Adds `MarketCap` member to `FineFundamental` class that represents the aggregate market value of a company represented in dollar amount.
Changes `CoarseFineFundamentalRegressionAlgorithm` (C# and Python) to select securities based in its market capitalization. Same result as selecting by P/E ratio.
In `PeriodCountConsolidatorBase`, differentiate the type of the `PyObject` and create an `IPeriodSpecification` accordingly. If the `PyObject` is a C# `Func<DateTime, CalendarInfo>` or a convertable Python method, we create a `FuncPeriodSpecification` with it as a parameter. If the `PyObject` is a `datetime.timedelta`, we create a `TimeSpanPeriodSpecification` with it as a parameter after a conversion to `TimeSpan`.
- Adds example in `DataConsolidationAlgorithm`
- Adds unit test for `timedelta` overload.
In the Coarse Universe Selection of the following algorithms
- ContingentClaimsAnalysisDefaultPredictionAlpha
- GreenblattMagicFormulaAlpha
- PriceGapMeanReversionAlpha
- SykesShortMicroCapAlpha
Universe.Unchanged is now used when the universe is not changed instead of saving a list of symbol and returning it.
Other minor refactoring.
* Write files by `UpdatedAt` date instead of the `CreatedAt` date
* Added ability to append to compressed files if file does not exist
* Cleaned up code as per review
* misc. documentation changes
* RSS converter now lives at: https://gist.github.com/gsalaz98/b87992cd5a5a214d01c63dbbefbbf12c
* Removed BenzingaNewsFactory as it is no longer needed
* Cleaned up code as per review to make this more maintainable
* Included new documentation
* Filter articles with no Symbols from being written
* General code improvements (duplication reduction, etc.)
- Renaming `PreSelected` to `Constituents`
- Adding base `ConstituentsUniverse`
- Adding Py and C# regression algorithm
- Fixing bug in `UniverseSelection`, it wasn't removing pending to be
removed securities unless the universe selection changed
- Adding test data
* Make Benzinga News Converter more efficient (and cleaner!)
* Implement JSON converter for serialization/deserialization process
* Fix bug where duplicate symbols would be added to news.Symbols
* Modify algorithm strategies
* Cleaned up code inside BenzingaDataConverter
* Added more documentation to potentially confusing bits around the code
Previously, we would be storing Benzinga time in local time (i.e. with
about a 7hr offset from UTC since I'm in Pacific Time). But now, we
instruct Json.NET to serialize and deserialize dates as UTC.
Additionally, because the data resolution was not set to `Second`,
EndTime would be rounded down with `Time` to the closest minute. We
fixed that by specifying that the data is in `Second` resolution.
- EventProvider will receive start date during initialization, this will
be used by the `MappingEventProvider` to correctly set current mapped
symbol
- Adding unit test, updating existing regression test
- 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
- Adds Python version of `TrainingInitializeRegressionAlgorithm`;
- Adds C# version of `TrainingExampleAlgorithm`;
- Removes `TrainingScheduledRegressionAlgorithm`.
- Adds PyObject overload to `ScheduleManager.TrainingNow` and `ScheduleManager.Training`
- Adds `QCAlgorithm.Train` helper method
- Adds Python algorithm showing how to use the helper method.
- Adding new `ConfidenceWeightedPortfolioConstructionModel` (C# / Py) that will
generate percent `Targets` based on the latest active `Insight` `Confidence` per
`Symbol`.
- Will ignore `Insights` that have no `Confidence`.(unit tested)
- 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. (unit tested)
- Adding unit tests
- Adding a new regression test framework algorithm (C#/Py)
-**Note**: `ConfidenceWeightedPortfolioConstructionModel` inherits from the `InsightWeightingPortfolioConstructionModel`. Protect method `GetValue` was implemented in `IWPCM` to enable the choice of `Insight` member.
- `PortfolioTargetCollection` avoid calling `Count` on
ConcurrentDictionary directly -> has to take all locks
- `SecurityChanges` change Union for Concat since constructor will call
HashSet
- Make `DynamicSecurityData` hold lazy data objects
- `RegisteredSecurityDataTypesProvider` avoid looping over all
registered types, adding `TryGetType`
- `Security.Update()` will no call group by on data since this data is
already grouped by type. Adding `ContainsFillForwardData` will allows to
be lazy and not re loop through the data unless necessary
- `DefaultAlphaHandler` will use the `static`
`Enumerable.Empty<Insight>` instance when possible
- `SubscriptionSynchronizer` will be lazy to construct the
`universeData` dictionary which is not used in most of the times. Will
use `Count` vs `Any` -> `Count` is known by the dictionary
- For python algorithms `JobQueue` will respect `AlgorithmLocation`, was
using unexisting `"algorithm-path-python"`
Jared