CANSLIM Screener

Screen US stocks using William O’Neil’s proven CANSLIM growth stock methodology. Phase 3 implements all 7 components for 100% methodology coverage.

Download Skill Package (.skill) View Source on GitHub

Table of Contents

1. Overview

The CANSLIM Screener applies William O’Neil’s growth stock selection system – developed from studying every major stock winner from 1953 to the present. The methodology identifies 7 common traits that multi-bagger stocks exhibit before their major price advances.

What it solves:

  • Systematically scores stocks across the 7 CANSLIM dimensions instead of relying on subjective judgment
  • Provides composite ratings (0-100) with clear interpretation bands
  • Protects against buying in bear markets (M component gates all recommendations)
  • Automates the labor-intensive process of analyzing earnings, growth, momentum, institutional activity, and market conditions

Phase 3 implements all 7 components:

Component Weight What It Measures
C - Current Earnings 15% Quarterly EPS and revenue growth (YoY)
A - Annual Growth 20% 3-year EPS CAGR and stability
N - Newness 15% Distance from 52-week high, breakout detection
S - Supply/Demand 15% Volume-based accumulation/distribution
L - Leadership 20% Multi-period RS (3m/6m/12m vs configurable benchmark, default ^GSPC)
I - Institutional 10% Holder count + ownership % (with Finviz fallback)
M - Market Direction 5% S&P 500 trend vs 50-day EMA

2. Prerequisites

FMP API key is required. The free tier (250 calls/day) supports up to 35 stocks per run. Use --max-candidates 35 to stay within the limit.

API Requirements:

Python Dependencies:

  • Python 3.7+
  • requests (FMP API calls)
  • beautifulsoup4 (Finviz web scraping for I component fallback)
  • lxml (HTML parsing)
pip install requests beautifulsoup4 lxml

API Budget (Phase 3):

  • 40 stocks x 7 FMP calls/stock = 280 FMP calls
  • Market data (S&P 500 quote, VIX, 52-week history): 3 calls
  • Total: ~283 FMP calls per run (exceeds 250 free tier)
  • Recommendation: Use --max-candidates 35 for free tier (248 calls), or upgrade to Starter

3. Quick Start 

# Set your API key
export FMP_API_KEY=your_key_here

# Run with default S&P 500 universe (top 40 by market cap)
python3 skills/canslim-screener/scripts/screen_canslim.py --output-dir reports/

# Free tier optimization (35 stocks max)
python3 skills/canslim-screener/scripts/screen_canslim.py \
  --max-candidates 35 --output-dir reports/

Or simply tell Claude:

Run a CANSLIM screen on the top 35 S&P 500 stocks

4. How It Works

The screener operates in a two-stage pipeline:

Stage 1 – Data Collection & Scoring (FMP API + Finviz):

  1. Fetch S&P 500 historical data for M component (market direction) and L component (relative strength benchmark)
  2. For each stock, make 7 FMP API calls: profile, quote, income statement (2 periods), 90-day history, 365-day history, institutional holders
  3. Calculate all 7 component scores using dedicated calculator modules
  4. When FMP institutional data is incomplete, automatically fall back to Finviz web scraping for ownership percentage

Stage 2 – Ranking & Reporting:

  1. Calculate composite weighted score: C(15%) + A(20%) + N(15%) + S(15%) + L(20%) + I(10%) + M(5%)
  2. Rank all stocks by composite score (highest first)
  3. Generate JSON output for programmatic use and Markdown report for human review

Finviz fallback behavior:

  • Triggers automatically when FMP sharesOutstanding is unavailable
  • Scrapes institutional ownership % from Finviz.com (free, no API key needed)
  • Rate-limited at 2.0 seconds per request
  • Improves I component accuracy from 35/100 (partial data) to 60-100/100 (full data)

5. Usage Examples

Example 1: Default S&P 500 Screening

Prompt:

Screen the S&P 500 for CANSLIM stocks

What happens: Claude runs the screener against the default universe of 40 stocks (top S&P 500 by market cap). Takes approximately 2 minutes. Generates a ranked report with composite scores and component breakdowns.

Why useful: The quickest way to identify the strongest growth stocks in the large-cap universe using a proven methodology.

Example 2: Semiconductor Sector Focus

Command:

python3 skills/canslim-screener/scripts/screen_canslim.py \
  --universe NVDA AMD QCOM AVGO TXN INTC MU MRVL AMAT LRCX \
  --output-dir reports/

Why useful: Narrows the analysis to a single high-growth sector. Useful when you already have a sector thesis and want to rank the best candidates within it using CANSLIM criteria.

Example 3: Free Tier Optimization

Command:

python3 skills/canslim-screener/scripts/screen_canslim.py \
  --max-candidates 35 --top 10 --output-dir reports/

Why useful: Stays within the FMP free tier limit (250 calls/day) while still analyzing a meaningful universe. The --top 10 flag keeps the report focused on the strongest candidates.

