融合宏观、情绪与技术指标，提升比特币短期预测精度
CryptoPulse: Short-Term Cryptocurrency Forecasting with Dual-Prediction and Cross-Correlated Market Indicators
- 双预测机制结合宏观波动、情绪和指标数据
- 在多个数据集上超越10种对比模型，显著提升准确率
- 适合量化交易者与加密市场分析人员使用
加密货币市场波动剧烈，给投资者带来挑战。现有预测系统多依赖历史价格模式，但常忽视三大关键因素：1）宏观投资环境，体现为主要币种波动引发的协同投资行为；2）整体市场情绪，受新闻影响显著；3）技术指标，揭示超买/超卖、动量与趋势，对短期价格变动至关重要。本文提出双预测机制，通过整合宏观经济波动、技术指标及个别币种价格变化，预测次日收盘价。同时引入基于市场情绪的重标定与融合优化机制。实验表明，该模型性能达当前最优，持续优于十种对比方法。
原文摘要 · Abstract (English)
Cryptocurrencies fluctuate in markets with high price volatility, posing significant challenges for investors. To aid in informed decision-making, systems predicting cryptocurrency market movements have been developed, typically focusing on historical patterns. However, these methods often overlook three critical factors influencing market dynamics: 1) the macro investing environment, reflected in major cryptocurrency fluctuations affecting collaborative investor behaviors; 2) overall market sentiment, heavily influenced by news impacting investor strategies; and 3) technical indicators, offering insights into overbought or oversold conditions, momentum, and market trends, which are crucial for short-term price movements. This paper proposes a dual prediction mechanism that forecasts the next day's closing price by incorporating macroeconomic fluctuations, technical indicators, and individual cryptocurrency price changes. Additionally, a novel refinement mechanism enhances predictions through market sentiment-based rescaling and fusion. Experiments demonstrate that the proposed model achieves state-of-the-art performance, consistently outperforming ten comparison methods.
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