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

• URL: http://arxiv.org/abs/2603.17692v1
• Date: Wed, 18 Mar 2026 13:09:11 GMT
• Title: Can Blindfolded LLMs Still Trade? An Anonymization-First Framework for Portfolio Optimization
• Abstract summary: LLM trading agents must demonstrate understanding of market dynamics rather than exploitation of memorized ticker associations.<n>We address two sources of spurious performance: memorization bias from ticker-specific pre-training, and survivorship bias from flawed backtesting.<n>Our approach is to blindfold the agents--anonymizing all identifiers--and verify whether meaningful signals persist.
• Score: 1.0957528713294875
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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