Technology Category
Cognitive Modeling & Cognitive Systems: Adaptive BehaviorMultiagent Systems: Adversarial AgentsGame Theory and Economic Paradigms: Auctions and Market-Based Systems
Systematic Trend-Following with Adaptive Portfolio Construction: Enhancing Risk-Adjusted Alpha in Cryptocurrency Markets
🤖 AI Summary
This study addresses the challenges posed by pronounced momentum effects and regime-dependent volatility in cryptocurrency markets, which often undermine traditional systematic strategies’ ability to balance risk control with consistent alpha generation. To this end, the paper proposes AdaptiveTrend, a novel framework that integrates high-frequency trend signals sampled at six-hour intervals, market-cap-aware asset selection optimized via rolling Sharpe ratios, a dynamic volatility-adjusted trailing stop-loss mechanism, and an asymmetric 70/30 long–short allocation informed by the market’s positive drift characteristics. In out-of-sample backtests spanning 2022–2024 across more than 150 cryptocurrency pairs, the strategy achieves an annualized Sharpe ratio of 2.41, a maximum drawdown of −12.7%, and a Calmar ratio of 3.18, substantially outperforming benchmark approaches.
Technology Category
Application Category
📝 Abstract
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.
Problem
Research questions and friction points this paper is trying to address.
cryptocurrency markets
trend-following
risk-adjusted alpha
regime-dependent volatility
systematic trading
Innovation
Methods, ideas, or system contributions that make the work stand out.
AdaptiveTrend
dynamic trailing stop
asymmetric long-short allocation
rolling Sharpe-ratio selection
regime-dependent volatility
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