Universe Selection

ETF Constituents Universes

Introduction

The ETFConstituentsUniverseSelectionModel selects a universe of US Equities based on the constituents of an ETF. These Universe Selection models rely on the US ETF Constituents dataset. They run on a daily schedule by default. To adjust the selection schedule, see Schedule.

Add ETF Constituents Universe Selection

To add an ETFConstituentsUniverseSelectionModel to your algorithm, in the Initializeinitialize method, call the AddUniverseSelectionadd_universe_selection method. The ETFConstituentsUniverseSelectionModel constructor expects an ETF ticker.

// Run universe selection asynchronously to speed up your algorithm.
UniverseSettings.Asynchronous = true;
AddUniverseSelection(new ETFConstituentsUniverseSelectionModel("SPY"));
# Run universe selection asynchronously to speed up your algorithm.
self.universe_settings.asynchronous = True
self.add_universe_selection(ETFConstituentsUniverseSelectionModel("SPY"))

The following table describes the arguments the model accepts:

ArgumentData TypeDescriptionDefault Value
etfTickeretf_tickerstringTicker of the ETF to get constituents for. To view the available ETFs, see Supported ETFs.
universeSettingsuniverse_settingsUniverseSettingsThe universe settings. If you don't provide an argument, the model uses the algorithm.UniverseSettingsalgorithm.universe_settings by default.None
universeFilterFuncuniverse_filter_funcFunc<IEnumerable<ETFConstituentUniverse>, IEnumerable<Symbol>>Callable[[List[ETFConstituentUniverse]], List[Symbol]]Function to filter ETF constituents. If you don't provide an argument, the model selects all of the ETF constituents by default.Nonenull

If you provide a universeFilterFuncuniverse_filter_func argument, you can use the following attributes of the ETFConstituentUniverse objects to select your universe:

The following example shows how to select the 10 Equities with the largest weight in the SPY ETF:

// Initialize asynchronous settings for speed and use the ETFConstituentsUniverseSelectionModel 
// to select the top 10 SPY constituents by weight, focusing on blue-chip stocks with minimal risk.
public override void Initialize()
{
    UniverseSettings.Asynchronous = true;
    AddUniverseSelection(
        new ETFConstituentsUniverseSelectionModel("SPY", universeFilterFunc: ETFConstituentsFilter)
    );
}

private IEnumerable<Symbol> ETFConstituentsFilter(IEnumerable<ETFConstituentUniverse> constituents)
{
    // Select the 10 largest Equities in the ETF.
    return constituents.OrderByDescending(c => c.Weight).Take(10).Select(c => c.Symbol);
}
# Initialize asynchronous settings for speed and use the ETFConstituentsUniverseSelectionModel 
# to select the top 10 SPY constituents by weight, focusing on blue-chip stocks with minimal risk.
def initialize(self) -> None:
    self.universe_settings.asynchronous = True   
    self.add_universe_selection(
        ETFConstituentsUniverseSelectionModel("SPY", universe_filter_func=self._etf_constituents_filter)
    )

def _etf_constituents_filter(self, constituents: List[ETFConstituentUniverse]) -> List[Symbol]:
    # Select the 10 largest Equities in the ETF.
    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.

// Initialize asynchronous settings for speed and use the LargestWeightSPYETFUniverseSelectionModel 
// to select the top 10 blue-chip SPY constituents by weight, focusing on stocks with minimal risk.
UniverseSettings.Asynchronous = true;
AddUniverseSelection(new LargestWeightSPYETFUniverseSelectionModel());

// Outside of the algorithm class
class LargestWeightSPYETFUniverseSelectionModel : ETFConstituentsUniverseSelectionModel
{
    public LargestWeightSPYETFUniverseSelectionModel(UniverseSettings universeSettings = null)
        : base("SPY", universeFilterFunc: ETFConstituentsFilter)
    {
    }

    private static IEnumerable<Symbol> ETFConstituentsFilter(IEnumerable<ETFConstituentUniverse> constituents)
    {
        // Select the 10 largest Equities in the ETF.
        return constituents.OrderByDescending(c => c.Weight).Take(10).Select(c => c.Symbol);
    }
}
# Initialize asynchronous settings for speed and use the LargestWeightSPYETFUniverseSelectionModel 
# to select the top 10 blue-chip SPY constituents by weight, focusing on stocks with minimal risk.
self.universe_settings.asynchronous = True
self.add_universe_selection(LargestWeightSPYETFUniverseSelectionModel())

# Outside of the algorithm class
class LargestWeightSPYETFUniverseSelectionModel(ETFConstituentsUniverseSelectionModel):
    def __init__(self) -> None:
        super().__init__(symbol, universe_filter_func=self._etf_constituents_filter)

    def _etf_constituents_filter(self, constituents: List[ETFConstituentUniverse]) -> List[Symbol]:
        # Select the 10 largest Equities in the ETF.
        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.UnchangedUNCHANGED.

To view the implementation of this model, see the LEAN GitHub repositoryLEAN GitHub repository.

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