利用 Synthesizer Transformer 模型，基于巨鲸交易和 CryptoQuant 数据预测比特币波动率飙升
Forecasting Bitcoin volatility spikes from whale transactions and CryptoQuant data using Synthesizer Transformer models
AI总结：
提出融合 CryptoQuant 链上数据与 whale-alert 巨鲸交易信息的 Synthesizer Transformer，提升比特币次日极端波动率飙升预测效果，并经可解释性分析与策略回测验证其风险管理价值。
摘要：
与传统金融市场相比，加密货币市场波动性极高。因此，预测其波动率对于风险管理至关重要。本文研究了 CryptoQuant 数据（例如链上分析、交易所和矿工数据）以及 whale-alert 推文，并探讨它们与比特币次日波动率之间的关系，重点关注极端波动率飙升。我们提出了一种用于预测波动率的深度学习 Synthesizer Transformer 模型。结果表明，在利用 CryptoQuant 数据以及 whale-alert 推文预测比特币极端波动率飙升时，该模型优于现有的最先进模型。我们使用 Captum XAI 库分析了模型，以研究哪些特征最为重要。我们还使用不同的基线交易策略对预测结果进行了回测，结果表明，我们能够在保持稳定利润的同时最大限度地减少回撤。研究结果强调，所提出的方法是预测比特币市场极端波动走势的有用工具。
英文摘要：
The cryptocurrency market is highly volatile compared to traditional financial markets. Hence, forecasting its volatility is crucial for risk management. In this paper, we investigate CryptoQuant data (e.g. on-chain analytics, exchange and miner data) and whale-alert tweets, and explore their relationship to Bitcoin's next-day volatility, with a focus on extreme volatility spikes. We propose a deep learning Synthesizer Transformer model for forecasting volatility. Our results show that the model outperforms existing state-of-the-art models when forecasting extreme volatility spikes for Bitcoin using CryptoQuant data as well as whale-alert tweets. We analysed our model with the Captum XAI library to investigate which features are most important. We also backtested our prediction results with different baseline trading strategies and the results show that we are able to minimize drawdown while keeping steady profits. Our findings underscore that the proposed method is a useful tool for forecasting extreme volatility movements in the Bitcoin market.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
