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基于置信度风险控制的比特币走势模型

文章 arXiv papers · 作者: Nathan Crone et al.
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总结

本研究使用比特币网络内部数据和外部特征，通过多个机器学习模型预测每日价格方向。研究使用2021年第一季度收集的未见观测数据，通过交易策略评估这些预测。在为期三个月的测试期内，二元信号产生的收益据报告与买入并持有相当。

该策略还将预测置信度作为风险容忍度输入，并根据模型的确定程度调整交易决策。作者报告称，这一版本在所述时期的表现优于买入并持有。结果仅限于一种资产和较短的历史评估窗口；摘录没有提供成本、风险调整后表现、模型选择或对其他市场状态的稳健性细节。因此，报告结果提示还需进一步测试，不能证明其具有持久的实盘交易表现。

核心观点

  • 这些模型结合比特币网络特征和外部输入，对每日走势进行分类。
  • 研究使用2021年第一季度的未见数据，通过交易策略评估表现。
  • 据报告，利用预测置信度调整风险容忍度，比买入并持有取得了更好的结果。
  • 这项仅涵盖一个短期的评估无法证明模型对不同市场状态或实盘交易具有稳健性。

标签

加密资产机器学习风险管理回测链上数据
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# Exploration of Algorithmic Trading Strategies for the Bitcoin Market


# Exploration of Algorithmic Trading Strategies for the Bitcoin Market









Bitcoin is firmly becoming a mainstream asset in our global society. Its highly volatile nature has traders and speculators flooding into the market to take advantage of its significant price swings in the hope of making money. This work brings an algorithmic trading approach to the Bitcoin market to exploit the variability in its price on a day-to-day basis through the classification of its direction. Building on previous work, in this paper, we utilise both features internal to the Bitcoin network and external features to inform the prediction of various machine learning models. As an empirical test of our models, we evaluate them using a real-world trading strategy on completely unseen data collected throughout the first quarter of 2021. Using only a binary predictor, at the end of our three-month trading period, our models showed an average profit of 86\%, matching the results of the more traditional buy-and-hold strategy. However, after incorporating a risk tolerance score into our trading strategy by utilising the model's prediction confidence scores, our models were 12.5\% more profitable than the simple buy-and-hold strategy. These results indicate the credible potential that machine learning models have in extracting profit from the Bitcoin market and act as a front-runner for further research into real-world Bitcoin trading.

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。