About US ETF Constituents

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.

About QuantConnect

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.

Add US ETF Constituents

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About QuantConnect

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 50,000 quants are served every month.

Algorithm Example

from AlgorithmImports import *
from QuantConnect.DataSource import *

class ETFConstituentUniverseAlgorithm(QCAlgorithm):

    def initialize(self) -> None:
        self.set_start_date(2016, 1, 1)
        self.set_end_date(2021, 1, 1)
        self.set_cash(100000)
        self.universe_settings.asynchronous = True
        self.universe_settings.resolution = Resolution.MINUTE
        
        self.spy = self.add_equity("SPY").symbol
        # Add universe selection on SPY's constituents to select only from large cap stocks
        # Save the universe to access its members for historical data call
        self._universe = self.add_universe(self.universe.etf(self.spy, self.universe_settings, self.etf_constituents_filter))
        
        # Historical Universe data, so you can work on the selection longitudinally
        history = self.history(self._universe, 30, Resolution.DAILY)
        for (universe_symbol, time), constituents in history.items():
            for constituent in constituents:
                self.debug(f'{constituent.symbol}: {constituent.weight}')

        self.weight_by_symbol = {}
        
        # Rebalance daily using scheduled event since selection is on daily basis
        self.schedule.on(
            self.date_rules.every_day(self.spy),
            self.time_rules.after_market_open(self.spy, 1),
            self.rebalance)

    def etf_constituents_filter(self, constituents: List[ETFConstituentUniverse]) -> List[Symbol]:
        # The top 10 weighted securities are considered better active selections
        # Save the weights for position sizing
        selected = sorted([c for c in constituents if c.weight],
            key=lambda c: c.weight, reverse=True)[:10]
        self.weight_by_symbol = {c.symbol: c.weight for c in selected}
        
        return list(self.weight_by_symbol.keys())

    def rebalance(self) -> None:
        spy_weight = sum(self.weight_by_symbol.values())

        # Liquidate the ones not in top 10 weights
        if spy_weight > 0:
            for symbol in self.portfolio.Keys:
                if symbol not in self.weight_by_symbol:
                    self.liquidate(symbol)

            # Create a long-short portfolio to earn excess return of the top 10 weighted stocks from SPY
            for symbol, weight in self.weight_by_symbol.items():
                self.set_holdings(symbol, 0.5 * weight / spy_weight)
            self.set_holdings(self.spy, -0.5)

    def on_securities_changed(self, changes: SecurityChanges) -> None:
        # Liquidate the ones not in top 10 weights
        for security in changes.removed_securities:
            if security.invested:
                self.liquidate(security.symbol, 'Removed From Universe')

        for security in changes.added_securities:
            # Historical data
            history = self.history(security.symbol, 7, Resolution.DAILY)
            self.debug(f'We got {len(history)} from our history request for {security.symbol}')

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:

Pricing

Provider offers 4 licensing options

Pricing