Reinforcement learning for finance : solve problems in finance with CNN and RNN using the TensorFlow library

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Author
Ahlawat, Samit,
Published
Berkeley, CA : Apress L. P., [2023].
Status
Available Online

Description

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, and loss 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.

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Format
Language
English
ISBN
9781484288351, 1484288351
UPC
10.1007/978-1-4842-8835-1

Notes

Bibliography
Includes bibliographical references and index.
Description
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, and loss 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.
Local note
O'Reilly O'Reilly Online Learning: Academic/Public Library Edition

Table of Contents

Intro
Table of Contents
About the Author
Acknowledgments
Preface
Introduction
Chapter 1: Overview
1.1 Methods for Training Neural Networks
1.2 Machine Learning in Finance
1.3 Structure of the Book
Chapter 2: Introduction to TensorFlow
2.1 Tensors and Variables
2.2 Graphs, Operations, and Functions
2.3 Modules
2.4 Layers
2.5 Models
2.6 Activation Functions
2.7 Loss Functions
2.8 Metrics
2.9 Optimizers
2.10 Regularizers
2.11 TensorBoard
2.12 Dataset Manipulation
2.13 Gradient Tape
Chapter 3: Convolutional Neural Networks
3.1 A Simple CNN
3.2 Neural Network Layers Used in CNNs
3.3 Output Shapes and Trainable Parameters of CNNs
3.4 Classifying Fashion MNIST Images
3.5 Identifying Technical Patterns in Security Prices
3.6 Using CNNs for Recognizing Handwritten Digits
Chapter 4: Recurrent Neural Networks
4.1 Simple RNN Layer
4.2 LSTM Layer
4.3 GRU Layer
4.4 Customized RNN Layers
4.5 Stock Price Prediction
4.6 Correlation in Asset Returns
Chapter 5: Reinforcement Learning Theory
5.1 Basics
5.2 Methods for Estimating the Markov Decision Problem
5.3 Value Estimation Methods
5.3.1 Dynamic Programming
Finding the Optimal Path in a Maze
European Call Option Valuation
Valuation of a European Barrier Option
5.3.2 Generalized Policy Iteration
Policy Improvement Theorem
Policy Evaluation
Policy Improvement
5.3.3 Monte Carlo Method
Pricing an American Put Option
5.3.4 Temporal Difference (TD) Learning
SARSA
Valuation of an American Barrier Option
Least Squares Temporal Difference (LSTD)
Least Squares Policy Evaluation (LSPE)
Least Squares Policy Iteration (LSPI)
Q-Learning
Double Q-Learning
Eligibility Trace
5.3.5 Cartpole Balancing
5.4 Policy Learning
5.4.1 Policy Gradient Theorem
5.4.2 REINFORCE Algorithm
5.4.3 Policy Gradient with State-Action Value Function Approximation
5.4.4 Policy Learning Using Cross Entropy
5.5 Actor-Critic Algorithms
5.5.1 Stochastic Gradient-Based Actor-Critic Algorithms
5.5.2 Building a Trading Strategy
5.5.3 Natural Actor-Critic Algorithms
5.5.4 Cross Entropy-Based Actor-Critic Algorithms
Chapter 6: Recent RL Algorithms
6.1 Double Deep Q-Network: DDQN
6.2 Balancing a Cartpole Using DDQN
6.3 Dueling Double Deep Q-Network
6.4 Noisy Networks
6.5 Deterministic Policy Gradient
6.5.1 Off-Policy Actor-Critic Algorithm
6.5.2 Deterministic Policy Gradient Theorem
6.6 Trust Region Policy Optimization: TRPO
6.7 Natural Actor-Critic Algorithm: NAC
6.8 Proximal Policy Optimization: PPO
6.9 Deep Deterministic Policy Gradient: DDPG
6.10 D4PG
6.11 TD3PG
6.12 Soft Actor-Critic: SAC
6.13 Variational Autoencoder
6.14 VAE for Dimensionality Reduction
6.15 Generative Adversarial Networks
Bibliography
Index

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Citations

APA Citation, 7th Edition (style guide)

Ahlawat, S. (2023). Reinforcement learning for finance: solve problems in finance with CNN and RNN using the TensorFlow library . Apress L. P..

Chicago / Turabian - Author Date Citation, 17th Edition (style guide)

Ahlawat, Samit. 2023. Reinforcement Learning for Finance: Solve Problems in Finance With CNN and RNN Using the TensorFlow Library. Berkeley, CA: Apress L. P.

Chicago / Turabian - Humanities (Notes and Bibliography) Citation, 17th Edition (style guide)

Ahlawat, Samit. Reinforcement Learning for Finance: Solve Problems in Finance With CNN and RNN Using the TensorFlow Library Berkeley, CA: Apress L. P, 2023.

