nautilus_trader 1.228.0

Production-grade Rust-native trading engine with deterministic event-driven architecture

Branch	Version	Status
master
nightly
develop

Platform	Rust	Python
Linux (x86_64)	1.96.0	3.12-3.14
Linux (ARM64)	1.96.0	3.12-3.14
macOS (ARM64)	1.96.0	3.12-3.14
Windows (x86_64)	1.96.0	3.12-3.14

• Docs: https://nautilustrader.io/docs/
• Website: https://nautilustrader.io
• Support: [e-mail]

Introduction

NautilusTrader is an open-source, production-grade, Rust-native engine for multi-asset, multi-venue trading systems.

The system spans research, deterministic simulation, and live execution within a single event-driven architecture, with Python serving as the control plane for strategy logic, configuration, and orchestration.

This separation provides the performance and safety of a compiled trading engine with the flexibility of Python for system composition and strategy development. Trading systems can also be written entirely in Rust for mission-critical workloads.

The same execution semantics and deterministic time model operate in both research and live systems. Strategies deploy from research to production with no code changes, providing research-to-live parity and reducing the divergence that typically introduces deployment risk.

NautilusTrader is asset-class-agnostic. Any venue with a REST API or WebSocket feed can be integrated through modular adapters. Current integrations span crypto exchanges (CEX and DEX), traditional markets (FX, equities, futures, options), and betting exchanges.

Features

• Fast: Rust core with asynchronous networking using tokio.
• Reliable: Type- and thread-safety backed by Rust, with optional Redis-backed state persistence.
• Portable: Runs on Linux, macOS, and Windows. Deploy using Docker.
• Flexible: Modular adapters integrate any REST API or WebSocket feed.
• Advanced: Time in force IOC, FOK, GTC, GTD, DAY, AT_THE_OPEN, AT_THE_CLOSE, advanced order types and conditional triggers. Execution instructions post-only, reduce-only, and icebergs. Contingency orders including OCO, OUO, OTO.
• Customizable: User-defined components, or assemble entire systems from scratch using the cache and message bus.
• Backtesting: Multiple venues, instruments, and strategies simultaneously using historical quote tick, trade tick, bar, order book, and custom data with nanosecond resolution.
• Live: Identical strategy implementations between research and live deployment.
• Multi-venue: Run market-making and cross-venue strategies across multiple venues simultaneously.
• AI Training: Engine fast enough 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?

Trading strategy research is often conducted in Python using vectorized approaches, while production trading systems are implemented separately using event-driven architectures in compiled languages.

NautilusTrader removes this separation.

A Rust-native core provides a deterministic event-driven runtime for both research and live execution, while Python serves as the control plane. The same architecture, execution semantics, and time model operate across both environments, allowing strategies to move from research to production without reimplementation.

Python bindings are provided via PyO3, with an ongoing migration from Cython. No Rust toolchain is required at install time.

This project makes the Soundness Pledge:

“The intent of this project is to be free of soundness bugs. The developers will do their best to avoid them, and welcome help in analyzing and fixing them.”

[!NOTE]

MSRV: NautilusTrader relies heavily on improvements in the Rust language and compiler. As a result, the Minimum Supported Rust Version (MSRV) is generally equal to the latest stable release of Rust.

Integrations

NautilusTrader is modularly designed to work with adapters, enabling connectivity to trading venues and data providers by translating their raw APIs into a unified interface and normalized domain model.

The following integrations are currently supported; see docs/integrations/ for details:

Name	ID	Type	Status	Docs
AX Exchange	AX	Perpetuals Exchange		Guide
Betfair	BETFAIR	Sports Betting Exchange		Guide
Binance	BINANCE	Crypto Exchange (CEX)		Guide
BitMEX	BITMEX	Crypto Exchange (CEX)		Guide
Bybit	BYBIT	Crypto Exchange (CEX)		Guide
Coinbase	COINBASE	Crypto Exchange (CEX)		Guide
Databento	DATABENTO	Data Provider		Guide
Deribit	DERIBIT	Crypto Exchange (CEX)		Guide
Derive	DERIVE	Crypto Exchange (DEX)		Guide
dYdX	DYDX	Crypto Exchange (DEX)		Guide
Hyperliquid	HYPERLIQUID	Crypto Exchange (DEX)		Guide
Interactive Brokers	INTERACTIVE_BROKERS	Brokerage (multi-venue)		Guide
Kraken	KRAKEN	Crypto Exchange (CEX)		Guide
Lighter	LIGHTER	Crypto Exchange (DEX)		Guide
OKX	OKX	Crypto Exchange (CEX)		Guide
Polymarket	POLYMARKET	Prediction Market (DEX)		Guide
Tardis	TARDIS	Crypto Data Provider		Guide

• ID: The default client ID for the integrations adapter clients.
• Type: The type of integration (often the venue type).

