Cryptocurrency markets are highly volatile and influenced by both price trends and market sentiment, making effective portfolio management challenging. This paper proposes a dynamic cryptocurrency portfolio strategy that integrates technical indicators and sentiment analysis to enhance investment decision-making. Market momentum is captured using the 14-day Relative Strength Index (RSI) and Simple Moving Average (SMA), while sentiment signals are extracted from news articles with VADER and further validated using the Google Gemini large language model. These signals are incorporated into expected return estimates and used in a constrained mean-variance optimization framework. Backtesting across multiple cryptocurrencies shows that the integrated approach outperforms traditional benchmarks, including momentum strategy, Bitcoin Long-Short strategy, and an equal-weighted portfolio, achieving stronger risk-adjusted returns and more consistent cumulative growth. Furthermore, comparing the sentiment-only and technical-only strategies shows that incorporating sentiment information alongside technical indicators can lead to more consistent performance gains. However, the strategies exhibit substantial drawdowns that coincide with known periods of market stress, indicating that additional risk-management components are required to improve stability.

翻译：加密货币市场具有高度波动性，且同时受到价格趋势和市场情绪的影响，这使得有效的投资组合管理颇具挑战。本文提出一种动态加密货币投资组合策略，该策略整合技术指标与情感分析以增强投资决策。市场动量通过14日相对强弱指数（RSI）和简单移动平均线（SMA）捕捉，而情感信号则从新闻文章中利用VADER提取，并进一步使用Google Gemini大语言模型进行验证。这些信号被纳入预期收益估计，并用于一个带约束的均值-方差优化框架。在多种加密货币上的回测表明，该整合方法优于传统基准策略，包括动量策略、比特币多空策略以及等权重投资组合，实现了更强的风险调整后收益和更一致的累积增长。此外，通过比较纯情感策略与纯技术策略发现，将情感信息与技术指标结合能够带来更持续的性能提升。然而，这些策略在已知的市场压力时期表现出显著的回撤，这表明需要引入额外的风险管理组件以提升稳定性。