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* Implement indicator-based option price model
This model uses IV and Greeks indicators to implement Lean's own option pricing model
* Minor fixes
* Address peer review
* Minor tests fixes
* Make the indicator based price model the default for options
* Address peer review
* Cleanup and minor changes
* Support indicators configuration for new pricing model
* Some cleanup
* Add QL option price model example algorithm
* Return lean models from static helpers
* Minor tests fixes
* Minor test fixes
* Address peer review
* Cleanup
* Fix unit tests
* Move QL models to OptionPriceModels.QuantLib.*
* Add forward tree helper method
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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
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