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

本書介紹了使用數學理論和實際例子從量化金融角度使用TensorFlow庫進行強化學習。《金融強化學習》首先描述了訓練神經網絡的方法。接下來，它討論了CNN和RNN - 這兩種神經網絡在強化學習中作為深度學習網絡使用。此外，本書深入探討了強化學習理論，解釋了馬爾可夫決策過程、價值函數、策略和策略梯度，以及它們的數學公式和學習算法。它涵蓋了從雙深度Q網絡到雙延遲深度確定性策略梯度和生成對抗網絡的最新強化學習算法，並使用TensorFlow Python庫進行了示例。它還作為TensorFlow編程的快速實踐指南，涵蓋了從變量和圖形到自動微分、層、模型和損失函數的概念。完成本書後，您將了解如何使用TensorFlow庫進行深度Q和生成對抗網絡的強化學習。本書適合數據科學家、機器學習工程師和Python程序員，他們希望應用強化學習來解決問題。

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.

Samit Ahlawat是美國紐約J.P. Morgan Chase的量化研究部門資本建模的高級副總裁。在他目前的職位上，他負責為資產管理建立交易策略和風險管理模型。他的研究興趣包括人工智能、風險管理和算法交易策略。他曾在CQF學院就人工智能發表演講，並在金融領域撰寫了多篇研究論文，並擁有一項關於人臉識別技術的專利。在閒暇時間，他也參與開源程式碼的貢獻。