Version: nightly

Message Bus

The MessageBus is a fundamental part of the platform, enabling communication between system components through message passing. This design creates a loosely coupled architecture where components can interact without direct dependencies.

The messaging patterns include:

Point-to-Point
Publish/Subscribe
Request/Response

Messages exchanged via the message bus fall into three categories:

Data
Events
Commands

Data and signal publishing

While the MessageBus is a lower-level component that users typically interact with indirectly, Actor and Strategy classes provide convenient methods built on top of it:

def publish_data(self, data_type: DataType, data: Data) -> None:
def publish_signal(self, name: str, value, ts_event: int | None = None) -> None:

These methods allow you to publish custom data and signals efficiently without needing to work directly with the MessageBus interface.

Direct access

For advanced users or specialized use cases, direct access to the message bus is available within Actor and Strategy classes through the self.msgbus reference, which provides the full message bus interface.

To publish a custom message directly, you can specify a topic as a str and any Python object as the message payload, for example:

self.msgbus.publish("MyTopic", "MyMessage")

Messaging styles

NautilusTrader is an event-driven framework where components communicate by sending and receiving messages. Understanding the different messaging styles is crucial for building effective trading systems.

This guide explains the three primary messaging patterns available in NautilusTrader:

Messaging Style	Purpose	Best For
MessageBus - Publish/Subscribe to topics	Low-level, direct access to the message bus	Custom events, system-level communication
Actor-Based - Publish/Subscribe Data	Structured trading data exchange	Trading metrics, indicators, data needing persistence
Actor-Based - Publish/Subscribe Signal	Lightweight notifications	Simple alerts, flags, status updates

Each approach serves different purposes and offers unique advantages. This guide will help you decide which messaging pattern to use in your NautilusTrader applications.

MessageBus publish/subscribe to topics

Concept

The MessageBus is the central hub for all messages in NautilusTrader. It enables a publish/subscribe pattern where components can publish events to named topics, and other components can subscribe to receive those messages. This decouples components, allowing them to interact indirectly via the message bus.

Key benefits and use cases

The message bus approach is ideal when you need:

Cross-component communication within the system.
Flexibility to define any topic and send any type of payload (any Python object).
Decoupling between publishers and subscribers who don't need to know about each other.
Global Reach where messages can be received by multiple subscribers.
Working with events that don't fit within the predefined Actor model.
Advanced scenarios requiring full control over messaging.

Considerations

You must track topic names manually (typos could result in missed messages).
You must define handlers manually.

Quick overview code

from nautilus_trader.core.message import Event

# Define a custom event
class Each10thBarEvent(Event):
    TOPIC = "each_10th_bar"  # Topic name
    def __init__(self, bar):
        self.bar = bar

# Subscribe in a component (in Strategy)
self.msgbus.subscribe(Each10thBarEvent.TOPIC, self.on_each_10th_bar)

# Publish an event (in Strategy)
event = Each10thBarEvent(bar)
self.msgbus.publish(Each10thBarEvent.TOPIC, event)

# Handler (in Strategy)
def on_each_10th_bar(self, event: Each10thBarEvent):
    self.log.info(f"Received 10th bar: {event.bar}")

Full example

MessageBus Example

Actor-based publish/subscribe data

Concept

This approach provides a way to exchange trading specific data between Actors in the system. (note: each Strategy inherits from Actor). It inherits from Data, which ensures proper timestamping and ordering of events - crucial for correct backtest processing.

Key Benefits and Use Cases

The Data publish/subscribe approach excels when you need:

Exchange of structured trading data like market data, indicators, custom metrics, or option greeks.
Proper event ordering via built-in timestamps (ts_event, ts_init) crucial for backtest accuracy.
Data persistence and serialization through the @customdataclass decorator, integrating seamlessly with NautilusTrader's data catalog system.
Standardized trading data exchange between system components.

Considerations

Requires defining a class that inherits from Data or uses @customdataclass.

Inheriting from `Data` vs. using `@customdataclass`

Inheriting from Data class:

Defines abstract properties ts_event and ts_init that must be implemented by the subclass. These ensure proper data ordering in backtests based on timestamps.

The @customdataclass decorator:

Adds ts_event and ts_init attributes if they are not already present.
Provides serialization functions: to_dict(), from_dict(), to_bytes(), to_arrow(), etc.
Enables data persistence and external communication.

