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AlgoSeek

US Future Options

Introduction

The US Future Options dataset by AlgoSeek provides Option data on US Future contracts, including prices, strikes, and expires. The data covers 16 Monthly Future contracts, starts in January 2012, and is delivered on a minute frequency. This dataset is created by monitoring the trading activity on the CME, CBOT, NYMEX, and COMEX markets.

This dataset depends on the following datasets:

For more information about the US Future Options dataset, including CLI commands and pricing, see the dataset listing.

About the Provider

AlgoSeek is a leading historical intraday US market data provider offering the most comprehensive and detailed market data and analytics products in the financial industry covering equities, futures, options, cash forex, and cryptocurrencies. AlgoSeek data is built for quantitative trading and machine learning. For more information about AlgoSeek, visit algoseek.com.

Getting Started

The following snippet demonstrates how to request data from the US Future Options dataset:

future = self.add_future(Futures.Metals.GOLD, Resolution.MINUTE)
future.set_filter(0, 90)
self.add_future_option(future.symbol, lambda universe: universe.strikes(-5, +5))
var future = AddFuture(Futures.Metals.Gold, Resolution.Minute);
future.SetFilter(0, 90);
AddFutureOption(future.Symbol, universe => universe.Strikes(-5, +5));

Data Summary

The following table describes the dataset properties:

PropertyValue
Start DateJanuary 2012
Asset Coverage16 Monthly Future Contracts. Standard expires only*.
Data DensityDense
ResolutionMinute, Hourly, & Daily
TimezoneNew York
Market HoursRegular and Extended
* No weeklies or 0DTE contracts.

Requesting Data

To add US Future Options data to your algorithm, call the AddFutureOptionadd_future_option method.

class FutureOptionDataAlgorithm(QCAlgorithm):

    def initialize(self) -> None:
        self.set_start_date(2020, 1, 28)
        self.set_end_date(2020, 6, 1)
        self.set_cash(100000)
        self.universe_settings.asynchronous = True
        future = self.add_future(Futures.Metals.GOLD, Resolution.MINUTE)
        future.set_filter(0, 90)
        self.add_future_option(future.symbol, lambda universe: universe.strikes(-5, +5))
public class FutureOptionDataAlgorithm : QCAlgorithm
{
    public override void Initialize()
    {
        SetStartDate(2020, 1, 28);
        SetEndDate(2020, 6, 1);
        SetCash(100000);
        UniverseSettings.Asynchronous = True;
        var future = AddFuture(Futures.Metals.Gold, Resolution.Minute);
        future.SetFilter(0, 90);
        AddFutureOption(future.Symbol, universe => universe.Strikes(-5, +5));
    }
}

The Future resolution must be less than or equal to the Future Option resolution. For example, if you set the Future resolution to minute, then the Future Option resolution must be minute, hour, or daily.

For more information about creating Future Options subscriptions, see Requesting Data or Future Options Universes.

Accessing Data

To get the current Future Options data, iterate through the OptionChainsoption_chains property of the current Slice. Slice objects deliver unique events to your algorithm as they happen, but the Slice may not contain data for your Future Options at every time step.

def on_data(self, slice: Slice) -> None:
    for canonical_fop_symbol, chain in slice.option_chains.items():
        for contract in chain:
            self.log(f"{contract.symbol} price at {slice.time}: {contract.last_price}")
public override void OnData(Slice slice)
{
    foreach (var kvp in slice.OptionChains)
    {
        var canonicalFOPSymbol = kvp.Key;
        var chain = kvp.Value;
        foreach (var contract in chain)
        {
            Log($"{contract.Symbol} price at {slice.Time}: {contract.LastPrice}");
        }
    }
}

You can also iterate through the FuturesChainsfutures_chains in the current Slice first.

