How would you build an automated commentary engine for daily trade attribution at scale? [R]

**Automated Trade Attribution Commentary Engine Architecture** A Reddit practitioner post describes a system design problem: building a pipeline that ingests thousands of trades at varying frequencies, runs deterministic attribution calculations, and generates accurate natural-language commentary explaining P&L drivers (e.g., "portfolio variance +$50k, driven by Equities, largest contributor Trade XYZ"). The proposed constraint is strict separation of concerns — Python/Polars handles all quantitative attribution math to avoid LLM hallucination, while the LLM operates only on pre-computed, structured outputs to generate prose. This pattern (deterministic compute → structured intermediate representation → LLM narration) is the practical standard for any financial reporting use case where numerical accuracy is auditable, and the post surfaces real engineering tradeoffs: hardcoded template logic doesn't scale across asset classes and regions, but unconstrained LLM math is unreliable. The relevant design question for practitioners is how to structure the intermediate data contract — likely a JSON schema of ranked attribution factors — that constrains the LLM's input space tightly enough to make output validation tractable at scale.