--- title: "Handling Data" description: "LEAN passes the data you request to the OnData method so you can make trading decisions. The Slice object groups the data together with a timestamp." canonical: https://www.quantconnect.com/docs/v2/writing-algorithms/securities/asset-classes/future-options/handling-data markdown: https://www.quantconnect.com/docs/v2/writing-algorithms/securities/asset-classes/future-options/handling-data.md language: python source: https://github.com/QuantConnect/Documentation/tree/master/03%20Writing%20Algorithms/03%20Securities/99%20Asset%20Classes/07%20Future%20Options/02%20Handling%20Data modified: 2026-10-29 index: https://www.quantconnect.com/llms.txt --- # Handling Data [Documentation](https://www.quantconnect.com/docs/v2.md) > [Writing Algorithms](https://www.quantconnect.com/docs/v2/writing-algorithms.md) > Securities > [Asset Classes](https://www.quantconnect.com/docs/v2/writing-algorithms/securities/asset-classes.md) > [Future Options](https://www.quantconnect.com/docs/v2/writing-algorithms/securities/asset-classes/future-options.md) > Handling Data > Code examples are shown in Python. Append `?lang=csharp` to this URL for the C# version of this page. ## 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](https://www.quantconnect.com/docs/v2/writing-algorithms/securities/asset-classes/future-options/requesting-data/individual-contracts.md#03-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. ![Tradebar decomposition](https://cdn.quantconnect.com/docs/i/dataformat-tradebar.png) 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`. ```python 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. ```python 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: *Members of `QuantConnect.Data.Market.TradeBar` (JSON): https://www.quantconnect.com/services/inspector?type=T:QuantConnect.Data.Market.TradeBar&language=python* ## 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. ![Quotebar decomposition](https://cdn.quantconnect.com/docs/i/dataformat-quotebar.png) 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`. ```python 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. ```python 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](https://www.quantconnect.com/docs/v2/writing-algorithms/reality-modeling/trade-fills/key-concepts.md) to make backtest results more realistic. `QuoteBar` objects have the following properties: *Members of `QuantConnect.Data.Market.QuoteBar` (JSON): https://www.quantconnect.com/services/inspector?type=T:QuantConnect.Data.Market.QuoteBar&language=python* ## 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`. ```python 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`. ```python 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 ``` ### Filter Chains `FuturesChain` objects provide the same filter methods as [Futures universe selection](https://www.quantconnect.com/docs/v2/writing-algorithms/universes/futures.md#13-Filter-Contracts), with the same names and the same behavior. Each method returns a new `FuturesChain` and leaves the source chain unchanged, so you can chain the calls together. ```python # Select the front contract that expires at least 90 days out. contract = next(iter(chain.expiring_after(self.time + timedelta(days=90)).front_month()), None) # Select the quarterly contracts. quarterlies = chain.contract_months(FutureExpirationCycles.MARCH) ``` To filter the chain with your own predicate, call the `where` method. ```python liquid_contracts = chain.open_interest(100000, sys.maxsize).where(lambda contract: contract.volume > 0) ``` The `standards_only` and `weeklys_only` methods of a chain apply to the contracts the chain already holds, so you can combine them with the expiration filters in any order. In universe selection, these two filters always run last. ### Properties `FuturesChain` objects have the following properties: *Members of `QuantConnect.Data.Market.FuturesChain` (JSON): https://www.quantconnect.com/services/inspector?type=T:QuantConnect.Data.Market.FuturesChain&language=python* ## 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`. ```python 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: *Members of `QuantConnect.Data.Market.FuturesContract` (JSON): https://www.quantconnect.com/services/inspector?type=T:QuantConnect.Data.Market.FuturesContract&language=python* ## 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`. ```python 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`. ```python 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 ``` ### Filter Chains `OptionChain` objects provide the same filter methods as [Option universe selection](https://www.quantconnect.com/docs/v2/writing-algorithms/universes/future-options.md), with the same names and the same behavior. Each method returns a new `OptionChain` and leaves the source chain unchanged, so you can chain the calls together. ```python # Select the call contracts that expire in 20-40 days and are within 3 strikes of the underlying price. contracts = chain.calls_only().expiration(20, 40).strikes(-3, 3) ``` The contract property filters, the Greeks and implied volatility filters, and the [Option strategy](https://www.quantconnect.com/docs/v2/writing-algorithms/trading-and-orders/option-strategies.md) filters are all available. The strategy filters select the legs of the strategy, or return an empty chain when no set of contracts matches the criteria. ```python # Select the four legs of an