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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
- Updates `DropboxUniverseSelectionAlgorithm` and `DropboxBaseDataUniverseSelectionAlgorithm` with new links to Dropbox files and date range to match the dates in the files.
- Adds copy of files in `TestData` folder.
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.
Some python algorithms suffered corrections to run under the new python framework (pythonnet).
Others were deleted because some features will be supported in futures implementations.
Adds a method in AlgorithmPythonUtil to transform C# DateTime into Python datetime
Louis Szeto