基于状态感知的多模态融合社会情绪与技术特征的比特币价格方向预测

摘要

比特币次日内时间尺度价格预测是计算金融领域一项极具挑战性的开放性问题。比特币呈现肥尾收益、非平稳动态特性，且其价格发现过程受Reddit和Twitter社交舆论影响。传统方法通过静态拼接将OHLCV技术特征与情绪特征进行融合，无论市场状态如何均采用相同的融合权重。这与行为金融学文献的发现不一致——后者表明，零售情绪在波动时期最具预测力，而在平静时期则充满噪音。本文提出“状态感知多模态学习”（Regime-Aware Multi-Modal Learning, RAML），该方法将情绪特征与价格特征的融合置于动态检测的二元市场状态条件下。滚动24小时波动率将观测样本划分为稳定状态与波动状态；一个可学习的sigmoid门控机制可调整情绪嵌入相对于价格嵌入的权重，在波动期更信任情绪特征，在稳定期则更信任价格动态。该系统基于3,491个小时观测数据（2024年7月至2025年9月）进行评估，结合了比特币OHLCV数据与Reddit /r/Bitcoin子论坛的FinBERT情绪。对比了四种模型——仅价格BiLSTM、仅情绪分类器、静态拼接BiLSTM以及RAML——分别针对3小时和6小时预测窗口，并通过消融实验单独检验情绪分支、状态检测与自适应融合的效果。RAML在3小时窗口上达到0.5474的宏平均F1分数，在6小时窗口上达到0.5513，并在3小时窗口上取得最高AUC（0.5084），表明其校准更优。消融实验证实每个组件均不可或缺，若将自适应加权替换为静态拼接，则6小时窗口上的召回率崩溃（F1: 0.14）。这些结果确立了状态条件自适应融合作为多模态金融预测的必要设计原则。

English

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.