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 Equity Option Universe

Introduction

The US Equity Option Universe dataset by QuantConnect lists the available US Equity Options contracts and the current Implied Volatility and Greeks. The data covers 4,000 Symbols, starts in January 2012, and is delivered on a daily update frequency. To create this dataset, we use our implementation of the forward tree pricing model, which accounts for the interest rate, dividend payments, and daily closing prices. The values in this dataset are the same values you can get from daily indicators with mirror Options.

This dataset depends on the US Equity Security Master dataset because the US Equity Security Master dataset contains information on splits, dividends, and symbol changes of the underlying security.

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

For more information about the US Equity 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 Equity Options Universe dataset:

Select Language:

option = self.add_option("GOOG")
self.option_symbol = option.symbol
option.set_filter(lambda universe: universe.delta(0.4, 0.6))

var option = AddOption("GOOG");
_optionSymbol = option.Symbol;
option.SetFilter(universe => universe.delta(0.4m, 0.6m));

Data Summary

The following table describes the dataset properties:

Property	Value
Start Date	January 2012
Asset Coverage	4,000 Symbols
Data Density	Dense
Resolution	Daily
Timezone	New York

Example Applications

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

Buying put Options to hedge against downward price movement in positive Equity positions
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 Equity 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 Equity Options Universe dataset, see the Data Explorer.

Requesting Data

To add US Equity Options Universe data to your algorithm, call the AddOptionadd_option method. Save a reference to the Equity Option Symbol so you can access the data later in your algorithm. To define which contracts should be in your universe, call the SetFilterset_filter method of the Option object.

The AddOptionadd_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 Equity Symbol. The OptionChainoption_chain method returns data on all the tradable contracts, not just the contracts that pass your universe filter.

Select Language:

class USEquityOptionsDataAlgorithm(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
        option = self.add_option("GOOG")
        self.option_symbol = option.symbol
        # Set our strike/expiry filter for this option chain
        option.set_filter(self._option_filter)
        # Get the entire Option chain for the current day.
        chain = self.option_chain(option.symbol.underlying, flatten=True).data_frame

    def _option_filter(self, universe: OptionFilterUniverse) -> OptionFilterUniverse:
        # Contracts can be filtered by greeks, implied volatility, open interest:
        return universe \
            .delta(0.5, 1.5) \
            .gamma(0.0001, 0.0006) \
            .vega(0.01, 1.5) \
            .theta(-2.0, -0.5) \
            .rho(0.5, 3.0) \
            .implied_volatility(1, 3) \
            .open_interest(100,500)

namespace QuantConnect
{
    public class USEquityOptionsDataAlgorithm : QCAlgorithm
    {
        private Symbol _optionSymbol;
        
        public override void Initialize()
        {
            SetStartDate(2020, 6, 1);
            SetEndDate(2021, 6, 1);
            SetCash(100000);
            UniverseSettings.Asynchronous = True;
            // Requesting data
            var option = AddOption("GOOG");
            _optionSymbol = option.Symbol;
            // Set our strike/expiry filter for this option chain
            option.SetFilter(OptionFilter);
            // Get the entire Option chain for the current day.
            var chain = OptionChain(option.Symbol.Underlying);
        }

        private virtual OptionFilterUniverse OptionFilter(OptionFilterUniverse universe)
        {
            // Contracts can be filtered by greeks, implied volatility, open interest:
            return universe
                .Delta(0.5m, 1.5m)
                .Gamma(0.0001m, 0.0006m)
                .Vega(0.01m, 1.5m)
                .Theta(-2.0m, -0.5m)
                .Rho(0.5m, 3.0m)
                .ImpliedVolatility(1.0m, 3.0m)
                .OpenInterest(100m, 500m);
        }
    }
}

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

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

Accessing Data

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

Historical Data

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

Historical Data In Algorithms

To get historical US Equity Options Universe data in an algorithm, call the History<OptionUniverse>history method with the canonical Equity 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:

# DataFrame
history_df = self.history(self.option_symbol, timedelta(3), flatten=True)

# OptionUniverse objects
history = self.history[OptionUniverse](self.option_symbol, timedelta(3))

// OptionUniverse objects 
var history = History<OptionUniverse>(_optionSymbol, 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 Equity 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()
option = qb.add_option("GOOG")
history = qb.history(option.symbol, datetime(2020, 6, 1), datetime(2020, 6, 5), flatten=True)

var qb = new QuantBook();
var option = qb.AddOption("GOOG");
var history = qb.History<OptionUniverse>(option.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 Equity Options Universe data in the Research Environment, see Universes.

Example Applications

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

Buying put Options to hedge against downward price movement in positive Equity positions
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.

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Hello

QuantConnect

US Equity 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