Leveragıng On Contınual Machıne Learnıng For Real Tıme Tradıng In Fınancıal Markets

Year 2024, Volume: 9 Issue: Issue: 2, 178 - 185

Emmanuel Ndidi Osegi , Vincent Ike Anıreh , Ifeanyi. C Emeto , Noble. A Ibiobu , Osu Joshua Orove , Uchechukwu Agi

https://doi.org/10.53070/bbd.1575411

Abstract

In this research paper, a novel neural inspired approach called the Auditory Machine Intelligence (AMI) is proposed for the prediction of cryptocurrency price - the ripple (XRP/USD) trading pair. The AMI is implemented as part of an LUNO API systems app and real-time data generated and captured. Comparisons were also made with a popular method used in cryptocurrency trading called the Non-linear Auto-Regressive Neural Network (NARX-net). The experimental results considering the 40%, 60% and 80% training were reported for the both techniques. From the experimental results, it was clearly seen that on the average, the NeuroAMI will outperform the NARXnet technique at the 60% and 80% input training data levels; however, the NARXnet technique fared better on the average at the 40% input training data level.

Keywords

Blockchain, cryptocurrency price, machine learning, neural network, prediction

LEVERAGING ON CONTINUAL MACHINE LEARNING FOR REAL TIME TRADING IN FINANCIAL MARKETS

Abstract

In this research paper, a novel neural inspired approach called the Auditory Machine Intelligence (AMI) is proposed for the prediction of cryptocurrency price - the ripple (XRP/USD) trading pair. The AMI is implemented as part of an LUNO API systems app and real-time data generated and captured. Comparisons were also made with a popular method used in cryptocurrency trading called the Non-linear Auto-Regressive Neural Network (NARX-net). The experimental results considering the 40%, 60% and 80% training were reported for the both techniques. From the experimental results, it was clearly seen that on the average, the NeuroAMI will outperform the NARXnet technique at the 60% and 80% input training data levels; however, the NARXnet technique fared better on the average at the 40% input training data level.

Keywords

Blockchain, cryptocurrency price, machine learning, neural network, prediction

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