Financial Theory with Python: A Gentle Introduction - PDF, ePUB

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Financial Theory with Python: A Gentle Introduction by Yves Hilpisch

Technological trends like online trading platforms, open source software, and open financial data have significantly lowered or even completely removed the barriers of entry to the global financial markets. Individuals with only limited amounts of cash at their free disposal can get started, for example, with algorithmic trading within hours. Students and academics in financial disciplines with a little bit of background knowledge in programming can easily apply cutting-edge innovations in machine and deep learning to financial data—on the notebooks they bring to their finance classes. On the hardware side, cloud providers offer professional compute and data processing capabilities starting at 5 USD per month, billed by the hour and with almost unlimited scalability. So far, academic and professional finance education has only partly reacted to these trends.

This book teaches both finance and the Python programming language from the ground up. Nowadays, finance and programming in general are closely intertwined disciplines, with Python being one of the most widely used programming languages in the financial industry. The book presents relevant foundations—from mathematics, finance, and programming—in an integrated but not-too-technical fashion. Traditionally, theoretical finance and computational finance have been more or less separate disciplines. The fact that programming classes (for example, in Python but also in C++) have become an integral part of Master of Financial Engineering and similar university programs shows how important programming skills have become in the field.

However, mathematical foundations, theoretical finance, and basic programming techniques are still quite often taught independently from one another and only later in combination with computational finance. This book takes a different approach in that the mathematical concepts—for example, from linear algebra and probability theory—provide the common background against which financial ideas and programming techniques alike are introduced. Abstract mathematical concepts are thereby motivated from two different angles: finance and programming. In addition, this approach allows for a new learning experience since both mathematical and financial concepts can directly be translated into executable code that can then be explored interactively.

Several readers of one of my other books, Python for Finance (2nd ed., 2018, O’Reilly), pointed out that it teaches neither finance nor Python from the ground up. Indeed, the reader of that book is expected to have at least some experience in both finance and (Python) programming. Financial Theory with Python closes this gap in that it focuses on more fundamental concepts from both finance and Python programming. In that sense, readers who finish this book can naturally progress to Python for Finance to further build and improve their Python skills as applied to finance. More guidance is provided in the final chapter.



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