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Requesting Data

Universes

Introduction

The add_future method enables you to select a basket of Future contracts for an underlying Future. To form your universe of contracts, you can filter them down by their expiry. If you want to subscribe to individual contracts one-by-one instead of a set of contracts, see Individual Contracts.

Create Universes

To add a universe of Future contracts, in the initialize method, call the add_future method. This method returns an Future object, which has a set_filter method you can call to filter the set of tradable contract down to just the contracts you want.

Select Language: 
class BasicFutureAlgorithm(QCAlgorithm):

    def initialize(self):
        self.future = self.add_future(
            Futures.Indices.SP_500_E_MINI,
            Resolution.DAILY,
            extended_market_hours=True,
            data_mapping_mode=DataMappingMode.OPEN_INTEREST,
            data_normalization_mode=DataNormalizationMode.BACKWARDS_RATIO,
            contract_depth_offset=0
        )
        self.future.set_filter(0, 182)
    
    def on_data(self, data):
        for continuous_symbol, chain in data.future_chains.items():
            for symbol, contract in chain.contracts.items():
                expiry = contract.expiry

For more information about the add_future method, see Create Universes.

Continous Contracts

By default, LEAN only subscribes to the continuous Future contract. A continuous Future contract represents a series of separate contracts stitched together to form a continuous price. If you need a lot of historical data to warm up an indicator, apply the indicator to the continuous contract price series. The Future object has a symbol property and a mapped property. The price of the symbol property is the adjusted price of the continuous contract. The price of the mapped property is the raw price of the currently selected contract in the continuous contract series.

Select Language: 
# Get the adjusted price of the continuous contract.
adjusted_price = self.securities[self._future.symbol].price 

# Get the raw price of the currently selected contract in the continuous contract series.
raw_price = self.securities[self._future.mapped].price

The continuous Futures contract isn't a tradable security. You must place orders for a specific Futures contract. To access the currently selected contract in the continuous contract series, use the mapped property of the Future object.

Select Language: 
# Place a market order for the currently selected contract in the continuous contract series.
self.market_order(self._future.mapped, 1)

For more information, see Continous Contracts.

Filter by Contract Properties

To set a contract filter, in the initialize method, call the set_filter method of the Future object. The following table describes the available filter techniques:

set_filter(minExpiry: int, maxExpiry: int)

Selects the contracts that expire within the range you set. This filter runs asynchronously by default.

set_filter(universeFunc: Callable[[FutureFilterUniverse], FutureFilterUniverse])

Selects the contracts that a function selects.
Select Language: 
# Select the contracts that expire within 182 days.
self._future.set_filter(0, 182)

# Select the front month contract.
self._future.set_filter(lambda future_filter_universe: future_filter_universe.front_month())

The following table describes the filter methods of the FutureFilterUniverse class:

standards_only()

Selects standard contracts

include_weeklys()

Selects non-standard weekly contracts

weeklys_only()

Selects weekly contracts

front_month()

Selects the front month contract

back_months()

Selects the non-front month contracts

back_month()

Selects the back month contracts

expiration(min_expiry: timedelta, max_expiry: timedelta)

Selects contracts that expire within a range of dates relative to the current day

expiration(min_expiry_days: int, max_expiry_days: int)

Selects contracts that expire within a range of dates relative to the current day

contracts(contracts: list[Symbol])

Selects a list of contracts

contracts(contractSelector: Callable[[list[Symbol]], list[Symbol]])

Selects contracts that a selector function selects

The preceding methods return an FutureFilterUniverse, so you can chain the methods together.

Select Language: 
# Select the front month standard contracts
self._future.set_filter(lambda future_filter_universe: future_filter_universe.standards_only().front_month())

You can also define an isolated filter method.

Select Language: 
# In Initialize
self._future.set_filter(self._contract_selector)
    
def _contract_selector(self, 
    future_filter_universe: Callable[[FutureFilterUniverse], FutureFilterUniverse]) -> FutureFilterUniverse:
    return future_filter_universe.standards_only().front_month()

Default Filter

By default, LEAN doesn't add any contracts to the FuturesChain it passes to the on_data method.

Historical Data

To get historical data for a Futures universe, see Historical Data.

Examples

The following examples demonstrate some common practices for requesting Futures universe data.

Example 1: Rollover

Future contracts expire monthly or quarterly in most cases. Hence, if we hold Future position in month or quarter end, we must consider rolling over to the mapped contract. The following algorithm shows how to buy and roll over to the next front month Future contract. We make use of the universe selection filter to select the front month contract and order the next mapped contract during the previous one expires.

