Define the Uncertainty C. The Black-Litterman model makes the assumption that the structure of C is proportional to the covariance Σ. As the number of observations grows, the posterior distribution improves, and the algorithm becomes more certain of which regions in parameter space are worth exploring and which are not, as seen in the picture below. Both components are optimally accounted for in the Tangent Portfolio: where the SML and efficient frontier meet. 6+ years’ experience in quantitative investment research [portfolio optimization, multi factor and asset allocation] across all asset categories; Demonstrated experience with statistical time-series data analysis and backtesting of investment strategies; Must have strong computer skills (Java or C++, Python, Numpy and Pandas). Bayesian Optimization Python The bayesian optimization framework uses a surrogate model to approximate the objective function and chooses to optimize it according to some acquisition function. This framework gives a lot of freedom to the user in terms of optimization choices: We will use a multimodal problem with five peaks, calculated as: y = x^2 * sin (5 * PI * x)^6. Bayesian Optimization provides a probabilistically principled method for global optimization. Bayesian portfolio optimization GitHub. Portfolio