Example 4: Component Deep Dive

After running a screen, examine the component breakdown for a top-scoring stock:

Score: 92.3 / 100 (Exceptional+)
  C (Current Earnings): 100/100 - EPS +58% QoQ, Revenue +32%
  A (Annual Growth):     95/100 - 3yr EPS CAGR 42%, consistent growth
  N (Newness):           98/100 - Within 2% of 52-week high, volume breakout
  S (Supply/Demand):     85/100 - Up/Down Volume Ratio 1.65 (Accumulation)
  L (Leadership):        92/100 - 52wk: +45% (+22% vs S&P 500), RS 88
  I (Institutional):     90/100 - 6,199 holders, 68.3% ownership
  M (Market Direction): 100/100 - Strong uptrend, S&P above 50-day EMA

Why useful: Understanding which components drive the score helps you assess whether the stock’s strength is broad-based or concentrated in one area. Stocks with high scores across all components are the most reliable.

Example 5: Bear Market Scenario

When the M component detects a bear market (S&P 500 below 50-day EMA, VIX elevated):

Market Condition: BEAR MARKET DETECTED
M Score: 0/100

WARNING: CANSLIM methodology recommends raising 80-100% cash in bear markets.
3 out of 4 stocks follow the market trend. Do NOT initiate new positions.

Why useful: The M component acts as a circuit breaker. Even if individual stocks score 90+ on other components, CANSLIM’s historical data shows that buying in confirmed bear markets has a poor win rate. This protects capital.

Example 6: Interpreting Score Ranges

Rating Score Guidance Position Sizing
Exceptional+ 90-100 All components near-perfect. Aggressive buy 15-20% of portfolio
Exceptional 80-89 Outstanding fundamentals + momentum. Strong buy 10-15% of portfolio
Strong 70-79 Solid across components, minor weaknesses. Standard buy 8-12% of portfolio
Above Average 60-69 Meets thresholds with one weak component. Buy on pullback 5-8% of portfolio

Why useful: The interpretation bands translate abstract scores into concrete position sizing guidance, aligning risk exposure with conviction level.

6. Understanding the Output

The screener generates two files:

  • canslim_screener_YYYY-MM-DD_HHMMSS.json – Structured data for programmatic use
  • canslim_screener_YYYY-MM-DD_HHMMSS.md – Human-readable report

Report sections:

  1. Market Condition Summary – Current trend, M score, and warnings
  2. Top N CANSLIM Candidates – Ranked by composite score with:
    • Composite score and rating band
    • Individual component scores with explanatory details
    • Data source notes (e.g., “Institutional data from Finviz”)
    • Weakest component identification
  3. Summary Statistics – Distribution of ratings (how many Exceptional+, Exceptional, Strong, etc.)
  4. Methodology Note – Phase 3: 7 components, 100% coverage

Quality warnings to watch for:

  • “Revenue declining despite EPS growth” – Possible buyback distortion
  • “Using Finviz institutional ownership data” – Data source switched (still accurate)
  • “Bear market detected” – M component = 0, do not buy

7. Tips & Best Practices

  • Always check the M component first. If M = 0, the rest of the scores are irrelevant from a CANSLIM perspective. Raise cash and wait.
  • Look for broad strength. A score of 85 with all components above 70 is more reliable than a score of 85 with one component at 100 and another at 40.
  • Use --max-candidates 35 for free tier. This is the sweet spot for the 250 calls/day FMP free tier limit.
  • Run weekly, not daily. CANSLIM is a weekly screening methodology. Earnings data updates quarterly, and most components are stable week-to-week.
  • Cross-reference with charts. CANSLIM identifies fundamentally strong stocks, but entry timing matters. Use the Technical Analyst skill for chart-based entry confirmation.
  • Finviz fallback is reliable. Testing showed 100% success rate (39/39 stocks) with ~2.5 seconds per request. The data quality is equivalent to FMP.

8. Combining with Other Skills

Workflow Steps
Full growth stock pipeline CANSLIM Screener (rank candidates) > Technical Analyst (confirm chart setup) > Position Sizer (calculate shares)
CANSLIM + VCP Run CANSLIM to identify growth leaders, then check if top candidates also show VCP patterns via VCP Screener
Pre-filter with FinViz Use FinViz Screener (fa_epsqoq_o25,ta_sma200_pa,ta_highlow52w_b0to10h) to build a custom universe, then pass those tickers to CANSLIM Screener with --universe
Earnings confirmation After CANSLIM ranks candidates, check Earnings Calendar for upcoming report dates to avoid entering before volatile events
Bear market protection When CANSLIM M = 0, switch to Market Environment Analysis and Breadth Chart Analyst to monitor for recovery signals

9. Troubleshooting

FMP API rate limit exceeded (429 error)

The script automatically retries after 60 seconds. If the error persists:

  • Reduce the universe: --max-candidates 30
  • Check daily usage: free tier resets at midnight UTC
  • Upgrade to FMP Starter ($29.99/mo) for 750 calls/day

Missing Python libraries 

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

Install all dependencies:

pip install requests beautifulsoup4 lxml

Finviz web scraping failure (403 error)

