CIRJE-F-1090 "Bitcoin Technical Trading with Articial Neural Network"
Author Name Nakano, Masafumi, Akihiko Takahashi, and Soichiro Takahashi
Date July 2018
Full Paper  
Remarks

Revised version of CIRJE-F-1078; Subsequently, published in “Physica A: Statistical Mechanics and its Applications” .

Abstract

This paper explores Bitcoin intraday technical trading based on artificial neural networks for the return prediction. In particular, our deep learning method successfully discovers trading signals through a seven layered neural network structure for given input data of technical indicators, which are calculated by the past time-series data over every 15 minutes. Under feasible settings of execution costs, the numerical experiments demonstrate that our approach significantly improves the performance of a buy-and-hold strategy. Especially, our model performs well for a challenging period from December 2017 to January 2018, during which Bitcoin suffers from substantial minus returns. Furthermore, various sensitivity analysis is implemented for the change of the number of layers, activation functions, input data and output classification to confirm the robustness of our approach.