggers: "Find CANSLIM stocks" "Screen for growth stocks using O'Neil's method" "Which stocks have strong earnings and momentum?" "Identify stocks near 52-week highs with accelerating earnings" "Run a CANSLIM screener on [sector/universe]" Implicit Triggers: User wants to identify multi-bagger candidates User is looking for growth stocks with proven fundamentals User wants systematic stock selection based on historical winners User needs a ranked list of stocks meeting O'Neil's criteria When NOT to Use: Value investing focus (use value-dividend-screener instead) Income/dividend focus (use dividend-growth-pullback-screener instead) Bear market conditions (M component will flag - consider raising cash) Prerequisites API Requirements: FMP API key (free tier: 250 calls/day, sufficient for 35 stocks; Starter tier $29.99/mo for 40+ stocks) Sign up: https://site.financialmodelingprep.com/developer/docs Set via environment variable: export FMP_API_KEY=your_key_here Python Dependencies: Python 3.7+ requests (FMP API calls) beautifulsoup4 (Finviz web scraping) lxml (HTML parsing) Installation: pip install requests beautifulsoup4 lxml Output Output Directory: reports/ (default) or custom via --output-dir Generated Files: canslim_screener_YYYY-MM-DD_HHMMSS.json - Structured data for programmatic 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 Workflow Step 1: Verify API Access and Requirements Check if user has FMP API key configured:

Check environment variable

echo $FMP_API_KEY

If not set, prompt user to provide it

Requirements:

FMP API key

(free tier: 250 calls/day, sufficient for 40 stocks)

Python 3.7+

with required libraries:

requests

(FMP API calls)

beautifulsoup4

(Finviz web scraping)

lxml

(HTML parsing)

Installation:

pip

install

requests beautifulsoup4 lxml

If API key is missing, guide user to:

Sign up at

https://site.financialmodelingprep.com/developer/docs

Get free API key (250 calls/day)

Set environment variable:

export FMP_API_KEY=your_key_here

Step 2: Determine Stock Universe

Option A: Default Universe (Recommended)

Use top 40 S&P 500 stocks by market cap (predefined in script):

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

Option B: Custom Universe

User provides specific symbols or sector:

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

\

--universe

AAPL MSFT GOOGL AMZN NVDA META TSLA

Option C: Sector-Specific

User can provide sector-focused list (Technology, Healthcare, etc.)

API Budget Considerations (Phase 3):

40 stocks × 7 FMP calls/stock = 280 API calls

FMP: 7 calls/stock (profile, quote, income×2, historical_90d, historical_365d, institutional)

Finviz: ~1.8 calls/stock (institutional ownership fallback, 2s rate limit, not counted in FMP budget)

Market data (^GSPC quote, ^VIX quote, ^GSPC 52-week history): 3 FMP calls

Total: ~283 FMP calls per screening run (exceeds 250 free tier)

Recommendation

Use --max-candidates 35 for free tier (35 × 7 + 3 = 248 calls), or upgrade to FMP Starter tier ($29.99/mo, 750 calls/day) for full 40-stock screening Step 3: Execute CANSLIM Screening Script Run the main screening script with appropriate parameters: cd skills/canslim-screener/scripts

Basic run (40 stocks, top 20 in report)

python3 screen_canslim.py --api-key $FMP_API_KEY

Custom parameters

python3 screen_canslim.py

\

--api-key

$FMP_API_KEY

\

--max-candidates

40

\

--top

20

\

--output-dir

..

/

..

/

..

