Cryptocurrency trading increasingly depends on timely integration of heterogeneous web information and market microstructure signals to support short-horizon decision making under extreme volatility. However, existing trading systems struggle to jointly reason over noisy multi-source web evidence while maintaining robustness to rapid price shocks at sub-second timescales. The first challenge lies in synthesizing unstructured web content, social sentiment, and structured OHLCV signals into coherent and interpretable trading decisions without amplifying spurious correlations, while the second challenge concerns risk control, as slow deliberative reasoning pipelines are ill-suited for handling abrupt market shocks that require immediate defensive responses. To address these challenges, we propose WebCryptoAgent, an agentic trading framework that decomposes web-informed decision making into modality-specific agents and consolidates their outputs into a unified evidence document for confidence-calibrated reasoning. We further introduce a decoupled control architecture that separates strategic hourly reasoning from a real-time second-level risk model, enabling fast shock detection and protective intervention independent of the trading loop. Extensive experiments on real-world cryptocurrency markets demonstrate that WebCryptoAgent improves trading stability, reduces spurious activity, and enhances tail-risk handling compared to existing baselines. Code will be available at https://github.com/AIGeeksGroup/WebCryptoAgent.

翻译：加密货币交易日益依赖于及时整合异构网络信息与市场微观结构信号，以支持极端波动性下的短期决策。然而，现有交易系统难以在嘈杂的多源网络证据上进行联合推理，同时保持对亚秒级时间尺度下快速价格冲击的鲁棒性。第一个挑战在于将非结构化网络内容、社交情绪与结构化OHLCV信号综合为连贯且可解释的交易决策，同时避免放大虚假相关性；第二个挑战则涉及风险控制，因为缓慢的审慎推理流程难以应对需要立即采取防御性响应的突发市场冲击。为应对这些挑战，我们提出WebCryptoAgent，一种智能体化交易框架。该框架将基于网络信息的决策分解为面向特定模态的智能体，并将其输出整合为统一的证据文档，以进行置信度校准的推理。我们进一步引入一种解耦的控制架构，将战略性的小时级推理与实时秒级风险模型分离，从而实现独立于交易循环的快速冲击检测与保护性干预。在真实世界加密货币市场上的大量实验表明，相较于现有基线方法，WebCryptoAgent提升了交易稳定性，减少了虚假交易活动，并增强了对尾部风险的处理能力。代码将在 https://github.com/AIGeeksGroup/WebCryptoAgent 提供。