Future Options
Handling Data
Introduction
LEAN passes the data you request to the on_data
method so you can make trading decisions. The Slice
object that the on_data
method receives groups all the data together at a single moment in time. To access the Slice
outside of the on_data
method, use the current_slice
property of your algorithm.
All the data formats use DataDictionary
objects to group data by Symbol
and provide easy access to information. The plural of the type denotes the collection of objects. For instance, the TradeBars
DataDictionary
is made up of TradeBar
objects. To access individual data points in the dictionary, you can index the dictionary with the Futures Option contract ticker or symbol
, but we recommend you use the symbol
.
To view the resolutions that are available for Future Options data, see Resolutions.
Trades
TradeBar
objects are price bars that consolidate individual trades from the exchanges. They contain the open, high, low, close, and volume of trading activity over a period of time.

To get the TradeBar
objects in the Slice
, index the Slice
or index the bars
property of the Slice
with the Option contract symbol
. If the Option contract doesn't actively trade or you are in the same time step as when you added the Option contract subscription, the Slice
may not contain data for your symbol
. To avoid issues, check if the Slice
contains data for your Option contract before you index the Slice
with the Option contract symbol
.
def on_data(self, slice: Slice) -> None: # Obtain the mapped TradeBar of the symbol if any trade_bar = slice.bars.get(self._option_contract_symbol) # None if not found
You can also iterate through the TradeBars
dictionary. The keys of the dictionary are the Symbol
objects and the values are the TradeBar
objects.
def on_data(self, slice: Slice) -> None: # Iterate all received Symbol-TradeBar key-value pairs for symbol, trade_bar in slice.bars.items(): close_price = trade_bar.close
TradeBar
objects have the following properties:
Quotes
QuoteBar
objects are bars that consolidate NBBO quotes from the exchanges. They contain the open, high, low, and close prices of the bid and ask. The open
, high
, low
, and close
properties of the QuoteBar
object are the mean of the respective bid and ask prices. If the bid or ask portion of the QuoteBar
has no data, the open
, high
, low
, and close
properties of the QuoteBar
copy the values of either the bid
or ask
instead of taking their mean.

To get the QuoteBar
objects in the Slice
, index the QuoteBars
property of the Slice
with the Option contract symbol
. If the Option contract doesn't actively get quotes or you are in the same time step as when you added the Option contract subscription, the Slice
may not contain data for your symbol
. To avoid issues, check if the Slice
contains data for your Option contract before you index the Slice
with the Option contract symbol
.
def on_data(self, slice: Slice) -> None: # Obtain the mapped QuoteBar of the symbol if any quote_bar = slice.quote_bars.get(self._option_contract_symbol) # None if not found
You can also iterate through the QuoteBars
dictionary. The keys of the dictionary are the Symbol
objects and the values are the QuoteBar
objects.
def on_data(self, slice: Slice) -> None: # Iterate all received Symbol-QuoteBar key-value pairs for symbol, quote_bar in slice.quote_bars.items(): ask_price = quote_bar.ask.close
QuoteBar
objects let LEAN incorporate spread costs into your simulated trade fills to make backtest results more realistic.
QuoteBar
objects have the following properties:
Futures Chains
FuturesChain
objects represent an entire chain of contracts for a single underlying Future.
To get the FuturesChain
, index the futures_chains
property of the Slice
with the continuous contract Symbol
.
def on_data(self, slice: Slice) -> None: # Try to get the FutureChain using the canonical symbol (None if no FutureChain return) chain = slice.futures_chains.get(self._future_contract_symbol.canonical) if chain: # Get all contracts if the FutureChain contains any member contracts = chain.contracts
You can also loop through the futures_chains
property to get each FuturesChain
.
def on_data(self, slice: Slice) -> None: # Iterate all received Canonical Symbol-FutureChain key-value pairs for continuous_contract_symbol, chain in slice.futures_chains.items(): contracts = chain.contracts
FuturesChain
objects have the following properties:
Futures Contracts
FuturesContract
objects represent the data of a single Futures contract in the market.
