Universe Selection

public class FrameworkScheduledUniverseSelectionAlgorithm : QCAlgorithm
{
    public override void Initialize()
    {
        SetStartDate(2023, 6, 1);
        SetEndDate(2023, 8, 1);
        SetCash(10000000);
        
        // Add a universe of that read from a Dropbox source to select the stocks 30 minutes before the week's first trading day.
        var spy = QuantConnect.Symbol.Create("SPY", SecurityType.Equity, Market.USA);
        AddUniverseSelection(new ScheduledUniverseSelectionModel(
            DateRules.WeekStart(spy),
            TimeRules.BeforeMarketOpen(spy, 10),
            SelectSymbols)
        );
        // Add Alpha model to trade based on the selections; the signals last for a week until the next selection.
        AddAlpha(new ConstantAlphaModel(InsightType.Price, InsightDirection.Up, TimeSpan.FromDays(7)));
        // Equally invest in insights to dissipate the capital risk evenly.
        SetPortfolioConstruction(new EqualWeightingPortfolioConstructionModel());
    }

    private string DownloadUniverseFile(DateTime dt)
    {
        // Download the universe CSV file. Dropbox links require the "dl=1" URL parameter.
        var file = Download(
            "https://www.dropbox.com/scl/fi/fbrxitk4ec3w91nse1raa/df.csv?rlkey=7r042rukzkthp7y1srloyhkov&st=5r4sdfwd&dl=1"
        );
        // Convert the CSV file data into a dictionary where the key is the date and
        // the value is a comma-separated string of stock tickers.
        foreach (var line in file.Split('\n').Skip(1))
        {
            // Skip empty lines.
            if (line.IsNullOrEmpty())
            {
                continue;
            }
            var items = line.Split("\"");
            var date = Parse.DateTimeExact(items[0].Split(",")[0], "yyyy-MM-dd").Date;
            // Return the universe if the date matches.
            if (date == dt.Date)
            {
                return items[1];
            }
        }
        return string.Empty;
    }

    private IEnumerable<Symbol> SelectSymbols(DateTime dt)
    {
        // Re-download the CSV file each day to get today's row.
        var tickers = DownloadUniverseFile(dt);
        // If there isn't an entry for the current date, return an empty universe.
        if (tickers.IsNullOrEmpty())
        {
            return Enumerable.Empty<Symbol>();
        }
        // Convert the stock tickers in the CSV file to Symbol objects.
        return tickers
            .Split(',')
            .Select(x => QuantConnect.Symbol.Create(x, SecurityType.Equity, Market.USA));
    }
}

from io import StringIO

class FrameworkScheduledUniverseSelectionAlgorithm(QCAlgorithm):
    def initialize(self) -> None:
        self.set_start_date(2023, 6, 1)
        self.set_end_date(2023, 8, 1)
        self.set_cash(10000000)

        # Add a universe of that read from a Dropbox source to select the stocks 30 minutes before the week's first trading day.
        spy = Symbol.create("SPY", SecurityType.EQUITY, Market.USA)
        self.add_universe_selection(ScheduledUniverseSelectionModel(
            self.date_rules.week_start(spy),
            self.time_rules.before_market_open(spy, 10),
            self.select_symbols
        ))
        # Add Alpha model to trade based on the selections; the signals last for a week until the next selection.
        self.add_alpha(ConstantAlphaModel(InsightType.PRICE, InsightDirection.UP, timedelta(7)))
        # Equally invest in insights to dissipate the capital risk evenly.
        self.set_portfolio_construction(EqualWeightingPortfolioConstructionModel())
    
    def _download_universe_file(self, dt: datetime) -> str:
        # Download the universe CSV file. Dropbox links require the "dl=1" URL parameter.
        file = self.download(
            "https://www.dropbox.com/scl/fi/fbrxitk4ec3w91nse1raa/df.csv?rlkey=7r042rukzkthp7y1srloyhkov&st=5r4sdfwd&dl=1"
        )
        # Convert the CSV file data into a dictionary where the key is the date and
        # the value is a comma-separated string of stock tickers.
        df = pd.read_csv(StringIO(file), index_col=0).iloc[:, 0]
        df.index = pd.to_datetime(df.index).date
        date = dt.date()
        # Return the universe if the date matches.
        if date in df:
            return df.loc[dt.date()]
        return ''

    def select_symbols(self, dt: datetime) -> list[Symbol]:
        # Re-download the CSV file each day to get today's row.
        tickers = self._download_universe_file(dt)
        # If there isn't an entry for the current date, return an empty universe.
        if not tickers:
            return []
        # Convert the stock tickers in the CSV file to Symbol objects.
        return [Symbol.create(x, SecurityType.EQUITY, Market.USA) for x in tickers.split(",")]

public class FrameworkScheduledUniverseSelectionAlgorithm : QCAlgorithm
{
    public override void Initialize()
    {
        SetStartDate(2019, 1, 1);
        SetEndDate(2024, 1, 1);
        
        // Add a universe of that select based on the time to trade seasonality.
        AddUniverseSelection(new ScheduledUniverseSelectionModel(
            DateRules.WeekStart(),
            TimeRules.At(0, 0),
            SelectSymbols)
        );
        // Add an Alpha model to trade based on the selections; the signals will last until the next selection.
        AddAlpha(new ConstantAlphaModel(InsightType.Price, InsightDirection.Up, TimeSpan.FromDays(7)));
        // Equally invest in insights to dissipate the capital risk evenly.
        SetPortfolioConstruction(new EqualWeightingPortfolioConstructionModel());
    }

    private IEnumerable<Symbol> SelectSymbols(DateTime dateTime)
    {
        // May/Jun/Jul/Nov/Dec are statistically better-performing months of SPY.
        if (new[] { 5, 6, 7, 11, 12 }.Contains(dateTime.Month))
        {
            return new[] { QuantConnect.Symbol.Create("SPY", SecurityType.Equity, Market.USA) };
        }
        // In other months, hold short-term bonds to preserve funds while earning interest.
        return new[] { QuantConnect.Symbol.Create("SHV", SecurityType.Equity, Market.USA) };
    }
}

class FrameworkScheduledUniverseSelectionAlgorithm(QCAlgorithm):
    def initialize(self) -> None:
        self.set_start_date(2019, 1, 1)
        self.set_end_date(2024, 1, 1)

        # Add a universe of that select based on the time to trade seasonality.
        self.add_universe_selection(ScheduledUniverseSelectionModel(
            self.date_rules.week_start(),
            self.time_rules.at(0, 0),
            self.select_symbols
        ))
        # Add an Alpha model to trade based on the selections; the signals will last until the next selection.
        self.add_alpha(ConstantAlphaModel(InsightType.PRICE, InsightDirection.UP, timedelta(7)))
        # Equally invest in insights to dissipate the capital risk evenly.
        self.set_portfolio_construction(EqualWeightingPortfolioConstructionModel())

    def select_symbols(self, date_time: datetime) -> list[Symbol]:
        # May/Jun/Jul/Nov/Dec are statistically better-performing months of SPY.
        if date_time.month in [5, 6, 7, 11, 12]:
            return [Symbol.create("SPY", SecurityType.EQUITY, Market.USA)]
        # In other months, hold short-term bonds to preserve funds while earning interest.
        return [Symbol.create("SHV", SecurityType.EQUITY, Market.USA)]