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The Promise:
Stop chasing accuracy and start building evidence-driven trading systems that survive real-world costs and regime changes.
Chapter 1: Preface
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A trader builds a model that predicts next-day returns with 58% accuracy. He deploys it, sizes positions aggressively, and watches it lose money for three straight months. The model was not wrong in the way he expected. It was right about direction, but its wins were small and its losses were large.
This is the default outcome when a practitioner treats ML trading as a modeling problem instead of a systems problem. The model is one component. The data pipeline, the validation logic, the risk controls, and the deployment infrastructure all carry equal weight. If any one of them is weak, the system fails regardless of how sophisticated the learner is.
