- `FutureMarginModel` and `PatternDayTradingMarginModel` will adjust
margin requirements before market closes using the new `Exchange.ClosingSoon` property
- Adding unit tests and regression algorithm
* Refactors AlgorithmManager loop
* Refactors StatisticsBuilder methods and strategy for series alignment
* Move sampling logic to the corresponding IResultHandler
* Changes benchmark resolution to Resolution.Hour
* Modifies IResultHandler to enable external sampling
* Adds BacktestResultHandler unit tests
* Adds ResolutionSwitchingAlgorithm to test misalignment
* Adds support to AlgorithmRunner to store algorithm IResultHandler
Warning: this commit breaks accurate calculations for algorithms that
only make use of `Daily` resolution data. Previously, because
the benchmark was added in Daily resolution in backtesting, any
algorithm that only made use of daily data would have an accurate
calculation for beta and various other statistics.
These changes serve to fix the statistics calculations of non-daily
resolution algorithms, with daily resolution to be revisited at a later
time.
Shows how to read/save object store entires. In this case, it shows a
use case where a potentially time intensive operation's result is saved
in the object store and on subsequent runs the result is pulled directly
from the object store to enable faster run times
- `UserDefinedUniverse` will no longer be removed as a data subscription.
- When `algorithm.AddData()` is called a universe selection data point
will be added to the `UserDefinedUniverse` subscription to trigger
selection and add the requested data.
- `DataManager` will make sure an active subscriptions
`SubscriptionDataConfig` will be present in the configuration collection
- Adding unit and regression tests
- Renaming `PreSelected` to `Constituents`
- Adding base `ConstituentsUniverse`
- Adding Py and C# regression algorithm
- Fixing bug in `UniverseSelection`, it wasn't removing pending to be
removed securities unless the universe selection changed
- Adding test data
- `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.
Martin Molinero