SOURCE RECORD · semantic_scholar
Deep Reinforcement Learning XAUUSD Trading Bot with PPO Algorithm and Real-Time Risk Management
The present study provides the design and development of a deep reinforcement learning-based automated trading system of XAUUSD through the Proximal Policy Optimization (PPO) algorithm with built-in real-time risk management flow. The proposed system will help solve the issues of volatility and price changes in the gold market which change at a fast rate with the purpose of learning optimal trading strategies directly with the help of historical market information. The trading agent is optimized with multi-timeframe OHLCV data and the popular technical analysis indicators, such as RSI, MACD, EMA, SMA, Bollinger Bands, and ATR, to understand both the short term and the long-term markets. A realistic simulation of trading environment through the use of custom reinforcement learning environment where transaction costs, market spread and account balance constraints are incorporated. This reward functionality is well structured to achieve a balance between profitability and capital conservation by punishing large drawdowns and volatile trading patterns. The decision-making process includes dynamic position sizing, adaptive stop-loss, and take-profit mechanisms, which are required to provide a strong risk control in live trading conditions. The system produces actionable buy, sell or hold signals and constantly tracks the performance metrics like profit and loss, win rate and maximum drawdown. It has a web-based dashboard to monitor in real-time and keep records of the trades. The combination of deep reinforcement learning and structured risk management shows that the proposed methodology has a systematic form of developing intelligent and adaptive trading bots based on realistic requirements under the conditions of the XAUUSD market.