From tradermonty-claude-trading-skills
Screens US stocks using full CANSLIM methodology (C,A,N,S,L,I,M) with FMP API and Finviz scraping. Ranks growth stocks by composite score for earnings, momentum, and market direction analysis.
npx claudepluginhub joshuarweaver/cascade-business-ops --plugin tradermonty-claude-trading-skillsThis skill uses the workspace's default tool permissions.
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.
references/canslim_methodology.mdreferences/fmp_api_endpoints.mdreferences/interpretation_guide.mdreferences/scoring_system.mdscripts/calculators/earnings_calculator.pyscripts/calculators/growth_calculator.pyscripts/calculators/institutional_calculator.pyscripts/calculators/leadership_calculator.pyscripts/calculators/market_calculator.pyscripts/calculators/new_highs_calculator.pyscripts/calculators/supply_demand_calculator.pyscripts/check_institutional_endpoint.pyscripts/finviz_stock_client.pyscripts/fmp_client.pyscripts/report_generator.pyscripts/scorer.pyscripts/screen_canslim.pyscripts/tests/conftest.pyscripts/tests/test_canslim_fixes.pyscripts/tests/test_fmp_fallback.pyGenerates design tokens/docs from CSS/Tailwind/styled-components codebases, audits visual consistency across 10 dimensions, detects AI slop in UI.
Records polished WebM UI demo videos of web apps using Playwright with cursor overlay, natural pacing, and three-phase scripting. Activates for demo, walkthrough, screen recording, or tutorial requests.
Delivers idiomatic Kotlin patterns for null safety, immutability, sealed classes, coroutines, Flows, extensions, DSL builders, and Gradle DSL. Use when writing, reviewing, refactoring, or designing Kotlin code.
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 Component Weights (Original O'Neil weights):
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 ../../../
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:
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 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)