We didn't experience the expected performance improvements. Locally under
unit test there was aboout an order of magnitude throughput increase, but
when run against the history benchmark, this new approach was 60% slower.
We're reverting this for now to perform further analysis and better
understand the performance profiling of the python history stack.
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.
- The algorithm has been renamed to CoarseFundamentalTop3Algorithm and updated to select the Top 3 instead of Top 5.
- The only new data required is daily, map and factor file for FB and has been added (map and factor files are dated 6/4/2018, as required by all regression tests).
- The coarse fundamental open source data has been updated.
- The expected regression statistics for the algorithm have been updated.
This algorithm shows how to implement a futures strategy in a framework algorithm.
`FutureUniverseSelectionModel` portfolio selection model was implemented to provide a base class to help create other futures universe selection models.
- 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.
- Moves `PearsonCorrelationPairsTradingAlphaModel` class to its own file in order to make it available as a framework model.
- Follows existing pattern design that alpha models receive a lookback and a `Resolution` object.
- Algorithm implements `IRegressionAlgorithmDefinition`.
- Since it will be used as a base class for other pairs trading models, it was ranamed as BasePairsTradingAlphaModel
- Use a tuple of symbols are key of BasePairsTradingAlphaModel._pairs dictionary.
Instead of using a single, pre-defined, pair set in the class constructor, the pair is defined when securities are changed, therefore depending on the universe selection model.
Michael Handschuh