Build a modular algorithmic trading research stack in Python, covering data handling, signal libraries, vectorized and event-based backtests, and paper-trading workflows.
Duration6 weeks
LevelIntermediate
Modules6
Lessons24
This course takes you from scattered notebooks to a structured Python codebase for algorithmic trading research. You will design data loaders, reusable signal functions, position sizing and cost models, then compare vectorized and loop-based backtests. Later modules cover paper-trading architecture, logging and testing so that research code is reproducible and easier to reason about.
6 modules · 24 lessons · 6 weeks. Curriculum preview; the final syllabus may change before enrollment opens.
Package a signal library of momentum and mean-reversion indicators with unit tests
Build a vectorized backtester and validate it against a simple event-loop version
Create a paper-trading simulator that replays historical bars and logs every decision
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.
4 weeks4 modulesLearn how algorithmic trading systems are structured, from market data and signals to orders, risk checks and evaluation, with a clear view of their limits.
6 weeks6 modulesBuild accurate, bias-aware backtesting engines in Python, from vectorized prototypes to event-driven simulators with realistic costs, fills and portfolio accounting.
8 weeks8 modulesDevelop multi-signal, multi-asset algorithmic strategies with rigorous research workflows, realistic execution modeling and disciplined robustness testing.
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