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WebCryptoAgent: Combining Web Evidence with Fast Crypto Risk Controls

Article arXiv papers · Author: Ali Kurban et al.
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Summary

WebCryptoAgent is a framework for short-horizon cryptocurrency trading that combines web content, social sentiment, and OHLCV market data. It assigns modality-specific agents to analyze different information sources, then consolidates their assessments into an evidence document for confidence-calibrated decisions. The design aims to limit decisions driven by noisy or spurious correlations.

The framework also separates strategic reasoning on an hourly schedule from a second-level risk model. This lets the risk component detect abrupt shocks and intervene without waiting for the slower trading loop. The authors report experiments on real-world cryptocurrency markets and say the approach improves trading stability, reduces unnecessary activity, and handles tail risk better than baseline systems. The supplied description does not state datasets, evaluation metrics, numerical results, or the conditions under which these gains hold, so the strength and generality of the evidence cannot be assessed from this summary alone.

Key ideas

  • Separate agents analyze web, sentiment, and OHLCV inputs before their evidence is consolidated.
  • Confidence-calibrated reasoning is intended to reduce decisions based on spurious signals.
  • Hourly strategic decisions are paired with a faster, independent risk model.
  • The authors report greater stability and improved tail-risk handling in real-market experiments.

Tags

CryptoSentimentMarket microstructureRisk managementMachine learning
Full text
# WebCryptoAgent: Agentic Crypto Trading with Web Informatics


# WebCryptoAgent: Agentic Crypto Trading with Web Informatics









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.

Shown in full with attribution under the source's licence. Licence: abstract CC0

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.