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From Hypotheses to Factors: Constrained LLM Agents in Cryptocurrency Markets

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  • Yikuan Huang
  • Zheqi Fan
  • Kaiqi Hu
  • Yifan Ye

Abstract

LLM agents are promising tools for empirical discovery, but their flexibility can also turn discovery into uncontrolled search. We study how to use agents under a reproducible protocol through cryptocurrency factor discovery. Our framework casts the task as sequential hypothesis search: an agent reads an append-only experiment trace, proposes falsifiable factor hypotheses, and maps them to executable recipes, while a deterministic engine enforces fixed data splits, selection gates, transaction costs, and portfolio tests. Candidate actions are restricted to a point-in-time factor DSL, making both successful and failed hypotheses auditable. A ridge-combined portfolio trained only on 2020--2022 data achieves a 44.55% annualized return and Sharpe ratio of 1.55 in the 2024--2026 pure out-of-sample period after a 5 basis point one-way trading cost.

Suggested Citation

  • Yikuan Huang & Zheqi Fan & Kaiqi Hu & Yifan Ye, 2026. "From Hypotheses to Factors: Constrained LLM Agents in Cryptocurrency Markets," Papers 2604.26747, arXiv.org.
    • Handle: RePEc:arx:papers:2604.26747
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    File URL: http://arxiv.org/pdf/2604.26747
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