Universe Selection

// Run universe selection asynchronously to speed up your algorithm. 
// In this case, you can't rely on the method or algorithm state between filter calls.
UniverseSettings.Asynchronous = true;
// Add a universe of SPY Options.
AddUniverseSelection(
    new OptionUniverseSelectionModel(
        // Refresh the universe daily.
        TimeSpan.FromDays(1), 
        _ => new [] { QuantConnect.Symbol.Create("SPY", SecurityType.Option, Market.USA) }
    )
);

from Selection.OptionUniverseSelectionModel import OptionUniverseSelectionModel 

# Run universe selection asynchronously to speed up your algorithm. 
# In this case, you can't rely on the method or algorithm state between filter calls.
self.universe_settings.asynchronous = True
# Add a universe of SPY Options.
self.set_universe_selection(
    OptionUniverseSelectionModel(
        # Refresh the universe daily.
        timedelta(1), lambda _: [Symbol.create("SPY", SecurityType.OPTION, Market.USA)]
    )
)

// In the Initialize method, add the OptionUniverseSelectionModel with a custom selection function.
public override void Initialize()
{
    AddUniverseSelection(
        new OptionUniverseSelectionModel(TimeSpan.FromDays(1), SelectOptionChainSymbols)
    );
}

// Define the selection function.
private IEnumerable<Symbol> SelectOptionChainSymbols(DateTime utcTime)
{
    // Equity Options example:
    //var tickers = new[] {"SPY", "QQQ", "TLT"};
    //return tickers.Select(ticker => QuantConnect.Symbol.Create(ticker, SecurityType.Option, Market.USA));

    // Index Options example:
    //var tickers = new[] {"VIX", "SPX"};
    //return tickers.Select(ticker => QuantConnect.Symbol.Create(ticker, SecurityType.IndexOption, Market.USA));

    // Future Options example:
    var futureSymbol = QuantConnect.Symbol.Create(Futures.Indices.SP500EMini, SecurityType.Future, Market.CME);
    var futureContractSymbols = FutureChainProvider.GetFutureContractList(futureSymbol, Time);
    foreach (var symbol in futureContractSymbols)
    {
        yield return QuantConnect.Symbol.CreateCanonicalOption(symbol);
    }
}

from Selection.OptionUniverseSelectionModel import OptionUniverseSelectionModel 

# In the initialize method, add the OptionUniverseSelectionModel with a custom selection function.
def initialize(self) -> None:
    self.add_universe_selection(
        OptionUniverseSelectionModel(timedelta(days=1), self.select_option_chain_symbols)
    )

# Define the selection function.
def select_option_chain_symbols(self, utc_time: datetime) -> List[Symbol]:
    # Equity Options example:
    #tickers = ["SPY", "QQQ", "TLT"]
    #return [Symbol.create(ticker, SecurityType.OPTION, Market.USA) for ticker in tickers]

    # Index Options example:
    #tickers = ["VIX", "SPX"]
    #return [Symbol.create(ticker, SecurityType.INDEX_OPTION, Market.USA) for ticker in tickers]

    # Future Options example:
    future_symbol = Symbol.create(Futures.Indices.SP500E_MINI, SecurityType.FUTURE, Market.CME)
    future_contract_symbols = self.future_chain_provider.get_future_contract_list(future_symbol, self.time)
    return [Symbol.create_canonical_option(symbol) for symbol in future_contract_symbols]

// In the Initialize method, define the universe settings and add data.
UniverseSettings.Asynchronous = true;
AddUniverseSelection(new EarliestExpiringAtTheMoneyCallOptionUniverseSelectionModel(this));