Status

• planned: Planned for future development.
• building: Under construction and likely not in a usable state.
• beta: Completed to a minimally working state and in a beta testing phase.
• stable: Stabilized feature set and API, the integration has been tested by both developers and users to a reasonable level (some bugs may still remain).

See the Integrations documentation for further details.

Roadmap

The Roadmap outlines NautilusTrader's strategic direction. Current priorities include completing the Rust-native core, improving documentation, and enhancing code ergonomics.

The open-source project focuses on single-node backtesting and live trading for individual and small-team quantitative traders. UI dashboards, distributed orchestration, and built-in AI/ML tooling are out of scope to maintain focus on the core engine and ecosystem sustainability.

New integration proposals should start with an RFC issue to discuss suitability before submitting a PR. See Community-contributed integrations for guidelines.

Security

The OpenSSF Scorecard badge tracks automated repository-health signals. It complements manual review, CI hardening, and security audits rather than replacing them.

NautilusTrader ships with signed releases, continuous vulnerability management, and transparent development practices, applying layered controls across the development and release lifecycle:

• Supply chain: dependencies are pinned in lock files with checksums, third-party Python packages install from wheels only (never built from source) and observe a publication cooldown before entering the lock file, cargo-vet audits Rust dependency provenance, production Rust dependencies come only from crates.io, and cargo-deny enforces Rust license compatibility.
• Code and dependency scanning: CodeQL static analysis, cargo-audit, cargo-deny, pip-audit, and OSV Scanner run on pull requests and nightly, alongside Gitleaks secret screening. cargo-fuzz targets cover selected adapter and signing surfaces.
• Build and release integrity: GitHub Actions are pinned to commit SHAs, CI runners are hardened with egress allow-listing, Python wheels and sdists carry SLSA build provenance, container images are Sigstore-signed with attested SPDX SBOMs, and PyPI and crates.io publishing uses Trusted Publishing (OIDC, no long-lived tokens), gated to the protected release environment that never runs code from pull requests or forks.
• Runtime cryptography: TLS and most runtime cryptography use aws-lc-rs, the Rust binding for AWS-LC, with Ed25519 signing via ed25519-dalek.

Reporting a vulnerability

Report privately through GitHub Security Advisories, or email [e-mail] (PGP key available on request). We acknowledge reports within 48 hours and patch critical vulnerabilities within 30 days.

A careful vulnerability report takes real time and effort. We appreciate that, and unless you prefer to remain anonymous, we credit reporters in the relevant security advisory and release notes.

The Security Policy details scope, coordinated disclosure, and step-by-step release verification. For the full policies, see the Responsible Disclosure and Supply Chain Security policies; CI/CD security is documented in .github/OVERVIEW.md.

Versioning and releases

[!WARNING]

NautilusTrader is still under active development. Some features may be incomplete, and while the API is becoming more stable, breaking changes can occur between releases. We strive to document these changes in the release notes on a best-effort basis.

We aim to follow a bi-weekly release schedule, though experimental or larger features may cause delays.

Branches

We aim to maintain a stable, passing build across all branches.

• master: Reflects the source code for the latest released version; recommended for production use.
• nightly: Daily snapshots of the develop branch for early testing; merged at 14:00 UTC and as required.
• develop: Active development branch for contributors and feature work.

[!NOTE]

Our roadmap aims to achieve a stable API for version 2.x (likely after the Rust port). Once this milestone is reached, we plan to implement a formal deprecation process for any API changes. This approach allows us to maintain a rapid development pace for now.

Precision mode

NautilusTrader supports two precision modes for its core value types (Price, Quantity, Money), which differ in their internal bit-width and maximum decimal precision.

• High-precision: 128-bit integers with up to 16 decimals of precision, and a larger value range.
• Standard-precision: 64-bit integers with up to 9 decimals of precision, and a smaller value range.

