Artificial Intelligence in Finance: A Python-Based Guide

by Yves Hilpisch

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

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 practiceApply neural networks and reinforcement learning to discover statistical inefficiencies in financial marketsIdentify and exploit economic inefficiencies through backtesting and algorithmic trading--the automated execution of trading strategiesUnderstand how AI will influence the competitive dynamics in the financial industry and what the potential emergence of a financial singularity might bring about

Genres: Finance, Programming, Artificial Intelligence

480 pages, Kindle Edition

Published October 14, 2020

Reviews

[Sierra · Rating 3 out of 5]

First few chapter were AMAZING. Then the concepts got so difficult and was hard to keep up in python.

[Ayush · Rating 2 out of 5]

Part II where author gives an overview of normative finance along with code snippets is quite helpful. Having said that, the book overall misses on the target audience. It gets technical fast w/o explaining fundamentals (so you'll have to go buy another book to learn that) and then ends too soon. The author tries level best to give both an overview as well as code to show what works. However, the reader will be left with only a high level idea of what everything means and no deep value. e.g. author convinces that data driven finance is the way to go and jumps to Neural networks rather than showing how traditional time-series models (ARIMA) work on the dataset, another example is backtesting which is shown to work using code snippets but left a warning that shown snippet won't work on production level systems.

Overall, neither the book gets too subjective and goes deep in theory/fundamentals nor does it tell the reader what it takes to build production level systems with AI.