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用决策树生成个股日内交易规则

文章 arXiv papers · 作者: Prajwal Naga et al.
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总结

该文介绍如何使用决策树，为NIFTY50指数中的个股生成日内交易规则。该方法不将交易者设计的一组技术指标组合应用于所有股票，而是尝试从现有指标中学习针对个股的规则。随后通过回测评估每只股票生成的规则，以决定是否值得采用。

文中将这些策略与简单的买入并持有进行比较：论文称决策树策略在许多股票上表现优于该基准，同时承认并非所有股票都如此。这支持按个股逐一评估，而不是假设存在一套通用规则。摘录未说明指标、训练与测试设计、交易成本或绩效衡量指标，因此不足以判断其稳健性或实盘交易可行性。

核心观点

  • 决策树可将现有技术指标转化为适用于个股的日内规则。
  • 该方法将学习得到的个股规则与交易者设计的固定指标组合进行对比。
  • 所提工作流程会先逐只股票回测规则，再决定是否采用。
  • 据报告，该方法在许多股票上优于买入并持有，但并非普遍如此。
  • 摘录没有提供足够细节来评估成本或样本外稳健性。

标签

股票机器学习技术指标回测
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# Decision Trees for Intuitive Intraday Trading Strategies


# Decision Trees for Intuitive Intraday Trading Strategies









This research paper aims to investigate the efficacy of decision trees in constructing intraday trading strategies using existing technical indicators for individual equities in the NIFTY50 index. Unlike conventional methods that rely on a fixed set of rules based on combinations of technical indicators developed by a human trader through their analysis, the proposed approach leverages decision trees to create unique trading rules for each stock, potentially enhancing trading performance and saving time. By extensively backtesting the strategy for each stock, a trader can determine whether to employ the rules generated by the decision tree for that specific stock. While this method does not guarantee success for every stock, decision treebased strategies outperform the simple buy-and-hold strategy for many stocks. The results highlight the proficiency of decision trees as a valuable tool for enhancing intraday trading performance on a stock-by-stock basis and could be of interest to traders seeking to improve their trading strategies.

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。