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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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* Add ClrBubbleExceptionInterpreter
* Add ClrBubbledExceptionInterpreter tests
* Bump pythonnet version to 2.0.23
* Minor changes
* Minor change
* Minor fix
* Fix failing unit tests
* Set default always open market hours entry for base security without subscription
Also return proper matching subscription for custom data symbols wihtout subscription.
* Minor changes
* Minor change
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* Support sourcing custom data from ObjectStore
* Add Python regression algorithm
* Wire engine to pass ObjectStore down to the stream readers
* Minor changes
* Minor unit tests fixes
* Remove unused SetupHandlerParameters.ObjectStore
* Minor changes
* Minor changes
* Support single-file zipped data to be sourced from object store
* Assert object store custom data history requests in regression algorithms
* Add custom object store data live data feed unit test
* Add multi-file object store custom data regression algorithms
* Minor fix
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Calling UniverseDefinition.ETF("TICKER",...) in Python was resolving the UserDefinition.ETF(Symbol...) overload due to Symbol's implicit string operator, causing the symbol to be wrongly created.
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* Deprecates IAlgorithmSettings.DataSubscriptionLimit
We will let the brokerage check and notify users that they have subscribed to datafeeds beyond their quota.
* Removes Unit Test
Also remove usage in `EmptyEquityAndOptions400Benchmark`.
* Removed platformId support from C2.
Modernized C2 API calls to APIv4.
Added C2 rate limiters (RateGate).
Added response logging so users can easily debug their code.
Added a few symbols for the demo.
* Address requested changes
* Address requested changes
---------
Co-authored-by: Francis Gingras <francis@collective2.com>
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* Candlestick charts base implementation
* Series and Candlestick series json serialization
* Some cleanup
* Add AddPlot method for candlestick series to QCAlgorithm
* Remove Values property from ISeriesPoint
* Add candlestick QCAlgorithm.Plot trade bar methods
* Implement candlestick series re-sampling
* Add more SeriesSampler unit tests
* Add examples of candlestick charts usage to exisiting charting algorithm
* Address peer review
* Address peer review
* Derive Candlestick from Bar
* Sampler changes
* Add new series types from the cloud
* Add more candlestick series sampler tests
* Minor cleanup
* Minor changes
* Add trailing stop orders base implementation
* Handle trailing stop order prices rounding
* Implement trailing stop orders fill logic
* Minor fill model changes
* Add ApplySplit to fill model interface for models that might need to be aware of splits.
Filling trailing stop orders require keeping track of min/max prices, which need to be split adjusted.
* Add brokerage order updated event for communicating certain order types prices changes
* Add order update event args class for brokerage side order updates
* Revert IFillModel.ApplySplit
* Add trailing stop orders regression algorithm
* Updated order ticket demo algorithm to include trailing stop orders
* Some cleanup
* Support trailing stop orders in IB brokerage model
* Some cleanup
* Fix failing tests
* Fix failing regression algorithm
* Address peer review
* Add trailing stop price calculation unit tests
* Minor changes
* Minor change
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* Fix bug and add unit tests
- Modify `InsightWeightingPortfolioConstructionModel.cs/py` to take the
absolute value in its `GetValue()` method
- Modifiy `InsightsReturnsTargetsConsistentWithDirection()` unit test to
consider also the case where the direction is Down and the weight is
negative
* Nit changes
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* Pandas frame include all ticks
- Pandas data framde history response will include all ticks. Updating
existing and adding new tests
* Python pandas converter performance improvement
* MHDB will merge common entry
- The MHDB will merge the market and security common entry holidays,
early closes and late opens
* Normalize & reuse future US holidays
- Normalize & reuse future US holidays
* Update existing unit tests expected stats
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* Implement Trailing FreePortfolioValue
- Implement Trailing FreePortfolioValue by default, users will be able
to set it to a fixed number if desired. Adding regression algorithm
- Setting the default 'MinimumOrderMarginPortfolioPercentage' from 0 to
0.1% of the TPV to avoud tiny trades by default
* Update existing regression algorithms
* Address reviews
- Send warning message to the user if a trade does not happen due to the
default setting of the minimum order margin percentage value
* Address reivews
* Rename TotalPortfolioValueLessFreeBuffer
* Update new regression algorithm
* Minor fix CrunchDao Symbology
- Minor fix for CrunchDao Symbology. Updating existing tests
* Add missing symbol mapping
- Add IAlgorithm.Ticker(Symbol) functionality which will return the
latest ticker for the requested symbol at the current algorithm time
- SignalExporters will use Ticker to get the current symbol ticker
* Rename SecId GetTicker to Ticker
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* Solve bug and add regression test
The bug was raised because, when trying to use C#
MeanVarianceOptimizationPortfolioConstructionModel from a Python
algorithm, there wasn't a constructor that accepted a portfolio optimizer
as a PyObject. Additionally, there wasn't also a Python Wrapper to wrapp
that portfolio optimizer.
