Artificial Intelligence in Finance
ΠΡΠΈΡΠΊΠΎ Π·Π° ΡΠ°Π·ΠΈ Π΅Π»Π΅ΠΊΡΡΠΎΠ½Π½Π° ΠΊΠ½ΠΈΠ³Π°
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
ΠΡΠ΅Π½ΠΊΠΈ ΠΈ ΠΎΡΠ·ΠΈΠ²ΠΈ
ΠΠ° Π°Π²ΡΠΎΡΠ°
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).
