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* add data
(cherry picked from commit 814011d89e5316d150f88ffca5f48d8d5f0ea7d9)
* update market hours for index
(cherry picked from commit edac40732c120eb84d27de00594b59eebb4983f5)
* add index algorithms
(cherry picked from commit b22d27b4fa98172c435f7c26de4a3a297c49a6b7)
* update statistics
* add cash
* Add india market
* add leverage for index
* can subscribe to index
* update format
* fix wrong cash
* fix ticker names
* update data
* update ticker and stats
* update docs
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* Add india market data
* use local data for algo
* Add India market regression algo
* update india market data
* Update readme
* add python algo for BasicTemplateIndiaAlgorithm
* Add India data regression python algo
* data india data files
* update tickers
* Fix algo template
* remove data
* fix stats
* update stats
* remove unused data
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* Continuous Futures Refactor. Live Mappings
- Adding support for live mappings. LiveTradingDataFeed will handle any
symbol mapping at the configuration layer and resubscribe through the
IDQH
- Refactoring continuous futures adding ContinuousFutureUniverse that
will select the currently mapped security
* Minor fixes
- Remove addition of configurations in UniverseSelection step, leave
resposability for universe.
- LiveTradingDF future unit test will only assert slice data for non
internal feeds.
- ContinuousContractUniverse will respect internal option interest
subscription
* Address review
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* Fixes for FutureOptions support in LeanData
* Add CreateCanonicalOption() utility function for Symbol.cs
* Add aggregated Futures/FuturesOptions data to Lean
* Add FutureOptions regressions for daily/hourly data
* Allow Futures to be added with low resolution
* Add Future regressions using hour/daily data
* Nit - Python Class names
* Add reviews
* Add alias into CreateCanonicalOption
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* Move processing of delistings to Brokerage
* Deal with case that exchange is not open on OptionSymbol.ID.Date
* Refactor solution to use DelistingNotification event
* Adjust some regression expected liquidation time
* Mark some todos on deprecated functions
* Update expected liqudation time for Py regressions
* Update regressions that have been validated
* Use HandlePositionAssigned for assignment orders
* Update regressions
* Update some missed unit tests; remove one that is already covered by regression
* Cleanup deprecated backend functions
* nit - small cleanup adjustment
* Post rebase fix
* Address review
* Minor tweak to py regression
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* Create generic writing for LeanDataWriter, + notes on todos
* Make Options Daily/Hourly data store by year
* Refactor Generic Write
* Permit hour and daily resolutions for options
* Refactor writer to merge when needed with other files
* Cleanup redundancies, run write tasks in parallel
* Make needed classes/vars available
* Update tests to reflect new naming convention for daily hourly options data
* Add Byte[] overloads for ZipData functions in compression
* Implemented Store() for ZipDataCacheProvider
* Have LeanDataWriter use a DataCacheProvider
* ZipDataCacheProvider cleanup
* ZipDataCacheProvider tweaks, doesn't support storing non-zips
* Test adjustments
* Update LeanDataWriter to use Write instead of SaveDailyHourly/SaveMinuteSecond
* Implement tests to verify DownloadAndSave behavior
* Nit cleanup on DownloadAndSave tests
* Fix for options daily/hourly underlying equity subscription read
* Add daily/hourly options data and regressions
* Add missing open interest for hourly
* Fix writing of OpenInterest Daily/Hourly data
* Update data
* Fix Date typo in regression
* Use daily algorithm to test delisting
* Revisions part 1
* Expand test for DataCacheProviders; refactor DiskDataCacheProvider
* nit - test adjustments
* ZipDataCacheProvider test setup refactor
* Adjust multithreaded read/write test; fixes for ZipDataCacheProvider
* Move DiskDataCacheProvider to its own file and add write test
* Remove _appendToZips; always overwrite entry or create zip
* Add mapping regression for daily options
* nit - add license to regression
* Fix Tick write case where more than one data point for a DateTime
* Fix data issue
* Address review
* Tweaks for tests
* Stop Store() early if no entry name is given
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* Implement IIndicatorWarmUpPeriodProvider
- Implement IIndicatorWarmUpPeriodProvider in PythonIndicator.cs
- Make a unit test to check whether the WarmUpPeriod is working as expected
- Make a regression test to check the new feature at a system level
* Nit change
* Change Period parameter for WarmUpPeriod parameter
