NautilusTrader is an open-source, high-performance, production-grade algorithmic trading platform, providing quantitative traders with the ability to backtest portfolios of automated trading strategies on historical data with an event-driven engine, and also deploy those same strategies live, with no code changes.

The platform is ‘AI-first’, designed to develop and deploy algorithmic trading strategies within a highly performant and robust Python native environment. This helps to address the parity challenge of keeping the Python research/backtest environment, consistent with the production live trading environment.

NautilusTraders design, architecture and implementation philosophy holds software correctness and safety at the highest level, with the aim of supporting Python native, mission-critical, trading system backtesting and live deployment workloads.

The platform is also universal and asset class agnostic - with any REST, WebSocket or FIX API able to be integrated via modular adapters. Thus, it can handle high-frequency trading operations for any asset classes including FX, Equities, Futures, Options, CFDs, Crypto and Betting - across multiple venues simultaneously.


  • Fast: C-level speed through Rust and Cython. Asynchronous networking with uvloop

  • Reliable: Type safety through Rust and Cython. Redis backed performant state persistence

  • Flexible: OS independent, runs on Linux, macOS, Windows. Deploy using Docker

  • Integrated: Modular adapters mean any REST, WebSocket, or FIX API can be integrated

  • Advanced: Time in force IOC , FOK , GTD , AT_THE_OPEN , AT_THE_CLOSE , advanced order types and conditional triggers. Execution instructions post-only , reduce-only , and icebergs. Contingency order lists including OCO , OTO

  • Backtesting: Run with multiple venues, instruments and strategies simultaneously using historical quote tick, trade tick, bar, order book and custom data with nanosecond resolution

  • Live: Use identical strategy implementations between backtesting and live deployments

  • Multi-venue: Multiple venue capabilities facilitate market making and statistical arbitrage strategies

  • AI Agent Training: Backtest engine fast enough to be used to train AI trading agents (RL/ES)


nautilus - from ancient Greek ‘sailor’ and naus ‘ship’.

The nautilus shell consists of modular chambers with a growth factor which approximates a logarithmic spiral. The idea is that this can be translated to the aesthetics of design and architecture.

Why NautilusTrader?

  • Highly performant event-driven Python - native binary core components

  • Parity between backtesting and live trading - identical strategy code

  • Reduced operational risk - risk management functionality, logical correctness and type safety

  • Highly extendable - message bus, custom components and actors, custom data, custom adapters

Traditionally, trading strategy research and backtesting might be conducted in Python (or other suitable language) using vectorized methods, with the strategy then needing to be reimplemented in a more event-drive way using C++, C#, Java or other statically typed language(s). The reasoning here is that vectorized backtesting code cannot express the granular time and event dependent complexity of real-time trading, where compiled languages have proven to be more suitable due to their inherently higher performance, and type safety.

One of the key advantages of NautilusTrader here, is that this reimplementation step is now circumvented - as the critical core components of the platform have all been written entirely in Rust or Cython. This means we’re using the right tools for the job, where systems programming languages compile performant binaries, with CPython C extension modules then able to offer a Python native environment, suitable for professional quantitative traders and trading firms.

Use cases

There are three main use cases for this software package:

  • Backtesting trading systems with historical data ( backtest )

  • Testing trading systems with real-time data and simulated execution ( sandbox )

  • Deploying trading systems with real-time data and executing on venues with real (or paper) accounts ( live )

The projects codebase provides a framework for implementing the software layer of systems which achieve the above. You will find the default backtest and live system implementations in their respectively named subpackages. A sandbox environment can be built using the sandbox adapter.


  • All examples will utilize these default system implementations.

  • We consider trading strategies to be subcomponents of end-to-end trading systems, these systems include the application and infrastructure layers.


The platform is designed to be easily integrated into a larger distributed system. To facilitate this, nearly all configuration and domain objects can be serialized using JSON, MessagePack or Apache Arrow (Feather) for communication over the network.

Common core

The common system core is utilized by all node environment contexts ( backtest , sandbox , and live ). User-defined Actor, Strategy and ExecAlgorithm components are managed consistently across these environment contexts.


Backtesting can be achieved by first making data available to a BacktestEngine either directly or via a higher level BacktestNode and ParquetDataCatalog , and then running the data through the system with nanosecond resolution.

Live trading

A TradingNode can ingest data and events from multiple data and execution clients. Live deployments can use both demo/paper trading accounts, or real accounts.

For live trading, a TradingNode can ingest data and events from multiple data and execution clients. The platform supports both demo/paper trading accounts and real accounts. High performance can be achieved by running asynchronously on a single event loop , with the potential to further boost performance by leveraging the uvloop implementation (available for Linux and macOS).


Python 3.11 offers improved run-time performance, while Python 3.12 additionally offers improved asyncio performance.

Domain model

The platform features a comprehensive trading domain model that includes various value types such as Price and Quantity , as well as more complex entities such as Order and Position objects, which are used to aggregate multiple events to determine state.

Data Types

The following market data types can be requested historically, and also subscribed to as live streams when available from a venue / data provider, and implemented in an integrations adapter.

  • OrderBookDelta (L1/L2/L3)

  • OrderBookDeltas (container type)

  • OrderBookDepth10 (fixed depth of 10 levels per side)

  • QuoteTick

  • TradeTick

  • Bar

  • Instrument

  • InstrumentStatus

  • InstrumentClose

  • VenueStatus

The following PriceType options can be used for bar aggregations;

  • BID

  • ASK

  • MID

  • LAST

The following BarAggregation options are possible;




  • HOUR

  • DAY

  • WEEK


  • TICK


  • VALUE (a.k.a Dollar bars)







The price types and bar aggregations can be combined with step sizes >= 1 in any way through a BarSpecification . This enables maximum flexibility and now allows alternative bars to be aggregated for live trading.

Account Types

The following account types are available for both live and backtest environments;

  • Cash single-currency (base currency)

  • Cash multi-currency

  • Margin single-currency (base currency)

  • Margin multi-currency

  • Betting single-currency

Order Types

The following order types are available (when possible on a venue);