Harvard Citation (style guide)

Ahlawat, S. (2023). Reinforcement learning for finance: solve problems in finance with CNN and RNN using the tensorflow library. Berkeley, CA: Apress L. P.

MLA Citation, 9th Edition (style guide)

Ahlawat, Samit. Reinforcement Learning for Finance: Solve Problems in Finance With CNN and RNN Using the TensorFlow Library Apress L. P., 2023.

Note! Citations contain only title, author, edition, publisher, and year published. Citations should be used as a guideline and should be double checked for accuracy. Citation formats are based on standards as of August 2021.

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Grouped Work ID9739e8e7-9b85-046b-f6ea-36f7ed9a2c61-eng
Full titlereinforcement learning for finance solve problems in finance with cnn and rnn using the tensorflow library
Authorahlawat samit
Grouping Categorybook
Last Update2025-01-24 12:33:29PM
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504 |a Includes bibliographical references and index.
5050 |a Intro -- Table of Contents -- About the Author -- Acknowledgments -- Preface -- Introduction -- Chapter 1: Overview -- 1.1 Methods for Training Neural Networks -- 1.2 Machine Learning in Finance -- 1.3 Structure of the Book -- Chapter 2: Introduction to TensorFlow -- 2.1 Tensors and Variables -- 2.2 Graphs, Operations, and Functions -- 2.3 Modules -- 2.4 Layers -- 2.5 Models -- 2.6 Activation Functions -- 2.7 Loss Functions -- 2.8 Metrics -- 2.9 Optimizers -- 2.10 Regularizers -- 2.11 TensorBoard -- 2.12 Dataset Manipulation -- 2.13 Gradient Tape -- Chapter 3: Convolutional Neural Networks
5058 |a 3.1 A Simple CNN -- 3.2 Neural Network Layers Used in CNNs -- 3.3 Output Shapes and Trainable Parameters of CNNs -- 3.4 Classifying Fashion MNIST Images -- 3.5 Identifying Technical Patterns in Security Prices -- 3.6 Using CNNs for Recognizing Handwritten Digits -- Chapter 4: Recurrent Neural Networks -- 4.1 Simple RNN Layer -- 4.2 LSTM Layer -- 4.3 GRU Layer -- 4.4 Customized RNN Layers -- 4.5 Stock Price Prediction -- 4.6 Correlation in Asset Returns -- Chapter 5: Reinforcement Learning Theory -- 5.1 Basics -- 5.2 Methods for Estimating the Markov Decision Problem
5058 |a 5.3 Value Estimation Methods -- 5.3.1 Dynamic Programming -- Finding the Optimal Path in a Maze -- European Call Option Valuation -- Valuation of a European Barrier Option -- 5.3.2 Generalized Policy Iteration -- Policy Improvement Theorem -- Policy Evaluation -- Policy Improvement -- 5.3.3 Monte Carlo Method -- Pricing an American Put Option -- 5.3.4 Temporal Difference (TD) Learning -- SARSA -- Valuation of an American Barrier Option -- Least Squares Temporal Difference (LSTD) -- Least Squares Policy Evaluation (LSPE) -- Least Squares Policy Iteration (LSPI) -- Q-Learning -- Double Q-Learning
5058 |a Eligibility Trace -- 5.3.5 Cartpole Balancing -- 5.4 Policy Learning -- 5.4.1 Policy Gradient Theorem -- 5.4.2 REINFORCE Algorithm -- 5.4.3 Policy Gradient with State-Action Value Function Approximation -- 5.4.4 Policy Learning Using Cross Entropy -- 5.5 Actor-Critic Algorithms -- 5.5.1 Stochastic Gradient-Based Actor-Critic Algorithms -- 5.5.2 Building a Trading Strategy -- 5.5.3 Natural Actor-Critic Algorithms -- 5.5.4 Cross Entropy-Based Actor-Critic Algorithms -- Chapter 6: Recent RL Algorithms -- 6.1 Double Deep Q-Network: DDQN -- 6.2 Balancing a Cartpole Using DDQN
5058 |a 6.3 Dueling Double Deep Q-Network -- 6.4 Noisy Networks -- 6.5 Deterministic Policy Gradient -- 6.5.1 Off-Policy Actor-Critic Algorithm -- 6.5.2 Deterministic Policy Gradient Theorem -- 6.6 Trust Region Policy Optimization: TRPO -- 6.7 Natural Actor-Critic Algorithm: NAC -- 6.8 Proximal Policy Optimization: PPO -- 6.9 Deep Deterministic Policy Gradient: DDPG -- 6.10 D4PG -- 6.11 TD3PG -- 6.12 Soft Actor-Critic: SAC -- 6.13 Variational Autoencoder -- 6.14 VAE for Dimensionality Reduction -- 6.15 Generative Adversarial Networks -- Bibliography -- Index
520 |a 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, and loss 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.
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