Iron Condor that expires in at least 30 days. legs = chain.iron_condor(30, 5, 10) ``` To filter the chain with your own predicate, call the `where` method. ```python liquid_contracts = chain.where(lambda contract: contract.open_interest > 100) ``` The `standards_only` and `weeklys_only` methods of a chain apply to the contracts the chain already holds, so you can combine them with the expiration filters in any order. In universe selection, these two filters always run last. ### Select Contracts To get a single contract from the chain, call the `select` method. It returns the contract that best matches the criteria you provide, or `None` when the chain holds no match. ```python # Get the put contract at 85% of the underlying price, from the expiration closest to 30 days out. contract = chain.select(OptionRight.PUT, target_dte=30, moneyness=-0.15) ``` These methods take a *target* number of days to expiration and return the closest expiration within the optional `min_dte` and `max_dte` window. The Option strategy filters instead take a *minimum* number of days to expiration and return the first expiration at or after it. The following table describes the contract selection methods of the `OptionChain` class: `select(right: OptionRight = None, target_dte: int = None, min_dte: int = None, max_dte: int = None, moneyness: float = 0)` Selects the contract with the strike closest to `moneyness` as a signed fraction of the underlying price. For example, -0.15 targets 85% of the underlying price and 0 targets the at-the-money strike. `pick(right: OptionRight = None, target_dte: int = None, min_dte: int = None, max_dte: int = None, moneyness: float = 0)` A synonym of the `select` method. `select_by_strike_distance(strike_from_atm: float, right: OptionRight = None, target_dte: int = None, min_dte: int = None, max_dte: int = None)` Selects the contract with the strike closest to a signed distance from the underlying price, in price units. This is how the Option strategy filters express their strikes. `select_by_delta(target_delta: float, right: OptionRight = None, target_dte: int = None, min_dte: int = None, max_dte: int = None)` Selects the contract with the absolute delta closest to the absolute target, so a 30 delta put is 0.3 or -0.3. Contracts without Greeks are ignored. `at_the_money(right: OptionRight = None)` Selects the contract with the strike closest to the underlying price. Ties go to the lower strike, then to the nearest expiration. `closest_expiry(target_dte: int = None, min_dte: int = None, max_dte: int = None)` Returns the expiration date closest to the target number of days out, or `None` when no expiration falls in the window. `at(expiry: datetime)` Returns a new chain with the contracts of the given expiration date. The time of day is ignored. The expiration methods compose with the chain filters, so you can narrow a single expiration down further. ```python expiry = chain.closest_expiry(target_dte=45, min_dte=30, max_dte=60) contracts = chain.at(expiry).puts_only().strikes(-2, 0) ``` `OptionChain` objects also expose read-only views of their contracts: `calls` and `puts`, sorted by expiration and then strike; `expiries`, the distinct expiration dates in ascending order; and `strike_prices`, the distinct strikes in ascending order. `strike_prices` is a `StrikeList` object, which adds the `closest_to`, `first_above`, and `first_below` methods. Each one takes a reference price and returns `None` when no strike matches. ```python strike = chain.strike_prices.first_above(chain.underlying.price) ``` ### Properties `OptionChain` objects have the following properties: *Members of `QuantConnect.Data.Market.OptionChain` (JSON): https://www.quantconnect.com/services/inspector?type=T:QuantConnect.Data.Market.OptionChain&language=python* ## 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`. ```python 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. ```python 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. ```python 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](https://www.quantconnect.com/docs/v2/writing-algorithms/reality-modeling/options-models/pricing.md). ### 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`. ```python 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: *Members of `QuantConnect.Data.Market.OptionContract` (JSON): https://www.quantconnect.com/services/inspector?type=T:QuantConnect.Data.Market.OptionContract&language=python* ## 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](https://www.quantconnect.com/docs/v2/writing-algorithms/trading-and-orders/option-strategies/protective-put.md) 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. ```python class FutureOptionExampleAlgorithm(QCAlgorithm): def initialize(self) -> None: self.set_start_date(2024, 9, 1) self.set_end_date(2024, 12, 31) self.set_cash(1000000000) # Seed the security price to ensure the underlying price is available at the initial filtering. self.settings.seed_initial_prices = True # 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](https://www.quantconnect.com/docs/v2/writing-algorithms/trading-and-orders/option-strategies/covered-call.md) 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. ```python class FutureOptionExampleAlgorithm(QCAlgorithm): def initialize(self) -> None: self.set_start_date(2024, 9, 1) self.set_end_date(2024, 12, 31) 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.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.