Decision Trees for Intuitive Intraday Trading Strategies

Prajwal Naga, Dinesh Balivada, Sharath Chandra Nirmala, Poornoday Tiruveedi
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Abstract

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.
直观日内交易策略的决策树
本文旨在研究决策树利用现有技术指标为 NIFTY50 指数中的个股构建盘中交易策略的功效。传统方法依赖于一套固定的规则,这些规则基于人类交易员通过分析制定的技术指标组合,与之不同的是,本文提出的方法利用决策树为每只股票创建独特的交易规则,从而提高交易绩效并节省时间。通过对每只股票的策略进行广泛的回溯测试,交易者可以决定是否对该特定股票采用决策树生成的规则。虽然这种方法不能保证每只股票都能成功,但基于决策树的策略在许多股票上都优于简单的买入并持有策略。这些结果凸显了决策树作为一种有价值的工具,在逐个股票的基础上提高日内交易绩效方面的能力,并可能引起寻求改进其交易策略的交易者的兴趣。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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