CNN-DRL with Shuffled Features in Finance

TL;DR

This paper introduces a novel CNN-DRL approach with shuffled features for financial data, significantly improving reward performance by strategically positioning relevant features.

Contribution

It proposes a new feature permutation technique in CNN-DRL that enhances reward outcomes in financial data analysis.

Findings

Substantial reward improvement demonstrated in experiments

Feature shuffling enhances CNN-DRL effectiveness

Strategic feature positioning benefits financial data modeling

Abstract

In prior methods, it was observed that the application of Convolutional Neural Networks agent in Deep Reinforcement Learning to financial data resulted in an enhanced reward. In this study, a specific permutation was applied to the feature vector, thereby generating a CNN matrix that strategically positions more pertinent features in close proximity. Our comprehensive experimental evaluations unequivocally demonstrate a substantial enhancement in reward attainment.