- Add PortfolioOptimizerPythonWrapper.cs
- Add constructor in
MeanVarianceOptimizationPortfolioConstructionModel.cs that accepts
portfolio optimizer as a PyObject
- Add regression algorithms to cover the changes
* Improve constructor overload implementation
* Change implementation to follow API pattern
* Enhance implementation and add unit tests
* Enhance implementation and add more unit tests
* Enhance implementation
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* Add forward data only fill model example
- Add forward data only custom fill model C# & PY example.
- Minor adjustment for 'MarketOrderFillTimeout' to be zero always in
backtesting
* Address reviews
* Add support for brokerage side new orders
- Add support for brokerage side new order events for liquidation cases
* Minor cash delta fix
* Improve account cash logging
* Fix null reference exception for open orders
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* Implement ShortableProviderPythonWrapper.cs
- Modify AllShortableSymbolsCoarseSelectionRegressionAlgorithm.cs and ShortableProviderOrdersRejectedRegressionAlgorithm.cs to use ShortableProvider from Security and not from the Brokerage model
- Add SetShortableProvider() overload method in Security.cs to set a custom shortable provider from Python
- Remove AllShortableSymbols() method from LocalDiskShortableProvider.cs
- Remove DefaultShortableProvider class
- Add regresion algorithms in C# to cover the changes done
* Implement ShortableProviderPythonWrapper.cs
- Modify AllShortableSymbolsCoarseSelectionRegressionAlgorithm.cs and ShortableProviderOrdersRejectedRegressionAlgorithm.cs to use ShortableProvider from Security and not from the Brokerage model
- Add SetShortableProvider() overload method in Security.cs to set a custom shortable provider from Python
- Remove AllShortableSymbols() method from LocalDiskShortableProvider.cs
- Remove DefaultShortableProvider class
- Add regresion algorithms in C# to cover the changes done
* Solve bugs and nit change
* Address review
---------
Co-authored-by: Martin-Molinero <martin@quantconnect.com>
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* Add dataMappingMode parameter to every history api method overload
* Minor unit tests fixes
* Update regression algorithm stats
* Minor changes
* Minor changes
* Add extendedMarket parameter to every history api method overload
* Rename extendedMarketHours parameter
New name is extendedHours as in the History API to standarize parameters naming
* Update generic history overloads to use every matching subscription
* Update regression algorithms stats
* Centralize period-based history error for tick resolution
* Rename extended market hours parameter to extendedMarketHours
* Minor changes
* Minor changes
* Minor unit tests changes
* Minor unit tests changes
* Minor changes
* Minor unit tests changes
* Minor unit tests changes
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* Add fillForward parameters to every History method
* Remove conflicting Python history method
* Undo removing conflicting Python history method
* Minor changes
* Minor changes
* Minor changes
* Add fillForward and extendedMarket parameters to history request factory
* Minor changes
* Minor changes
* Minor changes
* Minor changes
* Minor unit tests changes
* Rename fillForward parameter in History API
New name if fillDataForward as in the Add*Security API to standarize
parameters naming
* Rename fillForward parameter
Using the shorter fillForward in every API
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* Add Collective2SignalExportClass
Add SignalExportTarget interface
* Collective2SignalExport test working
Add SignalExportManager
Add draft of CrunchDAOSignalExport
* Modify SignalExportManager
Instantiate SignalExportManager in QCAlgorithm constructor
Draft of CrunchDAOSignalExport
* Improve SignalExportManager
- Add regression tests SignalExportDemonstrationAlgorithm in C# and
Python
* Improve SignalExportDemonstrationAlgorithm
Address requested changes in Collective2SignalExport, SignalExportManger and SignalExportTargetTests.cs
* Add CrunchDAOSignalExport.cs
Add CrunchDAOSignalExport unit tests in SignalExportTargetTests.cs
* Add NumeraiSignalExport.cs
Modify SignalExportDemonstrationAlgorithm.cs to test NumeraiSignalExport
Add unit test in SignalExportTargetTests to test NumeraiSignalExport
* Address required changes
- Add BaseSignalExport.cs
- Add SignalExportParameters.cs
- Add PortfolioSignalExportDemonstrationAlgorithm.cs/py
- Improve Error handling in SignalExport provider classes
- Collective2SignalExport now gets the correct number of shares for each holding
- SignalExportManager now computes the correct holding percentage of each portfolio target