To get the Futures contracts in the Slice
, use the contracts
property of the FuturesChain
.
def on_data(self, slice: Slice) -> None: # Try to get the FutureChain using the canonical symbol chain = slice.future_chains.get(self._future_contract_symbol.canonical) if chain: # Get individual contract data (None if not contained) contract = chain.contracts.get(self._future_contract_symbol) if contract: price = contract.last_price
FuturesContract
objects have the following properties:
Option Chains
OptionChain
objects represent an entire chain of Option contracts for a single underlying security.
To get the OptionChain
, index the option_chains
property of the Slice
with the canonical Symbol
.
def on_data(self, slice: Slice) -> None: # Try to get the OptionChain using the canonical symbol (None if no OptionChain return) chain = slice.option_chains.get(self._option_contract_symbol.Canonical) if chain: # Get all contracts if the OptionChain contains any member contracts = chain.contracts
You can also loop through the option_chains
property to get each OptionChain
.
def on_data(self, slice: Slice) -> None: # Iterate all received Canonical Symbol-OptionChain key-value pairs for canonical_fop_symbol, chain in slice.option_chains.items(): contracts = chain.contracts
OptionChain
objects have the following properties:
Option Contracts
OptionContract
objects represent the data of a single Option contract in the market.
To get the Option contracts in the Slice
, use the contracts
property of the OptionChain
.
def on_data(self, slice: Slice) -> None: # Try to get the OptionChain using the canonical symbol chain = slice.option_chains.get(self._option_contract_symbol.canonical) if chain: # Get individual contract data contract = chain.contracts.get(self._option_contract_symbol) if contract: price = contract.price
You can also iterate through the futures_chains
first.
def on_data(self, slice: Slice) -> None: # Iterate all received Canonical Symbol-FuturesChain key-value pairs for continuous_future_symbol, futures_chain in slice.futures_chains.items(): # Select a Future Contract and create its canonical FOP Symbol futures_contract = [contract for contract in futures_chain][0] canonical_fop_symbol = Symbol.create_canonical_option(futures_contract.symbol) # Try to get the OptionChain using the canonical FOP symbol option_chain = slice.option_chains.get(canonical_fop_symbol) if option_chain: # Get individual contract data option_contract = option_chain.contracts.get(self._option_contract_symbol) if option_contract: price = option_contract.price
Greeks and Implied Volatility
To get the Greeks and implied volatility of an Option contract, use the greeks
and implied_volatility
members.
def on_data(self, slice: Slice) -> None: # Try to get the OptionChain using the canonical symbol chain = slice.option_chains.get(self._option_contract_symbol.canonical) if chain: # Get individual contract data contract = chain.contracts.get(self._option_contract_symbol) if contract: # Get greeks data of the selected contract delta = contract.greeks.delta iv = contract.implied_volatility
LEAN only calculates Greeks and implied volatility when you request them because they are expensive operations. If you invoke the greeks
property, the Greeks aren't calculated. However, if you invoke the greeks.delta
, LEAN calculates the delta. To avoid unecessary computation in your algorithm, only request the Greeks and implied volatility when you need them. For more information about the Greeks and implied volatility, see Options Pricing.
Open Interest
Open interest is the number of outstanding contracts that haven't been settled. It provides a measure of investor interest and the market liquidity, so it's a popular metric to use for contract selection. Open interest is calculated once per day. To get the latest open interest value, use the open_interest
property of the Option
or option_contract
.
def on_data(self, slice: Slice) -> None: # Try to get the option_chains using the canonical symbol chain = slice.option_chains.get(self._contract_symbol.canonical) if chain: # Get individual contract data contract = chain.contracts.get(self._contract_symbol) if contract: # Get the open interest of the selected contracts open_interest = contract.open_interest
Properties
OptionContract
objects have the following properties:
Examples
The following examples demonstrate some common practices for handling Future Option data.
