* Implements Equity Fill Model
This commit sets the base to create a new equity fill model and the `EquityFillModel` is just a copy of `FillModel`.
* Adds Summary to FillModelPythonWrapper.GetPricesInternal
Adds summary to FillModelPythonWrapper.GetPricesInternal with remarks that it's a temporarily method to help the refactoring of fill models.
* DataConsolidator Wrapper for Python Consolidators
* Regression Unit Test
* Refactor Regression test
* Bad test fix
* pre review
* self review
* Add RegisterIndicator for Python Consolidator
* Python base class for consolidators
* Modify regression algo to register indicator
* unit test - attach event
* Test fix
* Fix test python imports
* Add license header file and null check
Co-authored-by: Martin Molinero <martin.molinero1@gmail.com>
* When a BaseData instance has Nullable fields, the number of data
points per Series is inconsistent, and results in a Series with
a length different from the other Series we produce, resulting
in an error "ValueError: cannot handle a non-unique multi-index!"
when we were constructing the final DataFrame.
Use the original `pandas.Series` to create `Series` objects before `DataFrame` (wrapper version) creation.
It improves the speed because it avoids unnecessary and expensive index wrapping operations of the `DataFrame` creation.
In this implementation, we dynamically create new classes that wraps key functions and properties. The wrappers will map/convert any parameter that are convertible to the string representation of Symbol.ID before they are used by the original function/property.
Methods: 'diff', 'div', 'divide', 'drop', 'drop_duplicates', 'droplevel', 'dropna', 'dtypes', 'duplicated'
`BackwardsCompatibilityDataFrame_binary_operator` replaces `BackwardsCompatibilityDataFrame_add` to handle all operations (more to be added in future commits)
`Remapper.__getitem__` was not returns a `Remapper` object when the result was `pandas.DataFrame`. It is needed for a sequence of `.loc.` calls.
Refactors `Remapper._self_mapper` to handle tuples where the key can be found in both first and second position.
Update unit tests that should test `Symbol` object as key, but were using `str(Symbol)`.
- AlgorithmPythonWrapper will directly call base OnFrameworkData()
implementation skipping going through python and it's overhead
- Small performance improvement for adding Tick data points into a Ticks
collection
- For python always wrap slice with PythonSlice, so that slice.Get()
works even when no custom data is present, adding test.
- Fixing Python slice enumeration which would not happen when custom
data was present. Adding unit test
- Fix Slice constructor which would fail to create data collections due
to relying on non-deterministic 'slice._data'. Adding unit test
- Improving custom data retrieval for symbols which have more than 1
data type in a slice. Adding unit test
- First step refactoring slice internally, no behavior changed
- Remove unrequired `GetBuyingPower`
- Making `BuyingPowerModel.GetMaintenanceMarginRequirement` protected
instead of public
- Adding `GetMaximumOrderQuantityForDeltaBuyingPower` to replace
public `GetMaintenanceMarginRequirement` and improve API experience for
consumers like the `DefaultMarginCallModel`
- Adding new unit tests
- Add support for Symbol key access for pandas ix and iloc results
- Wrapp pdf merge, join, concat method results
- Wrapp pandas.concat method result with `Remapper`
- Adding unit tests
- Replacing `BaseData.AdjustResolution` for `DefaultResolution` and
`SupportedResolutions`
- Making `Resolution` nullable for `Algorithm.AddData` methods
- The `ISubscriptionDataConfigService` will set the default resolution
if none was provided and assert it is supported
- Fix bug with `PythonData` `IsSparseData` and `RequiresMapping`
resolution
- Adding `BaseData.AdjustResolution()` that should return a valid
resolution for the given data and security type.
This allows us to set a limitation which is useful to avoid invalid data
requests or unnecessary fill forward situations. The user will be
notified through a console message.
- Adding unit and regression test
- Updating example algorithms custom data resolution
- Some performance improvements. Wont change console color if
`SelectedOptimization` is defined
- Covers another level on inheritance of Market Data by using `Type.IsAssignableFrom`
- Caches the list of `MethodInfo` for custom data types to avoid redefining that list.
When custom data classes inherited from market data classes such as `TradeBar`, it created duplicate entries. Therefore, we need to exclude the common properties in the private field `PandasData._members`.
- Moving mapper from C# to Python since some cases did not work when
implemented in C#
- Small changes to `PandasDataFrameHistoryAlgorithm` which runs till the
end with no errors
- Adding more backwards compatible unit tests
- Add Pandas backwards compatibility shim
- Adding `MappingExtensions` which will remove data type from the
`Symbol.ID.Symbol` value to resolve the `MapFile`
- `SecurityIdentifier.TryParse()` will throw when given an invalid
`SecurityType`
- Custom data types will know whether or not Lean should use map files
- Updating regression test with sample custom data using map files,
which can run locally
- Adding unit tests for the `SubscriptionDataReaderHistoryProvider`,
checking it mappes equities and options correctly
Alexandre Catarino