GitHub Actions Scripts

This directory contains automation scripts used by GitHub Actions workflows.

Table of Contents

• Ticker Fetchers
• Utilities
• Requirements
• Environment Variables
• Testing

Ticker Fetchers

fetch_tickers.py

Purpose: Fetches high-quality US stock tickers for the main trading systems.

Target Criteria:

• US stocks from major exchanges (NASDAQ, NYSE, NYSEARCA, NYSEMKT)
• Price: $0.50+ (excludes penny stocks)
• Volume: 25,000+ shares/day
• Market Cap: $25M+
• Quality score: 45+
• Max tickers: 2,500

Usage:

# Production run
python fetch_tickers.py

# Dry run (no database writes)
python fetch_tickers.py --dry-run

# Verbose output
python fetch_tickers.py --verbose

# Disable quality filter
python fetch_tickers.py --disable-quality-filter

# Include global markets (default: US-only)
python fetch_tickers.py --include-global

# Custom quality score threshold
python fetch_tickers.py --min-quality-score 50.0

# Custom ticker limit
python fetch_tickers.py --max-tickers 3000

# Disable YFinance validation (not recommended)
python fetch_tickers.py --disable-yfinance-validation

Workflow: .github/workflows/fetch-tickers.yml
Schedule: Sundays at 6:00 AM UTC
Database Table: tickers

fetch_penny_tickers.py

Purpose: Fetches penny stock tickers (stocks under $5) for specialized trading strategies.

Target Criteria:

• Price: $0.10 - $5.00 (penny stock range)
• Market Cap: $5M - $300M (micro to small cap)
• Volume: 10,000+ shares/day
• Includes OTC markets (OTCQX, OTCQB, PINK)
• Max tickers: 2,000

Usage:

# Production run
python fetch_penny_tickers.py

# Dry run (no database writes)
python fetch_penny_tickers.py --dry-run

# Verbose output
python fetch_penny_tickers.py --verbose

# Custom ticker limit
python fetch_penny_tickers.py --max-tickers 1500

# Disable YFinance validation (not recommended)
python fetch_penny_tickers.py --disable-yfinance-validation

Workflow: .github/workflows/fetch-penny-tickers.yml
Schedule: Mondays at 7:00 AM UTC
Database Table: penny_tickers

Documentation: Penny Tickers Guide

Utilities

utils/efficient_ticker_filter.py

Purpose: High-performance ticker filtering with batch processing.

Features:

• CFD (Contract for Difference) detection
• S&P 500 symbol prioritization
• Exchange filtering (US markets)
• Heuristic-based quality scoring
• Batch API calls to multiple data sources
• Symbol validation patterns

Key Classes:

• EfficientTickerFilter: Main filtering logic
• EfficientTickerMetrics: Lightweight ticker metrics

utils/yfinance_validator.py

Purpose: Validates ticker data quality on Yahoo Finance.

Features:

• Historical data availability checks
• OHLC (Open-High-Low-Close) validation
• Volume data verification
• Price range validation
• Data completeness scoring
• Gap detection
• Parallel batch processing with rate limiting

Key Classes:

• YFinanceValidator: Main validation logic
• YFinanceValidation: Validation result data class

test_yfinance_validation.py

Purpose: Test suite for YFinance validator.

Usage:

cd .github/scripts
python test_yfinance_validation.py

Tests:

• Known valid tickers (AAPL, MSFT, GOOGL)
• Known invalid tickers (fake symbols)
• Batch validation performance
• Rate limiting behavior

Requirements

Python Dependencies

File: requirements.txt

python-dotenv==1.0.0
requests==2.31.0
supabase==2.0.3
yfinance==0.2.32
pandas==2.1.3
numpy==1.26.2

Installation

# Install dependencies
pip install -r requirements.txt

# Or with virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install -r requirements.txt

Environment Variables

All scripts require these environment variables:

Required for Database Access

NEXT_PUBLIC_SUPABASE_URL=https://your-project.supabase.co
NEXT_PUBLIC_SUPABASE_SERVICE_ROLE_KEY=your_service_role_key

Required for Data Sources

# Alpha Vantage (US stock listings)
ALPHA_VANTAGE_API_KEY=your_alpha_vantage_key

# Financial Modeling Prep (global markets, penny stocks)
FMP_API_KEY=your_fmp_key

Setting Up Environment

Local Development

Create .env file in project root:

cat > .env << EOF
NEXT_PUBLIC_SUPABASE_URL=<your_supabase_url>
NEXT_PUBLIC_SUPABASE_SERVICE_ROLE_KEY=<your_service_role_key>
ALPHA_VANTAGE_API_KEY=<your_alpha_vantage_key>
FMP_API_KEY=<your_fmp_key>
EOF