- Change regression test to check if the new parameter keep backwards compatibility with indicators that do not set WarmUpPeriod
* Documentation change
* Fix tests bugs
- In CommonIndicatorTests.cs before finish the test it checks the period.value with the number of samples but for default the period.value was set to -1
* Change names
* Change WarmUp and RegisterIndicator methods
- Lean WarmUp indicator skip custom python indicators that don't define WarmUpPeriod parameter
* Call WarmUpIndicator manually
- Add a new "bridge" method called WarmUpIndicator in QCAlgorithm.Python.cs to set up everything to call WarmUpIndicator in QCAlgorithm.Indicators.cs
- Change the regression algorithm to warm up the indicators manually
* Remove unnecessary code and add more tests
* Nit change
* Revert "Nit change"
This reverts commit da411f59c9e4295d75a11c6c581f615a938dd2c6.
* Fix bugs
* Try fix bugs
* Add C# regression test
- More nit changes
- Fix bugs
* Requested changes
* Remove unnecessary code
* Requested changes
* Nit changes
- Add new Python class to check a custom indicator, which doesn't inherits from PythonIndicator, warms up properly
* Reduce redundant code
* Fix bug and add more unit and regression tests
* - Add more unit tests
* Nit change
* Test cleanup
Co-authored-by: Martin-Molinero <martin@quantconnect.com>
* Implement IIndicatorWarmUpPeriodProvider
- Implement IIndicatorWarmUpPeriodProvider in PythonIndicator.cs
- Make a unit test to check whether the WarmUpPeriod is working as expected
- Make a regression test to check the new feature at a system level
* Nit change
* Change Period parameter for WarmUpPeriod parameter
- Change regression test to check if the new parameter keep backwards compatibility with indicators that do not set WarmUpPeriod
* Documentation change
* Fix tests bugs
- In CommonIndicatorTests.cs before finish the test it checks the period.value with the number of samples but for default the period.value was set to -1
* Change names
* Change WarmUp and RegisterIndicator methods
- Lean WarmUp indicator skip custom python indicators that don't define WarmUpPeriod parameter
* Call WarmUpIndicator manually
- Add a new "bridge" method called WarmUpIndicator in QCAlgorithm.Python.cs to set up everything to call WarmUpIndicator in QCAlgorithm.Indicators.cs
- Change the regression algorithm to warm up the indicators manually
* Remove unnecessary code and add more tests
* Nit change
* Revert "Nit change"
This reverts commit da411f59c9e4295d75a11c6c581f615a938dd2c6.
* Fix bugs
* Try fix bugs
* Add C# regression test
- More nit changes
- Fix bugs
* Requested changes
* Remove unnecessary code
* Requested changes
* Nit changes
- Add new Python class to check a custom indicator, which doesn't inherits from PythonIndicator, warms up properly
* Reduce redundant code
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* Implement scheduled event sampling solution
* Use UTC time, only update daily portfolio value once a day
* For daily resolutions sample chart always
* Cleanup
* Drop resample daily all together
* Force final sample
* Regression updates
* FIx LiveResultHandler to update portfolio and benchmark values outside of sampling event
* Name the daily sampling event
* Address review pt 1
* Drop force and use reference wrapper
* Adjust tests
* Fix warning for Benchmark Timezone Misalignment and also add test
* Fix for daily resolution orders and test adjustments
* Also warn on universe settings with daily resolution
* Update missed regression
* Fix reference wrapper use
* Update regression after rebase
* Add values back in for Daylight Algo
* Have statistics builder skip day 1 performance
* Regression adjustments
* Test adjustments
* Update regression unit test
* Adjust some regressions starts to show performance values
* Add hourly algorithm for beta comparison
* Address missing Python regression changes
* Remove null comment
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* Rename Renko to ClassicRenko
* Rename WickedRenko to Renko
* Adjust tests and usages
* Backwards support WickedRenkoConsolidator
* Renames for consistency
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- After https://github.com/QuantConnect/Lean/pull/5872 trading API
changes numpy float64 was not converted correctly by pythonNet and
used an int. Reverting API changes and adding regression test. This
should be fixed at pythonNet layer
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* Adds ETF(...) to UniverseDefinitions
* Adds ETF constituents universe framework regression algorithm
for C#/Python
* Address review: adds test cases for ticker/Symbol ETF universe additions
* Fixes bug where null Market would result in null dereference exception
* Address review: add missing Index tests
* Address review: don't hardcode market when creating constituent universe
* Uses Brokerage Model's default markets collection to determine
the market for the given security type
* Address review: restore QC500 and DollarVolume.Top(...)
