Universe Selection
ETF Constituents Universes
Introduction
The ETFConstituentsUniverseSelectionModel
selects a daily universe of US Equities based on the constituents of an ETF. These Universe Selection models rely on the US ETF Constituents dataset.
Add ETF Constituents Universe Selection
To add an ETFConstituentsUniverseSelectionModel
to your algorithm, in the Initialize
method, call the AddUniverseSelection
method. The ETFConstituentsUniverseSelectionModel
constructor expects an ETF Symbol
.
AddUniverseSelection(new ETFConstituentsUniverseSelectionModel(etfSymbol));
self.AddUniverseSelection(ETFConstituentsUniverseSelectionModel(etfSymbol))
The following table describes the arguments the model accepts:
Argument | Data Type | Description | Default Value |
---|---|---|---|
etfSymbol | Symbol | Symbol of the ETF to get constituents for | |
universeSettings | UniverseSettings | The universe settings. If you don't provide an argument, the model uses the algorithm.UniverseSettings by default. | null None |
universeFilterFunc | Func<IEnumerable<ETFConstituentData>, IEnumerable<Symbol>> Callable[[List[ETFConstituentData]], List[Symbol]] | Function to filter ETF constituents. If you don't provide an argument, the model selects all of the ETF constituents by default. | null None |
If you provide a universeFilterFunc
argument, you can use the following attributes of the ETFConstituentData
objects to select your universe:
The following example shows how to select the 10 Equities with the largest weight in the SPY ETF:
public override void Initialize() { var symbol = QuantConnect.Symbol.Create("SPY", SecurityType.Equity, Market.USA); AddUniverseSelection(new ETFConstituentsUniverseSelectionModel(symbol, UniverseSettings, ETFConstituentsFilter)); } private IEnumerable<Symbol> ETFConstituentsFilter(IEnumerable<ETFConstituentData> constituents) { return constituents.OrderByDescending(c => c.Weight).Take(10).Select(c => c.Symbol); }
def Initialize(self) -> None: symbol = Symbol.Create("SPY", SecurityType.Equity, Market.USA) universe = ETFConstituentsUniverseSelectionModel(symbol, self.UniverseSettings, self.ETFConstituentsFilter) self.AddUniverseSelection(universe) def ETFConstituentsFilter(self, constituents: List[ETFConstituentData]) -> List[Symbol]: selected = sorted([c for c in constituents if c.Weight], key=lambda c: c.Weight, reverse=True)[:10] return [c.Symbol for c in selected]
To move the ETF Symbol
and the selection function outside of the algorithm class, create a universe selection model that inherits the ETFConstituentsUniverseSelectionModel
class.
// In Initialize AddUniverseSelection(new LargestWeightSPYETFUniverseSelectionModel(UniverseSettings)); // Outside of the algorithm class class LargestWeightSPYETFUniverseSelectionModel : ETFConstituentsUniverseSelectionModel { public LargestWeightSPYETFUniverseSelectionModel(UniverseSettings universeSettings = null) : base(QuantConnect.Symbol.Create("SPY", SecurityType.Equity, Market.USA), universeSettings, ETFConstituentsFilter) { } private static IEnumerable<Symbol> ETFConstituentsFilter(IEnumerable<ETFConstituentData> constituents) { return constituents.OrderByDescending(c => c.Weight).Take(10).Select(c => c.Symbol); } }
# In Initialize self.AddUniverseSelection(LargestWeightSPYETFUniverseSelectionModel(self.UniverseSettings)) # Outside of the algorithm class class LargestWeightSPYETFUniverseSelectionModel(ETFConstituentsUniverseSelectionModel): def __init__(self, universe_settings: UniverseSettings = None) -> None: symbol = Symbol.Create("SPY", SecurityType.Equity, Market.USA) super().__init__(symbol, universe_settings, self.ETFConstituentsFilter) def ETFConstituentsFilter(self, constituents: List[ETFConstituentData]) -> List[Symbol]: selected = sorted([c for c in constituents if c.Weight], key=lambda c: c.Weight, reverse=True)[:10] return [c.Symbol for c in selected]
To return the current universe constituents from the selection function, return Universe.Unchanged
.
To view the implementation of this model, see the LEAN GitHub repositoryLEAN GitHub repository.