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
CustomCharting: did not initialize self.lastprice
QuandFuturesData: algorithm file name and class didn't match
UpdateOrderRegression: did not cast quantity (int) to decimal
UserDefinedUniverse: selector function returns C# List
Adds overloads to Plot and PlotIndicator to handle calls from python.
Adds CustomChartingAlgorithm and changes MACDTrendAlgorithm to showcase the feature
Some python algorithms suffered corrections to run under the new python framework (pythonnet).
Others were deleted because some features will be supported in futures implementations.
Adds a method in AlgorithmPythonUtil to transform C# DateTime into Python datetime
Jack Simonson