* Restores algorithms related to both helper universe
definition methods
* Address review: remove copy to output directory for python algos
* Add example algorithms for ETF constituent universes using custom RSI alpha model
* Address review: adjust algorithm to use cache + algo RSI & clean up code
* Address review: make ETF Constituent RSI Alpha Model algo a regression test
* Address review: increase trade count and remove single trade logic
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* Add basic template Atreyu algorithm
- Add C# and Python basic template atreyu algorithm. Show casing how to
specify exchange to execute in different ways.
- Adjust trading API to allow specifying order properties to use
* Lean Exchange improvements
- Rename PrimaryExchange to Exchange
- OrderPropeties will use Exchange enum instead of string
- Adding BSE exchange value
* Regression tests fixes
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* Adds support for ETF constituent universes
* Adds filtering for universe data if it doesn't match the
universe subscription type
* Update mapping for ALL underlying Symbols if
`Symbol.UpdateMappedSymbol(...)` is called. Required to support
constituent ETF universes that might have mapping events
* Delistings of composite constituent universe Symbol will result in
removal of universe securities.
* Added regression algorithms for ETF constituent mappings (C#/Python),
along with data required to run locally
* Refactor universe delistings in SubscriptionSynchronizer -
big thank you to @Martin-Molinero :)
* Address review: update regression algorithms and add explanatory comments
* Address review: add additional checks to delisting regression algorithms
* Adds new regression algorithm testing the addition of a universe
without calling AddEquity() and asserts same behavior
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* WIP removal of custom data references & tests updates
* Regression algos updated and python algorithms moved to DataSource repos
* Fixes failing unit tests
* Add "LiveDataTypes" field to LiveNodePacket
* Adds Initialize() to IDataChannelProvider
* Adds new extension method to convert
HistoryRequest -> SubscriptionDataConfig
* Address review: Add protobuf definitions for Iconic data types
* Address review: re-adds DynamicSecurityDataAlgorithm as regression algo
* Small adjustments to variable naming and documentation
* Move test files to respective DataSource repos
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* Filter out small orders based on Setting
- BuyingPowerModel will filter out small orders based on algorithm
setting, a % of PTV, instead of hard coded 1 share value. Addin unit
and regression tests
- Updating regression algorithms to use new setting, reduce order trades
* Update regression algorithms
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* Updates Equity Market Data
* Updates Unit Tests
* Updates Regression Tests
In this commit we include regression tests with small changes (slightly different CAGR, Alpha, etc, but same number of trades) due to the data update.
