Decision Trees for Intuitive Intraday Trading Strategies
Prajwal Naga, Dinesh Balivada, Sharath Chandra Nirmala, Poornoday, Tiruveedi

TL;DR
This paper explores using decision trees to develop personalized intraday trading strategies based on technical indicators, showing they can outperform buy-and-hold for many stocks in the NIFTY50 index.
Contribution
It introduces a novel approach of applying decision trees to generate stock-specific intraday trading rules, improving performance over traditional fixed-rule methods.
Findings
Decision tree strategies outperform buy-and-hold for many stocks.
The method creates unique, stock-specific trading rules.
Decision trees are effective tools for intraday trading enhancement.
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsStock Market Forecasting Methods
MethodsSparse Evolutionary Training