// Outside of the algorithm class, define the universe selection model.
class EarliestExpiringAtTheMoneyCallOptionUniverseSelectionModel : OptionUniverseSelectionModel
{
    public EarliestExpiringAtTheMoneyCallOptionUniverseSelectionModel(QCAlgorithm algorithm)
            : base(TimeSpan.FromDays(1), utcTime => SelectOptionChainSymbols(algorithm, utcTime)) {}
    
    private static IEnumerable<Symbol> SelectOptionChainSymbols(QCAlgorithm algorithm, DateTime utcTime)
    {
        // Equity Options example:
        //var tickers = new[] {"SPY", "QQQ", "TLT"};
        //return tickers.Select(ticker => QuantConnect.Symbol.Create(ticker, SecurityType.Option, Market.USA));

        // Index Options example:
        //var tickers = new[] {"VIX", "SPX"};
        //return tickers.Select(ticker => QuantConnect.Symbol.Create(ticker, SecurityType.IndexOption, Market.USA));

        // Future Options example:
        var futureSymbol = QuantConnect.Symbol.Create(Futures.Indices.SP500EMini, SecurityType.Future, Market.CME);
        var futureContractSymbols = algorithm.FutureChainProvider.GetFutureContractList(futureSymbol, algorithm.Time);
        foreach (var symbol in futureContractSymbols)
        {
            yield return QuantConnect.Symbol.CreateCanonicalOption(symbol);
        }
    }
    
    // Create a filter to select contracts that have the strike price within 1 strike level and expire within 7 days.
    protected override OptionFilterUniverse Filter(OptionFilterUniverse filter)
    {
        return filter.Strikes(-1, -1).Expiration(0, 7).CallsOnly();
    }
}

# In the initialize method, define the universe settings and add data.
self.universe_settings.asynchronous = True
self.add_universe_settings(EarliestExpiringAtTheMoneyCallOptionUniverseSelectionModel(self))

# Outside of the algorithm class, define the universe selection model.
class EarliestExpiringAtTheMoneyCallOptionUniverseSelectionModel(OptionUniverseSelectionModel):
    def __init__(self, algorithm):
        self.algo = algorithm
        super().__init__(timedelta(1), self.select_option_chain_symbols)
    
    def select_option_chain_symbols(self, utc_time: datetime) -> List[Symbol]:
        # Equity Options example:
        #tickers = ["SPY", "QQQ", "TLT"]
        #return [Symbol.create(ticker, SecurityType.OPTION, Market.USA) for ticker in tickers]

        # Index Options example:
        #tickers = ["VIX", "SPX"]
        #return [Symbol.create(ticker, SecurityType.INDEX_OPTION, Market.USA) for ticker in tickers]

        # Future Options example:
        future_symbol = Symbol.create(Futures.Indices.SP500E_MINI, SecurityType.FUTURE, Market.CME)
        future_contract_symbols = self.algo.future_chain_provider.get_future_contract_list(future_symbol, self.algo.time)
        return [Symbol.create_canonical_option(symbol) for symbol in future_contract_symbols]
        
    # Create a filter to select contracts that have the strike price within 1 strike level and expire within 7 days.
    def Filter(self, option_filter_universe: OptionFilterUniverse) -> OptionFilterUniverse:
        return option_filter_universe.strikes(-1, -1).expiration(0, 7).calls_only()

// Configure the universe to use price data unadjusted for splits and dividends ("raw") into the algorithm. 
// Options require raw Equity prices.
UniverseSettings.DataNormalizationMode = DataNormalizationMode.Raw;
UniverseSettings.Asynchronous = true;
AddUniverseSelection(
    new OptionChainedUniverseSelectionModel(
        // Add a universe of the 10 most liquid US Equities.
        AddUniverse(Universe.DollarVolume.Top(10)),
        // Select call Option contracts on the underlying Equities that have the strike price within 2 strike levels.
        optionFilterUniverse => optionFilterUniverse.Strikes(-2, +2).FrontMonth().CallsOnly()
    )
);

# Configure the universe to use price data unadjusted for splits and dividends ("raw") into the algorithm. 
# Options require raw Equity prices.
self.universe_settings.data_normalization_mode = DataNormalizationMode.RAW
self.universe_settings.asynchronous = True
self.add_universe_selection(
    OptionChainedUniverseSelectionModel(
        # Add a universe of the 10 most liquid US Equities.
        self.add_universe(self.universe.dollar_volume.top(10)),
        # Select call Option contracts on the underlying Equities that have the strike price within 2 strike levels.
        lambda option_filter_universe: option_filter_universe.strikes(-2, +2).front_month().calls_only()
    )
)

