Reinforcement Learning for Finance: Solve Problems in Finance with CNN and RNN Using the TensorFlow Library
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Berkeley, CA
Apress L. P.
2022
|
| Ausgabe: | 1st ed |
| Schlagworte: | |
| Online-Zugang: | DE-2070s |
| Beschreibung: | Description based on publisher supplied metadata and other sources |
| Beschreibung: | 1 Online-Ressource (435 Seiten) |
| ISBN: | 9781484288351 |
Internformat
MARC
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| 100 | 1 | |a Ahlawat, Samit |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Reinforcement Learning for Finance |b Solve Problems in Finance with CNN and RNN Using the TensorFlow Library |
| 250 | |a 1st ed | ||
| 264 | 1 | |a Berkeley, CA |b Apress L. P. |c 2022 | |
| 264 | 4 | |c ©2023 | |
| 300 | |a 1 Online-Ressource (435 Seiten) | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b c |2 rdamedia | ||
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| 500 | |a Description based on publisher supplied metadata and other sources | ||
| 505 | 8 | |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 -- 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 | |
| 505 | 8 | |a 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 | |
| 650 | 4 | |a Reinforcement learning | |
| 776 | 0 | 8 | |i Erscheint auch als |n Druck-Ausgabe |a Ahlawat, Samit |t Reinforcement Learning for Finance |d Berkeley, CA : Apress L. P.,c2022 |z 9781484288344 |
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Datensatz im Suchindex
| _version_ | 1810600618113368064 |
|---|---|
| adam_text | |
| any_adam_object | |
| author | Ahlawat, Samit |
| author_facet | Ahlawat, Samit |
| author_role | aut |
| author_sort | Ahlawat, Samit |
| author_variant | s a sa |
| building | Verbundindex |
| bvnumber | BV049872851 |
| collection | ZDB-30-PQE |
| 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 |
| ctrlnum | (ZDB-30-PQE)EBC7164636 (ZDB-30-PAD)EBC7164636 (ZDB-89-EBL)EBL7164636 (OCoLC)1356574193 (DE-599)BVBBV049872851 |
| dewey-full | 332.0285631 |
| dewey-hundreds | 300 - Social sciences |
| dewey-ones | 332 - Financial economics |
| dewey-raw | 332.0285631 |
| dewey-search | 332.0285631 |
| dewey-sort | 3332.0285631 |
| dewey-tens | 330 - Economics |
| discipline | Wirtschaftswissenschaften |
| edition | 1st ed |
| format | Electronic eBook |
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| id | DE-604.BV049872851 |
| illustrated | Not Illustrated |
| indexdate | 2024-09-19T05:21:46Z |
| institution | BVB |
| isbn | 9781484288351 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-035212309 |
| oclc_num | 1356574193 |
| open_access_boolean | |
| owner | DE-2070s |
| owner_facet | DE-2070s |
| physical | 1 Online-Ressource (435 Seiten) |
| psigel | ZDB-30-PQE ZDB-30-PQE HWR_PDA_PQE |
| publishDate | 2022 |
| publishDateSearch | 2022 |
| publishDateSort | 2022 |
| publisher | Apress L. P. |
| record_format | marc |
| spelling | Ahlawat, Samit Verfasser aut Reinforcement Learning for Finance Solve Problems in Finance with CNN and RNN Using the TensorFlow Library 1st ed Berkeley, CA Apress L. P. 2022 ©2023 1 Online-Ressource (435 Seiten) txt rdacontent c rdamedia cr rdacarrier Description based on publisher supplied metadata and other sources 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 Reinforcement learning Erscheint auch als Druck-Ausgabe Ahlawat, Samit Reinforcement Learning for Finance Berkeley, CA : Apress L. P.,c2022 9781484288344 |
| spellingShingle | Ahlawat, Samit Reinforcement Learning for Finance Solve Problems in Finance with CNN and RNN Using the TensorFlow Library 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 Reinforcement learning |
| title | Reinforcement Learning for Finance Solve Problems in Finance with CNN and RNN Using the TensorFlow Library |
| title_auth | Reinforcement Learning for Finance Solve Problems in Finance with CNN and RNN Using the TensorFlow Library |
| title_exact_search | Reinforcement Learning for Finance Solve Problems in Finance with CNN and RNN Using the TensorFlow Library |
| title_full | Reinforcement Learning for Finance Solve Problems in Finance with CNN and RNN Using the TensorFlow Library |
| title_fullStr | Reinforcement Learning for Finance Solve Problems in Finance with CNN and RNN Using the TensorFlow Library |
| title_full_unstemmed | Reinforcement Learning for Finance Solve Problems in Finance with CNN and RNN Using the TensorFlow Library |
| title_short | Reinforcement Learning for Finance |
| title_sort | reinforcement learning for finance solve problems in finance with cnn and rnn using the tensorflow library |
| title_sub | Solve Problems in Finance with CNN and RNN Using the TensorFlow Library |
| topic | Reinforcement learning |
| topic_facet | Reinforcement learning |
| work_keys_str_mv | AT ahlawatsamit reinforcementlearningforfinancesolveproblemsinfinancewithcnnandrnnusingthetensorflowlibrary |