def on_data(self, slice: Slice) -> None:
    for continuous_future_symbol, futures_chain in slice.futures_chains.items():
        # Select a Future Contract and create its canonical FOP Symbol
        futures_contract = [contract for contract in futures_chain][0]
        canonical_fop_symbol = Symbol.create_canonical_option(futures_contract.symbol)
        option_chain = slice.option_chains.get(canonical_fop_symbol)
        if option_chain:
            for fop_contract in option_chain:
                 self.log(f"{fop_contract.symbol} price at {slice.time}: {fop_contract.last_price}")
public override void OnData(Slice slice)
{
    foreach (var kvp in slice.FuturesChains)
    {
        var continuousContractSymbol = kvp.Key;
        var futuresChain = kvp.Value;
        
        // Select a Future Contract and create its canonical FOP Symbol
        var futuresContract = futuresChain.First();
        var canonicalFOPSymbol = QuantConnect.Symbol.CreateCanonicalOption(futuresContract.Symbol);
        if (slice.OptionChains.TryGetValue(canonicalFOPSymbol, out var optionChain))
        {
            foreach (var fopContract in optionChain)
            {
                Log($"{fopContract.Symbol} price at {slice.Time}: {fopContract.LastPrice}");
            }
        }
    }
}

For more information about accessing Future Options data, see Handling Data.

Historical Data

You can get historical US Future Options data in an algorithm and the Research Environment.

Historical Data In Algorithms

To get historical US Future Options data in an algorithm, call the Historyhistory method with the Future Option contract Symbol. If there is no data in the period you request, the history result is empty.

# DataFrame of trade and quote data
history_df = self.history(contract.symbol, 100, Resolution.MINUTE)

# DataFrame of open interest data
history_oi_df = self.history(OpenInterest, contract.symbol, 100, Resolution.MINUTE)

# TradeBar objects
history_trade_bars = self.history[TradeBar](contract.symbol, 100, Resolution.MINUTE)

# QuoteBar objects
history_quote_bars = self.history[QuoteBar](contract.symbol, 100, Resolution.MINUTE)

# OpenInterest objects
history_oi = self.history[OpenInterest](contract.symbol, 100, Resolution.MINUTE)
// TradeBar objects 
var historyTradeBars = History(contract.Symbol, 100, Resolution.Minute);

// QuoteBar objects 
var historyQuoteBars = History<QuoteBar>(contract.Symbol, 100, Resolution.Minute);

// OpenInterest objects 
var historyOpenInterest = History<OpenInterest >(contract.Symbol, 100, Resolution.Minute);

For more information about historical data in algorithms, see History Requests.

Historical Data In Research

To get historical US Future Options data in the Research Environment, call the Historyhistory or OptionHistoryoption_history method. The Historyhistory method returns the price, volume, and open interest history for some given Future Option contract(s). The OptionHistoryoption_history method returns the price and volume history for the contracts that pass your daily universe filter.

qb = QuantBook()
future = qb.add_future(Futures.Indices.SP_500_E_MINI)
start_date = datetime(2024, 1, 2)
future_contract_symbol = sorted(
    qb.future_chain_provider.get_future_contract_list(future.symbol, start_date), 
    key=lambda s: s.id.date
)[0]
history = qb.option_history(
    future_contract_symbol, start_date, future_contract_symbol.id.date, Resolution.HOUR
)
history_df = history.data_frame
expiries = history.get_expiry_dates()
strikes = history.get_strikes()
var qb = new QuantBook();
var future = qb.AddFuture(Futures.Indices.SP500EMini);
var startDate = new DateTime(2024, 1, 2);
var futureContractSymbol = qb.FutureChainProvider.GetFutureContractList(future.Symbol, startDate)
    .OrderBy(x => x.ID.Date)
    .First();
var history = qb.OptionHistory(
    futureContractSymbol, startDate, futureContractSymbol.ID.Date, Resolution.Hour
);

var contracts = history
    .SelectMany(x => x.OptionChains.SelectMany(y => y.Value.Contracts.Keys))
    .Distinct().ToList();
var expiries = contracts.Select(x => x.ID.Date).Distinct().ToList();
var strikes = contracts.Select(x => x.ID.StrikePrice).Distinct().ToList();

To get historical data for arbitrary US Equity Option contracts instead of just the that pass your universe filter, call the Historyhistory method like you would in an algorithm, but on the QuantBook object. For more information about historical data in the Research Environment, see Key Concepts.

