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利用Reddit情绪智能体进行高频加密货币交易

文章 arXiv papers · 作者: Qiuhan Han et al.
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总结

PulseReddit将大规模Reddit讨论与高频加密货币市场统计数据对齐，以研究社交信息能否支持短期交易。本文评估整合这些数据的LLM多智能体系统，并将其表现与传统基准进行比较。

报告的实验发现，加入情绪信息的系统交易表现更好，尤其是在牛市中，并描述了其跨市场状态的适应能力。研究还考察了模型表现与效率之间的权衡，以帮助选择模型。所提供的描述没有说明数据集构建细节、资产、评估时期、成本或数值结果，因此仅凭这段文字无法更准确地评估这些结论。

核心观点

  • 该数据集将Reddit讨论数据与高频加密货币市场统计数据配对。
  • 基于LLM的多智能体系统利用社交数据研究短期交易表现。
  • 报告的优势在牛市中最明显，研究也考察了系统在不同市场状态下的适应能力。
  • 模型选择需要权衡交易表现与计算效率。

标签

加密资产高频交易市场情绪机器学习
全文
# PulseReddit: A Novel Reddit Dataset for Benchmarking MAS in High-Frequency Cryptocurrency Trading


# PulseReddit: A Novel Reddit Dataset for Benchmarking MAS in High-Frequency Cryptocurrency Trading









High-Frequency Trading (HFT) is pivotal in cryptocurrency markets, demanding rapid decision-making. Social media platforms like Reddit offer valuable, yet underexplored, information for such high-frequency, short-term trading. This paper introduces \textbf{PulseReddit}, a novel dataset that is the first to align large-scale Reddit discussion data with high-frequency cryptocurrency market statistics for short-term trading analysis. We conduct an extensive empirical study using Large Language Model (LLM)-based Multi-Agent Systems (MAS) to investigate the impact of social sentiment from PulseReddit on trading performance. Our experiments conclude that MAS augmented with PulseReddit data achieve superior trading outcomes compared to traditional baselines, particularly in bull markets, and demonstrate robust adaptability across different market regimes. Furthermore, our research provides conclusive insights into the performance-efficiency trade-offs of different LLMs, detailing significant considerations for practical model selection in HFT applications. PulseReddit and our findings establish a foundation for advanced MAS research in HFT, demonstrating the tangible benefits of integrating social media.

在遵守原作品许可的前提下，附作者信息全文展示。 许可协议: abstract CC0

此摘要由 Stratmill 研究智能体根据原文撰写，并非原文副本。