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TA-Lib Integration and Optimization [mercury-ta]

Overview​

Optimize Mercury TA's technical analysis capabilities by migrating from custom Python implementations to the industry-standard TA-Lib library. This will improve performance, reduce maintenance burden, and ensure more accurate calculations.

Status​

  • Planning
  • Implementation
  • Testing
  • Documentation
  • Deployment

Components​

Core Components​

  • Technical Analysis Engine
    • RSI Implementation
    • ATR Implementation
    • Keltner Channels
    • Volatility Calculations
    • Moving Averages

Supporting Components​

  • Data Validation
    • Update validation rules for TA-Lib output
    • Add quality checks for calculations
  • Performance Monitoring
    • Add timing metrics for calculations
    • Compare performance with previous implementation

Technical Implementation​

Phase 1: Setup and Migration​

  1. Add TA-Lib to project dependencies

    • Update requirements.txt
    • Add installation instructions to Dockerfile
    • Update development setup docs

  2. Create parallel implementations

    • Keep existing code
    • Add TA-Lib implementations alongside
    • Enable A/B testing via feature flag

  3. Validation Framework

    • Add output validation for TA-Lib results
    • Compare results with current implementation
    • Document any calculation differences

Phase 2: Testing and Verification​

  1. Performance Testing

    • Benchmark both implementations
    • Document performance improvements
    • Identify any bottlenecks

  2. Integration Testing

    • Update test suite for TA-Lib
    • Add comparison tests
    • Verify calculation accuracy

Phase 3: Rollout​

  1. Gradual Migration

    • Enable TA-Lib for non-critical indicators first
    • Monitor performance and accuracy
    • Roll out to critical indicators

  2. Cleanup

    • Remove old implementations
    • Update documentation
    • Clean up dependencies

Configuration​

# Feature flag for TA-Lib implementation
USE_TALIB = os.getenv("USE_TALIB", "false").lower() == "true"

# Performance monitoring
ENABLE_PERF_MONITORING = True
PERF_SAMPLE_RATE = 0.1  # Monitor 10% of calculations

# Validation thresholds
RESULT_DIFF_THRESHOLD = 0.0001  # Maximum allowed difference in results

Development Guidelines​

Code Organization​

  • Keep TA-Lib implementations in separate modules
  • Use factory pattern for indicator selection
  • Maintain clear separation of concerns

Testing Requirements​

  • Must include performance benchmarks
  • Must verify calculation accuracy
  • Must test edge cases and error handling

Documentation Requirements​

  • Update API documentation
  • Document any calculation differences
  • Provide migration guide for clients

Performance Requirements​

  • Must be at least 2x faster than current implementation
  • Memory usage should not increase
  • Response times must remain under 100ms

Migration Notes​

Current Pain Points​

  1. Custom implementations require significant maintenance
  2. Performance issues with large datasets
  3. Potential calculation inaccuracies
  4. High CPU usage during peak times

Expected Benefits​

  1. Improved calculation speed
  2. Reduced maintenance burden
  3. Industry-standard implementations
  4. Better resource utilization

Risks and Mitigations​

  1. Risk: Installation complexity

    • Mitigation: Detailed setup documentation
    • Mitigation: Docker container with pre-installed TA-Lib

  2. Risk: Calculation differences

    • Mitigation: Extensive validation
    • Mitigation: Gradual rollout with monitoring

  3. Risk: Performance regression

    • Mitigation: Comprehensive benchmarking
    • Mitigation: Feature flag for rollback

Dependencies​

Required Packages​

ta-lib==0.6.1
numpy>=1.26.3
pandas>=2.2.0

System Requirements​

  • TA-Lib C library
  • Sufficient CPU for parallel calculations
  • Adequate memory for large datasets

Metrics and Monitoring​

Performance Metrics​

  • Calculation time per indicator
  • Memory usage during calculations
  • CPU utilization
  • Cache hit rates

Quality Metrics​

  • Calculation accuracy vs previous implementation
  • Error rates
  • Response times
  • Resource utilization

Rollback Plan​

  1. Keep feature flag infrastructure
  2. Maintain old implementation until full verification
  3. Monitor error rates and performance
  4. Prepare rollback scripts and procedures

Future Considerations​

  1. Additional Indicators

    • More technical indicators from TA-Lib
    • Custom indicators using TA-Lib primitives

  2. Performance Optimizations

    • Parallel processing for multiple symbols
    • Improved caching strategies
    • Resource allocation optimization

  3. Integration Opportunities

    • Direct integration with trading systems
    • Real-time calculation capabilities
    • Advanced analytical features