TensorTrade is still in Alpha, meaning it should not be used in production systems yet, and it may contain bugs.
TensorTrade is an open source Python framework for building, training, evaluating, and deploying robust trading algorithms using reinforcement learning. The framework focuses on being highly composable and extensible, to allow the system to scale from simple trading strategies on a single CPU, to complex investment strategies run on a distribution of HPC machines.
Under the hood, the framework uses many of the APIs from existing machine learning libraries to maintain high quality data pipelines and learning models. One of the main goals of TensorTrade is to enable fast experimentation with algorithmic trading strategies, by leveraging the existing tools and pipelines provided by
Every piece of the framework is split up into re-usable components, allowing you to take advantage of the general use components built by the community, while keeping your proprietary features private. The aim is to simplify the process of testing and deploying robust trading agents using deep reinforcement learning, to allow you and I to focus on creating profitable strategies.
The goal of this framework is to enable fast experimentation, while maintaining production-quality data pipelines.
Inspired by Keras' guiding principles.
User friendliness. TensorTrade is an API designed for human beings, not machines. It puts user experience front and center. TensorTrade follows best practices for reducing cognitive load: it offers consistent & simple APIs, it minimizes the number of user actions required for common use cases, and it provides clear and actionable feedback upon user error.
Modularity. A trading environment is a conglomeration of fully configurable modules that can be plugged together with as few restrictions as possible. In particular, exchanges, feature pipelines, action schemes, reward schemes, trading agents, and performance reports are all standalone modules that you can combine to create new trading environments.
Easy extensibility. New modules are simple to add (as new classes and functions), and existing modules provide ample examples. To be able to easily create new modules allows for total expressiveness, making TensorTrade suitable for advanced research and production use.
TensorTrade Hall of Fame
TensorTrade is entirely community funded. We appreciate all of our great sponsors! If you would like to be featured on our Hall of Fame, or sponsor the framework in any other way, visit the Sponsorship section below.
You can get started testing on Google Colab or your local machine, by viewing our many examples
TensorTrade requires Python >= 3.6 for all functionality to work as expected.
pip install -r requirements.txt
To run the commands below, ensure Docker is installed. Visit https://docs.docker.com/install/ for more information.
Run Jupyter Notebooks
To run a jupyter notebook in your browser, execute the following command and visit the
http://127.0.0.1:8888/?token=... link printed to the command line.
To build the HTML documentation, execute the following command.
Run Test Suite
To run the test suite, execute the following command.
You can ask questions and join the development discussion:
If you would like to support this project financially, there are a few ways you can contribute. Your contributions are greatly appreciated and help to keep TensorTrade maintained and always improving.
Github Sponsors: https://github.com/sponsors/notadamking
All Github Sponsors donations are matched 1:1 by Github up to $5,000!
All Gitcoin Grants donations go directly towards funding our Gitcoin issues.
Contributions are encouraged and welcomed. This project is meant to grow as the community around it grows. Let me know on Discord in the #suggestions channel if there is anything that you would like to see in the future, or if there is anything you feel is missing.
Working on your first Pull Request? You can learn how from this free series How to Contribute to an Open Source Project on GitHub