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加密货币市场的自适应趋势跟踪与组合配置

文章 arXiv papers · 作者: Duc Bui et al.
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总结

AdaptiveTrend 是一种系统化加密货币策略，将六小时周期的趋势信号与每月一次的投资组合更新相结合。它采用按波动率调整的移动止损，通过滚动夏普比率和市值筛选资产，并在多头与空头头寸之间非对称地分配资金。作者以市场历史上的正向漂移为做多倾向提供依据，并将该系统描述为一种随波动率和资产表现变化而调整敞口的方法。

本文报告了对超过 150 个加密货币交易对进行的样本外回测，涵盖 36 个月，时间从 2022 年至 2024 年。年化夏普比率为 2.41，最大回撤为 -12.7%，卡玛比率为 3.18；作者称该策略优于时间序列动量和等权买入持有基准。文中还报告了参数敏感性、交易成本建模和市场状态分析。这些结论来自回测；摘录没有提供实现细节、底层数据或独立复现，因此实盘表现和稳健性仍不确定。

核心观点

  • 该框架将六小时趋势跟踪信号与每月投资组合配置相结合。
  • 移动止损会根据日内波动状态进行调整。
  • 资产筛选采用滚动夏普比率和市值过滤条件。
  • 资金在多头与空头头寸之间非对称配置。
  • 所报告的样本外回测包含交易成本和市场状态分析，但不能证明实盘效果。

标签

加密资产趋势跟踪动量投资组合构建回测风险管理
全文
# Systematic Trend-Following with Adaptive Portfolio Construction: Enhancing Risk-Adjusted Alpha in Cryptocurrency Markets


# Systematic Trend-Following with Adaptive Portfolio Construction: Enhancing Risk-Adjusted Alpha in Cryptocurrency Markets









Cryptocurrency markets exhibit pronounced momentum effects and regime-dependent volatility, presenting both opportunities and challenges for systematic trading strategies. We propose AdaptiveTrend, a multi-component algorithmic trading framework that integrates high-frequency trend-following on 6-hour intervals with monthly adaptive portfolio construction and asymmetric long-short capital allocation. Our framework introduces three key innovations: (1) a dynamic trailing stop mechanism calibrated to intra-day volatility regimes, (2) a rolling Sharpe-ratio-based asset selection procedure with market-capitalization-aware filtering, and (3) a theoretically motivated asymmetric 70/30 long-short allocation scheme grounded in the empirical positive drift of crypto markets. Through extensive out-of-sample backtesting across 150+ cryptocurrency pairs over a 36-month evaluation window (2022-2024), AdaptiveTrend achieves an annualized Sharpe ratio of 2.41, a maximum drawdown of -12.7%, and a Calmar ratio of 3.18, significantly outperforming benchmark trend-following strategies (TSMOM, time-series momentum) and equal-weighted buy-and-hold portfolios. We further conduct rigorous robustness analyses including parameter sensitivity, transaction cost modeling, and regime-conditional performance decomposition, demonstrating the strategy's resilience across bull, bear, and sideways market conditions.

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。