2 citations · 2 across the 5 of their papers we have counts for
5 papers
Finding Optimal Trading History in Reinforcement Learning for Stock Market Trading
Sina Montazeri, Haseebullah Jumakhan, Amir Mirzaeinia
This paper investigates the optimization of temporal windows in Financial Deep Reinforcement Learning (DRL) models using 2D Convolutional Neural Networks (CNNs). We introduce a nov…
Gradient Reduction Convolutional Neural Network Policy for Financial Deep Reinforcement Learning
Sina Montazeri, Haseebullah Jumakhan, Sonia Abrasiabian +1
Building on our prior explorations of convolutional neural networks (CNNs) for financial data processing, this paper introduces two significant enhancements to refine our CNN model…
Deep Reinforcement Learning Strategies in Finance: Insights into Asset Holding, Trading Behavior, and Purchase Diversity
Alireza Mohammadshafie, Akram Mirzaeinia, Haseebullah Jumakhan +1
Recent deep reinforcement learning (DRL) methods in finance show promising outcomes. However, there is limited research examining the behavior of these DRL algorithms. This paper a…
Wireguard: An Efficient Solution for Securing IoT Device Connectivity
Haseebullah Jumakhan, Amir Mirzaeinia
The proliferation of vulnerable Internet-of-Things (IoT) devices has enabled large-scale cyberattacks. Solutions like Hestia and HomeSnitch have failed to comprehensively address I…
CNN-DRL for Scalable Actions in Finance
Sina Montazeri, Akram Mirzaeinia, Haseebullah Jumakhan +1
The published MLP-based DRL in finance has difficulties in learning the dynamics of the environment when the action scale increases. If the buying and selling increase to one thous…