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.
NautilusTrader is AI/ML first, designed to deploy models for algorithmic trading strategies developed using the Python ecosystem - within a highly performant and robust Python native environment.
The platform aims to be universal - with any REST/WebSocket/FIX API able to be integrated via modular adapters. Thus the platform can handle high-frequency trading operations for any asset classes including FX, Equities, Futures, Options, CFDs, Crypto and Betting - across multiple venues simultaneously.
- Fast: C-level speed and type safety provided through Cython. Asynchronous networking utilizing uvloop.
- Reliable: Redis backed performant state persistence for live implementations.
- Flexible: Any REST/WebSocket/FIX API can be integrated into the platform.
- Backtesting: Multiple instruments and strategies simultaneously with historical quote tick, trade tick, bar and order book data.
- Multi-venue: Multiple venue capabilities facilitate market making and statistical arbitrage strategies.
- AI Agent Training: Backtest engine fast enough to be used to train AI trading agents (RL/ES).
One of the key value propositions of NautilusTrader is that it addresses the challenge of keeping the research/backtest environment consistent with the production live trading environment.
Normally research and backtesting may be conducted in Python (or other suitable language), with trading strategies traditionally then needing to be reimplemented in C++/C#/Java or other statically typed language(s). The reasoning here is to enjoy the performance a compiled language can offer, along with the tooling and support which has made these languages historically more suitable for large enterprise systems.
The value of NautilusTrader here is that this re-implementation step is circumvented, as the platform was designed from the ground up to hold its own in terms of performance and quality.
Python has simply caught up in performance (via Cython offering C-level speed) and general tooling, making it a suitable language for building a large system such as this. The benefit being that a Python native environment can be offered, suitable for professional quantitative traders and hedge funds.
Python was originally created decades ago as a simple scripting language with a clean straight forward syntax. It has since evolved into a fully fledged general purpose object-oriented programming language. Not only that, Python has become the de facto lingua franca of data science, machine learning, and artificial intelligence.
The language out of the box is not without its drawbacks however, especially in the context of implementing large systems. Cython has addressed a lot of these issues, offering all the advantages of a statically typed language, embedded into Pythons rich ecosystem of software libraries and developer/user communities.
Cython is a compiled programming language that aims to be a superset of the Python programming language, designed to give C-like performance with code that is written mostly in Python with optional additional C-inspired syntax.
The project heavily utilizes Cython to provide static type safety and increased performance for Python through C extension modules. The vast majority of the production code is actually written in Cython, however the libraries can be accessed from both pure Python and Cython.
- Reliability
- Testability
- Performance
- Modularity
- Maintainability
- Deployability
NautilusTrader is designed to work with modular adapters which provide integrations with data publishers and/or trading venues (exchanges/brokers).
Refer to the integrations documentation.
To install the latest binary wheel (or sdist package) from PyPI:
pip install -U nautilus_trader
Refer to the installation guide.
Examples of both backtest and live trading launch scripts are available in the examples directory.
We aim to make the developer experience for this hybrid codebase of Cython and Python as pleasant as possible. Please refer to the developer guide for helpful information.
Involvement from the trading community is a goal for this project. All help is welcome! Developers can open issues on GitHub to discuss proposed enhancements/changes, or to make bug reports.
Refer to the CONTRIBUTING.md for further information.
Please make all pull requests to the develop branch.
NautilusTrader is currently following a bi-weekly release schedule.
NautilusTrader is licensed under the LGPL v3.0 as found in the LICENSE file.
Contributors are also required to sign a standard Contributor License Agreement (CLA), which is administered automatically through CLA Assistant.
Copyright (C) 2015-2021 Nautech Systems Pty Ltd. All rights reserved.
