- 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.
- 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.
- Missing `event` keyword prevented pythonnet to recognize `DataConsolidated` as a event handler.
- Adds python version of `RenkoConsolidatorAlgorithm`.
This framework algorithm alpha model is HistoricalReturnsAlphaModel and the portfolio construction model is MeanVarianceOptimizationPortfolioConstructionModel.
This examples implements an algorithm that rebalances the portfolio according to modern portfolio theory.
- Adds log to display the python version the algorithm is using.
- Fixes python algorithms that were failing because of small subtleties
like leading zeroes.
- Updates pythonnet with a version compiled with python 3.6 flags
- Changes in DockerfileFoundation: we now use miniconda to manage the python
environment.
- Took the opportunity to add NTLK (#1349), Tensorforce (#1369) and
PyTorch/Pyro (#1385).
- Changes readme in Algorithm.Python to show steps to install miniconda
Creates a python wrapper for volatility models created in python algorithms and adds a method to the Security object to set such models.
Adds an algorithm to show how volatility models can be implemented.
In this update, methods overloads with decimal parameters accept python float.
- Fixes FractionalQuantityRegressionAlgorithm:
With the pythonnet update we can pass a python float where a decimal is required.
Stefano Raggi