想知道LLM交易模型到底能不能赚钱？这篇论文给了诊断工具TradeLens，实测DeepSeek-V3.2选股不行，GLM-4.7择时也翻车，不吹准确率而看利润转化。
现有LLM交易系统评估忽视智能体经济可行性。论文提出TradeLens工具，从交易记录、运行时轨迹和部署配置重建交易轨迹，归因利润与成本。在DeepSeek-V3.2、GLM-4.7等骨干模型及多种资本规模、交易频率上测试，发现智能能否盈利取决于“智能到利润”转换效率：DeepSeek-V3.2资产选择差，GLM-4.7时机判断为负。资本规模和频率仅放大或削弱决策时机价值，并不改变根本模式。
Can Agentic Trading Systems Pay for Their Own Intelligence?
Large language model (LLM) agents are increasingly used in trading systems, where model reasoning, tool use, and continual decisions incur costs that are expected to produce trading value. Existing evaluations typically report performance metrics, but rarely examine agentic viability: whether dynamic LLM-mediated decisions convert their induced costs into measurable incremental profit. To apply this criterion, we introduce TradeLens, a trace-grounded diagnostic toolkit for evaluating agentic trading systems from their trading records, runtime traces, and deployment configurations. It reconstructs trading trajectories, attributes profit and cost to interpretable evidence, and diagnoses whether and why an agent pays for its own intelligence. We conduct extensive analysis across backbone models, capital scales, trading frequencies, and system architectures, together with deployment discussion. Our results show that viability hinges on intelligence-to-profit conversion: models exhibit different failure patterns, such as poor asset selection in DeepSeek-V3.2 and negative timing in GLM-4.7, while capital scale, trading frequency, and architecture matter only by amplifying or degrading decision-attributed timing value. These findings reframe the evaluation of LLM-based trading agents from capability-centric performance ranking to trace-grounded diagnosis of intelligence-to-profit conversion. Our code is available at https://anonymous.4open.science/r/TradeLens.