Develop multi-signal, multi-asset algorithmic strategies with rigorous research workflows, realistic execution modeling and disciplined robustness testing.
Duration8 weeks
LevelIntermediate
Modules8
Lessons32
This course focuses on moving beyond single-indicator rules to strategies that combine signals, manage portfolios of instruments and account for execution realities. You will study signal combination, regime awareness, cross-sectional ranking, execution cost modeling and capacity, while applying walk-forward analysis and stress testing throughout. The emphasis is on research discipline: every design choice is tested, documented and challenged.
8 modules · 32 lessons · 8 weeks. Curriculum preview; the final syllabus may change before enrollment opens.
Build a multi-signal composite score and test its stability across time periods
Develop a cross-sectional ranking strategy with turnover and cost constraints
Run a walk-forward study with stress scenarios and present findings in a research report
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 a modular algorithmic trading research stack in Python, covering data handling, signal libraries, vectorized and event-based backtests, and paper-trading workflows.
7 weeks7 modulesApply rigorous validation methods to trading strategies: walk-forward and cross-validation, overfitting diagnostics, multiple testing control and robust parameter optimization.
8 weeks8 modulesResearch, construct and evaluate factor models using cross-sectional regressions, portfolio sorts and risk attribution, with careful handling of biases and multiple testing.
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