PyTA is a modern, user-friendly alternative to TA-Lib for technical analysis leveraging pandas, numpy and scipy for ease of use. Designed to be compatible with Python 3.10 and later, PyTA provides a comprehensive set of financial indicators and tools without the need for third-party build tools or outdated library versions. Ideal for developers and analysts seeking a straightforward and maintainable solution for the technical analysis of financial data.
- Modern and User-Friendly: A contemporary alternative to TA-Lib designed for ease of use and integration with modern Python environments.
- Compatibility: Supports Python 3.10 and later versions, ensuring compatibility with recent Python releases.
- Comprehensive Financial Indicators: Provides a wide range of financial indicators and tools essential for technical analysis.
- Dependency-Free: Does not require third-party build tools or outdated libraries, simplifying the installation and setup process.
- Integration with Pandas, Numpy, and Scipy: Leverages these popular libraries for robust and efficient data handling and analysis.
- Straightforward and Maintainable: Offers a clean and maintainable codebase, making it easier for developers and analysts to use and contribute.
- Technical Analysis: Designed specifically for the technical analysis of financial data, offering relevant features and tools for this purpose.
From your terminal, use pip to install with the following command:
pip install git+https://github.com/saviornt/PyTA
- Once you've installed pyta, import it into your project with
import pyta - Load in your DataFrame, ex:
data - Create a new column that equals a called PyTA Indicator, for example:
data['EMA] = pyta.EMA[data]
def preprocess_data(data):
data['EMA'] = pyta.EMA(data)
data['RSI'] = pyta.RSI(data)
data['VWAP'] = pyta.VWAP(data)
return data
For more detailed documentation, visit the Wiki.
This project is licensed under the MIT License - see the LICENSE file for details.