- 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
This was added to confirm no changes of substance happened as a result of
altering the subscription synchronization code. Leaving these in as debug
aids in case of further failures w/ this algorithm.
In this update, methods overloads with decimal parameters accept python float.
- Fixes FractionalQuantityRegressionAlgorithm:
With the pythonnet update we can pass a python float where a decimal is required.
In this PR we are disabling the default security seeding (automatically getting the last price for a security when added to the algorithm) for a couple reasons, both when using large universes:
- In live trading, these history requests are sent to a history server, potentially causing timeouts
- In backtesting, depending on the algorithm this could also cause slowdowns up to 30%
Requiring a period here forces signal models to place a time frame on
when their signal is valid. This also allows consumers of signals to
have some expectation of when a prediction should come to fruition.
The risk management model is intended to check the algorithm's positions
at the end of each time step to potentially exit positions that are losing
too much.
This change includes a check to prevent users from overriding methods required
by the framework. This is non-ideal and we should perhaps look into alternatives
to this approach, which could involve additional methods on IAlgorithm. In order
to not lose access to these events at the algorithm level, we could expose them
as C# events (not sure python compatibility?)
This algorithm had a couple of issues:
- it was using a coarse universe with no data files available, so it has been changed to use a custom universe
- it was using algorithm time instead of selection time in the selector function
The regression stats have also been updated to match the new algorithm code.
Juan José D'Ambrosio