# nautechsystems/nautilus_trader

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

Repository: https://github.com/nautechsystems/nautilus_trader
Canonical: https://ross.abutalabs.com/products/nautilus_trader
Homepage: https://nautilustrader.io
Language: Rust
License: LGPL-3.0
License Family: copyleft
Topics: algorithmic-trading-engine, trading-platform, crypto-trading, artificial-intelligence, machine-learning, trading, python, equity-trading, forex, futures-trading, sports-betting, rust, options-trading
Last push: 2026-08-27T00:16:01+00:00

## Health v2 (maintenance only)
Score: 98/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 96, longevity 100
- inputs: {"age_days": 2991, "days_push": 7, "days_rel": 31, "gap_med": 17, "n_releases_24m": 32}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 27886, forks 3600 (observed 2026-08-28T04:11:47.998213+00:00)

## What it is
NautilusTrader is an open-source, production-grade algorithmic trading platform with a Rust-native core and Python API, built on a deterministic event-driven architecture with nanosecond resolution. It supports multi-asset, multi-venue trading with identical strategy code running in both backtesting and live trading environments.

## Use cases
- backtest trading strategies on historical market data
- deploy algorithmic trading strategies to live markets
- build crypto trading bots
- simulate order book execution with configurable latency and fill models
- run high-throughput parameter sweeps for strategy research
- trade multiple asset classes from a single engine
- write trading strategies in Python on a Rust core
- replay and analyze historical market data

## When to choose
- you need research-to-live parity so backtested strategies run unchanged in production
- you require nanosecond-resolution, deterministic event-driven backtesting
- you trade multiple asset classes (equities, futures, forex, crypto, options) across venues
- you want Rust-level performance for latency-sensitive execution with a Python strategy API
- you need granular order book, tick, and bar data handling with a Parquet data catalog

## When to avoid
- you only need simple charting or manual trading rather than systematic/algorithmic trading
- you want a plug-and-play bot with no programming
- your strategies are simple enough that a lighter-weight Python backtester suffices
- you need a turnkey hosted solution without self-managed infrastructure (consider the vendor's paid offerings)
- LGPL-3.0 licensing is incompatible with your distribution model

## Facets
- artifact type: framework
- maturity: stable
- function: trading, simulation, streaming, webhook, serialization
- domain: fintech, machine-learning
- platform: rust, python, windows, cross-platform
- tags: algorithmic-trading, backtesting, event-driven, multi-asset, multi-venue, low-latency, order-book, market-making, crypto-trading, forex, futures, options, equities, sports-betting, parquet-data-catalog, research-to-live-parity, nanosecond-resolution, quantitative-trading, quantitative-finance, cryptocurrency, linux, macos

## Member repositories
- nautechsystems/nautilus_trader (main) score 98

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:11:47.998213+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T16:54:29.240707+00:00, confidence not recorded.
  - readme: https://github.com/nautechsystems/nautilus_trader (fetched 2026-08-28T04:11:47.998213+00:00, sha 8748e8a4f7a2)
  - homepage: https://nautilustrader.io (fetched 2026-08-29T07:51:27.452637+00:00, sha 86d0ec3e7518)
  - site_page: https://nautilustrader.io/about (fetched 2026-08-29T07:51:27.462124+00:00, sha d60ecea12877)
  - site_page: https://nautilustrader.io/docs (fetched 2026-08-29T07:51:27.467422+00:00, sha 07b551383546)
  - site_page: https://nautilustrader.io/docs/latest/concepts/data (fetched 2026-08-29T07:51:27.469527+00:00, sha 629f0312fa8f)
  - site_page: https://nautilustrader.io/docs/latest/concepts/strategies (fetched 2026-08-29T07:51:27.472711+00:00, sha 94c65a10eff0)
  - site_page: https://nautilustrader.io/docs/latest/concepts/backtesting (fetched 2026-08-29T07:51:27.475209+00:00, sha d0fd380d9ce3)
  - site_page: https://nautilustrader.io/docs/latest/concepts/configuration (fetched 2026-08-29T07:51:27.476826+00:00, sha a0159b838e99)
  - site_page: https://nautilustrader.io/docs/latest/concepts/live (fetched 2026-08-29T07:51:27.478554+00:00, sha ba499e5433e4)
  - registry_crates: https://crates.io/api/v1/crates/nautilus_trader (fetched 2026-08-29T07:51:27.480735+00:00, sha 2af75faf3db4)
- Data as of 2026-08-30T08:39:29.467469+00:00.