---
title: "International Future Universe"
description: "International Future Universe dataset from QuantConnect."
canonical: https://www.quantconnect.com/docs/v2/writing-algorithms/datasets/quantconnect/international-future-universe
markdown: https://www.quantconnect.com/docs/v2/writing-algorithms/datasets/quantconnect/international-future-universe.md
language: python
source: https://github.com/QuantConnect/Documentation/tree/master/03%20Writing%20Algorithms/14%20Datasets/02%20QuantConnect/16%20International%20Future%20Universe
modified: 2026-11-01
index: https://www.quantconnect.com/llms.txt
---

# International Future Universe

[Documentation](https://www.quantconnect.com/docs/v2.md) > [Writing Algorithms](https://www.quantconnect.com/docs/v2/writing-algorithms.md) > Datasets > [QuantConnect](https://www.quantconnect.com/docs/v2/writing-algorithms/datasets/quantconnect.md) > International Future Universe

> Code examples are shown in Python. Append `?lang=csharp` to this URL for the C# version of this page.

## Introduction

The International Future Universe dataset by QuantConnect lists the available International Future contracts, their daily trading volume, and Open Interest. The data covers 5 contracts (DAX, FESX, HSI, KM, 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, KRX, and CME exchanges.

This dataset depends on the [US Futures Security Master](https://www.quantconnect.com/datasets/quantconnect-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](https://www.quantconnect.com/datasets/tickdata-international-futures) and [US Futures by AlgoSeek](https://www.quantconnect.com/datasets/algoseek-us-future) for NKD.

For more information about the International Future Universe dataset, including CLI commands and pricing, see the [dataset listing](https://www.quantconnect.com/datasets/quantconnect-international-future-universe).

## 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:

```python
hsi = self.add_future(Futures.Indices.HANG_SENG, Resolution.MINUTE)           # "HSI"
hsi.set_filter(0, 90)
dax = self.add_future(Futures.Indices.DAX, Resolution.MINUTE)                 # "DAX"
dax.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)
km = self.add_future(Futures.Indices.KOSPI_200, Resolution.MINUTE)            # "KM"
km.set_filter(0, 90)
```

## Data Summary

The following table describes the dataset properties:

| Property | Value |
| --- | --- |
| Start Date | July 1998 (for details, see `Supported Assets`) |
| Coverage | 5 Contracts |
| Data Density | Dense |
| Resolution | Daily |
| Timezone | Various (for details, see `Supported Assets`) |
| Market Hours | [Regular and Extended](https://www.quantconnect.com/docs/v2/writing-algorithms/securities/asset-classes/futures/market-hours.md) |

## Requesting Data

To add International Future Universe data to your algorithm, call the `add_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 `add_future` method provides a daily stream of Future chain data. To get the most recent daily chain, call the `futures_chain` method with the underlying Future Symbol. The `futures_chain` method returns data on all the tradable contracts, not just the contracts that pass your universe filter.

```python
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
```

For more information about creating Future Universes, see [Futures](https://www.quantconnect.com/docs/v2/writing-algorithms/universes/futures.md).

## Accessing Data

For information about accessing International Future Universe data, see [Futures](https://www.quantconnect.com/docs/v2/writing-algorithms/securities/asset-classes/futures/requesting-data/universes.md).

## 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 `history` method with continuous Future `Symbol` and a lookback period. This method returns the all the available contracts for each trading day, not the subset of contracts that pass your universe filter. If there is no data for the period you requested, the history result is empty.

```python
# Add the Future and save a reference to it.
future = self.add_future(Futures.Indices.HANG_SENG)

# DataFrame example where the columns are the FutureUniverse attributes:
history_df = self.history(FutureUniverse, future.symbol, 5, flatten=True)

# Series example where the values are lists of FutureUniverse objects:
history_series = self.history(FutureUniverse, future.symbol, 5, flatten=False)

# FutureUniverse objects example:
history = self.history[FutureUniverse](future.symbol, 5)
```

For more information about historical International Future Universe data in algorithms, see [Contracts](https://www.quantconnect.com/docs/v2/writing-algorithms/historical-data/asset-classes/futures.md#09-Contracts).

