Data Prep and Baseline Benchmarks Deliver Quick Wins

Load S&P 500 prices via skfolio.datasets.load_sp500_dataset(), convert to returns with prices_to_returns(), and split chronologically (train_test_split(shuffle=False, test_size=0.33)) to prevent look-ahead bias—training spans ~67% historical days, testing the rest. Baselines like EqualWeighted(), InverseVolatility(), and Random() fit on train, predict on test, yielding metrics like annualized Sharpe (printed via ptf.annualized_sharpe_ratio), mean return, and volatility. These expose naive strategies' flaws: equal-weight ignores volatility, random adds noise—use them to benchmark any optimizer.

Mean-Variance, Risk Measures, and Clustering Beat Baselines

MeanRisk(risk_measure=RiskMeasure.VARIANCE) minimizes variance or maximizes Sharpe (ObjectiveFunction.MAXIMIZE_RATIO), generating efficient frontiers (efficient_frontier_size=20) plotted by risk vs. Sharpe. Swap risks to CVaR (95%), SEMI_VARIANCE, CDAR, or MAX_DRAWDOWN for tail-focused portfolios that cut CVaR@95% and max drawdown vs. variance. RiskBudgeting() equalizes contributions (variance or CVaR). Hierarchical methods shine: HierarchicalRiskParity() clusters assets via dendrograms for stable weights; NestedClustersOptimization() nests MeanRisk(CVAR) inside RiskBudgeting(VARIANCE) with 5-fold CV, capturing correlations without covariance pitfalls.

Robust Priors, Constraints, and Views Stabilize Real-World Use

Replace EmpiricalCovariance()/EmpiricalMu() with DenoiseCovariance(), ShrunkMu(), GerberCovariance(), or EWMu(alpha=0.1) in EmpiricalPrior() for max-Sharpe portfolios resilient to estimation error. Add realism via min_weights=0.0, max_weights=0.20, transaction_costs=0.0005, groups (e.g., GroupA <=0.6, GroupB>=0.2), l2_coef=0.01. BlackLitterman(views=["AAPL == 0.0008", "JPM - BAC == 0.0002"]) blends market priors with views. FactorModel() on load_factors_dataset() explains returns via external factors, boosting Sharpe. Pipelines like SelectKExtremes(k=8) + MeanRisk() prune to top performers.

Walk-Forward CV and Tuning Ensure Out-of-Sample Performance

cross_val_predict() with WalkForward(train_size=252*2, test_size=63) simulates rolling 2-year trains/3-month tests, computing portfolio Sharpe/Calmar. GridSearchCV() tunes l2_coef=[0.0,0.01,0.1] and mu_estimator__alpha=[0.05,0.1,0.2,0.5] on max-Sharpe, selecting best CV Sharpe. Final Population() of 18 strategies compares annualized mean/vol/Sharpe/Sortino/CVaR@95%/drawdowns (sorted by test Sharpe), with plots for cumulative returns, weights, risk contributions—revealing hierarchical/risk-parity often top variance-based in stability.