- Improving OrderSizing.Value and Volume to include code in consumers
- OrderSizing.GetUnorderedQuantity() will adjust result by lot size
- ImmediateExecutionModels will use OrderSizing.GetUnorderedQuantity()
- OrderSizing.Value() will take into account ContractMultiplier
- Adding unit tests
- `TimeSliceFactory` will avoid creating empty collections
- `ExecutionModels` will check target collection count before trying to
enumerate
- Reduce calls to .`TotalPortfolioValue`
- `SecurityValues` will only be created when required
- `TimeKeeper` will use TimeZone unique Id as dictionary key. The
TimeZone hash is expensive.
- `AlgorithmManager` will avoid calling `DateTime.UtcNow`,
`ConvertFromUtc()` and `RoundDownInTimeZone()`
This model was assuming that the history request used to warm up the indicators contains the 'close' column which is only valid for Equity.
The models were also refactored to update the indicators without a consolidator since the last data point from the history request was not pushed throught the indicators.
- Removing `using QCAlgorithmFramework = QuantConnect.Algorithm.QCAlgorithm`
- Removing `QCAlgorithmFrameworkBridge`
- Removing `IsFrameworkAlgorithm`
- Making `EmitInsightBasedOnFill` private. Adding new
`IOrderEventProvider` exposing an `event` to which `QCAlgorithm` will
subscribe.
- `AccountType.Cash` algorithms will be allowed to manually trade and
emight insights manually or with alpha model.
- This commit is related to PR 19 in QC/pythonnet
- C# decimal will be cast to C# double and converted into python
float
- Adding new `decimal.py` into the python algorithm project. This is
required for backwards compatibility with users performing operations
over expected decimal types (like `Price`)
- Updating two python regression test algorithms using custom python
execution models to be aware and ignore floating point precision errors
when handling order sizing.
The volume weighted average price execution model had its own implementation
of VWAP since the existing VWAP didn't perform the daily reset. The intraday
VWAP indicator has since been added to the indicators project so this is just
duplicate code.
- Typoes were fixed in `ImmediateExecutionModel.cs`;
- Refactors `PriceIsFavorable` methods in `StandardDeviationExecutionModel` and `VolumeWeightedAveragePriceExecutionModel` C# models;
- Adds python version of C# execution models
Three methods were added to enable easier consumption of an enumerable
of slices returned from a history request. The dictionary version is
most likely to be used by python, while the functional versions allow
for lots of flexibility in how you'd like to consume the stream of slices.
After much review, it was determined that this was much more confusing than it
was helpful. The removed implementation hinged on the thinking that the construction
model would be emitting targets as quantities of cash and not quantities of virtual
positions. A previous commit added a warning message for cash modelling and this
commit removes a (bad) attempt at making cash modelling work as expected. For now,
if you want to use cash modelling with the algorithm framework, careful thought will
need to be applied to the implementation of the portofio construction model AND the
execution model. They'll each need to be speaking on the same terms. As of this point,
we're unsure of a means to address all concerns, and so are leaving it as a warning
message coupled with decent documentation in the commit history regarding our
discussions/thoughts on the topics.
Again, the key for cash modelling working properly is just that the portfolio
construction model and the execution model agree on what each target means and
also agree on how virual positions vs currency balances are handled and managed.
The execution models should maintain references to symbol data as long as
the security is a member of at least one universe. It's important to note
here that 'membership' in the universe is not the same as 'recently selected'
by the universe. A security remains a member even after it's been deselected
until it has zero holdings and zero open orders.
In the next commit we'll add an extra condition which will confirm that there
are also zero outstanding portfolio targets for the security.
These methods were phrased in the negative sense. This change
reads how one would expect, if certain conditons are met, then
buy -- vs, if certain condition is not met, don't buy.
VWAP will submit market orders while the current price is more favorable than VWAP.
STD will submit market orders while the current price is a configured number of
standard deviations away from the mean in the favorable direction.
Adds method overload that accept a `PyObject` to `SetAlpha`, `SetExecution`, `SetPortfolioConstruction`, `SetPortfolioSelection` and `SetRiskManagement`. In these methods, a custom model written in python will be wrapped around the respective `PythonWrapper`.
When focusing on generating alpha signals we don't need t both with execution or
portfolio construction models. Instead we can judge how well we do based on our
generated alphas. By not submitting orders, backtests and live performance is
greatly improved.
This forces the quantity computation to be performed from the portfolio construction model.
As a result of this change, we've removed the Percent and Quantity implementations and
replaced them with just a PortfolioTarget implementation that is equivalent to the previous
Quantity implementation. Users can still use the static Percent method to generate the
correct quantities for a target for the common case of a percent weighted portfolio.
Martin Molinero