quantlab.portfolio.predefined.approximate.black_litterman.decide#

quantlab.portfolio.predefined.approximate.black_litterman.decide(rule, panel, prices=None, stopping=Stopping(tolerance=1e-05, max_iterations=20000))[source]#

Return every bar’s Black-Litterman weights of a decision panel.

The approximate mean-variance (mean_variance.decide) on each bar’s posterior (see the module docstring): every selectable symbol a candidate, candidate_top_k ranking the posterior mu.

Parameters:

ruleBlackLittermanOptimizer: The rule, bound to the predicted labels (its span read).
panelDecisionPanel: The bars’ decision inputs, the market factor among the factors.
pricesPanelPrices, optional: The book before the first bar and the prices it drifts with.
stoppingStopping: The kernels’ stopping rule.

Returns:

PanelSolution

Examples

With rule a bound BlackLittermanOptimizer over a factor risk model and panel a decision panel of its declared inputs:

>>> decide(rule, panel).weights.sum(axis=1)
array([1., 1.])