Feedback request: structured strategy representation and provenance layer for financial agents

## Background

I maintain an early-stage open-source project called QST.

QST tries to represent quantitative trading strategies in a structured format that is easier to transmit, validate, store, compare, and process by agents or automated research systems.

It is not a reinforcement learning environment, backtesting engine, trading engine, broker adapter, or replacement for FinRL / FinRobot / AI4Finance components. Its scope is narrower: strategy representation, validation, and provenance.

## Core idea

Many strategies are currently stored as Python code, configs, notebooks, or informal descriptions. These formats are flexible, but they are not always easy to validate, compare, audit, or process reliably by agents.

QST explores whether strategies can be represented as structured, canonical, hashable records, with outputs such as validation diagnostics, deterministic canonical JSON, graph / parameter / instance hashes, and provenance metadata for experiments or agent workflows.

## Possible integration path

QST could start as a sidecar tool or external adapter, without requiring changes to existing FinRL, FinRobot, or AI4Finance workflows.

A first step could simply be:

strategy idea or config -> QST record -> validate -> canonicalize -> hash -> provenance report

This may be useful for agent-generated strategies, strategy memory, experiment reproducibility, or tracking strategy evolution.

## Questions

1. Does this kind of structured strategy representation layer seem useful for financial agent or quant research workflows?
2. Would strategy memory, agent-generated strategy validation, or experiment provenance be a reasonable first use case?
3. Are there existing papers, standards, or systems I should study before continuing?
4. Are there any design risks or wrong assumptions I should be aware of?

Project link: https://github.com/waswrsis/Quant-Strategy-Tokenizer