融合技术指标与财报情绪分析，提升量化交易策略表现
Algorithmic Trading Strategy Development and Optimisation
- 整合均线、动量、波动率与FinBERT情绪分析构建新策略
- 总收益、夏普比率显著优于基线模型，回撤更小
- 适合量化交易、金融科技研究者参考
本报告基于历史S&P 500市场数据和财报电话会议情绪分析，开发并优化了一种增强型算法交易策略。该策略融合移动平均线、动量、波动率及基于FinBERT的情绪分析，以提升交易决策质量。结果表明，该增强策略在总回报、夏普比率和回撤等指标上均显著优于基线模型，验证了技术指标、情绪分析与计算优化相结合在算法交易系统中的有效性。
原文摘要 · Abstract (English)
The report presents with the development and optimisation of an enhanced algorithmic trading strategy through the use of historical S&P 500 market data and earnings call sentiment analysis. The proposed strategy integrates various technical indicators such as moving averages, momentum, volatility, and FinBERT-based sentiment analysis to improve overall trades being taken. The results show that the enhanced strategy significantly outperforms the baseline model in terms of total return, Sharpe ratio, and drawdown amongst other factors. The findings helped demonstrate the relevance and effectiveness of combining technical indicators, sentiment analysis, and computational optimisation in algorithmic trading systems.
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