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Black-Litterman Posterior Returns

Hard · portfolio_optimization · Quant Researcher interview question · portfolio-optimization, black-litterman, bayesian, matrix-algebra, factor-models, numpy

The Black-Litterman model addresses the sensitivity of mean-variance optimization to input errors by blending market equilibrium returns with an investor's specific views. This Bayesian approach uses matrix algebra to produce stable, intuitive posterior expected returns that serve as robust inputs for portfolio allocation. Task Implement the function solution(mu_prior, sigma, P, Q, omega, tau) to calculate posterior expected returns using the Black-Litterman model. The function takes a prior re