- 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
- Adding new `SetAccountCurrency()` for backtesting. Has to be called
before adding any `Security` or calling `SetCash()`, else will throw.
- Adding new Non account currency unit tests for `CashBuyingPower`,
`SecurityPortfolioModel`, `SecurityMarginModel`,
`SecurityPortfolioManager`, `Future/OptionMarginBuyingPowerModels`
- Adding new C# regression test using `SetAccountCurrency()`, one for
`CashBuyingPowerModel` and one for `SecurityMarginModel`
- Adding new Py and C# basic regression algorithms using
`SetAccountCurrency()`
- `Options` and `Futures` will use not use `AccountCurrency` as quote
Cash.
- `SecurityBenchmark` value will be in account currency
- 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 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.
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.
- 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.
AlexCatarino