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.
- Removed usages of algorithm.Securities.key as a parameter for the
`ManualUniverseSelectionModel()` since those securities, added through
`AddXXXX` calls will be managed by the `UserDefinedUniverse`. This was
causing for Universes to try to add the same subscription requests
- Adding new empty constructor for ManualUniverseSelectionModel,
required for Python
- ManualUniverse will return any existing SDC for the
symbol. This is for maintaining existing behavior and
preventing breaking changes: Specifically motivated by usages of
Algorithm.Securities.Keys as constructor parameter of the
ManualUniverseSelectionModel, since those Symbols added by Addxxx()
calls will already be managed by the UserDefinedUniverse
- Making some format modifications to aling with used Lean formatting
- Some formatting, comments changes
- Removing SubscriptionManager from new FuturesChainUniverse constructor
- Making Option and Future properties in FutureChainUniverse and
OptionChainUniverse
- Obsolete Universe.CreateSecurity, Universe.SetSecurityInitializer,
Universe.GetSubscriptionRequests(Security security, DateTime currentTimeUtc, DateTime maximumEndTimeUtc)
- Adding new GetSubscriptionRequests() overload that will receive
instance which implementes new ISubscriptionService
- UserDefinedUniverse will stop using Security.Subscriptions
`RsiAlphaModel` and `BlackLittermanOptimizationPortfolioConstructionModel` didn't have a key check after a history request. If a history request retuns no data for a given symbol, trying to access the pandas dataframe results in a `KeyError`.
`RsiAlphaModel` and `BlackLittermanOptimizationPortfolioConstructionModel` didn't have a key check after a history request. If a history request retuns no data for a given symbol, trying to access the pandas dataframe results in a `KeyError`.
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.
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.
- 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.
The allocation should be calculated for symbols which last insights are not `InsightDirection.Flat`. For example, if the last insight of SPY and of IBM are Up, and of AIG is flat, then 50% of equity should be allocated in SPY, 50% in IBM and 0% in AIG. Before this fix, we would have 33% SPY, 33%, IBM and 0% AIG.
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.
Securities from different types may have different timezones. In this case, daily resolution data is split in two slices in a history request, so these slices are grouped together to determined whether we have data from all the symbols in the same date.
- 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`.
Michael Handschuh