论文标题
使用合成器变压器模型的鲸鱼交易和加密数据的预测比特币波动率峰值
Forecasting Bitcoin volatility spikes from whale transactions and CryptoQuant data using Synthesizer Transformer models
论文作者
论文摘要
与传统金融市场相比，加密货币市场高度波动。因此，预测其波动性对于风险管理至关重要。在本文中，我们研究了加密数据（例如，链分析，交换和矿工数据）和鲸鱼 - 出售推文，并探索它们与比特币第二天波动率的关系，重点是极端波动率。我们提出了一个深度学习合成器变压器模型，以预测波动。我们的结果表明，该模型在使用加密数据以及鲸鱼 - 艾尔特推文预测比特币的极端波动率时要优于现有的最新模型。我们使用Captum XAI库分析了我们的模型，以研究哪些功能最重要。我们还通过不同的基线交易策略对我们的预测结果进行了回测，结果表明，我们能够在保持稳定的利润的同时最大程度地减少下降。我们的发现强调了所提出的方法是预测比特币市场中极端波动运动的有用工具。
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.
