Trading Strategy Validation Using Forwardtesting with Deep Neural Networks

Ivan Letteri, Giuseppe Della Penna, Giovanni De Gasperis, Abeer Dyoub

2023

Abstract

Traders commonly test their trading strategies by applying them on the historical market data (backtesting), and then reuse on their (future) trades the strategy that achieved the maximum profit on such past data. In this paper we propose a novel technique, that we shall call forwardtesting, that determines the strategy to apply by testing it on the possible future predicted by a deep neural network that has been designed to perform stock price forecasts and trained with the market historical data. Our results confirm that neural networks outperform classical statistical techniques when performing such forecasts, and their predictions allow to select a trading strategy that, when applied to the real future, results equally or more profitable than the strategy that would be selected through the traditional backtesting.

Paper Citation

in Harvard Style

Letteri I., Della Penna G., De Gasperis G. and Dyoub A. (2023). Trading Strategy Validation Using Forwardtesting with Deep Neural Networks. In Proceedings of the 5th International Conference on Finance, Economics, Management and IT Business - Volume 1: FEMIB, ISBN 978-989-758-646-0, SciTePress, pages 15-25. DOI: 10.5220/0011715300003494

in Bibtex Style

@conference{femib23,
author={Ivan Letteri and Giuseppe Della Penna and Giovanni De Gasperis and Abeer Dyoub},
title={Trading Strategy Validation Using Forwardtesting with Deep Neural Networks},
booktitle={Proceedings of the 5th International Conference on Finance, Economics, Management and IT Business - Volume 1: FEMIB,},
year={2023},
pages={15-25},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011715300003494},
isbn={978-989-758-646-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 5th International Conference on Finance, Economics, Management and IT Business - Volume 1: FEMIB,
TI - Trading Strategy Validation Using Forwardtesting with Deep Neural Networks
SN - 978-989-758-646-0
AU - Letteri I.
AU - Della Penna G.
AU - De Gasperis G.
AU - Dyoub A.
PY - 2023
SP - 15
EP - 25
DO - 10.5220/0011715300003494
PB - SciTePress