Nautilus Trader
Production-grade Rust-native trading engine with deterministic event-driven architecture
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About
](https://codecov.io/gh/nautechsystems/nautilus_trader) [
](https://pepy.tech/project/nautilus-trader) [
| Branch | Version | Status |
|---|---|---|
master | [](https://packages.nautechsystems.io/simple/nautilus-trader/index.html) | [](https://github.com/nautechsystems/nautilus_trader/actions/workflows/build.yml) |
nightly | [](https://packages.nautechsystems.io/simple/nautilus-trader/index.html) | [](https://github.com/nautechsystems/nautilus_trader/actions/workflows/build.yml) |
develop | [](https://packages.nautechsystems.io/simple/nautilus-trader/index.html) | [](https://github.com/nautechsystems/nautilus_trader/actions/workflows/build.yml) |
| Platform | Rust | Python |
|---|---|---|
Linux (x86_64) | 1.94.1 | 3.12-3.14 |
Linux (ARM64) | 1.94.1 | 3.12-3.14 |
macOS (ARM64) | 1.94.1 | 3.12-3.14 |
Windows (x86_64) | 1.94.1 | 3.12-3.14 |
- Docs:
- Website:
- Support: support@nautilustrader.io
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.
Don't lose this
Three weeks from now, you'll want Nautilus Trader again. Will you remember where to find it?
Save it to your library and the next time you need Nautilus Trader, it’s one tap away — from any AI app you use. Group it into a bench with the rest of the team for that kind of task and you can pull the whole stack at once.
⚡ Pro tip for geeks: add a-gnt 🤵🏻♂️ as a custom connector in Claude or a custom GPT in ChatGPT — one click and your library is right there in the chat. Or, if you’re in an editor, install the a-gnt MCP server and say “use my [bench name]” in Claude Code, Cursor, VS Code, or Windsurf.
a-gnt's Take
Our honest review
This plugs directly into your AI and gives it new abilities it didn't have before. Production-grade Rust-native trading engine with deterministic event-driven architecture. Once connected, just ask your AI to use it. It's completely free and works across most major AI apps. This one just landed in the catalog — worth trying while it's fresh.
Tips for getting started
Tap "Get" above, pick your AI app, and follow the steps. Most installs take under 30 seconds.
Your data stays between you and your AI — nothing is shared with us or anyone else.
What's New
Imported from GitHub
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