In This Page

Introduction

About the Provider

Getting Started

Data Summary

Example Applications

Data Point Attributes

Supported Assets

Requesting Data

Accessing Data

Historical Data

Example Applications

QuantConnect

US Future Option Universe

Introduction

The US Future Option Universe dataset by QuantConnect lists the available US Future Options contracts and the current open interest. The data covers 16 Monthly Future contracts, starting in January 2012, and is delivered on a daily update frequency. This dataset is created by monitoring the trading activity on the CME, CBOT, NYMEX, and COMEX markets.

This dataset does not contain market data. For market data, see US Future Options by AlgoSeek.

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

About the Provider

QuantConnect was founded in 2012 to serve quants everywhere with the best possible algorithmic trading technology. Seeking to disrupt a notoriously closed-source industry, QuantConnect takes a radically open-source approach to algorithmic trading. Through the QuantConnect web platform, more than 50,000 quants are served every month.

Getting Started

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

Select Language:

future = self.add_future(Futures.Metals.GOLD, Resolution.MINUTE)
future.set_filter(0, 90)
self.add_future_option(future.symbol, lambda universe: universe.strikes(-1, 1))

var future = AddFuture(Futures.Metals.Gold, Resolution.Minute);
future.SetFilter(0, 90);
AddFutureOption(future.Symbol, universe => universe.Strikes(-1, 1));

Data Summary

The following table describes the dataset properties:

Property	Value
Start Date	January 2012
Asset Coverage	16 Monthly Future Contracts. Standard expires only*
Data Density	Dense
Resolution	Minute, Hourly, & Daily
Timezone	New York
Market Hours	Regular and Extended

* No weeklies or 0DTE contracts.

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 deviate from their theoretical value

For more example algorithms, see Examples.

Data Point Attributes

The US Future Options Universe dataset provides OptionUniverse objects, which have the following attributes:

OptionUniverse Select Language:

The security identifier of the option symbol

id: SecurityIdentifier

Price of the option/underlying

value: decimal

Open price of the option/underlying

open: decimal

High price of the option/underlying

high: decimal

Low price of the option/underlying

low: decimal

Close price of the option/underlying

close: decimal

Volume of the option/underlying

volume: decimal

Open interest value of the option

open_interest: decimal

Implied volatility value of the option

implied_volatility: decimal

Greeks values of the option

greeks: Greeks

Time that the data became available to use

end_time: DateTime

The associated underlying price data if any

underlying: BaseData

Gets or sets the contracts selected by the universe

filtered_contracts: HashSet<Symbol>

Gets the data list

data: List<BaseData>

Market Data Type of this data - does it come in individual price packets or is it grouped into OHLC.

data_type: MarketDataType

True if this is a fill forward piece of data

is_fill_forward: bool

Current time marker of this data packet.

time: DateTime

Symbol representation for underlying Security

symbol: Symbol

As this is a backtesting platform we'll provide an alias of value as price.

price: decimal

Supported Assets

To view the supported assets in the US Future Options Universe dataset, see the Data Explorer.

Requesting Data

To add US Future Options Universe data to your algorithm, call the AddFutureOptionadd_future_option method. To define which contracts should be in your universe, specify the filter when requesting the FutureOption data.

The AddFutureOptionadd_future_option method provides a daily stream of Option chain data. To get the most recent daily chain, call the OptionChainoption_chain method with the underlying Future Symbol. The OptionChainoption_chain method returns data on all the tradable contracts, not just the contracts that pass your universe filter.

Select Language:

class USFutureOptionsDataAlgorithm(QCAlgorithm):

    def initialize(self) -> None:
        self.set_start_date(2020, 6, 1)
        self.set_end_date(2021, 6, 1)
        self.set_cash(100000)
        self.universe_settings.asynchronous = True
        
        self.future = self.add_future(Futures.Metals.GOLD, Resolution.MINUTE)
        self.future.set_filter(0, 90)
        # Set our strike/expiry filter for this option chain
        self.add_future_option(self.future.symbol, self._option_filter)

    def on_data(self, slice: Slice) -> None:
        # Get the entire Option chain for the current day.
        symbol = Symbol.create_canonical_option(self.future.mapped)
        chain = self.option_chain(symbol, flatten=True).data_frame

    def _option_filter(self, universe: OptionFilterUniverse) -> OptionFilterUniverse:
        # Contracts can be filtered by strike, and expiration
        return universe.strikes(-1, 1)

namespace QuantConnect
{
    public class USFutureOptionsDataAlgorithm : QCAlgorithm
    {
        private Future _future;
        
        public override void Initialize()
        {
            SetStartDate(2020, 6, 1);
            SetEndDate(2021, 6, 1);
            SetCash(100000);
            UniverseSettings.Asynchronous = True;
            
            _future = AddFuture(Futures.Metals.Gold, Resolution.Minute);
            _future.SetFilter(0, 90);
            // Set our strike/expiry filter for this option chain
            AddFutureOption(_future.Symbol, OptionFilter);
        }

        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(_future.Mapped);
            // Get the entire Option chain for the current day.
            var chain = OptionChain(symbol);
        }

        private virtual OptionFilterUniverse OptionFilter(OptionFilterUniverse universe)
        {
            // Contracts can be filtered by strike, and expiration
            return universe.Strikes(-1, 1);
        }
    }
}

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 you must set the Future Option resolution to minute, hour, or daily.

