Regime-Gated Fusion of Bitcoin Sentiment and Technical Features
Summary
The paper proposes a model for predicting Bitcoin’s short-horizon price direction by combining hourly price and volume features with sentiment from Reddit. A rolling volatility measure labels each observation as stable or volatile, and a learned gate changes the relative weight given to sentiment and price information: sentiment receives more weight in volatile conditions, while price dynamics receive more weight in stable conditions. The study compares this approach with price-only, sentiment-only, and static-fusion models across three-hour and six-hour forecasts, and reports ablation analyses of the components.
Using 3,491 hourly observations from July 2024 through September 2025, the regime-aware model records macro-F1 scores of 0.5474 and 0.5513 at the two horizons, and the highest three-hour AUC at 0.5084. Removing adaptive weighting is associated with a sharp six-hour recall decline. These results come from a limited sample and specific data sources; the reported AUC is close to chance, and the excerpt does not establish durable profitability or generalization to other assets and periods.
Key ideas
- Rolling 24-hour volatility divides observations into stable and volatile regimes.
- A learned gate shifts weight between social sentiment and price features based on the detected regime.
- The study compares regime-aware fusion with single-source and static-concatenation models at two forecast horizons.
- The reported scores are modest and come from a limited Bitcoin sample, so they do not establish trading profitability.
Tags
Full text
# 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.
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