{"title":"直观日内交易策略的决策树","authors":"Prajwal Naga, Dinesh Balivada, Sharath Chandra Nirmala, Poornoday Tiruveedi","doi":"arxiv-2405.13959","DOIUrl":null,"url":null,"abstract":"This research paper aims to investigate the efficacy of decision trees in\nconstructing intraday trading strategies using existing technical indicators\nfor individual equities in the NIFTY50 index. Unlike conventional methods that\nrely on a fixed set of rules based on combinations of technical indicators\ndeveloped by a human trader through their analysis, the proposed approach\nleverages decision trees to create unique trading rules for each stock,\npotentially enhancing trading performance and saving time. By extensively\nbacktesting the strategy for each stock, a trader can determine whether to\nemploy the rules generated by the decision tree for that specific stock. While\nthis method does not guarantee success for every stock, decision treebased\nstrategies outperform the simple buy-and-hold strategy for many stocks. The\nresults highlight the proficiency of decision trees as a valuable tool for\nenhancing intraday trading performance on a stock-by-stock basis and could be\nof interest to traders seeking to improve their trading strategies.","PeriodicalId":501139,"journal":{"name":"arXiv - QuantFin - Statistical Finance","volume":"37 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Decision Trees for Intuitive Intraday Trading Strategies\",\"authors\":\"Prajwal Naga, Dinesh Balivada, Sharath Chandra Nirmala, Poornoday Tiruveedi\",\"doi\":\"arxiv-2405.13959\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This research paper aims to investigate the efficacy of decision trees in\\nconstructing intraday trading strategies using existing technical indicators\\nfor individual equities in the NIFTY50 index. Unlike conventional methods that\\nrely on a fixed set of rules based on combinations of technical indicators\\ndeveloped by a human trader through their analysis, the proposed approach\\nleverages decision trees to create unique trading rules for each stock,\\npotentially enhancing trading performance and saving time. By extensively\\nbacktesting the strategy for each stock, a trader can determine whether to\\nemploy the rules generated by the decision tree for that specific stock. While\\nthis method does not guarantee success for every stock, decision treebased\\nstrategies outperform the simple buy-and-hold strategy for many stocks. The\\nresults highlight the proficiency of decision trees as a valuable tool for\\nenhancing intraday trading performance on a stock-by-stock basis and could be\\nof interest to traders seeking to improve their trading strategies.\",\"PeriodicalId\":501139,\"journal\":{\"name\":\"arXiv - QuantFin - Statistical Finance\",\"volume\":\"37 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-05-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - QuantFin - Statistical Finance\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2405.13959\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - QuantFin - Statistical Finance","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2405.13959","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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.