QuantConnect Research Project

Currently we have two ways to use QuantConnect research notebooks, you can either install and run locally or just use our docker image (Recommended).

The up to date docker image is available at quantconnect/research. You can pull this image with docker pull quantconnect/research.

Using the Docker Image

Starting the Container

The docker image we created can be started using the included .bat/.sh file in this directory (Lean/Research). These scripts take care of all the work required to get the notebook container setup and started for use. Including launching a browser to the notebook lab environment for you.

From a terminal launch the run_docker_notebook.bat/.sh script; there are a few options on how to launch this:

1. Launch with no parameters and answer the questions regarding configuration (Press enter for defaults) ex: ./run_docker_notebook.bat

   *   Enter docker image [default: quantconnect/research:latest]:
   *   Enter absolute path to Data folder [default: ~yourpathtolean~\Lean\Data\]:
   *   Enter absolute path to store notebooks [default: ~yourpathtolean~\Lean\Research\Notebooks]:
   

2. Using the docker.cfg to store args for repeated use; any blank entries will resort to default values! ex: ./run_docker_notebook.bat docker.cfg

    IMAGE=quantconnect/research:latest
    DATA_DIR=
    NOTEBOOK_DIR=
   

3. Inline arguments; anything you don't enter will use the default args! ex: ./run_docker.bat IMAGE=quantconnect/research:latest

   • Accepted args for inline include all listed in the file docker.cfg

Once the docker image starts, the script will attempt to open your browser to the Jupyter notebook web app, if this fails open your browser and go to localhost:8888

C# Notebook

When using C# for research notebooks it requires that you load our setup script CSX file QuantConnect.csx into your notebook. This will load our QuantConnect libraries into your C# Kernel. In this setup, the file is one directory above the notebooks dir. Be sure to use the following line in your first cell to load in this csx file:

load "../QuantConnect.csx"

After this the environment is ready to use; take a look at our reference notebook KitchenSinkCSharpQuantBookTemplate.ipynb for an example of how to use our QuantBook interface!

Python Notebook

With Python we have a setup script that will automatically load QuantBooks libraries into the Python kernel so there is no need to import them.

You notebook is ready to use; take a look at our reference notebook KitchenSinkQuantBookTemplate.ipynb for an example of how to use our QuantBook interface!

Using the Web Api from Notebook

Both of our setup scripts for Python & C# include a instantiated Api object under the variable name api. Before you can use this api object to interact with the cloud you must edit your config in the root of your Notebook directory. Once this has been done once, it does not need to be done again.

In config.json add the following entries with your respective values

job-user-id: 12345, // Your id here
api-access-token: "token13432", // Your api token here

Once this has been done, you may restart your kernel and begin to use the api variable. Reference our examples mentioned above for practical uses of this object.

Shutting Down the Notebook Lab

When you are done with the research environment be sure to stop the container with either Docker's dashboard or through the Docker CLI with docker kill LeanResearch.

Build a new image

For most users this will not be necessary, simply use docker pull quantconnect/research to get the latest image.

docker build -t quantconnect/research - < DockerfileJupyter will build a new docker image using the latest version of lean. To build from particular tag of lean a build arg can be provided, for example --build-arg LEAN_TAG=8631.

Running Jupyter Locally

Note: we recommend using the above approach with our Docker container, where the setup and evironment is tested and stable.

Before we enable Jupyter support, follow Lean installation and Python installation to get LEAN running Python algorithms in your machine.

1. Installation:

1. Install JupyterLab:

    pip install jupyterlab

2. Install QuantConnect Python API

   pip install quantconnect

3. Linux and macOS: Copy pythonnet binaries for jupyter

 cp Lean/Launcher/bin/Debug/jupyter/* Lean/Launcher/bin/Debug

2. Run Jupyter:

1. Update the config.json file in Lean/Launcher/bin/Debug/ folder

   "composer-dll-directory": ".",

2. Run Jupyter from the command line

    cd Lean/Launcher/bin/Debug
    jupyter lab