About this item
Highlights
- The widespread adoption of AI and machine learning is revolutionizing many industries today.
- About the Author: Dr. Yves J. Hilpisch is founder and managing partner of The Python Quants (http: //tpq.io), a group that focuses on the use of open source technologies for financial data science, algorithmic trading and computational finance.
- 475 Pages
- Computers + Internet, Programming Languages
Description
Book Synopsis
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
About the Author
Dr. Yves J. Hilpisch is founder and managing partner of The Python Quants (http: //tpq.io), a group that focuses on the use of open source technologies for financial data science, algorithmic trading and computational finance. He is the author of the books Python for Finance (O'Reilly, 2014), Derivatives Analytics with Python (Wiley, 2015) and Listed Volatility and Variance Derivatives (Wiley, 2017). Yves lectures on computational finance at the CQF Program (http: //cqf.com), on data science at htw saar University of Applied Sciences (http: //htwsaar.de), and is the director for the online training program leading to the first Python for Finance University Certificate (awarded by htw saar).