In This Page

Introduction

About the Provider

Getting Started

Data Summary

Example Applications

Supported Assets

Data Point Attributes

Requesting Data

Accessing Data

Historical Data

Example Applications

QuantConnect

International Future Universe

Introduction

The International Future Universe dataset by QuantConnect lists the available International Future contracts, their daily trading volume, and Open Interest. The data covers 3 contracts (FESX, HSI, and NKD), starts in July 1998, and is delivered on daily frequency. This dataset is created by monitoring the trading activity on the EUREX, HKFE, and CME.

This dataset depends on the US Futures Security Master dataset because the US Futures Security Master dataset contains information on symbol changes of the contracts.

This dataset does not contain market data. For market data, see International Futures by TickData and US Futures by AlgoSeek for NKD.

For more information about the International Future 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 International Futures Universe dataset provides data for contract filtering/selection:

Select Language:

hsi = self.add_future(Futures.Indices.HANG_SENG, Resolution.MINUTE)            # "HSI"
hsi.set_filter(0, 90)
fesx = self.add_future(Futures.Indices.EURO_STOXX_50, Resolution.MINUTE)       # "FESX"
fesx.set_filter(0, 90)
nkd = self.add_future(Futures.Indices.NIKKEI_225_DOLLAR, Resolution.MINUTE)    # "NKD"
nkd.set_filter(0, 90)

var hsi= AddFuture(Futures.Indices.HangSeng, Resolution.Minute)            // "HSI";
hsi.SetFilter(0, 90);
var fesx = AddFuture(Futures.Indices.EuroStoxx50, Resolution.Minute)       // "FESX";
fesx.SetFilter(0, 90);
var nkd = AddFuture(Futures.Indices.Nikkei225Dollar, Resolution.Minute)    // "NKD";
nkd.SetFilter(0, 90);

Data Summary

The following table describes the dataset properties:

Property	Value
Start Date	July 1998 (for details, see `Supported Assets`)
Coverage	3 Contracts
Data Density	Dense
Resolution	Daily
Timezone	Various (for details, see `Supported Assets`)
Market Hours	Regular and Extended

Example Applications

The International Futures Universe dataset enables you to design Futures strategies accurately. Examples include the following strategies:

Buying the Futures contract with the most open interest to reduce slippage and market impact
Trade speculation on an International Index
Trading bull calendar spreads to reduce volatility and margin requirements

For more example algorithms, see Examples.

Supported Assets

The following table shows the available Futures:

Ticker	Future	Start Date	Time Zone	Currency
HSI	Hang Seng Index Futures	Jan 2010	Asia/Hong Kong	HKD
FESX	EURO STOXX 50 Index Futures	Jul 1998	Europe/Berlin	EUR
NKD	Nikkei 225 Index Futures	Jan 2007	America/Chicago	USD

Data Point Attributes

The International Future Universe dataset provides FutureFilterUniverse and FuturesChain objects.

FutureFilterUniverse Attributes

FutureFilterUniverse objects have the following attributes:

FuturesChain Attributes

FuturesChain objects have the following attributes:

Requesting Data

To add International Future Universe data to your algorithm, call the AddFutureadd_future method. Save a reference to the Future object so you can access the data later in your algorithm. To define which contracts should be in your universe, specify the filter when requesting the Future data.

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

Select Language:

class InternationalFuturesDataAlgorithm(QCAlgorithm):

    def initialize(self) -> None:
        self.set_start_date(2013, 12, 20) 
        self.set_end_date(2014, 2, 20) 
        self.set_cash(1000000) 
        self.universe_settings.asynchronous = True
        self._future = self.add_future(Futures.Indices.HANG_SENG)
        # Set our contract filter for this Future chain.
        self._future.set_filter(lambda universe: universe.standards_only().front_month())
        # Get the entire Futures chain for the current day.
        chain = self.futures_chain(self._future.symbol, flatten=True).data_frame

public class InternationalFuturesDataAlgorithm : QCAlgorithm
{
    private Future _future;
        
    public override void Initialize()
    {
        SetStartDate(2013, 12, 20);
        SetEndDate(2014, 2, 20);
        SetCash(1000000);
        UniverseSettings.Asynchronous = True;
        var future = AddFuture(Futures.Indices.HangSeng);
        // Set our contract filter for this Future chain.
        _future.SetFilter((universe) => universe.StandardsOnly().FrontMonth());
        // Get the entire Futures chain for the current day.
        var chain = FuturesChain(_future.Symbol);
    }
}

For more information about creating Future Universes, see Futures.

