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Documentation Index

Fetch the complete documentation index at: https://docs.quanux.org/llms.txt

Use this file to discover all available pages before exploring further.

QuanuX supports three installation paths depending on your workflow. Conda is the recommended approach for most users — it handles Python dependencies, manages your environment alongside data science tools, and installs from the official QuanuX channel with no manual dependency resolution. If you’re already working in a pip-based environment or want to contribute to the codebase, the venv and source build paths are fully supported.

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Prerequisites

Before installing, confirm you have the following available on your system:
DependencyVersionRequired for
Python3.10+Server and strategy runtime
Node.jsv20+Cockpit client
pnpmLatestCockpit client build and dev
Rust & CargoStableTauri desktop app (dev builds only)
Linux is the primary target platform for execution nodes. The server and CLI run on Linux and macOS. The Tauri desktop cockpit runs on Linux, macOS, and Windows. If you’re setting up a production execution node, use a Linux server.
QuanuX is the first quantitative trading platform to offer an official Conda channel. Installing through Conda places QuanuX in a managed environment alongside your Python data science stack — pandas, polars, numpy, DuckDB, and more — and lets you launch QuanuX directly from Anaconda Navigator.
1

Add the QuanuX and conda-forge channels

conda config --add channels conda-forge
conda config --add channels QuanuX
2

Install QuanuX

conda install quanux
This creates the quanux environment with Python 3.11 and all required server-side dependencies.
3

Activate the environment

conda activate quanux
Once the QuanuX channel is added, you can manage upgrades with conda update quanux. The Conda environment also gives you direct access to ML tools like PyTorch without isolation conflicts.

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Option 2: pip with a virtual environment

Use this path if you’re integrating QuanuX into an existing pip-managed project or prefer not to use Conda.
1

Create and activate a virtual environment

python3 -m venv .venv
source .venv/bin/activate
2

Install Python dependencies

pip install -r requirements.txt
This installs the full server dependency set, including FastAPI, uvicorn, keyring, the Rithmic async client, NATS, MCP, and AI provider SDKs (OpenAI, Google Generative AI).
3

Install Node.js dependencies

pnpm install
The requirements.txt is the pip fallback. Conda is preferred because it manages native binary dependencies (DuckDB, numpy, polars) more reliably. If you encounter compilation errors with pip, switch to the Conda path.

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Option 3: Source build

Use the source build path if you want to contribute to QuanuX, access unreleased features, or build the Tauri desktop app from source.
1

Clone the repository

git clone https://github.com/quantdiy/QuanuX.git
cd QuanuX
2

Set up the Python environment

conda env create -f environment.yml
conda activate quanux
3

Install Node.js dependencies

pnpm install
4

Build the Tauri desktop app (optional)

To run the desktop cockpit in development mode, you need Rust and Cargo installed. Then:
pnpm -C client/desktop/tauri-app tauri dev
To produce a production desktop build:
pnpm -C client/desktop/tauri-app tauri build

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Platform notes

Linux is the primary target for execution nodes. All bare-metal execution features — including Solarflare EF_VI kernel bypass, CPU core pinning, and quanuxctl nest deployments — require Linux. Production execution nodes must be compiled natively; cross-compilation and Docker are not supported for execution nodes. macOS is fully supported for running the server, cockpit, and CLI locally. It is the recommended development environment on Apple hardware. Windows is supported for the Tauri desktop cockpit only. Server and CLI functionality on Windows is not officially supported.

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Verify your installation

After installing, confirm the server starts correctly: 
uvicorn server.app.main:app --host 0.0.0.0 --port 8080
You should see uvicorn report that it’s listening on 0.0.0.0:8080. If you see import errors, check that your Python environment is activated and all dependencies from requirements.txt or environment.yml are installed.

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Next steps

Once QuanuX is installed, follow the Quickstart to configure your secrets, launch the cockpit, and run your first backtest.