Textbook in PDF format

Deep Learning is rapidly gaining momentum in the world of finance and trading. But for many professional traders, this sophisticated field has a reputation for being complex and difficult. This hands-on guide teaches you how to develop a deep learning trading model from scratch using Python, and it also helps you create and backtest trading algorithms based on Machine Learning and reinforcement learning.
Sofien Kaabar—financial author, trading consultant, and institutional market strategist—introduces Deep Learning strategies that combine technical and quantitative analyses. By fusing Deep Learning concepts with technical analysis, this unique book presents outside-the-box ideas in the world of financial trading. This A-Z guide also includes a full introduction to technical analysis, evaluating machine learning algorithms, and algorithm optimization.
Deep Learning is a slightly more complex and more detailed field than Machine Learning. Machine Learning and Deep Learning both fall under the umbrella of Data Science. As you will see, Deep Learning is mostly about neural networks, a highly sophisticated and powerful algorithm that has enjoyed a lot of coverage and hype, and for good reason: it is very powerful and able to catch highly complex nonlinear relationships between different variables.
• Understand and create Machine Learning and Deep Learning models
• Explore the details behind reinforcement learning and see how it's used in time series
• Understand how to interpret performance evaluation metrics
• Examine technical analysis and learn how it works in financial markets
• Create technical indicators in Python and combine them with ML models for optimization
• Evaluate the models' profitability and predictability to understand their limitations and potential
Who Should Read It?
This book is intended for a wide audience, including professionals and academics in finance, data scientists, quantitative traders, and students of finance of any level. It provides a thorough introduction to the use of machine and deep learning in time series prediction, and it is an essential resource for anyone who wants to understand and apply these powerful techniques.
The book assumes you have basic background knowledge in both Python programming (professional Python users will find the code very straightforward) and financial trading. I take a clear and simple approach that focuses on the key concepts so that you understand the purpose of every idea.
Contents:
Preface
1. Introducing Data Science and Trading
2. Essential Probabilistic Methods for Deep Learning
3. Descriptive Statistics and Data Analysis
4. Linear Algebra and Calculus for Deep Learning
5. Introducing Technical Analysis
6. Introductory Python for Data Science
7. Machine Learning Models for Time Series Prediction
8. Deep Learning for Time Series Prediction I
9. Deep Learning for Time Series Prediction II
10. Deep Reinforcement Learning for Time Series Prediction
11. Advanced Techniques and Strategies
12. Market Drivers and Risk Management
Index