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 January 2009 and is delivered on a daily basis. This dataset is created by tracking the host ETF websites and can be delayed by up to 1 week.
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.
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
class ETFConstituentsDataAlgorithm(QCAlgorithm):
def Initialize(self) -> None:
self.SetStartDate(2016, 1, 1)
self.SetEndDate(2021, 1, 1)
self.SetCash(100000)
self.UniverseSettings.Resolution = Resolution.Minute
# Requesting data
self.spy = self.AddEquity("SPY").Symbol
self.AddUniverse(self.Universe.ETF(self.spy, self.UniverseSettings, self.ETFConstituentsFilter))
self.weightBySymbol = {}
self.Schedule.On(
self.DateRules.EveryDay(self.spy),
self.TimeRules.AfterMarketOpen(self.spy, 1),
self.Rebalance)
def ETFConstituentsFilter(self, constituents: List[ETFConstituentData]) -> List[Symbol]:
# Get the 10 securities with the largest weight in the index
selected = sorted([c for c in constituents if c.Weight],
key=lambda c: c.Weight, reverse=True)[:10]
self.weightBySymbol = {c.Symbol: c.Weight for c in selected}
return list(self.weightBySymbol.keys())
def Rebalance(self) -> None:
spyWeight = sum(self.weightBySymbol.values())
if spyWeight > 0:
for symbol in self.Portfolio.Keys:
if symbol not in self.weightBySymbol:
self.Liquidate(symbol)
for symbol, weight in self.weightBySymbol.items():
self.SetHoldings(symbol, 0.5 * weight / spyWeight)
self.SetHoldings(self.spy, -0.5)
def OnSecurityChanged(self, changes: SecurityChanges) -> None:
for security in changes.RemovedSecurities:
if security.Invested:
algorithm.Liquidate(security.Symbol, 'Removed From Universe')
for security in changes.AddedSecurities:
# 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:
- Creating an index-tracking algorithm for customized passive portfolio management
- Performing statistical arbitrage with the base ETF
Pricing
Cloud Access
Free access for universe selection strategies on the QuantConnect Cloud. Create custom filters using price and volume for the ETF constituents.
On Premise Download
On premise download of ETF constituent data files, including closing price and volume for the day for local backtesting.
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