Название: An Introduction To Python For Quantitative Finance: From Scratch To Productivity
Автор: Paul Alexander Bilokon, Antoine Jacquier, Ewan Mackie, Aitor Muguruza
Издательство: World Scientific Publishing
Год: 2026
Страниц: 278
Язык: английский
Формат: pdf (true)
Размер: 10.1 MB

This book is written for both newcomers and experienced practitioners working at the intersection of Data Science, Machine Learning, and finance. It is designed to allow readers with no formal prerequisites to enter these fields with confidence, while also providing sufficient depth to be valuable to professionals. Beginning with a gentle introduction to Python, the book gradually progresses to more advanced language features and the mathematical foundations required to understand key models in quantitative finance. Throughout, the emphasis is on developing both conceptual understanding and practical skills. The material strikes a careful balance between the mathematics underpinning modern financial models and the practical considerations of Data Science and Machine Learning. Concepts are introduced and reinforced through hands-on case studies based on real financial datasets, enabling readers to gain experience working with realistic data and workflows. The contents of this book have been refined over many years of teaching to students and practitioners with diverse backgrounds at Imperial College London and the Thalesians Intensive Summer School in Artificial Intelligence, and reflects both academic rigor and real-world relevance.

Python is one of the most important programming languages in modern financial institutions. It is widely used for workflow automation, data management, risk analysis, dashboarding, and other fundamental day-to-day operations. Its success stems partly from its accessibility to both non-programmers and professional developers. Python emphasises readability and simplicity, making it suitable even in roles not traditionally associated with writing production code. For example, it is not uncommon to see quants, data scientists, and even traders building end-to-end applications or processes in Python to add new functional- ity or automate repetitive tasks. Python is ideal for shipping high-quality code quickly.

A key driver, and perhaps also a consequence, of Python’s widespread adoption is the wealth of open-source libraries, or packages, available to support specific tasks. In particular, Data Science and quantitative fields have benefited from fast, low-level numerical computation tools, advanced statistical modelling libraries, and even cutting-edge machine learning frameworks being open-sourced. In recent years, there has also been a trend towards open-sourcing data and crowd-sourcing better predictive models via platforms such as Kaggle.com. Once again, Python is the dominant language in this space. Today, many packages that demonstrate numerical or statistical techniques also include toy datasets, which allow practitioners to test ideas and reproduce others’ results with ease.