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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
* OnEndOfDayRegressionAlgorithm - Since the EndTime of the hourly benchmark is during the day,
the OnEndOfDay method gets called one less time than usual. Updates statistics
* CustomUniverseWithBenchmarkRegressionAlgorithm.cs - modified algorithm so
that it works with hourly benchmark. Previously only tested for Daily benchmark
* BasicTemplateAlgorithm.py - Modified resolution to be
Resolution.Minute, just like it is in C#
* CustomDataRegressionAlgorithm.py - Remove warmup call from Initialize
* IndicatorSuiteAlgorithm.py - Adds PythonQuandl import to fix import error
By using the python object parant class, which is either `PythonQuandl` or `PythonData`, instead of `DynamicData`, the `AlgorithmManager.Stream` method can find a matching subcription data configuration used to create a data feed packet.
Closes#2694
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