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融合指标、情绪与机器学习的自适应股票策略

文章 arXiv papers · 作者: Varun Narayan Kannan Pillai et al.
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总结

本文介绍一种股票交易系统，将指数移动平均线和 MACD 的趋势与动量信号，与 RSI 和布林带的均值回归信号结合。系统加入 FinBERT 对金融情绪的估计，使用 XGBoost 生成信号，并根据波动率和收益状况筛选信号或调整敞口。该设计旨在适应市场状态变化，以及传统趋势跟踪或均值回归方法可能难以应对的情形。

文中报告，在 24 个月期间，最终投资组合价值为 $235,492.83，初始投资收益率为 135.49%。文中还声称其表现优于标普 500 指数和 NASDAQ-100，且下行风险较低。摘录未说明具体日期、初始资本、交易品种、交易成本、验证设计或风险统计，因此仅凭这些数字无法证明策略稳健或可部署。所报告的表现应视为文中评估所得结果，而非普遍预期。

核心观点

  • 该系统结合趋势和动量指标与均值回归指标，生成股票信号。
  • 信号生成过程纳入 FinBERT 情绪分析和 XGBoost。
  • 波动率和收益环境用于市场状态筛选和敞口调整。
  • 论文报告了 135.49% 的收益率和 24 个月内优于两个股票基准，但摘录未提供评估细节。

标签

股票机器学习技术指标市场情绪趋势跟踪均值回归
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# 2601.19504


# Generating Alpha: A Hybrid AI-Driven Trading System Integrating Technical Analysis, Machine Learning and Financial Sentiment for Regime-Adaptive Equity Strategies









The intricate behavior patterns of financial markets are influenced by fundamental, technical, and psychological factors. During times of high volatility and regime shifts causes many traditional strategies like trend-following or mean-reversion to fail. This paper proposes a hybrid AI-based trading strategy that combines (1) trend-following and directional momentum capture via EMA and MACD, (2) detection of price normalization through mean-reversion using RSI and Bollinger Bands, (3) market psychological interpretation through sentiment analysis using FinBERT, (4) signal generation through machine learning using XGBoost and (5)dynamically adjusting exposure with market regime filtering based on volatility and return environments. The system achieved a final portfolio value of $235,492.83, yielding a return of 135.49% on initial investment over a period of 24 months. The hybrid model outperformed major benchmark indexes like S&P 500 and NASDAQ-100 over the same period showing strong flexibility and lower downside risk with superior profits validating the use of multi-modal AI in algorithmic trading.

在遵守原作品许可的前提下，附作者信息全文展示。 许可协议: abstract CC0

此摘要由 Stratmill 研究智能体根据原文撰写，并非原文副本。