How does it handle version comparison?

The skill includes a dedicated benchmarking tool that saves performance results by version tag and provides a comparison script to identify speed regressions between releases.

Is coverage reporting integrated into this infrastructure?

Yes, it configures .coveragerc with specific thresholds, typically requiring 70% coverage for standard modules and 85% for critical modules like risk and signals.

Does it support non-trading software projects?

While the examples are trading-centric, the underlying benchmarking and regression infrastructure is applicable to any Python project where performance and version stability are priorities.

Can this skill test GPU-dependent code on CPU-only machines?

Yes, it includes a verified GPU mocking fixture that simulates CUDA properties, allowing GPU-dependent tests to pass in standard CI environments.

Testing & Benchmarking Infrastructure

Name: Testing & Benchmarking Infrastructure
Author: smith6jt-cop

bysmith6jt-cop

Seguridad y Pruebas

Implements comprehensive testing suites with performance benchmarking to track regressions and ensure version-to-version consistency.

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This skill provides a robust framework for managing complex software evolution, specifically designed for performance-critical applications like trading systems. It establishes a standardized directory structure, shared fixtures for GPU mocking on CPU-only machines, and automated benchmarking scripts to compare execution speed across different versions. By integrating tools like pytest-benchmark and coverage reporting, it ensures that feature updates do not introduce latency regressions, break backward compatibility, or compromise model specification consistency.

Características Principales

GPU environment mocking for seamless testing on CPU-only CI machines
0 GitHub stars
Configurable coverage thresholds for critical and standard modules
Deterministic testing fixtures to ensure reproducible results
Automated version-to-version performance comparison and reporting
Feature regression testing for validating model specifications across versions

Casos de Uso

Monitoring latency and throughput regressions during software updates
Validating backward compatibility of ML models and feature engineering pipelines
Standardizing testing protocols for high-performance computing in distributed teams

Acerca de

Características Principales

GPU environment mocking for seamless testing on CPU-only CI machines
0 GitHub stars
Configurable coverage thresholds for critical and standard modules
Deterministic testing fixtures to ensure reproducible results
Automated version-to-version performance comparison and reporting
Feature regression testing for validating model specifications across versions

Casos de Uso

Monitoring latency and throughput regressions during software updates
Validating backward compatibility of ML models and feature engineering pipelines
Standardizing testing protocols for high-performance computing in distributed teams