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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>
* Feature combo orders
- Add support for combo orders
* Make fill model wait for all grouped orders to emit fills
* Add ComboFill to model multiple fills for combo orders
* Fill combo limit orders
Add some regression algorithms
* Add fill implementation for combo leg limit orders
* Add IFill as common interface for Fill and ComboFill
* Refactor combo orders removing IGroupOrder interface
Move the group order manager to the base Order class
* Update algorithms
* Handle combo order events atomically
* Refactor brokerage transaction event handler
* Refactor combo fill models
* Process fills in batch
* Combo orders fill model tests
* Combo leg limit orders algorithm
* Regression algorithms cleanup
* Fill and combo fill classes cleanup
* Housekeeping
* Refactor equity fill model to derive from base fill model
* Address review changes request
* Handling the new types of orders in the OrderJsonConverter
* Add regression algorithm to test combo orders update/cancel
* Add regression algorithm to test combo orders update/cancel
* Housekeeping
* Address review changes request
* Minor changes
* Security transaction handler method for setting order request id
* Extend public interface for placing combo orders
* Combo order tickets demo algorithm python version
* Tweaks and updates
* Minor fixes
* Minor changes
* Minor fixes
* Address reviews minor fixes
* Minor fixes
Co-authored-by: Martin-Molinero <martin@quantconnect.com>
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* CryptoFutures adjustments
- Address reviews of CryptoFutures:
- Add new Slice MarginInterestRates collection
- Add new regression tests asserting funding rate application behaves
the same no matter the resolution
- Add auxiliary data by type into the security cache
- Revert BuyingPowerModel changes
* Make interest rate application time deterministic
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* WIP
* Add base currency cash
* Symbol properties and data processing
* Add basic template algorithm
* Add hourly crypto future algorithm
* Minor fixes after live trading testing
* CoinApiDataQueueHandler CryptoFuture support
* Address reviews
* Fix regression algorithms after update
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* Allows Market-On-Close Orders Outside Buffer Period
Market-On-Close orders can be submitted before and after the buffer period from 15:45 to 16:00 (Tested with TWS) meaning that we can submit MOC when the market is closed and, consequently, use daily resolution data.
* Adds Regression Test with Extended Market Hours
- Removes `nextMarketClose > Time` condition since it's unnecessary. If the algorithm Time is greater than the close of that day, `nextMarektClose` refers to the next day.
* Updates Unit Test
Updates `OrderQuantityConversionTest` because the MOC orders are submitted. They are placed at 7 pm and invalid before this pull request.
* Updates Summary of new Regression Tests
* Using IV to calculate Greeks, and remove single-step stochastic approximation
* Correct calculation for theta, vega, rho
* Add calculation from Black Calculator and peer review
* Address peer review and added unit test
* Update some tests and correct vega/eho
* Fix Unit Test and Improve Comments
Fixes `IndexOptionCallITMGreeksExpiryRegressionAlgorithm` since `Vega` was really non-zero.
* Fix regression test and add IV calculation
* refactor and bug fixing on peer review
* refactor and bug fixing on peer review
* for rerun test
* add warning on IV estimation not coveraged and edit speed unit test to not exceed 2s per 1000 iteration
* update logging
* update logging and description
* Add default option pricing models and unit tests
* address review
* Added Fed interest rate as risk-free rate with unit tests and set as default for option greeks calculation, added regression algorithms, addressed peer review
* refactor structure of interest rate
* Skip Saturday and Sunday
* regression test fix
* peer review
* Fixes Interest Rate Provider Logic
* Minor tweaks
* Fix start date
* Minor test tweak
* Update interest rates
* Fix unit tests
* Add minor log
Co-authored-by: Alexandre Catarino <AlexCatarino@users.noreply.github.com>
Co-authored-by: Martin-Molinero <martin@quantconnect.com>
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Use `SetEndDate` to set `EndDate` in `QCAlgorithm` constructor. It will ensure that the `EndDate` is independent of the time the algorithm is executed if `SetEndDate` is not called in `Initilaize`.
Some users don't implement `SetEndDate` to run the algorithm to the latest datapoint, but this is not true if we run the algorithm during the day as the latest datapoint will be 24 hours before the execution time while there is data until the current day midnight.
Jhonathan Abreu