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* Adds ETF(...) to UniverseDefinitions
* Adds ETF constituents universe framework regression algorithm
for C#/Python
* Address review: adds test cases for ticker/Symbol ETF universe additions
* Fixes bug where null Market would result in null dereference exception
* Address review: add missing Index tests
* Address review: don't hardcode market when creating constituent universe
* Uses Brokerage Model's default markets collection to determine
the market for the given security type
* Address review: restore QC500 and DollarVolume.Top(...)
* Restores algorithms related to both helper universe
definition methods
* Address review: remove copy to output directory for python algos
* Add example algorithms for ETF constituent universes using custom RSI alpha model
* Address review: adjust algorithm to use cache + algo RSI & clean up code
* Address review: make ETF Constituent RSI Alpha Model algo a regression test
* Address review: increase trade count and remove single trade logic
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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
In this new UniverseDefintions, a member, QC500, defines the QC500 Universe. This helper can be used to add that custom universe to the user algorithm (both in C# and Python).
- ConstituentsQC500GeneratorAlgorithm:
- Change monthly flag to be consistent with Selection Model that cannot use Schedule events.
- Use a Dictionary keyed by `Symbol` instead of `string`.
- Selector functions return `Universe.Unchanged` instead of empty list;
-Refactoring and more informative logging.
- QC500UniverseSelectionModel
- SelectFine methods were performing all the logics every day and it should be only once per month
- Log and return `Universe.Unchanged` before division by zero if universe drops to zero members after filtering before selection by sector.
- Refactoring
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.
Louis Szeto