[!NOTE]

By default, the official Python wheels ship in high-precision (128-bit) mode on Linux and macOS. On Windows, only standard-precision (64-bit) Python wheels are available because MSVC's C/C++ frontend does not support __int128, preventing the Cython/FFI layer from handling 128-bit integers.

For pure Rust crates, high-precision works on all platforms (including Windows) since Rust handles i128/u128 via software emulation. The default is standard-precision unless you explicitly enable the high-precision feature flag.

See the Installation Guide for further details.

Rust feature flag: To enable high-precision mode in Rust, add the high-precision feature to your Cargo.toml:

[dependencies]
nautilus_model = { version = "*", features = ["high-precision"] }

Installation

We recommend using the latest supported version of Python and installing nautilus_trader inside a virtual environment to isolate dependencies.

There are two supported ways to install:

1. Pre-built binary wheel from PyPI or the Nautech Systems package index.
2. Build from source.

[!TIP]

We highly recommend installing using the uv package manager with a "vanilla" CPython.

Conda and other Python distributions may work but aren’t officially supported.

From PyPI

To install the latest binary wheel (or sdist package) from PyPI using Python's pip package manager:

pip install -U nautilus_trader

Install optional dependencies as 'extras' for specific integrations (e.g., betfair, docker, dydx, ib, polymarket, visualization):

pip install -U "nautilus_trader[docker,ib]"

See the Installation Guide for the full list of available extras.

From the Nautech Systems package index

The Nautech Systems package index (packages.nautechsystems.io) complies with PEP-503 and hosts both stable and development binary wheels for nautilus_trader. This enables users to install either the latest stable release or pre-release versions for testing.

Stable wheels

Stable wheels correspond to official releases of nautilus_trader on PyPI, and use standard versioning.

To install the latest stable release:

pip install -U nautilus_trader --index-url=https://packages.nautechsystems.io/simple

[!TIP]

Use --extra-index-url instead of --index-url if you want pip to fall back to PyPI automatically.

Development wheels

Development wheels are published from both the nightly and develop branches, allowing users to test features and fixes ahead of stable releases.

This process also helps preserve compute resources and provides easy access to the exact binaries tested in CI pipelines, while adhering to PEP-440 versioning standards:

• develop wheels use the version format dev{date}+{build_number} (e.g., 1.208.0.dev20241212+7001).
• nightly wheels use the version format a{date} (alpha) (e.g., 1.208.0a20241212).

Platform	Nightly	Develop
Linux (x86_64)	✓	✓
Linux (ARM64)	✓	-
macOS (ARM64)	✓	-
Windows (x86_64)	✓	-

Note: Development wheels from the develop branch publish for Linux x86_64 only. Windows, macOS, and Linux ARM64 builds run on the nightly schedule to keep CI feedback fast.

[!WARNING]

We do not recommend using development wheels in production environments, such as live trading controlling real capital.

Installation commands

By default, pip will install the latest stable release. Adding the --pre flag ensures that pre-release versions, including development wheels, are considered.

To install the latest available pre-release (including development wheels):

pip install -U nautilus_trader --pre --index-url=https://packages.nautechsystems.io/simple

To install a specific development wheel (e.g., 1.221.0a20251026 for October 26, 2025):

pip install nautilus_trader==1.221.0a20251026 --index-url=https://packages.nautechsystems.io/simple

Available versions

You can view all available versions of nautilus_trader on the package index.

To programmatically fetch and list available versions:

curl -s https://packages.nautechsystems.io/simple/nautilus-trader/index.html | sed -n 's/.*<a href="\([^"]*\)".*/\1/p' | awk -F'#' '{print $1}' | sort

[!NOTE]

On Linux, confirm your glibc version with ldd --version and ensure it reports 2.35 or newer before installing binary wheels.

Branch updates

• develop branch wheels (.dev): Build and publish continuously with every merged commit.
• nightly branch wheels (a): Build and publish daily when we automatically merge the develop branch at 14:00 UTC (if there are changes).

Retention policies

• develop branch wheels (.dev): We retain only the most recent wheel build.
• nightly branch wheels (a): We retain only the 30 most recent wheel builds.