* Updates Regression Tests 2
The following regression tests were adapt because of verification of hard-coded market data values:
- `AdjustedVolumeRegressionAlgorithm`
- `HistoryWithSymbolChangesRegressionAlgorithm`
- `OptionRenameRegressionAlgorithm`
- `RawDataRegressionAlgorithm`
- `SwitchDataModeRegressionAlgorithm`
The following regression tests have more trades since adjusted prices allowed more 1-2 shares trades that were rounded down to zero before:
- `AddUniverseSelectionModelCoarseAlgorithm` 23 -> 35
- `MeanVarianceOptimizationFrameworkAlgorithm` 12 -> 14
- `PortfolioRebalanceOnDateRulesRegressionAlgorithm` 298 -> 324
- `PortfolioRebalanceOnInsightChangesRegressionAlgorithm` 83 -> 86
- `ScheduledUniverseSelectionModelRegressionAlgorithm` 86 -> 90
- `SectorExposureRiskFrameworkAlgorithm` 17 -> 22
- `SetHoldingsMultipleTargetsRegressionAlgorithm` 8 -> 9
- `StandardDeviationExecutionModelRegressionAlgorithm` 196 -> 199
- `UniverseUnchangedRegressionAlgorithm` 11 -> 17
- `VolumeWeightedAveragePriceExecutionModelRegressionAlgorithm` 237 -> 238
Especial cases:
- `BlackLittermanPortfolioOptimizationFrameworkAlgorithm` 18 -> 17
- BLM model sensibility
- `OptionChainedAndUniverseSelectionRegressionAlgorithm`
The following regression tests have different Capacity because of different volume from lowest capacity asset, except:
- `OptionEquityCoveredCallRegressionAlgorithm` New lowest capacity asset is underlying
- `OptionEquityCoveredPutRegressionAlgorithm` New lowest capacity asset is underlying
* Revert File Update for SPWR and SPWRA
* Fix Regression Tests
Temporarily removes python regression test for `MeanVarianceOptimizationFrameworkAlgorithm` as the `MeanVarianceOptimizationPortfolioConstructionModel` for each version are yeilding different results. If we use C# version in `MeanVarianceOptimizationPortfolioConstructionModel.py`, the results match.
* Changes Optimization Method in MinimumVariancePortfolioOptimizer [Py]
Uses `trust-constr` method.
See https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html
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* Python research import improvements
- Improve start.py for research env
- Remove unrequired imports
* Centralize algorithm imports
* Add regression test GH action
* Unit test python import clean up
* Join research and main imports
* More python import clean up
* Fix failing skipped regression algorithm
Build & Test Lean / build (push) Has been cancelled
* Use lean data key as param for request
* key -> filePath rename and some cleanup
* Refactor
* Add Organizations Endpoints
* Add some organization api wrapper objects
* Address namespace issue
* Reorganize Api Test into seperate files using one ApiTestBase
* Add Organization tests
* Use capitalized "API" test namespace to reduce amount of file changes
* Add License to test base
* Update /data endpoint functions and response objects
* Update ApiDataProvider Logic
* Handle deserialization of organization products
* Simplify converter
* Only throw for equity requests when not subscribed to map/factor files
* Add missing header
* Make arguement exception
* Api adjustments
* Add Zip factor and map file providers
- Common project will now reference Compression project and not the other way
round.
- Adding Zip FactorFile and MapFile providers
* Refactor FactorFileProvider to use DataProvider to fetch files
* Use resulting MinimumDate in construction of FactorFile
* Nit FactorFile comments and arrangement
* Refactor MapFileProviders to use DataProvider for fetching files
* Refactor ZipFileProvider
* Clean up
* Refactor Backtesting Future/Option chain providers to use dataprovider
* Fixes for data/ endpoints and test adjustments
* Response objects adjustments/cleanups
* ApiDateProvider fixes and testing
* Add LocalZipFactorFileTests
* Update ApiDataProvider download test to verify stream is not null
* Implement posting of agreement summary and signed time
* Mark all Api related tests as explicit and document details on running
* Clarify default token on ApiTestBase
* Adjust summary
* Update Api responses for QCC, except org products which are sold in USD
* Implement cache expiration for zip MapFile and FactorFiles. Adding unit tests
* Fix multiple markets for ZipFactorFile provider
* Use Symbol as cache key
* Api.cs review
* Dispose of factorFileStream after reading
* Use zip.EntryFileNames
* Address a few reviews
* Few more fixes
* Address Api Review
* Add Job Org id to config
* Minor tweaks
* Compare with invariant culture
* Fixes Option Universe selection
* ZipEntryNameSubscriptionDataSourceReader will use IDataProvider
* Fix research
* Fix null reference exception
* Make duplicate log debug
Co-authored-by: Martin-Molinero <martin@quantconnect.com>
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* Attempt to remove SecurityInitializer from UniverseSelection stack entirely
* Fix broken Python Universe Selection model
* Remove mistake change (meant for another branch)
Build & Test Lean / build (push) Has been cancelled
* Apply changes from #2146 on current master
Credit for most of these changes goes to https://github.com/viliwonka.
