Deep Learning for Finance: Creating Machine & Deep Learning Models for Trading in Python

Deep learning is gaining momentum in finance and trading, but it can be complex and difficult for professional traders. This hands-on guide teaches you how to develop a deep learning trading model from scratch using Python and create and backtest trading algorithms based on machine learning and reinforcement learning. It also includes a full introduction to technical analysis, evaluating machine learning algorithms, and algorithm optimization.

Format: Paperback / softback
Length: 350 pages
Publication date: 31 January 2024
Publisher: O'Reilly Media

Deep learning is gaining significant traction in the world of finance and trading, attracting the attention of professionals seeking to leverage its advanced capabilities. However, this sophisticated field is often perceived as complex and challenging, making it daunting for many traders. This comprehensive hands-on guide aims to demystify deep learning and empower traders with the skills to develop their own trading models using Python.

In addition to providing a step-by-step tutorial on building deep learning trading models, the guide also delves into the creation and backtesting of trading algorithms based on machine learning and reinforcement learning. Author Sofien Kaabar, a financial author, trading consultant, and institutional market strategist, brings his expertise to the forefront, introducing innovative deep learning strategies that seamlessly integrate technical and quantitative analyses.

By fusing deep learning concepts with technical analysis, this unique book offers fresh and unconventional ideas in the realm of financial trading. It serves as an A-Z guide, providing a thorough introduction to technical analysis, evaluating machine learning algorithms, and optimizing algorithm performance.

Throughout the book, readers will gain a deep understanding of machine learning and deep learning models, exploring the intricacies behind reinforcement learning and its application in time series analysis. They will learn how to interpret performance evaluation metrics, examine the workings of technical analysis in financial markets, and create technical indicators in Python, seamlessly integrating them with ML models for optimization.

The guide also evaluates the profitability and predictability of the models, shedding light on their limitations and potential applications. By empowering traders with the knowledge and tools to harness the power of deep learning, this guide prepares them to stay ahead of the curve in an ever-evolving financial landscape. Whether you are a seasoned trader or a newcomer to the field, this comprehensive guide will provide you with the foundation you need to succeed in the world of deep learning trading.

Weight: 634g
Dimension: 235 x 178 x 21 (mm)
ISBN-13: 9781098148393