Each course is an interactive book — every chapter runs Python in your browser, every concept has an editable example, and every chapter ends with spaced-repetition flashcards to lock in what you read. Courses are sold individually or bundled into a six-week paced cohort.
From DataFrames to decision models. Covers the Python data-science stack (pandas, NumPy, SciPy, Matplotlib), DataFrames in depth, linear regression with CAPM and Fama-French factors, and clustering for customer segmentation. Worked examples use real market data.
Build, backtest, and evaluate systematic trading strategies. Signal construction, portfolio optimisation under Markowitz and Black-Litterman frameworks, transaction-cost modelling, walk-forward validation, and live-trading hygiene. Code runs on bundled historical price data.
The full risk-manager's toolkit. Parametric and historical VaR, Expected Shortfall, copula models for tail dependence, stress testing under Basel III, and the regulatory framework that determines a bank's capital requirement. Every model fits to real bank-level data.
Graph theory applied to business: customer-influence networks, recommendation systems, community detection, viral-cascade models, and modern graph neural networks. Worked examples use sanitised social and transactional data.
Want a course on a topic not listed here? Email contact@xuhuwan.com or message @xuhuwan on X — the next course is voted on by current cohort members.