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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
- Do not reuse parameter variables `start` and `end`. Create new variables with meaningful names and rename them to `startUtc` and `endUtc`.
- Use `EachTradeableDayInTimeZone` to calculate `tradableDates`.
- Adds another test/assertion in the regression algorithms to ensure tests in the scheduled event were performed.
The tradable days of the history request should respect the data time zone since the data source files also do.
Upgrade `BasicTemplateFuturesHistoryAlgorithm` to a regression algorithm and add a schedule event to test history requests every hour.
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