Stochastic Neural Networks-Based Algorithmic Trading for the Cryptocurrency Market

【Author】 Kalariya, Vasu; Parmar, Pushpendra; Jay, Patel; Tanwar, Sudeep; Raboaca, Maria Simona; Alqahtani, Fayez; Tolba, Amr; Neagu, Bogdan-Constantin

【Source】MATHEMATICS

【影响因子】2.592

【Abstract】Throughout the history of modern finance, very few financial instruments have been as strikingly volatile as cryptocurrencies. The long-term prospects of cryptocurrencies remain uncertain; however, taking advantage of recent advances in neural networks and volatility, we show that the trading algorithms reinforced by short-term price predictions are bankable. Traditional trading algorithms and indicators are often based on mean reversal strategies that do not advantage price predictions. Furthermore, deterministic models cannot capture market volatility even after incorporating price predictions. Thus motivated by these issues, we integrate randomness in the price prediction models to simulate stochastic behavior. This paper proposes hybrid trading strategies that take advantage of the traditional mean reversal strategies alongside robust price predictions from stochastic neural networks. We trained stochastic neural networks to predict prices based on market data and social sentiment. The backtesting was conducted on three cryptocurrencies: Bitcoin, Ethereum, and Litecoin, for over 600 days from August 2017 to December 2019. We show that the proposed trading algorithms are better when compared to the traditional buy and hold strategy in terms of both stability and returns.

【Keywords】Bollinger bands; pairs trading; Awesome Oscillator; stochastic neural networks; cryptocurrency

【发表时间】2022 MAY

【收录时间】2022-05-20

【文献类型】实证性文章

【主题类别】

区块链治理-市场治理-数字货币