- Classic Algorithms will emight insights based on order fills.
- To be able to update generated insights closed time, we will not
clone emitted insights.
- `InsightAnalysisContext` will update `AnalysisEndTimeUtc` when the
Insight period is closed and the period is `EndOfTimeTimeSpan`
- Adding new regression algorithm asserting on the new emitted insights
- Adding unit tests
- `LiveTradingResultHandler` will store `AlphaRunTimeStatistics`
- Making `DefaultAlphaHandler.ProcessAsynchronousEvents` virtual to
facilitate cloud changes
- Updates PythonNet to 1.0.5.17
- Improve performance by adding new `interop` `type` cache holding a `bool`, true if its an `exception`. And adding a `setter` and `getter` cache for the `propertyobject`. Closes#2925.
- Decimal parsing allows numeric string in exponential notation. Closes#2918#2919.
Closes#2929
- Adding new `SetAccountCurrency()` for backtesting. Has to be called
before adding any `Security` or calling `SetCash()`, else will throw.
- Adding new Non account currency unit tests for `CashBuyingPower`,
`SecurityPortfolioModel`, `SecurityMarginModel`,
`SecurityPortfolioManager`, `Future/OptionMarginBuyingPowerModels`
- Adding new C# regression test using `SetAccountCurrency()`, one for
`CashBuyingPowerModel` and one for `SecurityMarginModel`
- Adding new Py and C# basic regression algorithms using
`SetAccountCurrency()`
- `Options` and `Futures` will use not use `AccountCurrency` as quote
Cash.
- `SecurityBenchmark` value will be in account currency
- Requires a new PythonNet 1.0.5.15 package where the different `.dll` are in a
specific folder: `\win` `\linux` and `\osx`
- Removed not present `decimal.py` from `Algorithm.Python` project. It
was moved into `Common`.
- Replace `xbuild` for `msbuild` required for using the `System.Runtime.InteropServices`.
Also note the `xbuild` on travis prints:
> >>>> xbuild tool is deprecated and will be removed in future updates, use msbuild instead <<<<
In the new package:
- C# decimal conversion will use C# double and python float due to the big performance impact of converting C# decimal to python decimal;
- `AlgorithmPythonWrapper` will now keep reference to the `OnData` and
`OnOrderEvent` `PyObject` method, giving a performance improvement,
since it does not have to resolve it in each loop.
- Moving `UniverseSelection.EnsureCurrencyDataFeeds` call into the
`IResultHandler` implementation through usage of the new `SetupHandlerHelper`
class, that will also set an initial conversion rate if none present.
- Adding regression test, that reproduces original issue
- Adding new `IAlgorithm.AccountCurrency { get; }` that will point to the
`Portfolio.CashBook.AccountCurrency`. Setter will be added in a
following PR.
- Base `Brokerage` class will now have a `AccountCurrency { get }`
pointing to the `IAccountCurrencyProvider`. Will be used by the different
brokerages implementations.
IAlgorithm exposed a means of setting the CurrentSlice but not a
means for consumers to get the value. This was because until now
all consumers were within the QCAlgorithm scope and had access to
the member variable. This change makes the CurrentSlice available
to LEAN engine code, where it's first use will be in PaperBrokerage
to detect and apply dividend distributions.
When the algorithm type in config.json was not found, Loader.cs
would throw a confusing error message. Modified the error message
to make it clearer.
Previous error message: "Unable to resolve multiple algorithm types
to a single type...". New error message: "Algorithm type name not
found,or unable to resolve multiple algorithm types to a single type...".
Improves the message when the Loader cannot resolve the algorithm to load. It happens when the assemblies don't have a QCAlgorithm class that match the algorithm name or you have 2-of them so Lean doesn't know which one to backtest.
The possible Loader exceptions are thrown as `AlgorithmSetupException` to mach the pattern for exceptions during initialization.
Launcher project is packed as `QuantConnect.Lean`but as a mean ot having all Lean features just calling one package.
In the same sense, `QuantConnect.Algorithm.CSharp` is included as package and added as dependecy in the `QuantConnect.Lean` package just to have a working example aailable out-of-the-box.
This type is just used as a container for generatd insights. Renaming in
preparation for a new InsightCollection to mirror the PortfolioTargetCollection
`AlgorithmPythonWrapper.OnFrameworkData` should call the base class method (`QCAlgorithmFramework.OnFrameworkData`) directly instead of trying to call this method from the python script
We had an issue with the data feed picking up universe/security changes
too quickly, thereby preventing user code from being able to configure the
security object properly. Specifically, users were having an issue setting
the data normalization mode of options and underlying equity securities. By
the time the user code had set the data mode, the data feed had already
created a subscription and began processing it, so the changes were never
seen in the data feed.
This change moves all security/universe changes into pending lists and at
the end of the time step applies those changes. Security objects are still
added directly to the SecurityManager for instance access, but we delay in
adding the security to the universe and the universe to the UniverseManager.
Once added to the universe manager, an event is fired and the data feed will
process the new subscriptions.
This event was not properly wrapped.
When `IAlgorithm.InsightsGenerated` is set in `IAlphaHandler`, it should be directed to the base algorithm, whereas it was set to a null variable.
Move the logic of importing the module into AlgorithmPythonWrapper where it is wrapped.
Throws an exception if the script does not have a class that inherits from either QCAlgorithm not QCAlgorithmFramework.
Adds a check for OnData being defined in the module. If not, OnData from the base class will not be called (it causes stack overflow otherwise)
The term 'alpha' is used to describe the entire algorithm. Therefore, 'alpha'
produces insights. From this we have things like IAlphaModel, which is the model
defining how insights are produced. We have IAlphaHandler, which defines how the
insights from a single 'alpha' (the algorithm) are managed, analyzed, and stored.
Types closer to the individual prediction level, such as InsightDirection, or
InsightScore relate directly to exactly 1 insight. The distinction between the
two became more clear as we developed the insights API, and from that effort it
was decided to harmonize alpha/insight terminology across the various QC systems.
Use interpreter.GetMessageHeader(e) instead of e.Message to produce to collated exception message containing the messages from inner exceptions.
Add the DllNotFoundPythonExceptionInterpreterTests to the test project.
The error message it was used to bypass the the error logging has been changed from `takes exactly x arguments (y given)` to `takes x positional argument but y were given`.
In order to access the custom data classes, the module containing them was added to the ObjectActivator. This was unnecessary if it wasn't a custom data algorithm.
Also, this operation would not be taken into account if the custom data class were defined after the algorithm was created: this is the case for QuantBook.
We refactor how custom data is handled: a new class was added to provide a instance creation factory that creates an instance of each python custom type.
- 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
With this constructor, SetConverter method, that was not respecting Lean pattern, is removed. The initialization of _converter field is done once the type is loaded.
Python algorithms with custom data requires an operation that converts a dictionary key into a attribute. In the current implementation the Slice object was converted into a python dictionary. This was not optimal, since we just need to make this conversion when the value of a key in the Slice is accessed.
This implementation proposes a wrapper for the Slice object, PythonSlice, that would just perform the operation described above when needed.
This method is being added to allow algorithms to complete initialization tasks that cannot be executed during Initialize, such as cancelling existing open orders in live trading.
This method will be called only once, when the warmup task is complete.
Closes#1043
Jared