Saket Sarin¹, Sunil K. Singh¹, Sudhakar Kumar¹, Shivam Goyal¹, Brij Bhooshan Gupta^2,3,4,8,*, Wadee Alhalabi⁵, Varsha Arya^6,7

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 3123-3138, 2024, DOI:10.32604/cmc.2024.051599

Abstract In the rapidly evolving landscape of today’s digital economy, Financial Technology (Fintech) emerges as a transformative force, propelled by the dynamic synergy between Artificial Intelligence (AI) and Algorithmic Trading. Our in-depth investigation delves into the intricacies of merging Multi-Agent Reinforcement Learning (MARL) and Explainable AI (XAI) within Fintech, aiming to refine Algorithmic Trading strategies. Through meticulous examination, we uncover the nuanced interactions of AI-driven agents as they collaborate and compete within the financial realm, employing sophisticated deep learning techniques to enhance the clarity and adaptability of trading decisions. These AI-infused Fintech platforms harness collective intelligence More >

Yutong Tian¹, Minghan Gao², Qiang Gao^1,*, Xiao-Hong Peng³

Intelligent Automation & Soft Computing, Vol.39, No.2, pp. 175-188, 2024, DOI:10.32604/iasc.2024.042762

Abstract How to find an effective trading policy is still an open question mainly due to the nonlinear and non-stationary dynamics in a financial market. Deep reinforcement learning, which has recently been used to develop trading strategies by automatically extracting complex features from a large amount of data, is struggling to deal with fast-changing markets due to sample inefficiency. This paper applies the meta-reinforcement learning method to tackle the trading challenges faced by conventional reinforcement learning (RL) approaches in non-stationary markets for the first time. In our work, the history trading data is divided into multiple… More >

Omer Berat Sezer^*, Ahmet Murat Ozbayoglu

Intelligent Automation & Soft Computing, Vol.26, No.2, pp. 323-334, 2020, DOI:10.31209/2018.100000065

Abstract Even though computational intelligence techniques have been extensively utilized in financial trading systems, almost all developed models use the time series data for price prediction or identifying buy-sell points. However, in this study we decided to use 2-D stock bar chart images directly without introducing any additional time series associated with the underlying stock. We propose a novel algorithmic trading model CNN-BI (Convolutional Neural Network with Bar Images) using a 2-D Convolutional Neural Network. We generated 2-D images of sliding windows of 30-day bar charts for Dow 30 stocks and trained a deep Convolutional Neural More >