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* Initial solution
* Solve review comments
* Add PythonSelectionModelHandler to reduce code duplication
* Refactor universe selection models
* Add python instance to Selection Models with virtual/override methods
* Add python instance to Alpha Models
* Add python instance to Execution models
* Solve review comments
* Solve new review comments
* Fix calling SetPythonInstance only when method exists and is callable
* Use unit test instead of regression algorithms
* Solve review comments
* Set python instance to the models
* Initialize Python containers only when instance is set
* Replace try-catch with explicit method existence check
* Initialize containers in BasePythonWrapper only when needed
* Add null instance check before method invocation
* Refactor TryExecuteMethod
* Refactor Python wrappers which inherit from BasePythonWrapper<>
* Solve review comments
* Remove ununsed methods
* Solve review comments
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* Some fixes for new C# enums handling in Pythonnet
* Minor changes and cleanup
* Update Pythonnet version to 2.0.45
* Minor changes
* Minor fix
* Minor fix
* Minor change
* Minor change
* Minor unit test fix
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* Portfolio state chart
- Cleanup and renames
- Add series.IndexName. Uodating unit tests
- Add Chart.LegendDisabled. Adding new unit tests
- Add ChartPoint.Tooltip. Updating unit tests
- Minor compression tweak. Adding unit test
- Add ChartJsonConverter. Adding unit tests
* Minor chart serialization order tweak
* Refactor portfolio state sampling and storing
* Move PortfolioMargin into a lean side chart
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* Update regression test to check number of insights
* Update model to have insight manager ref and cancel insight when signal goes flat
* Update unit tests and regression tests
* Address peer review: using Insights.Clear
* Updates Regression Algorithm to Assert the Number of Insights
* Updates HistoricalReturnsAlphaModel to Cancel Insights Not Emit Flat
We expect fewer insights after this change, but no changes to any regression algorithm.
* Updates Regression Algorithm
Assert the new expected number of generated insights.
* Use InsightCollection Clear Method
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* Updates the PearsonCorrelationPairsTradingAlphaModelFrameworkAlgorithm
Change the `PearsonCorrelationPairsTradingAlphaModelFrameworkAlgorithm` logic to show that it doesn't remove the consolidators used in the Alpha Model's indicators.
* Fixes `BasePairsTradingAlphaModel`
The `BasePairsTradingAlphaModel` will create indicators with class constructors and register them to consolidators that will be removed when the security is removed from the universe.
* Addresses Peer-Review
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* Warm up EmaCrossAlphaModel
Warm Up EmaCrossAlphaModel indicators
* Fix regression test bug
When using the default `EmaCrossAlphaModel()` the period of both indicators to be ready is bigger than the difference between the start date and the end date of the algorithm. Then, as the algorithm didn't warm up the data both indicators of EmaCrossAlpha never were ready, but now as the model warms up the data both indicators are ready so we get different statistics
* Requested change
* Fix unit tests
As there wasn't items in `AddedSecurities`, when trying to remove the items in ´RemovedSecurities´ there was nothing to remove because there was never a security in `_symbolDataBySymbol`. That's why, in order to test, the behavior of `EmaCrossAlphaModel` when removing a security we need to first add one to then remove it.
* Requested changes in Python
- Requested changes in Python
- Nit changes
* Nit change
* Requested Changes
* Add RemoveConsolidators() method in Python version
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- Add new CustomWeight PCM for alpha streams
- Add new AlphaStreams AlphaModule that will handle security additions
and removals, removing this logic from AlphaStreamsBasicTemplateAlgo
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* Warm up MACD indicators
- When a security is added in the MACD alpha model, it's warm up at once
* Add unit tests
* Nit change
* Code style and nit changes
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* Order handling improvements
- Execution model will only trigger market order if they are above the
minimum order margin portfolio percetage value
- SecurityCache.Reset is complete
* Python Import fixes
- Add regression test for ImmediateExecutionModel minimum order margin
check
* 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.
JosueNina