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* Normalize universe data frames
Universe and (generically BaseDataCollection) data frames are not normalize and unpacked into a data frame, instead of just creating data frames with the universe lists within it
* Fix unit tests and algorithms to expecte new universe dataframe format
* Fixes
* Add PandasConverter.DataFrameGenerator class
* Pandas data frame generator class fixes
* Add comments
* Housekeeping
* Add attributes to mark classes and properties for pandas processing
* Improve pandas properties expanding
Allow and handle duplicate names
* Use PandasData generalization for Lean common data types
* Add points time as column when converting base data collections to data frames
* Cleanup and minor changes
* Minor change
* Pandas data to get type members on demand
* Move Pandas helper classes to their own files
* Minor changes
* Add flatten argument to python history api
This allows users to decide whether they want fully expanded dataframes for universe and other collection data types. Else, master behavior is kept
* Adding missing changes to last commit
* Update Pythonnet version to 2.0.40
* Add flattent argument to algorithm's OptionChain api
* Minor changes
* Housekeeping
* Minor changes
* Bug fix skipping data collection data points
* Add comment
* Set correct exchange time to OptionUniverse instances
* Address peer review and cleanup
* Cleanup
* Minor changes
* Add Scheduled Universe Selection
- Adding `UniverseSettings.Schedule` that will allow users to
specify a custom selection schedule which is independent of the
underlying data frequency
- Adding unit and regression tests
* Add live trading schedule time shift
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* Refactor universe historical data source
- Add new universe history API methods
- Refactor QuantBook UniverseHistory to use the universe selection
itself instead of a given func
- Refactor and rename fundamental types
- Refactor AddUniverse API to handle universe collection data which
holds another type internally, like fundamental
* Fix minor bug causing ApiDataProvider not to serve Bitfinex universe data
* Further improvements to add universe API
* Handle no selection function
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* New Fundamental Data
* Minor CIK lookup fix
* Handle live mode & delete unexisting properties
* Minor coarse fundamental adjustment
* Add fundamental history support
* Fix unit tests
* Performance improvements
* Fixes
* Minor regression algorithm fix
* Improvements. Add FundamentalUniverseSelectionModel
* Change default values
* Fix unit test
* Minor tweaks
* Fix unit test
* Minor error handling improvement
* Fix rebase
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- Validate cache folder exists before using it
- Avoid duplication fetching factor & map files path
- Adding helper method to check if directory is empty. Adding unit tests
Uses `MarketCap = Value * EarningReports.BasicAverageShares.ThreeMonths`. Since the previous calculations as using `BasicEPS` that can be negative, market cap got negative values which is not realistic.
Moves `MarketCap` code from the generated file to another one that shares the partial class.
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.
- Will use an array as store, the most efficient collection memory wise.
Holding a custom internal `struct PeriodField`
- Will store period as a byte number, not a string
- By default the collection will be null and created on demand
- Adding more unit tests
- Reduce the amount of `Path.Combine()` usages -> it has a peformance
overhead
- Improving `FineFundamentalSubscriptionFactory` GetSource algorithm,
now it will not check if each file exists while finding the appropriate,
since we already iterated the directory before
- `DefaultDataProvider` will not check if file exists since `new
FileStream` performance the same operation internally
- When subscribing to `Coarse` data the `LiveTradingDataFeed` will use
the normalized `CoarseFundamental Universe Symbol` for that market ->
not using the random GUID
- Adding unit tests which reproduce issue.
- Implementing `QCAlgorithm.AddUniverseSelectionModel`
- Adding C#/Py regression algorithms using the new API
- Modifying `ManualUniverSelectionModels` symbol, adding hash of
the selected `Symbol.Values`
- Modifying `Coarse` and `Fine` symbol, adding random GUID
- Adding `NullUniverseSelectionModel`
Universe data is piped through the TimeSlice and saved in the
security cache in the algorithm manager for consumption by the
algorithm.
This change includes an update to SecurityCache.AddData to preclude
us from setting the security price using auxiliary data. Tests were
updated accordingly.
JosueNina