Master quantitative finance from mathematical foundations through derivatives pricing, risk management, portfolio theory, and algorithmic trading — all with hands-on Python implementations.
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Build the mathematical and statistical foundation needed for quantitative finance — random walks, probability distributions, and market efficiency.
Understand the major categories of financial derivatives — forwards, futures, options, and swaps — and how they are used for hedging and speculation.
Master the core options pricing models — binomial trees, Black-Scholes, the Greeks, implied volatility, and put-call parity.
Learn the quantitative tools for measuring and managing financial risk — Value at Risk, Expected Shortfall, stress testing, credit risk, and operational risk.
Master bond mathematics, term structure models, yield curve construction, swap pricing, and mortgage-backed securities.
Master modern portfolio theory — mean-variance optimization, CAPM, multi-factor models, APT, and the Black-Litterman framework for combining quantitative models with investor views.
Explore the world of algorithmic trading — from market microstructure and order types to momentum strategies, mean reversion, and rigorous backtesting methodology.