Filtering away symbols with earnings today, when already have add_universe by market cap

Here’s how to modernize your universe selection using Coarse + Fine selection functions, making your code more efficient and fully compatible with QuantConnect's recommended universe model:

from AlgorithmImports import *

class CoarseFineUniverseAlgorithm(QCAlgorithm):
    def initialize(self):
        # 1. Backtest parameters
        self.set_start_date(2021, 1, 1)
        self.set_end_date(2022, 1, 1)
        self.set_cash(100000)

        # 2. Store the selected universe
        self._universe_symbols: Set[Symbol] = set()

        # 3. Attach the Coarse + Fine universe selection delegates
        self.add_universe(self._coarse_selection, self._fine_selection)

    def _coarse_selection(self, coarse: List[CoarseFundamental]) -> List[Symbol]:
        """Return the 1,000 most liquid US stocks that have fundamental data."""
        if not coarse:
            return []

        # Filter for securities that have fundamental data and a positive price
        filtered = [c for c in coarse if c.has_fundamental_data and c.price > 0]

        # Sort by dollar volume and take the top 1,000
        top_liquid = sorted(filtered, key=lambda c: c.dollar_volume, reverse=True)[:1000]

        return [c.symbol for c in top_liquid]

    def _fine_selection(self, fine: List[FineFundamental]) -> List[Symbol]:
        """Filter to NASDAQ stocks with positive MarketCap and return the top 100 by MarketCap."""
        if not fine:
            self._universe_symbols.clear()
            return []

        # Keep only NASDAQ listings with positive market cap
        filtered = [f for f in fine
                    if f.market_cap > 0 and f.security_reference.exchange_id in ("NAS", "NAQ")]

        # Sort by market cap and keep the top 100
        top_by_marketcap = sorted(filtered, key=lambda f: f.market_cap, reverse=True)[:100]

        # Persist the selected universe
        self._universe_symbols = set(f.symbol for f in top_by_marketcap)

        return [f.symbol for f in top_by_marketcap]

    def on_data(self, data: Slice):
        # Trading logic would go here. For now we simply pass.
        pass

Usage:

• Replace your legacy self.add_universe(self._select_universe_legacy) call with this new setup.
• This method is scalable, efficient, and avoids history requests or unnecessary filtering in OnData.
• The symbols currently in your universe are always in self._universe_symbols for any further logic.

Let me know if you want further refinements or portfolio logic integration!

Sources: Coarse Fine Fundamental Combo Algorithm, Coarse Fundamental Top3 Algorithm, Legacy Fundamental Universes - QuantConnect.com, Coarse Fine Fundamental Regression Algorithm, Legacy Fundamental Universes - QuantConnect.com