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结合深度学习与进化方法进行ETF投资

文章 arXiv papers · 作者: Prasang Gupta et al.
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总结

论文提出一种系统化投资方法，旨在通过一系列短期ETF买入决策实现较长期的财富积累。该方法将进化算法与深度学习模型结合，以改进相较于传统每日定投的投入时机。其目标是随着时间调整买入决策，而不是在每个预定日期都采用相同的投资方式。

作者报告称，对于某只ETF，其收益比传统每日定投高约百分之一。他们表示，证据来自通过零售经纪平台执行的实盘算法交易决策，而不只是模拟回测。简要描述没有说明ETF、评估期、风险指标、成本，也没有说明该比较是否适用于其他基金和市场环境。因此，报告的差异只是一个案例研究，不能广泛保证这种集成方法会改善长期投资结果。

核心观点

  • 该方法结合进化算法和深度学习来指导ETF买入决策。
  • 该方法通过一系列短期投资选择，着眼于长期财富积累。
  • 比较对象是针对某只ETF进行的传统每日定投。
  • 作者报告称，采用实盘算法决策后收益高约百分之一，但关于结果能否推广的信息有限。

标签

股票机器学习投资组合构建仓位规模回测
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# Intelligent Systematic Investment Agent: an ensemble of deep learning and evolutionary strategies


# Intelligent Systematic Investment Agent: an ensemble of deep learning and evolutionary strategies









Machine learning driven trading strategies have garnered a lot of interest over the past few years. There is, however, limited consensus on the ideal approach for the development of such trading strategies. Further, most literature has focused on trading strategies for short-term trading, with little or no focus on strategies that attempt to build long-term wealth. Our paper proposes a new approach for developing long-term investment strategies using an ensemble of evolutionary algorithms and a deep learning model by taking a series of short-term purchase decisions. Our methodology focuses on building long-term wealth by improving systematic investment planning (SIP) decisions on Exchange Traded Funds (ETF) over a period of time. We provide empirical evidence of superior performance (around 1% higher returns) using our ensemble approach as compared to the traditional daily systematic investment practice on a given ETF. Our results are based on live trading decisions made by our algorithm and executed on the Robinhood trading platform.

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。