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* Adds ETF(...) to UniverseDefinitions
* Adds ETF constituents universe framework regression algorithm
for C#/Python
* Address review: adds test cases for ticker/Symbol ETF universe additions
* Fixes bug where null Market would result in null dereference exception
* Address review: add missing Index tests
* Address review: don't hardcode market when creating constituent universe
* Uses Brokerage Model's default markets collection to determine
the market for the given security type
* Address review: restore QC500 and DollarVolume.Top(...)
* Restores algorithms related to both helper universe
definition methods
* Address review: remove copy to output directory for python algos
* Add example algorithms for ETF constituent universes using custom RSI alpha model
* Address review: adjust algorithm to use cache + algo RSI & clean up code
* Address review: make ETF Constituent RSI Alpha Model algo a regression test
* Address review: increase trade count and remove single trade logic
Regression Tests / build (push) Has been cancelled
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* WIP removal of custom data references & tests updates
* Regression algos updated and python algorithms moved to DataSource repos
* Fixes failing unit tests
* Add "LiveDataTypes" field to LiveNodePacket
* Adds Initialize() to IDataChannelProvider
* Adds new extension method to convert
HistoryRequest -> SubscriptionDataConfig
* Address review: Add protobuf definitions for Iconic data types
* Address review: re-adds DynamicSecurityDataAlgorithm as regression algo
* Small adjustments to variable naming and documentation
* Move test files to respective DataSource repos
* Remove python stubs directory
- Removing python stubs directory since after https://github.com/QuantConnect/Lean/pull/4899
it was been replaced by a python package `quantconnect-stubs`.
- Reverting IDE settings using the stubs folder PR https://github.com/QuantConnect/Lean/pull/4657
* Revert "Adds Python stubs location definition for PyCharm and Visual Studio Code (#4657)"
This reverts commit aded66ec5b.
* Address self-review: Provide list of imports and refactor python readme
Co-authored-by: Gerardo Salazar <gsalaz9800@gmail.com>
* Adds CustomBuyingPowerModelAlgorithm
This algorithms is an example on how to implement a custom buying power model.
In this particular case, it shows how to override `HasSufficientBuyingPowerForOrder` in order to place orders without sufficient buying power according to the default model.
* Upgrades CustomModelsAlgorithm to Include CustomBuyingPowerModel
The custom buying power model overrides `HasSufficientBuyingPowerForOrderResult` but it doesn't change the trades and, consequently, the regression statistics.
This example shows how to create an EMA cross algorithm for a futures' front contract. Once the contract is added, the indicators are registered to a new consolidator and warmed up with historical data. When a contract is removed, the consolidator is removed and the indicators are reseted. We don't need to liquidate it, because it's liquidated automatically since it has expired.
`BaseDictionary` is an abstract implementation of `IExtendedDictionary` keyed by `Symbol` that implements Python `dict` methods. `Slice`, `DataDictionary`, `SecurityManager`, and `SecurityPortfolioManager` derives from it in order to behave like Python `dict`.
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.
- Adds log to display the python version the algorithm is using.
- Fixes python algorithms that were failing because of small subtleties
like leading zeroes.
- Updates pythonnet with a version compiled with python 3.6 flags
- Changes in DockerfileFoundation: we now use miniconda to manage the python
environment.
- Took the opportunity to add NTLK (#1349), Tensorforce (#1369) and
PyTorch/Pyro (#1385).
- Changes readme in Algorithm.Python to show steps to install miniconda
Creates a python wrapper for volatility models created in python algorithms and adds a method to the Security object to set such models.
Adds an algorithm to show how volatility models can be implemented.
This algorithm serves as an example for the SetSecurityInilializer for python feature
The date range for the C# version is changed to match existing data
Adds support for fee, fill and slippage custom modelling.
Adds CustomModelsAlgorithm to showcase the new feature
Modifies C# version of CustomModelsAlgorithm to match existing data in github
Gerardo Salazar