Unlocking Profit Potential: Maximizing Returns with Bayesian Optimization of Supertrend Indicator Parameters

ArXiv ID: 2405.14262 “View on arXiv”

Authors: Unknown

Abstract

This paper investigates the potential of Bayesian optimization (BO) to optimize the atr multiplier and atr period -the parameters of the Supertrend indicator for maximizing trading profits across diverse stock datasets. By employing BO, the thesis aims to automate the identification of optimal parameter settings, leading to a more data-driven and potentially more profitable trading strategy compared to relying on manually chosen parameters. The effectiveness of the BO-optimized Supertrend strategy will be evaluated through backtesting on a variety of stock datasets.

Keywords: Bayesian optimization, Supertrend indicator, parameter optimization, backtesting, trading profits, Equities (Stocks)

Complexity vs Empirical Score

• Math Complexity: 5.5/10
• Empirical Rigor: 6.5/10
• Quadrant: Holy Grail
• Why: The paper applies Bayesian optimization, which involves probabilistic modeling and Gaussian processes (advanced math), while also evaluating the optimized strategy via backtesting on multiple stock datasets with performance metrics like profit factor and maximum drawdown, indicating significant empirical implementation.

  flowchart TD
    A["Research Goal:<br>Optimize Supertrend Parameters<br>via Bayesian Optimization"] --> B["Input: Diverse Stock Datasets"]
    B --> C["Method: Bayesian Optimization<br>Search Space: ATR Multiplier & Period"]
    C --> D["Process: Backtesting<br>Evaluated for Trading Profits"]
    D --> E{"Comparison: BO-Optimized vs<br>Manual Parameters"}
    E -->|Outcome| F["Key Finding:<br>Data-Driven Strategy with<br>Maximized Returns"]
    E -->|Outcome| G["Key Finding:<br>Automated Parameter Identification"]