### Historical Data In Research

To get historical International Future Universe data in the Research Environment, call the `history` method with continuous Future `Symbol` and a time period. This method returns the all the available contracts for each trading day, not the subset of contracts that pass your universe filter. If there is no data for the period you requested, the history result is empty.

```python
# Add the Future and save a reference to it.
future = qb.add_future(Futures.Indices.HANG_SENG)

# DataFrame example where the columns are the FutureUniverse attributes:
history_df = qb.history(FutureUniverse, future.symbol, datetime(2025, 1, 1), datetime(2025, 4, 1), flatten=True)

# Series example where the values are lists of FutureUniverse objects:
history_series = qb.history(FutureUniverse, future.symbol, 5, flatten=False)

# FutureUniverse objects example:
history = qb.history[FutureUniverse](future.symbol, datetime(2025, 1, 1), datetime(2025, 4, 1))
```

For more information about historical International Future Universe data in the Research Environment, see [Daily Prices History](https://www.quantconnect.com/docs/v2/research-environment/datasets/futures/universes.md#06-Daily-Prices-History).

## Supported Assets

The following table shows the available Futures:

| Ticker | Future | Start Date | Time Zone | Currency |
| --- | --- | --- | --- | --- |
| DAX | German Index Futures | Jan 2010 | Europe/Berlin | EUR |
| FESX | EURO STOXX 50 Index Futures | Jul 1998 | Europe/Berlin | EUR |
| HSI | Hang Seng Index Futures | Jan 2010 | Asia/Hong Kong | HKD |
| KM | Kospi 200 Index Futures | Jan 2007 | Asia/Seoul | KRW |
| NKD | Nikkei 225 Index Futures | Jan 2007 | America/Chicago | USD |

## 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

### Classic Algorithm Example

The following example algorithm uses the [ZigZag indicator](https://www.quantconnect.com/docs/v2/writing-algorithms/indicators/supported-indicators/zig-zag.md) to determine the trend of Hang Seng Index. It then trades the Index with HSI Futures.

```python
from AlgorithmImports import *

class InternationalFuturesDataAlgorithm(QCAlgorithm):

    def initialize(self) -> None:
        self.set_start_date(2024, 9, 1)
        self.set_end_date(2024, 12, 31)
        # Set the time zone to HKT to make it more comparable with the exchange.
        self.set_time_zone(TimeZones.HONG_KONG)
        # Set the account currency as HKD to trade HSI Futures.
        self.set_account_currency("HKD", 1000000)
        # Seed the last price of the contracts for filling.
        self.settings.seed_initial_prices = True

        # Request HSI Futures to trade.
        # Note that we will trade the contract with the highest open interest for liquidity.
        self.hsi_future = self.add_future(
            Futures.Indices.HANG_SENG,
            extended_market_hours=True,
            data_mapping_mode=DataMappingMode.LAST_TRADING_DAY,
            contract_depth_offset=0
        )
        # Adds contracts that expiry within 90 days. We will trade the farthest contract
        self.hsi_future.set_filter(0,90)
        # Request the corresponding underlying Index for feeding indicator for trade signal generation.
        hsi_index = self.add_index("HSI").symbol

        # Create a ZigZag indicator to trade Hang Seng Index price pivot points.
        self._zz = self.zz(hsi_index, 0.15, 5, Resolution.DAILY)
        # Warm up indicator for immediate readiness to trade.
        self.warm_up_indicator(hsi_index, self._zz, Resolution.DAILY)

    def on_data(self, slice: Slice) -> None:
        # Only place trade if the Future contracts is in market opening hours to avoid stale fills.
        if self.is_market_open(self.hsi_future.symbol) and self._zz.is_ready:
            pivot = self._zz.pivot_type
            # If the last pivot point is a low point, the current trend is increasing after this low point.
            if pivot == PivotPointType.LOW:
                contracts = sorted([x.symbol for x in slice.future_chains.get(self.hsi_future.symbol)],
                    key=lambda x: x.id.date)
                self.set_holdings(contracts[-1], 0.2)
            # If the last pivot point is a high point, the current trend is decreasing after this high point.
            if pivot == PivotPointType.HIGH:
                contracts = sorted([x.symbol for x in slice.future_chains.get(self.hsi_future.symbol)],
                    key=lambda x: x.id.date)
                self.set_holdings(contracts[-1], -0.2)
```

## Data Point Attributes

The International Future Universe dataset provides `FutureFilterUniverse` and `FutureUniverse` objects.

### FutureFilterUniverse Attributes

`FutureFilterUniverse` objects have the following attributes:

*Members of `QuantConnect.Securities.FutureFilterUniverse` (JSON): https://www.quantconnect.com/services/inspector?type=T:QuantConnect.Securities.FutureFilterUniverse&language=python*

### FutureUniverse Attributes

`FutureUniverse` objects have the following attributes:

*Members of `QuantConnect.Data.UniverseSelection.FutureUniverse` (JSON): https://www.quantconnect.com/services/inspector?type=T:QuantConnect.Data.UniverseSelection.FutureUniverse&language=python*