Research Article
Automated Forex Trading Bot Using MQL4: A Reinforcement Learning-based Approach
Jamari Markus,
Baku Agyo Raphael,
Ismaila Jesse Mazadu*
Issue:
Volume 13, Issue 3, September 2025
Pages:
49-62
Received:
29 April 2025
Accepted:
14 May 2025
Published:
27 August 2025
Abstract: Foreign exchange (Forex) trading is a high-liquidity, high-volatility global market that requires rapid decision-making. Manual trading often suffers from human limitations, including emotional biases and inconsistent decision-making. This paper presents the design and implementation of an automated Forex trading bot developed using MetaQuotes Language 4 (MQL4) and reinforcement learning (RL) strategies. The system integrates real-time market data analysis, technical indicators (MACD, RSI, Bollinger Bands), and dynamic risk management. Leveraging a custom RL environment, the bot adapts to changing market conditions, learns optimal strategies, and executes trades with reduced human intervention. Simulation results show improved trading performance, higher Sharpe ratios, and reduced drawdown compared to manual strategies. The bot architecture consisted of distinct layers, including the market data input layer, decision engine, order execution module, and risk management system. The bot was tested in a demo trading environment over a one-week period. Results demonstrated a win rate of 62%, a profit factor of 1.45, and a maximum drawdown of 4.2%. These outcomes validate the bot's ability to achieve stable performance under simulated market conditions. This study underscores the potential of AI-driven automation for enhancing algorithmic trading efficacy.
Abstract: Foreign exchange (Forex) trading is a high-liquidity, high-volatility global market that requires rapid decision-making. Manual trading often suffers from human limitations, including emotional biases and inconsistent decision-making. This paper presents the design and implementation of an automated Forex trading bot developed using MetaQuotes Lang...
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Research Article
Emotionally Intelligent User Interfaces of Social Media: The Role of Emotional Intelligence in User Interface Design and UX Pattern on Social Media on Human Behaviour
Munim Ahsan Chowdhury Primon*
Issue:
Volume 13, Issue 3, September 2025
Pages:
63-68
Received:
26 March 2025
Accepted:
9 April 2025
Published:
2 September 2025
DOI:
10.11648/j.acis.20251303.12
Downloads:
Views:
Abstract: Social Media platforms are increasing user engagements with their attractive User Interfaces (UI) design playing a crucial role in maintaining user involvement. Use of Emotional Intelligence (EI) into the UI transforming user interaction and engagement pattern significantly. With an emphasis on how design decisions affect user behaviour, this study explores the nuanced function of EIUIs (Emotionally Inteligence User Interfaces) in the context of social media. It specifically draws attention to the dangers of manipulative techniques like "dark patterns"-purposeful interface strategies intended to influence users to take actions they might not have otherwise taken-as well as the possibility of constructive involvement. By investigating these practices, the paper hopes to clarify the wider ethical consequences of interface design as well as the duty of platforms and designers to promote transparency, autonomy, and confidence in digital interactions. This paper aims to contribute to the discourse on user interface design by providing a comprehensive understanding of EIUIs' potential and pitfalls in the context of social media interactions. Additionally, this study focusses on finding information of the pattern of Emotionally Intelligent User Interfaces (EIUI) design to enhance user engagements emotionally and tend to take decisions and the impact of the design pattern in user experience.
Abstract: Social Media platforms are increasing user engagements with their attractive User Interfaces (UI) design playing a crucial role in maintaining user involvement. Use of Emotional Intelligence (EI) into the UI transforming user interaction and engagement pattern significantly. With an emphasis on how design decisions affect user behaviour, this study...
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