Multi-Agent Analysis of Off-Exchange Public Information for Cryptocurrency Market Trend Prediction

2025-10-09

AI Summary

Cryptocurrency markets exhibit extreme volatility, operate 24/7, react strongly to news, and pose challenges in identifying sideways trends—limitations inadequately addressed by existing methods in heterogeneous information fusion and dynamic risk modeling. This paper proposes a multi-agent trend forecasting framework that innovatively integrates news semantics (extracted via large language models), market technical indicators, and state-aware mechanisms. We design an information-preserving news analysis system, an adaptive volatility-conditioned fusion module, and a low-overhead distributed agent coordination architecture—enabling theoretically grounded market impact quantification and real-time signal integration. Experiments on Bitcoin data demonstrate statistically significant improvements over state-of-the-art NLP-based baselines across short-, medium-, and long-term forecasting tasks, validating the framework’s effectiveness and advancement in financial time-series prediction and real-time risk management.

Technology Category

• Multiagent Systems: Adversarial Agents
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)

Application Category

• Economics, Online Markets and Human Computation: Economic aspects of blockchain and cryptocurrencies
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Cryptocurrency markets present unique prediction challenges due to their extreme volatility, 24/7 operation, and hypersensitivity to news events, with existing approaches suffering from key information extraction and poor sideways market detection critical for risk management. We introduce a theoretically-grounded multi-agent cryptocurrency trend prediction framework that advances the state-of-the-art through three key innovations: (1) an information-preserving news analysis system with formal theoretical guarantees that systematically quantifies market impact, regulatory implications, volume dynamics, risk assessment, technical correlation, and temporal effects using large language models; (2) an adaptive volatility-conditional fusion mechanism with proven optimal properties that dynamically combines news sentiment and technical indicators based on market regime detection; (3) a distributed multi-agent coordination architecture with low communication complexity enabling real-time processing of heterogeneous data streams. Comprehensive experimental evaluation on Bitcoin across three prediction horizons demonstrates statistically significant improvements over state-of-the-art natural language processing baseline, establishing a new paradigm for financial machine learning with broad implications for quantitative trading and risk management systems.

Problem

• Predicting cryptocurrency trends using multi-agent analysis
• Overcoming volatility and news sensitivity challenges
• Improving information extraction and market regime detection

Innovation

• Multi-agent framework with information-preserving news analysis
• Adaptive fusion mechanism combining sentiment and indicators
• Distributed coordination architecture for real-time data processing