documentation VS ta

Compare documentation vs ta and see what are their differences.

documentation

This repository contains the documentation for the current Quantiacs project. Check it out at: https://quantiacs.com/documentation/en/ (by quantiacs)
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documentation ta
1 10
2 4,036
- -
6.2 6.9
12 days ago about 1 month ago
Stylus Jupyter Notebook
MIT License MIT License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

documentation

Posts with mentions or reviews of documentation. We have used some of these posts to build our list of alternatives and similar projects.

ta

Posts with mentions or reviews of ta. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-02-15.

What are some alternatives?

When comparing documentation and ta you can also consider the following projects:

finta - Common financial technical indicators implemented in Pandas.

pandas-ta - Technical Analysis Indicators - Pandas TA is an easy to use Python 3 Pandas Extension with 150+ Indicators

mlfinlab - MlFinLab helps portfolio managers and traders who want to leverage the power of machine learning by providing reproducible, interpretable, and easy to use tools.

ta-lib-python - Python wrapper for TA-Lib (http://ta-lib.org/).

machine-learning-for-trading - Code for Machine Learning for Algorithmic Trading, 2nd edition.

python-binance - Binance Exchange API python implementation for automated trading

ib_insync - Python sync/async framework for Interactive Brokers API

borb-google-colab-examples - This repository contains some examples of using borb in google colab. These examples enable you to try out the features of borb without installing it on your system. They also ensure the system requirements and imports are all taken care of.

Lean - Lean Algorithmic Trading Engine by QuantConnect (Python, C#)

ydata-quality - Data Quality assessment with one line of code

backtrader - Python Backtesting library for trading strategies

benford_py - Python implementation of Benford's Law tests.