Rebasing three years of changes didn't go so well, so I manually
applied the changes in #2146 on the current master branch.
This commit attempts to represent the changes in #2146 as closely as
possible, no cleanup has been performed yet. Despite this, some changes
were inevitable because part of #2146 has been implemented another way
in the past three years.
* Clean up
* Process review comments
* Add tests
* symbol -> potentialConversionRateSymbol
* Ensure regression tests run/get data
* Fix broken test
* Process review comments
* Process review comments
* Simplify currency conversion logic
* Performance improvement, doc updates and test rename
* Rebase fixes
* Remove obsolete properties
Co-authored-by: Martin-Molinero <martin@quantconnect.com>
Build & Test Lean / build (push) Has been cancelled
* Use UniverseSettings.DataNormalizationMode for securities added in Algorithm
* Stop SubscriptionUtils from forcing Adjusted mode
* Return behavior to original and add comments
* nit typo
* Add regression
* Add unit test that verifies DataNormalizationMode can be altered manually by security
* Cleanup and add Py version of regression
* Update projects to use .NET 5.0, the successor to .NET Core
* Fix ambiguous errors. Add IBAutomator net5
* Remove FXCM
* Upgrade IBAutomater to v1.0.51
ignored, and an empty message aborts the commit.
* Fix rebase
- Fix ambiguous Index
- Remove StrategyCapacity.cs
- Update System.Threading.Tasks.Extensionsy
* Remove unrequired references
* Fixes
- Travis will use dotnet, not nunit nor mono
- Remove mono from foundation image
- Fix python setup in research
- Fix unit tests
* Don't call ReadKey when input is redirected
* Fix ConsoleLeanOptimizer
* Research fixes
* Update comment
* Add vsdbg to Dockerfile
* Fixes
- Revert dockerfile FROM custom changes
- Adjust and fix regression algorithms
- Option assignment will be deterministic in the order
- 'Rolling Averaged Population' is calculated using doubles, updating
expected values.
- Update readme, removing references to mono
- Add missing Py.Gil lock
* Replace ICSharp with .NET Interactive
* Fixes after rebase
* CSharp research fixes
- Adding new Initialize.csx that pre loads all assemblies
- Adjusting template research file
- Moving steps in dockerfilejupyter
- Fix unit tests and regression tests after rebase
Co-authored-by: Gerardo Salazar <gsalaz9800@gmail.com>
Co-authored-by: Stefano Raggi <stefano.raggi67@gmail.com>
Co-authored-by: Jasper van Merle <jaspervmerle@gmail.com>
* Refactor HasSufficientBuyingPowerForOrder implementations
Adds Sufficient and Insufficient helper methods to HashSufficientbuyingPowerForOrderParameters
enabling syntax like:
return paraeeters.Sufficient()
returnparameters.Insufficient(reason)
The next change will add the initial margin required which will simply require
updating both of these helper methods to accept the value.
* IBuyingPowerModel: Add margin functions Maintenance/Initial/ForOrder
These were originally hidden in an effort to only expose what's necessary
for the engine to perform its work. Additionally, we encapsulated all of
the method arguments into parameters classes to prevent having to break
anyone in the future. Not including these foundational methods turns out to
be an oversight. These methods are not required by the engine, but rather by
other models. Another possible solution here is to add an additional abstraction
and include these methods on this new abstraction. BuyingPowerModel would then
explicitly implement these methods and models that depend on them would require
two code paths, one for when the buying power model implements this interface
and another for when it doesn't.
