https://github.com/saviornt/pyta
PyTA is a Python library for technical analysis, offering a range of functions to compute indicators like moving averages, momentum, volatility, and patterns. Designed as a user-friendly alternative to TA-Lib, it leverages pandas, numpy and scipy for ease of use.
https://github.com/saviornt/pyta
Last synced: about 1 year ago
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PyTA is a Python library for technical analysis, offering a range of functions to compute indicators like moving averages, momentum, volatility, and patterns. Designed as a user-friendly alternative to TA-Lib, it leverages pandas, numpy and scipy for ease of use.
- Host: GitHub
- URL: https://github.com/saviornt/pyta
- Owner: saviornt
- License: mit
- Created: 2024-08-17T00:27:01.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-10-02T04:18:08.000Z (over 1 year ago)
- Last Synced: 2025-02-08T01:52:38.622Z (over 1 year ago)
- Language: Python
- Homepage:
- Size: 104 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# PyTA
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PyTA provides a comprehensive set of financial indicators and tools without the need for third-party build tools or outdated library versions.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.
## Features
- 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- Dependency-Free: Does not require third-party build tools or outdated libraries, simplifying the installation and setup process.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.
## Installation
From your terminal, use pip to install with the following command:
`pip install git+https://github.com/saviornt/PyTA`
## Example Usage
1. Once you've installed pyta, import it into your project with `import pyta`
2. Load in your DataFrame, ex: `data`
3. Create a new column that equals a called PyTA Indicator, for example: `data['EMA] = pyta.EMA[data]`
## Example Code
def preprocess_data(data):
data['EMA'] = pyta.EMA(data)
data['RSI'] = pyta.RSI(data)
data['VWAP'] = pyta.VWAP(data)
return data
## Documentation
For more detailed documentation, visit the [Wiki](https://github.com/saviornt/PyTA/wiki).
## License
This project is licensed under the MIT License - see the [LICENSE](https://github.com/saviornt/PyTA/blob/main/LICENSE) file for details.