arXiv cs.MA· Junzhou Su, Yichen Luo, Yebo Feng, Teng Li, Jiahua Xu·· 6 天前AI 评分22
SwingGuard：风险条件化的多智能体波段交易框架，采用双时间尺度治理
SwingGuard: Risk-Conditioned Multi-Agent Swing Trading with Dual-Timescale Governance
AI 导读
SwingGuard 是一个面向加密货币波段交易的双时间尺度、仅做多多智能体框架，在策略生成前引入当前风险条件，并由每五分钟运行一次的 Risk Controller 持续约束持仓。
正文 · 原文
Abstract:Cryptocurrency swing trading spans mismatched timescales: positions remain active while market and event risk can change within minutes. However, many predictive and large language model (LLM)-based trading systems assess risk after strategy generation or run both functions at the same cadence, leaving proposals unconditioned by current risk and exposure weakly governed between revisions. We propose SwingGuard, a dual-timescale, long-only multi-agent framework for risk-conditioned strategy generation. At the strategy timescale, specialized analysts synthesize point-in-time technical, sentiment, on-chain, and macroeconomic evidence through bullish-bearish deliberation. The resulting evidence and current risk context are then combined by a Portfolio Architect to form a target position. At the governance timescale, a five-minute Risk Controller performs conditional diagnosis, maintains persistent risk states with staged recovery, audits the proposal, and constrains execution until the next strategy revision. This design allows risk to shape the feasible strategy before proposal finalization while continuing to govern the active position afterward. Evaluations on Bitcoin (BTC) and Ether (ETH) show that SwingGuard preserves positive returns after transaction costs while maintaining shallow drawdowns and strong risk-adjusted performance on both assets. The exposure-capped joint portfolio achieves a Sharpe ratio of 2.98, compared with 2.79 for an otherwise identical Post-hoc Risk variant. Structured traces further link risk evidence and state transitions to strategy constraints and executed position changes, providing an auditable framework for asynchronous cryptocurrency swing trading. Overall, the results indicate that conditioning strategy formation on current risk, together with persistent high-frequency governance, improves risk-adjusted performance without sacrificing auditability.
| Comments: | 9 pages, 4 figures |
| Subjects: | Multiagent Systems (cs.MA) |
| Cite as: | arXiv:2610.12988 [cs.MA] |
| (or arXiv:2610.12988v1 [cs.MA] for this version) | |
| https://doi.org/10.48550/arXiv.2610.12988 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Junzhou Su [view email]
[v1]
Fri, 9 Oct 2026 04:41:05 UTC (2,043 KB)
来源：arXiv cs.MA · arxiv.org