quantlab.portfolio.predefined.approximate.mean_variance

The approximate backtest’s mean-variance: every decision bar’s Markowitz weights in compiled code.

The implementation quantlab.portfolio.approximate.implementation chooses for a MeanVarianceOptimizer or an ActiveRiskOptimizer: decide reads the rule’s config and the span its bind read, and maximises the per-bar rule’s objective

w @ mu - risk_aversion / 2 * (w - b) @ Sigma @ (w - b) - turnover_penalty * |w - holdings|_1

(b the active-risk rule’s benchmark weights, zero otherwise) by accelerated proximal gradient (_support.mean_variance_kernels, numba) rather than cvxpy: forward in time when the decision reads the holdings (the turnover penalty, or candidate_top_k keeping held names), in parallel over the bars otherwise. mu and Sigma are those the per-bar rule computes on each bar’s context with nothing locked: the covariance estimator’s (quantlab.portfolio.approximate.estimate_all) scaled to the expected-return label’s span, with volatility_label its volatilities the prediction over sqrt(span); a symbol selectable when it is tradable and covered; and on the selectable symbols with a prediction mu the rule’s expected_return model (ExpectedReturnModel) gives, the same array code the per-bar rule calls, handed sigma (the covariance’s diagonal on the span) when it needs it.

Classes

PanelProblem

What every bar's decision reads but its holdings: mu, the covariance and the candidates.

Functions

decide

Return every bar's mean-variance weights of a decision panel: in time order when they read the holdings, at once otherwise.