Adaptive Trend-Following and Portfolio Allocation for Cryptocurrency Markets

Summary

AdaptiveTrend is a systematic cryptocurrency strategy that combines trend signals on six-hour intervals with monthly portfolio updates. It uses volatility-adjusted trailing stops, selects assets using rolling Sharpe ratios with market-capitalization filters, and allocates capital asymmetrically between long and short positions. The authors motivate the long bias by the market’s positive historical drift and frame the system as a way to adapt exposure as volatility and asset performance change.

The document reports out-of-sample backtests on more than 150 cryptocurrency pairs over 36 months from 2022 to 2024. It gives an annualized Sharpe ratio of 2.41, maximum drawdown of -12.7%, and Calmar ratio of 3.18, and says the strategy beat time-series momentum and equal-weighted buy-and-hold benchmarks. Parameter sensitivity, transaction cost modeling, and market-regime analysis are also reported. These are backtest claims; the excerpt does not provide implementation details, underlying data, or independent replication, so live performance and robustness remain uncertain.

Key ideas

• The framework pairs six-hour trend-following signals with monthly portfolio construction.
• Trailing stops adjust to intraday volatility regimes.
• Asset selection uses rolling Sharpe ratios and market-capitalization filters.
• Capital is allocated asymmetrically between long and short positions.
• The reported out-of-sample backtest includes transaction-cost and regime analyses, but does not establish live results.

Tags

# 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.

Shown in full with attribution under the source's licence. Licence: abstract CC0

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.