Execution

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_orders(...)
• 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).

This diagram illustrates message flow (commands and events) across the Nautilus execution components.

                  ┌───────────────────┐
                  │                   │
                  │                   │
                  │                   │
          ┌───────►   OrderEmulator   ├────────────┐
          │       │                   │            │
          │       │                   │            │
          │       │                   │            │
┌─────────┴──┐    └─────▲──────┬──────┘            │
│            │          │      │           ┌───────▼────────┐   ┌─────────────────────┐   ┌─────────────────────┐
│            │          │      │           │                │   │                     │   │                     │
│            ├──────────┼──────┼───────────►                ├───►                     ├───►                     │
│  Strategy  │          │      │           │                │   │                     │   │                     │
│            │          │      │           │   RiskEngine   │   │   ExecutionEngine   │   │   ExecutionClient   │
│            ◄──────────┼──────┼───────────┤                ◄───┤                     ◄───┤                     │
│            │          │      │           │                │   │                     │   │                     │
│            │          │      │           │                │   │                     │   │                     │
└─────────┬──┘    ┌─────┴──────▼──────┐    └───────▲────────┘   └─────────────────────┘   └─────────────────────┘
          │       │                   │            │
          │       │                   │            │
          │       │                   │            │
          └───────►   ExecAlgorithm   ├────────────┘
                  │                   │
                  │                   │
                  │                   │
                  └───────────────────┘

Order Management System (OMS)

An order management system (OMS) type refers to the method used for assigning orders to positions and tracking those positions for an instrument. OMS types apply to both strategies and venues (simulated and real). Even if a venue doesn't explicitly state the method in use, an OMS type is always in effect. The OMS type for a component can be specified using the OmsType enum.

The OmsType enum has three variants:

• UNSPECIFIED: The OMS type defaults based on where it is applied (details below)
• NETTING: Positions are combined into a single position per instrument ID
• HEDGING: Multiple positions per instrument ID are supported (both long and short)

The table below describes different configuration combinations and their applicable scenarios. When the strategy and venue OMS types differ, the ExecutionEngine handles this by overriding or assigning position_id values for received OrderFilled events. A "virtual position" refers to a position ID that exists within the Nautilus system but not on the venue in reality.

Strategy OMS	Venue OMS	Description
NETTING	NETTING	The strategy uses the venues native OMS type, with a single position ID per instrument ID.
HEDGING	HEDGING	The strategy uses the venues native OMS type, with multiple position IDs per instrument ID (both LONG and SHORT).
NETTING	HEDGING	The strategy overrides the venues native OMS type. The venue tracks multiple positions per instrument ID, but Nautilus maintains a single position ID.
HEDGING	NETTING	The strategy overrides the venues native OMS type. The venue tracks a single position per instrument ID, but Nautilus maintains multiple position IDs.

note

Configuring OMS types separately for strategies and venues increases platform complexity but allows for a wide range of trading styles and preferences (see below).

OMS config examples:

• Most cryptocurrency exchanges use a NETTING OMS type, representing a single position per market. It may be desirable for a trader to track multiple "virtual" positions for a strategy.
• Some FX ECNs or brokers use a HEDGING OMS type, tracking multiple positions both LONG and SHORT. The trader may only care about the NET position per currency pair.

info

Nautilus does not yet support venue-side hedging modes such as Binance BOTH vs. LONG/SHORT where the venue nets per direction. It is advised to keep Binance account configurations as BOTH so that a single position is netted.

OMS configuration

If a strategy OMS type is not explicitly set using the oms_type configuration option, it will default to UNSPECIFIED. This means the ExecutionEngine will not override any venue position_ids, and the OMS type will follow the venue's OMS type.

tip

When configuring a backtest, you can specify the oms_type for the venue. To enhance backtest accuracy, it is recommended to match this with the actual OMS type used by the venue in practice.

Risk engine

The RiskEngine is a core component of every Nautilus system, including backtest, sandbox, and live environments. Every order command and event passes through the RiskEngine unless specifically bypassed in the RiskEngineConfig.

