Deep Learning for Trading Signals

Deep learning for trading has a credibility problem. Many published models look impressive in a diagram and fall apart on honest market data. If you know Python and basic machine learning, this book is about telling the difference between a good architecture and a good signal.

You build a complete deep signal pipeline, evaluating every step against one question: does it improve trading decisions when tested honestly? You start with the institutional signal substrate, including data preprocessing and denoising. Next you compare advanced network architectures for markets, then look at post-signal engineering and deployment.

Later chapters bring in large language models for financial signal generation and reinforcement learning for signal optimisation.