Pandas TA

A Comprehensive Python Technical Analysis Library for Traders and Investors

Description

Pandas Technical Analysis (Pandas TA) is a Popular Comprehensive and Easy to Use Python 3 Technical Analysis Library with primary dependencies: numpy for accuracy, numba for performance, and pandas for brevity and bulk processing.

Features

• A Free & Open Source library with a LARGE flat library structure similar to TA Lib.
  • 150+ indicators and utilities.
  • 60+ Candelstick Patterns with TA Lib installed.
• Performance improvements with numba
• A Pandas DataFrame Extension named "ta", that provides additional properties, methods, and indicators to simplify time series calculations of ohlcv columns.
• Indicator Equivalence
  • Primarily: TA Lib
  • Secondarily: TradingView
  • Tertiary: Sierra Chart | MQL5 | FM Labs | Pro Real Code | User 42 | Technical Traders | etc
• And more ...

About Churn

The state and status of this library and it's slowversioning increments seem to be having an impact on it's long term viability and support.

This adapted variation from Intercooler JS clarifies why Pandas TA does not have frequent versioning.

"This is not because it is dead, but rather because it is (mostly) right: the basic idea is right, and the implementation at least right enough.

Thus there will not be constant activity and churn on the project, but rather a stewardship relationship, where the main goal now is to not screw it up. Documentation and testing will be improved, features/indicators will be added/removed as needed, but there will be no massive rewrite or constant updating. This is in contrast with the software industry in general, which often has comical levels of churn."

Support :broken_heart:

A big thank you to all current and past sponsors, whose generous support has been and continues to be essential to the success of the project! :pray:

Despite what the usage metrics indicate, current and past levels of support are unsustainable for maintaining and improving the library. Unless significant additional support is provided by July 1st, 2026, this widely used library will be archived. Support through contributions, donations & sponsorships is necessary to ensure the project's continued success and development.

:stop_sign: Please* DO NOT email me personally with Pandas TA Bugs, Issues or Feature Requests that are best handled via Github Issues.

Installation

Before installing, review the installation requirements.txt to ensure your system is set up properly.

From pypi

$ pip install pandas-ta

From Github

$ pip install git+https://github.com/twopirllc/pandas-ta

Development Version

$ pip install -U git+https://github.com/twopirllc/pandas-ta.git@development

• The development version includes numerous bug fixes, speed improvements and better documentation since release, 0.3.14b.

Local Installation

Click on green <> Code button to download the source zip and unzip in your application directory. Then perform a local install.

Bugs, Indicators and Feature Requests

• Some bugs and features may already be fixed or implemented in the development version. Please try the development version first before making an issue!
• If the development version does not resolve the bug, search both Open and Closed Issues before opening a new Issue.

Contributors

Thank you all for your help! It has been and continues to be integral to this project. :sunglasses:

Made with contrib.rocks.

Contributing

Anyone who wishes to contribute to this project - be it documentation, features, bug fixes, code cleanup, testing, open issue help, or code reviews - are encouraged to do so. These contributions are crucial to the longevity of this project.

Based on your skill level, select from the following issue topics:

Good First Issue | Bugs | Help Wanted | Enhancements

Code of Conduct

See the Code of Conduct regarding handling yourself and dealing with others in and around the Pandas TA community. Ensure you completely understand the "common sense" Guidelines outlined there.

License

MIT

Copyright © 2024 - Pandas TA