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结合比特币情绪与技术指标进行机器学习

文章 arXiv papers · 作者: Arthur Emanuel de Oliveira Carosia
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总结

这项初步研究探讨能否结合市场情绪与技术分析来预测比特币价格走势。情绪输入为恐惧与贪婪指数，并与技术指标和机器学习算法一同使用。研究的动机是，情绪已被认为与价格波动有关，而将情绪指标与技术指标结合起来的研究较少。作者报告称，初步实验产生了可观的投资回报，并超过买入并持有基准。所提供的描述未指出算法、指标、数据时期、验证流程或风险调整后结果。因此，它提供的是一个研究方向和初步表现主张，细节不足以判断稳健性、复现策略，或确定结果能否推广到实验之外。

核心观点

  • 本研究将恐惧与贪婪指数和技术指标结合，用于建模比特币价格走势。
  • 研究使用机器学习算法探索加密货币预测。
  • 作者称这项工作仍处于初步阶段，并报告回报超过买入并持有基准。
  • 所提供的描述缺少评估稳健性所需的方法和验证细节。

标签

加密资产市场情绪技术指标机器学习回测
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# Using Sentiment and Technical Analysis to Predict Bitcoin with Machine Learning


# Using Sentiment and Technical Analysis to Predict Bitcoin with Machine Learning









Cryptocurrencies have gained significant attention in recent years due to their decentralized nature and potential for financial innovation. Thus, the ability to accurately predict its price has become a subject of great interest for investors, traders, and researchers. Some works in the literature show how Bitcoin's market sentiment correlates with its price fluctuations in the market. However, papers that consider the sentiment of the market associated with financial Technical Analysis indicators in order to predict Bitcoin's price are still scarce. In this paper, we present a novel approach for predicting Bitcoin price movements by combining the Fear & Greedy Index, a measure of market sentiment, Technical Analysis indicators, and the potential of Machine Learning algorithms. This work represents a preliminary study on the importance of sentiment metrics in cryptocurrency forecasting. Our initial experiments demonstrate promising results considering investment returns, surpassing the Buy & Hold baseline, and offering valuable insights about the combination of indicators of sentiment and market in a cryptocurrency prediction model.

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。