Select Language: 
class FutureExampleAlgorithm(QCAlgorithm):
    def initialize(self) -> None:
        future = self.add_future(Futures.Indices.SP_500_E_MINI, extended_market_hours=True)
        self._future = future.symbol
        # We only want to hold position of the front month contract.
        future.set_filter(lambda u: u.front_month())
    
    def on_securities_changed(self, changes: SecurityChanges) -> None:
        # Liquidate if expired (or not being the front month contract anymore) and exit universe.
        for removed in changes.removed_securities:
            self.liquidate(removed.symbol)
        
        for added in changes.added_securities:
            # Make sure the newly added contract is an actual mapped tradable contract.
            if not added.symbol.is_canonical():
                # Roll over by ordering the same quantity.
                # Use limit order since market on open order is not supported on Future and avoid extreme quote filling.
                self.limit_order(added.symbol, 1, self.securities[self._future].price)

Example 2: Continuous Future Indicator

One of the major applications of Continuous Future is to obtain smooth price series to feed into indicators. This can ensure the indicator gets the correct price data that is comparable to the current mapped Future contract. In this example, we demonstrate a 252-day Exponential Moving Average indicator update using continuous ES contract data.

Select Language: 
class FutureExampleAlgorithm(QCAlgorithm):
    def initialize(self) -> None:
        # Use backward ratio normalization for continuous contract to feed smooth, comparable price series to the indicator.
        self._future = self.add_future(Futures.Indices.SP_500_E_MINI,
            data_normalization_mode=DataNormalizationMode.BACKWARDS_RATIO,
            extended_market_hours=True)
        # We only want to hold position of the front month contract.
        self._future.set_filter(lambda u: u.front_month())
        # Create a 252-day EMA indicator as a trend estimator.
        self._future.ema = self.ema(self._future.symbol, 252, Resolution.DAILY)
        # Warm up the EMA indicator to make it readily available.
        self.warm_up_indicator(self._future.symbol, self._future.ema)
    
    def on_data(self, slice: Slice) -> None:
        # Ensure the TradeBar data is available for the Future. Only use updated price data to update the indicator and make trading decision.
        bar = slice.bars.get(self._future.symbol)
        if bar:
            # Buy the mapped contract if the trend is estimated to go up (price above EMA).
            if self._future.ema.current.value >= bar.close:
                self.set_holdings(self._future.mapped, 0.1)
            # Short the mapped contract if the trend is estimated to go down (price below EMA).
            else:
                self.set_holdings(self._future.mapped, -0.1)
    
    def on_securities_changed(self, changes: SecurityChanges) -> None:
        # Liquidate if expired (or not being the front month contract anymore) and exit universe.
        for removed in changes.removed_securities:
            self.liquidate(removed.symbol)

Example 3: Contango

In Future trading, contango refers to the far-to-expiry Future contract price is higher than the spot price due to various reasons, such as storage fee and insurance of the commodities. The following example shows a contango trading by shorting the far contract that the price is above a threshold compared to the front month contract price and buying the front month contract to earn the premium in between.

Select Language: 
class FutureExampleAlgorithm(QCAlgorithm):
    def initialize(self) -> None:
        # Allow extended market hours trade, which is common for Future since extended market hour is still popular.
        future = self.add_future(Futures.Metals.MICRO_GOLD, extended_market_hours=True)
        self._future = future.symbol
        # Limit the expiration to within 6 months, as the longer the expiration, the higher the price uncertainty.
        future.set_filter(lambda u: u.expiration(0, 183))
    
    def on_data(self, slice: Slice) -> None:
        # Get Future chain only for the selected Future contract.
        chain = slice.future_chains.get(self._future)
        if not self.portfolio.invested and chain:
            # It takes 2 contracts with different expiries to form a horizontal spread arbitration to earn price difference in contango.
            if len(list(chain)) < 2:
                return
            sorted_by_expiry = sorted(chain, key=lambda x: x.expiry)
            far_contract = sorted_by_expiry[-1]
            near_contract = sorted_by_expiry[0]
    
            # Check if the far contract price is 1% higher than the near one.
            # If so, short the far contract and buy the near one to earn the horizontal spread premium.
            if far_contract.bid_price >= near_contract.ask_price * 1.01:
                self.market_order(far_contract.symbol, -1)
                self.market_order(near_contract.symbol, 1)
    
    def on_securities_changed(self, changes: SecurityChanges) -> None:
        # Liquidate if expired (or not being the front month contract anymore) and exit universe.
        for removed in changes.removed_securities:
            self.liquidate()

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