1. Apply the pattern used in `EqualWeightingPortfolioConstructionModel`
2. Change the logic to compute the views from the insights.
3. Change the logis to compute the posterior mean and covariance
Use `UnconstrainedMeanVariancePortfolioOptimizer` in `BlackLittermanPortfolioOptimizationFrameworkAlgorithm` to bypass the difference in regression tests with `IPortfolioOptimizer` that rely on different algorithms in C# and python.
Adds unit tests for BLOPCV to test the implementation against Black and Litterman 1999 paper.
- Creates `MinimumVariancePortfolioOptimizer` and `MaximumSharpeRatioPortfolioOptimizer` portfolio optimizer. They implement `Optimize` method that returns a array of float representing the portfolio weights.
- Refactors `BlackLittermanOptimizationPortfolioConstructionModel` and `MeanVarianceOptimizationPortfolioConstructionModel` to use the portfolio optimizers. Part of the logic in BLOPC was changed to match the MVOPC one.
- Adds `BlackLittermanPortfolioOptimizationFrameworkAlgorithm` similar to `MeanVarianceOptimizationFrameworkAlgorithm` that uses BLOPC.
AlexCatarino