/

Script Workflow (Phase 3 - Full CANSLIM):

Market Direction (M)

Analyze S&P 500 trend vs 50-day EMA (using real historical data for accurate EMA)

If bear market detected (M=0), warn user to raise cash

S&P 500 Historical Data

Fetch 52-week data for M component EMA and L component RS calculation

Stock Analysis

For each stock, calculate:

C Component

Quarterly EPS/revenue growth (YoY)

A Component

3-year EPS CAGR and stability

N Component

Distance from 52-week high, breakout detection

S Component

Volume-based accumulation/distribution (up-day vs down-day volume)

L Component

52-week Relative Strength vs S&P 500

I Component

Institutional holder count + ownership % (with Finviz fallback)

Composite Scoring

Weighted average with all 7 component breakdown

Ranking

Sort by composite score (highest first)

Reporting

Generate JSON + Markdown outputs Expected Execution Time (Phase 3): 40 stocks: ~2 minutes (additional 52-week history fetch per stock for L component) Finviz fallback adds ~2 seconds per stock (rate limiting) L component requires 365-day historical data for each stock Finviz Fallback Behavior: Triggers automatically when FMP sharesOutstanding unavailable Scrapes institutional ownership % from Finviz.com (free, no API key) Increases I component accuracy from 35/100 (partial data) to 60-100/100 (full data) User sees: ✅ Using Finviz institutional ownership for NVDA: 68.3% Step 4: Read and Parse Screening Results The script generates two output files: canslim_screener_YYYY-MM-DD_HHMMSS.json - Structured data canslim_screener_YYYY-MM-DD_HHMMSS.md - Human-readable report Read the Markdown report to identify top candidates:

Find the latest report

ls -lt canslim_screener_*.md | head -1

Read the report

cat

canslim_screener_YYYY-MM-DD_HHMMSS.md

Report Structure (Phase 3 - Full CANSLIM):

Market Condition Summary (trend, M score, warnings)

Top N CANSLIM Candidates (ranked, N = --top parameter)

For each stock:

Composite Score and Rating (Exceptional+/Exceptional/Strong/etc.)

Component Breakdown (C, A, N, S, L, I, M scores with details)

Interpretation (rating description, guidance, weakest component)

Warnings (quality issues, market conditions, data source notes)

Summary Statistics (rating distribution)

Methodology note (Phase 3: 7 components, 100% coverage)

Component Details in Report:

S Component

"Up/Down Volume Ratio: 1.06 ✓ Accumulation"

L Component

"52wk: +45.2% (+22.1% vs S&P) RS: 88"

I Component

"6199 holders, 68.3% ownership ⭐ Superinvestor" Step 5: Analyze Top Candidates and Provide Recommendations Review the top-ranked stocks and cross-reference with knowledge bases: Reference Documents to Consult: references/interpretation_guide.md - Understand rating bands and portfolio sizing references/canslim_methodology.md - Deep dive into component meanings (now includes S and I) references/scoring_system.md - Understand scoring formulas (Phase 3 weights) Analysis Framework: For Exceptional+ stocks (90-100 points) : All components near-perfect (C≥85, A≥85, N≥85, S≥80, L≥85, I≥80, M≥80) Guidance: Immediate buy, aggressive position sizing (15-20% of portfolio) Example: "NVDA scores 97.2 - explosive quarterly earnings (100), strong 3-year growth (95), at new highs (98), volume accumulation (85), RS leader (92), strong institutional support (90), uptrend market (100)" For Exceptional stocks (80-89 points) : Outstanding fundamentals + strong momentum Guidance: Strong buy, standard sizing (10-15% of portfolio) For Strong stocks (70-79 points) : Solid across all components, minor weaknesses Guidance: Buy, standard sizing (8-12% of portfolio) Phase 3 Example: "Stock scores 77.5 - strong earnings (85), solid growth (80), near high (70), accumulation (60), RS leader (75), good institutions (60), uptrend (90)" For Above Average stocks (60-69 points) : Meets thresholds, one component weak Guidance: Buy on pullback, conservative sizing (5-8% of portfolio) Bear Market Override: If M component = 0 (bear market detected), do NOT buy regardless of other scores Guidance: Raise 80-100% cash, wait for market recovery CANSLIM does not work in bear markets (3 out of 4 stocks follow market trend) Step 6: Generate User-Facing Report Create a concise, actionable summary for the user: Report Format:

