Background

Like common stocks, Bitcoin price fluctuations are non-stationary and highly noisy. Due to attractiveness of Bitcoin in terms of returns and risk, Bitcoin price prediction is attracting a growing attention from both investors and researchers. Indeed, with the development of machine learning and especially deep learning, forecasting Bitcoin is receiving a particular interest.

Methods

We implement and apply deep forward neural network (DFFNN) for the analysis and forecasting Bitcoin high-frequency price data. Importantly, we seek to investigate the effect of standard numerical training algorithms on the accuracy obtained by DFFNN; namely, the conjugate gradient with Powell-Beale restarts, the resilient algorithm, and Levenberg-Marquardt algorithm. The DFFNN was applied to a big dataset composed of 65,535 samples.

Results

In terms of root mean of squared errors (RMSEs), the simulation results show that the DFFNN trained with the Levenberg-Marquardt algorithm outperforms DFFNN trained with Powell-Beale restarts algorithm and DFFNN trained with resilient algorithm. In addition, the resilient algorithm is fast which suggests that it could be promising in online training and trading.

Conclusions

The DFFNN trained with Levenberg-Marquardt algorithm is effective and easy to implement for Bitcoin high-frequency price data forecasting.

中文翻译：

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加密货币高频交易中的深度学习预测

背景

像普通股一样，比特币的价格波动是非平稳的且高度嘈杂。由于比特币在回报和风险方面具有吸引力，因此比特币价格预测正吸引着投资者和研究人员越来越多的关注。确实，随着机器学习（尤其是深度学习）的发展，预测比特币正引起特别的兴趣。

方法

我们实施并应用深度前向神经网络（DFFNN）进行分析和预测比特币高频价格数据。重要的是，我们试图研究标准数值训练算法对DFFNN获得的准确性的影响。即，重新启动Powell-Beale的共轭梯度，弹性算法和Levenberg-Marquardt算法。DFFNN被应用于包含65,535个样本的大型数据集。

结果

在平方根均方根（RMSE）方面，仿真结果表明，使用Levenberg-Marquardt算法训练的DFFNN优于使用Powell-Beale重新启动算法训练的DFFNN和使用弹性算法训练的DFFNN。此外，弹性算法速度快，这表明它在在线培训和交易中很有前途。

结论

用Levenberg-Marquardt算法训练的DFFNN对于比特币高频价格数据预测是有效且易于实现的。