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比特币情绪与技术特征的状态门控融合

文章 arXiv papers · 作者: Muhammad Abdullah Haroon
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总结

本文提出一种预测比特币短期价格方向的模型，将小时级价格和成交量特征与 Reddit 情绪结合。滚动波动率指标将每条观测标记为稳定或波动状态，训练得到的门控机制则调整情绪与价格信息的相对权重：波动状态下赋予情绪更高权重，稳定状态下则更重视价格动态。研究将该方法与仅使用价格、仅使用情绪和静态融合模型进行比较，预测时距为三小时和六小时，并报告了各组成部分的消融分析。

使用3,491条小时级观测，时间跨度为2024年7月至2025年9月。状态感知模型在两个预测时距下的宏平均F1得分分别为0.5474和0.5513，三小时预测的AUC最高，为0.5084。移除自适应加权后，六小时召回率明显下降。这些结果来自有限样本和特定数据源；报告的AUC接近随机水平，摘要也未能证明其盈利能力持久，或可推广到其他资产和时期。

核心观点

  • 滚动24小时波动率将观测划分为稳定和波动状态。
  • 训练得到的门控机制根据检测到的状态，在社交情绪和价格特征之间调整权重。
  • 研究在两个预测时距下，将状态感知融合模型与单一信息源模型和静态拼接模型进行比较。
  • 报告的得分不高，且来自有限的比特币样本，因此不能证明交易盈利能力。

标签

加密资产机器学习市场情绪技术指标波动率
全文
# Bitcoin Price Direction Prediction via Regime-Aware Multi-Modal Fusion of Social Sentiment and Technical Features


# Bitcoin Price Direction Prediction via Regime-Aware Multi-Modal Fusion of Social Sentiment and Technical Features









Bitcoin price prediction on sub-daily timescales is a hard open problem in computational finance. Bitcoin exhibits fat-tailed returns, non-stationary dynamics, and a price discovery process influenced by social discourse on Reddit and Twitter. Conventional approaches fuse OHLCV technical features with sentiment via static concatenation, applying identical fusion weights regardless of market state. This is inconsistent with the behavioural finance literature, which shows that retail sentiment is most predictive during volatile periods and noisy during calm ones. This paper proposes Regime-Aware Multi-Modal Learning (RAML), which conditions fusion of sentiment and price features on a dynamically detected binary market regime. Rolling 24-hour volatility partitions observations into stable and volatile regimes; a learnable sigmoid gate adjusts the weight of the sentiment embedding relative to the price embedding, trusting sentiment more during volatility and price dynamics more during stable phases. The system is evaluated on 3,491 hourly observations (July 2024-September 2025), combining Bitcoin OHLCV data with Reddit /r/Bitcoin FinBERT sentiment. Four models are compared - price-only BiLSTM, sentiment-only classifier, static-concatenation BiLSTM, and RAML - across 3-hour and 6-hour horizons, with an ablation study isolating the sentiment branch, regime detection, and adaptive fusion. RAML achieves macro-F1 of 0.5474 (3h) and 0.5513 (6h), with the highest AUC at 3 hours (0.5084), indicating better calibration. Ablation confirms every component is necessary, and replacing adaptive weighting with concatenation causes recall collapse at 6 hours (F1: 0.14). These results establish regime-conditioned adaptive fusion as a necessary design principle for multi-modal financial forecasting.

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。