CLQT: A Closed-Loop, Cost-Aware, Strategy-Consistent Benchmark for Diagnostic Evaluation of LLM Portfolio-Management Agents
创建于 更新于
摘要
CLQT reframes closed-loop trading evaluation as a diagnostic instrument rather than a simple leaderboard, introducing a five-stage loop (gather, synthesize, construct a mandate-aware portfolio, execute with cost discipline, reflect) and a tamper-evident audit trail that enables reconstruction of every metric from the decision trail. It yields a five-axis capability scorecard (APM-CS): Coherence, Acuity, Composure, Discipline, Reliability, calibrated against held-out judges and ablations, and validated through backtest and a live broker track. The contribution is a durable map of agent competencies and limitations—across both backtest and live—rather than a single, once-off ranking. This diagnostic framework is designed to scale with more models, regimes, and iterations, aiming to inform growth and improvement of AI-driven portfolio-management agents [page::0][page::1].
