An overview of technical analysis in systematic trading strategies returns and a novel systematic strategy yielding positive significant returns

Marco Basanisi () and Roberto Torresetti ()

Journal of Contemporary Research in Business, Economics and Finance, 2023, vol. 5, issue 1, 12-24

Abstract: This paper contributes to the literature on systematic trading strategies, in particular technical analysis profitability. We measure the profitability and forecasting power of a trend following strategy implemented in Python on a wide perimeter (205 European stocks, 11 industries, 7 major stock exchanges) over 8 years: from 2015 to 2022. The strategy signal is based on 4 moving averages and a trailing stop loss. We also introduce a mechanism based on trailing upper and lower price bounds to avoid false signals and limit transaction costs during lateral movements. We calibrate the iper-parameters to all stocks belonging to the same industry. The returns of the strategy applied to the constituents of the top performing industries provides a total return of 20% net of transaction costs, with an annualized Sharpe ratio of 0.54, in the out of sample time window from 2020 to 2022.

Keywords: Algorithm calibration; Cross-validation; Forecasting power; Python; Sharpe ratio; Systematic trading; Technical analysis. (search for similar items in EconPapers)
Date: 2023
References: Add references at CitEc
Citations:

Downloads: (external link)
http://learning-gate.com/index.php/2641-0265/article/view/204/96 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ajp:jcrbef:v:5:y:2023:i:1:p:12-24:id:204

Access Statistics for this article

More articles in Journal of Contemporary Research in Business, Economics and Finance from Learning Gate
Bibliographic data for series maintained by Michael Laurence ().