Testing Best Practices

In quantitative finance, a single bug can cost millions. This guide covers comprehensive testing strategies to ensure your trading systems are bulletproof, from unit tests to production validation.

Testing Philosophy

VectorAlpha follows a multi-layered testing approach that validates correctness, performance, and edge cases at every level. Our goal is to catch issues before they impact trading decisions.

Testing Pyramid

  • ▪ Unit Tests (70%): Fast, isolated tests for individual functions
  • ▪ Integration Tests (20%): Component interaction validation
  • ▪ End-to-End Tests (10%): Full system validation with real data

Unit Testing Best Practices

Numerical Accuracy Testing

Financial calculations require extreme precision. Use epsilon-based comparisons for floating-point values:

Learn more: Comparing Floating Point Numbers

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Full code examples with syntax highlighting will be available in the next update.

Property-Based Testing

Use property-based testing to discover edge cases automatically:

Learn more: Proptest Documentation

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Full code examples with syntax highlighting will be available in the next update.

Integration Testing

Market Data Pipeline Testing

Test the entire data flow from market feeds to indicator calculations:

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Full code examples with syntax highlighting will be available in the next update.

Backtesting Validation

Ensure backtesting results are reproducible and statistically valid:

Code Example Coming Soon

Full code examples with syntax highlighting will be available in the next update.

Performance Testing

Latency Benchmarks

Measure and track performance metrics to prevent regressions:

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Full code examples with syntax highlighting will be available in the next update.

Performance Regression Testing

Set up automated performance regression tests that fail if latency increases by more than 5%. This catches performance issues before they reach production.

Financial Domain Testing

Market Edge Cases

Test handling of extreme market conditions and data anomalies:

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Statistical Validation

Verify that trading strategies produce statistically significant results:

Learn more: Statistical Methods for Backtesting

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Full code examples with syntax highlighting will be available in the next update.

Test Data Management

Synthetic Data Generation

Generate realistic test data for comprehensive coverage:

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Full code examples with syntax highlighting will be available in the next update.

Production Testing

Shadow Mode Testing

Run new strategies in shadow mode alongside production systems:

  1. Execute calculations without placing orders
  2. Compare results with production strategies
  3. Monitor for divergences or errors
  4. Validate performance under real market conditions
  5. Graduate to production after validation period

Testing Checklist

Before Each Commit

  • □ Run unit tests
  • □ Check code coverage (>90%)
  • □ Run linter and formatter
  • □ Verify no performance regression

Before Release

  • □ Full integration test suite
  • □ Stress testing with edge cases
  • □ Historical data validation
  • □ Shadow mode verification

Next Steps

Contributing Guidelines →

Learn how to contribute code with proper testing

Performance Optimization →

Optimize your code for production environments