Verifying build provenance

All release artifacts published by the project carry cryptographic attestations generated by the CI/CD pipeline:

• Python wheels and source distribution (PyPI, GitHub Releases, Nautech Systems package index): SLSA build provenance.
• Docker images (ghcr.io/nautechsystems/nautilus_trader, ghcr.io/nautechsystems/jupyterlab): keyless cosign signatures plus SPDX SBOM attestations.

Both are issued via Sigstore and bound to a specific commit SHA, so verification ensures the artifact was produced by the official NautilusTrader GitHub Actions workflow and has not been tampered with since.

For step-by-step verification commands, see Verifying releases in SECURITY.md.

[!NOTE]

Verification requires the GitHub CLI (gh) for Python artifacts and cosign for Docker images. Development wheels from develop and nightly branches are also attested.

From source

It's possible to install from source using pip if you first install the build dependencies as specified in the pyproject.toml.

1. Install rustup (the Rust toolchain installer):

   • Linux and macOS:

     curl https://sh.rustup.rs -sSf | sh
     

   • Windows:

     • Download and install rustup-init.exe
     • Install "Desktop development with C++" using Build Tools for Visual Studio 2022

   • Verify (any system): from a terminal session run: rustc --version

2. Enable cargo in the current shell:

   • Linux and macOS:

     source $HOME/.cargo/env
     

   • Windows:

     • Start a new PowerShell

3. Install clang (a C language frontend for LLVM):

   • Linux (also installs lld, used as the Rust linker for faster builds):

     sudo apt-get install clang lld
     

   • macOS:

     xcode-select --install
     

   • Windows:

     1. Add Clang to your Build Tools for Visual Studio 2022:

        • Start | Visual Studio Installer | Modify | C++ Clang tools for Windows (latest) = checked | Modify

     2. Enable clang in the current shell:

        [System.Environment]::SetEnvironmentVariable('path', "C:\Program Files\Microsoft Visual Studio\2022\BuildTools\VC\Tools\Llvm\x64\bin\;" + $env:Path,"User")
        

   • Verify (any system): from a terminal session run: clang --version

4. Install uv (see the uv installation guide for more details):

   • Linux and macOS:

     curl -LsSf https://astral.sh/uv/install.sh | sh
     

   • Windows (PowerShell):

     irm https://astral.sh/uv/install.ps1 | iex
     

5. Clone the source with git, and install from the project's root directory:

   git clone --branch develop --depth 1 https://github.com/nautechsystems/nautilus_trader
   cd nautilus_trader
   uv sync --all-extras
   

[!NOTE]

The --depth 1 flag fetches just the latest commit for a faster, lightweight clone.

6. Set environment variables for PyO3 compilation (Linux and macOS only). Run these commands from the repository root after uv sync:

   # Set the Python executable path for PyO3
   export PYO3_PYTHON="$PWD/.venv/bin/python"
   
   # Linux only: Set the library path for the uv-managed Python runtime
   PYTHON_LIB_DIR="$("$PYO3_PYTHON" -c 'import sysconfig; print(sysconfig.get_config_var("LIBDIR"))')"
   export LD_LIBRARY_PATH="$PYTHON_LIB_DIR${LD_LIBRARY_PATH:+:$LD_LIBRARY_PATH}"
   
   # Required for Rust tests when using uv-installed Python
   export PYTHONHOME="$("$PYO3_PYTHON" -c 'import sys; print(sys.base_prefix)')"
   

[!NOTE]

The LD_LIBRARY_PATH export is Linux-specific and not needed on macOS.

The PYTHONHOME variable is required when running make cargo-test with a uv-installed Python. Without it, tests that depend on PyO3 may fail to locate the Python runtime.

See the Installation Guide for other options and further details.

Redis

Using Redis with NautilusTrader is optional and only required if configured as the backend for a cache database or message bus. See the Redis section of the Installation Guide for further details.

Makefile

A Makefile is provided to automate most installation and build tasks for development. Some of the targets include:

• make install: Installs in release build mode with all dependency groups and extras.
• make install-debug: Same as make install but with debug build mode.
• make install-just-deps: Installs just the main, dev and test dependencies (does not install package).
• make build: Runs the build script in release build mode (default).
• make build-debug: Runs the build script in debug build mode.
• make build-wheel: Runs uv build with a wheel format in release mode.
• make build-wheel-debug: Runs uv build with a wheel format in debug mode.
• make cargo-test: Runs all Rust crate tests using cargo-nextest.
• make clean: Deletes all build results, such as .so or .dll files.
• make distclean: CAUTION Removes all artifacts not in the git index from the repository. This includes source files which have not been git added.
• make docs: Builds the documentation HTML using Sphinx.
• make pre-commit: Runs the pre-commit checks over all files.
• make ruff: Runs ruff over all files using the pyproject.toml config (with autofix).
• make pytest: Runs all tests with pytest.
• make test-performance: Runs performance tests with codspeed.

