Title: From On-chain to Macro: Assessing the Importance of Data Source Diversity in Cryptocurrency Market Forecasting Show Chinese title

Title: 从链上到宏观：评估数据源多样性在加密货币市场预测中的重要性

Abstract: This study investigates the impact of data source diversity on the performance of cryptocurrency forecasting models by integrating various data categories, including technical indicators, on-chain metrics, sentiment and interest metrics, traditional market indices, and macroeconomic indicators. We introduce the Crypto100 index, representing the top 100 cryptocurrencies by market capitalization, and propose a novel feature reduction algorithm to identify the most impactful and resilient features from diverse data sources. Our comprehensive experiments demonstrate that data source diversity significantly enhances the predictive performance of forecasting models across different time horizons. Key findings include the paramount importance of on-chain metrics for both short-term and long-term predictions, the growing relevance of traditional market indices and macroeconomic indicators for longer-term forecasts, and substantial improvements in model accuracy when diverse data sources are utilized. These insights help demystify the short-term and long-term driving factors of the cryptocurrency market and lay the groundwork for developing more accurate and resilient forecasting models. Show Chinese abstract

Abstract: 本研究通过整合各种数据类别，包括技术指标、链上指标、情感和兴趣指标、传统市场指数以及宏观经济指标，探讨数据源多样性对加密货币预测模型性能的影响。 我们引入了Crypto100指数，该指数代表按市值排名前100的加密货币，并提出了一种新颖的特征约简算法，以从多样化的数据源中识别最具影响力和稳健的特征。 我们的全面实验表明，数据源多样性显著提高了不同时间范围内的预测模型的预测性能。 关键发现包括链上指标对于短期和长期预测的重要性，传统市场指数和宏观经济指标对于长期预测的相关性日益增加，以及在使用多样化数据源时模型准确性的显著提升。 这些见解有助于揭示加密货币市场的短期和长期驱动因素，并为开发更准确和稳健的预测模型奠定基础。