DOI: 10.47836/pjst.30.4.22
Thank you for reading this post, don’t forget to subscribe!ABSTRACT: A year ago, one thousand USD invested in Bitcoin (BTC) alone would have appreciated to three thousand five hundred USD. Deep reinforcement learning (DRL) recent outstanding performance has opened up the possibilities to predict price fluctuations in changing markets and determine effective trading points, making a significant contribution to the finance sector. Several DRL methods have been tested in the trading domain. However, this research proposes implementing the proximal policy optimisation (PPO) algorithm, which has not been integrated into an automated trading system (ATS). Furthermore, behavioural biases in human decision-making often cloud one’s judgement to perform emotionally. ATS may alleviate these problems by identifying and using the best potential strategy for maximising profit over time. Motivated by the factors mentioned, this research aims to develop a stable, accurate, and robust automated trading system that implements a deep neural network and reinforcement learning to predict price movements to maximise investment returns by performing optimal trading points. Experiments and evaluations illustrated that this research model has outperformed the baseline buy and hold method and exceeded models of other similar works.
– The research proposes a new automated cryptocurrency trading system integrated with DRL.
– The experimental analysis of the model showed exceptional results, surpassing similar works.
– The research proposes a new automated cryptocurrency trading system integrated with DRL.
– The experimental analysis of the model showed exceptional results, surpassing similar works.
– The research proposes a new automated cryptocurrency trading system integrated with DRL.
– The experimental analysis of the model showed exceptional results, surpassing similar works.
– Humans have limitations in terms of availability and rational thinking.
– Humans tend to be their own greatest enemy due to emotions.
Methods used: – Humans have limitations in terms of availability and rational thinking.
– Humans tend to be their own greatest enemy due to emotions.
– Resolves human hindrance in automated trading.
– Demonstrates exceptional results surpassing similar works.
– The research model outperformed the baseline buy and hold method.
– The research model exceeded models of other similar works.
– Proposed implementation of PPO algorithm in an automated trading system.
– Aims to develop a stable, accurate, and robust trading system using DRL.
– The paper proposes implementing the PPO algorithm in an automated trading system.
– The research aims to develop a stable and accurate trading system using DRL.
In this paper , a stable, accurate, and robust automated trading system that implements a deep neural network and reinforcement learning to predict price movements to maximize investment returns by performing optimal trading points is proposed.