Hands-On Deep Learning for Finance: Implement deep learning techniques and algorithms to create powerful trading strategies

English | 2020 | ISBN: 978-1789613179 | 390 Pages | PDF, EPUB | 77 MB

Take your quantitative strategies to the next level by exploring nine examples that make use of cutting-edge deep learning technologies, including CNNs, LSTM, GANs, reinforcement learning, and CapsNets
Quantitative methods are the vanguard of the investment management industry. With this book, you’ll learn how you can use deep learning models to capture insights from financial data and implement deep learning models using Python libraries such as TensorFlow and Keras.
Starting with an overview of deep learning in the finance domain, you’ll use neural network architectures such as CNNs, RNNs, and LSTM to develop, test, and validate trading-based models. You’ll enhance your understanding of financial models by applying deep learning algorithms and exploit them systematically. With a practical approach, this book will cover different aspects of asset management and guide you in enhancing financial trading strategies. As you advance, you’ll perform index replication and forecasting using autoencoders and LSTM, respectively, and move on to using advanced NLP techniques and BLSTM to process newsfeed for specific stocks. This deep learning book will initially take you through using CNNs to develop a trading signal with simple technical indicators and then using CapsNets to improve their performance. Toward the end, you’ll even learn how to use generative adversarial networks (GANs) to perform risk management and implement deep reinforcement learning for automated trading.
What you will learn

• Implement quantitative financial models using the various building blocks of a deep neural network
• Build, train, and optimize deep networks from scratch
• Use LSTM to process data sequences such as time series and news feeds
• Implement convolutional neural networks (CNNs), CapsNets, and other models to create trading strategies
• Adapt popular neural networks for pattern recognition in finance using transfer learning
• Automate investment decisions by using reinforcement learning
• Discover how a risk model can be constructed using D-GAN