CLQT: A Closed-Loop, Cost-Aware, Strategy-Consistent Benchmark for Diagnostic Evaluation of LLM Portfolio-Management Agents

Abstract

LLM agents are increasingly cast as autonomous portfolio managers, yet the dominant evaluation idiom, a leaderboard of returns over a fixed window, certifies neither the soundness of an agent's process nor the durability of its edge: one period's return is dominated by the market path, and apparent alpha can dissolve once look-ahead bias and trading costs are controlled. We introduce CLQT, a closed-loop benchmark that reframes LLM trading evaluation as diagnosis rather than ranking. CLQT enforces point-in-time data access through a hard TimeGate, models institutional transaction and financing costs, scores strategy consistency across rounds, and seals every gather-analyze-decide-execute-reflect cycle into a recompute-verifiable audit chain; the same model runs as a constrained investment committee or a single autonomous orchestrator, making scaffolding an experimental variable. From the audit trail CLQT computes a five-axis capability scorecard (Coherence, Acuity, Composure, Discipline, Reliability), with coherence scored partly by a held-out LLM judge to curb self-preference bias. We validate CLQT on a contamination-controlled, year-long multi-model backtest campaign with a 13-configuration ablation grid and a four-week live broker paper-trading track on post-cutoff data. The diagnosis-first read shows that the capability leader is not the Sharpe leader; that agents' allocations systematically fail to follow their own stated analysis, a stating-versus-doing gap stable across both tracks (+0.30 backtest, +0.23 live); and that module value registers on the capability and behavioral axes when returns alone cannot separate it. Net of realistic costs, agents clear defensive baselines but do not cleanly beat the index. Credible evaluation of LLM investment agents must therefore diagnose the process rather than rank a period's return, the standard CLQT operationalizes and makes auditable.