[!TIP]

Run make help for documentation on all available make targets.

[!TIP]

See the crates/infrastructure/TESTS.md file for running the infrastructure integration tests.

Examples

Indicators and strategies can be developed in Python, Cython, or Rust. For performance and latency-sensitive applications, we recommend Rust. Below are some examples:

• indicator example written in Python.
• indicator implementations written in Cython.
• strategy examples written in Python.
• backtest examples using a BacktestEngine directly.

Docker

Docker containers are built with the following variant tags:

• nautilus_trader:latest has the latest release version installed.
• nautilus_trader:nightly has the head of the nightly branch installed.
• jupyterlab:latest has the latest release version installed along with jupyterlab and an example backtest notebook with accompanying data.
• jupyterlab:nightly has the head of the nightly branch installed along with jupyterlab and an example backtest notebook with accompanying data.

You can pull the container images as follows:

docker pull ghcr.io/nautechsystems/<image_variant_tag> --platform linux/amd64

You can launch the backtest example container by running:

docker pull ghcr.io/nautechsystems/jupyterlab:nightly --platform linux/amd64
docker run -p 8888:8888 ghcr.io/nautechsystems/jupyterlab:nightly

Then open your browser at the following address:

http://127.0.0.1:8888/lab

[!WARNING]

Examples use log_level="ERROR" because Nautilus logging exceeds Jupyter's stdout rate limit, causing notebooks to hang at lower log levels.

Development

We aim to provide the most pleasant developer experience possible for this hybrid codebase of Rust, Python, and Cython. See the Developer Guide for helpful information.

[!TIP]

Run make build-debug to compile after changes to Rust or Cython code for the most efficient development workflow.

Testing with Rust

cargo-nextest is the standard Rust test runner for NautilusTrader. Its key benefit is isolating each test in its own process, ensuring test reliability by avoiding interference.

You can install cargo-nextest by running:

cargo install cargo-nextest

[!TIP]

Run Rust tests with make cargo-test, which uses cargo-nextest with an efficient profile.

Contributing

Thank you for considering contributing to NautilusTrader! We welcome any and all help to improve the project. If you have an idea for an enhancement or a bug fix, the first step is to open an issue on GitHub to discuss it with the team. This helps to ensure that your contribution will be well-aligned with the goals of the project and avoids duplication of effort.

Before getting started, be sure to review the open-source scope outlined in the project’s roadmap to understand what’s in and out of scope.

Once you're ready to start working on your contribution, make sure to follow the guidelines outlined in the CONTRIBUTING.md file. This includes signing a Contributor License Agreement (CLA) to ensure that your contributions can be included in the project.

[!NOTE]

Pull requests should target the develop branch (the default branch). This is where new features and improvements are integrated before release.

Thank you again for your interest in NautilusTrader! We look forward to reviewing your contributions and working with you to improve the project.

Community

Join our community of users and contributors on Discord to chat and stay up-to-date with the latest announcements and features of NautilusTrader. Whether you're a developer looking to contribute or just want to learn more about the platform, all are welcome on our Discord server.

[!WARNING]

NautilusTrader does not issue, promote, or endorse any cryptocurrency tokens. Any claims or communications suggesting otherwise are unauthorized and false.

All official updates and communications from NautilusTrader will be shared exclusively through https://nautilustrader.io, our GitHub, our Discord server, or our verified X (Twitter) account: @NautilusTrader.

If you encounter any suspicious activity, please report it to the appropriate platform and contact us at [e-mail].

License

The source code for NautilusTrader is available on GitHub under the GNU Lesser General Public License v3.0. Contributions to the project are welcome and require the completion of a standard Contributor License Agreement (CLA).

---

NautilusTrader™ is developed and maintained by Nautech Systems, a technology company specializing in the development of high-performance trading systems. For more information, visit https://nautilustrader.io.

Use of this software is subject to the Disclaimer.

© 2015-2026 Nautech Systems Pty Ltd. All rights reserved.