Beyond Agent Architecture: Execution Assumptions and Reproducibility in LLM-Based Trading Systems
arXiv:2606.08285v1 Announce Type: new Abstract: Large language models (LLMs) and agentic systems are increasingly proposed for financial trading, yet their reported performance remains difficult to compare because studies vary in data provenance, temporal split discipline, execution timing, turnover treatment, and transaction-cost modeling. This article presents a targeted topical review and reproducibility audit of execution realism in LLM-based trading research. A coded evidence matrix covering 30 trade-relevant primary studies is used to assess point-in-time controls, split transparency, he
The increasing proliferation of LLMs and agentic systems in financial applications necessitates a critical review of real-world applicability and comparability, highlighting current reproducibility challenges.
This research provides a framework for assessing the reliability and true performance of LLM-based trading systems, which is crucial for institutional adoption and risk management.
The transparency and methodological rigor for evaluating AI-driven financial strategies will improve, shifting focus from headline performance to robust, reproducible execution assumptions.
- · Transparent AI/ML research
- · Financial institutions with strong validation processes
- · Quantitative traders focused on real-world execution
- · Robust AI agent developers
- · Unsubstantiated AI trading claims
- · Researchers with poor reproducibility standards
- · Speculative AI investment firms
- · Black-box AI financial products
Increased scrutiny on the practical implementation details and execution environments of LLM-based trading strategies.
Demand for standardized benchmarks and open-source validation tools for AI agents in finance, driving a 'trust but verify' approach.
Consolidation in the AI finance sector as only systems demonstrating true robustness and transparent execution gain market traction and regulatory approval.
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Read at arXiv cs.AI