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 bots ability to achieve stable performance under simulated market conditions. This study underscores the potential of AI-driven automation for enhancing algorithmic trading efficacy.
Markus et al. (Wed,) studied this question.
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