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 :)
- This version serves two purposes: example of universe selection model and base class for other universe selection models, since the pythonnet doesn't deal well with inheritance of abstract classes.
- Adds PyObject overload to `CoarseFundamentalUniverse`.
- Use `MaximumDrawdownPercentPerSecurity` as `RiskManagementModel`.
- Modifies regression test to reflect risk model choice
- Use SetXXX to set models in python version
The PairsTradingAlphaModel is a simple example of defining an insight
grouping. Insights that are grouped together are assigned a unique
group-id that can be used by the portfolio construction model.
Updates were made to the CommonAlphaModelTests to give more control to
derived types. Some changes are still needed here to give securities
unique prices. I would recommend using a psuedo-random walk approach
by using Random with a constant seed value.
David