GitHub Actions

1. Go to repository Settings → Secrets and variables → Actions
2. Add repository secrets:
   • NEXT_PUBLIC_SUPABASE_URL
   • NEXT_PUBLIC_SUPABASE_SERVICE_ROLE_KEY
   • ALPHA_VANTAGE_API_KEY
   • FMP_API_KEY

Testing

Dry Run Mode

Test scripts without writing to database:

# Regular tickers
python fetch_tickers.py --dry-run --verbose

# Penny tickers
python fetch_penny_tickers.py --dry-run --verbose

Validation Testing

Test YFinance validator:

python test_yfinance_validation.py

Manual Workflow Triggers

Test workflows via GitHub UI:

1. Go to Actions tab
2. Select workflow:
   • "Weekly Global Market Tickers Update" (regular tickers)
   • "Weekly Penny Stock Tickers Update" (penny tickers)
3. Click "Run workflow"
4. Set parameters:
   • dry_run: true (test without database writes)
   • verbose: true (detailed logging)
   • max_tickers: Custom limit (optional)

Expected Results

fetch_tickers.py

Typical Output:

Total Candidates: 10,000 - 15,000 tickers
After Pre-Filtering: 5,000 - 8,000 tickers
After Quality Filtering: 3,000 - 5,000 tickers
After YFinance Validation: 2,000 - 2,500 tickers
Pass Rate: 20% - 25%
Execution Time: 15-25 minutes

fetch_penny_tickers.py

Typical Output:

Total Candidates: 5,000 - 10,000 tickers
After Deduplication: 4,000 - 8,000 tickers
After YFinance Validation: 1,000 - 2,000 tickers
Pass Rate: 20% - 40%
Execution Time: 10-20 minutes

Common Issues

API Rate Limiting

Symptoms:

• 401/429 HTTP errors
• "Too many requests" messages
• Incomplete results

Solutions:

• Wait and retry (limits reset hourly/daily)
• Reduce max_tickers parameter
• Use --verbose to see detailed errors
• Check API usage quotas

YFinance Validation Failures

Symptoms:

• Very low pass rates (< 10%)
• "No historical data" errors
• Connection timeouts

Solutions:

• Check internet connectivity
• Reduce batch size (built-in: 100 tickers/batch)
• Increase timeout (workflow default: 30-45 minutes)
• Disable validation if necessary (not recommended)

Supabase Connection Issues

Symptoms:

• "Missing Supabase credentials" error
• Database write failures
• Timeout during inserts

Solutions:

• Verify environment variables
• Check Supabase project status
• Verify service role key permissions
• Check database row limits

Best Practices

1. Always test with dry-run first

   python fetch_tickers.py --dry-run --verbose

2. Monitor workflow runs in GitHub Actions

   • Check logs for warnings
   • Review automated issues
   • Track success rates over time

3. Use appropriate ticker limits

   • Regular tickers: 2,000 - 3,000 recommended
   • Penny tickers: 1,500 - 2,000 recommended

4. Respect API rate limits

   • Don't run scripts too frequently
   • Use built-in rate limiting
   • Stagger different scripts (separate days)

5. Keep dependencies updated

   pip list --outdated
   pip install --upgrade <package>

Monitoring

GitHub Actions

• View workflow runs: Actions tab
• Check logs: Click on workflow run → job → step
• Download artifacts: Logs are saved for 30 days

Automated Issues

Failed workflows create GitHub issues automatically:

• Tagged with automation, tickers, bug
• Include run details and error information
• Should be reviewed and resolved promptly

Database Health

Monitor ticker data quality:

-- Check last update times
SELECT 
  'tickers' as table_name,
  COUNT(*) as ticker_count,
  MAX(last_fetched) as last_update
FROM tickers
WHERE is_active = true
UNION ALL
SELECT 
  'penny_tickers' as table_name,
  COUNT(*) as ticker_count,
  MAX(last_fetched) as last_update
FROM penny_tickers
WHERE is_active = true;

-- Check data freshness
SELECT 
  symbol, 
  name, 
  last_fetched,
  NOW() - last_fetched as age
FROM tickers
WHERE is_active = true
AND last_fetched < NOW() - INTERVAL '7 days'
ORDER BY last_fetched
LIMIT 10;

Contributing

When modifying scripts:

1. Test locally with --dry-run
2. Update documentation
3. Add tests for new features
4. Update requirements.txt if adding dependencies
5. Update workflow files if changing parameters

Support

For issues or questions:

1. Check this README
2. Review script logs (--verbose flag)
3. Check GitHub Actions logs
4. Review data source documentation:
   • FMP API Docs
   • Alpha Vantage Docs
   • YFinance Docs
   • Supabase Docs

Related Documentation

• Database Documentation
• Penny Tickers Guide
• GitHub Actions Workflows