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* Filter out small orders based on Setting
- BuyingPowerModel will filter out small orders based on algorithm
setting, a % of PTV, instead of hard coded 1 share value. Addin unit
and regression tests
- Updating regression algorithms to use new setting, reduce order trades
* Update regression algorithms
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* Python research import improvements
- Improve start.py for research env
- Remove unrequired imports
* Centralize algorithm imports
* Add regression test GH action
* Unit test python import clean up
* Join research and main imports
* More python import clean up
* Fix failing skipped regression algorithm
We didn't experience the expected performance improvements. Locally under
unit test there was aboout an order of magnitude throughput increase, but
when run against the history benchmark, this new approach was 60% slower.
We're reverting this for now to perform further analysis and better
understand the performance profiling of the python history stack.
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.
- 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.