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.

翻译：加密货币市场呈现出显著的动量效应和依赖于市场状态的波动性，这为系统性交易策略带来了机遇与挑战。我们提出了AdaptiveTrend，一个多组分的算法交易框架。该框架将6小时区间的高频趋势跟踪与月度自适应组合构建以及非对称多空资本配置相结合。我们的框架引入了三项关键创新：(1) 一种根据日内波动状态校准的动态追踪止损机制，(2) 一种基于滚动夏普比率并结合市值感知过滤的资产选择流程，以及(3) 一种基于加密货币市场经验性正向漂移理论推导的非对称70/30多空配置方案。通过对超过150个加密货币交易对在36个月评估窗口（2022-2024年）内进行广泛的样本外回测，AdaptiveTrend实现了2.41的年化夏普比率、-12.7%的最大回撤以及3.18的卡尔玛比率，显著优于基准趋势跟踪策略（TSMOM，时间序列动量）和等权重买入持有组合。我们进一步进行了严格的稳健性分析，包括参数敏感性、交易成本建模和市场状态条件下的绩效分解，证明了该策略在牛市、熊市和横盘市场条件下均具有稳健性。