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结合比特币市场数据与社交信号进行交易

文章 arXiv papers · 作者: David Garcia et al.
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总结

本研究结合比特币市场指标与社交及行为信号，探讨程序化交易。输入数据包括交易所价格和成交量、技术普及程度及比特币交易量，以及搜索活动、口碑传播量、推文情绪和观点极化程度。分析涵盖三年多的比特币相关数据，并考察这些信号与价格走势的关系。

作者报告称，观点极化程度上升和交易所成交量增加先于比特币价格上涨，而情绪倾向则先于观点极化变化和交易所成交量增加。作者据此构建交易策略，并报告称不到一年的期间内取得了高额利润。他们还表示，统计检验已考虑风险和交易成本。文中未提供具体策略规则、绩效数据或验证细节，因此这些发现是该方法在所研究样本中的证据，并不能保证社交信号将持续具有预测能力。

核心观点

  • 分析结合比特币市场活动、普及程度、交易数据和社交信号。
  • 据报告，观点极化和交易所成交量会先于比特币价格上涨。
  • 据报告，情绪倾向会先于观点极化和交易所成交量增加。
  • 作者利用这些关系构建策略，并报告了盈利结果。
  • 描述缺少足够细节，无法独立评估策略规则或其持续性。

标签

加密资产市场情绪机器学习回测
全文
# Social signals and algorithmic trading of Bitcoin


# Social signals and algorithmic trading of Bitcoin









The availability of data on digital traces is growing to unprecedented sizes, but inferring actionable knowledge from large-scale data is far from being trivial. This is especially important for computational finance, where digital traces of human behavior offer a great potential to drive trading strategies. We contribute to this by providing a consistent approach that integrates various datasources in the design of algorithmic traders. This allows us to derive insights into the principles behind the profitability of our trading strategies. We illustrate our approach through the analysis of Bitcoin, a cryptocurrency known for its large price fluctuations. In our analysis, we include economic signals of volume and price of exchange for USD, adoption of the Bitcoin technology, and transaction volume of Bitcoin. We add social signals related to information search, word of mouth volume, emotional valence, and opinion polarization as expressed in tweets related to Bitcoin for more than 3 years. Our analysis reveals that increases in opinion polarization and exchange volume precede rising Bitcoin prices, and that emotional valence precedes opinion polarization and rising exchange volumes. We apply these insights to design algorithmic trading strategies for Bitcoin, reaching very high profits in less than a year. We verify this high profitability with robust statistical methods that take into account risk and trading costs, confirming the long-standing hypothesis that trading based social media sentiment has the potential to yield positive returns on investment.

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。