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nautilus_trader

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

With conditionsPyPI LibrariesReleased Aug 2026253.6K downloads / moLGPL-3.0-or-laterPlatform wheel

Decision gist · record as of 2026-08-14

platform wheels — nautilus_trader-1.231.0-cp312-cp312-macosx_26_0_arm64.whl · nautilus_trader-1.231.0-cp312-cp312-manylinux_2_35_aarch64.whl · nautilus_trader-1.231.0-cp312-cp312-manylinux_2_35_x86_64.whl
v1.231.0 · released 2026-08-02 · Python <3.15,>=3.12 · 11 runtime deps: click, fsspec, msgspec, numpy, pandas, portion, pyarrow, pytz

Yes, with conditions. NautilusTrader is worth installing if you need research-to-production parity in algorithmic trading and are comfortable with Python 3.12–3.14 and LGPL-3.0-or-later licensing. Active maintenance, no known vulnerabilities, and broad platform support make it production-ready. The medium install friction (11 dependencies, compiled wheels) is typical for data-heavy trading systems. Not suitable if you require Python <3.12 or cannot accept copyleft licensing on library modifications.AI-flagged interpretation of the facts on this page — verify before relying

Before you install

  • Requires Python 3.12–3.14; Rust 1.97.1 is used internally but not required at install time.
  • PyO3 bindings wrap the Rust v2 runtime.
  • Medium install friction: compiled wheels available for Python 3.12–3.14 across Linux (x86_64, ARM64), macOS (ARM64), and Windows (x86_64).

License · maintenance · safety

LGPL-3.0-or-later (copyleft) — Licensed under LGPL-3.0-or-later (copyleft). Any modifications to the library itself must be shared under the same license; applications using it as a dependency are not restricted, but redistribution of modified library code requires source disclosure.

last release 2026-08-02 (12 days) · last repo commit 2026-08-14 · 25,494 stars

0 known vulnerabilities (OSV.dev, 2026-08-14) · 253,604 downloads/mo, #8,514 on PyPI

Verify before relying

pip install nautilus-trader

from nautilus_trader.trading.strategy import Strategy
from nautilus_trader.core.data import Bar

# Define strategy logic in Python; engine handles execution deterministically
  • Specific performance benchmarks (latency, throughput) for live execution vs. backtesting
  • Details on supported order types beyond IOC, FOK, GTC, GTD, DAY, AT_THE_OPEN, AT_THE_CLOSE
  • Current list of production-ready venue adapters and their stability status
  • Persistence and recovery semantics when using optional Redis-backed state
Same gist for agents: .md · .json

What it is and what it does

NautilusTrader bridges research and production trading by providing a single Rust-native event-driven runtime that executes strategies identically in both backtesting and live environments. Python serves as the control plane for strategy logic, configuration, and orchestration, while the Rust core handles deterministic execution, order management, and multi-venue coordination. The system supports research workflows using historical tick, bar, and order-book data at nanosecond resolution, then deploys the same strategy code to live trading without reimplementation.

The engine is asset-class-agnostic and connects to any venue via modular REST or WebSocket adapters. It includes advanced order types (IOC, FOK, GTC, GTD, DAY, AT_THE_OPEN, AT_THE_CLOSE), contingency orders (OCO, OUO, OTO), execution instructions (post-only, reduce-only, icebergs), and multi-venue market-making. The Rust core provides type and thread safety, optional Redis-backed persistence, and performance sufficient for AI agent training. Python bindings are provided via PyO3; the legacy Cython v1 core remains supported during the v2 release-candidate phase.

Use it for

  • Backtest multi-venue strategies on historical tick and bar data, then deploy the same code live without code changes
  • Build market-making or cross-venue arbitrage strategies that coordinate orders across multiple exchanges simultaneously
  • Train reinforcement learning or evolutionary strategy agents using the engine's fast deterministic runtime
  • Integrate custom adapters for new venues or data providers via the modular adapter interface
  • Run production trading systems with Redis-backed state persistence and type-safe Rust execution

Worth the install?

AI-flagged interpretation of the facts on this page. Verify before relying on it.

With conditions

Yes, with conditions.