This occurs when Finviz blocks scraping requests. The script degrades gracefully:

  • Falls back to FMP holder count only
  • I component score is capped at 70/100 with a 50% penalty
  • Wait a few minutes and retry, or verify your network access to finviz.com

All stocks scoring below 60

This may indicate bear market conditions or a universe lacking growth stocks:

  • Check the M component – if M = 0, follow bear market protocol (raise cash)
  • Try a different sector or expand the universe
  • In weak markets, scores in the 55-65 range may still be the relative best available

10. Reference

CLI Arguments

Argument Required Default Description
--api-key No $FMP_API_KEY FMP API key
--max-candidates No 40 Max stocks to analyze (use 35 for free tier)
--top No 20 Number of top results in the report
--output-dir No . Output directory for JSON and Markdown reports
--universe No S&P 500 top 40 Custom list of ticker symbols
--rs-benchmark No ^GSPC Benchmark symbol for L-component RS (e.g. SPY, QQQ, IWM). M component still uses ^GSPC for EMA scale consistency.
--disable-rs No false Skip L component calculation. Saves the per-stock 365-day price fetch and the custom benchmark fetch (when applicable). L is fixed at neutral 50.

Default Universe

The default universe includes the top 40 S&P 500 stocks by market cap:

AAPL, MSFT, GOOGL, AMZN, NVDA, META, TSLA, BRK.B, UNH, JNJ,
XOM, V, PG, JPM, MA, HD, CVX, MRK, ABBV, PEP, COST, AVGO, KO,
ADBE, LLY, TMO, WMT, MCD, CSCO, ACN, ORCL, ABT, NKE, CRM, DHR,
VZ, TXN, AMD, QCOM, INTC

Scoring Formula 

Composite = C x 0.15 + A x 0.20 + N x 0.15 + S x 0.15 + L x 0.20 + I x 0.10 + M x 0.05

Weighted RS Calculation (Phase 3.1)

The L component now uses a multi-period weighted relative strength against a configurable benchmark (default ^GSPC):

Weighted RS = 0.40 × rel_3m + 0.30 × rel_6m + 0.30 × rel_12m
  • Periods: 3m = 63 trading bars, 6m = 126 bars, 12m = 252 bars.
  • When some periods are missing (insufficient history), the weights are re-normalized over the available periods so they still sum to 1.0.
  • Fallback hierarchy when full multi-period data is not available:
    1. No benchmark → score from the weighted absolute stock performance with a 20% penalty (preserves the legacy fallback behavior).
    2. All multi-period windows missing but >=50 bars of price history available (e.g. fewer than 63 trading bars but a longer-than-50-bar series) → fall back to the legacy 365-day full-window absolute return as the scoring input. The 20% penalty still applies when no benchmark is present.
    3. <50 bars of price history → score=0 with error set; no scoring is performed.
  • Pass --rs-benchmark SPY (or QQQ, IWM, etc.) to swap the benchmark. The M component always uses ^GSPC to keep its EMA calculation in scale.

Output Schema (Phase 3.1)

The L-component sub-object adds the following fields. Existing fields are preserved for backward compatibility.

Field Type Description
rs_3m_return / rs_6m_return / rs_12m_return float | null Stock absolute return over 63 / 126 / 252 trading bars.
benchmark_3m_return / benchmark_6m_return / benchmark_12m_return float | null Benchmark absolute return over the same windows.
rel_3m / rel_6m / rel_12m float | null Stock minus benchmark (per period). Null when no benchmark.
weighted_stock_performance float | null Weighted absolute return (used as scoring input when no benchmark).
weighted_relative_performance float | null Weighted relative return (scoring input when benchmark available).
benchmark_52w_performance float | null Symbol-neutral successor to sp500_52w_performance.
rs_benchmark string Benchmark symbol used (e.g. ^GSPC, SPY).
rs_benchmark_relative_return float | null Equal to rel_12m (252-bar relative). Note: this is not the same as legacy relative_performance, which uses the full 365-day window.
rs_rating string Short label derived from rs_rank_percentile: Market Leader / Strong / Above Average / Average / Laggard / Weak.
rs_component_score int Alias of score (0–100).
rs_rank_percentile int | null Estimated percentile derived from relative-performance thresholds. Not a cross-sectional percentile within the screened universe.

Deprecation notes:

  • sp500_52w_performance is retained for backward compatibility. When --rs-benchmark is set to a non-^GSPC symbol, this field contains that benchmark’s return (the field name lies). Prefer benchmark_52w_performance.
  • relative_performance continues to mean the 365-day full-window stock − benchmark return. It differs from rs_benchmark_relative_return (252-bar) and from weighted_relative_performance (multi-period weighted).

Rating Bands

Band Score Interpretation
Exceptional+ 90-100 All components near-perfect
Exceptional 80-89 Outstanding fundamentals + momentum
Strong 70-79 Solid across components
Above Average 60-69 Meets thresholds with minor weaknesses
Average 50-59 Mixed signals
Below Average < 50 Does not meet CANSLIM criteria