- SignalExportManager now takes into account if the algorithm is in live mode
- Demonstration algorithms now are more simple
* Address last required changes
- PortfolioSignalExportDemonstrationAlgorithm.cs/py now inherits from SignalExportDemonstrationAlgorithm.cs/py
- Add more unit tests to assert SignalExportManager gets the correct percentage quantity for each holding
- Change Collective2SignalExport, CrunchDAOSignalExport and NumeraiSignalExport Send() method to return true if there was no error while sending the signals and false otherwise
- Nit changes
* Remove exceptions thrown
- Add more unit tests and more test cases
- Enhance BaseSignalExport.Dispose() method
* Add Collective2SignalExportClass
Add SignalExportTarget interface
* Collective2SignalExport test working
Add SignalExportManager
Add draft of CrunchDAOSignalExport
* Modify SignalExportManager
Instantiate SignalExportManager in QCAlgorithm constructor
Draft of CrunchDAOSignalExport
* Improve SignalExportManager
- Add regression tests SignalExportDemonstrationAlgorithm in C# and
Python
* Improve SignalExportDemonstrationAlgorithm
Address requested changes in Collective2SignalExport, SignalExportManger and SignalExportTargetTests.cs
* Add CrunchDAOSignalExport.cs
Add CrunchDAOSignalExport unit tests in SignalExportTargetTests.cs
* Add NumeraiSignalExport.cs
Modify SignalExportDemonstrationAlgorithm.cs to test NumeraiSignalExport
Add unit test in SignalExportTargetTests to test NumeraiSignalExport
* Address required changes
- Add BaseSignalExport.cs
- Add SignalExportParameters.cs
- Add PortfolioSignalExportDemonstrationAlgorithm.cs/py
- Improve Error handling in SignalExport provider classes
- Collective2SignalExport now gets the correct number of shares for each holding
- SignalExportManager now computes the correct holding percentage of each portfolio target
- SignalExportManager now takes into account if the algorithm is in live mode
- Demonstration algorithms now are more simple
* Address last required changes
- PortfolioSignalExportDemonstrationAlgorithm.cs/py now inherits from SignalExportDemonstrationAlgorithm.cs/py
- Add more unit tests to assert SignalExportManager gets the correct percentage quantity for each holding
- Change Collective2SignalExport, CrunchDAOSignalExport and NumeraiSignalExport Send() method to return true if there was no error while sending the signals and false otherwise
- Nit changes
* Remove exceptions thrown
- Add more unit tests and more test cases
- Enhance BaseSignalExport.Dispose() method
* Fix failing regression tests
* Fix failing unit tests
* Nit changes
* Nit change
* Nit change
* Fix failing unit tests
* Changes required
- Break regression algos `SignalExportDemonstrationAlgorithm.cs/py` nad `PortfolioSignalExportDemonstrationAlgorithm.cs/`y` into three ones, one for each signal export provider
- Change SignalExportManager constructor to receive current algorithm as a parameter
- Fix bug in `SignalExportManager.GetPortfolioTargets()`, now it computes the correct percentage for each holding
- Make `BaseSignalExport.DefaultAllowedSecurityTypes` overrdible
- Handle case were `Collective2SignalExport.ConvertPercentageToQuantity()` returns null
- Clean unnecessary code in `Collective2SignalExport()`, `CrunchDAOSignalExport()` and `NumeraiSignalExport()`
* Nit change
* Nit change
* Minor tweaks after review
* Remove indexes from signal exports
* Required changes
- Change EMA indicators period from 200, 300 to 10,100 in regression algorithms
- Remove Indices from regression algorithms
- Add more XML documentation to regression algorithms
- Change `Log.Error` to `_algorithm.Error` in Signal export providers. Besides, fix error message format
- Change default value for `platformId` parameter in `Collective2SignalExport.cs` constructor
- Solve small bugs in SignalExportProvider when verificating the amount of porfolio targets is greater than zero and each portfolio target is allowed
- Handle case when `PortfolioTarget.Percent()` returns null in `Collective2SignalExport.ConvertPercentageToQuantity()`
- Handle error format message from Collective2 API
- Check every ticker signal is between 0 and 1 (inclusive) in `CrunchDAOSignalExport.cs`
- Modifiy `NumeraiSignalExport.cs` constructor to take into account filename given in the arguments
- Fix small bug with the return value of `ConvertTargetsToNumerai()` method in `NumeraiSignalExport.cs`
- Modify `SignalExportManager.cs` to return true when the algorithm being ran is not in live mode
- Remove indices from CrunchDAO unit tests
* Enhance ´CrunchDAOSignalExport.cs´ implementation
---------
Co-authored-by: Martin-Molinero <martin@quantconnect.com>
* Fix EMA indicator first value calculation
As done by TALib and TradingView, the first EMA value after warmup is
the SMA of the first period.
