* Add open interest data to future chains contracts
Even though futures chains and contracts are not created from internal subscriptions data (like OI subscriptions or continuous futures contracts subscriptions), we still want to add OI data to already created contracts given that OI subscriptions are always internal but trade an quote ones are not.
* Minor changes
Regression Tests / build (push) Has been cancelled
Build & Test Lean / build (push) Has been cancelled
* 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
* Filter values that are before subscription start time; also adjust starttime for OpenInterest
* Use data EndTime for comparison
* Allow Auxiliary data through
* Fix OpenInterest DataReader Logic
* Add regression
* Address review
* Ignore open interest for time slice
- TimeSliceFactory will directly ignore open interest for determining if
the slice has data or not. Open interest will still be available
through the Tick collection. Reverting some of the previous commits
changes since they are no longer required.
- HistoryRequests and SubscriptionRequest will use AlwaysOpen exchange
for open interest requests. Adding unit test reproducing issue
- Adding `BaseDataRequest` to avoid duplication logic.
* Make OpenInterest an internal feed and ignored by default in history
- Adding unit tests
* Revert SubscriptionFilterEnumerator Start time addition
Co-authored-by: Martin Molinero <martin.molinero1@gmail.com>
- Fix Slice.Get OpenInterest type. Adding unit test
- Fix for SecurityCache that wasn't storing OpenInterest types
- Updateing regression tests to covere these usages
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
Jhonathan Abreu