- Changes `ExpectedStatistics` in MVOFA
- All regression tests now
- Removes unnecessary constructor arguments in `ReturnsSymbolData`
- Tide up code and add method summaries.
- 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.
This change allows the universe selection model to select different universe
definitions as time proceeds. This enables the definition of a universe model
that, for example, could add option chains for securities selected by a different
universe model.
The BasicTemplateOptionsFrameworkAlgorithm was added to showcase and provide
regression for a universe model that selects different universes.
Provides demonstration algorithm showing the steps required to convert a
QCAlgorithm into the framework with minimal code changes.
1. Subclass QCAlgorithmFrameworkBridge
2. Add EmitInsights calls to where orders are placed
3. Profit :)
- This version serves two purposes: example of universe selection model and base class for other universe selection models, since the pythonnet doesn't deal well with inheritance of abstract classes.
- Adds PyObject overload to `CoarseFundamentalUniverse`.
- Use `MaximumDrawdownPercentPerSecurity` as `RiskManagementModel`.
- Modifies regression test to reflect risk model choice
- Use SetXXX to set models in python version
The PairsTradingAlphaModel is a simple example of defining an insight
grouping. Insights that are grouped together are assigned a unique
group-id that can be used by the portfolio construction model.
Updates were made to the CommonAlphaModelTests to give more control to
derived types. Some changes are still needed here to give securities
unique prices. I would recommend using a psuedo-random walk approach
by using Random with a constant seed value.
- Typoes were fixed in `ImmediateExecutionModel.cs`;
- Refactors `PriceIsFavorable` methods in `StandardDeviationExecutionModel` and `VolumeWeightedAveragePriceExecutionModel` C# models;
- Adds python version of C# execution models
The composite model combines multiple alpha models into a singular model and
properly sets each insight's SourceModel property to the name of the model that
generated the insight
Alpha models can choose to implement the Name property, if not, the system
will use the model's type name as the Insight.SourceModel.
Existing tests were updated to also assert expected model names
Martin Molinero