QuantLab, part 5 of 6
Portfolio and risk.
Optimisation from first principles, risk metrics that matter, stress testing, explainability.
5 articles, about 34 minutes in order. Foundational first, then applied, then advanced.
- 01
Portfolio optimisation from first principles
Mean-variance optimisation, the efficient frontier, equal weight as a baseline, hierarchical risk parity, risk parity and CVaR optimisation.
- 02
Risk and return metrics that matter
Annualised return and volatility, Sharpe, Sortino, Calmar, information ratio, beta and alpha, VaR/CVaR and capture ratios, with formulas and failure modes.
- 03
Scenario and stress testing your allocation
Historical replay versus hypothetical shock versus reverse stress testing, the six modelled episodes, and why daily bars miss an intraday liquidity event.
- 04
Explainability: knowing why a model said what it said
Permutation importance, partial dependence and linear coefficients, computed out of sample, and what none of them can tell you about a trading decision.
- 05
Drawdown, volatility and the limits of historical risk
How maximum, average and duration-of-drawdown are computed, why volatility scaling breaks, and what a purely historical risk measure cannot see coming.
Other parts of QuantLab
- 01FoundationsWhat quantitative research is, the loop it runs in, and how a result fools you.
- 02Data and featuresPreparing financial time series, building features without leakage, choosing a benchmark.
- 03ModelsModel families and when to use them, linear against trees, hyperparameters, forecasting.
- 04ValidationChronological splits, purging and embargoing, walk-forward folds, the naive baseline, overfitting.
- 06PracticeReproducibility, checkable reports, re-runnable workflows, presenting work, teaching a term.
Read it here, then run it in the workspace.
Every article links to the platform page that implements the idea. Beta access is free and opens by cohort.