Build accurate, bias-aware backtesting engines in Python, from vectorized prototypes to event-driven simulators with realistic costs, fills and portfolio accounting.
Duration6 weeks
LevelIntermediate
Modules6
Lessons24
Backtests are only as good as their assumptions, and this course shows how to make those assumptions explicit and correct. You will build a vectorized backtester and an event-driven engine in Python, implement portfolio accounting, cost and fill models, and test the engine itself. The course closes with diagnostics that detect look-ahead bias, data errors and fragile results before they reach a research report.
6 modules · 24 lessons · 6 weeks. Curriculum preview; the final syllabus may change before enrollment opens.
Build a vectorized multi-asset backtester with cost and lag controls
Implement an event-driven engine with order, fill and portfolio components
Create a test suite and bias-detection report for your own backtesting engine
Quantzivo certificates are completion credentials issued by Quantzivo, not accredited qualifications. Courses teach technical and research skills; they do not promise investment returns or provide investment advice.
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