Quick overview code

from nautilus_trader.core.data import Data
from nautilus_trader.model.custom import customdataclass

@customdataclass
class GreeksData(Data):
    delta: float
    gamma: float

# Publish data (in Actor / Strategy)
data = GreeksData(delta=0.75, gamma=0.1, ts_event=1_630_000_000_000_000_000, ts_init=1_630_000_000_000_000_000)
self.publish_data(GreeksData, data)

# Subscribe to receiving data  (in Actor / Strategy)
self.subscribe_data(GreeksData)

# Handler (this is static callback function with fixed name)
def on_data(self, data: Data):
    if isinstance(data, GreeksData):
        self.log.info(f"Delta: {data.delta}, Gamma: {data.gamma}")

Full example

Actor-Based Data Example

Actor-based publish/subscribe signal

Concept

Signals are a lightweight way to publish and subscribe to simple notifications within the actor framework. This is the simplest messaging approach, requiring no custom class definitions.

Key Benefits and Use Cases

The Signal messaging approach shines when you need:

Simple, lightweight notifications/alerts like "RiskThresholdExceeded" or "TrendUp".
Quick, on-the-fly messaging without defining custom classes.
Broadcasting alerts or flags as primitive data (int, float, or str).
Easy API integration with straightforward methods (publish_signal, subscribe_signal).
Multiple subscriber communication where all subscribers receive signals when published.
Minimal setup overhead with no class definitions required.

Considerations

Each signal can contain only single value of type: int, float, and str. That means no support for complex data structures or other Python types.
In the on_signal handler, you can only differentiate between signals using signal.value, as the signal name is not accessible in the handler.

Quick overview code

# Define signal constants for better organization (optional but recommended)
import types
signals = types.SimpleNamespace()
signals.NEW_HIGHEST_PRICE = "NewHighestPriceReached"
signals.NEW_LOWEST_PRICE = "NewLowestPriceReached"

# Subscribe to signals (in Actor/Strategy)
self.subscribe_signal(signals.NEW_HIGHEST_PRICE)
self.subscribe_signal(signals.NEW_LOWEST_PRICE)

# Publish a signal (in Actor/Strategy)
self.publish_signal(
    name=signals.NEW_HIGHEST_PRICE,
    value=signals.NEW_HIGHEST_PRICE,  # value can be the same as name for simplicity
    ts_event=bar.ts_event,  # timestamp from triggering event
)

# Handler (this is static callback function with fixed name)
def on_signal(self, signal):
    # IMPORTANT: We match against signal.value, not signal.name
    match signal.value:
        case signals.NEW_HIGHEST_PRICE:
            self.log.info(
                f"New highest price was reached. | "
                f"Signal value: {signal.value} | "
                f"Signal time: {unix_nanos_to_dt(signal.ts_event)}",
                color=LogColor.GREEN
            )
        case signals.NEW_LOWEST_PRICE:
            self.log.info(
                f"New lowest price was reached. | "
                f"Signal value: {signal.value} | "
                f"Signal time: {unix_nanos_to_dt(signal.ts_event)}",
                color=LogColor.RED
            )

Full example

Actor-Based Signal Example

Summary and decision guide

Here's a quick reference to help you decide which messaging style to use:

Decision guide: Which style to choose?

Use Case	Recommended Approach	Setup required
Custom events or system-level communication	`MessageBus` + Pub/Sub to topic	Topic + Handler management
Structured trading data	`Actor` + Pub/Sub Data + optional `@customdataclass` if serialization is needed	New class definition inheriting from `Data` (handler `on_data` is predefined)
Simple alerts/notifications	`Actor` + Pub/Sub Signal	Just signal name

External publishing

The MessageBus can be backed with any database or message broker technology which has an integration written for it, this then enables external publishing of messages.

info

Redis is currently supported for all serializable messages which are published externally. The minimum supported Redis version is 6.2 (required for streams functionality).

Under the hood, when a backing database (or any other compatible technology) is configured, all outgoing messages are first serialized, then transmitted via a Multiple-Producer Single-Consumer (MPSC) channel to a separate thread (implemented in Rust). In this separate thread, the message is written to its final destination, which is presently Redis streams.

