Contents

tensortrade 1.0.4

0

TensorTrade: A reinforcement learning library for training, evaluating, and deploying robust trading

TensorTrade: A reinforcement learning library for training, evaluating, and deploying robust trading agents.

Stars: 5959, Watchers: 5959, Forks: 1190, Open Issues: 41

The tensortrade-org/tensortrade repo was created 6 years ago and the last code push was Yesterday.
The project is extremely popular with a mindblowing 5959 github stars!

How to Install tensortrade

You can install tensortrade using pip

pip install tensortrade

or add it to a project with poetry

poetry add tensortrade

Package Details

Author
Adam King <[email protected]>, Matthew Brulhardt <[email protected]>
License
Apache 2.0
Homepage
https://github.com/tensortrade-org/tensortrade
PyPi:
https://pypi.org/project/tensortrade/
GitHub Repo:
https://github.com/tensortrade-org/tensortrade

Classifiers

  • Office/Business/Financial
  • Office/Business/Financial/Investment
  • Scientific/Engineering/Artificial Intelligence
  • Scientific/Engineering/Information Analysis
  • Software Development/Libraries
  • Software Development/Libraries/Python Modules
  • System/Distributed Computing
No  tensortrade  pypi packages just yet.

Errors

A list of common tensortrade errors.

Code Examples

Here are some tensortrade code examples and snippets.

Related Packages & Articles

pyfolio 0.9.2

pyfolio is a Python library for performance and risk analysis of financial portfolios

PyAlgoTrade 0.20

PyAlgoTrade is an event driven algorithmic trading Python library.

finta 1.3

Common financial technical indicators implemented in Pandas.

pandas-ta 0.4.71b0

A Comprehensive Python 3 Technical Analysis Library with Pandas Dataframe Extension for Quantitative Researchers, Traders, and Investors.

ta 0.11.0

ta is a Python module that provides a technical analysis library, designed to enable feature engineering from financial time series datasets. It is built on the pandas and numpy libraries and offers a wide range of indicators such as volume, volatility, trend, and momentum indicators. ta is designed for Python developers working in the financial sector, making it a valuable asset in the field of Financial Software and Fintech Solutions, particularly for those developing trading algorithms or investment strategies.