Computational Astrology is an interdisciplinary field at the intersection of computer science and traditional astrology. It encompasses the development of computational tools, the algorithmic and empirical evaluation of astrological techniques, and their application to practical problem-solving, such as decision-support systems.
Core Areas
Algorithms & Software Engineering: Developing open-source tools and high-precision engines for astronomical calculations, chart generation, planetary dignities, and predictive timing techniques (such as primary directions and profections).
Statistics & Data Analysis: Applying statistical methods and data science to historical and biographical datasets to test, calibrate, and validate astrological models and traditional rules.
Machine Learning & Language Models (LLMs): Using generative AI and natural language processing to synthesize complex chart data into structured interpretations, simulate scenarios, and assist in human decision-making.
Reference Implementation
PyAstra is the reference open-source Python library for computational astrology. It provides accurate astronomical calculations, traditional astrology techniques and tools for integrating astrological data with modern LLMs.