Intraday algorithmic trading strategies for cryptocurrencies

Gil Cohen ()
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Gil Cohen: Western Galilee Academic College

Review of Quantitative Finance and Accounting, 2023, vol. 61, issue 1, No 12, 395-409

Abstract: Abstract This research is the first attempt to create Machine Learning (ML) algorithmic systems that would be able to intraday trade automatically popular cryptocurrencies using oscillators that are commonly used to trade other financial assets. It uses intraday price data of Bitcoin, Ethereum, Binance Coin, Cardano, and XRP with different trading time frames that vary from 5 to 180 min. Our results show that the RSI system is the best algorithmic trading system for cryptocurrency intraday trading. The RSI-based system has out beat the B&H strategy for all five cryptocurrencies. Moreover, it has proven its ability to improve trading performances under any market conditions up or down trends. The other two trading systems that were based on the MACD and Keltner Channels oscillators were found to outperform the B&H strategy for Bitcoin, Binance Coin, and XRP while it was beaten by the B&H strategy for Ethereum and Cardano. Although cryptocurrencies are known for their high volatility, this research has proven that longer time frames such as 60 and 120 min have produced better trading results than shorter time frames such as 5 and 15 min.

Keywords: Cryptocurrencies; Intraday trading; Algorithmic; Artificial intelligence (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s11156-023-01139-2

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