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

https://doi.org/10.55214/jcrbef.v5i1.204

Authors

  • Marco Basanisi Università degli Studi di Milano, Italy.
  • Roberto Torresetti Università degli Studi di Milano, Italy.

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.

How to Cite

Basanisi, M. ., & Torresetti, R. . (2023). An overview of technical analysis in systematic trading strategies returns and a novel systematic strategy yielding positive significant returns. Journal of Contemporary Research in Business, Economics and Finance, 5(1), 12–24. https://doi.org/10.55214/jcrbef.v5i1.204
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