Using TensorTrade-NG with JupyterLab¶

Explore the power of TensorTrade-NG, a flexible framework designed to facilitate the development of reinforcement learning algorithms in financial trading environments. This tutorial will guide you through setting up your environment in JupyterLab, ensuring you have everything you need to start experimenting with custom trading strategies.

Prerequisites¶

Python 3.12
JupyterLab

Preparation¶

Create a new Python virtual environment and activate it
```
cd /my/project/directory
python -m venv venv
source venv/bin/activate
```
This step isolates your TensorTrade-NG environment, ensuring no conflicts with other Python packages.
Install TensorTrade-NG with JupyterLab extra packages and create a new kernel for Jupyter
```
pip install "tensortrade-ng[jlab]"
ipython kernel install --user --name tensortrade-ng
```
By including the jlab extras, you ensure compatibility with JupyterLab and streamline your development process by having the necessary tools and dependencies readily available.
Restart JupyterLab and use the new kernel
After restarting JupyterLab, you should see the new kernel “tensortrade-ng” available. Select it to begin your TensorTrade-NG projects. This kernel is pre-configured with all the necessary dependencies to make your first steps in TensorTrade-NG effortless.

What You Learned¶

In this tutorial, you learned how to set up a dedicated Python environment for TensorTrade-NG, ensuring that your development environment is clean, organized, and tailored for machine learning projects. By installing TensorTrade-NG with JupyterLab extras, you ensured that your environment is equipped to handle the interactive development of financial trading strategies using reinforcement learning.