QuantConnect

US ETF Constituents

Introduction

The US ETF Constituents dataset by QuantConnect tracks the constituents and weighting of US Equities in 2,650 ETF listings. The data starts in June 2009 and is delivered on a daily basis (monthly basis before January 2015). This dataset is created by tracking the host ETF websites and can be delayed by up to 1 week.

This dataset depends on the US Equity Security Master dataset because the US Equity Security Master dataset contains information on splits, dividends, and symbol changes.

For more information about the US ETF Constituents dataset, including CLI commands and pricing, see the dataset listing.

About the Provider

QuantConnect was founded in 2012 to serve quants everywhere with the best possible algorithmic trading technology. Seeking to disrupt a notoriously closed-source industry, QuantConnect takes a radically open-source approach to algorithmic trading. Through the QuantConnect web platform, more than 160,000 quants are served every month.

Getting Started

The following snippet demonstrates how to request data from the US ETF Constituents dataset:

def initialize(self) -> None:
    self.universe_settings.asynchronous = True
    # Use the following method for a Classic Algorithm
    self._universe = self.add_universe(self.universe.etf("SPY", Market.USA, self.universe_settings, self.etf_constituents_filter))

    symbol = Symbol.create("SPY", SecurityType.EQUITY, Market.USA)
    # Use the following method for a Framework Algorithm
    self.add_universe_selection(ETFConstituentsUniverseSelectionModel(symbol, self.universe_settings, self.etf_constituents_filter))

    def etf_constituents_filter(self, constituents: List[ETFConstituentUniverse]) -> List[Symbol]:
        # Add all Symbols of the ETFConstituentUniverse
       return [x.symbol for x in constituents]
public override void Initialize()
{
    UniverseSettings.Asynchronous = True;
    // Use the following method for a Classic Algorithm
    _universe = AddUniverse(Universe.ETF("SPY", Market.USA, UniverseSettings, ETFConstituentsFilter));

    var symbol = QuantConnect.Symbol.Create("SPY", SecurityType.Equity, Market.USA);
    // Use the following method for a Framework Algorithm
    AddUniverseSelection(new ETFConstituentsUniverseSelectionModel(symbol, UniverseSettings, ETFConstituentsFilter));
}
private IEnumerable<Symbol> ETFConstituentsFilter(IEnumerable <ETFConstituentUniverse> constituents)
{
    // Add all Symbols of the ETFConstituentUniverse
    return constituents.Select(x => x.Symbol);
}

Data Summary

The following table describes the dataset properties:

PropertyValue
Start DateJune 2009
Asset Coverage2,650 US ETF Listings
Data DensityDense
ResolutionDaily (Monthly before Jan 2015)
TimezoneNew York

Example Applications

The ETF Constituents dataset provides an excellent source of tradable universes for strategies without selection bias. When you use an ETF universe, the original ETF can serve as an excellent benchmark for your strategy performance. Other use cases include the following:

  • Creating an index-tracking algorithm for customized passive portfolio management
  • Performing statistical arbitrage with the base ETF

For more example algorithms, see Examples.

Data Point Attributes

The ETF Constituents dataset provides ETFConstituentUniverse objects, which have the following attributes:

Supported ETFs

The following table shows the available ETFs:

Requesting Data

To add US ETF Constituents data to your algorithm, call the AddUniverseadd_universe and Universe.ETFuniverse.etf methods. To select which constituents occupy the universe, provide the ETF Symbol and a selection function. 

class ETFConstituentUniverseAlgorithm(QCAlgorithm):

    def initialize(self) -> None:
        self.set_start_date(2018, 1, 1)
        self.set_end_date(2020, 8, 25)
        self.set_cash(100000)
        self.universe_settings.asynchronous = True
        self._universe = self.add_universe(self.universe.etf("SPY", self.universe_settings, self.etf_constituents_filter))
namespace QuantConnect
{
   public class ETFConstituentUniverseAlgorithm : QCAlgorithm
   {
       public override void Initialize()
       {
           SetStartDate(2018, 1, 1);
           SetEndDate(2020, 8, 25);
           SetCash(100000);
           UniverseSettings.Asynchronous = True;
           _universe = AddUniverse(Universe.ETF("SPY", UniverseSettings, ETFConstituentsFilter));
       }
    }
}

For more information about universe settings, see Settings.

Accessing Data

To access the US ETF Constituent data, use the ETFConstituentUniverse objects in your selection function. The data is available in daily resolution. The Symbol objects you return from your selection function defines the universe constituents.

def etf_constituents_filter(self, constituents: List[ETFConstituentUniverse]) -> List[Symbol]:
    for c in constituents:
        self.debug(f'{c.end_time} :: {c.last_update} :: {c.weight} :: {c.shares_held} :: {c.market_value}')
    return [x.symbol for x in constituents]
public IEnumerable<Symbol> ETFConstituentsFilter(IEnumerable<ETFConstituentUniverse> constituents)
{
    foreach (var c in constituents)
    {
        Debug($"{c.EndTime} :: {c.LastUpdate} :: {c.Weight} :: {c.SharesHeld} :: {c.MarketValue}");
    }

    return constituents.Select(c => c.Symbol);
}

Historical Data

You can get historical universe data in an algorithm and in the Research Environment.

Historical Universe Data in Algorithms

To get historical universe data in an algorithm, call the Historyhistory method with the Universe object and the lookback period. If there is no data in the period you request, the history result is empty.

var history = History(_universe, 30, Resolution.Daily);
foreach (var constituents in history)
{
    foreach (ETFConstituentUniverse constituent in constituents)
    {
        Log($"{constituent.Symbol} weight at {constituent.EndTime}: {constituent.Weight}");
    }
}
# DataFrame example where the columns are the ETFConstituentUniverse attributes: 
df_history = self.history(self.universe, 30, Resolution.DAILY, flatten=True)

# Series example where the values are lists of ETFConstituentUniverse objects: 
series_history = self.history(self.universe, 30, Resolution.DAILY)
for (universe_symbol, time), constituents in series_history.items():
    for constituent in constituents:
        self.log(f'{constituent.symbol} weight at {constituent.end_time}: {constituent.weight}')

Historical Universe Data in Research

To get historical universe data in research, call the UniverseHistoryuniverse_history method with the Universe object and the lookback period. The UniverseHistoryuniverse_history returns the filtered universe. If there is no data in the period you request, the history result is empty.

var universeHistory = qb.UniverseHistory(universe, qb.Time.AddDays(-30), qb.Time);
foreach (var constituents in universeHistory )
{
    foreach (ETFConstituentUniverse constituent in constituents)
    {
        Console.WriteLine($"{constituent.Symbol} weight at {constituent.EndTime}: {constituent.Weight}");
    }
}
# DataFrame example where the columns are the ETFConstituentUniverse attributes: 
df_history = qb.universe_history(universe, qb.time-timedelta(30), qb.time, flatten=True)

# Series example where the values are lists of ETFConstituentUniverse objects: 
series_history = qb.universe_history(universe, qb.time-timedelta(30), qb.time)
for (universe_symbol, time), constituents in series_history.items():
    for constituent in constituents:
       print(f"{constituent.symbol} weight at {constituent.end_time}: {constituent.weight}")

You can call the Historyhistory method in Research.

Example Applications

The ETF Constituents dataset provides an excellent source of tradable universes for strategies without selection bias. When you use an ETF universe, the original ETF can serve as an excellent benchmark for your strategy performance. Other use cases include the following:

  • Creating an index-tracking algorithm for customized passive portfolio management
  • Performing statistical arbitrage with the base ETF

For more example algorithms, see Examples.

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