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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
- Removes `UniverseSettings.DataNormalizationMode` (it will ne addressed in a dedicated issue: https://github.com/QuantConnect/Lean/issues/3082)
- Adds examples/tests for Tick resolution, Forex (QuoteBar data) and Custom data.
- Tick resolution is not allowed: logs a message
- Custom data example/test added in `CustomDataNiftyAlgorithm`
- Adds support for ATR and VWAP since they are, respectively, a bar and a trade bar indicator.
- Adds consolidators to handle difference between data resolution and indicator resolution.
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.
Implements Quandl support for Python.
It was not possible to derive from Quandl in order to select the column. If the data did not have "close", it would thrown an exception since it would look for this work in a dictionary.
It is now possible to select the column.
See example QuandFuturesDataAlgorithm.py
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
Martin-Molinero