* Update TSI indicator test data
Data was exported from TradingView
* Update TRIX indicator test data from TradingView
* Update AccumulationDistributionOscillator indicator test data from TradingView
* Update Double EMA indicator test data from TradingView
* Update McClellanSummationIndex indicator test data
* Update SchaffTrendCycle indicator test data
* Update TripleExponentialMovingAverage indicator test data from TradingView
* Update stats for algorithms using EMA
* Update failing unit tests
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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
* Improves MinimumVariancePortfolioOptimizer Values Handling
The C# version of the `MinimumVariancePortfolioOptimizer` generated NaN resulting in unit tests failing.
If the solver returns NaN for an item, we set it to zero. if all items are NaN or Zero, we return the initial guess, since the sum cannot be zero.
* Fixes `RiskParityPortfolioConstructionModelTests`
We need to add insights to insight manager before we call `CreateTargets`.
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* Asserts Number of Insights In One PCM Regression Test
If the `EqualWeightingPortfolioConstructionModel` interacts with the `QCAlgorithm.Insights`, the number of elements in the collection should not the sum of emitted insights.
* Refactor Portfolio Construction Models to Use Insight Manager
`PortfolioConstructionModel` will use `QCAlgorithm.Insights" instead of class property `InsightCollection` to manage the insights. It no longer adds insights to the collection, but it removes them if they expire or the securities are removed from the universe.
Updates PCMs that were affected by the change.
* Updates Unit Tests
We need to add the insights to the insight manager before we call `PortfolioConstruction.CreateTargets`
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* Performance Improvements
- Remove Immutable collection from position groups to improve
performance. Adding more tests
- Remove ConcurrentDictionary from PortfolioTargetCollection &
DataManager. Adding more tests
- Add Securities enumerator keys & values cache
* Add test for PortfolioTargetCollection remove by reference
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* Apply splits and dividends to volatility models
* Apply splits and dividends to volatility models using history requests
* Add new ScaleRaw data normalization mode
Handling the new mode in the price scale enumerator.
* DataNormalizationMode.ScaledRaw history requests
* Minor changes
* Minor changes
* Disable new normalization mode in AddSecurity methods and other minor changes
* Peer review
* Minor changes
* Peer review
* Minor changes
* Peer review
* Peer review
* Peer review
* Add scaled raw history regression algorithm
* Add more regression algorithms
* Add more regression algorithms
* Add Slice.TryGet unit tests
* Peer review
* Peer review
* Peer review
* Peer review
* Peer review
* Update algorithms stats
* Peer review
* Peer review
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* Refactor alpha statistics
- Refactor alpha statistics, cleaning up and simplifying no longer required calculations and scoring
- Adding new InsightEvaluator abstraction, adding C# & PY regression
algorithms
* Optimization backtest result json converter update
* Address reviews
- Remove IAlphaHandler, move insight storage responsability to IResultHandler
and centralizing insight collection on the QCAlgorithm.Insights to be
reused by the framework models
- Fix portfolio turnover single day backtests and duplicate time
sampling handling. Updating regression algorithms
* Add InsightCollection tests and minor fixes
* Adding more & improved tests
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* remove annualization
* Update python optimizer to best fit in convex problem
* rerun test
* test metric
* Add comment to explain using simple return
* Add unit test on PR
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The calculations of `CloseTimeUtc` are correct and consistent: if the exchange is closed when the insight expires, `CloseTimeUtc` is the next open.
However, the definition of the `Period` was not consistent. Normally, it is defined by the different between `CloseTimeUtc` and `GeneratedTimeUtc`, but when we create an Insight with the Resolution and bar count overload, the period was defined by them:
```csharp
insight.Period = Resolution.ToTimeSpan().Multiply(BarCount);
```
which is incorrect, since it doesn't take the days the exchange is closed into account. The `ComputePeriod` method enforced consistency between the Resolution and bar Count overload and the `DateTime` overload, so the period for this overload was also incorrect.
* Custom data type history request in python
* Potential solutions
* Minor changes
* Use Slice.Get(Type) for getting python custom data history
* Minor changes
* Add unit tests
* Add unit tests
* Udpdate regression algorithms
* Peer review
* Peer review
* Add research regression tests
* Minor changes
* Minor changes
* Minor tweaks
---------
Co-authored-by: Martin-Molinero <martin@quantconnect.com>
Jhonathan Abreu