Supported Assets

The following list shows the available (16) Futures Options:

  • Futures.Dairy.ClassIIIMilkFutures.Dairy.CLASS_III_MILK: Class III Milk Futures (CME: DC)
  • Futures.Energy.CrudeOilWTIFutures.Energy.CRUDE_OIL_WTI: Crude Oil WTI Futures (NYMEX: LO | Underlying: CL)
  • Futures.Energy.GasolineFutures.Energy.GASOLINE: Gasoline RBOB Futures (NYMEX: OB | Underlying: RB)
  • Futures.Energy.HeatingOilFutures.Energy.HEATING_OIL: Heating Oil Futures (NYMEX: OH | Underlying: HO)
  • Futures.Energy.NaturalGasFutures.Energy.NATURAL_GAS: Natural Gas Futures (NYMEX: ON | Underlying: NG)
  • Futures.Financials.Y10TreasuryNoteFutures.Financials.Y_10_TREASURY_NOTE: 10Y U.S. Treasury Note Futures (CBOT: OZN | Underlying: ZN)
  • Futures.Financials.Y2TreasuryNoteFutures.Financials.Y_2_TREASURY_NOTE: 2Y U.S. Treasury Note Futures (CBOT: OZT | Underlying: ZT)
  • Futures.Financials.Y30TreasuryBondFutures.Financials.Y_30_TREASURY_BOND: 30Y U.S. Treasury Bond Futures (CBOT: OZB | Underlying: ZB)
  • Futures.Grains.CornFutures.Grains.CORN: Corn Futures (CBOT: OZC | Underlying: ZC)
  • Futures.Grains.SoybeansFutures.Grains.SOYBEANS: Soybeans Futures (CBOT: OZS | Underlying: ZS)
  • Futures.Grains.WheatFutures.Grains.WHEAT: Default wheat contract is SRWWheat (CBOT: OZW | Underlying: ZW)
  • Futures.Indices.NASDAQ100EMiniFutures.Indices.NASDAQ_100_E_MINI: E-mini NASDAQ 100 Futures (CME: NQ)
  • Futures.Indices.SP500EMiniFutures.Indices.SP_500_E_MINI: E-mini S&P 500 Futures (CME: ES)
  • Futures.Metals.CopperFutures.Metals.COPPER: Copper Futures (COMEX: HXE | Underlying: HG)
  • Futures.Metals.GoldFutures.Metals.GOLD: Gold Futures (COMEX: OG | Underlying: GC)
  • Futures.Metals.SilverFutures.Metals.SILVER: Silver Futures (COMEX: SO | Underlying: SI)

Example Applications

The US Future Options dataset enables you to accurately design Future Option strategies. Examples include the following strategies:

  • Selling out of the money Future Option contracts to collect the premium that the Option buyer pays
  • Buying put Options to hedge against downward price movement in Future contracts you bought
  • Exploiting arbitrage opportunities that arise when the price of Option contracts deviates from their theoretical value

Classic Algorithm Example

The following example demonstrates a weekly-renewing covered call strategy to collect credit of selling the option. It filters the ATM call contract that expires within the current week at week start using SetFilter filtering function.

from AlgorithmImports import *

class FutureOptionExampleAlgorithm(QCAlgorithm):

    def initialize(self) -> None:
        # Subscribe the underlying since the updated price is needed for filtering
        self.underlying = self.add_future(Futures.Indices.SP_500_E_MINI,
            extended_market_hours=True,
            data_mapping_mode=DataMappingMode.OPEN_INTEREST,
            data_normalization_mode=DataNormalizationMode.BACKWARDS_RATIO,
            contract_depth_offset=0)
        # Filter the underlying continuous Futures to narrow the FOP spectrum
        self.underlying.set_filter(0, 182)
        # Filter for the current-week-expiring calls to formulate a covered call that expires at the end of week
        self.add_future_option(self.underlying.symbol, lambda u: u.include_weeklys().calls_only().expiration(0, 5))

    def on_data(self, slice: Slice) -> None:
        # Create canonical symbol for the mapped future contract, since option chains are mapped by canonical symbol
        symbol = Symbol.create_canonical_option(self.underlying.mapped)