The RiskEngine includes several built-in pre-trade risk checks, including:

• Price precisions correct for the instrument
• Prices are positive (unless an option type instrument)
• Quantity precisions correct for the instrument
• Below maximum notional for the instrument
• Within maximum or minimum quantity for the instrument
• Only reducing position when a reduce_only execution instruction is specified for the order

If any risk check fails, an OrderDenied event is generated, effectively closing the order and preventing it from progressing further. This event includes a human-readable reason for the denial.

Trading state

Additionally, the current trading state of a Nautilus system affects order flow.

The TradingState enum has three variants:

• ACTIVE: The system operates normally
• HALTED: The system will not process further order commands until the state changes
• REDUCING: The system will only process cancels or commands that reduce open positions

info

See the RiskEngineConfig API Reference for further details.

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()
engine.add_exec_algorithm(exec_algorithm)

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(
    instrument_id=ETHUSDT_BINANCE.id,
    bar_type=BarType.from_str("ETHUSDT.BINANCE-250-TICK-LAST-INTERNAL"),
    trade_size=Decimal("0.05"),
    fast_ema_period=10,
    slow_ema_period=20,
    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.

info

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].

warning

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.

from nautilus_trader.model.orders.base import Order

def on_order(self, order: Order) -> None:  # noqa (too complex)
    # 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)

note

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).

warning

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)

{exec_spawn_id}-E{spawn_sequence}

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

note

The "primary" and "secondary" / "spawn" terminology was specifically chosen to avoid conflict or confusion with the "parent" and "child" contingent 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. Calling the below method will return all execution algorithm orders for the given query filters.

def orders_for_exec_algorithm(
    self,
    exec_algorithm_id: ExecAlgorithmId,
    venue: Venue | None = None,
    instrument_id: InstrumentId | None = None,
    strategy_id: StrategyId | None = None,
    side: OrderSide = OrderSide.NO_ORDER_SIDE,
) -> list[Order]:

As well as more specifically querying the orders for a certain execution series/spawn. Calling the below method will return all orders for the given exec_spawn_id (if found).

def orders_for_exec_spawn(self, exec_spawn_id: ClientOrderId) -> list[Order]:

note

This also includes the primary (original) order.

Execution reconciliation

Execution reconciliation is the process of aligning the external state of reality for orders and positions (both closed and open) with the current system state built from events. This process is primarily applicable to live trading, which is why only the LiveExecutionEngine includes reconciliation capabilities.

There are two main scenarios for reconciliation:

• Previous Cached Execution State: If cached execution state exists, information from reports is used to generate events to align the state
• No Previous Cached Execution State: If there is no cached state, all orders and positions that exist externally are generated from scratch

Reconciliation configuration

Unless reconciliation is disabled by setting the reconciliation configuration parameter to false, the execution engine will perform the execution reconciliation procedure for each venue. Additionally, you can specify the lookback window for reconciliation by setting the reconciliation_lookback_mins configuration parameter.

Each strategy can also be configured to claim any external orders for an instrument ID generated during reconciliation using the external_order_claims configuration parameter. This is useful in situations where, at system start, there is no cached state or it is desirable for a strategy to resume its operations and continue managing existing open orders at the venue for an instrument.

info

See the LiveExecEngineConfig API Reference for further details.

Reconciliation procedure

The reconciliation procedure is standardized for all adapter execution clients and uses the following methods to produce an execution mass status:

• generate_order_status_reports
• generate_fill_reports
• generate_position_status_reports

The system state is then reconciled with the reports, which represent the external reality:

• Duplicate Check:
  • Check for duplicate order IDs and trade IDs
• Order Reconciliation:
  • Generate and apply events necessary to update orders from any currently cached state to the current state
  • If any trade reports are missing, inferred OrderFilled events are generated
  • If any client order ID is not recognized or an order report lacks a client order ID, an external order is generated
• Position Reconciliation:
  • Ensure the net position per instrument matches the position reports returned from the venue

If reconciliation fails, the system will not continue to start, and an error will be logged.

tip

The current reconciliation procedure can experience state mismatches if the lookback window is misconfigured or if the venue omits certain order or trade reports due to filter conditions. If you encounter reconciliation issues, drop any cached state or ensure the account is flat at system shutdown and startup.