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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
- Reduce MinimumVariancePortfolioOptimizar precision goal so that both
CSharp and Py MeanVarianceOptimizationFrameworkAlgorithm return the same
results
- Limit factor file dates in factor file generator unit test
- Adding new unit test for python PCM implementations, asserting each
method is correctly called
- Reverting some unrequired changes in the
`MeanVarianceOptimizationFrameworkAlgorithm`
- Refactor shared logic from `EqualWeightingPortfolioConstructionModel`
into base `PortfolioConstructionModel` implementation
- `MeanVarianceOptimizationPortfolioConstructionModel` will respect
rebalancing period and will use all active inisights, not just the last
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.
- 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.
Martin-Molinero