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Bayesian Optimization of Supertrend Parameters for Stock Trading

Article arXiv papers · Author: Abdul Rahman
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Summary

The document proposes using Bayesian optimization to select the ATR multiplier and ATR period in the Supertrend indicator. These settings determine how the indicator tracks price and generates trading signals, and the aim is to automate their selection rather than rely on manually chosen values.

The proposed strategy would be evaluated through backtests across several stock datasets. The document describes the research objective and evaluation plan, but it provides no results, dataset details, validation procedure, or comparison with fixed parameters. Its claim of potential improvement therefore remains untested in the material provided; backtest performance alone would also not establish that optimized settings generalize to future markets.

Key ideas

  • Bayesian optimization is proposed to tune the Supertrend ATR multiplier and period.
  • The indicator parameters are intended to be selected for trading profitability.
  • The strategy is planned for evaluation through backtests on multiple stock datasets.
  • No findings or validation details are provided in the document.

Tags

EquitiesTechnical indicatorsMachine learningBacktesting
Full text
# Unlocking Profit Potential: Maximizing Returns with Bayesian Optimization of Supertrend Indicator Parameters


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









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.

Shown in full with attribution under the source's licence. Licence: abstract CC0

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.