Implement binomial trees, Black-Scholes-Merton and Monte Carlo option pricers in Python, then test them against each other and against known analytical results.
Duration6 weeks
LevelIntermediate
Modules6
Lessons24
This course turns option pricing theory into working, tested Python code. You will build binomial and trinomial trees, implement the Black-Scholes-Merton model, price path-dependent options with Monte Carlo simulation and solve for implied volatility numerically. Every model is validated against closed-form benchmarks and convergence checks, so you learn both the math and the engineering discipline of numerical pricing.
6 modules · 24 lessons · 6 weeks. Curriculum preview; the final syllabus may change before enrollment opens.
Build a small Python pricing library with tree, analytical and Monte Carlo engines behind one interface
Run a convergence study showing how tree steps and simulation paths affect pricing error
Create an implied volatility solver that handles deep in-the-money, far out-of-the-money and near-expiry edge cases
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.
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