使用情绪分析和集体学习来预测股票市场
Dr Archana Y Chaudhari1, Dr Smita Mahajan1
1Artificial Intelligence & Machine Learning Department, Symbiosis Institute of Technology, Pune Campus, Symbiosis International (Deemed University), Pune, India.
MethodsX
|April 10, 2025
概括
这项研究使用深度强化学习 (DRL) 模型提高了股票市场预测的准确性. 新的政策调整与信托地区优化 (PACTRO) 方法通过分析技术指标来改善投资决策.
科学领域:
- * 计算金融和人工智能.
- * 金融市场分析和算法交易.
背景情况:
- * 股票市场投资提供了收入潜力,但由于固有的波动性和不可预测性而受到阻碍.
- * 传统的预测方法与金融市场的动态性质作斗争.
- * 需要先进的技术来提高投资者对股票市场模式的可预测性.
研究的目的:
- * 通过尖端的深度强化学习 (DRL) 方法提高股票市场模式的可预测性.
- * 开发一个可操作的投资洞察力的计算框架,指导最佳的买入或卖出决策.
- * 引入和评估新的政策调整与信托地区优化 (PACTRO) 技术.
主要方法:
- * 深度强化学习 (DRL) 模型的实施:优势行为者批评 (A2C),近接政策优化 (PPO2) 和软行为者批评 (SAC).
- * 利用来自雅虎金融的历史股票数据.
- * 关键技术指标的整合:价格图,移动平均线收差异 (MACD),波林杰波段 (BB) 和相对强度指数 (RSI).
- * 拟议的政策调整与信托地区优化 (PACTRO) 的应用,用于信托地区内的政策调整.
主要成果:
- * DRL模型在预测股票市场动态方面表现出更强大的能力.
- * PACTRO技术有效优化了政策调整,提高了预测可靠性.
- * 该框架提供了用于定制培训和交易场景的综合历史数据,产生了可操作的见解.
- *关键技术指标对于DRL模型的预测准确性至关重要.
结论:
- *深度强化学习,特别是PACTRO方法,提供了一种强大的方法来应对股票市场波动.
- * 该研究成功创建了一个框架,帮助投资者做出更明智的决策.
- * 技术指标是强大的股票市场预测模型的重要输入.
- * 这项研究有助于提高股票市场投资的可预测性和利能力.
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