- `TimeSliceFactory` will avoid creating empty collections
- `ExecutionModels` will check target collection count before trying to
enumerate
- Reduce calls to .`TotalPortfolioValue`
- `SecurityValues` will only be created when required
- `TimeKeeper` will use TimeZone unique Id as dictionary key. The
TimeZone hash is expensive.
- `AlgorithmManager` will avoid calling `DateTime.UtcNow`,
`ConvertFromUtc()` and `RoundDownInTimeZone()`
- Adding new `AlgorithmSettings` Min and Max absolute portfolio target
percentage
- Adding new `PortfolioConstructionModel.FilterInvalidInsightMagnitude()`
helper method that will be used by the `BlackLitterman` and
`MeanVariance` optiomization portfolio construction models to skip
insights with extreme magnitudes that will cause exceptions
- `PortfolioTarget.Percentage()` will now verify requested percent is
withing the settings values
- 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`
This model was assuming that the history request used to warm up the indicators contains the 'close' column which is only valid for Equity.
The models were also refactored to update the indicators without a consolidator since the last data point from the history request was not pushed throught the indicators.
- Removing `using QCAlgorithmFramework = QuantConnect.Algorithm.QCAlgorithm`
- Removing `QCAlgorithmFrameworkBridge`
- Removing `IsFrameworkAlgorithm`
- Making `EmitInsightBasedOnFill` private. Adding new
`IOrderEventProvider` exposing an `event` to which `QCAlgorithm` will
subscribe.
- `AccountType.Cash` algorithms will be allowed to manually trade and
emight insights manually or with alpha model.
- ConstituentsQC500GeneratorAlgorithm:
- Change monthly flag to be consistent with Selection Model that cannot use Schedule events.
- Use a Dictionary keyed by `Symbol` instead of `string`.
- Selector functions return `Universe.Unchanged` instead of empty list;
-Refactoring and more informative logging.
- QC500UniverseSelectionModel
- SelectFine methods were performing all the logics every day and it should be only once per month
- Log and return `Universe.Unchanged` before division by zero if universe drops to zero members after filtering before selection by sector.
- Refactoring
`MaximumSectorExposureRiskModel` needs `IndustryTemplateCode` which is only found in Equity data with Fundamental data. Thus, we check whether all active securities have such information.
- 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;
- This commit is related to PR 19 in QC/pythonnet
- C# decimal will be cast to C# double and converted into python
float
- Adding new `decimal.py` into the python algorithm project. This is
required for backwards compatibility with users performing operations
over expected decimal types (like `Price`)
- Updating two python regression test algorithms using custom python
execution models to be aware and ignore floating point precision errors
when handling order sizing.
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.
Gerardo Salazar