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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
Adds `MarketCap` member to `FineFundamental` class that represents the aggregate market value of a company represented in dollar amount.
Changes `CoarseFineFundamentalRegressionAlgorithm` (C# and Python) to select securities based in its market capitalization. Same result as selecting by P/E ratio.
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.
- Adds log to display the python version the algorithm is using.
- Fixes python algorithms that were failing because of small subtleties
like leading zeroes.
- Updates pythonnet with a version compiled with python 3.6 flags
- Changes in DockerfileFoundation: we now use miniconda to manage the python
environment.
- Took the opportunity to add NTLK (#1349), Tensorforce (#1369) and
PyTorch/Pyro (#1385).
- Changes readme in Algorithm.Python to show steps to install miniconda
Martin-Molinero