Tests were additionally updated to remove test model implementations created for
the sole purpose of exposing these private methods.
* Add ConstantBuyingPowerModel
Provides an implementation of IBuyingPowerModel that returns the same
constant value
* Update BuyingPowerModelPythonWrapper to use reflection for method names
Having a bunch of hard-coded strings is a sure fire way for someone to
overlook when changing methods. This change ensures that noone needs to
remember that this code exists :)
Cleans up the syntax around verifying a python object implements a particular
C# interface via the ValidateImplementationOf<T> method by having it return a
value since the only use cases are in constructors when setting the models.
I was initially going to update ALL python wrappers to validate the passed
in models, but such a change could break many things that are 'working' right
now. Such an effort should be saved for its own dedicated PR.
* Add Parameters/Result types for new buying power model methods
* Support computing maintenance margin for arbitrary quantities
The existing GetMaintenanceMargin function assumes that we're only interested
in the maintenance margin for the entirety of the provided security's holdings.
This makes it impossible to perform what-if analysis or to even ask how much
maintenance margin is devoted to a particular subset of the security's holdings.
This change adds the quantity to the MaintenanceMarginParameters class. Futures
and Options models also depend on holdings cost and holdings value, so they have
also been added to the parameters type. Finally, static factory methods were
added to improve discernment of intent: ForCurrentHoldings provides the existing
behavior and then ForQuantityAtCurrentPrice to support what-if scenarios where
we're looking for the change in maintenance margin if we were to execute an order
for the securiy at the current time step. Obviously a constructor is provided to
set all of the values explicitly, using any price metric the caller desires.
* Address review
- Fix BPM xml documentation
- Fix python unit tests and PythonWrapper validate method
* Add SecurityHolding.QuantityChanged event
Adding event handlers will allow us to orchestrate complex
events from distant parts of the codebase through wiring
them up. If we continue down this path, it will move us away
from the current, very 'mechanical' data flows expressed in
LEAN and towards a more modern, event processing based system.
This is but a baby step in that direction and the initial use
case is using this QuantityChanged event to trigger resolution
of the algoritm's positions groups. This is part of an effort
to improve the fidelity of options margin modeling where we'll
model an OptionStrategy as an IPositionGroup. This will allow
us to compute the margin requirements of an OptionStrategy as
a unit instead of computing margin of each security individually
in isolation.
See #4065
* PortfolioManager: Group fields and remove unused field
This codebase generally places fields as the first members, but
this class had some fields at the top, then some properties, and
then some more fields. This change brings all the fields together
at the top of the file and also removes pointless comments placed
directly above some of the fields. Additionally, an unused field
was removed.
* Remove unused _currencyConverter from Security
Looks like at some point the only code using this member variable was removed
and the necessary clean up was overlooked.
* Add Parse.Enum functions
* Support disabling regression algorithms by language via config.json
Adds 'regression-test-languages' to config.json and filters regerssion algorithms to
run based on this value. When cycling on a particular feature, it's nice to be able
to run the entire regression set while ignoring the python algorithms. Once the C#
algorithms are all passing, one can then go back and run C# and Python in a final run,
since 99% of feature work doesn't impact python specifically.
* Implement IComparable in SecurityIdentitfier
This can be used to deterministically sort securities and symbols
* Add .editorconfig to enforce common formatting for json/sh files
* Fix typo in IBuyingPowerModel.GetBuyingPower xml docs
* Add ListEquals/GetListHashCode and OrderDirection.Closes(PositionSide)
ListEquals and GetListHashCode are designed to be used together as they
complement each other according to C#'s requirements for Equals and
GetHashCode functions.
PositionSide.ToOrderDirection() extension simply converts a PositionSide
to its logical equivalent OrderDirection. Long->Buy, Short->Sell, None->Hold
OrderDirection.Closes(PositionSide) determines if a particular OrderDirection
would have the effect of reducing a position's absolute size. This function
greatly improves the readability of buying power functions that must provide
adjustments when an order/contemplated trade reduces/closes an existing position.