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

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

Top 5 CANSLIM Candidates

1. [SYMBOL] - [Company Name] ⭐⭐⭐ ** Score: ** [X.X]/100 ([Rating]) ** Price: ** $[XXX.XX] | ** Sector: ** [Sector] ** Component Breakdown: ** - C (Earnings): [X]/100 - [EPS growth]% QoQ, [Revenue growth]% revenue - A (Growth): [X]/100 - [CAGR]% 3yr EPS CAGR - N (Newness): [X]/100 - [Distance]% from 52wk high - S (Supply/Demand): [X]/100 - Up/Down Volume Ratio: [X.XX] - L (Leadership): [X]/100 - 52wk: [+X.X]% ([+X.X]% vs S&P) RS: [XX] - I (Institutional): [X]/100 - [N] holders, [X.X]% ownership [⭐ Superinvestor if present] - M (Market): [X]/100 - [Trend] ** Interpretation: ** [Rating description and guidance] ** Weakest Component: ** [X] ([score]) ** Data Source Note: ** [If Finviz used: "Institutional data from Finviz"] [Repeat for top 5 stocks]

Investment Recommendations ** Immediate Buy List (90+ score): ** - [List stocks with exceptional+ ratings] - Position sizing: 15-20% each ** Strong Buy List (80-89 score): ** - [List stocks with exceptional ratings] - Position sizing: 10-15% each ** Watchlist (70-79 score): ** - [List stocks with strong ratings] - Buy on pullback

Risk Factors

[Identify any quality warnings from components]

[Market condition warnings]

[Sector concentration risks if applicable]

[Data source reliability notes if Finviz heavily used]

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

**

Note:

**

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

Resources

Scripts Directory (

scripts/

)

Main Scripts:

screen_canslim.py

- Main orchestrator script

Entry point for screening workflow

Handles argument parsing, API coordination, ranking, reporting

Usage:

python3 screen_canslim.py --api-key KEY [options]

fmp_client.py

- FMP API client wrapper

Rate limiting (0.3s between calls)

429 error handling with 60s retry

Session-based caching

Methods:

get_income_statement()

,

get_quote()

,

get_historical_prices()

,

get_institutional_holders()

finviz_stock_client.py

- Finviz web scraping client ←

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

Troubleshooting

Issue 1: FMP API Rate Limit Exceeded

Symptoms:

ERROR: 429 Too Many Requests - Rate limit exceeded

Retrying in 60 seconds...

Causes:

Running multiple screenings within short time window

Exceeding 250 calls/day (free tier limit)

Other applications using same API key

Solutions:

Wait and Retry

Script auto-retries after 60s

Reduce Universe

Use

--max-candidates 30

to lower API usage

Check Daily Usage

Free tier resets at midnight UTC

Upgrade Plan

FMP Starter ($29.99/month) provides 750 calls/day Issue 2: Missing Required Libraries Symptoms: ERROR: required libraries not found. Install with: pip install beautifulsoup4 requests lxml Solutions:

Install all required libraries

pip install requests beautifulsoup4 lxml

Or install individually

pip

install

beautifulsoup4

pip

install

requests

pip

install

lxml

Issue 3: Finviz Fallback Slow Execution

Symptoms:

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

Causes:

Finviz rate limiting (2.0s per request)

All stocks triggering fallback due to FMP data gaps

Solutions:

Accept Delay

1-2 minutes for 40 stocks is normal with Finviz fallback

Monitor Fallback Usage

Check logs for "Using Finviz institutional ownership" messages

Reduce Rate Limit

(advanced): Edit

finviz_stock_client.py

, change

rate_limit_seconds=2.0

to

1.5

(risk: IP ban)

Note:

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

Issue 4: Finviz Web Scraping Failure

Symptoms:

WARNING: Finviz request failed with status 403 for NVDA

⚠️ Using Finviz institutional ownership data - FMP shares outstanding unavailable. Finviz fallback also unavailable. Score reduced by 50%.

