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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
- After https://github.com/QuantConnect/Lean/pull/4000
CoarseFineFundamentalRegressionAlgorithm started using `MarketCap`, but
this value was always 0 in existing data, so it caused undeterministic
results. Adding new data and update expected result.
- `AccumulativeInsightFrameworkAlgorithm` expected statistic were not
correct, updating.
* 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
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.
Jhonathan Abreu