Cryptocurrency direction forecasting using deep learning algorithms

Abstract

Recently, the deep learning architecture has been used with an increasing rate for forecasting in financial markets. In this paper, the LSTM model is used to forecast the daily closing price direction of the BTC/USD. Both model accuracy and the profit or loss of the trades made based on the proposed model are analyzed. In addition, the effects of the MACD indicator and the input matrix dimension on forecasting accuracy are evaluated. The potential risks and actual risks encountered by the trader who trades based on the proposed model were also analyzed. The obtained results indicate that the optimization of the LSTM parameters using the Bayesian optimization model has enhanced the model’s accuracy. The results obtained from analyzing the drawdown and reward/risk resulting from the trades made based on the model show that the model enables the trader to trade with peace of mind due to the low level of actual risks and potential risks.

KEYWORDS:

• Cryptocurrency forecasting
• deep learning
• BTC/USD forecasting
• BPNN

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 Particle swarm optimization.

2 Convolutional Neural Networks.

3 Recurrent Neural Networks.

4 Event embedding input and CNN prediction model.

5 Deep Neural Network.

6 Long-short term memory neural network.

7 Auto Regressive Integrated Moving Average.

8 Artificial Neural Network-Generalized AutoRegressive Conditional Heteroskedasticity.