Trading cryptocurrencies using algorithmic average true range systems

Abstract

Abstract This research makes the first attempt to design, optimize and use average true range (ATR)‐based trading systems for five popular cryptocurrencies. We used particle swarm optimization procedures to optimize systems with multiple objectives that are based on the ATR concept. Our aim was to determine the best configurations for each system that would maximize net profits, the profit factor, and the percentage of profitable trades. We demonstrate that the ATR‐based systems can predict the price trends of the examined cryptocurrencies. Our results also indicate that optimized Keltner Channel‐based systems improve the ability of the stand‐alone optimized ATR systems to forecast trends, net profits, and the profit factor. Finally, both systems perform better for long trades than for short trades.