        # Get option chain data for the mapped future, as both the underlying and FOP have the highest liquidity among all other contracts
        chain = slice.option_chains.get(symbol)
        if not self.portfolio.invested and chain:
            # Obtain the ATM call that expires at the end of week, such that both underlying and the FOP expires the same time
            expiry = max(x.expiry for x in chain)
            atm_call = sorted([x for x in chain if x.expiry == expiry],
                key=lambda x: abs(x.strike - x.underlying_last_price))[0]

            # Use abstraction method to order a covered call to avoid manual error
            option_strategy = OptionStrategies.covered_call(symbol, atm_call.strike,expiry)
            self.buy(option_strategy, 1)
        
    def on_securities_changed(self, changes: SecurityChanges) -> None:
        for security in changes.added_securities:
            if security.type == SecurityType.FUTURE_OPTION:
                # Historical data
                history = self.history(security.symbol, 10, Resolution.MINUTE)
                self.debug(f"We got {len(history)} from our history request for {security.symbol}")
public class FutureOptionExampleAlgorithm : QCAlgorithm
{
    private Future _underlying;

    public override void Initialize()
    {
        // Subscribe the underlying since the updated price is needed for filtering
        _underlying = AddFuture(Futures.Indices.SP500EMini,
            extendedMarketHours: True,
            dataMappingMode: DataMappingMode.OpenInterest,
            dataNormalizationMode: DataNormalizationMode.BackwardsRatio,
            contractDepthOffset: 0);
        // Filter the underlying continuous Futures to narrow the FOP spectrum
        _underlying.SetFilter(0, 182);
        // Filter for the current-week-expiring calls to formulate a covered call that expires at the end of week
        AddFutureOption(_underlying.Symbol, (u) => u.IncludeWeeklys().CallsOnly().Expiration(0, 5));
    }

    public override void OnData(Slice slice)
    {
        // Create canonical symbol for the mapped future contract, since option chains are mapped by canonical symbol
        var symbol = QuantConnect.Symbol.CreateCanonicalOption(_underlying.Mapped);

        // Get option chain data for the mapped future, as both the underlying and FOP have the highest liquidity among all other contracts
        if (!Portfolio.Invested && 
            slice.OptionChains.TryGetValue(symbol, out var chain))
        {
            // Obtain the ATM call that expires at the end of week, such that both underlying and the FOP expires the same time
            var expiry = chain.Max(x => x.Expiry);
            var atmCall = chain.Where(x => x.Expiry == expiry)
                .OrderBy(x => Math.Abs(x.Strike - x.UnderlyingLastPrice))
                .First();

            // Use abstraction method to order a covered call to avoid manual error
            var optionStrategy = OptionStrategies.CoveredCall(symbol, atmCall.Strike, expiry);
            Buy(optionStrategy, 1);
        }
    }

    public override void OnSecuritiesChanged(SecurityChanges changes)
    {
        foreach (var security in changes.AddedSecurities)
        {
            if (security.Type == SecurityType.FutureOption)
            {
                // Historical data
                var history = History(security.Symbol, 10, Resolution.Minute);
                Debug($"We got {history.Count()} from our history request for {security.Symbol}");
            }
        }
    }
}

Framework Algorithm Example

The following example demonstrates a daily renewing speculation trade on upward movement of Gold with FOP using algorithm framework. Using FOP, traders can achieve lower cost to get similar absolute profit, given the FOP is selected to be deep ITM with high Delta value.

