How do you verify AI-generated trading strategies?

Treat an AI-generated strategy as an unverified hypothesis. Convert it into explicit rules, confirm the required data exists, remove future-looking assumptions, model costs and execution, test robustness, and require human review before any trading decision.

The first step is to replace vague model output with a strategy spec: universe, inputs, signal formula, rebalance schedule, sizing, risk limits, and conditions where the strategy should not trade.

The verification step should treat the AI output as a draft produced by an assistant, not as an authority. Every data field, rule, and expected edge needs to be connected to something the system can actually observe and test.

AI systems can invent data fields, assume fills that are not realistic, ignore transaction costs, or use information that would not have existed at the decision time. Every assumption needs to be checked before testing.

A strategy should be tested against realistic costs, multiple windows, out-of-sample periods, stress scenarios, and risk constraints. If the idea only works under one fragile set of assumptions, it is not ready for capital.

Human review should happen before a generated idea becomes a backtest, before a backtest becomes a paper strategy, and before any later trading workflow is allowed to act. The reviewer should be able to see the strategy spec, assumptions, validation artifacts, and known failure modes in one place.

Alphora is designed for AI-assisted research workflows where generated ideas still need explicit strategy specs, validation artifacts, review steps, and controlled execution paths.