NautilusTrader can handle trade execution and order management for multiple strategies and venues simultaneously (per instance). Several interacting components are involved in execution, making it crucial to understand the possible flows of execution messages (commands and events).

The main execution-related components include:

  • Strategy

  • ExecAlgorithm (execution algorithms)

  • OrderEmulator

  • RiskEngine

  • ExecutionEngine or LiveExecutionEngine

  • ExecutionClient or LiveExecutionClient

Execution flow

The Strategy base class inherits from Actor and so contains all of the common data related methods. It also provides methods for managing orders and trade execution:

  • submit_order(...)

  • submit_order_list(...)

  • modify_order(...)

  • cancel_order(...)

  • cancel_all_orders(...)

  • close_position(...)

  • close_all_positions(...)

  • query_order(...)

These methods create the necessary execution commands under the hood and send them on the message bus to the relevant components (point-to-point), as well as publishing any events (such as the initialization of new orders i.e. OrderInitialized events).

The general execution flow looks like the following (each arrow indicates movement across the message bus):

Strategy -> OrderEmulator -> ExecAlgorithm -> RiskEngine -> ExecutionEngine -> ExecutionClient

The OrderEmulator and ExecAlgorithm (s) components are optional in the flow, depending on individual order parameters (as explained below).

Submitting orders

An OrderFactory is provided on the base class for every Strategy as a convenience, reducing the amount of boilerplate required to create different Order objects (although these objects can still be initialized directly with the Order.__init__ constructor if the trader prefers).

The component an order flows to when submitted for execution depends on the following:

  • If an emulation_trigger is specified, the order will first be sent to the OrderEmulator

  • If an exec_algorithm_id is specified, the order will first be sent to the relevant ExecAlgorithm (assuming it exists and has been registered correctly)

  • Otherwise, the order is sent to the RiskEngine

The following examples show method implementations for a Strategy .

This example submits a LIMIT BUY order for emulation (see OrderEmulator ):

    def buy(self) -> None:
        Users simple buy method (example).
        order: LimitOrder = self.order_factory.limit(



It’s possible to specify both order emulation, and an execution algorithm.

This example submits a MARKET BUY order to a TWAP execution algorithm:

    def buy(self) -> None:
        Users simple buy method (example).
        order: MarketOrder = self.order_factory.market(
            exec_algorithm_params={"horizon_secs": 20, "interval_secs": 2.5},


Execution algorithms

The platform supports customized execution algorithm components and provides some built-in algorithms, such as the Time-Weighted Average Price (TWAP) algorithm.

TWAP (Time-Weighted Average Price)

The TWAP execution algorithm aims to execute orders by evenly spreading them over a specified time horizon. The algorithm receives a primary order representing the total size and direction then splits this by spawning smaller child orders, which are then executed at regular intervals throughout the time horizon.

This helps to reduce the impact of the full size of the primary order on the market, by minimizing the concentration of trade size at any given time.

The algorithm will immediately submit the first order, with the final order submitted being the primary order at the end of the horizon period.

Using the TWAP algorithm as an example (found in /examples/algorithms/twap.py ), this example demonstrates how to initialize and register a TWAP execution algorithm directly with a BacktestEngine (assuming an engine is already initialized):

from nautilus_trader.examples.strategies.ema_cross_twap import EMACrossTWAP
from nautilus_trader.examples.strategies.ema_cross_twap import EMACrossTWAPConfig

# Instantiate and add your execution algorithm
exec_algorithm = TWAPExecAlgorithm()

For this particular algorithm, two parameters must be specified:

  • horizon_secs

  • interval_secs

The horizon_secs parameter determines the time period over which the algorithm will execute, while the interval_secs parameter sets the time between individual order executions. These parameters determine how a primary order is split into a series of spawned orders.

