An Empirical Study of a Dynamic Stop Loss Strategy with Deep Reinforcement Learning on the NASDAQ Stock Market

Mateusz Anders; Jozef Zurada; Paweł Weichbroth

doi:10.1007/978-3-031-78465-1_15

An Empirical Study of a Dynamic Stop Loss Strategy with Deep Reinforcement Learning on the NASDAQ Stock Market

Abstract

The objective of this paper is to empirically investigate the efficacy of using Deep Reinforcement Learning (DRL) to maximize investment returns by incorporating expected optimal closing prices of long positions into a daily strategy. This paper extends existing research on the impact of stop-loss orders on investment strategy results and brings contribution of these orders to trading strategies into a completely new perspective. We propose a novel approach using DRL, in contrast to fixed-price stop-loss strategies, trailing-stop strategies, or other machine learning approaches. In the backtesting experiment, daily OHLCV data for stocks from the NASDAQ-100 index (as of May 2024) were used for the period spanning from January 2014 to January 2024. The strategy is compared with buy-and-hold, stop-loss, and trailing stop-loss strategies. Significant effort was made to accurately reflect market conditions in the simulation. We found a positive impact of using DRL compared to other tested strategies when encountering entirely new data, suggesting positive serial market correlations. The results suggest that appropriate closing rules and active management of stop levels can increase investment returns without necessarily reducing portfolio return volatility.

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Category:

Conference activity

Type:

publikacja w wydawnictwie zbiorowym recenzowanym (także w materiałach konferencyjnych)

Language:

English

Publication year:

2024

Bibliographic description:

Anders M., Zurada J., Weichbroth P.: An Empirical Study of a Dynamic Stop Loss Strategy with Deep Reinforcement Learning on the NASDAQ Stock Market// / : , 2024,

DOI: