Learn how algorithmic trading systems are structured, from market data and signals to orders, risk checks and evaluation, with a clear view of their limits.
Duration4 weeks
LevelBeginner
Modules4
Lessons16
This course introduces the building blocks of algorithmic trading as an engineering discipline: market data, rule-based signals, order types, execution and risk controls. You will turn plain-language trading rules into precise logic, run simple simulations in Python, and learn why costs, slippage and overfitting matter before any strategy is taken seriously. The goal is a solid mental model of how automated systems work and how to evaluate them responsibly.
4 modules · 16 lessons · 4 weeks. Curriculum preview; the final syllabus may change before enrollment opens.
Write a formal specification for a moving-average crossover rule, including edge cases
Build a bar-by-bar simulator in pandas that applies fixed transaction costs
Produce a short evaluation memo comparing results with and without costs and slippage
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.
5 weeks5 modulesLearn a structured process for designing trading strategies: forming hypotheses, defining rules, choosing data and evaluation criteria, and documenting research honestly.
4 weeks4 modulesLearn to identify, measure and communicate market, credit, liquidity and operational risk using volatility, drawdown, VaR and stress testing concepts.
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