Options Models
Exercise
Introduction
If you exercise a long Option position or are assigned on your short Option position, LEAN processes an Option exercise order. The Option exercise model converts the Option exercise order into an OrderEvent.
Set Models
To set the exercise model of an Option, call the set_option_exercise_model
method of the Option
object inside a security initializer.
class BrokerageModelExampleAlgorithm(QCAlgorithm): def initialize(self) -> None: # In the Initialize method, set the security initializer to seed initial the prices and models of assets. self.set_security_initializer(MySecurityInitializer(self.brokerage_model, FuncSecuritySeeder(self.get_last_known_prices))) # Outside of the algorithm class class MySecurityInitializer(BrokerageModelSecurityInitializer): def __init__(self, brokerage_model: IBrokerageModel, security_seeder: ISecuritySeeder) -> None: super().__init__(brokerage_model, security_seeder) def initialize(self, security: Security) -> None: # First, call the superclass definition. # This method sets the reality models of each security using the default reality models of the brokerage model. super().initialize(security) # Next, overwrite the Option exercise model if security.Type == SecurityType.OPTION: # Option type security.set_option_exercise_model(DefaultExerciseModel())
Default Behavior
The default Option exercise model is the DefaultExerciseModel
. The DefaultExerciseModel
fills exercise orders to the full quantity with zero fees and applies an order tag to represent if the order is an exercise or assignment. To view the implementation of this model, see the LEAN GitHub repository.
Model Structure
Option exercise models should extend the DefaultExerciseModel
class. Extensions of the DefaultExerciseModel
must implement the option_exercise
method, which receives Option
and OptionExerciseOrder
objects and then returns a list of OrderEvent
objects that contain the order fill information.
class CustomOptionExerciseModelExampleAlgorithm(QCAlgorithm): def initialize(self) -> None: security = self.add_option("SPY") # Set custom option exercise model for mimicking specific Brokerage most realistic actions security.set_option_exercise_model(MyOptionExerciseModel()) # Define the custom Option exercise model outside of the algorithm class MyOptionExerciseModel(DefaultExerciseModel): def option_exercise(self, option: Option, order: OptionExerciseOrder) -> List[OrderEvent]: in_the_money = option.is_auto_exercised(option.underlying.close) is_assignment = in_the_money and option.holdings.is_short order_event = OrderEvent( order.id, option.symbol, Extensions.convert_to_utc(option.local_time, option.exchange.time_zone), OrderStatus.FILLED, Extensions.get_order_direction(order.quantity), 0.0, order.quantity, OrderFee.zero, "Tag" ) order_event.is_assignment = is_assignment return [ order_event ]
For a full example algorithm, see this backtest .
OptionExerciseOrder
objects have the following properties:
The following table describes the arguments of the OrderEvent
constructor:
Argument Details |
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Argument: |
Argument: |
Argument: |
Argument: |
Argument: |
Argument: |
Argument: |
Argument: |
OrderEvent
objects have the following attributes:
Examples
The following examples demonstrate some common practices for implementing a custom option exercise model.
Example 1: Cash Settlement
The following algorithm trades GOOG 30-day expiring straddle. Yet, instead of settling with the underlying stock, some brokerages will settle with cash for ITM options. To simulate this behavior, we can create a custom option exercise model.
class OptionExerciseModelAlgorithm(QCAlgorithm): def initialize(self) -> None: self.set_start_date(2017, 4, 1) self.set_end_date(2017, 6, 30) # Request GOOG option data for trading. security = self.add_option("GOOG") self.goog = security.symbol # Filter for the 2 ATM contracts expiring in 30 days to form a straddle strategy. security.set_filter(lambda universe: universe.include_weeklys().straddle(30)) # Set custom option exercise model for disabling exercise through security initializer. self.set_security_initializer(MySecurityInitializer(self.brokerage_model, FuncSecuritySeeder(self.get_last_known_prices))) def on_data(self, slice: Slice) -> None: # Open position on updated option chain data. chain = slice.option_chains.get(self.goog) if chain and not self.portfolio.invested: # Only one strike and expiry for the straddle universe. strike = min(x.strike for x in chain) expiry = min(x.expiry for x in chain) # Open the straddle position. option_straddle = OptionStrategies.straddle(self.goog, strike, expiry) self.buy(option_straddle, 5) class MySecurityInitializer(BrokerageModelSecurityInitializer): def __init__(self, brokerageModel, securitySeeder): super().__init__(brokerageModel, securitySeeder) def initialize(self, security: Security) -> None: super().initialize(security) # Set the custom Option exercise model for Option securities if security.type == SecurityType.OPTION: security.set_option_exercise_model(MyOptionExerciseModel()) # Define the custom Option exercise model outside of the algorithm class MyOptionExerciseModel(DefaultExerciseModel): def option_exercise(self, option: Option, order: OptionExerciseOrder) -> List[OrderEvent]: underlying = option.underlying utc_time = Extensions.convert_to_utc(option.local_time, option.exchange.time_zone) in_the_money = option.is_auto_exercised(underlying.close) # Cash settle: using payoff. payoff = option.get_intrinsic_value(underlying.close) # Only liquidate option positions, but do not add equity positions. order_event = OrderEvent( order.id, option.symbol, utc_time, OrderStatus.FILLED, Extensions.get_order_direction(order.quantity), payoff, order.quantity, OrderFee.ZERO, "Option Settlement" ) order_event.is_in_the_money = in_the_money return [ order_event ]
Other Examples
For more examples, see the following algorithms: