- 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
- 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.
- 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.
We are not using python lists instead of generator (yield) because we get better exception information in this case. The aim is to lead users to avoid using generators and/or know its limitations.
1. `HistoricalReturnsAlphaModel`:
1. Adds lookback period for return calculation
2. Adds return-depend direction to insights
3. Refactors indicator history warm-up
2. `MeanVarianceOptimizationPortfolioConstructionModel`:
1. Adds lookback period for return calculation
2. Adds exception for null magnitude
3. Refactors indicator history warm-up
3. Other minor fixes:
1. Default target return was 2 instead of 0.02 (2%)
2. Proper removal of consolidator subscriptions
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.
Martin Molinero