Accessing Data

For information about accessing International Future Universe data, see Futures.

Historical Data

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

Historical Data In Algorithms

To get historical International Future Universe data in an algorithm, call the Historyhistory method with the list Future contract Symbol objects. You may obtain all available Future contracts on a date by calling the FuturesChainfutures_chain method. Note that this method will return all available contracts despite your previous filter. If there is no data for the period you requested, the history result is empty.

Select Language:

# Subscribe to the underlying Future and save a reference to the Symbol.
symbol = self.add_future(Futures.Indices.HANG_SENG).symbol
# Get the contracts available on this day.
contracts = [x.symbol for x in self.futures_chain(symbol)]

# Request the historical data to obtain the data.
# DataFrame objects
history_df = self.history(contracts, 10, Resolution.DAILY, flatten=True)
open_interest = self.history(OpenInterest, contracts, 10, Resolution.DAILY, flatten=True)

# Open Interest objects
open_interest = self.history[OpenInterest](contracts, 10, Resolution.DAILY)

// Subscribe to the underlying Future and save a reference to the Symbol.
var symbol = AddFuture(Futures.Indices.HangSeng).Symbol
// Get the contracts available on this day.
var contracts = contracts = FuturesChain(symbol).Select(x => x.Symbol).ToList();

// Request the historical data to obtain the data.
// Slice objects
var history = History(contracts, 10, Resolution.Daily);

// Open Interest objects
var openInterest = History<OpenInterest>(contracts, 10, Resolution.Daily);

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

Historical Data In Research

To get historical International Future Universe data in the Research Environment for an entire Futures chain, call the FutureHistoryfuture_history method with the canonical Future Symbol.

Select Language:

qb = QuantBook()
future = qb.add_future(Futures.Indices.HANG_SENG) 
future.set_filter(0, 90)
history = qb.future_history(future.symbol, datetime(2020, 6, 1), datetime(2020, 6, 5), Resolution.DAILY)
history_df = history.data_frame
all_history = history.get_all_data()
expiries = history.get_expiry_dates()

var qb = new QuantBook();
var future = qb.AddFuture(Futures.Indices.HangSeng);
future.SetFilter(0, 90);
var history = qb.FutureHistory(future.Symbol, new DateTime(2020, 6, 1), new DateTime(2020, 6, 5));

You can also do similar in the research environment like in the algorithm to obtain the price and open interest data.

Select Language:

qb = QuantBook()
end = datetime(2020, 6, 5)
qb.set_start_date(end)
symbol = qb.add_future(Futures.Indices.HANG_SENG).symbol
# Get the contracts available on this day.
contracts = [x.symbol for x in qb.futures_chain(symbol)]

# Request the historical data to obtain the data.
history_df = qb.history(contracts, datetime(2020, 6, 1), end, Resolution.DAILY, flatten=True)
open_interest = qb.history(OpenInterest, contracts, datetime(2020, 6, 1), end, Resolution.DAILY, flatten=True)

var qb = new QuantBook();
var end = new DateTime(2020, 6, 5);
qb.SetStartDate(end)
var future = qb.AddFuture(Futures.Indices.HangSeng);
// Get the contracts available on the day.
var contracts = qb.FuturesChain(future.Symbol).Select(x => x.Symbol);

// Request the historical data to obtain the data.
var history = qb.History(contracts, new DateTime(2020, 6, 1), end, Resolution.Daily);
var openInterest = qb.History<OpenInterest>(contracts, new DateTime(2020, 6, 1), end, Resolution.Daily);

For more information about historical International Future Universe data in the Research Environment, see Futures.

Example Applications

The International Futures Universe dataset enables you to design Futures strategies accurately. Examples include the following strategies:

Buying the Futures contract with the most open interest to reduce slippage and market impact
Trade speculation on an International Index
Trading bull calendar spreads to reduce volatility and margin requirements

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

International Future Universe

Introduction

About the Provider

Getting Started

Data Summary

Example Applications

Supported Assets

Data Point Attributes

FutureFilterUniverse Attributes

FuturesChain Attributes

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