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.
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
Implements Quandl support for Python.
It was not possible to derive from Quandl in order to select the column. If the data did not have "close", it would thrown an exception since it would look for this work in a dictionary.
It is now possible to select the column.
See example QuandFuturesDataAlgorithm.py
Michael Handschuh