For more information about creating US Future Option Universes, see Future Options.

Accessing Data

For information about accessing US Future Options Universe data, see Future Options.

Historical Data

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

Historical Data In Algorithms

To get historical US Future Options Universe data in an algorithm, call the History<OptionUniverse>history method with the canonical mapped Future Option Symbol. This method returns data on all of the tradable contracts, not just the contracts that pass your universe filter. If there is no data in the period you request, the history result is empty.

Select Language:

self.future = self.add_future(Futures.Metals.GOLD, Resolution.MINUTE)
self.future.set_filter(0, 90)
self.add_future_option(self.future.symbol)

future_option_symbol = Symbol.create_canonical_option(self.future.mapped)
# DataFrame
history_df = self.history(future_option_symbol, timedelta(3), flatten=True)
# OptionUniverse objects
history = self.history[OptionUniverse](future_option_symbol, timedelta(3))

_future = AddFuture(Futures.Metals.Gold, Resolution.Minute);
_future.SetFilter(0, 90);
AddFutureOption(_future.Symbol);
var futureOptionSymbol = QuantConnect.Symbol.CreateCanonicalOption(_future.Mapped);
var history = History<OptionUniverse>(futureOptionSymbol, TimeSpan.FromDays(3)).ToList();

For more information about historical Equity Options Universe data in algorithms, see Historical Data.

Historical Data In Research

To get historical US Future Options Universe data in the Research Environment, call the History<OptionUniverse>history method with the canonical Option Symbol. This method returns data on all of the tradable contracts, not just the contracts that pass your universe filter.

Select Language:

qb = QuantBook()
future = qb.add_future(Futures.Metals.GOLD, Resolution.MINUTE)
future.set_filter(0, 90)
symbol = Symbol.create_canonical_option(future.mapped)
history = qb.history(symbol, datetime(2020, 6, 1), datetime(2020, 6, 5), flatten=True)

var qb = new QuantBook();
var future = qb.AddFuture(Futures.Metals.Gold, Resolution.Minute);
var symbol = QuantConnect.Symbol.CreateCanonicalOption(future.Mapped);
var history = qb.History<OptionUniverse>(symbol, new DateTime(2020, 6, 1), new DateTime(2020, 6, 6));
foreach (var chain in history)
{
    var endTime = chain.EndTime;
    var filteredContracts = chain.Data
        .Select(contract => contract as OptionUniverse)
        .Where(contract => contract.Greeks.Delta > 0.3m);
    foreach (var contract in filteredContracts)
    {
        var price = contract.Price;
        var iv = contract.ImpliedVolatility;
    }
}

For more information about historical Future Options Universe data in the Research Environment, see Universes.

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 deviate from their theoretical value

For more example algorithms, see Examples.

You can also see our Videos. You can also get in touch with us via Discord.

Did you find this page helpful?

Contribute to the documentation:

SIGN IN

Browse

Cloud Platform

Writing Algorithms

▶Key Concepts

Initialization

▶Securities

▶Portfolio

▶Universes

▶Datasets

▶Importing Data

▶Consolidating Data

▶Historical Data

▶Trading and Orders

▶Reality Modeling

Scheduled Events

▶Indicators

Object Store

▶Optimization

▶Machine Learning

▶Algorithm Framework

Charting

Logging

▶Statistics

▶Live Trading

Strategy Library

API Reference

▶Migrations

Research Environment

Local Platform

LEAN CLI

LEAN Engine

Hello

QuantConnect

US Future Option Universe

Introduction

About the Provider

Getting Started

Data Summary

Example Applications

Data Point Attributes

Supported Assets

Requesting Data

Accessing Data

Historical Data

Historical Data In Algorithms

Historical Data In Research

Example Applications

▶
Key Concepts

▶
Securities

▶
Portfolio

▶
Universes

▶
Datasets

▶
Importing Data

▶
Consolidating Data

▶
Historical Data

▶
Trading and Orders

▶
Reality Modeling

▶
Indicators

▶
Optimization

▶
Machine Learning

▶
Algorithm Framework

▶
Statistics

▶
Live Trading

▶
Migrations