nautechsystems/nautilus_trader
Production-grade Rust-native trading engine with deterministic event-driven architecture
Build with Backblaze B2
SDKs, agent skills, IDE extensions, and reference pipelines from Backblaze Labs. All open source.
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Where it stands
Rank, growth pace and release cadence relative to everything GStars tracks.
5 releases in the last 90 days, the latest 9 days ago, with 227 contributors on GitHub.
Peaked at #2 in the weekly ranking the week of Aug 16, its best in the past year.
Use Cases & Benefits
- Provides a production-grade, Rust-native algorithmic trading engine with a deterministic event-driven architecture for backtesting and live trading.
- Ensures parity between backtesting and live trading by allowing identical Python-native strategy implementations with high performance and type safety.
- Use for developing and deploying high-frequency trading strategies across multiple asset classes including crypto, equities, futures, and options.
- Use for integrating diverse market data and trading venues via modular adapters supporting REST APIs and WebSocket feeds for multi-venue trading.
- Use for training AI trading agents with fast backtesting capabilities suitable for reinforcement learning and evolutionary strategies.
Should you depend on it?
License terms, project hygiene and risks, from GitHub metadata and GStars' snapshots.
License
LGPL 3.0
Weak copyleft: changes to the licensed code itself must be shared when you distribute it, but code that merely uses it can stay under your own license.
Community files
- README: present
- Contributing guide: present
- Code of conduct: present
Watch-outs
No watch-outs from GStars' checks.
General guidance from repository metadata, not legal advice. How we score
From the README
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.
NautilusTrader's design, architecture, and implementation philosophy prioritizes 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 API or WebSocket feed able to be integrated via modular adapters. It supports high-frequency trading across a wide range of asset classes and instrument types including FX, Equities, Futures, Options, Crypto, DeFi, and Betting, enabling seamless operations across multiple venues simultaneously.
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.
Daily snapshots
Stars gained per day, and how forks and open issues moved, from GStars' daily snapshots
+5,884 stars over 87 days across 42 snapshot days; best day +443 on Aug 18.
Forks
4.2K+899
Open issues
165+62
Compared with peers in AI
Fastest-growing repositories GStars tracks in this cohort for the week of Nov 8, by 7-day star growth
| Repository | Stars | +7d | Health | Last release |
|---|---|---|---|---|
| D4Vinci/Scrapling | 101.1K | +6.3K | 75 | 6 days ago |
| thedotmack/claude-mem | 115.8K | +4.7K | 75 | today |
| heygen-com/hyperframes | 82.3K | +4.0K | 75 | today |
| harry0703/MoneyPrinterTurbo | 142.0K | +3.8K | 75 | 3 days ago |
| github/spec-kit | 149.2K | +2.6K | 75 | today |
| nautechsystems/nautilus_trader | 31.3K | — | 65 | 9 days ago |
| Category median | 115.8K | +4.0K | 75 | — |
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