Reinforcement Learning for Finance

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Beschreibung

This book introduces reinforcement learning with mathematical theory and practical examples from quantitative finance using the TensorFlow library.

Reinforcement Learning for Finance begins by describing methods for training neural networks. Next, it discusses CNN and RNN ¿ two kinds of neural networks used as deep learning networks in reinforcement learning. Further, the book dives into reinforcement learning theory, explaining the Markov decision process, value function, policy, and policy gradients, with their mathematical formulations and learning algorithms. It covers recent reinforcement learning algorithms from double deep-Q networks to twin-delayed deep deterministic policy gradients and generative adversarial networks with examples using the TensorFlow Python library. It also serves as a quick hands-on guide to TensorFlow programming, covering concepts ranging from variables and graphs to automatic differentiation, layers, models, andloss functions.

After completing this book, you will understand reinforcement learning with deep q and generative adversarial networks using the TensorFlow library.

What You Will Learn

Understand the fundamentals of reinforcement learning

Apply reinforcement learning programming techniques to solve quantitative-finance problems

Gain insight into convolutional neural networks and recurrent neural networks

Understand the Markov decision process

Who This Book Is For

Data Scientists, Machine Learning engineers and Python programmers who want to apply reinforcement learning to solve problems.

This book introduces reinforcement learning with mathematical theory and practical examples from quantitative finance using the TensorFlow library.

After completing this book, you will understand reinforcement learning with deep q and generative adversarial networks using the TensorFlow library.

What You Will Learn

Understand the fundamentals of reinforcement learning

Apply reinforcement learning programming techniques to solve quantitative-finance problems

Gain insight into convolutional neural networks and recurrent neural networks

Understand the Markov decision process

Who This Book Is For

Data Scientists, Machine Learning engineers and Python programmers who want to apply reinforcement learning to solve problems.

Über den Autor

Samit Ahlawat is a Senior Vice President in Quantitative Research, Capital Modeling at J.P. Morgan Chase in New York, US. In his current role, he is responsible for building trading strategies for asset management and for building risk management models. His research interests include artificial intelligence, risk management and algorithmic trading strategies. He has given CQF institute talks on artificial intelligence, has authored several research papers in finance and holds a patent for facial recognition technology. In his spare time, he contributes to open source code.

Zusammenfassung

Covers reinforcement learning concepts with mathematical theory and practical application

Explains cutting-edge advances in reinforcement learning algorithms.

Covers convolutional neural networks and recurrent neural networks in reinforcement learning

Inhaltsverzeichnis

Chapter 1 Overview.- Chapter 2 Introduction to TensorFlow.- Chapter 3 Convolutional Neural Networks.- Chapter 4 Recurrent Neural Networks.- Chapter 5 Reinforcement Learning - Theory.- Chapter 6 Recent RL Algorithms.

Details

Erscheinungsjahr:	2022
Genre:	Importe , Informatik
Rubrik:	Naturwissenschaften & Technik
Medium:	Taschenbuch
Inhalt:	xv 423 S. 1 s/w Illustr. 84 farbige Illustr. 423 p. 85 illus. 84 illus. in color.
ISBN-13:	9781484288344
ISBN-10:	1484288343
Sprache:	Englisch
Einband:	Kartoniert / Broschiert
Autor:	Ahlawat, Samit
Auflage:	1st edition
Hersteller:	APRESS
Verantwortliche Person für die EU:	APress in Springer Science + Business Media, Heidelberger Platz 3, D-14197 Berlin, juergen.hartmann@springer.com
Maße:	235 x 155 x 24 mm
Von/Mit:	Samit Ahlawat
Erscheinungsdatum:	27.12.2022
Gewicht:	0,663 kg

Artikel-ID: 123641289