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A profitable trading algorithm for cryptocurrencies using a Neural Network model
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DOI：10.1016/j.eswa.2023.121806.png)
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摘要
En 中文
Algorithmic trading enables the execution of orders using a set of rules determined by a computer program. Orders are submitted based on an asset's expected price in the future, an approach well suited for high-volatility markets, such as those trading in cryptocurrencies. The goal of this study is to find a reliable and profitable model to predict the future direction of a crypto asset's price based on publicly available historical data. We first develop a novel labeling scheme and map this problem into a Machine Learning classification problem. The model is then validated on three major cryptocurrencies through an extensive backtest over a bull, bear and flat market. Finally, the contribution of each feature to the classification output is analyzed.
Keyword:
Cryptocurrencies
Machine learning
Neural network
Price prediction
Algorithmic trading
Explainable AI
Backtesting
Shapley values
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期刊
IF:
7.5
论文数:
3.0W
被引数:
10.2W
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