Example 1: Monthly Protective Put
The following algorithm shows how to perform monthly selection on individual ES Future Option contract to implement a protective put option strategy to hedge speculation on S&P500 Future. It is a useful tool to hedge the excessive risk on leverage using Futures to trade.
class FutureOptionExampleAlgorithm(QCAlgorithm): def initialize(self) -> None: self.set_start_date(2021, 1, 1) self.set_end_date(2022, 1, 1) self.set_cash(1000000000) # Seed the security price to ensure the underlying price is available at the initial filtering. self.set_security_initializer(BrokerageModelSecurityInitializer(self.brokerage_model, FuncSecuritySeeder(self.get_last_known_prices))) # Set data normalization mode to raw for fair strike price comparison. self.universe_settings.data_normalization_mode = DataNormalizationMode.RAW # Subscribe the underlying since the updated price is needed for filtering. self.underlying = self.add_future(Futures.Indices.SP_500_E_MINI, data_mapping_mode=DataMappingMode.OPEN_INTEREST, data_normalization_mode=DataNormalizationMode.RAW, contract_depth_offset=0) # Filter the underlying continuous Futures to narrow the FOP spectrum. self.underlying.set_filter(0, 31) # Schedule a monthly event on selection of future-future option pair, since the portfolio rebalance on a monthly basis. self.schedule.on( self.date_rules.month_start(self.underlying.symbol), self.time_rules.after_market_open(self.underlying.symbol, 0), self.selection_and_rebalance ) def selection_and_rebalance(self) -> None: # Get all available put FOP contract for the mapped underlying contract, since the trade liquidity and volatility is the highest. contract_symbols = self.option_chain(self.underlying.mapped) contract_symbols = [symbol for symbol in contract_symbols if symbol.id.option_right == OptionRight.PUT] # Select the ATM put expires the same date as the underlying. The max expiry of the FOP will expire the same time as the front month future. expiry = max(symbol.id.date for symbol in contract_symbols) filtered_symbols = [symbol for symbol in contract_symbols if symbol.id.date == expiry] selected = sorted(filtered_symbols, key=lambda symbol: abs(symbol.id.strike_price - self.securities[self.underlying.mapped].price))[0] # Request the FOP contract data for trading. contract = self.add_future_option_contract(selected) # A Protective Put consists of long a lot of the underlying, and long a put contract. self.market_order(self.underlying.mapped, contract.symbol_properties.contract_multiplier) self.market_order(contract.symbol, 1)
Example 2: Weekly Covered Call
The below example demonstrates a weekly-renewing
covered call
strategy to collect credit of selling the option. It filters the ATM call contract that expires within the current week at week start using
set_filter
filtering function.
class FutureOptionExampleAlgorithm(QCAlgorithm): def initialize(self) -> None: self.set_start_date(2021, 1, 1) self.set_end_date(2022, 1, 1) self.set_cash(1000000000) # Set data normalization mode to raw for fair strike price comparison. self.universe_settings.data_normalization_mode = DataNormalizationMode.RAW # Subscribe the underlying since the updated price is needed for filtering. 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.RAW, contract_depth_offset=0) # Filter the underlying continuous Futures to narrow the FOP spectrum. self.underlying.set_filter(0, 182) # Filter for the current-week-expiring calls to formulate a covered call that expires at the end of week. self.add_future_option(self.underlying.symbol, lambda u: u.include_weeklys().calls_only().expiration(0, 5)) def on_data(self, slice: Slice) -> None: # Create canonical symbol for the mapped future contract, since option chains are mapped by canonical symbol. symbol = Symbol.create_canonical_option(self.underlying.mapped) # Get option chain data for the mapped future, as both the underlying and FOP have the highest liquidity among all other contracts. chain = slice.option_chains.get(symbol) if not self.portfolio.invested and chain: # Obtain the ATM call that expires at the end of week, such that both underlying and the FOP expires the same time. expiry = max(x.expiry for x in chain) atm_call = sorted([x for x in chain if x.expiry == expiry], key=lambda x: abs(x.strike - x.underlying_last_price))[0] # Use abstraction method to order a covered call to avoid manual error. option_strategy = OptionStrategies.covered_call(symbol, atm_call.strike,expiry) self.buy(option_strategy, 1)
Note that since both the underlying Future and the Future Option are expiring on the same day and are cash-settling in most cases, Lean can exercise the Future Option into account cash automatically at expiry and we do not need to handle the option exercise/assignment event.