By using the python object parant class, which is either `PythonQuandl` or `PythonData`, instead of `DynamicData`, the `AlgorithmManager.Stream` method can find a matching subcription data configuration used to create a data feed packet.
Closes#2694
The C# version was supposed to handle python modules, but when they inherit from a C# module, pythonnet send them as C# objects. Consequently, they are not wrapped and cannot be used. The python version of `CompositeRiskManagementModel` solves the issue.
- Implements python version of `MaximumUnrealizedProfitPercentPerSecurity`
- Updates `CompositeRiskManagementModelFrameworkAlgorithm` in order to use python risk model.
`DataDictionary.GetValue` is meant to be used as an alternative to `DataDictionary.TryGetValue`. It was created due to limitations to the python implementation.
- Removed usages of algorithm.Securities.key as a parameter for the
`ManualUniverseSelectionModel()` since those securities, added through
`AddXXXX` calls will be managed by the `UserDefinedUniverse`. This was
causing for Universes to try to add the same subscription requests
- Adding new empty constructor for ManualUniverseSelectionModel,
required for Python
- ManualUniverse will return any existing SDC for the
symbol. This is for maintaining existing behavior and
preventing breaking changes: Specifically motivated by usages of
Algorithm.Securities.Keys as constructor parameter of the
ManualUniverseSelectionModel, since those Symbols added by Addxxx()
calls will already be managed by the UserDefinedUniverse
- Making some format modifications to aling with used Lean formatting
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.
Stefano Raggi