Quant research & backtesting · Open-source community (NautilusTrader Ltd.)

NautilusTrader

Open-source, event-driven algorithmic trading platform in Python/Rust for high-performance backtesting and live deployment.

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NautilusTrader is an open-source algorithmic trading platform designed so that the same strategy code runs unchanged in backtesting and live trading, avoiding the common gap between research and production logic. Its core engine is written in Rust with a Python API, aimed at nanosecond-resolution event processing for high-frequency and systematic strategies across equities, FX, futures, options and crypto. It supports multiple venue adapters for live market data and execution, parallelized backtesting across large historical datasets, and a modular architecture for custom data types and order routing. It is self-hosted, run either locally or on infrastructure the user manages, with no vendor cloud or subscription layer.

At a glance

Vendor Open-source community (NautilusTrader Ltd.)
Pricing model Open source + paid options
Free tier Yes
Deployment Self-hosted
Open source Yes (LGPL-3.0)
Best for Systematic and high-frequency traders who need backtest and live-trading code to be identical.

Pricing

Free, open-source platform with no paid tier or hosted offering.

Pricing has not been verified yet — see the vendor's site.

Features

  • Rust core engine with Python strategy API
  • Identical strategy code for backtest and live trading
  • Nanosecond-resolution event processing
  • Parallelized backtesting across large datasets
  • Multiple exchange/broker venue adapters
  • Support for equities, FX, futures, options and crypto

Integrations

Profile last reviewed September 21, 2026

Head to head

Alternatives

QuantConnect Cloud algorithmic trading platform built on the open-source LEAN engine, with research notebooks and live deployment. Backtrader Open-source Python backtesting and live-trading framework for building and evaluating trading strategies. vectorbt Open-source Python library for vectorized backtesting, using NumPy/pandas array operations to test thousands of parameter combinations fast.

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Terms to know

Time series forecasting Predicting future values of a metric using patterns found in its own historical sequence over time. Algorithmic trading Using computer programs to automatically place and manage trade orders based on predefined rules. Backtesting Testing a trading or investment strategy against historical data to see how it would have performed.

Related guides

How to backtest a trading strategy Data quality, look-ahead and survivorship bias, realistic costs, and the evaluation metrics that separate a real edge from an overfit curve. How to choose a quant research and backtesting platform The choice is really about who owns the code — a library you control, a desktop license, or a hosted platform with data included.