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加密货币市场的动态协整配对交易

文章 arXiv papers · 作者: Masood Tadi et al.
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总结

本研究评估一种动态加密货币配对交易策略，该策略使用协整检验和估算的均值回归速度来选择资产并调整回看窗口。研究在不同情景下采用Engle-Granger、Kapetanios-Snell-Shin和Johansen检验，并使用Ornstein-Uhlenbeck过程估算价差半衰期，以指导资产选择和窗口设定。

作者报告称，该策略在BitMEX上的表现优于简单的买入并持有策略，且最大回撤相对较低。他们还发现，在模型价差中组合多种币种能改善风险调整后盈利能力，某些币种组合似乎提供了更好的套利机会。回测使用按分钟分箱的建仓数据，并采用最佳买卖报价和市场成交数据模拟交易。只有在报价数量充足时才计入成交，并在信号触发后设置一段间隔。这些设计选择考虑了市场微观结构，但报告结果仍仅适用于受测交易所、资产、情景和历史数据。

核心观点

  • 该策略使用多种协整检验，识别不同情景下的加密货币关系。
  • Ornstein-Uhlenbeck过程估算的半衰期用于指导资产选择和回看窗口长度。
  • 研究报告称，该策略在BitMEX上的收益优于买入并持有策略，且最大回撤相对较低。
  • 据报告，在价差中使用多种币种能提高风险调整后盈利能力。
  • 回测根据报价和信号生成后的可用挂单量模拟成交。

标签

加密资产配对交易均值回归套利回测交易执行
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# Evaluation of Dynamic Cointegration-Based Pairs Trading Strategy in the Cryptocurrency Market


# Evaluation of Dynamic Cointegration-Based Pairs Trading Strategy in the Cryptocurrency Market









This research aims to demonstrate a dynamic cointegration-based pairs trading strategy, including an optimal look-back window framework in the cryptocurrency market, and evaluate its return and risk by applying three different scenarios. We employ the Engle-Granger methodology, the Kapetanios-Snell-Shin (KSS) test, and the Johansen test as cointegration tests in different scenarios. We calibrate the mean-reversion speed of the Ornstein-Uhlenbeck process to obtain the half-life used for the asset selection phase and look-back window estimation. By considering the main limitations in the market microstructure, our strategy exceeds the naive buy-and-hold approach in the Bitmex exchange. Another significant finding is that we implement a numerous collection of cryptocurrency coins to formulate the model's spread, which improves the risk-adjusted profitability of the pairs trading strategy. Besides, the strategy's maximum drawdown level is reasonably low, which makes it useful to be deployed. The results also indicate that a class of coins has better potential arbitrage opportunities than others. This research has some noticeable advantages, making it stand out from similar studies in the cryptocurrency market. First is the accuracy of data in which minute-binned data create the signals in the formation period. Besides, to backtest the strategy during the trading period, we simulate the trading signals using best bid/ask quotes and market trades. We exclusively take the order execution into account when the asset size is already available at its quoted price (with one or more period gaps after signal generation). This action makes the backtesting much more realistic.

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。