The widespread adoption of AI and machine learning is revolutionizing many industries today.
Once these technologies are combined with the programmatic availability of historical and
real-time financial data the financial industry will also change fundamentally. With this
practical book you'll learn how to use AI and machine learning to discover statistical
inefficiencies in financial markets and exploit them through algorithmic trading. Author Yves
Hilpisch shows practitioners students and academics in both finance and data science
practical ways to apply machine learning and deep learning algorithms to finance. Thanks to
lots of self-contained Python examples you'll be able to replicate all results and figures
presented in the book. In five parts this guide helps you: Learn central notions and
algorithms from AI including recent breakthroughs on the way to artificial general
intelligence (AGI) and superintelligence (SI) Understand why data-driven finance AI and
machine learning will have a lasting impact on financial theory and practice Apply neural
networks and reinforcement learning to discover statistical inefficiencies in financial markets
Identify and exploit economic inefficiencies through backtesting and algorithmic trading--the
automated execution of trading strategies Understand how AI will influence the competitive
dynamics in the financial industry and what the potential emergence of a financial singularity
might bring about