OrderDirection.Buy.Closes(PositionSide.Short)
OrderDirection.Sell.Closes(PositionSide.Long)
All other combinations return false
Adds ToArray/ToImmutableArray convenience functions that combine a call
to Select followed by To(Immutable)Array all in one function call.
* Add decimal.DiscretelyRoundBy extension method
Supports rounding a decimal value by an arbitrarily chosen maximum precision,
or 'quanta'
* Update FutureMarginBuyingPowerModelTests to respect the security's lot size
* Add core position group classes and abstractions
* Add initial/maintenance margin support, buying power model consistency tests
* Add SufficientBuyingPower and GetReservedBuyingPower to position group model
Includes update to BrokerageTransactionHandler to use position group BPM for
sufficient buying power checks.
* Resolve position groups on each fill
We need to update the state of our position groups on each fill so that
we can properly handle multiple orders within the same time step. We
also limit the number of positions sent into the resolver by removing
securities without any holdings.
* fixup! Add SufficientBuyingPower and GetReservedBuyingPower to position group model
* Add GetMaximumLotsFor{Target|Delta}BuyingPower
Instead of computing order quantity, these functions compute the
maximum number of position group lots, which is the position group
quantity, and is guaranteed to be a whole number, for the provided
target/delta buying power parameters.
The SecurityPositionGroupBuyingPowerModel delegates to the security's
IBuyingPowerModel by applying a scaling factor equal to the security's
lot size.
This change also updates references to IBuyingPowerModel.GetMaximum...
to use the new position group model methods.
* Convert remaining IBuyingPowerModel call sites to position groups
* Rename PositionManasger.CreateDefaultGroup -> GetOrCreateDefaultGroup
Better describes its behavior
* Add Position Groups readme.md
* Add Option Strategy BuyingPowerModel
- Adding CompositePrositionGroupResolver and
OptionStrategyPositionGroupResolver
- Adding OptionStrategyPositionGroupBuyingPowerModel handling option
strategies based on IBs margin table. Adding regression algorithms
- Few changes so that option strategies executed by multiple orders are
detected
- Adjust OptionStrategyDefinitionMatch to include equity legs in the
matching result
- Minor tweaks fixing previous rebase
- Minor fixes for existing option strategies definitions, adding new
missing strategies.
- Fixing minor bugs in option strategy matcher. Adding more unit tests
* Address self reviews
- Fixing bug in 'PositionGroupCollection'
- Few minor simplificaitons
- Adding BasicTemplateOptionEquityStrategyAlgorithm
* Address reviews
- Improve regression algorithms margin remaining and used assert logic to be exact. Taking into account spread and fees
Co-authored-by: Michael Handschuh <mhandschuh@gmail.com>
* Improves greeks configurability and defaults for all option asset types
* Makes `StandardDeviationOfReturns` configurable by users, so that
greeks can be loaded according to user expectations and the series
of returns that they'd like to compute for `n` periods and timespan
of `T`, as well as resolution of the data in live mode.
* Changes resolution to max resolution available for the default
volatility model created for the security. Usually this only applies
to live mode, but if creating an instance of the
`StandardDeviationOfReturns` volatility model and no `updateFrequency`
is provided, the resolution's time span will be used as the default
value. Backwards compatibility for equities is maintained.
* Changes defaults for `StandardDeviationOfReturnsVolatilityModel`
to warmup greeks faster for other derivative asset types
* Improves comments on `StandardDeviationOfReturns` for clarity on how
to use the volatility model for end users
* Fixes bug where TradeBar could not have proper Symbol set when getting
max resolution
* Applies to QCAlgorithm.Universe and StandardDeviationOfReturnsVolatilityModel
* Adds tests to check volatility model is updated at specified config intervals
* Address review: add shared method for (Relative)StandardDeviation
volatility models
* Adjusts logic to determine bar type
* Address review: order by TickType when getting configs inside volatility models
Ronit Jain