Harnessing technical indicators with deep learning based price forecasting for cryptocurrency trading

Mingu Kang, Joongi Hong and Suntae Kim

Physica A: Statistical Mechanics and its Applications, 2025, vol. 660, issue C

Abstract: The rapid development and significant volatility of the cryptocurrency market make price trend prediction highly challenging. Accurate price predictions are crucial for making informed investment decisions that can lead to higher returns. However, few studies have focused on integrating predictions into actionable trading strategies. This study aims to enhance cryptocurrency trading strategies by integrating deep learning-based price forecasting with technical indicators. Twelve deep learning models were developed and their performance in generating trading signals was compared across various cryptocurrencies and forecast periods. These signals were combined with technical indicators and backtested to identify the optimal strategy, evaluated using the Sharpe ratio. Results show that SegRNN outperformed other models in price forecasting, while a strategy combining TimesNet and Bollinger Bands (BB) achieved the highest trading performance in the ETH market with a returns of 3.19, a maximum drawdown (MDD) of -7.46, and Sharpe ratio of 3.56. Additionally, the integration of technical indicators and AI models demonstrated significant improvements at mid-range intervals, particularly at the 4-hour interval, although no improvement was observed at shorter intervals such as 30 minutes. The study concludes that integrating deep learning with technical indicators can significantly improve the robustness and performance of trading strategies in volatile markets.

Keywords: Cryptocurrency; Price forecasting; Technical indicator; Trading strategy (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:660:y:2025:i:c:s0378437125000111

DOI: 10.1016/j.physa.2025.130359

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