// In the Initialize method, define the universe settings and add the universe selection model.
public override void Initialize()
{
    UniverseSettings.DataNormalizationMode = DataNormalizationMode.Raw;
    UniverseSettings.Asynchronous = true;
    AddUniverseSelection(
        new OptionChainedUniverseSelectionModel(
            AddUniverse(Universe.DollarVolume.Top(10)), OptionFilterFunction
        )
    );
}

// Define the contract filter function to select front month call contracts with a strike price within 2 strike levels.
private OptionFilterUniverse OptionFilterFunction(OptionFilterUniverse optionFilterUniverse)
{
    return optionFilterUniverse.Strikes(-2, +2).FrontMonth().CallsOnly();
}

# In the initialize method, define the universe settings and add the universe selection model.
def initialize(self) -> None:
    self.universe_settings.data_normalization_mode = DataNormalizationMode.RAW
    self.universe_settings.asynchronous = True
    self.add_universe_selection(
        OptionChainedUniverseSelectionModel(
            self.add_universe(self.universe.dollar_volume.top(10)), self.option_filter_function
        )
    )

# Define the contract filter function to select front month call contracts with a strike price within 2 strike levels.
def option_filter_function(self, option_filter_universe: OptionFilterUniverse) -> OptionFilterUniverse:
    return option_filter_universe.strikes(-2, +2).front_month().calls_only()

// Example code to chain a fundamental universe and an Equity Options universe by selecting top 10 stocks with lowest PE, indicating potentially undervalued stocks and then selecting their from-month call Option contracts to target contracts with high liquidity.
using QuantConnect.Data;
using QuantConnect.Data.Fundamental;
using QuantConnect.Data.UniverseSelection;
using QuantConnect.Securities;
using QuantConnect.Securities.Option;
using QuantConnect.Util;
using System;
using System.Collections.Generic;
using System.Linq;
namespace QuantConnect.Algorithm.CSharp
{
    public class ETFUniverseOptions : QCAlgorithm
    {
        private int _day;
        public override void Initialize()
        {
            SetStartDate(2023, 2, 2);
            SetCash(100000);
            UniverseSettings.Asynchronous = true;
            UniverseSettings.DataNormalizationMode = DataNormalizationMode.Raw;
            SetSecurityInitializer(new CustomSecurityInitializer(this));

            var universe = AddUniverse(FundamentalFunction);
            AddUniverseOptions(universe, OptionFilterFunction);
        }

        private IEnumerable<Symbol> FundamentalFunction(IEnumerable<Fundamental> fundamental)
        {
            return fundamental
                .Where(f => !double.IsNaN(f.ValuationRatios.PERatio))
                .OrderBy(f => f.ValuationRatios.PERatio)
                .Take(10)
                .Select(x => x.Symbol);
        }

        private OptionFilterUniverse OptionFilterFunction(OptionFilterUniverse optionFilterUniverse)
        {
            return optionFilterUniverse.Strikes(-2, +2).FrontMonth().CallsOnly();
        }

        public override void OnData(Slice data)
        {
            if (IsWarmingUp || _day == Time.Day) return;

            foreach (var (symbol, chain) in data.OptionChains)
            {
                if (Portfolio[chain.Underlying.Symbol].Invested)
                    Liquidate(chain.Underlying.Symbol);

                var spot = chain.Underlying.Price;
                var contract = chain.OrderBy(x => Math.Abs(spot-x.Strike)).FirstOrDefault();
                var tag = $"IV: {contract.ImpliedVolatility:F3} Δ: {contract.Greeks.Delta:F3}";
                MarketOrder(contract.Symbol, 1, true, tag);
                _day = Time.Day;
            }
        }
    }

    internal class CustomSecurityInitializer : BrokerageModelSecurityInitializer
    {
        private QCAlgorithm _algorithm;
        public CustomSecurityInitializer(QCAlgorithm algorithm)
            : base(algorithm.BrokerageModel, new FuncSecuritySeeder(algorithm.GetLastKnownPrices))
        {
            _algorithm = algorithm;
        }    
    
        public override void Initialize(Security security)
        {
            // First, call the superclass definition
            // This method sets the reality models of each security using the default reality models of the brokerage model
            base.Initialize(security);