from AlgorithmImports import *

class USFuturesDataAlgorithm(QCAlgorithm):

    def initialize(self) -> None:

        self.set_start_date(2013, 12, 20) 
        self.set_end_date(2014, 2, 20)
        self.set_cash(100000)
        # Asynchronous to save computation resources
        self.universe_settings.asynchronous = True
        # Override an option universe selection model to select the FOPs
        self.set_universe_selection(GoldFOPUniverseSelectionModel())
        # Handle the FOP trading logic in an alpha model
        self.add_alpha(ConstantFutureOptionsAlphaModel())
        # To order a single contract per insight, use a custom portfolio construction model
        self.set_portfolio_construction(SingleSharePortfolioConstructionModel())
        

class GoldFOPUniverseSelectionModel(OptionUniverseSelectionModel):

    # Daily renewed universe since option contract list is updated on a daily basis
    def __init__(self,) -> None:
        super().__init__(timedelta(1), self.select_fop_symbols)

    def select_fop_symbols(self, algorithm: QCAlgorithm, utcTime: datetime) -> List[Symbol]:
        #  Create the underlying symbol to get the FOP contracts later
        future_symbol = Symbol.create(Futures.Metals.GOLD, SecurityType.FUTURE, Market.COMEX)
        # Get all gold FOP contracts of the front month gold contract expiring within 3 months, since their liquidity is the highest
        future_contract_list = algorithm.future_chain_provider.get_future_contract_list(future_symbol, algorithm.time)
        return [Symbol.create_canonical_option(x) for x in future_contract_list 
                    if x.id.date <= algorithm.time + timedelta(90)]

    def filter(self, option_filter_universe: OptionFilterUniverse) -> OptionFilterUniverse:
        # Filter for the ATM calls that expires latest (same expiry as the underlying)
        # +/-5 strike range buffer for price movement
        return option_filter_universe.back_month().strikes(-5, +5).calls_only();


class ConstantFutureOptionsAlphaModel(AlphaModel):

    # A dictionary to cache the mapped FOP of the underlying Future for filtering wanted option chain data and check if invested
    option_contract_by_future_underlying_contract = {}

    def update(self, algorithm: QCAlgorithm, slice: Slice) -> List[Insight]:
        insights = []

        for kvp in slice.option_chains:
            # Liquidate underlying Future contract after Option assignment
            underlying_future_contract = kvp.key.underlying
            if algorithm.portfolio[underlying_future_contract].invested:
                algorithm.insights.cancel([underlying_future_contract])
                del self.option_contract_by_future_underlying_contract[underlying_future_contract]

            # Do not repeatly invest in the same underlying's FOP
            chain = [contract for contract in kvp.Value if algorithm.securities[contract.symbol].is_tradable)]
            if not chain or underlying_future_contract in self.option_contract_by_future_underlying_contract:
                continue
            
            # Select the Option contract with the lowest strike price to speculate trade the underlying with lowest cost and highest delta
            contract = sorted(chain, key=lambda x: x.strike)[0]
            insights.append(Insight.price(contract.symbol, contract.expiry + timedelta(1), InsightDirection.UP))
            self.option_contract_by_future_underlying_contract[underlying_future_contract] = contract
        
        return insights


class SingleSharePortfolioConstructionModel(PortfolioConstructionModel):

    def create_targets(self, algorithm: QCAlgorithm, insights: List[Insight]) -> List[PortfolioTarget]:
        targets = []
        for insight in insights:
            if algorithm.securities[insight.symbol].is_tradable:
                # Use integer target to create a portfolio target to trade a single contract
                targets.append(PortfolioTarget(insight.symbol, insight.direction))
        return targets
public class FutureOptionDataAlgorithm : QCAlgorithm
{
    public override void Initialize()
    {
        SetStartDate(2020, 1, 28);
        SetEndDate(2020, 6, 1);
        SetCash(100000);
        // Asynchronous to save computation resources
        UniverseSettings.Asynchronous = True;
        // Override an option universe selection model to select the FOPs
        AddUniverseSelection(new GoldFOPUniverseSelectionModel(this));
        // Handle the FOP trading logic in an alpha model
        SetAlpha(new ConstantFutureOptionsAlphaModel());
        // To order a single contract per insight, use a custom portfolio construction model
        SetPortfolioConstruction(new SingleSharePortfolioConstructionModel());
    }
}

