Profiling, Refactoring, and Versioning

Under development

This lesson is part of the course scaffold and is being actively written. The learning objectives and outline below define its final scope.

Learning objectives

By the end of this lesson you will be able to:

• Profile a Python trading pipeline with cProfile and py-spy and identify the true hot path from measured data rather than intuition
• Optimize a hot path through vectorization, caching, or algorithmic change, and quantify the speedup with before/after benchmarks
• Refactor legacy research code safely using characterization tests and incremental extraction, demonstrating unchanged backtest output at each step
• Apply semantic versioning and a release process — changelog, tags, deprecation policy — to the trading platform

Outline

1. Measure before optimizing — benchmarks, baselines, and representative workloads
2. cProfile — call-graph profiling and reading the output honestly
3. py-spy — sampling a live process without stopping it
4. Optimizing hot paths — vectorization, caching, better algorithms, when to drop to native code
5. Refactoring legacy research code — characterization tests and finding seams
6. Incremental refactors — the strangler pattern applied to research pipelines
7. Semantic versioning — what counts as a breaking change in a trading platform
8. Releases — changelogs, tagging, deprecation windows, and rollbacks

Prerequisites

• Testing and CI/CD