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
Who This Book Is For
If you’re a finance or investment professional who wants to lead the development of quantitative strategies, this book is for you. With this practical guide, you’ll be able to use deep learning methods for building financial models and incorporating them in your investment process. Anyone who wants to enter the fascinating domain of quantitative finance using the power of deep learning algorithms and techniques will also find this book useful. Basic knowledge of machine learning and Python programming is required.