QQQ Live Universe Constituent Issue

from AlgorithmImports import *

class ETFConstituentDebugAlgorithm(QCAlgorithm):
    def initialize(self) -> None:
        """
        Creates two ETF-constituent universes (QQQ and XLF) and logs every daily
        update so you can compare when constituent data is missing, empty, or stale.
        """

        # ------------------------------------------------------------------ #
        # Basic configuration
        # ------------------------------------------------------------------ #
        self.set_start_date(2021, 1, 1)
        self.set_end_date(2022, 1, 1)
        self.set_cash(100000)

        # Work with daily resolution universe data
        self.universe_settings.resolution = Resolution.DAILY

        # ETFs to monitor: first is the target (QQQ), second is the reference (XLF)
        self._etf_tickers: list[str] = ["QQQ", "XLF"]

        # ------------------------------------------------------------------ #
        # Add ETF universes
        # ------------------------------------------------------------------ #
        for ticker in self._etf_tickers:

            # Wrap the selector so the ticker is available inside the callback
            def _selector(constituents, _ticker=ticker):
                return self._select_constituents(_ticker, constituents)

            universe = self.universe.etf(ticker, self.universe_settings, _selector)
            self.add_universe(universe)

    # ---------------------------------------------------------------------- #
    # Universe selector / logger
    # ---------------------------------------------------------------------- #
    def _select_constituents(
        self,
        ticker: str,
        constituents: list[ETFConstituentUniverse]
    ) -> list[Symbol]:
        """
        Logs the list of constituents and highlights any missing or stale data.
        Returns an empty list because this algorithm only performs diagnostics.
        """

        constituents_list: list[ETFConstituentUniverse] = list(constituents)
        count: int = len(constituents_list)

        # Header for the day
        self.debug(f"{self.time.date()} | {ticker} | constituents returned: {count}")

        # -------------------------------------------------------------- #
        # 1) No constituent data received
        # -------------------------------------------------------------- #
        if count == 0:
            self.debug(
                f"{self.time.date()} | {ticker} | *** MISSING constituent data ***"
            )
            return []  # Nothing to select—diagnostics only

        # -------------------------------------------------------------- #
        # 2) Constituent data received – log details
        # -------------------------------------------------------------- #
        now: datetime = self.time
        stale_threshold: datetime = now - timedelta(days=1)
        stale_found: bool = False

        for c in constituents_list:
            # Defensive guard in case LastUpdate is missing
            last_update: datetime = getattr(c, "last_update", c.end_time)

            if last_update < stale_threshold:
                stale_found = True

            # Detailed per-constituent log entry
            self.debug(
                f"{self.time.date()} | {ticker} | {c.symbol.value:>6} | "
                f"weight: {c.weight:.6f} | last_update: {last_update:%Y-%m-%d} | "
                f"end_time: {c.end_time:%Y-%m-%d}"
            )

        # Highlight stale data
        if stale_found:
            self.debug(
                f"{self.time.date()} | {ticker} | "
                f"*** WARNING: Stale constituent data detected ***"
            )

        # Diagnostics only → select nothing for trading
        return []

Instructions for Zachary:

• Use this algorithm as-is in live or backtest environments.
• The algorithm will log, each day, how many constituents it receives for QQQ and XLF, and print each constituent’s symbol, weight, and update dates.
• It will highlight if data is missing (empty) or stale (older than 1 day).
• Compare logs for QQQ and XLF to see if QQQ’s data is missing/delayed in live mode (while XLF works), which you can share with QuantConnect support for troubleshooting or documentation.

Sources: docs writing algorithms datasets quantconnect us etf constituents, docs writing algorithms universes equity etf constituents universes, ETF Constituents Universes - QuantConnect.com, Constituents Universe Regression Algorithm, docs writing algorithms datasets quantconnect us equity coarse universe