A professional track for quant developers: fast Python, market data structures, backtesting engine design, research infrastructure and production-ready simulated execution.
Duration10 weeks
LevelAdvanced
Modules10
Lessons40
Quant developers turn research ideas into fast, correct and maintainable software. This track covers the full stack of that work, from performance-oriented Python and market data structures to backtesting engine design, experiment infrastructure and risk components. It finishes with production-readiness practices and a capstone where you deliver a reviewed, tested system that runs against simulated feeds.
10 modules · 40 lessons · 10 weeks. Curriculum preview; the final syllabus may change before enrollment opens.
Build an event-driven backtesting engine with cost and slippage models and deterministic replays
Optimize a slow factor computation pipeline using profiling, vectorization and Numba
Deliver a paper-trading service on a simulated feed with monitoring and kill switch controls
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.
6 weeks6 modulesWrite fast, well-structured and well-tested Python for quantitative work: performance profiling, vectorization, concurrency, typing, packaging and design patterns.
8 weeks8 modulesDesign and build an event-driven trading system architecture with message queues, state management, risk services and replayable simulations shared between research and paper trading.
6 weeks6 modulesBuild accurate, bias-aware backtesting engines in Python, from vectorized prototypes to event-driven simulators with realistic costs, fills and portfolio accounting.
Reserve your place in a few steps, or ask us anything about the curriculum, schedule and format first.
