Apply rigorous validation methods to trading strategies: walk-forward and cross-validation, overfitting diagnostics, multiple testing control and robust parameter optimization.
Duration7 weeks
LevelIntermediate
Modules7
Lessons28
Many strategies look convincing in a backtest and fail under scrutiny. This course teaches the statistical and procedural tools used to challenge results, including purged cross-validation, walk-forward optimization, bootstrap and Monte Carlo methods, and corrections for multiple testing. You will also learn how to optimize parameters without fitting noise and how to report validated results with appropriate uncertainty.
7 modules · 28 lessons · 7 weeks. Curriculum preview; the final syllabus may change before enrollment opens.
Run a walk-forward optimization study and compare it with a single in-sample fit
Build a Monte Carlo and bootstrap toolkit for drawdown and Sharpe ratio uncertainty
Produce a validation report applying multiple testing adjustments to a set of strategy variants
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 modulesBuild accurate, bias-aware backtesting engines in Python, from vectorized prototypes to event-driven simulators with realistic costs, fills and portfolio accounting.
6 weeks6 modulesLearn the probability and statistics used in quantitative research, from distributions and estimation to hypothesis testing, regression and resampling, applied to market data.
10 weeks10 modulesPractice quant research the way professional teams structure it: rigorous hypotheses, point-in-time data, factor-aware testing, validation, peer review and decision memos.
Reserve your place in a few steps, or ask us anything about the curriculum, schedule and format first.
