Understand where AI fits in quantitative research: defining hypotheses, working with code and examining evidence.
Begin with the task a model performs, then examine the rules and evidence needed to evaluate its output.
AI quant trading uses machine-learning or language-model methods within a rule-based research and trading workflow. The useful question is which task a model performs and how that task is evaluated.
A testable strategy describes what is known, when a decision is made and how an order is modeled. Natural language is a starting point for that specification.
AI output should be checked against the task it was meant to complete. Code correctness, forecast quality and trading performance require different evidence.