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

Reddit r/MachineLearning / 4/25/2026

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Key Points

The post asks how to architect an automated commentary engine that produces precise, human-readable daily trade attribution from high-volume time-series trade data at scale.
The author highlights a key constraint: attribution math must be deterministic and accurate, so they cannot rely on LLMs to compute results due to potential hallucinations.
They describe a tension between flexibility and rigidity—hardcoding all attribution scenarios in an ETL pipeline makes the system too inflexible for new business cases.
The question invites concrete design approaches, including whether to use agentic workflows that generate and execute Python/Polars in a sandbox versus using pre-calculated cubes and structured prompts for natural-language generation.
They also request recommendations on specific frameworks and design patterns that have worked in financial reporting (e.g., LangChain, LlamaIndex, PandasAI).

Hey everyone,

I'm currently working through a problem in the market risk reporting space and would love to hear how you all would architect this.

The Use Case: > I have thousands of trades coming in at varying frequencies (daily, monthly). I need to build a system that automatically analyzes this time-series data and generates a precise, human-readable commentary detailing exactly what changed and why.

For example, the output needs to be a judgment like: "The portfolio variance today was +$50k, driven primarily by a shift in the Equities asset class, with the largest single contributor being Trade XYZ."

The Dilemma:

The Math: Absolute precision is non-negotiable. I know I can't just dump raw data into an LLM and ask it to calculate attribution, because it will hallucinate the math. I usually rely on Python and Polars for the high-performance deterministic crunching.
The Rigidity: If I hardcode every single attribution scenario (by asset class, by region, by specific trade) into a static ETL pipeline before feeding it to an LLM for summarization, the system becomes too rigid to handle new business scenarios automatically.

My Question:

How would you strike the balance between deterministic mathematical precision and dynamic natural language generation?

Are you using Agentic workflows (e.g., having an LLM dynamically write and execute Polars/pandas code in a sandbox)? Or are you sticking to pre-calculated cubes and heavily structured context prompts? Any specific frameworks (LangChain, LlamaIndex, PandasAI, etc.) or design patterns you've had success within financial reporting?

Appreciate any insights!

submitted by /u/Problemsolver_11
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