| | 資料類型 | 狀態 | 應還日期 | 預約人數 | 館藏地 | 索書號 | 條碼號 | | 預約 | 圖書 | 借出中
(可召回，7天還書) | 2024/09/09 | 0 | 總館
西文圖書區 | HG4523 .T76 2020 | W112747 |
| 內容簡介 | Take your quantitative strategies to the next level by exploring nine examples that make use of cutting-edge deep learning technologies, including CNNs, LSTMs, GANs, reinforcement learning, and CapsNets Key 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 Book Description 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 pro |
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