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. 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.