# Configure your strategy
config = EMACrossTWAPConfig(
    twap_horizon_secs=10.0,  # <-- execution algorithm param
    twap_interval_secs=2.5,  # <-- execution algorithm param

# Instantiate and add your strategy
strategy = EMACrossTWAP(config=config)

Alternatively, you can specify these parameters dynamically per order, determining them based on actual market conditions. In this case, the strategy configuration parameters could be provided to an execution model which determines the horizon and interval.


There is no limit to the number of execution algorithm parameters you can create. The parameters just need to be a dictionary with string keys and primitive values (values that can be serialized over the wire, such as ints, floats, and strings).

Writing execution algorithms

To implement a custom execution algorithm you must define a class which inherits from ExecAlgorithm .

An execution algorithm is a type of Actor , so it’s capable of the following:

  • Request and subscribe to data

  • Access the Cache

  • Set time alerts and/or timers using a Clock

Additionally it can:

  • Access the central Portfolio

  • Spawn secondary orders from a received primary (original) order

Once an execution algorithm is registered, and the system is running, it will receive orders off the messages bus which are addressed to its ExecAlgorithmId via the exec_algorithm_id order parameter. The order may also carry the exec_algorithm_params being a dict[str, Any] .


Because of the flexibility of the exec_algorithm_params dictionary. It’s important to thoroughly validate all of the key value pairs for correct operation of the algorithm (for starters that the dictionary is not None and all necessary parameters actually exist).

Received orders will arrive via the following on_order(...) method. These received orders are know as “primary” (original) orders when being handled by an execution algorithm.

def on_order(self, order: Order) -> None:  # noqa (too complex)
    Actions to be performed when running and receives an order.

    order : Order
        The order to be handled.

    System method (not intended to be called by user code).

    # Handle the order here

When the algorithm is ready to spawn a secondary order, it can use one of the following methods:

  • spawn_market(...) (spawns a MARKET order)

  • spawn_market_to_limit(...) (spawns a MARKET_TO_LIMIT order)

  • spawn_limit(...) (spawns a LIMIT order)


Additional order types will be implemented in future versions, as the need arises.

Each of these methods takes the primary (original) Order as the first argument. The primary order quantity will be reduced by the quantity passed in (becoming the spawned orders quantity).


There must be enough primary order quantity remaining (this is validated).

Once the desired number of secondary orders have been spawned, and the execution routine is over, the intention is that the algorithm will then finally send the primary (original) order.

Spawned orders

All secondary orders spawned from an execution algorithm will carry a exec_spawn_id which is simply the ClientOrderId of the primary (original) order, and whose client_order_id derives from this original identifier with the following convention:

  • exec_spawn_id (primary order client_order_id value)

  • spawn_sequence (the sequence number for the spawned order)


e.g. O-20230404-001-000-E1 (for the first spawned order)


The “primary” and “secondary” / “spawn” terminology was specifically chosen to avoid conflict or confusion with the “parent” and “child” contingency orders terminology (an execution algorithm may also deal with contingent orders).

Managing execution algorithm orders

The Cache provides several methods to aid in managing (keeping track of) the activity of an execution algorithm:

cpdef list orders_for_exec_algorithm(
    ExecAlgorithmId exec_algorithm_id,
    Venue venue = None,
    InstrumentId instrument_id = None,
    StrategyId strategy_id = None,
    OrderSide side = OrderSide.NO_ORDER_SIDE,
    Return all execution algorithm orders for the given query filters.

    exec_algorithm_id : ExecAlgorithmId
        The execution algorithm ID.
    venue : Venue, optional
        The venue ID query filter.
    instrument_id : InstrumentId, optional
        The instrument ID query filter.
    strategy_id : StrategyId, optional
        The strategy ID query filter.
    side : OrderSide, default ``NO_ORDER_SIDE`` (no filter)
        The order side query filter.



As well as more specifically querying the orders for a certain execution series/spawn:

cpdef list orders_for_exec_spawn(self, ClientOrderId client_order_id):
    Return all orders for the given execution spawn ID (if found).

    Will also include the primary (original) order.

    client_order_id : ClientOrderId
        The execution algorithm spawning primary (original) client order ID.