- 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.
Securities from different types may have different timezones. In this case, daily resolution data is split in two slices in a history request, so these slices are grouped together to determined whether we have data from all the symbols in the same date.
- 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.
The ConfigureOptionChainSecurity method somewhat was overshadowing the security
initializer's responsibilities. This method has been trimmed down into a contract
filter function sub classes can use to define what option contracts they want to
select.
These times were being set AFTER they were copied, so the insights sent into
the portfolio construction model were the originals (uncopied) and then copies
that are sent into the event are then piped to the insight manager/alpha handler
for scoring, so interestingly, the scoring remained the same so regression tests
continued to pass because no types were written with a dependency on that data
other than the scoring, which as mentioned, got the copies with the correct times.
A recent change made use of this time and showcases this heinous bug in all its
glory.
This refactoring overrides the event invocator and seals the method to force
derived types to use the event handler, thereby guaranteeing that the event is
always invoked AFTER the generated/close times are properly set, also ensuring
that the copy is performed after the times are set as well.
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.
`UniverseSelectionModelPythonWrapper.CreateUniverses` method modifier should be `override` instead of `virtual`, since that class inherit from `UniverseSelectionModel`.
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 :)
`EqualEeightingPortfolioConstructionModel` (C# and Python) allocates all cash to the stocks who have insights in universe.
- Fixes regression tests to reflect the model logic change
- Fixes imports in python algorithms to use python models when available
Martin Molinero