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利用链上数据与巨鲸数据预测比特币波动率飙升

文章 arXiv papers · 作者: Dorien Herremans et al.
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总结

本文研究CryptoQuant信号（包括链上数据、交易所数据和矿工数据）以及巨鲸警报帖文能否帮助预测次日比特币波动率，尤其是极端飙升。研究提出Synthesizer Transformer预测模型，并将其表现与现有方法进行比较。作者报告称，使用这些数据源时，该模型对极端波动率的预测表现更好，但说明未提供样本期、资产市场细节或数值比较结果。

研究使用特征归因分析考察哪些输入因素发挥作用，并通过几种基准交易策略对预测结果进行回测。研究报告称，回撤有所降低且利润稳定，但摘要未提供具体指标、策略规则或交易成本细节。因此，这些结果表明其可能用于风险管理和交易，但现有说明并未证明其在不同市场环境或样本外情形下具有稳健性。

核心观点

  • 目标是预测次日比特币波动率，尤其关注极端飙升。
  • 输入数据包括CryptoQuant数据和巨鲸警报帖文。
  • 据报告，Synthesizer Transformer在极端波动率预测方面优于对比模型。
  • 研究采用特征归因分析检查影响较大的输入因素。
  • 回测报告称回撤更低且利润稳定，但未提供指标或稳健性细节。

标签

加密资产波动率机器学习链上数据回测风险管理
全文
# Forecasting Bitcoin volatility spikes from whale transactions and CryptoQuant data using Synthesizer Transformer models


# Forecasting Bitcoin volatility spikes from whale transactions and CryptoQuant data using Synthesizer Transformer models









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.

在遵守原作品许可的前提下，附作者信息全文展示。 许可协议: abstract CC0

此摘要由 Stratmill 研究智能体根据原文撰写，并非原文副本。