In the previous implementation, each one of bars.Select statement causes an extra enumeration of the bars enumerable. To make matters worse, this is all invoked 4*N times, where N is the number of data points.
Another bottleneck was related to the way we concatenate pandas.DataFrames: we would add a new data frame to an existing data frame individually. Now, we collect all data frames and concatenate them in one operation.
With this new implementation, we have accomplshed a reduction of 90% of memory usage in a history request of 1000 symbols.
Creates a class that wraps a PyObject that represents a ISecurityInitializer object for the case where we use a class to initialize securities
Adds SetSecurityInitializer method that accepts a PyObject that may represent a method, a function or class that is used to initialize securities
Adds support for fee, fill and slippage custom modelling.
Adds CustomModelsAlgorithm to showcase the new feature
Modifies C# version of CustomModelsAlgorithm to match existing data in github
With simple commands, we can use Lean indicators in QuantBook. It fetchs the historical data from the symbol, calculates the indicator and saves the output in a pandas.DataFrame.
History requests should not return a dictionary with a dataframe, but a multi-index dataframe.
It is more common to work with multi-index dataframes rather than multi-column.
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
This flag will be used to speed python algorithms execution, since it avoids a wrapping operation that is only required when there is custom data in python algorithms.
AlexCatarino