AI Agents 相关度: 9/10

Can Blindfolded LLMs Still Trade? An Anonymization-First Framework for Portfolio Optimization

Joohyoung Jeon, Hongchul Lee
arXiv: 2603.17692v1 发布: 2026-03-18 更新: 2026-03-18

AI 摘要

该论文提出了一种匿名化优先的框架BlindTrade,验证LLM交易代理的信号有效性,避免记忆和幸存者偏差。

主要贡献

  • 提出了BlindTrade框架,用于评估LLM交易代理的泛化能力。
  • 通过匿名化股票代码和公司名称,降低了记忆偏差的影响。
  • 使用GNN和PPO-DSR策略进行交易,并在实际数据上进行了验证。

方法论

匿名化股票代码和公司名称,LLM生成交易分数和推理,构建GNN,用PPO-DSR策略进行交易,并进行负控制实验。

原文摘要

For LLM trading agents to be genuinely trustworthy, they must demonstrate understanding of market dynamics rather than exploitation of memorized ticker associations. Building responsible multi-agent systems demands rigorous signal validation: proving that predictions reflect legitimate patterns, not pre-trained recall. We address two sources of spurious performance: memorization bias from ticker-specific pre-training, and survivorship bias from flawed backtesting. Our approach is to blindfold the agents--anonymizing all identifiers--and verify whether meaningful signals persist. BlindTrade anonymizes tickers and company names, and four LLM agents output scores along with reasoning. We construct a GNN graph from reasoning embeddings and trade using PPO-DSR policy. On 2025 YTD (through 2025-08-01), we achieved Sharpe 1.40 +/- 0.22 across 20 seeds and validated signal legitimacy through negative control experiments. To assess robustness beyond a single OOS window, we additionally evaluate an extended period (2024--2025), revealing market-regime dependency: the policy excels in volatile conditions but shows reduced alpha in trending bull markets.

标签

LLM Trading Agent Anonymization Portfolio Optimization GNN PPO

arXiv 分类

cs.LG cs.AI q-fin.CP q-fin.PM