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Screen US stocks using William O'Neil's CANSLIM growth stock methodology. Use when user requests CANSLIM stock screening, growth stock analysis, momentum stock identification, or wants to find stocks with strong earnings and price momentum following O'Neil's investment system.
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| name | canslim-screener |
| description | Screen US stocks using William O'Neil's CANSLIM growth stock methodology. Use when user requests CANSLIM stock screening, growth stock analysis, momentum stock identification, or wants to find stocks with strong earnings and price momentum following O'Neil's investment system. |
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:
Key Features:
Phase 3.1 Component Weights (Original O'Neil weights):
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:
error set.Future Phases:
Explicit Triggers:
Implicit Triggers:
When NOT to Use:
API Requirements:
export FMP_API_KEY=your_key_herePython Dependencies:
requests (FMP API calls)beautifulsoup4 (Finviz web scraping)lxml (HTML parsing)Installation:
pip install requests beautifulsoup4 lxml
Output Directory: reports/ (default) or custom via --output-dir
Generated Files:
canslim_screener_YYYY-MM-DD_HHMMSS.json - Structured data for programmatic usecanslim_screener_YYYY-MM-DD_HHMMSS.md - Human-readable reportReport Contents:
Rating Bands:
Check if user has FMP API key configured:
# Check environment variable
echo $FMP_API_KEY
# If not set, prompt user to provide it
Requirements:
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:
export FMP_API_KEY=your_key_hereOption 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):
--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 screeningRun 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):
Expected Execution Time (Phase 3):
Finviz Fallback Behavior:
sharesOutstanding unavailable✅ Using Finviz institutional ownership for NVDA: 68.3%The script generates two output files:
canslim_screener_YYYY-MM-DD_HHMMSS.json - Structured datacanslim_screener_YYYY-MM-DD_HHMMSS.md - Human-readable reportRead 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):
Component Details in Report:
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.
Review the top-ranked stocks and cross-reference with knowledge bases:
Reference Documents to Consult:
references/interpretation_guide.md - Understand rating bands and portfolio sizingreferences/canslim_methodology.md - Deep dive into component meanings (now includes S and I)references/scoring_system.md - Understand scoring formulas (Phase 3 weights)Analysis Framework:
For Exceptional+ stocks (90-100 points):
For Exceptional stocks (80-89 points):
For Strong stocks (70-79 points):
For Above Average stocks (60-69 points):
Bear Market Override:
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).
scripts/)Main Scripts:
screen_canslim.py - Main orchestrator script
python3 screen_canslim.py --api-key KEY [options]fmp_client.py - FMP API client wrapper
get_income_statement(), get_quote(), get_historical_prices(), get_institutional_holders()finviz_stock_client.py - Finviz web scraping client ← NEW
get_institutional_ownership(), get_stock_data()Calculators (scripts/calculators/):
earnings_calculator.py - C component (Current Earnings)
growth_calculator.py - A component (Annual Growth)
new_highs_calculator.py - N component (Newness)
supply_demand_calculator.py - S component (Supply/Demand) ← NEW
leadership_calculator.py - L component (Leadership/Relative Strength)
institutional_calculator.py - I component (Institutional)
market_calculator.py - M component (Market Direction)
Supporting Modules:
scorer.py - Composite score calculation
report_generator.py - Output generation
references/)Knowledge Bases:
references/canslim_methodology.md (27KB) - Complete CANSLIM explanation
references/scoring_system.md (21KB) - Technical scoring specification (Phase 3)
references/fmp_api_endpoints.md (18KB) - API integration guide (Phase 3)
references/interpretation_guide.md (18KB) - User guidance
How to Use References:
references/canslim_methodology.md first to understand O'Neil's system (now includes S and I)references/interpretation_guide.md when analyzing resultsreferences/scoring_system.md if scores seem unexpectedreferences/fmp_api_endpoints.md for API troubleshooting or Finviz fallback issuesSymptoms:
ERROR: 429 Too Many Requests - Rate limit exceeded
Retrying in 60 seconds...
Causes:
Solutions:
--max-candidates 30 to lower API usageSymptoms:
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
Symptoms:
Execution time: 2 minutes 30 seconds for 40 stocks (slower than expected)
Causes:
Solutions:
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).
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:
Solutions:
Graceful Degradation:
Symptoms:
✓ Successfully analyzed 40 stocks
Top 5 Stocks:
1. AAPL - 58.3 (Average)
2. MSFT - 55.1 (Average)
...
Causes:
Solutions:
Symptoms:
⚠️ Revenue declining despite EPS growth (possible buyback distortion)
⚠️ Using Finviz institutional ownership data (68.3%) - FMP shares outstanding unavailable.
Interpretation:
Actions:
This is Phase 3 implementing all 7 of 7 CANSLIM components:
Implications:
Automatic Fallback System:
sharesOutstanding, Finviz automatically activatesData Source Priority:
Tested Reliability:
Phase 4 (Planned):
This screener is for educational and informational purposes only.
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).
Evaluate account-level drawdown circuit breaker rules from trader-memory-core state and decide whether new trade risk is allowed today. Uses realized P&L, losing-streak cooldowns, and weekly/monthly drawdown limits without any external API.
Judge whether a market FAILED to react to news favorable to a crowded speculative position — step 2 of Jason Shapiro's COT contrarian process. Consumes a cot-contrarian-detector report (or an explicit direction) plus a Claude-curated events JSON, fetches the underlying price series with a documented fallback chain, and produces a fail-closed CONFIRMED / NOT_CONFIRMED / INSUFFICIENT_EVIDENCE verdict using a statistically validated drift-significance test (not a naive failure-ratio, which false-confirms on pure noise). Generic beyond COT — reusable for PEAD and macro-crowding news-failure checks. Use when the user asks to check news-failure confirmation, whether a crowded market "shrugged off" good/bad news, or wants to run Shapiro step 2 on a CROWDED_LONG/CROWDED_SHORT market.
Track investment theses across their lifecycle — from screening idea to closed position with postmortem. Register theses from screener outputs, manage state transitions, attach position sizing, review due dates, and generate postmortem reports with P&L and MAE/MFE analysis. Trigger when user says "register thesis", "track this idea", "thesis status", "review due", "close position", "postmortem", or "trading journal".
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