发表机构
Virginia Tech（弗吉尼亚理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过合成交易基准，发现执行假设（如收盘价成交）会显著改变短期夏普比率排名，排名稳定性应作为基准报告的一部分。
AI 中文摘要
LLM交易代理的回测通常假设每笔订单都能以收盘价成交。我们探究这一选择是否仅改变报告的收益，还是也会改变代理的排名。五个提示式LLM信号策略和七个经典基线在六种执行设置下交易相同的合成价格路径，这些设置从接近理想的成交到延迟、价差、参与度和冲击压力不等。主实验包含2,462次运行，具有匹配的决策频率和配对的市场路径。在压缩的双资产棋盘上，高波动制度下接近理想与默认压力排名之间的一致性降至Kendall τ_b=0.21，而平静制度下为0.82。种子自助法区间[0.00,0.52]和[0.48,0.94]较宽且重叠。在固定的11策略棋盘上，一致性从双资产时的0.24上升到十资产时的0.85；双资产点估计与我们测试的更广泛设置存在显著差异。排名变化与换手率相关，与买入持有策略的比较也取决于该锚点的初始化方式。实验并不比较LLM交易技能。它表明，在短期内，执行约定可能成为基准的显著特征。执行假设和排名稳定性应与收益一同报告。
英文摘要
Backtests of LLM trading agents often assume that every order fills at the closing price. We ask whether this choice changes only reported returns or also the order of the agents. Five prompted LLM signal policies and seven classical baselines trade the same synthetic price paths under six execution settings, from near-ideal fills to latency, spread, participation, and impact stresses. The main experiment contains $2{,}462$ runs with matched decision frequencies and paired market paths. On the compressed two-asset board, agreement between the near-ideal and default-stress rankings falls to Kendall $τ_b=0.21$ in the high-volatility regime, compared with $0.82$ in the calm regime. The seed-bootstrap intervals, $[0.00,0.52]$ and $[0.48,0.94]$, are wide and overlap. On a fixed 11-policy board, agreement rises from 0.24 with two assets to 0.85 with ten; the two-asset point estimate differs substantially from the wider settings we tested. Rank changes are related to turnover, and comparisons with buy-and-hold also depend on how that anchor is initialized. The experiment does not compare LLM trading skill. It shows that, on a short horizon, an execution convention can become part of the benchmark's headline. Execution assumptions and rank stability should be reported alongside returns.
