By the same authors

Particle Swarm Optimization of Bollinger Bands

Research output: Contribution to conferencePaper

Author(s)

Department/unit(s)

Publication details

DatePublished - 2010
Original languageUndefined/Unknown

Abstract

The use of technical indicators to derive stock trading signals is a foundation of financial technical analysis. Many of these indicators have several parameters which creates a difficult optimization problem given the highly non-linear and non-stationary nature of a financial time-series. This study investigates a popular financial indicator, Bollinger Bands, and the fine tuning of its parameters via particle swarm optimization under 4 different fitness functions: profitability, Sharpe ratio, Sortino ratio and accuracy. The experiment results show that the parameters optimized through PSO using the profitability fitness function produced superior out-of-sample trading results which includes transaction costs when compared to the default parameters.

Discover related content

Find related publications, people, projects, datasets and more using interactive charts.

View graph of relations