Causes:

Finviz blocking scraping requests (User-Agent detection)

Rate limit exceeded (too many requests)

Network issues or Finviz downtime

Solutions:

Wait and Retry

Rate limit resets after a few minutes

Check Internet Connection

Verify network access to finviz.com

Fallback Accepted

Script continues with FMP holder count only (I score capped at 70/100)

Manual Verification

Check Finviz website manually for blocked IP

Graceful Degradation:

Script never fails due to Finviz issues

Falls back to FMP holder count only

User sees quality warning in report

Issue 5: No Stocks Meet Minimum Thresholds

Symptoms:

✓ Successfully analyzed 40 stocks

Top 5 Stocks:

1. AAPL - 58.3 (Average)

2. MSFT - 55.1 (Average)

...

Causes:

Bear market conditions (M component low)

Selected universe lacks growth stocks

Market rotation away from growth

Solutions:

Check M Component

If M=0 (bear market), raise cash per CANSLIM rules

Expand Universe

Try different sectors or market cap ranges

Lower Expectations

Average scores (55-65) may still be actionable in weak markets

Wait for Better Setup

CANSLIM works best in bull markets

Issue 6: Data Quality Warnings

Symptoms:

⚠️ Revenue declining despite EPS growth (possible buyback distortion)

⚠️ Using Finviz institutional ownership data (68.3%) - FMP shares outstanding unavailable.

Interpretation:

These are

not errors

- they are quality flags from calculators

Revenue warning: EPS growth may be from share buybacks, not organic growth

Finviz warning: Data source switched from FMP to Finviz (still accurate)

Actions:

Review component details in full report

Cross-check with fundamental analysis

Adjust position sizing based on risk level

Finviz data is reliable - no action needed for data source warnings

Important Notes

Phase 3 Implementation Status

This is

Phase 3

implementing all 7 of 7 CANSLIM components:

✅

C

(Current Earnings) - Implemented

✅

A

(Annual Growth) - Implemented

✅

N

(Newness) - Implemented

✅

S

(Supply/Demand) - Implemented

✅

L

(Leadership/RS Rank) - Implemented

✅

I

(Institutional) - Implemented

✅

M

(Market Direction) - Implemented

Implications:

Composite scores represent

100% of full CANSLIM methodology

Uses original O'Neil component weights (C 15%, A 20%, N 15%, S 15%, L 20%, I 10%, M 5%)

L component (20% weight) is the largest individual factor alongside A, emphasizing relative strength leadership

M component uses real 50-day EMA from historical data (not fallback estimate)

Finviz Integration Benefits

Automatic Fallback System:

When FMP API doesn't provide

sharesOutstanding

, Finviz automatically activates

Scrapes institutional ownership % from Finviz.com (free, no API key)

Improves I component accuracy from 35/100 (partial) to 60-100/100 (full)

Data Source Priority:

FMP API

(primary): Institutional holder count + shares outstanding calculation

Finviz

(fallback): Direct institutional ownership % from web page

Partial Data

(last resort): Holder count only, 50% penalty applied

Tested Reliability:

39/39 stocks successfully retrieved ownership % via Finviz (100% success rate)

Average execution time: 2.54 seconds per stock

No errors or IP blocks during testing

Future Enhancements

Phase 4 (Planned):

FINVIZ Elite integration for pre-screening

Execution time: 2 minutes → 10-15 seconds

FMP API usage reduction: 90%

Larger universe possible (100+ stocks)

Data Source Attribution

FMP API

Income statements, quotes, historical prices, key metrics, institutional holders

Finviz

Institutional ownership % (fallback), market data

Methodology

William O'Neil's "How to Make Money in Stocks" (4th edition)

Scoring System

Adapted from IBD MarketSmith proprietary system Disclaimer This screener is for educational and informational purposes only. Not investment advice Past performance does not guarantee future results CANSLIM methodology works best in bull markets (M component confirms) Conduct your own research and consult a financial advisor before making investment decisions O'Neil's historical winners include AAPL (2009: +1,200%), NFLX (2013: +800%), but many stocks fail to perform Version: Phase 3 Last Updated: 2026-02-20 API Requirements: FMP API (free tier: up to 35 stocks; Starter tier recommended for 40 stocks) + BeautifulSoup/requests/lxml for Finviz Execution Time: ~2 minutes for 40 stocks Output Formats: JSON + Markdown Components Implemented: C, A, N, S, L, I, M (7 of 7, 100% coverage)