Preface
Python was quickly becoming the de-facto language for data science, machine learning and natural language processing; it would unlock new sources of innovation. Python would allow us to engage with its sizeable open source community, bringing state-of-the-art technology in-house quickly, while allowing for customization.1
Kindman and Taylor (2021)
Why This Book?
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 ...
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