canslim-screener

Cuándo usarla

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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Qué necesitas antes

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

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Errores que conviene evitar

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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