public class GoldFOPUniverseSelectionModel : OptionUniverseSelectionModel
{
    // Daily renewed universe since option contract list is updated on a daily basis
    public GoldFOPUniverseSelectionModel(QCAlgorithm algorithm)
        : base(TimeSpan.FromDays(1), _ => OptionChainSymbolSelector(algorithm, _))
    {
    }

    private static IEnumerable<Symbol> OptionChainSymbolSelector(QCAlgorithm algorithm, DateTime utcTime)
    {
        // Create the underlying symbol to get the FOP contracts later
        var futureSymbol = QuantConnect.Symbol.Create(Futures.Metals.Gold, SecurityType.Future, Market.COMEX);
        // Get all gold FOP contracts of the front month gold contract expiring within 3 months, since their liquidity is the highest
        return algorithm.FutureChainProvider.GetFutureContractList(futureSymbol, algorithm.Time)
            .Where(futureContractSymbol => futureContractSymbol.ID.Date <= algorithm.Time + TimeSpan.FromDays(90))
            .Select(futureContractSymbol => QuantConnect.Symbol.CreateCanonicalOption(futureContractSymbol));
    }

    protected override OptionFilterUniverse Filter(OptionFilterUniverse optionFilterUniverse)
    {
        // Filter for the ATM calls that expires latest (same expiry as the underlying)
        // +/-5 strike range buffer for price movement
        return optionFilterUniverse.BackMonth().Strikes(-5, +5).CallsOnly();
    }
}


class ConstantFutureOptionsAlphaModel : AlphaModel
{
    // A dictionary to cache the mapped FOP of the underlying Future for filtering wanted option chain data and check if invested
    private Dictionary<Symbol, OptionContract> optionContractByUnderlyingFutureContract = new Dictionary<Symbol, OptionContract>();
    
    public override IEnumerable<Insight> Update(QCAlgorithm algorithm, Slice slice)
    {
        var insights = new List<Insight>();
        
        foreach (var kvp in slice.OptionChains)
        {
            // Liquidate underlying Future contract after Option assignment
            var underlyingFutureContract = kvp.Key.Underlying;
            if (algorithm.Portfolio[underlyingFutureContract].Invested)
            {
                algorithm.Insights.Cancel(new[] { underlyingFutureContract });
                optionContractByUnderlyingFutureContract.Remove(underlyingFutureContract);
            }
            
            // Do not repeatly invest in the same underlying's FOP
            var chain = kvp.Value.Where(contract => algorithm.Securities[contract.Symbol].IsTradable);
            if (chain.Count() == 0 || optionContractByUnderlyingFutureContract.ContainsKey(underlyingFutureContract))
            {
                continue;
            }

            // Select the Option contract with the lowest strike price to speculate trade the underlying with lowest cost and highest delta
            var contract = chain.MinBy(contract => contract.Strike);
            insights.Add(Insight.Price(contract.Symbol, contract.Expiry.AddDays(1), InsightDirection.Up));
            optionContractByUnderlyingFutureContract.Add(kvp.Key.Underlying, contract);
        }            
        return insights;
    }
}


class SingleSharePortfolioConstructionModel : PortfolioConstructionModel
{
    public override IEnumerable<PortfolioTarget> CreateTargets(QCAlgorithm algorithm, Insight[] insights)
    {
        var targets = new List<PortfolioTarget>();
        foreach (var insight in insights)
        {
            if (algorithm.Securities[insight.Symbol].IsTradable)
            {
                // Use integer target to create a portfolio target to trade a single contract
                targets.Add(new PortfolioTarget(insight.Symbol, (int) insight.Direction));
            }
        }
        return targets;
    }
}

Data Point Attributes

The US Future Options dataset provides TradeBar, QuoteBar, and OpenInterest objects.

TradeBar Attributes

TradeBar objects have the following attributes:

QuoteBar Attributes

QuoteBar objects have the following attributes:

OpenInterest Attributes

OpenInterest objects have the following attributes:

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