Reliable Before Profitable: QuantHarness for Governing Financial Strategy Development with LLM Agents

Key points

• LLM agents can turn natural-language financial research requests into executable and backtested strategies, but a successful run may still implement the wrong rule, use information unavailable at decision time, or obscure how repairs and candidate selection changed the strategy.1
• Because these failures arise throughout development, final code and performance metrics alone are insufficient to establish either fidelity to the request or the validity of the development process.1
• We therefore address the reliability of financial strategy development at the trajectory level: the system must control how intermediate outputs become downstream state and preserve the evidence needed to justify the terminal outcome.1
• We also introduce FinRelBench, a benchmark that evaluates strategy-development reliability through execution success, requirement satisfaction, and runtime evidence.1

Sources (1)

• [1]Reliable Before Profitable: QuantHarness for Governing Financial Strategy Development with LLM Agents
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 19, 09:59 AM

  We introduce QuantHarness, a two-layer agent harness in which a finance-specific Workflow structures specification, data grounding, construction, testing, and review, while Harness Core enforces stage-specific information, action, and commitment boundaries.

  LLM agents can turn natural-language financial research requests into executable and backtested strategies, but a successful run may still implement the wrong rule, use information unavailable at decision time, or obscure how repairs and candidate selection changed the strategy.

Extractive summary: sentences quoted from the sources.