The Alpha Illusion: Reported Alpha from LLM Trading Agents Should Not Be Treated as Deployment Evidence

2026-05-16

AI Summary

This study critically examines the evaluation paradigms of existing end-to-end large language model (LLM) trading agents, whose reported excess returns are often misinterpreted as deployable evidence despite lacking rigorous validation of temporal consistency, real-world market frictions, and robustness. We demonstrate that such gains may stem from temporal leakage, unmodeled transaction costs, or overfitting to narrative patterns rather than genuine predictive ability. To address these issues, we propose a tiered minimum reporting protocol (P1–P6) and introduce a conservative modular architecture that decouples the LLM—used solely as an auditable information interface—from independently calibrated risk management, signal validation, and execution modules. Through structured validity tests encompassing temporal integrity, trading frictions, and counterfactual robustness, we find insufficient public evidence to support deployable predictive capabilities in current LLM-based agents and release open-source replication tools to foster more rigorous evaluation standards.

Abstract

End-to-end LLM trading agents have moved quickly from research curiosity to a small ecosystem of named systems, including FinCon, FinMem, TradingAgents, FinAgent, QuantAgent, and FLAG-Trader. Several of these report headline Sharpe ratios that would be material if read at face value on a deployment desk, and associated benchmarks such as FinBen report trading-task Sharpe statistics in the same range. The gap between architecture research and deployment claim has been crossed too freely on both sides of the academia--industry divide. We take a position on that gap: reported alpha from end-to-end LLM trading agents should not be treated as deployment evidence. Before such returns can support claims of deployable trading capability, they must survive structural validity tests for temporal integrity, real-world frictions, counterfactual robustness, predictive calibration, numerical execution, and multi-agent disaggregation. Current public evidence cannot yet distinguish robust predictive ability from temporal contamination, unmodeled frictions, short-window Sharpe uncertainty, narrative fitting, and parametric priors. The problem is not only evaluative but structural. Language confidence is not tradable probability, narrative reasoning is not numerical execution, and model priors may become undisclosed implicit factor exposures. We contribute a minimum reporting protocol suite, P1--P6, with tiered applicability by claim strength, and a conservative modular alternative that uses LLMs as auditable information interfaces upstream of independent calibration, risk, and execution modules. Code and reproduction harness: \url{https://github.com/hj1650782738/Trading}.

Problem

• alpha illusion
• LLM trading agents
• deployment evidence
• temporal contamination
• Sharpe ratio

Innovation

• LLM trading agents
• alpha illusion
• structural validity
• temporal integrity
• modular trading architecture