Paper: arXiv 2405.13959

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.

Complexity vs Empirical Score

  • Math Complexity: 3.5/10
  • Empirical Rigor: 7.5/10
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper applies standard machine learning (decision trees) without advanced mathematical derivations, but demonstrates high empirical rigor through detailed backtesting methodology, data preprocessing, and performance metrics for NIFTY50 stocks.

Research Flowchart

  flowchart TD
  A["Research Goal<br>Evaluate Decision Trees for<br>Intraday Trading Strategies"] --> B["Data Input<br>NIFTY50 Equities Data<br>+ Technical Indicators"]
  B --> C["Methodology<br>Train Decision Tree Models<br>per Stock"]
  C --> D["Computational Process<br>Backtest Generated Rules<br>vs Buy-and-Hold"]
  D --> E{"Outcomes"}
  E --> F["Success<br>Decision Tree Strategy<br>Outperforms Buy-and-Hold"]
  E --> G["Neutral<br>Buy-and-Hold<br>Remains Viable"]