- Adding new `InsightWeightingPortfolioConstructionModel` that will
generate percent `Targets` based on the latest active `Insight` `Weight` per
`Symbol`.
- Will ignore `Insights` that have no `Weight`.
- If the sum of all the last active `Insight` per `Symbol` is bigger than 1, it
will factor down each target percent holdings proportionally so the sum is 1.
- Adding unit tests
- Adding a new regression test framework algorithm
- Note most of the code, including tests, are reused from the
`EqualWeightingPortfolioConstructionModel`
- Updates PythonNet to 1.0.5.17
- Improve performance by adding new `interop` `type` cache holding a `bool`, true if its an `exception`. And adding a `setter` and `getter` cache for the `propertyobject`. Closes#2925.
- Decimal parsing allows numeric string in exponential notation. Closes#2918#2919.
Closes#2929
- Requires a new PythonNet 1.0.5.15 package where the different `.dll` are in a
specific folder: `\win` `\linux` and `\osx`
- Removed not present `decimal.py` from `Algorithm.Python` project. It
was moved into `Common`.
- Replace `xbuild` for `msbuild` required for using the `System.Runtime.InteropServices`.
Also note the `xbuild` on travis prints:
> >>>> xbuild tool is deprecated and will be removed in future updates, use msbuild instead <<<<
In the new package:
- C# decimal conversion will use C# double and python float due to the big performance impact of converting C# decimal to python decimal;
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.
Restructured
Update message
Added removal of trailing highs for unnecessary securities
Add logging message
Improvements
Rename
Add regression Algorithm
Changed to use TradeBar values instead of only current price
Cleaned msg layout
Update Regression test
This is the inverse of the MaximumDrawdownPercentPerSecurity risk model.
It's goal is to liquidate holdings for a security when the unrealized profit
passes a specified threshold. This can viewed as a 'take the money and run'
risk model.
Add a multi-dimensional array of double representing the covariance. Some models, e.g., Black-Litterman may want to optimize a covariance that is different from the historical one.
Adds UnconstrainedMeanVariancePortfolioOptimizer: a simple optimizer that has a solution, therefore no numerical optimization method is required.
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.
- 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 :)
Jared