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

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 netwo...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Ahlawat, Samit (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Berkeley, CA : Apress L. P., [2023]
Temas:
Finance > Mathematical models > Data processing.
Reinforcement learning.
Python (Computer program language)
Finances > Modèles mathématiques > Informatique.
Apprentissage par renforcement (Intelligence artificielle)
Python (Langage de programmation)
Finance > Mathematical models > Data processing
Reinforcement learning
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Tabla de Contenidos:
  • 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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