This design is primarily driven by performance considerations. By offloading the I/O operations to a separate thread, we ensure that the main thread remains unblocked and can continue its tasks without being hindered by the potentially time-consuming operations involved in interacting with a database or client.

Serialization

Nautilus supports serialization for:

All Nautilus built-in types (serialized as dictionaries dict[str, Any] containing serializable primitives).
Python primitive types (str, int, float, bool, bytes).

You can add serialization support for custom types by registering them through the serialization subpackage.

def register_serializable_type(
    cls,
    to_dict: Callable[[Any], dict[str, Any]],
    from_dict: Callable[[dict[str, Any]], Any],
):
    ...

cls: The type to register.
to_dict: The delegate to instantiate a dict of primitive types from the object.
from_dict: The delegate to instantiate the object from a dict of primitive types.

Configuration

The message bus external backing technology can be configured by importing the MessageBusConfig object and passing this to your TradingNodeConfig. Each of these config options will be described below.

...  # Other config omitted
message_bus=MessageBusConfig(
    database=DatabaseConfig(),
    encoding="json",
    timestamps_as_iso8601=True,
    buffer_interval_ms=100,
    autotrim_mins=30,
    use_trader_prefix=True,
    use_trader_id=True,
    use_instance_id=False,
    streams_prefix="streams",
    types_filter=[QuoteTick, TradeTick],
)
...

Database config

A DatabaseConfig must be provided, for a default Redis setup on the local loopback you can pass a DatabaseConfig(), which will use defaults to match.

Encoding

Two encodings are currently supported by the built-in Serializer used by the MessageBus:

JSON (json)
MessagePack (msgpack)

Use the encoding config option to control the message writing encoding.

tip

The msgpack encoding is used by default as it offers the most optimal serialization and memory performance. We recommend using json encoding for human readability when performance is not a primary concern.

Timestamp formatting

By default timestamps are formatted as UNIX epoch nanosecond integers. Alternatively you can configure ISO 8601 string formatting by setting the timestamps_as_iso8601 to True.

Message stream keys

Message stream keys are essential for identifying individual trader nodes and organizing messages within streams. They can be tailored to meet your specific requirements and use cases. In the context of message bus streams, a trader key is typically structured as follows:

trader:{trader_id}:{instance_id}:{streams_prefix}

The following options are available for configuring message stream keys:

Trader prefix

If the key should begin with the trader string.

Trader ID

If the key should include the trader ID for the node.

Instance ID

Each trader node is assigned a unique 'instance ID,' which is a UUIDv4. This instance ID helps distinguish individual traders when messages are distributed across multiple streams. You can include the instance ID in the trader key by setting the use_instance_id configuration option to True. This is particularly useful when you need to track and identify traders across various streams in a multi-node trading system.

Streams prefix

The streams_prefix string enables you to group all streams for a single trader instance or organize messages for multiple instances. Configure this by passing a string to the streams_prefix configuration option, ensuring other prefixes are set to false.

Stream per topic

Indicates whether the producer will write a separate stream for each topic. This is particularly useful for Redis backings, which do not support wildcard topics when listening to streams. If set to False, all messages will be written to the same stream.

info

Redis does not support wildcard stream topics. For better compatibility with Redis, it is recommended to set this option to False.

Types filtering

When messages are published on the message bus, they are serialized and written to a stream if a backing for the message bus is configured and enabled. To prevent flooding the stream with data like high-frequency quotes, you may filter out certain types of messages from external publication.

To enable this filtering mechanism, pass a list of type objects to the types_filter parameter in the message bus configuration, specifying which types of messages should be excluded from external publication.

from nautilus_trader.config import MessageBusConfig
from nautilus_trader.data import TradeTick
from nautilus_trader.data import QuoteTick

# Create a MessageBusConfig instance with types filtering
message_bus = MessageBusConfig(
    types_filter=[QuoteTick, TradeTick]
)

Stream auto-trimming

The autotrim_mins configuration parameter allows you to specify the lookback window in minutes for automatic stream trimming in your message streams. Automatic stream trimming helps manage the size of your message streams by removing older messages, ensuring that the streams remain manageable in terms of storage and performance.

info

The current Redis implementation will maintain the autotrim_mins as a maximum width (plus roughly a minute, as streams are trimmed no more than once per minute). Rather than a maximum lookback window based on the current wall clock time.

