- `SetHoldings` will take `OnMarketOpen` ordes into account when
determining order quantity
- Adding new regression test. Updating existing algorithms which
suffered of the issue
- Adding a performance improvement, will avoid margin and portfolio
calculations for MarketOnOpen orders that wont be able to fill
- 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
- `ITimeRules` are expected to yield time date in UTC, fixing `Noon`,
`Midnight` and `Every`
- `ScheduledUniverseSelectionModel` will use UTC time zone by default
since that is the default expected time zone `ITimeRule` provides
- Adding regression test
- Adding `Security.NullLeverage` value to determine when the
`SecurityInitializer` leverage should be used or not
- Adding regression algorithm which reproduces the issue
- Adds Python version of `TrainingInitializeRegressionAlgorithm`;
- Adds C# version of `TrainingExampleAlgorithm`;
- Removes `TrainingScheduledRegressionAlgorithm`.
- Adding `FreePortfolioValue` to be set after algorithm initialize based
on the `TotalPortfolioValue` and the `FreePortfolioValuePercentage`
- Updating regression tests
- Adding new regression test
- Adding check for minimum order value at `BuyingPowerModel`
We restrict each algorithm time loop to a pre-determined amount of time.
Exceeding this limit will cause the algorithm to immediately terminate.
This quickly becomes an issue when considering users running trainable
models that have a long initialization period that exceeds the time loop
maximum.
This change provides a mechanism through which a long-running scheduled
event is permitted to keep running and is permitted to avoid the time loop
permitted by requesting additional time. Requests for additional time are
limited according to a leaky bucket implementation whose parameters are
set via the job's controls structure. The fundamental time unit for the
algorithm is a single minute.
Here's how it works. If a scheduled event takes longer than one full wall
clock second then a request is made to the leaky bucket for one more minute.
If the scheduled event continues to take more time, it will continue to
request additional minutes. Each requested minute will prevent the algorithm's
time loop check from terminating the algorithm. When the bucket is empty and
no more minutes are available to be requested, a TimeoutException is thrown
causing a cascade that ends in the algorithm's termination and status being
flipped to RuntimeError.
Additionally, this applies equally to ALL scheduled events. While some helpers
were added with the naming of Train and TrainNow to the ScheduleManager, these
methods don't do anything special and the infrastructure doesn't otherwise
flag them as different, so this feature becomes part of the core Scheduled
Event feature set.
Further, the live scheduled events were not touched and are still pending
further discussion regarding the value added by enforcing a time restriction
when simulation time and wall clock time are equivalent.
Fixes#3319
- 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.
- Add `TiingoNews.HistoricalCrawlOffset`, timespan to add for
backtesting
- Rename: remove `Data` from `TiingoNewsData` and rename `TiingoDailyData` to `TiingoPrice`
Provides dynamic access to cached security data keyed by the type's name.
For example, `security.Data.GetAll<Tick>()` would yield a list of ticks.
Likewise, using the dynamic accessors, `((dynamic)security.Data).Tick`
would return the same list. In C# you'll need to cast security.Data to
a dynamic. In python, all C# objects are viewed as dynamic, so python can
simply access `security.Data.Tick` directly.
See #3620
* Added better documentation for AddData methods
* Added new regression algorithms for adding in OnSecuritiesChanged
* Changed regression algorithms to add data that exists
* Styling and logging fixes
* Deleted regression algorithms because they tested behavior similar to
other existing regression algorithms
* Fixed new bug in regression algorithm due to AddData changes
* Added unit tests for wrapt version and package existence
* Fix issue where data would be set to raw normalization mode
Adds static methods of the form Parse.<TypeName>(string str) that use
CultureInfo.InvariantCulture. These are to be used when parsing strings.
It's still safe (from the CA1304/CA1305 perspective) to use the ToDecimal
extension method for decimals.
Adds string extension methods for common operations that will now require
CultureInfo.InvariantCulture. These are to be used when converting values
to strings, such as ToStringInvariant()/ToStringInvariant(format), but also
useful for searching within strings, StartsWithInvariant, EndsWithInvariant
and IndexOfInvariant.
Martin Molinero