1. Apply the pattern used in `EqualWeightingPortfolioConstructionModel`
2. Change the logic to compute the views from the insights.
3. Change the logis to compute the posterior mean and covariance
Use `UnconstrainedMeanVariancePortfolioOptimizer` in `BlackLittermanPortfolioOptimizationFrameworkAlgorithm` to bypass the difference in regression tests with `IPortfolioOptimizer` that rely on different algorithms in C# and python.
Adds unit tests for BLOPCV to test the implementation against Black and Litterman 1999 paper.
- When there are or aren'tt new insights, the EqualWeightingPortfolioConstructionModel will creates a target to flatten delisted securities from the universe of expired insights.
- Helper methods were added to deal with removing expired insights and getting active ones and used in `EqualWeightingPortfolioConstructionModel`
- Adds unit test
- Updates framework algorithms
Fixes a bug where we were using the security's data resolution to compute
the insight's close time. This led a case such as insight.Period == 20days
to step 20days worth of tradable minutes (assuming minute data resolution),
yielding a close time that was very far in the future.
We also add different means of specifying an insight's period/close time:
1. Specify insight period as a TimeSpan and we compute close time
2. Specify insight period and a resolution and bar count and we compute close time
3. Specify insight close time local directly and we compute the insight period
The key here is maintaining consistency between the three different approaches
which is heavily validated with the corresponding unit tests.
Edits also made to trust the insight's close time as the analysis end time in
the case where the analysis period == insight period (extra analysis period = 0).
Given the current setup (extra analysis period == 0), this guarantees that close
and analysis end times are equivalent.
Regression statistics were updated and expectedly we get many more insights that
have completed analysis, and as such, average scores have also changed.
- 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.
Instead of using a single, pre-defined, pair set in the class constructor, the pair is defined when securities are changed, therefore depending on the universe selection model.
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.
Pipes universe data from the data feed, through TimeSlice.Create and adds
TimeSlice.UniverseData dictionary property for read access from the algo
manager, where the data will be placed onto the correct security object.
This happens when users pass Securities.Keys into the manual model,
causing the SecurityChanges object to have references to the canonical
securities, thereby leading to indicators and other things being
done to them unknowningly.
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.
Alpha models can choose to implement the Name property, if not, the system
will use the model's type name as the Insight.SourceModel.
Existing tests were updated to also assert expected model names
This identifier is used to determine the alpha model that generated.
This is NOT ideal, since it requires users to specify the value, more
thought will be givent to how we can resolve this value automatically
Adds test for surviving roundtrip copy operation.
Implements `Insight.Price` method to make it easier to create new instances of `Insight` of `InsightType.Price`.
Standardize the parameter order to `Symbol`, `TimeSpan`, `InsightType`, `InsightDirection`, `Double`, `Double`.
Martin Molinero