NautilusTrader is worth installing if you need research-to-production parity in algorithmic trading and are comfortable with Python 3.12–3.14 and LGPL-3.0-or-later licensing. Active maintenance, no known vulnerabilities, and broad platform support make it production-ready. The medium install friction (11 dependencies, compiled wheels) is typical for data-heavy trading systems. Not suitable if you require Python <3.12 or cannot accept copyleft licensing on library modifications.

Install

nautilus-trader on PyPI

Before you install

Medium install friction: compiled wheels available for Python 3.12–3.14 across Linux (x86_64, ARM64), macOS (ARM64), and Windows (x86_64). Active maintenance with latest release 12 days old and 25494 repository stars. Requires 11 runtime dependencies including numpy, pandas, pyarrow, and uvloop.

Requires Python 3.12–3.14; Rust 1.97.1 is used internally but not required at install time. PyO3 bindings wrap the Rust v2 runtime.

License in practice

Licensed under LGPL-3.0-or-later (copyleft). Any modifications to the library itself must be shared under the same license; applications using it as a dependency are not restricted, but redistribution of modified library code requires source disclosure.

Quickstart

pip install nautilus-trader

from nautilus_trader.trading.strategy import Strategy
from nautilus_trader.core.data import Bar

# Define strategy logic in Python; engine handles execution deterministically

Verify before relying

  • Specific performance benchmarks (latency, throughput) for live execution vs. backtesting
  • Details on supported order types beyond IOC, FOK, GTC, GTD, DAY, AT_THE_OPEN, AT_THE_CLOSE
  • Current list of production-ready venue adapters and their stability status
  • Persistence and recovery semantics when using optional Redis-backed state

Package facts

LicenseLGPL-3.0-or-later copyleft
Python supportSupports the current Python release <3.15,>=3.12
Install frictionMedium. Platform-specific wheel
Runtime dependencies
11 packages
clickfsspecmsgspecnumpypandasportionpyarrowpytztqdmtzdatauvloop
MaintenanceActively maintained 12 days since the last release
Last repo commit
First released
Downloads253,604 / month, #8,514 on PyPI 30-day window, as of 2026-08-14
Known vulnerabilitiesNone known OSV.dev, checked 2026-08-14
Classifiers
Development Status :: 4 - BetaIntended Audience :: DevelopersIntended Audience :: Financial and Insurance IndustryIntended Audience :: Science/ResearchLicense :: OSI Approved :: GNU Lesser General Public License v3 or later (LGPLv3+)Operating System :: OS IndependentProgramming Language :: Python :: 3Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: Office/Business :: FinancialTopic :: Office/Business :: Financial :: InvestmentTopic :: Scientific/EngineeringTopic :: Software Development :: LibrariesTopic :: Software Development :: Libraries :: Python Modules

Evidence: nautilus_trader-1.231.0-cp312-cp312-macosx_26_0_arm64.whl; nautilus_trader-1.231.0-cp312-cp312-manylinux_2_35_aarch64.whl; nautilus_trader-1.231.0-cp312-cp312-manylinux_2_35_x86_64.whl; nautilus_trader-1.231.0-cp312-cp312-win_amd64.whl; nautilus_trader-1.231.0-cp313-cp313-macosx_26_0_arm64.whl; nautilus_trader-1.231.0-cp313-cp313-manylinux_2_35_aarch64.whl; nautilus_trader-1.231.0-cp313-cp313-manylinux_2_35_x86_64.whl; nautilus_trader-1.231.0-cp313-cp313-win_amd64.whl; nautilus_trader-1.231.0-cp314-cp314-macosx_26_0_arm64.whl; nautilus_trader-1.231.0-cp314-cp314-manylinux_2_35_aarch64.whl; nautilus_trader-1.231.0-cp314-cp314-manylinux_2_35_x86_64.whl; nautilus_trader-1.231.0-cp314-cp314-win_amd64.whl

Tags

Capabilities
algorithmic trading enginebacktesting frameworkmulti-venue trading systemrust trading platformevent-driven tradingresearch to production tradingquantitative trading framework
Topics
trading-enginebacktestingrust-native

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See also betfairlightweight · freqtrade · backtesting · ccxt · backtrader · lumibot · vectorbt · quantconnect-stubs · ib_async · python-kraken-sdk