* Reconcile duplicated code
* Add License header
* CS0219 Fixes: Value assigned, but never used
* CA1507: Use nameof in place of string literals
* CS0108 : Hides Inherited Member; Use new keyword to overwrite formally
* CS0114: Hides inherited member; use override keyword
* CS0168: Variable is declared but never used
* Tests CS1062; using obsolete implicit Symbol -> String; fix via .ToString()
* CS0472: Non Nullable Obj getting Null Checked
* CS0067 Member not used; ignore all cases for future use
* CS00162 : Unreachable code; either removed or ignored for debugging and test cases
* CS0169 Remove non-used fields; ignore those that may be used in future
* CS0414; Field is assigned but never used.
* CS0618; Obsolete properties and members; Only fixes simple ones, rest will have to broken up
* CS0649; Field never assigned too
* CS0659 & CS0661 ; Overwrite operators and equals but not hashcode; I don't really override it but just call base
* Small comment fix
* Cleanup pragma statement
- We will now check if python selection method returned `Universe.Unchanged`
- Removing `ToList()` call on fine and coarse data before sending it to
the python algorithm
- Adding regression algorithms
- Adding new `InsightWeightingPortfolioConstructionModel` that will
generate percent `Targets` based on the latest active `Insight` `Weight` per
`Symbol`.
- Will ignore `Insights` that have no `Weight`.
- If the sum of all the last active `Insight` per `Symbol` is bigger than 1, it
will factor down each target percent holdings proportionally so the sum is 1.
- Adding unit tests
- Adding a new regression test framework algorithm
- Note most of the code, including tests, are reused from the
`EqualWeightingPortfolioConstructionModel`
- 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.
`RsiAlphaModel` and `BlackLittermanOptimizationPortfolioConstructionModel` didn't have a key check after a history request. If a history request retuns no data for a given symbol, trying to access the pandas dataframe results in a `KeyError`.
`RsiAlphaModel` and `BlackLittermanOptimizationPortfolioConstructionModel` didn't have a key check after a history request. If a history request retuns no data for a given symbol, trying to access the pandas dataframe results in a `KeyError`.
Securities from different types may have different timezones. In this case, daily resolution data is split in two slices in a history request, so these slices are grouped together to determined whether we have data from all the symbols in the same date.
- Moves `PearsonCorrelationPairsTradingAlphaModel` class to its own file in order to make it available as a framework model.
- Follows existing pattern design that alpha models receive a lookback and a `Resolution` object.
- Algorithm implements `IRegressionAlgorithmDefinition`.
- Since it will be used as a base class for other pairs trading models, it was ranamed as BasePairsTradingAlphaModel
- Use a tuple of symbols are key of BasePairsTradingAlphaModel._pairs dictionary.
Instead of using a single, pre-defined, pair set in the class constructor, the pair is defined when securities are changed, therefore depending on the universe selection model.
We are not using python lists instead of generator (yield) because we get better exception information in this case. The aim is to lead users to avoid using generators and/or know its limitations.
The exception in ignored because the generator isn't closed until it is being deleted (automatically in this case, when Python exits); the generator __del__ handler closes the generator, which triggers an exception of there is one.
Colton Sellers