            // Next, overwrite the price model        
            if (security.Type == SecurityType.Option) // Option type
            {
                (security as Option).PriceModel = OptionPriceModels.CrankNicolsonFD();
            }

            // Overwrite the volatility model and warm it up
            if (security.Type == SecurityType.Equity)
            {
                security.VolatilityModel = new StandardDeviationOfReturnsVolatilityModel(30);
                var tradeBars = _algorithm.History(security.Symbol, 30, Resolution.Daily);
                foreach (var tradeBar in tradeBars)
                    security.VolatilityModel.Update(security, tradeBar);
            }    
        }
    }
}

# Example code to chain a fundamental universe and an Equity Options universe by selecting top 10 stocks with lowest PE, indicating potentially undervalued stocks and then selecting their from-month call Option contracts to target contracts with high liquidity.
from AlgorithmImports import *

    class ChainedUniverseAlgorithm(QCAlgorithm):
    def initialize(self):
        self.set_start_date(2023, 2, 2)
        self.set_cash(100000)
        self.universe_settings.asynchronous = True
        self.universe_settings.data_normalization_mode = DataNormalizationMode.RAW
        self.set_security_initializer(CustomSecurityInitializer(self))

        universe = self.add_universe(self.fundamental_function)
        self.add_universe_options(universe, self.option_filter_function)
        self.day = 0

    def fundamental_function(self, fundamental: List[Fundamental]) -> List[Symbol]:
        filtered = (f for f in fundamental if not np.isnan(f.valuation_ratios.pe_ratio))
        sorted_by_pe_ratio = sorted(filtered, key=lambda f: f.valuation_ratios.pe_ratio)
        return [f.symbol for f in sorted_by_pe_ratio[:10]]

    def option_filter_function(self, option_filter_universe: OptionFilterUniverse) -> OptionFilterUniverse:
        return option_filter_universe.strikes(-2, +2).front_month().calls_only()

    def on_data(self, data: Slice) -> None:
        if self.is_warming_up or self.day == self.time.day:
            return
        
        for symbol, chain in data.option_chains.items():
            if self.portfolio[chain.underlying.symbol].invested:
                self.liquidate(chain.underlying.symbol)

            spot = chain.underlying.price
            contract = sorted(chain, key=lambda x: abs(spot-x.strike))[0]
            tag = f"IV: {contract.implied_volatility:.3f} Δ: {contract.greeks.delta:.3f}"
            self.market_order(contract.symbol, 1, True, tag)
            self.day = self.time.day

class CustomSecurityInitializer(BrokerageModelSecurityInitializer):
    def __init__(self, algorithm: QCAlgorithm) -> None:
        super().__init__(algorithm.brokerage_model, FuncSecuritySeeder(algorithm.get_last_known_prices))
        self.algorithm = algorithm

    def initialize(self, security: Security) -> None:
        # First, call the superclass definition
        # This method sets the reality models of each security using the default reality models of the brokerage model
        super().initialize(security)

        # Overwrite the price model        
        if security.type == SecurityType.OPTION: # Option type
            security.price_model = OptionPriceModels.crank_nicolson_fd()

        # Overwrite the volatility model and warm it up
        if security.type == SecurityType.EQUITY:
            security.volatility_model = StandardDeviationOfReturnsVolatilityModel(30)
            trade_bars = self.algorithm.history[TradeBar](security.symbol, 30, Resolution.DAILY)
            for trade_bar in trade_bars:
                security.volatility_model.update(security, trade_bar)