Troiano L Hands-On Deep Learning for Finance 2020 torrent

Textbook in PDF format

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
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
About
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.
Features
Implement deep learning techniques and algorithms to build financial models
Apply modern AI techniques in quantitative market modeling and investment decision making
Leverage Python libraries for rapid development and prototyping