About US Future Option Universe

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, starts 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.

The US Future Option Universe dataset depends on the US Future Universe dataset because the US Future Universe dataset contains the universe of underlying Futures contracts. This dataset also 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 US Future Options by AlgoSeek.

About QuantConnect

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.

Add US Future Option Universe

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About QuantConnect

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.

Algorithm Example

class FutureOptionAlgorithm(QCAlgorithm):
    def initialize(self) -> None:
        self.set_start_date(2020,1,1)
        # Filter the underlying continuous Futures to narrow the FOP spectrum.
        self.underlying = self.add_future(Futures.Indices.SP_500_E_MINI,
            extended_market_hours=True,
            data_mapping_mode=DataMappingMode.OPEN_INTEREST,
            data_normalization_mode=DataNormalizationMode.BACKWARDS_RATIO,
            contract_depth_offset=0)
        self.underlying.set_filter(0, 182)
        # Use CallSpread filter to obtain the 2 best-matched contracts that forms a call spread.
        # It simplifies from further filtering and reduce computation on redundant subscription.
        self.add_future_option(self.underlying.symbol, lambda u: u.call_spread(5, 5, -5))
    
    def on_data(self, slice: Slice) -> None:
        if self.portfolio.invested:
            return
        # Create canonical symbol for the mapped future contract, since we need that to access the option chain.
        symbol = Symbol.create_canonical_option(self.underlying.mapped)
    
        # Get option chain data for the mapped future only.
        # It requires 2 contracts with different strikes to form a call spread, so we make sure at least 2 contracts are present.
        chain = slice.option_chains.get(symbol)
        if not chain or len(list(chain)) < 2:
            return
            
        # Separate the contracts by strike, as we need to access their strike.
        expiry = min([x.expiry for x in chain])
        sorted_by_strike = sorted([x.strike for x in chain])
        itm_strike = sorted_by_strike[0]
        otm_strike = sorted_by_strike[-1]
        
        # Use abstraction method to order a bull call spread to avoid manual error.
        option_strategy = OptionStrategies.bull_call_spread(symbol, itm_strike, otm_strike, expiry)
        self.buy(option_strategy, 1)

Example Applications

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

Pricing

Provider offers 4 licensing options

Pricing