Hands-On Deep Learning for Finance by Elena Mejuto Villa, Arjun Bhandari, Luigi Troiano

Quantitative methods are the vanguard of the investment management industry. This book shows how to enhance trading strategies and investments in financial markets using deep learning algorithms.

This book is an excellent reference to understand how deep learning models can be leveraged to capture insights from financial data. You will implement deep learning models using Python libraries such as TensorFlow and Keras. You will learn various deep learning algorithms to build models for understanding financial market dynamics and exploiting them in a systematic manner. This book takes a pragmatic approach to address various aspects of asset management. The information content in non-structured data like news flow is crystalized using BLSTM. Autoencoders for efficient index replication is discussed in detail. You will use CNN to develop a trading signal with simple technical indicators, and improvements offered by more complex techniques such as CapsNets. Volatility is given due emphasis by demonstrating the superiority of forecasts employing LSTM, and Monte Carlo simulations using GAN for value at risk computations. These are then brought together by implementing deep reinforcement learning for automated trading.

This book will serve as a continuing reference for implementing deep learning models to build investment strategies.

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 LSTMs 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.

Table of Contents
Deep learning for finance 101
Designing neural network architectures
Construction, testing and validation of financial models
Index replication by auto-encoders
Volatility forecasting by LSTM
Trading rule identification by CNN
Asset allocation by LSTM over CNN
Digesting news by NLP with BLSTM
Risk Measurement Using GAN
Chart visual analysis by transfer learning
Better chart analysis using CapsNet
Training trader robots by deep reinforcement learning
What’s next ?