External streams

The message bus within a TradingNode (node) is referred to as the "internal message bus". A producer node is one which publishes messages onto an external stream (see external publishing). The consumer node listens to external streams to receive and publish deserialized message payloads on its internal message bus.

                  ┌───────────────────────────┐
                  │                           │
                  │                           │
                  │                           │
                  │      Producer Node        │
                  │                           │
                  │                           │
                  │                           │
                  │                           │
                  │                           │
                  │                           │
                  └─────────────┬─────────────┘
                                │
                                │
┌───────────────────────────────▼──────────────────────────────┐
│                                                              │
│                            Stream                            │
│                                                              │
└─────────────┬────────────────────────────────────┬───────────┘
              │                                    │
              │                                    │
┌─────────────▼───────────┐          ┌─────────────▼───────────┐
│                         │          │                         │
│                         │          │                         │
│     Consumer Node 1     │          │     Consumer Node 2     │
│                         │          │                         │
│                         │          │                         │
│                         │          │                         │
│                         │          │                         │
│                         │          │                         │
│                         │          │                         │
│                         │          │                         │
└─────────────────────────┘          └─────────────────────────┘

tip

Set the LiveDataEngineConfig.external_clients with the list of client_ids intended to represent the external streaming clients. The DataEngine will filter out subscription commands for these clients, ensuring that the external streaming provides the necessary data for any subscriptions to these clients.

Example configuration

The following example details a streaming setup where a producer node publishes Binance data externally, and a downstream consumer node publishes these data messages onto its internal message bus.

Producer node

We configure the MessageBus of the producer node to publish to a "binance" stream. The settings use_trader_id, use_trader_prefix, and use_instance_id are all set to False to ensure a simple and predictable stream key that the consumer nodes can register for.

message_bus=MessageBusConfig(
    database=DatabaseConfig(timeout=2),
    use_trader_id=False,
    use_trader_prefix=False,
    use_instance_id=False,
    streams_prefix="binance",  # <---
    stream_per_topic=False,
    autotrim_mins=30,
),

Consumer node

We configure the MessageBus of the consumer node to receive messages from the same "binance" stream. The node will listen to the external stream keys to publish these messages onto its internal message bus. Additionally, we declare the client ID "BINANCE_EXT" as an external client. This ensures that the DataEngine does not attempt to send data commands to this client ID, as we expect these messages to be published onto the internal message bus from the external stream, to which the node has subscribed to the relevant topics.

data_engine=LiveDataEngineConfig(
    external_clients=[ClientId("BINANCE_EXT")],
),
message_bus=MessageBusConfig(
    database=DatabaseConfig(timeout=2),
    external_streams=["binance"],  # <---
),

Data and signal publishing​

Direct access​

Messaging styles​

MessageBus publish/subscribe to topics​

Concept​

Key benefits and use cases​

Considerations​

Quick overview code​

Full example​

Actor-based publish/subscribe data​

Concept​

Key Benefits and Use Cases​

Considerations​

Inheriting from Data vs. using @customdataclass​

Quick overview code​

Full example​

Actor-based publish/subscribe signal​

Concept​

Key Benefits and Use Cases​

Considerations​

Quick overview code​

Full example​

Summary and decision guide​

Decision guide: Which style to choose?​

External publishing​

Serialization​

Configuration​

Database config​

Encoding​

Timestamp formatting​

Message stream keys​

Trader prefix​

Trader ID​

Instance ID​

Streams prefix​

Stream per topic​

Types filtering​

Stream auto-trimming​

External streams​

Example configuration​

Producer node​

Consumer node​

Data and signal publishing

Direct access

Messaging styles

MessageBus publish/subscribe to topics

Concept

Key benefits and use cases

Considerations

Quick overview code

Full example

Actor-based publish/subscribe data

Concept

Key Benefits and Use Cases

Considerations

Inheriting from `Data` vs. using `@customdataclass`

Quick overview code

Full example

Actor-based publish/subscribe signal

Concept

Key Benefits and Use Cases

Considerations

Quick overview code

Full example

Summary and decision guide

Decision guide: Which style to choose?

External publishing

Serialization

Configuration

Database config

Encoding

Timestamp formatting

Message stream keys

Trader prefix

Trader ID

Instance ID

Streams prefix

Stream per topic

Types filtering

Stream auto-trimming

External streams

Example configuration

Producer node

Consumer node