Getting started

1. Install

Set up a Python 3.12-3.14 environment and install the package:

bash

pip install -U nautilus_trader

See the Installation guide for platform support, source builds, and Docker images.

2. Run the quickstart

The Quickstart runs your first backtest in five minutes using synthetic data. No downloads, no catalog setup.

The getting-started tutorials all use a simple EMA crossover strategy. This is deliberate. The trading logic is not the focus. These tutorials teach how the engine operates: data loading, venue simulation, order lifecycle, and reporting. The tutorials introduce different strategies (mean reversion, order book imbalance, grid market making) once the engine mechanics are clear.

3. Choose your path

Backtesting - learn the two API levels below, then work through the tutorials for strategy pattern walkthroughs.
Live trading - see the Configure a live trading node how-to and Integrations for supported venues.
Data workflows - see the how-to guides for loading external data and setting up the Parquet data catalog.
Building adapters - see the Developer guide.

Backtesting API levels

NautilusTrader provides two API levels for backtesting:

API level	Entry point	Best for
Low‑level API	`BacktestEngine`	Direct component access, library development
High‑level API	`BacktestNode`	Production workflows, easier transition to live trading (recommended)

The high‑level API requires a Parquet‑based data catalog. The low‑level API works with in‑memory data but has no live‑trading path.

:::warning[One node per process] Running multiple BacktestNode or TradingNode instances concurrently in the same process is not supported due to global singleton state. Sequential execution with proper disposal between runs is supported.

See Processes and threads for details. :::

See the Backtesting concept guide for help choosing an API level.

Examples in the repository

The online documentation shows a subset of examples. For the full set, see the repository on GitHub:

Directory	Contains
examples/	Fully runnable, self‑contained Python examples
docs/tutorials/	Tutorials demonstrating common workflows
docs/concepts/	Concept guides with code snippets illustrating key features
nautilus_trader/examples/	Pure‑Python examples of strategies, indicators, and exec algos
tests/unit_tests/	Unit tests covering core functionality and edge cases

Running in Docker

A self-contained dockerized Jupyter notebook server provides the fastest way to try NautilusTrader with no local setup. Deleting the container deletes any data.

bash

# Pull the latest image
docker pull ghcr.io/nautechsystems/jupyterlab:nightly --platform linux/amd64

# Run the container
docker run -p 8888:8888 ghcr.io/nautechsystems/jupyterlab:nightly

Then open http://localhost:8888 in your browser.

:::warning Examples use log_level="ERROR" because Nautilus logging exceeds Jupyter's stdout rate limit, causing notebooks to hang at lower log levels. :::